a-priori</italic>\n knowledge on traffic randomness, for light-weight, and delay-tolerant application scenarios. Stochastic gradient descent is exploited to decouple the optimizations between time slots. A graph matching problem is then formulated for every time slot by decoupling and unifying the nonuniform cardinalities, and solved in a distributed manner by developing a new linear (1/2)-approximation method. We prove that the optimality loss resulting from the distributed approximate graph matching method can be compensated and diminish by increasing the learning time. Corroborated by simulations, the proposed distributed online learning is asymptotically optimal and superior to the state of the art in terms of throughput and energy efficiency.","Title: Reliable and real-time data gathering in multi-hop linear wireless sensor networks\nYear: 2006\n\nAbstract: Data gathering is a critical operation in wireless sensor networks for extracting useful information from the operating environment. In this paper, we study the problem of data gathering in multi-hop linear sensor networks. We employ a simple model based on random channel access scheme to tackle the high degree of channel contention and high probability of packet collision induced by bursty traffic. In our model, each node optimally attempts a transmission, and our goal is to tune the attempt probability for each sensor node with the objective to minimize the data gathering duration on condition that each link can provide guaranteed per-hop packet delivery reliability. We formulate this problem as an optimization problem and propose a distributed solution which relies on only two hop neighbors information. Based on this model, a simple and scalable protocol RADG (Random Access Data Gathering) is designed. Simulation results show that our algorithm has fast convergence speed. Moreover, RADG is robust to link error in essence and particularly suitable to monitor environments with high degree of interference.","Title: The Self-Adaptive Adjustment Method Of Clustering Center In Multi-Spectral Remote Sensing Image Classification Of Land Use\nYear: 2011\n\nAbstract: As one kind of remote sensing images of land use composed by various categories of surface objects difficult to obtain multi-distribution model of class spectral feature, analyzing the spectral characteristics of LU of multispectral RS imagery, this paper presents a self-adaptive adjustment of clustering center method. Depending on the intercepted situation of the cluster centers between different features to conduct split, the sub-centers obtained are as the sub-category features and the cluster centers assemble to characterize category model which is better to deal with the problems of LU category composed by various surface objects and category model not satisfying multivariate normal distribution. As there are much differences between the many centers features in the unit of category area, so the selection of training area and the determinants of rules are easy. The results of experiment indicate that the LU classification accuracy is increased between 4% and 6% with this method.","Title: A Novel Connection Correlation Scheme Based on Threshold Secret Sharing.\nYear: 2016\n\nAbstract: Detecting correlated network flows aids with exposing the attackers who hide behind anonymous networks or stepping stones. More recent flow watermarking schemes can link flows, but are often impossible to achieve invisibility and robustness concurrently. This letter proposes an innovative connection correlation scheme capable of remaining invisible to attackers. The idea is that we exploit the burstiness and chaos inherent in network traffic to collect some unique and stable traffic traits, which are used later to construct an identifier based on Shamir's (t, n) threshold secret sharing scheme. Experimental results indicate that the proposed scheme is robust to network jitter, packet insertions, and losses.","Title: Social-Aware Resource Allocation for Content Dissemination Networks: An Evolutionary Game Approach.\nYear: 2017\n\nAbstract: Recently, the increasing popularity of local area services, such as YouTube, Facebook, and Twitter, on which thousands of clients subscribe and download popular contents all the while, has drawn more and more attention on the study of content dissemination networks. Device-to-device (D2D) communication, which allows two devices to directly communicate with each other, has become an effective content dissemination method. With the goal of reducing the delay of content dissemination process with D2D communication, we propose an evolutionary game (EG)-based distributed resource allocation scheme. Moreover, we theoretically prove the existence and stability of game equilibrium and further propose a global search algorithm to achieve equilibrium. In the algorithm, all D2D links will select resource adaptively based on the predicted contact duration Aiming at improving the prediction accuracy of contact duration, we propose social trajectory similarity (STS) to represent the overlapping of history trajectory among all the mobile users by mining user behavior patterns. Numerical results show that our proposed STS increases nearly 20% correlation with history trajectory overlap compared with jaccard index. Furthermore, our EG-based scheme reduces the delay over 15% compared with the coalition game scheme, and over 30% compared with the random selection scheme. In addition, our proposed scheme also increases throughput efficiency with nearly 20% and achieves better fairness.","Title: Propagation Modeling and Defending of a Mobile Sensor Worm in Wireless Sensor and Actuator Networks.\nYear: 2017\n\nAbstract: WSANs (Wireless Sensor and Actuator Networks) are derived from traditional wireless sensor networks by introducing mobile actuator elements. Previous studies indicated that mobile actuators can improve network performance in terms of data collection, energy supplementation, etc. However, according to our experimental simulations, the actuator's mobility also causes the sensor worm to spread faster if an attacker launches worm attacks on an actuator and compromises it successfully. Traditional worm propagation models and defense strategies did not consider the diffusion with a mobile worm carrier. To address this new problem, we first propose a microscopic mathematical model to describe the propagation dynamics of the sensor worm. Then, a two-step local defending strategy (LDS) with a mobile patcher (a mobile element which can distribute patches) is designed to recover the network. In LDS, all recovering operations are only taken in a restricted region to minimize the cost. Extensive experimental results demonstrate that our model estimations are rather accurate and consistent with the actual spreading scenario of the mobile sensor worm. Moreover, on average, the LDS outperforms other algorithms by approximately 50% in terms of the cost.","Title: Detecting Steganography of Adaptive Multirate Speech with Unknown Embedding Rate.\nYear: 2017\n\nAbstract: Steganalysis of adaptive multirate (AMR) speech is a significant research topic for preventing cybercrimes based on steganography in mobile speech services. Differing from the state-of-the-art works, this paper focuses on steganalysis of AMR speech with unknown embedding rate, where we present three schemes based on support-vector-machine to address the concern. The first two schemes evolve from the existing image steganalysis schemes, which adopt different global classifiers. One is trained on a comprehensive speech sample set including original samples and steganographic samples with various embedding rates, while the other is trained on a particular speech sample set containing original samples and steganographic samples with uniform distributions of embedded information. Further, we present a hybrid steganalysis scheme, which employs Dempster-Shafer theory (DST) to fuse all the evidence from multiple specific classifiers and provide a synthesized detection result. All the steganalysis schemes are evaluated using the well-selected feature set based on statistical characteristics of pulse pairs and compared with the optimal steganalysis that adopts specialized classifiers for corresponding embedding rates. The experimental results demonstrate that all the three steganalysis schemes are feasible and effective for detecting the existing steganographic methods with unknown embedding rates in AMR speech streams, while the DST-based scheme outperforms the others overall.","Title: A Social-Energy Based Cluster Management Scheme for User-Centric Ultra-Dense Networks.\nYear: 2017\n\nAbstract: With the exponential growth of traffic demand, ultra-dense networks are proposed to increase the network capacity. However, the high-density access point (AP) deployment will increase the complexity of AP coordination, and AP cluster (APC) needs to be considered in practical implementations. Due to the dynamic changes in spatiotemporal distribution of users and service demand, we propose a social-energy-based cluster management (SECM) scheme in order to reduce APC update frequency. Specifically, in social domain, we propose a congeniality-based personalized recommendation (CPR) algorithm to predict users' incoming requests. We further propose a CPR-based AP cluster algorithm to solve the matching problem among users, APs, and content. In energy domain, we propose an inter-cluster energy cooperation scheme to avoid the shutting down of members in AP cluster and reduce the update of clusters. Numerical results demonstrate that our proposed scheme can achieve a gain of 77.8% in the APC management utility averagely, without loss of fairness compared with the other state-of-the-art schemes.","Title: Joint Beamforming and User Selection in Multiuser Collaborative MIMO SWIPT Systems With Nonnegligible Circuit Energy Consumption.\nYear: 2018\n\nAbstract: Multiantenna beamforming has potential to improve the efficiency of simultaneous wireless information and power transfer (SWIPT). Existing designs are focused on the downlink of multiple-input-single-output under the assumption of single-antenna users and negligible energy consumption in users' circuitry, despite the fact that using multiple antennas on the user side can further improve system eff...","Title: Weather prediction with multiclass support vector machines in the fault detection of photovoltaic system\nYear: 2017\n\nAbstract: Since the efficiency of photovoltaic (PV) power is closely related to the weather, many PV enterprises install weather instruments to monitor the working state of the PV power system. With the development of the soft measurement technology, the instrumental method seems obsolete and involves high cost. This paper proposes a novel method for predicting the types of weather based on the PV power dat...","Title: Data collection from WSNs to the cloud based on mobile Fog elements\nYear: 2020\n\nAbstract: The powerful computing and storage capability of cloud computing can inject new vitality into wireless sensor networks (WSNs) and have motivated a series of new applications. However, data collection from WSNs to the Cloud is a bottleneck because the poor communication ability of WSNs, especially in delay-sensitive applications, limits their further development and applications. We propose a fog structure composed of multiple mobile sinks. Mobile sinks act as fog nodes to bridge the gap between WSNs and the Cloud. They cooperate with each other to set up a multi-input multi-output (MIMO) network, aiming to maximize the throughput and minimize the transmission latency. We district collecting zones for all sinks and then assign sensors to the corresponding sinks. For those assigned sensors, hops and energy consumption are considered to solve the hopspot problem. Sensor data are uploaded to the Cloud synchronously through sinks. The problem is proved to be NP-hard, and we design an approximation algorithm to solve this problem with several provable properties. We also designed a detailed routing algorithm for sensors considering hops and energy consumption. We compare our method to several traditional solutions. Extensive experimental results suggest that the proposed method significantly outperforms traditional solutions.","Title: Secure Public Audit for Operation Behavior Logs in Shared Cloud Storage\nYear: 2017\n\nAbstract: In cloud storage, the data owner and data users can easily perform cooperative work on shared data. In this paper, we focus on operation behavior auditing in shared cloud storage, which is significant for the avoidance of potential crimes in the cloud and equitable accountability determination for the forensic investigation in shared cloud. We first introduce a novel secure public auditing model for operation behavior in shared cloud. Based on this, a secure auditing scheme for operation behavior logs (OBLs) is presented. Specifically, by using the certificateless signature, our scheme provides public privacy preserving auditing. The experiment analysis proves that our scheme can audit the validity of OBLs in the cloud efficiently. To the best of our knowledge, our scheme is the first solution of public auditing for OBLs in shared cloud.","Title: Energy efficiency optimization in large-scale distributed MIMO systems over K fading channels\nYear: 2017\n\nAbstract: In this paper, we investigate the energy efficiency (EE) of large-scale distributed multiple-input multiple-output (D-MIMO) systems. Firstly, we derive an analytical closed-form expression of the lower bound on the ergodic capacity for D-MIMO systems, and the asymptotic performance for the large-scale antenna arrays systems over K fading channels is proposed. Utilizing these results, we elaborate on the EE optimization problem with transmitted power constraint. To tackle this multi-objective optimization problem, we propose an one-dimension iterative search algorithm with low complexity to obtain the optimal number of receive antennas, transmitted power and achievable sum rate with optimum EE. Simulation results verify the performance gain of the proposed scheme along with the increasing number of transmit antennas.","Title: Improving k-anonymity based privacy preservation for collaborative filtering.\nYear: 2018\n\nAbstract: Collaborative Filtering (CF) is applied in recommender systems to predict users’ preference through filtering the information or patterns. Privacy Preserving Collaborative Filtering (PPCF) aims to achieve privacy protection in the recommendation process, which has an increasing significance in recommender systems and thus attracted much interests in recent years. Existing PPCF methods are mainly based on cryptography, obfuscation, perturbation and differential privacy. They have high computational cost, low data quality and difficulties in calibrating the magnitude of noise. This paper proposes a (p, l, α)-diversity method that improves the existing k-anonymity method in PPCF, where p is attacker’s prior knowledge about users’ ratings and (l, α) is the diversity among users in each group to improve the level of privacy preserving. To achieve (l, α)-diversity, users in each equivalence class shall come from at least l (l < k) clusters in α clustering results. Therefore, we firstly apply Latent Factor Model (LFM) to reduce matrix sparsity. Then we propose an improved Maximum Distance to Average Vector (MDAV) microaggregation algorithm based on importance partitioning to increase the homogeneity among the records in each group which can retain better data quality in (p, l, α)-diversity model. Finally, we apply t-closeness in PPCF. Theoretical analysis and experimental results demonstrate our approach assures a higher level of privacy preserving and less information loss than existing methods.","Title: Research on precision of modular machine tool based on computer aided multi-body system method\nYear: 2019\n\nAbstract: Modularity of machine tool, an engineering technology that deals with cost-effective and quick reaction to all the market changes, is urgently needed in the modern manufacturing companies. However, a general accuracy analytical method of modular machine tool is seldom concerned. The basic module error of the machine tool is synthesized by the theory of computer aided multi-body system, a universal accuracy model for modular machine tools has been established. The developed mathematical model is used to calculate and predict the resultant position error at the tool–workpiece interface for error compensation. Results show the method based on the computer aided multi-body system only depends on the motion of module and fits for rapid modeling of computer technology, and error factors such as thermal expansion and deforming under loads can be involved in the analysis processing except the geometric error.","Title: A Social Centrality-Aware D2D Multicast Scheme for Content Dissemination\nYear: 2017\n\nAbstract: Device-to-device (D2D) communication is conceived as a vital component for 5G cellular networks to improve spectral reuse, and enhance system capacity. For content dissemination, D2D communication also provides an efficient way, and the influence of social interactions among mobile devices has attracted substantial attention due to its potential impact on resource allocation. Aiming to enhance the dissemination efficiency and link stability, a social-aware D2D multicast scheme that exploits social network property of centrality for content dissemination is proposed. Based on the multi-dimensional centrality we defined, a multicast clustering scheme is proposed. Then, a bipartite graph is constructed to represent the matching problem of multicast clusters and channel resource, and Kuhn-Munkres (KM) algorithm is introduced to solve the channel allocation problem optimally. Simulation results demonstrate the effectiveness of our proposed scheme, which significantly improves the efficiency of content dissemination.","Title: Radio over Cloud (RoC): Cloud-Assisted Distributed Beamforming for Multi-class Traffic\nYear: 2019\n\nAbstract: Cloud has yet to be applied to computationally intensive radio signal processing, due to closely coupled computing tasks resulting from interference. This paper presents a new cloud-assisted joint beamforming architecture, where computations are decoupled for individual wireless users and pipelined for cloud execution, using Difference of Convex (DC), $\\ell _1$ℓ1-norm approximations, and dual decompositions. User-specific tasks are constructed and aligned with the cloud to leverage computation reuses and minimize overhead. The time-complexity is dramatically improved to support networks with tens to hundreds of base stations and users, without compromising the sum rate and quality-of-service. Further, the superiority of DC to the state-of-the-art Weighted Minimum Mean Square Error (WMMSE) in terms of convex relaxation is observed and discussed. Corroborated by simulations, the reason is revealed as WMMSE aggressively increases the data rate at interim stages, hence adversely interacting with $\\ell _1$ℓ1-norm approximation and reducing the feasible solution regions at later stages.","Title: Traffic-Aware Relay Vehicle Selection in Millimeter-Wave Vehicle-to-Vehicle Communication\nYear: 2019\n\nAbstract: Line-of-sight (LOS) blockage is a crucial problem in millimeter-wave vehicle-to-vehicle communication due to its severe penetration loss and high vehicle mobility. In order to overcome the LOS blockage problem, in this letter, we propose using neighbor vehicles as relays to forward the blocked traffic flows. Specifically, a traffic-aware relay vehicle selection is investigated. First, analytic hie...","Title: Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised Algorithm.\nYear: 2019\n\nAbstract: The parametric fault diagnosis of analog circuits is very crucial for condition-based maintenance (CBM) in prognosis and health management. In order to improve the diagnostic rate of parametric faults in engineering applications, a semi-supervised machine learning algorithm was used to classify the parametric fault. A lifting wavelet transform was used to extract fault features, a local preserving mapping algorithm was adopted to optimize the Fisher linear discriminant analysis, and a semi-supervised cooperative training algorithm was utilized for fault classification. In the proposed method, the fault values were randomly selected as training samples in a range of parametric fault intervals, for both optimizing the generalization of the model and improving the fault diagnosis rate. Furthermore, after semi-supervised dimensionality reduction and semi-supervised classification were applied, the diagnosis rate was slightly higher than the existing training model by fixing the value of the analyzed component.","Title: Public Auditing for Trusted Cloud Storage Services\nYear: 2019\n\nAbstract: Cloud storage can provide on-demand outsourcing of data services for organizations and individuals. However, because customers may not fully trust that cloud service providers meet their legal expectations for data security, techniques for auditing the cloud have attracted increasing attention. Here, we present an architecture of public data auditing, review existing methods or mechanisms for various auditing objectives, and discuss trends and possible future developments.","Title: Space-Time Spectrum Sharing for Unmanned Aerial Vehicle Networks\nYear: 2018\n\nAbstract: In this paper, we study the spectrum sharing of unmanned aerial vehicle (UAV) networks. Multiple primary users (PUs) are distributed on the ground. UAV networks distributed in the three-dimensional (3D) space share the spectrum of PUs. The 3D space is divided into constrained region and unconstrained region by planes. In constrained region, UAVs have temporal spectrum access opportunities. While in the unconstrained region, there is a spatial spectrum hole that allows UAVs to access PUs spectrum without constraints. Moreover, we analyzed the condition of existing a transition region between constrained region and unconstrained region. The bounds of two regions over fading channel are calculated by considering the constraints of the miss detection probability, false alarm probability and the aggregation interference of UAVs to PUs. UAVs can use the height information to decide the way of spectrum sharing.","Title: Electronic Nose-Based Technique for Rapid Detection and Recognition of Moldy Apples.\nYear: 2019\n\nAbstract: In this study, the PEN3 electronic nose was used to detect and recognize fresh and moldy apples inoculated with Penicillium expansum and Aspergillus niger, taking Golden Delicious apples as the model subject. Firstly, the apples were divided into two groups: individual apple inoculated only with/without different molds (Group A) and mixed apples of inoculated apples with fresh apples (Group B). Then, the characteristic gas sensors of the PEN3 electronic nose that were most closely correlated with the flavor information of the moldy apples were optimized and determined to simplify the analysis process and improve the accuracy of the results. Four pattern recognition methods, including linear discriminant analysis (LDA), backpropagation neural network (BPNN), support vector machines (SVM), and radial basis function neural network (RBFNN), were applied to analyze the data obtained from the characteristic sensors, aiming at establishing the prediction model of the flavor information and fresh/moldy apples. The results showed that only the gas sensors of W1S, W2S, W5S, W1W, and W2W in the PEN3 electronic nose exhibited a strong signal response to the flavor information, indicating most were closely correlated with the characteristic flavor of apples and thus the data obtained from these characteristic sensors were used for modeling. The results of the four pattern recognition methods showed that BPNN had the best prediction performance for the training and testing sets for both Groups A and B, with prediction accuracies of 96.3% and 90.0% (Group A), 77.7% and 72.0% (Group B), respectively. Therefore, we demonstrate that the PEN3 electronic nose not only effectively detects and recognizes fresh and moldy apples, but also can distinguish apples inoculated with different molds.","Title: Public audit for operation behavior logs with error locating in cloud storage\nYear: 2019\n\nAbstract: To ensure the creditability of audit for operation behaviors in cloud storage scenarios, it is indispensable to verify the integrity of log files prior to forensic analysis. Thus, in this paper, we mainly focus on how to achieve effective public audits for operation behavior logs. To achieve this goal, we first propose a new block-based logging method to satisfy all necessary requirements for security and performance, i.e., tamper resistance of log files, non-repudiation of behaviors and selective verification of log blocks. Next, we give a privacy-preserving public auditing method for a single log block, which can support an unlimited number of effective auditing operations. Further, we present a binary auditing tree-based public auditing method, which can achieve error locating while supporting selective verification for multiple log blocks. The security of the proposed scheme is formally proven. Moreover, its performance for verification is evaluated by comprehensive experiments and comparisons with existing schemes. The experimental results demonstrate that our scheme can efficiently achieve public verification for operation behavior logs in the cloud storage scenario and outperforms the existing ones in computation and communication costs.","Title: A novel steganographic method for algebraic-code-excited-linear-prediction speech streams based on fractional pitch delay search\nYear: 2019\n\nAbstract: Although a large number of steganography algorithms based on algebraic-code-excited-linear-prediction have been proposed, their performance, such as embedding capacity, embedding transparency and capability for resisting detection, can be further enhanced. Therefore, we are motivated to present a new steganographic scheme based on pitch delay search, which can achieve better performance by embedding secret information into the fractional pitch delay parameters while keeping the integer pitch delay parameters unchanged. Specifically, we treat all fractional pitch delays as replaceable cover bits to achieve maximum embedding capacity. Further, the steganographic process is encrypted and guided by m sequences for flexibility and security. The proposed method is evaluated with a large number of adaptive multi-rate speech samples and compared with the existing works. Experimental results show that the proposed method can provide larger embedding capacity than the existing works while achieving excellent embedding transparency. Moreover, we evaluate the performance of the proposed method for resisting the state-of-the-art steganalysis methods. The experimental results demonstrate that the proposed method is highly secure, since the state-of-the-art steganalysis methods cannot detect it efficiently.","Title: A Low-Complexity QR-based Precoding for Massive MIMO Systems with Channel Update\nYear: 2018\n\nAbstract: In this paper, we investigate the precoding design for massive multiple-input multiple-output (MIMO) system after a new channel changes for a given user, i.e., channel update. Specifically, we develop a modified two-stage precoding based on channel extension to balance the multi-user interference and noise effect. Additionally, utilizing the matrix transform method of the QR decomposition, a low-complexity precoding algorithm is proposed with channel update into three cases of position. The computation complexity analysis and simulation results in terms of the system capacity are evaluated to demonstrate performance gains of the proposed algorithms.","Title: A Neighborhood-Assisted Framework for Differential Evolution.\nYear: 2019\n\nAbstract: Differential evolution (DE), as a powerful and efficient evolutionary algorithm (EA), has shown its advantages in solving the complex optimization problems. In the literature, the utilization of neighborhood information has been attracting wide attention in the DE community due to its effectiveness in enhancing the search ability of DE. However, we have observed that no general framework is presented to provide a comprehensive way of studying the neighborhood-based DE variants. Therefore, this paper suggests a three-layer mechanism neighborhood-assisted (TLNA) DE framework to facilitate the utilization of neighborhood information. In TLNA, the mechanisms of using neighborhood information are generalized into a three-layer cooperative structure, i.e., the interaction mechanism (IM) layer, the organization mechanism (OM) layer, and utilization mechanism (UM) layer. Thus, TLNA is built to provide a synergistic effect of different layers of mechanisms for systematically utilizing neighborhood information. As a general framework, TLNA can be realized with different implementations of the three-layer mechanism. Furthermore, to demonstrate the practicality of the proposed framework, a TLNA instantiation (iTLNA) is given in detail. The performance of iTLNA is extensively evaluated on a suite of benchmark functions. The experimental results have confirmed the competitiveness of iTLNA to other DE variants and EAs, which shows that the proposed TLNA framework can pave an effective way to improve the performance of DE with neighborhood information.","Title: Profitable Cooperative Region for Distributed Online Edge Caching\nYear: 2019\n\nAbstract: Cooperative caching can unify network storage to improve efficiency, but the effective placement and search of contents are challenging especially in distributed edge clouds with neither \n<italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">a-priori</italic>\n knowledge on content requests nor instantaneous global view. This paper establishes a new profitable cooperative region for every content request admitted at an edge server, within which the content, if cached, can be retrieved with guaranteed profit against a direct retrieval from the network backbone. This narrows down the search for the content. The caching density of the content can also be significantly reduced, e.g., to a cached copy per region. The regions are based on a novel distributed framework which allows individual servers to spontaneously admit/dispatch requests and deliver/forward contents, while asymptotically maximizing the time-average profit of caching. The cooperative region for content is erected at individual servers by comparing the upper and lower bounds for the backlogs of unsatisfied requests of the content. Simulations show the substantially improved profit of the proposed approach over existing solutions. The regions can help automate the placement of contents with reduced density and improved efficiency.","Title: State feedback design for set stabilization of probabilistic Boolean control networks\nYear: 2019\n\nAbstract: Set stabilization of probabilistic Boolean control networks (PBCNs) is investigated in this paper and some interesting results are derived. The main results consist of three parts. (1) A definition of set stabilizability with probability one by closed-loop control is proposed for PBCNs, which is not a natural extension from deterministic Boolean control networks to PBCNs due to the random feature of PBCNs. (2) A necessary and sufficient set stabilizability condition is provided for PBCNs. (3) An algorithm for designing a state feedback controller is developed. It is guaranteed that all designed controllers can stabilize a PBCN to a given subset with probability one. The design method is constructive, so it is convenient to use this method in practical application. The results derived above are fundamental and important, since based on them many problems about PBCNs can be solved, for example partial stabilization, synchronization, and so on. Finally, a practical example is employed to show the effectiveness of our results.","Title: Vulnerability analysis of public transit systems from the perspective of urban residential communities\nYear: 2019\n\nAbstract: •A methodology to analyze the vulnerability of PTSs from the perspective of RC.•The functionality of PTSs is measured by the accessibility of RC.•An algorithm is proposed to calculate the accessibilities of each RC.•The vulnerability of PTSs is analyzed under several daily disruption modes.","Title: Differential Evolution With Adaptive Guiding Mechanism Based on Heuristic Rules.\nYear: 2019\n\nAbstract: This paper proposes to resolve the limitation of differential evolution (DE) that the difference between the individuals in search behavior has not yet been utilized effectively for guiding the evolution of the population. An adaptive guiding mechanism (AGM) based on the heuristic rules is thus suggested to make possible, individual-dependent guidance. The AGM mainly comprises three stages: construction, separation, and guidance. In the construction stage, the elite leadership team (ELT) is established with an adaptive control scheme by using good information of the population. In the separation stage, the ELT is divided into distinct elite groups that are allocated to different individuals based on their search behaviors. In the guidance stage, the leader that is chosen from the respective elite group, as well as the promising directions extracted from the population, are used together to guide the search of each individual. By incorporating AGM into DE, a novel algorithm framework, named DE with AGM (DE-AGM), is proposed to enhance the performance of DE. As a general framework, DE-AGM can be easily and seamlessly applied to most DE variants. The experimental results on 58 benchmark functions have demonstrated the competitive performance of DE-AGM.","Title: Person Re-identification via Adaptive Verification Loss\nYear: 2019\n\nAbstract: Person re-identification (re-ID) is the task of associating the image patches of the identical person across disjoint cameras, which is a very challenging topic in computer vision. Recently, with the advance of deep learning techniques and the public of huge datasets, person re-ID has achieved a great success. However, an obvious but ignored issue is that the sample pairs are biased, where the negative sample pairs are far more than the positive ones. In this paper, we develop an adaptive verification loss, termed as ADV-Loss to handle the imbalance of sample pairs. Our ADV-Loss empowers the popular verification loss with the flexibility of adaptively addressed the hard triple units. Specifically, we learn a re-weighted strategy from the triplet loss to balance the sample pairs. According to the objective values of triple loss, the hard triple units will be endowed with larger weights, while the less important triple units are de-emphasized or simply dropped. Thus, ADV-Loss, on the basis of the cross-entropy loss, handles the biased issue by only holding the equal number but informative sample pairs. Moreover, theoretical analysis of our designed ADV-Loss is provided to guarantee the important sample pairs not to be despised during the backpropagation. Ablation analysis verifies the potential efficacy of the proposed loss. Moreover, experiments of person re-ID on three large-scale benchmark datasets show that two popular deep networks with the proposed loss achieve satisfactory performance against several well-established person re-ID methods.","Title: A Novel Information and Energy Cooperation Transmission Scheme in Cognitive Spectrum Sharing-Based D2D Communication Systems\nYear: 2019\n\nAbstract: Spectrum efficiency and energy efficiency are two critical issues in designing wireless communication networks. In this paper, we consider cognitive spectrum sharing-based cooperation between a single-antenna primary system and a multi-antenna wireless-powered device-to-device (D2D) communication system. A novel joint time division and power splitting-based three-phase information and energy cooperation transmission scheme are proposed for cognitive spectrum sharing-based D2D communication system. In the proposed scheme, the cognitive D2D transceiver pair adopts the power splitting technique to receive information and energy simultaneously from the primary transmitter (PT) in the first phase. Then, they utilize the beamforming technique and the harvested energy as half-duplex (HD) relays to jointly forward the primary user (PU) signal in the second phase. Eventually, in the third phase, they can communicate directly with each other by using the licensed spectrum and their remaining energy. We focus on the design of time division ratios, power splitting ratios, and beamforming vectors, with the objective to maximize the data rate of the cognitive D2D communication system, subject to the quality of service (QoS) requirement of the primary system, and the cognitive D2D communication system's minimum energy harvesting requirements and maximum power consumption constraints. The simulation results demonstrate that our proposed scheme is the best and significantly improves the average data rate of the cognitive D2D communication system under different PU target rate requirements or PT rated transmit power. Furthermore, a large number of simulations are demonstrated to verify the feasibility and effectiveness of the proposed scheme.","Title: Optimized Lossless Data Hiding in JPEG Bitstream and Relay Transfer-Based Extension\nYear: 2021\n\nAbstract: This paper proposes a new framework of lossless data hiding (LDH) in JPEG images. The proposed framework contains two algorithms, i.e., the optimized basic LDH and the relay transfer based extension. In the basic algorithm, we aim to preserve the filesize after data embedding. The data hiding process is optimized by variable-length-code (VLC) mapping, combination and permutation. In the extended algorithm, we focus on embedding more bits into the bitstream with a condition that the filesize increment is allowed. To decrease the filesize increment, we propose a relay transfer based algorithm to preprocess the JPEG bitstream. Subsequently, we embed data into the processed bitstream using the basic LDH. Both algorithms provide better performances than previous arts. After lossless data hiding, the marked JPEG bitstream is compliant to common JPEG decoders. Since all operations are implemented on VLCs and the Huffman codes, no distortion is generated on the image pixels. Experimental results demonstrate that the proposed approach outperforms previous methods.","Title: STAR-RIS Integrated Nonorthogonal Multiple Access and Over-the-Air Federated Learning: Framework, Analysis, and Optimization\nYear: 2022\n\nAbstract: This article integrates nonorthogonal multiple access (NOMA) and over-the-air federated learning (AirFL) into a unified framework using one simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). The STAR-RIS plays an important role in adjusting the decoding order of hybrid users for efficient interference mitigation and omnidirectional coverage extension. To capture the impact of nonideal wireless channels on AirFL, a closed-form expression for the optimality gap (also known as the convergence upper bound) between the actual loss and the optimal loss is derived. This analysis reveals that the learning performance is significantly affected by the active and passive beamforming schemes, as well as wireless noise. Furthermore, when the learning rate diminishes as the training proceeds, the optimality gap is explicitly shown to converge with a linear rate. To accelerate convergence while satisfying quality-of-service requirements, a mixed-integer nonlinear programming (MINLP) problem is formulated by jointly designing the transmit power at users and the configuration mode of STAR-RIS. Next, a trust-region-based successive convex approximation method and a penalty-based semidefinite relaxation approach are proposed to handle the decoupled nonconvex subproblems iteratively. An alternating optimization algorithm is then developed to find a suboptimal solution for the original MINLP problem. Extensive simulation results show that: 1) the proposed framework can efficiently support NOMA and AirFL users via concurrent uplink communications; 2) our algorithms achieve a faster convergence rate on independent and identically distributed (IID) and non-IID settings compared to the existing baselines; and 3) both the spectrum efficiency and learning performance are significantly improved with the aid of the well-tuned STAR-RIS.","Title: Online Offloading Scheduling for NOMA-Aided MEC Under Partial Device Knowledge\nYear: 2022\n\nAbstract: By exploiting the superiority of nonorthogonal multiple access (NOMA), NOMA-aided mobile-edge computing (MEC) can provide scalable and low-latency computing services for the Internet of Things. However, given the prevalent stochasticity of wireless networks and sophisticated signal processing of NOMA, it is critical but challenging to design an efficient task offloading algorithm for NOMA-aided ME...","Title: Over-the-Air Federated Learning and Non-Orthogonal Multiple Access Unified by Reconfigurable Intelligent Surface\nYear: 2021\n\nAbstract: With the aim of integrating over-the-air federated learning (AirFL) and non-orthogonal multiple access (NOMA) into an on-demand universal framework, this paper proposes a novel reconfigurable intelligent surface (RIS)-unified network by leveraging the RIS to flexibly adjust the signal processing order of hybrid users. The objective of this work is to maximize the achievable hybrid rate of all users by jointly optimizing the transmit power at users, controlling the receive scalar at the base station, and designing the phase shifts at the RIS. Due to the fact that all signals of computation and communication are combined into one concurrent transmission, the formulated resource allocation problem is very challenging. To solve this problem, the alternating optimization is invoked to address non-convex subproblems iteratively for finding a suboptimal solution with low complexity. Simulation results demonstrate that i) the proposed RIS-unified network can support the on-demand communication and computation efficiently, and ii) the designed algorithms are also applicable to conventional networks with only AirFL or NOMA users.","Title: Tail-Heaviness, Asymmetry, And Profitability Forecasting By Quantile Regression\nYear: 2021\n\nAbstract: We show that quantile regression is better than ordinary-least-squares (OLS) regression in forecasting profitability for a range of profitability measures following the conventional setup of the accounting literature, including the mean absolute forecast error (MAFE) evaluation criterion. Moreover, we perform both a simulated-data and an archival-data analysis to examine how the forecasting performance of quantile regression against OLS changes with the shape of the profitability distribution. Considering the MAFE and mean squared forecast error (MSFE) criteria together, we see that the quantile regression is more accurate relative to OLS when the profitability to be forecast has a heavier-tailed distribution. In addition, the asymmetry of the profitability distribution has either a U-shape or an inverted-U-shape effect on the forecasting accuracy of quantile regression. An application of the distributional shape analysis framework to cash flow forecasting demonstrates the usefulness of the framework beyond profitability forecasting, providing additional empirical evidence on the positive effect of tail-heaviness and supporting the notion of an inverted-U-shape effect of asymmetry.","Title: Maintenance of Structural Hole Spanners in Dynamic Networks\nYear: 2021\n\nAbstract: Structural Hole (SH) spanners are the set of users who bridge different groups of users and are vital in numerous applications. Despite their importance, existing work for identifying SH spanners focuses only on static networks. However, real-world networks are highly dynamic where the underlying structure of the network evolves continuously. Consequently, we study SH spanner problem for dynamic n...","Title: Particle Filter Reinforcement via Context-Sensing for Smartphone-Based Pedestrian Dead Reckoning\nYear: 2021\n\nAbstract: Pedestrian dead reckoning based on particle filter is commonly used for enabling seamless smartphone-based indoor positioning. However, compass directions indoor are heavily distorted due to the presence of ferromagnetic materials. Conventional particle filters convert the raw compass direction to a distribution adding a constant variance noise and leveraging a particle swarm to simulate the distr...","Title: Energy-Efficient Uplink Power Control in NOMA Enhanced Cell-Free Massive MIMO Networks\nYear: 2021\n\nAbstract: The ever-increasing communication requirement of the next generation network imposes great challenges in the existing cellular architecture and requires energy-efficient resource allocation. In this paper, we consider a cell-free (CF) massive multiple-input multiple-output (MIMO) network, where a large number of distributed access points with multiple antennas simultaneously serve a smaller number...","Title: Rate Splitting Multiple Access for Joint Communication and Sensing Systems with Unmanned Aerial Vehicles\nYear: 2021\n\nAbstract: This paper investigates the problem of resource allocation for joint communication and radar sensing system on rate-splitting multiple access (RSMA) based unmanned aerial vehicle (UAV) system. UAV simultaneously communicates with multiple users and probes signals to targets of interest to exploit cooperative sensing ability and achieve substantial gains in size, cost and power consumption. By virt...","Title: Communication-Aware Path Design for Indoor Robots Exploiting Federated Deep Reinforcement Learning\nYear: 2021\n\nAbstract: Robots have been widely used in the era of Internet of Things (IoT) and become an important driver behind 6G systems. However, due to the mobility of robots and the complicated wireless environment, it is challenging to control robots without prior knowledge, especially for the dynamic multi-robot systems. This paper investigates data transmission and path planning problems in an indoor multi-robot navigation system, where non-orthogonal multiple access (NOMA) is adopted to provide massive connectivity and improve spectrum efficiency. The considered system is designed for the long-term throughput maximization by jointly designing the downlink transmit power at the access point (AP) and the motion paths of the robots, while satisfying the power budget and mobility constraints. In this paper, a novel federated deep reinforcement learning approach called federated deep Q-network learning (F-DQN) algorithm is proposed to tackle the formulated problem. Simulation results demonstrate that the proposed algorithm not only speeds up the convergence rate and promotes the system throughput, but also is scalable for the number of robots.","Title: Reconfigurable Intelligent Surface Aided Secure UAV Communications\nYear: 2021\n\nAbstract: This paper investigates the problem of secure communication in the unmanned aerial vehicle (UAV) enabled networks aided by a reconfigurable intelligent surface (RIS) from the physical layer security perspective. Specifically, the RIS is deployed to assist the wireless transmission from the UAV to the ground user in the presence of an eavesdropper. The objective of this work is to maximize the secrecy rate by jointly optimizing the phase shifts at the RIS as well as the transmit beamforming vector and location of the UAV. However, the formulated problem is difficult to solve directly due to the non-linear and non-convex objective function and constraints. By invoking the successive convex approximation and fractional programming techniques, the intractable original problem is transformed into convex ones, then an alternating algorithm is proposed to solve the challenging problem effectively. Simulations results demonstrate that the designed algorithm for RIS-aided UAV communications can achieve higher secrecy rate than benchmarks.","Title: Learning Event Guided Network For Salient Object Detection\nYear: 2021\n\nAbstract: Salient object detection (SOD) focuses on mimicking the attention mechanism in human vision system. Due to the limited information provided from the traditional image data, the image-based SOD advances are very challenging in some complex scenes. Inspired by the emerging event cameras that provide asynchronous measurements of local temporal contrast over a large dynamic range, we propose a new idea to extract more effective information from the combination of event flow and RGB images. In this paper, we construct an end-to-end joint network for salient object detection (ERSOD-Net), which simultaneously supervise the RGB image and the event data within the corresponding image exposure time. To fully exploit temporal information of event data, Long Short-Term Memory module is utilized to effectively process event and learn salient object event surfaces. Moreover, multi-level feature interaction is designed to fuse two complementary branches of image and event and to predict significance mapping. Finally, to demonstrate the effectiveness of our model, a real Event-RGB SOD dataset (ERSOD) is built by DAVIS camera. Experiments on both benchmark and ERSOD datasets show that the proposed event guided network greatly improves the SOD performance in different evaluation metrics. The code and datasets will be released later at https://github.com/jxr326/ERSOD-Net. (c) 2021 Elsevier B.V. All rights reserved.","Title: Joint Optimization of Control and Communication in Autonomous UAV Swarms: Challenges, Potentials, and Framework\nYear: 2021\n\nAbstract: The unmanned aerial vehicle (UAV) swarm has drawn much attention for its wide range of potential applications. However, most UAV swarms rely on manual remote control or preprogramming, leaving autonomous cooperation as a challenging issue. Control and communication are two important pillars intra-autonomous-swarm, and the competition for limited resources between control and communication needs to...","Title: The Comprehensive Contributions of Endpoint Degree and Coreness in Link Prediction\nYear: 2021\n\nAbstract: AbstractIn past studies, researchers find that endpoint degree, H-index, and coreness can quantify the influence of endpoints in link prediction, especially the synthetical endpoint degree and H-index improve prediction performances compared with the traditional link prediction models. However, neither endpoint degree nor H-index can describe the aggregation degree of neighbors, which results in inaccurate expression of the endpoint influence intensity. Through abundant investigations, we find that researchers ignore the importance of coreness for the influence of endpoints. Meanwhile, we also find that the synthetical endpoint degree and coreness can not only describe the maximal connected subgraph of endpoints accurately but also express the endpoint influence intensity. In this paper, we propose the DCHI model by synthesizing endpoint degree and coreness and the HCHI model by synthesizing H-index and coreness on SRW-based models, respectively. Extensive simulations on twelve real benchmark datasets show that, in most cases, DCHI shows better prediction performances in link prediction than HCHI and other traditional models.","Title: Online delay-guaranteed workload scheduling to minimize power cost in cloud data centers using renewable energy\nYear: 2022\n\nAbstract: •Model: Proposed a new model to handle different SLAs of workloads and guarantee the worst-case delay of delay-tolerant workload.•Algorithm: Designed an O(1) time online control algorithm OSDG for each slot to achieve the long term optimization objective.•Analysis and Experiment: Proved our algorithm achieves an explicit [O(1/V),O(V)] cost-delay tradeoff and validated its performance.","Title: Secrecy Performance Analysis of IRS-Aided UAV Relay System\nYear: 2021\n\nAbstract: In this letter, we study the secrecy performance of an intelligent reflecting surface (IRS)-aided unmanned aerial vehicle (UAV) relay communication system in the presence of multiple ground eavesdroppers (Eves). Specifically, a UAV equipped with an IRS is deployed as a passive relay to forward the signals transmitted from the base station to users. By modeling the distribution of Eves with stochas...","Title: Carrier Phase-Based Synchronization and High-Accuracy Positioning in 5G New Radio Cellular Networks\nYear: 2022\n\nAbstract: Inspired by excellent precision of carrier phase positioning, this paper presents a new carrier phase positioning technique for 5G new radio cellular networks with a focus on clock synchronization and integer ambiguity resolution. A carrier-phase based clock offset estimation method is first proposed to achieve precise clock synchronization among base stations, and proved to achieve the Cramér–Rao...","Title: Securing Aerial Offloading via Intelligent Omni-Surface\nYear: 2021\n\nAbstract: Different from conventional reflecting-only metasurfaces, intelligent omni-surfaces (IOSs) are capable of reflecting and transmitting the received signals simultaneously. As such, users located at both sides of IOSs can be served efficiently. In this paper, a novel IOS-enhanced aerial offloading system is proposed in the presence of one ground eavesdropper. To maximize the secrecy energy efficienc...","Title: Enabling Ubiquitous Non-Orthogonal Multiple Access and Pervasive Federated Learning via STAR-RIS\nYear: 2021\n\nAbstract: This paper proposes a new, compatible, unified framework which integrates non-orthogonal multiple access (NOMA) and over-the-air federated learning (AirFL) via concurrent communication. In particular, a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is leveraged to adjust the signal processing order for efficient interference mitigation and omni-directional coverage extension. With the aim of investigating the impact of non-ideal wireless communication on AirFL, we provide a closed-form expression for the optimality gap over a given number of communication rounds. This result reveals that the learning performance is significantly affected by the resource allocation scheme and channel noise. To minimize the derived optimality gap, a mixed-integer non-linear programming (MINLP) problem is formulated by jointly designing the transmit power at users and configuration mode at the STAR-RIS. Through developing an alternating optimization algorithm, a suboptimal solution for the original MINLP problem is obtained. Simulation results show that the learning performance in terms of training loss and test accuracy can be effectively improved with the aid of the STAR-RIS.","Title: A Digital Twin Based Design of the Semi-physical Marine Engine Room Simulator for Remote Maintenance Assistance\nYear: 2021\n\nAbstract: The semi-physical marine engine room simulator is one of the necessary equipment for the training of marine engineers worldwide. However, its maintenance has become a problem for a long time due to its complexity and vulnerability. Although the semi-physical marine engine room simulator fails frequently, its failures are usually small-scale communication failures, which are easy to solve for experts. For such kind of micro faults, the cost of field maintenance by experts is very high due to the long journey. Generally, most manufactures will choose to assist the users’ maintenance by telephone or email firstly. If it does not work, they will have to send experts for field maintenance which is expensive for users. In this paper, we propose a digital twin architecture of the semi-physical marine engine room simulator to assist users in remote maintenance. The digital twin system is a multi-twins architecture consisting of the user digital twin and the manufacturer digital twin. The user digital twin is a technical guidance platform for information reception and display, through which the user can get technical adviser from the experts in the 3D virtual environment. The manufacture digital twin is a platform for monitoring and troubleshooting by experts. The communication faults are the main faults of the semi-physical marine engine room simulator, which are easy to monitor. Consequently, it is possible to realize remote maintenance assistance through the digital twin system without a lot of time cost and economic cost.","Title: Detection of Covert Timing Channel Based on Time Series Symbolization\nYear: 2021\n\nAbstract: Covert Timing Channels(CTCs) is a technique to leak information. CTCs only modify inter-arrival time sequence(IATs) between packets, consequently, traditional network security mechanisms, such as firewalls and proxies, can not effectively detect CTCs. If CTCs are maliciously utilized by criminals, will pose a great threat to network security. Classic CTCs detection methods, such as KS-test, Entropy-test, etc, not only have less universality and robustness, but also require more sampled IATs to detect CTCs, therefore, how to improve performance of detection methods against CTCs, has became a popular research in recent years. In this paper, a new CTCs detection method based on time series symbolization is proposed. It firstly converts the sampled IATs to symbolic time series, and regards each discrete value as a status. Then counts the times of transition for each status to status, and calculates the status transition probability matrix(STPM). Finally, it differentiates the label(overt or covert) of sampled IATs, by calculating similarity score. Experimental results about detection accuracy show that, in an ideal network environment, compared with classic methods, our method has better performance, with average accuracy of about 96%. Besides, our method has better performance as well, with the existence of network interference.","Title: Covert Timing Channels Detection Based on Auxiliary Classifier Generative Adversarial Network\nYear: 2021\n\nAbstract: Covert timing channels (CTCs) are defined as a mechanism that embeds covert information into network traffic. In a manner, information leakage caused by CTCs brings serious threat to network security. In recent years, detection of CTCs is a focus and a challenging task in the field of covert channel research. However, existing detection schemes based on statistical methods have poor performance in detecting multiple CTCs, and require so many inter-arrival times of packets that these schemes cannot detect CTCs in real time. In this paper, we propose a novel deep learning approach for CTCs detection, namely, covert timing channels detection based on auxiliary classifier generative adversarial network (CD-ACGAN). The network structure and loss function of CD-ACGAN are designed to be suitable for CTCs detection task. We first encode traffic flows into single-channel Gramian Angular Field (GAF) images. Then we use CD-ACGAN to learn features from GAF images and predict the classes of CTCs. Our experimental results show that our approach has high accuracy and strong robustness in detecting various CTCs.","Title: Information Propagation Influenced by Population Heterogeneity Behavioral Adoption on Weighted Network\nYear: 2022\n\nAbstract: In the realistic world, various individuals have distinct personalities, preferences, and attitudes toward new information and behavior acceptance, called population heterogeneity. It is seldom taken into account and theoretically analyzed in information propagation on a weighted network. Therefore, we divide individuals into fashionable and conservative individuals according to their passion degree and willingness for novel behaviors acceptance. Then, we build two behavior adoption threshold models corresponding to fashionable and conservative individuals on the weighted network to explore the effect of population heterogeneity on information propagation. Next, a partition theory based on edge weight and population heterogeneity is proposed to qualitatively analyze the information propagation mechanism. The theoretical analyses and simulation results show that fashionable individuals promote information propagation and behavior adoption. More importantly, the crossover phenomena of phase transition appear. When the fraction of fashionable individuals is relatively large, the increasing pattern of the final adoption size shows a second-order continuous phase transition. In comparison, the increasing pattern alters to first-order discontinuous phase transition with the decrease of the fraction of fashionable individuals. Moreover, reducing weight distribution heterogeneity promotes information propagation and slightly accelerates the change of the phase transition pattern from the first-order discontinuous to the second-order continuous. Besides, increasing the degree distribution heterogeneity accelerates the change of the phase transition pattern. Finally, our theoretical analyses coincide well with the simulation results.","Title: Toward Communication-Learning Trade-Off for Federated Learning at the Network Edge\nYear: 2022\n\nAbstract: In this letter, we study a wireless federated learning (FL) system where network pruning is applied to local users with limited resources. Although pruning is beneficial to reduce FL latency, it also deteriorates learning performance due to the information loss. Thus, a trade-off problem between communication and learning is raised. To address this challenge, we quantify the effects of network pruning and packet error on the learning performance by deriving the convergence rate of FL with a non-convex loss function. Then, closed-form solutions for pruning control and bandwidth allocation are proposed to minimize the weighted sum of FL latency and FL performance. Finally, numerical results demonstrate that i) our proposed solution can outperform benchmarks in terms of cost reduction and accuracy guarantee, and ii) a higher pruning rate would bring less communication overhead but also worsen FL accuracy, which is consistent with our theoretical analysis.","Title: Safeguarding NOMA Networks via Reconfigurable Dual-Functional Surface Under Imperfect CSI\nYear: 2022\n\nAbstract: This paper investigates the use of the reconfigurable dual-functional surface to guarantee the full-space secure transmission in non-orthogonal multiple access (NOMA) networks. In the presence of eavesdroppers, the downlink communication from the base station to the legitimate users is safeguarded by the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), where three practical operating protocols, namely energy splitting (ES), mode selection (MS), and time splitting (TS), are studied. The joint optimization of power allocation, active and passive beamforming is investigated to maximize the secrecy energy efficiency (SEE), taking into account the imperfect channel state information (CSI) of all channels. For ES, by approximating the semi-infinite constraints with the \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><tex-math notation=\"LaTeX\">$\\mathcal {S}$</tex-math></inline-formula>\n-procedure and general sign-definiteness, the problem is solved by an alternating optimization framework. Besides, the proposed algorithm is extended to the MS protocol by solving a mixed-integer non-convex problem. While for TS, a two-layer iterative method is proposed. Simulation results show that: 1) The proposed STAR-RIS assisted NOMA networks are able to provide up to 33.6% higher SEE than conventional RIS counterparts; 2) TS and ES protocols are generally preferable for low and high power domain, respectively; 3) The accuracy of CSI estimation and the bit resolution power consumption are crucial to reap the SEE benefits offered by STAR-RIS.","Title: Cooperative Federated Learning and Model Update Verification in Blockchain-Empowered Digital Twin Edge Networks\nYear: 2022\n\nAbstract: With the rapid development of Internet of Things (IoT), the digital twin is emerging as one of the most promising technologies to connect physical components with digital space for better optimization of physical systems. However, the limited wireless resource and security concerns impede the deployment of the digital twin in IoT. In this article, we exploit blockchain to propose a new digital twin edge networks framework for enabling flexible and secure digital twin construction. We first develop cooperative federated learning through an access point (AP) to help resource-limited smart devices in constructing digital twin at the network edges belonging to different mobile network operators (MNOs). Then, we propose a model update chain by leveraging directed acyclic graph (DAG) blockchain to secure both local model updates and global model updates. In order to incentivize the APs to help in local models training for resource-limited smart devices and also encourage the APs to contribute resource in local model update verification, we design an iterative double auction-based joint cooperative federated learning and local model update verification scheme. The optimal unified time for cooperative federated learning and local model update verification is solved to maximize social welfare. Numerical results illustrate that the proposed scheme is efficient in digital twin construction.","Title: Federated Learning in Multi-RIS-Aided Systems\nYear: 2022\n\nAbstract: The fundamental communication paradigms in the next-generation mobile networks are shifting from connected things to connected intelligence. The potential result is that current communication-centric wireless systems are greatly stressed when supporting computation-centric intelligent services with distributed big data. This is one reason that makes federated learning come into being, it allows collaborative training over many edge devices while avoiding the transmission of raw data. To tackle the problem of model aggregation in federated learning systems, this article resorts to multiple reconfigurable intelligent surfaces (RISs) to achieve efficient and reliable learning-oriented wireless connectivity. The seamless integration of communication and computation is actualized by over-the-air computation (AirComp), which can be deemed as one of the uplink nonorthogonal multiple access (NOMA) techniques without individual information decoding. Since all local parameters are uploaded via noisy concurrent transmissions, the unfavorable propagation error inevitably deteriorates the accuracy of the aggregated global model. The goals of this work are to 1) alleviate the signal distortion of AirComp over shared wireless channels and 2) speed up the convergence rate of federated learning. More specifically, both the mean-square error (MSE) and the device set in the model uploading process are optimized by jointly designing transceivers, tuning reflection coefficients, and selecting clients. Compared to baselines, extensive simulation results show that 1) the proposed algorithms can aggregate model more accurately and accelerate convergence and 2) the training loss and inference accuracy of federated learning can be improved significantly with the aid of multiple RISs.","Title: Efficient Traffic Scheduling for Coexistence of eMBB and uRLLC in Industrial IoT Networks\nYear: 2022\n\nAbstract: Ultra-reliable low-latency communication (uRLLC) is envisioned to efficiently support mission-critical scenarios, especially for industrial Internet of Things (IIoT). Considering the requirements of high throughput and massive connectivity in machine-type communications, uRLLC traffic is usually coexisted with enhanced mobile broadband (eMBB) services for the data-intensive industrial cases. To strike a balance between the two distinct tasks, this paper investigates a multi-objective optimization problem by taking into account the performance of both uRLLC and eMBB. Specifically, we aim at maximizing eMBB data rate and uRLLC reliability, while minimizing the communication overhead of control channels caused by uRLLC puncturing. To solve this challenging problem, an analytic hierarchy process method is adopted to estimate the importance of each objective with expert knowledge. Then, a coalitional game is invoked to evaluate the preference degree of resource blocks allocated to uRLLC devices. Following this, an improved Gale-Shapley algorithm is proposed for efficient traffic scheduling. Simulation results demonstrate that the proposed algorithm can achieve better performance in terms of eMBB throughput and uRLLC reliability with the reduced signal overhead.","Title: A multimodal differential privacy framework based on fusion representation learning\nYear: 2022\n\nAbstract: Differential privacy mechanisms vary in modalities, and there have been many methods implementing differential privacy on unimodal data. Few studies focus on unifying them to protect multimodal data, though privacy protection of multimodal data is of great significance. In our work, we propose a multimodal differential privacy protection framework. Firstly, we use multimodal representation learning to fuse different modalities and map them to the same subspace. Then based on this representation, we use the Local Differential Privacy (LDP) mechanism to protect data. We propose two protection methods for low-dimensional and high-dimensional fusion tensors respectively. The former is based on Binary Encoding, and the latter is based on multi-dimensional Fourier Transform. To the best of our knowledge, we are the first to propose LDP-based methods for the representation learning of multimodal fusion. Experimental results demonstrate the flexibility of our framework where both approaches show efficient performance as well as high data utility.","Title: Mobile Feature Enhanced High-Accuracy Positioning Based on Carrier Phase and Bayesian Estimation\nYear: 2022\n\nAbstract: The mobile feature and the moving positions are important for mobile communications and Internet of Things. Inspired by the potentially excellent precision of carrier phase positioning, this article proposes a mobile feature enhanced high-accuracy positioning algorithm based on the carrier phase and Bayesian estimation. The mobile feature is first estimated using time-differential carrier phase measurements. By combining the estimated position changes with instant ranging and carrier phase measurements, the factorized posterior probability of positioning is established based on the Bayes theorem. In turn, a factor graph-based positioning method is proposed to obtain precise positioning results. The numerical simulation results show that the proposed algorithm can achieve centimeter-level accuracy, and the positioning error can approach the Cramer–Rao lower bound.","Title: A Novel Group Paging Control Method for Massive MTC Accesses in LTE Networks\nYear: 2022\n\nAbstract: The fifth-generation (5G) systems have to deal with massive deployment of machine-type-communication (MTC) devices. System overload may occur during a random access procedure under a limited number of preamble resources and physical uplink shared channel resources especially when there exist massive MTC devices in a cell. In order to resolve the system overload (caused by the massive MTC deployment), the 3GPP proposed the adopted a group paging (GP)-based uplink access technique. But its performances dramatically decrease as the number of MTC devices increases. In this paper we propose a novel method, named ACB-based group paging overload control method (AGO). To reduce the number of simultaneous access MTC devices, AGO first scatters the MTC devices over a GP interval, and then automatically adjusts ACB parameters according to the load conditions. By doing so, AGO achieves high-channel access probability for MTC devices. Simulation results show that this method is superior to the GP and Pre-backoff mechanisms in terms of success and collision probability, average access latency and resource utilization rate.","Title: Steganalysis of adaptive multi-rate speech streams with distributed representations of codewords\nYear: 2022\n\nAbstract: With the wide application of adaptive multi-rate (AMR) speech coder, steganography and steganalysis based on AMR coded speech streams have become a hot spot in the field of information hiding. Quantization Index Modulation (QIM)-based steganography is one of the most effective approaches to hide secret information into AMR coded speech streams with excellent imperceptibility and robustness. So far, accurate detection for QIM-based steganography in short-length or low-embedding-rate speech streams remains an open problem, though some approaches can complete detection in a short time. To address this challenge, we first analyze and verify the characteristics of QIM-based steganography, and present a novel and high time-efficient steganalysis model based on distributed representations. Specifically, a codeword embedding layer is introduced to capture distributed representations with a denser space; then we introduce a bidirectional Long Short-Term Memory (LSTM) layer and propose a gated attention mechanism to provide contextual distribution features with better generalization capabilities; finally, a Multi-Layer Perceptron (MLP) classifier is designed to distinguish normal or steganographic objects. The experimental results demonstrate that the proposed model can effectively detect QIM-based steganography in AMR speech streams and outperform the state-of-the-art ones.","Title: Spatial-Temporal Attention Network for Crime Prediction with Adaptive Graph Learning\nYear: 2022\n\nAbstract: It is important but challenging to accurately predict urban crimes. Existing studies rely on domain knowledge specific, pre-defined inter-dependency graphs using extra urban data and have many disadvantages. We propose a novel framework, AGL-STAN, to efficiently capture complex spatial-temporal correlations of urban crimes with higher prediction accuracy but without extra data. In AGL-STAN, we design an adaptive graph learning method to learn the inter-dependencies among communities, and a time-aware self-attention method to accurately model the influence of time-varying crime incidents with a multihead attention mechanism. We demonstrate the superiority of AGL-STAN over the state-of-the-art methods through extensive experiments.","Title: Battery-less Massive Access for Simultaneous Information Transmission and Federated Learning in WPT Networks\nYear: 2022\n\nAbstract: One of the key visions for 6G is to enable Internet of Intelligence at the network edge. However, many battery-less devices face the dilemmas of energy shortage and spectrum deficiency. To tackle these challenges, we propose a simultaneous information transmission and federated learning (SITFL) scheme for the purpose of overcoming communication bottlenecks and accelerating data processing in wireless power transfer networks. For mean-square-error minimization, a low-complexity solution is developed to optimize the transmit and receive beamforming jointly. Simulation results demonstrate the effectiveness of the proposed solution for wireless powered SITFL networks.","Title: Visualizing Multi-Agent Reinforcement Learning for Robotic Communication in Industrial IoT Networks\nYear: 2022\n\nAbstract: With its mobility and flexibility, autonomous robots have received extensive attention in industrial Internet of Things (IoT). In this paper, we adopt non-orthogonal multiple access and multi-antenna technology to enhance the connectivity of sensors and the throughput of data collection through taking advantage of the power and spatial domains. For average sumrate maximization, we optimize the transmit power of sensors and the trajectories of robots jointly. To deal with uncertainty and dynamics in the industrial environment, we propose a multiagent reinforcement learning (MARL) algorithm with experience exchange. Next, we present the visualization of robotic communication and mobility to analyze the learning behavior intuitively. From the software implementation results, we observe that the proposed MARL algorithm can effectively adjust the communication strategies of sensors and control the trajectories of robots in a fully distributed manner. The code and demonstrations can be found at https://github.com/lry-bupt/Visual_MARL.","Title: Elastic Routing Mechanism for Flying Ad Hoc Network\nYear: 2022\n\nAbstract: Flying Ad-Hoc Network (FANET) is a hot topic in current research. The design of routing mechanism is challenging because when the scale of Unmanned Aerial Vehicle (UAV) nodes is large, vast amount of routing overhead may lead to network collapse. An elastic routing mechanism is proposed for large-scale small UAVs multitasking scenarios. Firstly, the New-Unifying Connected Dominating Set (N-UCDS) algorithm is proposed to construct a virtual backbone network based on the connected dominating set. The number of neighboring nodes, remaining energy and link duration are considered to influence the UAV network performance when electing backbone nodes. Secondly, by deploying and running the New Better Approach to Mobile Ad-Hoc Network-Advanced (NBATMAN-ADV) routing protocol on the backbone nodes, the link quality can be evaluated by using the received signal strength index and signal-to-noise ratio of the physical layer data. In this way, the change of the link can be quickly sensed while reducing the routing overhead. The simulation results show that the routing protocol proposed in this paper has significantly improved average packet delivery rate, end-to-end delay and received throughput compared with other traditional proactive routing protocols.","Title: STFF-SM: Steganalysis Model Based on Spatial and Temporal Feature Fusion for Speech Streams\nYear: 2023\n\nAbstract: The real-time detection of speech steganography in Voice-over-Internet-Protocol (VoIP) scenarios remains an open problem, as it requires steganalysis methods to perform for low-intensity embeddings and short-sample inputs, as well as provide rapid detection results. To address these challenges, this paper presents a novel steganalysis model based on spatial and temporal feature fusion (STFF-SM). Differing from the existing methods, we take both the integer and fractional pitch delays as input, and design subframe-stitch module to organically integrate subframe-wise integer delays and frame-wise fractional pitch delays. Further, we design a spatial fusion module based on pre-activation residual convolution to extract the pitch spatial features and gradually increase their dimensions to discover finer steganographic distortions to enhance the detection effect, where a Group-Squeeze-Weighting block is introduced to alleviate the information loss in the process of increasing the feature dimension. In addition, we design a temporal fusion module to extract pitch temporal features using the stacked LSTM, where a Gated Feed-Forward Network is introduced to learn the interaction between different feature maps while suppressing the features that are not useful for detection. We evaluated the performance of STFF-SM through comprehensive experiments and comparisons with the state-of-the-art solutions. The experimental results demonstrate that STFF-SM can well meet the needs of real-time detection of speech steganography in VoIP streams, and outperforms the existing methods in detection performance, especially with low embedding strengths and short window sizes."],"string":"[\n \"Title: A High-Capacity Covering Code for Voice-Over-IP Steganography.\\nYear: 2015\\n\\nAbstract: Although steganographic transparency and embedding capacity are considered to be two conflicting objectives in the design of steganographic systems, it is possible and necessary to strike a good balance between them in Voice-over-IP steganography. In this paper, to improve steganographic transparency while maintaining relatively large embedding capacity, the authors present a 2n-1, 2n covering code, which can hide 2n-1 bits of secret messages into 2n bits of cover messages with not more than n-bit changed. Specifically, each 2n-1-bit secret message is first transformed into two 2n-bit candidate codewords. In embedding process, the cover message is replaced with the optimal codeword more similar with it. In this way, the embedding distortion can be largely reduced. The proposed method is evaluated by comparing with existing ones with a large number of ITU-T G.729a encoded speech samples. The experimental results show that the authors' scheme can provide good performance on both steganographic transparency and embedding capacity, and achieve better balance between the two objectives than the existing ones.\",\n \"Title: Beam axis detection and alignment for uniform circular array-based orbital angular momentum wireless communication.\\nYear: 2016\\n\\nAbstract: Orbital angular momentum (OAM) provides a new dimension for multiplexing and consequently can improve the link capacity remarkably. In the scope of radio communication, uniform circular array (UCA) is usually used to generate OAM signals, where beam axis detection and alignment is a key factor that influences the detection of OAM signal and directly determines the capacity of OAM links. This study analyses the beam axis detection and alignment issues under the ideal (noiseless) condition. The authors first clarify the fact that for single-mode OAM transmission, the intensity of electric field in radial direction is characterised by the Bessel function of the first kind. Inspired by this property, they propose three practical methods for beam axis detection and alignment, that is, double parallelogram array method, double UCA method and single UCA method. Iterative translation and rotation strategy are applied to enhance the precision. Finally, the effectiveness of the proposed methods is justified by simulation.\",\n \"Title: Anomaly detection and localization by diffusion wavelet-based analysis on traffic matrix.\\nYear: 2015\\n\\nAbstract: Diffusion wavelets (DW) transform has been successfully used in Multi-Resolution Analysis (MRA) of traffic matrices because it inherently adapts to the structure of the underlying network. There are many potential applications based on DW analysis such as anomaly detection, routing optimization and capacity plan, which, however, have not been well developed. This paper shows how to apply two-dimensional DW transform in traffic matrix analysis and anomaly detection. The experimental results demonstrate the effectiveness of DW-based technique in traffic matrix analysis and anomaly detection in practical networks. It also shows this new technique is potential to be used in many other network applications.\",\n \"Title: Differential Multiply-and-Forward and Selective Combining Based Multi-relay TDBC Protocol.\\nYear: 2015\\n\\nAbstract: In this paper, we propose a differential multiplyand-forward (DMF) and selective combining (SC) based multirelay time division broadcasting (TDBC) protocol, named as DMF-SC, in rayleigh fading channels. Different from amplify and-forward relaying (AF) and decode-and-forward (DF) relaying, in DMF multi-relaying, each relay broadcasts the product of two signals received from two terminals. The decision variables from the direct and cascaded links at the terminal node are selective combined to obtain the cooperative diversity. Theoretical analyses and simulation results show that the proposed DMFSC protocol can obtain a order N + 1 cooperative diversity, and outperforms AF, DF and multiplicative network coding protocols with differential modulation. Meanwhile, no channel state information is needed at the terminals with the proposed DMF-SC based multi-relay TDBC protocol.\",\n \"Title: An Efficient Content Delivery Algorithm for Intermittently Connected Mobile Ad Hoc Networks\\nYear: 2010\\n\\nAbstract: The content dissemination among mobile users can be solved by self-facilitated mobile ad hoc networks (MANET) in an infrastructure environment. But the devices used to make up the mobile ad hoc networks usually have a limited radio transmission range, sporadic node density and power limitation, which makes MANET become intermittently connected. In this paper we focus on the content delivery problem in intermittently connected ad hoc networks (ICMANET). We propose an efficient model for this problem in ICMANET to maximize the total number of requests to be met. We have proposed a distributed algorithm based on the model which can be implemented by each node in ICMANET. We compare our algorithm with the existing ones and the simulation results demonstrate that our algorithm outperforms other algorithms significantly.\",\n \"Title: Continuous tracking for mobile targets with mobility nodes in WSNs\\nYear: 2014\\n\\nAbstract: Tracking mobile targets is one of the most important applications in wireless sensor networks (WSNs). Traditional tracking solutions are based on fixed sensor nodes and have two critical problems. First, in WSNs, the energy constraint is a main concern, but due to the mobility of targets, lots of sensor nodes in WSNs have to switch between active and sleep states frequently, which causes excessive energy consumption. Second, when there are holes in the deployment area, targets may fail to be detected while moving in the holes. To solve these problems, this paper exploits a few of mobile sensor nodes to continuously track mobile targets because the energy capacity of mobile nodes is less constrained. Based on a realistic detection model, a solution for scheduling mobile nodes to cooperate with ordinary fixed nodes is proposed. When targets move, mobile nodes move along with them for tracking. The results of extensive simulations show that mobile nodes help to track the target when holes appears in the coverage area and extend the effective monitoring time. Moreover, the proposed solution can effectively reduce the energy consumption of sensor nodes and prolong the lifetime of the networks.\",\n \"Title: Qos-Aware Distributed Cell Sleep Algorithm For Ofdma Small Cell Networks\\nYear: 2015\\n\\nAbstract: This paper aims at improving the energy efficiency of small cell networks via cell sleep mechanism. The traditional power consumption minimization problem with quality of service (QoS) constraints is reestablished from the distributed small cell eNodeB (SeNB) sleep algorithm. The proposed algorithm consists of a Rate-based Access and Scheduling (RAS) algorithm and a distributed Diluted Load based Sleep (DLS) algorithm. In RAS algorithm, a many-to-many matching model is adopted to deal with the UE access and resource allocation problem. In DLS algorithm, a metric called Diluted Load is introduced to determine the sleep SeNB and solve the sleep optimization problem in a distributed way. By alternately conducting RAS and DLS algorithms, the energy efficiency of small cells keeps improving. Simulation results show that our algorithm can achieve lower power consumption as well as lower UE blocking ratio compared with existing sleep algorithms.\",\n \"Title: A Novel Vertical Handover Algorithm in a Hybrid Visible Light Communication and LTE System\\nYear: 2015\\n\\nAbstract: Visible light communication (VLC) is considered as a promising high speed wireless access technology. However, the line-of-sight (LOS) nature of visible light limits VLC coverage and user mobility. On the contrary, radio frequency (RF) provides much more extensive coverage, though its link bit rate is lower by contrast. A cooperation of VLC and RF can allow them to enhance each other in certain aspects. Therefore, following existing studies, a hybrid VLC-LTE system is employed. One big challenge for realizing such a heterogeneous network is mobility management due to dynamic traffic and network conditions. Based on two basic vertical handover (VHO) schemes, this paper proposes a VHO algorithm through prediction (PVHO). Interruption durations, message sizes and access delays are critical metrics for prejudging the system state. A mobile terminal (MT) records these key parameters in real time and then processes them to offer guidance for proper handover decisions. Through simulation, the advantages of PVHO are validated by comparing with existing algorithms. Analysis of the numeric results gives an indication that the performance of PVHO is superior under various circumstances.\",\n \"Title: Coordinated Transmission Based Interference Mitigation In Vlc Network\\nYear: 2015\\n\\nAbstract: Nowadays, multiple access points and subcarrier transmission are widely used in visible light communication(VLC) to achieve seamless coverage and high spectral efficiency. However, the reuse of subcarriers may cause severe interference for users located in overlapping areas using co-channel. Those existing methods utilize interference managements to solve this issue, but at the cost of bandwidth inefficiency. Hence, a designated resource allocation that includes interference mitigation is needed without degrading the performance of the network. In this paper, a graph theory based resource allocation algorithm in the downlink is proposed, which not only mitigates the interference, but also improves the capacity and fairness of the system. Numeric results demonstrate that the proposed scheme exhibits a 133.6% and a 25.0% average system throughput improvement compared to the Round Robin scheme and proportional fairness scheme. Additionally, a better performance in fairness is obtained by the proposed scheme.\",\n \"Title: Detecting Targets Based On A Realistic Detection And Decision Model In Wireless Sensor Networks\\nYear: 2015\\n\\nAbstract: Target detection applications in Wireless sensor networks (WSNs) need to detect whether the target is present or not. For accurate detection, sensors need to collaborate with each other to determine the target's presence. However, most of existing works use the \\\"simple disk model\\\" and the \\\"majority rule\\\" to model sensed signal and to make the final decision, respectively, which may not obtain the true judgment. In this work, we utilize a realistic signal model and a probabilistic decision model to describe the sensing process of sensors as well as to make the final decision. Moreover, we propose a realistic decision algorithm in which local signal measurements of sensors are converted to corresponding local detection probabilities in order to make a rational final decision. The effectiveness of the proposed model and algorithm are both validated by simulation results, which show high detection probabilities and acceptable false alarm probabilities.\",\n \"Title: Game Theoretic Approach For Smallcell Resource Management\\nYear: 2015\\n\\nAbstract: Smallcells can provide high-quality radio coverage in indoor environment. However, resource management and interference mitigation are key challenges, resulting from the heterogeneous backhaul and the uncoordinated nature of smallcells. We propose a new game theoretic approach, where the transmit power of smallcells evolve in a distributed manner, adapting to traffic demands and mitigating interference. The key idea is to formulate smallcells' clustering as an evolutionary game, where the smallcells independently switch to less interfered clusters. At every evolutionary stage, a non-cooperative game is cast to balance the transmit powers within each cluster. Limited signalling is exchanged between the clusters, adjusting future evolution. Compared to the prior art, simulations show that our approach can improve the network throughput by 10.2%.\",\n \"Title: Energy-Efficient Sleep Strategy For Distributed Mimo Systems\\nYear: 2014\\n\\nAbstract: This paper focuses on the optimization of energy efficiency (EE) for BSs with distributed multiple-input multiple-output (MIMO) using an efficient antenna sleep strategy. A novel metric called sleep EE gain is proposed in this paper to evaluate the energy efficiency performance of sleep strategy in distributed MIMO systems. A related closed-form expression is developed to determine the optimum antenna sleep period and to optimize the proposed metric. Based on our analysis, we propose an efficient sleep strategy for distributed MIMO systems. Simulation results show that the proposed strategy can significantly improve the overall sleep energy efficiency gain of distributed MIMO systems.\",\n \"Title: Steganalysis of Low Bit-Rate Speech Based on Statistic Characteristics of Pulse Positions\\nYear: 2015\\n\\nAbstract: Steganography in low bit-rare speech streams is an important branch of Voice-over-IP steganography. From the point of preventing cybercrimes, it is significant to design effective steganalysis methods. In this paper, we present a support-vector-machine based steganalysis of low bit-rate speech exploiting statistic characteristics of pulse positions. Specifically, we utilize the probability distribution of pulse positions as a long-time distribution feature, extract Markov transition probabilities of pulse positions according to the short-time invariance characteristic of speech signals, and employ joint probability matrices to characterize the pulse-to-pulse correlation. We evaluate the performance of the proposed method with a large number of G.729a encoded samples, and compare it with the state-of-the-art methods. The experimental results demonstrate that our method significantly outperforms the previous ones on detection accuracy at any given embedding rates or with any sample lengths. Particularly, this method can successfully detect steganography employing only one or a few of the potential cover bits, which is hard to be effectively detected by the existing methods.\",\n \"Title: A Component Carrier Selection Scheme In Macro And Csg Femtocells Co-Channel Deployment For Lte-Advanced Downlink Systems\\nYear: 2014\\n\\nAbstract: In macro and Closed Subscriber Group (CSG) femtocells co-channel deployment, femtocells and Macrocell User Equipments (MUEs) suffer strong interference from neighboring femtocells. The interference seriously degrades MUEs' throughput performance or even causes outage. To solve this issue, we provide a centralized component carrier selection scheme. By globally optimizing femtocells' and MUEs' component carrier selection, the proposed scheme can not only avoid the interference among femtocells and the MUEs' interference from femtocells simultaneously, but also maximize the system spectral efficiency and improve MUEs' throughput performance on this basis. Simulation results demonstrate under high data rate demands the proposed scheme can greatly improve MUEs' performance both in total throughput and outage rate than the classical universal frequency reuse scheme. Furthermore, the proposed scheme can achieve better femtocells' total throughput performance than the hard frequency reuse 1/2 scheme.\",\n \"Title: Joint Illumination-Communication Optimization in Visible Light Communication\\nYear: 2015\\n\\nAbstract: One major characteristic of visible light communication (VLC) is its two-fold functionality of illumination and communication. Therefore, this paper considers joint optimization of illumination-communication in an indoor VLC system where light-emitting diode (LED) transmit-power can be adjusted. The aim is to render uniform illumination levels while guaranteeing users' communicating and illumination requirements. So the problem is formulated as optimization under constraints. A novel harmony annealing (HA) intelligent search algorithm is developed to solve it. HA adequately utilizes previous optimized information in next search trials. Meanwhile, multiple search directions are enhanced to avoid solutions being trapped in local optimum. Thus, HA's search efficiency and global search ability are improved. Through simulation, advantages of HA are identified and validated by comparing with conventional intelligent search algorithms. Analysis of the numeric results gives an indication that the illumination uniformity is increased while user's received signal-to-noise ratio (SNR) is sufficient for keeping BER-6.\",\n \"Title: Evolutionary radio resource management in distributed femtocell networks\\nYear: 2015\\n\\nAbstract: Femtocells are an integrating part of future cellular systems, where radio resource management is a challenge due to unbalanced backhaul delays and the hotspot nature of femtocells. We propose a new game theoretic framework, where the channel allocation and transmit powers evolve in a distributed manner, adapting to femtocells' topology and traffic demands. The key idea is to cast femtocells' clustering as an evolutionary game, where the femtocells independently switch to less interfered clusters. Within each cluster, we design a non-cooperative game to implement power control in the absence of centralized coordination. Simulations show that our approach is effective for timely interference mitigation and reliable topology management. The stability and scalability of our approach are also demonstrated.\",\n \"Title: An Efficient VoIP Steganography Based on Random Binary Matrix.\\nYear: 2015\\n\\nAbstract: To reduce the distortion while maintaining relatively large embedding capacity, a steganographic scheme based on random binary matrix is presented in this paper. We first design a 3x4 covering code, which can hide 3 bits of secret messages into 4 bits of cover messages with not more than 2-bit changed. Further, we construct a random binary matrix with cover bits at diagonal lines for steganography, and conceal secret messages into each four cover bits encrypted with the other binary bits of their rows or columns using the above 3x4 covering code. The proposed scheme is evaluated with a large number of G.711 encode speech samples. The experimental results show that our scheme can provide good performance on both steganographic transparency and embedding capacity.\",\n \"Title: Extracting target detection knowledge based on spatiotemporal information in wireless sensor networks\\nYear: 2016\\n\\nAbstract: AbstractWireless sensor networks (WSNs) have been deployed for many applications of target detection, such as intrusion detection and wildlife protection. In these applications, the first step is to detect whether the target is present or not. However, most of the existing work uses the \\\"simple disk model\\\" as signal model, which may not capture the sensing environment. In this work, we utilize a more realistic signal model to describe sensing process of sensors. On the other hand, the \\\"majority rule\\\" is widely used to make the final decision, which may not obtain the true judgment. To this end, we utilize a more realistic signal model and also use a probabilistic decision model to make the final decision. Moreover, we propose a probabilistic detection algorithm in which all sensors' local measurement values are fully used. This algorithm does not need any artificial threshold compared with traditional algorithms. It makes the most of spatiotemporal information to obtain the final decision. For the spatial perspective, sensors are distributed in different locations cooperating with each other. Meanwhile, for the temporal perspective, multiround subdecisions are fused. The effectiveness of the proposed method is validated by extensive simulation results, which show high detection probabilities and low false alarm probabilities.\",\n \"Title: Resource Allocation in a Generalized Framework for Virtualized Heterogeneous Wireless Network.\\nYear: 2016\\n\\nAbstract: As a prevailing concept in 5G, virtualization provides efficient coordination among multiple radio access technologies (RATs) and enables multiple service providers (SPs) to share different RATs' infrastructure. This paper proposes a generic framework for virtualizing heterogeneous wireless network with different RATs. A novel \\\"VMAC\\\" (virtualized medium access control) concept is introduced to converge different RAT protocols and perform inter-RAT resource allocation. To suit the proposed framework, a virtualization based resource allocation scheme is devised. We formulate the problem as a mixed combinatorial optimization, which jointly considers network access and rate allocation. First, to solve the network access problem, \\\"adaptability ratio\\\" is developed to model the fact that different RATs possess different adaptability to different services. And a Grey Relational Analysis (GRA) method is adopted to calculate the adaptability ratio. Second, services are modeled as players, bargaining for RAT resources in a Nash bargaining game. And a closed-form Nash bargaining solution (NBS) is derived. Combining adaptability ratio with NBS, a novel resource allocation algorithm is devised. Through simulation, the superiority and feasibility of the proposed algorithm are validated.\",\n \"Title: OM2DNC: Opportunistic Max2-Degree Network Coding for wireless data broadcasting.\\nYear: 2016\\n\\nAbstract: In wireless broadcasting systems, multiple retransmissions are commonly employed to guarantee the correct reception of each packet. The traditional hybrid automatic retransmission request protocol retransmits one packet per slot. Hence, a large amount of retransmissions is required to correctly receive all the packets, which leads to low spectrum efficiency. In this paper, we propose an Opportunistic Max2-Degree Network Coding (OM2DNC) based wireless broadcasting (WBC) protocol. Specifically, to reduce the overall number of retransmissions, lost packets of different user equipments (UEs) are combined by performing Max-Degree network coding (NC) at the access node. Then, NC combined packets are broadcasted by utilizing the Max-Degree based opportunistic retransmission protocol. At each UE, the lost packet can be recovered by using the proposed joint network recursive systematic convolution decoder. Theoretical analyses and simulation results show that the average number of transmissions performance of the proposed OM2DNC based WBC protocol outperforms that of the traditional NC based WBC protocols.\",\n \"Title: A Study Of The Characteristics Of Memd For Fractional Gaussian Noise\\nYear: 2016\\n\\nAbstract: The dyadic filter bank property of multivariate empirical mode decomposition (MEMD) for white Gaussian noise (WGN) is well established. In order to investigate the way MEMD behaves in the presence of fractional Gaussian noise (fGn), we conduct thorough numerical experiments for MEMD for fGn inputs. It turns out that similar to WGN, MEMD follows dyadic filter bank structure for fGn inputs, which is more stable than empirical mode decomposition (EMD) regardless of the Hurst exponent. Moreover, the estimation of the Hurst exponent of fGn contaminated with different kinds of signals is also presented via MEMD in this work.\",\n \"Title: Critical size of ego communication networks.\\nYear: 2016\\n\\nAbstract: With the help of information and communication technologies, studies on the overall social networks have been extensively reported recently. However, investigations on the directed Ego Communication Networks (ECNs) remain insufficient, where an ECN stands for a sub network composed of a centralized individual and his/her direct contacts. In this paper, the directed ECNs are built on the Call Detail Records (CDRs), which cover more than 7 million people of a provincial capital city in China for half a year. Results show that there is a critical size for ECN at about 150, above which the average emotional closeness between ego and alters drops, the balanced relationship between ego and network collapses, and the proportion of strong ties decreases. This paper not only demonstrate the significance of ECN size in affecting its properties, but also shows accordance with the \\\" Dunbar's Number\\\". These results can be viewed as a cross-culture supportive evidence to the well-known Social Brain Hypothesis (SBH). Copyright (C) EPLA, 2016\",\n \"Title: Energy Efficient Cell Selection in Small Cell Networks With Constrained Backhaul Links.\\nYear: 2016\\n\\nAbstract: This letter aims at providing a cell selection scheme to improve the system energy efficiency of small cell networks under both user equipment and backhaul link data rate constraints. The system energy efficiency optimization problem is formulated as a mixed integer linear fractional problem, which is NP-hard. To reduce the complexity, a stable sub-optimal solution named energy efficient cell sele...\",\n \"Title: Hybrid user association for maximising energy efficiency in heterogeneous networks with human-to-human/machine-to-machine coexistence.\\nYear: 2016\\n\\nAbstract: In this study, a hybrid user association scheme for maximising energy efficiency is proposed in a wireless uplink heterogeneous network with human-to-human and machine-to-machine communications coexistence. Different from conventional user association algorithms, the authors consider access control protocol for both the human-type-communications (HTCs) users equipments' (UEs) and machine-type-comm...\",\n \"Title: Resource allocation in a generalized LTE air interface virtualization framework exploiting user behavior.\\nYear: 2016\\n\\nAbstract: Wireless network virtualization (WNV) is a promising technique to solve the ossification of current networks. In this paper, a generalized Long Term Evolution (LTE) air interface virtualization framework is proposed where virtual operators (VOs) are enabled to share the physical resources owned by the infrastructure provider (InP). This user-centric feature provides VOs with the flexibility to manage their own virtualized networks according to different user traffic demand. Hence, we introduce the “Gini Coefficient” to quantitatively characterize the user traffic behavior among different VOs. In addition, we consider bandwidth-power allocation to optimize system energy efficiency (EE). The resource allocation problem is formulated as a mixed combinatorial and non-convex optimization problem, which is extremely difficult to solve. To reduce the computational complexity, we decouple the problem into two steps. First, for a given power allocation, we obtain the bandwidth allocation. Adopting bankruptcy game model and the well-known Shapley value, a heuristic bandwidth allocation algorithm is devised. Second, under the assumption of known bandwidth allocation, we transform the original optimization problem into an equivalent convex optimization problem and obtain the optimal solution via fractional programming. Through simulation, the results of user behavior and resource allocation are jointly analyzed. The user behavior is proved to be effective and the proposed resource allocation outperforms conventional schemes.\",\n \"Title: Social-aware energy harvesting device-to-device communications in 5G networks.\\nYear: 2016\\n\\nAbstract: With ever increasing demands for local area services in 5G cellular networks, solutions are necessary to deliver local area data in a spectrum- and energy-efficient manner. In this article we propose a new cellular communication architecture that integrates energy harvesting technologies and social networking characteristics into D2D communications for local data dissemination. The proposed archit...\",\n \"Title: Achieving Probabilistic Anonymity in a Linear and Hybrid Randomization Model.\\nYear: 2016\\n\\nAbstract: The randomization methods that are applied for privacy-preserving data mining are commonly subject to reconstruction, linkage, and semantic-related attacks. Some existing works employed random noise addition to realize probabilistic anonymity, aiming only at linkage attacks. Random noise addition is vulnerable to reconstruction attacks, and is unable to achieve semantic closeness, particularly on ...\",\n \"Title: Adaptive sequential offloading game for multi-cell Mobile Edge Computing\\nYear: 2016\\n\\nAbstract: Mobile Edge Computing (MEC) provides computation capabilities within the radio access network and in close proximity to mobile users, so as to enrich user experience. In this paper, we consider a multi-cell MEC scenario, where the small cells are densely deployed and serve multiple mobile users. However, the finite computation resources of MEC server and severe inter-cell interference limit the scalability of offloading. We propose an adaptive sequential offloading game approach to resolve these problems, where the mobile users sequentially make offloading decisions based on the current interference environment and available computation resources. Besides, we adjust the number of offloaded users adaptively to avoid the unexpected queuing delay due to the competition for limited computation resources among offloaded users. Numerical results show that our proposed algorithm can achieve efficient performance in terms of both the experienced latency and energy consumption and scale well as the network system size increases.\",\n \"Title: A Dynamic Channel Allocation Algorithm in Random Access Process for LTE System.\\nYear: 2016\\n\\nAbstract: Random access technology is necessary in media access control of telecommunication system. Its function is directly related to the performance of the whole system. How to use time and frequency resource of LTE system effectively and reduce access delay as far as possible is the focus of this paper. This paper proposes a dynamic random access channel allocation algorithm for competitive conditions. The algorithm focuses on improving the utilization of time and frequency resource in LTE system and avoiding waste caused by idle resource.\",\n \"Title: Fine-Granularity Based Application Offloading Policy In Cloud-Enhanced Small Cell Networks\\nYear: 2016\\n\\nAbstract: Mobile users run complex applications increasingly, requiring much more computing power and energy. Cloud-enhanced small cell networks, endowing small cells with cloud computing abilities, extend mobile devices capabilities and battery lifetime by providing them with the advantages of instant and high-rate access to the cloud servers. However, conventional offloading policy can not fully exploit these benefits. In this paper, we present a fine-granularity offloading policy, aiming at minimizing the energy consumption while satisfying a strict delay constraint. We consider a practical application consisting of a set of tasks and model it as a generic graph topology. Then, the energy-efficient task offloading problem is mathematically formulated as a constrained 0-1 programming. To solve the problem with a low computing load, we adopt BPSO algorithm. Simulation results show the proposed offloading policy saves up to 25% energy consumption compared with local execution while delay constraint is satisfied. Moreover, it outperforms conventional rough-granularity offloading policy in energy-saving.\",\n \"Title: Adaptive Sampling-Based Information Collection for Wireless Body Area Networks.\\nYear: 2016\\n\\nAbstract: To collect important health information, WBAN applications typically sense data at a high frequency. However, limited by the quality of wireless link, the uploading of sensed data has an upper frequency. To reduce upload frequency, most of the existing WBAN data collection approaches collect data with a tolerable error. These approaches can guarantee precision of the collected data, but they are not able to ensure that the upload frequency is within the upper frequency. Some traditional sampling based approaches can control upload frequency directly, however, they usually have a high loss of information. Since the core task of WBAN applications is to collect health information, this paper aims to collect optimized information under the limitation of upload frequency. The importance of sensed data is defined according to information theory for the first time. Information-aware adaptive sampling is proposed to collect uniformly distributed data. Then we propose Adaptive Sampling-based Information Collection (ASIC) which consists of two algorithms. An adaptive sampling probability algorithm is proposed to compute sampling probabilities of different sensed values. A multiple uniform sampling algorithm provides uniform samplings for values in different intervals. Experiments based on a real dataset show that the proposed approach has higher performance in terms of data coverage and information quantity. The parameter analysis shows the optimized parameter settings and the discussion shows the underlying reason of high performance in the proposed approach.\",\n \"Title: Quality of Protection in Cloud-Assisted Cognitive Machine-to-Machine Communications for Industrial Systems.\\nYear: 2016\\n\\nAbstract: Cloud-assisted cognitive machine-to-machine co- mmunications (CM2M) is a new paradigm to improve the mobile services, which have drawn considerable attention in industry and academia. In this paper, we consider the quality of protection (QoP) of information transmission in cloud-assisted CM2M communications. In such an environment, the secondary M2M system intends to share the primary spectrum on the condition that the secondary transmitter (ST) has to relay the primary message. However, the ST is a low-energy device which adopts the energy harvesting technique to power itself. In particular, we focus on secure information transmission for the primary system when the secondary users (SUs) are the potential eavesdroppers. We aim to jointly design power splitting and secure beamforming to maximize the secondary M2M system data rate subject to the secrecy requirement of the primary system and the ST power constraint. To solve this non-convex problem, we propose a computationally efficient two-stage optimization approach. Simulation results demonstrate that our proposed scheme achieves a significant transmission rate of the secondary M2M system while provides a high secrecy rate for the primary system compared to the scheme without energy harvesting.\",\n \"Title: Steganalysis of analysis-by-synthesis speech exploiting pulse-position distribution characteristics.\\nYear: 2016\\n\\nAbstract: Steganography in low bit-rate speech streams is an important branch of Voice-over-Internet Protocol steganography. From the point of preventing cybercrimes, it is significant to design effective steganalysis methods. In this paper, we present a support-vector-machine-based steganalysis of low bit-rate speech exploiting statistic characteristics of pulse positions. Specifically, we utilize the probability distribution of pulse positions as a long-time distribution feature, extract Markov transition probabilities of pulse positions according to the short-time invariance characteristic of speech signals, and employ joint probability matrices to characterize the pulse-to-pulse correlation. We evaluate the performance of the proposed method with a large number of G.729a-encoded speech samples and compare it with the state-of-the-art methods. The experimental results demonstrate that our method significantly outperforms the previous ones on detection accuracy, false positive rate, and false negative rate at any given embedding rates or with any sample lengths. Particularly, this method can successfully detect steganography employing only one or a few of the potential cover bits, which is hard to be effectively detected by the existing methods. Copyright (c) 2016 John Wiley & Sons, Ltd.\",\n \"Title: Sparse-Graph Codes And Peeling Decoder For Compressed Sensing\\nYear: 2016\\n\\nAbstract: In this paper, we propose a compressed sensing scheme using sparse-graph codes and peeling decoder (SGPD). By using a mix method for construction of sensing matrices proposed by Pawar and Ramchandran, it generates local sensing matrices and implements sensing and signal recovery in an adaptive manner. Then, we show how to optimize the construction of local sensing matrices using the theory of sparse-graph codes. Like the existing compressed sensing schemes based on sparse-graph codes with \\\"good\\\" degree profile, SGPD requires only O(k) measurements to recover a k-sparse signal of dimension n in the noiseless setting. In the presence of noise, SGPD performs better than the existing compressed sensing schemes based on sparse-graph codes, still with a similar implementation cost. Furthermore, the average variable node degree for sensing matrices is empirically minimized for SGPD among various existing CS schemes, which can reduce the sensing computational complexity.\",\n \"Title: Beamforming Design in Relay-Based Full-Duplex MISO Wireless Powered Communication Networks.\\nYear: 2016\\n\\nAbstract: In this letter, we jointly design the beamformers of relay and source in full-duplex relay-based wireless powered communication networks. Equipped with multiple antennas, the relay/source is designed to transmit information and receive information/energy simultaneously. Furthermore, the hybrid access point (H-AP) acts as relay to assist the information transfer from source to destination. Moreover, source transmits signals only with the energy harvested from H-AP/relay and recycled energy from its own transmitted signals. In this setup, we propose a novel one-phase co-channel protocol and jointly design the beamforming of relay and source to maximize the system throughput. As the problem is non-convex, we first use zero forcing (ZF) method to cancel the self-interference at relay and then propose a low-complexity algorithm to obtain the global optimum when the ZF constraint is assumed. Numerical results show that our proposed optimal beamforming design is superior to other reference schemes.\",\n \"Title: Joint Effect of User Activity Sensing and Biased Cell Association in Energy Efficient HetNets.\\nYear: 2016\\n\\nAbstract: In heterogeneous networks, the active/sleep strategy for base stations (BSs) provides an effective way to match capacity to demand and also improve energy efficiency. At the same time, environmental awareness and self-organizing features are expected to play important roles in improving the network performance. In this letter, we propose an active/sleep scheme based on user activity sensing of small cell BSs considering biased cell association. The effect on coverage probability, user throughput, and energy consumption of the proposed scheme in $K$ -tier HetNets is analyzed using stochastic geometry theory. The performance analysis can be effectively used to design an energy-efficient active/sleep scheme for multi-tier HetNets.\",\n \"Title: Mobility-Aware Load Balancing Scheme in Hybrid VLC-LTE Networks.\\nYear: 2016\\n\\nAbstract: Visible light communication (VLC) can offload the increasingly heavy traffic in LTE network, improving the spectral and energy efficiency without introducing additional interference. However, developing a scalable load balancing scheme with less handover overheads becomes an issue. In this letter, leveraging the location-sensitive feature of VLC channel, we propose a mobility-aware load balancing scheme to maximize the system utility. The load balance problem is solved by introducing college admission model in a matching theory. The preferences are the measures of system throughput, users' trajectories, and the proposed load fairness index. A system-level simulation results demonstrate that the proposed scheme outperforms the considered benchmarks in terms of throughput, handover overheads, and network fairness significantly.\",\n \"Title: User-Centric Precoding Designs for the Non-Regenerative MIMO Two-Way Relay Systems.\\nYear: 2016\\n\\nAbstract: In this letter, we jointly design the source and relay precoders for non-regenerative multi-input multi-output two-way relay systems from the user-centric perspective. The two sources are assumed to be both energy efficiency (EE) desirable and spectral efficiency (SE) desirable. We aim at studying the EE-SE tradeoff between the two sources. The resulting problem is a non-convex multi-objective problem. To address this issue, an iterative algorithm is proposed to design the relay precoding matrix and source beamforming vectors alternatively. We also show that the proposed iterative algorithm is guaranteed to converge.\",\n \"Title: A Generic Framework For Heterogeneous Wireless Network Virtualization: Virtual Mac Design\\nYear: 2016\\n\\nAbstract: Virtualization is a promising technique to solve the ossification of current wireless networks and meet the ever-increasing mobile data volume. This paper analyses the requirements in wireless network virtualization and proposes a generalized virtualization framework to overcome these challenges. A novel VMAC (virtual medium access control) concept is devised to perform resource virtualization and management, and the functionalities of VMAC are presented in detail, including user plane as well as data control plane. Through VMAC, heterogeneous RANs (radio access networks) can be aggregated with a unified protocol stack, packaged in the form of a service, reconfigured to provide end-to-end user services. The paper is concluded by identifying important open issues to be studied in future research.\",\n \"Title: Improving the Localization Probability and Decreasing Communication Cost for Mobile Users.\\nYear: 2016\\n\\nAbstract: This paper studies the problem of localization for mobile users with low communication cost. Due to the sparse deployment of anchors, the localization probabilities achieved by the traditional fixed anchors-based methods are not acceptable. To solve this problem, we propose to exploit the localized users as the mobile anchors for localizing the non-localized users. In this way, the localization probability can be improved. Moreover, an algorithm for electing mobile anchors is designed to decrease the communication cost, with several provable properties. This electing algorithm is a distributed method, without negotiation among mobile users. Extensive experimental results demonstrate that in terms of localization probability, our method outperforms the traditional fixed anchors-based methods by approximately 30 %similar to 60 % with a small increment of communication cost.\",\n \"Title: Forward and Backhaul Link Optimization for Energy Efficient OFDMA Small Cell Networks.\\nYear: 2017\\n\\nAbstract: This paper aims at improving the system energy efficiency of orthogonal frequency division multiple access small cell networks under user data rate requirements. Differently, in this paper, we take into account the effect of both forward link and backhaul link when formulating the system energy efficiency optimization problem. The optimal solution to the system energy efficiency problem needs the joint consideration of the scheduling, the transmission power optimization, and the backhaul link data rate control, which is NP-hard and generates huge signaling overhead. To this end, we first compute and obtain the lower bound and upper bound of the optimal system energy efficiency based on the property analysis of the formulated system energy efficiency problem. Then, to reduce the signaling overhead and calculation complexity, we propose a forward and backhaul link energy efficiency optimization scheme (FBEEOS) to approach the achieved system energy efficiency bounds. The system energy efficiency is proved to be convergent in a non-decreasing manner in FBEEOS. The simulation results confirm the properties of FBEEOS. In addition, our results show that the transmission power optimization is necessary to achieve higher system energy efficiency though its contribution to total energy consumption is negligible. This is a result of the effect of transmission power on interference and throughput, and needs to be taken into consideration when we optimize the system energy efficiency.\",\n \"Title: Reliable wireless connections for fast-moving rail users based on a chained fog structure.\\nYear: 2017\\n\\nAbstract: Currently, 3G and 4G networks provide customers with high-speed wireless services almost everywhere. However, the wireless connection is often unstable and unreliable, especially for fast-moving end users (e.g., those on trains and buses). To investigate the severity of this problem, we conducted real experiments on fast-moving trains to investigate the quality of 3G connections. From the results, we found that 1) from the temporal perspective, the 3G connections were not stable and suffered from frequent disruptions of connectivity, and 2) from the spatial perspective, the connections that were established in different train compartments were largely independent. These two findings motivate us to propose a brand-new fog computing structure, which acts as an intermediate layer between the end users and the 3G infrastructure. This new fog structure introduces a series of mutually chained network gateways that are located in different compartments. This structure addresses the aforementioned problem of unstable connectivity and thus ensures reliable wireless service for fast-moving users, such as passengers on trains. We performed a series of theoretical and empirical analyses to evaluate the performance of the newly proposed structure. All of the experimental results suggest that our proposed fog structure greatly improves the reliability of wireless connections on fast-moving trains.\",\n \"Title: Steganalysis of adaptive multi-rate speech using statistical characteristics of pulse pairs.\\nYear: 2017\\n\\nAbstract: In this work, we concentrate on the steganalysis of adaptive multi-rate (AMR) speech, which widely exists in mobile Voice-over-IP services. Compared with the state of the arts, the most significant advantage of this work is that more accurate and more complete steganalysis features are presented. To avoid the impact of the possible interchange of pulse positions in one track, we characterize AMR speech exploiting the statistical properties of pulse pairs. Specifically, we employ the probability distributions of pulse pairs as long-term distribution features, extract Markov transition probabilities of pulse pairs as short-term invariant features, and adopt joint probability matrices of pulse pairs as features based on track-to-track correlations. Moreover, to optimize the feature set and reduce its dimension, we introduce a feature selection mechanism using adaptive boosting technique. Exploiting the well-selected features, we further present a steganalysis of AMR speech based on support-vector-machine. The proposed method is evaluated with a good supply of AMR-encoded speech samples, and compared with the state-of-the-art methods. The experimental results demonstrate that the proposed method can effectively detect the state-of-the-art steganography methods for AMR speech, and significantly outperforms the state-of-the-art steganalysis methods on detection accuracy, false-positive rate and false-negative rate for any given embedding rate or speech samples with any given length. In particular, the proposed method can provide accurate detecting results for the existing steganographic methods in a timely manner, and thereby be applied in the steganalysis scenario for real-time speech streams. We characterize AMR speech exploiting the statistical properties of pulse pairs.Three types of steganalysis features are presented.We present an SVM-based steganalysis scheme with feature selection using AdaBoost.Our method significantly outperforms the state-of-the-art steganalysis methods.Our method can be applied in steganalysis scenarios for real-time speech streams.\",\n \"Title: Secure Outage Analysis Of Buffer-Aided Cognitive Relay Networks With Multiple Primary Users\\nYear: 2016\\n\\nAbstract: This letter investigates the physical layer security for a buffer-aided underlay cooperative cognitive radio network in the presence of an eavesdropper, wherein, the relay is equipped with a buffer so that it can store packets received from the secondary source. To improve the secure performance of cognitive radio networks, we propose a novel cognitive secure link selection scheme which incorporates the instantaneous strength of the wireless links as well as the status of relay's buffer, the proposed scheme adapts the link selection decision on the strongest available link by dynamically switching between relay reception and transmission. Closed-form expressions of secrecy outage probability (SOP) for cognitive radio network is obtained based on the Markov chain. Numerical results demonstrate that the proposed scheme can significantly enhance the secure performance compared to the conventional relay selection scheme.\",\n \"Title: Efficient Data Persistence Scheme Based On Compressive Sensing In Wireless Sensor Networks\\nYear: 2017\\n\\nAbstract: This paper investigates the data persistence problem with compressive sensing (CS) in wireless sensor networks (WSNs) where the sensed readings should be temporarily stored among the entire network in a distributed manner until gathered by a mobile sink. Since there is an energy-performance tradeoff, conventional CS-based schemes only focus on reducing the energy consumption or improving the CS construction performance. In this paper, we propose an efficient Compressive Sensing based Data Persistence (CSDP) scheme to achieve the optimum balance between energy consumption and reconstruction performance. Unlike most existing CS-based schemes which require packets visiting the entire network to reach the equilibrium distribution, in our proposed scheme information exchange is only performed among neighboring nodes. Therefore, such an approach will result in a non-uniform distribution of measurements, and the CS measurement matrix depends heavily on the node degree. The CS reconstruction performance and energy consumption are analyzed. Simulation results confirm that the proposed CSDP scheme consumes the least energy and computational overheads compared with other representative schemes, while almost without sacrificing the CS reconstruction performance.\",\n \"Title: Vulnerability effects of passengers' intermodal transfer distance preference and subway expansion on complementary urban public transportation systems.\\nYear: 2017\\n\\nAbstract: The vulnerability studies on urban public transportation systems have attracted growing attentions in recent years, due to their important role in the economy development of a city and the well-beings of its citizens. This paper proposes a vulnerability model of complementary urban public transportation systems (CUPTSs) composed of bus systems and subway systems, with the consideration of passengers’ intermodal transfer distance preference (PITDP) to capture different levels of complementary strength between the two systems. Based on the model, this paper further introduces a CUPTSs-aimed vulnerability analysis method from two specific aspects: (a) vulnerability effects of different PITDP values, which facilitate the design of policies to change PITDP to reduce system vulnerability; (b) vulnerability effects of different subway expansion plans, which facilitate the vulnerability investigation of current expansion plan and the identification of the optimal expansion plan from the system vulnerability perspective. The proposed CUPTSs-aimed vulnerability analysis method is applied to investigate the complementary bus and subway systems in the city of Wuhan, China. The insights from this study are helpful to analyze other CUPTSs for valuable planning suggestions from the vulnerability perspective.\",\n \"Title: Virtual MAC concept and its protocol design in virtualised heterogeneous wireless network.\\nYear: 2017\\n\\nAbstract: As a prevailing concept in 5G, virtualisation provides efficient coordination among multiple radio access technologies (RATs) and enables multiple service providers to share different RATs' physical infrastructures. This study proposes a generic framework for virtualising heterogeneous networks with different RATs. A novel virtual medium access control concept is introduced to realise resource off...\",\n \"Title: egoPortray: Visual Exploration of Mobile Communication Signature from Egocentric Network Perspective.\\nYear: 2017\\n\\nAbstract: The coming big data era calls for new methodologies to process and analyze the huge volumes of data. Visual analytics is becoming increasingly crucial in data analysis, presentation, and exploring. Communication data is significant in studying human interactions and social relationships. In this paper, we propose a visual analytics system named egoPortray to interactively analyze the communication data based on directed weighted ego network model. Ego network (EN) is composed of a centered individual, its direct contacts (alters), and the interactions among them. Based on the EN model, egoPortray presents an overall statistical view to grasp the entire EN features distributions and correlations, and a glyph-based group view to illustrate the key EN features for comparing different egos. The proposed system and the idea of ego network can be generalized and applied in other fields where network structure exits.\",\n \"Title: Multiobjective Economic Model Predictive Control Of Mechanical Pulping Processes\\nYear: 2016\\n\\nAbstract: We present a multiobjective economic model predictive control (m-econ MPC) strategy to mitigate high electrical energy demands of a two-stage mechanical pulping (MP) process. The nonlinear MP process considered in this paper consists of a primary and a secondary refiner. In the proposed m-econ MPC technique, economic performance is optimized during dynamic transitions while a stabilizing constraint is used to ensure convergence to steady-state operating points. Through simulations, we demonstrate that the m-econ MPC can reduce the energy consumed by mechanical pulping processes by up to 27%.\",\n \"Title: On the Max2-Min network coding for wireless data broadcasting\\nYear: 2015\\n\\nAbstract: In wireless broadcasting systems, lost packets are retransmitted to guarantee the correct reception of each packet. Traditional automatic retransmission request (ARQ) protocol retransmits one packet per slot, which leads to low spectrum efficiency. In this paper, we propose a Max2-Min Network Coding based Wireless BroadCasting (WBC) protocol. Specifically, to reduce the overall number of retransmissions, lost packets of different user equipments (UEs) are combined by performing Max2-Min network coding (NC) at the base station. Then, NC combined packets are broadcasted to all the UEs. At each UE, lost packets can be recovered by using the proposed joint network and recursive systematic convolution (RSC) decoder (JNRD). Theoretical analyses and simulation results show that the proposed Max2-Min NC based WBC protocol outperforms traditional NC based WBC protocol on the average number of transmissions performance.\",\n \"Title: A Novel Botnet Detection Method Based on Preprocessing Data Packet by Graph Structure Clustering\\nYear: 2016\\n\\nAbstract: Botnets are one of the most serious threats in the Internet, and thus the effective detection of the botnet becomes more and more important. In this paper, inspired by IP tracing technology, we propose a novel botnet detection method that can analyze the data packets, based on graph structure clustering. This method analyzes the comprehensive information of packages content and timestamp flow. Such a capability is achieved by improving the HEMST(Hierarchical Euclidean Minimun Spanning Tree) clustering algorithm. It performs a similarity matching process to find the sender of each cluster that is the controlled host in botnet. Experimental results show that the clustering correct rate can reach to 97% which demonstrates the effectiveness of our method, having a better detection rate.\",\n \"Title: Rumor restraining based on propagation prediction with limited observations in large-scale social networks.\\nYear: 2017\\n\\nAbstract: In order to minimize the negative effect of malicious rumors in large-scale OSNs (Online Social Networks), researchers have proposed numerous solutions, such as controlling important user nodes and controlling bridges of social communities. However, these methods rarely take the space-time dynamic of rumor propagation into consideration. The selected controlled user nodes or bridges may be unable to influence the propagation process actually at current time if they are far from the rumor source or they have already undergone the rumor before. In the above mentioned two scenarios, it will be meaningless to control so-called important users and community bridges. In our work, we aim to restrain the rumor by predicting propagation dynamic from the microscopic perspective and collecting the boundary users who are most likely to be contagious at the moment. Moreover, to predict rumor's propagation dynamic practicably and efficiently, we adopt the sensor observation and meanwhile assume that there exist some short propagation paths which are explicit. We experimentally demonstrate that the proposed microscopic model's estimations rather accurately predict the propagation dynamic of the rumor. Moreover, the proposed rumor restraining method outperforms evidently classic Degree Centrality and Target Immunization approaches regarding the immunity scale and the immunity speed.\",\n \"Title: Cooperative Load Balancing in OFDMA Small Cell Networks\\nYear: 2016\\n\\nAbstract: This paper aims at balancing the load of OFDMA small cell networks. Different with the existing works that define the load as the number of service user equipments or the occupied amount of resources, we model the load as the service ability of small cells. The system load balancing problem that adopts the max-min fairness measure is formulated as a lexicographical minimization problem whose solution is NP-hard. To reduce the complexity and obtain a satisfying performance, a sub-optimal solution named as cooperative load balancing scheme (CLBS) is proposed. By sequentially conducting the cooperative user association, cooperative scheduling operation and cooperative transmission power optimization in CLBS, the system load is balanced to a stable state in a lexicographical non-increasing manner. Simulations results show that CLBS guarantees a satisfying load balancing performance in small cell networks.\",\n \"Title: Hiding Information Into Voice-Over-IP Streams Using Adaptive Bitrate Modulation\\nYear: 2017\\n\\nAbstract: Information hiding based on bitrate switching is a novel steganographic scheme for voice over IP, which can resist traditional steganalysis methods. However, the state-of-the-art method has not achieved its best embedding performance. Thus, in this letter, we present two adaptive bitrate modulating-based steganographic methods introducing Hamming matrix encoding (HME) strategy. The first one can conceal ${\\\\lfloor \\\\log _{2}(n+1)\\\\rfloor }$ bits of secret data into ${n}$ speech frames with at most one bitrate switching by applying the HME strategy to each frame group, while the second one, compared with the state-of-the-art method, can embed ${(1+\\\\lfloor \\\\log _{2}(n+1)\\\\rfloor)/2}$ bits of secret data into ${n}$ speech frames without increasing the number of bitrate switches by only employing the HME strategy for the frame groups with bitrate switching. The theoretical analysis and experimental evaluation demonstrate that the proposed methods are feasible, and outperform the existing one on both covert bandwidth and embedding efficiency while achieving the comparably good steganographic transparency.\",\n \"Title: Multiuser Joint Task Offloading and Resource Optimization in Proximate Clouds\\nYear: 2017\\n\\nAbstract: Proximate cloud computing enables computationally intensive applications on mobile devices, providing a rich user experience. However, remote resource bottlenecks limit the scalability of offloading, requiring optimization of the offloading decision and resource utilization. To this end, in this paper, we leverage the variability in capabilities of mobile devices and user preferences. Our system u...\",\n \"Title: Fronthaul Load Balancing in Energy Harvesting Powered Cloud Radio Access Networks.\\nYear: 2017\\n\\nAbstract: Enhanced with wireless power transfer capability, cloud radio access network (C-RAN) enables energy-restrained mobile devices to function uninterruptedly. Beamforming of C-RAN has potential to improve the efficiency of wireless power transfer, in addition to transmission data rates. In this paper, we design the beamforming jointly for data transmission and energy transfer, under finite fronthaul capacity of C-RAN. A non-convex problem is formulated to balance the fronthaul requirements of different remote radio heads (RRHs). Norm approximations and relaxations are carried out to convexify the problem to second order cone programming (SOCP). To improve the scalability of the design to large networks, we further decentralize the SOCP problem using the alternating direction multiplier method (ADMM). A series of reformulations and transformations are conducted, such that the resultant problem conforms to the state-of-the-art ADMM solver and can be efficiently solved in real time. Simulation results show that the distributed algorithm can remarkably reduce the time complexity without compromising the fronthaul load balancing of its centralized counterpart. The proposed algorithms can also reduce the fronthaul bandwidth requirements by 25% to 50%, compared with the prior art.\",\n \"Title: Joint Rate Maximization of Downlink and Uplink in Multiuser MIMO SWIPT Systems.\\nYear: 2017\\n\\nAbstract: Beamforming has the potential to improve the efficiency of simultaneous wireless information and power transfer (SWIPT) systems. Existing beamforming techniques have been focused on the downlink of SWIPT systems. In this paper, we optimize the beamformers and transmit duration to maximize the weighted sum rate of both the downlink and uplink in a multiuser multiple-input multiple-output (MIMO) SWIPT system. Specifically, we formulate and transform the problem into a weighted sum mean square error minimization, conduct difference of convex programming to decouple the downlink and uplink, and convert the problem to quadratic programming (QP), which can be solved iteratively in a centralized fashion. We also decentralize the QP problem using dual decompositions, and reduce the time-complexity without compromising the data rate. Moreover, our algorithms are extended to the case under imperfect channel state information. Confirmed by simulations, the proposed decentralization can dramatically reduce the time complexity by orders of magnitude. The scalability of the proposed approach can be substantially enhanced to support medium to large networks.\",\n \"Title: Trajectory Privacy Preservation Based on a Fog Structure for Cloud Location Services.\\nYear: 2017\\n\\nAbstract: The development of mobile cloud computing technology has made location-based service (LBS) increasingly more popular. Given the continuous requests to cloud LBS servers, the amounts of location and trajectory information collected by LBS servers are continuously increasing. Privacy awareness for LBS has been extensively studied in recent years. Among the privacy concerns about LBS, trajectory privacy preservation is particularly important. Based on privacy preservation models, previous work have mainly focused on peer-to-peer and centralized architectures. However, the burden on users is heavy in peer-to-peer architectures, because user devices need to communicate with LBS servers directly. In centralized architectures, a trusted third party (TTP) is introduced, and acts as a bridge between users and the LBS server. Anonymity technologies, such as k-anonymity, mix-zone, and dummy technologies, are usually implemented by the TTP to ensure safety. There are certain drawbacks in TTP architectures: Users have no physical control of the TTP. Moreover, the TTP is more attractive to adversaries, because substantially more sensitive information is stored by the TTP. To solve the above-mentioned problems, in this paper, we propose a fog structure to store partial important data with the dummy anonymity technology to ensure physical control, which can be considered as absolutely trust. Compared with cloud computing, fog computing is a promising technique that extends the cloud computing to the edge of a network. Moreover, fog computing provides local computation and storage abilities, wide geo-distribution, and support for mobility. Therefore, mobile users' partial important information can be stored on a fog server to ensure better management. We take the principles of similarity, intersection, practicability, and correlation into consideration and design a dummy rotation algorithm with several properties. The effectiveness of the proposed method is validated through extensive simulations, which show that the proposed method can provide enhanced privacy preservation.\",\n \"Title: Energy-Saving Offloading by Jointly Allocating Radio and Computational Resources for Mobile Edge Computing.\\nYear: 2017\\n\\nAbstract: Mobile edge computing (MEC) providing information technology and cloud-computing capabilities within the radio access network is an emerging technique in fifth-generation networks. MEC can extend the computational capacity of smart mobile devices (SMDs) and economize SMDs' energy consumption by migrating the computation-intensive task to the MEC server. In this paper, we consider a multi-mobile-users MEC system, where multiple SMDs ask for computation offloading to a MEC server. In order to minimize the energy consumption on SMDs, we jointly optimize the offloading selection, radio resource allocation, and computational resource allocation coordinately. We formulate the energy consumption minimization problem as a mixed interger nonlinear programming (MINLP) problem, which is subject to specific application latency constraints. In order to solve the problem, we propose a reformulation linearization-technique-based Branch-and-Bound (RLTBB) method, which can obtain the optimal result or a suboptimal result by setting the solving accuracy. Considering the complexity of RTLBB cannot be guaranteed, we further design a Gini coefficient-based greedy heuristic (GCGH) to solve the MINLP problem in polynomial complexity by degrading the MINLP problem into the convex problem. Many simulation results demonstrate the energy saving enhancements of RLTBB and GCGH.\",\n \"Title: Enabling public auditability for operation behaviors in cloud storage.\\nYear: 2017\\n\\nAbstract: In this paper, we focus on auditing for users' operation behaviors, which is significant for the avoidance of potential crimes in the cloud and equitable accountability determination in the forensic. We first present a public model for operation behaviors in cloud storage, in which a trusted third party is introduced to verify the integrity of operation behavior logs to enhance the credibility of forensic results as well as alleviate the burden of the forensic investigator. Further, we design a block-based logging approach to support selective verification and a hash-chain-based structure for each log block to ensure the forward security and append-only properties for log entries. Moreover, to achieve the tamper resistance of log blocks and non-repudiation of auditing proofs, we employ Merkle hash tree (MHT) to record the hash values of the aggregation authentication block tags sequentially and publish the root of MHT to the public once a block has been appended. Meanwhile, using the authentication property of MHT, our scheme can provide log-less verification with privacy preservation. We formally prove the security of the proposed scheme and evaluate its performance on entry appending and verification by concrete experiments and comparisons with the state-of-the-art schemes. The results demonstrate that the proposed scheme can effectively achieve secure auditing for log files of operation behaviors in cloud storage and outperforms the previous ones in computation complexity and communication overhead.\",\n \"Title: Interoperable localization for mobile group users.\\nYear: 2017\\n\\nAbstract: This paper studies the problem of real-time wireless localization for mobile group users, which can be used in many applications such as fire rescue and safety management in construction sites. However, due to the mobility of users and the instability of wireless signals, we find that traditional localization methods do not perform well, and the localization accuracy is thus not acceptable. To solve this problem, we propose to localize mobile users by exploiting their interrelated information. The mutual distance information among the users are exploited to get better localization performance for all the users. We first theoretically prove that the proposed scheme can improve the localization probability and accuracy. Furthermore, an Interoperable Localization Method (ILM) is designed, which extends the Kalman filter to alleviate the influence of noisy and unstable wireless signals. Finally, extensive experimental results demonstrate that our method outperforms the fixed anchor-based methods by 20%45% in terms of localization accuracy and 20%50% in terms of localization probability.\",\n \"Title: A Radio Resource Virtualization-Based RAT Selection Scheme in Heterogeneous Networks.\\nYear: 2017\\n\\nAbstract: In the future heterogeneous wireless networks, heterogeneity of radio resources from different radio access technologies (RATs) still exists. The heterogeneity, especially for networks with the coexistence of non-orthogonal and orthogonal resources, makes the radio resources difficult to be uniformly measured, and thus hinders the efficient utilization of radio resources. To overcome this limitation, this letter firstly proposes a radio resource virtualization approach in heterogeneous networks. Based on the accumulated historical data of resource utilization information, heterogeneous radio resources are virtualized into normalized resources using deep learning method. Secondly, the consumption difference of virtualized resources under different situations of network load and user demand is modeled. Moreover, aiming at efficiently utilizing radio resources and reducing access blocking rate, a RAT selection scheme based on the radio resource virtualization is proposed. Through simulation, the validity of the proposed scheme is evaluated.\",\n \"Title: Edge Popularity Prediction Based on Social-Driven Propagation Dynamics.\\nYear: 2017\\n\\nAbstract: Caching contents in edge networks can reduce latency and lighten the burden on backhaul links. Since the capacity of cache nodes is limited, accurate content popularity distribution is crucial to the effectual usage of cache capacity. However, existing popularity prediction models stem from big data and, hence, may suffer poor accuracy due to the small population in edge caching. In this letter, we propose a social-driven propagation dynamics-based prediction model, which requires neither training phases nor prior knowledge. Specifically, we first explore social relationships to bridge the gap between small population and prediction accuracy under susceptible-infected-recovery model. Then, a discrete-time markov chain approach is proposed to predict the viewing probability of certain contents from the perspective of individuals. Simulations validate that our proposed model outperforms other solutions significantly, by improving up to 94 in accuracy and 99 less runtime overhead.\",\n \"Title: Precoding Designs In Non-Regenerative Mimo Two-Way Relay Systems For Maximizing Weighted Sum Energy Efficiencies\\nYear: 2016\\n\\nAbstract: In this paper, we investigate the energy efficient optimization for non-regenerative multiple-input-multiple-output (MIMO) Two-Way Relay (TWR) network. Contrary to existing energy efficiency (EE) optimal precoding designs, we maximize the weighted sum per-source energy efficiencies (WSEE) subject to the maximum transmit power constraints of sources and relay, such that different EE requirements from the sources can be investigated. The resulting optimization problem is a non-convex sum of ratios problem. To address this issue, we first reformulate the objective as a more tractable parametric subtraction form by introducing some auxiliary variables. Then we develop an efficient block coordinate decent method to solve the equivalent problem with respect to the individual optimization variable. It is shown that the proposed iterative algorithm is guaranteed to converge.\",\n \"Title: Partial Critical Path Based Greedy Offloading In Small Cell Cloud\\nYear: 2016\\n\\nAbstract: With mobile applications sharply developing, the battery technology becomes the bottleneck. Meanwhile, mobile users are increasingly sensitive to the latency of an application. The computation offloading in Small Cell Cloud (SCC) can economize the energy consumption of mobile devices efficiently and guarantee the makespan of an application. In this paper, we model the mobile application as a directed acyclic graph (DAG), and formulate an optimization problem of collaborative task execution to minimize the energy consumption on the mobile device while meeting a prescribed latency constraint. In order to solve this NP-hard problem, we propose a greedy algorithm based on partial critical path (GA-PCP) which can solve the problem approximately. The algorithm partitions the DAG into chains and processes these chains with the \\\"Add-Compare-Select\\\" strategy to obtain the execution strategy. The algorithm can obtain a polynomial time complexity. Simulation results show that the solution of the GA-PCP is close to the optimal solution of the enumeration algorithm. Besides, the GA-PCP execution strategy can significantly save the energy consumption on the mobile device thereby prolonging its battery life, compared to the local execution.\",\n \"Title: Distributed Beamforming and BS Sleep under Small Cell Network\\nYear: 2016\\n\\nAbstract: Green telecommunication is becoming an important part in designing the wireless communication systems. Small Cell Network (SCN) is considered as one of the most promising techniques in the future cellular networks. In order to reduce the high power consumption caused by dense deployment of small base stations (BSs), we first propose a new decentralized precoding algorithm which aims to maximize the energy efficiency of the SCN and the suboptimal solution can be obtained by two times of iterations. For further consideration, when the quantity of UEs is low in the network, it is a waste of energy to open a BS to serve only a few UEs or provide a low data rate to the UEs. So we introduce a BS closing strategy, which takes advantage of the property of SCN that different BSs cover more overlapping areas. Simulation results show that the proposed decentralized precoding algorithm outperforms the traditional precoding algorithms such as BD precoding, and the sleep strategy does not only take linear time complexity but also improve the energy efficiency of the SCN obviously.\",\n \"Title: Study on Network Anomaly Localization Techniques\\nYear: 2016\\n\\nAbstract: With the increased network scale and complexity of network topology, especially the constant variation of attacking modes, it becomes urgent to find efficient methods to localize anomalies quickly and accurately. Present studies on network performance are mostly limited to detecting anomalies, or balancing the missed rate and false alarm rate of anomaly detection. The existing small amount of localization researches are divided into two categories: localization methods based on end to end path probe and wavelet transform. This paper introduces some existing network anomaly localization methods and analyzes the performance of them. Finally, we discuss the existing technical difficulties in anomaly localization.\",\n \"Title: Diffusion Wavelet-Based Anomaly Detection in Networks\\nYear: 2016\\n\\nAbstract: Traffic Matrix (TM) can contain information about irregular network topology structure and depict the traffic characteristics of global network. It is a critical parameter to network traffic engineering and attracts significant research interests. Diffusion Wavelet (DW) can perform an effective Multi-Resolution Analysis (MRA)on TM in both temporaland space domains because it intrinsically adapts to the underlying network structure. This paper shows how to apply DW to TM analysis and anomaly detection. By comparing with other anomaly detection methods, it is confirmed thatour method can detect anomaly effectively due to combining with the analysis results by DW.\",\n \"Title: Comparison on Network Simulation Techniques\\nYear: 2016\\n\\nAbstract: Network simulation is an important technique for verifying new algorithms, analyzing network performance and deploying the practical networks. Different network simulation softwares are applied for different scenarios. In this paper, their performance in different applications are discussed in detail. Three kinds of main network simulation softwares are introduced in this paper: OPNET, Network Simulator (NS) and Objective Modular Network Testbed in C++ (OMNeT++). NS is widely used in network research. How to apply NS in network traffic analysis is discussed in this paper.\",\n \"Title: Weighted Ensemble Classification of Multi-label Data Streams.\\nYear: 2017\\n\\nAbstract: Many real world applications involve classification of multi-label data streams. However, most existing classification models mostly focused on classifying single-label data streams. Learning in multi-label data stream scenarios is more challenging, as the classification systems should be able to consider several properties, such as large data volumes, label correlations and concept drifts. In this paper, we propose an efficient and effective ensemble model for multi-label stream classification based on ML-KNN (Multi-Label KNN) [31] and propose a balance AdjustWeight function to combine the predictions which can efficiently process high-speed multi-label stream data with concept drifts. The empirical results indicate that our approach achieves a high accuracy and low storage cost, and outperforms the existing methods ML-KNN and SMART [14].\",\n \"Title: Energy efficient illumination optimization for indoor visible light communication\\nYear: 2016\\n\\nAbstract: Communication, illumination, and energy saving are three basic objectives of visible light communication (VLC) system. However, it's difficult to simultaneously optimize these performances. Therefore, this paper jointly optimizes energy consumption and illumination while communication and other constraints are satisfied. The problem is formulated using multi-objective and multi-control-variable optimization. A novel p-optimality based clonal selection algorithm (CLONALG) is developed to solve it. Compared with conventional Pareto optimality method, our algorithm exerts prior preference on optimization objectives, enabling multiple search directions towards the optimal solution set. Simulation results prove that illumination uniformity is improved while LED power consumption is reduced. Also, advantages of p-optimality are identified and validated by comparing with Pareto optimality method.\",\n \"Title: Secure SWIPT in MISO downlink system with energy efficiency constraint\\nYear: 2017\\n\\nAbstract: Energy efficiency (EE) is a key metric in future green wireless networks. In this paper, we study the secure simultaneous wireless information and power transfer (SWIPT) in a downlink multiple-input-single-output (MISO) system, where the energy efficiency (EE) of the system is guaranteed. By jointly designing the transmit beamforming (BF), artificial noise (AN) and power splitting (PS) ratio, the total transmit power is minimized under given quality of service (QoS) and energy harvesting requirement at the legitimate user. To tackle the non-convex problem, we first transform it into a convex one by applying successive convex approximation (SCA), relax the problem into a tractable alternative, and then employ the semi-definite relaxation (SDR) technique. Finally, we use one-dimensional search to obtain the optimal PS ratio. Moreover, the tightness of the relaxation is proved. Simulation results show our proposed algorithm outperforms other schemes such as zero forcing (ZF) or without utilizing AN.\",\n \"Title: Neighborhood-adaptive differential evolution for global numerical optimization.\\nYear: 2017\\n\\nAbstract: •The effect of different neighborhood topologies on different functions is considered.•Multiple neighborhood topologies are available for each individual on the function.•The synergy among multiple neighborhood topologies is revealed in NaDE.•The framework of NaDE is easy to implement and realize with most DE algorithms.\",\n \"Title: Neighborhood guided differential evolution.\\nYear: 2017\\n\\nAbstract: Differential evolution (DE) relies mainly on its mutation mechanism to guide its search. Generally, the parents involved in mutation are randomly selected from the current population. Although such a mutation strategy is easy to use, it is inefficient for solving complex problems. Hence, how to utilize population information to further enhance the search ability of the mutation operator has become one of the most salient and active topics in DE. To address this issue, a new DE framework with the concept of index-based neighborhood, is proposed in this study. The proposed framework is named as neighborhood guided DE (NGDE). In NGDE, a neighborhood guided selection (NGS) is introduced to guide the mutation process by extracting the promising search directions with the neighborhood information. NGS includes four main operators: neighborhood construction, neighbors grouping, two-level neighbors ranking, and parents selection. With these four operators, NGS can utilize the topology and fitness information of population simultaneously. To evaluate the effectiveness of the proposed approach, NGS is applied to several original and advanced DE algorithms. Experimental results have shown that NGDE generally outperforms most of the corresponding DE algorithms on different kinds of optimization problems.\",\n \"Title: Improved Face Detection and Alignment using Cascade Deep Convolutional Network.\\nYear: 2017\\n\\nAbstract: Real-world face detection and alignment demand an advanced discriminative model to address challenges by pose, lighting and expression. Illuminated by the deep learning algorithm, some convolutional neural networks based face detection and alignment methods have been proposed. Recent studies have utilized the relation between face detection and alignment to make models computationally efficiency, however they ignore the connection between each cascade CNNs. In this paper, we propose an structure to propose higher quality training data for End-to-End cascade network training, which give computers more space to automatic adjust weight parameter and accelerate convergence. Experiments demonstrate considerable improvement over existing detection and alignment models.\",\n \"Title: Multi-Attribute Vertical Handover Decision-Making Algorithm in a Hybrid VLC-Femto System.\\nYear: 2017\\n\\nAbstract: Due to the complementary natures of visible light communication (VLC) and radio frequency, a combined application is regarded as a promising approach of supporting both hyperspeed data transmission and reliable signal connectivity. Vertical handover (VHO) is critical for guaranteeing seamless service in such a heterogeneous network. This letter employs a family hybrid VLC-Femto system and proposes a VHO algorithm based on decision selection. Our approach considers multiple attributes, including dynamic network parameters and actual traffic preferences by utilizing analytic hierarchy process (AHP). Additionally, it can compensate for the weakness of classic AHP in comparing the criteria values of different candidates by applying a two-person cooperative game (CG) model. Finally, the rationality degree (RD) of each decision is calculated by combining the results of AHP and CG, and the decision with the higher RD is selected. The simulation results validate the superiority of the proposed AHP-CG VHO algorithm over existing schemes under various circumstances.\",\n \"Title: Dynamic Incentive Design in Content Dissemination Process Through D2D Communication.\\nYear: 2017\\n\\nAbstract: With the development in machine learning area, future networks are better at predicting user demands. By proactively disseminating content into target users' caches through device-to-device communication, wireless traffic can be greatly offloaded. However, practical users are selfish and incentive needs are to be provided to encourage them to help spread the content. In this letter, we consider the incentive design in content dissemination process with awareness of link failure. A dynamic incentive mechanism is proposed based on Markov decision process and the optimal incentives are determined for different network states. Simulation results show that our dynamic scheme achieves a higher network utility than the optimal fixed incentive scheme with an average gain of 7.23% under information asymmetry and 48.23% with partial information.\",\n \"Title: Stabilization of k-valued logical control networks by open-loop control via the reverse-transfer method.\\nYear: 2017\\n\\nAbstract: A reverse-transfer method is proposed to study the stabilization problem of k-valued logical control networks (LCNs). Main results consist of two parts. (1) Two necessary and sufficient stabilization conditions are derived for k-valued LCNs. The stabilization results are much superior to the existing relevant results in the literature. (2) Based on the above conditions, some algorithms for solving the open-loop stabilization problem of k-valued LCNs are developed. These algorithms cut useless control paths off as many as possible, which, compared with existing results, reduce the related computational cost markedly.\",\n \"Title: Secure Logging and Public Audit for Operation Behavior in Cloud Storage\\nYear: 2017\\n\\nAbstract: In cloud storage, auditing for operation behavior logs (OBLs) plays an increasingly important role in the field of cloud auditing. In this paper, we first introduce our third party auditing model for user's operation behavior. Further, a novel secure logging approach is proposed to ensure the OBLs security in cloud. Then, we present a secure public auditing scheme for OBLs. Specifically, BLS signature and random mask technique are used by third party auditor to verify the validity of OBLs in auditing process, by which our scheme achieves secure public privacy-preserving verification for OBLs. The experimental results show that our scheme provides secure and high efficiency in auditing performance.\",\n \"Title: Non-homeostatic body weight regulation through a brainstem-restricted receptor for GDF15.\\nYear: 2017\\n\\nAbstract: Under homeostatic conditions, animals use well-defined hypothalamic neural circuits to help maintain stable body weight, by integrating metabolic and hormonal signals from the periphery to balance food consumption and energy expenditure(1,2). In stressed or disease conditions, however, animals use alternative neuronal pathways to adapt to the metabolic challenges of altered energy demand(3). Recent studies have identified brain areas outside the hypothalamus that are activated under these 'non-homeostatic' conditions(4-6), but the molecular nature of the peripheral signals and brain-localized receptors that activate these circuits remains elusive. Here we identify glial cell-derived neurotrophic factor (GDNF) receptor alpha-like (GFRAL) as a brainstem-restricted receptor for growth and differentiation factor 15 (GDF15). GDF15 regulates food intake, energy expenditure and body weight in response to metabolic and toxin-induced stresses; we show that Gfral knockout mice are hyperphagic under stressed conditions and are resistant to chemotherapy-induced anorexia and body weight loss. GDF15 activates GFRAL-expressing neurons localized exclusively in the area postrema and nucleus tractus solitarius of the mouse brainstem. It then triggers the activation of neurons localized within the parabrachial nucleus and central amygdala, which constitute part of the 'emergency circuit' that shapes feeding responses to stressful conditions(7). GDF15 levels increase in response to tissue stress and injury, and elevated levels are associated with body weight loss in numerous chronic human diseases(8,9). By isolating GFRAL as the receptor for GDF15-induced anorexia and weight loss, we identify a mechanistic basis for the non-homeostatic regulation of neural circuitry by a peripheral signal associated with tissue damage and stress. These findings provide opportunities to develop therapeutic agents for the treatment of disorders with altered energy demand.\",\n \"Title: Adaptive Centralized Clustering Framework for Software-Defined Ultra-Dense Wireless Networks.\\nYear: 2017\\n\\nAbstract: This paper develops a new centralized clustering framework to mitigate strong intercell interference in a software-defined ultra-dense network, where the number of clusters can be adapting to network conditions. A directed interference graph is designed to capture the dominant interference resulting from user mobility. An asymptotically optimal Max-K-Cut method is proposed to partition the graph, ...\",\n \"Title: Dynamic-Hash-Table Based Public Auditing for Secure Cloud Storage.\\nYear: 2017\\n\\nAbstract: Cloud storage is an increasingly popular application of cloud computing, which can provide on-demand outsourcing data services for both organizations and individuals. However, users may not fully trust the cloud service providers (CSPs) in that it is difficult to determine whether the CSPs meet their legal expectations for data security. Therefore, it is critical to develop efficient auditing tech...\",\n \"Title: Mainlobe Anti-Jamming Via Eigen-Projection Processing And Covariance Matrix Reconstruction\\nYear: 2017\\n\\nAbstract: In this letter, a novel mainlobe anti-jamming method via eigen-projection processing and covariance matrix reconstruction is proposed. The present work mainly focuses on two aspects: the first aspect is to obtain the eigenvector of the mainlobe interference accurately in order to form the eigen-projection matrix to suppress the mainlobe interference. The second aspect is to reconstruct the covariance matrix which is uesd to calculate the adaptive weight vector for forming an ideal beam pattern. Additionally, the self-null effect caused by the signal of interest and the side lobe interferences elimination are also considered in the proposed method. Theoretical analysis and simulation results demonstrate that the proposed method can suppress the mainlobe interference effectively and achieve a superior performance.\",\n \"Title: An Improved Multivariate Wavelet Denoising Method Using Subspace Projection\\nYear: 2017\\n\\nAbstract: An improved multivariate wavelet denoising algorithm combined with subspace and principal component analysis is presented in this paper. The key element is deriving an optimal orthogonal matrix that can project the multivariate observation signal to a signal subspace from observation space. Univariate wavelet shrinkage operator is then applied to the projected signals channel-wise resulting in the improvement of the output SNR. Finally, principal component analysis is performed on the denoised signal in the observation space to further improve the denoising performance. Experimental results based on synthesized and real world ECG data verify the effectiveness of the proposed algorithm.\",\n \"Title: Promising techniques for anomaly detection on network traffic.\\nYear: 2017\\n\\nAbstract: In various networks, anomaly may happen due to network breakdown, intrusion detection, and end-to-end traffic changes. To detect these anomalies is important in diagnosis, fault report, capacity plan and so on. However, it's challenging to detect these anomalies with high accuracy rate and time efficiency. Existing works are mainly classified into two streams, anomaly detection on link traffic and on global traffic. In this paper we discuss various anomaly detection methods on both types of traffic and compare their performance.\",\n \"Title: Fog-Based Evaluation Approach for Trustworthy Communication in Sensor-Cloud System.\\nYear: 2017\\n\\nAbstract: A sensor-cloud system is a combination of wireless sensor networks and cloud computing that is equipped with ubiquitous physical sensing ability, high-speed computation, huge storage, and so on. However, sensor-cloud systems suffer from various types of malicious attacks that can cause sensor communications to become unreliable. Establishing a trust evaluation method to ensure members reliability in sensor cloud is an effective way to resist malicious attacks. However, most current trust evaluation methods are constrained to specific attacks or applications, and they lack compatibility, verifiability, and scalability. To solve these problems, we formulated the trust evaluation issue as a multiple linear regression problem. Considering energy restrictions, we adopt fog nodes to assist in the trust computation. Moreover, the least squares algorithm is used to find the fitting function between the communication feature and the trust value. The experimental results show that our approach can find the best trust evaluation model and improve the compatibility, verifiability, and accuracy of trust evaluation.\",\n \"Title: The Chaotic Video Surveillance in Mobile Camera Sensor Networks: Improving the Efficiency and Security.\\nYear: 2017\\n\\nAbstract: Recent years have witnessed the rapid development of camera sensor applications. Sensors and cameras are deployed everywhere. However, there are several problems in traditional camera sensor surveillance applications. First, the positions of the camera sensors are fixed so that the surveillance range is limited. Second, there is a potential security issue since criminal suspects can make an on-the-spot investigation in advance and then circumvent the surveillance area while committing criminal activities. To solve these problems, we introduced a mobile video surveillance method in this paper, which can not only extend the monitoring range, but also improve the security. We firstly design a Stadia Ant Colony Algorithm (SACA) to improve the surveillance efficiency. Based on this algorithm, a Chaotic Path Planning Algorithm (CPPA) is designed to make the moving paths of camera sensors unpredictable, which ensures the security of surveillance. Moreover, a prototype system with mobile cameras is implemented. Extensive simulation experiments validate the effectiveness of the proposed method which is an improvement of existed fixed surveillance systems.\",\n \"Title: An annulus-based access class barring scheme for stationary machine-type communications devices\\nYear: 2016\\n\\nAbstract: The 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) cellular network is a promising system for machine-type communications (MTC). However, random access overload becomes a severe issue with the emerging massive number of MTC devices (MTCDs) in LTE network. To solve the problem, access class barring (ACB) and timing advance (TA) command comparison are two effective schemes by limiting the number of random access requests. In this paper, we propose an annulus-based ACB (AB-ACB) scheme and combine it with TA command comparison in stationary MTCDs scenario. Aiming to exploit the devices distribution potential in stationary scenario, AB-ACB divides cell coverage into some annuluses with equal width and adjusts ACB parameters based on the distribution of MTCDs in these annuluses. In the combined scheme, the optimal number of annuluses and ACB parameters are adaptively configured according to LTE cell deployment, so as to maximize the expected number of MTCDs served in a random access slot (RAS). Simulation results show that, comparing to the scheme jointly using ordinary ACB and TA command comparison, our proposed combined scheme outperforms in random access efficiency.\",\n \"Title: Distributed Cache Placement and User Association in Multicast-Aided Heterogeneous Networks.\\nYear: 2017\\n\\nAbstract: The small cells have been equipped with caching and multicast capabilities to save energy and ease backhaul burden. However, given the increasing diversity of user association in heterogeneous networks, traditional schemes may fail to exploit the energy-saving potential of caching and multicast. In this paper, we propose the model to minimize total power consumption by jointly optimizing the user association and cache deployment. The formulated joint optimization problem is decomposed and reformulated as a NP-complete Set Partition Problem (SPP). Motivated by the idea of the tactile networks, the devices can find the candidate user grouping based on its own utility. The utility function is judiciously designed to reduce the searching space without compromising optimality. Then, a heuristic caching algorithm is proposed by rigorously deriving the upper and lower bounds of cache placement. Simulation results show that our proposed scheme outperforms the other existing multicast and caching algorithms in terms of power consumption by up to 28%, while keeping the load among base stations balanced.\",\n \"Title: Optimal Schedule of Mobile Edge Computing for Internet of Things Using Partial Information.\\nYear: 2017\\n\\nAbstract: Mobile edge computing is of particular interest to Internet of Things (IoT), where inexpensive simple devices can get complex tasks offloaded to and processed at powerful infrastructure. Scheduling is challenging due to stochastic task arrivals and wireless channels, congested air interface, and more prominently, prohibitive feedbacks from thousands of devices. In this paper, we generate asymptotically optimal schedules tolerant to out-of-date network knowledge, thereby relieving stringent requirements on feedbacks. A perturbed Lyapunov function is designed to stochastically maximize a network utility balancing throughput and fairness. A knapsack problem is solved per slot for the optimal schedule, provided up-to-date knowledge on the data and energy backlogs of all devices. The knapsack problem is relaxed to accommodate out-of-date network states. Encapsulating the optimal schedule under up-to-date network knowledge, the solution under partial out-of-date knowledge preserves asymptotic optimality, and allows devices to self-nominate for feedback. Corroborated by simulations, our approach is able to dramatically reduce feedbacks at no cost of optimality. The number of devices that need to feed back is reduced to less than 60 out of a total of 5000 IoT devices.\",\n \"Title: An Improved (k, p, l)-Anonymity Method for Privacy Preserving Collaborative Filtering.\\nYear: 2017\\n\\nAbstract: Collaborative Filtering (CF) is a successful technique that has been implemented in recommender systems and Privacy Preserving Collaborative Filtering (PPCF) aroused increasing concerns of the society. Current solutions mainly focus on cryptographic methods, obfuscation methods, perturbation methods and differential privacy methods. But these methods have some shortcomings, such as unnecessary computational cost, lower data quality and hard to calibrate the magnitude of noise. This paper proposes a (k, p, l)-anonymity method that improves the existing k-anonymity method in PPCF. The method works as follows: First, it applies Latent Factor Model (LFM) to reduce matrix sparsity. Then it improves Maximum Distance to Average Vector (MDAV) microaggregation algorithm based on importance partitioning to increase homogeneity among records in each group which can retain better data quality and (p, l)-diversity model where p is attacker's prior knowledge about users' ratings and l is the diversity among users in each group to improve the level of privacy preserving. Theoretical and experimental analyses show that our approach ensures a higher level of privacy preserving based on lower information loss.\",\n \"Title: Selective Offloading in Mobile Edge Computing for the Green Internet of Things.\\nYear: 2018\\n\\nAbstract: Mobile edge computing provides the radio access networks with cloud computing capabilities to fulfill the requirements of the Internet of Things services such as high reliability and low latency. Offloading services to edge servers can alleviate the storage and computing limitations and prolong the lifetimes of the IoT devices. However, offloading in MEC faces scalability problems due to the massive number of IoT devices. In this article, we present a new integration architecture of the cloud, MEC, and IoT, and propose a lightweight request and admission framework to resolve the scalability problem. Without coordination among devices, the proposed framework can be operated at the IoT devices and computing servers separately, by encapsulating latency requirements in offloading requests. Then a selective offloading scheme is designed to minimize the energy consumption of devices, where the signaling overhead can be further reduced by enabling the devices to be self-nominated or self-denied for offloading. Simulation results show that our proposed selective offloading scheme can satisfy the latency requirements of different services and reduce the energy consumption of IoT devices.\",\n \"Title: Handover Control in Wireless Systems via Asynchronous Multi-User Deep Reinforcement Learning.\\nYear: 2018\\n\\nAbstract: In this paper, we propose a two-layer framework to learn the optimal handover (HO) controllers in possibly large-scale wireless systems supporting mobile Internet-of-Things users or traditional cellular users, where the user mobility patterns could be heterogeneous. In particular, our proposed framework first partitions the user equipments (UEs) with different mobility patterns into clusters, where the mobility patterns are similar in the same cluster. Then, within each cluster, an asynchronous multiuser deep reinforcement learning (RL) scheme is developed to control the HO processes across the UEs in each cluster, in the goal of lowering the HO rate while ensuring certain system throughput. In this scheme, we use a deep neural network (DNN) as an HO controller learned by each UE via RL in a collaborative fashion. Moreover, we use supervised learning in initializing the DNN controller before the execution of RL to exploit what we already know with traditional HO schemes and to mitigate the negative effects of random exploration at the initial stage. Furthermore, we show that the adopted global-parameter-based asynchronous framework enables us to train faster with more UEs, which could nicely address the scalability issue to support large systems. Finally, simulation results demonstrate that the proposed framework can achieve better performance than the state-of-art online schemes, in terms of HO rates.\",\n \"Title: Load balancing in small cell networks using user equipment transfer.\\nYear: 2017\\n\\nAbstract: This paper aims at balancing the load of small cell networks via user equipment (UE) transfer. To guarantee the fairness of both small cells and UEs, a load definition that jointly considers the small cells' service ability and UEs' data rate requirements is adopted. The system load balancing issue is formulated as an integer lexicographical minimization problem whose calculation complexity is NP-hard. Based on the property analysis of the constructed problem, an lexicographical bound of the system load vector is found and the optimal sub-channel allocation strategy is obtained. To reduce the complexity and achieve a satisfying load balancing performance, a UE transfer based load balancing (UTBLB) scheme is proposed by managing the load distribution in a lexicographical non-increasing manner. Simulation results indicate that the performance of UTBLB is approaching the optimal solution and the fairness of both small cells and UEs is improved.\",\n \"Title: An Effective Authentication Scheme Using DCT for Mobile Devices.\\nYear: 2018\\n\\nAbstract: This paper proposes an image authentication scheme for mobile devices. The proposed scheme generates an image watermark by using discrete cosine transform (DCT) and hides the watermark in the spatial pixels for image authentication and tamper detection. The hiding operator used in this paper is very simple in a mobile environment allowing high-speed authentication using a low-power mobile device. The quality of the stego-image and the recovered image becomes excellent as a result of the proposed scheme.\",\n \"Title: Social learning differential evolution.\\nYear: 2018\\n\\nAbstract: Differential evolution (DE) has attracted much attention in the field of evolutionary computation and has proved to be one of the most successful evolutionary algorithms (EAs) for global optimization. Mutation, as the core operator of DE, is essential for guiding the search of DE. In this study, inspired by the phenomenon of social learning in animal societies, we propose an adaptive social learning (ASL) strategy for DE to extract the neighborhood relationship information of individuals in the current population. The new DE framework is named social learning DE (SL-DE). Unlike the classical DE algorithms where the parents in mutation are randomly selected from the current population, SL-DE uses the ASL strategy to intelligently guide the selection of parents. With ASL, each individual is only allowed to interact with its neighbors and the parents in mutation will be selected from its neighboring solutions. To evaluate the effectiveness of the proposed framework, SL-DE is applied to several classical and advanced DE algorithms. The simulation results on forty-three real-parameter functions and seventeen real-world application problems have demonstrated the advantages of SL-DE over several representative DE variants and the state-of-the-art EAs.\",\n \"Title: Energy-efficient relay tracking with multiple mobile camera sensors.\\nYear: 2018\\n\\nAbstract: Camera Sensor Networks (CSNs) has been deployed for many applications of video surveillance. In urban road or large buildings, camera sensors are deployed in public places for social security, and target tracking is a typical application of CSNs. Recently, development in electronic technology and image sensors makes it possible to deploy mobile cameras in CSNs. Due to the constraints of limited sensing range, it is necessary for mobile cameras to cooperate with each other to improve tracking quality and reduce moving distance. In this paper, a cooperative relay tracking problem is proposed and proved to be NP-hard. For single target tracking, we design a cooperative relay algorithm, which can shorten the total moving distance of cameras and ensure a continuous tracking for mobile target. Moreover, different from single target tracking problem, we design a genetic algorithm to obtain an available schedule for tracking multiple targets. The effectiveness of the proposed methods are validated by extensive simulation results, which shows that the energy consumed by cameras can be obviously reduced while guaranteeing continuous tracking.\",\n \"Title: DIntrusion Detection in WSN with an Improved NSA Based on the DE-CMOP.\\nYear: 2017\\n\\nAbstract: Inspired by the idea of Artificial Immune System, many researches of wireless sensor network (WSN) intrusion detection is based on the artificial intelligent system (AIS). However, a large number of generated detectors, black hole, overlap problem of NSA have impeded further used in WSN. In order to improve the anomaly detection performance for WSN, detector generation mechanism need to be improved. Therefore, in this paper, a Differential Evolution Constraint Multi-objective Optimization Problem based Negative Selection Algorithm (DE-CMOP based NSA) is proposed to optimize the distribution and effectiveness of the detector. By combining the constraint handling and multi-objective optimization technique, the algorithm is able to generate the detector set with maximized coverage of non-self space and minimized overlap among detectors. By employing differential evolution, the algorithm can reduce the black hole effectively. The experiment results show that our proposed scheme provides improved NSA algorithm in-terms, the detectors generated by the DE-CMOP based NSA more uniform with less overlap and minimum black hole, thus effectively improves the intrusion detection performance. At the same time, the new algorithm reduces the number of detectors which reduces the complexity of detection phase. Thus, this makes it suitable for intrusion detection in WSN.\",\n \"Title: MMSE-Based Precoding for Rate Splitting Systems With Finite Feedback.\\nYear: 2018\\n\\nAbstract: In this letter, to tackle the inevitable effects of imperfect channel state information caused by finite feedback, we investigate a rate splitting transmission scheme proposed recently, which splits one symbol into a common part and a private part, and transmits signals simultaneously after superposition. First, we propose a minimum mean square error precoding to further suppress the imperfectness of the channel, in which random vector quantization is considered and a closed-form expression is derived. Then, utilizing an approximate expression of the quantization error, we develop a modified power allocation scheme for different parts of symbols by minimizing the sum-rate loss. Numerical results demonstrate the performance gains of the proposed schemes.\",\n \"Title: A Hybrid Network Traffic Prediction Model Based on Optimized Neural Network\\nYear: 2017\\n\\nAbstract: With growth of networks, it's demanding to predict the development of network traffic. In this paper, we analyze the network traffic based on the hybrid neural network model. The chaotic property of traffic data is verified by analyzing the chaos characteristics of the data. Based on the study of artificial neural network, wavelet transform theory and quantum genetic algorithm, we propose a neural network optimization method based on efficient global search capability of quantum genetic algorithm. The proposed quantum genetic artificial neural network model can predict the network traffic more accurately. The prediction results can be used to monitor the network anomaly in network security field, and improve the quality of service. The results will also benefit to search efficient network optimization solutions by predicting network behavior.\",\n \"Title: Efficient Algorithms for VM Placement in Cloud Data Centers\\nYear: 2017\\n\\nAbstract: The virtual machine (VM) placement problem is a major issue in optimizing resource ulitization of cloud data centers. With the rapid development of cloud computing, efficient algorithms are needed to reduce the power consumption and save energy in data centers. Many models and algorithms are designed with a objective to minimize the number of physical machines (PMs) used in a cloud data center. In this paper, we take into account the execution time of the PM, and formulat a new optimization problem of VM placement, which aims to minimize the total execution time of the PMs. We discuss the NP-hardness of the problem, and present heuristic algorithms to solve it in both offline and online scenarios. Furthermore, we conduct experiments to evaluate the performance of the proposed algorithms and the result show that our methods are able to perform better than other commonly used algorithms.\",\n \"Title: Fault detection of discrete-time T-S fuzzy affine systems based on piecewise Lyapunov functions.\\nYear: 2014\\n\\nAbstract: This paper investigates the problem of robust fault detection for a class of discrete-time nonlinear systems, which are represented by Takagi–Sugeno (T–S) fuzzy affine dynamic models with norm-bounded uncertainties. The objective is to design an admissible fault detection filter guaranteeing the asymptotic stability of the resulting residual system with prescribed performances. It is assumed that the plant premise variables, which are often the state variables or their functions, are not measurable so that the fault detection filter implementation with state-space partition may not be synchronized with the state trajectories of the plant. Based on a piecewise quadratic Lyapunov function combined with S-procedure and some matrix inequality convexification techniques, the results are formulated in the form of linear matrix inequalities. Finally, a simulation example is provided to illustrate the effectiveness of the proposed approach.\",\n \"Title: Differential evolution with individual-dependent topology adaptation.\\nYear: 2018\\n\\nAbstract: Differential evolution (DE) is an efficient and robust evolutionary algorithm (EA), that has been widely and successfully applied to solve global optimization problems in diverse real-world applications. As the population structure has a major influence on the behavior of an EA, effectively incorporating population topology into DE has recently attracted increasing attention. Previous works have shown the effectiveness of different topologies in improving the performance of DE and revealed that different topologies can have different effects on the population’s ability to solve optimization problems. However, the synergy of different topologies for the problems being solved has not been systematically investigated in most DE variants. Moreover, individuals with different fitness values play different roles in guiding the search during the evolutionary process. Nevertheless, the individual-dependent roles are not considered in most DE variants that consider the population topology. To overcome these drawbacks and utilize the information that is derived from the differences between the fitness values of individuals for topology adaption, we propose a multi-topology-based DE (MTDE) algorithm that includes an ensemble of multiple population topologies (MPT), an individual-dependent adaptive topology selection (ITS) scheme, and a topology-dependent mutation (TDM) strategy. In the ensemble of MPT, multiple population topologies with different degrees of connectivity are employed. In the ITS scheme, each individual adaptively selects the topology that is most compatible its role in guiding the search based on its fitness value. In the TDM strategy, the parents for mutation are chosen from the neighborhood of the current individual based on the corresponding topology to generate offspring. The effectiveness of the proposed algorithm is extensively evaluated on a suite of benchmark functions. Experimental results demonstrate the competitive performance of MTDE when compared with other state-of-the-art DE variants and EAs.\",\n \"Title: Explainable Matrix Factorization with Constraints on Neighborhood in the Latent Space.\\nYear: 2018\\n\\nAbstract: Nowadays, recommender systems are widely used to solve the problem of information overload in modern society. And most of the previous studies focus overwhelmingly on high accuracy in the recommender systems. But in a real system, the high accuracy does not always satisfy overall user experience. The explainability has a great impact on the user experience. We mainly focus on the explainability of recommender systems in this paper. To the best of our knowledge, it is the first time that the neighborhood information in the latent space is integrated into the Explainable Matrix Factorization. We change the method of calculation of the explainability matrix and consider the neighbors’ weight to further improve performance. We use the benchmark data set (MovieLens) to demonstrate the effectiveness of the proposed Neighborhood-based Explainable Matrix Factorization. And the result shows a great improvement for accuracy and explainability.\",\n \"Title: Optimization Matrix Factorization Recommendation Algorithm Based on Rating Centrality.\\nYear: 2018\\n\\nAbstract: Matrix factorization (MF) is extensively used to mine the user preference from explicit ratings in recommender systems. However, the reliability of explicit ratings is not always consistent, because many factors may affect the user’s final evaluation on an item, including commercial advertising and a friend’s recommendation. Therefore, mining the reliable ratings of user is critical to further improve the performance of the recommender system. In this work, we analyze the deviation degree of each rating in overall rating distribution of user and item, and propose the notion of user-based rating centrality and item-based rating centrality, respectively. Moreover, based on the rating centrality, we measure the reliability of each user rating and provide an optimized matrix factorization recommendation algorithm. Experimental results on two popular recommendation datasets reveal that our method gets better performance compared with other matrix factorization recommendation algorithms, especially on sparse datasets.\",\n \"Title: Distributed Optimization of Collaborative Regions in Large-Scale Inhomogeneous Fog Computing.\\nYear: 2018\\n\\nAbstract: Fog computing enables resource-limited network devices to help each other with computationally demanding tasks, but has yet to be implemented in large scales due to sophisticated control and network inhomogeneity. This paper presents a new fully distributed online optimization to asymptotically minimize the time-average cost of fog computing, where tasks are selected to be offloaded and processed ...\",\n \"Title: Energy-Efficient Admission of Delay-Sensitive Tasks for Mobile Edge Computing.\\nYear: 2018\\n\\nAbstract: Task admission is critical to delay-sensitive applications in mobile edge computing, but is technically challenging due to its combinatorial mixed nature and consequently limited scalability. We propose an asymptotically optimal task admission approach which is able to guarantee task delays and achieve (1-ϵ)-approximation of the computationally prohibitive maximum energy saving at a time-complexit...\",\n \"Title: Constrained Stochastic Game in Licensed-Assisted Access for Dynamic Contention Window Adaptation.\\nYear: 2018\\n\\nAbstract: Licensed-assisted access (LAA) allows operations on unlicensed spectrum for long term evolution. Enhanced listen-before-talk (LBT) scheme is required to improve LAA performance. In this letter, we investigate the LAA dynamic contention window adaptation scheme for individual and cooperative throughput optimization under buffer length constraint. We formulate a constrained stochastic game for this ...\",\n \"Title: Detecting Bitrate Modulation-Based Covert Voice-Over-IP Communication.\\nYear: 2018\\n\\nAbstract: Steganography based on bitrate modulation is a new type of information hiding approach for Voice over IP (VoIP), which can naturally resist state-of-the-art steganalysis schemes, due to inducing no modifications on the involved protocols and payloads of VoIP packets. To fill this gap, this letter presents a new corresponding detection scheme. Specifically, for the existing three methods based on b...\",\n \"Title: Information Prediction and Dynamic Programming-Based RAN Slicing for Mobile Edge Computing.\\nYear: 2018\\n\\nAbstract: In 5G networks, mobile edge computing (MEC) and radio access network (RAN) slicing can support the evolving services and satisfy the diverse service requirements efficiently, thereby, being promising technologies. In this letter, the work is focused on the RAN slicing between MEC services and traditional services. An information prediction and dynamic programming based RAN slicing algorithm is pro...\",\n \"Title: A Social-Aware Virtual MAC Protocol for Energy-Efficient D2D Communications Underlying Heterogeneous Cellular Networks.\\nYear: 2018\\n\\nAbstract: In this paper, a social-aware virtual medium access control (SV-MAC) is devised to integrate virtualization and social awareness to D2D communications underlying heterogeneous cellular networks. SV-MAC is a converged network layer. Through SV-MAC, heterogeneous networks can be unified in a single protocol stack, providing a common abstraction for users and mobile network operators (MNOs) to share ...\",\n \"Title: Steganalysis of Inactive Voice-Over-IP Frames Based on Poker Test.\\nYear: 2018\\n\\nAbstract: This paper concentrates on the detection of steganography in inactive frames of low bit rate audio streams in Voice over Internet Protocol (VoIP) scenarios. Both theoretical and experimental analyses demonstrate that the distribution of 0 and 1 in encoding parameter bits becomes symmetric after a steganographic process. Moreover, this symmetry affects the frequency of each subsequence of parameter bits, and accordingly changes the poker test statistical features of encoding parameter bits. Employing the poker test statistics of each type of encoding parameter bits as detection features, we present a steganalysis method based on a support vector machine. We evaluate the proposed method with a large quantity of speech samples encoded by G.723.1 and compare it with the entropy test. The experimental results show that the proposed method is effective, and largely outperforms the entropy test in any cases.\",\n \"Title: Adjacency-Hash-Table Based Public Auditing for Data Integrity in Mobile Cloud Computing.\\nYear: 2018\\n\\nAbstract: Cloud storage, one of the core services of cloud computing, provides an effective way to solve the problems of storage and management caused by high-speed data growth. Thus, a growing number of organizations and individuals tend to store their data in the cloud. However, due to the separation of data ownership and management, it is difficult for users to check the integrity of data in the traditional way. Therefore, many researchers focus on developing several protocols, which can remotely check the integrity of data in the cloud. In this paper, we propose a novel public auditing protocol based on the adjacency-hash table, where dynamic auditing and data updating are more efficient than those of the state of the arts. Moreover, with such an authentication structure, computation and communication costs can be reduced effectively. The security analysis and performance evaluation based on comprehensive experiments demonstrate that our protocol can achieve all the desired properties and outperform the state-of-the-art ones in computing overheads for updating and verification.\",\n \"Title: Public auditing for shared cloud data with efficient and secure group management.\\nYear: 2019\\n\\nAbstract: •A full scheme aiming to achieve all necessary demands for shared-data public auditing.•A novel lazy-revocation strategy for efficient and secure user revocation.•Enable identity traceability of malicious users using a modification record table.•Enable dynamic operations of shared data using an extended dynamic hash table.•The scheme outperforms the state-of-the-art ones in computational costs.\",\n \"Title: Distributed Online Optimization of Fog Computing for Selfish Devices With Out-of-Date Information.\\nYear: 2018\\n\\nAbstract: By performing fog computing, a device can offload delay-tolerant computationally demanding tasks to its peers for processing, and the results can be returned and aggregated. In distributed wireless networks, the challenges of fog computing include lack of central coordination, selfish behaviors of devices, and multi-hop signaling delays, which can result in outdated network knowledge and prevent e...\",\n \"Title: Multi-Timescale Decentralized Online Orchestration of Software-Defined Networks.\\nYear: 2018\\n\\nAbstract: Decentralized orchestration of the control plane is critical to the scalability and reliability of software-defined network (SDN). However, existing orchestrations of SDN are either one-off or centralized, and would be inefficient the presence of temporal and spatial variations in traffic requests. In this paper, a fully distributed orchestration is proposed to minimize the time-average cost of SD...\",\n \"Title: Exploiting Incidence Relation Between Subgroups for Improving Clustering-Based Recommendation Model.\\nYear: 2019\\n\\nAbstract: Matrix factorization (MF) has been attracted much attention in recommender systems due to its extensibility and high accuracy. Recently, some clustering-based MF recommendation methods have been proposed in succession to capture the associations between related users (items). However, these methods only use the subgroup data to build local models, so they will suffer the over-fitting problem caused by insufficient data in the process of training. In this paper, we analyse the incidence relation between subgroups of users (items) and then propose two single improved clustering-based MF models. Through exploiting these relations between subgroups, the local model in each subgroup can obtain global information from other subgroups, which can mitigate the over-fitting problem. Above all, we generate an ensemble model by combining the two single models for capturing associations between users and associations between items at the same time. Experimental results on different scales of MovieLens datasets demonstrate that our method outperforms state-of-the-art clustering-based recommendation methods, especially on sparse datasets.\",\n \"Title: Mobile Features Enhanced Indoor Positioning Based on Bayesian Estimation\\nYear: 2018\\n\\nAbstract: In this paper, we propose an indoor positioning model using the principle of Bayesian estimation. By analyzing the mobile features of the target and combining these features with state transition probability, we narrow down the belief region of the location distribution. To make our system model more intuitive, some basic concepts about the factor graph and the corresponding sum-product algorithm are introduced. Then, we map our system model onto the factor graph and make basic rules for message propagation. Based on the probability graphic model, mobile features enhanced sum-product algorithm is implemented to calculate the posterior probability of the target given range measurement observations. Simulation results show that the positioning precision of the proposed algorithm is higher than that of the existing algorithms at different speed levels, and the proposed algorithm also performs more robust in noisy indoor environment.\",\n \"Title: Motion Feature And Millimeter Wave Multi-Path Aoa-Toa Based 3d Indoor Positioning\\nYear: 2018\\n\\nAbstract: As location information becomes vitally important, positioning has been a highly desirable feature of 5G system which enables a huge amount of location-based applications and services. Millimeter wave (mmWave) is the promising technology for both offering better spectrum resource and positioning performance. A virtualized indoor office scenario with only one mmWave base station (BS) is considered in this paper. User equipment (UE) motion feature, mmWave line-of-sight (LoS) and first order reflection paths' AoA-ToA are fused for indoor positioning Firstly, an improved least mean square (LMS) algorithm that combines motion message is proposed to refine the multi-path AoA estimation. Furthermore, a modified multi path unscented Kalman filter (UKF) is proposed to track UE's position in the scenario. The information exchanges of the two stages not only consist of estimates(position, AoA) but also variance of position. Based on the simulation results, the proposed methods provide 2 times LoS-AoA estimation gains and centimeter 3D positioning accuracy respectively. Besides, this strategy is capable of positioning task with insufficient anchor nodes (AN).\",\n \"Title: Dynamic Adaptive Compressive Sensing-Based Multi-User Detection in Uplink URLLC\\nYear: 2018\\n\\nAbstract: Ultra reliable and low latency communication (URLLC) is one of the three typical service scenarios in the fifth generation mobile communications (5G) system, which supports mission-critical machine-type communication. Grant-free non-orthogonal multiple access (NOMA) system is a promising candidate technology for uplink URLLC scenario but it causes the problem of multi-user detection (MUD). In this paper, we propose a dynamic adaptive compressive sensing (DACS)-based MUD algorithm to realize MUD in URLLC scenario by exploiting user activity sparsity. Different from most of the state-of-the-art compressive sensing (CS)-based MUD algorithms, this algorithm needs no input of user activity sparsity level which may be unknown in practical system. Particularly, this algorithm adopts a stage-wise approach to increase estimated number of active users stage by stage for adaptively acquiring the true user activity sparsity level, introduces a backtracking idea to refine the estimated active user set for more accurate detection, and exploits the temporal correlation between active user sets in adjacent time slots for reducing computational complexity. Simulation results demonstrate that, although the proposed DACS-based MUD algorithm lacks the information of user activity sparsity level, it achieves better bit error rate (BER) performance than the conventional CS-based MUD algorithm.\",\n \"Title: Multi-path Routing Based QoS-aware Fairness Backhaul-Access Scheduling in mmWave UDN\\nYear: 2018\\n\\nAbstract: Due to the severe propagation loss nature of millimeter wave (mmWave), multi-hop relay transmission is a promising mmWave backhaul solution in ultra dense network. In this paper, we propose a multi-path routing scheme taking all small cells' backhaul into account to increase the system throughput. Under multi-hop constraint, we aim to optimize the downlink resource allocation of mmWave band. A two-objective link scheduling problem is formulated, where one objective is to ensure fairness among user equipments (UEs), and the other is to increase the system throughput. Since the two objectives are contradictory, we cannot obtain their optimal solutions simultaneously. To solve this problem, we propose a concurrent transmission based heuristic algorithm, which is called QoS (i.e., quality of service) -aware fairness scheduling (QFS). The proposed QFS scheme aims at transmitting as much data as possible in each time slot and scheduling appropriate links to guarantee the fairness. Simulation results show that QFS scheme can provide better fairness than conventional schemes. The percentage of UEs capable of achieving the minimum data rate requirements by the proposed QFS scheme is about 92.4%, which is greatly higher than those of the conventional schemes. In addition, the throughput of QFS scheme is about 3.18 times higher than Time Division Multiple Access (TDMA) scheme.\",\n \"Title: Improving the Security of a Public Auditing Scheme for Multiple-Replica Dynamic Data.\\nYear: 2018\\n\\nAbstract: Cloud auditing is a significant technique for determining the security of data owners’ data in cloud. However, multiple-replica places greater demands on cloud auditing, where not only the integrity of each data replica but also the number of replicas should be checked. Moreover, it is also significant to support dynamical updating for multiple-replica data. Therefore, how to achieve secure and effective multiple-replica dynamic data auditing is a cutting-edge issue for cloud auditing. Recently, an efficient multi-replica dynamic data auditing scheme (IEEE Transactions on Information Forensics u0026 Security, DOI: 10.1109/TIFS.2014.2384391) was presented to address this issue. Unfortunately, there is a security defect in this protocol, as we demonstrate in this paper. Specifically, a dishonest cloud storage provider can store an aggregation of all data copies instead of each replica itself without being detected by an auditor. Accordingly, we suggest a solution to resolve the problem while preserving all the properties of the original protocol.\",\n \"Title: Privacy-preserving public auditing for secure data storage in fog-to-cloud computing.\\nYear: 2019\\n\\nAbstract: With increasing popularity of fog-to-cloud based Internet of Things (IoT), how to ensure the integrity of IoT data outsourced in clouds has become one of the biggest security challenges. However, little effort has been put into addressing the problem. To fill this gap, this paper presents a tailor-made public auditing scheme for data storage in fog-to-cloud based IoT scenarios, which can achieve all indispensable performance and security requirements. Particularly, we design a tag-transforming strategy based on the bilinear mapping technique to convert the tags generated by mobile sinks to the ones created by the fog nodes in the phase of proof generation, which cannot only effectively protect the identity privacy, but also reduce the communication and computational costs in the verification phase; moreover, we present a zero-knowledge proof mechanism to verify the integrity of IoT data from various generators (e.g., mobile sinks and fog nodes) while achieving perfect data-privacy preserving. We formally prove the security of our scheme and evaluate its performance by theoretical analysis and comprehensive experiments. The results demonstrate that our scheme can efficiently achieve secure auditing for data storage in fog-to-cloud based IoT scenarios, and outperforms the straight-forward solution in communication and computational costs as well as energy consumption.\",\n \"Title: Joint Uplink and Downlink Scheduling in HetNets with Wireless Self-backhaul.\\nYear: 2018\\n\\nAbstract: Wireless self-backhaul is an efficient solution to release the backhaul bottleneck of 5G Heterogeneous Networks (HetNets). This paper aims at jointly optimizing uplink and downlink scheduling to improve system throughput of time division duplex (TDD) HetNets. The problem that considers the uplink-downlink requirements and access-backhaul link coordination constraints is formulated, which has coupled variables and non-polynomial calculation complexity. Based on the structure property of the constructed problem, a joint uplink and downlink scheduling (JUDS) scheme is proposed to manage the complex interference and fully obtain the flexibility of wireless self-backhaul. In JUDS scheme, the original problem is decomposed into a transmission time slot (TTI) orchestration sub-problem and a sub-band scheduling sub-problem, both of which owns high efficient solutions. Simulation results address the effectiveness of JUDS scheme. On the one hand, the total system downlink throughput is significantly improved by JUDS scheme comparing with other existing scheduling schemes. On the other hand, The data rates of access links and backhaul links are well matched.\",\n \"Title: Energy-efficient Cache Updating Scheme with Popularity Prediction.\\nYear: 2018\\n\\nAbstract: Content popularity evolution impacts optimization of cache placement and therefore influences design of caching policies. In this paper, we propose a cache updating scheme with popularity prediction and energy cost consideration. Firstly, we use linear regression method to predict future content popularity. Furthermore, we update the preceding cache placement according to predicted popularity aiming at minimizing energy cost. The problem is formulated as a convex optimization problem. Then we transform cache updating cost function, such that the optimal cache updating solution can be gained by Karush-Kuhn-Tucker (KKT) conditions. At last, the simulation results show that our scheme gets better performance in terms of energy consumption compared to the scheme without cache updating energy cost consideration.\",\n \"Title: Hierarchical Auction and Dynamic Programming Based Resource Allocation (HA&DP-RA) Algorithm for 5G RAN Slicing.\\nYear: 2018\\n\\nAbstract: The emerging fifth generation (5G) wireless system will have different requirements in terms of rate, latency and reliability. Network slicing which addresses the deployment of multiple logical networks as independent business operations on a common physical infrastructure has attracted more attentions. However, problems such as isolation and resource allocation of wireless network slices still exist. The paper focuses on the allocation of spectrum resources for 5G RAN slices. Firstly, The hierarchical auction model can satisfy both inter-slice isolation and intra-slice customization. Then, we propose a Hierarchical Auction and Dynamic Programming based Resource Allocation (HA&DP-RA) algorithm to allocate resources for the slices. The dynamic programming algorithm is adopted to obtain a stable solution. Finally, simulation results show the effectiveness of the proposed scheme.\",\n \"Title: Received Signal Strength Prediction Based Multi-Connectivity Handover Scheme for Ultra-Dense Networks.\\nYear: 2018\\n\\nAbstract: Ultra reliable and low latency communication (URLLC) is envisioned as a new paradigm for the fifth generation (5G) mobile communications. To meet its stringent requirement, multi-connectivity attracts attention from both the academy and industrial, especially in Ultra-Dense Networks (UDN) where the user is under the coverage of several base stations (BSs). The dense deployment of BSs leads to a frequent unnecessary handover of moving user equipment (UE), which results in higher handover latency and more signal overhead. To overcome this problem, a received signal strength (RSS) prediction based multi-connectivity handover (RPMCH) scheme is proposed. In the scheme, a new handover triggering mechanism is adopted and the target serving BSs are selected based on RSS prediction. The simulation shows that the PRMCH scheme reduces the number of handovers by more than 50% compared with state-of-the-art handover scheme. In addition, our scheme contributes to a higher average throughput for medium and high mobility cases.\",\n \"Title: Joint Slot and Sub-channel Allocation Scheme in Wireless Self-Backhaul HetNets\\nYear: 2018\\n\\nAbstract: Due to the low cost and flexible deployment, wireless self-backhaul can be a promising backhaul solution for ultradense heterogeneous networks (HetNets). In this paper, we investigate the scheduling issues in wireless self-backhaul HetNets. To coordinate the inter-cell interference among small base stations (SBSs), a graph coloring algorithm is used to divide the SBSs into multiple SBS sets. SBSs belonging to the same set can receive or transmit data at the same time. We formulate a slot and sub-channel allocation optimization problem to maximize the downlink (DL) throughput of all SBSs subject to access-backhaul link matching constraints and user data rate requirements. Since the formulated problem is Integer Non Linear Program (INLP) whose solution is intractable, the solution is obtained by dividing the problem into two sub-problems. Firstly, a Linear Problem (LP) is formulated for solving the slot allocation problem, which roughly guarantee user data rate and access-backhaul link matching requirements. Secondly, two low-complexity heuristic algorithms for sub-channel allocation are proposed. Each base station (BS) will use corresponding sub-channel allocation algorithm in a distributed manner at working slot.\",\n \"Title: A Hybrid Access Scheme in LTE Licensed Assisted Access Networks\\nYear: 2018\\n\\nAbstract: In order to deploy Long Term Evolution (LTE) on the unlicensed spectrum, Licensed-Assisted Access (LAA) has been proposed by 3rd Generation Partnership Project (3GPP). Since LAA is not the only technology deploying on the unlicensed band, an urgent issue for improving the performance of LAA is reducing the influence of interference from WiFi networks. Common access methods such as scheduling access and random access are not suitable for LAA and WiFi coexistence system since the radio channels may be occupied by other technologies meanwhile. To ensure the quality of service and improve the utilization of spectrum resource in LAA networks, a hybrid access scheme is proposed for uplink transmission in this paper. The hybrid access scheme can select an access scheme in each subframe between the scheduling based access scheme and the random access scheme taking the load condition of the networks into account. By the means of choosing the suitable access scheme dynamically, the hybrid access scheme may improve the access success rate of UE, which is superior to the random access scheme and the scheduling access scheme.\",\n \"Title: Context-aware Data Caching and Resource Allocation in HetNets with Self-backhaul\\nYear: 2018\\n\\nAbstract: Wireless self-backhaul technology is an effective solution to the backhaul bottleneck of 5G Heterogeneous Networks (HetNets). To effectively utilize the spectrum resource as well as reduce the transmission load of backhaul links, we propose a joint Context-aware Data Caching and Resource Allocation (CDCRA) scheme for self-backhauled HetNets aiming at maximizing the total system throughput. Context-aware technology combined with many-to-many matching theory is utilized for data caching in small base stations (SBSs). Then interference management based on graph theory is considered when allocating spectrum resource for both access links and backhaul links. Different comparison schemes are designed to verify the performance of our proposed scheme. Simulation results indicate that the total system throughput is significantly improved by CDCRA scheme comparing with other data caching and resource allocation schemes.\",\n \"Title: Energy-Efficient Inter-Frequency Small Cell Discovery in Dense Urban Environments\\nYear: 2019\\n\\nAbstract: This letter aims at providing an energy-efficient inter-frequency small cell discovery scheme in dense urban environments under the maximum inter-frequency measurement power (IMP) constraint and minimum traffic offloading opportunity utilization (TOOU) requirement. The problem is formulated as a multi-objective optimization problem, which is hard to solve. Therefore, a context-aware inter-frequency small cell discovery (CISCD) scheme is proposed. Based on particle swarm optimizer, the CISCD scheme enables joint optimization of the average IMP and TOOU. Simulations validate that the proposed CISCD scheme is feasible and its performance is superior to existing well-known mechanisms.\",\n \"Title: Distributed Online Learning of Fog Computing Under Nonuniform Device Cardinality\\nYear: 2018\\n\\nAbstract: Processing data around the point of capture, fog computing can support computationally demanding Internet-of-Things (IoT) services. Distributed online optimization is important given the size of IoT, but challenging due to time variations of random traffic and nonuniform connectivity (or cardinality) of edge servers and IoT devices. This paper presents a distributed online learning approach to asymptotically minimizing the time-average cost of fog computing in the absence of the \\n<italic xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">a-priori</italic>\\n knowledge on traffic randomness, for light-weight, and delay-tolerant application scenarios. Stochastic gradient descent is exploited to decouple the optimizations between time slots. A graph matching problem is then formulated for every time slot by decoupling and unifying the nonuniform cardinalities, and solved in a distributed manner by developing a new linear (1/2)-approximation method. We prove that the optimality loss resulting from the distributed approximate graph matching method can be compensated and diminish by increasing the learning time. Corroborated by simulations, the proposed distributed online learning is asymptotically optimal and superior to the state of the art in terms of throughput and energy efficiency.\",\n \"Title: Reliable and real-time data gathering in multi-hop linear wireless sensor networks\\nYear: 2006\\n\\nAbstract: Data gathering is a critical operation in wireless sensor networks for extracting useful information from the operating environment. In this paper, we study the problem of data gathering in multi-hop linear sensor networks. We employ a simple model based on random channel access scheme to tackle the high degree of channel contention and high probability of packet collision induced by bursty traffic. In our model, each node optimally attempts a transmission, and our goal is to tune the attempt probability for each sensor node with the objective to minimize the data gathering duration on condition that each link can provide guaranteed per-hop packet delivery reliability. We formulate this problem as an optimization problem and propose a distributed solution which relies on only two hop neighbors information. Based on this model, a simple and scalable protocol RADG (Random Access Data Gathering) is designed. Simulation results show that our algorithm has fast convergence speed. Moreover, RADG is robust to link error in essence and particularly suitable to monitor environments with high degree of interference.\",\n \"Title: The Self-Adaptive Adjustment Method Of Clustering Center In Multi-Spectral Remote Sensing Image Classification Of Land Use\\nYear: 2011\\n\\nAbstract: As one kind of remote sensing images of land use composed by various categories of surface objects difficult to obtain multi-distribution model of class spectral feature, analyzing the spectral characteristics of LU of multispectral RS imagery, this paper presents a self-adaptive adjustment of clustering center method. Depending on the intercepted situation of the cluster centers between different features to conduct split, the sub-centers obtained are as the sub-category features and the cluster centers assemble to characterize category model which is better to deal with the problems of LU category composed by various surface objects and category model not satisfying multivariate normal distribution. As there are much differences between the many centers features in the unit of category area, so the selection of training area and the determinants of rules are easy. The results of experiment indicate that the LU classification accuracy is increased between 4% and 6% with this method.\",\n \"Title: A Novel Connection Correlation Scheme Based on Threshold Secret Sharing.\\nYear: 2016\\n\\nAbstract: Detecting correlated network flows aids with exposing the attackers who hide behind anonymous networks or stepping stones. More recent flow watermarking schemes can link flows, but are often impossible to achieve invisibility and robustness concurrently. This letter proposes an innovative connection correlation scheme capable of remaining invisible to attackers. The idea is that we exploit the burstiness and chaos inherent in network traffic to collect some unique and stable traffic traits, which are used later to construct an identifier based on Shamir's (t, n) threshold secret sharing scheme. Experimental results indicate that the proposed scheme is robust to network jitter, packet insertions, and losses.\",\n \"Title: Social-Aware Resource Allocation for Content Dissemination Networks: An Evolutionary Game Approach.\\nYear: 2017\\n\\nAbstract: Recently, the increasing popularity of local area services, such as YouTube, Facebook, and Twitter, on which thousands of clients subscribe and download popular contents all the while, has drawn more and more attention on the study of content dissemination networks. Device-to-device (D2D) communication, which allows two devices to directly communicate with each other, has become an effective content dissemination method. With the goal of reducing the delay of content dissemination process with D2D communication, we propose an evolutionary game (EG)-based distributed resource allocation scheme. Moreover, we theoretically prove the existence and stability of game equilibrium and further propose a global search algorithm to achieve equilibrium. In the algorithm, all D2D links will select resource adaptively based on the predicted contact duration Aiming at improving the prediction accuracy of contact duration, we propose social trajectory similarity (STS) to represent the overlapping of history trajectory among all the mobile users by mining user behavior patterns. Numerical results show that our proposed STS increases nearly 20% correlation with history trajectory overlap compared with jaccard index. Furthermore, our EG-based scheme reduces the delay over 15% compared with the coalition game scheme, and over 30% compared with the random selection scheme. In addition, our proposed scheme also increases throughput efficiency with nearly 20% and achieves better fairness.\",\n \"Title: Propagation Modeling and Defending of a Mobile Sensor Worm in Wireless Sensor and Actuator Networks.\\nYear: 2017\\n\\nAbstract: WSANs (Wireless Sensor and Actuator Networks) are derived from traditional wireless sensor networks by introducing mobile actuator elements. Previous studies indicated that mobile actuators can improve network performance in terms of data collection, energy supplementation, etc. However, according to our experimental simulations, the actuator's mobility also causes the sensor worm to spread faster if an attacker launches worm attacks on an actuator and compromises it successfully. Traditional worm propagation models and defense strategies did not consider the diffusion with a mobile worm carrier. To address this new problem, we first propose a microscopic mathematical model to describe the propagation dynamics of the sensor worm. Then, a two-step local defending strategy (LDS) with a mobile patcher (a mobile element which can distribute patches) is designed to recover the network. In LDS, all recovering operations are only taken in a restricted region to minimize the cost. Extensive experimental results demonstrate that our model estimations are rather accurate and consistent with the actual spreading scenario of the mobile sensor worm. Moreover, on average, the LDS outperforms other algorithms by approximately 50% in terms of the cost.\",\n \"Title: Detecting Steganography of Adaptive Multirate Speech with Unknown Embedding Rate.\\nYear: 2017\\n\\nAbstract: Steganalysis of adaptive multirate (AMR) speech is a significant research topic for preventing cybercrimes based on steganography in mobile speech services. Differing from the state-of-the-art works, this paper focuses on steganalysis of AMR speech with unknown embedding rate, where we present three schemes based on support-vector-machine to address the concern. The first two schemes evolve from the existing image steganalysis schemes, which adopt different global classifiers. One is trained on a comprehensive speech sample set including original samples and steganographic samples with various embedding rates, while the other is trained on a particular speech sample set containing original samples and steganographic samples with uniform distributions of embedded information. Further, we present a hybrid steganalysis scheme, which employs Dempster-Shafer theory (DST) to fuse all the evidence from multiple specific classifiers and provide a synthesized detection result. All the steganalysis schemes are evaluated using the well-selected feature set based on statistical characteristics of pulse pairs and compared with the optimal steganalysis that adopts specialized classifiers for corresponding embedding rates. The experimental results demonstrate that all the three steganalysis schemes are feasible and effective for detecting the existing steganographic methods with unknown embedding rates in AMR speech streams, while the DST-based scheme outperforms the others overall.\",\n \"Title: A Social-Energy Based Cluster Management Scheme for User-Centric Ultra-Dense Networks.\\nYear: 2017\\n\\nAbstract: With the exponential growth of traffic demand, ultra-dense networks are proposed to increase the network capacity. However, the high-density access point (AP) deployment will increase the complexity of AP coordination, and AP cluster (APC) needs to be considered in practical implementations. Due to the dynamic changes in spatiotemporal distribution of users and service demand, we propose a social-energy-based cluster management (SECM) scheme in order to reduce APC update frequency. Specifically, in social domain, we propose a congeniality-based personalized recommendation (CPR) algorithm to predict users' incoming requests. We further propose a CPR-based AP cluster algorithm to solve the matching problem among users, APs, and content. In energy domain, we propose an inter-cluster energy cooperation scheme to avoid the shutting down of members in AP cluster and reduce the update of clusters. Numerical results demonstrate that our proposed scheme can achieve a gain of 77.8% in the APC management utility averagely, without loss of fairness compared with the other state-of-the-art schemes.\",\n \"Title: Joint Beamforming and User Selection in Multiuser Collaborative MIMO SWIPT Systems With Nonnegligible Circuit Energy Consumption.\\nYear: 2018\\n\\nAbstract: Multiantenna beamforming has potential to improve the efficiency of simultaneous wireless information and power transfer (SWIPT). Existing designs are focused on the downlink of multiple-input-single-output under the assumption of single-antenna users and negligible energy consumption in users' circuitry, despite the fact that using multiple antennas on the user side can further improve system eff...\",\n \"Title: Weather prediction with multiclass support vector machines in the fault detection of photovoltaic system\\nYear: 2017\\n\\nAbstract: Since the efficiency of photovoltaic (PV) power is closely related to the weather, many PV enterprises install weather instruments to monitor the working state of the PV power system. With the development of the soft measurement technology, the instrumental method seems obsolete and involves high cost. This paper proposes a novel method for predicting the types of weather based on the PV power dat...\",\n \"Title: Data collection from WSNs to the cloud based on mobile Fog elements\\nYear: 2020\\n\\nAbstract: The powerful computing and storage capability of cloud computing can inject new vitality into wireless sensor networks (WSNs) and have motivated a series of new applications. However, data collection from WSNs to the Cloud is a bottleneck because the poor communication ability of WSNs, especially in delay-sensitive applications, limits their further development and applications. We propose a fog structure composed of multiple mobile sinks. Mobile sinks act as fog nodes to bridge the gap between WSNs and the Cloud. They cooperate with each other to set up a multi-input multi-output (MIMO) network, aiming to maximize the throughput and minimize the transmission latency. We district collecting zones for all sinks and then assign sensors to the corresponding sinks. For those assigned sensors, hops and energy consumption are considered to solve the hopspot problem. Sensor data are uploaded to the Cloud synchronously through sinks. The problem is proved to be NP-hard, and we design an approximation algorithm to solve this problem with several provable properties. We also designed a detailed routing algorithm for sensors considering hops and energy consumption. We compare our method to several traditional solutions. Extensive experimental results suggest that the proposed method significantly outperforms traditional solutions.\",\n \"Title: Secure Public Audit for Operation Behavior Logs in Shared Cloud Storage\\nYear: 2017\\n\\nAbstract: In cloud storage, the data owner and data users can easily perform cooperative work on shared data. In this paper, we focus on operation behavior auditing in shared cloud storage, which is significant for the avoidance of potential crimes in the cloud and equitable accountability determination for the forensic investigation in shared cloud. We first introduce a novel secure public auditing model for operation behavior in shared cloud. Based on this, a secure auditing scheme for operation behavior logs (OBLs) is presented. Specifically, by using the certificateless signature, our scheme provides public privacy preserving auditing. The experiment analysis proves that our scheme can audit the validity of OBLs in the cloud efficiently. To the best of our knowledge, our scheme is the first solution of public auditing for OBLs in shared cloud.\",\n \"Title: Energy efficiency optimization in large-scale distributed MIMO systems over K fading channels\\nYear: 2017\\n\\nAbstract: In this paper, we investigate the energy efficiency (EE) of large-scale distributed multiple-input multiple-output (D-MIMO) systems. Firstly, we derive an analytical closed-form expression of the lower bound on the ergodic capacity for D-MIMO systems, and the asymptotic performance for the large-scale antenna arrays systems over K fading channels is proposed. Utilizing these results, we elaborate on the EE optimization problem with transmitted power constraint. To tackle this multi-objective optimization problem, we propose an one-dimension iterative search algorithm with low complexity to obtain the optimal number of receive antennas, transmitted power and achievable sum rate with optimum EE. Simulation results verify the performance gain of the proposed scheme along with the increasing number of transmit antennas.\",\n \"Title: Improving k-anonymity based privacy preservation for collaborative filtering.\\nYear: 2018\\n\\nAbstract: Collaborative Filtering (CF) is applied in recommender systems to predict users’ preference through filtering the information or patterns. Privacy Preserving Collaborative Filtering (PPCF) aims to achieve privacy protection in the recommendation process, which has an increasing significance in recommender systems and thus attracted much interests in recent years. Existing PPCF methods are mainly based on cryptography, obfuscation, perturbation and differential privacy. They have high computational cost, low data quality and difficulties in calibrating the magnitude of noise. This paper proposes a (p, l, α)-diversity method that improves the existing k-anonymity method in PPCF, where p is attacker’s prior knowledge about users’ ratings and (l, α) is the diversity among users in each group to improve the level of privacy preserving. To achieve (l, α)-diversity, users in each equivalence class shall come from at least l (l < k) clusters in α clustering results. Therefore, we firstly apply Latent Factor Model (LFM) to reduce matrix sparsity. Then we propose an improved Maximum Distance to Average Vector (MDAV) microaggregation algorithm based on importance partitioning to increase the homogeneity among the records in each group which can retain better data quality in (p, l, α)-diversity model. Finally, we apply t-closeness in PPCF. Theoretical analysis and experimental results demonstrate our approach assures a higher level of privacy preserving and less information loss than existing methods.\",\n \"Title: Research on precision of modular machine tool based on computer aided multi-body system method\\nYear: 2019\\n\\nAbstract: Modularity of machine tool, an engineering technology that deals with cost-effective and quick reaction to all the market changes, is urgently needed in the modern manufacturing companies. However, a general accuracy analytical method of modular machine tool is seldom concerned. The basic module error of the machine tool is synthesized by the theory of computer aided multi-body system, a universal accuracy model for modular machine tools has been established. The developed mathematical model is used to calculate and predict the resultant position error at the tool–workpiece interface for error compensation. Results show the method based on the computer aided multi-body system only depends on the motion of module and fits for rapid modeling of computer technology, and error factors such as thermal expansion and deforming under loads can be involved in the analysis processing except the geometric error.\",\n \"Title: A Social Centrality-Aware D2D Multicast Scheme for Content Dissemination\\nYear: 2017\\n\\nAbstract: Device-to-device (D2D) communication is conceived as a vital component for 5G cellular networks to improve spectral reuse, and enhance system capacity. For content dissemination, D2D communication also provides an efficient way, and the influence of social interactions among mobile devices has attracted substantial attention due to its potential impact on resource allocation. Aiming to enhance the dissemination efficiency and link stability, a social-aware D2D multicast scheme that exploits social network property of centrality for content dissemination is proposed. Based on the multi-dimensional centrality we defined, a multicast clustering scheme is proposed. Then, a bipartite graph is constructed to represent the matching problem of multicast clusters and channel resource, and Kuhn-Munkres (KM) algorithm is introduced to solve the channel allocation problem optimally. Simulation results demonstrate the effectiveness of our proposed scheme, which significantly improves the efficiency of content dissemination.\",\n \"Title: Radio over Cloud (RoC): Cloud-Assisted Distributed Beamforming for Multi-class Traffic\\nYear: 2019\\n\\nAbstract: Cloud has yet to be applied to computationally intensive radio signal processing, due to closely coupled computing tasks resulting from interference. This paper presents a new cloud-assisted joint beamforming architecture, where computations are decoupled for individual wireless users and pipelined for cloud execution, using Difference of Convex (DC), $\\\\ell _1$ℓ1-norm approximations, and dual decompositions. User-specific tasks are constructed and aligned with the cloud to leverage computation reuses and minimize overhead. The time-complexity is dramatically improved to support networks with tens to hundreds of base stations and users, without compromising the sum rate and quality-of-service. Further, the superiority of DC to the state-of-the-art Weighted Minimum Mean Square Error (WMMSE) in terms of convex relaxation is observed and discussed. Corroborated by simulations, the reason is revealed as WMMSE aggressively increases the data rate at interim stages, hence adversely interacting with $\\\\ell _1$ℓ1-norm approximation and reducing the feasible solution regions at later stages.\",\n \"Title: Traffic-Aware Relay Vehicle Selection in Millimeter-Wave Vehicle-to-Vehicle Communication\\nYear: 2019\\n\\nAbstract: Line-of-sight (LOS) blockage is a crucial problem in millimeter-wave vehicle-to-vehicle communication due to its severe penetration loss and high vehicle mobility. In order to overcome the LOS blockage problem, in this letter, we propose using neighbor vehicles as relays to forward the blocked traffic flows. Specifically, a traffic-aware relay vehicle selection is investigated. First, analytic hie...\",\n \"Title: Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised Algorithm.\\nYear: 2019\\n\\nAbstract: The parametric fault diagnosis of analog circuits is very crucial for condition-based maintenance (CBM) in prognosis and health management. In order to improve the diagnostic rate of parametric faults in engineering applications, a semi-supervised machine learning algorithm was used to classify the parametric fault. A lifting wavelet transform was used to extract fault features, a local preserving mapping algorithm was adopted to optimize the Fisher linear discriminant analysis, and a semi-supervised cooperative training algorithm was utilized for fault classification. In the proposed method, the fault values were randomly selected as training samples in a range of parametric fault intervals, for both optimizing the generalization of the model and improving the fault diagnosis rate. Furthermore, after semi-supervised dimensionality reduction and semi-supervised classification were applied, the diagnosis rate was slightly higher than the existing training model by fixing the value of the analyzed component.\",\n \"Title: Public Auditing for Trusted Cloud Storage Services\\nYear: 2019\\n\\nAbstract: Cloud storage can provide on-demand outsourcing of data services for organizations and individuals. However, because customers may not fully trust that cloud service providers meet their legal expectations for data security, techniques for auditing the cloud have attracted increasing attention. Here, we present an architecture of public data auditing, review existing methods or mechanisms for various auditing objectives, and discuss trends and possible future developments.\",\n \"Title: Space-Time Spectrum Sharing for Unmanned Aerial Vehicle Networks\\nYear: 2018\\n\\nAbstract: In this paper, we study the spectrum sharing of unmanned aerial vehicle (UAV) networks. Multiple primary users (PUs) are distributed on the ground. UAV networks distributed in the three-dimensional (3D) space share the spectrum of PUs. The 3D space is divided into constrained region and unconstrained region by planes. In constrained region, UAVs have temporal spectrum access opportunities. While in the unconstrained region, there is a spatial spectrum hole that allows UAVs to access PUs spectrum without constraints. Moreover, we analyzed the condition of existing a transition region between constrained region and unconstrained region. The bounds of two regions over fading channel are calculated by considering the constraints of the miss detection probability, false alarm probability and the aggregation interference of UAVs to PUs. UAVs can use the height information to decide the way of spectrum sharing.\",\n \"Title: Electronic Nose-Based Technique for Rapid Detection and Recognition of Moldy Apples.\\nYear: 2019\\n\\nAbstract: In this study, the PEN3 electronic nose was used to detect and recognize fresh and moldy apples inoculated with Penicillium expansum and Aspergillus niger, taking Golden Delicious apples as the model subject. Firstly, the apples were divided into two groups: individual apple inoculated only with/without different molds (Group A) and mixed apples of inoculated apples with fresh apples (Group B). Then, the characteristic gas sensors of the PEN3 electronic nose that were most closely correlated with the flavor information of the moldy apples were optimized and determined to simplify the analysis process and improve the accuracy of the results. Four pattern recognition methods, including linear discriminant analysis (LDA), backpropagation neural network (BPNN), support vector machines (SVM), and radial basis function neural network (RBFNN), were applied to analyze the data obtained from the characteristic sensors, aiming at establishing the prediction model of the flavor information and fresh/moldy apples. The results showed that only the gas sensors of W1S, W2S, W5S, W1W, and W2W in the PEN3 electronic nose exhibited a strong signal response to the flavor information, indicating most were closely correlated with the characteristic flavor of apples and thus the data obtained from these characteristic sensors were used for modeling. The results of the four pattern recognition methods showed that BPNN had the best prediction performance for the training and testing sets for both Groups A and B, with prediction accuracies of 96.3% and 90.0% (Group A), 77.7% and 72.0% (Group B), respectively. Therefore, we demonstrate that the PEN3 electronic nose not only effectively detects and recognizes fresh and moldy apples, but also can distinguish apples inoculated with different molds.\",\n \"Title: Public audit for operation behavior logs with error locating in cloud storage\\nYear: 2019\\n\\nAbstract: To ensure the creditability of audit for operation behaviors in cloud storage scenarios, it is indispensable to verify the integrity of log files prior to forensic analysis. Thus, in this paper, we mainly focus on how to achieve effective public audits for operation behavior logs. To achieve this goal, we first propose a new block-based logging method to satisfy all necessary requirements for security and performance, i.e., tamper resistance of log files, non-repudiation of behaviors and selective verification of log blocks. Next, we give a privacy-preserving public auditing method for a single log block, which can support an unlimited number of effective auditing operations. Further, we present a binary auditing tree-based public auditing method, which can achieve error locating while supporting selective verification for multiple log blocks. The security of the proposed scheme is formally proven. Moreover, its performance for verification is evaluated by comprehensive experiments and comparisons with existing schemes. The experimental results demonstrate that our scheme can efficiently achieve public verification for operation behavior logs in the cloud storage scenario and outperforms the existing ones in computation and communication costs.\",\n \"Title: A novel steganographic method for algebraic-code-excited-linear-prediction speech streams based on fractional pitch delay search\\nYear: 2019\\n\\nAbstract: Although a large number of steganography algorithms based on algebraic-code-excited-linear-prediction have been proposed, their performance, such as embedding capacity, embedding transparency and capability for resisting detection, can be further enhanced. Therefore, we are motivated to present a new steganographic scheme based on pitch delay search, which can achieve better performance by embedding secret information into the fractional pitch delay parameters while keeping the integer pitch delay parameters unchanged. Specifically, we treat all fractional pitch delays as replaceable cover bits to achieve maximum embedding capacity. Further, the steganographic process is encrypted and guided by m sequences for flexibility and security. The proposed method is evaluated with a large number of adaptive multi-rate speech samples and compared with the existing works. Experimental results show that the proposed method can provide larger embedding capacity than the existing works while achieving excellent embedding transparency. Moreover, we evaluate the performance of the proposed method for resisting the state-of-the-art steganalysis methods. The experimental results demonstrate that the proposed method is highly secure, since the state-of-the-art steganalysis methods cannot detect it efficiently.\",\n \"Title: A Low-Complexity QR-based Precoding for Massive MIMO Systems with Channel Update\\nYear: 2018\\n\\nAbstract: In this paper, we investigate the precoding design for massive multiple-input multiple-output (MIMO) system after a new channel changes for a given user, i.e., channel update. Specifically, we develop a modified two-stage precoding based on channel extension to balance the multi-user interference and noise effect. Additionally, utilizing the matrix transform method of the QR decomposition, a low-complexity precoding algorithm is proposed with channel update into three cases of position. The computation complexity analysis and simulation results in terms of the system capacity are evaluated to demonstrate performance gains of the proposed algorithms.\",\n \"Title: A Neighborhood-Assisted Framework for Differential Evolution.\\nYear: 2019\\n\\nAbstract: Differential evolution (DE), as a powerful and efficient evolutionary algorithm (EA), has shown its advantages in solving the complex optimization problems. In the literature, the utilization of neighborhood information has been attracting wide attention in the DE community due to its effectiveness in enhancing the search ability of DE. However, we have observed that no general framework is presented to provide a comprehensive way of studying the neighborhood-based DE variants. Therefore, this paper suggests a three-layer mechanism neighborhood-assisted (TLNA) DE framework to facilitate the utilization of neighborhood information. In TLNA, the mechanisms of using neighborhood information are generalized into a three-layer cooperative structure, i.e., the interaction mechanism (IM) layer, the organization mechanism (OM) layer, and utilization mechanism (UM) layer. Thus, TLNA is built to provide a synergistic effect of different layers of mechanisms for systematically utilizing neighborhood information. As a general framework, TLNA can be realized with different implementations of the three-layer mechanism. Furthermore, to demonstrate the practicality of the proposed framework, a TLNA instantiation (iTLNA) is given in detail. The performance of iTLNA is extensively evaluated on a suite of benchmark functions. The experimental results have confirmed the competitiveness of iTLNA to other DE variants and EAs, which shows that the proposed TLNA framework can pave an effective way to improve the performance of DE with neighborhood information.\",\n \"Title: Profitable Cooperative Region for Distributed Online Edge Caching\\nYear: 2019\\n\\nAbstract: Cooperative caching can unify network storage to improve efficiency, but the effective placement and search of contents are challenging especially in distributed edge clouds with neither \\n<italic xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">a-priori</italic>\\n knowledge on content requests nor instantaneous global view. This paper establishes a new profitable cooperative region for every content request admitted at an edge server, within which the content, if cached, can be retrieved with guaranteed profit against a direct retrieval from the network backbone. This narrows down the search for the content. The caching density of the content can also be significantly reduced, e.g., to a cached copy per region. The regions are based on a novel distributed framework which allows individual servers to spontaneously admit/dispatch requests and deliver/forward contents, while asymptotically maximizing the time-average profit of caching. The cooperative region for content is erected at individual servers by comparing the upper and lower bounds for the backlogs of unsatisfied requests of the content. Simulations show the substantially improved profit of the proposed approach over existing solutions. The regions can help automate the placement of contents with reduced density and improved efficiency.\",\n \"Title: State feedback design for set stabilization of probabilistic Boolean control networks\\nYear: 2019\\n\\nAbstract: Set stabilization of probabilistic Boolean control networks (PBCNs) is investigated in this paper and some interesting results are derived. The main results consist of three parts. (1) A definition of set stabilizability with probability one by closed-loop control is proposed for PBCNs, which is not a natural extension from deterministic Boolean control networks to PBCNs due to the random feature of PBCNs. (2) A necessary and sufficient set stabilizability condition is provided for PBCNs. (3) An algorithm for designing a state feedback controller is developed. It is guaranteed that all designed controllers can stabilize a PBCN to a given subset with probability one. The design method is constructive, so it is convenient to use this method in practical application. The results derived above are fundamental and important, since based on them many problems about PBCNs can be solved, for example partial stabilization, synchronization, and so on. Finally, a practical example is employed to show the effectiveness of our results.\",\n \"Title: Vulnerability analysis of public transit systems from the perspective of urban residential communities\\nYear: 2019\\n\\nAbstract: •A methodology to analyze the vulnerability of PTSs from the perspective of RC.•The functionality of PTSs is measured by the accessibility of RC.•An algorithm is proposed to calculate the accessibilities of each RC.•The vulnerability of PTSs is analyzed under several daily disruption modes.\",\n \"Title: Differential Evolution With Adaptive Guiding Mechanism Based on Heuristic Rules.\\nYear: 2019\\n\\nAbstract: This paper proposes to resolve the limitation of differential evolution (DE) that the difference between the individuals in search behavior has not yet been utilized effectively for guiding the evolution of the population. An adaptive guiding mechanism (AGM) based on the heuristic rules is thus suggested to make possible, individual-dependent guidance. The AGM mainly comprises three stages: construction, separation, and guidance. In the construction stage, the elite leadership team (ELT) is established with an adaptive control scheme by using good information of the population. In the separation stage, the ELT is divided into distinct elite groups that are allocated to different individuals based on their search behaviors. In the guidance stage, the leader that is chosen from the respective elite group, as well as the promising directions extracted from the population, are used together to guide the search of each individual. By incorporating AGM into DE, a novel algorithm framework, named DE with AGM (DE-AGM), is proposed to enhance the performance of DE. As a general framework, DE-AGM can be easily and seamlessly applied to most DE variants. The experimental results on 58 benchmark functions have demonstrated the competitive performance of DE-AGM.\",\n \"Title: Person Re-identification via Adaptive Verification Loss\\nYear: 2019\\n\\nAbstract: Person re-identification (re-ID) is the task of associating the image patches of the identical person across disjoint cameras, which is a very challenging topic in computer vision. Recently, with the advance of deep learning techniques and the public of huge datasets, person re-ID has achieved a great success. However, an obvious but ignored issue is that the sample pairs are biased, where the negative sample pairs are far more than the positive ones. In this paper, we develop an adaptive verification loss, termed as ADV-Loss to handle the imbalance of sample pairs. Our ADV-Loss empowers the popular verification loss with the flexibility of adaptively addressed the hard triple units. Specifically, we learn a re-weighted strategy from the triplet loss to balance the sample pairs. According to the objective values of triple loss, the hard triple units will be endowed with larger weights, while the less important triple units are de-emphasized or simply dropped. Thus, ADV-Loss, on the basis of the cross-entropy loss, handles the biased issue by only holding the equal number but informative sample pairs. Moreover, theoretical analysis of our designed ADV-Loss is provided to guarantee the important sample pairs not to be despised during the backpropagation. Ablation analysis verifies the potential efficacy of the proposed loss. Moreover, experiments of person re-ID on three large-scale benchmark datasets show that two popular deep networks with the proposed loss achieve satisfactory performance against several well-established person re-ID methods.\",\n \"Title: A Novel Information and Energy Cooperation Transmission Scheme in Cognitive Spectrum Sharing-Based D2D Communication Systems\\nYear: 2019\\n\\nAbstract: Spectrum efficiency and energy efficiency are two critical issues in designing wireless communication networks. In this paper, we consider cognitive spectrum sharing-based cooperation between a single-antenna primary system and a multi-antenna wireless-powered device-to-device (D2D) communication system. A novel joint time division and power splitting-based three-phase information and energy cooperation transmission scheme are proposed for cognitive spectrum sharing-based D2D communication system. In the proposed scheme, the cognitive D2D transceiver pair adopts the power splitting technique to receive information and energy simultaneously from the primary transmitter (PT) in the first phase. Then, they utilize the beamforming technique and the harvested energy as half-duplex (HD) relays to jointly forward the primary user (PU) signal in the second phase. Eventually, in the third phase, they can communicate directly with each other by using the licensed spectrum and their remaining energy. We focus on the design of time division ratios, power splitting ratios, and beamforming vectors, with the objective to maximize the data rate of the cognitive D2D communication system, subject to the quality of service (QoS) requirement of the primary system, and the cognitive D2D communication system's minimum energy harvesting requirements and maximum power consumption constraints. The simulation results demonstrate that our proposed scheme is the best and significantly improves the average data rate of the cognitive D2D communication system under different PU target rate requirements or PT rated transmit power. Furthermore, a large number of simulations are demonstrated to verify the feasibility and effectiveness of the proposed scheme.\",\n \"Title: Optimized Lossless Data Hiding in JPEG Bitstream and Relay Transfer-Based Extension\\nYear: 2021\\n\\nAbstract: This paper proposes a new framework of lossless data hiding (LDH) in JPEG images. The proposed framework contains two algorithms, i.e., the optimized basic LDH and the relay transfer based extension. In the basic algorithm, we aim to preserve the filesize after data embedding. The data hiding process is optimized by variable-length-code (VLC) mapping, combination and permutation. In the extended algorithm, we focus on embedding more bits into the bitstream with a condition that the filesize increment is allowed. To decrease the filesize increment, we propose a relay transfer based algorithm to preprocess the JPEG bitstream. Subsequently, we embed data into the processed bitstream using the basic LDH. Both algorithms provide better performances than previous arts. After lossless data hiding, the marked JPEG bitstream is compliant to common JPEG decoders. Since all operations are implemented on VLCs and the Huffman codes, no distortion is generated on the image pixels. Experimental results demonstrate that the proposed approach outperforms previous methods.\",\n \"Title: STAR-RIS Integrated Nonorthogonal Multiple Access and Over-the-Air Federated Learning: Framework, Analysis, and Optimization\\nYear: 2022\\n\\nAbstract: This article integrates nonorthogonal multiple access (NOMA) and over-the-air federated learning (AirFL) into a unified framework using one simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). The STAR-RIS plays an important role in adjusting the decoding order of hybrid users for efficient interference mitigation and omnidirectional coverage extension. To capture the impact of nonideal wireless channels on AirFL, a closed-form expression for the optimality gap (also known as the convergence upper bound) between the actual loss and the optimal loss is derived. This analysis reveals that the learning performance is significantly affected by the active and passive beamforming schemes, as well as wireless noise. Furthermore, when the learning rate diminishes as the training proceeds, the optimality gap is explicitly shown to converge with a linear rate. To accelerate convergence while satisfying quality-of-service requirements, a mixed-integer nonlinear programming (MINLP) problem is formulated by jointly designing the transmit power at users and the configuration mode of STAR-RIS. Next, a trust-region-based successive convex approximation method and a penalty-based semidefinite relaxation approach are proposed to handle the decoupled nonconvex subproblems iteratively. An alternating optimization algorithm is then developed to find a suboptimal solution for the original MINLP problem. Extensive simulation results show that: 1) the proposed framework can efficiently support NOMA and AirFL users via concurrent uplink communications; 2) our algorithms achieve a faster convergence rate on independent and identically distributed (IID) and non-IID settings compared to the existing baselines; and 3) both the spectrum efficiency and learning performance are significantly improved with the aid of the well-tuned STAR-RIS.\",\n \"Title: Online Offloading Scheduling for NOMA-Aided MEC Under Partial Device Knowledge\\nYear: 2022\\n\\nAbstract: By exploiting the superiority of nonorthogonal multiple access (NOMA), NOMA-aided mobile-edge computing (MEC) can provide scalable and low-latency computing services for the Internet of Things. However, given the prevalent stochasticity of wireless networks and sophisticated signal processing of NOMA, it is critical but challenging to design an efficient task offloading algorithm for NOMA-aided ME...\",\n \"Title: Over-the-Air Federated Learning and Non-Orthogonal Multiple Access Unified by Reconfigurable Intelligent Surface\\nYear: 2021\\n\\nAbstract: With the aim of integrating over-the-air federated learning (AirFL) and non-orthogonal multiple access (NOMA) into an on-demand universal framework, this paper proposes a novel reconfigurable intelligent surface (RIS)-unified network by leveraging the RIS to flexibly adjust the signal processing order of hybrid users. The objective of this work is to maximize the achievable hybrid rate of all users by jointly optimizing the transmit power at users, controlling the receive scalar at the base station, and designing the phase shifts at the RIS. Due to the fact that all signals of computation and communication are combined into one concurrent transmission, the formulated resource allocation problem is very challenging. To solve this problem, the alternating optimization is invoked to address non-convex subproblems iteratively for finding a suboptimal solution with low complexity. Simulation results demonstrate that i) the proposed RIS-unified network can support the on-demand communication and computation efficiently, and ii) the designed algorithms are also applicable to conventional networks with only AirFL or NOMA users.\",\n \"Title: Tail-Heaviness, Asymmetry, And Profitability Forecasting By Quantile Regression\\nYear: 2021\\n\\nAbstract: We show that quantile regression is better than ordinary-least-squares (OLS) regression in forecasting profitability for a range of profitability measures following the conventional setup of the accounting literature, including the mean absolute forecast error (MAFE) evaluation criterion. Moreover, we perform both a simulated-data and an archival-data analysis to examine how the forecasting performance of quantile regression against OLS changes with the shape of the profitability distribution. Considering the MAFE and mean squared forecast error (MSFE) criteria together, we see that the quantile regression is more accurate relative to OLS when the profitability to be forecast has a heavier-tailed distribution. In addition, the asymmetry of the profitability distribution has either a U-shape or an inverted-U-shape effect on the forecasting accuracy of quantile regression. An application of the distributional shape analysis framework to cash flow forecasting demonstrates the usefulness of the framework beyond profitability forecasting, providing additional empirical evidence on the positive effect of tail-heaviness and supporting the notion of an inverted-U-shape effect of asymmetry.\",\n \"Title: Maintenance of Structural Hole Spanners in Dynamic Networks\\nYear: 2021\\n\\nAbstract: Structural Hole (SH) spanners are the set of users who bridge different groups of users and are vital in numerous applications. Despite their importance, existing work for identifying SH spanners focuses only on static networks. However, real-world networks are highly dynamic where the underlying structure of the network evolves continuously. Consequently, we study SH spanner problem for dynamic n...\",\n \"Title: Particle Filter Reinforcement via Context-Sensing for Smartphone-Based Pedestrian Dead Reckoning\\nYear: 2021\\n\\nAbstract: Pedestrian dead reckoning based on particle filter is commonly used for enabling seamless smartphone-based indoor positioning. However, compass directions indoor are heavily distorted due to the presence of ferromagnetic materials. Conventional particle filters convert the raw compass direction to a distribution adding a constant variance noise and leveraging a particle swarm to simulate the distr...\",\n \"Title: Energy-Efficient Uplink Power Control in NOMA Enhanced Cell-Free Massive MIMO Networks\\nYear: 2021\\n\\nAbstract: The ever-increasing communication requirement of the next generation network imposes great challenges in the existing cellular architecture and requires energy-efficient resource allocation. In this paper, we consider a cell-free (CF) massive multiple-input multiple-output (MIMO) network, where a large number of distributed access points with multiple antennas simultaneously serve a smaller number...\",\n \"Title: Rate Splitting Multiple Access for Joint Communication and Sensing Systems with Unmanned Aerial Vehicles\\nYear: 2021\\n\\nAbstract: This paper investigates the problem of resource allocation for joint communication and radar sensing system on rate-splitting multiple access (RSMA) based unmanned aerial vehicle (UAV) system. UAV simultaneously communicates with multiple users and probes signals to targets of interest to exploit cooperative sensing ability and achieve substantial gains in size, cost and power consumption. By virt...\",\n \"Title: Communication-Aware Path Design for Indoor Robots Exploiting Federated Deep Reinforcement Learning\\nYear: 2021\\n\\nAbstract: Robots have been widely used in the era of Internet of Things (IoT) and become an important driver behind 6G systems. However, due to the mobility of robots and the complicated wireless environment, it is challenging to control robots without prior knowledge, especially for the dynamic multi-robot systems. This paper investigates data transmission and path planning problems in an indoor multi-robot navigation system, where non-orthogonal multiple access (NOMA) is adopted to provide massive connectivity and improve spectrum efficiency. The considered system is designed for the long-term throughput maximization by jointly designing the downlink transmit power at the access point (AP) and the motion paths of the robots, while satisfying the power budget and mobility constraints. In this paper, a novel federated deep reinforcement learning approach called federated deep Q-network learning (F-DQN) algorithm is proposed to tackle the formulated problem. Simulation results demonstrate that the proposed algorithm not only speeds up the convergence rate and promotes the system throughput, but also is scalable for the number of robots.\",\n \"Title: Reconfigurable Intelligent Surface Aided Secure UAV Communications\\nYear: 2021\\n\\nAbstract: This paper investigates the problem of secure communication in the unmanned aerial vehicle (UAV) enabled networks aided by a reconfigurable intelligent surface (RIS) from the physical layer security perspective. Specifically, the RIS is deployed to assist the wireless transmission from the UAV to the ground user in the presence of an eavesdropper. The objective of this work is to maximize the secrecy rate by jointly optimizing the phase shifts at the RIS as well as the transmit beamforming vector and location of the UAV. However, the formulated problem is difficult to solve directly due to the non-linear and non-convex objective function and constraints. By invoking the successive convex approximation and fractional programming techniques, the intractable original problem is transformed into convex ones, then an alternating algorithm is proposed to solve the challenging problem effectively. Simulations results demonstrate that the designed algorithm for RIS-aided UAV communications can achieve higher secrecy rate than benchmarks.\",\n \"Title: Learning Event Guided Network For Salient Object Detection\\nYear: 2021\\n\\nAbstract: Salient object detection (SOD) focuses on mimicking the attention mechanism in human vision system. Due to the limited information provided from the traditional image data, the image-based SOD advances are very challenging in some complex scenes. Inspired by the emerging event cameras that provide asynchronous measurements of local temporal contrast over a large dynamic range, we propose a new idea to extract more effective information from the combination of event flow and RGB images. In this paper, we construct an end-to-end joint network for salient object detection (ERSOD-Net), which simultaneously supervise the RGB image and the event data within the corresponding image exposure time. To fully exploit temporal information of event data, Long Short-Term Memory module is utilized to effectively process event and learn salient object event surfaces. Moreover, multi-level feature interaction is designed to fuse two complementary branches of image and event and to predict significance mapping. Finally, to demonstrate the effectiveness of our model, a real Event-RGB SOD dataset (ERSOD) is built by DAVIS camera. Experiments on both benchmark and ERSOD datasets show that the proposed event guided network greatly improves the SOD performance in different evaluation metrics. The code and datasets will be released later at https://github.com/jxr326/ERSOD-Net. (c) 2021 Elsevier B.V. All rights reserved.\",\n \"Title: Joint Optimization of Control and Communication in Autonomous UAV Swarms: Challenges, Potentials, and Framework\\nYear: 2021\\n\\nAbstract: The unmanned aerial vehicle (UAV) swarm has drawn much attention for its wide range of potential applications. However, most UAV swarms rely on manual remote control or preprogramming, leaving autonomous cooperation as a challenging issue. Control and communication are two important pillars intra-autonomous-swarm, and the competition for limited resources between control and communication needs to...\",\n \"Title: The Comprehensive Contributions of Endpoint Degree and Coreness in Link Prediction\\nYear: 2021\\n\\nAbstract: AbstractIn past studies, researchers find that endpoint degree, H-index, and coreness can quantify the influence of endpoints in link prediction, especially the synthetical endpoint degree and H-index improve prediction performances compared with the traditional link prediction models. However, neither endpoint degree nor H-index can describe the aggregation degree of neighbors, which results in inaccurate expression of the endpoint influence intensity. Through abundant investigations, we find that researchers ignore the importance of coreness for the influence of endpoints. Meanwhile, we also find that the synthetical endpoint degree and coreness can not only describe the maximal connected subgraph of endpoints accurately but also express the endpoint influence intensity. In this paper, we propose the DCHI model by synthesizing endpoint degree and coreness and the HCHI model by synthesizing H-index and coreness on SRW-based models, respectively. Extensive simulations on twelve real benchmark datasets show that, in most cases, DCHI shows better prediction performances in link prediction than HCHI and other traditional models.\",\n \"Title: Online delay-guaranteed workload scheduling to minimize power cost in cloud data centers using renewable energy\\nYear: 2022\\n\\nAbstract: •Model: Proposed a new model to handle different SLAs of workloads and guarantee the worst-case delay of delay-tolerant workload.•Algorithm: Designed an O(1) time online control algorithm OSDG for each slot to achieve the long term optimization objective.•Analysis and Experiment: Proved our algorithm achieves an explicit [O(1/V),O(V)] cost-delay tradeoff and validated its performance.\",\n \"Title: Secrecy Performance Analysis of IRS-Aided UAV Relay System\\nYear: 2021\\n\\nAbstract: In this letter, we study the secrecy performance of an intelligent reflecting surface (IRS)-aided unmanned aerial vehicle (UAV) relay communication system in the presence of multiple ground eavesdroppers (Eves). Specifically, a UAV equipped with an IRS is deployed as a passive relay to forward the signals transmitted from the base station to users. By modeling the distribution of Eves with stochas...\",\n \"Title: Carrier Phase-Based Synchronization and High-Accuracy Positioning in 5G New Radio Cellular Networks\\nYear: 2022\\n\\nAbstract: Inspired by excellent precision of carrier phase positioning, this paper presents a new carrier phase positioning technique for 5G new radio cellular networks with a focus on clock synchronization and integer ambiguity resolution. A carrier-phase based clock offset estimation method is first proposed to achieve precise clock synchronization among base stations, and proved to achieve the Cramér–Rao...\",\n \"Title: Securing Aerial Offloading via Intelligent Omni-Surface\\nYear: 2021\\n\\nAbstract: Different from conventional reflecting-only metasurfaces, intelligent omni-surfaces (IOSs) are capable of reflecting and transmitting the received signals simultaneously. As such, users located at both sides of IOSs can be served efficiently. In this paper, a novel IOS-enhanced aerial offloading system is proposed in the presence of one ground eavesdropper. To maximize the secrecy energy efficienc...\",\n \"Title: Enabling Ubiquitous Non-Orthogonal Multiple Access and Pervasive Federated Learning via STAR-RIS\\nYear: 2021\\n\\nAbstract: This paper proposes a new, compatible, unified framework which integrates non-orthogonal multiple access (NOMA) and over-the-air federated learning (AirFL) via concurrent communication. In particular, a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is leveraged to adjust the signal processing order for efficient interference mitigation and omni-directional coverage extension. With the aim of investigating the impact of non-ideal wireless communication on AirFL, we provide a closed-form expression for the optimality gap over a given number of communication rounds. This result reveals that the learning performance is significantly affected by the resource allocation scheme and channel noise. To minimize the derived optimality gap, a mixed-integer non-linear programming (MINLP) problem is formulated by jointly designing the transmit power at users and configuration mode at the STAR-RIS. Through developing an alternating optimization algorithm, a suboptimal solution for the original MINLP problem is obtained. Simulation results show that the learning performance in terms of training loss and test accuracy can be effectively improved with the aid of the STAR-RIS.\",\n \"Title: A Digital Twin Based Design of the Semi-physical Marine Engine Room Simulator for Remote Maintenance Assistance\\nYear: 2021\\n\\nAbstract: The semi-physical marine engine room simulator is one of the necessary equipment for the training of marine engineers worldwide. However, its maintenance has become a problem for a long time due to its complexity and vulnerability. Although the semi-physical marine engine room simulator fails frequently, its failures are usually small-scale communication failures, which are easy to solve for experts. For such kind of micro faults, the cost of field maintenance by experts is very high due to the long journey. Generally, most manufactures will choose to assist the users’ maintenance by telephone or email firstly. If it does not work, they will have to send experts for field maintenance which is expensive for users. In this paper, we propose a digital twin architecture of the semi-physical marine engine room simulator to assist users in remote maintenance. The digital twin system is a multi-twins architecture consisting of the user digital twin and the manufacturer digital twin. The user digital twin is a technical guidance platform for information reception and display, through which the user can get technical adviser from the experts in the 3D virtual environment. The manufacture digital twin is a platform for monitoring and troubleshooting by experts. The communication faults are the main faults of the semi-physical marine engine room simulator, which are easy to monitor. Consequently, it is possible to realize remote maintenance assistance through the digital twin system without a lot of time cost and economic cost.\",\n \"Title: Detection of Covert Timing Channel Based on Time Series Symbolization\\nYear: 2021\\n\\nAbstract: Covert Timing Channels(CTCs) is a technique to leak information. CTCs only modify inter-arrival time sequence(IATs) between packets, consequently, traditional network security mechanisms, such as firewalls and proxies, can not effectively detect CTCs. If CTCs are maliciously utilized by criminals, will pose a great threat to network security. Classic CTCs detection methods, such as KS-test, Entropy-test, etc, not only have less universality and robustness, but also require more sampled IATs to detect CTCs, therefore, how to improve performance of detection methods against CTCs, has became a popular research in recent years. In this paper, a new CTCs detection method based on time series symbolization is proposed. It firstly converts the sampled IATs to symbolic time series, and regards each discrete value as a status. Then counts the times of transition for each status to status, and calculates the status transition probability matrix(STPM). Finally, it differentiates the label(overt or covert) of sampled IATs, by calculating similarity score. Experimental results about detection accuracy show that, in an ideal network environment, compared with classic methods, our method has better performance, with average accuracy of about 96%. Besides, our method has better performance as well, with the existence of network interference.\",\n \"Title: Covert Timing Channels Detection Based on Auxiliary Classifier Generative Adversarial Network\\nYear: 2021\\n\\nAbstract: Covert timing channels (CTCs) are defined as a mechanism that embeds covert information into network traffic. In a manner, information leakage caused by CTCs brings serious threat to network security. In recent years, detection of CTCs is a focus and a challenging task in the field of covert channel research. However, existing detection schemes based on statistical methods have poor performance in detecting multiple CTCs, and require so many inter-arrival times of packets that these schemes cannot detect CTCs in real time. In this paper, we propose a novel deep learning approach for CTCs detection, namely, covert timing channels detection based on auxiliary classifier generative adversarial network (CD-ACGAN). The network structure and loss function of CD-ACGAN are designed to be suitable for CTCs detection task. We first encode traffic flows into single-channel Gramian Angular Field (GAF) images. Then we use CD-ACGAN to learn features from GAF images and predict the classes of CTCs. Our experimental results show that our approach has high accuracy and strong robustness in detecting various CTCs.\",\n \"Title: Information Propagation Influenced by Population Heterogeneity Behavioral Adoption on Weighted Network\\nYear: 2022\\n\\nAbstract: In the realistic world, various individuals have distinct personalities, preferences, and attitudes toward new information and behavior acceptance, called population heterogeneity. It is seldom taken into account and theoretically analyzed in information propagation on a weighted network. Therefore, we divide individuals into fashionable and conservative individuals according to their passion degree and willingness for novel behaviors acceptance. Then, we build two behavior adoption threshold models corresponding to fashionable and conservative individuals on the weighted network to explore the effect of population heterogeneity on information propagation. Next, a partition theory based on edge weight and population heterogeneity is proposed to qualitatively analyze the information propagation mechanism. The theoretical analyses and simulation results show that fashionable individuals promote information propagation and behavior adoption. More importantly, the crossover phenomena of phase transition appear. When the fraction of fashionable individuals is relatively large, the increasing pattern of the final adoption size shows a second-order continuous phase transition. In comparison, the increasing pattern alters to first-order discontinuous phase transition with the decrease of the fraction of fashionable individuals. Moreover, reducing weight distribution heterogeneity promotes information propagation and slightly accelerates the change of the phase transition pattern from the first-order discontinuous to the second-order continuous. Besides, increasing the degree distribution heterogeneity accelerates the change of the phase transition pattern. Finally, our theoretical analyses coincide well with the simulation results.\",\n \"Title: Toward Communication-Learning Trade-Off for Federated Learning at the Network Edge\\nYear: 2022\\n\\nAbstract: In this letter, we study a wireless federated learning (FL) system where network pruning is applied to local users with limited resources. Although pruning is beneficial to reduce FL latency, it also deteriorates learning performance due to the information loss. Thus, a trade-off problem between communication and learning is raised. To address this challenge, we quantify the effects of network pruning and packet error on the learning performance by deriving the convergence rate of FL with a non-convex loss function. Then, closed-form solutions for pruning control and bandwidth allocation are proposed to minimize the weighted sum of FL latency and FL performance. Finally, numerical results demonstrate that i) our proposed solution can outperform benchmarks in terms of cost reduction and accuracy guarantee, and ii) a higher pruning rate would bring less communication overhead but also worsen FL accuracy, which is consistent with our theoretical analysis.\",\n \"Title: Safeguarding NOMA Networks via Reconfigurable Dual-Functional Surface Under Imperfect CSI\\nYear: 2022\\n\\nAbstract: This paper investigates the use of the reconfigurable dual-functional surface to guarantee the full-space secure transmission in non-orthogonal multiple access (NOMA) networks. In the presence of eavesdroppers, the downlink communication from the base station to the legitimate users is safeguarded by the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), where three practical operating protocols, namely energy splitting (ES), mode selection (MS), and time splitting (TS), are studied. The joint optimization of power allocation, active and passive beamforming is investigated to maximize the secrecy energy efficiency (SEE), taking into account the imperfect channel state information (CSI) of all channels. For ES, by approximating the semi-infinite constraints with the \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"><tex-math notation=\\\"LaTeX\\\">$\\\\mathcal {S}$</tex-math></inline-formula>\\n-procedure and general sign-definiteness, the problem is solved by an alternating optimization framework. Besides, the proposed algorithm is extended to the MS protocol by solving a mixed-integer non-convex problem. While for TS, a two-layer iterative method is proposed. Simulation results show that: 1) The proposed STAR-RIS assisted NOMA networks are able to provide up to 33.6% higher SEE than conventional RIS counterparts; 2) TS and ES protocols are generally preferable for low and high power domain, respectively; 3) The accuracy of CSI estimation and the bit resolution power consumption are crucial to reap the SEE benefits offered by STAR-RIS.\",\n \"Title: Cooperative Federated Learning and Model Update Verification in Blockchain-Empowered Digital Twin Edge Networks\\nYear: 2022\\n\\nAbstract: With the rapid development of Internet of Things (IoT), the digital twin is emerging as one of the most promising technologies to connect physical components with digital space for better optimization of physical systems. However, the limited wireless resource and security concerns impede the deployment of the digital twin in IoT. In this article, we exploit blockchain to propose a new digital twin edge networks framework for enabling flexible and secure digital twin construction. We first develop cooperative federated learning through an access point (AP) to help resource-limited smart devices in constructing digital twin at the network edges belonging to different mobile network operators (MNOs). Then, we propose a model update chain by leveraging directed acyclic graph (DAG) blockchain to secure both local model updates and global model updates. In order to incentivize the APs to help in local models training for resource-limited smart devices and also encourage the APs to contribute resource in local model update verification, we design an iterative double auction-based joint cooperative federated learning and local model update verification scheme. The optimal unified time for cooperative federated learning and local model update verification is solved to maximize social welfare. Numerical results illustrate that the proposed scheme is efficient in digital twin construction.\",\n \"Title: Federated Learning in Multi-RIS-Aided Systems\\nYear: 2022\\n\\nAbstract: The fundamental communication paradigms in the next-generation mobile networks are shifting from connected things to connected intelligence. The potential result is that current communication-centric wireless systems are greatly stressed when supporting computation-centric intelligent services with distributed big data. This is one reason that makes federated learning come into being, it allows collaborative training over many edge devices while avoiding the transmission of raw data. To tackle the problem of model aggregation in federated learning systems, this article resorts to multiple reconfigurable intelligent surfaces (RISs) to achieve efficient and reliable learning-oriented wireless connectivity. The seamless integration of communication and computation is actualized by over-the-air computation (AirComp), which can be deemed as one of the uplink nonorthogonal multiple access (NOMA) techniques without individual information decoding. Since all local parameters are uploaded via noisy concurrent transmissions, the unfavorable propagation error inevitably deteriorates the accuracy of the aggregated global model. The goals of this work are to 1) alleviate the signal distortion of AirComp over shared wireless channels and 2) speed up the convergence rate of federated learning. More specifically, both the mean-square error (MSE) and the device set in the model uploading process are optimized by jointly designing transceivers, tuning reflection coefficients, and selecting clients. Compared to baselines, extensive simulation results show that 1) the proposed algorithms can aggregate model more accurately and accelerate convergence and 2) the training loss and inference accuracy of federated learning can be improved significantly with the aid of multiple RISs.\",\n \"Title: Efficient Traffic Scheduling for Coexistence of eMBB and uRLLC in Industrial IoT Networks\\nYear: 2022\\n\\nAbstract: Ultra-reliable low-latency communication (uRLLC) is envisioned to efficiently support mission-critical scenarios, especially for industrial Internet of Things (IIoT). Considering the requirements of high throughput and massive connectivity in machine-type communications, uRLLC traffic is usually coexisted with enhanced mobile broadband (eMBB) services for the data-intensive industrial cases. To strike a balance between the two distinct tasks, this paper investigates a multi-objective optimization problem by taking into account the performance of both uRLLC and eMBB. Specifically, we aim at maximizing eMBB data rate and uRLLC reliability, while minimizing the communication overhead of control channels caused by uRLLC puncturing. To solve this challenging problem, an analytic hierarchy process method is adopted to estimate the importance of each objective with expert knowledge. Then, a coalitional game is invoked to evaluate the preference degree of resource blocks allocated to uRLLC devices. Following this, an improved Gale-Shapley algorithm is proposed for efficient traffic scheduling. Simulation results demonstrate that the proposed algorithm can achieve better performance in terms of eMBB throughput and uRLLC reliability with the reduced signal overhead.\",\n \"Title: A multimodal differential privacy framework based on fusion representation learning\\nYear: 2022\\n\\nAbstract: Differential privacy mechanisms vary in modalities, and there have been many methods implementing differential privacy on unimodal data. Few studies focus on unifying them to protect multimodal data, though privacy protection of multimodal data is of great significance. In our work, we propose a multimodal differential privacy protection framework. Firstly, we use multimodal representation learning to fuse different modalities and map them to the same subspace. Then based on this representation, we use the Local Differential Privacy (LDP) mechanism to protect data. We propose two protection methods for low-dimensional and high-dimensional fusion tensors respectively. The former is based on Binary Encoding, and the latter is based on multi-dimensional Fourier Transform. To the best of our knowledge, we are the first to propose LDP-based methods for the representation learning of multimodal fusion. Experimental results demonstrate the flexibility of our framework where both approaches show efficient performance as well as high data utility.\",\n \"Title: Mobile Feature Enhanced High-Accuracy Positioning Based on Carrier Phase and Bayesian Estimation\\nYear: 2022\\n\\nAbstract: The mobile feature and the moving positions are important for mobile communications and Internet of Things. Inspired by the potentially excellent precision of carrier phase positioning, this article proposes a mobile feature enhanced high-accuracy positioning algorithm based on the carrier phase and Bayesian estimation. The mobile feature is first estimated using time-differential carrier phase measurements. By combining the estimated position changes with instant ranging and carrier phase measurements, the factorized posterior probability of positioning is established based on the Bayes theorem. In turn, a factor graph-based positioning method is proposed to obtain precise positioning results. The numerical simulation results show that the proposed algorithm can achieve centimeter-level accuracy, and the positioning error can approach the Cramer–Rao lower bound.\",\n \"Title: A Novel Group Paging Control Method for Massive MTC Accesses in LTE Networks\\nYear: 2022\\n\\nAbstract: The fifth-generation (5G) systems have to deal with massive deployment of machine-type-communication (MTC) devices. System overload may occur during a random access procedure under a limited number of preamble resources and physical uplink shared channel resources especially when there exist massive MTC devices in a cell. In order to resolve the system overload (caused by the massive MTC deployment), the 3GPP proposed the adopted a group paging (GP)-based uplink access technique. But its performances dramatically decrease as the number of MTC devices increases. In this paper we propose a novel method, named ACB-based group paging overload control method (AGO). To reduce the number of simultaneous access MTC devices, AGO first scatters the MTC devices over a GP interval, and then automatically adjusts ACB parameters according to the load conditions. By doing so, AGO achieves high-channel access probability for MTC devices. Simulation results show that this method is superior to the GP and Pre-backoff mechanisms in terms of success and collision probability, average access latency and resource utilization rate.\",\n \"Title: Steganalysis of adaptive multi-rate speech streams with distributed representations of codewords\\nYear: 2022\\n\\nAbstract: With the wide application of adaptive multi-rate (AMR) speech coder, steganography and steganalysis based on AMR coded speech streams have become a hot spot in the field of information hiding. Quantization Index Modulation (QIM)-based steganography is one of the most effective approaches to hide secret information into AMR coded speech streams with excellent imperceptibility and robustness. So far, accurate detection for QIM-based steganography in short-length or low-embedding-rate speech streams remains an open problem, though some approaches can complete detection in a short time. To address this challenge, we first analyze and verify the characteristics of QIM-based steganography, and present a novel and high time-efficient steganalysis model based on distributed representations. Specifically, a codeword embedding layer is introduced to capture distributed representations with a denser space; then we introduce a bidirectional Long Short-Term Memory (LSTM) layer and propose a gated attention mechanism to provide contextual distribution features with better generalization capabilities; finally, a Multi-Layer Perceptron (MLP) classifier is designed to distinguish normal or steganographic objects. The experimental results demonstrate that the proposed model can effectively detect QIM-based steganography in AMR speech streams and outperform the state-of-the-art ones.\",\n \"Title: Spatial-Temporal Attention Network for Crime Prediction with Adaptive Graph Learning\\nYear: 2022\\n\\nAbstract: It is important but challenging to accurately predict urban crimes. Existing studies rely on domain knowledge specific, pre-defined inter-dependency graphs using extra urban data and have many disadvantages. We propose a novel framework, AGL-STAN, to efficiently capture complex spatial-temporal correlations of urban crimes with higher prediction accuracy but without extra data. In AGL-STAN, we design an adaptive graph learning method to learn the inter-dependencies among communities, and a time-aware self-attention method to accurately model the influence of time-varying crime incidents with a multihead attention mechanism. We demonstrate the superiority of AGL-STAN over the state-of-the-art methods through extensive experiments.\",\n \"Title: Battery-less Massive Access for Simultaneous Information Transmission and Federated Learning in WPT Networks\\nYear: 2022\\n\\nAbstract: One of the key visions for 6G is to enable Internet of Intelligence at the network edge. However, many battery-less devices face the dilemmas of energy shortage and spectrum deficiency. To tackle these challenges, we propose a simultaneous information transmission and federated learning (SITFL) scheme for the purpose of overcoming communication bottlenecks and accelerating data processing in wireless power transfer networks. For mean-square-error minimization, a low-complexity solution is developed to optimize the transmit and receive beamforming jointly. Simulation results demonstrate the effectiveness of the proposed solution for wireless powered SITFL networks.\",\n \"Title: Visualizing Multi-Agent Reinforcement Learning for Robotic Communication in Industrial IoT Networks\\nYear: 2022\\n\\nAbstract: With its mobility and flexibility, autonomous robots have received extensive attention in industrial Internet of Things (IoT). In this paper, we adopt non-orthogonal multiple access and multi-antenna technology to enhance the connectivity of sensors and the throughput of data collection through taking advantage of the power and spatial domains. For average sumrate maximization, we optimize the transmit power of sensors and the trajectories of robots jointly. To deal with uncertainty and dynamics in the industrial environment, we propose a multiagent reinforcement learning (MARL) algorithm with experience exchange. Next, we present the visualization of robotic communication and mobility to analyze the learning behavior intuitively. From the software implementation results, we observe that the proposed MARL algorithm can effectively adjust the communication strategies of sensors and control the trajectories of robots in a fully distributed manner. The code and demonstrations can be found at https://github.com/lry-bupt/Visual_MARL.\",\n \"Title: Elastic Routing Mechanism for Flying Ad Hoc Network\\nYear: 2022\\n\\nAbstract: Flying Ad-Hoc Network (FANET) is a hot topic in current research. The design of routing mechanism is challenging because when the scale of Unmanned Aerial Vehicle (UAV) nodes is large, vast amount of routing overhead may lead to network collapse. An elastic routing mechanism is proposed for large-scale small UAVs multitasking scenarios. Firstly, the New-Unifying Connected Dominating Set (N-UCDS) algorithm is proposed to construct a virtual backbone network based on the connected dominating set. The number of neighboring nodes, remaining energy and link duration are considered to influence the UAV network performance when electing backbone nodes. Secondly, by deploying and running the New Better Approach to Mobile Ad-Hoc Network-Advanced (NBATMAN-ADV) routing protocol on the backbone nodes, the link quality can be evaluated by using the received signal strength index and signal-to-noise ratio of the physical layer data. In this way, the change of the link can be quickly sensed while reducing the routing overhead. The simulation results show that the routing protocol proposed in this paper has significantly improved average packet delivery rate, end-to-end delay and received throughput compared with other traditional proactive routing protocols.\",\n \"Title: STFF-SM: Steganalysis Model Based on Spatial and Temporal Feature Fusion for Speech Streams\\nYear: 2023\\n\\nAbstract: The real-time detection of speech steganography in Voice-over-Internet-Protocol (VoIP) scenarios remains an open problem, as it requires steganalysis methods to perform for low-intensity embeddings and short-sample inputs, as well as provide rapid detection results. To address these challenges, this paper presents a novel steganalysis model based on spatial and temporal feature fusion (STFF-SM). Differing from the existing methods, we take both the integer and fractional pitch delays as input, and design subframe-stitch module to organically integrate subframe-wise integer delays and frame-wise fractional pitch delays. Further, we design a spatial fusion module based on pre-activation residual convolution to extract the pitch spatial features and gradually increase their dimensions to discover finer steganographic distortions to enhance the detection effect, where a Group-Squeeze-Weighting block is introduced to alleviate the information loss in the process of increasing the feature dimension. In addition, we design a temporal fusion module to extract pitch temporal features using the stacked LSTM, where a Gated Feed-Forward Network is introduced to learn the interaction between different feature maps while suppressing the features that are not useful for detection. We evaluated the performance of STFF-SM through comprehensive experiments and comparisons with the state-of-the-art solutions. The experimental results demonstrate that STFF-SM can well meet the needs of real-time detection of speech steganography in VoIP streams, and outperforms the existing methods in detection performance, especially with low embedding strengths and short window sizes.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":200,"string":"200"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13651,"string":"13,651"}}},{"rowIdx":13652,"cells":{"question_id":{"kind":"string","value":"Hideitsu Hino"},"question":{"kind":"string","value":"Generate an abstract for the title \"EEG dipole source localization with information criteria for multiple particle filters.\" using the following items: 1. Electroencephalography 2. Dipole source localization 3. Information criterion 4. Particle filtering 5. Auditory short-term memory task"},"answer":{"kind":"string","value":"Electroencephalography (EEG) is a non-invasive brain imaging technique that describes neural electrical activation with good temporal resolution. Source localization is required for clinical and functional interpretations of EEG signals, and most commonly is achieved via the dipole model; however, the number of dipoles in the brain should be determined for a reasonably accurate interpretation. In this paper, we propose a dipole source localization (DSL) method that adaptively estimates the dipole number by using a novel information criterion. Since the particle filtering process is nonparametric, it is not clear whether conventional information criteria such as Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) can be applied. In the proposed method, multiple particle filters run in parallel, each of which respectively estimates the dipole locations and moments, with the assumption that the dipole number is known and fixed; at every time step, the most predictive particle filter is selected by using an information criterion tailored for particle filters. We tested the proposed information criterion first through experiments on artificial datasets; these experiments supported the hypothesis that the proposed information criterion would outperform both AIC and BIC. We then analyzed real human EEG datasets collected during an auditory short-term memory task using the proposed method. We found that the alpha-band dipoles were localized to the right and left auditory areas during the auditory short-term memory task, which is consistent with previous physiological findings. These analyses suggest the proposed information criterion can work well in both model and real-world situations."},"correct_answer":{"kind":"string","value":"Electroencephalography (EEG) is a non-invasive brain imaging technique that describes neural electrical activation with good temporal resolution. Source localization is required for clinical and functional interpretations of EEG signals, and most commonly is achieved via the dipole model; however, the number of dipoles in the brain should be determined for a reasonably accurate interpretation. In this paper, we propose a dipole source localization (DSL) method that adaptively estimates the dipole number by using a novel information criterion. Since the particle filtering process is nonparametric, it is not clear whether conventional information criteria such as Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) can be applied. In the proposed method, multiple particle filters run in parallel, each of which respectively estimates the dipole locations and moments, with the assumption that the dipole number is known and fixed; at every time step, the most predictive particle filter is selected by using an information criterion tailored for particle filters. We tested the proposed information criterion first through experiments on artificial datasets; these experiments supported the hypothesis that the proposed information criterion would outperform both AIC and BIC. We then analyzed real human EEG datasets collected during an auditory short-term memory task using the proposed method. We found that the alpha-band dipoles were localized to the right and left auditory areas during the auditory short-term memory task, which is consistent with previous physiological findings. These analyses suggest the proposed information criterion can work well in both model and real-world situations."},"documents":{"kind":"list like","value":["Title: Item Preference Parameters from Grouped Ranking Observations\nYear: 2009\n\nAbstract: Given a set of rating data for a set of items, determining the values of items is a matter of importance. Various probability models have been proposed to solve this task. The Plackett-Luce model is one of such models, which parametrizes the value of each item by a real valued preference parameter. In this paper, the Plackett-Luce model is generalized to cope with the grouped ranking observations such as movies or restaurants ratings. Since the maximization of the likelihood of the proposed model is computationally intractable, the lower bound of the likelihood which is easy to evaluate is derived, and the em algorithm is adopted to the maximization of the lower bound.","Title: Robust hypersurface fitting based on random sampling approximations\nYear: 2012\n\nAbstract: This paper considers N−1-dimensional hypersurface fitting based on L2 distance in N-dimensional input space. The problem is usually reduced to hyperplane fitting in higher dimension. However, because feature mapping is generally a nonlinear mapping, it does not preserve the order of lengthes, and this derives an unacceptable fitting result. To avoid it, JNLPCA is introduced. JNLPCA defines the L2 distance in the feature space as a weighted L2 distance to reflect the metric in the input space. In the fitting, random sampling approximation of least k-th power deviation, and least α-percentile of squares are introduced to make estimation robust. The proposed hypersurface fitting method is evaluated by quadratic curve fitting and quadratic curve segments extraction from artificial data and a real image.","Title: Self-Calibration of Radially Symmetric Distortion by Model Selection\nYear: 2010\n\nAbstract: For self-calibration of general radially symmetric distortion (RSD) of omni directional cameras such as fish-eye lenses, calibration parameters are usually estimated so that curved lines, which are supposed to be straight in the real-world, are mapped to straight lines in the calibrated image, which is assumed to be taken by an ideal pin-hole camera. In this paper, a method of calibrating RSD is introduced base on the notion of principal component analysis (PCA). In the proposed method, the distortion function, which maps a distorted image to an ideal pin-hole camera image, is assumed to be a linear combination of a certain class of basis functions, and an algorithm for solving its coefficients by using line patterns is given. Then a method of selecting good basis functions is proposed, which aims to realize appropriate calibration in practice. Experimental results for synthetic data and real images are presented to emonstrate the performance of our calibration method.","Title: Item-user Preference Mapping with Mixture Models - Data Visualization for Item Preference\nYear: 2009\n\nAbstract: In this paper, we propose a visualization technique of a statistical relation of users and preference of items based on a mixture model. In our visualization, items are given as points in a few dimensional preference space, and user specific preferences are given as lines in the same space. The relationship between items and user preferences are intuitively interpreted via projections from points onto lines. As a primitive implementation, we introduce a mixture of the Bradley-Terry models, and visualize the relation between items and user preferences with benchmark data sets.","Title: Multiple kernel learning with gaussianity measures\nYear: 2012\n\nAbstract: Kernel methods are known to be effective for nonlinear multivariate analysis. One of the main issues in the practical use of kernel methods is the selection of kernel. There have been a lot of studies on kernel selection and kernel learning. Multiple kernel learning (MKL) is one of the promising kernel optimization approaches. Kernel methods are applied to various classifiers including Fisher discriminant analysis (FDA). FDA gives the Bayes optimal classification axis if the data distribution of each class in the feature space is a gaussian with a shared covariance structure. Based on this fact, an MKL framework based on the notion of gaussianity is proposed. As a concrete implementation, an empirical characteristic function is adopted to measure gaussianity in the feature space associated with a convex combination of kernel functions, and two MKL algorithms are derived. From experimental results on some data sets, we show that the proposed kernel learning followed by FDA offers strong classification power.","Title: Information estimators for weighted observations.\nYear: 2013\n\nAbstract: The Shannon information content is a valuable numerical characteristic of probability distributions. The problem of estimating the information content from an observed dataset is very important in the fields of statistics, information theory, and machine learning. The contribution of the present paper is in proposing information estimators, and showing some of their applications. When the given data are associated with weights, each datum contributes differently to the empirical average of statistics. The proposed estimators can deal with this kind of weighted data. Similar to other conventional methods, the proposed information estimator contains a parameter to be tuned, and is computationally expensive. To overcome these problems, the proposed estimator is further modified so that it is more computationally efficient and has no tuning parameter. The proposed methods are also extended so as to estimate the cross-entropy, entropy, and Kullback-Leibler divergence. Simple numerical experiments show that the information estimators work properly. Then, the estimators are applied to two specific problems, distribution-preserving data compression, and weight optimization for ensemble regression.","Title: An improved entropy-based multiple kernel learning\nYear: 2012\n\nAbstract: Kernel methods have been successfully used in many practical machine learning problems. However, the problem of choosing a suitable kernel is left to practitioners. One method to select the optimal kernel is to learn a linear combination of element kernels. A framework of multiple kernel learning based on conditional entropy minimization criterion (MCEM) has been proposed and it has been shown to work well for, e.g., speaker recognition tasks. In this paper, a computationally efficient implementation for MCEM, which utilizes sequential quadratic programming, is formulated. Through a comparative experiment to conventional MCEM algorithm on a speaker verification task, the proposed method is shown to offer comparable verification accuracy with considerable improvement in computational speed.","Title: Extraction of Basic Patterns of Household Energy Consumption\nYear: 2011\n\nAbstract: Solar power, wind power, and co-generation (combined heat and power) systems are possible candidate for household power generation. These systems have their advantages and disadvantages. To propose the optimal combination of the power generation systems, the extraction of basic patterns of energy consumption of the house is required. In this study, energy consumption patterns are modeled by mixtures of Gaussian distributions. Then, using the symmetrized Kullback-Leibler divergence as a distance measure of the distributions, the basic pattern of energy consumption is extracted by means of hierarchical clustering. By an experiment using the Annex 42 dataset, it is shown that the proposed method is able to extract typical energy consumption patterns.","Title: Entropy-based sliced inverse regression.\nYear: 2013\n\nAbstract: The importance of dimension reduction has been increasing according to the growth of the size of available data in many fields. An appropriate dimension reduction method of raw data helps to reduce computational time and to expose the intrinsic structure of complex data. Sliced inverse regression is a well-known dimension reduction method for regression, which assumes an elliptical distribution for the explanatory variable, and ingeniously reduces the problem of dimension reduction to a simple eigenvalue problem. Sliced inverse regression is based on the strong assumptions on the data distribution and the form of regression function, and there are a number of methods to relax or remove these assumptions to extend the applicability of the inverse regression method. However, each method is known to have its drawbacks either theoretically or empirically. To alleviate drawbacks in the existing methods, a dimension reduction method for regression based on the notion of conditional entropy minimization is proposed. Using entropy as a measure of dispersion of data, a low dimensional subspace is estimated without assuming any specific distribution nor any regression function. The proposed method is shown to perform comparable or superior to the conventional methods through experiments using artificial and real-world datasets.","Title: Robust hyperplane fitting based on k-th power deviation and α-quantile\nYear: 2011\n\nAbstract: In this paper, two methods for one-dimensional reduction of data by hyperplane fitting are proposed. One is least a-percentile of squares, which is an extension of least median of squares estimation and minimizes the a-percentile of squared Euclidean distance. The other is least k-th power deviation, which is an extension of least squares estimation and minimizes the k-th power deviation of squared Euclidean distance. Especially, for least k-th power deviation of 0 k ≤ 1, it is proved that a useful property, called optimal sampling property, holds in one-dimensional reduction of data by hyperplane fitting. The optimal sampling property is that the global optimum for affine hyperplane fitting passes through N data points when an N-1-dimensional hyperplane is fitted to the N-dimensional data. The performance of the proposed methods is evaluated by line fitting to artificial data and a real image.","Title: Multiple Kernel Learning by Conditional Entropy Minimization\nYear: 2010\n\nAbstract: Kernel methods have been successfully used in many practical machine learning problems. Choosing a suitable kernel is left to the practitioner. A common way to an automatic selection of optimal kernels is to learn a linear combination of element kernels. In this paper, a novel framework of multiple kernel learning is proposed based on conditional entropy minimization criterion. For the proposed framework, three multiple kernel learning algorithms are derived. The algorithms are experimentally shown to be comparable to or outperform kernel Fisher discriminant analysis and other multiple kernel learning algorithms on benchmark data sets.","Title: New Probabilistic Bounds on Eigenvalues and Eigenvectors of Random Kernel Matrices\nYear: 2012\n\nAbstract: Kernel methods are successful approaches for different machine learning problems. This success is mainly rooted in using feature maps and kernel matrices. Some methods rely on the eigenvalues/eigenvectors of the kernel matrix, while for other methods the spectral information can be used to estimate the excess risk. An important question remains on how close the sample eigenvalues/eigenvectors are to the population values. In this paper, we improve earlier results on concentration bounds for eigenvalues of general kernel matrices. For distance and inner product kernel functions, e.g. radial basis functions, we provide new concentration bounds, which are characterized by the eigenvalues of the sample covariance matrix. Meanwhile, the obstacles for sharper bounds are accounted for and partially addressed. As a case study, we derive a concentration inequality for sample kernel target-alignment.","Title: Sliced inverse regression with conditional entropy minimization\nYear: 2012\n\nAbstract: An appropriate dimension reduction of raw data helps to reduce computational time and to reveal the intrinsic structure of complex data. In this paper, a dimension reduction method for regression is proposed. The method is based on the well-known sliced inverse regression and conditional entropy minimization. Using entropy as a measure of dispersion of data distribution, dimension reduction subspace is estimated without assuming regression function form nor data distribution, unlike conventional sliced inverse regression. The proposed method is shown to perform well compared to some conventional methods through experiments using both artificial and real-world data sets.","Title: A Measure of Credibility of Solar Power Prediction\nYear: 2011\n\nAbstract: Recently, remarkable developments of new energy technologies have been achieved against various energy problems. Photovoltaic (PV) system, one of such technologies, has an advantage of utilizing infinite and clean energy. On the contrary, it also has a disadvantage of unreliable power supply mainly caused by unstable weather. The fluctuation of the power supply of PV systems are considerably large because of rapid insulation changes and rapid weather changes, and in some cases, it seems impossible to realize high-accuracy prediction even with sophisticated prediction models. In this paper, using recently proposed estimator for the Shannon information content, a method to output a measure of credibility for prediction is proposed. With the proposed method, it is possible to judge whether the energy supply at a certain future time is unpredictably fluctuate compared to the current value or not, and it is possible to take measures against the rapid change of solar energy generation in advance. From an experimental result using solar energy supply data, we see that the proposed measure of credibility reflects the difficulty of predicting solar energy supply.","Title: Gaussian multiple kernel learning with entropy power inequality\nYear: 2013\n\nAbstract: Kernel methods have become a standard solution for a large number of data analysis, and extensively utilized in the field of signal processing including analysis of speech, image, time series, and DNA sequences. The main difficulty in applying the kernel method is in designing the appropriate kernel for the specific data, and multiple kernel learning (MKL) is one of the principled approaches for kernel design problem. In this paper, a novel multiple kernel learning method based on the notion of Gaussianity evaluated by entropy power inequality is proposed. The notable characteristics of the proposed method are in utilizing the entropy power inequality for kernel learning, and in realizing an MKL algorithm which only optimizes the kernel combination coefficients, while the conventional methods need optimizing both combination coefficients and classifier parameters. The proposed MKL algorithm is experimentally shown to have good classification accuracy.","Title: A computationally efficient information estimator for weighted data\nYear: 2011\n\nAbstract: The Shannon information content is a fundamental quantity and it is of great importance to estimate it from observed dataset in the field of statistics, information theory, and machine learning. In this study, an estimator for the information content using a given set of weighted data is proposed. The empirical data distribution varies depending on the weight. The notable features of the proposed estimator are its computational efficiency and its ability to deal with weighted data. The proposed estimator is extended in order to estimate cross entropy, entropy and KL divergence with weighted data. Then, the estimators are applied to classification with one-class samples, and distribution preserving data compression problems.","Title: A Versatile Clustering Method for Electricity Consumption Pattern Analysis in Households.\nYear: 2013\n\nAbstract: Analysis and modeling of electric energy demand is indispensable for power planning, operation, facility investment, and urban planning. Because of recent development of renewable energy generation systems oriented for households, there is also a great demand for analysing the electricity usage and optimizing the way to install electricity generation systems for each household. In this study, empl...","Title: Speaker Verification Robust To Talking Style Variation Using Multiple Kernel Learning Based On Conditional Entropy Minimization\nYear: 2011\n\nAbstract: We developed a new speaker verification system that is robust to intra-speaker variation. There is a strong likelihood that intra-speaker variations will occur due to changes in talking styles, the periods when an individual speaks, and so on. It is well known that such variation generally degrades the performance of speaker verification systems. To solve this problem, we applied multiple kernel learning (MKL) based on conditional entropy minimization, which impose the data to be compactly aggregated for each speaker class and ensure that the different speaker classes were far apart from each other. Experimental results showed that the proposed speaker verification system achieved a robust performance to intra-speaker variation derived from changes in the talking styles compared to the conventional maximum margin-based system.","Title: Calibration of radially symmetric distortion based on linearity in the calibrated image\nYear: 2011\n\nAbstract: For calibration of general radially symmetric distortion of omnidirectional cameras such as fish-eye lenses, calibration parameters are usually estimated so that curved lines, which are supposed to be straight in the real-world, are mapped to straight lines in the calibrated image, which is called plumbline principle. Under the principle, the camera with radially symmetric distortion can be calibrated by at least one distorted line in a image, theoretically, and the calibrated image is equivalent to the image taken by an ideal pin-hole camera. In this paper, the method to optimize the calibration parameters by maximizing the sum of the straightness, which is invariant under translation, rotation and magnification (scaling), of distorted lines on calibrated image is proposed. The performance of the proposed method is evaluated by artificial data and a real image.","Title: An estimation of generalized bradley-terry models based on the em algorithm.\nYear: 2011\n\nAbstract: The Bradley-Terry model is a statistical representation for one's preference or ranking data by using pairwise comparison results of items. For estimation of the model, several methods based on the sum of weighted Kullback-Leibler divergences have been proposed from various contexts. The purpose of this letter is to interpret an estimation mechanism of the Bradley-Terry model from the viewpoint of flatness, a fundamental notion used in information geometry. Based on this point of view, a new estimation method is proposed on a framework of the em algorithm. The proposed method is different in its objective function from that of conventional methods, especially in treating unobserved comparisons, and it is consistently interpreted in a probability simplex. An estimation method with weight adaptation is also proposed from a viewpoint of the sensitivity. Experimental results show that the proposed method works appropriately, and weight adaptation improves accuracy of the estimate.","Title: An Information Theoretic Perspective of the Sparse Coding\nYear: 2009\n\nAbstract: The sparse coding method is formulated as an information theoretic optimization problem. The rate distortion theory leads to an objective functional which can be interpreted as an information theoretic formulation of the sparse coding. Viewing as an entropy minimization problem, the rate distortion theory and consequently the sparse coding are extended to discriminative variants. As a concrete example of this information theoretic sparse coding, a discriminative non-linear sparse coding algorithm with neural networks is proposed. Experimental results of gender classification by face images show that the discriminative sparse coding is more robust to noise, compared to the conventional method which directly uses images as inputs to a linear support vector machine.","Title: Sparse Coding Approach for Multi-Frame Image Super Resolution.\nYear: 2014\n\nAbstract: An image super-resolution method from multiple observation of low-resolution images is proposed. The method is based on sub-pixel accuracy block matching for estimating relative displacements of observed images, and sparse signal representation for estimating the corresponding high-resolution image. Relative displacements of small patches of observed low-resolution images are accurately estimated by a computationally efficient block matching method. Since the estimated displacements are also regarded as a warping component of image degradation process, the matching results are directly utilized to generate low-resolution dictionary for sparse image representation. The matching scores of the block matching are used to select a subset of low-resolution patches for reconstructing a high-resolution patch, that is, an adaptive selection of informative low-resolution images is realized. When there is only one low-resolution image, the proposed method works as a single-frame super-resolution method. The proposed method is shown to perform comparable or superior to conventional single- and multi-frame super-resolution methods through experiments using various real-world datasets.","Title: A conditional entropy minimization criterion for dimensionality reduction and multiple kernel learning.\nYear: 2010\n\nAbstract: Reducing the dimensionality of high-dimensional data without losing its essential information is an important task in information processing. When class labels of training data are available, Fisher discriminant analysis (FDA) has been widely used. However, the optimality of FDA is guaranteed only in a very restricted ideal circumstance, and it is often observed that FDA does not provide a good classification surface for many real problems. This letter treats the problem of supervised dimensionality reduction from the viewpoint of information theory and proposes a framework of dimensionality reduction based on class-conditional entropy minimization. The proposed linear dimensionality-reduction technique is validated both theoretically and experimentally. Then, through kernel Fisher discriminant analysis (KFDA), the multiple kernel learning problem is treated in the proposed framework, and a novel algorithm, which iteratively optimizes the parameters of the classification function and kernel combination coefficients, is proposed. The algorithm is experimentally shown to be comparable to or outperforms KFDA for large-scale benchmark data sets, and comparable to other multiple kernel learning techniques on the yeast protein function annotation task.","Title: Integration of MKL-Based and I-Vector-Based Speaker Verification by Short Utterances\nYear: 2013\n\nAbstract: We developed a speaker verification system that is efficient for short utterances. The i-vector-based speaker representation has helped realize highly accurate speaker verification systems, however, it might be not robust against short utterances because the reliability of statistics required for extracting i-vectors is low. On the other hand, multiple kernel learning based on conditional entropy minimization has also achieved high accuracy in speaker verification that is robust against intra-speaker variability. To improve the robustness of speaker verification systems against short utterances, we attempted to integrate the above-mentioned complementary systems. Our experimental results showed that the proposed system integration achieved high-accuracy speaker verification systems, irrespective of the utterance lengths, even for very short utterances (e.g., less than two seconds).","Title: A tree search approach to sparse coding\nYear: 2012\n\nAbstract: Sparse coding is an important optimization problem with numerous applications. In this paper, we describe the problem and the commonly used pursuit methods, and propose a best-first tree search algorithm employing multiple queues for unexplored tree nodes. We assess the effectiveness of our method in an extensive computational experiment, showing its superiority over other methods even for modest computational time.","Title: Calibration of Radially Symmetric Distortion by Fitting Principal Component\nYear: 2009\n\nAbstract: To calibrate radially symmetric distortion of omnidirectional cameras such as fish-eye lenses, calibration parameters are usually estimated so that lines, which are supposed to be straight in the 3D real scene, are mapped to straight lines in the calibrated image. In this paper, this problem is treated as a fitting problem of the principal component in uncalibrated images, and an estimation procedure of calibration parameters is proposed based on the principal component analysis. Experimental results for synthetic data and real images are presented to demonstrate the performance of our calibration method.","Title: A grouped ranking model for item preference parameter.\nYear: 2010\n\nAbstract: Given a set of rating data for a set of items, determining preference levels of items is a matter of importance. Various probability models have been proposed to solve this task. One such model is the Plackett-Luce model, which parameterizes the preference level of each item by a real value. In this letter, the Plackett-Luce model is generalized to cope with grouped ranking observations such as movie or restaurant ratings. Since it is difficult to maximize the likelihood of the proposed model directly, a feasible approximation is derived, and the em algorithm is adopted to find the model parameter by maximizing the approximate likelihood which is easily evaluated. The proposed model is extended to a mixture model, and two applications are proposed. To show the effectiveness of the proposed model, numerical experiments with real-world data are carried out.","Title: Combination of Multiple Distance Measures for Protein Fold Classification\nYear: 2013\n\nAbstract: In structural biology, measuring the similarity between two protein structures is an essential task. The most common approach is to find the best alignment between two protein backbone structures and use the root mean square deviation (RMSD) of the superimposed alpha-carbon atom coordinates as the distance measurement. Other approaches extract features of the protein structures and the similarity measure is based on the extracted features. However, there is no single best approach, as each has its own advantages and limitations. One intuitive idea is that a better result can be obtained by combining complementary approaches. In this paper, we propose a new approach to protein fold classification, by introducing the concept of large margin nearest neighbor for combining multiple measures of distance between protein structures. We combine the Euclidean distance matrices of 12 features extracted from the amino acid sequence of the protein, the RMSD obtained from the geometrical alignment using Combinatorial Extension, and the canonical angles between the subspaces generated from the synthesized multi-view protein structure images. We demonstrate the effectiveness of the proposed method by classifying 27 fold classes of proteins in the Ding Dubchak dataset.","Title: Entropy Power Inequality for Learning Optimal Combination of Kernel Functions\nYear: 2015\n\nAbstract: Kernel methods have become a standard solution for a large number of data analyses, and extensively utilized in the field of signal processing including analysis of speech, image, time series, and DNA sequences. The main difficulty in applying the kernel method is in designing the appropriate kernel for the specific data, and multiple kernel learning (MKL) is one of the principled approaches for kernel design problem. In this paper, a novel multiple kernel learning method based on the notion of Gaussianity evaluated by entropy power inequality is proposed. The notable characteristics of the proposed method are in utilizing the entropy power inequality for kernel learning, and in realizing an MKL algorithm which only optimizes the kernel combination coefficients, while the conventional methods need optimizing both combination coefficients and classifier parameters. The proposed kernel learning approach is shown to have good classification accuracy through a set of standard datasets for dichotomy classification.","Title: A nonparametric clustering algorithm with a quantile-based likelihood estimator.\nYear: 2014\n\nAbstract: Clustering is a representative of unsupervised learning and one of the important approaches in exploratory data analysis. By its very nature, clustering without strong assumption on data distribution is desirable. Information-theoretic clustering is a class of clustering methods that optimize information-theoretic quantities such as entropy and mutual information. These quantities can be estimated in a nonparametric manner, and information-theoretic clustering algorithms are capable of capturing various intrinsic data structures. It is also possible to estimate information-theoretic quantities using a data set with sampling weight for each datum. Assuming the data set is sampled from a certain cluster and assigning different sampling weights depending on the clusters, the cluster-conditional information-theoretic quantities are estimated. In this letter, a simple iterative clustering algorithm is proposed based on a nonparametric estimator of the log likelihood for weighted data sets. The clustering algorithm is also derived from the principle of conditional entropy minimization with maximum entropy regularization. The proposed algorithm does not contain a tuning parameter. The algorithm is experimentally shown to be comparable to or outperform conventional nonparametric clustering methods.","Title: Sensing Visual Attention by Sequential Patterns\nYear: 2014\n\nAbstract: A method for sensing human visual attention is proposed. The method is based on the analysis of sequential image patterns of faces and irises observed at regular time intervals. The basic concept is to represent the set of image patterns produced by the action of gazing at a certain area as a nonlinear subspace in a high-dimensional pattern vector space. Such a space is called an attention subspace. In this framework, an input subspace from an unknown action is classified into an attention subspace of gazing at a certain area or into attention subspaces of gazing at other areas (named non-attention subspaces) by measuring the canonical angles between the input subspace and pre-computed dictionary subspaces. To maintain performance even in the presence of head movement, two mechanisms are introduced: 1) the kernel orthogonal mutual subspace method, which is suitable for classifying sets of multiple images, and 2) a kernel function for considering the head position in addition to a kernel function for pixel values. The stable performance of the proposed method including situations with head movements is demonstrated through experiments.","Title: A Kernel Method to Extract Common Features Based on Mutual Information.\nYear: 2014\n\nAbstract: Kernel canonical correlation analysis (CCA) aims to extract common features from a pair of multivariate data sets by maximizing a linear correlation between nonlinear mappings of the data. However, the kernel CCA tends to obtain the features that have only small information of original multivariates in spite of their high correlation, because it considers only statistics of the extracted features and the nonlinear mappings have high degree of freedom. We propose a kernel method for common feature extraction based on mutual information that maximizes a new objective function. The objective function is a linear combination of two kinds of mutual information, one between the extracted features and the other between the multivariate and its feature. A large value of the former mutual information provides strong dependency to the features, and the latter prevents loss of the feature's information related to the multivariate. We maximize the objective function by using the Parallel Tempering MCMC in order to overcome a local maximum problem. We show the effectiveness of the proposed method via numerical experiments.","Title: Intrinsic Graph Structure Estimation Using Graph Laplacian\nYear: 2014\n\nAbstract: A graph is a mathematical representation of a set of variables where some pairs of the variables are connected by edges. Common examples of graphs are railroads, the Internet, and neural networks. It is both theoretically and practically important to estimate the intensity of direct connections between variables. In this study, a problem of estimating the intrinsic graph structure from observed data is considered. The observed data in this study are a matrix with elements representing dependency between nodes in the graph. The dependency represents more than direct connections because it includes influences of various paths. For example, each element of the observed matrix represents a co-occurrence of events at two nodes or a correlation of variables corresponding to two nodes. In this setting, spurious correlations make the estimation of direct connection difficult. To alleviate this difficulty, a digraph Laplacian is used for characterizing a graph. A generative model of this observed matrix is proposed, and a parameter estimation algorithm for the model is also introduced. The notable advantage of the proposed method is its ability to deal with directed graphs, while conventional graph structure estimation methods such as covariance selections are applicable only to undirected graphs. The algorithm is experimentally shown to be able to identify the intrinsic graph structure.","Title: Change-Point Detection in a Sequence of Bags-of-Data\nYear: 2016\n\nAbstract: In this paper, the limitation that is prominent in most existing works of change-point detection methods is addressed by proposing a nonparametric, computationally efficient method. The limitation is that most works assume that each data point observed at each time step is a single multi-dimensional vector. However, there are many situations where this does not hold. Therefore, a setting where each observation is a collection of random variables, which we call a bag of data, is considered.","Title: Learning Ancestral Atom via Sparse Coding\nYear: 2013\n\nAbstract: Sparse signal models have been the focus of recent research. In sparse coding, signals are represented with a linear combination of a small number of elementary signals called atoms, and the collection of atoms is called a dictionary. Design of the dictionary has strong influence on the signal approximation performance. Recently, to put prior information into dictionary learning, several methods imposing a certain kind of structure on the dictionary are proposed. In this paper, like wavelet analysis, a dictionary for sparse signal representation is assumed to be generated from an ancestral atom, and a method for learning the ancestral atom is proposed. The proposed algorithm updates the ancestral atom by iterating dictionary update in unstructured dictionary space and projection of the updated dictionary onto the structured dictionary space. The algorithm allows a simple differential geometric interpretation. Numerical experiments are performed to show the characteristics and advantages of the proposed algorithm.","Title: Non-parametric entropy estimators based on simple linear regression.\nYear: 2015\n\nAbstract: Estimators for differential entropy are proposed. The estimators are based on the second order expansion of the probability mass around the inspection point with respect to the distance from the point. Simple linear regression is utilized to estimate the values of density function and its second derivative at a point. After estimating the values of the probability density function at each of the given sample points, by taking the empirical average of the negative logarithm of the density estimates, two entropy estimators are derived. Other entropy estimators which directly estimate entropy by linear regression, are also proposed. The proposed four estimators are shown to perform well through numerical experiments for various probability distributions.","Title: Multi-frame image super resolution based on sparse coding\nYear: 2015\n\nAbstract: An image super-resolution method from multiple observation of low-resolution images is proposed. The method is based on sub-pixel accuracy block matching for estimating relative displacements of observed images, and sparse signal representation for estimating the corresponding high-resolution image, where correspondence between high- and low-resolution images are modeled by a certain degradation process. Relative displacements of small patches of observed low-resolution images are accurately estimated by a computationally efficient block matching method. The matching scores of the block matching are used to select a subset of low-resolution patches for reconstructing a high-resolution patch, that is, an adaptive selection of informative low-resolution images is realized. The proposed method is shown to perform comparable or superior to conventional super-resolution methods through experiments using various images.","Title: Protein Fold Classification Using Large Margin Combination Of Distance Metrics\nYear: 2016\n\nAbstract: Many methods have been proposed for measuring the structural similarity between two protein folds. However, it is difficult to select one best method from them for the classification task, as each method has its own strength and weakness. Intuitively, combining multiple methods is one solution to get the optimal classification results. In this paper, by generalizing the concept of the large margin nearest neighbor (LMNN), a method for combining multiple distance metrics from different types of protein structure comparison methods for protein fold classification task is proposed. While LMNN is limited to Mahalanobis-based distance metric learning from a set of feature vectors of training data, the proposed method learns an optimal combination of metrics from a set of distance metrics by minimizing the distances between intra-class data and enlarging the distances of different classes' data. The main advantage of the proposed method is the capability in finding an optimal weight coefficient for combination of many metrics, possibly including poor metrics, avoiding the difficulties in selecting which metrics to be included for the combination. The effectiveness of the proposed method is demonstrated on classification experiments using two public protein datasets, namely, Ding Dubchak dataset and ENZYMES dataset.","Title: Double Sparse Multi-Frame Image Super Resolution.\nYear: 2015\n\nAbstract: A large number of image super resolution algorithms based on the sparse coding are proposed, and some algorithms realize the multi-frame super resolution. In multi-frame super resolution based on the sparse coding, both accurate image registration and sparse coding are required. Previous study on multi-frame super resolution based on sparse coding firstly apply block matching for image registration, followed by sparse coding to enhance the image resolution. In this paper, these two problems are solved by optimizing a single objective function. The results of numerical experiments support the effectiveness of the proposed approch.","Title: Patchworking multiple pairwise distances for learning with distance matrices\nYear: 2015\n\nAbstract: A classification framework using only a set of distance matrices is proposed. The proposed algorithm can learn a classifier only from a set of distance matrices or similarity matrices, hence applicable to structured data, which do not have natural vector representation such as time series and graphs. Random forest is used to explore ideal feature representation based on the distance between points defined by a set of given distance matrices. The effectiveness of the proposed method is evaluated through experiments with point process data and graph structured data.","Title: Linear Discriminant Analysis with Adherent Regularization.\nYear: 2015\n\nAbstract: Sparse modeling have become one of the standard approaches for latent variable analysis in the literature of statistics, machine learning and signal processing. This paper considers a supervised dimension reduction, which is a fundamental problem in data science. Particularly, the problem of linear discriminant analysis is considered. Extending the previous attempt to impose sparsity invoking regularization for Fisher's discriminant model, the proposed method bridges two different formulations of linear discriminant analysis, namely, the Fisher's discriminant model and the normal model, via a particular form of regularization. The proposed discriminant problem is efficiently solved by using the proximal point algorithm. The proposed method is shown to work well through experiments using both artificial and real-world datasets.","Title: Personal Authentication Based on 3D Configuration of Micro-feature Points on Facial Surface.\nYear: 2015\n\nAbstract: This paper proposes a personal authentication method based on the 3D configuration of micro-feature points such as moles and freckles, plus common feature points such as corners of eyes, edges of mouth and nostrils. The basic idea behind the proposed method is the assumption that such 3D configuration can be uniquely determined by individuals. To compare two configurations of feature points effectively, the concept of 3D shape subspace in a high-dimensional vector space is introduced. With this idea, the task of comparing the sets of feature points is converted to that of measuring the structural similarity between the corresponding shape subspaces. The validity of the proposed method is demonstrated through experiments with feature points from actual face images. In addition, the performance limit of the method is explored using sets of artificially generated feature points.","Title: Group Sparsity Tensor Factorization for De-anonymization of Mobility Traces\nYear: 2015\n\nAbstract: The de-anonymization attack using personalized transition matrices is known as one of the most successful approaches to link anonymized traces with users. However, since many users disclose only a small amount of location information to the public in their daily lives, the amount of training data available to the adversary can be very small. The aim of this paper is to quantify the risk of de-anonymization in this realistic situation. To achieve this aim, we utilize the fact that spatial data can form group structure, and propose group sparsity tensor factorization to train the personalized transition matrices that capture spatial group structure from a small amount of training data. We apply our training method to the de-anonymization attack, and evaluate it using the Geolife dataset. The results show that the training method using tensor factorization outperforms the Maximum Likelihood estimation method, and is further improved by incorporating group sparsity regularization.","Title: Spotting fingerspelled words from sign language video by temporally regularized canonical component analysis\nYear: 2016\n\nAbstract: A method for spotting specific words in sign language video is proposed. In classes and talks given using Japanese Sign Language, words that do not have a defined sign, such as the names of people, objects, and places, are represented by sets of multiple characters from the Japanese finger alphabet. The difficulty of recognizing these words has created strong demand for the ability to spot specific words in order to help interpreters and the audience to follow a talk. We address the spotting task by employing the basic idea of temporal regularized canonical correlation analysis (TRCCA), which can simultaneously handle shape and motion information about a 3D object. The classification accuracy of TRCCA is enhanced by incorporating two functions: 1) parallel processing with multiple time scales, 2) strong implicit feature mapping by nonlinear orthogonalization. The enhanced TRCCA is called \"kernel orthogonal TRCCA (KOTRCCA)\". The effectiveness of the proposed method using KOTRCCA is demonstrated through experiments spotting eight different words in sign language videos.","Title: Nonparametric e-Mixture Estimation.\nYear: 2016\n\nAbstract: This study considers the common situation in data analysis when there are few observations of the distribution of interest or the target distribution, while abundant observations are available from auxiliary distributions. In this situation, it is natural to compensate for the lack of data from the target distribution by using data sets from these auxiliary distributions-in other words, approximating the target distribution in a subspace spanned by a set of auxiliary distributions. Mixture modeling is one of the simplest ways to integrate information from the target and auxiliary distributions in order to express the target distribution as accurately as possible. There are two typical mixtures in the context of information geometry: the [Formula: see text]- and [Formula: see text]-mixtures. The [Formula: see text]-mixture is applied in a variety of research fields because of the presence of the well-known expectation-maximazation algorithm for parameter estimation, whereas the [Formula: see text]-mixture is rarely used because of its difficulty of estimation, particularly for nonparametric models. The [Formula: see text]-mixture, however, is a well-tempered distribution that satisfies the principle of maximum entropy. To model a target distribution with scarce observations accurately, this letter proposes a novel framework for a nonparametric modeling of the [Formula: see text]-mixture and a geometrically inspired estimation algorithm. As numerical examples of the proposed framework, a transfer learning setup is considered. The experimental results show that this framework works well for three types of synthetic data sets, as well as an EEG real-world data set.","Title: Doubly sparse structure in image super resolution\nYear: 2016\n\nAbstract: There are a large number of image super resolution algorithms based on the sparse coding, and some algorithms realize multi-frame super resolution. For utilizing multiple low resolution observations, both accurate image registration and sparse coding are required. Previous study on multi-frame super resolution based on sparse coding firstly apply block matching for image registration, followed by sparse coding to enhance the image resolution. In this paper, these two problems are solved by optimizing a single objective function. The proposed formulation not only has a mathematically interesting structure called the double sparsity, but also offers improved numerical performance.","Title: Non-parametric e-mixture of Density Functions.\nYear: 2016\n\nAbstract: Mixture modeling is one of the simplest ways to represent complicated probability density functions, and to integrate information from different sources. There are two typical mixtures in the context of information geometry, the m- and e-mixtures. This paper proposes a novel framework of non-parametric e-mixture modeling by using a simple estimation algorithm based on geometrical insights into the characteristics of the e-mixture. An experimental result supports the proposed framework.","Title: An Entropy Estimator Based on Polynomial Regression with Poisson Error Structure.\nYear: 2016\n\nAbstract: A method for estimating Shannon differential entropy is proposed based on the second order expansion of the probability mass around the inspection point with respect to the distance from the point. Polynomial regression with Poisson error structure is utilized to estimate the values of density function. The density estimates at every given data points are averaged to obtain entropy estimators. The proposed estimator is shown to perform well through numerical experiments for various probability distributions.","Title: Group Sparsity Tensor Factorization for Re-Identification of Open Mobility Traces.\nYear: 2017\n\nAbstract: Re-identification attacks based on a Markov chain model have been widely studied to understand how anonymized traces are linked to users. This approach is known to enable users to be re-identified with high accuracy when an adversary trains a personalized transition matrix for each target user using a large amount of training data, and when all of the anonymized traces are from the target users. In reality, however, the amount of training data for each target user can be very small, since many users disclose only a small amount of their location information to the public. In addition, many of the anonymized traces are from “non-target” users, whose personalized transition matrices cannot be trained in advance. This paper aims to quantify the risk of re-identification in the realistic situation explained earlier. We first utilize the fact that spatial data can form a group structure, and propose group sparsity tensor factorization to effectively train the personalized transition matrices from a small number of training traces. We second formulate a re-identification attack in an “open” scenario, where many of the anonymized traces are from non-target users. Specifically, we regard this type of attack as a biometric verification (or identification) task, and propose a framework and an algorithm for performing this task using a population transition matrix, which is computed from personalized transition matrices. Our experimental results using three real data sets show that a training method using tensor factorization significantly outperforms the maximum likelihood estimation method, and is further improved by incorporating group sparsity regularization.","Title: Double sparsity for multi-frame super resolution.\nYear: 2017\n\nAbstract: A number of image super resolution algorithms based on the sparse coding have successfully implemented multi-frame super resolution in recent years. In order to utilize multiple low-resolution observations, both accurate image registration and sparse coding are required. Previous study on multi-frame super resolution based on sparse coding firstly apply block matching for image registration, followed by sparse coding to enhance the image resolution. In this paper, these two problems are solved by optimizing a single objective function. The proposed formulation not only has a mathematically interesting structure, called the double sparsity, but also yields comparable or improved numerical performance to conventional methods.","Title: 3d Object Recognition With Enhanced Grassmann Discriminant Analysis\nYear: 2016\n\nAbstract: Subspace representation has become a promising choice in the classification of 3D objects such as face and hand shape, as it can model compactly the appearance of an object, represent effectively the variations such as the change in pose and illumination condition. Subspace based methods tend to require complicated formulation, though, we can utilize the notion of Grassmann manifold to cast the complicated formulation into a simple one in a unified manner. Each subspace is represented by a point on the manifold. Thank to this useful correspondence, various types of conventional methods have been constructed on a manifold by the kernel trick using a Grassmann kernel. In particular, discriminant analysis on Grassmann manifold (GDA) have been known as one of the useful tools for image set classification. GDA can work as a powerful feature extraction method on the manifold. However, there remains room to improve its ability in that the discriminative space is determined depending on the set of data points on the manifold. This suggests that if the data on a manifold are not so discriminative, the ability of GDA may be limited. To overcome this limitation, we construct a set of more discriminative class subspaces as the input for GDA. For this purpose, we propose to project class subspaces onto a generalized difference subspace (GDS), before mapping class subspaces onto the manifold. The GDS projection can magnify the angles between class subspaces. As a result, the separability of data points between different classes is improved and the ability of GDA is enhanced. The effectiveness of our enhanced GDA is demonstrated through classification experiments with CMU face database and hand shape database.","Title: Local Intrinsic Dimension Estimation by Generalized Linear Modeling.\nYear: 2017\n\nAbstract: We propose a method for intrinsic dimension estimation. By fitting the power of distance from an inspection point and the number of samples included inside a ball with a radius equal to the distance, to a regression model, we estimate the goodness of fit. Then, by using the maximum likelihood method, we estimate the local intrinsic dimension around the inspection point. The proposed method is show...","Title: Classification Of Volcanic Ash Particles Using A Convolutional Neural Network And Probability\nYear: 2018\n\nAbstract: Analyses of volcanic ash are typically performed either by qualitatively classifying ash particles by eye or by quantitatively parameterizing its shape and texture. While complex shapes can be classified through qualitative analyses, the results are subjective due to the difficulty of categorizing complex shapes into a single class. Although quantitative analyses are objective, selection of shape parameters is required. Here, we applied a convolutional neural network (CNN) for the classification of volcanic ash. First, we defined four basal particle shapes (blocky, vesicular, elongated, rounded) generated by different eruption mechanisms (e.g., brittle fragmentation), and then trained the CNN using particles composed of only one basal shape. The CNN could recognize the basal shapes with over 90% accuracy. Using the trained network, we classified ash particles composed of multiple basal shapes based on the output of the network, which can be interpreted as a mixing ratio of the four basal shapes. Clustering of samples by the averaged probabilities and the intensity is consistent with the eruption type. The mixing ratio output by the CNN can be used to quantitatively classify complex shapes in nature without categorizing forcibly and without the need for shape parameters, which may lead to a new taxonomy.","Title: Localizing Current Dipoles From Eeg Data Using A Birth-Death Process\nYear: 2018\n\nAbstract: A common approach to the electroencephalogram (EEG) source localization problem is to estimate the states of current dipoles. However, the dipole estimation problem is difficult because not only is it an inverse problem but also the number of dipoles can change over time. In this paper, we model the relationship between current dipoles and EEG observations using a state-space model where the creation and annihilation of dipoles is represented as a birth-death process. We estimate the dipoles' positions and moments with a Rao-Blackwellized particle filter and estimate whether a new dipole has been created or an existing one annihilated via the Bayesian information criterion. Experiments on both synthetic and real data show that the proposed model and estimation method can effectively estimate the number and positions of the dipoles.","Title: Estimation of neural connections from partially observed neural spikes.\nYear: 2018\n\nAbstract: Plasticity is one of the most important properties of the nervous system, which enables animals to adjust their behavior to the ever-changing external environment. Changes in synaptic efficacy between neurons constitute one of the major mechanisms of plasticity. Therefore, estimation of neural connections is crucial for investigating information processing in the brain. Although many analysis methods have been proposed for this purpose, most of them suffer from one or all the following mathematical difficulties: (1) only partially observed neural activity is available; (2) correlations can include both direct and indirect pseudo-interactions; and (3) biological evidence that a neuron typically has only one type of connection (excitatory or inhibitory) should be considered. To overcome these difficulties, a novel probabilistic framework for estimating neural connections from partially observed spikes is proposed in this paper. First, based on the property of a sum of random variables, the proposed method estimates the influence of unobserved neurons on observed neurons and extracts only the correlations among observed neurons. Second, the relationship between pseudo-correlations and target connections is modeled by neural propagation in a multiplicative manner. Third, a novel information-theoretic framework is proposed for estimating neuron types. The proposed method was validated using spike data generated by artificial neural networks. In addition, it was applied to multi-unit data recorded from the CA1 area of a rat’s hippocampus. The results confirmed that our estimates are consistent with previous reports. These findings indicate that the proposed method is useful for extracting crucial interactions in neural signals as well as in other multi-probed point process data.","Title: mmpp: A package for calculating similarity and distance metrics for simple and marked temporal point processes\nYear: 2015\n\nAbstract: A simple temporal point process (SPP) is an important class of time series, where the sample realization of the process is solely composed of the times at which events occur. Particular examples of point process data are neuronal spike patterns or spike trains, and a large number of distance and similarity metrics for those data have been proposed. A marked point process (MPP) is an extension of a simple temporal point process, in which a certain vector valued mark is associated with each of the temporal points in the SPP. Analyses of MPPs are of practical importance because instances of MPPs include recordings of natural disasters such as earthquakes and tornadoes. In this paper, we introduce the R package mmpp, which implements a number of distance and similarity metrics for SPPs, and also extends those metrics for dealing with MPPs.","Title: ider: Intrinsic Dimension Estimation with R\nYear: 2017\n\nAbstract: In many data analyses, the dimensionality of the observed data is high while its intrinsic dimension remains quite low. Estimating the intrinsic dimension of an observed dataset is an essential preliminary step for dimensionality reduction, manifold learning, and visualization. This paper introduces an R package, named ider, that implements eight intrinsic dimension estimation methods, including a recently proposed method based on a second-order expansion of a probability mass function and a generalized linear model. The usage of each function in the package is explained with datasets generated using a function that is also included in the package.","Title: Bayesian Optimization for Multi-objective Optimization and Multi-point Search.\nYear: 2019\n\nAbstract: Bayesian optimization is an effective method to efficiently optimize unknown objective functions with high evaluation costs. Traditional Bayesian optimization algorithms select one point per iteration for single objective function, whereas in recent years, Bayesian optimization for multi-objective optimization or multi-point search per iteration have been proposed. However, Bayesian optimization that can deal with them at the same time in non-heuristic way is not known at present. We propose a Bayesian optimization algorithm that can deal with multi-objective optimization and multi-point search at the same time. First, we define an acquisition function that considers both multi-objective and multi-point search problems. It is difficult to analytically maximize the acquisition function as the computational cost is prohibitive even when approximate calculations such as sampling approximation are performed; therefore, we propose an accurate and computationally efficient method for estimating gradient of the acquisition function, and develop an algorithm for Bayesian optimization with multi-objective and multi-point search. It is shown via numerical experiments that the performance of the proposed method is comparable or superior to those of heuristic methods.","Title: Expectation Variables On A Para-Contact Metric Manifold Exactly Derived From Master Equations\nYear: 2019\n\nAbstract: Based on information and para-contact metric geometries, in this paper a class of dynamical systems is formulated for describing time-development of expectation variables. Here such systems for expectation variables are exactly derived from continuous-time master equations describing nonequilibrium processes.","Title: Bayesian Dynamic Mode Decomposition With Variational Matrix Factorization\nYear: 2021\n\nAbstract: Dynamic mode decomposition (DMD) and its extensions are data-driven methods that have substantially contributed to our understanding of dynamical systems. However, because DMD and most of its extensions are deterministic, it is difficult to treat probabilistic representations of parameters and predictions. In this work, we propose a novel formulation of a Bayesian DMD model. Our Bayesian DMD model is consistent with the procedure of standard DMD, which is to first determine the subspace of observations, and then compute the modes on that subspace. Variational matrix factorization makes it possible to realize a fully-Bayesian scheme of DMD. Moreover, we derive a Bayesian DMD model for incomplete data, which demonstrates the advantage of probabilistic modeling. Finally, both of nonlinear simulated and real-world datasets are used to illustrate the potential of the proposed method.","Title: One-bit Submission for Locally Private Quasi-MLE: Its Asymptotic Normality and Limitation\nYear: 2022\n\nAbstract: Local differential privacy (LDP) is an information-theoretic privacy definition suitable for statistical surveys that involve an untrusted data curator. An LDP version of quasi-maximum likelihood estimator (QMLE) has been developed, but the existing method to build LDP QMLE is difficult to implement for a large-scale survey system in the real world due to long waiting time, expensive communication cost, and the boundedness assumption of derivative of a log-likelihood function. We provided an alternative LDP protocol without those issues, which is potentially much easily deployable to a large-scale survey. We also provided sufficient conditions for the consistency and asymptotic normality and limitations of our protocol. Our protocol is less burdensome for the users, and the theoretical guarantees cover more realistic cases than those for the existing method.","Title: Detecting cell assemblies by NMF-based clustering from calcium imaging data\nYear: 2022\n\nAbstract: A large number of neurons form cell assemblies that process information in the brain. Recent developments in measurement technology, one of which is calcium imaging, have made it possible to study cell assemblies. In this study, we aim to extract cell assemblies from calcium imaging data. We propose a clustering approach based on non-negative matrix factorization (NMF). The proposed approach first obtains a similarity matrix between neurons by NMF and then performs spectral clustering on it. The application of NMF entails the problem of model selection. The number of bases in NMF affects the result considerably, and a suitable selection method is yet to be established. We attempt to resolve this problem by model averaging with a newly defined estimator based on NMF. Experiments on simulated data suggest that the proposed approach is superior to conventional correlation-based clustering methods over a wide range of sampling rates. We also analyzed calcium imaging data of sleeping/waking mice and the results suggest that the size of the cell assembly depends on the degree and spatial extent of slow wave generation in the cerebral cortex.","Title: Information Geometrically Generalized Covariate Shift Adaptation\nYear: 2022\n\nAbstract: Many machine learning methods assume that the training and test data follow the same distribution. However, in the real world, this assumption is often violated. In particular, the marginal distribution of the data changes, called covariate shift, is one of the most important research topics in machine learning. We show that the well-known family of covariate shift adaptation methods is unified in the framework of information geometry. Furthermore, we show that parameter search for a geometrically generalized covariate shift adaptation method can be achieved efficiently. Numerical experiments show that our generalization can achieve better performance than the existing methods it encompasses.","Title: Unsupervised Domain Adaptation for Extra Features in the Target Domain Using Optimal Transport.\nYear: 2022\n\nAbstract: Domain adaptation aims to transfer knowledge of labeled instances obtained from a source domain to a target domain to fill the gap between the domains. Most domain adaptation methods assume that the source and target domains have the same dimensionality. Methods that are applicable when the number of features is different in each domain have rarely been studied, especially when no label information is given for the test data obtained from the target domain. In this letter, it is assumed that common features exist in both domains and that extra (new additional) features are observed in the target domain; hence, the dimensionality of the target domain is higher than that of the source domain. To leverage the homogeneity of the common features, the adaptation between these source and target domains is formulated as an optimal transport (OT) problem. In addition, a learning bound in the target domain for the proposed OT-based method is derived. The proposed algorithm is validated using both simulated and real-world data."],"string":"[\n \"Title: Item Preference Parameters from Grouped Ranking Observations\\nYear: 2009\\n\\nAbstract: Given a set of rating data for a set of items, determining the values of items is a matter of importance. Various probability models have been proposed to solve this task. The Plackett-Luce model is one of such models, which parametrizes the value of each item by a real valued preference parameter. In this paper, the Plackett-Luce model is generalized to cope with the grouped ranking observations such as movies or restaurants ratings. Since the maximization of the likelihood of the proposed model is computationally intractable, the lower bound of the likelihood which is easy to evaluate is derived, and the em algorithm is adopted to the maximization of the lower bound.\",\n \"Title: Robust hypersurface fitting based on random sampling approximations\\nYear: 2012\\n\\nAbstract: This paper considers N−1-dimensional hypersurface fitting based on L2 distance in N-dimensional input space. The problem is usually reduced to hyperplane fitting in higher dimension. However, because feature mapping is generally a nonlinear mapping, it does not preserve the order of lengthes, and this derives an unacceptable fitting result. To avoid it, JNLPCA is introduced. JNLPCA defines the L2 distance in the feature space as a weighted L2 distance to reflect the metric in the input space. In the fitting, random sampling approximation of least k-th power deviation, and least α-percentile of squares are introduced to make estimation robust. The proposed hypersurface fitting method is evaluated by quadratic curve fitting and quadratic curve segments extraction from artificial data and a real image.\",\n \"Title: Self-Calibration of Radially Symmetric Distortion by Model Selection\\nYear: 2010\\n\\nAbstract: For self-calibration of general radially symmetric distortion (RSD) of omni directional cameras such as fish-eye lenses, calibration parameters are usually estimated so that curved lines, which are supposed to be straight in the real-world, are mapped to straight lines in the calibrated image, which is assumed to be taken by an ideal pin-hole camera. In this paper, a method of calibrating RSD is introduced base on the notion of principal component analysis (PCA). In the proposed method, the distortion function, which maps a distorted image to an ideal pin-hole camera image, is assumed to be a linear combination of a certain class of basis functions, and an algorithm for solving its coefficients by using line patterns is given. Then a method of selecting good basis functions is proposed, which aims to realize appropriate calibration in practice. Experimental results for synthetic data and real images are presented to emonstrate the performance of our calibration method.\",\n \"Title: Item-user Preference Mapping with Mixture Models - Data Visualization for Item Preference\\nYear: 2009\\n\\nAbstract: In this paper, we propose a visualization technique of a statistical relation of users and preference of items based on a mixture model. In our visualization, items are given as points in a few dimensional preference space, and user specific preferences are given as lines in the same space. The relationship between items and user preferences are intuitively interpreted via projections from points onto lines. As a primitive implementation, we introduce a mixture of the Bradley-Terry models, and visualize the relation between items and user preferences with benchmark data sets.\",\n \"Title: Multiple kernel learning with gaussianity measures\\nYear: 2012\\n\\nAbstract: Kernel methods are known to be effective for nonlinear multivariate analysis. One of the main issues in the practical use of kernel methods is the selection of kernel. There have been a lot of studies on kernel selection and kernel learning. Multiple kernel learning (MKL) is one of the promising kernel optimization approaches. Kernel methods are applied to various classifiers including Fisher discriminant analysis (FDA). FDA gives the Bayes optimal classification axis if the data distribution of each class in the feature space is a gaussian with a shared covariance structure. Based on this fact, an MKL framework based on the notion of gaussianity is proposed. As a concrete implementation, an empirical characteristic function is adopted to measure gaussianity in the feature space associated with a convex combination of kernel functions, and two MKL algorithms are derived. From experimental results on some data sets, we show that the proposed kernel learning followed by FDA offers strong classification power.\",\n \"Title: Information estimators for weighted observations.\\nYear: 2013\\n\\nAbstract: The Shannon information content is a valuable numerical characteristic of probability distributions. The problem of estimating the information content from an observed dataset is very important in the fields of statistics, information theory, and machine learning. The contribution of the present paper is in proposing information estimators, and showing some of their applications. When the given data are associated with weights, each datum contributes differently to the empirical average of statistics. The proposed estimators can deal with this kind of weighted data. Similar to other conventional methods, the proposed information estimator contains a parameter to be tuned, and is computationally expensive. To overcome these problems, the proposed estimator is further modified so that it is more computationally efficient and has no tuning parameter. The proposed methods are also extended so as to estimate the cross-entropy, entropy, and Kullback-Leibler divergence. Simple numerical experiments show that the information estimators work properly. Then, the estimators are applied to two specific problems, distribution-preserving data compression, and weight optimization for ensemble regression.\",\n \"Title: An improved entropy-based multiple kernel learning\\nYear: 2012\\n\\nAbstract: Kernel methods have been successfully used in many practical machine learning problems. However, the problem of choosing a suitable kernel is left to practitioners. One method to select the optimal kernel is to learn a linear combination of element kernels. A framework of multiple kernel learning based on conditional entropy minimization criterion (MCEM) has been proposed and it has been shown to work well for, e.g., speaker recognition tasks. In this paper, a computationally efficient implementation for MCEM, which utilizes sequential quadratic programming, is formulated. Through a comparative experiment to conventional MCEM algorithm on a speaker verification task, the proposed method is shown to offer comparable verification accuracy with considerable improvement in computational speed.\",\n \"Title: Extraction of Basic Patterns of Household Energy Consumption\\nYear: 2011\\n\\nAbstract: Solar power, wind power, and co-generation (combined heat and power) systems are possible candidate for household power generation. These systems have their advantages and disadvantages. To propose the optimal combination of the power generation systems, the extraction of basic patterns of energy consumption of the house is required. In this study, energy consumption patterns are modeled by mixtures of Gaussian distributions. Then, using the symmetrized Kullback-Leibler divergence as a distance measure of the distributions, the basic pattern of energy consumption is extracted by means of hierarchical clustering. By an experiment using the Annex 42 dataset, it is shown that the proposed method is able to extract typical energy consumption patterns.\",\n \"Title: Entropy-based sliced inverse regression.\\nYear: 2013\\n\\nAbstract: The importance of dimension reduction has been increasing according to the growth of the size of available data in many fields. An appropriate dimension reduction method of raw data helps to reduce computational time and to expose the intrinsic structure of complex data. Sliced inverse regression is a well-known dimension reduction method for regression, which assumes an elliptical distribution for the explanatory variable, and ingeniously reduces the problem of dimension reduction to a simple eigenvalue problem. Sliced inverse regression is based on the strong assumptions on the data distribution and the form of regression function, and there are a number of methods to relax or remove these assumptions to extend the applicability of the inverse regression method. However, each method is known to have its drawbacks either theoretically or empirically. To alleviate drawbacks in the existing methods, a dimension reduction method for regression based on the notion of conditional entropy minimization is proposed. Using entropy as a measure of dispersion of data, a low dimensional subspace is estimated without assuming any specific distribution nor any regression function. The proposed method is shown to perform comparable or superior to the conventional methods through experiments using artificial and real-world datasets.\",\n \"Title: Robust hyperplane fitting based on k-th power deviation and α-quantile\\nYear: 2011\\n\\nAbstract: In this paper, two methods for one-dimensional reduction of data by hyperplane fitting are proposed. One is least a-percentile of squares, which is an extension of least median of squares estimation and minimizes the a-percentile of squared Euclidean distance. The other is least k-th power deviation, which is an extension of least squares estimation and minimizes the k-th power deviation of squared Euclidean distance. Especially, for least k-th power deviation of 0 k ≤ 1, it is proved that a useful property, called optimal sampling property, holds in one-dimensional reduction of data by hyperplane fitting. The optimal sampling property is that the global optimum for affine hyperplane fitting passes through N data points when an N-1-dimensional hyperplane is fitted to the N-dimensional data. The performance of the proposed methods is evaluated by line fitting to artificial data and a real image.\",\n \"Title: Multiple Kernel Learning by Conditional Entropy Minimization\\nYear: 2010\\n\\nAbstract: Kernel methods have been successfully used in many practical machine learning problems. Choosing a suitable kernel is left to the practitioner. A common way to an automatic selection of optimal kernels is to learn a linear combination of element kernels. In this paper, a novel framework of multiple kernel learning is proposed based on conditional entropy minimization criterion. For the proposed framework, three multiple kernel learning algorithms are derived. The algorithms are experimentally shown to be comparable to or outperform kernel Fisher discriminant analysis and other multiple kernel learning algorithms on benchmark data sets.\",\n \"Title: New Probabilistic Bounds on Eigenvalues and Eigenvectors of Random Kernel Matrices\\nYear: 2012\\n\\nAbstract: Kernel methods are successful approaches for different machine learning problems. This success is mainly rooted in using feature maps and kernel matrices. Some methods rely on the eigenvalues/eigenvectors of the kernel matrix, while for other methods the spectral information can be used to estimate the excess risk. An important question remains on how close the sample eigenvalues/eigenvectors are to the population values. In this paper, we improve earlier results on concentration bounds for eigenvalues of general kernel matrices. For distance and inner product kernel functions, e.g. radial basis functions, we provide new concentration bounds, which are characterized by the eigenvalues of the sample covariance matrix. Meanwhile, the obstacles for sharper bounds are accounted for and partially addressed. As a case study, we derive a concentration inequality for sample kernel target-alignment.\",\n \"Title: Sliced inverse regression with conditional entropy minimization\\nYear: 2012\\n\\nAbstract: An appropriate dimension reduction of raw data helps to reduce computational time and to reveal the intrinsic structure of complex data. In this paper, a dimension reduction method for regression is proposed. The method is based on the well-known sliced inverse regression and conditional entropy minimization. Using entropy as a measure of dispersion of data distribution, dimension reduction subspace is estimated without assuming regression function form nor data distribution, unlike conventional sliced inverse regression. The proposed method is shown to perform well compared to some conventional methods through experiments using both artificial and real-world data sets.\",\n \"Title: A Measure of Credibility of Solar Power Prediction\\nYear: 2011\\n\\nAbstract: Recently, remarkable developments of new energy technologies have been achieved against various energy problems. Photovoltaic (PV) system, one of such technologies, has an advantage of utilizing infinite and clean energy. On the contrary, it also has a disadvantage of unreliable power supply mainly caused by unstable weather. The fluctuation of the power supply of PV systems are considerably large because of rapid insulation changes and rapid weather changes, and in some cases, it seems impossible to realize high-accuracy prediction even with sophisticated prediction models. In this paper, using recently proposed estimator for the Shannon information content, a method to output a measure of credibility for prediction is proposed. With the proposed method, it is possible to judge whether the energy supply at a certain future time is unpredictably fluctuate compared to the current value or not, and it is possible to take measures against the rapid change of solar energy generation in advance. From an experimental result using solar energy supply data, we see that the proposed measure of credibility reflects the difficulty of predicting solar energy supply.\",\n \"Title: Gaussian multiple kernel learning with entropy power inequality\\nYear: 2013\\n\\nAbstract: Kernel methods have become a standard solution for a large number of data analysis, and extensively utilized in the field of signal processing including analysis of speech, image, time series, and DNA sequences. The main difficulty in applying the kernel method is in designing the appropriate kernel for the specific data, and multiple kernel learning (MKL) is one of the principled approaches for kernel design problem. In this paper, a novel multiple kernel learning method based on the notion of Gaussianity evaluated by entropy power inequality is proposed. The notable characteristics of the proposed method are in utilizing the entropy power inequality for kernel learning, and in realizing an MKL algorithm which only optimizes the kernel combination coefficients, while the conventional methods need optimizing both combination coefficients and classifier parameters. The proposed MKL algorithm is experimentally shown to have good classification accuracy.\",\n \"Title: A computationally efficient information estimator for weighted data\\nYear: 2011\\n\\nAbstract: The Shannon information content is a fundamental quantity and it is of great importance to estimate it from observed dataset in the field of statistics, information theory, and machine learning. In this study, an estimator for the information content using a given set of weighted data is proposed. The empirical data distribution varies depending on the weight. The notable features of the proposed estimator are its computational efficiency and its ability to deal with weighted data. The proposed estimator is extended in order to estimate cross entropy, entropy and KL divergence with weighted data. Then, the estimators are applied to classification with one-class samples, and distribution preserving data compression problems.\",\n \"Title: A Versatile Clustering Method for Electricity Consumption Pattern Analysis in Households.\\nYear: 2013\\n\\nAbstract: Analysis and modeling of electric energy demand is indispensable for power planning, operation, facility investment, and urban planning. Because of recent development of renewable energy generation systems oriented for households, there is also a great demand for analysing the electricity usage and optimizing the way to install electricity generation systems for each household. In this study, empl...\",\n \"Title: Speaker Verification Robust To Talking Style Variation Using Multiple Kernel Learning Based On Conditional Entropy Minimization\\nYear: 2011\\n\\nAbstract: We developed a new speaker verification system that is robust to intra-speaker variation. There is a strong likelihood that intra-speaker variations will occur due to changes in talking styles, the periods when an individual speaks, and so on. It is well known that such variation generally degrades the performance of speaker verification systems. To solve this problem, we applied multiple kernel learning (MKL) based on conditional entropy minimization, which impose the data to be compactly aggregated for each speaker class and ensure that the different speaker classes were far apart from each other. Experimental results showed that the proposed speaker verification system achieved a robust performance to intra-speaker variation derived from changes in the talking styles compared to the conventional maximum margin-based system.\",\n \"Title: Calibration of radially symmetric distortion based on linearity in the calibrated image\\nYear: 2011\\n\\nAbstract: For calibration of general radially symmetric distortion of omnidirectional cameras such as fish-eye lenses, calibration parameters are usually estimated so that curved lines, which are supposed to be straight in the real-world, are mapped to straight lines in the calibrated image, which is called plumbline principle. Under the principle, the camera with radially symmetric distortion can be calibrated by at least one distorted line in a image, theoretically, and the calibrated image is equivalent to the image taken by an ideal pin-hole camera. In this paper, the method to optimize the calibration parameters by maximizing the sum of the straightness, which is invariant under translation, rotation and magnification (scaling), of distorted lines on calibrated image is proposed. The performance of the proposed method is evaluated by artificial data and a real image.\",\n \"Title: An estimation of generalized bradley-terry models based on the em algorithm.\\nYear: 2011\\n\\nAbstract: The Bradley-Terry model is a statistical representation for one's preference or ranking data by using pairwise comparison results of items. For estimation of the model, several methods based on the sum of weighted Kullback-Leibler divergences have been proposed from various contexts. The purpose of this letter is to interpret an estimation mechanism of the Bradley-Terry model from the viewpoint of flatness, a fundamental notion used in information geometry. Based on this point of view, a new estimation method is proposed on a framework of the em algorithm. The proposed method is different in its objective function from that of conventional methods, especially in treating unobserved comparisons, and it is consistently interpreted in a probability simplex. An estimation method with weight adaptation is also proposed from a viewpoint of the sensitivity. Experimental results show that the proposed method works appropriately, and weight adaptation improves accuracy of the estimate.\",\n \"Title: An Information Theoretic Perspective of the Sparse Coding\\nYear: 2009\\n\\nAbstract: The sparse coding method is formulated as an information theoretic optimization problem. The rate distortion theory leads to an objective functional which can be interpreted as an information theoretic formulation of the sparse coding. Viewing as an entropy minimization problem, the rate distortion theory and consequently the sparse coding are extended to discriminative variants. As a concrete example of this information theoretic sparse coding, a discriminative non-linear sparse coding algorithm with neural networks is proposed. Experimental results of gender classification by face images show that the discriminative sparse coding is more robust to noise, compared to the conventional method which directly uses images as inputs to a linear support vector machine.\",\n \"Title: Sparse Coding Approach for Multi-Frame Image Super Resolution.\\nYear: 2014\\n\\nAbstract: An image super-resolution method from multiple observation of low-resolution images is proposed. The method is based on sub-pixel accuracy block matching for estimating relative displacements of observed images, and sparse signal representation for estimating the corresponding high-resolution image. Relative displacements of small patches of observed low-resolution images are accurately estimated by a computationally efficient block matching method. Since the estimated displacements are also regarded as a warping component of image degradation process, the matching results are directly utilized to generate low-resolution dictionary for sparse image representation. The matching scores of the block matching are used to select a subset of low-resolution patches for reconstructing a high-resolution patch, that is, an adaptive selection of informative low-resolution images is realized. When there is only one low-resolution image, the proposed method works as a single-frame super-resolution method. The proposed method is shown to perform comparable or superior to conventional single- and multi-frame super-resolution methods through experiments using various real-world datasets.\",\n \"Title: A conditional entropy minimization criterion for dimensionality reduction and multiple kernel learning.\\nYear: 2010\\n\\nAbstract: Reducing the dimensionality of high-dimensional data without losing its essential information is an important task in information processing. When class labels of training data are available, Fisher discriminant analysis (FDA) has been widely used. However, the optimality of FDA is guaranteed only in a very restricted ideal circumstance, and it is often observed that FDA does not provide a good classification surface for many real problems. This letter treats the problem of supervised dimensionality reduction from the viewpoint of information theory and proposes a framework of dimensionality reduction based on class-conditional entropy minimization. The proposed linear dimensionality-reduction technique is validated both theoretically and experimentally. Then, through kernel Fisher discriminant analysis (KFDA), the multiple kernel learning problem is treated in the proposed framework, and a novel algorithm, which iteratively optimizes the parameters of the classification function and kernel combination coefficients, is proposed. The algorithm is experimentally shown to be comparable to or outperforms KFDA for large-scale benchmark data sets, and comparable to other multiple kernel learning techniques on the yeast protein function annotation task.\",\n \"Title: Integration of MKL-Based and I-Vector-Based Speaker Verification by Short Utterances\\nYear: 2013\\n\\nAbstract: We developed a speaker verification system that is efficient for short utterances. The i-vector-based speaker representation has helped realize highly accurate speaker verification systems, however, it might be not robust against short utterances because the reliability of statistics required for extracting i-vectors is low. On the other hand, multiple kernel learning based on conditional entropy minimization has also achieved high accuracy in speaker verification that is robust against intra-speaker variability. To improve the robustness of speaker verification systems against short utterances, we attempted to integrate the above-mentioned complementary systems. Our experimental results showed that the proposed system integration achieved high-accuracy speaker verification systems, irrespective of the utterance lengths, even for very short utterances (e.g., less than two seconds).\",\n \"Title: A tree search approach to sparse coding\\nYear: 2012\\n\\nAbstract: Sparse coding is an important optimization problem with numerous applications. In this paper, we describe the problem and the commonly used pursuit methods, and propose a best-first tree search algorithm employing multiple queues for unexplored tree nodes. We assess the effectiveness of our method in an extensive computational experiment, showing its superiority over other methods even for modest computational time.\",\n \"Title: Calibration of Radially Symmetric Distortion by Fitting Principal Component\\nYear: 2009\\n\\nAbstract: To calibrate radially symmetric distortion of omnidirectional cameras such as fish-eye lenses, calibration parameters are usually estimated so that lines, which are supposed to be straight in the 3D real scene, are mapped to straight lines in the calibrated image. In this paper, this problem is treated as a fitting problem of the principal component in uncalibrated images, and an estimation procedure of calibration parameters is proposed based on the principal component analysis. Experimental results for synthetic data and real images are presented to demonstrate the performance of our calibration method.\",\n \"Title: A grouped ranking model for item preference parameter.\\nYear: 2010\\n\\nAbstract: Given a set of rating data for a set of items, determining preference levels of items is a matter of importance. Various probability models have been proposed to solve this task. One such model is the Plackett-Luce model, which parameterizes the preference level of each item by a real value. In this letter, the Plackett-Luce model is generalized to cope with grouped ranking observations such as movie or restaurant ratings. Since it is difficult to maximize the likelihood of the proposed model directly, a feasible approximation is derived, and the em algorithm is adopted to find the model parameter by maximizing the approximate likelihood which is easily evaluated. The proposed model is extended to a mixture model, and two applications are proposed. To show the effectiveness of the proposed model, numerical experiments with real-world data are carried out.\",\n \"Title: Combination of Multiple Distance Measures for Protein Fold Classification\\nYear: 2013\\n\\nAbstract: In structural biology, measuring the similarity between two protein structures is an essential task. The most common approach is to find the best alignment between two protein backbone structures and use the root mean square deviation (RMSD) of the superimposed alpha-carbon atom coordinates as the distance measurement. Other approaches extract features of the protein structures and the similarity measure is based on the extracted features. However, there is no single best approach, as each has its own advantages and limitations. One intuitive idea is that a better result can be obtained by combining complementary approaches. In this paper, we propose a new approach to protein fold classification, by introducing the concept of large margin nearest neighbor for combining multiple measures of distance between protein structures. We combine the Euclidean distance matrices of 12 features extracted from the amino acid sequence of the protein, the RMSD obtained from the geometrical alignment using Combinatorial Extension, and the canonical angles between the subspaces generated from the synthesized multi-view protein structure images. We demonstrate the effectiveness of the proposed method by classifying 27 fold classes of proteins in the Ding Dubchak dataset.\",\n \"Title: Entropy Power Inequality for Learning Optimal Combination of Kernel Functions\\nYear: 2015\\n\\nAbstract: Kernel methods have become a standard solution for a large number of data analyses, and extensively utilized in the field of signal processing including analysis of speech, image, time series, and DNA sequences. The main difficulty in applying the kernel method is in designing the appropriate kernel for the specific data, and multiple kernel learning (MKL) is one of the principled approaches for kernel design problem. In this paper, a novel multiple kernel learning method based on the notion of Gaussianity evaluated by entropy power inequality is proposed. The notable characteristics of the proposed method are in utilizing the entropy power inequality for kernel learning, and in realizing an MKL algorithm which only optimizes the kernel combination coefficients, while the conventional methods need optimizing both combination coefficients and classifier parameters. The proposed kernel learning approach is shown to have good classification accuracy through a set of standard datasets for dichotomy classification.\",\n \"Title: A nonparametric clustering algorithm with a quantile-based likelihood estimator.\\nYear: 2014\\n\\nAbstract: Clustering is a representative of unsupervised learning and one of the important approaches in exploratory data analysis. By its very nature, clustering without strong assumption on data distribution is desirable. Information-theoretic clustering is a class of clustering methods that optimize information-theoretic quantities such as entropy and mutual information. These quantities can be estimated in a nonparametric manner, and information-theoretic clustering algorithms are capable of capturing various intrinsic data structures. It is also possible to estimate information-theoretic quantities using a data set with sampling weight for each datum. Assuming the data set is sampled from a certain cluster and assigning different sampling weights depending on the clusters, the cluster-conditional information-theoretic quantities are estimated. In this letter, a simple iterative clustering algorithm is proposed based on a nonparametric estimator of the log likelihood for weighted data sets. The clustering algorithm is also derived from the principle of conditional entropy minimization with maximum entropy regularization. The proposed algorithm does not contain a tuning parameter. The algorithm is experimentally shown to be comparable to or outperform conventional nonparametric clustering methods.\",\n \"Title: Sensing Visual Attention by Sequential Patterns\\nYear: 2014\\n\\nAbstract: A method for sensing human visual attention is proposed. The method is based on the analysis of sequential image patterns of faces and irises observed at regular time intervals. The basic concept is to represent the set of image patterns produced by the action of gazing at a certain area as a nonlinear subspace in a high-dimensional pattern vector space. Such a space is called an attention subspace. In this framework, an input subspace from an unknown action is classified into an attention subspace of gazing at a certain area or into attention subspaces of gazing at other areas (named non-attention subspaces) by measuring the canonical angles between the input subspace and pre-computed dictionary subspaces. To maintain performance even in the presence of head movement, two mechanisms are introduced: 1) the kernel orthogonal mutual subspace method, which is suitable for classifying sets of multiple images, and 2) a kernel function for considering the head position in addition to a kernel function for pixel values. The stable performance of the proposed method including situations with head movements is demonstrated through experiments.\",\n \"Title: A Kernel Method to Extract Common Features Based on Mutual Information.\\nYear: 2014\\n\\nAbstract: Kernel canonical correlation analysis (CCA) aims to extract common features from a pair of multivariate data sets by maximizing a linear correlation between nonlinear mappings of the data. However, the kernel CCA tends to obtain the features that have only small information of original multivariates in spite of their high correlation, because it considers only statistics of the extracted features and the nonlinear mappings have high degree of freedom. We propose a kernel method for common feature extraction based on mutual information that maximizes a new objective function. The objective function is a linear combination of two kinds of mutual information, one between the extracted features and the other between the multivariate and its feature. A large value of the former mutual information provides strong dependency to the features, and the latter prevents loss of the feature's information related to the multivariate. We maximize the objective function by using the Parallel Tempering MCMC in order to overcome a local maximum problem. We show the effectiveness of the proposed method via numerical experiments.\",\n \"Title: Intrinsic Graph Structure Estimation Using Graph Laplacian\\nYear: 2014\\n\\nAbstract: A graph is a mathematical representation of a set of variables where some pairs of the variables are connected by edges. Common examples of graphs are railroads, the Internet, and neural networks. It is both theoretically and practically important to estimate the intensity of direct connections between variables. In this study, a problem of estimating the intrinsic graph structure from observed data is considered. The observed data in this study are a matrix with elements representing dependency between nodes in the graph. The dependency represents more than direct connections because it includes influences of various paths. For example, each element of the observed matrix represents a co-occurrence of events at two nodes or a correlation of variables corresponding to two nodes. In this setting, spurious correlations make the estimation of direct connection difficult. To alleviate this difficulty, a digraph Laplacian is used for characterizing a graph. A generative model of this observed matrix is proposed, and a parameter estimation algorithm for the model is also introduced. The notable advantage of the proposed method is its ability to deal with directed graphs, while conventional graph structure estimation methods such as covariance selections are applicable only to undirected graphs. The algorithm is experimentally shown to be able to identify the intrinsic graph structure.\",\n \"Title: Change-Point Detection in a Sequence of Bags-of-Data\\nYear: 2016\\n\\nAbstract: In this paper, the limitation that is prominent in most existing works of change-point detection methods is addressed by proposing a nonparametric, computationally efficient method. The limitation is that most works assume that each data point observed at each time step is a single multi-dimensional vector. However, there are many situations where this does not hold. Therefore, a setting where each observation is a collection of random variables, which we call a bag of data, is considered.\",\n \"Title: Learning Ancestral Atom via Sparse Coding\\nYear: 2013\\n\\nAbstract: Sparse signal models have been the focus of recent research. In sparse coding, signals are represented with a linear combination of a small number of elementary signals called atoms, and the collection of atoms is called a dictionary. Design of the dictionary has strong influence on the signal approximation performance. Recently, to put prior information into dictionary learning, several methods imposing a certain kind of structure on the dictionary are proposed. In this paper, like wavelet analysis, a dictionary for sparse signal representation is assumed to be generated from an ancestral atom, and a method for learning the ancestral atom is proposed. The proposed algorithm updates the ancestral atom by iterating dictionary update in unstructured dictionary space and projection of the updated dictionary onto the structured dictionary space. The algorithm allows a simple differential geometric interpretation. Numerical experiments are performed to show the characteristics and advantages of the proposed algorithm.\",\n \"Title: Non-parametric entropy estimators based on simple linear regression.\\nYear: 2015\\n\\nAbstract: Estimators for differential entropy are proposed. The estimators are based on the second order expansion of the probability mass around the inspection point with respect to the distance from the point. Simple linear regression is utilized to estimate the values of density function and its second derivative at a point. After estimating the values of the probability density function at each of the given sample points, by taking the empirical average of the negative logarithm of the density estimates, two entropy estimators are derived. Other entropy estimators which directly estimate entropy by linear regression, are also proposed. The proposed four estimators are shown to perform well through numerical experiments for various probability distributions.\",\n \"Title: Multi-frame image super resolution based on sparse coding\\nYear: 2015\\n\\nAbstract: An image super-resolution method from multiple observation of low-resolution images is proposed. The method is based on sub-pixel accuracy block matching for estimating relative displacements of observed images, and sparse signal representation for estimating the corresponding high-resolution image, where correspondence between high- and low-resolution images are modeled by a certain degradation process. Relative displacements of small patches of observed low-resolution images are accurately estimated by a computationally efficient block matching method. The matching scores of the block matching are used to select a subset of low-resolution patches for reconstructing a high-resolution patch, that is, an adaptive selection of informative low-resolution images is realized. The proposed method is shown to perform comparable or superior to conventional super-resolution methods through experiments using various images.\",\n \"Title: Protein Fold Classification Using Large Margin Combination Of Distance Metrics\\nYear: 2016\\n\\nAbstract: Many methods have been proposed for measuring the structural similarity between two protein folds. However, it is difficult to select one best method from them for the classification task, as each method has its own strength and weakness. Intuitively, combining multiple methods is one solution to get the optimal classification results. In this paper, by generalizing the concept of the large margin nearest neighbor (LMNN), a method for combining multiple distance metrics from different types of protein structure comparison methods for protein fold classification task is proposed. While LMNN is limited to Mahalanobis-based distance metric learning from a set of feature vectors of training data, the proposed method learns an optimal combination of metrics from a set of distance metrics by minimizing the distances between intra-class data and enlarging the distances of different classes' data. The main advantage of the proposed method is the capability in finding an optimal weight coefficient for combination of many metrics, possibly including poor metrics, avoiding the difficulties in selecting which metrics to be included for the combination. The effectiveness of the proposed method is demonstrated on classification experiments using two public protein datasets, namely, Ding Dubchak dataset and ENZYMES dataset.\",\n \"Title: Double Sparse Multi-Frame Image Super Resolution.\\nYear: 2015\\n\\nAbstract: A large number of image super resolution algorithms based on the sparse coding are proposed, and some algorithms realize the multi-frame super resolution. In multi-frame super resolution based on the sparse coding, both accurate image registration and sparse coding are required. Previous study on multi-frame super resolution based on sparse coding firstly apply block matching for image registration, followed by sparse coding to enhance the image resolution. In this paper, these two problems are solved by optimizing a single objective function. The results of numerical experiments support the effectiveness of the proposed approch.\",\n \"Title: Patchworking multiple pairwise distances for learning with distance matrices\\nYear: 2015\\n\\nAbstract: A classification framework using only a set of distance matrices is proposed. The proposed algorithm can learn a classifier only from a set of distance matrices or similarity matrices, hence applicable to structured data, which do not have natural vector representation such as time series and graphs. Random forest is used to explore ideal feature representation based on the distance between points defined by a set of given distance matrices. The effectiveness of the proposed method is evaluated through experiments with point process data and graph structured data.\",\n \"Title: Linear Discriminant Analysis with Adherent Regularization.\\nYear: 2015\\n\\nAbstract: Sparse modeling have become one of the standard approaches for latent variable analysis in the literature of statistics, machine learning and signal processing. This paper considers a supervised dimension reduction, which is a fundamental problem in data science. Particularly, the problem of linear discriminant analysis is considered. Extending the previous attempt to impose sparsity invoking regularization for Fisher's discriminant model, the proposed method bridges two different formulations of linear discriminant analysis, namely, the Fisher's discriminant model and the normal model, via a particular form of regularization. The proposed discriminant problem is efficiently solved by using the proximal point algorithm. The proposed method is shown to work well through experiments using both artificial and real-world datasets.\",\n \"Title: Personal Authentication Based on 3D Configuration of Micro-feature Points on Facial Surface.\\nYear: 2015\\n\\nAbstract: This paper proposes a personal authentication method based on the 3D configuration of micro-feature points such as moles and freckles, plus common feature points such as corners of eyes, edges of mouth and nostrils. The basic idea behind the proposed method is the assumption that such 3D configuration can be uniquely determined by individuals. To compare two configurations of feature points effectively, the concept of 3D shape subspace in a high-dimensional vector space is introduced. With this idea, the task of comparing the sets of feature points is converted to that of measuring the structural similarity between the corresponding shape subspaces. The validity of the proposed method is demonstrated through experiments with feature points from actual face images. In addition, the performance limit of the method is explored using sets of artificially generated feature points.\",\n \"Title: Group Sparsity Tensor Factorization for De-anonymization of Mobility Traces\\nYear: 2015\\n\\nAbstract: The de-anonymization attack using personalized transition matrices is known as one of the most successful approaches to link anonymized traces with users. However, since many users disclose only a small amount of location information to the public in their daily lives, the amount of training data available to the adversary can be very small. The aim of this paper is to quantify the risk of de-anonymization in this realistic situation. To achieve this aim, we utilize the fact that spatial data can form group structure, and propose group sparsity tensor factorization to train the personalized transition matrices that capture spatial group structure from a small amount of training data. We apply our training method to the de-anonymization attack, and evaluate it using the Geolife dataset. The results show that the training method using tensor factorization outperforms the Maximum Likelihood estimation method, and is further improved by incorporating group sparsity regularization.\",\n \"Title: Spotting fingerspelled words from sign language video by temporally regularized canonical component analysis\\nYear: 2016\\n\\nAbstract: A method for spotting specific words in sign language video is proposed. In classes and talks given using Japanese Sign Language, words that do not have a defined sign, such as the names of people, objects, and places, are represented by sets of multiple characters from the Japanese finger alphabet. The difficulty of recognizing these words has created strong demand for the ability to spot specific words in order to help interpreters and the audience to follow a talk. We address the spotting task by employing the basic idea of temporal regularized canonical correlation analysis (TRCCA), which can simultaneously handle shape and motion information about a 3D object. The classification accuracy of TRCCA is enhanced by incorporating two functions: 1) parallel processing with multiple time scales, 2) strong implicit feature mapping by nonlinear orthogonalization. The enhanced TRCCA is called \\\"kernel orthogonal TRCCA (KOTRCCA)\\\". The effectiveness of the proposed method using KOTRCCA is demonstrated through experiments spotting eight different words in sign language videos.\",\n \"Title: Nonparametric e-Mixture Estimation.\\nYear: 2016\\n\\nAbstract: This study considers the common situation in data analysis when there are few observations of the distribution of interest or the target distribution, while abundant observations are available from auxiliary distributions. In this situation, it is natural to compensate for the lack of data from the target distribution by using data sets from these auxiliary distributions-in other words, approximating the target distribution in a subspace spanned by a set of auxiliary distributions. Mixture modeling is one of the simplest ways to integrate information from the target and auxiliary distributions in order to express the target distribution as accurately as possible. There are two typical mixtures in the context of information geometry: the [Formula: see text]- and [Formula: see text]-mixtures. The [Formula: see text]-mixture is applied in a variety of research fields because of the presence of the well-known expectation-maximazation algorithm for parameter estimation, whereas the [Formula: see text]-mixture is rarely used because of its difficulty of estimation, particularly for nonparametric models. The [Formula: see text]-mixture, however, is a well-tempered distribution that satisfies the principle of maximum entropy. To model a target distribution with scarce observations accurately, this letter proposes a novel framework for a nonparametric modeling of the [Formula: see text]-mixture and a geometrically inspired estimation algorithm. As numerical examples of the proposed framework, a transfer learning setup is considered. The experimental results show that this framework works well for three types of synthetic data sets, as well as an EEG real-world data set.\",\n \"Title: Doubly sparse structure in image super resolution\\nYear: 2016\\n\\nAbstract: There are a large number of image super resolution algorithms based on the sparse coding, and some algorithms realize multi-frame super resolution. For utilizing multiple low resolution observations, both accurate image registration and sparse coding are required. Previous study on multi-frame super resolution based on sparse coding firstly apply block matching for image registration, followed by sparse coding to enhance the image resolution. In this paper, these two problems are solved by optimizing a single objective function. The proposed formulation not only has a mathematically interesting structure called the double sparsity, but also offers improved numerical performance.\",\n \"Title: Non-parametric e-mixture of Density Functions.\\nYear: 2016\\n\\nAbstract: Mixture modeling is one of the simplest ways to represent complicated probability density functions, and to integrate information from different sources. There are two typical mixtures in the context of information geometry, the m- and e-mixtures. This paper proposes a novel framework of non-parametric e-mixture modeling by using a simple estimation algorithm based on geometrical insights into the characteristics of the e-mixture. An experimental result supports the proposed framework.\",\n \"Title: An Entropy Estimator Based on Polynomial Regression with Poisson Error Structure.\\nYear: 2016\\n\\nAbstract: A method for estimating Shannon differential entropy is proposed based on the second order expansion of the probability mass around the inspection point with respect to the distance from the point. Polynomial regression with Poisson error structure is utilized to estimate the values of density function. The density estimates at every given data points are averaged to obtain entropy estimators. The proposed estimator is shown to perform well through numerical experiments for various probability distributions.\",\n \"Title: Group Sparsity Tensor Factorization for Re-Identification of Open Mobility Traces.\\nYear: 2017\\n\\nAbstract: Re-identification attacks based on a Markov chain model have been widely studied to understand how anonymized traces are linked to users. This approach is known to enable users to be re-identified with high accuracy when an adversary trains a personalized transition matrix for each target user using a large amount of training data, and when all of the anonymized traces are from the target users. In reality, however, the amount of training data for each target user can be very small, since many users disclose only a small amount of their location information to the public. In addition, many of the anonymized traces are from “non-target” users, whose personalized transition matrices cannot be trained in advance. This paper aims to quantify the risk of re-identification in the realistic situation explained earlier. We first utilize the fact that spatial data can form a group structure, and propose group sparsity tensor factorization to effectively train the personalized transition matrices from a small number of training traces. We second formulate a re-identification attack in an “open” scenario, where many of the anonymized traces are from non-target users. Specifically, we regard this type of attack as a biometric verification (or identification) task, and propose a framework and an algorithm for performing this task using a population transition matrix, which is computed from personalized transition matrices. Our experimental results using three real data sets show that a training method using tensor factorization significantly outperforms the maximum likelihood estimation method, and is further improved by incorporating group sparsity regularization.\",\n \"Title: Double sparsity for multi-frame super resolution.\\nYear: 2017\\n\\nAbstract: A number of image super resolution algorithms based on the sparse coding have successfully implemented multi-frame super resolution in recent years. In order to utilize multiple low-resolution observations, both accurate image registration and sparse coding are required. Previous study on multi-frame super resolution based on sparse coding firstly apply block matching for image registration, followed by sparse coding to enhance the image resolution. In this paper, these two problems are solved by optimizing a single objective function. The proposed formulation not only has a mathematically interesting structure, called the double sparsity, but also yields comparable or improved numerical performance to conventional methods.\",\n \"Title: 3d Object Recognition With Enhanced Grassmann Discriminant Analysis\\nYear: 2016\\n\\nAbstract: Subspace representation has become a promising choice in the classification of 3D objects such as face and hand shape, as it can model compactly the appearance of an object, represent effectively the variations such as the change in pose and illumination condition. Subspace based methods tend to require complicated formulation, though, we can utilize the notion of Grassmann manifold to cast the complicated formulation into a simple one in a unified manner. Each subspace is represented by a point on the manifold. Thank to this useful correspondence, various types of conventional methods have been constructed on a manifold by the kernel trick using a Grassmann kernel. In particular, discriminant analysis on Grassmann manifold (GDA) have been known as one of the useful tools for image set classification. GDA can work as a powerful feature extraction method on the manifold. However, there remains room to improve its ability in that the discriminative space is determined depending on the set of data points on the manifold. This suggests that if the data on a manifold are not so discriminative, the ability of GDA may be limited. To overcome this limitation, we construct a set of more discriminative class subspaces as the input for GDA. For this purpose, we propose to project class subspaces onto a generalized difference subspace (GDS), before mapping class subspaces onto the manifold. The GDS projection can magnify the angles between class subspaces. As a result, the separability of data points between different classes is improved and the ability of GDA is enhanced. The effectiveness of our enhanced GDA is demonstrated through classification experiments with CMU face database and hand shape database.\",\n \"Title: Local Intrinsic Dimension Estimation by Generalized Linear Modeling.\\nYear: 2017\\n\\nAbstract: We propose a method for intrinsic dimension estimation. By fitting the power of distance from an inspection point and the number of samples included inside a ball with a radius equal to the distance, to a regression model, we estimate the goodness of fit. Then, by using the maximum likelihood method, we estimate the local intrinsic dimension around the inspection point. The proposed method is show...\",\n \"Title: Classification Of Volcanic Ash Particles Using A Convolutional Neural Network And Probability\\nYear: 2018\\n\\nAbstract: Analyses of volcanic ash are typically performed either by qualitatively classifying ash particles by eye or by quantitatively parameterizing its shape and texture. While complex shapes can be classified through qualitative analyses, the results are subjective due to the difficulty of categorizing complex shapes into a single class. Although quantitative analyses are objective, selection of shape parameters is required. Here, we applied a convolutional neural network (CNN) for the classification of volcanic ash. First, we defined four basal particle shapes (blocky, vesicular, elongated, rounded) generated by different eruption mechanisms (e.g., brittle fragmentation), and then trained the CNN using particles composed of only one basal shape. The CNN could recognize the basal shapes with over 90% accuracy. Using the trained network, we classified ash particles composed of multiple basal shapes based on the output of the network, which can be interpreted as a mixing ratio of the four basal shapes. Clustering of samples by the averaged probabilities and the intensity is consistent with the eruption type. The mixing ratio output by the CNN can be used to quantitatively classify complex shapes in nature without categorizing forcibly and without the need for shape parameters, which may lead to a new taxonomy.\",\n \"Title: Localizing Current Dipoles From Eeg Data Using A Birth-Death Process\\nYear: 2018\\n\\nAbstract: A common approach to the electroencephalogram (EEG) source localization problem is to estimate the states of current dipoles. However, the dipole estimation problem is difficult because not only is it an inverse problem but also the number of dipoles can change over time. In this paper, we model the relationship between current dipoles and EEG observations using a state-space model where the creation and annihilation of dipoles is represented as a birth-death process. We estimate the dipoles' positions and moments with a Rao-Blackwellized particle filter and estimate whether a new dipole has been created or an existing one annihilated via the Bayesian information criterion. Experiments on both synthetic and real data show that the proposed model and estimation method can effectively estimate the number and positions of the dipoles.\",\n \"Title: Estimation of neural connections from partially observed neural spikes.\\nYear: 2018\\n\\nAbstract: Plasticity is one of the most important properties of the nervous system, which enables animals to adjust their behavior to the ever-changing external environment. Changes in synaptic efficacy between neurons constitute one of the major mechanisms of plasticity. Therefore, estimation of neural connections is crucial for investigating information processing in the brain. Although many analysis methods have been proposed for this purpose, most of them suffer from one or all the following mathematical difficulties: (1) only partially observed neural activity is available; (2) correlations can include both direct and indirect pseudo-interactions; and (3) biological evidence that a neuron typically has only one type of connection (excitatory or inhibitory) should be considered. To overcome these difficulties, a novel probabilistic framework for estimating neural connections from partially observed spikes is proposed in this paper. First, based on the property of a sum of random variables, the proposed method estimates the influence of unobserved neurons on observed neurons and extracts only the correlations among observed neurons. Second, the relationship between pseudo-correlations and target connections is modeled by neural propagation in a multiplicative manner. Third, a novel information-theoretic framework is proposed for estimating neuron types. The proposed method was validated using spike data generated by artificial neural networks. In addition, it was applied to multi-unit data recorded from the CA1 area of a rat’s hippocampus. The results confirmed that our estimates are consistent with previous reports. These findings indicate that the proposed method is useful for extracting crucial interactions in neural signals as well as in other multi-probed point process data.\",\n \"Title: mmpp: A package for calculating similarity and distance metrics for simple and marked temporal point processes\\nYear: 2015\\n\\nAbstract: A simple temporal point process (SPP) is an important class of time series, where the sample realization of the process is solely composed of the times at which events occur. Particular examples of point process data are neuronal spike patterns or spike trains, and a large number of distance and similarity metrics for those data have been proposed. A marked point process (MPP) is an extension of a simple temporal point process, in which a certain vector valued mark is associated with each of the temporal points in the SPP. Analyses of MPPs are of practical importance because instances of MPPs include recordings of natural disasters such as earthquakes and tornadoes. In this paper, we introduce the R package mmpp, which implements a number of distance and similarity metrics for SPPs, and also extends those metrics for dealing with MPPs.\",\n \"Title: ider: Intrinsic Dimension Estimation with R\\nYear: 2017\\n\\nAbstract: In many data analyses, the dimensionality of the observed data is high while its intrinsic dimension remains quite low. Estimating the intrinsic dimension of an observed dataset is an essential preliminary step for dimensionality reduction, manifold learning, and visualization. This paper introduces an R package, named ider, that implements eight intrinsic dimension estimation methods, including a recently proposed method based on a second-order expansion of a probability mass function and a generalized linear model. The usage of each function in the package is explained with datasets generated using a function that is also included in the package.\",\n \"Title: Bayesian Optimization for Multi-objective Optimization and Multi-point Search.\\nYear: 2019\\n\\nAbstract: Bayesian optimization is an effective method to efficiently optimize unknown objective functions with high evaluation costs. Traditional Bayesian optimization algorithms select one point per iteration for single objective function, whereas in recent years, Bayesian optimization for multi-objective optimization or multi-point search per iteration have been proposed. However, Bayesian optimization that can deal with them at the same time in non-heuristic way is not known at present. We propose a Bayesian optimization algorithm that can deal with multi-objective optimization and multi-point search at the same time. First, we define an acquisition function that considers both multi-objective and multi-point search problems. It is difficult to analytically maximize the acquisition function as the computational cost is prohibitive even when approximate calculations such as sampling approximation are performed; therefore, we propose an accurate and computationally efficient method for estimating gradient of the acquisition function, and develop an algorithm for Bayesian optimization with multi-objective and multi-point search. It is shown via numerical experiments that the performance of the proposed method is comparable or superior to those of heuristic methods.\",\n \"Title: Expectation Variables On A Para-Contact Metric Manifold Exactly Derived From Master Equations\\nYear: 2019\\n\\nAbstract: Based on information and para-contact metric geometries, in this paper a class of dynamical systems is formulated for describing time-development of expectation variables. Here such systems for expectation variables are exactly derived from continuous-time master equations describing nonequilibrium processes.\",\n \"Title: Bayesian Dynamic Mode Decomposition With Variational Matrix Factorization\\nYear: 2021\\n\\nAbstract: Dynamic mode decomposition (DMD) and its extensions are data-driven methods that have substantially contributed to our understanding of dynamical systems. However, because DMD and most of its extensions are deterministic, it is difficult to treat probabilistic representations of parameters and predictions. In this work, we propose a novel formulation of a Bayesian DMD model. Our Bayesian DMD model is consistent with the procedure of standard DMD, which is to first determine the subspace of observations, and then compute the modes on that subspace. Variational matrix factorization makes it possible to realize a fully-Bayesian scheme of DMD. Moreover, we derive a Bayesian DMD model for incomplete data, which demonstrates the advantage of probabilistic modeling. Finally, both of nonlinear simulated and real-world datasets are used to illustrate the potential of the proposed method.\",\n \"Title: One-bit Submission for Locally Private Quasi-MLE: Its Asymptotic Normality and Limitation\\nYear: 2022\\n\\nAbstract: Local differential privacy (LDP) is an information-theoretic privacy definition suitable for statistical surveys that involve an untrusted data curator. An LDP version of quasi-maximum likelihood estimator (QMLE) has been developed, but the existing method to build LDP QMLE is difficult to implement for a large-scale survey system in the real world due to long waiting time, expensive communication cost, and the boundedness assumption of derivative of a log-likelihood function. We provided an alternative LDP protocol without those issues, which is potentially much easily deployable to a large-scale survey. We also provided sufficient conditions for the consistency and asymptotic normality and limitations of our protocol. Our protocol is less burdensome for the users, and the theoretical guarantees cover more realistic cases than those for the existing method.\",\n \"Title: Detecting cell assemblies by NMF-based clustering from calcium imaging data\\nYear: 2022\\n\\nAbstract: A large number of neurons form cell assemblies that process information in the brain. Recent developments in measurement technology, one of which is calcium imaging, have made it possible to study cell assemblies. In this study, we aim to extract cell assemblies from calcium imaging data. We propose a clustering approach based on non-negative matrix factorization (NMF). The proposed approach first obtains a similarity matrix between neurons by NMF and then performs spectral clustering on it. The application of NMF entails the problem of model selection. The number of bases in NMF affects the result considerably, and a suitable selection method is yet to be established. We attempt to resolve this problem by model averaging with a newly defined estimator based on NMF. Experiments on simulated data suggest that the proposed approach is superior to conventional correlation-based clustering methods over a wide range of sampling rates. We also analyzed calcium imaging data of sleeping/waking mice and the results suggest that the size of the cell assembly depends on the degree and spatial extent of slow wave generation in the cerebral cortex.\",\n \"Title: Information Geometrically Generalized Covariate Shift Adaptation\\nYear: 2022\\n\\nAbstract: Many machine learning methods assume that the training and test data follow the same distribution. However, in the real world, this assumption is often violated. In particular, the marginal distribution of the data changes, called covariate shift, is one of the most important research topics in machine learning. We show that the well-known family of covariate shift adaptation methods is unified in the framework of information geometry. Furthermore, we show that parameter search for a geometrically generalized covariate shift adaptation method can be achieved efficiently. Numerical experiments show that our generalization can achieve better performance than the existing methods it encompasses.\",\n \"Title: Unsupervised Domain Adaptation for Extra Features in the Target Domain Using Optimal Transport.\\nYear: 2022\\n\\nAbstract: Domain adaptation aims to transfer knowledge of labeled instances obtained from a source domain to a target domain to fill the gap between the domains. Most domain adaptation methods assume that the source and target domains have the same dimensionality. Methods that are applicable when the number of features is different in each domain have rarely been studied, especially when no label information is given for the test data obtained from the target domain. In this letter, it is assumed that common features exist in both domains and that extra (new additional) features are observed in the target domain; hence, the dimensionality of the target domain is higher than that of the source domain. To leverage the homogeneity of the common features, the adaptation between these source and target domains is formulated as an optimal transport (OT) problem. In addition, a learning bound in the target domain for the proposed OT-based method is derived. The proposed algorithm is validated using both simulated and real-world data.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":64,"string":"64"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13652,"string":"13,652"}}},{"rowIdx":13653,"cells":{"question_id":{"kind":"string","value":"Chi Xu"},"question":{"kind":"string","value":"Generate an abstract for the title \"Energy Efficient Multiband Cooperative Spectrum Sensing for Heterogeneous Cognitive Industrial Wireless Sensor Networks\" using the following items: 1. Industrial wireless sensor networks (IWSNs)\n2. Cognitive radio technology\n3. Multiband cooperative spectrum sensing scheme (MB-CSS)\n4. Spectrum efficiency \n5. Energy optimization"},"answer":{"kind":"string","value":"Industrial wireless sensor networks (IWSNs) are facing more and more interference and coexistence problems as there is an explosive growth of wireless networks working in the unlicensed ISM band. To address these serious problems, we employ cognitive radio technology and formulate a novel cognitive IWSN with heterogeneous nodes, where cognitive nodes and sensor nodes separately perform spectrum sensing and data transmission. Correspondingly, we present a frame structure that allows both kinds of nodes to take up the whole frame for continuous spectrum sensing and successive data transmission, respectively. Then, a multiband cooperative spectrum sensing scheme (MB-CSS) is proposed for reliable and successive spectrum access. In this way, spectrum efficiency is enhanced while primary users are sufficiently protected. Furthermore, with the aim of minimizing energy cost, we optimize MB-CSS by solving mixed integer programming problems. Numerical results validate the availability of MB-CSS in heterogeneous cognitive IWNs, and demonstrate that the energy cost can be minimized under the condition that primary users are fully protected."},"correct_answer":{"kind":"string","value":"Industrial wireless sensor networks (IWSNs) are facing more and more interference and coexistence problems as there is an explosive growth of wireless networks working in the unlicensed ISM band. To address these serious problems, we employ cognitive radio technology and formulate a novel cognitive IWSN with heterogeneous nodes, where cognitive nodes and sensor nodes separately perform spectrum sensing and data transmission. Correspondingly, we present a frame structure that allows both kinds of nodes to take up the whole frame for continuous spectrum sensing and successive data transmission, respectively. Then, a multiband cooperative spectrum sensing scheme (MB-CSS) is proposed for reliable and successive spectrum access. In this way, spectrum efficiency is enhanced while primary users are sufficiently protected. Furthermore, with the aim of minimizing energy cost, we optimize MB-CSS by solving mixed integer programming problems. Numerical results validate the availability of MB-CSS in heterogeneous cognitive IWNs, and demonstrate that the energy cost can be minimized under the condition that primary users are fully protected."},"documents":{"kind":"list like","value":["Title: A Robust O(n) Solution to the Perspective-n-Point Problem.\nYear: 2012\n\nAbstract: We propose a non-iterative solution for the perspective-n-point (PnP) problem, which can robustly retrieve the optimum by solving a seventh order polynomial. The central idea consists of three steps: (1) to divide the reference points into 3-point subsets in order to achieve a series of fourth order polynomials, (2) to compute the sum of the square of the polynomials so as to form a cost function, and (3) to find the roots of the derivative of the cost function in order to determine the optimum. The advantages of the proposed method are as follows: Firstly, it can stably deal with the planar case, ordinary 3D case and quasi-singular case, and it is as accurate as the state-of-the-art iterative algorithms with much less computational time. Secondly, it is the first non-iterative PnP solution that can achieve more accurate results than the iterative algorithms when no redundant reference points can be used (n \\le 5). Thirdly, Large-size point sets can be handled efficiently because its computational complexity is O(n).","Title: Performance and power modeling in a multi-programmed multi-core environment\nYear: 2010\n\nAbstract: This paper describes a fast, automated technique for accurate on-line estimation of the performance and power consumption of interacting processes in a multi-programmed, multi-core environment. The proposed technique does not require modifying hardware or applications. The performance model uses reuse distance histograms, cache access frequencies, and the relationship between the throughput and cache miss rate of each process to predict throughput. The system-level power model is derived using multi-variable linear regression, accounting for cache contention. Both models are validated on multiple real multi-core systems using SPEC CPU2000 benchmarks; their performance and power estimates are within 3.5% of measured values on average. We explain how to integrate the two models for power estimation during process assignment, helpful for power-aware assignment.","Title: Robust and efficient pose estimation from line correspondences\nYear: 2012\n\nAbstract: We propose a non-iterative solution for the Perspective-n-Line (PnL) problem, which can efficiently and accurately estimate the camera pose for both small number and large number of line correspondences. By selecting a rotation axis in the camera framework, the reference lines are divided into triplets to form a sixteenth order cost function, and then the optimum is retrieved from the roots of the derivative of the cost function by evaluating the orthogonal errors and the reprojected errors of the local minima. The final pose estimation is normalized by a 3D alignment approach. The advantages of the proposed method are as follows: (1) it stably retrieves the optimum of the solution with very little computational complexity and high accuracy; (2) small line sets can be robustly handled to achieve highly accurate results and; (3) large line sets can be efficiently handled because it is O(n).","Title: Occlusion Handling in Augmented Reality System for Human-Assisted Assembly Task\nYear: 2008\n\nAbstract: Augmented reality (AR) based human-machine interaction (HMI) provided a seamless interface between user and application environment, but occlusion handing remained as a tough problem in AR applications. Recently most AR occlusion handling algorithms aimed at general environment, more special research on AR occlusion handling for application in the context of industrial assembly, such as assembly training and assembly task guidance, is required. An occlusion handling method aimed at video see-through AR-based assembly system is presented based on the analysis of the occlusion between virtual part, visual assembly feature, navigation information, physical part and physical assembly environment in the context of virtual/physical assembly working space. The method was implemented in prototype AR-assembly system and was proved to be efficient in handling the virtual/physical occlusion in augmented assembly scene.","Title: Efficient lookup table based camera pose estimation for augmented reality\nYear: 2011\n\nAbstract: Until now, exising camera pose estimation methods for the widely used square marker-based augmented reality (AR) are either highly sensitive to noise or much time consuming, and developers have to work hard to find the proper trade-off between computational speed and quality in mobile AR applications where computational resources are limited. The major difficulty is that only the four corner points of the square AR marker are available, and no redundant point correspondences can be used for a stable estimation. To solve this problem, an efficient lookup table (LUT)-based non-iterative solution is presented in this paper that achieves high stability in the presence of noise better than the most robust and accurate iterative solutions in the field, with the same level of accuracy and a much lower computational complexity. Our central idea consists of extracting a key parameter β from the camera pose and creating a LUT for β by taking the symmetrical structure of the square marker into account, thereby exploiting additional information. Copyright © 2011 John Wiley & Sons, Ltd. (Nowadays, the marker-based mobile augmented reality should rely on time-consuming iterative scheme in pose estimation procedure for accuracy and stability due to the absence of redundant point correspondences. We present an efficient LUT-based pose estimation solution which achieves high stability in the presence of noise better than the most robust and accurate iterative solutions available in the field, with the same level of accuracy and a much lower computational complexity.)","Title: Memory access aware on-line voltage control for performance and energy optimization\nYear: 2010\n\nAbstract: This paper describes an off-chip memory access-aware runtime DVFS control technique that minimizes energy consumption subject to constraints on application execution times. We consider application phases and the implications of changing cache miss rates on the ideal power control state. We first propose a two-stage DVFS algorithm based on formulating the throughput-constrained energy minimization problem as a multiple-choice knapsack problem (MCKP). This algorithm uses a power model that adapts to application phase changes by observing processor hardware performance counter values. The solutions it produces provide upper bounds on the energy savings achievable under a performance constraint. However, this algorithm assumes a priori (oracle or profiling-based) knowledge of application phase change behavior. To relax this assumption, we propose P-DVFS, an predictive DVFS algorithm for on-line minimization of energy consumption under a performance constraint without requiring a priori knowledge of an application's behavior. P-DVFS uses hardware performance counter based performance and power models. It predicts remaining execution time online in order to control voltage and frequency settings to optimize energy consumption and performance. The P-DVFS problem is formulated as a multiple-choice knapsack problem, which can be efficiently and optimally solved online. We evaluated P-DVFS using direct measurement of a real DVFS-equipped system. When bounding performance loss to at most 20% of that at the maximum frequency and voltage, P-DVFS leads to energy consumptions within 1.83% of the optimal solution for our problem instances on average with a maximum deviation of 4.83%. In addition to producing results approaching those of an oracle formulation, P-DVFS reduces power consumption for our problem instances by 9.93% on average, and up to 25.64%, compared with the most advanced related work.","Title: A Stable Direct Solution Of Perspective-Three-Point Problem\nYear: 2011\n\nAbstract: The perspective-three-point problem (P3P) is a classical problem in computer vision. The existing direct solutions of P3P have at least three limitations: (1) the numerical instability when using different vertex permutations, (2) the degeneration in the geometric singularity case, and (3) the dependence on particular equation solvers.A new direct solution of P3P is presented to deal with these limitations. The main idea is to reduce the number of unknown parameters by using a geometric constraint we called \"perspective similar triangle\" (PST). The PST method achieves high stability in the permutation problem and in the presence of image noise, and does not rely on particular equation solvers. Furthermore, reliable results can be retrieved even in \"danger cylinder\", a typical kind of geometric singularity of P3P, where all existing direct solutions degenerate significantly.","Title: TOA estimator for UWB backscattering RFID system with clutter suppression capability\nYear: 2010\n\nAbstract: Time of arrival (TOA) estimation in multipath dense environment for UWB backscattering radio frequency identification (RFID) system is challenging due to the presence of strong clutter. In addition, the backscattering RFID system has peculiar signal transmission and modulation characteristics, which are considerably different from conventional communication and localization systems. The existing TOA estimators proposed for conventional UWB systems are inappropriate for the backscattering RFID system since they lack the required clutter suppression capability and do not account for the peculiar characteristics of backscattering system. In this paper, we derive a nondata-aided (NDA) least square (LS) TOA estimator for UWB backscattering RFID system. We show that the proposed estimator is inherently immune to clutter and is robust in under-sampling operation. The effects of various parameter settings on the TOA estimation accuracy are also studied via simulations.","Title: SVM based internal model control for nonlinear systems\nYear: 2006\n\nAbstract: In this paper, a design procedure of support vector machine (SVM) with RBF kernel function based internal model control (IMC) strategy for stable nonlinear systems with input-output form is proposed. The control scheme consists of two controllers: a SVM based controller which fulfils the direct inverse model control and a traditional controller which fulfils the close-loop control. And so the scheme can deal with the errors between the process and the SVM based internal model generated by model mismatch and additional disturbance. Simulations are given to illustrate the proposed design procedure and the properties of the SVM based internal model control scheme for unknown nonlinear systems with time delay.","Title: SVM based nonlinear self-tuning control\nYear: 2006\n\nAbstract: In this paper, a support vector machine (SVM) with polynomial kernel function enhanced nonlinear self-tuning controller is developed, which combines the SVM identifier and parameters’ modifier together. The inverse model of a nonlinear system is achieved by off-line black-box identification according to input and output data. Then parameters of the model are modified online using gradient descent algorithm. Simulation results show that SVM based self-tuning control can be well applied to nonlinear uncertain system. And the SVM based self-tuning control of nonlinear system has good robustness performance in tracking reference input with good generalization ability.","Title: Adaptive system of heterogeneous multi-agent investors in an artificial evolutionary double auction market\nYear: 2010\n\nAbstract: In this paper, an adaptive system is proposed which attempts to combine together the approaches of studies of historical data and researches of multi-agent artificial market by evolving a double auction market model with diversity of different traders The purpose of this research is to construct an artificial market which is more close to realistic one and more practical for future researches The model with heterogeneous agents and the environment with which agents and market interact is complicated but controllable by data mining the optimal proportion of the different agents at the input to the market that generates an output which can fit historical data curve The simulation results suggest that the system performance is close to the expecting values in the testing with adequate training in advance.","Title: Efficient Hand Pose Estimation from a Single Depth Image\nYear: 2013\n\nAbstract: We tackle the practical problem of hand pose estimation from a single noisy depth image. A dedicated three-step pipeline is proposed: Initial estimation step provides an initial estimation of the hand in-plane orientation and 3D location, Candidate generation step produces a set of 3D pose candidate from the Hough voting space with the help of the rotational invariant depth features, Verification step delivers the final 3D hand pose as the solution to an optimization problem. We analyze the depth noises, and suggest tips to minimize their negative impacts on the overall performance. Our approach is able to work with Kinect-type noisy depth images, and reliably produces pose estimations of general motions efficiently (12 frames per second). Extensive experiments are conducted to qualitatively and quantitatively evaluate the performance with respect to the state-of-the-art methods that have access to additional RGB images. Our approach is shown to deliver on par or even better results.","Title: Imitation of Real Market Dynamics by Construction of Multi-agent Based Evolutionary Artificial Market\nYear: 2011\n\nAbstract: In this paper, an adaptive system is proposed which attempts to imitate a real market dynamics by combining together the approaches of studies of historical data and researches of multi-agent artificial market. The proportion of different agents is evolved by genetic algorithm in an artificial double auction market. The purpose of this research is to construct an artificial market which generates the dynamics of real market as similar as possible. The model with heterogeneous agents and the environment with which agents and market interact is complicated but controllable by data mining the optimal proportion of the different agents at the input to the market that generates an output which can fit historical data curve. The simulation results suggest that the system performance is close to the expecting values in the testing with adequate training in advance.","Title: Strategy Tree Construction and Optimization with Genetic Programming.\nYear: 2013\n\nAbstract: We applied genetic programming (GP) to search for a strategy in a technical analysis (TA) indicator candidate pool for stock market trading and optimized it through historical data. The method provides decision rule optimization scheme to deal with problems in the real trading in financial market, and it optimizes strategies in relatively complicated contents. GP is used to construct the condition in decision rule with different logical operations. The method has been applied to the optimization of investment strategies with good return results in simulation experiments.","Title: Experimental Evaluation of UWB Ranging Performance for Correlation and ED Receivers in Dense Multipath Environment\nYear: 2007\n\nAbstract: UWB signal reception can be accomplished by coherent or non-coherent receivers. Correlation receiver is a type of coherent receiver while energy detection (ED) receiver belongs to non-coherent type. In this paper, we evaluate their ranging performance using the channel profiles measured in a typical indoor office environment. The effects of ranging parameters such as search window length are also investigated. The results show that correlation receiver outperforms ED receiver in the line-of-sight (LOS) scenario and smaller integration interval gives better performance for ED. For the non-line-of-sight (NLOS) scenario, surprisingly, ED receiver outperforms correlation receiver and larger integration interval brings better performance for ED when the detection threshold exceeds certain level. The study also shows that regardless of the receiver types used, the resultant ranging errors for the LOS or NLOS environments can be described by unified models with the parameters being distance dependent.","Title: Cache contention and application performance prediction for multi-core systems\nYear: 2010\n\nAbstract: The ongoing move to chip multiprocessors (CMPs) permits greater sharing of last-level cache by processor cores but this sharing aggravates the cache contention problem, potentially undermining performance improvements. Accurately modeling the impact of inter-process cache contention on performance and power consumption is required for optimized process assignment. However, techniques based on exhaustive consideration of process-to-processor mappings and cycle-accurate simulation are inefficient or intractable for CMPs, which often permit a large number of potential assignments. This paper proposes CAMP, a fast and accurate shared cache aware performance model for multi-core processors. CAMP estimates the performance degradation due to cache contention of processes running on CMPs. It uses reuse distance histograms, cache access frequencies, and the relationship between the throughput and cache miss rate of each process to predict its effective cache size when running concurrently and sharing cache with other processes, allowing instruction throughput estimation.We also provide an automated way to obtain process-dependent characteristics, such as reuse distance histograms, without offline simulation, operating system (OS) modification, or additional hardware. We tested the accuracy of CAMP using 55 different combinations of 10 SPEC CPU2000 benchmarks on a dual-core CMP machine. The average throughput prediction error was 1.57%.","Title: Fast online SVR algorithm based adaptive internal model control\nYear: 2006\n\nAbstract: Based on fast online support vector regression (SVR) algorithm, reverse model of system model is constructed, and adaptive internal model controller is developed. First, SVR model and its online training algorithm are introduced. A kernel cache method is used to accelerate the online training algorithm, which makes it suitable for real-time control application. Then it is used in internal model control (IMC) for online constructing internal model and designing the internal model controller. Output errors of the system are used to control online SVR algorithm, which made the whole control system a closed-loop one. Last, the fast online SVM based adaptive internal model control was used to control a benchmark nonlinear system. Simulation results show that the controller has simple structure, good control performance and robustness.","Title: Data synchronization with conflict resolution for RFID-based track and trace\nYear: 2012\n\nAbstract: In an RFID-assisted track and trace information network, the same set of data may be stored in distributed locations. Data conflicts occur when the values of distributed data copies are modified locally with different values causing data inconsistency. In order to restore data consistency, the values of distributed data copies have to be synchronized to the same value by resolving the conflicts. The conventional conflict resolutions do not consider the unique characteristics of RFID data and therefore are not able to maximize the benefit of information users or may even provide erroneous resolution result. In this paper, a data synchronization method with conflict resolution accommodated to RFID applications is proposed. The method resolves the conflicts based on multiple RFID data attributes and takes the dependent relationship between data into account. Simulations confirm the efficiency of the algorithm.","Title: A VSC method for MIMO systems based on SVM\nYear: 2006\n\nAbstract: A variable structure control (VSC) scheme for linear black-box multi-input/multi-output (MIMO) systems based on support vector machine (SVM) is developed. After analyzing character of MIMO system, an additional control is designed to track trajectory. Then VSC algorithm is adopted to eliminate the difference. By estimating outputs of next step, VSC inputs and additional inputs are obtained directly by two kinds of trained SVMs, and so recognition of system parameters is avoided. A linear MIMO system is introduced to prove the scheme, and simulation shows that the high identification precision and quick training speed.","Title: Pattern-Oriented Agent-Based Modeling for Financial Market Simulation\nYear: 2007\n\nAbstract: The paper presents a pattern-oriented agent-based model to simulate the dynamics of a stock market. The model generates satisfactory market macro-level trend and volatility while the agents obey simple rules but follow the behaviors of the neighbors closely. Both the market and the agents are made to evolve in an environment where Darwin's natural selection rules apply.","Title: Delay-Dependent Threshold Selection for UWB TOA Estimation\nYear: 2008\n\nAbstract: Precision ultra-wideband ranging requires accurate estimation of time of arrival (TOA). The threshold-based TOA estimation has attracted considerable interest due to its simplicity. In this paper, we propose a technique to set the threshold as a function of delay instead of a single value as in conventional methods. Simulation results show that the proposed method is superior to conventional methods.","Title: A Mixed Reality-Based Assembly Verification and Training Platform\nYear: 2009\n\nAbstract: Mixed reality (MR) based human-machine interaction provides a seamless interface between user and application environment, which synthesizes the advantages of the convenient interaction of virtual reality, and the strong realistic of augmented reality. In this paper, MR is applied in the context of industrial assembly process, and a MR based assembly verification and training platform is proposed. In the MR based assembly environment, virtual model, real images and augmented information are jointly displayed on the assembly scene, accessorizing multi-video display windows of different angles of view to browse the real assembly scene. Additionally, constraint proxies figuratively reconstruct part's constraint relationship in the MR environment, and avoid the complex calculation of constraint's match. By using a virtual hand with constraints guided to assemble, an effective and realistic assembly process experience is provided to the user.","Title: A novel shape-based image classification method by featuring radius histogram of dilating discs filled into regular and irregular shapes\nYear: 2010\n\nAbstract: In this paper, a novel feature for shape-based image classification is proposed, in which a set of randomly dilating discs are distributed on an unknown regular or irregular shape, and the radius histogram of discs is used to represent the target shape. By such doing, a shape can be modeled by the radius histogram. The proposed feature is rather effective for shape retrieval with rotation and scaling invariance. The proposed feature is particularly effective in the retrieval of string-linked objects in which conventional approaches may fail badly. Experimental results on seven shapes and string-linked objects show that our proposed new feature is very effective in shape classification and shape-based image retrieval.","Title: Image Recovery from Broken Image Streams\nYear: 2007\n\nAbstract: This paper presents an approach for image recovery from broken image streams based on an image continuity model. The information to be recovered includes the header of an image (width, height, color space etc.) and the data stream which might have been partially damaged. We begin our discussion on common image formats and necessary information for the image recovery. We then define several measures on image contents to facilitate the image recovery. Using these measures, an effective recovery algorithm is proposed. Experimental results show that the algorithm can successfully recover images of different formats, even multi-frame images from broken image streams in most cases.","Title: Healthy: A Diary System Based on Activity Recognition Using Smartphone\nYear: 2013\n\nAbstract: An activity-diary system, named Healthy, is presented in this paper. Healthy can infer users diary of physical activities and energy expenditure based on METS (Metabolic Equivalents) values via recognizing general human activities. In this system, we design a two-layer classifier which costs less energy and memory with satisfactory accuracy. Our classifier divides the activities into two categories: periodic and nonperiodic. And a different sub-classifier is applied for each category. Meanwhile, We design a state listener to recognize more complicated activities. To further improve recognition accuracy, in the second layer sub-classifier, we put forward an adaptive framing algorithm based on the period length of periodical activities to determine the time during which features are extracted. By testing Healthy in real situation, we obtained an average recognition accuracy of 98.0%.","Title: Construction of Evolutionary Multi-agent Double Auction Market for Data Mining Combinational Strategies with Stable Returns.\nYear: 2011\n\nAbstract: In this paper, we report a multi-agent artificial market scheme which evolves for imitating a real stock market as similar as better. The artificial market model can generate possible price trend curves those have high correlation coefficient with Hong Kong Hang Seng Index (HSI). The purpose of the imitation is to generate different possible market price dynamics, so the artificial markets can be reliable source to provide statistically analysis samples for a real market. The strategies optimized in the artificial markets can improve the stability and profitability in the real market. The aim of the experiment is to obtain a strategy that can provide profitable and relatively stable return without forecasting the future movements of the market.","Title: Optimal Spatial Matrix Filter Design for Array Signal Preprocessing.\nYear: 2014\n\nAbstract: An efficient technique of designing spatial matrix filter for array signal preprocessing based on convex programming was proposed. Five methods were considered for designing the filter. In design method 1, we minimized the passband fidelity subject to the controlled overall stopband attenuation level. In design method 2, the objective function and the constraint in the design method 1 were reversed. In design method 3, the optimal matrix filter which has the general mean square error was considered. In design method 4, the left stopband and the right stopband were constrained with specific attenuation level each, and the minimized passband fidelity was received. In design method 5, the optimization objective function was the sum of the left stopband and the right stopband attenuation levels with the weighting factors 1 and gamma, respectively, and the passband fidelity was the constraints. The optimal solution of the optimizations above was derived by the Lagrange multiplier theory. The relations between the optimal solutions were analyzed. The generalized singular value decomposition was introduced to simplify the optimal solution of design methods 1 and 2 and enhanced the efficiency of solving the Lagrange multipliers. By simulations, it could be found that the proposed method was effective for designing the spatial matrix filter.","Title: On the Interplay between Network Traffic and Energy Consumption in Virtualized Environment: An Empirical Study\nYear: 2014\n\nAbstract: Networking and virtualization are two key building blocks of modern cloud computing. The energy consumption of physical machines has been carefully examined in the past research, including the impact of network traffic. When it comes with virtual machines, the inter-play between energy consumption and network traffic however becomes much more complicated. The traffic are now generated by and exchanged between virtual machines (VMs), which could reside in different physical machines with their respective network interface cards (NICs), or share the same physical machine. When multiple VMs share a physical NIC, their traffic can interfere with each other, causing extra overhead. Yet the VM's allocation can be dynamic and they can even migrated across physical machines, thereby changing the traffic pattern. These factors combined make the network traffic highly diverse and dynamic, so is the corresponding energy consumption. A close examination on the network traffic and energy consumption in virtualized environments is thus of need. In this paper, we present an initial measurement study on the interplay between energy consumption and network traffic in representative virtualization environments. Our study reveals a series of unique energy consumption patterns of the network traffic in this context. We show that state-of-the-art virtualization designs noticeably increase the demand of CPU resources when handling networked transactions, generating excessive interrupt requests with ceaselessly context switching, which in turn increases energy consumption. Even when the physical machine is in an idle state, the VM network transactions will will incur remarkable energy consumption. Furthermore, even with identical number of VMs and amount of traffic on a physical machine, the energy consumptions vary significantly with different VM allocation strategies. Our close examination pinpoints the root cause, and offers new angles to revisit the existing resource usage and energy consumption models, so as to optimize the service provisioning as well as virtual machine placement and migration.","Title: Quantitative payoff evaluation for track and trace system in supply chains\nYear: 2014\n\nAbstract: The track and trace information increases the supply chain visibility which leads to the improvement of the operation efficiency and effectiveness. A performance evaluation method for track and trace system can help a company to select the best system design and identify the degree of improvement for an implemented system. Existing research works on the performance evaluation of track and trace have limitations such as only supporting single product state and lack of consideration for missing and erroneous insertion of information. In this paper, we take payoff as performance criteria and model the input and output of the system as unequal lengths of product state sequences. A novel pad-and-transit technique is proposed to identify and generate intermediate information transformations with missing and erroneous inserted information. Based on the identified transformations, the expected payoff is then evaluated using a closed form formula. A numerical case study is provided to illustrate the application of the proposed method.","Title: MIMO RF probe for wide-area indoor human motion monitoring\nYear: 2014\n\nAbstract: This paper addresses the problem of RF-based wide-area human motion monitoring in indoor multipath environments. A major challenge for conventional pulse-Doppler radar in multipath scenarios is the difficulty in discriminating direct-path targets from ghost returns due to multipath scattering. In this paper, the ability of a multiple-input multiple-output (MIMO) RF probe to discern both direction-of-departure (DoD) and direction-of-arrival (DoA) via “non-causal” beamforming is exploited for indoor motion monitoring. Preliminary results with real data are presented which demonstrate the sidelobe suppression and multipath mitigation achieved. Also, MIMO processing is analyzed using the bi-directional beampattern and spectrum defined herein.","Title: Lightweight construction of the information potential field in wireless sensor networks\nYear: 2014\n\nAbstract: The information gradient-based routing protocols have been proved to be economical and effective by adopting the principle of achieving the global objective through local decision, but lightweight methods to construct the information gradient should be fully investigated, especially in a large-scale network with high information dynamics. In this paper, we focus on the construction of the information gradient by balancing convergence conditions and energy consumption. Therefore, two algorithms, Hierarchical Skeleton-based Construction Algorithm (HSCA) and Estimate value Substitution Algorithm (ESA) are proposed to achieve the goal of fastening the convergence in an energy efficient way. Both of the algorithms obey the typical assumptions on WSNs settings and the gossip-styled propagation principle. Comprehensive simulation results show that the proposed algorithms can reduce iteration times to reach a convergence status by 80% and conserve 30-50% energy consumption on average.","Title: Analysis And Control For Matrix Rectifier By Circuit Dq Transformation\nYear: 2015\n\nAbstract: An input filter is indispensable for a matrix rectifier (MR), since it can improve the input current quality and reduce the ac supply voltage distortion. However the characteristics of input filter reduce the input power factor (IPF). Moreover, the unbalanced ac supply voltage could disturb dc output voltage. Consequently, this paper provides a novel approach to achieve both tight dc output voltage and unity IPF at the main ac power supply by applying circuit DQ transformation to MR. Analyzing the DQ model of MR, sliding mode control (SMC) based on reaching law is used to achieve tight dc output voltage regulation of MR and PI control is applied to control the q-axis current to be zero. Finally, simulation and experiment results are shown to verify the effectiveness of the control scheme proposed in this paper.","Title: Component segmentation of sketches used in 3D model retrieval\nYear: 2015\n\nAbstract: Sketching is a natural human practice. With the popularity of multi-touch tablets and styluses, sketching has become a more popular means of human-computer interaction. However, accurately recognizing sketches is rather challenging, especially when they are drawn by non-professionals. Therefore, automatic sketch understanding has attracted much research attention. To tackle the problem, we propose to segment sketch drawings before analyzing the semantic meanings of sketches for the purpose of developing a sketch-based 3D model retrieval system.","Title: Forecast UPC-level FMCG demand, Part I: Exploratory analysis and visualization\nYear: 2015\n\nAbstract: We are interested in forecasting a large collection of FMCG demand time series. As the demand of FMCG exists in a hierarchy (from manufacturers to distributors to retailers), the bottom level of the hierarchy may contain thousands or even millions of time series. Producing aggregate consistent forecasts while utilizing the unique features from each time series thus become a technical challenge. To achieve better forecasting results, exploratory analysis is often necessary to obtain insights on the underlying demand generating mechanism for each time series. Exploratory analysis aims at discovering those so-called \\\"exogenous factors\\\", such as price, demand of the complementary/substitutive goods and calendar events, which can help explain some of the demand fluctuation. During forecast accuracy evaluation, outlier detection is also important; a single anomalous time series can contribute much to the overall error. However, in a big data (such as retailing scanner data) enabled environment, exploratory analysis and visualization need much attention, because of the non-scalable nature of the existing methods. Scalability is essential for exogenous factor selection and outlier detection in big time series data. In Part I of this two-part paper, we introduce some exploratory analytics and visualization methods (from not scalable to very scalable) for big retailing time series. Forecasting of the hierarchical FMCG demand is addressed in Part II.","Title: Sparsity adjusted information gain for feature selection in sentiment analysis\nYear: 2015\n\nAbstract: The widespread use of social media and the internet are emerging trends that offer an additional interaction channel for companies to better understand customer sentiments about their brands and products. Sentiment analysis uses text data from social media such as customer comments and reviews, which has the nature of high dimensionality. Without selection, typically there are at least thousands of features (words or phrases) that can be extracted from a text corpus, among which there are many redundant or irrelevant features for sentiment classification task. Thus, it is critical to select a compact yet effective set of features to avoid the complex classifier design and slow running time of classification process. However, very few of existing metrics is able to improve efficacy of feature selection by addressing the issue of sparsity of feature matrix for text data, i.e., many features may appear only in a few documents. In this paper, an improved feature selection metric known as sparsity adjusted information gain (SAIG) is proposed, which modifies the conventional information gain metric and aims to adjust the feature ranking scores according to the sparsity of the feature vector. It is able to use less features to obtain a targeted performance level. The experiment results show that SAIG is able to improve the performance of sentiment classification.","Title: Space-Delay Adaptive Processing For Mimo Rf Indoor Motion Mapping\nYear: 2015\n\nAbstract: In this paper, space-delay adaptive processing (SDAP) is proposed for motion mapping using MIMO RF signals. Typically, indoor RF localization methods require ultra-wideband signals to achieve the time-delay resolution required for separation of returns in complex multipath environments. Due to spectrum use restrictions, however, there is a need for methods which can operate using the lower bandwidths allocated for so-called ISM use. In this paper, we develop SDAP methods for linear MIMO transmit-receive arrays which can achieve near ultra-wideband range resolution and multipath sidelobe suppression but which use significantly less bandwidth. Real data results for indoor motion monitoring are presented aimed at mapping patient repositioning activities in long-term care facilities for pressure ulcer prevention.","Title: A Novel Comac-Based Cooperative Spectrum Sensing Scheme In Cognitive Radio Networks\nYear: 2015\n\nAbstract: Conventional cooperative spectrum sensing (CSS) schemes in cognitive radio networks (CRNs) require that the secondary users (SUs) report their sensing data sequentially to the fusion center, which yields long reporting delay especially in the case of large number of cooperative SUs. By exploiting the computation over multiple-access channel (CoMAC) method, this paper proposes a novel CoMAC-based CSS scheme that allows the cooperative SUs to encode their local statistics in transmit power and to transmit simultaneously the modulated symbols sequence carrying transmit power information to the fusion center. The fusion center then makes the final decision on the presence of primary users by recovering the overall test statistic of energy detection from the energy of the received signal. Performance metrics of the CoMAC-based CSS scheme, i.e., detection probability and false alarm probability, are further derived based on the central limit theorem. Finally, based on the derived detection and false alarm probabilities, both energy detection threshold and spectrum sensing time are optimized to improve the average throughput of the CRN.","Title: Hand Action Detection from Ego-centric Depth Sequences with Error-correcting Hough Transform.\nYear: 2017\n\nAbstract: •An effective and efficient solution for hand action detection from mobile egocentric depth sequences.•A novel error- correcting mechanism to tackle the bottleneck issue of incorrect votes from Hough transform.•A comprehensive, in-house annotated ego-centric hand action dataset on which the proposed method is carefully evaluated.•A deep learning baseline.","Title: Outage Performance of Underlay Multihop Cognitive Relay Networks With Energy Harvesting.\nYear: 2016\n\nAbstract: This letter studies the outage performance of multihop cognitive relay networks with energy harvesting in underlay paradigms, wherein the secondary users are powered by a dedicated power beacon (PB) and their transmit powers are subject to the harvested energy from PB and the interference constraint from the primary user. We derive the exact outage probability for Rayleigh block fading and prove that the outage probability is monotonically decreasing with respect to the transmit power of PB. Furthermore, we derive the asymptotic outage probability to study the outage saturation phenomenon and propose an iterative algorithm that jointly optimizes the transmit power of PB and the harvest-to-transmit ratio to approximate the minimum outage probability. Simulation results validate the theoretical results.","Title: Mimo space-delay adaptive processing for wide-area indoor human motion classification.\nYear: 2016\n\nAbstract: This paper develops space-delay adaptive processing (SDAP) for multiple-input, multiple-output (MIMO) arrays with application to classifying human motion in wide-area indoor environments. The proposed adaptive method resolves motion returns using joint spatial-temporal processing on the received signals. Results with real data demonstrate that MIMO SDAP can mitigate ghost motion returns due to indoor multipath scattering and bandwidth limitation for better separation of micro-Doppler signatures from multiple targets. Human activities such as bed turnovers and arm or leg movements are then classified based on micro-Doppler analysis for patient pressure ulcer prevention.","Title: Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups.\nYear: 2017\n\nAbstract: Pose estimation, tracking, and action recognition of articulated objects from depth images are important and challenging problems, which are normally considered separately. In this paper, a unified paradigm based on Lie group theory is proposed, which enables us to collectively address these related problems. Our approach is also applicable to a wide range of articulated objects. Empirically it is evaluated on lab animals including mouse and fish, as well as on human hand. On these applications, it is shown to deliver competitive results compared to the state-of-the-arts, and non-trivial baselines including convolutional neural networks and regression forest methods. Moreover, new sets of annotated depth data of articulated objects are created which, together with our code, are made publicly available.","Title: Time-efficient cooperative spectrum sensing via analog computation over multiple-access channel.\nYear: 2017\n\nAbstract: Conventional cooperative spectrum sensing (CSS) schemes in cognitive radio networks (CRNs) require that the secondary users (SUs) report their sensing data separately in the time domain to the fusion center, which yields long reporting delay especially in the case of large number of cooperative SUs. By exploiting the computation over multiple-access channel (CoMAC) method, this paper proposes a novel CoMAC-based CSS scheme that allows the cooperative SUs to encode their local statistics in transmit power and to transmit simultaneously the modulated symbols sequence carrying transmit power information to the fusion center. The fusion center then makes the final decision on the presence of primary users by recovering the overall test statistic of energy detection from the energy of the received signal. Performance metrics of the CoMAC-based CSS scheme, i.e., detection probability and false alarm probability, are further derived based on the central limit theorem. Finally, based on the derived detection and false alarm probabilities, both energy detection threshold and spectrum sensing time are optimized to improve the average throughput of the CRN. Simulations demonstrate the efficiency of this work.","Title: Learning to Search on Manifolds for 3D Pose Estimation of Articulated Objects.\nYear: 2016\n\nAbstract: This paper focuses on the challenging problem of 3D pose estimation of a diverse spectrum of articulated objects from single depth images. A novel structured prediction approach is considered, where 3D poses are represented as skeletal models that naturally operate on manifolds. Given an input depth image, the problem of predicting the most proper articulation of underlying skeletal model is thus formulated as sequentially searching for the optimal skeletal configuration. This is subsequently addressed by convolutional neural nets trained end-to-end to render sequential prediction of the joint locations as regressing a set of tangent vectors of the underlying manifolds. Our approach is examined on various articulated objects including human hand, mouse, and fish benchmark datasets. Empirically it is shown to deliver highly competitive performance with respect to the state-of-the-arts, while operating in real-time (over 30 FPS).","Title: Multivariate Regression with Grossly Corrupted Observations: A Robust Approach and its Applications.\nYear: 2017\n\nAbstract: This paper studies the problem of multivariate linear regression where a portion of the observations is grossly corrupted or is missing, and the magnitudes and locations of such occurrences are unknown in priori. To deal with this problem, we propose a new approach by explicitly consider the error source as well as its sparseness nature. An interesting property of our approach lies in its ability of allowing individual regression output elements or tasks to possess their unique noise levels. Moreover, despite working with a non-smooth optimization problem, our approach still guarantees to converge to its optimal solution. Experiments on synthetic data demonstrate the competitiveness of our approach compared with existing multivariate regression models. In addition, empirically our approach has been validated with very promising results on two exemplar real-world applications: The first concerns the prediction of textit{Big-Five} personality based on user behaviors at social network sites (SNSs), while the second is 3D human hand pose estimation from depth images. The implementation of our approach and comparison methods as well as the involved datasets are made publicly available in support of the open-source and reproducible research initiatives.","Title: Memnet: Enhancing Throughput And Energy Efficiency For Hybrid Workloads Via Para-Virtualized Memory Sharing\nYear: 2016\n\nAbstract: Virtualization has become a building block for modern IT industry, and many datacenters are now highly virtualized. It is known that virtualization also introduces nontrivial overhead, which can cause severe self-interference inside a VM when CPU intensive tasks and bandwidth intensive tasks are co-located. Energy efficiency of the server can be affected as well. While such overhead is well-studied in application/protocol specific context, a more comprehensive solution is yet to be explored for general cloud services. In this paper, we present MemNet, a novel protocol-independent solution that enables para-virtualized memory sharing between host and guest VMs. This design successfully decouples I/O and computation operations and lifts the offered interface from the physical devices to highlevel network services. Our real-world implementation on KVM indicates that, MemNet can achieve 27% and 70% gain in terms of computing and networking performance, respectively, by resolving the self-interference. It also provides 32% improvement in terms of energy efficiency.","Title: End-to-End Throughput Maximization for Underlay Multi-Hop Cognitive Radio Networks With RF Energy Harvesting.\nYear: 2017\n\nAbstract: This paper studies a green paradigm for the underlay coexistence of primary users (PUs) and secondary users (SUs) in energy harvesting cognitive radio networks (EH-CRNs), wherein battery-free SUs capture both the spectrum and the energy of PUs to enhance spectrum efficiency and green energy utilization. To lower the transmit powers of SUs, we employ multi-hop transmission with time division multip...","Title: Joint Spatial-Temporal Access Scheme for Multihop Cognitive Relay Networks Over Nakagami-m Fading.\nYear: 2017\n\nAbstract: This paper investigates the cooperative multihop cognitive relay networks (CRNs) under a novel two-dimensional spatial-temporal opportunity model, in which free opportunity and sharing opportunity are defined to enhance the spectrum efficiency. Correspondingly, a joint spatial-temporal access scheme (JSTAS) is proposed to realize successive spectrum access for continuous data transmission. The multihop CRNs with fixed relaying employ decode-and-forward with and without signal-to-noise ratio selection, which are named as SDF and DF, respectively. Then, considering the interference constraints from multiple primary receivers, the interference of one primary transmitter and the maximum transmit powers of cognitive users, we study the outage performance of multihop CRNs with JSTAS over Nakagami-m fading. To comprehensively evaluate JSTAS, we further present the pure spatial access scheme (SAS) and the pure temporal access scheme (TAS) under the spatial-temporal opportunity model, and calculate their average network outage probabilities. Simulation results demonstrate that SDF outperforms DF, while JSTAS outperforms SAS and TAS under all considered scenarios.","Title: Pose Estimation from Line Correspondences: A Complete Analysis and a Series of Solutions.\nYear: 2017\n\nAbstract: In this paper we deal with the camera pose estimation problem from a set of 2D/3D line correspondences, which is also known as PnL (Perspective-n-Line) problem. We carry out our study by comparing PnL with the well-studied PnP (Perspective-n-Point) problem, and our contributions are three-fold: (1) We provide a complete 3D configuration analysis for P3L, which includes the well-known P3P problem as well as several existing analyses as special cases. (2) By exploring the similarity between PnL and PnP, we propose a new subset-based PnL approach as well as a series of linear-formulation-based PnL approaches inspired by their PnP counterparts. (3) The proposed linear-formulation-based methods can be easily extended to deal with the line and point features simultaneously.","Title: Gait Energy Response Function For Clothing-Invariant Gait Recognition\nYear: 2016\n\nAbstract: This paper describes a method of clothing-invariant gait recognition by modifying intensity response function of a silhouettebased gait feature. While a silhouette-based representation such as gait energy image (GEI) has been popular in gait recognition community due to its simple yet effective property, it is also well known that such a representation is susceptible to clothes variations since it significantly changes silhouettes (e. g., down jacket, long skirt). We therefore propose a gait energy response function (GERF) which transforms an original gait energy into another one in a nonlinear way, which increases discrimination capability under clothes variation. More specifically, the GERF is represented as a vector of components of a lookup table from an original gait energy to another one and its optimization process is formulated as a generalized eigenvalue problem considering discrimination capability as well as regularization on the GERF. In addition, we apply Gabor filters to the GEI transformed by the GERF and further apply a spatial metric learning method for better performance. In experiments, the OUISIR Treadmill dataset B with the largest clothing variation was used to measure the performance both in verification and identification scenarios. The experimental results show that the proposed method achieved stateof- the-art performance in verification scenarios and competitive performance in identification scenarios.","Title: Speed Invariance Vs. Stability: Cross-Speed Gait Recognition Using Single-Support Gait Energy Image\nYear: 2016\n\nAbstract: Gait recognition has recently attracted much attention since it can identify person at a distance without subject cooperation. Walking speed changes, however, cause gait changes in appearance, which significantly drops performance of gait recognition. Considering a speed-invariant property at single-support phases where stride change due to speed changes are mitigated, and a stability against phase estimation error and segmentation noise by aggregating multiple phases inspired by gait energy image (GEI), we propose a speed-invariant gait representation called single-support GEI (SSGEI), which realizes a good tradeoff between the speed invariance and the stability by combining singlesupport phases and GEI concept. For this purpose, we firstly find out the optimal duration around single support phases using a training set so as to well balance the speed invariance and the stability. We then extract SSGEI by aggregating multiple single-support frames. Finally, we combine the proposed SSGEI with subsequent Gabor filters and metric learning for better performance. Experiments on the publicly available OU-ISIR Treadmill Dataset A composed of the largest speed variations demonstrated that the proposed method yielded 99.33% rank-1 identification rate on average for cross-speed gait recognition, which outperforms the other state-of-the-arts, and realized a low computational cost as well.","Title: A Unified Latent Factor Model for Effective Category-Aware Recommendation.\nYear: 2017\n\nAbstract: Our data analysis on real-world datasets shows that user preferences are intimately related with item categories, implying the non-negligible of category information for effective recommendation. Thus, in this paper, step by step we propose a unified item-category latent factor model by considering user-category, item-category and category-category interactions. Our approach can be applied to both the situations where an item belongs to either a single category (one-to-one) or multiple categories (one-to-many). Finally, empirical studies on the real-world datasets demonstrate the superiority of our approach in comparison with other counterparts.","Title: MRLR: Multi-level Representation Learning for Personalized Ranking in Recommendation.\nYear: 2017\n\nAbstract: Representation learning (RL) has recently proven to be effective in capturing local item relationships by modeling item co-occurrence in individual useru0027s interaction record. However, the value of RL for recommendation has not reached the full potential due to two major drawbacks: 1) recommendation is modeled as a rating prediction problem but should essentially be a personalized ranking one; 2) multi-level organizations of items are neglected for fine-grained item relationships. We design a unified Bayesian framework MRLR to learn user and item embeddings from a multi-level item organization, thus benefiting from RL as well as achieving the goal of personalized ranking. Extensive validation on real-world datasets shows that MRLR consistently outperforms state-of-the-art algorithms.","Title: Joint Spatial-Temporal Access Scheme Of Multi-Hop Cognitive Radio Sensor Networks\nYear: 2017\n\nAbstract: This paper studies a novel channel access scheme for delay-sensitive multi-hop cognitive radio sensor networks (CRSNs) with fixed decode-and-forward relaying. We first formulate a two-dimensional spatial-temporal opportunity model wherein free and sharing opportunities are defined, and then propose a joint spatial-temporal access scheme (JSTAS) to achieve successive channel access for continuous data transmission. By spatial sensing and temporal sensing, the cognitive sensors with JSTAS control the transmission powers by fully considering their maximum transmission powers, the interference constraints of multiple primary receivers and the interference from one primary transmitter. To evaluate JSTAS, we derive the closed-form outage probability under Rayleigh fading channels and make comparisons among five different schemes. Simulation results demonstrate that JSTAS has better outage performance than that of pure spatial or temporal access scheme under all considered scenarios.","Title: The OU-ISIR Gait Database comprising the Large Population Dataset with Age and performance evaluation of age estimation.\nYear: 2017\n\nAbstract: In this paper, we describe the world’s largest gait database, the “OU-ISIR Gait Database, Large Population Dataset with Age (OULP-Age)” and its application to a statistically reliable performance evaluation of gait-based age estimation. Whereas existing gait databases include only 4016 subjects at most, we constructed an extremely large-scale gait database that includes 63,846 subjects (31,093 males and 32,753 females) with ages ranging from 2 to 90 years old. Benchmark algorithms of gait-based age estimation were then implemented to evaluate statistically significant performance differences. Additionally, the dependence of gait-based age estimation performance on gender and age group, in addition to the number of training subjects, was investigated to provide several insights for future research on the topic.","Title: Green-Energy-Powered Cognitive Radio Networks: Joint Time and Power Allocation.\nYear: 2018\n\nAbstract: This article studies a green-energy-powered cognitive radio network (GCRN) in an underlay paradigm, wherein multiple battery-free secondary users (SUs) capture both the spectrum and the energy of primary users (PUs) to communicate with an access point (AP). By time division multiple access, each SU transmits data to AP in the allocated time and harvests energy from the RF signals of PUs otherwise, all in the same licensed spectrum concurrently with PUs. Thus, the transmit power of each SU is jointly constrained by the peak interference power at PU and the harvested energy of SU. With the formulated green coexistence paradigm, we investigate the sum-throughput maximization problem with respect to time and power allocation, which is non-convex. To obtain the optimal resource allocation, we propose a joint optimal time and power allocation (JOTPA) algorithm that first transforms the original problem into a convex optimization problem with respect to time and energy allocation, and then solve it by iterative Lagrange dual decomposition. To comprehensively evaluate the performance of the GCRN with JOTPA, we deploy the GCRN in three typical scenarios and compare JOTPA with the equal time and optimal power allocation (ETOPA) algorithm. Extensive simulations show that the deployment of the GCRN significantly influences the throughput performance and JOTPA outperforms ETOPA under all considered scenarios.","Title: Hand joints-based gesture recognition for noisy dataset using nested interval unscented Kalman filter with LSTM network.\nYear: 2018\n\nAbstract: Hand joints-based gesture recognition using a neural network provides excellent performance in hand gesture recognition. However, during the collection of sequential skeletal datasets, joints identification through hand pose estimations usually includes noise and even errors, which often diminish the accuracy of gesture recognition. To promote the availability of hand gesture recognition for such noisy datasets, this paper presents a nested interval unscented Kalman filter (UKF) with long short-term memory (NIUKF-LSTM) network to improve the accuracy of hand gesture recognition from noisy datasets. This nested interval method with the UKF changes the distribution of the sigma points based on two sampling intervals. By considering the information of previous frames, the nested interval method helps the NIUKF-LSTM network revise the noise in the sequential hand skeletal data and improve the recognition accuracy. The experimental results showing the removal of noisy skeletal data from the dynamic hand gesture dataset demonstrate the effectiveness of our NIUKF-LSTM network, which achieves better performance than do other state-of-the-art methods.","Title: Correction to: Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups.\nYear: 2018\n\nAbstract: The original author list did not accurately reflect the contributions of the following colleagues.","Title: Recurrent knowledge graph embedding for effective recommendation.\nYear: 2018\n\nAbstract: Knowledge graphs (KGs) have proven to be effective to improve recommendation. Existing methods mainly rely on hand-engineered features from KGs (e.g., meta paths), which requires domain knowledge. This paper presents RKGE, a KG embedding approach that automatically learns semantic representations of both entities and paths between entities for characterizing user preferences towards items. Specifically, RKGE employs a novel recurrent network architecture that contains a batch of recurrent networks to model the semantics of paths linking a same entity pair, which are seamlessly fused into recommendation. It further employs a pooling operator to discriminate the saliency of different paths in characterizing user preferences towards items. Extensive validation on real-world datasets shows the superiority of RKGE against state-of-the-art methods. Furthermore, we show that RKGE provides meaningful explanations for recommendation results.","Title: Gait-based human age estimation using age group-dependent manifold learning and regression.\nYear: 2018\n\nAbstract: Human age estimation from gait is expected to be an important technology for a variety of applications such as automatic customer counting for marketing research or automatic age-based access control restriction for a specific area because the gait can be observable at a distance from a camera (e.g., CCTV). Although the aging process of gait significantly differs among age groups (e.g., children, adults, and the elderly), previous studies on gait-based human age estimation employ a single age group-independent estimation model that suffers from large estimation errors when the age variation increases. We therefore propose an age group-dependent gait-based human age estimation method for better accuracy. Specifically, in the training phase, we first compose age groups that are well-separated from each other by clustering gait features along with their age labels. We then learn a classifier that classifies the gait features for multiple age groups using a directed acyclic graph support vector machine. Next, we learn an age regression model for each age group using support vector regression with a Gaussian kernel in conjunction with a manifold learning technique, i.e., orthogonal locality preserving projection, to better characterize the gait feature. In the test phase, given a gait feature, it is first classified into an age group and then its age is estimated with the age regression model of the classified age group. Experimental results on a gait database that has the world’s largest population of participants ranging from 2 to 90 years old demonstrate the state-of-the-art performance of the proposed method.","Title: Gait Recognition by Deformable Registration\nYear: 2018\n\nAbstract: This paper describes a method of gait recognition robust against intra-subject posture changes. A person sometimes walks with changing his/her posture when looking down at a smartphone or carrying a heavy object, which makes intra-subject variation large and consequently makes gait recognition difficult. We therefore introduce a deformable registration model to mitigate the intra-subject posture changes. More specifically, we represent a deformation field by a set of deformation vectors on lattice-type control points allocated on an image, i.e., by free-form deformation (FFD) framework. Given a pair of a probe and a gallery, we compute the deformation field so as to minimize the difference between a probe morphed by the deformation field and the gallery, as well as to ensure the spatial smoothness of the deformation field. We then learn the intra-subject eigen deformation modes from a training set of the same subjects' pairs (e.g., bending the upper body forward and swinging arms more), which are relatively different from inter-subject deformation modes (e.g., body shape spread and stride change). Moreover, because the deformable registration is responsible for a preprocessing part before matching, it can be combined with any types of matching algorithms for gait recognition. Experiments with 1,334 subjects show that the proposed method improves the gait recognition accuracy in both cases without and with a state-of-the-art deep learning-based matcher, respectively.","Title: A Flexible Method for Time-of-Flight Camera Calibration Using Random Forest.\nYear: 2018\n\nAbstract: A learning based approach is proposed to calibrate the geometric distortion of a Time-of-Flight (ToF) camera. Our method is flexible as it requires only a ToF camera and a standard camera calibration chessboard. We treat the noise model of a ToF camera as a black box, and employ random forest to automatically learn the underlying unique noise model. The geometric property of the point-cloud can be effectively restored by the learned distortion model. The method can be used in a range of computer vision applications including e.g. hand pose estimation.","Title: Smartphone-Based Crowdsourcing for Panoramic Virtual Tour Construction\nYear: 2018\n\nAbstract: Virtual tour applications have gained popularity in recent years by providing immersed user experience and visual navigation hints. Typically, a virtual tour contains both image data and inter-scene spatial connections, and is usually presented as a constellation of images. The construction of virtual tours originally requires sophisticated devices and time-consuming site survey. Lately, the technology advance in smartphones facilitates the virtual tour capture on such versatile end devices. In this work, we introduce a crowdsourced approach toenable large-scale panoramic virtual tour construction with the active participation of smartphone users. We have designed and implemented the framework, as well as proposed further optimizations on constellation construction. Extensive experiments are conducted to evaluate the overall performance, including image quality, constellation accuracy, user experience, and energy efficiency.","Title: Harnessing Smartphone Users' Contribution For Virtual Tour Construction\nYear: 2018\n\nAbstract: Virtual tour applications have gained popularity in recent years by providing immersed user experience and visual navigation hints. Typically, a virtual tour contains both image data and inter-scene spatial connections, and is presented as a constellation of images. In this demo, we present a novel approach to enable large-scale panoramic virtual tour construction with the active contributions of smartphone users.","Title: Multi-Hop Cognitive Wireless Powered Networks: Outage Analysis and Optimization.\nYear: 2019\n\nAbstract: This paper analyzes and optimizes the outage performance of multi-hop cognitive wireless powered networks (CWPNs) in underlay paradigms. To be specific, there are multiple power beacons (PBs) performing wireless power transfer (WPT) for multiple battery-free secondary users (SUs). Correspondingly, the SUs first harvest energy from the RF signals of PBs and then execute multi-hop cognitive data transmission in the licensed channel concurrently with the primary users (PUs). Therefore, the transmit power of SUs are subject to the energy causality constraint imposed by WPT and the interference power constraint from multiple PUs. We derive and obtain the closed-form exact and asymptotic end-to-end outage probabilities for multi-hop CWPNs over Rayleigh block fading. Furthermore, we optimize the outage performance by studying the outage minimization problem with respect to the WPT power and the WPT time. Due to the complexity of outage probability, we propose a self-adaptive particle swarm optimization (SA-PSO)-based resource allocation algorithm to jointly optimize the power and time for WPT. Extensive simulations validate the correctness of theoretical analysis and the effectiveness of the proposed optimization algorithm.","Title: On Multiple Virtual NICs in Cloud Computing: Performance Bottleneck and Enhancement.\nYear: 2018\n\nAbstract: Multiple network interface card (NIC) configuration is the key to achieving service isolation and link aggregation in enterprise networks. Despite the increasing deployments of multiple virtual NICs (multi-vNIC) in cloud environments, the performance and hidden cost of such multi-vNIC configurations are yet to be understood. Our measurements in both Xen and KVM environments reveal that the network...","Title: Secure resource allocation for energy harvesting cognitive radio sensor networks without and with cooperative jamming.\nYear: 2018\n\nAbstract: This paper studies secure resource allocation for energy harvesting cognitive radio sensor networks (EH-CRSNs) underlaying a small cell network, wherein the small cell base station (SCBS) employs adaptive or fixed transmit power for cooperative jamming or not while serving cellular users (CUs). Correspondingly, cognitive sensors (CSs) first harvest energy from the RF signals of SCBS and then communicate with each other concurrently with CUs in the licensed cellular channel which is wiretapped by an eavesdropper. Fully considering the peak interference power at CUs and the energy causality imposed by energy harvesting and processing cost, we investigate two secrecy rate maximization problems for secure resource allocation. For EH-CRSNs without cooperative jamming, we first transform the original non-convex problem into a convex problem and then employ the primary decomposition method to derive the optimal harvesting time and power allocation, with which CSs can perform reactive secure resource allocation according to the instantaneous channel state and the setup of the small cell. For EH-CRSNs with cooperative jamming, we employ the iterative optimization method to optimize the charging time and power allocation, with which SCBS can perform proactive secure resource allocation according to the instantaneous channel state. Extensive simulations evaluate the performance of EH-CRSNs and validate the effectiveness of the proposed algorithms.","Title: Graphic IMC-PID tuning based on maximum sensitivity for uncertain systems\nYear: 2019\n\nAbstract: Obtaining all feasible parameters of the proportional-integral-differential (PID) controller is the key goal in uncertain systems. This paper proposes a graphical tuning method based on an internal model control (IMC) strategy for uncertain systems with time delay. Specifically, the Kharitonov theorem is introduced first to simplify the uncertain system into 32 polynomials. Then, for each polynomial, the IMC structure is applied to reduce the tuning parameters of the PID controller in order to rapidly determine the controller parameters. Finally, the maximum sensitivity (Ms) is used to further guarantee the controlled system with a certain robustness and dynamic performance, which can portray constant gain margin and phase margin boundaries, and can even determine the range of parameters of the proposed IMC filter. Three example results from simulations are presented to demonstrate the effectiveness and applicability of the proposed method.","Title: Gait-based age progression/regression: a baseline and performance evaluation by age group classification and cross-age gait identification\nYear: 2019\n\nAbstract: Gait is believed to be an advanced behavioral biometric that can be perceived at a large distance from a camera without subject cooperation and hence is favorable for many applications in surveillance and forensics. However, appearance differences caused by human aging may significantly reduce the performance of gait recognition. Modeling the aging process on gait features is one of the possible solutions to this problem, and it may inspire more potential applications, such as finding lost children and examining health status. To the best of our knowledge, this topic has not been studied in the literature. Motivated by the fact that aging effects are mainly reflected in the shape and appearance deformations of the gait feature, we propose a baseline algorithm for gait-based age progression and regression using a generic geometric transformation between different age groups, in conjunction with the gait energy image, which is an appearance-based gait feature frequently used in the gait analysis community, to render gait aging and reverse aging effects simultaneously. Various evaluations were conducted through gait-based age group classification and cross-age gait identification to validate the performance of the proposed method, in addition to providing several insights for future research on the subject.","Title: Lane scheduling around crossroads for edge computing based autonomous driving.\nYear: 2019\n\nAbstract: Autonomous driving, which can free our hands and feet, is of increasing importance in our daily lives. However, the capacity of onboard computation and communication limits the rapid development of autonomous driving. To address this issue, this paper proposes a novel model named the edge computing-based lanes scheduling system (ECLSS) to study lane scheduling for each vehicle around crossroads with real-time edge devices. There are several edge computing devices (ECD) deployed around crossroads in ECLSS that can collect information from vehicles and road conditions with short-range wired/wireless transmissions. Based on the strong computing power of ECDs and their real-time transmission performance, we propose two centralized management lane scheduling methods: the searching for efficient switching algorithm (SESA) and special vehicles lane switching algorithm (SVLSA). These edge computing-based autonomous driving methods aim to achieve high speed passing through crossroads and guarantee that special vehicles can pass through crossroads within a certain time. Extensive simulations are conducted, and the simulation results demonstrate the superiority of the proposed approaches over competing schemes in typical lane switching scenarios.","Title: Heterogeneous Slot Scheduling for Real-time Industrial Wireless Sensor Networks\nYear: 2019\n\nAbstract: Real-time performance and reliability are two critical indicators in an industrial wireless sensor network (IWSN). Several time-division multiple-address (TDMA)-based industrial standards such as WirelessHART and ISA100 are widely used in IWSNs. However, to simplify the analysis, standard TDMA supports only one or two slot types in each frame, and each slot is usually 10 ms, which severely limits transmissibility and real-time responses in TDMA-based IWSNs where the number of transmissions is large but the length of most packets is small. In this paper, we propose a TDMA frame containing slots of different lengths to address this issue. The key ideas are to waste fewer slot resources and achieve on-demand slot allocation. First, we study the matching problem in a TDMA frame; then, we propose two scheduling methods, the split scheduling algorithm (SSA) and the double plug-in algorithm (DPA), under our TDMA frame. Extensive simulations and real testbed results show that the proposed solution DPA can significantly improve network performance and reliability. Real-time comparisons with other existing scheduling schemes show that the proposed solution improves the acceptance ratio by 48.8% compared to the rate-monotonic scheme.","Title: Fast Top-N Personalized Recommendation On Item Graph\nYear: 2019\n\nAbstract: In the era of big data, traditional supply chain systems can not match the requirement of e-commerce. The analysis of customers' demands and behaviors are necessary to exploit the potential insights and to build intelligent supply chain systems, which can be achieved by recommender systems. Graph-based recommendation models work well for top-N recommender systems due to their capability to capture the potential relationships between entities. In this paper, we propose a novel graph-based recommendation model to achieve personalized item ranking. To be specific, we design an adapted semi-supervised learning method to capture item smoothness, item fitting, and item confidence. By exploiting the structure of item graph moderately, the proposed method achieves impressive effectiveness and efficiency. In addition, extensive experimental results on real-world datasets show that our proposed method consistently outperforms the state-of-the-art counterparts on the top-N recommendation task.","Title: Systemic In Silico Screening In Drug Discovery For Coronavirus Disease (Covid-19) With An Online Interactive Web Server\nYear: 2020\n\nAbstract: The emergence of the new coronavirus (nCoV-19) has impacted human health on a global scale, while the interaction between the virus and the host is the foundation of the disease. The viral genome codes a cluster of proteins, each with a unique function in the event of host invasion or viral development. Under the current adverse situation, we employ virtual screening tools in searching for drugs and natural products which have been already deposited in DrugBank in an attempt to accelerate the drug discovery process. This study provides an initial evaluation of current drug candidates from various reports using our systemic in silico drug screening based on structures of viral proteins and human ACE2 receptor. Additionally, we have built an interactive online platform (https://shennongproject.ai/) for browsing these results with the visual display of a small molecule docked on its potential target protein, without installing any specialized structural software. With continuous maintenance and incorporation of data from laboratory work, it may serve not only as the assessment tool for the new drug discovery but also an educational web site for the public.","Title: Real-Time Gait-Based Age Estimation And Gender Classification From A Single Image\nYear: 2021\n\nAbstract: In this paper, we propose a unified real-time framework for gait-based age estimation and gender classification that uses just a single image, which reduces the latency in video capturing compared with the existing methods based on a gait cycle. To cope with the problem of lacking motion information in the input single image, we first reconstruct a gait cycle of a silhouette sequence from the input image via a gait cycle reconstruction network. The reconstructed gait cycle is then fed into a state-of-the-art gait recognition network for feature representation learning, which is further used to obtain the class of the gender and the estimated probability distribution of integer age labels. Unlike the existing methods focusing on the gait sequences captured from the side view, the proposed method is applicable to the gait images from an arbitrary view with a single trained model, which is more suitable for real-world application scenarios (e.g., automatic access control). Stand-alone and client-server online systems were implemented based on the proposed method, which validates the real-time/online property in actual scenes. The experiments on the world's largest multi-view gait dataset demonstrate the effectiveness of the proposed method, which achieves performance improvement compared with the benchmark algorithms.","Title: The Obfuscation Method Of User Identification System\nYear: 2021\n\nAbstract: The research on the identification of network users has been continuous, and methods and algorithms emerge in endlessly. Of course, it solves the problem that it is difficult to identify the identity of network interactive users. Yet, user identification raises questions about user privacy. In the last paper [[, we proposed a user identification method based on Mining Web Usage Profiles from Proxy Logs. For the previous user identification system, westudy that which degree the surfing behavior must be obfuscated to prevent identification. Furthermore, we investigate directions in how far a user can hide her identity by obfuscating her web usage pattern through adding or hiding random HTTP connections. In experimental evaluation, we examine the time-period being necessary to obfuscate an identity based on a two-week log file being provided by our industrial partners.","Title: Uncertainty-Aware Gait-Based Age Estimation and its Applications\nYear: 2021\n\nAbstract: Gait-based age estimation is a key technique for many applications. It is well known that age estimation uncertainty is highly dependent on age (i.e., small for children and large for adults), and it is important to know the uncertainty for the above-mentioned applications. Therefore, we propose a method for uncertainty-aware gait-based age estimation by introducing a label distribution learning f...","Title: End-to-end Model-based Gait Recognition using Synchronized Multi-view Pose Constraint\nYear: 2021\n\nAbstract: We propose an end-to-end model-based cross-view gait recognition which employs pose sequences and shapes extracted by human model fitting. Specifically, we consider a problem setting where gait sequences from single different views are given as a pair to match in a test phase, while asynchronous multi-view gait sequences are given for each subject in a training phase. This work exploits multi-view constraint in the training phase to extract more consistent pose sequences from any views in the test phase, unlike the existing methods do not consider them. For this purpose, given asynchronous multi-view gait sequences, we introduce a phase synchronization step in the training phase so that we can impose pose consistency at each synchronized phase in a temporally up-sampled phase domain. We then train our network by minimizing a loss function based on the synchronized multi-view pose constraint as well as shape consistency, temporal pose smoothness, recognition accuracy, etc in an end-to-end manner. We also introduce the synchronization step in a test phase to reduce intra-subject variations caused by asynchronous pose features. Experimental results on the OU-MVLP and CASIA-B datasets show that the proposed method achieves the state-of-the-art performance for both gait identification and verification scenarios, especially a great improvement in terms of the pose representations.","Title: Avalanches during epithelial tissue growth; Uniform Growth and a drosophila eye disc model\nYear: 2022\n\nAbstract: Epithelial tissues constitute an exotic type of active matter with non-linear properties reminiscent of amorphous materials. In the context of a proliferating epithelium, modeled by the quasistatic vertex model, we identify novel discrete tissue scale rearrangements, i.e. cellular rearrangement avalanches, which are a form of collective cell movement. During the avalanches, the vast majority of cells retain their neighbors, and the resulting cellular trajectories are radial in the periphery, a vortex in the core. After the onset of these avalanches, the epithelial area grows discontinuously. The avalanches are found to be stochastic, and their strength is correlated with the density of cells in the tissue. Overall, avalanches redistribute accumulated local spatial pressure along the tissue. Furthermore, the distribution of avalanche magnitudes is found to obey a power law, with an exponent consistent with sheer induced avalanches in amorphous materials. To understand the role of avalanches in organ development, we simulate epithelial growth of the Drosophila eye disc during the third instar using a computational model, which includes both chemical and mechanistic signaling. During the third instar, the morphogenetic furrow (MF), a ~10 cell wide wave of apical area constriction propagates through the epithelium. These simulations are used to understand the details of the growth process, the effect of the MF on the growth dynamics on the tissue scale, and to make predictions for experimental observations. The avalanches are found to depend on the strength of the apical constriction of cells in the MF, with a stronger apical constriction leading to less frequent and more pronounced avalanches. The results herein highlight the dependence of simulated tissue growth dynamics on relaxation timescales, and serve as a guide for in vitro experiments.","Title: Multi-agent deep reinforcement learning for end-edge orchestrated resource allocation in industrial wireless networks\nYear: 2022\n\nAbstract: Edge artificial intelligence will empower the ever simple industrial wireless networks (IWNs) supporting complex and dynamic tasks by collaboratively exploiting the computation and communication resources of both machine-type devices (MTDs) and edge servers. In this paper, we propose a multi-agent deep reinforcement learning based resource allocation (MADRL-RA) algorithm for end-edge orchestrated IWNs to support computation-intensive and delay-sensitive applications. First, we present the system model of IWNs, wherein each MTD is regarded as a self-learning agent. Then, we apply the Markov decision process to formulate a minimum system overhead problem with joint optimization of delay and energy consumption. Next, we employ MADRL to defeat the explosive state space and learn an effective resource allocation policy with respect to computing decision, computation capacity, and transmission power. To break the time correlation of training data while accelerating the learning process of MADRL-RA, we design a weighted experience replay to store and sample experiences categorically. Furthermore, we propose a step-by-step epsilon-greedy method to balance exploitation and exploration. Finally, we verify the effectiveness of MADRL-RA by comparing it with some benchmark algorithms in many experiments, showing that MADRL-RA converges quickly and learns an effective resource allocation policy achieving the minimum system overhead.","Title: Mixed-Criticality Industrial Data Scheduling on 5G NR\nYear: 2022\n\nAbstract: Compared to industrial wired networks, 5G can improve device mobility and reduce the cost of networking. However, the real-time performance and reliability of 5G new radio (NR) still need to be improved to satisfy industrial applications’ requirements. In factories, the main factor that affects the performance of 5G NR is the unstable signal quality caused by high temperatures and metal. Although assigning dedicated resources to all transmissions and retransmissions is an effective method to improve the performance of 5G NR, the unstable signal quality causes the resources required for retransmissions to be uncertain. To address the problem, we introduce the mixed-criticality task model to 5G NR. When high-criticality packets cannot be transmitted, they are allowed to preempt the resources shared with low-criticality packets. The mixed-criticality scheduling problem of 5G NR is NP-hard. We formulate it as an optimization modulo theories (OMT) specification and propose a scheduling algorithm based on bin packing methods to make 5G NR satisfy industrial applications’ requirements. Finally, we conduct extensive evaluations based on an industrial 5G testbed and random test cases. The evaluation results indicate that our algorithm makes communication reliability greater than 99.9% on unlicensed spectrum, and for most test cases, our algorithm is close to optimal solutions.","Title: Self-supervised Depth Estimation in Laparoscopic Image Using 3D Geometric Consistency\nYear: 2022\n\nAbstract: Depth estimation is a crucial step for image-guided intervention in robotic surgery and laparoscopic imaging system. Since per-pixel depth ground truth is difficult to acquire for laparoscopic image data, it is rarely possible to apply supervised depth estimation to surgical applications. As an alternative, self-supervised methods have been introduced to train depth estimators using only synchronized stereo image pairs. However, most recent work focused on the left-right consistency in 2D and ignored valuable inherent 3D information on the object in real world coordinates, meaning that the left-right 3D geometric structural consistency is not fully utilized. To overcome this limitation, we present M3Depth, a self-supervised depth estimator to leverage 3D geometric structural information hidden in stereo pairs while keeping monocular inference. The method also removes the influence of border regions unseen in at least one of the stereo images via masking, to enhance the correspondences between left and right images in overlapping areas. Extensive experiments show that our method outperforms previous self-supervised approaches on both a public dataset and a newly acquired dataset by a large margin, indicating a good generalization across different samples and laparoscopes.","Title: Deep Reinforcement Learning-Based Multichannel Access for Industrial Wireless Networks With Dynamic Multiuser Priority\nYear: 2022\n\nAbstract: In Industry 4.0, massive heterogeneous industrial devices generate a great deal of data with different quality of service requirements, and communicate via industrial wireless networks (IWNs). However, the limited time-frequency resources of IWNs cannot well support the high concurrent access of massive industrial devices with strict real-time and reliable communication requirements. To address this problem, a deep reinforcement learning-based dynamic priority multichannel access (DRL-DPMCA) algorithm is proposed in this article. Firstly, according to the time-sensitivity of industrial data, industrial devices are assigned with different priorities, based on which their channel access probabilities are dynamically adjusted. Then, the Markov decision process is utilized to model the dynamic priority multichannel access problem. To cope with the explosion of state space caused by the multichannel access of massive industrial devices with dynamic priorities, DRL is used to establish the mapping from states to actions. Next, the long-term cumulative reward is maximized to obtain an effective policy. Especially, with joint consideration of the access reward and priority reward, a compound reward for multichannel access and dynamic priority is designed. For breaking the time correlation of training data while accelerating the convergence of DRL-DPMCA, an experience replay with experience-weight is proposed to store and sample experiences categorically. Besides, the gated recurrent unit, dueling architecture and step-by-step \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><tex-math notation=\"LaTeX\">$\\varepsilon$</tex-math></inline-formula>\n-greedy method are employed to make states more comprehensive and reduce model oscillation. Extensive experiments show that, compared with slotted-Aloha and deep Q network algorithms, DRL-DPMCA converges quickly, and guarantees the highest channel access probability and the minimum queuing delay for high-priority industrial devices in the context of minimum access conflict and nearly 100% channel utilization.","Title: A Comprehensive Comparison of Machine Learning and Feature Selection Methods for Maize Biomass Estimation Using Sentinel-1 SAR, Sentinel-2 Vegetation Indices, and Biophysical Variables\nYear: 2022\n\nAbstract: Rapid and accurate estimation of maize biomass is critical for predicting crop productivity. The launched Sentinel-1 (S-1) synthetic aperture radar (SAR) and Sentinel-2 (S-2) missions offer a new opportunity to map biomass. The selection of appropriate response variables is crucial for improving the accuracy of biomass estimation. We developed models from SAR polarization indices, vegetation indices (VIs), and biophysical variables (BPVs) based on gaussian process regression (GPR) and random forest (RF) with feature optimization to retrieve maize biomass in Changchun, Jilin province, Northeastern China. Three new predictors from each type of remote sensing data were proposed based on the correlations to biomass measured in June, July, and August 2018. The results showed that a predictor combined by vertical-horizontal polarization (VV), vertical-horizontal polarization (VH), and the difference of VH and VV (VH-VV) derived from S-1 images of June, July, and August, respectively, with GPR and RF, provided a more accurate estimation of biomass (R-2 = 0.81-0.83, RMSE = 0.40-0.41 kg/m(2)) than the models based on single SAR polarization indices or their combinations, or optimized features (R-2 = 0.04-0.39, RMSE = 0.84-1.08 kg/m(2)). Among the S-2 VIs, the GPR model using a combination of ratio vegetation index (RVI) of June, normalized different infrared index (NDII) of July, and normalized difference vegetation index (NDVI) of August achieved a result with R-2 = 0.83 and RMSE = 0.39 kg/m(2), much better than single VIs or their combination, or optimized features (R-2 of 0.31-0.77, RMSE of 0.47-0.87 kg/m(2)). A BPV predictor, combined with leaf chlorophyll content (CAB) in June, canopy water content (CWC) in July, and fractional vegetation cover (FCOVER) in August, with RF, also yielded the highest accuracy (R-2 = 0.85, RMSE = 0.38 kg/m(2)) compared to that of single BPVs or their combinations, or optimized subset. Overall, the three combined predictors were found to be significant contributors to improving the estimation accuracy of biomass with GPR and RF methods. This study clearly sheds new insights on the application of S-1 and S-2 data on maize biomass modeling.","Title: Multi-View Large Population Gait Database With Human Meshes and Its Performance Evaluation\nYear: 2022\n\nAbstract: Existing model-based gait databases provide the 2D poses (i.e., joint locations) extracted by general pose estimators as the human model. However, these 2D poses suffer from information loss and are of relatively low quality. In this paper, we consider a more informative 3D human mesh model with parametric pose and shape features, and propose a multi-view training framework for accurate mesh estimation. Unlike existing methods, which estimate a mesh from a single view and suffer from the ill-posed estimation problem in 3D space, the proposed framework takes asynchronous multi-view gait sequences as input and uses both multi-view and single-view streams to learn consistent and accurate mesh models for both multi-view and single-view sequences. After applying the proposed framework to the existing OU-MVLP database, we establish a large-scale gait database with human meshes (i.e., OUMVLP-Mesh), containing over 10,000 subjects and up to 14 view angles. Experimental results show that the proposed framework estimates human mesh models more accurately than similar methods, providing models of sufficient quality to improve the recognition performance of a baseline model-based gait recognition approach.","Title: Deep Regression with Spatial-Frequency Feature Coupling and Image Synthesis for Robot-Assisted Endomicroscopy\nYear: 2022\n\nAbstract: Probe-based confocal laser endomicroscopy (pCLE) allows in-situ visualisation of cellular morphology for intraoperative tissue characterization. Robotic manipulation of the pCLE probe can maintain the probe-tissue contact within micrometre working range to achieve the precision and stability required to capture good quality microscopic information. In this paper, we propose the first approach to automatically regress the distance between a pCLE probe and the tissue surface during robotic tissue scanning. The Spatial-Frequency Feature Coupling network (SFFC-Net) was designed to regress probe-tissue distance by extracting an enhanced data representation based on the fusion of spatial and frequency domain features. Image-level supervision is used in a novel fashion in regression to enable the network to effectively learn the relationship between the sharpness of the pCLE image and its distance from the tissue surface. Consequently, a novel Feedback Training (FT) module has been designed to synthesise unseen images to incorporate feedback into the training process. The first pCLE regression dataset (PRD) was generated which includes ex-vivo images with corresponding probe-tissue distance. Our performance evaluation verifies that the proposed network outperforms other state-of-the-art (SOTA) regression networks."],"string":"[\n \"Title: A Robust O(n) Solution to the Perspective-n-Point Problem.\\nYear: 2012\\n\\nAbstract: We propose a non-iterative solution for the perspective-n-point (PnP) problem, which can robustly retrieve the optimum by solving a seventh order polynomial. The central idea consists of three steps: (1) to divide the reference points into 3-point subsets in order to achieve a series of fourth order polynomials, (2) to compute the sum of the square of the polynomials so as to form a cost function, and (3) to find the roots of the derivative of the cost function in order to determine the optimum. The advantages of the proposed method are as follows: Firstly, it can stably deal with the planar case, ordinary 3D case and quasi-singular case, and it is as accurate as the state-of-the-art iterative algorithms with much less computational time. Secondly, it is the first non-iterative PnP solution that can achieve more accurate results than the iterative algorithms when no redundant reference points can be used (n \\\\le 5). Thirdly, Large-size point sets can be handled efficiently because its computational complexity is O(n).\",\n \"Title: Performance and power modeling in a multi-programmed multi-core environment\\nYear: 2010\\n\\nAbstract: This paper describes a fast, automated technique for accurate on-line estimation of the performance and power consumption of interacting processes in a multi-programmed, multi-core environment. The proposed technique does not require modifying hardware or applications. The performance model uses reuse distance histograms, cache access frequencies, and the relationship between the throughput and cache miss rate of each process to predict throughput. The system-level power model is derived using multi-variable linear regression, accounting for cache contention. Both models are validated on multiple real multi-core systems using SPEC CPU2000 benchmarks; their performance and power estimates are within 3.5% of measured values on average. We explain how to integrate the two models for power estimation during process assignment, helpful for power-aware assignment.\",\n \"Title: Robust and efficient pose estimation from line correspondences\\nYear: 2012\\n\\nAbstract: We propose a non-iterative solution for the Perspective-n-Line (PnL) problem, which can efficiently and accurately estimate the camera pose for both small number and large number of line correspondences. By selecting a rotation axis in the camera framework, the reference lines are divided into triplets to form a sixteenth order cost function, and then the optimum is retrieved from the roots of the derivative of the cost function by evaluating the orthogonal errors and the reprojected errors of the local minima. The final pose estimation is normalized by a 3D alignment approach. The advantages of the proposed method are as follows: (1) it stably retrieves the optimum of the solution with very little computational complexity and high accuracy; (2) small line sets can be robustly handled to achieve highly accurate results and; (3) large line sets can be efficiently handled because it is O(n).\",\n \"Title: Occlusion Handling in Augmented Reality System for Human-Assisted Assembly Task\\nYear: 2008\\n\\nAbstract: Augmented reality (AR) based human-machine interaction (HMI) provided a seamless interface between user and application environment, but occlusion handing remained as a tough problem in AR applications. Recently most AR occlusion handling algorithms aimed at general environment, more special research on AR occlusion handling for application in the context of industrial assembly, such as assembly training and assembly task guidance, is required. An occlusion handling method aimed at video see-through AR-based assembly system is presented based on the analysis of the occlusion between virtual part, visual assembly feature, navigation information, physical part and physical assembly environment in the context of virtual/physical assembly working space. The method was implemented in prototype AR-assembly system and was proved to be efficient in handling the virtual/physical occlusion in augmented assembly scene.\",\n \"Title: Efficient lookup table based camera pose estimation for augmented reality\\nYear: 2011\\n\\nAbstract: Until now, exising camera pose estimation methods for the widely used square marker-based augmented reality (AR) are either highly sensitive to noise or much time consuming, and developers have to work hard to find the proper trade-off between computational speed and quality in mobile AR applications where computational resources are limited. The major difficulty is that only the four corner points of the square AR marker are available, and no redundant point correspondences can be used for a stable estimation. To solve this problem, an efficient lookup table (LUT)-based non-iterative solution is presented in this paper that achieves high stability in the presence of noise better than the most robust and accurate iterative solutions in the field, with the same level of accuracy and a much lower computational complexity. Our central idea consists of extracting a key parameter β from the camera pose and creating a LUT for β by taking the symmetrical structure of the square marker into account, thereby exploiting additional information. Copyright © 2011 John Wiley & Sons, Ltd. (Nowadays, the marker-based mobile augmented reality should rely on time-consuming iterative scheme in pose estimation procedure for accuracy and stability due to the absence of redundant point correspondences. We present an efficient LUT-based pose estimation solution which achieves high stability in the presence of noise better than the most robust and accurate iterative solutions available in the field, with the same level of accuracy and a much lower computational complexity.)\",\n \"Title: Memory access aware on-line voltage control for performance and energy optimization\\nYear: 2010\\n\\nAbstract: This paper describes an off-chip memory access-aware runtime DVFS control technique that minimizes energy consumption subject to constraints on application execution times. We consider application phases and the implications of changing cache miss rates on the ideal power control state. We first propose a two-stage DVFS algorithm based on formulating the throughput-constrained energy minimization problem as a multiple-choice knapsack problem (MCKP). This algorithm uses a power model that adapts to application phase changes by observing processor hardware performance counter values. The solutions it produces provide upper bounds on the energy savings achievable under a performance constraint. However, this algorithm assumes a priori (oracle or profiling-based) knowledge of application phase change behavior. To relax this assumption, we propose P-DVFS, an predictive DVFS algorithm for on-line minimization of energy consumption under a performance constraint without requiring a priori knowledge of an application's behavior. P-DVFS uses hardware performance counter based performance and power models. It predicts remaining execution time online in order to control voltage and frequency settings to optimize energy consumption and performance. The P-DVFS problem is formulated as a multiple-choice knapsack problem, which can be efficiently and optimally solved online. We evaluated P-DVFS using direct measurement of a real DVFS-equipped system. When bounding performance loss to at most 20% of that at the maximum frequency and voltage, P-DVFS leads to energy consumptions within 1.83% of the optimal solution for our problem instances on average with a maximum deviation of 4.83%. In addition to producing results approaching those of an oracle formulation, P-DVFS reduces power consumption for our problem instances by 9.93% on average, and up to 25.64%, compared with the most advanced related work.\",\n \"Title: A Stable Direct Solution Of Perspective-Three-Point Problem\\nYear: 2011\\n\\nAbstract: The perspective-three-point problem (P3P) is a classical problem in computer vision. The existing direct solutions of P3P have at least three limitations: (1) the numerical instability when using different vertex permutations, (2) the degeneration in the geometric singularity case, and (3) the dependence on particular equation solvers.A new direct solution of P3P is presented to deal with these limitations. The main idea is to reduce the number of unknown parameters by using a geometric constraint we called \\\"perspective similar triangle\\\" (PST). The PST method achieves high stability in the permutation problem and in the presence of image noise, and does not rely on particular equation solvers. Furthermore, reliable results can be retrieved even in \\\"danger cylinder\\\", a typical kind of geometric singularity of P3P, where all existing direct solutions degenerate significantly.\",\n \"Title: TOA estimator for UWB backscattering RFID system with clutter suppression capability\\nYear: 2010\\n\\nAbstract: Time of arrival (TOA) estimation in multipath dense environment for UWB backscattering radio frequency identification (RFID) system is challenging due to the presence of strong clutter. In addition, the backscattering RFID system has peculiar signal transmission and modulation characteristics, which are considerably different from conventional communication and localization systems. The existing TOA estimators proposed for conventional UWB systems are inappropriate for the backscattering RFID system since they lack the required clutter suppression capability and do not account for the peculiar characteristics of backscattering system. In this paper, we derive a nondata-aided (NDA) least square (LS) TOA estimator for UWB backscattering RFID system. We show that the proposed estimator is inherently immune to clutter and is robust in under-sampling operation. The effects of various parameter settings on the TOA estimation accuracy are also studied via simulations.\",\n \"Title: SVM based internal model control for nonlinear systems\\nYear: 2006\\n\\nAbstract: In this paper, a design procedure of support vector machine (SVM) with RBF kernel function based internal model control (IMC) strategy for stable nonlinear systems with input-output form is proposed. The control scheme consists of two controllers: a SVM based controller which fulfils the direct inverse model control and a traditional controller which fulfils the close-loop control. And so the scheme can deal with the errors between the process and the SVM based internal model generated by model mismatch and additional disturbance. Simulations are given to illustrate the proposed design procedure and the properties of the SVM based internal model control scheme for unknown nonlinear systems with time delay.\",\n \"Title: SVM based nonlinear self-tuning control\\nYear: 2006\\n\\nAbstract: In this paper, a support vector machine (SVM) with polynomial kernel function enhanced nonlinear self-tuning controller is developed, which combines the SVM identifier and parameters’ modifier together. The inverse model of a nonlinear system is achieved by off-line black-box identification according to input and output data. Then parameters of the model are modified online using gradient descent algorithm. Simulation results show that SVM based self-tuning control can be well applied to nonlinear uncertain system. And the SVM based self-tuning control of nonlinear system has good robustness performance in tracking reference input with good generalization ability.\",\n \"Title: Adaptive system of heterogeneous multi-agent investors in an artificial evolutionary double auction market\\nYear: 2010\\n\\nAbstract: In this paper, an adaptive system is proposed which attempts to combine together the approaches of studies of historical data and researches of multi-agent artificial market by evolving a double auction market model with diversity of different traders The purpose of this research is to construct an artificial market which is more close to realistic one and more practical for future researches The model with heterogeneous agents and the environment with which agents and market interact is complicated but controllable by data mining the optimal proportion of the different agents at the input to the market that generates an output which can fit historical data curve The simulation results suggest that the system performance is close to the expecting values in the testing with adequate training in advance.\",\n \"Title: Efficient Hand Pose Estimation from a Single Depth Image\\nYear: 2013\\n\\nAbstract: We tackle the practical problem of hand pose estimation from a single noisy depth image. A dedicated three-step pipeline is proposed: Initial estimation step provides an initial estimation of the hand in-plane orientation and 3D location, Candidate generation step produces a set of 3D pose candidate from the Hough voting space with the help of the rotational invariant depth features, Verification step delivers the final 3D hand pose as the solution to an optimization problem. We analyze the depth noises, and suggest tips to minimize their negative impacts on the overall performance. Our approach is able to work with Kinect-type noisy depth images, and reliably produces pose estimations of general motions efficiently (12 frames per second). Extensive experiments are conducted to qualitatively and quantitatively evaluate the performance with respect to the state-of-the-art methods that have access to additional RGB images. Our approach is shown to deliver on par or even better results.\",\n \"Title: Imitation of Real Market Dynamics by Construction of Multi-agent Based Evolutionary Artificial Market\\nYear: 2011\\n\\nAbstract: In this paper, an adaptive system is proposed which attempts to imitate a real market dynamics by combining together the approaches of studies of historical data and researches of multi-agent artificial market. The proportion of different agents is evolved by genetic algorithm in an artificial double auction market. The purpose of this research is to construct an artificial market which generates the dynamics of real market as similar as possible. The model with heterogeneous agents and the environment with which agents and market interact is complicated but controllable by data mining the optimal proportion of the different agents at the input to the market that generates an output which can fit historical data curve. The simulation results suggest that the system performance is close to the expecting values in the testing with adequate training in advance.\",\n \"Title: Strategy Tree Construction and Optimization with Genetic Programming.\\nYear: 2013\\n\\nAbstract: We applied genetic programming (GP) to search for a strategy in a technical analysis (TA) indicator candidate pool for stock market trading and optimized it through historical data. The method provides decision rule optimization scheme to deal with problems in the real trading in financial market, and it optimizes strategies in relatively complicated contents. GP is used to construct the condition in decision rule with different logical operations. The method has been applied to the optimization of investment strategies with good return results in simulation experiments.\",\n \"Title: Experimental Evaluation of UWB Ranging Performance for Correlation and ED Receivers in Dense Multipath Environment\\nYear: 2007\\n\\nAbstract: UWB signal reception can be accomplished by coherent or non-coherent receivers. Correlation receiver is a type of coherent receiver while energy detection (ED) receiver belongs to non-coherent type. In this paper, we evaluate their ranging performance using the channel profiles measured in a typical indoor office environment. The effects of ranging parameters such as search window length are also investigated. The results show that correlation receiver outperforms ED receiver in the line-of-sight (LOS) scenario and smaller integration interval gives better performance for ED. For the non-line-of-sight (NLOS) scenario, surprisingly, ED receiver outperforms correlation receiver and larger integration interval brings better performance for ED when the detection threshold exceeds certain level. The study also shows that regardless of the receiver types used, the resultant ranging errors for the LOS or NLOS environments can be described by unified models with the parameters being distance dependent.\",\n \"Title: Cache contention and application performance prediction for multi-core systems\\nYear: 2010\\n\\nAbstract: The ongoing move to chip multiprocessors (CMPs) permits greater sharing of last-level cache by processor cores but this sharing aggravates the cache contention problem, potentially undermining performance improvements. Accurately modeling the impact of inter-process cache contention on performance and power consumption is required for optimized process assignment. However, techniques based on exhaustive consideration of process-to-processor mappings and cycle-accurate simulation are inefficient or intractable for CMPs, which often permit a large number of potential assignments. This paper proposes CAMP, a fast and accurate shared cache aware performance model for multi-core processors. CAMP estimates the performance degradation due to cache contention of processes running on CMPs. It uses reuse distance histograms, cache access frequencies, and the relationship between the throughput and cache miss rate of each process to predict its effective cache size when running concurrently and sharing cache with other processes, allowing instruction throughput estimation.We also provide an automated way to obtain process-dependent characteristics, such as reuse distance histograms, without offline simulation, operating system (OS) modification, or additional hardware. We tested the accuracy of CAMP using 55 different combinations of 10 SPEC CPU2000 benchmarks on a dual-core CMP machine. The average throughput prediction error was 1.57%.\",\n \"Title: Fast online SVR algorithm based adaptive internal model control\\nYear: 2006\\n\\nAbstract: Based on fast online support vector regression (SVR) algorithm, reverse model of system model is constructed, and adaptive internal model controller is developed. First, SVR model and its online training algorithm are introduced. A kernel cache method is used to accelerate the online training algorithm, which makes it suitable for real-time control application. Then it is used in internal model control (IMC) for online constructing internal model and designing the internal model controller. Output errors of the system are used to control online SVR algorithm, which made the whole control system a closed-loop one. Last, the fast online SVM based adaptive internal model control was used to control a benchmark nonlinear system. Simulation results show that the controller has simple structure, good control performance and robustness.\",\n \"Title: Data synchronization with conflict resolution for RFID-based track and trace\\nYear: 2012\\n\\nAbstract: In an RFID-assisted track and trace information network, the same set of data may be stored in distributed locations. Data conflicts occur when the values of distributed data copies are modified locally with different values causing data inconsistency. In order to restore data consistency, the values of distributed data copies have to be synchronized to the same value by resolving the conflicts. The conventional conflict resolutions do not consider the unique characteristics of RFID data and therefore are not able to maximize the benefit of information users or may even provide erroneous resolution result. In this paper, a data synchronization method with conflict resolution accommodated to RFID applications is proposed. The method resolves the conflicts based on multiple RFID data attributes and takes the dependent relationship between data into account. Simulations confirm the efficiency of the algorithm.\",\n \"Title: A VSC method for MIMO systems based on SVM\\nYear: 2006\\n\\nAbstract: A variable structure control (VSC) scheme for linear black-box multi-input/multi-output (MIMO) systems based on support vector machine (SVM) is developed. After analyzing character of MIMO system, an additional control is designed to track trajectory. Then VSC algorithm is adopted to eliminate the difference. By estimating outputs of next step, VSC inputs and additional inputs are obtained directly by two kinds of trained SVMs, and so recognition of system parameters is avoided. A linear MIMO system is introduced to prove the scheme, and simulation shows that the high identification precision and quick training speed.\",\n \"Title: Pattern-Oriented Agent-Based Modeling for Financial Market Simulation\\nYear: 2007\\n\\nAbstract: The paper presents a pattern-oriented agent-based model to simulate the dynamics of a stock market. The model generates satisfactory market macro-level trend and volatility while the agents obey simple rules but follow the behaviors of the neighbors closely. Both the market and the agents are made to evolve in an environment where Darwin's natural selection rules apply.\",\n \"Title: Delay-Dependent Threshold Selection for UWB TOA Estimation\\nYear: 2008\\n\\nAbstract: Precision ultra-wideband ranging requires accurate estimation of time of arrival (TOA). The threshold-based TOA estimation has attracted considerable interest due to its simplicity. In this paper, we propose a technique to set the threshold as a function of delay instead of a single value as in conventional methods. Simulation results show that the proposed method is superior to conventional methods.\",\n \"Title: A Mixed Reality-Based Assembly Verification and Training Platform\\nYear: 2009\\n\\nAbstract: Mixed reality (MR) based human-machine interaction provides a seamless interface between user and application environment, which synthesizes the advantages of the convenient interaction of virtual reality, and the strong realistic of augmented reality. In this paper, MR is applied in the context of industrial assembly process, and a MR based assembly verification and training platform is proposed. In the MR based assembly environment, virtual model, real images and augmented information are jointly displayed on the assembly scene, accessorizing multi-video display windows of different angles of view to browse the real assembly scene. Additionally, constraint proxies figuratively reconstruct part's constraint relationship in the MR environment, and avoid the complex calculation of constraint's match. By using a virtual hand with constraints guided to assemble, an effective and realistic assembly process experience is provided to the user.\",\n \"Title: A novel shape-based image classification method by featuring radius histogram of dilating discs filled into regular and irregular shapes\\nYear: 2010\\n\\nAbstract: In this paper, a novel feature for shape-based image classification is proposed, in which a set of randomly dilating discs are distributed on an unknown regular or irregular shape, and the radius histogram of discs is used to represent the target shape. By such doing, a shape can be modeled by the radius histogram. The proposed feature is rather effective for shape retrieval with rotation and scaling invariance. The proposed feature is particularly effective in the retrieval of string-linked objects in which conventional approaches may fail badly. Experimental results on seven shapes and string-linked objects show that our proposed new feature is very effective in shape classification and shape-based image retrieval.\",\n \"Title: Image Recovery from Broken Image Streams\\nYear: 2007\\n\\nAbstract: This paper presents an approach for image recovery from broken image streams based on an image continuity model. The information to be recovered includes the header of an image (width, height, color space etc.) and the data stream which might have been partially damaged. We begin our discussion on common image formats and necessary information for the image recovery. We then define several measures on image contents to facilitate the image recovery. Using these measures, an effective recovery algorithm is proposed. Experimental results show that the algorithm can successfully recover images of different formats, even multi-frame images from broken image streams in most cases.\",\n \"Title: Healthy: A Diary System Based on Activity Recognition Using Smartphone\\nYear: 2013\\n\\nAbstract: An activity-diary system, named Healthy, is presented in this paper. Healthy can infer users diary of physical activities and energy expenditure based on METS (Metabolic Equivalents) values via recognizing general human activities. In this system, we design a two-layer classifier which costs less energy and memory with satisfactory accuracy. Our classifier divides the activities into two categories: periodic and nonperiodic. And a different sub-classifier is applied for each category. Meanwhile, We design a state listener to recognize more complicated activities. To further improve recognition accuracy, in the second layer sub-classifier, we put forward an adaptive framing algorithm based on the period length of periodical activities to determine the time during which features are extracted. By testing Healthy in real situation, we obtained an average recognition accuracy of 98.0%.\",\n \"Title: Construction of Evolutionary Multi-agent Double Auction Market for Data Mining Combinational Strategies with Stable Returns.\\nYear: 2011\\n\\nAbstract: In this paper, we report a multi-agent artificial market scheme which evolves for imitating a real stock market as similar as better. The artificial market model can generate possible price trend curves those have high correlation coefficient with Hong Kong Hang Seng Index (HSI). The purpose of the imitation is to generate different possible market price dynamics, so the artificial markets can be reliable source to provide statistically analysis samples for a real market. The strategies optimized in the artificial markets can improve the stability and profitability in the real market. The aim of the experiment is to obtain a strategy that can provide profitable and relatively stable return without forecasting the future movements of the market.\",\n \"Title: Optimal Spatial Matrix Filter Design for Array Signal Preprocessing.\\nYear: 2014\\n\\nAbstract: An efficient technique of designing spatial matrix filter for array signal preprocessing based on convex programming was proposed. Five methods were considered for designing the filter. In design method 1, we minimized the passband fidelity subject to the controlled overall stopband attenuation level. In design method 2, the objective function and the constraint in the design method 1 were reversed. In design method 3, the optimal matrix filter which has the general mean square error was considered. In design method 4, the left stopband and the right stopband were constrained with specific attenuation level each, and the minimized passband fidelity was received. In design method 5, the optimization objective function was the sum of the left stopband and the right stopband attenuation levels with the weighting factors 1 and gamma, respectively, and the passband fidelity was the constraints. The optimal solution of the optimizations above was derived by the Lagrange multiplier theory. The relations between the optimal solutions were analyzed. The generalized singular value decomposition was introduced to simplify the optimal solution of design methods 1 and 2 and enhanced the efficiency of solving the Lagrange multipliers. By simulations, it could be found that the proposed method was effective for designing the spatial matrix filter.\",\n \"Title: On the Interplay between Network Traffic and Energy Consumption in Virtualized Environment: An Empirical Study\\nYear: 2014\\n\\nAbstract: Networking and virtualization are two key building blocks of modern cloud computing. The energy consumption of physical machines has been carefully examined in the past research, including the impact of network traffic. When it comes with virtual machines, the inter-play between energy consumption and network traffic however becomes much more complicated. The traffic are now generated by and exchanged between virtual machines (VMs), which could reside in different physical machines with their respective network interface cards (NICs), or share the same physical machine. When multiple VMs share a physical NIC, their traffic can interfere with each other, causing extra overhead. Yet the VM's allocation can be dynamic and they can even migrated across physical machines, thereby changing the traffic pattern. These factors combined make the network traffic highly diverse and dynamic, so is the corresponding energy consumption. A close examination on the network traffic and energy consumption in virtualized environments is thus of need. In this paper, we present an initial measurement study on the interplay between energy consumption and network traffic in representative virtualization environments. Our study reveals a series of unique energy consumption patterns of the network traffic in this context. We show that state-of-the-art virtualization designs noticeably increase the demand of CPU resources when handling networked transactions, generating excessive interrupt requests with ceaselessly context switching, which in turn increases energy consumption. Even when the physical machine is in an idle state, the VM network transactions will will incur remarkable energy consumption. Furthermore, even with identical number of VMs and amount of traffic on a physical machine, the energy consumptions vary significantly with different VM allocation strategies. Our close examination pinpoints the root cause, and offers new angles to revisit the existing resource usage and energy consumption models, so as to optimize the service provisioning as well as virtual machine placement and migration.\",\n \"Title: Quantitative payoff evaluation for track and trace system in supply chains\\nYear: 2014\\n\\nAbstract: The track and trace information increases the supply chain visibility which leads to the improvement of the operation efficiency and effectiveness. A performance evaluation method for track and trace system can help a company to select the best system design and identify the degree of improvement for an implemented system. Existing research works on the performance evaluation of track and trace have limitations such as only supporting single product state and lack of consideration for missing and erroneous insertion of information. In this paper, we take payoff as performance criteria and model the input and output of the system as unequal lengths of product state sequences. A novel pad-and-transit technique is proposed to identify and generate intermediate information transformations with missing and erroneous inserted information. Based on the identified transformations, the expected payoff is then evaluated using a closed form formula. A numerical case study is provided to illustrate the application of the proposed method.\",\n \"Title: MIMO RF probe for wide-area indoor human motion monitoring\\nYear: 2014\\n\\nAbstract: This paper addresses the problem of RF-based wide-area human motion monitoring in indoor multipath environments. A major challenge for conventional pulse-Doppler radar in multipath scenarios is the difficulty in discriminating direct-path targets from ghost returns due to multipath scattering. In this paper, the ability of a multiple-input multiple-output (MIMO) RF probe to discern both direction-of-departure (DoD) and direction-of-arrival (DoA) via “non-causal” beamforming is exploited for indoor motion monitoring. Preliminary results with real data are presented which demonstrate the sidelobe suppression and multipath mitigation achieved. Also, MIMO processing is analyzed using the bi-directional beampattern and spectrum defined herein.\",\n \"Title: Lightweight construction of the information potential field in wireless sensor networks\\nYear: 2014\\n\\nAbstract: The information gradient-based routing protocols have been proved to be economical and effective by adopting the principle of achieving the global objective through local decision, but lightweight methods to construct the information gradient should be fully investigated, especially in a large-scale network with high information dynamics. In this paper, we focus on the construction of the information gradient by balancing convergence conditions and energy consumption. Therefore, two algorithms, Hierarchical Skeleton-based Construction Algorithm (HSCA) and Estimate value Substitution Algorithm (ESA) are proposed to achieve the goal of fastening the convergence in an energy efficient way. Both of the algorithms obey the typical assumptions on WSNs settings and the gossip-styled propagation principle. Comprehensive simulation results show that the proposed algorithms can reduce iteration times to reach a convergence status by 80% and conserve 30-50% energy consumption on average.\",\n \"Title: Analysis And Control For Matrix Rectifier By Circuit Dq Transformation\\nYear: 2015\\n\\nAbstract: An input filter is indispensable for a matrix rectifier (MR), since it can improve the input current quality and reduce the ac supply voltage distortion. However the characteristics of input filter reduce the input power factor (IPF). Moreover, the unbalanced ac supply voltage could disturb dc output voltage. Consequently, this paper provides a novel approach to achieve both tight dc output voltage and unity IPF at the main ac power supply by applying circuit DQ transformation to MR. Analyzing the DQ model of MR, sliding mode control (SMC) based on reaching law is used to achieve tight dc output voltage regulation of MR and PI control is applied to control the q-axis current to be zero. Finally, simulation and experiment results are shown to verify the effectiveness of the control scheme proposed in this paper.\",\n \"Title: Component segmentation of sketches used in 3D model retrieval\\nYear: 2015\\n\\nAbstract: Sketching is a natural human practice. With the popularity of multi-touch tablets and styluses, sketching has become a more popular means of human-computer interaction. However, accurately recognizing sketches is rather challenging, especially when they are drawn by non-professionals. Therefore, automatic sketch understanding has attracted much research attention. To tackle the problem, we propose to segment sketch drawings before analyzing the semantic meanings of sketches for the purpose of developing a sketch-based 3D model retrieval system.\",\n \"Title: Forecast UPC-level FMCG demand, Part I: Exploratory analysis and visualization\\nYear: 2015\\n\\nAbstract: We are interested in forecasting a large collection of FMCG demand time series. As the demand of FMCG exists in a hierarchy (from manufacturers to distributors to retailers), the bottom level of the hierarchy may contain thousands or even millions of time series. Producing aggregate consistent forecasts while utilizing the unique features from each time series thus become a technical challenge. To achieve better forecasting results, exploratory analysis is often necessary to obtain insights on the underlying demand generating mechanism for each time series. Exploratory analysis aims at discovering those so-called \\\\\\\"exogenous factors\\\\\\\", such as price, demand of the complementary/substitutive goods and calendar events, which can help explain some of the demand fluctuation. During forecast accuracy evaluation, outlier detection is also important; a single anomalous time series can contribute much to the overall error. However, in a big data (such as retailing scanner data) enabled environment, exploratory analysis and visualization need much attention, because of the non-scalable nature of the existing methods. Scalability is essential for exogenous factor selection and outlier detection in big time series data. In Part I of this two-part paper, we introduce some exploratory analytics and visualization methods (from not scalable to very scalable) for big retailing time series. Forecasting of the hierarchical FMCG demand is addressed in Part II.\",\n \"Title: Sparsity adjusted information gain for feature selection in sentiment analysis\\nYear: 2015\\n\\nAbstract: The widespread use of social media and the internet are emerging trends that offer an additional interaction channel for companies to better understand customer sentiments about their brands and products. Sentiment analysis uses text data from social media such as customer comments and reviews, which has the nature of high dimensionality. Without selection, typically there are at least thousands of features (words or phrases) that can be extracted from a text corpus, among which there are many redundant or irrelevant features for sentiment classification task. Thus, it is critical to select a compact yet effective set of features to avoid the complex classifier design and slow running time of classification process. However, very few of existing metrics is able to improve efficacy of feature selection by addressing the issue of sparsity of feature matrix for text data, i.e., many features may appear only in a few documents. In this paper, an improved feature selection metric known as sparsity adjusted information gain (SAIG) is proposed, which modifies the conventional information gain metric and aims to adjust the feature ranking scores according to the sparsity of the feature vector. It is able to use less features to obtain a targeted performance level. The experiment results show that SAIG is able to improve the performance of sentiment classification.\",\n \"Title: Space-Delay Adaptive Processing For Mimo Rf Indoor Motion Mapping\\nYear: 2015\\n\\nAbstract: In this paper, space-delay adaptive processing (SDAP) is proposed for motion mapping using MIMO RF signals. Typically, indoor RF localization methods require ultra-wideband signals to achieve the time-delay resolution required for separation of returns in complex multipath environments. Due to spectrum use restrictions, however, there is a need for methods which can operate using the lower bandwidths allocated for so-called ISM use. In this paper, we develop SDAP methods for linear MIMO transmit-receive arrays which can achieve near ultra-wideband range resolution and multipath sidelobe suppression but which use significantly less bandwidth. Real data results for indoor motion monitoring are presented aimed at mapping patient repositioning activities in long-term care facilities for pressure ulcer prevention.\",\n \"Title: A Novel Comac-Based Cooperative Spectrum Sensing Scheme In Cognitive Radio Networks\\nYear: 2015\\n\\nAbstract: Conventional cooperative spectrum sensing (CSS) schemes in cognitive radio networks (CRNs) require that the secondary users (SUs) report their sensing data sequentially to the fusion center, which yields long reporting delay especially in the case of large number of cooperative SUs. By exploiting the computation over multiple-access channel (CoMAC) method, this paper proposes a novel CoMAC-based CSS scheme that allows the cooperative SUs to encode their local statistics in transmit power and to transmit simultaneously the modulated symbols sequence carrying transmit power information to the fusion center. The fusion center then makes the final decision on the presence of primary users by recovering the overall test statistic of energy detection from the energy of the received signal. Performance metrics of the CoMAC-based CSS scheme, i.e., detection probability and false alarm probability, are further derived based on the central limit theorem. Finally, based on the derived detection and false alarm probabilities, both energy detection threshold and spectrum sensing time are optimized to improve the average throughput of the CRN.\",\n \"Title: Hand Action Detection from Ego-centric Depth Sequences with Error-correcting Hough Transform.\\nYear: 2017\\n\\nAbstract: •An effective and efficient solution for hand action detection from mobile egocentric depth sequences.•A novel error- correcting mechanism to tackle the bottleneck issue of incorrect votes from Hough transform.•A comprehensive, in-house annotated ego-centric hand action dataset on which the proposed method is carefully evaluated.•A deep learning baseline.\",\n \"Title: Outage Performance of Underlay Multihop Cognitive Relay Networks With Energy Harvesting.\\nYear: 2016\\n\\nAbstract: This letter studies the outage performance of multihop cognitive relay networks with energy harvesting in underlay paradigms, wherein the secondary users are powered by a dedicated power beacon (PB) and their transmit powers are subject to the harvested energy from PB and the interference constraint from the primary user. We derive the exact outage probability for Rayleigh block fading and prove that the outage probability is monotonically decreasing with respect to the transmit power of PB. Furthermore, we derive the asymptotic outage probability to study the outage saturation phenomenon and propose an iterative algorithm that jointly optimizes the transmit power of PB and the harvest-to-transmit ratio to approximate the minimum outage probability. Simulation results validate the theoretical results.\",\n \"Title: Mimo space-delay adaptive processing for wide-area indoor human motion classification.\\nYear: 2016\\n\\nAbstract: This paper develops space-delay adaptive processing (SDAP) for multiple-input, multiple-output (MIMO) arrays with application to classifying human motion in wide-area indoor environments. The proposed adaptive method resolves motion returns using joint spatial-temporal processing on the received signals. Results with real data demonstrate that MIMO SDAP can mitigate ghost motion returns due to indoor multipath scattering and bandwidth limitation for better separation of micro-Doppler signatures from multiple targets. Human activities such as bed turnovers and arm or leg movements are then classified based on micro-Doppler analysis for patient pressure ulcer prevention.\",\n \"Title: Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups.\\nYear: 2017\\n\\nAbstract: Pose estimation, tracking, and action recognition of articulated objects from depth images are important and challenging problems, which are normally considered separately. In this paper, a unified paradigm based on Lie group theory is proposed, which enables us to collectively address these related problems. Our approach is also applicable to a wide range of articulated objects. Empirically it is evaluated on lab animals including mouse and fish, as well as on human hand. On these applications, it is shown to deliver competitive results compared to the state-of-the-arts, and non-trivial baselines including convolutional neural networks and regression forest methods. Moreover, new sets of annotated depth data of articulated objects are created which, together with our code, are made publicly available.\",\n \"Title: Time-efficient cooperative spectrum sensing via analog computation over multiple-access channel.\\nYear: 2017\\n\\nAbstract: Conventional cooperative spectrum sensing (CSS) schemes in cognitive radio networks (CRNs) require that the secondary users (SUs) report their sensing data separately in the time domain to the fusion center, which yields long reporting delay especially in the case of large number of cooperative SUs. By exploiting the computation over multiple-access channel (CoMAC) method, this paper proposes a novel CoMAC-based CSS scheme that allows the cooperative SUs to encode their local statistics in transmit power and to transmit simultaneously the modulated symbols sequence carrying transmit power information to the fusion center. The fusion center then makes the final decision on the presence of primary users by recovering the overall test statistic of energy detection from the energy of the received signal. Performance metrics of the CoMAC-based CSS scheme, i.e., detection probability and false alarm probability, are further derived based on the central limit theorem. Finally, based on the derived detection and false alarm probabilities, both energy detection threshold and spectrum sensing time are optimized to improve the average throughput of the CRN. Simulations demonstrate the efficiency of this work.\",\n \"Title: Learning to Search on Manifolds for 3D Pose Estimation of Articulated Objects.\\nYear: 2016\\n\\nAbstract: This paper focuses on the challenging problem of 3D pose estimation of a diverse spectrum of articulated objects from single depth images. A novel structured prediction approach is considered, where 3D poses are represented as skeletal models that naturally operate on manifolds. Given an input depth image, the problem of predicting the most proper articulation of underlying skeletal model is thus formulated as sequentially searching for the optimal skeletal configuration. This is subsequently addressed by convolutional neural nets trained end-to-end to render sequential prediction of the joint locations as regressing a set of tangent vectors of the underlying manifolds. Our approach is examined on various articulated objects including human hand, mouse, and fish benchmark datasets. Empirically it is shown to deliver highly competitive performance with respect to the state-of-the-arts, while operating in real-time (over 30 FPS).\",\n \"Title: Multivariate Regression with Grossly Corrupted Observations: A Robust Approach and its Applications.\\nYear: 2017\\n\\nAbstract: This paper studies the problem of multivariate linear regression where a portion of the observations is grossly corrupted or is missing, and the magnitudes and locations of such occurrences are unknown in priori. To deal with this problem, we propose a new approach by explicitly consider the error source as well as its sparseness nature. An interesting property of our approach lies in its ability of allowing individual regression output elements or tasks to possess their unique noise levels. Moreover, despite working with a non-smooth optimization problem, our approach still guarantees to converge to its optimal solution. Experiments on synthetic data demonstrate the competitiveness of our approach compared with existing multivariate regression models. In addition, empirically our approach has been validated with very promising results on two exemplar real-world applications: The first concerns the prediction of textit{Big-Five} personality based on user behaviors at social network sites (SNSs), while the second is 3D human hand pose estimation from depth images. The implementation of our approach and comparison methods as well as the involved datasets are made publicly available in support of the open-source and reproducible research initiatives.\",\n \"Title: Memnet: Enhancing Throughput And Energy Efficiency For Hybrid Workloads Via Para-Virtualized Memory Sharing\\nYear: 2016\\n\\nAbstract: Virtualization has become a building block for modern IT industry, and many datacenters are now highly virtualized. It is known that virtualization also introduces nontrivial overhead, which can cause severe self-interference inside a VM when CPU intensive tasks and bandwidth intensive tasks are co-located. Energy efficiency of the server can be affected as well. While such overhead is well-studied in application/protocol specific context, a more comprehensive solution is yet to be explored for general cloud services. In this paper, we present MemNet, a novel protocol-independent solution that enables para-virtualized memory sharing between host and guest VMs. This design successfully decouples I/O and computation operations and lifts the offered interface from the physical devices to highlevel network services. Our real-world implementation on KVM indicates that, MemNet can achieve 27% and 70% gain in terms of computing and networking performance, respectively, by resolving the self-interference. It also provides 32% improvement in terms of energy efficiency.\",\n \"Title: End-to-End Throughput Maximization for Underlay Multi-Hop Cognitive Radio Networks With RF Energy Harvesting.\\nYear: 2017\\n\\nAbstract: This paper studies a green paradigm for the underlay coexistence of primary users (PUs) and secondary users (SUs) in energy harvesting cognitive radio networks (EH-CRNs), wherein battery-free SUs capture both the spectrum and the energy of PUs to enhance spectrum efficiency and green energy utilization. To lower the transmit powers of SUs, we employ multi-hop transmission with time division multip...\",\n \"Title: Joint Spatial-Temporal Access Scheme for Multihop Cognitive Relay Networks Over Nakagami-m Fading.\\nYear: 2017\\n\\nAbstract: This paper investigates the cooperative multihop cognitive relay networks (CRNs) under a novel two-dimensional spatial-temporal opportunity model, in which free opportunity and sharing opportunity are defined to enhance the spectrum efficiency. Correspondingly, a joint spatial-temporal access scheme (JSTAS) is proposed to realize successive spectrum access for continuous data transmission. The multihop CRNs with fixed relaying employ decode-and-forward with and without signal-to-noise ratio selection, which are named as SDF and DF, respectively. Then, considering the interference constraints from multiple primary receivers, the interference of one primary transmitter and the maximum transmit powers of cognitive users, we study the outage performance of multihop CRNs with JSTAS over Nakagami-m fading. To comprehensively evaluate JSTAS, we further present the pure spatial access scheme (SAS) and the pure temporal access scheme (TAS) under the spatial-temporal opportunity model, and calculate their average network outage probabilities. Simulation results demonstrate that SDF outperforms DF, while JSTAS outperforms SAS and TAS under all considered scenarios.\",\n \"Title: Pose Estimation from Line Correspondences: A Complete Analysis and a Series of Solutions.\\nYear: 2017\\n\\nAbstract: In this paper we deal with the camera pose estimation problem from a set of 2D/3D line correspondences, which is also known as PnL (Perspective-n-Line) problem. We carry out our study by comparing PnL with the well-studied PnP (Perspective-n-Point) problem, and our contributions are three-fold: (1) We provide a complete 3D configuration analysis for P3L, which includes the well-known P3P problem as well as several existing analyses as special cases. (2) By exploring the similarity between PnL and PnP, we propose a new subset-based PnL approach as well as a series of linear-formulation-based PnL approaches inspired by their PnP counterparts. (3) The proposed linear-formulation-based methods can be easily extended to deal with the line and point features simultaneously.\",\n \"Title: Gait Energy Response Function For Clothing-Invariant Gait Recognition\\nYear: 2016\\n\\nAbstract: This paper describes a method of clothing-invariant gait recognition by modifying intensity response function of a silhouettebased gait feature. While a silhouette-based representation such as gait energy image (GEI) has been popular in gait recognition community due to its simple yet effective property, it is also well known that such a representation is susceptible to clothes variations since it significantly changes silhouettes (e. g., down jacket, long skirt). We therefore propose a gait energy response function (GERF) which transforms an original gait energy into another one in a nonlinear way, which increases discrimination capability under clothes variation. More specifically, the GERF is represented as a vector of components of a lookup table from an original gait energy to another one and its optimization process is formulated as a generalized eigenvalue problem considering discrimination capability as well as regularization on the GERF. In addition, we apply Gabor filters to the GEI transformed by the GERF and further apply a spatial metric learning method for better performance. In experiments, the OUISIR Treadmill dataset B with the largest clothing variation was used to measure the performance both in verification and identification scenarios. The experimental results show that the proposed method achieved stateof- the-art performance in verification scenarios and competitive performance in identification scenarios.\",\n \"Title: Speed Invariance Vs. Stability: Cross-Speed Gait Recognition Using Single-Support Gait Energy Image\\nYear: 2016\\n\\nAbstract: Gait recognition has recently attracted much attention since it can identify person at a distance without subject cooperation. Walking speed changes, however, cause gait changes in appearance, which significantly drops performance of gait recognition. Considering a speed-invariant property at single-support phases where stride change due to speed changes are mitigated, and a stability against phase estimation error and segmentation noise by aggregating multiple phases inspired by gait energy image (GEI), we propose a speed-invariant gait representation called single-support GEI (SSGEI), which realizes a good tradeoff between the speed invariance and the stability by combining singlesupport phases and GEI concept. For this purpose, we firstly find out the optimal duration around single support phases using a training set so as to well balance the speed invariance and the stability. We then extract SSGEI by aggregating multiple single-support frames. Finally, we combine the proposed SSGEI with subsequent Gabor filters and metric learning for better performance. Experiments on the publicly available OU-ISIR Treadmill Dataset A composed of the largest speed variations demonstrated that the proposed method yielded 99.33% rank-1 identification rate on average for cross-speed gait recognition, which outperforms the other state-of-the-arts, and realized a low computational cost as well.\",\n \"Title: A Unified Latent Factor Model for Effective Category-Aware Recommendation.\\nYear: 2017\\n\\nAbstract: Our data analysis on real-world datasets shows that user preferences are intimately related with item categories, implying the non-negligible of category information for effective recommendation. Thus, in this paper, step by step we propose a unified item-category latent factor model by considering user-category, item-category and category-category interactions. Our approach can be applied to both the situations where an item belongs to either a single category (one-to-one) or multiple categories (one-to-many). Finally, empirical studies on the real-world datasets demonstrate the superiority of our approach in comparison with other counterparts.\",\n \"Title: MRLR: Multi-level Representation Learning for Personalized Ranking in Recommendation.\\nYear: 2017\\n\\nAbstract: Representation learning (RL) has recently proven to be effective in capturing local item relationships by modeling item co-occurrence in individual useru0027s interaction record. However, the value of RL for recommendation has not reached the full potential due to two major drawbacks: 1) recommendation is modeled as a rating prediction problem but should essentially be a personalized ranking one; 2) multi-level organizations of items are neglected for fine-grained item relationships. We design a unified Bayesian framework MRLR to learn user and item embeddings from a multi-level item organization, thus benefiting from RL as well as achieving the goal of personalized ranking. Extensive validation on real-world datasets shows that MRLR consistently outperforms state-of-the-art algorithms.\",\n \"Title: Joint Spatial-Temporal Access Scheme Of Multi-Hop Cognitive Radio Sensor Networks\\nYear: 2017\\n\\nAbstract: This paper studies a novel channel access scheme for delay-sensitive multi-hop cognitive radio sensor networks (CRSNs) with fixed decode-and-forward relaying. We first formulate a two-dimensional spatial-temporal opportunity model wherein free and sharing opportunities are defined, and then propose a joint spatial-temporal access scheme (JSTAS) to achieve successive channel access for continuous data transmission. By spatial sensing and temporal sensing, the cognitive sensors with JSTAS control the transmission powers by fully considering their maximum transmission powers, the interference constraints of multiple primary receivers and the interference from one primary transmitter. To evaluate JSTAS, we derive the closed-form outage probability under Rayleigh fading channels and make comparisons among five different schemes. Simulation results demonstrate that JSTAS has better outage performance than that of pure spatial or temporal access scheme under all considered scenarios.\",\n \"Title: The OU-ISIR Gait Database comprising the Large Population Dataset with Age and performance evaluation of age estimation.\\nYear: 2017\\n\\nAbstract: In this paper, we describe the world’s largest gait database, the “OU-ISIR Gait Database, Large Population Dataset with Age (OULP-Age)” and its application to a statistically reliable performance evaluation of gait-based age estimation. Whereas existing gait databases include only 4016 subjects at most, we constructed an extremely large-scale gait database that includes 63,846 subjects (31,093 males and 32,753 females) with ages ranging from 2 to 90 years old. Benchmark algorithms of gait-based age estimation were then implemented to evaluate statistically significant performance differences. Additionally, the dependence of gait-based age estimation performance on gender and age group, in addition to the number of training subjects, was investigated to provide several insights for future research on the topic.\",\n \"Title: Green-Energy-Powered Cognitive Radio Networks: Joint Time and Power Allocation.\\nYear: 2018\\n\\nAbstract: This article studies a green-energy-powered cognitive radio network (GCRN) in an underlay paradigm, wherein multiple battery-free secondary users (SUs) capture both the spectrum and the energy of primary users (PUs) to communicate with an access point (AP). By time division multiple access, each SU transmits data to AP in the allocated time and harvests energy from the RF signals of PUs otherwise, all in the same licensed spectrum concurrently with PUs. Thus, the transmit power of each SU is jointly constrained by the peak interference power at PU and the harvested energy of SU. With the formulated green coexistence paradigm, we investigate the sum-throughput maximization problem with respect to time and power allocation, which is non-convex. To obtain the optimal resource allocation, we propose a joint optimal time and power allocation (JOTPA) algorithm that first transforms the original problem into a convex optimization problem with respect to time and energy allocation, and then solve it by iterative Lagrange dual decomposition. To comprehensively evaluate the performance of the GCRN with JOTPA, we deploy the GCRN in three typical scenarios and compare JOTPA with the equal time and optimal power allocation (ETOPA) algorithm. Extensive simulations show that the deployment of the GCRN significantly influences the throughput performance and JOTPA outperforms ETOPA under all considered scenarios.\",\n \"Title: Hand joints-based gesture recognition for noisy dataset using nested interval unscented Kalman filter with LSTM network.\\nYear: 2018\\n\\nAbstract: Hand joints-based gesture recognition using a neural network provides excellent performance in hand gesture recognition. However, during the collection of sequential skeletal datasets, joints identification through hand pose estimations usually includes noise and even errors, which often diminish the accuracy of gesture recognition. To promote the availability of hand gesture recognition for such noisy datasets, this paper presents a nested interval unscented Kalman filter (UKF) with long short-term memory (NIUKF-LSTM) network to improve the accuracy of hand gesture recognition from noisy datasets. This nested interval method with the UKF changes the distribution of the sigma points based on two sampling intervals. By considering the information of previous frames, the nested interval method helps the NIUKF-LSTM network revise the noise in the sequential hand skeletal data and improve the recognition accuracy. The experimental results showing the removal of noisy skeletal data from the dynamic hand gesture dataset demonstrate the effectiveness of our NIUKF-LSTM network, which achieves better performance than do other state-of-the-art methods.\",\n \"Title: Correction to: Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups.\\nYear: 2018\\n\\nAbstract: The original author list did not accurately reflect the contributions of the following colleagues.\",\n \"Title: Recurrent knowledge graph embedding for effective recommendation.\\nYear: 2018\\n\\nAbstract: Knowledge graphs (KGs) have proven to be effective to improve recommendation. Existing methods mainly rely on hand-engineered features from KGs (e.g., meta paths), which requires domain knowledge. This paper presents RKGE, a KG embedding approach that automatically learns semantic representations of both entities and paths between entities for characterizing user preferences towards items. Specifically, RKGE employs a novel recurrent network architecture that contains a batch of recurrent networks to model the semantics of paths linking a same entity pair, which are seamlessly fused into recommendation. It further employs a pooling operator to discriminate the saliency of different paths in characterizing user preferences towards items. Extensive validation on real-world datasets shows the superiority of RKGE against state-of-the-art methods. Furthermore, we show that RKGE provides meaningful explanations for recommendation results.\",\n \"Title: Gait-based human age estimation using age group-dependent manifold learning and regression.\\nYear: 2018\\n\\nAbstract: Human age estimation from gait is expected to be an important technology for a variety of applications such as automatic customer counting for marketing research or automatic age-based access control restriction for a specific area because the gait can be observable at a distance from a camera (e.g., CCTV). Although the aging process of gait significantly differs among age groups (e.g., children, adults, and the elderly), previous studies on gait-based human age estimation employ a single age group-independent estimation model that suffers from large estimation errors when the age variation increases. We therefore propose an age group-dependent gait-based human age estimation method for better accuracy. Specifically, in the training phase, we first compose age groups that are well-separated from each other by clustering gait features along with their age labels. We then learn a classifier that classifies the gait features for multiple age groups using a directed acyclic graph support vector machine. Next, we learn an age regression model for each age group using support vector regression with a Gaussian kernel in conjunction with a manifold learning technique, i.e., orthogonal locality preserving projection, to better characterize the gait feature. In the test phase, given a gait feature, it is first classified into an age group and then its age is estimated with the age regression model of the classified age group. Experimental results on a gait database that has the world’s largest population of participants ranging from 2 to 90 years old demonstrate the state-of-the-art performance of the proposed method.\",\n \"Title: Gait Recognition by Deformable Registration\\nYear: 2018\\n\\nAbstract: This paper describes a method of gait recognition robust against intra-subject posture changes. A person sometimes walks with changing his/her posture when looking down at a smartphone or carrying a heavy object, which makes intra-subject variation large and consequently makes gait recognition difficult. We therefore introduce a deformable registration model to mitigate the intra-subject posture changes. More specifically, we represent a deformation field by a set of deformation vectors on lattice-type control points allocated on an image, i.e., by free-form deformation (FFD) framework. Given a pair of a probe and a gallery, we compute the deformation field so as to minimize the difference between a probe morphed by the deformation field and the gallery, as well as to ensure the spatial smoothness of the deformation field. We then learn the intra-subject eigen deformation modes from a training set of the same subjects' pairs (e.g., bending the upper body forward and swinging arms more), which are relatively different from inter-subject deformation modes (e.g., body shape spread and stride change). Moreover, because the deformable registration is responsible for a preprocessing part before matching, it can be combined with any types of matching algorithms for gait recognition. Experiments with 1,334 subjects show that the proposed method improves the gait recognition accuracy in both cases without and with a state-of-the-art deep learning-based matcher, respectively.\",\n \"Title: A Flexible Method for Time-of-Flight Camera Calibration Using Random Forest.\\nYear: 2018\\n\\nAbstract: A learning based approach is proposed to calibrate the geometric distortion of a Time-of-Flight (ToF) camera. Our method is flexible as it requires only a ToF camera and a standard camera calibration chessboard. We treat the noise model of a ToF camera as a black box, and employ random forest to automatically learn the underlying unique noise model. The geometric property of the point-cloud can be effectively restored by the learned distortion model. The method can be used in a range of computer vision applications including e.g. hand pose estimation.\",\n \"Title: Smartphone-Based Crowdsourcing for Panoramic Virtual Tour Construction\\nYear: 2018\\n\\nAbstract: Virtual tour applications have gained popularity in recent years by providing immersed user experience and visual navigation hints. Typically, a virtual tour contains both image data and inter-scene spatial connections, and is usually presented as a constellation of images. The construction of virtual tours originally requires sophisticated devices and time-consuming site survey. Lately, the technology advance in smartphones facilitates the virtual tour capture on such versatile end devices. In this work, we introduce a crowdsourced approach toenable large-scale panoramic virtual tour construction with the active participation of smartphone users. We have designed and implemented the framework, as well as proposed further optimizations on constellation construction. Extensive experiments are conducted to evaluate the overall performance, including image quality, constellation accuracy, user experience, and energy efficiency.\",\n \"Title: Harnessing Smartphone Users' Contribution For Virtual Tour Construction\\nYear: 2018\\n\\nAbstract: Virtual tour applications have gained popularity in recent years by providing immersed user experience and visual navigation hints. Typically, a virtual tour contains both image data and inter-scene spatial connections, and is presented as a constellation of images. In this demo, we present a novel approach to enable large-scale panoramic virtual tour construction with the active contributions of smartphone users.\",\n \"Title: Multi-Hop Cognitive Wireless Powered Networks: Outage Analysis and Optimization.\\nYear: 2019\\n\\nAbstract: This paper analyzes and optimizes the outage performance of multi-hop cognitive wireless powered networks (CWPNs) in underlay paradigms. To be specific, there are multiple power beacons (PBs) performing wireless power transfer (WPT) for multiple battery-free secondary users (SUs). Correspondingly, the SUs first harvest energy from the RF signals of PBs and then execute multi-hop cognitive data transmission in the licensed channel concurrently with the primary users (PUs). Therefore, the transmit power of SUs are subject to the energy causality constraint imposed by WPT and the interference power constraint from multiple PUs. We derive and obtain the closed-form exact and asymptotic end-to-end outage probabilities for multi-hop CWPNs over Rayleigh block fading. Furthermore, we optimize the outage performance by studying the outage minimization problem with respect to the WPT power and the WPT time. Due to the complexity of outage probability, we propose a self-adaptive particle swarm optimization (SA-PSO)-based resource allocation algorithm to jointly optimize the power and time for WPT. Extensive simulations validate the correctness of theoretical analysis and the effectiveness of the proposed optimization algorithm.\",\n \"Title: On Multiple Virtual NICs in Cloud Computing: Performance Bottleneck and Enhancement.\\nYear: 2018\\n\\nAbstract: Multiple network interface card (NIC) configuration is the key to achieving service isolation and link aggregation in enterprise networks. Despite the increasing deployments of multiple virtual NICs (multi-vNIC) in cloud environments, the performance and hidden cost of such multi-vNIC configurations are yet to be understood. Our measurements in both Xen and KVM environments reveal that the network...\",\n \"Title: Secure resource allocation for energy harvesting cognitive radio sensor networks without and with cooperative jamming.\\nYear: 2018\\n\\nAbstract: This paper studies secure resource allocation for energy harvesting cognitive radio sensor networks (EH-CRSNs) underlaying a small cell network, wherein the small cell base station (SCBS) employs adaptive or fixed transmit power for cooperative jamming or not while serving cellular users (CUs). Correspondingly, cognitive sensors (CSs) first harvest energy from the RF signals of SCBS and then communicate with each other concurrently with CUs in the licensed cellular channel which is wiretapped by an eavesdropper. Fully considering the peak interference power at CUs and the energy causality imposed by energy harvesting and processing cost, we investigate two secrecy rate maximization problems for secure resource allocation. For EH-CRSNs without cooperative jamming, we first transform the original non-convex problem into a convex problem and then employ the primary decomposition method to derive the optimal harvesting time and power allocation, with which CSs can perform reactive secure resource allocation according to the instantaneous channel state and the setup of the small cell. For EH-CRSNs with cooperative jamming, we employ the iterative optimization method to optimize the charging time and power allocation, with which SCBS can perform proactive secure resource allocation according to the instantaneous channel state. Extensive simulations evaluate the performance of EH-CRSNs and validate the effectiveness of the proposed algorithms.\",\n \"Title: Graphic IMC-PID tuning based on maximum sensitivity for uncertain systems\\nYear: 2019\\n\\nAbstract: Obtaining all feasible parameters of the proportional-integral-differential (PID) controller is the key goal in uncertain systems. This paper proposes a graphical tuning method based on an internal model control (IMC) strategy for uncertain systems with time delay. Specifically, the Kharitonov theorem is introduced first to simplify the uncertain system into 32 polynomials. Then, for each polynomial, the IMC structure is applied to reduce the tuning parameters of the PID controller in order to rapidly determine the controller parameters. Finally, the maximum sensitivity (Ms) is used to further guarantee the controlled system with a certain robustness and dynamic performance, which can portray constant gain margin and phase margin boundaries, and can even determine the range of parameters of the proposed IMC filter. Three example results from simulations are presented to demonstrate the effectiveness and applicability of the proposed method.\",\n \"Title: Gait-based age progression/regression: a baseline and performance evaluation by age group classification and cross-age gait identification\\nYear: 2019\\n\\nAbstract: Gait is believed to be an advanced behavioral biometric that can be perceived at a large distance from a camera without subject cooperation and hence is favorable for many applications in surveillance and forensics. However, appearance differences caused by human aging may significantly reduce the performance of gait recognition. Modeling the aging process on gait features is one of the possible solutions to this problem, and it may inspire more potential applications, such as finding lost children and examining health status. To the best of our knowledge, this topic has not been studied in the literature. Motivated by the fact that aging effects are mainly reflected in the shape and appearance deformations of the gait feature, we propose a baseline algorithm for gait-based age progression and regression using a generic geometric transformation between different age groups, in conjunction with the gait energy image, which is an appearance-based gait feature frequently used in the gait analysis community, to render gait aging and reverse aging effects simultaneously. Various evaluations were conducted through gait-based age group classification and cross-age gait identification to validate the performance of the proposed method, in addition to providing several insights for future research on the subject.\",\n \"Title: Lane scheduling around crossroads for edge computing based autonomous driving.\\nYear: 2019\\n\\nAbstract: Autonomous driving, which can free our hands and feet, is of increasing importance in our daily lives. However, the capacity of onboard computation and communication limits the rapid development of autonomous driving. To address this issue, this paper proposes a novel model named the edge computing-based lanes scheduling system (ECLSS) to study lane scheduling for each vehicle around crossroads with real-time edge devices. There are several edge computing devices (ECD) deployed around crossroads in ECLSS that can collect information from vehicles and road conditions with short-range wired/wireless transmissions. Based on the strong computing power of ECDs and their real-time transmission performance, we propose two centralized management lane scheduling methods: the searching for efficient switching algorithm (SESA) and special vehicles lane switching algorithm (SVLSA). These edge computing-based autonomous driving methods aim to achieve high speed passing through crossroads and guarantee that special vehicles can pass through crossroads within a certain time. Extensive simulations are conducted, and the simulation results demonstrate the superiority of the proposed approaches over competing schemes in typical lane switching scenarios.\",\n \"Title: Heterogeneous Slot Scheduling for Real-time Industrial Wireless Sensor Networks\\nYear: 2019\\n\\nAbstract: Real-time performance and reliability are two critical indicators in an industrial wireless sensor network (IWSN). Several time-division multiple-address (TDMA)-based industrial standards such as WirelessHART and ISA100 are widely used in IWSNs. However, to simplify the analysis, standard TDMA supports only one or two slot types in each frame, and each slot is usually 10 ms, which severely limits transmissibility and real-time responses in TDMA-based IWSNs where the number of transmissions is large but the length of most packets is small. In this paper, we propose a TDMA frame containing slots of different lengths to address this issue. The key ideas are to waste fewer slot resources and achieve on-demand slot allocation. First, we study the matching problem in a TDMA frame; then, we propose two scheduling methods, the split scheduling algorithm (SSA) and the double plug-in algorithm (DPA), under our TDMA frame. Extensive simulations and real testbed results show that the proposed solution DPA can significantly improve network performance and reliability. Real-time comparisons with other existing scheduling schemes show that the proposed solution improves the acceptance ratio by 48.8% compared to the rate-monotonic scheme.\",\n \"Title: Fast Top-N Personalized Recommendation On Item Graph\\nYear: 2019\\n\\nAbstract: In the era of big data, traditional supply chain systems can not match the requirement of e-commerce. The analysis of customers' demands and behaviors are necessary to exploit the potential insights and to build intelligent supply chain systems, which can be achieved by recommender systems. Graph-based recommendation models work well for top-N recommender systems due to their capability to capture the potential relationships between entities. In this paper, we propose a novel graph-based recommendation model to achieve personalized item ranking. To be specific, we design an adapted semi-supervised learning method to capture item smoothness, item fitting, and item confidence. By exploiting the structure of item graph moderately, the proposed method achieves impressive effectiveness and efficiency. In addition, extensive experimental results on real-world datasets show that our proposed method consistently outperforms the state-of-the-art counterparts on the top-N recommendation task.\",\n \"Title: Systemic In Silico Screening In Drug Discovery For Coronavirus Disease (Covid-19) With An Online Interactive Web Server\\nYear: 2020\\n\\nAbstract: The emergence of the new coronavirus (nCoV-19) has impacted human health on a global scale, while the interaction between the virus and the host is the foundation of the disease. The viral genome codes a cluster of proteins, each with a unique function in the event of host invasion or viral development. Under the current adverse situation, we employ virtual screening tools in searching for drugs and natural products which have been already deposited in DrugBank in an attempt to accelerate the drug discovery process. This study provides an initial evaluation of current drug candidates from various reports using our systemic in silico drug screening based on structures of viral proteins and human ACE2 receptor. Additionally, we have built an interactive online platform (https://shennongproject.ai/) for browsing these results with the visual display of a small molecule docked on its potential target protein, without installing any specialized structural software. With continuous maintenance and incorporation of data from laboratory work, it may serve not only as the assessment tool for the new drug discovery but also an educational web site for the public.\",\n \"Title: Real-Time Gait-Based Age Estimation And Gender Classification From A Single Image\\nYear: 2021\\n\\nAbstract: In this paper, we propose a unified real-time framework for gait-based age estimation and gender classification that uses just a single image, which reduces the latency in video capturing compared with the existing methods based on a gait cycle. To cope with the problem of lacking motion information in the input single image, we first reconstruct a gait cycle of a silhouette sequence from the input image via a gait cycle reconstruction network. The reconstructed gait cycle is then fed into a state-of-the-art gait recognition network for feature representation learning, which is further used to obtain the class of the gender and the estimated probability distribution of integer age labels. Unlike the existing methods focusing on the gait sequences captured from the side view, the proposed method is applicable to the gait images from an arbitrary view with a single trained model, which is more suitable for real-world application scenarios (e.g., automatic access control). Stand-alone and client-server online systems were implemented based on the proposed method, which validates the real-time/online property in actual scenes. The experiments on the world's largest multi-view gait dataset demonstrate the effectiveness of the proposed method, which achieves performance improvement compared with the benchmark algorithms.\",\n \"Title: The Obfuscation Method Of User Identification System\\nYear: 2021\\n\\nAbstract: The research on the identification of network users has been continuous, and methods and algorithms emerge in endlessly. Of course, it solves the problem that it is difficult to identify the identity of network interactive users. Yet, user identification raises questions about user privacy. In the last paper [[, we proposed a user identification method based on Mining Web Usage Profiles from Proxy Logs. For the previous user identification system, westudy that which degree the surfing behavior must be obfuscated to prevent identification. Furthermore, we investigate directions in how far a user can hide her identity by obfuscating her web usage pattern through adding or hiding random HTTP connections. In experimental evaluation, we examine the time-period being necessary to obfuscate an identity based on a two-week log file being provided by our industrial partners.\",\n \"Title: Uncertainty-Aware Gait-Based Age Estimation and its Applications\\nYear: 2021\\n\\nAbstract: Gait-based age estimation is a key technique for many applications. It is well known that age estimation uncertainty is highly dependent on age (i.e., small for children and large for adults), and it is important to know the uncertainty for the above-mentioned applications. Therefore, we propose a method for uncertainty-aware gait-based age estimation by introducing a label distribution learning f...\",\n \"Title: End-to-end Model-based Gait Recognition using Synchronized Multi-view Pose Constraint\\nYear: 2021\\n\\nAbstract: We propose an end-to-end model-based cross-view gait recognition which employs pose sequences and shapes extracted by human model fitting. Specifically, we consider a problem setting where gait sequences from single different views are given as a pair to match in a test phase, while asynchronous multi-view gait sequences are given for each subject in a training phase. This work exploits multi-view constraint in the training phase to extract more consistent pose sequences from any views in the test phase, unlike the existing methods do not consider them. For this purpose, given asynchronous multi-view gait sequences, we introduce a phase synchronization step in the training phase so that we can impose pose consistency at each synchronized phase in a temporally up-sampled phase domain. We then train our network by minimizing a loss function based on the synchronized multi-view pose constraint as well as shape consistency, temporal pose smoothness, recognition accuracy, etc in an end-to-end manner. We also introduce the synchronization step in a test phase to reduce intra-subject variations caused by asynchronous pose features. Experimental results on the OU-MVLP and CASIA-B datasets show that the proposed method achieves the state-of-the-art performance for both gait identification and verification scenarios, especially a great improvement in terms of the pose representations.\",\n \"Title: Avalanches during epithelial tissue growth; Uniform Growth and a drosophila eye disc model\\nYear: 2022\\n\\nAbstract: Epithelial tissues constitute an exotic type of active matter with non-linear properties reminiscent of amorphous materials. In the context of a proliferating epithelium, modeled by the quasistatic vertex model, we identify novel discrete tissue scale rearrangements, i.e. cellular rearrangement avalanches, which are a form of collective cell movement. During the avalanches, the vast majority of cells retain their neighbors, and the resulting cellular trajectories are radial in the periphery, a vortex in the core. After the onset of these avalanches, the epithelial area grows discontinuously. The avalanches are found to be stochastic, and their strength is correlated with the density of cells in the tissue. Overall, avalanches redistribute accumulated local spatial pressure along the tissue. Furthermore, the distribution of avalanche magnitudes is found to obey a power law, with an exponent consistent with sheer induced avalanches in amorphous materials. To understand the role of avalanches in organ development, we simulate epithelial growth of the Drosophila eye disc during the third instar using a computational model, which includes both chemical and mechanistic signaling. During the third instar, the morphogenetic furrow (MF), a ~10 cell wide wave of apical area constriction propagates through the epithelium. These simulations are used to understand the details of the growth process, the effect of the MF on the growth dynamics on the tissue scale, and to make predictions for experimental observations. The avalanches are found to depend on the strength of the apical constriction of cells in the MF, with a stronger apical constriction leading to less frequent and more pronounced avalanches. The results herein highlight the dependence of simulated tissue growth dynamics on relaxation timescales, and serve as a guide for in vitro experiments.\",\n \"Title: Multi-agent deep reinforcement learning for end-edge orchestrated resource allocation in industrial wireless networks\\nYear: 2022\\n\\nAbstract: Edge artificial intelligence will empower the ever simple industrial wireless networks (IWNs) supporting complex and dynamic tasks by collaboratively exploiting the computation and communication resources of both machine-type devices (MTDs) and edge servers. In this paper, we propose a multi-agent deep reinforcement learning based resource allocation (MADRL-RA) algorithm for end-edge orchestrated IWNs to support computation-intensive and delay-sensitive applications. First, we present the system model of IWNs, wherein each MTD is regarded as a self-learning agent. Then, we apply the Markov decision process to formulate a minimum system overhead problem with joint optimization of delay and energy consumption. Next, we employ MADRL to defeat the explosive state space and learn an effective resource allocation policy with respect to computing decision, computation capacity, and transmission power. To break the time correlation of training data while accelerating the learning process of MADRL-RA, we design a weighted experience replay to store and sample experiences categorically. Furthermore, we propose a step-by-step epsilon-greedy method to balance exploitation and exploration. Finally, we verify the effectiveness of MADRL-RA by comparing it with some benchmark algorithms in many experiments, showing that MADRL-RA converges quickly and learns an effective resource allocation policy achieving the minimum system overhead.\",\n \"Title: Mixed-Criticality Industrial Data Scheduling on 5G NR\\nYear: 2022\\n\\nAbstract: Compared to industrial wired networks, 5G can improve device mobility and reduce the cost of networking. However, the real-time performance and reliability of 5G new radio (NR) still need to be improved to satisfy industrial applications’ requirements. In factories, the main factor that affects the performance of 5G NR is the unstable signal quality caused by high temperatures and metal. Although assigning dedicated resources to all transmissions and retransmissions is an effective method to improve the performance of 5G NR, the unstable signal quality causes the resources required for retransmissions to be uncertain. To address the problem, we introduce the mixed-criticality task model to 5G NR. When high-criticality packets cannot be transmitted, they are allowed to preempt the resources shared with low-criticality packets. The mixed-criticality scheduling problem of 5G NR is NP-hard. We formulate it as an optimization modulo theories (OMT) specification and propose a scheduling algorithm based on bin packing methods to make 5G NR satisfy industrial applications’ requirements. Finally, we conduct extensive evaluations based on an industrial 5G testbed and random test cases. The evaluation results indicate that our algorithm makes communication reliability greater than 99.9% on unlicensed spectrum, and for most test cases, our algorithm is close to optimal solutions.\",\n \"Title: Self-supervised Depth Estimation in Laparoscopic Image Using 3D Geometric Consistency\\nYear: 2022\\n\\nAbstract: Depth estimation is a crucial step for image-guided intervention in robotic surgery and laparoscopic imaging system. Since per-pixel depth ground truth is difficult to acquire for laparoscopic image data, it is rarely possible to apply supervised depth estimation to surgical applications. As an alternative, self-supervised methods have been introduced to train depth estimators using only synchronized stereo image pairs. However, most recent work focused on the left-right consistency in 2D and ignored valuable inherent 3D information on the object in real world coordinates, meaning that the left-right 3D geometric structural consistency is not fully utilized. To overcome this limitation, we present M3Depth, a self-supervised depth estimator to leverage 3D geometric structural information hidden in stereo pairs while keeping monocular inference. The method also removes the influence of border regions unseen in at least one of the stereo images via masking, to enhance the correspondences between left and right images in overlapping areas. Extensive experiments show that our method outperforms previous self-supervised approaches on both a public dataset and a newly acquired dataset by a large margin, indicating a good generalization across different samples and laparoscopes.\",\n \"Title: Deep Reinforcement Learning-Based Multichannel Access for Industrial Wireless Networks With Dynamic Multiuser Priority\\nYear: 2022\\n\\nAbstract: In Industry 4.0, massive heterogeneous industrial devices generate a great deal of data with different quality of service requirements, and communicate via industrial wireless networks (IWNs). However, the limited time-frequency resources of IWNs cannot well support the high concurrent access of massive industrial devices with strict real-time and reliable communication requirements. To address this problem, a deep reinforcement learning-based dynamic priority multichannel access (DRL-DPMCA) algorithm is proposed in this article. Firstly, according to the time-sensitivity of industrial data, industrial devices are assigned with different priorities, based on which their channel access probabilities are dynamically adjusted. Then, the Markov decision process is utilized to model the dynamic priority multichannel access problem. To cope with the explosion of state space caused by the multichannel access of massive industrial devices with dynamic priorities, DRL is used to establish the mapping from states to actions. Next, the long-term cumulative reward is maximized to obtain an effective policy. Especially, with joint consideration of the access reward and priority reward, a compound reward for multichannel access and dynamic priority is designed. For breaking the time correlation of training data while accelerating the convergence of DRL-DPMCA, an experience replay with experience-weight is proposed to store and sample experiences categorically. Besides, the gated recurrent unit, dueling architecture and step-by-step \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"><tex-math notation=\\\"LaTeX\\\">$\\\\varepsilon$</tex-math></inline-formula>\\n-greedy method are employed to make states more comprehensive and reduce model oscillation. Extensive experiments show that, compared with slotted-Aloha and deep Q network algorithms, DRL-DPMCA converges quickly, and guarantees the highest channel access probability and the minimum queuing delay for high-priority industrial devices in the context of minimum access conflict and nearly 100% channel utilization.\",\n \"Title: A Comprehensive Comparison of Machine Learning and Feature Selection Methods for Maize Biomass Estimation Using Sentinel-1 SAR, Sentinel-2 Vegetation Indices, and Biophysical Variables\\nYear: 2022\\n\\nAbstract: Rapid and accurate estimation of maize biomass is critical for predicting crop productivity. The launched Sentinel-1 (S-1) synthetic aperture radar (SAR) and Sentinel-2 (S-2) missions offer a new opportunity to map biomass. The selection of appropriate response variables is crucial for improving the accuracy of biomass estimation. We developed models from SAR polarization indices, vegetation indices (VIs), and biophysical variables (BPVs) based on gaussian process regression (GPR) and random forest (RF) with feature optimization to retrieve maize biomass in Changchun, Jilin province, Northeastern China. Three new predictors from each type of remote sensing data were proposed based on the correlations to biomass measured in June, July, and August 2018. The results showed that a predictor combined by vertical-horizontal polarization (VV), vertical-horizontal polarization (VH), and the difference of VH and VV (VH-VV) derived from S-1 images of June, July, and August, respectively, with GPR and RF, provided a more accurate estimation of biomass (R-2 = 0.81-0.83, RMSE = 0.40-0.41 kg/m(2)) than the models based on single SAR polarization indices or their combinations, or optimized features (R-2 = 0.04-0.39, RMSE = 0.84-1.08 kg/m(2)). Among the S-2 VIs, the GPR model using a combination of ratio vegetation index (RVI) of June, normalized different infrared index (NDII) of July, and normalized difference vegetation index (NDVI) of August achieved a result with R-2 = 0.83 and RMSE = 0.39 kg/m(2), much better than single VIs or their combination, or optimized features (R-2 of 0.31-0.77, RMSE of 0.47-0.87 kg/m(2)). A BPV predictor, combined with leaf chlorophyll content (CAB) in June, canopy water content (CWC) in July, and fractional vegetation cover (FCOVER) in August, with RF, also yielded the highest accuracy (R-2 = 0.85, RMSE = 0.38 kg/m(2)) compared to that of single BPVs or their combinations, or optimized subset. Overall, the three combined predictors were found to be significant contributors to improving the estimation accuracy of biomass with GPR and RF methods. This study clearly sheds new insights on the application of S-1 and S-2 data on maize biomass modeling.\",\n \"Title: Multi-View Large Population Gait Database With Human Meshes and Its Performance Evaluation\\nYear: 2022\\n\\nAbstract: Existing model-based gait databases provide the 2D poses (i.e., joint locations) extracted by general pose estimators as the human model. However, these 2D poses suffer from information loss and are of relatively low quality. In this paper, we consider a more informative 3D human mesh model with parametric pose and shape features, and propose a multi-view training framework for accurate mesh estimation. Unlike existing methods, which estimate a mesh from a single view and suffer from the ill-posed estimation problem in 3D space, the proposed framework takes asynchronous multi-view gait sequences as input and uses both multi-view and single-view streams to learn consistent and accurate mesh models for both multi-view and single-view sequences. After applying the proposed framework to the existing OU-MVLP database, we establish a large-scale gait database with human meshes (i.e., OUMVLP-Mesh), containing over 10,000 subjects and up to 14 view angles. Experimental results show that the proposed framework estimates human mesh models more accurately than similar methods, providing models of sufficient quality to improve the recognition performance of a baseline model-based gait recognition approach.\",\n \"Title: Deep Regression with Spatial-Frequency Feature Coupling and Image Synthesis for Robot-Assisted Endomicroscopy\\nYear: 2022\\n\\nAbstract: Probe-based confocal laser endomicroscopy (pCLE) allows in-situ visualisation of cellular morphology for intraoperative tissue characterization. Robotic manipulation of the pCLE probe can maintain the probe-tissue contact within micrometre working range to achieve the precision and stability required to capture good quality microscopic information. In this paper, we propose the first approach to automatically regress the distance between a pCLE probe and the tissue surface during robotic tissue scanning. The Spatial-Frequency Feature Coupling network (SFFC-Net) was designed to regress probe-tissue distance by extracting an enhanced data representation based on the fusion of spatial and frequency domain features. Image-level supervision is used in a novel fashion in regression to enable the network to effectively learn the relationship between the sharpness of the pCLE image and its distance from the tissue surface. Consequently, a novel Feedback Training (FT) module has been designed to synthesise unseen images to incorporate feedback into the training process. The first pCLE regression dataset (PRD) was generated which includes ex-vivo images with corresponding probe-tissue distance. Our performance evaluation verifies that the proposed network outperforms other state-of-the-art (SOTA) regression networks.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":84,"string":"84"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13653,"string":"13,653"}}},{"rowIdx":13654,"cells":{"question_id":{"kind":"string","value":"Chao Zhu"},"question":{"kind":"string","value":"Generate an abstract for the title \"Semantic Bilinear Pooling For Fine-Grained Recognition\" using the following items: deep learning, fine-grained recognition, hierarchical label tree, semantic bilinear pooling, state-of-the-art performance."},"answer":{"kind":"string","value":"Naturally, fine-grained recognition, e.g., vehicle identification or bird classification, has specific hierarchical labels, where fine categories are always harder to be classified than coarse categories. However, most of the recent deep learning based methods neglect the semantic structure of fine-grained objects and do not take advantage of the traditional fine-grained recognition techniques (e.g. coarse-to-fine classification). In this paper, we propose a novel framework with a two-branch network (coarse branch and fine branch), i.e., semantic bilinear pooling, for fine-grained recognition with a hierarchical label tree. This framework can adaptively learn the semantic information from the hierarchical levels. Specifically, we design a generalized cross-entropy loss for the training of the proposed framework to fully exploit the semantic priors via considering the relevance between adjacent levels and enlarge the distance between samples of different coarse classes. Furthermore, our method leverages only the fine branch when testing so that it adds no overhead to the testing time. Experimental results show that our proposed method achieves state-of-the-art performance on four public datasets."},"correct_answer":{"kind":"string","value":"Naturally, fine-grained recognition, e.g., vehicle identification or bird classification, has specific hierarchical labels, where fine categories are always harder to be classified than coarse categories. However, most of the recent deep learning based methods neglect the semantic structure of fine-grained objects and do not take advantage of the traditional fine-grained recognition techniques (e.g. coarse-to-fine classification). In this paper, we propose a novel framework with a two-branch network (coarse branch and fine branch), i.e., semantic bilinear pooling, for fine-grained recognition with a hierarchical label tree. This framework can adaptively learn the semantic information from the hierarchical levels. Specifically, we design a generalized cross-entropy loss for the training of the proposed framework to fully exploit the semantic priors via considering the relevance between adjacent levels and enlarge the distance between samples of different coarse classes. Furthermore, our method leverages only the fine branch when testing so that it adds no overhead to the testing time. Experimental results show that our proposed method achieves state-of-the-art performance on four public datasets."},"documents":{"kind":"list like","value":["Title: Texture Classification via Local Feature Representation of Multi-order Gradients.\nYear: 2013\n\nAbstract: This paper presents a novel method to texture classification using local feature representation of multiple order gradients. Different from the state of the art approaches in literature that make use of the widely-used first order gradient based local descriptors, e.g. LBP, HOG, DAISY, SIFT, etc., we claim that the second order gradient based ones also provide critical contribution to classification performance, and thus propose to use Histogram of Second Order Gradients (HSOG) to describe micro-texton patterns. Both the similarity measurements of first and second order gradients computed by Bag-of-Feature modeling and SVM classifier are combined for decision making. Experimental results achieved on the Outex TC dataset not only illustrate that the second order gradient based HSOG is effective to classify texture images, but also highlight that multiple order gradient based description by fusing complementary clues of the first and second order gradients is a promising solution to improve the accuracy in texture classification. © Springer International Publishing Switzerland 2013.","Title: Building a virtual factory\nYear: 2004\n\nAbstract: In this paper, we describe a visualisation system which helps simulation experts transform discrete simulation models and results into animated scenes in a virtual environment. The system aims to significantly reduce production costs and error sources during the generation process of visualisations. The means to achieve these goals are the development of a framework for the translation of simulation results into animations, reuse of animation elements, and the implementation of customization tools for animation elements and their behaviour. By utilizing advanced visualisation techniques, the system is also capable of directing the focus of the observer to interesting events, objects and timeframes.","Title: Cage-based tree deformation\nYear: 2011\n\nAbstract: Tree models are broadly used in multimedia applications, but it is a challenge task to deform trees with plenty of branches. We propose a novel method to deform the tree model interactively using cages based on trees' property, and we design a framework to control the hierarchy deformation of trees. The bounding box of the trunk is used as the global control cage, while the bounding boxes of branches are set as local cages. The linear combination of mean value coordinates computed from local cages and the global cage decides the vertices deformed positions of the deformed tree. This framework is straightforward and effective, which could also preserve local details of the tree. Experimental results show that our technique is suitable for deforming trees with many branches.","Title: Perturbation analysis for the reduced minimum modulus of bounded linear operator in Banach spaces\nYear: 2003\n\nAbstract: Let X and Y be two Banach spaces over the complex field C and let T:X->Y be a bounded linear operator with the generalized inverse T^+. Let [email protected] be a bounded linear operator with @__ __T^[email protected]__ [email protected]__ [email protected]@__ __<1/2. Let @c(T) denote the reduced minimum modulus of T. We first establish the upper semi-continuity theorem for @c(T). Furthermore, if dimKerT=dimKerT","Title: HSOG: a novel local descriptor based on histograms of second order gradients for object categorization\nYear: 2013\n\nAbstract: This paper presents a novel local image descriptor for object categorization that extracts the Histograms of the Second Order Gradients and is thereby named as HSOG. The HSOG descriptor is in contrast to the widely used ones in the literature, e.g. SIFT, DAISY, HOG, LBP, etc., which are based on the first order gradient information. The contributions of this work can be summarized as: (1) the design of HSOG; (2) the prove of its discriminative power and its complementation to the first order gradient based descriptors; (3) the analysis of performance variation caused by different parameter settings; and (4) the multi-scale extension which further improves the categorization accuracy. The experimental results achieved on the Caltech 101 and Caltech 256 databases clearly highlight the effectiveness of the proposed approach.","Title: An experimental study on four models of customer churn prediction\nYear: 2009\n\nAbstract: Decision tree, neural network and logistic regression were applied frequently as models of customer churn prediction, but the application of them has been mature and they are difficult to be improved. In this paper, Bayesian Networks, Support Vector Machines, Rough Sets and Survival Analysis were selected for experimental comparison study. An integrated contrast among the four models from the applicability of model in theory and experimental comparison has been processed. Overall, of the four models the Bayesian network model performed best while the Survival analysis did worst.","Title: Multimodal recognition of visual concepts using histograms of textual concepts and selective weighted late fusion scheme\nYear: 2013\n\nAbstract: The text associated with images provides valuable semantic meanings about image content that can hardly be described by low-level visual features. In this paper, we propose a novel multimodal approach to automatically predict the visual concepts of images through an effective fusion of textual features along with visual ones. In contrast to the classical Bag-of-Words approach which simply relies on term frequencies, we propose a novel textual descriptor, namely the Histogram of Textual Concepts (HTC), which accounts for the relatedness of semantic concepts in accumulating the contributions of words from the image caption toward a dictionary. In addition to the popular SIFT-like features, we also evaluate a set of mid-level visual features, aiming at characterizing the harmony, dynamism and aesthetic quality of visual content, in relationship with affective concepts. Finally, a novel selective weighted late fusion (SWLF) scheme is proposed to automatically select and weight the scores from the best features according to the concept to be classified. This scheme proves particularly useful for the image annotation task with a multi-label scenario. Extensive experiments were carried out on the MIR FLICKR image collection within the ImageCLEF 2011 photo annotation challenge. Our best model, which is a late fusion of textual and visual features, achieved a MiAP (Mean interpolated Average Precision) of 43.69% and ranked 2nd out of 79 runs. We also provide comprehensive analysis of the experimental results and give some insights for future improvements.","Title: Graph-based image segmentation using weighted color patch\nYear: 2013\n\nAbstract: Constructing a discriminative affinity graph plays an essential role in graph-based image segmentation, and feature directly influences the discriminative power of the affinity graph. In this paper, we propose a new method based on the weighted color patch to compute the weight of edges in an affinity graph. The proposed method intends to incorporate both color and neighborhood information by representing pixels with color patches. Furthermore, we assign both local and global weights adaptively for each pixel in a patch in order to alleviate the over-smooth effect of using patches. The normalized cut (NCut) algorithm is then applied on the resulting affinity graph to find partitions. We evaluate the proposed method on the Prague color texture image benchmark and the Berkeley image segmentation database. The extensive experiments show that our method is competitive compared to the other standard methods with multiple evaluation metrics.","Title: Image region description using orthogonal combination of local binary patterns enhanced with color information\nYear: 2013\n\nAbstract: Visual content description is a key issue for machine-based image analysis and understanding. A good visual descriptor should be both discriminative and computationally efficient while possessing some properties of robustness to viewpoint changes and lighting condition variations. In this paper, we propose a new operator called the orthogonal combination of local binary patterns (denoted as OC-LBP) and six new local descriptors based on OC-LBP enhanced with color information for image region description. The aim is to increase both discriminative power and photometric invariance properties of the original LBP operator while keeping its computational efficiency. The experiments in three different applications show that the proposed descriptors outperform the popular SIFT, CS-LBP, HOG and SURF, and achieve comparable or even better performances than the state-of-the-art color SIFT descriptors. Meanwhile, the proposed descriptors provide complementary information to color SIFT, because a fusion of these two kinds of descriptors is found to perform clearly better than either of the two separately. Moreover, the proposed descriptors are about four times faster to compute than color SIFT.","Title: Optimal control of the risk process in a regime-switching environment\nYear: 2011\n\nAbstract: This paper is concerned with cost optimization of an insurance company. The surplus of the insurance company is modeled by a controlled regime-switching diffusion, in which the regime-switching mechanism provides the fluctuations of the random environment. The goal is to find an optimal control that minimizes the total cost up to a stochastic exit time. A weaker sufficient condition than that of Fleming and Soner (2006, Section V.2) for the continuity of the value function is obtained. Further, the value function is shown to be a viscosity solution of a Hamilton-Jacobi-Bellman equation.","Title: High efficiency micro-bubble generator using carbon nanocoils as heater\nYear: 2010\n\nAbstract: This paper reports fabrication and properties of microbubble generator using carbon nanocoils as heater. The carbon nanocoils were dielectrophorestically bridged between microelectrodes in the microstructures. Behavior of the bubble generation was studied in an open DI water circumstance. Compared with traditional metal and polysilicon based microbubble generator, its power consumption was generally 2 orders of magnitude lower, e.g., <; 70μW for ~300μm diameter bubble, indicating high electro-thermal efficiency than ever before. Mechanism underneath the ultra low power consumption is probably due to the nano helical structure of the heating element which leads to the greatly decreased heat loss and will be further investigated.","Title: Numerical solutions of optimal risk control and dividend optimization policies under a generalized singular control formulation\nYear: 2012\n\nAbstract: This paper develops numerical methods for finding optimal dividend pay-out and reinsurance policies. A generalized singular control formulation of surplus and discounted payoff function is introduced, where the surplus is modeled by a regime-switching process subject to both regular and singular controls. To approximate the value function and optimal controls, Markov chain approximation techniques are used to construct a discrete-time controlled Markov chain. The proofs of the convergence of the approximation sequence to the surplus process and the value function are given. Examples of proportional and excess-of-loss reinsurance are presented to illustrate the applicability of numerical methods.","Title: Cultural heritage as digital experience: a Singaporean perspective\nYear: 2007\n\nAbstract: Interactive 3D computer graphics technology is now extremely popular, seen in the increasing interest in and use of 3D digitization of cultural heritage contents. This paper introduces a digital interactive cultural heritage system which embeds Virtual-Reality (VR) technology within the Peranakan culture, the Peranakan culture being a Singaporean unique ethnic culture. A prototype has undergone the full developmental process of being implemented, tested, and evaluated. This paper will also discuss the results of the usability test.","Title: Multi-scale Color Local Binary Patterns for Visual Object Classes Recognition\nYear: 2010\n\nAbstract: The Local Binary Pattern (LBP) operator is a computationally efficient yet powerful feature for analyzing local texture structures. While the LBP operator has been successfully applied to tasks as diverse as texture classification, texture segmentation, face recognition and facial expression recognition, etc., it has been rarely used in the domain of Visual Object Classes (VOC) recognition mainly due to its deficiency of power for dealing with various changes in lighting and viewing conditions in real-world scenes. In this paper, we propose six novel multi-scale color LBP operators in order to increase photometric invariance property and discriminative power of the original LBP operator. The experimental results on the PASCAL VOC 2007 image benchmark show significant accuracy improvement by the proposed operators as compared with both the original LBP and other popular texture descriptors such as Gabor filter.","Title: On Optimal Harvesting Problems in Random Environments\nYear: 2011\n\nAbstract: This paper investigates the optimal harvesting strategy for a single species living in random environments whose population growth is given by a regime-switching diffusion. Harvesting acts as a (stochastic) control on the size of the population. The objective is to find a harvesting strategy which maximizes the expected total discounted income from harvesting up to the time of extinction of the species; the income rate is allowed to be state- and environment-dependent. This is a singular stochastic control problem, with both the extinction time and the optimal harvesting policy depending on the initial condition. One aspect of receiving payments up to the random time of extinction is that small changes in the initial population size may significantly alter the extinction time when using the same harvesting policy. Consequently, one no longer obtains continuity of the value function using standard arguments for either regular or singular control problems having a fixed time horizon. This paper introduces a new sufficient condition under which the continuity of the value function for the regime-switching model is established. Further, it is shown that the value function is a viscosity solution of a coupled system of quasi-variational inequalities. The paper also establishes a verification theorem and, based on this theorem, an $\\varepsilon$-optimal harvesting strategy is constructed under certain conditions on the model. Two examples are analyzed in detail.","Title: Co-match: fast and efficient packet inspection for multiple flows\nYear: 2009\n\nAbstract: Packet inspection is widely employed in application-layer protocol analyzing systems to enable accurate protocol identification. Many existing systems, however, fail to meet the requirement of keeping up with wire speed in networking. There are two limitations: (1) software-based matching schemes are usually in a sequential manner which is slow and inefficient; (2) fast hardware-based matching schemes are inapplicable to network packet processing for lacking of intrinsic support for multiple flows. This paper proposes a novel approach for application-layer protocol identification called Co-Match, which combines software and hardware together to achieve fast and efficient signature matching for multiple flows. First, a grouping scheme is adopted to organize signatures into several matching sets. With this scheme, each packet is only matched against a subset of signatures, bringing about a remarkable improvement of matching speed in software. Second, an FPGA-based coprocessor is developed in order to support fast parallel regular expression matching for multiple flows in hardware. Moreover, a hardware-based flow-level traffic load balancer is employed to parallel multi-flow processing on multiple CPU cores. Experimental results show that our approach is efficient to handle multiple flows while system throughput can achieve the wire speed of Gigabit Ethernet links with moderate CPU usage.","Title: Optimal dividend payment problems in piecewise-deterministic compound Poisson risk models\nYear: 2012\n\nAbstract: This work deals with an optimal dividend payment problem for a piecewise-deterministic compound Poisson insurance risk model. The objective is to maximize the expected discounted dividend payout up to the time of ruin. When the dividend payment rate is restricted, the value function is shown to be a solution of the corresponding Hamilton-Jacobi-Bellman equation, which in turn leads to a tractable methodology to find an optimal threshold dividend payment policy. For the case of unrestricted payment rate, the value function and an optimal barrier strategy are determined explicitly with exponential claim size distributions. A comparison of two examples is provided to illustrate the main results.","Title: Encoding local binary descriptors by bag-of-features with hamming distance for visual object categorization\nYear: 2013\n\nAbstract: This paper presents a novel method for encoding local binary descriptors for Visual Object Categorization (VOC). Nowadays, local binary descriptors, e.g. LBP and BRIEF, have become very popular in image matching tasks because of their fast computation and matching using binary bitstrings. However, the bottleneck of applying them in the domain of VOC lies in the high dimensional histograms produced by encoding these binary bitstrings into decimal codes. To solve this problem, we propose to encode local binary bitstrings directly by the Bag-of-Features (BoF) model with Hamming distance. The advantages of this approach are two-fold: (1) It solves the high dimensionality issue of the traditional binary bitstring encoding methods, making local binary descriptors more feasible for the task of VOC, especially when more bits are considered; (2) It is computationally efficient because the Hamming distance, which is very suitable for comparing bitstrings, is based on bitwise XOR operations that can be fast computed on modern CPUs. The proposed method is validated by applying on LBP feature for the purpose of VOC. The experimental results on the PASCAL VOC 2007 benchmark show that our approach effectively improves the recognition accuracy compared to the traditional LBP feature.","Title: Efficient processing of monotonic linear progressive queries via dynamic materialized views\nYear: 2010\n\nAbstract: There is an increasing demand to process emerging types of queries, such as progressive queries (PQs), from numerous contemporary database applications including telematics, ecommerce, business intelligence, and decision support. Unlike a conventional query, a progressive query is formulated in several steps, i.e., consisting of a set of inter-related step-queries (SQ). A user formulates their SQs on the fly based on the results returned by the previous SQs. Processing such queries provides performance improvement opportunities for a database management system. In this paper, we study the efficient processing of a special type of PQ, called a monotonic linear progressive query (MLPQ). We present a technique to process such PQs based on dynamically materialized views. The key idea is to create a superior-relationship graph for step-queries from historical PQs, which can be used to estimate the benefit of materializing a current step-query. The materialized views are then used to improve the performance of future step-queries. Algorithms and strategies to create and maintain a superior-relationship graph, dynamically select materialized views (step-queries), and the search for a materialized view to process a given step-query are discussed. Experimental results demonstrate that our proposed technique is quite promising in efficiently processing this type of progressive query.","Title: Pixel to Patch Sampling Structure and Local Neighboring Intensity Relationship Patterns for Texture Classification\nYear: 2013\n\nAbstract: In this letter, we explore local image descriptors for texture classification. We mainly propose two novel contributions: an effective sampling structure based on Pixel To Patch (PTP) to mimic the retinal sampling pattern; and a novel Local Neighboring Intensity Relationship Pattern (LNIRP) descriptor to extract texture feature by exploring neighboring gray-scale properties. The LNIRP descriptor is extended by using the PTP sampling structure which aims to capture not only micro-patterns but also macro-patterns, while reducing feature dimensionality and improving computational efficiency. The proposed descriptor has advantages of computational simplicity, no texton dictionary learning step and training-free. Moreover, the LNIRP descriptor is complementary to the Local Binary Pattern (LBP) descriptor. Extensive experiments were conducted on Outex database to evaluate the proposed descriptor and sampling structure. The proposed descriptor can achieve superior classification performance compared to most of the state-of-the-art methods, including what we believe to be the best results reported for Outex, while offering a smallest feature dimension.","Title: Optimal Switching with Constraints and Utility Maximization of an Indivisible Market\nYear: 2012\n\nAbstract: This work focuses on optimal switching with constraints. Our motivation stems from utility maximization of an indivisible market. The dynamic programming approach is used; the value function is characterized as the unique viscosity solution of a quasi-variational inequality. The unbounded domain introduces new challenges. By studying the sample paths of the diffusion at the boundary, a sufficient condition for the continuity of the value function is provided, yielding the desired characterization. Not only are the results of this work applicable to the utility maximization problem, but also they can be used for general optimal switching problems with finite regimes.","Title: A materialized-view based technique to optimize progressive queries via dependency analysis\nYear: 2011\n\nAbstract: Progressive queries (PQ) are a new type of query emerging from numerous contemporary database applications, including e-commerce, social network, business intelligence, and decision support. Such a PQ is formulated in several steps via a set of inter-related step-queries (SQ). How to optimize such PQs represents a new challenge in the development of a database management system. In our previous work, we introduced a materialized-view based technique to process a special type of PQ, called monotonic linear PQs. In this paper, we present a new materialized-view based technique to efficiently process generic PQs. This technique allows an SQ in a given PQ to utilize the results of previous SQs not only from the same PQ but also from other in-process and completed PQs. Due to the storage constraint, it is impossible to retain the results of all the SQs of a completed PQ. Hence, a crucial issue is how to select popular SQs from completed PQs to keep their results as materialized views for optimizing future PQs. To tackle this issue, we introduce a multiple query dependency graph (MQDG) to capture the data source dependency relationships among SQs from multiple PQs. We then present a model to estimate the benefit of an SQ in the MQDG and discuss a procedure to choose critical SQs in the MQDG for materializing their results. The strategies for constructing the MQDG and maintaining the set of materialized views are also suggested. Experimental results demonstrate that our technique is quite promising in efficiently processing PQs.","Title: Visual object recognition using DAISY descriptor\nYear: 2011\n\nAbstract: Visual content description is a key issue for the task of machine-based visual object categorization (VOC). A good visual descriptor should be both discriminative enough and computationally efficient while possessing some properties of robustness to viewpoint changes and lighting condition variations. The recent literature has featured local image descriptors, e.g. SIFT, as the main trend in VOC. However, it is well known that SIFT is computationally expensive, especially when the number of objects/concepts and learning data increase significantly. In this paper, we investigate the DAISY, which is a new fast local descriptor introduced for wide baseline matching problem, in the context of VOC. We carefully evaluate and compare the DAISY descriptor with SIFT both in terms of recognition accuracy and computation complexity on two standard image benchmarks - Caltech 101 and PASCAL VOC 2007. The experimental results show that DAISY outperforms the state-of-the-art SIFT while using shorter descriptor length and operating 3 times faster. When displaying a similar recognition accuracy to SIFT, DAISY can operate 12 times faster.","Title: Visual Object Categorization via Sparse Representation\nYear: 2009\n\nAbstract: In this paper, we consider the problem of classifying a real world image to the corresponding object class based on its visual content via sparse representation, which is originally used as a powerful tool for acquiring, representing and compressing high-dimensional signals. Assuming the intuitive hypothesis that an image could be represented by a linear combination of the training images from the same class, we propose a novel approach for visual object categorization in which a sparse representation of the image is first of all obtained by solving a L1 (or L0)-minimization problem and then fed into a traditional classifier such as Support Vector Machine (SVM) to finally perform the specified task. Experimental results obtained on the SIMPLIcity database have shown that this new approach can improve the classification performance compared to standard SVM using directly features extracted from the image.","Title: An Embedded Load Balancing System for High Speed OC192 Networks\nYear: 2009\n\nAbstract: Parallel architecture has been used for packet processing of high speed links. Essential to such architecture is a load balancer which responsible for packet dispatching. In this paper, we design an embedded system for high speed OC192 network traffic load balancing. In the system, incoming traffic is load-balanced to 12 processing engines through Ethernet. In order to reasonably dispatch traffic while preserving the order of packets within a flow, we propose a novel feedback-based load balancing algorithm. For each packet, we calculate the hash value on source and destination IP addresses. The return value is used as an index to a lookup table to retrieve a target processing engine. The lookup table is adjusted according to feedback. Effectiveness of the algorithm is evaluated by simulations using real network trace. We implement the system in field programmable gate array (FPGA) and verify it by experiments. Experimental results show that the system sustains 10 Gbps throughputs with an average delay of 4.2 microseconds. The system has been used in a practical network monitoring application.","Title: Fan-Shaped Patch Local Binary Patterns For Texture Classification\nYear: 2013\n\nAbstract: In this paper, we present a new distinctive feature for texture classification, the fan-shaped patch local binary patterns (FP-LBP). The proposed FP-LBP operator extends the traditional LBP operator by encoding the difference between each central pixel with the average value of its neighboring fan-shaped patches, instead of only using its neighboring pixels. By this way, FP-LBP not only preserves more information of local structures than the traditional LBP, but also keeps relatively lower dimensionality, especially when larger radius and more neighboring pixels are considered. Moreover, the \"uniform\" and rotation invariant FP-LBP are also defined similarly to the traditional LBP. The proposed descriptors are evaluated on two popular texture databases: CUReT and KTH-TIPS, and the experimental results show that FP-LBP outperforms the traditional LBP descriptor with a smaller feature dimension. Moreover, the proposed method achieves higher classification accuracy than most of the state-of-the-art methods on both databases.","Title: Optimization of Monotonic Linear Progressive Queries Based on Dynamic Materialized Views.\nYear: 2014\n\nAbstract: There is an increasing demand to efficiently process emerging types of queries, such as progressive queries (PQs), from contemporary database applications including telematics, e-commerce and social media. Unlike conventional queries, a PQ consists of a set of step-queries (SQ). A user formulates a new SQ on the fly based on the result(s) from the previous SQ(s). Existing database management syste...","Title: DMVI: a dynamic materialized view index for efficiently discovering usable views for progressive queries\nYear: 2012\n\nAbstract: Progressive queries (PQ) are a new type of query emerged from numerous data intensive applications. A user formulates a PQ in several steps using a set of inter-related step-queries (SQ). Efficiently processing PQs in a DBMS is crucial in supporting these applications. In our previous work, we introduced a materialized view based approach to efficiently processing PQs, where our focus was on selection of promising materialized views. The problem of how to efficiently find usable views to answer SQs for a PQ remained open. In this paper, we present a new index technique, which is called the dynamic materialized view index (DMVI), to rapidly discover usable views to answer a given SQ. The structure of the index and the strategies to construct, maintain and use the DMVI are discussed. The Experimental results demonstrate that our technique is quite promising in improving the performance of the materialized view based query processing for PQs.","Title: Reconstruction of Tree Crown Shape from Scanned Data\nYear: 2008\n\nAbstract: Reconstruction of a real tree from scattered scanned points is a new challenge in virtual reality. Although many progresses are made on main branch structures and overall shape of a tree, reconstructions are still not satisfactory in terms of silhouette and details. We do think that 3D reconstruction of the tree crown shapes may help to constrain accurate reconstruction of complete real tree geometry. We propose here a novel approach for tree crown reconstruction based on an improvement of alpha shape modeling, where the data are points unevenly distributed in a volume rather than on a surface only. The result is an extracted silhouette mesh model, a concave closure of the input data. We suggest an appropriate scope of proper alpha values, so that the reconstruction of the silhouette mesh is a valid manifold surface. Experimental results show that our technique works well in extracting the crown shapes of real trees.","Title: Harvesting in Stochastic Environments: Optimal Policies in a Relaxed Model.\nYear: 2011\n\nAbstract: This paper examines the objective of optimally harvesting a single species in a stochastic environment. This problem has previously been analyzed in Alvarez (2000) using dynamic programming techniques and, due to the natural payoff structure of the price rate function (the price decreases as the population increases), no optimal harvesting policy exists. This paper establishes a relaxed formulation of the harvesting model in such a manner that existence of an optimal relaxed harvesting policy can not only be proven but also identified. The analysis embeds the harvesting problem in an infinite-dimensional linear program over a space of occupation measures in which the initial position enters as a parameter and then analyzes an auxiliary problem having fewer constraints. In this manner upper bounds are determined for the optimal value (with the given initial position); these bounds depend on the relation of the initial population size to a specific target size. The more interesting case occurs when the initial population exceeds this target size; a new argument is required to obtain a sharp upper bound. Though the initial population size only enters as a parameter, the value is determined in a closed-form functional expression of this parameter.","Title: A Selective Weighted Late Fusion for Visual Concept Recognition.\nYear: 2012\n\nAbstract: We propose in this paper a novel multimodal approach to automatically predict the visual concepts of images through an effective fusion of visual and textual features. It relies on a Selective Weighted Late Fusion (SWLF) scheme which, in optimizing an overall Mean interpolated Average Precision (MiAP), learns to automatically select and weight the best experts for each visual concept to be recognized. Experiments were conducted on the MIR Flickr image collection within the ImageCLEF 2011 Photo Annotation challenge. The results have brought to the fore the effectiveness of SWLF as it achieved a MiAP of 43.69 % for the detection of the 99 visual concepts which ranked 2nd out of the 79 submitted runs, while our new variant of SWLF allows to reach a MiAP of 43.93 %.","Title: LIRIS-Imagine at ImageCLEF 2012 Photo Annotation Task.\nYear: 2012\n\nAbstract: In this paper, we present the methods we have proposed and evaluated through the ImageCLEF 2012 Photo Annotation task. More precisely, we have proposed the Histogram of Textual Concepts (HTC) textual feature to capture the relatedness of semantic concepts. In con- trast to term frequency-based text representations mostly used for visual concept detection and annotation, HTC relies on the semantic similar- ity between the user tags and a concept dictionary. Moreover, a Selective Weighted Late Fusion (SWLF) is introduced to combine multiple sources of information which by iteratively selecting and weighting the best fea- tures for each concept at hand to be classied. The results have shown","Title: Analysis on electro-thermal property of micro-bubble generator using carbon nanotube heating elements\nYear: 2009\n\nAbstract: Localized high temperature due to Joule heating in Carbon Nanotubes (CNTs) can generate micron-sized bubbles. Electro-thermal property of CNTs-based microbubble generator is characterized by finite element analysis in this paper. Water circumstance with volume in micrometer scale covering solid elements of the microdevice was considered. Temperature distribution and power input corresponding to microbubble nucleation at the centre of the heating elements were evaluated. Free water convection was used to set thermal boundary condition at the liquid-solid interface before micobubble generation. The steady-state, time-transient and parametric simulations have been performed. The results of relationship between power consumption, material properties such as thermal conductivity, and device configuration ( such as the number of CNTs, electrode thickness, electrode gap, etc.) could be a guidance to the design and fabrication of this kind of microbubble generator.","Title: An experimental study of consumption behaviors in telecom social networks\nYear: 2010\n\nAbstract: Customers' social ties have played a crucial role in influencing their consumption behaviors. Generally, users can influence others' consumption behaviors via word-of-mouth. But in some special industry such as telecom, some kinds of indirect influences also exist between users and some applications such as social recommendation have been researched on the basis of these influences. In order to explore such influences, this paper selects some attributes to outline the users' consumption behaviors and explores the relationship of the social ties and the behaviors via some analysis experiments. The results show that (1) the social tie attributes have strong correlations with users' consumption behaviors similarity; (2) as the strength of the telecom social ties improve, the similarity value of the users' value-added services using behaviors get greater but (3) the communication behaviors don't have a strong correlation with the strength of telecom social ties.","Title: Regularity and Recurrence of Switching Diffusions\nYear: 2007\n\nAbstract: This work is concerned with switching diffusion processes, also known as regime-switching diffusions. Our attention focuses\n on regularity, recurrence, and positive recurrence of the underlying stochastic processes. The main effort is devoted to obtaining\n easily verifiable conditions for the aforementioned properties. Continuous-state-dependent jump processes are considered.\n First general criteria on regularity and recurrence using Liapunov functions are obtained. Then we focus on a class of problems,\n in which both the drift and the diffusion coefficients are “linearizable” with respect to the continuous state, and suppose\n that the generator of the jump part of the process can be approximated by a generator of an ergodic Markov chain. Sufficient\n conditions for regularity, recurrence, and positive recurrence are derived, which are linear combination of the averaged coefficients\n (averaged with respect to the stationary measure of the Markov chain).","Title: HSOG: A Novel Local Image Descriptor Based on Histograms of the Second-Order Gradients.\nYear: 2014\n\nAbstract: Recent investigations on human vision discover that the retinal image is a landscape or a geometric surface, consisting of features such as ridges and summits. However, most of existing popular local image descriptors in the literature, e. g., scale invariant feature transform (SIFT), histogram of oriented gradient (HOG), DAISY, local binary Patterns (LBP), and gradient location and orientation histogram, only employ the first-order gradient information related to the slope and the elasticity, i.e., length, area, and so on of a surface, and thereby partially characterize the geometric properties of a landscape. In this paper, we introduce a novel and powerful local image descriptor that extracts the histograms of second-order gradients (HSOGs) to capture the curvature related geometric properties of the neural landscape, i.e., cliffs, ridges, summits, valleys, basins, and so on. We conduct comprehensive experiments on three different applications, including the problem of local image matching, visual object categorization, and scene classification. The experimental results clearly evidence the discriminative power of HSOG as compared with its first-order gradient-based counterparts, e. g., SIFT, HOG, DAISY, and center-symmetric LBP, and the complementarity in terms of image representation, demonstrating the effectiveness of the proposed local descriptor.","Title: A Boosted Multi-Task Model for Pedestrian Detection with Occlusion Handling.\nYear: 2015\n\nAbstract: Pedestrian detection is a challenging problem in computer vision, and has achieved impressive progress in recent years. However, the current state-of-the-art methods suffer from significant performance decline with increasing occlusion level of pedestrians. A common approach for occlusion handling is to train a set of occlusion-specific detectors and merge their results directly, but these detecto...","Title: On hybrid diffusions\nYear: 2008\n\nAbstract: For the pressing needs in control and optimization using hybrid systems, this work focuses on weak stochastic stability and ergodicity of regime-switching diffusions. Using Liapunov functions, we derive necessary and sufficient conditions for weak stability. Then, ergodicity of weakly stable regime-switching diffusions is obtained by constructing cycles using the associated discrete-time Markov chains.","Title: A Measure Approach for Continuous Inventory Models: Discounted Cost Criterion.\nYear: 2015\n\nAbstract: This paper develops a new approach to the solution of impulse control problems for continuous inventory models under a discounted cost criterion. The analysis imbeds this stochastic problem in two different infinite-dimensional linear programs, parametrized by the initial inventory level x(0), by concentrating on particular functions and capturing the expected (discounted) behavior of the inventory level process and ordering decisions as measures. The first imbedding then naturally leads to the minimization of a nonlinear function representing the cost associated with an (s, S) ordering policy and an optimizing pair determines optimal levels (s*, S*). The lower bound arising from this imbedding is tight when x(0) >= s* but is a strict lower bound when x(0) < s*. Solving the first linear program determines the value function in the \"no order\" region and is critical to the formulation of the second linear program. The dual of the second linear program is then solved to provide a tight lower bound for all x(0), in particular for x(0) < s*, and thereby completely determines the value function. Existence of an optimal (s, S) policy in the admissible class of ordering policies (and its characterization) is a consequence of the method, not an a priori assumption. Also of note in this approach is that it solves piecewise in two regions the family of linear programs parametrized by x(0). No smoothness of the value function is required; instead, the level of smoothness results from its construction using the particular functions from which the linear programs are derived. This paper places minimal assumptions on a general stochastic differential equation model for the inventory level and illustrates the approach on two examples.","Title: Dynamic flow consolidation for energy savings in green DCNs.\nYear: 2015\n\nAbstract: Energy consumption of data center has become an important challenge due to high electric cost and carbon dioxide emissions. Previous work has mainly focused on saving energy cost of servers, though the energy consumption of data center networks (DCNs), consisting of networking equipments like switches, also takes a significant part of the overall energy consumption. In this paper, we propose ProCons, an energy saving mechanism that dynamically consolidates traffic flows onto a small set of networking equipments in order to shut down idle ones for energy saving. Different from previous works that assume the traffic demands to be stable, ProCons takes into account the variance of traffic demand over time, and predicts future demand based on historical statistics. The traffic flows are then scheduled based on the predicted future demands and the capacity of each link. We evaluate ProCons with real life traces collected from data centers using a flow-level simulator. Our experimental results show that using ProCons, 40% of energy savings for DCNs can be gained while maintaining the good performance of flow transmission.","Title: Group Cost-Sensitive Boosting for Multi-Resolution Pedestrian Detection.\nYear: 2016\n\nAbstract: As an important yet challenging problem in computer vision, pedestrian detection has achieved impressive progress in recent years. However, the significant performance decline with decreasing resolution is a major bottleneck of current state-of-the-art methods. For the popular boosting-based detectors, one of the main reasons is that low resolution samples, which are usually more difficult to detect than high resolution ones, are treated by equal costs in the boosting process, leading to the consequence that they are more easily being rejected in early stages and can hardly be recovered in late stages as false negatives. To address this problem, we propose in this paper a new multi-resolution detection approach based on a novel group cost-sensitive boosting algorithm, which extends the popular AdaBoost by exploring different costs for different resolution groups in the boosting process, and places more emphases on low resolution group in order to better handle detection of hard samples. The proposed approach is evaluated on the challenging Caltech pedestrian benchmark, and out-performs other state-of-the-art on different resolutionspecific test sets.","Title: LOGAN: Problem Diagnosis in the Cloud Using Log-Based Reference Models\nYear: 2016\n\nAbstract: Problem diagnosis is one crucial aspect in the cloud operation that is becoming increasingly challenging. On the one hand, the volume of logs generated in today's cloud is overwhelmingly large. On the other hand, cloud architecture becomes more distributed and complex, which makes it more difficult to troubleshoot failures. In order to address these challenges, we have developed a tool, called LOGAN, that enables operators to quickly identify the log entries that potentially lead to the root cause of a problem. It constructs behavioral reference models from logs that represent the normal patterns. When problem occurs, our tool enables operators to inspect the divergence of current logs from the reference model and highlight logs likely to contain the hints to the root cause. To support these capabilities we have designed and developed several mechanisms. First, we developed log correlation algorithms using various IDs embedded in logs to help identify and isolate log entries that belong to the failed request. Second, we provide efficient log comparison to help understand the differences between different executions. Finally we designed mechanisms to highlight critical log entries that are likely to contain information pertaining to the root cause of the problem. We have implemented the proposed approach in a popular cloud management system, OpenStack, and through case studies, we demonstrate this tool can help operators perform problem diagnosis quickly and effectively.","Title: Bloody Image Classification with Global and Local Features.\nYear: 2016\n\nAbstract: Object content understanding in images and videos draws more and more attention nowadays. However, only few existing methods have addressed the problem of bloody scene detection in images. Along with the widespread popularity of the Internet, violent contents have affected our daily life. In this paper, we propose region-based techniques to identify a color image being bloody or not. Firstly, we have established a new dataset containing 25431 bloody images and 25431 non-bloody images. These annotated images are derived from the Violent Scenes Dataset, a public shared dataset for violent scenes detection in Hollywood movies and web videos. Secondly, we design a bloody image classification method with global visual features using Support Vector Machines. Thirdly, we also construct a novel bloody region identification approach using Convolutional Neural Networks. Finally, comparative experiments show that bloody image classification with local features is more effective.","Title: Discriminative latent semantic feature learning for pedestrian detection.\nYear: 2017\n\nAbstract: Features act as a key factor in pedestrian detection task. Most widely-used ones like HOG are manually designed and hard to be adaptive, thus now more attention has been paid to the features automatically learned on data. In this paper, a novel approach of learning discriminative features is proposed, addressing two main limitations of the methods in the literature. On one hand, unlike those methods of learning features on low-level pixels, we propose to learn features via a particular sparse coding algorithm enhanced on mid-level image representation, in order to obtain higher-level latent semantics and robustness; On the other hand, those methods usually utilize label information in model training such as deformable part model (DPM) with high computation cost. Instead, we propose to extend the learning process via a maximum margin criterion, in order to better encode discriminative information directly in features by optimizing them to be close to each other if from the same class and far from each other if from different classes. Furthermore, a boosted detection framework rather than the complex DPM is adopted to achieve both high accuracy and efficiency. The proposed approach achieves promising results on several standard pedestrian detection benchmarks.","Title: On Feller and Strong Feller Properties and Exponential Ergodicity of Regime-Switching Jump Diffusion Processes with Countable Regimes.\nYear: 2017\n\nAbstract: This work focuses on a class of regime-switching jump diffusion processes, in which the switching component has countably infinite many states or regimes. The existence and uniqueness of the underlying process are obtained by an interlacing procedure. Then the Feller and strong Feller properties of such processes are derived by the coupling method and an appropriate Radon Nikodym derivative. Finally the paper studies exponential ergodicity of regime-switching jump diffusion processes.","Title: Tracking Based Multi-Orientation Scene Text Detection: A Unified Framework With Dynamic Programming.\nYear: 2017\n\nAbstract: There are a variety of grand challenges for multi-orientation text detection in scene videos, where the typical issues include skew distortion, low contrast, and arbitrary motion. Most conventional video text detection methods using individual frames have limited performance. In this paper, we propose a novel tracking based multi-orientation scene text detection method using multiple frames within a unified framework via dynamic programming. First, a multi-information fusion-based multi-orientation text detection method in each frame is proposed to extensively locate possible character candidates and extract text regions with multiple channels and scales. Second, an optimal tracking trajectory is learned and linked globally over consecutive frames by dynamic programming to finally refine the detection results with all detection, recognition, and prediction information. Moreover, the effectiveness of our proposed system is evaluated with the state-of-the-art performances on several public data sets of multi-orientation scene text images and videos, including MSRA-TD500, USTB-SV1K, and ICDAR 2015 Scene Videos.","Title: Vehicular fog computing: Vision and challenges\nYear: 2017\n\nAbstract: The emerging vehicular applications demand for a lot more computing and communication capacity to excel in their compute-intensive and latency-sensitive tasks. Fog computing, which focuses on moving computing resources to the edge of networks, complements cloud computing by solving the latency constraints and reducing ingress traffic to the cloud. This paper presents a visionary concept on vehicular fog computing that turns connected vehicles into mobile fog nodes and utilises mobility of vehicles for providing cost-effective and on-demand fog computing for vehicular applications. Besides system design, this paper also discusses the remained technical challenges.","Title: Diamond: Nesting the Data Center Network with Wireless Rings in 3D Space.\nYear: 2018\n\nAbstract: The introduction of wireless transmissions into the data center has been shown to be promising in improving the performance of data center networks (DCN) cost effectively. For high transmission flexibility and performance, a fundamental challenge is to increase the wireless availability and enable fully hybrid and seamless transmissions over both wired and wireless DCN components. Rather than limiting the number of wireless radios by the size of top-of-rack (ToR) switches, we propose a novel DCN architecture, Diamond, which nests the wired DCN with radios equipped on all servers. To harvest the gain allowed by the rich reconfigurable wireless resources, we propose the low-cost deployment of scalable 3D Ring Reflection Spaces (RRSs) which are interconnected with streamlined wired herringbone to enable large number of concurrent wireless transmissions through high-performance multi-reflection of radio signals over metal. To increase the number of concurrent wireless transmissions within each RRS, we propose a precise reflection method to reduce the wireless interference. We build a 60GHz-based testbed to demonstrate the function and transmission ability of our proposed architecture. We further perform extensive simulations to show the significant performance gain of Diamond, in supporting up to five times higher server-to-server capacity, enabling network-wide load balancing, and ensuring high fault tolerance.","Title: Almost Sure and Moment Exponential Stability of Regime-Switching Jump Diffusions.\nYear: 2017\n\nAbstract: This work is devoted to almost sure and moment exponential stability of regime-switching jump diffusions. The Lyapunov function method is used to derive sufficient conditions for stabilities for general nonlinear systems, which further helps to derive easily verifiable conditions for linear systems. For one-dimensional linear regime-switching jump diffusions, necessary and sufficient conditions for almost sure and pth moment exponential stabilities are presented. Several examples are provided for illustration.","Title: Effective Human Detection Via Multi-Model Classification And Adaptive Late Fusion\nYear: 2018\n\nAbstract: Human detection serves as an important basis to achieve certain video surveillance-oriented biometrics such as gait, face and actions since the first step is to find and locate human targets in surveillance scenes. In the literature, channel feature-based methods and deep neural network-based methods are two most popular kinds of human detection approaches, with their own advantages. However, there is not much effort on the study of their combination to take full advantage of these two kinds of approaches. Therefore in this paper, we propose an effective human detection approach by combining multiple state-of-the-art deep neural network-based and channel feature-based methods with an adaptive late fusion strategy. The key idea of our approach is to explore complementary information of different state-of-the-art detection methods and to find an appropriate way to combine their strong points for better performance. The proposed approach is evaluated on several standard human detection benchmarks, and shows its effectiveness by achieving superior performances to the other state-of-the-art methods on most evaluation settings.","Title: Fog Following Me: Latency and Quality Balanced Task Allocation in Vehicular Fog Computing\nYear: 2018\n\nAbstract: Emerging vehicular applications, such as real-time situational awareness and cooperative lane change, demand for sufficient computing resources at the edge to conduct time-critical and data-intensive tasks. This paper proposes Fog Following Me (Folo), a novel solution for latency and quality balanced task allocation in vehicular fog computing. Folo is designed to support the mobility of vehicles, including ones generating tasks and the others serving as fog nodes. We formulate the process of task allocation across stationary and mobile fog nodes into a joint optimization problem, with constraints on service latency, quality loss, and fog capacity. As it is a NP-hard problem, we linearize it and solve it using Mixed Integer Linear Programming. To evaluate the effectiveness of Folo, we simulate the mobility of fog nodes at different times of day based on real-world taxi traces, and implement two representative tasks, including video streaming and real-time object recognition. Compared with naive and random fog node selection, the latency and quality balanced task allocation provided by Folo achieves higher performance. More specifically, Folo shortens the average service latency by up to 41\\% while reducing the quality loss by up to 60\\%.","Title: Design Flow of Accelerating Hybrid Extremely Low Bit-Width Neural Network in Embedded FPGA\nYear: 2018\n\nAbstract: Neural network accelerators with low latency and low energy consumption are desirable for edge computing. To create such accelerators, we propose a design flow for accelerating the extremely low bit-width neural network (ELB-NN) in embedded FPGAs with hybrid quantization schemes. This flow covers both network training and FPGA-based network deployment, which facilitates the design space exploration and simplifies the tradeoff between network accuracy and computation efficiency. Using this flow helps hardware designers to deliver a network accelerator in edge devices under strict resource and power constraints. We present the proposed flow by supporting hybrid ELB settings within a neural network. Results show that our design can deliver very high performance peaking at 10.3 TOPS and classify up to 325.3 image/s/watt while running large-scale neural networks for less than 5W using embedded FPGA. To the best of our knowledge, it is the most energy efficient solution in comparison to GPU or other FPGA implementations reported so far in the literature.","Title: AccDNN: An IP-Based DNN Generator for FPGAs\nYear: 2018\n\nAbstract: Using FPGA to accelerate Deep Neural Networks (DNNs) requires RTL programming, hardware verification, and precise resource allocation, which is both time-consuming and challenging. To address this issue, we present AccDNN, an end-to-end automation tool that can generate high-performance DNN designs on FPGAs automatically. Highlights of this tool include high-quality RTL network layer IPs, a fine-grained layer-based pipeline architecture, and a column-based cache scheme for high throughput, low latency, and reduced on-chip memory utilization. AccDNN also includes an automatic design space exploration tool, called A-REALM, used to generate optimized parallelism schemes by considering external memory access bandwidth, data reuse behaviors, resource availability, and network complexity. We demonstrate AccDNN on four DNNs (Alexnet, ZF, VGG16, and YOLO) on two Xilinx FPGAs (ZC706 and KU115) for edge- and cloud-computing, respectively. AccDNN generates designs that deliver 263 GOPS and 36.4 GOPS/W on ZC706 without any batching and 2109 GOPS and 94.5 GOPS/W on KU115.","Title: Vehicular Fog Computing for Video Crowdsourcing: Applications, Feasibility, and Challenges.\nYear: 2018\n\nAbstract: With the growing adoption of dash cameras, we are seeing great potential for innovations by analyzing the video collected from vehicles. On the other hand, transmitting and analyzing a large amount of video, especially high-resolution video in real time, requires a lot of communications and computing resources. In this work, we investigate the feasibility and challenges of applying vehicular fog c...","Title: DNNBuilder: an automated tool for building high-performance DNN hardware accelerators for FPGAs\nYear: 2018\n\nAbstract: Building a high-performance EPGA accelerator for Deep Neural Networks (DNNs) often requires RTL programming, hardware verification, and precise resource allocation, all of which can be time-consuming and challenging to perform even for seasoned FPGA developers. To bridge the gap between fast DNN construction in software (e.g., Caffe, TensorFlow) and slow hardware implementation, we propose DNNBuilder for building high-performance DNN hardware accelerators on FPGAs automatically. Novel techniques are developed to meet the throughput and latency requirements for both cloud- and edge-devices. A number of novel techniques including high-quality RTL neural network components, a fine-grained layer-based pipeline architecture, and a column-based cache scheme are developed to boost throughput, reduce latency, and save FPGA on-chip memory. To address the limited resource challenge, we design an automatic design space exploration tool to generate optimized parallelism guidelines by considering external memory access bandwidth, data reuse behaviors, FPGA resource availability, and DNN complexity. DNNBuilder is demonstrated on four DNNs (Alexnet, ZF, VGG16, and YOLO) on two FPGAs (XC7Z045 and KU115) corresponding to the edge- and cloud-computing, respectively. The fine-grained layer-based pipeline architecture and the column-based cache scheme contribute to 7.7x and 43x reduction of the latency and BRAM utilization compared to conventional designs. We achieve the best performance (up to 5.15x faster) and efficiency (up to 5.88x more efficient) compared to published FPGA-based classification-oriented DNN accelerators for both edge and cloud computing cases. We reach 4218 GOPS for running object detection DNN which is the highest throughput reported to the best of our knowledge. DNNBuilder can provide millisecond-scale real-time performance for processing HD video input and deliver higher efficiency (up to 4.35x) than the GPU-based solutions.","Title: Detecting Multi-Resolution Pedestrians Using Group Cost-Sensitive Boosting with Channel Features.\nYear: 2019\n\nAbstract: Significant progress has been achieved in the past few years for the challenging task of pedestrian detection. Nevertheless, a major bottleneck of existing state-of-the-art approaches lies in a great drop in performance with reducing resolutions of the detected targets. For the boosting-based detectors which are popular in pedestrian detection literature, a possible cause for this drop is that in their boosting training process, low-resolution samples, which are usually more difficult to be detected due to the missing details, are still treated equally importantly as high-resolution samples, resulting in the false negatives since they are more easily rejected in the early stages and can hardly be recovered in the late stages. To address this problem, we propose in this paper a robust multi-resolution detection approach with a novel group cost-sensitive boosting algorithm, which is derived from the standard AdaBoost algorithm to further explore different costs for different resolution groups of the samples in the boosting process, and to place greater emphasis on low-resolution groups in order to better handle the detection of multi-resolution targets. The effectiveness of the proposed approach is evaluated on the Caltech pedestrian benchmark and KAIST (Korea Advanced Institute of Science and Technology) multispectral pedestrian benchmark, and validated by its promising performance on different resolution-specific test sets of both benchmarks.","Title: Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing\nYear: 2019\n\nAbstract: With the emerging vehicular applications, such as real-time situational awareness and cooperative lane change, there exist huge demands for sufficient computing resources at the edge to conduct time-critical and data-intensive tasks. This paper proposes Folo, a novel solution for latency and quality optimized task allocation in vehicular fog computing (VFC). Folo is designed to support the mobilit...","Title: Deep Neural Network-Based Severity Prediction of Bug Reports.\nYear: 2019\n\nAbstract: Software maintenance is an essential phase of software development. Developers employ issue tracking systems to collect bugs for software improvement. Users submit bugs through such issue tracking systems and decide the severity of reported bugs. The severity is an important attribute of a bug that decides how quickly it should be solved. It helps developers to solve important bugs on time. However, manual severity assessment is a tedious job and could be incorrect. To this end, in this paper, we propose a deep neural network-based automatic approach for the severity prediction of bug reports. First, we apply natural language processing techniques for text preprocessing of bug reports. Second, we compute and assign an emotion score for each bug report. Third, we create a vector for each preprocessed bug report. Forth, we pass the constructed vector and the emotion score of each bug report to a deep neural network based classifier for severity prediction. We also evaluate the proposed approach on the history-data of bug reports. The results of cross-product suggest that the proposed approach outperforms the state-of-the-art approaches. On average, it improves the f-measure by 7.90%.","Title: Adaptive Pattern-Parameter Matching For Robust Pedestrian Detection\nYear: 2021\n\nAbstract: Pedestrians with challenging patterns, e.g. small scale or heavy occlusion, appear frequently in practical applications like autonomous driving, which remains tremendous obstacle to higher robustness of detectors. Although plenty of previous works have been dedicated to these problems, properly matching patterns of pedestrian and parameters of detector, i.e., constructing a detector with proper parameter sizes for certain pedestrian patterns of different complexity, has been seldom investigated intensively. Pedestrian instances are usually handled equally with the same amount of parameters, which in our opinion is inadequate for those with more difficult patterns and leads to unsatisfactory performance. Thus, we propose in this paper a novel detection approach via adaptive pattern-parameter matching. The input pedestrian patterns, especially the complex ones, are first disentangled into simpler patterns for detection head by Pattern Disentangling Module (PDM) with various receptive fields. Then, Gating Feature Filtering Module (GFFM) dynamically decides the spatial positions where the patterns are still not simple enough and need further disentanglement by the next-level PDM. Co-operating with these two key components, our approach can adaptively select the best matched parameter size for the input patterns according to their complexity. Moreover, to further explore the relationship between parameter sizes and their performance on the corresponding patterns, two parameter selection policies are designed: 1) extending parameter size to maximum, aiming at more difficult patterns for different occlusion types; 2) specializing parameter size by group division, aiming at complex patterns for scale variations. Extensive experiments on two popular benchmarks, Caltech and CityPersons, show that our proposed method achieves superior performance compared with other state-of-the-art methods on subsets of different scales and occlusion types.","Title: Learning to Restore Hazy Video: A New Real-World Dataset and A New Method\nYear: 2021\n\nAbstract: Most of the existing deep learning-based dehazing methods are trained and evaluated on the image dehazing datasets, where the dehazed images are generated by only exploiting the information from the corresponding hazy ones. On the other hand, video dehazing algorithms, which can acquire more satisfying dehazing results by exploiting the temporal redundancy from neighborhood hazy frames, receive less attention due to the absence of the video dehazing datasets. Therefore, we propose the first REal-world VIdeo DEhazing (REVIDE) dataset which can be used for the supervised learning of the video dehazing algorithms. By utilizing a well-designed video acquisition system, we can capture paired real-world hazy and haze-free videos that are perfectly aligned by recording the same scene (with or without haze) twice. Considering the challenge of exploiting temporal redundancy among the hazy frames, we also develop a Confidence Guided and Improved Deformable Network (CG-IDN) for video dehazing. The experiments demonstrate that the hazy scenes in the REVIDE dataset are more realistic than the synthetic datasets and the proposed algorithm also performs favorably against state-of-the-art dehazing methods.","Title: Applying Edge Computing To Analyse Path Planning Algorithm In College Football Training\nYear: 2021\n\nAbstract: In order to assist college football training, an edge computing-based path planning algorithm for the football robot, and an improved PSO (particle swarm optimization) algorithm are proposed. First, the cartesian coordinate system is transformed into the polar coordinate system, and the coordinates of the robots' path coding are calculated and displayed in the polar coordinate system. Then, the robots' coordinates in the current, the next, and the obstacle point are measured and compared to determine whether path planning is needed. Consequently, the PSO, optimized by the \"concentration selection\" strategy in the artificial immune algorithm, is chosen for path planning. The experiments show that with the improved PSO, five robots of 0.4 m in radius and moving less than 2 m/s can be avoided easily. The static obstacle avoidance experiment shows that obstacles can be avoided more easily, using the path planned through improved PSO. The dynamic obstacle avoidance experiments indicate that the robot can avoid all moving obstacles, blocking in front, intercepting from all sides, and tracking from behind timely. The experiments prove that the proposed path planning is effective, which has reference value for promoting the application of path planning algorithms in college football training.","Title: Automatic Stem-Leaf Segmentation Of Maize Shoots Using Three-Dimensional Point Cloud\nYear: 2021\n\nAbstract: The application of 3D point cloud data in maize research is increasingly extensive. Currently, there are many approaches to acquiring three-dimensional (3D) point clouds of maize plants. However, automatic stem-leaf segmentation of maize shoots from 3D point clouds remains challenging, especially for new emerging leaves that are wrapped very closely together during the seedling stage. To address this issue, we propose an automatic segmentation method consisting of three steps: skeleton extraction, coarse segmentation based on the skeleton, and fine segmentation based on stem-leaf classification. The segmentation method was tested on 75 maize seedlings and compared with the manually obtained ground truth. The mean precision, mean recall, mean micro F1 score, and mean overall accuracy of our segmentation algorithm were 0.944, 0.956, 0.950 and 0.953, respectively. Using the segmentation results, two applications were also developed in this study, namely, phenotypic trait extraction and skeleton optimization. Six phenotypic parameters, namely, plant height, crown diameter, stem height and diameter, leaf width, and length, can be accurately and automatically measured. Furthermore, the values of R2 for the six phenotypic traits were all above 0.92. We also propose a skeleton optimization method that can extract the skeletons of the upper leaves completely and clearly. The results indicate that the proposed algorithm can automatically and precisely segment not only the fully expanded leaves but also the new leaves wrapped closely together. The proposed approach can play an important role in further maize research and applications, such as genotype-to-phenotype study, geometric reconstruction, and dynamic growth animation. We released the source code and test data at the web site https://github.com/syau-miao/seg4 maize.git.","Title: SiamPolar: Semi-supervised realtime video object segmentation with polar representation\nYear: 2022\n\nAbstract: •A novel polar representation is introduced for video object segmentation.•An asymmetric Siamese network is developed using similar backbones with different depths.•We design the repeated cross correlation and semi-FPN based on peeling convolutions.","Title: Geometric and semantic analysis of road image sequences for traffic scene construction\nYear: 2021\n\nAbstract: •A novel and simple geometric analysis network is proposed using heatmaps.•A FCN-based network is developed to perform semantic segmentation.•The scene construction is conducted by combining the geometric and semantic analysis.","Title: Deep Recurrent Neural Network with Multi-Scale Bi-directional Propagation for Video Deblurring.\nYear: 2022\n\nAbstract: The success of the state-of-the-art video deblurring methods stems mainly from implicit or explicit estimation of alignment among the adjacent frames for latent video restoration. However, due to the influence of the blur effect, estimating the alignment information from the blurry adjacent frames is not a trivial task. Inaccurate estimations will interfere the following frame restoration. Instead of estimating alignment information, we propose a simple and effective deep Recurrent Neural Network with Multi-scale Bi-directional Propagation (RNN-MBP) to effectively propagate and gather the information from unaligned neighboring frames for better video deblurring. Specifically, we build a Multi-scale Bi-directional Propagation (MBP) module with two U-Net RNN cells which can directly exploit the inter-frame information from unaligned neighboring hidden states by integrating them in different scales. Moreover, to better evaluate the proposed algorithm and existing state-of-the-art methods on real-world blurry scenes, we also create a Real-World Blurry Video Dataset (RBVD) by a well-designed Digital Video Acquisition System (DVAS) and use it as the training and evaluation dataset. Extensive experimental results demonstrate that the proposed RBVD dataset effectively improve the performance of existing algorithms on real-world blurry videos, and the proposed algorithm performs favorably against the state-of-the-art methods on three typical benchmarks. The code is available at https://github.com/XJTU-CVLAB-LOWLEVEL/RNN-MBP.","Title: Hysteretic Optimality of Container Warming Control in Serverless Computing Systems\nYear: 2021\n\nAbstract: Keeping containers warm for prompt service responses and reducing warm containers for light-weight system management exhibit a fundamental tradeoff in serverless computing systems. In this letter, we investigate the problem of container warming control for serverless computing, and we formulate it as a Markov decision process (MDP). By observing that the value functions corresponding to the MDP are partially submodular, we show that the derived optimal policy is hysteretic and partially non-decreasing. Our numerical results show that the derived optimal policy exhibits a hysteretic structure, which can be realized via switching-up/-down thresholds in practice.","Title: Exploring the role of sports APP in (campus fitness) intelligent solutions using data fusion algorithm and internet of things\nYear: 2022\n\nAbstract: The purposes are to study how the multi-sensor Internet of Things (IoT) data fusion algorithm calculates in data fusion systems and improves the systems' fusion efficiency. An improved Weighted Least-Squares (WLS) algorithm is proposed for IoT data fusion, and how it processes massive data in a multi-sensor system is studied. Accordingly, a multi-sensor system for sports fitness is designed based on a data fusion algorithm. First, the purposes and demand for sports Application (APP) are analysed, to understand the problems and necessary improvements of such APP. Then, the implementation of the IoT and the classification of the data fusion algorithm of the IoT are explored. The WLS method is selected through comparison, and its implementation process and the data processing process are analysed and explained. Finally, the sports fitness system based on the IoT data fusion algorithm is designed and analysed. The results show that the wireless communication of a multi-sensor data fusion system is feasible and reliable. The variances calculated through the WLS method and the Arithmetic Mean (AM) method in data fusion are compared. The former value is about one-thousandth of the latter value, indicating that the data fusion based on WLS is advantageous over the traditional data fusion.","Title: Multi-Objective Path-Decision Model of Multimodal Transport Considering Uncertain Conditions and Carbon Emission Policies\nYear: 2022\n\nAbstract: To develop low-carbon transport and promote sustainable economic development, this paper took the uncertainty in highway transport speed and transshipment time into account in the actual transport process and established multi-objective path-decision models of multimodal transport under different carbon policies. The expectation values of nonlinear uncertainties were estimated by Law of Large Numbers (LLN), and the models were solved by the K-shortest paths algorithm and non-dominated sorting algorithm (NSGA-II), whose advancement and effectiveness were verified through the comparison of SPEA2. Based on the Pareto theory, the optimally symmetrical compromise between the objectives and the influence of the transport speed uncertainty and carbon emission policies on path decisions were quantified and discussed. Taking the multimodal transport network of West Africa as the experimental background, the practicability of the path-decision results is analyzed, and a trade-off analysis is also conducted to provide the theoretical foundation for future freight transport planning.","Title: Age-Efficient Concurrent Information Update Scheduling in Edge-Native Systems\nYear: 2022\n\nAbstract: Age of information (AoI) has lately evolved as a promising performance metric to quantify the timeliness of information updates in communication systems. In this letter, we aim to address the concurrent information update scheduling problem in edge-native (EN) systems, where Internet-of-Things (IoT) devices provide information updates for the processing of EN tasks on edge servers. To this end, we...","Title: Software-Defined Maritime Fog Computing: Architecture, Advantages, and Feasibility\nYear: 2022\n\nAbstract: Advances in sensing and communication technologies have enabled emerging applications in ocean research. The stringent resource limitations, however, hinder local task processing on maritime devices. Cloud-based task offloading is also not feasible due to the unaffordable costs induced by satellite links. In this work, we propose a visionary architecture on software-defined maritime fog computing (SD-MFC), which adopts fog computing to provide localized services and uses software-defined networking technology to provide the abstraction of underlying resources. In SD-MFC, coastal infrastructures and large-size vessels are turned into maritime fog nodes to process the tasks generated by surrounding maritime devices. By exploration of a real-world vessel trajectory dataset, the feasibility and effectiveness of the proposed SD-MFC are verified.","Title: Occluded Pedestrian Detection via Distribution-Based Mutual-Supervised Feature Learning\nYear: 2022\n\nAbstract: Pedestrian detection is a very important task in intelligent transportation system. State-of-the-art detectors work well on non-occluded pedestrians, but they are still far from satisfactory for heavily occluded ones. Recently, to deal with occlusion problems, the popular two-stage approaches are to build a two-branch architecture with the help of additional visible body annotations. However, these methods still have disadvantages. Either the two branches only use score-level fusion, which cannot guarantee the detectors to learn more robust pedestrian features. Or they only focus on the features of visible part via the attention mechanisms. However, the visible body features of heavily occluded pedestrians are only concentrated in a relatively small area, which may easily lead to missed detections. To alleviate the above issues, we propose a novel Distribution-based Mutual-Supervised Feature Learning Network (DMSFLN), to better deal with occluded pedestrian detection. The key DMSFL module in our network is to learn more discriminative feature representations of pedestrians by minimizing the similarity loss between feature distributions of full body and visible body, which has two advantages: enhancing the feature representations of occluded pedestrians and reducing the intra-class variance in pedestrians. To facilitate the DMSFL module, we also propose a novel two-branch network architecture, which is trained in a mutual-supervised way with both full body and visible body annotations respectively. Extensive experiments are conducted on four challenging pedestrian datasets: Caltech, CityPersons, CrowdHuman and CUHK occlusion. Our approach achieves superior performance compared to other state-of-the-art methods, especially on heavy occlusion subsets.","Title: CAliC: Accurate and Efficient Image-Text Retrieval via Contrastive Alignment and Visual Contexts Modeling\nYear: 2022\n\nAbstract: ABSTRACTImage-text retrieval is an essential task of information retrieval, in which the models with the Vision-and-Language Pretraining(VLP) are able to achieve ideal accuracy compared with the ones without VLP. Among different VLP approaches, the single-stream models achieve the overall best retrieval accuracy, but slower inference speed. Recently, researchers have introduced the two-stage retrieval setting commonly used in the information retrieval field to the single-stream VLP model for a better accuracy/efficiency trade-off. However, the retrieval accuracy and efficiency are still unsatisfactory mainly due to the limitations of the patch-based visual unimodal encoder in these VLP models. The unimodal encoders are trained on pure visual data, so the visual features extracted by them are difficult to align with the textual features and it is also difficult for the multi-modal encoder to understand visual information. Under these circumstances, we propose an accurate and efficient two-stage image-text retrieval model via Contrastive Alignment and visual Contexts modeling(CAliC). In the first stage of the proposed model, the visual unimodal encoder is pretrained with cross-modal contrastive learning to extract easily aligned visual features, which improves the retrieval accuracy and the inference speed. In the second stage of the proposed model, we introduce a new visual contexts modeling task during pretraining to help the multi-modal encoder better understand the visual information and get more accurate predictions. Extensive experimental evaluation validates the effectiveness of our proposed approach, which achieves a higher retrieval accuracy while keeping a faster inference speed, and outperforms existing state-of-the-art retrieval methods on image-text retrieval tasks over Flickr30K and COCO benchmarks."],"string":"[\n \"Title: Texture Classification via Local Feature Representation of Multi-order Gradients.\\nYear: 2013\\n\\nAbstract: This paper presents a novel method to texture classification using local feature representation of multiple order gradients. Different from the state of the art approaches in literature that make use of the widely-used first order gradient based local descriptors, e.g. LBP, HOG, DAISY, SIFT, etc., we claim that the second order gradient based ones also provide critical contribution to classification performance, and thus propose to use Histogram of Second Order Gradients (HSOG) to describe micro-texton patterns. Both the similarity measurements of first and second order gradients computed by Bag-of-Feature modeling and SVM classifier are combined for decision making. Experimental results achieved on the Outex TC dataset not only illustrate that the second order gradient based HSOG is effective to classify texture images, but also highlight that multiple order gradient based description by fusing complementary clues of the first and second order gradients is a promising solution to improve the accuracy in texture classification. © Springer International Publishing Switzerland 2013.\",\n \"Title: Building a virtual factory\\nYear: 2004\\n\\nAbstract: In this paper, we describe a visualisation system which helps simulation experts transform discrete simulation models and results into animated scenes in a virtual environment. The system aims to significantly reduce production costs and error sources during the generation process of visualisations. The means to achieve these goals are the development of a framework for the translation of simulation results into animations, reuse of animation elements, and the implementation of customization tools for animation elements and their behaviour. By utilizing advanced visualisation techniques, the system is also capable of directing the focus of the observer to interesting events, objects and timeframes.\",\n \"Title: Cage-based tree deformation\\nYear: 2011\\n\\nAbstract: Tree models are broadly used in multimedia applications, but it is a challenge task to deform trees with plenty of branches. We propose a novel method to deform the tree model interactively using cages based on trees' property, and we design a framework to control the hierarchy deformation of trees. The bounding box of the trunk is used as the global control cage, while the bounding boxes of branches are set as local cages. The linear combination of mean value coordinates computed from local cages and the global cage decides the vertices deformed positions of the deformed tree. This framework is straightforward and effective, which could also preserve local details of the tree. Experimental results show that our technique is suitable for deforming trees with many branches.\",\n \"Title: Perturbation analysis for the reduced minimum modulus of bounded linear operator in Banach spaces\\nYear: 2003\\n\\nAbstract: Let X and Y be two Banach spaces over the complex field C and let T:X->Y be a bounded linear operator with the generalized inverse T^+. Let [email protected] be a bounded linear operator with @__ __T^[email protected]__ [email protected]__ [email protected]@__ __<1/2. Let @c(T) denote the reduced minimum modulus of T. We first establish the upper semi-continuity theorem for @c(T). Furthermore, if dimKerT=dimKerT\",\n \"Title: HSOG: a novel local descriptor based on histograms of second order gradients for object categorization\\nYear: 2013\\n\\nAbstract: This paper presents a novel local image descriptor for object categorization that extracts the Histograms of the Second Order Gradients and is thereby named as HSOG. The HSOG descriptor is in contrast to the widely used ones in the literature, e.g. SIFT, DAISY, HOG, LBP, etc., which are based on the first order gradient information. The contributions of this work can be summarized as: (1) the design of HSOG; (2) the prove of its discriminative power and its complementation to the first order gradient based descriptors; (3) the analysis of performance variation caused by different parameter settings; and (4) the multi-scale extension which further improves the categorization accuracy. The experimental results achieved on the Caltech 101 and Caltech 256 databases clearly highlight the effectiveness of the proposed approach.\",\n \"Title: An experimental study on four models of customer churn prediction\\nYear: 2009\\n\\nAbstract: Decision tree, neural network and logistic regression were applied frequently as models of customer churn prediction, but the application of them has been mature and they are difficult to be improved. In this paper, Bayesian Networks, Support Vector Machines, Rough Sets and Survival Analysis were selected for experimental comparison study. An integrated contrast among the four models from the applicability of model in theory and experimental comparison has been processed. Overall, of the four models the Bayesian network model performed best while the Survival analysis did worst.\",\n \"Title: Multimodal recognition of visual concepts using histograms of textual concepts and selective weighted late fusion scheme\\nYear: 2013\\n\\nAbstract: The text associated with images provides valuable semantic meanings about image content that can hardly be described by low-level visual features. In this paper, we propose a novel multimodal approach to automatically predict the visual concepts of images through an effective fusion of textual features along with visual ones. In contrast to the classical Bag-of-Words approach which simply relies on term frequencies, we propose a novel textual descriptor, namely the Histogram of Textual Concepts (HTC), which accounts for the relatedness of semantic concepts in accumulating the contributions of words from the image caption toward a dictionary. In addition to the popular SIFT-like features, we also evaluate a set of mid-level visual features, aiming at characterizing the harmony, dynamism and aesthetic quality of visual content, in relationship with affective concepts. Finally, a novel selective weighted late fusion (SWLF) scheme is proposed to automatically select and weight the scores from the best features according to the concept to be classified. This scheme proves particularly useful for the image annotation task with a multi-label scenario. Extensive experiments were carried out on the MIR FLICKR image collection within the ImageCLEF 2011 photo annotation challenge. Our best model, which is a late fusion of textual and visual features, achieved a MiAP (Mean interpolated Average Precision) of 43.69% and ranked 2nd out of 79 runs. We also provide comprehensive analysis of the experimental results and give some insights for future improvements.\",\n \"Title: Graph-based image segmentation using weighted color patch\\nYear: 2013\\n\\nAbstract: Constructing a discriminative affinity graph plays an essential role in graph-based image segmentation, and feature directly influences the discriminative power of the affinity graph. In this paper, we propose a new method based on the weighted color patch to compute the weight of edges in an affinity graph. The proposed method intends to incorporate both color and neighborhood information by representing pixels with color patches. Furthermore, we assign both local and global weights adaptively for each pixel in a patch in order to alleviate the over-smooth effect of using patches. The normalized cut (NCut) algorithm is then applied on the resulting affinity graph to find partitions. We evaluate the proposed method on the Prague color texture image benchmark and the Berkeley image segmentation database. The extensive experiments show that our method is competitive compared to the other standard methods with multiple evaluation metrics.\",\n \"Title: Image region description using orthogonal combination of local binary patterns enhanced with color information\\nYear: 2013\\n\\nAbstract: Visual content description is a key issue for machine-based image analysis and understanding. A good visual descriptor should be both discriminative and computationally efficient while possessing some properties of robustness to viewpoint changes and lighting condition variations. In this paper, we propose a new operator called the orthogonal combination of local binary patterns (denoted as OC-LBP) and six new local descriptors based on OC-LBP enhanced with color information for image region description. The aim is to increase both discriminative power and photometric invariance properties of the original LBP operator while keeping its computational efficiency. The experiments in three different applications show that the proposed descriptors outperform the popular SIFT, CS-LBP, HOG and SURF, and achieve comparable or even better performances than the state-of-the-art color SIFT descriptors. Meanwhile, the proposed descriptors provide complementary information to color SIFT, because a fusion of these two kinds of descriptors is found to perform clearly better than either of the two separately. Moreover, the proposed descriptors are about four times faster to compute than color SIFT.\",\n \"Title: Optimal control of the risk process in a regime-switching environment\\nYear: 2011\\n\\nAbstract: This paper is concerned with cost optimization of an insurance company. The surplus of the insurance company is modeled by a controlled regime-switching diffusion, in which the regime-switching mechanism provides the fluctuations of the random environment. The goal is to find an optimal control that minimizes the total cost up to a stochastic exit time. A weaker sufficient condition than that of Fleming and Soner (2006, Section V.2) for the continuity of the value function is obtained. Further, the value function is shown to be a viscosity solution of a Hamilton-Jacobi-Bellman equation.\",\n \"Title: High efficiency micro-bubble generator using carbon nanocoils as heater\\nYear: 2010\\n\\nAbstract: This paper reports fabrication and properties of microbubble generator using carbon nanocoils as heater. The carbon nanocoils were dielectrophorestically bridged between microelectrodes in the microstructures. Behavior of the bubble generation was studied in an open DI water circumstance. Compared with traditional metal and polysilicon based microbubble generator, its power consumption was generally 2 orders of magnitude lower, e.g., <; 70μW for ~300μm diameter bubble, indicating high electro-thermal efficiency than ever before. Mechanism underneath the ultra low power consumption is probably due to the nano helical structure of the heating element which leads to the greatly decreased heat loss and will be further investigated.\",\n \"Title: Numerical solutions of optimal risk control and dividend optimization policies under a generalized singular control formulation\\nYear: 2012\\n\\nAbstract: This paper develops numerical methods for finding optimal dividend pay-out and reinsurance policies. A generalized singular control formulation of surplus and discounted payoff function is introduced, where the surplus is modeled by a regime-switching process subject to both regular and singular controls. To approximate the value function and optimal controls, Markov chain approximation techniques are used to construct a discrete-time controlled Markov chain. The proofs of the convergence of the approximation sequence to the surplus process and the value function are given. Examples of proportional and excess-of-loss reinsurance are presented to illustrate the applicability of numerical methods.\",\n \"Title: Cultural heritage as digital experience: a Singaporean perspective\\nYear: 2007\\n\\nAbstract: Interactive 3D computer graphics technology is now extremely popular, seen in the increasing interest in and use of 3D digitization of cultural heritage contents. This paper introduces a digital interactive cultural heritage system which embeds Virtual-Reality (VR) technology within the Peranakan culture, the Peranakan culture being a Singaporean unique ethnic culture. A prototype has undergone the full developmental process of being implemented, tested, and evaluated. This paper will also discuss the results of the usability test.\",\n \"Title: Multi-scale Color Local Binary Patterns for Visual Object Classes Recognition\\nYear: 2010\\n\\nAbstract: The Local Binary Pattern (LBP) operator is a computationally efficient yet powerful feature for analyzing local texture structures. While the LBP operator has been successfully applied to tasks as diverse as texture classification, texture segmentation, face recognition and facial expression recognition, etc., it has been rarely used in the domain of Visual Object Classes (VOC) recognition mainly due to its deficiency of power for dealing with various changes in lighting and viewing conditions in real-world scenes. In this paper, we propose six novel multi-scale color LBP operators in order to increase photometric invariance property and discriminative power of the original LBP operator. The experimental results on the PASCAL VOC 2007 image benchmark show significant accuracy improvement by the proposed operators as compared with both the original LBP and other popular texture descriptors such as Gabor filter.\",\n \"Title: On Optimal Harvesting Problems in Random Environments\\nYear: 2011\\n\\nAbstract: This paper investigates the optimal harvesting strategy for a single species living in random environments whose population growth is given by a regime-switching diffusion. Harvesting acts as a (stochastic) control on the size of the population. The objective is to find a harvesting strategy which maximizes the expected total discounted income from harvesting up to the time of extinction of the species; the income rate is allowed to be state- and environment-dependent. This is a singular stochastic control problem, with both the extinction time and the optimal harvesting policy depending on the initial condition. One aspect of receiving payments up to the random time of extinction is that small changes in the initial population size may significantly alter the extinction time when using the same harvesting policy. Consequently, one no longer obtains continuity of the value function using standard arguments for either regular or singular control problems having a fixed time horizon. This paper introduces a new sufficient condition under which the continuity of the value function for the regime-switching model is established. Further, it is shown that the value function is a viscosity solution of a coupled system of quasi-variational inequalities. The paper also establishes a verification theorem and, based on this theorem, an $\\\\varepsilon$-optimal harvesting strategy is constructed under certain conditions on the model. Two examples are analyzed in detail.\",\n \"Title: Co-match: fast and efficient packet inspection for multiple flows\\nYear: 2009\\n\\nAbstract: Packet inspection is widely employed in application-layer protocol analyzing systems to enable accurate protocol identification. Many existing systems, however, fail to meet the requirement of keeping up with wire speed in networking. There are two limitations: (1) software-based matching schemes are usually in a sequential manner which is slow and inefficient; (2) fast hardware-based matching schemes are inapplicable to network packet processing for lacking of intrinsic support for multiple flows. This paper proposes a novel approach for application-layer protocol identification called Co-Match, which combines software and hardware together to achieve fast and efficient signature matching for multiple flows. First, a grouping scheme is adopted to organize signatures into several matching sets. With this scheme, each packet is only matched against a subset of signatures, bringing about a remarkable improvement of matching speed in software. Second, an FPGA-based coprocessor is developed in order to support fast parallel regular expression matching for multiple flows in hardware. Moreover, a hardware-based flow-level traffic load balancer is employed to parallel multi-flow processing on multiple CPU cores. Experimental results show that our approach is efficient to handle multiple flows while system throughput can achieve the wire speed of Gigabit Ethernet links with moderate CPU usage.\",\n \"Title: Optimal dividend payment problems in piecewise-deterministic compound Poisson risk models\\nYear: 2012\\n\\nAbstract: This work deals with an optimal dividend payment problem for a piecewise-deterministic compound Poisson insurance risk model. The objective is to maximize the expected discounted dividend payout up to the time of ruin. When the dividend payment rate is restricted, the value function is shown to be a solution of the corresponding Hamilton-Jacobi-Bellman equation, which in turn leads to a tractable methodology to find an optimal threshold dividend payment policy. For the case of unrestricted payment rate, the value function and an optimal barrier strategy are determined explicitly with exponential claim size distributions. A comparison of two examples is provided to illustrate the main results.\",\n \"Title: Encoding local binary descriptors by bag-of-features with hamming distance for visual object categorization\\nYear: 2013\\n\\nAbstract: This paper presents a novel method for encoding local binary descriptors for Visual Object Categorization (VOC). Nowadays, local binary descriptors, e.g. LBP and BRIEF, have become very popular in image matching tasks because of their fast computation and matching using binary bitstrings. However, the bottleneck of applying them in the domain of VOC lies in the high dimensional histograms produced by encoding these binary bitstrings into decimal codes. To solve this problem, we propose to encode local binary bitstrings directly by the Bag-of-Features (BoF) model with Hamming distance. The advantages of this approach are two-fold: (1) It solves the high dimensionality issue of the traditional binary bitstring encoding methods, making local binary descriptors more feasible for the task of VOC, especially when more bits are considered; (2) It is computationally efficient because the Hamming distance, which is very suitable for comparing bitstrings, is based on bitwise XOR operations that can be fast computed on modern CPUs. The proposed method is validated by applying on LBP feature for the purpose of VOC. The experimental results on the PASCAL VOC 2007 benchmark show that our approach effectively improves the recognition accuracy compared to the traditional LBP feature.\",\n \"Title: Efficient processing of monotonic linear progressive queries via dynamic materialized views\\nYear: 2010\\n\\nAbstract: There is an increasing demand to process emerging types of queries, such as progressive queries (PQs), from numerous contemporary database applications including telematics, ecommerce, business intelligence, and decision support. Unlike a conventional query, a progressive query is formulated in several steps, i.e., consisting of a set of inter-related step-queries (SQ). A user formulates their SQs on the fly based on the results returned by the previous SQs. Processing such queries provides performance improvement opportunities for a database management system. In this paper, we study the efficient processing of a special type of PQ, called a monotonic linear progressive query (MLPQ). We present a technique to process such PQs based on dynamically materialized views. The key idea is to create a superior-relationship graph for step-queries from historical PQs, which can be used to estimate the benefit of materializing a current step-query. The materialized views are then used to improve the performance of future step-queries. Algorithms and strategies to create and maintain a superior-relationship graph, dynamically select materialized views (step-queries), and the search for a materialized view to process a given step-query are discussed. Experimental results demonstrate that our proposed technique is quite promising in efficiently processing this type of progressive query.\",\n \"Title: Pixel to Patch Sampling Structure and Local Neighboring Intensity Relationship Patterns for Texture Classification\\nYear: 2013\\n\\nAbstract: In this letter, we explore local image descriptors for texture classification. We mainly propose two novel contributions: an effective sampling structure based on Pixel To Patch (PTP) to mimic the retinal sampling pattern; and a novel Local Neighboring Intensity Relationship Pattern (LNIRP) descriptor to extract texture feature by exploring neighboring gray-scale properties. The LNIRP descriptor is extended by using the PTP sampling structure which aims to capture not only micro-patterns but also macro-patterns, while reducing feature dimensionality and improving computational efficiency. The proposed descriptor has advantages of computational simplicity, no texton dictionary learning step and training-free. Moreover, the LNIRP descriptor is complementary to the Local Binary Pattern (LBP) descriptor. Extensive experiments were conducted on Outex database to evaluate the proposed descriptor and sampling structure. The proposed descriptor can achieve superior classification performance compared to most of the state-of-the-art methods, including what we believe to be the best results reported for Outex, while offering a smallest feature dimension.\",\n \"Title: Optimal Switching with Constraints and Utility Maximization of an Indivisible Market\\nYear: 2012\\n\\nAbstract: This work focuses on optimal switching with constraints. Our motivation stems from utility maximization of an indivisible market. The dynamic programming approach is used; the value function is characterized as the unique viscosity solution of a quasi-variational inequality. The unbounded domain introduces new challenges. By studying the sample paths of the diffusion at the boundary, a sufficient condition for the continuity of the value function is provided, yielding the desired characterization. Not only are the results of this work applicable to the utility maximization problem, but also they can be used for general optimal switching problems with finite regimes.\",\n \"Title: A materialized-view based technique to optimize progressive queries via dependency analysis\\nYear: 2011\\n\\nAbstract: Progressive queries (PQ) are a new type of query emerging from numerous contemporary database applications, including e-commerce, social network, business intelligence, and decision support. Such a PQ is formulated in several steps via a set of inter-related step-queries (SQ). How to optimize such PQs represents a new challenge in the development of a database management system. In our previous work, we introduced a materialized-view based technique to process a special type of PQ, called monotonic linear PQs. In this paper, we present a new materialized-view based technique to efficiently process generic PQs. This technique allows an SQ in a given PQ to utilize the results of previous SQs not only from the same PQ but also from other in-process and completed PQs. Due to the storage constraint, it is impossible to retain the results of all the SQs of a completed PQ. Hence, a crucial issue is how to select popular SQs from completed PQs to keep their results as materialized views for optimizing future PQs. To tackle this issue, we introduce a multiple query dependency graph (MQDG) to capture the data source dependency relationships among SQs from multiple PQs. We then present a model to estimate the benefit of an SQ in the MQDG and discuss a procedure to choose critical SQs in the MQDG for materializing their results. The strategies for constructing the MQDG and maintaining the set of materialized views are also suggested. Experimental results demonstrate that our technique is quite promising in efficiently processing PQs.\",\n \"Title: Visual object recognition using DAISY descriptor\\nYear: 2011\\n\\nAbstract: Visual content description is a key issue for the task of machine-based visual object categorization (VOC). A good visual descriptor should be both discriminative enough and computationally efficient while possessing some properties of robustness to viewpoint changes and lighting condition variations. The recent literature has featured local image descriptors, e.g. SIFT, as the main trend in VOC. However, it is well known that SIFT is computationally expensive, especially when the number of objects/concepts and learning data increase significantly. In this paper, we investigate the DAISY, which is a new fast local descriptor introduced for wide baseline matching problem, in the context of VOC. We carefully evaluate and compare the DAISY descriptor with SIFT both in terms of recognition accuracy and computation complexity on two standard image benchmarks - Caltech 101 and PASCAL VOC 2007. The experimental results show that DAISY outperforms the state-of-the-art SIFT while using shorter descriptor length and operating 3 times faster. When displaying a similar recognition accuracy to SIFT, DAISY can operate 12 times faster.\",\n \"Title: Visual Object Categorization via Sparse Representation\\nYear: 2009\\n\\nAbstract: In this paper, we consider the problem of classifying a real world image to the corresponding object class based on its visual content via sparse representation, which is originally used as a powerful tool for acquiring, representing and compressing high-dimensional signals. Assuming the intuitive hypothesis that an image could be represented by a linear combination of the training images from the same class, we propose a novel approach for visual object categorization in which a sparse representation of the image is first of all obtained by solving a L1 (or L0)-minimization problem and then fed into a traditional classifier such as Support Vector Machine (SVM) to finally perform the specified task. Experimental results obtained on the SIMPLIcity database have shown that this new approach can improve the classification performance compared to standard SVM using directly features extracted from the image.\",\n \"Title: An Embedded Load Balancing System for High Speed OC192 Networks\\nYear: 2009\\n\\nAbstract: Parallel architecture has been used for packet processing of high speed links. Essential to such architecture is a load balancer which responsible for packet dispatching. In this paper, we design an embedded system for high speed OC192 network traffic load balancing. In the system, incoming traffic is load-balanced to 12 processing engines through Ethernet. In order to reasonably dispatch traffic while preserving the order of packets within a flow, we propose a novel feedback-based load balancing algorithm. For each packet, we calculate the hash value on source and destination IP addresses. The return value is used as an index to a lookup table to retrieve a target processing engine. The lookup table is adjusted according to feedback. Effectiveness of the algorithm is evaluated by simulations using real network trace. We implement the system in field programmable gate array (FPGA) and verify it by experiments. Experimental results show that the system sustains 10 Gbps throughputs with an average delay of 4.2 microseconds. The system has been used in a practical network monitoring application.\",\n \"Title: Fan-Shaped Patch Local Binary Patterns For Texture Classification\\nYear: 2013\\n\\nAbstract: In this paper, we present a new distinctive feature for texture classification, the fan-shaped patch local binary patterns (FP-LBP). The proposed FP-LBP operator extends the traditional LBP operator by encoding the difference between each central pixel with the average value of its neighboring fan-shaped patches, instead of only using its neighboring pixels. By this way, FP-LBP not only preserves more information of local structures than the traditional LBP, but also keeps relatively lower dimensionality, especially when larger radius and more neighboring pixels are considered. Moreover, the \\\"uniform\\\" and rotation invariant FP-LBP are also defined similarly to the traditional LBP. The proposed descriptors are evaluated on two popular texture databases: CUReT and KTH-TIPS, and the experimental results show that FP-LBP outperforms the traditional LBP descriptor with a smaller feature dimension. Moreover, the proposed method achieves higher classification accuracy than most of the state-of-the-art methods on both databases.\",\n \"Title: Optimization of Monotonic Linear Progressive Queries Based on Dynamic Materialized Views.\\nYear: 2014\\n\\nAbstract: There is an increasing demand to efficiently process emerging types of queries, such as progressive queries (PQs), from contemporary database applications including telematics, e-commerce and social media. Unlike conventional queries, a PQ consists of a set of step-queries (SQ). A user formulates a new SQ on the fly based on the result(s) from the previous SQ(s). Existing database management syste...\",\n \"Title: DMVI: a dynamic materialized view index for efficiently discovering usable views for progressive queries\\nYear: 2012\\n\\nAbstract: Progressive queries (PQ) are a new type of query emerged from numerous data intensive applications. A user formulates a PQ in several steps using a set of inter-related step-queries (SQ). Efficiently processing PQs in a DBMS is crucial in supporting these applications. In our previous work, we introduced a materialized view based approach to efficiently processing PQs, where our focus was on selection of promising materialized views. The problem of how to efficiently find usable views to answer SQs for a PQ remained open. In this paper, we present a new index technique, which is called the dynamic materialized view index (DMVI), to rapidly discover usable views to answer a given SQ. The structure of the index and the strategies to construct, maintain and use the DMVI are discussed. The Experimental results demonstrate that our technique is quite promising in improving the performance of the materialized view based query processing for PQs.\",\n \"Title: Reconstruction of Tree Crown Shape from Scanned Data\\nYear: 2008\\n\\nAbstract: Reconstruction of a real tree from scattered scanned points is a new challenge in virtual reality. Although many progresses are made on main branch structures and overall shape of a tree, reconstructions are still not satisfactory in terms of silhouette and details. We do think that 3D reconstruction of the tree crown shapes may help to constrain accurate reconstruction of complete real tree geometry. We propose here a novel approach for tree crown reconstruction based on an improvement of alpha shape modeling, where the data are points unevenly distributed in a volume rather than on a surface only. The result is an extracted silhouette mesh model, a concave closure of the input data. We suggest an appropriate scope of proper alpha values, so that the reconstruction of the silhouette mesh is a valid manifold surface. Experimental results show that our technique works well in extracting the crown shapes of real trees.\",\n \"Title: Harvesting in Stochastic Environments: Optimal Policies in a Relaxed Model.\\nYear: 2011\\n\\nAbstract: This paper examines the objective of optimally harvesting a single species in a stochastic environment. This problem has previously been analyzed in Alvarez (2000) using dynamic programming techniques and, due to the natural payoff structure of the price rate function (the price decreases as the population increases), no optimal harvesting policy exists. This paper establishes a relaxed formulation of the harvesting model in such a manner that existence of an optimal relaxed harvesting policy can not only be proven but also identified. The analysis embeds the harvesting problem in an infinite-dimensional linear program over a space of occupation measures in which the initial position enters as a parameter and then analyzes an auxiliary problem having fewer constraints. In this manner upper bounds are determined for the optimal value (with the given initial position); these bounds depend on the relation of the initial population size to a specific target size. The more interesting case occurs when the initial population exceeds this target size; a new argument is required to obtain a sharp upper bound. Though the initial population size only enters as a parameter, the value is determined in a closed-form functional expression of this parameter.\",\n \"Title: A Selective Weighted Late Fusion for Visual Concept Recognition.\\nYear: 2012\\n\\nAbstract: We propose in this paper a novel multimodal approach to automatically predict the visual concepts of images through an effective fusion of visual and textual features. It relies on a Selective Weighted Late Fusion (SWLF) scheme which, in optimizing an overall Mean interpolated Average Precision (MiAP), learns to automatically select and weight the best experts for each visual concept to be recognized. Experiments were conducted on the MIR Flickr image collection within the ImageCLEF 2011 Photo Annotation challenge. The results have brought to the fore the effectiveness of SWLF as it achieved a MiAP of 43.69 % for the detection of the 99 visual concepts which ranked 2nd out of the 79 submitted runs, while our new variant of SWLF allows to reach a MiAP of 43.93 %.\",\n \"Title: LIRIS-Imagine at ImageCLEF 2012 Photo Annotation Task.\\nYear: 2012\\n\\nAbstract: In this paper, we present the methods we have proposed and evaluated through the ImageCLEF 2012 Photo Annotation task. More precisely, we have proposed the Histogram of Textual Concepts (HTC) textual feature to capture the relatedness of semantic concepts. In con- trast to term frequency-based text representations mostly used for visual concept detection and annotation, HTC relies on the semantic similar- ity between the user tags and a concept dictionary. Moreover, a Selective Weighted Late Fusion (SWLF) is introduced to combine multiple sources of information which by iteratively selecting and weighting the best fea- tures for each concept at hand to be classied. The results have shown\",\n \"Title: Analysis on electro-thermal property of micro-bubble generator using carbon nanotube heating elements\\nYear: 2009\\n\\nAbstract: Localized high temperature due to Joule heating in Carbon Nanotubes (CNTs) can generate micron-sized bubbles. Electro-thermal property of CNTs-based microbubble generator is characterized by finite element analysis in this paper. Water circumstance with volume in micrometer scale covering solid elements of the microdevice was considered. Temperature distribution and power input corresponding to microbubble nucleation at the centre of the heating elements were evaluated. Free water convection was used to set thermal boundary condition at the liquid-solid interface before micobubble generation. The steady-state, time-transient and parametric simulations have been performed. The results of relationship between power consumption, material properties such as thermal conductivity, and device configuration ( such as the number of CNTs, electrode thickness, electrode gap, etc.) could be a guidance to the design and fabrication of this kind of microbubble generator.\",\n \"Title: An experimental study of consumption behaviors in telecom social networks\\nYear: 2010\\n\\nAbstract: Customers' social ties have played a crucial role in influencing their consumption behaviors. Generally, users can influence others' consumption behaviors via word-of-mouth. But in some special industry such as telecom, some kinds of indirect influences also exist between users and some applications such as social recommendation have been researched on the basis of these influences. In order to explore such influences, this paper selects some attributes to outline the users' consumption behaviors and explores the relationship of the social ties and the behaviors via some analysis experiments. The results show that (1) the social tie attributes have strong correlations with users' consumption behaviors similarity; (2) as the strength of the telecom social ties improve, the similarity value of the users' value-added services using behaviors get greater but (3) the communication behaviors don't have a strong correlation with the strength of telecom social ties.\",\n \"Title: Regularity and Recurrence of Switching Diffusions\\nYear: 2007\\n\\nAbstract: This work is concerned with switching diffusion processes, also known as regime-switching diffusions. Our attention focuses\\n on regularity, recurrence, and positive recurrence of the underlying stochastic processes. The main effort is devoted to obtaining\\n easily verifiable conditions for the aforementioned properties. Continuous-state-dependent jump processes are considered.\\n First general criteria on regularity and recurrence using Liapunov functions are obtained. Then we focus on a class of problems,\\n in which both the drift and the diffusion coefficients are “linearizable” with respect to the continuous state, and suppose\\n that the generator of the jump part of the process can be approximated by a generator of an ergodic Markov chain. Sufficient\\n conditions for regularity, recurrence, and positive recurrence are derived, which are linear combination of the averaged coefficients\\n (averaged with respect to the stationary measure of the Markov chain).\",\n \"Title: HSOG: A Novel Local Image Descriptor Based on Histograms of the Second-Order Gradients.\\nYear: 2014\\n\\nAbstract: Recent investigations on human vision discover that the retinal image is a landscape or a geometric surface, consisting of features such as ridges and summits. However, most of existing popular local image descriptors in the literature, e. g., scale invariant feature transform (SIFT), histogram of oriented gradient (HOG), DAISY, local binary Patterns (LBP), and gradient location and orientation histogram, only employ the first-order gradient information related to the slope and the elasticity, i.e., length, area, and so on of a surface, and thereby partially characterize the geometric properties of a landscape. In this paper, we introduce a novel and powerful local image descriptor that extracts the histograms of second-order gradients (HSOGs) to capture the curvature related geometric properties of the neural landscape, i.e., cliffs, ridges, summits, valleys, basins, and so on. We conduct comprehensive experiments on three different applications, including the problem of local image matching, visual object categorization, and scene classification. The experimental results clearly evidence the discriminative power of HSOG as compared with its first-order gradient-based counterparts, e. g., SIFT, HOG, DAISY, and center-symmetric LBP, and the complementarity in terms of image representation, demonstrating the effectiveness of the proposed local descriptor.\",\n \"Title: A Boosted Multi-Task Model for Pedestrian Detection with Occlusion Handling.\\nYear: 2015\\n\\nAbstract: Pedestrian detection is a challenging problem in computer vision, and has achieved impressive progress in recent years. However, the current state-of-the-art methods suffer from significant performance decline with increasing occlusion level of pedestrians. A common approach for occlusion handling is to train a set of occlusion-specific detectors and merge their results directly, but these detecto...\",\n \"Title: On hybrid diffusions\\nYear: 2008\\n\\nAbstract: For the pressing needs in control and optimization using hybrid systems, this work focuses on weak stochastic stability and ergodicity of regime-switching diffusions. Using Liapunov functions, we derive necessary and sufficient conditions for weak stability. Then, ergodicity of weakly stable regime-switching diffusions is obtained by constructing cycles using the associated discrete-time Markov chains.\",\n \"Title: A Measure Approach for Continuous Inventory Models: Discounted Cost Criterion.\\nYear: 2015\\n\\nAbstract: This paper develops a new approach to the solution of impulse control problems for continuous inventory models under a discounted cost criterion. The analysis imbeds this stochastic problem in two different infinite-dimensional linear programs, parametrized by the initial inventory level x(0), by concentrating on particular functions and capturing the expected (discounted) behavior of the inventory level process and ordering decisions as measures. The first imbedding then naturally leads to the minimization of a nonlinear function representing the cost associated with an (s, S) ordering policy and an optimizing pair determines optimal levels (s*, S*). The lower bound arising from this imbedding is tight when x(0) >= s* but is a strict lower bound when x(0) < s*. Solving the first linear program determines the value function in the \\\"no order\\\" region and is critical to the formulation of the second linear program. The dual of the second linear program is then solved to provide a tight lower bound for all x(0), in particular for x(0) < s*, and thereby completely determines the value function. Existence of an optimal (s, S) policy in the admissible class of ordering policies (and its characterization) is a consequence of the method, not an a priori assumption. Also of note in this approach is that it solves piecewise in two regions the family of linear programs parametrized by x(0). No smoothness of the value function is required; instead, the level of smoothness results from its construction using the particular functions from which the linear programs are derived. This paper places minimal assumptions on a general stochastic differential equation model for the inventory level and illustrates the approach on two examples.\",\n \"Title: Dynamic flow consolidation for energy savings in green DCNs.\\nYear: 2015\\n\\nAbstract: Energy consumption of data center has become an important challenge due to high electric cost and carbon dioxide emissions. Previous work has mainly focused on saving energy cost of servers, though the energy consumption of data center networks (DCNs), consisting of networking equipments like switches, also takes a significant part of the overall energy consumption. In this paper, we propose ProCons, an energy saving mechanism that dynamically consolidates traffic flows onto a small set of networking equipments in order to shut down idle ones for energy saving. Different from previous works that assume the traffic demands to be stable, ProCons takes into account the variance of traffic demand over time, and predicts future demand based on historical statistics. The traffic flows are then scheduled based on the predicted future demands and the capacity of each link. We evaluate ProCons with real life traces collected from data centers using a flow-level simulator. Our experimental results show that using ProCons, 40% of energy savings for DCNs can be gained while maintaining the good performance of flow transmission.\",\n \"Title: Group Cost-Sensitive Boosting for Multi-Resolution Pedestrian Detection.\\nYear: 2016\\n\\nAbstract: As an important yet challenging problem in computer vision, pedestrian detection has achieved impressive progress in recent years. However, the significant performance decline with decreasing resolution is a major bottleneck of current state-of-the-art methods. For the popular boosting-based detectors, one of the main reasons is that low resolution samples, which are usually more difficult to detect than high resolution ones, are treated by equal costs in the boosting process, leading to the consequence that they are more easily being rejected in early stages and can hardly be recovered in late stages as false negatives. To address this problem, we propose in this paper a new multi-resolution detection approach based on a novel group cost-sensitive boosting algorithm, which extends the popular AdaBoost by exploring different costs for different resolution groups in the boosting process, and places more emphases on low resolution group in order to better handle detection of hard samples. The proposed approach is evaluated on the challenging Caltech pedestrian benchmark, and out-performs other state-of-the-art on different resolutionspecific test sets.\",\n \"Title: LOGAN: Problem Diagnosis in the Cloud Using Log-Based Reference Models\\nYear: 2016\\n\\nAbstract: Problem diagnosis is one crucial aspect in the cloud operation that is becoming increasingly challenging. On the one hand, the volume of logs generated in today's cloud is overwhelmingly large. On the other hand, cloud architecture becomes more distributed and complex, which makes it more difficult to troubleshoot failures. In order to address these challenges, we have developed a tool, called LOGAN, that enables operators to quickly identify the log entries that potentially lead to the root cause of a problem. It constructs behavioral reference models from logs that represent the normal patterns. When problem occurs, our tool enables operators to inspect the divergence of current logs from the reference model and highlight logs likely to contain the hints to the root cause. To support these capabilities we have designed and developed several mechanisms. First, we developed log correlation algorithms using various IDs embedded in logs to help identify and isolate log entries that belong to the failed request. Second, we provide efficient log comparison to help understand the differences between different executions. Finally we designed mechanisms to highlight critical log entries that are likely to contain information pertaining to the root cause of the problem. We have implemented the proposed approach in a popular cloud management system, OpenStack, and through case studies, we demonstrate this tool can help operators perform problem diagnosis quickly and effectively.\",\n \"Title: Bloody Image Classification with Global and Local Features.\\nYear: 2016\\n\\nAbstract: Object content understanding in images and videos draws more and more attention nowadays. However, only few existing methods have addressed the problem of bloody scene detection in images. Along with the widespread popularity of the Internet, violent contents have affected our daily life. In this paper, we propose region-based techniques to identify a color image being bloody or not. Firstly, we have established a new dataset containing 25431 bloody images and 25431 non-bloody images. These annotated images are derived from the Violent Scenes Dataset, a public shared dataset for violent scenes detection in Hollywood movies and web videos. Secondly, we design a bloody image classification method with global visual features using Support Vector Machines. Thirdly, we also construct a novel bloody region identification approach using Convolutional Neural Networks. Finally, comparative experiments show that bloody image classification with local features is more effective.\",\n \"Title: Discriminative latent semantic feature learning for pedestrian detection.\\nYear: 2017\\n\\nAbstract: Features act as a key factor in pedestrian detection task. Most widely-used ones like HOG are manually designed and hard to be adaptive, thus now more attention has been paid to the features automatically learned on data. In this paper, a novel approach of learning discriminative features is proposed, addressing two main limitations of the methods in the literature. On one hand, unlike those methods of learning features on low-level pixels, we propose to learn features via a particular sparse coding algorithm enhanced on mid-level image representation, in order to obtain higher-level latent semantics and robustness; On the other hand, those methods usually utilize label information in model training such as deformable part model (DPM) with high computation cost. Instead, we propose to extend the learning process via a maximum margin criterion, in order to better encode discriminative information directly in features by optimizing them to be close to each other if from the same class and far from each other if from different classes. Furthermore, a boosted detection framework rather than the complex DPM is adopted to achieve both high accuracy and efficiency. The proposed approach achieves promising results on several standard pedestrian detection benchmarks.\",\n \"Title: On Feller and Strong Feller Properties and Exponential Ergodicity of Regime-Switching Jump Diffusion Processes with Countable Regimes.\\nYear: 2017\\n\\nAbstract: This work focuses on a class of regime-switching jump diffusion processes, in which the switching component has countably infinite many states or regimes. The existence and uniqueness of the underlying process are obtained by an interlacing procedure. Then the Feller and strong Feller properties of such processes are derived by the coupling method and an appropriate Radon Nikodym derivative. Finally the paper studies exponential ergodicity of regime-switching jump diffusion processes.\",\n \"Title: Tracking Based Multi-Orientation Scene Text Detection: A Unified Framework With Dynamic Programming.\\nYear: 2017\\n\\nAbstract: There are a variety of grand challenges for multi-orientation text detection in scene videos, where the typical issues include skew distortion, low contrast, and arbitrary motion. Most conventional video text detection methods using individual frames have limited performance. In this paper, we propose a novel tracking based multi-orientation scene text detection method using multiple frames within a unified framework via dynamic programming. First, a multi-information fusion-based multi-orientation text detection method in each frame is proposed to extensively locate possible character candidates and extract text regions with multiple channels and scales. Second, an optimal tracking trajectory is learned and linked globally over consecutive frames by dynamic programming to finally refine the detection results with all detection, recognition, and prediction information. Moreover, the effectiveness of our proposed system is evaluated with the state-of-the-art performances on several public data sets of multi-orientation scene text images and videos, including MSRA-TD500, USTB-SV1K, and ICDAR 2015 Scene Videos.\",\n \"Title: Vehicular fog computing: Vision and challenges\\nYear: 2017\\n\\nAbstract: The emerging vehicular applications demand for a lot more computing and communication capacity to excel in their compute-intensive and latency-sensitive tasks. Fog computing, which focuses on moving computing resources to the edge of networks, complements cloud computing by solving the latency constraints and reducing ingress traffic to the cloud. This paper presents a visionary concept on vehicular fog computing that turns connected vehicles into mobile fog nodes and utilises mobility of vehicles for providing cost-effective and on-demand fog computing for vehicular applications. Besides system design, this paper also discusses the remained technical challenges.\",\n \"Title: Diamond: Nesting the Data Center Network with Wireless Rings in 3D Space.\\nYear: 2018\\n\\nAbstract: The introduction of wireless transmissions into the data center has been shown to be promising in improving the performance of data center networks (DCN) cost effectively. For high transmission flexibility and performance, a fundamental challenge is to increase the wireless availability and enable fully hybrid and seamless transmissions over both wired and wireless DCN components. Rather than limiting the number of wireless radios by the size of top-of-rack (ToR) switches, we propose a novel DCN architecture, Diamond, which nests the wired DCN with radios equipped on all servers. To harvest the gain allowed by the rich reconfigurable wireless resources, we propose the low-cost deployment of scalable 3D Ring Reflection Spaces (RRSs) which are interconnected with streamlined wired herringbone to enable large number of concurrent wireless transmissions through high-performance multi-reflection of radio signals over metal. To increase the number of concurrent wireless transmissions within each RRS, we propose a precise reflection method to reduce the wireless interference. We build a 60GHz-based testbed to demonstrate the function and transmission ability of our proposed architecture. We further perform extensive simulations to show the significant performance gain of Diamond, in supporting up to five times higher server-to-server capacity, enabling network-wide load balancing, and ensuring high fault tolerance.\",\n \"Title: Almost Sure and Moment Exponential Stability of Regime-Switching Jump Diffusions.\\nYear: 2017\\n\\nAbstract: This work is devoted to almost sure and moment exponential stability of regime-switching jump diffusions. The Lyapunov function method is used to derive sufficient conditions for stabilities for general nonlinear systems, which further helps to derive easily verifiable conditions for linear systems. For one-dimensional linear regime-switching jump diffusions, necessary and sufficient conditions for almost sure and pth moment exponential stabilities are presented. Several examples are provided for illustration.\",\n \"Title: Effective Human Detection Via Multi-Model Classification And Adaptive Late Fusion\\nYear: 2018\\n\\nAbstract: Human detection serves as an important basis to achieve certain video surveillance-oriented biometrics such as gait, face and actions since the first step is to find and locate human targets in surveillance scenes. In the literature, channel feature-based methods and deep neural network-based methods are two most popular kinds of human detection approaches, with their own advantages. However, there is not much effort on the study of their combination to take full advantage of these two kinds of approaches. Therefore in this paper, we propose an effective human detection approach by combining multiple state-of-the-art deep neural network-based and channel feature-based methods with an adaptive late fusion strategy. The key idea of our approach is to explore complementary information of different state-of-the-art detection methods and to find an appropriate way to combine their strong points for better performance. The proposed approach is evaluated on several standard human detection benchmarks, and shows its effectiveness by achieving superior performances to the other state-of-the-art methods on most evaluation settings.\",\n \"Title: Fog Following Me: Latency and Quality Balanced Task Allocation in Vehicular Fog Computing\\nYear: 2018\\n\\nAbstract: Emerging vehicular applications, such as real-time situational awareness and cooperative lane change, demand for sufficient computing resources at the edge to conduct time-critical and data-intensive tasks. This paper proposes Fog Following Me (Folo), a novel solution for latency and quality balanced task allocation in vehicular fog computing. Folo is designed to support the mobility of vehicles, including ones generating tasks and the others serving as fog nodes. We formulate the process of task allocation across stationary and mobile fog nodes into a joint optimization problem, with constraints on service latency, quality loss, and fog capacity. As it is a NP-hard problem, we linearize it and solve it using Mixed Integer Linear Programming. To evaluate the effectiveness of Folo, we simulate the mobility of fog nodes at different times of day based on real-world taxi traces, and implement two representative tasks, including video streaming and real-time object recognition. Compared with naive and random fog node selection, the latency and quality balanced task allocation provided by Folo achieves higher performance. More specifically, Folo shortens the average service latency by up to 41\\\\% while reducing the quality loss by up to 60\\\\%.\",\n \"Title: Design Flow of Accelerating Hybrid Extremely Low Bit-Width Neural Network in Embedded FPGA\\nYear: 2018\\n\\nAbstract: Neural network accelerators with low latency and low energy consumption are desirable for edge computing. To create such accelerators, we propose a design flow for accelerating the extremely low bit-width neural network (ELB-NN) in embedded FPGAs with hybrid quantization schemes. This flow covers both network training and FPGA-based network deployment, which facilitates the design space exploration and simplifies the tradeoff between network accuracy and computation efficiency. Using this flow helps hardware designers to deliver a network accelerator in edge devices under strict resource and power constraints. We present the proposed flow by supporting hybrid ELB settings within a neural network. Results show that our design can deliver very high performance peaking at 10.3 TOPS and classify up to 325.3 image/s/watt while running large-scale neural networks for less than 5W using embedded FPGA. To the best of our knowledge, it is the most energy efficient solution in comparison to GPU or other FPGA implementations reported so far in the literature.\",\n \"Title: AccDNN: An IP-Based DNN Generator for FPGAs\\nYear: 2018\\n\\nAbstract: Using FPGA to accelerate Deep Neural Networks (DNNs) requires RTL programming, hardware verification, and precise resource allocation, which is both time-consuming and challenging. To address this issue, we present AccDNN, an end-to-end automation tool that can generate high-performance DNN designs on FPGAs automatically. Highlights of this tool include high-quality RTL network layer IPs, a fine-grained layer-based pipeline architecture, and a column-based cache scheme for high throughput, low latency, and reduced on-chip memory utilization. AccDNN also includes an automatic design space exploration tool, called A-REALM, used to generate optimized parallelism schemes by considering external memory access bandwidth, data reuse behaviors, resource availability, and network complexity. We demonstrate AccDNN on four DNNs (Alexnet, ZF, VGG16, and YOLO) on two Xilinx FPGAs (ZC706 and KU115) for edge- and cloud-computing, respectively. AccDNN generates designs that deliver 263 GOPS and 36.4 GOPS/W on ZC706 without any batching and 2109 GOPS and 94.5 GOPS/W on KU115.\",\n \"Title: Vehicular Fog Computing for Video Crowdsourcing: Applications, Feasibility, and Challenges.\\nYear: 2018\\n\\nAbstract: With the growing adoption of dash cameras, we are seeing great potential for innovations by analyzing the video collected from vehicles. On the other hand, transmitting and analyzing a large amount of video, especially high-resolution video in real time, requires a lot of communications and computing resources. In this work, we investigate the feasibility and challenges of applying vehicular fog c...\",\n \"Title: DNNBuilder: an automated tool for building high-performance DNN hardware accelerators for FPGAs\\nYear: 2018\\n\\nAbstract: Building a high-performance EPGA accelerator for Deep Neural Networks (DNNs) often requires RTL programming, hardware verification, and precise resource allocation, all of which can be time-consuming and challenging to perform even for seasoned FPGA developers. To bridge the gap between fast DNN construction in software (e.g., Caffe, TensorFlow) and slow hardware implementation, we propose DNNBuilder for building high-performance DNN hardware accelerators on FPGAs automatically. Novel techniques are developed to meet the throughput and latency requirements for both cloud- and edge-devices. A number of novel techniques including high-quality RTL neural network components, a fine-grained layer-based pipeline architecture, and a column-based cache scheme are developed to boost throughput, reduce latency, and save FPGA on-chip memory. To address the limited resource challenge, we design an automatic design space exploration tool to generate optimized parallelism guidelines by considering external memory access bandwidth, data reuse behaviors, FPGA resource availability, and DNN complexity. DNNBuilder is demonstrated on four DNNs (Alexnet, ZF, VGG16, and YOLO) on two FPGAs (XC7Z045 and KU115) corresponding to the edge- and cloud-computing, respectively. The fine-grained layer-based pipeline architecture and the column-based cache scheme contribute to 7.7x and 43x reduction of the latency and BRAM utilization compared to conventional designs. We achieve the best performance (up to 5.15x faster) and efficiency (up to 5.88x more efficient) compared to published FPGA-based classification-oriented DNN accelerators for both edge and cloud computing cases. We reach 4218 GOPS for running object detection DNN which is the highest throughput reported to the best of our knowledge. DNNBuilder can provide millisecond-scale real-time performance for processing HD video input and deliver higher efficiency (up to 4.35x) than the GPU-based solutions.\",\n \"Title: Detecting Multi-Resolution Pedestrians Using Group Cost-Sensitive Boosting with Channel Features.\\nYear: 2019\\n\\nAbstract: Significant progress has been achieved in the past few years for the challenging task of pedestrian detection. Nevertheless, a major bottleneck of existing state-of-the-art approaches lies in a great drop in performance with reducing resolutions of the detected targets. For the boosting-based detectors which are popular in pedestrian detection literature, a possible cause for this drop is that in their boosting training process, low-resolution samples, which are usually more difficult to be detected due to the missing details, are still treated equally importantly as high-resolution samples, resulting in the false negatives since they are more easily rejected in the early stages and can hardly be recovered in the late stages. To address this problem, we propose in this paper a robust multi-resolution detection approach with a novel group cost-sensitive boosting algorithm, which is derived from the standard AdaBoost algorithm to further explore different costs for different resolution groups of the samples in the boosting process, and to place greater emphasis on low-resolution groups in order to better handle the detection of multi-resolution targets. The effectiveness of the proposed approach is evaluated on the Caltech pedestrian benchmark and KAIST (Korea Advanced Institute of Science and Technology) multispectral pedestrian benchmark, and validated by its promising performance on different resolution-specific test sets of both benchmarks.\",\n \"Title: Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing\\nYear: 2019\\n\\nAbstract: With the emerging vehicular applications, such as real-time situational awareness and cooperative lane change, there exist huge demands for sufficient computing resources at the edge to conduct time-critical and data-intensive tasks. This paper proposes Folo, a novel solution for latency and quality optimized task allocation in vehicular fog computing (VFC). Folo is designed to support the mobilit...\",\n \"Title: Deep Neural Network-Based Severity Prediction of Bug Reports.\\nYear: 2019\\n\\nAbstract: Software maintenance is an essential phase of software development. Developers employ issue tracking systems to collect bugs for software improvement. Users submit bugs through such issue tracking systems and decide the severity of reported bugs. The severity is an important attribute of a bug that decides how quickly it should be solved. It helps developers to solve important bugs on time. However, manual severity assessment is a tedious job and could be incorrect. To this end, in this paper, we propose a deep neural network-based automatic approach for the severity prediction of bug reports. First, we apply natural language processing techniques for text preprocessing of bug reports. Second, we compute and assign an emotion score for each bug report. Third, we create a vector for each preprocessed bug report. Forth, we pass the constructed vector and the emotion score of each bug report to a deep neural network based classifier for severity prediction. We also evaluate the proposed approach on the history-data of bug reports. The results of cross-product suggest that the proposed approach outperforms the state-of-the-art approaches. On average, it improves the f-measure by 7.90%.\",\n \"Title: Adaptive Pattern-Parameter Matching For Robust Pedestrian Detection\\nYear: 2021\\n\\nAbstract: Pedestrians with challenging patterns, e.g. small scale or heavy occlusion, appear frequently in practical applications like autonomous driving, which remains tremendous obstacle to higher robustness of detectors. Although plenty of previous works have been dedicated to these problems, properly matching patterns of pedestrian and parameters of detector, i.e., constructing a detector with proper parameter sizes for certain pedestrian patterns of different complexity, has been seldom investigated intensively. Pedestrian instances are usually handled equally with the same amount of parameters, which in our opinion is inadequate for those with more difficult patterns and leads to unsatisfactory performance. Thus, we propose in this paper a novel detection approach via adaptive pattern-parameter matching. The input pedestrian patterns, especially the complex ones, are first disentangled into simpler patterns for detection head by Pattern Disentangling Module (PDM) with various receptive fields. Then, Gating Feature Filtering Module (GFFM) dynamically decides the spatial positions where the patterns are still not simple enough and need further disentanglement by the next-level PDM. Co-operating with these two key components, our approach can adaptively select the best matched parameter size for the input patterns according to their complexity. Moreover, to further explore the relationship between parameter sizes and their performance on the corresponding patterns, two parameter selection policies are designed: 1) extending parameter size to maximum, aiming at more difficult patterns for different occlusion types; 2) specializing parameter size by group division, aiming at complex patterns for scale variations. Extensive experiments on two popular benchmarks, Caltech and CityPersons, show that our proposed method achieves superior performance compared with other state-of-the-art methods on subsets of different scales and occlusion types.\",\n \"Title: Learning to Restore Hazy Video: A New Real-World Dataset and A New Method\\nYear: 2021\\n\\nAbstract: Most of the existing deep learning-based dehazing methods are trained and evaluated on the image dehazing datasets, where the dehazed images are generated by only exploiting the information from the corresponding hazy ones. On the other hand, video dehazing algorithms, which can acquire more satisfying dehazing results by exploiting the temporal redundancy from neighborhood hazy frames, receive less attention due to the absence of the video dehazing datasets. Therefore, we propose the first REal-world VIdeo DEhazing (REVIDE) dataset which can be used for the supervised learning of the video dehazing algorithms. By utilizing a well-designed video acquisition system, we can capture paired real-world hazy and haze-free videos that are perfectly aligned by recording the same scene (with or without haze) twice. Considering the challenge of exploiting temporal redundancy among the hazy frames, we also develop a Confidence Guided and Improved Deformable Network (CG-IDN) for video dehazing. The experiments demonstrate that the hazy scenes in the REVIDE dataset are more realistic than the synthetic datasets and the proposed algorithm also performs favorably against state-of-the-art dehazing methods.\",\n \"Title: Applying Edge Computing To Analyse Path Planning Algorithm In College Football Training\\nYear: 2021\\n\\nAbstract: In order to assist college football training, an edge computing-based path planning algorithm for the football robot, and an improved PSO (particle swarm optimization) algorithm are proposed. First, the cartesian coordinate system is transformed into the polar coordinate system, and the coordinates of the robots' path coding are calculated and displayed in the polar coordinate system. Then, the robots' coordinates in the current, the next, and the obstacle point are measured and compared to determine whether path planning is needed. Consequently, the PSO, optimized by the \\\"concentration selection\\\" strategy in the artificial immune algorithm, is chosen for path planning. The experiments show that with the improved PSO, five robots of 0.4 m in radius and moving less than 2 m/s can be avoided easily. The static obstacle avoidance experiment shows that obstacles can be avoided more easily, using the path planned through improved PSO. The dynamic obstacle avoidance experiments indicate that the robot can avoid all moving obstacles, blocking in front, intercepting from all sides, and tracking from behind timely. The experiments prove that the proposed path planning is effective, which has reference value for promoting the application of path planning algorithms in college football training.\",\n \"Title: Automatic Stem-Leaf Segmentation Of Maize Shoots Using Three-Dimensional Point Cloud\\nYear: 2021\\n\\nAbstract: The application of 3D point cloud data in maize research is increasingly extensive. Currently, there are many approaches to acquiring three-dimensional (3D) point clouds of maize plants. However, automatic stem-leaf segmentation of maize shoots from 3D point clouds remains challenging, especially for new emerging leaves that are wrapped very closely together during the seedling stage. To address this issue, we propose an automatic segmentation method consisting of three steps: skeleton extraction, coarse segmentation based on the skeleton, and fine segmentation based on stem-leaf classification. The segmentation method was tested on 75 maize seedlings and compared with the manually obtained ground truth. The mean precision, mean recall, mean micro F1 score, and mean overall accuracy of our segmentation algorithm were 0.944, 0.956, 0.950 and 0.953, respectively. Using the segmentation results, two applications were also developed in this study, namely, phenotypic trait extraction and skeleton optimization. Six phenotypic parameters, namely, plant height, crown diameter, stem height and diameter, leaf width, and length, can be accurately and automatically measured. Furthermore, the values of R2 for the six phenotypic traits were all above 0.92. We also propose a skeleton optimization method that can extract the skeletons of the upper leaves completely and clearly. The results indicate that the proposed algorithm can automatically and precisely segment not only the fully expanded leaves but also the new leaves wrapped closely together. The proposed approach can play an important role in further maize research and applications, such as genotype-to-phenotype study, geometric reconstruction, and dynamic growth animation. We released the source code and test data at the web site https://github.com/syau-miao/seg4 maize.git.\",\n \"Title: SiamPolar: Semi-supervised realtime video object segmentation with polar representation\\nYear: 2022\\n\\nAbstract: •A novel polar representation is introduced for video object segmentation.•An asymmetric Siamese network is developed using similar backbones with different depths.•We design the repeated cross correlation and semi-FPN based on peeling convolutions.\",\n \"Title: Geometric and semantic analysis of road image sequences for traffic scene construction\\nYear: 2021\\n\\nAbstract: •A novel and simple geometric analysis network is proposed using heatmaps.•A FCN-based network is developed to perform semantic segmentation.•The scene construction is conducted by combining the geometric and semantic analysis.\",\n \"Title: Deep Recurrent Neural Network with Multi-Scale Bi-directional Propagation for Video Deblurring.\\nYear: 2022\\n\\nAbstract: The success of the state-of-the-art video deblurring methods stems mainly from implicit or explicit estimation of alignment among the adjacent frames for latent video restoration. However, due to the influence of the blur effect, estimating the alignment information from the blurry adjacent frames is not a trivial task. Inaccurate estimations will interfere the following frame restoration. Instead of estimating alignment information, we propose a simple and effective deep Recurrent Neural Network with Multi-scale Bi-directional Propagation (RNN-MBP) to effectively propagate and gather the information from unaligned neighboring frames for better video deblurring. Specifically, we build a Multi-scale Bi-directional Propagation (MBP) module with two U-Net RNN cells which can directly exploit the inter-frame information from unaligned neighboring hidden states by integrating them in different scales. Moreover, to better evaluate the proposed algorithm and existing state-of-the-art methods on real-world blurry scenes, we also create a Real-World Blurry Video Dataset (RBVD) by a well-designed Digital Video Acquisition System (DVAS) and use it as the training and evaluation dataset. Extensive experimental results demonstrate that the proposed RBVD dataset effectively improve the performance of existing algorithms on real-world blurry videos, and the proposed algorithm performs favorably against the state-of-the-art methods on three typical benchmarks. The code is available at https://github.com/XJTU-CVLAB-LOWLEVEL/RNN-MBP.\",\n \"Title: Hysteretic Optimality of Container Warming Control in Serverless Computing Systems\\nYear: 2021\\n\\nAbstract: Keeping containers warm for prompt service responses and reducing warm containers for light-weight system management exhibit a fundamental tradeoff in serverless computing systems. In this letter, we investigate the problem of container warming control for serverless computing, and we formulate it as a Markov decision process (MDP). By observing that the value functions corresponding to the MDP are partially submodular, we show that the derived optimal policy is hysteretic and partially non-decreasing. Our numerical results show that the derived optimal policy exhibits a hysteretic structure, which can be realized via switching-up/-down thresholds in practice.\",\n \"Title: Exploring the role of sports APP in (campus fitness) intelligent solutions using data fusion algorithm and internet of things\\nYear: 2022\\n\\nAbstract: The purposes are to study how the multi-sensor Internet of Things (IoT) data fusion algorithm calculates in data fusion systems and improves the systems' fusion efficiency. An improved Weighted Least-Squares (WLS) algorithm is proposed for IoT data fusion, and how it processes massive data in a multi-sensor system is studied. Accordingly, a multi-sensor system for sports fitness is designed based on a data fusion algorithm. First, the purposes and demand for sports Application (APP) are analysed, to understand the problems and necessary improvements of such APP. Then, the implementation of the IoT and the classification of the data fusion algorithm of the IoT are explored. The WLS method is selected through comparison, and its implementation process and the data processing process are analysed and explained. Finally, the sports fitness system based on the IoT data fusion algorithm is designed and analysed. The results show that the wireless communication of a multi-sensor data fusion system is feasible and reliable. The variances calculated through the WLS method and the Arithmetic Mean (AM) method in data fusion are compared. The former value is about one-thousandth of the latter value, indicating that the data fusion based on WLS is advantageous over the traditional data fusion.\",\n \"Title: Multi-Objective Path-Decision Model of Multimodal Transport Considering Uncertain Conditions and Carbon Emission Policies\\nYear: 2022\\n\\nAbstract: To develop low-carbon transport and promote sustainable economic development, this paper took the uncertainty in highway transport speed and transshipment time into account in the actual transport process and established multi-objective path-decision models of multimodal transport under different carbon policies. The expectation values of nonlinear uncertainties were estimated by Law of Large Numbers (LLN), and the models were solved by the K-shortest paths algorithm and non-dominated sorting algorithm (NSGA-II), whose advancement and effectiveness were verified through the comparison of SPEA2. Based on the Pareto theory, the optimally symmetrical compromise between the objectives and the influence of the transport speed uncertainty and carbon emission policies on path decisions were quantified and discussed. Taking the multimodal transport network of West Africa as the experimental background, the practicability of the path-decision results is analyzed, and a trade-off analysis is also conducted to provide the theoretical foundation for future freight transport planning.\",\n \"Title: Age-Efficient Concurrent Information Update Scheduling in Edge-Native Systems\\nYear: 2022\\n\\nAbstract: Age of information (AoI) has lately evolved as a promising performance metric to quantify the timeliness of information updates in communication systems. In this letter, we aim to address the concurrent information update scheduling problem in edge-native (EN) systems, where Internet-of-Things (IoT) devices provide information updates for the processing of EN tasks on edge servers. To this end, we...\",\n \"Title: Software-Defined Maritime Fog Computing: Architecture, Advantages, and Feasibility\\nYear: 2022\\n\\nAbstract: Advances in sensing and communication technologies have enabled emerging applications in ocean research. The stringent resource limitations, however, hinder local task processing on maritime devices. Cloud-based task offloading is also not feasible due to the unaffordable costs induced by satellite links. In this work, we propose a visionary architecture on software-defined maritime fog computing (SD-MFC), which adopts fog computing to provide localized services and uses software-defined networking technology to provide the abstraction of underlying resources. In SD-MFC, coastal infrastructures and large-size vessels are turned into maritime fog nodes to process the tasks generated by surrounding maritime devices. By exploration of a real-world vessel trajectory dataset, the feasibility and effectiveness of the proposed SD-MFC are verified.\",\n \"Title: Occluded Pedestrian Detection via Distribution-Based Mutual-Supervised Feature Learning\\nYear: 2022\\n\\nAbstract: Pedestrian detection is a very important task in intelligent transportation system. State-of-the-art detectors work well on non-occluded pedestrians, but they are still far from satisfactory for heavily occluded ones. Recently, to deal with occlusion problems, the popular two-stage approaches are to build a two-branch architecture with the help of additional visible body annotations. However, these methods still have disadvantages. Either the two branches only use score-level fusion, which cannot guarantee the detectors to learn more robust pedestrian features. Or they only focus on the features of visible part via the attention mechanisms. However, the visible body features of heavily occluded pedestrians are only concentrated in a relatively small area, which may easily lead to missed detections. To alleviate the above issues, we propose a novel Distribution-based Mutual-Supervised Feature Learning Network (DMSFLN), to better deal with occluded pedestrian detection. The key DMSFL module in our network is to learn more discriminative feature representations of pedestrians by minimizing the similarity loss between feature distributions of full body and visible body, which has two advantages: enhancing the feature representations of occluded pedestrians and reducing the intra-class variance in pedestrians. To facilitate the DMSFL module, we also propose a novel two-branch network architecture, which is trained in a mutual-supervised way with both full body and visible body annotations respectively. Extensive experiments are conducted on four challenging pedestrian datasets: Caltech, CityPersons, CrowdHuman and CUHK occlusion. Our approach achieves superior performance compared to other state-of-the-art methods, especially on heavy occlusion subsets.\",\n \"Title: CAliC: Accurate and Efficient Image-Text Retrieval via Contrastive Alignment and Visual Contexts Modeling\\nYear: 2022\\n\\nAbstract: ABSTRACTImage-text retrieval is an essential task of information retrieval, in which the models with the Vision-and-Language Pretraining(VLP) are able to achieve ideal accuracy compared with the ones without VLP. Among different VLP approaches, the single-stream models achieve the overall best retrieval accuracy, but slower inference speed. Recently, researchers have introduced the two-stage retrieval setting commonly used in the information retrieval field to the single-stream VLP model for a better accuracy/efficiency trade-off. However, the retrieval accuracy and efficiency are still unsatisfactory mainly due to the limitations of the patch-based visual unimodal encoder in these VLP models. The unimodal encoders are trained on pure visual data, so the visual features extracted by them are difficult to align with the textual features and it is also difficult for the multi-modal encoder to understand visual information. Under these circumstances, we propose an accurate and efficient two-stage image-text retrieval model via Contrastive Alignment and visual Contexts modeling(CAliC). In the first stage of the proposed model, the visual unimodal encoder is pretrained with cross-modal contrastive learning to extract easily aligned visual features, which improves the retrieval accuracy and the inference speed. In the second stage of the proposed model, we introduce a new visual contexts modeling task during pretraining to help the multi-modal encoder better understand the visual information and get more accurate predictions. Extensive experimental evaluation validates the effectiveness of our proposed approach, which achieves a higher retrieval accuracy while keeping a faster inference speed, and outperforms existing state-of-the-art retrieval methods on image-text retrieval tasks over Flickr30K and COCO benchmarks.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":72,"string":"72"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13654,"string":"13,654"}}},{"rowIdx":13655,"cells":{"question_id":{"kind":"string","value":"Jeffrey D Ullman"},"question":{"kind":"string","value":"Generate an abstract for the title \"Meta-MapReduce: A Technique for Reducing Communication in MapReduce Computations.\" using the following items: MapReduce, Cloud Services, Big Data, Meta-MapReduce, Communication Cost."},"answer":{"kind":"string","value":"MapReduce has proven to be one of the most useful paradigms in the revolution of distributed computing, where cloud services and cluster computing become the standard venue for computing. The federation of cloud and big data activities is the next challenge where MapReduce should be modified to avoid (big) data migration across remote (cloud) sites. This is exactly our scope of research, where only the very essential data for obtaining the result is transmitted, reducing communication, processing and preserving data privacy as much as possible. In this work, we propose an algorithmic technique for MapReduce algorithms, called Meta-MapReduce, that decreases the communication cost by allowing us to process and move metadata to clouds and from the map phase to reduce phase. In Meta-MapReduce, the reduce phase fetches only the required data at required iterations, which in turn, assists in preserving the data privacy."},"correct_answer":{"kind":"string","value":"MapReduce has proven to be one of the most useful paradigms in the revolution of distributed computing, where cloud services and cluster computing become the standard venue for computing. The federation of cloud and big data activities is the next challenge where MapReduce should be modified to avoid (big) data migration across remote (cloud) sites. This is exactly our scope of research, where only the very essential data for obtaining the result is transmitted, reducing communication, processing and preserving data privacy as much as possible. In this work, we propose an algorithmic technique for MapReduce algorithms, called Meta-MapReduce, that decreases the communication cost by allowing us to process and move metadata to clouds and from the map phase to reduce phase. In Meta-MapReduce, the reduce phase fetches only the required data at required iterations, which in turn, assists in preserving the data privacy."},"documents":{"kind":"list like","value":["Title: Flow graph reducibility\nYear: 1972\n\nAbstract: The structure of programs can often be described by a technique called “interval analysis” on their flow graphs. Here, we characterize the set of flow graphs that can be analyzed in this way in terms of two very simple transformation on graphs. We then give a necessary and sufficient condition for analyzability and apply it to “goto-less programs,” showing that they all meet the criterion.","Title: Fragments of relations\nYear: 1983\n\nAbstract: We develop a theory of relations that are constructed by the union and selection operations from fragment relations. Algorithms for inserting and deleting from relations that are composed of physical fragments are discussed, and we show when such insertions and deletions are meaningful. We also show how to find an access set for a relation, that is, a set of fragments sufficient to produce the relation, and we apply the test to the question of how the fragmentation of relations interacts with a query on the relation, showing that a selection on the relation can be implemented by retrieving a set of physical fragments that forms an access set for another particular relation.","Title: Parallel skyline queries\nYear: 2012\n\nAbstract: In this paper, we design and analyze parallel algorithms for skyline queries. The skyline of a multidimensional set consists of the points for which no other point exists that is at least as good along every dimension. As a framework for parallel computation, we use both the MP model proposed in (Koutris and Suciu, PODS 2011), which requires that the data is perfectly load-balanced, and a variation of the model in (Afrati and Ullman, EDBT 2010), the GMP model, which demands weaker load balancing constraints. In addition to load balancing, we want to minimize the number of blocking steps, where all processors must wait and synchronize. We propose a 2-step algorithm in the MP model for any dimension of the dataset, as well a 1-step algorithm for the case of 2 and 3 dimensions. Moreover, we present a 1-step algorithm in the GMP model for any number of dimensions.","Title: Updating Logical Databases\nYear: 1986\n\nAbstract: We suggest a new approach to database updates, in which a database is treated as a collection of theories. We investigate two issues: simultaneous multiple update operations and equivalence of databases under update operations.","Title: New Frontiers in Database System Research\nYear: 1992\n\nAbstract: After many years of development centered around business applications, database systems are now recognized as a key technology\n in many new application areas being addressed by Computer Science today. We shall mention some of these applications, and\n the general sorts of advances in technology needed to support these applications. For example, there is a need to deal with\n databases of progressively larger size, containing data of a wide variety of types, and there is a need to perform operations\n of nonstandard kinds on this data. There is a need to combine data from widely distributed databases and integrating this\n data to form a useful whole.","Title: Answering Queries Using Limited External Processors\nYear: 1996\n\nAbstract: When answering queries using external information sources, their contents can be describedby views. To answer a query, we must rewrite it using the set of views presented by the sources.When the external information sources also have the ability to answer some (perhaps limited)sets of queries that require performing operations on their data, the set of views presented bythe source may be infinite (albeit encoded in some finite fashion). Previous work on answeringqueries using views has...","Title: Connections in acyclic hypergraphs\nYear: 1984\n\nAbstract: We demonstrate a sense in which the equivalence between blocks (subgraphs without articulation points) and biconnected components (subgraphs in which there are two edge-disjoint paths between any pair of nodes) that holds in ordinary graph theory can be generalized to hypergraphs. The result has an interpretation for relational databases that the universal relations described by acyclic join dependencies are exactly those for which the connections among attributes are defined uniquely. We also exhibit a relationship between the process of Graham reduction (Graham, 1979) of hypergraphs and the process of tableau reduction (Aho, Sagiv and Ullman, 1979) that holds only for acyclic hypergraphs.","Title: Fuzzy Joins Using MapReduce\nYear: 2012\n\nAbstract: Fuzzy/similarity joins have been widely studied in the research community and extensively used in real-world applications. This paper proposes and evaluates several algorithms for finding all pairs of elements from an input set that meet a similarity threshold. The computation model is a single MapReduce job. Because we allow only one MapReduce round, the Reduce function must be designed so a given output pair is produced by only one task, for many algorithms, satisfying this condition is one of the biggest challenges. We break the cost of an algorithm into three components: the execution cost of the mappers, the execution cost of the reducers, and the communication cost from the mappers to reducers. The algorithms are presented first in terms of Hamming distance, but extensions to edit distance and Jaccard distance are shown as well. We find that there are many different approaches to the similarity-join problem using MapReduce, and none dominates the others when both communication and reducer costs are considered. Our cost analyses enable applications to pick the optimal algorithm based on their communication, memory, and cluster requirements.","Title: Designing good MapReduce algorithms\nYear: 2012\n\nAbstract: An introduction to designing algorithms for the MapReduce framework for parallel processing of big data.","Title: A communication-time tradeoff\nYear: 1987\n\nAbstract: We show a nontrivial tradeoff between the communication c and time t required to compute a collection of values whose dependencies form a grid, i.e., value (i,j) depends on the values (i-1,j) and (i,j-1). No matter how we share the responsibility for computing the nodes of the n x n grid among processors, the law ct = /spl Omega/(n/sup 3/) must hold. Further, there must be a single path through the grid along which there are d communication steps, where dt = /spl Omega/(n/sup 2/). Depending on the machine organization, either law may be the more significant.","Title: Adequacy of decompositions of relational databases\nYear: 1979\n\nAbstract: We consider conditions that have appeared in the literature with the purpose of defining a “good” decomposition of a relation scheme. We show that these notions are equivalent in the case that all constraints in the database are functional dependencies. This result solves an open problem of Rissanen. However, for arbitrary constraints the notions are shown to differ.","Title: Implementing data cubes efficiently\nYear: 1996\n\nAbstract: Decision support applications involve complex queries on very large databases. Since response times should be small, query optimization is critical. Users typically view the data as multidimensional data cubes. Each cell of the data cube is a view consisting of an aggregation of interest, like total sales. The values of many of these cells are dependent on the values of other cells in the data cube. A common and powerful query optimization technique is to materialize some or all of these cells rather than compute them from raw data each time. Commercial systems differ mainly in their approach to materializing the data cube. In this paper, we investigate the issue of which cells (views) to materialize when it is too expensive to materialize all views. A lattice framework is used to express dependencies among views. We present greedy algorithms that work off this lattice and determine a good set of views to materialize. The greedy algorithm performs within a small constant factor of optimal under a variety of models. We then consider the most common case of the hypercube lattice and examine the choice of materialized views for hypercubes in detail, giving some good tradeoffs between the space used and the average time to answer a query.","Title: Parsing algorithms with backtrack\nYear: 1970\n\nAbstract: Two classes of restricted top down parsing algorithms using backtrack are considered. We show that the smaller class recognizes all deterministic context free languages, and that both classes can be simulated in linear time on a random access machine. Certain generalizations of these parsing algorithms are shown equivalent to the larger class. Finally, some decision and closure properties of the classes of languages defined are given.","Title: Optimal wiring between rectangles\nYear: 1981\n\nAbstract: We consider the problem of wiring together two parallel rows of points under a variety of conditions. The options include whether we allow the rows to slide relative to one another, whether we use only rectilinear wires or arbitrary wires, and whether we can use wires in one layer or several layers. In almost all of these combinations of conditions, we can provide a polynomial-time algorithm to minimize the distance between the parallel rows of points. We also compare two fundamentally different wiring approaches, where one and two layers are used. We show that although the theoretical model implies that there can be great gains for the two-layer strategy, even in cases where no crossovers are required, when we consider typical design rules for laying out VLSI circuits there is no substantial advantage to the two-layer approach over the one-layer approach.","Title: Protection in operating systems\nYear: 1976\n\nAbstract: A model of protection mechanisms in computing systems is presented and its appropriateness is argued. The “safety” problem for protection systems under this model is to determine in a given situation whether a subject can acquire a particular right to an object. In restricted cases, it can be shown that this problem is decidable, i.e. there is an algorithm to determine whether a system in a particular configuration is safe. In general, and under surprisingly weak assumptions, it cannot be decided if a situation is safe. Various implications of this fact are discussed.","Title: An optimal synchronizer for the hypercube\nYear: 1989\n\nAbstract: The synchronizer is a simulation methodology introduced by Awerbuch [Al] for simulating a synchronous network by an asynchronous one, thus enabling the execution of a synchronous algorithm on an asynchronous network. In this paper we present a novel technique for constructing network synchronizers. This technique is developed from some basic relationships between synchronizers and the structure of a t-spanning subgraph over the network. As a special result, we obta.in a synchronizer for the hypercube with optimal time and communication complexi ties.","Title: Transitive closure and recursive Datalog implemented on clusters\nYear: 2012\n\nAbstract: Implementing recursive algorithms on computing clusters presents a number of new challenges. In particular, we consider the endgame problem: later rounds of a recursion often transfer only small amounts of data, causing high overhead for interprocessor communication. One way to deal with the endgame problem is to use an algorithm that reduces the number of rounds of the recursion. Especially, in an application like transitive closure (\"TC\") there are several recursive-doubling algorithms that use a logarithmic, rather than linear, number of rounds. Unfortunately, recursive-doubling algorithms can deduce many more facts than the linear TC algorithms, which could negate the cost savings from the elimination of the overhead due to the proliferation of small files. We are thus led to consider TC algorithms that, like the linear algorithms, have the unique decomposition property that assures paths are discovered only once. We find that many such algorithms exist, and we show that they are incomparable, in that any of them could prove best on some data --- even lower in cost than the linear algorithms in some cases. The recursive-doubling approach to TC extends to other recursions as well. However, it is not acceptable to reduce the number of rounds at the expense of a major increase in the number of facts that are deduced. In this paper, we prove it is possible to implement any Datalog program of right-linear chain rules with a logarithmic number of rounds and no order-of-magnitude increase in the number of facts deduced. On the other hand, there are linear recursions for which the two goals of reducing the number of rounds and maintaining the total number of deduced facts cannot be met simultaneously. We show that the reachability problem cannot be solved in logarithmic rounds without using a binary predicate, thus squaring the number of potential facts to be deduced. We also show that the samegeneration recursion cannot be solved in logarithmic rounds without using a predicate of arity three.","Title: Code Generation for Expressions with Common Subexpressions\nYear: 1976\n\nAbstract: This paper shows the problem of generating optimal code for expressions containing common subexpressions is computationally difficult, even for simple expressions and simple machines. Some heuristics for code generation are given and their worst-case behavior is analyzed. For one register machines, an optimal code generation algorithm is given whose time complexity is linear in the size of an expression and exponential only in the amount of sharing.","Title: Evaluating, combining and generalizing recommendations with prerequisites\nYear: 2010\n\nAbstract: We consider the problem of recommending the best set of k items when there is an inherent ordering between items, expressed as a set of prerequisites (e.g., the movie 'Godfather I' is a prerequisite of 'Godfather II'). Since this general problem is computationally intractable, we develop 3 approximation algorithms to solve this problem for various prerequisite structures (e.g., chain graphs, AND graphs, AND-OR graphs). We derive worst-case bounds for these algorithms for these structures, and experimentally evaluate these algorithms on synthetic data. We also develop an algorithm to combine solutions in order to generate even better solutions, and compare the performance of this algorithm with the other three.","Title: A Survey of New Directions in Database Systems\nYear: 2003\n\nAbstract: As database system research evolves, there are severalenduring themes. One, of course, is how we deal with thelargest possible amounts of data. A less obvious theme isoptimization - it is an essential ingredient of all modernforms of database system. Because we deal with large volumesof data, we are often forced to process that data inregular ways. But when operations are uniform, there isan opportunity for the use of very-high-level languages, ofwhich SQL is the primary example. However, to make avery-high-level language effective, we need to optimize itwell, that is, produce effective query plans from all sorts ofqueries.","Title: Enumerating subgraph instances using map-reduce\nYear: 2013\n\nAbstract: The theme of this paper is how to find all instances of a given “sample” graph in a larger “data graph,” using a single round of map-reduce. For the simplest sample graph, the triangle, we improve upon the best known such algorithm. We then examine the general case, considering both the communication cost between mappers and reducers and the total computation cost at the reducers. To minimize communication cost, we exploit the techniques of [1] for computing multiway joins (evaluating conjunctive queries) in a single map-reduce round. Several methods are shown for translating sample graphs into a union of conjunctive queries with as few queries as possible. We also address the matter of optimizing computation cost. Many serial algorithms are shown to be “convertible,” in the sense that it is possible to partition the data graph, explore each partition in a separate reducer, and have the total computation cost at the reducers be of the same order as the computation cost of the serial algorithm.","Title: A View of Directions in Relational Database Theory\nYear: 1981\n\nAbstract: We shall briefly survey what the author believes are some of the most fruitful directions in relational database theory. These directions include dependency inferences, support for the universal relation concept, null value semantics, and an exploration of the properties of acyclic database schemes.","Title: Induction variables in very high level languages\nYear: 1976\n\nAbstract: We explore the notion of an induction variable in the context of a set-theoretic programming langugage. An appropriate definition, we believe, involves both the necessity that changes in the variable around a loop be easily computable and that they be small. We attempt to justify these requirements and show why they are independent assumptions. Next the question of what operators on sets play the role of +, − and * for arithmetic languages is explored, and several theorems allowing us recursively to detect induction variables in a loop are given. It is shown that most of the usual set operations do fit nicely into the theory and help form induction variables. The reason most variables fail to be induction variables concerns the structure of control flow, more than it does the operators applied.","Title: Validating Constraints with Partial Information: Research Overview\nYear: 1994\n\nAbstract: We are interested in the problem of validating the consistency of integrity constraints whendata is modified. In particular, we consider how constraints can be checked with only "partialinformation". Partial information may include: (1) the constraint specifications only, (2) theconstraint specifications and the modified data, or (3) the constraint specifications, the modifieddata, and portions of the existing data. Methods for constraint checking with partial informationcan be much more...","Title: Correcting faults in write-once memory\nYear: 1984\n\nAbstract: No abstract available.","Title: Parallel Complexity of Logical Query Programs\nYear: 1988\n\nAbstract: We consider the parallel time complexity of logic programs without function symbols, called logical query programs, or Datalog\n programs. We give a PRAM algorithm for computing the minimum model of a logical query program, and show that for programs\n with the “polynomial fringe property,” this algorithm runs in time that is logarithmic in the input size, assuming that concurrent\n writes are allowed if they are consistent. As a result, the “linear” and “piecewise linear” classes of logic programs are\n inN\n C. Then we examine several nonlinear classes in which the program has a single recursive rule that is an “elementary chain.”\n We show that certain nonlinear programs are related to GSM mappings of a balanced parentheses language, and that this relationship\n implies the “polynomial fringe property;” hence such programs are inN\n C Finally, we describe an approach for demonstrating that certain logical query programs are log space complete forP, and apply it to both elementary single rule programs and nonelementary programs.","Title: Generating efficient plans for queries using views\nYear: 2001\n\nAbstract: We study the problem or generating efficient, equivalent rewritings using views to compute the answer to a query. We take the closed-world assumption, in which views are materialized from base relations, rather than views describing sources in terms of abstract predicates, as is common when the open-world assumption is used. In the closed-world model, there can be an infinite number of different rewritings that compute the same answer, yet have quite different performance. Query optimizers take a logical plan (a rewriting of the query) as an input, and generate efficient physical plans to compute the answer. Thus our goal is to generate a small subset of the possible logical plans without missing an optimal physical plan.We first consider a cost model that counts the number of subgoals in a physical plan, and show a search space that is guaranteed to include an optimal rewriting, if the query has a rewriting in terms of the views. We also develop an efficient algorithm for finding rewritings with the minimum number of subgoals. We then consider a cost model that counts the sizes of intermediate relations of a physical plan, without dropping any attributes, and give a search space for finding optimal rewritings. Our final cost model allows attributes to be dropped in intermediate relations. We show that, by careful variable renaming, it is possible to do better than the standard “supplementary relation” approach, by dropping attributes that the latter approach would retain. Experiments show that our algorithm of generating optimal rewritings has good efficiency and scalability.","Title: Query Flocks: A Generalization of Association-Rule Mining\nYear: 1998\n\nAbstract: Association-rule mining has proved a highly successful tech- nique for extracting useful information from very large databases. This success is attributed not only to the ap- propriateness of the objectives, but to the fact that a number of new query-optimization ideas, such as the \"a-priori\" trick, make association-rule mining run much faster than might be expected. In this paper we see that the same tricks can be extended to a much more general context, allowing ef- ficient mining of very large databases for many different kinds of patterns. The general idea, called \"query flocks,\" is a generate-and-test model for data-mining problems. We show how the idea can be used either in a general-purpose mining system or in a next generation of conventional query optimizers.","Title: The care and feeding of LR(k) grammars\nYear: 1971\n\nAbstract: We consider methods of modifying LR(k) parsers [1] while preserving the ability of that parsing method to detect errors at the earliest possible point on the input. Two transformations are developed, and the methods of Korenjak [2] and DeRemer [3] are expressed in terms of these transformations. The relation between these two methods is exposed. Proofs are for the most part omitted, but can be found in [4].","Title: Vision Paper: Towards an Understanding of the Limits of Map-Reduce Computation\nYear: 2012\n\nAbstract: A significant amount of recent research work has addressed the problem of solving various data management problems in the cloud. The major algorithmic challenges in map-reduce computations involve balancing a multitude of factors such as the number of machines available for mappers/reducers, their memory requirements, and communication cost (total amount of data sent from mappers to reducers). Most past work provides custom solutions to specific problems, e.g., performing fuzzy joins in map-reduce, clustering, graph analyses, and so on. While some problems are amenable to very efficient map-reduce algorithms, some other problems do not lend themselves to a natural distribution, and have provable lower bounds. Clearly, the ease of \"map-reducability\" is closely related to whether the problem can be partitioned into independent pieces, which are distributed across mappers/reducers. What makes a problem distributable? Can we characterize general properties of problems that determine how easy or hard it is to find efficient map-reduce algorithms? This is a vision paper that attempts to answer the questions described above.","Title: Improving the efficiency of database-system teaching\nYear: 2003\n\nAbstract: The education industry has a very poor record of productivity gains. In this brief article, I outline some of the ways the teaching of a college course in database systems could be made more efficient, and staff time used more productively. These ideas carry over to other programming-oriented courses, and many of them apply to any academic subject whatsoever. After proposing a number of things that could be done, I concentrate here on a system under development, called OTC (On-line Testing Center), and on its methodology of \"root questions.\" These questions encourage students to do homework of the long-answer type, yet we can have their work checked and graded automatically by a simple multiple-choice-question grader. OTC also offers some improvement in the way we handle SQL homework, and could be used with other languages as well.","Title: Relations Between Time and Tape Complexities\nYear: 1968\n\nAbstract: It is shown that if a language L is recognized by a (nondeterministic) single-tape Turing machine of time complexity T(n), then L is recognized by a (nondeterministic) offline Turing machine of tape complexity T1/2(n). If T(n) ≥ n2;, L is recognized by a (nondeterministic) single-tape Turing machine of tape complexity T1/2(n). If a language L is recognized by a (nondeterministic) offline Turing machine of time complexity (T(n), then L is recognized by a (nondeterministic) offline Turing machine of tape complexity (T(n) log n)1/2 and by a (nondeterministic) single-tape Turing machine of that tape complexity if T (n) ≥ n2/log n.","Title: A general scheme for the automatic inference of variable types\nYear: 1978\n\nAbstract: We present the best known algorithm for the determination of run-time types in a programming language requiring no type declarations. We demonstrate that it is superior to other published algorithms and that it is the best possible algorithm from among all those that use the same set of primitive operators.","Title: Analysis of a Simple Algorithm for Global Flow Problems\nYear: 1973\n\nAbstract: There is an ordering of the nodes of a flow graph G which topologically sorts the dominance relation and can be found in 0(edges) steps. This ordering is the reverse of the order in which a node is last visited while growing any depth-first spanning tree of G. Moreover, if G is reducible, then this ordering topologically sorts the \"dag\" of G. Thus, for a reducible flow graph (rfg) there is a simple algorithm to compute the dominators of each node in 0(edges) bit vector steps.The main result of this paper relates two parameters of an rfg. If G is reducible, d is the largest number of back edges found in any cycle-free path in G, and k is the length of the interval derived sequence of G, then k≥d. From this result it follows that there is a very simple bit propagation algorithm (indeed, the obvious one) which also uses the above ordering, and is at least as good as the interval algorithm for solving all known global data flow problems such as \"available expressions\" and \"live variables.\"","Title: A Scheme for the Automatic Inference of Variable Types\nYear: 1980\n\nAbstract: In this paper an algonthra for the determination of run-tune types m a programming language requiring no type declaratmns is presented It ~s demonstrated that this algorithm is superior to other pubhshed algortthms m the sense that it produces stronger assertions about the set of possible types for variables than do other known algorithms. In fact this algonthra is shown to be the best possible algorithm from among all those that use the same set of prumttve operators.","Title: The Theory Of Functional And Template Dependencies\nYear: 1982\n\nAbstract: Template dependencies were introduced by Sadri and Ullman [17] to generalize existing forms of data dependencies. It was hoped that by studying a large and natural class of dependencies, we could solve the inference problem for these dependencies, while that problem was elusive for restricted subsets of the template dependencies, such as embedded multivalued dependencies. At about the same time, other generalizations of known dependency forms were developed, such as the implicational dependencies of Fagin [11] and the algebraic dependencies of Yannakakis and Papadimitriou [20]. Unlike the template dependencies, the latter forms include the functional dependencies as special cases. In this paper we show that no nontrivial functional dependency follows from template dependencies, and we characterize those template dependencies that follow from functional dependencies. We then give a complete set of axioms for reasoning about combinations of functional and template dependencies. As a result, template dependencies augmented by functional dependencies can serve as a substitute for the more general implicational or algebraic dependencies, providing the same ability to represent those dependencies that appear ‘in nature’, while providing a somewhat simpler notation and set of axioms than the more general classes.","Title: Optimizing joins in a map-reduce environment\nYear: 2010\n\nAbstract: Implementations of map-reduce are being used to perform many operations on very large data. We examine strategies for joining several relations in the map-reduce environment. Our new approach begins by identifying the \"map-key,\" the set of attributes that identify the Reduce process to which a Map process must send a particular tuple. Each attribute of the map-key gets a \"share,\" which is the number of buckets into which its values are hashed, to form a component of the identifier of a Reduce process. Relations have their tuples replicated in limited fashion, the degree of replication depending on the shares for those map-key attributes that are missing from their schema. We study the problem of optimizing the shares, given a fixed number of Reduce processes. An algorithm for detecting and fixing problems where an attribute is \"mistakenly\" included in the map-key is given. Then, we consider two important special cases: chain joins and star joins. In each case we are able to determine the map-key and determine the shares that yield the least replication. While the method we propose is not always superior to the conventional way of using map-reduce to implement joins, there are some important cases involving large-scale data where our method wins, including: (1) analytic queries in which a very large fact table is joined with smaller dimension tables, and (2) queries involving paths through graphs with high out-degree, such as the Web or a social network.","Title: On notions of information transfer in VLSI circuits\nYear: 1983\n\nAbstract: Several papers have recently dealt with techniques for proving area-time lower bounds for VLSI computation by “crossing sequence” methods. A number of natural questions are raised by these definitions. 1.Is the fooling set approach the most powerful way to get information-transfer-based lower bounds? We shall show it is not, and offer a candidate for the title “most powerful.” Of course, without a precise definition of “information transfer argument,” there could be other contenders. 2.Are the notions of the three papers cited equivalent? We shall exhibit certain inequivalences among the three notions, although open questions remain. However, we can resolve an open question of Papadimitriou and Sipser [PS] concerning the relationship between nondeterministic and deterministic communication complexity.","Title: A model of statistical database their security\nYear: 1977\n\nAbstract: Considered here, for a particular model of databases in which only information about relatively large sets of records can be obtained, is the question of whether one can from statistical information obtain information about individuals. Under the assumption that the data in the database is taken from arbitrary integers, it is shown that essentially nothing can be inferred. It is also shown that when the values are known to be imprecise in some fixed range, one can often deduce the values of individual records.","Title: Gradiance On-Line Accelerated Learning\nYear: 2005\n\nAbstract: Gradiance On-Line Accelerated Learning (GOAL) is a system for creating and automatically grading homeworks, programming laboratories, and tests. Through the concept of \"root questions,\" Gradiance encourages students to solve complete problems, even though the homework appears to be in a multiple-choice format. Students are offered a hint or advice every time they get a wrong answer, and the student is allowed to try each question until getting it right. By grouping several questions together and telling students to try until they get a perfect score, GOAL turns homework into a true learning experience, rather than a \"mini-quiz.\"","Title: Logical query optimization by proof-tree transformation\nYear: 1993\n\nAbstract: For certain sets of logical rules, one can demonstrate that for every proof tree there is another tree proving the same fact and having a special “normal” form and thereby establish several useful properties of programs. Such a demonstration can be carried out in two parts: first establish that some containment holds between pieces of proof trees, and then show that repeated application of a transformation based on this containment converts every proof tree to the normal form. In this paper, we explore the power of arguments based on such proof-tree transformations and show that they provide a general tool for program optimization. We develop techniques to demonstrate useful proof-tree containments and show how certain program properties can be tested by using these containments in normal-form arguments. We discuss two techniques for demonstrating proof-tree containments. One is to reduce the question to one of conjunctive-query containment. A more powerful method is to test whether one conjunctive query is contained in the infinite union of conjunctive queries formed by expanding a set of recursive rules. We then consider two applications of these techniques. First, we give tests for commutativity of linear rules. When linear rules commute, we can reduce the complexity of “counting” methods for query evaluation from exponential to polynomial; commutativity is also an important element of separability in the sense of Naughton. A second application is to test whether certain non-linear rules are equivalent to linear rules that are obtained from them in a natural way.","Title: A complete axiomatization for a large class of dependencies in relational datatbases\nYear: 1980\n\nAbstract: Relational database theory has discovered complete axiomatizations for functional and multivalued dependencies. However, a database design system that makes use of dependencies declared by the user must deal with some more general kinds of dependencies than these— at least with embedded multivalued dependencies. Yet no axiomatization for embedded multivalued dependencies is known. In this paper, we define a more general class of dependencies, called “template dependencies” and give a complete axiomatization for these. We then discuss the interaction between functional dependencies and template dependencies.","Title: Equivalence of relational database schemes\nYear: 1979\n\nAbstract: We investigate the question of when two database schemes embody the same information. We argue that this question reduces to the equivalence of the sets of fixed points of the project-join mappings associated with the two database schemes in question. When data dependencies are given, we need only consider those fixed points that satisfy the dependencies. A polynomial algorithm to test the equivalence of database schemes, when there are no dependencies, is given. We also provide an exponential algorithm to handle the case where there are functional and/or multivalued dependencies. Furthermore, we give a polynomial time test to determine whether a project-join mapping preserves a set of functional dependencies, and a polynomial time algorithm for equivalence of database schemes whose project-join mappings do preserve the given set of functional dependencies. Lastly, we introduce the “update sets” approach to database design as an application of these results.","Title: Information integration using logical views\nYear: 2000\n\nAbstract: A number of ideas concerning information-integration tools can be thought of as constructing answers to queries using views that represent the capabilities of information sources. We review the formal basis of these techniques, which are closely related to containment algorithms for conjunctive queries and/or Datalog programs. Then we compare the approaches taken by AT&T Labs’ “Information Manifold” and the Stanford “Tsimmis” project in these terms.","Title: A technique for speeding up LR(k) parsers\nYear: 1972\n\nAbstract: We present a new transformation that reduces the size and increases the speed of LR(k) parsers. This transformation can be applied to all LR(k) parsers including those produced by Knuth's and DeRemer's techniques. The transformation causes the parser to avoid reductions by productions of the form A &ran; B where A and B are non-terminals.","Title: Big data: a research agenda\nYear: 2013\n\nAbstract: Recently, a great deal of interest for Big Data has risen, mainly driven from a widespread number of research problems strongly related to real-life applications and systems, such as representing, modeling, processing, querying and mining massive, distributed, large-scale repositories (mostly being of unstructured nature). Inspired by this main trend, in this paper we discuss three important aspects of Big Data research, namely OLAP over Big Data, Big Data Posting, and Privacy of Big Data. We also depict future research directions, hence implicitly defining a research agenda aiming at leading future challenges in this research field.","Title: Characterizations and Extensions of Pushdown Translations\nYear: 1971\n\nAbstract: A device called a pushdown assembler has been recently introduced and has been shown capable of defining exactly the syntax directed translations (SDT's). The output operation of the pushdown assembler can be extended in a natural way to obtain a more powerful device called a type B pushdown assembler (or B-machine). A B-machine can define SDT's more simply and directly than the original pushdown assembler. B-machines can also define many interesting translations which are not SDT's. In this paper the B-machine is defined and compared with the original pushdown assembler. The properties of B-machine translations are investigated and it is shown that, as with SDT's, there exists a natural infinite hierarchy of B-machine translations.","Title: Universality of data retrieval languages\nYear: 1979\n\nAbstract: We consider the question of how powerful a relational query language should be and state two principles that we feel any query language should satisfy. We show that although relational algebra and relational calculus satisfy these principles, there are certain queries involving least fixed points that cannot be expressed by these languages, yet that also satisfy the principles. We then consider various extensions of relational algebra to enable it to answer such queries. Finally, we discuss our extensions to relational algebra in terms of a new programming language oriented model for queries.","Title: Crossfertilization Of DBMS Research With Other Disciplines Of Computer Science (Panel Discussion)\nYear: 1978\n\nAbstract: The DBMS research is moving towards formalization of its problems and solutions. The DBMS researcher can learn and use the techniques that have been developed in formalizing the concepts in Operating systems, Artificial Intelligence, Adaptive systems and Pattern Recognition, and Programming Languages. The panel members are currently involved in research oriented towards cross fertilization of DBMS and various disciplines of computer science. The panel will try to identify commonality of problems, approach and solutions. We hope the discussion will stimulate research in the database area that can fully exploit what we have learned elsewhere.","Title: A General Theory of Translation\nYear: 1969\n\nAbstract: The concept of a translation is fundamental to any theory of compiling. Formally, atranslation is any set of pairs of words. Classes of finitely describable translations are considered in general, from the point of view of balloon automata [17, 18, 19].","Title: Deadlock-free packet switching networks\nYear: 1979\n\nAbstract: Deadlock is one of the most serious system failures that can occur in a computer system or a network. Deadlock states have been observed in existing computer networks emphasizing the need for carefully designed flow control procedures (controllers) to avoid deadlocks. Such a deadlock-free controller is readily found if we allow it global information about the overall network state. Generally, this assumption is not realistic, and we must resort to deadlock free local controllers using only packet and node information. We present here several types of such controllers, we study their relationship and give a proof of their optimality with respect to deadlock free controllers using the same set of local parameters.","Title: Efficient and Complete Tests for Database Integrity Constraint Checking\nYear: 1994\n\nAbstract: this paper we focus our discussion on constraints expressed as conjunctive queries witharithmetic comparisons, we suppose the only accessible relation is the updated relation, and weconsider updates that are insertions of a single tuple. The general form of a conjunctive queryconstraint is:","Title: A Version Numbering Scheme with a Useful Lexicographical Order\nYear: 1995\n\nAbstract: We describe a numbering scheme for versions with alternatives that has a useful lexicographical ordering. The version hierarchy is a tree. By inspection of the version numbers, we can easily determine whether one version is an ancestor of another. If so, we can determine the version sequence between these two versions. If not, we can determine the most recent common ancestor to these two versions (i.e., the least upper bound, lub). Sorting the version numbers lexicographically results in a version being followed by all descendants and preceded by all its ancestors. We use a representation of nonnegative integers that is self delimiting and whose lexicographical ordering matches the ordering by value.","Title: Database systems: achievements and opportunities\nYear: 1991\n\nAbstract: The history of database system research in the U.S. is one of exceptional productivity and startling economic impact. Barely twenty years old as a basic science research field, database research conducted with Federal support in the nation's universities and in its industrial research laboratories has fueled an information services industry estimated at $10 billion per year in the U.S. alone. This industry has grown at an average rate of 20 percent per year since 1965 and is continuing to expand at this rate. Achievements in database research underpin fundamental advances in communications systems, transportation and logistics, financial management, knowledge-based systems, accessibility to scientific literature, and a host of other civilian and defense applications. They also serve as the foundation for considerable progress in basic science in various fields ranging from computing to biology.","Title: Fast algorithms for the elimination of common subexpressions\nYear: 1973\n\nAbstract: We give two algorithms for computing the set of available expressions at entrance to the nodes of a flow graph. The first takes O(mn) isteps on a program flow graph (one in which no node has more than two successors), where n is the number of nodes and m the number of expressions which are ever computed. A modified version of this algorithm requires O(n2) steps of an extended type, where bit vector operations are regarded as one step. We present another algorithm which works only for reducible flow graphs. It requires O(n log n) extended steps.","Title: Error Detection in Precedence Parsers\nYear: 1973\n\nAbstract: We consider methods by which a precedence matrix may be modified without severely degrading the error-detecting capability of a parser utilizing the matrix. Two different definitions of “the same” error detection capability are considered. The first, called “exact equivalence”, permits only useless parts of the precedence matrix to be modified. The second, called simply “equivalence”, allows the modified matrix to perform some reductions, but never to shift an input symbol, after the original has detected an error.","Title: Cluster computing, recursion and datalog\nYear: 2010\n\nAbstract: The cluster-computing environment typified by Hadoop, the open-source implementation of map-reduce, is receiving serious attention as the way to execute queries and other operations on very large-scale data. Datalog execution presents several unusual issues for this enviroment. We discuss the best way to execute a round of seminaive evaluation on a computing cluster using the map-reduce. Using transitive closure as an example, we examine the cost of executing recursions in several different ways. Recursive processes such as evaluation of a recursive Datalog program do not fit the key map-reduce assumption that tasks deliver output only when they are completed. As a result, the resilience under compute-node failure that is a key element of the map-reduce framework is not supported for recursive programs. We discuss extensions to this framework that are suitable for executing recursive Datalog programs on very large-scale data in a way that allows progress to continue after node failures, without restarting the entire job.","Title: Constraint checking with partial information\nYear: 1994\n\nAbstract: Constraints are a valuable tool for managing information across multiple databases, as well as for general purposes of assuring data integrity. However, efficient implementation of constraint checking is difficult. In this paper we explore techniques for assuring constraint satisfaction without performing a complete evaluation of the constraints. We consider methods that use only constraint definitions, methods that use constraints and updates, and methods that use constraints, updates, and “local” data.","Title: Upper And Lower Bounds On The Cost Of A Map-Reduce Computation\nYear: 2012\n\nAbstract: In this paper we study the tradeoff between parallelism and communication cost in a map-reduce computation. For any problem that is not \"embarrassingly parallel,\" the finer we partition the work of the reducers so that more parallelism can be extracted, the greater will be the total communication between mappers and reducers. We introduce a model of problems that can be solved in a single round of map-reduce computation. This model enables a generic recipe for discovering lower bounds on communication cost as a function of the maximum number of inputs that can be assigned to one reducer. We use the model to analyze the tradeoff for three problems: finding pairs of strings at Hamming distance d, finding triangles and other patterns in a larger graph, and matrix multiplication. For finding strings of Hamming distance 1, we have upper and lower bounds that match exactly. For triangles and many other graphs, we have upper and lower bounds that are the same to within a constant factor. For the problem of matrix multiplication, we have matching upper and lower bounds for one-round map-reduce algorithms. We are also able to explore two-round map-reduce algorithms for matrix multiplication and show that these never have more communication, for a given reducer size, than the best one-round algorithm, and often have significantly less.","Title: Map-reduce extensions and recursive queries\nYear: 2011\n\nAbstract: We survey the recent wave of extensions to the popular map-reduce systems, including those that have begun to address the implementation of recursive queries using the same computing environment as map-reduce. A central problem is that recursive tasks cannot deliver their output only at the end, which makes recovery from failures much more complicated than in map-reduce and its nonrecursive extensions. We propose several algorithmic ideas for efficient implementation of recursions in the map-reduce environment and discuss several alternatives for supporting recovery from failures without restarting the entire job.","Title: The TSIMMIS Project: Integration of Heterogeneous Information Sources.\nYear: 1994\n\nAbstract: The goal of the Tsimmis Project is to develop tools that facilitate the rapid integration of heterogeneous information sources that may include both structured and unstructured data. This paper gives an overview of the project, describ-ing components that extract properties from unstructured objects, that translate information into a common object model, that combine information from several sources, that allow browsing of information, and that manage constraints across heterogeneous sites. Tsimmis is a joint project be-tween Stanford and the IBM Almaden Research Center.","Title: Answering queries using limited external query processors\nYear: 1999\n\nAbstract: When answering queries using external information sources, the contents of the queries can be described by views. To answer a query, we must rewrite it using the set of views presented by the sources. When the external information sources also have the ability to answer some (perhaps limited) sets of queries that require performing operations on their data, the set of views presented by the source may be infinite (albeit encoded in some finite fashion). Previous work on answering queries using views has only considered the case where the set of views is finite. In order to exploit the ability of information sources to answer more complex queries, we consider the problem of answering conjunctive queries using infinite sets of conjunctive views. Our first result is that an infinite set of conjunctive views can be partitioned into a finite number of equivalence classes, such that picking one view from every nonempty class is sufficient to determine whether the query can be answered using the views. Second, we show how to compute the set of equivalence classes for sets of conjunctive views encoded by a datalog program. Furthermore, we extend our results to the case when the query and the views use the built-in predicates <, ⩽, =, and ≠, and they are interpreted over a dense domain. Finally, we extend our results to conjunctive queries and views with the built-in predicates <, ⩽, and = interpreted over the integers. In doing so we present a result of independent interest, namely, an algorithm to minimize such queries.","Title: Answering Queries Using Templates with Binding Patterns\nYear: 1995\n\nAbstract: When integrating heterogeneous information resources, it is often the case that the source is rather limited in the kinds of queries it can answer. If a query is asked of the entire system, we have a new kind of optimization problem, in which we must try to express the given query in terms of the limit ed query templates that this source can answer. For the case of conjunctive queries, we show how to decide with a nondeterministic polynomial-time algorithm whether the given query can be answered. We then extend our results to allow arithmetic comparisons in the given query and in the templates.","Title: Properties of acyclic database schemes\nYear: 1981\n\nAbstract: There is a class of database descriptions, involving one “acyclic” join dependency and a collection of functional dependencies, and nothing else, that appears powerful enough to describe most any real-world body of data in relational database terms. Further, this class has many desirable properties. Some properties make operations like updates and the selection of joins to implement a query over a universal relation especially easy. Other properties of interest were studied by other researchers who described the same class in radically different terms, and found desirable properties in their own contexts. It is the purpose of this paper to define the class formally, to give its important properties and the equivalences with the other classes mentioned, and to explain the importance of each property. This paper is intended to summarize the results that will appear in more detail in [FMU] and [BFMY].","Title: Principles of Optimal Page Replacement\nYear: 1971\n\nAbstract: ABSTP~CT. A formal model is presented for paging algorithms under /-order nonstationary assumptions about program behavior. When processing a program under paging in a given memory, a given paging policy generates a certain (expected) number of page calls, i.e., its \"cost.\" Under usual assumptions about memory system organization, minimum cost is always achieved by a demand paging algorithm. The minimum cost for /-order program behavior assumptions is expressed as a dynamic programming problem whose solution yields an optimal replacement algorithm. Solutions are exhibited in several 0-order cases of interest. Paging al- gorithms that implement and approximate the minimum cost are discussed.","Title: On protection in operating systems\nYear: 1975\n\nAbstract: A model of protection mechanisms in computing systems is presented and its appropriateness is demonstrated. The “safety” problem for protection systems under our model is to determine in a given situation whether a subject can acquire a particular right to an object. In restricted cases, one can show that this problem is decidable, i.e., there is an algorithm to determine whether a system in a particular configuration is safe. In general, and under surprisingly weak assumptions, one cannot decide if a situation is safe. Various implications of this fact are discussed.","Title: Deductive databases: achievements and future directions\nYear: 1990\n\nAbstract: In the recent years, Deductive Databases have been the focus of intense research, which has brought dramatic advances in theory, systems and applications. A salient feature of deductive databases is their capability of supporting a declarative, rule-based style of expressing queries and applications on databases. As such, they find applications in disparate areas, such as knowledge mining from databases, and computer-aided design and manufacturing systems.In this paper, we briefly review the key concepts behind deductive databases and their newly developed enabling technology. Then, we describe current research on extending the functionality and usability of deductive databases and on providing a synthesis of deductive databases with procedural and object-oriented approaches.","Title: Monotone data flow analysis frameworks\nYear: 1977\n\nAbstract: We consider a generalization of Kildall's lattice theoretic approach to data flow analysis, which we call monotone data flow analysis frameworks. Many flow analysis problems which appear in practice meet the monotonicity condition but not Kildall's condition called distributivity. We show that the maximal fixed point solution exists for every instance of every monotone framework, and that it can be obtained by Kildall's algorithm. However, whenever the framework is monotone but not distributive, there are instances in which the desired solution--the \\\"meet over all paths solution\\\" -- differs from the maximal fixed point. Finally, we show the nonexistence of an algorithm to compute the meet over all paths solution for monotone frameworks.","Title: On the equivalence of logical databases\nYear: 1984\n\nAbstract: We suggest a new approach to database updates, in which a database is treated as a collection of theories. We investigate two issues: a) equivalence of databases under update operations, b) simultaneous multiple update operations.","Title: Implementation of logical query languages for databases\nYear: 1985\n\nAbstract: We examine methods of implementing queries about relational databases in the case where these queries are expressed in first-order logic as a collection of Horn clauses. Because queries may be defined recursively, straightforward methods of query evaluation do not always work, and a variety of strategies have been proposed to handle subsets of recursive queries. We express such query evaluation techniques as “capture rules” on a graph representing clauses and predicates. One essential property of capture rules is that they can be applied independently, thus providing a clean interface for query-evaluation systems that use several different strategies in different situations. Another is that there be an efficient test for the applicability of a given rule. We define basic capture rules corresponding to application of operators from relational algebra, a top-down capture rule corresponding to “backward chaining,” that is, repeated resolution of goals, a bottom-up rule, corresponding to “forward chaining,” where we attempt to deduce all true facts in a given class, and a “sideways” rule that allows us to pass results from one goal to another.","Title: On the convergence of query evaluation\nYear: 1989\n\nAbstract: We propose a “sideways information passing” method for evaluating Horn clause queries in the presence of monotonicity constraints on the database relations. We give a necessary and sufficient condition for its convergence and show that testing for the condition is polynomial when the arity of the relations is bounded, and PSPACE-complete otherwise. We also prove a related condition for the case of downward finite domains (e.g., Herbrand universes), thus improving an algorithm by Naish for a class of Prolog programs.","Title: The Asilomar report on database research\nYear: 1998\n\nAbstract: The database research community is rightly proud of success in basic research, and its remarkable record of technology transfer. Now the field needs to radically broaden its research focus to attack the issues of capturing, storing, analyzing, and presenting the vast array of online data. The database research community should embrace a broader research agenda — broadening the definition of database management to embrace all the content of the Web and other online data stores, and rethinking our fundamental assumptions in light of technology shifts. To accelerate this transition, we recommend changing the way research results are evaluated and presented. In particular, we advocate encouraging more speculative and long-range work, moving conferences to a poster format, and publishing all research literature on the Web.","Title: A Comparison between Deductive and Object-Oriented Database Systems\nYear: 1991\n\nAbstract: The author will not pretend to be unbiased in the matter of comparing the deductiveand object-oriented approaches to new database systems; he believes that despite someimportant concepts originating with the object-oriented approach, the deductive familyof systems will ultimately dominate. We shall explore some of the reasons why the twoapproaches do not mix well. Fortunately, most of the features of object-oriented databasesystems can be incorporated in deductive systems (but not...","Title: Database Systems: Achievements and Opportunities - The \"Lagunita\" Report of the NSF Invitational Workshop on the Future of Database System Research held in Palo Alto, California, February 22-23, 1990\nYear: 1990\n\nAbstract: The history of database system research in the U.S. is one of exceptional productivity and startling economic impact. Barely twenty years old as a basic science research field, database research conducted with Federal support in the nation's universities and in its industrial research laboratories has fueled an information services industry estimated at $10 billion per year in the U.S. alone. This industry has grown at an average rate of 20 percent per year since 1965 and is continuing to expand at this rate. Achievements in database research underpin fundamental advances in communications systems, transportation and logistics, financial management, knowledge-based systems, accessibility to scientific literature, and a host of other civilian and defense applications. They also serve as the foundation for considerable progress in basic science in various fields ranging from computing to biology.","Title: Designing good algorithms for MapReduce and beyond\nYear: 2012\n\nAbstract: As MapReduce/Hadoop grows in importance, we find more exotic applications being written this way. Not every program written for this platform performs as well as we might wish. There are several reasons why a MapReduce program can underperform expectations. One is the need to balance the communication cost of transporting data from the mappers to the reducers against the computation done at the mappers and reducers themselves. A second important issue is selecting the number of rounds of MapReduce. A third issue is that of skew. If wall-clock time is important, then using many different reduce-keys and many compute nodes may minimize the time to finish the job. Yet if the data is uncooperative, and no provision is made to distribute the data evenly, much of the work is done by a single node.","Title: The database approach to knowledge representation\nYear: 1996\n\nAbstract: The database theory community, centered around the PODS (Principles of Database Systems) conference has had a long-term interest in logic as a way to represent \"data\", \"information,\" and \"knowledge\" (take your pick on the term - it boils down to facts or atoms and rules, usually Horn clauses). The approach of this community has been \"slow and steady,\" preferring to build up carefully from simple special cases to more general ideas, always paying attention to how efficiently we can process queries and perform other operations on the facts and rules. A powerful theory has developed, and it is beginning to have some impact on applications, especially information-integration engines.","Title: The interaction between functional dependencies and template dependencies\nYear: 1980\n\nAbstract: A large class of dependencies, called template dependencies, was introduced in Sadri and Ullman [1979], and a complete set of inference rules (axioms) was given for it. In this paper, we investigate the interaction between template dependencies and functional dependencies. We develop techniques for partially deciding which template and functional dependencies are logically implied by a set of template and functional dependencies. Since template dependencies include all dependencies, besides functional dependencies, known to us (such as multivalued, join, mutual, generalized mutual, and subset dependencies, and their embedded versions), the results of this paper enable us to apply the techniques from Sadri and Ullman [1979] to any set of data dependencies.","Title: Design overview of the NAIL] system\nYear: 1986\n\nAbstract: We describe the design decisions made for the NAIL! (not another implementation of logic!) system, an advanced form of DBMS where queries may involve a large collection of Prolog-like rules used for query interpretation. A discussion of the ways NAIL! semantics differs from Prolog is followed by an exposition of the principal ideas in the system design. These points include the partition of predicates into strongly connected components to represent the structure of recursions and the capture rule organization for selecting query processing strategies. Other ideas include the way distinctions between bound and free arguments are capitalized upon and the persistence of previously discovered facts about the way to handle certain queries. We also survey the recent work on the processing of recursively defined queries conducted by the NAIL! group and others with similar computational models.","Title: On complementary and independent mappings on databases\nYear: 1984\n\nAbstract: We define the notion of independent views to indicate whether the range values of the two views may be achieved independently The concept of complementary views indicates when the domain element can be uniquely determined by the range values of the two complementary views We consider the relationship between independent and complementary views In unrestricted domains, a view (but not the identity or empty view) can have more than one complementary, independent view Databases, however, are more restricted domains They are finite power sets A view is monotonic if it preserves inclusion However, in finite power sets when all views are monotonic, if a given view has another view which is independent and complementary, then this view is unique.","Title: Efficient Optimization of a Class of Relational Expressions (Abstract)\nYear: 1978\n\nAbstract: Many useful database queries can be formulated in terms of expressions whose operands are relations and whose operators are the relational operations select, project, and join. This paper investigates the computational complexity of optimizing relational expressions of this form under a variety of cost measures. A matrix, called a tableau, is proposed as a conventient representative for the value of an expression. Functional dependencies can be used to imply additional equivalences among tableaux. The optimization problem is shown to be NP-complete, but we can give a polynomial time algorithm to optimize tableaux that correspond to an important subclass of expressions.","Title: Viewpoint: Advising students for success\nYear: 2009\n\nAbstract: Some advice for those doing the advising (and what the advisors can learn from the advisees).","Title: The Compilation of Regular Expressions into Integrated Circuits\nYear: 1982\n\nAbstract: We consider the design of integrated circuits to implement arbitrary regular expressions. In general, we may use the McNaughton-Yamada algorithm to convert a regular expression of length n into a nondeterministic finite automaton with at most 2n states and 4n transitions. Instead of converting the nondeterministic device to a deterministic one, we propose two ways of implementing the nondeterministic device directly. First, we could produce a PLA (programmable logic array) of approximate dimensions 4n 脳 4n by representing the states directly by columns, rather than coding the states in binary. This approach, while theoretically suboptimal, makes use of carefully developed technology and, because of the care with which PLA implementation has been done, may be the preferred technique in many real situations. Another approach is to use the hierarchical structure of the automaton produced from the regular expression to guide a hierarchical layout of the circuit. This method produces a circuit 0(√n) on a side and is, to within a constant factor, the best that can be done in general.","Title: A simplied universal relation assumption and its properties\nYear: 1982\n\nAbstract: One problem concerning the universal relation assumption is the inability of known methods to obtain a database scheme design in the general case, where the real-world constraints are given by a set of dependencies that includes embedded multivalued dependencies. We propose a simpler method of describing the real world, where constraints are given by functional dependencies and a single join dependency. The relationship between this method of defining the real world and the classical methods is exposed. We characterize in terms of hypergraphs those multivalued dependencies that are the consequence of a given join dependency. Also characterized in terms of hypergraphs are those join dependencies that are equivalent to a set of multivalued dependencies.","Title: Algorithms for the Compilation of Regular Expressions into PLAs\nYear: 1987\n\nAbstract: The language of regular expressions is a useful one for specifying certain sequential processes at a very high level. They\n allow easy modification of designs for circuits, like controllers, that are described by patterns of events they must recognize\n and the responses they must make to those patterns. This paper discusses the compilation of such expressions into specifications\n for programmable logic arrays (PLAs) that will implement the required function. A regular expression is converted into a nondeterministic\n finite automaton, and then the automaton states are encoded as values on wires that are inputs and outputs of a PLA. The translation\n of regular expressions into nondeterministic automata by two different methods is discussed, along with the advantages of\n each method. A major part of the compilation problem is selection of good state codes for the nondeterministic automata; one\n successful strategy and its application to microcode compaction is explained in the paper.","Title: Evaluating relational expressions with dense and sparse arguments\nYear: 1975\n\nAbstract: We consider expressions whose arguments are relations and whose operators are chosen from among ∪, ο, *, and -1. We further assume that operands may be designated \"sparse\" or \"dense\", in a manner to be made formal subsequently. Our aim is to determine whether the evaluation of such an expression is (a) as hard as general transitive closure (b) as hard as transitive closure for sparse graphs. (c) as hard as connected components of an undirected graph.","Title: Schema Design for Uncertain Databases\nYear: 2009\n\nAbstract: ABSTRACT We address schema design in uncertain databases. Since uncertain data is relational in nature, decomposition be- comes a key issue in design. Decomposition relies on depen- dency theory, and primarily on functional dependencies. We study the theory of functional dependencies (FDs) for uncertain relations. We dene,several kinds of horizontal FDs and vertical FDs, each of which is consistent with con- ventional FDs when,an uncertain relation doesn’t contain any uncertainty. In addition to standard forms of decom- positions allowed by ordinary relations, our FDs allow more complex decompositions specic,to uncertain data. We show how our theory of FDs can be used for lossless decomposition of uncertain relations. We then present algorithms and com- plexity results for three fundamental problems with respect to FDs over ordinary and uncertain relations: (1) Testing whether a relation instance satises,an FD; (2) Finding all FDs satised,by a relation instance; and (3) Inferring all FDs that hold in the result of a query over uncertain rela- tions with FDs. We also give a sound and complete axiom- atization of horizontal and vertical FDs. We look at keys as a special case of FDs. Finally, we briey consider uncertain data that contains condence,values.","Title: Selected database research at Stanford\nYear: 1990\n\nAbstract: This report describes seven projects at the Computer Science Department of Stanford University that may be relevant to SIGMOD community.","Title: The complexity of ordering subgoals\nYear: 1988\n\nAbstract: Selection of an appropriate order for the evaluation of subgoals in a logical rule frequently is essential for efficiency. We formulate the problem as one of feasible subgoal orders and show that the question is inherently exponential in time. The proof is by reduction from linear-space alternating Turing machine recognition, which appears to be far easier, in this case, than the more obvious reduction from exponential-time (ordinary) Turing machines","Title: On the semantics of updates in databases\nYear: 1983\n\nAbstract: We suggest here a methodology for updating databases with integrity constraints and rules for deriving inexphcit information. First we consider the problem of updating arbitrary theories by inserting into them or deleting from them arbitrary sentences. The solution involves two key ideas when replacing an old theory by a new one we wish to minimize the change in the theory, and when there are several theories that involve minimal changes, we look for a new theory that reflects that ambiguity. The methodology is also adapted to updating databases, where different facts can carry different priorities, and to updating user views.","Title: MedMaker: A Mediation System Based on Declarative Specifications\nYear: 1996\n\nAbstract: Mediators are used for integration of heterogeneous information sources. We present a system for declaratively specifying mediators. It is targeted for integration of sources with unstructured or semi-structured data and/or sources with changing schemas. We illustrate the main features of the Mediator Specification Language (MSL), show how they facilitate integration, and describe the implementation of the system that interprets the MSL specifications.","Title: The MIDAS Data-Mining Project at Stanford\nYear: 1999\n\nAbstract: The following notes summarize recent research into data-mining techniques that are in progress at Stanford: 1. The Google search engine: beating Yahoo et al. At their own game. 2. Query Flocks: Generalizing association rules/market baskets in a query precompiler that uses a relational DBMS effectively. 3. Synthesizing Knowledge from the Web: exploiting the Web's redundancy to extract data automatically. 4. Detecting Low-Frequency Events: Unlike marketing, where you only care about items that lots of people buy, extracting intelligence from text usually requires looking for a small number of unexpected juxtapositions of terms.","Title: The revenge of the JD\nYear: 1983\n\nAbstract: Manifestations of the \"universal relation assumption\" can be seen either as definitions of a one-relation user view of data, or as algorithms for answering queries about arbitrary sets of attributes. In this paper we explore equivalences between these two points of view. We show that if the user's view is the representative instance, then our ability to answer queries about the universal relation, by applying relational algebra to the actual database, is equivalent to a \"boundedness\" condition on the dependencies of the database scheme. Further, whenever this condition holds, there is a finite union of lossless tableau mappings that produces the desired relation.","Title: The U. R. strikes back\nYear: 1982\n\nAbstract: In this paper, we try to put to rest many of the objections to the universal relation concept that have appeared in the literature. First, we shall taxonomize the varieties of ideas that are sometimes called the \"universal relation assumption.\" Then, we consider some of the arguments pro and con. In some cases, the arguments against were expressed prematurely, and solutions to the problems they expose have since been found. In other cases, the arguments against are simply fallacious. In still other cases, the problems pointed out are real, but simply serve to point out that the advantages of the universal relation are not gotten for free. We shall conclude the paper with a description of the algorithm used to interpret queries in System/U, and the reasoning behind it.","Title: On finding lowest common ancestors in trees\nYear: 1973\n\nAbstract: Trees in an n node forest are to be merged according to instructions in a given sequence, while other instructions in the sequence ask for the lowest common ancestor of pairs of nodes. We show that any sequence of O(n) instructions can be processed “on line” in O(n log n) steps on a random access computer. If we can accept our answer “off-line”, that is, no answers need to be produced until the entire sequence of instructions has been seen seen, then we may perform the task in O(n G(n)) steps, where G(n) is the number of times we must apply log2 to n to obtain a number less than or equal to zero. A third algorithm solves a problem of intermediate complexity. We require the answers on line, but we suppose that all tree merging instructions precede the information requests. This algorithm requires O(n log log n) time. We apply the first on line algorithm to a problem in code optimization, that of computing immediate dominators in a reducible flow graph. We show how this computation can be performed in O(n log n) steps.","Title: Finding the Depth of a Flow Graph\nYear: 1976\n\nAbstract: The depth of a flow graph is the maximum number of back edges in a cycle free path, where a back edge is defined by some depth-first spanning tree for the flow graph. In the case of a reducible graph, the depth is independent of the DFST chosen. We show that the computation of the depth of a reducible flow graph may be done in polynomial time. Our algorithm is 0(ne) on a flow graph of n nodes and e edges. Since e≤2n for normal flow graphs, our algorithm is really 0(n2). While even an 0(n2) algorithm is not likely to be acceptable, it is suggestive of the possibility of a more efficient algorithm. Finally, we show that the general problem of computing the depth of an arbitrary flow graph with respect to an arbitrary DFST is NP-complete.","Title: Economy of description by parsers, DPDA's, and PDA's\nYear: 1975\n\nAbstract: It is shown that there is a sequence of languages E 1 , E 2 ,… such that every correct prefix parser (one which detects errors at the earliest possible moment, e.g., LR or LL parsers) for E n has size 2 cn , yet a deterministic PDA recognizing E n exists and has size O( n 2 ). There is another easily described sequence of languages N 1 , N 2 ,… for which N has a nondeterministic PDA of size O( n 2 ), but no deterministic PDA of size less than 2 cn . It is shown moreover, that this latter gap can be made arbitrarily large for different sequences of languages.","Title: Dynamic itemset counting and implication rules for market basket data\nYear: 1997\n\nAbstract: We consider the problem of analyzing market-basket data and present several important contributions. First, we present a new algorithm for finding large itemsets which uses fewer passes over the data than classic algorithms, and yet uses fewer candidate itemsets than methods based on sampling. We investigate the idea of item reordering, which can improve the low-level efficiency of the algorithm. Second, we present a new way of generating “implication rules,” which are normalized based on both the antecedent and the consequent and are truly implications (not simply a measure of co-occurrence), and we show how they produce more intuitive results than other methods. Finally, we show how different characteristics of real data, as opposed by synthetic data, can dramatically affect the performance of the system and the form of the results.","Title: Maximal objects and the semantics of universal relation databases\nYear: 1983\n\nAbstract: The universal relation concept is intended to provide the database user with a simplified model in which he can compose queries without regard to the underlying structure of the relations in the database. Frequently, the lossless join criterion provides the query interpreter with the clue needed to interpret the query as the user intended. However, some examples exist where interpretation by the lossless-join rule runs contrary to our intuition. To handle some of these cases, we propose a concept called maximal objects, which modifies the universal relation concept in exactly those situations where it appears to go awry—when the underlying relational structure has “cycles.” We offer examples of how the maximal object concept provides intuitively correct interpretations. We also consider how one might construct maximal objects mechanically from purely syntactic structural information—the relation schemes and functional dependencies—about the database.","Title: Using views to generate efficient evaluation plans for queries\nYear: 2007\n\nAbstract: We study the problem of generating efficient, equivalent rewritings using views to compute the answer to a query. We take the closed-world assumption, in which views are materialized from base relations, rather than views describing sources in terms of abstract predicates, as is common when the open-world assumption is used. In the closed-world model, there can be an infinite number of different rewritings that compute the same answer, yet have quite different performance. Query optimizers take a logical plan (a rewriting of the query) as an input, and generate efficient physical plans to compute the answer. Thus our goal is to generate a small subset of the possible logical plans without missing an optimal physical plan. We first consider a cost model that counts the number of subgoals in a physical plan, and show a search space that is guaranteed to include an optimal rewriting, if the query has a rewriting in terms of the views. We also develop an efficient algorithm for finding rewritings with the minimum number of subgoals. We then consider a cost model that counts the sizes of intermediate relations of a physical plan, without dropping any attributes, and give a search space for finding optimal rewritings. Our final cost model allows attributes to be dropped in intermediate relations. We show that, by careful variable renaming, it is possible to do better than the standard ''supplementary relation'' approach, by dropping attributes that the latter approach would retain. Experiments show that our algorithm of generating optimal rewritings has good efficiency and scalability.","Title: Global Data Flow Analysis and Iterative Algorithms\nYear: 1976\n\nAbstract: Kildall has developed data propagation algorithms for code optimization in a general lattice theoretic framework. In another direction, Hecht and Ullman gave a strong upper bound on the number of iterations required for propagation algorithms when the data is represented by bit vectors and depth-first ordering of the flow graph is used. The present paper combines the ideas of these two papers by considering conditions under which the bound of Hecht and Ullman applies to the depth-first version of Kildall's general data propagation algorithm. It is shown that the following condition is necessary and sufficient: Let ƒ and g be any two functions which could be associated with blocks of a flow graph, let x be an arbitrary lattice element, and let 0 be the lattice zero. Then (*) (∀ƒ,g,x) [ƒg(0) ≥ g(0) ∧ ƒ(x) ∧ x]. Then it is shown that several of the particular instances of the techniques Kildall found useful do not meet condition (*).","Title: A Query Translation Scheme for Rapid Implementation of Wrappers\nYear: 1995\n\nAbstract: Wrappers provide access to heterogeneous information sources by converting application queries into source speci c queries or commands. In this paper we present a wrapper implemen-tation toolkit that facilitates rapid development of wrappers. We focus on the query translation component of the toolkit, called the converter. The converter takes as input a Query Descrip-tion and Translation Language (QDTL) description of the queries that can be processed by the underlying source. Based on this description the converter decides if an application query is (a) directly supported, i. e., it can be translated to a query of the underlying system following instructions in the QDTL description; (b) logically supported, i. e., logically equivalent to a di-rectly supported query; (c) indirectly supported, i. e., it can be computed by applying a lter, automatically generated by the converter, to the result of a directly supported query.","Title: Efficient tests for top-down termination of logical rules\nYear: 1988\n\nAbstract: Considered is the question of whether top-down (Prolog-like) evaluation of a set of logical rules can be guaranteed to terminate. The NAIL! system is designed to process programs consisting of logical rules and to select, for each fragment of the program, the best from among many possible strategies for its evaluation. In the context of such a system, it is essential that termination tests be fast. Thus, the “uniqueness” property of logical rules is introduced. This property is satisfied by many of the common examples of rules and is easily recognized. For rules with this property, a set of inequalities, whose satisfaction is sufficient for termination of the rules, can be generated in polynomial time. Then a polynomial test for satisfaction of constraints generated by this process is given.","Title: Database Research: Achievements and Opportunities into the 21st Century\nYear: 1996\n\nAbstract: In May, 1995 an NSF workshop on the future of database management systems research was convened. This paper reports the conclusions of that meeting. Among the most important directions for future DBMS research recommended by the panel are: support for multimedia objects; managing distributed and loosely coupled information, as on the world-wide web; supporting new database applications such as data mining and warehousing; workflow and other complex transaction-management problems, and enhancing the ease-of-use of DBMS''s for both users and system managers.","Title: A Survey of Association-Rule Mining\nYear: 2000\n\nAbstract: The standard model for association-rule mining involves a set of \"items\" and a set of \"baskets.\" The baskets contain items that some customer has purchased at the same time. The problem is to find pairs, or perhaps larger sets, of items that frequently appear together in baskets. We mention the principal approaches to eefficient, large-scale discovery of the frequent itemsets, including the a-priori algorithm, improvements using hashing, and one- and two-pass probabilistic algorithms for finding frequent itemsets. We then turn to techniques for finding highly correlated, but infrequent, pairs of items. These notes were written for CS345 at Stanford University and are reprinted by permission of the author. http://www-db.stanford.edu/~ullman/mining/mining.html gives you access to the entire set of notes, including additional citations and on-line links.","Title: A survey of deductive database systems\nYear: 1995\n\nAbstract: The area of deductive databases has matured in recent years, and it now seems appropriate to reflect upon what has been achieved and what the future holds. In this paper, we provide an overview of the area, with a focus on implementation techniques, and briefly describe a number of projects that have led to implemented systems.","Title: Optimizing Multiway Joins in a Map-Reduce Environment\nYear: 2011\n\nAbstract: Implementations of map-reduce are being used to perform many operations on very large data. We examine strategies for joining several relations in the map-reduce environment. Our new approach begins by identifying the “map-key,” the set of attributes that identify the Reduce process to which a Map process must send a particular tuple. Each attribute of the map-key gets a “share,” which is the number of buckets into which its values are hashed, to form a component of the identifier of a Reduce process. Relations have their tuples replicated in limited fashion, the degree of replication depending on the shares for those map-key attributes that are missing from their schema. We study the problem of optimizing the shares, given a fixed number of Reduce processes. An algorithm for detecting and fixing problems where a variable is mistakenly included in the map-key is given. Then, we consider two important special cases: chain joins and star joins. In each case, we are able to determine the map-key and determine the shares that yield the least replication. While the method we propose is not always superior to the conventional way of using map-reduce to implement joins, there are some important cases involving large-scale data where our method wins, including: 1) analytic queries in which a very large fact table is joined with smaller dimension tables, and 2) queries involving paths through graphs with high out-degree, such as the Web or a social network.","Title: Flux, sorting, and supercomputer organization for AI applications\nYear: 1984\n\nAbstract: A central issue for the design of massively parallel computers is their ability to sort. We consider organizations that are suitable for fast sorting, both those that use point-to-point connections and those that connect processors with multipoint nets. We show that for fast sorting and minimal area, nets must connect at least √n nodes each, if the network has n nodes. We then discuss some of the ways that fast-sorting networks can be used to speed up other processes, such as combinatorial search.","Title: The Input/Output Complexity of Transitive Closure.\nYear: 1990\n\nAbstract: Suppose a directed graph has its arcs stored in secondary memory, and we wish to compute its transitive closure, also storing the result in secondary memory. We assume that an amount of main memory capable of holding “values” is available, and that lies between, the number of nodes of the graph, and, the number of arcs. The cost measure we use for algorithms is the of Kung and Hong, where we count 1 every time a value is moved into main memory from secondary memory, or vice versa.In the dense case, where is close to, we show that I/O equal to(/√) is sufficient to compute the transitive closure of an-node graph, using main memory of size. Moreover, it is necessary for any algorithm that is “standard,” in a sense to be defined precisely in the paper. Roughly, “standard” means that paths are constructed only by concatenating arcs and previously discovered paths. For the sparse case, we show that I/O equal to(√) is sufficient, although the algorithm we propose meets our definition of “standard” only if the underlying graph is acyclic. We also show that(√) is necessary for any standard algorithm in the sparse case. That settles the I/O complexity of the sparse/acyclic case, for standard algorithms. It is unknown whether this complexity can be achieved in the sparse, cyclic case, by a standard algorithm, and it is unknown whether the bound can be beaten by nonstandard algorithms.We then consider a special kind of standard algorithm, in which paths are constructed only by concatenating arcs and old paths, never by concatenating two old paths. This restriction seems essential if we are to take advantage of sparseness. Unfortunately, we show that almost another factor of I/O is necessary. That is, there is an algorithm in this class using I/O(√) for arbitrary sparse graphs, including cyclic ones. Moreover, every algorithm in the restricted class must use(√/log) I/O, on some cyclic graphs.","Title: A Characterization of Two-Way Deterministic Classes of Languages\nYear: 1970\n\nAbstract: It is shown that a class of languages is defined by a class of two-way deterministic balloon automata if and only if that class is closed under marked union, marked Kleene closure and the inverse mappings performed by deterministic GSMs that move two ways on the input. Hence, the context sensitive languages and various time and tape complexity classes are equivalent to classes of two-way deterministic balloon automata.","Title: Template Dependencies: A Large Class of Dependencies in Relational Databases and Its Complete Axiomatization\nYear: 1982\n\nAbstract: Relauonal database theory has d~scovered complete axlomauzations for functional and multivalued dependencies. However, a database design system based on dependencies must deal with some more general kinds of dependencies than these--at least with embedded multivalued dependencies Yet no axiomatizatton for embedded multivalued dependencies is known. A more general class of dependencies, called \"template dependencies,\" is detrmed in this paper, and a complete ax~omatizat~on is given for them.","Title: A recognition algorithm for pushdown store systems\nYear: 1968\n\nAbstract: A pushdown store is a list in which information can be accessed only on a last-in first-out principle of operation. The use of pushdown stores is an important technique in the construction of compilers and other language-processing devices. Of particular interest from both practical and theoretical considerations is how the time and memory required to process a language is functionally related to the length of the input sentence under consideration. In this paper we consider languages which can be defined by pushdown store systems. To obtain quantitative results, a two-way (off-line) nondeterministic pushdown automaton (2N PDA, for short) is used as an analytical model of a pushdown store system, and the two-way deterministic push-down automaton (2D PDA, for short) is also considered as a special case.","Title: Scalable Techniques for Mining Causal Structures\nYear: 2000\n\nAbstract: Mining for association rules in market basket data has proved a fruitful area of research. Measures such as conditional probability (confidence) and correlation have been used to infer rules of the form “the existence of item A implies the existence of item B.” However, such rules indicate only a statistical relationship between A and B. They do not specify the nature of the relationship: whether the presence of A causes the presence of B, or the converse, or some other attribute or phenomenon causes both to appear together. In applications, knowing such causal relationships is extremely useful for enhancing understanding and effecting change. While distinguishing causality from correlation is a truly difficult problem, recent work in statistics and Bayesian learning provide some avenues of attack. In these fields, the goal has generally been to learn complete causal models, which are essentially impossible to learn in large-scale data mining applications with a large number of variables.In this paper, we consider the problem of determining casual relationships, instead of mere associations, when mining market basket data. We identify some problems with the direct application of Bayesian learning ideas to mining large databases, concerning both the scalability of algorithms and the appropriateness of the statistical techniques, and introduce some initial ideas for dealing with these problems. We present experimental results from applying our algorithms on several large, real-world data sets. The results indicate that the approach proposed here is both computationally feasible and successful in identifying interesting causal structures. An interesting outcome is that it is perhaps easier to infer the lack of causality than to infer causality, information that is useful in preventing erroneous decision making.","Title: Storing and querying tree-structured records in Dremel\nYear: 2014\n\nAbstract: In Dremel, data is stored as nested relations. The schema for a relation is a tree, all of whose nodes are attributes, and whose leaf attributes hold values. We explore filter and aggregate queries that are given in the Dremel dialect of SQL. Complications arise because of repeated attributes, i.e., attributes that are allowed to have more than one value. We focus on the common class of Dremel queries that are processed on column-stored data in a way that results in query processing time that is linear on the size of the relevant data, i.e., data in the columns that participate in the query. We formally define the data model, the query language and the algorithms for query processing in column-stored data. The concepts of repetition context and semi-flattening are introduced here and play a central role in understanding this class of queries and their algorithms.","Title: Assignment of Different-Sized Inputs in MapReduce.\nYear: 2015\n\nAbstract: A MapReduce algorithm can be described by a mapping schema, which assigns inputs to a set of reducers, such that for each required output there exists a reducer that receives all the inputs that participate in the computation of this output. Reducers have a capacity, which limits the sets of inputs that they can be assigned. However, individual inputs may vary in terms of size. We consider, for the first time, mapping schemas where input sizes are part of the considerations and restrictions. One of the significant parameters to optimize in any MapReduce job is communication cost between the map and reduce phases. The communication cost can be optimized by minimizing the number of copies of inputs sent to the reducers. The communication cost is closely related to the number of reducers of constrained capacity that are used to accommodate appropriately the inputs, so that the requirement of how the inputs must meet in a reducer is satisfied. In this work, we consider a family of problems where it is required that each input meets with each other input in at least one reducer. We also consider a slightly different family of problems in which, each input of a set, X, is required to meet each input of another set, Y, in at least one reducer. We prove that finding an optimal mapping schema for these families of problem is NP-hard, and present several approximation algorithms for finding a near optimal mapping schema.","Title: GYM: A Multiround Join Algorithm In MapReduce.\nYear: 2014\n\nAbstract: Multiround algorithms are now commonly used in distributed data processing systems, yet the extent to which algorithms can benefit from running more rounds is not well understood. This paper answers this question for a spectrum of rounds for the problem of computing the equijoin of $n$ relations. Specifically, given any query $Q$ with width $\\w$, {\\em intersection width} $\\iw$, input size $\\mathrm{IN}$, output size $\\mathrm{OUT}$, and a cluster of machines with $M$ memory available per machine, we show that: (1) $Q$ can be computed in $O(n)$ rounds with $O(n\\frac{(\\mathrm{IN}^{\\w} + \\mathrm{OUT})^2}{M})$ communication cost. (2) $Q$ can be computed in $O(\\log(n))$ rounds with $O(n\\frac{(\\mathrm{IN}^{\\max(\\w, 3\\iw)} + \\mathrm{OUT})^2}{M})$ communication cost. \\end{itemize} Intersection width is a new notion of queries and generalized hypertree decompositions (GHDs) of queries we introduce to capture how connected the adjacent cyclic components of the GHDs are. We achieve our first result by introducing a distributed and generalized version of Yannakakis's algorithm, called GYM. GYM takes as input any GHD of $Q$ with width $\\w$ and depth $d$, and computes $Q$ in $O(d + \\log(n))$ rounds and $O(n\\frac{(\\mathrm{IN}^{\\w} + \\mathrm{OUT})^2}{M})$ communication cost. We achieve our second result by showing how to construct GHDs of $Q$ with width $\\max(\\w, 3\\iw)$ and depth $O(\\log(n))$. We describe another technique to construct GHDs with longer widths and shorter depths, demonstrating a spectrum of tradeoffs one can make between communication and the number of rounds.","Title: Handling Skew in Multiway Joins in Parallel Processing.\nYear: 2015\n\nAbstract: Handling skew is one of the major challenges in query processing. In distributed computational environments such as MapReduce, uneven distribution of the data to the servers is not desired. One of the dominant measures that we want to optimize in distributed environments is communication cost. In a MapReduce job this is the amount of data that is transferred from the mappers to the reducers. In this paper we will introduce a novel technique for handling skew when we want to compute a multiway join in one MapReduce round with minimum communication cost. This technique is actually an adaptation of the Shares algorithm [Afrati et. al, TKDE 2011].","Title: Bounds for Overlapping Interval Join on MapReduce.\nYear: 2015\n\nAbstract: We consider the problem of 2-way interval join, where we want to find all pairs of overlapping intervals, i.e., intervals that share at least one point in common. We present lower and upper bounds on the replication rate for this problem when it is implemented in MapReduce. We study three cases, where intervals in the input are: (i) unit-length and equally-spaced, (ii) variable-length and equally-spaced, and (iii) equally-spaced with specific distribution of the various lengths. Our algorithms offer intuition as how to build algorithms for other cases, especially when we have some statistical knowledge about the distribution of the lengths of the intervals. E.g., if mostly large intervals interact with small intervals and not within themselves, then we believe our techniques can be extended to achieve better replication rate.","Title: Representative objects: concise representations of semistructured, hierarchical data\nYear: 1997\n\nAbstract: Introduces the concept of representative objects, which uncover the inherent schema(s) in semi-structured, hierarchical data sources and provide a concise description of the structure of the data. Semi-structured data, unlike data stored in typical relational or object-oriented databases, does not have a fixed schema that is known in advance and stored separately from the data. With the rapid growth of the World Wide Web, semi-structured hierarchical data sources are becoming widely available to the casual user. The lack of external schema information currently makes browsing and querying these data sources inefficient at best, and impossible at worst. We show how representative objects make schema discovery efficient and facilitate the generation of meaningful queries over the data.","Title: Index selection for OLAP\nYear: 1997\n\nAbstract: On-line analytical processing (OLAP) is a recent and important application of database systems. Typically, OLAP data is presented as a multidimensional “data cube.” OLAP queries are complex and can take many hours or even days to run, if executed directly on the raw data. The most common method of reducing execution time is to precompute some of the queries into summary tables (subcubes of the data cube) and then to build indexes on these summary tables. In most commercial OLAP systems today, the summary tables that are to be precomputed are picked first, followed by the selection of the appropriate indexes on them. A trial-and-error approach is used to divide the space available between the summary tables and the indexes. This two-step process can perform very poorly. Since both summary tables and indexes consume the same resource-space-their selection should be done together for the most efficient use of space. The authors give algorithms that automate the selection of summary tables and indexes. In particular, they present a family of algorithms of increasing time complexities, and prove strong performance bounds for them. The algorithms with higher complexities have better performance bounds. However, the increase in the performance bound is diminishing, and they show that an algorithm of moderate complexity can perform fairly close to the optimal","Title: Some Pairs Problems.\nYear: 2016\n\nAbstract: A common form of MapReduce application involves discovering relationships between certain pairs of inputs. Similarity joins serve as a good example of this type of problem, which we call a \\\"some-pairs\\\" problem. In the framework of [4], algorithms are measured by the tradeoff between reducer size (maximum number of inputs a reducer can handle) and the replication rate (average number of reducers to which an input must be sent. There are two obvious approaches to solving some-pairs problems in general. We show that no general-purpose MapReduce algorithm can beat both of these two algorithms in the worst case. We then explore a recursive algorithm for solving some-pairs problems and heuristics for beating the lower bound on common instances of the some-pairs class of problems.","Title: Assignment Problems of Different-Sized Inputs in MapReduce\nYear: 2015\n\nAbstract: A MapReduce algorithm can be described by a mapping schema, which assigns inputs to a set of reducers, such that for each required output there exists a reducer that receives all the inputs participating in the computation of this output. Reducers have a capacity that limits the sets of inputs they can be assigned. However, individual inputs may vary in terms of size. We consider, for the first time, mapping schemas where input sizes are part of the considerations and restrictions. One of the significant parameters to optimize in any MapReduce job is communication cost between the map and reduce phases. The communication cost can be optimized by minimizing the number of copies of inputs sent to the reducers. The communication cost is closely related to the number of reducers of constrained capacity that are used to accommodate appropriately the inputs, so that the requirement of how the inputs must meet in a reducer is satisfied. In this work, we consider a family of problems where it is required that each input meets with each other input in at least one reducer. We also consider a slightly different family of problems in which each input of a list, X, is required to meet each input of another list, Y, in at least one reducer. We prove that finding an optimal mapping schema for these families of problems is NP-hard, and present a bin-packing-based approximation algorithm for finding a near optimal mapping schema.","Title: SharesSkew: An Algorithm to Handle Skew for Joins in MapReduce\nYear: 2015\n\nAbstract: •A novel algorithm to handle skew for multiway joins.•Introduction of residual joins to deal with skew.•Communication cost minimization based on reducer capacity.•Examination of important classes of joins.","Title: Computing Marginals Using MapReduce.\nYear: 2018\n\nAbstract: We consider the problem of computing data-cube marginals by a single round of MapReduce, focusing on the relationship between the reducer size and the replication rate. Initially, we simplify the problem by making the extent of each dimension the same. Several recursive constructions meet or come close to the minimum possible replication rate for a given reducer size. These ideas extend in two directions. We relax the assumption that the extents are all equal, and we consider how to compute marginals from lower-order marginals rather than from the raw data cube.","Title: Dynamic Memories with Rapid Random and Sequential Access\nYear: 1974\n\nAbstract: We propose a new architecture for dynamic memories in which the contents of any cell in memory can be accessed by applying a sequence of two primitive memory operations. The advantage of our memory over previous designs is its fast sequential access. Any word in an n cell memory can be accessed in 0(log n) steps. However, once two consecutive words have been accessed, following words can be accessed in one step per word.","Title: Experiments as research validation: have we gone too far?\nYear: 2015\n\nAbstract: Reconsidering conference paper reviewers' requirements for experimental evidence.","Title: Efficient And Private Approximations Of Distributed Databases Calculations\nYear: 2017\n\nAbstract: In recent years, an increasing amount of data is collected in different and often, not cooperative, databases. The problem of privacy-preserving, distributed calculations over separate databases and, a relative to it, the issue of private data release were intensively investigated. However, despite a considerable progress, computational complexity, due to an increasing size of data, remains a limiting factor in real-world deployments, especially in case of privacy-preserving computations.In this paper, we suggest sampling as a method of improving computational performance. Sampling was a topic of extensive research that recently received a boost of interest. We provide a sampling method targeted at separate, non-collaborating, vertically partitioned datasets. The method is exemplified and tested on approximation of intersection set both without and with privacy-preserving mechanism. An analysis of the bound on error as a function of the sample size is discussed and heuristic algorithm is suggested to further improve the performance. The algorithms were implemented and experimental results confirm the validity of the approach.","Title: Shasta: Interactive Reporting At Scale.\nYear: 2016\n\nAbstract: We describe Shasta, a middleware system built at Google to support interactive reporting in complex user-facing applications related to Google's Internet advertising business. Shasta targets applications with challenging requirements: First, user query latencies must be low. Second, underlying transactional data stores have complex \\\"read-unfriendly\\\" schemas, placing significant transformation logic between stored data and the read-only views that Shasta exposes to its clients. This transformation logic must be expressed in a way that scales to large and agile engineering teams. Finally, Shasta targets applications with strong data freshness requirements, making it challenging to precompute query results using common techniques such as ETL pipelines or materialized views. Instead, online queries must go all the way from primary storage to user-facing views, resulting in complex queries joining 50 or more tables. Designed as a layer on top of Google's F1 RDBMS and Mesa data warehouse, Shasta combines language and system techniques to meet these requirements. To help with expressing complex view specifications, we developed a query language called RVL, with support for modularized view templates that can be dynamically compiled into SQL. To execute these SQL queries with low latency at scale, we leveraged and extended F1's distributed query engine with facilities such as safe execution of C++ and Java UDFs. To reduce latency and increase read parallelism, we extended F1 storage with a distributed read-only in-memory cache. The system we describe is in production at Google, powering critical applications used by advertisers and internal sales teams. Shasta has significantly improved system scalability and software engineering efficiency compared to the middleware solutions it replaced.","Title: Portrait of an Indexer - Computing Pointers Into Instructional Videos.\nYear: 2016\n\nAbstract: We examine algorithms for creating indexes into ordered series ofinstructional lecture video transcripts. The goal is for students andindustry practitioners to use the indexes towards review orreference. Lecture videos differ from often-examined documentcollections such as newspaper articles in that the transcript orderinggenerally reflects pedagogical intent. One challenge is therefore toidentify where a concept is primarily introduced, and where theresulting index should thus direct students. The typically appliedTF-IDF approach gets tricked in this context by artifacts such asworked examples whose associated vocabulary may dominate a lecture,but should not be included in a good index. We contrast the TF-IDFapproach with algorithms that consult Wikipedia documents to vouch forterm importance. This method helps filter the harmful artifacts. Wemeasure the algorithms against three human-created indexes over the 90lecture videos of a popular database course. We found that (i) humans have low inter-rater reliability, whether they are experts inthe field or not, and that (em ii) one of the examined algorithmsapproaches the inter-rater reliability with humans.","Title: Panel: The State of Data: Invited Paper from panelists.\nYear: 2016\n\nAbstract: This panel critically examines the state of data: how its growth and ubiquity have confronted the computer science and particularly the database community, with new challenges. These challenges require practitioners and teachers to learn new skills and engage with other disciplines in ways they had not done before. Panelists will examine the impact of the 'bigness' of data, and its importance for an ever-increasing array of applications, as well as the implications for traditional ideas of privacy and person-hood. By bringing together data specialists with those trained in social and human sciences, this panel aims to initiate discussion about the new social role the computer scientist and the database community have begun to play.","Title: Computing Marginals Using MapReduce: Keynote talk paper.\nYear: 2016\n\nAbstract: We consider the problem of computing the data-cube marginals of a fixed order k (i.e., all marginals that aggregate over k dimensions), using a single round of MapReduce. The focus is on the relationship between the reducer size (number of inputs allowed at a single reducer) and the replication rate (number of reducers to which an input is sent). We show that the replication rate is minimized when the reducers receive all the inputs necessary to compute one marginal of higher order. That observation lets us view the problem as one of covering sets of k dimensions with sets of a larger size m, a problem that has been studied under the name \\\"covering numbers.\\\" We offer a number of constructions that, for different values of k and m meet or come close to yielding the minimum possible replication rate for a given reducer size.","Title: GYM: A Multiround Distributed Join Algorithm.\nYear: 2017\n\nAbstract: Multiround algorithms are now commonly used in distributed data processing systems, yet the extent to which algorithms can benefit from running more rounds is not well understood. This paper answers this question for several rounds for the problem of computing the equijoin of n relations. Given any query Q with width w, intersection width iw, input size IN, output size OUT, and a cluster of machines with M=Omega(IN frac{1}{epsilon}) memory available per machine, where epsilon u003e 1 and w ge 1 are constants, we show that:1. Q can be computed in O(n) rounds with O(n(INw + OUT)2/M) communication cost with high probability.Q can be computed in O(log(n)) rounds with O(n(INmax(w, 3iw) + OUT)2/M) communication cost with high probability.Intersection width is a new notion we introduce for queries and generalized hypertree decompositions (GHDs) of queries that captures how connected the adjacent components of the GHDs are. achieve our first result by introducing a distributed and generalized version of Yannakakisu0027s algorithm, called GYM. GYM takes as input any GHD of Q with width w and depth d, and computes Q in O(d + log(n)) rounds and O(n (INw + OUT)2/M) communication cost. We achieve our second result by showing how to construct GHDs of Q with width max(w, 3iw) and depth O(log(n)). We describe another technique to construct GHDs with longer widths and lower depths, demonstrating other tradeoffs one can make between communication and the number of rounds.","Title: Report from the third workshop on Algorithms and Systems for MapReduce and Beyond (BeyondMR'16).\nYear: 2017\n\nAbstract: This report summarizes the presentations and discussions of the third workshop on Algorithms and Systems for MapReduce and Beyond (BeyondMR'16). The BeyondMR workshop was held in conjunction with the 2016 SIGMOD conference in San Francisco, California, USA on July 1, 2016. The goal of the workshop was to bring together researchers and practitioners to explore algorithms, computational models, architectures, languages and interfaces for systems that need largescale parallelization and systems designed to support efficient parallelization and fault tolerance. These include specialized programming and data-management systems based on MapReduce and extensions, graph processing systems, data-intensive workflow and dataflow systems. The program featured two very well attended invited talks by Ion Stoica from AMPLab, University of California Berkeley and Carlos Guestrin from the University of Washington.","Title: Report from the Fourth Workshop on Algorithms andSystems for MapReduce and Beyond (BeyondMR '17).\nYear: 2017\n\nAbstract: This report summarizes the presentations and discussions of the fourth workshop on Algorithms and Systems for MapReduce and Beyond (BeyondMR '17). The BeyondMR workshop was held in conjunction with the 2017 SIGMOD/PODS conference in Chicago, Illinois, USA on Friday May 19, 2017. The goal of the workshop was to bring together researchers and practitioners to explore algorithms, computational models, languages and interfaces for systems that provide large-scale parallelization and fault tolerance. These include specialized programming and data-management systems based on MapReduce and extensions thereof, graph processing systems and data-intensive workflow systems. \n\nThe program featured two well-attended invited talks, the first on current and future development in big data processing by Matei Zaharia from Databricks and the University of Stanford, and the second on computational models for the analysis and development of big data processing algorithms by Ke Yi from the Hong Kong University of Science and Technology.","Title: Partitioned Data Security on Outsourced Sensitive and Non-sensitive Data.\nYear: 2018\n\nAbstract: Despite extensive research on cryptography, secure and efficient query processing over outsourced data remains an open challenge. This paper continues along the emerging trend in secure data processing that recognizes that the entire dataset may not be sensitive, and hence, non-sensitivity of data can be exploited to overcome limitations of existing encryption-based approaches. We propose a new secure approach, entitled query binning (QB) that allows non-sensitive parts of the data to be outsourced in clear-text while guaranteeing that no information is leaked by the joint processing of non-sensitive data (in clear-text) and sensitive data (in encrypted form). QB maps a query to a set of queries over the sensitive and non-sensitive data in a way that no leakage will occur due to the joint processing over sensitive and non-sensitive data. Interestingly, in addition to improve performance, we show that QB actually strengthens the security of the underlying cryptographic technique by preventing size, frequency-count, and workload-skew attacks.","Title: Argument Reduction by Factoring\nYear: 1989\n\nAbstract: We identify a useful property of a program with re- spect to a predicate, called factoring. While we prove that detecting factorability is undecidable in general, we show that for a large class of programs, the pro- gram obtained by applying the Magic Sets transfor- mation is factorable with respect to the recursive predicate. When the factoring property holds, a sim- ple optimization of the program generated by the Magic Sets transformation results in a new program that is never less efficient than the Magic Sets pro- gram and is often dramatically more efficient, due to the reduction of the arity of the recursive predi- cate. We show that the concept of factoring general- izes some previously identified special cases of recur- sions, including separable recursions and right- and left-linear recursions, and that the specialized evalua- tion algorithms and rewriting strategies developed for","Title: Optimizing Large Join Queries in Mediation Systems\nYear: 1999\n\nAbstract: In data integration systems, queries posed to a mediator need to be translated into a sequence of queries to the underlying data sources. In a heterogeneous environment, with sources of diverse and limited query capabilities, not all the translations are feasible. In this paper, we study the problem of finding feasible and efficient query plans for mediator systems. We consider conjunctive queries on mediators and model the source capabilities through attribute-binding adornments. We use a simple cost model that focuses on the major costs in mediation systems, those involved with sending queries to sources and getting answers back. Under this metric, we develop two algorithms for source query sequencing - one based on a simple greedy strategy and another based on a partitioning scheme. The first algorithm produces optimal plans in some scenarios, and we show a linear bound on its worst case performance when it misses optimal plans. The second algorithm generates optimal plans in more scenarios, while having no bound on the margin by which it misses the optimal plans. We also report on the results of the experiments that study the performance of the two algorithms.","Title: Scaling Cryptographic Techniques by Exploiting Data Sensitivity at a Public Cloud.\nYear: 2019\n\nAbstract: Despite extensive research on cryptography, secure and efficient query processing over outsourced data remains an open challenge. This poster continues along the emerging trend in secure data processing that recognizes that the entire dataset may not be sensitive, and hence, non-sensitivity of data can be exploited to overcome some of the limitations of existing encryption-based approaches. In particular, this poster outlines a new secure keyword search approach, called query keyword binning (QB) that allows non-sensitive parts of the data to be outsourced in clear-text while guaranteeing that no information is leaked by joint processing of non-sensitive data (in clear-text) and sensitive data (in encrypted form). QB improves the performance of and strengthens the security of the underlying cryptographic technique by preventing size, frequency-count, and workload-skew attacks.","Title: Efficient Multiway Hash Join on Reconfigurable Hardware.\nYear: 2019\n\nAbstract: We propose the algorithms for performing multiway joins using a new type of coarse grain reconfigurable hardware accelerator~-- ``Plasticine''~-- that, compared with other accelerators, emphasizes high compute capability and high on-chip communication bandwidth. Joining three or more relations in a single step, i.e. multiway join, is efficient when the join of any two relations yields too large an intermediate relation. We show at least 200X speedup for a sequence of binary hash joins execution on Plasticine over CPU. We further show that in some realistic cases, a Plasticine-like accelerator can make 3-way joins more efficient than a cascade of binary hash joins on the same hardware, by a factor of up to 45X.","Title: Efficient and Privacy Preserving Approximation of Distributed Statistical Queries\nYear: 2022\n\nAbstract: In recent years, an increasing amount of data is collected in different and often, not cooperative, databases. The problem of privacy-preserving, distributed calculations over separate databases and, a relative to it, the issue of private data release was intensively investigated. However, despite a considerable progress, computational complexity and consequently, the performance of the computations, due to an increasing size of data, remains a limiting factor in real-world deployments. Especially in the case of privacy-preserving computations. In this paper, we suggest sampling as a method of improving computational performance. Sampling was a topic of extensive research in the past that recently received a boost of interest. We provide a sampling method targeted at separate, non-collaborating, vertically partitioned datasets. The method is exemplified and tested on an approximation of intersection set both with and without a privacy-preserving mechanism. An analysis of the bound on the error as a function of the sample size is discussed and a heuristic algorithm is suggested to further improve the performance. The algorithms were implemented and experimental results confirm the validity of the approach.","Title: On the Nature of Data Science\nYear: 2021\n\nAbstract: ABSTRACTOne can hear \"Data Science\" defined as a synonym for machine learning or as a branch of Statistics. I shall argue that it is far more than that; it is the natural evolution of the technology of very large-scale data management to solve problems in scientific and commercial fields. To support my argument, I shall give a brief introduction to two algorithms that are important in data science but that are neither machine learning nor statistics: locality-sensitive hashing and counting distinct elements. Biography: Jeff Ullman is the Stanford W. Ascherman Professor of Engineering (Emeritus) in the Department of Computer Science at Stanford and CEO of Gradiance Corp. He received the B.S. degree from Columbia University in 1963 and the PhD from Princeton in 1966. Prior to his appointment at Stanford in 1979, he was a member of the technical staff of Bell Laboratories from 1966-1969, and on the faculty of Princeton University between 1969 and 1979. From 1990-1994, he was chair of the Stanford Computer Science Department. Ullman was elected to the National Academy of Engineering in 1989, the American Academy of Arts and Sciences in 2012, the National Academy of Sciences in 2020, and has held Guggenheim and Einstein Fellowships. He has received the Sigmod Contributions Award (1996), the ACM Karl V. Karlstrom Outstanding Educator Award (1998), the Knuth Prize (2000), the Sigmod E. F. Codd Innovations award (2006), the IEEE von Neumann medal (2010), the NEC C&C Foundation Prize (2017), and the ACM A.M. Turing Award (2020). He is the author of 16 books, including books on database systems, data mining, compilers, automata theory, and algorithms.","Title: Abstractions, Their Algorithms, and Their Compilers\nYear: 2022\n\nAbstract: COMPUTATIONAL THINKING, WHICH centers around devising abstractions for problems so they can be solved with computational steps and efficient algorithms, is a concept that serves not only computer science (CS) but progressively more of science and everyday life."],"string":"[\n \"Title: Flow graph reducibility\\nYear: 1972\\n\\nAbstract: The structure of programs can often be described by a technique called “interval analysis” on their flow graphs. Here, we characterize the set of flow graphs that can be analyzed in this way in terms of two very simple transformation on graphs. We then give a necessary and sufficient condition for analyzability and apply it to “goto-less programs,” showing that they all meet the criterion.\",\n \"Title: Fragments of relations\\nYear: 1983\\n\\nAbstract: We develop a theory of relations that are constructed by the union and selection operations from fragment relations. Algorithms for inserting and deleting from relations that are composed of physical fragments are discussed, and we show when such insertions and deletions are meaningful. We also show how to find an access set for a relation, that is, a set of fragments sufficient to produce the relation, and we apply the test to the question of how the fragmentation of relations interacts with a query on the relation, showing that a selection on the relation can be implemented by retrieving a set of physical fragments that forms an access set for another particular relation.\",\n \"Title: Parallel skyline queries\\nYear: 2012\\n\\nAbstract: In this paper, we design and analyze parallel algorithms for skyline queries. The skyline of a multidimensional set consists of the points for which no other point exists that is at least as good along every dimension. As a framework for parallel computation, we use both the MP model proposed in (Koutris and Suciu, PODS 2011), which requires that the data is perfectly load-balanced, and a variation of the model in (Afrati and Ullman, EDBT 2010), the GMP model, which demands weaker load balancing constraints. In addition to load balancing, we want to minimize the number of blocking steps, where all processors must wait and synchronize. We propose a 2-step algorithm in the MP model for any dimension of the dataset, as well a 1-step algorithm for the case of 2 and 3 dimensions. Moreover, we present a 1-step algorithm in the GMP model for any number of dimensions.\",\n \"Title: Updating Logical Databases\\nYear: 1986\\n\\nAbstract: We suggest a new approach to database updates, in which a database is treated as a collection of theories. We investigate two issues: simultaneous multiple update operations and equivalence of databases under update operations.\",\n \"Title: New Frontiers in Database System Research\\nYear: 1992\\n\\nAbstract: After many years of development centered around business applications, database systems are now recognized as a key technology\\n in many new application areas being addressed by Computer Science today. We shall mention some of these applications, and\\n the general sorts of advances in technology needed to support these applications. For example, there is a need to deal with\\n databases of progressively larger size, containing data of a wide variety of types, and there is a need to perform operations\\n of nonstandard kinds on this data. There is a need to combine data from widely distributed databases and integrating this\\n data to form a useful whole.\",\n \"Title: Answering Queries Using Limited External Processors\\nYear: 1996\\n\\nAbstract: When answering queries using external information sources, their contents can be describedby views. To answer a query, we must rewrite it using the set of views presented by the sources.When the external information sources also have the ability to answer some (perhaps limited)sets of queries that require performing operations on their data, the set of views presented bythe source may be infinite (albeit encoded in some finite fashion). Previous work on answeringqueries using views has...\",\n \"Title: Connections in acyclic hypergraphs\\nYear: 1984\\n\\nAbstract: We demonstrate a sense in which the equivalence between blocks (subgraphs without articulation points) and biconnected components (subgraphs in which there are two edge-disjoint paths between any pair of nodes) that holds in ordinary graph theory can be generalized to hypergraphs. The result has an interpretation for relational databases that the universal relations described by acyclic join dependencies are exactly those for which the connections among attributes are defined uniquely. We also exhibit a relationship between the process of Graham reduction (Graham, 1979) of hypergraphs and the process of tableau reduction (Aho, Sagiv and Ullman, 1979) that holds only for acyclic hypergraphs.\",\n \"Title: Fuzzy Joins Using MapReduce\\nYear: 2012\\n\\nAbstract: Fuzzy/similarity joins have been widely studied in the research community and extensively used in real-world applications. This paper proposes and evaluates several algorithms for finding all pairs of elements from an input set that meet a similarity threshold. The computation model is a single MapReduce job. Because we allow only one MapReduce round, the Reduce function must be designed so a given output pair is produced by only one task, for many algorithms, satisfying this condition is one of the biggest challenges. We break the cost of an algorithm into three components: the execution cost of the mappers, the execution cost of the reducers, and the communication cost from the mappers to reducers. The algorithms are presented first in terms of Hamming distance, but extensions to edit distance and Jaccard distance are shown as well. We find that there are many different approaches to the similarity-join problem using MapReduce, and none dominates the others when both communication and reducer costs are considered. Our cost analyses enable applications to pick the optimal algorithm based on their communication, memory, and cluster requirements.\",\n \"Title: Designing good MapReduce algorithms\\nYear: 2012\\n\\nAbstract: An introduction to designing algorithms for the MapReduce framework for parallel processing of big data.\",\n \"Title: A communication-time tradeoff\\nYear: 1987\\n\\nAbstract: We show a nontrivial tradeoff between the communication c and time t required to compute a collection of values whose dependencies form a grid, i.e., value (i,j) depends on the values (i-1,j) and (i,j-1). No matter how we share the responsibility for computing the nodes of the n x n grid among processors, the law ct = /spl Omega/(n/sup 3/) must hold. Further, there must be a single path through the grid along which there are d communication steps, where dt = /spl Omega/(n/sup 2/). Depending on the machine organization, either law may be the more significant.\",\n \"Title: Adequacy of decompositions of relational databases\\nYear: 1979\\n\\nAbstract: We consider conditions that have appeared in the literature with the purpose of defining a “good” decomposition of a relation scheme. We show that these notions are equivalent in the case that all constraints in the database are functional dependencies. This result solves an open problem of Rissanen. However, for arbitrary constraints the notions are shown to differ.\",\n \"Title: Implementing data cubes efficiently\\nYear: 1996\\n\\nAbstract: Decision support applications involve complex queries on very large databases. Since response times should be small, query optimization is critical. Users typically view the data as multidimensional data cubes. Each cell of the data cube is a view consisting of an aggregation of interest, like total sales. The values of many of these cells are dependent on the values of other cells in the data cube. A common and powerful query optimization technique is to materialize some or all of these cells rather than compute them from raw data each time. Commercial systems differ mainly in their approach to materializing the data cube. In this paper, we investigate the issue of which cells (views) to materialize when it is too expensive to materialize all views. A lattice framework is used to express dependencies among views. We present greedy algorithms that work off this lattice and determine a good set of views to materialize. The greedy algorithm performs within a small constant factor of optimal under a variety of models. We then consider the most common case of the hypercube lattice and examine the choice of materialized views for hypercubes in detail, giving some good tradeoffs between the space used and the average time to answer a query.\",\n \"Title: Parsing algorithms with backtrack\\nYear: 1970\\n\\nAbstract: Two classes of restricted top down parsing algorithms using backtrack are considered. We show that the smaller class recognizes all deterministic context free languages, and that both classes can be simulated in linear time on a random access machine. Certain generalizations of these parsing algorithms are shown equivalent to the larger class. Finally, some decision and closure properties of the classes of languages defined are given.\",\n \"Title: Optimal wiring between rectangles\\nYear: 1981\\n\\nAbstract: We consider the problem of wiring together two parallel rows of points under a variety of conditions. The options include whether we allow the rows to slide relative to one another, whether we use only rectilinear wires or arbitrary wires, and whether we can use wires in one layer or several layers. In almost all of these combinations of conditions, we can provide a polynomial-time algorithm to minimize the distance between the parallel rows of points. We also compare two fundamentally different wiring approaches, where one and two layers are used. We show that although the theoretical model implies that there can be great gains for the two-layer strategy, even in cases where no crossovers are required, when we consider typical design rules for laying out VLSI circuits there is no substantial advantage to the two-layer approach over the one-layer approach.\",\n \"Title: Protection in operating systems\\nYear: 1976\\n\\nAbstract: A model of protection mechanisms in computing systems is presented and its appropriateness is argued. The “safety” problem for protection systems under this model is to determine in a given situation whether a subject can acquire a particular right to an object. In restricted cases, it can be shown that this problem is decidable, i.e. there is an algorithm to determine whether a system in a particular configuration is safe. In general, and under surprisingly weak assumptions, it cannot be decided if a situation is safe. Various implications of this fact are discussed.\",\n \"Title: An optimal synchronizer for the hypercube\\nYear: 1989\\n\\nAbstract: The synchronizer is a simulation methodology introduced by Awerbuch [Al] for simulating a synchronous network by an asynchronous one, thus enabling the execution of a synchronous algorithm on an asynchronous network. In this paper we present a novel technique for constructing network synchronizers. This technique is developed from some basic relationships between synchronizers and the structure of a t-spanning subgraph over the network. As a special result, we obta.in a synchronizer for the hypercube with optimal time and communication complexi ties.\",\n \"Title: Transitive closure and recursive Datalog implemented on clusters\\nYear: 2012\\n\\nAbstract: Implementing recursive algorithms on computing clusters presents a number of new challenges. In particular, we consider the endgame problem: later rounds of a recursion often transfer only small amounts of data, causing high overhead for interprocessor communication. One way to deal with the endgame problem is to use an algorithm that reduces the number of rounds of the recursion. Especially, in an application like transitive closure (\\\"TC\\\") there are several recursive-doubling algorithms that use a logarithmic, rather than linear, number of rounds. Unfortunately, recursive-doubling algorithms can deduce many more facts than the linear TC algorithms, which could negate the cost savings from the elimination of the overhead due to the proliferation of small files. We are thus led to consider TC algorithms that, like the linear algorithms, have the unique decomposition property that assures paths are discovered only once. We find that many such algorithms exist, and we show that they are incomparable, in that any of them could prove best on some data --- even lower in cost than the linear algorithms in some cases. The recursive-doubling approach to TC extends to other recursions as well. However, it is not acceptable to reduce the number of rounds at the expense of a major increase in the number of facts that are deduced. In this paper, we prove it is possible to implement any Datalog program of right-linear chain rules with a logarithmic number of rounds and no order-of-magnitude increase in the number of facts deduced. On the other hand, there are linear recursions for which the two goals of reducing the number of rounds and maintaining the total number of deduced facts cannot be met simultaneously. We show that the reachability problem cannot be solved in logarithmic rounds without using a binary predicate, thus squaring the number of potential facts to be deduced. We also show that the samegeneration recursion cannot be solved in logarithmic rounds without using a predicate of arity three.\",\n \"Title: Code Generation for Expressions with Common Subexpressions\\nYear: 1976\\n\\nAbstract: This paper shows the problem of generating optimal code for expressions containing common subexpressions is computationally difficult, even for simple expressions and simple machines. Some heuristics for code generation are given and their worst-case behavior is analyzed. For one register machines, an optimal code generation algorithm is given whose time complexity is linear in the size of an expression and exponential only in the amount of sharing.\",\n \"Title: Evaluating, combining and generalizing recommendations with prerequisites\\nYear: 2010\\n\\nAbstract: We consider the problem of recommending the best set of k items when there is an inherent ordering between items, expressed as a set of prerequisites (e.g., the movie 'Godfather I' is a prerequisite of 'Godfather II'). Since this general problem is computationally intractable, we develop 3 approximation algorithms to solve this problem for various prerequisite structures (e.g., chain graphs, AND graphs, AND-OR graphs). We derive worst-case bounds for these algorithms for these structures, and experimentally evaluate these algorithms on synthetic data. We also develop an algorithm to combine solutions in order to generate even better solutions, and compare the performance of this algorithm with the other three.\",\n \"Title: A Survey of New Directions in Database Systems\\nYear: 2003\\n\\nAbstract: As database system research evolves, there are severalenduring themes. One, of course, is how we deal with thelargest possible amounts of data. A less obvious theme isoptimization - it is an essential ingredient of all modernforms of database system. Because we deal with large volumesof data, we are often forced to process that data inregular ways. But when operations are uniform, there isan opportunity for the use of very-high-level languages, ofwhich SQL is the primary example. However, to make avery-high-level language effective, we need to optimize itwell, that is, produce effective query plans from all sorts ofqueries.\",\n \"Title: Enumerating subgraph instances using map-reduce\\nYear: 2013\\n\\nAbstract: The theme of this paper is how to find all instances of a given “sample” graph in a larger “data graph,” using a single round of map-reduce. For the simplest sample graph, the triangle, we improve upon the best known such algorithm. We then examine the general case, considering both the communication cost between mappers and reducers and the total computation cost at the reducers. To minimize communication cost, we exploit the techniques of [1] for computing multiway joins (evaluating conjunctive queries) in a single map-reduce round. Several methods are shown for translating sample graphs into a union of conjunctive queries with as few queries as possible. We also address the matter of optimizing computation cost. Many serial algorithms are shown to be “convertible,” in the sense that it is possible to partition the data graph, explore each partition in a separate reducer, and have the total computation cost at the reducers be of the same order as the computation cost of the serial algorithm.\",\n \"Title: A View of Directions in Relational Database Theory\\nYear: 1981\\n\\nAbstract: We shall briefly survey what the author believes are some of the most fruitful directions in relational database theory. These directions include dependency inferences, support for the universal relation concept, null value semantics, and an exploration of the properties of acyclic database schemes.\",\n \"Title: Induction variables in very high level languages\\nYear: 1976\\n\\nAbstract: We explore the notion of an induction variable in the context of a set-theoretic programming langugage. An appropriate definition, we believe, involves both the necessity that changes in the variable around a loop be easily computable and that they be small. We attempt to justify these requirements and show why they are independent assumptions. Next the question of what operators on sets play the role of +, − and * for arithmetic languages is explored, and several theorems allowing us recursively to detect induction variables in a loop are given. It is shown that most of the usual set operations do fit nicely into the theory and help form induction variables. The reason most variables fail to be induction variables concerns the structure of control flow, more than it does the operators applied.\",\n \"Title: Validating Constraints with Partial Information: Research Overview\\nYear: 1994\\n\\nAbstract: We are interested in the problem of validating the consistency of integrity constraints whendata is modified. In particular, we consider how constraints can be checked with only "partialinformation". Partial information may include: (1) the constraint specifications only, (2) theconstraint specifications and the modified data, or (3) the constraint specifications, the modifieddata, and portions of the existing data. Methods for constraint checking with partial informationcan be much more...\",\n \"Title: Correcting faults in write-once memory\\nYear: 1984\\n\\nAbstract: No abstract available.\",\n \"Title: Parallel Complexity of Logical Query Programs\\nYear: 1988\\n\\nAbstract: We consider the parallel time complexity of logic programs without function symbols, called logical query programs, or Datalog\\n programs. We give a PRAM algorithm for computing the minimum model of a logical query program, and show that for programs\\n with the “polynomial fringe property,” this algorithm runs in time that is logarithmic in the input size, assuming that concurrent\\n writes are allowed if they are consistent. As a result, the “linear” and “piecewise linear” classes of logic programs are\\n inN\\n C. Then we examine several nonlinear classes in which the program has a single recursive rule that is an “elementary chain.”\\n We show that certain nonlinear programs are related to GSM mappings of a balanced parentheses language, and that this relationship\\n implies the “polynomial fringe property;” hence such programs are inN\\n C Finally, we describe an approach for demonstrating that certain logical query programs are log space complete forP, and apply it to both elementary single rule programs and nonelementary programs.\",\n \"Title: Generating efficient plans for queries using views\\nYear: 2001\\n\\nAbstract: We study the problem or generating efficient, equivalent rewritings using views to compute the answer to a query. We take the closed-world assumption, in which views are materialized from base relations, rather than views describing sources in terms of abstract predicates, as is common when the open-world assumption is used. In the closed-world model, there can be an infinite number of different rewritings that compute the same answer, yet have quite different performance. Query optimizers take a logical plan (a rewriting of the query) as an input, and generate efficient physical plans to compute the answer. Thus our goal is to generate a small subset of the possible logical plans without missing an optimal physical plan.We first consider a cost model that counts the number of subgoals in a physical plan, and show a search space that is guaranteed to include an optimal rewriting, if the query has a rewriting in terms of the views. We also develop an efficient algorithm for finding rewritings with the minimum number of subgoals. We then consider a cost model that counts the sizes of intermediate relations of a physical plan, without dropping any attributes, and give a search space for finding optimal rewritings. Our final cost model allows attributes to be dropped in intermediate relations. We show that, by careful variable renaming, it is possible to do better than the standard “supplementary relation” approach, by dropping attributes that the latter approach would retain. Experiments show that our algorithm of generating optimal rewritings has good efficiency and scalability.\",\n \"Title: Query Flocks: A Generalization of Association-Rule Mining\\nYear: 1998\\n\\nAbstract: Association-rule mining has proved a highly successful tech- nique for extracting useful information from very large databases. This success is attributed not only to the ap- propriateness of the objectives, but to the fact that a number of new query-optimization ideas, such as the \\\"a-priori\\\" trick, make association-rule mining run much faster than might be expected. In this paper we see that the same tricks can be extended to a much more general context, allowing ef- ficient mining of very large databases for many different kinds of patterns. The general idea, called \\\"query flocks,\\\" is a generate-and-test model for data-mining problems. We show how the idea can be used either in a general-purpose mining system or in a next generation of conventional query optimizers.\",\n \"Title: The care and feeding of LR(k) grammars\\nYear: 1971\\n\\nAbstract: We consider methods of modifying LR(k) parsers [1] while preserving the ability of that parsing method to detect errors at the earliest possible point on the input. Two transformations are developed, and the methods of Korenjak [2] and DeRemer [3] are expressed in terms of these transformations. The relation between these two methods is exposed. Proofs are for the most part omitted, but can be found in [4].\",\n \"Title: Vision Paper: Towards an Understanding of the Limits of Map-Reduce Computation\\nYear: 2012\\n\\nAbstract: A significant amount of recent research work has addressed the problem of solving various data management problems in the cloud. The major algorithmic challenges in map-reduce computations involve balancing a multitude of factors such as the number of machines available for mappers/reducers, their memory requirements, and communication cost (total amount of data sent from mappers to reducers). Most past work provides custom solutions to specific problems, e.g., performing fuzzy joins in map-reduce, clustering, graph analyses, and so on. While some problems are amenable to very efficient map-reduce algorithms, some other problems do not lend themselves to a natural distribution, and have provable lower bounds. Clearly, the ease of \\\"map-reducability\\\" is closely related to whether the problem can be partitioned into independent pieces, which are distributed across mappers/reducers. What makes a problem distributable? Can we characterize general properties of problems that determine how easy or hard it is to find efficient map-reduce algorithms? This is a vision paper that attempts to answer the questions described above.\",\n \"Title: Improving the efficiency of database-system teaching\\nYear: 2003\\n\\nAbstract: The education industry has a very poor record of productivity gains. In this brief article, I outline some of the ways the teaching of a college course in database systems could be made more efficient, and staff time used more productively. These ideas carry over to other programming-oriented courses, and many of them apply to any academic subject whatsoever. After proposing a number of things that could be done, I concentrate here on a system under development, called OTC (On-line Testing Center), and on its methodology of \\\"root questions.\\\" These questions encourage students to do homework of the long-answer type, yet we can have their work checked and graded automatically by a simple multiple-choice-question grader. OTC also offers some improvement in the way we handle SQL homework, and could be used with other languages as well.\",\n \"Title: Relations Between Time and Tape Complexities\\nYear: 1968\\n\\nAbstract: It is shown that if a language L is recognized by a (nondeterministic) single-tape Turing machine of time complexity T(n), then L is recognized by a (nondeterministic) offline Turing machine of tape complexity T1/2(n). If T(n) ≥ n2;, L is recognized by a (nondeterministic) single-tape Turing machine of tape complexity T1/2(n). If a language L is recognized by a (nondeterministic) offline Turing machine of time complexity (T(n), then L is recognized by a (nondeterministic) offline Turing machine of tape complexity (T(n) log n)1/2 and by a (nondeterministic) single-tape Turing machine of that tape complexity if T (n) ≥ n2/log n.\",\n \"Title: A general scheme for the automatic inference of variable types\\nYear: 1978\\n\\nAbstract: We present the best known algorithm for the determination of run-time types in a programming language requiring no type declarations. We demonstrate that it is superior to other published algorithms and that it is the best possible algorithm from among all those that use the same set of primitive operators.\",\n \"Title: Analysis of a Simple Algorithm for Global Flow Problems\\nYear: 1973\\n\\nAbstract: There is an ordering of the nodes of a flow graph G which topologically sorts the dominance relation and can be found in 0(edges) steps. This ordering is the reverse of the order in which a node is last visited while growing any depth-first spanning tree of G. Moreover, if G is reducible, then this ordering topologically sorts the \\\"dag\\\" of G. Thus, for a reducible flow graph (rfg) there is a simple algorithm to compute the dominators of each node in 0(edges) bit vector steps.The main result of this paper relates two parameters of an rfg. If G is reducible, d is the largest number of back edges found in any cycle-free path in G, and k is the length of the interval derived sequence of G, then k≥d. From this result it follows that there is a very simple bit propagation algorithm (indeed, the obvious one) which also uses the above ordering, and is at least as good as the interval algorithm for solving all known global data flow problems such as \\\"available expressions\\\" and \\\"live variables.\\\"\",\n \"Title: A Scheme for the Automatic Inference of Variable Types\\nYear: 1980\\n\\nAbstract: In this paper an algonthra for the determination of run-tune types m a programming language requiring no type declaratmns is presented It ~s demonstrated that this algorithm is superior to other pubhshed algortthms m the sense that it produces stronger assertions about the set of possible types for variables than do other known algorithms. In fact this algonthra is shown to be the best possible algorithm from among all those that use the same set of prumttve operators.\",\n \"Title: The Theory Of Functional And Template Dependencies\\nYear: 1982\\n\\nAbstract: Template dependencies were introduced by Sadri and Ullman [17] to generalize existing forms of data dependencies. It was hoped that by studying a large and natural class of dependencies, we could solve the inference problem for these dependencies, while that problem was elusive for restricted subsets of the template dependencies, such as embedded multivalued dependencies. At about the same time, other generalizations of known dependency forms were developed, such as the implicational dependencies of Fagin [11] and the algebraic dependencies of Yannakakis and Papadimitriou [20]. Unlike the template dependencies, the latter forms include the functional dependencies as special cases. In this paper we show that no nontrivial functional dependency follows from template dependencies, and we characterize those template dependencies that follow from functional dependencies. We then give a complete set of axioms for reasoning about combinations of functional and template dependencies. As a result, template dependencies augmented by functional dependencies can serve as a substitute for the more general implicational or algebraic dependencies, providing the same ability to represent those dependencies that appear ‘in nature’, while providing a somewhat simpler notation and set of axioms than the more general classes.\",\n \"Title: Optimizing joins in a map-reduce environment\\nYear: 2010\\n\\nAbstract: Implementations of map-reduce are being used to perform many operations on very large data. We examine strategies for joining several relations in the map-reduce environment. Our new approach begins by identifying the \\\"map-key,\\\" the set of attributes that identify the Reduce process to which a Map process must send a particular tuple. Each attribute of the map-key gets a \\\"share,\\\" which is the number of buckets into which its values are hashed, to form a component of the identifier of a Reduce process. Relations have their tuples replicated in limited fashion, the degree of replication depending on the shares for those map-key attributes that are missing from their schema. We study the problem of optimizing the shares, given a fixed number of Reduce processes. An algorithm for detecting and fixing problems where an attribute is \\\"mistakenly\\\" included in the map-key is given. Then, we consider two important special cases: chain joins and star joins. In each case we are able to determine the map-key and determine the shares that yield the least replication. While the method we propose is not always superior to the conventional way of using map-reduce to implement joins, there are some important cases involving large-scale data where our method wins, including: (1) analytic queries in which a very large fact table is joined with smaller dimension tables, and (2) queries involving paths through graphs with high out-degree, such as the Web or a social network.\",\n \"Title: On notions of information transfer in VLSI circuits\\nYear: 1983\\n\\nAbstract: Several papers have recently dealt with techniques for proving area-time lower bounds for VLSI computation by “crossing sequence” methods. A number of natural questions are raised by these definitions. 1.Is the fooling set approach the most powerful way to get information-transfer-based lower bounds? We shall show it is not, and offer a candidate for the title “most powerful.” Of course, without a precise definition of “information transfer argument,” there could be other contenders. 2.Are the notions of the three papers cited equivalent? We shall exhibit certain inequivalences among the three notions, although open questions remain. However, we can resolve an open question of Papadimitriou and Sipser [PS] concerning the relationship between nondeterministic and deterministic communication complexity.\",\n \"Title: A model of statistical database their security\\nYear: 1977\\n\\nAbstract: Considered here, for a particular model of databases in which only information about relatively large sets of records can be obtained, is the question of whether one can from statistical information obtain information about individuals. Under the assumption that the data in the database is taken from arbitrary integers, it is shown that essentially nothing can be inferred. It is also shown that when the values are known to be imprecise in some fixed range, one can often deduce the values of individual records.\",\n \"Title: Gradiance On-Line Accelerated Learning\\nYear: 2005\\n\\nAbstract: Gradiance On-Line Accelerated Learning (GOAL) is a system for creating and automatically grading homeworks, programming laboratories, and tests. Through the concept of \\\"root questions,\\\" Gradiance encourages students to solve complete problems, even though the homework appears to be in a multiple-choice format. Students are offered a hint or advice every time they get a wrong answer, and the student is allowed to try each question until getting it right. By grouping several questions together and telling students to try until they get a perfect score, GOAL turns homework into a true learning experience, rather than a \\\"mini-quiz.\\\"\",\n \"Title: Logical query optimization by proof-tree transformation\\nYear: 1993\\n\\nAbstract: For certain sets of logical rules, one can demonstrate that for every proof tree there is another tree proving the same fact and having a special “normal” form and thereby establish several useful properties of programs. Such a demonstration can be carried out in two parts: first establish that some containment holds between pieces of proof trees, and then show that repeated application of a transformation based on this containment converts every proof tree to the normal form. In this paper, we explore the power of arguments based on such proof-tree transformations and show that they provide a general tool for program optimization. We develop techniques to demonstrate useful proof-tree containments and show how certain program properties can be tested by using these containments in normal-form arguments. We discuss two techniques for demonstrating proof-tree containments. One is to reduce the question to one of conjunctive-query containment. A more powerful method is to test whether one conjunctive query is contained in the infinite union of conjunctive queries formed by expanding a set of recursive rules. We then consider two applications of these techniques. First, we give tests for commutativity of linear rules. When linear rules commute, we can reduce the complexity of “counting” methods for query evaluation from exponential to polynomial; commutativity is also an important element of separability in the sense of Naughton. A second application is to test whether certain non-linear rules are equivalent to linear rules that are obtained from them in a natural way.\",\n \"Title: A complete axiomatization for a large class of dependencies in relational datatbases\\nYear: 1980\\n\\nAbstract: Relational database theory has discovered complete axiomatizations for functional and multivalued dependencies. However, a database design system that makes use of dependencies declared by the user must deal with some more general kinds of dependencies than these— at least with embedded multivalued dependencies. Yet no axiomatization for embedded multivalued dependencies is known. In this paper, we define a more general class of dependencies, called “template dependencies” and give a complete axiomatization for these. We then discuss the interaction between functional dependencies and template dependencies.\",\n \"Title: Equivalence of relational database schemes\\nYear: 1979\\n\\nAbstract: We investigate the question of when two database schemes embody the same information. We argue that this question reduces to the equivalence of the sets of fixed points of the project-join mappings associated with the two database schemes in question. When data dependencies are given, we need only consider those fixed points that satisfy the dependencies. A polynomial algorithm to test the equivalence of database schemes, when there are no dependencies, is given. We also provide an exponential algorithm to handle the case where there are functional and/or multivalued dependencies. Furthermore, we give a polynomial time test to determine whether a project-join mapping preserves a set of functional dependencies, and a polynomial time algorithm for equivalence of database schemes whose project-join mappings do preserve the given set of functional dependencies. Lastly, we introduce the “update sets” approach to database design as an application of these results.\",\n \"Title: Information integration using logical views\\nYear: 2000\\n\\nAbstract: A number of ideas concerning information-integration tools can be thought of as constructing answers to queries using views that represent the capabilities of information sources. We review the formal basis of these techniques, which are closely related to containment algorithms for conjunctive queries and/or Datalog programs. Then we compare the approaches taken by AT&T Labs’ “Information Manifold” and the Stanford “Tsimmis” project in these terms.\",\n \"Title: A technique for speeding up LR(k) parsers\\nYear: 1972\\n\\nAbstract: We present a new transformation that reduces the size and increases the speed of LR(k) parsers. This transformation can be applied to all LR(k) parsers including those produced by Knuth's and DeRemer's techniques. The transformation causes the parser to avoid reductions by productions of the form A &ran; B where A and B are non-terminals.\",\n \"Title: Big data: a research agenda\\nYear: 2013\\n\\nAbstract: Recently, a great deal of interest for Big Data has risen, mainly driven from a widespread number of research problems strongly related to real-life applications and systems, such as representing, modeling, processing, querying and mining massive, distributed, large-scale repositories (mostly being of unstructured nature). Inspired by this main trend, in this paper we discuss three important aspects of Big Data research, namely OLAP over Big Data, Big Data Posting, and Privacy of Big Data. We also depict future research directions, hence implicitly defining a research agenda aiming at leading future challenges in this research field.\",\n \"Title: Characterizations and Extensions of Pushdown Translations\\nYear: 1971\\n\\nAbstract: A device called a pushdown assembler has been recently introduced and has been shown capable of defining exactly the syntax directed translations (SDT's). The output operation of the pushdown assembler can be extended in a natural way to obtain a more powerful device called a type B pushdown assembler (or B-machine). A B-machine can define SDT's more simply and directly than the original pushdown assembler. B-machines can also define many interesting translations which are not SDT's. In this paper the B-machine is defined and compared with the original pushdown assembler. The properties of B-machine translations are investigated and it is shown that, as with SDT's, there exists a natural infinite hierarchy of B-machine translations.\",\n \"Title: Universality of data retrieval languages\\nYear: 1979\\n\\nAbstract: We consider the question of how powerful a relational query language should be and state two principles that we feel any query language should satisfy. We show that although relational algebra and relational calculus satisfy these principles, there are certain queries involving least fixed points that cannot be expressed by these languages, yet that also satisfy the principles. We then consider various extensions of relational algebra to enable it to answer such queries. Finally, we discuss our extensions to relational algebra in terms of a new programming language oriented model for queries.\",\n \"Title: Crossfertilization Of DBMS Research With Other Disciplines Of Computer Science (Panel Discussion)\\nYear: 1978\\n\\nAbstract: The DBMS research is moving towards formalization of its problems and solutions. The DBMS researcher can learn and use the techniques that have been developed in formalizing the concepts in Operating systems, Artificial Intelligence, Adaptive systems and Pattern Recognition, and Programming Languages. The panel members are currently involved in research oriented towards cross fertilization of DBMS and various disciplines of computer science. The panel will try to identify commonality of problems, approach and solutions. We hope the discussion will stimulate research in the database area that can fully exploit what we have learned elsewhere.\",\n \"Title: A General Theory of Translation\\nYear: 1969\\n\\nAbstract: The concept of a translation is fundamental to any theory of compiling. Formally, atranslation is any set of pairs of words. Classes of finitely describable translations are considered in general, from the point of view of balloon automata [17, 18, 19].\",\n \"Title: Deadlock-free packet switching networks\\nYear: 1979\\n\\nAbstract: Deadlock is one of the most serious system failures that can occur in a computer system or a network. Deadlock states have been observed in existing computer networks emphasizing the need for carefully designed flow control procedures (controllers) to avoid deadlocks. Such a deadlock-free controller is readily found if we allow it global information about the overall network state. Generally, this assumption is not realistic, and we must resort to deadlock free local controllers using only packet and node information. We present here several types of such controllers, we study their relationship and give a proof of their optimality with respect to deadlock free controllers using the same set of local parameters.\",\n \"Title: Efficient and Complete Tests for Database Integrity Constraint Checking\\nYear: 1994\\n\\nAbstract: this paper we focus our discussion on constraints expressed as conjunctive queries witharithmetic comparisons, we suppose the only accessible relation is the updated relation, and weconsider updates that are insertions of a single tuple. The general form of a conjunctive queryconstraint is:\",\n \"Title: A Version Numbering Scheme with a Useful Lexicographical Order\\nYear: 1995\\n\\nAbstract: We describe a numbering scheme for versions with alternatives that has a useful lexicographical ordering. The version hierarchy is a tree. By inspection of the version numbers, we can easily determine whether one version is an ancestor of another. If so, we can determine the version sequence between these two versions. If not, we can determine the most recent common ancestor to these two versions (i.e., the least upper bound, lub). Sorting the version numbers lexicographically results in a version being followed by all descendants and preceded by all its ancestors. We use a representation of nonnegative integers that is self delimiting and whose lexicographical ordering matches the ordering by value.\",\n \"Title: Database systems: achievements and opportunities\\nYear: 1991\\n\\nAbstract: The history of database system research in the U.S. is one of exceptional productivity and startling economic impact. Barely twenty years old as a basic science research field, database research conducted with Federal support in the nation's universities and in its industrial research laboratories has fueled an information services industry estimated at $10 billion per year in the U.S. alone. This industry has grown at an average rate of 20 percent per year since 1965 and is continuing to expand at this rate. Achievements in database research underpin fundamental advances in communications systems, transportation and logistics, financial management, knowledge-based systems, accessibility to scientific literature, and a host of other civilian and defense applications. They also serve as the foundation for considerable progress in basic science in various fields ranging from computing to biology.\",\n \"Title: Fast algorithms for the elimination of common subexpressions\\nYear: 1973\\n\\nAbstract: We give two algorithms for computing the set of available expressions at entrance to the nodes of a flow graph. The first takes O(mn) isteps on a program flow graph (one in which no node has more than two successors), where n is the number of nodes and m the number of expressions which are ever computed. A modified version of this algorithm requires O(n2) steps of an extended type, where bit vector operations are regarded as one step. We present another algorithm which works only for reducible flow graphs. It requires O(n log n) extended steps.\",\n \"Title: Error Detection in Precedence Parsers\\nYear: 1973\\n\\nAbstract: We consider methods by which a precedence matrix may be modified without severely degrading the error-detecting capability of a parser utilizing the matrix. Two different definitions of “the same” error detection capability are considered. The first, called “exact equivalence”, permits only useless parts of the precedence matrix to be modified. The second, called simply “equivalence”, allows the modified matrix to perform some reductions, but never to shift an input symbol, after the original has detected an error.\",\n \"Title: Cluster computing, recursion and datalog\\nYear: 2010\\n\\nAbstract: The cluster-computing environment typified by Hadoop, the open-source implementation of map-reduce, is receiving serious attention as the way to execute queries and other operations on very large-scale data. Datalog execution presents several unusual issues for this enviroment. We discuss the best way to execute a round of seminaive evaluation on a computing cluster using the map-reduce. Using transitive closure as an example, we examine the cost of executing recursions in several different ways. Recursive processes such as evaluation of a recursive Datalog program do not fit the key map-reduce assumption that tasks deliver output only when they are completed. As a result, the resilience under compute-node failure that is a key element of the map-reduce framework is not supported for recursive programs. We discuss extensions to this framework that are suitable for executing recursive Datalog programs on very large-scale data in a way that allows progress to continue after node failures, without restarting the entire job.\",\n \"Title: Constraint checking with partial information\\nYear: 1994\\n\\nAbstract: Constraints are a valuable tool for managing information across multiple databases, as well as for general purposes of assuring data integrity. However, efficient implementation of constraint checking is difficult. In this paper we explore techniques for assuring constraint satisfaction without performing a complete evaluation of the constraints. We consider methods that use only constraint definitions, methods that use constraints and updates, and methods that use constraints, updates, and “local” data.\",\n \"Title: Upper And Lower Bounds On The Cost Of A Map-Reduce Computation\\nYear: 2012\\n\\nAbstract: In this paper we study the tradeoff between parallelism and communication cost in a map-reduce computation. For any problem that is not \\\"embarrassingly parallel,\\\" the finer we partition the work of the reducers so that more parallelism can be extracted, the greater will be the total communication between mappers and reducers. We introduce a model of problems that can be solved in a single round of map-reduce computation. This model enables a generic recipe for discovering lower bounds on communication cost as a function of the maximum number of inputs that can be assigned to one reducer. We use the model to analyze the tradeoff for three problems: finding pairs of strings at Hamming distance d, finding triangles and other patterns in a larger graph, and matrix multiplication. For finding strings of Hamming distance 1, we have upper and lower bounds that match exactly. For triangles and many other graphs, we have upper and lower bounds that are the same to within a constant factor. For the problem of matrix multiplication, we have matching upper and lower bounds for one-round map-reduce algorithms. We are also able to explore two-round map-reduce algorithms for matrix multiplication and show that these never have more communication, for a given reducer size, than the best one-round algorithm, and often have significantly less.\",\n \"Title: Map-reduce extensions and recursive queries\\nYear: 2011\\n\\nAbstract: We survey the recent wave of extensions to the popular map-reduce systems, including those that have begun to address the implementation of recursive queries using the same computing environment as map-reduce. A central problem is that recursive tasks cannot deliver their output only at the end, which makes recovery from failures much more complicated than in map-reduce and its nonrecursive extensions. We propose several algorithmic ideas for efficient implementation of recursions in the map-reduce environment and discuss several alternatives for supporting recovery from failures without restarting the entire job.\",\n \"Title: The TSIMMIS Project: Integration of Heterogeneous Information Sources.\\nYear: 1994\\n\\nAbstract: The goal of the Tsimmis Project is to develop tools that facilitate the rapid integration of heterogeneous information sources that may include both structured and unstructured data. This paper gives an overview of the project, describ-ing components that extract properties from unstructured objects, that translate information into a common object model, that combine information from several sources, that allow browsing of information, and that manage constraints across heterogeneous sites. Tsimmis is a joint project be-tween Stanford and the IBM Almaden Research Center.\",\n \"Title: Answering queries using limited external query processors\\nYear: 1999\\n\\nAbstract: When answering queries using external information sources, the contents of the queries can be described by views. To answer a query, we must rewrite it using the set of views presented by the sources. When the external information sources also have the ability to answer some (perhaps limited) sets of queries that require performing operations on their data, the set of views presented by the source may be infinite (albeit encoded in some finite fashion). Previous work on answering queries using views has only considered the case where the set of views is finite. In order to exploit the ability of information sources to answer more complex queries, we consider the problem of answering conjunctive queries using infinite sets of conjunctive views. Our first result is that an infinite set of conjunctive views can be partitioned into a finite number of equivalence classes, such that picking one view from every nonempty class is sufficient to determine whether the query can be answered using the views. Second, we show how to compute the set of equivalence classes for sets of conjunctive views encoded by a datalog program. Furthermore, we extend our results to the case when the query and the views use the built-in predicates <, ⩽, =, and ≠, and they are interpreted over a dense domain. Finally, we extend our results to conjunctive queries and views with the built-in predicates <, ⩽, and = interpreted over the integers. In doing so we present a result of independent interest, namely, an algorithm to minimize such queries.\",\n \"Title: Answering Queries Using Templates with Binding Patterns\\nYear: 1995\\n\\nAbstract: When integrating heterogeneous information resources, it is often the case that the source is rather limited in the kinds of queries it can answer. If a query is asked of the entire system, we have a new kind of optimization problem, in which we must try to express the given query in terms of the limit ed query templates that this source can answer. For the case of conjunctive queries, we show how to decide with a nondeterministic polynomial-time algorithm whether the given query can be answered. We then extend our results to allow arithmetic comparisons in the given query and in the templates.\",\n \"Title: Properties of acyclic database schemes\\nYear: 1981\\n\\nAbstract: There is a class of database descriptions, involving one “acyclic” join dependency and a collection of functional dependencies, and nothing else, that appears powerful enough to describe most any real-world body of data in relational database terms. Further, this class has many desirable properties. Some properties make operations like updates and the selection of joins to implement a query over a universal relation especially easy. Other properties of interest were studied by other researchers who described the same class in radically different terms, and found desirable properties in their own contexts. It is the purpose of this paper to define the class formally, to give its important properties and the equivalences with the other classes mentioned, and to explain the importance of each property. This paper is intended to summarize the results that will appear in more detail in [FMU] and [BFMY].\",\n \"Title: Principles of Optimal Page Replacement\\nYear: 1971\\n\\nAbstract: ABSTP~CT. A formal model is presented for paging algorithms under /-order nonstationary assumptions about program behavior. When processing a program under paging in a given memory, a given paging policy generates a certain (expected) number of page calls, i.e., its \\\"cost.\\\" Under usual assumptions about memory system organization, minimum cost is always achieved by a demand paging algorithm. The minimum cost for /-order program behavior assumptions is expressed as a dynamic programming problem whose solution yields an optimal replacement algorithm. Solutions are exhibited in several 0-order cases of interest. Paging al- gorithms that implement and approximate the minimum cost are discussed.\",\n \"Title: On protection in operating systems\\nYear: 1975\\n\\nAbstract: A model of protection mechanisms in computing systems is presented and its appropriateness is demonstrated. The “safety” problem for protection systems under our model is to determine in a given situation whether a subject can acquire a particular right to an object. In restricted cases, one can show that this problem is decidable, i.e., there is an algorithm to determine whether a system in a particular configuration is safe. In general, and under surprisingly weak assumptions, one cannot decide if a situation is safe. Various implications of this fact are discussed.\",\n \"Title: Deductive databases: achievements and future directions\\nYear: 1990\\n\\nAbstract: In the recent years, Deductive Databases have been the focus of intense research, which has brought dramatic advances in theory, systems and applications. A salient feature of deductive databases is their capability of supporting a declarative, rule-based style of expressing queries and applications on databases. As such, they find applications in disparate areas, such as knowledge mining from databases, and computer-aided design and manufacturing systems.In this paper, we briefly review the key concepts behind deductive databases and their newly developed enabling technology. Then, we describe current research on extending the functionality and usability of deductive databases and on providing a synthesis of deductive databases with procedural and object-oriented approaches.\",\n \"Title: Monotone data flow analysis frameworks\\nYear: 1977\\n\\nAbstract: We consider a generalization of Kildall's lattice theoretic approach to data flow analysis, which we call monotone data flow analysis frameworks. Many flow analysis problems which appear in practice meet the monotonicity condition but not Kildall's condition called distributivity. We show that the maximal fixed point solution exists for every instance of every monotone framework, and that it can be obtained by Kildall's algorithm. However, whenever the framework is monotone but not distributive, there are instances in which the desired solution--the \\\\\\\"meet over all paths solution\\\\\\\" -- differs from the maximal fixed point. Finally, we show the nonexistence of an algorithm to compute the meet over all paths solution for monotone frameworks.\",\n \"Title: On the equivalence of logical databases\\nYear: 1984\\n\\nAbstract: We suggest a new approach to database updates, in which a database is treated as a collection of theories. We investigate two issues: a) equivalence of databases under update operations, b) simultaneous multiple update operations.\",\n \"Title: Implementation of logical query languages for databases\\nYear: 1985\\n\\nAbstract: We examine methods of implementing queries about relational databases in the case where these queries are expressed in first-order logic as a collection of Horn clauses. Because queries may be defined recursively, straightforward methods of query evaluation do not always work, and a variety of strategies have been proposed to handle subsets of recursive queries. We express such query evaluation techniques as “capture rules” on a graph representing clauses and predicates. One essential property of capture rules is that they can be applied independently, thus providing a clean interface for query-evaluation systems that use several different strategies in different situations. Another is that there be an efficient test for the applicability of a given rule. We define basic capture rules corresponding to application of operators from relational algebra, a top-down capture rule corresponding to “backward chaining,” that is, repeated resolution of goals, a bottom-up rule, corresponding to “forward chaining,” where we attempt to deduce all true facts in a given class, and a “sideways” rule that allows us to pass results from one goal to another.\",\n \"Title: On the convergence of query evaluation\\nYear: 1989\\n\\nAbstract: We propose a “sideways information passing” method for evaluating Horn clause queries in the presence of monotonicity constraints on the database relations. We give a necessary and sufficient condition for its convergence and show that testing for the condition is polynomial when the arity of the relations is bounded, and PSPACE-complete otherwise. We also prove a related condition for the case of downward finite domains (e.g., Herbrand universes), thus improving an algorithm by Naish for a class of Prolog programs.\",\n \"Title: The Asilomar report on database research\\nYear: 1998\\n\\nAbstract: The database research community is rightly proud of success in basic research, and its remarkable record of technology transfer. Now the field needs to radically broaden its research focus to attack the issues of capturing, storing, analyzing, and presenting the vast array of online data. The database research community should embrace a broader research agenda — broadening the definition of database management to embrace all the content of the Web and other online data stores, and rethinking our fundamental assumptions in light of technology shifts. To accelerate this transition, we recommend changing the way research results are evaluated and presented. In particular, we advocate encouraging more speculative and long-range work, moving conferences to a poster format, and publishing all research literature on the Web.\",\n \"Title: A Comparison between Deductive and Object-Oriented Database Systems\\nYear: 1991\\n\\nAbstract: The author will not pretend to be unbiased in the matter of comparing the deductiveand object-oriented approaches to new database systems; he believes that despite someimportant concepts originating with the object-oriented approach, the deductive familyof systems will ultimately dominate. We shall explore some of the reasons why the twoapproaches do not mix well. Fortunately, most of the features of object-oriented databasesystems can be incorporated in deductive systems (but not...\",\n \"Title: Database Systems: Achievements and Opportunities - The \\\"Lagunita\\\" Report of the NSF Invitational Workshop on the Future of Database System Research held in Palo Alto, California, February 22-23, 1990\\nYear: 1990\\n\\nAbstract: The history of database system research in the U.S. is one of exceptional productivity and startling economic impact. Barely twenty years old as a basic science research field, database research conducted with Federal support in the nation's universities and in its industrial research laboratories has fueled an information services industry estimated at $10 billion per year in the U.S. alone. This industry has grown at an average rate of 20 percent per year since 1965 and is continuing to expand at this rate. Achievements in database research underpin fundamental advances in communications systems, transportation and logistics, financial management, knowledge-based systems, accessibility to scientific literature, and a host of other civilian and defense applications. They also serve as the foundation for considerable progress in basic science in various fields ranging from computing to biology.\",\n \"Title: Designing good algorithms for MapReduce and beyond\\nYear: 2012\\n\\nAbstract: As MapReduce/Hadoop grows in importance, we find more exotic applications being written this way. Not every program written for this platform performs as well as we might wish. There are several reasons why a MapReduce program can underperform expectations. One is the need to balance the communication cost of transporting data from the mappers to the reducers against the computation done at the mappers and reducers themselves. A second important issue is selecting the number of rounds of MapReduce. A third issue is that of skew. If wall-clock time is important, then using many different reduce-keys and many compute nodes may minimize the time to finish the job. Yet if the data is uncooperative, and no provision is made to distribute the data evenly, much of the work is done by a single node.\",\n \"Title: The database approach to knowledge representation\\nYear: 1996\\n\\nAbstract: The database theory community, centered around the PODS (Principles of Database Systems) conference has had a long-term interest in logic as a way to represent \\\"data\\\", \\\"information,\\\" and \\\"knowledge\\\" (take your pick on the term - it boils down to facts or atoms and rules, usually Horn clauses). The approach of this community has been \\\"slow and steady,\\\" preferring to build up carefully from simple special cases to more general ideas, always paying attention to how efficiently we can process queries and perform other operations on the facts and rules. A powerful theory has developed, and it is beginning to have some impact on applications, especially information-integration engines.\",\n \"Title: The interaction between functional dependencies and template dependencies\\nYear: 1980\\n\\nAbstract: A large class of dependencies, called template dependencies, was introduced in Sadri and Ullman [1979], and a complete set of inference rules (axioms) was given for it. In this paper, we investigate the interaction between template dependencies and functional dependencies. We develop techniques for partially deciding which template and functional dependencies are logically implied by a set of template and functional dependencies. Since template dependencies include all dependencies, besides functional dependencies, known to us (such as multivalued, join, mutual, generalized mutual, and subset dependencies, and their embedded versions), the results of this paper enable us to apply the techniques from Sadri and Ullman [1979] to any set of data dependencies.\",\n \"Title: Design overview of the NAIL] system\\nYear: 1986\\n\\nAbstract: We describe the design decisions made for the NAIL! (not another implementation of logic!) system, an advanced form of DBMS where queries may involve a large collection of Prolog-like rules used for query interpretation. A discussion of the ways NAIL! semantics differs from Prolog is followed by an exposition of the principal ideas in the system design. These points include the partition of predicates into strongly connected components to represent the structure of recursions and the capture rule organization for selecting query processing strategies. Other ideas include the way distinctions between bound and free arguments are capitalized upon and the persistence of previously discovered facts about the way to handle certain queries. We also survey the recent work on the processing of recursively defined queries conducted by the NAIL! group and others with similar computational models.\",\n \"Title: On complementary and independent mappings on databases\\nYear: 1984\\n\\nAbstract: We define the notion of independent views to indicate whether the range values of the two views may be achieved independently The concept of complementary views indicates when the domain element can be uniquely determined by the range values of the two complementary views We consider the relationship between independent and complementary views In unrestricted domains, a view (but not the identity or empty view) can have more than one complementary, independent view Databases, however, are more restricted domains They are finite power sets A view is monotonic if it preserves inclusion However, in finite power sets when all views are monotonic, if a given view has another view which is independent and complementary, then this view is unique.\",\n \"Title: Efficient Optimization of a Class of Relational Expressions (Abstract)\\nYear: 1978\\n\\nAbstract: Many useful database queries can be formulated in terms of expressions whose operands are relations and whose operators are the relational operations select, project, and join. This paper investigates the computational complexity of optimizing relational expressions of this form under a variety of cost measures. A matrix, called a tableau, is proposed as a conventient representative for the value of an expression. Functional dependencies can be used to imply additional equivalences among tableaux. The optimization problem is shown to be NP-complete, but we can give a polynomial time algorithm to optimize tableaux that correspond to an important subclass of expressions.\",\n \"Title: Viewpoint: Advising students for success\\nYear: 2009\\n\\nAbstract: Some advice for those doing the advising (and what the advisors can learn from the advisees).\",\n \"Title: The Compilation of Regular Expressions into Integrated Circuits\\nYear: 1982\\n\\nAbstract: We consider the design of integrated circuits to implement arbitrary regular expressions. In general, we may use the McNaughton-Yamada algorithm to convert a regular expression of length n into a nondeterministic finite automaton with at most 2n states and 4n transitions. Instead of converting the nondeterministic device to a deterministic one, we propose two ways of implementing the nondeterministic device directly. First, we could produce a PLA (programmable logic array) of approximate dimensions 4n 脳 4n by representing the states directly by columns, rather than coding the states in binary. This approach, while theoretically suboptimal, makes use of carefully developed technology and, because of the care with which PLA implementation has been done, may be the preferred technique in many real situations. Another approach is to use the hierarchical structure of the automaton produced from the regular expression to guide a hierarchical layout of the circuit. This method produces a circuit 0(√n) on a side and is, to within a constant factor, the best that can be done in general.\",\n \"Title: A simplied universal relation assumption and its properties\\nYear: 1982\\n\\nAbstract: One problem concerning the universal relation assumption is the inability of known methods to obtain a database scheme design in the general case, where the real-world constraints are given by a set of dependencies that includes embedded multivalued dependencies. We propose a simpler method of describing the real world, where constraints are given by functional dependencies and a single join dependency. The relationship between this method of defining the real world and the classical methods is exposed. We characterize in terms of hypergraphs those multivalued dependencies that are the consequence of a given join dependency. Also characterized in terms of hypergraphs are those join dependencies that are equivalent to a set of multivalued dependencies.\",\n \"Title: Algorithms for the Compilation of Regular Expressions into PLAs\\nYear: 1987\\n\\nAbstract: The language of regular expressions is a useful one for specifying certain sequential processes at a very high level. They\\n allow easy modification of designs for circuits, like controllers, that are described by patterns of events they must recognize\\n and the responses they must make to those patterns. This paper discusses the compilation of such expressions into specifications\\n for programmable logic arrays (PLAs) that will implement the required function. A regular expression is converted into a nondeterministic\\n finite automaton, and then the automaton states are encoded as values on wires that are inputs and outputs of a PLA. The translation\\n of regular expressions into nondeterministic automata by two different methods is discussed, along with the advantages of\\n each method. A major part of the compilation problem is selection of good state codes for the nondeterministic automata; one\\n successful strategy and its application to microcode compaction is explained in the paper.\",\n \"Title: Evaluating relational expressions with dense and sparse arguments\\nYear: 1975\\n\\nAbstract: We consider expressions whose arguments are relations and whose operators are chosen from among ∪, ο, *, and -1. We further assume that operands may be designated \\\"sparse\\\" or \\\"dense\\\", in a manner to be made formal subsequently. Our aim is to determine whether the evaluation of such an expression is (a) as hard as general transitive closure (b) as hard as transitive closure for sparse graphs. (c) as hard as connected components of an undirected graph.\",\n \"Title: Schema Design for Uncertain Databases\\nYear: 2009\\n\\nAbstract: ABSTRACT We address schema design in uncertain databases. Since uncertain data is relational in nature, decomposition be- comes a key issue in design. Decomposition relies on depen- dency theory, and primarily on functional dependencies. We study the theory of functional dependencies (FDs) for uncertain relations. We dene,several kinds of horizontal FDs and vertical FDs, each of which is consistent with con- ventional FDs when,an uncertain relation doesn’t contain any uncertainty. In addition to standard forms of decom- positions allowed by ordinary relations, our FDs allow more complex decompositions specic,to uncertain data. We show how our theory of FDs can be used for lossless decomposition of uncertain relations. We then present algorithms and com- plexity results for three fundamental problems with respect to FDs over ordinary and uncertain relations: (1) Testing whether a relation instance satises,an FD; (2) Finding all FDs satised,by a relation instance; and (3) Inferring all FDs that hold in the result of a query over uncertain rela- tions with FDs. We also give a sound and complete axiom- atization of horizontal and vertical FDs. We look at keys as a special case of FDs. Finally, we briey consider uncertain data that contains condence,values.\",\n \"Title: Selected database research at Stanford\\nYear: 1990\\n\\nAbstract: This report describes seven projects at the Computer Science Department of Stanford University that may be relevant to SIGMOD community.\",\n \"Title: The complexity of ordering subgoals\\nYear: 1988\\n\\nAbstract: Selection of an appropriate order for the evaluation of subgoals in a logical rule frequently is essential for efficiency. We formulate the problem as one of feasible subgoal orders and show that the question is inherently exponential in time. The proof is by reduction from linear-space alternating Turing machine recognition, which appears to be far easier, in this case, than the more obvious reduction from exponential-time (ordinary) Turing machines\",\n \"Title: On the semantics of updates in databases\\nYear: 1983\\n\\nAbstract: We suggest here a methodology for updating databases with integrity constraints and rules for deriving inexphcit information. First we consider the problem of updating arbitrary theories by inserting into them or deleting from them arbitrary sentences. The solution involves two key ideas when replacing an old theory by a new one we wish to minimize the change in the theory, and when there are several theories that involve minimal changes, we look for a new theory that reflects that ambiguity. The methodology is also adapted to updating databases, where different facts can carry different priorities, and to updating user views.\",\n \"Title: MedMaker: A Mediation System Based on Declarative Specifications\\nYear: 1996\\n\\nAbstract: Mediators are used for integration of heterogeneous information sources. We present a system for declaratively specifying mediators. It is targeted for integration of sources with unstructured or semi-structured data and/or sources with changing schemas. We illustrate the main features of the Mediator Specification Language (MSL), show how they facilitate integration, and describe the implementation of the system that interprets the MSL specifications.\",\n \"Title: The MIDAS Data-Mining Project at Stanford\\nYear: 1999\\n\\nAbstract: The following notes summarize recent research into data-mining techniques that are in progress at Stanford: 1. The Google search engine: beating Yahoo et al. At their own game. 2. Query Flocks: Generalizing association rules/market baskets in a query precompiler that uses a relational DBMS effectively. 3. Synthesizing Knowledge from the Web: exploiting the Web's redundancy to extract data automatically. 4. Detecting Low-Frequency Events: Unlike marketing, where you only care about items that lots of people buy, extracting intelligence from text usually requires looking for a small number of unexpected juxtapositions of terms.\",\n \"Title: The revenge of the JD\\nYear: 1983\\n\\nAbstract: Manifestations of the \\\"universal relation assumption\\\" can be seen either as definitions of a one-relation user view of data, or as algorithms for answering queries about arbitrary sets of attributes. In this paper we explore equivalences between these two points of view. We show that if the user's view is the representative instance, then our ability to answer queries about the universal relation, by applying relational algebra to the actual database, is equivalent to a \\\"boundedness\\\" condition on the dependencies of the database scheme. Further, whenever this condition holds, there is a finite union of lossless tableau mappings that produces the desired relation.\",\n \"Title: The U. R. strikes back\\nYear: 1982\\n\\nAbstract: In this paper, we try to put to rest many of the objections to the universal relation concept that have appeared in the literature. First, we shall taxonomize the varieties of ideas that are sometimes called the \\\"universal relation assumption.\\\" Then, we consider some of the arguments pro and con. In some cases, the arguments against were expressed prematurely, and solutions to the problems they expose have since been found. In other cases, the arguments against are simply fallacious. In still other cases, the problems pointed out are real, but simply serve to point out that the advantages of the universal relation are not gotten for free. We shall conclude the paper with a description of the algorithm used to interpret queries in System/U, and the reasoning behind it.\",\n \"Title: On finding lowest common ancestors in trees\\nYear: 1973\\n\\nAbstract: Trees in an n node forest are to be merged according to instructions in a given sequence, while other instructions in the sequence ask for the lowest common ancestor of pairs of nodes. We show that any sequence of O(n) instructions can be processed “on line” in O(n log n) steps on a random access computer. If we can accept our answer “off-line”, that is, no answers need to be produced until the entire sequence of instructions has been seen seen, then we may perform the task in O(n G(n)) steps, where G(n) is the number of times we must apply log2 to n to obtain a number less than or equal to zero. A third algorithm solves a problem of intermediate complexity. We require the answers on line, but we suppose that all tree merging instructions precede the information requests. This algorithm requires O(n log log n) time. We apply the first on line algorithm to a problem in code optimization, that of computing immediate dominators in a reducible flow graph. We show how this computation can be performed in O(n log n) steps.\",\n \"Title: Finding the Depth of a Flow Graph\\nYear: 1976\\n\\nAbstract: The depth of a flow graph is the maximum number of back edges in a cycle free path, where a back edge is defined by some depth-first spanning tree for the flow graph. In the case of a reducible graph, the depth is independent of the DFST chosen. We show that the computation of the depth of a reducible flow graph may be done in polynomial time. Our algorithm is 0(ne) on a flow graph of n nodes and e edges. Since e≤2n for normal flow graphs, our algorithm is really 0(n2). While even an 0(n2) algorithm is not likely to be acceptable, it is suggestive of the possibility of a more efficient algorithm. Finally, we show that the general problem of computing the depth of an arbitrary flow graph with respect to an arbitrary DFST is NP-complete.\",\n \"Title: Economy of description by parsers, DPDA's, and PDA's\\nYear: 1975\\n\\nAbstract: It is shown that there is a sequence of languages E 1 , E 2 ,… such that every correct prefix parser (one which detects errors at the earliest possible moment, e.g., LR or LL parsers) for E n has size 2 cn , yet a deterministic PDA recognizing E n exists and has size O( n 2 ). There is another easily described sequence of languages N 1 , N 2 ,… for which N has a nondeterministic PDA of size O( n 2 ), but no deterministic PDA of size less than 2 cn . It is shown moreover, that this latter gap can be made arbitrarily large for different sequences of languages.\",\n \"Title: Dynamic itemset counting and implication rules for market basket data\\nYear: 1997\\n\\nAbstract: We consider the problem of analyzing market-basket data and present several important contributions. First, we present a new algorithm for finding large itemsets which uses fewer passes over the data than classic algorithms, and yet uses fewer candidate itemsets than methods based on sampling. We investigate the idea of item reordering, which can improve the low-level efficiency of the algorithm. Second, we present a new way of generating “implication rules,” which are normalized based on both the antecedent and the consequent and are truly implications (not simply a measure of co-occurrence), and we show how they produce more intuitive results than other methods. Finally, we show how different characteristics of real data, as opposed by synthetic data, can dramatically affect the performance of the system and the form of the results.\",\n \"Title: Maximal objects and the semantics of universal relation databases\\nYear: 1983\\n\\nAbstract: The universal relation concept is intended to provide the database user with a simplified model in which he can compose queries without regard to the underlying structure of the relations in the database. Frequently, the lossless join criterion provides the query interpreter with the clue needed to interpret the query as the user intended. However, some examples exist where interpretation by the lossless-join rule runs contrary to our intuition. To handle some of these cases, we propose a concept called maximal objects, which modifies the universal relation concept in exactly those situations where it appears to go awry—when the underlying relational structure has “cycles.” We offer examples of how the maximal object concept provides intuitively correct interpretations. We also consider how one might construct maximal objects mechanically from purely syntactic structural information—the relation schemes and functional dependencies—about the database.\",\n \"Title: Using views to generate efficient evaluation plans for queries\\nYear: 2007\\n\\nAbstract: We study the problem of generating efficient, equivalent rewritings using views to compute the answer to a query. We take the closed-world assumption, in which views are materialized from base relations, rather than views describing sources in terms of abstract predicates, as is common when the open-world assumption is used. In the closed-world model, there can be an infinite number of different rewritings that compute the same answer, yet have quite different performance. Query optimizers take a logical plan (a rewriting of the query) as an input, and generate efficient physical plans to compute the answer. Thus our goal is to generate a small subset of the possible logical plans without missing an optimal physical plan. We first consider a cost model that counts the number of subgoals in a physical plan, and show a search space that is guaranteed to include an optimal rewriting, if the query has a rewriting in terms of the views. We also develop an efficient algorithm for finding rewritings with the minimum number of subgoals. We then consider a cost model that counts the sizes of intermediate relations of a physical plan, without dropping any attributes, and give a search space for finding optimal rewritings. Our final cost model allows attributes to be dropped in intermediate relations. We show that, by careful variable renaming, it is possible to do better than the standard ''supplementary relation'' approach, by dropping attributes that the latter approach would retain. Experiments show that our algorithm of generating optimal rewritings has good efficiency and scalability.\",\n \"Title: Global Data Flow Analysis and Iterative Algorithms\\nYear: 1976\\n\\nAbstract: Kildall has developed data propagation algorithms for code optimization in a general lattice theoretic framework. In another direction, Hecht and Ullman gave a strong upper bound on the number of iterations required for propagation algorithms when the data is represented by bit vectors and depth-first ordering of the flow graph is used. The present paper combines the ideas of these two papers by considering conditions under which the bound of Hecht and Ullman applies to the depth-first version of Kildall's general data propagation algorithm. It is shown that the following condition is necessary and sufficient: Let ƒ and g be any two functions which could be associated with blocks of a flow graph, let x be an arbitrary lattice element, and let 0 be the lattice zero. Then (*) (∀ƒ,g,x) [ƒg(0) ≥ g(0) ∧ ƒ(x) ∧ x]. Then it is shown that several of the particular instances of the techniques Kildall found useful do not meet condition (*).\",\n \"Title: A Query Translation Scheme for Rapid Implementation of Wrappers\\nYear: 1995\\n\\nAbstract: Wrappers provide access to heterogeneous information sources by converting application queries into source speci c queries or commands. In this paper we present a wrapper implemen-tation toolkit that facilitates rapid development of wrappers. We focus on the query translation component of the toolkit, called the converter. The converter takes as input a Query Descrip-tion and Translation Language (QDTL) description of the queries that can be processed by the underlying source. Based on this description the converter decides if an application query is (a) directly supported, i. e., it can be translated to a query of the underlying system following instructions in the QDTL description; (b) logically supported, i. e., logically equivalent to a di-rectly supported query; (c) indirectly supported, i. e., it can be computed by applying a lter, automatically generated by the converter, to the result of a directly supported query.\",\n \"Title: Efficient tests for top-down termination of logical rules\\nYear: 1988\\n\\nAbstract: Considered is the question of whether top-down (Prolog-like) evaluation of a set of logical rules can be guaranteed to terminate. The NAIL! system is designed to process programs consisting of logical rules and to select, for each fragment of the program, the best from among many possible strategies for its evaluation. In the context of such a system, it is essential that termination tests be fast. Thus, the “uniqueness” property of logical rules is introduced. This property is satisfied by many of the common examples of rules and is easily recognized. For rules with this property, a set of inequalities, whose satisfaction is sufficient for termination of the rules, can be generated in polynomial time. Then a polynomial test for satisfaction of constraints generated by this process is given.\",\n \"Title: Database Research: Achievements and Opportunities into the 21st Century\\nYear: 1996\\n\\nAbstract: In May, 1995 an NSF workshop on the future of database management systems research was convened. This paper reports the conclusions of that meeting. Among the most important directions for future DBMS research recommended by the panel are: support for multimedia objects; managing distributed and loosely coupled information, as on the world-wide web; supporting new database applications such as data mining and warehousing; workflow and other complex transaction-management problems, and enhancing the ease-of-use of DBMS''s for both users and system managers.\",\n \"Title: A Survey of Association-Rule Mining\\nYear: 2000\\n\\nAbstract: The standard model for association-rule mining involves a set of \\\"items\\\" and a set of \\\"baskets.\\\" The baskets contain items that some customer has purchased at the same time. The problem is to find pairs, or perhaps larger sets, of items that frequently appear together in baskets. We mention the principal approaches to eefficient, large-scale discovery of the frequent itemsets, including the a-priori algorithm, improvements using hashing, and one- and two-pass probabilistic algorithms for finding frequent itemsets. We then turn to techniques for finding highly correlated, but infrequent, pairs of items. These notes were written for CS345 at Stanford University and are reprinted by permission of the author. http://www-db.stanford.edu/~ullman/mining/mining.html gives you access to the entire set of notes, including additional citations and on-line links.\",\n \"Title: A survey of deductive database systems\\nYear: 1995\\n\\nAbstract: The area of deductive databases has matured in recent years, and it now seems appropriate to reflect upon what has been achieved and what the future holds. In this paper, we provide an overview of the area, with a focus on implementation techniques, and briefly describe a number of projects that have led to implemented systems.\",\n \"Title: Optimizing Multiway Joins in a Map-Reduce Environment\\nYear: 2011\\n\\nAbstract: Implementations of map-reduce are being used to perform many operations on very large data. We examine strategies for joining several relations in the map-reduce environment. Our new approach begins by identifying the “map-key,” the set of attributes that identify the Reduce process to which a Map process must send a particular tuple. Each attribute of the map-key gets a “share,” which is the number of buckets into which its values are hashed, to form a component of the identifier of a Reduce process. Relations have their tuples replicated in limited fashion, the degree of replication depending on the shares for those map-key attributes that are missing from their schema. We study the problem of optimizing the shares, given a fixed number of Reduce processes. An algorithm for detecting and fixing problems where a variable is mistakenly included in the map-key is given. Then, we consider two important special cases: chain joins and star joins. In each case, we are able to determine the map-key and determine the shares that yield the least replication. While the method we propose is not always superior to the conventional way of using map-reduce to implement joins, there are some important cases involving large-scale data where our method wins, including: 1) analytic queries in which a very large fact table is joined with smaller dimension tables, and 2) queries involving paths through graphs with high out-degree, such as the Web or a social network.\",\n \"Title: Flux, sorting, and supercomputer organization for AI applications\\nYear: 1984\\n\\nAbstract: A central issue for the design of massively parallel computers is their ability to sort. We consider organizations that are suitable for fast sorting, both those that use point-to-point connections and those that connect processors with multipoint nets. We show that for fast sorting and minimal area, nets must connect at least √n nodes each, if the network has n nodes. We then discuss some of the ways that fast-sorting networks can be used to speed up other processes, such as combinatorial search.\",\n \"Title: The Input/Output Complexity of Transitive Closure.\\nYear: 1990\\n\\nAbstract: Suppose a directed graph has its arcs stored in secondary memory, and we wish to compute its transitive closure, also storing the result in secondary memory. We assume that an amount of main memory capable of holding “values” is available, and that lies between, the number of nodes of the graph, and, the number of arcs. The cost measure we use for algorithms is the of Kung and Hong, where we count 1 every time a value is moved into main memory from secondary memory, or vice versa.In the dense case, where is close to, we show that I/O equal to(/√) is sufficient to compute the transitive closure of an-node graph, using main memory of size. Moreover, it is necessary for any algorithm that is “standard,” in a sense to be defined precisely in the paper. Roughly, “standard” means that paths are constructed only by concatenating arcs and previously discovered paths. For the sparse case, we show that I/O equal to(√) is sufficient, although the algorithm we propose meets our definition of “standard” only if the underlying graph is acyclic. We also show that(√) is necessary for any standard algorithm in the sparse case. That settles the I/O complexity of the sparse/acyclic case, for standard algorithms. It is unknown whether this complexity can be achieved in the sparse, cyclic case, by a standard algorithm, and it is unknown whether the bound can be beaten by nonstandard algorithms.We then consider a special kind of standard algorithm, in which paths are constructed only by concatenating arcs and old paths, never by concatenating two old paths. This restriction seems essential if we are to take advantage of sparseness. Unfortunately, we show that almost another factor of I/O is necessary. That is, there is an algorithm in this class using I/O(√) for arbitrary sparse graphs, including cyclic ones. Moreover, every algorithm in the restricted class must use(√/log) I/O, on some cyclic graphs.\",\n \"Title: A Characterization of Two-Way Deterministic Classes of Languages\\nYear: 1970\\n\\nAbstract: It is shown that a class of languages is defined by a class of two-way deterministic balloon automata if and only if that class is closed under marked union, marked Kleene closure and the inverse mappings performed by deterministic GSMs that move two ways on the input. Hence, the context sensitive languages and various time and tape complexity classes are equivalent to classes of two-way deterministic balloon automata.\",\n \"Title: Template Dependencies: A Large Class of Dependencies in Relational Databases and Its Complete Axiomatization\\nYear: 1982\\n\\nAbstract: Relauonal database theory has d~scovered complete axlomauzations for functional and multivalued dependencies. However, a database design system based on dependencies must deal with some more general kinds of dependencies than these--at least with embedded multivalued dependencies Yet no axiomatizatton for embedded multivalued dependencies is known. A more general class of dependencies, called \\\"template dependencies,\\\" is detrmed in this paper, and a complete ax~omatizat~on is given for them.\",\n \"Title: A recognition algorithm for pushdown store systems\\nYear: 1968\\n\\nAbstract: A pushdown store is a list in which information can be accessed only on a last-in first-out principle of operation. The use of pushdown stores is an important technique in the construction of compilers and other language-processing devices. Of particular interest from both practical and theoretical considerations is how the time and memory required to process a language is functionally related to the length of the input sentence under consideration. In this paper we consider languages which can be defined by pushdown store systems. To obtain quantitative results, a two-way (off-line) nondeterministic pushdown automaton (2N PDA, for short) is used as an analytical model of a pushdown store system, and the two-way deterministic push-down automaton (2D PDA, for short) is also considered as a special case.\",\n \"Title: Scalable Techniques for Mining Causal Structures\\nYear: 2000\\n\\nAbstract: Mining for association rules in market basket data has proved a fruitful area of research. Measures such as conditional probability (confidence) and correlation have been used to infer rules of the form “the existence of item A implies the existence of item B.” However, such rules indicate only a statistical relationship between A and B. They do not specify the nature of the relationship: whether the presence of A causes the presence of B, or the converse, or some other attribute or phenomenon causes both to appear together. In applications, knowing such causal relationships is extremely useful for enhancing understanding and effecting change. While distinguishing causality from correlation is a truly difficult problem, recent work in statistics and Bayesian learning provide some avenues of attack. In these fields, the goal has generally been to learn complete causal models, which are essentially impossible to learn in large-scale data mining applications with a large number of variables.In this paper, we consider the problem of determining casual relationships, instead of mere associations, when mining market basket data. We identify some problems with the direct application of Bayesian learning ideas to mining large databases, concerning both the scalability of algorithms and the appropriateness of the statistical techniques, and introduce some initial ideas for dealing with these problems. We present experimental results from applying our algorithms on several large, real-world data sets. The results indicate that the approach proposed here is both computationally feasible and successful in identifying interesting causal structures. An interesting outcome is that it is perhaps easier to infer the lack of causality than to infer causality, information that is useful in preventing erroneous decision making.\",\n \"Title: Storing and querying tree-structured records in Dremel\\nYear: 2014\\n\\nAbstract: In Dremel, data is stored as nested relations. The schema for a relation is a tree, all of whose nodes are attributes, and whose leaf attributes hold values. We explore filter and aggregate queries that are given in the Dremel dialect of SQL. Complications arise because of repeated attributes, i.e., attributes that are allowed to have more than one value. We focus on the common class of Dremel queries that are processed on column-stored data in a way that results in query processing time that is linear on the size of the relevant data, i.e., data in the columns that participate in the query. We formally define the data model, the query language and the algorithms for query processing in column-stored data. The concepts of repetition context and semi-flattening are introduced here and play a central role in understanding this class of queries and their algorithms.\",\n \"Title: Assignment of Different-Sized Inputs in MapReduce.\\nYear: 2015\\n\\nAbstract: A MapReduce algorithm can be described by a mapping schema, which assigns inputs to a set of reducers, such that for each required output there exists a reducer that receives all the inputs that participate in the computation of this output. Reducers have a capacity, which limits the sets of inputs that they can be assigned. However, individual inputs may vary in terms of size. We consider, for the first time, mapping schemas where input sizes are part of the considerations and restrictions. One of the significant parameters to optimize in any MapReduce job is communication cost between the map and reduce phases. The communication cost can be optimized by minimizing the number of copies of inputs sent to the reducers. The communication cost is closely related to the number of reducers of constrained capacity that are used to accommodate appropriately the inputs, so that the requirement of how the inputs must meet in a reducer is satisfied. In this work, we consider a family of problems where it is required that each input meets with each other input in at least one reducer. We also consider a slightly different family of problems in which, each input of a set, X, is required to meet each input of another set, Y, in at least one reducer. We prove that finding an optimal mapping schema for these families of problem is NP-hard, and present several approximation algorithms for finding a near optimal mapping schema.\",\n \"Title: GYM: A Multiround Join Algorithm In MapReduce.\\nYear: 2014\\n\\nAbstract: Multiround algorithms are now commonly used in distributed data processing systems, yet the extent to which algorithms can benefit from running more rounds is not well understood. This paper answers this question for a spectrum of rounds for the problem of computing the equijoin of $n$ relations. Specifically, given any query $Q$ with width $\\\\w$, {\\\\em intersection width} $\\\\iw$, input size $\\\\mathrm{IN}$, output size $\\\\mathrm{OUT}$, and a cluster of machines with $M$ memory available per machine, we show that: (1) $Q$ can be computed in $O(n)$ rounds with $O(n\\\\frac{(\\\\mathrm{IN}^{\\\\w} + \\\\mathrm{OUT})^2}{M})$ communication cost. (2) $Q$ can be computed in $O(\\\\log(n))$ rounds with $O(n\\\\frac{(\\\\mathrm{IN}^{\\\\max(\\\\w, 3\\\\iw)} + \\\\mathrm{OUT})^2}{M})$ communication cost. \\\\end{itemize} Intersection width is a new notion of queries and generalized hypertree decompositions (GHDs) of queries we introduce to capture how connected the adjacent cyclic components of the GHDs are. We achieve our first result by introducing a distributed and generalized version of Yannakakis's algorithm, called GYM. GYM takes as input any GHD of $Q$ with width $\\\\w$ and depth $d$, and computes $Q$ in $O(d + \\\\log(n))$ rounds and $O(n\\\\frac{(\\\\mathrm{IN}^{\\\\w} + \\\\mathrm{OUT})^2}{M})$ communication cost. We achieve our second result by showing how to construct GHDs of $Q$ with width $\\\\max(\\\\w, 3\\\\iw)$ and depth $O(\\\\log(n))$. We describe another technique to construct GHDs with longer widths and shorter depths, demonstrating a spectrum of tradeoffs one can make between communication and the number of rounds.\",\n \"Title: Handling Skew in Multiway Joins in Parallel Processing.\\nYear: 2015\\n\\nAbstract: Handling skew is one of the major challenges in query processing. In distributed computational environments such as MapReduce, uneven distribution of the data to the servers is not desired. One of the dominant measures that we want to optimize in distributed environments is communication cost. In a MapReduce job this is the amount of data that is transferred from the mappers to the reducers. In this paper we will introduce a novel technique for handling skew when we want to compute a multiway join in one MapReduce round with minimum communication cost. This technique is actually an adaptation of the Shares algorithm [Afrati et. al, TKDE 2011].\",\n \"Title: Bounds for Overlapping Interval Join on MapReduce.\\nYear: 2015\\n\\nAbstract: We consider the problem of 2-way interval join, where we want to find all pairs of overlapping intervals, i.e., intervals that share at least one point in common. We present lower and upper bounds on the replication rate for this problem when it is implemented in MapReduce. We study three cases, where intervals in the input are: (i) unit-length and equally-spaced, (ii) variable-length and equally-spaced, and (iii) equally-spaced with specific distribution of the various lengths. Our algorithms offer intuition as how to build algorithms for other cases, especially when we have some statistical knowledge about the distribution of the lengths of the intervals. E.g., if mostly large intervals interact with small intervals and not within themselves, then we believe our techniques can be extended to achieve better replication rate.\",\n \"Title: Representative objects: concise representations of semistructured, hierarchical data\\nYear: 1997\\n\\nAbstract: Introduces the concept of representative objects, which uncover the inherent schema(s) in semi-structured, hierarchical data sources and provide a concise description of the structure of the data. Semi-structured data, unlike data stored in typical relational or object-oriented databases, does not have a fixed schema that is known in advance and stored separately from the data. With the rapid growth of the World Wide Web, semi-structured hierarchical data sources are becoming widely available to the casual user. The lack of external schema information currently makes browsing and querying these data sources inefficient at best, and impossible at worst. We show how representative objects make schema discovery efficient and facilitate the generation of meaningful queries over the data.\",\n \"Title: Index selection for OLAP\\nYear: 1997\\n\\nAbstract: On-line analytical processing (OLAP) is a recent and important application of database systems. Typically, OLAP data is presented as a multidimensional “data cube.” OLAP queries are complex and can take many hours or even days to run, if executed directly on the raw data. The most common method of reducing execution time is to precompute some of the queries into summary tables (subcubes of the data cube) and then to build indexes on these summary tables. In most commercial OLAP systems today, the summary tables that are to be precomputed are picked first, followed by the selection of the appropriate indexes on them. A trial-and-error approach is used to divide the space available between the summary tables and the indexes. This two-step process can perform very poorly. Since both summary tables and indexes consume the same resource-space-their selection should be done together for the most efficient use of space. The authors give algorithms that automate the selection of summary tables and indexes. In particular, they present a family of algorithms of increasing time complexities, and prove strong performance bounds for them. The algorithms with higher complexities have better performance bounds. However, the increase in the performance bound is diminishing, and they show that an algorithm of moderate complexity can perform fairly close to the optimal\",\n \"Title: Some Pairs Problems.\\nYear: 2016\\n\\nAbstract: A common form of MapReduce application involves discovering relationships between certain pairs of inputs. Similarity joins serve as a good example of this type of problem, which we call a \\\\\\\"some-pairs\\\\\\\" problem. In the framework of [4], algorithms are measured by the tradeoff between reducer size (maximum number of inputs a reducer can handle) and the replication rate (average number of reducers to which an input must be sent. There are two obvious approaches to solving some-pairs problems in general. We show that no general-purpose MapReduce algorithm can beat both of these two algorithms in the worst case. We then explore a recursive algorithm for solving some-pairs problems and heuristics for beating the lower bound on common instances of the some-pairs class of problems.\",\n \"Title: Assignment Problems of Different-Sized Inputs in MapReduce\\nYear: 2015\\n\\nAbstract: A MapReduce algorithm can be described by a mapping schema, which assigns inputs to a set of reducers, such that for each required output there exists a reducer that receives all the inputs participating in the computation of this output. Reducers have a capacity that limits the sets of inputs they can be assigned. However, individual inputs may vary in terms of size. We consider, for the first time, mapping schemas where input sizes are part of the considerations and restrictions. One of the significant parameters to optimize in any MapReduce job is communication cost between the map and reduce phases. The communication cost can be optimized by minimizing the number of copies of inputs sent to the reducers. The communication cost is closely related to the number of reducers of constrained capacity that are used to accommodate appropriately the inputs, so that the requirement of how the inputs must meet in a reducer is satisfied. In this work, we consider a family of problems where it is required that each input meets with each other input in at least one reducer. We also consider a slightly different family of problems in which each input of a list, X, is required to meet each input of another list, Y, in at least one reducer. We prove that finding an optimal mapping schema for these families of problems is NP-hard, and present a bin-packing-based approximation algorithm for finding a near optimal mapping schema.\",\n \"Title: SharesSkew: An Algorithm to Handle Skew for Joins in MapReduce\\nYear: 2015\\n\\nAbstract: •A novel algorithm to handle skew for multiway joins.•Introduction of residual joins to deal with skew.•Communication cost minimization based on reducer capacity.•Examination of important classes of joins.\",\n \"Title: Computing Marginals Using MapReduce.\\nYear: 2018\\n\\nAbstract: We consider the problem of computing data-cube marginals by a single round of MapReduce, focusing on the relationship between the reducer size and the replication rate. Initially, we simplify the problem by making the extent of each dimension the same. Several recursive constructions meet or come close to the minimum possible replication rate for a given reducer size. These ideas extend in two directions. We relax the assumption that the extents are all equal, and we consider how to compute marginals from lower-order marginals rather than from the raw data cube.\",\n \"Title: Dynamic Memories with Rapid Random and Sequential Access\\nYear: 1974\\n\\nAbstract: We propose a new architecture for dynamic memories in which the contents of any cell in memory can be accessed by applying a sequence of two primitive memory operations. The advantage of our memory over previous designs is its fast sequential access. Any word in an n cell memory can be accessed in 0(log n) steps. However, once two consecutive words have been accessed, following words can be accessed in one step per word.\",\n \"Title: Experiments as research validation: have we gone too far?\\nYear: 2015\\n\\nAbstract: Reconsidering conference paper reviewers' requirements for experimental evidence.\",\n \"Title: Efficient And Private Approximations Of Distributed Databases Calculations\\nYear: 2017\\n\\nAbstract: In recent years, an increasing amount of data is collected in different and often, not cooperative, databases. The problem of privacy-preserving, distributed calculations over separate databases and, a relative to it, the issue of private data release were intensively investigated. However, despite a considerable progress, computational complexity, due to an increasing size of data, remains a limiting factor in real-world deployments, especially in case of privacy-preserving computations.In this paper, we suggest sampling as a method of improving computational performance. Sampling was a topic of extensive research that recently received a boost of interest. We provide a sampling method targeted at separate, non-collaborating, vertically partitioned datasets. The method is exemplified and tested on approximation of intersection set both without and with privacy-preserving mechanism. An analysis of the bound on error as a function of the sample size is discussed and heuristic algorithm is suggested to further improve the performance. The algorithms were implemented and experimental results confirm the validity of the approach.\",\n \"Title: Shasta: Interactive Reporting At Scale.\\nYear: 2016\\n\\nAbstract: We describe Shasta, a middleware system built at Google to support interactive reporting in complex user-facing applications related to Google's Internet advertising business. Shasta targets applications with challenging requirements: First, user query latencies must be low. Second, underlying transactional data stores have complex \\\\\\\"read-unfriendly\\\\\\\" schemas, placing significant transformation logic between stored data and the read-only views that Shasta exposes to its clients. This transformation logic must be expressed in a way that scales to large and agile engineering teams. Finally, Shasta targets applications with strong data freshness requirements, making it challenging to precompute query results using common techniques such as ETL pipelines or materialized views. Instead, online queries must go all the way from primary storage to user-facing views, resulting in complex queries joining 50 or more tables. Designed as a layer on top of Google's F1 RDBMS and Mesa data warehouse, Shasta combines language and system techniques to meet these requirements. To help with expressing complex view specifications, we developed a query language called RVL, with support for modularized view templates that can be dynamically compiled into SQL. To execute these SQL queries with low latency at scale, we leveraged and extended F1's distributed query engine with facilities such as safe execution of C++ and Java UDFs. To reduce latency and increase read parallelism, we extended F1 storage with a distributed read-only in-memory cache. The system we describe is in production at Google, powering critical applications used by advertisers and internal sales teams. Shasta has significantly improved system scalability and software engineering efficiency compared to the middleware solutions it replaced.\",\n \"Title: Portrait of an Indexer - Computing Pointers Into Instructional Videos.\\nYear: 2016\\n\\nAbstract: We examine algorithms for creating indexes into ordered series ofinstructional lecture video transcripts. The goal is for students andindustry practitioners to use the indexes towards review orreference. Lecture videos differ from often-examined documentcollections such as newspaper articles in that the transcript orderinggenerally reflects pedagogical intent. One challenge is therefore toidentify where a concept is primarily introduced, and where theresulting index should thus direct students. The typically appliedTF-IDF approach gets tricked in this context by artifacts such asworked examples whose associated vocabulary may dominate a lecture,but should not be included in a good index. We contrast the TF-IDFapproach with algorithms that consult Wikipedia documents to vouch forterm importance. This method helps filter the harmful artifacts. Wemeasure the algorithms against three human-created indexes over the 90lecture videos of a popular database course. We found that (i) humans have low inter-rater reliability, whether they are experts inthe field or not, and that (em ii) one of the examined algorithmsapproaches the inter-rater reliability with humans.\",\n \"Title: Panel: The State of Data: Invited Paper from panelists.\\nYear: 2016\\n\\nAbstract: This panel critically examines the state of data: how its growth and ubiquity have confronted the computer science and particularly the database community, with new challenges. These challenges require practitioners and teachers to learn new skills and engage with other disciplines in ways they had not done before. Panelists will examine the impact of the 'bigness' of data, and its importance for an ever-increasing array of applications, as well as the implications for traditional ideas of privacy and person-hood. By bringing together data specialists with those trained in social and human sciences, this panel aims to initiate discussion about the new social role the computer scientist and the database community have begun to play.\",\n \"Title: Computing Marginals Using MapReduce: Keynote talk paper.\\nYear: 2016\\n\\nAbstract: We consider the problem of computing the data-cube marginals of a fixed order k (i.e., all marginals that aggregate over k dimensions), using a single round of MapReduce. The focus is on the relationship between the reducer size (number of inputs allowed at a single reducer) and the replication rate (number of reducers to which an input is sent). We show that the replication rate is minimized when the reducers receive all the inputs necessary to compute one marginal of higher order. That observation lets us view the problem as one of covering sets of k dimensions with sets of a larger size m, a problem that has been studied under the name \\\\\\\"covering numbers.\\\\\\\" We offer a number of constructions that, for different values of k and m meet or come close to yielding the minimum possible replication rate for a given reducer size.\",\n \"Title: GYM: A Multiround Distributed Join Algorithm.\\nYear: 2017\\n\\nAbstract: Multiround algorithms are now commonly used in distributed data processing systems, yet the extent to which algorithms can benefit from running more rounds is not well understood. This paper answers this question for several rounds for the problem of computing the equijoin of n relations. Given any query Q with width w, intersection width iw, input size IN, output size OUT, and a cluster of machines with M=Omega(IN frac{1}{epsilon}) memory available per machine, where epsilon u003e 1 and w ge 1 are constants, we show that:1. Q can be computed in O(n) rounds with O(n(INw + OUT)2/M) communication cost with high probability.Q can be computed in O(log(n)) rounds with O(n(INmax(w, 3iw) + OUT)2/M) communication cost with high probability.Intersection width is a new notion we introduce for queries and generalized hypertree decompositions (GHDs) of queries that captures how connected the adjacent components of the GHDs are. achieve our first result by introducing a distributed and generalized version of Yannakakisu0027s algorithm, called GYM. GYM takes as input any GHD of Q with width w and depth d, and computes Q in O(d + log(n)) rounds and O(n (INw + OUT)2/M) communication cost. We achieve our second result by showing how to construct GHDs of Q with width max(w, 3iw) and depth O(log(n)). We describe another technique to construct GHDs with longer widths and lower depths, demonstrating other tradeoffs one can make between communication and the number of rounds.\",\n \"Title: Report from the third workshop on Algorithms and Systems for MapReduce and Beyond (BeyondMR'16).\\nYear: 2017\\n\\nAbstract: This report summarizes the presentations and discussions of the third workshop on Algorithms and Systems for MapReduce and Beyond (BeyondMR'16). The BeyondMR workshop was held in conjunction with the 2016 SIGMOD conference in San Francisco, California, USA on July 1, 2016. The goal of the workshop was to bring together researchers and practitioners to explore algorithms, computational models, architectures, languages and interfaces for systems that need largescale parallelization and systems designed to support efficient parallelization and fault tolerance. These include specialized programming and data-management systems based on MapReduce and extensions, graph processing systems, data-intensive workflow and dataflow systems. The program featured two very well attended invited talks by Ion Stoica from AMPLab, University of California Berkeley and Carlos Guestrin from the University of Washington.\",\n \"Title: Report from the Fourth Workshop on Algorithms andSystems for MapReduce and Beyond (BeyondMR '17).\\nYear: 2017\\n\\nAbstract: This report summarizes the presentations and discussions of the fourth workshop on Algorithms and Systems for MapReduce and Beyond (BeyondMR '17). The BeyondMR workshop was held in conjunction with the 2017 SIGMOD/PODS conference in Chicago, Illinois, USA on Friday May 19, 2017. The goal of the workshop was to bring together researchers and practitioners to explore algorithms, computational models, languages and interfaces for systems that provide large-scale parallelization and fault tolerance. These include specialized programming and data-management systems based on MapReduce and extensions thereof, graph processing systems and data-intensive workflow systems. \\n\\nThe program featured two well-attended invited talks, the first on current and future development in big data processing by Matei Zaharia from Databricks and the University of Stanford, and the second on computational models for the analysis and development of big data processing algorithms by Ke Yi from the Hong Kong University of Science and Technology.\",\n \"Title: Partitioned Data Security on Outsourced Sensitive and Non-sensitive Data.\\nYear: 2018\\n\\nAbstract: Despite extensive research on cryptography, secure and efficient query processing over outsourced data remains an open challenge. This paper continues along the emerging trend in secure data processing that recognizes that the entire dataset may not be sensitive, and hence, non-sensitivity of data can be exploited to overcome limitations of existing encryption-based approaches. We propose a new secure approach, entitled query binning (QB) that allows non-sensitive parts of the data to be outsourced in clear-text while guaranteeing that no information is leaked by the joint processing of non-sensitive data (in clear-text) and sensitive data (in encrypted form). QB maps a query to a set of queries over the sensitive and non-sensitive data in a way that no leakage will occur due to the joint processing over sensitive and non-sensitive data. Interestingly, in addition to improve performance, we show that QB actually strengthens the security of the underlying cryptographic technique by preventing size, frequency-count, and workload-skew attacks.\",\n \"Title: Argument Reduction by Factoring\\nYear: 1989\\n\\nAbstract: We identify a useful property of a program with re- spect to a predicate, called factoring. While we prove that detecting factorability is undecidable in general, we show that for a large class of programs, the pro- gram obtained by applying the Magic Sets transfor- mation is factorable with respect to the recursive predicate. When the factoring property holds, a sim- ple optimization of the program generated by the Magic Sets transformation results in a new program that is never less efficient than the Magic Sets pro- gram and is often dramatically more efficient, due to the reduction of the arity of the recursive predi- cate. We show that the concept of factoring general- izes some previously identified special cases of recur- sions, including separable recursions and right- and left-linear recursions, and that the specialized evalua- tion algorithms and rewriting strategies developed for\",\n \"Title: Optimizing Large Join Queries in Mediation Systems\\nYear: 1999\\n\\nAbstract: In data integration systems, queries posed to a mediator need to be translated into a sequence of queries to the underlying data sources. In a heterogeneous environment, with sources of diverse and limited query capabilities, not all the translations are feasible. In this paper, we study the problem of finding feasible and efficient query plans for mediator systems. We consider conjunctive queries on mediators and model the source capabilities through attribute-binding adornments. We use a simple cost model that focuses on the major costs in mediation systems, those involved with sending queries to sources and getting answers back. Under this metric, we develop two algorithms for source query sequencing - one based on a simple greedy strategy and another based on a partitioning scheme. The first algorithm produces optimal plans in some scenarios, and we show a linear bound on its worst case performance when it misses optimal plans. The second algorithm generates optimal plans in more scenarios, while having no bound on the margin by which it misses the optimal plans. We also report on the results of the experiments that study the performance of the two algorithms.\",\n \"Title: Scaling Cryptographic Techniques by Exploiting Data Sensitivity at a Public Cloud.\\nYear: 2019\\n\\nAbstract: Despite extensive research on cryptography, secure and efficient query processing over outsourced data remains an open challenge. This poster continues along the emerging trend in secure data processing that recognizes that the entire dataset may not be sensitive, and hence, non-sensitivity of data can be exploited to overcome some of the limitations of existing encryption-based approaches. In particular, this poster outlines a new secure keyword search approach, called query keyword binning (QB) that allows non-sensitive parts of the data to be outsourced in clear-text while guaranteeing that no information is leaked by joint processing of non-sensitive data (in clear-text) and sensitive data (in encrypted form). QB improves the performance of and strengthens the security of the underlying cryptographic technique by preventing size, frequency-count, and workload-skew attacks.\",\n \"Title: Efficient Multiway Hash Join on Reconfigurable Hardware.\\nYear: 2019\\n\\nAbstract: We propose the algorithms for performing multiway joins using a new type of coarse grain reconfigurable hardware accelerator~-- ``Plasticine''~-- that, compared with other accelerators, emphasizes high compute capability and high on-chip communication bandwidth. Joining three or more relations in a single step, i.e. multiway join, is efficient when the join of any two relations yields too large an intermediate relation. We show at least 200X speedup for a sequence of binary hash joins execution on Plasticine over CPU. We further show that in some realistic cases, a Plasticine-like accelerator can make 3-way joins more efficient than a cascade of binary hash joins on the same hardware, by a factor of up to 45X.\",\n \"Title: Efficient and Privacy Preserving Approximation of Distributed Statistical Queries\\nYear: 2022\\n\\nAbstract: In recent years, an increasing amount of data is collected in different and often, not cooperative, databases. The problem of privacy-preserving, distributed calculations over separate databases and, a relative to it, the issue of private data release was intensively investigated. However, despite a considerable progress, computational complexity and consequently, the performance of the computations, due to an increasing size of data, remains a limiting factor in real-world deployments. Especially in the case of privacy-preserving computations. In this paper, we suggest sampling as a method of improving computational performance. Sampling was a topic of extensive research in the past that recently received a boost of interest. We provide a sampling method targeted at separate, non-collaborating, vertically partitioned datasets. The method is exemplified and tested on an approximation of intersection set both with and without a privacy-preserving mechanism. An analysis of the bound on the error as a function of the sample size is discussed and a heuristic algorithm is suggested to further improve the performance. The algorithms were implemented and experimental results confirm the validity of the approach.\",\n \"Title: On the Nature of Data Science\\nYear: 2021\\n\\nAbstract: ABSTRACTOne can hear \\\"Data Science\\\" defined as a synonym for machine learning or as a branch of Statistics. I shall argue that it is far more than that; it is the natural evolution of the technology of very large-scale data management to solve problems in scientific and commercial fields. To support my argument, I shall give a brief introduction to two algorithms that are important in data science but that are neither machine learning nor statistics: locality-sensitive hashing and counting distinct elements. Biography: Jeff Ullman is the Stanford W. Ascherman Professor of Engineering (Emeritus) in the Department of Computer Science at Stanford and CEO of Gradiance Corp. He received the B.S. degree from Columbia University in 1963 and the PhD from Princeton in 1966. Prior to his appointment at Stanford in 1979, he was a member of the technical staff of Bell Laboratories from 1966-1969, and on the faculty of Princeton University between 1969 and 1979. From 1990-1994, he was chair of the Stanford Computer Science Department. Ullman was elected to the National Academy of Engineering in 1989, the American Academy of Arts and Sciences in 2012, the National Academy of Sciences in 2020, and has held Guggenheim and Einstein Fellowships. He has received the Sigmod Contributions Award (1996), the ACM Karl V. Karlstrom Outstanding Educator Award (1998), the Knuth Prize (2000), the Sigmod E. F. Codd Innovations award (2006), the IEEE von Neumann medal (2010), the NEC C&C Foundation Prize (2017), and the ACM A.M. Turing Award (2020). He is the author of 16 books, including books on database systems, data mining, compilers, automata theory, and algorithms.\",\n \"Title: Abstractions, Their Algorithms, and Their Compilers\\nYear: 2022\\n\\nAbstract: COMPUTATIONAL THINKING, WHICH centers around devising abstractions for problems so they can be solved with computational steps and efficient algorithms, is a concept that serves not only computer science (CS) but progressively more of science and everyday life.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":141,"string":"141"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13655,"string":"13,655"}}},{"rowIdx":13656,"cells":{"question_id":{"kind":"string","value":"Helge Janicke"},"question":{"kind":"string","value":"Generate an abstract for the title \"The Agile Incident Response for Industrial Control Systems (AIR4ICS) framework\" using the following items: cyber incident response, industrial control systems, Agile, framework, flexibility."},"answer":{"kind":"string","value":"AbstractAbstractCyber incident response within Industrial Control Systems (ICS) is characterised by high levels of uncertainty and unpredictability and requires a multi-disciplined team that encompasses personnel business operations, Operational Technology (OT), IT, security operations and media engagement to be effective. Such teams require a dynamic decision framework to allow ICS operators to maintain services during the recovery of full operating capability. There is empirical evidence that static incident response playbooks do not provide enough flexibility in their definition to support situations outside of the scope of their initial definition, and that they have been ignored when cyber incidents have occurred. A thematic analysis of semi-structured interviews with ICS incident response professional identified three main areas of concern: communication, information sharing between knowledge areas, and achieving external buy-in.The Agile Incident Response for Industrial Control Systems (AIR4ICS) framework has been developed to integrate Agile techniques into the Cyber Security domain of incident response. AIR4ICS provides a dynamic approach to improve situational awareness, information sharing, collective decision-making and response flexibility within the unique context of ICS. The techniques used in AIR4ICS were initially shaped by interviews with professionals with experience of protecting ICS, structured using the Scrum methodology, and refined through a series of Cyber Incident Response exercises with Incident Response professionals facing-off against specialist ICS Red Teams.AIR4ICS has resulted in a framework that provides a modular approach that can be adapted to fit the working practices, skillsets and priorities of individual organisations. The framework improves communication, promotes information sharing between knowledge areas, and increases external buy-in.Ultimately, AIR4ICS provides a dynamic decision framework that allows Incident Response Teams to manage uncertainty and unpredictability to reduce the time taken to restore normal operations."},"correct_answer":{"kind":"string","value":"AbstractAbstractCyber incident response within Industrial Control Systems (ICS) is characterised by high levels of uncertainty and unpredictability and requires a multi-disciplined team that encompasses personnel business operations, Operational Technology (OT), IT, security operations and media engagement to be effective. Such teams require a dynamic decision framework to allow ICS operators to maintain services during the recovery of full operating capability. There is empirical evidence that static incident response playbooks do not provide enough flexibility in their definition to support situations outside of the scope of their initial definition, and that they have been ignored when cyber incidents have occurred. A thematic analysis of semi-structured interviews with ICS incident response professional identified three main areas of concern: communication, information sharing between knowledge areas, and achieving external buy-in.The Agile Incident Response for Industrial Control Systems (AIR4ICS) framework has been developed to integrate Agile techniques into the Cyber Security domain of incident response. AIR4ICS provides a dynamic approach to improve situational awareness, information sharing, collective decision-making and response flexibility within the unique context of ICS. The techniques used in AIR4ICS were initially shaped by interviews with professionals with experience of protecting ICS, structured using the Scrum methodology, and refined through a series of Cyber Incident Response exercises with Incident Response professionals facing-off against specialist ICS Red Teams.AIR4ICS has resulted in a framework that provides a modular approach that can be adapted to fit the working practices, skillsets and priorities of individual organisations. The framework improves communication, promotes information sharing between knowledge areas, and increases external buy-in.Ultimately, AIR4ICS provides a dynamic decision framework that allows Incident Response Teams to manage uncertainty and unpredictability to reduce the time taken to restore normal operations."},"documents":{"kind":"list like","value":["Title: Controlling data dissemination\nYear: 2011\n\nAbstract: We present a policy-based approach for the control of information flow between services. The controlled dissemination of information shared between services is achieved by the communication and enforcement of policies associated with shared information. The presented approach integrates easily as a communication middle-ware in existing service-based information systems keeping the management and distribution of policies transparent to the service implementation. This article focuses on the architecture and presents initial results that show the feasibility of the approach.","Title: A note on the formalisation of UCON\nYear: 2007\n\nAbstract: Usage Control (UCON) Models, similar to Access Control Models, control and govern the users' access to resources and services that are available in the system. One of the major improvements of UCON over traditional access control models is the continuity of the control and the concept of attribute mutability. In this paper we provide an alternative formalisation of the UCON model that relaxes many of the assumptions made in earlier formalisations of the model. We question the enforceability of UCON policies as described by previous formalisations and improve on it.","Title: Decentralized XACML Overlay Network\nYear: 2010\n\nAbstract: We propose a novel approach for the collaborative enforcement of security policies in distributed systems that is based on the dynamic (re-) deployment of multiple PDPs. The policies enforced by the collaborating PDPs are analysed and decomposed from a system wide policy as present in current centralized approaches. The security policy is decomposed into sub-policies based on an object domain approach so the decisions are local to the object's domain. The classes of policies investigated are dynamic history-based access control policies, ie. the PDPs decision is dependent on the history of interaction between users and system resources. This type of policy can capture static and dynamic separation of duty policies, as are commonly found in commercial organisations. The distribution model of the PDP allows for the coordination and synchronisation of PDPs on the basis of events, where a decision is based on a previous history originating from other PDPs. The key contribution of this paper is the analysis of temporal dependencies between policies and an efficient PDP distribution strategy for object-based distributed systems as well as presenting a designed library that create and synchronize a network of PDP's in a peer to peer fashion.","Title: Optimizing Software Quality Assurance\nYear: 2010\n\nAbstract: A major concern for managers of software projects are the triple constraints of cost, schedule and quality due to the difficulties to quantify accurately the trade-offs between them. Project managers working for accredited companies with a high maturity will typically use software cost estimation models like COCOMO II and predict software quality by the estimated number of defects the product is likely to contain at release. However, most of these models are used separately and the interplay between cost/effort estimation, project scheduling and the resultant quality of the software product is not well understood. In this paper, we propose a regression-based model that allows project managers to estimate the trade-off between the quality, cost and development time of a software product, based on previously collected data.","Title: Efficient Data Processing for Large-Scale Cloud Services\nYear: 2012\n\nAbstract: The cloud concept and its implementations are gaining in importance for systems that connect evermore new devices which in turn require communication with each other. In scenarios where we can find large numbers of data providers on one side and data consumers on the other side, such as in the Internet of Things, large scale sensor networks, machine to machine communication or even in social media, one emerging requirement is to process, procure, and provide information efficiently and with almost zero latency. This work is introducing new concepts to describe the flow of data to and from sources to cloud services in a formal way by limiting information flow with filtering concepts and combining data processing techniques adopted from complex event processing.","Title: Dynamic Access Control Policies: Specification and Verification.\nYear: 2013\n\nAbstract: Security requirements deal with the protection of assets against unauthorized access (disclosure or modification) and their availability to authorized users. Temporal constraints of history-based access control policies are difficult to express naturally in traditional policy languages. We propose a compositional formal framework for the specification and verification of temporal access control po...","Title: Concurrent Enforcement of Usage Control Policies\nYear: 2008\n\nAbstract: Policy-based approaches to the management of systems distinguish between the specification of requirements, in the form of policies, and their enforcement on the system. In this work we focus on the latter aspect and investigate the enforcement of stateful policies in a concurrent environment. As a representative of stateful policies we use the UCON model and show how dependencies between policy rules affect their enforcement. We propose a technique for enforcing policies concurrently based on the static analysis of dependencies between policies. The potential of our technique for improving the efficacy of enforcement mechanisms is illustrated using a small, but representative example.","Title: Verification and enforcement of access control policies\nYear: 2013\n\nAbstract: Access control mechanisms protect critical resources of systems from unauthorized access. In a policy-based management approach, administrators define user privileges as rules that determine the conditions and the extent of users' access rights. As rules become more complex, analytical skills are required to identify conflicts and interactions within the rules that comprise a system policy--especially when rules are stateful and depend on event histories. Without adequate tool support such an analysis is error-prone and expensive. In consequence, many policy specifications are inconsistent or conflicting that render the system insecure. The security of the system, however, does not only depend on the correct specification of the security policy, but in a large part also on the correct interpretation of those rules by the system's enforcement mechanism.In this paper, we show how policy rules can be formalized in Fusion Logic, a temporal logic for the specification of behavior of systems. A symbolic decision procedure for Fusion Logic based on Binary Decision Diagrams (BDDs) is provided and we introduce a novel technique for the construction of enforcement mechanisms of access control policy rules that uses a BDD encoded enforcement automaton based on input traces which reflect state changes in the system. We provide examples of verification of policy rules, such as absence of conflicts, and dynamic separation of duty and of the enforcement of policies using our prototype implementation (FLCheck) for which we detail the underlying theory.","Title: Data Confidentiality in Mobile Ad hoc Networks\nYear: 2012\n\nAbstract: Mobile ad hoc networks (MANETs) are self-configuring infrastructure-less networks comprised of mobile nodes that communicate over wireless links without any central control on a peer-to-peer basis. These individual nodes act as routers to forward both their own data and also their neighbours' data by sending and receiving packets to and from other nodes in the network. The relatively easy configuration and the quick deployment make ad hoc networks suitable the emergency situations (such as human or natural disasters) and for military units in enemy territory. Securing data dissemination between these nodes in such networks, however, is a very challenging task. Exposing such information to anyone else other than the intended nodes could cause a privacy and confidentiality breach, particularly in military scenarios. In this paper we present a novel framework to enhance the privacy and data confidentiality in mobile ad hoc networks by attaching the originator policies to the messages as they are sent between nodes. We evaluate our framework using the Network Simulator (NS-2) to check whether the privacy and confidentiality of the originator are met. For this we implemented the Policy Enforcement Points (PEPs), as NS-2 agents that manage and enforce the policies attached to packets at every node in the MANET.","Title: Deriving Enforcement Mechanisms from Policies\nYear: 2007\n\nAbstract: Policies provide a flexible and scalable approach to the management of distributed systems by separating the specification of security requirements and their enforcement. Over the years the expressiveness of policy languages increased considerably making it possible to capture a variety of complex requirements that for example depend on the history of the system execution. The most important criteria for the successful operation of policy-managed systems is whether the deployed enforcement mechanisms can guarantee the compliance with the policies. With the expressiveness of policy languages this assurance is increasingly difficult to achieve. In this paper we therefore address the development of enforcement mechanisms from a theoretical perspective and show how enforcement code can be formally derived for compositional, history-dependent policies that can change dynamically over time or on the occurrence of events.","Title: Multi-Factor Authentication Using Hardware Information and User Profiling Techniques.\nYear: 2012\n\nAbstract: This paper presents a multi-factor authentication approach that extends traditional usernamepassword authentication with hardware and user behaviour profiling techniques. The aim of the approach is to improve the reliability of authentication by computing trust and confidence scores against user profiles. Based on the level of trust, the access control mechanisms may then choose to (un-)lock certain functions or even classify the access as an attack and redirect the user to a honey-pot to gather additional information about the attacker that can be used for a trace-back. The novelty of the approach is that it observes the correlation between usersu0027 behaviours and their hardware usage as implicit verification procedures to discriminate the usage of the user-name and password entry.","Title: An Introduction to Cyber Peacekeeping.\nYear: 2018\n\nAbstract: Cyber is the newest domain of war, and the topic of cyber warfare is one that is receiving increasing attention. Research efforts into cyber warfare are extensive, covering a range of issues such as legality, cyber weapons and deterrence. Despite all of the research activity around cyber warfare, one aspect has been largely overlooked: the restoration of peace and security in its aftermath. In this article, we present the argument that cyber warfare will threaten civilian peace and security long after a conflict has ended, and that existing peace operations will be required to evolve in order to address this threat. We explore how existing UN peacekeeping operations could be adapted, in ways that would be both feasible and valuable towards maintaining and restoring peace in a region. We conclude that the path to cyber peacekeeping will not be easy, but that it is an evolution that must begin today so that we can be prepared for the conflicts of the future.","Title: Analysis and run-time verification of dynamic security policies\nYear: 2005\n\nAbstract: Ensuring the confidentiality, integrity and availability of information is the key issue in the battle for information superiority and thus is a decisive factor in modern warfare. Security policies and security mechanisms govern the access to information and other resources. Their correct specification, i.e. denial of potentially dangerous access and adherence to all established need-to-know requirements, is critical. In this paper we present a security model that allows to express dynamic access control policies that can change on time or events. A simple agent system, simulating a platoon, is used to show the need and the advantages of our policy model. The paper finally presents how existing tool-support can be used for the analysis and verification of policies.","Title: A Model-Based Approach for RFID Application Testing\nYear: 2013\n\nAbstract: A new approach for testing Radio Frequency Identification applications is presented in this paper. Therefore, a set based semantics is introduced to describe RFID applications. The approach uses UML to model the characteristics of the system under test. The combination of three different model-types, which consist of a Domain Model, a Movement Model and Process Models, allows for the extraction of test cases and the evaluation of functional aspects of the application under test. This model-based test approach is validated on an example case study, of processes in a retail clothing shop.","Title: A Compositional Event & Time-Based Policy Model\nYear: 2006\n\nAbstract: Policies are increasingly used to govern the behaviour of complex distributed systems. Most policy models that allow policy composition, to address the complexity of policies, are only concerned with structural composition. In this paper we argue that it is natural to compose policies also along the temporal axis, i.e. express policies that can dynamically change over time or on the occurrence of events. We present a dynamic policy frame-work that has a sound semantics in Interval Temporal Logic and allows both structural and temporal composition. We provide examples of authorisation, delegation and obligation policies that can be activity-based, state-based or historybased, i.e. expressing decisions on the history of execution. Examples for the composition of policies show how the framework can be used to express policies for systems that operate in an environment that is characterised by uncertainty. Finally tool-support for the specification and analysis of dynamic policies is presented.","Title: A Property Based Framework for Trust and Reputation in Mobile Computing\nYear: 2009\n\nAbstract: In this paper we reinforce the benefits of a trust based approach in mobile multi-agent systems. Whilst we do not specify a trust model we provide a framework for property based trust, defining the observations which underpin many pre-existing trust models. This framework maintains subjectivity which should be the basis of any trust based system yet enables entities to communicate and collaborate trust information regardless of the trust model used for deliberation. We also introduce dasiatrust communitiespsila as a representation of property based trust information thus enabling the collaboration of reputation information.","Title: Autonomous agents and multi –agent systems (AAMAS) for the military – issues and challenges\nYear: 2005\n\nAbstract: The military domain is a very challenging environment and human endeavour in this domain is characterized by uncertainty and the need to be able to deal with significant and disruptive dynamic changes. In addition, activities are driven by human decision-makers who need support in making sense of the environment and with reasoning about, and effecting, possible futures. Hence, various unique factors need to be taken into account when considering the provision of applications, tools, devices and infrastructure for the military domain. This paper will itemize and discuss some of these factors in the context of autonomous agents and multi-agent systems. This paper is a desiderata for the research space.","Title: Position Paper: Safety and Security Monitoring in ICS/SCADA Systems\nYear: 2014\n\nAbstract: Supervisory control and Data Acquisition (SCADA) systems play a core role in a nation's critical infrastructure, overseeing the monitoring and control of systems in electricity, gas supply, logistics services, banks and hospitals. Monitoring safety and security properties in industrial control system (ICS) and SCADA environments faces unique challenges not found in typical enterprise networks. Novel monitoring solutions are desirable that take into account these differences. This paper presents a new approach for monitoring safety and security properties in industrial control systems. The approach is based on verifying a formal specification of ICS/SCADA components during runtime that is capable of detecting abnormal system behaviours. The solution is miniaturised and can be deployed at various points throughout the SCADA network, making masquerading and man-in-the-middle attacks more difficult to execute successfully.","Title: Critical Theory as an Approach to the Ethics of Information Security.\nYear: 2014\n\nAbstract: Information security can be of high moral value. It can equally be used for immoral purposes and have undesirable consequences. In this paper we suggest that critical theory can facilitate a better understanding of possible ethical issues and can provide support when finding ways of addressing them. The paper argues that critical theory has intrinsic links to ethics and that it is possible to identify concepts frequently used in critical theory to pinpoint ethical concerns. Using the example of UK electronic medical records the paper demonstrates that a critical lens can highlight issues that traditional ethical theories tend to overlook. These are often linked to collective issues such as social and organisational structures, which philosophical ethics with its typical focus on the individual does not tend to emphasise. The paper suggests that this insight can help in developing ways of researching and innovating responsibly in the area of information security.","Title: An Extended Stable Marriage Problem Algorithm for Clone Detection.\nYear: 2014\n\nAbstract: Code cloning negatively affects industrial software and threatens intellectual property. This paper presents a novel approach to detecting cloned software by using a bijective matching technique. The proposed approach focuses on increasing the range of similarity measures and thus enhancing the precision of the detection. This is achieved by extending a well-known stable-marriage problem (SMP) and demonstrating how matches between code fragments of different files can be expressed. A prototype of the proposed approach is provided using a proper scenario, which shows a noticeable improvement in several features of clone detection such as scalability and accuracy.","Title: Cyber warfare: Issues and challenges.\nYear: 2015\n\nAbstract: The topic of cyber warfare is a vast one, with numerous sub topics receiving attention from the research community. We first examine the most basic question of what cyber warfare is, comparing existing definitions to find common ground or disagreements. We discover that there is no widely adopted definition and that the terms cyber war and cyber warfare are not well enough differentiated. To address these issues, we present a definition model to help define both cyber warfare and cyber war. The paper then identifies nine research challenges in cyber warfare and analyses contemporary work carried out in each. We conclude by making suggestions on how the field may best be progressed by future efforts.","Title: Low-Latency Service Data Aggregation Using Policy Obligations\nYear: 2014\n\nAbstract: The Internet of Things, large scale sensor networks or even in social media, are now well established and their use is growing daily. Usage scenarios in these fields highlight the requirement to process, procure, and provide information with almost zero latency. This work is introducing new concepts for enabling fast communication by limiting information flow through filtering concepts combined with data processing techniques adopted from complex event processing. Specifically we introduce a novel mediation services architecture using filter policies to reduce latency. The filter policies define when and what data services need to provide to the mediator and thus save on bandwidth. The filter policies describe temporal conditions between two events removing the need to keep a complete history while still allowing temporal reasoning. Promising experimental results highlight the advantages to be gained from the approach.","Title: RFID systems from a cyber-physical systems perspective\nYear: 2013\n\nAbstract: This paper surveys and compares various definitions of cyber-physical systems occurring in contemporary research literature and relates them to RFID system research. In comparing the major characteristics of both we conclude that RFID systems are very similar to CPS and develop a topology of CPS that includes RFID. To frame the discussion of RFID systems closer we also develop a formal model that allows for the specification of RFID systems and explore the models' suitability to express a wider range of CPS.","Title: Social Internet of Vehicles for Smart Cities.\nYear: 2016\n\nAbstract: Digital devices are becoming increasingly ubiquitous and interconnected. Their evolution to intelligent parts of a digital ecosystem creates novel applications with so far unresolved security issues. A particular example is a vehicle. As vehicles evolve from simple means of transportation to smart entities with new sensing and communication capabilities, they become active members of a smart city. The Internet of Vehicles (IoV) consists of vehicles that communicate with each other and with public networks through V2V (vehicle-to-vehicle), V2I (vehicle-to-infrastructure) and V2P (vehicle-to-pedestrian) interactions, which enables both the collection and the real-time sharing of critical information about the condition on the road network. The Social Internet of Things (SIoT) introduces social relationships among objects, creating a social network where the participants are not humans, but intelligent objects. In this article, we explore the concept of the Social Internet of Vehicles (SIoV), a network that enables social interactions both among vehicles and among drivers. We discuss technologies and components of the SIoV, possible applications and issues of security, privacy and trust that are likely to arise.","Title: Human Behaviour as an aspect of Cyber Security Assurance.\nYear: 2016\n\nAbstract: There continue to be numerous breaches publicised pertaining to cyber security despite security practices being applied within industry for many years. This article is intended to be the first in a number of articles as research into cyber security assurance processes. This article is compiled based on current research related to cyber security assurance and the impact of the human element on it. The objective of this work is to identify elements of cyber security that would benefit from further research and development based on the literature review findings. The results outlined in this article present a need for the cyber security field to look in to established industry areas to benefit from effective practices such as human reliability assessment, along with improved methods of validation such as statistical quality control in order to obtain true assurance. The article proposes the development of a framework that will be based upon defined and repeatable quantification, specifically relating to the range of human aspect tasks that provide, or are intended not to negatively affect cyber security posture.","Title: A Robust Eco-Routing Protocol against Malicious Data in Vehicular Networks\nYear: 2015\n\nAbstract: Vehicular networks have a diverse range of applications that vary from safety, to traffic management and comfort. Vehicular communications (VC) can assist in the ecorouting of vehicles in order to reduce the overall mileage and CO2 emissions by the exchange of data among vehicle-entities. However, the trustworthiness of these data is crucial as false information can heavily affect the performance of applications. Hence, the devising of mechanisms that reassure the integrity of the exchanged data is of utmost importance. In this article we investigate how tweaked information originating from malicious nodes can affect the performance of a real time eco-routing mechanism that uses Dedicated Short Ranged Communications (DSRC), namely Erou Ve. We improve the routing decision mechanism of the original algorithm and also develop and evaluate defense mechanisms that exploit vehicular communications in order to filter out tweaked data. We prove that our proposed mechanisms can restore the performance of the Erou Ve to near its optimal operation and can be used as a basis for protecting other similar traffic management systems.","Title: User interface design for privacy awareness in eHealth technologies\nYear: 2016\n\nAbstract: In this paper we investigate privacy issues relating to Human Computer Interfaces for mobile eHealth technologies. We present the Inform-Alert-Mitigate (I-AM) cycle, a novel approach to address privacy concerns that are associated with the use of these technologies. The I-AM approach supports the responsible innovation of new technologies. We demonstrate the effectiveness of I-AM by applying it to examples taken from mobile applications relating to personal health. We discuss three classes of applications: a) fitness trackers b) personal wellbeing applications and c) medical applications, and evaluate the privacy exposure of their users using representative applications from these classes. The paper evaluates the current privacy enhancing features of these applications against the identified risks and demonstrates how the I-AM approach can be applied to yield additional and more effective privacy protection for these technologies.","Title: An Industrial Control Systems incident response decision framework\nYear: 2015\n\nAbstract: The integration of Industrial Control Systems (ICS) with Information and Communication Technologies (ICT) systems has resulted in tremendous increase in the number of ICS security incidents. Current ICS incident response practices can hardly be applied into ICS directly as the two systems are fundamentally different in terms of system complexity, criticality, flexibility and dependability. This paper addresses this challenges by proposing an ICS incident response decision framework. It is based on decision-making theory and has descriptive, predictive and prescriptive models that particularly address ICS incident response decision-making. This framework is implemented using a service-oriented architecture (SOA) that provides incident response decision making as services and offers standardized interfaces to integrate with existing incident management systems.","Title: Insecure by Design: Using Human Interface Devices to exploit SCADA systems.\nYear: 2015\n\nAbstract: Modern Supervisory Control and Data Acquisition (SCADA) systems which are used by the electric utility industry to monitor and control electric power generation, transmission and distribution, are recognized today as critical components of the electric power delivery infrastructure. SCADA systems are large, complex and incorporate increasingly large numbers of widely distributed components. Cyber-attacks usually target valuable infrastructures assets, taking advantage of architectural/technical vulnerabilities or even weaknesses in the defense systems. Even though novel intrusion detection systems are being implemented and used for defending cyber-attacks certain vulnerabilities of SCADA systems can still be exploited. In this article we present an attack scenario based on a Human Interface Device (HID) device which is used as a means of communication/exploitation tool to compromise SCADA systems. The attack, which is a normal series of commands that are sent from the HID to the PLC cannot be detected through current intrusion detection mechanisms.","Title: An Initial Investigation into Attribution in SCADA Systems\nYear: 2013\n\nAbstract: Supervisory control and Data Acquisition (SCADA) systems play a core role in a nation's critical infrastructure, overseeing the monitoring and control of systems in electricity, gas supply, logistics services, banks and hospitals. SCADA systems were once separated from other networks and used proprietary communications protocols, hardware and software. Nowadays modern SCADA systems are increasingly directly or indirectly connected to the Internet, use standardised protocols and commercial-off-the-shelf hardware and software. Attacks on these systems have the potential for devastating consequences and attribution of attacks against SCADA systems presents new challenges. This paper investigates the use of techniques to attribute cyber attacks against SCADA systems. We investigate the use of five known technical attribution techniques in SCADA systems.","Title: Towards Agile Industrial Control Systems Incident Response.\nYear: 2015\n\nAbstract: The integration of Industrial Control Systems (ICS) with IT systems has increased the ICS' exposure to cyber threats. We have seen a tremendous increase in the number of security incidents happened to ICS in the past five years. This requires the ICS to provide effective incident response capabilities to counteract security attacks. Previous research on ICS incident response has been focusing on incident detection and analysis. This paper examines the current ICS incident response procedure from a managerial perspective, identifies the unique characteristics of ICS incident response and proposes a framework to improve incident response capabilities. In particular, it evaluates the benefit of agile values to address specific characteristics of ICS Incident Response. This foundational work sets the scene for future research to apply agile practices into ICS Incident Response.","Title: A Security Evaluation Framework for U.K. E-Goverment Services Agile Software Development.\nYear: 2016\n\nAbstract: This study examines the traditional approach to software development within the United Kingdom Government and the accreditation process. Initially we look at the Waterfall methodology that has been used for several years. We discuss the pros and cons of Waterfall before moving onto the Agile Scrum methodology. Agile has been adopted by the majority of Government digital departments including the Government Digital Services. Agile, despite its ability to achieve high rates of productivity organized in short, flexible, iterations, has faced security professionalsu0027 disbelief when working within the U.K. Government. One of the major issues is that we develop in Agile but the accreditation process is conducted using Waterfall resulting in delays to go live dates. Taking a brief look into the accreditation process that is used within Government for I.T. systems and applications, we focus on giving the accreditor the assurance they need when developing new applications and systems. A framework has been produced by utilising the Open Web Application Security Projectu0027s (OWASP) Application Security Verification Standard (ASVS). This framework will allow security and Agile to work side by side and produce secure code.","Title: Root cause analysis (RCA) as a preliminary tool into the investigation of identity theft\nYear: 2016\n\nAbstract: Identity theft has been known for some centuries whereby falsified identity documents were misused as well as offences such as impersonating others were common in the society. However, the advent of technology changed the method used for conducting this crime, whereby through the use of the Internet, personal information is can be stolen and misused by criminals. The crime has its causes originating from human error and judgement to failure of computing and networking systems that allow unauthorized access to personal information. In order to provide a better tool of investigating this crime, there is the need to explore the causes of the crime thereby providing a better framework for investigating Identity theft crimes. This study uses Root Cause Analysis (RCA) as a preliminary tool that serves to provide a depicted identification of the causes of Identity theft paving the way into investigating the crime and creating incident response plans.","Title: Measuring the Risk of Cyber Attack in Industrial Control Systems.\nYear: 2016\n\nAbstract: Cyber attacks on industrial control systems (ICS) that underpin critical national infrastructure can be characterised as high-impact, low-frequency events. To date, the volume of attacks versus the overall global footprint of ICS is low, and as a result there is an insufficient dataset to adequately assess the risk to an ICS operator, yet the impacts are potentially catastrophic. This paper identifies key elements of existing decision science that can be used to inform and improve the cyber security of ICS against antagonistic threats and highlights the areas where further development is required to derive realistic risk assessments, as well as detailing how data from established safety processes may inform the decision-making process. The paper concludes by making recommendations as to how a validated dataset could be constructed to support investment in ICS cyber security.","Title: Using Gamification to Raise Awareness of Cyber Threats to Critical National Infrastructure.\nYear: 2016\n\nAbstract: Senior executives of critical national infrastructure facilities face competing requirements for investment budgets. Whilst the impact of a cyber attack upon such utilities is potentially catastrophic, the risks to continued operations from failing to upgrade ageing infrastructure, or not meeting mandated regulatory regimes, are considered higher given the demonstrable impact of such circumstances. As cyber attacks on critical national infrastructure remain low-frequency events, there is little to motivate business leaders to increase their investment in cyber defences to comparable levels. This paper describes SCIPS, a gamified environment in which senior executives experience the impact of a cyber attack on an electric power generation plant, demonstrating how it can strategically affect shareholder value, and allows them to form their own views on the relative importance of cyber security investment.","Title: Semantics-aware detection of targeted attacks: a survey.\nYear: 2017\n\nAbstract: In today’s interconnected digital world, targeted attacks have become a serious threat to conventional computer systems and critical infrastructure alike. Many researchers contribute to the fight against network intrusions or malicious software by proposing novel detection systems or analysis methods. However, few of these solutions have a particular focus on Advanced Persistent Threats or similarly sophisticated multi-stage attacks. This turns finding domain-appropriate methodologies or developing new approaches into a major research challenge. To overcome these obstacles, we present a structured review of semantics-aware works that have a high potential for contributing to the analysis or detection of targeted attacks. We introduce a detailed literature evaluation schema in addition to a highly granular model for article categorization. Out of 123 identified papers, 60 were found to be relevant in the context of this study. The selected articles are comprehensively reviewed and assessed in accordance to Kitchenham’s guidelines for systematic literature reviews. In conclusion, we combine new insights and the status quo of current research into the concept of an ideal systemic approach capable of semantically processing and evaluating information from different observation points.","Title: Human behaviour as an aspect of cybersecurity assurance.\nYear: 2016\n\nAbstract: There continue to be numerous breaches publicised pertaining to cybersecurity despite security practices being applied within industry for many years. This paper is intended to be the first in a number of papers as research into cybersecurity assurance processes. This paper is compiled based on current research related to cybersecurity assurance and the impact of the human element on it. The objective of this work is to identify elements of cybersecurity that would benefit from further research and development based on the literature review findings. The results outlined in this paper present a need for the cybersecurity field to look in to established industry areas to benefit from effective practices such as human reliability assessment, along with improved methods of validation such as statistical quality control in order to obtain true assurance. The paper proposes the development of a framework that will be based upon defined and repeatable quantification, specifically relating to the range of human aspect tasks that provide or are intended not to negatively affect cybersecurity assurance. Copyright © 2016 John Wiley & Sons, Ltd.","Title: Authentication Protocols for Internet of Things: A Comprehensive Survey.\nYear: 2017\n\nAbstract: In this paper, a comprehensive survey of authentication protocols for Internet of Things (IoT) is presented. Specifically more than forty authentication protocols developed for or applied in the context of the IoT are selected and examined in detail. These protocols are categorized based on the target environment: (1) Machine to Machine Communications (M2M), (2) Internet of Vehicles (IoV), (3) Internet of Energy (IoE), and (4) Internet of Sensors (IoS). Threat models, countermeasures, and formal security verification techniques used in authentication protocols for the IoT are presented. In addition a taxonomy and comparison of authentication protocols that are developed for the IoT in terms of network model, specific security goals, main processes, computation complexity, and communication overhead are provided. Based on the current survey, open issues are identified and future research directions are proposed.","Title: A Survey on Privacy-preserving Schemes for Smart Grid Communications.\nYear: 2016\n\nAbstract: In this paper, we present a comprehensive survey of privacy-preserving schemes for Smart Grid communications. Specifically, we select and in-detail examine thirty privacy preserving schemes developed for or applied in the context of Smart Grids. Based on the communication and system models, we classify these schemes that are published between 2013 and 2016, in five categories, including, 1) Smart grid with the advanced metering infrastructure, 2) Data aggregation communications, 3) Smart grid marketing architecture, 4) Smart community of home gateways, and 5) Vehicle-to grid architecture. For each scheme, we survey the attacks of leaking privacy, countermeasures, and game theoretic approaches. In addition, we review the survey articles published in the recent years that deal with Smart Grids communications, applications, standardization, and security. Based on the current survey, several recommendations for further research are discussed at the end of this paper.","Title: Towards data privacy in heterogeneous cloud environments: An extension to the SANTA policy language\nYear: 2017\n\nAbstract: This paper addresses the pressing concern of insufficient and inflexible support for data privacy in hybrid cloud environments, where potentially sensitive data is dynamically transferred between remote (i.e. public) and local (i.e. private) data centres, as well as personal computers and portable/mobile devices. As sensitive data is being transferred across various administrative domains with differing data protection levels, legislations, and security mechanisms in place, it is important to enable a differentiated approach to implement a flexible access control mechanism tailored to the current context and circumstances. Accordingly, this paper raises awareness on the issues of data privacy in such heterogeneous cloud ecosystems, and presents an on-going research work targeted at devising an access control policy language, which serves to support various types of policies, applicable across a wide range of hybrid cloud scenarios. The presented work is based on and extends the current capabilities of SANTA - a policy language, which, however, currently does not support data privacy concerns associated with clouds, including mobile and hybrid clouds.","Title: Poster: Design of an Anomaly-based Threat Detection & Explication System\nYear: 2017\n\nAbstract: The poster corresponding to this summary depicts a proposition of a system able to explain anomalous behavior within a user session by considering anomalies identified through their deviation from a set of baseline process graphs. We adapt star structures, a bipartite representation used to approximate the edit distance between two graphs. Relevant processes are selected from a dictionary of benign and malicious traces generated through a sentiment-like bigram extraction and scoring system based on the log likelihood ratio test. We prototypically implemented smart anomaly explication through a number of competency questions derived and evaluated by a decision tree. The determined key factors are ultimately mapped to a dedicated APT attack stage ontology that considers actions, actors, as well as target assets.","Title: SCIPS: Using Experiential Learning to Raise Cyber Situational Awareness in Industrial Control System\nYear: 2017\n\nAbstract: AbstractThe cyber threat to industrial control systems is an acknowledged security issue, but a qualified dataset to quantify the risk remains largely unavailable. Senior executives of facilities that operate these systems face competing requirements for investment budgets, but without an understanding of the nature of the threat, cyber security may not be a high priority. Education and awareness campaigns are established methods of raising the profile of security issues with stakeholders, but traditional techniques typically deliver generic messages to wide audiences, rather than tailoring the communications to those who understand the impact of organisational risks. This paper explores the use of experiential learning through serious games for senior executives, to develop mental models within which participants can frame the nature of the threat, thereby raising their cyber security awareness, and increasing their motivation to address the issue.","Title: On Data Leakage From Non-Production Systems\nYear: 2017\n\nAbstract: Purpose - This study is an exploration of areas pertaining to the use of production data in non-production environments. During the software development life cycle, non-production environments are used to serve various purposes to include unit, component, integration, system, user acceptance, performance and configuration testing. Organisations and third parties have been and are continuing to use copies of production data in non-production environments. This can lead to personal and sensitive data being accidentally leaked if appropriate and rigorous security guidelines are not implemented. This paper aims to propose a comprehensive framework for minimising data leakage from non-production environments. The framework was evaluated using guided interviews and was proven effective in helping organisation manage sensitive data in non-production environments.Design/methodology/approach - Authors conducted a thorough literature review on areas related to data leakage from non-production systems. By doing an analysis of advice, guidelines and frameworks that aims at finding a practical solution for selecting and implementing a de-identification solution of sensitive data, the authors managed to highlight the importance of all areas related to sensitive data protection. Based on these areas, a framework was proposed which was evaluated by conducting set of guided interviews.Findings - This paper has researched the background information and produced a framework for an organisation to manage sensitive data in its non-production environments. This paper presents a proposed framework that describes a process flow from the legal and regulatory requirements to data treatment and protection, gained through understanding the organisation's business, the production system, the purpose and the requirements of the non-production environment. The paper shows that there is some conflict between security and perceived usability, which may be addressed by challenging the perceptions of usability or identifying the compromise required. Non-production environments need not be the sole responsibility of the IT section, they should be of interest to the business area that is responsible for the data held.Originality/value - This paper proposes a simplified business model and framework. The proposed model diagrammatically describes the interactions of elements affecting the organisation. It highlights how non-production environments may be perceived as separate from the business systems, but despite the perceptions, these are still subject to the same legal requirements and constraints. It shows the interdependency of data, software, technical infrastructure and human interaction and how the change of one element may affect the others. The proposed framework describes the process flow and forms a practical solution in assisting the decision-making process and providing documentary evidence for assurance and audit purposes. It looks at the requirements of the non-production system in relation to the legal and regulatory constraints, as well as the organisational requirements and business systems. The impact of human factors on the data is also considered to bring a holistic approach to the protection of non-production environments.","Title: The industrial control system cyber defence triage process.\nYear: 2017\n\nAbstract: The threat to Industrial Control Systems (ICS) from cyber attacks is widely acknowledged by governments and literature. Operators of ICS are looking to address these threats in an effective and cost-sensitive manner that does not expose their operations to additional risks through invasive testing. Whilst existing standards and guidelines offer comprehensive advice for reviewing the security of ICS infrastructure, resource and time limitations can lead to incomplete assessments or undesirably long countermeasure implementation schedules.","Title: Can a Network Attack Be Simulated in an Emulated Environment for Network Security Training?\nYear: 2017\n\nAbstract: This paper outlines a tool developed with the purpose of creating a simple configurable emulated network environment that can be used in cyber defence exercises. Research has been conducted into the various related subject areas: cyber defence exercises, network threats, network emulation, network traffic replay, network topologies, and common network services. From this research a requirements specification was produced to encapsulate the features required to create this tool. A network, containing many of the aspects researched, was designed and implemented using Netkit-NG to act as a blueprint for the tool and to further knowledge in the construction of an emulated network. Following this the tool was developed and tested to ensure requirements were met.","Title: Internet of Cloud: Security and Privacy issues.\nYear: 2017\n\nAbstract: The synergy between Cloud and IoT has emerged largely due to the Cloud having attributes which directly benefit IoT and enable its continued growth. IoT adoptingCloud services has brought newsecurity challenges. In this book chapter, we pursue two main goals: (1) to analyse the different components of Cloud computing and IoT and (2) to present security and privacy problems that these systems face. We thoroughly investigate current security and privacy preservation solutions that exist in this area, with an eye on the Industrial Internet of Things, discuss open issues and propose future directions.","Title: Security for 4G and 5G Cellular Networks: A Survey of Existing Authentication and Privacy-preserving Schemes.\nYear: 2018\n\nAbstract: This paper presents a comprehensive survey of existing authentication and privacy-preserving schemes for 4G and 5G cellular networks. We start by providing an overview of existing surveys that deal with 4G and 5G cellular networks. Then, we give a classification of threat models in 4G and 5G cellular networks in four categories, including, attacks against privacy, attacks against integrity, attacks against availability, and attacks against authentication. We also provide a classification of countermeasures into three types of categories, including, cryptography methods, humans factors, and intrusion detection methods. The countermeasures and informal and formal security analysis techniques used by the authentication and privacy preserving schemes are summarized in form of tables. Based on the categorization of the authentication and privacy models, we classify these schemes in seven types, including, handover authentication with privacy, mutual authentication with privacy, RFID authentication with privacy, deniable authentication with privacy, authentication with mutual anonymity, authentication and key agreement with privacy, and three-factor authentication with privacy. In addition, we provide a taxonomy and comparison of authentication and privacy-preserving schemes for 4G and 5G cellular networks in form of tables. Based on the current survey, several recommendations for further research are discussed at the end of this paper.","Title: A Security Architectural Pattern For Risk Management Of Industry Control Systems Within Critical National Infrastructure\nYear: 2017\n\nAbstract: SCADA and ICS security have been focusing on addressing issues such as vulnerability discovery and intrusion detection within critical national infrastructure. Less attention has been paid to architectural solutions to the cyber security risks from an information assurance perspective. Security controls are not always traced back to the business requirements. This paper presents a holistic end-to-end view of the requirements, medium to high severity risks and proposes a generic security architectural pattern to address them. The architectural pattern is developed based on the Sherwood Applied Business Security Architecture (SABSA) top two layers, contextual and conceptual, which are responsible for understanding the business requirements and development of a concept architecture and strategy. Moreover, this research is motivated by industrial practices and has reflected the recent changes of GCHQ's mission. This research also contributes to the SCADA/ICS risk assessment by deriving holistic sets of risk management and architectural design requirements for SCADA/ICS.","Title: Vulnerability Analysis of Network Scanning on SCADA Systems.\nYear: 2018\n\nAbstract: Supervisory Control and Data Acquisition (SCADA) systems and Industrial Control Systems (ICSs) have controlled the regulation and management of Critical National Infrastructure environments for decades. With the demand for remote facilities to be controlled and monitored, industries have continued to adopt Internet technology into their ICS and SCADA systems so that their enterprise can span across international borders in order to meet the demand of modern living. Although this is a necessity, it could prove to be potentially dangerous. The devices that make up ICS and SCADA systems have bespoke purposes and are often inherently vulnerable and difficult to merge with newer technologies. The focus of this article is to explore, test, and critically analyse the use of network scanning tools against bespoke SCADA equipment in order to identify the issues with conducting asset discovery or service detection on SCADA systems with the same tools used on conventional IP networks. The observations and results of the experiments conducted are helpful in evaluating their feasibility and whether they have a negative impact on how they operate. This in turn helps deduce whether network scanners open a new set of vulnerabilities unique to SCADA systems.","Title: Blockchain Technologies for the Internet of Things: Research Issues and Challenges.\nYear: 2019\n\nAbstract: This paper presents a comprehensive survey of the existing blockchain protocols for the Internet of Things (IoT) networks. We start by describing the blockchains and summarizing the existing surveys that deal with blockchain technologies. Then, we provide an overview of the application domains of blockchain technologies in IoT, e.g., Internet of Vehicles, Internet of Energy, Internet of Cloud, Edg...","Title: Class Balanced Similarity-Based Instance Transfer Learning for Botnet Family Classification.\nYear: 2018\n\nAbstract: The use of Transfer Learning algorithms for enhancing the performance of machine learning algorithms has gained attention over the last decade. In this paper we introduce an extension and evaluation of our novel approach Similarity Based Instance Transfer Learning (SBIT). The extended version is denoted Class Balanced SBIT (or CB-SBIT for short) because it ensures the dataset resulting after instance transfer does not contain class imbalance. We compare the performance of CB-SBIT against the original SBIT algorithm. In addition, we compare its performance against that of the classical Synthetic Minority Over-sampling Technique (SMOTE) using network traffic data. We also compare the performance of CB-SBIT against the performance of the open source transfer learning algorithm TransferBoost using text data. Our results show that CB-SBIT outperforms the original SBIT and SMOTE using varying sizes of network traffic data but falls short when compared to TransferBoost using text data.","Title: SEQUIN: a grammar inference framework for analyzing malicious system behavior.\nYear: 2018\n\nAbstract: Targeted attacks on IT systems are a rising threat to the confidentiality of sensitive data and the availability of critical systems. The emergence of Advanced Persistent Threats (APTs) made it paramount to fully understand the particulars of such attacks in order to improve or devise effective defense mechanisms. Grammar inference paired with visual analytics (VA) techniques offers a powerful foundation for the automated extraction of behavioral patterns from sequential event traces. To facilitate the interpretation and analysis of APTs, we present SEQUIN, a grammar inference system based on the Sequitur compression algorithm that constructs a context-free grammar (CFG) from string-based input data. In addition to recursive rule extraction, we expanded the procedure through automated assessment routines capable of dealing with multiple input sources and types. This automated assessment enables the accurate identification of interesting frequent or anomalous patterns in sequential corpora of arbitrary quantity and origin. On the formal side, we extended the CFG with attributes that help describe the extracted (malicious) actions. Discovery-focused pattern visualization of the output is provided by our dedicated KAMAS VA prototype.","Title: HEART-IS: A novel technique for evaluating human error-related information security incidents.\nYear: 2019\n\nAbstract: Organisations continue to suffer information security incidents and breaches as a result of human error even though humans are recognised as the weakest link with regard to information security. Despite this level of understanding organisations continue to focus their attention on technical security controls rather than the human factor and have not incorporated methods such as Human Reliability Analysis (HRA) which are used within high reliability sectors such as rail, aviation and energy. The objectives of our research are to define a human error related information security incident and create the novel HEART of Information Security (HEART-IS) technique which is an adaptation of the Human Error Assessment and Reduction Technique (HEART). We conducted a case study within a private sector organisation using HEART-IS to establish if HRA is applicable to information security. The novel HEART-IS technique comprises of a mapping component and an analysis component. In the case study, we applied HEART-IS to map HEART Error Producing Conditions (EPC) to twelve months of reported information security incidents and analysed the volumes of human error and underlying causes. We found that HEART-IS is applicable to the information security field with some minor amendments to the terminology. The mapping of information security incident causes to the HEART Error Producing Conditions (EPC) was successful but the in-built HEART human error probability calculations did not match the actual volumes of reported human error related incidents.","Title: Threats, Protection and Attribution of Cyber Attacks on Critical Infrastructures.\nYear: 2019\n\nAbstract: As Critical National Infrastructures are becoming more vulnerable to cyber attacks, their protection becomes a significant issue for any organization as well as a nation. Moreover, the ability to attribute is a vital element of avoiding impunity in cyberspace. In this article, we present main threats to critical infrastructures along with protective measures that one nation can take, and which are classified according to legal, technical, organizational, capacity building, and cooperation aspects. Finally we provide an overview of current methods and practices regarding cyber attribution and cyber peace keeping","Title: Developing Cyber Buffer Zones.\nYear: 2018\n\nAbstract: The United Nations conducts peace operations around the world, aiming tomaintain peace and security in conflict torn areas. Whilst early operations werelargely successful, the changing nature of warfare and conflict has often left peaceoperations strugglingto adapt. In this article, we make a contribution towardsefforts to plan for the next evolution in both intra and inter-state conflict: cyberwarfare. It is now widely accepted that cyber warfare will be a component offuture conflicts, and much researchhas been devoted to how governments andmilitaries can prepare for and fight in this new domain [1]. Despite the vastamount of research relating to cyber warfare, there has been less discussion onits impact towards successful peace operations. This is agap in knowledge thatis important to address, since the restoration of peace following conflict of anykind is of global importance. It is however a complex topic requiring discussionacross multiple domains. Input from the technical, political, governmental andsocietal domains are critical in forming the concept of cyber peacekeeping.Previous work on this topic has sought to define the concept of cyber peacekeeping[2, 3, 4]. We build upon this work by exploring the practicalities ofstarting up a cyber peacekeeping component and setting up a Cyber Buffer Zone (CBZ).","Title: MIMO Techniques for Jamming Threat Suppression in Vehicular Networks.\nYear: 2016\n\nAbstract: Vehicular ad hoc networks have emerged as a promising field of research and development, since they will be able to accommodate a variety of applications, ranging from infotainment to traffic management and road safety. A specific security-related concern that vehicular ad hoc networks face is how to keep communication alive in the presence of radio frequency jamming, especially during emergency situations. Multiple Input Multiple Output techniques are proven to be able to improve some crucial parameters of vehicular communications such as communication range and throughput. In this article, we investigate how Multiple Input Multiple Output techniques can be used in vehicular ad hoc networks as active defense mechanisms in order to avoid jamming threats. For this reason, a variation of spatial multiplexing is proposed, namely, vSP4, which achieves not only high throughput but also a stable diversity gain upon the interference of a malicious jammer.","Title: Design Of An Anomaly-Based Threat Detection & Explication System\nYear: 2017\n\nAbstract: Current signature-based malware detection systems are heavily reliant on fixed patterns that struggle with unknown or evasive applications, while behavior-based solutions usually leave most of the interpretative work to a human analyst. In this paper, we propose a system able to explain anomalous behavior within a user session by considering anomalies identified through their deviation from a set of baseline process graphs. To minimize computational requirements we adapt star structures, a bipartite representation used to approximate the edit distance between two graphs. Baseline templates are generated automatically and adapt to the nature of the respective process. We prototypically implement smart anomaly explication through a number of competency questions derived and evaluated using the decision tree algorithm. The determined key factors are ultimately mapped to a dedicated APT attack stage ontology that considers actions, actors, as well as target assets.","Title: Route Optimization of Electric Vehicles based on Dynamic Wireless Charging.\nYear: 2018\n\nAbstract: One of the barriers for the adoption of electric vehicles (EVs) is the anxiety around the limited driving range. Recent proposals have explored charging EVs on the move, using dynamic wireless charging which enables power exchange between the vehicle and the grid while the vehicle is moving. In this paper, we focus on the intelligent routing of EVs in need of charging so that they can make most efficient use of the so-called mobile energy disseminators (MEDs) which operate as mobile charging stations. We present a method for routing EVs around MEDs on the road network, which is based on constraint logic programming and optimization using a graph-based shortest path algorithm. The proposed method exploits inter-vehicle communications in order to eco-route electric vehicles. We argue that combining modern communications between vehicles and state of the art technologies on energy transfer, the driving range of EVs can be extended without the need for larger batteries or overtly costly infrastructure. We present extensive simulations in city conditions that show the driving range and consequently the overall travel time of electric vehicles is improved with intelligent routing in the presence of MEDs.","Title: Cyber security of critical infrastructures\nYear: 2018\n\nAbstract: Modern Supervisory Control and Data Acquisition (SCADA) systems are essential for monitoring and managing electric power generation, transmission and distribution. In the age of the Internet of Things, SCADA has evolved into big, complex and distributed systems that are prone to be conventional in addition to new threats. Many security methods can be applied to such systems, having in mind that both high efficiency, real time intrusion identification and low overhead are required.","Title: Exploring the role of work identity and work locus of control in information security awareness.\nYear: 2019\n\nAbstract: A growing body of research evidence has been focused on exploring aspects of individual differences in the context of human factors and adherence to organisational information security. The present study aimed to extend this research by exploring three individual variables related directly to the individual's perceived control within the workplace, their commitment to current work identity, and the extent to which they are reconsidering commitment to work. A total 1003 participants aged between 18–65 (Mean = 40.29; SD = 12.28), who were in full or part-time employment took part in the study. The results demonstrated that work locus of control acted as a significant predictor for total scores on a measure of information security awareness. Those individuals who demonstrated more externality had weaker engagement in accepted information security within the workplace. The findings from the current study are discussed in the context of potential links to counterproductive work behaviours, as well as presenting possible practical routes for intervention strategies to help mitigate poor engagement in information security awareness.","Title: Evaluating information security core human error causes (IS-CHEC) technique in public sector and comparison with the private sector.\nYear: 2019\n\nAbstract: •This research identified that human error proportions are higher than currently understood in the literature.•The IS-CHEC technique is applicable to information security, in a participating public sector organisation providing healthcare services.•The majority of information security incidents pertain to human error.•The use of IS-CHEC technique provides insight into the common causes of human error.•The IS-CHEC technique has been improved based upon a comparison of case study findings within public and private sector organisations.","Title: AIDIS: Detecting and classifying anomalous behavior in ubiquitous kernel processes\nYear: 2019\n\nAbstract: Targeted attacks on IT systems are a rising threat against the confidentiality, integrity, and availability of critical information and infrastructures. With the rising prominence of advanced persistent threats (APTs), identifying and understanding such attacks has become increasingly important. Current signature-based systems are heavily reliant on fixed patterns that struggle with unknown or evasive applications, while behavior-based solutions usually leave most of the interpretative work to a human analyst.","Title: Dying Of A Hundred Good Symptoms: Why Good Security Can Still Fail-A Literature Review And Analysis\nYear: 2021\n\nAbstract: Many organizations suffer serious information security incidents, despite having taken positive steps towards achieving good security standards. The authors hypothesize that these issues are often as a result of security arrangements not being sufficiently integrated with businesses. We believe that adopting an enterprise architecture (EA) approach to implementing information security - commonly referred to as an 'Enterprise Information Security Architecture' (EISA) - will deliver substantial benefits. Our paper has reviewed and analyzed literature concerning the root causes of information security incidents and describes a novel approach with 8 domains for ensuring critical factors are considered when building an EISA framework.","Title: Security Visualization: Detecting Denial of Service.\nYear: 2016\n\nAbstract: Denial Of Service attacks are notorious attack methods used to target servers of IT systems and Industrial Control Systems to prevent them from working or to reduce efficiency, hence decreasing user experience. Visualization is the method of taking data, processing and displaying data in an easy to view format. Visualization could be used to identify Denial Of Service attacks by monitoring the data sent to clients and being displayed to the users. Manipulating the type of data shown and the format it is shown in can help users spot potential attacks by seeing outliers in the data sets. This research develops novel software that can run on an web server. It processes the web access logs, displays the data to users and identify potential attacks in access logs. The software has been tested, with the majority of tests passing. Further development of the project is discussed and the main areas for development are also explored.","Title: Effect of Network Architecture Changes on OCSVM Based Intrusion Detection System.\nYear: 2016\n\nAbstract: Intrusion Detection Systems are becoming an important defense mechanism for (supervisory control and data acquisition (SCADA) systems. SCADA systems are likely to become more dynamic leading to a need for research into how changes to the network architecture that is monitored, affect the performance of defense mechanisms. This article investigates how changes in the network architecture of the SCADA system affect the performance of an IDS that is based on the One class Support Vector Machine (OCSVM). Also the article proposes an adaptive mechanism that can cope with such changes and can work in real time situations.","Title: Published Incidents And Their Proportions Of Human Error\nYear: 2019\n\nAbstract: Purpose This paper aims to provide an understanding of the proportions of incidents that relate to human error. The information security field experiences a continuous stream of information security incidents and breaches, which are publicised by the media, public bodies and regulators. Despite the need for information security practices being recognised and in existence for some time, the underlying general information security affecting tasks and causes of these incidents and breaches are not consistently understood, particularly with regard to human error. Design/methodology/approach This paper analyses recent published incidents and breaches to establish the proportions of human error and where possible subsequently uses the HEART (human error assessment and reduction technique) human reliability analysis technique, which is established within the safety field. Findings This analysis provides an understanding of the proportions of incidents and breaches that relate to human error, as well as the common types of tasks that result in these incidents and breaches through adoption of methods applied within the safety field. Originality/value This research provides original contribution to knowledge through the analysis of recent public sector information security incidents and breaches to understand the proportions that relate to human error.","Title: A Deep Learning-based Penetration Testing Framework for Vulnerability Identification in Internet of Things Environments.\nYear: 2021\n\nAbstract: The Internet of Things (IoT) paradigm has displayed tremendous growth in recent years, resulting in innovations like Industry 4.0 and smart environments that provide improvements to efficiency, management of assets and facilitate intelligent decision making. However, these benefits are offset by considerable cybersecurity concerns that arise due to inherent vulnerabilities, which hinder IoT-based systems' Confidentiality, Integrity, and Availability. Security vulnerabilities can be detected through the application of penetration testing, and specifically, a subset of the information-gathering stage, known as vulnerability identification. Yet, existing penetration testing solutions can not discover zero-day vulnerabilities from IoT environments, due to the diversity of generated data, hardware constraints, and environmental complexity. Thus, it is imperative to develop effective penetration testing solutions for the detection of vulnerabilities in smart IoT environments. In this paper, we propose a deep learning-based penetration testing framework, namely Long Short-Term Memory Recurrent Neural Network-Enabled Vulnerability Identification (LSTM-EVI). We utilize this framework through a novel cybersecurity-oriented testbed, which is a smart airport-based testbed comprised of both physical and virtual elements. The framework was evaluated using this testbed and on real-time data sources. Our results revealed that the proposed framework achieves about 99% detection accuracy for scanning attacks, outperforming other four peer techniques.","Title: A Novel Two-Factor Honeytoken Authentication Mechanism\nYear: 2021\n\nAbstract: The majority of systems rely on user authentication on passwords, but passwords have so many weaknesses and widespread use that easily raise significant security concerns, regardless of their encrypted form. Users hold the same password for different accounts, administrators never check password files for flaws that might lead to a successful cracking, and the lack of a tight security policy regarding regular password replacement are a few problems that need to be addressed. The proposed research work aims at enhancing this security mechanism, prevent penetrations, password theft, and attempted break-ins towards securing computing systems. The selected solution approach is two-folded; it implements a two-factor authentication scheme to prevent unauthorized access, accompanied by Honeyword principles to detect corrupted or stolen tokens. Both can be integrated into any platform or web application with the use of QR codes and a mobile phone.","Title: Digital Twins and Cyber Security – solution or challenge?\nYear: 2021\n\nAbstract: Digital twin technology today is diverse and emerging and its full potential is not yet widely understood. The concept of a digital twin allows for the analysis, design, optimisation and evolution of systems to take place fully digital, or in conjunction with a cyber-physical system to improve speed, accuracy and efficiency when compared to traditional engineering approaches. Digital twins continu...","Title: SmartValidator: A framework for automatic identification and classification of cyber threat data\nYear: 2022\n\nAbstract: A wide variety of Cyber Threat Information (CTI) is used by Security Operation Centres (SOCs) to perform validation of security incidents and alerts. Security experts manually define different types of rules and scripts based on CTI to perform validation tasks. These rules and scripts need to be updated continuously due to evolving threats, changing SOCs’ requirements and dynamic nature of CTI. The manual process of updating rules and scripts delays the response to attacks. To reduce the burden of human experts and accelerate response, we propose a novel Artificial Intelligence (AI) based framework, SmartValidator. SmartValidator leverages Machine Learning (ML) techniques to enable automated validation of alerts. It consists of three layers to perform the tasks of data collection, model building and alert validation. It projects the validation task as a classification problem. Instead of building and saving models for all possible requirements, we propose to automatically construct the validation models based on SOC’s requirements and CTI. We built a Proof of Concept (PoC) system with eight ML algorithms, two feature engineering techniques and 18 requirements to investigate the effectiveness and efficiency of SmartValidator. The evaluation results showed that when prediction models were built automatically for classifying cyber threat data, the F1-score of 75% of the models were above 0.8, which indicates adequate performance of the PoC for use in a real-world organization. The results further showed that dynamic construction of prediction models required 99% less models to be built than pre-building models for all possible requirements. Thus, SmartValidator is much more efficient to use when SOCs’ requirements and threat behaviour are constantly evolving. The framework can be followed by various industries to accelerate and automate the validation of alerts and incidents based on their CTI and SOC’s preferences.","Title: Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning\nYear: 2022\n\nAbstract: In this paper, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems in two different modes, namely, centralized and federated learning. Specifically, the dataset has been generated using a purpose-built IoT/IIoT testbed with a large representative set of devices, sensors, protocols and cloud/edge configurations. The IoT data are generated from various IoT devices (more than 10 types) such as Low-cost digital sensors for sensing temperature and humidity, Ultrasonic sensor, Water level detection sensor, pH Sensor Meter, Soil Moisture sensor, Heart Rate Sensor, Flame Sensor, etc.). Furthermore, we identify and analyze fourteen attacks related to IoT and IIoT connectivity protocols, which are categorized into five threats, including, DoS/DDoS attacks, Information gathering, Man in the middle attacks, Injection attacks, and Malware attacks. In addition, we extract features obtained from different sources, including alerts, system resources, logs, network traffic, and propose new 61 features with high correlations from 1176 found features. After processing and analyzing the proposed realistic cyber security dataset, we provide a primary exploratory data analysis and evaluate the performance of machine learning approaches (i.e., traditional machine learning as well as deep learning) in both centralized and federated learning modes. The Edge-IIoTset dataset can be publicly accessed from http://ieee-dataport.org/8939.","Title: Reliability, Security, and Privacy in Power Grids\nYear: 2022\n\nAbstract: As we move from traditional power grids to smart grids, new threats arise. We focus on cybersecurity in power grids, highlighting the threats to and vulnerabilities of those cyberphysical systems and presenting future solutions.","Title: Security Revisited: Honeytokens meet Google Authenticator\nYear: 2022\n\nAbstract: In contrast to the rapid advancement and evolution of technology, computer systems rely significantly on passwords to maintain their security for an unreasonably long time. Although the introduction of two or more factors improved existing authentication procedures and gave rise to new multi-factor authentication schemes, more advanced and focused assaults have revealed they are also sensitive and ineffective to assure only authorized access to systems. This study suggests a revolutionary two-factor authentication mechanism that uses QR codes and honeytokens. In the current implementation, honeywords and Google authenticator work together. In order to speed up the identification process, the honeywords technique is utilized as a second factor on a different computing device. By using a stronger and more effective authentication mechanism than a single password mechanism or a common two-factor authentication scheme, the novel and straightforward approach presented here intends to add additional layers of security and protection into a system, thereby increasing its security."],"string":"[\n \"Title: Controlling data dissemination\\nYear: 2011\\n\\nAbstract: We present a policy-based approach for the control of information flow between services. The controlled dissemination of information shared between services is achieved by the communication and enforcement of policies associated with shared information. The presented approach integrates easily as a communication middle-ware in existing service-based information systems keeping the management and distribution of policies transparent to the service implementation. This article focuses on the architecture and presents initial results that show the feasibility of the approach.\",\n \"Title: A note on the formalisation of UCON\\nYear: 2007\\n\\nAbstract: Usage Control (UCON) Models, similar to Access Control Models, control and govern the users' access to resources and services that are available in the system. One of the major improvements of UCON over traditional access control models is the continuity of the control and the concept of attribute mutability. In this paper we provide an alternative formalisation of the UCON model that relaxes many of the assumptions made in earlier formalisations of the model. We question the enforceability of UCON policies as described by previous formalisations and improve on it.\",\n \"Title: Decentralized XACML Overlay Network\\nYear: 2010\\n\\nAbstract: We propose a novel approach for the collaborative enforcement of security policies in distributed systems that is based on the dynamic (re-) deployment of multiple PDPs. The policies enforced by the collaborating PDPs are analysed and decomposed from a system wide policy as present in current centralized approaches. The security policy is decomposed into sub-policies based on an object domain approach so the decisions are local to the object's domain. The classes of policies investigated are dynamic history-based access control policies, ie. the PDPs decision is dependent on the history of interaction between users and system resources. This type of policy can capture static and dynamic separation of duty policies, as are commonly found in commercial organisations. The distribution model of the PDP allows for the coordination and synchronisation of PDPs on the basis of events, where a decision is based on a previous history originating from other PDPs. The key contribution of this paper is the analysis of temporal dependencies between policies and an efficient PDP distribution strategy for object-based distributed systems as well as presenting a designed library that create and synchronize a network of PDP's in a peer to peer fashion.\",\n \"Title: Optimizing Software Quality Assurance\\nYear: 2010\\n\\nAbstract: A major concern for managers of software projects are the triple constraints of cost, schedule and quality due to the difficulties to quantify accurately the trade-offs between them. Project managers working for accredited companies with a high maturity will typically use software cost estimation models like COCOMO II and predict software quality by the estimated number of defects the product is likely to contain at release. However, most of these models are used separately and the interplay between cost/effort estimation, project scheduling and the resultant quality of the software product is not well understood. In this paper, we propose a regression-based model that allows project managers to estimate the trade-off between the quality, cost and development time of a software product, based on previously collected data.\",\n \"Title: Efficient Data Processing for Large-Scale Cloud Services\\nYear: 2012\\n\\nAbstract: The cloud concept and its implementations are gaining in importance for systems that connect evermore new devices which in turn require communication with each other. In scenarios where we can find large numbers of data providers on one side and data consumers on the other side, such as in the Internet of Things, large scale sensor networks, machine to machine communication or even in social media, one emerging requirement is to process, procure, and provide information efficiently and with almost zero latency. This work is introducing new concepts to describe the flow of data to and from sources to cloud services in a formal way by limiting information flow with filtering concepts and combining data processing techniques adopted from complex event processing.\",\n \"Title: Dynamic Access Control Policies: Specification and Verification.\\nYear: 2013\\n\\nAbstract: Security requirements deal with the protection of assets against unauthorized access (disclosure or modification) and their availability to authorized users. Temporal constraints of history-based access control policies are difficult to express naturally in traditional policy languages. We propose a compositional formal framework for the specification and verification of temporal access control po...\",\n \"Title: Concurrent Enforcement of Usage Control Policies\\nYear: 2008\\n\\nAbstract: Policy-based approaches to the management of systems distinguish between the specification of requirements, in the form of policies, and their enforcement on the system. In this work we focus on the latter aspect and investigate the enforcement of stateful policies in a concurrent environment. As a representative of stateful policies we use the UCON model and show how dependencies between policy rules affect their enforcement. We propose a technique for enforcing policies concurrently based on the static analysis of dependencies between policies. The potential of our technique for improving the efficacy of enforcement mechanisms is illustrated using a small, but representative example.\",\n \"Title: Verification and enforcement of access control policies\\nYear: 2013\\n\\nAbstract: Access control mechanisms protect critical resources of systems from unauthorized access. In a policy-based management approach, administrators define user privileges as rules that determine the conditions and the extent of users' access rights. As rules become more complex, analytical skills are required to identify conflicts and interactions within the rules that comprise a system policy--especially when rules are stateful and depend on event histories. Without adequate tool support such an analysis is error-prone and expensive. In consequence, many policy specifications are inconsistent or conflicting that render the system insecure. The security of the system, however, does not only depend on the correct specification of the security policy, but in a large part also on the correct interpretation of those rules by the system's enforcement mechanism.In this paper, we show how policy rules can be formalized in Fusion Logic, a temporal logic for the specification of behavior of systems. A symbolic decision procedure for Fusion Logic based on Binary Decision Diagrams (BDDs) is provided and we introduce a novel technique for the construction of enforcement mechanisms of access control policy rules that uses a BDD encoded enforcement automaton based on input traces which reflect state changes in the system. We provide examples of verification of policy rules, such as absence of conflicts, and dynamic separation of duty and of the enforcement of policies using our prototype implementation (FLCheck) for which we detail the underlying theory.\",\n \"Title: Data Confidentiality in Mobile Ad hoc Networks\\nYear: 2012\\n\\nAbstract: Mobile ad hoc networks (MANETs) are self-configuring infrastructure-less networks comprised of mobile nodes that communicate over wireless links without any central control on a peer-to-peer basis. These individual nodes act as routers to forward both their own data and also their neighbours' data by sending and receiving packets to and from other nodes in the network. The relatively easy configuration and the quick deployment make ad hoc networks suitable the emergency situations (such as human or natural disasters) and for military units in enemy territory. Securing data dissemination between these nodes in such networks, however, is a very challenging task. Exposing such information to anyone else other than the intended nodes could cause a privacy and confidentiality breach, particularly in military scenarios. In this paper we present a novel framework to enhance the privacy and data confidentiality in mobile ad hoc networks by attaching the originator policies to the messages as they are sent between nodes. We evaluate our framework using the Network Simulator (NS-2) to check whether the privacy and confidentiality of the originator are met. For this we implemented the Policy Enforcement Points (PEPs), as NS-2 agents that manage and enforce the policies attached to packets at every node in the MANET.\",\n \"Title: Deriving Enforcement Mechanisms from Policies\\nYear: 2007\\n\\nAbstract: Policies provide a flexible and scalable approach to the management of distributed systems by separating the specification of security requirements and their enforcement. Over the years the expressiveness of policy languages increased considerably making it possible to capture a variety of complex requirements that for example depend on the history of the system execution. The most important criteria for the successful operation of policy-managed systems is whether the deployed enforcement mechanisms can guarantee the compliance with the policies. With the expressiveness of policy languages this assurance is increasingly difficult to achieve. In this paper we therefore address the development of enforcement mechanisms from a theoretical perspective and show how enforcement code can be formally derived for compositional, history-dependent policies that can change dynamically over time or on the occurrence of events.\",\n \"Title: Multi-Factor Authentication Using Hardware Information and User Profiling Techniques.\\nYear: 2012\\n\\nAbstract: This paper presents a multi-factor authentication approach that extends traditional usernamepassword authentication with hardware and user behaviour profiling techniques. The aim of the approach is to improve the reliability of authentication by computing trust and confidence scores against user profiles. Based on the level of trust, the access control mechanisms may then choose to (un-)lock certain functions or even classify the access as an attack and redirect the user to a honey-pot to gather additional information about the attacker that can be used for a trace-back. The novelty of the approach is that it observes the correlation between usersu0027 behaviours and their hardware usage as implicit verification procedures to discriminate the usage of the user-name and password entry.\",\n \"Title: An Introduction to Cyber Peacekeeping.\\nYear: 2018\\n\\nAbstract: Cyber is the newest domain of war, and the topic of cyber warfare is one that is receiving increasing attention. Research efforts into cyber warfare are extensive, covering a range of issues such as legality, cyber weapons and deterrence. Despite all of the research activity around cyber warfare, one aspect has been largely overlooked: the restoration of peace and security in its aftermath. In this article, we present the argument that cyber warfare will threaten civilian peace and security long after a conflict has ended, and that existing peace operations will be required to evolve in order to address this threat. We explore how existing UN peacekeeping operations could be adapted, in ways that would be both feasible and valuable towards maintaining and restoring peace in a region. We conclude that the path to cyber peacekeeping will not be easy, but that it is an evolution that must begin today so that we can be prepared for the conflicts of the future.\",\n \"Title: Analysis and run-time verification of dynamic security policies\\nYear: 2005\\n\\nAbstract: Ensuring the confidentiality, integrity and availability of information is the key issue in the battle for information superiority and thus is a decisive factor in modern warfare. Security policies and security mechanisms govern the access to information and other resources. Their correct specification, i.e. denial of potentially dangerous access and adherence to all established need-to-know requirements, is critical. In this paper we present a security model that allows to express dynamic access control policies that can change on time or events. A simple agent system, simulating a platoon, is used to show the need and the advantages of our policy model. The paper finally presents how existing tool-support can be used for the analysis and verification of policies.\",\n \"Title: A Model-Based Approach for RFID Application Testing\\nYear: 2013\\n\\nAbstract: A new approach for testing Radio Frequency Identification applications is presented in this paper. Therefore, a set based semantics is introduced to describe RFID applications. The approach uses UML to model the characteristics of the system under test. The combination of three different model-types, which consist of a Domain Model, a Movement Model and Process Models, allows for the extraction of test cases and the evaluation of functional aspects of the application under test. This model-based test approach is validated on an example case study, of processes in a retail clothing shop.\",\n \"Title: A Compositional Event & Time-Based Policy Model\\nYear: 2006\\n\\nAbstract: Policies are increasingly used to govern the behaviour of complex distributed systems. Most policy models that allow policy composition, to address the complexity of policies, are only concerned with structural composition. In this paper we argue that it is natural to compose policies also along the temporal axis, i.e. express policies that can dynamically change over time or on the occurrence of events. We present a dynamic policy frame-work that has a sound semantics in Interval Temporal Logic and allows both structural and temporal composition. We provide examples of authorisation, delegation and obligation policies that can be activity-based, state-based or historybased, i.e. expressing decisions on the history of execution. Examples for the composition of policies show how the framework can be used to express policies for systems that operate in an environment that is characterised by uncertainty. Finally tool-support for the specification and analysis of dynamic policies is presented.\",\n \"Title: A Property Based Framework for Trust and Reputation in Mobile Computing\\nYear: 2009\\n\\nAbstract: In this paper we reinforce the benefits of a trust based approach in mobile multi-agent systems. Whilst we do not specify a trust model we provide a framework for property based trust, defining the observations which underpin many pre-existing trust models. This framework maintains subjectivity which should be the basis of any trust based system yet enables entities to communicate and collaborate trust information regardless of the trust model used for deliberation. We also introduce dasiatrust communitiespsila as a representation of property based trust information thus enabling the collaboration of reputation information.\",\n \"Title: Autonomous agents and multi –agent systems (AAMAS) for the military – issues and challenges\\nYear: 2005\\n\\nAbstract: The military domain is a very challenging environment and human endeavour in this domain is characterized by uncertainty and the need to be able to deal with significant and disruptive dynamic changes. In addition, activities are driven by human decision-makers who need support in making sense of the environment and with reasoning about, and effecting, possible futures. Hence, various unique factors need to be taken into account when considering the provision of applications, tools, devices and infrastructure for the military domain. This paper will itemize and discuss some of these factors in the context of autonomous agents and multi-agent systems. This paper is a desiderata for the research space.\",\n \"Title: Position Paper: Safety and Security Monitoring in ICS/SCADA Systems\\nYear: 2014\\n\\nAbstract: Supervisory control and Data Acquisition (SCADA) systems play a core role in a nation's critical infrastructure, overseeing the monitoring and control of systems in electricity, gas supply, logistics services, banks and hospitals. Monitoring safety and security properties in industrial control system (ICS) and SCADA environments faces unique challenges not found in typical enterprise networks. Novel monitoring solutions are desirable that take into account these differences. This paper presents a new approach for monitoring safety and security properties in industrial control systems. The approach is based on verifying a formal specification of ICS/SCADA components during runtime that is capable of detecting abnormal system behaviours. The solution is miniaturised and can be deployed at various points throughout the SCADA network, making masquerading and man-in-the-middle attacks more difficult to execute successfully.\",\n \"Title: Critical Theory as an Approach to the Ethics of Information Security.\\nYear: 2014\\n\\nAbstract: Information security can be of high moral value. It can equally be used for immoral purposes and have undesirable consequences. In this paper we suggest that critical theory can facilitate a better understanding of possible ethical issues and can provide support when finding ways of addressing them. The paper argues that critical theory has intrinsic links to ethics and that it is possible to identify concepts frequently used in critical theory to pinpoint ethical concerns. Using the example of UK electronic medical records the paper demonstrates that a critical lens can highlight issues that traditional ethical theories tend to overlook. These are often linked to collective issues such as social and organisational structures, which philosophical ethics with its typical focus on the individual does not tend to emphasise. The paper suggests that this insight can help in developing ways of researching and innovating responsibly in the area of information security.\",\n \"Title: An Extended Stable Marriage Problem Algorithm for Clone Detection.\\nYear: 2014\\n\\nAbstract: Code cloning negatively affects industrial software and threatens intellectual property. This paper presents a novel approach to detecting cloned software by using a bijective matching technique. The proposed approach focuses on increasing the range of similarity measures and thus enhancing the precision of the detection. This is achieved by extending a well-known stable-marriage problem (SMP) and demonstrating how matches between code fragments of different files can be expressed. A prototype of the proposed approach is provided using a proper scenario, which shows a noticeable improvement in several features of clone detection such as scalability and accuracy.\",\n \"Title: Cyber warfare: Issues and challenges.\\nYear: 2015\\n\\nAbstract: The topic of cyber warfare is a vast one, with numerous sub topics receiving attention from the research community. We first examine the most basic question of what cyber warfare is, comparing existing definitions to find common ground or disagreements. We discover that there is no widely adopted definition and that the terms cyber war and cyber warfare are not well enough differentiated. To address these issues, we present a definition model to help define both cyber warfare and cyber war. The paper then identifies nine research challenges in cyber warfare and analyses contemporary work carried out in each. We conclude by making suggestions on how the field may best be progressed by future efforts.\",\n \"Title: Low-Latency Service Data Aggregation Using Policy Obligations\\nYear: 2014\\n\\nAbstract: The Internet of Things, large scale sensor networks or even in social media, are now well established and their use is growing daily. Usage scenarios in these fields highlight the requirement to process, procure, and provide information with almost zero latency. This work is introducing new concepts for enabling fast communication by limiting information flow through filtering concepts combined with data processing techniques adopted from complex event processing. Specifically we introduce a novel mediation services architecture using filter policies to reduce latency. The filter policies define when and what data services need to provide to the mediator and thus save on bandwidth. The filter policies describe temporal conditions between two events removing the need to keep a complete history while still allowing temporal reasoning. Promising experimental results highlight the advantages to be gained from the approach.\",\n \"Title: RFID systems from a cyber-physical systems perspective\\nYear: 2013\\n\\nAbstract: This paper surveys and compares various definitions of cyber-physical systems occurring in contemporary research literature and relates them to RFID system research. In comparing the major characteristics of both we conclude that RFID systems are very similar to CPS and develop a topology of CPS that includes RFID. To frame the discussion of RFID systems closer we also develop a formal model that allows for the specification of RFID systems and explore the models' suitability to express a wider range of CPS.\",\n \"Title: Social Internet of Vehicles for Smart Cities.\\nYear: 2016\\n\\nAbstract: Digital devices are becoming increasingly ubiquitous and interconnected. Their evolution to intelligent parts of a digital ecosystem creates novel applications with so far unresolved security issues. A particular example is a vehicle. As vehicles evolve from simple means of transportation to smart entities with new sensing and communication capabilities, they become active members of a smart city. The Internet of Vehicles (IoV) consists of vehicles that communicate with each other and with public networks through V2V (vehicle-to-vehicle), V2I (vehicle-to-infrastructure) and V2P (vehicle-to-pedestrian) interactions, which enables both the collection and the real-time sharing of critical information about the condition on the road network. The Social Internet of Things (SIoT) introduces social relationships among objects, creating a social network where the participants are not humans, but intelligent objects. In this article, we explore the concept of the Social Internet of Vehicles (SIoV), a network that enables social interactions both among vehicles and among drivers. We discuss technologies and components of the SIoV, possible applications and issues of security, privacy and trust that are likely to arise.\",\n \"Title: Human Behaviour as an aspect of Cyber Security Assurance.\\nYear: 2016\\n\\nAbstract: There continue to be numerous breaches publicised pertaining to cyber security despite security practices being applied within industry for many years. This article is intended to be the first in a number of articles as research into cyber security assurance processes. This article is compiled based on current research related to cyber security assurance and the impact of the human element on it. The objective of this work is to identify elements of cyber security that would benefit from further research and development based on the literature review findings. The results outlined in this article present a need for the cyber security field to look in to established industry areas to benefit from effective practices such as human reliability assessment, along with improved methods of validation such as statistical quality control in order to obtain true assurance. The article proposes the development of a framework that will be based upon defined and repeatable quantification, specifically relating to the range of human aspect tasks that provide, or are intended not to negatively affect cyber security posture.\",\n \"Title: A Robust Eco-Routing Protocol against Malicious Data in Vehicular Networks\\nYear: 2015\\n\\nAbstract: Vehicular networks have a diverse range of applications that vary from safety, to traffic management and comfort. Vehicular communications (VC) can assist in the ecorouting of vehicles in order to reduce the overall mileage and CO2 emissions by the exchange of data among vehicle-entities. However, the trustworthiness of these data is crucial as false information can heavily affect the performance of applications. Hence, the devising of mechanisms that reassure the integrity of the exchanged data is of utmost importance. In this article we investigate how tweaked information originating from malicious nodes can affect the performance of a real time eco-routing mechanism that uses Dedicated Short Ranged Communications (DSRC), namely Erou Ve. We improve the routing decision mechanism of the original algorithm and also develop and evaluate defense mechanisms that exploit vehicular communications in order to filter out tweaked data. We prove that our proposed mechanisms can restore the performance of the Erou Ve to near its optimal operation and can be used as a basis for protecting other similar traffic management systems.\",\n \"Title: User interface design for privacy awareness in eHealth technologies\\nYear: 2016\\n\\nAbstract: In this paper we investigate privacy issues relating to Human Computer Interfaces for mobile eHealth technologies. We present the Inform-Alert-Mitigate (I-AM) cycle, a novel approach to address privacy concerns that are associated with the use of these technologies. The I-AM approach supports the responsible innovation of new technologies. We demonstrate the effectiveness of I-AM by applying it to examples taken from mobile applications relating to personal health. We discuss three classes of applications: a) fitness trackers b) personal wellbeing applications and c) medical applications, and evaluate the privacy exposure of their users using representative applications from these classes. The paper evaluates the current privacy enhancing features of these applications against the identified risks and demonstrates how the I-AM approach can be applied to yield additional and more effective privacy protection for these technologies.\",\n \"Title: An Industrial Control Systems incident response decision framework\\nYear: 2015\\n\\nAbstract: The integration of Industrial Control Systems (ICS) with Information and Communication Technologies (ICT) systems has resulted in tremendous increase in the number of ICS security incidents. Current ICS incident response practices can hardly be applied into ICS directly as the two systems are fundamentally different in terms of system complexity, criticality, flexibility and dependability. This paper addresses this challenges by proposing an ICS incident response decision framework. It is based on decision-making theory and has descriptive, predictive and prescriptive models that particularly address ICS incident response decision-making. This framework is implemented using a service-oriented architecture (SOA) that provides incident response decision making as services and offers standardized interfaces to integrate with existing incident management systems.\",\n \"Title: Insecure by Design: Using Human Interface Devices to exploit SCADA systems.\\nYear: 2015\\n\\nAbstract: Modern Supervisory Control and Data Acquisition (SCADA) systems which are used by the electric utility industry to monitor and control electric power generation, transmission and distribution, are recognized today as critical components of the electric power delivery infrastructure. SCADA systems are large, complex and incorporate increasingly large numbers of widely distributed components. Cyber-attacks usually target valuable infrastructures assets, taking advantage of architectural/technical vulnerabilities or even weaknesses in the defense systems. Even though novel intrusion detection systems are being implemented and used for defending cyber-attacks certain vulnerabilities of SCADA systems can still be exploited. In this article we present an attack scenario based on a Human Interface Device (HID) device which is used as a means of communication/exploitation tool to compromise SCADA systems. The attack, which is a normal series of commands that are sent from the HID to the PLC cannot be detected through current intrusion detection mechanisms.\",\n \"Title: An Initial Investigation into Attribution in SCADA Systems\\nYear: 2013\\n\\nAbstract: Supervisory control and Data Acquisition (SCADA) systems play a core role in a nation's critical infrastructure, overseeing the monitoring and control of systems in electricity, gas supply, logistics services, banks and hospitals. SCADA systems were once separated from other networks and used proprietary communications protocols, hardware and software. Nowadays modern SCADA systems are increasingly directly or indirectly connected to the Internet, use standardised protocols and commercial-off-the-shelf hardware and software. Attacks on these systems have the potential for devastating consequences and attribution of attacks against SCADA systems presents new challenges. This paper investigates the use of techniques to attribute cyber attacks against SCADA systems. We investigate the use of five known technical attribution techniques in SCADA systems.\",\n \"Title: Towards Agile Industrial Control Systems Incident Response.\\nYear: 2015\\n\\nAbstract: The integration of Industrial Control Systems (ICS) with IT systems has increased the ICS' exposure to cyber threats. We have seen a tremendous increase in the number of security incidents happened to ICS in the past five years. This requires the ICS to provide effective incident response capabilities to counteract security attacks. Previous research on ICS incident response has been focusing on incident detection and analysis. This paper examines the current ICS incident response procedure from a managerial perspective, identifies the unique characteristics of ICS incident response and proposes a framework to improve incident response capabilities. In particular, it evaluates the benefit of agile values to address specific characteristics of ICS Incident Response. This foundational work sets the scene for future research to apply agile practices into ICS Incident Response.\",\n \"Title: A Security Evaluation Framework for U.K. E-Goverment Services Agile Software Development.\\nYear: 2016\\n\\nAbstract: This study examines the traditional approach to software development within the United Kingdom Government and the accreditation process. Initially we look at the Waterfall methodology that has been used for several years. We discuss the pros and cons of Waterfall before moving onto the Agile Scrum methodology. Agile has been adopted by the majority of Government digital departments including the Government Digital Services. Agile, despite its ability to achieve high rates of productivity organized in short, flexible, iterations, has faced security professionalsu0027 disbelief when working within the U.K. Government. One of the major issues is that we develop in Agile but the accreditation process is conducted using Waterfall resulting in delays to go live dates. Taking a brief look into the accreditation process that is used within Government for I.T. systems and applications, we focus on giving the accreditor the assurance they need when developing new applications and systems. A framework has been produced by utilising the Open Web Application Security Projectu0027s (OWASP) Application Security Verification Standard (ASVS). This framework will allow security and Agile to work side by side and produce secure code.\",\n \"Title: Root cause analysis (RCA) as a preliminary tool into the investigation of identity theft\\nYear: 2016\\n\\nAbstract: Identity theft has been known for some centuries whereby falsified identity documents were misused as well as offences such as impersonating others were common in the society. However, the advent of technology changed the method used for conducting this crime, whereby through the use of the Internet, personal information is can be stolen and misused by criminals. The crime has its causes originating from human error and judgement to failure of computing and networking systems that allow unauthorized access to personal information. In order to provide a better tool of investigating this crime, there is the need to explore the causes of the crime thereby providing a better framework for investigating Identity theft crimes. This study uses Root Cause Analysis (RCA) as a preliminary tool that serves to provide a depicted identification of the causes of Identity theft paving the way into investigating the crime and creating incident response plans.\",\n \"Title: Measuring the Risk of Cyber Attack in Industrial Control Systems.\\nYear: 2016\\n\\nAbstract: Cyber attacks on industrial control systems (ICS) that underpin critical national infrastructure can be characterised as high-impact, low-frequency events. To date, the volume of attacks versus the overall global footprint of ICS is low, and as a result there is an insufficient dataset to adequately assess the risk to an ICS operator, yet the impacts are potentially catastrophic. This paper identifies key elements of existing decision science that can be used to inform and improve the cyber security of ICS against antagonistic threats and highlights the areas where further development is required to derive realistic risk assessments, as well as detailing how data from established safety processes may inform the decision-making process. The paper concludes by making recommendations as to how a validated dataset could be constructed to support investment in ICS cyber security.\",\n \"Title: Using Gamification to Raise Awareness of Cyber Threats to Critical National Infrastructure.\\nYear: 2016\\n\\nAbstract: Senior executives of critical national infrastructure facilities face competing requirements for investment budgets. Whilst the impact of a cyber attack upon such utilities is potentially catastrophic, the risks to continued operations from failing to upgrade ageing infrastructure, or not meeting mandated regulatory regimes, are considered higher given the demonstrable impact of such circumstances. As cyber attacks on critical national infrastructure remain low-frequency events, there is little to motivate business leaders to increase their investment in cyber defences to comparable levels. This paper describes SCIPS, a gamified environment in which senior executives experience the impact of a cyber attack on an electric power generation plant, demonstrating how it can strategically affect shareholder value, and allows them to form their own views on the relative importance of cyber security investment.\",\n \"Title: Semantics-aware detection of targeted attacks: a survey.\\nYear: 2017\\n\\nAbstract: In today’s interconnected digital world, targeted attacks have become a serious threat to conventional computer systems and critical infrastructure alike. Many researchers contribute to the fight against network intrusions or malicious software by proposing novel detection systems or analysis methods. However, few of these solutions have a particular focus on Advanced Persistent Threats or similarly sophisticated multi-stage attacks. This turns finding domain-appropriate methodologies or developing new approaches into a major research challenge. To overcome these obstacles, we present a structured review of semantics-aware works that have a high potential for contributing to the analysis or detection of targeted attacks. We introduce a detailed literature evaluation schema in addition to a highly granular model for article categorization. Out of 123 identified papers, 60 were found to be relevant in the context of this study. The selected articles are comprehensively reviewed and assessed in accordance to Kitchenham’s guidelines for systematic literature reviews. In conclusion, we combine new insights and the status quo of current research into the concept of an ideal systemic approach capable of semantically processing and evaluating information from different observation points.\",\n \"Title: Human behaviour as an aspect of cybersecurity assurance.\\nYear: 2016\\n\\nAbstract: There continue to be numerous breaches publicised pertaining to cybersecurity despite security practices being applied within industry for many years. This paper is intended to be the first in a number of papers as research into cybersecurity assurance processes. This paper is compiled based on current research related to cybersecurity assurance and the impact of the human element on it. The objective of this work is to identify elements of cybersecurity that would benefit from further research and development based on the literature review findings. The results outlined in this paper present a need for the cybersecurity field to look in to established industry areas to benefit from effective practices such as human reliability assessment, along with improved methods of validation such as statistical quality control in order to obtain true assurance. The paper proposes the development of a framework that will be based upon defined and repeatable quantification, specifically relating to the range of human aspect tasks that provide or are intended not to negatively affect cybersecurity assurance. Copyright © 2016 John Wiley & Sons, Ltd.\",\n \"Title: Authentication Protocols for Internet of Things: A Comprehensive Survey.\\nYear: 2017\\n\\nAbstract: In this paper, a comprehensive survey of authentication protocols for Internet of Things (IoT) is presented. Specifically more than forty authentication protocols developed for or applied in the context of the IoT are selected and examined in detail. These protocols are categorized based on the target environment: (1) Machine to Machine Communications (M2M), (2) Internet of Vehicles (IoV), (3) Internet of Energy (IoE), and (4) Internet of Sensors (IoS). Threat models, countermeasures, and formal security verification techniques used in authentication protocols for the IoT are presented. In addition a taxonomy and comparison of authentication protocols that are developed for the IoT in terms of network model, specific security goals, main processes, computation complexity, and communication overhead are provided. Based on the current survey, open issues are identified and future research directions are proposed.\",\n \"Title: A Survey on Privacy-preserving Schemes for Smart Grid Communications.\\nYear: 2016\\n\\nAbstract: In this paper, we present a comprehensive survey of privacy-preserving schemes for Smart Grid communications. Specifically, we select and in-detail examine thirty privacy preserving schemes developed for or applied in the context of Smart Grids. Based on the communication and system models, we classify these schemes that are published between 2013 and 2016, in five categories, including, 1) Smart grid with the advanced metering infrastructure, 2) Data aggregation communications, 3) Smart grid marketing architecture, 4) Smart community of home gateways, and 5) Vehicle-to grid architecture. For each scheme, we survey the attacks of leaking privacy, countermeasures, and game theoretic approaches. In addition, we review the survey articles published in the recent years that deal with Smart Grids communications, applications, standardization, and security. Based on the current survey, several recommendations for further research are discussed at the end of this paper.\",\n \"Title: Towards data privacy in heterogeneous cloud environments: An extension to the SANTA policy language\\nYear: 2017\\n\\nAbstract: This paper addresses the pressing concern of insufficient and inflexible support for data privacy in hybrid cloud environments, where potentially sensitive data is dynamically transferred between remote (i.e. public) and local (i.e. private) data centres, as well as personal computers and portable/mobile devices. As sensitive data is being transferred across various administrative domains with differing data protection levels, legislations, and security mechanisms in place, it is important to enable a differentiated approach to implement a flexible access control mechanism tailored to the current context and circumstances. Accordingly, this paper raises awareness on the issues of data privacy in such heterogeneous cloud ecosystems, and presents an on-going research work targeted at devising an access control policy language, which serves to support various types of policies, applicable across a wide range of hybrid cloud scenarios. The presented work is based on and extends the current capabilities of SANTA - a policy language, which, however, currently does not support data privacy concerns associated with clouds, including mobile and hybrid clouds.\",\n \"Title: Poster: Design of an Anomaly-based Threat Detection & Explication System\\nYear: 2017\\n\\nAbstract: The poster corresponding to this summary depicts a proposition of a system able to explain anomalous behavior within a user session by considering anomalies identified through their deviation from a set of baseline process graphs. We adapt star structures, a bipartite representation used to approximate the edit distance between two graphs. Relevant processes are selected from a dictionary of benign and malicious traces generated through a sentiment-like bigram extraction and scoring system based on the log likelihood ratio test. We prototypically implemented smart anomaly explication through a number of competency questions derived and evaluated by a decision tree. The determined key factors are ultimately mapped to a dedicated APT attack stage ontology that considers actions, actors, as well as target assets.\",\n \"Title: SCIPS: Using Experiential Learning to Raise Cyber Situational Awareness in Industrial Control System\\nYear: 2017\\n\\nAbstract: AbstractThe cyber threat to industrial control systems is an acknowledged security issue, but a qualified dataset to quantify the risk remains largely unavailable. Senior executives of facilities that operate these systems face competing requirements for investment budgets, but without an understanding of the nature of the threat, cyber security may not be a high priority. Education and awareness campaigns are established methods of raising the profile of security issues with stakeholders, but traditional techniques typically deliver generic messages to wide audiences, rather than tailoring the communications to those who understand the impact of organisational risks. This paper explores the use of experiential learning through serious games for senior executives, to develop mental models within which participants can frame the nature of the threat, thereby raising their cyber security awareness, and increasing their motivation to address the issue.\",\n \"Title: On Data Leakage From Non-Production Systems\\nYear: 2017\\n\\nAbstract: Purpose - This study is an exploration of areas pertaining to the use of production data in non-production environments. During the software development life cycle, non-production environments are used to serve various purposes to include unit, component, integration, system, user acceptance, performance and configuration testing. Organisations and third parties have been and are continuing to use copies of production data in non-production environments. This can lead to personal and sensitive data being accidentally leaked if appropriate and rigorous security guidelines are not implemented. This paper aims to propose a comprehensive framework for minimising data leakage from non-production environments. The framework was evaluated using guided interviews and was proven effective in helping organisation manage sensitive data in non-production environments.Design/methodology/approach - Authors conducted a thorough literature review on areas related to data leakage from non-production systems. By doing an analysis of advice, guidelines and frameworks that aims at finding a practical solution for selecting and implementing a de-identification solution of sensitive data, the authors managed to highlight the importance of all areas related to sensitive data protection. Based on these areas, a framework was proposed which was evaluated by conducting set of guided interviews.Findings - This paper has researched the background information and produced a framework for an organisation to manage sensitive data in its non-production environments. This paper presents a proposed framework that describes a process flow from the legal and regulatory requirements to data treatment and protection, gained through understanding the organisation's business, the production system, the purpose and the requirements of the non-production environment. The paper shows that there is some conflict between security and perceived usability, which may be addressed by challenging the perceptions of usability or identifying the compromise required. Non-production environments need not be the sole responsibility of the IT section, they should be of interest to the business area that is responsible for the data held.Originality/value - This paper proposes a simplified business model and framework. The proposed model diagrammatically describes the interactions of elements affecting the organisation. It highlights how non-production environments may be perceived as separate from the business systems, but despite the perceptions, these are still subject to the same legal requirements and constraints. It shows the interdependency of data, software, technical infrastructure and human interaction and how the change of one element may affect the others. The proposed framework describes the process flow and forms a practical solution in assisting the decision-making process and providing documentary evidence for assurance and audit purposes. It looks at the requirements of the non-production system in relation to the legal and regulatory constraints, as well as the organisational requirements and business systems. The impact of human factors on the data is also considered to bring a holistic approach to the protection of non-production environments.\",\n \"Title: The industrial control system cyber defence triage process.\\nYear: 2017\\n\\nAbstract: The threat to Industrial Control Systems (ICS) from cyber attacks is widely acknowledged by governments and literature. Operators of ICS are looking to address these threats in an effective and cost-sensitive manner that does not expose their operations to additional risks through invasive testing. Whilst existing standards and guidelines offer comprehensive advice for reviewing the security of ICS infrastructure, resource and time limitations can lead to incomplete assessments or undesirably long countermeasure implementation schedules.\",\n \"Title: Can a Network Attack Be Simulated in an Emulated Environment for Network Security Training?\\nYear: 2017\\n\\nAbstract: This paper outlines a tool developed with the purpose of creating a simple configurable emulated network environment that can be used in cyber defence exercises. Research has been conducted into the various related subject areas: cyber defence exercises, network threats, network emulation, network traffic replay, network topologies, and common network services. From this research a requirements specification was produced to encapsulate the features required to create this tool. A network, containing many of the aspects researched, was designed and implemented using Netkit-NG to act as a blueprint for the tool and to further knowledge in the construction of an emulated network. Following this the tool was developed and tested to ensure requirements were met.\",\n \"Title: Internet of Cloud: Security and Privacy issues.\\nYear: 2017\\n\\nAbstract: The synergy between Cloud and IoT has emerged largely due to the Cloud having attributes which directly benefit IoT and enable its continued growth. IoT adoptingCloud services has brought newsecurity challenges. In this book chapter, we pursue two main goals: (1) to analyse the different components of Cloud computing and IoT and (2) to present security and privacy problems that these systems face. We thoroughly investigate current security and privacy preservation solutions that exist in this area, with an eye on the Industrial Internet of Things, discuss open issues and propose future directions.\",\n \"Title: Security for 4G and 5G Cellular Networks: A Survey of Existing Authentication and Privacy-preserving Schemes.\\nYear: 2018\\n\\nAbstract: This paper presents a comprehensive survey of existing authentication and privacy-preserving schemes for 4G and 5G cellular networks. We start by providing an overview of existing surveys that deal with 4G and 5G cellular networks. Then, we give a classification of threat models in 4G and 5G cellular networks in four categories, including, attacks against privacy, attacks against integrity, attacks against availability, and attacks against authentication. We also provide a classification of countermeasures into three types of categories, including, cryptography methods, humans factors, and intrusion detection methods. The countermeasures and informal and formal security analysis techniques used by the authentication and privacy preserving schemes are summarized in form of tables. Based on the categorization of the authentication and privacy models, we classify these schemes in seven types, including, handover authentication with privacy, mutual authentication with privacy, RFID authentication with privacy, deniable authentication with privacy, authentication with mutual anonymity, authentication and key agreement with privacy, and three-factor authentication with privacy. In addition, we provide a taxonomy and comparison of authentication and privacy-preserving schemes for 4G and 5G cellular networks in form of tables. Based on the current survey, several recommendations for further research are discussed at the end of this paper.\",\n \"Title: A Security Architectural Pattern For Risk Management Of Industry Control Systems Within Critical National Infrastructure\\nYear: 2017\\n\\nAbstract: SCADA and ICS security have been focusing on addressing issues such as vulnerability discovery and intrusion detection within critical national infrastructure. Less attention has been paid to architectural solutions to the cyber security risks from an information assurance perspective. Security controls are not always traced back to the business requirements. This paper presents a holistic end-to-end view of the requirements, medium to high severity risks and proposes a generic security architectural pattern to address them. The architectural pattern is developed based on the Sherwood Applied Business Security Architecture (SABSA) top two layers, contextual and conceptual, which are responsible for understanding the business requirements and development of a concept architecture and strategy. Moreover, this research is motivated by industrial practices and has reflected the recent changes of GCHQ's mission. This research also contributes to the SCADA/ICS risk assessment by deriving holistic sets of risk management and architectural design requirements for SCADA/ICS.\",\n \"Title: Vulnerability Analysis of Network Scanning on SCADA Systems.\\nYear: 2018\\n\\nAbstract: Supervisory Control and Data Acquisition (SCADA) systems and Industrial Control Systems (ICSs) have controlled the regulation and management of Critical National Infrastructure environments for decades. With the demand for remote facilities to be controlled and monitored, industries have continued to adopt Internet technology into their ICS and SCADA systems so that their enterprise can span across international borders in order to meet the demand of modern living. Although this is a necessity, it could prove to be potentially dangerous. The devices that make up ICS and SCADA systems have bespoke purposes and are often inherently vulnerable and difficult to merge with newer technologies. The focus of this article is to explore, test, and critically analyse the use of network scanning tools against bespoke SCADA equipment in order to identify the issues with conducting asset discovery or service detection on SCADA systems with the same tools used on conventional IP networks. The observations and results of the experiments conducted are helpful in evaluating their feasibility and whether they have a negative impact on how they operate. This in turn helps deduce whether network scanners open a new set of vulnerabilities unique to SCADA systems.\",\n \"Title: Blockchain Technologies for the Internet of Things: Research Issues and Challenges.\\nYear: 2019\\n\\nAbstract: This paper presents a comprehensive survey of the existing blockchain protocols for the Internet of Things (IoT) networks. We start by describing the blockchains and summarizing the existing surveys that deal with blockchain technologies. Then, we provide an overview of the application domains of blockchain technologies in IoT, e.g., Internet of Vehicles, Internet of Energy, Internet of Cloud, Edg...\",\n \"Title: Class Balanced Similarity-Based Instance Transfer Learning for Botnet Family Classification.\\nYear: 2018\\n\\nAbstract: The use of Transfer Learning algorithms for enhancing the performance of machine learning algorithms has gained attention over the last decade. In this paper we introduce an extension and evaluation of our novel approach Similarity Based Instance Transfer Learning (SBIT). The extended version is denoted Class Balanced SBIT (or CB-SBIT for short) because it ensures the dataset resulting after instance transfer does not contain class imbalance. We compare the performance of CB-SBIT against the original SBIT algorithm. In addition, we compare its performance against that of the classical Synthetic Minority Over-sampling Technique (SMOTE) using network traffic data. We also compare the performance of CB-SBIT against the performance of the open source transfer learning algorithm TransferBoost using text data. Our results show that CB-SBIT outperforms the original SBIT and SMOTE using varying sizes of network traffic data but falls short when compared to TransferBoost using text data.\",\n \"Title: SEQUIN: a grammar inference framework for analyzing malicious system behavior.\\nYear: 2018\\n\\nAbstract: Targeted attacks on IT systems are a rising threat to the confidentiality of sensitive data and the availability of critical systems. The emergence of Advanced Persistent Threats (APTs) made it paramount to fully understand the particulars of such attacks in order to improve or devise effective defense mechanisms. Grammar inference paired with visual analytics (VA) techniques offers a powerful foundation for the automated extraction of behavioral patterns from sequential event traces. To facilitate the interpretation and analysis of APTs, we present SEQUIN, a grammar inference system based on the Sequitur compression algorithm that constructs a context-free grammar (CFG) from string-based input data. In addition to recursive rule extraction, we expanded the procedure through automated assessment routines capable of dealing with multiple input sources and types. This automated assessment enables the accurate identification of interesting frequent or anomalous patterns in sequential corpora of arbitrary quantity and origin. On the formal side, we extended the CFG with attributes that help describe the extracted (malicious) actions. Discovery-focused pattern visualization of the output is provided by our dedicated KAMAS VA prototype.\",\n \"Title: HEART-IS: A novel technique for evaluating human error-related information security incidents.\\nYear: 2019\\n\\nAbstract: Organisations continue to suffer information security incidents and breaches as a result of human error even though humans are recognised as the weakest link with regard to information security. Despite this level of understanding organisations continue to focus their attention on technical security controls rather than the human factor and have not incorporated methods such as Human Reliability Analysis (HRA) which are used within high reliability sectors such as rail, aviation and energy. The objectives of our research are to define a human error related information security incident and create the novel HEART of Information Security (HEART-IS) technique which is an adaptation of the Human Error Assessment and Reduction Technique (HEART). We conducted a case study within a private sector organisation using HEART-IS to establish if HRA is applicable to information security. The novel HEART-IS technique comprises of a mapping component and an analysis component. In the case study, we applied HEART-IS to map HEART Error Producing Conditions (EPC) to twelve months of reported information security incidents and analysed the volumes of human error and underlying causes. We found that HEART-IS is applicable to the information security field with some minor amendments to the terminology. The mapping of information security incident causes to the HEART Error Producing Conditions (EPC) was successful but the in-built HEART human error probability calculations did not match the actual volumes of reported human error related incidents.\",\n \"Title: Threats, Protection and Attribution of Cyber Attacks on Critical Infrastructures.\\nYear: 2019\\n\\nAbstract: As Critical National Infrastructures are becoming more vulnerable to cyber attacks, their protection becomes a significant issue for any organization as well as a nation. Moreover, the ability to attribute is a vital element of avoiding impunity in cyberspace. In this article, we present main threats to critical infrastructures along with protective measures that one nation can take, and which are classified according to legal, technical, organizational, capacity building, and cooperation aspects. Finally we provide an overview of current methods and practices regarding cyber attribution and cyber peace keeping\",\n \"Title: Developing Cyber Buffer Zones.\\nYear: 2018\\n\\nAbstract: The United Nations conducts peace operations around the world, aiming tomaintain peace and security in conflict torn areas. Whilst early operations werelargely successful, the changing nature of warfare and conflict has often left peaceoperations strugglingto adapt. In this article, we make a contribution towardsefforts to plan for the next evolution in both intra and inter-state conflict: cyberwarfare. It is now widely accepted that cyber warfare will be a component offuture conflicts, and much researchhas been devoted to how governments andmilitaries can prepare for and fight in this new domain [1]. Despite the vastamount of research relating to cyber warfare, there has been less discussion onits impact towards successful peace operations. This is agap in knowledge thatis important to address, since the restoration of peace following conflict of anykind is of global importance. It is however a complex topic requiring discussionacross multiple domains. Input from the technical, political, governmental andsocietal domains are critical in forming the concept of cyber peacekeeping.Previous work on this topic has sought to define the concept of cyber peacekeeping[2, 3, 4]. We build upon this work by exploring the practicalities ofstarting up a cyber peacekeeping component and setting up a Cyber Buffer Zone (CBZ).\",\n \"Title: MIMO Techniques for Jamming Threat Suppression in Vehicular Networks.\\nYear: 2016\\n\\nAbstract: Vehicular ad hoc networks have emerged as a promising field of research and development, since they will be able to accommodate a variety of applications, ranging from infotainment to traffic management and road safety. A specific security-related concern that vehicular ad hoc networks face is how to keep communication alive in the presence of radio frequency jamming, especially during emergency situations. Multiple Input Multiple Output techniques are proven to be able to improve some crucial parameters of vehicular communications such as communication range and throughput. In this article, we investigate how Multiple Input Multiple Output techniques can be used in vehicular ad hoc networks as active defense mechanisms in order to avoid jamming threats. For this reason, a variation of spatial multiplexing is proposed, namely, vSP4, which achieves not only high throughput but also a stable diversity gain upon the interference of a malicious jammer.\",\n \"Title: Design Of An Anomaly-Based Threat Detection & Explication System\\nYear: 2017\\n\\nAbstract: Current signature-based malware detection systems are heavily reliant on fixed patterns that struggle with unknown or evasive applications, while behavior-based solutions usually leave most of the interpretative work to a human analyst. In this paper, we propose a system able to explain anomalous behavior within a user session by considering anomalies identified through their deviation from a set of baseline process graphs. To minimize computational requirements we adapt star structures, a bipartite representation used to approximate the edit distance between two graphs. Baseline templates are generated automatically and adapt to the nature of the respective process. We prototypically implement smart anomaly explication through a number of competency questions derived and evaluated using the decision tree algorithm. The determined key factors are ultimately mapped to a dedicated APT attack stage ontology that considers actions, actors, as well as target assets.\",\n \"Title: Route Optimization of Electric Vehicles based on Dynamic Wireless Charging.\\nYear: 2018\\n\\nAbstract: One of the barriers for the adoption of electric vehicles (EVs) is the anxiety around the limited driving range. Recent proposals have explored charging EVs on the move, using dynamic wireless charging which enables power exchange between the vehicle and the grid while the vehicle is moving. In this paper, we focus on the intelligent routing of EVs in need of charging so that they can make most efficient use of the so-called mobile energy disseminators (MEDs) which operate as mobile charging stations. We present a method for routing EVs around MEDs on the road network, which is based on constraint logic programming and optimization using a graph-based shortest path algorithm. The proposed method exploits inter-vehicle communications in order to eco-route electric vehicles. We argue that combining modern communications between vehicles and state of the art technologies on energy transfer, the driving range of EVs can be extended without the need for larger batteries or overtly costly infrastructure. We present extensive simulations in city conditions that show the driving range and consequently the overall travel time of electric vehicles is improved with intelligent routing in the presence of MEDs.\",\n \"Title: Cyber security of critical infrastructures\\nYear: 2018\\n\\nAbstract: Modern Supervisory Control and Data Acquisition (SCADA) systems are essential for monitoring and managing electric power generation, transmission and distribution. In the age of the Internet of Things, SCADA has evolved into big, complex and distributed systems that are prone to be conventional in addition to new threats. Many security methods can be applied to such systems, having in mind that both high efficiency, real time intrusion identification and low overhead are required.\",\n \"Title: Exploring the role of work identity and work locus of control in information security awareness.\\nYear: 2019\\n\\nAbstract: A growing body of research evidence has been focused on exploring aspects of individual differences in the context of human factors and adherence to organisational information security. The present study aimed to extend this research by exploring three individual variables related directly to the individual's perceived control within the workplace, their commitment to current work identity, and the extent to which they are reconsidering commitment to work. A total 1003 participants aged between 18–65 (Mean = 40.29; SD = 12.28), who were in full or part-time employment took part in the study. The results demonstrated that work locus of control acted as a significant predictor for total scores on a measure of information security awareness. Those individuals who demonstrated more externality had weaker engagement in accepted information security within the workplace. The findings from the current study are discussed in the context of potential links to counterproductive work behaviours, as well as presenting possible practical routes for intervention strategies to help mitigate poor engagement in information security awareness.\",\n \"Title: Evaluating information security core human error causes (IS-CHEC) technique in public sector and comparison with the private sector.\\nYear: 2019\\n\\nAbstract: •This research identified that human error proportions are higher than currently understood in the literature.•The IS-CHEC technique is applicable to information security, in a participating public sector organisation providing healthcare services.•The majority of information security incidents pertain to human error.•The use of IS-CHEC technique provides insight into the common causes of human error.•The IS-CHEC technique has been improved based upon a comparison of case study findings within public and private sector organisations.\",\n \"Title: AIDIS: Detecting and classifying anomalous behavior in ubiquitous kernel processes\\nYear: 2019\\n\\nAbstract: Targeted attacks on IT systems are a rising threat against the confidentiality, integrity, and availability of critical information and infrastructures. With the rising prominence of advanced persistent threats (APTs), identifying and understanding such attacks has become increasingly important. Current signature-based systems are heavily reliant on fixed patterns that struggle with unknown or evasive applications, while behavior-based solutions usually leave most of the interpretative work to a human analyst.\",\n \"Title: Dying Of A Hundred Good Symptoms: Why Good Security Can Still Fail-A Literature Review And Analysis\\nYear: 2021\\n\\nAbstract: Many organizations suffer serious information security incidents, despite having taken positive steps towards achieving good security standards. The authors hypothesize that these issues are often as a result of security arrangements not being sufficiently integrated with businesses. We believe that adopting an enterprise architecture (EA) approach to implementing information security - commonly referred to as an 'Enterprise Information Security Architecture' (EISA) - will deliver substantial benefits. Our paper has reviewed and analyzed literature concerning the root causes of information security incidents and describes a novel approach with 8 domains for ensuring critical factors are considered when building an EISA framework.\",\n \"Title: Security Visualization: Detecting Denial of Service.\\nYear: 2016\\n\\nAbstract: Denial Of Service attacks are notorious attack methods used to target servers of IT systems and Industrial Control Systems to prevent them from working or to reduce efficiency, hence decreasing user experience. Visualization is the method of taking data, processing and displaying data in an easy to view format. Visualization could be used to identify Denial Of Service attacks by monitoring the data sent to clients and being displayed to the users. Manipulating the type of data shown and the format it is shown in can help users spot potential attacks by seeing outliers in the data sets. This research develops novel software that can run on an web server. It processes the web access logs, displays the data to users and identify potential attacks in access logs. The software has been tested, with the majority of tests passing. Further development of the project is discussed and the main areas for development are also explored.\",\n \"Title: Effect of Network Architecture Changes on OCSVM Based Intrusion Detection System.\\nYear: 2016\\n\\nAbstract: Intrusion Detection Systems are becoming an important defense mechanism for (supervisory control and data acquisition (SCADA) systems. SCADA systems are likely to become more dynamic leading to a need for research into how changes to the network architecture that is monitored, affect the performance of defense mechanisms. This article investigates how changes in the network architecture of the SCADA system affect the performance of an IDS that is based on the One class Support Vector Machine (OCSVM). Also the article proposes an adaptive mechanism that can cope with such changes and can work in real time situations.\",\n \"Title: Published Incidents And Their Proportions Of Human Error\\nYear: 2019\\n\\nAbstract: Purpose This paper aims to provide an understanding of the proportions of incidents that relate to human error. The information security field experiences a continuous stream of information security incidents and breaches, which are publicised by the media, public bodies and regulators. Despite the need for information security practices being recognised and in existence for some time, the underlying general information security affecting tasks and causes of these incidents and breaches are not consistently understood, particularly with regard to human error. Design/methodology/approach This paper analyses recent published incidents and breaches to establish the proportions of human error and where possible subsequently uses the HEART (human error assessment and reduction technique) human reliability analysis technique, which is established within the safety field. Findings This analysis provides an understanding of the proportions of incidents and breaches that relate to human error, as well as the common types of tasks that result in these incidents and breaches through adoption of methods applied within the safety field. Originality/value This research provides original contribution to knowledge through the analysis of recent public sector information security incidents and breaches to understand the proportions that relate to human error.\",\n \"Title: A Deep Learning-based Penetration Testing Framework for Vulnerability Identification in Internet of Things Environments.\\nYear: 2021\\n\\nAbstract: The Internet of Things (IoT) paradigm has displayed tremendous growth in recent years, resulting in innovations like Industry 4.0 and smart environments that provide improvements to efficiency, management of assets and facilitate intelligent decision making. However, these benefits are offset by considerable cybersecurity concerns that arise due to inherent vulnerabilities, which hinder IoT-based systems' Confidentiality, Integrity, and Availability. Security vulnerabilities can be detected through the application of penetration testing, and specifically, a subset of the information-gathering stage, known as vulnerability identification. Yet, existing penetration testing solutions can not discover zero-day vulnerabilities from IoT environments, due to the diversity of generated data, hardware constraints, and environmental complexity. Thus, it is imperative to develop effective penetration testing solutions for the detection of vulnerabilities in smart IoT environments. In this paper, we propose a deep learning-based penetration testing framework, namely Long Short-Term Memory Recurrent Neural Network-Enabled Vulnerability Identification (LSTM-EVI). We utilize this framework through a novel cybersecurity-oriented testbed, which is a smart airport-based testbed comprised of both physical and virtual elements. The framework was evaluated using this testbed and on real-time data sources. Our results revealed that the proposed framework achieves about 99% detection accuracy for scanning attacks, outperforming other four peer techniques.\",\n \"Title: A Novel Two-Factor Honeytoken Authentication Mechanism\\nYear: 2021\\n\\nAbstract: The majority of systems rely on user authentication on passwords, but passwords have so many weaknesses and widespread use that easily raise significant security concerns, regardless of their encrypted form. Users hold the same password for different accounts, administrators never check password files for flaws that might lead to a successful cracking, and the lack of a tight security policy regarding regular password replacement are a few problems that need to be addressed. The proposed research work aims at enhancing this security mechanism, prevent penetrations, password theft, and attempted break-ins towards securing computing systems. The selected solution approach is two-folded; it implements a two-factor authentication scheme to prevent unauthorized access, accompanied by Honeyword principles to detect corrupted or stolen tokens. Both can be integrated into any platform or web application with the use of QR codes and a mobile phone.\",\n \"Title: Digital Twins and Cyber Security – solution or challenge?\\nYear: 2021\\n\\nAbstract: Digital twin technology today is diverse and emerging and its full potential is not yet widely understood. The concept of a digital twin allows for the analysis, design, optimisation and evolution of systems to take place fully digital, or in conjunction with a cyber-physical system to improve speed, accuracy and efficiency when compared to traditional engineering approaches. Digital twins continu...\",\n \"Title: SmartValidator: A framework for automatic identification and classification of cyber threat data\\nYear: 2022\\n\\nAbstract: A wide variety of Cyber Threat Information (CTI) is used by Security Operation Centres (SOCs) to perform validation of security incidents and alerts. Security experts manually define different types of rules and scripts based on CTI to perform validation tasks. These rules and scripts need to be updated continuously due to evolving threats, changing SOCs’ requirements and dynamic nature of CTI. The manual process of updating rules and scripts delays the response to attacks. To reduce the burden of human experts and accelerate response, we propose a novel Artificial Intelligence (AI) based framework, SmartValidator. SmartValidator leverages Machine Learning (ML) techniques to enable automated validation of alerts. It consists of three layers to perform the tasks of data collection, model building and alert validation. It projects the validation task as a classification problem. Instead of building and saving models for all possible requirements, we propose to automatically construct the validation models based on SOC’s requirements and CTI. We built a Proof of Concept (PoC) system with eight ML algorithms, two feature engineering techniques and 18 requirements to investigate the effectiveness and efficiency of SmartValidator. The evaluation results showed that when prediction models were built automatically for classifying cyber threat data, the F1-score of 75% of the models were above 0.8, which indicates adequate performance of the PoC for use in a real-world organization. The results further showed that dynamic construction of prediction models required 99% less models to be built than pre-building models for all possible requirements. Thus, SmartValidator is much more efficient to use when SOCs’ requirements and threat behaviour are constantly evolving. The framework can be followed by various industries to accelerate and automate the validation of alerts and incidents based on their CTI and SOC’s preferences.\",\n \"Title: Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning\\nYear: 2022\\n\\nAbstract: In this paper, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems in two different modes, namely, centralized and federated learning. Specifically, the dataset has been generated using a purpose-built IoT/IIoT testbed with a large representative set of devices, sensors, protocols and cloud/edge configurations. The IoT data are generated from various IoT devices (more than 10 types) such as Low-cost digital sensors for sensing temperature and humidity, Ultrasonic sensor, Water level detection sensor, pH Sensor Meter, Soil Moisture sensor, Heart Rate Sensor, Flame Sensor, etc.). Furthermore, we identify and analyze fourteen attacks related to IoT and IIoT connectivity protocols, which are categorized into five threats, including, DoS/DDoS attacks, Information gathering, Man in the middle attacks, Injection attacks, and Malware attacks. In addition, we extract features obtained from different sources, including alerts, system resources, logs, network traffic, and propose new 61 features with high correlations from 1176 found features. After processing and analyzing the proposed realistic cyber security dataset, we provide a primary exploratory data analysis and evaluate the performance of machine learning approaches (i.e., traditional machine learning as well as deep learning) in both centralized and federated learning modes. The Edge-IIoTset dataset can be publicly accessed from http://ieee-dataport.org/8939.\",\n \"Title: Reliability, Security, and Privacy in Power Grids\\nYear: 2022\\n\\nAbstract: As we move from traditional power grids to smart grids, new threats arise. We focus on cybersecurity in power grids, highlighting the threats to and vulnerabilities of those cyberphysical systems and presenting future solutions.\",\n \"Title: Security Revisited: Honeytokens meet Google Authenticator\\nYear: 2022\\n\\nAbstract: In contrast to the rapid advancement and evolution of technology, computer systems rely significantly on passwords to maintain their security for an unreasonably long time. Although the introduction of two or more factors improved existing authentication procedures and gave rise to new multi-factor authentication schemes, more advanced and focused assaults have revealed they are also sensitive and ineffective to assure only authorized access to systems. This study suggests a revolutionary two-factor authentication mechanism that uses QR codes and honeytokens. In the current implementation, honeywords and Google authenticator work together. In order to speed up the identification process, the honeywords technique is utilized as a second factor on a different computing device. By using a stronger and more effective authentication mechanism than a single password mechanism or a common two-factor authentication scheme, the novel and straightforward approach presented here intends to add additional layers of security and protection into a system, thereby increasing its security.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":73,"string":"73"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13656,"string":"13,656"}}},{"rowIdx":13657,"cells":{"question_id":{"kind":"string","value":"Ming Zhu"},"question":{"kind":"string","value":"Generate an abstract for the title \"Determination of the Data Model for Heterogeneous Data Processing Based on Cost Estimation.\" using the following items: heterogeneous data processing, data model, cost estimation, intermediate representation, efficiency."},"answer":{"kind":"string","value":"In heterogeneous data processing, various data model often make analytic task too hard to achieve optimal performance, it is necessary to unify heterogeneous data into the same data model. How to determine the proper intermediate data model and unify the involved heterogeneous data models for the analytical task is an urgent problem need to be solved. In this paper, we proposed a model determination method based on cost estimation. It evaluates the execution cost of query tasks on different data models, which taken as the criterion to measure the data model, and chooses a data model with the least cost as the intermediate representation during data processing. The experimental results of BigBench datasets showed that the proposed cost estimation based method could appropriately determine the data model, which made heterogeneous data processing efficiently."},"correct_answer":{"kind":"string","value":"In heterogeneous data processing, various data model often make analytic task too hard to achieve optimal performance, it is necessary to unify heterogeneous data into the same data model. How to determine the proper intermediate data model and unify the involved heterogeneous data models for the analytical task is an urgent problem need to be solved. In this paper, we proposed a model determination method based on cost estimation. It evaluates the execution cost of query tasks on different data models, which taken as the criterion to measure the data model, and chooses a data model with the least cost as the intermediate representation during data processing. The experimental results of BigBench datasets showed that the proposed cost estimation based method could appropriately determine the data model, which made heterogeneous data processing efficiently."},"documents":{"kind":"list like","value":["Title: Supporting Flexibility of the CMMI Process Framework with a Multi-layered Process Model\nYear: 2013\n\nAbstract: CMMI(Capability Maturity Model Integration) is a process framework which is organized by goals, practice and process area. The Maturity Level 3 stressed that the project level process must be tailored from the organization level process, that is to say, no matter how the project level process changes, it must satisfy the constraints of the organization level process. However, companies usually prefer to choose detailed and operational single-layered process models for applying CMMI. If some activities in process have been changed, the process model must be totally redesigned, this reduces the flexibility and increases the cost of adopting CMMI. In order to solve the above problems, this paper presents a multi-layered model, in which the upper layer is used to describe the CMMI organization level process (i.e., the software development lifecycle) and the lower layer is used to describe the project level process model (i.e., the procedure of each development group). At the same time, we provide delegation relationship in multi-layered process model to build the constraints between the upper layer and the lower layer process model, it brings the flexibility that the lower layered model (the project process) can change its activities on the premise that obeying the constraints of upper layered model (the organization process). Finally, in order to validate the practical effectiveness of this model, a CMMI case from GUODIAN Nanjing Automation Cd., LTD is provided.","Title: The Performance Of Parallel Matrix Algorithms On A Broadcast-Based Architecture\nYear: 2006\n\nAbstract: Due to advances in fiber-optics and very large scale integration (VLSI) technology, interconnection networks which allow multiple simultaneous broadcasts are becoming feasible. This paper summarizes one such multiprocessor architecture called the Simultaneous Optical Multiprocessor Exchange Bus (SOME-Bus). It also presents enhancements to the network interface and the cache and directory controllers which support cache block combining, capture and prefetch, and allow complete overlap of processing time with the communication time due to compulsory misses. The paper uses two fundamental matrix algorithms to characterize the impact of each enhancement on performance. Cache miss analysis and results from the execution of these programs on a SOME-Bus simulator show that block capture and prefetch combined with an effective block replacement policy succeed in significantly reducing the miss rate due to compulsory misses as the cache size increases, while a similar increase of cache size in traditional architectures leaves the miss rate due to compulsory misses unaffected. Copyright (c) 2005 John Wiley & Sons, Ltd.","Title: A Remote Monitoring System of IDC Room Based on ZigBee Wireless Sensor Networks\nYear: 2009\n\nAbstract: This paper introduces an intelligent remote monitor system based on ZigBee sensor networks for IDC (Internet Data Center) room. The system consists of two parts: three types of ZigBee modules and the server module. The three types of ZigBee modules contain first-cluster node, second-cluster node and sensor end-node. The system can control remote IDC room based on the connection of the ZigBee protocols and traditional Internet networks. The data collected by the sensor end-nodes in the IDC room are transmitted through ZigBee networks to the server that can communicate with the remote monitor PC with Internet connection. The functions the system implements include data collection, analysis, management, storage, automatic alarm, display and control. By monitoring the IDC room unmanned, the proposed system reduces the energy consumption and the number of management staff, at the same time, provides reliable and robust monitoring, then control the equipments in real-time. Consequently, it ensures the safety and stable operation for the IDC room.","Title: A green solution for intelligent metropolitan heating system with uSDCards\nYear: 2012\n\nAbstract: In this paper1, we present the architecture, design, and simulation of an intelligent system for Temperature Monitoring used in metropolitan heating. The system consists of several TelosB-compatible motes, a Nokia uSDCard, and a smart phone. We use TelosB Motes to collect temperature data, and to transport the data to the smart phone. The uSDCard, as the middle layer, connects the smart phone with the ZigBee compatible devices. The smart phone, as the terminal, processes data and manages the TelosB Motes. We use the smart phone as the final terminal because it has a rich set of user interfaces and has access to various kinds of networks, allowing our system to be extended more easily and more user-friendly. In real scenes, our system can reduce the temperature reading fluctuation, and ultimately save the energy consumption for heating companies, providing a better living environment for indoor users.","Title: Quaternion Fourier-Mellin moments for color images\nYear: 2011\n\nAbstract: The quaternion Fourier-Mellin moments for describing color images are introduced, which can be seen as the generalization of traditional Fourier-Mellin moments for gray-level images. Then, the quaternion Fourier-Mellin moment invariants are derived, which could be a useful tool in color object recognition tasks that require the similarity invariance. In addition, the problem of color image registration using quaternion Fourier-Mellin moments is discussed. The registration method can match color images differing in rotation and scaling. The advantage of our method is that it can process color image directly, without losing color information. Experimental results validate the effectiveness of the method we proposed.","Title: An Improved Gps/Rfid Integration Method Based On Sequential Iterated Reduced Sigma Point Kalman Filter\nYear: 2012\n\nAbstract: In this paper, an improved GPS/RFID integration method based on Sequential Iterated Reduced Sigma Point Kalman Filter (SIR-SPKF) is proposed for vehicle navigation applications. It is applied to improve the accuracy, reliability and availability of satellite positioning in the areas where the satellite visibility is limited. An RFID system is employed to assist the GPS system in achieving high accuracy positioning. Further, to reduce the measurement noise and decrease the computational complexity caused by the integrated GPS/RFID, SIRSPET is investigated as the dominant filter for the proposed integration. Performances and computational complexities of different integration scenarios with different filters are compared in this paper. A field experiment shows that both accuracy and availability of positioning can be improved significantly by this low-cost GPS/RFID integration method with the reduced computational load.","Title: Load Migrating for the Hot Spots in Wireless Sensor Networks Using CTP\nYear: 2011\n\nAbstract: To suit the needs of data collection, routing protocols in WSN are normally required to form a collection tree where data flows from the source nodes to the sink nodes. These protocols, such as CTP and Multihop LQI, generally target at reducing the packet delivery cost without considering load balancing issues. We argue that load balancing is crucial for WSNs because load imbalance may cause certain nodes, which we call hot spots, to run out of energy faster than expected. The load imbalance may lead to holes and prominently degrade the performance of the network. In this paper, we propose BCTP (Balanced Collection Tree Protocol), which enhances CTP by enabling the network to migrate the load of the node under heavy traffic. BCTP uses the average transmission rate as the metric to measure a node's long term traffic load. Once a node is found heavily loaded, BCTP adopts a stochastic routing strategy to balance the load. BCTP is evaluated by test bed experiments with 9 Telosb motes. Experiment result shows that BCTP can reduce the load of the hot spot by up to 61.9\\% in a densely deployed network.","Title: Adaptive Trajectory Tracking of Stratospheric Airship Based on Input-output Stability Theory\nYear: 2011\n\nAbstract: Based on input-output stability theory a new approach is introduced about cruise trajectory tracking adaptive control of a fully-actuated stratospheric airship. Firstly the dynamic model is expressed by the generalized coordinates. In view of the imprecisely unknown inertial parameters, online inertial parameter estimator and trajectory tracking law are designed to realize autonomous tracking of any continuous time-varying desired trajectory from an arbitrary initial condition. The stability of the closed-loop control system is proved by using the Lyapunov stability theory. Finally, simulation results of tracking a typical trajectory for such a fully-actuated airship show the validity of the proposed method.","Title: An efficient similarity-based validity index for kernel clustering algorithm\nYear: 2006\n\nAbstract: The qualities of clustering, including those obtained by the kernel-based methods should be assessed. In this paper, by investigating the inherent pairwise similarities in kernel matrix implicitly defined by the kernel function, we define two statistical similarity coefficients which can be used to describe the within-cluster and between-cluster similarities between the data items, respectively. And then, an efficient cluster validity index and a self-adaptive kernel clustering (SAKC) algorithm are proposed based on these two similarity coefficients. The performance and effectiveness of the proposed validity index and SAKC algorithm are demonstrated, compared with some existing methods, on two synthetic datasets and four UCI real databases. And the robustness of this new index with Gaussian kernel width is also explored tentatively.","Title: ISA: a source code static vulnerability detection system based on data fusion\nYear: 2007\n\nAbstract: Static analysis is a kind of effective method to detect the vulnerabilities in the software. Without running the programs, static analysis tools can be used to automatically discover unknown bugs. To cope with the problem of high false positives and false negatives in source code static analysis methods, this paper presents a source code static analysis technology for vulnerability detection based on data fusion. By parsing and making data fusion on the outcome of different static analysis methods, this technology lets different results validate each other, which greatly decreases the false positives and false negatives. Brief explanations are given to support this method. A prototype system of scalable source code analysis system (ISA for short) is designed and implemented which also can automatically search for the best result based on feedback of the user interaction. The whole system is scalable and platform-independent. It is proved by experiment that this method has a better performance with lower false positives and false negatives and higher efficiency compared with one single method.","Title: Affinity-aware Virtual Cluster Optimization for MapReduce Applications\nYear: 2012\n\nAbstract: Infrastructure-as-a-Service clouds are becoming ubiquitous for provisioning virtual machines on demand. Cloud service providers expect to use least resources to deliver best services. As users frequently request virtual machines to build virtual clusters and run MapReduce-like jobs for big data processing, cloud service providers intend to place virtual machines closely to minimize network latency and subsequently reduce data movement cost. In this paper we focus on the virtual machine placement issue for provisioning virtual clusters with minimum network latency in clouds. We define distance as the latency between virtual machines and use it to measure the affinity of virtual clusters. Such metric of distance indicates the considerations of virtual machine placement and topology of physical nodes in clouds. Then we formulate our problem as the classical shortest distance problem and solve it by modeling to integer programming problem. A greedy virtual machine placement algorithm is designed to get a compact virtual cluster. Furthermore, an improved heuristic algorithm is also presented for achieving a global resource optimization. The simulation results verify our algorithms and the experiment results validate the improvement achieved by our approaches.","Title: Improved Minimum Hop Routing Protocol in WSN\nYear: 2012\n\nAbstract: Wireless Sensor Network (WSN) is a real-time monitor network [1]. It can sense and collect environmental information within the network or monitor objects. These data are processed to obtain detailed and accurate information. Research focusing on Wireless Sensor Network is routing, positioning technology, energy control and security guarantees. Routing technology is the basis for other research directions, and now a lot of routing algorithms have been proposed. This paper is based on the needs of the small transmission delay. The Minimum Hop Routing (MHR) algorithm is studied. MHR algorithm is mainly based on the node updating the routing table, so as to build each routing table, so as to build a path from source node to destination node. MHR has the advantage of network delay is small, but MHR also has shortcomings-with the data transmission, some path will soon run out of energy, the death of some nodes will depart the network into several parts, reduce network connecting. In order to solve the problem, this paper presents an improved minimum hop routing algorithm. First, sensor nodes are randomly distributed by region, Second, selecting the maximum energy node as the target node for transmission. Through analysis and experiment the approach has effectively reduced the transmission hops, and compared with the Flooding, the node energy consumption has increased.","Title: New Mix codes for multiple bit upsets mitigation in fault-secure memories\nYear: 2011\n\nAbstract: Nowadays, multibit error correction codes (MECCs) are effective approaches to mitigate multiple bit upsets (MBUs) in memories. As technology scales, combinational circuits have become more susceptible to radiation induced single event transient (SET). Therefore, transient faults in encoding and decoding circuits are more frequent than before. Firstly, this paper proposes a new MECC, which is called Mix code, to mitigate MBUs in fault-secure memories. Considering the structure characteristic of MECC, Euclidean Geometry Low Density Parity Check (EG-LDPC) codes and Hamming codes are combined in the proposed Mix codes to protect memories against MBUs with low redundancy overheads. Then, the fault-secure scheme is presented, which can tolerate transient faults in both the storage cell and the encoding and decoding circuits. The proposed fault-secure scheme has remarkably lower redundancy overheads than the existing fault-secure schemes. Furthermore, the proposed scheme is suitable for ordinary accessed data width (e.g., 2^n bits) between system bus and memory. Finally, the proposed scheme has been implemented in Verilog and validated through a wide set of simulations. The experiment results reveal that the proposed scheme can effectively mitigate multiple errors in whole memory systems. They can not only reduce the redundancy overheads of the storage array but also improve the performance of MECC circuits in fault-secure memory systems.","Title: Adaptive Pixel/Patch-Based Stereo Matching For 2d Face Recognition\nYear: 2013\n\nAbstract: In this paper, we propose using adaptive pixel/patch-based stereo matching for 2D face recognition. We don't perform 3D reconstruction but define a measure of the similarity of two 2D face images. After rectifying the two images by epipolar geometry, we match them using the similarity for face recognition. The proposed approach has been tested on the CMU PIE and FERET database and demonstrates superior performance compared to existing methods in real-world situations including changes in pose and illumination.","Title: Mining spatial association rules with multi-relational approach\nYear: 2010\n\nAbstract: Working on spatial data models for geographic phenomena have always been viewed from a spatial context and emphasizing spatial change. But the problem is that spatial relationships are embedded in space, unknown a priori. To achieve such issue, spatial association rules mining techniques are needed. In this paper, we propose a multi-relational mining method to deal with it. We use a non-parametric way by using Vironoi-diagram based neighborhood, classification method is implemented to pre-process the rules condition, association rules are pre-defined, and a close Apriori-base algorithm is proposed to cope with it. Then the framework is evaluated by the real-world dataset, and some thoughtful association rules are given.","Title: A Deep-Space Trail Forecast Method Based on the Enlargement of Field of View\nYear: 2006\n\nAbstract: During the procedure of target tracing, lots of stars will cross the field of view. These stars will disturb the procedure and, the precision of system will be degraded. At the same time, because the optical surveillance system is limited by environment, the targets are often covered by clouds. The above situations will seriously degrade the system's robustness, and once the target is disappeared, the traditional system can't track the target again. Traditional forecast methods merely use coder information, so the precision is low. In order to upgrade the precision, we combine the coder information and the missdistance to forecast the next frame target position. The method is based on field range enlargement. Proved by practical experiments, this method can effectively improve the robustness of the target tracking system.","Title: Gatewaying the Wireless Sensor Networks\nYear: 2013\n\nAbstract: With the development of Internet of Things (IoT), bridging wireless sensor networks (WSNs) and other networks has become important. We divide bridging solutions into two categories: hardware solutions and middleware solutions. Hardware solutions have both low power short distance wireless interfaces and other types of transmission interfaces, e.g. GPRS, 3G/4G, via hardware implementations. This kind of solutions is more stable and more applicable for deployed sensor networks. In middleware solutions, the whole system processes appropriate protocol conversion. This kind of solutions is more independent of hardware, making it easier to be reused in different networks. This paper briefly presents key points of each solution and evaluates advantages and disadvantages of them in terms of different criteria. Eventually, we derive the most appropriate situation for each solution from our comparisons and discussions.","Title: Mining Similarities for Clustering Web Video Clips\nYear: 2008\n\nAbstract: With the widespread use of online video application, the amount of online video clips becomes huge. Web video search engines can help users to locate video clips they are interested in. However, most video search engines return similar or near-duplicate videos together in the result lists, which is inconvenient for users to browse. This paper proposes a novel approach to cluster similar web searched videos based on video visual similarities mining. The visual information is extracted for each video clip at first, then the video clips are clustered according to the pair-wise similarities among them. To evaluate the effectiveness of the proposed method, experiments are conducted on YouTube video search results.","Title: A distributed Key Message algorithm to optimize the communication in clusters\nYear: 2009\n\nAbstract: The Key Message (KM) approach prioritizes communications along the critical path to speed up the execution of parallel applications in a cluster environment. However, the current KM approaches (i.e, the Static KM (SKM) and the dynamic KM with a central predictor (CDKM)) either lack the capability of adapting to dynamic environment or suffer from the expensive centralized control to predict dynamic critical path rendering them to be suboptimal. In this paper, we introduce a dynamic and distributed KM algorithm (DDKM) that takes this dynamic nature into account while avoiding overheads of central control, thus, overcoming the drawbacks of its predecessors. To evaluate the DDKM algorithm, we implement it and simulate it in a cluster environment whose communication network (e.g., shared bus) can be modeled as an M/D/1. We compare DDKM with current KM approaches. The performance of DDKM is close to that of CDKM, and much better than SKM up to 28.9% when the network is highly congested. These results demonstrate that DDKM is a promising optimization in a real cluster environment.","Title: Efficient Two-Dimensional Error Codes for Multiple Bit Upsets Mitigation in Memory.\nYear: 2010\n\nAbstract: As technology scales, more and more memory cells can be placed in a die. Multiple bit upsets (MBUs) induced by a single event in adjacent memory cells gets significantly increased. This paper proposes the efficient two-dimensional error codes to mitigate radiation-induced MBUs in memory. This scheme can correct MBUs with any possible width and assure the reliability of memory with very low cost overhead. Firstly, each word is transformed to a matrix form. Then, low complexity multibit error detection codes are applied in each row for error detection while parity codes are applied in each column for error correction. Furthermore, the layout architecture of two-dimensional error codes is used to mitigate MBUs in the redundancy bits. The proposed scheme has been implemented in Verilog and validated through a wide set of simulations. The experiment results reveal that the proposed method has a superior protection level. Compared with the existing multibit error correction codes, it has lower encoding and decoding overheads, even for Hamming codes.","Title: Bayesian network based on FTA for safety evaluation on coalmine haulage system\nYear: 2010\n\nAbstract: Safety evaluation is one of the most effective countermeasures to improve the safety in enterprises. Bayesian network based on FTA is a new safety evaluation method. On the basis of FTA, combining with the advantages of Bayesian network, the new method relax the harsh conditions of FTA which make it used in a wider area. This method is applied to the safety evaluation of underground haulage system in one mining area of Kailuan Coal Group Corporation Ltd. Through the result of the analysis we point out the most important factors influencing the safety state. The corresponding countermeasures are put forward too. The evaluation result indicates that this method is easy and can be popularized.","Title: Patient-specific computational fluid dynamic simulation of a bilateral bidirectional Glenn connection.\nYear: 2008\n\nAbstract: Computational fluid dynamics (CFD) have been used to investigate the hemodynamic performance in cavopulmonary anastomosis and resulted in improved operative design. In this study, CFD simulations were performed in a patient-specific bilateral bidirectional Glenn (BBDG) connection model and the power losses as well as flow features at different levels of predetermined pulmonary flow splits were calculated and compared. The control volume power loss varied between 0.64 and 1.02 mW when the flow ratio of left pulmonary artery/right pulmonary artery changed from 80:20 to 20:80. The flow patterns within the connection area and the static pressures in the four vessels differed from each other as the pulmonary flow split changed. Power loss and flow patterns of this BBDG connection were influenced by the pulmonary flow split.","Title: Incremental DFT based search algorithm for similar sequence\nYear: 2005\n\nAbstract: This paper begins with a new algorithm for computing time sequence data expansion distance on the time domain that, with a time complexity of O(n×m), solves the problem of retained similarity after the shifting and scaling of time sequence on the Y axis. After this, another algorithm is proposed for computing time sequence data expansion distance on frequency domain and searching similar subsequence in long time sequence, with a time complexity of merely O(n×fc), suitable for online implementation for its high efficiency, and adaptable to the extended definition of time sequence data expansion distance. An incremental DFT algorithm is also provided for time sequence data and linear weighted time sequence data, which allows dimension reduction on each window of a long sequence, simplifying the traditional O(n×m×fc) to O(n×fc).","Title: Prior based cardiac valve segmentation in echocardiographic sequences: geodesic active contour guided by region and shape prior\nYear: 2005\n\nAbstract: This paper presents a segmentation of cardiac valve structure method in ultrasound sequence. Prior knowledge on certain complex object is a powerful guidance in image segmentation. We represent region and shape prior of the cardiac valve in a form of speed field and incorporate it into image segmentation process within level set framework. Region prior constrains the zero level set evolving in certain region and shape prior pulls the curve to the ideal contour. Experiments on a large quantity of 3D valve sequences show that the algorithm improves accuracy of segmentation and reduces the manual intervention.","Title: Compressed Sensing Based on Random Symmetric Bernoulli Matrix\nYear: 2012\n\nAbstract: The task of compressed sensing is to recover a sparse vector from a small number of linear and non-adaptive measurements, and the problem of finding a suitable measurement matrix is very important in this field. While most recent works focused on random matrices with entries drawn independently from certain probability distributions, in this paper we show that a partial random symmetric Bernoulli matrix whose entries are not independent, can be used to recover signal from observations successfully with high probability. The experimental results also show that the proposed matrix is a suitable measurement matrix.","Title: Quaternion Based Color Image Quality Assessment Index\nYear: 2011\n\nAbstract: A quaternion model for describing color image is proposed in order to evaluate its quality. Local variance distribution of luminance layer is calculated. Color information is taken into account by using quaternion matrix. The description method is a combination of luminance layer and color information. The angle between the singular value feature vectors of the quaternion matrices corresponding to the reference image and the distorted image is used to measure the structural similarity of the two color images. When the reference image and distorted images are of unequal size it can also assess their quality. Results from experiments show that the proposed method is better consistent with the human visual characteristics than MSE, PSNR and MSSIM. The resized distorted images can also be assessed rationally by this method.","Title: A Hybrid Analysis of an Optimization Approach for Cluster Applications\nYear: 2005\n\nAbstract: Cluster/distributed computing has become a popular, cost-effective alternative to high-performance parallel computers. Many parallel programming languages and related programming models have become widely accepted on clusters. However, the high communication overhead is a major shortcoming of running parallel applications on cluster/distributed computing environments. To reduce the communication overhead and thus the completion time of a parallel application, this paper introduces and evaluates an efficient Key Message (KM) approach to support parallel computing on cluster computing environments. In this paper, we briefly present the model and algorithm, and then analytical and simulation methods are adopted to evaluate the performance of the algorithm. It demonstrates that when network background load increases or the computation to communication ratio decreases, the analysis results show better improvement on communication of a parallel application over the system which does not use the KM approach.","Title: Poster: a green solution for intelligent metropolitan heating system with uSDcard\nYear: 2011\n\nAbstract: In this poster, we present the architecture, design, and the preliminary simulation results of a system for Metropolitan Heating, via intelligent temperature monitoring. The system consists of several Crossbow TelosB-compatible motes, a Nokia uSDCard, and a smart phone. The motes are used to collect indoor temperature data, which is further relayed to the smart phone's uSD interface. The uSDCard connects the phone with IEEE 802.15.4/ZigBee compatible devices. We use the smart phone as the sink terminal because it has a rich set of user interfaces and can access to different kinds of networks, such as GPRS and 3G. In real scenes, our system can reduce the reading fluctuation, and ultimately save the energy consumed for heating company, achieving a better living condition in the houses.","Title: Anisotropic 3d reconstruction and restoration for rotation-scanning 4d echocardiographic images based on MAP-MRF\nYear: 2005\n\nAbstract: An anisotropic method of 3D reconstruction method for time sequence echocardiographic images is proposed in this paper. First, a Bayesian model based on MAP-MRF is described to reconstruct 3D volume, and extended to deal with the images acquired by rotation scanning method. Second, the spatial and temporal nature of ultrasound images is taken into account for the selection of parameter of energy function, which makes this statistical model anisotropic. Hence not only can this method reconstruct 3D ultrasound images, but also remove the speckle noise anisotropically. Finally, we illustrate the experiments of our method on the synthetic and medical images and compare with the isotropic reconstruction method.","Title: Promotion Analysis Based on Fitting in Multi-Level Space\nYear: 2011\n\nAbstract: This paper addresses an interesting problem, which is to find top-k L-Regions of a given object in a fact table with multiple dimensions and each dimension has one hierarchy with multiple levels. L-Region is a variant of combination of members from each dimension. To tackle the problem of the large solution space, we propose a materialized strategy called LR-Cube, which balances storage and time overhead by model fitting. The LRAPQuery designed in this paper is a query guaranteeing completeness and uniqueness in checking L-Regions. The experiments show that our proposed method is both feasible and of high efficiency.","Title: Mixed Compressed Sensing Based on Random Graphs.\nYear: 2013\n\nAbstract: Finding a suitable measurement matrix is an important topic in compressed sensing. Though the known random matrix, whose entries are drawn independently from a certain probability distribution, can be used as a measurement matrix and recover signal well, in most cases, we hope the measurement matrix imposed with some special structure. In this paper, based on random graph models, we show that the mixed symmetric random matrices, whose diagonal entries obey a distribution and non-diagonal entries obey another distribution, can be used to recover signal successfully with high probability.","Title: An efficient pixel-wise method for moving object detection in complex scenes\nYear: 2013\n\nAbstract: Moving object detection is often one of the most basic and important stages in computer vision applications. In this paper, a novel background model is proposed to extract moving foreground objects from videos that may contain different kinds of disturbance such as illumination changes, camera parameter variations, noises and dynamic backgrounds, etc. For each frame, a local frequency response map is generated using short-term Fourier transformation (STFT) in local regions, and by extracting the relations among neighborhoods of the response map, a compact pixel feature is introduced as local frequency pattern. Then, an adaptive probabilistic estimation of pixel feature sequence modified from kernel density estimation is performed to estimate the probability of a pixel being background. Experimental evaluations on complex scenes of surveillance videos demonstrate that the proposed method has archived satisfactory results.","Title: An Area-Time Efficient Architecture for 16 x 16 Decimal Multiplications\nYear: 2013\n\nAbstract: With growing demands of decimal computations in scientific, financial and many other key applications, area-time efficient hardware implementation of decimal arithmetic is desired. In this paper, we present a parallel architecture for the fixed-point decimal multiplications based on the 8421 BCD representation. By reducing the entries of partial product pre-computations and using a tree structure with carry-look ahead adders (CLAs) as opposed to carry-save adders (CSAs), a significant speedup of the partial product generations (PPGs) and partial product accumulations can be achieved, while at the same time, the hardware overhead can be reduced. The × decimal multiplier using the proposed architecture with a TSMC 90nm technology compares favorably against three other best known decimal multiplier designs in terms of delay-area product.","Title: Region and shape prior based geodesic active contour and application in cardiac valve segmentation\nYear: 2005\n\nAbstract: Geodesic active contour is a useful image segmentation method. But it may fail to segment objects disturbed by complex noises. Prior knowledge on certain object is a powerful guidance in image segmentation. We represent region and shape prior of certain object in a form of speed field and incorporate it into Geodesic Active Contours. Region prior constrains the zero level set evolving in certain region and shape prior pulls the curve to the ideal contour. Applications in a large quantity of cardiac valve echocardiographic sequences have shown that the algorithm is a more accurate and efficient image segmentation method.","Title: Occlusion-Robust Face Recognition Using Iterative Stacked Denoising Autoencoder.\nYear: 2013\n\nAbstract: This paper investigates how to recognize faces with partial occlusions using iterative stacked denoising autoencoder (ISDAE). We introduce a mapping-autoencoder (MAE) for occlusion detection, which requires no prior knowledge of occlusion. Inspired by stacked denoising autoencoder (SDAE)'s capability to learn patterns from noisy data, we propose a novel iterative structure of SDAE for occluded faces restoration. Deep neural network (DNN) is used for final recognition. Compared with the state-of-the-art approaches (e.g. sparse representation), ISDAE achieves competitive results under serious occlusion conditions. © Springer-Verlag 2013.","Title: Quaternion moment and its invariants for color object classification.\nYear: 2014\n\nAbstract: Moment invariants are important shape descriptors in computer vision. In this study, we propose new sets of quaternion moment descriptors for color image. They are constructed in the quaternion framework and are an extension of complex moment invariants for grayscale images. This is a useful tool in color image processing and color object recognition tasks that require the similarity invariance. The advantage of the proposed quaternion moment invariants is that they can not only process color image in a holistic manner but also grayscale one. In addition, the computational complexity of the proposed method is much lower than the quaternion Zernike moments defined in the polar coordinates. An example of using the quaternion moment invariants as pattern features for a color object classification application is given. Theoretical and experimental results show that the proposed descriptors perform better than the other competing moment-based methods.","Title: New SEC-DED-DAEC codes for multiple bit upsets mitigation in memory.\nYear: 2011\n\nAbstract: Nowadays, multiple bit upsets (MBUs) have been widely investigated in memories. Conventional single error correction and double error detection (SEC-DED) codes are capable of correcting one error and detecting all possible double errors. However, they may not provide adequate protection against MBUs. This paper proposes new single-error-correction, double-error-detection double-adjacent-error-correction (SEC-DED-DAEC) codes to mitigate radiation or noise source induced MBUs in memories. The proposed SEC-DED-DAEC codes are obtained from conventional SEC-DED codes according to the mathematics model established in this paper. They can detect and correct all adjacent double bit errors and assure a lower miscorrection probability for non-adjacent double bit errors compared with other SEC-DED-DAEC codes. Furthermore, the redundancy bits of the proposed scheme are the same as those of conventional SEC-DED codes. This means that the increase of correct-capability do not cause additional hardware overhead for the memory system. Finally, the experiment results reveal that the proposed scheme reduces the miscorrection probability of non-adjacent double bit errors by 12% compared to the best known SEC-DED-DAEC codes. Moreover, compared to the well known BCH codes, the proposed scheme reduces 40% hardware redundancy and keeps an acceptable reliability. © 2011 IEEE.","Title: Dynamic key messaging for cluster computing\nYear: 2004\n\nAbstract: Over the past decades, distributed computing has been gaining popularity. It provides more computing power and memory space for parallel applications. On the other hand, such applications fight back and challenge the architectures of the distributed systems for more efficiency. To face the challenge, a key messaging (KM) scheme was proposed to realize the optimization of communication at a system architecture level in our previous papers. The contribution of KM is that it performs the optimization in both the underlying communication system and high level application model. The performance of an application is always determined by its critical path. Currently, messages along the critical path can be easily blocked by non-critical path messages, which degrade the performance. To solve this problem, KM provides an algorithm to identify the critical-path messages and optimizes them by introducing a prioritized protocol layer. Thus, these messages are served first before any low priority messages. Shorter processing time for the messages results in faster completion time of the critical path. Although KM was proved to be effective by a prototype system on the IBM SP2, there are limitations in its optimization procedures where dynamic natures of an underlying network should be considered when running parallel applications on cluster/distributed computing environments. To address the problem, a dynamic key message (DKM) algorithm is introduced. DKM takes into considerations the changes of network traffic load while tasks are running and dynamically updates the critical path on the fly. A comprehensive simulation method is adopted to evaluate the performance of this algorithm and the results show that under the same workload, DKM exhibits much more stability than the static key message (SKM) algorithm when the network traffic load changes.","Title: Analysis to Effects on Conceptual Parameters of Stratospheric Airship with Specified Factors.\nYear: 2011\n\nAbstract: Stratospheric airship is capable of stationkeeping at high altitude in precondition of the balance of buoyancy and weight, thrust and drag. Based on specific computation process, when some hypotheses are given, the length of airship can be calculated and it is emphasized to analyze the impacts on payload capability performance and conceptual parameters (such as length, surface area and volume) with altitude, latitude of station-keeping and pressure difference, temperature difference, helium purity. It is shown from the analyses that pressure difference, temperature difference and helium purity have fewer effects on payload capability and length of airship, and in contrast, altitude and latitude of stationkeeping have the larger effects. On the other hand, effects on payload capability and length with each technology guideline are also discussed when specified operation parameters keep constant, such as altitude and design wind of station-keeping. It is concluded that the benefit to length and payload capability is the largest with improvement of envelope mass/area ratio but the least with improvement of propeller system efficiency. © 2011 ACADEMY PUBLISHER.","Title: A Novel Threat Assessment Method for DDoS Early Warning Using Network Vulnerability Analysis\nYear: 2010\n\nAbstract: Distributed Denial of Service (DDoS) attack is one of main threats to Internet security. Due to the spatio-temporal properties of the attack, it is possible to detect the attack at its early stage. In this paper, we propose a novel method of DDoS threat assessment based on network vulnerability analysis. Both the multi-phase character in the temporal dimension and the impacts in the spatial dimension are concerned in our method. We use three metrics to assess threat, namely the ratio of progress, botnet size, and bots distribution. Experimental results show that our method is sensitive to the changes of attack states, and is easy to be implemented in an early warning system because of its simplicity.","Title: Model optimization techniques in a verification platform for classified properties\nYear: 2004\n\nAbstract: In system functional verification, collaborative method is an effective technique. Co-SAM verification platform employs three verification methods, static analysis, logic simulation and model checking, to implement verification process. With the collaborative platform, system properties are classified and verified. A whole model for a system inclines to ineffectiveness and states explosion during verification. Model refinement is indispensable. This paper presents special optimization techniques for verification model refinement, i.e. property grouping, signals reordering, and model hierarchizing. Experimental results demonstrate the validity of these optimization techniques in system functional verification.","Title: Transparent Bilateral Teleoperation Under Position And Rate Control\nYear: 2000\n\nAbstract: A four-channel control architecture has been suggested in the literature to achieve transparency for master-slave teleoperator systems under position control. In this paper the result is generalized to include teleoperator systems that are under rate control or move general master-slave kinematic correspondence laws such as a mixed-position/rate mode. A one-degree-of-freedom example is given to outline the design and analysis of such a system for transparency and stability! The validity of the theory has been verified through simulations and experiments.","Title: A Geographic Routing Algorithm in Duty-Cycled Sensor Networks with Mobile Sinks\nYear: 2011\n\nAbstract: In this paper, we focus on achieving better energy conservation for geographic routing algorithms in duty-cycled WSNs when there is a mobile sink. We simplify the problem as a topology coverage one, and propose a multi-metric geographic algorithm (MMGR) which uses multi-metric candidates (MMCs) for geographic routing. The analysis and extensive simulation results show that MMGR can achieve better energy conservations than McTPGF, while retaining good performance of end-to-end delay and hop counts.","Title: Cooperative Multi-Versioning Technique Based on Version Replication\nYear: 2006\n\nAbstract: This paper first discusses the progress and existing problems in the study of cooperative graphics editing systems (CGES), and then a new distributed multi-versioning model based on version replication is proposed for consistency maintenance of discordant conflicting operations according to the conflicting features and types of concurrent operations in CGES. Based on this model, the scheme of expressing versions is further discussed in detail, including the method of expressing versions and showing execution effect of versions. Then an algorithm of multiple versions incremental creation based on version replication is devised, and an application example is given. The discussion ends up with the future research","Title: A Fast Handoff Mechanism with Pre-scanning for Wireless Mesh Networks\nYear: 2011\n\nAbstract: With the development of real-time business, the original traditional WMN has been difficult to satisfy the needs of the real-time business. This article is in support of this delay sensitive type under the background of real-time application business in WMN. This improved mechanism is proposing an pre-scanning algorithm focusing on the higher delay in WMN link layer scanning which based on traditional WMN link layer switching mechanism.","Title: Downlink Packets Scheduling in enterprise WLAN\nYear: 2013\n\nAbstract: Enterprise WLAN consists of many APs connected to wired backbone network. Conventional DCF mechanism can not completely prevent conflicts between APs which cause large quantities of transmission failure and retransmission overhead. However, to keep compatible with 802.11-compliant clients, it is difficult to discard DCF. In this paper, we proposed a framework called DPS, which incorporates centralized scheduling with DCF. In DPS, downlink packets are not forwarded as soon as they arrived at APs. Instead, a central controller schedules their forwarding. APs still use DCF to send downlink packets according to the controller's instructions. DPS preserves DCF and requires no modifications to 802.11-compliant clients. Under DPS framework, we proposed a scheduling algorithm, ensuring downlink packets be sent with high success rate. In this way, we can significantly reduce transmission failure and retransmission overhead. Experiment results indicated that DPS can significantly improve the performance of enterprise WLAN compared with DCF without any scheduling.","Title: Design of micropillar array for cell capture.\nYear: 2011\n\nAbstract: Micropillar based fluidic chip is an attractive platform for cell isolation, capture and detection. This paper is focused on the design of micropillar array. Its flow situation is simulated by using finite element analysis method. The relationships between the geometry and arrangement of micropillars in the array and the distribution of fluid velocity, shear stress, dynamic pressure which have to be accorded to the cell migration and capture are studied. An optimized water drop shape is obtained for micropillar design. In addition, a silicon micropillar array is proposed to be fabricated with surface functionalization for actin capture. © 2011 IEEE.","Title: Key Message Algorithm: A Communication Optimization Algorithm in Cluster-Based Parallel Computing\nYear: 1999\n\nAbstract: Parallel computing using Network of Workstations (NOWs) has become very popular recently. However, the execution of parallel applications on such systems has been hampered by the high communication overhead. To reduce the communication overhead and to speedup the execution of parallel applications on NOWs, this paper proposes a Key Message approach that minimizes the cost of message-passing in a parallel application by prioritizing communications in the underlying shared communication network. In this paper, we first describe the queuing network model on which our approach is based, then introduce the algorithm that identifies the messages to be prioritized in a parallel application, and finally discuss the results obtained. Our preliminary analysis of the algorithm on randomly generated task graphs shows improvement over the system without using the prioritization scheme.","Title: Spatial Optimization Method For Sustainable Multiobjective Land Use Allocation\nYear: 2010\n\nAbstract: As one of the core research aspect in Land use and land cover change, the optimal allocation of land use is soon becoming the key measure for sustainable land utilization. In this paper, we designed a method that could implement the multiobjective optimization model for sustainable land use in space automatically, thus we could obtain spatial optimal allocation. Yangshan Town was taken as the research district. First of all, analyzed land use structure which used to build a Digital Elevation Model in GIS technique, extracted land use pattern, aspect, slope from it, land suitability evaluation was calculated by altitude, soil quality and other eco-factors. Subsequently the research area was divided into four parts namely optimizing development zone, major development zone, limited development zone, and prohibited development zone based on their suitability. Thirdly, in accordance with the base map, adopted the depth-first search strategy to implement the structure optimal allocation in graphs with land suitability evaluation as threshold. The result showed that: (1) this method proposed a simple way that used quantity optimal outcomes to allocate in space, it could reduce the complexity in building a land use spatial optimization model, if dynamic elements such as timing, human behavior, etc. were less important risk factors while we took into account. (2) For considering the connection of graph, it could efficiently solve fragmentation compared with nothing but land suitability evaluation. (3) It could provide a convenient and precise approach for government land use planning.","Title: An Experimental Study of BATMAN Performance in a Campus Deployment of Wireless Mesh Networks\nYear: 2011\n\nAbstract: Based on a fundamental understanding of a high-quality routing protocol called Better Approach To Mobile Ad-hoc Networking or BATMAN, we provide an experimental study of its performance considering a representative set of meaningful measures with a real Wireless Mesh Network (WMN) test bed deploying in our campus. Analysis and experiments results show that BATMAN is not only an efficient and stable routing protocol, but also can satisfy the requirements of multimedia communication in mesh networks.","Title: Developed Acoustic Multi-relaxation Algorithm for Humidity Measurement in Gas Mixtures\nYear: 2007\n\nAbstract: In this paper, we focus on the relaxation process of acoustic waves in polyatomic gases, and build up a multi-relaxation algorithmic model of a gas mixture of nitrogen, oxygen and water vapor. The algorithmic is basing on the complicated transfer mechanism of vibrational-translational, vibrational-vibrational degrees of freedom. By computing acoustic velocity and relaxation attenuation, we notice that changes in water vapor concentration have great influence on the relaxation frequency of the mixture, thus proving the feasibility of detecting humidity by means of relaxation method. Finally, we describe a detailed algorithm to measure proportion of water vapor. This novel approach not only determines the concentration of water vapor, but also reflects the proportional fluctuation of the mixture.","Title: Symmetric Toeplitz-Structured Compressed Sensing Matrices.\nYear: 2013\n\nAbstract: How to construct a suitable measurement matrix is an important topic in compressed sensing. A significant part of the recent work is that the measurement matrices are not completely random on the entries but exhibit some considerable structures. In this paper, we proved that a symmetric Toeplitz matrix and its variant can be used as measurement matrices and recovery signal with high probability. Compared with random matrices (e.g. Gaussian and Bernoulli matrices) and some structured matrices (e.g. Toeplitz and circulant matrices), we need to generate fewer independent entries to obtain the measurement matrix while the effectiveness of the recovery keeps good.","Title: Enhanced multicast performance for a 60-GHz gigabit wireless service over optical access network based on 16-QAM-OFDM hierarchical modulation\nYear: 2013\n\nAbstract: We propose and experimentally demonstrate a 60-GHz ultra-high throughput (VHT) wireless over fiber multicast system. A novel 16-QAM-OFDM hierarchical modulation is implemented and 2.5-dB performance improvement is achieved comparing with conventional I-Q modulation. © 2013 OSA.","Title: A bidirectional multi-band 60-GHz wireless-over-fiber transmission system with compacted base station and mobile terminal using a robust mm-Wave LO free technique\nYear: 2013\n\nAbstract: For the first time, bidirectional multi-band multi-gigabit wireless-over-fiber transmission at 60-GHz mm-wave band has been accomplished with centrally supplied LO service. It does not require mm-wave LO devices in each base stations and mobile terminals. © 2013 OSA.","Title: Finite element analysis of the contact interface between trans-femoral stump and prosthetic socket.\nYear: 2013\n\nAbstract: Transfemoral amputees need prosthetic devices after amputation surgery, and the interface pressure between the residual limb and prosthetic socket has a significant effect on an amputee's satisfaction and comfort. The purpose of this study was to build a nonlinear finite element model to investigate the interface pressure between the above-knee residual limb and its prosthetic socket. The model was three-dimensional (3D) with consideration of nonlinear boundary conditions. Contact analysis was used to simulate the friction conditions between skin and the socket. The normal stresses up to 80.57 kPa at the distal end of the soft tissue. The longitudinal and circumferential shear stress distributions at the limb-socket interface were also simulated. This study explores the influences of load transfer between trans-femoral residual limb and its prosthetic socket.","Title: Real-time obstacle detection with motion features using monocular vision\nYear: 2015\n\nAbstract: Real-time obstacle detection by monocular vision is a challenging problem in autonomous navigation of vehicles and driver-assistance systems. In this paper, we introduce an approach of real-time obstacle detection which can effectively tell apart obstacles from shadows and road surface markings. We propose the followings: (1) a two consecutive frames (TCF) model to find the differences between obstacles and the ground plane by motion features; (2) a filter to increase probabilities of obstacle regions; (3) an updating process to reduce false positives and update the algorithm when the vehicle moves on. We perform experiments on two datasets and our autonomous vehicle. The results show that our method is effective in various conditions and meets the real-time requirement.","Title: Local image descriptor based on spectral embedding\nYear: 2015\n\nAbstract: This study presents a local image descriptor based on spectral embedding. Specifically, the spectra of line graph are used to represent image edges, corners and edge points with big curvature. The authors theoretically analyse and experimentally verify that the spectra of line graph are robust to noise and are invariant to rotation and linear intensity changes. Based on such a fact, some local image descriptors are constructed using the spectra of line graph. Comparative experiments demonstrate the effectiveness of the proposed descriptor and its superiority to some state-of-the-art descriptors under image rotation, image blur, viewpoint change, illumination change, JPEG compression and noise.","Title: On high-performance parallel decimal fixed-point multiplier designs.\nYear: 2014\n\nAbstract: •Simplified logic and structure for 8421–5421 BCD multiplier are proposed.•With a novel 4221 BCD full adder, 4221–8421 conversion is no longer needed.•4221 BCD multiplier using the new 4221 BCD full adder is proposed.•The proposed 8421–5421 multiplier gives the best performance ever.","Title: Identity Recognition Of Multi-Haptic Pressure Feature Based On Sparse Representation\nYear: 2014\n\nAbstract: Herein, a new identity recognition method of multi-haptic pressure feature based on sparse representation was investigated. According to the common dynamic features, the regional feature and the ratio of length vs. width of external bounding rectangle (extracted by using the least area method) were extracted. The subset of dynamic feature was optimized by correlation criterion, the sparse representation of haptic pressure was obtained according to the sparse basis (i.e., wavelet basis), and the sparse feature vector was calculated by the Topelitz measurement matrix. After that, the haptic pressure feature set was created by combining dynamic feature subset and sparse feature subset linearly. Furthermore, Support Vector Machine (SVM) classifier identified more than two objects following the one to many rule and output the identification result according to the rule of majority voting, and the stability of features is studied by calculating the intraclass correlation coefficient (ICC) and coefficient of variation (C.V). Overall, the improved acuracy of identity recognition demonstrating the effectiveness and stability of the multihaptic pressure feature.","Title: Fall detection for elderly person care in a vision-based home surveillance environment using a monocular camera.\nYear: 2014\n\nAbstract: Fall detection is one of the most important health care issues for elderly people at home, which can lead to severe injuries. With the advances and conveniences in computer vision in the last few decades, computer vision-based methods provide a promising way for detecting falls. In this paper, we propose a novel vision-based fall detection method for monitoring elderly people in house care environment. The foreground human silhouette is extracted via background modeling and tracked throughout the video sequence. The human body is represented with ellipse fitting, and the silhouette motion is modeled by an integrated normalized motion energy image computed over a short-term video sequence. Then, the shape deformation quantified from the fitted silhouettes is used as the features to distinguish different postures of the human. Finally, different postures are classified via a multi-class support vector machine and a context-free grammar-based method that provides longer range temporal constraints can verify the detected falls. Extensive experiments show that the proposed method has achieved a reliable result compared with other common methods.","Title: The charging-scheduling problem for electric vehicle networks\nYear: 2014\n\nAbstract: Electric vehicle (EV) is a promising transportation with plenty of advantages, e.g., low carbon emission, high energy efficiency. However, it requires frequent and long time charging. In public charging stations, EVs spend long time on queuing especially during peak hours. Hence, it requires an efficient method to reduce the total charging time for EVs. We study the Electric Vehicle Charging-Scheduling (EVCS) problem in this paper. First we prove that EVCS is NP-Complete, which can be reduced from one Parallel Machine Scheduling (PMS) problem. Then two heuristic algorithms are proposed: the Earliest Start Time (EST) algorithm, and the Earliest Finish Time (EFT) algorithm. EST tries to advance the start charging time to get customers in service as early as possible, while EFT focuses on the possible finish charging time to get customers served as soon as possible. Finally simulations show that, the proposed algorithms outperform the classic greedy nearest scheduling algorithm: assign each EV to its nearest charging station, then choose the outlet where the fewest EVs are queuing. Typically, under our simulation settings, the average finish time and maximum finish time can be reduced by about one hour, and six hours respectively.","Title: The control algorithm of craniofacial plastic surgical robot\nYear: 2014\n\nAbstract: This paper mainly introduces a kind of auxiliary robot for craniofacial plastic surgery. It contains mechanism design of the robot, the design of the control system, and experimental verification in order to prove the effectiveness of drilling bone. The robot make use of the stability and accuracy of the motor movement, combining with the 2D/3D imaging techniques, through the pressure sensor, position sensor, speed sensor real-time measure stress and displacement. By the force feedback, know the depth of the drill hole. In the drilling bone, the robot solve the error and injury problems because of the hand shaking in the surgery, improve the stability of the operation. In the motor control, because of the robot have nonlinear mechanical structure, the traditional PID control could not bring stability speed and fast response. Due to the fuzzy control need not to establish accurate model, so design a fuzzy controller to make motor run more stable and accurate. Finally the animal experiment was carried out, obtain the force during the process of drilling the bone, the results prove experiment achieve the goal.","Title: Supporting “One Big AP” illusion in enterprise WLAN: An SDN-based solution\nYear: 2014\n\nAbstract: IEEE 802.11 standard leaves client to determine AP association selection and initiate AP handoff. This often leads to sub-optimal results in enterprise WLAN with a large number of users. Therefore, network operators expects the ability to dominate AP association selection and AP handoff initiation so that better AP/client association can be achieved. In addition, AP handoff procedure involves time-consuming packets exchange. Although many methods have been proposed to simplify AP handoff procedures, there is at least hundreds of milliseconds during which client's data transmission is blocked. In this paper, we proposed the abstraction of “One Big AP” in which unmodified 802.11-compliant client is tricked into believing that there is one access point with very large coverage, while the AP that sends and receives its packets has changed. To support the“One Big AP” illusion, we leveraged the emerging idea of Software-Defined Networking (SDN) to reform the architecture of enterprise WLAN. In the SDN-based architecture, we can easily realize network-controlled AP association and client-transparent AP handoff. We demonstrated the feasibility and efficiency of our solution through simulation.","Title: Using Category Theory and Data Flow Analysis for Modeling and Verifying Properties of Communications in the Process-Oriented Language Erasmus\nYear: 2014\n\nAbstract: The increasing significance of concurrency and the need to address the challenges presented by concurrency motivate its formalization. In this paper, a methodology for modeling and verifying communications in a concurrent system developed in Erasmus, a process-oriented programming language, is proposed. The innovative aspects of this methodology include the use of category theory to model and verify properties of communications in Erasmus, and the use of data flow to analyze and abstract an implementation of Erasmus. The methodology is illustrated by a simple example.","Title: Closed-form analysis of intra/inter-band cross-modulation in multiband radio-over-fiber systems\nYear: 2013\n\nAbstract: A theoretical and experimental investigation of the nonlinear behaviors of intra- and inter-band cross-modulation (XM) in a multiband radio-over-fiber (RoF) system is presented for the first time. A closed-form theoretical analysis is proposed to characterize and validate the data XM induced by the nonlinearity of optical Mach-Zehnder modulator (MZM). Nonlinear behaviors of the intra-band XM between the in-phase and quadrature (I/Q) components of a single-band vector signal, as well as the inter-band XM between multiband combinations of scalar and vector signals are investigated, and consistent agreement has been verified between the theory and experiments. It was confirmed that scalar signals only suffer from inter-band XM; whereas vector signals suffer from both intra- and inter-band XMs. Intra-band XM induces constellation compression to vector signals; while inter-band XM induces eye diagram degradation to scalar signals, as well as nonlinear constellation scaling to vector signals. Moreover, QPSK shows superior tolerance to MZM nonlinearity than higher order QAM formats due to it intrinsic resistance to intra-band XM.","Title: Centralized small-cell radio access network with shared millimeter-wave radio-over-fiber resources.\nYear: 2013\n\nAbstract: Centralized radio access networks (RANs) based on radio-over-fiber (RoF) techniques can simplify the remote access units (RAUs) and maximize the resource sharing among services and operators, thus improving the system efficiency and reducing the operational cost. A scheme of resource sharing is proposed for centralized RANs based on millimeter-wave (MMW) RoF system. Both RoF infrastructure resources and microwave-photonics resources are shared by multiple operators in the heterogeneous system. High-throughput services carried on both V-band (60GHz) and W-band (100GHz) are delivered to RAUs through flexible configuration at the central office (CO). This proposed multi-service centralized RAN with shared infrastructure is successfully demonstrated via both simulations and experiments.","Title: Violence Detection in Video by Using 3D Convolutional Neural Networks.\nYear: 2014\n\nAbstract: Whereas most researches are about the action recognition problem, the detection of fights has been comparatively less involved. Such capability may be of great importance. Typical methods mostly rely on domain knowledge to construct complex handcraft features from inputs. On the contrary, deep models can act directly on the raw inputs and automatically extracts features. So we developed in this paper a novel 3D ConvNets model for violence detection in video without using any prior knowledge. To evaluate our method, experimental validation conducted in the context of the Hockey dataset. The results show that the method achieves superior performance without relying on handcrafted features.","Title: TED: Inter-domain traffic engineering via deflection\nYear: 2014\n\nAbstract: As inter-domain routing on today's Internet does not and basically cannot consider traffic load when determining best traffic forwarding paths, it is not always optimal for a router to forward packets along its default path, especially when the router's default output port incurs a long queuing delay. In this paper, we design a new approach called TED in which border routers of autonomous systems (AS) adaptively deflect outbound traffic from a congested default path to an alternative path to significantly improve inter-domain traffic engineering (TE) and end-to-end throughput. With TED, every router only needs to examine the queue length of its own outgoing ports to orchestrate its deflection operation and ensure traffic forwarding is at line speed. It does not need to communicate or coordinate with other TED-capable routers or modify packet content, making TED incrementally deployable. Our evaluation shows that TED significantly increases the average throughput of traffic flows, and the improvement is comparable to directly upgrading router hardware and capacity. Finally, a prototype of TED on NetFPGA is also implemented.","Title: Using multiple unmanned aerial vehicles to maintain connectivity of MANETs\nYear: 2014\n\nAbstract: Unmanned Aerial Vehicles (UAVs) have emerged as promising relay platforms to improve the connectivity of ground Mobile Ad Hoc Networks (MANETs). Due to the relatively high cost of UAVs, a lot of efforts have been made to optimize the deployment of UAVs so that the number of UAVs needed in maintaining the connectivity of ground nodes can be minimized. However, existing work on optimization of UAVs' deployment hasn't considered the situation that there are already some UAVs deployed in the field. In this paper, we study the problem of deploying minimum number of UAVs to maintain the connectivity of ground MANETs under the condition that some UAVs have already been deployed in the field. We formulate this problem as a Minimum Steiner Tree problem with Existing Mobile Steiner points under Edge Length Bound constraints (MST-EMSELB) and prove that the problem is NP-Complete. We also propose an Existing UAVs Aware (EUA) polynomial time approximate algorithm for the MST-EMSELB problem that uses a maximum match heuristic to compute new positions for existing UAVs. Simulation results demonstrate that the proposed EUA method has bester performance than a non-EUA method in the term of needed new UAV numbers. Compared with the non-EUA method, the EUA method can reduce at most 60% of the new UAVs number.","Title: CDRDN: Content Driven Routing in Datacenter Network\nYear: 2014\n\nAbstract: A major challenge in data center networks is to provide enough network capacity to meet the ever increasing demand of large-scale distributed computing. While existing proposals focus on adding more switches and links, which cost extra hardware and energy, we explore another dimension in which spare disk space at servers is used for caching data, so as to increase network throughput without additional hardware. Leveraging the Named Data Networking (NDN) architecture and unique characteristics of data centers, we design a novel Content Driven Routing in Datacenter Network (CDRDN) that enables universal caching in data center networks in an efficient and scalable way. First, rather than using switches for “on-path” caching like in native NDN, CDRDN uses the large storage space at servers to do “off-path” caching. Second, by taking advantage of data center's regular and hierarchical network topology, CDRDN switches are able to direct requests to nearby server caches even under high dynamics of caches. Third, given the vast amount of data in data centers, the full name-based routing table would be difficult to fit in switch's limited fast memory. CDRDN adopts a compound content and location routing to ensure packet delivery while benefiting from name-based routing as much as the switches can afford. CDRDN extends NDN's adaptive forwarding mechanism to deal with cache misses, link failures, and congestion without running routing protocols or cache exchange protocols. Our packet-level simulations show that CDRDN can almost double the network throughput compared with shortest-path routing under the same setting, and CDRDN can effectively deal with link failures using adaptive forwarding.","Title: UPMAC: A localized load-adaptive MAC protocol for underwater acoustic networks\nYear: 2014\n\nAbstract: Unlike terrestrial networks that mainly rely on radio waves for communications, underwater networks utilize acoustic waves, which have comparatively lower loss and longer range in underwater environments. However, acoustic waves incurs long propagation delays that typically lead to low throughput especially in protocols that require receiver feedback such as multimedia stream delivery. In addition, energy cost of transmission underwater is much higher than reception (almost 125:1 [1]). Thus, collision and retransmission should be reduced in order to reduce energy cost and improve throughput Receiver-based protocols, like RIPT and COS-TS, can significantly reduce collision and retransmission. But they are time and energy consuming because nodes are controlled to turn into receiver mode by control packets or a timer regardless of load. In this paper, we propose an underwater practical MAC protocol, called UPMAC. The main objective of UPMAC is to adapt to the network load conditions by providing two modes (high and low load modes) and switching between them based on different offered load. Turn-around time overhead is reduced and it is less vulnerable to control packet corruption, since we reduce the use of control packets by the technique of piggyback. UPMAC provides a low data collision rate in both one-hop and multi-hop situation because we use Receiver-based approach in high load mode. Extensive simulations show that our approach can achieve significantly better performance in both general and Sea Swarm (tree) topologies.","Title: Investigation of intra/inter-band cross-modulation in multiband radio-over-fiber systems\nYear: 2013\n\nAbstract: We theoretically and experimentally investigated the intra/inter-band cross-modulation (XM) induced by the nonlinearity of Mach-Zehnder modulator (MZM) in a multiband radio-over-fiber (RoF) system. A closed-form theoretical analysis was proposed to model and predict the nonlinear XM behaviors, for both single-band vector signals and multiband combination of scalar and vector signals. To verify the feasibility of the theoretical model, both simulation and experimental investigations were carried out, and according to the theoretical analysis, scalar signals only suffer from inter-band XM; whereas vector signals suffer from both intra- and inter-band XM. The signal quality degradation induced by MZM nonlinearity for a single-band 16QAM, QPSK, and two bands of OOK signals are investigated and characterized, and good agreement between theory and experiments was achieved. It was observed that QPSK shows superior performances compared with higher order QAM formats due to its intrinsic resistance to intra-band XM.","Title: A novel approach for spectrum mobility games with priority in Cognitive Radio networks\nYear: 2014\n\nAbstract: In recent years, the problem of spectrum mobility in Cognitive Radio (CR) Networks has been widely investigated. In order to fully utilize spectrum resources, many spectrum handoff techniques based on game theory have been proposed, but most studies only concern how to achieve better payoffs for users, without paying much attention to the Quality of Service (QoS). Thus, we propose a new channel switching model based on game theory, using a prioritized approach to meet the diverse needs of users, such as bandwidth, delay, and jitter. Once the Nash equilibrium is achieved, our model will provide different QoSes by setting different priorities to different users. We also propose two acceleration methods to reach the Nash equilibrium more quickly. Finally, we evaluate the performance of the proposed schemes using real channel availability measurements. Experiments results show that our model can provide differentiated services and our algorithm is guaranteed to reach an approximate Nash equilibrium within polynomial time.","Title: Adaptive photonic-assisted M-ary ASK/M-QAM millimeter-wave synthesis in multi-antenna Radio-over-Fiber system\nYear: 2014\n\nAbstract: We propose and experimentally verify novel photonic-assisted multi-antenna RoF system to achieve multiplexing gain by synthesizing two 1/0.5-Gb/s OOK/4-QAM mm-wave signals into a 2/1-Gb/s 4-ary ASK/16-QAM signal, or SNR gain by combining two identical channels.","Title: Building an inverse model of MR damper based on Dahl model\nYear: 2014\n\nAbstract: Magnetorheological (MR) fluid dampers are potential intelligent devices in vibration semi-active control application. But its model is difficult to get for its strong nonlinearity. In this works, the modified Dahl model is proposed with fewer parameters and clear physical meanings. The parameters are identified by crossover particle swarm optimization (PSO) and recursive least-square optimization method based on experiment data efficiently. A simple open-loop inverse model which can efficiently and accurately obtain expected control current intensity is proposed. The effectiveness and precision of proposed models are verified by simulation in wide conditions.","Title: Parallel matrix algorithms on a broadcast-based architecture\nYear: 2004\n\nAbstract: Summary form only given. Due to advances in fiber-optics and VLSI technology, interconnection networks which allow multiple simultaneous broadcasts are becoming feasible. This paper summarizes one such multiprocessor architecture called the Simultaneous Optical Multiprocessor Exchange Bus (SOME-Bus). It also presents the design of the network interface and the cache and directory controllers which support cache block combining, capture and prefetch and allow complete overlap of processing time with the communication time due to compulsory misses. The paper uses two fundamental matrix algorithms to characterize the architecture performance. Cache miss analysis and results from the execution of these programs on a SOME-Bus simulator show that block capture and prefetch combined with an effective block replacement policy succeed in significantly reducing the miss rate due to compulsory misses as the cache size increases, while a similar increase of cache size in traditional architectures leaves the miss rate due to compulsory misses unaffected.","Title: On a parallel decimal multiplier based on hybrid 8421–5421 BCD recoding\nYear: 2013\n\nAbstract: Parallel decimal multiplications can be broadly divided into two major steps: partial product generations (PPGs) and partial product accumulations (PPAs). Although most operands in decimal multipliers are represented in popular 8421 BCD codes, alternative 4221 and 5211 BCD codes are also sometimes employed, alone or mixed with 8421 codes, to represent the partial products, with a hope that by doing so, the multipliers' area/time efficiency will be improved. However, study shows that multipliers based on unsuitable mixing of codes, like 4221-8421, can have worse area and timing performance than 8421 multipliers due to the long carry propagation delay of 4221 PPA and the extra 4221-8421 conversions as required. Consequently, in this paper, we propose an 8421-5421 BCD multiplier that is equipped with a simplified PPG based on 8421-5421 recoding and an improved 8421 carry-lookahead adder tree for PPA, and compare it against the two best known multiplier designs using 90nm TSMC technology. The synthesis results have confirmed that the proposed 8421-5421 multiplier achieves the lowest delay and is the most time-area efficient design.","Title: Obstacle detection in single images with deep neural networks\nYear: 2016\n\nAbstract: Abstract Obstacle detection in single images is a challenging problem in autonomous navigation on low-cost condition. In this paper, we introduce an approach for obstacle detection in single images with deep neural networks. We propose the followings: (1) a deep model combined with other deep neural network for obstacle detection; (2) a method to segment obstacles and infer their depths. Among others, both local and global information are generated in our method for better classification and portability. Experiments are performed on the open datasets and images captured by our autonomous vehicle. The results show that our method is effective in both obstacle detection and depth inference.","Title: A localisation algorithm based on region partition of cellular network in wireless sensor networks\nYear: 2016\n\nAbstract: AbstractIn this paper, anchor nodes are deployed in the vertices and the centre of hexagon, which is the basic architecture of cellular network. Unknown nodes are distributed randomly in the hexagon. Because of inerratic cellular network, the geometrical characteristic is made full use of and the network is divided into several regions. The minimal connecting hop between nodes is different in different regions. According to corresponding relationship between unknown nodes hop information and divided regions, the unknown node is determined in a very small region. Through calculating centroid of the small region, the localisation based on range-free is well completed. On the basis of the simulation results, it is demonstrated that the localisation algorithm based on region partition of cellular network we proposed has better positioning performances, compared with the typical processing.","Title: A new extragradient-type method for mixed variational inequalities.\nYear: 2015\n\nAbstract: In this paper, a new projection method for mixed variational inequalities is introduced in Euclidean spaces. The Armijo-type linesearch is similar to that of He’s method for variational inequalities. Under some suitable assumptions, we prove that the sequence generated by the proposed method is globally convergent to a solution of the problem. If, in addition, a certain error bound holds, we analyze the convergence rate of the iterative sequence. The results presented in this paper generalize and improve some known results given in literatures.","Title: An innovative implementation for directory-based cache coherence in shared memory multiprocessors\nYear: 1997\n\nAbstract: Directory-based cache coherence protocol is accepted as the common technique in large scale shared memory multiprocessors because of its scalability. Although it was extensively studied in the past, however, the memory overhead and long miss penalty entailed by this protocol are the major obstacles to scale for large scale multiprocessors. On the other hand, the ever-increasing cache line size makes the false sharing problem more serious than before, which will lead to high miss rate. Based on the scope consistency, we propose a lock-specific home-based cache coherence protocol. In this new cache coherence protocol, all directory memory overhead are eleminated completely and false sharing problem and high miss rate will be sloved greatly at the cost of small write notice buffer in each processor.","Title: Battery optimal scheduling based on energy balance in wireless sensor networks\nYear: 2015\n\nAbstract: In wireless sensor networks, sensors are usually equipped with limited lifetime batteries. To face the short lifetime, some sensor nodes have the option to connect an extra battery, or to use smart battery-packs with multiple cells. In these cases, a major technique to save the energy is to schedule the batteries or battery cells through the properties of the batteries. This study presents a sched...","Title: Using Category Theory to Verify Implementation Against Design in Concurrent Systems\nYear: 2015\n\nAbstract: The research has shown that process-oriented programming languages provide a suitable means for developing concurrent systems. However, in the development of a concurrent system, there is a challenge to manage consistency between design and implementation. To deal with such a challenge, we propose a new formal verification methodology and illustrate it by a running example. In this methodology, a concurrent system is designed using a process algebra, namely communicating sequential processes, and implemented in a process-oriented programming language, namely Erasmus. The consistency between the design and the implementation of such a concurrent system is verified formally using category theory.","Title: MIFO: Multi-path Interdomain Forwarding\nYear: 2015\n\nAbstract: Today's interdomain routing is traffic agnostic when determining the single, best forwarding path. Naturally, as it does not adapt to congestion, the path chosen is not always optimal. In this paper, we focus on designing a multi-path interdomain forwarding (MIFO) mechanism, where AS border routers adaptively forward outbound traffic from a congested default path to an alternative path, without touching the interdomain routing protocols. Different from previous efforts which enable multi-path on control plane, MIFO achieves multi-path on data plane. The multiple alternative forwarding paths are obtained by exploring local BGP RIB. Multi-path forwarding on data plane can create a loop even within a stable network. MIFO solves this problem with a simple and practical approach. Several other challenges are also addressed including preventing cycling packet between iBGP peers and choosing the best alternative path from among multiple candidates. Our evaluations show that MIFO significantly improves the end-to-end throughput at the AS level, compared to traditional BGP and MIRO. For example, with only 50% of the ASes being MIFO capable, a significant percentage of the flows (about 40%) can use at least 50% of the inter-AS link capacity. In contrast, BGP and MIRO routing make less effective use of the inter-AS links, with only 7% and 17% of the flows can be so. Finally, we have developed a prototype implementation of MIFO on Linux with the forwarding engine in the kernel, with the routing daemon developed on XORP platform. The experiments on a test bed built with prototypes show that MIFO can improves the aggregate throughput by 81% compared with BGP routing.","Title: Spectrum Sublet Game Among Secondary Users In Cognitive Radio Networks\nYear: 2015\n\nAbstract: Dynamic spectrum sharing can improve the efficiency of spectrum utilization. Spectrum trading between primary users (PUs) and secondary users (SUs) is a popular and efficient way to fulfill this kind of spectrum sharing. In this paper we present a novel spectrum trading mechanism which operate among secondary users. More specifically, some secondary users which has leased spectrum from PUs can sublet it to other SUs to reduce their own leasing cost. Then all of the SUs can share these spectrum bands to conduct data transmission respectively. This leads to a new multi-leader multi-follower (MLMF) game which is different from existing works. The existence of Nash equilibrium of this formulated game is proven by redefining it as a shared MLMF constraint game. A decentralized algorithm is then proposed to find Nash equilibrium of this two tiers game with only local information. Simulations are provided to illustrate the convergence and effectiveness of the proposed algorithm.","Title: Sdn-Based Routing For Efficient Message Propagation In Vanet\nYear: 2015\n\nAbstract: Vehicular Ad hoc Network (VANET) is an intermittently connected mobile network in which message propagation is quite challenging. Conventional routing protocols proposed for VANET are usually in greedy or optimum fashion. Geographical forwarding only uses local information to make the routing decision which may lead to long packet delay, while link-based forwarding has better performance but requires much more overheads. To disseminate message efficiently in VANET, a routing protocol which has both short delivery delay time and low routing overhead is required. In this paper, we proposed a SDN-based routing framework for efficiently message propagation in VANET. Software-Defined Networking (SDN) is an emerging technology that decouples the control plane from the data forwarding plane in switches and collects all the control planes into a central controller. In SDN-based routing framework, the central controller gathers network information from switches and computes optimal routing paths for switches based on the global network information. Since switches don't need to exchange routing information with each other, the routing overhead is much lower. This paper is the first to propose a SDN-based routing framework for efficiently message propagation in VANET. A new algorithm is developed to find the global optimal route from the source to the destination in VANET with dynamic network density. We demonstrate, through the simulation results, that our proposed framework significantly outperforms the related protocols in terms of both delivery delay time and routing overhead.","Title: A sociality-aware online AP association algorithm based on spectral clustering\nYear: 2015\n\nAbstract: Imbalanced load distribution among multiple APs (access points) has become a serious problem in enterprise WLANs. Load imbalance causes inefficiency of network usage and unreasonable resource allocation. We find that users tend to come and leave together, which can lead to fluctuation of network load. We propose a novel online AP allocation scheme (ASC) to keep load balanced when users come and leave together. We study the homophily of users in social network, where users with tight social relationships will have more similar network usage and more opportunity to leave in union. When users come, we collect user data first, then we adopt the spectral clustering to classify users according to the collected user data. The controller distribute users from the same cluster to different APs. Through the simulation, we find that our scheme improve the overall balancing performance by at least 22% compared with the least load first selection algorithm (LLF) and 58% compared with random selection algorithm (RS).","Title: A measurement study of a campus Wi-Fi network with mixed handheld and non-handheld traffic\nYear: 2015\n\nAbstract: Handheld and non-handheld devices, which are simultaneously online, constitute the regular clients of the campus wireless local area networks (WLAN). It is necessary to understand the patterns of the user distribution and the user behavior, and adopt better strategies to optimize the service, as well as to understand the students' daily life. This paper introduces a approach of understanding how campus WLAN performs and how the users behave. In the analysis, many interesting phenomenons and problems are discovered. We capture all of the packets in a campus free Wi-Fi during the experimental period. We articulate an approach to describe the user distribution from the view of the traffic variation tendency and the network performance. And the user behavior patterns are described from two aspects, the popular websites and the behavior of downloading files. Furthermore, after revealing the problems of the network, practical suggestions is proposed for improvements in different aspects.","Title: Optimized Periodical Charging in Large-Scale Deployed WSNs.\nYear: 2015\n\nAbstract: Restricted by finite battery energy, traditional wireless sensor networks (WSNs) can only maintain for a limited period of time, resulting in serious performance bottleneck in long-term deployment of WSN. Fortunately, the advancement in the wireless energy transfer technology provides a potential to free WSNs from limited energy supply and remain perpetual operational. A mobile charger called wireless charging vehicle (WCV) is employed to periodically charge each sensor node and keep its energy level above the minimum threshold. Aiming at maximizing the ratio of the WCV's vocation time over the cycle time as well as guaranteeing the perpetual operation of networks, we proposes a feasible and optimal solution to this issue within the context of a real-time large-scale deployed WSN.","Title: Acoustic Source Localization with Distributed Smartphone Arrays.\nYear: 2015\n\nAbstract: Acoustic source localization in sensor network is a challenging task because of severe constraints on cost, energy, and effective range of sensor devices. To overcome limitations in existing solutions, this paper formally describes, designs, implements, and evaluates a Hamming Distance-based Method for Acoustic Source Localization, i.e., HammingLoc, in distributed smartphone networks. The key idea behind HammingLoc is to turn the localization problem into search problem in Hamming space. Time Differences of Arrival (TDOAs) of signals pertaining the same smartphone are estimated through the simple Generalized Cross-Correlation method. After the quantization with a bit for the TDOA measurement from the smartphone nodes, source localization is performed by minimizing the Hamming distance between the measured binary sequence and the binary vectors in a database. The proposed design is evaluated through theoretical analysis, extensive simulations, and physical experiments (an indoor test-bed with 30 smartphone nodes). Evaluation results demonstrate that HammingLoc can effectively localize the acoustic source with good robustness.","Title: A Public Vehicle System with Multiple Origin-Destination Pairs on Traffic Networks.\nYear: 2015\n\nAbstract: Substantial technology advances have been made in areas of autonomous and connected vehicles, which open a wide landscape for future transportation systems. We propose a new type of transportation system, Public Vehicle (PV) system, to provide effective, comfortable, and convenient service. The PV system is to improve the efficiency of current transportation systems, e.g., taxi system. Meanwhile, the design of such a system targets on significant reduction in energy consumption, traffic congestion, and provides solutions with affordable cost. The key issue of implementing an effective PV system is to design efficient scheduling algorithms. We formulate it as the PV Path (PVP) problem, and prove it is NP-Complete. Then we introduce a real time approach, which is based on solutions of the Traveling Salesman Problem (TSP) and it can serve people efficiently with lower costs. Our results show that to achieve the same performance (e.g., the total time: waiting and travel time), the number of vehicles can be reduced by 47%-69%, compared with taxis. The number of vehicles on roads is reduced, thus traffic congestion is relieved.","Title: An HD IPTV system based on scalable video coding\nYear: 2015\n\nAbstract: With the development of mobile Internet technology and smart phones, IPTV is faced with a greatly increasing demand for adaptive media contents. An IPTV system is designed for HD video based on scalable video coding (SVC), which could serve the increasing smart phone users with different quality videos by using SVC encoder while do not consume more computing resource, storage and bandwidth. Due to the hot of HD video, our system could run with HD video steadily in real-time. Unlike other approaches, this system executes rewrite operation on the edges server instead of end devices with limited computing power. The system is implemented in real equipment and the results show a well performance.","Title: A Single Access Point Based Traffic Control System with Passive Measurement\nYear: 2015\n\nAbstract: The nature of wireless radio enables the unwanted anomaly users beyond the service area grabbing the bandwidth and lowering the throughput of the normal users in the service area. Conventional cryptographic solutions such as WPA and WPA2 have been cracked by many means. Existed non-cryptographic solutions require non-trivial costs such as multiple APs, special antenna, or fingerprint server. This paper focuses on designing an easy-to-use traffic control system. First, we propose an RSS window scheme to gain less fickle RSS value. Second, we design Libra, a dynamic traffic control scheme based on realtime passive measurement. Libra limits the traffic of invader users and protects the normal users' priority of using the channel. Finally, the scheme is deployed on a pervasive AP with OpenWrt system, as an additional tool to enhance the WiFi security. Benefiting from the user friendliness, the user can install the system just as installing a pervasive wireless router.","Title: Impact of Directional Antenna on Physical Layer Authentication\nYear: 2015\n\nAbstract: Many existed physical layer based schemes have been proposed to enhance the traditional authentication in wireless networks. However earlier schemes did not discuss directional antenna issue, and lacked of real measurement based evaluation. In this paper, we take an insight into the threat of directional antenna on physical layer authentication by real measurement observation. We give a brief overview of the mainstream physical layer authentication schemes, and build a real-time experimental platform to collect the channel state information, and evaluate the performance of some mainstream schemes by collecting real data in different typical environments, especially in the situation when the attacker uses directional antenna. We show the physical layer authentication schemes are effective in most cases, but may fail to detect the third party attacker equipped with directional antenna. Based on our discovery, we suggest that directional antenna impact should be seriously considered in physical layer based applications, especially in security domain.","Title: Higher bandwidth may sometimes reduce cloud application performance\nYear: 2014\n\nAbstract: Cloud computing has experienced explosive growth in last ten years. Since cloud applications usually come up with large amounts of data transfer, the network capability becomes one of the dominant performance factors. Judging from common sense, it seems obvious that the higher quantity of bandwidth guaranteed for a job, the higher performance we will achieve. Rather, in this paper, we found that the effect of providing higher bandwidth guarantee turns to be uncertain. Without efficient work and flow scheduling mechanism, merely increasing the bandwidth allocated for the job may actually be as harmful as reducing the bandwidth.","Title: CCOF: Congestion control on the fly for Inter-Domain routing\nYear: 2014\n\nAbstract: We design a new routing system called CCOF in which border routers of autonomous systems (AS) adaptively redirect outbound traffic from a congested default path to an alternative path in favor of Inter Domain traffic engineering (TE). We show how this can be done simply, by examining outgoing port's queuing size, and efficiently, that maintains line speed forwarding. In addition, CCOF-router is completely compatible with legacy routers since it neither requires cooperation nor modifies the packet content. Our evaluation shows that CCOF significantly increases the average throughput of traffic flows, and has similar improvement as directly upgrading device capacity. Finally, prototype implementation is achieved on NetFPGA.","Title: ABR-Tree: An Efficient Distributed Multidimensional Indexing Approach for Massive Data.\nYear: 2015\n\nAbstract: In the big data era, there many application scenarios urgently need efficient distributed multidimensional indexing approach to accelerate the data analytics. To address this issue, in this paper, we propose ABR-Tree, a multidimensional distributed indexing approach. ABR-Tree consist of two components, the global append-efficient Bï¾ź+ï¾ź-Tree, and the local R*-Tree. Both of them are layered over the cloud database as the index and data store, which not only make ABR-Tree is easy to implement and inherently become a distributed cloud index, but also enable ABR-Tree can sustain high throughput workload and large data volumes, meanwhile, ensuring fault-tolerance, and high availability. We conducted extensive experiments over 1ï¾źTB real data set to evaluate its efficiency of processing multidimensional range queries, the results show that it is significantly fast than the existing representative distributed multidimensional cloud index method.","Title: Enhancing Parallel Data Loading for Large Scale Scientific Database.\nYear: 2015\n\nAbstract: The rapidly increased data size make large scale scientific database often have a huge time delay between loading data into the system and ready for receiving query request. To solve this problem, we proposed an efficient parallel data loading approach named FASTLoad. It is designed to maximize the given resource e.g., network bandwidth, main memory utilization for optimizing the data loading in large scale array model based scientific database system. To verify the efficiency of FASTLoad, we implemented it in our Adaptable Data Loading System and evaluate its performance over various sizes of large scientific data sets. Our experimental results show that the performance of FASTLoad can be 4 to 6 times fast than the built-in loading techniques of states-of-the-arts array model based scientific database system.","Title: FASTDB: An Array Database System for Efficient Storing and Analyzing Massive Scientific Data.\nYear: 2015\n\nAbstract: With the development of science and technology, the data size and complexity of scientific data are increased rapidly, which made efficient data storage and parallel analysis of scientific data become a big challenge. The previous techniques that combine the traditional relational database with analysis software tends cannot efficiently meet the performance requirement of large scale scientific data based analysis. In this paper, we present FASTDB, a distributed array database system that optimized for massive scientific data management and provide a share-nothing, parallel array processing analysis. In order to demonstrate the intrinsic performance characteristics of FASTDB, we applied it into the interactive analysis of data from astronomical surveys, and designed a series of experiments with scientific analysis tasks. According to the experimental results, we found FASTDB can be significantly fast than traditional database based SkyServer in many typical analytical scenarios.","Title: Design Efficient In-Database Video Storage Approach by Learning from Performance Evaluation of BLOB.\nYear: 2015\n\nAbstract: Multimedia application designers often required to make the choice of whether storing multimedia objects as files in the file system, or as BLOBs (Binary Large Objects) in a database, or a combination of both. Towards small multimedia data, it often suggested to store them as BLOB data of database. However, previous study indicated that the efficiency of BLOB based video storage not always suffice. In this paper, By learning from the issues discovered from our previous performance evaluation, we proposed an efficient in-database video storage approach named TIViS (Temporal Interval based Video Storage). When a video object store into database by TIViS, it will be decomposed into temporal intervals and each interval will be sequential stored based on its temporal information. Additionally, in TIViS approach, a specialized buffer management mechanism is also developed to optimize the data access of multimedia objects. In our work, we implemented TIViS approach into the open source database system PostgreSQL 8.4. We conducted a series of experiments to verify the efficiency of TIViS approach, the results demonstrate that TIViS based video storage is significantly superior to traditional database system’s built-in BLOB approach (e.g., PostgreSQL’s Bytea).","Title: Performance Prediction for Concurrent Workloads in Distributed Database Systems.\nYear: 2015\n\nAbstract: In order to store and process data at large-scale, distributed databases are built to partition data and process it in parallel on distributed nodes in a cluster. When the database concurrently execute heterogeneous query workloads, performance prediction is needed. However, running queries in a distributed database heavily touches upon the network overhead as the data transmission between cluster nodes. Hence, in this work, we take network latency into account when predict concurrent query performance. We propose a linear regression model to estimate the interaction when execute concurrent query for analytical workloads in distributed database system. Since network latency and overheads of local processing are the two most significant factors for query execution, we analyze the query behavior with multivariate regression on both of them at different degree of concurrency. In addition, we use sampling techniques to obtain various query mixes as concurrency level increasing. The experiments for evaluation the performance of our prediction model are conducted over a PostgreSQL database cluster with a representative analytical workloads of TPC-H, the experimental results demonstrates that the query latency predictions of our model can minimize the relative error within 14 % on average.","Title: Design of the multiple Neural Network compensator for a billiard robot\nYear: 2015\n\nAbstract: In this paper, a multiple Neural Network (NN) compensator is designed for a billiard robot to finish a task, in which the trained robot is commanded to control the cue ball to a specific target point along a trajectory with multiple cushion rebounds. A novel pyramid classification has been established to sort out the pattern of trajectory and its segments. For each trajectory pattern, a corresponding Back Propagation Neural Network (BPNN) model has been established to fit the deviation between theoretical direction point and actual one. The pyramid classification and a finite number of BPNN models composited the multiple NN compensator. In the test, the robot will calculate the deviation and work out the actual direction point for potting. The test results have verified the reliability and workability of the multiple NN compensator.","Title: Local feature descriptor using entropy rate.\nYear: 2016\n\nAbstract: Over the past decades, an increasing number of local feature descriptors have been proposed in the community of computer vision and pattern recognition. Although they have achieved impressive results in many applications, how to find a balance between accuracy and computational efficiency is still an open issue. To address this issue, we present a local feature descriptor using entropy rate (FDER), which is robust to a variety of image transformations. We first employ the nonsubsampled Contourlet transform to produce multiple support regions and design a graph structure to describe the sub-region. We then use the entropy rate of random walks on the designed graph to build the FDER descriptor. Extensive experiments demonstrate the superiority of proposed descriptor dealing with various image transformations in comparison with the existing state-of-the-art descriptors.","Title: Providing flexible services for heterogeneous vehicles: an NFV-based approach.\nYear: 2016\n\nAbstract: Currently, many automotive manufacturers including Mercedes Benz and BMW have released new products equipped with intelligent onboard systems (IOSs) that support interconnection between vehicles and the remote service center. With the help of IOSs, vehicles can obtain traffic information and enjoy location-based services, such as restaurant and nearby gas station recommendations. However, IOSs adopt a closed architecture and are very difficult to upgrade to support new services. NFV is introduced as a new methodology that offers a potential way out of this bottleneck. Utilizing virtualization technologies, NFV may execute services as software applications on standard IT platforms like servers, switches, and storage. Using NFV technology, closed IOSs can be transformed to open ones and upgraded with lower cost. In this article, we introduce an NFV-based framework for vehicular ad hoc networks, and discuss the benefits and challenges. We also present two use cases to demonstrate its strength.","Title: Local feature descriptor invariant to monotonic illumination changes.\nYear: 2016\n\nAbstract: This paper presents a monotonic invariant intensity descriptor (MIID) via spectral embedding and nonsubsampled contourlet transform (NSCT). To make the proposed descriptor discriminative, NSCT is used for the construction of multiple support regions. Specifically, the directed graph and the spectral feature vectors of the signless Laplacian matrix are exploited to construct the MIID. We theoretically demonstrate that the proposed descriptor is able to tackle monotonic illumination changes and many other geometric and photometric transformations. We conduct extensive experiments on the standard Oxford dataset and the complex illumination dataset to demonstrate the superiority of proposed descriptor over the existing state-of-the-art descriptors in dealing with image blur, viewpoint changes, illumination changes, and JPEG compression. (C) 2015 SPIE and IS&T","Title: Using Failures and Category Theory to Verify Process Communications between Design and Implementation of Concurrent Systems.\nYear: 2016\n\nAbstract: The process-oriented design and implementation of concurrent systems have important advantages. However, it is challenging to verify the consistency of process communications between the design and the implementation. To deal with such a challenge, we construct a formal framework for designing, implementing and verifying the consistency of process communications. In this framework, we use Failures in Communicating Sequential Processes (CSP), Erasmus and Category Theory as the foundation. The framework is illustrated by using a running example.","Title: Guided Filter based Edge-preserving Image Non-blind Deconvolution.\nYear: 2016\n\nAbstract: In this work, we propose a new approach for efficient edge-preserving image deconvolution. Our algorithm is based on a novel type of explicit image filter - guided filter. The guided filter can be used as an edge-preserving smoothing operator like the popular bilateral filter, but has better behaviors near edges. We propose an efficient iterative algorithm with the decouple of deblurring and denoising steps in the restoration process. In deblurring step, we proposed two cost function which could be computed with fast Fourier transform efficiently. The solution of the first one is used as the guidance image, and another solution will be filtered in next step. In the denoising step, the guided filter is used with the two obtained images for efficient edge-preserving filtering. Furthermore, we derive a simple and effective method to automatically adjust the regularization parameter at each iteration. We compare our deconvolution algorithm with many competitive deconvolution techniques in terms of ISNR and visual quality.","Title: Public Vehicles for Future Urban Transportation.\nYear: 2016\n\nAbstract: This paper advocates a new paradigm of transportation systems for future smart cities, namely, public vehicles (PVs), that provides dynamic ridesharing trips at requests. Passengers will enjoy more convenient and flexible transportation services with much less expense. In the PV system, both the number of vehicles and required parking spaces will be significantly reduced. There will be less traffic congestion, less energy consumption, and less pollution. In this paper, the concept, method, and algorithm for the PV system are described. The key issue of effectively implementing the PV system is to design efficient planning and scheduling algorithms. The PV-path problem is formulated, which is NP-complete. Then, a practical approach is proposed, which can serve people anywhere and anytime. The simulation results show that, to achieve the same performance (e.g., total time, waiting time, and travel time), the number of vehicles in the PV system can be reduced by around 90% and 57% compared with the conventional vehicle system and Uber Pool, respectively, and the total traveling distance can be reduced by 34% and 14%.","Title: Transfer Problem in a Cloud-based Public Vehicle System with Sustainable Discomfort.\nYear: 2016\n\nAbstract: The increasing population in urban areas gives rise to a huge traffic pressure. A cloud-based industrial system, public vehicle (PV) system, is promising to mitigate the traffic congestion in smart cities, where passengers can share PVs and transfer among them with scheduling decisions made by the cloud. This paper studies the transfer problem in the PV system due to that transfer can improve the whole traffic efficiency with sacrificing a little comfort with the corporation of all the PVs. The transfer problem is NP-Complete through our analysis. Our work can be separated into three steps. First, we introduce several factors to guarantee the comfort of passengers during transfer. Second, we propose two algorithms through the graph-based scheduling problem aiming at reducing the travel distance of all the PVs with service guarantee. Third, simulations based on the Shanghai (China) urban road network show that, the total travel distance of PVs is reduced under the quality of service for passengers, and the traffic efficiency is improved.","Title: A Novel Link Scheduling Algorithm For Wireless Networks Using Directional Antenna\nYear: 2016\n\nAbstract: For a given set of communication links whose senders transmit at a fixed power level, it is a hot problem to select a maximum set of links that can be transmitted simultaneously, which is known to be NP-hard. The existing algorithm only apply to the condition of omnidirectional transmission. This paper addresses the problem in a plane wireless network where the nodes use directional antennas under physical interference model. We develop a directional interference model applicable to such networks, and first propose the approximation algorithm to solve scheduling problem under this model. We proved the correctness of the algorithm by mathematical analysis. We have also proved the great advantages of using directional antenna by extensive simulations.","Title: Characterizing the Scalability and Performance of Analytic Database System.\nYear: 2016\n\nAbstract: Analytic database is designed for analytical applications which aim to explore the value of massive data. It has been widely used in many areas, from business analytics to scientific data discovery. In order to efficiently processing massive data, analytic database often can be scaled-out to achieve high performance. In this paper, in order to understand the intrinsic performance characteristics of analytic database, we take the popular Greenplum database as the representative analytic database system, and conduct a series of comprehensive performance evaluation over it to characterize its scalability and performance features. According to the experimental results and analysis, we obtained an series of initial insights of the factors which may significantly affect the performance paradigm of Greenplum, which will be helpful for analytic database system users to obtain better analytical performance.","Title: Accelerating Concurrent Analytic Tasks with Cost-Conscious Result Set Replacement Algorithm.\nYear: 2016\n\nAbstract: High concurrent analytic applications such as SaaS based BI services face the problem of how to meet performance SLAs (Service Level Agreement) when the number of users and concurrency increased. In order to reduce the task processing overheads and services response time, analytic applications tend heavily rely on various main-memory data management and cache techniques. In this paper, we designed a cost-conscious cache replacement approach named CRSR (Cost-conscious Result Sets Replacement), which take task result sets as the essential data unit and replace the existing result set by a specialized cost estimation strategy. We conduct a series of evaluation to compare the proposed CRSR approach with representative cache management methods, the experiments show that in most cases, the proposed CRSR algorithm can efficiently reduce the response time of high concurrency analysis service and outperform its competitors.","Title: Traffic efficiency improvement and passengers comfort in ridesharing systems in VANETs\nYear: 2015\n\nAbstract: Ridesharing has become one of the most attention-grabbing trends in modern transportation recent years. This paper studies traffic efficiency improvement and comfort of passengers in ridesharing systems in smart cities. We introduce several improvement methods, and some tradeoffs involving comfort in ridematching strategies. The traffic efficiency can be improved with preserving the service level of passengers through several methods, e.g., recommending pickup or dropoff locations, transfers, and corporation with other transportation systems. The total travel distance of vehicles and the total travel time of passengers can be dropped. Therefore, passengers can enjoy their trips with sustaining a little discomfort, and the whole traffic efficiency in smart cities is improved.","Title: MRAMsim: A Simulator for Magnetoresistive RAM\nYear: 2016\n\nAbstract: Compute requirements are increasing rapidly in systems ranging from desktops to servers. The conventional memory system greatly impacts the system efficiency and can't be improved anymore because of the fabrication process. Magnetoresistive Random Access Memory (MRAM) provides many benefits, such as non-volatility, low leakage, high-speed operation and unlimited endurance. Because of these attractive features, MRAM is regarded as one of the most promising technologies to incorporate into the memory hierarchy. Since the manufacturing cost of real MRAM device is high, the simulation tool is rather important. In this paper, We design and evaluate a trace-driven simulator for MRAM, called MRAMsim. We have proposed various Address Mapping Schemes to improve the efficiency of MRAM memory system. We show that MRAM is a promising candidate for main memory system. The experimental results reveal that MRAM-based main memory system is able to achieve fast response time and high bandwidth.","Title: CCDN: Content-Centric Data Center Networks.\nYear: 2016\n\nAbstract: Data center networks continually seek higher network performance to meet the ever increasing application demand. Recently, researchers are exploring the method to enhance the data center network performance by intelligent caching and increasing the access points for hot data chunks. Motivated by this, we come up with a simple yet useful caching mechanism for generic data centers, i.e., a server caches a data chunk after an application on it reads the chunk from the file system, and then uses the cached chunk to serve subsequent chunk requests from nearby servers. To turn the basic idea above into a practical system and address the challenges behind it, we design content-centric data center networks CCDNs, which exploits an innovative combination of content-based forwarding and location [Internet Protocol IP]-based forwarding in switches, to correctly locate the target server for a data chunk on a fully distributed basis. Furthermore, CCDN enhances traditional content-based forwarding to determine the nearest target server, and enhances traditional location IP-based forwarding to make high utilization of the precious memory space in switches. Extensive simulations based on real-world workloads and experiments on a test bed built with NetFPGA prototypes show that, even with a small portion of the server’s storage as cache e.g., 3% and with a modest content forwarding information base size e.g., 1000 entries in switches, CCDN can improve the average throughput to get data chunks by 43% compared with a pure Hadoop File System HDFS system in a real data center.","Title: A Novel Framework for Analysis of Global Network Neutrality Based on Packet Loss Rate.\nYear: 2015\n\nAbstract: Network neutrality is a policy made by the government. It emphasizes that Internet Service Providers (ISPs) should treat all users equally, regardless of users' behavior, as long as they are legal. In this sense, it aims to build a fair and healthy network. Packet Loss Rate (PLR) is an important metric to evaluate a network's condition. In this paper, we compare PLR of two different kinds of tests, which are \\\"speedtest\\\" and \\\"bittorrent\\\", to judge network neutrality of ISPs. The difference of PLR of two tests is used to evaluate the level of network neutrality violations. We propose a general framework for analysis of network neutrality. First, by transforming the well-known Mathis model, we build a model that uses public data, collected by the network neutrality bot (Neubot), to estimate the PLR of countries all over the world. Second, a composite function model, named PECF, is proposed for fitting to get the lower limit of PLR (L-PLR). When the value of PLR is L-PLR, the frequencies of PLR of two tests differ most and a network neutrality violation appears. By mathematical inference, we draw the conclusion that ISPs violate network neutrality when PLR is larger than 0.10. Third, based on this conclusion, we concentrate on the case when PLR is larger than 0.10 to study network neutrality violations. We use R-PLR (Ranged PLR) to describe PLR within a certain range. By comparing the PLR of \\\"speedtest\\\" with that of \\\"bittorrent\\\" within a certain R-PLR, we further determine the level of network neutrality violation in time and space. Network neutrality violation is more likely to happen under network peaks. The geographical distribution of PLR shows China (CN) and South Africa (ZA) suffer the most severe network neutrality violations among the eight regions studied. Finally, we present the histogram of PLR worldwide from year 2012 to 2015, which shows a relationship between network development and user demand.","Title: Video Anomaly Detection Based on Adaptive Multiple Auto-Encoders.\nYear: 2016\n\nAbstract: Anomaly detection in surveillance videos is a challenging problem in computer vision community. In this paper, a novel unsupervised learning framework is proposed to detect and localize abnormal events in real-time manner. Typical methods mainly rely on extracting complex handcraft features and learning only a fitting model for prediction. In contrast, normal events are represented using simple spatio-temporal volume (STV) in our method, then adaptive multiple auto-encoders (AMAE) are constructed to handle the inter-class variation in normal events. When testing on an unknown frame, reconstruction errors of multiple auto-encoders are utilized for prediction. Experiments are performed on UCSD Ped2 and UMN datasets. Experimental results show that our method is effective to detect and localize abnormal events at a speed of 70 fps.","Title: Adaptive Sliding Mode Relative Motion Control for Autonomous Carrier Landing of Fixed-Wing Unmanned Aerial Vehicles.\nYear: 2017\n\nAbstract: In this paper, relative motion model and control strategy for autonomous fixed-wing unmanned aerial vehicle (UAV) carrier landing are addressed. First, a coupled six-degrees-of-freedom (6-DOF) non-linear relative motion model is established from 6-DOF UAV and carrier models. Second, because of the under-actuated characteristic of two vehicles, the 6-DOF relative motion model is simplified to a four-degree-of-freedom (4-DOF) model to facilitate the control design. Third, an adaptive sliding mode control law is proposed to track desired landing trajectory and maintain constant relative pitch and roll angles. Finally, simulation results demonstrate the effectiveness of the proposed control method.","Title: Optimized Charging in Large-Scale Deployed WSNs with Mobile Charger.\nYear: 2016\n\nAbstract: Restricted by finite battery energy, traditional wireless sensor networks (WSNs) can only maintain for a limited period of time, resulting in serious performance bottleneck in long-term deployment of WSN. Fortunately, the advancement in the wireless energy transfer technology provides a potential to free WSNs from limited energy supply and remain perpetual operational. A mobile charger called wireless charging vehicle (WCV) is employed to periodically charge each sensor node and keep its energy level above the minimum threshold. Aiming at maximizing the ratio of the WCV's vocation time over the cycle time as well as guaranteeing the perpetual operation of networks, we propose a feasible and optimal solution to this issue within the context of a real-time large-scale deployed WSN. First, we develop two different types of charging cycles: initialization cycles and renewable cycles and give relevant algorithms to construct these two cycles for each sensor node. We then formulate the optimization problem into an optimal construction algorithm and prove its correctness through theoretical analysis. Finally, we conduct extensive simulations to demonstrate the effectiveness of our proposed algorithms.","Title: Near space airship conceptual design and optimization.\nYear: 2016\n\nAbstract: Near space airships have gained widespread attention owing to the unique characteristics of long endurance at high altitudes; because of this characteristic, such airships have great potential for use in the fields of communication relay, early warning, earth observation etc. Near space airships can substitute near-earth geostationary satellites owing to the advantages of high load and long endurance at altitude exceeding 20 km. Buoyancy against gravity is generated by the airship hull, which is completely filled with helium, while the near space airship is equipped with an electrical propulsion system powered by solar energy. Unlike conventional aircraft, nearspace airships are a new type of vehicles with less design experience; coupled multi-disciplinary problems related to aerodynamics, structure, energy, control, etc. exist. With the aim of finding a solution for optimal near-space airship design, this study proposed a design using a multidisciplinary design optimization method.","Title: A Novel Two-stage Learning Pipeline for Deep Neural Networks.\nYear: 2017\n\nAbstract: In this work, a training method was proposed for Deep Neural Networks (DNNs) based on a two-stage structure. Local DNN models are trained in all local machines and uploaded to the center with partial training data. These local models are integrated as a new DNN model (combination DNN). With another DNN model (optimization DNN) connected, the combination DNN forms a global DNN model in the center. This results in greater accuracy than local DNN models with smaller amounts of data uploaded. In this case, the bandwidth of the uploaded data is saved, and the accuracy is maintained as well. Experiments are conducted on MNIST dataset, CIFAR-10 dataset and LFW dataset. The results show that with less training data uploaded, the global model produces greater accuracy than local models. Specifically, this method focuses on condition of big data.","Title: A New Image Spatial Color Gamut Mapping Algorithm\nYear: 2017\n\nAbstract: Spatial color gamut mapping algorithm is researched to improve the mapping quality by solving halo artifacts and to improve its efficiency. This paper firstly uses guide filtering to filter L channel and get base layer of L channel, and then use the difference between original L channel value with base layer, to get detail layer of L channel. At the same time, makes statistics on distance relationship between each pixel point with target and source color gamut boundary, according to the statistical data to find out the best threshold value. Make the part of detail layer larger than the threshold value compensates to original image. The experiment proved that efficiency of single mapping operation is higher than conventional two-level mapping algorithm and for most of the images, processing effect is better. What's more, halo artifact is also solved.","Title: Single sample per person face recognition with KPCANet and a weighted voting scheme.\nYear: 2017\n\nAbstract: Most current methods of facial recognition rely on the condition of having multiple samples per person available for feature extraction. In practical applications, however, only one sample may be available for each person to train a model with. As a result, many of the traditional methods fall short, leaving the challenge of facial recognition greater than ever. To deal with this challenge, this study addresses a face recognition algorithm based on a kernel principal component analysis network (KPCANet) and then proposes a weighted voting method. First, the aligned face image is partitioned into several non-overlapping patches to form the training set. Next, a KPCANet is used to obtain filters and feature banks. Finally, the identification of the unlabeled probe occurs through the application of the weighted voting method. Based on several widely used face datasets, the results of the experiments demonstrate the superiority of the proposed method.","Title: Exploring Relationships between Syntax and Semantics of a Process-Oriented Language by Category Theory.\nYear: 2017\n\nAbstract: Recent research findings demonstrate that process-oriented programming languages provide a suitable means to develop concurrent systems. In this paper, we propose to use mandatory/optional trace sets to model the semantics of a process-oriented programming language named Erasmus. Inspired by Hoare's view of category theory and obtained research results towards validating the view, category theory is used to explore relationships between syntax and semantics of Erasmus. To illustrate the research activities, several examples are developed. In doing so, it is shown that a relationship between Erasmus commands can lead to a corresponding relationship between semantics for commands.","Title: Abnormal event detection and localization in crowded scenes based on PCANet.\nYear: 2017\n\nAbstract: Detecting and localizing abnormal events in crowded scenes still remains a challenging task among computer vision community. An unsupervised framework is proposed in this paper to address the problem. Low-level features and optical flows (OF) of video sequences are extracted to represent motion information in the temporal domain. Moreover, abnormal events usually occur in local regions and are closely linked to their surrounding areas in the spatial domain. To extract high-level information from local regions and model the relationship in spatial domain, the first step is to calculate optical flow maps and divide them into a set of non-overlapping sub-maps. Next, corresponding PCANet models are trained using the sub-maps at same spatial location in the optical flow maps. Based on the block-wise histograms extracted by PCANet models, a set of one-class classifiers are trained to predict the anomaly scores of test frames. The framework is completely unsupervised because it utilizes only normal videos. Experiments were carried out on UCSD Ped2 and UMN datasets, and the results show competitive performance of this framework when compared with other state-of-the-art methods.","Title: Artificial compound eye: a survey of the state-of-the-art.\nYear: 2017\n\nAbstract: An artificial compound eye system is the bionic system of natural compound eyes with much wider field-of-view, better capacity to detect moving objects and higher sensitivity to light intensity than ordinary single-aperture eyes. In recent years, renewed attention has been paid to the artificial compound eyes, due to their better characteristics inheriting from insect compound eyes than ordinary optical imaging systems. This paper provides a comprehensive survey of the state-of-the-art work on artificial compound eyes. This review starts from natural compound eyes to artificial compound eyes including their system design, theoretical development and applications. The survey of artificial compound eyes is developed in terms of two main types: planar and curved artificial compound eyes. Finally, the most promising future research developments are highlighted.","Title: BELIA: bandwidth estimate-based link increase algorithm for MPTCP.\nYear: 2017\n\nAbstract: As a set of extensions to the regular Transmission Control Protocol (TCP), Multipath TCP (MPTCP) enables the multi-homed devices to access one data stream through multiple paths. MPTCP congestion window control is the crucial part of maintaining the fair network resource allocation, especially in the coexist scene of MPTCP applications and regular TCP applications. However, the current MPTCP conge...","Title: Joint Transportation and Charging Scheduling in Public Vehicle Systems - A Game Theoretic Approach.\nYear: 2018\n\nAbstract: Public vehicle (PV) systems are promising transportation systems for future smart cities which provide dynamic ride-sharing services according to passengers' requests. PVs are driverless/self-driving electric vehicles which require frequent recharging from smart grids. For such systems, the challenge lies in both the efficient scheduling scheme to satisfy transportation demands with service guaran...","Title: An Online Ride-Sharing Path Planning Strategy for Public Vehicle Systems.\nYear: 2017\n\nAbstract: As efficient traffic-management platforms, public vehicle (PV) systems are envisioned to be a promising approach to solving traffic congestions and pollutions for future smart cities. PV systems provide online/dynamic peer-to-peer ride-sharing services with the goal of serving sufficient number of customers with minimum number of vehicles and lowest possible cost. A key component of the PV system is the online ride-sharing scheduling strategy. In this paper, we propose an efficient path planning strategy that focuses on a limited potential search area for each vehicle by filtering out the requests that violate passenger service quality level, so that the global search is reduced to local search. We analyze the performance of the proposed solution such as reduction ratio of computational complexity. Simulations based on the Manhattan taxi data set show that, the computing time is reduced by 22% compared with the exhaustive search method under the same service quality performance.","Title: HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection.\nYear: 2018\n\nAbstract: The development of an anomaly-based intrusion detection system (IDS) is a primary research direction in the field of intrusion detection. An IDS learns normal and anomalous behavior by analyzing network traffic and can detect unknown and new attacks. However, the performance of an IDS is highly dependent on feature design, and designing a feature set that can accurately characterize network traffic is still an ongoing research issue. Anomaly-based IDSs also have the problem of a high false alarm rate (FAR), which seriously restricts their practical applications. In this paper, we propose a novel IDS called the hierarchical spatial-temporal features-based intrusion detection system (HAST-IDS), which first learns the low-level spatial features of network traffic using deep convolutional neural networks (CNNs) and then learns high-level temporal features using long short-term memory networks. The entire process of feature learning is completed by the deep neural networks automatically; no feature engineering techniques are required. The automatically learned traffic features effectively reduce the FAR. The standard DARPA1998 and ISCX2012 data sets are used to evaluate the performance of the proposed system. The experimental results show that the HAST-IDS outperforms other published approaches in terms of accuracy, detection rate, and FAR, which successfully demonstrates its effectiveness in both feature learning and FAR reduction.","Title: Enhancing Stratified Graph Sampling Algorithms based on Approximate Degree Distribution.\nYear: 2018\n\nAbstract: Sampling technique has become one of the recent research focuses in the graph-related fields. Most of the existing graph sampling algorithms tend to sample the high degree or low degree nodes in the complex networks because of the characteristic of scale-free. Scale-free means that degrees of different nodes are subject to a power law distribution. So, there is a significant difference in the degrees between the overall sampling nodes. In this paper, we propose a concept of approximate degree distribution and devise a stratified strategy using it in the complex networks. We also develop two graph sampling algorithms combining the node selection method with the stratified strategy. The experimental results show that our sampling algorithms preserve several properties of different graphs and behave more accurately than other algorithms. Further, we prove the proposed algorithms are superior to the off-the-shelf algorithms in terms of the unbiasedness of the degrees and more efficient than state-of-the-art FFS and ES-i algorithms.","Title: An in-network service system based on smart routers and edge devices\nYear: 2017\n\nAbstract: Watching and downloading of the online highdefinition video has become one of our daily needs. The user experience of traditional service system based on content delivery network (CDN) and peer to peer (P2P) is limited by the bandwidth and the service ability. These technologies mostly concentrate on content servers and personal computers rather than routers and devices in the network. In this paper, we propose an in-network service system by taking full advantage of the smart routers and edge devices to relieve the load of CDN. We mainly do the following works. 1) We propose the basic architecture of the in-network service system and deploy it on a real network environment. 2) We introduce a hybrid node management algorithm for large scale, flexible, high churn network to manage the smart routers with the edge devices. 3) We design a file self-diffusion algorithm for content delivery. The evaluation shows the high performance of the hit rate of service using the proposed system. In the experiment, up to half of the user requests can be serviced by the proposed system without accessing the CDN.","Title: Multiscale Fully Convolutional Network for Foreground Object Detection in Infrared Videos.\nYear: 2018\n\nAbstract: Accurate and fast infrared (IR) foreground object detection is one of the most significant issues to be solved due to its important meaning for IR target recognition, IR precise guidance, IR video surveillance, and so on. A common approach for such tasks is “background subtraction,” which aims to detect foreground object through background modeling. Thus far, many background subtraction methods ha...","Title: Background Subtraction Using Multiscale Fully Convolutional Network.\nYear: 2018\n\nAbstract: Background modeling and subtraction based on change detection are the first step in many high-level computer vision applications. Many background subtraction methods have been proposed in the recent past and their efforts mainly focus on two aspects: more advanced background models and more complex feature representations. Recently, hierarchical features learned from deep convolutional neural networks have been shown to be effective for many computer vision tasks, such as classification and recognition. However, few researchers try to learn the deep features to address the background subtraction problem. Therefore, in this paper, we propose a novel multiscale fully convolutional network (MFCN) architecture which takes advantage of different layer features for background subtraction. We show that the foreground detection accuracy can be greatly improved by using the deep features learned from the MFCN and instead of building highly complex background models, and the complexity of the background subtraction process can be easily solved during the subtraction operation itself. Experimental results on CDnet 2014 data set and SBM-RGBD data set show that the proposed MFCN-based method achieves state-of-the-art performance while operating at real time.","Title: Part-Based Feature Extraction For Person Re-Identification\nYear: 2018\n\nAbstract: In this paper, we propose a new part-based CNN feature extraction method for end-to-end person re-identification. In our method, the input images are first divided into two different non-overlapping parts, and then two different CNN models are trained for classifying them respectively. Next, in testing phase, we use these two trained CNN models to extract features from different parts of persons separately and then combine them together to construct the overall features of the input images. Finally, metric is utilized to rank the distances between the overall features of the different persons. To make maximize performance, we also try different types of CNN models with various distance metrics. Experiment on dataset Market-1501 demonstrates that our proposed method for person re-identification significantly outperforms state-of-the-art models. And experiment on dataset MARS shows that our method can also get competitive performance on large dataset.","Title: Geometry Based Three-Dimensional Image Processing Method For Electronic Cluster Eye\nYear: 2018\n\nAbstract: In recent years, much attention has been paid to the electronic cluster eye (eCley), a new type of artificial compound eyes, because of its small size, wide field of view (FOV) and sensitivity to motion objects. An eCley is composed of a certain number of optical channels organized as an array. Each optical channel spans a small and fixed field of view (FOV). To obtain a complete image with a full FOV, the images from all the optical channels are required to be fused together. The parallax from unparallel neighboring optical channels in eCley may lead to reconstructed image blurring and incorrectly estimated depth. To solve this problem, this paper proposes a geometry based three-dimensional image processing method (G3D) for eCley to obtain a complete focused image and dense depth map. In G3D, we derive the geometry relationship of optical channels in eCley to obtain the mathematical relation between the parallax and depth among unparallel neighboring optical channels. Based on the geometry relationship, all of the optical channels are used to estimate the depth map and reconstruct a focused image. Subsequently, by using an edge-aware interpolation method, we can further gain a sharply focused image and a depth map. The effectiveness of the proposed method is verified by the experimental results.","Title: Generation of Walking Motions for the Biped Ascending Slopes Based on Genetic Algorithm.\nYear: 2018\n\nAbstract: This study aims to generate the optimal trajectories for the biped walking up sloping surfaces after ensuring the minimum energy consumption by using genetic algorithm (GA) and motion/force control scheme. During optimization, the step length, the maximum height of swing foot and walking speed were optimized with the seven-link biped model. The impactless bipedal walking was investigated for walking on the ground level and slopes with different gradients, respectively. The results showed that the biped consumed more energy when the optimal walking speed increased for walking on the same slopes. There were no great differences in optimal step length when the biped changed the walking speed. The results showed that the proposed approach is able to generate optimal gaits for the biped simply by changing boundary conditions with GA.","Title: Accelerating Massive Astronomical Cross-Match Based on Roaring Bitmap over Parallel Database System.\nYear: 2018\n\nAbstract: In order to reduce the large network overhead and the heavy cost of cross-match on the astronomical catalog in the database cluster, we proposed a novel method of cross-matches based on Roaring Bitmap. Firstly, we store astronomical catalog data in column-oriented storage with compression setup to reduce I/O overhead of accessing field in the parallel database system. Secondly, we create the spatial index, which maps the 2D coordinates into integer number. Then, using Roaring Bitmap convert the spatial index into a bitmap index. Finally, the received spatial range search of cross-match is translated into bitmap operations to achieve batch processing. The experiments over the real large-scale astronomical data show that the proposed method is 4 to 10 times faster than traditional method, meanwhile, only consume less than 10% of memory resource.","Title: DACC: A Data Exploration Method for High-Dimensional Data Sets.\nYear: 2018\n\nAbstract: Data exploration has been proved to be an efficient solution to learn interesting new insights from dataset in an intuitional way. Typically, discovering interesting patterns and objects over high-dimensional dataset is often very difficult due to its large search space. In this paper, we developed a data exploration method named Decision Analysis of Cross Clustering (DACC) based on subspace clustering. It characterize the data objects in the representation of decision trees over divided clustering subspace, which help users quickly understand the patterns of the data and then make interactive exploration easier. We conducted a series of experiments over the real-world datasets and the results showed that, DACC is superior to the representative data explorative approach in term of efficiency and accuracy, and it is applicable for interactive exploration analysis of high-dimensional data sets.","Title: MIC-KMeans: A Maximum Information Coefficient Based High-Dimensional Clustering Algorithm.\nYear: 2018\n\nAbstract: Clustering algorithms often use distance measure as the measure of similarity between point pairs. Such clustering algorithms are difficult to deal with the curse of dimensionality in high-dimension space. In order to address this issue which is common in clustering algorithms, we proposed to use MIC instead of distance measure in k-means clustering algorithm and implemented the novel MIC-kmeans algorithm for high-dimension clustering. MIC-kmeans can cluster the data with correlation to avoid the problem of distance failure in high-dimension space. The experimental results over the synthetic data and real datasets show that MIC-kmeans is superior to k-means clustering algorithm based on distance measure.","Title: Correction to \"Background Subtraction Using Multiscale Fully Convolutional Network\".\nYear: 2018\n\nAbstract: In the above paper [1] , the authors did not cite a reference that was crucial to the paper. Please note that the following citation [2] should have been cited in the following paragraph (page 16014): “From the output binary masks we can see that there is a great contrast between the foreground objects and their back-grounds, and in most cases the foreground objects are uniform connected areas, which means that in the input frames, there should also have a big difference of the features between the foreground and the background [2] .” and “So in order to extract this kind of contrast information, we add a contrast layer [2] after the feature layer X i .”","Title: Multi-bit quantisation for similarity-preserving hashing.\nYear: 2018\n\nAbstract: As a promising alternative to traditional search techniques, hashing-based approximate nearest neighbour search provides an applicable solution for big data. Most existing efforts are devoted to finding better projections to preserve the neighbouring structure of original data points in Hamming space, but ignore the quantisation procedure which may lead to the breakdown of the neighbouring structu...","Title: A Compressed Graph Representation For Services Composition\nYear: 2018\n\nAbstract: Service-oriented computing enhances business scalability and flexibility. Providers who expect to benefit from it may bring the explosive growth of web services. As a result, the huge storage required in searching a composition solution may be too much to afford. In this paper, we apply a compressed data structure to tackle this challenging issue. We propose a system model to solves web service composition (WSC) problem. In this model, we use a compressed tree to represent the search graph. This work innovatively solve the composition problem with compressed graph representation. The proposed model is verified through theoretical analysis and experimental study. The obtained experimental results show that this system may handle a large number of web services with small space requirement.","Title: Combining Background Subtraction Algorithms with Convolutional Neural Network.\nYear: 2018\n\nAbstract: Accurate and fast extraction of foreground objects is a key prerequisite for a wide range of computer vision applications, such as object tracking and recognition. Thus, many background subtraction (BGS) methods for foreground object detection have been proposed in recent decades. However, this is still regarded as a tough problem due to a variety of challenges, such as illumination variations, camera jitter, dynamic backgrounds, and shadows. Currently, there is no single method that can handle all the challenges in a robust way. We try to solve this problem from a perspective of combining different state-of-the-art BGS algorithms to create a more robust and more advanced foreground detection algorithm. More specifically, an encoder–decoder fully convolutional neural network architecture is adapted and trained to automatically learn how to leverage the characteristics of different algorithms to fuse the results produced by different BGS algorithms and produce a more precise result. Comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that the proposed method outperforms all the considered single BGS algorithms. We show that our solution is more efficient than other BGS combination strategies.","Title: A Scalable Parameterized NoC Emulator Built Upon Xilinx Virtex-7 FPGA\nYear: 2017\n\nAbstract: A number of critical design decisions, such as network topology, buffer sizes, flow control mechanism and so on so forth, have to be evaluated in any NoC the design. Designs and verifications of NoCs are based on either software simulations, which are extremely slow and inaccurate for complex models, or hardware emulations using low/mid-class FPGAs, where the scalability of the NoC system is intensively restricted by the limited on-chip resources. In this paper, we implement a parameterized NoC emulation system, capable of verifying complete functionality of routers and monitoring network performance and buffer usages in real time, on a hardware platform featuring a super large FPGA chip, Xilinx Virtex-7. This FPGA-based emulator also shall be configured to support multiple routing algorithms and packet transferring mechanisms. Compared to the existing emulators, it requires less user effort to measure the performance under various application scenarios, and it scales well to emulate large NoC designs. Currently, this emulator has been used to study NoCs with sizes of 4x4 and 8x8. For the case of 4x4 (8X8) NoC emulator, data transfers between routers can run at over 50MHz, and only occupies about 6% (25%) of the FPGA logic block resources.","Title: Nonlinear Dynamic Analysis of Air Traffic Flow at Different Temporal Scales: Nonlinear Analysis Approach versus Complex Networks Approach\nYear: 2018\n\nAbstract: Insights into the temporal-spatial characteristics of air traffic flow is the prerequisite of air traffic flow modeling, prediction and optimization. The intrinsic dynamics of air traffic flow were analyzed according to radar track data by classical nonlinear analysis and complex network methods. The nonlinear dynamic characteristics of air traffic flow were revealed from multiple perspectives by means of phase space reconstruction, recurrence plot, Lyapunov exponent, correlation dimension and Hurst exponent. The experimental results show that the example traffic flow became the most chaotic and the state persistence was poorest at time scale of 30 min. Besides, this paper explored the application of a complex network to analyze the nonlinearity of air traffic flow, and verified the chaotic and fractal characteristics by analyzing the degree distribution of the converted network. The degree distribution of the network derived from the traffic flow time series was approximately fitted to the exponential function and featured a strength level of chaos that was positively correlated with the size of network degree distribution power exponent. The linear relationship between network degree distribution power exponent and Hurst exponent was revealed, and the nonlinearity of air route traffic flow was systematically characterized. Particularly, the complex network-based analysis method helped examine air traffic flow from a new perspective, enriching the research means. These novel findings will provide theoretical basis for aggregated air traffic flow modeling, decision support system design and tactical flow management.","Title: Erratum to \"Multiscale Fully Convolutional Network for Foreground Object Detection in Infrared Videos\".\nYear: 2018\n\nAbstract: The architecture of the proposed multiscale fully convolutional network (MFCN) in our paper [1] is mainly derived from a salient object method [2] and a semantic segmentation method [3] . We missed these two reference papers in the original paper. The MFCN is an encoder-decoder architecture; the output has the same size with the input. For the encoder part, we use the pretrained VGG16 network, and for the decoder part, we upsample the features with deconvolution operations. The contrast layer for feature extraction is derived from the salient detection method [2] .","Title: Extended scale invariant local binary pattern for background subtraction.\nYear: 2018\n\nAbstract: Background subtraction based on change detection is the first step in many video surveillance systems, an effective background subtraction algorithm should distinguish foreground from the background sensitively, and adapt to the variation of background scenes robustly. In this study, the authors propose a robust background subtraction algorithm which takes advantages of local texture features repr...","Title: Discriminative Correlation Quantization for Cross-Modal Similarity Retrieval.\nYear: 2018\n\nAbstract: Due to their superior performance on efficient search and discrete loss, quantization methods have attracted considerable attention for approximate nearest neighbor (ANN) search on large-scale multimedia data. In this paper, we aim to introduce quantization into cross-modal similarity search with a focus on learning discriminative binary codes. Different from existing cross-modal quantization algorithms that transform heterogeneous data to a common subspace by unsupervised approaches, we propose a novel cross-modal quantization method embedded in a supervised framework by exploring the discriminative property of label information. The proposed approach learns common semantic space from label information by linear classification, enabling the generated category-specific features to produce more discriminative quantization codes. Furthermore, the unified codebooks and quantization codes are adopted to preserve the correlation of similar inter-modal pairs in the learned semantic space. The overall optimization algorithm jointly learns the linear classifiers, category-specific features and the unified quantizer in an alternated strategy. Extensive comparative experiments on three benchmark datasets show the superiority of our approach over some state-of-the-art methods.","Title: A Robust Local Binary Similarity Pattern for Foreground Object Detection.\nYear: 2018\n\nAbstract: Accurate and fast extraction of the foreground object is one of the most significant issues to be solved due to its important meaning for object tracking and recognition in video surveillance. Although many foreground object detection methods have been proposed in the recent past, it is still regarded as a tough problem due to illumination variations and dynamic backgrounds challenges. In this paper, we propose a robust foreground object detection method with two aspects of contributions. First, we propose a robust texture operator named Robust Local Binary Similarity Pattern (RLBSP), which shows strong robustness to illumination variations and dynamic backgrounds. Second, a combination of color and texture features are used to characterize pixel representations, which compensate each other to make full use of their own advantages. Comprehensive experiments evaluated on the CDnet 2012 dataset demonstrate that the proposed method performs favorably against state-of-the-art methods.","Title: Background Subtraction with Real-time Semantic Segmentation.\nYear: 2018\n\nAbstract: Accurate and fast foreground object extraction is very important for object tracking and recognition in video surveillance. Although many background subtraction (BGS) methods have been proposed in the recent past, it is still regarded as a tough problem due to the variety of challenging situations that occur in real-world scenarios. In this paper, we explore this problem from a new perspective and propose a novel background subtraction framework with real-time semantic segmentation (RTSS). Our proposed framework consists of two components, a traditional BGS segmenter $mathcal{B}$ and a real-time semantic segmenter $mathcal{S}$. The BGS segmenter $mathcal{B}$ aims to construct background models and segments foreground objects. The real-time semantic segmenter $mathcal{S}$ is used to refine the foreground segmentation outputs as feedbacks for improving the model updating accuracy. $mathcal{B}$ and $mathcal{S}$ work in parallel on two threads. For each input frame $I_t$, the BGS segmenter $mathcal{B}$ computes a preliminary foreground/background (FG/BG) mask $B_t$. At the same time, the real-time semantic segmenter $mathcal{S}$ extracts the object-level semantics ${S}_t$. Then, some specific rules are applied on ${B}_t$ and ${S}_t$ to generate the final detection ${D}_t$. Finally, the refined FG/BG mask ${D}_t$ is fed back to update the background model. Comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that our proposed method achieves state-of-the-art performance among all unsupervised background subtraction methods while operating at real-time, and even performs better than some deep learning based supervised algorithms. In addition, our proposed framework is very flexible and has the potential for generalization.","Title: Multi-Kernel Supervised Hashing With Graph Regularization For Cross-Modal Retrieval\nYear: 2018\n\nAbstract: Hashing based approximate nearest neighbor search has received considerable attention due to the demand of fast query for big multimedia data. Cross-modal hashing focuses on retrieval tasks across different modalities, which is more useful in practical applications. In this paper, we propose a novel two-stage cross-modal hashing method, referred to as Multi-Kernel Supervised Hashing with Graph Regularization (MKSRH). To better capture the essential attribute of original data, MKSRH first maps original data to a kernel space constructed by a linear combination of multiple kernel functions. Then the preliminary hash functions are learned using the Adaboost framework in the kernel space. To produce more accurate hash codes, the obtained hash function are then refined using a graph regularization based strategy. Experimental results on two canonical datasets show that MKSRH significantly outperforms than some typical cross-modal hashing methods, demonstrating the effectiveness and the superiority of the proposed approach.","Title: A Memory-Efficient Approach to the Scalability of Recommender System With Hit Improvement.\nYear: 2018\n\nAbstract: With the rapid increase of users and resources on the Internet, the scale of the recommender system becomes larger and larger. There are three major challenges facing in recommender system: sparsity, scalability, and cold start. In this paper, we mainly focus on the scalability issue and propose a recommender system based on the memory-efficient recurrent neural network. First, we allocate an item table for items and use a pair of embedding vectors to represent each item. Thus, we can use a few vectors to represent numerous items and decrease the memory used for the storage of embedding vectors. Second, we present a similarity-based initialization method for the item table to get a better representation of items. Third, we further design the loss function and the adjustment method to adjust the placement of items in the item table to speed up the training procedure of the model and get a better performance. The experimental results demonstrate the effectiveness of our approach. It can clearly improve the performance of recommendation, such as hit rate and normalized discounted cumulative gain, when compared to the state-of-the-art recommender algorithm. In addition, our approach can also handle the cold start problem and supply new users with the same quality of service as the old users.","Title: An Approach for QoS-aware Service Composition with GraphPlan and Fuzzy Logic.\nYear: 2018\n\nAbstract: With the rapid development of Information and Communication Technology, the growth of data is set to explode and brings us to an era of cloud computing. As a result, more and more services are posted on the web. In such situation, searching and composing services become a computationally demanding work. To address this problem, we design a system which combines fuzzy logic with GraphPlan algorithm. Fuzzy rules are employed to evaluate and rank services according to users’ preferences, then, the solution with the optimal QoS value is selected and used in GraphPlan construction. We evaluate our approach by experiments with web service challenge datasets.","Title: Modeling and Verification of Response Time of QoS-aware Web Service Composition by Timed CSP.\nYear: 2018\n\nAbstract: Web service composition enables the provision/reusing of existing services in different business processes to satisfy different business requirements without investing in new infrastructure. QoS-aware web service composition seeks to help users find the optimal solution with maximization of users’ satisfaction. A number of approaches based on Communicating Sequential Processes (CSP) have been proposed to model and verify properties of web service composition. However, little work has been done in verifying inputs, outputs and QoS criteria of web service composition.","Title: An Online Ride-Sharing Path-Planning Strategy for Public Vehicle Systems.\nYear: 2019\n\nAbstract: As efficient traffic-management platforms, public vehicle (PV) systems are envisioned to be a promising approach to solving traffic congestion and pollution for future smart cities. PV systems provide online/dynamic peer-to-peer ride-sharing services with the goal of serving a sufficient number of customers with a minimum number of vehicles and the lowest possible cost. A key component of the PV s...","Title: Human Pose Estimation Based on Step Deep Convolution Neural Network.\nYear: 2018\n\nAbstract: To improve the accuracy and robustness of human pose estimation, a step deep convolution neural network is proposed, which consists of a feed forward module and several step modules. Image features output from the last four layers of feed forward module are fused with the context features, the fused features are used as the input of the first step module; Image features of each step module fused with context features are used as the input of the next step module; The confidence map output from the last step module is used to predict joint position. This stepwise approach increases the receptive fields, which is good to learn the long-distance relationship between joints and predicting the position of occluded joints. At the same time, the confidence calculated by the previous module provides more and more accurate estimation of the position of each joint in the subsequent modules. In addition, the network also provides a way to strengthen the intermediate supervision by learning the objective function, so as to supplement the gradient of back-propagation and adjust the learning process, which effectively solves problem that the gradient disappears during training. Our approach is tested on two standard datasets of Leeds Sports Poses (LSP) and Frames Labeled In Cinema (FLIC), whose results indicate that our network has better performance in pose estimation of human body.","Title: SAR Target Image Classification Based on Transfer Learning and Model Compression\nYear: 2019\n\nAbstract: When convolutional neural networks (CNNs) are applied to the synthetic aperture radar (SAR) image classification, they are prone to overfitting due to scarce SAR image data, and CNNs require a large amount of storage and long computing time, so it is difficult to deploy them on resource constrained devices. This letter proposes a simple and feasible approach that can effectively solve these problems. First, the convolutional layers of the pretrained model on the ImageNet data set are transferred, and a new convolutional layer and global pooling layer are added afterward. Then, fine-tuning is performed on the new network from the SAR image data set. Finally, a filter-based pruning method is used on the convolutional layers to obtain a compact network. Compared with the all-convolutional network (A-ConvNets) which is the state-of-the-art method on the moving and stationary target acquisition and recognition data set, our method achieves about \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$3.6\\times $ </tex-math></inline-formula>\n speedup during forward propagation and \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$3.7\\times $ </tex-math></inline-formula>\n compression of the parameters, with only a 1.42% decrease in the accuracy.","Title: Key Message Approach to Optimize Communication of Parallel Applications on Clusters\nYear: 2003\n\nAbstract: Over the past few years, cluster/distributed computing has been gaining popularity. The proliferation of the cluster/distributed computing is due to the improved performance and increased reliability of these systems. Many parallel programming languages and related parallel programming models have become widely accepted. However, one of the major shortcomings of running parallel applications on cluster/distributed computing environments is the high communication overhead incurred. To reduce the communication overhead, and thus the completion time of a parallel application, this paper describes a simple, efficient and portable Key Message (KM) approach to support parallel computing on cluster/distributed computing environments. To demonstrate the advantage of the KM approach, a prototype runtime system has been implemented and evaluated. Our preliminary experimental results show that the KM approach has better improvement on communication of a parallel application when network background load increases or the computation to communication ratio of the application decreases.","Title: A Data and Task Co-scheduling Algorithm for Scientific Cloud Workflows\nYear: 2020\n\nAbstract: Cloud computing has emerged as a promising computational infrastructure for cost-efficient workflow execution by provisioning on-demand resources in a pay-as-you-go manner. While scientific workflows require accessing community-wide resources, they usually need to be performed in collaborative cloud environments composed of multiple datacenters. Although such environments facilitate scientific col...","Title: A Multi-Aperture Optical Flow Estimation Method For An Artificial Compound Eye\nYear: 2019\n\nAbstract: An artificial compound eye (ACE) is a bio-inspired vision sensor which mimics a natural compound eye (typical of insects). This artificial eye is able to visualize large fields of the outside world through multi-aperture. Due to its functioning, the ACE is subject to optical flow, that is an apparent motion of the object visualized by the eye. This paper proposes a method to estimate the optical flow based on capturing multiple images (multi-aperture).In this method, based on descriptors-based initial optical flows, a unified global energy function is presented to incorporate the information of multi-aperture and simultaneously recover the optical flows of multi-aperture. The energy function imposes a compound flow fields consistency assumption along with the brightness constancy and piecewise smoothness assumptions. This formula efficiently binds the flow field in time and space, and further enables view-consistent optical flow estimation. Experimental results on real and synthetic data demonstrate that the proposed method recovers view-consistent optical flows crossed multi-aperture and performs better than other optical flow methods on the multi-aperture images.","Title: Trajectory tracking control for a marine surface vessel with asymmetric saturation actuators.\nYear: 2017\n\nAbstract: This paper investigates the problem of trajectory tracking control for an unmanned marine surface vessel (MSV) with external disturbances and asymmetric saturation actuators. An adaptive radial basis function neural network (RBFNN) is constructed to provide an estimation of the unknown disturbances and is applied to design the trajectory tracking controller through a backstepping technique. To handle the effect of nonsmooth asymmetric saturation nonlinearity, a Gaussian error function-based continuous differentiable asymmetric saturation model is employed such that the backstepping technique can be used in the control design. It is proved that all the states in the closed-loop system are semiglobally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of origin by appropriately choosing design parameters. Simulation results and comparisons illustrate the effectiveness of the proposed controller and its robustness to external disturbances and asymmetric saturation actuators.","Title: TOUSE: A Fair User Selection Mechanism Based on Dynamic Time Warping for MU-MIMO Networks.\nYear: 2017\n\nAbstract: Multi-user Multiple-Input and Multiple-Output (MU-MIMO) has potential for prominently enhancing the capacity of wireless network by simultaneously transmitting to multiple users. User selection is an unavoidable problem which bottlenecks the gain of MU-MIMO to a great extent. Major state-of-the-art works are focusing on improving network throughput by using Channel State Information (CSI), however, the overhead of CSI feedback becomes unacceptable when the number of users is large. Some work does well in balancing tradeoff between complexity and achievable throughput but is lack of consideration of fairness. Current works universally ignore the rational utilizing of time resources, which may lead the improvements of network throughput to a standstill. In this paper, we propose TOUSE, a scalable and fair user selection scheme for MU-MIMO. The core design is dynamic-timewarping- based user selection mechanism for downlink MU-MIMO, which could make full use of concurrent transmitting time. TOUSE also presents a novel data-rate estimation method without any CSI feedback, providing supports for user selections. Simulation result shows that TOUSE significantly outperforms traditional contention-based user selection schemes in both throughput and fairness in an indoor condition.","Title: An Approach For Detecting Fault Lines In A Small Current Grounding System Using Fuzzy Reasoning Spiking Neural P Systems\nYear: 2018\n\nAbstract: This paper presents a novel approach for detecting fault lines in a small current grounding system using fuzzy reasoning spiking neural P systems. In this approach, six features of current/voltage signals in a small current grounding system are analyzed by considering transient and steady components, respectively; a fault measure is used to quantify the possibility that a line is faulty; information gain degree is discussed to weight the importance of each of the six features; rough set theory is applied to reduce the features; and finally a fuzzy reasoning spiking neural P system is used to construct fault line detection models. Six cases in a small current grounding system prove the effectiveness of the introduced approach.","Title: A Hierarchical Attention Retrieval Model for Healthcare Question Answering\nYear: 2019\n\nAbstract: The growth of the Web in recent years has resulted in the development of various online platforms that provide healthcare information services. These platforms contain an enormous amount of information, which could be beneficial for a large number of people. However, navigating through such knowledgebases to answer specific queries of healthcare consumers is a challenging task. A majority of such queries might be non-factoid in nature, and hence, traditional keyword-based retrieval models do not work well for such cases. Furthermore, in many scenarios, it might be desirable to get a short answer that sufficiently answers the query, instead of a long document with only a small amount of useful information. In this paper, we propose a neural network model for ranking documents for question answering in the healthcare domain. The proposed model uses a deep attention mechanism at word, sentence, and document levels, for efficient retrieval for both factoid and non-factoid queries, on documents of varied lengths. Specifically, the word-level cross-attention allows the model to identify words that might be most relevant for a query, and the hierarchical attention at sentence and document levels allows it to do effective retrieval on both long and short documents. We also construct a new large-scale healthcare question-answering dataset, which we use to evaluate our model. Experimental evaluation results against several state-of-the-art baselines show that our model outperforms the existing retrieval techniques.","Title: Periodic motion generation for the impactless biped walking up slopes via genetic algorithm\nYear: 2020\n\nAbstract: The angular positions of the lower limb joints play important roles onthe energy consumption and stability for the bipedal walking up slopes. In this study, the ranges of angular position of lower limb joints are confined and thevelocity of swing foot is zero when it touches the ground, which result in theconstruction of the impactless planar bipedal model. Motion/force control scheme combined with genetic algorithm (GA) is used to ensure stability and low energy cost of bipedal walking at different speeds. The optimized parameters of gaitsare obtained by GA, which include walking speed, step length and the maximumheight of swing ankle joint. The results demonstrate that more energy isconsumed when the optimal walking speed increases for the biped walking on slopes with the same gradient. There are no great differences in optimal steplength of the biped when the walking speed changes. The optimal step lengthdeclines as the slope increases at the same walking speed. The ankle torques of standing leg have higher values in single support phase at fast speed comparedto those at slow and normal speeds. Modifications of boundary conditions can not only be used to realize the stable walking for the biped negotiating slopes, but also be applied to analyze bipedal gaits for walking on stairs and uneven Surfaces.","Title: Deep Reinforcement Learning for Unmanned Aerial Vehicle-Assisted Vehicular Networks.\nYear: 2019\n\nAbstract: Unmanned aerial vehicles (UAVs) are envisioned to complement the 5G communication infrastructure in future smart cities. Hot spots easily appear in road intersections, where effective communication among vehicles is challenging. UAVs may serve as relays with the advantages of low price, easy deployment, line-of-sight links, and flexible mobility. In this paper, we study a UAV-assisted vehicular network where the UAV jointly adjusts its transmission power and bandwidth allocation under 3D flight to maximize the total throughput. First, we formulate a Markov Decision Process (MDP) problem by modeling the mobility of vehicles and the state transitions caused by the UAV's 3D flight. Secondly, we solve the target problem using a deep reinforcement learning method, namely, the deep deterministic policy gradient, and propose three solutions with different control objectives. Thirdly, in a simplified model with small state and action spaces, we verify the optimality of proposed algorithms. Comparing with two baseline schemes, we demonstrate the effectiveness of proposed algorithms in a realistic model.","Title: Low-light Image Enhancement Algorithm Based on Retinex and Generative Adversarial Network.\nYear: 2019\n\nAbstract: Low-light image enhancement is generally regarded as a challenging task in image processing, especially for the complex visual tasks at night or weakly illuminated. In order to reduce the blurs or noises on the low-light images, a large number of papers have contributed to applying different technologies. Regretfully, most of them had served little purposes in coping with the extremely poor illumination parts of images or test in practice. In this work, the authors propose a novel approach for processing low-light images based on the Retinex theory and generative adversarial network (GAN), which is composed of the decomposition part for splitting the image into illumination image and reflected image, and the enhancement part for generating high-quality image. Such a discriminative network is expected to make the generated image clearer. Couples of experiments have been implemented under the circumstance of different lighting strength on the basis of Converted See-In-the-Dark (CSID) datasets, and the satisfactory results have been achieved with exceeding expectation that much encourages the authors. In a word, the proposed GAN-based network and employed Retinex theory in this work have proven to be effective in dealing with the low-light image enhancement problems, which will benefit the image processing with no doubt.","Title: Top-N Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph Embedding.\nYear: 2019\n\nAbstract: The traditional collaborative filtering recommendation algorithm only uses the item-user rating matrix without considering the semantic information of the item itself, resulting in a problem that the recommendation accuracy is not high. This paper proposes a Top-N collaborative filtering recommendation algorithm based on knowledge graph embedding. The knowledge graph embedding is used to learn a low-dimensional vector for each entity and relationship in the knowledge graph, while maintaining the structure and semantic information of the original graph in the vector. By calculating the semantic similarity between items, the semantic information of the item itself is incorporated into the collaborative filtering recommendation. The algorithm makes up for the defect that the collaborative filtering recommendation algorithm does not consider the knowledge information of the item itself, and enhances the effect of collaborative filtering recommendation on the semantic level. The experimental results on the MovieLens dataset show that the algorithm can get higher values on precision, recall and F1 measure.","Title: Talents Evaluation Modeling Based on Fuzzy Mathematics.\nYear: 2019\n\nAbstract: Fuzzy synthetic evaluation is a comprehensive assessment method based on fuzzy mathematics. Using the jurisdiction degree theory in fuzzy mathematics, it transforms qualitative evaluation into quantitative evaluation, and has been widely applied in many fields. In order to make the recruitment process of HRM (Human Resource Management) more systematic and rational, this paper introduces a method to evaluate talents by applying fuzzy synthetic evaluation, and discussed how to implement such mathematical model in our prototype system based on J2EE. Furthermore, the enterprise evaluation process with customizable resume is also proposed in this paper.","Title: A Dataset And Benchmark Of Underwater Object Detection For Robot Picking\nYear: 2021\n\nAbstract: Underwater object detection for robot picking has attracted a lot of interest. However, it is still an unsolved problem due to several challenges. We take steps towards making it more realistic by addressing the following challenges. Firstly, the currently available datasets basically lack the test set annotations, causing researchers must compare their method with other SOTAs on a self-divided te...","Title: PointAugmenting: Cross-Modal Augmentation for 3D Object Detection\nYear: 2021\n\nAbstract: Camera and LiDAR are two complementary sensors for 3D object detection in the autonomous driving context. Camera provides rich texture and color cues while LiDAR specializes in relative distance sensing. The challenge of 3D object detection lies in effectively fusing 2D camera images with 3D LiDAR points. In this paper, we present a novel cross-modal 3D object detection algorithm, named PointAugmenting. On one hand, PointAugmenting decorates point clouds with corresponding point-wise CNN features extracted by pretrained 2D detection models, and then performs 3D object detection over the decorated point clouds. In comparison with highly abstract semantic segmentation scores to decorate point clouds, CNN features from detection networks adapt to object appearance variations, achieving significant improvement. On the other hand, PointAugmenting benefits from a novel cross modal data augmentation algorithm, which consistently pastes virtual objects into images and point clouds during network training. Extensive experiments on the large-scale nuScenes and Waymo datasets demonstrate the effectiveness and efficiency of our PointAugmenting. Notably, PointAugmenting outperforms the LiDAR-only baseline detector by +6.5% mAP and achieves the new state-of-the-art results on the nuScenes leaderboard to date.","Title: A Hybrid Pre-Joined Service Network In Graph Database And Memory For Service Composition\nYear: 2021\n\nAbstract: To make full use of the large space and persistency provided by a database and the fast processing speed of main memory, we propose a hybrid pre-joined service network method combining in-graph-database and in-memory calculations. This method firstly stores services' information and combinations in a graph database. Then, for a user's request, it retrieves candidate services from the database and constructs a service network in memory. After that, services are picked and composed to fulfil an optimal solution in memory. We assess the performance of our method by conducting experiments and comparing the approach with other database-based methods. Experiment results indicate the efficiency of this method in comparison, and it can always find solutions and lead to higher users' satisfaction.","Title: A Fast Stereo Matching Network With Multi-Cross Attention\nYear: 2021\n\nAbstract: Stereo matching networks based on deep learning are widely developed and can obtain excellent disparity estimation. We present a new end-to-end fast deep learning stereo matching network in this work that aims to determine the corresponding disparity from two stereo image pairs. We extract the characteristics of the low-resolution feature images using the stacked hourglass structure feature extractor and build a multi-level detailed cost volume. We also use the edge of the left image to guide disparity optimization and sub-sample with the low-resolution data, ensuring excellent accuracy and speed at the same time. Furthermore, we design a multi-cross attention model for binocular stereo matching to improve the matching accuracy and achieve end-to-end disparity regression effectively. We evaluate our network on Scene Flow, KITTI2012, and KITTI2015 datasets, and the experimental results show that the speed and accuracy of our method are excellent.","Title: A Novel Computer Vision-Based Data Driven Modelling Approach For Person Specific Fall Detection\nYear: 2021\n\nAbstract: In this paper, we propose a novel person specific fall detection system based on a monocular camera, which can be applied for assisting the independent living of an older adult living alone at home. A single camera covering the living area is used for video recordings of an elderly person's normal daily activities. From the recorded video data, the human silhouette regions in every frame are then extracted based on the codebook background subtraction technique. Low-dimensionality representative features of extracted silhouetted are then extracted by convolutional neural network-based autoencoder (CNN-AE). Features obtained from the CNN-AE are applied to construct an one class support vector machine (OCSVM) model, which is a data driven model based on the video recordings and can be applied for fall detection. From the comprehensive experimental evaluations on different people in a real home environment, it is shown that the proposed fall detection system can successfully detect different types of falls (falls towards different orientations at different positions in a real home environment) with small false alarms.","Title: Scaling up Mobile Service Selection in Edge Computing Environment with Cuckoo Optimization Algorithm\nYear: 2021\n\nAbstract: Nowadays, instead of providing computational capabilities in the cloud, mobile edge computing pushes computational capabilities to network edges. It is common for mobile device users to request services located on edge servers. Mobile devices are usually resource-constrained with dynamic locations of users, and an unstable mobile environment may lead to complex mobile service requirements. Therefo...","Title: A Top-N-Balanced Sequential Recommendation Based On Recurrent Network\nYear: 2019\n\nAbstract: To solve the low accuracy problem of the recommender system for long term users, in this paper, we propose a top-N-balanced sequential recommendation based on recurrent neural network. We postulated and verified that the interactions between users and items is time-dependent in the long term, but in the short term, it is time-independent. We balance the top-N recommendation and sequential recommendation to generate a better recommender list by improving the loss function and generation method. The experimental results demonstrate the effectiveness of our method. Compared with a state-of-the-art recommender algorithm, our method clearly improves the performance of the recommendation on hit rate. Besides the improvement of the basic performance, our method can also handle the cold start problem and supply new users with the same quality of service as the old users.","Title: Optimal Design Of 10-Bit Single-Slope Adc For Cmos Image Sensor Based On Swarm Intelligent Optimization Algorithm\nYear: 2021\n\nAbstract: As one of the most critical blocks of the CMOS image sensor (CIS), the accuracy and power consumption of the analogue-to-digital converter (ADC) play an important role in its performance. However, due to the mutually restrictive relationship between power consumption and accuracy in the single-slope ADC (SS ADC), it is difficult to improve these performance indexes meantime without an efficient optimal design method. Here, an optimal design methodology based on an improved artificial fish swarm optimization algorithm (IAFSOA) is proposed for a 10-bit SS ADC in CIS. First, a voltage storage structure and offset calibration circuit are developed for promoting the performance of the comparator. Then, IAFSOA is developed for the circuit parameters optimization with faster convergence rate and higher computational accuracy. The proposed multi-objectives optimization algorithm can obtain the optimal parameters of the kernel circuit components and lead to an enhancement of both power consumption and accuracy. The optimized SS ADC is designed based on 0.18 mu m process to verify the effectiveness of the proposed method. Compared to the earlier reported work, the IAFSOA-based optimal design method can promote the performance of the SS ADC by an improvement of effective number of bits and a reduction of power consumption.","Title: Classification of Giemsa staining chromosome using input-aware deep convolutional neural network with integrated uncertainty estimates\nYear: 2022\n\nAbstract: A fully automated chromosome analysis system can substitute cytogenetic experts for the task of chromosome karyotype analysis, which in turn can substantially increase the efficiency of disease diagnosis. However, the construction of such a system is most crucially restricted by the accuracy of chromosome classification, during karyotype analysis. To facilitate the construction of an automatic chromosome analysis system, an input-aware and probabilistic prediction convolutional neural network (IAPP-CNN) is presented in this paper for high accuracy of chromosome classification. The approach follows three stages and consists of one input-aware module, one feature extractor module and one probabilistic prediction module. In the first stage, the input-aware module develops raw images automatically into the global-scale image, the object-scale image and the part-scale image, by introducing an attention mechanism. In the second stage, the three scale images are input into the feature extraction module through three branches, then the respective feature operators are obtained via their independent CNN feature extractors. In the third stage, the probabilistic prediction module uses three dynamic probabilistic parameters to estimate the prediction of each CNN branch separately, and then combined the three CNN votes for the final decision. The feature expression ability of the key feature was improved and the network was enabled to focus on the recognizable regions in the image. Evaluation results from a large dataset of healthy patients showed that the proposed IAPP-CNN achieved the highest accuracy of 99.2% for the chromosome classification task, surpassing the performance of a competitive baseline created by state-of-the-art methods.","Title: Quadrotor Autonomous Navigation in Semi-Known Environments Based on Deep Reinforcement Learning\nYear: 2021\n\nAbstract: In the application scenarios of quadrotors, it is expected that only part of the obstacles can be identified and located in advance. In order to make quadrotors fly safely in this situation, we present a deep reinforcement learning-based framework to realize autonomous navigation in semi-known environments. Specifically, the proposed framework utilizes the dueling double deep recurrent Q-learning, which can implement global path planning with the obstacle map as input. Moreover, the proposed framework combined with contrastive learning-based feature extraction can conduct real-time autonomous obstacle avoidance with monocular vision effectively. The experimental results demonstrate that our framework exhibits remarkable performance for both global path planning and autonomous obstacle avoidance.","Title: Residual Spatial and Channel Attention Networks for Single Image Dehazing\nYear: 2021\n\nAbstract: Single image dehazing is a highly challenging ill-posed problem. Existing methods including both prior-based and learning-based heavily rely on the conceptual simplified atmospheric scattering model by estimating the so-called medium transmission map and atmospheric light. However, the formation of haze in the real world is much more complicated and inaccurate estimations further degrade the dehazing performance with color distortion, artifacts and insufficient haze removal. Moreover, most dehazing networks treat spatial-wise and channel-wise features equally, but haze is practically unevenly distributed across an image, thus regions with different haze concentrations require different attentions. To solve these problems, we propose an end-to-end trainable densely connected residual spatial and channel attention network based on the conditional generative adversarial framework to directly restore a haze-free image from an input hazy image, without explicitly estimation of any atmospheric scattering parameters. Specifically, a novel residual attention module is proposed by combining spatial attention and channel attention mechanism, which could adaptively recalibrate spatial-wise and channel-wise feature weights by considering interdependencies among spatial and channel information. Such a mechanism allows the network to concentrate on more useful pixels and channels. Meanwhile, the dense network can maximize the information flow along features from different levels to encourage feature reuse and strengthen feature propagation. In addition, the network is trained with a multi-loss function, in which contrastive loss and registration loss are novel refined to restore sharper structures and ensure better visual quality. Experimental results demonstrate that the proposed method achieves the state-of-the-art performance on both public synthetic datasets and real-world images with more visually pleasing dehazed results.","Title: Cross-Modal 3D Object Detection and Tracking for Auto-Driving\nYear: 2021\n\nAbstract: Detecting and tracking objects in 3D scenes play crucial roles in autonomous driving. Successfully recognizing objects through space and time hinges on a strong detector and a reliable association scheme. Recent 3D detection and tracking approaches widely represent objects as points when associating detection results with trajectories. Despite the demonstrated success, these approaches do not fully exploit the rich appearance information of objects. In this paper, we present a conceptually simple yet effective algorithm, named AlphaTrack, which considers both the location and appearance changes to perform joint 3D object detection and tracking. To achieve this, we propose a cross-modal fusion scheme that fuses camera appearance feature with LiDAR feature to facilitate 3D detection and tracking. We further attach an additional branch to the 3D detector to output instance-aware appearance embedding, which significantly improves tracking performance with our designed association mechanisms. Extensive validations on large-scale autonomous driving dataset demonstrate the effectiveness of the proposed algorithm in comparison with state-of-the-art approaches. Notably, the proposed algorithm ranks first on the nuScenes tracking leaderboard to date.","Title: Multilingual Code Snippets Training for Program Translation.\nYear: 2022\n\nAbstract: Program translation aims to translate source code from one programming language to another. It is particularly useful in applications such as multiple-platform adaptation and legacy code migration. Traditional rule-based program translation methods usually rely on meticulous manual rule-crafting, which is costly both in terms of time and effort. Recently, neural network based methods have been developed to address this problem. However, the absence of high-quality parallel code data is one of the main bottlenecks which impedes the development of program translation models. In this paper, we introduce CoST, a new multilingual Code Snippet Translation dataset that contains parallel data from 7 commonly used programming languages. The dataset is parallel at the level of code snippets, which provides much more fine-grained alignments between different languages than the existing translation datasets. We also propose a new program translation model that leverages multilingual snippet denoising auto-encoding and Multilingual Snippet Translation (MuST) pre-training. Extensive experiments show that the multilingual snippet training is effective in improving program translation performance, especially for low-resource languages. Moreover, our training method shows good generalizability and consistently improves the translation performance of a number of baseline models. The proposed model outperforms the baselines on both snippet-level and program-level translation, and achieves state-of-the-art performance on CodeXGLUE translation task. The code, data, and appendix for this paper can be found at https://github.com/reddy-lab-code-research/MuST-CoST.","Title: Homography-Based Visual Servoing for Underactuated VTOL UAVs Tracking a 6-DOF Moving Ship\nYear: 2022\n\nAbstract: This paper develops a novel homography-based visual servo control method for the vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) tracking the trajectory of a 6 degrees of freedom (6-DOF) moving ship. Different from the classical homography-based visual servoing which is only suitable for the trajectory tracking of a planar moving target, an extended homography-based visual servo...","Title: IMAGE ENHANCEMENT ALGORITHM BASED ON IMAGE SPATIAL DOMAIN SEGMENTATION\nYear: 2021\n\nAbstract: This paper proposes an image enhancement algorithm based on the theory of image segmentation and image frequency. First, a mathematical model corresponding to the pixel frequency is established by using the difference of pigment values between the pixels in the image and the surrounding pixels (i.e., pixel receptive field). Then, the image is divided into the low frequency region (background area), low-medium frequency region (foreground area), medium-high frequency region (target area) and high frequency region (detail area) by a pixel frequency characteristic graph. Gamma correction, MSRCR, MSR, top hat + bottom hat are used for image enhancement for each area, and then the parts are merged. Three indicators of PSNR, SSIM, and MSE are introduced to evaluate the quality of the enhanced image. The results show that the image enhanced by this algorithm has the highest PSNR and SSIM values and the lowest MSE value, indicating that the enhancement effect of this algorithm is better. Compared with traditional algorithms, the image enhancement algorithm in this paper produces higher image quality and richer details.","Title: Deep Learning-Based Monocular 3D Object Detection with Refinement of Depth Information\nYear: 2022\n\nAbstract: Recently, the research on monocular 3D target detection based on pseudo-LiDAR data has made some progress. In contrast to LiDAR-based algorithms, the robustness of pseudo-LiDAR methods is still inferior. After conducting in-depth experiments, we realized that the main limitations are due to the inaccuracy of the target position and the uncertainty in the depth distribution of the foreground target. These two problems arise from the inaccurate depth estimation. To deal with the aforementioned problems, we propose two innovative solutions. The first is a novel method based on joint image segmentation and geometric constraints, used to predict the target depth and provide the depth prediction confidence measure. The predicted target depth is fused with the overall depth of the scene and results in the optimal target position. For the second, we utilize the target scale, normalized with the Gaussian function, as a priori information. The uncertainty of depth distribution, which can be visualized as long-tail noise, is reduced. With the refined depth information, we convert the optimized depth map into the point cloud representation, called a pseudo-LiDAR point cloud. Finally, we input the pseudo-LiDAR point cloud to the LiDAR-based algorithm to detect the 3D target. We conducted extensive experiments on the challenging KITTI dataset. The results demonstrate that our proposed framework outperforms various state-of-the-art methods by more than 12.37% and 5.34% on the easy and hard settings of the KITTI validation subset, respectively. On the KITTI test set, our framework also outperformed state-of-the-art methods by 5.1% and 1.76% on the easy and hard settings, respectively.","Title: Event-Triggered Dynamic Coverage Control for Multiple Stratospheric Airships\nYear: 2022\n\nAbstract: This article first investigates the dynamic coverage control problem for the multiple stratospheric airships (MSAs) system considering its practical application scenarios. A dynamic coverage control framework is put forward, in which the MSA system can be guided and controlled to fully cover the observation target region. Once a specific target is detected, the coverage target can be switched. First, the location information of the monitored target is predicted by an autoregressive model against processing delay. Second, the coverage control scheme consists of two layers: a novel potential field-based virtual control law to generate the desired velocity and angular velocity and an adaptive tracking controller to track them. In the virtual control law, a dynamic artificial potential field is introduced to adapt to the dynamic scenarios. In the tracking controller, which is combined with the adaptive control technique and the saturation compensator theory, the external disturbances and input saturation are addressed. Third, the event-triggered mechanism is designed to reduce the control frequency to prolong the actuator life. The simulation results are given to substantiate the capability of the proposed dynamic coverage control framework.","Title: A Layered Spiking Neural System for Classification Problems\nYear: 2022\n\nAbstract: Biological brains have a natural capacity for resolving certain classification tasks. Studies on biologically plausible spiking neurons, architectures and mechanisms of artificial neural systems that closely match biological observations while giving high classification performance are gaining momentum. Spiking neural P systems (SN P systems) are a class of membrane computing models and third-generation neural networks that are based on the behavior of biological neural cells and have been used in various engineering applications. Furthermore, SN P systems are characterized by a highly flexible structure that enables the design of a machine learning algorithm by mimicking the structure and behavior of biological cells without the over-simplification present in neural networks. Based on this aspect, this paper proposes a novel type of SN P system, namely, layered SN P system (LSN P system), to solve classification problems by supervised learning. The proposed LSN P system consists of a multi-layer network containing multiple weighted fuzzy SN P systems with adaptive weight adjustment rules. The proposed system employs specific ascending dimension techniques and a selection method of output neurons for classification problems. The experimental results obtained using benchmark datasets from the UCI machine learning repository and MNIST dataset demonstrated the feasibility and effectiveness of the proposed LSN P system. More importantly, the proposed LSN P system presents the first SN P system that demonstrates sufficient performance for use in addressing real-world classification problems.","Title: Unsupervised Low-Light Image Enhancement by Extracting Structural Similarity and Color Consistency\nYear: 2022\n\nAbstract: A novel structure-aware unsupervised network is proposed to deal with low-light image enhancement issues based on the inspiration of Retinex theory and self-supervised perceptual loss. It comprises four main components, namely the original structural similarity module, the novel color consistency module, the attentional enhancement module, and the naturalness discriminator module. Specially for the structural similarity module, an embedded structural feature extractor (SFE) model capable of generating structure correspondence is well designed and pre-trained by employing the contrastive learning technique, and a multi-scale structural similarity distance is introduced to optimize the SFE network. Besides, a self-supervised color consistency module is established by using a degraded estimation algorithm for recovering the missing colors. The whole enhancement framework operates in unsupervised manners and finally obtains the best naturalness image quality evaluator metric. Experimental results demonstrate that the proposed unsupervised network is able to recover natural structure and color images more effectively, which would also help to enlarge the practical application without collecting paired datasets in advance.","Title: Fast Momentum Contrast Learning for Unsupervised Person Re-Identification\nYear: 2021\n\nAbstract: Person re-identification (re-ID) aims to identify the same persons' images across different cameras by deploying a trained re-ID model. Nevertheless, the domain bias between different datasets can lead to considerably low identification accuracy when directly applying a trained re-ID model from one dataset to another. Therefore, in order to develop a kind of more transferable re-ID methods with strong robustness, this letter proposes a fast momentum contrast learning framework, which consists of a teacher encoder and a fast momentum encoder, from a reinforcing visual representation learning point of view. The fast momentum encoder produced by the teacher encoder is employed to build a dynamic dictionary and then design the contrast learning loss function together with the teacher encoder. In addition, both the individual loss and the clustering loss are adopted for obtaining the optimal performances in unsupervised person re-ID models. The proposed framework achieves one stage of end-to-end training and experimental results demonstrate that our identification accuracy outperforms existing advanced methods. The code of this work is available at https://github.com/binquanwang/FMCL_UDA.","Title: On homomorphic images of the Szilard languages of matrix insertion–deletion systems with matrices of size 2\nYear: 2022\n\nAbstract: A Szilard language is a well-known tool in formal language theory to express the derivation process in a grammar system or grammar. Matrix InsDel (insertion–deletion) system is a well-known variant of InsDel system, where the idea of matrix control is combined with InsDel systems. The size of a matrix InsDel system is represented by a septuple of integers \n \n \n \n $$(k; p, m, m^{'};$$\n \n \n \n \n \n $$q, n, n^{'})$$\n \n , where k represents maximum number of rules in a matrix, i.e., size of a matrix. The parameters p, m and \n \n \n \n $$m^{'}$$\n \n represent the maximal length of the strings inserted, the maximal length of the left context and the maximal length of the right context of the insertion rules, respectively. The parameters \n \n \n \n $$q, n, n^{'}$$\n \n represent the same for deletion rules. In this paper, we investigate the Szilard languages of matrix InsDel systems with matrices of size 2. We give examples of regular, context-free and context-sensitive languages which cannot be the Szilard language of any matrix InsDel system. We show that any regular language can be represented as a homomorphic image of Szilard language of matrix InsDel system of size (2; 2, 0, 0; 1, 0, 0). Any linear language, meta-linear language and rational (or regular) closure of linear language can be obtained as the homomorphic image of Szilard language of matrix InsDel systems of size (2; 1, 1, 0; 1, 1, 0), and (2; 1, 0, 1; 1, 0, 1). Moreover, any recursively enumerable language can be obtained as the homomorphic image of the Szilard language of matrix InsDel systems of size (2; 1, 1, 0; 1, 1, 1), (2; 1, 0, 1; 1, 1, 1), (2; 1, 1, 1; 1, 1, 0), (2; 1, 1, 1; 1, 0, 1), (2; 1, 1, 0; 2, 0, 0), (2; 1, 0, 1; 2, 0, 0), (2; 2, 0, 0; 1, 1, 0), and (2; 2, 0, 0; 1, 0, 1).","Title: Single image dehazing based on multi-scale segmentation and deep learning\nYear: 2022\n\nAbstract: Existing image dehazing methods suffer from problems of insufficient dehazing, distortion, and low color contrast. Aiming at this problem, a deep learning single-image dehazing method based on multi-scale segmentation is proposed in this paper. The study found that the haze information in the haze image will decrease with the increase of frequency. Therefore, the haze image is first decomposed into four sub-images of different frequency domains through image segmentation in this article. A dehazing network model composed of four sub-network channels with different complexity is then constructed to extract the haze information contained in each sub-image. After the transmission sub-images are generated, the image fusion technology is used to obtain the final transmittance map. Finally, the haze-free image is obtained based on the physical model of atmospheric scattering. Experimental results on the synthetic and real images dataset show that the proposed method achieves significant dehazing effect and high color contrast with no distortion, showing superior performance than other dehazing methods.","Title: A distributed adaptive optimization spiking neural P system for approximately solving combinatorial optimization problems\nYear: 2022\n\nAbstract: •Proposes a distributed adaptive optimization spiking neural P system with a distributed population structure and a new adaptive learning rate considering population diversity.•Extensive experiments on knapsack problems show that DAOSNPS gains much better and more stable solutions than OSNPS, AOSNPS and other two optimization algorithms.","Title: Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation\nYear: 2022\n\nAbstract: In recent years, image segmentation techniques based on deep learning have achieved many applications in remote sensing, medical, and autonomous driving fields. In space exploration, the segmentation of spacecraft objects by monocular images can support space station on-orbit assembly tasks and space target position and attitude estimation tasks, which has essential research value and broad application prospects. However, there is no segmentation network designed for spacecraft targets. This paper proposes an end-to-end spacecraft image segmentation network using the semantic segmentation network DeepLabv3+ as the basic framework. We develop a multi-scale neural network based on sparse convolution. First, the feature extraction capability is improved by the dilated convolutional network. Second, we introduce the channel attention mechanism into the network to recalibrate the feature responses. Finally, we design a parallel atrous spatial pyramid pooling (ASPP) structure that enhances the contextual information of the network. To verify the effectiveness of the method, we built a spacecraft segmentation dataset on which we conduct experiments on the segmentation algorithm. The experimental results show that the encoder+ attention+ decoder structure proposed in this paper, which focuses on high-level and low-level features, can obtain clear and complete masks of spacecraft targets with high segmentation accuracy. Compared with DeepLabv3+, our method is a significant improvement. We also conduct an ablation study to research the effectiveness of our network framework.","Title: Mobile service selection in edge and cloud computing environment with grey wolf algorithm\nYear: 2022\n\nAbstract: The proliferation of mobile devices has resulted in the tremendous development of edge computing. A common mechanism is that once the requests from the users are too complex to be afforded by a single service, then edge computing services ought to step in. Nevertheless, services on edge servers are most commonly resource-constrained and unstable. To this end, we propose a novel solution for the mobile service selection problem with edge and cloud computing. An extended grey wolf algorithm is presented; specifically, crossover operator and roulette wheel are applied in reproduction and selection operations. Comparative experiments are implemented between our approach and other nature-inspired algorithms to verify the effectiveness and efficiency, which demonstrate our method may find a solution with better QoS values.","Title: AC-LSTM: Anomaly State Perception of Infrared Point Targets Based on CNN plus LSTM\nYear: 2022\n\nAbstract: Anomaly perception of infrared point targets has high application value in many fields, such as maritime surveillance, airspace surveillance, and early warning systems. This kind of abnormality includes the explosion of the target, the separation between stages, the disintegration caused by the abnormal strike, etc. By extracting the radiation characteristics of continuous frame targets, it is possible to analyze and warn the target state in time. Most anomaly detection methods adopt traditional outlier detection, which has the problems of poor accuracy and a high false alarm rate. Driven by data, this paper proposes a new network structure, called AC-LSTM, which combines Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM), and embeds the Periodic Time Series Data Attention module (PTSA). The network can better extract the spatial and temporal characteristics of one-dimensional time series data, and the PTSA module can consider the periodic characteristics of the target in the process of continuous movement, and focus on abnormal data. In addition, this paper also proposes a new time series data enhancement method, which slices and re-amplifies the long time series data. This method significantly improves the accuracy of anomaly detection. Through a large number of experiments, AC-LSTM has achieved higher scores on our collected datasets than other methods.","Title: PSNet: LiDAR and Camera Registration Using Parallel Subnetworks\nYear: 2022\n\nAbstract: The working environment of autonomous driving and robot navigation is so complex and dynamic that a single type of sensor is insufficient for performing object detection. Thus, in many perception schemes, the LiDAR-camera fusion strategy is preferred. However, the performance of a LiDAR-camera fusion heavily relies on a set of accurately calibrated extrinsic parameters. We propose PSNet, an end-to-end convolutional neural network (CNN) for calibration; this is the first calibration network to use parallel subnetworks to obtain multiresolution features and fuse them adaptively to encourage robustness against different initial error ranges. The method has three key characteristics: (i) Addition of a downsampling block to improve suitability for sparse projected depth maps; (ii) Connection of the high-to-low resolution convolution streams in parallel to obtain multiresolution features that are spatially more precise and contain richer semantic information; (iii) Fusion of multiresolution streams by the multiscale feature aggregation module. The network corrects errors from initial calibration to the ground truth online, rather than directly obtaining the accurate parameters. We evaluated our model on the KITTI datasets and it outperformed other CNN-based methods. In addition, extensive experiments evaluating the model with untrained and unfamiliar datasets demonstrated that our method exhibited good generalization ability.","Title: Cyclic Global Guiding Network for Point Cloud Completion\nYear: 2022\n\nAbstract: The application of 3D scenes has gradually expanded in recent years. A 3D point cloud is unreliable when it is acquired because of the performance of the sensor. Therefore, it causes difficulties in utilization. Point cloud completion can reconstruct and restore sparse and incomplete point clouds to a more realistic shape. We propose a cyclic global guiding network structure and apply it to point cloud completion tasks. While learning the local details of the whole cloud, our network structure can play a guiding role and will not ignore the overall characteristics of the whole cloud. Based on global guidance, we propose a variety of fitting planes and layered folding attention modules to strengthen the local effect. We use the relationship between the point and the plane to increase the compatibility between the network learning and the original sparse point cloud. We use the attention mechanism of the layer overlay to strengthen the local effect between the encode and decode. Therefore, point clouds are more accurate. Our experiments indicate the effectiveness of our method on the ShapeNet, KITTI, and MVP datasets and are superior to other networks.","Title: An Efficient Information-Reinforced Lidar Deep Completion Network without RGB Guided\nYear: 2022\n\nAbstract: Due to the sparsity of point clouds obtained by LIDAR, the depth information is usually not complete and dense. The depth completion task is to recover dense depth information from sparse depth information. However, most of the current deep completion networks use RGB images as guidance, which are more like a processing method of information fusion. They are not valid when there is only sparse depth data and no other color information. Therefore, this paper proposes an information-reinforced completion network for a single sparse depth input. We use a multi-resolution dense progressive fusion structure to maximize the multi-scale information and optimize the global situation by point folding. At the same time, we re-aggregate the confidence and impose another depth constraint on the pixel depth to make the depth estimation closer to the ground trues. Our experimental results on KITTI and NYU Depth v2 datasets show that the proposed network achieves better results than other unguided deep completion methods. And it is excellent in both accuracy and real-time performance.","Title: Application of Random Forest Algorithm on Tornado Detection\nYear: 2022\n\nAbstract: Tornadoes are highly destructive small-scale extreme weather processes in the troposphere. The weather radar is one of the most effective remote sensing devices for the monitoring and early warning of tornadoes. The existing tornado detection algorithms based on radar data are unsupervised and have strict multi-altitude constraints, such as the tornado detection algorithm based on tornado vortex signatures (TDA-TVS), which may lead to high false alarm rates, and the performance of the detection algorithm is greatly affected by the radar data quality control algorithm. A novel TDA-RF algorithm based on the random forest (RF) classification algorithm is proposed for real-time tornado identification of the S-band China new generation of Doppler weather radar (CINRAD-SA). The TDA-RF algorithm uses velocity features to identify tornadoes and adds features related to reflectivity and velocity spectrum width in radar level-II data. Historical CINRAD-SA tornado data from 2006-2015 are used to construct the tornado dataset and train the TDA-RF model. The performance of TDA-RF is evaluated using CINRAD-SA data from five tornadoes of 2016-2020 with enhanced Fujita(EF) scale ratings ranging from EF0 to EF4 and distances from 10 to 130 km to the radar. TDA-RF performs well overall with the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) of 71%, 29%, and 55%, respectively. Moreover, the TDA-RF improves POD and CSI, and reduces FAR compared to the TDA-TVS. The maximum tornado early-warning time of TDA-RF is 17 min, and the average is 6 min; TDA-RF can provide classification probability according to the tornado generation and development process to facilitate tracking ability."],"string":"[\n \"Title: Supporting Flexibility of the CMMI Process Framework with a Multi-layered Process Model\\nYear: 2013\\n\\nAbstract: CMMI(Capability Maturity Model Integration) is a process framework which is organized by goals, practice and process area. The Maturity Level 3 stressed that the project level process must be tailored from the organization level process, that is to say, no matter how the project level process changes, it must satisfy the constraints of the organization level process. However, companies usually prefer to choose detailed and operational single-layered process models for applying CMMI. If some activities in process have been changed, the process model must be totally redesigned, this reduces the flexibility and increases the cost of adopting CMMI. In order to solve the above problems, this paper presents a multi-layered model, in which the upper layer is used to describe the CMMI organization level process (i.e., the software development lifecycle) and the lower layer is used to describe the project level process model (i.e., the procedure of each development group). At the same time, we provide delegation relationship in multi-layered process model to build the constraints between the upper layer and the lower layer process model, it brings the flexibility that the lower layered model (the project process) can change its activities on the premise that obeying the constraints of upper layered model (the organization process). Finally, in order to validate the practical effectiveness of this model, a CMMI case from GUODIAN Nanjing Automation Cd., LTD is provided.\",\n \"Title: The Performance Of Parallel Matrix Algorithms On A Broadcast-Based Architecture\\nYear: 2006\\n\\nAbstract: Due to advances in fiber-optics and very large scale integration (VLSI) technology, interconnection networks which allow multiple simultaneous broadcasts are becoming feasible. This paper summarizes one such multiprocessor architecture called the Simultaneous Optical Multiprocessor Exchange Bus (SOME-Bus). It also presents enhancements to the network interface and the cache and directory controllers which support cache block combining, capture and prefetch, and allow complete overlap of processing time with the communication time due to compulsory misses. The paper uses two fundamental matrix algorithms to characterize the impact of each enhancement on performance. Cache miss analysis and results from the execution of these programs on a SOME-Bus simulator show that block capture and prefetch combined with an effective block replacement policy succeed in significantly reducing the miss rate due to compulsory misses as the cache size increases, while a similar increase of cache size in traditional architectures leaves the miss rate due to compulsory misses unaffected. Copyright (c) 2005 John Wiley & Sons, Ltd.\",\n \"Title: A Remote Monitoring System of IDC Room Based on ZigBee Wireless Sensor Networks\\nYear: 2009\\n\\nAbstract: This paper introduces an intelligent remote monitor system based on ZigBee sensor networks for IDC (Internet Data Center) room. The system consists of two parts: three types of ZigBee modules and the server module. The three types of ZigBee modules contain first-cluster node, second-cluster node and sensor end-node. The system can control remote IDC room based on the connection of the ZigBee protocols and traditional Internet networks. The data collected by the sensor end-nodes in the IDC room are transmitted through ZigBee networks to the server that can communicate with the remote monitor PC with Internet connection. The functions the system implements include data collection, analysis, management, storage, automatic alarm, display and control. By monitoring the IDC room unmanned, the proposed system reduces the energy consumption and the number of management staff, at the same time, provides reliable and robust monitoring, then control the equipments in real-time. Consequently, it ensures the safety and stable operation for the IDC room.\",\n \"Title: A green solution for intelligent metropolitan heating system with uSDCards\\nYear: 2012\\n\\nAbstract: In this paper1, we present the architecture, design, and simulation of an intelligent system for Temperature Monitoring used in metropolitan heating. The system consists of several TelosB-compatible motes, a Nokia uSDCard, and a smart phone. We use TelosB Motes to collect temperature data, and to transport the data to the smart phone. The uSDCard, as the middle layer, connects the smart phone with the ZigBee compatible devices. The smart phone, as the terminal, processes data and manages the TelosB Motes. We use the smart phone as the final terminal because it has a rich set of user interfaces and has access to various kinds of networks, allowing our system to be extended more easily and more user-friendly. In real scenes, our system can reduce the temperature reading fluctuation, and ultimately save the energy consumption for heating companies, providing a better living environment for indoor users.\",\n \"Title: Quaternion Fourier-Mellin moments for color images\\nYear: 2011\\n\\nAbstract: The quaternion Fourier-Mellin moments for describing color images are introduced, which can be seen as the generalization of traditional Fourier-Mellin moments for gray-level images. Then, the quaternion Fourier-Mellin moment invariants are derived, which could be a useful tool in color object recognition tasks that require the similarity invariance. In addition, the problem of color image registration using quaternion Fourier-Mellin moments is discussed. The registration method can match color images differing in rotation and scaling. The advantage of our method is that it can process color image directly, without losing color information. Experimental results validate the effectiveness of the method we proposed.\",\n \"Title: An Improved Gps/Rfid Integration Method Based On Sequential Iterated Reduced Sigma Point Kalman Filter\\nYear: 2012\\n\\nAbstract: In this paper, an improved GPS/RFID integration method based on Sequential Iterated Reduced Sigma Point Kalman Filter (SIR-SPKF) is proposed for vehicle navigation applications. It is applied to improve the accuracy, reliability and availability of satellite positioning in the areas where the satellite visibility is limited. An RFID system is employed to assist the GPS system in achieving high accuracy positioning. Further, to reduce the measurement noise and decrease the computational complexity caused by the integrated GPS/RFID, SIRSPET is investigated as the dominant filter for the proposed integration. Performances and computational complexities of different integration scenarios with different filters are compared in this paper. A field experiment shows that both accuracy and availability of positioning can be improved significantly by this low-cost GPS/RFID integration method with the reduced computational load.\",\n \"Title: Load Migrating for the Hot Spots in Wireless Sensor Networks Using CTP\\nYear: 2011\\n\\nAbstract: To suit the needs of data collection, routing protocols in WSN are normally required to form a collection tree where data flows from the source nodes to the sink nodes. These protocols, such as CTP and Multihop LQI, generally target at reducing the packet delivery cost without considering load balancing issues. We argue that load balancing is crucial for WSNs because load imbalance may cause certain nodes, which we call hot spots, to run out of energy faster than expected. The load imbalance may lead to holes and prominently degrade the performance of the network. In this paper, we propose BCTP (Balanced Collection Tree Protocol), which enhances CTP by enabling the network to migrate the load of the node under heavy traffic. BCTP uses the average transmission rate as the metric to measure a node's long term traffic load. Once a node is found heavily loaded, BCTP adopts a stochastic routing strategy to balance the load. BCTP is evaluated by test bed experiments with 9 Telosb motes. Experiment result shows that BCTP can reduce the load of the hot spot by up to 61.9\\\\% in a densely deployed network.\",\n \"Title: Adaptive Trajectory Tracking of Stratospheric Airship Based on Input-output Stability Theory\\nYear: 2011\\n\\nAbstract: Based on input-output stability theory a new approach is introduced about cruise trajectory tracking adaptive control of a fully-actuated stratospheric airship. Firstly the dynamic model is expressed by the generalized coordinates. In view of the imprecisely unknown inertial parameters, online inertial parameter estimator and trajectory tracking law are designed to realize autonomous tracking of any continuous time-varying desired trajectory from an arbitrary initial condition. The stability of the closed-loop control system is proved by using the Lyapunov stability theory. Finally, simulation results of tracking a typical trajectory for such a fully-actuated airship show the validity of the proposed method.\",\n \"Title: An efficient similarity-based validity index for kernel clustering algorithm\\nYear: 2006\\n\\nAbstract: The qualities of clustering, including those obtained by the kernel-based methods should be assessed. In this paper, by investigating the inherent pairwise similarities in kernel matrix implicitly defined by the kernel function, we define two statistical similarity coefficients which can be used to describe the within-cluster and between-cluster similarities between the data items, respectively. And then, an efficient cluster validity index and a self-adaptive kernel clustering (SAKC) algorithm are proposed based on these two similarity coefficients. The performance and effectiveness of the proposed validity index and SAKC algorithm are demonstrated, compared with some existing methods, on two synthetic datasets and four UCI real databases. And the robustness of this new index with Gaussian kernel width is also explored tentatively.\",\n \"Title: ISA: a source code static vulnerability detection system based on data fusion\\nYear: 2007\\n\\nAbstract: Static analysis is a kind of effective method to detect the vulnerabilities in the software. Without running the programs, static analysis tools can be used to automatically discover unknown bugs. To cope with the problem of high false positives and false negatives in source code static analysis methods, this paper presents a source code static analysis technology for vulnerability detection based on data fusion. By parsing and making data fusion on the outcome of different static analysis methods, this technology lets different results validate each other, which greatly decreases the false positives and false negatives. Brief explanations are given to support this method. A prototype system of scalable source code analysis system (ISA for short) is designed and implemented which also can automatically search for the best result based on feedback of the user interaction. The whole system is scalable and platform-independent. It is proved by experiment that this method has a better performance with lower false positives and false negatives and higher efficiency compared with one single method.\",\n \"Title: Affinity-aware Virtual Cluster Optimization for MapReduce Applications\\nYear: 2012\\n\\nAbstract: Infrastructure-as-a-Service clouds are becoming ubiquitous for provisioning virtual machines on demand. Cloud service providers expect to use least resources to deliver best services. As users frequently request virtual machines to build virtual clusters and run MapReduce-like jobs for big data processing, cloud service providers intend to place virtual machines closely to minimize network latency and subsequently reduce data movement cost. In this paper we focus on the virtual machine placement issue for provisioning virtual clusters with minimum network latency in clouds. We define distance as the latency between virtual machines and use it to measure the affinity of virtual clusters. Such metric of distance indicates the considerations of virtual machine placement and topology of physical nodes in clouds. Then we formulate our problem as the classical shortest distance problem and solve it by modeling to integer programming problem. A greedy virtual machine placement algorithm is designed to get a compact virtual cluster. Furthermore, an improved heuristic algorithm is also presented for achieving a global resource optimization. The simulation results verify our algorithms and the experiment results validate the improvement achieved by our approaches.\",\n \"Title: Improved Minimum Hop Routing Protocol in WSN\\nYear: 2012\\n\\nAbstract: Wireless Sensor Network (WSN) is a real-time monitor network [1]. It can sense and collect environmental information within the network or monitor objects. These data are processed to obtain detailed and accurate information. Research focusing on Wireless Sensor Network is routing, positioning technology, energy control and security guarantees. Routing technology is the basis for other research directions, and now a lot of routing algorithms have been proposed. This paper is based on the needs of the small transmission delay. The Minimum Hop Routing (MHR) algorithm is studied. MHR algorithm is mainly based on the node updating the routing table, so as to build each routing table, so as to build a path from source node to destination node. MHR has the advantage of network delay is small, but MHR also has shortcomings-with the data transmission, some path will soon run out of energy, the death of some nodes will depart the network into several parts, reduce network connecting. In order to solve the problem, this paper presents an improved minimum hop routing algorithm. First, sensor nodes are randomly distributed by region, Second, selecting the maximum energy node as the target node for transmission. Through analysis and experiment the approach has effectively reduced the transmission hops, and compared with the Flooding, the node energy consumption has increased.\",\n \"Title: New Mix codes for multiple bit upsets mitigation in fault-secure memories\\nYear: 2011\\n\\nAbstract: Nowadays, multibit error correction codes (MECCs) are effective approaches to mitigate multiple bit upsets (MBUs) in memories. As technology scales, combinational circuits have become more susceptible to radiation induced single event transient (SET). Therefore, transient faults in encoding and decoding circuits are more frequent than before. Firstly, this paper proposes a new MECC, which is called Mix code, to mitigate MBUs in fault-secure memories. Considering the structure characteristic of MECC, Euclidean Geometry Low Density Parity Check (EG-LDPC) codes and Hamming codes are combined in the proposed Mix codes to protect memories against MBUs with low redundancy overheads. Then, the fault-secure scheme is presented, which can tolerate transient faults in both the storage cell and the encoding and decoding circuits. The proposed fault-secure scheme has remarkably lower redundancy overheads than the existing fault-secure schemes. Furthermore, the proposed scheme is suitable for ordinary accessed data width (e.g., 2^n bits) between system bus and memory. Finally, the proposed scheme has been implemented in Verilog and validated through a wide set of simulations. The experiment results reveal that the proposed scheme can effectively mitigate multiple errors in whole memory systems. They can not only reduce the redundancy overheads of the storage array but also improve the performance of MECC circuits in fault-secure memory systems.\",\n \"Title: Adaptive Pixel/Patch-Based Stereo Matching For 2d Face Recognition\\nYear: 2013\\n\\nAbstract: In this paper, we propose using adaptive pixel/patch-based stereo matching for 2D face recognition. We don't perform 3D reconstruction but define a measure of the similarity of two 2D face images. After rectifying the two images by epipolar geometry, we match them using the similarity for face recognition. The proposed approach has been tested on the CMU PIE and FERET database and demonstrates superior performance compared to existing methods in real-world situations including changes in pose and illumination.\",\n \"Title: Mining spatial association rules with multi-relational approach\\nYear: 2010\\n\\nAbstract: Working on spatial data models for geographic phenomena have always been viewed from a spatial context and emphasizing spatial change. But the problem is that spatial relationships are embedded in space, unknown a priori. To achieve such issue, spatial association rules mining techniques are needed. In this paper, we propose a multi-relational mining method to deal with it. We use a non-parametric way by using Vironoi-diagram based neighborhood, classification method is implemented to pre-process the rules condition, association rules are pre-defined, and a close Apriori-base algorithm is proposed to cope with it. Then the framework is evaluated by the real-world dataset, and some thoughtful association rules are given.\",\n \"Title: A Deep-Space Trail Forecast Method Based on the Enlargement of Field of View\\nYear: 2006\\n\\nAbstract: During the procedure of target tracing, lots of stars will cross the field of view. These stars will disturb the procedure and, the precision of system will be degraded. At the same time, because the optical surveillance system is limited by environment, the targets are often covered by clouds. The above situations will seriously degrade the system's robustness, and once the target is disappeared, the traditional system can't track the target again. Traditional forecast methods merely use coder information, so the precision is low. In order to upgrade the precision, we combine the coder information and the missdistance to forecast the next frame target position. The method is based on field range enlargement. Proved by practical experiments, this method can effectively improve the robustness of the target tracking system.\",\n \"Title: Gatewaying the Wireless Sensor Networks\\nYear: 2013\\n\\nAbstract: With the development of Internet of Things (IoT), bridging wireless sensor networks (WSNs) and other networks has become important. We divide bridging solutions into two categories: hardware solutions and middleware solutions. Hardware solutions have both low power short distance wireless interfaces and other types of transmission interfaces, e.g. GPRS, 3G/4G, via hardware implementations. This kind of solutions is more stable and more applicable for deployed sensor networks. In middleware solutions, the whole system processes appropriate protocol conversion. This kind of solutions is more independent of hardware, making it easier to be reused in different networks. This paper briefly presents key points of each solution and evaluates advantages and disadvantages of them in terms of different criteria. Eventually, we derive the most appropriate situation for each solution from our comparisons and discussions.\",\n \"Title: Mining Similarities for Clustering Web Video Clips\\nYear: 2008\\n\\nAbstract: With the widespread use of online video application, the amount of online video clips becomes huge. Web video search engines can help users to locate video clips they are interested in. However, most video search engines return similar or near-duplicate videos together in the result lists, which is inconvenient for users to browse. This paper proposes a novel approach to cluster similar web searched videos based on video visual similarities mining. The visual information is extracted for each video clip at first, then the video clips are clustered according to the pair-wise similarities among them. To evaluate the effectiveness of the proposed method, experiments are conducted on YouTube video search results.\",\n \"Title: A distributed Key Message algorithm to optimize the communication in clusters\\nYear: 2009\\n\\nAbstract: The Key Message (KM) approach prioritizes communications along the critical path to speed up the execution of parallel applications in a cluster environment. However, the current KM approaches (i.e, the Static KM (SKM) and the dynamic KM with a central predictor (CDKM)) either lack the capability of adapting to dynamic environment or suffer from the expensive centralized control to predict dynamic critical path rendering them to be suboptimal. In this paper, we introduce a dynamic and distributed KM algorithm (DDKM) that takes this dynamic nature into account while avoiding overheads of central control, thus, overcoming the drawbacks of its predecessors. To evaluate the DDKM algorithm, we implement it and simulate it in a cluster environment whose communication network (e.g., shared bus) can be modeled as an M/D/1. We compare DDKM with current KM approaches. The performance of DDKM is close to that of CDKM, and much better than SKM up to 28.9% when the network is highly congested. These results demonstrate that DDKM is a promising optimization in a real cluster environment.\",\n \"Title: Efficient Two-Dimensional Error Codes for Multiple Bit Upsets Mitigation in Memory.\\nYear: 2010\\n\\nAbstract: As technology scales, more and more memory cells can be placed in a die. Multiple bit upsets (MBUs) induced by a single event in adjacent memory cells gets significantly increased. This paper proposes the efficient two-dimensional error codes to mitigate radiation-induced MBUs in memory. This scheme can correct MBUs with any possible width and assure the reliability of memory with very low cost overhead. Firstly, each word is transformed to a matrix form. Then, low complexity multibit error detection codes are applied in each row for error detection while parity codes are applied in each column for error correction. Furthermore, the layout architecture of two-dimensional error codes is used to mitigate MBUs in the redundancy bits. The proposed scheme has been implemented in Verilog and validated through a wide set of simulations. The experiment results reveal that the proposed method has a superior protection level. Compared with the existing multibit error correction codes, it has lower encoding and decoding overheads, even for Hamming codes.\",\n \"Title: Bayesian network based on FTA for safety evaluation on coalmine haulage system\\nYear: 2010\\n\\nAbstract: Safety evaluation is one of the most effective countermeasures to improve the safety in enterprises. Bayesian network based on FTA is a new safety evaluation method. On the basis of FTA, combining with the advantages of Bayesian network, the new method relax the harsh conditions of FTA which make it used in a wider area. This method is applied to the safety evaluation of underground haulage system in one mining area of Kailuan Coal Group Corporation Ltd. Through the result of the analysis we point out the most important factors influencing the safety state. The corresponding countermeasures are put forward too. The evaluation result indicates that this method is easy and can be popularized.\",\n \"Title: Patient-specific computational fluid dynamic simulation of a bilateral bidirectional Glenn connection.\\nYear: 2008\\n\\nAbstract: Computational fluid dynamics (CFD) have been used to investigate the hemodynamic performance in cavopulmonary anastomosis and resulted in improved operative design. In this study, CFD simulations were performed in a patient-specific bilateral bidirectional Glenn (BBDG) connection model and the power losses as well as flow features at different levels of predetermined pulmonary flow splits were calculated and compared. The control volume power loss varied between 0.64 and 1.02 mW when the flow ratio of left pulmonary artery/right pulmonary artery changed from 80:20 to 20:80. The flow patterns within the connection area and the static pressures in the four vessels differed from each other as the pulmonary flow split changed. Power loss and flow patterns of this BBDG connection were influenced by the pulmonary flow split.\",\n \"Title: Incremental DFT based search algorithm for similar sequence\\nYear: 2005\\n\\nAbstract: This paper begins with a new algorithm for computing time sequence data expansion distance on the time domain that, with a time complexity of O(n×m), solves the problem of retained similarity after the shifting and scaling of time sequence on the Y axis. After this, another algorithm is proposed for computing time sequence data expansion distance on frequency domain and searching similar subsequence in long time sequence, with a time complexity of merely O(n×fc), suitable for online implementation for its high efficiency, and adaptable to the extended definition of time sequence data expansion distance. An incremental DFT algorithm is also provided for time sequence data and linear weighted time sequence data, which allows dimension reduction on each window of a long sequence, simplifying the traditional O(n×m×fc) to O(n×fc).\",\n \"Title: Prior based cardiac valve segmentation in echocardiographic sequences: geodesic active contour guided by region and shape prior\\nYear: 2005\\n\\nAbstract: This paper presents a segmentation of cardiac valve structure method in ultrasound sequence. Prior knowledge on certain complex object is a powerful guidance in image segmentation. We represent region and shape prior of the cardiac valve in a form of speed field and incorporate it into image segmentation process within level set framework. Region prior constrains the zero level set evolving in certain region and shape prior pulls the curve to the ideal contour. Experiments on a large quantity of 3D valve sequences show that the algorithm improves accuracy of segmentation and reduces the manual intervention.\",\n \"Title: Compressed Sensing Based on Random Symmetric Bernoulli Matrix\\nYear: 2012\\n\\nAbstract: The task of compressed sensing is to recover a sparse vector from a small number of linear and non-adaptive measurements, and the problem of finding a suitable measurement matrix is very important in this field. While most recent works focused on random matrices with entries drawn independently from certain probability distributions, in this paper we show that a partial random symmetric Bernoulli matrix whose entries are not independent, can be used to recover signal from observations successfully with high probability. The experimental results also show that the proposed matrix is a suitable measurement matrix.\",\n \"Title: Quaternion Based Color Image Quality Assessment Index\\nYear: 2011\\n\\nAbstract: A quaternion model for describing color image is proposed in order to evaluate its quality. Local variance distribution of luminance layer is calculated. Color information is taken into account by using quaternion matrix. The description method is a combination of luminance layer and color information. The angle between the singular value feature vectors of the quaternion matrices corresponding to the reference image and the distorted image is used to measure the structural similarity of the two color images. When the reference image and distorted images are of unequal size it can also assess their quality. Results from experiments show that the proposed method is better consistent with the human visual characteristics than MSE, PSNR and MSSIM. The resized distorted images can also be assessed rationally by this method.\",\n \"Title: A Hybrid Analysis of an Optimization Approach for Cluster Applications\\nYear: 2005\\n\\nAbstract: Cluster/distributed computing has become a popular, cost-effective alternative to high-performance parallel computers. Many parallel programming languages and related programming models have become widely accepted on clusters. However, the high communication overhead is a major shortcoming of running parallel applications on cluster/distributed computing environments. To reduce the communication overhead and thus the completion time of a parallel application, this paper introduces and evaluates an efficient Key Message (KM) approach to support parallel computing on cluster computing environments. In this paper, we briefly present the model and algorithm, and then analytical and simulation methods are adopted to evaluate the performance of the algorithm. It demonstrates that when network background load increases or the computation to communication ratio decreases, the analysis results show better improvement on communication of a parallel application over the system which does not use the KM approach.\",\n \"Title: Poster: a green solution for intelligent metropolitan heating system with uSDcard\\nYear: 2011\\n\\nAbstract: In this poster, we present the architecture, design, and the preliminary simulation results of a system for Metropolitan Heating, via intelligent temperature monitoring. The system consists of several Crossbow TelosB-compatible motes, a Nokia uSDCard, and a smart phone. The motes are used to collect indoor temperature data, which is further relayed to the smart phone's uSD interface. The uSDCard connects the phone with IEEE 802.15.4/ZigBee compatible devices. We use the smart phone as the sink terminal because it has a rich set of user interfaces and can access to different kinds of networks, such as GPRS and 3G. In real scenes, our system can reduce the reading fluctuation, and ultimately save the energy consumed for heating company, achieving a better living condition in the houses.\",\n \"Title: Anisotropic 3d reconstruction and restoration for rotation-scanning 4d echocardiographic images based on MAP-MRF\\nYear: 2005\\n\\nAbstract: An anisotropic method of 3D reconstruction method for time sequence echocardiographic images is proposed in this paper. First, a Bayesian model based on MAP-MRF is described to reconstruct 3D volume, and extended to deal with the images acquired by rotation scanning method. Second, the spatial and temporal nature of ultrasound images is taken into account for the selection of parameter of energy function, which makes this statistical model anisotropic. Hence not only can this method reconstruct 3D ultrasound images, but also remove the speckle noise anisotropically. Finally, we illustrate the experiments of our method on the synthetic and medical images and compare with the isotropic reconstruction method.\",\n \"Title: Promotion Analysis Based on Fitting in Multi-Level Space\\nYear: 2011\\n\\nAbstract: This paper addresses an interesting problem, which is to find top-k L-Regions of a given object in a fact table with multiple dimensions and each dimension has one hierarchy with multiple levels. L-Region is a variant of combination of members from each dimension. To tackle the problem of the large solution space, we propose a materialized strategy called LR-Cube, which balances storage and time overhead by model fitting. The LRAPQuery designed in this paper is a query guaranteeing completeness and uniqueness in checking L-Regions. The experiments show that our proposed method is both feasible and of high efficiency.\",\n \"Title: Mixed Compressed Sensing Based on Random Graphs.\\nYear: 2013\\n\\nAbstract: Finding a suitable measurement matrix is an important topic in compressed sensing. Though the known random matrix, whose entries are drawn independently from a certain probability distribution, can be used as a measurement matrix and recover signal well, in most cases, we hope the measurement matrix imposed with some special structure. In this paper, based on random graph models, we show that the mixed symmetric random matrices, whose diagonal entries obey a distribution and non-diagonal entries obey another distribution, can be used to recover signal successfully with high probability.\",\n \"Title: An efficient pixel-wise method for moving object detection in complex scenes\\nYear: 2013\\n\\nAbstract: Moving object detection is often one of the most basic and important stages in computer vision applications. In this paper, a novel background model is proposed to extract moving foreground objects from videos that may contain different kinds of disturbance such as illumination changes, camera parameter variations, noises and dynamic backgrounds, etc. For each frame, a local frequency response map is generated using short-term Fourier transformation (STFT) in local regions, and by extracting the relations among neighborhoods of the response map, a compact pixel feature is introduced as local frequency pattern. Then, an adaptive probabilistic estimation of pixel feature sequence modified from kernel density estimation is performed to estimate the probability of a pixel being background. Experimental evaluations on complex scenes of surveillance videos demonstrate that the proposed method has archived satisfactory results.\",\n \"Title: An Area-Time Efficient Architecture for 16 x 16 Decimal Multiplications\\nYear: 2013\\n\\nAbstract: With growing demands of decimal computations in scientific, financial and many other key applications, area-time efficient hardware implementation of decimal arithmetic is desired. In this paper, we present a parallel architecture for the fixed-point decimal multiplications based on the 8421 BCD representation. By reducing the entries of partial product pre-computations and using a tree structure with carry-look ahead adders (CLAs) as opposed to carry-save adders (CSAs), a significant speedup of the partial product generations (PPGs) and partial product accumulations can be achieved, while at the same time, the hardware overhead can be reduced. The × decimal multiplier using the proposed architecture with a TSMC 90nm technology compares favorably against three other best known decimal multiplier designs in terms of delay-area product.\",\n \"Title: Region and shape prior based geodesic active contour and application in cardiac valve segmentation\\nYear: 2005\\n\\nAbstract: Geodesic active contour is a useful image segmentation method. But it may fail to segment objects disturbed by complex noises. Prior knowledge on certain object is a powerful guidance in image segmentation. We represent region and shape prior of certain object in a form of speed field and incorporate it into Geodesic Active Contours. Region prior constrains the zero level set evolving in certain region and shape prior pulls the curve to the ideal contour. Applications in a large quantity of cardiac valve echocardiographic sequences have shown that the algorithm is a more accurate and efficient image segmentation method.\",\n \"Title: Occlusion-Robust Face Recognition Using Iterative Stacked Denoising Autoencoder.\\nYear: 2013\\n\\nAbstract: This paper investigates how to recognize faces with partial occlusions using iterative stacked denoising autoencoder (ISDAE). We introduce a mapping-autoencoder (MAE) for occlusion detection, which requires no prior knowledge of occlusion. Inspired by stacked denoising autoencoder (SDAE)'s capability to learn patterns from noisy data, we propose a novel iterative structure of SDAE for occluded faces restoration. Deep neural network (DNN) is used for final recognition. Compared with the state-of-the-art approaches (e.g. sparse representation), ISDAE achieves competitive results under serious occlusion conditions. © Springer-Verlag 2013.\",\n \"Title: Quaternion moment and its invariants for color object classification.\\nYear: 2014\\n\\nAbstract: Moment invariants are important shape descriptors in computer vision. In this study, we propose new sets of quaternion moment descriptors for color image. They are constructed in the quaternion framework and are an extension of complex moment invariants for grayscale images. This is a useful tool in color image processing and color object recognition tasks that require the similarity invariance. The advantage of the proposed quaternion moment invariants is that they can not only process color image in a holistic manner but also grayscale one. In addition, the computational complexity of the proposed method is much lower than the quaternion Zernike moments defined in the polar coordinates. An example of using the quaternion moment invariants as pattern features for a color object classification application is given. Theoretical and experimental results show that the proposed descriptors perform better than the other competing moment-based methods.\",\n \"Title: New SEC-DED-DAEC codes for multiple bit upsets mitigation in memory.\\nYear: 2011\\n\\nAbstract: Nowadays, multiple bit upsets (MBUs) have been widely investigated in memories. Conventional single error correction and double error detection (SEC-DED) codes are capable of correcting one error and detecting all possible double errors. However, they may not provide adequate protection against MBUs. This paper proposes new single-error-correction, double-error-detection double-adjacent-error-correction (SEC-DED-DAEC) codes to mitigate radiation or noise source induced MBUs in memories. The proposed SEC-DED-DAEC codes are obtained from conventional SEC-DED codes according to the mathematics model established in this paper. They can detect and correct all adjacent double bit errors and assure a lower miscorrection probability for non-adjacent double bit errors compared with other SEC-DED-DAEC codes. Furthermore, the redundancy bits of the proposed scheme are the same as those of conventional SEC-DED codes. This means that the increase of correct-capability do not cause additional hardware overhead for the memory system. Finally, the experiment results reveal that the proposed scheme reduces the miscorrection probability of non-adjacent double bit errors by 12% compared to the best known SEC-DED-DAEC codes. Moreover, compared to the well known BCH codes, the proposed scheme reduces 40% hardware redundancy and keeps an acceptable reliability. © 2011 IEEE.\",\n \"Title: Dynamic key messaging for cluster computing\\nYear: 2004\\n\\nAbstract: Over the past decades, distributed computing has been gaining popularity. It provides more computing power and memory space for parallel applications. On the other hand, such applications fight back and challenge the architectures of the distributed systems for more efficiency. To face the challenge, a key messaging (KM) scheme was proposed to realize the optimization of communication at a system architecture level in our previous papers. The contribution of KM is that it performs the optimization in both the underlying communication system and high level application model. The performance of an application is always determined by its critical path. Currently, messages along the critical path can be easily blocked by non-critical path messages, which degrade the performance. To solve this problem, KM provides an algorithm to identify the critical-path messages and optimizes them by introducing a prioritized protocol layer. Thus, these messages are served first before any low priority messages. Shorter processing time for the messages results in faster completion time of the critical path. Although KM was proved to be effective by a prototype system on the IBM SP2, there are limitations in its optimization procedures where dynamic natures of an underlying network should be considered when running parallel applications on cluster/distributed computing environments. To address the problem, a dynamic key message (DKM) algorithm is introduced. DKM takes into considerations the changes of network traffic load while tasks are running and dynamically updates the critical path on the fly. A comprehensive simulation method is adopted to evaluate the performance of this algorithm and the results show that under the same workload, DKM exhibits much more stability than the static key message (SKM) algorithm when the network traffic load changes.\",\n \"Title: Analysis to Effects on Conceptual Parameters of Stratospheric Airship with Specified Factors.\\nYear: 2011\\n\\nAbstract: Stratospheric airship is capable of stationkeeping at high altitude in precondition of the balance of buoyancy and weight, thrust and drag. Based on specific computation process, when some hypotheses are given, the length of airship can be calculated and it is emphasized to analyze the impacts on payload capability performance and conceptual parameters (such as length, surface area and volume) with altitude, latitude of station-keeping and pressure difference, temperature difference, helium purity. It is shown from the analyses that pressure difference, temperature difference and helium purity have fewer effects on payload capability and length of airship, and in contrast, altitude and latitude of stationkeeping have the larger effects. On the other hand, effects on payload capability and length with each technology guideline are also discussed when specified operation parameters keep constant, such as altitude and design wind of station-keeping. It is concluded that the benefit to length and payload capability is the largest with improvement of envelope mass/area ratio but the least with improvement of propeller system efficiency. © 2011 ACADEMY PUBLISHER.\",\n \"Title: A Novel Threat Assessment Method for DDoS Early Warning Using Network Vulnerability Analysis\\nYear: 2010\\n\\nAbstract: Distributed Denial of Service (DDoS) attack is one of main threats to Internet security. Due to the spatio-temporal properties of the attack, it is possible to detect the attack at its early stage. In this paper, we propose a novel method of DDoS threat assessment based on network vulnerability analysis. Both the multi-phase character in the temporal dimension and the impacts in the spatial dimension are concerned in our method. We use three metrics to assess threat, namely the ratio of progress, botnet size, and bots distribution. Experimental results show that our method is sensitive to the changes of attack states, and is easy to be implemented in an early warning system because of its simplicity.\",\n \"Title: Model optimization techniques in a verification platform for classified properties\\nYear: 2004\\n\\nAbstract: In system functional verification, collaborative method is an effective technique. Co-SAM verification platform employs three verification methods, static analysis, logic simulation and model checking, to implement verification process. With the collaborative platform, system properties are classified and verified. A whole model for a system inclines to ineffectiveness and states explosion during verification. Model refinement is indispensable. This paper presents special optimization techniques for verification model refinement, i.e. property grouping, signals reordering, and model hierarchizing. Experimental results demonstrate the validity of these optimization techniques in system functional verification.\",\n \"Title: Transparent Bilateral Teleoperation Under Position And Rate Control\\nYear: 2000\\n\\nAbstract: A four-channel control architecture has been suggested in the literature to achieve transparency for master-slave teleoperator systems under position control. In this paper the result is generalized to include teleoperator systems that are under rate control or move general master-slave kinematic correspondence laws such as a mixed-position/rate mode. A one-degree-of-freedom example is given to outline the design and analysis of such a system for transparency and stability! The validity of the theory has been verified through simulations and experiments.\",\n \"Title: A Geographic Routing Algorithm in Duty-Cycled Sensor Networks with Mobile Sinks\\nYear: 2011\\n\\nAbstract: In this paper, we focus on achieving better energy conservation for geographic routing algorithms in duty-cycled WSNs when there is a mobile sink. We simplify the problem as a topology coverage one, and propose a multi-metric geographic algorithm (MMGR) which uses multi-metric candidates (MMCs) for geographic routing. The analysis and extensive simulation results show that MMGR can achieve better energy conservations than McTPGF, while retaining good performance of end-to-end delay and hop counts.\",\n \"Title: Cooperative Multi-Versioning Technique Based on Version Replication\\nYear: 2006\\n\\nAbstract: This paper first discusses the progress and existing problems in the study of cooperative graphics editing systems (CGES), and then a new distributed multi-versioning model based on version replication is proposed for consistency maintenance of discordant conflicting operations according to the conflicting features and types of concurrent operations in CGES. Based on this model, the scheme of expressing versions is further discussed in detail, including the method of expressing versions and showing execution effect of versions. Then an algorithm of multiple versions incremental creation based on version replication is devised, and an application example is given. The discussion ends up with the future research\",\n \"Title: A Fast Handoff Mechanism with Pre-scanning for Wireless Mesh Networks\\nYear: 2011\\n\\nAbstract: With the development of real-time business, the original traditional WMN has been difficult to satisfy the needs of the real-time business. This article is in support of this delay sensitive type under the background of real-time application business in WMN. This improved mechanism is proposing an pre-scanning algorithm focusing on the higher delay in WMN link layer scanning which based on traditional WMN link layer switching mechanism.\",\n \"Title: Downlink Packets Scheduling in enterprise WLAN\\nYear: 2013\\n\\nAbstract: Enterprise WLAN consists of many APs connected to wired backbone network. Conventional DCF mechanism can not completely prevent conflicts between APs which cause large quantities of transmission failure and retransmission overhead. However, to keep compatible with 802.11-compliant clients, it is difficult to discard DCF. In this paper, we proposed a framework called DPS, which incorporates centralized scheduling with DCF. In DPS, downlink packets are not forwarded as soon as they arrived at APs. Instead, a central controller schedules their forwarding. APs still use DCF to send downlink packets according to the controller's instructions. DPS preserves DCF and requires no modifications to 802.11-compliant clients. Under DPS framework, we proposed a scheduling algorithm, ensuring downlink packets be sent with high success rate. In this way, we can significantly reduce transmission failure and retransmission overhead. Experiment results indicated that DPS can significantly improve the performance of enterprise WLAN compared with DCF without any scheduling.\",\n \"Title: Design of micropillar array for cell capture.\\nYear: 2011\\n\\nAbstract: Micropillar based fluidic chip is an attractive platform for cell isolation, capture and detection. This paper is focused on the design of micropillar array. Its flow situation is simulated by using finite element analysis method. The relationships between the geometry and arrangement of micropillars in the array and the distribution of fluid velocity, shear stress, dynamic pressure which have to be accorded to the cell migration and capture are studied. An optimized water drop shape is obtained for micropillar design. In addition, a silicon micropillar array is proposed to be fabricated with surface functionalization for actin capture. © 2011 IEEE.\",\n \"Title: Key Message Algorithm: A Communication Optimization Algorithm in Cluster-Based Parallel Computing\\nYear: 1999\\n\\nAbstract: Parallel computing using Network of Workstations (NOWs) has become very popular recently. However, the execution of parallel applications on such systems has been hampered by the high communication overhead. To reduce the communication overhead and to speedup the execution of parallel applications on NOWs, this paper proposes a Key Message approach that minimizes the cost of message-passing in a parallel application by prioritizing communications in the underlying shared communication network. In this paper, we first describe the queuing network model on which our approach is based, then introduce the algorithm that identifies the messages to be prioritized in a parallel application, and finally discuss the results obtained. Our preliminary analysis of the algorithm on randomly generated task graphs shows improvement over the system without using the prioritization scheme.\",\n \"Title: Spatial Optimization Method For Sustainable Multiobjective Land Use Allocation\\nYear: 2010\\n\\nAbstract: As one of the core research aspect in Land use and land cover change, the optimal allocation of land use is soon becoming the key measure for sustainable land utilization. In this paper, we designed a method that could implement the multiobjective optimization model for sustainable land use in space automatically, thus we could obtain spatial optimal allocation. Yangshan Town was taken as the research district. First of all, analyzed land use structure which used to build a Digital Elevation Model in GIS technique, extracted land use pattern, aspect, slope from it, land suitability evaluation was calculated by altitude, soil quality and other eco-factors. Subsequently the research area was divided into four parts namely optimizing development zone, major development zone, limited development zone, and prohibited development zone based on their suitability. Thirdly, in accordance with the base map, adopted the depth-first search strategy to implement the structure optimal allocation in graphs with land suitability evaluation as threshold. The result showed that: (1) this method proposed a simple way that used quantity optimal outcomes to allocate in space, it could reduce the complexity in building a land use spatial optimization model, if dynamic elements such as timing, human behavior, etc. were less important risk factors while we took into account. (2) For considering the connection of graph, it could efficiently solve fragmentation compared with nothing but land suitability evaluation. (3) It could provide a convenient and precise approach for government land use planning.\",\n \"Title: An Experimental Study of BATMAN Performance in a Campus Deployment of Wireless Mesh Networks\\nYear: 2011\\n\\nAbstract: Based on a fundamental understanding of a high-quality routing protocol called Better Approach To Mobile Ad-hoc Networking or BATMAN, we provide an experimental study of its performance considering a representative set of meaningful measures with a real Wireless Mesh Network (WMN) test bed deploying in our campus. Analysis and experiments results show that BATMAN is not only an efficient and stable routing protocol, but also can satisfy the requirements of multimedia communication in mesh networks.\",\n \"Title: Developed Acoustic Multi-relaxation Algorithm for Humidity Measurement in Gas Mixtures\\nYear: 2007\\n\\nAbstract: In this paper, we focus on the relaxation process of acoustic waves in polyatomic gases, and build up a multi-relaxation algorithmic model of a gas mixture of nitrogen, oxygen and water vapor. The algorithmic is basing on the complicated transfer mechanism of vibrational-translational, vibrational-vibrational degrees of freedom. By computing acoustic velocity and relaxation attenuation, we notice that changes in water vapor concentration have great influence on the relaxation frequency of the mixture, thus proving the feasibility of detecting humidity by means of relaxation method. Finally, we describe a detailed algorithm to measure proportion of water vapor. This novel approach not only determines the concentration of water vapor, but also reflects the proportional fluctuation of the mixture.\",\n \"Title: Symmetric Toeplitz-Structured Compressed Sensing Matrices.\\nYear: 2013\\n\\nAbstract: How to construct a suitable measurement matrix is an important topic in compressed sensing. A significant part of the recent work is that the measurement matrices are not completely random on the entries but exhibit some considerable structures. In this paper, we proved that a symmetric Toeplitz matrix and its variant can be used as measurement matrices and recovery signal with high probability. Compared with random matrices (e.g. Gaussian and Bernoulli matrices) and some structured matrices (e.g. Toeplitz and circulant matrices), we need to generate fewer independent entries to obtain the measurement matrix while the effectiveness of the recovery keeps good.\",\n \"Title: Enhanced multicast performance for a 60-GHz gigabit wireless service over optical access network based on 16-QAM-OFDM hierarchical modulation\\nYear: 2013\\n\\nAbstract: We propose and experimentally demonstrate a 60-GHz ultra-high throughput (VHT) wireless over fiber multicast system. A novel 16-QAM-OFDM hierarchical modulation is implemented and 2.5-dB performance improvement is achieved comparing with conventional I-Q modulation. © 2013 OSA.\",\n \"Title: A bidirectional multi-band 60-GHz wireless-over-fiber transmission system with compacted base station and mobile terminal using a robust mm-Wave LO free technique\\nYear: 2013\\n\\nAbstract: For the first time, bidirectional multi-band multi-gigabit wireless-over-fiber transmission at 60-GHz mm-wave band has been accomplished with centrally supplied LO service. It does not require mm-wave LO devices in each base stations and mobile terminals. © 2013 OSA.\",\n \"Title: Finite element analysis of the contact interface between trans-femoral stump and prosthetic socket.\\nYear: 2013\\n\\nAbstract: Transfemoral amputees need prosthetic devices after amputation surgery, and the interface pressure between the residual limb and prosthetic socket has a significant effect on an amputee's satisfaction and comfort. The purpose of this study was to build a nonlinear finite element model to investigate the interface pressure between the above-knee residual limb and its prosthetic socket. The model was three-dimensional (3D) with consideration of nonlinear boundary conditions. Contact analysis was used to simulate the friction conditions between skin and the socket. The normal stresses up to 80.57 kPa at the distal end of the soft tissue. The longitudinal and circumferential shear stress distributions at the limb-socket interface were also simulated. This study explores the influences of load transfer between trans-femoral residual limb and its prosthetic socket.\",\n \"Title: Real-time obstacle detection with motion features using monocular vision\\nYear: 2015\\n\\nAbstract: Real-time obstacle detection by monocular vision is a challenging problem in autonomous navigation of vehicles and driver-assistance systems. In this paper, we introduce an approach of real-time obstacle detection which can effectively tell apart obstacles from shadows and road surface markings. We propose the followings: (1) a two consecutive frames (TCF) model to find the differences between obstacles and the ground plane by motion features; (2) a filter to increase probabilities of obstacle regions; (3) an updating process to reduce false positives and update the algorithm when the vehicle moves on. We perform experiments on two datasets and our autonomous vehicle. The results show that our method is effective in various conditions and meets the real-time requirement.\",\n \"Title: Local image descriptor based on spectral embedding\\nYear: 2015\\n\\nAbstract: This study presents a local image descriptor based on spectral embedding. Specifically, the spectra of line graph are used to represent image edges, corners and edge points with big curvature. The authors theoretically analyse and experimentally verify that the spectra of line graph are robust to noise and are invariant to rotation and linear intensity changes. Based on such a fact, some local image descriptors are constructed using the spectra of line graph. Comparative experiments demonstrate the effectiveness of the proposed descriptor and its superiority to some state-of-the-art descriptors under image rotation, image blur, viewpoint change, illumination change, JPEG compression and noise.\",\n \"Title: On high-performance parallel decimal fixed-point multiplier designs.\\nYear: 2014\\n\\nAbstract: •Simplified logic and structure for 8421–5421 BCD multiplier are proposed.•With a novel 4221 BCD full adder, 4221–8421 conversion is no longer needed.•4221 BCD multiplier using the new 4221 BCD full adder is proposed.•The proposed 8421–5421 multiplier gives the best performance ever.\",\n \"Title: Identity Recognition Of Multi-Haptic Pressure Feature Based On Sparse Representation\\nYear: 2014\\n\\nAbstract: Herein, a new identity recognition method of multi-haptic pressure feature based on sparse representation was investigated. According to the common dynamic features, the regional feature and the ratio of length vs. width of external bounding rectangle (extracted by using the least area method) were extracted. The subset of dynamic feature was optimized by correlation criterion, the sparse representation of haptic pressure was obtained according to the sparse basis (i.e., wavelet basis), and the sparse feature vector was calculated by the Topelitz measurement matrix. After that, the haptic pressure feature set was created by combining dynamic feature subset and sparse feature subset linearly. Furthermore, Support Vector Machine (SVM) classifier identified more than two objects following the one to many rule and output the identification result according to the rule of majority voting, and the stability of features is studied by calculating the intraclass correlation coefficient (ICC) and coefficient of variation (C.V). Overall, the improved acuracy of identity recognition demonstrating the effectiveness and stability of the multihaptic pressure feature.\",\n \"Title: Fall detection for elderly person care in a vision-based home surveillance environment using a monocular camera.\\nYear: 2014\\n\\nAbstract: Fall detection is one of the most important health care issues for elderly people at home, which can lead to severe injuries. With the advances and conveniences in computer vision in the last few decades, computer vision-based methods provide a promising way for detecting falls. In this paper, we propose a novel vision-based fall detection method for monitoring elderly people in house care environment. The foreground human silhouette is extracted via background modeling and tracked throughout the video sequence. The human body is represented with ellipse fitting, and the silhouette motion is modeled by an integrated normalized motion energy image computed over a short-term video sequence. Then, the shape deformation quantified from the fitted silhouettes is used as the features to distinguish different postures of the human. Finally, different postures are classified via a multi-class support vector machine and a context-free grammar-based method that provides longer range temporal constraints can verify the detected falls. Extensive experiments show that the proposed method has achieved a reliable result compared with other common methods.\",\n \"Title: The charging-scheduling problem for electric vehicle networks\\nYear: 2014\\n\\nAbstract: Electric vehicle (EV) is a promising transportation with plenty of advantages, e.g., low carbon emission, high energy efficiency. However, it requires frequent and long time charging. In public charging stations, EVs spend long time on queuing especially during peak hours. Hence, it requires an efficient method to reduce the total charging time for EVs. We study the Electric Vehicle Charging-Scheduling (EVCS) problem in this paper. First we prove that EVCS is NP-Complete, which can be reduced from one Parallel Machine Scheduling (PMS) problem. Then two heuristic algorithms are proposed: the Earliest Start Time (EST) algorithm, and the Earliest Finish Time (EFT) algorithm. EST tries to advance the start charging time to get customers in service as early as possible, while EFT focuses on the possible finish charging time to get customers served as soon as possible. Finally simulations show that, the proposed algorithms outperform the classic greedy nearest scheduling algorithm: assign each EV to its nearest charging station, then choose the outlet where the fewest EVs are queuing. Typically, under our simulation settings, the average finish time and maximum finish time can be reduced by about one hour, and six hours respectively.\",\n \"Title: The control algorithm of craniofacial plastic surgical robot\\nYear: 2014\\n\\nAbstract: This paper mainly introduces a kind of auxiliary robot for craniofacial plastic surgery. It contains mechanism design of the robot, the design of the control system, and experimental verification in order to prove the effectiveness of drilling bone. The robot make use of the stability and accuracy of the motor movement, combining with the 2D/3D imaging techniques, through the pressure sensor, position sensor, speed sensor real-time measure stress and displacement. By the force feedback, know the depth of the drill hole. In the drilling bone, the robot solve the error and injury problems because of the hand shaking in the surgery, improve the stability of the operation. In the motor control, because of the robot have nonlinear mechanical structure, the traditional PID control could not bring stability speed and fast response. Due to the fuzzy control need not to establish accurate model, so design a fuzzy controller to make motor run more stable and accurate. Finally the animal experiment was carried out, obtain the force during the process of drilling the bone, the results prove experiment achieve the goal.\",\n \"Title: Supporting “One Big AP” illusion in enterprise WLAN: An SDN-based solution\\nYear: 2014\\n\\nAbstract: IEEE 802.11 standard leaves client to determine AP association selection and initiate AP handoff. This often leads to sub-optimal results in enterprise WLAN with a large number of users. Therefore, network operators expects the ability to dominate AP association selection and AP handoff initiation so that better AP/client association can be achieved. In addition, AP handoff procedure involves time-consuming packets exchange. Although many methods have been proposed to simplify AP handoff procedures, there is at least hundreds of milliseconds during which client's data transmission is blocked. In this paper, we proposed the abstraction of “One Big AP” in which unmodified 802.11-compliant client is tricked into believing that there is one access point with very large coverage, while the AP that sends and receives its packets has changed. To support the“One Big AP” illusion, we leveraged the emerging idea of Software-Defined Networking (SDN) to reform the architecture of enterprise WLAN. In the SDN-based architecture, we can easily realize network-controlled AP association and client-transparent AP handoff. We demonstrated the feasibility and efficiency of our solution through simulation.\",\n \"Title: Using Category Theory and Data Flow Analysis for Modeling and Verifying Properties of Communications in the Process-Oriented Language Erasmus\\nYear: 2014\\n\\nAbstract: The increasing significance of concurrency and the need to address the challenges presented by concurrency motivate its formalization. In this paper, a methodology for modeling and verifying communications in a concurrent system developed in Erasmus, a process-oriented programming language, is proposed. The innovative aspects of this methodology include the use of category theory to model and verify properties of communications in Erasmus, and the use of data flow to analyze and abstract an implementation of Erasmus. The methodology is illustrated by a simple example.\",\n \"Title: Closed-form analysis of intra/inter-band cross-modulation in multiband radio-over-fiber systems\\nYear: 2013\\n\\nAbstract: A theoretical and experimental investigation of the nonlinear behaviors of intra- and inter-band cross-modulation (XM) in a multiband radio-over-fiber (RoF) system is presented for the first time. A closed-form theoretical analysis is proposed to characterize and validate the data XM induced by the nonlinearity of optical Mach-Zehnder modulator (MZM). Nonlinear behaviors of the intra-band XM between the in-phase and quadrature (I/Q) components of a single-band vector signal, as well as the inter-band XM between multiband combinations of scalar and vector signals are investigated, and consistent agreement has been verified between the theory and experiments. It was confirmed that scalar signals only suffer from inter-band XM; whereas vector signals suffer from both intra- and inter-band XMs. Intra-band XM induces constellation compression to vector signals; while inter-band XM induces eye diagram degradation to scalar signals, as well as nonlinear constellation scaling to vector signals. Moreover, QPSK shows superior tolerance to MZM nonlinearity than higher order QAM formats due to it intrinsic resistance to intra-band XM.\",\n \"Title: Centralized small-cell radio access network with shared millimeter-wave radio-over-fiber resources.\\nYear: 2013\\n\\nAbstract: Centralized radio access networks (RANs) based on radio-over-fiber (RoF) techniques can simplify the remote access units (RAUs) and maximize the resource sharing among services and operators, thus improving the system efficiency and reducing the operational cost. A scheme of resource sharing is proposed for centralized RANs based on millimeter-wave (MMW) RoF system. Both RoF infrastructure resources and microwave-photonics resources are shared by multiple operators in the heterogeneous system. High-throughput services carried on both V-band (60GHz) and W-band (100GHz) are delivered to RAUs through flexible configuration at the central office (CO). This proposed multi-service centralized RAN with shared infrastructure is successfully demonstrated via both simulations and experiments.\",\n \"Title: Violence Detection in Video by Using 3D Convolutional Neural Networks.\\nYear: 2014\\n\\nAbstract: Whereas most researches are about the action recognition problem, the detection of fights has been comparatively less involved. Such capability may be of great importance. Typical methods mostly rely on domain knowledge to construct complex handcraft features from inputs. On the contrary, deep models can act directly on the raw inputs and automatically extracts features. So we developed in this paper a novel 3D ConvNets model for violence detection in video without using any prior knowledge. To evaluate our method, experimental validation conducted in the context of the Hockey dataset. The results show that the method achieves superior performance without relying on handcrafted features.\",\n \"Title: TED: Inter-domain traffic engineering via deflection\\nYear: 2014\\n\\nAbstract: As inter-domain routing on today's Internet does not and basically cannot consider traffic load when determining best traffic forwarding paths, it is not always optimal for a router to forward packets along its default path, especially when the router's default output port incurs a long queuing delay. In this paper, we design a new approach called TED in which border routers of autonomous systems (AS) adaptively deflect outbound traffic from a congested default path to an alternative path to significantly improve inter-domain traffic engineering (TE) and end-to-end throughput. With TED, every router only needs to examine the queue length of its own outgoing ports to orchestrate its deflection operation and ensure traffic forwarding is at line speed. It does not need to communicate or coordinate with other TED-capable routers or modify packet content, making TED incrementally deployable. Our evaluation shows that TED significantly increases the average throughput of traffic flows, and the improvement is comparable to directly upgrading router hardware and capacity. Finally, a prototype of TED on NetFPGA is also implemented.\",\n \"Title: Using multiple unmanned aerial vehicles to maintain connectivity of MANETs\\nYear: 2014\\n\\nAbstract: Unmanned Aerial Vehicles (UAVs) have emerged as promising relay platforms to improve the connectivity of ground Mobile Ad Hoc Networks (MANETs). Due to the relatively high cost of UAVs, a lot of efforts have been made to optimize the deployment of UAVs so that the number of UAVs needed in maintaining the connectivity of ground nodes can be minimized. However, existing work on optimization of UAVs' deployment hasn't considered the situation that there are already some UAVs deployed in the field. In this paper, we study the problem of deploying minimum number of UAVs to maintain the connectivity of ground MANETs under the condition that some UAVs have already been deployed in the field. We formulate this problem as a Minimum Steiner Tree problem with Existing Mobile Steiner points under Edge Length Bound constraints (MST-EMSELB) and prove that the problem is NP-Complete. We also propose an Existing UAVs Aware (EUA) polynomial time approximate algorithm for the MST-EMSELB problem that uses a maximum match heuristic to compute new positions for existing UAVs. Simulation results demonstrate that the proposed EUA method has bester performance than a non-EUA method in the term of needed new UAV numbers. Compared with the non-EUA method, the EUA method can reduce at most 60% of the new UAVs number.\",\n \"Title: CDRDN: Content Driven Routing in Datacenter Network\\nYear: 2014\\n\\nAbstract: A major challenge in data center networks is to provide enough network capacity to meet the ever increasing demand of large-scale distributed computing. While existing proposals focus on adding more switches and links, which cost extra hardware and energy, we explore another dimension in which spare disk space at servers is used for caching data, so as to increase network throughput without additional hardware. Leveraging the Named Data Networking (NDN) architecture and unique characteristics of data centers, we design a novel Content Driven Routing in Datacenter Network (CDRDN) that enables universal caching in data center networks in an efficient and scalable way. First, rather than using switches for “on-path” caching like in native NDN, CDRDN uses the large storage space at servers to do “off-path” caching. Second, by taking advantage of data center's regular and hierarchical network topology, CDRDN switches are able to direct requests to nearby server caches even under high dynamics of caches. Third, given the vast amount of data in data centers, the full name-based routing table would be difficult to fit in switch's limited fast memory. CDRDN adopts a compound content and location routing to ensure packet delivery while benefiting from name-based routing as much as the switches can afford. CDRDN extends NDN's adaptive forwarding mechanism to deal with cache misses, link failures, and congestion without running routing protocols or cache exchange protocols. Our packet-level simulations show that CDRDN can almost double the network throughput compared with shortest-path routing under the same setting, and CDRDN can effectively deal with link failures using adaptive forwarding.\",\n \"Title: UPMAC: A localized load-adaptive MAC protocol for underwater acoustic networks\\nYear: 2014\\n\\nAbstract: Unlike terrestrial networks that mainly rely on radio waves for communications, underwater networks utilize acoustic waves, which have comparatively lower loss and longer range in underwater environments. However, acoustic waves incurs long propagation delays that typically lead to low throughput especially in protocols that require receiver feedback such as multimedia stream delivery. In addition, energy cost of transmission underwater is much higher than reception (almost 125:1 [1]). Thus, collision and retransmission should be reduced in order to reduce energy cost and improve throughput Receiver-based protocols, like RIPT and COS-TS, can significantly reduce collision and retransmission. But they are time and energy consuming because nodes are controlled to turn into receiver mode by control packets or a timer regardless of load. In this paper, we propose an underwater practical MAC protocol, called UPMAC. The main objective of UPMAC is to adapt to the network load conditions by providing two modes (high and low load modes) and switching between them based on different offered load. Turn-around time overhead is reduced and it is less vulnerable to control packet corruption, since we reduce the use of control packets by the technique of piggyback. UPMAC provides a low data collision rate in both one-hop and multi-hop situation because we use Receiver-based approach in high load mode. Extensive simulations show that our approach can achieve significantly better performance in both general and Sea Swarm (tree) topologies.\",\n \"Title: Investigation of intra/inter-band cross-modulation in multiband radio-over-fiber systems\\nYear: 2013\\n\\nAbstract: We theoretically and experimentally investigated the intra/inter-band cross-modulation (XM) induced by the nonlinearity of Mach-Zehnder modulator (MZM) in a multiband radio-over-fiber (RoF) system. A closed-form theoretical analysis was proposed to model and predict the nonlinear XM behaviors, for both single-band vector signals and multiband combination of scalar and vector signals. To verify the feasibility of the theoretical model, both simulation and experimental investigations were carried out, and according to the theoretical analysis, scalar signals only suffer from inter-band XM; whereas vector signals suffer from both intra- and inter-band XM. The signal quality degradation induced by MZM nonlinearity for a single-band 16QAM, QPSK, and two bands of OOK signals are investigated and characterized, and good agreement between theory and experiments was achieved. It was observed that QPSK shows superior performances compared with higher order QAM formats due to its intrinsic resistance to intra-band XM.\",\n \"Title: A novel approach for spectrum mobility games with priority in Cognitive Radio networks\\nYear: 2014\\n\\nAbstract: In recent years, the problem of spectrum mobility in Cognitive Radio (CR) Networks has been widely investigated. In order to fully utilize spectrum resources, many spectrum handoff techniques based on game theory have been proposed, but most studies only concern how to achieve better payoffs for users, without paying much attention to the Quality of Service (QoS). Thus, we propose a new channel switching model based on game theory, using a prioritized approach to meet the diverse needs of users, such as bandwidth, delay, and jitter. Once the Nash equilibrium is achieved, our model will provide different QoSes by setting different priorities to different users. We also propose two acceleration methods to reach the Nash equilibrium more quickly. Finally, we evaluate the performance of the proposed schemes using real channel availability measurements. Experiments results show that our model can provide differentiated services and our algorithm is guaranteed to reach an approximate Nash equilibrium within polynomial time.\",\n \"Title: Adaptive photonic-assisted M-ary ASK/M-QAM millimeter-wave synthesis in multi-antenna Radio-over-Fiber system\\nYear: 2014\\n\\nAbstract: We propose and experimentally verify novel photonic-assisted multi-antenna RoF system to achieve multiplexing gain by synthesizing two 1/0.5-Gb/s OOK/4-QAM mm-wave signals into a 2/1-Gb/s 4-ary ASK/16-QAM signal, or SNR gain by combining two identical channels.\",\n \"Title: Building an inverse model of MR damper based on Dahl model\\nYear: 2014\\n\\nAbstract: Magnetorheological (MR) fluid dampers are potential intelligent devices in vibration semi-active control application. But its model is difficult to get for its strong nonlinearity. In this works, the modified Dahl model is proposed with fewer parameters and clear physical meanings. The parameters are identified by crossover particle swarm optimization (PSO) and recursive least-square optimization method based on experiment data efficiently. A simple open-loop inverse model which can efficiently and accurately obtain expected control current intensity is proposed. The effectiveness and precision of proposed models are verified by simulation in wide conditions.\",\n \"Title: Parallel matrix algorithms on a broadcast-based architecture\\nYear: 2004\\n\\nAbstract: Summary form only given. Due to advances in fiber-optics and VLSI technology, interconnection networks which allow multiple simultaneous broadcasts are becoming feasible. This paper summarizes one such multiprocessor architecture called the Simultaneous Optical Multiprocessor Exchange Bus (SOME-Bus). It also presents the design of the network interface and the cache and directory controllers which support cache block combining, capture and prefetch and allow complete overlap of processing time with the communication time due to compulsory misses. The paper uses two fundamental matrix algorithms to characterize the architecture performance. Cache miss analysis and results from the execution of these programs on a SOME-Bus simulator show that block capture and prefetch combined with an effective block replacement policy succeed in significantly reducing the miss rate due to compulsory misses as the cache size increases, while a similar increase of cache size in traditional architectures leaves the miss rate due to compulsory misses unaffected.\",\n \"Title: On a parallel decimal multiplier based on hybrid 8421–5421 BCD recoding\\nYear: 2013\\n\\nAbstract: Parallel decimal multiplications can be broadly divided into two major steps: partial product generations (PPGs) and partial product accumulations (PPAs). Although most operands in decimal multipliers are represented in popular 8421 BCD codes, alternative 4221 and 5211 BCD codes are also sometimes employed, alone or mixed with 8421 codes, to represent the partial products, with a hope that by doing so, the multipliers' area/time efficiency will be improved. However, study shows that multipliers based on unsuitable mixing of codes, like 4221-8421, can have worse area and timing performance than 8421 multipliers due to the long carry propagation delay of 4221 PPA and the extra 4221-8421 conversions as required. Consequently, in this paper, we propose an 8421-5421 BCD multiplier that is equipped with a simplified PPG based on 8421-5421 recoding and an improved 8421 carry-lookahead adder tree for PPA, and compare it against the two best known multiplier designs using 90nm TSMC technology. The synthesis results have confirmed that the proposed 8421-5421 multiplier achieves the lowest delay and is the most time-area efficient design.\",\n \"Title: Obstacle detection in single images with deep neural networks\\nYear: 2016\\n\\nAbstract: Abstract Obstacle detection in single images is a challenging problem in autonomous navigation on low-cost condition. In this paper, we introduce an approach for obstacle detection in single images with deep neural networks. We propose the followings: (1) a deep model combined with other deep neural network for obstacle detection; (2) a method to segment obstacles and infer their depths. Among others, both local and global information are generated in our method for better classification and portability. Experiments are performed on the open datasets and images captured by our autonomous vehicle. The results show that our method is effective in both obstacle detection and depth inference.\",\n \"Title: A localisation algorithm based on region partition of cellular network in wireless sensor networks\\nYear: 2016\\n\\nAbstract: AbstractIn this paper, anchor nodes are deployed in the vertices and the centre of hexagon, which is the basic architecture of cellular network. Unknown nodes are distributed randomly in the hexagon. Because of inerratic cellular network, the geometrical characteristic is made full use of and the network is divided into several regions. The minimal connecting hop between nodes is different in different regions. According to corresponding relationship between unknown nodes hop information and divided regions, the unknown node is determined in a very small region. Through calculating centroid of the small region, the localisation based on range-free is well completed. On the basis of the simulation results, it is demonstrated that the localisation algorithm based on region partition of cellular network we proposed has better positioning performances, compared with the typical processing.\",\n \"Title: A new extragradient-type method for mixed variational inequalities.\\nYear: 2015\\n\\nAbstract: In this paper, a new projection method for mixed variational inequalities is introduced in Euclidean spaces. The Armijo-type linesearch is similar to that of He’s method for variational inequalities. Under some suitable assumptions, we prove that the sequence generated by the proposed method is globally convergent to a solution of the problem. If, in addition, a certain error bound holds, we analyze the convergence rate of the iterative sequence. The results presented in this paper generalize and improve some known results given in literatures.\",\n \"Title: An innovative implementation for directory-based cache coherence in shared memory multiprocessors\\nYear: 1997\\n\\nAbstract: Directory-based cache coherence protocol is accepted as the common technique in large scale shared memory multiprocessors because of its scalability. Although it was extensively studied in the past, however, the memory overhead and long miss penalty entailed by this protocol are the major obstacles to scale for large scale multiprocessors. On the other hand, the ever-increasing cache line size makes the false sharing problem more serious than before, which will lead to high miss rate. Based on the scope consistency, we propose a lock-specific home-based cache coherence protocol. In this new cache coherence protocol, all directory memory overhead are eleminated completely and false sharing problem and high miss rate will be sloved greatly at the cost of small write notice buffer in each processor.\",\n \"Title: Battery optimal scheduling based on energy balance in wireless sensor networks\\nYear: 2015\\n\\nAbstract: In wireless sensor networks, sensors are usually equipped with limited lifetime batteries. To face the short lifetime, some sensor nodes have the option to connect an extra battery, or to use smart battery-packs with multiple cells. In these cases, a major technique to save the energy is to schedule the batteries or battery cells through the properties of the batteries. This study presents a sched...\",\n \"Title: Using Category Theory to Verify Implementation Against Design in Concurrent Systems\\nYear: 2015\\n\\nAbstract: The research has shown that process-oriented programming languages provide a suitable means for developing concurrent systems. However, in the development of a concurrent system, there is a challenge to manage consistency between design and implementation. To deal with such a challenge, we propose a new formal verification methodology and illustrate it by a running example. In this methodology, a concurrent system is designed using a process algebra, namely communicating sequential processes, and implemented in a process-oriented programming language, namely Erasmus. The consistency between the design and the implementation of such a concurrent system is verified formally using category theory.\",\n \"Title: MIFO: Multi-path Interdomain Forwarding\\nYear: 2015\\n\\nAbstract: Today's interdomain routing is traffic agnostic when determining the single, best forwarding path. Naturally, as it does not adapt to congestion, the path chosen is not always optimal. In this paper, we focus on designing a multi-path interdomain forwarding (MIFO) mechanism, where AS border routers adaptively forward outbound traffic from a congested default path to an alternative path, without touching the interdomain routing protocols. Different from previous efforts which enable multi-path on control plane, MIFO achieves multi-path on data plane. The multiple alternative forwarding paths are obtained by exploring local BGP RIB. Multi-path forwarding on data plane can create a loop even within a stable network. MIFO solves this problem with a simple and practical approach. Several other challenges are also addressed including preventing cycling packet between iBGP peers and choosing the best alternative path from among multiple candidates. Our evaluations show that MIFO significantly improves the end-to-end throughput at the AS level, compared to traditional BGP and MIRO. For example, with only 50% of the ASes being MIFO capable, a significant percentage of the flows (about 40%) can use at least 50% of the inter-AS link capacity. In contrast, BGP and MIRO routing make less effective use of the inter-AS links, with only 7% and 17% of the flows can be so. Finally, we have developed a prototype implementation of MIFO on Linux with the forwarding engine in the kernel, with the routing daemon developed on XORP platform. The experiments on a test bed built with prototypes show that MIFO can improves the aggregate throughput by 81% compared with BGP routing.\",\n \"Title: Spectrum Sublet Game Among Secondary Users In Cognitive Radio Networks\\nYear: 2015\\n\\nAbstract: Dynamic spectrum sharing can improve the efficiency of spectrum utilization. Spectrum trading between primary users (PUs) and secondary users (SUs) is a popular and efficient way to fulfill this kind of spectrum sharing. In this paper we present a novel spectrum trading mechanism which operate among secondary users. More specifically, some secondary users which has leased spectrum from PUs can sublet it to other SUs to reduce their own leasing cost. Then all of the SUs can share these spectrum bands to conduct data transmission respectively. This leads to a new multi-leader multi-follower (MLMF) game which is different from existing works. The existence of Nash equilibrium of this formulated game is proven by redefining it as a shared MLMF constraint game. A decentralized algorithm is then proposed to find Nash equilibrium of this two tiers game with only local information. Simulations are provided to illustrate the convergence and effectiveness of the proposed algorithm.\",\n \"Title: Sdn-Based Routing For Efficient Message Propagation In Vanet\\nYear: 2015\\n\\nAbstract: Vehicular Ad hoc Network (VANET) is an intermittently connected mobile network in which message propagation is quite challenging. Conventional routing protocols proposed for VANET are usually in greedy or optimum fashion. Geographical forwarding only uses local information to make the routing decision which may lead to long packet delay, while link-based forwarding has better performance but requires much more overheads. To disseminate message efficiently in VANET, a routing protocol which has both short delivery delay time and low routing overhead is required. In this paper, we proposed a SDN-based routing framework for efficiently message propagation in VANET. Software-Defined Networking (SDN) is an emerging technology that decouples the control plane from the data forwarding plane in switches and collects all the control planes into a central controller. In SDN-based routing framework, the central controller gathers network information from switches and computes optimal routing paths for switches based on the global network information. Since switches don't need to exchange routing information with each other, the routing overhead is much lower. This paper is the first to propose a SDN-based routing framework for efficiently message propagation in VANET. A new algorithm is developed to find the global optimal route from the source to the destination in VANET with dynamic network density. We demonstrate, through the simulation results, that our proposed framework significantly outperforms the related protocols in terms of both delivery delay time and routing overhead.\",\n \"Title: A sociality-aware online AP association algorithm based on spectral clustering\\nYear: 2015\\n\\nAbstract: Imbalanced load distribution among multiple APs (access points) has become a serious problem in enterprise WLANs. Load imbalance causes inefficiency of network usage and unreasonable resource allocation. We find that users tend to come and leave together, which can lead to fluctuation of network load. We propose a novel online AP allocation scheme (ASC) to keep load balanced when users come and leave together. We study the homophily of users in social network, where users with tight social relationships will have more similar network usage and more opportunity to leave in union. When users come, we collect user data first, then we adopt the spectral clustering to classify users according to the collected user data. The controller distribute users from the same cluster to different APs. Through the simulation, we find that our scheme improve the overall balancing performance by at least 22% compared with the least load first selection algorithm (LLF) and 58% compared with random selection algorithm (RS).\",\n \"Title: A measurement study of a campus Wi-Fi network with mixed handheld and non-handheld traffic\\nYear: 2015\\n\\nAbstract: Handheld and non-handheld devices, which are simultaneously online, constitute the regular clients of the campus wireless local area networks (WLAN). It is necessary to understand the patterns of the user distribution and the user behavior, and adopt better strategies to optimize the service, as well as to understand the students' daily life. This paper introduces a approach of understanding how campus WLAN performs and how the users behave. In the analysis, many interesting phenomenons and problems are discovered. We capture all of the packets in a campus free Wi-Fi during the experimental period. We articulate an approach to describe the user distribution from the view of the traffic variation tendency and the network performance. And the user behavior patterns are described from two aspects, the popular websites and the behavior of downloading files. Furthermore, after revealing the problems of the network, practical suggestions is proposed for improvements in different aspects.\",\n \"Title: Optimized Periodical Charging in Large-Scale Deployed WSNs.\\nYear: 2015\\n\\nAbstract: Restricted by finite battery energy, traditional wireless sensor networks (WSNs) can only maintain for a limited period of time, resulting in serious performance bottleneck in long-term deployment of WSN. Fortunately, the advancement in the wireless energy transfer technology provides a potential to free WSNs from limited energy supply and remain perpetual operational. A mobile charger called wireless charging vehicle (WCV) is employed to periodically charge each sensor node and keep its energy level above the minimum threshold. Aiming at maximizing the ratio of the WCV's vocation time over the cycle time as well as guaranteeing the perpetual operation of networks, we proposes a feasible and optimal solution to this issue within the context of a real-time large-scale deployed WSN.\",\n \"Title: Acoustic Source Localization with Distributed Smartphone Arrays.\\nYear: 2015\\n\\nAbstract: Acoustic source localization in sensor network is a challenging task because of severe constraints on cost, energy, and effective range of sensor devices. To overcome limitations in existing solutions, this paper formally describes, designs, implements, and evaluates a Hamming Distance-based Method for Acoustic Source Localization, i.e., HammingLoc, in distributed smartphone networks. The key idea behind HammingLoc is to turn the localization problem into search problem in Hamming space. Time Differences of Arrival (TDOAs) of signals pertaining the same smartphone are estimated through the simple Generalized Cross-Correlation method. After the quantization with a bit for the TDOA measurement from the smartphone nodes, source localization is performed by minimizing the Hamming distance between the measured binary sequence and the binary vectors in a database. The proposed design is evaluated through theoretical analysis, extensive simulations, and physical experiments (an indoor test-bed with 30 smartphone nodes). Evaluation results demonstrate that HammingLoc can effectively localize the acoustic source with good robustness.\",\n \"Title: A Public Vehicle System with Multiple Origin-Destination Pairs on Traffic Networks.\\nYear: 2015\\n\\nAbstract: Substantial technology advances have been made in areas of autonomous and connected vehicles, which open a wide landscape for future transportation systems. We propose a new type of transportation system, Public Vehicle (PV) system, to provide effective, comfortable, and convenient service. The PV system is to improve the efficiency of current transportation systems, e.g., taxi system. Meanwhile, the design of such a system targets on significant reduction in energy consumption, traffic congestion, and provides solutions with affordable cost. The key issue of implementing an effective PV system is to design efficient scheduling algorithms. We formulate it as the PV Path (PVP) problem, and prove it is NP-Complete. Then we introduce a real time approach, which is based on solutions of the Traveling Salesman Problem (TSP) and it can serve people efficiently with lower costs. Our results show that to achieve the same performance (e.g., the total time: waiting and travel time), the number of vehicles can be reduced by 47%-69%, compared with taxis. The number of vehicles on roads is reduced, thus traffic congestion is relieved.\",\n \"Title: An HD IPTV system based on scalable video coding\\nYear: 2015\\n\\nAbstract: With the development of mobile Internet technology and smart phones, IPTV is faced with a greatly increasing demand for adaptive media contents. An IPTV system is designed for HD video based on scalable video coding (SVC), which could serve the increasing smart phone users with different quality videos by using SVC encoder while do not consume more computing resource, storage and bandwidth. Due to the hot of HD video, our system could run with HD video steadily in real-time. Unlike other approaches, this system executes rewrite operation on the edges server instead of end devices with limited computing power. The system is implemented in real equipment and the results show a well performance.\",\n \"Title: A Single Access Point Based Traffic Control System with Passive Measurement\\nYear: 2015\\n\\nAbstract: The nature of wireless radio enables the unwanted anomaly users beyond the service area grabbing the bandwidth and lowering the throughput of the normal users in the service area. Conventional cryptographic solutions such as WPA and WPA2 have been cracked by many means. Existed non-cryptographic solutions require non-trivial costs such as multiple APs, special antenna, or fingerprint server. This paper focuses on designing an easy-to-use traffic control system. First, we propose an RSS window scheme to gain less fickle RSS value. Second, we design Libra, a dynamic traffic control scheme based on realtime passive measurement. Libra limits the traffic of invader users and protects the normal users' priority of using the channel. Finally, the scheme is deployed on a pervasive AP with OpenWrt system, as an additional tool to enhance the WiFi security. Benefiting from the user friendliness, the user can install the system just as installing a pervasive wireless router.\",\n \"Title: Impact of Directional Antenna on Physical Layer Authentication\\nYear: 2015\\n\\nAbstract: Many existed physical layer based schemes have been proposed to enhance the traditional authentication in wireless networks. However earlier schemes did not discuss directional antenna issue, and lacked of real measurement based evaluation. In this paper, we take an insight into the threat of directional antenna on physical layer authentication by real measurement observation. We give a brief overview of the mainstream physical layer authentication schemes, and build a real-time experimental platform to collect the channel state information, and evaluate the performance of some mainstream schemes by collecting real data in different typical environments, especially in the situation when the attacker uses directional antenna. We show the physical layer authentication schemes are effective in most cases, but may fail to detect the third party attacker equipped with directional antenna. Based on our discovery, we suggest that directional antenna impact should be seriously considered in physical layer based applications, especially in security domain.\",\n \"Title: Higher bandwidth may sometimes reduce cloud application performance\\nYear: 2014\\n\\nAbstract: Cloud computing has experienced explosive growth in last ten years. Since cloud applications usually come up with large amounts of data transfer, the network capability becomes one of the dominant performance factors. Judging from common sense, it seems obvious that the higher quantity of bandwidth guaranteed for a job, the higher performance we will achieve. Rather, in this paper, we found that the effect of providing higher bandwidth guarantee turns to be uncertain. Without efficient work and flow scheduling mechanism, merely increasing the bandwidth allocated for the job may actually be as harmful as reducing the bandwidth.\",\n \"Title: CCOF: Congestion control on the fly for Inter-Domain routing\\nYear: 2014\\n\\nAbstract: We design a new routing system called CCOF in which border routers of autonomous systems (AS) adaptively redirect outbound traffic from a congested default path to an alternative path in favor of Inter Domain traffic engineering (TE). We show how this can be done simply, by examining outgoing port's queuing size, and efficiently, that maintains line speed forwarding. In addition, CCOF-router is completely compatible with legacy routers since it neither requires cooperation nor modifies the packet content. Our evaluation shows that CCOF significantly increases the average throughput of traffic flows, and has similar improvement as directly upgrading device capacity. Finally, prototype implementation is achieved on NetFPGA.\",\n \"Title: ABR-Tree: An Efficient Distributed Multidimensional Indexing Approach for Massive Data.\\nYear: 2015\\n\\nAbstract: In the big data era, there many application scenarios urgently need efficient distributed multidimensional indexing approach to accelerate the data analytics. To address this issue, in this paper, we propose ABR-Tree, a multidimensional distributed indexing approach. ABR-Tree consist of two components, the global append-efficient Bï¾ź+ï¾ź-Tree, and the local R*-Tree. Both of them are layered over the cloud database as the index and data store, which not only make ABR-Tree is easy to implement and inherently become a distributed cloud index, but also enable ABR-Tree can sustain high throughput workload and large data volumes, meanwhile, ensuring fault-tolerance, and high availability. We conducted extensive experiments over 1ï¾źTB real data set to evaluate its efficiency of processing multidimensional range queries, the results show that it is significantly fast than the existing representative distributed multidimensional cloud index method.\",\n \"Title: Enhancing Parallel Data Loading for Large Scale Scientific Database.\\nYear: 2015\\n\\nAbstract: The rapidly increased data size make large scale scientific database often have a huge time delay between loading data into the system and ready for receiving query request. To solve this problem, we proposed an efficient parallel data loading approach named FASTLoad. It is designed to maximize the given resource e.g., network bandwidth, main memory utilization for optimizing the data loading in large scale array model based scientific database system. To verify the efficiency of FASTLoad, we implemented it in our Adaptable Data Loading System and evaluate its performance over various sizes of large scientific data sets. Our experimental results show that the performance of FASTLoad can be 4 to 6 times fast than the built-in loading techniques of states-of-the-arts array model based scientific database system.\",\n \"Title: FASTDB: An Array Database System for Efficient Storing and Analyzing Massive Scientific Data.\\nYear: 2015\\n\\nAbstract: With the development of science and technology, the data size and complexity of scientific data are increased rapidly, which made efficient data storage and parallel analysis of scientific data become a big challenge. The previous techniques that combine the traditional relational database with analysis software tends cannot efficiently meet the performance requirement of large scale scientific data based analysis. In this paper, we present FASTDB, a distributed array database system that optimized for massive scientific data management and provide a share-nothing, parallel array processing analysis. In order to demonstrate the intrinsic performance characteristics of FASTDB, we applied it into the interactive analysis of data from astronomical surveys, and designed a series of experiments with scientific analysis tasks. According to the experimental results, we found FASTDB can be significantly fast than traditional database based SkyServer in many typical analytical scenarios.\",\n \"Title: Design Efficient In-Database Video Storage Approach by Learning from Performance Evaluation of BLOB.\\nYear: 2015\\n\\nAbstract: Multimedia application designers often required to make the choice of whether storing multimedia objects as files in the file system, or as BLOBs (Binary Large Objects) in a database, or a combination of both. Towards small multimedia data, it often suggested to store them as BLOB data of database. However, previous study indicated that the efficiency of BLOB based video storage not always suffice. In this paper, By learning from the issues discovered from our previous performance evaluation, we proposed an efficient in-database video storage approach named TIViS (Temporal Interval based Video Storage). When a video object store into database by TIViS, it will be decomposed into temporal intervals and each interval will be sequential stored based on its temporal information. Additionally, in TIViS approach, a specialized buffer management mechanism is also developed to optimize the data access of multimedia objects. In our work, we implemented TIViS approach into the open source database system PostgreSQL 8.4. We conducted a series of experiments to verify the efficiency of TIViS approach, the results demonstrate that TIViS based video storage is significantly superior to traditional database system’s built-in BLOB approach (e.g., PostgreSQL’s Bytea).\",\n \"Title: Performance Prediction for Concurrent Workloads in Distributed Database Systems.\\nYear: 2015\\n\\nAbstract: In order to store and process data at large-scale, distributed databases are built to partition data and process it in parallel on distributed nodes in a cluster. When the database concurrently execute heterogeneous query workloads, performance prediction is needed. However, running queries in a distributed database heavily touches upon the network overhead as the data transmission between cluster nodes. Hence, in this work, we take network latency into account when predict concurrent query performance. We propose a linear regression model to estimate the interaction when execute concurrent query for analytical workloads in distributed database system. Since network latency and overheads of local processing are the two most significant factors for query execution, we analyze the query behavior with multivariate regression on both of them at different degree of concurrency. In addition, we use sampling techniques to obtain various query mixes as concurrency level increasing. The experiments for evaluation the performance of our prediction model are conducted over a PostgreSQL database cluster with a representative analytical workloads of TPC-H, the experimental results demonstrates that the query latency predictions of our model can minimize the relative error within 14 % on average.\",\n \"Title: Design of the multiple Neural Network compensator for a billiard robot\\nYear: 2015\\n\\nAbstract: In this paper, a multiple Neural Network (NN) compensator is designed for a billiard robot to finish a task, in which the trained robot is commanded to control the cue ball to a specific target point along a trajectory with multiple cushion rebounds. A novel pyramid classification has been established to sort out the pattern of trajectory and its segments. For each trajectory pattern, a corresponding Back Propagation Neural Network (BPNN) model has been established to fit the deviation between theoretical direction point and actual one. The pyramid classification and a finite number of BPNN models composited the multiple NN compensator. In the test, the robot will calculate the deviation and work out the actual direction point for potting. The test results have verified the reliability and workability of the multiple NN compensator.\",\n \"Title: Local feature descriptor using entropy rate.\\nYear: 2016\\n\\nAbstract: Over the past decades, an increasing number of local feature descriptors have been proposed in the community of computer vision and pattern recognition. Although they have achieved impressive results in many applications, how to find a balance between accuracy and computational efficiency is still an open issue. To address this issue, we present a local feature descriptor using entropy rate (FDER), which is robust to a variety of image transformations. We first employ the nonsubsampled Contourlet transform to produce multiple support regions and design a graph structure to describe the sub-region. We then use the entropy rate of random walks on the designed graph to build the FDER descriptor. Extensive experiments demonstrate the superiority of proposed descriptor dealing with various image transformations in comparison with the existing state-of-the-art descriptors.\",\n \"Title: Providing flexible services for heterogeneous vehicles: an NFV-based approach.\\nYear: 2016\\n\\nAbstract: Currently, many automotive manufacturers including Mercedes Benz and BMW have released new products equipped with intelligent onboard systems (IOSs) that support interconnection between vehicles and the remote service center. With the help of IOSs, vehicles can obtain traffic information and enjoy location-based services, such as restaurant and nearby gas station recommendations. However, IOSs adopt a closed architecture and are very difficult to upgrade to support new services. NFV is introduced as a new methodology that offers a potential way out of this bottleneck. Utilizing virtualization technologies, NFV may execute services as software applications on standard IT platforms like servers, switches, and storage. Using NFV technology, closed IOSs can be transformed to open ones and upgraded with lower cost. In this article, we introduce an NFV-based framework for vehicular ad hoc networks, and discuss the benefits and challenges. We also present two use cases to demonstrate its strength.\",\n \"Title: Local feature descriptor invariant to monotonic illumination changes.\\nYear: 2016\\n\\nAbstract: This paper presents a monotonic invariant intensity descriptor (MIID) via spectral embedding and nonsubsampled contourlet transform (NSCT). To make the proposed descriptor discriminative, NSCT is used for the construction of multiple support regions. Specifically, the directed graph and the spectral feature vectors of the signless Laplacian matrix are exploited to construct the MIID. We theoretically demonstrate that the proposed descriptor is able to tackle monotonic illumination changes and many other geometric and photometric transformations. We conduct extensive experiments on the standard Oxford dataset and the complex illumination dataset to demonstrate the superiority of proposed descriptor over the existing state-of-the-art descriptors in dealing with image blur, viewpoint changes, illumination changes, and JPEG compression. (C) 2015 SPIE and IS&T\",\n \"Title: Using Failures and Category Theory to Verify Process Communications between Design and Implementation of Concurrent Systems.\\nYear: 2016\\n\\nAbstract: The process-oriented design and implementation of concurrent systems have important advantages. However, it is challenging to verify the consistency of process communications between the design and the implementation. To deal with such a challenge, we construct a formal framework for designing, implementing and verifying the consistency of process communications. In this framework, we use Failures in Communicating Sequential Processes (CSP), Erasmus and Category Theory as the foundation. The framework is illustrated by using a running example.\",\n \"Title: Guided Filter based Edge-preserving Image Non-blind Deconvolution.\\nYear: 2016\\n\\nAbstract: In this work, we propose a new approach for efficient edge-preserving image deconvolution. Our algorithm is based on a novel type of explicit image filter - guided filter. The guided filter can be used as an edge-preserving smoothing operator like the popular bilateral filter, but has better behaviors near edges. We propose an efficient iterative algorithm with the decouple of deblurring and denoising steps in the restoration process. In deblurring step, we proposed two cost function which could be computed with fast Fourier transform efficiently. The solution of the first one is used as the guidance image, and another solution will be filtered in next step. In the denoising step, the guided filter is used with the two obtained images for efficient edge-preserving filtering. Furthermore, we derive a simple and effective method to automatically adjust the regularization parameter at each iteration. We compare our deconvolution algorithm with many competitive deconvolution techniques in terms of ISNR and visual quality.\",\n \"Title: Public Vehicles for Future Urban Transportation.\\nYear: 2016\\n\\nAbstract: This paper advocates a new paradigm of transportation systems for future smart cities, namely, public vehicles (PVs), that provides dynamic ridesharing trips at requests. Passengers will enjoy more convenient and flexible transportation services with much less expense. In the PV system, both the number of vehicles and required parking spaces will be significantly reduced. There will be less traffic congestion, less energy consumption, and less pollution. In this paper, the concept, method, and algorithm for the PV system are described. The key issue of effectively implementing the PV system is to design efficient planning and scheduling algorithms. The PV-path problem is formulated, which is NP-complete. Then, a practical approach is proposed, which can serve people anywhere and anytime. The simulation results show that, to achieve the same performance (e.g., total time, waiting time, and travel time), the number of vehicles in the PV system can be reduced by around 90% and 57% compared with the conventional vehicle system and Uber Pool, respectively, and the total traveling distance can be reduced by 34% and 14%.\",\n \"Title: Transfer Problem in a Cloud-based Public Vehicle System with Sustainable Discomfort.\\nYear: 2016\\n\\nAbstract: The increasing population in urban areas gives rise to a huge traffic pressure. A cloud-based industrial system, public vehicle (PV) system, is promising to mitigate the traffic congestion in smart cities, where passengers can share PVs and transfer among them with scheduling decisions made by the cloud. This paper studies the transfer problem in the PV system due to that transfer can improve the whole traffic efficiency with sacrificing a little comfort with the corporation of all the PVs. The transfer problem is NP-Complete through our analysis. Our work can be separated into three steps. First, we introduce several factors to guarantee the comfort of passengers during transfer. Second, we propose two algorithms through the graph-based scheduling problem aiming at reducing the travel distance of all the PVs with service guarantee. Third, simulations based on the Shanghai (China) urban road network show that, the total travel distance of PVs is reduced under the quality of service for passengers, and the traffic efficiency is improved.\",\n \"Title: A Novel Link Scheduling Algorithm For Wireless Networks Using Directional Antenna\\nYear: 2016\\n\\nAbstract: For a given set of communication links whose senders transmit at a fixed power level, it is a hot problem to select a maximum set of links that can be transmitted simultaneously, which is known to be NP-hard. The existing algorithm only apply to the condition of omnidirectional transmission. This paper addresses the problem in a plane wireless network where the nodes use directional antennas under physical interference model. We develop a directional interference model applicable to such networks, and first propose the approximation algorithm to solve scheduling problem under this model. We proved the correctness of the algorithm by mathematical analysis. We have also proved the great advantages of using directional antenna by extensive simulations.\",\n \"Title: Characterizing the Scalability and Performance of Analytic Database System.\\nYear: 2016\\n\\nAbstract: Analytic database is designed for analytical applications which aim to explore the value of massive data. It has been widely used in many areas, from business analytics to scientific data discovery. In order to efficiently processing massive data, analytic database often can be scaled-out to achieve high performance. In this paper, in order to understand the intrinsic performance characteristics of analytic database, we take the popular Greenplum database as the representative analytic database system, and conduct a series of comprehensive performance evaluation over it to characterize its scalability and performance features. According to the experimental results and analysis, we obtained an series of initial insights of the factors which may significantly affect the performance paradigm of Greenplum, which will be helpful for analytic database system users to obtain better analytical performance.\",\n \"Title: Accelerating Concurrent Analytic Tasks with Cost-Conscious Result Set Replacement Algorithm.\\nYear: 2016\\n\\nAbstract: High concurrent analytic applications such as SaaS based BI services face the problem of how to meet performance SLAs (Service Level Agreement) when the number of users and concurrency increased. In order to reduce the task processing overheads and services response time, analytic applications tend heavily rely on various main-memory data management and cache techniques. In this paper, we designed a cost-conscious cache replacement approach named CRSR (Cost-conscious Result Sets Replacement), which take task result sets as the essential data unit and replace the existing result set by a specialized cost estimation strategy. We conduct a series of evaluation to compare the proposed CRSR approach with representative cache management methods, the experiments show that in most cases, the proposed CRSR algorithm can efficiently reduce the response time of high concurrency analysis service and outperform its competitors.\",\n \"Title: Traffic efficiency improvement and passengers comfort in ridesharing systems in VANETs\\nYear: 2015\\n\\nAbstract: Ridesharing has become one of the most attention-grabbing trends in modern transportation recent years. This paper studies traffic efficiency improvement and comfort of passengers in ridesharing systems in smart cities. We introduce several improvement methods, and some tradeoffs involving comfort in ridematching strategies. The traffic efficiency can be improved with preserving the service level of passengers through several methods, e.g., recommending pickup or dropoff locations, transfers, and corporation with other transportation systems. The total travel distance of vehicles and the total travel time of passengers can be dropped. Therefore, passengers can enjoy their trips with sustaining a little discomfort, and the whole traffic efficiency in smart cities is improved.\",\n \"Title: MRAMsim: A Simulator for Magnetoresistive RAM\\nYear: 2016\\n\\nAbstract: Compute requirements are increasing rapidly in systems ranging from desktops to servers. The conventional memory system greatly impacts the system efficiency and can't be improved anymore because of the fabrication process. Magnetoresistive Random Access Memory (MRAM) provides many benefits, such as non-volatility, low leakage, high-speed operation and unlimited endurance. Because of these attractive features, MRAM is regarded as one of the most promising technologies to incorporate into the memory hierarchy. Since the manufacturing cost of real MRAM device is high, the simulation tool is rather important. In this paper, We design and evaluate a trace-driven simulator for MRAM, called MRAMsim. We have proposed various Address Mapping Schemes to improve the efficiency of MRAM memory system. We show that MRAM is a promising candidate for main memory system. The experimental results reveal that MRAM-based main memory system is able to achieve fast response time and high bandwidth.\",\n \"Title: CCDN: Content-Centric Data Center Networks.\\nYear: 2016\\n\\nAbstract: Data center networks continually seek higher network performance to meet the ever increasing application demand. Recently, researchers are exploring the method to enhance the data center network performance by intelligent caching and increasing the access points for hot data chunks. Motivated by this, we come up with a simple yet useful caching mechanism for generic data centers, i.e., a server caches a data chunk after an application on it reads the chunk from the file system, and then uses the cached chunk to serve subsequent chunk requests from nearby servers. To turn the basic idea above into a practical system and address the challenges behind it, we design content-centric data center networks CCDNs, which exploits an innovative combination of content-based forwarding and location [Internet Protocol IP]-based forwarding in switches, to correctly locate the target server for a data chunk on a fully distributed basis. Furthermore, CCDN enhances traditional content-based forwarding to determine the nearest target server, and enhances traditional location IP-based forwarding to make high utilization of the precious memory space in switches. Extensive simulations based on real-world workloads and experiments on a test bed built with NetFPGA prototypes show that, even with a small portion of the server’s storage as cache e.g., 3% and with a modest content forwarding information base size e.g., 1000 entries in switches, CCDN can improve the average throughput to get data chunks by 43% compared with a pure Hadoop File System HDFS system in a real data center.\",\n \"Title: A Novel Framework for Analysis of Global Network Neutrality Based on Packet Loss Rate.\\nYear: 2015\\n\\nAbstract: Network neutrality is a policy made by the government. It emphasizes that Internet Service Providers (ISPs) should treat all users equally, regardless of users' behavior, as long as they are legal. In this sense, it aims to build a fair and healthy network. Packet Loss Rate (PLR) is an important metric to evaluate a network's condition. In this paper, we compare PLR of two different kinds of tests, which are \\\\\\\"speedtest\\\\\\\" and \\\\\\\"bittorrent\\\\\\\", to judge network neutrality of ISPs. The difference of PLR of two tests is used to evaluate the level of network neutrality violations. We propose a general framework for analysis of network neutrality. First, by transforming the well-known Mathis model, we build a model that uses public data, collected by the network neutrality bot (Neubot), to estimate the PLR of countries all over the world. Second, a composite function model, named PECF, is proposed for fitting to get the lower limit of PLR (L-PLR). When the value of PLR is L-PLR, the frequencies of PLR of two tests differ most and a network neutrality violation appears. By mathematical inference, we draw the conclusion that ISPs violate network neutrality when PLR is larger than 0.10. Third, based on this conclusion, we concentrate on the case when PLR is larger than 0.10 to study network neutrality violations. We use R-PLR (Ranged PLR) to describe PLR within a certain range. By comparing the PLR of \\\\\\\"speedtest\\\\\\\" with that of \\\\\\\"bittorrent\\\\\\\" within a certain R-PLR, we further determine the level of network neutrality violation in time and space. Network neutrality violation is more likely to happen under network peaks. The geographical distribution of PLR shows China (CN) and South Africa (ZA) suffer the most severe network neutrality violations among the eight regions studied. Finally, we present the histogram of PLR worldwide from year 2012 to 2015, which shows a relationship between network development and user demand.\",\n \"Title: Video Anomaly Detection Based on Adaptive Multiple Auto-Encoders.\\nYear: 2016\\n\\nAbstract: Anomaly detection in surveillance videos is a challenging problem in computer vision community. In this paper, a novel unsupervised learning framework is proposed to detect and localize abnormal events in real-time manner. Typical methods mainly rely on extracting complex handcraft features and learning only a fitting model for prediction. In contrast, normal events are represented using simple spatio-temporal volume (STV) in our method, then adaptive multiple auto-encoders (AMAE) are constructed to handle the inter-class variation in normal events. When testing on an unknown frame, reconstruction errors of multiple auto-encoders are utilized for prediction. Experiments are performed on UCSD Ped2 and UMN datasets. Experimental results show that our method is effective to detect and localize abnormal events at a speed of 70 fps.\",\n \"Title: Adaptive Sliding Mode Relative Motion Control for Autonomous Carrier Landing of Fixed-Wing Unmanned Aerial Vehicles.\\nYear: 2017\\n\\nAbstract: In this paper, relative motion model and control strategy for autonomous fixed-wing unmanned aerial vehicle (UAV) carrier landing are addressed. First, a coupled six-degrees-of-freedom (6-DOF) non-linear relative motion model is established from 6-DOF UAV and carrier models. Second, because of the under-actuated characteristic of two vehicles, the 6-DOF relative motion model is simplified to a four-degree-of-freedom (4-DOF) model to facilitate the control design. Third, an adaptive sliding mode control law is proposed to track desired landing trajectory and maintain constant relative pitch and roll angles. Finally, simulation results demonstrate the effectiveness of the proposed control method.\",\n \"Title: Optimized Charging in Large-Scale Deployed WSNs with Mobile Charger.\\nYear: 2016\\n\\nAbstract: Restricted by finite battery energy, traditional wireless sensor networks (WSNs) can only maintain for a limited period of time, resulting in serious performance bottleneck in long-term deployment of WSN. Fortunately, the advancement in the wireless energy transfer technology provides a potential to free WSNs from limited energy supply and remain perpetual operational. A mobile charger called wireless charging vehicle (WCV) is employed to periodically charge each sensor node and keep its energy level above the minimum threshold. Aiming at maximizing the ratio of the WCV's vocation time over the cycle time as well as guaranteeing the perpetual operation of networks, we propose a feasible and optimal solution to this issue within the context of a real-time large-scale deployed WSN. First, we develop two different types of charging cycles: initialization cycles and renewable cycles and give relevant algorithms to construct these two cycles for each sensor node. We then formulate the optimization problem into an optimal construction algorithm and prove its correctness through theoretical analysis. Finally, we conduct extensive simulations to demonstrate the effectiveness of our proposed algorithms.\",\n \"Title: Near space airship conceptual design and optimization.\\nYear: 2016\\n\\nAbstract: Near space airships have gained widespread attention owing to the unique characteristics of long endurance at high altitudes; because of this characteristic, such airships have great potential for use in the fields of communication relay, early warning, earth observation etc. Near space airships can substitute near-earth geostationary satellites owing to the advantages of high load and long endurance at altitude exceeding 20 km. Buoyancy against gravity is generated by the airship hull, which is completely filled with helium, while the near space airship is equipped with an electrical propulsion system powered by solar energy. Unlike conventional aircraft, nearspace airships are a new type of vehicles with less design experience; coupled multi-disciplinary problems related to aerodynamics, structure, energy, control, etc. exist. With the aim of finding a solution for optimal near-space airship design, this study proposed a design using a multidisciplinary design optimization method.\",\n \"Title: A Novel Two-stage Learning Pipeline for Deep Neural Networks.\\nYear: 2017\\n\\nAbstract: In this work, a training method was proposed for Deep Neural Networks (DNNs) based on a two-stage structure. Local DNN models are trained in all local machines and uploaded to the center with partial training data. These local models are integrated as a new DNN model (combination DNN). With another DNN model (optimization DNN) connected, the combination DNN forms a global DNN model in the center. This results in greater accuracy than local DNN models with smaller amounts of data uploaded. In this case, the bandwidth of the uploaded data is saved, and the accuracy is maintained as well. Experiments are conducted on MNIST dataset, CIFAR-10 dataset and LFW dataset. The results show that with less training data uploaded, the global model produces greater accuracy than local models. Specifically, this method focuses on condition of big data.\",\n \"Title: A New Image Spatial Color Gamut Mapping Algorithm\\nYear: 2017\\n\\nAbstract: Spatial color gamut mapping algorithm is researched to improve the mapping quality by solving halo artifacts and to improve its efficiency. This paper firstly uses guide filtering to filter L channel and get base layer of L channel, and then use the difference between original L channel value with base layer, to get detail layer of L channel. At the same time, makes statistics on distance relationship between each pixel point with target and source color gamut boundary, according to the statistical data to find out the best threshold value. Make the part of detail layer larger than the threshold value compensates to original image. The experiment proved that efficiency of single mapping operation is higher than conventional two-level mapping algorithm and for most of the images, processing effect is better. What's more, halo artifact is also solved.\",\n \"Title: Single sample per person face recognition with KPCANet and a weighted voting scheme.\\nYear: 2017\\n\\nAbstract: Most current methods of facial recognition rely on the condition of having multiple samples per person available for feature extraction. In practical applications, however, only one sample may be available for each person to train a model with. As a result, many of the traditional methods fall short, leaving the challenge of facial recognition greater than ever. To deal with this challenge, this study addresses a face recognition algorithm based on a kernel principal component analysis network (KPCANet) and then proposes a weighted voting method. First, the aligned face image is partitioned into several non-overlapping patches to form the training set. Next, a KPCANet is used to obtain filters and feature banks. Finally, the identification of the unlabeled probe occurs through the application of the weighted voting method. Based on several widely used face datasets, the results of the experiments demonstrate the superiority of the proposed method.\",\n \"Title: Exploring Relationships between Syntax and Semantics of a Process-Oriented Language by Category Theory.\\nYear: 2017\\n\\nAbstract: Recent research findings demonstrate that process-oriented programming languages provide a suitable means to develop concurrent systems. In this paper, we propose to use mandatory/optional trace sets to model the semantics of a process-oriented programming language named Erasmus. Inspired by Hoare's view of category theory and obtained research results towards validating the view, category theory is used to explore relationships between syntax and semantics of Erasmus. To illustrate the research activities, several examples are developed. In doing so, it is shown that a relationship between Erasmus commands can lead to a corresponding relationship between semantics for commands.\",\n \"Title: Abnormal event detection and localization in crowded scenes based on PCANet.\\nYear: 2017\\n\\nAbstract: Detecting and localizing abnormal events in crowded scenes still remains a challenging task among computer vision community. An unsupervised framework is proposed in this paper to address the problem. Low-level features and optical flows (OF) of video sequences are extracted to represent motion information in the temporal domain. Moreover, abnormal events usually occur in local regions and are closely linked to their surrounding areas in the spatial domain. To extract high-level information from local regions and model the relationship in spatial domain, the first step is to calculate optical flow maps and divide them into a set of non-overlapping sub-maps. Next, corresponding PCANet models are trained using the sub-maps at same spatial location in the optical flow maps. Based on the block-wise histograms extracted by PCANet models, a set of one-class classifiers are trained to predict the anomaly scores of test frames. The framework is completely unsupervised because it utilizes only normal videos. Experiments were carried out on UCSD Ped2 and UMN datasets, and the results show competitive performance of this framework when compared with other state-of-the-art methods.\",\n \"Title: Artificial compound eye: a survey of the state-of-the-art.\\nYear: 2017\\n\\nAbstract: An artificial compound eye system is the bionic system of natural compound eyes with much wider field-of-view, better capacity to detect moving objects and higher sensitivity to light intensity than ordinary single-aperture eyes. In recent years, renewed attention has been paid to the artificial compound eyes, due to their better characteristics inheriting from insect compound eyes than ordinary optical imaging systems. This paper provides a comprehensive survey of the state-of-the-art work on artificial compound eyes. This review starts from natural compound eyes to artificial compound eyes including their system design, theoretical development and applications. The survey of artificial compound eyes is developed in terms of two main types: planar and curved artificial compound eyes. Finally, the most promising future research developments are highlighted.\",\n \"Title: BELIA: bandwidth estimate-based link increase algorithm for MPTCP.\\nYear: 2017\\n\\nAbstract: As a set of extensions to the regular Transmission Control Protocol (TCP), Multipath TCP (MPTCP) enables the multi-homed devices to access one data stream through multiple paths. MPTCP congestion window control is the crucial part of maintaining the fair network resource allocation, especially in the coexist scene of MPTCP applications and regular TCP applications. However, the current MPTCP conge...\",\n \"Title: Joint Transportation and Charging Scheduling in Public Vehicle Systems - A Game Theoretic Approach.\\nYear: 2018\\n\\nAbstract: Public vehicle (PV) systems are promising transportation systems for future smart cities which provide dynamic ride-sharing services according to passengers' requests. PVs are driverless/self-driving electric vehicles which require frequent recharging from smart grids. For such systems, the challenge lies in both the efficient scheduling scheme to satisfy transportation demands with service guaran...\",\n \"Title: An Online Ride-Sharing Path Planning Strategy for Public Vehicle Systems.\\nYear: 2017\\n\\nAbstract: As efficient traffic-management platforms, public vehicle (PV) systems are envisioned to be a promising approach to solving traffic congestions and pollutions for future smart cities. PV systems provide online/dynamic peer-to-peer ride-sharing services with the goal of serving sufficient number of customers with minimum number of vehicles and lowest possible cost. A key component of the PV system is the online ride-sharing scheduling strategy. In this paper, we propose an efficient path planning strategy that focuses on a limited potential search area for each vehicle by filtering out the requests that violate passenger service quality level, so that the global search is reduced to local search. We analyze the performance of the proposed solution such as reduction ratio of computational complexity. Simulations based on the Manhattan taxi data set show that, the computing time is reduced by 22% compared with the exhaustive search method under the same service quality performance.\",\n \"Title: HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection.\\nYear: 2018\\n\\nAbstract: The development of an anomaly-based intrusion detection system (IDS) is a primary research direction in the field of intrusion detection. An IDS learns normal and anomalous behavior by analyzing network traffic and can detect unknown and new attacks. However, the performance of an IDS is highly dependent on feature design, and designing a feature set that can accurately characterize network traffic is still an ongoing research issue. Anomaly-based IDSs also have the problem of a high false alarm rate (FAR), which seriously restricts their practical applications. In this paper, we propose a novel IDS called the hierarchical spatial-temporal features-based intrusion detection system (HAST-IDS), which first learns the low-level spatial features of network traffic using deep convolutional neural networks (CNNs) and then learns high-level temporal features using long short-term memory networks. The entire process of feature learning is completed by the deep neural networks automatically; no feature engineering techniques are required. The automatically learned traffic features effectively reduce the FAR. The standard DARPA1998 and ISCX2012 data sets are used to evaluate the performance of the proposed system. The experimental results show that the HAST-IDS outperforms other published approaches in terms of accuracy, detection rate, and FAR, which successfully demonstrates its effectiveness in both feature learning and FAR reduction.\",\n \"Title: Enhancing Stratified Graph Sampling Algorithms based on Approximate Degree Distribution.\\nYear: 2018\\n\\nAbstract: Sampling technique has become one of the recent research focuses in the graph-related fields. Most of the existing graph sampling algorithms tend to sample the high degree or low degree nodes in the complex networks because of the characteristic of scale-free. Scale-free means that degrees of different nodes are subject to a power law distribution. So, there is a significant difference in the degrees between the overall sampling nodes. In this paper, we propose a concept of approximate degree distribution and devise a stratified strategy using it in the complex networks. We also develop two graph sampling algorithms combining the node selection method with the stratified strategy. The experimental results show that our sampling algorithms preserve several properties of different graphs and behave more accurately than other algorithms. Further, we prove the proposed algorithms are superior to the off-the-shelf algorithms in terms of the unbiasedness of the degrees and more efficient than state-of-the-art FFS and ES-i algorithms.\",\n \"Title: An in-network service system based on smart routers and edge devices\\nYear: 2017\\n\\nAbstract: Watching and downloading of the online highdefinition video has become one of our daily needs. The user experience of traditional service system based on content delivery network (CDN) and peer to peer (P2P) is limited by the bandwidth and the service ability. These technologies mostly concentrate on content servers and personal computers rather than routers and devices in the network. In this paper, we propose an in-network service system by taking full advantage of the smart routers and edge devices to relieve the load of CDN. We mainly do the following works. 1) We propose the basic architecture of the in-network service system and deploy it on a real network environment. 2) We introduce a hybrid node management algorithm for large scale, flexible, high churn network to manage the smart routers with the edge devices. 3) We design a file self-diffusion algorithm for content delivery. The evaluation shows the high performance of the hit rate of service using the proposed system. In the experiment, up to half of the user requests can be serviced by the proposed system without accessing the CDN.\",\n \"Title: Multiscale Fully Convolutional Network for Foreground Object Detection in Infrared Videos.\\nYear: 2018\\n\\nAbstract: Accurate and fast infrared (IR) foreground object detection is one of the most significant issues to be solved due to its important meaning for IR target recognition, IR precise guidance, IR video surveillance, and so on. A common approach for such tasks is “background subtraction,” which aims to detect foreground object through background modeling. Thus far, many background subtraction methods ha...\",\n \"Title: Background Subtraction Using Multiscale Fully Convolutional Network.\\nYear: 2018\\n\\nAbstract: Background modeling and subtraction based on change detection are the first step in many high-level computer vision applications. Many background subtraction methods have been proposed in the recent past and their efforts mainly focus on two aspects: more advanced background models and more complex feature representations. Recently, hierarchical features learned from deep convolutional neural networks have been shown to be effective for many computer vision tasks, such as classification and recognition. However, few researchers try to learn the deep features to address the background subtraction problem. Therefore, in this paper, we propose a novel multiscale fully convolutional network (MFCN) architecture which takes advantage of different layer features for background subtraction. We show that the foreground detection accuracy can be greatly improved by using the deep features learned from the MFCN and instead of building highly complex background models, and the complexity of the background subtraction process can be easily solved during the subtraction operation itself. Experimental results on CDnet 2014 data set and SBM-RGBD data set show that the proposed MFCN-based method achieves state-of-the-art performance while operating at real time.\",\n \"Title: Part-Based Feature Extraction For Person Re-Identification\\nYear: 2018\\n\\nAbstract: In this paper, we propose a new part-based CNN feature extraction method for end-to-end person re-identification. In our method, the input images are first divided into two different non-overlapping parts, and then two different CNN models are trained for classifying them respectively. Next, in testing phase, we use these two trained CNN models to extract features from different parts of persons separately and then combine them together to construct the overall features of the input images. Finally, metric is utilized to rank the distances between the overall features of the different persons. To make maximize performance, we also try different types of CNN models with various distance metrics. Experiment on dataset Market-1501 demonstrates that our proposed method for person re-identification significantly outperforms state-of-the-art models. And experiment on dataset MARS shows that our method can also get competitive performance on large dataset.\",\n \"Title: Geometry Based Three-Dimensional Image Processing Method For Electronic Cluster Eye\\nYear: 2018\\n\\nAbstract: In recent years, much attention has been paid to the electronic cluster eye (eCley), a new type of artificial compound eyes, because of its small size, wide field of view (FOV) and sensitivity to motion objects. An eCley is composed of a certain number of optical channels organized as an array. Each optical channel spans a small and fixed field of view (FOV). To obtain a complete image with a full FOV, the images from all the optical channels are required to be fused together. The parallax from unparallel neighboring optical channels in eCley may lead to reconstructed image blurring and incorrectly estimated depth. To solve this problem, this paper proposes a geometry based three-dimensional image processing method (G3D) for eCley to obtain a complete focused image and dense depth map. In G3D, we derive the geometry relationship of optical channels in eCley to obtain the mathematical relation between the parallax and depth among unparallel neighboring optical channels. Based on the geometry relationship, all of the optical channels are used to estimate the depth map and reconstruct a focused image. Subsequently, by using an edge-aware interpolation method, we can further gain a sharply focused image and a depth map. The effectiveness of the proposed method is verified by the experimental results.\",\n \"Title: Generation of Walking Motions for the Biped Ascending Slopes Based on Genetic Algorithm.\\nYear: 2018\\n\\nAbstract: This study aims to generate the optimal trajectories for the biped walking up sloping surfaces after ensuring the minimum energy consumption by using genetic algorithm (GA) and motion/force control scheme. During optimization, the step length, the maximum height of swing foot and walking speed were optimized with the seven-link biped model. The impactless bipedal walking was investigated for walking on the ground level and slopes with different gradients, respectively. The results showed that the biped consumed more energy when the optimal walking speed increased for walking on the same slopes. There were no great differences in optimal step length when the biped changed the walking speed. The results showed that the proposed approach is able to generate optimal gaits for the biped simply by changing boundary conditions with GA.\",\n \"Title: Accelerating Massive Astronomical Cross-Match Based on Roaring Bitmap over Parallel Database System.\\nYear: 2018\\n\\nAbstract: In order to reduce the large network overhead and the heavy cost of cross-match on the astronomical catalog in the database cluster, we proposed a novel method of cross-matches based on Roaring Bitmap. Firstly, we store astronomical catalog data in column-oriented storage with compression setup to reduce I/O overhead of accessing field in the parallel database system. Secondly, we create the spatial index, which maps the 2D coordinates into integer number. Then, using Roaring Bitmap convert the spatial index into a bitmap index. Finally, the received spatial range search of cross-match is translated into bitmap operations to achieve batch processing. The experiments over the real large-scale astronomical data show that the proposed method is 4 to 10 times faster than traditional method, meanwhile, only consume less than 10% of memory resource.\",\n \"Title: DACC: A Data Exploration Method for High-Dimensional Data Sets.\\nYear: 2018\\n\\nAbstract: Data exploration has been proved to be an efficient solution to learn interesting new insights from dataset in an intuitional way. Typically, discovering interesting patterns and objects over high-dimensional dataset is often very difficult due to its large search space. In this paper, we developed a data exploration method named Decision Analysis of Cross Clustering (DACC) based on subspace clustering. It characterize the data objects in the representation of decision trees over divided clustering subspace, which help users quickly understand the patterns of the data and then make interactive exploration easier. We conducted a series of experiments over the real-world datasets and the results showed that, DACC is superior to the representative data explorative approach in term of efficiency and accuracy, and it is applicable for interactive exploration analysis of high-dimensional data sets.\",\n \"Title: MIC-KMeans: A Maximum Information Coefficient Based High-Dimensional Clustering Algorithm.\\nYear: 2018\\n\\nAbstract: Clustering algorithms often use distance measure as the measure of similarity between point pairs. Such clustering algorithms are difficult to deal with the curse of dimensionality in high-dimension space. In order to address this issue which is common in clustering algorithms, we proposed to use MIC instead of distance measure in k-means clustering algorithm and implemented the novel MIC-kmeans algorithm for high-dimension clustering. MIC-kmeans can cluster the data with correlation to avoid the problem of distance failure in high-dimension space. The experimental results over the synthetic data and real datasets show that MIC-kmeans is superior to k-means clustering algorithm based on distance measure.\",\n \"Title: Correction to \\\"Background Subtraction Using Multiscale Fully Convolutional Network\\\".\\nYear: 2018\\n\\nAbstract: In the above paper [1] , the authors did not cite a reference that was crucial to the paper. Please note that the following citation [2] should have been cited in the following paragraph (page 16014): “From the output binary masks we can see that there is a great contrast between the foreground objects and their back-grounds, and in most cases the foreground objects are uniform connected areas, which means that in the input frames, there should also have a big difference of the features between the foreground and the background [2] .” and “So in order to extract this kind of contrast information, we add a contrast layer [2] after the feature layer X i .”\",\n \"Title: Multi-bit quantisation for similarity-preserving hashing.\\nYear: 2018\\n\\nAbstract: As a promising alternative to traditional search techniques, hashing-based approximate nearest neighbour search provides an applicable solution for big data. Most existing efforts are devoted to finding better projections to preserve the neighbouring structure of original data points in Hamming space, but ignore the quantisation procedure which may lead to the breakdown of the neighbouring structu...\",\n \"Title: A Compressed Graph Representation For Services Composition\\nYear: 2018\\n\\nAbstract: Service-oriented computing enhances business scalability and flexibility. Providers who expect to benefit from it may bring the explosive growth of web services. As a result, the huge storage required in searching a composition solution may be too much to afford. In this paper, we apply a compressed data structure to tackle this challenging issue. We propose a system model to solves web service composition (WSC) problem. In this model, we use a compressed tree to represent the search graph. This work innovatively solve the composition problem with compressed graph representation. The proposed model is verified through theoretical analysis and experimental study. The obtained experimental results show that this system may handle a large number of web services with small space requirement.\",\n \"Title: Combining Background Subtraction Algorithms with Convolutional Neural Network.\\nYear: 2018\\n\\nAbstract: Accurate and fast extraction of foreground objects is a key prerequisite for a wide range of computer vision applications, such as object tracking and recognition. Thus, many background subtraction (BGS) methods for foreground object detection have been proposed in recent decades. However, this is still regarded as a tough problem due to a variety of challenges, such as illumination variations, camera jitter, dynamic backgrounds, and shadows. Currently, there is no single method that can handle all the challenges in a robust way. We try to solve this problem from a perspective of combining different state-of-the-art BGS algorithms to create a more robust and more advanced foreground detection algorithm. More specifically, an encoder–decoder fully convolutional neural network architecture is adapted and trained to automatically learn how to leverage the characteristics of different algorithms to fuse the results produced by different BGS algorithms and produce a more precise result. Comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that the proposed method outperforms all the considered single BGS algorithms. We show that our solution is more efficient than other BGS combination strategies.\",\n \"Title: A Scalable Parameterized NoC Emulator Built Upon Xilinx Virtex-7 FPGA\\nYear: 2017\\n\\nAbstract: A number of critical design decisions, such as network topology, buffer sizes, flow control mechanism and so on so forth, have to be evaluated in any NoC the design. Designs and verifications of NoCs are based on either software simulations, which are extremely slow and inaccurate for complex models, or hardware emulations using low/mid-class FPGAs, where the scalability of the NoC system is intensively restricted by the limited on-chip resources. In this paper, we implement a parameterized NoC emulation system, capable of verifying complete functionality of routers and monitoring network performance and buffer usages in real time, on a hardware platform featuring a super large FPGA chip, Xilinx Virtex-7. This FPGA-based emulator also shall be configured to support multiple routing algorithms and packet transferring mechanisms. Compared to the existing emulators, it requires less user effort to measure the performance under various application scenarios, and it scales well to emulate large NoC designs. Currently, this emulator has been used to study NoCs with sizes of 4x4 and 8x8. For the case of 4x4 (8X8) NoC emulator, data transfers between routers can run at over 50MHz, and only occupies about 6% (25%) of the FPGA logic block resources.\",\n \"Title: Nonlinear Dynamic Analysis of Air Traffic Flow at Different Temporal Scales: Nonlinear Analysis Approach versus Complex Networks Approach\\nYear: 2018\\n\\nAbstract: Insights into the temporal-spatial characteristics of air traffic flow is the prerequisite of air traffic flow modeling, prediction and optimization. The intrinsic dynamics of air traffic flow were analyzed according to radar track data by classical nonlinear analysis and complex network methods. The nonlinear dynamic characteristics of air traffic flow were revealed from multiple perspectives by means of phase space reconstruction, recurrence plot, Lyapunov exponent, correlation dimension and Hurst exponent. The experimental results show that the example traffic flow became the most chaotic and the state persistence was poorest at time scale of 30 min. Besides, this paper explored the application of a complex network to analyze the nonlinearity of air traffic flow, and verified the chaotic and fractal characteristics by analyzing the degree distribution of the converted network. The degree distribution of the network derived from the traffic flow time series was approximately fitted to the exponential function and featured a strength level of chaos that was positively correlated with the size of network degree distribution power exponent. The linear relationship between network degree distribution power exponent and Hurst exponent was revealed, and the nonlinearity of air route traffic flow was systematically characterized. Particularly, the complex network-based analysis method helped examine air traffic flow from a new perspective, enriching the research means. These novel findings will provide theoretical basis for aggregated air traffic flow modeling, decision support system design and tactical flow management.\",\n \"Title: Erratum to \\\"Multiscale Fully Convolutional Network for Foreground Object Detection in Infrared Videos\\\".\\nYear: 2018\\n\\nAbstract: The architecture of the proposed multiscale fully convolutional network (MFCN) in our paper [1] is mainly derived from a salient object method [2] and a semantic segmentation method [3] . We missed these two reference papers in the original paper. The MFCN is an encoder-decoder architecture; the output has the same size with the input. For the encoder part, we use the pretrained VGG16 network, and for the decoder part, we upsample the features with deconvolution operations. The contrast layer for feature extraction is derived from the salient detection method [2] .\",\n \"Title: Extended scale invariant local binary pattern for background subtraction.\\nYear: 2018\\n\\nAbstract: Background subtraction based on change detection is the first step in many video surveillance systems, an effective background subtraction algorithm should distinguish foreground from the background sensitively, and adapt to the variation of background scenes robustly. In this study, the authors propose a robust background subtraction algorithm which takes advantages of local texture features repr...\",\n \"Title: Discriminative Correlation Quantization for Cross-Modal Similarity Retrieval.\\nYear: 2018\\n\\nAbstract: Due to their superior performance on efficient search and discrete loss, quantization methods have attracted considerable attention for approximate nearest neighbor (ANN) search on large-scale multimedia data. In this paper, we aim to introduce quantization into cross-modal similarity search with a focus on learning discriminative binary codes. Different from existing cross-modal quantization algorithms that transform heterogeneous data to a common subspace by unsupervised approaches, we propose a novel cross-modal quantization method embedded in a supervised framework by exploring the discriminative property of label information. The proposed approach learns common semantic space from label information by linear classification, enabling the generated category-specific features to produce more discriminative quantization codes. Furthermore, the unified codebooks and quantization codes are adopted to preserve the correlation of similar inter-modal pairs in the learned semantic space. The overall optimization algorithm jointly learns the linear classifiers, category-specific features and the unified quantizer in an alternated strategy. Extensive comparative experiments on three benchmark datasets show the superiority of our approach over some state-of-the-art methods.\",\n \"Title: A Robust Local Binary Similarity Pattern for Foreground Object Detection.\\nYear: 2018\\n\\nAbstract: Accurate and fast extraction of the foreground object is one of the most significant issues to be solved due to its important meaning for object tracking and recognition in video surveillance. Although many foreground object detection methods have been proposed in the recent past, it is still regarded as a tough problem due to illumination variations and dynamic backgrounds challenges. In this paper, we propose a robust foreground object detection method with two aspects of contributions. First, we propose a robust texture operator named Robust Local Binary Similarity Pattern (RLBSP), which shows strong robustness to illumination variations and dynamic backgrounds. Second, a combination of color and texture features are used to characterize pixel representations, which compensate each other to make full use of their own advantages. Comprehensive experiments evaluated on the CDnet 2012 dataset demonstrate that the proposed method performs favorably against state-of-the-art methods.\",\n \"Title: Background Subtraction with Real-time Semantic Segmentation.\\nYear: 2018\\n\\nAbstract: Accurate and fast foreground object extraction is very important for object tracking and recognition in video surveillance. Although many background subtraction (BGS) methods have been proposed in the recent past, it is still regarded as a tough problem due to the variety of challenging situations that occur in real-world scenarios. In this paper, we explore this problem from a new perspective and propose a novel background subtraction framework with real-time semantic segmentation (RTSS). Our proposed framework consists of two components, a traditional BGS segmenter $mathcal{B}$ and a real-time semantic segmenter $mathcal{S}$. The BGS segmenter $mathcal{B}$ aims to construct background models and segments foreground objects. The real-time semantic segmenter $mathcal{S}$ is used to refine the foreground segmentation outputs as feedbacks for improving the model updating accuracy. $mathcal{B}$ and $mathcal{S}$ work in parallel on two threads. For each input frame $I_t$, the BGS segmenter $mathcal{B}$ computes a preliminary foreground/background (FG/BG) mask $B_t$. At the same time, the real-time semantic segmenter $mathcal{S}$ extracts the object-level semantics ${S}_t$. Then, some specific rules are applied on ${B}_t$ and ${S}_t$ to generate the final detection ${D}_t$. Finally, the refined FG/BG mask ${D}_t$ is fed back to update the background model. Comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that our proposed method achieves state-of-the-art performance among all unsupervised background subtraction methods while operating at real-time, and even performs better than some deep learning based supervised algorithms. In addition, our proposed framework is very flexible and has the potential for generalization.\",\n \"Title: Multi-Kernel Supervised Hashing With Graph Regularization For Cross-Modal Retrieval\\nYear: 2018\\n\\nAbstract: Hashing based approximate nearest neighbor search has received considerable attention due to the demand of fast query for big multimedia data. Cross-modal hashing focuses on retrieval tasks across different modalities, which is more useful in practical applications. In this paper, we propose a novel two-stage cross-modal hashing method, referred to as Multi-Kernel Supervised Hashing with Graph Regularization (MKSRH). To better capture the essential attribute of original data, MKSRH first maps original data to a kernel space constructed by a linear combination of multiple kernel functions. Then the preliminary hash functions are learned using the Adaboost framework in the kernel space. To produce more accurate hash codes, the obtained hash function are then refined using a graph regularization based strategy. Experimental results on two canonical datasets show that MKSRH significantly outperforms than some typical cross-modal hashing methods, demonstrating the effectiveness and the superiority of the proposed approach.\",\n \"Title: A Memory-Efficient Approach to the Scalability of Recommender System With Hit Improvement.\\nYear: 2018\\n\\nAbstract: With the rapid increase of users and resources on the Internet, the scale of the recommender system becomes larger and larger. There are three major challenges facing in recommender system: sparsity, scalability, and cold start. In this paper, we mainly focus on the scalability issue and propose a recommender system based on the memory-efficient recurrent neural network. First, we allocate an item table for items and use a pair of embedding vectors to represent each item. Thus, we can use a few vectors to represent numerous items and decrease the memory used for the storage of embedding vectors. Second, we present a similarity-based initialization method for the item table to get a better representation of items. Third, we further design the loss function and the adjustment method to adjust the placement of items in the item table to speed up the training procedure of the model and get a better performance. The experimental results demonstrate the effectiveness of our approach. It can clearly improve the performance of recommendation, such as hit rate and normalized discounted cumulative gain, when compared to the state-of-the-art recommender algorithm. In addition, our approach can also handle the cold start problem and supply new users with the same quality of service as the old users.\",\n \"Title: An Approach for QoS-aware Service Composition with GraphPlan and Fuzzy Logic.\\nYear: 2018\\n\\nAbstract: With the rapid development of Information and Communication Technology, the growth of data is set to explode and brings us to an era of cloud computing. As a result, more and more services are posted on the web. In such situation, searching and composing services become a computationally demanding work. To address this problem, we design a system which combines fuzzy logic with GraphPlan algorithm. Fuzzy rules are employed to evaluate and rank services according to users’ preferences, then, the solution with the optimal QoS value is selected and used in GraphPlan construction. We evaluate our approach by experiments with web service challenge datasets.\",\n \"Title: Modeling and Verification of Response Time of QoS-aware Web Service Composition by Timed CSP.\\nYear: 2018\\n\\nAbstract: Web service composition enables the provision/reusing of existing services in different business processes to satisfy different business requirements without investing in new infrastructure. QoS-aware web service composition seeks to help users find the optimal solution with maximization of users’ satisfaction. A number of approaches based on Communicating Sequential Processes (CSP) have been proposed to model and verify properties of web service composition. However, little work has been done in verifying inputs, outputs and QoS criteria of web service composition.\",\n \"Title: An Online Ride-Sharing Path-Planning Strategy for Public Vehicle Systems.\\nYear: 2019\\n\\nAbstract: As efficient traffic-management platforms, public vehicle (PV) systems are envisioned to be a promising approach to solving traffic congestion and pollution for future smart cities. PV systems provide online/dynamic peer-to-peer ride-sharing services with the goal of serving a sufficient number of customers with a minimum number of vehicles and the lowest possible cost. A key component of the PV s...\",\n \"Title: Human Pose Estimation Based on Step Deep Convolution Neural Network.\\nYear: 2018\\n\\nAbstract: To improve the accuracy and robustness of human pose estimation, a step deep convolution neural network is proposed, which consists of a feed forward module and several step modules. Image features output from the last four layers of feed forward module are fused with the context features, the fused features are used as the input of the first step module; Image features of each step module fused with context features are used as the input of the next step module; The confidence map output from the last step module is used to predict joint position. This stepwise approach increases the receptive fields, which is good to learn the long-distance relationship between joints and predicting the position of occluded joints. At the same time, the confidence calculated by the previous module provides more and more accurate estimation of the position of each joint in the subsequent modules. In addition, the network also provides a way to strengthen the intermediate supervision by learning the objective function, so as to supplement the gradient of back-propagation and adjust the learning process, which effectively solves problem that the gradient disappears during training. Our approach is tested on two standard datasets of Leeds Sports Poses (LSP) and Frames Labeled In Cinema (FLIC), whose results indicate that our network has better performance in pose estimation of human body.\",\n \"Title: SAR Target Image Classification Based on Transfer Learning and Model Compression\\nYear: 2019\\n\\nAbstract: When convolutional neural networks (CNNs) are applied to the synthetic aperture radar (SAR) image classification, they are prone to overfitting due to scarce SAR image data, and CNNs require a large amount of storage and long computing time, so it is difficult to deploy them on resource constrained devices. This letter proposes a simple and feasible approach that can effectively solve these problems. First, the convolutional layers of the pretrained model on the ImageNet data set are transferred, and a new convolutional layer and global pooling layer are added afterward. Then, fine-tuning is performed on the new network from the SAR image data set. Finally, a filter-based pruning method is used on the convolutional layers to obtain a compact network. Compared with the all-convolutional network (A-ConvNets) which is the state-of-the-art method on the moving and stationary target acquisition and recognition data set, our method achieves about \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"> <tex-math notation=\\\"LaTeX\\\">$3.6\\\\times $ </tex-math></inline-formula>\\n speedup during forward propagation and \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"> <tex-math notation=\\\"LaTeX\\\">$3.7\\\\times $ </tex-math></inline-formula>\\n compression of the parameters, with only a 1.42% decrease in the accuracy.\",\n \"Title: Key Message Approach to Optimize Communication of Parallel Applications on Clusters\\nYear: 2003\\n\\nAbstract: Over the past few years, cluster/distributed computing has been gaining popularity. The proliferation of the cluster/distributed computing is due to the improved performance and increased reliability of these systems. Many parallel programming languages and related parallel programming models have become widely accepted. However, one of the major shortcomings of running parallel applications on cluster/distributed computing environments is the high communication overhead incurred. To reduce the communication overhead, and thus the completion time of a parallel application, this paper describes a simple, efficient and portable Key Message (KM) approach to support parallel computing on cluster/distributed computing environments. To demonstrate the advantage of the KM approach, a prototype runtime system has been implemented and evaluated. Our preliminary experimental results show that the KM approach has better improvement on communication of a parallel application when network background load increases or the computation to communication ratio of the application decreases.\",\n \"Title: A Data and Task Co-scheduling Algorithm for Scientific Cloud Workflows\\nYear: 2020\\n\\nAbstract: Cloud computing has emerged as a promising computational infrastructure for cost-efficient workflow execution by provisioning on-demand resources in a pay-as-you-go manner. While scientific workflows require accessing community-wide resources, they usually need to be performed in collaborative cloud environments composed of multiple datacenters. Although such environments facilitate scientific col...\",\n \"Title: A Multi-Aperture Optical Flow Estimation Method For An Artificial Compound Eye\\nYear: 2019\\n\\nAbstract: An artificial compound eye (ACE) is a bio-inspired vision sensor which mimics a natural compound eye (typical of insects). This artificial eye is able to visualize large fields of the outside world through multi-aperture. Due to its functioning, the ACE is subject to optical flow, that is an apparent motion of the object visualized by the eye. This paper proposes a method to estimate the optical flow based on capturing multiple images (multi-aperture).In this method, based on descriptors-based initial optical flows, a unified global energy function is presented to incorporate the information of multi-aperture and simultaneously recover the optical flows of multi-aperture. The energy function imposes a compound flow fields consistency assumption along with the brightness constancy and piecewise smoothness assumptions. This formula efficiently binds the flow field in time and space, and further enables view-consistent optical flow estimation. Experimental results on real and synthetic data demonstrate that the proposed method recovers view-consistent optical flows crossed multi-aperture and performs better than other optical flow methods on the multi-aperture images.\",\n \"Title: Trajectory tracking control for a marine surface vessel with asymmetric saturation actuators.\\nYear: 2017\\n\\nAbstract: This paper investigates the problem of trajectory tracking control for an unmanned marine surface vessel (MSV) with external disturbances and asymmetric saturation actuators. An adaptive radial basis function neural network (RBFNN) is constructed to provide an estimation of the unknown disturbances and is applied to design the trajectory tracking controller through a backstepping technique. To handle the effect of nonsmooth asymmetric saturation nonlinearity, a Gaussian error function-based continuous differentiable asymmetric saturation model is employed such that the backstepping technique can be used in the control design. It is proved that all the states in the closed-loop system are semiglobally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of origin by appropriately choosing design parameters. Simulation results and comparisons illustrate the effectiveness of the proposed controller and its robustness to external disturbances and asymmetric saturation actuators.\",\n \"Title: TOUSE: A Fair User Selection Mechanism Based on Dynamic Time Warping for MU-MIMO Networks.\\nYear: 2017\\n\\nAbstract: Multi-user Multiple-Input and Multiple-Output (MU-MIMO) has potential for prominently enhancing the capacity of wireless network by simultaneously transmitting to multiple users. User selection is an unavoidable problem which bottlenecks the gain of MU-MIMO to a great extent. Major state-of-the-art works are focusing on improving network throughput by using Channel State Information (CSI), however, the overhead of CSI feedback becomes unacceptable when the number of users is large. Some work does well in balancing tradeoff between complexity and achievable throughput but is lack of consideration of fairness. Current works universally ignore the rational utilizing of time resources, which may lead the improvements of network throughput to a standstill. In this paper, we propose TOUSE, a scalable and fair user selection scheme for MU-MIMO. The core design is dynamic-timewarping- based user selection mechanism for downlink MU-MIMO, which could make full use of concurrent transmitting time. TOUSE also presents a novel data-rate estimation method without any CSI feedback, providing supports for user selections. Simulation result shows that TOUSE significantly outperforms traditional contention-based user selection schemes in both throughput and fairness in an indoor condition.\",\n \"Title: An Approach For Detecting Fault Lines In A Small Current Grounding System Using Fuzzy Reasoning Spiking Neural P Systems\\nYear: 2018\\n\\nAbstract: This paper presents a novel approach for detecting fault lines in a small current grounding system using fuzzy reasoning spiking neural P systems. In this approach, six features of current/voltage signals in a small current grounding system are analyzed by considering transient and steady components, respectively; a fault measure is used to quantify the possibility that a line is faulty; information gain degree is discussed to weight the importance of each of the six features; rough set theory is applied to reduce the features; and finally a fuzzy reasoning spiking neural P system is used to construct fault line detection models. Six cases in a small current grounding system prove the effectiveness of the introduced approach.\",\n \"Title: A Hierarchical Attention Retrieval Model for Healthcare Question Answering\\nYear: 2019\\n\\nAbstract: The growth of the Web in recent years has resulted in the development of various online platforms that provide healthcare information services. These platforms contain an enormous amount of information, which could be beneficial for a large number of people. However, navigating through such knowledgebases to answer specific queries of healthcare consumers is a challenging task. A majority of such queries might be non-factoid in nature, and hence, traditional keyword-based retrieval models do not work well for such cases. Furthermore, in many scenarios, it might be desirable to get a short answer that sufficiently answers the query, instead of a long document with only a small amount of useful information. In this paper, we propose a neural network model for ranking documents for question answering in the healthcare domain. The proposed model uses a deep attention mechanism at word, sentence, and document levels, for efficient retrieval for both factoid and non-factoid queries, on documents of varied lengths. Specifically, the word-level cross-attention allows the model to identify words that might be most relevant for a query, and the hierarchical attention at sentence and document levels allows it to do effective retrieval on both long and short documents. We also construct a new large-scale healthcare question-answering dataset, which we use to evaluate our model. Experimental evaluation results against several state-of-the-art baselines show that our model outperforms the existing retrieval techniques.\",\n \"Title: Periodic motion generation for the impactless biped walking up slopes via genetic algorithm\\nYear: 2020\\n\\nAbstract: The angular positions of the lower limb joints play important roles onthe energy consumption and stability for the bipedal walking up slopes. In this study, the ranges of angular position of lower limb joints are confined and thevelocity of swing foot is zero when it touches the ground, which result in theconstruction of the impactless planar bipedal model. Motion/force control scheme combined with genetic algorithm (GA) is used to ensure stability and low energy cost of bipedal walking at different speeds. The optimized parameters of gaitsare obtained by GA, which include walking speed, step length and the maximumheight of swing ankle joint. The results demonstrate that more energy isconsumed when the optimal walking speed increases for the biped walking on slopes with the same gradient. There are no great differences in optimal steplength of the biped when the walking speed changes. The optimal step lengthdeclines as the slope increases at the same walking speed. The ankle torques of standing leg have higher values in single support phase at fast speed comparedto those at slow and normal speeds. Modifications of boundary conditions can not only be used to realize the stable walking for the biped negotiating slopes, but also be applied to analyze bipedal gaits for walking on stairs and uneven Surfaces.\",\n \"Title: Deep Reinforcement Learning for Unmanned Aerial Vehicle-Assisted Vehicular Networks.\\nYear: 2019\\n\\nAbstract: Unmanned aerial vehicles (UAVs) are envisioned to complement the 5G communication infrastructure in future smart cities. Hot spots easily appear in road intersections, where effective communication among vehicles is challenging. UAVs may serve as relays with the advantages of low price, easy deployment, line-of-sight links, and flexible mobility. In this paper, we study a UAV-assisted vehicular network where the UAV jointly adjusts its transmission power and bandwidth allocation under 3D flight to maximize the total throughput. First, we formulate a Markov Decision Process (MDP) problem by modeling the mobility of vehicles and the state transitions caused by the UAV's 3D flight. Secondly, we solve the target problem using a deep reinforcement learning method, namely, the deep deterministic policy gradient, and propose three solutions with different control objectives. Thirdly, in a simplified model with small state and action spaces, we verify the optimality of proposed algorithms. Comparing with two baseline schemes, we demonstrate the effectiveness of proposed algorithms in a realistic model.\",\n \"Title: Low-light Image Enhancement Algorithm Based on Retinex and Generative Adversarial Network.\\nYear: 2019\\n\\nAbstract: Low-light image enhancement is generally regarded as a challenging task in image processing, especially for the complex visual tasks at night or weakly illuminated. In order to reduce the blurs or noises on the low-light images, a large number of papers have contributed to applying different technologies. Regretfully, most of them had served little purposes in coping with the extremely poor illumination parts of images or test in practice. In this work, the authors propose a novel approach for processing low-light images based on the Retinex theory and generative adversarial network (GAN), which is composed of the decomposition part for splitting the image into illumination image and reflected image, and the enhancement part for generating high-quality image. Such a discriminative network is expected to make the generated image clearer. Couples of experiments have been implemented under the circumstance of different lighting strength on the basis of Converted See-In-the-Dark (CSID) datasets, and the satisfactory results have been achieved with exceeding expectation that much encourages the authors. In a word, the proposed GAN-based network and employed Retinex theory in this work have proven to be effective in dealing with the low-light image enhancement problems, which will benefit the image processing with no doubt.\",\n \"Title: Top-N Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph Embedding.\\nYear: 2019\\n\\nAbstract: The traditional collaborative filtering recommendation algorithm only uses the item-user rating matrix without considering the semantic information of the item itself, resulting in a problem that the recommendation accuracy is not high. This paper proposes a Top-N collaborative filtering recommendation algorithm based on knowledge graph embedding. The knowledge graph embedding is used to learn a low-dimensional vector for each entity and relationship in the knowledge graph, while maintaining the structure and semantic information of the original graph in the vector. By calculating the semantic similarity between items, the semantic information of the item itself is incorporated into the collaborative filtering recommendation. The algorithm makes up for the defect that the collaborative filtering recommendation algorithm does not consider the knowledge information of the item itself, and enhances the effect of collaborative filtering recommendation on the semantic level. The experimental results on the MovieLens dataset show that the algorithm can get higher values on precision, recall and F1 measure.\",\n \"Title: Talents Evaluation Modeling Based on Fuzzy Mathematics.\\nYear: 2019\\n\\nAbstract: Fuzzy synthetic evaluation is a comprehensive assessment method based on fuzzy mathematics. Using the jurisdiction degree theory in fuzzy mathematics, it transforms qualitative evaluation into quantitative evaluation, and has been widely applied in many fields. In order to make the recruitment process of HRM (Human Resource Management) more systematic and rational, this paper introduces a method to evaluate talents by applying fuzzy synthetic evaluation, and discussed how to implement such mathematical model in our prototype system based on J2EE. Furthermore, the enterprise evaluation process with customizable resume is also proposed in this paper.\",\n \"Title: A Dataset And Benchmark Of Underwater Object Detection For Robot Picking\\nYear: 2021\\n\\nAbstract: Underwater object detection for robot picking has attracted a lot of interest. However, it is still an unsolved problem due to several challenges. We take steps towards making it more realistic by addressing the following challenges. Firstly, the currently available datasets basically lack the test set annotations, causing researchers must compare their method with other SOTAs on a self-divided te...\",\n \"Title: PointAugmenting: Cross-Modal Augmentation for 3D Object Detection\\nYear: 2021\\n\\nAbstract: Camera and LiDAR are two complementary sensors for 3D object detection in the autonomous driving context. Camera provides rich texture and color cues while LiDAR specializes in relative distance sensing. The challenge of 3D object detection lies in effectively fusing 2D camera images with 3D LiDAR points. In this paper, we present a novel cross-modal 3D object detection algorithm, named PointAugmenting. On one hand, PointAugmenting decorates point clouds with corresponding point-wise CNN features extracted by pretrained 2D detection models, and then performs 3D object detection over the decorated point clouds. In comparison with highly abstract semantic segmentation scores to decorate point clouds, CNN features from detection networks adapt to object appearance variations, achieving significant improvement. On the other hand, PointAugmenting benefits from a novel cross modal data augmentation algorithm, which consistently pastes virtual objects into images and point clouds during network training. Extensive experiments on the large-scale nuScenes and Waymo datasets demonstrate the effectiveness and efficiency of our PointAugmenting. Notably, PointAugmenting outperforms the LiDAR-only baseline detector by +6.5% mAP and achieves the new state-of-the-art results on the nuScenes leaderboard to date.\",\n \"Title: A Hybrid Pre-Joined Service Network In Graph Database And Memory For Service Composition\\nYear: 2021\\n\\nAbstract: To make full use of the large space and persistency provided by a database and the fast processing speed of main memory, we propose a hybrid pre-joined service network method combining in-graph-database and in-memory calculations. This method firstly stores services' information and combinations in a graph database. Then, for a user's request, it retrieves candidate services from the database and constructs a service network in memory. After that, services are picked and composed to fulfil an optimal solution in memory. We assess the performance of our method by conducting experiments and comparing the approach with other database-based methods. Experiment results indicate the efficiency of this method in comparison, and it can always find solutions and lead to higher users' satisfaction.\",\n \"Title: A Fast Stereo Matching Network With Multi-Cross Attention\\nYear: 2021\\n\\nAbstract: Stereo matching networks based on deep learning are widely developed and can obtain excellent disparity estimation. We present a new end-to-end fast deep learning stereo matching network in this work that aims to determine the corresponding disparity from two stereo image pairs. We extract the characteristics of the low-resolution feature images using the stacked hourglass structure feature extractor and build a multi-level detailed cost volume. We also use the edge of the left image to guide disparity optimization and sub-sample with the low-resolution data, ensuring excellent accuracy and speed at the same time. Furthermore, we design a multi-cross attention model for binocular stereo matching to improve the matching accuracy and achieve end-to-end disparity regression effectively. We evaluate our network on Scene Flow, KITTI2012, and KITTI2015 datasets, and the experimental results show that the speed and accuracy of our method are excellent.\",\n \"Title: A Novel Computer Vision-Based Data Driven Modelling Approach For Person Specific Fall Detection\\nYear: 2021\\n\\nAbstract: In this paper, we propose a novel person specific fall detection system based on a monocular camera, which can be applied for assisting the independent living of an older adult living alone at home. A single camera covering the living area is used for video recordings of an elderly person's normal daily activities. From the recorded video data, the human silhouette regions in every frame are then extracted based on the codebook background subtraction technique. Low-dimensionality representative features of extracted silhouetted are then extracted by convolutional neural network-based autoencoder (CNN-AE). Features obtained from the CNN-AE are applied to construct an one class support vector machine (OCSVM) model, which is a data driven model based on the video recordings and can be applied for fall detection. From the comprehensive experimental evaluations on different people in a real home environment, it is shown that the proposed fall detection system can successfully detect different types of falls (falls towards different orientations at different positions in a real home environment) with small false alarms.\",\n \"Title: Scaling up Mobile Service Selection in Edge Computing Environment with Cuckoo Optimization Algorithm\\nYear: 2021\\n\\nAbstract: Nowadays, instead of providing computational capabilities in the cloud, mobile edge computing pushes computational capabilities to network edges. It is common for mobile device users to request services located on edge servers. Mobile devices are usually resource-constrained with dynamic locations of users, and an unstable mobile environment may lead to complex mobile service requirements. Therefo...\",\n \"Title: A Top-N-Balanced Sequential Recommendation Based On Recurrent Network\\nYear: 2019\\n\\nAbstract: To solve the low accuracy problem of the recommender system for long term users, in this paper, we propose a top-N-balanced sequential recommendation based on recurrent neural network. We postulated and verified that the interactions between users and items is time-dependent in the long term, but in the short term, it is time-independent. We balance the top-N recommendation and sequential recommendation to generate a better recommender list by improving the loss function and generation method. The experimental results demonstrate the effectiveness of our method. Compared with a state-of-the-art recommender algorithm, our method clearly improves the performance of the recommendation on hit rate. Besides the improvement of the basic performance, our method can also handle the cold start problem and supply new users with the same quality of service as the old users.\",\n \"Title: Optimal Design Of 10-Bit Single-Slope Adc For Cmos Image Sensor Based On Swarm Intelligent Optimization Algorithm\\nYear: 2021\\n\\nAbstract: As one of the most critical blocks of the CMOS image sensor (CIS), the accuracy and power consumption of the analogue-to-digital converter (ADC) play an important role in its performance. However, due to the mutually restrictive relationship between power consumption and accuracy in the single-slope ADC (SS ADC), it is difficult to improve these performance indexes meantime without an efficient optimal design method. Here, an optimal design methodology based on an improved artificial fish swarm optimization algorithm (IAFSOA) is proposed for a 10-bit SS ADC in CIS. First, a voltage storage structure and offset calibration circuit are developed for promoting the performance of the comparator. Then, IAFSOA is developed for the circuit parameters optimization with faster convergence rate and higher computational accuracy. The proposed multi-objectives optimization algorithm can obtain the optimal parameters of the kernel circuit components and lead to an enhancement of both power consumption and accuracy. The optimized SS ADC is designed based on 0.18 mu m process to verify the effectiveness of the proposed method. Compared to the earlier reported work, the IAFSOA-based optimal design method can promote the performance of the SS ADC by an improvement of effective number of bits and a reduction of power consumption.\",\n \"Title: Classification of Giemsa staining chromosome using input-aware deep convolutional neural network with integrated uncertainty estimates\\nYear: 2022\\n\\nAbstract: A fully automated chromosome analysis system can substitute cytogenetic experts for the task of chromosome karyotype analysis, which in turn can substantially increase the efficiency of disease diagnosis. However, the construction of such a system is most crucially restricted by the accuracy of chromosome classification, during karyotype analysis. To facilitate the construction of an automatic chromosome analysis system, an input-aware and probabilistic prediction convolutional neural network (IAPP-CNN) is presented in this paper for high accuracy of chromosome classification. The approach follows three stages and consists of one input-aware module, one feature extractor module and one probabilistic prediction module. In the first stage, the input-aware module develops raw images automatically into the global-scale image, the object-scale image and the part-scale image, by introducing an attention mechanism. In the second stage, the three scale images are input into the feature extraction module through three branches, then the respective feature operators are obtained via their independent CNN feature extractors. In the third stage, the probabilistic prediction module uses three dynamic probabilistic parameters to estimate the prediction of each CNN branch separately, and then combined the three CNN votes for the final decision. The feature expression ability of the key feature was improved and the network was enabled to focus on the recognizable regions in the image. Evaluation results from a large dataset of healthy patients showed that the proposed IAPP-CNN achieved the highest accuracy of 99.2% for the chromosome classification task, surpassing the performance of a competitive baseline created by state-of-the-art methods.\",\n \"Title: Quadrotor Autonomous Navigation in Semi-Known Environments Based on Deep Reinforcement Learning\\nYear: 2021\\n\\nAbstract: In the application scenarios of quadrotors, it is expected that only part of the obstacles can be identified and located in advance. In order to make quadrotors fly safely in this situation, we present a deep reinforcement learning-based framework to realize autonomous navigation in semi-known environments. Specifically, the proposed framework utilizes the dueling double deep recurrent Q-learning, which can implement global path planning with the obstacle map as input. Moreover, the proposed framework combined with contrastive learning-based feature extraction can conduct real-time autonomous obstacle avoidance with monocular vision effectively. The experimental results demonstrate that our framework exhibits remarkable performance for both global path planning and autonomous obstacle avoidance.\",\n \"Title: Residual Spatial and Channel Attention Networks for Single Image Dehazing\\nYear: 2021\\n\\nAbstract: Single image dehazing is a highly challenging ill-posed problem. Existing methods including both prior-based and learning-based heavily rely on the conceptual simplified atmospheric scattering model by estimating the so-called medium transmission map and atmospheric light. However, the formation of haze in the real world is much more complicated and inaccurate estimations further degrade the dehazing performance with color distortion, artifacts and insufficient haze removal. Moreover, most dehazing networks treat spatial-wise and channel-wise features equally, but haze is practically unevenly distributed across an image, thus regions with different haze concentrations require different attentions. To solve these problems, we propose an end-to-end trainable densely connected residual spatial and channel attention network based on the conditional generative adversarial framework to directly restore a haze-free image from an input hazy image, without explicitly estimation of any atmospheric scattering parameters. Specifically, a novel residual attention module is proposed by combining spatial attention and channel attention mechanism, which could adaptively recalibrate spatial-wise and channel-wise feature weights by considering interdependencies among spatial and channel information. Such a mechanism allows the network to concentrate on more useful pixels and channels. Meanwhile, the dense network can maximize the information flow along features from different levels to encourage feature reuse and strengthen feature propagation. In addition, the network is trained with a multi-loss function, in which contrastive loss and registration loss are novel refined to restore sharper structures and ensure better visual quality. Experimental results demonstrate that the proposed method achieves the state-of-the-art performance on both public synthetic datasets and real-world images with more visually pleasing dehazed results.\",\n \"Title: Cross-Modal 3D Object Detection and Tracking for Auto-Driving\\nYear: 2021\\n\\nAbstract: Detecting and tracking objects in 3D scenes play crucial roles in autonomous driving. Successfully recognizing objects through space and time hinges on a strong detector and a reliable association scheme. Recent 3D detection and tracking approaches widely represent objects as points when associating detection results with trajectories. Despite the demonstrated success, these approaches do not fully exploit the rich appearance information of objects. In this paper, we present a conceptually simple yet effective algorithm, named AlphaTrack, which considers both the location and appearance changes to perform joint 3D object detection and tracking. To achieve this, we propose a cross-modal fusion scheme that fuses camera appearance feature with LiDAR feature to facilitate 3D detection and tracking. We further attach an additional branch to the 3D detector to output instance-aware appearance embedding, which significantly improves tracking performance with our designed association mechanisms. Extensive validations on large-scale autonomous driving dataset demonstrate the effectiveness of the proposed algorithm in comparison with state-of-the-art approaches. Notably, the proposed algorithm ranks first on the nuScenes tracking leaderboard to date.\",\n \"Title: Multilingual Code Snippets Training for Program Translation.\\nYear: 2022\\n\\nAbstract: Program translation aims to translate source code from one programming language to another. It is particularly useful in applications such as multiple-platform adaptation and legacy code migration. Traditional rule-based program translation methods usually rely on meticulous manual rule-crafting, which is costly both in terms of time and effort. Recently, neural network based methods have been developed to address this problem. However, the absence of high-quality parallel code data is one of the main bottlenecks which impedes the development of program translation models. In this paper, we introduce CoST, a new multilingual Code Snippet Translation dataset that contains parallel data from 7 commonly used programming languages. The dataset is parallel at the level of code snippets, which provides much more fine-grained alignments between different languages than the existing translation datasets. We also propose a new program translation model that leverages multilingual snippet denoising auto-encoding and Multilingual Snippet Translation (MuST) pre-training. Extensive experiments show that the multilingual snippet training is effective in improving program translation performance, especially for low-resource languages. Moreover, our training method shows good generalizability and consistently improves the translation performance of a number of baseline models. The proposed model outperforms the baselines on both snippet-level and program-level translation, and achieves state-of-the-art performance on CodeXGLUE translation task. The code, data, and appendix for this paper can be found at https://github.com/reddy-lab-code-research/MuST-CoST.\",\n \"Title: Homography-Based Visual Servoing for Underactuated VTOL UAVs Tracking a 6-DOF Moving Ship\\nYear: 2022\\n\\nAbstract: This paper develops a novel homography-based visual servo control method for the vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) tracking the trajectory of a 6 degrees of freedom (6-DOF) moving ship. Different from the classical homography-based visual servoing which is only suitable for the trajectory tracking of a planar moving target, an extended homography-based visual servo...\",\n \"Title: IMAGE ENHANCEMENT ALGORITHM BASED ON IMAGE SPATIAL DOMAIN SEGMENTATION\\nYear: 2021\\n\\nAbstract: This paper proposes an image enhancement algorithm based on the theory of image segmentation and image frequency. First, a mathematical model corresponding to the pixel frequency is established by using the difference of pigment values between the pixels in the image and the surrounding pixels (i.e., pixel receptive field). Then, the image is divided into the low frequency region (background area), low-medium frequency region (foreground area), medium-high frequency region (target area) and high frequency region (detail area) by a pixel frequency characteristic graph. Gamma correction, MSRCR, MSR, top hat + bottom hat are used for image enhancement for each area, and then the parts are merged. Three indicators of PSNR, SSIM, and MSE are introduced to evaluate the quality of the enhanced image. The results show that the image enhanced by this algorithm has the highest PSNR and SSIM values and the lowest MSE value, indicating that the enhancement effect of this algorithm is better. Compared with traditional algorithms, the image enhancement algorithm in this paper produces higher image quality and richer details.\",\n \"Title: Deep Learning-Based Monocular 3D Object Detection with Refinement of Depth Information\\nYear: 2022\\n\\nAbstract: Recently, the research on monocular 3D target detection based on pseudo-LiDAR data has made some progress. In contrast to LiDAR-based algorithms, the robustness of pseudo-LiDAR methods is still inferior. After conducting in-depth experiments, we realized that the main limitations are due to the inaccuracy of the target position and the uncertainty in the depth distribution of the foreground target. These two problems arise from the inaccurate depth estimation. To deal with the aforementioned problems, we propose two innovative solutions. The first is a novel method based on joint image segmentation and geometric constraints, used to predict the target depth and provide the depth prediction confidence measure. The predicted target depth is fused with the overall depth of the scene and results in the optimal target position. For the second, we utilize the target scale, normalized with the Gaussian function, as a priori information. The uncertainty of depth distribution, which can be visualized as long-tail noise, is reduced. With the refined depth information, we convert the optimized depth map into the point cloud representation, called a pseudo-LiDAR point cloud. Finally, we input the pseudo-LiDAR point cloud to the LiDAR-based algorithm to detect the 3D target. We conducted extensive experiments on the challenging KITTI dataset. The results demonstrate that our proposed framework outperforms various state-of-the-art methods by more than 12.37% and 5.34% on the easy and hard settings of the KITTI validation subset, respectively. On the KITTI test set, our framework also outperformed state-of-the-art methods by 5.1% and 1.76% on the easy and hard settings, respectively.\",\n \"Title: Event-Triggered Dynamic Coverage Control for Multiple Stratospheric Airships\\nYear: 2022\\n\\nAbstract: This article first investigates the dynamic coverage control problem for the multiple stratospheric airships (MSAs) system considering its practical application scenarios. A dynamic coverage control framework is put forward, in which the MSA system can be guided and controlled to fully cover the observation target region. Once a specific target is detected, the coverage target can be switched. First, the location information of the monitored target is predicted by an autoregressive model against processing delay. Second, the coverage control scheme consists of two layers: a novel potential field-based virtual control law to generate the desired velocity and angular velocity and an adaptive tracking controller to track them. In the virtual control law, a dynamic artificial potential field is introduced to adapt to the dynamic scenarios. In the tracking controller, which is combined with the adaptive control technique and the saturation compensator theory, the external disturbances and input saturation are addressed. Third, the event-triggered mechanism is designed to reduce the control frequency to prolong the actuator life. The simulation results are given to substantiate the capability of the proposed dynamic coverage control framework.\",\n \"Title: A Layered Spiking Neural System for Classification Problems\\nYear: 2022\\n\\nAbstract: Biological brains have a natural capacity for resolving certain classification tasks. Studies on biologically plausible spiking neurons, architectures and mechanisms of artificial neural systems that closely match biological observations while giving high classification performance are gaining momentum. Spiking neural P systems (SN P systems) are a class of membrane computing models and third-generation neural networks that are based on the behavior of biological neural cells and have been used in various engineering applications. Furthermore, SN P systems are characterized by a highly flexible structure that enables the design of a machine learning algorithm by mimicking the structure and behavior of biological cells without the over-simplification present in neural networks. Based on this aspect, this paper proposes a novel type of SN P system, namely, layered SN P system (LSN P system), to solve classification problems by supervised learning. The proposed LSN P system consists of a multi-layer network containing multiple weighted fuzzy SN P systems with adaptive weight adjustment rules. The proposed system employs specific ascending dimension techniques and a selection method of output neurons for classification problems. The experimental results obtained using benchmark datasets from the UCI machine learning repository and MNIST dataset demonstrated the feasibility and effectiveness of the proposed LSN P system. More importantly, the proposed LSN P system presents the first SN P system that demonstrates sufficient performance for use in addressing real-world classification problems.\",\n \"Title: Unsupervised Low-Light Image Enhancement by Extracting Structural Similarity and Color Consistency\\nYear: 2022\\n\\nAbstract: A novel structure-aware unsupervised network is proposed to deal with low-light image enhancement issues based on the inspiration of Retinex theory and self-supervised perceptual loss. It comprises four main components, namely the original structural similarity module, the novel color consistency module, the attentional enhancement module, and the naturalness discriminator module. Specially for the structural similarity module, an embedded structural feature extractor (SFE) model capable of generating structure correspondence is well designed and pre-trained by employing the contrastive learning technique, and a multi-scale structural similarity distance is introduced to optimize the SFE network. Besides, a self-supervised color consistency module is established by using a degraded estimation algorithm for recovering the missing colors. The whole enhancement framework operates in unsupervised manners and finally obtains the best naturalness image quality evaluator metric. Experimental results demonstrate that the proposed unsupervised network is able to recover natural structure and color images more effectively, which would also help to enlarge the practical application without collecting paired datasets in advance.\",\n \"Title: Fast Momentum Contrast Learning for Unsupervised Person Re-Identification\\nYear: 2021\\n\\nAbstract: Person re-identification (re-ID) aims to identify the same persons' images across different cameras by deploying a trained re-ID model. Nevertheless, the domain bias between different datasets can lead to considerably low identification accuracy when directly applying a trained re-ID model from one dataset to another. Therefore, in order to develop a kind of more transferable re-ID methods with strong robustness, this letter proposes a fast momentum contrast learning framework, which consists of a teacher encoder and a fast momentum encoder, from a reinforcing visual representation learning point of view. The fast momentum encoder produced by the teacher encoder is employed to build a dynamic dictionary and then design the contrast learning loss function together with the teacher encoder. In addition, both the individual loss and the clustering loss are adopted for obtaining the optimal performances in unsupervised person re-ID models. The proposed framework achieves one stage of end-to-end training and experimental results demonstrate that our identification accuracy outperforms existing advanced methods. The code of this work is available at https://github.com/binquanwang/FMCL_UDA.\",\n \"Title: On homomorphic images of the Szilard languages of matrix insertion–deletion systems with matrices of size 2\\nYear: 2022\\n\\nAbstract: A Szilard language is a well-known tool in formal language theory to express the derivation process in a grammar system or grammar. Matrix InsDel (insertion–deletion) system is a well-known variant of InsDel system, where the idea of matrix control is combined with InsDel systems. The size of a matrix InsDel system is represented by a septuple of integers \\n \\n \\n \\n $$(k; p, m, m^{'};$$\\n \\n \\n \\n \\n \\n $$q, n, n^{'})$$\\n \\n , where k represents maximum number of rules in a matrix, i.e., size of a matrix. The parameters p, m and \\n \\n \\n \\n $$m^{'}$$\\n \\n represent the maximal length of the strings inserted, the maximal length of the left context and the maximal length of the right context of the insertion rules, respectively. The parameters \\n \\n \\n \\n $$q, n, n^{'}$$\\n \\n represent the same for deletion rules. In this paper, we investigate the Szilard languages of matrix InsDel systems with matrices of size 2. We give examples of regular, context-free and context-sensitive languages which cannot be the Szilard language of any matrix InsDel system. We show that any regular language can be represented as a homomorphic image of Szilard language of matrix InsDel system of size (2; 2, 0, 0; 1, 0, 0). Any linear language, meta-linear language and rational (or regular) closure of linear language can be obtained as the homomorphic image of Szilard language of matrix InsDel systems of size (2; 1, 1, 0; 1, 1, 0), and (2; 1, 0, 1; 1, 0, 1). Moreover, any recursively enumerable language can be obtained as the homomorphic image of the Szilard language of matrix InsDel systems of size (2; 1, 1, 0; 1, 1, 1), (2; 1, 0, 1; 1, 1, 1), (2; 1, 1, 1; 1, 1, 0), (2; 1, 1, 1; 1, 0, 1), (2; 1, 1, 0; 2, 0, 0), (2; 1, 0, 1; 2, 0, 0), (2; 2, 0, 0; 1, 1, 0), and (2; 2, 0, 0; 1, 0, 1).\",\n \"Title: Single image dehazing based on multi-scale segmentation and deep learning\\nYear: 2022\\n\\nAbstract: Existing image dehazing methods suffer from problems of insufficient dehazing, distortion, and low color contrast. Aiming at this problem, a deep learning single-image dehazing method based on multi-scale segmentation is proposed in this paper. The study found that the haze information in the haze image will decrease with the increase of frequency. Therefore, the haze image is first decomposed into four sub-images of different frequency domains through image segmentation in this article. A dehazing network model composed of four sub-network channels with different complexity is then constructed to extract the haze information contained in each sub-image. After the transmission sub-images are generated, the image fusion technology is used to obtain the final transmittance map. Finally, the haze-free image is obtained based on the physical model of atmospheric scattering. Experimental results on the synthetic and real images dataset show that the proposed method achieves significant dehazing effect and high color contrast with no distortion, showing superior performance than other dehazing methods.\",\n \"Title: A distributed adaptive optimization spiking neural P system for approximately solving combinatorial optimization problems\\nYear: 2022\\n\\nAbstract: •Proposes a distributed adaptive optimization spiking neural P system with a distributed population structure and a new adaptive learning rate considering population diversity.•Extensive experiments on knapsack problems show that DAOSNPS gains much better and more stable solutions than OSNPS, AOSNPS and other two optimization algorithms.\",\n \"Title: Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation\\nYear: 2022\\n\\nAbstract: In recent years, image segmentation techniques based on deep learning have achieved many applications in remote sensing, medical, and autonomous driving fields. In space exploration, the segmentation of spacecraft objects by monocular images can support space station on-orbit assembly tasks and space target position and attitude estimation tasks, which has essential research value and broad application prospects. However, there is no segmentation network designed for spacecraft targets. This paper proposes an end-to-end spacecraft image segmentation network using the semantic segmentation network DeepLabv3+ as the basic framework. We develop a multi-scale neural network based on sparse convolution. First, the feature extraction capability is improved by the dilated convolutional network. Second, we introduce the channel attention mechanism into the network to recalibrate the feature responses. Finally, we design a parallel atrous spatial pyramid pooling (ASPP) structure that enhances the contextual information of the network. To verify the effectiveness of the method, we built a spacecraft segmentation dataset on which we conduct experiments on the segmentation algorithm. The experimental results show that the encoder+ attention+ decoder structure proposed in this paper, which focuses on high-level and low-level features, can obtain clear and complete masks of spacecraft targets with high segmentation accuracy. Compared with DeepLabv3+, our method is a significant improvement. We also conduct an ablation study to research the effectiveness of our network framework.\",\n \"Title: Mobile service selection in edge and cloud computing environment with grey wolf algorithm\\nYear: 2022\\n\\nAbstract: The proliferation of mobile devices has resulted in the tremendous development of edge computing. A common mechanism is that once the requests from the users are too complex to be afforded by a single service, then edge computing services ought to step in. Nevertheless, services on edge servers are most commonly resource-constrained and unstable. To this end, we propose a novel solution for the mobile service selection problem with edge and cloud computing. An extended grey wolf algorithm is presented; specifically, crossover operator and roulette wheel are applied in reproduction and selection operations. Comparative experiments are implemented between our approach and other nature-inspired algorithms to verify the effectiveness and efficiency, which demonstrate our method may find a solution with better QoS values.\",\n \"Title: AC-LSTM: Anomaly State Perception of Infrared Point Targets Based on CNN plus LSTM\\nYear: 2022\\n\\nAbstract: Anomaly perception of infrared point targets has high application value in many fields, such as maritime surveillance, airspace surveillance, and early warning systems. This kind of abnormality includes the explosion of the target, the separation between stages, the disintegration caused by the abnormal strike, etc. By extracting the radiation characteristics of continuous frame targets, it is possible to analyze and warn the target state in time. Most anomaly detection methods adopt traditional outlier detection, which has the problems of poor accuracy and a high false alarm rate. Driven by data, this paper proposes a new network structure, called AC-LSTM, which combines Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM), and embeds the Periodic Time Series Data Attention module (PTSA). The network can better extract the spatial and temporal characteristics of one-dimensional time series data, and the PTSA module can consider the periodic characteristics of the target in the process of continuous movement, and focus on abnormal data. In addition, this paper also proposes a new time series data enhancement method, which slices and re-amplifies the long time series data. This method significantly improves the accuracy of anomaly detection. Through a large number of experiments, AC-LSTM has achieved higher scores on our collected datasets than other methods.\",\n \"Title: PSNet: LiDAR and Camera Registration Using Parallel Subnetworks\\nYear: 2022\\n\\nAbstract: The working environment of autonomous driving and robot navigation is so complex and dynamic that a single type of sensor is insufficient for performing object detection. Thus, in many perception schemes, the LiDAR-camera fusion strategy is preferred. However, the performance of a LiDAR-camera fusion heavily relies on a set of accurately calibrated extrinsic parameters. We propose PSNet, an end-to-end convolutional neural network (CNN) for calibration; this is the first calibration network to use parallel subnetworks to obtain multiresolution features and fuse them adaptively to encourage robustness against different initial error ranges. The method has three key characteristics: (i) Addition of a downsampling block to improve suitability for sparse projected depth maps; (ii) Connection of the high-to-low resolution convolution streams in parallel to obtain multiresolution features that are spatially more precise and contain richer semantic information; (iii) Fusion of multiresolution streams by the multiscale feature aggregation module. The network corrects errors from initial calibration to the ground truth online, rather than directly obtaining the accurate parameters. We evaluated our model on the KITTI datasets and it outperformed other CNN-based methods. In addition, extensive experiments evaluating the model with untrained and unfamiliar datasets demonstrated that our method exhibited good generalization ability.\",\n \"Title: Cyclic Global Guiding Network for Point Cloud Completion\\nYear: 2022\\n\\nAbstract: The application of 3D scenes has gradually expanded in recent years. A 3D point cloud is unreliable when it is acquired because of the performance of the sensor. Therefore, it causes difficulties in utilization. Point cloud completion can reconstruct and restore sparse and incomplete point clouds to a more realistic shape. We propose a cyclic global guiding network structure and apply it to point cloud completion tasks. While learning the local details of the whole cloud, our network structure can play a guiding role and will not ignore the overall characteristics of the whole cloud. Based on global guidance, we propose a variety of fitting planes and layered folding attention modules to strengthen the local effect. We use the relationship between the point and the plane to increase the compatibility between the network learning and the original sparse point cloud. We use the attention mechanism of the layer overlay to strengthen the local effect between the encode and decode. Therefore, point clouds are more accurate. Our experiments indicate the effectiveness of our method on the ShapeNet, KITTI, and MVP datasets and are superior to other networks.\",\n \"Title: An Efficient Information-Reinforced Lidar Deep Completion Network without RGB Guided\\nYear: 2022\\n\\nAbstract: Due to the sparsity of point clouds obtained by LIDAR, the depth information is usually not complete and dense. The depth completion task is to recover dense depth information from sparse depth information. However, most of the current deep completion networks use RGB images as guidance, which are more like a processing method of information fusion. They are not valid when there is only sparse depth data and no other color information. Therefore, this paper proposes an information-reinforced completion network for a single sparse depth input. We use a multi-resolution dense progressive fusion structure to maximize the multi-scale information and optimize the global situation by point folding. At the same time, we re-aggregate the confidence and impose another depth constraint on the pixel depth to make the depth estimation closer to the ground trues. Our experimental results on KITTI and NYU Depth v2 datasets show that the proposed network achieves better results than other unguided deep completion methods. And it is excellent in both accuracy and real-time performance.\",\n \"Title: Application of Random Forest Algorithm on Tornado Detection\\nYear: 2022\\n\\nAbstract: Tornadoes are highly destructive small-scale extreme weather processes in the troposphere. The weather radar is one of the most effective remote sensing devices for the monitoring and early warning of tornadoes. The existing tornado detection algorithms based on radar data are unsupervised and have strict multi-altitude constraints, such as the tornado detection algorithm based on tornado vortex signatures (TDA-TVS), which may lead to high false alarm rates, and the performance of the detection algorithm is greatly affected by the radar data quality control algorithm. A novel TDA-RF algorithm based on the random forest (RF) classification algorithm is proposed for real-time tornado identification of the S-band China new generation of Doppler weather radar (CINRAD-SA). The TDA-RF algorithm uses velocity features to identify tornadoes and adds features related to reflectivity and velocity spectrum width in radar level-II data. Historical CINRAD-SA tornado data from 2006-2015 are used to construct the tornado dataset and train the TDA-RF model. The performance of TDA-RF is evaluated using CINRAD-SA data from five tornadoes of 2016-2020 with enhanced Fujita(EF) scale ratings ranging from EF0 to EF4 and distances from 10 to 130 km to the radar. TDA-RF performs well overall with the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) of 71%, 29%, and 55%, respectively. Moreover, the TDA-RF improves POD and CSI, and reduces FAR compared to the TDA-TVS. The maximum tornado early-warning time of TDA-RF is 17 min, and the average is 6 min; TDA-RF can provide classification probability according to the tornado generation and development process to facilitate tracking ability.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":200,"string":"200"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13657,"string":"13,657"}}},{"rowIdx":13658,"cells":{"question_id":{"kind":"string","value":"Chao Feng"},"question":{"kind":"string","value":"Generate an abstract for the title \"On the embedding of cycles in pancake graphs\" using the following items: 1. Star networks\n2. Pancake networks\n3. Parallel computers\n4. Embedding algorithms\n5. Hamiltonian cycles."},"answer":{"kind":"string","value":"In recent times the use of star and pancake networks as a viable interconnection scheme for parallel computers has been examined by a number of researchers. An attractive feature of these two classes of graphs is that they have sublogarithmic diameter and have a great deal of symmetry akin to the binary hypercube. In this paper we describe new algorithms for embedding: (a) Hamiltonian cycles along with ranking and unranking algorithms with respect to them, and (b) The set of cycles. The analogous problems for star graphs has been solved recently [5]."},"correct_answer":{"kind":"string","value":"In recent times the use of star and pancake networks as a viable interconnection scheme for parallel computers has been examined by a number of researchers. An attractive feature of these two classes of graphs is that they have sublogarithmic diameter and have a great deal of symmetry akin to the binary hypercube. In this paper we describe new algorithms for embedding: (a) Hamiltonian cycles along with ranking and unranking algorithms with respect to them, and (b) The set of cycles. The analogous problems for star graphs has been solved recently [5]."},"documents":{"kind":"list like","value":["Title: Adaptive System-Level Diagnosis for Hypercube Multiprocessors\nYear: 1996\n\nAbstract: System-level diagnosis is an important technique for fault detection and location in multiprocessor computing systems. Efficient diagnosis is highly desirable for sustaining the original system power. Moreover, effective diagnosis is particularly important for a multiprocessor system with high scalability but low connectivity. Most of the existing results are not applicable in practice because of the high diagnosis cost and limited diagnosability. Over-d fault diagnosis, where d is the diagnosability, has only been addressed using a probabilistic method in the literature. Aiming at these two issues, we propose a hierarchical adaptive system-level diagnosis approach for hypercube systems using a divide-and-conquer strategy. We first propose a conceptual algorithm HADA to formulate a rigorous analysis. Then we present its practical variant IHADA. In HADA and IHADA, the over-d fault problem is inherently tackled through a deterministic method. Three measures for diagnosis cost (diagnosis time, number of tests, and number of test links) are analyzed for the proposed algorithms. It is proved that the diagnosis cost required by our approach is lower than in previous diagnosis algorithms. It is shown that the diagnosis cost for the proposed algorithms depends on the number and location of faulty units in the system and the cost is extremely low when only a small number of faulty units exist. It is also shown that our algorithms are characterized by lower costs than a pessimistic diagnosis algorithm which trades lower diagnosis cost for a lower degree of accuracy. Experimental results on the nCUBE are provided to substantiate the practicality of the proposed approach.","Title: Asymptotic performance of amplify-and-forward MIMO relaying with transmit antenna selection.\nYear: 2010\n\nAbstract: This paper proposes two schemes, TAS-SD&RD and TAS-SR&RD, in which transmit antenna selection (TAS) strategies are combined\n with amplify-and-forward (AF) protocol in MIMO relaying scenario considering the destination has only limited channel state\n information (CSI) of the source-to-relay channel, and analyzes asymptotic performance of the two schemes. The source, destination,\n and relay are equipped with multiple antennas, denoted by N\n S, N\n D, and N\n R, respectively. Specifically, the paper derives the asymptotic outage probability and symbol error rate (SER) of the proposed\n schemes in the cooperative AF MIMO relaying scenario. These analyses in high SNR regime lead to the conclusion that TAS-SD&RD\n and TAS-SR&RD can achieve full diversity order, N\n S\n N\n D + NR min {N\n S,N\n D}, in some antenna configurations of the cooperative scenario. Numerical results that confirm our analyses are also presented\n in the paper.","Title: Dynamical Gaussian mixture model for tracking elliptical living objects\nYear: 2006\n\nAbstract: In this letter, we present a novel dynamical Gaussian mixture model (DGMM) for tracking elliptical living objects in video frames. The parameters, which inform object position and shape, are estimated by using a traditional Gaussian mixture model (GMM) for the first frame. Instead of simply using the parameters of one frame as the initial state for the next frame, DGMM employs Kalman filter to predict these parameters to initialize the evolution of GMM for the rest of sequel frames. Compared with the simple way, the estimate by Kalman filter is usually much closer to the true state, the computation load for tracking is reduced significantly. In addition, ''split'', ''merge'' and ''delete'' operations are integrated into DGMM to account for object splitting, merging and vanishing. This scheme can suppress the chance of getting stuck at a local maximum, led by a mismatch between the number of objects in the model and the actual number of objects. Results obtained from experiments on both simulated and real-world data demonstrate the effectiveness of our algorithm. DGMM has been successfully applied to track the early development process of urchin embryo in which the events of motility and cleavage occur.","Title: Classification of tea using LED-induced fluorescence\nYear: 2013\n\nAbstract: LED-induced fluorescence was used to evaluate the classification of teas. In this paper, the fluorescence spectra of six different types of Chinese Chaoqing teas were excited by power LED (375nm), then the fluorescence spectra were recorded by a compact spectrometer. Principal component analysis was used to discriminate the teas. The result shows the potential application of this method in the classification of teas.","Title: Diversity order analysis of a sub-optimal transmit antenna selection strategy for non-regenerative MIMO relay channel\nYear: 2009\n\nAbstract: This paper proposes a novel sub-optimal transmit antenna selection (TAS) strategy for non-regenerative half-duplex MIMO relay channel. While antenna selection is a sub-optimal form of beamforming, it still enjoys the advantage of tractable optimization and low feedback overhead. The diversity order of this proposed strategy is derived to prove that it can achieve the full diversity order as the optimal strategy with only a small performance loss. Furthermore, it is shown that our scheme lead to a reduction of complexity in terms of time slots for SNR estimation when the number of the transmit antennas are more than two. Monte Carlo simulation results of outage probability are presented to demonstrate its advantage over the other two conventional TAS schemes.","Title: An adaptive system-level diagnosis approach for hypercube multiprocessors\nYear: 1993\n\nAbstract: This paper proposes a hierarchical adaptive system-level diagnosis approach for hypercube systems. Three measures for diagnosis cost (diagnosis time, number of tests and number of test links) are analyzed for the proposed algorithm. It is proved that the diagnosis cost required by this algorithm is lower than in the previous diagnosis algorithms in most of the fault cases. It is shown that the diagnosis cost for the proposed algorithm depends on the number of faulty units in the system and the cost is extremely low when only a small number of faulty units exist. It is shown that this algorithm is even characterized by lower costs than a pessimistic diagnosis algorithm which trades lower diagnosis cost for a lower degree of accuracy.","Title: A Kinship Function Approach to Robust and Probabilistic Optimization Under Polynomial Uncertainty\nYear: 2011\n\nAbstract: In this paper, we study a class of robust design problems with polynomial dependence on the uncertainty. One of the main motivations for considering these problems comes from robust controller design, where one often encounters systems that depend polynomially on the uncertain parameters. This paper can be seen as integrated in the emerging area of probabilistic robustness, where a probabilistic relaxation of the original robust problem is adopted, thus requiring the satisfaction of the constraints not for all possible values of the uncertainty, but for most of them. Different from the randomized approach for tackling probabilistic relaxations, which is only guaranteed to provide soft bounds on the probability of satisfaction, we present a deterministic approach based on the novel concept of kinship function introduced in this paper. This allows the development of an original framework, which leads to easily computable deterministic convex relaxations of the probabilistic problem. In particular, optimal polynomial kinship functions are introduced, which can be computed a priori and once for all and provide the “best convex bound” on the probability of constraint violation. More importantly, it is proven that the solution of the relaxed problem converges to that of the original robust optimization problem as the degree of the optimal polynomial kinship function increases. Furthermore, by relying on quadrature formulas for computation of integrals of polynomials, it is shown that the computational complexity of the proposed approach is polynomial in the number of uncertain parameters. Finally, unlike other deterministic approaches to robust polynomial optimization, the number of variables in the ensuing optimization problem is not increased by the proposed approximation. An important feature of this approach is that a significant amount of the computational burden is shifted to a one-time offline computation whose results can be stored and provided to - - the end-user.","Title: Distributional robustness analysis for polynomial uncertainty\nYear: 2009\n\nAbstract: This paper addresses the problem of computing the worst-case expected value of a polynomial function, over a class of admissible distributions. It is shown that this problem, for the class of distributions considered, is equivalent to a convex optimization problem for which efficient linear matrix inequality (LMI) relaxations are available. In case that the performance function is continuous (not necessarily polynomial), the worst-case expected value can be approximated by using its polynomial approximations. Moreover, the proposed approach is applied to compute hard bounds of the worst-case probability of a polynomial being negative. Numerical examples are presented which illustrate the application of the results in this paper.","Title: On the testability of array structures for FFT computation\nYear: 1993\n\nAbstract: This article presents new approaches for testing VLSI array architectures used in the computation of the complexN-point Fast Fourier Transform. Initially, an unrestricted single cell-level fault model is considered. The first proposed approach is based on a process whose complexity is independent (or C- as constant) of the number of cells in the FFT architecture. This is accomplished by showing a topological equivalence between the FFT array and a linear (one-dimensional) array. The process of fault location is also analyzed. The second proposed method is based on a testing process whose complexity is linear with respect to the number of stages (columns) of the FFT array. A component-level fault model is also proposed and analyzed. The implications of this model on the C-testability process are fully described.","Title: Toolset to explore visual motion designs in a video game\nYear: 2012\n\nAbstract: We describe a research toolset to explore visual designs in a video game. We focus specifically on visual motion, defined by attributes of motion, and their effect on accessibility, which may lead to a diminished experience for novice players. Eight expert game designers evaluated the tool embedded into a simple point and click game. Specifically they controlled attributes of speed, size of game elements, and amount of elements on screen associated to game targets, distractions, and feedback. The tool allowed experts to define difficulty settings and expose patterns, which they verified. As a game, we then investigated the effect of visual motion on accessibility in a formal user study comprised of 105 participants. As a follow-up to this work, we expanded the toolset to include 8 additional attributes of motion.","Title: A new diagnosis approach for short faults in interconnects\nYear: 1995\n\nAbstract: Existing one-step diagnosis approaches for faults in interconnects either yield a long test sequence, or use a non-generalized procedure to generate a shorter test sequence. We propose a new diagnosis approach for short faults in interconnects. The pin-adjacency fault model is assumed. By using a divide-and-conquer strategy, our approach can generate a very compact test vector sequence which can diagnose an unrestricted number of short faults. Our experiments for three benchmarks as well as large random interconnects (up to 50,000 nets) show that our approach can achieve more than 50% savings in the length of the generated test sequence. This can significantly save the diagnosis cost for boundary-scan testing. An adaptive diagnosis approach is further proposed to dynamically truncate the originally generated test sequence based on the current information of faulty nets. The performance of our adaptive approach in terms of the on-line test generation time and the resulting test sequence length is better than for existing adaptive diagnosis approaches when the fault rate is not very small, such as in a new product line. If a low complexity for the ATE is of major importance, then the proposed one-step approach is the best choice.<>","Title: Uniform sample generations from contractive block Toeplitz matrices\nYear: 2006\n\nAbstract: This note deals with generating a series of random matrices uniformly and independently from contractive lower triangular block Toeplitz (LTBT) matrices. All the contractive LTBT matrices are parameterized nonlinearly but recursively in a closed-form by a sequence of contractive unstructured matrices. Moreover, the conditional probability density function (PDF) is obtained in a closed-form for all the matrix parameters, as well as the squares of their singular values. It has been made clear that the conditional PDFs of the matrix parameters are unitarily invariant. A procedure is developed to accurately produce the required random matrix samples. © 2006 IEEE.","Title: Evaluating affective features of 3D motionscapes\nYear: 2014\n\nAbstract: Abstract motion textures are widely applied in visual design and immersive environments such as games to imbue the environment or presentation with affect. While visual designers and artists carefully manipulate visual elements such as colour, form and motion to evoke affect, understanding what aspects of motion contribute to this still remains a matter of designer craft rather than validated principle. We report an empirical study of how simple features of motion in 3D textures, or motionscapes, contribute to the elicitation of affect. 12 university students were recruited to evaluate a series of 3D motionscapes. Results showed basic motion properties including speed, direction, path curvature and shape had significant influence on affective impressions such as valence, comfort, urgency and intensity, suggesting further directions for applications and explorations in this design space.","Title: A divide-and-conquer methodology for system-level diagnosis of processor arrays\nYear: 1994\n\nAbstract: System-level diagnosis in a multiprocessor system should be both effective and efficient. Most of the existing results are not applicable in practice because of the high diagnosis cost and limited diagnosability. We propose a divide-and-conquer methodology of fault diagnosis for a general class of processor arrays, which is one of the most popular architectures in today's multiprocessor systems. Parallel testing and adaptive diagnosis are our major strategies. The over-d fault problem is inherently tackled through a deterministic method. Based on this unified methodology, we first propose an algorithm for 2D arrays. Moreover, our methodology can be fully explored to those multi-dimensional arrays with high degree of hierarchy like hypercubes. We thus propose another diagnosis algorithm for this kind of architectures. Analytical and simulation results show that our approaches are superior.","Title: CMOS 1.2V bandgap voltage reference design\nYear: 2013\n\nAbstract: A typical bandgap voltage reference based on CSMC 0.35 μm CMOS technology is designed and fabricated. The overall bandgap architecture is optimized to achieve high accuracy temperature and power supply independent voltage reference. The design consists of the bandgap core circuit, op-amp, and start-up circuit. The test results show that the bandgap reference circuit provides reference voltage of 1.2128 V to 1.2175 V with 3.3 V power supply and temperature range from -40°C to 80°C simultaneously, and the temperature coefficient is 32.2ppm/°C. The total layout area including dummy structures is 135 um×236 um.","Title: Drift Detection for Multi-label Data Streams Based on Label Grouping and Entropy\nYear: 2014\n\nAbstract: Many real-world applications involve multi-label data streams, so effective concept drift detection methods should be able to consider the unique properties of multi-label stream data, such as label dependence. To deal with these challenges, we proposed an efficient and effective method to detect concept drift based on label grouping and entropy for multi-label data. Two methods are proposed to group the set of class labels into different subsets and a multi-label version of entropy was adjusted to measure the distribution of multi-label data. Concept drift was detected by comparing the entropies of the older and the most recent data. The experiments are run on three synthetic datasets and two real-world datasets and the experimental results illustrate the better classification performance of the proposed method for detecting drift.","Title: Hybrid system identification via sparse polynomial optimization\nYear: 2010\n\nAbstract: In this paper, the problem of identifying discrete time affine hybrid systems with measurement noise is considered. Given a finite collection of measurements and a bound on the noise, the objective is to identify a hybrid system with the smallest number of sub-systems that is compatible with the a priori information. While this problem has been addressed in the literature if the input/output data is noise-free or corrupted by process noise, it remains open for the case of measurement noise. To handle this case, we propose a new approach based on recasting the problem into a polynomial optimization form and exploiting its inherent sparse structure to obtain computationally tractable problems. Combining these ideas with a randomized Hit and Run type approach leads to further computational complexity reduction, allowing for solving realistically sized problems. Numerical examples are provided, illustrating the effectiveness of the algorithm and its potential to handle large size problems.","Title: Hybrid system identification: An SDP approach\nYear: 2010\n\nAbstract: The problem of identifying discrete time affine hybrid systems with noisy measurements is addressed in this paper. Given a finite number of measurements of input/output and a bound on the measurement noise, the objective is to identify a switching sequence and a set of affine models that are compatible with the a priori information, while minimizing the number of affine models. While this problem has been successfully addressed in the literature if the input/output data is noise-free or corrupted by process noise, results for the case of measurement noise are limited, e.g., a randomized algorithm has been proposed in a previous paper. In this paper, we develop a deterministic approach. Namely, by recasting the identification problem as polynomial optimization, we develop deterministic algorithms, in which the inherent sparse structure is exploited. A finite dimensional semi-definite problem is then given which is equivalent to the identification problem. Moreover, to address computational complexity issues, an equivalent rank minimization problem subject to deterministic LMI constraints is provided, as efficient convex relaxations for rank minimization are available in the literature. Numerical examples are provided, illustrating the effectiveness of the algorithms.","Title: On Control System Design Using Random Samples Of Contractive Block Toeplitz Matrices\nYear: 2007\n\nAbstract: Contractive lower triangular block Toeplitz (LTBT) matrices have recently been parametrized in a closed-form by a sequence of unstructured contractive matrices. A problem naturally arises between uniform samplings over contractive LTBT matrices and their matrix parameters, that is, which one is more appropriate in control system analysis and synthesis. This paper makes it clear that uniform sampling over matrix parameters results in a contractive LTBT matrix with a more rapid concentration speed towards the boundary of the uncertainty set. This is not an attractive property in control-related problems. Numerical comparisons also show that uniform sampling directly over contractive LTBT matrices outperforms that over the matrix parameter set in robustness analysis for a closed-loop system, as well as in strongly stable closed-loop system design with complexity constraints on the controller. As a sacrifice, uniform sampling over contractive LTBT matrices is more mathematically complicated and more time consuming.","Title: Mixed-precision models for calculation of high-order virial coefficients on GPUs\nYear: 2014\n\nAbstract: The virial equation of state (VEOS) is a density expansion of the thermodynamic pressure with respect to an ideal-gas reference. Its coefficients can be computed from a molecular model, and become more expensive to calculate at higher order. In this paper, we use GPU to calculate the 8th, 9th and 10th virial coefficients of the Lennard-Jones (LJ) potential model by the Mayer Sampling Monte Carlo (MSMC) method and Wheatley's algorithm. Two mixed-precision models are proposed to overcome a potential precision limitation of current GPUs while maintaining the performance benefit. On the latest Kepler architecture GPU Tesla K40, an average speedup of 20 to 40 is achieved for these calculations.","Title: A Method Based on Meta-model for the Translation from UML into Event-B\nYear: 2016\n\nAbstract: UML model has become the standard in the field of modeling. However, UML is a kind of semi-formal language that lacks precise semantic description. In addition, UML models cannot be verified formally. The problem above could be solved through the translation from UML model into a formal model that could then be verified formally. However, the traditional translation is on the layer of model which can't realize the automatic translation and the rules of translation are complex. To address this need, we propose a method based on meta-model for the translation from UML into Event-B. We created the rules of translation on the layer of meta-model and realized automated translation on the layer of model. To do this, the meta-models of UML and Event-B were created by meta-modeling. Next, the translation rules were constructed by ATL. Finally, a representative case study is presented to illustrate the feasibility and effectiveness of this method.","Title: Testing Network Protocol Binary Software with Selective Symbolic Execution\nYear: 2016\n\nAbstract: The vulnerabilities existing in network protocol implementations are difficult to detect. The main reason is that the state space of complex protocol binary software is too large to explore. This paper proposes a novel approach that leverages selective symbolic execution to test network protocol binary software directly, which confines symbolic execution in the secure-sensitive area. This paper also builds a prototype system, S2EProtocol, upon the Selective Symbolic Execution (S2E) platform and uses it to test several real network protocol binary software. The evaluation results show that the proposed method can be used to find vulnerabilities efficiently and effectively.","Title: The recognition of traffic speed limit sign in hazy weather.\nYear: 2017\n\nAbstract: Hazy weather affects drivers' sightline seriously and causes a high potential safety hazard. This paper proposes a novel approach for recognizing the speed limit sign in hazy weather. It consists of three major modules: haze removal, speed limit sign location, and sign recognition. In haze removal, this paper proposes to dehaze image with the dark channel prior. The speed limit sign is located by Histogram of Oriented Gradient (HOG) feature extraction and Support Vector Machine (SVM) classification and is recognized by the seven layers Convolutional Neural Networks (CNN). Experimental results show that the proposed method has better performance than the state-of-art dehazing methods and the processing time is also reduced. The recognition rate of the speed limit signs is 98.51% that is better than the human performance, and the classifier can recognize the speed limit sign with rotation, shift, scale and other distortions.","Title: Demo: Material Identification with Commodity RFID Devices.\nYear: 2017\n\nAbstract: Target material identification plays an important role in many real-life applications. This paper introduces a system that can identify the material type with cheap commercial off-the-shelf (COTS) RFID devices. The key intuition is that different materials cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. However, without knowing either material type, trying to obtain the information is challenging. We propose a method to address this challenge and evaluate the method's performance in real-world environment. The results show that we achieve higher than 94% material identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi.","Title: Improving the Cooperation of Fuzzing and Symbolic Execution by Test-cases Prioritizing.\nYear: 2017\n\nAbstract: Nowadays much attention is paid to the threat of vulnerabilities on the software security. Fuzzing and symbolic execution, complementary to each other, are two effective techniques in software testing. In this paper, we develop the prototype called FAS(Fuzzing and Symbolic) for software testing under both fuzzing and symbolic execution. In our method, the test cases are prioritized through deep-oriented strategy and large-distance-first strategy, in order to get a higher path-coverage with the condition of limited resource","Title: S2f: Discover Hard-To-Reach Vulnerabilities By Semi-Symbolic Fuzz Testing\nYear: 2017\n\nAbstract: Fuzz testing is a popular program testing technique. However, it is difficult to find hard-to-reach vulnerabilities that are nested with complex branches. In this paper, we propose semi-symbolic fuzz testing to discover hard-to-reach vulnerabilities. Our method groups inputs into high frequency and low frequency ones. Then symbolic execution is utilized to solve only uncovered branches to mitigate the path explosion problem. Especially, in order to play the advantages of fuzz testing, our method locates critical branch for each low frequency input and corrects the generated test cases to comfort the branch condition. We also implemented a prototype-S2F, and the experimental results show that S2F can gain 17.70% coverage performance and discover more hard-to-reach vulnerabilities than other vulnerability detection tools for our benchmark.","Title: Target Material Identification with Commodity RFID Devices\nYear: 2017\n\nAbstract: BSTRACTTarget material identification plays an important role in many real-life applications. This paper introduces a system that can identify the material type with cheap commercial off-the-shelf (COTS) RFID devices. The key intuition is that different materials cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. However, without knowing either material type, trying to obtain the information is challenging. We propose a method to address this challenge and evaluate the method's performance in real-world environment. The results show that we achieve higher than 94% material identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi.","Title: An Exploitability Analysis Technique for Binary Vulnerability Based on Automatic Exception Suppression.\nYear: 2018\n\nAbstract: To quickly verify and fix vulnerabilities, it is necessary to judge the exploitability of the massive crash generated by the automated vulnerability mining tool. While the current manual analysis of the crash process is inefficient and time-consuming, the existing automated tools can only handle execute exceptions and some write exceptions but cannot handle common read exceptions. To address this problem, we propose a method of determining the exploitability based on the exception type suppression. This method enables the program to continue to execute until an exploitable exception is triggered. The method performs a symbolic replay of the crash sample, constructing and reusing data gadget, to bypass the complex exception, thereby improving the efficiency and accuracy of vulnerability exploitability analysis. The testing of typical CGC/RHG binary software shows that this method can automatically convert a crash that cannot be judged by existing analysis tools into a different crash type and judge the exploitability successfully.","Title: Perfect Gaussian Integer Sequence Pairs From Cyclic Difference Set Pairs\nYear: 2018\n\nAbstract: The concept of Gaussian integer sequence pair is generalized from a single Gaussian integer sequence. In this letter, by adopting cyclic difference set pairs, a new construction method for perfect Gaussian integer sequence pairs is presented. Furthermore, the necessary and sufficient conditions for constructing perfect Gaussian integer sequence pairs are given. Through the research in this paper, a large number of perfect Gaussian integer sequence pairs can be obtained, which can greatly extend the existence of perfect sequence pairs.","Title: Construction of User Concurrent Profile Based on Usage Logs\nYear: 2018\n\nAbstract: With the wide application of software systems based on Service-Oriented Architecture (SOA), the reliability of such software has received more and more attention. Software reliability testing and evaluation is an important means to improve reliability of SOA systems, the first step of which is the construction of an operational profile. However, using traditional operational profile is difficult to describe the characteristics of user concurrency and the real usage scenarios of SOA systems. Moreover, it is also difficult to ensure the accuracy of operational probabilities because of the subjectivity of expert experience in determining the probability of an operation. In this paper, a construction method of user concurrent profile based on usage logs is proposed. In the user concurrent profile, the characteristics of user concurrency in SOA software systems is embodied; besides, the accuracy of usage probability can be ensured due to the data from usage logs.","Title: Analyzing Network Protocol Binary Software With Joint Symbolic Execution\nYear: 2016\n\nAbstract: Network protocol binary software is threaten by vulnerabilities. Current analyzing methods do not make full use of the interaction information in network protocol and do not treat both sides of network protocol as a whole system. This paper proposes a novel method based on joint symbolic execution to test synchronously both server and client of the whole network protocol binary software stacks. Moreover, this paper presents a prototype system, S2EProtocol-joint, upon Selective Symbolic Execution (S2E) platform to automatically test network protocol binary software. The experiment results validate that this approach is effective and efficient in detecting vulnerabilities.","Title: Exploring Scalable Computing Architectures For Interactions Analysis\nYear: 2018\n\nAbstract: Characterization of pharmacological signal transductions leading to drug-induced expressions of genes and proteins requires the capability to identify interactions among different potential predictor components, e.g. genomic data, clinical data, and environmental data. The detection of these gene-gene and gene-environment interactions remains challenging due to the exponential computational complexity and high dimensionality of the interaction problem. The problem is further exacerbated due to the involvement of very large-scale epidemiological datasets. Efficient high-order interaction analysis of such large-scale data is not feasible with the traditional frameworks. Parallel implementations of such applications in traditional cluster environments are often inefficient due to the storage bandwidth and network I/O limitations. Scalable distributed platforms can offer better scalability to such problems compared to the cluster architectures. Moreover, such data- and compute-intensive problems can benefit even further from data-intensive supercomputing (DISC) architectures that have been shown to yield superior performance compared to the regularly used cluster platforms. In this paper, we evaluate the applicability of different architectures such as traditional server based distributed architectures supported on commodity hardware and shared nothing architectures with massively parallel processing capabilities, towards the Interaction Analysis problem. Our experiments show that the massively parallel processing, shared-nothing architecture outweigh the benefits often realized through traditional server based and even distributed computing architectures. We conclude that the rapidly growing class of shared nothing architectures offers a potentially efficient and viable alternative to facilitate high-order interaction analysis involving extremely large scale biological datasets and is well suited to this category of data- and compute-intensive problems that cannot be addressed effectively using traditional frameworks.","Title: Wired/Wireless Integrated Multi-Screen Display Endoscopic System\nYear: 2016\n\nAbstract: Medical electronic endoscope has a wide range of applications in medical diagnostic. Most of the traditional medical electronic endoscopes are wired endoscopes which are unavailable in many circumstances. Considering the weaknesses of the traditional video endoscope and combining with its future development trend, we design and implement the wired/wireless multi-screen display endoscopic system. The video signal can not only be transmitted to the Liquid Crystal Display by cable, but also be transmitted to the mobile terminals via Wi-Fi. In this work, a video endoscopic system prototype was built which uses H.264 video compression to reduce the data size needed to be transmitted by the Wi-Fi transmitter. The ARM9 based video processor performs the main task of video compression and transmission, CC3100 to establish the wireless link and the camera used is analog camera.","Title: Research on Method of Emergency Cases Retrieval Based on Case-Based Reasoning\nYear: 2017\n\nAbstract: Aiming at the problem of emergency cases retrieval, this paper proposes a method of emergency cases retrieval considering the predator behavior of birds. Based on case-based reasoning (CBR) theory, this method introduces the particle swarm optimization (PSO) algorithm and tabu search (TS) into the calculation of attribute feature weight, and then calculates the similarity between target case and historical case for finding the most similar historical case. The validity and feasibility of the proposed method are verified by the collected data.","Title: Discover deeper bugs with dynamic symbolic execution and coverage-based fuzz testing.\nYear: 2018\n\nAbstract: Coverage-based fuzz testing and dynamic symbolic execution are both popular program testing techniques. However, on their own, both techniques suffer from scalability problems when considering the complexity of modern software. Hybrid testing methods attempt to mitigate these problems by leveraging dynamic symbolic execution to assist fuzz testing. Unfortunately, the efficiency of such methods is ...","Title: The establishment of diabetes diet classification model based on BL_WSmote\nYear: 2018\n\nAbstract: This paper analyzes the daily diet recipe of diabetes from traditional Chinese medicine diet, and establishes a model to determine the type of diabetes. The process of building a model is as follows. Firstly, use improved Smote algorithm to do data preprocessing. Secondly, using GA to optimize Random Forest do attribute reduction. Finally, using Bagging establish model. We establish 3 sets of comparison experiments. The comparison of 4 different algorithms between Random Forest, GDBT, Ada Booster, Bagging 4 different algorithms; The comparison between raw data, Smote improved data, BL-Smote improved data, BL-WSmote improved; The comparison between the whole attributes and the important attributes. The model accuracy is as high as 92%, it is proved that this model has certain applicable value in determining diabetes classification.","Title: Material Identification with Commodity Wi-Fi Devices.\nYear: 2018\n\nAbstract: Target material identification is playing an important role in our everyday life. This paper introduces a device-free target material identification system, implemented on ubiquitous and cheap commercial off-the-shelf (COTS) Wi-Fi devices. The intuition is that different materials produce different amounts of phase and amplitude changes when a target appears on the line-of-sight (LoS) of a radio frequency (RF) link. However, due to multipath and hardware imperfection, the measured phase and amplitude of the channel state information (CSI) are very noisy. We thus present novel CSI pre-processing schemes to address the multipath and hardware noise issues before they can be used for accurate material sensing. Comprehensive real-life experiments demonstrate that we can identify 10 commonly seen liquids at an overall accuracy higher than 95% with strong multipath indoors.","Title: Product-Aware Helpfulness Prediction of Online Reviews\nYear: 2019\n\nAbstract: Helpful reviews are essential for e-commerce and review websites, as they can help customers make quick purchase decisions and merchants to increase profits. Due to a great number of online reviews with unknown helpfulness, it recently leads to promising research on building automatic mechanisms to assess review helpfulness. The mainstream methods generally extract various linguistic and embedding features solely from the text of a review as the evidence for helpfulness prediction. We, however, consider that the helpfulness of a review should be fully aware of the metadata (such as the title, the brand, the category, and the description) of its target product, besides the textual content of the review itself. Hence, in this paper we propose an end-to-end deep neural architecture directly fed by both the metadata of a product and the raw text of its reviews to acquire product-aware review representations for helpfulness prediction. The learned representations do not require tedious labor on feature engineering and are expected to be more informative as the target-aware evidence to assess the helpfulness of online reviews. We also construct two large-scale datasets which are a portion of the real-world web data in Amazon and Yelp, respectively, to train and test our approach. Experiments are conducted on two different tasks: helpfulness identification and regression of online reviews, and results demonstrate that our approach can achieve state-of-the-art performance with substantial improvements.","Title: Preliminary Study on Efficient Named Entity Recognition\nYear: 2019\n\nAbstract: In this paper, we describe our preliminary study for efficient recognition and extraction of named entities on mobile phones. The methodology investigation and experiment verification are focusing on two parts. The first part is a compressed named entities recognition (NER)-model-based named entity recognizer to reduce the memory usage. The second part is an NER classifier. When the result from compressed named entity recognizer is blank, the NER classifier will help to further check if the result is correct to increase the final accuracy. In addition, the framework is described. The study shows that the proposed named entity recognizer remains almost the same recognition performance but reduce the storage cost evidently. Therefore, latter research can dig further in terms of memory usage reduction and performance optimization.","Title: Program State Sensitive Parallel Fuzzing for Real World Software.\nYear: 2019\n\nAbstract: Fuzz testing is a widely used technique for software vulnerability detection, but it is still limited in finding bugs nested in the deep program states. Parallel testing is an augmented method aiming to make the best of the computing resource to expose more deep program bugs. However, current parallel testing methods cannot deal well with task slicing so that the parallel nodes show serious duplication with each other, thus decreasing the efficiency in total. For instance, the original parallel mode of the well-known fuzzer American fuzzy lop (AFL) does not split the task and just synchronizes the interesting seeds without any internal execution information. In this paper, we put forward a novel program state sensitive parallel testing method, which: 1) splits the task into low correlated subtasks according to the program states and 2) adjusts the mutation engine to confine one instance's testing among its subtask-related code region as more as possible. Our method is an objective to reduce the testing collision between parallel instances and therefore improve the performance. We developed a new fuzzer called PAFL and implemented some experiments to investigate that if our parallel testing framework is positive when deploying multiple instances and if it shows a better path discovery compared with two state-of-the-art fuzzers, AFL and AFLFast. In the experiments, we employed PAFL with 1/2/4/8/16 to observe their path discovery and conclude that our new parallel framework is positive when using more multiple instances. We also compared PAFL with AFL and AFLFast by employing eight parallel instances for each fuzzer, and the results prove that our tool has the best path discovery among the three fuzzers. Compared with the original parallel AFL, PAFL can achieve averaged performance gains of 3.98, 3.04, 4.18, and 1.45 on the c + +filt, objdump, readelf, and tcpdump, respectively. Besides, we took PAFL on binutils and libtiff, and finally, we found ten new bugs.","Title: Unsupervised Feature Selection by Pareto Optimization\nYear: 2019\n\nAbstract: Dimensionality reduction is often employed to deal with the data with a huge number of features, which can be generally divided into two categories: feature transformation and feature selection. Due to the interpretability, the efficiency during inference and the abundance of unlabeled data, unsupervised feature selection has attracted much attention. In this paper, we consider its natural formulation, column subset selection (CSS), which is to minimize the reconstruction error of a data matrix by selecting a subset of features. We propose an anytime randomized iterative approach POCSS, which minimizes the reconstruction error and the number of selected features simultaneously. Its approximation guarantee is well bounded. Empirical results exhibit the superior performance of POCSS over the state-of-the-art algorithms.","Title: A Queueing-Theoretic Framework for Vehicle Dispatching in Dynamic Car-Hailing\nYear: 2019\n\nAbstract: With the rapid development of smart mobile devices, the car-hailing platforms (e.g., Uber or Lyft) have attracted much attention from both the academia and the industry. In this paper, we consider an important dynamic car-hailing problem, namely maximum revenue vehicle dispatching (MRVD), in which rider requests dynamically arrive and drivers need to serve as many riders as possible such that the entire revenue of the platform is maximized. We prove that the MRVD problem is NP-hard and intractable. To handle the MRVD problem, we propose a queueing-based vehicle dispatching framework, which first uses existing machine learning algorithms to predict the future vehicle demand of each region, then estimates the idle time periods of drivers through a queueing model for each region. With the information of the predicted vehicle demands and estimated idle time periods of drivers, we propose one batch-based vehicle dispatching algorithm to efficiently assign suitable drivers to riders such that the expected entire revenue of the platform is maximized during each batch processing. Through experiments over real data sets, we demonstrate the efficiency and effectiveness of our proposed framework.","Title: NeuralTaint: A Key Segment Marking Tool Based on Neural Network.\nYear: 2019\n\nAbstract: Dynamic taint analysis techniques are a popular dynamic software analysis method. Marking a key segment of program function by dynamic taint analysis is an important part of software vulnerability research. Key segment marking usually related to the control flow taint analysis, however, several specific program structure may cause failure in key segment marking due to the control flow dependence, and overtainting and undertainting problem. In this paper, we proposed a novel method to mark a key segment accurately and efficiently with deep learning technology. Firstly, we fit the program function execution into a continuous function by the convoluntional network, and then mark the key segment roughly through derivative information of fitted nerual network. Finally, we mark the key segment of specific program function completely and accurately by filtering and diffusion algorithm. We developed the key segment marking tool NeuralTaint on this principle. We design an experiment to select the specific neural network structure of NeuralTaint. Our extensive evaluations demonstrate that NeuralTaint significantly outperforms the two state-of-the-art traditional dynamic taint analysis tool on seven popular real-world programs.","Title: Sensing Our World Using Wireless Signals\nYear: 2019\n\nAbstract: In the last few years, pervasive wireless signals have been established as a powerful medium for ubiquitous sensing. Wireless sensing quickly becomes an active cross-disciplinary research area that involves wireless communication, signal processing, human-computer interaction, machine learning, and even hardware design. This paper provides an accessible introduction to this exciting area and presents the key technologies enabling it. It highlights the latest achievements in this field, and the unaddressed research challenges as well as how emerging wireless standards can better support this promising technique.","Title: A Lightweight Assisted Vulnerability Discovery Method Using Deep Neural Networks.\nYear: 2019\n\nAbstract: Thousands of vulnerabilities are discovered in programs every day, which is extremely harmful to software security. Thus, discovering vulnerabilities in projects has become a central issue. Facing a sustained growth of software complexity and large code size, manual code auditing becomes time-consuming and labor-intensive. With more open source programs available and a high degree of code formalization, it is possible to study features from source code to guide vulnerability discovery work. In this paper, we present a lightweight-assisted vulnerability discovery method using a deep neural network (LAVDNN) to detect weakness and to provide guidance for manual auditing. The method proposed in this paper leverages function names as semantics features to uncover weak functions in large-scale open source programs. First, we extract function names and classify into weak and benign datasets. Then, we construct deep neural networks and compare the performances of different models. According to the experimental results, our method performs well for both C/C++ and Python programs, with the F-2-score reaching 0.91 and 0.915, respectively. Ultimately, we evaluate the method by comparing with other approaches using the libraries FFmpeg 0.6 and LibTIFF 4.0.6. The results show that the LAVDNN could narrow the range of functions to be analyzed and report more weak functions without any prior vulnerability information. As a lightweight-assisted tool, the LAVDNN significantly reduces the false positive rate and hardly misses weak functions.","Title: Multi-Objective Submodular Maximization By Regret Ratio Minimization With Theoretical Guarantee\nYear: 2021\n\nAbstract: Submodular maximization has attracted much attention due to its wide application and attractive property. Previous works mainly considered one single objective function, while there can be multiple ones in practice. As the objectives are usually conflicting, there exists a set of Pareto optimal solutions, attaining different optimal trade-offs among multiple objectives. In this paper, we consider the problem of minimizing the regret ratio in multi-objective submodular maximization, which is to find at most k solutions to approximate the whole Pareto set as well as possible. We propose a new algorithm RRMS by sampling representative weight vectors and solving the corresponding weighted sums of objective functions using some given alpha-approximation algorithm for single-objective submodular maximization. We prove that the regret ratio of the output of RRMS is upper bounded by 1-alpha + O(root d-1).(d/k-d)(d1-d). where d is the number of objectives. This is the first theoretical guarantee for the situation with more than two objectives. When d = 2, it reaches the (1 - alpha + O(1/k))-guarantee of the only existing algorithm POLYTOPE. Empirical results on the applications of multiobjective weighted maximum coverage and Max-Cut show the superior performance of RRMS over POLYTOPE.","Title: An Effective Nuclear Extraction Mask Method for SVM Classification\nYear: 2020\n\nAbstract: With the development of medical technology, the automatic cell analysis system plays an important role in medical diagnosis and medical image processing. The kernel recognition theory and technology based on support vector machine (SVM) classifier are mainly optimized from the perspective of the kernel segmentation algorithm to improve the recognition accuracy of the SVM classifier. Unfortunately, the nuclear overlap treatment can not accurately separate the nuclear gelling impurities in the dyeing process, resulting in the low classification accuracy of SVM. To solve the above image segmentation problems in the process of nuclear imaging processing, an effective nuclear extraction method based on the mask method for the SVM classifier is proposed. Compared with related work, the proposed method enables one to achieve a higher accuracy of SVM cross-validation.","Title: Wireless Communication with Extremely Large-Scale Intelligent Reflecting Surface\nYear: 2021\n\nAbstract: Intelligent reflecting surface (IRS) is a promising technology for wireless communications, thanks to its potential capability to engineer the radio environment. However, in practice, such an envisaged benefit is attainable only when the passive IRS is of a sufficiently large size, for which the conventional uniform plane wave (UPW)-based channel model may become inaccurate. In this paper, we purs...","Title: Reducing Test Cases with Attention Mechanism of Neural Networks\nYear: 2021\n\nAbstract: As fuzzing techniques become more effective at triggering program crashes, how to triage crashes with less human efforts has become increasingly imperative. To this aim, test case reduction which reduces a crashing input to its minimal form plays an important role, especially when analyzing programs with random, complex, or large inputs. However, existing solutions rely on random algorithms or pre-defined rules, which are inaccurate and error-prone in many cases because of the implementation variance in program internals. In this paper, we present SCREAM, a new approach that leverages neural networks to reduce test cases. In particular, by feeding the network with a program's crashing inputs and non-crashing inputs, the network learns to approximate the computation from the program entry point to the crash point and implicitly denotes the input bytes that are significant to the crash. With the invisibility of the trained network's parameters, we leverage the attention mechanism to explain the network, namely extracting the significance of each input byte to the crash. At the end, the significant input bytes are re-assembled as the failure-inducing input. The cost of our approach is to design a proper dataset augmentation algorithm and a suitable network structure. To this end, we develop a unique dataset augmentation technique that can generate adequate and highly-differentiable samples and expand the search space of crashing input. Highlights of our research also include a novel network structure that can capture dependence of input blocks in long sequences. We evaluated SCREAM on 41 representative programs. The results show that SCREAM outperforms state-of-the-art solutions regarding accuracy and efficiency. Such improvement is made possible by the network's capability to summarize the significance of input bytes from multiple rounds of mutation, which tolerates perturbation occurred in random reduction of single crashing input.","Title: Automated Detection On The Security Of The Linked-List Operations\nYear: 2022\n\nAbstract: 1 Introduction\r\nAs measures taken by OS(operation system)to mitigate exploit getting more perfect,the stack-related vulnerabilities are reduced gradually and heap-related vulnerabilities have become increasingly prominent in most C/C++memory vulnerabilities.","Title: Android malicious application detection based on Support Filtering and Lasso LR Algorithm\nYear: 2021\n\nAbstract: With the vigorous development of the mobile Internet, the Android system, which accounts for 76% of the drama mobile operating system, has also been widely promoted and popularized. However, due to its own open source characteristics, various parts of the Android system are facing serious threats from hacker attacks, and the main threat comes from malicious applications. In order to cope with the ...","Title: Pushing the Limits of Respiration Sensing with Reconfigurable Metasurface\nYear: 2021\n\nAbstract: ABSTRACTHuman respiration monitoring acts as a crucial role to indicate people's daily health. Compared with traditional respiration monitoring methods, device-free wireless respiration sensing technology is emerging as a promising modality due to the less privacy intrusive and without on-body sensors. However, due to the intrinsic nature of relying on weak reflection signals for sensing, the sensing range is limited. Meanwhile, reliable sensing performance only can be achieved when the environment with little or even no interference. In this work, we propose a WiFi-based respiration system to simultaneously enlarge the sensing range and mitigate the interference. The basic idea is to employ a reconfigurable metasurface to dynamically manipulate electromagnetic waves in the environment to achieve beamforming and beam steering. Our system thus enhances the sensing range and reduces the energy of reflected signals from interferers to ensure reliable performance. Proof-of-concept experiments demonstrate the effectiveness of our scheme.","Title: AVA-AVD: Audio-visual Speaker Diarization in the Wild\nYear: 2022\n\nAbstract: ABSTRACTAudio-visual speaker diarization aims at detecting \"who spoke when'' using both auditory and visual signals. Existing audio-visual diarization datasets are mainly focused on indoor environments like meeting rooms or news studios, which are quite different from in-the-wild videos in many scenarios such as movies, documentaries, and audience sitcoms. To develop diarization methods for these challenging videos, we create the AVA Audio-Visual Diarization (AVA-AVD) dataset. Our experiments demonstrate that adding AVA-AVD into training set can produce significantly better diarization models for in-the-wild videos despite that the data is relatively small. Moreover, this benchmark is challenging due to the diverse scenes, complicated acoustic conditions, and completely off-screen speakers. As a first step towards addressing the challenges, we design the Audio-Visual Relation Network (AVR-Net) which introduces a simple yet effective modality mask to capture discriminative information based on face visibility. Experiments show that our method not only can outperform state-of-the-art methods but is more robust as varying the ratio of off-screen speakers. Our data and code has been made publicly available at \\textcolormagenta \\urlhttps://github.com/showlab/AVA-AVD .","Title: Game of Hide-and-Seek: Exposing Hidden Interfaces in Embedded Web Applications of IoT Devices\nYear: 2022\n\nAbstract: ABSTRACTRecent years have seen increased attacks targeting embedded web applications of IoT devices. An important target of such attacks is the hidden interface of embedded web applications, which employs no protection but exposes security-critical actions and sensitive information to illegitimate users. With the severity and the pervasiveness of this issue, it is crucial to identify the vulnerable hidden interfaces, shed light on best practices and raise public awareness. In this paper, we present, a new approach that automatically exposes hidden web interfaces of IoT devices. Specifically, constructs probing requests through firmware analysis to test physical devices, and narrows down the scope of identification by filtering out irrelevant requests and interfaces through differential analysis. It pinpoints hidden interfaces by attaching various device-setting parameters in the probing requests and matching keywords of sensitive information. Evaluated on 17 IoT devices, successfully identified 44 vulnerabilities, including 43 previously unknown ones. also demonstrates surprising efficiency: on average, it delivered 151438 probing requests, taking only 47 minutes on each target device.","Title: A data hiding scheme based on the difference of image interpolation algorithms\nYear: 2022\n\nAbstract: Interpolation is a mature technique used in image processing and many data hiding schemes based on image interpolation have been proposed. In this paper, a novel efficient data hiding scheme based on the difference between image interpolation algorithms is proposed. The interpolated values obtained by neighboring points using different interpolation algorithms are generally different, and the difference can be used for encoding secret data. 2k different interpolated values calculated by 2k different interpolation algorithms respectively in the ideal case, and they are numbered in increasing order to correspond to the k bits secret data in each interpolated pixel. At the receiver's side, the secret data can be obtained based on the matching of the sorting results of these interpolation values of the accepted interpolation values. In fact, different algorithms may produce the same interpolation values, it is necessary to modify the appropriate number of interpolation value so that the number of different interpolation values reaches 2k, then the following image hiding procedures can employ the method of ideal case. The experimental results show that the proposed hiding scheme provides high embedding capacity with good visual quality.","Title: Forest-based Deep Recommender\nYear: 2022\n\nAbstract: With the development of deep learning techniques, deep recommendation models also achieve remarkable improvements in terms of recommendation accuracy. However, due to the large number of candidate items in practice and the high cost of preference computation, these methods also suffer from low efficiency of recommendation. The recently proposed tree-based deep recommendation models alleviate the problem by directly learning tree structure and representations under the guidance of recommendation objectives. However, such models have two shortcomings. First, the max-heap assumption in the hierarchical tree, in which the preference for a parent node should be the maximum between the preferences for its children, is difficult to satisfy in their binary classification objectives. Second, the learned index only includes a single tree, which is different from the widely-used multiple trees index, providing an opportunity to improve the accuracy of recommendation. To this end, we propose a Deep Forest-based Recommender (DeFoRec for short) for an efficient recommendation. In DeFoRec, all the trees generated during training process are retained to form the forest. When learning node representation of each tree, we have to satisfy the max-heap assumption as much as possible and mimic beam search behavior over the tree in the training stage. This is achieved by DeFoRec to regard the training task as multi-classification over tree nodes at the same level. However, the number of tree nodes grows exponentially with levels, making us to train the preference model by the guidance of sampled-softmax technique. The experiments are conducted on real-world datasets, validating the effectiveness of the proposed preference model learning method and tree learning method.","Title: Default: Mutual Information-based Crash Triage for Massive Crashes\nYear: 2022\n\nAbstract: With the considerable success achieved by modern fuzzing in-frastructures, more crashes are produced than ever before. To dig out the root cause, rapid and faithful crash triage for large numbers of crashes has always been attractive. However, hindered by the practical difficulty of reducing analysis imprecision without compromising efficiency, this goal has not been accomplished. In this paper, we present an end-to-end crash triage solution Default, for accurately and quickly pinpointing unique root cause from large numbers of crashes. In particular, we quantify the “crash relevance” of program entities based on mutual information, which serves as the criterion of unique crash bucketing and allows us to bucket massive crashes without pre-analyzing their root cause. The quantification of “crash relevance” is also used in the shortening of long crashing traces. On this basis, we use the interpretability of neural networks to precisely pinpoint the root cause in the shortened traces by evaluating each basic block's impact on the crash label. Evaluated with 20 programs with 22216 crashes in total, Default demonstrates remarkable accuracy and performance, which is way beyond what the state-of-the-art techniques can achieve: crash de-duplication was achieved at a super-fast processing speed - 0.017 seconds per crashing trace, without missing any unique bugs. After that, it identifies the root cause of 43 unique crashes with no false negatives and an average false positive rate of 9.2%.","Title: a low-cost and reconfigurable metasurface for mmWave networks: poster abstract\nYear: 2022\n\nAbstract: BSTRACTMillimeter-wave (mmWave) technology is emerging as the most promising candidate to support a wide range of applications with high data rate demand. However, due to the high directivity of mmWaves, its links are highly susceptible to barriers from walls and the movement of people. To address these issues, this paper introduces a low-cost and reconfigurable metasurface placed in the environment to reshape and resteer mmWave beams. The metasurface consists of many unit-cells, each acting as a phase shifter for signals going through it. By encoding the phase shifting values, the metasurface can reshape and resteer mmWave beams, thereby enabling a fast mmWave beam relay through the wall or redirects the beam power to another direction when a human body blocks the line-of-sight path. Preliminary simulated results show our designed metasurface can perform accurate beam steering within a field-of-view of [-60°, 60°]. And even with the small-size prototype (16 × 16 array of unit-cells), the metasurface enables up to 10.8 dB signal strength improvement.","Title: Branch Identification and Junction Points Location for Apple Trees Based on Deep Learning\nYear: 2022\n\nAbstract: Branch identification is key to the robotic pruning system for apple trees. High identification accuracy and the positioning of junction points between branch and trunk are important prerequisites for pruning with a robotic arm. Recently, with the development of deep learning, Transformer has been gradually applied to the field of computer vision and achieved good results. However, the effect of branch identification based on Transformer has not been verified so far. Taking Swin-T and Resnet50 as a backbone, this study detected and segmented the trunk, primary branch and support of apple trees on the basis of Mask R-CNN and Cascade Mask R-CNN. The results show that, when Intersection over Union (IoU) is 0.5, the bbox mAP and segm mAP of Cascade Mask R-CNN Swin-T are the highest, which are 0.943 and 0.940; as for the each category identification, Cascade Mask R-CNN Swin-T shows no significant difference with the other three algorithms in trunk and primary branch; when the identified object is a support, the bbox AP and segm AP of Cascade Mask R-CNN Swin-T is significantly higher than that of other algorithms, which are 0.879 and 0.893. Next, Cascade Mask R-CNN SW-T is combined with Zhang & Suen to obtain the junction point. Compared with the direct application of Zhang & Suen algorithm, the skeleton obtained by this method is advantaged by trunk diameter information, and its shape and junction points position are closer to the actual apple trees. This model and method can be applied to follow-up research and offer a new solution to the robotic pruning system for apple trees."],"string":"[\n \"Title: Adaptive System-Level Diagnosis for Hypercube Multiprocessors\\nYear: 1996\\n\\nAbstract: System-level diagnosis is an important technique for fault detection and location in multiprocessor computing systems. Efficient diagnosis is highly desirable for sustaining the original system power. Moreover, effective diagnosis is particularly important for a multiprocessor system with high scalability but low connectivity. Most of the existing results are not applicable in practice because of the high diagnosis cost and limited diagnosability. Over-d fault diagnosis, where d is the diagnosability, has only been addressed using a probabilistic method in the literature. Aiming at these two issues, we propose a hierarchical adaptive system-level diagnosis approach for hypercube systems using a divide-and-conquer strategy. We first propose a conceptual algorithm HADA to formulate a rigorous analysis. Then we present its practical variant IHADA. In HADA and IHADA, the over-d fault problem is inherently tackled through a deterministic method. Three measures for diagnosis cost (diagnosis time, number of tests, and number of test links) are analyzed for the proposed algorithms. It is proved that the diagnosis cost required by our approach is lower than in previous diagnosis algorithms. It is shown that the diagnosis cost for the proposed algorithms depends on the number and location of faulty units in the system and the cost is extremely low when only a small number of faulty units exist. It is also shown that our algorithms are characterized by lower costs than a pessimistic diagnosis algorithm which trades lower diagnosis cost for a lower degree of accuracy. Experimental results on the nCUBE are provided to substantiate the practicality of the proposed approach.\",\n \"Title: Asymptotic performance of amplify-and-forward MIMO relaying with transmit antenna selection.\\nYear: 2010\\n\\nAbstract: This paper proposes two schemes, TAS-SD&RD and TAS-SR&RD, in which transmit antenna selection (TAS) strategies are combined\\n with amplify-and-forward (AF) protocol in MIMO relaying scenario considering the destination has only limited channel state\\n information (CSI) of the source-to-relay channel, and analyzes asymptotic performance of the two schemes. The source, destination,\\n and relay are equipped with multiple antennas, denoted by N\\n S, N\\n D, and N\\n R, respectively. Specifically, the paper derives the asymptotic outage probability and symbol error rate (SER) of the proposed\\n schemes in the cooperative AF MIMO relaying scenario. These analyses in high SNR regime lead to the conclusion that TAS-SD&RD\\n and TAS-SR&RD can achieve full diversity order, N\\n S\\n N\\n D + NR min {N\\n S,N\\n D}, in some antenna configurations of the cooperative scenario. Numerical results that confirm our analyses are also presented\\n in the paper.\",\n \"Title: Dynamical Gaussian mixture model for tracking elliptical living objects\\nYear: 2006\\n\\nAbstract: In this letter, we present a novel dynamical Gaussian mixture model (DGMM) for tracking elliptical living objects in video frames. The parameters, which inform object position and shape, are estimated by using a traditional Gaussian mixture model (GMM) for the first frame. Instead of simply using the parameters of one frame as the initial state for the next frame, DGMM employs Kalman filter to predict these parameters to initialize the evolution of GMM for the rest of sequel frames. Compared with the simple way, the estimate by Kalman filter is usually much closer to the true state, the computation load for tracking is reduced significantly. In addition, ''split'', ''merge'' and ''delete'' operations are integrated into DGMM to account for object splitting, merging and vanishing. This scheme can suppress the chance of getting stuck at a local maximum, led by a mismatch between the number of objects in the model and the actual number of objects. Results obtained from experiments on both simulated and real-world data demonstrate the effectiveness of our algorithm. DGMM has been successfully applied to track the early development process of urchin embryo in which the events of motility and cleavage occur.\",\n \"Title: Classification of tea using LED-induced fluorescence\\nYear: 2013\\n\\nAbstract: LED-induced fluorescence was used to evaluate the classification of teas. In this paper, the fluorescence spectra of six different types of Chinese Chaoqing teas were excited by power LED (375nm), then the fluorescence spectra were recorded by a compact spectrometer. Principal component analysis was used to discriminate the teas. The result shows the potential application of this method in the classification of teas.\",\n \"Title: Diversity order analysis of a sub-optimal transmit antenna selection strategy for non-regenerative MIMO relay channel\\nYear: 2009\\n\\nAbstract: This paper proposes a novel sub-optimal transmit antenna selection (TAS) strategy for non-regenerative half-duplex MIMO relay channel. While antenna selection is a sub-optimal form of beamforming, it still enjoys the advantage of tractable optimization and low feedback overhead. The diversity order of this proposed strategy is derived to prove that it can achieve the full diversity order as the optimal strategy with only a small performance loss. Furthermore, it is shown that our scheme lead to a reduction of complexity in terms of time slots for SNR estimation when the number of the transmit antennas are more than two. Monte Carlo simulation results of outage probability are presented to demonstrate its advantage over the other two conventional TAS schemes.\",\n \"Title: An adaptive system-level diagnosis approach for hypercube multiprocessors\\nYear: 1993\\n\\nAbstract: This paper proposes a hierarchical adaptive system-level diagnosis approach for hypercube systems. Three measures for diagnosis cost (diagnosis time, number of tests and number of test links) are analyzed for the proposed algorithm. It is proved that the diagnosis cost required by this algorithm is lower than in the previous diagnosis algorithms in most of the fault cases. It is shown that the diagnosis cost for the proposed algorithm depends on the number of faulty units in the system and the cost is extremely low when only a small number of faulty units exist. It is shown that this algorithm is even characterized by lower costs than a pessimistic diagnosis algorithm which trades lower diagnosis cost for a lower degree of accuracy.\",\n \"Title: A Kinship Function Approach to Robust and Probabilistic Optimization Under Polynomial Uncertainty\\nYear: 2011\\n\\nAbstract: In this paper, we study a class of robust design problems with polynomial dependence on the uncertainty. One of the main motivations for considering these problems comes from robust controller design, where one often encounters systems that depend polynomially on the uncertain parameters. This paper can be seen as integrated in the emerging area of probabilistic robustness, where a probabilistic relaxation of the original robust problem is adopted, thus requiring the satisfaction of the constraints not for all possible values of the uncertainty, but for most of them. Different from the randomized approach for tackling probabilistic relaxations, which is only guaranteed to provide soft bounds on the probability of satisfaction, we present a deterministic approach based on the novel concept of kinship function introduced in this paper. This allows the development of an original framework, which leads to easily computable deterministic convex relaxations of the probabilistic problem. In particular, optimal polynomial kinship functions are introduced, which can be computed a priori and once for all and provide the “best convex bound” on the probability of constraint violation. More importantly, it is proven that the solution of the relaxed problem converges to that of the original robust optimization problem as the degree of the optimal polynomial kinship function increases. Furthermore, by relying on quadrature formulas for computation of integrals of polynomials, it is shown that the computational complexity of the proposed approach is polynomial in the number of uncertain parameters. Finally, unlike other deterministic approaches to robust polynomial optimization, the number of variables in the ensuing optimization problem is not increased by the proposed approximation. An important feature of this approach is that a significant amount of the computational burden is shifted to a one-time offline computation whose results can be stored and provided to - - the end-user.\",\n \"Title: Distributional robustness analysis for polynomial uncertainty\\nYear: 2009\\n\\nAbstract: This paper addresses the problem of computing the worst-case expected value of a polynomial function, over a class of admissible distributions. It is shown that this problem, for the class of distributions considered, is equivalent to a convex optimization problem for which efficient linear matrix inequality (LMI) relaxations are available. In case that the performance function is continuous (not necessarily polynomial), the worst-case expected value can be approximated by using its polynomial approximations. Moreover, the proposed approach is applied to compute hard bounds of the worst-case probability of a polynomial being negative. Numerical examples are presented which illustrate the application of the results in this paper.\",\n \"Title: On the testability of array structures for FFT computation\\nYear: 1993\\n\\nAbstract: This article presents new approaches for testing VLSI array architectures used in the computation of the complexN-point Fast Fourier Transform. Initially, an unrestricted single cell-level fault model is considered. The first proposed approach is based on a process whose complexity is independent (or C- as constant) of the number of cells in the FFT architecture. This is accomplished by showing a topological equivalence between the FFT array and a linear (one-dimensional) array. The process of fault location is also analyzed. The second proposed method is based on a testing process whose complexity is linear with respect to the number of stages (columns) of the FFT array. A component-level fault model is also proposed and analyzed. The implications of this model on the C-testability process are fully described.\",\n \"Title: Toolset to explore visual motion designs in a video game\\nYear: 2012\\n\\nAbstract: We describe a research toolset to explore visual designs in a video game. We focus specifically on visual motion, defined by attributes of motion, and their effect on accessibility, which may lead to a diminished experience for novice players. Eight expert game designers evaluated the tool embedded into a simple point and click game. Specifically they controlled attributes of speed, size of game elements, and amount of elements on screen associated to game targets, distractions, and feedback. The tool allowed experts to define difficulty settings and expose patterns, which they verified. As a game, we then investigated the effect of visual motion on accessibility in a formal user study comprised of 105 participants. As a follow-up to this work, we expanded the toolset to include 8 additional attributes of motion.\",\n \"Title: A new diagnosis approach for short faults in interconnects\\nYear: 1995\\n\\nAbstract: Existing one-step diagnosis approaches for faults in interconnects either yield a long test sequence, or use a non-generalized procedure to generate a shorter test sequence. We propose a new diagnosis approach for short faults in interconnects. The pin-adjacency fault model is assumed. By using a divide-and-conquer strategy, our approach can generate a very compact test vector sequence which can diagnose an unrestricted number of short faults. Our experiments for three benchmarks as well as large random interconnects (up to 50,000 nets) show that our approach can achieve more than 50% savings in the length of the generated test sequence. This can significantly save the diagnosis cost for boundary-scan testing. An adaptive diagnosis approach is further proposed to dynamically truncate the originally generated test sequence based on the current information of faulty nets. The performance of our adaptive approach in terms of the on-line test generation time and the resulting test sequence length is better than for existing adaptive diagnosis approaches when the fault rate is not very small, such as in a new product line. If a low complexity for the ATE is of major importance, then the proposed one-step approach is the best choice.<>\",\n \"Title: Uniform sample generations from contractive block Toeplitz matrices\\nYear: 2006\\n\\nAbstract: This note deals with generating a series of random matrices uniformly and independently from contractive lower triangular block Toeplitz (LTBT) matrices. All the contractive LTBT matrices are parameterized nonlinearly but recursively in a closed-form by a sequence of contractive unstructured matrices. Moreover, the conditional probability density function (PDF) is obtained in a closed-form for all the matrix parameters, as well as the squares of their singular values. It has been made clear that the conditional PDFs of the matrix parameters are unitarily invariant. A procedure is developed to accurately produce the required random matrix samples. © 2006 IEEE.\",\n \"Title: Evaluating affective features of 3D motionscapes\\nYear: 2014\\n\\nAbstract: Abstract motion textures are widely applied in visual design and immersive environments such as games to imbue the environment or presentation with affect. While visual designers and artists carefully manipulate visual elements such as colour, form and motion to evoke affect, understanding what aspects of motion contribute to this still remains a matter of designer craft rather than validated principle. We report an empirical study of how simple features of motion in 3D textures, or motionscapes, contribute to the elicitation of affect. 12 university students were recruited to evaluate a series of 3D motionscapes. Results showed basic motion properties including speed, direction, path curvature and shape had significant influence on affective impressions such as valence, comfort, urgency and intensity, suggesting further directions for applications and explorations in this design space.\",\n \"Title: A divide-and-conquer methodology for system-level diagnosis of processor arrays\\nYear: 1994\\n\\nAbstract: System-level diagnosis in a multiprocessor system should be both effective and efficient. Most of the existing results are not applicable in practice because of the high diagnosis cost and limited diagnosability. We propose a divide-and-conquer methodology of fault diagnosis for a general class of processor arrays, which is one of the most popular architectures in today's multiprocessor systems. Parallel testing and adaptive diagnosis are our major strategies. The over-d fault problem is inherently tackled through a deterministic method. Based on this unified methodology, we first propose an algorithm for 2D arrays. Moreover, our methodology can be fully explored to those multi-dimensional arrays with high degree of hierarchy like hypercubes. We thus propose another diagnosis algorithm for this kind of architectures. Analytical and simulation results show that our approaches are superior.\",\n \"Title: CMOS 1.2V bandgap voltage reference design\\nYear: 2013\\n\\nAbstract: A typical bandgap voltage reference based on CSMC 0.35 μm CMOS technology is designed and fabricated. The overall bandgap architecture is optimized to achieve high accuracy temperature and power supply independent voltage reference. The design consists of the bandgap core circuit, op-amp, and start-up circuit. The test results show that the bandgap reference circuit provides reference voltage of 1.2128 V to 1.2175 V with 3.3 V power supply and temperature range from -40°C to 80°C simultaneously, and the temperature coefficient is 32.2ppm/°C. The total layout area including dummy structures is 135 um×236 um.\",\n \"Title: Drift Detection for Multi-label Data Streams Based on Label Grouping and Entropy\\nYear: 2014\\n\\nAbstract: Many real-world applications involve multi-label data streams, so effective concept drift detection methods should be able to consider the unique properties of multi-label stream data, such as label dependence. To deal with these challenges, we proposed an efficient and effective method to detect concept drift based on label grouping and entropy for multi-label data. Two methods are proposed to group the set of class labels into different subsets and a multi-label version of entropy was adjusted to measure the distribution of multi-label data. Concept drift was detected by comparing the entropies of the older and the most recent data. The experiments are run on three synthetic datasets and two real-world datasets and the experimental results illustrate the better classification performance of the proposed method for detecting drift.\",\n \"Title: Hybrid system identification via sparse polynomial optimization\\nYear: 2010\\n\\nAbstract: In this paper, the problem of identifying discrete time affine hybrid systems with measurement noise is considered. Given a finite collection of measurements and a bound on the noise, the objective is to identify a hybrid system with the smallest number of sub-systems that is compatible with the a priori information. While this problem has been addressed in the literature if the input/output data is noise-free or corrupted by process noise, it remains open for the case of measurement noise. To handle this case, we propose a new approach based on recasting the problem into a polynomial optimization form and exploiting its inherent sparse structure to obtain computationally tractable problems. Combining these ideas with a randomized Hit and Run type approach leads to further computational complexity reduction, allowing for solving realistically sized problems. Numerical examples are provided, illustrating the effectiveness of the algorithm and its potential to handle large size problems.\",\n \"Title: Hybrid system identification: An SDP approach\\nYear: 2010\\n\\nAbstract: The problem of identifying discrete time affine hybrid systems with noisy measurements is addressed in this paper. Given a finite number of measurements of input/output and a bound on the measurement noise, the objective is to identify a switching sequence and a set of affine models that are compatible with the a priori information, while minimizing the number of affine models. While this problem has been successfully addressed in the literature if the input/output data is noise-free or corrupted by process noise, results for the case of measurement noise are limited, e.g., a randomized algorithm has been proposed in a previous paper. In this paper, we develop a deterministic approach. Namely, by recasting the identification problem as polynomial optimization, we develop deterministic algorithms, in which the inherent sparse structure is exploited. A finite dimensional semi-definite problem is then given which is equivalent to the identification problem. Moreover, to address computational complexity issues, an equivalent rank minimization problem subject to deterministic LMI constraints is provided, as efficient convex relaxations for rank minimization are available in the literature. Numerical examples are provided, illustrating the effectiveness of the algorithms.\",\n \"Title: On Control System Design Using Random Samples Of Contractive Block Toeplitz Matrices\\nYear: 2007\\n\\nAbstract: Contractive lower triangular block Toeplitz (LTBT) matrices have recently been parametrized in a closed-form by a sequence of unstructured contractive matrices. A problem naturally arises between uniform samplings over contractive LTBT matrices and their matrix parameters, that is, which one is more appropriate in control system analysis and synthesis. This paper makes it clear that uniform sampling over matrix parameters results in a contractive LTBT matrix with a more rapid concentration speed towards the boundary of the uncertainty set. This is not an attractive property in control-related problems. Numerical comparisons also show that uniform sampling directly over contractive LTBT matrices outperforms that over the matrix parameter set in robustness analysis for a closed-loop system, as well as in strongly stable closed-loop system design with complexity constraints on the controller. As a sacrifice, uniform sampling over contractive LTBT matrices is more mathematically complicated and more time consuming.\",\n \"Title: Mixed-precision models for calculation of high-order virial coefficients on GPUs\\nYear: 2014\\n\\nAbstract: The virial equation of state (VEOS) is a density expansion of the thermodynamic pressure with respect to an ideal-gas reference. Its coefficients can be computed from a molecular model, and become more expensive to calculate at higher order. In this paper, we use GPU to calculate the 8th, 9th and 10th virial coefficients of the Lennard-Jones (LJ) potential model by the Mayer Sampling Monte Carlo (MSMC) method and Wheatley's algorithm. Two mixed-precision models are proposed to overcome a potential precision limitation of current GPUs while maintaining the performance benefit. On the latest Kepler architecture GPU Tesla K40, an average speedup of 20 to 40 is achieved for these calculations.\",\n \"Title: A Method Based on Meta-model for the Translation from UML into Event-B\\nYear: 2016\\n\\nAbstract: UML model has become the standard in the field of modeling. However, UML is a kind of semi-formal language that lacks precise semantic description. In addition, UML models cannot be verified formally. The problem above could be solved through the translation from UML model into a formal model that could then be verified formally. However, the traditional translation is on the layer of model which can't realize the automatic translation and the rules of translation are complex. To address this need, we propose a method based on meta-model for the translation from UML into Event-B. We created the rules of translation on the layer of meta-model and realized automated translation on the layer of model. To do this, the meta-models of UML and Event-B were created by meta-modeling. Next, the translation rules were constructed by ATL. Finally, a representative case study is presented to illustrate the feasibility and effectiveness of this method.\",\n \"Title: Testing Network Protocol Binary Software with Selective Symbolic Execution\\nYear: 2016\\n\\nAbstract: The vulnerabilities existing in network protocol implementations are difficult to detect. The main reason is that the state space of complex protocol binary software is too large to explore. This paper proposes a novel approach that leverages selective symbolic execution to test network protocol binary software directly, which confines symbolic execution in the secure-sensitive area. This paper also builds a prototype system, S2EProtocol, upon the Selective Symbolic Execution (S2E) platform and uses it to test several real network protocol binary software. The evaluation results show that the proposed method can be used to find vulnerabilities efficiently and effectively.\",\n \"Title: The recognition of traffic speed limit sign in hazy weather.\\nYear: 2017\\n\\nAbstract: Hazy weather affects drivers' sightline seriously and causes a high potential safety hazard. This paper proposes a novel approach for recognizing the speed limit sign in hazy weather. It consists of three major modules: haze removal, speed limit sign location, and sign recognition. In haze removal, this paper proposes to dehaze image with the dark channel prior. The speed limit sign is located by Histogram of Oriented Gradient (HOG) feature extraction and Support Vector Machine (SVM) classification and is recognized by the seven layers Convolutional Neural Networks (CNN). Experimental results show that the proposed method has better performance than the state-of-art dehazing methods and the processing time is also reduced. The recognition rate of the speed limit signs is 98.51% that is better than the human performance, and the classifier can recognize the speed limit sign with rotation, shift, scale and other distortions.\",\n \"Title: Demo: Material Identification with Commodity RFID Devices.\\nYear: 2017\\n\\nAbstract: Target material identification plays an important role in many real-life applications. This paper introduces a system that can identify the material type with cheap commercial off-the-shelf (COTS) RFID devices. The key intuition is that different materials cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. However, without knowing either material type, trying to obtain the information is challenging. We propose a method to address this challenge and evaluate the method's performance in real-world environment. The results show that we achieve higher than 94% material identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi.\",\n \"Title: Improving the Cooperation of Fuzzing and Symbolic Execution by Test-cases Prioritizing.\\nYear: 2017\\n\\nAbstract: Nowadays much attention is paid to the threat of vulnerabilities on the software security. Fuzzing and symbolic execution, complementary to each other, are two effective techniques in software testing. In this paper, we develop the prototype called FAS(Fuzzing and Symbolic) for software testing under both fuzzing and symbolic execution. In our method, the test cases are prioritized through deep-oriented strategy and large-distance-first strategy, in order to get a higher path-coverage with the condition of limited resource\",\n \"Title: S2f: Discover Hard-To-Reach Vulnerabilities By Semi-Symbolic Fuzz Testing\\nYear: 2017\\n\\nAbstract: Fuzz testing is a popular program testing technique. However, it is difficult to find hard-to-reach vulnerabilities that are nested with complex branches. In this paper, we propose semi-symbolic fuzz testing to discover hard-to-reach vulnerabilities. Our method groups inputs into high frequency and low frequency ones. Then symbolic execution is utilized to solve only uncovered branches to mitigate the path explosion problem. Especially, in order to play the advantages of fuzz testing, our method locates critical branch for each low frequency input and corrects the generated test cases to comfort the branch condition. We also implemented a prototype-S2F, and the experimental results show that S2F can gain 17.70% coverage performance and discover more hard-to-reach vulnerabilities than other vulnerability detection tools for our benchmark.\",\n \"Title: Target Material Identification with Commodity RFID Devices\\nYear: 2017\\n\\nAbstract: BSTRACTTarget material identification plays an important role in many real-life applications. This paper introduces a system that can identify the material type with cheap commercial off-the-shelf (COTS) RFID devices. The key intuition is that different materials cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. However, without knowing either material type, trying to obtain the information is challenging. We propose a method to address this challenge and evaluate the method's performance in real-world environment. The results show that we achieve higher than 94% material identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi.\",\n \"Title: An Exploitability Analysis Technique for Binary Vulnerability Based on Automatic Exception Suppression.\\nYear: 2018\\n\\nAbstract: To quickly verify and fix vulnerabilities, it is necessary to judge the exploitability of the massive crash generated by the automated vulnerability mining tool. While the current manual analysis of the crash process is inefficient and time-consuming, the existing automated tools can only handle execute exceptions and some write exceptions but cannot handle common read exceptions. To address this problem, we propose a method of determining the exploitability based on the exception type suppression. This method enables the program to continue to execute until an exploitable exception is triggered. The method performs a symbolic replay of the crash sample, constructing and reusing data gadget, to bypass the complex exception, thereby improving the efficiency and accuracy of vulnerability exploitability analysis. The testing of typical CGC/RHG binary software shows that this method can automatically convert a crash that cannot be judged by existing analysis tools into a different crash type and judge the exploitability successfully.\",\n \"Title: Perfect Gaussian Integer Sequence Pairs From Cyclic Difference Set Pairs\\nYear: 2018\\n\\nAbstract: The concept of Gaussian integer sequence pair is generalized from a single Gaussian integer sequence. In this letter, by adopting cyclic difference set pairs, a new construction method for perfect Gaussian integer sequence pairs is presented. Furthermore, the necessary and sufficient conditions for constructing perfect Gaussian integer sequence pairs are given. Through the research in this paper, a large number of perfect Gaussian integer sequence pairs can be obtained, which can greatly extend the existence of perfect sequence pairs.\",\n \"Title: Construction of User Concurrent Profile Based on Usage Logs\\nYear: 2018\\n\\nAbstract: With the wide application of software systems based on Service-Oriented Architecture (SOA), the reliability of such software has received more and more attention. Software reliability testing and evaluation is an important means to improve reliability of SOA systems, the first step of which is the construction of an operational profile. However, using traditional operational profile is difficult to describe the characteristics of user concurrency and the real usage scenarios of SOA systems. Moreover, it is also difficult to ensure the accuracy of operational probabilities because of the subjectivity of expert experience in determining the probability of an operation. In this paper, a construction method of user concurrent profile based on usage logs is proposed. In the user concurrent profile, the characteristics of user concurrency in SOA software systems is embodied; besides, the accuracy of usage probability can be ensured due to the data from usage logs.\",\n \"Title: Analyzing Network Protocol Binary Software With Joint Symbolic Execution\\nYear: 2016\\n\\nAbstract: Network protocol binary software is threaten by vulnerabilities. Current analyzing methods do not make full use of the interaction information in network protocol and do not treat both sides of network protocol as a whole system. This paper proposes a novel method based on joint symbolic execution to test synchronously both server and client of the whole network protocol binary software stacks. Moreover, this paper presents a prototype system, S2EProtocol-joint, upon Selective Symbolic Execution (S2E) platform to automatically test network protocol binary software. The experiment results validate that this approach is effective and efficient in detecting vulnerabilities.\",\n \"Title: Exploring Scalable Computing Architectures For Interactions Analysis\\nYear: 2018\\n\\nAbstract: Characterization of pharmacological signal transductions leading to drug-induced expressions of genes and proteins requires the capability to identify interactions among different potential predictor components, e.g. genomic data, clinical data, and environmental data. The detection of these gene-gene and gene-environment interactions remains challenging due to the exponential computational complexity and high dimensionality of the interaction problem. The problem is further exacerbated due to the involvement of very large-scale epidemiological datasets. Efficient high-order interaction analysis of such large-scale data is not feasible with the traditional frameworks. Parallel implementations of such applications in traditional cluster environments are often inefficient due to the storage bandwidth and network I/O limitations. Scalable distributed platforms can offer better scalability to such problems compared to the cluster architectures. Moreover, such data- and compute-intensive problems can benefit even further from data-intensive supercomputing (DISC) architectures that have been shown to yield superior performance compared to the regularly used cluster platforms. In this paper, we evaluate the applicability of different architectures such as traditional server based distributed architectures supported on commodity hardware and shared nothing architectures with massively parallel processing capabilities, towards the Interaction Analysis problem. Our experiments show that the massively parallel processing, shared-nothing architecture outweigh the benefits often realized through traditional server based and even distributed computing architectures. We conclude that the rapidly growing class of shared nothing architectures offers a potentially efficient and viable alternative to facilitate high-order interaction analysis involving extremely large scale biological datasets and is well suited to this category of data- and compute-intensive problems that cannot be addressed effectively using traditional frameworks.\",\n \"Title: Wired/Wireless Integrated Multi-Screen Display Endoscopic System\\nYear: 2016\\n\\nAbstract: Medical electronic endoscope has a wide range of applications in medical diagnostic. Most of the traditional medical electronic endoscopes are wired endoscopes which are unavailable in many circumstances. Considering the weaknesses of the traditional video endoscope and combining with its future development trend, we design and implement the wired/wireless multi-screen display endoscopic system. The video signal can not only be transmitted to the Liquid Crystal Display by cable, but also be transmitted to the mobile terminals via Wi-Fi. In this work, a video endoscopic system prototype was built which uses H.264 video compression to reduce the data size needed to be transmitted by the Wi-Fi transmitter. The ARM9 based video processor performs the main task of video compression and transmission, CC3100 to establish the wireless link and the camera used is analog camera.\",\n \"Title: Research on Method of Emergency Cases Retrieval Based on Case-Based Reasoning\\nYear: 2017\\n\\nAbstract: Aiming at the problem of emergency cases retrieval, this paper proposes a method of emergency cases retrieval considering the predator behavior of birds. Based on case-based reasoning (CBR) theory, this method introduces the particle swarm optimization (PSO) algorithm and tabu search (TS) into the calculation of attribute feature weight, and then calculates the similarity between target case and historical case for finding the most similar historical case. The validity and feasibility of the proposed method are verified by the collected data.\",\n \"Title: Discover deeper bugs with dynamic symbolic execution and coverage-based fuzz testing.\\nYear: 2018\\n\\nAbstract: Coverage-based fuzz testing and dynamic symbolic execution are both popular program testing techniques. However, on their own, both techniques suffer from scalability problems when considering the complexity of modern software. Hybrid testing methods attempt to mitigate these problems by leveraging dynamic symbolic execution to assist fuzz testing. Unfortunately, the efficiency of such methods is ...\",\n \"Title: The establishment of diabetes diet classification model based on BL_WSmote\\nYear: 2018\\n\\nAbstract: This paper analyzes the daily diet recipe of diabetes from traditional Chinese medicine diet, and establishes a model to determine the type of diabetes. The process of building a model is as follows. Firstly, use improved Smote algorithm to do data preprocessing. Secondly, using GA to optimize Random Forest do attribute reduction. Finally, using Bagging establish model. We establish 3 sets of comparison experiments. The comparison of 4 different algorithms between Random Forest, GDBT, Ada Booster, Bagging 4 different algorithms; The comparison between raw data, Smote improved data, BL-Smote improved data, BL-WSmote improved; The comparison between the whole attributes and the important attributes. The model accuracy is as high as 92%, it is proved that this model has certain applicable value in determining diabetes classification.\",\n \"Title: Material Identification with Commodity Wi-Fi Devices.\\nYear: 2018\\n\\nAbstract: Target material identification is playing an important role in our everyday life. This paper introduces a device-free target material identification system, implemented on ubiquitous and cheap commercial off-the-shelf (COTS) Wi-Fi devices. The intuition is that different materials produce different amounts of phase and amplitude changes when a target appears on the line-of-sight (LoS) of a radio frequency (RF) link. However, due to multipath and hardware imperfection, the measured phase and amplitude of the channel state information (CSI) are very noisy. We thus present novel CSI pre-processing schemes to address the multipath and hardware noise issues before they can be used for accurate material sensing. Comprehensive real-life experiments demonstrate that we can identify 10 commonly seen liquids at an overall accuracy higher than 95% with strong multipath indoors.\",\n \"Title: Product-Aware Helpfulness Prediction of Online Reviews\\nYear: 2019\\n\\nAbstract: Helpful reviews are essential for e-commerce and review websites, as they can help customers make quick purchase decisions and merchants to increase profits. Due to a great number of online reviews with unknown helpfulness, it recently leads to promising research on building automatic mechanisms to assess review helpfulness. The mainstream methods generally extract various linguistic and embedding features solely from the text of a review as the evidence for helpfulness prediction. We, however, consider that the helpfulness of a review should be fully aware of the metadata (such as the title, the brand, the category, and the description) of its target product, besides the textual content of the review itself. Hence, in this paper we propose an end-to-end deep neural architecture directly fed by both the metadata of a product and the raw text of its reviews to acquire product-aware review representations for helpfulness prediction. The learned representations do not require tedious labor on feature engineering and are expected to be more informative as the target-aware evidence to assess the helpfulness of online reviews. We also construct two large-scale datasets which are a portion of the real-world web data in Amazon and Yelp, respectively, to train and test our approach. Experiments are conducted on two different tasks: helpfulness identification and regression of online reviews, and results demonstrate that our approach can achieve state-of-the-art performance with substantial improvements.\",\n \"Title: Preliminary Study on Efficient Named Entity Recognition\\nYear: 2019\\n\\nAbstract: In this paper, we describe our preliminary study for efficient recognition and extraction of named entities on mobile phones. The methodology investigation and experiment verification are focusing on two parts. The first part is a compressed named entities recognition (NER)-model-based named entity recognizer to reduce the memory usage. The second part is an NER classifier. When the result from compressed named entity recognizer is blank, the NER classifier will help to further check if the result is correct to increase the final accuracy. In addition, the framework is described. The study shows that the proposed named entity recognizer remains almost the same recognition performance but reduce the storage cost evidently. Therefore, latter research can dig further in terms of memory usage reduction and performance optimization.\",\n \"Title: Program State Sensitive Parallel Fuzzing for Real World Software.\\nYear: 2019\\n\\nAbstract: Fuzz testing is a widely used technique for software vulnerability detection, but it is still limited in finding bugs nested in the deep program states. Parallel testing is an augmented method aiming to make the best of the computing resource to expose more deep program bugs. However, current parallel testing methods cannot deal well with task slicing so that the parallel nodes show serious duplication with each other, thus decreasing the efficiency in total. For instance, the original parallel mode of the well-known fuzzer American fuzzy lop (AFL) does not split the task and just synchronizes the interesting seeds without any internal execution information. In this paper, we put forward a novel program state sensitive parallel testing method, which: 1) splits the task into low correlated subtasks according to the program states and 2) adjusts the mutation engine to confine one instance's testing among its subtask-related code region as more as possible. Our method is an objective to reduce the testing collision between parallel instances and therefore improve the performance. We developed a new fuzzer called PAFL and implemented some experiments to investigate that if our parallel testing framework is positive when deploying multiple instances and if it shows a better path discovery compared with two state-of-the-art fuzzers, AFL and AFLFast. In the experiments, we employed PAFL with 1/2/4/8/16 to observe their path discovery and conclude that our new parallel framework is positive when using more multiple instances. We also compared PAFL with AFL and AFLFast by employing eight parallel instances for each fuzzer, and the results prove that our tool has the best path discovery among the three fuzzers. Compared with the original parallel AFL, PAFL can achieve averaged performance gains of 3.98, 3.04, 4.18, and 1.45 on the c + +filt, objdump, readelf, and tcpdump, respectively. Besides, we took PAFL on binutils and libtiff, and finally, we found ten new bugs.\",\n \"Title: Unsupervised Feature Selection by Pareto Optimization\\nYear: 2019\\n\\nAbstract: Dimensionality reduction is often employed to deal with the data with a huge number of features, which can be generally divided into two categories: feature transformation and feature selection. Due to the interpretability, the efficiency during inference and the abundance of unlabeled data, unsupervised feature selection has attracted much attention. In this paper, we consider its natural formulation, column subset selection (CSS), which is to minimize the reconstruction error of a data matrix by selecting a subset of features. We propose an anytime randomized iterative approach POCSS, which minimizes the reconstruction error and the number of selected features simultaneously. Its approximation guarantee is well bounded. Empirical results exhibit the superior performance of POCSS over the state-of-the-art algorithms.\",\n \"Title: A Queueing-Theoretic Framework for Vehicle Dispatching in Dynamic Car-Hailing\\nYear: 2019\\n\\nAbstract: With the rapid development of smart mobile devices, the car-hailing platforms (e.g., Uber or Lyft) have attracted much attention from both the academia and the industry. In this paper, we consider an important dynamic car-hailing problem, namely maximum revenue vehicle dispatching (MRVD), in which rider requests dynamically arrive and drivers need to serve as many riders as possible such that the entire revenue of the platform is maximized. We prove that the MRVD problem is NP-hard and intractable. To handle the MRVD problem, we propose a queueing-based vehicle dispatching framework, which first uses existing machine learning algorithms to predict the future vehicle demand of each region, then estimates the idle time periods of drivers through a queueing model for each region. With the information of the predicted vehicle demands and estimated idle time periods of drivers, we propose one batch-based vehicle dispatching algorithm to efficiently assign suitable drivers to riders such that the expected entire revenue of the platform is maximized during each batch processing. Through experiments over real data sets, we demonstrate the efficiency and effectiveness of our proposed framework.\",\n \"Title: NeuralTaint: A Key Segment Marking Tool Based on Neural Network.\\nYear: 2019\\n\\nAbstract: Dynamic taint analysis techniques are a popular dynamic software analysis method. Marking a key segment of program function by dynamic taint analysis is an important part of software vulnerability research. Key segment marking usually related to the control flow taint analysis, however, several specific program structure may cause failure in key segment marking due to the control flow dependence, and overtainting and undertainting problem. In this paper, we proposed a novel method to mark a key segment accurately and efficiently with deep learning technology. Firstly, we fit the program function execution into a continuous function by the convoluntional network, and then mark the key segment roughly through derivative information of fitted nerual network. Finally, we mark the key segment of specific program function completely and accurately by filtering and diffusion algorithm. We developed the key segment marking tool NeuralTaint on this principle. We design an experiment to select the specific neural network structure of NeuralTaint. Our extensive evaluations demonstrate that NeuralTaint significantly outperforms the two state-of-the-art traditional dynamic taint analysis tool on seven popular real-world programs.\",\n \"Title: Sensing Our World Using Wireless Signals\\nYear: 2019\\n\\nAbstract: In the last few years, pervasive wireless signals have been established as a powerful medium for ubiquitous sensing. Wireless sensing quickly becomes an active cross-disciplinary research area that involves wireless communication, signal processing, human-computer interaction, machine learning, and even hardware design. This paper provides an accessible introduction to this exciting area and presents the key technologies enabling it. It highlights the latest achievements in this field, and the unaddressed research challenges as well as how emerging wireless standards can better support this promising technique.\",\n \"Title: A Lightweight Assisted Vulnerability Discovery Method Using Deep Neural Networks.\\nYear: 2019\\n\\nAbstract: Thousands of vulnerabilities are discovered in programs every day, which is extremely harmful to software security. Thus, discovering vulnerabilities in projects has become a central issue. Facing a sustained growth of software complexity and large code size, manual code auditing becomes time-consuming and labor-intensive. With more open source programs available and a high degree of code formalization, it is possible to study features from source code to guide vulnerability discovery work. In this paper, we present a lightweight-assisted vulnerability discovery method using a deep neural network (LAVDNN) to detect weakness and to provide guidance for manual auditing. The method proposed in this paper leverages function names as semantics features to uncover weak functions in large-scale open source programs. First, we extract function names and classify into weak and benign datasets. Then, we construct deep neural networks and compare the performances of different models. According to the experimental results, our method performs well for both C/C++ and Python programs, with the F-2-score reaching 0.91 and 0.915, respectively. Ultimately, we evaluate the method by comparing with other approaches using the libraries FFmpeg 0.6 and LibTIFF 4.0.6. The results show that the LAVDNN could narrow the range of functions to be analyzed and report more weak functions without any prior vulnerability information. As a lightweight-assisted tool, the LAVDNN significantly reduces the false positive rate and hardly misses weak functions.\",\n \"Title: Multi-Objective Submodular Maximization By Regret Ratio Minimization With Theoretical Guarantee\\nYear: 2021\\n\\nAbstract: Submodular maximization has attracted much attention due to its wide application and attractive property. Previous works mainly considered one single objective function, while there can be multiple ones in practice. As the objectives are usually conflicting, there exists a set of Pareto optimal solutions, attaining different optimal trade-offs among multiple objectives. In this paper, we consider the problem of minimizing the regret ratio in multi-objective submodular maximization, which is to find at most k solutions to approximate the whole Pareto set as well as possible. We propose a new algorithm RRMS by sampling representative weight vectors and solving the corresponding weighted sums of objective functions using some given alpha-approximation algorithm for single-objective submodular maximization. We prove that the regret ratio of the output of RRMS is upper bounded by 1-alpha + O(root d-1).(d/k-d)(d1-d). where d is the number of objectives. This is the first theoretical guarantee for the situation with more than two objectives. When d = 2, it reaches the (1 - alpha + O(1/k))-guarantee of the only existing algorithm POLYTOPE. Empirical results on the applications of multiobjective weighted maximum coverage and Max-Cut show the superior performance of RRMS over POLYTOPE.\",\n \"Title: An Effective Nuclear Extraction Mask Method for SVM Classification\\nYear: 2020\\n\\nAbstract: With the development of medical technology, the automatic cell analysis system plays an important role in medical diagnosis and medical image processing. The kernel recognition theory and technology based on support vector machine (SVM) classifier are mainly optimized from the perspective of the kernel segmentation algorithm to improve the recognition accuracy of the SVM classifier. Unfortunately, the nuclear overlap treatment can not accurately separate the nuclear gelling impurities in the dyeing process, resulting in the low classification accuracy of SVM. To solve the above image segmentation problems in the process of nuclear imaging processing, an effective nuclear extraction method based on the mask method for the SVM classifier is proposed. Compared with related work, the proposed method enables one to achieve a higher accuracy of SVM cross-validation.\",\n \"Title: Wireless Communication with Extremely Large-Scale Intelligent Reflecting Surface\\nYear: 2021\\n\\nAbstract: Intelligent reflecting surface (IRS) is a promising technology for wireless communications, thanks to its potential capability to engineer the radio environment. However, in practice, such an envisaged benefit is attainable only when the passive IRS is of a sufficiently large size, for which the conventional uniform plane wave (UPW)-based channel model may become inaccurate. In this paper, we purs...\",\n \"Title: Reducing Test Cases with Attention Mechanism of Neural Networks\\nYear: 2021\\n\\nAbstract: As fuzzing techniques become more effective at triggering program crashes, how to triage crashes with less human efforts has become increasingly imperative. To this aim, test case reduction which reduces a crashing input to its minimal form plays an important role, especially when analyzing programs with random, complex, or large inputs. However, existing solutions rely on random algorithms or pre-defined rules, which are inaccurate and error-prone in many cases because of the implementation variance in program internals. In this paper, we present SCREAM, a new approach that leverages neural networks to reduce test cases. In particular, by feeding the network with a program's crashing inputs and non-crashing inputs, the network learns to approximate the computation from the program entry point to the crash point and implicitly denotes the input bytes that are significant to the crash. With the invisibility of the trained network's parameters, we leverage the attention mechanism to explain the network, namely extracting the significance of each input byte to the crash. At the end, the significant input bytes are re-assembled as the failure-inducing input. The cost of our approach is to design a proper dataset augmentation algorithm and a suitable network structure. To this end, we develop a unique dataset augmentation technique that can generate adequate and highly-differentiable samples and expand the search space of crashing input. Highlights of our research also include a novel network structure that can capture dependence of input blocks in long sequences. We evaluated SCREAM on 41 representative programs. The results show that SCREAM outperforms state-of-the-art solutions regarding accuracy and efficiency. Such improvement is made possible by the network's capability to summarize the significance of input bytes from multiple rounds of mutation, which tolerates perturbation occurred in random reduction of single crashing input.\",\n \"Title: Automated Detection On The Security Of The Linked-List Operations\\nYear: 2022\\n\\nAbstract: 1 Introduction\\r\\nAs measures taken by OS(operation system)to mitigate exploit getting more perfect,the stack-related vulnerabilities are reduced gradually and heap-related vulnerabilities have become increasingly prominent in most C/C++memory vulnerabilities.\",\n \"Title: Android malicious application detection based on Support Filtering and Lasso LR Algorithm\\nYear: 2021\\n\\nAbstract: With the vigorous development of the mobile Internet, the Android system, which accounts for 76% of the drama mobile operating system, has also been widely promoted and popularized. However, due to its own open source characteristics, various parts of the Android system are facing serious threats from hacker attacks, and the main threat comes from malicious applications. In order to cope with the ...\",\n \"Title: Pushing the Limits of Respiration Sensing with Reconfigurable Metasurface\\nYear: 2021\\n\\nAbstract: ABSTRACTHuman respiration monitoring acts as a crucial role to indicate people's daily health. Compared with traditional respiration monitoring methods, device-free wireless respiration sensing technology is emerging as a promising modality due to the less privacy intrusive and without on-body sensors. However, due to the intrinsic nature of relying on weak reflection signals for sensing, the sensing range is limited. Meanwhile, reliable sensing performance only can be achieved when the environment with little or even no interference. In this work, we propose a WiFi-based respiration system to simultaneously enlarge the sensing range and mitigate the interference. The basic idea is to employ a reconfigurable metasurface to dynamically manipulate electromagnetic waves in the environment to achieve beamforming and beam steering. Our system thus enhances the sensing range and reduces the energy of reflected signals from interferers to ensure reliable performance. Proof-of-concept experiments demonstrate the effectiveness of our scheme.\",\n \"Title: AVA-AVD: Audio-visual Speaker Diarization in the Wild\\nYear: 2022\\n\\nAbstract: ABSTRACTAudio-visual speaker diarization aims at detecting \\\"who spoke when'' using both auditory and visual signals. Existing audio-visual diarization datasets are mainly focused on indoor environments like meeting rooms or news studios, which are quite different from in-the-wild videos in many scenarios such as movies, documentaries, and audience sitcoms. To develop diarization methods for these challenging videos, we create the AVA Audio-Visual Diarization (AVA-AVD) dataset. Our experiments demonstrate that adding AVA-AVD into training set can produce significantly better diarization models for in-the-wild videos despite that the data is relatively small. Moreover, this benchmark is challenging due to the diverse scenes, complicated acoustic conditions, and completely off-screen speakers. As a first step towards addressing the challenges, we design the Audio-Visual Relation Network (AVR-Net) which introduces a simple yet effective modality mask to capture discriminative information based on face visibility. Experiments show that our method not only can outperform state-of-the-art methods but is more robust as varying the ratio of off-screen speakers. Our data and code has been made publicly available at \\\\textcolormagenta \\\\urlhttps://github.com/showlab/AVA-AVD .\",\n \"Title: Game of Hide-and-Seek: Exposing Hidden Interfaces in Embedded Web Applications of IoT Devices\\nYear: 2022\\n\\nAbstract: ABSTRACTRecent years have seen increased attacks targeting embedded web applications of IoT devices. An important target of such attacks is the hidden interface of embedded web applications, which employs no protection but exposes security-critical actions and sensitive information to illegitimate users. With the severity and the pervasiveness of this issue, it is crucial to identify the vulnerable hidden interfaces, shed light on best practices and raise public awareness. In this paper, we present, a new approach that automatically exposes hidden web interfaces of IoT devices. Specifically, constructs probing requests through firmware analysis to test physical devices, and narrows down the scope of identification by filtering out irrelevant requests and interfaces through differential analysis. It pinpoints hidden interfaces by attaching various device-setting parameters in the probing requests and matching keywords of sensitive information. Evaluated on 17 IoT devices, successfully identified 44 vulnerabilities, including 43 previously unknown ones. also demonstrates surprising efficiency: on average, it delivered 151438 probing requests, taking only 47 minutes on each target device.\",\n \"Title: A data hiding scheme based on the difference of image interpolation algorithms\\nYear: 2022\\n\\nAbstract: Interpolation is a mature technique used in image processing and many data hiding schemes based on image interpolation have been proposed. In this paper, a novel efficient data hiding scheme based on the difference between image interpolation algorithms is proposed. The interpolated values obtained by neighboring points using different interpolation algorithms are generally different, and the difference can be used for encoding secret data. 2k different interpolated values calculated by 2k different interpolation algorithms respectively in the ideal case, and they are numbered in increasing order to correspond to the k bits secret data in each interpolated pixel. At the receiver's side, the secret data can be obtained based on the matching of the sorting results of these interpolation values of the accepted interpolation values. In fact, different algorithms may produce the same interpolation values, it is necessary to modify the appropriate number of interpolation value so that the number of different interpolation values reaches 2k, then the following image hiding procedures can employ the method of ideal case. The experimental results show that the proposed hiding scheme provides high embedding capacity with good visual quality.\",\n \"Title: Forest-based Deep Recommender\\nYear: 2022\\n\\nAbstract: With the development of deep learning techniques, deep recommendation models also achieve remarkable improvements in terms of recommendation accuracy. However, due to the large number of candidate items in practice and the high cost of preference computation, these methods also suffer from low efficiency of recommendation. The recently proposed tree-based deep recommendation models alleviate the problem by directly learning tree structure and representations under the guidance of recommendation objectives. However, such models have two shortcomings. First, the max-heap assumption in the hierarchical tree, in which the preference for a parent node should be the maximum between the preferences for its children, is difficult to satisfy in their binary classification objectives. Second, the learned index only includes a single tree, which is different from the widely-used multiple trees index, providing an opportunity to improve the accuracy of recommendation. To this end, we propose a Deep Forest-based Recommender (DeFoRec for short) for an efficient recommendation. In DeFoRec, all the trees generated during training process are retained to form the forest. When learning node representation of each tree, we have to satisfy the max-heap assumption as much as possible and mimic beam search behavior over the tree in the training stage. This is achieved by DeFoRec to regard the training task as multi-classification over tree nodes at the same level. However, the number of tree nodes grows exponentially with levels, making us to train the preference model by the guidance of sampled-softmax technique. The experiments are conducted on real-world datasets, validating the effectiveness of the proposed preference model learning method and tree learning method.\",\n \"Title: Default: Mutual Information-based Crash Triage for Massive Crashes\\nYear: 2022\\n\\nAbstract: With the considerable success achieved by modern fuzzing in-frastructures, more crashes are produced than ever before. To dig out the root cause, rapid and faithful crash triage for large numbers of crashes has always been attractive. However, hindered by the practical difficulty of reducing analysis imprecision without compromising efficiency, this goal has not been accomplished. In this paper, we present an end-to-end crash triage solution Default, for accurately and quickly pinpointing unique root cause from large numbers of crashes. In particular, we quantify the “crash relevance” of program entities based on mutual information, which serves as the criterion of unique crash bucketing and allows us to bucket massive crashes without pre-analyzing their root cause. The quantification of “crash relevance” is also used in the shortening of long crashing traces. On this basis, we use the interpretability of neural networks to precisely pinpoint the root cause in the shortened traces by evaluating each basic block's impact on the crash label. Evaluated with 20 programs with 22216 crashes in total, Default demonstrates remarkable accuracy and performance, which is way beyond what the state-of-the-art techniques can achieve: crash de-duplication was achieved at a super-fast processing speed - 0.017 seconds per crashing trace, without missing any unique bugs. After that, it identifies the root cause of 43 unique crashes with no false negatives and an average false positive rate of 9.2%.\",\n \"Title: a low-cost and reconfigurable metasurface for mmWave networks: poster abstract\\nYear: 2022\\n\\nAbstract: BSTRACTMillimeter-wave (mmWave) technology is emerging as the most promising candidate to support a wide range of applications with high data rate demand. However, due to the high directivity of mmWaves, its links are highly susceptible to barriers from walls and the movement of people. To address these issues, this paper introduces a low-cost and reconfigurable metasurface placed in the environment to reshape and resteer mmWave beams. The metasurface consists of many unit-cells, each acting as a phase shifter for signals going through it. By encoding the phase shifting values, the metasurface can reshape and resteer mmWave beams, thereby enabling a fast mmWave beam relay through the wall or redirects the beam power to another direction when a human body blocks the line-of-sight path. Preliminary simulated results show our designed metasurface can perform accurate beam steering within a field-of-view of [-60°, 60°]. And even with the small-size prototype (16 × 16 array of unit-cells), the metasurface enables up to 10.8 dB signal strength improvement.\",\n \"Title: Branch Identification and Junction Points Location for Apple Trees Based on Deep Learning\\nYear: 2022\\n\\nAbstract: Branch identification is key to the robotic pruning system for apple trees. High identification accuracy and the positioning of junction points between branch and trunk are important prerequisites for pruning with a robotic arm. Recently, with the development of deep learning, Transformer has been gradually applied to the field of computer vision and achieved good results. However, the effect of branch identification based on Transformer has not been verified so far. Taking Swin-T and Resnet50 as a backbone, this study detected and segmented the trunk, primary branch and support of apple trees on the basis of Mask R-CNN and Cascade Mask R-CNN. The results show that, when Intersection over Union (IoU) is 0.5, the bbox mAP and segm mAP of Cascade Mask R-CNN Swin-T are the highest, which are 0.943 and 0.940; as for the each category identification, Cascade Mask R-CNN Swin-T shows no significant difference with the other three algorithms in trunk and primary branch; when the identified object is a support, the bbox AP and segm AP of Cascade Mask R-CNN Swin-T is significantly higher than that of other algorithms, which are 0.879 and 0.893. Next, Cascade Mask R-CNN SW-T is combined with Zhang & Suen to obtain the junction point. Compared with the direct application of Zhang & Suen algorithm, the skeleton obtained by this method is advantaged by trunk diameter information, and its shape and junction points position are closer to the actual apple trees. This model and method can be applied to follow-up research and offer a new solution to the robotic pruning system for apple trees.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":59,"string":"59"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13658,"string":"13,658"}}},{"rowIdx":13659,"cells":{"question_id":{"kind":"string","value":"Haitao Wang"},"question":{"kind":"string","value":"Generate an abstract for the title \"Computing L Shortest Paths Among Polygonal Obstacles in the Plane.\" using the following items: 1. Obstacle-avoiding path \n2. Shortest path map \n3. Time complexity \n4. Space complexity \n5. L1 distance"},"answer":{"kind":"string","value":"Given a point s and a set of h pairwise disjoint polygonal obstacles with a total of n vertices in the plane, suppose a triangulation of the space outside the obstacles is given; we present an \n \n \n \n $$O(n+h\\log h)$$\n \n \n \n time and O(n) space algorithm for building a data structure (called shortest path map) of size O(n) such that for any query point t, the length of an \n \n \n \n $$L_1$$\n \n \n \n shortest obstacle-avoiding path from s to t can be computed in \n \n \n \n $$O(\\log n)$$\n \n \n \n time and the actual path can be reported in additional time proportional to the number of edges of the path. The previously best algorithm computes such a shortest path map in \n \n \n \n $$O(n\\log n)$$\n \n \n \n time and O(n) space. So our algorithm is faster when h is relatively small. Further, our techniques can be extended to obtain improved results for other related problems, e.g., computing the \n \n \n \n $$L_1$$\n \n \n \n geodesic Voronoi diagram for a set of point sites among the obstacles."},"correct_answer":{"kind":"string","value":"Given a point s and a set of h pairwise disjoint polygonal obstacles with a total of n vertices in the plane, suppose a triangulation of the space outside the obstacles is given; we present an \n \n \n \n $$O(n+h\\log h)$$\n \n \n \n time and O(n) space algorithm for building a data structure (called shortest path map) of size O(n) such that for any query point t, the length of an \n \n \n \n $$L_1$$\n \n \n \n shortest obstacle-avoiding path from s to t can be computed in \n \n \n \n $$O(\\log n)$$\n \n \n \n time and the actual path can be reported in additional time proportional to the number of edges of the path. The previously best algorithm computes such a shortest path map in \n \n \n \n $$O(n\\log n)$$\n \n \n \n time and O(n) space. So our algorithm is faster when h is relatively small. Further, our techniques can be extended to obtain improved results for other related problems, e.g., computing the \n \n \n \n $$L_1$$\n \n \n \n geodesic Voronoi diagram for a set of point sites among the obstacles."},"documents":{"kind":"list like","value":["Title: Quell.\nYear: 2015\n\nAbstract: We study the computational complexity of the puzzle Quell. The goal is to collect pearls by sliding a droplet of water over them in a map that is a two-dimensional grid. Each cell of the grid is either a free space (possibly with a pearl in it) or an obstacle. In each move, the droplet slides in one of the four directions to the maximal extent, through the free spaces of the map and collecting all pearls along the way, until it is stopped by an obstacle. We show that any-Moves-all-Pearls (deciding whether it is possible to collect all the pearls using any number of moves) can be solved in polynomial time. In contrast, both any-Moves-max-Pearls (finding the maximum number of pearls that can be collected using any number of moves) and min-Moves-all-Pearls (finding the minimum number of moves required to collect all the pearls) are APX-hard, although the corresponding decision problems are in FPT. We also present a simple 2-approximation for any-Moves-max-Pearls, and leave open the question whether min-Moves-all-Pearls admits a polynomial-time constant approximation.","Title: Generalized quotient image\nYear: 2004\n\nAbstract: In this paper, we present a unified framework for modeling intrinsic properties of face images for recognition. It is based on the quotient image (QI) concept, in particular on the existing works of QI [1, 2], Spherical Harmonic[13, 14, 15], [16, 17], Image Ratio [3, 5, 6, 7]and Retinex [4, 9]. Under this framework, we generalize these previous works into two new algorithms: (1) Non-Point Light Quotient Image (NPL-QI) extends QI to deal with nonpoint light sources by modeling non-point light directions using spherical harmonic bases; (2) Self-Quotient Image (S-QI) extends QI to perform illumination subtraction without the need for alignment and no shadow assumption. Experimental results show that our algorithms can significantly improve the performance of face recognition under varying illumination conditions.","Title: Online Rectangle Filling\nYear: 2011\n\nAbstract: We study an online geometric problem arising in channel-aware scheduling of wireless networks, which we call online rectangle filling. We present an online algorithm for this problem with a competitive ratio of 1.848. We also prove a lower bound of 1.6358\n for the competitive ratio of the problem. In addition, we give an O(n\n 2)-time optimal offline algorithm, where n is the size of the input. All three results are significant improvements on the previous results. Our techniques are based\n on new observations of the combinatorial structures of the problem.","Title: Visibility and ray shooting queries in polygonal domains\nYear: 2013\n\nAbstract: Given a polygonal domain (or polygon with holes) in the plane, we study the problem of computing the visibility polygon of any query point. As a special case of visibility problems, we also study the ray-shooting problem of finding the first point on the polygon boundaries that is hit by any query ray. These are fundamental problems in computational geometry and have been studied extensively. We present new algorithms and data structures that improve the previous results.","Title: Computing Shortest Paths among Curved Obstacles in the Plane\nYear: 2013\n\nAbstract: A fundamental problem in computational geometry is to compute an obstacle-avoiding Euclidean shortest path between two points in the plane. The case of this problem on polygonal obstacles is well studied. In this article, we consider the problem version on curved obstacles, which are commonly modeled as splinegons. A splinegon can be viewed as replacing each edge of a polygon by a convex curved edge (polygons are special splinegons), and the combinatorial complexity of each curved edge is assumed to be O(1). Given in the plane two points s and t and a set s of h pairwise disjoint splinegons with a total of n vertices, after a bounded degree decomposition of S is obtained, we compute a shortest s-to-t path avoiding the splinegons in O(n + h log h + k) time, where k is a parameter sensitive to the geometric structures of the input and is upper bounded by O(h2). The bounded degree decomposition of S, which is similar to the triangulation of the polygonal domains, can be computed in O(n log n) time or O(n + h log 1 + εh) time for any ε > 0. In particular, when all splinegons are convex, the decomposition can be computed in O(n + h log h) time and k is linear to the number of common tangents in the free space (called “free common tangents”) among the splinegons. Our techniques also improve several previous results: (1) For the polygon case (i.e., when all splinegons are polygons), the shortest path problem was previously solved in O(n log n) time, or in O(n + h2log n) time. Thus, our algorithm improves the O(n + h2log n) time result, and is faster than the O(n log n) time solution for sufficiently small h, for example, h=o(&sqrt;n,logn. (2) Our techniques produce an optimal output-sensitive algorithm for a basic visibility problem of computing all free common tangents among h pairwise disjoint convex splinegons with a total of n vertices. Our algorithm runs in O(n + h log h + k) time and O(n) working space, where k is the number of all free common tangents. Note that k = O(h2). Even for the special case where all splinegons are convex polygons, the previously best algorithm for this visibility problem takes O(n + h2log n) time. (3) We improve the previous work for computing the shortest path between two points among convex pseudodisks of O(1) complexity each. In addition, a by-product of our techniques is an optimal O(n + h log h) time and O(n) space algorithm for computing the Voronoi diagram of a set of h pairwise disjoint convex splinegons with a total of n vertices.","Title: Principal Observation Ray Calibration for Tiled-Lens-Array Integral Imaging Display\nYear: 2013\n\nAbstract: Integral imaging display (IID) is a promising technology to provide realistic 3D image without glasses. To achieve a large screen IID with a reasonable fabrication cost, a potential solution is a tiled-lens-array IID (TLA-IID). However, TLA-IIDs are subject to 3D image artifacts when there are even slight misalignments between the lens arrays. This work aims at compensating these artifacts by calibrating the lens array poses with a camera and including them in a ray model used for rendering the 3D image. Since the lens arrays are transparent, this task is challenging for traditional calibration methods. In this paper, we propose a novel calibration method based on defining a set of principle observation rays that pass lens centers of the TLA and the camera's optical center. The method is able to determine the lens array poses with only one camera at an arbitrary unknown position without using any additional markers. The principle observation rays are automatically extracted using a structured light based method from a dense correspondence map between the displayed and captured pixels. Experiments show that lens array misalignments can be estimated with a standard deviation smaller than 0.4 pixels. Based on this, 3D image artifacts are shown to be effectively removed in a test TLA-IID with challenging misalignments.","Title: Outlier respecting points approximation\nYear: 2014\n\nAbstract: In this paper, we consider a generalized problem formulation of computing a functional curve to approximate a point set in the plane with outliers. The goal is to seek a solution that not only optimizes its original objectives, but also somehow accommodates the impact of the outliers. Based on a new model of accommodating outliers, we present efficient geometric algorithms for various versions of this problem (e.g., the approximating functions are step functions or piecewise linear functions, the points are unweighted or weighted, etc). All our results are first known. Our new model and techniques for handling outliers may be useful to other applications as well.","Title: Style preserving Chinese character synthesis based on hierarchical representation of character\nYear: 2008\n\nAbstract: with English, and they are not suitable for Chinese character synthesis. In this paper, we propose an unified approach for modeling and synthesizing Chinese characters. Using a three-level hierarchical representation, each character is decomposed into basic components, which forms the stroke database and radical database. In the synthesis process, we use a wavelet-based approach to select proper strokes and radicals, and some aesthetic constraints are defined based on the relationships between components, then genetic algorithm is employed to search for the optimal results which best match the aesthetic constraints. Experimental results demonstrates the effectiveness of our method.","Title: Expression and application of geospatial relation ontology\nYear: 2013\n\nAbstract: Spatial relation is one of the most important features of geographic space information. The in-depth research on spatial relation is the base of realizing GIS spatial reasoning. Recently GIS software could only express few spatial relations and is difficult to be understood by computer, and also the researches on spatial relation ignore commonsense spatial cognition of people. The process on spatial relation reasoning causes serious semantic collision. Ontology theory was introduced to research spatial relation, which accorded with the commonsense spatial cognition of people. The paper designed a whole frame of space ontology and geospatial relation ontology model, which realized the expression of that model with TBC software. The paper built geographical ontology instance base based on Region data of Henan province and realized the expression of spatial relation ontology with OWL language. Some application instances were also done to validate the theories and technologies of the paper. The final aim is to promote the share and reuse of spatial relation knowledge.","Title: Computing the L 1 geodesic diameter and center of a simple polygon in linear time\nYear: 2014\n\nAbstract: In this paper, we show that the L 1 geodesic diameter and center of a simple polygon can be computed in linear time. For the purpose, we focus on revealing basic geometric properties of the L 1 geodesic balls, that is, the metric balls with respect to the L 1 geodesic distance. More specifically, in this paper we show that any family of L 1 geodesic balls in any simple polygon has Helly number two, and the L 1 geodesic center consists of midpoints of shortest paths between diametral pairs. These properties are crucial for our linear-time algorithms, and do not hold for the Euclidean case.","Title: Minmax regret 1-facility location on uncertain path networks.\nYear: 2013\n\nAbstract: •We propose an algorithm to compute the minmax regret.•We solve the minmax 1-facility location problem on uncertain path network.•Our algorithm improves the previous work.•We discover new observations on the problem.","Title: Detecting and tracking motion of Myxococcus xanthus bacteria in swarms.\nYear: 2012\n\nAbstract: Automatically detecting and tracking the motion of Myxococcus xanthus bacteria provide essential information for studying bacterial cell motility mechanisms and collective behaviors. However, this problem is difficult due to the low contrast of microscopy images, cell clustering and colliding behaviors, etc. To overcome these difficulties, our approach starts with a level set based pre-segmentation of cell clusters, followed by an enhancement of the rod-like cell features and detection of individual bacterium within each cluster. A novel method based on \"spikes\" of the outer medial axis is applied to divide touching (colliding) cells. The tracking of cell motion is accomplished by a non-crossing bipartite graph matching scheme that matches not only individual cells but also the neighboring structures around each cell. Our approach was evaluated on image sequences of moving M. xanthus bacteria close to the edge of their swarms, achieving high accuracy on the test data sets.","Title: RAUU: rate adaptation for unreliable unicast traffic in high speed networks\nYear: 2007\n\nAbstract: While long term throughput not exceeding TCP with Reno congestion control algorithm is widely accepted as the criterion of weighing TCP friendliness, this may lead to resource waste in high speed networks due to Reno's known performance limits. Inspired by FAST TCP, a congestion control algorithm named Rate Adaptation for Unreliable Unicast traffic (RAUU) is proposed for unreliable unicast traffic in high speed networks to improve its efficiency while still holding friendliness to TCP. Being a rate-based approach to best fit unreliable unicast traffic, RAUU has made special design choices to alleviate the inherent contiguous loss problem of rate adaptation algorithms. Like FAST, it also tries to maintain appropriate number of extra packets in networks, and for that purpose it combines loss and delay as congestion signals. Theoretical analysis shows that in ideal networks RAUU has and will converge to its one and only equilibrium state where the number of extra packets is equal to the preset value. Plentiful simulation experiments confirmed that it could achieve similar performance to FAST as well as comparable throughput smoothness to TFRC while keeping TCP-friendliness at the same time.","Title: Computing Shortest Paths amid Convex Pseudodisks.\nYear: 2013\n\nAbstract: Multiple objects in the plane are called pseudodisks if the boundaries of any two of them intersect transversely at most twice. Given a set of n (possibly intersecting) convex pseudodisks of O(1) complexity each and two points s and t in the plane, we present an efficient algorithm for computing a shortest s-to-t path avoiding the pseudodisks. After the union of the pseudodisks is computed, which can be done in O(n log n) randomized time or O(n log(2) n) deterministic time, our algorithm runs in O(n log n + k) deterministic time, where k is the size of the extended visibility graph of the union of the pseudodisks. Note that k = O(n(2)) in the worst case. In over two decades, the previously best algorithms for this problem have not improved on the bound of O(n(2) log n) time, even when all the pseudodisks are pairwise disjoint disks. Our technique is also applicable to a motion planning problem of finding a shortest path to translate a convex object in the plane from one location to another avoiding a given set of polygonal obstacles, improving the previously best known solution and settling an open problem posed in 1988. Our algorithm actually solves a more general version of the open problem. Further, as a byproduct of our approach, we present an O(n log n + k)-time algorithm for computing the extended visibility graph of a set of n (possibly intersecting) convex pseudodisks in the plane. The previously best known time bound for this visibility problem is O(n(2) log n).","Title: Computing the visibility polygon of an island in a polygonal domain\nYear: 2012\n\nAbstract: Given a set $\\mathcal{P}$ of h pairwise-disjoint polygonal obstacles of totally n vertices in the plane, we study the problem of computing the (weakly) visibility polygon from a polygonal obstacle P* (an island) in $\\mathcal{P}$. We give an O(n2h2) time algorithm for it. Previously, the special case where P* is a line segment was solved in O(n4) time, which is worst-case optimal. In addition, when all obstacles in $\\mathcal{P}$ (including P*) are convex, our algorithm runs in O(n+h4) time.","Title: Computing L1 Shortest Paths among Polygonal Obstacles in the Plane\nYear: 2012\n\nAbstract: Given a point $s$ and a set of $h$ pairwise disjoint polygonal obstacles of totally $n$ vertices in the plane, we present a new algorithm for building an $L_1$ shortest path map of size O(n) in $O(T)$ time and O(n) space such that for any query point $t$, the length of the $L_1$ shortest obstacle-avoiding path from $s$ to $t$ can be reported in $O(\\log n)$ time and the actual shortest path can be found in additional time proportional to the number of edges of the path, where $T$ is the time for triangulating the free space. It is currently known that $T=O(n+h\\log^{1+\\epsilon}h)$ for an arbitrarily small constant $\\epsilon>0$. If the triangulation can be done optimally (i.e., $T=O(n+h\\log h)$), then our algorithm is optimal. Previously, the best algorithm computes such an $L_1$ shortest path map in $O(n\\log n)$ time and O(n) space. Our techniques can be extended to obtain improved results for other related problems, e.g., computing the $L_1$ geodesic Voronoi diagram for a set of point sites in a polygonal domain, finding shortest paths with fixed orientations, finding approximate Euclidean shortest paths, etc.","Title: Context-Restricted, role-oriented emotion knowledge acquisition and representation\nYear: 2005\n\nAbstract: Emotion knowledge is a fundamental part of human commonsense knowledge. For the lack of solid knowledge extraction work in this domain, this paper focuses on acquiring and representing the antecedent situations of various emotions as well as the emotion subjects' interpretation to these situations in real-world emotion-eliciting scenarios. After a comprehensive analysis on the meaning structure of three critical notions in emotion understanding and modeling (i.e. role, context and event), this paper introduces a pragmatic emotion knowledge acquisition method and presents a context-restricted, role-oriented, frame-based representational model of emotion knowledge, with a case study which demonstrates the applicability of the model.","Title: Computing maximum non-crossing matching in convex bipartite graphs\nYear: 2012\n\nAbstract: We consider computing a maximum non-crossing matching in convex bipartite graphs. For a convex bipartite graph of n vertices and m edges, we present an O ( n log n ) time algorithm for finding a maximum non-crossing matching in the graph. The previous best algorithm takes O ( m + n log n ) time (Malucelli et¿al., 1993). Since m = ¿ ( n 2 ) in the worst case, our result improves the previous work.","Title: Text Learning and Hierarchical Feature Selection in Webpage Classification\nYear: 2008\n\nAbstract: One of the solutions of retrieving information from the Internet is by classifying web pages automatically. In almost all classification methods that have been published, feature selection is a very important issue. Although there are many feature selection methods has been proposed. Most of them focus on the features within a category and ignore that the hierarchy of categories also plays an important role in achieving accurate classification results. This paper proposes a new feature selection method that incorporates hierarchical information, which prevents the classifying process from going through every node in the hierarchy. Our test results show that our classification algorithm using hierarchical information reduces the search complexity from n to log(n) and increases the accuracy by 6.2% comparing to a related algorithm.","Title: Nexir: A Novel Web Extraction Rule Language Toward A Three-Stage Web Data Extraction Model\nYear: 2013\n\nAbstract: As the most popular information publishing platform, the Web contains a lot of valued data information of interests to users or applications. Nowadays, although a lot of data mining or analysis techniques have been studied in last decade, there are still not many easy-to-use web data mining tools available for users to extract useful data information from the Web. The web information extraction is a whole process involving web page navigation, data extraction and data integration. Unfortunately most of existing studies or systems lack of sufficient consideration toward the three-stage process. Also most of them lack the powerful rules to express the flexible extraction logic to extract data records with complicate structure. In this paper, we propose a novel web data extraction language, NEXIR, toward a three-stage web data extraction model. First of all, the language can define rules for system to automate the navigation process of the web pages, including deep web pages that need interactions from users. Then the language allows users to define flexible and complicated rules to extract data records from web pages and integrate extracted data into a pre-defined structure. A language engine and a prototype extraction system have been implemented based on the proposed language. The experimental results show that our language and system work effective and powerful compared with existing data extraction approaches.","Title: An improved algorithm for reconstructing a simple polygon from its visibility angles\nYear: 2012\n\nAbstract: We study the problem of reconstructing a simple polygon: Given a cyclically ordered vertex sequence of an unknown simple polygon P of n vertices and, for each vertex v of P, the sequence of angles defined by all the visible vertices of v in P, reconstruct the polygon P (up to similarity). An O(n3logn) time algorithm has been proposed for this problem. We present an improved algorithm with running time O(n2), based on new observations on the geometric structures of the problem. Since the input size (i.e., the total number of input visibility angles) is Θ(n2) in the worst case, our algorithm is worst-case optimal.","Title: Face recognition system using multiple face model of hybrid Fourier feature under uncontrolled illumination variation.\nYear: 2011\n\nAbstract: The authors present a robust face recognition system for large-scale data sets taken under uncontrolled illumination variations. The proposed face recognition system consists of a novel illumination-insensitive preprocessing method, a hybrid Fourier-based facial feature extraction, and a score fusion scheme. First, in the preprocessing stage, a face image is transformed into an illumination-insensitive image, called an \"integral normalized gradient image,\" by normalizing and integrating the smoothed gradients of a facial image. Then, for feature extraction of complementary classifiers, multiple face models based upon hybrid Fourier features are applied. The hybrid Fourier features are extracted from different Fourier domains in different frequency bandwidths, and then each feature is individually classified by linear discriminant analysis. In addition, multiple face models are generated by plural normalized face images that have different eye distances. Finally, to combine scores from multiple complementary classifiers, a log likelihood ratio-based score fusion scheme is applied. The proposed system using the face recognition grand challenge (FRGC) experimental protocols is evaluated; FRGC is a large available data set. Experimental results on the FRGC version 2.0 data sets have shown that the proposed method shows an average of 81.49% verification rate on 2-D face images under various environmental variations such as illumination changes, expression changes, and time elapses.","Title: \"Is It Really a Defect?\" An Empirical Study on Measuring and Improving the Process of Software Defect Reporting\nYear: 2011\n\nAbstract: Testing is one of the most labor-intensive activities in software development life cycle and consumes between 30% and 50% of total development costs according to many studies. The communication gap between testers and developers that is caused by unclear or even invalid defect reporting usually makes the testing schedule delay, and contributes large amount of testing effort to rework and re-communication. Defect reporting's efficiency directly influences testing efficiency and effectiveness. From our observation and improvement practices on a software product with recent 4 increments: 1) the percentage of unclear defect reports is around 23%, but more than 82% testers, more or less, reported unclear defect reports, 2) the percentage of invalid defect reports is around 26%, but more than 35% testers reported invalid defect reports. Improvement practices that we took on training testers on how to report defect in a clearer and more efficient way further bridged the communication gap between testers and developers, and it can reduce 15.7% unclear defect reports and 19.53% invalid defect reports, which finally led to the improvement of testing efficiency.","Title: Efficient algorithms for the one-dimensional k-center problem\nYear: 2013\n\nAbstract: We consider the problem of finding k centers for n weighted points on a real line. This (weighted) k-center problem was solved in O ( n log ¿ n ) time previously by using Cole's parametric search and other complicated approaches. In this paper, we present an easier O ( n log ¿ n ) time algorithm that avoids the parametric search, and in certain special cases our algorithm solves the problem in O ( n ) time. In addition, our techniques involve developing interesting data structures for processing queries that find a lowest point in the common intersection of a certain subset of half-planes. This subproblem is interesting in its own right and our solution for it may find other applications as well.","Title: Shape And Texture Preserved Non-Photorealistic Rendering\nYear: 2004\n\nAbstract: Two approaches, image-space and object-space, exist for non-photorealistic rendering WPM. The object-space approach has an advantage that it is able to access the 3D shape information of the scene with a 3D model. In this paper, we present a non-stroke based image-space technique that has the strength similar to that of the object-space approach, but without involving explicit 3D models. The basic idea is to factorise the Lambertian model to obtain the shape and texture information implicitly contained in the image. We also develop a specific anisotropic low-pass image filter, which can smooth an image without blurring important features. With the developed techniques, we present two NPR styles, the sketching style and the painting style. Although our work does not aim to mimic any specific existing art form, the sketching style can generate effects similar to those of the pen-and-ink and the painting style is able to produce the colour permeating effects of watercolour. Copyright (C) 2004 John Wiley Sons, Ltd.","Title: Processing an Offline Insertion-Query Sequence with Applications\nYear: 2011\n\nAbstract: In this paper, we present techniques and algorithms for processing an offline sequence of insertion and query operations and\n for related problems. Most problems we consider are solved optimally in linear time by our algorithms, which are based on\n a new geometric modeling and interesting techniques. We also discuss some applications to which our algorithms and techniques\n can be applied to yield efficient solutions.","Title: Weak visibility queries of line segments in simple polygons\nYear: 2012\n\nAbstract: Given a simple polygon P in the plane, we present new data structures for computing the weak visibility polygon from any query line segment in P. We build a data structure in O ( n ) time and space that can compute the visibility polygon for any query line segment s in O ( k log ¿ n ) time, where k is the size of the visibility polygon of s and n is the number of vertices of P. Alternatively, we build a data structure in O ( n 3 ) time and space that can compute the visibility polygon for any query line segment in O ( k + log ¿ n ) time. In order to develop these data structures, we obtain many other results that may be interesting in their own right.","Title: Multimodal Preserving Embedding For Face Recognition\nYear: 2008\n\nAbstract: Traditional dimension reduction approaches always consider the samples in a class are uni-modal. In real world, samples in a class are usually multi-modal, for instance, the manifold of the facial appearance of a person under different illumination, expression, and poses is multi-modal. Recently, dimension reduction approaches based on manifold learning are presented, the main purpose is to preserve the manifold structure on low dimensional space. In this paper, by analyzing the manifold learning methods and the traditional dimension reduction methods, we show that most of these methods can be summarized into a general framework. Based on this framework, we propose a novel dimension reduction approach, called multi-modal preserving embedding (MPE) by utilizing path-based similarity measure. We also describe two useful extensions of our method: kernel MPE and Tensor MPE. Comprehensive comparisons and extensive experiments on face recognition are included to demonstrate the effectiveness of our method.","Title: The topology aware file distribution problem\nYear: 2011\n\nAbstract: We present theoretical results for large-file distribution on general networks of known topology (known link bandwidths and\n router locations). We show that the problem of distributing a file in minimum time is NP-hard in this model, and we give an\n O(logn) approximation algorithm, where n is the number of workstations that require the file. We also characterize our method as optimal amongst the class of “no-link-sharing”\n algorithms.","Title: Computing Shortest Paths amid Pseudodisks\nYear: 2011\n\nAbstract: Multiple objects in the plane are called pseudodisks if they are convex and the boundaries of any two of them intersect transversely at most twice. Given a set of n (possibly intersecting) pseudodisks of O(1) complexity each and two points s and t in the plane, we develop an O(n2) time algorithm for computing a shortest s-to-t path avoiding the pseudodisks. In over two decades, the previously best algorithms for this problem take O(n2 log n) time, even when all pseudodisks are pairwise-disjoint disks. Our technique is also applicable to a motion planning problem of finding a shortest path to translate a convex object in the plane from one location to another avoiding a given set of polygonal obstacles, improving the previously best known solution. Our algorithm actually solves a more general version of the motion planning problem. Further, as a by-product of our approach, we present an O(n2) time algorithm for computing the visibility graph of a set of n (possibly intersecting) pseudodisks in the plane. The previously best known time bound of this visibility problem is O(n2 log n). In addition, for n pairwise disjoint (non-polygonal) convex objects of O(1) complexity each in the plane, we compute a shortest s-to-t path avoiding all objects in O(n log n + k) time, where k is the size of the visibility graph of the objects.","Title: Two-dimensional fuzzy PID switching power supply controller based on FPGA\nYear: 2013\n\nAbstract: This paper proposed a control method combining fuzzy PI and fuzzy PD to implement the phase-shifted full-bridge switching power supply control. The method can meet the control demands of the object's time-varying characteristics. It does not rely on a fixed mathematical model, and it can adjust control parameters on-line. The system simulation model is built via Matlab/Simulink, and fuzzy control rules are determined. The results of simulation analysis show that the control effect using this method is better than traditional PID algorithm in switching power supply. Two-dimensional fuzzy PID controller based on FPGA has been designed, and it could effectively be applied to control of 3KW phase-shifted full-bridge switching power supply.","Title: New algorithms for 1-D facility location and path equipartition problems\nYear: 2011\n\nAbstract: We study the one-dimensional facility location problems. Given a set of n customers on the real line, each customer having a cost for setting up a facility at its position, and an integer k, we seek to find at most k of the customers to set up facilities for serving all n customers such that the total cost for facility set-up and service transportation is minimized. We consider several problem variations including k-median and k-coverage and a linear model. We also study a related path equipartition problem: Given a vertex-weighted path and an integer k, remove k-1 edges so that the weights of the resulting k sub-paths are as equal as possible. Based on new problem modeling and observations, we present improved algorithms for these problems over the previous work.","Title: Efficient algorithms for the weighted k-center problem on a real line\nYear: 2011\n\nAbstract: We present $O(\\min\\{n\\log^{1.5} n, n\\log n+k^2\\log^2\\frac{n}{k}\\log^2 n\\})$ time algorithms for the weighted k-problem on a real line. Previously, the best known algorithms for this problem take O(nlog2n) time, or O(knlogn) time, or a time linear in n but exponential in k. Our techniques involve developing efficient data structures for processing queries that find a lowest point in the common intersection of a certain subset of half-planes. This subproblem is interesting in its own right.","Title: Illumination modeling and normalization for face recognition\nYear: 2003\n\nAbstract: We present a general framework for face modeling under varying lighting conditions. First, we show that a face lighting subspace can be constructed based on three or more training face images illuminated by noncoplanar lights. The lighting of any face image can be represented as a point in this subspace. Second, we show that the extreme rays, i.e. the boundary of an illumination cone, cover the entire light sphere. Therefore, a relatively sparsely sampled face images can be used to build a face model instead of calculating each extremely illuminated face image. Third, we present a face normalization algorithm, illumination alignment, i.e. changing the lighting of one face image to that of another face image. Experiments are presented.","Title: Improved algorithms for path partition and related problems.\nYear: 2011\n\nAbstract: We study the L∞ path partition problem: given a path of n weighted vertices and an integer k, remove k−1 edges from the path so that the maximum absolute deviation of the weights of the resulting k sub-paths from their mean is minimized. Previously, the best algorithm solves this problem in O(nklogk) time. We present an O(nk) time algorithm. We also give improved solutions for two related problems: the Ld path partition problem and the web proxies placement problem.","Title: Physical Modeling and Configuration Simulation for Constrained Cables of Electromechanical Products\nYear: 2013\n\nAbstract: To solve the problem of low cable assembly efficiency for current electromechanical products, a digital modeling method is proposed based on nonlinear mechanics of a thin elastic rod. Considering the flexibility, cable configuration is embodied by translation and rotation of cross-section along the centerline. According to the minimum potential energy principle in Elasticity, Kirchhoff equations in the form of Euler angles are gained, which are used to describe spatial flexural configuration of deformable cables. For the specific boundary condition that cables are fixed at both ends, analytical integrals of Euler angles are achieved on the base of kinetic analogy. Furthermore, to deal with the difficulty of getting Cartesian coordinate integrals, a cylindrical system is introduced to describe spatial position of flexural cables on the base of Saint-Venant of Elasticity. Finally, a configuration simulation platform is developed and an experimental system using 3D laser scan technology is founded to do effective verification of the relevant model and algorithm.","Title: Pyramid surface reconstruction from normal.\nYear: 2004\n\nAbstract: In this paper, a novel method is introduced to estimate height from surface normal and implemented in a photometric 3D reconstruction system, base on the improved optimization function in the height domain. To avoid the global distortion from local integration, a pyramid reconstruction ideal is proposed, which sample the surface every certain points to form the separated point sets called sub-surfaces, and then step by step the coarser sub-surfaces is combined into finer ones and eventually derive the whole surface. Experiment shows that our method is faster and more accurate than some of the main method in this field.","Title: Fitting A Step Function To A Point Set With Outliers Based On Simplicial Thickness Data Structures\nYear: 2012\n\nAbstract: Given a real epsilon > 0, an integer g >= 0 and a set of points in the plane, we study the problem of computing a piecewise linear functional curve with minimum number of line segments to approximate all points after removing g outliers such that the approximate error is at most epsilon. We give an improved algorithm over the previous work. The algorithm is based on two dynamic data structures developed in this paper for the simplicial thickness queries, which are of independent interest. For a set S of simplices in the d-dimensional space R-d (d >= 2 is a constant), the simplicial thickness of a point p is defined as the number of simplices in S that contain p. Given a set P of n points in R-d, we develop two dynamic data structures to support the following operations. (1) Simplex insertion: Insert a simplex into S. (2) Simplex deletion: Delete a simplex from S. (3) Simplicial thickness query: Given a query simplex sigma, compute the minimum simplicial thickness among all points in sigma boolean AND P. The first data structure is constructed in O(n(1+delta)) time, for any constant delta > 0, and can support each operation in O(n(1-1/d)) time; the second one is constructed in O(n log n) time and can support each operation in O(n(1-1/d)(log n)(O(1))) time. Both data structures use O(n) space. These data structures may find other applications as well.","Title: The peak point of LURR and its significance\nYear: 2010\n\nAbstract: The Load-Unload Response Ratio (LURR) value fluctuates around one during the early stage of the seismogenic process and then rises and reaches its peak point before the occurrence of a strong earthquake; the strong earthquake however does not onset at that time, but after the peak point LURR decreases sharply at the eve of the main shock before the final event outbreak. Thus, the peak point of LURR is ahead of the occurrence of an earthquake. We denote the lead time as T2. The discovery of peak point for LURR and the relationship between T2 and magnitude M is of great significance as the peak point is usually easy to determine and then we can predict the occurrence time for the coming event according to Equation (3) so that we can enhance the precision of time for earthquake prediction in terms of LURR from ‘year’ scale to ‘month’ scale. The variation of LURR around the Wenchuan earthquake and its lesson to us are depicted in the paper. Copyright © 2009 John Wiley & Sons, Ltd.","Title: New algorithms for online rectangle filling with k-lookahead\nYear: 2011\n\nAbstract: We study the online rectangle filling problem which arises in channel aware scheduling of wireless networks, and present deterministic and randomized results for algorithms that are allowed a k-lookahead for k驴2. Our main result is a deterministic min驴{1.848,1+2/(k驴1)}-competitive online algorithm. This is the first algorithm for this problem with a competitive ratio approaching 1 as k approaches +驴. The previous best-known solution for this problem has a competitive ratio of 2 for any k驴2. We also present a randomized online algorithm with a competitive ratio of 1+1/(k+1). Our final result is a closely matching lower bound (also proved in this paper) of $1+1/(\\sqrt{k+2}+\\sqrt{k+1})^{2}1+1/(4(k+2))$ on the competitive ratio of any randomized online algorithm against an oblivious adversary. These are the first known results for randomized algorithms for this problem.","Title: Matroid and knapsack center problems\nYear: 2013\n\nAbstract: In the classic k-center problem, we are given a metric graph, and the objective is to select k nodes as centers such that the maximum distance from any vertex to its closest center is minimized. In this paper, we consider two important generalizations of k-center, the matroid center problem and the knapsack center problem. Both problems are motivated by recent content distribution network applications. Our contributions can be summarized as follows: (1) We consider the matroid center problem in which the centers are required to form an independent set of a given matroid. We show this problem is NP-hard even on a line. We present a 3-approximation algorithm for the problem on general metrics. We also consider the outlier version of the problem where a given number of vertices can be excluded as outliers from the solution. We present a 7-approximation for the outlier version. (2) We consider the (multi-)knapsack center problem in which the centers are required to satisfy one (or more) knapsack constraint(s). It is known that the knapsack center problem with a single knapsack constraint admits a 3-approximation. However, when there are at least two knapsack constraints, we show this problem is not approximable at all. To complement the hardness result, we present a polynomial time algorithm that gives a 3-approximate solution such that one knapsack constraint is satisfied and the others may be violated by at most a factor of $$1+epsilon $$1+∈. We also obtain a 3-approximation for the outlier version that may violate the knapsack constraint by $$1+epsilon $$1+∈.","Title: An Environment for Multi-domain Ontology Development and Knowledge Acquisition\nYear: 2002\n\nAbstract: Ontology is used widely as an analytical tool in many domains in recent years, and adopted as a fundamental basis for knowledge acquisition and sharing. However, it becomes difficult for knowledge engineers to share a large ontology base created by many engineers from different domains, and thus ontologies may not genuinely support knowledge engineers in knowledge acquisition and analysis. In order to promote multi-domain ontology sharing and to facilitate knowledge acquisition and analysis, we have implemented an ontology and knowledge engineering environment (OKEE) for multi-domain ontology development and knowledge acquisition. OKEE provides a list of functionalities for ontology design and for knowledge acquisition.","Title: Algorithms on minimizing the maximum sensor movement for barrier coverage of a linear domain\nYear: 2013\n\nAbstract: In this paper, we study the problem of moving n sensors on a line to form a barrier coverage of a specified segment of the line such that the maximum moving distance of the sensors is minimized. Previously, it was an open question whether this problem on sensors with arbitrary sensing ranges is solvable in polynomial time. We settle this open question positively by giving an O(n2lognloglogn) time algorithm. Further, if all sensors have the same-size sensing range, we give an O(nlogn) time algorithm, which improves the previous best O(n2) time solution.","Title: Trajectory tracking control for uninhabited air vehicles\nYear: 2006\n\nAbstract: In this paper a simplified control system framework is presented to solve maneuvering trajectory tracking problem of uninhabited air vehicles. The flight control system is divided into three subsystems including command generation, transformation and allocation. According to the kinematics equations of the aircraft, flight path angle commands can be generated by desired 3D position from path planning. These commands are transformed to body angular rates through direct nonlinear mapping, which is simpler than common multi-loop method based on time scale separation assumption. Then, by using weighted pseudo-inverse method, the control surface deflections are allocated to follow body angular rates from the previous step. Additionally, an on-line multilayer neural network is used to provide robustness to the uncertainty of system. Numerical simulation results show the viability of this approach.","Title: Face representation and reconstruction under different illumination conditions\nYear: 2003\n\nAbstract: To deal with image variations due to illumination problem, Ramamoorthi and Basri have independently derived a spherical harmonic analysis for the Lambertian reflectance and linear subspace. Their theoretical work provided a new approach for face representation, however both of them assume that the 3D surface normals and albedo (or unit albedo) are known, which limit this algorithm's application. We present a novel method for modelling 3D face shape and albedo from only three images and this work will fill the blank which Ramamoorthi and Basri left. Our work is closely related to photometric stereo, but conditions of photometric stereo for estimating albedo and surface normal are too strict to be applied for real application. Moreover, the conventionally used singular value decomposition (SVD) approach leads to the notorious linear ambiguity and the solution to this problem needs to introduce more constraints or more images. By taking advantage of similar 3D shape of all human faces, the highlight of the new method is that it circumambulates the linear ambiguity by 3D alignment. The experiment results show that our estimated model can be perfectly employed to face recognition and 3D reconstruction.","Title: New Algorithms for Facility Location Problems on the Real Line.\nYear: 2014\n\nAbstract: In this paper, we study the facility location problems on the real line. Given a set of customers on the real line, each customer having a cost for setting up a facility at its position, and an integer , we seek to find at most of the customers to set up facilities for serving all customers such that the total cost for facility set-up and service transportation is minimized. We consider several problem variations including the -median, the -coverage, and the linear model. The previously best algorithms for these problems all take () time. Our new algorithms break the () time bottleneck and solve these problems in sub-quadratic time. Our algorithms are based on a new problem modeling and interesting algorithmic techniques, which may find other applications as well.","Title: Face representation under different illumination conditions\nYear: 2003\n\nAbstract: To deal with image variations due to illumination problem, recently Ramamoorthi and Basri have independently derived a spherical harmonic analysis for the Lambertian reflectance and linear subspace. Their theoretical work provided a new approach for face representation, however both of them had the assumption that the 3D surface normal and albedo are known. This assumption limits this algorithm's application. In this paper, we present a novel method for modeling 3D face shape and albedo from only three images with unknown light directions and this work well fills the blank, which Ramamoorthi and Basri left. By taking the advantage of similar 3D shape of all human faces, the highlight of the new method is that it circumambulates the linear ambiguity by 3D alignment. The experiment results show that our estimated model can be perfectly employed to face recognition and 3D reconstruction.","Title: Knowledge modeling and acquisition of traditional Chinese herbal drugs and formulae from text.\nYear: 2004\n\nAbstract: Traditional Chinese medicine has developed over more than 4000 years. A tremendous amount of medical knowledge has been accumulated, among which herbal drugs and formulae are an important portion. This paper presents an ontology for traditional Chinese drugs and formulae, and an ontology-based system for extracting knowledge of drugs and formulae from semi-structured text. The system consists of two components: an executable knowledge extraction language (or EKEL) for specifying knowledge-extracting agents, and a support machine for executing EKEL programs. Experiments show that the system is adequate of extracting knowledge of herbal drugs and formulae from semi-structured text.","Title: A simulation approach for impact analysis of requirement volatility considering dependency change\nYear: 2012\n\nAbstract: Requirement volatility is a common and inevitable project risk which has severe consequences on software projects. When requirement change occurs, a project manager wants to analyze its impact so as to better cope with it. As the modification to one requirement can cause changes in its dependent requirements and its dependency relationship, the impact analysis can be very complex. This paper proposes a simulation approach DepRVSim (Requirement Volatility Simulation considering Dependency relationship) to assessing this sort of impact. We abstract the general patterns of the influence mechanism, which may trigger modification in its dependency relationship and bring changes in other requirements through dependency. DepRVSim can generate such information as the probability distribution of effort deviation and schedule deviation. As a proof-of-concept, the applicability of DepRVSim is demonstrated with an illustrative case study of a real software project. Results indicate that DepRVSim is able to provide experimental evidence for decision making when requirement changes.","Title: Cell affinity of PHBV thin films evaluated by a parallel plate flow chamber\nYear: 2009\n\nAbstract: The aim of the study is to evaluate the cell affinity of poly(3-hydroxybutyric acid-co-3-hydroxyvaleric acid) (PHBV) thin films using a parallel plate flow chamber system. The PHBV thin films were modified by anhydrous ammonia gaseous plasma treatment. Mouse 3T3 fibroblast cells were seeded on bare and plasma modified PHBV thin films, respectively. A laminar flow enforces the cells on these films in a parallel plate flow chamber system. The percentage of initial cells remaining on the films after serials flows was used to evaluate the cell affinity of films. The results showed that, compared with control groups, ammonia plasma treatment could promote the cell affinity of PHBV films. It also indicated that the parallel plate flow chamber system can be used to evaluate the cell affinity of a material.","Title: Extraction Rule Language for Web Information Extraction and Integration\nYear: 2013\n\nAbstract: The Web is the largest data source that contains a lot of valuable information of interests to users or applications. However, how to automatically navigate and extract useful data from web pages is an important issue to study. There have been a number of existing studies on this area. However, most of them do not take enough consideration on complete process of web information extraction and lack of powerful rule expression ability to describe the navigation, extraction and integration rules. In this paper, we study and propose a new web information extraction rule language toward a general model for web information extraction and integration. We first introduce a source data objects to extract different type of web data records. Then we adopt the XML to define the target data entity structure and use scripts to perform target data record integration. The results show that our extraction rule language can provide powerful and flexible ability to describe extraction logic to achieve accurate web data records extraction from complex web pages.","Title: Locating an Obnoxious Line among Planar Objects\nYear: 2012\n\nAbstract: Given a set P of n points in the plane such that each point has a positive weight, we study the problem of finding an obnoxious line that intersects the convex hull of P and maximizes the minimum weighted Euclidean distance to all points of P. We also consider a variant of this problem whose input is a set of m polygons with totally n vertices in the plane such that each polygon has a positive weight and whose goal is to locate an obnoxious line with respect to the weighted polygons. We improve the previous results for both problems. Our algorithms are based on new geometric observations and interesting algorithmic techniques.","Title: Approximating Spanning Trees with Inner Nodes Cost\nYear: 2005\n\nAbstract: We consider the practical NP-complete problem of finding a minimum weight spanning tree with both edge weights and inner nodes weights. We present two polynomial time algorithms with approximation factors of 2.35 · ln n and 2Hn, respectively, where n is the number of nodes in the graph and Hn is the n-th Harmonic number. This nearly matches the lower bound of (l-\\in )Hn, for any \\in \\ge 0. We also give an approximation algorithm with approximation factor \\Delta - 1, where \\Delta is the maximum degree of the graph. For metric spaces, we give a 3.105-approximation algorithm and show that an approximation factor of 1.463 is impossible unless {NP \\subseteq DTIME[n^{O(\\log longn)} ]}.","Title: A map ontology driven approach to natural language traffic information processing and services\nYear: 2006\n\nAbstract: This paper proposes a map ontology driven approach to natural language traffic information processing, and also describes its evaluation results Traffic congestion is considered a major urban problem whose solution has long been sought for by engineers and researchers Recently, the idea of gathering traffic information from mobile users via short message service appears promising However, the traffic information is difficult to process to achieve a high accuracy because of its direct, indirect and connotative expressions The proposed map ontology consists of a set of concepts, attributes, relations and constraints on them The map ontology plays two key roles: 1) a basis for natural language traffic information analysis, and 2) a basis for user query analysis In this paper we present the major information processing modules and services for mobile users Experimental results show that the proposed method can improve the traffic information processing accuracy to 93%–95%.","Title: Face recognition under varying lighting conditions using self quotient image\nYear: 2004\n\nAbstract: In this paper, we introduce the concept of self-quotient image (SQI) for robust face recognition under varying lighting conditions. It is based on the quotient image method (Amnon Shashua et al., 2001) (T. Riklin-Raviv et al., 1999) to achieve lighting invariance. However, the SQI has three advantages: (1) it needs only one face image for extraction of intrinsic lighting invariant property of a face while removing extrinsic factor corresponding to the lighting; (2) no alignment is needed; (3) it works in shadow regions. The theoretical analysis on conditions where the algorithm is applicable and a non-iterative filtering algorithm for computing SQI are presented. Experiment results demonstrate the effectiveness of our method for robust face recognition under varying lighting conditions.","Title: Optimal Point Movement for Covering Circular Regions\nYear: 2012\n\nAbstract: Given n points in a circular region C in the plane, we study the problems of moving the n points to the boundary of G to form a regular n-gon such that the maximum (min-max) or the sum (min-sum) of the Euclidean distances traveled by the points is minimized. These problems have applications, e.g., in mobile sensor barrier coverage of wireless sensor networks. The min-max problem further has two versions: the decision version and the optimization version. For the min-max problem, we present an O(nlog2n) time algorithm for the decision version and an O(nlog3n) time algorithm for the optimization version. The previously best algorithms for the two problem versions take O(n3.5) time and O(n3.5logn) time, respectively. For the min-sum problem we show that a special case with all points initially lying on the boundary of the circular region can be solved in O(n2) time, improving a previous O(n4) time solution. For the general min-sum problem, we present a 3-approximation O(n2) time algorithm. In addition, a by-product of our techniques is an algorithm for dynamically maintaining the maximum matching of a circular convex bipartite graph; our algorithm can handle each vertex insertion or deletion on the graph in O(log2n) time. This result may be interesting in its own right.","Title: Representing a Functional Curve by Curves with Fewer Peaks\nYear: 2011\n\nAbstract: In this paper, we study the problems of (approximately) representing a functional curve in 2-D by a set of curves with fewer peaks. Representing a function (or its curve) by certain classes of structurally simpler functions (or their curves) is a basic mathematical problem. Problems of this kind also find applications in applied areas such as intensity-modulated radiation therapy (IMRT). Let $\\bf f$ be an input piecewise linear functional curve of size n. We consider several variations of the problems. (1) Uphill–downhill pair representation (UDPR): Find two nonnegative piecewise linear curves, one nondecreasing (uphill) and one nonincreasing (downhill), such that their sum exactly or approximately represents $\\bf f$. (2) Unimodal representation (UR): Find a set of unimodal (single-peak) curves such that their sum exactly or approximately represents $\\bf f$. (3) Fewer-peak representation (FPR): Find a piecewise linear curve with at most k peaks that exactly or approximately represents $\\bf f$. Furthermore, for each problem, we consider two versions. For the UDPR problem, we study its feasibility version: Given ε0, determine whether there is a feasible UDPR solution for $\\bf f$ with an approximation error ε; its min-ε version: Compute the minimum approximation error ε ∗ such that there is a feasible UDPR solution for $\\bf f$ with error ε ∗. For the UR problem, we study its min-k version: Given ε0, find a feasible solution with the minimum number k ∗ of unimodal curves for $\\bf f$ with an error ε; its min-ε version: given k0, compute the minimum error ε ∗ such that there is a feasible solution with at most k unimodal curves for $\\bf f$ with error ε ∗. For the FPR problem, we study its min-k version: Given ε0, find one feasible curve with the minimum number k ∗ of peaks for $\\bf f$ with an error ε; its min-ε version: given k≥0, compute the minimum error ε ∗ such that there is a feasible curve with at most k peaks for $\\bf f$ with error ε ∗. Little work has been done previously on solving these functional curve representation problems. We solve all the problems (except the UR min-ε version) in optimal O(n) time, and the UR min-ε version in O(n+mlog m) time, where mn is the number of peaks of $\\bf f$. Our algorithms are based on new geometric observations and interesting techniques.","Title: Improved points approximation algorithms based on simplicial thickness data structures\nYear: 2010\n\nAbstract: Given a real ε 0, an integer g ≥ 0 and a set of points in the plane, we study the problem of computing a piecewise linear functional curve with minimum number of line segments to approximate all points after removing g outliers such that the approximation error is at most ε. We give an improved algorithm over the previous work. The algorithm is based on two dynamic data structures developed in this paper for the simplicial thickness queries, which are of independent interest. For a set S of simplices in the d-D space Ed (d ≥ 2 is a constant), the simplicial thickness of a point p is defined as the number of simplices in S that contain p. Given a set P of n points in Ed, we develop two linear-space dynamic data structures to support the following operations. (1) Simplex insertion: Insert a simplex into S. (2) Simplex deletion: Delete a simplex from S. (3) Simplicial thickness query: Given a query simplex σ, compute the minimum simplicial thickness among all points in σ ∩ P. The first data structure supports each operation in O(n1-1/d) time with O(n1+δ) time preprocessing, for any constant δ 0; the second one supports each operation in O(n1-1/d(log n)O(1)) time with O(n log n) time preprocessing. These data structures may also find other applications.","Title: A note on searching line arrangements and applications\nYear: 2013\n\nAbstract: We study the problem of searching for a vertex with a desired property in the arrangement of a set of lines in the plane. We show that this problem can be solved efficiently by modifying (and simplifying) two slope selection algorithms without using parametric search. We apply this result to a points approximation problem and obtain an optimal solution for it without using parametric search. Since this line arrangement searching problem is quite natural, our result may find other applications as well.","Title: Segment-Tree Based Cost Aggregation for Stereo Matching\nYear: 2013\n\nAbstract: This paper presents a novel tree-based cost aggregation method for dense stereo matching. Instead of employing the minimum spanning tree (MST) and its variants, a new tree structure, \"Segment-Tree\", is proposed for non-local matching cost aggregation. Conceptually, the segment-tree is constructed in a three-step process: first, the pixels are grouped into a set of segments with the reference color or intensity image, second, a tree graph is created for each segment, and in the final step, these independent segment graphs are linked to form the segment-tree structure. In practice, this tree can be efficiently built in time nearly linear to the number of the image pixels. Compared to MST where the graph connectivity is determined with local edge weights, our method introduces some 'non-local' decision rules: the pixels in one perceptually consistent segment are more likely to share similar disparities, and therefore their connectivity within the segment should be first enforced in the tree construction process. The matching costs are then aggregated over the tree within two passes. Performance evaluation on 19 Middlebury data sets shows that the proposed method is comparable to previous state-of-the-art aggregation methods in disparity accuracy and processing speed. Furthermore, the tree structure can be refined with the estimated disparities, which leads to consistent scene segmentation and significantly better aggregation results.","Title: Stereo Matching with Reliable Disparity Propagation\nYear: 2011\n\nAbstract: In this paper, we propose a novel propagation-based stereo matching algorithm. Starting from an initial disparity map, our algorithm selects highly reliable pixels and propagates their disparities along the scan line to produce dense disparity results. The key idea is to construct a line segment region for each pixel with local color and connectivity constraints. The pixel wise line segments are efficiently used to compute initial disparities, select reliable pixels and determine proper propagation regions. Streaking artifacts are effectively removed in a refinement process. Experimental results demonstrate the performance of the proposed method: it ranks 5th in the Middlebury benchmark, and the results can be computed within a few seconds.","Title: Aggregate-Max Nearest Neighbor Searching in the Plane.\nYear: 2013\n\nAbstract: We study the aggregate/group nearest neighbor searching for the MAX operator in the plane. For a set $P$ of $n$ points and a query set $Q$ of $m$ points, the query asks for a point of $P$ whose maximum distance to the points in $Q$ is minimized. We present data structures for answering such queries for both $L_1$ and $L_2$ distance measures. Previously, only heuristic and approximation algorithms were given for both versions. For the $L_1$ version, we build a data structure of O(n) size in $O(n\\log n)$ time, such that each query can be answered in $O(m+\\log n)$ time. For the $L_2$ version, we build a data structure in $O(n\\log n)$ time and $O(n\\log \\log n)$ space, such that each query can be answered in $O(m\\sqrt{n}\\log^{O(1)} n)$ time, and alternatively, we build a data structure in $O(n^{2+\\epsilon})$ time and space for any $\\epsilon>0$, such that each query can be answered in $O(m\\log n)$ time. Further, we extend our result for the $L_1$ version to the top-$k$ queries where each query asks for the $k$ points of $P$ whose maximum distances to $Q$ are the smallest for any $k$ with $1\\leq k\\leq n$: We build a data structure of O(n) size in $O(n\\log n)$ time, such that each top-$k$ query can be answered in $O(m+k\\log n)$ time.","Title: MFC: a method of co-referent relation acquisition from large-scale chinese corpora\nYear: 2006\n\nAbstract: This paper proposes a multi-feature constrained method (MFC) to acquire co-referent relations from large-scale Chinese corpora. The MFC has two phases: candidate relations extraction and verification. The extraction phase uses distribution distance, pattern homogeneity and coordination distribution features of co-referent target words to extract candidate relations from Chinese corpora. In the verification phase, we define an ontology for co-referent token words, and build a relation graph for all candidate relations. Both the ontology and the graph are integrated to generate individual, joint and reinforced strategies to verify candidate relations. Comprehensive experiments have shown that the MFC is practical, and can also be extended to acquire other types of relations.","Title: Self quotient image for face recognition\nYear: 2004\n\nAbstract: The reliability of facial recognition techniques is often affected by the variation of illumination, such as shadows and illumination direction changes. In this paper, we present a novel framework, called the self-quotient image, for the elimination of the lighting effect in the image. Although this method has a similar invariant form to the quotient image by Shashua etc. (2001), it does not need the alignment and bootstrap images. Our method combines the image processing technique of edge-preserved filtering with the Retinex applications of by Jobson, et al., (1997) and Gross and Brajovie (2003). We have analyzed this algorithm with a 3D imaging model and formulated the conditions where illumination-invariant and -variant properties can be realized, respectively. A fast anisotropic filter is also presented. The experiment results show that our method is effective in removing the effect of illumination for robust face recognition.","Title: A survey of computational emotion research\nYear: 2005\n\nAbstract: Emotion reserch covers a multi-disciplinary domain. In the past three decades, a number of theoretical models and computer applications have been proposed from different perspectives. There are models of appraisal theory, such as OCC and Frijda's model, as well as computational systems such as Affective Reasoner, EMA and Cathexis, to make agents believable and human-like. This paper conducts a comprehensive analysis of those state-of-the-art based on the following six criteria, each of which we believe represents a critical aspect in a computational emotion system.","Title: Strategy of Data Processing for GPS Rover and Reference Receivers Using Different Sampling Rates.\nYear: 2011\n\nAbstract: In Global Positioning System (GPS) applications, the double-difference (DD) model is often employed, which requires that the reference station and the rover receivers adopt the same sampling rate. On the premise of the need fulfillment, could the existing static receivers of Global Navigation Satellite System networks with a low sampling rate be used as the references for the rover with a high sam...","Title: On Building An Accurate Stereo Matching System On Graphics Hardware\nYear: 2011\n\nAbstract: This paper presents a GPU-based stereo matching system with good performance in both accuracy and speed. The matching cost volume is initialized with an AD-Census measure, aggregated in dynamic cross-based regions, and updated in a scan line optimization framework to produce the disparity results. Various errors in the disparity results are effectively handled in a multi-step refinement process. Each stage of the system is designed with parallelism considerations such that the computations can be accelerated with CUDA implementations. Experimental results demonstrate the accuracy and the efficiency of the system: currently it is the top performer in the Middlebury benchmark, and the results are achieved on GPU within 0.1 seconds. We also provide extra examples on stereo video sequences and discuss the limitations of the system.","Title: Two-Point L1 Shortest Path Queries in the Plane\nYear: 2014\n\nAbstract: Let P be a set of h pairwise-disjoint polygonal obstacles with a total of n vertices in the plane. In this paper, we consider the problem of building a data structure that can quickly compute an L1 shortest obstacle-avoiding path between any two query points s and t. We build a data structure of size O(n + h2 · log h · 4√log h) in O(n + h2 · log2 h · 4√log h) time that answers each query in O(log n + k) time, where k is the number of edges of the output path. Note that n + h2 · log2 h · 4√log h = O(n+h2+&epsis;) for any constant &epsis; 0. We also extend our techniques to the weighted rectilinear version in which the \"obstacles\" of P are rectilinear regions with \"weights\" and allow L1 paths to travel through them with weighted costs. Our algorithm answers each query in O(log n + k) time with a data structure of size O(n2 · log n · 4√log n) that is built in O(n2 · log2 n · 4√log n) time.","Title: A Network Emergent Computing Model Based on Cellular Automaton\nYear: 2009\n\nAbstract: Emergent computation is a relatively new approach for understanding the behaviors of complex systems. Central to this approach is the idea that system-level behavior emerges from interaction among individual elements. This paper proposes a network emergent computation model based on cellular automaton, which introduces network operating mechanism, such as store-and-forward, adjacency interaction, rate adjustment, resource competition and delayed feedback as the interactions among individual cells. The simulation results show that the system-level behaviors, such as power-law, self-similarity and noise, can be generated spontaneously by local nonlinear interaction among cells, which have led to a better understanding of the macro-behavior of the system from the perspective of microscopic mechanism.","Title: A 57∼66GHz CMOS voltage-controlled oscillator using tunable differential inductor\nYear: 2012\n\nAbstract: Short-range wireless data transmission around the 60GHz band has been flourishing for various biomedical applications due to its features of high speed, high security and small device size. Wireless monitoring of human body with such transceivers has enabled numerous research areas in healthcare and biomedical engineering, such as long-term real-time recording of the Electroencephalography signal while the subject is in real-life scenario. Among the design challenges of such transceiver, the voltage-controlled oscillator (VCO) design plays a key role. Large tuning range of the VCO enables the effective utilization of the spectrum while small area is desired for biomedical application with implantation. This paper proposes a novel circuit topology for 60GHz VCO. It covers the full unlicensed 57~66GHz frequency band and supports multi-band operation. Simulated in UMC 1P9M 65nm CMOS technology, a tuning range of 14% has been achieved with low power consumption. The VCO core only occupies an area of 60×80um2, which is the smallest for 60-GHz applications published ever.","Title: A sample-and-hold circuit for 10-bit 100MS/s pipelined ADC\nYear: 2011\n\nAbstract: In this paper a fully differential sample-and-hold (S/H) circuit for the pipelined analog-to-digital converter (ADC) was presented. The S/H circuit is based on capacitor flip-around S/H architecture with gain-boosted differential folded cascode operational transconductance amplifier (OTA), which can achieve high DC gain and large bandwidth. Bootstrapped switch and bottom plate sampling techniques are used to reduce the nonlinear distortion. The entire S/H circuit was designed in CSMC 1.8V/3.3V 0.18μm 1P6M CMOS technology. Simulation results show the S/H circuit achieves SNDR of 84dB, ENOB of 13.7, THD of -85dB, and SFDR of 85.4dB at the input frequency 9.86328125M Hz, which is suitable for a 10bit 100MS/s pipelined ADC.","Title: Computing the L1 geodesic diameter and center of a simple polygon in linear time.\nYear: 2015\n\nAbstract: In this paper, we show that the L1 geodesic diameter and center of a simple polygon can be computed in linear time. For the purpose, we focus on revealing basic geometric properties of the L1 geodesic balls, that is, the metric balls with respect to the L1 geodesic distance. More specifically, in this paper we show that any family of L1 geodesic balls in any simple polygon has Helly number two, and the L1 geodesic center consists of midpoints of shortest paths between diametral pairs. These properties are crucial for our linear-time algorithms, and do not hold for the Euclidean case.","Title: Minimizing the Aggregate Movements for Interval Coverage.\nYear: 2014\n\nAbstract: We consider an interval coverage problem. Given n intervals of the same length on a line L and a line segment B on L, we want to move the intervals along L such that every point of B is covered by at least one interval and the sum of the moving distances of all intervals is minimized. As a basic geometry problem, it has applications in mobile sensor barrier coverage in wireless sensor networks. The previous work solved the problem in $$O(n^2)$$O(n2) time. In this paper, by discovering many interesting observations and developing new algorithmic techniques, we present an $$O(n\\\\log n)$$O(nlogn) time algorithm. We also show an $$\\\\varOmega (n\\\\log n)$$Ω(nlogn) time lower bound for this problem, which implies the optimality of our algorithm.","Title: Range Queries on Uncertain Data.\nYear: 2015\n\nAbstract: Given a set P of n uncertain points on the real line, each represented by its one-dimensional probability density function, we consider the problem of building data structures on P to answer range queries of the following three types: (1) top-1 query: find the point in P that lies in I with the highest probability, (2) top-k query: given any integer k <= n as part of the query, return the k points in P that lie in I with the highest probabilities, and (3) threshold query: given any threshold tau as part of the query, return all points of P that lie in I with probabilities at least tau. We present data structures for these range queries with linear or near linear space and efficient query time.","Title: Real-time local stereo via edge-aware disparity propagation.\nYear: 2014\n\nAbstract: This letter presents a novel method for real-time local stereo matching. Different from previous methods which have spent many efforts on cost aggregation, the proposed method re-solves the stereo problem by propagating disparities in the cost domain. It is started by pre-detecting the disparity priors, on which a new cost volume is built for disparity assignment. Then the reliable disparities are propagated via filtering on this cost volume. Specially, a new O(1) geodesic filter is proposed and demonstrated effective for the task of edge-aware disparity propagation. As can be expected, the proposed framework is highly efficient, due to leaving double aggregation on left–right views, as well as costly post-processing steps, out of account. Moreover, by properly designing a quadric cost function, our method could be extended to good sub-pixel accuracy with a simple quadratic polynomial interpolation. Quantitative evaluation shows that it outperforms all the other local methods both in terms of accuracy and speed on Middlebury benchmark. It ranks 8th out of over 150 submissions if sub-pixel precision is considered, and the average runtime is only 9ms on a NVIDIA GeForce GTX 580GPU.","Title: Real time light field reconstruction for sub-pixel based integral imaging display\nYear: 2014\n\nAbstract: Integral imaging (II) display provides a promising 3D display technology for users to see natural 3D color images with stereo and motion parallax. However, they often suffers from the limitations of both the insufficient spatial resolution and lack of real-time content generation strategies. In this paper, we advance the traditional II display with an efficient sub-pixel based light field reconstruction scheme, to achieve 3D imagery with much higher spatial resolution in real-time speed.","Title: Value-based portfolio scoping: an industrial case study\nYear: 2012\n\nAbstract: Customization is considered as a promising way for better satisfying diversity of customer needs. In organizations short of resources, it is a frequent challenge to get balance between development and customization workload in order to ensure product success as well as customer satisfaction. In this paper, we proposed a value-based product portfolio scoping approach to determine optimal product scale for planning software product line adoption. The approach blends existing methods in domain analysis, requirements clustering, and valuation theory. An industrial case study is presented to demonstrate the application of the approach and its effectiveness.","Title: Shortest color-spanning intervals\nYear: 2014\n\nAbstract: Given a set of n points on a line, where each point has one of k colors, and given an integer si≥1 for each color i, 1≤i≤k, the problem Shortest Color-Spanning t Intervals (SCSI-t) aims at finding t intervals to cover at least si points of each color i, such that the maximum length of the intervals is minimized. Chen and Misiolek introduced the problem SCSI-1, and presented an algorithm running in O(n) time if the input points are sorted. Khanteimouri et al. gave an O(n2logn) time algorithm for the special case of SCSI-2 with si=1 for all colors i. In this paper, we present an improved algorithm with running time of O(n2) for SCSI-2 with arbitrary si≥1. We also obtain some interesting results for the general problem SCSI-t. From the negative direction, we show that approximating SCSI-t within any ratio is NP-hard when t is part of the input, is W[2]-hard when t is the parameter, and is W[1]-hard with both t and k as parameters. Moreover, the NP-hardness and the W[2]-hardness with parameter t hold even if si=1 for all i. From the positive direction, we show that SCSI-t with si=1 for all i is fixed-parameter tractable with k as the parameter, and admits an exact algorithm running in O(2kn⋅max{k,logn}) time.","Title: One-Dimensional k-Center on Uncertain Data.\nYear: 2014\n\nAbstract: Problems on uncertain data have attracted significant attention due to the imprecise nature of many measurement data. In this paper, we consider the k-center problem on one-dimensional uncertain data. The input is a set P of (weighted) uncertain points on a real line, and each uncertain point is specified by its probability density function (pdf) which is a piecewise-uniform function (i.e., a histogram). The goal is to find a set Q of k points on the line to minimize the maximum expected distance from the uncertain points of P to their expected closest points in Q. We present efficient algorithms for this uncertain k-center problem and their running times almost match those for the \\\"deterministic\\\" k-center problem.","Title: Maple: scalable multi-dimensional range search over encrypted cloud data with tree-based index\nYear: 2014\n\nAbstract: Cloud computing promises users massive scale outsourced data storage services with much lower costs than traditional methods. However, privacy concerns compel sensitive data to be stored on the cloud server in an encrypted form. This posts a great challenge for effectively utilizing cloud data, such as executing common SQL queries. A variety of searchable encryption techniques have been proposed to solve this issue; yet efficiency and scalability are still the two main obstacles for their adoptions in real-world datasets, which are multi-dimensional in general. In this paper, we propose a tree-based public-key Multi-Dimensional Range Searchable Encryption (MDRSE) to overcome the above limitations. Specifically, we first formally define the leakage function and security of a tree-based MDRSE. Then, by leveraging an existing predicate encryption in a novel way, our tree-based MDRSE efficiently indexes and searches over encrypted cloud data with multi-dimensional tree structures (i.e., R-trees). Moreover, our scheme is able to protect single-dimensional privacy while previous efficient solutions fail to achieve. Our scheme is selectively secure, and through extensive experimental evaluation on a large-scale real-world dataset, we show the efficiency and scalability of our scheme.","Title: Line-Constrained k -Median, k -Means, and k -Center Problems in the Plane.\nYear: 2014\n\nAbstract: The (weighted) k-median, k-means, and k-center problems in the plane are known to be NP-hard. In this paper, we study these problems with an additional constraint that requires the sought k facilities to be on a given line. We present efficient algorithms for various distance metrics such as L-1, L-2, L-infinity. Assume all n weighted points are given sorted by their projections on the given line. For k-median, our algorithms for L1 and L-infinity metrics run in O(min{nk, n root k log n log n, n2(O(n root k log n log n)) log n}) time and O(min{nk log n, n root k log n log(2) n, n2(O(n root k log n log n)) log(2) n}) time, respectively. For k-means, which is defined only on the L-2 metric, we give an O(min{nk, nn root k log n, n2(O(root log k log log n))}) time algorithm. For kcenter, our algorithms run in O(n log n) time for all three metrics, and in O(n) time for the unweighted version under L-1 and L infinity metrics.","Title: Hierarchical gaze estimation based on adaptive feature learning\nYear: 2014\n\nAbstract: Existing appearance-based gaze estimation methods suffer from tedious calibration and appearance variation caused by head movement. In this paper, to handle this problem, we propose a novel appearance-based gaze estimation method by introducing supervised adaptive feature extraction and hierarchical mapping model. Firstly, an adaptive feature learning method is proposed to extract topology-preserving (TOP) feature individually. Then hierarchical mapping method is proposed to localize gaze position based on coarse-to-fine strategy. Appearance synthesis approach is used to increase the refer sample density. Experiments show that under the condition of sparse calibration, proposed method has better performance in accuracy than existing methods under fixed head pose without chinrest. Moreover, our method can be easily extended for head pose-varying gaze estimation.","Title: Efficient algorithms for the one-dimensional k-center problem.\nYear: 2015\n\nAbstract: We consider the problem of finding k centers for n weighted points on a real line. This (weighted) k-center problem was solved in O(nlogn) time previously by using Cole's parametric search and other complicated approaches. In this paper, we present an easier O(nlogn) time algorithm that avoids the parametric search, and in certain special cases our algorithm solves the problem in O(n) time. In addition, our techniques involve developing interesting data structures for processing queries that find a lowest point in the common intersection of a certain subset of half-planes. This subproblem is interesting in its own right and our solution for it may find other applications as well.","Title: One-dimensional k-center on uncertain data.\nYear: 2015\n\nAbstract: Problems on uncertain data have attracted significant attention due to the imprecise nature of many measurement data. In this paper, we consider the k-center problem on one-dimensional uncertain data. The input is a set P of (weighted) uncertain points on a real line, and each uncertain point is specified by its probability density function (pdf) which is a piecewise-uniform function (i.e., a histogram). The goal is to find a set Q of k points on the line to minimize the maximum expected distance from the uncertain points of P to their expected closest points in Q. We present efficient algorithms for this uncertain k-center problem and their running times almost match those for the “deterministic” k-center problem.","Title: A mechanical chest compressor closed-loop controller with an effective trade-off between blood flow improvement and ribs fracture reduction.\nYear: 2015\n\nAbstract: Chest compression (CC) is a significant emergency medical procedure for maintaining circulation during cardiac arrest. Although CC produces the necessary blood flow for patients with heart arrest, improperly deep CC will contribute significantly to the risk of chest injury. In this paper, an optimal CC closed-loop controller for a mechanical chest compressor (OCC-MCC) was developed to provide an effective trade-off between the benefit of improved blood perfusion and the risk of ribs fracture. The trade-off performance of the OCC-MCC during real automatic mechanical CCs was evaluated by comparing the OCC-MCC and the traditional mechanical CC method (TMCM) with a human circulation hardware model based on hardware simulations. A benefit factor (BF), risk factor (RF) and benefit versus risk index (BRI) were introduced in this paper for the comprehensive evaluation of risk and benefit. The OCC-MCC was developed using the LabVIEW control platform and the mechanical chest compressor (MCC) controller. PID control is also employed by MCC for effective compression depth regulation. In addition, the physiological parameters model for MCC was built based on a digital signal processor for hardware simulations. A comparison between the OCC-MCC and TMCM was then performed based on the simulation test platform which is composed of the MCC, LabVIEW control platform, physiological parameters model for MCC and the manikin. Compared with the TMCM, the OCC-MCC obtained a better trade-off and a higher BRI in seven out of a total of nine cases. With a higher mean value of cardiac output (1.35 L/min) and partial pressure of end-tidal CO2 (15.7 mmHg), the OCC-MCC obtained a larger blood flow and higher BF than TMCM (5.19 vs. 3.41) in six out of a total of nine cases. Although it is relatively difficult to maintain a stable CC depth when the chest is stiff, the OCC-MCC is still superior to the TMCM for performing safe and effective CC during CPR. The OCC-MCC is superior to the TMCM in performing safe and effective CC during CPR and can be incorporated into the current version of mechanical CC devices for high quality CPR, in both in-hospital and out-of-hospital CPR settings.","Title: Balanced splitting on weighted intervals\nYear: 2015\n\nAbstract: We study a problem on splitting intervals. Let I be a set of n intervals on a line L , with non-negative weights. Given any integer k ¿ 1 , we want to find k points on L to partition L into k + 1 segments, such that the maximum cost of these segments is minimized, where the cost of each segment s is the sum of the weights of the intervals in I intersecting s . We present an O ( n log n ) time algorithm for this problem.","Title: Aggregate-MAX Top-k Nearest Neighbor Searching in the L1 Plane.\nYear: 2015\n\nAbstract: We study the aggregate/group top-k nearest neighbor searching for the Max operator in the plane, where the distances are measured by the L1 metric. Let P be a set of n points in the plane. Given a query set Q of m points, for each point p ∈ P, the aggregate-max distance from p to Q is defined to be the maximum distance from p to all points in Q. Given Q and an integer k with 1 ≤ k ≤ n, the query asks for the k points of P that have the smallest aggregate-max distances to Q. We build a data structure of O(n) size in O(n log n) time, such that each query can be answered in O(m+k log n) time and the k points are reported in sorted order by their aggregate-max distances to Q. Alternatively, we build a data structure of O(n log n) size in O(n log2 n) time that can answer each query in O(m + k + log3 n) time.","Title: Unknown environment exploration of multi-robot system with the FORDPSO.\nYear: 2016\n\nAbstract: Effective environment exploration in unknown environment is precondition of constructing the environment map and carrying out other tasks for multi-robot system. Due to its excellent performance, particle swarm optimization (PSO) has been widely used in multi-robot exploration field. To deal with its drawback – easily trapped in local optima, Darwinian PSO (DPSO) optimization is proposed by Tillett et al. [1] with the natural selection function and first used in real world robot exploration by Couceiro et al. [2], forming the robotic DPSO (RDPSO). To increase the algorithm performance and control its convergence rate, fractional calculus is used to replace inertia component in RDPSO for its “memory” ability and forming the fractional order RDPSO (FORDPSO). This paper presents a formal analysis of RDPSO and studies the influence of the coefficients on FORDPSO algorithm. To satisfy the requirement of dynamically changing robots׳ behaviors during the exploration, fuzzy inferring system is designed to achieve better control coefficients. Experiment results obtained in two complex simulated environments illustrate that biological and sociological inspiration is effective to meet the challenges of multi-robot system application in unknown environment exploration, and the exploration effect of the fuzzy adaptive FORDPSO is better than that of the fixed coefficient FORDPSO. Furthermore, the performance of FORDPSO with different neighborhood topologies are studied and compared with other six PSO variations. All the results demonstrate the effect of the FORDPSO on the multi-robot environment exploration.","Title: Geometric Range Search on Encrypted Spatial Data.\nYear: 2016\n\nAbstract: Geometric range search is a fundamental primitive for spatial data analysis in SQL and NoSQL databases. It has extensive applications in location-based services, computer-aided design, and computational geometry. Due to the dramatic increase in data size, it is necessary for companies and organizations to outsource their spatial data sets to third-party cloud services (e.g., Amazon) in order to reduce storage and query processing costs, but, meanwhile, with the promise of no privacy leakage to the third party. Searchable encryption is a technique to perform meaningful queries on encrypted data without revealing privacy. However, geometric range search on spatial data has not been fully investigated nor supported by existing searchable encryption schemes. In this paper, we design a symmetric-key searchable encryption scheme that can support geometric range queries on encrypted spatial data. One of our major contributions is that our design is a general approach, which can support different types of geometric range queries. In other words, our design on encrypted data is independent from the shapes of geometric range queries. Moreover, we further extend our scheme with the additional use of tree structures to achieve search complexity that is faster than linear. We formally define and prove the security of our scheme with indistinguishability under selective chosen-plaintext attacks, and demonstrate the performance of our scheme with experiments in a real cloud platform (Amazon EC2).","Title: An Optimal Algorithm for Minimum-Link Rectilinear Paths in Triangulated Rectilinear Domains.\nYear: 2015\n\nAbstract: We present a new algorithm for finding minimum-link rectilinear paths among rectilinear obstacles in the plane. Given a triangulated rectilinear domain of h pairwise-disjoint rectilinear obstacles with a total of n vertices, our algorithm can find a minimum-link rectilinear path between any two points in \\(O(n+ h \\log h)\\) time. Further, within the same time our algorithm can build an O(n)-size data structure for any source point s, such that given any query point t, the number of edges of a minimum-link rectilinear path from s to t can be computed in \\(O(\\log n)\\) time and the actual path can be output in additional time linear in the number of the edges of the path. The previously best algorithms for the problems run in \\(O(n \\log n)\\) time.","Title: Algorithms for Minimizing the Movements of Spreading Points in Linear Domains.\nYear: 2015\n\nAbstract: We study a problem on spreading points. Given a set P of n points sorted on a line L and a distance value , we wish to move the points of P along L such that the distance of any two points ofP is at least and the maximum movement of all points is minimized. We present an O(n) time algorithm for this problem. Further, we extend our algorithm to solve (in O(n) time) the cycle version of the problem where all points of P are on a cycle C. Previously, only weakly polynomial-time algorithms were known for these problems based on linear programming. In addition, we present a linear-time algorithm for a similar facility-location moving problem, which improves the previous work.","Title: Parallel Approach and Platform for Large-Scale WEB Data Extraction\nYear: 2013\n\nAbstract: As the most popular information publishing platform, the Web contains a lot of valued information of interests to users or applications. Although a lot of data extraction techniques have been studied in the last decade, it is still far away from meeting the need of real data extraction. On the one hand, most of them cannot support the whole web information extraction process involving three stages: web page navigation, data extraction and data integration, On the other hand, they cannot support parallel data extraction process for large-scale web pages. In this paper, we propose a parallel approach and platform based on the Hadoop MapReduce for large-scale web data extraction. Our approach can perform the whole three-stage web data extraction process in parallel. Experimental results show that our approach is efficient and can achieve linear speedup.","Title: Linear Time Approximation Schemes for Geometric Maximum Coverage\nYear: 2015\n\nAbstract: We study approximation algorithms for the following geometric version of the maximum coverage problem: Let P be a set of n weighted points in the plane. Let D represent a planar object, such as a rectangle, or a disk. We want to place m copies of D such that the sum of the weights of the points in P covered by these copies is maximized. For any fixed ε>0, we present efficient approximation schemes that can find a (1−ε)-approximation to the optimal solution. In particular, for m=1 and for the special case where D is a rectangle, our algorithm runs in time O(nlog(1ε)), improving on the previous result. For m>1 and the rectangular case, our algorithm runs in O(nεlog(1ε)+mεlogm+m(1ε)O(min(m,1ε))) time. For a more general class of shapes (including disks, polygons with O(1) edges), our algorithm runs in O(n(1ε)O(1)+mϵlogm+m(1ε)O(min(m,1ε2))) time.","Title: Computing the L1 Geodesic Diameter and Center of a Polygonal Domain.\nYear: 2017\n\nAbstract: For a polygonal domain with h holes and a total of n vertices, we present algorithms that compute the L-1 geodesic diameter in O(n(2)+h(4)) time and the L-1 geodesic center in O((n(4)+n(2) h(4))alpha(n)) time, where alpha(.) denotes the inverse Ackermann function. No algorithms were known for these problems before. For the Euclidean counterpart, the best algorithms compute the geodesic diameter in O(n(7.)(73)) or O(n(7)(h+log n)) time, and compute the geodesic center in O(n(12+is an element of)) time. Therefore, our algorithms are much faster than the algorithms for the Euclidean problems. Our algorithms are based on several interesting observations on L-1 shortest paths in polygonal domains.","Title: LBP Discriminant Analysis for Face Verification\nYear: 2005\n\nAbstract: Pedestrian detection is a challenging vision task, especially applied to the automotive field where the background changes as the vehicle moves. This paper presents an extensive study upon human body models and the techniques suitable for being used ...","Title: Survivability Evaluation Index Systems And Evaluation Models For Wireless Sensor Networks\nYear: 2015\n\nAbstract: Survivability evaluation index systems and evaluation models are important basis for evaluating network survivability. The Analytic Hierarchy Process (AHP) is a common system evaluation method, but it cannot reflect the mutual influence among evaluation indexes. In order to overcome the defects of AHP, the survivability evaluation index set of Wireless Sensor Network (WSN) in emergency communications is selected comprehensively, and establishes a survivability index system of WSN based on the Analytic Network Process (ANP). Then, a survivability evaluation model for clustering WSN based on semi-Markov Process (SMP) is put forward in this paper, and a SMP-based survival state transition diagram of cluster head is given. Survivability evaluation index systems and evaluation models are not only helpful to accessing the survivability of WSN in emergency communications objectively and effectively, but also to providing reference basis for the actual deployment and application of WSN.","Title: Minimizing the Maximum Moving Cost of Interval Coverage.\nYear: 2015\n\nAbstract: In this paper, we study an interval coverage problem. We are given n intervals of the same length on a line L and a line segment B on L. Each interval has a nonnegative weight. The goal is to move the intervals along L such that every point of B is covered by at least one interval and the maximum moving cost of all intervals is minimized, where the moving cost of each interval is its moving distance times its weight. Algorithms for the \"unweighted\" version of this problem have been given before. In this paper, we present a first-known algorithm for this weighted version and our algorithm runs in O(n(2) log n log log n) time. The problem has applications in mobile sensor barrier coverage.","Title: Preliminary Testing of a Hand Gesture Recognition Wristband Based on EMG and Inertial Sensor Fusion.\nYear: 2015\n\nAbstract: Electromyography EMG is well suited for capturing static hand features involving relatively long and stable muscle activations. At the same time, inertial sensing can inherently capture dynamic features related to hand rotation and translation. This paper introduces a hand gesture recognition wristband based on combined EMG and IMU signals. Preliminary testing was performed on four healthy subjects to evaluate a classification algorithm for identifying four surface pressing gestures at two force levels and eight air gestures. Average classification accuracy across all subjects was 88% for surface gestures and 96% for air gestures. Classification accuracy was significantly improved when both EMG and inertial sensing was used in combination as compared to results based on either single sensing modality.","Title: Computing the Center of Uncertain Points on Tree Networks.\nYear: 2017\n\nAbstract: Uncertain data has been very common in many applications. In this paper, we consider the one-center problem for uncertain data on tree networks. In this problem, we are given a tree T and n (weighted) uncertain points each of which has m possible locations on T associated with probabilities. The goal is to find a point $$x^*$$xź on T such that the maximum (weighted) expected distance from $$x^*$$xź to all uncertain points is minimized. To the best of our knowledge, this problem has not been studied before. We propose a refined prune-and-search technique that solves the problem in linear time.","Title: Numerical Simulation of 3-D Elastic Moduli with Elliptical Cracks Using FM-DBEM\nYear: 2014\n\nAbstract: In the paper, the fast multiple dual boundary element method is applied to simulate the model of a elastic three-dimensional (3-D) isotropic solid with elliptical micro cracks in various sizes and estimate the global effective elastic moduli of the micro crack-weakened solid. Following the previous simulation of solids with circular micro cracks, the present analyses require no interactions of elliptical micro cracks, and investigate the relations between the effective elastic moduli and the micro crack density parameter. Some representative elliptical micro crack models (including randomly distributed micro cracks and a family of parallel micro cracks) are simulated, and compared with some analytic approximate micromechanical solutions. Numerical results shows the elliptical micro cracks can be approximated with good accuracy to be circular cracks in the same crack density. Besides, it can also present an alternative simulation method of structure containing elliptical micro cracks or micro cracks of other shapes in complicate distributing states, and present an accurate effective moduli prediction.","Title: Tree-Based Multi-dimensional Range Search on Encrypted Data with Enhanced Privacy.\nYear: 2014\n\nAbstract: With searchable encryption, a data user is able to perform meaningful search on encrypted data stored in the public cloud without revealing data privacy. Besides handling simple queries (e.g., keyword queries), complex search functions, such as multi-dimensional (conjunctive) range queries, have also been studied in several approaches to provide search functionalities over multi-dimensional data. However, current works supporting multi-dimensional range queries either only achieve linear search complexity or reveal additional private information to the public cloud. In this paper, we propose a tree-based symmetric-key searchable encryption to support multi-dimensional range queries on encrypted data. Besides protecting data privacy, our proposed scheme is able to achieve faster-than-linear search, query privacy and single-dimensional privacy simultaneously compared to previous solutions. More specifically, we formally define the security of our proposed scheme, prove that it is selectively secure, and demonstrate its faster-than-linear efficiency with experiments over a real-world dataset.","Title: A note on computing the center of uncertain data on the real line.\nYear: 2016\n\nAbstract: We consider the one-dimensional one-center problem on uncertain data. We are given a set P of n (weighted) uncertain points on a real line L and each uncertain point is specified by a probability density function that is a piecewise-uniform function (i.e., a histogram). The goal is to find a point c (the center) on L such that the maximum expected distance from c to all uncertain points of P is minimized. We present a linear-time algorithm for this problem.","Title: On The Geodesic Centers Of Polygonal Domains\nYear: 2016\n\nAbstract: In this paper, we study the problem of computing Euclidean geodesic centers of a polygonal domain P with a total of n vertices. We give a necessary condition for a point being a geodesic center. We show that there is at most one geodesic center among all points of P that have topologically-equivalent shortest path maps. This implies that the total number of geodesic centers is bounded by the combinatorial size of the shortest path map equivalence decomposition of P, which is known to be O(n(10)). One key observation is a pi-range property on shortest path lengths when points are moving. With these observations, we propose an algorithm that can compute all geodesic centers in O(n(11) log n) time. Previously, an algorithm of O(n(12+is an element of)) time was known for this problem, for any is an element of > 0.","Title: Database Calibration For Outdoor Wi-Fi Positioning System\nYear: 2016\n\nAbstract: The limitation of the GPS in urban canyon has led to the rapid development of Wi-Fi positioning system (WPS). The fingerprint based WPS could be divided into calibration and positioning stages. In calibration stage, several grid points (GPs) are selected, and their position tags and featured access points (APs) are collected to build fingerprint database. In positioning stage, real time measurement of APs are compared with the feature of each GP in the database. The k weighted nearest neighbors (KWNN) algorithm is used as pattern matching algorithm to estimate the final positioning result. However, the performance of outdoor fingerprint-based WPS is not good enough for pedestrian navigation. The main challenge is to build a robust fingerprint database. The received number of APs in outdoor environments has large variation. In addition, positioning result estimated by GPS receiver is used as position tag of each GP to automatically build the fingerprint database. This paper studies the lifecycle of fingerprint database in outdoor environment. We also shows that using long time collected data to build database could improve the positioning accuracy. Moreover, a new 3D-GNSS (3D building models aided GNSS) positioning method is used to provide accurate position tags. In this paper, the fingerprint-based WPS has been developed in an outdoor environment near the center of Tokyo city. The proposed WPS can achieve around 17 meters positioning accuracy in urban canyon.","Title: An Improved Algorithm for Diameter-Optimally Augmenting Paths in a Metric Space.\nYear: 2017\n\nAbstract: Let P be a path graph of n vertices embedded in a metric space. We consider the problem of adding a new edge to P such that the diameter of the resulting graph is minimized. Previously (in ICALP 2015) the problem was solved in O(nlog3n) time. In this paper, based on new observations and different algorithmic techniques, we present an O(nlogn) time algorithm.","Title: Separating Overlapped Intervals on a Line.\nYear: 2016\n\nAbstract: Given $n$ intervals on a line $ell$, we consider the problem of moving these intervals on $ell$ such that no two intervals overlap and the maximum moving distance of the intervals is minimized. The difficulty for solving the problem lies in determining the order of the intervals in an optimal solution. By interesting observations, we show that it is sufficient to consider at most $n$ candidate lists of ordered intervals. Further, although explicitly maintaining these lists takes $Omega(n^2)$ time and space, by more observations and a pruning technique, we present an algorithm that can compute an optimal solution in $O(nlog n)$ time and $O(n)$ space. We also prove an $Omega(nlog n)$ time lower bound for solving the problem, which implies the optimality of our algorithm.","Title: Improved Algorithms For Structured Sparse Recovery.\nYear: 2017\n\nAbstract: It is known that certain structures of the signal in addition to the standard notion of sparsity (called structured sparsity) can improve the sample complexity in several compressive sensing applications. Recently, Hegde et al. proposed a framework, called approximation-tolerant model-based compressive sensing, for recovering signals with structured sparsity. Their framework requires two oracles, the head- and the tail-approximation projection oracles. The two oracles should return approximate solutions in the model which is closest to the query signal. In this paper, we consider two structured sparsity models and obtain improved projection algorithms. The first one is the tree sparsity model, which captures the support structure in the wavelet decomposition of piecewise-smooth signals. We propose a linear time $(1-epsilon)$-approximation algorithm for head-approximation projection and a linear time $(1+epsilon)$-approximation algorithm for tail-approximation projection. The best previous result is an $tilde{O}(nlog n)$ time bicriterion approximation algorithm (meaning that their algorithm may return a solution of sparsity larger than $k$) by Hegde et al. Our result provides an affirmative answer to the open problem mentioned in the survey of Hegde and Indyk. As a corollary, we can recover a constant approximate $k$-sparse signal. The other is the Constrained Earth Mover Distance (CEMD) model, which is useful to model the situation where the positions of the nonzero coefficients of a signal do not change significantly as a function of spatial (or temporal) locations. We obtain the first single criterion constant factor approximation algorithm for the head-approximation projection. The previous best known algorithm is a bicriterion approximation. Using this result, we can get a faster constant approximation algorithm with fewer measurements for the recovery problem in CEMD model.","Title: Dispersing Points on Intervals.\nYear: 2016\n\nAbstract: We consider a problem of dispersing points on disjoint intervals on a line. Given n pairwise disjoint intervals sorted on a line, we want to find a point in each interval such that the minimum pairwise distance of these points is maximized. Based on a greedy strategy, we present a linear time algorithm for the problem. Further, we also solve in linear time the cycle version of the problem where the intervals are given on a cycle.","Title: Method of Consistency Judgment for App Software's User Comments.\nYear: 2016\n\nAbstract: With the popularity of mobile intelligent terminal, user comments of App software is viewed as one of the research interests of social computing. Faced with the massive App software, most users usually view the other users’ comments and marks to selecting the desired App software. Due to the freedom and randomness of the network comments, the inconsistence between the user’s comment and mark makes it difficult to choose App software. This paper presents a method by analyzing the relationships among user’s comment information, the user’s mark and App software information. Firstly, the consistency between user’s comment information and App software information is judged. Then, through analyzing the grammar relationships among the feature-words, adverbs and the feature-sentiment-words in App software’s feature-sentiment-word-pairs, the user’s emotional tendency about App software is quantified combining with the dictionary and the network sentiment words. After calculating the user’s comprehensive score of App software, the consistency of App software’s user comment is judged by comparing this score and the user’s mark. Finally, the experimental results show that the method is effective.","Title: Efficient reconstruction of density matrices for high dimensional quantum state tomography.\nYear: 2017\n\nAbstract: The conventional quantum state tomography (QST) needs large number of measurements to reconstruct the quantum state. Thanks to the compressive sensing (CS) theory, one can recover a pure or nearly pure quantum state with an acceptable accuracy given much fewer number of measurements. However, most existing algorithms for CS based QST are rather slow and difficult to be implemented in practice. To fill the gap between the CS theory and practical QST, this paper firstly applies an improved Alternating Direction Multiplier Method (ADMM) combining with the Iterative Shrinkage-Thresholding Algorithm (ISTA), ISTADMM for short, aiming at improving the efficiency of QST problem in particular with much lower number of measurements. The ISTADMM avoids computing the inverse of large-scale matrix, reduces the computational time and required memory space. The computation complexity is reduced from O(d6) for least square (widely used in QST), and O(md4) for Fixed Point-ADMM in our previous work, to ISTADMMs O(md2). The proposed algorithm makes it practical to reconstruct high dimensional quantum states provided fewer number of measurements. The simulations verify the superiority of the proposed algorithm, where it takes 3.13 minutes to reconstruct an 8-qubit density matrix with 96.17% accuracy, which is faster than many existing and our previous work.","Title: Viscous Scalar Rarefaction Waves.\nYear: 2017\n\nAbstract: We study the pointwise behavior of rarefaction waves for scalar viscous conservation laws. Our primary goal is the analysis of the initial layer and large time behavior of the solution of the Riemann problem. We use the Green's function approach, making essential use of the Hopf Cole transformation for the Burgers equation.","Title: Line-Constrained k-Median, k-Means, and k-Center Problems in the Plane.\nYear: 2016\n\nAbstract: The (weighted) k-median, k-means, and k-center problems in the plane are known to be NP-hard. In this paper, we study these problems with an additional constraint that requires the sought k facilities to be on a given line. We present efficient algorithms for various distance measures such as L1,L2,L∞. We assume that all n weighted points are given sorted by their projections on the given line. For k-median, our algorithms for L1 and L∞ metrics run in O(min{nk,nklog nlog n,n2O(log klog log n)log n}) time and O(min{nklog n,nklog nlog2n,n2O(log klog log n)log2n}) time, respectively. For k-means, which is defined only on the squared L2 distance, we give an O(min{nk,nklog n,n2O(log klog log n)}) time algorithm. For k-center, our algorithms run in O(nlog n) time for all three metrics, and in O(n) time for the unweighted version under L1 and L∞ metrics. While our results for the k-center problem are optimal, the results for the k-median problem almost match the best algorithms for the corresponding one-dimensio...","Title: Mobile Multimedia Classroom Construction For Rhythmic Gymnastics Based On Apt Teaching Model\nYear: 2017\n\nAbstract: APT teaching model is an information-based teaching model which includes Assessment, Pedagogy and Technology. It can improve students' learning initiative and autonomous learning ability. In this study, mobile multimedia classroom based on APT teaching model was designed for Rhythmic Gymnastics. With APT teaching model, learners' learning is no longer limited to time and place. It can motivate learners' learning interest and enhance learning efficiency. The mobile multimedia classroom was constructed from three aspects: assessment, pedagogy and technology. The final teaching test shows that, learners are very interested in the teaching mode of mobile multimedia classroom and express a strong wish for such teaching mode. Mobile multimedia classroom for Rhythmic Gymnastics based on APT teaching model has a promising application prospect in actual teaching process.","Title: Quickest Visibility Queries in Polygonal Domains.\nYear: 2017\n\nAbstract: Let s be a point in a polygonal domain \n \n \n \n $${\\mathcal {P}}$$\n \n \n \n of \n \n \n \n $$h-1$$\n \n \n \n holes and n vertices. We consider a quickest visibility query problem. Given a query point q in \n \n \n \n $${\\mathcal {P}}$$\n \n \n \n , the goal is to find a shortest path in \n \n \n \n $${\\mathcal {P}}$$\n \n \n \n to move from s to seeq as quickly as possible. Previously, Arkin et al. (SoCG 2015) built a data structure of size \n \n \n \n $$O(n^22^{\\alpha (n)}\\log n)$$\n \n \n \n that can answer each query in \n \n \n \n $$O(K\\log ^2 n)$$\n \n \n \n time, where \n \n \n \n $$\\alpha (n)$$\n \n \n \n is the inverse Ackermann function and K is the size of the visibility polygon of q in \n \n \n \n $${\\mathcal {P}}$$\n \n \n \n (and K can be \n \n \n \n $$\\varTheta (n)$$\n \n \n \n in the worst case). In this paper, we present a new data structure of size \n \n \n \n $$O(n\\log h + h^2)$$\n \n \n \n that can answer each query in \n \n \n \n $$O(h\\log h\\log n)$$\n \n \n \n time. Our result improves the previous work when h is relatively small. In particular, if h is a constant, then our result even matches the best result for the simple polygon case (i.e., \n \n \n \n $$h=1$$\n \n \n \n ), which is optimal. As a by-product, we also have a new algorithm for a shortest-path-to-segment query problem. Given a query line segment \n \n \n \n $$\\tau $$\n \n \n \n in \n \n \n \n $${\\mathcal {P}}$$\n \n \n \n , the query seeks a shortest path from s to all points of \n \n \n \n $$\\tau $$\n \n \n \n . Previously, Arkin et al. gave a data structure of size \n \n \n \n $$O(n^22^{\\alpha (n)}\\log n)$$\n \n \n \n that can answer each query in \n \n \n \n $$O(\\log ^2 n)$$\n \n \n \n time, and another data structure of size \n \n \n \n $$O(n^3\\log n)$$\n \n \n \n with \n \n \n \n $$O(\\log n)$$\n \n \n \n query time. We present a data structure of size O(n) with query time \n \n \n \n $$O\\big (h\\log \\frac{n}{h}\\big )$$\n \n \n \n , which also favors small values of h and is optimal when \n \n \n \n $$h=O(1)$$\n \n \n \n .","Title: Algorithms for Covering Multiple Barriers.\nYear: 2017\n\nAbstract: In this paper, we consider the problems for covering multiple intervals on a line. Given a set B of m line segments (called \"barriers\") on a horizontal line L and another set S of n horizontal line segments of the same length in the plane, we want to move all segments of S to L so that their union covers all barriers and the maximum movement of all segments of S is minimized. Previously, an O(n(3) log n)-time algorithm was given for the problem but only for the special case m = 1. In this paper, we propose an O(n(2) log n log log n + nm log m)-time algorithm for any m, which improves the previous work even for m = 1. We then consider a line-constrained version of the problem in which the segments of S are all initially on the line L. Previously, an O(n log n)-time algorithm was known for the case m = 1. We present an algorithm of O((n+ m) log(n+ m)) time for any m. These problems may have applications in mobile sensor barrier coverage in wireless sensor networks.","Title: COPSS-lite: Lightweight ICN Based Pub/Sub for IoT Environments.\nYear: 2017\n\nAbstract: Information Centric Networking is a new networking paradigm that treats content as first class entity. It provides content to users without regards to the current location of the content. The publish/subscribe systems have gained popularity in Internet. Pub/sub systems dismisses the need for users to request every content of their interest. Instead, the content is supplied to interested users (subscribers) as and when it is published. CCN/NDN are popular ICN proposals widely accepted in the ICN community however, they do not provide an efficient pub/sub mechanism. COPSS enhances CCN/NDN with an efficient pub/sub capability. Internet of Things (IoT) is a growing topic of interest in both Academia and Industry. The current designs for IoT relies on IP. However, the IoT devices are constrained in their available resources and IP is heavy for their operation.We observed that IoT's are information centric in nature and hence ICN is a more suitable candidate to support IoT environments. Although NDN and COPSS work well for the Internet, their current full fledged implementations cannot be used by the resource constrained IoT devices. CCN-lite is a light weight, inter-operable version of the CCNx protocol for supporting the IoT devices. However, CCN-lite like its ancestors lacks the support for an efficient pub/sub mechanism. In this paper, we developed COPSS-lite, an efficient and light weight implementation of pub/sub for IoT. COPSS-lite is developed to enhance CCN-lite and also support multi-hop connection by incorporating the famous RPL protocol for low power and lossy networks. We provide a preliminary evaluation to show proof of operability with real world sensor devices in IoT lab. Our results show that COPSS-lite is compact, operates on all platforms that support CCN-lite and we observe significant performance benefits with COPSS-lite in IoT environments.","Title: An O(n\\log n)-Time Algorithm for the k-Center Problem in Trees.\nYear: 2018\n\nAbstract: We consider a classical k-center problem in trees. Let T be a tree of n vertices and every vertex has a nonnegative weight. The problem is to find k centers on the edges of T such that the maximum weighted distance from all vertices to their closest centers is minimized. Megiddo and Tamir (SIAM J. Comput., 1983) gave an algorithm that can solve the problem in O(n log^2 n) time by using Cole’s parametric search. Since then it has been open for over three decades whether the problem can be solved in O(n log n) time. In this paper, we present an O(n log n) time algorithm for the problem and thus settle the open problem affirmatively.","Title: Bicriteria Rectilinear Shortest Paths among Rectilinear Obstacles in the Plane.\nYear: 2017\n\nAbstract: Given a rectilinear domain P of h pairwise-disjoint rectilinear obstacles with a total of n vertices in the plane, we study the problem of computing bicriteria rectilinear shortest paths between two points s and t in P. Three types of bicriteria rectilinear paths are considered: minimum-link shortest paths, shortest minimum-link paths, and minimum-cost paths where the cost of a path is a non-decreasing function of both the number of edges and the length of the path. The one-point and two-point path queries are also considered. Algorithms for these problems have been given previously. Our contributions are threefold. First, we find a critical error in all previous algorithms. Second, we correct the error in a not-so-trivial way. Third, we further improve the algorithms so that they are even faster than the previous (incorrect) algorithms when h is relatively small. For example, for computing a minimum-link shortest s-t path, the previous algorithm runs in O(n log^{3/2} n) time while the time of our new algorithm is O(n + h log^{3/2} h).","Title: Hardware Acceleration for Machine Learning.\nYear: 2017\n\nAbstract: This paper presents an approach to enhance the performance of machine learning applications based on hardware acceleration. This approach is based on parameterised architectures designed for Convolutional Neural Network (CNN) and Support Vector Machine (SVM), and the associated design flow common to both. This approach is illustrated by two case studies including object detection and satellite data analysis. The potential of the proposed approach is presented.","Title: A Web3D forest geo-visualization and user interface evaluation.\nYear: 2017\n\nAbstract: In this study, we developed a workflow for the construction of WebGL rendering and interactive visualization of forest landscapes from GIS and forest simulation datasets. We constructed a realistic virtual forest environment for our study site in Virginia. This Web3D virtual environment, built with X3DOM and HTML5, is capable of fast real-time rendering and walk-through simulation based on two Level of Details (LOD) switching techniques, and interactive over mainstream web browsers without a plugin. We explored the performance of a navigation aid and two types of LOD cut-off functions in the forest geo-visualization. We used a hunting game search task to test the effect of our user interface features. According to the paired t-test results, the navigation aid significantly increasing participants' performance, while the exponential cut-off and linear cut-off functions showed no significant difference from participants' perspective.","Title: k-Regret Minimizing Set: Efficient Algorithms and Hardness.\nYear: 2017\n\nAbstract: We study the k-regret minimizing query (k-RMS), which is a useful operator for supporting multi-criteria decision-making. Given two integers k and r, a k-RMS returns r tuples from the database which minimize the k-regret ratio, defined as one minus the worst ratio between the k-th maximum utility score among all tuples in the database and the maximum utility score of the r tuples returned. A solution set contains only r tuples, enjoying the benefits of both top-k queries and skyline queries. Proposed in 2012, the query has been studied extensively in recent years. In this paper, we advance the theory and the practice of k-RMS in the following aspects. First, we develop efficient algorithms for k-RMS (and its decision version) when the dimensionality is 2. The running time of our algorithms outperforms those of previous ones. Second, we show that k-RMS is NP-hard even when the dimensionality is 3. This provides a complete characterization of the complexity of k-RMS, and answers an open question in previous studies. In addition, we present approximation algorithms for the problem when the dimensionality is 3 or larger.","Title: An Efficient Polarity Optimization Approach for Fixed Polarity Reed-Muller Logic Circuits Based on Novel Binary Differential Evolution Algorithm.\nYear: 2017\n\nAbstract: The bottleneck of integrated circuit design could potentially be alleviated by using Reed-Muller (RM) logic circuits due to their remarkable superiority in power, area and testability. In this paper, we propose a Novel Binary Differential Evolution (DE) algorithm (NBDE) to solve the discrete binary-encoded combination optimization problem. Moreover, based on the NBDE, we propose an Efficient Polarity Optimization Approach (EPOA) for Fixed Polarity RM (FPRM) logic circuits, which uses the NBDE to search the best polarity under a performance constraint. To the best of our knowledge, we are the first to use DE to optimize RM circuits. The experimental results on 24 MCNC benchmark circuits show the effectiveness and superiority of EPOA.","Title: Balancing Global and Local Fairness Allocation Model in Heterogeneous Data Center.\nYear: 2017\n\nAbstract: Aiming at the problem that the bandwidth allocation method in heterogeneous data center can't take into account the global and local fairness, we propose an allocation method called GLA that satisfies fair attributes and can improve the utilization of system resources by nearly 20% when the number of clients are more than six contrasting with the existing methods. The algorithm is also protable and scalable that can adapt to most of systems.","Title: Time-Delay And Amplitude Modified Bp Imaging Algorithm Of Multiple Targets For Uwb Through-The-Wall Radar Imaging\nYear: 2017\n\nAbstract: In order to solve the undetected probability of multiple targets in ultra-wideband (UWB) through-the-wall radar imaging (TWRI), a time-delay and amplitude modified back projection (BP) algorithm is proposed. The refraction point is found by Fermat's principle in the presence of a wall, and the time-delay is correctly compensated. On this basis, transmission loss of the electromagnetic wave, the absorption loss of the refraction wave, and the diffusion loss of the spherical wave are analyzed in detail. Amplitude compensation is deduced and tested on a model with a single-layer wall. The simulating results by finite difference time domain (FDTD) show that it is effective in increasing the scattering intensity of the targets behind the wall. Compensation for the diffusion loss in the spherical wave also plays a main role. Additionally, the two-layer wall model is simulated. Then, the calculating time and the imaging quality are compared between a single-ayer wall model and a two-layer wall model. The results illustrate the performance of the time-delay and amplitude-modified BP algorithm with multiple targets and multiple-layer walls of UWB TWRI.","Title: An online learning neural network ensembles with random weights for regression of sequential data stream.\nYear: 2017\n\nAbstract: An ensemble of neural networks has been proved to be an effective machine learning framework. However, very limited studies in the current literature examined the neural network ensemble for online regression; furthermore, these methods were combination of online individual models and did not consider the ensemble diversity. In this paper, a novel online sequential learning algorithm for neural network ensembles for online regression is proposed. The algorithm is built upon the decorrelated neural network ensembles (DNNE) and thus referred to as Online-DNNE; so it uses single-hidden layer feed-forward neural networks with random hidden nodes’ parameters as ensemble components and introduces negative correlation learning to train base models simultaneously in a cooperative manner which can effectively maintain the ensemble diversity. The Online-DNNE only learns the newly arrived data, and the computation complexity is thus reduced. The results of the experiments with benchmarks show the effectiveness and significant advantages of the proposed approach.","Title: A Construction Method for the Semantic Relation Corpus of Traditional Chinese Medicine.\nYear: 2017\n\nAbstract: It is difficult to use a search engine to acquire knowledge directly due to the complexity of Web resources. In this paper we proposed a method for semantic relation corpus construction of traditional Chinese medicine based on combination of multiple encyclopedias. For a known conceptual pair, we got some search results by automatically constructing search requests based on URLs' characteristics of the encyclopedia search engine, and used regular expressions to extract meaningful texts from the search results to form semantic relation corpus. The experiment result shows that the precision and recall are 92.1% and 65.3%, respectively.","Title: N2DLOF: A New Local Density-Based Outlier Detection Approach for Scattered Data\nYear: 2017\n\nAbstract: Since the Local Outlier Factor (LOF) was first proposed, there is a large family of approaches that is derived from it. For the reason that the existing local outliers detection approaches only focus on the extent of overall separation between an object and its neighbors, and do not pay attention to the degree of dispersion between them, the precision of these approaches will be seriously affected in the scattered data sets for outlier detection. In this paper, we redefine the local outliers by combining the degree of dispersion of the object and its neighbors, and propose a new local outlier detection approach (N2DLOF). Compared to conventional approaches, the outliers obtained by N2DLOF are more sensitive to the degree of anomaly of the scattered data sets. Experiments show that our approach has a significant improvement on outlier detection precision in the case of scattered datasets with similar time complexity. In short, we extend the ecosystem of the local outlier detection approaches from a new perspective.","Title: Maximization of link capacity by joint power and spectrum allocation for smart satellite transponder.\nYear: 2017\n\nAbstract: The contradiction between ever increasing satellite communication traffic and limited satellite transponder resources motivates a more dynamic allocation and more effective utilization of satellite transponders' resources. In this paper, the link capacity for a smart satellite transponder is maximized with limited available power and spectrum resource at satellite transponder. Specifically, given that the satellite transponder broadcasts the signals from the gateway station to the multiple satellite terminals with minimum transmission rate requirement, the satellite transponder needs to provide as large link capacity as possible to gateway station for the amount data transmission of special demands. The problem is formulated with aim of maximizing link capacity, and subject to minimum transmission rate requirement of link to satellite terminals and available resource allocation. The finely-matched dynamic power and spectrum allocation scheme is proposed to achieve maximization of both target link capacity and transponder resource utilization. Simulations results demonstrate that the proposed scheme outperforms tradition schemes and the superiority is even more remarkable in multi-constrained situations.","Title: Timetable-aware opportunistic DTN routing for vehicular communications in battlefield environments.\nYear: 2018\n\nAbstract: Delay tolerant network (namely DTN) is regarded as a promising technology for communications in challenging environments for its resilience and adaptability. However, efficient and reliable routing algorithm design is still one of many difficulties it faces in achieving broad and fruitful applications. Due to reasons like electromagnetic inference, lack of infrastructure and constantly moving, communication among vehicles in battlefield environments is difficult and makes a prospective field for DTN to bring effective improvements. In this paper, a timetable-aware opportunistic DTN routing algorithm combining limited message copies and biased message spray is proposed for this scenario. Firstly, a mobility model named timetable based aggregation and spread (ASMM) is abstracted, which captures both the integrity and individuality in vehicles’ typical moving behavior on military missions. Then, from the time and coordination information contained in the preset timetables, the meeting chances for different node groups can be predicted. This meeting opportunity is used as a utility for message transmission evaluation in our proposed routing algorithm called GenericSpray. GenericSpray is able to make more accurate and motivated message transferring decisions by exploiting contact opportunity prediction in choosing among possible message carriers and deciding appropriate number of copies to spray. To investigate its performance, both ASMM and GenericSpray are implemented on The ONE, a specialized DTN simulation platform. Results of simulation experiments indicate that GenericSpray outperforms all other classical DTN routing algorithms chosen as contrast, in terms of delivery ratio, delivery delay and transmission overhead.","Title: Routing Algorithm with Virtual Topology Toward to Huge Numbers of LEO Mobile Satellite Network Based on SDN.\nYear: 2018\n\nAbstract: SDN network is a dynamic, controllable, cost-effective and adaptable system. It is suitable for communication networks with high bandwidth and high dynamic characteristics. Therefore, combining SDN ideas with the new generation of LEO satellite networks can achieve more flexible monitoring and management of the network, and can make the network expansion more convenient. Joint the Depth-First-Search (DFS) idea and Dijkstra algorithm for the huge numbers of LEO mobile satellite network based on SDN is proposed to improve the computational efficiency and the reliability of calculation result. Moreover, the communication performance of space-based network based on SDN and traditional space-based network is compared and analyzed. The simulation results show that the huge numbers of LEO mobile satellite network based on SDN breaks through the performance limitations of the traditional network architecture, and it can achieve better performance of the network.","Title: Feasibility of Wrist-Worn, Real-Time Hand, and Surface Gesture Recognition via sEMG and IMU Sensing.\nYear: 2018\n\nAbstract: While most wearable gesture recognition approaches focus on the forearm or fingers, the wrist may be a more suitable location for practical use. We present the design and validation of a real-time gesture recognition wristband based on surface electromyography and inertial measurement unit sensing fusion, which can recognize 8 air gestures and 4 surface gestures with 2 distinct force levels. Ten h...","Title: Real-Time Monitoring for BDS Signal-In-Space Anomalies Using Ground Observation Data.\nYear: 2018\n\nAbstract: Signal-in-space (SIS) User Range Error (URE) is one of the major error sources for BeiDou Navigation Satellite System (BDS) applications and can reach tens of meters or even more. Therefore, real-time monitoring of SIS anomalies has a great realistic significance to guarantee the safety of users. According to an analysis of the BDS navigation messages, it showed that the User Range Accuracy (URA) index could not reflect the change of URE when it was abnormal. The conventional models using the relationship between URA and URE to monitor SIS anomalies are not suitable to the present BDS. Therefore, we use a prior information of SIS URE derived from ground observational data instead of URA to monitor BDS SIS anomalies. In order to realize the corresponding functions, we analysed the distribution of SIS UREs and obtained their prior models. Then, the monitoring threshold is determined using the prior models and a confidence interval instead of URA. The scheme was tested by applying to BDS SIS anomalies monitoring based on 13 ground tracking stations. The performance of this method was assessed by comparison with the satellite-health indicators from broadcast ephemeris. The results confirm that the method developed in this paper can rightly and timely detect abnormal SIS.","Title: RTTV: a dynamic CFI measurement tool based on TPM.\nYear: 2018\n\nAbstract: In programme dynamic analysis, control flow integrity (CFI) is an efficient way to investigate programme's behaviour. By detecting these CF instructions, researchers can obtain programme's runtime information and execution status accurately. This feature makes CFI a sharp and sensitive approach to detect programme abnormal conditions and malicious attacks such as stack overflow and return-oriented...","Title: Inferring large-scale gene regulatory networks using a randomized algorithm based on singular value decomposition.\nYear: 2019\n\nAbstract: Reconstructing large-scale gene regulatory networks (GRNs) is a challenging problem in the field of computational biology. Various methods for inferring GRNs have been developed, but they fail to accurately infer GRNs with a large number of genes. Additionally, the existing evaluation indexes for evaluating the constructed networks have obvious disadvantages because GRNs in most biological systems are sparse. In this paper, we develop a new method for inferring GRNs based on randomized singular value decomposition (RSVD) and ordinary differential equation (ODE)-based optimization, denoted as IGRSVD, from large-scale time series data with noise. The three major contributions of this paper are as follows. First, the IGRSVD algorithm uses the RSVD to handle the noise and reduce the original large-scale data into small-scale problems. Second, we propose two new evaluated indexes, the expected value accuracy (EVA) and the expected value error (EVE), to evaluate the performance of inferred networks by considering the sparse features in the network. Finally, the proposed IGRSVD algorithm is compared with the existing SVD algorithm and PCA_CMI algorithm using four subsets from E. coli and datasets from DREAM challenge. The experimental results demonstrate that the IGRSVD algorithm is effective and more suitable for reconstructing large-scale networks.","Title: A Two-Level Classifier Model for Sentiment Analysis.\nYear: 2017\n\nAbstract: This paper proposes a fast and high performance classifier model for sentiment analysis of textual reviews. The key contribution is three fold. First, a two-level classifier model consists of three base classifiers is proposed, and theory proves that the model could be better than the strongest classifier among the base classifiers in both classification performance and time cost of predict. Second, this paper proposes a lexicon-based classifier as a base classifier using a new part of speech (POS) which is called \"weaken words\". Finally, we implemented several two-level classifiers by combining the lexicon-based classifier with several machine learning classifiers. Experiments on Chinese reviews dataset show that the two-level classifier model is effective and efficient.","Title: HSAStore - A Hierarchical Storage Architecture for Computing Systems Containing Large-Scale Intermediate Data.\nYear: 2017\n\nAbstract: This paper introduces HSAStore, a newly designed storage system whose goal goes to build an efficient storage system for computing systems that containing large-scale intermediate data. HSAStore involves three sub-systems to work together, a distributed file system which is in charge of the intermediate data, a centralized Network Attached Storage (NAS) which stores the raw input data and the results data, and a local file system that serves for the local data. The HSAStore takes full advantage of the network bandwidth of the computing cluster. Moreover, HSAStore adopts the distributed file system, so it is helpful for efficient execution of parallel programs. Experiments show that HSAStore has a significant improvement on efficiency in the computing systems that containing large-scale intermediate data.","Title: Human-Like ZMP Generator and Walking Stabilizer Based on Divergent Component of Motion\nYear: 2018\n\nAbstract: To realize natural and fast bipedal walking, we mimic the ZMP trajectory from human walking data based on the concept of Divergent Component of Motion (DCM). This paper presents an omnidirectional gait generator that is lightweight and universal. In addition, we integrate DCM tracking and foot placement adjustment into our control framework to ensure a stable walking motion and push recovery. The method proposed was validated in RoboCup2018 AdultSize soccer competition, where our robot WALKER+ walked on grass field and kept balance after the push from other robots.","Title: L-Fcn: A Lightweight Fully Convolutional Network For Biomedical Semantic Segmentation\nYear: 2018\n\nAbstract: For the past few years, deep learning-based methods have been widely used in the field of biomedical imaging. In biomedical image processing, the typical application of deep learning is semantic segmentation. However, the classical deep learning methods require higher hardware consumption and computational costs. In order to resolve this problem, we propose a new lightweight fully convolutional network (L-FCN). L-FCN consists of traditional watershed algorithm and fully convolutional network, which eliminates useless non-edge pixels to improves the efficiency of semantic segmentation. Experimental evaluations on ISBI 2012 dataset indicate that our L-FCN algorithm outperforms U-net in time e square ciency and computational costs, meanwhile, maintain approximate image segmentation effect. Due to its portability, L-FCN can be applied to many biomedical areas easily.","Title: Localization Performance Under Middle And Low Frequency Sound Source Based On Time Reversal Method In Enclosed Space\nYear: 2019\n\nAbstract: Multipath interference, reflection, sound absorption, and obstacles are important factors affect the sound source localization performance in enclosed space. Time reversal has the advantages of adaptive focusing, strong anti-reverberation capability, and the ability to overcome multipath effects, which are quite suitable for sound source localization in enclosed space due to its complex environment that many methods (e.g., beamforming, high-resolution spectral estimation, and the time delay of arrival) are not applicable. Therefore, this paper studies the localization performance under middle and low frequency sound sources cover single-frequency sinusoidal signal, bandwidth signal, and impulse signal through dual-channel matching based on time reversal method. When the frequency is below 1 kHz, the media's absorption is generally negligible. Therefore, this paper only studies frequencies within 1 kHz. In this paper, numerical simulations are used to verify the feasibility of dual-channel matching under middle and low frequency sound sources in ordinary enclosed space. Then, actual experiments are conducted in an ordinary office on the case of different sound sources. The numerical simulation and experiment results show that dual-channel matching method has satisfying localization effect. The localization effect of medium frequency signal is superior to low frequency signal. Bandwidth signals have better localization effect than single-frequency sinusoidal signals, and steady state sound source has better localization effect than impulse sound source. In general, dual-channel matching method has strong robustness and applicability for sound source localization in enclosed space.","Title: Self-Organizing Approximation Command Filtered Backstepping Control for Higher Order SISO Systems in Internet of Things.\nYear: 2019\n\nAbstract: With the rapid development of the Internet of Things (IOT) and increasing research on tracking control of the affine IOT systems with uncertainty, a nonlinear control method of command filtering backstepping with self-organizing approximation is proposed. A command filter with a saturation structure is used to eliminate the analytic computation of command derivatives, and tracking errors are redefined to compensate the discrepancy between the command filtered signals and the analytic computation of command derivatives. Command filtered backstepping is used to decouple the higher IOT system and simplify the structure of the IOT system. Self-organizing approximation is used to online approximate the uncertain and eliminate the effect of the uncertain in the IOT system. The approximation for the uncertain is finished by a linear product between a set of basis functions and weighted functions on the local region. The weighted sum of compensated tracking errors is used as the prespecified tracking accuracy performance. A new local approximator is added to the control system according to the criterion based on the Lyapunov theory to enhance the capacity of the approximator when the prespecified tracking accuracy performance cannot be achieved. Finally, an application for a simple IOT system is analyzed by simulations. The simulation results show that the control method for the IOT system is effective.","Title: Field Measurement And Numerical Simulation Of The Influence Of Blasting Excavation On Adjacent Buried Pipelines\nYear: 2019\n\nAbstract: The blasting seismic waves generated by tunnel blasting will have an adverse effect on adjacent buried pipelines, which increasing the difficulty of subway construction. In this paper, taking the section of blasting excavation in Dalian Metro Line 1 as the engineering background, the attenuation law of blasting seismic wave in the soil and the vibration response of the buried pipeline are systematically studied by means of field measurement and numerical simulation. The deformation monitoring of the pipeline shows that the time curve of the pipeline and the surface settlement are distributed exponentially, and the trend of them is the same. The attenuation law of seismic wave obtained by numerical simulation is basically consistent with the measured results, which indicates that it is feasible to study the blasting vibration effect by numerical simulation. The Sa Rodolfo J Ki prediction formula is established according to the measured data, which can reflect the propagation law of the blasting seismic wave. It can provide some basis for the construction of subway tunnels at the subsequent stage.","Title: Approximating Points by a Piecewise Linear Function: II. Dealing with Outliers\nYear: 2009\n\nAbstract: In this paper, we study the violation versions of the planar points approximation problems, which deal with outliers in the input points. We present efficient algorithms for both the step function and the more general piecewise linear function cases, and for both non-weighted and weighted points. Most of our results are first-known. Our algorithms are based on interesting and nontrivial geometric techniques and data structures, which may find other applications.","Title: Circular range search on encrypted spatial data\nYear: 2015\n\nAbstract: Searchable encryption is a promising technique enabling meaningful search operations to be performed on encrypted databases while protecting user privacy from untrusted third-party service providers. However, while most of the existing works focus on common SQL queries, geometric queries on encrypted spatial data have not been well studied. Especially, circular range search is an important type of geometric query on spatial data which has wide applications, such as proximity testing in Location-Based Services and Delaunay triangulation in computational geometry. In this paper, we propose two novel symmetric-key searchable encryption schemes supporting circular range search. Informally, both of our schemes can correctly verify whether a point is inside a circle on encrypted spatial data without revealing data privacy or query privacy to a semi-honest cloud server. We formally define the security of our proposed schemes, prove that they are secure under Selective Chosen-Plaintext Attacks, and evaluate their performance through experiments in a real-world cloud platform (Amazon EC2).","Title: Passive Moving Target Classification Via Spectra Multiplication Method.\nYear: 2017\n\nAbstract: Traditional feature extractions, such as mel-frequency cepstral coefficients (MFCCs), are susceptible to acoustic channel effects, reverberation, and additive environmental noises when applied to passive moving target classification in underwater environment. A spectra multiplication method (SMM) is proposed in this letter to replace the estimated spectrum of MFCCs. SMM suppresses the time-variant...","Title: Design and performance analysis of high response actuator using magnetic shape memory alloy\nYear: 2017\n\nAbstract: Magnetic shape memory alloy (MSMA) has recently emerged as a new type of multifunctional material exhibiting excellent performance as fast response and large strain. Constitution equations are derived based on the magnet-strain effect of MSMA. Experiments are conducted to explore the magnetic field induced strain, and data are applied to establish neural network based models to build the nonlinear relationship between strain, stress and other related parameters. Actuator design, magnetic field analysis and dynamic analysis are carried out. With a combination of the characteristics of ordinary magnetostrictive materials and conventional shape memory alloy, it is qualified for high performance actuator.","Title: Optimization Of Index-Based Method Of Metadata Search For Large-Scale File Systems\nYear: 2017\n\nAbstract: With the vigorous development of the information era, the volume of data in file systems has grown to TB or EB. Metadata search determines the speed of retrieving required files in such large-scale file systems. There are many researches on metadata search based on different theories, and have made great achievements. Some researches show that the distribution of metadata presents sub-tree locality and horizontal locality, and metadata search presents heavy-tailed distribution. However, current methods do not make full use of the spatial locality and load characteristics in metadata search. In this paper, we put forward a metadata search method based on keyword-index. Our method fully exploits characteristics of metadata distribution and metadata search by reasonably partitioning indexes. The experimental result shows that our partitioning method is more efficient than current non partitioning method.","Title: Intelligent Particle Blind Source Separation Research\nYear: 2018\n\nAbstract: A new sequential blind source separation algorithm is proposed based on intelligent single particle optimization. The signal variability is used as the objective function and the separation vector is transformed by using the spherical coordinate transform method. The intelligent single particle optimization is used for solving the objective function. By de-correlation method, the separated source signal component is removed from the mixed signals, and source signal could be separated out respectively according to the decreasing order of the signal variability. Simulation results show that the proposed separation algorithm can realize sequential separation of source signal efficiently and the separation precision is very high.","Title: A New Static Routing Algorithm of GEO/LEO Hybrid Satellite Networks\nYear: 2017\n\nAbstract: With the rapid development of satellite mobile communication, building a satellite/terrestrial integrated network is the inevitable trend of satellite communication. Based on the network of backbone and access, a global coverage of GEO/LEO hybrid satellite constellations are designed. As the backbone network, GEO constellation realize relay and management. As the access network, LEO constellation realize the communication with the terrestrial users. A new static routing strategy based on virtual topology is proposed, which provides significant advantages of low complexity, high efficiency and low routing overhead. The satellite networks can provide the features of flexible structure, easy management, efficient transferring, and potential in scalability.","Title: A Resource Allocation Scheme with Excellent Energy Efficiency Based on the Channel Matrix Sparse\nYear: 2017\n\nAbstract: This paper introduces a C-RAN scenario which is overlapping covered with the macrocell, residential microcells, office microcells under different activeness. Due to the large scare-fading in the urban environment, the signal to interference and noise ratio (SINR) between some offices and the family micro base stations are too small to care. Therefore, some interference between micro base stations and user are not considerable. In order to improve the energy efficiency in the CoMP process, this paper presents selective beamforming with channel matrix sparse algorithm. This algorithm greatly reduces the complexity of matrix inversion calculation during beamforming and improves the energy efficiency of the system. Simulation results show that the proposed algorithm can effectively reduce the energy consumption of the baseband and improve the energy efficiency of the system.","Title: M-estimator based robust unscented Kalman filter through statistical linearization\nYear: 2019\n\nAbstract: The robust unscented Kalman filter (UKF) is revisited in this paper from a new point of view, namely the statistical linear regression (SLR) perspective of the unscented transformation (UT). When the actual distribution of the observation noise deviates from the assumed Gaussian distribution, the performance of the filter may degrade significantly owing to lacking robustness. After linearizing the nonlinear observation equation using the SLR perspective, the M-estimator is employed to replace the weighted least-squares one, resulting in the robust UKF. Besides providing new theoretical aspects, the advantageous performance of the proposed filter is re-emphasized in terms of the following three: (1) robust, the proposed filter is robust against deviations from the assumed Gaussian distribution; (2) efficient, by elaborately selecting the tuning parameter of the M-estimator, the robust filter will have a slight efficiency loss compared with the UKF when the Gaussian assumption does hold; (3) accurate, the proposed filter achieves robustness without compromising the accuracy of the UT.","Title: Location-Assistant Content Distribution Scheme For Emergency Rescue\nYear: 2018\n\nAbstract: In order to optimize the timeliness and pertinence of message delivery in emergency rescue scenarios and improve the service performance of emergency communications network, we propose a location-assistant content distribution scheme based on delay tolerant network. First of all, considering that the movement patterns of rescue teams tend to follow a predetermined course of action, we design a location-based group mobility model. Due to the intermittent network connectivity and variety of emergency service, a content-classification-based publish/subscribe architecture and a GenericSpray routing algorithm based on the prediction of overlap opportunity in spatio-temporal positions are proposed. Furthermore, we also give a cache management strategy based on the content significance. Since locationassistant content distribution scheme can predict the overlap of activity between rescue teams through the course of action, not only can the number of copy forwarding and message delivery delays be significantly reduced but also the priority delivery of important messages can be ensured by message classification. Simulation experiments show that compared with the traditional delay tolerant network routing algorithm and the classic first-in-first-out caching strategy, location-assistant content distribution scheme improves the performance of message delivery rate, transmission delay, and control overhead significantly.","Title: Spatio-Temporal Change of the Cover and Management Factor in the Soil Erosion Prediction Model in Hunan Province, China * This research was supported by the Project of National Natural Science Foundation of China (41371184) and Water Science and Technology Program of Hunan Province in China (2017-230-40).\nYear: 2018\n\nAbstract: Soil erosion is an important research issue because it can result in serious hazards, such as land quality degrading, flood frequency increasing and deposit in rivers and lakes. The factor of cover and management is a parameter of the soil erosion prediction model and it is the ratio of soil erosion of the present cover and management to continuous clear tillage. Good vegetation cover and the effective land management can control soil erosion to some extent. Quantitative assessment the factor of cover and management in a region scale can provide the cover and management data and some good suggestion for the land use and soil conservation planning. This study chooses Hunan Province in the central and southern China which has no relevant research till now. With the land use and vegetation coverage data of 30 meters resolution extracted from Landsat images in 2000, 2005, 2010 and 2015, the cover and management factor of this region is evaluated and the spatial-temporal characteristics of land use and the factor is analyzed by using the evaluation method, GIS spatial analysis and statistical analysis. The results are as follows. Because the main terrains are mountains and hills which account for 70 percent of the whole province area, the main land use types are forest land and grassland. The mean of cover and management factor is 0.06739, 0.06783, 0.06876 and 0.07421 in 2000, 2005, 2010 and 2015 respectively. The factor increased about 10 percent from 2000 to 2015. The change of cover and management factor may increase soil losses risk if the other factors affecting soil losses are not taken into account.","Title: Covering Uncertain Points in a Tree.\nYear: 2017\n\nAbstract: In this paper, we consider a coverage problem for uncertain points in a tree. Let T be a tree containing a set $${\\mathcal {P}}$$P of n (weighted) demand points, and the location of each demand point $$P_i\\in {{\\mathcal {P}}}$$Pi?P is uncertain but is known to appear in one of $$m_i$$mi points on T each associated with a probability. Given a covering range$$\\lambda $$?, the problem is to find a minimum number of points (called centers) on T to build facilities for serving (or covering) these demand points in the sense that for each uncertain point $$P_i\\in {{\\mathcal {P}}}$$Pi?P, the expected distance from $$P_i$$Pi to at least one center is no more than $$\\lambda $$?. The problem has not been studied before. We present an $$O(|T|+M\\log ^2 M)$$O(|T|+Mlog2M) time algorithm for the problem, where |T| is the number of vertices of T and M is the total number of locations of all uncertain points of $${{\\mathcal {P}}}$$P, i.e., $$M=\\sum _{P_i\\in {{\\mathcal {P}}}}m_i$$M=?Pi?Pmi. In addition, by using this algorithm, we solve a k-center problem on T for the uncertain points of $${{\\mathcal {P}}}$$P.","Title: OC-Cache: An Open-channel SSD Based Cache for Multi-Tenant Systems\nYear: 2018\n\nAbstract: In a multi-tenant cloud environment, tenants are usually hosted by virtual machines. Cloud providers deploy multiple virtual machines on a physical server to better utilize physical resources including CPU, memory, and storage devices. SSDs are often used as an I/O cache shared among the tenants for large storage systems using hard disk drives (HDDs) as their main storage devices, which can receive much of SSD's performance benefit and HDD's cost advantage. A key challenge in the use of the shared cache is to ensure strong performance isolation and maintain its high utilization at the same time. However, conventional SSD cache management approaches cannot effectively address this challenge. In this paper, we propose OC-Cache, an open-channel SSD cache framework which utilizes SSD'd internal parallelism to adaptively allocate cache to tenants for both good performance isolation and high SSD utilization. In particular, OC-Cache uses a tenant's miss ratio curve to determine the amount of cache space allocation and where the allocation is (in dedicated or shared SSD channels) and dynamically manages cache space according to the workload characteristics. Experiments show that OC-Cache significantly reduces interference among tenants, and maintains high utilization of the SSD cache.","Title: Study on the Thermal Characteristics of In-Wheel Motor Drive System Based on Driving Cycles.\nYear: 2019\n\nAbstract: The driving conditions of vehicles, such as rapid acceleration, lasting downhill and so on, which demand for drive power, are rapidly increasing. For an electric vehicle driven by in-wheel motor (IWM), the IWM needs to provide greater power to maintain the normal operation of the vehicle. At present, the pursuit of high power density for IWM reduces the volume of motor under the same power. But the loss density is increased correspondingly, which will lead to overheating of IWM. In this paper, a 15-kW IWM drive system is taken as the research object. Based on the establishment of the temperature analysis model, instantaneous thermal characteristics of the IWM drive system are analyzed under the long climbing condition with low-speed and rapid acceleration driving cycles. The results show that the temperature of the stator winding and stator core is relatively high under the two driving cycles. The temperature of the stator winding is always in the highest level, and the maximum all appear on the end winding. The highest temperature of the end winding can reach 229.49 degrees C under the rapid acceleration driving cycles, which significantly exceeds the limitation for the insulation (155 degrees C) and will seriously affect the normal operation of the IWM and the vehicle. Therefore, in the follow-up design of cooling system, spray cooling with good local cooling effect can be adopted to focus on the cooling for the end winding. This paper can provide theory for the design of the feasibility thermal cooling solution.","Title: Near-Optimal Algorithms For Shortest Paths In Weighted Unit-Disk Graphs\nYear: 2019\n\nAbstract: We revisit a classical graph-theoretic problem, the single-source shortest-path (SSSP) problem, in weighted unit-disk graphs. We first propose an exact (and deterministic) algorithm which solves the problem in O(n log(2)n) time using linear space, where n is the number of the vertices of the graph. This significantly improves the previous deterministic algorithm by Cabello and Jejcic [CGTA'15] which uses O(n(1+delta)) time and O(n(1+delta)) space (for any constantd delta > 0) and the previous randomized algorithm by Kaplan et al. [SODA'17] which uses O(n log(12+o(1))n) expected time and O(n log(3) n) space. More specifically, we show that if the 2D offline insertion-only (additively) weighted nearest-neighbor problem with k operations (i.e., insertions and queries) can be solved in f (k) time, then the SSSP problem in weighted unit-disk graphs can be solved in O(n log n + f (n)) time. Using the same framework with some new ideas, we also obtain a (1 + epsilon)-approximate algorithm for the problem, using O(n log n + n log(2) (1/epsilon)) time and linear space. This improves the previous (1 + epsilon)-approximate algorithm by Chan and Skrepetos [SoCG'18] which uses O((1/epsilon)(2)n log n) time and O((1/epsilon)(2)n) space. More specifically, we show that if the 2D offline insertion-only weighted nearest-neighbor problem with k(1) operations in which at most k(2) operations are insertions can be solved in f (k(1), k(2)) time, then the (1 + epsilon)-approximate SSSP problem in weighted unit-disk graphs can be solved in O(n log n+ f (n, O(epsilon(-2)))) time. Because of the Omega(n log n)-time lower bound of the problem (even when approximation is allowed), both of our algorithms are almost optimal.","Title: A Divide-and-Conquer Algorithm for Two-Point $L_1$ Shortest Path Queries in Polygonal Domains.\nYear: 2019\n\nAbstract: Let $mathcal{P}$ be a polygonal domain of $h$ holes and $n$ vertices. We study the problem of constructing a data structure that can compute a shortest path between $s$ and $t$ in $mathcal{P}$ under the $L_1$ metric for any two query points $s$ and $t$. To do so, a standard approach is to first find a set of $n_s$ gateways for $s$ and a set of $n_t$ gateways for $t$ such that there exist a shortest $s$-$t$ path containing a gateway of $s$ and a gateway of $t$, and then compute a shortest $s$-$t$ path using these gateways. Previous algorithms all take quadratic $O(n_scdot n_t)$ time to solve this problem. In this paper, we propose a divide-and-conquer technique that solves the problem in $O(n_s + n_t log n_s)$ time. As a consequence, we construct a data structure of $O(n+(h^2log^3 h/loglog h))$ size in $O(n+(h^2log^4 h/loglog h))$ time such that each query can be answered in $O(log n)$ time.","Title: A Linear-Time Algorithm for Radius-Optimally Augmenting Paths in a Metric Space.\nYear: 2019\n\nAbstract: Let $P$ be a path graph of $n$ vertices embedded in a metric space. We consider the problem of adding a new edge to $P$ to minimize the radius of the resulting graph. Previously, a similar problem for minimizing the diameter of the graph was solved in $O(nlog n)$ time. To the best of our knowledge, the problem of minimizing the radius has not been studied before. In this paper, we present an $O(n)$ time algorithm for the problem, which is optimal.","Title: Improved Algorithms for the Bichromatic Two-Center Problem for Pairs of Points.\nYear: 2019\n\nAbstract: We consider a bichromatic two-center problem for pairs of points. Given a set $S$ of $n$ pairs of points in the plane, for every pair, we want to assign a red color to one point and a blue color to the other, in such a way that the value $max{r_1,r_2}$ is minimized, where $r_1$ (resp., $r_2$) is the radius of the smallest enclosing disk of all red (resp., blue) points. Previously, an exact algorithm of $O(n^3log^2 n)$ time and a $(1+varepsilon)$-approximate algorithm of $O(n + (1/varepsilon)^6 log^2 (1/varepsilon))$ time were known. In this paper, we propose a new exact algorithm of $O(n^2log^2 n)$ time and a new $(1+varepsilon)$-approximate algorithm of $O(n + (1/varepsilon)^3 log^2 (1/varepsilon))$ time.","Title: UCSCXenaShiny: an R/CRAN package for interactive analysis of UCSC Xena data\nYear: 2022\n\nAbstract: UCSC Xena platform provides huge amounts of processed cancer omics data from large cancer research projects (e.g. TCGA, CCLE and PCAWG) or individual research groups and enables unprecedented research opportunities. However, a graphical user interface-based tool for interactively analyzing UCSC Xena data and generating elegant plots is still lacking, especially for cancer researchers and clinicians with limited programming experience. Here, we present UCSCXenaShiny, an R Shiny package for quickly searching, downloading, exploring, analyzing and visualizing data from UCSC Xena data hubs. This tool could effectively promote the practical use of public data, and can serve as an important complement to the current Xena genomics explorer.","Title: Resource Allocation for Full-Duplex M2M Networks with Imperfect CSI\nYear: 2021\n\nAbstract: In this paper, we investigate the resource allocation problem for a full-duplex network with machine-to-machine devices and cellular users. Our objective is to maximize the network sum rate while achieving individual quality-of-service (QoS) requirements, considering imperfect and partial channel state information. The sum rate maximization problem is formulated as a mixed-integer nonlinear programming problem. We decouple it into a subcarrier assignment subproblem and a power allocation subproblem to make it more tractable. The non-convex subcarrier assignment problem is transformed to a convex problem by applying majorization minimization method, and then solved by the interior-point method. While the power allocation problem is solved by applying the Lagrange dual method. Simulation results demonstrate the impact of channel estimation error on sum rate performance and the superiority of the proposed algorithm. Furthermore, the fairness is improved among users by QoS guarantee.","Title: A Bilateral Dual-Arm Teleoperation Robot System with a Unified Control Architecture\nYear: 2021\n\nAbstract: The teleoperation system can transmit human intention to the remote robot, so that the system combines excellent robot operation performance and human intelligence. In this article, we have established a bilateral teleoperation system with force feedback from the arm and gripper. That is, the slave robot system can provide force feedback on both the wrist and the fingers, while the master robot sy...","Title: Microblog-oriented Multi-scale CNN Multi-label Sentiment Classification Model\nYear: 2020\n\nAbstract: With the rapid development of the Internet, social networks have become an important platform for people to express their opinions. Through the sentiment analysis of the text, the user's emotional tendency can be captured in time. Multi-label sentiment classification is one of the difficult issues. Therefore, the paper proposes a multi-label sentiment classification model for short texts of Weibo ...","Title: Chinese Real-word Error Automatic Detection and Correction Based on Confusion Set and Generalization Model\nYear: 2020\n\nAbstract: With the development of informatization, there are many errors in Chinese texts, and one kind of error is called “Real-word Error” which like English Real-word Error. The wrong word itself is also a right word in dictionary which confirm by the specific context. While using data resource of corpus to proofread text, data Sparseness will bring the miscarriage of justice problem. In view of the abov...","Title: Apparatus and methods for mouse behavior recognition on foot contact features\nYear: 2021\n\nAbstract: Behavior recognition of model animals such as mice, rats, and monkeys, is of great significance in medical research and vital in the development in medicines or drugs. Studies of the animal’s responses to specific stimulations consisted of visual imaging methods that was easily affected by cage lighting conditions and shooting angle. In this study, we designed a behavior classification system that focused on object-floor contact features extracted from touch images of the frustrated total internal reflection injected with infrared lights. A mouse behavioral dataset (IMBD) was then established, and a support vector machine was applied to conduct this classification. To improve its performance, an improved particle swarm optimization was proposed for the optimization of parameters. Based on IMBD and the support vector machine, we tested different feature extraction methods and classifiers. The system performance of the current study showed more accuracy and efficacy compared with performance of other prevalent particle swarm optimizations. Our study resulted in a recognition rate of up to 94.37% for individual behavior. The average rate of behavioral recognition for all tested mice reached 83.09%. The results suggested that foot contact features of mice were more effective than regular video features in behavior recognition.","Title: AMMASurv - Asymmetrical Multi-Modal Attention for Accurate Survival Analysis with Whole Slide Images and Gene Expression Data.\nYear: 2021\n\nAbstract: The use of multi-modal data such as the combination of whole slide images (WSIs) and gene expression data for survival analysis can lead to more accurate survival predictions. Previous multi-modal survival models are not able to efficiently excavate the intrinsic information within each modality. Moreover, despite experimental results show that WSIs provide more effective information than gene expression data, previous methods regard the information from different modalities as similarly important so they cannot flexibly utilize the potential connection between the modalities. To address the above problems, we propose a new asymmetrical multi-modal method, termed as AMMASurv. Specifically, we design an asymmetrical multi-modal attention mechanism (AMMA) in Transformer encoder for multi-modal data to enable a more flexible multi-modal information fusion for survival prediction. Different from previous works, AMMASurv can effectively utilize the intrinsic information within every modality and flexibly adapts to the modalities of different importance. Extensive experiments are conducted to validate the effectiveness of the proposed model. Encouraging results demonstrate the superiority of our method over other state-of-the-art methods.","Title: Integration of Patch Features Through Self-supervised Learning and Transformer for Survival Analysis on Whole Slide Images\nYear: 2021\n\nAbstract: Survival prediction using whole slide images (WSIs) can provide guidance for better treatment of diseases and patient care. Previous methods usually extract and process only image features from patches of WSIs. However, they ignore the significant role of spatial information of patches and the correlation between the patches of WSIs. Furthermore, those methods extract the patch features through the model pre-trained on ImageNet, overlooking the huge gap between WSIs and natural images. Therefore, we propose a new method, called SeTranSurv, for survival prediction. SeTranSurv extracts patch features from WSIs through self-supervised learning and adaptively aggregates these features according to their spatial information and correlation between patches using the Transformer. Experiments on three large cancer datasets indicate the effectiveness of our model. More importantly, SeTranSurv has better interpretability in locating important patterns and features that contribute to accurate cancer survival prediction.","Title: Research On The Characteristics Of Hand Shape In Different Countries\nYear: 2019\n\nAbstract: For a long time, science has been dedicated to revealing various indicators of the human body, including height and weight. The same purpose is also for this paper. Based on data on the hand of people from ten different countries, this paper do some research with the help of multi-variate analysis tools. According to the different characteristics of the data, this paper first carried out the analysis of variance on hand length. Then, according to the different dimension of hand shape, including the vertical length of the palm, the metacarpal and the length of the index finger, factor analysis was carried out, and two influence factors were extracted. Then, multiple regressions were performed on these variables, at the same time, stepwise regression method was adopted, and the main independent variables were chose from the constructed model. Finally, gender is set to a dummy variable to study the impact of gender differences on the hand length.","Title: H-Infinity Sliding Mode Control For Pdt-Switched Nonlinear Systems Under The Dynamic Event-Triggered Mechanism\nYear: 2022\n\nAbstract: In this paper, the H-infinity sliding mode control problem of persistent dwell-time switched non-linear systems is investigated. Due to the limited bandwidth resources, a dynamic event-triggered mechanism is introduced to alleviate the transmission burden. Considering that the system parameters are switched in accordance with the persistent dwell-time switching strategy, a novel switched sliding mode control law is constructed. Then, drawing on the Lyapunov function approach, sufficient conditions are derived, which not only ensure the reachability of the sliding region around the specified sliding surface but also guarantee the globally uniform exponential stability of the system with an H-infinity performance. Moreover, the specific form of the controller gains is derived by utilizing an efficient decoupling method. Eventually, the validity of the proposed method is validated by two numerical examples. (C) 2021 Elsevier Inc. All rights reserved.","Title: Constructing Many Faces in Arrangements of Lines and Segments\nYear: 2022\n\nAbstract: We present new algorithms for computing many faces in arrangements of lines and segments. Given a set $S$ of $n$ lines (resp., segments) and a set $P$ of $m$ points in the plane, the problem is to compute the faces of the arrangements of $S$ that contain at least one point of $P$. For the line case, we give a deterministic algorithm of $O(m^{2/3}n^{2/3}\\log^{2/3} (n/\\sqrt{m})+(m+n)\\log n)$ time. This improves the previously best deterministic algorithm [Agarwal, 1990] by a factor of $\\log^{2.22}n$ and improves the previously best randomized algorithm [Agarwal, Matou\\v{s}ek, and Schwarzkopf, 1998] by a factor of $\\log^{1/3}n$ in certain cases (e.g., when $m=\\Theta(n)$). For the segment case, we present a deterministic algorithm of $O(n^{2/3}m^{2/3}\\log n+\\tau(n\\alpha^2(n)+n\\log m+m)\\log n)$ time, where $\\tau=\\min\\{\\log m,\\log (n/\\sqrt{m})\\}$ and $\\alpha(n)$ is the inverse Ackermann function. This improves the previously best deterministic algorithm [Agarwal, 1990] by a factor of $\\log^{2.11}n$ and improves the previously best randomized algorithm [Agarwal, Matou\\v{s}ek, and Schwarzkopf, 1998] by a factor of $\\log n$ in certain cases (e.g., when $m=\\Theta(n)$). We also give a randomized algorithm of $O(m^{2/3}K^{1/3}\\log n+\\tau(n\\alpha(n)+n\\log m+m)\\log n\\log K)$ expected time, where $K$ is the number of intersections of all segments of $S$. In addition, we consider the query version of the problem, that is, preprocess $S$ to compute the face of the arrangement of $S$ that contains any query point. We present new results that improve the previous work for both the line and the segment cases.","Title: Algorithms for diameters of unicycle graphs and diameter-optimally augmenting trees\nYear: 2021\n\nAbstract: We consider the problem of computing the diameter of a unicycle graph (i.e., a graph with a unique cycle). We present an O(n) time algorithm for the problem, where n is the number of vertices of the graph. This improves the previous best O(n log n) time solution [Oh and Ahn, ISAAC 2016]. Using this algorithm as a subroutine, we solve the problem of adding a shortcut to a tree so that the diameter of the new graph (which is a unicycle graph) is minimized; our algorithm takes O(n(2)logn) time and O(n) space. The previous best algorithms solve the problem in O(n(2)log(3)n) time and O(n) space [Oh and Ahn, ISAAC 2016], or in O(n(2)) time and O(n(2)) space [Bilo, ISAAC 2018]. (C) 2021 Elsevier B.V. All rights reserved.","Title: Indoor Crowd Density Estimation Based on Sound Field Disturbance Analysis\nYear: 2021\n\nAbstract: With the frequent occurrence of large-scale indoor stampedes, people pay more and more attention to the problem of indoor crowd density, and indoor crowd density estimation has become a very important security work. This paper proposes an indoor crowd density estimation method based on acoustic means. This method makes use of the reverberation effect and spatial distribution of sound wave propagat...","Title: Vehicle Object Detection Method Based On Candidate Region Aggregation\nYear: 2021\n\nAbstract: Multi-scale vehicle detection is an important application in the field of object detection, and Feature Pyramid Network (FPN) is an important means to deal with multi-scale object detection tasks. However, baseline method is the common method used in most of the existing network structure, which represents the input image information by selecting one from the output layer of FPN, and discard other layers. This not only limits the performance of the network structure, but also performs poorly when dealing with the problem of excessive scale differences. To solve this problem, a novelty candidate region aggregation network (CRAN) is proposed in this paper. The candidate regions of different feature layers are effectively aggregated to improve the network generalization performance. Specifically, calculate the similarity between different feature layers through a feature quality score module, and use this as a quantity factor to determine the number of candidate regions reserved for the corresponding feature layer. Finally, they are aggregated into a more comprehensive candidate region group. Further, in order to improve the detection efficiency of small objects, an area cross entropy loss function is proposed. It makes the model pay more attention to small targets by adding a monotonic decrease based on the area. Finally, the proposed CRAN and the area cross entropy loss function are applied to the advanced detectors. The experimental results in the KITTI and UA-DETRAC datasets show that this method has good performance on vehicle objects in different scenarios, and can meet the requirements of practical application.","Title: A Simple Algorithm for Computing the Zone of a Line in an Arrangement of Lines.\nYear: 2022\n\nAbstract: Let $L$ be a set of $n$ lines in the plane. The zone $Z(\\ell)$ of a line $\\ell$ in the arrangement $\\mathcal{A}(L)$ of $L$ is the set of faces of $\\mathcal{A}(L)$ whose closure intersects $\\ell$. It is known that the combinatorial size of $Z(\\ell)$ is $O(n)$. Given $L$ and $\\ell$, computing $Z(\\ell)$ is a fundamental problem. Linear-time algorithms exist for computing $Z(\\ell)$ if $\\mathcal{A}(L)$ has already been built, but building $\\mathcal{A}(L)$ takes $O(n^2)$ time. On the other hand, $O(n\\log n)$-time algorithms are also known for computing $Z(\\ell)$ without relying on $\\mathcal{A}(L)$, but these algorithms are relatively complicated. In this paper, we present a simple algorithm that can compute $Z(\\ell)$ in $O(n\\log n)$ time. More specifically, once the sorted list of the intersections between $\\ell$ and the lines of $L$ is known, the algorithm runs in $O(n)$ time. A big advantage of our algorithm, which mainly involves a Graham's scan style procedure, is its simplicity.","Title: A Prior Information Enhanced Extraction Framework For Document-Level Financial Event Extraction\nYear: 2021\n\nAbstract: Document-level financial event extraction (DFEE) is the task of detecting events and extracting the corresponding event arguments in financial documents, which plays an important role in information extraction in the financial domain. This task is challenging as the financial documents are generally long text and event arguments of one event may be scattered in different sentences. To address this issue, we proposed a novel Prior Information Enhanced Extraction framework (PIEE) for DFEE, leveraging prior information from both event types and pre-trained language models. Specifically, PIEE consists of three components: event detection, event argument extraction, and event table filling. In event detection, we identify the event type. Then, the event type is explicitly used for event argument extraction. Meanwhile, the implicit information within language models also provides considerable cues for event arguments localization. Finally, all the event arguments are filled in an event table by a set of predefined heuristic rules. To demonstrate the effectiveness of our proposed framework, we participated in the share task of CCKS2020 Task 4-2: Document-level Event Arguments Extraction. On both Leaderboard A and Leaderboard B, PIEE took the first place and significantly outperformed the other systems.","Title: A Novel Hybrid Subset-Learning Method For Predicting Risk Factors Of Atherosclerosis\nYear: 2017\n\nAbstract: Cardiovascular disease (CVD) caused by atherosclerosis is one of the major causes of death world-wide. Currently diverse machine learning models have been applied to disease prediction and classification. However, most of them tend to focus on the performance of the algorithm and neglect the underlying variables for patients in different carotid atherosclerotic stages. In this paper, we propose a novel hybrid machine learning method named Subset Learning (S-learning) to predict and discover the risk factors of these different stages. The S-learning algorithm can elucidate the variables that have significant influence on the outcome of carotid atherosclerotic. Performance comparisons are based on the dataset collected from both Shanghai Renji and Shanghai Huashan Hospital. The result shows that the proposed method has superior classification performance than other classification algorithms. Our findings point to the utility of predictive machine learning and the discovery of risk factors to refine the treatment plans.","Title: Wobtree: A Write-Optimized B Plus -Tree For Non-Volatile Memory\nYear: 2021\n\nAbstract: The emergence of non-volatile memory (NVM) has introduced new opportunities for performance optimizations in existing storage systems. To better utilize its byte-addressability and near-DRAM performance, NVM can be attached on the memory bus and accessed via load/store memory instructions rather than the conventional block interface. In this scenario, a cache line (usually 64 bytes) becomes the data transfer unit between volatile and non-volatile devices. However, the failure-atomicity of write on NVM is the memory bit width (usually 8 bytes). This mismatch between the data transfer unit and the atomicity unit may introduce write amplification and compromise data consistency of node-based data structures such as B+-trees. In this paper, we propose WOBTree, a Write-Optimized B+-Tree for NVM to address the mismatch problem without expensive logging. WOBTree minimizes the update granularity from a tree node to a much smaller subnode and carefully arranges the write operations in it to ensure crash consistency and reduce write amplification. Experimental results show that compared with previous persistent B+-tree solutions, WOBTree reduces the write amplification by up to 86x and improves write performance by up to 61x while maintaining similar search performance.","Title: Improved algorithms for the bichromatic two-center problem for pairs of points\nYear: 2022\n\nAbstract: We consider a bichromatic two-center problem for pairs of points. Given a set S of n pairs of points in the plane, for every pair, we want to assign a red color to one point and a blue color to the other, in such a way that the value max{r1,r2} is minimized, where r1 (resp., r2) is the radius of the smallest enclosing disk of all red (resp., blue) points. Previously, an exact algorithm of O(n3log2n) time and a (1+ε)-approximate algorithm of O(n+(1/ε)6log2(1/ε)) time were known. In this paper, we propose a new exact algorithm of O(n2log2n) time and a new (1+ε)-approximate algorithm of O(n+(1/ε)3log2(1/ε)) time.","Title: Constraint Loss for Rotated Object Detection in Remote Sensing Images\nYear: 2021\n\nAbstract: Rotated object detection is an extension of object detection that uses an oriented bounding box instead of a general horizontal bounding box to define the object position. It is widely used in remote sensing images, scene text, and license plate recognition. The existing rotated object detection methods usually add an angle prediction channel in the bounding box prediction branch, and smooth L1 loss is used as the regression loss function. However, we argue that smooth L1 loss causes a sudden change in loss and slow convergence due to the angle solving mechanism of open CV (the angle between the horizontal line and the first side of the bounding box in the counter-clockwise direction is defined as the rotation angle), and this problem exists in most existing regression loss functions. To solve the above problems, we propose a decoupling modulation mechanism to overcome the problem of sudden changes in loss. On this basis, we also proposed a constraint mechanism, the purpose of which is to accelerate the convergence of the network and ensure optimization toward the ideal direction. In addition, the proposed decoupling modulation mechanism and constraint mechanism can be integrated into the popular regression loss function individually or together, which further improves the performance of the model and makes the model converge faster. The experimental results show that our method achieves 75.2% performance on the aerial image dataset DOTA (OBB task), and saves more than 30% of computing resources. The method also achieves a state-of-the-art performance in HRSC2016, and saved more than 40% of computing resources, which confirms the applicability of the approach.","Title: Joint Road Side Units Selection and Resource Allocation in Vehicular Edge Computing\nYear: 2021\n\nAbstract: With powerful storage and computation capability, vehicular edge computing is considered as a promising paradigm to enhance the safety and quality-of-service of vehicles in intelligent transportation systems. In this work, we investigate a joint road side units (RSUs) selection and resource allocation problem, with the objective of minimizing the total task offloading delay subject to the bandwidt...","Title: Co-Channel Interference Suppression for LTE Passive Radar Based on Spatial Feature Cognition\nYear: 2022\n\nAbstract: Passive radars based on long-term evolution (LTE) signals suffer from sever interferences. The interferences are not only from the base station used as the illuminator of opportunity (BS-IoO), but also from the other co-channel base stations (CCBS) working at the same frequency with the BS-IoO. Because the reference signals of the co-channel interferences are difficult to obtain, cancellation performance degrades seriously when traditional interference suppression methods are applied in LTE-based passive radar. This paper proposes a cascaded cancellation method based on the spatial spectrum cognition of interference. It consists of several cancellation loops. In each loop, the spatial spectrum of strong interferences is first recognized by using the cyclostationary characteristic of LTE signal and the compressed sensing technique. A clean reference signal of each interference is then reconstructed according to the spatial spectrum previously obtained. With the reference signal, the interferences are cancelled. At the end of each loop, the energy of the interference residual is estimated. If the interference residual is still strong, then the cancellation loop continues; otherwise it terminates. The proposed method can get good cancellation performance with a small-sized antenna array. Theoretical and simulation results demonstrate the effectiveness of the proposed method.","Title: Sandwich Integration Technique for the Pressure Sensor Detection of Occlusal Force In Vitro\nYear: 2022\n\nAbstract: Bite force measurement is an important parameter when checking the function and integrity of the masticatory system, whereas it is currently very difficult to measure bite force during functional movement. Hence, the purpose of this study is to explore the potential technique and device for the measurement and intervention of the continuous bite forces on functional and dynamic occlusal condition. A portable biosensor by sandwich technique was designed, and the validity, reliability, and sensitivity were determined by mechanical pressure loading tests; meanwhile, the pressure signal is acquired by, and transmitted to, voltage changes by the electrical measurements of the sensors. The result is that, when the mechanical stress detection device is thicker than 3.5 mm, it shows relatively ideal mechanical properties; however, when the thickness is less than 3.0 mm, there is a risk of cracking. Mechanical stress changing and voltage variation had a regularity and positive relationship in this study. The mechanical stress-measuring device made by medical and industrial cross has a good application prospect for the measurement of bite force during function.","Title: Evaluation of the Integrity Risk for Precise Point Positioning\nYear: 2022\n\nAbstract: Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP) is an attractive positioning technology due to its high precision and flexibility. However, the vulnerability of PPP brings a safety risk to its application in the field of life safety, which must be evaluated quantitatively to provide integrity for PPP users. Generally, PPP solutions are processed recursively based on the extended Kalman filter (EKF) estimator, utilizing both the previous and current measurements. Therefore, the integrity risk should be qualified considering the effects of all the potential observation faults in history. However, this will cause the calculation load to explode over time, which is impractical for long-time missions. This study used the innovations in a time window to detect the faults in the measurements, quantifying the integrity risk by traversing the fault modes in the window to maintain a stable computation cost. A non-zero bias was conservatively introduced to encapsulate the effect of the faults before the window. Coping with the multiple simultaneous faults, the worst-case integrity risk was calculated to overbound the real risk in the multiple fault modes. In order to verify the proposed method, simulation and experimental tests were carried out in this study. The results showed that the fixed and hold mode adopted for ambiguity resolution is critical to an integrity risk evaluation, which can improve the observation redundancy and remove the influence of the biased predicted ambiguities on the integrity risk. Increasing the length of the window can weaken the impact of the conservative assumption on the integrity risk due to the smoothing effect of the EKF estimator. In addition, improving the accuracy of observations can also reduce the integrity risk, which indicates that establishing a refined PPP random model can improve the integrity performance.","Title: Adaptive sliding mode control for persistent dwell-time switched nonlinear systems with matched/mismatched uncertainties and its application\nYear: 2022\n\nAbstract: In this paper, the adaptive sliding mode control issue for switched nonlinear systems with matched and mismatched uncertainties is addressed, where the persistent dwell-time switching rule is introduced to describe the switching of parameters. Besides, considering the case that the upper bound of the matched uncertainty is unknown, the purpose of this paper is to utilize an adaptive control method to estimate its upper bound parameters. To begin with, a linear sliding surface is constructed, and then the reduced-order sliding mode dynamics can be obtained through a reduced-order method. Next, sufficient conditions can be derived based on the Lyapunov stability and the persistent dwell-time switching analysis techniques ensuring that the reduced-order sliding mode dynamics is globally uniformly exponentially stable. Moreover, a switched adaptive sliding mode control law is designed, which can not only ensure the reachability of the sliding surface but also estimate the upper bound parameters of the matched uncertainty. Finally, a numerical example and a circuit model are introduced to verify the effectiveness of the proposed method.","Title: Target Detection and DOA Estimation for Passive Bistatic Radar in the Presence of Residual Interference\nYear: 2022\n\nAbstract: With the development of radio technology, passive bistatic radar (PBR) will suffer from interferences not only from the base station that is used as the illuminator of opportunity (BS-IoO), but also from the base station with co-frequency or adjacent frequency (BS-CF/AF). It is difficult for clutter cancellation algorithm to suppress all the interferences, especially the interferences from BS-CF/AF. The residual interferences will seriously affect target detection and DOA estimation. To solve this problem, a novel target detection and DOA estimation method for PBR based on compressed sensing sparse reconstruction is proposed. Firstly, clutter cancellation algorithm is used to suppress the interferences from BS-IoO. Secondly, the residual interferences and target echo are separated in spatial domain based on the azimuth sparse reconstruction. Finally, target detection and DOA estimation method are given. The proposed method can achieve not only target detection and DOA estimation in the presence of residual interferences, but also better anti-mainlobe interferences and high-resolution DOA estimation performance. Numerical simulation and experimental results verify the effectiveness of the proposed algorithm.","Title: Image super-resolution reconstruction based on generative adversarial network model with feedback and attention mechanisms\nYear: 2022\n\nAbstract: Despite the rapid development of single-image super-resolution (SISR) methods of generative adversarial networks (GAN), which can reconstruct visually realistic images, the problem of high discrepancy between the recovered details or textures and the ground truth persists. To address this issue, an SISR reconstruction GAN based on a feedback and attention mechanism (FBSRGAN) is proposed. Specifically, we select a network with a feedback mechanism as the generator, which can gradually create high-resolution images through the feedback connection. The attention mechanism is combined with the feedback block to adaptively select useful feature information and effectively process the feedback stream and enhance the image output quality. We use the relative average least squares GAN loss to reduce the instability of the optimization generator process to guide GAN to obtain more realistic results. The results show that compared with the ESRGAN method, when the amplification factor is 4, PSNR and SSIM of the proposed method increase by 0.386 and 0.0141, respectively, and PI decreases by 0.284, while the number of parameters is only 18.5% of that of ESRGAN, when tested on the Set5 dataset. Compared with existing GAN-based SR methods, FBSRGAN achieves superior performance in terms of both perceptual ability and image distortion.","Title: Unit-Disk Range Searching and Applications.\nYear: 2022\n\nAbstract: Given a set $P$ of $n$ points in the plane, we consider the problem of computing the number of points of $P$ in a query unit disk (i.e., all query disks have the same radius). We show that the main techniques for simplex range searching in the plane can be adapted to this problem. For example, by adapting Matou\\v{s}ek's results, we can build a data structure of $O(n)$ space so that each query can be answered in $O(\\sqrt{n})$ time. Our techniques lead to improvements for several other classical problems, such as batched range searching, counting/reporting intersecting pairs of unit circles, distance selection, discrete 2-center, etc. For example, given a set of $n$ unit disks and a set of $n$ points in the plane, the batched range searching problem is to compute for each disk the number of points in it. Previous work [Katz and Sharir, 1997] solved the problem in $O(n^{4/3}\\log n)$ time while our new algorithm runs in $O(n^{4/3})$ time.","Title: Autonomous Electric Vehicle Battery Disassembly Based on NeuroSymbolic Computing\nYear: 2022\n\nAbstract: The booming of electric vehicles demands efficient battery disassembly for recycling to be environment-friendly. Due to the unstructured environment and high uncertainties, battery disassembly is still primarily done by humans, probably assisted by robots. It is highly desirable to design autonomous solutions to improve work efficiency and lower human risks in high voltage and toxic environments. This paper proposes a novel framework of the NeuroSymbolic task and motion planning method to disassemble batteries in an unstructured environment using robots automatically. It enables robots to independently locate and disassemble battery bolts, with or without obstacles. This study not only provides a solution for intelligently disassembling electric vehicle batteries but also verifies its feasibility through a set of test results with the robot accomplishing the disassembly tasks in a complex and dynamic environment.","Title: Computing the Minimum Bottleneck Moving Spanning Tree\nYear: 2022\n\nAbstract: Given a set $P$ of $n$ points that are moving in the plane, we consider the problem of computing a spanning tree for these moving points that does not change its combinatorial structure during the point movement. The objective is to minimize the bottleneck weight of the spanning tree (i.e., the largest Euclidean length of all edges) during the whole movement. The problem was solved in $O(n^2)$ time previously [Akitaya, Biniaz, Bose, De Carufel, Maheshwari, Silveira, and Smid, WADS 2021]. In this paper, we present a new algorithm of $O(n^{4/3} \\log^3 n)$ time.","Title: Algorithms for the line-constrained disk coverage and related problems\nYear: 2022\n\nAbstract: Given a set P of n points and a set S of m weighted disks in the plane, the disk coverage problem asks for a subset of disks of minimum total weight that cover all points of P. The problem is NP-hard. In this paper, we consider a line-constrained version in which all disks are centered on a line L (while points of P can be anywhere in the plane). We present an O ((m + n) log(m + n) + kappa log m) time algorithm for the problem, where kappa is the number of pairs of disks whose boundaries intersect. Alternatively, we can also solve the problem in O (nm log(m + n)) time. For the unit-disk case where all disks have the same radius, the running time can be reduced to O ((n + m) log(m + n)). In addition, we solve in O ((m + n) log(m + n)) time the L & INFIN; and L1 cases of the problem, in which the disks are squares and diamonds, respectively. We further demonstrate that our techniques can also be used to solve other geometric coverage problems. For example, given in the plane a set P of n points and a set S of n weighted half-planes, we solve in O (n4 log n) time the problem of finding a subset of half-planes to cover P so that their total weight is minimized. This improves the previous best algorithm of O (n(5)) time by almost a linear factor. If all half-planes are lower ones, then our algorithm runs in O(n(2) log n) time, which improves the previous best algorithm of O (n(4)) time by almost a quadratic factor. (C)& nbsp;2022 Elsevier B.V. All rights reserved.","Title: Pansharpening via Triplet Attention Network With Information Interaction\nYear: 2022\n\nAbstract: Pansharpening aims to obtain high spatial resolution multispectral (MS) images by fusing the spatial and spectral information in low spatial resolution (LR) MS and panchromatic (PAN) images. Recently, deep neural network (DNN) based pansharpening methods have been advanced extensively. Although most DNN-based methods show good performance, it is difficult for them to preserve the spatial details in the fused image. In this article, we propose a new pansharpening method based on a triplet attention network with information interaction to efficiently enhance the spatial and spectral information in the fused image. First, different attention mechanisms are designed to model the spatial and spectral feature properties in LR MS and PAN images. Then, the complementarity among different feature maps is enhanced by information interaction, which promotes the compatibility of features from subnetworks. Finally, we utilize a graph attention module to capture the similarity within feature maps. According to the graph, the informative feature maps are selected to provide more details for the reconstruction of the fused image. Extensive experiments on QuickBird and GeoEye-1 satellite datasets show that the proposed method can produce competitive fused images when compared with some state-of-the-art methods.","Title: A Tracking Imaging Control Method for Dual-FSM 3D GISC LiDAR\nYear: 2022\n\nAbstract: In this paper, a tracking and pointing control system with dual-FSM (fast steering mirror) composite axis is proposed. It is applied to the target-tracking accuracy control in a 3D GISC LiDAR (three-dimensional ghost imaging LiDAR via sparsity constraint) system. The tracking and pointing imaging control system of the dual-FSM 3D GISC LiDAR proposed in this paper is a staring imaging method with multiple measurements, which mainly solves the problem of high-resolution remote-sensing imaging of high-speed moving targets when the technology is transformed into practical applications. In the research of this control system, firstly, we propose a method that combines motion decoupling and sensor decoupling to solve the mechanical coupling problem caused by the noncoaxial sensor installation of the FSM. Secondly, we suppress the inherent mechanical resonance of the FSM in the control system. Thirdly, we propose the optical path design of a dual-FSM 3D GISC LiDAR tracking imaging system to solve the problem of receiving aperture constraint. Finally, after sufficient experimental verification, our method is shown to successfully reduce the coupling from 7% to 0.6%, and the precision tracking bandwidth reaches 300 Hz. Moreover, when the distance between the GISC system and the target is 2.74 km and the target flight speed is 7 m/s, the tracking accuracy of the system is improved from 15.7 mu rad (sigma) to 2.2 mu rad (sigma), and at the same time, the system recognizes the target contour clearly. Our research is valuable to put the GISC technology into practical applications.","Title: Eliminating the Resonance Interference for an Inductive Speed Sensor Subjected to an Early-Time HEMP Field\nYear: 2022\n\nAbstract: In the present study, the mechanism of electromagnetic resonance interference on an inductive speed sensor is investigated. To this end, a second-order equivalent resonance model is established based on the lumped parameter of the speed sensor. Then the effects of different parameters on the interference signal are analyzed. The effect of terminal loads and filter capacitances are simulated in the CST software and the obtained results for the resonance interference signal are analyzed. Moreover, an experiment is carried out and the oscillation of the voltage interference signal is produced by the speed sensor under the testing of early-time high altitude nuclear electromagnetic pulse (HEMP) field. The GTEM cell is used to produce the plane wave for the experiment. Finally, the advantages and disadvantages of different resonance elimination methods are analyzed, and the method is proposed to eliminate the oscillation of the voltage interference signal.","Title: AG600 Maritime Base Location Decision Based on the Interval Intuitionistic Fuzzy TOPSIS Method\nYear: 2022\n\nAbstract: Aiming at the location research blank of the AG600 amphibious aircraft marine rescue base, the location decision index system of the AG600 is established, and the AG600 location decision model based on the interval intuitionistic fuzzy theory and improved TOPSIS method is constructed. Through investigation and statistical analysis, the evaluation system with 10 indices is determined based on applicability, safety, and accessibility, and in the process of quantization of index assignment, a fuzzy conversion method for different indices is proposed. The interval intuitionistic fuzzy number of each index is obtained by combining objective calculation and subjective assignment, and the interval intuitionistic fuzzy decision matrix is constructed. The fuzzy entropy method is used to calculate the index weight, and then the improved Technique for Order of Preference by Similarity (TOPSIS) is used to evaluate and rank the rescue bases, the scientific and reasonable location decision results are obtained. Moreover, STOPSIS and WS are used to analyze and demonstrate the sorting inversion problem, and the value is 0.908, which further proves the effectiveness of the method proposed in this paper. Finally, taking the South China Sea islands as an example, the correctness and reliability of the proposed model is verified by comparison with the calculation results of the score function method and the projection method.","Title: High-Efficiency Multihomed Multimedia Transmission in Wireless Sensors\nYear: 2022\n\nAbstract: For real-time video transmission in multimedia sensor networks, efficient and reliable communication is very important. However, under actual conditions, when the sensor transmits data to the sink node, the link between the sensor and the sink node usually becomes unstable due to bandwidth limitations and unstable transmission links. Unstable data transmission will affect the normal data transmission of sensor nodes and even cause sensor nodes to fail to communicate with sink nodes. These problems make users who use multimedia sensor networks have a poor user experience. In order to solve these problems, a brand-new, multi-homed, multi-connection system was designed for wireless multimedia sensors, and a prototype system was implemented. Compared with the traditional single-homed connection mode, the system can maintain a more stable connection under the same bandwidth, memory, and CPU resource constraints.","Title: Short-term wind power probabilistic forecasting using a new neural computing approach: GMC-DeepNN-PF\nYear: 2022\n\nAbstract: With the increasing penetration of renewable energy in power generation, the increasing uncertainty of renewable energy has significant influence on the stability of energy system. In order to handle the system uncertainty as well as satisfying the energy demand and supply, a short-term wind power probabilistic forecasting under uncertainty using the Gaussian mixed clustering-Deep neural network probabilistic forecasting (GMC-DeepNN-PF) is proposed. To illustrate the applicability of the proposed method, a case study of a wind power data set from Kaggle is presented and comparisons among four different data situations and four different forecasting models are shown in this paper. The raw wind power data is firstly pre-processed based on Gaussian Mixture Model reducing data defects. After that, the cleaned data is extended for more information which can improve the output accuracy and time as a property is added to forecasting model. According to DeepNN-PF, deep conventional neural network concatenated with T distribution is then utilized for forecasting using extended data. The simulations and comparisons demonstrate the advancement of the proposed method.","Title: Near-Field Multiple Target Localization in Frequency Diverse Array Based on Tensor Decomposition\nYear: 2022\n\nAbstract: Target localization is a fundamental problem in array signal processing. The problem of locating near-field targets with multiple-input multiple-output (MIMO) radar has been studied extensively; however, most of the conventional matrix-based approaches suffer from limitations in terms of the representation and exploitation of the multidimensional nature of MIMO radar signals. In this paper, we addressed the problem of localizing multiple targets in the near-field region, aiming at pursuing a solution applicable for multidimensional signal that is able to achieve sufficient accuracy. A tensor-based signal model impinging on a monostatic frequency diverse array multiple-input multiple-output (FDA-MIMO) radar was formulated, and a corresponding tensor decomposition-based localization algorithm (TenDLA) that showcases the connection between the tensor-based analysis and the localization problem was developed. Additionally, a correction procedure to mitigate the estimation deviations on the range and angle was presented, yielding significant improvements in estimation accuracy. Numerical examples demonstrated the validity and effectiveness of the proposed approach, and it was shown that this approach is superior to conventional methods due to its high-resolution estimation accuracy as well as its relatively low computational costs.","Title: A text classification method based on LSTM and graph attention network\nYear: 2022\n\nAbstract: Text classification is a popular research topic in the natural language processing. Recently solving text classification problems with graph neural network (GNN) has received increasing attention. However, current graph-based studies ignore the hidden information in text syntax and sequence structure, and it is difficult to use the model directly for processing new documents because the text graph is built based on the whole corpus including the test set. To address the above problems, we propose a text classification model based on long short-term memory network (LSTM) and graph attention network (GAT). The model builds a separate graph based on the syntactic structure of each document, generates word embeddings with contextual information using LSTM, then learns the inductive representation of words by GAT, and finally fuses all the nodes in the graph together into the document embedding. Experimental results on four datasets show that our model outperforms existing text classification methods with faster convergence and less memory consumption than other graph-based methods. In addition, our model shows a more notable improvement when using less training data. Our model proves the importance of text syntax and sequence information for classification results.","Title: Fuzzy <inline-formula><tex-math notation=\"LaTeX\">$\\mathcal {H}_{\\infty }$</tex-math></inline-formula> Sliding Mode Control of Persistent Dwell-Time Switched Nonlinear Systems\nYear: 2022\n\nAbstract: In this article, the fuzzy \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><tex-math notation=\"LaTeX\">$\\mathcal {H}_{\\infty }$</tex-math></inline-formula>\n sliding mode control problem for continuous-time switched nonlinear systems is investigated. The switching signal conforms to the persistent dwell-time switching mechanism. The first objective of this article is to construct a switched integral sliding surface that not only accommodates the switched nonlinear model, but ensures that the sliding mode dynamics are globally uniformly exponentially stable and have an \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><tex-math notation=\"LaTeX\">$\\mathcal {H}_{\\infty }$</tex-math></inline-formula>\n performance by the equivalent controller derived from the sliding surface. Another aim is to integrate the switched sliding mode control law to force the system trajectories to the sliding surface in a finite amount of time. Then, on the basis of the Lyapunov function technique, the specific form of the sliding mode control law gains is given first, and later the finite-time reachability of the sliding surface is ensured. Finally, the validity of the proposed switched sliding mode control method is validated by a numerical example."],"string":"[\n \"Title: Quell.\\nYear: 2015\\n\\nAbstract: We study the computational complexity of the puzzle Quell. The goal is to collect pearls by sliding a droplet of water over them in a map that is a two-dimensional grid. Each cell of the grid is either a free space (possibly with a pearl in it) or an obstacle. In each move, the droplet slides in one of the four directions to the maximal extent, through the free spaces of the map and collecting all pearls along the way, until it is stopped by an obstacle. We show that any-Moves-all-Pearls (deciding whether it is possible to collect all the pearls using any number of moves) can be solved in polynomial time. In contrast, both any-Moves-max-Pearls (finding the maximum number of pearls that can be collected using any number of moves) and min-Moves-all-Pearls (finding the minimum number of moves required to collect all the pearls) are APX-hard, although the corresponding decision problems are in FPT. We also present a simple 2-approximation for any-Moves-max-Pearls, and leave open the question whether min-Moves-all-Pearls admits a polynomial-time constant approximation.\",\n \"Title: Generalized quotient image\\nYear: 2004\\n\\nAbstract: In this paper, we present a unified framework for modeling intrinsic properties of face images for recognition. It is based on the quotient image (QI) concept, in particular on the existing works of QI [1, 2], Spherical Harmonic[13, 14, 15], [16, 17], Image Ratio [3, 5, 6, 7]and Retinex [4, 9]. Under this framework, we generalize these previous works into two new algorithms: (1) Non-Point Light Quotient Image (NPL-QI) extends QI to deal with nonpoint light sources by modeling non-point light directions using spherical harmonic bases; (2) Self-Quotient Image (S-QI) extends QI to perform illumination subtraction without the need for alignment and no shadow assumption. Experimental results show that our algorithms can significantly improve the performance of face recognition under varying illumination conditions.\",\n \"Title: Online Rectangle Filling\\nYear: 2011\\n\\nAbstract: We study an online geometric problem arising in channel-aware scheduling of wireless networks, which we call online rectangle filling. We present an online algorithm for this problem with a competitive ratio of 1.848. We also prove a lower bound of 1.6358\\n for the competitive ratio of the problem. In addition, we give an O(n\\n 2)-time optimal offline algorithm, where n is the size of the input. All three results are significant improvements on the previous results. Our techniques are based\\n on new observations of the combinatorial structures of the problem.\",\n \"Title: Visibility and ray shooting queries in polygonal domains\\nYear: 2013\\n\\nAbstract: Given a polygonal domain (or polygon with holes) in the plane, we study the problem of computing the visibility polygon of any query point. As a special case of visibility problems, we also study the ray-shooting problem of finding the first point on the polygon boundaries that is hit by any query ray. These are fundamental problems in computational geometry and have been studied extensively. We present new algorithms and data structures that improve the previous results.\",\n \"Title: Computing Shortest Paths among Curved Obstacles in the Plane\\nYear: 2013\\n\\nAbstract: A fundamental problem in computational geometry is to compute an obstacle-avoiding Euclidean shortest path between two points in the plane. The case of this problem on polygonal obstacles is well studied. In this article, we consider the problem version on curved obstacles, which are commonly modeled as splinegons. A splinegon can be viewed as replacing each edge of a polygon by a convex curved edge (polygons are special splinegons), and the combinatorial complexity of each curved edge is assumed to be O(1). Given in the plane two points s and t and a set s of h pairwise disjoint splinegons with a total of n vertices, after a bounded degree decomposition of S is obtained, we compute a shortest s-to-t path avoiding the splinegons in O(n + h log h + k) time, where k is a parameter sensitive to the geometric structures of the input and is upper bounded by O(h2). The bounded degree decomposition of S, which is similar to the triangulation of the polygonal domains, can be computed in O(n log n) time or O(n + h log 1 + εh) time for any ε > 0. In particular, when all splinegons are convex, the decomposition can be computed in O(n + h log h) time and k is linear to the number of common tangents in the free space (called “free common tangents”) among the splinegons. Our techniques also improve several previous results: (1) For the polygon case (i.e., when all splinegons are polygons), the shortest path problem was previously solved in O(n log n) time, or in O(n + h2log n) time. Thus, our algorithm improves the O(n + h2log n) time result, and is faster than the O(n log n) time solution for sufficiently small h, for example, h=o(&sqrt;n,logn. (2) Our techniques produce an optimal output-sensitive algorithm for a basic visibility problem of computing all free common tangents among h pairwise disjoint convex splinegons with a total of n vertices. Our algorithm runs in O(n + h log h + k) time and O(n) working space, where k is the number of all free common tangents. Note that k = O(h2). Even for the special case where all splinegons are convex polygons, the previously best algorithm for this visibility problem takes O(n + h2log n) time. (3) We improve the previous work for computing the shortest path between two points among convex pseudodisks of O(1) complexity each. In addition, a by-product of our techniques is an optimal O(n + h log h) time and O(n) space algorithm for computing the Voronoi diagram of a set of h pairwise disjoint convex splinegons with a total of n vertices.\",\n \"Title: Principal Observation Ray Calibration for Tiled-Lens-Array Integral Imaging Display\\nYear: 2013\\n\\nAbstract: Integral imaging display (IID) is a promising technology to provide realistic 3D image without glasses. To achieve a large screen IID with a reasonable fabrication cost, a potential solution is a tiled-lens-array IID (TLA-IID). However, TLA-IIDs are subject to 3D image artifacts when there are even slight misalignments between the lens arrays. This work aims at compensating these artifacts by calibrating the lens array poses with a camera and including them in a ray model used for rendering the 3D image. Since the lens arrays are transparent, this task is challenging for traditional calibration methods. In this paper, we propose a novel calibration method based on defining a set of principle observation rays that pass lens centers of the TLA and the camera's optical center. The method is able to determine the lens array poses with only one camera at an arbitrary unknown position without using any additional markers. The principle observation rays are automatically extracted using a structured light based method from a dense correspondence map between the displayed and captured pixels. Experiments show that lens array misalignments can be estimated with a standard deviation smaller than 0.4 pixels. Based on this, 3D image artifacts are shown to be effectively removed in a test TLA-IID with challenging misalignments.\",\n \"Title: Outlier respecting points approximation\\nYear: 2014\\n\\nAbstract: In this paper, we consider a generalized problem formulation of computing a functional curve to approximate a point set in the plane with outliers. The goal is to seek a solution that not only optimizes its original objectives, but also somehow accommodates the impact of the outliers. Based on a new model of accommodating outliers, we present efficient geometric algorithms for various versions of this problem (e.g., the approximating functions are step functions or piecewise linear functions, the points are unweighted or weighted, etc). All our results are first known. Our new model and techniques for handling outliers may be useful to other applications as well.\",\n \"Title: Style preserving Chinese character synthesis based on hierarchical representation of character\\nYear: 2008\\n\\nAbstract: with English, and they are not suitable for Chinese character synthesis. In this paper, we propose an unified approach for modeling and synthesizing Chinese characters. Using a three-level hierarchical representation, each character is decomposed into basic components, which forms the stroke database and radical database. In the synthesis process, we use a wavelet-based approach to select proper strokes and radicals, and some aesthetic constraints are defined based on the relationships between components, then genetic algorithm is employed to search for the optimal results which best match the aesthetic constraints. Experimental results demonstrates the effectiveness of our method.\",\n \"Title: Expression and application of geospatial relation ontology\\nYear: 2013\\n\\nAbstract: Spatial relation is one of the most important features of geographic space information. The in-depth research on spatial relation is the base of realizing GIS spatial reasoning. Recently GIS software could only express few spatial relations and is difficult to be understood by computer, and also the researches on spatial relation ignore commonsense spatial cognition of people. The process on spatial relation reasoning causes serious semantic collision. Ontology theory was introduced to research spatial relation, which accorded with the commonsense spatial cognition of people. The paper designed a whole frame of space ontology and geospatial relation ontology model, which realized the expression of that model with TBC software. The paper built geographical ontology instance base based on Region data of Henan province and realized the expression of spatial relation ontology with OWL language. Some application instances were also done to validate the theories and technologies of the paper. The final aim is to promote the share and reuse of spatial relation knowledge.\",\n \"Title: Computing the L 1 geodesic diameter and center of a simple polygon in linear time\\nYear: 2014\\n\\nAbstract: In this paper, we show that the L 1 geodesic diameter and center of a simple polygon can be computed in linear time. For the purpose, we focus on revealing basic geometric properties of the L 1 geodesic balls, that is, the metric balls with respect to the L 1 geodesic distance. More specifically, in this paper we show that any family of L 1 geodesic balls in any simple polygon has Helly number two, and the L 1 geodesic center consists of midpoints of shortest paths between diametral pairs. These properties are crucial for our linear-time algorithms, and do not hold for the Euclidean case.\",\n \"Title: Minmax regret 1-facility location on uncertain path networks.\\nYear: 2013\\n\\nAbstract: •We propose an algorithm to compute the minmax regret.•We solve the minmax 1-facility location problem on uncertain path network.•Our algorithm improves the previous work.•We discover new observations on the problem.\",\n \"Title: Detecting and tracking motion of Myxococcus xanthus bacteria in swarms.\\nYear: 2012\\n\\nAbstract: Automatically detecting and tracking the motion of Myxococcus xanthus bacteria provide essential information for studying bacterial cell motility mechanisms and collective behaviors. However, this problem is difficult due to the low contrast of microscopy images, cell clustering and colliding behaviors, etc. To overcome these difficulties, our approach starts with a level set based pre-segmentation of cell clusters, followed by an enhancement of the rod-like cell features and detection of individual bacterium within each cluster. A novel method based on \\\"spikes\\\" of the outer medial axis is applied to divide touching (colliding) cells. The tracking of cell motion is accomplished by a non-crossing bipartite graph matching scheme that matches not only individual cells but also the neighboring structures around each cell. Our approach was evaluated on image sequences of moving M. xanthus bacteria close to the edge of their swarms, achieving high accuracy on the test data sets.\",\n \"Title: RAUU: rate adaptation for unreliable unicast traffic in high speed networks\\nYear: 2007\\n\\nAbstract: While long term throughput not exceeding TCP with Reno congestion control algorithm is widely accepted as the criterion of weighing TCP friendliness, this may lead to resource waste in high speed networks due to Reno's known performance limits. Inspired by FAST TCP, a congestion control algorithm named Rate Adaptation for Unreliable Unicast traffic (RAUU) is proposed for unreliable unicast traffic in high speed networks to improve its efficiency while still holding friendliness to TCP. Being a rate-based approach to best fit unreliable unicast traffic, RAUU has made special design choices to alleviate the inherent contiguous loss problem of rate adaptation algorithms. Like FAST, it also tries to maintain appropriate number of extra packets in networks, and for that purpose it combines loss and delay as congestion signals. Theoretical analysis shows that in ideal networks RAUU has and will converge to its one and only equilibrium state where the number of extra packets is equal to the preset value. Plentiful simulation experiments confirmed that it could achieve similar performance to FAST as well as comparable throughput smoothness to TFRC while keeping TCP-friendliness at the same time.\",\n \"Title: Computing Shortest Paths amid Convex Pseudodisks.\\nYear: 2013\\n\\nAbstract: Multiple objects in the plane are called pseudodisks if the boundaries of any two of them intersect transversely at most twice. Given a set of n (possibly intersecting) convex pseudodisks of O(1) complexity each and two points s and t in the plane, we present an efficient algorithm for computing a shortest s-to-t path avoiding the pseudodisks. After the union of the pseudodisks is computed, which can be done in O(n log n) randomized time or O(n log(2) n) deterministic time, our algorithm runs in O(n log n + k) deterministic time, where k is the size of the extended visibility graph of the union of the pseudodisks. Note that k = O(n(2)) in the worst case. In over two decades, the previously best algorithms for this problem have not improved on the bound of O(n(2) log n) time, even when all the pseudodisks are pairwise disjoint disks. Our technique is also applicable to a motion planning problem of finding a shortest path to translate a convex object in the plane from one location to another avoiding a given set of polygonal obstacles, improving the previously best known solution and settling an open problem posed in 1988. Our algorithm actually solves a more general version of the open problem. Further, as a byproduct of our approach, we present an O(n log n + k)-time algorithm for computing the extended visibility graph of a set of n (possibly intersecting) convex pseudodisks in the plane. The previously best known time bound for this visibility problem is O(n(2) log n).\",\n \"Title: Computing the visibility polygon of an island in a polygonal domain\\nYear: 2012\\n\\nAbstract: Given a set $\\\\mathcal{P}$ of h pairwise-disjoint polygonal obstacles of totally n vertices in the plane, we study the problem of computing the (weakly) visibility polygon from a polygonal obstacle P* (an island) in $\\\\mathcal{P}$. We give an O(n2h2) time algorithm for it. Previously, the special case where P* is a line segment was solved in O(n4) time, which is worst-case optimal. In addition, when all obstacles in $\\\\mathcal{P}$ (including P*) are convex, our algorithm runs in O(n+h4) time.\",\n \"Title: Computing L1 Shortest Paths among Polygonal Obstacles in the Plane\\nYear: 2012\\n\\nAbstract: Given a point $s$ and a set of $h$ pairwise disjoint polygonal obstacles of totally $n$ vertices in the plane, we present a new algorithm for building an $L_1$ shortest path map of size O(n) in $O(T)$ time and O(n) space such that for any query point $t$, the length of the $L_1$ shortest obstacle-avoiding path from $s$ to $t$ can be reported in $O(\\\\log n)$ time and the actual shortest path can be found in additional time proportional to the number of edges of the path, where $T$ is the time for triangulating the free space. It is currently known that $T=O(n+h\\\\log^{1+\\\\epsilon}h)$ for an arbitrarily small constant $\\\\epsilon>0$. If the triangulation can be done optimally (i.e., $T=O(n+h\\\\log h)$), then our algorithm is optimal. Previously, the best algorithm computes such an $L_1$ shortest path map in $O(n\\\\log n)$ time and O(n) space. Our techniques can be extended to obtain improved results for other related problems, e.g., computing the $L_1$ geodesic Voronoi diagram for a set of point sites in a polygonal domain, finding shortest paths with fixed orientations, finding approximate Euclidean shortest paths, etc.\",\n \"Title: Context-Restricted, role-oriented emotion knowledge acquisition and representation\\nYear: 2005\\n\\nAbstract: Emotion knowledge is a fundamental part of human commonsense knowledge. For the lack of solid knowledge extraction work in this domain, this paper focuses on acquiring and representing the antecedent situations of various emotions as well as the emotion subjects' interpretation to these situations in real-world emotion-eliciting scenarios. After a comprehensive analysis on the meaning structure of three critical notions in emotion understanding and modeling (i.e. role, context and event), this paper introduces a pragmatic emotion knowledge acquisition method and presents a context-restricted, role-oriented, frame-based representational model of emotion knowledge, with a case study which demonstrates the applicability of the model.\",\n \"Title: Computing maximum non-crossing matching in convex bipartite graphs\\nYear: 2012\\n\\nAbstract: We consider computing a maximum non-crossing matching in convex bipartite graphs. For a convex bipartite graph of n vertices and m edges, we present an O ( n log n ) time algorithm for finding a maximum non-crossing matching in the graph. The previous best algorithm takes O ( m + n log n ) time (Malucelli et¿al., 1993). Since m = ¿ ( n 2 ) in the worst case, our result improves the previous work.\",\n \"Title: Text Learning and Hierarchical Feature Selection in Webpage Classification\\nYear: 2008\\n\\nAbstract: One of the solutions of retrieving information from the Internet is by classifying web pages automatically. In almost all classification methods that have been published, feature selection is a very important issue. Although there are many feature selection methods has been proposed. Most of them focus on the features within a category and ignore that the hierarchy of categories also plays an important role in achieving accurate classification results. This paper proposes a new feature selection method that incorporates hierarchical information, which prevents the classifying process from going through every node in the hierarchy. Our test results show that our classification algorithm using hierarchical information reduces the search complexity from n to log(n) and increases the accuracy by 6.2% comparing to a related algorithm.\",\n \"Title: Nexir: A Novel Web Extraction Rule Language Toward A Three-Stage Web Data Extraction Model\\nYear: 2013\\n\\nAbstract: As the most popular information publishing platform, the Web contains a lot of valued data information of interests to users or applications. Nowadays, although a lot of data mining or analysis techniques have been studied in last decade, there are still not many easy-to-use web data mining tools available for users to extract useful data information from the Web. The web information extraction is a whole process involving web page navigation, data extraction and data integration. Unfortunately most of existing studies or systems lack of sufficient consideration toward the three-stage process. Also most of them lack the powerful rules to express the flexible extraction logic to extract data records with complicate structure. In this paper, we propose a novel web data extraction language, NEXIR, toward a three-stage web data extraction model. First of all, the language can define rules for system to automate the navigation process of the web pages, including deep web pages that need interactions from users. Then the language allows users to define flexible and complicated rules to extract data records from web pages and integrate extracted data into a pre-defined structure. A language engine and a prototype extraction system have been implemented based on the proposed language. The experimental results show that our language and system work effective and powerful compared with existing data extraction approaches.\",\n \"Title: An improved algorithm for reconstructing a simple polygon from its visibility angles\\nYear: 2012\\n\\nAbstract: We study the problem of reconstructing a simple polygon: Given a cyclically ordered vertex sequence of an unknown simple polygon P of n vertices and, for each vertex v of P, the sequence of angles defined by all the visible vertices of v in P, reconstruct the polygon P (up to similarity). An O(n3logn) time algorithm has been proposed for this problem. We present an improved algorithm with running time O(n2), based on new observations on the geometric structures of the problem. Since the input size (i.e., the total number of input visibility angles) is Θ(n2) in the worst case, our algorithm is worst-case optimal.\",\n \"Title: Face recognition system using multiple face model of hybrid Fourier feature under uncontrolled illumination variation.\\nYear: 2011\\n\\nAbstract: The authors present a robust face recognition system for large-scale data sets taken under uncontrolled illumination variations. The proposed face recognition system consists of a novel illumination-insensitive preprocessing method, a hybrid Fourier-based facial feature extraction, and a score fusion scheme. First, in the preprocessing stage, a face image is transformed into an illumination-insensitive image, called an \\\"integral normalized gradient image,\\\" by normalizing and integrating the smoothed gradients of a facial image. Then, for feature extraction of complementary classifiers, multiple face models based upon hybrid Fourier features are applied. The hybrid Fourier features are extracted from different Fourier domains in different frequency bandwidths, and then each feature is individually classified by linear discriminant analysis. In addition, multiple face models are generated by plural normalized face images that have different eye distances. Finally, to combine scores from multiple complementary classifiers, a log likelihood ratio-based score fusion scheme is applied. The proposed system using the face recognition grand challenge (FRGC) experimental protocols is evaluated; FRGC is a large available data set. Experimental results on the FRGC version 2.0 data sets have shown that the proposed method shows an average of 81.49% verification rate on 2-D face images under various environmental variations such as illumination changes, expression changes, and time elapses.\",\n \"Title: \\\"Is It Really a Defect?\\\" An Empirical Study on Measuring and Improving the Process of Software Defect Reporting\\nYear: 2011\\n\\nAbstract: Testing is one of the most labor-intensive activities in software development life cycle and consumes between 30% and 50% of total development costs according to many studies. The communication gap between testers and developers that is caused by unclear or even invalid defect reporting usually makes the testing schedule delay, and contributes large amount of testing effort to rework and re-communication. Defect reporting's efficiency directly influences testing efficiency and effectiveness. From our observation and improvement practices on a software product with recent 4 increments: 1) the percentage of unclear defect reports is around 23%, but more than 82% testers, more or less, reported unclear defect reports, 2) the percentage of invalid defect reports is around 26%, but more than 35% testers reported invalid defect reports. Improvement practices that we took on training testers on how to report defect in a clearer and more efficient way further bridged the communication gap between testers and developers, and it can reduce 15.7% unclear defect reports and 19.53% invalid defect reports, which finally led to the improvement of testing efficiency.\",\n \"Title: Efficient algorithms for the one-dimensional k-center problem\\nYear: 2013\\n\\nAbstract: We consider the problem of finding k centers for n weighted points on a real line. This (weighted) k-center problem was solved in O ( n log ¿ n ) time previously by using Cole's parametric search and other complicated approaches. In this paper, we present an easier O ( n log ¿ n ) time algorithm that avoids the parametric search, and in certain special cases our algorithm solves the problem in O ( n ) time. In addition, our techniques involve developing interesting data structures for processing queries that find a lowest point in the common intersection of a certain subset of half-planes. This subproblem is interesting in its own right and our solution for it may find other applications as well.\",\n \"Title: Shape And Texture Preserved Non-Photorealistic Rendering\\nYear: 2004\\n\\nAbstract: Two approaches, image-space and object-space, exist for non-photorealistic rendering WPM. The object-space approach has an advantage that it is able to access the 3D shape information of the scene with a 3D model. In this paper, we present a non-stroke based image-space technique that has the strength similar to that of the object-space approach, but without involving explicit 3D models. The basic idea is to factorise the Lambertian model to obtain the shape and texture information implicitly contained in the image. We also develop a specific anisotropic low-pass image filter, which can smooth an image without blurring important features. With the developed techniques, we present two NPR styles, the sketching style and the painting style. Although our work does not aim to mimic any specific existing art form, the sketching style can generate effects similar to those of the pen-and-ink and the painting style is able to produce the colour permeating effects of watercolour. Copyright (C) 2004 John Wiley Sons, Ltd.\",\n \"Title: Processing an Offline Insertion-Query Sequence with Applications\\nYear: 2011\\n\\nAbstract: In this paper, we present techniques and algorithms for processing an offline sequence of insertion and query operations and\\n for related problems. Most problems we consider are solved optimally in linear time by our algorithms, which are based on\\n a new geometric modeling and interesting techniques. We also discuss some applications to which our algorithms and techniques\\n can be applied to yield efficient solutions.\",\n \"Title: Weak visibility queries of line segments in simple polygons\\nYear: 2012\\n\\nAbstract: Given a simple polygon P in the plane, we present new data structures for computing the weak visibility polygon from any query line segment in P. We build a data structure in O ( n ) time and space that can compute the visibility polygon for any query line segment s in O ( k log ¿ n ) time, where k is the size of the visibility polygon of s and n is the number of vertices of P. Alternatively, we build a data structure in O ( n 3 ) time and space that can compute the visibility polygon for any query line segment in O ( k + log ¿ n ) time. In order to develop these data structures, we obtain many other results that may be interesting in their own right.\",\n \"Title: Multimodal Preserving Embedding For Face Recognition\\nYear: 2008\\n\\nAbstract: Traditional dimension reduction approaches always consider the samples in a class are uni-modal. In real world, samples in a class are usually multi-modal, for instance, the manifold of the facial appearance of a person under different illumination, expression, and poses is multi-modal. Recently, dimension reduction approaches based on manifold learning are presented, the main purpose is to preserve the manifold structure on low dimensional space. In this paper, by analyzing the manifold learning methods and the traditional dimension reduction methods, we show that most of these methods can be summarized into a general framework. Based on this framework, we propose a novel dimension reduction approach, called multi-modal preserving embedding (MPE) by utilizing path-based similarity measure. We also describe two useful extensions of our method: kernel MPE and Tensor MPE. Comprehensive comparisons and extensive experiments on face recognition are included to demonstrate the effectiveness of our method.\",\n \"Title: The topology aware file distribution problem\\nYear: 2011\\n\\nAbstract: We present theoretical results for large-file distribution on general networks of known topology (known link bandwidths and\\n router locations). We show that the problem of distributing a file in minimum time is NP-hard in this model, and we give an\\n O(logn) approximation algorithm, where n is the number of workstations that require the file. We also characterize our method as optimal amongst the class of “no-link-sharing”\\n algorithms.\",\n \"Title: Computing Shortest Paths amid Pseudodisks\\nYear: 2011\\n\\nAbstract: Multiple objects in the plane are called pseudodisks if they are convex and the boundaries of any two of them intersect transversely at most twice. Given a set of n (possibly intersecting) pseudodisks of O(1) complexity each and two points s and t in the plane, we develop an O(n2) time algorithm for computing a shortest s-to-t path avoiding the pseudodisks. In over two decades, the previously best algorithms for this problem take O(n2 log n) time, even when all pseudodisks are pairwise-disjoint disks. Our technique is also applicable to a motion planning problem of finding a shortest path to translate a convex object in the plane from one location to another avoiding a given set of polygonal obstacles, improving the previously best known solution. Our algorithm actually solves a more general version of the motion planning problem. Further, as a by-product of our approach, we present an O(n2) time algorithm for computing the visibility graph of a set of n (possibly intersecting) pseudodisks in the plane. The previously best known time bound of this visibility problem is O(n2 log n). In addition, for n pairwise disjoint (non-polygonal) convex objects of O(1) complexity each in the plane, we compute a shortest s-to-t path avoiding all objects in O(n log n + k) time, where k is the size of the visibility graph of the objects.\",\n \"Title: Two-dimensional fuzzy PID switching power supply controller based on FPGA\\nYear: 2013\\n\\nAbstract: This paper proposed a control method combining fuzzy PI and fuzzy PD to implement the phase-shifted full-bridge switching power supply control. The method can meet the control demands of the object's time-varying characteristics. It does not rely on a fixed mathematical model, and it can adjust control parameters on-line. The system simulation model is built via Matlab/Simulink, and fuzzy control rules are determined. The results of simulation analysis show that the control effect using this method is better than traditional PID algorithm in switching power supply. Two-dimensional fuzzy PID controller based on FPGA has been designed, and it could effectively be applied to control of 3KW phase-shifted full-bridge switching power supply.\",\n \"Title: New algorithms for 1-D facility location and path equipartition problems\\nYear: 2011\\n\\nAbstract: We study the one-dimensional facility location problems. Given a set of n customers on the real line, each customer having a cost for setting up a facility at its position, and an integer k, we seek to find at most k of the customers to set up facilities for serving all n customers such that the total cost for facility set-up and service transportation is minimized. We consider several problem variations including k-median and k-coverage and a linear model. We also study a related path equipartition problem: Given a vertex-weighted path and an integer k, remove k-1 edges so that the weights of the resulting k sub-paths are as equal as possible. Based on new problem modeling and observations, we present improved algorithms for these problems over the previous work.\",\n \"Title: Efficient algorithms for the weighted k-center problem on a real line\\nYear: 2011\\n\\nAbstract: We present $O(\\\\min\\\\{n\\\\log^{1.5} n, n\\\\log n+k^2\\\\log^2\\\\frac{n}{k}\\\\log^2 n\\\\})$ time algorithms for the weighted k-problem on a real line. Previously, the best known algorithms for this problem take O(nlog2n) time, or O(knlogn) time, or a time linear in n but exponential in k. Our techniques involve developing efficient data structures for processing queries that find a lowest point in the common intersection of a certain subset of half-planes. This subproblem is interesting in its own right.\",\n \"Title: Illumination modeling and normalization for face recognition\\nYear: 2003\\n\\nAbstract: We present a general framework for face modeling under varying lighting conditions. First, we show that a face lighting subspace can be constructed based on three or more training face images illuminated by noncoplanar lights. The lighting of any face image can be represented as a point in this subspace. Second, we show that the extreme rays, i.e. the boundary of an illumination cone, cover the entire light sphere. Therefore, a relatively sparsely sampled face images can be used to build a face model instead of calculating each extremely illuminated face image. Third, we present a face normalization algorithm, illumination alignment, i.e. changing the lighting of one face image to that of another face image. Experiments are presented.\",\n \"Title: Improved algorithms for path partition and related problems.\\nYear: 2011\\n\\nAbstract: We study the L∞ path partition problem: given a path of n weighted vertices and an integer k, remove k−1 edges from the path so that the maximum absolute deviation of the weights of the resulting k sub-paths from their mean is minimized. Previously, the best algorithm solves this problem in O(nklogk) time. We present an O(nk) time algorithm. We also give improved solutions for two related problems: the Ld path partition problem and the web proxies placement problem.\",\n \"Title: Physical Modeling and Configuration Simulation for Constrained Cables of Electromechanical Products\\nYear: 2013\\n\\nAbstract: To solve the problem of low cable assembly efficiency for current electromechanical products, a digital modeling method is proposed based on nonlinear mechanics of a thin elastic rod. Considering the flexibility, cable configuration is embodied by translation and rotation of cross-section along the centerline. According to the minimum potential energy principle in Elasticity, Kirchhoff equations in the form of Euler angles are gained, which are used to describe spatial flexural configuration of deformable cables. For the specific boundary condition that cables are fixed at both ends, analytical integrals of Euler angles are achieved on the base of kinetic analogy. Furthermore, to deal with the difficulty of getting Cartesian coordinate integrals, a cylindrical system is introduced to describe spatial position of flexural cables on the base of Saint-Venant of Elasticity. Finally, a configuration simulation platform is developed and an experimental system using 3D laser scan technology is founded to do effective verification of the relevant model and algorithm.\",\n \"Title: Pyramid surface reconstruction from normal.\\nYear: 2004\\n\\nAbstract: In this paper, a novel method is introduced to estimate height from surface normal and implemented in a photometric 3D reconstruction system, base on the improved optimization function in the height domain. To avoid the global distortion from local integration, a pyramid reconstruction ideal is proposed, which sample the surface every certain points to form the separated point sets called sub-surfaces, and then step by step the coarser sub-surfaces is combined into finer ones and eventually derive the whole surface. Experiment shows that our method is faster and more accurate than some of the main method in this field.\",\n \"Title: Fitting A Step Function To A Point Set With Outliers Based On Simplicial Thickness Data Structures\\nYear: 2012\\n\\nAbstract: Given a real epsilon > 0, an integer g >= 0 and a set of points in the plane, we study the problem of computing a piecewise linear functional curve with minimum number of line segments to approximate all points after removing g outliers such that the approximate error is at most epsilon. We give an improved algorithm over the previous work. The algorithm is based on two dynamic data structures developed in this paper for the simplicial thickness queries, which are of independent interest. For a set S of simplices in the d-dimensional space R-d (d >= 2 is a constant), the simplicial thickness of a point p is defined as the number of simplices in S that contain p. Given a set P of n points in R-d, we develop two dynamic data structures to support the following operations. (1) Simplex insertion: Insert a simplex into S. (2) Simplex deletion: Delete a simplex from S. (3) Simplicial thickness query: Given a query simplex sigma, compute the minimum simplicial thickness among all points in sigma boolean AND P. The first data structure is constructed in O(n(1+delta)) time, for any constant delta > 0, and can support each operation in O(n(1-1/d)) time; the second one is constructed in O(n log n) time and can support each operation in O(n(1-1/d)(log n)(O(1))) time. Both data structures use O(n) space. These data structures may find other applications as well.\",\n \"Title: The peak point of LURR and its significance\\nYear: 2010\\n\\nAbstract: The Load-Unload Response Ratio (LURR) value fluctuates around one during the early stage of the seismogenic process and then rises and reaches its peak point before the occurrence of a strong earthquake; the strong earthquake however does not onset at that time, but after the peak point LURR decreases sharply at the eve of the main shock before the final event outbreak. Thus, the peak point of LURR is ahead of the occurrence of an earthquake. We denote the lead time as T2. The discovery of peak point for LURR and the relationship between T2 and magnitude M is of great significance as the peak point is usually easy to determine and then we can predict the occurrence time for the coming event according to Equation (3) so that we can enhance the precision of time for earthquake prediction in terms of LURR from ‘year’ scale to ‘month’ scale. The variation of LURR around the Wenchuan earthquake and its lesson to us are depicted in the paper. Copyright © 2009 John Wiley & Sons, Ltd.\",\n \"Title: New algorithms for online rectangle filling with k-lookahead\\nYear: 2011\\n\\nAbstract: We study the online rectangle filling problem which arises in channel aware scheduling of wireless networks, and present deterministic and randomized results for algorithms that are allowed a k-lookahead for k驴2. Our main result is a deterministic min驴{1.848,1+2/(k驴1)}-competitive online algorithm. This is the first algorithm for this problem with a competitive ratio approaching 1 as k approaches +驴. The previous best-known solution for this problem has a competitive ratio of 2 for any k驴2. We also present a randomized online algorithm with a competitive ratio of 1+1/(k+1). Our final result is a closely matching lower bound (also proved in this paper) of $1+1/(\\\\sqrt{k+2}+\\\\sqrt{k+1})^{2}1+1/(4(k+2))$ on the competitive ratio of any randomized online algorithm against an oblivious adversary. These are the first known results for randomized algorithms for this problem.\",\n \"Title: Matroid and knapsack center problems\\nYear: 2013\\n\\nAbstract: In the classic k-center problem, we are given a metric graph, and the objective is to select k nodes as centers such that the maximum distance from any vertex to its closest center is minimized. In this paper, we consider two important generalizations of k-center, the matroid center problem and the knapsack center problem. Both problems are motivated by recent content distribution network applications. Our contributions can be summarized as follows: (1) We consider the matroid center problem in which the centers are required to form an independent set of a given matroid. We show this problem is NP-hard even on a line. We present a 3-approximation algorithm for the problem on general metrics. We also consider the outlier version of the problem where a given number of vertices can be excluded as outliers from the solution. We present a 7-approximation for the outlier version. (2) We consider the (multi-)knapsack center problem in which the centers are required to satisfy one (or more) knapsack constraint(s). It is known that the knapsack center problem with a single knapsack constraint admits a 3-approximation. However, when there are at least two knapsack constraints, we show this problem is not approximable at all. To complement the hardness result, we present a polynomial time algorithm that gives a 3-approximate solution such that one knapsack constraint is satisfied and the others may be violated by at most a factor of $$1+epsilon $$1+∈. We also obtain a 3-approximation for the outlier version that may violate the knapsack constraint by $$1+epsilon $$1+∈.\",\n \"Title: An Environment for Multi-domain Ontology Development and Knowledge Acquisition\\nYear: 2002\\n\\nAbstract: Ontology is used widely as an analytical tool in many domains in recent years, and adopted as a fundamental basis for knowledge acquisition and sharing. However, it becomes difficult for knowledge engineers to share a large ontology base created by many engineers from different domains, and thus ontologies may not genuinely support knowledge engineers in knowledge acquisition and analysis. In order to promote multi-domain ontology sharing and to facilitate knowledge acquisition and analysis, we have implemented an ontology and knowledge engineering environment (OKEE) for multi-domain ontology development and knowledge acquisition. OKEE provides a list of functionalities for ontology design and for knowledge acquisition.\",\n \"Title: Algorithms on minimizing the maximum sensor movement for barrier coverage of a linear domain\\nYear: 2013\\n\\nAbstract: In this paper, we study the problem of moving n sensors on a line to form a barrier coverage of a specified segment of the line such that the maximum moving distance of the sensors is minimized. Previously, it was an open question whether this problem on sensors with arbitrary sensing ranges is solvable in polynomial time. We settle this open question positively by giving an O(n2lognloglogn) time algorithm. Further, if all sensors have the same-size sensing range, we give an O(nlogn) time algorithm, which improves the previous best O(n2) time solution.\",\n \"Title: Trajectory tracking control for uninhabited air vehicles\\nYear: 2006\\n\\nAbstract: In this paper a simplified control system framework is presented to solve maneuvering trajectory tracking problem of uninhabited air vehicles. The flight control system is divided into three subsystems including command generation, transformation and allocation. According to the kinematics equations of the aircraft, flight path angle commands can be generated by desired 3D position from path planning. These commands are transformed to body angular rates through direct nonlinear mapping, which is simpler than common multi-loop method based on time scale separation assumption. Then, by using weighted pseudo-inverse method, the control surface deflections are allocated to follow body angular rates from the previous step. Additionally, an on-line multilayer neural network is used to provide robustness to the uncertainty of system. Numerical simulation results show the viability of this approach.\",\n \"Title: Face representation and reconstruction under different illumination conditions\\nYear: 2003\\n\\nAbstract: To deal with image variations due to illumination problem, Ramamoorthi and Basri have independently derived a spherical harmonic analysis for the Lambertian reflectance and linear subspace. Their theoretical work provided a new approach for face representation, however both of them assume that the 3D surface normals and albedo (or unit albedo) are known, which limit this algorithm's application. We present a novel method for modelling 3D face shape and albedo from only three images and this work will fill the blank which Ramamoorthi and Basri left. Our work is closely related to photometric stereo, but conditions of photometric stereo for estimating albedo and surface normal are too strict to be applied for real application. Moreover, the conventionally used singular value decomposition (SVD) approach leads to the notorious linear ambiguity and the solution to this problem needs to introduce more constraints or more images. By taking advantage of similar 3D shape of all human faces, the highlight of the new method is that it circumambulates the linear ambiguity by 3D alignment. The experiment results show that our estimated model can be perfectly employed to face recognition and 3D reconstruction.\",\n \"Title: New Algorithms for Facility Location Problems on the Real Line.\\nYear: 2014\\n\\nAbstract: In this paper, we study the facility location problems on the real line. Given a set of customers on the real line, each customer having a cost for setting up a facility at its position, and an integer , we seek to find at most of the customers to set up facilities for serving all customers such that the total cost for facility set-up and service transportation is minimized. We consider several problem variations including the -median, the -coverage, and the linear model. The previously best algorithms for these problems all take () time. Our new algorithms break the () time bottleneck and solve these problems in sub-quadratic time. Our algorithms are based on a new problem modeling and interesting algorithmic techniques, which may find other applications as well.\",\n \"Title: Face representation under different illumination conditions\\nYear: 2003\\n\\nAbstract: To deal with image variations due to illumination problem, recently Ramamoorthi and Basri have independently derived a spherical harmonic analysis for the Lambertian reflectance and linear subspace. Their theoretical work provided a new approach for face representation, however both of them had the assumption that the 3D surface normal and albedo are known. This assumption limits this algorithm's application. In this paper, we present a novel method for modeling 3D face shape and albedo from only three images with unknown light directions and this work well fills the blank, which Ramamoorthi and Basri left. By taking the advantage of similar 3D shape of all human faces, the highlight of the new method is that it circumambulates the linear ambiguity by 3D alignment. The experiment results show that our estimated model can be perfectly employed to face recognition and 3D reconstruction.\",\n \"Title: Knowledge modeling and acquisition of traditional Chinese herbal drugs and formulae from text.\\nYear: 2004\\n\\nAbstract: Traditional Chinese medicine has developed over more than 4000 years. A tremendous amount of medical knowledge has been accumulated, among which herbal drugs and formulae are an important portion. This paper presents an ontology for traditional Chinese drugs and formulae, and an ontology-based system for extracting knowledge of drugs and formulae from semi-structured text. The system consists of two components: an executable knowledge extraction language (or EKEL) for specifying knowledge-extracting agents, and a support machine for executing EKEL programs. Experiments show that the system is adequate of extracting knowledge of herbal drugs and formulae from semi-structured text.\",\n \"Title: A simulation approach for impact analysis of requirement volatility considering dependency change\\nYear: 2012\\n\\nAbstract: Requirement volatility is a common and inevitable project risk which has severe consequences on software projects. When requirement change occurs, a project manager wants to analyze its impact so as to better cope with it. As the modification to one requirement can cause changes in its dependent requirements and its dependency relationship, the impact analysis can be very complex. This paper proposes a simulation approach DepRVSim (Requirement Volatility Simulation considering Dependency relationship) to assessing this sort of impact. We abstract the general patterns of the influence mechanism, which may trigger modification in its dependency relationship and bring changes in other requirements through dependency. DepRVSim can generate such information as the probability distribution of effort deviation and schedule deviation. As a proof-of-concept, the applicability of DepRVSim is demonstrated with an illustrative case study of a real software project. Results indicate that DepRVSim is able to provide experimental evidence for decision making when requirement changes.\",\n \"Title: Cell affinity of PHBV thin films evaluated by a parallel plate flow chamber\\nYear: 2009\\n\\nAbstract: The aim of the study is to evaluate the cell affinity of poly(3-hydroxybutyric acid-co-3-hydroxyvaleric acid) (PHBV) thin films using a parallel plate flow chamber system. The PHBV thin films were modified by anhydrous ammonia gaseous plasma treatment. Mouse 3T3 fibroblast cells were seeded on bare and plasma modified PHBV thin films, respectively. A laminar flow enforces the cells on these films in a parallel plate flow chamber system. The percentage of initial cells remaining on the films after serials flows was used to evaluate the cell affinity of films. The results showed that, compared with control groups, ammonia plasma treatment could promote the cell affinity of PHBV films. It also indicated that the parallel plate flow chamber system can be used to evaluate the cell affinity of a material.\",\n \"Title: Extraction Rule Language for Web Information Extraction and Integration\\nYear: 2013\\n\\nAbstract: The Web is the largest data source that contains a lot of valuable information of interests to users or applications. However, how to automatically navigate and extract useful data from web pages is an important issue to study. There have been a number of existing studies on this area. However, most of them do not take enough consideration on complete process of web information extraction and lack of powerful rule expression ability to describe the navigation, extraction and integration rules. In this paper, we study and propose a new web information extraction rule language toward a general model for web information extraction and integration. We first introduce a source data objects to extract different type of web data records. Then we adopt the XML to define the target data entity structure and use scripts to perform target data record integration. The results show that our extraction rule language can provide powerful and flexible ability to describe extraction logic to achieve accurate web data records extraction from complex web pages.\",\n \"Title: Locating an Obnoxious Line among Planar Objects\\nYear: 2012\\n\\nAbstract: Given a set P of n points in the plane such that each point has a positive weight, we study the problem of finding an obnoxious line that intersects the convex hull of P and maximizes the minimum weighted Euclidean distance to all points of P. We also consider a variant of this problem whose input is a set of m polygons with totally n vertices in the plane such that each polygon has a positive weight and whose goal is to locate an obnoxious line with respect to the weighted polygons. We improve the previous results for both problems. Our algorithms are based on new geometric observations and interesting algorithmic techniques.\",\n \"Title: Approximating Spanning Trees with Inner Nodes Cost\\nYear: 2005\\n\\nAbstract: We consider the practical NP-complete problem of finding a minimum weight spanning tree with both edge weights and inner nodes weights. We present two polynomial time algorithms with approximation factors of 2.35 · ln n and 2Hn, respectively, where n is the number of nodes in the graph and Hn is the n-th Harmonic number. This nearly matches the lower bound of (l-\\\\in )Hn, for any \\\\in \\\\ge 0. We also give an approximation algorithm with approximation factor \\\\Delta - 1, where \\\\Delta is the maximum degree of the graph. For metric spaces, we give a 3.105-approximation algorithm and show that an approximation factor of 1.463 is impossible unless {NP \\\\subseteq DTIME[n^{O(\\\\log longn)} ]}.\",\n \"Title: A map ontology driven approach to natural language traffic information processing and services\\nYear: 2006\\n\\nAbstract: This paper proposes a map ontology driven approach to natural language traffic information processing, and also describes its evaluation results Traffic congestion is considered a major urban problem whose solution has long been sought for by engineers and researchers Recently, the idea of gathering traffic information from mobile users via short message service appears promising However, the traffic information is difficult to process to achieve a high accuracy because of its direct, indirect and connotative expressions The proposed map ontology consists of a set of concepts, attributes, relations and constraints on them The map ontology plays two key roles: 1) a basis for natural language traffic information analysis, and 2) a basis for user query analysis In this paper we present the major information processing modules and services for mobile users Experimental results show that the proposed method can improve the traffic information processing accuracy to 93%–95%.\",\n \"Title: Face recognition under varying lighting conditions using self quotient image\\nYear: 2004\\n\\nAbstract: In this paper, we introduce the concept of self-quotient image (SQI) for robust face recognition under varying lighting conditions. It is based on the quotient image method (Amnon Shashua et al., 2001) (T. Riklin-Raviv et al., 1999) to achieve lighting invariance. However, the SQI has three advantages: (1) it needs only one face image for extraction of intrinsic lighting invariant property of a face while removing extrinsic factor corresponding to the lighting; (2) no alignment is needed; (3) it works in shadow regions. The theoretical analysis on conditions where the algorithm is applicable and a non-iterative filtering algorithm for computing SQI are presented. Experiment results demonstrate the effectiveness of our method for robust face recognition under varying lighting conditions.\",\n \"Title: Optimal Point Movement for Covering Circular Regions\\nYear: 2012\\n\\nAbstract: Given n points in a circular region C in the plane, we study the problems of moving the n points to the boundary of G to form a regular n-gon such that the maximum (min-max) or the sum (min-sum) of the Euclidean distances traveled by the points is minimized. These problems have applications, e.g., in mobile sensor barrier coverage of wireless sensor networks. The min-max problem further has two versions: the decision version and the optimization version. For the min-max problem, we present an O(nlog2n) time algorithm for the decision version and an O(nlog3n) time algorithm for the optimization version. The previously best algorithms for the two problem versions take O(n3.5) time and O(n3.5logn) time, respectively. For the min-sum problem we show that a special case with all points initially lying on the boundary of the circular region can be solved in O(n2) time, improving a previous O(n4) time solution. For the general min-sum problem, we present a 3-approximation O(n2) time algorithm. In addition, a by-product of our techniques is an algorithm for dynamically maintaining the maximum matching of a circular convex bipartite graph; our algorithm can handle each vertex insertion or deletion on the graph in O(log2n) time. This result may be interesting in its own right.\",\n \"Title: Representing a Functional Curve by Curves with Fewer Peaks\\nYear: 2011\\n\\nAbstract: In this paper, we study the problems of (approximately) representing a functional curve in 2-D by a set of curves with fewer peaks. Representing a function (or its curve) by certain classes of structurally simpler functions (or their curves) is a basic mathematical problem. Problems of this kind also find applications in applied areas such as intensity-modulated radiation therapy (IMRT). Let $\\\\bf f$ be an input piecewise linear functional curve of size n. We consider several variations of the problems. (1) Uphill–downhill pair representation (UDPR): Find two nonnegative piecewise linear curves, one nondecreasing (uphill) and one nonincreasing (downhill), such that their sum exactly or approximately represents $\\\\bf f$. (2) Unimodal representation (UR): Find a set of unimodal (single-peak) curves such that their sum exactly or approximately represents $\\\\bf f$. (3) Fewer-peak representation (FPR): Find a piecewise linear curve with at most k peaks that exactly or approximately represents $\\\\bf f$. Furthermore, for each problem, we consider two versions. For the UDPR problem, we study its feasibility version: Given ε0, determine whether there is a feasible UDPR solution for $\\\\bf f$ with an approximation error ε; its min-ε version: Compute the minimum approximation error ε ∗ such that there is a feasible UDPR solution for $\\\\bf f$ with error ε ∗. For the UR problem, we study its min-k version: Given ε0, find a feasible solution with the minimum number k ∗ of unimodal curves for $\\\\bf f$ with an error ε; its min-ε version: given k0, compute the minimum error ε ∗ such that there is a feasible solution with at most k unimodal curves for $\\\\bf f$ with error ε ∗. For the FPR problem, we study its min-k version: Given ε0, find one feasible curve with the minimum number k ∗ of peaks for $\\\\bf f$ with an error ε; its min-ε version: given k≥0, compute the minimum error ε ∗ such that there is a feasible curve with at most k peaks for $\\\\bf f$ with error ε ∗. Little work has been done previously on solving these functional curve representation problems. We solve all the problems (except the UR min-ε version) in optimal O(n) time, and the UR min-ε version in O(n+mlog m) time, where mn is the number of peaks of $\\\\bf f$. Our algorithms are based on new geometric observations and interesting techniques.\",\n \"Title: Improved points approximation algorithms based on simplicial thickness data structures\\nYear: 2010\\n\\nAbstract: Given a real ε 0, an integer g ≥ 0 and a set of points in the plane, we study the problem of computing a piecewise linear functional curve with minimum number of line segments to approximate all points after removing g outliers such that the approximation error is at most ε. We give an improved algorithm over the previous work. The algorithm is based on two dynamic data structures developed in this paper for the simplicial thickness queries, which are of independent interest. For a set S of simplices in the d-D space Ed (d ≥ 2 is a constant), the simplicial thickness of a point p is defined as the number of simplices in S that contain p. Given a set P of n points in Ed, we develop two linear-space dynamic data structures to support the following operations. (1) Simplex insertion: Insert a simplex into S. (2) Simplex deletion: Delete a simplex from S. (3) Simplicial thickness query: Given a query simplex σ, compute the minimum simplicial thickness among all points in σ ∩ P. The first data structure supports each operation in O(n1-1/d) time with O(n1+δ) time preprocessing, for any constant δ 0; the second one supports each operation in O(n1-1/d(log n)O(1)) time with O(n log n) time preprocessing. These data structures may also find other applications.\",\n \"Title: A note on searching line arrangements and applications\\nYear: 2013\\n\\nAbstract: We study the problem of searching for a vertex with a desired property in the arrangement of a set of lines in the plane. We show that this problem can be solved efficiently by modifying (and simplifying) two slope selection algorithms without using parametric search. We apply this result to a points approximation problem and obtain an optimal solution for it without using parametric search. Since this line arrangement searching problem is quite natural, our result may find other applications as well.\",\n \"Title: Segment-Tree Based Cost Aggregation for Stereo Matching\\nYear: 2013\\n\\nAbstract: This paper presents a novel tree-based cost aggregation method for dense stereo matching. Instead of employing the minimum spanning tree (MST) and its variants, a new tree structure, \\\"Segment-Tree\\\", is proposed for non-local matching cost aggregation. Conceptually, the segment-tree is constructed in a three-step process: first, the pixels are grouped into a set of segments with the reference color or intensity image, second, a tree graph is created for each segment, and in the final step, these independent segment graphs are linked to form the segment-tree structure. In practice, this tree can be efficiently built in time nearly linear to the number of the image pixels. Compared to MST where the graph connectivity is determined with local edge weights, our method introduces some 'non-local' decision rules: the pixels in one perceptually consistent segment are more likely to share similar disparities, and therefore their connectivity within the segment should be first enforced in the tree construction process. The matching costs are then aggregated over the tree within two passes. Performance evaluation on 19 Middlebury data sets shows that the proposed method is comparable to previous state-of-the-art aggregation methods in disparity accuracy and processing speed. Furthermore, the tree structure can be refined with the estimated disparities, which leads to consistent scene segmentation and significantly better aggregation results.\",\n \"Title: Stereo Matching with Reliable Disparity Propagation\\nYear: 2011\\n\\nAbstract: In this paper, we propose a novel propagation-based stereo matching algorithm. Starting from an initial disparity map, our algorithm selects highly reliable pixels and propagates their disparities along the scan line to produce dense disparity results. The key idea is to construct a line segment region for each pixel with local color and connectivity constraints. The pixel wise line segments are efficiently used to compute initial disparities, select reliable pixels and determine proper propagation regions. Streaking artifacts are effectively removed in a refinement process. Experimental results demonstrate the performance of the proposed method: it ranks 5th in the Middlebury benchmark, and the results can be computed within a few seconds.\",\n \"Title: Aggregate-Max Nearest Neighbor Searching in the Plane.\\nYear: 2013\\n\\nAbstract: We study the aggregate/group nearest neighbor searching for the MAX operator in the plane. For a set $P$ of $n$ points and a query set $Q$ of $m$ points, the query asks for a point of $P$ whose maximum distance to the points in $Q$ is minimized. We present data structures for answering such queries for both $L_1$ and $L_2$ distance measures. Previously, only heuristic and approximation algorithms were given for both versions. For the $L_1$ version, we build a data structure of O(n) size in $O(n\\\\log n)$ time, such that each query can be answered in $O(m+\\\\log n)$ time. For the $L_2$ version, we build a data structure in $O(n\\\\log n)$ time and $O(n\\\\log \\\\log n)$ space, such that each query can be answered in $O(m\\\\sqrt{n}\\\\log^{O(1)} n)$ time, and alternatively, we build a data structure in $O(n^{2+\\\\epsilon})$ time and space for any $\\\\epsilon>0$, such that each query can be answered in $O(m\\\\log n)$ time. Further, we extend our result for the $L_1$ version to the top-$k$ queries where each query asks for the $k$ points of $P$ whose maximum distances to $Q$ are the smallest for any $k$ with $1\\\\leq k\\\\leq n$: We build a data structure of O(n) size in $O(n\\\\log n)$ time, such that each top-$k$ query can be answered in $O(m+k\\\\log n)$ time.\",\n \"Title: MFC: a method of co-referent relation acquisition from large-scale chinese corpora\\nYear: 2006\\n\\nAbstract: This paper proposes a multi-feature constrained method (MFC) to acquire co-referent relations from large-scale Chinese corpora. The MFC has two phases: candidate relations extraction and verification. The extraction phase uses distribution distance, pattern homogeneity and coordination distribution features of co-referent target words to extract candidate relations from Chinese corpora. In the verification phase, we define an ontology for co-referent token words, and build a relation graph for all candidate relations. Both the ontology and the graph are integrated to generate individual, joint and reinforced strategies to verify candidate relations. Comprehensive experiments have shown that the MFC is practical, and can also be extended to acquire other types of relations.\",\n \"Title: Self quotient image for face recognition\\nYear: 2004\\n\\nAbstract: The reliability of facial recognition techniques is often affected by the variation of illumination, such as shadows and illumination direction changes. In this paper, we present a novel framework, called the self-quotient image, for the elimination of the lighting effect in the image. Although this method has a similar invariant form to the quotient image by Shashua etc. (2001), it does not need the alignment and bootstrap images. Our method combines the image processing technique of edge-preserved filtering with the Retinex applications of by Jobson, et al., (1997) and Gross and Brajovie (2003). We have analyzed this algorithm with a 3D imaging model and formulated the conditions where illumination-invariant and -variant properties can be realized, respectively. A fast anisotropic filter is also presented. The experiment results show that our method is effective in removing the effect of illumination for robust face recognition.\",\n \"Title: A survey of computational emotion research\\nYear: 2005\\n\\nAbstract: Emotion reserch covers a multi-disciplinary domain. In the past three decades, a number of theoretical models and computer applications have been proposed from different perspectives. There are models of appraisal theory, such as OCC and Frijda's model, as well as computational systems such as Affective Reasoner, EMA and Cathexis, to make agents believable and human-like. This paper conducts a comprehensive analysis of those state-of-the-art based on the following six criteria, each of which we believe represents a critical aspect in a computational emotion system.\",\n \"Title: Strategy of Data Processing for GPS Rover and Reference Receivers Using Different Sampling Rates.\\nYear: 2011\\n\\nAbstract: In Global Positioning System (GPS) applications, the double-difference (DD) model is often employed, which requires that the reference station and the rover receivers adopt the same sampling rate. On the premise of the need fulfillment, could the existing static receivers of Global Navigation Satellite System networks with a low sampling rate be used as the references for the rover with a high sam...\",\n \"Title: On Building An Accurate Stereo Matching System On Graphics Hardware\\nYear: 2011\\n\\nAbstract: This paper presents a GPU-based stereo matching system with good performance in both accuracy and speed. The matching cost volume is initialized with an AD-Census measure, aggregated in dynamic cross-based regions, and updated in a scan line optimization framework to produce the disparity results. Various errors in the disparity results are effectively handled in a multi-step refinement process. Each stage of the system is designed with parallelism considerations such that the computations can be accelerated with CUDA implementations. Experimental results demonstrate the accuracy and the efficiency of the system: currently it is the top performer in the Middlebury benchmark, and the results are achieved on GPU within 0.1 seconds. We also provide extra examples on stereo video sequences and discuss the limitations of the system.\",\n \"Title: Two-Point L1 Shortest Path Queries in the Plane\\nYear: 2014\\n\\nAbstract: Let P be a set of h pairwise-disjoint polygonal obstacles with a total of n vertices in the plane. In this paper, we consider the problem of building a data structure that can quickly compute an L1 shortest obstacle-avoiding path between any two query points s and t. We build a data structure of size O(n + h2 · log h · 4√log h) in O(n + h2 · log2 h · 4√log h) time that answers each query in O(log n + k) time, where k is the number of edges of the output path. Note that n + h2 · log2 h · 4√log h = O(n+h2+&epsis;) for any constant &epsis; 0. We also extend our techniques to the weighted rectilinear version in which the \\\"obstacles\\\" of P are rectilinear regions with \\\"weights\\\" and allow L1 paths to travel through them with weighted costs. Our algorithm answers each query in O(log n + k) time with a data structure of size O(n2 · log n · 4√log n) that is built in O(n2 · log2 n · 4√log n) time.\",\n \"Title: A Network Emergent Computing Model Based on Cellular Automaton\\nYear: 2009\\n\\nAbstract: Emergent computation is a relatively new approach for understanding the behaviors of complex systems. Central to this approach is the idea that system-level behavior emerges from interaction among individual elements. This paper proposes a network emergent computation model based on cellular automaton, which introduces network operating mechanism, such as store-and-forward, adjacency interaction, rate adjustment, resource competition and delayed feedback as the interactions among individual cells. The simulation results show that the system-level behaviors, such as power-law, self-similarity and noise, can be generated spontaneously by local nonlinear interaction among cells, which have led to a better understanding of the macro-behavior of the system from the perspective of microscopic mechanism.\",\n \"Title: A 57∼66GHz CMOS voltage-controlled oscillator using tunable differential inductor\\nYear: 2012\\n\\nAbstract: Short-range wireless data transmission around the 60GHz band has been flourishing for various biomedical applications due to its features of high speed, high security and small device size. Wireless monitoring of human body with such transceivers has enabled numerous research areas in healthcare and biomedical engineering, such as long-term real-time recording of the Electroencephalography signal while the subject is in real-life scenario. Among the design challenges of such transceiver, the voltage-controlled oscillator (VCO) design plays a key role. Large tuning range of the VCO enables the effective utilization of the spectrum while small area is desired for biomedical application with implantation. This paper proposes a novel circuit topology for 60GHz VCO. It covers the full unlicensed 57~66GHz frequency band and supports multi-band operation. Simulated in UMC 1P9M 65nm CMOS technology, a tuning range of 14% has been achieved with low power consumption. The VCO core only occupies an area of 60×80um2, which is the smallest for 60-GHz applications published ever.\",\n \"Title: A sample-and-hold circuit for 10-bit 100MS/s pipelined ADC\\nYear: 2011\\n\\nAbstract: In this paper a fully differential sample-and-hold (S/H) circuit for the pipelined analog-to-digital converter (ADC) was presented. The S/H circuit is based on capacitor flip-around S/H architecture with gain-boosted differential folded cascode operational transconductance amplifier (OTA), which can achieve high DC gain and large bandwidth. Bootstrapped switch and bottom plate sampling techniques are used to reduce the nonlinear distortion. The entire S/H circuit was designed in CSMC 1.8V/3.3V 0.18μm 1P6M CMOS technology. Simulation results show the S/H circuit achieves SNDR of 84dB, ENOB of 13.7, THD of -85dB, and SFDR of 85.4dB at the input frequency 9.86328125M Hz, which is suitable for a 10bit 100MS/s pipelined ADC.\",\n \"Title: Computing the L1 geodesic diameter and center of a simple polygon in linear time.\\nYear: 2015\\n\\nAbstract: In this paper, we show that the L1 geodesic diameter and center of a simple polygon can be computed in linear time. For the purpose, we focus on revealing basic geometric properties of the L1 geodesic balls, that is, the metric balls with respect to the L1 geodesic distance. More specifically, in this paper we show that any family of L1 geodesic balls in any simple polygon has Helly number two, and the L1 geodesic center consists of midpoints of shortest paths between diametral pairs. These properties are crucial for our linear-time algorithms, and do not hold for the Euclidean case.\",\n \"Title: Minimizing the Aggregate Movements for Interval Coverage.\\nYear: 2014\\n\\nAbstract: We consider an interval coverage problem. Given n intervals of the same length on a line L and a line segment B on L, we want to move the intervals along L such that every point of B is covered by at least one interval and the sum of the moving distances of all intervals is minimized. As a basic geometry problem, it has applications in mobile sensor barrier coverage in wireless sensor networks. The previous work solved the problem in $$O(n^2)$$O(n2) time. In this paper, by discovering many interesting observations and developing new algorithmic techniques, we present an $$O(n\\\\\\\\log n)$$O(nlogn) time algorithm. We also show an $$\\\\\\\\varOmega (n\\\\\\\\log n)$$Ω(nlogn) time lower bound for this problem, which implies the optimality of our algorithm.\",\n \"Title: Range Queries on Uncertain Data.\\nYear: 2015\\n\\nAbstract: Given a set P of n uncertain points on the real line, each represented by its one-dimensional probability density function, we consider the problem of building data structures on P to answer range queries of the following three types: (1) top-1 query: find the point in P that lies in I with the highest probability, (2) top-k query: given any integer k <= n as part of the query, return the k points in P that lie in I with the highest probabilities, and (3) threshold query: given any threshold tau as part of the query, return all points of P that lie in I with probabilities at least tau. We present data structures for these range queries with linear or near linear space and efficient query time.\",\n \"Title: Real-time local stereo via edge-aware disparity propagation.\\nYear: 2014\\n\\nAbstract: This letter presents a novel method for real-time local stereo matching. Different from previous methods which have spent many efforts on cost aggregation, the proposed method re-solves the stereo problem by propagating disparities in the cost domain. It is started by pre-detecting the disparity priors, on which a new cost volume is built for disparity assignment. Then the reliable disparities are propagated via filtering on this cost volume. Specially, a new O(1) geodesic filter is proposed and demonstrated effective for the task of edge-aware disparity propagation. As can be expected, the proposed framework is highly efficient, due to leaving double aggregation on left–right views, as well as costly post-processing steps, out of account. Moreover, by properly designing a quadric cost function, our method could be extended to good sub-pixel accuracy with a simple quadratic polynomial interpolation. Quantitative evaluation shows that it outperforms all the other local methods both in terms of accuracy and speed on Middlebury benchmark. It ranks 8th out of over 150 submissions if sub-pixel precision is considered, and the average runtime is only 9ms on a NVIDIA GeForce GTX 580GPU.\",\n \"Title: Real time light field reconstruction for sub-pixel based integral imaging display\\nYear: 2014\\n\\nAbstract: Integral imaging (II) display provides a promising 3D display technology for users to see natural 3D color images with stereo and motion parallax. However, they often suffers from the limitations of both the insufficient spatial resolution and lack of real-time content generation strategies. In this paper, we advance the traditional II display with an efficient sub-pixel based light field reconstruction scheme, to achieve 3D imagery with much higher spatial resolution in real-time speed.\",\n \"Title: Value-based portfolio scoping: an industrial case study\\nYear: 2012\\n\\nAbstract: Customization is considered as a promising way for better satisfying diversity of customer needs. In organizations short of resources, it is a frequent challenge to get balance between development and customization workload in order to ensure product success as well as customer satisfaction. In this paper, we proposed a value-based product portfolio scoping approach to determine optimal product scale for planning software product line adoption. The approach blends existing methods in domain analysis, requirements clustering, and valuation theory. An industrial case study is presented to demonstrate the application of the approach and its effectiveness.\",\n \"Title: Shortest color-spanning intervals\\nYear: 2014\\n\\nAbstract: Given a set of n points on a line, where each point has one of k colors, and given an integer si≥1 for each color i, 1≤i≤k, the problem Shortest Color-Spanning t Intervals (SCSI-t) aims at finding t intervals to cover at least si points of each color i, such that the maximum length of the intervals is minimized. Chen and Misiolek introduced the problem SCSI-1, and presented an algorithm running in O(n) time if the input points are sorted. Khanteimouri et al. gave an O(n2logn) time algorithm for the special case of SCSI-2 with si=1 for all colors i. In this paper, we present an improved algorithm with running time of O(n2) for SCSI-2 with arbitrary si≥1. We also obtain some interesting results for the general problem SCSI-t. From the negative direction, we show that approximating SCSI-t within any ratio is NP-hard when t is part of the input, is W[2]-hard when t is the parameter, and is W[1]-hard with both t and k as parameters. Moreover, the NP-hardness and the W[2]-hardness with parameter t hold even if si=1 for all i. From the positive direction, we show that SCSI-t with si=1 for all i is fixed-parameter tractable with k as the parameter, and admits an exact algorithm running in O(2kn⋅max{k,logn}) time.\",\n \"Title: One-Dimensional k-Center on Uncertain Data.\\nYear: 2014\\n\\nAbstract: Problems on uncertain data have attracted significant attention due to the imprecise nature of many measurement data. In this paper, we consider the k-center problem on one-dimensional uncertain data. The input is a set P of (weighted) uncertain points on a real line, and each uncertain point is specified by its probability density function (pdf) which is a piecewise-uniform function (i.e., a histogram). The goal is to find a set Q of k points on the line to minimize the maximum expected distance from the uncertain points of P to their expected closest points in Q. We present efficient algorithms for this uncertain k-center problem and their running times almost match those for the \\\\\\\"deterministic\\\\\\\" k-center problem.\",\n \"Title: Maple: scalable multi-dimensional range search over encrypted cloud data with tree-based index\\nYear: 2014\\n\\nAbstract: Cloud computing promises users massive scale outsourced data storage services with much lower costs than traditional methods. However, privacy concerns compel sensitive data to be stored on the cloud server in an encrypted form. This posts a great challenge for effectively utilizing cloud data, such as executing common SQL queries. A variety of searchable encryption techniques have been proposed to solve this issue; yet efficiency and scalability are still the two main obstacles for their adoptions in real-world datasets, which are multi-dimensional in general. In this paper, we propose a tree-based public-key Multi-Dimensional Range Searchable Encryption (MDRSE) to overcome the above limitations. Specifically, we first formally define the leakage function and security of a tree-based MDRSE. Then, by leveraging an existing predicate encryption in a novel way, our tree-based MDRSE efficiently indexes and searches over encrypted cloud data with multi-dimensional tree structures (i.e., R-trees). Moreover, our scheme is able to protect single-dimensional privacy while previous efficient solutions fail to achieve. Our scheme is selectively secure, and through extensive experimental evaluation on a large-scale real-world dataset, we show the efficiency and scalability of our scheme.\",\n \"Title: Line-Constrained k -Median, k -Means, and k -Center Problems in the Plane.\\nYear: 2014\\n\\nAbstract: The (weighted) k-median, k-means, and k-center problems in the plane are known to be NP-hard. In this paper, we study these problems with an additional constraint that requires the sought k facilities to be on a given line. We present efficient algorithms for various distance metrics such as L-1, L-2, L-infinity. Assume all n weighted points are given sorted by their projections on the given line. For k-median, our algorithms for L1 and L-infinity metrics run in O(min{nk, n root k log n log n, n2(O(n root k log n log n)) log n}) time and O(min{nk log n, n root k log n log(2) n, n2(O(n root k log n log n)) log(2) n}) time, respectively. For k-means, which is defined only on the L-2 metric, we give an O(min{nk, nn root k log n, n2(O(root log k log log n))}) time algorithm. For kcenter, our algorithms run in O(n log n) time for all three metrics, and in O(n) time for the unweighted version under L-1 and L infinity metrics.\",\n \"Title: Hierarchical gaze estimation based on adaptive feature learning\\nYear: 2014\\n\\nAbstract: Existing appearance-based gaze estimation methods suffer from tedious calibration and appearance variation caused by head movement. In this paper, to handle this problem, we propose a novel appearance-based gaze estimation method by introducing supervised adaptive feature extraction and hierarchical mapping model. Firstly, an adaptive feature learning method is proposed to extract topology-preserving (TOP) feature individually. Then hierarchical mapping method is proposed to localize gaze position based on coarse-to-fine strategy. Appearance synthesis approach is used to increase the refer sample density. Experiments show that under the condition of sparse calibration, proposed method has better performance in accuracy than existing methods under fixed head pose without chinrest. Moreover, our method can be easily extended for head pose-varying gaze estimation.\",\n \"Title: Efficient algorithms for the one-dimensional k-center problem.\\nYear: 2015\\n\\nAbstract: We consider the problem of finding k centers for n weighted points on a real line. This (weighted) k-center problem was solved in O(nlogn) time previously by using Cole's parametric search and other complicated approaches. In this paper, we present an easier O(nlogn) time algorithm that avoids the parametric search, and in certain special cases our algorithm solves the problem in O(n) time. In addition, our techniques involve developing interesting data structures for processing queries that find a lowest point in the common intersection of a certain subset of half-planes. This subproblem is interesting in its own right and our solution for it may find other applications as well.\",\n \"Title: One-dimensional k-center on uncertain data.\\nYear: 2015\\n\\nAbstract: Problems on uncertain data have attracted significant attention due to the imprecise nature of many measurement data. In this paper, we consider the k-center problem on one-dimensional uncertain data. The input is a set P of (weighted) uncertain points on a real line, and each uncertain point is specified by its probability density function (pdf) which is a piecewise-uniform function (i.e., a histogram). The goal is to find a set Q of k points on the line to minimize the maximum expected distance from the uncertain points of P to their expected closest points in Q. We present efficient algorithms for this uncertain k-center problem and their running times almost match those for the “deterministic” k-center problem.\",\n \"Title: A mechanical chest compressor closed-loop controller with an effective trade-off between blood flow improvement and ribs fracture reduction.\\nYear: 2015\\n\\nAbstract: Chest compression (CC) is a significant emergency medical procedure for maintaining circulation during cardiac arrest. Although CC produces the necessary blood flow for patients with heart arrest, improperly deep CC will contribute significantly to the risk of chest injury. In this paper, an optimal CC closed-loop controller for a mechanical chest compressor (OCC-MCC) was developed to provide an effective trade-off between the benefit of improved blood perfusion and the risk of ribs fracture. The trade-off performance of the OCC-MCC during real automatic mechanical CCs was evaluated by comparing the OCC-MCC and the traditional mechanical CC method (TMCM) with a human circulation hardware model based on hardware simulations. A benefit factor (BF), risk factor (RF) and benefit versus risk index (BRI) were introduced in this paper for the comprehensive evaluation of risk and benefit. The OCC-MCC was developed using the LabVIEW control platform and the mechanical chest compressor (MCC) controller. PID control is also employed by MCC for effective compression depth regulation. In addition, the physiological parameters model for MCC was built based on a digital signal processor for hardware simulations. A comparison between the OCC-MCC and TMCM was then performed based on the simulation test platform which is composed of the MCC, LabVIEW control platform, physiological parameters model for MCC and the manikin. Compared with the TMCM, the OCC-MCC obtained a better trade-off and a higher BRI in seven out of a total of nine cases. With a higher mean value of cardiac output (1.35 L/min) and partial pressure of end-tidal CO2 (15.7 mmHg), the OCC-MCC obtained a larger blood flow and higher BF than TMCM (5.19 vs. 3.41) in six out of a total of nine cases. Although it is relatively difficult to maintain a stable CC depth when the chest is stiff, the OCC-MCC is still superior to the TMCM for performing safe and effective CC during CPR. The OCC-MCC is superior to the TMCM in performing safe and effective CC during CPR and can be incorporated into the current version of mechanical CC devices for high quality CPR, in both in-hospital and out-of-hospital CPR settings.\",\n \"Title: Balanced splitting on weighted intervals\\nYear: 2015\\n\\nAbstract: We study a problem on splitting intervals. Let I be a set of n intervals on a line L , with non-negative weights. Given any integer k ¿ 1 , we want to find k points on L to partition L into k + 1 segments, such that the maximum cost of these segments is minimized, where the cost of each segment s is the sum of the weights of the intervals in I intersecting s . We present an O ( n log n ) time algorithm for this problem.\",\n \"Title: Aggregate-MAX Top-k Nearest Neighbor Searching in the L1 Plane.\\nYear: 2015\\n\\nAbstract: We study the aggregate/group top-k nearest neighbor searching for the Max operator in the plane, where the distances are measured by the L1 metric. Let P be a set of n points in the plane. Given a query set Q of m points, for each point p ∈ P, the aggregate-max distance from p to Q is defined to be the maximum distance from p to all points in Q. Given Q and an integer k with 1 ≤ k ≤ n, the query asks for the k points of P that have the smallest aggregate-max distances to Q. We build a data structure of O(n) size in O(n log n) time, such that each query can be answered in O(m+k log n) time and the k points are reported in sorted order by their aggregate-max distances to Q. Alternatively, we build a data structure of O(n log n) size in O(n log2 n) time that can answer each query in O(m + k + log3 n) time.\",\n \"Title: Unknown environment exploration of multi-robot system with the FORDPSO.\\nYear: 2016\\n\\nAbstract: Effective environment exploration in unknown environment is precondition of constructing the environment map and carrying out other tasks for multi-robot system. Due to its excellent performance, particle swarm optimization (PSO) has been widely used in multi-robot exploration field. To deal with its drawback – easily trapped in local optima, Darwinian PSO (DPSO) optimization is proposed by Tillett et al. [1] with the natural selection function and first used in real world robot exploration by Couceiro et al. [2], forming the robotic DPSO (RDPSO). To increase the algorithm performance and control its convergence rate, fractional calculus is used to replace inertia component in RDPSO for its “memory” ability and forming the fractional order RDPSO (FORDPSO). This paper presents a formal analysis of RDPSO and studies the influence of the coefficients on FORDPSO algorithm. To satisfy the requirement of dynamically changing robots׳ behaviors during the exploration, fuzzy inferring system is designed to achieve better control coefficients. Experiment results obtained in two complex simulated environments illustrate that biological and sociological inspiration is effective to meet the challenges of multi-robot system application in unknown environment exploration, and the exploration effect of the fuzzy adaptive FORDPSO is better than that of the fixed coefficient FORDPSO. Furthermore, the performance of FORDPSO with different neighborhood topologies are studied and compared with other six PSO variations. All the results demonstrate the effect of the FORDPSO on the multi-robot environment exploration.\",\n \"Title: Geometric Range Search on Encrypted Spatial Data.\\nYear: 2016\\n\\nAbstract: Geometric range search is a fundamental primitive for spatial data analysis in SQL and NoSQL databases. It has extensive applications in location-based services, computer-aided design, and computational geometry. Due to the dramatic increase in data size, it is necessary for companies and organizations to outsource their spatial data sets to third-party cloud services (e.g., Amazon) in order to reduce storage and query processing costs, but, meanwhile, with the promise of no privacy leakage to the third party. Searchable encryption is a technique to perform meaningful queries on encrypted data without revealing privacy. However, geometric range search on spatial data has not been fully investigated nor supported by existing searchable encryption schemes. In this paper, we design a symmetric-key searchable encryption scheme that can support geometric range queries on encrypted spatial data. One of our major contributions is that our design is a general approach, which can support different types of geometric range queries. In other words, our design on encrypted data is independent from the shapes of geometric range queries. Moreover, we further extend our scheme with the additional use of tree structures to achieve search complexity that is faster than linear. We formally define and prove the security of our scheme with indistinguishability under selective chosen-plaintext attacks, and demonstrate the performance of our scheme with experiments in a real cloud platform (Amazon EC2).\",\n \"Title: An Optimal Algorithm for Minimum-Link Rectilinear Paths in Triangulated Rectilinear Domains.\\nYear: 2015\\n\\nAbstract: We present a new algorithm for finding minimum-link rectilinear paths among rectilinear obstacles in the plane. Given a triangulated rectilinear domain of h pairwise-disjoint rectilinear obstacles with a total of n vertices, our algorithm can find a minimum-link rectilinear path between any two points in \\\\(O(n+ h \\\\log h)\\\\) time. Further, within the same time our algorithm can build an O(n)-size data structure for any source point s, such that given any query point t, the number of edges of a minimum-link rectilinear path from s to t can be computed in \\\\(O(\\\\log n)\\\\) time and the actual path can be output in additional time linear in the number of the edges of the path. The previously best algorithms for the problems run in \\\\(O(n \\\\log n)\\\\) time.\",\n \"Title: Algorithms for Minimizing the Movements of Spreading Points in Linear Domains.\\nYear: 2015\\n\\nAbstract: We study a problem on spreading points. Given a set P of n points sorted on a line L and a distance value , we wish to move the points of P along L such that the distance of any two points ofP is at least and the maximum movement of all points is minimized. We present an O(n) time algorithm for this problem. Further, we extend our algorithm to solve (in O(n) time) the cycle version of the problem where all points of P are on a cycle C. Previously, only weakly polynomial-time algorithms were known for these problems based on linear programming. In addition, we present a linear-time algorithm for a similar facility-location moving problem, which improves the previous work.\",\n \"Title: Parallel Approach and Platform for Large-Scale WEB Data Extraction\\nYear: 2013\\n\\nAbstract: As the most popular information publishing platform, the Web contains a lot of valued information of interests to users or applications. Although a lot of data extraction techniques have been studied in the last decade, it is still far away from meeting the need of real data extraction. On the one hand, most of them cannot support the whole web information extraction process involving three stages: web page navigation, data extraction and data integration, On the other hand, they cannot support parallel data extraction process for large-scale web pages. In this paper, we propose a parallel approach and platform based on the Hadoop MapReduce for large-scale web data extraction. Our approach can perform the whole three-stage web data extraction process in parallel. Experimental results show that our approach is efficient and can achieve linear speedup.\",\n \"Title: Linear Time Approximation Schemes for Geometric Maximum Coverage\\nYear: 2015\\n\\nAbstract: We study approximation algorithms for the following geometric version of the maximum coverage problem: Let P be a set of n weighted points in the plane. Let D represent a planar object, such as a rectangle, or a disk. We want to place m copies of D such that the sum of the weights of the points in P covered by these copies is maximized. For any fixed ε>0, we present efficient approximation schemes that can find a (1−ε)-approximation to the optimal solution. In particular, for m=1 and for the special case where D is a rectangle, our algorithm runs in time O(nlog(1ε)), improving on the previous result. For m>1 and the rectangular case, our algorithm runs in O(nεlog(1ε)+mεlogm+m(1ε)O(min(m,1ε))) time. For a more general class of shapes (including disks, polygons with O(1) edges), our algorithm runs in O(n(1ε)O(1)+mϵlogm+m(1ε)O(min(m,1ε2))) time.\",\n \"Title: Computing the L1 Geodesic Diameter and Center of a Polygonal Domain.\\nYear: 2017\\n\\nAbstract: For a polygonal domain with h holes and a total of n vertices, we present algorithms that compute the L-1 geodesic diameter in O(n(2)+h(4)) time and the L-1 geodesic center in O((n(4)+n(2) h(4))alpha(n)) time, where alpha(.) denotes the inverse Ackermann function. No algorithms were known for these problems before. For the Euclidean counterpart, the best algorithms compute the geodesic diameter in O(n(7.)(73)) or O(n(7)(h+log n)) time, and compute the geodesic center in O(n(12+is an element of)) time. Therefore, our algorithms are much faster than the algorithms for the Euclidean problems. Our algorithms are based on several interesting observations on L-1 shortest paths in polygonal domains.\",\n \"Title: LBP Discriminant Analysis for Face Verification\\nYear: 2005\\n\\nAbstract: Pedestrian detection is a challenging vision task, especially applied to the automotive field where the background changes as the vehicle moves. This paper presents an extensive study upon human body models and the techniques suitable for being used ...\",\n \"Title: Survivability Evaluation Index Systems And Evaluation Models For Wireless Sensor Networks\\nYear: 2015\\n\\nAbstract: Survivability evaluation index systems and evaluation models are important basis for evaluating network survivability. The Analytic Hierarchy Process (AHP) is a common system evaluation method, but it cannot reflect the mutual influence among evaluation indexes. In order to overcome the defects of AHP, the survivability evaluation index set of Wireless Sensor Network (WSN) in emergency communications is selected comprehensively, and establishes a survivability index system of WSN based on the Analytic Network Process (ANP). Then, a survivability evaluation model for clustering WSN based on semi-Markov Process (SMP) is put forward in this paper, and a SMP-based survival state transition diagram of cluster head is given. Survivability evaluation index systems and evaluation models are not only helpful to accessing the survivability of WSN in emergency communications objectively and effectively, but also to providing reference basis for the actual deployment and application of WSN.\",\n \"Title: Minimizing the Maximum Moving Cost of Interval Coverage.\\nYear: 2015\\n\\nAbstract: In this paper, we study an interval coverage problem. We are given n intervals of the same length on a line L and a line segment B on L. Each interval has a nonnegative weight. The goal is to move the intervals along L such that every point of B is covered by at least one interval and the maximum moving cost of all intervals is minimized, where the moving cost of each interval is its moving distance times its weight. Algorithms for the \\\"unweighted\\\" version of this problem have been given before. In this paper, we present a first-known algorithm for this weighted version and our algorithm runs in O(n(2) log n log log n) time. The problem has applications in mobile sensor barrier coverage.\",\n \"Title: Preliminary Testing of a Hand Gesture Recognition Wristband Based on EMG and Inertial Sensor Fusion.\\nYear: 2015\\n\\nAbstract: Electromyography EMG is well suited for capturing static hand features involving relatively long and stable muscle activations. At the same time, inertial sensing can inherently capture dynamic features related to hand rotation and translation. This paper introduces a hand gesture recognition wristband based on combined EMG and IMU signals. Preliminary testing was performed on four healthy subjects to evaluate a classification algorithm for identifying four surface pressing gestures at two force levels and eight air gestures. Average classification accuracy across all subjects was 88% for surface gestures and 96% for air gestures. Classification accuracy was significantly improved when both EMG and inertial sensing was used in combination as compared to results based on either single sensing modality.\",\n \"Title: Computing the Center of Uncertain Points on Tree Networks.\\nYear: 2017\\n\\nAbstract: Uncertain data has been very common in many applications. In this paper, we consider the one-center problem for uncertain data on tree networks. In this problem, we are given a tree T and n (weighted) uncertain points each of which has m possible locations on T associated with probabilities. The goal is to find a point $$x^*$$xź on T such that the maximum (weighted) expected distance from $$x^*$$xź to all uncertain points is minimized. To the best of our knowledge, this problem has not been studied before. We propose a refined prune-and-search technique that solves the problem in linear time.\",\n \"Title: Numerical Simulation of 3-D Elastic Moduli with Elliptical Cracks Using FM-DBEM\\nYear: 2014\\n\\nAbstract: In the paper, the fast multiple dual boundary element method is applied to simulate the model of a elastic three-dimensional (3-D) isotropic solid with elliptical micro cracks in various sizes and estimate the global effective elastic moduli of the micro crack-weakened solid. Following the previous simulation of solids with circular micro cracks, the present analyses require no interactions of elliptical micro cracks, and investigate the relations between the effective elastic moduli and the micro crack density parameter. Some representative elliptical micro crack models (including randomly distributed micro cracks and a family of parallel micro cracks) are simulated, and compared with some analytic approximate micromechanical solutions. Numerical results shows the elliptical micro cracks can be approximated with good accuracy to be circular cracks in the same crack density. Besides, it can also present an alternative simulation method of structure containing elliptical micro cracks or micro cracks of other shapes in complicate distributing states, and present an accurate effective moduli prediction.\",\n \"Title: Tree-Based Multi-dimensional Range Search on Encrypted Data with Enhanced Privacy.\\nYear: 2014\\n\\nAbstract: With searchable encryption, a data user is able to perform meaningful search on encrypted data stored in the public cloud without revealing data privacy. Besides handling simple queries (e.g., keyword queries), complex search functions, such as multi-dimensional (conjunctive) range queries, have also been studied in several approaches to provide search functionalities over multi-dimensional data. However, current works supporting multi-dimensional range queries either only achieve linear search complexity or reveal additional private information to the public cloud. In this paper, we propose a tree-based symmetric-key searchable encryption to support multi-dimensional range queries on encrypted data. Besides protecting data privacy, our proposed scheme is able to achieve faster-than-linear search, query privacy and single-dimensional privacy simultaneously compared to previous solutions. More specifically, we formally define the security of our proposed scheme, prove that it is selectively secure, and demonstrate its faster-than-linear efficiency with experiments over a real-world dataset.\",\n \"Title: A note on computing the center of uncertain data on the real line.\\nYear: 2016\\n\\nAbstract: We consider the one-dimensional one-center problem on uncertain data. We are given a set P of n (weighted) uncertain points on a real line L and each uncertain point is specified by a probability density function that is a piecewise-uniform function (i.e., a histogram). The goal is to find a point c (the center) on L such that the maximum expected distance from c to all uncertain points of P is minimized. We present a linear-time algorithm for this problem.\",\n \"Title: On The Geodesic Centers Of Polygonal Domains\\nYear: 2016\\n\\nAbstract: In this paper, we study the problem of computing Euclidean geodesic centers of a polygonal domain P with a total of n vertices. We give a necessary condition for a point being a geodesic center. We show that there is at most one geodesic center among all points of P that have topologically-equivalent shortest path maps. This implies that the total number of geodesic centers is bounded by the combinatorial size of the shortest path map equivalence decomposition of P, which is known to be O(n(10)). One key observation is a pi-range property on shortest path lengths when points are moving. With these observations, we propose an algorithm that can compute all geodesic centers in O(n(11) log n) time. Previously, an algorithm of O(n(12+is an element of)) time was known for this problem, for any is an element of > 0.\",\n \"Title: Database Calibration For Outdoor Wi-Fi Positioning System\\nYear: 2016\\n\\nAbstract: The limitation of the GPS in urban canyon has led to the rapid development of Wi-Fi positioning system (WPS). The fingerprint based WPS could be divided into calibration and positioning stages. In calibration stage, several grid points (GPs) are selected, and their position tags and featured access points (APs) are collected to build fingerprint database. In positioning stage, real time measurement of APs are compared with the feature of each GP in the database. The k weighted nearest neighbors (KWNN) algorithm is used as pattern matching algorithm to estimate the final positioning result. However, the performance of outdoor fingerprint-based WPS is not good enough for pedestrian navigation. The main challenge is to build a robust fingerprint database. The received number of APs in outdoor environments has large variation. In addition, positioning result estimated by GPS receiver is used as position tag of each GP to automatically build the fingerprint database. This paper studies the lifecycle of fingerprint database in outdoor environment. We also shows that using long time collected data to build database could improve the positioning accuracy. Moreover, a new 3D-GNSS (3D building models aided GNSS) positioning method is used to provide accurate position tags. In this paper, the fingerprint-based WPS has been developed in an outdoor environment near the center of Tokyo city. The proposed WPS can achieve around 17 meters positioning accuracy in urban canyon.\",\n \"Title: An Improved Algorithm for Diameter-Optimally Augmenting Paths in a Metric Space.\\nYear: 2017\\n\\nAbstract: Let P be a path graph of n vertices embedded in a metric space. We consider the problem of adding a new edge to P such that the diameter of the resulting graph is minimized. Previously (in ICALP 2015) the problem was solved in O(nlog3n) time. In this paper, based on new observations and different algorithmic techniques, we present an O(nlogn) time algorithm.\",\n \"Title: Separating Overlapped Intervals on a Line.\\nYear: 2016\\n\\nAbstract: Given $n$ intervals on a line $ell$, we consider the problem of moving these intervals on $ell$ such that no two intervals overlap and the maximum moving distance of the intervals is minimized. The difficulty for solving the problem lies in determining the order of the intervals in an optimal solution. By interesting observations, we show that it is sufficient to consider at most $n$ candidate lists of ordered intervals. Further, although explicitly maintaining these lists takes $Omega(n^2)$ time and space, by more observations and a pruning technique, we present an algorithm that can compute an optimal solution in $O(nlog n)$ time and $O(n)$ space. We also prove an $Omega(nlog n)$ time lower bound for solving the problem, which implies the optimality of our algorithm.\",\n \"Title: Improved Algorithms For Structured Sparse Recovery.\\nYear: 2017\\n\\nAbstract: It is known that certain structures of the signal in addition to the standard notion of sparsity (called structured sparsity) can improve the sample complexity in several compressive sensing applications. Recently, Hegde et al. proposed a framework, called approximation-tolerant model-based compressive sensing, for recovering signals with structured sparsity. Their framework requires two oracles, the head- and the tail-approximation projection oracles. The two oracles should return approximate solutions in the model which is closest to the query signal. In this paper, we consider two structured sparsity models and obtain improved projection algorithms. The first one is the tree sparsity model, which captures the support structure in the wavelet decomposition of piecewise-smooth signals. We propose a linear time $(1-epsilon)$-approximation algorithm for head-approximation projection and a linear time $(1+epsilon)$-approximation algorithm for tail-approximation projection. The best previous result is an $tilde{O}(nlog n)$ time bicriterion approximation algorithm (meaning that their algorithm may return a solution of sparsity larger than $k$) by Hegde et al. Our result provides an affirmative answer to the open problem mentioned in the survey of Hegde and Indyk. As a corollary, we can recover a constant approximate $k$-sparse signal. The other is the Constrained Earth Mover Distance (CEMD) model, which is useful to model the situation where the positions of the nonzero coefficients of a signal do not change significantly as a function of spatial (or temporal) locations. We obtain the first single criterion constant factor approximation algorithm for the head-approximation projection. The previous best known algorithm is a bicriterion approximation. Using this result, we can get a faster constant approximation algorithm with fewer measurements for the recovery problem in CEMD model.\",\n \"Title: Dispersing Points on Intervals.\\nYear: 2016\\n\\nAbstract: We consider a problem of dispersing points on disjoint intervals on a line. Given n pairwise disjoint intervals sorted on a line, we want to find a point in each interval such that the minimum pairwise distance of these points is maximized. Based on a greedy strategy, we present a linear time algorithm for the problem. Further, we also solve in linear time the cycle version of the problem where the intervals are given on a cycle.\",\n \"Title: Method of Consistency Judgment for App Software's User Comments.\\nYear: 2016\\n\\nAbstract: With the popularity of mobile intelligent terminal, user comments of App software is viewed as one of the research interests of social computing. Faced with the massive App software, most users usually view the other users’ comments and marks to selecting the desired App software. Due to the freedom and randomness of the network comments, the inconsistence between the user’s comment and mark makes it difficult to choose App software. This paper presents a method by analyzing the relationships among user’s comment information, the user’s mark and App software information. Firstly, the consistency between user’s comment information and App software information is judged. Then, through analyzing the grammar relationships among the feature-words, adverbs and the feature-sentiment-words in App software’s feature-sentiment-word-pairs, the user’s emotional tendency about App software is quantified combining with the dictionary and the network sentiment words. After calculating the user’s comprehensive score of App software, the consistency of App software’s user comment is judged by comparing this score and the user’s mark. Finally, the experimental results show that the method is effective.\",\n \"Title: Efficient reconstruction of density matrices for high dimensional quantum state tomography.\\nYear: 2017\\n\\nAbstract: The conventional quantum state tomography (QST) needs large number of measurements to reconstruct the quantum state. Thanks to the compressive sensing (CS) theory, one can recover a pure or nearly pure quantum state with an acceptable accuracy given much fewer number of measurements. However, most existing algorithms for CS based QST are rather slow and difficult to be implemented in practice. To fill the gap between the CS theory and practical QST, this paper firstly applies an improved Alternating Direction Multiplier Method (ADMM) combining with the Iterative Shrinkage-Thresholding Algorithm (ISTA), ISTADMM for short, aiming at improving the efficiency of QST problem in particular with much lower number of measurements. The ISTADMM avoids computing the inverse of large-scale matrix, reduces the computational time and required memory space. The computation complexity is reduced from O(d6) for least square (widely used in QST), and O(md4) for Fixed Point-ADMM in our previous work, to ISTADMMs O(md2). The proposed algorithm makes it practical to reconstruct high dimensional quantum states provided fewer number of measurements. The simulations verify the superiority of the proposed algorithm, where it takes 3.13 minutes to reconstruct an 8-qubit density matrix with 96.17% accuracy, which is faster than many existing and our previous work.\",\n \"Title: Viscous Scalar Rarefaction Waves.\\nYear: 2017\\n\\nAbstract: We study the pointwise behavior of rarefaction waves for scalar viscous conservation laws. Our primary goal is the analysis of the initial layer and large time behavior of the solution of the Riemann problem. We use the Green's function approach, making essential use of the Hopf Cole transformation for the Burgers equation.\",\n \"Title: Line-Constrained k-Median, k-Means, and k-Center Problems in the Plane.\\nYear: 2016\\n\\nAbstract: The (weighted) k-median, k-means, and k-center problems in the plane are known to be NP-hard. In this paper, we study these problems with an additional constraint that requires the sought k facilities to be on a given line. We present efficient algorithms for various distance measures such as L1,L2,L∞. We assume that all n weighted points are given sorted by their projections on the given line. For k-median, our algorithms for L1 and L∞ metrics run in O(min{nk,nklog nlog n,n2O(log klog log n)log n}) time and O(min{nklog n,nklog nlog2n,n2O(log klog log n)log2n}) time, respectively. For k-means, which is defined only on the squared L2 distance, we give an O(min{nk,nklog n,n2O(log klog log n)}) time algorithm. For k-center, our algorithms run in O(nlog n) time for all three metrics, and in O(n) time for the unweighted version under L1 and L∞ metrics. While our results for the k-center problem are optimal, the results for the k-median problem almost match the best algorithms for the corresponding one-dimensio...\",\n \"Title: Mobile Multimedia Classroom Construction For Rhythmic Gymnastics Based On Apt Teaching Model\\nYear: 2017\\n\\nAbstract: APT teaching model is an information-based teaching model which includes Assessment, Pedagogy and Technology. It can improve students' learning initiative and autonomous learning ability. In this study, mobile multimedia classroom based on APT teaching model was designed for Rhythmic Gymnastics. With APT teaching model, learners' learning is no longer limited to time and place. It can motivate learners' learning interest and enhance learning efficiency. The mobile multimedia classroom was constructed from three aspects: assessment, pedagogy and technology. The final teaching test shows that, learners are very interested in the teaching mode of mobile multimedia classroom and express a strong wish for such teaching mode. Mobile multimedia classroom for Rhythmic Gymnastics based on APT teaching model has a promising application prospect in actual teaching process.\",\n \"Title: Quickest Visibility Queries in Polygonal Domains.\\nYear: 2017\\n\\nAbstract: Let s be a point in a polygonal domain \\n \\n \\n \\n $${\\\\mathcal {P}}$$\\n \\n \\n \\n of \\n \\n \\n \\n $$h-1$$\\n \\n \\n \\n holes and n vertices. We consider a quickest visibility query problem. Given a query point q in \\n \\n \\n \\n $${\\\\mathcal {P}}$$\\n \\n \\n \\n , the goal is to find a shortest path in \\n \\n \\n \\n $${\\\\mathcal {P}}$$\\n \\n \\n \\n to move from s to seeq as quickly as possible. Previously, Arkin et al. (SoCG 2015) built a data structure of size \\n \\n \\n \\n $$O(n^22^{\\\\alpha (n)}\\\\log n)$$\\n \\n \\n \\n that can answer each query in \\n \\n \\n \\n $$O(K\\\\log ^2 n)$$\\n \\n \\n \\n time, where \\n \\n \\n \\n $$\\\\alpha (n)$$\\n \\n \\n \\n is the inverse Ackermann function and K is the size of the visibility polygon of q in \\n \\n \\n \\n $${\\\\mathcal {P}}$$\\n \\n \\n \\n (and K can be \\n \\n \\n \\n $$\\\\varTheta (n)$$\\n \\n \\n \\n in the worst case). In this paper, we present a new data structure of size \\n \\n \\n \\n $$O(n\\\\log h + h^2)$$\\n \\n \\n \\n that can answer each query in \\n \\n \\n \\n $$O(h\\\\log h\\\\log n)$$\\n \\n \\n \\n time. Our result improves the previous work when h is relatively small. In particular, if h is a constant, then our result even matches the best result for the simple polygon case (i.e., \\n \\n \\n \\n $$h=1$$\\n \\n \\n \\n ), which is optimal. As a by-product, we also have a new algorithm for a shortest-path-to-segment query problem. Given a query line segment \\n \\n \\n \\n $$\\\\tau $$\\n \\n \\n \\n in \\n \\n \\n \\n $${\\\\mathcal {P}}$$\\n \\n \\n \\n , the query seeks a shortest path from s to all points of \\n \\n \\n \\n $$\\\\tau $$\\n \\n \\n \\n . Previously, Arkin et al. gave a data structure of size \\n \\n \\n \\n $$O(n^22^{\\\\alpha (n)}\\\\log n)$$\\n \\n \\n \\n that can answer each query in \\n \\n \\n \\n $$O(\\\\log ^2 n)$$\\n \\n \\n \\n time, and another data structure of size \\n \\n \\n \\n $$O(n^3\\\\log n)$$\\n \\n \\n \\n with \\n \\n \\n \\n $$O(\\\\log n)$$\\n \\n \\n \\n query time. We present a data structure of size O(n) with query time \\n \\n \\n \\n $$O\\\\big (h\\\\log \\\\frac{n}{h}\\\\big )$$\\n \\n \\n \\n , which also favors small values of h and is optimal when \\n \\n \\n \\n $$h=O(1)$$\\n \\n \\n \\n .\",\n \"Title: Algorithms for Covering Multiple Barriers.\\nYear: 2017\\n\\nAbstract: In this paper, we consider the problems for covering multiple intervals on a line. Given a set B of m line segments (called \\\"barriers\\\") on a horizontal line L and another set S of n horizontal line segments of the same length in the plane, we want to move all segments of S to L so that their union covers all barriers and the maximum movement of all segments of S is minimized. Previously, an O(n(3) log n)-time algorithm was given for the problem but only for the special case m = 1. In this paper, we propose an O(n(2) log n log log n + nm log m)-time algorithm for any m, which improves the previous work even for m = 1. We then consider a line-constrained version of the problem in which the segments of S are all initially on the line L. Previously, an O(n log n)-time algorithm was known for the case m = 1. We present an algorithm of O((n+ m) log(n+ m)) time for any m. These problems may have applications in mobile sensor barrier coverage in wireless sensor networks.\",\n \"Title: COPSS-lite: Lightweight ICN Based Pub/Sub for IoT Environments.\\nYear: 2017\\n\\nAbstract: Information Centric Networking is a new networking paradigm that treats content as first class entity. It provides content to users without regards to the current location of the content. The publish/subscribe systems have gained popularity in Internet. Pub/sub systems dismisses the need for users to request every content of their interest. Instead, the content is supplied to interested users (subscribers) as and when it is published. CCN/NDN are popular ICN proposals widely accepted in the ICN community however, they do not provide an efficient pub/sub mechanism. COPSS enhances CCN/NDN with an efficient pub/sub capability. Internet of Things (IoT) is a growing topic of interest in both Academia and Industry. The current designs for IoT relies on IP. However, the IoT devices are constrained in their available resources and IP is heavy for their operation.We observed that IoT's are information centric in nature and hence ICN is a more suitable candidate to support IoT environments. Although NDN and COPSS work well for the Internet, their current full fledged implementations cannot be used by the resource constrained IoT devices. CCN-lite is a light weight, inter-operable version of the CCNx protocol for supporting the IoT devices. However, CCN-lite like its ancestors lacks the support for an efficient pub/sub mechanism. In this paper, we developed COPSS-lite, an efficient and light weight implementation of pub/sub for IoT. COPSS-lite is developed to enhance CCN-lite and also support multi-hop connection by incorporating the famous RPL protocol for low power and lossy networks. We provide a preliminary evaluation to show proof of operability with real world sensor devices in IoT lab. Our results show that COPSS-lite is compact, operates on all platforms that support CCN-lite and we observe significant performance benefits with COPSS-lite in IoT environments.\",\n \"Title: An O(n\\\\log n)-Time Algorithm for the k-Center Problem in Trees.\\nYear: 2018\\n\\nAbstract: We consider a classical k-center problem in trees. Let T be a tree of n vertices and every vertex has a nonnegative weight. The problem is to find k centers on the edges of T such that the maximum weighted distance from all vertices to their closest centers is minimized. Megiddo and Tamir (SIAM J. Comput., 1983) gave an algorithm that can solve the problem in O(n log^2 n) time by using Cole’s parametric search. Since then it has been open for over three decades whether the problem can be solved in O(n log n) time. In this paper, we present an O(n log n) time algorithm for the problem and thus settle the open problem affirmatively.\",\n \"Title: Bicriteria Rectilinear Shortest Paths among Rectilinear Obstacles in the Plane.\\nYear: 2017\\n\\nAbstract: Given a rectilinear domain P of h pairwise-disjoint rectilinear obstacles with a total of n vertices in the plane, we study the problem of computing bicriteria rectilinear shortest paths between two points s and t in P. Three types of bicriteria rectilinear paths are considered: minimum-link shortest paths, shortest minimum-link paths, and minimum-cost paths where the cost of a path is a non-decreasing function of both the number of edges and the length of the path. The one-point and two-point path queries are also considered. Algorithms for these problems have been given previously. Our contributions are threefold. First, we find a critical error in all previous algorithms. Second, we correct the error in a not-so-trivial way. Third, we further improve the algorithms so that they are even faster than the previous (incorrect) algorithms when h is relatively small. For example, for computing a minimum-link shortest s-t path, the previous algorithm runs in O(n log^{3/2} n) time while the time of our new algorithm is O(n + h log^{3/2} h).\",\n \"Title: Hardware Acceleration for Machine Learning.\\nYear: 2017\\n\\nAbstract: This paper presents an approach to enhance the performance of machine learning applications based on hardware acceleration. This approach is based on parameterised architectures designed for Convolutional Neural Network (CNN) and Support Vector Machine (SVM), and the associated design flow common to both. This approach is illustrated by two case studies including object detection and satellite data analysis. The potential of the proposed approach is presented.\",\n \"Title: A Web3D forest geo-visualization and user interface evaluation.\\nYear: 2017\\n\\nAbstract: In this study, we developed a workflow for the construction of WebGL rendering and interactive visualization of forest landscapes from GIS and forest simulation datasets. We constructed a realistic virtual forest environment for our study site in Virginia. This Web3D virtual environment, built with X3DOM and HTML5, is capable of fast real-time rendering and walk-through simulation based on two Level of Details (LOD) switching techniques, and interactive over mainstream web browsers without a plugin. We explored the performance of a navigation aid and two types of LOD cut-off functions in the forest geo-visualization. We used a hunting game search task to test the effect of our user interface features. According to the paired t-test results, the navigation aid significantly increasing participants' performance, while the exponential cut-off and linear cut-off functions showed no significant difference from participants' perspective.\",\n \"Title: k-Regret Minimizing Set: Efficient Algorithms and Hardness.\\nYear: 2017\\n\\nAbstract: We study the k-regret minimizing query (k-RMS), which is a useful operator for supporting multi-criteria decision-making. Given two integers k and r, a k-RMS returns r tuples from the database which minimize the k-regret ratio, defined as one minus the worst ratio between the k-th maximum utility score among all tuples in the database and the maximum utility score of the r tuples returned. A solution set contains only r tuples, enjoying the benefits of both top-k queries and skyline queries. Proposed in 2012, the query has been studied extensively in recent years. In this paper, we advance the theory and the practice of k-RMS in the following aspects. First, we develop efficient algorithms for k-RMS (and its decision version) when the dimensionality is 2. The running time of our algorithms outperforms those of previous ones. Second, we show that k-RMS is NP-hard even when the dimensionality is 3. This provides a complete characterization of the complexity of k-RMS, and answers an open question in previous studies. In addition, we present approximation algorithms for the problem when the dimensionality is 3 or larger.\",\n \"Title: An Efficient Polarity Optimization Approach for Fixed Polarity Reed-Muller Logic Circuits Based on Novel Binary Differential Evolution Algorithm.\\nYear: 2017\\n\\nAbstract: The bottleneck of integrated circuit design could potentially be alleviated by using Reed-Muller (RM) logic circuits due to their remarkable superiority in power, area and testability. In this paper, we propose a Novel Binary Differential Evolution (DE) algorithm (NBDE) to solve the discrete binary-encoded combination optimization problem. Moreover, based on the NBDE, we propose an Efficient Polarity Optimization Approach (EPOA) for Fixed Polarity RM (FPRM) logic circuits, which uses the NBDE to search the best polarity under a performance constraint. To the best of our knowledge, we are the first to use DE to optimize RM circuits. The experimental results on 24 MCNC benchmark circuits show the effectiveness and superiority of EPOA.\",\n \"Title: Balancing Global and Local Fairness Allocation Model in Heterogeneous Data Center.\\nYear: 2017\\n\\nAbstract: Aiming at the problem that the bandwidth allocation method in heterogeneous data center can't take into account the global and local fairness, we propose an allocation method called GLA that satisfies fair attributes and can improve the utilization of system resources by nearly 20% when the number of clients are more than six contrasting with the existing methods. The algorithm is also protable and scalable that can adapt to most of systems.\",\n \"Title: Time-Delay And Amplitude Modified Bp Imaging Algorithm Of Multiple Targets For Uwb Through-The-Wall Radar Imaging\\nYear: 2017\\n\\nAbstract: In order to solve the undetected probability of multiple targets in ultra-wideband (UWB) through-the-wall radar imaging (TWRI), a time-delay and amplitude modified back projection (BP) algorithm is proposed. The refraction point is found by Fermat's principle in the presence of a wall, and the time-delay is correctly compensated. On this basis, transmission loss of the electromagnetic wave, the absorption loss of the refraction wave, and the diffusion loss of the spherical wave are analyzed in detail. Amplitude compensation is deduced and tested on a model with a single-layer wall. The simulating results by finite difference time domain (FDTD) show that it is effective in increasing the scattering intensity of the targets behind the wall. Compensation for the diffusion loss in the spherical wave also plays a main role. Additionally, the two-layer wall model is simulated. Then, the calculating time and the imaging quality are compared between a single-ayer wall model and a two-layer wall model. The results illustrate the performance of the time-delay and amplitude-modified BP algorithm with multiple targets and multiple-layer walls of UWB TWRI.\",\n \"Title: An online learning neural network ensembles with random weights for regression of sequential data stream.\\nYear: 2017\\n\\nAbstract: An ensemble of neural networks has been proved to be an effective machine learning framework. However, very limited studies in the current literature examined the neural network ensemble for online regression; furthermore, these methods were combination of online individual models and did not consider the ensemble diversity. In this paper, a novel online sequential learning algorithm for neural network ensembles for online regression is proposed. The algorithm is built upon the decorrelated neural network ensembles (DNNE) and thus referred to as Online-DNNE; so it uses single-hidden layer feed-forward neural networks with random hidden nodes’ parameters as ensemble components and introduces negative correlation learning to train base models simultaneously in a cooperative manner which can effectively maintain the ensemble diversity. The Online-DNNE only learns the newly arrived data, and the computation complexity is thus reduced. The results of the experiments with benchmarks show the effectiveness and significant advantages of the proposed approach.\",\n \"Title: A Construction Method for the Semantic Relation Corpus of Traditional Chinese Medicine.\\nYear: 2017\\n\\nAbstract: It is difficult to use a search engine to acquire knowledge directly due to the complexity of Web resources. In this paper we proposed a method for semantic relation corpus construction of traditional Chinese medicine based on combination of multiple encyclopedias. For a known conceptual pair, we got some search results by automatically constructing search requests based on URLs' characteristics of the encyclopedia search engine, and used regular expressions to extract meaningful texts from the search results to form semantic relation corpus. The experiment result shows that the precision and recall are 92.1% and 65.3%, respectively.\",\n \"Title: N2DLOF: A New Local Density-Based Outlier Detection Approach for Scattered Data\\nYear: 2017\\n\\nAbstract: Since the Local Outlier Factor (LOF) was first proposed, there is a large family of approaches that is derived from it. For the reason that the existing local outliers detection approaches only focus on the extent of overall separation between an object and its neighbors, and do not pay attention to the degree of dispersion between them, the precision of these approaches will be seriously affected in the scattered data sets for outlier detection. In this paper, we redefine the local outliers by combining the degree of dispersion of the object and its neighbors, and propose a new local outlier detection approach (N2DLOF). Compared to conventional approaches, the outliers obtained by N2DLOF are more sensitive to the degree of anomaly of the scattered data sets. Experiments show that our approach has a significant improvement on outlier detection precision in the case of scattered datasets with similar time complexity. In short, we extend the ecosystem of the local outlier detection approaches from a new perspective.\",\n \"Title: Maximization of link capacity by joint power and spectrum allocation for smart satellite transponder.\\nYear: 2017\\n\\nAbstract: The contradiction between ever increasing satellite communication traffic and limited satellite transponder resources motivates a more dynamic allocation and more effective utilization of satellite transponders' resources. In this paper, the link capacity for a smart satellite transponder is maximized with limited available power and spectrum resource at satellite transponder. Specifically, given that the satellite transponder broadcasts the signals from the gateway station to the multiple satellite terminals with minimum transmission rate requirement, the satellite transponder needs to provide as large link capacity as possible to gateway station for the amount data transmission of special demands. The problem is formulated with aim of maximizing link capacity, and subject to minimum transmission rate requirement of link to satellite terminals and available resource allocation. The finely-matched dynamic power and spectrum allocation scheme is proposed to achieve maximization of both target link capacity and transponder resource utilization. Simulations results demonstrate that the proposed scheme outperforms tradition schemes and the superiority is even more remarkable in multi-constrained situations.\",\n \"Title: Timetable-aware opportunistic DTN routing for vehicular communications in battlefield environments.\\nYear: 2018\\n\\nAbstract: Delay tolerant network (namely DTN) is regarded as a promising technology for communications in challenging environments for its resilience and adaptability. However, efficient and reliable routing algorithm design is still one of many difficulties it faces in achieving broad and fruitful applications. Due to reasons like electromagnetic inference, lack of infrastructure and constantly moving, communication among vehicles in battlefield environments is difficult and makes a prospective field for DTN to bring effective improvements. In this paper, a timetable-aware opportunistic DTN routing algorithm combining limited message copies and biased message spray is proposed for this scenario. Firstly, a mobility model named timetable based aggregation and spread (ASMM) is abstracted, which captures both the integrity and individuality in vehicles’ typical moving behavior on military missions. Then, from the time and coordination information contained in the preset timetables, the meeting chances for different node groups can be predicted. This meeting opportunity is used as a utility for message transmission evaluation in our proposed routing algorithm called GenericSpray. GenericSpray is able to make more accurate and motivated message transferring decisions by exploiting contact opportunity prediction in choosing among possible message carriers and deciding appropriate number of copies to spray. To investigate its performance, both ASMM and GenericSpray are implemented on The ONE, a specialized DTN simulation platform. Results of simulation experiments indicate that GenericSpray outperforms all other classical DTN routing algorithms chosen as contrast, in terms of delivery ratio, delivery delay and transmission overhead.\",\n \"Title: Routing Algorithm with Virtual Topology Toward to Huge Numbers of LEO Mobile Satellite Network Based on SDN.\\nYear: 2018\\n\\nAbstract: SDN network is a dynamic, controllable, cost-effective and adaptable system. It is suitable for communication networks with high bandwidth and high dynamic characteristics. Therefore, combining SDN ideas with the new generation of LEO satellite networks can achieve more flexible monitoring and management of the network, and can make the network expansion more convenient. Joint the Depth-First-Search (DFS) idea and Dijkstra algorithm for the huge numbers of LEO mobile satellite network based on SDN is proposed to improve the computational efficiency and the reliability of calculation result. Moreover, the communication performance of space-based network based on SDN and traditional space-based network is compared and analyzed. The simulation results show that the huge numbers of LEO mobile satellite network based on SDN breaks through the performance limitations of the traditional network architecture, and it can achieve better performance of the network.\",\n \"Title: Feasibility of Wrist-Worn, Real-Time Hand, and Surface Gesture Recognition via sEMG and IMU Sensing.\\nYear: 2018\\n\\nAbstract: While most wearable gesture recognition approaches focus on the forearm or fingers, the wrist may be a more suitable location for practical use. We present the design and validation of a real-time gesture recognition wristband based on surface electromyography and inertial measurement unit sensing fusion, which can recognize 8 air gestures and 4 surface gestures with 2 distinct force levels. Ten h...\",\n \"Title: Real-Time Monitoring for BDS Signal-In-Space Anomalies Using Ground Observation Data.\\nYear: 2018\\n\\nAbstract: Signal-in-space (SIS) User Range Error (URE) is one of the major error sources for BeiDou Navigation Satellite System (BDS) applications and can reach tens of meters or even more. Therefore, real-time monitoring of SIS anomalies has a great realistic significance to guarantee the safety of users. According to an analysis of the BDS navigation messages, it showed that the User Range Accuracy (URA) index could not reflect the change of URE when it was abnormal. The conventional models using the relationship between URA and URE to monitor SIS anomalies are not suitable to the present BDS. Therefore, we use a prior information of SIS URE derived from ground observational data instead of URA to monitor BDS SIS anomalies. In order to realize the corresponding functions, we analysed the distribution of SIS UREs and obtained their prior models. Then, the monitoring threshold is determined using the prior models and a confidence interval instead of URA. The scheme was tested by applying to BDS SIS anomalies monitoring based on 13 ground tracking stations. The performance of this method was assessed by comparison with the satellite-health indicators from broadcast ephemeris. The results confirm that the method developed in this paper can rightly and timely detect abnormal SIS.\",\n \"Title: RTTV: a dynamic CFI measurement tool based on TPM.\\nYear: 2018\\n\\nAbstract: In programme dynamic analysis, control flow integrity (CFI) is an efficient way to investigate programme's behaviour. By detecting these CF instructions, researchers can obtain programme's runtime information and execution status accurately. This feature makes CFI a sharp and sensitive approach to detect programme abnormal conditions and malicious attacks such as stack overflow and return-oriented...\",\n \"Title: Inferring large-scale gene regulatory networks using a randomized algorithm based on singular value decomposition.\\nYear: 2019\\n\\nAbstract: Reconstructing large-scale gene regulatory networks (GRNs) is a challenging problem in the field of computational biology. Various methods for inferring GRNs have been developed, but they fail to accurately infer GRNs with a large number of genes. Additionally, the existing evaluation indexes for evaluating the constructed networks have obvious disadvantages because GRNs in most biological systems are sparse. In this paper, we develop a new method for inferring GRNs based on randomized singular value decomposition (RSVD) and ordinary differential equation (ODE)-based optimization, denoted as IGRSVD, from large-scale time series data with noise. The three major contributions of this paper are as follows. First, the IGRSVD algorithm uses the RSVD to handle the noise and reduce the original large-scale data into small-scale problems. Second, we propose two new evaluated indexes, the expected value accuracy (EVA) and the expected value error (EVE), to evaluate the performance of inferred networks by considering the sparse features in the network. Finally, the proposed IGRSVD algorithm is compared with the existing SVD algorithm and PCA_CMI algorithm using four subsets from E. coli and datasets from DREAM challenge. The experimental results demonstrate that the IGRSVD algorithm is effective and more suitable for reconstructing large-scale networks.\",\n \"Title: A Two-Level Classifier Model for Sentiment Analysis.\\nYear: 2017\\n\\nAbstract: This paper proposes a fast and high performance classifier model for sentiment analysis of textual reviews. The key contribution is three fold. First, a two-level classifier model consists of three base classifiers is proposed, and theory proves that the model could be better than the strongest classifier among the base classifiers in both classification performance and time cost of predict. Second, this paper proposes a lexicon-based classifier as a base classifier using a new part of speech (POS) which is called \\\"weaken words\\\". Finally, we implemented several two-level classifiers by combining the lexicon-based classifier with several machine learning classifiers. Experiments on Chinese reviews dataset show that the two-level classifier model is effective and efficient.\",\n \"Title: HSAStore - A Hierarchical Storage Architecture for Computing Systems Containing Large-Scale Intermediate Data.\\nYear: 2017\\n\\nAbstract: This paper introduces HSAStore, a newly designed storage system whose goal goes to build an efficient storage system for computing systems that containing large-scale intermediate data. HSAStore involves three sub-systems to work together, a distributed file system which is in charge of the intermediate data, a centralized Network Attached Storage (NAS) which stores the raw input data and the results data, and a local file system that serves for the local data. The HSAStore takes full advantage of the network bandwidth of the computing cluster. Moreover, HSAStore adopts the distributed file system, so it is helpful for efficient execution of parallel programs. Experiments show that HSAStore has a significant improvement on efficiency in the computing systems that containing large-scale intermediate data.\",\n \"Title: Human-Like ZMP Generator and Walking Stabilizer Based on Divergent Component of Motion\\nYear: 2018\\n\\nAbstract: To realize natural and fast bipedal walking, we mimic the ZMP trajectory from human walking data based on the concept of Divergent Component of Motion (DCM). This paper presents an omnidirectional gait generator that is lightweight and universal. In addition, we integrate DCM tracking and foot placement adjustment into our control framework to ensure a stable walking motion and push recovery. The method proposed was validated in RoboCup2018 AdultSize soccer competition, where our robot WALKER+ walked on grass field and kept balance after the push from other robots.\",\n \"Title: L-Fcn: A Lightweight Fully Convolutional Network For Biomedical Semantic Segmentation\\nYear: 2018\\n\\nAbstract: For the past few years, deep learning-based methods have been widely used in the field of biomedical imaging. In biomedical image processing, the typical application of deep learning is semantic segmentation. However, the classical deep learning methods require higher hardware consumption and computational costs. In order to resolve this problem, we propose a new lightweight fully convolutional network (L-FCN). L-FCN consists of traditional watershed algorithm and fully convolutional network, which eliminates useless non-edge pixels to improves the efficiency of semantic segmentation. Experimental evaluations on ISBI 2012 dataset indicate that our L-FCN algorithm outperforms U-net in time e square ciency and computational costs, meanwhile, maintain approximate image segmentation effect. Due to its portability, L-FCN can be applied to many biomedical areas easily.\",\n \"Title: Localization Performance Under Middle And Low Frequency Sound Source Based On Time Reversal Method In Enclosed Space\\nYear: 2019\\n\\nAbstract: Multipath interference, reflection, sound absorption, and obstacles are important factors affect the sound source localization performance in enclosed space. Time reversal has the advantages of adaptive focusing, strong anti-reverberation capability, and the ability to overcome multipath effects, which are quite suitable for sound source localization in enclosed space due to its complex environment that many methods (e.g., beamforming, high-resolution spectral estimation, and the time delay of arrival) are not applicable. Therefore, this paper studies the localization performance under middle and low frequency sound sources cover single-frequency sinusoidal signal, bandwidth signal, and impulse signal through dual-channel matching based on time reversal method. When the frequency is below 1 kHz, the media's absorption is generally negligible. Therefore, this paper only studies frequencies within 1 kHz. In this paper, numerical simulations are used to verify the feasibility of dual-channel matching under middle and low frequency sound sources in ordinary enclosed space. Then, actual experiments are conducted in an ordinary office on the case of different sound sources. The numerical simulation and experiment results show that dual-channel matching method has satisfying localization effect. The localization effect of medium frequency signal is superior to low frequency signal. Bandwidth signals have better localization effect than single-frequency sinusoidal signals, and steady state sound source has better localization effect than impulse sound source. In general, dual-channel matching method has strong robustness and applicability for sound source localization in enclosed space.\",\n \"Title: Self-Organizing Approximation Command Filtered Backstepping Control for Higher Order SISO Systems in Internet of Things.\\nYear: 2019\\n\\nAbstract: With the rapid development of the Internet of Things (IOT) and increasing research on tracking control of the affine IOT systems with uncertainty, a nonlinear control method of command filtering backstepping with self-organizing approximation is proposed. A command filter with a saturation structure is used to eliminate the analytic computation of command derivatives, and tracking errors are redefined to compensate the discrepancy between the command filtered signals and the analytic computation of command derivatives. Command filtered backstepping is used to decouple the higher IOT system and simplify the structure of the IOT system. Self-organizing approximation is used to online approximate the uncertain and eliminate the effect of the uncertain in the IOT system. The approximation for the uncertain is finished by a linear product between a set of basis functions and weighted functions on the local region. The weighted sum of compensated tracking errors is used as the prespecified tracking accuracy performance. A new local approximator is added to the control system according to the criterion based on the Lyapunov theory to enhance the capacity of the approximator when the prespecified tracking accuracy performance cannot be achieved. Finally, an application for a simple IOT system is analyzed by simulations. The simulation results show that the control method for the IOT system is effective.\",\n \"Title: Field Measurement And Numerical Simulation Of The Influence Of Blasting Excavation On Adjacent Buried Pipelines\\nYear: 2019\\n\\nAbstract: The blasting seismic waves generated by tunnel blasting will have an adverse effect on adjacent buried pipelines, which increasing the difficulty of subway construction. In this paper, taking the section of blasting excavation in Dalian Metro Line 1 as the engineering background, the attenuation law of blasting seismic wave in the soil and the vibration response of the buried pipeline are systematically studied by means of field measurement and numerical simulation. The deformation monitoring of the pipeline shows that the time curve of the pipeline and the surface settlement are distributed exponentially, and the trend of them is the same. The attenuation law of seismic wave obtained by numerical simulation is basically consistent with the measured results, which indicates that it is feasible to study the blasting vibration effect by numerical simulation. The Sa Rodolfo J Ki prediction formula is established according to the measured data, which can reflect the propagation law of the blasting seismic wave. It can provide some basis for the construction of subway tunnels at the subsequent stage.\",\n \"Title: Approximating Points by a Piecewise Linear Function: II. Dealing with Outliers\\nYear: 2009\\n\\nAbstract: In this paper, we study the violation versions of the planar points approximation problems, which deal with outliers in the input points. We present efficient algorithms for both the step function and the more general piecewise linear function cases, and for both non-weighted and weighted points. Most of our results are first-known. Our algorithms are based on interesting and nontrivial geometric techniques and data structures, which may find other applications.\",\n \"Title: Circular range search on encrypted spatial data\\nYear: 2015\\n\\nAbstract: Searchable encryption is a promising technique enabling meaningful search operations to be performed on encrypted databases while protecting user privacy from untrusted third-party service providers. However, while most of the existing works focus on common SQL queries, geometric queries on encrypted spatial data have not been well studied. Especially, circular range search is an important type of geometric query on spatial data which has wide applications, such as proximity testing in Location-Based Services and Delaunay triangulation in computational geometry. In this paper, we propose two novel symmetric-key searchable encryption schemes supporting circular range search. Informally, both of our schemes can correctly verify whether a point is inside a circle on encrypted spatial data without revealing data privacy or query privacy to a semi-honest cloud server. We formally define the security of our proposed schemes, prove that they are secure under Selective Chosen-Plaintext Attacks, and evaluate their performance through experiments in a real-world cloud platform (Amazon EC2).\",\n \"Title: Passive Moving Target Classification Via Spectra Multiplication Method.\\nYear: 2017\\n\\nAbstract: Traditional feature extractions, such as mel-frequency cepstral coefficients (MFCCs), are susceptible to acoustic channel effects, reverberation, and additive environmental noises when applied to passive moving target classification in underwater environment. A spectra multiplication method (SMM) is proposed in this letter to replace the estimated spectrum of MFCCs. SMM suppresses the time-variant...\",\n \"Title: Design and performance analysis of high response actuator using magnetic shape memory alloy\\nYear: 2017\\n\\nAbstract: Magnetic shape memory alloy (MSMA) has recently emerged as a new type of multifunctional material exhibiting excellent performance as fast response and large strain. Constitution equations are derived based on the magnet-strain effect of MSMA. Experiments are conducted to explore the magnetic field induced strain, and data are applied to establish neural network based models to build the nonlinear relationship between strain, stress and other related parameters. Actuator design, magnetic field analysis and dynamic analysis are carried out. With a combination of the characteristics of ordinary magnetostrictive materials and conventional shape memory alloy, it is qualified for high performance actuator.\",\n \"Title: Optimization Of Index-Based Method Of Metadata Search For Large-Scale File Systems\\nYear: 2017\\n\\nAbstract: With the vigorous development of the information era, the volume of data in file systems has grown to TB or EB. Metadata search determines the speed of retrieving required files in such large-scale file systems. There are many researches on metadata search based on different theories, and have made great achievements. Some researches show that the distribution of metadata presents sub-tree locality and horizontal locality, and metadata search presents heavy-tailed distribution. However, current methods do not make full use of the spatial locality and load characteristics in metadata search. In this paper, we put forward a metadata search method based on keyword-index. Our method fully exploits characteristics of metadata distribution and metadata search by reasonably partitioning indexes. The experimental result shows that our partitioning method is more efficient than current non partitioning method.\",\n \"Title: Intelligent Particle Blind Source Separation Research\\nYear: 2018\\n\\nAbstract: A new sequential blind source separation algorithm is proposed based on intelligent single particle optimization. The signal variability is used as the objective function and the separation vector is transformed by using the spherical coordinate transform method. The intelligent single particle optimization is used for solving the objective function. By de-correlation method, the separated source signal component is removed from the mixed signals, and source signal could be separated out respectively according to the decreasing order of the signal variability. Simulation results show that the proposed separation algorithm can realize sequential separation of source signal efficiently and the separation precision is very high.\",\n \"Title: A New Static Routing Algorithm of GEO/LEO Hybrid Satellite Networks\\nYear: 2017\\n\\nAbstract: With the rapid development of satellite mobile communication, building a satellite/terrestrial integrated network is the inevitable trend of satellite communication. Based on the network of backbone and access, a global coverage of GEO/LEO hybrid satellite constellations are designed. As the backbone network, GEO constellation realize relay and management. As the access network, LEO constellation realize the communication with the terrestrial users. A new static routing strategy based on virtual topology is proposed, which provides significant advantages of low complexity, high efficiency and low routing overhead. The satellite networks can provide the features of flexible structure, easy management, efficient transferring, and potential in scalability.\",\n \"Title: A Resource Allocation Scheme with Excellent Energy Efficiency Based on the Channel Matrix Sparse\\nYear: 2017\\n\\nAbstract: This paper introduces a C-RAN scenario which is overlapping covered with the macrocell, residential microcells, office microcells under different activeness. Due to the large scare-fading in the urban environment, the signal to interference and noise ratio (SINR) between some offices and the family micro base stations are too small to care. Therefore, some interference between micro base stations and user are not considerable. In order to improve the energy efficiency in the CoMP process, this paper presents selective beamforming with channel matrix sparse algorithm. This algorithm greatly reduces the complexity of matrix inversion calculation during beamforming and improves the energy efficiency of the system. Simulation results show that the proposed algorithm can effectively reduce the energy consumption of the baseband and improve the energy efficiency of the system.\",\n \"Title: M-estimator based robust unscented Kalman filter through statistical linearization\\nYear: 2019\\n\\nAbstract: The robust unscented Kalman filter (UKF) is revisited in this paper from a new point of view, namely the statistical linear regression (SLR) perspective of the unscented transformation (UT). When the actual distribution of the observation noise deviates from the assumed Gaussian distribution, the performance of the filter may degrade significantly owing to lacking robustness. After linearizing the nonlinear observation equation using the SLR perspective, the M-estimator is employed to replace the weighted least-squares one, resulting in the robust UKF. Besides providing new theoretical aspects, the advantageous performance of the proposed filter is re-emphasized in terms of the following three: (1) robust, the proposed filter is robust against deviations from the assumed Gaussian distribution; (2) efficient, by elaborately selecting the tuning parameter of the M-estimator, the robust filter will have a slight efficiency loss compared with the UKF when the Gaussian assumption does hold; (3) accurate, the proposed filter achieves robustness without compromising the accuracy of the UT.\",\n \"Title: Location-Assistant Content Distribution Scheme For Emergency Rescue\\nYear: 2018\\n\\nAbstract: In order to optimize the timeliness and pertinence of message delivery in emergency rescue scenarios and improve the service performance of emergency communications network, we propose a location-assistant content distribution scheme based on delay tolerant network. First of all, considering that the movement patterns of rescue teams tend to follow a predetermined course of action, we design a location-based group mobility model. Due to the intermittent network connectivity and variety of emergency service, a content-classification-based publish/subscribe architecture and a GenericSpray routing algorithm based on the prediction of overlap opportunity in spatio-temporal positions are proposed. Furthermore, we also give a cache management strategy based on the content significance. Since locationassistant content distribution scheme can predict the overlap of activity between rescue teams through the course of action, not only can the number of copy forwarding and message delivery delays be significantly reduced but also the priority delivery of important messages can be ensured by message classification. Simulation experiments show that compared with the traditional delay tolerant network routing algorithm and the classic first-in-first-out caching strategy, location-assistant content distribution scheme improves the performance of message delivery rate, transmission delay, and control overhead significantly.\",\n \"Title: Spatio-Temporal Change of the Cover and Management Factor in the Soil Erosion Prediction Model in Hunan Province, China * This research was supported by the Project of National Natural Science Foundation of China (41371184) and Water Science and Technology Program of Hunan Province in China (2017-230-40).\\nYear: 2018\\n\\nAbstract: Soil erosion is an important research issue because it can result in serious hazards, such as land quality degrading, flood frequency increasing and deposit in rivers and lakes. The factor of cover and management is a parameter of the soil erosion prediction model and it is the ratio of soil erosion of the present cover and management to continuous clear tillage. Good vegetation cover and the effective land management can control soil erosion to some extent. Quantitative assessment the factor of cover and management in a region scale can provide the cover and management data and some good suggestion for the land use and soil conservation planning. This study chooses Hunan Province in the central and southern China which has no relevant research till now. With the land use and vegetation coverage data of 30 meters resolution extracted from Landsat images in 2000, 2005, 2010 and 2015, the cover and management factor of this region is evaluated and the spatial-temporal characteristics of land use and the factor is analyzed by using the evaluation method, GIS spatial analysis and statistical analysis. The results are as follows. Because the main terrains are mountains and hills which account for 70 percent of the whole province area, the main land use types are forest land and grassland. The mean of cover and management factor is 0.06739, 0.06783, 0.06876 and 0.07421 in 2000, 2005, 2010 and 2015 respectively. The factor increased about 10 percent from 2000 to 2015. The change of cover and management factor may increase soil losses risk if the other factors affecting soil losses are not taken into account.\",\n \"Title: Covering Uncertain Points in a Tree.\\nYear: 2017\\n\\nAbstract: In this paper, we consider a coverage problem for uncertain points in a tree. Let T be a tree containing a set $${\\\\mathcal {P}}$$P of n (weighted) demand points, and the location of each demand point $$P_i\\\\in {{\\\\mathcal {P}}}$$Pi?P is uncertain but is known to appear in one of $$m_i$$mi points on T each associated with a probability. Given a covering range$$\\\\lambda $$?, the problem is to find a minimum number of points (called centers) on T to build facilities for serving (or covering) these demand points in the sense that for each uncertain point $$P_i\\\\in {{\\\\mathcal {P}}}$$Pi?P, the expected distance from $$P_i$$Pi to at least one center is no more than $$\\\\lambda $$?. The problem has not been studied before. We present an $$O(|T|+M\\\\log ^2 M)$$O(|T|+Mlog2M) time algorithm for the problem, where |T| is the number of vertices of T and M is the total number of locations of all uncertain points of $${{\\\\mathcal {P}}}$$P, i.e., $$M=\\\\sum _{P_i\\\\in {{\\\\mathcal {P}}}}m_i$$M=?Pi?Pmi. In addition, by using this algorithm, we solve a k-center problem on T for the uncertain points of $${{\\\\mathcal {P}}}$$P.\",\n \"Title: OC-Cache: An Open-channel SSD Based Cache for Multi-Tenant Systems\\nYear: 2018\\n\\nAbstract: In a multi-tenant cloud environment, tenants are usually hosted by virtual machines. Cloud providers deploy multiple virtual machines on a physical server to better utilize physical resources including CPU, memory, and storage devices. SSDs are often used as an I/O cache shared among the tenants for large storage systems using hard disk drives (HDDs) as their main storage devices, which can receive much of SSD's performance benefit and HDD's cost advantage. A key challenge in the use of the shared cache is to ensure strong performance isolation and maintain its high utilization at the same time. However, conventional SSD cache management approaches cannot effectively address this challenge. In this paper, we propose OC-Cache, an open-channel SSD cache framework which utilizes SSD'd internal parallelism to adaptively allocate cache to tenants for both good performance isolation and high SSD utilization. In particular, OC-Cache uses a tenant's miss ratio curve to determine the amount of cache space allocation and where the allocation is (in dedicated or shared SSD channels) and dynamically manages cache space according to the workload characteristics. Experiments show that OC-Cache significantly reduces interference among tenants, and maintains high utilization of the SSD cache.\",\n \"Title: Study on the Thermal Characteristics of In-Wheel Motor Drive System Based on Driving Cycles.\\nYear: 2019\\n\\nAbstract: The driving conditions of vehicles, such as rapid acceleration, lasting downhill and so on, which demand for drive power, are rapidly increasing. For an electric vehicle driven by in-wheel motor (IWM), the IWM needs to provide greater power to maintain the normal operation of the vehicle. At present, the pursuit of high power density for IWM reduces the volume of motor under the same power. But the loss density is increased correspondingly, which will lead to overheating of IWM. In this paper, a 15-kW IWM drive system is taken as the research object. Based on the establishment of the temperature analysis model, instantaneous thermal characteristics of the IWM drive system are analyzed under the long climbing condition with low-speed and rapid acceleration driving cycles. The results show that the temperature of the stator winding and stator core is relatively high under the two driving cycles. The temperature of the stator winding is always in the highest level, and the maximum all appear on the end winding. The highest temperature of the end winding can reach 229.49 degrees C under the rapid acceleration driving cycles, which significantly exceeds the limitation for the insulation (155 degrees C) and will seriously affect the normal operation of the IWM and the vehicle. Therefore, in the follow-up design of cooling system, spray cooling with good local cooling effect can be adopted to focus on the cooling for the end winding. This paper can provide theory for the design of the feasibility thermal cooling solution.\",\n \"Title: Near-Optimal Algorithms For Shortest Paths In Weighted Unit-Disk Graphs\\nYear: 2019\\n\\nAbstract: We revisit a classical graph-theoretic problem, the single-source shortest-path (SSSP) problem, in weighted unit-disk graphs. We first propose an exact (and deterministic) algorithm which solves the problem in O(n log(2)n) time using linear space, where n is the number of the vertices of the graph. This significantly improves the previous deterministic algorithm by Cabello and Jejcic [CGTA'15] which uses O(n(1+delta)) time and O(n(1+delta)) space (for any constantd delta > 0) and the previous randomized algorithm by Kaplan et al. [SODA'17] which uses O(n log(12+o(1))n) expected time and O(n log(3) n) space. More specifically, we show that if the 2D offline insertion-only (additively) weighted nearest-neighbor problem with k operations (i.e., insertions and queries) can be solved in f (k) time, then the SSSP problem in weighted unit-disk graphs can be solved in O(n log n + f (n)) time. Using the same framework with some new ideas, we also obtain a (1 + epsilon)-approximate algorithm for the problem, using O(n log n + n log(2) (1/epsilon)) time and linear space. This improves the previous (1 + epsilon)-approximate algorithm by Chan and Skrepetos [SoCG'18] which uses O((1/epsilon)(2)n log n) time and O((1/epsilon)(2)n) space. More specifically, we show that if the 2D offline insertion-only weighted nearest-neighbor problem with k(1) operations in which at most k(2) operations are insertions can be solved in f (k(1), k(2)) time, then the (1 + epsilon)-approximate SSSP problem in weighted unit-disk graphs can be solved in O(n log n+ f (n, O(epsilon(-2)))) time. Because of the Omega(n log n)-time lower bound of the problem (even when approximation is allowed), both of our algorithms are almost optimal.\",\n \"Title: A Divide-and-Conquer Algorithm for Two-Point $L_1$ Shortest Path Queries in Polygonal Domains.\\nYear: 2019\\n\\nAbstract: Let $mathcal{P}$ be a polygonal domain of $h$ holes and $n$ vertices. We study the problem of constructing a data structure that can compute a shortest path between $s$ and $t$ in $mathcal{P}$ under the $L_1$ metric for any two query points $s$ and $t$. To do so, a standard approach is to first find a set of $n_s$ gateways for $s$ and a set of $n_t$ gateways for $t$ such that there exist a shortest $s$-$t$ path containing a gateway of $s$ and a gateway of $t$, and then compute a shortest $s$-$t$ path using these gateways. Previous algorithms all take quadratic $O(n_scdot n_t)$ time to solve this problem. In this paper, we propose a divide-and-conquer technique that solves the problem in $O(n_s + n_t log n_s)$ time. As a consequence, we construct a data structure of $O(n+(h^2log^3 h/loglog h))$ size in $O(n+(h^2log^4 h/loglog h))$ time such that each query can be answered in $O(log n)$ time.\",\n \"Title: A Linear-Time Algorithm for Radius-Optimally Augmenting Paths in a Metric Space.\\nYear: 2019\\n\\nAbstract: Let $P$ be a path graph of $n$ vertices embedded in a metric space. We consider the problem of adding a new edge to $P$ to minimize the radius of the resulting graph. Previously, a similar problem for minimizing the diameter of the graph was solved in $O(nlog n)$ time. To the best of our knowledge, the problem of minimizing the radius has not been studied before. In this paper, we present an $O(n)$ time algorithm for the problem, which is optimal.\",\n \"Title: Improved Algorithms for the Bichromatic Two-Center Problem for Pairs of Points.\\nYear: 2019\\n\\nAbstract: We consider a bichromatic two-center problem for pairs of points. Given a set $S$ of $n$ pairs of points in the plane, for every pair, we want to assign a red color to one point and a blue color to the other, in such a way that the value $max{r_1,r_2}$ is minimized, where $r_1$ (resp., $r_2$) is the radius of the smallest enclosing disk of all red (resp., blue) points. Previously, an exact algorithm of $O(n^3log^2 n)$ time and a $(1+varepsilon)$-approximate algorithm of $O(n + (1/varepsilon)^6 log^2 (1/varepsilon))$ time were known. In this paper, we propose a new exact algorithm of $O(n^2log^2 n)$ time and a new $(1+varepsilon)$-approximate algorithm of $O(n + (1/varepsilon)^3 log^2 (1/varepsilon))$ time.\",\n \"Title: UCSCXenaShiny: an R/CRAN package for interactive analysis of UCSC Xena data\\nYear: 2022\\n\\nAbstract: UCSC Xena platform provides huge amounts of processed cancer omics data from large cancer research projects (e.g. TCGA, CCLE and PCAWG) or individual research groups and enables unprecedented research opportunities. However, a graphical user interface-based tool for interactively analyzing UCSC Xena data and generating elegant plots is still lacking, especially for cancer researchers and clinicians with limited programming experience. Here, we present UCSCXenaShiny, an R Shiny package for quickly searching, downloading, exploring, analyzing and visualizing data from UCSC Xena data hubs. This tool could effectively promote the practical use of public data, and can serve as an important complement to the current Xena genomics explorer.\",\n \"Title: Resource Allocation for Full-Duplex M2M Networks with Imperfect CSI\\nYear: 2021\\n\\nAbstract: In this paper, we investigate the resource allocation problem for a full-duplex network with machine-to-machine devices and cellular users. Our objective is to maximize the network sum rate while achieving individual quality-of-service (QoS) requirements, considering imperfect and partial channel state information. The sum rate maximization problem is formulated as a mixed-integer nonlinear programming problem. We decouple it into a subcarrier assignment subproblem and a power allocation subproblem to make it more tractable. The non-convex subcarrier assignment problem is transformed to a convex problem by applying majorization minimization method, and then solved by the interior-point method. While the power allocation problem is solved by applying the Lagrange dual method. Simulation results demonstrate the impact of channel estimation error on sum rate performance and the superiority of the proposed algorithm. Furthermore, the fairness is improved among users by QoS guarantee.\",\n \"Title: A Bilateral Dual-Arm Teleoperation Robot System with a Unified Control Architecture\\nYear: 2021\\n\\nAbstract: The teleoperation system can transmit human intention to the remote robot, so that the system combines excellent robot operation performance and human intelligence. In this article, we have established a bilateral teleoperation system with force feedback from the arm and gripper. That is, the slave robot system can provide force feedback on both the wrist and the fingers, while the master robot sy...\",\n \"Title: Microblog-oriented Multi-scale CNN Multi-label Sentiment Classification Model\\nYear: 2020\\n\\nAbstract: With the rapid development of the Internet, social networks have become an important platform for people to express their opinions. Through the sentiment analysis of the text, the user's emotional tendency can be captured in time. Multi-label sentiment classification is one of the difficult issues. Therefore, the paper proposes a multi-label sentiment classification model for short texts of Weibo ...\",\n \"Title: Chinese Real-word Error Automatic Detection and Correction Based on Confusion Set and Generalization Model\\nYear: 2020\\n\\nAbstract: With the development of informatization, there are many errors in Chinese texts, and one kind of error is called “Real-word Error” which like English Real-word Error. The wrong word itself is also a right word in dictionary which confirm by the specific context. While using data resource of corpus to proofread text, data Sparseness will bring the miscarriage of justice problem. In view of the abov...\",\n \"Title: Apparatus and methods for mouse behavior recognition on foot contact features\\nYear: 2021\\n\\nAbstract: Behavior recognition of model animals such as mice, rats, and monkeys, is of great significance in medical research and vital in the development in medicines or drugs. Studies of the animal’s responses to specific stimulations consisted of visual imaging methods that was easily affected by cage lighting conditions and shooting angle. In this study, we designed a behavior classification system that focused on object-floor contact features extracted from touch images of the frustrated total internal reflection injected with infrared lights. A mouse behavioral dataset (IMBD) was then established, and a support vector machine was applied to conduct this classification. To improve its performance, an improved particle swarm optimization was proposed for the optimization of parameters. Based on IMBD and the support vector machine, we tested different feature extraction methods and classifiers. The system performance of the current study showed more accuracy and efficacy compared with performance of other prevalent particle swarm optimizations. Our study resulted in a recognition rate of up to 94.37% for individual behavior. The average rate of behavioral recognition for all tested mice reached 83.09%. The results suggested that foot contact features of mice were more effective than regular video features in behavior recognition.\",\n \"Title: AMMASurv - Asymmetrical Multi-Modal Attention for Accurate Survival Analysis with Whole Slide Images and Gene Expression Data.\\nYear: 2021\\n\\nAbstract: The use of multi-modal data such as the combination of whole slide images (WSIs) and gene expression data for survival analysis can lead to more accurate survival predictions. Previous multi-modal survival models are not able to efficiently excavate the intrinsic information within each modality. Moreover, despite experimental results show that WSIs provide more effective information than gene expression data, previous methods regard the information from different modalities as similarly important so they cannot flexibly utilize the potential connection between the modalities. To address the above problems, we propose a new asymmetrical multi-modal method, termed as AMMASurv. Specifically, we design an asymmetrical multi-modal attention mechanism (AMMA) in Transformer encoder for multi-modal data to enable a more flexible multi-modal information fusion for survival prediction. Different from previous works, AMMASurv can effectively utilize the intrinsic information within every modality and flexibly adapts to the modalities of different importance. Extensive experiments are conducted to validate the effectiveness of the proposed model. Encouraging results demonstrate the superiority of our method over other state-of-the-art methods.\",\n \"Title: Integration of Patch Features Through Self-supervised Learning and Transformer for Survival Analysis on Whole Slide Images\\nYear: 2021\\n\\nAbstract: Survival prediction using whole slide images (WSIs) can provide guidance for better treatment of diseases and patient care. Previous methods usually extract and process only image features from patches of WSIs. However, they ignore the significant role of spatial information of patches and the correlation between the patches of WSIs. Furthermore, those methods extract the patch features through the model pre-trained on ImageNet, overlooking the huge gap between WSIs and natural images. Therefore, we propose a new method, called SeTranSurv, for survival prediction. SeTranSurv extracts patch features from WSIs through self-supervised learning and adaptively aggregates these features according to their spatial information and correlation between patches using the Transformer. Experiments on three large cancer datasets indicate the effectiveness of our model. More importantly, SeTranSurv has better interpretability in locating important patterns and features that contribute to accurate cancer survival prediction.\",\n \"Title: Research On The Characteristics Of Hand Shape In Different Countries\\nYear: 2019\\n\\nAbstract: For a long time, science has been dedicated to revealing various indicators of the human body, including height and weight. The same purpose is also for this paper. Based on data on the hand of people from ten different countries, this paper do some research with the help of multi-variate analysis tools. According to the different characteristics of the data, this paper first carried out the analysis of variance on hand length. Then, according to the different dimension of hand shape, including the vertical length of the palm, the metacarpal and the length of the index finger, factor analysis was carried out, and two influence factors were extracted. Then, multiple regressions were performed on these variables, at the same time, stepwise regression method was adopted, and the main independent variables were chose from the constructed model. Finally, gender is set to a dummy variable to study the impact of gender differences on the hand length.\",\n \"Title: H-Infinity Sliding Mode Control For Pdt-Switched Nonlinear Systems Under The Dynamic Event-Triggered Mechanism\\nYear: 2022\\n\\nAbstract: In this paper, the H-infinity sliding mode control problem of persistent dwell-time switched non-linear systems is investigated. Due to the limited bandwidth resources, a dynamic event-triggered mechanism is introduced to alleviate the transmission burden. Considering that the system parameters are switched in accordance with the persistent dwell-time switching strategy, a novel switched sliding mode control law is constructed. Then, drawing on the Lyapunov function approach, sufficient conditions are derived, which not only ensure the reachability of the sliding region around the specified sliding surface but also guarantee the globally uniform exponential stability of the system with an H-infinity performance. Moreover, the specific form of the controller gains is derived by utilizing an efficient decoupling method. Eventually, the validity of the proposed method is validated by two numerical examples. (C) 2021 Elsevier Inc. All rights reserved.\",\n \"Title: Constructing Many Faces in Arrangements of Lines and Segments\\nYear: 2022\\n\\nAbstract: We present new algorithms for computing many faces in arrangements of lines and segments. Given a set $S$ of $n$ lines (resp., segments) and a set $P$ of $m$ points in the plane, the problem is to compute the faces of the arrangements of $S$ that contain at least one point of $P$. For the line case, we give a deterministic algorithm of $O(m^{2/3}n^{2/3}\\\\log^{2/3} (n/\\\\sqrt{m})+(m+n)\\\\log n)$ time. This improves the previously best deterministic algorithm [Agarwal, 1990] by a factor of $\\\\log^{2.22}n$ and improves the previously best randomized algorithm [Agarwal, Matou\\\\v{s}ek, and Schwarzkopf, 1998] by a factor of $\\\\log^{1/3}n$ in certain cases (e.g., when $m=\\\\Theta(n)$). For the segment case, we present a deterministic algorithm of $O(n^{2/3}m^{2/3}\\\\log n+\\\\tau(n\\\\alpha^2(n)+n\\\\log m+m)\\\\log n)$ time, where $\\\\tau=\\\\min\\\\{\\\\log m,\\\\log (n/\\\\sqrt{m})\\\\}$ and $\\\\alpha(n)$ is the inverse Ackermann function. This improves the previously best deterministic algorithm [Agarwal, 1990] by a factor of $\\\\log^{2.11}n$ and improves the previously best randomized algorithm [Agarwal, Matou\\\\v{s}ek, and Schwarzkopf, 1998] by a factor of $\\\\log n$ in certain cases (e.g., when $m=\\\\Theta(n)$). We also give a randomized algorithm of $O(m^{2/3}K^{1/3}\\\\log n+\\\\tau(n\\\\alpha(n)+n\\\\log m+m)\\\\log n\\\\log K)$ expected time, where $K$ is the number of intersections of all segments of $S$. In addition, we consider the query version of the problem, that is, preprocess $S$ to compute the face of the arrangement of $S$ that contains any query point. We present new results that improve the previous work for both the line and the segment cases.\",\n \"Title: Algorithms for diameters of unicycle graphs and diameter-optimally augmenting trees\\nYear: 2021\\n\\nAbstract: We consider the problem of computing the diameter of a unicycle graph (i.e., a graph with a unique cycle). We present an O(n) time algorithm for the problem, where n is the number of vertices of the graph. This improves the previous best O(n log n) time solution [Oh and Ahn, ISAAC 2016]. Using this algorithm as a subroutine, we solve the problem of adding a shortcut to a tree so that the diameter of the new graph (which is a unicycle graph) is minimized; our algorithm takes O(n(2)logn) time and O(n) space. The previous best algorithms solve the problem in O(n(2)log(3)n) time and O(n) space [Oh and Ahn, ISAAC 2016], or in O(n(2)) time and O(n(2)) space [Bilo, ISAAC 2018]. (C) 2021 Elsevier B.V. All rights reserved.\",\n \"Title: Indoor Crowd Density Estimation Based on Sound Field Disturbance Analysis\\nYear: 2021\\n\\nAbstract: With the frequent occurrence of large-scale indoor stampedes, people pay more and more attention to the problem of indoor crowd density, and indoor crowd density estimation has become a very important security work. This paper proposes an indoor crowd density estimation method based on acoustic means. This method makes use of the reverberation effect and spatial distribution of sound wave propagat...\",\n \"Title: Vehicle Object Detection Method Based On Candidate Region Aggregation\\nYear: 2021\\n\\nAbstract: Multi-scale vehicle detection is an important application in the field of object detection, and Feature Pyramid Network (FPN) is an important means to deal with multi-scale object detection tasks. However, baseline method is the common method used in most of the existing network structure, which represents the input image information by selecting one from the output layer of FPN, and discard other layers. This not only limits the performance of the network structure, but also performs poorly when dealing with the problem of excessive scale differences. To solve this problem, a novelty candidate region aggregation network (CRAN) is proposed in this paper. The candidate regions of different feature layers are effectively aggregated to improve the network generalization performance. Specifically, calculate the similarity between different feature layers through a feature quality score module, and use this as a quantity factor to determine the number of candidate regions reserved for the corresponding feature layer. Finally, they are aggregated into a more comprehensive candidate region group. Further, in order to improve the detection efficiency of small objects, an area cross entropy loss function is proposed. It makes the model pay more attention to small targets by adding a monotonic decrease based on the area. Finally, the proposed CRAN and the area cross entropy loss function are applied to the advanced detectors. The experimental results in the KITTI and UA-DETRAC datasets show that this method has good performance on vehicle objects in different scenarios, and can meet the requirements of practical application.\",\n \"Title: A Simple Algorithm for Computing the Zone of a Line in an Arrangement of Lines.\\nYear: 2022\\n\\nAbstract: Let $L$ be a set of $n$ lines in the plane. The zone $Z(\\\\ell)$ of a line $\\\\ell$ in the arrangement $\\\\mathcal{A}(L)$ of $L$ is the set of faces of $\\\\mathcal{A}(L)$ whose closure intersects $\\\\ell$. It is known that the combinatorial size of $Z(\\\\ell)$ is $O(n)$. Given $L$ and $\\\\ell$, computing $Z(\\\\ell)$ is a fundamental problem. Linear-time algorithms exist for computing $Z(\\\\ell)$ if $\\\\mathcal{A}(L)$ has already been built, but building $\\\\mathcal{A}(L)$ takes $O(n^2)$ time. On the other hand, $O(n\\\\log n)$-time algorithms are also known for computing $Z(\\\\ell)$ without relying on $\\\\mathcal{A}(L)$, but these algorithms are relatively complicated. In this paper, we present a simple algorithm that can compute $Z(\\\\ell)$ in $O(n\\\\log n)$ time. More specifically, once the sorted list of the intersections between $\\\\ell$ and the lines of $L$ is known, the algorithm runs in $O(n)$ time. A big advantage of our algorithm, which mainly involves a Graham's scan style procedure, is its simplicity.\",\n \"Title: A Prior Information Enhanced Extraction Framework For Document-Level Financial Event Extraction\\nYear: 2021\\n\\nAbstract: Document-level financial event extraction (DFEE) is the task of detecting events and extracting the corresponding event arguments in financial documents, which plays an important role in information extraction in the financial domain. This task is challenging as the financial documents are generally long text and event arguments of one event may be scattered in different sentences. To address this issue, we proposed a novel Prior Information Enhanced Extraction framework (PIEE) for DFEE, leveraging prior information from both event types and pre-trained language models. Specifically, PIEE consists of three components: event detection, event argument extraction, and event table filling. In event detection, we identify the event type. Then, the event type is explicitly used for event argument extraction. Meanwhile, the implicit information within language models also provides considerable cues for event arguments localization. Finally, all the event arguments are filled in an event table by a set of predefined heuristic rules. To demonstrate the effectiveness of our proposed framework, we participated in the share task of CCKS2020 Task 4-2: Document-level Event Arguments Extraction. On both Leaderboard A and Leaderboard B, PIEE took the first place and significantly outperformed the other systems.\",\n \"Title: A Novel Hybrid Subset-Learning Method For Predicting Risk Factors Of Atherosclerosis\\nYear: 2017\\n\\nAbstract: Cardiovascular disease (CVD) caused by atherosclerosis is one of the major causes of death world-wide. Currently diverse machine learning models have been applied to disease prediction and classification. However, most of them tend to focus on the performance of the algorithm and neglect the underlying variables for patients in different carotid atherosclerotic stages. In this paper, we propose a novel hybrid machine learning method named Subset Learning (S-learning) to predict and discover the risk factors of these different stages. The S-learning algorithm can elucidate the variables that have significant influence on the outcome of carotid atherosclerotic. Performance comparisons are based on the dataset collected from both Shanghai Renji and Shanghai Huashan Hospital. The result shows that the proposed method has superior classification performance than other classification algorithms. Our findings point to the utility of predictive machine learning and the discovery of risk factors to refine the treatment plans.\",\n \"Title: Wobtree: A Write-Optimized B Plus -Tree For Non-Volatile Memory\\nYear: 2021\\n\\nAbstract: The emergence of non-volatile memory (NVM) has introduced new opportunities for performance optimizations in existing storage systems. To better utilize its byte-addressability and near-DRAM performance, NVM can be attached on the memory bus and accessed via load/store memory instructions rather than the conventional block interface. In this scenario, a cache line (usually 64 bytes) becomes the data transfer unit between volatile and non-volatile devices. However, the failure-atomicity of write on NVM is the memory bit width (usually 8 bytes). This mismatch between the data transfer unit and the atomicity unit may introduce write amplification and compromise data consistency of node-based data structures such as B+-trees. In this paper, we propose WOBTree, a Write-Optimized B+-Tree for NVM to address the mismatch problem without expensive logging. WOBTree minimizes the update granularity from a tree node to a much smaller subnode and carefully arranges the write operations in it to ensure crash consistency and reduce write amplification. Experimental results show that compared with previous persistent B+-tree solutions, WOBTree reduces the write amplification by up to 86x and improves write performance by up to 61x while maintaining similar search performance.\",\n \"Title: Improved algorithms for the bichromatic two-center problem for pairs of points\\nYear: 2022\\n\\nAbstract: We consider a bichromatic two-center problem for pairs of points. Given a set S of n pairs of points in the plane, for every pair, we want to assign a red color to one point and a blue color to the other, in such a way that the value max{r1,r2} is minimized, where r1 (resp., r2) is the radius of the smallest enclosing disk of all red (resp., blue) points. Previously, an exact algorithm of O(n3log2n) time and a (1+ε)-approximate algorithm of O(n+(1/ε)6log2(1/ε)) time were known. In this paper, we propose a new exact algorithm of O(n2log2n) time and a new (1+ε)-approximate algorithm of O(n+(1/ε)3log2(1/ε)) time.\",\n \"Title: Constraint Loss for Rotated Object Detection in Remote Sensing Images\\nYear: 2021\\n\\nAbstract: Rotated object detection is an extension of object detection that uses an oriented bounding box instead of a general horizontal bounding box to define the object position. It is widely used in remote sensing images, scene text, and license plate recognition. The existing rotated object detection methods usually add an angle prediction channel in the bounding box prediction branch, and smooth L1 loss is used as the regression loss function. However, we argue that smooth L1 loss causes a sudden change in loss and slow convergence due to the angle solving mechanism of open CV (the angle between the horizontal line and the first side of the bounding box in the counter-clockwise direction is defined as the rotation angle), and this problem exists in most existing regression loss functions. To solve the above problems, we propose a decoupling modulation mechanism to overcome the problem of sudden changes in loss. On this basis, we also proposed a constraint mechanism, the purpose of which is to accelerate the convergence of the network and ensure optimization toward the ideal direction. In addition, the proposed decoupling modulation mechanism and constraint mechanism can be integrated into the popular regression loss function individually or together, which further improves the performance of the model and makes the model converge faster. The experimental results show that our method achieves 75.2% performance on the aerial image dataset DOTA (OBB task), and saves more than 30% of computing resources. The method also achieves a state-of-the-art performance in HRSC2016, and saved more than 40% of computing resources, which confirms the applicability of the approach.\",\n \"Title: Joint Road Side Units Selection and Resource Allocation in Vehicular Edge Computing\\nYear: 2021\\n\\nAbstract: With powerful storage and computation capability, vehicular edge computing is considered as a promising paradigm to enhance the safety and quality-of-service of vehicles in intelligent transportation systems. In this work, we investigate a joint road side units (RSUs) selection and resource allocation problem, with the objective of minimizing the total task offloading delay subject to the bandwidt...\",\n \"Title: Co-Channel Interference Suppression for LTE Passive Radar Based on Spatial Feature Cognition\\nYear: 2022\\n\\nAbstract: Passive radars based on long-term evolution (LTE) signals suffer from sever interferences. The interferences are not only from the base station used as the illuminator of opportunity (BS-IoO), but also from the other co-channel base stations (CCBS) working at the same frequency with the BS-IoO. Because the reference signals of the co-channel interferences are difficult to obtain, cancellation performance degrades seriously when traditional interference suppression methods are applied in LTE-based passive radar. This paper proposes a cascaded cancellation method based on the spatial spectrum cognition of interference. It consists of several cancellation loops. In each loop, the spatial spectrum of strong interferences is first recognized by using the cyclostationary characteristic of LTE signal and the compressed sensing technique. A clean reference signal of each interference is then reconstructed according to the spatial spectrum previously obtained. With the reference signal, the interferences are cancelled. At the end of each loop, the energy of the interference residual is estimated. If the interference residual is still strong, then the cancellation loop continues; otherwise it terminates. The proposed method can get good cancellation performance with a small-sized antenna array. Theoretical and simulation results demonstrate the effectiveness of the proposed method.\",\n \"Title: Sandwich Integration Technique for the Pressure Sensor Detection of Occlusal Force In Vitro\\nYear: 2022\\n\\nAbstract: Bite force measurement is an important parameter when checking the function and integrity of the masticatory system, whereas it is currently very difficult to measure bite force during functional movement. Hence, the purpose of this study is to explore the potential technique and device for the measurement and intervention of the continuous bite forces on functional and dynamic occlusal condition. A portable biosensor by sandwich technique was designed, and the validity, reliability, and sensitivity were determined by mechanical pressure loading tests; meanwhile, the pressure signal is acquired by, and transmitted to, voltage changes by the electrical measurements of the sensors. The result is that, when the mechanical stress detection device is thicker than 3.5 mm, it shows relatively ideal mechanical properties; however, when the thickness is less than 3.0 mm, there is a risk of cracking. Mechanical stress changing and voltage variation had a regularity and positive relationship in this study. The mechanical stress-measuring device made by medical and industrial cross has a good application prospect for the measurement of bite force during function.\",\n \"Title: Evaluation of the Integrity Risk for Precise Point Positioning\\nYear: 2022\\n\\nAbstract: Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP) is an attractive positioning technology due to its high precision and flexibility. However, the vulnerability of PPP brings a safety risk to its application in the field of life safety, which must be evaluated quantitatively to provide integrity for PPP users. Generally, PPP solutions are processed recursively based on the extended Kalman filter (EKF) estimator, utilizing both the previous and current measurements. Therefore, the integrity risk should be qualified considering the effects of all the potential observation faults in history. However, this will cause the calculation load to explode over time, which is impractical for long-time missions. This study used the innovations in a time window to detect the faults in the measurements, quantifying the integrity risk by traversing the fault modes in the window to maintain a stable computation cost. A non-zero bias was conservatively introduced to encapsulate the effect of the faults before the window. Coping with the multiple simultaneous faults, the worst-case integrity risk was calculated to overbound the real risk in the multiple fault modes. In order to verify the proposed method, simulation and experimental tests were carried out in this study. The results showed that the fixed and hold mode adopted for ambiguity resolution is critical to an integrity risk evaluation, which can improve the observation redundancy and remove the influence of the biased predicted ambiguities on the integrity risk. Increasing the length of the window can weaken the impact of the conservative assumption on the integrity risk due to the smoothing effect of the EKF estimator. In addition, improving the accuracy of observations can also reduce the integrity risk, which indicates that establishing a refined PPP random model can improve the integrity performance.\",\n \"Title: Adaptive sliding mode control for persistent dwell-time switched nonlinear systems with matched/mismatched uncertainties and its application\\nYear: 2022\\n\\nAbstract: In this paper, the adaptive sliding mode control issue for switched nonlinear systems with matched and mismatched uncertainties is addressed, where the persistent dwell-time switching rule is introduced to describe the switching of parameters. Besides, considering the case that the upper bound of the matched uncertainty is unknown, the purpose of this paper is to utilize an adaptive control method to estimate its upper bound parameters. To begin with, a linear sliding surface is constructed, and then the reduced-order sliding mode dynamics can be obtained through a reduced-order method. Next, sufficient conditions can be derived based on the Lyapunov stability and the persistent dwell-time switching analysis techniques ensuring that the reduced-order sliding mode dynamics is globally uniformly exponentially stable. Moreover, a switched adaptive sliding mode control law is designed, which can not only ensure the reachability of the sliding surface but also estimate the upper bound parameters of the matched uncertainty. Finally, a numerical example and a circuit model are introduced to verify the effectiveness of the proposed method.\",\n \"Title: Target Detection and DOA Estimation for Passive Bistatic Radar in the Presence of Residual Interference\\nYear: 2022\\n\\nAbstract: With the development of radio technology, passive bistatic radar (PBR) will suffer from interferences not only from the base station that is used as the illuminator of opportunity (BS-IoO), but also from the base station with co-frequency or adjacent frequency (BS-CF/AF). It is difficult for clutter cancellation algorithm to suppress all the interferences, especially the interferences from BS-CF/AF. The residual interferences will seriously affect target detection and DOA estimation. To solve this problem, a novel target detection and DOA estimation method for PBR based on compressed sensing sparse reconstruction is proposed. Firstly, clutter cancellation algorithm is used to suppress the interferences from BS-IoO. Secondly, the residual interferences and target echo are separated in spatial domain based on the azimuth sparse reconstruction. Finally, target detection and DOA estimation method are given. The proposed method can achieve not only target detection and DOA estimation in the presence of residual interferences, but also better anti-mainlobe interferences and high-resolution DOA estimation performance. Numerical simulation and experimental results verify the effectiveness of the proposed algorithm.\",\n \"Title: Image super-resolution reconstruction based on generative adversarial network model with feedback and attention mechanisms\\nYear: 2022\\n\\nAbstract: Despite the rapid development of single-image super-resolution (SISR) methods of generative adversarial networks (GAN), which can reconstruct visually realistic images, the problem of high discrepancy between the recovered details or textures and the ground truth persists. To address this issue, an SISR reconstruction GAN based on a feedback and attention mechanism (FBSRGAN) is proposed. Specifically, we select a network with a feedback mechanism as the generator, which can gradually create high-resolution images through the feedback connection. The attention mechanism is combined with the feedback block to adaptively select useful feature information and effectively process the feedback stream and enhance the image output quality. We use the relative average least squares GAN loss to reduce the instability of the optimization generator process to guide GAN to obtain more realistic results. The results show that compared with the ESRGAN method, when the amplification factor is 4, PSNR and SSIM of the proposed method increase by 0.386 and 0.0141, respectively, and PI decreases by 0.284, while the number of parameters is only 18.5% of that of ESRGAN, when tested on the Set5 dataset. Compared with existing GAN-based SR methods, FBSRGAN achieves superior performance in terms of both perceptual ability and image distortion.\",\n \"Title: Unit-Disk Range Searching and Applications.\\nYear: 2022\\n\\nAbstract: Given a set $P$ of $n$ points in the plane, we consider the problem of computing the number of points of $P$ in a query unit disk (i.e., all query disks have the same radius). We show that the main techniques for simplex range searching in the plane can be adapted to this problem. For example, by adapting Matou\\\\v{s}ek's results, we can build a data structure of $O(n)$ space so that each query can be answered in $O(\\\\sqrt{n})$ time. Our techniques lead to improvements for several other classical problems, such as batched range searching, counting/reporting intersecting pairs of unit circles, distance selection, discrete 2-center, etc. For example, given a set of $n$ unit disks and a set of $n$ points in the plane, the batched range searching problem is to compute for each disk the number of points in it. Previous work [Katz and Sharir, 1997] solved the problem in $O(n^{4/3}\\\\log n)$ time while our new algorithm runs in $O(n^{4/3})$ time.\",\n \"Title: Autonomous Electric Vehicle Battery Disassembly Based on NeuroSymbolic Computing\\nYear: 2022\\n\\nAbstract: The booming of electric vehicles demands efficient battery disassembly for recycling to be environment-friendly. Due to the unstructured environment and high uncertainties, battery disassembly is still primarily done by humans, probably assisted by robots. It is highly desirable to design autonomous solutions to improve work efficiency and lower human risks in high voltage and toxic environments. This paper proposes a novel framework of the NeuroSymbolic task and motion planning method to disassemble batteries in an unstructured environment using robots automatically. It enables robots to independently locate and disassemble battery bolts, with or without obstacles. This study not only provides a solution for intelligently disassembling electric vehicle batteries but also verifies its feasibility through a set of test results with the robot accomplishing the disassembly tasks in a complex and dynamic environment.\",\n \"Title: Computing the Minimum Bottleneck Moving Spanning Tree\\nYear: 2022\\n\\nAbstract: Given a set $P$ of $n$ points that are moving in the plane, we consider the problem of computing a spanning tree for these moving points that does not change its combinatorial structure during the point movement. The objective is to minimize the bottleneck weight of the spanning tree (i.e., the largest Euclidean length of all edges) during the whole movement. The problem was solved in $O(n^2)$ time previously [Akitaya, Biniaz, Bose, De Carufel, Maheshwari, Silveira, and Smid, WADS 2021]. In this paper, we present a new algorithm of $O(n^{4/3} \\\\log^3 n)$ time.\",\n \"Title: Algorithms for the line-constrained disk coverage and related problems\\nYear: 2022\\n\\nAbstract: Given a set P of n points and a set S of m weighted disks in the plane, the disk coverage problem asks for a subset of disks of minimum total weight that cover all points of P. The problem is NP-hard. In this paper, we consider a line-constrained version in which all disks are centered on a line L (while points of P can be anywhere in the plane). We present an O ((m + n) log(m + n) + kappa log m) time algorithm for the problem, where kappa is the number of pairs of disks whose boundaries intersect. Alternatively, we can also solve the problem in O (nm log(m + n)) time. For the unit-disk case where all disks have the same radius, the running time can be reduced to O ((n + m) log(m + n)). In addition, we solve in O ((m + n) log(m + n)) time the L & INFIN; and L1 cases of the problem, in which the disks are squares and diamonds, respectively. We further demonstrate that our techniques can also be used to solve other geometric coverage problems. For example, given in the plane a set P of n points and a set S of n weighted half-planes, we solve in O (n4 log n) time the problem of finding a subset of half-planes to cover P so that their total weight is minimized. This improves the previous best algorithm of O (n(5)) time by almost a linear factor. If all half-planes are lower ones, then our algorithm runs in O(n(2) log n) time, which improves the previous best algorithm of O (n(4)) time by almost a quadratic factor. (C)& nbsp;2022 Elsevier B.V. All rights reserved.\",\n \"Title: Pansharpening via Triplet Attention Network With Information Interaction\\nYear: 2022\\n\\nAbstract: Pansharpening aims to obtain high spatial resolution multispectral (MS) images by fusing the spatial and spectral information in low spatial resolution (LR) MS and panchromatic (PAN) images. Recently, deep neural network (DNN) based pansharpening methods have been advanced extensively. Although most DNN-based methods show good performance, it is difficult for them to preserve the spatial details in the fused image. In this article, we propose a new pansharpening method based on a triplet attention network with information interaction to efficiently enhance the spatial and spectral information in the fused image. First, different attention mechanisms are designed to model the spatial and spectral feature properties in LR MS and PAN images. Then, the complementarity among different feature maps is enhanced by information interaction, which promotes the compatibility of features from subnetworks. Finally, we utilize a graph attention module to capture the similarity within feature maps. According to the graph, the informative feature maps are selected to provide more details for the reconstruction of the fused image. Extensive experiments on QuickBird and GeoEye-1 satellite datasets show that the proposed method can produce competitive fused images when compared with some state-of-the-art methods.\",\n \"Title: A Tracking Imaging Control Method for Dual-FSM 3D GISC LiDAR\\nYear: 2022\\n\\nAbstract: In this paper, a tracking and pointing control system with dual-FSM (fast steering mirror) composite axis is proposed. It is applied to the target-tracking accuracy control in a 3D GISC LiDAR (three-dimensional ghost imaging LiDAR via sparsity constraint) system. The tracking and pointing imaging control system of the dual-FSM 3D GISC LiDAR proposed in this paper is a staring imaging method with multiple measurements, which mainly solves the problem of high-resolution remote-sensing imaging of high-speed moving targets when the technology is transformed into practical applications. In the research of this control system, firstly, we propose a method that combines motion decoupling and sensor decoupling to solve the mechanical coupling problem caused by the noncoaxial sensor installation of the FSM. Secondly, we suppress the inherent mechanical resonance of the FSM in the control system. Thirdly, we propose the optical path design of a dual-FSM 3D GISC LiDAR tracking imaging system to solve the problem of receiving aperture constraint. Finally, after sufficient experimental verification, our method is shown to successfully reduce the coupling from 7% to 0.6%, and the precision tracking bandwidth reaches 300 Hz. Moreover, when the distance between the GISC system and the target is 2.74 km and the target flight speed is 7 m/s, the tracking accuracy of the system is improved from 15.7 mu rad (sigma) to 2.2 mu rad (sigma), and at the same time, the system recognizes the target contour clearly. Our research is valuable to put the GISC technology into practical applications.\",\n \"Title: Eliminating the Resonance Interference for an Inductive Speed Sensor Subjected to an Early-Time HEMP Field\\nYear: 2022\\n\\nAbstract: In the present study, the mechanism of electromagnetic resonance interference on an inductive speed sensor is investigated. To this end, a second-order equivalent resonance model is established based on the lumped parameter of the speed sensor. Then the effects of different parameters on the interference signal are analyzed. The effect of terminal loads and filter capacitances are simulated in the CST software and the obtained results for the resonance interference signal are analyzed. Moreover, an experiment is carried out and the oscillation of the voltage interference signal is produced by the speed sensor under the testing of early-time high altitude nuclear electromagnetic pulse (HEMP) field. The GTEM cell is used to produce the plane wave for the experiment. Finally, the advantages and disadvantages of different resonance elimination methods are analyzed, and the method is proposed to eliminate the oscillation of the voltage interference signal.\",\n \"Title: AG600 Maritime Base Location Decision Based on the Interval Intuitionistic Fuzzy TOPSIS Method\\nYear: 2022\\n\\nAbstract: Aiming at the location research blank of the AG600 amphibious aircraft marine rescue base, the location decision index system of the AG600 is established, and the AG600 location decision model based on the interval intuitionistic fuzzy theory and improved TOPSIS method is constructed. Through investigation and statistical analysis, the evaluation system with 10 indices is determined based on applicability, safety, and accessibility, and in the process of quantization of index assignment, a fuzzy conversion method for different indices is proposed. The interval intuitionistic fuzzy number of each index is obtained by combining objective calculation and subjective assignment, and the interval intuitionistic fuzzy decision matrix is constructed. The fuzzy entropy method is used to calculate the index weight, and then the improved Technique for Order of Preference by Similarity (TOPSIS) is used to evaluate and rank the rescue bases, the scientific and reasonable location decision results are obtained. Moreover, STOPSIS and WS are used to analyze and demonstrate the sorting inversion problem, and the value is 0.908, which further proves the effectiveness of the method proposed in this paper. Finally, taking the South China Sea islands as an example, the correctness and reliability of the proposed model is verified by comparison with the calculation results of the score function method and the projection method.\",\n \"Title: High-Efficiency Multihomed Multimedia Transmission in Wireless Sensors\\nYear: 2022\\n\\nAbstract: For real-time video transmission in multimedia sensor networks, efficient and reliable communication is very important. However, under actual conditions, when the sensor transmits data to the sink node, the link between the sensor and the sink node usually becomes unstable due to bandwidth limitations and unstable transmission links. Unstable data transmission will affect the normal data transmission of sensor nodes and even cause sensor nodes to fail to communicate with sink nodes. These problems make users who use multimedia sensor networks have a poor user experience. In order to solve these problems, a brand-new, multi-homed, multi-connection system was designed for wireless multimedia sensors, and a prototype system was implemented. Compared with the traditional single-homed connection mode, the system can maintain a more stable connection under the same bandwidth, memory, and CPU resource constraints.\",\n \"Title: Short-term wind power probabilistic forecasting using a new neural computing approach: GMC-DeepNN-PF\\nYear: 2022\\n\\nAbstract: With the increasing penetration of renewable energy in power generation, the increasing uncertainty of renewable energy has significant influence on the stability of energy system. In order to handle the system uncertainty as well as satisfying the energy demand and supply, a short-term wind power probabilistic forecasting under uncertainty using the Gaussian mixed clustering-Deep neural network probabilistic forecasting (GMC-DeepNN-PF) is proposed. To illustrate the applicability of the proposed method, a case study of a wind power data set from Kaggle is presented and comparisons among four different data situations and four different forecasting models are shown in this paper. The raw wind power data is firstly pre-processed based on Gaussian Mixture Model reducing data defects. After that, the cleaned data is extended for more information which can improve the output accuracy and time as a property is added to forecasting model. According to DeepNN-PF, deep conventional neural network concatenated with T distribution is then utilized for forecasting using extended data. The simulations and comparisons demonstrate the advancement of the proposed method.\",\n \"Title: Near-Field Multiple Target Localization in Frequency Diverse Array Based on Tensor Decomposition\\nYear: 2022\\n\\nAbstract: Target localization is a fundamental problem in array signal processing. The problem of locating near-field targets with multiple-input multiple-output (MIMO) radar has been studied extensively; however, most of the conventional matrix-based approaches suffer from limitations in terms of the representation and exploitation of the multidimensional nature of MIMO radar signals. In this paper, we addressed the problem of localizing multiple targets in the near-field region, aiming at pursuing a solution applicable for multidimensional signal that is able to achieve sufficient accuracy. A tensor-based signal model impinging on a monostatic frequency diverse array multiple-input multiple-output (FDA-MIMO) radar was formulated, and a corresponding tensor decomposition-based localization algorithm (TenDLA) that showcases the connection between the tensor-based analysis and the localization problem was developed. Additionally, a correction procedure to mitigate the estimation deviations on the range and angle was presented, yielding significant improvements in estimation accuracy. Numerical examples demonstrated the validity and effectiveness of the proposed approach, and it was shown that this approach is superior to conventional methods due to its high-resolution estimation accuracy as well as its relatively low computational costs.\",\n \"Title: A text classification method based on LSTM and graph attention network\\nYear: 2022\\n\\nAbstract: Text classification is a popular research topic in the natural language processing. Recently solving text classification problems with graph neural network (GNN) has received increasing attention. However, current graph-based studies ignore the hidden information in text syntax and sequence structure, and it is difficult to use the model directly for processing new documents because the text graph is built based on the whole corpus including the test set. To address the above problems, we propose a text classification model based on long short-term memory network (LSTM) and graph attention network (GAT). The model builds a separate graph based on the syntactic structure of each document, generates word embeddings with contextual information using LSTM, then learns the inductive representation of words by GAT, and finally fuses all the nodes in the graph together into the document embedding. Experimental results on four datasets show that our model outperforms existing text classification methods with faster convergence and less memory consumption than other graph-based methods. In addition, our model shows a more notable improvement when using less training data. Our model proves the importance of text syntax and sequence information for classification results.\",\n \"Title: Fuzzy <inline-formula><tex-math notation=\\\"LaTeX\\\">$\\\\mathcal {H}_{\\\\infty }$</tex-math></inline-formula> Sliding Mode Control of Persistent Dwell-Time Switched Nonlinear Systems\\nYear: 2022\\n\\nAbstract: In this article, the fuzzy \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"><tex-math notation=\\\"LaTeX\\\">$\\\\mathcal {H}_{\\\\infty }$</tex-math></inline-formula>\\n sliding mode control problem for continuous-time switched nonlinear systems is investigated. The switching signal conforms to the persistent dwell-time switching mechanism. The first objective of this article is to construct a switched integral sliding surface that not only accommodates the switched nonlinear model, but ensures that the sliding mode dynamics are globally uniformly exponentially stable and have an \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"><tex-math notation=\\\"LaTeX\\\">$\\\\mathcal {H}_{\\\\infty }$</tex-math></inline-formula>\\n performance by the equivalent controller derived from the sliding surface. Another aim is to integrate the switched sliding mode control law to force the system trajectories to the sliding surface in a finite amount of time. Then, on the basis of the Lyapunov function technique, the specific form of the sliding mode control law gains is given first, and later the finite-time reachability of the sliding surface is ensured. Finally, the validity of the proposed switched sliding mode control method is validated by a numerical example.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":199,"string":"199"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13659,"string":"13,659"}}},{"rowIdx":13660,"cells":{"question_id":{"kind":"string","value":"Rajesh Kumar"},"question":{"kind":"string","value":"Generate an abstract for the title \"Hardware Implementation for Lower Limb Surface EMG Measurement and Analysis Using Explainable AI for Activity Recognition\" using the following items: EMG signals, Lower limb activity recognition, Data acquisition system, WD-EEMD approach, Explainable artificial intelligence (XAI)"},"answer":{"kind":"string","value":"Electromyography (EMG) signals are gaining popularity for several biomedical applications, including pattern recognition, disease detection, human-machine interfaces, medical image processing, and robotic limb or exoskeleton fabrication. In this study, a two-channel data acquisition system for measuring EMG signals is proposed for human lower limb activity recognition. Five leg activities have been accomplished to measure EMG signals from two lower limb muscles to validate the developed hardware. Five subjects (three males and two females) were chosen to acquire EMG signals during these activities. The raw EMG signal was first denoised using a hybrid of Wavelet Decomposition with Ensemble Empirical Mode Decomposition (WD-EEMD) approach to classify the recorded EMG dataset. Then, eight time-domain (TD) features were extracted using the overlapping windowing technique. An investigation into the comparative effectiveness of several classifiers is presented, although it was hard to distinguish how the classifiers predicted the activities. Having a trustworthy explanation for the outcomes of these classifiers would be quite beneficial overall. An approach known as explainable artificial intelligence (XAI) was introduced to produce trustworthy predictive modeling results and applied the XAI technique known as local interpretable model-agnostic explanations (LIME) to a straightforward human interpretation. LIME investigates how extracted features are anticipated and which features are most responsible for each action. The accuracy of the extra tree classifier gives the highest accuracy of the other studied algorithms for identifying different human lower limb activities from sEMG signals."},"correct_answer":{"kind":"string","value":"Electromyography (EMG) signals are gaining popularity for several biomedical applications, including pattern recognition, disease detection, human-machine interfaces, medical image processing, and robotic limb or exoskeleton fabrication. In this study, a two-channel data acquisition system for measuring EMG signals is proposed for human lower limb activity recognition. Five leg activities have been accomplished to measure EMG signals from two lower limb muscles to validate the developed hardware. Five subjects (three males and two females) were chosen to acquire EMG signals during these activities. The raw EMG signal was first denoised using a hybrid of Wavelet Decomposition with Ensemble Empirical Mode Decomposition (WD-EEMD) approach to classify the recorded EMG dataset. Then, eight time-domain (TD) features were extracted using the overlapping windowing technique. An investigation into the comparative effectiveness of several classifiers is presented, although it was hard to distinguish how the classifiers predicted the activities. Having a trustworthy explanation for the outcomes of these classifiers would be quite beneficial overall. An approach known as explainable artificial intelligence (XAI) was introduced to produce trustworthy predictive modeling results and applied the XAI technique known as local interpretable model-agnostic explanations (LIME) to a straightforward human interpretation. LIME investigates how extracted features are anticipated and which features are most responsible for each action. The accuracy of the extra tree classifier gives the highest accuracy of the other studied algorithms for identifying different human lower limb activities from sEMG signals."},"documents":{"kind":"list like","value":["Title: System assurance of subsea petroleum production systems: a case study mapping factors influencing the sourcing strategy.\nYear: 2012\n\nAbstract: Subsea systems are increasingly used in the production of offshore oil and gas. However, the life cycle management of such systems is not without risks as there are many factors that could result in increased costs, lower quality, delay in delivery, as well as increased health, safety or environmental risks. In addition to engineering related risks, there are substantial risks related to the sourcing of the systems, the execution of the offshore installation process, project execution and logistics, as well as the system operation and maintenance. Often many companies with various specialized expertize needs to be coordinated in the project and the cost may become huge if something goes wrong or of the technology does not perform. The sourcing strategy in subsea systems is based on the corporate vision, management and business philosophy of the operator and the regulatory authorities in the life cycle. The purpose of this paper is to identify and discuss various factors that will influence a sourcing strategy and the decision making process previous to establishing a contractual agreement in complex industrial projects. A case study is performed in the Norwegian oil and gas industry to identify, analyze and validate factors and attributes that impact the sourcing strategy of subsea production systems. The factors are ranked according to importance and as practiced in the industry.","Title: Performance of Robotic Augmentation in Microsurgery-Scale Motions\nYear: 1999\n\nAbstract: This paper is part of the development process of a microsurgical \"cooperating\" assistant. To evaluate its applicability to augment fine surgical motions, we test precision and operator perception in simple microsurgical scale pick and place motions. Such motions are common in microsurgical pro- cedures(e.g. micro-vascular anastomosis). The experiments test the users' ability to position a common surgical tool to 250, 200 and 150 micrometer ac- curacy. These experiments were performed using two test platforms. The new \"steady hand\" robot designed for microsurgery and the LARS robot (a laparo- scopic camera holding robot) adapted for this purpose. Comparative results for several parameters including time, success rate, error rate, number of attempts are included. Comparison of performance of the two robots for these tasks is also included. The results support our claim that the new \"steady hand\" robot augments human performance for microsurgery-scale motion","Title: ANFIS Based Intelligent Control of Vector Controlled Induction Motor Drive\nYear: 2009\n\nAbstract: This paper presents Adaptive Neuro-Fuzzy Inference System (ANFIS) based intelligent control of vector controlled induction motor drive. The proposed intelligent control scheme consists of sensorless adaptive neuro-fuzzy speed controller with speed estimation based on adaptive neuro-fuzzy inverse model. The proposed neuro-fuzzy speed controller incorporates fuzzy logic algorithm with a five-layer artificial neural network (ANN) structure. The conventional PI speed controller is replaced by Adaptive Neuro-Fuzzy Inference System (ANFIS), which tunes the fuzzy inference system with hybrid learning algorithm. This makes fuzzy system to learn. The structure of speed estimator is also same as speed controller but it incorporates inverse model identification algorithm for speed estimation. The performance of the proposed scheme is investigated at different operating conditions. The simulation study indicates robustness and suitability of drive for high performance drives applications.","Title: Artificial intelligence applications in Permanent Magnet Brushless DC motor drives\nYear: 2010\n\nAbstract: Permanent Magnet Brushless DC (PMBLDC) machines are more popular due its simple structure and low cost. Improvements in permanent magnetic materials and power electronic devices have resulted in reliable, cost effective PMBLDC drives, for many applications. Advances in artificial intelligent applications like neural network, fuzzy logic, Genetic algorithm etc. have made tremendous impact on electric motor drives. The brushless DC motor is a multivariable and non-linear system. In conventional PMBLDC drives speed and position sensing of brushless DC motors require high degree of accuracy. Unfortunately, traditional methods of control require detailed modelling of all the motor parameters to achieve this. The Intelligent control techniques like, fuzzy logic control/Neural network control etc. uses heuristic input---output relations to deal with vague and complex situations. This paper presents a literature survey on the intelligent control techniques for PMBLDC motor drives. Various AI techniques for PMBLDC motor drives are described. Attempt is made to provide a guideline and quick reference for the researchers and practicing engineers those are working in the area of PMBLDC motor drives.","Title: Dependency analysis for component-based software systems\nYear: 2009\n\nAbstract: Interaction in component-based systems (CBS) happens when a component provides an interface and other components use it, and also when a component submits an event and other component receives it. Interactions promote dependencies. Higher dependency leads to a complex system, which results in poor understanding and a higher maintenance cost. Usually, dependency is represented by an adjacency matrix used in graph theory. However, this representation can check only for the presence of dependency between components and does not consider the type of interactions between these components. Interaction type can have a significant contribution to the complexity of the system. This paper proposes a link-list based dependency representation and implements it by using Hash Map in Java. This representation can store the dependency along with other information like, provided and required interfaces of components along with their types. This information can be used to analyze several interaction and dependency related issues. This paper also presents the results of an experiment of the proposed approach and measures the interaction densities and dependency level of an individual component and for the system The results show that the proposed metrics can also be used to identify the most critical and isolated components in the system, which can lead to better understanding and easy system maintenance.","Title: Covalent attachment of actin filaments to Tween 80 coated polystyrene beads for cargo transportation.\nYear: 2008\n\nAbstract: In this manuscript, a new strategy has been reported for circumscribed covalent attachment of barbed and pointed ends of actin filaments to polystyrene beads. A comparative study of attachment of actin filaments to polystyrene beads was performed by blocking functionally active sites on polystyrene beads with nonionic detergents such as Tween 20, Tween 80 and polyethylene glycol (PEG). Effective blocking of active sites was obtained with Tween 80 at 0.1% concentration. Attachment of single bundle of actin filament to bead was assessed by rotational motion of bead tailed actin in front and lateral view. Velocity of actin filaments attached to different size of beads in in-vitro motility assay was calculated to ascertain their attachments. Velocity of actin attached to 1.0 and 3.0μm polystyrene beads was reduced to 3.0–4.0 and 0.0–1.0μm/s, respectively as compared to free actin velocity of 4.0–6.0μm/s. Single point attachment of actin filaments to different size of beads was assessed by decrease in sliding velocity. Present study provides insight into the actin–myosin based molecular motor systems for drug delivery and biosensors applications.","Title: Simple Biomanipulation Tasks with 'Steady Hand' Cooperative Manipulator\nYear: 2003\n\nAbstract: This paper reports preliminary experiments in the development of our Augmented Micromanipulation System using the JHU \"Steady Hand\" cooperative robot system to augment single cell manipulation tasks. The need for robotic augmentation of biomanipulatuon tasks is discussed. The JHU \"Steady-Hand\" robot configuration for cell manipulation is reported. Augmentation strategies for stable cooperative insertion of a micropipette in a mouse embryo are developed and preliminary experiments validating these strategies are presented. These preliminary experiments demonstrate promise of cooperative robotic augmentation in single cell manipulation tasks.","Title: An Augmentation System for Fine Manipulation\nYear: 2000\n\nAbstract: Augmented surgical manipulation tasks can be viewed as a sequence of smaller, simpler steps driven primarily by the surgeon's input. These steps can be abstracted as controlled interaction of the tool/end-effector with the environment. The basic research problem here is performing a sequence of control primitives. In computing terms, each of the primitives is a predefined computational routine(e.g. compliant motion or some other \"macro\") with initiation and termination predicates. The sequencing of these primitives depends upon user control and effects of the environmental interaction. We explore a sensor driven system to perform simple manipulation tasks. The system is composed of a core set of \"safe\" system states and task specific states and transitions. Using the \"steady hand\" robot as the experimental platform we investigate using such a system.","Title: Target Counting under Minimal Sensing: Complexity and Approximations\nYear: 2008\n\nAbstract: We consider the problem of counting a set of discrete point targets using a network of sensors under a minimalistic model. Each sensor outputs a single integer, the number of distinct targets in its range, but targets are otherwise indistinguishable to sensors: no angles, distances, coordinates, or other target-identifying measurements are available. This minimalistic model serves to explore the fundamental performance limits of low-cost sensors for such surveillance tasks as estimating the number of people, vehicles or ships in a field of interest to first degree of approximation, to be followed by more expensive sensing and localization if needed. This simple abstract setting allows us to explore the intrinsic complexity of a fundamental problem, and derive rigorous worst-case performance bounds. We show that even in the 1-dimensional setting (for instance, sensors counting vehicles on a road), the problem is non-trivial: target count can be estimated within relative accuracy of factor $\\sqrt{2}$ and this is the best possible in the worst-case. We then address additional questions related to constructing feasible target placements, and noisy counters. In two dimensions, the problem is considerably more complicated: a constant-factor approximation is impossible. Our algorithms and analysis can easily handle some of the non-idealities of real sensors, such as asymmetric ranges and non-exact target counts.","Title: A quantitative evaluation of aspect-oriented software quality model (AOSQUAMO)\nYear: 2009\n\nAbstract: Aspect-Oriented (AO) technology is a new paradigm and emerging field of software development. Aspect-Oriented Programming (AOP) cleanly encapsulates crosscutting concerns which cannot be encapsulated or modularized by traditional programming approaches like Module-Oriented (MO) and Object-Oriented (OO). In order to evaluate quality of software systems, researchers and practitioners have proposed their software quality characteristics and models. As AO is a new abstraction, there is no dedicated software quality model, which can describe and include new features of AO technology. In this paper, a new Aspect-Oriented Software Quality Model (AOSQUAMO) has been proposed. Analytic Hierarchy Process (AHP) is used to evaluate quality of AO software systems as a single parameter. This proposed quality model further may be used to compare AO software systems which will help consumer to choose better quality software.","Title: Rearrangement of Recognized Strokes in Online Handwritten Gurmukhi Words Recognition\nYear: 2009\n\nAbstract: This paper presents a system to recognize online handwritten Gurmukhi words. We have proposed a new step as rearrangement of recognized strokes in online handwriting recognition procedure. The rearrangement of recognized strokes includes: strokes identification as dependent and major dependent strokes; the rearrangement of strokes with respect to their positions; the combination of strokes to recognize character. We have achieved an overall recognition rate as 81.02% in online handwritten cursive handwriting for a set of 2576 Gurmukhi dictionary words.","Title: Economic analysis and power management of a stand-alone wind/photovoltaic hybrid energy system using biogeography based optimization algorithm.\nYear: 2013\n\nAbstract: The stand-alone energy system having a photovoltaic (PV) panels or wind turbines have low reliability and high cost as compared with wind/PV hybrid energy system. In this study, Biogeography Based Optimization (BBO) algorithm is developed for the prediction of the optimal sizing coefficient of wind/PV hybrid energy system in remote areas. BBO algorithm is used to evaluate optimal component sizing and operational strategy by minimizing the total cost of hybrid energy system, while guaranteeing the availability of energy. A diesel generator is added to ensure uninterrupted power supply due to the intermittent nature of wind and solar resources. Due to the complexity of the hybrid energy system design with nonlinear integral planning, BBO algorithm is used to solve the problem. The developed BBO Algorithm has been applied to design the wind/ PV hybrid energy systems to supply a located in the area of Jaipur, Rajasthan (India). Conventional methods require calculation at every single combination of sizing, operation strategy and the data for each variation of component needs to be entered manually and execute separately. Results show that the hybrid energy systems can deliver energy in a stand-alone installation with an acceptable cost. It is clear from the results that the proposed BBO method has excellent convergence property, require less computational time and can avoid the shortcoming of premature convergence of other optimization techniques to obtain the better solution.","Title: Quality aspects for component-based systems: A metrics based approach\nYear: 2012\n\nAbstract: In component-based development, software systems are built by assembling components already developed and prepared for integration. To estimate the quality of components, complexity, reusability, dependability, and maintainability are the key aspects. The quality of an individual component influences the quality of the overall system. Therefore, there is a strong need to select the best quality component, both from functional and nonfunctional aspects. The present paper produces a critical analysis of metrics for various quality aspects for components and component-based systems. These aspects include four main quality factors: complexity, dependency, reusability, and maintainability. A systematic study is applied to find as much literature as possible. A total of 49 papers were found suitable after a defined search criteria. The analysis provided in this paper has a different objective as we focused on efficiency and practical ability of the proposed approach in the selected papers. The various key attributes from these two are defined. Each paper is evaluated based on the various key parameters viz. metrics definition, implementation technique, validation, usability, data source, comparative analysis, practicability, and extendibility. The paper critically examines various quality aspects and their metrics for component-based systems. In some papers, authors have also compared the results with other techniques. For characteristics like complexity and dependency, most of the proposed metrics are analytical. Soft computing and evolutionary approaches are either not being used or much less explored so far for these aspects, which may be the future concern for the researchers. In addition, hybrid approaches like neuro-fuzzy, neuro-genetic, etc., may also be examined for evaluation of these aspects. However, to conclude that one particular technique is better than others may not be appropriate. It may be true for one characteristic by considering different set of inputs and dataset but may not be true for the same with different inputs. The intension in the proposed work is to give a score for each metric proposed by the researchers based on the selected parameters, but certainly not to criticize any research contribution by authors. Copyright © 2012 John Wiley & Sons, Ltd.","Title: Forensic Detection of Fraudulent Alteration in Ball-Point Pen Strokes\nYear: 2012\n\nAbstract: Alteration and addition to valuable data on paper documents are among the fastest growing crimes around the globe. The loss due to these crimes is huge and is increasing with an alarming rate. The techniques, which are used by forensic document examiners, to examine such cases are still limited to manual examination of physical, chemical and microscopic characteristics. Moreover, it is very difficult to detect an alteration when the ink of similar color is involved. We could not find much in the literature to deal with this problem in an automated pattern recognition framework. In this paper, we restrict ourselves to alterations made with ball-point pen strokes and propose a scheme for detection of such alterations using pattern recognition tools. For this, a large set of color and texture based features is extracted. To choose an adequate set of useful features from the extracted ones, a multilayer perceptron (MLP)-based feature analysis technique is used. For detection of the alteration, three different classifiers, namely, ${\\\\rm K}$-nearest neighbor, MLP and support vector machines are used. The results are quite promising.","Title: Stochastic Thermal Free Vibration Response of Laminated Composite Plates Resting on Elastic Foundation with Uncertain Material Properties.\nYear: 2008\n\nAbstract: This study deals with the stochastic free vibration response of a laminated composite plate resting on a elastic foundation subjected to a temperature loading with uncertain material properties. The material properties are modeled as independent basic random variables. The temperature field considered is assumed to be a uniform distribution over the plate surface and through the plate thickness. The material properties are assumed to be independent of temperature. Based on the higher order deformation theory, C0 finite element method is employed to derive the eigen value problems in term of fundamental frequency. A mean-centered first order perturbation technique (FOPT) is adopted to evaluate the mean and standard derivation of the fundamental frequency with all edges simply supported boundary conditions. The effects of foundation parameters on the critical temperature are analyzed. The results have been validated with independent Monte Carlo simulation (MCS) and those available in the literature.","Title: Generalized coupling measure for aspect-oriented systems\nYear: 2009\n\nAbstract: Coupling is an internal software attribute that can be used to indicate the degree of interdependence among the components of a software system. Coupling in software has been linked to predict external software quality attributes such as fault-proneness, ripple effects, changeability, impact analysis etc. Despite an interesting body of work for measuring coupling in Aspect-Oriented (AO) Systems, there is no complete generic coupling framework for AO systems. In this paper, we have proposed a generic coupling measurement framework that takes into account three, the most well known families of Aspect-Oriented Programming (AOP) languages, AspectJ, CaesarJ and Hyper/J. This paper contributes to an enhanced understanding of coupling in AO systems, which in turn helps to (i) define new coupling metrics which permit the analysis and comparison of Java, AspectJ, CaesarJ and Hyper/J implementations, and (ii) integrate different existing measures and examine same concepts from different perspectives.","Title: A Fast Compressive Sensing Based Digital Image Encryption Technique using Structurally Random Matrices and Arnold Transform.\nYear: 2014\n\nAbstract: A new digital image encryption method based on fast compressed sensing approach using structurally random matrices and Arnold transform is proposed. Considering the natural images to be compressed in any domain, the fast compressed sensing based approach saves computational time, increases the quality of the image and reduces the dimension of the digital image by choosing even 25 % of the measurements. First, dimension reduction is utilized to compress the digital image with scrambling effect. Second, Arnold transformation is used to give the reduced digital image into more complex form. Then, the complex image is again encrypted by double random phase encoding process embedded with a host image; two random keys with fractional Fourier transform are been used as a secret keys. At the receiver, the decryption process is recovered by using TwIST algorithm. Experimental results including peak-to-peak signal-to-noise ratio between the original and reconstructed image are shown to analyze the validity of this technique and demonstrated our proposed method to be secure, fast, complex and robust.","Title: Numerical simulation and convergence analysis of a finite volume scheme for solving general breakage population balance equations\nYear: 2013\n\nAbstract: This paper presents a finite volume scheme (FVS) for solving general breakage population balance equations (BPBEs). In particular, the number density based BPBE is transformed to the form of a mass conservation law. Then it becomes easy to apply the well known FVSs that have an important property of mass conservation. Following Kumar and Warnecke for fixed pivot (FP) method [16] and cell average technique (CAT) [15], the stability and the convergence analysis of the semi-discretized FVS are studied. Unlike the CAT and the FP method, the FVS is second order consistent independently of the type of meshes. We also observe that FVS gives second order convergence rate on four different types of uniform and non-uniform meshes with non-decreasing behavior of mesh width. Nevertheless, one order better accuracy than the FP method is achieved on locally uniform meshes. It is also noticed that on non-uniform random meshes the FVS shows one order and two orders higher accuracy than the CAT and the FP method, respectively. The mathematical results of convergence analysis are validated numerically by taking three test problems. The numerical simulations are also compared with the results obtained by the CAT and the FP method.","Title: Assessment of Crohn's disease lesions in wireless capsule endoscopy images.\nYear: 2012\n\nAbstract: Capsule endoscopy (CE) provides noninvasive access to a large part of the small bowel that is otherwise inaccessible without invasive and traumatic treatment. However, it also produces large amounts of data (approximately 50 000 images) that must be then manually reviewed by a clinician. Such large datasets provide an opportunity for application of image analysis and supervised learning methods. A...","Title: Finding An Operating Region For A Bio-Inspired Robotic Fish Underwater Vehicle In The Lighthill Framework\nYear: 2013\n\nAbstract: Sir J. Lighthill mathematical slender body swimming model formulates the biological fish propulsion mechanism (undulation) in fluid environment. The present research has focused on the relevance of Lighthill (LH) based biomimetic robotic propulsion. A 2-joint, 3-link multibody vehicle model biologically inspired by a Body Caudal Fin (BCF) carangiform fish propulsion mechanism is designed. Different mathematical propulsive waveforms are proposed in LH frame-work to generate posterior body undulation. These functions are combined with inverse kinematics to generate various bio-inspired trajectories for the robotic fish vehicle motion. The robotic fish model (kinematics and dynamics) is integrated with the Lighthill (LH) mathematical model framework. Comparative studies are undertaken among a LH model and the proposed propulsive wave models. LH Cubic and NURB quadratic functions are found to be 16.32 % and 17.94 % efficient than a non-LH function respectively. Based on the simulation results of critical kinematic parameters TBF and Propulsive wavelength, an operating region is established to facilitate the open-loop (manual) control experiments.","Title: Long cycles and long paths in the Kronecker product of a cycle and a tree\nYear: 1997\n\nAbstract: Let C m × T denote the Kronecker product of a cycle C m and a tree T . If m is odd, then C m × T is connected, otherwise this graph consists of two isomorphic components. This paper presents a scheme which constructs a long cycle in each component of C m × T . If T satisfies certain degree constraints, then the cycle thus traced is shown to be a dominating set, and in some cases, a vertex cover of that component. The procedure builds on (i) results on longest cycles in C m × P n , and (ii) a path factor of T . Additional results include characterizations for the existence of a Hamiltonian cycle and for that of a Hamiltonian path in C m × T .","Title: A Family of 32 nm IA Processors\nYear: 2011\n\nAbstract: Westmere is the latest IA processor family for mobile, desktop and server market segments, implemented on Intel's second-generation high-k metal gate 32 nm process. Westmere not only increases core count, cache size, and frequency within the previous generation's power envelope, it also provides further improvements in power efficiency, feature set, and support for combo DDR3 and low voltage DDR3 despite using a thin gate technology.","Title: A Testbed System for Robotically Assisted Percutaneous Pattern Therapy\nYear: 1999\n\nAbstract: This paper presents a second generation prototype image-guided robotic system for percutaneous delivery of surgical devices\n and therapeutic agents, with potential applications in the treatment of liver cancer and other malignancies. The system is\n intended to deliver these devices and therapies more consistently and accurately than a physician can achieve freehand. This\n capability will permit the treatment of smaller lesions, will enable the physician to better achieve optimized patterns of\n therapy distribution, and will allow for more rapid re-targeting for multiple lesions. The system will allow treatment of\n patients for whom surgery is contraindicated and could potentially replace major surgery, reducing patient morbidity and mortality,\n as well as the cost of treatment. The new prototype system uses new, modular hardware and software components, which improve\n its accuracy, usability, and flexibility as compared to the first generation prototype. Techniques for image processing, both\n in 2D and 3D images, planning, and fiducial registration have also been developed. In vitro, the system achieves submillimetric accuracy in the placement of simulated treatment devices from a preoperative plan generated\n from 3D imagery. Some initial in vivo considerations have also been addressed, and work is ongoing in this area.","Title: Real-Time Implementation of a Harmony Search Algorithm-Based Clustering Protocol for Energy-Efficient Wireless Sensor Networks\nYear: 2014\n\nAbstract: In real-life applications of wireless sensor networks (WSNs), optimization of the network operation is required to extend its lifetime. A framework is proposed that enables practical development of centralized cluster-based protocols supported by optimization methods for the WSNs. Based on this framework, a protocol using harmony search algorithm (HSA), a music-based meta-heuristic optimization method, is designed and implemented in real time for the WSNs. It is expected to minimize the intra-cluster distances between the cluster members and their cluster heads (CHs) and optimize the energy distribution of the WSNs. The study of HSA cluster-based protocol is carried out in a real case where the WSNs equipped with the proposed protocol are deployed in an indoor environment to monitor the ambient temperature for fire detection. A comparison is made with the well-known cluster-based protocols developed for WSNs such as low-energy adaptive clustering hierarchy-centralized (LEACH-C) and a cluster-based protocol using Fuzzy C-Means (FCM) clustering algorithm. Experimental results demonstrate that the proposed protocol using HSA can be realized in centralized cluster-based WSNs for safety and surveillance applications in building environments. From the obtained experimental test results, it can be seen that the WSNs lifetime has been extended using the proposed HSA protocol in comparison with that of LEACH-C and FCM protocols.","Title: A novel approach to identification of power quality disturbances using continuous wavelet transform\nYear: 2010\n\nAbstract: This paper presents a novel approach that is able to detect the power quality disturbances present in transient as well as steady-stable signals with the information of magnitude of the disturbances. This method uses the continuous wavelet transform (CWT). A new difference coefficient matrix (DCM) is proposed which is calculated from the difference of the CWT coefficients of the pure sinusoidal signal and the disturbed signal. Then, the scale wise sum of rows of DCM gives unique feature matrix (UFM). This paper shows that UFM posses the unique features that can be used to generate the unique signatures of various disturbances. These signatures not only reveal the information which can be used for the classification of the power quality disturbance but also provide the information about the magnitude of the disturbance of the signal. A case study is presented which verifies the proposed approach.","Title: Tracking multiple targets using binary proximity sensors\nYear: 2007\n\nAbstract: Recent work has shown that, despite the minimal information provided by a binary proximity sensor, a network of such sensors can provide remarkably good target tracking performance. In this paper, we examine the performance of such a sensor network for tracking multiple targets. We begin with geometric arguments that address the problem of counting the number of distinct targets, given a snapshot of the sensor readings. We provide necessary and sufficient criteria for an accurate target count in a one-dimensional setting, and provide a greedy algorithm that determines the minimum number of targets that is consistent with the sensor readings. While these combinatorial arguments bring out the difficulty of target counting based on sensor readings at a given time, they leave open the possibility of accurate counting and tracking by exploiting the evolution of the sensor readings across time. To this end, we develop a particle filtering algorithm based on a cost function that penalizes changes in velocity. An extensive set of simulations, as well as experiments with passive infrared sensors, are reported. We conclude that, despite the combinatorial complexity of target counting, probabilistic approaches based on fairly generic models for the trajectories yield respectable tracking performance.","Title: A new hybrid multi-agent-based particle swarm optimisation technique\nYear: 2009\n\nAbstract: This paper presents a multi-agent-based hybrid particle swarm optimisation technique. The algorithm integrates the deterministic, the multi-agent system (MAS) and the particle swarm optimisation (PSO) algorithm. An agent in hybrid multi-agent PSO (HMAPSO) represents a particle to PSO and a candidate solution to optimisation problem. All agents search parallel in an equally distributed lattice-like structure to save energy and computational time. The best solution is obtained through bee decision making process. Thus making use of deterministic search, multi-agent and bee PSO, the HMAPSO realises the purpose of optimisation. The proposed algorithm has been tested on various optimisation problems. The experimental results obtained show the robustness and accuracy of proposed HMAPSO. It also has been concluded that the proposed HMAPSO is able to generate a unique and optimal solution than the earlier reported approaches and hence can be a better option for real-time online optimisation problems.","Title: Mapping factors influencing the selection of subsea petroleum production systems: a case study.\nYear: 2012\n\nAbstract: The development, design, and selection of subsea petroleum production equipment and facilities are critical activities, as the decisions made will impact the success and profitability of the project. Pre-studies need to be conducted to design the best subsea concept and to assess the development costs in order to succeed in the project execution and to keep the production rate and profits as expected. Technical, economic, and government regulations are some of the factors that need to be evaluated in order to contract subsea services and procurement activities. However, there are many other factors that should be addressed before selecting the concept. By identifying and studying these aspects, one will be able to develop and select the best equipment to cover the functions needed, as well as to identify health, safety, environmental and quality requirements and spare parts’ availability for maintenance interventions. Based on a literature study and an industrial case study, in this paper we identify, map, validate and systematically rank various aspects, factors, and design criteria that need to be addressed in the design and selection of subsea oil and gas production systems.","Title: Reusability assessment for software components\nYear: 2009\n\nAbstract: Software reuse has been used as a tool to reduce the development cost and time of the software. Nowadays, in fact, majority of software systems are being developed from an assembly of existing reusable components. In order to assess the reuse of components effectively, it is necessary to measure the reusability of these components. Paper proposes Artificial Neural Network based approach to assess the reusability of software component. This work will help developers to select the best component in terms of its reusability, which will improve the maintainability of the overall system.","Title: DESIGN OF TWO-DIMENSIONAL INFINITE IMPULSE RESPONSE RECURSIVE FILTERS USING HYBRID MULTIAGENT PARTICLE SWARM OPTIMIZATION\nYear: 2010\n\nAbstract: We incorporate the optimization problem of two-dimensional infinite impulse response (IIR) recursive filters and the optimization methodology of hybrid multiagent particle swarm optimization (HMAPSO) and then apply the resultant optimized IIR filter in image processing for justifying HMAPSO robustness over other algorithm and its role in optimizing real-time situations. The design of the 2-D IIR filter is reduced to a constrained minimization problem whose robust solution is being achieved by a novel and optimal algorithm HMAPSO. This algorithm integrates the deterministic solution by the multiagent system, the particle swarm optimization (PSO) algorithm, and bee decision-making process. All agents search parallel in an equally distributed lattice-like structure to save energy and computational time as done by the bees in their hive selection process. Thus making use of deterministic search, multiagent PSO, and bee, the HMAPSO realizes the purpose of optimization. Experimental results and the application of the designed filters to focusing the defocused image show that the HMAPSO approach provides better upshots than the previous design methods.","Title: Fingerprint matching using multi-dimensional ANN\nYear: 2010\n\nAbstract: Fingerprint matching is one of the most widely used biometric technique for personal identification. This identification is achieved in this work by using the concept that every fingerprint has a unique pattern of distribution of the minutiae points. In this paper, a new method of recognition of this pattern of distribution of the minutiae points of an enhanced image is considered by using a multi-dimensional artificial neural network (MDANN). The proposed technique has the distinct advantage of using the entire resized minutiae image as an input at once. It is capable of excellent pattern recognition properties as the distribution of the minutiae points are used directly for recognition. The proposed approach shows significant promise and potential for improvement, compared with the other conventional matching techniques with regard to time and efficiency of results.","Title: Synthesis and characterization of luminescent Eu(HMAP)3·2H2O and Tb(HMAP)3·2H2O complexes.\nYear: 2009\n\nAbstract: The novel complexes Eu(HMAP)3·2H2O and Tb(HMAP)3·2H2O (where HMAP=2′-hydroxy-4′-methoxyacetophenone) are synthesized and characterized by infra red spectroscopy, 1H NMR spectroscopy, TGA/DTA, SEM and elemental analysis. The qualitative analysis of the complexes to detect the presence of rare earth (Eu, Tb), have been performed by energy dispersive analysis (EDX). The Eu(HMAP)3·2H2O on excitation at 354nm emits bright red luminescence with main peak at 612nm and Tb(HMAP)3·2H2O on excitation at 354nm emits bright green luminescence with main peak at 547nm. The luminescence intensity of Eu(HMAP)3·2H2O is higher than Tb(HMAP)3·2H2O. These complexes emitting red and green luminescence might be used to make the electroluminescent (EL) devices for display applications.","Title: A Novel Approach for Detection of Alteration in Ball Pen Writings\nYear: 2009\n\nAbstract: Addition or alteration to documents that have profound implication is very common. The technique that Forensic Document Examiners (FDEs) use for the examination of such documents is basically a physical examination. In this paper we consider the alteration detection as a two-class pattern recognition problem. Image processing techniques are used for feature extraction and a neural network based feature analysis technique is used for finding a set of discriminatory features. The results using a nearest neighbor classifier are very encouraging. The results also demonstrate the effectiveness of feature analysis.","Title: A novel sparse model based forensic writer identification\nYear: 2014\n\nAbstract: The paper presents a novel method for writer identification based on sparse representation of handwritten structural primitives, called graphemes or fraglets. The proposed method is different from the existing grapheme based methods as the earlier methods use vector quantization based coding (clustering method) to get a document descriptor, while the proposed method uses sparse coding for the same. Literature shows that the sparse coding outperforms vector quantization in many real life applications including face recognition. Sparse coding can achieve comparatively much lower reconstruction error. Secondly, the sparsity allows representation to be specialized and can capture a writer specific features more accurately. Graphemes (fraglets) extracted from a document are represented in terms of Fourier and wavelet descriptors because the fraglet contour may be well described by its global as well as local characteristics. Wavelet descriptors also give a multi-resolution representation of the shape. Results have shown that even with a smaller codebook (than the earlier reported systems), the proposed method achieves better performance.","Title: A meta registration framework for lesion matching.\nYear: 2009\n\nAbstract: A variety of pixel and feature based methods have been proposed for registering multiple views of anatomy visible in studies obtained using diagnostic, minimally invasive imaging. A given registration method may outperform another depending on anatomical variations, imaging conditions, and imaging sensor performance, and it is often difficult a priori to determine the best registration method for a particular application. To address this problem, we propose a registration framework that pools the results of multiple registration methods using a decision function for validating registrations. We refer to this as meta registration. We demonstrate that our framework outperforms several individual registration methods on the task of registering multiple views of Crohn's disease lesions sampled from a Capsule Endoscopy (CE) study database. We also report on preliminary work on assessing the quality of registrations obtained, and the possibility of using such assessment in the registration framework.","Title: DaVinci canvas: a telerobotic surgical system with integrated, robot-assisted, laparoscopic ultrasound capability.\nYear: 2005\n\nAbstract: We present daVinci Canvas: a telerobotic surgical system with integrated robot-assisted laparoscopic ultrasound capability. DaVinci Canvas consists of the integration of a rigid laparoscopic ultrasound probe with the daVinci robot, video tracking of ultrasound probe motions, endoscope and ultrasound calibration and registration, autonomous robot motions, and the display of registered 2D and 3D ultrasound images. Although we used laparoscopic liver cancer surgery as a focusing application, our broader aim was the development of a versatile system that would be useful for many procedures.","Title: Analysis of Crohn's disease lesions in capsule endoscopy images\nYear: 2009\n\nAbstract: Capsule endoscopy (CE) is aimed at diagnosing disease in areas of the gastrointestinal (GI) tract beyond the reach of conventional endoscopy. Recent work has addressed various methods for reducing the complexity of CE diagnosis and the time needed for analyzing the data. This includes detection of lumen and its contractions, fluids such as blood and intestinal juices, as well as extraneous matter such as food and bubbles. This paper outlines our ongoing work to segment lesions (in particular Crohn's disease) and other abnormalities in CE images. In particular, here we describe the data collection and clinical analysis for our project and preliminary results for segmenting abnormal and extraneous images from a set of 10 CE studies.","Title: An Intelligent Tuned Harmony Search algorithm for optimisation\nYear: 2012\n\nAbstract: The Harmony Search (HS) algorithm is a population-based metaheuristic optimisation algorithm. This algorithm is inspired by the music improvisation process in which the musician searches for harmony and continues to polish the pitches to obtain a better harmony. Although several variants of the HS algorithm have been proposed, their effectiveness in dealing with diverse problems is still unsatisfactory. The performances of these variants mainly depend on the selection of different parameters of the algorithm. In this paper, a new variant of the HS algorithm is proposed that maintains a proper balance between diversification and intensification throughout the search process by automatically selecting the proper pitch adjustment strategy based on its Harmony Memory. However, the performance of the proposed Intelligent Tuned Harmony Search (ITHS) algorithm is influenced by other parameters, such as the Harmony Memory Size (HMS) and the Harmony Memory Considering Rate (HMCR). The effects that varying these parameters have on the performance of the ITHS algorithm is also analysed in detail. The performance of the proposed ITHS algorithm is investigated and compared with eight state-of-the-art HS variants over 17 benchmark functions. Furthermore, to investigate the robustness of the proposed algorithm at higher dimensions, a scalability study is also performed. Finally, the numerical results obtained reflect the superiority of the proposed ITHS algorithm in terms of accuracy, convergence speed, and robustness when compared with other state-of-the-art HS variants.","Title: Fuzzy ideals and fuzzy semiprime ideals: some ring theoretic analogues\nYear: 1992\n\nAbstract: An attempt is made to examine the algebraic nature of fuzzy semiprime ideals under homomorphisms. Also, the fuzzy nil radical of the nth power of a fuzzy prime ideal is studied and a sufficient condition for a fuzzy ideal to be fuzzy maximal is given.","Title: Writer-independent off-line signature verification using surroundedness feature\nYear: 2012\n\nAbstract: The paper presents a novel set of features based on surroundedness property of a signature (image in binary form) for off-line signature verification. The proposed feature set describes the shape of a signature in terms of spatial distribution of black pixels around a candidate pixel (on the signature). It also provides a measure of texture through the correlation among signature pixels in the neighborhood of that candidate pixel. So the proposed feature set is unique in the sense that it contains both shape and texture property unlike most of the earlier proposed features for off-line signature verification. Since the features are proposed based on intuitive idea of the problem, evaluation of features by various feature selection techniques has also been sought to get a compact set of features. To examine the efficacy of the proposed features, two popular classifiers namely, multilayer perceptron and support vector machine are implemented and tested on two publicly available database namely, GPDS300 corpus and CEDAR signature database.","Title: Few useful considerations for maintaining software components and component-based systems\nYear: 2007\n\nAbstract: Component-Based Systems (CBS) maintenance may require several activities different than normal applications, such as upgrading the functionality of black-box components (for which code may not be available), replacement of older version components with the new ones for better and improved functionality, tracing the problem of compatibility between the new components with system, and so on. The focus of this paper is on investigating several issues and concerns about maintainability of component-based systems. It also explores the acceptance of maintainability characteristics and sub-characteristics as defined in ISO9126 quality model for CBS. The paper proposes two new sub-characteristics, namely trackability and portability, to be included under the maintenance activity.","Title: Applying Soft Computing Approaches to Predict Defect Density in Software Product Releases: An Empirical Study.\nYear: 2013\n\nAbstract: There is non-linear relationship between software metrics and defects, which results to a complex mapping. Therefore, to focus on the defect density area, it is a critical business requirement of effective and practical approach, which can help find the defect density in software releases. Soft computing provides a better platform to solve the non-linear and complex mapping problem. The aim of this paper is to formulate, build, evaluate, validate and compare two main sections of soft computing, fuzzy logic and artificial neural network approaches in prediction of defect density of subsequent software product releases. In this research, these two approaches are formulated and applied to predict the existence of a defect in file of software release. Both approaches have also been validated against various releases of two commercial software product release data sets. The validation criteria include mean absolute error, root mean square error and graphical analysis. The analysis of the study shows that artificial neural network provides better results compared to Fuzzy Inference System; but applicability of best approach depends on the data availability and the quantum of data.","Title: Multiple-Target Tracking With Binary Proximity Sensors\nYear: 2011\n\nAbstract: Recent work has shown that, despite the minimal information provided by a binary proximity sensor, a network of these sensors can provide remarkably good target tracking performance. In this article, we examine the performance of such a sensor network for tracking multiple targets. We begin with geometric arguments that address the problem of counting the number of distinct targets, given a snapshot of the sensor readings. We provide necessary and sufficient criteria for an accurate target count in a one-dimensional setting, and provide a greedy algorithm that determines the minimum number of targets that is consistent with the sensor readings. While these combinatorial arguments bring out the difficulty of target counting based on sensor readings at a given time, they leave open the possibility of accurate counting and tracking by exploiting the evolution of the sensor readings over time. To this end, we develop a particle filtering algorithm based on a cost function that penalizes changes in velocity. An extensive set of simulations, as well as experiments with passive infrared sensors, are reported. We conclude that, despite the combinatorial complexity of target counting, probabilistic approaches based on fairly generic models of trajectories yield respectable tracking performance.","Title: Empirical Evaluation And Validation Of Interface Complexity Metrics For Software Components\nYear: 2008\n\nAbstract: The major drivers for complex applications are cost, efficiency, development time, understandability, usability and more importantly the maintainability. Due to their black-box nature, complexity of software components is more crucial for component-based systems. This paper discusses various complexity concerns for these systems and reviews a number of complexity metrics for software components and component-based systems. As interfaces are the only source of information to know about the black-box components, this paper proposes a new interface complexity metric or these components. This metric is based on the information available in he interfaces like interface methods and properties. It also discusses the methodology to assign the weight values to these methods and properties to finally evaluate the complexity of the component. This paper validates the proposed metric against standard Weyukar's properties and empirically evaluates he metric for several Java Bean components. Finally a correlation analysis between proposed metrics and several other metrics like performance, customizability and readability is done to validate the metric.","Title: Industrial Service Innovation Growth And Barriers\nYear: 2008\n\nAbstract: Industrial Business-to-Business (B2B) services are becoming increasingly important for industrial growth and competition. As the markets are becoming more competitive and globalised, companies need to focus on improving existing services and creating new services. Innovation in services is a complex process and many barriers are encountered for companies that do not have experience in service innovation. Based on a study in the Norwegian oil and gas industry, this paper maps recent industrial B2B service growth and innovations. A number of innovation barriers are identified and discussed. Furthermore, the paper draws attention to the need for development in industrial service sourcing strategies, relationships, as well as contractual and performance issues.","Title: Debris removal in Pap-smear images.\nYear: 2013\n\nAbstract: Since its introduction in the 1940s the Pap-smear test has helped reduce the incidence of cervical cancer dramatically in countries where regular screening is standard. The automation of this procedure is an open problem that has been ongoing for over fifty years without reaching satisfactory results. Existing systems are discouragingly expensive and yet they are only able to make a correct distinction between normal and abnormal samples in a fraction of cases. Therefore, they are limited to acting as support for the cytotechnicians as they perform their manual screening. The main reason for the current limitations is that the automated systems struggle to overcome the complexity of the cell structures. Samples are covered in artefacts such as blood cells, overlapping and folded cells, and bacteria, that hamper the segmentation processes and generate large number of suspicious objects. The classifiers designed to differentiate between normal cells and pre-cancerous cells produce unpredictable results when classifying artefacts. In this paper, we propose a sequential classification scheme focused on removing unwanted objects, debris, from an initial segmentation result, intended to be run before the actual normal/abnormal classifier. The method has been evaluated using three separate datasets obtained from cervical samples prepared using both the standard Pap-smear approach as well as the more recent liquid based cytology sample preparation technique. We show success in removing more than 99% of the debris without loosing more than around one percent of the epithelial cells detected by the segmentation process.","Title: A Steady-Hand Robotic System for Microsurgical Augmentation\nYear: 1999\n\nAbstract: This paper reports the development of a robotic system designed to extend a human’s ability to perform small-scale (sub-millimeter)\n manipulation tasks requiring human judgement, sensory integration and hand-eye coordination. Our novel approach, which we\n call “steady hand” micromanipulation, is for tools to be held simultaneously both by the operator’s hand and a specially designed\n actively controlled robot arm. The robot’s controller senses forces exerted by the operator on the tool and by the tool on\n the environment, and uses this information in various control modes to provide smooth, tremor-free precise positional control\n and force scaling. Our goal is to develop a manipulation system with the precision and sensitivity of a machine, but with\n the manipulative transparency and immediacy of handheld tools for tasks characterized by compliant or semi-rigid contacts\n with the environment.","Title: Towards integrating task information in skills assessment for dexterous tasks in surgery and simulation.\nYear: 2011\n\nAbstract: With the increasing popularity of robotic surgery, several studies in the literature have investigated automatically assessing skill measures based on motion and video data captured from these systems. A range of simulation environments for robotic surgery are now in development. Skill assessment in these environments has so far only focused on evaluating the utility and validity of statistics such as task completion time, and instrument distance measured during a simulated task. We present the first work using motion data from a robotic surgery simulation environment in development for classifying users of varying skills and detecting completion of trainee. Given the standardized environment of the simulator, and the availability of the ground truth, skill measurements and feedback based on task motion hold the promise of effective automated objective assessment. Based on motion data of a simulated manipulation task from 17 users of varying skills, we demonstrate binary classification (proficient vs. trainee) of user skill with 87.5% accuracy. Alternate measures based on instrument pose more relevant in the simulated environment including a new measure of motion efficiency are also presented and evaluated.","Title: Evaluation of Precipitation Features in High-Frequency SSM/I Measurements Over Indian Land and Oceanic Regions\nYear: 2009\n\nAbstract: Evaluation of the Special Sensor Microwave Imager (SSM/I) precipitation features is presented from various passes over Indian oceanic and land regions under the framework of radiative transfer simulations for both emission and scattering atmospheres. There is considerable uncertainty in the interpretation of the SSM/I high-frequency scattering features for development of rainfall algorithms. Speci...","Title: Estimation of quality for software components: an empirical approach\nYear: 2008\n\nAbstract: Component-Based Development (CBD) approach now is widely accepted in software industry. This approach enables efficient application development through the integration of already developed software components. The success of these applications heavily depends upon the selection of appropriate components to fit customer requirements. Therefore it is very necessary to evaluate the quality of components before using them in the final system. Quality models proposed so far can not be fully implemented as-it-is on components and component-based systems (CBS) due to architectural differences in the development approach. Present paper surveys a number of quality models for traditional and component-based systems and proposes a new model for CBS by proposing some new characteristics, which may be very relevant in the context of components. All the quality characteristics may not be of prime importance for an application to be developed for a specific domain. Therefore, it is necessary to identify only those characteristics/sub-characteristics, which may have higher priorities over the others. The present work uses Analytical Hierarchy Process (AHP) to assign the weight values to the characteristics for the proposed model. These weight values are then used to evaluate the quality contribution of sub-characteristics, characteristics and then finally the overall quality of the component by using the appropriate metrics. This approach can be used to identify and select better quality component among several others which can be used in the final system.","Title: An ontology-based approach to heterogeneous verification of embedded control systems\nYear: 2005\n\nAbstract: This paper presents an ontology-based approach to heterogeneous verification of embedded systems, that is, the integration of verification results from different tools and different models of embedded system applications. We present an overview of our proposed framework and explain the key components. We then describe an initial ontology for embedded control applications and its mapping to a knowledge base. We illustrate this initial framework using an example of an automotive power window controller. The concluding discussion describes our current work and future research directions.","Title: Preliminary Experiments in Cooperative Human/Robert Force Control for Robot Assisted Microsurgical Manipulation\nYear: 2000\n\nAbstract: This paper reports preliminary experiments with a new robot system designed to cooperatively extend a human's ability to perform fine manipulation tasks requiring human judgement, sensory integration and hand-eye coordination. A recently completed steady-hand robot is reported. A sta- ble force control law is reviewed. Preliminary experiments validate theoretical predictions of stable one-dimensional control of tool-tip forces in contact with both linearly and nonlinearly compliant objects. Preliminary feasibility ex- periments demonstrate stable one-dimensional robotic augmentation and \"force scaling\" of a human operator's tactile input.","Title: Towards a Unified Framework for Cohesion Measurement in Aspect-Oriented Systems\nYear: 2008\n\nAbstract: Aspect-Oriented Programming (AOP) is an emerging technique that provides a means to cleanly encapsulate and implement aspects that crosscut other modules. However, despite an interesting body of work for measuring cohesion in Aspect-Oriented (AO) Systems, there is poor understanding of cohesion in the context of AOP. Most of the proposed cohesion assessment framework and metrics for AOP are for AspectJ programming language. In this paper we have defined a generic cohesion framework that takes into account two, the most well known families of available AOP languages, AspectJ and CaesarJ. This unified framework contributes in better understanding of cohesion in AOP, witch can contribute in (i) comparing measures and their potential use, (ii) integrating different existing measures which examine the same concept in different ways, and (iii) defining new cohesion metrics, which in turn permits the analysis and comparison of Java, AspectJ and CaesarJ implementations.","Title: Computer-integrated revision total hip replacement surgery: concept and preliminary results\nYear: 1999\n\nAbstract: This paper describes an ongoing project to develop a computer-integrated system to assist surgeons in revision total hip replacement (RTHR) surgery. In RTHR surgery, a failing orthopedic hip implant, typically cemented, is replaced with a new one by removing the old implant, removing the cement and fitting a new implant into an enlarged canal broached in the femur. RTHR surgery is a difficult procedure fraught with technical challenges and a high incidence of complications. The goals of the computer-based system are the significant reduction of cement removal labor and time, the elimination of cortical wall penetration and femur fracture, the improved positioning and fit of the new implant resulting from precise, high-quality canal milling and the reduction of bone sacrificed to fit the new implant. Our starting points are the ROBODOC(R) system for primary hip replacement surgery and the manual RTHR surgical protocol. We first discuss the main difficulties of computer-integrated RTHR surgery and identify key issues and possible solutions. We then describe possible system architectures and protocols for preoperative planning and intraoperative execution. We present a summary of methods and preliminary results in CT image metal artifact removal, interactive cement cut-volume definition and cement machining, anatomy-based registration using fluoroscopic X-ray images and clinical trials using an extended RTHR version of ROBODOC. We conclude with a summary of lessons learned and a discussion of current and future work.","Title: Writer Identification for Handwritten Telugu Documents Using Directional Morphological Features\nYear: 2010\n\nAbstract: Linking a person based on handwritten documents is one of the oldest techniques that is used by crime investigators and forensic scientists. The importance of writer recognition in anthrax letter cases has made this examination popular in recent years. In this paper we propose four feature set namely directional opening, directional closing, direction erosion and k-curvature features for writer recognition on Telugu handwritten documents. Each of the features is extracted from the words after dividing them into a number of cells and then subjected to a nearest neighbor classifier for writer recognition. Although the results of each of the feature set is quite encouraging, the directional opening feature outperforms other feature sets.","Title: A Fuzzy Logic Approach To Measure Complexity Of Generic Aspect-Oriented Systems\nYear: 2010\n\nAbstract: Aspect-oriented programming (AOP) is an emerging technique that provides a mechanism to clearly encapsulate and implement concerns that crosscut other modules. It is claimed that this technique improves code modularization and therefore reduces complexity of object-oriented programs (OOP). Most of the proposed complexity measurement frameworks for AOP are for AspectJ programming language. In this paper, a generalized framework for assessment of complexity of aspect-oriented (AO) systems, has been defined that takes into account three, the most well known families of available AOP languages, AspectJ, CaesarJ and Hyper/J. In order to automate complexity measurement, a tool has been developed using fuzzy logic, in which some set of rules have been defined as rule base. Using this tool, complexity of majority of AOP languages can be measured, which will further help in the measurement of external software qualities, such as maintainability, reusability, adaptability and understandability.","Title: Luminescent properties of europium and terbium complexes with 2′-hydroxy-4′, 6′-dimethoxyacetophenone.\nYear: 2010\n\nAbstract: The novel complexes Eu(L)3·2H2O and Tb(L)3·2H2O (where L=2′-hydroxy-4′,6′-dimethoxyacetophenone) were synthesized and characterized by infrared spectroscopy, 1HNMR spectroscopy, TGA/DTA, scanning electron microscopy, elemental analysis and energy dispersive analysis (EDX). The Eu(L)3·2H2O on excitation at 354nm emits bright red luminescent with main peak at 612nm and Tb(L)3·2H2O on excitation at 354nm emits bright green luminescence with main peak at 547nm. The luminescence intensity of Eu(L)3·2H2O is higher than Tb(L)3·2H2O. These complexes emitting red and green luminescence might be used to make the OLEDs for display applications.","Title: A Progressive Cut Refinement Scheme for Revision Total Hip Replacement Surgery Using C-arm Fluoroscopy\nYear: 1999\n\nAbstract: We describe a new method to cut a precise, high quality cavity in Revision Total Hip Replacement surgery (RTHR) using a set of intra-operative C-arm fluoro- scopic images. With respect to previous approaches, our method provides the fol- lowing new features: (1) a novel checkerboard plate was designed to correct the geo- metric distortion within fluoroscopic images. Unlike previous distortion correction devices, the plate doesn't completely obscure any part of the image, and the distortion correction algorithm works well even when there are some overlaid objects in field of view; (2) a novel corkscrew fiducial object attached to the robot end-effector was de- signed, and a 6D pose estimation algorithm based on the 2D projection of the cork- screw is developed and used in robot-imager registration and imager co-registration; (3) we propose a progressive cut refinement scheme and an iterative cut location al- gorithm which utilizes image subtraction and 2D anatomy contour matching tech- niques. Several cutting experiments and some simulated experiments have been con- ducted to assess our techniques. The results indicate that our scheme is a promising method for RTHR application.","Title: A decision fusion strategy for polyp detection in capsule endoscopy.\nYear: 2012\n\nAbstract: Wireless capsule endoscopy (CE) is now routinely used for non-invasive diagnosis of small bowel diseases. But, it still requires manual assessment of the approximately 50,000 study images. Literature has recently investigated automated methods to detect and analyze various anomalies in CE images to improve reading efficiency and reduce variability. We propose such a computer aided diagnosis (CAD) approach to detect small bowel polyps. For supervised classification of polyps, we investigated fusing multiple statistical classifiers based on color, texture and edge features. The combined boosted classifier when evaluated using 1200 CE images outperformed all individual classifiers and achieved a ~90% classification accuracy.","Title: Numerical methods for solving two-dimensional aggregation population balance equations\nYear: 2011\n\nAbstract: Abstract The cell average technique (CAT) and the fixed pivot (FP) method for solving two-dimensional aggregation population balance equations using a rectangular grid were implemented in Kumar et al. (2008) . Recently, Chakraborty and Kumar (2007) have studied the FP scheme for the same problem on two different types of triangular grids and found that the method shows better results for number density as compared to the rectangular grids. However, they did not discuss the results for higher moments. Therefore, our first aim in this work is to compare different moments calculated by the FP technique on rectangular and triangular meshes with the analytical moments. Further we introduce a new mathematical formulation of the CAT for the two different types of triangular grids as considered by Chakraborty and Kumar (2007) . The new formulation is simple to implement and gives better accuracy as compared to the rectangular grids. Three different test problems are considered to analyze the accuracy of both schemes by comparing the analytical and numerical solutions. The new formulation shows good agreement with the analytical results for number density and higher moments.","Title: Distributed Modular Computer-Integrated Surgical Robotic Systems: Architecture for Intelligent Object Distribution\nYear: 2000\n\nAbstract: This paper presents intelligent object distribution architecture to maximize the performance and intelligence of a distributed surgical robotics system and its preliminary implementation in an MR-guided surgical robot system in an open-configuration MRI scanner. The method enables networked integration of a robot control server and multiple clients with minimum engineering overhead but maximum flexibility and performance. The clients in this study include an intraoperative imager, high-performance image processing computer(s), and surgical navigation host. The first contribution of the paper is to propose the use of object distribution by common object request broker architecture (CORBA), in which a robot control object on the robot control server can be remotely but transparently invoked from the clients regardless of their hardware, operating systems, or programming language. Second, we propose a technique to achieve additional flexibility by reporting the robot configuration information, i.e. geometry and kinematics of the robot, to the clients upon connection. Third, we ensure protection against an unauthorized entity by introducing a security control host that authorized the clientsaccess to the robot server. In a prototype implementation of an MR-guided surgical robot system, the robot was controlled by surgical navigation software (the 3D Slicer) on a UNIX client by invoking the distributed control object on a robot control server on a PC. The method was evaluated in performance studies; and the result indicated 3.6 milliseconds for retrieving positions of the robot stages and 25.5 milliseconds to send a frame-based motion command, which are satisfactory for surgical robot control. In conclusion, the proposed method shows the potential usefulness of flexibly integrating the legacy software to a surgical robot system with minimum engineering overhead, thereby achieving highly complex and intelligent tasks in robot-assisted surgery.","Title: Measuring changeability for generic aspect-oriented systems\nYear: 2008\n\nAbstract: Maintenance of software systems has become a major concern for software developers and users. In environments, where software changes are frequently required to improve software quality, chan-geability is an important characteristic of maintainability in ISO/IEC 9126 quality standards. Many researchers and practition-ers have proposed changeability assessment techniques for Object-Oriented Programming (OOP) and Aspect-Oriented Programming (AOP). To the best of our knowledge, no one has proposed chan-geability assessment technique for generic Aspect-Oriented (AO) Systems. AOP is an emerging technique that provides a means to clearly encapsulate and implement aspects that crosscut other modules. In this paper, we have defined a generic changeability assessment technique that takes into account two well known fami-lies of available AOP languages viz, AspectJ and CaesarJ. A co-relation analysis between changeability and dependency has been performed. Result shows that highly dependent AO systems will absorb low changeability.","Title: Visualization of anatomical information in near-infrared imaging for robotic urological surgery\nYear: 2011\n\nAbstract: Commercial telerobotic surgery systems for soft tissue surgery are generally limited to visual imaging, though it is possible to simultaneously view picture-in-picture visualization of another workstation. However, it is not easy to correlate such information with the primary endoscopic view since it may not relate to the surface visible in the visual endoscopic images. As critical surfaces as well as surgical targets often lie subsurface, a range of techniques (e.g. ultrasound and near-infrared imaging) and registration methods have been investigated as robotic surgery gains popularity. While investigation of nerves, blood vessels, and tumors has received prior attention, we present a new prototype system for real-time multimodal image registration that focuses on the visualization of the urinary tract. By providing an accurate registration between stereo video images and a near infrared imager, we aim to enhance surgical awareness and make critical uretary tasks such as mobilization of the ureters easier. Engineering validation experiments with a prototype imager, and in-vivo experiments using a prototype Hamamatsu Photodynamic Eye (PDE) imager together with the da Vinci surgical system demonstrating feasibility are presented. These initial experiments have also shown encouraging response from the clinicians.","Title: A Cooperative Network Framework for Multi-UAV Guided Ground Ad Hoc Networks\nYear: 2015\n\nAbstract: Cooperative ad hoc networks are becoming very important in various military and civilian applications. The interfacing between different ad hoc networks provides large applications in field of surveillance, navigation, disaster monitoring and homeland security. This paper focuses on implementation of UAV (unmanned aerial vehicles) ad hoc network that forms a guidance system for ground ad hoc network. The network framework proposed in the paper uses neural network to form cognitive and topology maps. Indirect and Bayesian Kalman Filter are used for estimations. These estimations allows updating of pre-constructed cognitive map to form ideal final search map that is shared among all nodes to perform search and track operations. The analysis showed that the proposed framework is capable of forming a search maps that is able to define multiple way points for each UAV in the network to follow a non-redundant path for searching and identifying various user nodes and geographical territories. The effectiveness of the model is demonstrated using simulations.","Title: A Computational Approach to Enzyme Design: Predicting ω-Aminotransferase Catalytic Activity Using Docking and MM-GBSA Scoring.\nYear: 2014\n\nAbstract: Enzyme design is an important area of ongoing research with a broad range of applications in protein therapeutics, biocatalysis, bioengineering, and other biomedical areas; however, significant challenges exist in the design of enzymes to catalyze specific reactions of interest. Here, we develop a computational protocol using an approach that combines molecular dynamics, docking, and MM-GBSA scoring to predict the catalytic activity of enzyme variants. Our primary focuses are to understand the molecular basis of substrate recognition and binding in an S-stereoselective omega-aminotransferase (omega-AT), which naturally catalyzes the transamination of pyruvate into alanine, and to predict mutations that enhance the catalytic efficiency of the enzyme. The conversion of (R)-ethyl 5-methyl-3-oxooctanoate to (3S,5R)-ethyl 3-amino-5-methyloctanoate in the context of several omega-AT mutants was evaluated using the computational protocol developed in this work. We correctly identify the mutations that yield the greatest improvements in enzyme activity (20-60-fold improvement over wild type) and confirm that the computationally predicted structure of a highly active mutant reproduces key structural aspects of the variant, including side chain conformational changes, as determined by X-ray crystallography. Overall, the protocol developed here yields encouraging results and suggests that computational approaches can aid in the redesign of enzymes with improved catalytic efficiency.","Title: Group based Swarm evolution algorithm (GSEA) driven mental task classifier.\nYear: 2015\n\nAbstract: Brain computer interface (BCI) system is based on non-invasive electroencephalographic signals. It is an augmented communication channel for disabled persons which translates neural activity from the brain into control signals. In this paper, a novel group based Swarm evolution algorithm (GSEA) driven neural classifier has been proposed to classify mental tasks. These mental tasks consists of left and right hand movement imagination and thinking of word generation. GSEA is a hybrid of swarm intelligence based computational methods with differential evolution based techniques. Hybridization of both algorithms ensures that pitfalls of both are overcome thus enhancing results. A concept of grouping has also been introduced to increase both exploration and exploitation performance of the algorithm. GSEA has been tested on publicly available BCI Competition 3 dataset 5. Experimental results shows that the proposed algorithm exhibits better result than individual swarm or differential search based algorithms and many other algorithms.","Title: A general framework for wireless capsule endoscopy study synopsis.\nYear: 2015\n\nAbstract: We present a general framework for analysis of wireless capsule endoscopy (CE) studies. The current available workstations provide a time-consuming and labor-intense work-flow for clinicians which requires the inspection of the full-length video. The development of a computer-aided diagnosis (CAD) CE workstation will have a great potential to reduce the diagnostic time and improve the accuracy of assessment. We propose a general framework based on hidden Markov models (HMMs) for study synopsis that forms the computational engine of our CAD workstation. Color, edge and texture features are first extracted and analyzed by a Support Vector Machine classifier, and then encoded as the observations for the HMM, uniquely combining the temporal information during the assessment. Experiments were performed on 13 full-length CE studies, instead of selected images previously reported. The results (e.g. 0.933 accuracy with 0.933 recall for detection of polyps) show that our framework achieved promising performance for multiple classification. We also report the patient-level CAD assessment of complete CE studies for multiple abnormalities, and the patient-level validation demonstrates the effectiveness and robustness of our methods.","Title: Geometrical Localization Algorithm for Three Dimensional Wireless Sensor Networks\nYear: 2014\n\nAbstract: The issue of localization has been addressed in many research areas such as vehicle navigation systems, virtual reality systems, user localization in wireless sensor networks (WSNs). In this paper, we have proposed an efficient range-free localization algorithm: Geometrical Localization Algorithm (GLA) for large scale three dimensional WSNs. GLA uses moving anchors to localize static sensors. GLA consists of beacon message selection, circular cross section selection. Three beacon messages are used to compute the center of circular cross section using vector method and perpendicular bisector method. The static sensors are localized with help of the center of circular cross section and geometrical rules for sphere. GLA is simulated in SINALGO software and results have been compared with existing methods namely chord selection and point localization. GLA outperforms both the compared methods in terms of average localization time and beacon overhead.","Title: Directed Bee Colony Optimization Algorithm.\nYear: 2014\n\nAbstract: The paper presents a new optimization algorithm inspired by group decision-making process of honey bees. The honeybees search for the best nest site among many possible sites taking care of both speed and accuracy. The nest site selection is analogous to finding the optimality in an optimization process. Such similarities between two processes have been used to cultivate a new algorithm by learning from each other. Various experiments have been conducted for better understanding of the algorithm. A comprehensive experimental investigation on the choice of various parameters such as number of bees, starting point for exploration, choice of decision process etc. has been made, discussed and used to formulate a more accurate and robust algorithm. The proposed Directed Bee Colony algorithm (DBC) has been tested on various benchmark optimization problems. To investigate the robustness of DBC, the scalability study is also conducted. The experiments conducted clearly show that the DBC generally outperformed the other approaches. The proposed algorithm has exceptional property of generating a unique optimal solution in comparison to earlier nature inspired approaches and therefore, can be a better option for real-time online optimization problems.","Title: A novel two-level particle swarm optimization approach to train the transformational grammar based hidden Markov models for performing structural alignment of pseudoknotted RNA.\nYear: 2015\n\nAbstract: A two-level particle swarm optimization (TL-PSO) algorithm is proposed for training stochastic context-sensitive hidden Markov model (cs-HMM), that addresses a thrust area of bioinformatics i.e. structural alignment of pseudoknotted non-coding RNAs (ncRNAs). Due to the well-conserved sequences and corresponding secondary structures of ncRNAs, the structural information becomes imperative for performing their alignments. Proposed approach is unique in the sense: it is the first idea so far which works on optimization of the model length; also it is the first swarm intelligence technique that is proposed for training csHMM. The two-level strategy with training and cross training sets helps in increasing the diversity of the particles so as to avoid trapping in local optima, yields more accurate estimation parameters, preserves the structure of the model and provides the best compression from the model. TL-PSO yields a trained stochastic model with position-dependent probabilities that achieves high prediction ratios than the compared non-stochastic scoring matrix based csHMM approaches. TL-PSO is also tested solely for sequence alignment of proteins, by training the conventional HMMs. TLPSO-HMM produces an effective framework for sequence alignment in terms of alignment quality and prediction accuracy than the competitive state-of-the-art and family of PSO based algorithms. Conjointly, TLPSO-csHMM finds higher prediction measures than competitive RNA structural alignment techniques for pseudoknotted and non-pseudoknotted RNA structures of diverse complexities.","Title: Realisation of a cluster-based protocol using fuzzy C-means algorithm for wireless sensor networks\nYear: 2013\n\nAbstract: In this study, real-time design and implementation of an energy-efficient cluster-based protocol for wireless sensor networks (WSNs) are presented. The formation of suitable clusters is of prime importance to balance energy usage by sensor nodes within each cluster of the cluster-based WSNs. This leads to energy savings for the sensor nodes resulting in longer network lifetime. To obtain this objective, the fuzzy C-means (FCM) clustering algorithm is incorporated in the protocol. The protocol is realised on a hardware test-bed with the support of the embedded operating system, TinyOS. Experimental results obtained from a scale-down laboratory based test-bed with up to 50 sensor nodes are provided to illustrate the efficacy of the WSNs using the proposed protocol and compared with the well-known cluster-based protocols such as Low Energy Adaptive Clustering Hierarchy. It has been shown that the FCM protocol is able to achieve better organisation of the network and thus can extend its lifetime under varying operating conditions and with different number of nodes.","Title: A boundary restricted adaptive particle swarm optimization for data clustering.\nYear: 2013\n\nAbstract: Data clustering is the most popular data analysis method in data mining. It is the method that parts the data object to meaningful groups. It has been applied into many areas such as image processing, pattern recognition and machine learning where the data sets are of many shapes and sizes. The most popular K-means and other classical algorithms suffer from drawback of their initial choice of centroid selection and local optima. This paper presents a new improved algorithm named as Boundary Restricted Adaptive Particle Swam Optimization (BR-APSO) algorithm with boundary restriction strategy. The proposed BR-APSO algorithm is tested on nine data sets, and its results are compared with those of PSO, NM-PSO, K-PSO and K-means clustering algorithms. It has been found that the proposed algorithm is robust, generates more accurate results and its convergence speed is also fast compared to other algorithms.","Title: Haswell: The Fourth-Generation Intel Core Processor\nYear: 2014\n\nAbstract: Haswell, Intel's fourth-generation core processor architecture, delivers a range of client parts, a converged core for the client and server, and technologies used across many products. It uses an optimized version of Intel 22-nm process technology. Haswell provides enhancements in power-performance efficiency, power management, form factor and cost, core and uncore microarchitecture, and the core's instruction set.","Title: Applicability of Landsat 8 data for characterizing glacier facies and supraglacial debris.\nYear: 2015\n\nAbstract: •Use of Landsat 8 OLI and TIRS bands for glacier facies and debris cover mapping.•A spectral profile based approach for increasing the efficiency of mapping.•Crevasses and slush zone on glacier also got mapped accurately.•A lapse rate based determination of transient freezing line was done.•OLI bands 1, 9, 5 and 6 along with TIRS band 10 were better to capture the facies.","Title: A lake detection algorithm (LDA) using Landsat 8 data: A comparative approach in glacial environment.\nYear: 2015\n\nAbstract: •Use of Landsat 8 coastal & cirrus bands for glacial lake detection and mapping.•Proposes MI (moisture index) & LDA (lake detection algorithm).•Proposes LDI (lake detection index) & LF (lake fraction) to check accuracy.•Performances of 7 pre-existing methods are also tested for glacial lake detection.•LDA performs best in case of both, mixed lake pixels and pure lake pixels.","Title: Beware, Your Hands Reveal Your Secrets!\nYear: 2014\n\nAbstract: Research on attacks which exploit video-based side-channels to decode text typed on a smartphone has traditionally assumed that the adversary is able to leverage some information from the screen display (say, a reflection of the screen or a low resolution video of the content typed on the screen). This paper introduces a new breed of side-channel attack on the PIN entry process on a smartphone which entirely relies on the spatio-temporal dynamics of the hands during typing to decode the typed text. Implemented on a dataset of 200 videos of the PIN entry process on an HTC One phone, we show, that the attack breaks an average of over 50% of the PINs on the first attempt and an average of over 85% of the PINs in ten attempts. Because the attack can be conducted in such a way not to raise suspicion (i.e., since the adversary does not have to direct the camera at the screen), we believe that it is very likely to be adopted by adversaries who seek to stealthily steal sensitive private information. As users conduct more and more of their computing transactions on mobile devices in the open, the paper calls for the community to take a closer look at the risks posed by the now ubiquitous camera-enabled devices.","Title: Fuzzy entropy-based framework for multi-faceted test case classification and selection: an empirical study\nYear: 2014\n\nAbstract: Software testing is complex, ambiguous, labour-intensive, costly, error prone and a core activity of software development. Devising the cost-effective and adequate strategies for software test cases optimisation has been one of the research issues in software testing for a long time. Existing techniques of test case optimisation are not providing the optimal solution to the test cases optimisation problem in terms of precision, completeness, cost and adequacy. The authors have already proposed a fuzzy logic-based multi-faceted measurement framework for test cases classification and fitness evaluation. Though, it reduces testing efforts, cost, incompleteness and increases adequacy, but, still there is ambiguity in classification and selection of some test cases due to ambiguity in fitness of test cases. Hence, there is a strong need to devise a technique to measure suitably and resolve the ambiguity in software test cases classification and selection problem. In this paper, the authors have unified their earlier proposed framework by introducing fuzzy entropy-based approach. The proposed unified framework chunks out the high ambiguity test cases and selects low ambiguity test cases for exercising on SUT (Software under Test). The proposed unified framework is tested on artefacts of benchmark applications, and the results show that the proposed unified framework enhances the classification accuracy by reducing ambiguity, and increases the number of test cases classified accurately.","Title: Optimal Thrust Allocation for Semisubmersible Oil Rig Platforms Using Improved Harmony Search Algorithm\nYear: 2012\n\nAbstract: Deep-water offshore drilling vessels, such as a semisubmersible drilling rig, use the dynamic positioning (DP) system and the thruster-assisted position-mooring system for maintaining a stationary position. In the DP system, the thrust allocator is used to distribute the desired generalized forces computed by the motion controller among the thrusters. However, to ensure safe operation of the vessel despite the thruster failure, the vessel is equipped with redundant thruster configuration and, therefore, is overactuated. For overactuated vessels, the choice of a particular solution for thrust allocation is found using some kind of optimization process. In this paper, the thrust allocator tries to minimize the power consumption and takes forbidden/spoil zones into account. The formulated thrust allocation problem becomes nonconvex due to thrust direction constraints on azimuth thrusters. The conventional methods get trapped in local minima and fail to find the optimum solution for the formulated nonconvex thrust allocation problem. In this paper, an improved harmony search (IHS) algorithm for solving the nonconvex thrust allocation problem is proposed. IHS is a variant of the harmony search (HS) algorithm. The HS algorithm is a music-based meta-heuristic optimization method, which is analogous with the music improvisation process where a musician continues to polish the pitches to obtain better harmony. Obtained numerical results show that the IHS algorithm has better convergence property when compared to the HS algorithm and the genetic algorithm (GA). Moreover, the power consumption for thrust allocation using the IHS algorithm is lower when compared with HS, GA, and Mincon (sequential quadratic programming) algorithms. The percentage savings in total power consumption for thruster allocation as compared to the Mincon algorithm for GA, HS, and IHS methods are 44.96%, 48.39%, and 51.58%, respectively.","Title: Optimal data aggregation tree in wireless sensor networks based on intelligent water drops algorithm\nYear: 2012\n\nAbstract: Energy conservation is an important aspect in wireless sensor networks (WSNs) to extend the network lifetime. In order to obtain energy-efficient data transmission within the network, sensor nodes can be organised into an optimal data aggregation tree with optimally selected aggregation nodes to transfer data. Various nature-inspired optimisation methods have been shown to outperform conventional methods when solving this problem in a distributed manner, that is, each sensor node makes its own decision on routing the data. In this study, a novel optimisation algorithm called intelligent water drops (IWDs) is adopted to construct the optimal data aggregation trees for the WSNs. Further enhancement of the basic IWD algorithm is proposed to improve the construction of the tree by attempting to increase the probability of selecting optimum aggregation nodes. The computational experiment results show that the IWD algorithm is able to obtain a better data aggregation tree with a smaller number of edges representing direct communication between two nodes when compared with the well-known optimisation method such as ant colony optimisation. In addition, the proposed improved version of the IWD algorithm provides better performance in comparison with the basic IWD algorithm for saving the energy of WSNs.","Title: Economic Analysis and Power Management of a Small Autonomous Hybrid Power System (SAHPS) Using Biogeography Based Optimization (BBO) Algorithm\nYear: 2013\n\nAbstract: In this study, Biogeography Based Optimization (BBO) algorithm is developed for the prediction of the optimal sizing coefficient of Small Autonomous Hybrid Power System (SAHPS) in remote areas. BBO algorithm is used to evaluate optimal component sizing and operational strategy by minimizing the total cost of SAHPS, while guaranteeing the availability of energy. Due to the complexity of the SAHPS design with nonlinear integral planning, BBO algorithm is used to solve the problem. The developed BBO Algorithm has been applied to design the wind/PV/hydro hybrid energy systems to supply a colony located in the area of Jaipur, Rajasthan (India) during the period of January, 2010 to January 2011. It is clear from the results that the proposed BBO method has excellent convergence property, requires less computational time and can avoid the shortcoming of premature convergence of other optimization techniques to obtain a better solution.","Title: An efficient pulse flip-flop based launch-on-shift scan cell\nYear: 2010\n\nAbstract: At-speed testing is essential for VLSI ICs implemented in nanometer technologies, operating at high clock speeds. Traditional scan based methodologies can be used for at-speed testing using a transition delay fault model. There are two common techniques to launch the transition-launch-on-shift (LOS) and launch-on-capture (LOC). LOS gives better fault coverage than LOC, but the main drawback of LOS is its requirement of a global at-speed scan enable (SE) signal that needs to be distributed across the IC. In this paper, we propose a pulsed flip-flop based LOS scan cell (PUFLOS cell) and a fast local scan enable generation circuit Our pulsed flip-flop based scan cell has 23.2% lower power dissipation and 27.3% better timing than a conventional muxed D-flip-flop based LOS scan cell. The layout area of our PUFLOS cell is 21% smaller than conventional LOS scan cell. Monte Carlo simulations demonstrate that our design is more robust to process variations than the conventional scan cell.","Title: Optimal placement of Phasor Measurement Units using Particle swarm Optimization\nYear: 2009\n\nAbstract: This paper solves the Phasor Measurement Unit (PMU) placement problem by Particle swarm Optimization (PSO). The PSO algorithm is implemented on three bus systems namely the 7, 14, 57 IEEE standard Bus systems. In this paper it has been proved that the placing of Phasor measurement units only at buses with the highest number of incident branches surely doesn't yield the optimal placement of the PMUs. This encouraged the idea of random placement of PMUs in the system and selection of the specific arrangement of PMUs which accomplished the task of complete observability of the system with minimum number of PMUs thus optimizing the PMU placement problem. The performance is compared with that of Adaptive GA, CLONALG and Adaptive CLONALG. The proposed method was found better.","Title: A study on inertia weight schemes with modified particle swarm optimization algorithm for multiple sequence alignment\nYear: 2013\n\nAbstract: In this study a modified particle swarm optimization (MPSO) algorithm has been proposed for obtaining optimal multiple sequence alignment (MSA). Due to the NP-complete nature of MSA problems, it is always a challenging task to obtain a suitable approach for optimal alignment of specific family of sequences. Proposed MPSO approach takes the random sequence length (with gaps), with non-repetitive gap positions in each iteration. Proposed approach applies both integer and binary matrix for incorporating gaps in sequences so as to avoid overlapping of gaps with sequences. Seven inertia weight schemes have been tested so as to check the effect of weight on convergence speed and time. Here the weight effect has been studied for different problem sizes i.e. 5 & 10 sequences with maximum sequence length of 10 & 212 respectively. MPSO is found to be performing well than standard PSO (S-PSO) at the same parameter set. Exponentially decreasing weight scheme is found performing significantly better at time and convergence criteria than other six weight approaches.","Title: Vector Control Techniques For Induction Motor Drive: A Review\nYear: 2009\n\nAbstract: This paper presents a literature survey on the vector control techniques for the induction motor drive. Various vector control techniques based on microprocessor/digital control, model adaptive reference control, intelligent control, sliding mode control and decoupling control are described. Uncertainties of parameter are important issue in vector control. Parameter needs to be identified clearly. Different parameter identification methods are discussed. Attempt is made to provide a guideline and quick reference for the researchers and practicing engineers those are working in the area of vector control.","Title: Classification of mental tasks from EEG data using backtracking search optimization based neural classifier\nYear: 2015\n\nAbstract: Brain Computer Interface (BCI) has been applied to augment impaired human cognitive function by converting mental signals into control signals. This paper presents a neural classifier optimized using Backtracking Search optimization Algorithm (BSANN) to classify three mental tasks consisting of right or left hand movement imagination and generation of word. BSA is an Evolutionary Algorithm (EA) which is suitable for deciphering non-linear and non-differentiable problems. Single control parameter gives BSA an upshot over other EA due to the lower degree of randomness. BSA keeps memory of old population to generate a new candidate set i.e. solution, so it gets the advantage of utilizing the search results of the previous population. The proposed method (BSANN) has been tested on the publicly available datasets of BCI Competition 3-5. Experimental result shows that BSANN exhibits better results than 21 other algorithms for classification of mental tasks in terms of classification accuracy.","Title: Social Media As A Catalyst For Civil Society Movements In India: A Study In Dehradun City\nYear: 2015\n\nAbstract: In India, online availability of information has created several new ways of communication and interaction through Internet relay chats, messaging email, video and voice chat, file sharing, blogging and discussion groups. Social media, particularly the Social Networking Sites, have enabled communication anywhere in the world and to anyone who shares interests and activities across political, social, economic and geographical boundaries. Of late, there has been intensive discourse and debate among intelligentsia about the perceptible impact of social media on opinion building and expression. Some civil society movements are also said to be accelerated by social media. This paper, through an empirical study, attempts to understand and analyse how social media, especially social networking sites, change the ways of communication, allow the public to critique and discuss fearlessly different social and political issues thereby becoming a force to reckon with for civil society movements and agitations in India. To study this phenomenon, Dehradun city, an education hub of India having a sizeable number of young active new media users, was chosen as a reference point. The study suggests that social media is yet to emerge as a definite force multiplier for civil society movements in India.","Title: A novel two-level particle swarm optimization approach for efficient multiple sequence alignment\nYear: 2015\n\nAbstract: This paper presents two-level particle swarm optimization (TL-PSO) algorithm as an effective framework for providing the solution of complex natured problems. Proposed approach is employed to solve a challenging problem of bioinformatics i.e. multiple sequence alignment (MSA) of proteins. The major challenge in MSA is the increasing complexity of the problem as soon as the number of sequences increases and average pairwise sequence identity (APSI) score decreases. Proposed TLPSO-MSA firstly maximizes the matched columns in level one followed by maximization of pairwise similarities in level two at the gbest solutions of level one. TLPSO-MSA efficiently handles the premature convergence and trapping in local optima related issues. The benchmark dataset for MSA of protein sequences are extracted from BAliBASE3.0. The special features of proposed algorithm is its prediction accuracy at very lower APSI scores. Proposed approach significantly outperforms the compared state-of-art competitive algorithms i.e. ALIGNER, MUSCLE, T-Coffee, MAFFT, ClustalW, DIALIGN-TX, ProbAlign and standard PSO algorithm. The claim is supported by the statistical significance testing using one way ANOVA followed by Bonferroni post-hoc analysis.","Title: An efficient two-level swarm intelligence approach for RNA secondary structure prediction with bi-objective minimum free energy scores.\nYear: 2016\n\nAbstract: This paper introduces a set-based two-level particle swarm optimization algorithm (TL-PSOfold) with multiple swarms for finding secondary structure of RNA with prediction accuracy. First objective is concerned with maximizing number of stacked loops at hydrogen bond, whereas, second objective deals with minimum free energy (MFE) at standard nearest neighbor database (NNDB). First level of the algorithm works on the entire search space for the best solution of each swarm, whereas, the second level works at the gbest solution of each swarm. The set based PSO approach has been applied at both levels to represent and update the set of ordered pairs of the folded RNA sequence. Improved weight parameter schemes with mutation operators are implemented for better convergence and to overcome the stagnation problem. Bi-objectives nature of TL-PSOfold enables the algorithm to achieve maximum matching pairs as well as optimum structure at respective levels. The performance of TL-PSOfold is compared with a family of PSO based algorithms i.e. HelixPSO v1, HelixPSO v2, PSOfold, SetPSO, IPSO, FPSO, popular secondary structure prediction software RNAfold, mfold and other metaheuristics RNAPredict, SARNA-Predict at the criteria of sensitivity, specificity and F-measure. Simulation results for TL-PSOfold show that it yields higher prediction accuracy than all the compared approaches. The claim is supported by the non-parametric statistical significance testing using Kruskal–Wallis test followed by post-hoc analysis.","Title: Authenticating users through their arm movement patterns.\nYear: 2016\n\nAbstract: In this paper, we propose four continuous authentication designs by using the characteristics of arm movements while individuals walk. The first design uses acceleration of arms captured by a smartwatchu0027s accelerometer sensor, the second design uses the rotation of arms captured by a smartwatchu0027s gyroscope sensor, third uses the fusion of both acceleration and rotation at the feature-level and fourth uses the fusion at score-level. Each of these designs is implemented by using four classifiers, namely, k nearest neighbors (k-NN) with Euclidean distance, Logistic Regression, Multilayer Perceptrons, and Random Forest resulting in a total of sixteen authentication mechanisms. These authentication mechanisms are tested under three different environments, namely an intra-session, inter-session on a dataset of 40 users and an inter-phase on a dataset of 12 users. The sessions of data collection were separated by at least ten minutes, whereas the phases of data collection were separated by at least three months. Under the intra-session environment, all of the twelve authentication mechanisms achieve a mean dynamic false accept rate (DFAR) of 0% and dynamic false reject rate (DFRR) of 0%. For the inter-session environment, feature level fusion-based design with classifier k-NN achieves the best error rates that are a mean DFAR of 2.2% and DFRR of 4.2%. The DFAR and DFRR increased from 5.68% and 4.23% to 15.03% and 14.62% respectively when feature level fusion-based design with classifier k-NN was tested under the inter-phase environment on a dataset of 12 users.","Title: Self-Healing Neural Model for Stabilization Against Failures Over Networked UAVs\nYear: 2015\n\nAbstract: Unmanned aerial vehicles (UAVs) allow formation of wide range ad hoc networks. These ad hoc formations with unmanned vehicles provide coverage of vast areas of applications involving mission dependent activities. Such networks can solve various issues related to civilian and military activities. One of the main applications of these networks is continuous surveillance. Surveillance by multiple nodes in ad hoc mode is directly dependent upon the continuous data sharing, cooperative decision making and stabilized network formation. Failures in network can hinder the performance and can decrease its operability. It is difficult to aloof network from discrete failures. Therefore, stabilized model is required which can provide stability to the whole network. For this, a self-healing neural model is developed which is capable of handling uncertain failures. It also provides provision for recovery of nodes from failure to stabilized state.","Title: Binary Fireworks Algorithm Based Thermal Unit Commitment\nYear: 2015\n\nAbstract: This paper presents the first application of fireworks algorithm to solve thermal unit commitment and scheduling problem. The scheduling problem accompanied by many constraints i.e., equality constraints like load balance and inequality constraints like system reserve and bounds like power generation, up/down time and ramp rate limits, finally shapes into a complex optimization problem. In this work, the scheduling and commitment problem is solved using binary fireworks algorithm BFWA, which mimics explosion of fireworks in the sky to define search space and distance between associated sparks to evaluate global minimum. Further, the effectiveness of fireworks pertaining to problem dimension, wide range of generation units from 10 to 100 are considered and evaluated. In addition, simulations results are compared to the existing optimization techniques in literature used for unit commitment and scheduling problem and it is observed that, BFWA is superior to some of the profound existing algorithms in achieving near optimal scheduling.","Title: An empirical evaluation of a three‐tier conduit framework for multifaceted test case classification and selection using fuzzy‐ant colony optimisation approach\nYear: 2015\n\nAbstract: The test case optimisation is an NP-complete, knowledge-driven, data-driven, and multidimensional search space partitioning and dimension reduction problem. In the multifaceted test case classification, partitioning and reducing the multidimensional test case fitness search space is the critical problem. The vague nature of fitness parameters, conflicting nature objectives, and ambiguity in the test case fitness evaluation have created and increased the uncertainty, the imprecision, and the incompleteness in the test case classification and selection. Because of the increasing ambiguity, the complexity, and the cost of software testing, automated test case classification and selection has emerged as an appropriate tool to classify test cases into predefined categories using the multifaceted concept. Most of the test cases affecting the performance of the classifier are irrelevant and redundant. A strong need therefore exists to devise an intelligent technique to identify and remove test cases affecting the performance of the classifier. For increasing the performance of classifier, multifaceted test case selection is used to reduce fitness search space to be searched. In this paper, a three-tier sequential framework is proposed for a multifaceted test case classification and selection. The first stage of the proposed framework is the fuzzy synthesis-based filtration approach for multifaceted test case fitness evaluation and classification. The second stage of the proposed framework is the fuzzy entropy-based filtration technique with a backward search strategy, used for estimating and reducing the ambiguity in test case fitness evaluation, classification, and selection. The third stage of the proposed framework is the ant colony optimisation-based wrapper technique with a forward search strategy, employed to select test cases from the output (reduced) test suite by the second stage. The proposed framework is tested on artefacts of benchmark applications. The results of the empirical study clearly show that the third stage of our proposed method outperforms the second and first stages, and the performance of the algorithms used in all three stages increases on average as the stages are escalating. The classification accuracy is enhanced by reducing the ambiguity in fitness and the classification of test cases, increasing the number of test cases accurately classified, and reducing the number in the test case pool to be exercised. Copyright (c) 2014 John Wiley & Sons, Ltd.","Title: A 45 nm CMOS-SOI Monolithic Photonics Platform With Bit-Statistics-Based Resonant Microring Thermal Tuning.\nYear: 2016\n\nAbstract: The microring resonator is critical for dense wavelength division multiplexed (DWDM) chip-to-chip optical I/O, enabling modulation and channel selection at the μm-scale suitable for a VLSI chip. Microring-based links, however, require active tuning to counteract process and thermo-optic variations. Here, we present a bit-statistical tuner that decouples tracking of optical one and zero-levels to r...","Title: Quantitative Attack Tree Analysis via Priced Timed Automata\nYear: 2015\n\nAbstract: The success of a security attack crucially depends on the resources available to an attacker: time, budget, skill level, and risk appetite. Insight in these dependencies and the most vulnerable system parts is key to providing effective counter measures. This paper considers attack trees, one of the most prominent security formalisms for threat analysis. We provide an effective way to compute the resources needed for a successful attack, as well as the associated attack paths. These paths provide the optimal ways, from the perspective of the attacker, to attack the system, and provide a ranking of the most vulnerable system parts. By exploiting the priced timed automaton model checker Uppaal CORA, we realize important advantages over earlier attack tree analysis methods: we can handle more complex gates, temporal dependencies between attack steps, shared subtrees, and realistic, multi-parametric cost structures. Furthermore, due to its compositionality, our approach is flexible and easy to extend. We illustrate our approach with several standard case studies from the literature, showing that our method agrees with existing analyses of these cases, and can incorporate additional data, leading to more informative results.","Title: A Novel Heterogeneous Framework for Local Dependency Dynamic Programming Problems\nYear: 2015\n\nAbstract: Dynamic programming is a well known technique to solve combinatorial search and optimization problems. LDDP (Local Dependency DP) problems are those DP problems where each update to an entry in the DP table is determined by the contents of its neigh boring cells. The amount of parallelism available for a given LDDP problem may vary depending on the position and number of neigh boring cells involved in the computation. In this paper, we develop a parallel heterogeneous (CPU+GPU) framework for solving LDDP problems. We use GPU and CPU specific optimizations and investigate if the given problem is a good candidate for heterogeneous computation. We also devise techniques to distribute the workload between CPU and GPU. We show how our framework can be used by considering several case study problems.","Title: Treadmill attack on gait-based authentication systems\nYear: 2015\n\nAbstract: In this paper, we demonstrate that gait patterns of an individual captured through a smartphone accelerometer can be imitated with the support of a digital treadmill. Furthermore, we design an attack for a baseline gait based authentication system (GBAS) and rigorously test its performance over an eighteen user data-set. By employing only two imitators and using a simple digital treadmill with speed control functionality, the attack increases the average false acceptance rate (FAR) from 5.8% to 43.66% for random forest, the best performing classifier in our experiments. More specifically, the FAR of eleven out of eighteen users increased to 70% or more. Our results call for a revisit of the design of the GBAS to make them resilient to such attacks.","Title: Observations From Study Of Pre-Islanding Behaviour In A Solar Pv System Connected To A Distribution Network\nYear: 2015\n\nAbstract: Unintentional islanding is a more relevant issue today due to rising penetration of solar photo voltaic systems on distribution grids. As the smart grid matures, better techniques for mitigating this problem have to evolve. Commercially available, inverter-resident, islanding detection based methods are aplenty and a lot others are being developed at research level. This work is oriented towards trying to understand the distributed-generation system dynamics that build up to the island forming situation. It is felt that inverter dynamics along with power system transitory events can provide a clue to an impending unintentional islanding event. Approaches resulting from such a study can train the system with predictive intelligence with regards to the possibility of an unwanted islanding condition. The study also results into interesting observations regarding the impact of capacitive reactive power present on the network, in excess of load requirement, on the grid-side current in the load-containing phase of the islandable feeder section. Arcing current inside the island and distortion in this current before islanding are the effects that are recorded.","Title: A 45nm SOI monolithic photonics chip-to-chip link with bit-statistics-based resonant microring thermal tuning\nYear: 2015\n\nAbstract: A new thermal tuning circuit for optical ring modulators enables demonstration of an optical chip-to-chip link for the first time with monolithically integrated photonic devices in a commercial 45nm SOI process, without any process changes. The tuning circuit uses independent 1/0 level-tracking and 1/0 bit counting to remain resilient against laser self-heating transients caused by non-DC-balanced transmit data. A 30fJ/bit transmitter and 374fJ/bit receiver with 6 mu A(pk-pk) photocurrent sensitivity complete the 5Gb/s link. The thermal tuner consumes 275fJ/bit and achieves a 600 GHz tuning range with a heater tuning efficiency of 3.8 mu W/GHz.","Title: Energy Efficient Data Dissemination in Multi-UAV Coordinated Wireless Sensor Networks.\nYear: 2016\n\nAbstract: With the requirement of better connectivity and enhanced coverage, collaborative networks are gaining a lot of popularity these days. One of such collaborative networks is formed between the wireless sensor networks (WSNs) and the unmanned aerial vehicles (UAVs). WSNs comprise static nodes arranged in a flat grid topology which may be randomly deployed or by some particular distribution. With the sensors operating on batteries, WSNs face a crucial issue of energy depletion during network operations. Integration of WSNs with UAVs can provide a solution to this excessive utilization of energy resources. UAVs provide a maneuvering support by playing a pivotal role of a manager node in these networks. However, integration of these networks demands an improved approach for data dissemination for effective utilization of network resources. For this, we propose a new data dissemination approach, which utilizes the attraction properties of fire fly optimization algorithm to provide energy efficient relaying. The proposed approach provides continuous connectivity, better lifetime, and improved coverage in the UAV coordinated WSNs. The performance of the proposed model is presented in terms of significant gains attained for parameters, namely, throughput, coverage, mean hops, lifetime, and delays, in comparison with the EEGA, ERIDSR, and I-ERIDSR approaches.","Title: Multivariate Statistics and Supervised Learning for Predictive Detection of Unintentional Islanding in Grid-Tied Solar PV Systems.\nYear: 2016\n\nAbstract: Integration of solar photovoltaic PV generation with power distribution networks leads to many operational challenges and complexities. Unintentional islanding is one of them which is of rising concern given the steady increase in grid-connected PV power. This paper builds up on an exploratory study of unintentional islanding on a modeled radial feeder having large PV penetration. Dynamic simulations, also run in real time, resulted in exploration of unique potential causes of creation of accidental islands. The resulting voltage and current data underwent dimensionality reduction using principal component analysis PCA which formed the basis for the application of Q statistic control charts for detecting the anomalous currents that could island the system. For reducing the false alarm rate of anomaly detection, Kullback-Leibler K-L divergence was applied on the principal component projections which concluded that Q statistic based approach alone is not reliable for detection of the symptoms liable to cause unintentional islanding. The obtained data was labeled and a K-nearest neighbor K-NN binomial classifier was then trained for identification and classification of potential islanding precursors from other power system transients. The three-phase short-circuit fault case was successfully identified as statistically different from islanding symptoms.","Title: Ageist Spider Monkey Optimization algorithm.\nYear: 2016\n\nAbstract: Swarm Intelligence (SI) is quite popular in the field of numerical optimization and has enormous scope for research. A number of algorithms based on decentralized and self-organized swarm behavior of natural as well as artificial systems have been proposed and developed in last few years. Spider Monkey Optimization (SMO) algorithm, inspired by the intelligent behavior of spider monkeys, is one such recently proposed algorithm. The algorithm along with some of its variants has proved to be very successful and efficient.","Title: Toward Robotic Robbery on the Touch Screen.\nYear: 2016\n\nAbstract: Despite the tremendous amount of research fronting the use of touch gestures as a mechanism of continuous authentication on smart phones, very little research has been conducted to evaluate how these systems could behave if attacked by sophisticated adversaries. In this article, we present two Lego-driven robotic attacks on touch-based authentication: a population statistics--driven attack and a user-tailored attack. The population statistics--driven attack is based on patterns gleaned from a large population of users, whereas the user-tailored attack is launched based on samples stolen from the victim. Both attacks are launched by a Lego robot that is trained on how to swipe on the touch screen. Using seven verification algorithms and a large dataset of users, we show that the attacks cause the system’s mean false acceptance rate (FAR) to increase by up to fivefold relative to the mean FAR seen under the standard zero-effort impostor attack. The article demonstrates the threat that robots pose to touch-based authentication and provides compelling evidence as to why the zero-effort attack should cease to be used as the benchmark for touch-based authentication systems.","Title: UAV-Assisted Heterogeneous Networks for Capacity Enhancement.\nYear: 2016\n\nAbstract: Modern day wireless networks have tremendously evolved driven by a sharp increase in user demands, continuously requesting more data and services. This puts significant strain on infrastructure-based macro cellular networks due to the inefficiency in handling these traffic demands, cost effectively. A viable solution is the use of unmanned aerial vehicles (UAVs) as intermediate aerial nodes betwee...","Title: Forensic Authentication of Bank Checks.\nYear: 2016\n\nAbstract: This chapter describes an automated methodology for the forensic authentication of bank checks. The problem of check authentication is modeled as a two-class pattern recognition problem. Color and texture features are extracted from images of genuine and counterfeit checks. A support vector machine is utilized to determine check authenticity. Classification experiments involving a dataset of 50 bank checks yielded a detection accuracy of 99.0%. The automated methodology can be used by non-specialist personnel to detect check counterfeiting in a banking environment where large numbers of checks are handled on a daily basis.","Title: Using Attack-Defense Trees to Analyze Threats and Countermeasures in an ATM: A Case Study.\nYear: 2016\n\nAbstract: Securing automated teller machines (ATMs), as critical and complex infrastructure, requires a precise understanding of the associated threats. This paper reports on the application of attack-defense trees to model and analyze the security of ATMs. We capture the most dangerous multi-stage attack scenarios applicable to ATM structures, and establish a practical experience report, where we reflect on the process of modeling ATM threats via attack-defense trees. In particular, we share our insights into the benefits and drawbacks of attack-defense tree modeling, as well as best practices and lessons learned.","Title: Validation Of Industrial Cyber-Physical Systems: An Application To Hvac Systems\nYear: 2016\n\nAbstract: We describe a validation approach for Simulink models of industrial cyber-physical systems (CPS), based on an adaptation of a coverage-guided test generation method for hybrid systems. Modelling an industrial CPS requires integrating heterogeneous components, which introduces high complexity in model verification. Using Simulink, which has become a de-facto industrial tool, heterogeneity comes from combining different formalisms (Simulink blocks, Stateflow diagrams, Matlab and C functions, etc.) and mixing different types of dynamics (discrete, continuous). Since the interactions between such components are often too complex to be faithfully captured in an existing mathematical modelling paradigm, we resort to treating them as black box systems while trying to exploit asmuch as possible a-priori knowledge about them. We first describe our approach: extracting from a Simulink model the information to define the main ingredients of the test generation framework, in particular environment inputs in which faults could be injected and critical states that require good coverage. We then illustrate the approach with an industrial model of an HVAC (Heating, Ventilation and Air Conditioning) system.","Title: Continuous authentication of smartphone users by fusing typing, swiping, and phone movement patterns\nYear: 2016\n\nAbstract: We studied the fusion of three biometric authentication modalities, namely, swiping gestures, typing patterns and the phone movement patterns observed during typing or swiping. A web browser was customized to collect the data generated from the aforementioned modalities over four to seven days in an unconstrained environment. Several features were extracted by using sliding window mechanism for each modality and analyzed by using information gain, correlation, and symmetric uncertainty. Finally, five features from windows of continuous swipes, thirty features from windows of continuously typed letters, and nine features from corresponding phone movement patterns while swiping/typing were used to build the authentication system. We evaluated the performance of each modality and their fusion over a dataset of 28 users. The feature-level fusion of swiping and the corresponding phone movement patterns achieved an authentication accuracy of 93.33%, whereas, the score-level fusion of typing behaviors and the corresponding phone movement patterns achieved an authentication accuracy of 89.31 %.","Title: A fourth order PDE based fuzzy c- means approach for segmentation of microscopic biopsy images in presence of Poisson noise for cancer detection.\nYear: 2017\n\nAbstract: •Presents a method for segmentation of microscopic biopsy images for cancer detection.•The proposed method can segment the cells and nuclei of the image in presence of Poisson noise.•The comparative analysis is presented with various standard methods in literature on various data sets.•The proposed method is working well and of use to the Histopathologist for second opinion.","Title: Intelligent Resource Inquisition Framework on Internet-of-Things.\nYear: 2017\n\nAbstract: •Develop an Intelligent Resource inquisition Framework on Internet-of-Things (IRIF-IoT).•Propose an optimized SMEBG algorithm for resource discovery.•Demonstrate its validity on an application, named Electronic Toll Collection (ETC).•The algorithm minimizes traffic congestion and enhances system performance significantly.","Title: Lyapunov stability-based control and identification of nonlinear dynamical systems using adaptive dynamic programming.\nYear: 2017\n\nAbstract: This paper presents a novel control and identification scheme based on adaptive dynamic programming for nonlinear dynamical systems. The aim of control in this paper is to make output of the plant to follow the desired reference trajectory. The dynamics of plants are assumed to be unknown, and to tackle the problem of unknown plant's dynamics, parameter variations and disturbance signal effects, a separate neural network-based identification model is set up which will work in parallel to the plant and the control scheme. Weights update equations of all neural networks present in the proposed scheme are derived using both gradient descent (GD) and Lyapunov stability (LS) criterion methods. Stability proof of LS-based algorithm is also given. Weight update equations derived using LS criterion ensure the global stability of the system, whereas those obtained through GD principle do not. Further, adaptive learning rate is employed in weight update equation instead of constant one in order to have fast learning of weight vectors. Also, LS- and GD-based weight update equations are also tested against parameter variation and disturbance signal. Three nonlinear dynamical systems (of different complexity) including the forced rigid pendulum trajectory control are used in this paper on which the proposed scheme is applied. The results obtained with LS method are found more accurate than those obtained with the GD-based method.","Title: Modeling and adaptive control of nonlinear dynamical systems using radial basis function network.\nYear: 2017\n\nAbstract: In this paper, the use of radial basis function network (RBFN) for simultaneous online identification and indirect adaptive control of nonlinear dynamical systems is demonstrated. The motivation of using RBFN comes from the simplicity of its structure and simpler mathematical formulation, which gives it an advantage over multi-layer feed-forward neural network (MLFFNN). Since most processes are nonlinear, the use of conventional proportional-integral-derivative controller is not useful. Most of the time plantu0027s dynamics information is not available. This creates another limitation on the use of conventional control techniques, which works only if plantu0027s dynamics information is available. The proposed controller is tested for parameter variations and disturbance effects. Simulation results showed that RBFN is able to capture the unknown dynamics as well as simultaneously able to adaptively control the plant. It is also found to compensate the effects of parameter variations and disturbances. The comparative analysis is also done with MLFFNN in each simulation example, and it is found that performance of RBFN is better than that of MLFFNN.","Title: Multi-objective two-level swarm intelligence approach for multiple RNA sequence-structure alignment.\nYear: 2017\n\nAbstract: This paper proposes a novel two-level particle swarm optimization algorithm for multi-objective optimization (MO-TLPSO) employed to a challenging problem of bioinformatics i.e. RNA sequence-structure alignment. Level one of the proposed approach optimizes the dimension of each swarm which is sequence length for the addressed problem, whereas level two optimizes the particle positions and then evaluates both the conflicting objectives. The conflicting objectives of the addressed problem are obtaining optimal multiple sequence alignment as well as optimal secondary structure. Optimal secondary structure is obtained by TL-PSOfold, the structure is further used for computing the contribution of base pairing of individual sequence and the co-variation between aligned positions of sequences so as to make the structure closer to the natural one. The results are tested against the popular softwares for pairwise and multiple alignment at BRAlibase benchmark datasets. Proposed work is so far the first multi-objective optimization based approach for structural alignment of multiple RNA sequences without converting the problem into single objective. Also, it is the first swarm intelligence based approach that addresses sequence-structure alignment issue of RNA sequences. Simulation results are compared with the state-of-the-art and competitive approaches. MO-TLPSO is found well competent in producing pairwise as well as multiple sequence-structure alignment of RNA. The claim is supported by performing statistical significance testing using one way ANOVA followed by Bonferroni post-hoc analysis for both kind of alignments.","Title: Termite spatial correlation based particle swarm optimization for unconstrained optimization.\nYear: 2017\n\nAbstract: In last few years, swarm intelligence has become the mainstay in the field of continuous optimization with many researchers developing algorithms simulating swarm behavior for the purpose of numerical optimization. This work proposes a new Termite Spatial Correlation based Particle Swarm Optimization (TSC-PSO) algorithm inspired by the movement strategy shown within Termites (Cornitermes cumulans). TSC-PSO modifies the velocity equation in the original PSO algorithm by replicating the step correlation based termite motion mechanism that exhibits individually in nature and works with decentralized control to collectively perform the overall task. Further, the algorithm incorporates the mutation strategy within it to make it suitable to avoid stagnation conditions while performing optimization in complex search spaces. For deriving its utility various benchmark functions of different geometric properties have been used. Experiments clearly demonstrate the success of the proposed algorithm in different benchmark conditions against various state-of-the-art optimization algorithms.","Title: G-FANET: an ambient network formation between ground and flying ad hoc networks.\nYear: 2017\n\nAbstract: Networking with aerial vehicles has evolved considerably over a period of time. Its applications range across a wide spectrum covering areas of military and civilian activities. Connectivity between aerial vehicles in ad hoc mode allows formation of multiple control units in the sky which have an ability to handle complex tasks. One of the major applications of these aerial vehicles is to coordinate simultaneously with another ad hoc network operating on the ground. This formation is termed as cooperative ad hoc networking. These networks operate on multiple data-sharing in form of cognitive maps. Thus, an efficient traffic management strategy is required to form a robust network. In this paper, an ambient network framework for coordination between ground and flying ad hoc network is presented. A fault-tolerant and robust connectivity strategy is proposed using neural, fuzzy and genetic modules. quaternion Kalman filter and its variant $$\\\\alpha -\\\\beta -\\\\gamma $$¿-ß-¿ filter is used to form the neural and decision system for guided aerial network. Effectiveness of the proposed traffic management framework for aerial vehicles is presented using mathematical simulations.","Title: Cooperative frameworks and network models for flying ad hoc networks: a survey.\nYear: 2017\n\nAbstract: SummaryIntegrated frameworks have extended the applications of networks beyond a simple data sharing unit. Simultaneously, operating networks can form a layered structure that can operate as homogeneous as well as dissociated units. Networks using unmanned aerial vehicles follow similar criteria in their operability. Unmanned aerial vehicles can act as single searching unit controlled by human or can form an aerial swarm that can fly autonomously with the capability of forming an aerial network. Such aerial swarms are categorized as aerial ad hoc networks. Cooperation amongst different networks can be realized using various frameworks, models, architectures and middlewares. Several solutions have been developed that can provide easy network deployment of aerial nodes. However, a combined literature is not present that provides a comparison between these approaches. Keeping this in view, various cooperative approaches for similar formation using aerial vehicles have been discussed in this paper. The detailed study and comparative analysis of these approaches have been included. Further, the paper also includes various software solutions and their comparisons based on common parameters. Finally, various open issues have been discussed that can provide insight of ongoing research and problems that are yet to be resolved in these networks. Copyright © 2016 John Wiley u0026 Sons, Ltd.","Title: SERVmegh: framework for green cloud.\nYear: 2017\n\nAbstract: With the growing popularity of cloud computing, different infrastructure as a service cloud frameworks do exists. Each framework has significant impact on robustness, scalability, fault-tolerance, energy efficiency, and so on of the cloud. To bring good characteristics of open source clouds and commercial clouds under a roof, a six layered green cloud framework namely SERVmegh is proposed. Resource management, green power management, workload analyzer and manager, and on/off control components of SERVmegh framework are designed and developed. Energy-efficient resource wastage reduction methodology for resource management is proposed. Algorithms for resource management, virtual machine placement, and minimization of virtual machine migrations using resource wastage reduction methodology are implemented. The performance of proposed algorithms is evaluated in CloudSim simulation and OpenNubula open source cloud environment. The results depict significant energy savings under different scenarios. Copyright (C) 2016 John Wiley & Sons, Ltd.","Title: Strategies for replica consistency in data grid - a comprehensive survey.\nYear: 2017\n\nAbstract: Data grid provides an efficient solution for data-oriented applications that need to manage and process large data sets located at geographically distributed storage resources. Data grid relies on data replicas to enhance the performance and to ensure the fault tolerant results to the users. Replicas are developed to increase the availability of data and to provide better data access. Replicas have their own advantages, but there are a number of issues that must be resolved. Among various existing issues, the critical concern is replica consistency. Various replica consistency strategies are available in the literature. These strategies rationalize and investigate various parameters like bandwidth consumption, access cost, scalability, execution time, storage consumption, staleness, and freshness of replicas. In this paper, several asynchronous replica consistencies are classified and analyzed based on various strategies such as topology, level of abstraction, update propagation, and locality. Some other strategies are also discussed and analyzed like adaptive consistency, quorum-based consistency, load balancing, and agent-based economically efficient, check-pointing, fault tolerance, and conflict management. Parameters on which these strategies are analyzed are methodology, replication classification, consistency, grid topology, environment, evaluation parameters, and performance. Copyright (C) 2016 John Wiley & Sons, Ltd.","Title: Estimation-Based Queue Scheduling Model to Improve QoS for End Users in MANETs.\nYear: 2016\n\nAbstract: Using MANETs for real time applications is always a challenge as the network is extremely dynamic with brisk topology changes. Despite this, several real time schedulers have been developed that aimed at providing QoS to ad hoc nodes. The quality of service (QoS) is standardized in terms of capacity, reliability, link quality, delays/jitters, and network cost. Thus, for QoS, the better transmission should be maintained at end user as well as at the transmitting unit. QoS of a network is affected by delays and bandwidth allocated for transmission. For an efficient network, it is required to predict these metrics during transmission. For this, in this paper, integration of quaternion-based Kalman filter is performed that predicts the required bandwidth and the network delays with higher accuracy. From the analysis, it is shown that bandwidth can be optimized but it is not possible to aloof delays in the network. Thus, while implementing such admission control procedures, estimation process allows control over delays and sustain them from going beyond a certain threshold value. The model proposed is a novel approach and has not been formulated in any of previous work related to QoS in MANETs. The effectiveness of model is demonstrated using both simulation and real time results.","Title: Efficient Two-Level Swarm Intelligence Approach for Multiple Sequence Alignment.\nYear: 2016\n\nAbstract: This paper proposes two-level particle swarm optimization (TL-PSO), an efficient PSO variant that addresses two levels of optimization problem. Level one works on optimizing dimension for entire swarm, whereas level two works for optimizing each particle's position. The issue addressed here is one of the most challenging multiple sequence alignment (MSA) problem. TL-PSO deals with the arduous task of determination of exact sequence length with most suitable gap positions in MSA. The two levels considered here are: to obtain optimal sequence length in level one and to attain optimum gap positions for maximal alignment score in level two. The performance of TL-PSO has been assessed through a comparative study with two kinds of benchmark dataset of DNA and RNA. The efficiency of the proposed approach is evaluated with four popular scoring schemes at spe-cific parameters. TL-PSO alignments are compared with four PSO variants, i.e. S-PSO, M-PSO, ED-MPSO and CPSO-Sk, and two leading alignment software, i.e. ClustalW and T-Coffee, at different alignment scores. Hence obtained results prove the competence of TL-PSO at accuracy aspects and conclude better score scheme.","Title: Efficient cooperative relaying in flying ad hoc networks using fuzzy-bee colony optimization.\nYear: 2017\n\nAbstract: Coordination among multiple ad hoc systems can extend the applications of the networks operating without any requirement of centralized devices. One of the coordination systems is formed by the collaboration of aerial ad hoc network and ground ad hoc network that performs cognitive coordination to form an effective guidance system despite operating with different configurations and dynamics. For guidance system, unmanned aerial vehicles form a cognitive map and relay them effectively to the intended node which is a part of ground ad hoc unit. The data sharing is difficult between the networks operating under different circumstances and configurations. An efficient approach is required to provide low-complex relaying for faster communications in these coordinated networks. One of the solutions can be found from the fuzzy formation of the problem that can be applied to some optimization algorithms to efficiently select the cognitive corridors intended for enhanced transmission. In this paper, a fuzzy-based bee colony optimization algorithm is proposed which operates over cooperative trust value, cognitive relaying value and situational awareness to provide efficient cognitive relaying between the two ad hoc units, and uses a cloud server to provide coordination between the nodes. The effectiveness of the proposed scheme and its applicability is tested using simulations.","Title: Driver behaviour detection and vehicle rating using multi-UAV coordinated vehicular networks.\nYear: 2017\n\nAbstract: Road accidents account for more than 2% of the total deaths across the globe with more than a million deaths each year due to road mishaps and improper traffic management. Traffic management is a major problem faced by modern cities due to a large number of vehicles operating at the same time. One of the major issues for road mishaps is driver's behaviour and skills. Thus, tracking vehicles and analyzing the driver behaviour is required for proper regulation of traffic. Some of the solutions are provided using infrastructure-based vehicular ad hoc networks (VANETs). However, their operations are constrained by the traffic itself, thus, limiting their scope. A new solution by forming VANETs using multiple unmanned aerial vehicles (UAVs) is proposed which is independent of using road side units (RSUs) for communications. The proposed approach is analyzed using simulations as well as real-time dataset. UAVs-coordinated VANETs is first proposed in this paper.Next, multi-UAVs coordinated behaviour analysis approach is presented.The approach can be scaled to n number of parameters based on the requirement.The proposed approach is analyzed using both simulative and real time data sets.This approach helps in lowering the number of accidents because of naive drivers.","Title: Quantitative Security and Safety Analysis with Attack-Fault Trees\nYear: 2017\n\nAbstract: Cyber physical systems, like power plants, medical devices and data centers have to meet high standards, both in terms of safety (i.e. absence of unintentional failures) and security(i.e. no disruptions due to malicious attacks). This paper presents attack fault trees (AFTs), a formalism thatmarries fault trees (safety) and attack trees (security). We equipAFTs with stochastic model checking techniques, enabling a rich plethora of qualitative and quantitative analyses. Qualitative metrics pinpoint to root causes of the system failure, while quantitative metrics concern the likelihood, cost, and impact of a disruption. Examples are: (1) the most likely attack path, (2) the most costly system failure, (3) the expected impact of an attack. Each of these metrics can be constrained, i.e., we can provide the most likely disruption within time t and/or budget B. Finally, we can use sensitivity analysis to find the attack step that has the most influence on a given metric. We demonstrate our approach through three realistic cases studies.","Title: A Soft Computing Approach for Modeling of Nonlinear Dynamical Systems.\nYear: 2016\n\nAbstract: A procedure based on the use of radial basis function network (RBFN) is presented for black box modeling of nonlinear dynamical systems. The generalization ability of RBFN is invoked to approximate the mathematical model of the given unknown nonlinear plant. This approximate model will then be used to predict the output of the plant at any given time instant. The parameters associated with RBFN are updated using the recursive equations obtained through the gradient-descent principle. The other benefit of using gradient descent principle is that it exhibits the clustering effect while adjusting the radial centers of RBFN. Real-time data of two benchmark problems: Box-Jenkins gas furnace data and Chemical process (polymer production), were used to show the application of RBFN for modeling purpose. Simulation results show that RBFN is well suited as a modeling tool for capturing the unknown nonlinear dynamics of the plant.","Title: State-of-charge estimation for li-ion battery using extended Kalman filter (EKF) and central difference Kalman filter (CDKF).\nYear: 2017\n\nAbstract: A precise estimation of the state-of-charge (SOC) is of major importance in battery electric vehicles (BEVs) for prolonging the lifetime of the battery. Firstly, an equivalent circuit using the first-order RC for describing the dynamic behavior of the battery has been developed. Parameters of the battery are identified using the Ageist Spider Monkey Optimization (ASMO) technique. The optimization method uses the anticipated terminal voltage of the battery during operation and error between the anticipated and measured voltage for identification of parameters. The focus of this paper is the implementation of recursive estimation of battery SOC using extended Kalman filter (EKF) and Central Difference Kalman Filter (CDKF) approach. The estimation has an absolute root-mean-square error (RMSE) of less than 4% and an absolute maximum error less than 6% in all circumstances. The test results indicate that CDKF has good performance compared to EKF for the estimation of battery SOC.","Title: A review on remotely sensed land surface temperature anomaly as an earthquake precursor.\nYear: 2017\n\nAbstract: •A systematic survey of recent advances in LST precursory approaches.•Evaluates LST anomaly as a probable marker of an earthquake.•Analyses the appearance of LST anomaly within various seismic strain fields.•Highlights the needed improvements for geostatistical robustness.","Title: QUAT-DEM: Quaternion-DEMATEL based neural model for mutual coordination between UAVs.\nYear: 2017\n\nAbstract: Networking between autonomously flying aerial vehicles requires efficient control and sustainable path for data transmission. Aerial nodes are extremely dynamic and provide a vast range of applications especially focusing on surveillance and data acquisition. However, these dynamic nodes are prone to unstable connectivity which hinders network operations, and also decreases the network lifetime. Such issues directly influence the control and relaying over aerial vehicles. Thus, it becomes essential to have an efficient strategy for controller selection and autonomous relaying. In this paper, a quaternion based neural model is proposed, which uses Decision Making Trial and Evaluation Laboratory Technique (DEMATEL) for the selection of network controllers and relays. The proposed algorithms are capable of selecting controller with a complexity O(n), and provide cooperative relaying with a complexity O(n × k). The effectiveness of the proposed model is demonstrated by using simulations. The results show that the proposed approach reduces delays by 25% during the selection of controller, and increases the transfer speed by 20% compared to existing approaches.","Title: Continuous authentication using one-class classifiers and their fusion\nYear: 2018\n\nAbstract: While developing continuous authentication systems (CAS), we generally assume that samples from both genuine and impostor classes are readily available. However, the assumption may not be true in certain circumstances. Therefore, we explore the possibility of implementing CAS using only genuine samples. Specifically, we investigate the usefulness of four one-class classifiers OCC (elliptic envelope, isolation forest, local outliers factor, and one-class support vector machines) and their fusion. The performance of these classifiers was evaluated on four distinct behavioral biometric datasets, and compared with eight multi-class classifiers (MCC). The results demonstrate that if we have sufficient training data from the genuine user the OCC, and their fusion can closely match the performance of the majority of MCC. Our findings encourage the research community to use OCC in order to build CAS as it does not require knowledge of impostor class during the enrollment process.","Title: Demand side management with consumer clusters in cyber-physical smart distribution system considering price-based and reward-based scheduling programs.\nYear: 2017\n\nAbstract: This study presents a demand side management (DSM) strategy for a cyber-physical smart distribution system (CPSDS). The proposed approach uses the price-based as well as reward-based DSM schemes as a part dual objective function. The objectives of the proposed scheduling approach comprise the profit maximisation objectives of demand response aggregator agent (DRAA) and network performance objectiv...","Title: Performance Of Vertical Multi Junction Solar Cell For Long Distance Wireless Power Transfer Using High Intensity Laser Beam\nYear: 2017\n\nAbstract: Wireless Power Transfer to long distance objects has been a much awaited task for researchers and scientists but old technologies like magnetic resonance coupling method were failed to provide any satisfactory result for long distance power transfer though it was good for small or very near distance power transfer. Far distant moving objects like space stations, lunar modules, rovers, unmanned aerial vehicles or drones largely suffer from lack of power providing mechanism resulting in short flight time of these vehicles. Major limitation is poor endurance for helicopters and quad-copters due to their very less efficient design and nature of vertical take-off and landing in comparison to those vehicle having fixed wings. Unlimited flight endurance can be achieved by using laser power beaming to recharge any target having optical tracking system done by beam riding method. Here wireless power transmission using a high intensity laser beaming (HILB) system by illuminating vertical multi-junction solar cell (VMJSC) has been discussed and its performance is studied and analyzed. Many of the HILB systems have been used to examine the performance of VMJSC under different types of conditions. Here many of the methods like optimal receiver geometry, parallel cell back-feeding, laser wavelength, thermal effects and non-uniform illumination at high intensities are studied and analyzed. Suggestions are made to further improve the system and to achieve sufficient power densities. Study shows that VMJSC mechanism can be used for high intensity laser beaming to power transfer without being damaged and retaining its efficiency.","Title: Recent developments in human gait research: parameters, approaches, applications, machine learning techniques, datasets and challenges.\nYear: 2018\n\nAbstract: Human gait provides a way of locomotion by combined efforts of the brain, nerves, and muscles. Conventionally, the human gait has been considered subjectively through visual observations but now with advanced technology, human gait analysis can be done objectively and empirically for the better quality of life. In this paper, the literature of the past survey on gait analysis has been discussed. This is followed by discussion on gait analysis methods. Vision-based human motion analysis has the potential to provide an inexpensive, non-obtrusive solution for the estimation of body poses. Data parameters for gait analysis have been discussed followed by preprocessing steps. Then the implemented machine learning techniques have been discussed in detail. The objective of this survey paper is to present a comprehensive analysis of contemporary gait analysis. This paper presents a framework (parameters, techniques, available database, machine learning techniques, etc.) for researchers in identifying the infertile areas of gait analysis. The authors expect that the overview presented in this paper will help advance the research in the field of gait analysis. Introduction to basic taxonomies of human gait is presented. Applications in clinical diagnosis, geriatric care, sports, biometrics, rehabilitation, and industrial area are summarized separately. Available machine learning techniques are also presented with available datasets for gait analysis. Future prospective in gait analysis are discussed in the end.","Title: Binary Grey Wolf Optimizer for large scale unit commitment problem.\nYear: 2018\n\nAbstract: The unit commitment problem belongs to the class of complex large scale, hard bound and constrained optimization problem involving operational planning of power system generation assets. This paper presents a heuristic binary approach to solve unit commitment problem (UC). The proposed approach applies Binary Grey Wolf Optimizer (BGWO) to determine the commitment schedule of UC problem. The grey wolf optimizer belongs to the class of bio-inspired heuristic optimization approaches and mimics the hierarchical and hunting principles of grey wolves. The binarization of GWO is owing to the UC problem characteristic binary/discrete search space. The binary string representation of BGWO is analogous to the commitment and de-committed status of thermal units constrained by minimum up/down times. Two models of Binary Grey Wolf Optimizer are presented to solve UC problem. The first approach includes upfront binarization of wolf update process towards the global best solution (s) followed by crossover operation. While, the second approach estimates continuous valued update of wolves towards global best solution(s) followed by sigmoid transformation. The Lambda-Iteration method to solve the convex economic load dispatch (ELD) problem. The constraint handling is carried out using the heuristic adjustment procedure. The BGWO models are experimented extensively using various well known illustrations from literature. In addition, the numerical experiments are also carried out for different circumstances of power system operation. The solution quality of BGWO are compared to existing classical as well as heuristic approaches to solve UC problem. The simulation results demonstrate the superior performance of BGWO in solving UC problem for small, medium and large scale systems successfully compared to other well established heuristic and binary approaches.","Title: Multi agent system: concepts, platforms and applications in power systems.\nYear: 2018\n\nAbstract: The power system is presently experiencing vital changes: it is advancing from a centralized structure to a decentralized one, primarily because of the enormous advancement of distributed renewable energy sources, so future power system obliges new control strategies. These systems must have the capacity to withstand new requirements, for example, the exceedingly disseminated nature, the irregularity of renewable energy sources and the restricted data transfer capacity for communications. Multi agent systems (MAS) have attributes that meet these prerequisites. A certain degree of distributed or collective intelligence can be accomplished through the connection of these agents with one another, participating or contending to achieve their objectives. This paper introduces outline of the fundamental ideas of MAS and its different platforms. Also, it provides a comprehensive survey on the power system applications in which MAS technique has been applied. For each power system application, technical details are also discussed.","Title: Optimal Offering of Demand Response Aggregation Company in Price-Based Energy and Reserve Market Participation.\nYear: 2018\n\nAbstract: This paper investigates the combined price-based scheduling/participation of generation company (GENCO) and demand response aggregation company (DRACO) in energy and reserve markets. The temporally coupled customer behavior can be better represented using the load profile attributes, when compared to the traditional approach with random willingness assignment. The proposed cost models for energy a...","Title: Adaptive artificial intelligence for automatic identification of defect in the angular contact bearing.\nYear: 2018\n\nAbstract: A reliable process making use of adaptive filtering, adaptive wavelet transform (AWT) and adaptive neuro-fuzzy inference system (ANFIS) is proposed for automatic identification of defect in the bearing. The process starts with denoising the signal using a filter designed from the noise part in the signal itself using the least mean square algorithm. Thereafter, AWT is applied to denoised signal to produce AWT scalogram. AWT scalogram clearly gives time location information corresponding to burst produced due to defect. For carrying out AWT, a wavelet is designed from the burst produced to defect using the least square fitting method. Then instantaneous energy (IE) of AWT scalogram is computed. For modeling of ANFIS, features are extracted from signal denoised by adaptive filtering, AWT scalogram and IE graph. The dimension of features is reduced by kernel principal component analysis. ANFIS modeling is carried out using low-dimensional kernel principal components. Finally, defect identification is carried out by applying test features kernel principal components to ANFIS. The proposed method has a recognition rate of 100%. Major benefit of the proposed method is that it requires very less training data and also does not require any optimization algorithm, usually required to find optimum parameters of a pattern recognition model. The method proposed in this work can fulfill the industrial requirement of having sophisticated and accurate automatic defect identification model.","Title: Diagonal recurrent neural network based identification of nonlinear dynamical systems with Lyapunov stability based adaptive learning rates.\nYear: 2018\n\nAbstract: This paper proposes a diagonal recurrent neural network (DRNN) based identification model for approximating the unknown dynamics of the nonlinear plants. The proposed model offers deeper memory and a simpler structure. Thereafter, we have developed a dynamic back-propagation learning algorithm for tuning the parameters of DRNN. Further, to guarantee the faster convergence and stability of the overall system, dynamic (adaptive) learning rates are developed in the sense of Lyapunov stability method. The proposed scheme is also compared with multi-layer feed forward neural network (MLFFNN) and radial basis function network (RBFN) based identification models. Numerical experiments reveal that DRNN has performed much better in approximating the dynamics of the plant and have also shown more robustness toward system uncertainties.","Title: AntiTbPdb: a knowledgebase of anti-tubercular peptides.\nYear: 2018\n\nAbstract: Tuberculosis is a global menace, caused by Mycobacterium tuberculosis, responsible for millions of premature deaths every year. In the era of drug-resistant tuberculosis, peptide-based therapeutics may provide alternate to small molecule based drugs. In order to create knowledgebase, AntiTbPdb (http://webs.iiitd.edu.in/raghava/antitbpdb/), experimentally validated anti-tubercular and anti-mycobacterial peptides were compiled from literature. We curate 10 652 research articles and 35 patents to extract antitubercular peptides and annotate these peptides manually. This knowledgebase has 1010 entries, each entry provides extensive information about an anti-tubercular peptide such as sequence, chemical modification, chirality, nature and source of origin. The tertiary structure of these anti-tubercular peptides containing natural as well as chemically modified residues was predicted using PEPstrMOD and I-TASSER. In addition to structural information, database maintains other properties of peptides like physiochemical properties. Numerous web-based tools have been integrated for data retrieval, browsing, sequence similarity search and peptide mapping. In order to assist wide range of user, we developed a responsive website suitable for smartphone, tablet and desktop.","Title: Electric Vehicle Scheduling Strategy in Residential Demand Response Programs With Neighbor Connection.\nYear: 2018\n\nAbstract: As smart grid enablers, demand response programs for household purposes have been in highlight recently. This paper presents a detailed structure of a household user, which is capable of energy transactions among consumer and load-serving entity. The proposed household model comprises several assets including nonshiftable appliances, shiftable appliances, and electric vehicle (EV). The EV can furt...","Title: Three-tier neural model for service provisioning over collaborative flying ad hoc networks.\nYear: 2018\n\nAbstract: Collaboration among different ad hoc networks allows the formation of efficient guided networks. One of the such collaborative networks is formed between aerial ad hoc network and terrestrial ad hoc network. Simultaneous operation of both the networks can resolve complex missions collaboratively. Such cooperation can be achieved by deploying service models that allow efficient integration of distributed networks. The performance of service model can be enhanced by incorporating neural schema that provides a layered interface and cognitive transfers with higher accuracy. In this paper, a 3-tier neural model is proposed that allows the formation of guided ad hoc network. A layered network model is formulated that provides service-based integration of different network components required for the functioning of aerial guided ad hoc network along with provision for efficient service provisioning over flying ad hoc networks. The effectiveness and utility of the proposed model are demonstrated using simulations. Simulation study demonstrates that the proposed approach yields significant improvements in terms of service identification accuracy up to 13.6 %, fault identification up to 17.7 %, cognitive receival up to 23.56 %, fault tolerance up to 13.5 %, network errors reduction up to 34.11 %, and overheads up to 10 % compared to existing approaches.","Title: Offrp: Optimised Fruit Fly Based Routing Protocol With Congestion Control For Uavs Guided Ad Hoc Networks\nYear: 2018\n\nAbstract: Unmanned aerial vehicles (UAVs) have gained a lot of attention from the researchers across the globe. Their autonomous capabilities provide vast range of applications such as disaster management, surveillance, border security and inter-civilian activities. UAV networks have a major issue regarding the data flow and routing. UAVs operate in different band as compared to the ground nodes. Hence, there is a chance of congestion that may fail the whole network. Prediction of such congestion states can provide continuous connectivity. For this, a new optimised fruit fly routing protocol (OFFRP) is proposed which uses the properties of existing fruit fly optimisation algorithm for finding appropriate routes. The search of food sources by fruit flies using their smell index forms the basis of the proposed protocol. The proposed protocol is evaluated using standard network simulations and is compared with all the major routing protocols as well as existing TCP-oriented congestion control algorithms.","Title: Investigating the Impact of Load Profile Attributes on Demand Response Exchange.\nYear: 2018\n\nAbstract: This paper investigates the impact of load profiling attributes on the demand response exchange (DRX) mechanism. Modeling of demand response (DR) seller bids in traditional approach/model includes a customer willingness factor assigned in a random/arbitrary manner. The proposed model of DR bids takes into account, the load attributes such as utilization and availability factors. The criticality an...","Title: Solution to unit commitment in power system operation planning using binary coded modified moth flame optimization algorithm (BMMFOA): A flame selection based computational technique.\nYear: 2018\n\nAbstract: •Modifications in flame selection approach in basic moth-flame approach are introduced.•Binary variants of basic and modified moth-flame approach are developed.•Application of proposed approaches to solve various test cases of unit commitment are simulated.•Comparative analysis with existing classical and heuristic approaches.•Statistical significance of proposed approaches is demonstrated.","Title: Energy Aware Resource Allocation for Clouds Using Two Level Ant Colony Optimization.\nYear: 2018\n\nAbstract: In cloud environment resources are dynamically allocated, adjusted, and deallocated. When to allocate and how many resources to allocate is a challenging task. Resources allocated optimally and at the right time not only improve the utilization of resources but also increase energy efficiency, provider's profit and customers' satisfaction. This paper presents ant colony optimization (ACO) based energy aware solution for resource allocation problem. The proposed energy aware resource allocation (EARA) methodology strives to optimize allocation of resources in order to improve energy efficiency of the cloud infrastructure while satisfying quality of service (QoS) requirements of the end users. Resources are allocated to jobs according to their QoS requirements. For energy efficient and QoS aware allocation of resources, EARA uses ACO at two levels. First level ACO allocates Virtual Machines (VMs) resources to jobs whereas second level ACO allocates Physical Machines (PMs) resources to VMs. Server consolidation and dynamic performance scaling of PMs are employed to conserve energy. The proposed methodology is implemented in CloudSim and the results are compared with existing popular resource allocation methods. Simulation results demonstrate that EARA achieves desired QoS and superior energy gains through better utilization of resources. EARA outperforms major existing resource allocation methods and achieves up to 10.56 % saving in energy consumption.","Title: Computational Intelligence for Demand Response Exchange Considering Temporal Characteristics of Load Profile via Adaptive Fuzzy Inference System.\nYear: 2018\n\nAbstract: This paper presents a computationally intelligent hybrid approach to incorporate the temporal characteristics of customer baseline load (CBL) in demand response exchange (DRX) mechanism using adaptive fuzzy inference system (FIS). The proposed hybrid approach considers the temporal characteristics of load profile using utilization factor, availability factor alongside the conventional/traditional ...","Title: Dynamic trafile congestion detection in VANETS using a Fuzzy rule-based system and K-means clustering\nYear: 2017\n\nAbstract: Vehicular traffic congestion poses a serious challenge to a green environment by contributing to air pollution, noise pollution, and unnecessary fuel consumption. This paper aims to meet one of the requirements of a smart city that detects traffic congestion in an area so that the traffic can be diverted on an alternate route in a vehicular adhoc network (VANET). VANET is a medium of wireless communication among vehicular commuters, which enables them to intimate their instantaneous physical characteristics such as speed, brake frequency, rain, fog, acceleration, and position to surrounding vehicles within a periphery so as to determine the level of congestion and find suitable ways to divert the traffic. The dynamic nature of the vehicular nodes makes the topology unpredictable, which is constantly monitored using a vehicular adhoc network (VANET). In this work, an integration of fuzzy inference rule based system (FRBS) and K-means clustering technique is explored to detect the traffic congestion under a dynamic traffic environment. FRBS is implemented through an Arduino Uno microcontroller. In this paper, four different physical vehicle attributes such as rain or fog, speed and brake frequency are considered for traffic congestion detection. This paper presents a detailed description regarding the co-ordination between vehicular units and a web server, which maintains a cloud database that preserves the data for future use.","Title: Effective Analysis of Attack Trees: A Model-Driven Approach.\nYear: 2018\n\nAbstract: Attack trees (ATs) are a popular formalism for security analysis, and numerous variations and tools have been developed around them. These were mostly developed independently, and offer little interoperability or ability to combine various AT features.","Title: Online modeling and adaptive control of robotic manipulators using Gaussian radial basis function networks.\nYear: 2018\n\nAbstract: Radial basis function network (RBFN) is used in this paper for predefined trajectory control of both one-link and two-link robotic manipulators. The updating equations for the RBFN parameters were derived using the gradient descent principle. The other advantage of using this principle is that it shows the clustering effect in distributing the radial centres. To increase the complexity, the dynamics of robotic manipulator is assumed to be unknown, and hence, simultaneous control and identification steps were performed using the RBFNs. The performance of the RBFN is compared with the multilayer feed-forward neural network (MLFFNN) in terms of mean square error, tolerance to disturbance and parameter variations in the system. The efficacy of RBFN as a controller and identification tool is verified by performing the simulation study, and the results obtained reveal the superior performance of RBFN over MLFFNN in both identification and control aspects for one-link and two-link robotic manipulators.","Title: An agent-based architecture for production scheduling in dynamic job-shop manufacturing system.\nYear: 2018\n\nAbstract: In a highly competitive environment, effective production is one of the key issues which can be addressed by efficient production planning and scheduling in the manufacturing system. This paper develops an agent-based architecture which enables integration of production planning and scheduling. In addition, this architecture will facilitate real time production scheduling as well as provide a multi-agent system (MAS) platform on which multiple agents will interact to each other. A case study of job-shop manufacturing system (JMS) has been considered in this paper for implementing the concept of MAS. The modeling of JMS has been created in Sim-Events which integrates an agent-based architecture developed by Stateflow to transform into dynamic JMS. Finally, the agent-based architecture is evaluated using utilization of each machine in the shop floor with respect to time.","Title: LOCKS: a property specification language for security goals.\nYear: 2018\n\nAbstract: We introduce a formal specification language LOCKS, that allow security practitioners to express as well as compose security goals in a convenient manner. LOCKS supports the specification of the most common security properties over generic attributes, both for qualitative and quantitative goals. To make our language independent of a specific security framework, we evaluate LOCKS over a generic attack model, namely the structural attack model (SAM), which over-arches the most prominent graphical threat models. Furthermore, we equip our language with a concise grammar, type rules and denotational semantics, thus laying the foundations of an automated tool. We take a number of informal security goals from the literature and show how they can be formally expressed in our language.","Title: Multiaggregator Collaborative Electric Vehicle Charge Scheduling Under Variable Energy Purchase and EV Cancelation Events.\nYear: 2018\n\nAbstract: With the growth of electric vehicles across the globe, it is essential to have a corresponding charging infrastructure developed. Several research works highlight the benefits of aggregator-based scheduling, where all aggregators operate in similar conditions with the same amount of purchased energy. However, there are unscheduled EVs left due to unavailability of sufficient energy and/or charging...","Title: Intelligent Grey Wolf Optimizer - Development and application for strategic bidding in uniform price spot energy market.\nYear: 2018\n\nAbstract: •A new variant of GWO, named as Intelligent Grey Wolf Optimizer (IGWO) is proposed.•The performance of IGWO is compared with other algorithms on benchmark functions.•A Monte Carlo Simulation based method is employed on two cost models based on normal probability distribution of rival bids to solve SBP.•The IGWO is applied to frame bidding strategy for a generating company in uniform price spot market.•The IGWO gives competitive results for single and multi-trading hours.","Title: HMADSO: a novel hill Myna and desert Sparrow optimization algorithm for cooperative rendezvous and task allocation in FANETs.\nYear: 2018\n\nAbstract: Cooperative task allocation and decision are important aspects of networks that involve heterogeneous nodes operating in ad hoc mode like flying ad hoc networks (FANETs). The task allocation can be either mission based or simple utilization of available resources. In networks, including mission critical resources, cooperative task allocation and rendezvous are the key factors that drive the mission as well as optimize the performance. Many optimization algorithms have been designed and developed which focus on the cooperative behavior of nodes and also handle resources efficiently. Cooperative allocation and rendezvous both can be achieved by taking an example from biological world. In this paper, a new hill Myna and desert Sparrow optimization algorithm, namely HMADSO, is proposed for cooperative rendezvous and efficient task allocation. The application and analysis of proposed algorithm are shown for FANETs. To validate the proposed HMADSO, onboard processors, as well as simulation-based analysis, are carried out. The libraries for the proposed approach are provided at http://bit.ly/HMADSOcode.","Title: EQUAL: Energy and QoS Aware Resource Allocation Approach for Clouds.\nYear: 2018\n\nAbstract: The popularity of cloud computing is increasing by leaps and bounds. To cope with resource demands of increasing number of cloud users, the cloud market players establish large sized data centers. The huge energy consumption by the data centers and liability of fulfilling Quality of Service (QoS) requirements of the end users have made resource allocation a challenging task. In this paper, energy and QoS aware resource allocation approach which employs Antlion optimization for allocation of resources to virtual machines (VMs) is proposed. It can operate in three modes, namely power aware, performance aware, and balanced mode. The proposed approach enhances energy efficiency of the cloud infrastructure by improving the utilization of resources while fulfilling QoS requirements of the end users. The proposed approach is implemented in CloudSim. The simulation results have shown improvement in QoS and energy efficiency of the cloud.","Title: Optimized Clustering Techniques For Gait Profiling In Children With Cerebral Palsy For Rehabilitation\nYear: 2018\n\nAbstract: Cerebral palsy (CP) is a neuro-development disease in children. It is quite an intricate task to categorize gait pattern into normal and CP based pathology. In this study, nature-inspired meta-heuristic algorithms are explored on a publicly available gait dataset of 156 subjects for automatic gait profiling of children with cerebral palsy. Five cases are considered to explore the feature selection criteria before applying clustering technique. Finding the optimal number of clusters is a challenging task in the unsupervised learning area. In this study, an optimal number of gait profiles in the datasets is identified based on voting from mean square error, silhouette coefficient and Dunn index. The study demonstrates that optimized based gait profile clusters could assist quantitatively in clinical rehabilitation evaluation for the children affected by CP.","Title: Comparative study of neural networks for dynamic nonlinear systems identification.\nYear: 2019\n\nAbstract: In this paper, a comparative study is performed to test the approximation ability of different neural network structures. It involves three neural networks multilayer feedforward neural network (MLFFNN), diagonal recurrent neural network (DRNN), and nonlinear autoregressive with exogenous inputs (NARX) neural network. Their robustness is also tested and compared when the system is subjected to parameter variations and disturbance signals. Further, dynamic back-propagation algorithm is used to update the parameters associated with these neural networks. Four dynamical systems of different complexities including motor-driven robotic link are considered on which the comparative study is performed. The simulation results show the superior performance of DRNN identification model over NARX and MLFFNN identification models.","Title: An SDN-Based Secure Mobility Model for UAV-Ground Communications.\nYear: 2017\n\nAbstract: Multi-UAV collaborative networks provide with the opportunity to exploit civil, chemical, biological, radiological, nuclear and geographical reconnaissance, survey, management, and control. For the collaborative network formation, coverage is of prime paramountcy. Alongside coverage, possession of information and communication security is withal a major challenge. The coverage quandary can be resolved by a perspicacious selection of UAV waypoints. But the security paradigm which can be an effect of faulty node, intrusion or even choice of erroneous communication channels needs to be taken care of through efficacious strategies. Consequently, both a specialized UAV mobility model and a security mechanism are required in order to establish prosperous collaborative networks. In this article, an SDN-based secure mobility model is proposed which takes into account the topological density and restricts the UAV and ground node (Wireless Sensor Networks (WSNs)) transmissions to authenticity. Significant gains are observed for throughput, coverage, and latency by establishing a simulated network between multiple UAVs and WSN motes.","Title: β-Chaotic map enabled Grey Wolf Optimizer.\nYear: 2019\n\nAbstract: The diversification (exploration) and intensification (exploitation) are two main attributes of any population-based metaheuristic algorithm. It is essential for any algorithm that in exploration phase the search space is utilized and explored properly through random behavior, on the other hand, the progression of the search in a viable direction to obtain global minima, should be performed through strategic behavior in exploitation phase. A proper balance between these two can be achieved by an adaptive mechanism in every algorithm. Robustness of an algorithm is judged by the efficacy of these two attributes along with the efficiency of the bridging mechanism. In literature, the positive impact of inculcation of chaotic sequences on the efficacy of these attributes has been reported. With this motivation, the paper presents an adaptive bridging mechanism based on β-chaotic sequence for the improvement of Grey Wolf Optimizer (GWO). The control vector of classical GWO is integrated with the β-chaotic sequence for better exploration and exploitation virtues. The new variant β-GWO is benchmarked on two benchmark suites 1 and 2 that include 12 shifted and biased functions and 29 Congress on Evolutionary Computation-2017 (CEC-2017) functions. Sensitivity Dependence of Initial Conditions (SDIC) is performed for tuning the initial parameters. The comparison of the proposed variant with other contemporary algorithms is carried out and different statistical tests are performed to judge the efficacy of the proposed variant. Further, the applicability of the proposed variant is checked with two real engineering problems namely frequency modulated sound waves parameter estimation problem and strategic bidding in the energy market. Results reveal that the proposed chaotic variant exhibits better exploration and exploitation qualities.","Title: Prediction of Land-Surface Temperatures of Jaipur City Using Linear Time Series Model.\nYear: 2016\n\nAbstract: All cities of the world have undergone rapid urbanization. Consequently, urban areas encounter higher surface and air temperatures than the surrounding nonurbanized areas and exhibit urban heat island (UHI) effect. Surface temperature derived from remote sensing data has been used for analyzing the UHI effect over a number of cities. This study has been carried out to predict the land-surface temp...","Title: ccPDB 2.0: an updated version of datasets created and compiled from Protein Data Bank.\nYear: 2018\n\nAbstract: ccPDB 2.0 (http://webs.iiitd.edu.in/raghava/ccpdb) is an updated version of the manually curated database ccPDB that maintains datasets required for developing methods to predict the structure and function of proteins. The number of datasets compiled from literature increased from 45 to 141 in ccPDB 2.0. Similarly, the number of protein structures used for creating datasets also increased from similar to 74 000 to similar to 137 000 (PDB March 2018 release). ccPDB 2.0 provides the same web services and flexible tools which were present in the previous version of the database. In the updated version, links of the number of methods developed in the past few years have also been incorporated. This updated resource is built on responsive templates which is compatible with smartphones (mobile, iPhone, iPad, tablets etc.) and large screen gadgets. In summary, ccPDB 2.0 is a user-friendly web-based platform that provides comprehensive as well as updated information about datasets.","Title: Uavs Assisted Queue Scheduling In Ground Ad Hoc Networks\nYear: 2019\n\nAbstract: Hybrid networks provide a vast range of applications in areas of military and civilian activities. One of the examples of hybrid network formations is unmanned aerial vehicles (UAVs) assisted ground ad hoc networks. Efficient coordination in these networks can resolve complex issues such as coverage, proper connectivity, scalability, and QoS. However, reliable data transmission with enhanced QoS is one of the key challenges in these types of hybrid ad hoc formations. In this paper, an efficient queue scheduling approach is proposed that allows improved QoS to end users. The proposed queue scheduling approach is developed in two parts. The first part utilises the Quaternion based Kalman Filter to find the appropriate locations for placement of each UAV. In the second part, satisfied importance analysis (SIA) is used to find the governing rules for the selection of appropriate queue for transmission. The effectiveness of the proposed model is demonstrated using simulations.","Title: A Gray Level Feature Detector and Its Hardware Architecture\nYear: 2008\n\nAbstract: This chapter describes a fast real time gray scale based feature point detector and its hardware architecture for FPGA based realization. The implementation is based on a new efficient technique which is fusion of both affine transformation\n invariance and robustness to noise. The novelty of the proposed approach lies in its highly accurate localization and realization\n only in terms of addition, subtraction and logic operations. The algorithm is designed to keep the high throughput requirements\n of today's feature point detectors and applications in Silicon. The proposed implementation is highly modular with custom\n scalability to fit devices like FPGAs etc, with different resource capacity. The implementation can be ported to any real time vision processing systems where\n power and speed are of utmost concern.","Title: Distributed Modular Computer-Integrated Surgical Robotic Systems: Implementation Using Modular Software and Networked Systems\nYear: 2000\n\nAbstract: We seek to build CIS research systems within a flexible, open architecture. In this paper, we outline our solutions to the problems of system design, construction, and integration in this environment: building distributed, modular systems for sensing, control, and processing. Based on our experience building these systems, we utilize distributed network architectures, modular software components, and intelligent object distribution to maximize flexibility while complying with the particular needs and interfaces of specific components. While a work in progress, these approaches have been integrated into several systems in development and have demonstrated significant utility, based on construction time and system flexibility. This paper will discuss the first two aspects: modular software and basic network architectures, while intelligent object distribution is presented in a companion paper. Based on the applications we have targeted and technologies we have used, this paper introduces our architecture and its components. It then presents the system's current state and finally, discusses plans for future improvements and extensions.","Title: Predictive Analytics for Breast Cancer Survivability - A Comparison of Five Predictive Models.\nYear: 2016\n\nAbstract: Predicting the statistics of survival from the innumerable reports of breast cancer is a difficult job to perform, one that every researcher tackles in the field of medicals science. Since the birth stages of the related research, many new techniques have advanced. This point is evident from the groundbreaking biomedical technologies and discovery of better self-explanatory predictive factors. Due to supply of cheaper computer hardware and easy availability of software technologies, large amount of qualitative data is being stored automatically. The developed ways of analyzing huge data quantities have bestowed upon this field efficient and effective means of processing. Five renowned data mining algorithms (Naive Bayes classification, decision trees, IBK, support vector machine (using SMO) and OneR,) were used for our prediction models applied to a substantiate dataset (more than 683cases). The10-fold cross-validation method was also taken into due consideration to determine the prediction accuracy of the five prognostic designs in comparing their credibility. Through a sensitivity analysis of support vector machines, we were able to gauge the prioritized importance of predictive factors in this work.","Title: Microscopic Biopsy Image Segmentation Using Hybrid Color K-Means Approach.\nYear: 2017\n\nAbstract: The color image segmentation is a fundamental requirement for microscopic biopsy image analysis and disease detection. In this paper, a hybrid combination of color k-means and marker control watershed based segmentation approach is proposed to be applied for the segmentation of cell and nuclei of microscopic biopsy images. The proposed approach is tested on breast cancer microscopic data set with ROI segmented ground truth images. Finally, the results obtained from proposed framework are compared with the results of popular segmentation algorithms such as Fuzzy c-means, color k-means, texture based segmentation as well as adaptive thresholding approaches. The experimental analysis shows that the proposed approach is providing better results in terms of accuracy, sensitivity, specificity, FPR false positive rate, global consistency error GCE, probability random index PRI, and variance of information VOI as compared to other segmentation approaches.","Title: Microprocessor Chip With Photonic I/O\nYear: 2017\n\nAbstract: In this work, we provide an overview of the technology and architecture of a microprocessor chip with optical I/O. Zero-change photonics integration enabled the chip to be fabricated in a commercial electronics CMOS foundry.","Title: Simple and Efficient Computational Intelligence Strategies for Effective Collaborative Decisions.\nYear: 2019\n\nAbstract: We approach scalability and cold start problems of collaborative recommendation in this paper. An intelligent hybrid filtering framework that maximizes feature engineering and solves cold start problem for personalized recommendation based on deep learning is proposed in this paper. Present e-commerce sites mainly recommend pertinent items or products to a lot of users through personalized recommendation. Such personalization depends on large extent on scalable systems which strategically responds promptly to the request of the numerous users accessing the site (new users). Tensor Factorization (TF) provides scalable and accurate approach for collaborative filtering in such environments. In this paper, we propose a hybrid-based system to address scalability problems in such environments. We propose to use a multi-task approach which represent multiview data from users, according to their purchasing and rating history. We use a Deep Learning approach to map item and user inter-relationship to a low dimensional feature space where item-user resemblance and their preferred items is maximized. The evaluation results from real world datasets show that, our novel deep learning multitask tensor factorization (NeuralFil) analysis is computationally less expensive, scalable and addresses the cold-start problem through explicit multi-task approach for optimal recommendation decision making.","Title: A dual objective approach for aggregator managed demand side management (DSM) in cloud based cyber physical smart distribution system\nYear: 2020\n\nAbstract: This paper presents an incentive based framework for demand response scheduling in smart distribution networks with commercial, residential and industrial load sectors using designated cloud storage in cyber physical cloud based smart distribution system. The proposed approach consists of a two stage procedure where dual objective problem is modeled to achieve both responsive load agent (RLA) economic benefits as well as distribution system operator (DSO) preferences. The two stage coordinated scheduling of RLAs with DSO network operation preferences is carried out by demand response aggregation agent (DRAA) through incentives. The cloud based information exchange between different stakeholders provides the necessary communication at different stages of responsive load scheduling. The concept of private, shared and community clouds are incorporated into the cyber physical cloud based DAM framework proposed in this work. The first stage scheduling of responsive loads is carried out by RLAs with electricity payment minimization objective looking into the tariff structure. Whereas, the second stage scheduling is carried out by DRAA in which incentives are offered to RLAs for the deviation from personalized schedules of first stage. The customer characterization and behavior (risk averse, incentive preferences etc.) of scheduling the responsive loads is considered through incentive range based clusters. The proposed DRAA coordinated DR scheduling framework is simulated using IEEE 37 bus distribution test system with commercial, residential and industrial sectors. The impact of stage 1 and stage 2 scheduling of responsive loads is analyzed with respect to system performance indices such as total operational cost, load factor, total network losses etc. The sensitivity analysis is performed to examine the impact of weightage assignment to DSO and RLA objectives on system performance aspects. The proposed DRAA coordinated two stage framework has shown considerable improvement in techno-economic aspects of responsive load scheduling in distribution network that can be appended to real world applications at distribution level/retail markets.","Title: Identification of potential islanding initiators in radial feeders with high solar PV penetration for preemptive preparedness\nYear: 2017\n\nAbstract: Reverse power flow is one of the anticipated implications of integrating large amount of distributed solar photovoltaic power with distribution feeders. Such an occurrence can transform into anomalies that can potentially affect the integrity of the network. Unintentional islanding is one such issue that has not been comprehensively addressed, with the consensus of all the stakeholders of distribution networks. It still remains a cause of concern among utilities, arising out of large penetration of distributed solar power. In this work, an anomalous over-current spike, attributed to reverse power flow was discovered on a radial feeder modeled with emulator hardware. Load-inverter interaction alongside grid-end disturbances in a high penetration scenario resulted into the anomaly that was found to be an islanding initiator. This paper uses suitable machine learning models to detect such occurrences so as to preemptively prepare and respond to any imminent islanding condition. Results of offline testing on a dedicated Raspberry Pi microcomputer have been presented. This paper presents a preliminary analysis of the feasibility of such a framework to be carried forward for online testing on hardware-generated data.","Title: Combination of Advanced Robotics and Computer Vision for Shelf Analytics in a Retail Store\nYear: 2017\n\nAbstract: Large-scale retail store associates are constantly faced with the challenge of managing store operations smoothly and maintaining the products in full stock on the product support devices (retail shelves). Keeping track of the quantities of each individual Stock Keeping Unit (Retail Product), replenishing them when depleted, and identifying and replacing misplaced products are few tasks that require continuous monitoring and large amount of manual effort. The solution being presented here aims at automating the tasks performed by the store associates which result in reducing the manual effort. The solution proposes the use of a Double Robot to patrol the store over a fixed path and capture images of the retail shelves at real time. These images are processed using developed solution to address various retail store challenges such as stock out problems and misplaced products. An alert generating mechanism has also been incorporated into the solution to alert the store associate via email or a text message, when a product is completely out of stock/misplaced. The solution approaches use classification techniques, deep learning techniques along with computer vision algorithms to automate these processes in retail stores.","Title: Analysis of ResNet and GoogleNet models for malware detection\nYear: 2019\n\nAbstract: We have utilized two distinct models to identify the obscure or new sort of malware in this paper. GoogleNet and ResNet models are researched and tried which belong to two different platforms i.e. ResNet belongs to Microsoft and GoogleNet is the intellectual property of Google. Two sorts of datasets are utilized for training and validation the models. One of the dataset was downloaded from Microsoft which is the combination of 10,868 records and these records are binary records. These records are additionally isolated in nine diverse classes. Second dataset is considerate dataset and it contains 3000 benign files. The said datasets were initially in the form of EXE files and were changed over into opcode, after that changed over into images. We got a testing accuracy of 74.5% on GoogleNet and 88.36% precision on ResNet.","Title: Binary grey wolf optimizer models for profit based unit commitment of price-taking GENCO in electricity market.\nYear: 2019\n\nAbstract: The restructuration of electric power sector has renovated the power system operational planning. In the deregulated market, electricity is treated as an entity unlike in the traditional vertically integrated market model where it is treated as a service provided by the generation companies (GENCOs). The task of GENCO is to perform self-scheduling of available units so as to achieve maximum profit in restructured power sector. Therefore, the problem of profit based unit commitment (PBUC) in deregulated markets can be formulated as a commitment and generation allocation through self-scheduling procedure. The commitment schedule optimization, i.e whether the status of a thermal unit is to be changed to on or off state, is a binary problem. Thus, the PBUC problem requires binary optimization for commitment and real valued optimization for generation allocation. In this paper, three binary grey wolf optimizer (BGWO) models are presented to solve the profit based self-scheduling problem of GENCO. The BGWO models proposed in this paper differ with respect to the transformation function used to map real valued wolf position into a binary variable. The binary mapping of commitment status is carried out using sigmoid and tangent hyperbolic transfer functions. Also, in the sigmoidal transfer function, two binary transformation procedures, namely crossover and conventional sigmoidal transfer function, are presented. The effectiveness of the proposed models in improving the solution quality of PBUC problem is examined using two test systems, a 3 unit and a 10 unit test system. In addition, two cases of GENCO market participation with and without reserve market participation are simulated. In the test case with reserve market participation, two commonly used reserve payment models are examined. Simulation results are presented and compared to existing approaches that have been used to solve the PBUC problem. The simulation results and statistical analysis demonstrate the improved solution quality (profit or fitness value) of the PBUC problem and statistical significance of the BGWO models with respect to solution quality obtained as compared to other established meta-heuristic approaches.","Title: Grey Based Decision Making For Evaluating Sustainable Performance Of Indian Marble Industries\nYear: 2019\n\nAbstract: This article focuses on the environmental, economic and social impact of marble industries in the north-western region of India. It presents a grey-based decision-making model for evaluating the extent of sustainability in three marble processing industries. The goal of this article is twofold. First, to identify the important criteria of sustainable performance in marble sector and second to compare three marble processing firms on the basis of sustainability criteria using grey based decision-making approach. A detailed questionnaire was sent to three marble processing firms and the analysis is done on the basis of the received responses.","Title: Fog computing: from architecture to edge computing and big data processing.\nYear: 2019\n\nAbstract: Cloud computing plays a vital role in processing a large amount of data. However, with the arrival of the Internet of Things, huge data are generated from these devices. Thus, there is the need for bringing characteristics of cloud closer to the request generator, so that processing of these huge data takes place at one-hop distance closer to that end user. This led to the emergence of fog computing with the aim to provide storage and computation at the edge of the network that reduces network traffic and overcomes many cloud computing drawbacks. Fog computing technology helps to overcome challenges of big data processing. The paper discusses the taxonomy of fog computing, how this is different from cloud computing and edge computing technologies, its applications, emerging key technologies (i.e., communication technologies and storage technologies) and various challenges involved in fog technology.","Title: Eff-Fas: Enhanced Fruit Fly Optimisation Based Search And Tracking By Flying Ad Hoc Swarm\nYear: 2019\n\nAbstract: Flying ad hoc swarms are networks formed by autonomously operated aerial nodes. These nodes can be simple high altitude platforms or specifically configured unmanned aircraft (UA). Such networks are autonomous, temporary and mission dependent. One of the major applications of this ad hoc swarm formations is the efficient search and tracking of an area without any redundancy and overlapping. Non-redundant cell tracking is a computationally expensive task, which requires optimisation strategies to be adopted during a search process. Incorporating fruit fly optimisation (FOA) algorithm to a strategic search and track operation simplifies the complexity of the overall system. In this paper, FOA is extended in terms of its applicability by modifying the procedures of the algorithm to allow its applicability to an aerial swarm for non-redundant search over a predefined area with lower complexity. Network simulations demonstrate the effectiveness of the proposed approach towards search and track operations.","Title: Delay Tolerant Network assisted flying Ad-Hoc network scenario: modeling and analytical perspective\nYear: 2019\n\nAbstract: Flying Ad-Hoc networks (FANET) are the extended paradigm of the mobile Ad-Hoc networks and, perhaps, one of the most emerging research domains in the current era. A huge number of tangible applications have been developed in this domain. The main advantages of such networks are their easy deployment, scalability, and robustness. However, the sparseness of these networks is an inherent characteristic that is known to be a bottleneck. The main objective of this work was to provide an alternative solution for the intermittently connected FANET by considering the philosophy of the Delay Tolerant Network (DTN) approach. To realize the functionality of the DTN protocols in a three-dimensional (3D) space, a social FANET model is proposed. FANET nodes are supposed to have a sparse node density. Fundamentally, the proposed DTN assisted Flying Ad hoc Network exploits the DTN routing and mobility features. The new mobility modeling for 3D spaces was re-engineered and tested with well-known routing protocols to analyze the performance of the model based on node speed, density, buffer, latency, message overhead, and power consumption. The effectiveness of 3D mobility models has also been compared against the one of classical models. The obtained results reflect a significant enhanced performance of the suggested DTN protocol for sparse FANET in a social scenario.","Title: Multi Agent based Autonomous Energy Management System with Self-Healing Capabilities for a Microgrid\nYear: 2019\n\nAbstract: Due to the inherent intermittent nature of renewable energy sources (RES), the control and management of microgrids become complex and require modern control techniques and management strategies to cope up with the changes in the dynamics of the system. This paper focuses on improving the dynamic performance of a microgrid by developing different energy management and control strategies employing ...","Title: Efficient Discrete Firefly Algorithm For Ctrie Based Caching Of Multiple Sequence Alignment On Optimally Scheduled Parallel Machines\nYear: 2019\n\nAbstract: This study introduces a two-level strategy for efficient execution of multiple sequence alignment (MSA) of complex heterogeneous sequences. The two levels of the proposed technique are comprised of: designing the discrete firefly algorithm (DFFA) for the formation and implementation of makespan minimisation on parallel machines, followed by performing Ctrie-based caching for pairwise alignment to reduce the load on the data servers for handling multiple queries. The proposed strategy addresses a multi-client problem that aims to acquire the full advantage of the computational power of parallel connected machines. Further, it is shown that the inclusion of Ctrie as caching mechanism successively improves the performance of the system with accretion in several sequences. Performance of proposed DFFA is also compared with discrete versions of four swarm intelligence based algorithms at the criteria of makespan minimisation and the rate of convergence on two kinds of complex and diverse datasets. The work is unique in this sense: it is the first swarm-intelligence-based implementation for the addressed problem; it is so far the first approach for Ctrie based caching of the MSA on the scheduled parallel machines; hybridisation of DFFA with Ctrie for caching the MSA results is also a novel implementation.","Title: A Multimodal Malware Detection Technique for Android IoT Devices Using Various Features.\nYear: 2019\n\nAbstract: Internet of things (IoT) is revolutionizing this world with its evolving applications in various aspects of life such as sensing, healthcare, remote monitoring, and so on. Android devices and applications are working hand to hand to realize dreams of the IoT. Recently, there is a rapid increase in threats and malware attacks on Android-based devices. Moreover, due to extensive exploitation of the Android platform in the IoT devices creates a task challenging of securing such kind of malware activities. This paper presents a novel framework that combines the advantages of both machine learning techniques and blockchain technology to improve the malware detection for Android IoT devices. The proposed technique is implemented using a sequential approach, which includes clustering, classification, and blockchain. Machine learning automatically extracts the malware information using clustering and classification technique and store the information into the blockchain. Thereby, all malware information stored in the blockchain history can be communicated through the network, and therefore any latest malware can be detected effectively. The implementation of the clustering technique includes calculation of weights for each feature set, the development of parametric study for optimization and simultaneously iterative reduction of unnecessary features having small weights. The classification algorithm is implemented to extract the various features of Android malware using naive Bayes classifier. Moreover, the naive Bayes classifier is based on decision trees for extracting more important features to provide classification and regression for achieving high accuracy and robustness. Finally, our proposed framework uses the permissioned blockchain to store authentic information of extracted features in a distributed malware database blocks to increase the run-time detection of malware with more speed and accuracy, and further to announce malware information for all users.","Title: Defending Touch-based Continuous Authentication Systems from Active Adversaries Using Generative Adversarial Networks\nYear: 2021\n\nAbstract: Previous studies have demonstrated that commonly studied (vanilla) touch-based continuous authentication systems (V-TCAS) are susceptible to population attack. This paper proposes a novel Generative Adversarial Network assisted TCAS (G-TCAS) framework, which showed more resilience to the population attack. G-TCAS framework was tested on a dataset of 117 users who interacted with a smartphone and tablet pair. On average, the increase in the false accept rates (FARs) for V-TCAS was much higher (22%) than G-TCAS (13%) for the smartphone. Likewise, the increase in the FARs for V-TCAS was 25% compared to G-TCAS (6%) for the tablet.","Title: A comprehensive survey of physical layer security over fading channels: Classifications, applications, and challenges\nYear: 2021\n\nAbstract: AbstractAbstractDue to the open nature of wireless communication systems, the data transmission over these networks are vulnerable to malicious attack. To safeguard the transmitted data, physical layer security (PLS) has been emerged as a promising technique because of its unique feature of utilizing randomness of the wireless channel to secure the information. The preeminent idea of utilizing the channel imperfections such as fading and noise into the security providing resource is the basic approach behind PLS. For exploiting the randomness of the propagation medium, the analysis of the secrecy performance over the fading channel is of utmost importance. With the explanation of basic theory and technology, this paper presents an elaborated survey on the PLS research over the fading channels. To exploit the performance gains, we discuss the optimization approaches in different network design problems, for example, secrecy rate maximization, power minimization, and secure energy efficiency problems. We present a comparative study of the expressions of various performance metrics of the PLS along with classification depending upon the channel state information of the eavesdropper. The application and challenges of the PLS in the emerging wireless technologies are also reviewed and discussed. This paper incorporates almost all the aspects of PLS implemented over the fading channels. Furthermore, the paper is concluded with open research directions for the PLS system in various fields that can be explored to design a flexible and robust security system to satisfy the security requirements of the next‐generation networks.Due to the open nature of wireless systems, the data transmission over these networks are vulnerable to malicious attack. To safeguard the data, PLS has been emerged as a promising technique because of its unique feature of utilizing randomness of the wireless channel to secure the information. View Figure","Title: Binary Fireworks Algorithm Application For Optimal Schedule Of Electric Vehicle Reserve In Traditional And Restructured Electricity Markets\nYear: 2021\n\nAbstract: The multipurpose use of electric vehicles (EVs) makes it a special entity in the transportation sector and in the modern smart grid for use as a reserve service. This paper presents an application of binary fireworks algorithm for solving a multi-beneficiary model in which conventional thermal units are scheduled considering EV aggregator as an identical entity to GENCO in offering reserve services. The fast response of the EV can be exploited for providing reserve capacity alongside traditional unit commitment in regulated and price-based unit commitment in deregulated electricity markets, respectively. It was found that, compared to the energy market participation, reserve market profit for EV increased by $3.59/MW and $3.96/MW in deregulated and traditional markets respectively. To further explore the role of EV reserve, battery electric vehicle (BEV), and fuel cell electric vehicle are used to carry out cost-benefit comparisons where the market participation proved much profitable for BEV.","Title: An Online Semantic-Enhanced Graphical Model for Evolving Short Text Stream Clustering\nYear: 2022\n\nAbstract: Due to the popularity of social media and online fora, such as Twitter, Reddit, Facebook, and Wechat, short text stream clustering has gained significant attention in recent years. However, most existing short text stream clustering approaches usually work on static data and tend to cause a “term ambiguity” problem due to the sparse word representation. Beyond, they often exploit short text streams in a batch way and are difficult to find evolving topics in term-changing subspaces. In this article, we propose an online semantic-enhanced graphical model for evolving short text stream clustering (OSGM), by exploiting the word-occurrence semantic information and dynamically maintaining evolving active topics in term-changing subspaces in an online way. Compared to the existing approaches, our online model is not only free of determining the optimal batch size but also lends itself to handling large-scale data streams efficiently. It is also able to handle the “term ambiguity” problem without incorporating features from external resources. More importantly, to the best of our knowledge, it is the first work to extract evolving topics in term-changing subspaces automatically in an online way. Extensive experiments demonstrate that the proposed model yields better performance compared to many state-of-the-art algorithms on both synthetic and real-world datasets.","Title: Maintainable Stochastic Communication Network Reliability Within Tolerable Packet Error Rate\nYear: 2021\n\nAbstract: The communication networks are complex and considered a stochastic flow network. This throws enormous challenges for the service provider to provide a desired Quality of Service (QoS) to the customer. Due to the stochastic behavior of the network, ensuring QoS requires serious efforts to make the entire system work within the given time constraint, cost, and failure rate. When the communication problem is time-bound, providing a quick and reasonable solution with a permissible error rate is of great importance. Therefore, in this paper, fast solutions to data communication problems are proposed to send the required d units of user's data from the desired source node to the destination node within some constraints such as permissible error rate , time constraint T and maintenance budget constraints B. System reliability and the performance of the proposed approaches are evaluated using minimal paths. All the minimal flow vectors evaluated from minimal paths that satisfy all the above-mentioned constraints are considered for network reliability evaluation. Further, the proposed approaches are observed as faster concerning computation time than the existing approaches.","Title: A Systematic Review On Task Scheduling In Fog Computing: Taxonomy, Tools, Challenges, And Future Directions\nYear: 2021\n\nAbstract: The biggest challenge of task scheduling in Fog computing is to satisfy users' dynamic requirements in real-time with Fog nodes' limited resource capacities. Fog nodes' heterogeneity and an obligation to complete tasks by the deadline while minimizing cost and energy consumption makes the scheduling process more challenging. This article facilitates a deeper understanding of the research issues through a detailed taxonomy and distinguishes significant challenges in existing work. Furthermore, the paper investigates existing solutions for various challenges, presents a meta-analysis on quality of service parameters and tools used to implement Fog task scheduling algorithms. This systematic review will help potential researchers easily identify specific research problems and future directions to enhance scheduling efficiency.","Title: A Review of Transmission Rate over Wireless Fading Channels: Classifications, Applications, and Challenges\nYear: 2022\n\nAbstract: To address the explosive traffic demands, the capacity of the fading channel is increasingly becoming a prime concern in the designing of the wireless communication system. The channel capacity is an extremely important quantity, since it allows the transmission of the data through the channel with an arbitrarily small probability of error. In other words, capacity dictates the maximum rate of information transmission, called as 'capacity' of channel, determined by the intrinsic properties of the channel and is independent of the content of the transmitted information. In this paper, we present a comprehensive survey of the existing work related to the channel capacity model over various fading channels. With an elaborated explanation of the theory of channel capacity, definitions of channel capacity based on the channel state information are reviewed. To compliment this, review of the technique to enhance the channel capacity is discussed and reviewed. An effective capacity model to overcome the channel capacity limitation is also explained. Furthermore, as the secure transmission of data is of utmost importance, to address this physical layer security model is also reviewed. We also summarize the work related to channel capacity in various types of wireless networks. We finally cover the future research directions, including less explored aspects of the channel capacity that can be studied to design efficient communication systems.","Title: A Resource-Blockchain Framework for Safeguarding IoT\nYear: 2021\n\nAbstract: Privacy concerning resource discovery and selection on the Internet of Things (IoT) platform has emerged as a crucial challenge due to constrained network environment, poor throughput, and access control system in the Resource Directory (RD), which could potentially lead to information breaches among intelligent devices. Blockchain is indeed the latest technological model for global information management, point-to-point exchange, consensus process, asymmetric authentication, cognitive agreement, and perhaps other computing innovations to address the challenge. Considering these advantages, a resource-blockchain framework for safeguarding IoT is proposed in this article. The framework facilitates efficient access of information among devices, gathers information about the constrained environment, accepts data from heterogeneous resources and stores them on CoAP-based RD. Moreover, it provides secure transactions and records authorised user's information via Blockchain-based centralised server which acts as the frontend of CoAP based REST, dispatcher or repository and proxy server. The study demonstrates that Blockchain technologies help in strengthening privacy capabilities and safeguarding intelligent devices.","Title: Multiscale and Direction Target Detecting in Remote Sensing Images via Modified YOLO-v4\nYear: 2022\n\nAbstract: Traditional target detection algorithms have difficulty to adapt complex environmental changes and have limited applicable scenarios. However, the deep-learning-based target detection model can automatically learn with strong generalization capability. In this article, we choose a single-stage deep-learning-based target detection model for research based on the model’s real-time processing requirements and to improve the accuracy and the robustness of target detection in remote sensing images. In addition, we improve the YOLOv4 network and present a new approach. First, we propose a classification setting of the nonmaximum suppression threshold to increase the accuracy without affecting the speed. Second, we study the anchor frame allocation problem in YOLOv4 and propose two allocation schemes. The proposed anchor frame scheme also improves the detection performance, and experimental results on the DOTA dataset validate their effectiveness.","Title: Breast cancer detection from histopathology images with deep inception and residual blocks\nYear: 2022\n\nAbstract: Research Highlights With an increase in the availability of huge pixel whole slide image (WSI) of tissues has given bloom to microscopic pathology application of deep learning and the possibility of loading the scanned images onto the machines, it has become easier for the researchers to develop an automated system for analyzing such images. The paper presents the deep learning-based approach for breast cancer for binary class classification. The proposed model has exploited the inception block of Inception V3 and residual block of Resnet. The proposed model is verified experimentally on both the dataset large (BHI) and small (BreakHis). The contribution of the paper can be summarized as- Remarkable classification accuracy is achieved while working on the recent dataset. The two-step feature map extraction model is trained by combining different magnification levels. The dataset is classified into benign and malignant class. The result is discussed on various performance measures for both the benchmark dataset. The experimental outcomes are compared with the result of the conventional model of Inception and Resnet along with the latest work reported in the literature of deep learning for breast cancer. It is concluded from the experimental result that the proposed model works competently with a large dataset as well as the small dataset. And magnification plays an important role. Abstract The rapid growth of the network in deep learning, a subset of artificial intelligence has motivated the researchers to develop the tools for medical imaging analysis. Here we have introduced computer aided diagnosis tools for binary class classification and detection for breast cancer on histopathological images. In this paper, we have proposed a hybrid deep neural network for image level cancer detection in cancerous and non-cancerous category of histopathology images. The hybrid deep neural network comprises of inception and residual block. The network incorporates the advance multilevel feature map for histopathological images and involve the advantages of inception and residual block. The model proposed combines the sturdiness of inception block and residual block and shows the stability in performance against the existing start-of the art algorithms. The proposed method is trained and validated on two publicly available dataset i.e., Breast Histopathology Images (BHI) and BreakHis. The image level classification has been performed at different magnification level in case of BreakHis dataset. The experimental outcome is evaluated on different performance measures and compared with the conventional Inception model and ResNet model as well as state-of-art breast cancer detection techniques. The proposed approach shows the training accuracy of 0.9642 for Breakhis and 0.8017 for BHI dataset. The model proposed outperforms the existing cancer detection algorithms as well as conventional deep neural networks with obtained accuracy of 0.8521 for BHI and 0.8080,0.8276,0.8655and 0.8580 for 40X,100X,200X and 400X respectively for BreakHis dataset.","Title: AFMT: Maintaining the safety-security of industrial control systems\nYear: 2022\n\nAbstract: Modern day industrial control systems are overwhelmingly complex. These systems feature intricate interactions between the cyber and the physical components. At the same time, they need to be trustworthy and deliver their services continuously. Underpinning, a crucial industrial activity to ensure the dependability of such critical systems is through timely maintenance, inspections and repairs. Several strategies exist here: \"fix it when it breaks\" (reactive maintenance), monitor and maintain a system in pre-established time intervals (preventive maintenance), preventive action based upon detected symptoms of failures condition-based maintenance (CBM), etc. In literature, the question of optimal maintenance frequency have been a subject of intense study. However, most papers, do not take information security aspects into account. This paper provides an automated tool-supported quantitative risk analysis framework, Attack-FaultMaintenance Trees, AFMTs, that will enable practitioners to make informed choice on: (a) identifying the critical component(s) necessary for uninterrupted systems; (b) a decision support system that will provide informed choices on policy measures, countermeasures and safeguards that will reduce the disruptions; (c) run the \"what-if\" scenarios to find the optimal trade-offs between system attributes (safety, security, usability and maintenance). The front-end of the tool is a domain-specific language geared to represent the system architecture using graphical-constructs. The back-end of the framework remains hidden to the practitioner. It consists of a mathematical engine based on statistical model-checking techniques. A case study of oil-pipeline is used to demonstrate the efficacy of our framework. (C) 2021 Elsevier B.V. All rights reserved.","Title: Zero-Day Malware Detection and Effective Malware Analysis Using Shapley Ensemble Boosting and Bagging Approach\nYear: 2022\n\nAbstract: Software products from all vendors have vulnerabilities that can cause a security concern. Malware is used as a prime exploitation tool to exploit these vulnerabilities. Machine learning (ML) methods are efficient in detecting malware and are state-of-art. The effectiveness of ML models can be augmented by reducing false negatives and false positives. In this paper, the performance of bagging and boosting machine learning models is enhanced by reducing misclassification. Shapley values of features are a true representation of the amount of contribution of features and help detect top features for any prediction by the ML model. Shapley values are transformed to probability scale to correlate with a prediction value of ML model and to detect top features for any prediction by a trained ML model. The trend of top features derived from false negative and false positive predictions by a trained ML model can be used for making inductive rules. In this work, the best performing ML model in bagging and boosting is determined by the accuracy and confusion matrix on three malware datasets from three different periods. The best performing ML model is used to make effective inductive rules using waterfall plots based on the probability scale of features. This work helps improve cyber security scenarios by effective detection of false-negative zero-day malware.","Title: IDeAuth: A novel behavioral biometric-based implicit deauthentication scheme for smartphones\nYear: 2022\n\nAbstract: •IDeAuth is a novel implicit deauthentication scheme.•Design Android-based prototype to acquire usersmicro hand-movement.•New dataset collected from 41 users for a period of one month.•One-class classification approach to verify legitimate owner of a smartphone.","Title: Cloud Energy Storage Based Embedded Battery Technology Architecture for Residential Users Cost Minimization\nYear: 2022\n\nAbstract: This paper presents a cloud energy storage (CES) architecture for reducing energy costs for residential microgrid users. The former of this article concentrates on identifying an appropriate battery technology from various battery technologies with the aid of a simulation study. The later part addresses the economic feasibility of the storage architecture with three different scenarios namely grid connected energy storage, distributed energy storage (DES) and CES. The performance of the proposed architecture has been evaluated by considering five residential users with suitable battery technology identified from the former part of the study. For the purpose of the analysis, PV and load profiles including seasonal effects and grid price were taken from IIT Mumbai, India and IEX portal, respectively. In addition, this article also examines the impact of increased number of users with CES. The value of this study is that the proposed CES architecture is capable of reducing the cost of electricity experienced by the user by 11.37% as compared to DES. With this, CES operator's revenue can be increased by 6.70% in summer and 16.97% in winter in the case of fixed number of users. Finally, based on the analysis and simulation results, this paper recommends CES with Li-ion battery technology for residential application.","Title: Residual-based fault detection isolation and recovery of a greenhouse\nYear: 2022\n\nAbstract: This paper is concerned with fault detection, isolation and recovery (FDIR) of the greenhouse whose temperature is regulated by the model predictive controller (MPC). The residual generation approach is adopted for fault detection and isolation. The new considerations in the proposed FDIR approach are the residual generation for actuator faults, regulation failure detection as the indication of inappropriate regulation by the controller, below threshold actuator fault detection strategy, and recovery operation updating model used by MPC once the FDIR isolates the actuator fault. The proposed control strategy FDIR with MPC was compared with fixed model information MPC for simulated scenarios of the actuator fault. It has been shown that FDIR successfully detects, isolates and updates model information with low computation burden for non-delayed control evaluation.","Title: Minimizing the Effect of Cascade Failure in Multilayer Networks with Optimal Redistribution of Link Loads\nYear: 2021\n\nAbstract: Modern real-world infrastructure systems consist of a coupled and interdependent subsystems. These system can be modelled as interdependent multilayer networks such as communication networks, power networks, transport networks, etc. These networks are prone to cascade failure of intra-layer and inter-layer links due to overload. For example, in power distribution networks, the flow of electric current beyond their capacities may cause to fail the distribution lines. In multilayer network systems, any damage to the links (intra-layer and inter-layer) may cause cascade failure due to redistribution of loads of failed links to the live links. If it is not controlled, it may damage the entire system. However, optimally redistribution of loads of the failed links to live links can minimize the damage of live links due to overload. In this work, we propose a method to optimally redistribute loads of failed links (intra-layer and inter-layer) to the live links in the interdependent multilayer network in the event of cascade failure. For this purpose, we consider three variants of synthetic and two empirical dataset multilayer networks. Our simulation results reveal that optimal redistribution of loads reduces the number of failed links. Besides, it also reduces the amount of extra load (due to failed links) to be redistributed on the live links. It leads to enhance the number of live links and maintaining the robustness of the entire multilayer networks.","Title: APT attacks on industrial control systems: A tale of three incidents\nYear: 2022\n\nAbstract: Modern-day industries are complex socio-technical entities. Understanding the risks associated with the operation of such systems requires proper consideration of budget constraints, security expertise and evaluating the effects of legacy services. A relatively newer and unorthodox form of cyber-attacks against such systems are Advanced Persistent Threats (APTs). APTs are resourceful and strategic, aiming at maximum damage by stalling critical services and stealing sensitive information. In this article, we demonstrate how attack trees can be used as a common language to model APT attacks in a practitioner-friendly manner. We do so by modelling three prominent APT attacks, namely Stuxnet, Blackenergy and Triton. Each attack is described in a systematic and structured way following the attack tree modelling language. We show that, because attack trees are compositional models, one can reuse them to model other complex attack scenarios. We illustrate this compositional feature by modelling attacks on an industrial oil-pipeline.","Title: What changed in the cyber-security after COVID-19?\nYear: 2022\n\nAbstract: This paper examines the transition in the cyber-security discipline induced by the ongoing COVID-19 pandemic. Using the classical information retrieval techniques, a more than twenty thousand documents are analyzed for the cyber content. In particular, we build the topic models using the Latent Dirichlet Allocation (LDA) unsupervised machine learning algorithm. The literature corpus is build through a uniform keyword search process made on the scholarly and the non-scholarly platforms filtered through the years 2010-2021. To qualitatively know the impact of COVID-19 pandemic on cyber-security, and perform a trend analysis of key themes, we organize the entire corpus into various (combination of) categories based on time period and whether the literature has undergone peer review process. Based on the weighted distribution of keywords in the aggregated corpus, we identify the key themes. While in the pre-COVID-19 period, the topics of cyber-threats to technology, privacy policy, blockchain remain popular, in the post-COVID-19 period, focus has shifted to challenges directly or indirectly brought by the pandemic. In particular, we observe post-COVID-19 cyber-security themes of privacy in healthcare, cyber insurance, cyber risks in supply chain gaining recognition. Few cyber-topics such as of malware, control system security remain important in perpetuity.","Title: A Lyapunov-stability-based context-layered recurrent pi-sigma neural network for the identification of nonlinear systems\nYear: 2022\n\nAbstract: A novel higher-order context-layered recurrent pi-sigma neural network (CLRPSNN) is presented for the identification of nonlinear dynamical systems. The proposed model is the modified form of the classical pi-sigma neural network (PSNN) and contains an additional layer (known as the context layer) of the context nodes. Pi-sigma networks involve a product operator/unit in their output layer which indirectly incorporates in them the capability of higher-order networks and also reduces their network complexity. For tuning the weights of the proposed CLRPSNN model, a learning procedure is developed by combining the Back-Propagation (BP) and Lyapunov-stability method. The performance of the proposed model is compared with other models such as PSNN, Feed-forward neural network (FFNN) (containing single hidden layer), and various popular recurrent neural network (RNN) like Elman recurrent neural network (ERNN), Jordan recurrent neural network (JRNN), Diagonal recurrent neural network (DRNN), and a deep neural network (DNN). The simulation study showed that the proposed model has given better results as compared to the other models.","Title: A secure and robust multilayer network with optimum inter layer links under budget constraints\nYear: 2022\n\nAbstract: Most of the complex systems consist of multiple subsystems and can be modeled as multilayer networks. These networks are prone to random or strategical attacks and end up disintegrating the entire multilayer network. These attacks can’t be avoided, but a model can be proposed to restore the network to make it secure and robust. In multilayer networks, each inter-layer link has its own cost (in terms of money) associated with it. The total cost of the inter-layer links is considered as the available budget. In the present work, a method is proposed to introduce the optimal number of inter-layer links (under budget constraints) to maintain the robustness of the multilayer network. For the simulation purpose, three variants of the artificial multilayer networks and data set EU-Air Transport Networks are considered. Simulation results reveal that for the multilayer network constructed with random network layers, approx. 65% of the available budget is utilized. Nearly 70% of the total nodes are connected to a mutually connected giant component (MCGC) via inter-layer links. However, for the Configuration model, using the almost total available budget, nearly all the nodes from the considered network layers are present in MCGC. Finally, in the case of the empirical dataset, by using approx. 75% of the available budget, almost 80% of the total nodes from the considered network layers are connected to MCGC via inter-layer links.","Title: A survey on IRS NOMA integrated communication networks\nYear: 2022\n\nAbstract: Intelligent reflecting surfaces aided communication have been emerging as strong candidates to support the 6G wireless physical platforms. IRS has shown promising qualities in enhancing the spectral efficiency of wireless networks because of its capability to alter the conduct of interacting electromagnetic waves through intelligent handling of the reflections phase shifts. Also, NOMA proves itself to be superior among the other multiple access techniques as it supports a greater number of users using non-orthogonal resource allocation. This paper brings a survey over the IRS-assisted NOMA networks. The IRS and NOMA technologies, and their physical working principles are first introduced in the paper. The state-of-the-art of the IRS-assisted NOMA communication networks is next presented followed by a discussion of related performance parameters for analysis. Afterward, it discusses the resource allocation, and secrecy requirements in the IRS–NOMA networks. Furthermore, it presents the relevant work related to the optimization of energy efficiency, power efficiency and coverage. A comparison of IRS–NOMA network with MIMO–NOMA, and relay aided NOMA network is provided. Finally, a few exciting open challenges for IRS-assisted NOMA networks are identified including optimization problem using ML, identifying implementing scenarios of NOMA or OMA with IRS, PLS, and terahertz communication.","Title: Spatio-Temporal Monitoring of Atmospheric Pollutants Using Earth Observation Sentinel 5P TROPOMI Data: Impact of Stubble Burning a Case Study\nYear: 2022\n\nAbstract: The problems of atmospheric pollutants are causing significant concern across the globe and in India. The aggravated level of atmospheric pollutants in the surrounding environment poses serious threats to normal living conditions by deteriorating air quality and causing adverse health impacts. Pollutant concentration increases during harvesting seasons of Kharif/Rabi due to stubble burning and is aggravated by other points or mobile sources. The present study is intended to monitor the spatio-temporal variation of the major atmospheric pollutants using Sentinel-5P TROPOMI data through cloud computing. Land Use/Land Cover (LULC-categorization or classification of human activities and natural coverage on the landscape) was utilised to extract the agricultural area in the study site. It involves the cloud computing of MOD64A1 (MODIS Burned monthly gridded data) and Sentinel-5P TROPOMI (S5P Tropomi) data for major atmospheric pollutants, such as CH4, NO2, SOX, CO, aerosol, and HCHO. The burned area output provided information regarding the stubble burning period, which has seen post-harvesting agricultural residue burning after Kharif crop harvesting (i.e., rice from April to June) and Rabi crop harvesting (i.e., wheat from September to November). The long duration of stubble burning is due to variation in farmers' harvesting and burning stubble/biomass remains in the field for successive crops. This period was used as criteria for considering the cloud computing of the Sentinel-5P TROPOMI data for atmospheric pollutants concentration in the study site. The results showed a significant increase in CH4, SO2, SOX, CO, and aerosol concentration during the AMJ months (stubble burning of Rabi crops) and OND months (stubble burning of Kharif crops) of each year. The results are validated with the ground control station data for PM2.5/PM10. and patterns of precipitation and temperature-gridded datasets. The trajectory frequency for air mass movement using the HYSPLIT model showed that the highest frequency and concentration were observed during OND months, followed by the AMJ months of each year (2018, 2019, 2020, and 2021). This study supports the role and robustness of Earth observation Sentinel-5P TROPOMI to monitor and evaluate air quality and pollutants distribution.","Title: A novel optimized decomposition method for Smoluchowski’s aggregation equation\nYear: 2023\n\nAbstract: The Smoluchowski’s aggregation equation has applications in the field of bio-pharmaceuticals (Zidar et al., 2018 [1]), financial sector (Pushkin et al., 2004 [2]), aerosol science (Shen et al., 2020 [3]) and many others. Several analytical, numerical and semi-analytical approaches have been devised to calculate the solutions of this equation. Semi-analytical methods are commonly employed since they do not require discretization of the space variable. The article deals with the introduction of a novel semi-analytical technique called the optimized decomposition method (ODM) (see Odibat (2020)) to compute solutions of this relevant integro-partial differential equation. The series solution computed using ODM is shown to converge to the exact solution. The theoretical results are validated using numerical examples for scientifically relevant aggregation kernels for which the exact solutions are available. Additionally, the ODM approximated results are compared with the solutions obtained using the Adomian decomposition method (ADM) in Singh et al., (2015). The novel method is shown to be superior to ADM for the examples considered and thus establishes as an improved and efficient method for solving the Smoluchowski’s equation."],"string":"[\n \"Title: System assurance of subsea petroleum production systems: a case study mapping factors influencing the sourcing strategy.\\nYear: 2012\\n\\nAbstract: Subsea systems are increasingly used in the production of offshore oil and gas. However, the life cycle management of such systems is not without risks as there are many factors that could result in increased costs, lower quality, delay in delivery, as well as increased health, safety or environmental risks. In addition to engineering related risks, there are substantial risks related to the sourcing of the systems, the execution of the offshore installation process, project execution and logistics, as well as the system operation and maintenance. Often many companies with various specialized expertize needs to be coordinated in the project and the cost may become huge if something goes wrong or of the technology does not perform. The sourcing strategy in subsea systems is based on the corporate vision, management and business philosophy of the operator and the regulatory authorities in the life cycle. The purpose of this paper is to identify and discuss various factors that will influence a sourcing strategy and the decision making process previous to establishing a contractual agreement in complex industrial projects. A case study is performed in the Norwegian oil and gas industry to identify, analyze and validate factors and attributes that impact the sourcing strategy of subsea production systems. The factors are ranked according to importance and as practiced in the industry.\",\n \"Title: Performance of Robotic Augmentation in Microsurgery-Scale Motions\\nYear: 1999\\n\\nAbstract: This paper is part of the development process of a microsurgical \\\"cooperating\\\" assistant. To evaluate its applicability to augment fine surgical motions, we test precision and operator perception in simple microsurgical scale pick and place motions. Such motions are common in microsurgical pro- cedures(e.g. micro-vascular anastomosis). The experiments test the users' ability to position a common surgical tool to 250, 200 and 150 micrometer ac- curacy. These experiments were performed using two test platforms. The new \\\"steady hand\\\" robot designed for microsurgery and the LARS robot (a laparo- scopic camera holding robot) adapted for this purpose. Comparative results for several parameters including time, success rate, error rate, number of attempts are included. Comparison of performance of the two robots for these tasks is also included. The results support our claim that the new \\\"steady hand\\\" robot augments human performance for microsurgery-scale motion\",\n \"Title: ANFIS Based Intelligent Control of Vector Controlled Induction Motor Drive\\nYear: 2009\\n\\nAbstract: This paper presents Adaptive Neuro-Fuzzy Inference System (ANFIS) based intelligent control of vector controlled induction motor drive. The proposed intelligent control scheme consists of sensorless adaptive neuro-fuzzy speed controller with speed estimation based on adaptive neuro-fuzzy inverse model. The proposed neuro-fuzzy speed controller incorporates fuzzy logic algorithm with a five-layer artificial neural network (ANN) structure. The conventional PI speed controller is replaced by Adaptive Neuro-Fuzzy Inference System (ANFIS), which tunes the fuzzy inference system with hybrid learning algorithm. This makes fuzzy system to learn. The structure of speed estimator is also same as speed controller but it incorporates inverse model identification algorithm for speed estimation. The performance of the proposed scheme is investigated at different operating conditions. The simulation study indicates robustness and suitability of drive for high performance drives applications.\",\n \"Title: Artificial intelligence applications in Permanent Magnet Brushless DC motor drives\\nYear: 2010\\n\\nAbstract: Permanent Magnet Brushless DC (PMBLDC) machines are more popular due its simple structure and low cost. Improvements in permanent magnetic materials and power electronic devices have resulted in reliable, cost effective PMBLDC drives, for many applications. Advances in artificial intelligent applications like neural network, fuzzy logic, Genetic algorithm etc. have made tremendous impact on electric motor drives. The brushless DC motor is a multivariable and non-linear system. In conventional PMBLDC drives speed and position sensing of brushless DC motors require high degree of accuracy. Unfortunately, traditional methods of control require detailed modelling of all the motor parameters to achieve this. The Intelligent control techniques like, fuzzy logic control/Neural network control etc. uses heuristic input---output relations to deal with vague and complex situations. This paper presents a literature survey on the intelligent control techniques for PMBLDC motor drives. Various AI techniques for PMBLDC motor drives are described. Attempt is made to provide a guideline and quick reference for the researchers and practicing engineers those are working in the area of PMBLDC motor drives.\",\n \"Title: Dependency analysis for component-based software systems\\nYear: 2009\\n\\nAbstract: Interaction in component-based systems (CBS) happens when a component provides an interface and other components use it, and also when a component submits an event and other component receives it. Interactions promote dependencies. Higher dependency leads to a complex system, which results in poor understanding and a higher maintenance cost. Usually, dependency is represented by an adjacency matrix used in graph theory. However, this representation can check only for the presence of dependency between components and does not consider the type of interactions between these components. Interaction type can have a significant contribution to the complexity of the system. This paper proposes a link-list based dependency representation and implements it by using Hash Map in Java. This representation can store the dependency along with other information like, provided and required interfaces of components along with their types. This information can be used to analyze several interaction and dependency related issues. This paper also presents the results of an experiment of the proposed approach and measures the interaction densities and dependency level of an individual component and for the system The results show that the proposed metrics can also be used to identify the most critical and isolated components in the system, which can lead to better understanding and easy system maintenance.\",\n \"Title: Covalent attachment of actin filaments to Tween 80 coated polystyrene beads for cargo transportation.\\nYear: 2008\\n\\nAbstract: In this manuscript, a new strategy has been reported for circumscribed covalent attachment of barbed and pointed ends of actin filaments to polystyrene beads. A comparative study of attachment of actin filaments to polystyrene beads was performed by blocking functionally active sites on polystyrene beads with nonionic detergents such as Tween 20, Tween 80 and polyethylene glycol (PEG). Effective blocking of active sites was obtained with Tween 80 at 0.1% concentration. Attachment of single bundle of actin filament to bead was assessed by rotational motion of bead tailed actin in front and lateral view. Velocity of actin filaments attached to different size of beads in in-vitro motility assay was calculated to ascertain their attachments. Velocity of actin attached to 1.0 and 3.0μm polystyrene beads was reduced to 3.0–4.0 and 0.0–1.0μm/s, respectively as compared to free actin velocity of 4.0–6.0μm/s. Single point attachment of actin filaments to different size of beads was assessed by decrease in sliding velocity. Present study provides insight into the actin–myosin based molecular motor systems for drug delivery and biosensors applications.\",\n \"Title: Simple Biomanipulation Tasks with 'Steady Hand' Cooperative Manipulator\\nYear: 2003\\n\\nAbstract: This paper reports preliminary experiments in the development of our Augmented Micromanipulation System using the JHU \\\"Steady Hand\\\" cooperative robot system to augment single cell manipulation tasks. The need for robotic augmentation of biomanipulatuon tasks is discussed. The JHU \\\"Steady-Hand\\\" robot configuration for cell manipulation is reported. Augmentation strategies for stable cooperative insertion of a micropipette in a mouse embryo are developed and preliminary experiments validating these strategies are presented. These preliminary experiments demonstrate promise of cooperative robotic augmentation in single cell manipulation tasks.\",\n \"Title: An Augmentation System for Fine Manipulation\\nYear: 2000\\n\\nAbstract: Augmented surgical manipulation tasks can be viewed as a sequence of smaller, simpler steps driven primarily by the surgeon's input. These steps can be abstracted as controlled interaction of the tool/end-effector with the environment. The basic research problem here is performing a sequence of control primitives. In computing terms, each of the primitives is a predefined computational routine(e.g. compliant motion or some other \\\"macro\\\") with initiation and termination predicates. The sequencing of these primitives depends upon user control and effects of the environmental interaction. We explore a sensor driven system to perform simple manipulation tasks. The system is composed of a core set of \\\"safe\\\" system states and task specific states and transitions. Using the \\\"steady hand\\\" robot as the experimental platform we investigate using such a system.\",\n \"Title: Target Counting under Minimal Sensing: Complexity and Approximations\\nYear: 2008\\n\\nAbstract: We consider the problem of counting a set of discrete point targets using a network of sensors under a minimalistic model. Each sensor outputs a single integer, the number of distinct targets in its range, but targets are otherwise indistinguishable to sensors: no angles, distances, coordinates, or other target-identifying measurements are available. This minimalistic model serves to explore the fundamental performance limits of low-cost sensors for such surveillance tasks as estimating the number of people, vehicles or ships in a field of interest to first degree of approximation, to be followed by more expensive sensing and localization if needed. This simple abstract setting allows us to explore the intrinsic complexity of a fundamental problem, and derive rigorous worst-case performance bounds. We show that even in the 1-dimensional setting (for instance, sensors counting vehicles on a road), the problem is non-trivial: target count can be estimated within relative accuracy of factor $\\\\sqrt{2}$ and this is the best possible in the worst-case. We then address additional questions related to constructing feasible target placements, and noisy counters. In two dimensions, the problem is considerably more complicated: a constant-factor approximation is impossible. Our algorithms and analysis can easily handle some of the non-idealities of real sensors, such as asymmetric ranges and non-exact target counts.\",\n \"Title: A quantitative evaluation of aspect-oriented software quality model (AOSQUAMO)\\nYear: 2009\\n\\nAbstract: Aspect-Oriented (AO) technology is a new paradigm and emerging field of software development. Aspect-Oriented Programming (AOP) cleanly encapsulates crosscutting concerns which cannot be encapsulated or modularized by traditional programming approaches like Module-Oriented (MO) and Object-Oriented (OO). In order to evaluate quality of software systems, researchers and practitioners have proposed their software quality characteristics and models. As AO is a new abstraction, there is no dedicated software quality model, which can describe and include new features of AO technology. In this paper, a new Aspect-Oriented Software Quality Model (AOSQUAMO) has been proposed. Analytic Hierarchy Process (AHP) is used to evaluate quality of AO software systems as a single parameter. This proposed quality model further may be used to compare AO software systems which will help consumer to choose better quality software.\",\n \"Title: Rearrangement of Recognized Strokes in Online Handwritten Gurmukhi Words Recognition\\nYear: 2009\\n\\nAbstract: This paper presents a system to recognize online handwritten Gurmukhi words. We have proposed a new step as rearrangement of recognized strokes in online handwriting recognition procedure. The rearrangement of recognized strokes includes: strokes identification as dependent and major dependent strokes; the rearrangement of strokes with respect to their positions; the combination of strokes to recognize character. We have achieved an overall recognition rate as 81.02% in online handwritten cursive handwriting for a set of 2576 Gurmukhi dictionary words.\",\n \"Title: Economic analysis and power management of a stand-alone wind/photovoltaic hybrid energy system using biogeography based optimization algorithm.\\nYear: 2013\\n\\nAbstract: The stand-alone energy system having a photovoltaic (PV) panels or wind turbines have low reliability and high cost as compared with wind/PV hybrid energy system. In this study, Biogeography Based Optimization (BBO) algorithm is developed for the prediction of the optimal sizing coefficient of wind/PV hybrid energy system in remote areas. BBO algorithm is used to evaluate optimal component sizing and operational strategy by minimizing the total cost of hybrid energy system, while guaranteeing the availability of energy. A diesel generator is added to ensure uninterrupted power supply due to the intermittent nature of wind and solar resources. Due to the complexity of the hybrid energy system design with nonlinear integral planning, BBO algorithm is used to solve the problem. The developed BBO Algorithm has been applied to design the wind/ PV hybrid energy systems to supply a located in the area of Jaipur, Rajasthan (India). Conventional methods require calculation at every single combination of sizing, operation strategy and the data for each variation of component needs to be entered manually and execute separately. Results show that the hybrid energy systems can deliver energy in a stand-alone installation with an acceptable cost. It is clear from the results that the proposed BBO method has excellent convergence property, require less computational time and can avoid the shortcoming of premature convergence of other optimization techniques to obtain the better solution.\",\n \"Title: Quality aspects for component-based systems: A metrics based approach\\nYear: 2012\\n\\nAbstract: In component-based development, software systems are built by assembling components already developed and prepared for integration. To estimate the quality of components, complexity, reusability, dependability, and maintainability are the key aspects. The quality of an individual component influences the quality of the overall system. Therefore, there is a strong need to select the best quality component, both from functional and nonfunctional aspects. The present paper produces a critical analysis of metrics for various quality aspects for components and component-based systems. These aspects include four main quality factors: complexity, dependency, reusability, and maintainability. A systematic study is applied to find as much literature as possible. A total of 49 papers were found suitable after a defined search criteria. The analysis provided in this paper has a different objective as we focused on efficiency and practical ability of the proposed approach in the selected papers. The various key attributes from these two are defined. Each paper is evaluated based on the various key parameters viz. metrics definition, implementation technique, validation, usability, data source, comparative analysis, practicability, and extendibility. The paper critically examines various quality aspects and their metrics for component-based systems. In some papers, authors have also compared the results with other techniques. For characteristics like complexity and dependency, most of the proposed metrics are analytical. Soft computing and evolutionary approaches are either not being used or much less explored so far for these aspects, which may be the future concern for the researchers. In addition, hybrid approaches like neuro-fuzzy, neuro-genetic, etc., may also be examined for evaluation of these aspects. However, to conclude that one particular technique is better than others may not be appropriate. It may be true for one characteristic by considering different set of inputs and dataset but may not be true for the same with different inputs. The intension in the proposed work is to give a score for each metric proposed by the researchers based on the selected parameters, but certainly not to criticize any research contribution by authors. Copyright © 2012 John Wiley & Sons, Ltd.\",\n \"Title: Forensic Detection of Fraudulent Alteration in Ball-Point Pen Strokes\\nYear: 2012\\n\\nAbstract: Alteration and addition to valuable data on paper documents are among the fastest growing crimes around the globe. The loss due to these crimes is huge and is increasing with an alarming rate. The techniques, which are used by forensic document examiners, to examine such cases are still limited to manual examination of physical, chemical and microscopic characteristics. Moreover, it is very difficult to detect an alteration when the ink of similar color is involved. We could not find much in the literature to deal with this problem in an automated pattern recognition framework. In this paper, we restrict ourselves to alterations made with ball-point pen strokes and propose a scheme for detection of such alterations using pattern recognition tools. For this, a large set of color and texture based features is extracted. To choose an adequate set of useful features from the extracted ones, a multilayer perceptron (MLP)-based feature analysis technique is used. For detection of the alteration, three different classifiers, namely, ${\\\\\\\\rm K}$-nearest neighbor, MLP and support vector machines are used. The results are quite promising.\",\n \"Title: Stochastic Thermal Free Vibration Response of Laminated Composite Plates Resting on Elastic Foundation with Uncertain Material Properties.\\nYear: 2008\\n\\nAbstract: This study deals with the stochastic free vibration response of a laminated composite plate resting on a elastic foundation subjected to a temperature loading with uncertain material properties. The material properties are modeled as independent basic random variables. The temperature field considered is assumed to be a uniform distribution over the plate surface and through the plate thickness. The material properties are assumed to be independent of temperature. Based on the higher order deformation theory, C0 finite element method is employed to derive the eigen value problems in term of fundamental frequency. A mean-centered first order perturbation technique (FOPT) is adopted to evaluate the mean and standard derivation of the fundamental frequency with all edges simply supported boundary conditions. The effects of foundation parameters on the critical temperature are analyzed. The results have been validated with independent Monte Carlo simulation (MCS) and those available in the literature.\",\n \"Title: Generalized coupling measure for aspect-oriented systems\\nYear: 2009\\n\\nAbstract: Coupling is an internal software attribute that can be used to indicate the degree of interdependence among the components of a software system. Coupling in software has been linked to predict external software quality attributes such as fault-proneness, ripple effects, changeability, impact analysis etc. Despite an interesting body of work for measuring coupling in Aspect-Oriented (AO) Systems, there is no complete generic coupling framework for AO systems. In this paper, we have proposed a generic coupling measurement framework that takes into account three, the most well known families of Aspect-Oriented Programming (AOP) languages, AspectJ, CaesarJ and Hyper/J. This paper contributes to an enhanced understanding of coupling in AO systems, which in turn helps to (i) define new coupling metrics which permit the analysis and comparison of Java, AspectJ, CaesarJ and Hyper/J implementations, and (ii) integrate different existing measures and examine same concepts from different perspectives.\",\n \"Title: A Fast Compressive Sensing Based Digital Image Encryption Technique using Structurally Random Matrices and Arnold Transform.\\nYear: 2014\\n\\nAbstract: A new digital image encryption method based on fast compressed sensing approach using structurally random matrices and Arnold transform is proposed. Considering the natural images to be compressed in any domain, the fast compressed sensing based approach saves computational time, increases the quality of the image and reduces the dimension of the digital image by choosing even 25 % of the measurements. First, dimension reduction is utilized to compress the digital image with scrambling effect. Second, Arnold transformation is used to give the reduced digital image into more complex form. Then, the complex image is again encrypted by double random phase encoding process embedded with a host image; two random keys with fractional Fourier transform are been used as a secret keys. At the receiver, the decryption process is recovered by using TwIST algorithm. Experimental results including peak-to-peak signal-to-noise ratio between the original and reconstructed image are shown to analyze the validity of this technique and demonstrated our proposed method to be secure, fast, complex and robust.\",\n \"Title: Numerical simulation and convergence analysis of a finite volume scheme for solving general breakage population balance equations\\nYear: 2013\\n\\nAbstract: This paper presents a finite volume scheme (FVS) for solving general breakage population balance equations (BPBEs). In particular, the number density based BPBE is transformed to the form of a mass conservation law. Then it becomes easy to apply the well known FVSs that have an important property of mass conservation. Following Kumar and Warnecke for fixed pivot (FP) method [16] and cell average technique (CAT) [15], the stability and the convergence analysis of the semi-discretized FVS are studied. Unlike the CAT and the FP method, the FVS is second order consistent independently of the type of meshes. We also observe that FVS gives second order convergence rate on four different types of uniform and non-uniform meshes with non-decreasing behavior of mesh width. Nevertheless, one order better accuracy than the FP method is achieved on locally uniform meshes. It is also noticed that on non-uniform random meshes the FVS shows one order and two orders higher accuracy than the CAT and the FP method, respectively. The mathematical results of convergence analysis are validated numerically by taking three test problems. The numerical simulations are also compared with the results obtained by the CAT and the FP method.\",\n \"Title: Assessment of Crohn's disease lesions in wireless capsule endoscopy images.\\nYear: 2012\\n\\nAbstract: Capsule endoscopy (CE) provides noninvasive access to a large part of the small bowel that is otherwise inaccessible without invasive and traumatic treatment. However, it also produces large amounts of data (approximately 50 000 images) that must be then manually reviewed by a clinician. Such large datasets provide an opportunity for application of image analysis and supervised learning methods. A...\",\n \"Title: Finding An Operating Region For A Bio-Inspired Robotic Fish Underwater Vehicle In The Lighthill Framework\\nYear: 2013\\n\\nAbstract: Sir J. Lighthill mathematical slender body swimming model formulates the biological fish propulsion mechanism (undulation) in fluid environment. The present research has focused on the relevance of Lighthill (LH) based biomimetic robotic propulsion. A 2-joint, 3-link multibody vehicle model biologically inspired by a Body Caudal Fin (BCF) carangiform fish propulsion mechanism is designed. Different mathematical propulsive waveforms are proposed in LH frame-work to generate posterior body undulation. These functions are combined with inverse kinematics to generate various bio-inspired trajectories for the robotic fish vehicle motion. The robotic fish model (kinematics and dynamics) is integrated with the Lighthill (LH) mathematical model framework. Comparative studies are undertaken among a LH model and the proposed propulsive wave models. LH Cubic and NURB quadratic functions are found to be 16.32 % and 17.94 % efficient than a non-LH function respectively. Based on the simulation results of critical kinematic parameters TBF and Propulsive wavelength, an operating region is established to facilitate the open-loop (manual) control experiments.\",\n \"Title: Long cycles and long paths in the Kronecker product of a cycle and a tree\\nYear: 1997\\n\\nAbstract: Let C m × T denote the Kronecker product of a cycle C m and a tree T . If m is odd, then C m × T is connected, otherwise this graph consists of two isomorphic components. This paper presents a scheme which constructs a long cycle in each component of C m × T . If T satisfies certain degree constraints, then the cycle thus traced is shown to be a dominating set, and in some cases, a vertex cover of that component. The procedure builds on (i) results on longest cycles in C m × P n , and (ii) a path factor of T . Additional results include characterizations for the existence of a Hamiltonian cycle and for that of a Hamiltonian path in C m × T .\",\n \"Title: A Family of 32 nm IA Processors\\nYear: 2011\\n\\nAbstract: Westmere is the latest IA processor family for mobile, desktop and server market segments, implemented on Intel's second-generation high-k metal gate 32 nm process. Westmere not only increases core count, cache size, and frequency within the previous generation's power envelope, it also provides further improvements in power efficiency, feature set, and support for combo DDR3 and low voltage DDR3 despite using a thin gate technology.\",\n \"Title: A Testbed System for Robotically Assisted Percutaneous Pattern Therapy\\nYear: 1999\\n\\nAbstract: This paper presents a second generation prototype image-guided robotic system for percutaneous delivery of surgical devices\\n and therapeutic agents, with potential applications in the treatment of liver cancer and other malignancies. The system is\\n intended to deliver these devices and therapies more consistently and accurately than a physician can achieve freehand. This\\n capability will permit the treatment of smaller lesions, will enable the physician to better achieve optimized patterns of\\n therapy distribution, and will allow for more rapid re-targeting for multiple lesions. The system will allow treatment of\\n patients for whom surgery is contraindicated and could potentially replace major surgery, reducing patient morbidity and mortality,\\n as well as the cost of treatment. The new prototype system uses new, modular hardware and software components, which improve\\n its accuracy, usability, and flexibility as compared to the first generation prototype. Techniques for image processing, both\\n in 2D and 3D images, planning, and fiducial registration have also been developed. In vitro, the system achieves submillimetric accuracy in the placement of simulated treatment devices from a preoperative plan generated\\n from 3D imagery. Some initial in vivo considerations have also been addressed, and work is ongoing in this area.\",\n \"Title: Real-Time Implementation of a Harmony Search Algorithm-Based Clustering Protocol for Energy-Efficient Wireless Sensor Networks\\nYear: 2014\\n\\nAbstract: In real-life applications of wireless sensor networks (WSNs), optimization of the network operation is required to extend its lifetime. A framework is proposed that enables practical development of centralized cluster-based protocols supported by optimization methods for the WSNs. Based on this framework, a protocol using harmony search algorithm (HSA), a music-based meta-heuristic optimization method, is designed and implemented in real time for the WSNs. It is expected to minimize the intra-cluster distances between the cluster members and their cluster heads (CHs) and optimize the energy distribution of the WSNs. The study of HSA cluster-based protocol is carried out in a real case where the WSNs equipped with the proposed protocol are deployed in an indoor environment to monitor the ambient temperature for fire detection. A comparison is made with the well-known cluster-based protocols developed for WSNs such as low-energy adaptive clustering hierarchy-centralized (LEACH-C) and a cluster-based protocol using Fuzzy C-Means (FCM) clustering algorithm. Experimental results demonstrate that the proposed protocol using HSA can be realized in centralized cluster-based WSNs for safety and surveillance applications in building environments. From the obtained experimental test results, it can be seen that the WSNs lifetime has been extended using the proposed HSA protocol in comparison with that of LEACH-C and FCM protocols.\",\n \"Title: A novel approach to identification of power quality disturbances using continuous wavelet transform\\nYear: 2010\\n\\nAbstract: This paper presents a novel approach that is able to detect the power quality disturbances present in transient as well as steady-stable signals with the information of magnitude of the disturbances. This method uses the continuous wavelet transform (CWT). A new difference coefficient matrix (DCM) is proposed which is calculated from the difference of the CWT coefficients of the pure sinusoidal signal and the disturbed signal. Then, the scale wise sum of rows of DCM gives unique feature matrix (UFM). This paper shows that UFM posses the unique features that can be used to generate the unique signatures of various disturbances. These signatures not only reveal the information which can be used for the classification of the power quality disturbance but also provide the information about the magnitude of the disturbance of the signal. A case study is presented which verifies the proposed approach.\",\n \"Title: Tracking multiple targets using binary proximity sensors\\nYear: 2007\\n\\nAbstract: Recent work has shown that, despite the minimal information provided by a binary proximity sensor, a network of such sensors can provide remarkably good target tracking performance. In this paper, we examine the performance of such a sensor network for tracking multiple targets. We begin with geometric arguments that address the problem of counting the number of distinct targets, given a snapshot of the sensor readings. We provide necessary and sufficient criteria for an accurate target count in a one-dimensional setting, and provide a greedy algorithm that determines the minimum number of targets that is consistent with the sensor readings. While these combinatorial arguments bring out the difficulty of target counting based on sensor readings at a given time, they leave open the possibility of accurate counting and tracking by exploiting the evolution of the sensor readings across time. To this end, we develop a particle filtering algorithm based on a cost function that penalizes changes in velocity. An extensive set of simulations, as well as experiments with passive infrared sensors, are reported. We conclude that, despite the combinatorial complexity of target counting, probabilistic approaches based on fairly generic models for the trajectories yield respectable tracking performance.\",\n \"Title: A new hybrid multi-agent-based particle swarm optimisation technique\\nYear: 2009\\n\\nAbstract: This paper presents a multi-agent-based hybrid particle swarm optimisation technique. The algorithm integrates the deterministic, the multi-agent system (MAS) and the particle swarm optimisation (PSO) algorithm. An agent in hybrid multi-agent PSO (HMAPSO) represents a particle to PSO and a candidate solution to optimisation problem. All agents search parallel in an equally distributed lattice-like structure to save energy and computational time. The best solution is obtained through bee decision making process. Thus making use of deterministic search, multi-agent and bee PSO, the HMAPSO realises the purpose of optimisation. The proposed algorithm has been tested on various optimisation problems. The experimental results obtained show the robustness and accuracy of proposed HMAPSO. It also has been concluded that the proposed HMAPSO is able to generate a unique and optimal solution than the earlier reported approaches and hence can be a better option for real-time online optimisation problems.\",\n \"Title: Mapping factors influencing the selection of subsea petroleum production systems: a case study.\\nYear: 2012\\n\\nAbstract: The development, design, and selection of subsea petroleum production equipment and facilities are critical activities, as the decisions made will impact the success and profitability of the project. Pre-studies need to be conducted to design the best subsea concept and to assess the development costs in order to succeed in the project execution and to keep the production rate and profits as expected. Technical, economic, and government regulations are some of the factors that need to be evaluated in order to contract subsea services and procurement activities. However, there are many other factors that should be addressed before selecting the concept. By identifying and studying these aspects, one will be able to develop and select the best equipment to cover the functions needed, as well as to identify health, safety, environmental and quality requirements and spare parts’ availability for maintenance interventions. Based on a literature study and an industrial case study, in this paper we identify, map, validate and systematically rank various aspects, factors, and design criteria that need to be addressed in the design and selection of subsea oil and gas production systems.\",\n \"Title: Reusability assessment for software components\\nYear: 2009\\n\\nAbstract: Software reuse has been used as a tool to reduce the development cost and time of the software. Nowadays, in fact, majority of software systems are being developed from an assembly of existing reusable components. In order to assess the reuse of components effectively, it is necessary to measure the reusability of these components. Paper proposes Artificial Neural Network based approach to assess the reusability of software component. This work will help developers to select the best component in terms of its reusability, which will improve the maintainability of the overall system.\",\n \"Title: DESIGN OF TWO-DIMENSIONAL INFINITE IMPULSE RESPONSE RECURSIVE FILTERS USING HYBRID MULTIAGENT PARTICLE SWARM OPTIMIZATION\\nYear: 2010\\n\\nAbstract: We incorporate the optimization problem of two-dimensional infinite impulse response (IIR) recursive filters and the optimization methodology of hybrid multiagent particle swarm optimization (HMAPSO) and then apply the resultant optimized IIR filter in image processing for justifying HMAPSO robustness over other algorithm and its role in optimizing real-time situations. The design of the 2-D IIR filter is reduced to a constrained minimization problem whose robust solution is being achieved by a novel and optimal algorithm HMAPSO. This algorithm integrates the deterministic solution by the multiagent system, the particle swarm optimization (PSO) algorithm, and bee decision-making process. All agents search parallel in an equally distributed lattice-like structure to save energy and computational time as done by the bees in their hive selection process. Thus making use of deterministic search, multiagent PSO, and bee, the HMAPSO realizes the purpose of optimization. Experimental results and the application of the designed filters to focusing the defocused image show that the HMAPSO approach provides better upshots than the previous design methods.\",\n \"Title: Fingerprint matching using multi-dimensional ANN\\nYear: 2010\\n\\nAbstract: Fingerprint matching is one of the most widely used biometric technique for personal identification. This identification is achieved in this work by using the concept that every fingerprint has a unique pattern of distribution of the minutiae points. In this paper, a new method of recognition of this pattern of distribution of the minutiae points of an enhanced image is considered by using a multi-dimensional artificial neural network (MDANN). The proposed technique has the distinct advantage of using the entire resized minutiae image as an input at once. It is capable of excellent pattern recognition properties as the distribution of the minutiae points are used directly for recognition. The proposed approach shows significant promise and potential for improvement, compared with the other conventional matching techniques with regard to time and efficiency of results.\",\n \"Title: Synthesis and characterization of luminescent Eu(HMAP)3·2H2O and Tb(HMAP)3·2H2O complexes.\\nYear: 2009\\n\\nAbstract: The novel complexes Eu(HMAP)3·2H2O and Tb(HMAP)3·2H2O (where HMAP=2′-hydroxy-4′-methoxyacetophenone) are synthesized and characterized by infra red spectroscopy, 1H NMR spectroscopy, TGA/DTA, SEM and elemental analysis. The qualitative analysis of the complexes to detect the presence of rare earth (Eu, Tb), have been performed by energy dispersive analysis (EDX). The Eu(HMAP)3·2H2O on excitation at 354nm emits bright red luminescence with main peak at 612nm and Tb(HMAP)3·2H2O on excitation at 354nm emits bright green luminescence with main peak at 547nm. The luminescence intensity of Eu(HMAP)3·2H2O is higher than Tb(HMAP)3·2H2O. These complexes emitting red and green luminescence might be used to make the electroluminescent (EL) devices for display applications.\",\n \"Title: A Novel Approach for Detection of Alteration in Ball Pen Writings\\nYear: 2009\\n\\nAbstract: Addition or alteration to documents that have profound implication is very common. The technique that Forensic Document Examiners (FDEs) use for the examination of such documents is basically a physical examination. In this paper we consider the alteration detection as a two-class pattern recognition problem. Image processing techniques are used for feature extraction and a neural network based feature analysis technique is used for finding a set of discriminatory features. The results using a nearest neighbor classifier are very encouraging. The results also demonstrate the effectiveness of feature analysis.\",\n \"Title: A novel sparse model based forensic writer identification\\nYear: 2014\\n\\nAbstract: The paper presents a novel method for writer identification based on sparse representation of handwritten structural primitives, called graphemes or fraglets. The proposed method is different from the existing grapheme based methods as the earlier methods use vector quantization based coding (clustering method) to get a document descriptor, while the proposed method uses sparse coding for the same. Literature shows that the sparse coding outperforms vector quantization in many real life applications including face recognition. Sparse coding can achieve comparatively much lower reconstruction error. Secondly, the sparsity allows representation to be specialized and can capture a writer specific features more accurately. Graphemes (fraglets) extracted from a document are represented in terms of Fourier and wavelet descriptors because the fraglet contour may be well described by its global as well as local characteristics. Wavelet descriptors also give a multi-resolution representation of the shape. Results have shown that even with a smaller codebook (than the earlier reported systems), the proposed method achieves better performance.\",\n \"Title: A meta registration framework for lesion matching.\\nYear: 2009\\n\\nAbstract: A variety of pixel and feature based methods have been proposed for registering multiple views of anatomy visible in studies obtained using diagnostic, minimally invasive imaging. A given registration method may outperform another depending on anatomical variations, imaging conditions, and imaging sensor performance, and it is often difficult a priori to determine the best registration method for a particular application. To address this problem, we propose a registration framework that pools the results of multiple registration methods using a decision function for validating registrations. We refer to this as meta registration. We demonstrate that our framework outperforms several individual registration methods on the task of registering multiple views of Crohn's disease lesions sampled from a Capsule Endoscopy (CE) study database. We also report on preliminary work on assessing the quality of registrations obtained, and the possibility of using such assessment in the registration framework.\",\n \"Title: DaVinci canvas: a telerobotic surgical system with integrated, robot-assisted, laparoscopic ultrasound capability.\\nYear: 2005\\n\\nAbstract: We present daVinci Canvas: a telerobotic surgical system with integrated robot-assisted laparoscopic ultrasound capability. DaVinci Canvas consists of the integration of a rigid laparoscopic ultrasound probe with the daVinci robot, video tracking of ultrasound probe motions, endoscope and ultrasound calibration and registration, autonomous robot motions, and the display of registered 2D and 3D ultrasound images. Although we used laparoscopic liver cancer surgery as a focusing application, our broader aim was the development of a versatile system that would be useful for many procedures.\",\n \"Title: Analysis of Crohn's disease lesions in capsule endoscopy images\\nYear: 2009\\n\\nAbstract: Capsule endoscopy (CE) is aimed at diagnosing disease in areas of the gastrointestinal (GI) tract beyond the reach of conventional endoscopy. Recent work has addressed various methods for reducing the complexity of CE diagnosis and the time needed for analyzing the data. This includes detection of lumen and its contractions, fluids such as blood and intestinal juices, as well as extraneous matter such as food and bubbles. This paper outlines our ongoing work to segment lesions (in particular Crohn's disease) and other abnormalities in CE images. In particular, here we describe the data collection and clinical analysis for our project and preliminary results for segmenting abnormal and extraneous images from a set of 10 CE studies.\",\n \"Title: An Intelligent Tuned Harmony Search algorithm for optimisation\\nYear: 2012\\n\\nAbstract: The Harmony Search (HS) algorithm is a population-based metaheuristic optimisation algorithm. This algorithm is inspired by the music improvisation process in which the musician searches for harmony and continues to polish the pitches to obtain a better harmony. Although several variants of the HS algorithm have been proposed, their effectiveness in dealing with diverse problems is still unsatisfactory. The performances of these variants mainly depend on the selection of different parameters of the algorithm. In this paper, a new variant of the HS algorithm is proposed that maintains a proper balance between diversification and intensification throughout the search process by automatically selecting the proper pitch adjustment strategy based on its Harmony Memory. However, the performance of the proposed Intelligent Tuned Harmony Search (ITHS) algorithm is influenced by other parameters, such as the Harmony Memory Size (HMS) and the Harmony Memory Considering Rate (HMCR). The effects that varying these parameters have on the performance of the ITHS algorithm is also analysed in detail. The performance of the proposed ITHS algorithm is investigated and compared with eight state-of-the-art HS variants over 17 benchmark functions. Furthermore, to investigate the robustness of the proposed algorithm at higher dimensions, a scalability study is also performed. Finally, the numerical results obtained reflect the superiority of the proposed ITHS algorithm in terms of accuracy, convergence speed, and robustness when compared with other state-of-the-art HS variants.\",\n \"Title: Fuzzy ideals and fuzzy semiprime ideals: some ring theoretic analogues\\nYear: 1992\\n\\nAbstract: An attempt is made to examine the algebraic nature of fuzzy semiprime ideals under homomorphisms. Also, the fuzzy nil radical of the nth power of a fuzzy prime ideal is studied and a sufficient condition for a fuzzy ideal to be fuzzy maximal is given.\",\n \"Title: Writer-independent off-line signature verification using surroundedness feature\\nYear: 2012\\n\\nAbstract: The paper presents a novel set of features based on surroundedness property of a signature (image in binary form) for off-line signature verification. The proposed feature set describes the shape of a signature in terms of spatial distribution of black pixels around a candidate pixel (on the signature). It also provides a measure of texture through the correlation among signature pixels in the neighborhood of that candidate pixel. So the proposed feature set is unique in the sense that it contains both shape and texture property unlike most of the earlier proposed features for off-line signature verification. Since the features are proposed based on intuitive idea of the problem, evaluation of features by various feature selection techniques has also been sought to get a compact set of features. To examine the efficacy of the proposed features, two popular classifiers namely, multilayer perceptron and support vector machine are implemented and tested on two publicly available database namely, GPDS300 corpus and CEDAR signature database.\",\n \"Title: Few useful considerations for maintaining software components and component-based systems\\nYear: 2007\\n\\nAbstract: Component-Based Systems (CBS) maintenance may require several activities different than normal applications, such as upgrading the functionality of black-box components (for which code may not be available), replacement of older version components with the new ones for better and improved functionality, tracing the problem of compatibility between the new components with system, and so on. The focus of this paper is on investigating several issues and concerns about maintainability of component-based systems. It also explores the acceptance of maintainability characteristics and sub-characteristics as defined in ISO9126 quality model for CBS. The paper proposes two new sub-characteristics, namely trackability and portability, to be included under the maintenance activity.\",\n \"Title: Applying Soft Computing Approaches to Predict Defect Density in Software Product Releases: An Empirical Study.\\nYear: 2013\\n\\nAbstract: There is non-linear relationship between software metrics and defects, which results to a complex mapping. Therefore, to focus on the defect density area, it is a critical business requirement of effective and practical approach, which can help find the defect density in software releases. Soft computing provides a better platform to solve the non-linear and complex mapping problem. The aim of this paper is to formulate, build, evaluate, validate and compare two main sections of soft computing, fuzzy logic and artificial neural network approaches in prediction of defect density of subsequent software product releases. In this research, these two approaches are formulated and applied to predict the existence of a defect in file of software release. Both approaches have also been validated against various releases of two commercial software product release data sets. The validation criteria include mean absolute error, root mean square error and graphical analysis. The analysis of the study shows that artificial neural network provides better results compared to Fuzzy Inference System; but applicability of best approach depends on the data availability and the quantum of data.\",\n \"Title: Multiple-Target Tracking With Binary Proximity Sensors\\nYear: 2011\\n\\nAbstract: Recent work has shown that, despite the minimal information provided by a binary proximity sensor, a network of these sensors can provide remarkably good target tracking performance. In this article, we examine the performance of such a sensor network for tracking multiple targets. We begin with geometric arguments that address the problem of counting the number of distinct targets, given a snapshot of the sensor readings. We provide necessary and sufficient criteria for an accurate target count in a one-dimensional setting, and provide a greedy algorithm that determines the minimum number of targets that is consistent with the sensor readings. While these combinatorial arguments bring out the difficulty of target counting based on sensor readings at a given time, they leave open the possibility of accurate counting and tracking by exploiting the evolution of the sensor readings over time. To this end, we develop a particle filtering algorithm based on a cost function that penalizes changes in velocity. An extensive set of simulations, as well as experiments with passive infrared sensors, are reported. We conclude that, despite the combinatorial complexity of target counting, probabilistic approaches based on fairly generic models of trajectories yield respectable tracking performance.\",\n \"Title: Empirical Evaluation And Validation Of Interface Complexity Metrics For Software Components\\nYear: 2008\\n\\nAbstract: The major drivers for complex applications are cost, efficiency, development time, understandability, usability and more importantly the maintainability. Due to their black-box nature, complexity of software components is more crucial for component-based systems. This paper discusses various complexity concerns for these systems and reviews a number of complexity metrics for software components and component-based systems. As interfaces are the only source of information to know about the black-box components, this paper proposes a new interface complexity metric or these components. This metric is based on the information available in he interfaces like interface methods and properties. It also discusses the methodology to assign the weight values to these methods and properties to finally evaluate the complexity of the component. This paper validates the proposed metric against standard Weyukar's properties and empirically evaluates he metric for several Java Bean components. Finally a correlation analysis between proposed metrics and several other metrics like performance, customizability and readability is done to validate the metric.\",\n \"Title: Industrial Service Innovation Growth And Barriers\\nYear: 2008\\n\\nAbstract: Industrial Business-to-Business (B2B) services are becoming increasingly important for industrial growth and competition. As the markets are becoming more competitive and globalised, companies need to focus on improving existing services and creating new services. Innovation in services is a complex process and many barriers are encountered for companies that do not have experience in service innovation. Based on a study in the Norwegian oil and gas industry, this paper maps recent industrial B2B service growth and innovations. A number of innovation barriers are identified and discussed. Furthermore, the paper draws attention to the need for development in industrial service sourcing strategies, relationships, as well as contractual and performance issues.\",\n \"Title: Debris removal in Pap-smear images.\\nYear: 2013\\n\\nAbstract: Since its introduction in the 1940s the Pap-smear test has helped reduce the incidence of cervical cancer dramatically in countries where regular screening is standard. The automation of this procedure is an open problem that has been ongoing for over fifty years without reaching satisfactory results. Existing systems are discouragingly expensive and yet they are only able to make a correct distinction between normal and abnormal samples in a fraction of cases. Therefore, they are limited to acting as support for the cytotechnicians as they perform their manual screening. The main reason for the current limitations is that the automated systems struggle to overcome the complexity of the cell structures. Samples are covered in artefacts such as blood cells, overlapping and folded cells, and bacteria, that hamper the segmentation processes and generate large number of suspicious objects. The classifiers designed to differentiate between normal cells and pre-cancerous cells produce unpredictable results when classifying artefacts. In this paper, we propose a sequential classification scheme focused on removing unwanted objects, debris, from an initial segmentation result, intended to be run before the actual normal/abnormal classifier. The method has been evaluated using three separate datasets obtained from cervical samples prepared using both the standard Pap-smear approach as well as the more recent liquid based cytology sample preparation technique. We show success in removing more than 99% of the debris without loosing more than around one percent of the epithelial cells detected by the segmentation process.\",\n \"Title: A Steady-Hand Robotic System for Microsurgical Augmentation\\nYear: 1999\\n\\nAbstract: This paper reports the development of a robotic system designed to extend a human’s ability to perform small-scale (sub-millimeter)\\n manipulation tasks requiring human judgement, sensory integration and hand-eye coordination. Our novel approach, which we\\n call “steady hand” micromanipulation, is for tools to be held simultaneously both by the operator’s hand and a specially designed\\n actively controlled robot arm. The robot’s controller senses forces exerted by the operator on the tool and by the tool on\\n the environment, and uses this information in various control modes to provide smooth, tremor-free precise positional control\\n and force scaling. Our goal is to develop a manipulation system with the precision and sensitivity of a machine, but with\\n the manipulative transparency and immediacy of handheld tools for tasks characterized by compliant or semi-rigid contacts\\n with the environment.\",\n \"Title: Towards integrating task information in skills assessment for dexterous tasks in surgery and simulation.\\nYear: 2011\\n\\nAbstract: With the increasing popularity of robotic surgery, several studies in the literature have investigated automatically assessing skill measures based on motion and video data captured from these systems. A range of simulation environments for robotic surgery are now in development. Skill assessment in these environments has so far only focused on evaluating the utility and validity of statistics such as task completion time, and instrument distance measured during a simulated task. We present the first work using motion data from a robotic surgery simulation environment in development for classifying users of varying skills and detecting completion of trainee. Given the standardized environment of the simulator, and the availability of the ground truth, skill measurements and feedback based on task motion hold the promise of effective automated objective assessment. Based on motion data of a simulated manipulation task from 17 users of varying skills, we demonstrate binary classification (proficient vs. trainee) of user skill with 87.5% accuracy. Alternate measures based on instrument pose more relevant in the simulated environment including a new measure of motion efficiency are also presented and evaluated.\",\n \"Title: Evaluation of Precipitation Features in High-Frequency SSM/I Measurements Over Indian Land and Oceanic Regions\\nYear: 2009\\n\\nAbstract: Evaluation of the Special Sensor Microwave Imager (SSM/I) precipitation features is presented from various passes over Indian oceanic and land regions under the framework of radiative transfer simulations for both emission and scattering atmospheres. There is considerable uncertainty in the interpretation of the SSM/I high-frequency scattering features for development of rainfall algorithms. Speci...\",\n \"Title: Estimation of quality for software components: an empirical approach\\nYear: 2008\\n\\nAbstract: Component-Based Development (CBD) approach now is widely accepted in software industry. This approach enables efficient application development through the integration of already developed software components. The success of these applications heavily depends upon the selection of appropriate components to fit customer requirements. Therefore it is very necessary to evaluate the quality of components before using them in the final system. Quality models proposed so far can not be fully implemented as-it-is on components and component-based systems (CBS) due to architectural differences in the development approach. Present paper surveys a number of quality models for traditional and component-based systems and proposes a new model for CBS by proposing some new characteristics, which may be very relevant in the context of components. All the quality characteristics may not be of prime importance for an application to be developed for a specific domain. Therefore, it is necessary to identify only those characteristics/sub-characteristics, which may have higher priorities over the others. The present work uses Analytical Hierarchy Process (AHP) to assign the weight values to the characteristics for the proposed model. These weight values are then used to evaluate the quality contribution of sub-characteristics, characteristics and then finally the overall quality of the component by using the appropriate metrics. This approach can be used to identify and select better quality component among several others which can be used in the final system.\",\n \"Title: An ontology-based approach to heterogeneous verification of embedded control systems\\nYear: 2005\\n\\nAbstract: This paper presents an ontology-based approach to heterogeneous verification of embedded systems, that is, the integration of verification results from different tools and different models of embedded system applications. We present an overview of our proposed framework and explain the key components. We then describe an initial ontology for embedded control applications and its mapping to a knowledge base. We illustrate this initial framework using an example of an automotive power window controller. The concluding discussion describes our current work and future research directions.\",\n \"Title: Preliminary Experiments in Cooperative Human/Robert Force Control for Robot Assisted Microsurgical Manipulation\\nYear: 2000\\n\\nAbstract: This paper reports preliminary experiments with a new robot system designed to cooperatively extend a human's ability to perform fine manipulation tasks requiring human judgement, sensory integration and hand-eye coordination. A recently completed steady-hand robot is reported. A sta- ble force control law is reviewed. Preliminary experiments validate theoretical predictions of stable one-dimensional control of tool-tip forces in contact with both linearly and nonlinearly compliant objects. Preliminary feasibility ex- periments demonstrate stable one-dimensional robotic augmentation and \\\"force scaling\\\" of a human operator's tactile input.\",\n \"Title: Towards a Unified Framework for Cohesion Measurement in Aspect-Oriented Systems\\nYear: 2008\\n\\nAbstract: Aspect-Oriented Programming (AOP) is an emerging technique that provides a means to cleanly encapsulate and implement aspects that crosscut other modules. However, despite an interesting body of work for measuring cohesion in Aspect-Oriented (AO) Systems, there is poor understanding of cohesion in the context of AOP. Most of the proposed cohesion assessment framework and metrics for AOP are for AspectJ programming language. In this paper we have defined a generic cohesion framework that takes into account two, the most well known families of available AOP languages, AspectJ and CaesarJ. This unified framework contributes in better understanding of cohesion in AOP, witch can contribute in (i) comparing measures and their potential use, (ii) integrating different existing measures which examine the same concept in different ways, and (iii) defining new cohesion metrics, which in turn permits the analysis and comparison of Java, AspectJ and CaesarJ implementations.\",\n \"Title: Computer-integrated revision total hip replacement surgery: concept and preliminary results\\nYear: 1999\\n\\nAbstract: This paper describes an ongoing project to develop a computer-integrated system to assist surgeons in revision total hip replacement (RTHR) surgery. In RTHR surgery, a failing orthopedic hip implant, typically cemented, is replaced with a new one by removing the old implant, removing the cement and fitting a new implant into an enlarged canal broached in the femur. RTHR surgery is a difficult procedure fraught with technical challenges and a high incidence of complications. The goals of the computer-based system are the significant reduction of cement removal labor and time, the elimination of cortical wall penetration and femur fracture, the improved positioning and fit of the new implant resulting from precise, high-quality canal milling and the reduction of bone sacrificed to fit the new implant. Our starting points are the ROBODOC(R) system for primary hip replacement surgery and the manual RTHR surgical protocol. We first discuss the main difficulties of computer-integrated RTHR surgery and identify key issues and possible solutions. We then describe possible system architectures and protocols for preoperative planning and intraoperative execution. We present a summary of methods and preliminary results in CT image metal artifact removal, interactive cement cut-volume definition and cement machining, anatomy-based registration using fluoroscopic X-ray images and clinical trials using an extended RTHR version of ROBODOC. We conclude with a summary of lessons learned and a discussion of current and future work.\",\n \"Title: Writer Identification for Handwritten Telugu Documents Using Directional Morphological Features\\nYear: 2010\\n\\nAbstract: Linking a person based on handwritten documents is one of the oldest techniques that is used by crime investigators and forensic scientists. The importance of writer recognition in anthrax letter cases has made this examination popular in recent years. In this paper we propose four feature set namely directional opening, directional closing, direction erosion and k-curvature features for writer recognition on Telugu handwritten documents. Each of the features is extracted from the words after dividing them into a number of cells and then subjected to a nearest neighbor classifier for writer recognition. Although the results of each of the feature set is quite encouraging, the directional opening feature outperforms other feature sets.\",\n \"Title: A Fuzzy Logic Approach To Measure Complexity Of Generic Aspect-Oriented Systems\\nYear: 2010\\n\\nAbstract: Aspect-oriented programming (AOP) is an emerging technique that provides a mechanism to clearly encapsulate and implement concerns that crosscut other modules. It is claimed that this technique improves code modularization and therefore reduces complexity of object-oriented programs (OOP). Most of the proposed complexity measurement frameworks for AOP are for AspectJ programming language. In this paper, a generalized framework for assessment of complexity of aspect-oriented (AO) systems, has been defined that takes into account three, the most well known families of available AOP languages, AspectJ, CaesarJ and Hyper/J. In order to automate complexity measurement, a tool has been developed using fuzzy logic, in which some set of rules have been defined as rule base. Using this tool, complexity of majority of AOP languages can be measured, which will further help in the measurement of external software qualities, such as maintainability, reusability, adaptability and understandability.\",\n \"Title: Luminescent properties of europium and terbium complexes with 2′-hydroxy-4′, 6′-dimethoxyacetophenone.\\nYear: 2010\\n\\nAbstract: The novel complexes Eu(L)3·2H2O and Tb(L)3·2H2O (where L=2′-hydroxy-4′,6′-dimethoxyacetophenone) were synthesized and characterized by infrared spectroscopy, 1HNMR spectroscopy, TGA/DTA, scanning electron microscopy, elemental analysis and energy dispersive analysis (EDX). The Eu(L)3·2H2O on excitation at 354nm emits bright red luminescent with main peak at 612nm and Tb(L)3·2H2O on excitation at 354nm emits bright green luminescence with main peak at 547nm. The luminescence intensity of Eu(L)3·2H2O is higher than Tb(L)3·2H2O. These complexes emitting red and green luminescence might be used to make the OLEDs for display applications.\",\n \"Title: A Progressive Cut Refinement Scheme for Revision Total Hip Replacement Surgery Using C-arm Fluoroscopy\\nYear: 1999\\n\\nAbstract: We describe a new method to cut a precise, high quality cavity in Revision Total Hip Replacement surgery (RTHR) using a set of intra-operative C-arm fluoro- scopic images. With respect to previous approaches, our method provides the fol- lowing new features: (1) a novel checkerboard plate was designed to correct the geo- metric distortion within fluoroscopic images. Unlike previous distortion correction devices, the plate doesn't completely obscure any part of the image, and the distortion correction algorithm works well even when there are some overlaid objects in field of view; (2) a novel corkscrew fiducial object attached to the robot end-effector was de- signed, and a 6D pose estimation algorithm based on the 2D projection of the cork- screw is developed and used in robot-imager registration and imager co-registration; (3) we propose a progressive cut refinement scheme and an iterative cut location al- gorithm which utilizes image subtraction and 2D anatomy contour matching tech- niques. Several cutting experiments and some simulated experiments have been con- ducted to assess our techniques. The results indicate that our scheme is a promising method for RTHR application.\",\n \"Title: A decision fusion strategy for polyp detection in capsule endoscopy.\\nYear: 2012\\n\\nAbstract: Wireless capsule endoscopy (CE) is now routinely used for non-invasive diagnosis of small bowel diseases. But, it still requires manual assessment of the approximately 50,000 study images. Literature has recently investigated automated methods to detect and analyze various anomalies in CE images to improve reading efficiency and reduce variability. We propose such a computer aided diagnosis (CAD) approach to detect small bowel polyps. For supervised classification of polyps, we investigated fusing multiple statistical classifiers based on color, texture and edge features. The combined boosted classifier when evaluated using 1200 CE images outperformed all individual classifiers and achieved a ~90% classification accuracy.\",\n \"Title: Numerical methods for solving two-dimensional aggregation population balance equations\\nYear: 2011\\n\\nAbstract: Abstract The cell average technique (CAT) and the fixed pivot (FP) method for solving two-dimensional aggregation population balance equations using a rectangular grid were implemented in Kumar et al. (2008) . Recently, Chakraborty and Kumar (2007) have studied the FP scheme for the same problem on two different types of triangular grids and found that the method shows better results for number density as compared to the rectangular grids. However, they did not discuss the results for higher moments. Therefore, our first aim in this work is to compare different moments calculated by the FP technique on rectangular and triangular meshes with the analytical moments. Further we introduce a new mathematical formulation of the CAT for the two different types of triangular grids as considered by Chakraborty and Kumar (2007) . The new formulation is simple to implement and gives better accuracy as compared to the rectangular grids. Three different test problems are considered to analyze the accuracy of both schemes by comparing the analytical and numerical solutions. The new formulation shows good agreement with the analytical results for number density and higher moments.\",\n \"Title: Distributed Modular Computer-Integrated Surgical Robotic Systems: Architecture for Intelligent Object Distribution\\nYear: 2000\\n\\nAbstract: This paper presents intelligent object distribution architecture to maximize the performance and intelligence of a distributed surgical robotics system and its preliminary implementation in an MR-guided surgical robot system in an open-configuration MRI scanner. The method enables networked integration of a robot control server and multiple clients with minimum engineering overhead but maximum flexibility and performance. The clients in this study include an intraoperative imager, high-performance image processing computer(s), and surgical navigation host. The first contribution of the paper is to propose the use of object distribution by common object request broker architecture (CORBA), in which a robot control object on the robot control server can be remotely but transparently invoked from the clients regardless of their hardware, operating systems, or programming language. Second, we propose a technique to achieve additional flexibility by reporting the robot configuration information, i.e. geometry and kinematics of the robot, to the clients upon connection. Third, we ensure protection against an unauthorized entity by introducing a security control host that authorized the clientsaccess to the robot server. In a prototype implementation of an MR-guided surgical robot system, the robot was controlled by surgical navigation software (the 3D Slicer) on a UNIX client by invoking the distributed control object on a robot control server on a PC. The method was evaluated in performance studies; and the result indicated 3.6 milliseconds for retrieving positions of the robot stages and 25.5 milliseconds to send a frame-based motion command, which are satisfactory for surgical robot control. In conclusion, the proposed method shows the potential usefulness of flexibly integrating the legacy software to a surgical robot system with minimum engineering overhead, thereby achieving highly complex and intelligent tasks in robot-assisted surgery.\",\n \"Title: Measuring changeability for generic aspect-oriented systems\\nYear: 2008\\n\\nAbstract: Maintenance of software systems has become a major concern for software developers and users. In environments, where software changes are frequently required to improve software quality, chan-geability is an important characteristic of maintainability in ISO/IEC 9126 quality standards. Many researchers and practition-ers have proposed changeability assessment techniques for Object-Oriented Programming (OOP) and Aspect-Oriented Programming (AOP). To the best of our knowledge, no one has proposed chan-geability assessment technique for generic Aspect-Oriented (AO) Systems. AOP is an emerging technique that provides a means to clearly encapsulate and implement aspects that crosscut other modules. In this paper, we have defined a generic changeability assessment technique that takes into account two well known fami-lies of available AOP languages viz, AspectJ and CaesarJ. A co-relation analysis between changeability and dependency has been performed. Result shows that highly dependent AO systems will absorb low changeability.\",\n \"Title: Visualization of anatomical information in near-infrared imaging for robotic urological surgery\\nYear: 2011\\n\\nAbstract: Commercial telerobotic surgery systems for soft tissue surgery are generally limited to visual imaging, though it is possible to simultaneously view picture-in-picture visualization of another workstation. However, it is not easy to correlate such information with the primary endoscopic view since it may not relate to the surface visible in the visual endoscopic images. As critical surfaces as well as surgical targets often lie subsurface, a range of techniques (e.g. ultrasound and near-infrared imaging) and registration methods have been investigated as robotic surgery gains popularity. While investigation of nerves, blood vessels, and tumors has received prior attention, we present a new prototype system for real-time multimodal image registration that focuses on the visualization of the urinary tract. By providing an accurate registration between stereo video images and a near infrared imager, we aim to enhance surgical awareness and make critical uretary tasks such as mobilization of the ureters easier. Engineering validation experiments with a prototype imager, and in-vivo experiments using a prototype Hamamatsu Photodynamic Eye (PDE) imager together with the da Vinci surgical system demonstrating feasibility are presented. These initial experiments have also shown encouraging response from the clinicians.\",\n \"Title: A Cooperative Network Framework for Multi-UAV Guided Ground Ad Hoc Networks\\nYear: 2015\\n\\nAbstract: Cooperative ad hoc networks are becoming very important in various military and civilian applications. The interfacing between different ad hoc networks provides large applications in field of surveillance, navigation, disaster monitoring and homeland security. This paper focuses on implementation of UAV (unmanned aerial vehicles) ad hoc network that forms a guidance system for ground ad hoc network. The network framework proposed in the paper uses neural network to form cognitive and topology maps. Indirect and Bayesian Kalman Filter are used for estimations. These estimations allows updating of pre-constructed cognitive map to form ideal final search map that is shared among all nodes to perform search and track operations. The analysis showed that the proposed framework is capable of forming a search maps that is able to define multiple way points for each UAV in the network to follow a non-redundant path for searching and identifying various user nodes and geographical territories. The effectiveness of the model is demonstrated using simulations.\",\n \"Title: A Computational Approach to Enzyme Design: Predicting ω-Aminotransferase Catalytic Activity Using Docking and MM-GBSA Scoring.\\nYear: 2014\\n\\nAbstract: Enzyme design is an important area of ongoing research with a broad range of applications in protein therapeutics, biocatalysis, bioengineering, and other biomedical areas; however, significant challenges exist in the design of enzymes to catalyze specific reactions of interest. Here, we develop a computational protocol using an approach that combines molecular dynamics, docking, and MM-GBSA scoring to predict the catalytic activity of enzyme variants. Our primary focuses are to understand the molecular basis of substrate recognition and binding in an S-stereoselective omega-aminotransferase (omega-AT), which naturally catalyzes the transamination of pyruvate into alanine, and to predict mutations that enhance the catalytic efficiency of the enzyme. The conversion of (R)-ethyl 5-methyl-3-oxooctanoate to (3S,5R)-ethyl 3-amino-5-methyloctanoate in the context of several omega-AT mutants was evaluated using the computational protocol developed in this work. We correctly identify the mutations that yield the greatest improvements in enzyme activity (20-60-fold improvement over wild type) and confirm that the computationally predicted structure of a highly active mutant reproduces key structural aspects of the variant, including side chain conformational changes, as determined by X-ray crystallography. Overall, the protocol developed here yields encouraging results and suggests that computational approaches can aid in the redesign of enzymes with improved catalytic efficiency.\",\n \"Title: Group based Swarm evolution algorithm (GSEA) driven mental task classifier.\\nYear: 2015\\n\\nAbstract: Brain computer interface (BCI) system is based on non-invasive electroencephalographic signals. It is an augmented communication channel for disabled persons which translates neural activity from the brain into control signals. In this paper, a novel group based Swarm evolution algorithm (GSEA) driven neural classifier has been proposed to classify mental tasks. These mental tasks consists of left and right hand movement imagination and thinking of word generation. GSEA is a hybrid of swarm intelligence based computational methods with differential evolution based techniques. Hybridization of both algorithms ensures that pitfalls of both are overcome thus enhancing results. A concept of grouping has also been introduced to increase both exploration and exploitation performance of the algorithm. GSEA has been tested on publicly available BCI Competition 3 dataset 5. Experimental results shows that the proposed algorithm exhibits better result than individual swarm or differential search based algorithms and many other algorithms.\",\n \"Title: A general framework for wireless capsule endoscopy study synopsis.\\nYear: 2015\\n\\nAbstract: We present a general framework for analysis of wireless capsule endoscopy (CE) studies. The current available workstations provide a time-consuming and labor-intense work-flow for clinicians which requires the inspection of the full-length video. The development of a computer-aided diagnosis (CAD) CE workstation will have a great potential to reduce the diagnostic time and improve the accuracy of assessment. We propose a general framework based on hidden Markov models (HMMs) for study synopsis that forms the computational engine of our CAD workstation. Color, edge and texture features are first extracted and analyzed by a Support Vector Machine classifier, and then encoded as the observations for the HMM, uniquely combining the temporal information during the assessment. Experiments were performed on 13 full-length CE studies, instead of selected images previously reported. The results (e.g. 0.933 accuracy with 0.933 recall for detection of polyps) show that our framework achieved promising performance for multiple classification. We also report the patient-level CAD assessment of complete CE studies for multiple abnormalities, and the patient-level validation demonstrates the effectiveness and robustness of our methods.\",\n \"Title: Geometrical Localization Algorithm for Three Dimensional Wireless Sensor Networks\\nYear: 2014\\n\\nAbstract: The issue of localization has been addressed in many research areas such as vehicle navigation systems, virtual reality systems, user localization in wireless sensor networks (WSNs). In this paper, we have proposed an efficient range-free localization algorithm: Geometrical Localization Algorithm (GLA) for large scale three dimensional WSNs. GLA uses moving anchors to localize static sensors. GLA consists of beacon message selection, circular cross section selection. Three beacon messages are used to compute the center of circular cross section using vector method and perpendicular bisector method. The static sensors are localized with help of the center of circular cross section and geometrical rules for sphere. GLA is simulated in SINALGO software and results have been compared with existing methods namely chord selection and point localization. GLA outperforms both the compared methods in terms of average localization time and beacon overhead.\",\n \"Title: Directed Bee Colony Optimization Algorithm.\\nYear: 2014\\n\\nAbstract: The paper presents a new optimization algorithm inspired by group decision-making process of honey bees. The honeybees search for the best nest site among many possible sites taking care of both speed and accuracy. The nest site selection is analogous to finding the optimality in an optimization process. Such similarities between two processes have been used to cultivate a new algorithm by learning from each other. Various experiments have been conducted for better understanding of the algorithm. A comprehensive experimental investigation on the choice of various parameters such as number of bees, starting point for exploration, choice of decision process etc. has been made, discussed and used to formulate a more accurate and robust algorithm. The proposed Directed Bee Colony algorithm (DBC) has been tested on various benchmark optimization problems. To investigate the robustness of DBC, the scalability study is also conducted. The experiments conducted clearly show that the DBC generally outperformed the other approaches. The proposed algorithm has exceptional property of generating a unique optimal solution in comparison to earlier nature inspired approaches and therefore, can be a better option for real-time online optimization problems.\",\n \"Title: A novel two-level particle swarm optimization approach to train the transformational grammar based hidden Markov models for performing structural alignment of pseudoknotted RNA.\\nYear: 2015\\n\\nAbstract: A two-level particle swarm optimization (TL-PSO) algorithm is proposed for training stochastic context-sensitive hidden Markov model (cs-HMM), that addresses a thrust area of bioinformatics i.e. structural alignment of pseudoknotted non-coding RNAs (ncRNAs). Due to the well-conserved sequences and corresponding secondary structures of ncRNAs, the structural information becomes imperative for performing their alignments. Proposed approach is unique in the sense: it is the first idea so far which works on optimization of the model length; also it is the first swarm intelligence technique that is proposed for training csHMM. The two-level strategy with training and cross training sets helps in increasing the diversity of the particles so as to avoid trapping in local optima, yields more accurate estimation parameters, preserves the structure of the model and provides the best compression from the model. TL-PSO yields a trained stochastic model with position-dependent probabilities that achieves high prediction ratios than the compared non-stochastic scoring matrix based csHMM approaches. TL-PSO is also tested solely for sequence alignment of proteins, by training the conventional HMMs. TLPSO-HMM produces an effective framework for sequence alignment in terms of alignment quality and prediction accuracy than the competitive state-of-the-art and family of PSO based algorithms. Conjointly, TLPSO-csHMM finds higher prediction measures than competitive RNA structural alignment techniques for pseudoknotted and non-pseudoknotted RNA structures of diverse complexities.\",\n \"Title: Realisation of a cluster-based protocol using fuzzy C-means algorithm for wireless sensor networks\\nYear: 2013\\n\\nAbstract: In this study, real-time design and implementation of an energy-efficient cluster-based protocol for wireless sensor networks (WSNs) are presented. The formation of suitable clusters is of prime importance to balance energy usage by sensor nodes within each cluster of the cluster-based WSNs. This leads to energy savings for the sensor nodes resulting in longer network lifetime. To obtain this objective, the fuzzy C-means (FCM) clustering algorithm is incorporated in the protocol. The protocol is realised on a hardware test-bed with the support of the embedded operating system, TinyOS. Experimental results obtained from a scale-down laboratory based test-bed with up to 50 sensor nodes are provided to illustrate the efficacy of the WSNs using the proposed protocol and compared with the well-known cluster-based protocols such as Low Energy Adaptive Clustering Hierarchy. It has been shown that the FCM protocol is able to achieve better organisation of the network and thus can extend its lifetime under varying operating conditions and with different number of nodes.\",\n \"Title: A boundary restricted adaptive particle swarm optimization for data clustering.\\nYear: 2013\\n\\nAbstract: Data clustering is the most popular data analysis method in data mining. It is the method that parts the data object to meaningful groups. It has been applied into many areas such as image processing, pattern recognition and machine learning where the data sets are of many shapes and sizes. The most popular K-means and other classical algorithms suffer from drawback of their initial choice of centroid selection and local optima. This paper presents a new improved algorithm named as Boundary Restricted Adaptive Particle Swam Optimization (BR-APSO) algorithm with boundary restriction strategy. The proposed BR-APSO algorithm is tested on nine data sets, and its results are compared with those of PSO, NM-PSO, K-PSO and K-means clustering algorithms. It has been found that the proposed algorithm is robust, generates more accurate results and its convergence speed is also fast compared to other algorithms.\",\n \"Title: Haswell: The Fourth-Generation Intel Core Processor\\nYear: 2014\\n\\nAbstract: Haswell, Intel's fourth-generation core processor architecture, delivers a range of client parts, a converged core for the client and server, and technologies used across many products. It uses an optimized version of Intel 22-nm process technology. Haswell provides enhancements in power-performance efficiency, power management, form factor and cost, core and uncore microarchitecture, and the core's instruction set.\",\n \"Title: Applicability of Landsat 8 data for characterizing glacier facies and supraglacial debris.\\nYear: 2015\\n\\nAbstract: •Use of Landsat 8 OLI and TIRS bands for glacier facies and debris cover mapping.•A spectral profile based approach for increasing the efficiency of mapping.•Crevasses and slush zone on glacier also got mapped accurately.•A lapse rate based determination of transient freezing line was done.•OLI bands 1, 9, 5 and 6 along with TIRS band 10 were better to capture the facies.\",\n \"Title: A lake detection algorithm (LDA) using Landsat 8 data: A comparative approach in glacial environment.\\nYear: 2015\\n\\nAbstract: •Use of Landsat 8 coastal & cirrus bands for glacial lake detection and mapping.•Proposes MI (moisture index) & LDA (lake detection algorithm).•Proposes LDI (lake detection index) & LF (lake fraction) to check accuracy.•Performances of 7 pre-existing methods are also tested for glacial lake detection.•LDA performs best in case of both, mixed lake pixels and pure lake pixels.\",\n \"Title: Beware, Your Hands Reveal Your Secrets!\\nYear: 2014\\n\\nAbstract: Research on attacks which exploit video-based side-channels to decode text typed on a smartphone has traditionally assumed that the adversary is able to leverage some information from the screen display (say, a reflection of the screen or a low resolution video of the content typed on the screen). This paper introduces a new breed of side-channel attack on the PIN entry process on a smartphone which entirely relies on the spatio-temporal dynamics of the hands during typing to decode the typed text. Implemented on a dataset of 200 videos of the PIN entry process on an HTC One phone, we show, that the attack breaks an average of over 50% of the PINs on the first attempt and an average of over 85% of the PINs in ten attempts. Because the attack can be conducted in such a way not to raise suspicion (i.e., since the adversary does not have to direct the camera at the screen), we believe that it is very likely to be adopted by adversaries who seek to stealthily steal sensitive private information. As users conduct more and more of their computing transactions on mobile devices in the open, the paper calls for the community to take a closer look at the risks posed by the now ubiquitous camera-enabled devices.\",\n \"Title: Fuzzy entropy-based framework for multi-faceted test case classification and selection: an empirical study\\nYear: 2014\\n\\nAbstract: Software testing is complex, ambiguous, labour-intensive, costly, error prone and a core activity of software development. Devising the cost-effective and adequate strategies for software test cases optimisation has been one of the research issues in software testing for a long time. Existing techniques of test case optimisation are not providing the optimal solution to the test cases optimisation problem in terms of precision, completeness, cost and adequacy. The authors have already proposed a fuzzy logic-based multi-faceted measurement framework for test cases classification and fitness evaluation. Though, it reduces testing efforts, cost, incompleteness and increases adequacy, but, still there is ambiguity in classification and selection of some test cases due to ambiguity in fitness of test cases. Hence, there is a strong need to devise a technique to measure suitably and resolve the ambiguity in software test cases classification and selection problem. In this paper, the authors have unified their earlier proposed framework by introducing fuzzy entropy-based approach. The proposed unified framework chunks out the high ambiguity test cases and selects low ambiguity test cases for exercising on SUT (Software under Test). The proposed unified framework is tested on artefacts of benchmark applications, and the results show that the proposed unified framework enhances the classification accuracy by reducing ambiguity, and increases the number of test cases classified accurately.\",\n \"Title: Optimal Thrust Allocation for Semisubmersible Oil Rig Platforms Using Improved Harmony Search Algorithm\\nYear: 2012\\n\\nAbstract: Deep-water offshore drilling vessels, such as a semisubmersible drilling rig, use the dynamic positioning (DP) system and the thruster-assisted position-mooring system for maintaining a stationary position. In the DP system, the thrust allocator is used to distribute the desired generalized forces computed by the motion controller among the thrusters. However, to ensure safe operation of the vessel despite the thruster failure, the vessel is equipped with redundant thruster configuration and, therefore, is overactuated. For overactuated vessels, the choice of a particular solution for thrust allocation is found using some kind of optimization process. In this paper, the thrust allocator tries to minimize the power consumption and takes forbidden/spoil zones into account. The formulated thrust allocation problem becomes nonconvex due to thrust direction constraints on azimuth thrusters. The conventional methods get trapped in local minima and fail to find the optimum solution for the formulated nonconvex thrust allocation problem. In this paper, an improved harmony search (IHS) algorithm for solving the nonconvex thrust allocation problem is proposed. IHS is a variant of the harmony search (HS) algorithm. The HS algorithm is a music-based meta-heuristic optimization method, which is analogous with the music improvisation process where a musician continues to polish the pitches to obtain better harmony. Obtained numerical results show that the IHS algorithm has better convergence property when compared to the HS algorithm and the genetic algorithm (GA). Moreover, the power consumption for thrust allocation using the IHS algorithm is lower when compared with HS, GA, and Mincon (sequential quadratic programming) algorithms. The percentage savings in total power consumption for thruster allocation as compared to the Mincon algorithm for GA, HS, and IHS methods are 44.96%, 48.39%, and 51.58%, respectively.\",\n \"Title: Optimal data aggregation tree in wireless sensor networks based on intelligent water drops algorithm\\nYear: 2012\\n\\nAbstract: Energy conservation is an important aspect in wireless sensor networks (WSNs) to extend the network lifetime. In order to obtain energy-efficient data transmission within the network, sensor nodes can be organised into an optimal data aggregation tree with optimally selected aggregation nodes to transfer data. Various nature-inspired optimisation methods have been shown to outperform conventional methods when solving this problem in a distributed manner, that is, each sensor node makes its own decision on routing the data. In this study, a novel optimisation algorithm called intelligent water drops (IWDs) is adopted to construct the optimal data aggregation trees for the WSNs. Further enhancement of the basic IWD algorithm is proposed to improve the construction of the tree by attempting to increase the probability of selecting optimum aggregation nodes. The computational experiment results show that the IWD algorithm is able to obtain a better data aggregation tree with a smaller number of edges representing direct communication between two nodes when compared with the well-known optimisation method such as ant colony optimisation. In addition, the proposed improved version of the IWD algorithm provides better performance in comparison with the basic IWD algorithm for saving the energy of WSNs.\",\n \"Title: Economic Analysis and Power Management of a Small Autonomous Hybrid Power System (SAHPS) Using Biogeography Based Optimization (BBO) Algorithm\\nYear: 2013\\n\\nAbstract: In this study, Biogeography Based Optimization (BBO) algorithm is developed for the prediction of the optimal sizing coefficient of Small Autonomous Hybrid Power System (SAHPS) in remote areas. BBO algorithm is used to evaluate optimal component sizing and operational strategy by minimizing the total cost of SAHPS, while guaranteeing the availability of energy. Due to the complexity of the SAHPS design with nonlinear integral planning, BBO algorithm is used to solve the problem. The developed BBO Algorithm has been applied to design the wind/PV/hydro hybrid energy systems to supply a colony located in the area of Jaipur, Rajasthan (India) during the period of January, 2010 to January 2011. It is clear from the results that the proposed BBO method has excellent convergence property, requires less computational time and can avoid the shortcoming of premature convergence of other optimization techniques to obtain a better solution.\",\n \"Title: An efficient pulse flip-flop based launch-on-shift scan cell\\nYear: 2010\\n\\nAbstract: At-speed testing is essential for VLSI ICs implemented in nanometer technologies, operating at high clock speeds. Traditional scan based methodologies can be used for at-speed testing using a transition delay fault model. There are two common techniques to launch the transition-launch-on-shift (LOS) and launch-on-capture (LOC). LOS gives better fault coverage than LOC, but the main drawback of LOS is its requirement of a global at-speed scan enable (SE) signal that needs to be distributed across the IC. In this paper, we propose a pulsed flip-flop based LOS scan cell (PUFLOS cell) and a fast local scan enable generation circuit Our pulsed flip-flop based scan cell has 23.2% lower power dissipation and 27.3% better timing than a conventional muxed D-flip-flop based LOS scan cell. The layout area of our PUFLOS cell is 21% smaller than conventional LOS scan cell. Monte Carlo simulations demonstrate that our design is more robust to process variations than the conventional scan cell.\",\n \"Title: Optimal placement of Phasor Measurement Units using Particle swarm Optimization\\nYear: 2009\\n\\nAbstract: This paper solves the Phasor Measurement Unit (PMU) placement problem by Particle swarm Optimization (PSO). The PSO algorithm is implemented on three bus systems namely the 7, 14, 57 IEEE standard Bus systems. In this paper it has been proved that the placing of Phasor measurement units only at buses with the highest number of incident branches surely doesn't yield the optimal placement of the PMUs. This encouraged the idea of random placement of PMUs in the system and selection of the specific arrangement of PMUs which accomplished the task of complete observability of the system with minimum number of PMUs thus optimizing the PMU placement problem. The performance is compared with that of Adaptive GA, CLONALG and Adaptive CLONALG. The proposed method was found better.\",\n \"Title: A study on inertia weight schemes with modified particle swarm optimization algorithm for multiple sequence alignment\\nYear: 2013\\n\\nAbstract: In this study a modified particle swarm optimization (MPSO) algorithm has been proposed for obtaining optimal multiple sequence alignment (MSA). Due to the NP-complete nature of MSA problems, it is always a challenging task to obtain a suitable approach for optimal alignment of specific family of sequences. Proposed MPSO approach takes the random sequence length (with gaps), with non-repetitive gap positions in each iteration. Proposed approach applies both integer and binary matrix for incorporating gaps in sequences so as to avoid overlapping of gaps with sequences. Seven inertia weight schemes have been tested so as to check the effect of weight on convergence speed and time. Here the weight effect has been studied for different problem sizes i.e. 5 & 10 sequences with maximum sequence length of 10 & 212 respectively. MPSO is found to be performing well than standard PSO (S-PSO) at the same parameter set. Exponentially decreasing weight scheme is found performing significantly better at time and convergence criteria than other six weight approaches.\",\n \"Title: Vector Control Techniques For Induction Motor Drive: A Review\\nYear: 2009\\n\\nAbstract: This paper presents a literature survey on the vector control techniques for the induction motor drive. Various vector control techniques based on microprocessor/digital control, model adaptive reference control, intelligent control, sliding mode control and decoupling control are described. Uncertainties of parameter are important issue in vector control. Parameter needs to be identified clearly. Different parameter identification methods are discussed. Attempt is made to provide a guideline and quick reference for the researchers and practicing engineers those are working in the area of vector control.\",\n \"Title: Classification of mental tasks from EEG data using backtracking search optimization based neural classifier\\nYear: 2015\\n\\nAbstract: Brain Computer Interface (BCI) has been applied to augment impaired human cognitive function by converting mental signals into control signals. This paper presents a neural classifier optimized using Backtracking Search optimization Algorithm (BSANN) to classify three mental tasks consisting of right or left hand movement imagination and generation of word. BSA is an Evolutionary Algorithm (EA) which is suitable for deciphering non-linear and non-differentiable problems. Single control parameter gives BSA an upshot over other EA due to the lower degree of randomness. BSA keeps memory of old population to generate a new candidate set i.e. solution, so it gets the advantage of utilizing the search results of the previous population. The proposed method (BSANN) has been tested on the publicly available datasets of BCI Competition 3-5. Experimental result shows that BSANN exhibits better results than 21 other algorithms for classification of mental tasks in terms of classification accuracy.\",\n \"Title: Social Media As A Catalyst For Civil Society Movements In India: A Study In Dehradun City\\nYear: 2015\\n\\nAbstract: In India, online availability of information has created several new ways of communication and interaction through Internet relay chats, messaging email, video and voice chat, file sharing, blogging and discussion groups. Social media, particularly the Social Networking Sites, have enabled communication anywhere in the world and to anyone who shares interests and activities across political, social, economic and geographical boundaries. Of late, there has been intensive discourse and debate among intelligentsia about the perceptible impact of social media on opinion building and expression. Some civil society movements are also said to be accelerated by social media. This paper, through an empirical study, attempts to understand and analyse how social media, especially social networking sites, change the ways of communication, allow the public to critique and discuss fearlessly different social and political issues thereby becoming a force to reckon with for civil society movements and agitations in India. To study this phenomenon, Dehradun city, an education hub of India having a sizeable number of young active new media users, was chosen as a reference point. The study suggests that social media is yet to emerge as a definite force multiplier for civil society movements in India.\",\n \"Title: A novel two-level particle swarm optimization approach for efficient multiple sequence alignment\\nYear: 2015\\n\\nAbstract: This paper presents two-level particle swarm optimization (TL-PSO) algorithm as an effective framework for providing the solution of complex natured problems. Proposed approach is employed to solve a challenging problem of bioinformatics i.e. multiple sequence alignment (MSA) of proteins. The major challenge in MSA is the increasing complexity of the problem as soon as the number of sequences increases and average pairwise sequence identity (APSI) score decreases. Proposed TLPSO-MSA firstly maximizes the matched columns in level one followed by maximization of pairwise similarities in level two at the gbest solutions of level one. TLPSO-MSA efficiently handles the premature convergence and trapping in local optima related issues. The benchmark dataset for MSA of protein sequences are extracted from BAliBASE3.0. The special features of proposed algorithm is its prediction accuracy at very lower APSI scores. Proposed approach significantly outperforms the compared state-of-art competitive algorithms i.e. ALIGNER, MUSCLE, T-Coffee, MAFFT, ClustalW, DIALIGN-TX, ProbAlign and standard PSO algorithm. The claim is supported by the statistical significance testing using one way ANOVA followed by Bonferroni post-hoc analysis.\",\n \"Title: An efficient two-level swarm intelligence approach for RNA secondary structure prediction with bi-objective minimum free energy scores.\\nYear: 2016\\n\\nAbstract: This paper introduces a set-based two-level particle swarm optimization algorithm (TL-PSOfold) with multiple swarms for finding secondary structure of RNA with prediction accuracy. First objective is concerned with maximizing number of stacked loops at hydrogen bond, whereas, second objective deals with minimum free energy (MFE) at standard nearest neighbor database (NNDB). First level of the algorithm works on the entire search space for the best solution of each swarm, whereas, the second level works at the gbest solution of each swarm. The set based PSO approach has been applied at both levels to represent and update the set of ordered pairs of the folded RNA sequence. Improved weight parameter schemes with mutation operators are implemented for better convergence and to overcome the stagnation problem. Bi-objectives nature of TL-PSOfold enables the algorithm to achieve maximum matching pairs as well as optimum structure at respective levels. The performance of TL-PSOfold is compared with a family of PSO based algorithms i.e. HelixPSO v1, HelixPSO v2, PSOfold, SetPSO, IPSO, FPSO, popular secondary structure prediction software RNAfold, mfold and other metaheuristics RNAPredict, SARNA-Predict at the criteria of sensitivity, specificity and F-measure. Simulation results for TL-PSOfold show that it yields higher prediction accuracy than all the compared approaches. The claim is supported by the non-parametric statistical significance testing using Kruskal–Wallis test followed by post-hoc analysis.\",\n \"Title: Authenticating users through their arm movement patterns.\\nYear: 2016\\n\\nAbstract: In this paper, we propose four continuous authentication designs by using the characteristics of arm movements while individuals walk. The first design uses acceleration of arms captured by a smartwatchu0027s accelerometer sensor, the second design uses the rotation of arms captured by a smartwatchu0027s gyroscope sensor, third uses the fusion of both acceleration and rotation at the feature-level and fourth uses the fusion at score-level. Each of these designs is implemented by using four classifiers, namely, k nearest neighbors (k-NN) with Euclidean distance, Logistic Regression, Multilayer Perceptrons, and Random Forest resulting in a total of sixteen authentication mechanisms. These authentication mechanisms are tested under three different environments, namely an intra-session, inter-session on a dataset of 40 users and an inter-phase on a dataset of 12 users. The sessions of data collection were separated by at least ten minutes, whereas the phases of data collection were separated by at least three months. Under the intra-session environment, all of the twelve authentication mechanisms achieve a mean dynamic false accept rate (DFAR) of 0% and dynamic false reject rate (DFRR) of 0%. For the inter-session environment, feature level fusion-based design with classifier k-NN achieves the best error rates that are a mean DFAR of 2.2% and DFRR of 4.2%. The DFAR and DFRR increased from 5.68% and 4.23% to 15.03% and 14.62% respectively when feature level fusion-based design with classifier k-NN was tested under the inter-phase environment on a dataset of 12 users.\",\n \"Title: Self-Healing Neural Model for Stabilization Against Failures Over Networked UAVs\\nYear: 2015\\n\\nAbstract: Unmanned aerial vehicles (UAVs) allow formation of wide range ad hoc networks. These ad hoc formations with unmanned vehicles provide coverage of vast areas of applications involving mission dependent activities. Such networks can solve various issues related to civilian and military activities. One of the main applications of these networks is continuous surveillance. Surveillance by multiple nodes in ad hoc mode is directly dependent upon the continuous data sharing, cooperative decision making and stabilized network formation. Failures in network can hinder the performance and can decrease its operability. It is difficult to aloof network from discrete failures. Therefore, stabilized model is required which can provide stability to the whole network. For this, a self-healing neural model is developed which is capable of handling uncertain failures. It also provides provision for recovery of nodes from failure to stabilized state.\",\n \"Title: Binary Fireworks Algorithm Based Thermal Unit Commitment\\nYear: 2015\\n\\nAbstract: This paper presents the first application of fireworks algorithm to solve thermal unit commitment and scheduling problem. The scheduling problem accompanied by many constraints i.e., equality constraints like load balance and inequality constraints like system reserve and bounds like power generation, up/down time and ramp rate limits, finally shapes into a complex optimization problem. In this work, the scheduling and commitment problem is solved using binary fireworks algorithm BFWA, which mimics explosion of fireworks in the sky to define search space and distance between associated sparks to evaluate global minimum. Further, the effectiveness of fireworks pertaining to problem dimension, wide range of generation units from 10 to 100 are considered and evaluated. In addition, simulations results are compared to the existing optimization techniques in literature used for unit commitment and scheduling problem and it is observed that, BFWA is superior to some of the profound existing algorithms in achieving near optimal scheduling.\",\n \"Title: An empirical evaluation of a three‐tier conduit framework for multifaceted test case classification and selection using fuzzy‐ant colony optimisation approach\\nYear: 2015\\n\\nAbstract: The test case optimisation is an NP-complete, knowledge-driven, data-driven, and multidimensional search space partitioning and dimension reduction problem. In the multifaceted test case classification, partitioning and reducing the multidimensional test case fitness search space is the critical problem. The vague nature of fitness parameters, conflicting nature objectives, and ambiguity in the test case fitness evaluation have created and increased the uncertainty, the imprecision, and the incompleteness in the test case classification and selection. Because of the increasing ambiguity, the complexity, and the cost of software testing, automated test case classification and selection has emerged as an appropriate tool to classify test cases into predefined categories using the multifaceted concept. Most of the test cases affecting the performance of the classifier are irrelevant and redundant. A strong need therefore exists to devise an intelligent technique to identify and remove test cases affecting the performance of the classifier. For increasing the performance of classifier, multifaceted test case selection is used to reduce fitness search space to be searched. In this paper, a three-tier sequential framework is proposed for a multifaceted test case classification and selection. The first stage of the proposed framework is the fuzzy synthesis-based filtration approach for multifaceted test case fitness evaluation and classification. The second stage of the proposed framework is the fuzzy entropy-based filtration technique with a backward search strategy, used for estimating and reducing the ambiguity in test case fitness evaluation, classification, and selection. The third stage of the proposed framework is the ant colony optimisation-based wrapper technique with a forward search strategy, employed to select test cases from the output (reduced) test suite by the second stage. The proposed framework is tested on artefacts of benchmark applications. The results of the empirical study clearly show that the third stage of our proposed method outperforms the second and first stages, and the performance of the algorithms used in all three stages increases on average as the stages are escalating. The classification accuracy is enhanced by reducing the ambiguity in fitness and the classification of test cases, increasing the number of test cases accurately classified, and reducing the number in the test case pool to be exercised. Copyright (c) 2014 John Wiley & Sons, Ltd.\",\n \"Title: A 45 nm CMOS-SOI Monolithic Photonics Platform With Bit-Statistics-Based Resonant Microring Thermal Tuning.\\nYear: 2016\\n\\nAbstract: The microring resonator is critical for dense wavelength division multiplexed (DWDM) chip-to-chip optical I/O, enabling modulation and channel selection at the μm-scale suitable for a VLSI chip. Microring-based links, however, require active tuning to counteract process and thermo-optic variations. Here, we present a bit-statistical tuner that decouples tracking of optical one and zero-levels to r...\",\n \"Title: Quantitative Attack Tree Analysis via Priced Timed Automata\\nYear: 2015\\n\\nAbstract: The success of a security attack crucially depends on the resources available to an attacker: time, budget, skill level, and risk appetite. Insight in these dependencies and the most vulnerable system parts is key to providing effective counter measures. This paper considers attack trees, one of the most prominent security formalisms for threat analysis. We provide an effective way to compute the resources needed for a successful attack, as well as the associated attack paths. These paths provide the optimal ways, from the perspective of the attacker, to attack the system, and provide a ranking of the most vulnerable system parts. By exploiting the priced timed automaton model checker Uppaal CORA, we realize important advantages over earlier attack tree analysis methods: we can handle more complex gates, temporal dependencies between attack steps, shared subtrees, and realistic, multi-parametric cost structures. Furthermore, due to its compositionality, our approach is flexible and easy to extend. We illustrate our approach with several standard case studies from the literature, showing that our method agrees with existing analyses of these cases, and can incorporate additional data, leading to more informative results.\",\n \"Title: A Novel Heterogeneous Framework for Local Dependency Dynamic Programming Problems\\nYear: 2015\\n\\nAbstract: Dynamic programming is a well known technique to solve combinatorial search and optimization problems. LDDP (Local Dependency DP) problems are those DP problems where each update to an entry in the DP table is determined by the contents of its neigh boring cells. The amount of parallelism available for a given LDDP problem may vary depending on the position and number of neigh boring cells involved in the computation. In this paper, we develop a parallel heterogeneous (CPU+GPU) framework for solving LDDP problems. We use GPU and CPU specific optimizations and investigate if the given problem is a good candidate for heterogeneous computation. We also devise techniques to distribute the workload between CPU and GPU. We show how our framework can be used by considering several case study problems.\",\n \"Title: Treadmill attack on gait-based authentication systems\\nYear: 2015\\n\\nAbstract: In this paper, we demonstrate that gait patterns of an individual captured through a smartphone accelerometer can be imitated with the support of a digital treadmill. Furthermore, we design an attack for a baseline gait based authentication system (GBAS) and rigorously test its performance over an eighteen user data-set. By employing only two imitators and using a simple digital treadmill with speed control functionality, the attack increases the average false acceptance rate (FAR) from 5.8% to 43.66% for random forest, the best performing classifier in our experiments. More specifically, the FAR of eleven out of eighteen users increased to 70% or more. Our results call for a revisit of the design of the GBAS to make them resilient to such attacks.\",\n \"Title: Observations From Study Of Pre-Islanding Behaviour In A Solar Pv System Connected To A Distribution Network\\nYear: 2015\\n\\nAbstract: Unintentional islanding is a more relevant issue today due to rising penetration of solar photo voltaic systems on distribution grids. As the smart grid matures, better techniques for mitigating this problem have to evolve. Commercially available, inverter-resident, islanding detection based methods are aplenty and a lot others are being developed at research level. This work is oriented towards trying to understand the distributed-generation system dynamics that build up to the island forming situation. It is felt that inverter dynamics along with power system transitory events can provide a clue to an impending unintentional islanding event. Approaches resulting from such a study can train the system with predictive intelligence with regards to the possibility of an unwanted islanding condition. The study also results into interesting observations regarding the impact of capacitive reactive power present on the network, in excess of load requirement, on the grid-side current in the load-containing phase of the islandable feeder section. Arcing current inside the island and distortion in this current before islanding are the effects that are recorded.\",\n \"Title: A 45nm SOI monolithic photonics chip-to-chip link with bit-statistics-based resonant microring thermal tuning\\nYear: 2015\\n\\nAbstract: A new thermal tuning circuit for optical ring modulators enables demonstration of an optical chip-to-chip link for the first time with monolithically integrated photonic devices in a commercial 45nm SOI process, without any process changes. The tuning circuit uses independent 1/0 level-tracking and 1/0 bit counting to remain resilient against laser self-heating transients caused by non-DC-balanced transmit data. A 30fJ/bit transmitter and 374fJ/bit receiver with 6 mu A(pk-pk) photocurrent sensitivity complete the 5Gb/s link. The thermal tuner consumes 275fJ/bit and achieves a 600 GHz tuning range with a heater tuning efficiency of 3.8 mu W/GHz.\",\n \"Title: Energy Efficient Data Dissemination in Multi-UAV Coordinated Wireless Sensor Networks.\\nYear: 2016\\n\\nAbstract: With the requirement of better connectivity and enhanced coverage, collaborative networks are gaining a lot of popularity these days. One of such collaborative networks is formed between the wireless sensor networks (WSNs) and the unmanned aerial vehicles (UAVs). WSNs comprise static nodes arranged in a flat grid topology which may be randomly deployed or by some particular distribution. With the sensors operating on batteries, WSNs face a crucial issue of energy depletion during network operations. Integration of WSNs with UAVs can provide a solution to this excessive utilization of energy resources. UAVs provide a maneuvering support by playing a pivotal role of a manager node in these networks. However, integration of these networks demands an improved approach for data dissemination for effective utilization of network resources. For this, we propose a new data dissemination approach, which utilizes the attraction properties of fire fly optimization algorithm to provide energy efficient relaying. The proposed approach provides continuous connectivity, better lifetime, and improved coverage in the UAV coordinated WSNs. The performance of the proposed model is presented in terms of significant gains attained for parameters, namely, throughput, coverage, mean hops, lifetime, and delays, in comparison with the EEGA, ERIDSR, and I-ERIDSR approaches.\",\n \"Title: Multivariate Statistics and Supervised Learning for Predictive Detection of Unintentional Islanding in Grid-Tied Solar PV Systems.\\nYear: 2016\\n\\nAbstract: Integration of solar photovoltaic PV generation with power distribution networks leads to many operational challenges and complexities. Unintentional islanding is one of them which is of rising concern given the steady increase in grid-connected PV power. This paper builds up on an exploratory study of unintentional islanding on a modeled radial feeder having large PV penetration. Dynamic simulations, also run in real time, resulted in exploration of unique potential causes of creation of accidental islands. The resulting voltage and current data underwent dimensionality reduction using principal component analysis PCA which formed the basis for the application of Q statistic control charts for detecting the anomalous currents that could island the system. For reducing the false alarm rate of anomaly detection, Kullback-Leibler K-L divergence was applied on the principal component projections which concluded that Q statistic based approach alone is not reliable for detection of the symptoms liable to cause unintentional islanding. The obtained data was labeled and a K-nearest neighbor K-NN binomial classifier was then trained for identification and classification of potential islanding precursors from other power system transients. The three-phase short-circuit fault case was successfully identified as statistically different from islanding symptoms.\",\n \"Title: Ageist Spider Monkey Optimization algorithm.\\nYear: 2016\\n\\nAbstract: Swarm Intelligence (SI) is quite popular in the field of numerical optimization and has enormous scope for research. A number of algorithms based on decentralized and self-organized swarm behavior of natural as well as artificial systems have been proposed and developed in last few years. Spider Monkey Optimization (SMO) algorithm, inspired by the intelligent behavior of spider monkeys, is one such recently proposed algorithm. The algorithm along with some of its variants has proved to be very successful and efficient.\",\n \"Title: Toward Robotic Robbery on the Touch Screen.\\nYear: 2016\\n\\nAbstract: Despite the tremendous amount of research fronting the use of touch gestures as a mechanism of continuous authentication on smart phones, very little research has been conducted to evaluate how these systems could behave if attacked by sophisticated adversaries. In this article, we present two Lego-driven robotic attacks on touch-based authentication: a population statistics--driven attack and a user-tailored attack. The population statistics--driven attack is based on patterns gleaned from a large population of users, whereas the user-tailored attack is launched based on samples stolen from the victim. Both attacks are launched by a Lego robot that is trained on how to swipe on the touch screen. Using seven verification algorithms and a large dataset of users, we show that the attacks cause the system’s mean false acceptance rate (FAR) to increase by up to fivefold relative to the mean FAR seen under the standard zero-effort impostor attack. The article demonstrates the threat that robots pose to touch-based authentication and provides compelling evidence as to why the zero-effort attack should cease to be used as the benchmark for touch-based authentication systems.\",\n \"Title: UAV-Assisted Heterogeneous Networks for Capacity Enhancement.\\nYear: 2016\\n\\nAbstract: Modern day wireless networks have tremendously evolved driven by a sharp increase in user demands, continuously requesting more data and services. This puts significant strain on infrastructure-based macro cellular networks due to the inefficiency in handling these traffic demands, cost effectively. A viable solution is the use of unmanned aerial vehicles (UAVs) as intermediate aerial nodes betwee...\",\n \"Title: Forensic Authentication of Bank Checks.\\nYear: 2016\\n\\nAbstract: This chapter describes an automated methodology for the forensic authentication of bank checks. The problem of check authentication is modeled as a two-class pattern recognition problem. Color and texture features are extracted from images of genuine and counterfeit checks. A support vector machine is utilized to determine check authenticity. Classification experiments involving a dataset of 50 bank checks yielded a detection accuracy of 99.0%. The automated methodology can be used by non-specialist personnel to detect check counterfeiting in a banking environment where large numbers of checks are handled on a daily basis.\",\n \"Title: Using Attack-Defense Trees to Analyze Threats and Countermeasures in an ATM: A Case Study.\\nYear: 2016\\n\\nAbstract: Securing automated teller machines (ATMs), as critical and complex infrastructure, requires a precise understanding of the associated threats. This paper reports on the application of attack-defense trees to model and analyze the security of ATMs. We capture the most dangerous multi-stage attack scenarios applicable to ATM structures, and establish a practical experience report, where we reflect on the process of modeling ATM threats via attack-defense trees. In particular, we share our insights into the benefits and drawbacks of attack-defense tree modeling, as well as best practices and lessons learned.\",\n \"Title: Validation Of Industrial Cyber-Physical Systems: An Application To Hvac Systems\\nYear: 2016\\n\\nAbstract: We describe a validation approach for Simulink models of industrial cyber-physical systems (CPS), based on an adaptation of a coverage-guided test generation method for hybrid systems. Modelling an industrial CPS requires integrating heterogeneous components, which introduces high complexity in model verification. Using Simulink, which has become a de-facto industrial tool, heterogeneity comes from combining different formalisms (Simulink blocks, Stateflow diagrams, Matlab and C functions, etc.) and mixing different types of dynamics (discrete, continuous). Since the interactions between such components are often too complex to be faithfully captured in an existing mathematical modelling paradigm, we resort to treating them as black box systems while trying to exploit asmuch as possible a-priori knowledge about them. We first describe our approach: extracting from a Simulink model the information to define the main ingredients of the test generation framework, in particular environment inputs in which faults could be injected and critical states that require good coverage. We then illustrate the approach with an industrial model of an HVAC (Heating, Ventilation and Air Conditioning) system.\",\n \"Title: Continuous authentication of smartphone users by fusing typing, swiping, and phone movement patterns\\nYear: 2016\\n\\nAbstract: We studied the fusion of three biometric authentication modalities, namely, swiping gestures, typing patterns and the phone movement patterns observed during typing or swiping. A web browser was customized to collect the data generated from the aforementioned modalities over four to seven days in an unconstrained environment. Several features were extracted by using sliding window mechanism for each modality and analyzed by using information gain, correlation, and symmetric uncertainty. Finally, five features from windows of continuous swipes, thirty features from windows of continuously typed letters, and nine features from corresponding phone movement patterns while swiping/typing were used to build the authentication system. We evaluated the performance of each modality and their fusion over a dataset of 28 users. The feature-level fusion of swiping and the corresponding phone movement patterns achieved an authentication accuracy of 93.33%, whereas, the score-level fusion of typing behaviors and the corresponding phone movement patterns achieved an authentication accuracy of 89.31 %.\",\n \"Title: A fourth order PDE based fuzzy c- means approach for segmentation of microscopic biopsy images in presence of Poisson noise for cancer detection.\\nYear: 2017\\n\\nAbstract: •Presents a method for segmentation of microscopic biopsy images for cancer detection.•The proposed method can segment the cells and nuclei of the image in presence of Poisson noise.•The comparative analysis is presented with various standard methods in literature on various data sets.•The proposed method is working well and of use to the Histopathologist for second opinion.\",\n \"Title: Intelligent Resource Inquisition Framework on Internet-of-Things.\\nYear: 2017\\n\\nAbstract: •Develop an Intelligent Resource inquisition Framework on Internet-of-Things (IRIF-IoT).•Propose an optimized SMEBG algorithm for resource discovery.•Demonstrate its validity on an application, named Electronic Toll Collection (ETC).•The algorithm minimizes traffic congestion and enhances system performance significantly.\",\n \"Title: Lyapunov stability-based control and identification of nonlinear dynamical systems using adaptive dynamic programming.\\nYear: 2017\\n\\nAbstract: This paper presents a novel control and identification scheme based on adaptive dynamic programming for nonlinear dynamical systems. The aim of control in this paper is to make output of the plant to follow the desired reference trajectory. The dynamics of plants are assumed to be unknown, and to tackle the problem of unknown plant's dynamics, parameter variations and disturbance signal effects, a separate neural network-based identification model is set up which will work in parallel to the plant and the control scheme. Weights update equations of all neural networks present in the proposed scheme are derived using both gradient descent (GD) and Lyapunov stability (LS) criterion methods. Stability proof of LS-based algorithm is also given. Weight update equations derived using LS criterion ensure the global stability of the system, whereas those obtained through GD principle do not. Further, adaptive learning rate is employed in weight update equation instead of constant one in order to have fast learning of weight vectors. Also, LS- and GD-based weight update equations are also tested against parameter variation and disturbance signal. Three nonlinear dynamical systems (of different complexity) including the forced rigid pendulum trajectory control are used in this paper on which the proposed scheme is applied. The results obtained with LS method are found more accurate than those obtained with the GD-based method.\",\n \"Title: Modeling and adaptive control of nonlinear dynamical systems using radial basis function network.\\nYear: 2017\\n\\nAbstract: In this paper, the use of radial basis function network (RBFN) for simultaneous online identification and indirect adaptive control of nonlinear dynamical systems is demonstrated. The motivation of using RBFN comes from the simplicity of its structure and simpler mathematical formulation, which gives it an advantage over multi-layer feed-forward neural network (MLFFNN). Since most processes are nonlinear, the use of conventional proportional-integral-derivative controller is not useful. Most of the time plantu0027s dynamics information is not available. This creates another limitation on the use of conventional control techniques, which works only if plantu0027s dynamics information is available. The proposed controller is tested for parameter variations and disturbance effects. Simulation results showed that RBFN is able to capture the unknown dynamics as well as simultaneously able to adaptively control the plant. It is also found to compensate the effects of parameter variations and disturbances. The comparative analysis is also done with MLFFNN in each simulation example, and it is found that performance of RBFN is better than that of MLFFNN.\",\n \"Title: Multi-objective two-level swarm intelligence approach for multiple RNA sequence-structure alignment.\\nYear: 2017\\n\\nAbstract: This paper proposes a novel two-level particle swarm optimization algorithm for multi-objective optimization (MO-TLPSO) employed to a challenging problem of bioinformatics i.e. RNA sequence-structure alignment. Level one of the proposed approach optimizes the dimension of each swarm which is sequence length for the addressed problem, whereas level two optimizes the particle positions and then evaluates both the conflicting objectives. The conflicting objectives of the addressed problem are obtaining optimal multiple sequence alignment as well as optimal secondary structure. Optimal secondary structure is obtained by TL-PSOfold, the structure is further used for computing the contribution of base pairing of individual sequence and the co-variation between aligned positions of sequences so as to make the structure closer to the natural one. The results are tested against the popular softwares for pairwise and multiple alignment at BRAlibase benchmark datasets. Proposed work is so far the first multi-objective optimization based approach for structural alignment of multiple RNA sequences without converting the problem into single objective. Also, it is the first swarm intelligence based approach that addresses sequence-structure alignment issue of RNA sequences. Simulation results are compared with the state-of-the-art and competitive approaches. MO-TLPSO is found well competent in producing pairwise as well as multiple sequence-structure alignment of RNA. The claim is supported by performing statistical significance testing using one way ANOVA followed by Bonferroni post-hoc analysis for both kind of alignments.\",\n \"Title: Termite spatial correlation based particle swarm optimization for unconstrained optimization.\\nYear: 2017\\n\\nAbstract: In last few years, swarm intelligence has become the mainstay in the field of continuous optimization with many researchers developing algorithms simulating swarm behavior for the purpose of numerical optimization. This work proposes a new Termite Spatial Correlation based Particle Swarm Optimization (TSC-PSO) algorithm inspired by the movement strategy shown within Termites (Cornitermes cumulans). TSC-PSO modifies the velocity equation in the original PSO algorithm by replicating the step correlation based termite motion mechanism that exhibits individually in nature and works with decentralized control to collectively perform the overall task. Further, the algorithm incorporates the mutation strategy within it to make it suitable to avoid stagnation conditions while performing optimization in complex search spaces. For deriving its utility various benchmark functions of different geometric properties have been used. Experiments clearly demonstrate the success of the proposed algorithm in different benchmark conditions against various state-of-the-art optimization algorithms.\",\n \"Title: G-FANET: an ambient network formation between ground and flying ad hoc networks.\\nYear: 2017\\n\\nAbstract: Networking with aerial vehicles has evolved considerably over a period of time. Its applications range across a wide spectrum covering areas of military and civilian activities. Connectivity between aerial vehicles in ad hoc mode allows formation of multiple control units in the sky which have an ability to handle complex tasks. One of the major applications of these aerial vehicles is to coordinate simultaneously with another ad hoc network operating on the ground. This formation is termed as cooperative ad hoc networking. These networks operate on multiple data-sharing in form of cognitive maps. Thus, an efficient traffic management strategy is required to form a robust network. In this paper, an ambient network framework for coordination between ground and flying ad hoc network is presented. A fault-tolerant and robust connectivity strategy is proposed using neural, fuzzy and genetic modules. quaternion Kalman filter and its variant $$\\\\\\\\alpha -\\\\\\\\beta -\\\\\\\\gamma $$¿-ß-¿ filter is used to form the neural and decision system for guided aerial network. Effectiveness of the proposed traffic management framework for aerial vehicles is presented using mathematical simulations.\",\n \"Title: Cooperative frameworks and network models for flying ad hoc networks: a survey.\\nYear: 2017\\n\\nAbstract: SummaryIntegrated frameworks have extended the applications of networks beyond a simple data sharing unit. Simultaneously, operating networks can form a layered structure that can operate as homogeneous as well as dissociated units. Networks using unmanned aerial vehicles follow similar criteria in their operability. Unmanned aerial vehicles can act as single searching unit controlled by human or can form an aerial swarm that can fly autonomously with the capability of forming an aerial network. Such aerial swarms are categorized as aerial ad hoc networks. Cooperation amongst different networks can be realized using various frameworks, models, architectures and middlewares. Several solutions have been developed that can provide easy network deployment of aerial nodes. However, a combined literature is not present that provides a comparison between these approaches. Keeping this in view, various cooperative approaches for similar formation using aerial vehicles have been discussed in this paper. The detailed study and comparative analysis of these approaches have been included. Further, the paper also includes various software solutions and their comparisons based on common parameters. Finally, various open issues have been discussed that can provide insight of ongoing research and problems that are yet to be resolved in these networks. Copyright © 2016 John Wiley u0026 Sons, Ltd.\",\n \"Title: SERVmegh: framework for green cloud.\\nYear: 2017\\n\\nAbstract: With the growing popularity of cloud computing, different infrastructure as a service cloud frameworks do exists. Each framework has significant impact on robustness, scalability, fault-tolerance, energy efficiency, and so on of the cloud. To bring good characteristics of open source clouds and commercial clouds under a roof, a six layered green cloud framework namely SERVmegh is proposed. Resource management, green power management, workload analyzer and manager, and on/off control components of SERVmegh framework are designed and developed. Energy-efficient resource wastage reduction methodology for resource management is proposed. Algorithms for resource management, virtual machine placement, and minimization of virtual machine migrations using resource wastage reduction methodology are implemented. The performance of proposed algorithms is evaluated in CloudSim simulation and OpenNubula open source cloud environment. The results depict significant energy savings under different scenarios. Copyright (C) 2016 John Wiley & Sons, Ltd.\",\n \"Title: Strategies for replica consistency in data grid - a comprehensive survey.\\nYear: 2017\\n\\nAbstract: Data grid provides an efficient solution for data-oriented applications that need to manage and process large data sets located at geographically distributed storage resources. Data grid relies on data replicas to enhance the performance and to ensure the fault tolerant results to the users. Replicas are developed to increase the availability of data and to provide better data access. Replicas have their own advantages, but there are a number of issues that must be resolved. Among various existing issues, the critical concern is replica consistency. Various replica consistency strategies are available in the literature. These strategies rationalize and investigate various parameters like bandwidth consumption, access cost, scalability, execution time, storage consumption, staleness, and freshness of replicas. In this paper, several asynchronous replica consistencies are classified and analyzed based on various strategies such as topology, level of abstraction, update propagation, and locality. Some other strategies are also discussed and analyzed like adaptive consistency, quorum-based consistency, load balancing, and agent-based economically efficient, check-pointing, fault tolerance, and conflict management. Parameters on which these strategies are analyzed are methodology, replication classification, consistency, grid topology, environment, evaluation parameters, and performance. Copyright (C) 2016 John Wiley & Sons, Ltd.\",\n \"Title: Estimation-Based Queue Scheduling Model to Improve QoS for End Users in MANETs.\\nYear: 2016\\n\\nAbstract: Using MANETs for real time applications is always a challenge as the network is extremely dynamic with brisk topology changes. Despite this, several real time schedulers have been developed that aimed at providing QoS to ad hoc nodes. The quality of service (QoS) is standardized in terms of capacity, reliability, link quality, delays/jitters, and network cost. Thus, for QoS, the better transmission should be maintained at end user as well as at the transmitting unit. QoS of a network is affected by delays and bandwidth allocated for transmission. For an efficient network, it is required to predict these metrics during transmission. For this, in this paper, integration of quaternion-based Kalman filter is performed that predicts the required bandwidth and the network delays with higher accuracy. From the analysis, it is shown that bandwidth can be optimized but it is not possible to aloof delays in the network. Thus, while implementing such admission control procedures, estimation process allows control over delays and sustain them from going beyond a certain threshold value. The model proposed is a novel approach and has not been formulated in any of previous work related to QoS in MANETs. The effectiveness of model is demonstrated using both simulation and real time results.\",\n \"Title: Efficient Two-Level Swarm Intelligence Approach for Multiple Sequence Alignment.\\nYear: 2016\\n\\nAbstract: This paper proposes two-level particle swarm optimization (TL-PSO), an efficient PSO variant that addresses two levels of optimization problem. Level one works on optimizing dimension for entire swarm, whereas level two works for optimizing each particle's position. The issue addressed here is one of the most challenging multiple sequence alignment (MSA) problem. TL-PSO deals with the arduous task of determination of exact sequence length with most suitable gap positions in MSA. The two levels considered here are: to obtain optimal sequence length in level one and to attain optimum gap positions for maximal alignment score in level two. The performance of TL-PSO has been assessed through a comparative study with two kinds of benchmark dataset of DNA and RNA. The efficiency of the proposed approach is evaluated with four popular scoring schemes at spe-cific parameters. TL-PSO alignments are compared with four PSO variants, i.e. S-PSO, M-PSO, ED-MPSO and CPSO-Sk, and two leading alignment software, i.e. ClustalW and T-Coffee, at different alignment scores. Hence obtained results prove the competence of TL-PSO at accuracy aspects and conclude better score scheme.\",\n \"Title: Efficient cooperative relaying in flying ad hoc networks using fuzzy-bee colony optimization.\\nYear: 2017\\n\\nAbstract: Coordination among multiple ad hoc systems can extend the applications of the networks operating without any requirement of centralized devices. One of the coordination systems is formed by the collaboration of aerial ad hoc network and ground ad hoc network that performs cognitive coordination to form an effective guidance system despite operating with different configurations and dynamics. For guidance system, unmanned aerial vehicles form a cognitive map and relay them effectively to the intended node which is a part of ground ad hoc unit. The data sharing is difficult between the networks operating under different circumstances and configurations. An efficient approach is required to provide low-complex relaying for faster communications in these coordinated networks. One of the solutions can be found from the fuzzy formation of the problem that can be applied to some optimization algorithms to efficiently select the cognitive corridors intended for enhanced transmission. In this paper, a fuzzy-based bee colony optimization algorithm is proposed which operates over cooperative trust value, cognitive relaying value and situational awareness to provide efficient cognitive relaying between the two ad hoc units, and uses a cloud server to provide coordination between the nodes. The effectiveness of the proposed scheme and its applicability is tested using simulations.\",\n \"Title: Driver behaviour detection and vehicle rating using multi-UAV coordinated vehicular networks.\\nYear: 2017\\n\\nAbstract: Road accidents account for more than 2% of the total deaths across the globe with more than a million deaths each year due to road mishaps and improper traffic management. Traffic management is a major problem faced by modern cities due to a large number of vehicles operating at the same time. One of the major issues for road mishaps is driver's behaviour and skills. Thus, tracking vehicles and analyzing the driver behaviour is required for proper regulation of traffic. Some of the solutions are provided using infrastructure-based vehicular ad hoc networks (VANETs). However, their operations are constrained by the traffic itself, thus, limiting their scope. A new solution by forming VANETs using multiple unmanned aerial vehicles (UAVs) is proposed which is independent of using road side units (RSUs) for communications. The proposed approach is analyzed using simulations as well as real-time dataset. UAVs-coordinated VANETs is first proposed in this paper.Next, multi-UAVs coordinated behaviour analysis approach is presented.The approach can be scaled to n number of parameters based on the requirement.The proposed approach is analyzed using both simulative and real time data sets.This approach helps in lowering the number of accidents because of naive drivers.\",\n \"Title: Quantitative Security and Safety Analysis with Attack-Fault Trees\\nYear: 2017\\n\\nAbstract: Cyber physical systems, like power plants, medical devices and data centers have to meet high standards, both in terms of safety (i.e. absence of unintentional failures) and security(i.e. no disruptions due to malicious attacks). This paper presents attack fault trees (AFTs), a formalism thatmarries fault trees (safety) and attack trees (security). We equipAFTs with stochastic model checking techniques, enabling a rich plethora of qualitative and quantitative analyses. Qualitative metrics pinpoint to root causes of the system failure, while quantitative metrics concern the likelihood, cost, and impact of a disruption. Examples are: (1) the most likely attack path, (2) the most costly system failure, (3) the expected impact of an attack. Each of these metrics can be constrained, i.e., we can provide the most likely disruption within time t and/or budget B. Finally, we can use sensitivity analysis to find the attack step that has the most influence on a given metric. We demonstrate our approach through three realistic cases studies.\",\n \"Title: A Soft Computing Approach for Modeling of Nonlinear Dynamical Systems.\\nYear: 2016\\n\\nAbstract: A procedure based on the use of radial basis function network (RBFN) is presented for black box modeling of nonlinear dynamical systems. The generalization ability of RBFN is invoked to approximate the mathematical model of the given unknown nonlinear plant. This approximate model will then be used to predict the output of the plant at any given time instant. The parameters associated with RBFN are updated using the recursive equations obtained through the gradient-descent principle. The other benefit of using gradient descent principle is that it exhibits the clustering effect while adjusting the radial centers of RBFN. Real-time data of two benchmark problems: Box-Jenkins gas furnace data and Chemical process (polymer production), were used to show the application of RBFN for modeling purpose. Simulation results show that RBFN is well suited as a modeling tool for capturing the unknown nonlinear dynamics of the plant.\",\n \"Title: State-of-charge estimation for li-ion battery using extended Kalman filter (EKF) and central difference Kalman filter (CDKF).\\nYear: 2017\\n\\nAbstract: A precise estimation of the state-of-charge (SOC) is of major importance in battery electric vehicles (BEVs) for prolonging the lifetime of the battery. Firstly, an equivalent circuit using the first-order RC for describing the dynamic behavior of the battery has been developed. Parameters of the battery are identified using the Ageist Spider Monkey Optimization (ASMO) technique. The optimization method uses the anticipated terminal voltage of the battery during operation and error between the anticipated and measured voltage for identification of parameters. The focus of this paper is the implementation of recursive estimation of battery SOC using extended Kalman filter (EKF) and Central Difference Kalman Filter (CDKF) approach. The estimation has an absolute root-mean-square error (RMSE) of less than 4% and an absolute maximum error less than 6% in all circumstances. The test results indicate that CDKF has good performance compared to EKF for the estimation of battery SOC.\",\n \"Title: A review on remotely sensed land surface temperature anomaly as an earthquake precursor.\\nYear: 2017\\n\\nAbstract: •A systematic survey of recent advances in LST precursory approaches.•Evaluates LST anomaly as a probable marker of an earthquake.•Analyses the appearance of LST anomaly within various seismic strain fields.•Highlights the needed improvements for geostatistical robustness.\",\n \"Title: QUAT-DEM: Quaternion-DEMATEL based neural model for mutual coordination between UAVs.\\nYear: 2017\\n\\nAbstract: Networking between autonomously flying aerial vehicles requires efficient control and sustainable path for data transmission. Aerial nodes are extremely dynamic and provide a vast range of applications especially focusing on surveillance and data acquisition. However, these dynamic nodes are prone to unstable connectivity which hinders network operations, and also decreases the network lifetime. Such issues directly influence the control and relaying over aerial vehicles. Thus, it becomes essential to have an efficient strategy for controller selection and autonomous relaying. In this paper, a quaternion based neural model is proposed, which uses Decision Making Trial and Evaluation Laboratory Technique (DEMATEL) for the selection of network controllers and relays. The proposed algorithms are capable of selecting controller with a complexity O(n), and provide cooperative relaying with a complexity O(n × k). The effectiveness of the proposed model is demonstrated by using simulations. The results show that the proposed approach reduces delays by 25% during the selection of controller, and increases the transfer speed by 20% compared to existing approaches.\",\n \"Title: Continuous authentication using one-class classifiers and their fusion\\nYear: 2018\\n\\nAbstract: While developing continuous authentication systems (CAS), we generally assume that samples from both genuine and impostor classes are readily available. However, the assumption may not be true in certain circumstances. Therefore, we explore the possibility of implementing CAS using only genuine samples. Specifically, we investigate the usefulness of four one-class classifiers OCC (elliptic envelope, isolation forest, local outliers factor, and one-class support vector machines) and their fusion. The performance of these classifiers was evaluated on four distinct behavioral biometric datasets, and compared with eight multi-class classifiers (MCC). The results demonstrate that if we have sufficient training data from the genuine user the OCC, and their fusion can closely match the performance of the majority of MCC. Our findings encourage the research community to use OCC in order to build CAS as it does not require knowledge of impostor class during the enrollment process.\",\n \"Title: Demand side management with consumer clusters in cyber-physical smart distribution system considering price-based and reward-based scheduling programs.\\nYear: 2017\\n\\nAbstract: This study presents a demand side management (DSM) strategy for a cyber-physical smart distribution system (CPSDS). The proposed approach uses the price-based as well as reward-based DSM schemes as a part dual objective function. The objectives of the proposed scheduling approach comprise the profit maximisation objectives of demand response aggregator agent (DRAA) and network performance objectiv...\",\n \"Title: Performance Of Vertical Multi Junction Solar Cell For Long Distance Wireless Power Transfer Using High Intensity Laser Beam\\nYear: 2017\\n\\nAbstract: Wireless Power Transfer to long distance objects has been a much awaited task for researchers and scientists but old technologies like magnetic resonance coupling method were failed to provide any satisfactory result for long distance power transfer though it was good for small or very near distance power transfer. Far distant moving objects like space stations, lunar modules, rovers, unmanned aerial vehicles or drones largely suffer from lack of power providing mechanism resulting in short flight time of these vehicles. Major limitation is poor endurance for helicopters and quad-copters due to their very less efficient design and nature of vertical take-off and landing in comparison to those vehicle having fixed wings. Unlimited flight endurance can be achieved by using laser power beaming to recharge any target having optical tracking system done by beam riding method. Here wireless power transmission using a high intensity laser beaming (HILB) system by illuminating vertical multi-junction solar cell (VMJSC) has been discussed and its performance is studied and analyzed. Many of the HILB systems have been used to examine the performance of VMJSC under different types of conditions. Here many of the methods like optimal receiver geometry, parallel cell back-feeding, laser wavelength, thermal effects and non-uniform illumination at high intensities are studied and analyzed. Suggestions are made to further improve the system and to achieve sufficient power densities. Study shows that VMJSC mechanism can be used for high intensity laser beaming to power transfer without being damaged and retaining its efficiency.\",\n \"Title: Recent developments in human gait research: parameters, approaches, applications, machine learning techniques, datasets and challenges.\\nYear: 2018\\n\\nAbstract: Human gait provides a way of locomotion by combined efforts of the brain, nerves, and muscles. Conventionally, the human gait has been considered subjectively through visual observations but now with advanced technology, human gait analysis can be done objectively and empirically for the better quality of life. In this paper, the literature of the past survey on gait analysis has been discussed. This is followed by discussion on gait analysis methods. Vision-based human motion analysis has the potential to provide an inexpensive, non-obtrusive solution for the estimation of body poses. Data parameters for gait analysis have been discussed followed by preprocessing steps. Then the implemented machine learning techniques have been discussed in detail. The objective of this survey paper is to present a comprehensive analysis of contemporary gait analysis. This paper presents a framework (parameters, techniques, available database, machine learning techniques, etc.) for researchers in identifying the infertile areas of gait analysis. The authors expect that the overview presented in this paper will help advance the research in the field of gait analysis. Introduction to basic taxonomies of human gait is presented. Applications in clinical diagnosis, geriatric care, sports, biometrics, rehabilitation, and industrial area are summarized separately. Available machine learning techniques are also presented with available datasets for gait analysis. Future prospective in gait analysis are discussed in the end.\",\n \"Title: Binary Grey Wolf Optimizer for large scale unit commitment problem.\\nYear: 2018\\n\\nAbstract: The unit commitment problem belongs to the class of complex large scale, hard bound and constrained optimization problem involving operational planning of power system generation assets. This paper presents a heuristic binary approach to solve unit commitment problem (UC). The proposed approach applies Binary Grey Wolf Optimizer (BGWO) to determine the commitment schedule of UC problem. The grey wolf optimizer belongs to the class of bio-inspired heuristic optimization approaches and mimics the hierarchical and hunting principles of grey wolves. The binarization of GWO is owing to the UC problem characteristic binary/discrete search space. The binary string representation of BGWO is analogous to the commitment and de-committed status of thermal units constrained by minimum up/down times. Two models of Binary Grey Wolf Optimizer are presented to solve UC problem. The first approach includes upfront binarization of wolf update process towards the global best solution (s) followed by crossover operation. While, the second approach estimates continuous valued update of wolves towards global best solution(s) followed by sigmoid transformation. The Lambda-Iteration method to solve the convex economic load dispatch (ELD) problem. The constraint handling is carried out using the heuristic adjustment procedure. The BGWO models are experimented extensively using various well known illustrations from literature. In addition, the numerical experiments are also carried out for different circumstances of power system operation. The solution quality of BGWO are compared to existing classical as well as heuristic approaches to solve UC problem. The simulation results demonstrate the superior performance of BGWO in solving UC problem for small, medium and large scale systems successfully compared to other well established heuristic and binary approaches.\",\n \"Title: Multi agent system: concepts, platforms and applications in power systems.\\nYear: 2018\\n\\nAbstract: The power system is presently experiencing vital changes: it is advancing from a centralized structure to a decentralized one, primarily because of the enormous advancement of distributed renewable energy sources, so future power system obliges new control strategies. These systems must have the capacity to withstand new requirements, for example, the exceedingly disseminated nature, the irregularity of renewable energy sources and the restricted data transfer capacity for communications. Multi agent systems (MAS) have attributes that meet these prerequisites. A certain degree of distributed or collective intelligence can be accomplished through the connection of these agents with one another, participating or contending to achieve their objectives. This paper introduces outline of the fundamental ideas of MAS and its different platforms. Also, it provides a comprehensive survey on the power system applications in which MAS technique has been applied. For each power system application, technical details are also discussed.\",\n \"Title: Optimal Offering of Demand Response Aggregation Company in Price-Based Energy and Reserve Market Participation.\\nYear: 2018\\n\\nAbstract: This paper investigates the combined price-based scheduling/participation of generation company (GENCO) and demand response aggregation company (DRACO) in energy and reserve markets. The temporally coupled customer behavior can be better represented using the load profile attributes, when compared to the traditional approach with random willingness assignment. The proposed cost models for energy a...\",\n \"Title: Adaptive artificial intelligence for automatic identification of defect in the angular contact bearing.\\nYear: 2018\\n\\nAbstract: A reliable process making use of adaptive filtering, adaptive wavelet transform (AWT) and adaptive neuro-fuzzy inference system (ANFIS) is proposed for automatic identification of defect in the bearing. The process starts with denoising the signal using a filter designed from the noise part in the signal itself using the least mean square algorithm. Thereafter, AWT is applied to denoised signal to produce AWT scalogram. AWT scalogram clearly gives time location information corresponding to burst produced due to defect. For carrying out AWT, a wavelet is designed from the burst produced to defect using the least square fitting method. Then instantaneous energy (IE) of AWT scalogram is computed. For modeling of ANFIS, features are extracted from signal denoised by adaptive filtering, AWT scalogram and IE graph. The dimension of features is reduced by kernel principal component analysis. ANFIS modeling is carried out using low-dimensional kernel principal components. Finally, defect identification is carried out by applying test features kernel principal components to ANFIS. The proposed method has a recognition rate of 100%. Major benefit of the proposed method is that it requires very less training data and also does not require any optimization algorithm, usually required to find optimum parameters of a pattern recognition model. The method proposed in this work can fulfill the industrial requirement of having sophisticated and accurate automatic defect identification model.\",\n \"Title: Diagonal recurrent neural network based identification of nonlinear dynamical systems with Lyapunov stability based adaptive learning rates.\\nYear: 2018\\n\\nAbstract: This paper proposes a diagonal recurrent neural network (DRNN) based identification model for approximating the unknown dynamics of the nonlinear plants. The proposed model offers deeper memory and a simpler structure. Thereafter, we have developed a dynamic back-propagation learning algorithm for tuning the parameters of DRNN. Further, to guarantee the faster convergence and stability of the overall system, dynamic (adaptive) learning rates are developed in the sense of Lyapunov stability method. The proposed scheme is also compared with multi-layer feed forward neural network (MLFFNN) and radial basis function network (RBFN) based identification models. Numerical experiments reveal that DRNN has performed much better in approximating the dynamics of the plant and have also shown more robustness toward system uncertainties.\",\n \"Title: AntiTbPdb: a knowledgebase of anti-tubercular peptides.\\nYear: 2018\\n\\nAbstract: Tuberculosis is a global menace, caused by Mycobacterium tuberculosis, responsible for millions of premature deaths every year. In the era of drug-resistant tuberculosis, peptide-based therapeutics may provide alternate to small molecule based drugs. In order to create knowledgebase, AntiTbPdb (http://webs.iiitd.edu.in/raghava/antitbpdb/), experimentally validated anti-tubercular and anti-mycobacterial peptides were compiled from literature. We curate 10 652 research articles and 35 patents to extract antitubercular peptides and annotate these peptides manually. This knowledgebase has 1010 entries, each entry provides extensive information about an anti-tubercular peptide such as sequence, chemical modification, chirality, nature and source of origin. The tertiary structure of these anti-tubercular peptides containing natural as well as chemically modified residues was predicted using PEPstrMOD and I-TASSER. In addition to structural information, database maintains other properties of peptides like physiochemical properties. Numerous web-based tools have been integrated for data retrieval, browsing, sequence similarity search and peptide mapping. In order to assist wide range of user, we developed a responsive website suitable for smartphone, tablet and desktop.\",\n \"Title: Electric Vehicle Scheduling Strategy in Residential Demand Response Programs With Neighbor Connection.\\nYear: 2018\\n\\nAbstract: As smart grid enablers, demand response programs for household purposes have been in highlight recently. This paper presents a detailed structure of a household user, which is capable of energy transactions among consumer and load-serving entity. The proposed household model comprises several assets including nonshiftable appliances, shiftable appliances, and electric vehicle (EV). The EV can furt...\",\n \"Title: Three-tier neural model for service provisioning over collaborative flying ad hoc networks.\\nYear: 2018\\n\\nAbstract: Collaboration among different ad hoc networks allows the formation of efficient guided networks. One of the such collaborative networks is formed between aerial ad hoc network and terrestrial ad hoc network. Simultaneous operation of both the networks can resolve complex missions collaboratively. Such cooperation can be achieved by deploying service models that allow efficient integration of distributed networks. The performance of service model can be enhanced by incorporating neural schema that provides a layered interface and cognitive transfers with higher accuracy. In this paper, a 3-tier neural model is proposed that allows the formation of guided ad hoc network. A layered network model is formulated that provides service-based integration of different network components required for the functioning of aerial guided ad hoc network along with provision for efficient service provisioning over flying ad hoc networks. The effectiveness and utility of the proposed model are demonstrated using simulations. Simulation study demonstrates that the proposed approach yields significant improvements in terms of service identification accuracy up to 13.6 %, fault identification up to 17.7 %, cognitive receival up to 23.56 %, fault tolerance up to 13.5 %, network errors reduction up to 34.11 %, and overheads up to 10 % compared to existing approaches.\",\n \"Title: Offrp: Optimised Fruit Fly Based Routing Protocol With Congestion Control For Uavs Guided Ad Hoc Networks\\nYear: 2018\\n\\nAbstract: Unmanned aerial vehicles (UAVs) have gained a lot of attention from the researchers across the globe. Their autonomous capabilities provide vast range of applications such as disaster management, surveillance, border security and inter-civilian activities. UAV networks have a major issue regarding the data flow and routing. UAVs operate in different band as compared to the ground nodes. Hence, there is a chance of congestion that may fail the whole network. Prediction of such congestion states can provide continuous connectivity. For this, a new optimised fruit fly routing protocol (OFFRP) is proposed which uses the properties of existing fruit fly optimisation algorithm for finding appropriate routes. The search of food sources by fruit flies using their smell index forms the basis of the proposed protocol. The proposed protocol is evaluated using standard network simulations and is compared with all the major routing protocols as well as existing TCP-oriented congestion control algorithms.\",\n \"Title: Investigating the Impact of Load Profile Attributes on Demand Response Exchange.\\nYear: 2018\\n\\nAbstract: This paper investigates the impact of load profiling attributes on the demand response exchange (DRX) mechanism. Modeling of demand response (DR) seller bids in traditional approach/model includes a customer willingness factor assigned in a random/arbitrary manner. The proposed model of DR bids takes into account, the load attributes such as utilization and availability factors. The criticality an...\",\n \"Title: Solution to unit commitment in power system operation planning using binary coded modified moth flame optimization algorithm (BMMFOA): A flame selection based computational technique.\\nYear: 2018\\n\\nAbstract: •Modifications in flame selection approach in basic moth-flame approach are introduced.•Binary variants of basic and modified moth-flame approach are developed.•Application of proposed approaches to solve various test cases of unit commitment are simulated.•Comparative analysis with existing classical and heuristic approaches.•Statistical significance of proposed approaches is demonstrated.\",\n \"Title: Energy Aware Resource Allocation for Clouds Using Two Level Ant Colony Optimization.\\nYear: 2018\\n\\nAbstract: In cloud environment resources are dynamically allocated, adjusted, and deallocated. When to allocate and how many resources to allocate is a challenging task. Resources allocated optimally and at the right time not only improve the utilization of resources but also increase energy efficiency, provider's profit and customers' satisfaction. This paper presents ant colony optimization (ACO) based energy aware solution for resource allocation problem. The proposed energy aware resource allocation (EARA) methodology strives to optimize allocation of resources in order to improve energy efficiency of the cloud infrastructure while satisfying quality of service (QoS) requirements of the end users. Resources are allocated to jobs according to their QoS requirements. For energy efficient and QoS aware allocation of resources, EARA uses ACO at two levels. First level ACO allocates Virtual Machines (VMs) resources to jobs whereas second level ACO allocates Physical Machines (PMs) resources to VMs. Server consolidation and dynamic performance scaling of PMs are employed to conserve energy. The proposed methodology is implemented in CloudSim and the results are compared with existing popular resource allocation methods. Simulation results demonstrate that EARA achieves desired QoS and superior energy gains through better utilization of resources. EARA outperforms major existing resource allocation methods and achieves up to 10.56 % saving in energy consumption.\",\n \"Title: Computational Intelligence for Demand Response Exchange Considering Temporal Characteristics of Load Profile via Adaptive Fuzzy Inference System.\\nYear: 2018\\n\\nAbstract: This paper presents a computationally intelligent hybrid approach to incorporate the temporal characteristics of customer baseline load (CBL) in demand response exchange (DRX) mechanism using adaptive fuzzy inference system (FIS). The proposed hybrid approach considers the temporal characteristics of load profile using utilization factor, availability factor alongside the conventional/traditional ...\",\n \"Title: Dynamic trafile congestion detection in VANETS using a Fuzzy rule-based system and K-means clustering\\nYear: 2017\\n\\nAbstract: Vehicular traffic congestion poses a serious challenge to a green environment by contributing to air pollution, noise pollution, and unnecessary fuel consumption. This paper aims to meet one of the requirements of a smart city that detects traffic congestion in an area so that the traffic can be diverted on an alternate route in a vehicular adhoc network (VANET). VANET is a medium of wireless communication among vehicular commuters, which enables them to intimate their instantaneous physical characteristics such as speed, brake frequency, rain, fog, acceleration, and position to surrounding vehicles within a periphery so as to determine the level of congestion and find suitable ways to divert the traffic. The dynamic nature of the vehicular nodes makes the topology unpredictable, which is constantly monitored using a vehicular adhoc network (VANET). In this work, an integration of fuzzy inference rule based system (FRBS) and K-means clustering technique is explored to detect the traffic congestion under a dynamic traffic environment. FRBS is implemented through an Arduino Uno microcontroller. In this paper, four different physical vehicle attributes such as rain or fog, speed and brake frequency are considered for traffic congestion detection. This paper presents a detailed description regarding the co-ordination between vehicular units and a web server, which maintains a cloud database that preserves the data for future use.\",\n \"Title: Effective Analysis of Attack Trees: A Model-Driven Approach.\\nYear: 2018\\n\\nAbstract: Attack trees (ATs) are a popular formalism for security analysis, and numerous variations and tools have been developed around them. These were mostly developed independently, and offer little interoperability or ability to combine various AT features.\",\n \"Title: Online modeling and adaptive control of robotic manipulators using Gaussian radial basis function networks.\\nYear: 2018\\n\\nAbstract: Radial basis function network (RBFN) is used in this paper for predefined trajectory control of both one-link and two-link robotic manipulators. The updating equations for the RBFN parameters were derived using the gradient descent principle. The other advantage of using this principle is that it shows the clustering effect in distributing the radial centres. To increase the complexity, the dynamics of robotic manipulator is assumed to be unknown, and hence, simultaneous control and identification steps were performed using the RBFNs. The performance of the RBFN is compared with the multilayer feed-forward neural network (MLFFNN) in terms of mean square error, tolerance to disturbance and parameter variations in the system. The efficacy of RBFN as a controller and identification tool is verified by performing the simulation study, and the results obtained reveal the superior performance of RBFN over MLFFNN in both identification and control aspects for one-link and two-link robotic manipulators.\",\n \"Title: An agent-based architecture for production scheduling in dynamic job-shop manufacturing system.\\nYear: 2018\\n\\nAbstract: In a highly competitive environment, effective production is one of the key issues which can be addressed by efficient production planning and scheduling in the manufacturing system. This paper develops an agent-based architecture which enables integration of production planning and scheduling. In addition, this architecture will facilitate real time production scheduling as well as provide a multi-agent system (MAS) platform on which multiple agents will interact to each other. A case study of job-shop manufacturing system (JMS) has been considered in this paper for implementing the concept of MAS. The modeling of JMS has been created in Sim-Events which integrates an agent-based architecture developed by Stateflow to transform into dynamic JMS. Finally, the agent-based architecture is evaluated using utilization of each machine in the shop floor with respect to time.\",\n \"Title: LOCKS: a property specification language for security goals.\\nYear: 2018\\n\\nAbstract: We introduce a formal specification language LOCKS, that allow security practitioners to express as well as compose security goals in a convenient manner. LOCKS supports the specification of the most common security properties over generic attributes, both for qualitative and quantitative goals. To make our language independent of a specific security framework, we evaluate LOCKS over a generic attack model, namely the structural attack model (SAM), which over-arches the most prominent graphical threat models. Furthermore, we equip our language with a concise grammar, type rules and denotational semantics, thus laying the foundations of an automated tool. We take a number of informal security goals from the literature and show how they can be formally expressed in our language.\",\n \"Title: Multiaggregator Collaborative Electric Vehicle Charge Scheduling Under Variable Energy Purchase and EV Cancelation Events.\\nYear: 2018\\n\\nAbstract: With the growth of electric vehicles across the globe, it is essential to have a corresponding charging infrastructure developed. Several research works highlight the benefits of aggregator-based scheduling, where all aggregators operate in similar conditions with the same amount of purchased energy. However, there are unscheduled EVs left due to unavailability of sufficient energy and/or charging...\",\n \"Title: Intelligent Grey Wolf Optimizer - Development and application for strategic bidding in uniform price spot energy market.\\nYear: 2018\\n\\nAbstract: •A new variant of GWO, named as Intelligent Grey Wolf Optimizer (IGWO) is proposed.•The performance of IGWO is compared with other algorithms on benchmark functions.•A Monte Carlo Simulation based method is employed on two cost models based on normal probability distribution of rival bids to solve SBP.•The IGWO is applied to frame bidding strategy for a generating company in uniform price spot market.•The IGWO gives competitive results for single and multi-trading hours.\",\n \"Title: HMADSO: a novel hill Myna and desert Sparrow optimization algorithm for cooperative rendezvous and task allocation in FANETs.\\nYear: 2018\\n\\nAbstract: Cooperative task allocation and decision are important aspects of networks that involve heterogeneous nodes operating in ad hoc mode like flying ad hoc networks (FANETs). The task allocation can be either mission based or simple utilization of available resources. In networks, including mission critical resources, cooperative task allocation and rendezvous are the key factors that drive the mission as well as optimize the performance. Many optimization algorithms have been designed and developed which focus on the cooperative behavior of nodes and also handle resources efficiently. Cooperative allocation and rendezvous both can be achieved by taking an example from biological world. In this paper, a new hill Myna and desert Sparrow optimization algorithm, namely HMADSO, is proposed for cooperative rendezvous and efficient task allocation. The application and analysis of proposed algorithm are shown for FANETs. To validate the proposed HMADSO, onboard processors, as well as simulation-based analysis, are carried out. The libraries for the proposed approach are provided at http://bit.ly/HMADSOcode.\",\n \"Title: EQUAL: Energy and QoS Aware Resource Allocation Approach for Clouds.\\nYear: 2018\\n\\nAbstract: The popularity of cloud computing is increasing by leaps and bounds. To cope with resource demands of increasing number of cloud users, the cloud market players establish large sized data centers. The huge energy consumption by the data centers and liability of fulfilling Quality of Service (QoS) requirements of the end users have made resource allocation a challenging task. In this paper, energy and QoS aware resource allocation approach which employs Antlion optimization for allocation of resources to virtual machines (VMs) is proposed. It can operate in three modes, namely power aware, performance aware, and balanced mode. The proposed approach enhances energy efficiency of the cloud infrastructure by improving the utilization of resources while fulfilling QoS requirements of the end users. The proposed approach is implemented in CloudSim. The simulation results have shown improvement in QoS and energy efficiency of the cloud.\",\n \"Title: Optimized Clustering Techniques For Gait Profiling In Children With Cerebral Palsy For Rehabilitation\\nYear: 2018\\n\\nAbstract: Cerebral palsy (CP) is a neuro-development disease in children. It is quite an intricate task to categorize gait pattern into normal and CP based pathology. In this study, nature-inspired meta-heuristic algorithms are explored on a publicly available gait dataset of 156 subjects for automatic gait profiling of children with cerebral palsy. Five cases are considered to explore the feature selection criteria before applying clustering technique. Finding the optimal number of clusters is a challenging task in the unsupervised learning area. In this study, an optimal number of gait profiles in the datasets is identified based on voting from mean square error, silhouette coefficient and Dunn index. The study demonstrates that optimized based gait profile clusters could assist quantitatively in clinical rehabilitation evaluation for the children affected by CP.\",\n \"Title: Comparative study of neural networks for dynamic nonlinear systems identification.\\nYear: 2019\\n\\nAbstract: In this paper, a comparative study is performed to test the approximation ability of different neural network structures. It involves three neural networks multilayer feedforward neural network (MLFFNN), diagonal recurrent neural network (DRNN), and nonlinear autoregressive with exogenous inputs (NARX) neural network. Their robustness is also tested and compared when the system is subjected to parameter variations and disturbance signals. Further, dynamic back-propagation algorithm is used to update the parameters associated with these neural networks. Four dynamical systems of different complexities including motor-driven robotic link are considered on which the comparative study is performed. The simulation results show the superior performance of DRNN identification model over NARX and MLFFNN identification models.\",\n \"Title: An SDN-Based Secure Mobility Model for UAV-Ground Communications.\\nYear: 2017\\n\\nAbstract: Multi-UAV collaborative networks provide with the opportunity to exploit civil, chemical, biological, radiological, nuclear and geographical reconnaissance, survey, management, and control. For the collaborative network formation, coverage is of prime paramountcy. Alongside coverage, possession of information and communication security is withal a major challenge. The coverage quandary can be resolved by a perspicacious selection of UAV waypoints. But the security paradigm which can be an effect of faulty node, intrusion or even choice of erroneous communication channels needs to be taken care of through efficacious strategies. Consequently, both a specialized UAV mobility model and a security mechanism are required in order to establish prosperous collaborative networks. In this article, an SDN-based secure mobility model is proposed which takes into account the topological density and restricts the UAV and ground node (Wireless Sensor Networks (WSNs)) transmissions to authenticity. Significant gains are observed for throughput, coverage, and latency by establishing a simulated network between multiple UAVs and WSN motes.\",\n \"Title: β-Chaotic map enabled Grey Wolf Optimizer.\\nYear: 2019\\n\\nAbstract: The diversification (exploration) and intensification (exploitation) are two main attributes of any population-based metaheuristic algorithm. It is essential for any algorithm that in exploration phase the search space is utilized and explored properly through random behavior, on the other hand, the progression of the search in a viable direction to obtain global minima, should be performed through strategic behavior in exploitation phase. A proper balance between these two can be achieved by an adaptive mechanism in every algorithm. Robustness of an algorithm is judged by the efficacy of these two attributes along with the efficiency of the bridging mechanism. In literature, the positive impact of inculcation of chaotic sequences on the efficacy of these attributes has been reported. With this motivation, the paper presents an adaptive bridging mechanism based on β-chaotic sequence for the improvement of Grey Wolf Optimizer (GWO). The control vector of classical GWO is integrated with the β-chaotic sequence for better exploration and exploitation virtues. The new variant β-GWO is benchmarked on two benchmark suites 1 and 2 that include 12 shifted and biased functions and 29 Congress on Evolutionary Computation-2017 (CEC-2017) functions. Sensitivity Dependence of Initial Conditions (SDIC) is performed for tuning the initial parameters. The comparison of the proposed variant with other contemporary algorithms is carried out and different statistical tests are performed to judge the efficacy of the proposed variant. Further, the applicability of the proposed variant is checked with two real engineering problems namely frequency modulated sound waves parameter estimation problem and strategic bidding in the energy market. Results reveal that the proposed chaotic variant exhibits better exploration and exploitation qualities.\",\n \"Title: Prediction of Land-Surface Temperatures of Jaipur City Using Linear Time Series Model.\\nYear: 2016\\n\\nAbstract: All cities of the world have undergone rapid urbanization. Consequently, urban areas encounter higher surface and air temperatures than the surrounding nonurbanized areas and exhibit urban heat island (UHI) effect. Surface temperature derived from remote sensing data has been used for analyzing the UHI effect over a number of cities. This study has been carried out to predict the land-surface temp...\",\n \"Title: ccPDB 2.0: an updated version of datasets created and compiled from Protein Data Bank.\\nYear: 2018\\n\\nAbstract: ccPDB 2.0 (http://webs.iiitd.edu.in/raghava/ccpdb) is an updated version of the manually curated database ccPDB that maintains datasets required for developing methods to predict the structure and function of proteins. The number of datasets compiled from literature increased from 45 to 141 in ccPDB 2.0. Similarly, the number of protein structures used for creating datasets also increased from similar to 74 000 to similar to 137 000 (PDB March 2018 release). ccPDB 2.0 provides the same web services and flexible tools which were present in the previous version of the database. In the updated version, links of the number of methods developed in the past few years have also been incorporated. This updated resource is built on responsive templates which is compatible with smartphones (mobile, iPhone, iPad, tablets etc.) and large screen gadgets. In summary, ccPDB 2.0 is a user-friendly web-based platform that provides comprehensive as well as updated information about datasets.\",\n \"Title: Uavs Assisted Queue Scheduling In Ground Ad Hoc Networks\\nYear: 2019\\n\\nAbstract: Hybrid networks provide a vast range of applications in areas of military and civilian activities. One of the examples of hybrid network formations is unmanned aerial vehicles (UAVs) assisted ground ad hoc networks. Efficient coordination in these networks can resolve complex issues such as coverage, proper connectivity, scalability, and QoS. However, reliable data transmission with enhanced QoS is one of the key challenges in these types of hybrid ad hoc formations. In this paper, an efficient queue scheduling approach is proposed that allows improved QoS to end users. The proposed queue scheduling approach is developed in two parts. The first part utilises the Quaternion based Kalman Filter to find the appropriate locations for placement of each UAV. In the second part, satisfied importance analysis (SIA) is used to find the governing rules for the selection of appropriate queue for transmission. The effectiveness of the proposed model is demonstrated using simulations.\",\n \"Title: A Gray Level Feature Detector and Its Hardware Architecture\\nYear: 2008\\n\\nAbstract: This chapter describes a fast real time gray scale based feature point detector and its hardware architecture for FPGA based realization. The implementation is based on a new efficient technique which is fusion of both affine transformation\\n invariance and robustness to noise. The novelty of the proposed approach lies in its highly accurate localization and realization\\n only in terms of addition, subtraction and logic operations. The algorithm is designed to keep the high throughput requirements\\n of today's feature point detectors and applications in Silicon. The proposed implementation is highly modular with custom\\n scalability to fit devices like FPGAs etc, with different resource capacity. The implementation can be ported to any real time vision processing systems where\\n power and speed are of utmost concern.\",\n \"Title: Distributed Modular Computer-Integrated Surgical Robotic Systems: Implementation Using Modular Software and Networked Systems\\nYear: 2000\\n\\nAbstract: We seek to build CIS research systems within a flexible, open architecture. In this paper, we outline our solutions to the problems of system design, construction, and integration in this environment: building distributed, modular systems for sensing, control, and processing. Based on our experience building these systems, we utilize distributed network architectures, modular software components, and intelligent object distribution to maximize flexibility while complying with the particular needs and interfaces of specific components. While a work in progress, these approaches have been integrated into several systems in development and have demonstrated significant utility, based on construction time and system flexibility. This paper will discuss the first two aspects: modular software and basic network architectures, while intelligent object distribution is presented in a companion paper. Based on the applications we have targeted and technologies we have used, this paper introduces our architecture and its components. It then presents the system's current state and finally, discusses plans for future improvements and extensions.\",\n \"Title: Predictive Analytics for Breast Cancer Survivability - A Comparison of Five Predictive Models.\\nYear: 2016\\n\\nAbstract: Predicting the statistics of survival from the innumerable reports of breast cancer is a difficult job to perform, one that every researcher tackles in the field of medicals science. Since the birth stages of the related research, many new techniques have advanced. This point is evident from the groundbreaking biomedical technologies and discovery of better self-explanatory predictive factors. Due to supply of cheaper computer hardware and easy availability of software technologies, large amount of qualitative data is being stored automatically. The developed ways of analyzing huge data quantities have bestowed upon this field efficient and effective means of processing. Five renowned data mining algorithms (Naive Bayes classification, decision trees, IBK, support vector machine (using SMO) and OneR,) were used for our prediction models applied to a substantiate dataset (more than 683cases). The10-fold cross-validation method was also taken into due consideration to determine the prediction accuracy of the five prognostic designs in comparing their credibility. Through a sensitivity analysis of support vector machines, we were able to gauge the prioritized importance of predictive factors in this work.\",\n \"Title: Microscopic Biopsy Image Segmentation Using Hybrid Color K-Means Approach.\\nYear: 2017\\n\\nAbstract: The color image segmentation is a fundamental requirement for microscopic biopsy image analysis and disease detection. In this paper, a hybrid combination of color k-means and marker control watershed based segmentation approach is proposed to be applied for the segmentation of cell and nuclei of microscopic biopsy images. The proposed approach is tested on breast cancer microscopic data set with ROI segmented ground truth images. Finally, the results obtained from proposed framework are compared with the results of popular segmentation algorithms such as Fuzzy c-means, color k-means, texture based segmentation as well as adaptive thresholding approaches. The experimental analysis shows that the proposed approach is providing better results in terms of accuracy, sensitivity, specificity, FPR false positive rate, global consistency error GCE, probability random index PRI, and variance of information VOI as compared to other segmentation approaches.\",\n \"Title: Microprocessor Chip With Photonic I/O\\nYear: 2017\\n\\nAbstract: In this work, we provide an overview of the technology and architecture of a microprocessor chip with optical I/O. Zero-change photonics integration enabled the chip to be fabricated in a commercial electronics CMOS foundry.\",\n \"Title: Simple and Efficient Computational Intelligence Strategies for Effective Collaborative Decisions.\\nYear: 2019\\n\\nAbstract: We approach scalability and cold start problems of collaborative recommendation in this paper. An intelligent hybrid filtering framework that maximizes feature engineering and solves cold start problem for personalized recommendation based on deep learning is proposed in this paper. Present e-commerce sites mainly recommend pertinent items or products to a lot of users through personalized recommendation. Such personalization depends on large extent on scalable systems which strategically responds promptly to the request of the numerous users accessing the site (new users). Tensor Factorization (TF) provides scalable and accurate approach for collaborative filtering in such environments. In this paper, we propose a hybrid-based system to address scalability problems in such environments. We propose to use a multi-task approach which represent multiview data from users, according to their purchasing and rating history. We use a Deep Learning approach to map item and user inter-relationship to a low dimensional feature space where item-user resemblance and their preferred items is maximized. The evaluation results from real world datasets show that, our novel deep learning multitask tensor factorization (NeuralFil) analysis is computationally less expensive, scalable and addresses the cold-start problem through explicit multi-task approach for optimal recommendation decision making.\",\n \"Title: A dual objective approach for aggregator managed demand side management (DSM) in cloud based cyber physical smart distribution system\\nYear: 2020\\n\\nAbstract: This paper presents an incentive based framework for demand response scheduling in smart distribution networks with commercial, residential and industrial load sectors using designated cloud storage in cyber physical cloud based smart distribution system. The proposed approach consists of a two stage procedure where dual objective problem is modeled to achieve both responsive load agent (RLA) economic benefits as well as distribution system operator (DSO) preferences. The two stage coordinated scheduling of RLAs with DSO network operation preferences is carried out by demand response aggregation agent (DRAA) through incentives. The cloud based information exchange between different stakeholders provides the necessary communication at different stages of responsive load scheduling. The concept of private, shared and community clouds are incorporated into the cyber physical cloud based DAM framework proposed in this work. The first stage scheduling of responsive loads is carried out by RLAs with electricity payment minimization objective looking into the tariff structure. Whereas, the second stage scheduling is carried out by DRAA in which incentives are offered to RLAs for the deviation from personalized schedules of first stage. The customer characterization and behavior (risk averse, incentive preferences etc.) of scheduling the responsive loads is considered through incentive range based clusters. The proposed DRAA coordinated DR scheduling framework is simulated using IEEE 37 bus distribution test system with commercial, residential and industrial sectors. The impact of stage 1 and stage 2 scheduling of responsive loads is analyzed with respect to system performance indices such as total operational cost, load factor, total network losses etc. The sensitivity analysis is performed to examine the impact of weightage assignment to DSO and RLA objectives on system performance aspects. The proposed DRAA coordinated two stage framework has shown considerable improvement in techno-economic aspects of responsive load scheduling in distribution network that can be appended to real world applications at distribution level/retail markets.\",\n \"Title: Identification of potential islanding initiators in radial feeders with high solar PV penetration for preemptive preparedness\\nYear: 2017\\n\\nAbstract: Reverse power flow is one of the anticipated implications of integrating large amount of distributed solar photovoltaic power with distribution feeders. Such an occurrence can transform into anomalies that can potentially affect the integrity of the network. Unintentional islanding is one such issue that has not been comprehensively addressed, with the consensus of all the stakeholders of distribution networks. It still remains a cause of concern among utilities, arising out of large penetration of distributed solar power. In this work, an anomalous over-current spike, attributed to reverse power flow was discovered on a radial feeder modeled with emulator hardware. Load-inverter interaction alongside grid-end disturbances in a high penetration scenario resulted into the anomaly that was found to be an islanding initiator. This paper uses suitable machine learning models to detect such occurrences so as to preemptively prepare and respond to any imminent islanding condition. Results of offline testing on a dedicated Raspberry Pi microcomputer have been presented. This paper presents a preliminary analysis of the feasibility of such a framework to be carried forward for online testing on hardware-generated data.\",\n \"Title: Combination of Advanced Robotics and Computer Vision for Shelf Analytics in a Retail Store\\nYear: 2017\\n\\nAbstract: Large-scale retail store associates are constantly faced with the challenge of managing store operations smoothly and maintaining the products in full stock on the product support devices (retail shelves). Keeping track of the quantities of each individual Stock Keeping Unit (Retail Product), replenishing them when depleted, and identifying and replacing misplaced products are few tasks that require continuous monitoring and large amount of manual effort. The solution being presented here aims at automating the tasks performed by the store associates which result in reducing the manual effort. The solution proposes the use of a Double Robot to patrol the store over a fixed path and capture images of the retail shelves at real time. These images are processed using developed solution to address various retail store challenges such as stock out problems and misplaced products. An alert generating mechanism has also been incorporated into the solution to alert the store associate via email or a text message, when a product is completely out of stock/misplaced. The solution approaches use classification techniques, deep learning techniques along with computer vision algorithms to automate these processes in retail stores.\",\n \"Title: Analysis of ResNet and GoogleNet models for malware detection\\nYear: 2019\\n\\nAbstract: We have utilized two distinct models to identify the obscure or new sort of malware in this paper. GoogleNet and ResNet models are researched and tried which belong to two different platforms i.e. ResNet belongs to Microsoft and GoogleNet is the intellectual property of Google. Two sorts of datasets are utilized for training and validation the models. One of the dataset was downloaded from Microsoft which is the combination of 10,868 records and these records are binary records. These records are additionally isolated in nine diverse classes. Second dataset is considerate dataset and it contains 3000 benign files. The said datasets were initially in the form of EXE files and were changed over into opcode, after that changed over into images. We got a testing accuracy of 74.5% on GoogleNet and 88.36% precision on ResNet.\",\n \"Title: Binary grey wolf optimizer models for profit based unit commitment of price-taking GENCO in electricity market.\\nYear: 2019\\n\\nAbstract: The restructuration of electric power sector has renovated the power system operational planning. In the deregulated market, electricity is treated as an entity unlike in the traditional vertically integrated market model where it is treated as a service provided by the generation companies (GENCOs). The task of GENCO is to perform self-scheduling of available units so as to achieve maximum profit in restructured power sector. Therefore, the problem of profit based unit commitment (PBUC) in deregulated markets can be formulated as a commitment and generation allocation through self-scheduling procedure. The commitment schedule optimization, i.e whether the status of a thermal unit is to be changed to on or off state, is a binary problem. Thus, the PBUC problem requires binary optimization for commitment and real valued optimization for generation allocation. In this paper, three binary grey wolf optimizer (BGWO) models are presented to solve the profit based self-scheduling problem of GENCO. The BGWO models proposed in this paper differ with respect to the transformation function used to map real valued wolf position into a binary variable. The binary mapping of commitment status is carried out using sigmoid and tangent hyperbolic transfer functions. Also, in the sigmoidal transfer function, two binary transformation procedures, namely crossover and conventional sigmoidal transfer function, are presented. The effectiveness of the proposed models in improving the solution quality of PBUC problem is examined using two test systems, a 3 unit and a 10 unit test system. In addition, two cases of GENCO market participation with and without reserve market participation are simulated. In the test case with reserve market participation, two commonly used reserve payment models are examined. Simulation results are presented and compared to existing approaches that have been used to solve the PBUC problem. The simulation results and statistical analysis demonstrate the improved solution quality (profit or fitness value) of the PBUC problem and statistical significance of the BGWO models with respect to solution quality obtained as compared to other established meta-heuristic approaches.\",\n \"Title: Grey Based Decision Making For Evaluating Sustainable Performance Of Indian Marble Industries\\nYear: 2019\\n\\nAbstract: This article focuses on the environmental, economic and social impact of marble industries in the north-western region of India. It presents a grey-based decision-making model for evaluating the extent of sustainability in three marble processing industries. The goal of this article is twofold. First, to identify the important criteria of sustainable performance in marble sector and second to compare three marble processing firms on the basis of sustainability criteria using grey based decision-making approach. A detailed questionnaire was sent to three marble processing firms and the analysis is done on the basis of the received responses.\",\n \"Title: Fog computing: from architecture to edge computing and big data processing.\\nYear: 2019\\n\\nAbstract: Cloud computing plays a vital role in processing a large amount of data. However, with the arrival of the Internet of Things, huge data are generated from these devices. Thus, there is the need for bringing characteristics of cloud closer to the request generator, so that processing of these huge data takes place at one-hop distance closer to that end user. This led to the emergence of fog computing with the aim to provide storage and computation at the edge of the network that reduces network traffic and overcomes many cloud computing drawbacks. Fog computing technology helps to overcome challenges of big data processing. The paper discusses the taxonomy of fog computing, how this is different from cloud computing and edge computing technologies, its applications, emerging key technologies (i.e., communication technologies and storage technologies) and various challenges involved in fog technology.\",\n \"Title: Eff-Fas: Enhanced Fruit Fly Optimisation Based Search And Tracking By Flying Ad Hoc Swarm\\nYear: 2019\\n\\nAbstract: Flying ad hoc swarms are networks formed by autonomously operated aerial nodes. These nodes can be simple high altitude platforms or specifically configured unmanned aircraft (UA). Such networks are autonomous, temporary and mission dependent. One of the major applications of this ad hoc swarm formations is the efficient search and tracking of an area without any redundancy and overlapping. Non-redundant cell tracking is a computationally expensive task, which requires optimisation strategies to be adopted during a search process. Incorporating fruit fly optimisation (FOA) algorithm to a strategic search and track operation simplifies the complexity of the overall system. In this paper, FOA is extended in terms of its applicability by modifying the procedures of the algorithm to allow its applicability to an aerial swarm for non-redundant search over a predefined area with lower complexity. Network simulations demonstrate the effectiveness of the proposed approach towards search and track operations.\",\n \"Title: Delay Tolerant Network assisted flying Ad-Hoc network scenario: modeling and analytical perspective\\nYear: 2019\\n\\nAbstract: Flying Ad-Hoc networks (FANET) are the extended paradigm of the mobile Ad-Hoc networks and, perhaps, one of the most emerging research domains in the current era. A huge number of tangible applications have been developed in this domain. The main advantages of such networks are their easy deployment, scalability, and robustness. However, the sparseness of these networks is an inherent characteristic that is known to be a bottleneck. The main objective of this work was to provide an alternative solution for the intermittently connected FANET by considering the philosophy of the Delay Tolerant Network (DTN) approach. To realize the functionality of the DTN protocols in a three-dimensional (3D) space, a social FANET model is proposed. FANET nodes are supposed to have a sparse node density. Fundamentally, the proposed DTN assisted Flying Ad hoc Network exploits the DTN routing and mobility features. The new mobility modeling for 3D spaces was re-engineered and tested with well-known routing protocols to analyze the performance of the model based on node speed, density, buffer, latency, message overhead, and power consumption. The effectiveness of 3D mobility models has also been compared against the one of classical models. The obtained results reflect a significant enhanced performance of the suggested DTN protocol for sparse FANET in a social scenario.\",\n \"Title: Multi Agent based Autonomous Energy Management System with Self-Healing Capabilities for a Microgrid\\nYear: 2019\\n\\nAbstract: Due to the inherent intermittent nature of renewable energy sources (RES), the control and management of microgrids become complex and require modern control techniques and management strategies to cope up with the changes in the dynamics of the system. This paper focuses on improving the dynamic performance of a microgrid by developing different energy management and control strategies employing ...\",\n \"Title: Efficient Discrete Firefly Algorithm For Ctrie Based Caching Of Multiple Sequence Alignment On Optimally Scheduled Parallel Machines\\nYear: 2019\\n\\nAbstract: This study introduces a two-level strategy for efficient execution of multiple sequence alignment (MSA) of complex heterogeneous sequences. The two levels of the proposed technique are comprised of: designing the discrete firefly algorithm (DFFA) for the formation and implementation of makespan minimisation on parallel machines, followed by performing Ctrie-based caching for pairwise alignment to reduce the load on the data servers for handling multiple queries. The proposed strategy addresses a multi-client problem that aims to acquire the full advantage of the computational power of parallel connected machines. Further, it is shown that the inclusion of Ctrie as caching mechanism successively improves the performance of the system with accretion in several sequences. Performance of proposed DFFA is also compared with discrete versions of four swarm intelligence based algorithms at the criteria of makespan minimisation and the rate of convergence on two kinds of complex and diverse datasets. The work is unique in this sense: it is the first swarm-intelligence-based implementation for the addressed problem; it is so far the first approach for Ctrie based caching of the MSA on the scheduled parallel machines; hybridisation of DFFA with Ctrie for caching the MSA results is also a novel implementation.\",\n \"Title: A Multimodal Malware Detection Technique for Android IoT Devices Using Various Features.\\nYear: 2019\\n\\nAbstract: Internet of things (IoT) is revolutionizing this world with its evolving applications in various aspects of life such as sensing, healthcare, remote monitoring, and so on. Android devices and applications are working hand to hand to realize dreams of the IoT. Recently, there is a rapid increase in threats and malware attacks on Android-based devices. Moreover, due to extensive exploitation of the Android platform in the IoT devices creates a task challenging of securing such kind of malware activities. This paper presents a novel framework that combines the advantages of both machine learning techniques and blockchain technology to improve the malware detection for Android IoT devices. The proposed technique is implemented using a sequential approach, which includes clustering, classification, and blockchain. Machine learning automatically extracts the malware information using clustering and classification technique and store the information into the blockchain. Thereby, all malware information stored in the blockchain history can be communicated through the network, and therefore any latest malware can be detected effectively. The implementation of the clustering technique includes calculation of weights for each feature set, the development of parametric study for optimization and simultaneously iterative reduction of unnecessary features having small weights. The classification algorithm is implemented to extract the various features of Android malware using naive Bayes classifier. Moreover, the naive Bayes classifier is based on decision trees for extracting more important features to provide classification and regression for achieving high accuracy and robustness. Finally, our proposed framework uses the permissioned blockchain to store authentic information of extracted features in a distributed malware database blocks to increase the run-time detection of malware with more speed and accuracy, and further to announce malware information for all users.\",\n \"Title: Defending Touch-based Continuous Authentication Systems from Active Adversaries Using Generative Adversarial Networks\\nYear: 2021\\n\\nAbstract: Previous studies have demonstrated that commonly studied (vanilla) touch-based continuous authentication systems (V-TCAS) are susceptible to population attack. This paper proposes a novel Generative Adversarial Network assisted TCAS (G-TCAS) framework, which showed more resilience to the population attack. G-TCAS framework was tested on a dataset of 117 users who interacted with a smartphone and tablet pair. On average, the increase in the false accept rates (FARs) for V-TCAS was much higher (22%) than G-TCAS (13%) for the smartphone. Likewise, the increase in the FARs for V-TCAS was 25% compared to G-TCAS (6%) for the tablet.\",\n \"Title: A comprehensive survey of physical layer security over fading channels: Classifications, applications, and challenges\\nYear: 2021\\n\\nAbstract: AbstractAbstractDue to the open nature of wireless communication systems, the data transmission over these networks are vulnerable to malicious attack. To safeguard the transmitted data, physical layer security (PLS) has been emerged as a promising technique because of its unique feature of utilizing randomness of the wireless channel to secure the information. The preeminent idea of utilizing the channel imperfections such as fading and noise into the security providing resource is the basic approach behind PLS. For exploiting the randomness of the propagation medium, the analysis of the secrecy performance over the fading channel is of utmost importance. With the explanation of basic theory and technology, this paper presents an elaborated survey on the PLS research over the fading channels. To exploit the performance gains, we discuss the optimization approaches in different network design problems, for example, secrecy rate maximization, power minimization, and secure energy efficiency problems. We present a comparative study of the expressions of various performance metrics of the PLS along with classification depending upon the channel state information of the eavesdropper. The application and challenges of the PLS in the emerging wireless technologies are also reviewed and discussed. This paper incorporates almost all the aspects of PLS implemented over the fading channels. Furthermore, the paper is concluded with open research directions for the PLS system in various fields that can be explored to design a flexible and robust security system to satisfy the security requirements of the next‐generation networks.Due to the open nature of wireless systems, the data transmission over these networks are vulnerable to malicious attack. To safeguard the data, PLS has been emerged as a promising technique because of its unique feature of utilizing randomness of the wireless channel to secure the information. View Figure\",\n \"Title: Binary Fireworks Algorithm Application For Optimal Schedule Of Electric Vehicle Reserve In Traditional And Restructured Electricity Markets\\nYear: 2021\\n\\nAbstract: The multipurpose use of electric vehicles (EVs) makes it a special entity in the transportation sector and in the modern smart grid for use as a reserve service. This paper presents an application of binary fireworks algorithm for solving a multi-beneficiary model in which conventional thermal units are scheduled considering EV aggregator as an identical entity to GENCO in offering reserve services. The fast response of the EV can be exploited for providing reserve capacity alongside traditional unit commitment in regulated and price-based unit commitment in deregulated electricity markets, respectively. It was found that, compared to the energy market participation, reserve market profit for EV increased by $3.59/MW and $3.96/MW in deregulated and traditional markets respectively. To further explore the role of EV reserve, battery electric vehicle (BEV), and fuel cell electric vehicle are used to carry out cost-benefit comparisons where the market participation proved much profitable for BEV.\",\n \"Title: An Online Semantic-Enhanced Graphical Model for Evolving Short Text Stream Clustering\\nYear: 2022\\n\\nAbstract: Due to the popularity of social media and online fora, such as Twitter, Reddit, Facebook, and Wechat, short text stream clustering has gained significant attention in recent years. However, most existing short text stream clustering approaches usually work on static data and tend to cause a “term ambiguity” problem due to the sparse word representation. Beyond, they often exploit short text streams in a batch way and are difficult to find evolving topics in term-changing subspaces. In this article, we propose an online semantic-enhanced graphical model for evolving short text stream clustering (OSGM), by exploiting the word-occurrence semantic information and dynamically maintaining evolving active topics in term-changing subspaces in an online way. Compared to the existing approaches, our online model is not only free of determining the optimal batch size but also lends itself to handling large-scale data streams efficiently. It is also able to handle the “term ambiguity” problem without incorporating features from external resources. More importantly, to the best of our knowledge, it is the first work to extract evolving topics in term-changing subspaces automatically in an online way. Extensive experiments demonstrate that the proposed model yields better performance compared to many state-of-the-art algorithms on both synthetic and real-world datasets.\",\n \"Title: Maintainable Stochastic Communication Network Reliability Within Tolerable Packet Error Rate\\nYear: 2021\\n\\nAbstract: The communication networks are complex and considered a stochastic flow network. This throws enormous challenges for the service provider to provide a desired Quality of Service (QoS) to the customer. Due to the stochastic behavior of the network, ensuring QoS requires serious efforts to make the entire system work within the given time constraint, cost, and failure rate. When the communication problem is time-bound, providing a quick and reasonable solution with a permissible error rate is of great importance. Therefore, in this paper, fast solutions to data communication problems are proposed to send the required d units of user's data from the desired source node to the destination node within some constraints such as permissible error rate , time constraint T and maintenance budget constraints B. System reliability and the performance of the proposed approaches are evaluated using minimal paths. All the minimal flow vectors evaluated from minimal paths that satisfy all the above-mentioned constraints are considered for network reliability evaluation. Further, the proposed approaches are observed as faster concerning computation time than the existing approaches.\",\n \"Title: A Systematic Review On Task Scheduling In Fog Computing: Taxonomy, Tools, Challenges, And Future Directions\\nYear: 2021\\n\\nAbstract: The biggest challenge of task scheduling in Fog computing is to satisfy users' dynamic requirements in real-time with Fog nodes' limited resource capacities. Fog nodes' heterogeneity and an obligation to complete tasks by the deadline while minimizing cost and energy consumption makes the scheduling process more challenging. This article facilitates a deeper understanding of the research issues through a detailed taxonomy and distinguishes significant challenges in existing work. Furthermore, the paper investigates existing solutions for various challenges, presents a meta-analysis on quality of service parameters and tools used to implement Fog task scheduling algorithms. This systematic review will help potential researchers easily identify specific research problems and future directions to enhance scheduling efficiency.\",\n \"Title: A Review of Transmission Rate over Wireless Fading Channels: Classifications, Applications, and Challenges\\nYear: 2022\\n\\nAbstract: To address the explosive traffic demands, the capacity of the fading channel is increasingly becoming a prime concern in the designing of the wireless communication system. The channel capacity is an extremely important quantity, since it allows the transmission of the data through the channel with an arbitrarily small probability of error. In other words, capacity dictates the maximum rate of information transmission, called as 'capacity' of channel, determined by the intrinsic properties of the channel and is independent of the content of the transmitted information. In this paper, we present a comprehensive survey of the existing work related to the channel capacity model over various fading channels. With an elaborated explanation of the theory of channel capacity, definitions of channel capacity based on the channel state information are reviewed. To compliment this, review of the technique to enhance the channel capacity is discussed and reviewed. An effective capacity model to overcome the channel capacity limitation is also explained. Furthermore, as the secure transmission of data is of utmost importance, to address this physical layer security model is also reviewed. We also summarize the work related to channel capacity in various types of wireless networks. We finally cover the future research directions, including less explored aspects of the channel capacity that can be studied to design efficient communication systems.\",\n \"Title: A Resource-Blockchain Framework for Safeguarding IoT\\nYear: 2021\\n\\nAbstract: Privacy concerning resource discovery and selection on the Internet of Things (IoT) platform has emerged as a crucial challenge due to constrained network environment, poor throughput, and access control system in the Resource Directory (RD), which could potentially lead to information breaches among intelligent devices. Blockchain is indeed the latest technological model for global information management, point-to-point exchange, consensus process, asymmetric authentication, cognitive agreement, and perhaps other computing innovations to address the challenge. Considering these advantages, a resource-blockchain framework for safeguarding IoT is proposed in this article. The framework facilitates efficient access of information among devices, gathers information about the constrained environment, accepts data from heterogeneous resources and stores them on CoAP-based RD. Moreover, it provides secure transactions and records authorised user's information via Blockchain-based centralised server which acts as the frontend of CoAP based REST, dispatcher or repository and proxy server. The study demonstrates that Blockchain technologies help in strengthening privacy capabilities and safeguarding intelligent devices.\",\n \"Title: Multiscale and Direction Target Detecting in Remote Sensing Images via Modified YOLO-v4\\nYear: 2022\\n\\nAbstract: Traditional target detection algorithms have difficulty to adapt complex environmental changes and have limited applicable scenarios. However, the deep-learning-based target detection model can automatically learn with strong generalization capability. In this article, we choose a single-stage deep-learning-based target detection model for research based on the model’s real-time processing requirements and to improve the accuracy and the robustness of target detection in remote sensing images. In addition, we improve the YOLOv4 network and present a new approach. First, we propose a classification setting of the nonmaximum suppression threshold to increase the accuracy without affecting the speed. Second, we study the anchor frame allocation problem in YOLOv4 and propose two allocation schemes. The proposed anchor frame scheme also improves the detection performance, and experimental results on the DOTA dataset validate their effectiveness.\",\n \"Title: Breast cancer detection from histopathology images with deep inception and residual blocks\\nYear: 2022\\n\\nAbstract: Research Highlights With an increase in the availability of huge pixel whole slide image (WSI) of tissues has given bloom to microscopic pathology application of deep learning and the possibility of loading the scanned images onto the machines, it has become easier for the researchers to develop an automated system for analyzing such images. The paper presents the deep learning-based approach for breast cancer for binary class classification. The proposed model has exploited the inception block of Inception V3 and residual block of Resnet. The proposed model is verified experimentally on both the dataset large (BHI) and small (BreakHis). The contribution of the paper can be summarized as- Remarkable classification accuracy is achieved while working on the recent dataset. The two-step feature map extraction model is trained by combining different magnification levels. The dataset is classified into benign and malignant class. The result is discussed on various performance measures for both the benchmark dataset. The experimental outcomes are compared with the result of the conventional model of Inception and Resnet along with the latest work reported in the literature of deep learning for breast cancer. It is concluded from the experimental result that the proposed model works competently with a large dataset as well as the small dataset. And magnification plays an important role. Abstract The rapid growth of the network in deep learning, a subset of artificial intelligence has motivated the researchers to develop the tools for medical imaging analysis. Here we have introduced computer aided diagnosis tools for binary class classification and detection for breast cancer on histopathological images. In this paper, we have proposed a hybrid deep neural network for image level cancer detection in cancerous and non-cancerous category of histopathology images. The hybrid deep neural network comprises of inception and residual block. The network incorporates the advance multilevel feature map for histopathological images and involve the advantages of inception and residual block. The model proposed combines the sturdiness of inception block and residual block and shows the stability in performance against the existing start-of the art algorithms. The proposed method is trained and validated on two publicly available dataset i.e., Breast Histopathology Images (BHI) and BreakHis. The image level classification has been performed at different magnification level in case of BreakHis dataset. The experimental outcome is evaluated on different performance measures and compared with the conventional Inception model and ResNet model as well as state-of-art breast cancer detection techniques. The proposed approach shows the training accuracy of 0.9642 for Breakhis and 0.8017 for BHI dataset. The model proposed outperforms the existing cancer detection algorithms as well as conventional deep neural networks with obtained accuracy of 0.8521 for BHI and 0.8080,0.8276,0.8655and 0.8580 for 40X,100X,200X and 400X respectively for BreakHis dataset.\",\n \"Title: AFMT: Maintaining the safety-security of industrial control systems\\nYear: 2022\\n\\nAbstract: Modern day industrial control systems are overwhelmingly complex. These systems feature intricate interactions between the cyber and the physical components. At the same time, they need to be trustworthy and deliver their services continuously. Underpinning, a crucial industrial activity to ensure the dependability of such critical systems is through timely maintenance, inspections and repairs. Several strategies exist here: \\\"fix it when it breaks\\\" (reactive maintenance), monitor and maintain a system in pre-established time intervals (preventive maintenance), preventive action based upon detected symptoms of failures condition-based maintenance (CBM), etc. In literature, the question of optimal maintenance frequency have been a subject of intense study. However, most papers, do not take information security aspects into account. This paper provides an automated tool-supported quantitative risk analysis framework, Attack-FaultMaintenance Trees, AFMTs, that will enable practitioners to make informed choice on: (a) identifying the critical component(s) necessary for uninterrupted systems; (b) a decision support system that will provide informed choices on policy measures, countermeasures and safeguards that will reduce the disruptions; (c) run the \\\"what-if\\\" scenarios to find the optimal trade-offs between system attributes (safety, security, usability and maintenance). The front-end of the tool is a domain-specific language geared to represent the system architecture using graphical-constructs. The back-end of the framework remains hidden to the practitioner. It consists of a mathematical engine based on statistical model-checking techniques. A case study of oil-pipeline is used to demonstrate the efficacy of our framework. (C) 2021 Elsevier B.V. All rights reserved.\",\n \"Title: Zero-Day Malware Detection and Effective Malware Analysis Using Shapley Ensemble Boosting and Bagging Approach\\nYear: 2022\\n\\nAbstract: Software products from all vendors have vulnerabilities that can cause a security concern. Malware is used as a prime exploitation tool to exploit these vulnerabilities. Machine learning (ML) methods are efficient in detecting malware and are state-of-art. The effectiveness of ML models can be augmented by reducing false negatives and false positives. In this paper, the performance of bagging and boosting machine learning models is enhanced by reducing misclassification. Shapley values of features are a true representation of the amount of contribution of features and help detect top features for any prediction by the ML model. Shapley values are transformed to probability scale to correlate with a prediction value of ML model and to detect top features for any prediction by a trained ML model. The trend of top features derived from false negative and false positive predictions by a trained ML model can be used for making inductive rules. In this work, the best performing ML model in bagging and boosting is determined by the accuracy and confusion matrix on three malware datasets from three different periods. The best performing ML model is used to make effective inductive rules using waterfall plots based on the probability scale of features. This work helps improve cyber security scenarios by effective detection of false-negative zero-day malware.\",\n \"Title: IDeAuth: A novel behavioral biometric-based implicit deauthentication scheme for smartphones\\nYear: 2022\\n\\nAbstract: •IDeAuth is a novel implicit deauthentication scheme.•Design Android-based prototype to acquire usersmicro hand-movement.•New dataset collected from 41 users for a period of one month.•One-class classification approach to verify legitimate owner of a smartphone.\",\n \"Title: Cloud Energy Storage Based Embedded Battery Technology Architecture for Residential Users Cost Minimization\\nYear: 2022\\n\\nAbstract: This paper presents a cloud energy storage (CES) architecture for reducing energy costs for residential microgrid users. The former of this article concentrates on identifying an appropriate battery technology from various battery technologies with the aid of a simulation study. The later part addresses the economic feasibility of the storage architecture with three different scenarios namely grid connected energy storage, distributed energy storage (DES) and CES. The performance of the proposed architecture has been evaluated by considering five residential users with suitable battery technology identified from the former part of the study. For the purpose of the analysis, PV and load profiles including seasonal effects and grid price were taken from IIT Mumbai, India and IEX portal, respectively. In addition, this article also examines the impact of increased number of users with CES. The value of this study is that the proposed CES architecture is capable of reducing the cost of electricity experienced by the user by 11.37% as compared to DES. With this, CES operator's revenue can be increased by 6.70% in summer and 16.97% in winter in the case of fixed number of users. Finally, based on the analysis and simulation results, this paper recommends CES with Li-ion battery technology for residential application.\",\n \"Title: Residual-based fault detection isolation and recovery of a greenhouse\\nYear: 2022\\n\\nAbstract: This paper is concerned with fault detection, isolation and recovery (FDIR) of the greenhouse whose temperature is regulated by the model predictive controller (MPC). The residual generation approach is adopted for fault detection and isolation. The new considerations in the proposed FDIR approach are the residual generation for actuator faults, regulation failure detection as the indication of inappropriate regulation by the controller, below threshold actuator fault detection strategy, and recovery operation updating model used by MPC once the FDIR isolates the actuator fault. The proposed control strategy FDIR with MPC was compared with fixed model information MPC for simulated scenarios of the actuator fault. It has been shown that FDIR successfully detects, isolates and updates model information with low computation burden for non-delayed control evaluation.\",\n \"Title: Minimizing the Effect of Cascade Failure in Multilayer Networks with Optimal Redistribution of Link Loads\\nYear: 2021\\n\\nAbstract: Modern real-world infrastructure systems consist of a coupled and interdependent subsystems. These system can be modelled as interdependent multilayer networks such as communication networks, power networks, transport networks, etc. These networks are prone to cascade failure of intra-layer and inter-layer links due to overload. For example, in power distribution networks, the flow of electric current beyond their capacities may cause to fail the distribution lines. In multilayer network systems, any damage to the links (intra-layer and inter-layer) may cause cascade failure due to redistribution of loads of failed links to the live links. If it is not controlled, it may damage the entire system. However, optimally redistribution of loads of the failed links to live links can minimize the damage of live links due to overload. In this work, we propose a method to optimally redistribute loads of failed links (intra-layer and inter-layer) to the live links in the interdependent multilayer network in the event of cascade failure. For this purpose, we consider three variants of synthetic and two empirical dataset multilayer networks. Our simulation results reveal that optimal redistribution of loads reduces the number of failed links. Besides, it also reduces the amount of extra load (due to failed links) to be redistributed on the live links. It leads to enhance the number of live links and maintaining the robustness of the entire multilayer networks.\",\n \"Title: APT attacks on industrial control systems: A tale of three incidents\\nYear: 2022\\n\\nAbstract: Modern-day industries are complex socio-technical entities. Understanding the risks associated with the operation of such systems requires proper consideration of budget constraints, security expertise and evaluating the effects of legacy services. A relatively newer and unorthodox form of cyber-attacks against such systems are Advanced Persistent Threats (APTs). APTs are resourceful and strategic, aiming at maximum damage by stalling critical services and stealing sensitive information. In this article, we demonstrate how attack trees can be used as a common language to model APT attacks in a practitioner-friendly manner. We do so by modelling three prominent APT attacks, namely Stuxnet, Blackenergy and Triton. Each attack is described in a systematic and structured way following the attack tree modelling language. We show that, because attack trees are compositional models, one can reuse them to model other complex attack scenarios. We illustrate this compositional feature by modelling attacks on an industrial oil-pipeline.\",\n \"Title: What changed in the cyber-security after COVID-19?\\nYear: 2022\\n\\nAbstract: This paper examines the transition in the cyber-security discipline induced by the ongoing COVID-19 pandemic. Using the classical information retrieval techniques, a more than twenty thousand documents are analyzed for the cyber content. In particular, we build the topic models using the Latent Dirichlet Allocation (LDA) unsupervised machine learning algorithm. The literature corpus is build through a uniform keyword search process made on the scholarly and the non-scholarly platforms filtered through the years 2010-2021. To qualitatively know the impact of COVID-19 pandemic on cyber-security, and perform a trend analysis of key themes, we organize the entire corpus into various (combination of) categories based on time period and whether the literature has undergone peer review process. Based on the weighted distribution of keywords in the aggregated corpus, we identify the key themes. While in the pre-COVID-19 period, the topics of cyber-threats to technology, privacy policy, blockchain remain popular, in the post-COVID-19 period, focus has shifted to challenges directly or indirectly brought by the pandemic. In particular, we observe post-COVID-19 cyber-security themes of privacy in healthcare, cyber insurance, cyber risks in supply chain gaining recognition. Few cyber-topics such as of malware, control system security remain important in perpetuity.\",\n \"Title: A Lyapunov-stability-based context-layered recurrent pi-sigma neural network for the identification of nonlinear systems\\nYear: 2022\\n\\nAbstract: A novel higher-order context-layered recurrent pi-sigma neural network (CLRPSNN) is presented for the identification of nonlinear dynamical systems. The proposed model is the modified form of the classical pi-sigma neural network (PSNN) and contains an additional layer (known as the context layer) of the context nodes. Pi-sigma networks involve a product operator/unit in their output layer which indirectly incorporates in them the capability of higher-order networks and also reduces their network complexity. For tuning the weights of the proposed CLRPSNN model, a learning procedure is developed by combining the Back-Propagation (BP) and Lyapunov-stability method. The performance of the proposed model is compared with other models such as PSNN, Feed-forward neural network (FFNN) (containing single hidden layer), and various popular recurrent neural network (RNN) like Elman recurrent neural network (ERNN), Jordan recurrent neural network (JRNN), Diagonal recurrent neural network (DRNN), and a deep neural network (DNN). The simulation study showed that the proposed model has given better results as compared to the other models.\",\n \"Title: A secure and robust multilayer network with optimum inter layer links under budget constraints\\nYear: 2022\\n\\nAbstract: Most of the complex systems consist of multiple subsystems and can be modeled as multilayer networks. These networks are prone to random or strategical attacks and end up disintegrating the entire multilayer network. These attacks can’t be avoided, but a model can be proposed to restore the network to make it secure and robust. In multilayer networks, each inter-layer link has its own cost (in terms of money) associated with it. The total cost of the inter-layer links is considered as the available budget. In the present work, a method is proposed to introduce the optimal number of inter-layer links (under budget constraints) to maintain the robustness of the multilayer network. For the simulation purpose, three variants of the artificial multilayer networks and data set EU-Air Transport Networks are considered. Simulation results reveal that for the multilayer network constructed with random network layers, approx. 65% of the available budget is utilized. Nearly 70% of the total nodes are connected to a mutually connected giant component (MCGC) via inter-layer links. However, for the Configuration model, using the almost total available budget, nearly all the nodes from the considered network layers are present in MCGC. Finally, in the case of the empirical dataset, by using approx. 75% of the available budget, almost 80% of the total nodes from the considered network layers are connected to MCGC via inter-layer links.\",\n \"Title: A survey on IRS NOMA integrated communication networks\\nYear: 2022\\n\\nAbstract: Intelligent reflecting surfaces aided communication have been emerging as strong candidates to support the 6G wireless physical platforms. IRS has shown promising qualities in enhancing the spectral efficiency of wireless networks because of its capability to alter the conduct of interacting electromagnetic waves through intelligent handling of the reflections phase shifts. Also, NOMA proves itself to be superior among the other multiple access techniques as it supports a greater number of users using non-orthogonal resource allocation. This paper brings a survey over the IRS-assisted NOMA networks. The IRS and NOMA technologies, and their physical working principles are first introduced in the paper. The state-of-the-art of the IRS-assisted NOMA communication networks is next presented followed by a discussion of related performance parameters for analysis. Afterward, it discusses the resource allocation, and secrecy requirements in the IRS–NOMA networks. Furthermore, it presents the relevant work related to the optimization of energy efficiency, power efficiency and coverage. A comparison of IRS–NOMA network with MIMO–NOMA, and relay aided NOMA network is provided. Finally, a few exciting open challenges for IRS-assisted NOMA networks are identified including optimization problem using ML, identifying implementing scenarios of NOMA or OMA with IRS, PLS, and terahertz communication.\",\n \"Title: Spatio-Temporal Monitoring of Atmospheric Pollutants Using Earth Observation Sentinel 5P TROPOMI Data: Impact of Stubble Burning a Case Study\\nYear: 2022\\n\\nAbstract: The problems of atmospheric pollutants are causing significant concern across the globe and in India. The aggravated level of atmospheric pollutants in the surrounding environment poses serious threats to normal living conditions by deteriorating air quality and causing adverse health impacts. Pollutant concentration increases during harvesting seasons of Kharif/Rabi due to stubble burning and is aggravated by other points or mobile sources. The present study is intended to monitor the spatio-temporal variation of the major atmospheric pollutants using Sentinel-5P TROPOMI data through cloud computing. Land Use/Land Cover (LULC-categorization or classification of human activities and natural coverage on the landscape) was utilised to extract the agricultural area in the study site. It involves the cloud computing of MOD64A1 (MODIS Burned monthly gridded data) and Sentinel-5P TROPOMI (S5P Tropomi) data for major atmospheric pollutants, such as CH4, NO2, SOX, CO, aerosol, and HCHO. The burned area output provided information regarding the stubble burning period, which has seen post-harvesting agricultural residue burning after Kharif crop harvesting (i.e., rice from April to June) and Rabi crop harvesting (i.e., wheat from September to November). The long duration of stubble burning is due to variation in farmers' harvesting and burning stubble/biomass remains in the field for successive crops. This period was used as criteria for considering the cloud computing of the Sentinel-5P TROPOMI data for atmospheric pollutants concentration in the study site. The results showed a significant increase in CH4, SO2, SOX, CO, and aerosol concentration during the AMJ months (stubble burning of Rabi crops) and OND months (stubble burning of Kharif crops) of each year. The results are validated with the ground control station data for PM2.5/PM10. and patterns of precipitation and temperature-gridded datasets. The trajectory frequency for air mass movement using the HYSPLIT model showed that the highest frequency and concentration were observed during OND months, followed by the AMJ months of each year (2018, 2019, 2020, and 2021). This study supports the role and robustness of Earth observation Sentinel-5P TROPOMI to monitor and evaluate air quality and pollutants distribution.\",\n \"Title: A novel optimized decomposition method for Smoluchowski’s aggregation equation\\nYear: 2023\\n\\nAbstract: The Smoluchowski’s aggregation equation has applications in the field of bio-pharmaceuticals (Zidar et al., 2018 [1]), financial sector (Pushkin et al., 2004 [2]), aerosol science (Shen et al., 2020 [3]) and many others. Several analytical, numerical and semi-analytical approaches have been devised to calculate the solutions of this equation. Semi-analytical methods are commonly employed since they do not require discretization of the space variable. The article deals with the introduction of a novel semi-analytical technique called the optimized decomposition method (ODM) (see Odibat (2020)) to compute solutions of this relevant integro-partial differential equation. The series solution computed using ODM is shown to converge to the exact solution. The theoretical results are validated using numerical examples for scientifically relevant aggregation kernels for which the exact solutions are available. Additionally, the ODM approximated results are compared with the solutions obtained using the Adomian decomposition method (ADM) in Singh et al., (2015). The novel method is shown to be superior to ADM for the examples considered and thus establishes as an improved and efficient method for solving the Smoluchowski’s equation.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":200,"string":"200"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13660,"string":"13,660"}}},{"rowIdx":13661,"cells":{"question_id":{"kind":"string","value":"Bing Han"},"question":{"kind":"string","value":"Generate an abstract for the title \"Action Tree Convolutional Networks: Skeleton-Based Human Action Recognition.\" using the following items: - Skeleton-based human activity recognition\n- Useless information in activity data\n- ATCNs (Action Tree Convolutional Networks)\n- Relations and semantics of nodes/body-parts\n- Improved performance over"},"answer":{"kind":"string","value":"This paper is mainly about addressing the problem of skeleton-based human activity recognition: ignoring the structure and relationship between skeleton joints and body-parts, the existence of a large amount of useless information in the activity data, and poor generalization ability. In order to solve the shortcomings of existing mainstream methods used for human action recognition, we propose a novel method named Action Tree Convolutional Networks (ATCNs). This method uses a data based auto-designed Action Tree network to dynamically generate a tree of nodes/body-parts and a semantic attention center, profoundly emphasizing the relations and semantics of nodes/body-parts. This method we introduced has a great improvement on the previous algorithm's neglect of the importance of nodes/body-parts relation, and improves the generalization ability of the algorithm. Through experiments on Kinetics and NTU-RGB+D datasets, our method achieves better performance improvements over other state-of-the-art methods."},"correct_answer":{"kind":"string","value":"This paper is mainly about addressing the problem of skeleton-based human activity recognition: ignoring the structure and relationship between skeleton joints and body-parts, the existence of a large amount of useless information in the activity data, and poor generalization ability. In order to solve the shortcomings of existing mainstream methods used for human action recognition, we propose a novel method named Action Tree Convolutional Networks (ATCNs). This method uses a data based auto-designed Action Tree network to dynamically generate a tree of nodes/body-parts and a semantic attention center, profoundly emphasizing the relations and semantics of nodes/body-parts. This method we introduced has a great improvement on the previous algorithm's neglect of the importance of nodes/body-parts relation, and improves the generalization ability of the algorithm. Through experiments on Kinetics and NTU-RGB+D datasets, our method achieves better performance improvements over other state-of-the-art methods."},"documents":{"kind":"list like","value":["Title: Application Of Artificial Intelligence In Autonomous English Learning Among College Students\nYear: 2019\n\nAbstract: To improve the English proficiency of college students, this paper attempts to promote autonomous English learning among these students using artificial intelligence technology. Specifically, the principles, features and application fields of artificial intelligence were introduced in details, and the requirements, structure, database and processes of autonomous English learning systems were discussed at length. On this basis, the backpropagation (BP) neural network, a typical artificial intelligence technique, was selected to design the modules of the autonomous English learning system for college students. The proposed system was then implemented in integrated open environment of Visual Studio, the architecture of the browser/server system and the SQL Server database on the Windows platform. The results show that the proposed system provides college students with intelligent and open self-evaluation tests and effective learning feedbacks. The research findings shed new light on improving the English learning of college students and reliving the pressure on college English teachers.","Title: Universal learning network and its application for nonlinear system with long time delay\nYear: 2006\n\nAbstract: This paper discusses universal learning network (ULN) and its application to control a class of nonlinear systems with long time delay. Two control architectures, model predictive control based on ULN model and single neuron PID (SN-PID) controller based on ULN predictor, are designed to control pH neutralization process, respectively. In addition, to verify the performance of ULN, two comparisons are also made. One is the generalization ability between ULN and back-propagation (BP) network, the other comparison is ULN predictor and Smith predictor, in which the same controller is used. Simulation results prove the applicability and effectiveness of the ULN model. The special architecture and its learning algorithm give ULN more representing abilities to model and control complicated nonlinear systems with long time delay.","Title: An iterative joint channel estimation and symbol detection algorithm applied in OFDM system with high data to pilot power ratio\nYear: 2003\n\nAbstract: For orthogonal frequency-division multiplexing (OFDM) systems with pilot-symbol-aided channel estimation, a straightforward way to overcome system throughput deterioration is by decreasing the power and reducing the number of pilots, which unfortunately, leads directly to the degradation of BER performance. For the purpose of improving the BER performance, an iterative joint channel estimation and symbol detection algorithm with low complexity and fast convergence is proposed. Both theoretical analysis and computer simulation show that this algorithm gives better BER performance and saves the overhead of OFDM systems.","Title: A Compensation Method With a Standard Square Wave for Precise DC Measurement of Mutual Inductance for Joule Balance\nYear: 2012\n\nAbstract: The precise dc-based measurement of the mutual inductance is one of the key issues in the Joule Balance method used to determine Planck's constant h. Here, a new compensation method with a standard square wave is provided. The dc value of the mutual inductance is obtained by measuring the integral area of the induced voltage. To increase the accuracy of this integral method, we just accurately measure the residual area observed between the induced voltage of the mutual inductor and a synchronously generated standard square wave. Experimental results indicate that this approach is consistent with the previous frequency extrapolation method within 1 ppm. The measurement uncertainty is estimated to be about 0.22 μH/H for the 340-mH mutual inductor at room temperature. A better result of 0.1 ppm could be obtained when the superconducting coil is used.","Title: Joint channel estimation and symbol detection for SFBC-OFDM systems via the EM algorithm\nYear: 2004\n\nAbstract: Joint channel estimation and symbol detection for space-frequency block coded OFDM (SFBC-OFDM) systems is considered. The expectation-maximization (EM) algorithm is employed to devise both soft-in-hard-out (SIHO) maximum likelihood (ML) iterative receivers and soft-in-soft-out (SISO) maximum a posteriori (MAP) iterative receivers for SFBC-OFDM systems. Besides the low computational complexity and fast convergence, the EM-based iterative receivers also have the ability of tracing the variation of fast fading channels, and thus provide quite satisfying performance and bandwidth efficiency even in high-vehicular-speed environment. Numerical results are provided to corroborate the theoretical designs.","Title: Mutual Inductance Measurement of the Superconducting Coil for the Joule Balance\nYear: 2013\n\nAbstract: The joule balance, an approach for precisely measuring the Planck constant at the National Institute of Metrology (NIM), China, is currently limited by the self-heating of the coil when the measuring current flows. To solve this heating problem, a superconducting coil has been built to replace the existing fixed coil wound with enameled copper wire. The movable coil set is formed by two enameled coils connected oppositely for easy alignment in air. However, the large self-inductance of the superconducting coil brings new challenges for the a mutual inductance measurement. Mutual inductance measurement of superconducting coil based on standard square wave compensation method is presented and discussed in this paper.","Title: Social Networks, Information Acquisition, and Asset Prices.\nYear: 2013\n\nAbstract: We analyze a rational expectations equilibrium model to explore the implications of information networks for the financial market. When information is exogenous, social communication improves market efficiency. However, social communication crowds out information production due to traders' incentives to \"free ride\" on informed friends and on a more informative price system. Overall, social communication hurts market efficiency when information is endogenous. The network effects on the cost of capital, liquidity, trading volume, and welfare are also sensitive to whether information is endogenous. Our analysis highlights the importance of information acquisition in examining the implications of information networks for financial markets.","Title: A Bernstein-type estimator for decreasing density with application to p-value adjustments\nYear: 2012\n\nAbstract: The nonparametric maximum likelihood estimator (NPMLE) is a popular approach to estimating decreasing densities, i.e., f(s)=f(t),s@?t. A less ideal feature of NPMLE is its step-function form. In this paper, we propose two nonparametric density estimators based on the Bernstein-type polynomials of the NPMLE. The proposed estimators have relatively simple forms and easy implementation. They have satisfactory smoothness as well as estimation efficiency. Numerical examples demonstrate the superior performance of the proposed estimators compared to existing methods. Decreasing densities have been applied in simultaneous inference to estimate the proportion of true null hypotheses and the local false discovery rate. We applied the proposed estimators to conduct simultaneous tests for a gene expression data set.","Title: Hard multidimensional multiple choice knapsack problems, an empirical study\nYear: 2010\n\nAbstract: Recent advances in algorithms for the multidimensional multiple choice knapsack problems have enabled us to solve rather large problem instances. However, these algorithms are evaluated with very limited benchmark instances. In this study, we propose new methods to systematically generate comprehensive benchmark instances. Some instances with special correlation properties between parameters are found to be several orders of magnitude harder than those currently used for benchmarking the algorithms. Experiments on an existing exact algorithm and two generic solvers show that instances whose weights are uncorrelated with the profits are easier compared with weakly or strongly correlated cases. Instances with classes containing similar set of profits for items and with weights strongly correlated to the profits are the hardest among all instance groups investigated. These hard instances deserve further study and understanding their properties may shed light to better algorithms.","Title: Sweep-Line analysis of TCP connection management\nYear: 2005\n\nAbstract: Despite the widespread use of the Transmission Control Protocol (TCP) as the main transport protocol in the Internet, the procedures for connection establishment and release are still not fully understood. This paper extends the analysis of a Coloured Petri net model of TCP’s Connection Management procedures by applying the state explosion alleviation technique known as the sweep-line method. The protocol is assumed to be operating over a reordering lossless channel. Termination and absence of deadlock properties are investigated for many scenarios, including client-server and simultaneous connection establishment, orderly release and abortion. The sweep-line method provides a reduction in memory usage of around a factor of 10 and allows investigation of many scenarios that were previously out of the reach of conventional methods.","Title: Neuromorphic models on a GPGPU cluster\nYear: 2010\n\nAbstract: There is currently a strong push in the research community to develop biological scale implementations of neuron based vision models. Systems at this scale are computationally demanding and have generally utilized more accurate neuron models, such as the Izhikevich and Hodgkin- Huxley models, in favor of the integrate and fire model. This paper examines the feasibility of using a cluster of NVIDIA General Purpose Graphics Processing Units (GPGPUs) for accelerating a spiking neural network based character recognition network based on the Izhikevich and Hodgkin-Huxley models to enable such large scale systems. We utilized a 32 node cluster at NCSA containing an NVIDIA Tesla S1070 GPGPU on each node. Based on a thorough review of the literature, this is the first study examining the use of a cluster of GPGPUs for accelerating neuromorphic models. Our results show that the GPGPU can provide speedups of 24.6 and 177.0 times over a dual core 2.4 GHz AMD Opteron processor for the Izhikevich and Hodgkin-Huxley models respectively. Additionally, the MPI implementations of the models scaled almost linearly, with 16 GPGPUs providing throughputs of 14.1 and 15.9 times that of a single GPGPU for the Izhikevich and Hodgkin-Huxley models respectively. This indicates that clusters of GPGPUs are well suited for this application domain.","Title: An Automatic MSRM Method with a Feedback Based on Shape Information for Auroral Oval Segmentation.\nYear: 2013\n\nAbstract: Auroral oval segmentation is of great significance to the study of auroral activities. In this paper, we propose an automatic maximal similarity based region merging (MSRM) method with a feedback based on shape information. Firstly, K-means method is employed to mark auroral oval points and background points, thus guiding the process of MSRM to obtain the initial segmentation result. Then the direct least-square ellipse fitting method is used to fit an ellipse on the initial boundary and points in the fitted ellipse are set as adjusted markers of auroral oval. Finally, the MSRM mechanism is used again to get the final segmentation result. Experimental results show that the proposed algorithm obtains a good performance. s © 2013 Springer-Verlag Berlin Heidelberg.","Title: Comparing quantitative trait Loci and gene expression data.\nYear: 2008\n\nAbstract: We develop methods to compare the positions of quantitative trait loci (QTL) with a set of genes selected by other methods, such as microarray experiments, from a sequenced genome. We apply our methods to QTL for addictive behavior in mouse, and a set of genes upregulated in a region of the brain associated with addictive behavior, the nucleus accumbens (NA). The association between the QTL and NA genes is not significantly stronger than expected by chance. However, chromosomes 2 and 16 do show strong associations suggesting that genes on these chromosomes might be associated with addictive behavior. The statistical methodology developed for this study can be applied to similar studies to assess the mutual information in microarray and QTL analyses.","Title: Medium-Earth-orbit SAR imaging based on keystone transform and azimuth perturbation\nYear: 2012\n\nAbstract: Due to the significant azimuth variance property in medium-Earth-orbit (MEO) synthetic aperture radar (SAR) echo, it is difficult for the conventional SAR algorithms to achieve a good compromise between accuracy and efficiency. A novel algorithm based on Keystone transform (KT) and azimuth perturbation (AP) is introduced in this paper to handle this problem. The function of KT is to correct the range walk and thus to mitigate the azimuth variance effect on range processing. The function of AP is to equalize the Doppler histories in each range gate and thus to mitigate the azimuth variance effect on azimuth compressing. Simulation results of an L-band MEO SAR with 5 m resolution at 10,000 km altitude demonstrate the capability of our algorithm.","Title: Predictive control method of improved double-controller scheme based on neural networks\nYear: 2006\n\nAbstract: This paper considers the problem of stabilizing a black-box plant with time delay using an improved double controller scheme. The PID parameters of the load controller of the double-controller scheme are obtained by a neural network controller with back propagation algorithm. Based on the adaptive algorithm of Universal Learning Network (ULN), ULN is adopted for modeling the plant and being a predictor of the control system. Simulation results prove the applicability and effectiveness of the improved double-controller scheme. ULN and the neural network controller give the double-controller scheme more representing abilities and robust ability.","Title: Dayside Corona Aurora Classification Based On X-Grey Level Aura Matrices And Feature Selection\nYear: 2011\n\nAbstract: Dayside corona aurora is the main form of aurora at magnetic noon, which is generated by the dynamics process of the interaction of the sun and earth's magnetosphere. Hence the study of dayside corona aurora is of great importance to the analysis of ionosphere and its dynamic feature. This paper proposes a novel aurora texture extraction method based static image classification of dayside aurora, in which X-grey level aura matrices are designed to extract the features of the original aurora images. Besides, a dayside aurora classification algorithm for dayside aurora based on feature selection is proposed to handle the large quantities of aurora samples. To eliminate the effect of noise and solve the problem of high feature dimensionality, the ReliefF is adopted to select the effective feature vector. For classification, texture models are learned by using the support vector machine, then a given texture of aurora image can be classified into one of the pre-learned classes. All of the aurora images in the experiment are obtained from the all-sky aurora image system in the Chinese Arctic Yellow River Station. The experimental results illustrate the high effectiveness of the proposed dayside aurora classification algorithm.","Title: Termination properties of TCP's connection management procedures\nYear: 2005\n\nAbstract: The Transmission Control Protocol (TCP) is the most widely used transport protocol in the Internet, providing a reliable data transfer service to many applications. This paper analyses TCP's Connection Management procedures for correct termination and absence of deadlocks. The protocol is assumed to be operating over a reordering lossless channel and is modelled using Coloured Petri nets. The following connection management scenarios are examined using state space analysis: client-server and simultaneous opening; orderly release; and abortion. The results demonstrate that TCP terminates correctly for client-server and simultaneous connection establishment, orderly release after the connection is established and aborting of connections. However, we discover a deadlock when connection release is initiated before the connection has been fully established when operating over a reordering lossless channel.","Title: A novel feature weighted clustering algorithm based on rough sets for shot boundary detection\nYear: 2006\n\nAbstract: Shot boundary detection as the crucial step attracts much more research interests in recent years. To partition news video into shots, many metrics were constructed to measure the similarity among video frames based on all the available video features. However, too many features will reduce the efficiency of the shot boundary detection. Therefore, it is necessary to perform feature reduction before shot boundary detection. For this purpose, the classification method based on clustering algorithm of Variable Precision Rough-Fuzzy Sets and Variable Precision Rough Sets for feature reduction and feature weighting is proposed. According to the particularity of news scenes, shot transition can be divided into three types: cut transition, gradual transition and no transition. The efficiency of the proposed method is extensively tested on UCI data sets and more than 3 h of news programs and 96.2% recall with 96.3% precision have been achieved.","Title: A novel clustering algorithm based on variable precision rough-fuzzy sets\nYear: 2006\n\nAbstract: In the field of cluster analysis and data mining, fuzzy c-means algorithm is one of effective methods, which has widely used in unsupervised pattern classification. However, the above algorithm assumes that each feature of the samples plays a uniform contribution for cluster analysis. To consider the different contribution of each dimensional feature of the given samples to be classified, this paper presents a novel fuzzy c-means clustering algorithm based on feature weighted, in which the Variable Precision Rough-Fuzzy Sets is used to assign the weights to each feature. Due to the advantages of Rough Sets for feature reduction, we can obtain the better results than the traditional one, which enriches the theory of FCM-type algorithms. Then, we apply the proposed method into video data to detect shot boundary in video indexing and browsing. The test experiment with UCI data and the video data from CCTV demonstrate the effectiveness of the novel algorithm.","Title: Taking the road less traveled by: Does conversation eradicate pernicious cascades?\nYear: 2011\n\nAbstract: We offer a model in which sequences of individuals often converge upon poor decisions and are prone to fads, despite communication of the payoff outcomes from past choices. This reflects both direct and indirect action-based information externalities. In contrast with previous cascades literature, cascades here are spontaneously dislodged and in general have a probability less than one of lasting forever. Furthermore, the ability of individuals to communicate can reduce average decision accuracy and welfare.","Title: A Markov blanket-based method for detecting causal SNPs in GWAS.\nYear: 2010\n\nAbstract: Detecting epistatic interactions associated with complex and common diseases can help to improve prevention, diagnosis and treatment of these diseases. With the development of genome-wide association studies (GWAS), designing powerful and robust computational method for identifying epistatic interactions associated with common diseases becomes a great challenge to bioinformatics society, because the study of epistatic interactions often deals with the large size of the genotyped data and the huge amount of combinations of all the possible genetic factors. Most existing computational detection methods are based on the classification capacity of SNP sets, which may fail to identify SNP sets that are strongly associated with the diseases and introduce a lot of false positives. In addition, most methods are not suitable for genome-wide scale studies due to their computational complexity.We propose a new Markov Blanket-based method, DASSO-MB (Detection of ASSOciations using Markov Blanket) to detect epistatic interactions in case-control GWAS. Markov blanket of a target variable T can completely shield T from all other variables. Thus, we can guarantee that the SNP set detected by DASSO-MB has a strong association with diseases and contains fewest false positives. Furthermore, DASSO-MB uses a heuristic search strategy by calculating the association between variables to avoid the time-consuming training process as in other machine-learning methods. We apply our algorithm to simulated datasets and a real case-control dataset. We compare DASSO-MB to other commonly-used methods and show that our method significantly outperforms other methods and is capable of finding SNPs strongly associated with diseases.Our study shows that DASSO-MB can identify a minimal set of causal SNPs associated with diseases, which contains less false positives compared to other existing methods. Given the huge size of genomic dataset produced by GWAS, this is critical in saving the potential costs of biological experiments and being an efficient guideline for pathogenesis research.","Title: A method of airborne InSAR DEM reconstruction in layover areas\nYear: 2012\n\nAbstract: The layover phenomenon of SAR imaging arises when different height contributions collapse in the same range-azimuth resolution cell, due to the presence of strong terrain slopes or discontinuities in the scenarios. Because of the phase discontinuities, for single baseline interferometric SAR, it is difficult to recover the accurate unwrapped phase in layover areas by traditional phase unwrapping methods. In this paper, according to the phase characteristic of layover areas, we propose a new method to retrieve unwrapped phase based on local frequency estimate. It can avoid the unwrapping error resulted from the phase jump at the edge of layover areas. As a result, relative accurate DEM of layover areas can be reconstructed, which is beneficial to afterward geocoding in InSAR topographic mapping.","Title: A saliency map method with cortex-like mechanisms and sparse representation\nYear: 2010\n\nAbstract: In the field of visual attention, bottom-up or saliency-based visual attention allows primates to detect non-specific conspicuous objects or targets in cluttered scenes. Simple multi-scale \"feature maps\" detect local spatial discontinuities in intensity, color, orientation, and are combined into a \"saliency\" map. HMAX is a feature extraction method and this method is motivated by a quantitative model of visual cortex. In this paper, we introduce the Saliency Criteria to measure the perspective fields. This model is based on cortex-like mechanisms and sparse representation, Saliency Criteria is obtained from Shannon's self-information and entropy. We demonstrate that the proposed model achieves superior accuracy with the comparison to classical approach in static saliency map generation on real data of natural scenes and psychology stimuli patterns.","Title: FEPI-MB: identifying SNPs-disease association using a Markov Blanket-based approach.\nYear: 2011\n\nAbstract: The interactions among genetic factors related to diseases are called epistasis. With the availability of genotyped data from genome-wide association studies, it is now possible to computationally unravel epistasis related to the susceptibility to common complex human diseases such as asthma, diabetes, and hypertension. However, the difficulties of detecting epistatic interaction arose from the large number of genetic factors and the enormous size of possible combinations of genetic factors. Most computational methods to detect epistatic interactions are predictor-based methods and can not find true causal factor elements. Moreover, they are both time-consuming and sample-consuming.We propose a new and fast Markov Blanket-based method, FEPI-MB (Fast EPistatic Interactions detection using Markov Blanket), for epistatic interactions detection. The Markov Blanket is a minimal set of variables that can completely shield the target variable from all other variables. Learning of Markov blankets can be used to detect epistatic interactions by a heuristic search for a minimal set of SNPs, which may cause the disease. Experimental results on both simulated data sets and a real data set demonstrate that FEPI-MB significantly outperforms other existing methods and is capable of finding SNPs that have a strong association with common diseases.FEPI-MB algorithm outperforms other computational methods for detection of epistatic interactions in terms of both the power and sample-efficiency. Moreover, compared to other Markov Blanket learning methods, FEPI-MB is more time-efficient and achieves a better performance.","Title: Saliency Based On Cortex-Like Mechanisms\nYear: 2011\n\nAbstract: A visual attention system should preferentially locate the most informative spots in complex environments. Feature-integration theory of attention plays an important role in bottom-up computational model for visual attention. This point extremely decreases the detection accuracy and also impacts the performance of the automatic visual attention model. To improve the accuracy of saliency detection in human visual attention, we propose a model based on cortex-like mechanisms. Saliency Criteria are obtained from two pathways: Shannon's entropy and sparse representation. And our model not only substantiates the bottom-up model proposed by Itti and HMAX model by Paggio, but also enriches the theory of saliency detection. We demonstrate that the proposed model achieves superior accuracy in comparison to the classical approach in static saliency map generation on real data of natural scenes and psychology stimuli patterns.","Title: Large-scale Protein-Protein Interaction prediction using novel kernel methods.\nYear: 2008\n\nAbstract: Knowledge of Protein-Protein Interactions (PPIs) can give us new insights into molecular mechanisms and properties of the cell. In this paper, we propose a novel domain-based kernel method to predict PPIs. A new kernel that measures the similarity between protein pairs based on a new feature representation is developed and applied to a large scale PPI database. Experimental results demonstrate its effectiveness. Furthermore, we evaluate the problem of cross-species PPI prediction and the effect of the number of negative samples on the performance of PPI predictions, which are two fundamental problems in most in silico PPI methods.","Title: A Quantum Potential Strategy by Quantum Genetic Algorithm.\nYear: 2012\n\nAbstract: In this papers, The potential of a quantum bit is defined and a novel quantum evolutionary algorithm based on potential of the quantum bit is proposed. The character of quantum entangled interference based on potential of the quantum bit is introduced. And convergence analysis and rationality analysis are implemented.","Title: Genetic studies of complex human diseases: Characterizing SNP-disease associations using Bayesian networks.\nYear: 2012\n\nAbstract: Detecting epistatic interactions plays a significant role in improving pathogenesis, prevention, diagnosis, and treatment of complex human diseases. Applying machine learning or statistical methods to epistatic interaction detection will encounter some common problems, e.g., very limited number of samples, an extremely high search space, a large number of false positives, and ways to measure the association between disease markers and the phenotype.To address the problems of computational methods in epistatic interaction detection, we propose a score-based Bayesian network structure learning method, EpiBN, to detect epistatic interactions. We apply the proposed method to both simulated datasets and three real disease datasets. Experimental results on simulation data show that our method outperforms some other commonly-used methods in terms of power and sample-efficiency, and is especially suitable for detecting epistatic interactions with weak or no marginal effects. Furthermore, our method is scalable to real disease data.We propose a Bayesian network-based method, EpiBN, to detect epistatic interactions. In EpiBN, we develop a new scoring function, which can reflect higher-order epistatic interactions by estimating the model complexity from data, and apply a fast Branch-and-Bound algorithm to learn the structure of a two-layer Bayesian network containing only one target node. To make our method scalable to real data, we propose the use of a Markov chain Monte Carlo (MCMC) method to perform the screening process. Applications of the proposed method to some real GWAS (genome-wide association studies) datasets may provide helpful insights into understanding the genetic basis of Age-related Macular Degeneration, late-onset Alzheimer's disease, and autism.","Title: Closed form expressions for the state space of TCP's Data Transfer Service operating over unbounded channels\nYear: 2004\n\nAbstract: The Internet is a very complex system, comprising a dynamically changing network of networks. The Internet's original designers created the Transmission Control Protocol (TCP) to provide a reliable end to end data service to its users, that would operate correctly in the face of failures in the network infrastructure. TCP handles the vast majority of the ever increasing traffic over the Internet and it is therefore of utmost importance that it operates correctly as the Internet grows and as speeds on the Internet increase. This paper provides a step towards its formal verification by providing a general formalisation of TCP requirements in the knowledge that it operates over a medium of very large and unknown capacity. These requirements are expressed in what is termed a service specification. Once the intent of the service that TCP is to provide to its users is defined in the service specification, TCP can be verified against this intent. A central part of the service specification is the definition of the sequences of user observable events (known as service primitives) that can occur at the user/TCP boundary. This is known as the service language. An important verification task is then to prove that TCP complies with this service language. The formal verification of a protocol as complex as TCP is an ambitious undertaking. To simplify the task, we consider the connection management and data transfer parts of the protocol and service separately. In this paper, we are concerned with the data transfer part of the service. Unfortunately the TCP Data Transfer Service language grows exponentially with the size of the medium over which it operates, which in general is unbounded. This implies that the automaton that represents the Data Transfer Service language is infinite. To tackle this problem, we parameterise the Data Transfer Service by the size of the medium. We provide a Coloured Petri Net model of the Data Transfer Service based on previous work and establish closed form expressions for its state space parameterised by the medium capacity. The state space is the automaton that represents the Data Transfer Service language. These expressions eliminate the need for reachability analysis and determine a parameterised automaton that embodies the Data Transfer Service language for arbitrary medium capacity.","Title: The evolution and developing tendency of DAC design methods\nYear: 2012\n\nAbstract: There are many different methods in DAC (digital-analog-convertor) design to meet the fact requirements. The methods of DAC design can be divided into several classifications. From the papers published before, the history of the evolution and limitations of each method can be summarized. The real requirements drive the innovation and change of DAC development. Base on the discourse above, it is possible to make a good optimization and find developing tendency of DAC design methods.","Title: Query range problem in wireless sensor networks\nYear: 2009\n\nAbstract: Wireless sensor networks with multiple users extracting data directly from nearby sensors have many potential applications. An important problem in such a network is how to allocate the multi-hop query range for each user such that a certain global optimality is achieved. We introduce this problem and show it is NP-complete in its generic form. Distributed heuristic is proposed and evaluated with simulations. Interesting behaviors of the network when optimized with different global objectives are observed from the simulation results.","Title: Robust detection of sources based on clustering in spatially correlated noise fields\nYear: 2006\n\nAbstract: The research of sources number detection is still open and challenging issue in array signal processing. The accurate estimation may be very essential to those high resolution direction finding algorithms. Three crucial issues are discussed for the application of cluster method to the source-number detection. A detection method based on Fuzzy-c-Means clustering algorithm has been proposed, which uses canonical correlation coefficients of the joint covariance matrix as the feature to be classified. Compared with the classical methods, our algorithm has better performance in low SNR and angular resolution. In the spatially correlated noise fields, the simulation results demonstrate the effectiveness and robustness of the proposed scheme.","Title: DASSO-MB: Detection of Epistatic Interactions in Genome-Wide Association Studies Using Markov Blankets\nYear: 2009\n\nAbstract: With the development of genome-wide association studies (GWAS), computationally identifying the epistatic interactions associated with common diseases presents a significant challenge to bioinformatics society. Most existing computational detection methods are based on the classification capacity of SNP sets, which may fail to identify SNP sets that are strongly associated with the diseases. In addition, most methods are not suitable for genome-wide scale studies due to their computational complexity. To address these issues, we propose the use of a Markov Blanket-based method, DASSO-MB, for epistatic interaction detection. We apply our method to both simulated data sets and a real data set, and demonstrate that DASSO-MB significantly outperforms other existing methods and is capable of finding SNPs that have a strong association with common diseases","Title: A case study on REST-style architecture for cyber-physical systems: Restful smart gateway.\nYear: 2011\n\nAbstract: Due to several key factors, Cyber-physical systems (CPS) pose great challenges in software system design, which are dynamic composition, heterogeneous, adaptation and uncertain in environmental factors. In this paper we present our research on the development of REST-style architecture for CPS. We propose a path towards solving requirements of CPS architecture through Restful principles. By using this architectural style, we have built a prototyping system called the restful smart gateway, which seamlessly integrates conceptual and physical resources into the Web and scale better. Some experiments on the smart gateway are given to illustrate its performance.","Title: Formalising TCP's Data Transfer Service Language: A Symbolic Automaton and its Properties\nYear: 2007\n\nAbstract: The TCP/IP protocol suite defines the procedures governing the movement of data within the Internet. A major component of the suite is the Transmission Control Protocol (TCP), which ensures data flows reliably between Internet applications. We use Coloured Petri Nets (CPNs) to model the application's view of the service provided by TCP for the transfer of data. This service depends on the storage capacity available in the Internet. We firstly establish exact expressions for the size of the CPN model's reachability graph (or state space) in terms of the storage capacity. They indicate that both the nodes and arcs of the state space grow exponentially in the storage capacity. Secondly,we derive a symbolic state space which represents an infinite family of state spaces, one for each value of the capacity. We prove that each member of this family is a strongly connected graph and that its associated finite state automaton is deterministic and minimum. Finally, we formulate the TCP data transfer service language for arbitrary capacity, from the symbolic representation of the family of automata. This service language is the yardstick against which the TCP data transfer protocol can be verified with respect to user observable sequences of events.","Title: Frequency synchronization for TDS-OFDM system with transmit diversity\nYear: 2009\n\nAbstract: This paper proposes a frequency synchronization algorithm for time domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) system with multiple antenna transmit diversity. The training sequences used in the transmit diversity TDS-OFDM system are orthogonal and have near-optimal autocorrelation property, and the carrier frequency offset (CFO) estimation algorithm is based on these properties. From computer simulations, the proposed frequency synchronization method performs better when increasing the number of the transmit antenna in AWGN environment, and the estimated CFO can make the carrier-to-interference ratio be greater than 20 dB even over time-varying multipath channels with SNR=10 dB.","Title: Forecast Accuracy Uncertainty and Momentum\nYear: 2009\n\nAbstract: We demonstrate that stock price momentum and earnings momentum can result from uncertainty surrounding the accuracy of cash flow forecasts. Our model has multiple information sources issuing cash flow forecasts for a stock. The investor combines these forecasts into an aggregate cash flow estimate that has minimal mean-squared forecast error. This aggregate estimate weights each cash flow forecast by the estimated accuracy of its issuer, which is obtained from their past forecast errors. Momentum arises from the investor gradually learning about the relative accuracy of the information sources and updating their weights. Empirical tests validate the model's prediction of stronger momentum in stocks with large information weight fluctuations and high forecast dispersion. We also identify return predictability attributable to changes in the information weights.","Title: A Novel Feature Combination Methods for Saliency-Based Visual Attention\nYear: 2009\n\nAbstract: In the field of visual attention, bottom-up or saliency-based visual attention allows primates to detect non-specific conspicuous objects or targets in cluttered scenes. Simple multi-scale “feature maps” detect local spatial discontinuities in intensity, color, orientation, and are combined into a “saliency” map. In this paper, we propose a Saliency Map based on feature weighted, in which the Rough Sets is used to assign the weighting for every feature. This method measures the contribution of each conspicuity map obtained from the feature maps to saliency map. And it also carries out a dynamic weighting of individual conspicuity maps. We obtain results, which enrich the theory of Saliency detection. We use the real data of natural scenes to demonstrate the effectiveness of the algorithm.","Title: Applications of FBG and ZigBee in telemetering of vortex-induced vibration for pipelines\nYear: 2013\n\nAbstract: Under the influence of sustained flood scouring, buried pipelines are often exposed out of earth surface and this will result in vortex-induced vibration of suspended pipeline, which is one of the main reasons of fatigue failure of pipelines. In order to realize the remote monitoring of pipelines threatened by floods, we have designed a new kind of telemetering system based on fiber Bragg grating and wireless network technologies, which can satisfy the requirements automatic and real-time monitoring of vortex-induced vibration of suspended pipelines subjected to flood impacting. The telemetering system has been put into practical application in a monitoring project, the measurement results indicate that it is of such advantages as real-time capability, high accuracy, unattended operation, and it can well meet the present requirements of safety monitoring of oil and gas pipelines.","Title: PPP: prefix-based popularity prediction for effective caching in content-centric networking\nYear: 2014\n\nAbstract: In the Content-Centric Networking (CCN) architecture, popular content can be cached in some intermediate network devices while being delivered, and the following requests for the cached content can be efficiently handled by the caches. Thus, how to design in-network caching is important for reducing both the traffic load and the delivery delay. In this paper, we propose a caching framework of Prefix-based Popularity Prediction (PPP) for efficient caching in CCN. PPP assigns a lifetime (in a cache) to the prefix of a name (of each cached object) based on its access history (or popularity), which is represented as a Prefix-Tree (PT). We demonstrate PPP's predictability of content popularity in CCN by both traces and simulations. The evaluation results show that PPP can achieve higher cache hits and less traffic load than traditional caching algorithms (i.e., LRU and LFU). Also, its performance gain increases with users of high mobility.","Title: Multiple Hierarchical Image Transmission Over Rayleigh Fading Channels\nYear: 2000\n\nAbstract: In this paper, Ne present a multiple hierarchical protection scheme applied in for Rayleigh fading channels using multilevel coding (MLC) system in which both capacity rule and block partitioning (BP) have been taken into account. As a result, the non-uniform set-partitioning design trades the performance of the less important data for better performance of the most important ones, and thus improves the multiple hierarchical protection ability of MLC schemes.","Title: Image representation by compressive sensing for visual sensor networks\nYear: 2010\n\nAbstract: This paper addresses the image representation problem in visual sensor networks. We propose a new image representation method for visual sensor networks based on compressive sensing (CS). CS is a new sampling method for sparse signals, which is able to compress the input data in the sampling process. Combining both signal sampling and data compression, CS is more capable of image representation for reducing the computation complexity in image/video encoder in visual sensor networks where computation resource is extremely limited. Since CS is more efficient for sparse signals, in our scheme, the input image is firstly decomposed into two components, i.e., dense and sparse components; then the dense component is encoded by the traditional approach (JPEG or JPEG 2000) while the sparse component is encoded by a CS technique. In order to improve the rate distortion performance, we leverage the strong correlation between dense and sparse components by using a piecewise autoregressive model to construct a prediction of the sparse component from the corresponding dense component. Given the measurements and the prediction of the sparse component as initial guess, we use projection onto convex set (POCS) to reconstruct the sparse component. Our method considerably reduces the number of random measurements needed for CS reconstruction and the decoding computational complexity, compared to the existing CS methods. In addition, our experimental results show that our method may achieves up to 2dB gain in PSNR over the existing CS based schemes, for the same number of measurements.","Title: Design and Implementation of University Focused Crawler Based on BP Network Classifier\nYear: 2009\n\nAbstract: The rapid growth of the World-Wide web poses unprecedented scaling challenges for general-purpose crawlers and search engines. Crawling the Web quickly and entirely is an expensive, unrealistic goal because of the required hardware and network resources. A focused crawler is an agent that targets a particular topic and visits and gathers only a relevant, narrow web segment while trying not to waste resources on irrelevant material. It can be used to build domain-specific web search portals and online personalized search tools. In this paper, we describe the design and implementation of a university focused crawler that runs on BP network classifier for prediction of the links leading to relevant pages. We present the flow of the system, discuss the performance, report the experimental results based on it. Our experiments show that the BP classifier performs very well in obtaining accurate relevant university Web resources.","Title: A Compensation Method to Measure the Mutual Inductance at Low Frequency\nYear: 2011\n\nAbstract: Traditionally, the most precise way to get the mutual inductance value is by a Campbell calculable mutual inductor. Here, an approach of measuring the mutual inductance at low frequency with compensation method is described. A multi-channel direct digital frequency synthesis source is developed to provide stable and precise phase. The mutual inductance is traced to the value of the frequency and resistance directly. The measurement uncertainty is 0.43 ppm at present and could be improved further.","Title: On defining the service provided by TCP\nYear: 2003\n\nAbstract: The Transmission Control Protocol (TCP) is an important transport layer protocol providing a reliable data transfer service to support many applications running over the Internet such as the World Wide Web. It is therefore important to define the intent of TCP. This is achieved by describing the services it is intended to provide in an abstract way, known as a service definition. Several attempts have been made to define the services provided by TCP, however, they are all inadequate in various ways. In particular, most of them follow the applications interface defined in the TCP Request for Comments (RFC 793) too closely, making them rather implementation specific. The aim of our work is to provide an appropriately abstract definition of the TCP service, following the guidance of the Open Systems Interconnection service conventions, and to make it as general and as complete as possible. We define the service in terms of a set of 15 service primitives and their sequences, for TCP's main features of connection establishment, data transfer, urgent data transfer, orderly connection release, and aborting connections. The choice of primitives is discussed in terms of their relationship with the Interface Definition of RFC 793. We formalise the TCP service using Coloured Petri Nets, and from it generate the global sequences of service primitives for TCP's Connection Management services (including aborts). We believe this is the first time this has been achieved.","Title: A Quantum Genetic Algorithm to Solve the Problem of Multivariate.\nYear: 2011\n\nAbstract: In order to apply quantum genetic algorithm to solve multi-peak continuous function optimal problem, a novel quantum genetic algorithm referring to multi-variable problem is proposed. It operates variables upon a universal quantum chromosome collectively. Thus it can adjust evolutionary intensity dynamically and consider evolutionary orientation depending on generated function of rotation angle. Theoretical and experimental results show that, this quantum genetic algorithm can obtain better evolutionary speed and better performance of convergence on multi-variable problem.","Title: 3D dense reconstruction from 2D video sequence via 3D geometric segmentation\nYear: 2011\n\nAbstract: 3D reconstruction is a major problem in computer vision. This paper considers the problem of reconstructing 3D structures, given a 2D video sequence. This problem is challenging since it is difficult to identify the trajectory of each object point/pixel over time. Traditional stereo 3D reconstruction methods and volumetric 3D reconstruction methods suffer from the blank wall problem, and the estimated dense depth map is not smooth, resulting in loss of actual geometric structures such as planes. To retain geometric structures embedded in the 3D scene, this paper proposes a novel surface fitting approach for 3D dense reconstruction. Specifically, we develop an expanded deterministic annealing algorithm to decompose 3D point cloud to multiple geometric structures, and estimate the parameters of each geometric structure. In this paper, we only consider plane structure, but our methodology can be extended to other parametric geometric structures such as spheres, cylinders, and cones. The experimental results show that the new approach is able to segment 3D point cloud into appropriate geometric structures and generate accurate 3D dense depth map.","Title: Recent Development on the Joule Balance at NIM.\nYear: 2011\n\nAbstract: The precise measurement of Planck constant h to establish a kind of quantum mass standard is an important but very difficult work. The Joule Balance method, for the same purpose but different from Watt Balance method, has been going on at the National Institute of Metrology for three years. All the measurements were carried out in the atmosphere of an air-conditioned room. The uncertainty is sever...","Title: A measurement method of the DTMB modulator\nYear: 2011\n\nAbstract: In this paper, a measurement method for DTMB modulator which performs the estimation of timing and frequency offsets, channel impulse response (CIR), as well as modulation error ratio (MER) by using only two consecutive frames in an iterative manner is proposed to precisely measure the modulator performance in short time. Both simulation and lab testing results show that the measurement method could reflect the performance of DTMB modulator with high accuracy.","Title: A Remark on Distance Regular Graph with k = 10, a 1 = 1.\nYear: 2012\n\nAbstract: Distance-regular graph is a kind of highly regular graph. It is studied actively in recent years. The classification is one of the important contents in the study of Distance-regular graph. In this thesis we discuss the distance-regular graph with k = 10,a(1) = 1 by mean of intersection diagrams, circuit chasing techniques and properties of distance-regular graphs. We prove the following conclusion: Let Gamma be a distance-regular graph of k = 10,a(1) = 1, if c(r+2) = 4 and a(r+2) = 5, then c(d) = 10. It is useful to a classification of distance-regular graph of k = 10,a(1) =1","Title: A Revised Research Approach to the Defining of DEILPHI Weight Value Based on Objective Predilections\nYear: 2009\n\nAbstract: This paper analyzes the characteristics of the traditional DEILPHI method, pointing out that the method has the shortcomings of strong subjectivity, complex data acquisition, and lack of real-time circumstance-specific solutions. It proposes an improved DEILPHI method based on objective preferences. According to the characteristics of evaluation indicators and the specific situation of actual evaluation work, this method takes different preference data to build preference matrix and amend the original DEILPHI judging matrix. In addition, based on this improved method, the article also designs the experimental environment to authenticate the example.","Title: On compatibility of uncertain additive linguistic preference relations and its application in the group decision making\nYear: 2011\n\nAbstract: We develop a new compatibility for the uncertain additive linguistic preference relations and utilize it to determine the optimal weights of experts in the group decision making (GDM). Based on some operational laws for the uncertain additive linguistic preference labels, we propose some new concepts of the compatibility degree and acceptable compatibility index for the two uncertain additive linguistic preference relations. We also prove the properties that the synthetic preference relation is also of acceptable compatibility under the condition that additive linguistic preference relations provided by experts are all of acceptable compatibility with the specific linguistic preference relation, which provides a theoretic basis for the application of the uncertain additive linguistic preference relations in the GDM. Furthermore, we establish a mathematical model to obtain the weights of experts based on the criterion of minimizing the compatibility in the GDM, and we discuss the solution to the model. Finally, we give a numerical example to make comparative analysis on compatibility index using the optimal experts' weights approach and the equal experts' weights approach, which indicates that the model is feasible and effective.","Title: A shot boundary detection method for news video based on rough sets and fuzzy clustering\nYear: 2005\n\nAbstract: With the rapid growing amount of multimedia, content-based infomation retrieval has become more and more important. As a crucial step in content-based news video indexing and retrieval system, shot boundary detection attracts much more research interests in recent years. To partition news video into shots, many metrics were constructed to measure the similarity among video frames based on all the available video features. However, too many features will reduce the efficiency of the shot boundary detection. Therefore, it is necessary to perform feature reduction for every decision of shot boundary. For this purpose, the classification method based on rough sets and fuzzy c-means clustering for feature reduction and rule generation is proposed. According to the particularity of news scenes, shot transition can be divided into three types: cut transition, gradual transition and no transition. The efficacy of the proposed method is extensively tested on more than 2 h of news programs and 98.0% recall with 96.6% precision have been achieved.","Title: A Coloured Petri Net Approach to Protocol Verification\nYear: 2003\n\nAbstract: The correct operation of communication and co-operation protocols, including signalling systems in various networks, is essential for the reliability of the many distributed systems that facilitate our global economy. This paper presents a methodology for the formal specification, analysis and verification of protocols based on the use of Coloured Petri nets and automata theory. The methodology is illustrated using two case studies. The first belongs to the category of data transfer protocols, called Stop-and-Wait Protocols, while the second investigates the connection management part of the Internet's Transmission Control Protocol (TCP). Stop-and-Wait protocols (SWP) incorporate retransmission strategies to recover from data transmission errors that occur on noisy transmission media. Although relatively simple, their basic mechanisms are important for practical protocols such as the data transfer procedures of TCP. The SWP case study is quite detailed. It considers a class of protocols characterized by two parameters: the maximum sequence number (MaxSeqNo) and the maximum number of retransmissions (MaxRetrans). We investigate the operation of the protocol over (lossy) in-sequence (FIFO) channels, and then over (lossy) re-ordering media, such as that provided by the Internet Protocol. Four properties are considered: the bound on the number of messages that can be in the communication channels; whether or not the protocol provides the expected service of alternating sends and receives; (unknowing) loss of messages (i.e. data sent but not received, and not detected as lost by the protocol); and the acceptance of duplicates as new messages. The model is analysed using a combination of hand proofs and automatic techniques. A new result for the bound of the channels (2MaxRetrans+1) is proved for FIFO channels. It is further shown that for re-ordering channels, the channels are unbounded, loss and duplication can occur, and that the SWP does not provide the expected service. We discuss the relevance of these results to the Transmission Control Protocol and indicate the limitations of our approach and the need for further work. The second case study (TCP) illustrates the use of hierarchies to provide a compact and readable CPN model for a complex protocol. We advocate an incremental approach to both modelling and analysis. The importance of stating the assumptions involved is emphasised and we illustrate how they affect the abstractions that can be made to simplify the model. The incremental approach to analysis allows us to validate the model against the TCP definition and to show how errors in the connection establishment procedures can be found. Finally we provide some observations and tips on the how the methodology can be used based on many years of experience. The emphasis of the paper is on providing a tutorial style introduction to the methodology, examining case studies in depth, rather than breadth, and giving some insight into the process while noting its limitations.","Title: Generalized intuitionistic fuzzy geometric interaction operators and their application to decision making\nYear: 2014\n\nAbstract: Considering that there may exist some interactions between membership function and non-membership function of different intuitionistic fuzzy sets, we present some new operational laws from the probability point of view and give a geometric interpretation of the new operations. Based on which, a new class of generalized intuitionistic fuzzy aggregation operators are developed, including the generalized intuitionistic fuzzy weighted geometric interaction averaging (GIFWGIA) operator, the generalized intuitionistic fuzzy ordered weighted geometric interaction averaging (GIFOWGIA) operator and the generalized intuitionistic fuzzy hybrid geometric interaction averaging (GIFHGIA) operator. The properties of these new generalized aggregation operators are investigated. Moreover, approaches to multiple attributes decision making are given based on the generalized aggregation operators under intuitionistic fuzzy environment, and an example is illustrated to show the validity and feasibility of new approach. Finally, we give a systematic comparison between the work of this paper and that of other papers.","Title: An adaptive dynamic evolution feedforward neural network on modified particle swarm optimization\nYear: 2009\n\nAbstract: In order to improve the generalization capacity of neural networks for poorly known nonlinear dynamic system with long time-delay, a novel adaptive dynamic feedforward neural network on modified Particle Swarm Optimization (PSO) algorithm is proposed. The adaptive time delay operator is adopted between input layer and the first hidden layer, and also the last hidden layer and output layer. Utilizing these dynamic time delay parameters, the proposed structure can adequately identify different classes of nonlinear systems expressed in the input-output representation form and pure time delay. Otherwise, to overcome the particles' premature convergence, the white noise and Logistic mapping are used to enhance the particles' search performance. Furthermore, the parameters in the dynamic feedforward neural network are trained by the modified PSO method. The proposed neural network shows a satisfactory global search and quick convergence capability, avoiding the complexity of gradient calculation. Simulation results demonstrate that the proposed algorithm is effective and accurate in identifying long-time delay nonlinear systems through the comparison with other methods.","Title: Modelling and analysing the functional behaviour of TCP’s connection management procedures\nYear: 2007\n\nAbstract: The transmission control protocol (TCP) is the most widely used transport protocol in the Internet. It provides a reliable data transfer service to many applications. In this paper, Coloured Petri Nets are used to model TCP’s connection management procedures. The model is created to verify TCP’s functional correctness (e.g. the absence of deadlocks and livelocks). We discuss different modelling approaches to motivate the approach taken. The paper defines the termination property of TCP’s connection management procedures, including the notions of desired and acceptable terminal states. Finally, we analyse TCP’s connection management procedures operating over re-ordering non-lossy and lossy channels. This is done incrementally and considers 11 different configurations. The analysis provides some insights into TCP’s behaviour where in certain circumstances the protocol fails to establish or terminate successfully.","Title: GReplica: A Web-based Data Grid Replica Management System\nYear: 2005\n\nAbstract: In wide-area distributed computing systems, it is often desirable to create remote copies (replicas) of files. File replication can be used to reduce access latency, improve data locality, and/or increase robustness, scalability and performance for distributed applications. In this paper, we describe a Web-based data grid replica management system (we call it GReplica) developed in Globus Toolkit.","Title: An Adaptive Location Service for Wireless Sensor Networks with Mobile Sinks\nYear: 2006\n\nAbstract: How to provide the sensor nodes with the locations of mobile sinks with minimum energy consumption is a key issue to the performance of the geographic location based routing protocols. In this paper, we investigate three location update methods: Neighbour update (NUPD), Flooding update (FUPD) and Unicast update (UUPD). We get the analytical expression of energy consumption of each method. Based on the comparison results of the performance of these methods under various network conditions, we propose an adaptive location service for mobile sinks in WSN, which enables the mobile sinks to send their current location to the sensor nodes efficiently according to various data traffic characteristics, network size and node density. We evaluate the performance of our location service by comprehensive simulations and compared it with the TTDD protocol. The results show that while keeping the packet delay below an upper bound, our location update algorithm can effectively decrease the energy consumption thus prolong the network lifetime.","Title: Optimizing Multi-hop Queries in ZigBee Based Multi-sink Sensor Networks\nYear: 2009\n\nAbstract: Wireless sensor networks with multiple users collecting data directly from the sensors have many potential applications. An important problem is to allocate for each user a query range to achieve certain global optimality while avoid congesting the sensors in the meanwhile. We study this problem for a ZigBee cluster tree by formulating it into a multi-dimensional multi-choice knapsack problem. Maximum overall query range and max-min fair query range objectives are investigated. Distributed algorithms are proposed which exploit the ZigBee cluster tree structure to keep the computation local. Extensive simulations show that the proposed methods achieve good approximation to the optimal solution with little overhead and improve the network performance.","Title: Automatic news audio classification based on selective ensemble SVMs\nYear: 2005\n\nAbstract: With the rapid growing amount of multimedia, content-based information retrieval has become more and more important. As a significant clue for video indexing and retrieval, audio detection and classification attracts much more attention and becomes a hot topic. On the basis of the priori model of news video structure, a selective ensemble support vector machines (SE-SVMs) is proposed to detect and classify the news audio into 4 types, i.e., silence, music, speech, and speech with music background. Experiments with news audio clips of 8514 seconds in total length illustrate that the average accuracy rate of the proposed audio classification method reaches to 98.9%, which is much better than that of the available SVM-based or traditional threshold-based method.","Title: Colorimetric characterisation of low-end digital camera and its application for on-screen texture visualisation\nYear: 2000\n\nAbstract: Along with the industrial success of precise colour communication using calibrated computer screens, images captured by digital cameras are widely used for realistic product simulation and true colour communication through the computer network. The need to simulate texture, through its precise micro-level colour variation characteristics, as opposed to simulating and presenting an overall pleasing picture of an object, cannot be over-stressed. This study compares the performance of a high-end and a low-end digital camera in terms of accuracy of colorimetric characterisation and WYSIWYG (what you see is what you get) colour texture simulation","Title: FPGA implementation of QR decomposition for MIMO-OFDM using four CORDIC cores\nYear: 2013\n\nAbstract: A low-resource implementation of QR decomposition (QRD) is proposed for MIMO-OFDM systems that employ turbo MMSE-IC (Minimum mean square error interference cancellation) detector. The proposed implementation uses only four CORDIC cores for QR decomposition of complex-valued matrices while existing systolic array architecture requires 15 to 38 CORDIC cores to achieve similar throughput. For 4-by-4 MIMO with 1024 subcarrier OFDM, the turbo MMSE-IC requires 1024 QRD of 4-by-4 complex matrices followed by back substitution. Each matrix QRD is accompanied by rotations of four vectors for the second and subsequent turbo iterations. The proposed architecture implements the multiple vector rotation by multipliers rather than CORDIC cores, thus saving resources. When combined with a 2-dimensional array of back substitution, the proposed QRD architecture can achieve 64-matrix pipelining and a throughput of seven clocks per matrix for MMSE-IC calculation. The proposed MMSE-IC architecture has been implemented in Altera Stratix IV FPGA with LDPC decoding and other baseband receiver components.","Title: Circle-Based Improvement Strategy of Simulated Annealing Genetic Algorithm.\nYear: 2012\n\nAbstract: Circular regulation is an important law of bionomics. Simulated annealing genetic algorithm is an effective method of improving genetic algorithm. Combining circular strategy with simulated annealing genetic algorithm efficiently, a novel simulated annealing genetic algorithm applying circular strategy is proposed. And it is justified according to schema evolution analysis and convergence analysis. It can not only assure the capability of global convergence, but also accelerate the evolution of colony and acquire the satisfactory global optimal solution.","Title: A biological inspired features based saliency map\nYear: 2012\n\nAbstract: A visual attention mechanism is believed to be responsible for the most informative spots in complex scenes. We proposed a novel biologically inspired attention model based on Cortex-like mechanisms and sparse representation. Biological Inspired model, HMAX model, is a feature extraction method and this method is motivated by a quantitative model of visual cortex. This biological inspired feature will be used to build the Saliency Criteria to measure the perspective fields. Saliency Criteria is obtained from Shannon's information entropy and sparse representation. We demonstrate that the proposed model achieves superior accuracy with the comparison to classical approach in static saliency map generation on real data of natural scenes and psychology stimuli patterns.","Title: A software to generate finite element meshes for patient-specific denture\nYear: 2012\n\nAbstract: Finite element modeling has been widely used in theoretical study of orthodontic biomechanics. However, the dental structure is complicate and it is time-consuming for traditional finite element meshes method. This restricts the application of finite element analysis of individual orthodontic treatment in clinic. In this paper, a novel software is developed, which includes the functions of semi-automatic teeth segmentation of patient-specific denture, 3D reconstruction and visualization of dental structure, 3D measurement of geometrical characteristics, simulation of tooth movement, and automatic generation of finite element meshes. This software may be used for patient-specific biomechanical analysis in orthodontics.","Title: Medium-Earth-Orbit SAR Focusing Using Range Doppler Algorithm With Integrated Two-Step Azimuth Perturbation\nYear: 2015\n\nAbstract: Existing low-Earth-orbit synthetic aperture radar (SAR) algorithms generally assume that the data are azimuth invariant. However, this assumption does not hold for the medium-Earth-orbit (MEO) SAR systems due to the significantly longer azimuth integration time and complex imaging geometries. As a result, the MEO SAR data cannot be processed accurately and efficiently using the existing algorithms. To solve this problem, this letter proposes a two-step azimuth perturbation (AP) method that uses the first-step AP to remove the bulk azimuth variance at the range processing stage and the second-step AP to remove the residual variance at the azimuth processing stage. As an example, an improved range Doppler algorithm with the integrated two-step AP is discussed in this letter. Simulations of an L-band MEO SAR with 5-m resolution at 10 000-km orbit height are used to demonstrate the validity and accuracy of this algorithm.","Title: Shape-Constrained Sparse and Low-Rank Decomposition for Auroral Substorm Detection.\nYear: 2016\n\nAbstract: An auroral substorm is an important geophysical phenomenon that reflects the interaction between the solar wind and the Earth's magnetosphere. Detecting substorms is of practical significance in order to prevent disruption to communication and global positioning systems. However, existing detection methods can be inaccurate or require time-consuming manual analysis and are therefore impractical for large-scale data sets. In this paper, we propose an automatic auroral substorm detection method based on a shape-constrained sparse and low-rank decomposition (SCSLD) framework. Our method automatically detects real substorm onsets in large-scale aurora sequences, which overcomes the limitations of manual detection. To reduce noise interference inherent in current SLD methods, we introduce a shape constraint to force the noise to be assigned to the low-rank part (stationary background), thus ensuring the accuracy of the sparse part (moving object) and improving the performance. Experiments conducted on aurora sequences in solar cycle 23 (1996-2008) show that the proposed SCSLD method achieves good performance for motion analysis of aurora sequences. Moreover, the obtained results are highly consistent with manual analysis, suggesting that the proposed automatic method is useful and effective in practice.","Title: Construction, Measurement, Shimming, and Performance of the NIST-4 Magnet System\nYear: 2014\n\nAbstract: The magnet system is one of the key elements of a watt balance. For the new watt balance currently under construction at the National Institute of Standards and Technology, a permanent magnet system was chosen. We describe the detailed construction of the magnet system, first measurements of the field profile, and shimming techniques that were used to achieve a flat field profile. The relative change of the radial magnetic flux density is <; 10-4 over a range of 5 cm. We further characterize the most important aspects of the magnet and give order of magnitude estimates for several systematic effects that originate from the magnet system.","Title: A shape-initialized and intensity-adaptive level set method for auroral oval segmentation.\nYear: 2014\n\nAbstract: •An intensity-adaptive level set is proposed to obtain the auroral oval boundary.•A saliency morphological map is constructed to initialize the level set.•The proposed method can segment not only full oval images, but also the gap oval ones.•The segmentation result is a complete boundary without any leak.","Title: An Iterative Algorithm To Process The Top-K Query For The Wireless Sensor Networks\nYear: 2015\n\nAbstract: The top-k query is an important type of query for the wireless sensor network. In this paper, we present an iterative algorithm to process the top-k query, which is a distributed algorithm combining the in-network aggregation and the trace back techniques. By using the in-network aggregation technique, the iterative algorithm calculates the current maximum value in the network. By using the trace back technology, the maximum value just calculated is removed from the network. The two steps are repeated k times. As the current maximum value in the network is selected per round, the answer to a top-k query can be obtained after k repetitions. Experimental results show that the iterative algorithm can reduce the number of messages transmitted during the procedure of the top-k query processing.","Title: Dayside aurora classification via BIFs-based sparse representation using manifold learning\nYear: 2014\n\nAbstract: Aurora is the typical ionosphere track generated by the interaction of solar wind and magnetosphere, whose modality and variation are significant to the study of space weather activity. A new aurora classification algorithm based on biologically inspired features (BIFs) and discriminative locality alignment (DLA) is proposed in this paper. First, an aurora image is represented by the BIFs, which combines the C1 units from the hierarchical model of object recognition in cortex and the gist features from the saliency map; then, the manifold learning method called DLA is used to obtain the effective sparse representation for auroras based on BIFs; finally, classification results using support vector machine and nearest neighbour with three sets of features: the C1 unit features, the gist features and the BIFs illustrate the effectiveness and robustness of our method on the real aurora image database from Chinese Arctic Yellow River Station.","Title: Improving scenario discovery using orthogonal rotations\nYear: 2013\n\nAbstract: Scenario discovery offers a new means to characterize and communicate the information in computer simulation models under conditions of deep uncertainty. The approach first defines scenarios as the future states of world where a proposed policy fails to meet its goals and then uses statistical algorithms to find concise descriptions of such regions in large databases of simulation model results. Current scenario discovery applications rely on the Patient Rule Induction Method (PRIM), a user-interactive bump-hunting algorithm that identifies hyper-rectangular regions in the input space of the simulation model. While often successful, scenario discovery applications have been limited because in general a policy's vulnerabilities are not well described by the PRIM's hyper-rectangular regions. This study proposes and evaluates improved scenario discovery algorithms that address this challenge with a Principal Component Analysis (PCA)-based preprocessing step that transforms the original model input parameters so that PRIM can then identify high quality hyper-rectangular scenarios in the new rotated coordination system. We explore two versions. PCA-PRIM allows rotations among all uncertain model input parameters and CPCA-PRIM (for constrained PCA-PRIM) only allows rotations among parameters within user-specified domains. The latter may provide more useful information to users, who may find scenario axes described by linear combinations of related domain parameters more interpretable than combinations of dissimilar parameters. We run two sets of tests on the PCA-PRIM and CPCA-PRIM algorithms, the first using simulated test date and the second results from a model used in a previous RAND study of the cost-effectiveness of renewable energy portfolio standards. We find that the new algorithms produce higher quality scenarios than PRIM alone as evaluated by several important measures of merit. In the test data, PCA-PRIM produces improvements averaging 37 percent, and CPCA-PRIM averaging 14 percent, over PRIM alone. In the renewable energy policy case study, PCA-PRIM and CPCA-PRIM exhibit similar improvements of about 16 percent over PRIM, and CPCA-PRIM generates scenarios interpretable by, and that provide useful information to, decision makers.","Title: Efficient implementation of iterative multi-input–multi-output orthogonal frequency-division multiplexing receiver using minimum-mean-square error interference cancellation\nYear: 2014\n\nAbstract: An efficient hardware implementation scheme is proposed for iterative multi-input-multi-output orthogonal frequency-division multiplexing receiver which includes an MMSE-IC (minimum-mean-square error interference cancellation) detector, a channel estimator, a low-density parity-check (LDPC) decoder and other supporting modules. The proposed implementation uses the QR decomposition (QRD) of the complex-valued matrices with four coordinate rotation digital computer (CORDIC) cores and a back substitution to solve the MMSE-IC equations while the existing systolic array architectures require 15-38 CORDIC cores to achieve a similar throughput. The proposed 4-CORDIC QRD architecture can be configured as a 16-matrix or a 64-matrix pipelining by using a different number of multipliers combined with one-dimensional (1D) or 2D arrays of the back substitution, respectively. The channel estimator implements a commonly-used frequency domain least squares channel estimation with the canonic-signed-digits method, thanks to the character of the Zadroff-Chu sequence used as the pilot. In the LDPC decoder, the min-sum algorithm is implemented for the quasicyclic LDPC decoding. The two schemes for the MMSE-IC detector with different throughput and resource usages have been implemented in a Field Programmable Gate Array for a complete baseband turbo receiver. Their resource usages, throughputs and latencies are compared with the classic systolic array architectures, which demonstrate that the proposed receiver architecture achieves the best tradeoff between the throughput and the resource usage.","Title: Anatomical Structure Sketcher For Cephalograms By Bimodal Deep Learning\nYear: 2013\n\nAbstract: The lateral cephalogram is a commonly used medium to acquire patient-specific morphology for diagnose and treatment planning in clinical dentistry. The robust anatomical structure detection and accurate annotation remain challenging considering the personal skeletal variations and image blurs caused by device-specific projection magnification, together with structure overlapping in the lateral cephalograms. We propose a novel cephalogram sketcher system, where the contour extraction of anatomical structures is formulated as a cross-modal morphology transfer from regular image patches to arbitrary curves. Specifically, the image patches of structures of interest are located by a hierarchical pictorial model. The automatic contour sketcher converts the image patch to a morphable boundary curve via a bimodal deep Boltzmann machine. The deep machine learns a joint representation of patch textures and contours, and forms a path from one modality (patches) to the other (contours). Thus, the sketcher can infer the contours by alternating Gibbs sampling along the path in a manner similar to the data completion. The proposed method is robust not only to structure detection, but also tends to produce accurate structure shapes and landmarks even in blurry X-ray images. The experiments performed on clinically captured cephalograms demonstrate the effectiveness of our method.","Title: Investigation of Degradation Factors for Adaptive Modulation and Coding in OFDM-MIMO Multiplexing\nYear: 2014\n\nAbstract: This paper investigates the throughput performance using adaptive modulation and coding (AMC) focusing on the causes of impairment to Orthogonal Frequency Division Multiplexing (OFDM) - Multiple-Input Multiple-Output (MIMO) multiplexing. We first investigate the influence of a limited number of modulation and coding schemes (MCSs) and channel estimation error for MCS selection based on mutual information (MI). Second, we clarify the effect of MCS selection error on an increasing maximum Doppler frequency due to the round trip delay time (RTT) in comparison to the throughput upper bound that is computed from the MCS combination providing the maximum throughput considering the block error. Third, we clarify the effect of channel estimation error of maximum likelihood detection (MLD) when using reference signal (RS) based channel estimation in comparison to ideal channel estimation. Through the evaluations, we clarify the impairment factors that degrade the achievable throughput for OFDM-MIMO multiplexing using AMC in an actual multipath fading channel.","Title: Effect of joint modulation and coding scheme selection on turbo SIC for OFDM-MIMO multiplexing\nYear: 2013\n\nAbstract: This paper presents the effect of joint modulation and coding scheme (MCS) selection on a turbo soft interference canceller (SIC) for OFDM-MIMO multiplexing using adaptive modulation and coding (AMC). We use a turbo SIC to detect signals by performing iterative interference cancellation using soft-symbol estimates based on the a posteriori log-likelihood ratio (LLR) at the Max-Log-MAP decoder output and linear minimum mean-square error (LMMSE)-based signal detection. Joint MCS section selects the best MCS combinations of all transmission streams so that the total throughput is maximized considering the residual interference from other streams for the turbo SIC. Computer simulations show that the joint MCS selection mitigates the decrease in throughput due to the large difference in the peak data rate between consecutive MCSs compared to independent MCS selection. Hence, we show that by using joint MCS selection, the total throughput with the number of MCSs of NMCS = 8 becomes almost identical to that for NMCS = 12 for 4-by-4 MIMO multiplexing using the turbo SIC.","Title: Evaluation Of Texture Mapping Algorithms\nYear: 2004\n\nAbstract: Five texture mapping algorithms were developed for simulating textures of physical objects on a CRT. Twenty-one thread winding cards with different colours were captured by a digital camera for generating texture profiles which were used to describe the statistical colour distributions of images. A pair comparison method was used to assess the performance of various texture mapping algorithms. A total of 42000 judgements were made by a panel of 10 normal colour vision observers. The results showed that realistic appearance can be achieved by mapping a single set of tristimulus values onto a texture profile specified by the colour distribution only in luminance factor channel.","Title: Higher Rank Principal Kronecker Model for Triply Selective Fading Channels With Experimental Validation\nYear: 2015\n\nAbstract: This paper proposes a higher rank principal Kronecker model (PKM) for simulating triply selective fading channels. To construct the PKM, the channel correlation matrices are decomposed using the higher order singular value decomposition (HOSVD) method. The proposed PKM-HOSVD model improves upon the original Kronecker model by using higher rank approximation of the channel correlation matrices rather than the rank-1 approximation. The proposed PKM-HOSVD model was validated by extensive field experiments conducted for 4 × 4 multiple-input-multiple-output (MIMO) systems in both indoor and outdoor environments. The carrier frequencies used included 800 MHz, 2.2 GHz, and 5.2 GHz. The channel correlation matrices calculated from the measured channel coefficients were then decomposed via the proposed PKM-HOSVD method. The quality of the decomposition was evaluated by not only the mean square error but also the correlation matrix distance. These results indicate that many practical channels must use higher rank approximation rather than the commonly used rank-1 approximation (or the Kermoal method) to achieve satisfactory decomposition accuracy. In addition, the predicted channel capacity by the proposed channel simulation model achieves better accuracy than the original rank-1 channel simulation model.","Title: Coils and the Electromagnet Used in the Joule Balance at the NIM\nYear: 2015\n\nAbstract: In the joule balance developed at National Institute of Metrology, the dynamic phase of a watt balance is replaced by the mutual inductance measurement in an attempt to provide an alternative method for the kilogram redefinition. However, for this method a rather large current in the exciting coil, is needed to offer the necessary magnetic field in the force weighing phase, and the coil heating becomes an important uncertainty source. To reduce coil heating, a new coil system, in which a ferromagnetic material is used to increase the magnetic field was designed recently. However, adopting the ferromagnetic material brings the difficulty from the nonlinear characteristic of material. This problem can be removed by measuring the magnetic flux linkage difference of the suspended coil at two vertical positions directly to replace the mutual inductance parameter. Some systematic effects of this magnet are discussed.","Title: Throughput Analyses Based On Practical Upper Bound For Adaptive Modulation And Coding In Ofdm Mimo Multiplexing\nYear: 2016\n\nAbstract: This paper proposes a practical throughput upper bound that considers physical layer techniques using adaptive modulation and coding (AMC) for orthogonal frequency division multiplexing (OFDM) multiple-input multiple-output (MIMO) multiplexing. The proposed upper bound is computed from the modulation and coding scheme (MCS) that provides the maximum throughput considering the required block error rate (BLER) at the respective received signal-to-noise power ratios as a constraint. Then, based on the practical throughput upper bound, we present the causes of impairment for selecting the best MCS based on the computed mutual information for OFDM MIMO multiplexing. More specifically, through the evaluations, we investigate the effect of MCS selection error on an increasing maximum Doppler frequency due to the round trip delay time and the effect of channel estimation error of maximum likelihood detection associated with reference signal based channel estimation.","Title: Mathematical model of precipitation of magnesium carbonate with carbon dioxide from the magnesium hydroxide slurry.\nYear: 2016\n\nAbstract: •A novel model for magnesium carbonation in heterogeneous reactor is included.•The model combining dissolution, absorption and precipitation is solved.•Nernst-Planck equation is used to maintain mass balance and electroneutrality.•The effects of size of dissolving particles and enhancement factor are included.•The interaction between the complex physical processes is analyzed.","Title: Monitoring the Weibull Shape Parameter with Type II Censored Data\nYear: 2015\n\nAbstract: In this article, we introduce a method for monitoring the Weibull shape parameter with type II (failure) censored data. The control limits depend on the sample size, the number of censored observations, the target average run length, and the stable value of . The method assumes that the scale parameter is constant during each sampling period, which is true under rational subgrouping. The proposed method utilizes the relationship between Weibull and smallest extreme value distribution. We propose an unbiased estimator of sigma=1/ as the monitoring statistic. We derive the control limits for one-sided and two-sided charts for several stable process average run lengths. We discuss two schemes, namely, the control-limits-only scheme and the control-limits-with-warning-lines scheme. The stable process average run length performance of the proposed charts is studied and compared with those of other charts for monitoring under similar assumptions. Copyright (c) 2014 John Wiley & Sons, Ltd.","Title: 2DPCANet: Dayside Aurora Classification Based on Deep Learning.\nYear: 2015\n\nAbstract: The mysterious and beautiful aurora represents various physical meaning, thus the classification of aurora images have significant scientific value to human beings. Principal component analysis network (PCANet) has achieved good results in classification. But when using PCANet to extract the image features, it transform original image into a vector, so that the structure information of the image are missing. Compared with PCA, 2DPCA is based on 2D image matrices rather than 1D vectors so that 2DPCA can use the structure information of original image more efficiently and reduce the computational complexity. Based on PCANet, we develop a classification method of aurora images, 2-dimension PCANet (2DPCANet). To evaluate 2DPCANet performance, a series of experiments were performed on two different aurora databases. The classification rate across all experiments was higher using 2DPCANet than PCANet. The experiment results also indicated that the classification time is shorter using 2DPCANet than PCANet.","Title: Auroral Oval Boundary Modeling Based on Deep Learning Method.\nYear: 2015\n\nAbstract: Research on the location of the auroral oval is important to understand the coupling processes of the Sun-Earth system. The equatorward boundary and poleward boundary of the auroral oval are significant parameters of the auroral oval location. Thus auroral oval boundary modeling is an efficient way to study the location of auroral oval. As the location of the auroral oval boundary is subject to a variety of geomagnetic factors, there are some limitations on traditional methods, which express the auroral oval boundary as a function of only one or several geomagnetic activity index. Deep learning method is used in this paper to learn the essential features of the inputs, which are a large number of geomagnetic parameters and the former locations of aurora boundary. Furthermore, a model is established to forecast the location of the auroral oval boundary. The experiment results show that our method can model and forecast the boundary of aurora oval efficiently on the data set obtained from Ultraviolet Imager UVI on Polar satellite and OMNI database on NASA.","Title: Self-Protecting JavaScript Based on Hierarchical Control\nYear: 2014\n\nAbstract: Self-protecting JavaScript can prevent malicious code execution by wrapping security relevant API calls and embedding security policy into the code. This mechanism is easy to use but hold high run-time overhead. This paper introduces hierarchical policy for self-protecting JavaScript. We make the division in accordance with the frequency and control mode of policy and mainly reduce the number of times policies were called. The new approach is able to ensure safety and decrease run-time overhead significantly.","Title: Public information and uninformed trading: Implications for market liquidity and price efficiency.\nYear: 2016\n\nAbstract: We develop a rational expectations equilibrium model in which noise trading comes from discretionary liquidity traders. The equilibrium quantity of aggregate noise trading is endogenously determined by the population size of liquidity traders active in the financial market. By improving market liquidity, public information reduces the expected trading loss of liquidity traders and thus attracts more such traders to the market, which negatively affects information aggregation. Analyzing an alternative setting that models noise trading as coming from hedgers yields similar insights. In a setting with endogenous information, public information can harm information aggregation both through crowding out private information and through attracting noise trading.","Title: Probabilistic Deep Spiking Neural Systems Enabled by Magnetic Tunnel Junction.\nYear: 2016\n\nAbstract: Deep spiking neural networks are becoming increasingly powerful tools for cognitive computing platforms. However, most of the existing studies on such computing models are developed with limited insights on the underlying hardware implementation, resulting in area and power expensive designs. Although several neuromimetic devices emulating neural operations have been proposed recently, their funct...","Title: BDSCyto: An Automated Approach for Identifying Cytokines Based on Best Dimension Searching.\nYear: 2016\n\nAbstract: We proposed an automated method for distinguishing cytokines from other proteins according to their primary sequences. Two strategies were employed to extract features from protein sequences. The first one is a single method, which includes autocorrelation and pseudo amino acid composition extracted feature methods based on composition and physical-chemical properties of proteins; while the second one is an optimal dimension searching method. Moreover, we developed BDSCyto as a web server to help researchers in classifying protein sequences efficiently and accurately. BDSCyto reduces the processing time and offers high accuracy by a series of efficient methods and multithreading technology based on Spark for large-scale data. Currently, numerous methods exceed 90 % accuracy in cytokine protein prediction, which is better than the existing single methods. BDSCyto is an open-source project and can be freely accessed by the public at http://bdscyto.sinaapp.com/","Title: An Approach To Linguistic Group Decision Making Based On Unbalanced Linguistic Generalized Power Aggregation Operator\nYear: 2016\n\nAbstract: Linguistic group decision making (GDM) always focuses on balanced linguistic terms sets, which are uniform and symmetrical. However, in real life there exist GDM problems that need to be assessed by linguistic terms sets with non-symmetrical and non-uniform distributions. The purpose of this paper is to present an unbalanced linguistic generalized power ordered weighted averaging (ULGPOWA) operator for aggregating unbalanced linguistic variables in GDM. The main characteristic of ULGPOWA operator is that the relationship among linguistic variables being aggregated is taken into account by using a support measure for each argument from others. Then properties of new operator are developed and an illustrative example is given to demonstrate the detailed process of proposed method, and the validity is verified by sensitivity analysis for the new approach.","Title: On Equivalence Of Two Approaches To Group Decision Making With Interval Multiplicative Preference Relation Based On Cowg Operator\nYear: 2016\n\nAbstract: We propose two new approaches to rank all the alternatives in the group decision making (GDM) with interval multiplicative preference relation (IMPR) and point out their equivalence on solutions, based on the continuous ordered weighted geometric (COWG) operator. First we introduced the COWG operator and used it to aggregate the individual IMPR into crisp one, which can be obtained the priority vector by eigenvector method. Then we develop two new approaches to derive the group priority vector. One is the weighted geometric mean method (WGMM) of all individual priority vectors under the condition of alpha - acceptable consistency of multiplicative preference relation. The other is the relative entropy method (REM), in which an optimization model is constructed to minimize the difference between the group priority vector and all individual priority vectors based on the conception of relative entropy. We find that the REM has the same solution with WGMM, which provides the optimal theory basis of WGMM. Finally, a numerical example is illustrated to rank the given alternatives in the GDM with IMPR and show that the REM is feasible.","Title: On the energy benefits of spiking deep neural networks: A case study.\nYear: 2016\n\nAbstract: Deep learning neural networks have achieved success in a large number of visual processing tasks and are currently utilized for many real-world applications like image search and speech recognition among others. However, in spite of achieving high accuracy in such classification problems, they involve significant computational resources. Over the past few years, artificial neural network models have evolved into the biologically realistic and event-driven spiking neural networks. Recent research efforts have been directed at developing mechanisms to convert traditional deep artificial nets to spiking nets where the neurons communicate by means of spikes. However, there have been limited studies providing insights on the specific power, area and energy benefits offered by deep spiking neural nets in comparison to their non-spiking counterparts. In this paper, we perform a case study for a hardware implementation of a spiking/non-spiking deep net on the MNIST dataset and clearly outline the design prospects involved in implementing neural computing platforms in the spiking mode of operation.","Title: Digitalizing Seismograms Using a Neighborhood Backtracking Method.\nYear: 2016\n\nAbstract: In this paper, we present a new algorithm for tracing waves on seismograms and digitalising the waves into single vectors in the form of a time series data. The algorithm consists of two main components that will be used to handle the smooth and complicate cases, respectively. The underlying feature of the algorithm lies in a novel searching process based on examining the context of pixels to ascertain how the tracing moves forward. The algorithm has been evaluated on a limited number of samples and the result demonstrates its competence. The work presented can be regarded as an effort of developing a uniform earthquake archive covering both the historical and the digital device periods for future reassessment of the seismic hazard cross the world. The archive developed will serve as an effective means for discovering precursor of earthquakes by characterising the spectral seismograms and the source parameters of less active sources, whereby permitting comparative studies on earthquakes and development of prediction models.","Title: A superpixel-based CRF saliency detection approach.\nYear: 2017\n\nAbstract: Salient object detection, experienced several decades, has been an active and popular topic in computer vision. Although a large amount of detection algorithms have been proposed, the obtained saliency maps are still not satisfying enough. To this end, we proposed a simple and novel supervised algorithm to detect a pure background saliency map using conditional random fields (CRF) and saliency cues. Most existing CRF approaches set up the probabilistic graphical models with pixel-wise eight neighborhood grid-shaped graph, while our superpixel level graph handling can not only simplify the model but also promote the performance due to the superpixel level two-ring with pseudo-background neighborhood system. It is intuitive and easy to interpret. As a result, the saliency maps generated by the proposed model have relatively pure background regions. Extensive experimental evaluations on six benchmark datasets with pixel-wise ground truths validated the robustness and effectiveness of the proposed saliency model.","Title: Enhanced Successive Cancellation List Decoding of Polar Codes.\nYear: 2017\n\nAbstract: Polar codes can be efficiently decoded by the successive cancellation list (SCL) algorithm in which the sorter plays an important role. The sorter, however, is time-consuming. We significantly improve this decoding scheme by developing a new non-sorting direct selection method for the SCL algorithm. In the non-sorting strategy, we design a new selector for choosing the L best surviving paths, which is achieved by exploiting the bitwise comparison of the candidate path metrics. The analysis and simulation results show that, for M-bit length path metrics, our direct selection method with time complexity O(ML) is superior to the latest simplified bubble sorter and the quick sorting strategy whose time complexity is O(ML 2 ) and O(ML log L), respectively. In the scenario of short codeword length and large list size, our selector can remarkably reduce the latency and the hardware resource consumption while not assuming that the metrics in the decoding are sorted.","Title: Development of a Semi-Analytical Algorithm for the Retrieval of Suspended Particulate Matter from Remote Sensing over Clear to Very Turbid Waters.\nYear: 2016\n\nAbstract: Remote sensing of suspended particulate matter, SPM, from space has long been used to assess its spatio-temporal variability in various coastal areas. The associated algorithms were generally site specific or developed over a relatively narrow range of concentration, which make them inappropriate for global applications (or at least over broad SPM range). In the frame of the GlobCoast project, a large in situ data set of SPM and remote sensing reflectance, R-rs(lambda), has been built gathering together measurements from various coastal areas around Europe, French Guiana, North Canada, Vietnam, and China. This data set covers various contrasting coastal environments diversely affected by different biogeochemical and physical processes such as sediment resuspension, phytoplankton bloom events, and rivers discharges (Amazon, Mekong, Yellow river, MacKenzie, etc.). The SPM concentration spans about four orders of magnitude, from 0.15 to 2626 g center dot m(-3). Different empirical and semi-analytical approaches developed to assess SPM from R-rs(lambda) were tested over this in situ data set. As none of them provides satisfactory results over the whole SPM range, a generic semi-analytical approach has been developed. This algorithm is based on two standard semi-analytical equations calibrated for low-to-medium and highly turbid waters, respectively. A mixing law has also been developed for intermediate environments. Sources of uncertainties in SPM retrieval such as the bio-optical variability, atmospheric correction errors, and spectral bandwidth have been evaluated. The coefficients involved in these different algorithms have been calculated for ocean color (SeaWiFS, MODIS-A/T, MERIS/OLCI, VIIRS) and high spatial resolution (LandSat8-OLI, and Sentinel2-MSI) sensors. The performance of the proposed algorithm varies only slightly from one sensor to another demonstrating the great potential applicability of the proposed approach over global and contrasting coastal waters.","Title: The Influence of Different Spatial Resolutions on the Retrieval Accuracy of Sea Surface Wind Speed With C-2PO Models Using Full Polarization C-Band SAR.\nYear: 2017\n\nAbstract: This paper presents a comparison strategy for investigating the influence of spatial resolutions on sea surface wind speed retrieval accuracy with cross-polarized synthetic aperture radar images. First, for wind speeds retrieved from vertical transmitting-vertical receiving (VV)-polarized images, the optimal geophysical C-band model (CMOD) function was selected among four CMOD functions. Second, t...","Title: Joint Monitoring of Pipeline-Flood Based on FBG and Zigbee Technologies.\nYear: 2017\n\nAbstract: A joint monitoring system of pipeline-flood on the basis of FBG and ZigBee is designed in this article by applying fiber Bragg grating sensing into hydrologic observation and mechanical vibration measurement field, which can realize multipoint and online monitoring of rainfall, river level, flow velocity and vibration frequency of pipeline. The influence of temperature on measurement results can be eliminated by using the dual-fiber pattern. In order to achieve the real-time and effective performances of data collection, the monitoring system is built on the basis of ZigBee wireless network system, which consists of the sensor nodes equipped with the FBG ombrometer, FBG fluviograph, FBG flowmeter and FBG vibrometer in the monitoring area, and they are connected to a network coordinator in a star topology structure. The communication between the ZigBee system and remote monitoring terminal has been realized by GPRS modules. This monitoring system has been put into practical application on a gas pipeline laid in a river valley. The experiment results have proved that the system has the characteristics of flexible and fast networking, real-time capability, low power consumption, high reliability and unattended operation, which can meet the requirements of safety monitoring of oil and gas pipelines subjected to flood impacting.","Title: Transformation Of Xml Data Sources For Sequential Path Mining\nYear: 2016\n\nAbstract: In recent years XML has become one of the most promising ways to define semi-structured data. Data mining techniques devised for detecting interesting patterns from semi-structure data have also grown in popularity, but carrying out such techniques on XML data can be problematic due to its hierarchical structure. Therefore, it has become necessary to transform XML into flattened, path data, so as to enable data mining to be carried out efficiently. However, problems may arise when the XML tree needs to be reconstructed from the traversal path. There are currently many transformation techniques for XML data, many of which take advantage of its tree-like hierarchical structure; but most of these approaches do not allow the XML tree to be reconstructed from the traversal path. In this paper we propose a new approach to the transformation of XML data into path data. The new approach employs a 5 step transformation process along with a new 'Postorder Sequencing' method of traversing the XML tree. The proposed method, on the one hand, can be seen an efficient and effective way of transforming XML data into collections of paths, and on the other hand enables XML trees to be generated from the traversal paths.","Title: A Multi-size Kernels CNN with Eye Movement Guided Task-Specific Initialization for Aurora Image Classification.\nYear: 2017\n\nAbstract: Aurora is the ionosphere track generated by the interaction of solar wind and magnetosphere. Different aurora correspond to different dynamics process in the magnetosphere. So aurora image classification is the basis for aurora analysis. Deep networks available that pretrained with large scale natural image dataset are not suitable for aurora classification due to the characteristics of aurora images. In this paper, a novel multi-size kernels CNN (MSKCNN) with eye movement guided task-specific initialization is proposed to classify aurora images. First, according to the human visual and cognitive mechanism, the patches are extracted guided by eye movement information of space physics. Then, a task-specific initialization scheme is design by using the features learned from aurora image patches to initialize the first layer of our proposed network. In addition, the network contains three parallel streams to learn features under different scale. The comparison experimental results illustrate the effectiveness of the proposed method.","Title: On the Processing of Very High Resolution Spaceborne SAR Data: A Chirp-Modulated Back Projection Approach.\nYear: 2018\n\nAbstract: A new image formation algorithm is proposed for processing very high resolution spaceborne sliding-spotlight synthetic aperture radar (SAR) data. Because of along-track antenna steering, the Doppler bandwidth of the received SAR data is expanded significantly beyond one pulse repetition frequency interval. Furthermore, the range histories become spatially dependent in both dimensions and cannot be...","Title: Three-stage electric vehicle scheduling considering stakeholders economic inconsistency and battery degradation.\nYear: 2017\n\nAbstract: This study proposes an electric vehicle (EV) aggregator operation mechanism in a residential community. The EV charging and discharging operation behaviours are scheduled to maximise the EV aggregator revenue, while EV aggregator provides reserve service for the grid. This study not only considers the energy and information interactions between three stakeholders: EV aggregator, EV owners, and pow...","Title: A new version of distance and similarity measures for hesitant fuzzy linguistic term sets and its application.\nYear: 2018\n\nAbstract: In this paper, a new version of distance measure for hesitant fuzzy linguistic term sets is developed. The new version of distance measure provides an objective way to handle the diverse dimensions of two HFLTSs, i.e., without shortening the HFLTS to be a linguistic interval or extending the shorter one by adding linguistic terms. The Hamming distance, the Euclidean distance and the corresponding forms are put forward. By the relationship between the distance measure and the similarity measure, some parallel similarity measures are also investigated. As applications of the proposed measures, the issues of pattern recognition, clustering analysis and multi-criteria decision making are considered.","Title: A Quality Assessment Method Based on Common Distributed Targets for GF-3 Polarimetric SAR Data.\nYear: 2018\n\nAbstract: The GaoFen-3 (GF-3) satellite, launched on 10 August 2016, is the first C-band polarimetric synthetic aperture radar (PolSAR) satellite in China. The PolSAR system of GF-3 can collect a significant wealth of information for geophysical research and applications. Being used for related applications, GF-3 PolSAR images must be of good quality. It is necessary to evaluate the quality of polarimetric data and achieve the normalized quality monitoring during 8-year designed life of GF-3. In this study, a new quality assessment method of PolSAR data based on common distributed targets is proposed, and the performance of the method is analyzed by simulations and GF-3 experiments. We evaluate the quality of GF-3 PolSAR data by this method. Results suggest that GF-3 antenna is highly isolated, and the quality of calibrated data satisfies the requests of quantitative applications.","Title: The GF-3 SAR Data Processor.\nYear: 2018\n\nAbstract: The Gaofen-3 (GF-3) data processor was developed as a workstation-based GF-3 synthetic aperture radar (SAR) data processing system. The processor consists of two vital subsystems of the GF-3 ground segment, which are referred to as data ingesting subsystem (DIS) and product generation subsystem (PGS). The primary purpose of DIS is to record and catalogue GF-3 raw data with a transferring format, and PGS is to produce slant range or geocoded imagery from the signal data. This paper presents a brief introduction of the GF-3 data processor, including descriptions of the system architecture, the processing algorithms and its output format.","Title: Fault Diagnosis Method of Diesel Engine Based on Improved Structure Preserving and K-NN Algorithm.\nYear: 2018\n\nAbstract: The diesel engine fault data is nonlinear and it's difficult to extract the characteristic information. Kernel Principal Component Analysis (KPCA) is used to extract features of nonlinear data, only considering global structure. Kernel Locality Preserving Projection (KLPP) considers the local feature structure. So an improved algorithm for global and local structure preserving is proposed to extract the feature of data. The improved feature extraction algorithm combining KPCA and KLPP, avoids the loss of information considering the global and local feature structure and then uses the modified K-NN algorithm for fault classification. In this paper, the software AVL BOOST is used to simulate the faults of diesel engine. The simulation experiments indicate the proposed method can extract the feature vectors effectively, and shows good classification performance.","Title: Active fault tolerant control design approach for the flexible spacecraft with sensor faults.\nYear: 2017\n\nAbstract: In this paper, the problem of active fault tolerant control (FTC) is studied for a class of flexible spacecraft attitude systems with Lipschitz nonlinearity and sensor fault. Firstly, a functional observer is designed for the attitude systems of flexible spacecraft in order to detect the time of unknown sensor fault occurred. Next, the sensor fault estimation is performed by filtering the output estimation error, as usually done in the residual generation framework. Then, a dynamic output feedback-based FTC approach is proposed to the flexible spacecraft in sensor faulty case, it not only attenuates flexible appendage disturbance with a given level γ, but also tolerates the effect of unknown sensor fault. Finally, the effectiveness of the proposed FTC method is demonstrated in the attitude systems of flexible spacecraft subject to a time-varying sensor fault.","Title: A High-Resolution SAR Focusing Experiment Based on GF-3 Staring Data.\nYear: 2018\n\nAbstract: Spotlight synthetic aperture radar (SAR) is a proven technique, which can provide high-resolution images as compared to those produced by traditional stripmap SAR. This paper addresses a high-resolution SAR focusing experiment based on Gaofen-3 satellite (GF-3) staring data with about 55 cm azimuth resolution and 240 MHz range bandwidth. In staring spotlight (ST) mode, the antenna always illuminates the same scene on the ground, which can extend the synthetic aperture. Based on a two-step processing algorithm, some special aspects such as curved-orbit model error correction, stop-and-go correction, and antenna pattern demodulation must be considered in image focusing. We provide detailed descriptions of all these aspects and put forward corresponding solutions. Using these suggested methods directly in an imaging module without any modification for other data processing software can make the most of the existing ground data processor. Finally, actual data acquired in GF-3 ST mode is used to validate these methodologies, and a well-focused, high-resolution image is obtained as a result of this focusing experiment.","Title: A Range Ambiguity Suppression Processing Method for Spaceborne SAR with Up and Down Chirp Modulation.\nYear: 2018\n\nAbstract: Range ambiguity is one of the factors which affect the SAR image quality. Alternately transmitting up and down chirp modulation pulses is one of the methods used to suppress the range ambiguity. However, the defocusing range ambiguous signal can still hold the stronger backscattering intensity than the mainlobe imaging area in some case, which has a severe impact on visual effects and subsequent applications. In this paper, a novel hybrid range ambiguity suppression method for up and down chirp modulation is proposed. The method can obtain the ambiguity area image and reduce the ambiguity signal power appropriately, by applying pulse compression using a contrary modulation rate and CFAR detecting method. The effectiveness and correctness of the approach is demonstrated by processing the archive images acquired by Chinese Gaofen-3 SAR sensor in full-polarization mode.","Title: A Preliminary Analysis of Wind Retrieval, Based on GF-3 Wave Mode Data.\nYear: 2018\n\nAbstract: This paper presents an analysis of measurements of the normalized radar cross-(NRCS) in Wave Mode for Chinese C-band Gaofen-3(GF-3) synthetic aperture radar (SAR). Based on 2779 images from GF-3 quad-polarization SAR in Wave Mode and collocated wind vectors from ERA-Interim, this experiment verifies the feasibility of using ocean surface wind fields and VV-polarized NRCS to perform normalized calibration. The method uses well-validated empirical C-band geophysical model function (CMOD4) to estimate the calibration constant for each beam. In addition, the relationship between cross-pol NRCS and wind vectors is discussed. The cross-pol NRCS increases linearly with wind speed and it is obviously modulated by the wind direction when the wind speed is greater than 8 m/s. Furthermore, the properties of the polarization ratio, denoted PR, are also investigated. The PR is dependent on incidence angle and azimuth angle. Two empirical models of the PR are fitted, one as a function of incidence angle only, the other with additional dependence on azimuth angle. Assessments show that the sigma(0)(VV) retrieved from new PR models as well as sigma(0)(HH) is in good agreement with sigma(0)(VV) extracted from SAR images directly.","Title: Eye Fixation Assisted Video Saliency Detection via Total Variation-Based Pairwise Interaction.\nYear: 2018\n\nAbstract: As human visual attention is naturally biased toward foreground objects in a scene, it can be used to extract salient objects in video clips. In this paper, we proposed a weakly supervised learning-based video saliency detection algorithm utilizing eye fixations information from multiple subjects. Our main idea is to extend eye fixations to saliency regions step by step. First, visual seeds are co...","Title: Xcel-RAM: Accelerating Binary Neural Networks in High-Throughput SRAM Compute Arrays.\nYear: 2018\n\nAbstract: Deep neural networks are a biologically-inspired class of algorithms that have recently demonstrated state-of-the-art accuracies involving large-scale classification and recognition tasks. Indeed, a major landmark that enables efficient hardware accelerators for deep networks is the recent advances from the machine learning community that have demonstrated aggressively scaled deep binary networks with state-of-the-art accuracies. In this paper, we demonstrate how deep binary networks can be accelerated in modified von-Neumann machines by enabling binary convolutions within the SRAM array. In general, binary convolutions consist of bit-wise XNOR followed by a population-count (popcount). We present a charge sharing XNOR and popcount operation in 10 transistor SRAM cells. We have employed multiple circuit techniques including dual-read-worldines (Dual-RWL) along with a dual-stage ADC that overcomes the inaccuracies of a low precision ADC, to achieve a fairly accurate popcount. In addition, a key highlight of the present work is the fact that we propose sectioning of the SRAM array by adding switches onto the read-bitlines, thereby achieving improved parallelism. This is beneficial for deep networks, where the kernel size grows and requires to be stored in multiple sub-banks. As such, one needs to evaluate the partial popcount from multiple sub-banks and sum them up for achieving the final popcount. For n-sections per sub-array, we can perform n convolutions within one particular sub-bank, thereby improving overall system throughput as well as the energy efficiency. Our results at the array level show that the energy consumption and delay per-operation was 1.914pJ and 45ns, respectively. Moreover, an energy improvement of 2.5x, and a performance improvement of 4x was achieved by using the proposed sectioned-SRAM, compared to a non-sectioned SRAM design.","Title: An Approach to Linguistic Multiple Attribute Decision-Making Based on Unbalanced Linguistic Generalized Heronian Mean Aggregation Operator\nYear: 2018\n\nAbstract: AbstractThis paper proposes an approach to linguistic multiple attribute decision-making problems with interactive unbalanced linguistic assessment information by unbalanced linguistic generalized Heronian mean aggregation operators. First, some generalized Heronian mean aggregation operators with unbalanced linguistic information are proposed, involving the unbalanced linguistic generalized arithmetic Heronian mean operator and the unbalanced linguistic generalized geometric Heronian mean operator. For the situation that the input arguments have different degrees of importance, the unbalanced linguistic generalized weighted arithmetic Heronian mean operator and the unbalanced linguistic generalized weighted geometric Heronian mean operator are developed. Then we investigate their properties and some particular cases. Finally, the effectiveness and universality of the developed approach are illustrated by a low-carbon tourist instance and comparison analysis. A sensitivity analysis is performed as well to test the robustness of proposed methods.","Title: Toward a spintronic deep learning spiking neural processor.\nYear: 2016\n\nAbstract: Deep Spiking neural architectures are becoming increasingly popular tools in complex pattern recognition tasks. However, implementation of such algorithms in conventional CMOS hardware entails huge area and power consumption due to the significant mismatch between the computational units and the corresponding CMOS devices. In this paper, we explore the design of an All-Spin Deep Spiking Neural Network where we demonstrate the mapping of synaptic and neuronal functionalities to domain wall dynamics in ferromagnets. We evaluate the potential advantages offered by such spintronic devices by performing micromagnetic simulations calibrated to experimental results. In order to investigate the benefits of such a spintronic design for large-scale neuromorphic systems, we perform device-circuit-algorithm co-design for a standard digit recognition problem on the MNIST dataset. Results indicate 250x improvement in energy consumption and 56x improvement in EDP of the spintronic deep network over a baseline CMOS implementation in commercial 45nm technology.","Title: Spectral Tailoring of Random Fiber Laser Based on the Multimode Interference Filter.\nYear: 2018\n\nAbstract: In this paper, we propose and experimentally demonstrate a novel scheme to tailor the spectrum of random fiber laser (RFL) based on the multimode interference effect. A fiber loop mirror (FLM) incorporating multimode interference filter (MMIF) with single mode fiber (SMF)-MMF-SMF structure acts as a wavelength-dependent point reflector in half-opened RFL. By varying the length of MMF, either single wavelength random lasing with specific wavelength or multiwavelength random lasing with selective wavelength spacing can be generated in MMIF-FLM-based RFL. Due to the wide wavelength range of the MMIF, cascaded multiwavelength random lasing can also be realized. The spectrum adjustable all fiberized RFL based on the MMIF shows distinct advantages, such as easy fabrication, wide operating wavelength range, and low-cost. Besides, the MMIF-FLM can has good power handling ability, by combining the MMIF-FLM and the short-length cavity, the proposed MMIF-based forward-pumping RFL also has a great potential to generate high power, high efficiency random lasing with the adjustable spectral property.","Title: On Intuitionistic Fuzzy Copula Aggregation Operators in Multiple- Attribute Decision Making.\nYear: 2018\n\nAbstract: Operations of intuitionistic fuzzy values have been widely studied and have attracted significant interest. In this paper, some other operations on intuitionistic fuzzy values on the basis of Archimedean copulas and corresponding co-copulas are introduced. Such novel operations can show the relevance between intuitionistic fuzzy values. A family of weighted aggregation operators are developed according to the proposed operations, i.e., the intuitionistic fuzzy copula aggregation operator. The properties of the novel operations and the weighted aggregation operators are also considered. In the end, we provide a modified maximizing deviation decision procedure for multiple attributes decision making under intuitionistic fuzzy environment, and show a case study to illustrate the application of the proposed approach.","Title: Interval-Valued 2-Tuple Linguistic Induced Continuous Ordered Weighted Distance Measure And Its Application To Multiple Attribute Group Decision Making\nYear: 2018\n\nAbstract: This paper aims to propose a new distance measure, the interval-valued 2-tuple linguistic induced continuous ordered weighted distance (IT-ICOWD) measure, which consists of the intervalvalued 2-tuple linguistic induced continuous ordered weighted averaging (IT-ICOWA) operator and the ordered weighted distance (OWD) measure. In these operators, we consider the risk attitude of decision maker. Furthermore, we discuss some desired properties and various special cases of the IT-ICOWD measure. Additionally, a method of multiple attribute group decision making (MAGDM) in interval-valued 2-tuple linguistic environment is developed on the basis of the IT-ICOWD measure. Through this method, we obtain three simple and exact formulae to determine the order-inducing variables of the IT-ICOWD measure, the weighting vector of decision makers and the weighting vector of attributes, respectively. At last, a numerical example is presented to illustrate the practicability and feasibility of proposed method.","Title: Screening of Port Enterprise Value Chain Routines Based on Evolution Equilibrium.\nYear: 2018\n\nAbstract: In order to find the screening mechanism of port enterprises value chain routines, based on describing the selection of port enterprises value chain routines, an evolutionary game model is presented and constructed. Using this model, we analyze the strategy of port enterprises value chain routines when they achieve the stability of the evolution. The results show that port enterprise value chain routine selection is a dynamic and repeated game. The expected revenue and convention cost of routines taking part in the game playing directly correlate with the evolutionary stable strategy and the selection of port enterprises value chain routines tend to be conservative strategy. Introducing evolutionary game theory provides a new perspective for the study on the formation, search and selection of port enterprises value chain routines and provides favorable theoretical support for further research in related fields.","Title: Predicting pathological response to neoadjuvant chemotherapy in breast cancer patients based on imbalanced clinical data.\nYear: 2018\n\nAbstract: Neoadjuvant chemotherapy (NAC) may help some breast cancer patients with subsequent surgery or radiotherapy. However, there are certain risks associated with NAC. To lower the risks, machine-learning methods can be used to assist the diagnosis of breast tumors based on clinical data. This study investigated the use of ensemble machine-learning models in the prediction of pathological response to NAC for breast cancer patients with actual clinical data. The ensemble k-nearest neighbor (EKNN) model was determined to predict pathological responses. The imbalanced clinical data of patients with NAC were reviewed retrospectively, and 11 clinicopathological variables were selected from all features to establish succinct EKNN model. A total of 259 patients’ clinical data was included in the model. The training and testing set for each single k-nearest neighbor (KNN) contained 27 and 9 patients, respectively. A total of 259 breast cancer patients in the database included 36 cases of pathological complete response, 157 cases of partial response, and 66 cases of stable disease. To solve the imbalanced clinical data problem, an ensemble-learning EKNN was designed, where the number of samples for each class in a base learner is set to equal to the minimum number 36. It showed that the classification accuracy of pathological response for breast cancer patients after NAC was 81.48% by EKNN model and the Kappa coefficient was 0.72, indicating that the robustness and generalization were better than the average prediction ability of single KNN model (average accuracy of single KNN model was 62.22% and Kappa coefficient was 0.43). Based on actual clinical data, important clinicopathological variables are selected, and the imbalanced problem are well solved by the ensemble EKNN model. The model improved the robustness and generalization for predicting the pathological response with imbalanced clinical data. It suggested that ensemble machine learning has possible practical applications for assisting cancer stage diagnoses and precision medicine.","Title: The Novel Computational Model Of Unbalanced Linguistic Variables Based On Archimedean Copula\nYear: 2018\n\nAbstract: We develop a novel computation model of unbalanced linguistic variables on the basis of Archimedean copulas and corresponding co-copulas, which provides a new tool to aggregate unbalanced linguistic information. The properties of the proposed computational model are also studied. We present the concepts of weighted unbalanced Archimedean copula arithmetic aggregation operators and weighted unbalanced Archimedean copula geometric aggregation operators. The properties of these aggregation operators are further investigated. Finally, a group decision making of sensory evaluation is introduced to illustrate the feasibility and validity of our proposed computational model.","Title: iQIYI-VID: A Large Dataset for Multi-modal Person Identification.\nYear: 2018\n\nAbstract: Person identification in the wild is very challenging due to great variation in poses, face quality, clothes, makeup and so on. Traditional research, such as face recognition, person re-identification, and speaker recognition, often focuses on a single modal of information, which is inadequate to handle all the situations in practice. Multi-modal person identification is a more promising way that we can jointly utilize face, head, body, audio features, and so on. In this paper, we introduce iQIYI-VID, the largest video dataset for multi-modal person identification. It is composed of 600K video clips of 5,000 celebrities. These video clips are extracted from 400K hours of online videos of various types, ranging from movies, variety shows, TV series, to news broadcasting. All video clips pass through a careful human annotation process, and the error rate of labels is lower than 0.2%. We evaluated the state-of-art models of face recognition, person re-identification, and speaker recognition on the iQIYI-VID dataset. Experimental results show that these models are still far from being perfect for task of person identification in the wild. We further demonstrate that a simple fusion of multi-modal features can improve person identification considerably. We have released the dataset online to promote multi-modal person identification research.","Title: Interval Type-2 Fuzzy Neural Networks for Chaotic Time Series Prediction: A Concise Overview.\nYear: 2019\n\nAbstract: Chaotic time series widely exists in nature and society (e.g., meteorology, physics, economics, etc.), which usually exhibits seemingly unpredictable features due to its inherent nonstationary and high complexity. Thankfully, multifarious advanced approaches have been developed to tackle the prediction issues, such as statistical methods, artificial neural networks (ANNs), and support vector machines. Among them, the interval type-2 fuzzy neural network (IT2FNN), which is a synergistic integration of fuzzy logic systems and ANNs, has received wide attention in the field of chaotic time series prediction. This paper begins with the structural features and superiorities of IT2FNN. Moreover, chaotic characters identification and phase-space reconstruction matters for prediction are presented. In addition, we also offer a comprehensive review of state-of-the-art applications of IT2FNN, with an emphasis on chaotic time series prediction and summarize their main contributions as well as some hardware implementations for computation speedup. Finally, this paper trends and extensions of this field, along with an outlook of future challenges are revealed. The primary objective of this paper is to serve as a tutorial or referee for interested researchers to have an overall picture on the current developments and identify their potential research direction to further investigation.","Title: Energy Efficient Neural Computing: A Study of Cross-Layer Approximations.\nYear: 2018\n\nAbstract: Deep neural networks (DNNs) have emerged as the state-of-the-art technique in a wide range of machine learning tasks for analytics and computer vision in the next generation of embedded (mobile, IoT, and wearable) devices. Despite their success, they suffer from high energy requirements. In recent years, the inherent error resiliency of DNNs has been exploited by introducing approximations at eith...","Title: Boundary constraint factor embedded localizing active contour model for medical image segmentation\nYear: 2019\n\nAbstract: Medical image segmentation has been widely used in clinical practice. It is an important basis for medical experts to diagnose the disease. However, weak edges and intensity inhomogeneity (Niu et al. in 2nd IEEE international conference on computational intelligence and applications (ICCIA). https://doi.org/10.1109/ciapp.2017.816722, 2017) in medical images may hinder the accuracy of any traditional active contour segmentation methods. In this paper, we propose an improved active contour method by embedding a boundary constraint factor and adding the local information of images to the energy function of Chan–Vese model. Then graph cuts method is used to optimize the new energy function of the improved active contour method in this paper. There are several salient features: (1) we can obtain more accurate boundaries by embedding a constraint factor and adding local information of images. (2) The energy function is not easily to fall into local optimum. (3) Finally, our method only need to adjust one parameter. The evaluation results on Magnetic Resonance Imaging and Computed Tomography, blood vessel images, and mammograms masses show that the proposed method leads to more accurate boundary detection results than the state-of-the-art edge-based and region-based active contour segmentation methods.","Title: Structural Reweight Sparse Subspace Clustering\nYear: 2019\n\nAbstract: Subspace clustering aims to segment a group of data points into a union of subspaces. Reweight sparse subspace clustering is one of the state-of-the-art algorithms which proposed an iterative weighted subspace clustering. The reweight matrix helps to improve the performance of the affinity matrix construction process but it easily falls into a local minimization. In this paper, we propose a structural reweight sparse subspace clustering algorithm which introduces the structural information into reweight subspace clustering. The structural information achieved in spectral clustering process is useful for the subsequent iterative optimization process which helps to obtain a better local minimization. The experimental results on the Extended Yale B, Hopkins 155, and COIL 20 datasets demonstrate that our algorithm achieves a better performance on subspace clustering problem.","Title: An Improved Imaging Algorithm for High-Resolution Spotlight SAR with Continuous PRI Variation Based on Modified Sinc Interpolation.\nYear: 2019\n\nAbstract: This paper focuses on an improved imaging algorithm for spotlight synthetic aperture radar (SAR) with continuous Pulse Repetition Interval (PRI) variation in extremely high-resolution. Conventional SAR systems are limited in that a wide swath cannot be achieved with a high azimuth resolution in the meantime. This limitation can be overcome by Pulse Repetition Frequency (PRF) variation in a SAR system. However, there are problems such as the ambiguities of point targets or extended targets caused by nonuniform sampling. A reconstructive method, Nonuniform Discrete Fourier Transform (NUDFT) has been presented in the current literature, but it is rather computationally expensive. In this paper, a modified sinc interpolation based on NUDFT is proposed, which is used to reconstruct the uniformly sampled echo in time domain. Since the interpolation kernel length is relatively short, it is more computationally efficient. Then, the two-step processing approach combined with the modified sinc interpolation is further presented, which has much better accuracy than that combined with the conventional sinc interpolation. Both the simulated data and the extracted GF-3 data experiment demonstrate the validity and accuracy of the proposed approach.","Title: Cross-Layer Design Exploration for Energy-Quality Tradeoffs in Spiking and Non-Spiking Deep Artificial Neural Networks.\nYear: 2018\n\nAbstract: Deep learning convolutional artificial neural networks have achieved success in a large number of visual processing tasks and are currently utilized for many real-world applications like image search and speech recognition among others. However, despite achieving high accuracy in such classification problems, they involve significant computational resources. Over the past few years, non-spiking de...","Title: DeltaFrame-BP: An Algorithm Using Frame Difference for Deep Convolutional Neural Networks Training and Inference on Video Data.\nYear: 2018\n\nAbstract: Inspired by the success of deep convolutional neural networks (CNNs) with back-propagation (BP) training on large-scale image recognition tasks, recent research efforts concentrated on expending deep CNNs toward more challenging automatized video analysis, such as video classification, object tracking, action recognition and optical flow detection. Video comprises a sequence of images (frames) cap...","Title: A Bi-Level Approach for Determining Optimal Dynamic Retail Electricity Pricing of Large Industrial Customers\nYear: 2019\n\nAbstract: In this paper, a bi-level optimization model is proposed for determining the optimal dynamic retail electricity price, which balances the monetary benefits between utility companies and large industrial customers. That is, the utility company at the upper level devotes to maximize the profit of electricity sale by tuning dynamic retail electricity prices, while large industrial customers at the lower level minimize their power consumption costs by optimally scheduling tasks and generation outputs of self-provided power plants. Specifically, the task scheduling problem of large industrial customers is formulated as a modified continuous-time mixed-integer linear programing (MILP) problem, for effectively handling the exact start time and cancellation of tasks as well as optimally deploying and executing tasks of large industrial customers. A hybrid optimization algorithm by integrating genetic algorithm (GA) and MILP is proposed for addressing computational complexity of the proposed bi-level optimization problem. That is, GA is used to solve the upper level problem, while the lower level scheduling problem is solved by a commercial MILP optimizer. Numerical case results show that the proposed method can effectively increase the profit of the utility company, while improving electricity consumption patterns and reducing average power consumption costs of large industrial customers.","Title: Structural and Hierarchical Partitioning of Virtual Microgrids in Power Distribution Network\nYear: 2019\n\nAbstract: Nowadays, the infrastructure and technologies in conventional distribution networks (CDNs) limit its further development to smart distribution networks. One way to overcome this is to develop CDNs using interconnected virtual microgrids (VMs); however, there is no consensus for an explicit definition of VMs. Therefore, in this paper, a comprehensive discussion on VMs is conducted, and a general framework for developing VMs is proposed. The core issue for developing VMs is to identify boundaries of VMs, and most of the suggested methods are based on analyzing operating states. In this paper, a different partitioning method, which is based on the analysis of structural characteristics, is proposed. The electrical coupling strength is defined as a composite weight coefficient for the analysis of electrical connection, and the electrical modularity is used to assess the quality of the partition so as to identify boundaries of VMs. This partitioning method is applied to the IEEE 33-bus distribution network and the PG&E 69-bus distribution network, and it can quickly evaluate the partitioning quality and effectively identify the boundaries of VMs.","Title: Low Phase Noise Co-Mb-Ofdm Optical Burst Transmitter For Time And Spectral Optical Aggregation\nYear: 2017\n\nAbstract: We demonstrate experimentally the feasibility of a low phase noise CO-MB-OFDM burst transmitter for time and spectral optical aggregation based on our proposition of a 100 kHz linewidth and 100 ns switching time laser source.","Title: Method of Tikhonov regularization for weighted frequency-difference electrical impedance tomography\nYear: 2017\n\nAbstract: Frequency-difference electrical impedance tomography (fdEIT) has attracted considerable attention, which can visualize anomalies without requiring any time-reference data obtained in the absence of anomalies and alleviate undesirable effects of modeling errors. Simple frequency-difference (SFD) method and weighted frequency-difference (WFD) method are two existing algorithms. Besides, WFD perform better due to its high resolution in the condition where the conductivity of background changes much. However, truncated singular value decomposition (TSVD) regularization adopted in WFD have several problems such as difficulties to choose the accurate truncation parameter, unsatisfactory image quality even in low noise condition and overwhelming computational tasks for large-scale problems. In this paper, an improved WFD algorithm using Tikhonov regularization (WFDTR) method as well as the selection method of regularization parameter is proposed. This method can figure out the appropriate regularization parameter more easily through L curve method and acquire reconstructed distribution images of good quality. What's more, the second corner of L curve is investigated. Finally, the proposed method has been compared with the conventional WFD method through numerical simulations. The proposed method works better than WFD in relatively low noise cases according to localization error, shape error as well as image noise indexes. In relatively high noise condition, they perform approximately equally.","Title: Low Phase Noise Optical Burst Transmitter For Time And Spectral Optical Aggregation Solution\nYear: 2017\n\nAbstract: Sub-Lambda Photonically Switched Network (SLPSN) solutions are able to improve the use of capacity in data networks while limiting the growth of energy consumption. Recently, we have proposed the TIme and Spectral optical Aggregation (TISA) concept in order to respond to operator's needs of flexibility in the allocation of network resources. Additionally, the TISA solution makes a better use of already deployed network resources. In TISA, each source sends data in the form of bursts that only occupies a spectral sub-band, thus offering purely optical aggregation and disaggregation in both time and spectral domains with the finest possible granularity. Wavelength-based routing is then performed passively in the network. A fast tunable laser with low phase noise is necessary to implement the TISA transmitter but, current existing commercial lasers can't meet these two specifications. In this paper we present a transmitter solution fulfilling the switching time and low phase noise requirements. We give experimental results of a CO-MB-OFDM transmitter realized by combining optical gates and low phase noise lasers and report its performance in a back-to-back configuration.","Title: The probabilistic ordered weighted continuous OWA operator and its application in group decision making\nYear: 2019\n\nAbstract: In this paper, the aim is to propose a new aggregation operator called the probabilistic ordered weighted continuous OWA (POW-COWA) operator to aggregate the continuous interval number. The primary character of the POW-COWA operator is that it unifies the probability and the continuous OWA (COWA) operator in the same formulation through considering various importance degrees of different concepts in the aggregation process. Simultaneously, the new operator can be employed to diminish uncertainty and lower the complexity of data processing. Furthermore, we discuss some desired properties and different special cases of the developed operator. Additionally, a method of group decision making (GDM) under interval number environment is proposed based on the POW-COWA operator. At last, a numerical example is presented to illustrate the proposed method.","Title: 100km Quasi-Lossless Fiber-Optic Transmission With A Novel Cascaded Random Raman Fiber Laser\nYear: 2018\n\nAbstract: Ultra-long distance (100km) quasi-lossless fiber-optic transmission is experimentally performed by using the 3rd-order Raman amplification pumped by a novel 1280nm cascaded random Raman fiber laser, which is the longest quasi-lossless transmission system reported to date.","Title: SLAM estimation method for uncertain model noise parameters\nYear: 2019\n\nAbstract: It is difficult to accurately obtain the statistical parameters of mobile robots motion parameters for its mobility. And it is also the same in observation system because of the environmental variability, which would cause the SLAM system noise statistical parameters uncertainty, and decrease the filtering performance. A SLAM adaptive filtering algorithm with noise statistical characteristics estimation is proposed, when the noise statistical parameters are constant but unknown, three additional maximum posteriori estimators are constructed, and adaptive CKF SLAM algorithm are designed. Compared with the standard CKF SLAM algorithm, the results show that under the uncertainty noise parameters, if the noise variance is Gaussian white noise distribution, the designed adaptive CKF SLAM with three maximum posterior can well estimate the state, track noise variance, and handle the problem of nonlinear systems. The simulation results verify the effectiveness of the proposed SLAM estimation method with the uncertainty of the model noise parameters.","Title: A Novel Ofdm Training Sequence Strategy Based On A Sliding Window For Optical Burst Traffic\nYear: 2018\n\nAbstract: In order to mitigate the transients occurring in the coherent receivers facing an absence of burst, a novel channel estimator, using a sliding window, is proposed here for optical DP-16QAM- OFDM burst traffic. Our experimental results show a significant performance gain of this new estimator resulting in an error threshold cancellation.","Title: Error Source Analysis and Correction of GF-3 Polarimetric Data.\nYear: 2018\n\nAbstract: The GaoFen-3 (GF-3) satellite is the first polarimetric synthetic aperture radar (PolSAR) satellite in China. With a designed in-orbit life of 8 years, it will provide large amounts of PolSAR data for ocean monitoring, disaster reduction, and many other applications. The polarimetric data quality is essential for all these applications, so the analysis and calibration of the polarimetric error sources are very important for GF-3. In this study, we established a full-link error model for GF-3 PolSAR system. Based on this model, we comprehensively analyzed the quantitative effects of the main error sources including the composition, figured out characteristics of the phase imbalance introduced by the antenna, and pointed out the error sources which have to be corrected. Furthermore, the polarimetric correction method for GF-3 PolSAR system is proposed. Finally, assisted by several external calibration experiments, polarimetric errors of GF-3 data are efficiently corrected during in-orbit-test phase.","Title: Who Owns Internet Of Thing Devices?\nYear: 2018\n\nAbstract: Although Internet of Things (IoT) has been recently receiving attention from the research community, undoubtedly, there still exists several privacy concerns about those devices. In particular, IoT devices in the cyberspace are reachable and visible through IP addresses. This article uniquely exploits to qualify the distribution of owner information of IoT devices based on the observation; consumers may write relevant details into the application-layer service on the IoT devices, such as company or usernames. We propose to automatically extract owner annotation by utilizing a set of techniques (network scanning, machine learning, and natural language processing). We use the probing and classifier to determine whether the response data come from an IoT device. The natural language-processing technique is used to extract owner information from IoT devices. We have conducted real-world experiments to evaluate our integrated approach empirically. The results show that the precision is 97% and the coverage is 96%. Furthermore, our approach is running on a more larger unlabeled dataset consisting of 93 million response packets from the whole IPv4 space. Our analysis has drawn upon nearly 4.3 million IoT devices exposed to the public, and it is a typical trail effect of the owner information distribution.","Title: R - Tree Index Construction of Dynamic K-means Algorithm.\nYear: 2018\n\nAbstract: In order to better construct the R - tree index structure and improve the efficiency of R - tree construction and retrieval, the R - tree construction algorithm is optimized from the following three aspects: the selection and determination of the initial center point, the weighted distance between data combined with the actual situation, and the division of redundant data by combining the theories of nearest neighbor, information entropy and probability statistics. The feasibility of the proposed algorithm is verified by comparing the objective function values, iteration times and time consumption under different cluster numbers. Furthermore, experimental results show that the index structure constructed by this method has smaller overlapping area and more efficient query efficiency.","Title: Power flow optimization for DC distribution grid with distributed energy access based on Newton–Raphson method through upper level control\nYear: 2019\n\nAbstract: DC distribution grids with distributed energy integration have been gaining a continually increasing interest in recent years both in academia and industry, power flow of the grids is a key issue to make use of these energy effectively and reliably, paper aims at optimizing the power flow of DC distribution through the upper level control, by considering the factors of distributed energy permeability and DC grid topology, without hardware investment increase nor using complex algorithm. firstly, the change rule of the network loss and voltage distribution are studied under different distributed energy permeability values and DC grid topology, Secondly, one OPF mathematical model of DC distribution network is established taking the index of network loss and voltage imbalance degree as objective function. According to the change of system flow, the optimized instruction value is provided by the system through calculation for each AC/DC, DC/DC converter; an optimization method of DC power flow is presented by controlling converter instruction. At last, two typical cases are demonstrated by simulation of PSCAD/EMTDC, indexes of the network loss and voltage imbalance degree of the DC distribution network are compared before and after the optimization, Simulation results show that the presented method is feasible to promote DC Distribution Grid.","Title: Optimization method for reducing network loss of dc distribution system with distributed resource\nYear: 2019\n\nAbstract: With mature of the direct current (dc) distribution grid technology, the distributed energy is embraced as much as possible, and reducing network loss becomes an effective way to increase energy efficiency and system stability. The conventional reducing loss approaches include complex algorithms, loss modeling in power devices and adding hardware, which lead to costs and system response time increase and system stability reduce. Considering the Intermittent characteristics of distributed energy, for reducing network loss, the effective option is to control error and response speed directly through the upper control, when distributed energy accesses on dc distribution network, which is no need to change grid topologies, run cumbersome power flow algorithms, nor add additional equipment. A reducing network loss method is proposed based on this idea. The details are as follows: Firstly, the network loss formula is derived based on power flow calculation, and the network loss change rules are analyzed. Secondly, one optimal power flow mathematical model of dc distribution network is established, which takes the minimum network loss as the objective functions and conforms to constraints of system security and components operating limit. The tide optimization is solved by using the artificial bees (ABC) algorithm. Thirdly, the network loss reduction method is proposed in the dc distribution network by using master–slave control through real-time control instruction optimization. The node voltage, branch current and main voltage source converter power are precisely regulated to control the power flow; thus, the network loss of multiple distributed energy access dc distribution network can be reduced. Fourthly, when wind and solar energy is connected to the dc distribution network, a typical IEEE16 node case is demonstrated to verify the feasibility of the proposed method using software MATLAB/SIMULINK. Fifthly, evaluation and prospect are made for the research.","Title: The region based MMTD energy function for image segmentation\nYear: 2019\n\nAbstract: This paper presents a region-based model based on measure of medium truth degree for image segmentation. Firstly, a new energy function based on measure of medium truth degree is constructed. To enhance the robustness against noise, a noise penalty term which is built by spatial distance measure is embedded to the conventional active contour energy function. Then local information is added to the internal and external energy term of the conventional active contour energy function to deal with intensity inhomogeneity images. Finally, to obtain more accurate and smoother boundary, a new stop function is introduced into the boundary smooth term of the conventional active contour energy function. Experiments results demonstrate that relatively complete and accurate boundaries can be obtained by the proposed model compared with the state-of-art methods on aurora images, images with intensity inhomogeneity, images with multi-objects, natural images, medical images.","Title: Safety Control Method of Robot-Assisted Cataract Surgery with Virtual Fixture and Virtual Force Feedback\nYear: 2020\n\nAbstract: Surgery is an effective means of treating cataracts and restoring vision. However, cataract surgery rate (CSR) in developing countries and regions is relatively low due to the lack of experienced high-level surgeons. In this paper, to reduce the reliance of surgery on physician experience and thereby increase CSR, a master-slave robotic system and safety control strategies with a virtual fixture and virtual force feedback are proposed to assist cataract surgery. First, the surgery is divided into four different stages with different robot control modes. Secondly, the virtual constraint area with virtual spring model in the operating stage is established, so that the doctor can distinguish the operation area where the end of the surgical instrument is located by feedback force. Thirdly, safety control algorithm guarantees that the surgical instrument strictly moves around the surgical incision point, which is regarded as a remote centre of motion, so that the cornea outside the incision point is not injured. Finally, the experimental results show that the proposed safety control strategy allows the robotic system to perform the procedure safely.","Title: Moving Target Detection with Modified Logarithm Background Subtraction and Its Application to the GF-3 Spotlight Mode.\nYear: 2019\n\nAbstract: Spaceborne spotlight SAR mode has drawn attention due to its high-resolution capability, however, the studies about moving target detection with this mode are less. The paper proposes an image sequence-based method entitled modified logarithm background subtraction to detect ground moving targets with Gaofen-3 Single Look Complex (SLC) spotlight SAR images. The original logarithm background subtraction method is designed by our team for airborne SAR. It uses the subaperture image sequence to generate a background image, then detects moving targets by using image sequence to subtract background. When we apply the original algorithm to the spaceborne spotlight SAR data, a high false alarm problem occurs. To tackle the high false alarm problem due to the target's low signal-to-noise-ratio (SNR) in spaceborne cases, several improvements are made. First, to preserve most of the moving target signatures, a low threshold CFAR (constant false alarm rate) detector is used to get the coarse detection. Second, because the moving target signatures have higher density than false detections in the coarse detection, a modified DBSCAN (density-based spatial-clustering-of-applications-with-noise) clustering method is then adopted to reduce false alarms. Third, the Kalman tracker is used to exclude the residual false detections, due to the real moving target signature having dynamic behavior. The proposed method is validated by real data, the shown results also prove the feasibility of the proposed method for both Gaofen-3 and other spaceborne systems.","Title: Fault Diagnosis of Complex Processes Using Sparse Kernel Local Fisher Discriminant Analysis.\nYear: 2020\n\nAbstract: As an outstanding discriminant analysis technique, Fisher discriminant analysis (FDA) gained extensive attention in supervised dimensionality reduction and fault diagnosis fields. However, it typically ignores the multimodality within the measured data, which may cause infeasibility in practice. In addition, it generally incorporates all process variables without emphasizing the key faulty ones when modeling the complex process, thus leading to degraded fault classification capability and poor model interpretability. To ease the above two drawbacks of conventional FDA, this brief presents an advantageously sparse local FDA (SLFDA) model, it first preserves the within-class multimodality by introducing local weighting factors into scatter matrix. Then, the responsible faulty variables are identified automatically through the elastic net algorithm, and the current optimization problem is subsequently settled through the feasible gradient direction method. Since then, the local data structure characteristics are exploited from both the sample dimension and variable dimension so that the fault diagnosis performance and model interpretability are significantly enhanced. In addition, we naturally extend SLFDA model to nonlinear variant (i.e., sparse kernel local FDA) by the kernel trick, which is substantially more resistant to strong nonlinearity. The simulation studies on Tennessee Eastman (TE) benchmark process and real-world diesel engine working process both validate that the novel diagnosis strategy is more accurate and reliable than the existing state-of-the-art methods.","Title: Research On Feature Extraction Method Of Indoor Visual Positioning Image Based On Area Division Of Foreground And Background\nYear: 2021\n\nAbstract: In the process of indoor visual positioning and navigation, difficult points often exist in corridors, stairwells, and other scenes that contain large areas of white walls, strong consistent background, and sparse feature points. Aiming at the problem of positioning and navigation in the real physical world where the walls with sparse feature points are difficult to be filled with pictures, this paper designs a feature extraction method, ARAC (Adaptive Region Adjustment based on Consistency) using Free and Open-Source Software and tools. It divides the image into foreground and background and extracts their features respectively, to achieve not only retain positioning information but also focus more energy on the foreground area which is favourable for navigation. In the test phase, under the combined conditions of illumination, scale and affine changes, the feature matching maps by the feature extraction algorithm proposed in this paper are compared with those by SIFT and SURF. Experiments show that the number of correctly matched feature pairs obtained by ARAC is better than SIFT and SURF, and whose time of feature extraction and matching is comparable to SURF, which verifies the accuracy and efficiency of the ARAC feature extraction method.","Title: Modeling And Performance Optimization Of Unmanned Aerial Vehicle Channels In Urban Emergency Management\nYear: 2021\n\nAbstract: With the development of smart cities, the use of unmanned aerial vehicles (UAVs) for interactive information exchange between air and ground can provide effective support for the deployment of emergency work. However, the existing UAV air-to-ground channels often use a single channel model. Considering that the density and distribution of obstructions on information transmission paths at different heights are different, only using a single channel model greatly affects the reliability of communications. Aiming at addressing the different channel characteristics of air-to-ground channels at different heights, a height-based adaptive SUUL-SULA channel model is proposed in this paper. Firstly, in the ultra-low altitude environment, the influence of large-scale fading and small-scale fading on the envelope of the received signal is discussed based on the classic LOO model, and the probability density function and bit error rate model of the received signal are derived. Secondly, a SULA channel model based on Jakes' model is proposed in the low-altitude environment. The uniform circular array beamforming technology is adopted to realize the design of the Doppler frequency shift compensation algorithm. Finally, the simulation results show that the SUUL-SULA model effectively reduces the bit error rate of the system and improves the reliability of communication. Therefore, this model can provide effective physical support for the application of UAV in smart city emergency management.","Title: Fighting Fake News: Two Stream Network for Deepfake Detection via Learnable SRM\nYear: 2021\n\nAbstract: Benefitting from the development of deep generative networks, modern fake news generation methods called Deepfake rapidly go viral over the Internet, calling for efficient detection methods. Existing Deepfake detection methods basically use binary classification networks trained on frame-level inputs and lack leveraging temporal information in videos. Besides, the accuracy of these methods will ra...","Title: SYNAUG: SYNTHESIS-BASED DATA AUGMENTATION FOR TEXT-DEPENDENT SPEAKER VERIFICATION\nYear: 2021\n\nAbstract: Text-dependent speaker verification systems trained on large amount of labelled data exhibit remarkable performance. However, collecting the speech from a lot of speakers with target transcript is a lengthy and expensive process. In this work, we propose a synthesis based data augmentation method (SynAug) to expand the training set with more speakers and text-controlled synthesized speech. The performance of SynAug is evaluated on the RSR2015 dataset. Experimental results show that for i-vector framework, the proposed methods can boost the system performance significantly, especially for the low-resource condition where the amount of genuine speech is extremely limited. Moreover, combined with traditional data augmentation methods such as adding noises and reverberation, the systems could be further strengthened in extremely limited resource situation.","Title: Distributed Fault-Tolerant Formation Control For Multiple Unmanned Aerial Vehicles Under Actuator Fault And Intermittent Communication Interrupt\nYear: 2021\n\nAbstract: This article develops a distributed adaptive fault-tolerant formation control scheme for the multiple unmanned aerial vehicles to counteract actuator faults and intermittent communication interrupt, where the issues on control input saturation and mismatched uncertainties are also addressed. The discontinuous communication protocol technique is exploited to achieve the stability of the formation system, if the conditions of dwell time and the rate of communication are satisfied. On the basis of the local information of neighboring unmanned aerial vehicles, a novel distributed adaptive mechanism is designed to estimate the bounds of actuator faults and uncertainties, where the input saturation is explicitly taken into consideration. The stability of the whole formation system under the designed fault-tolerant formation control strategy is analyzed using the Lyapunov approach. Finally, simulation results are presented to illustrate the effectiveness of the proposed scheme.","Title: Oscillatory Fourier Neural Network: A Compact and Efficient Architecture for Sequential Processing.\nYear: 2022\n\nAbstract: Tremendous progress has been made in sequential processing with the recent advances in recurrent neural networks. However, recurrent architectures face the challenge of exploding/vanishing gradients during training, and require significant computational resources to execute back-propagation through time. Moreover, large models are typically needed for executing complex sequential tasks. To address these challenges, we propose a novel neuron model that has cosine activation with a time varying component for sequential processing. The proposed neuron provides an efficient building block for projecting sequential inputs into spectral domain, which helps to retain long-term dependencies with minimal extra model parameters and computation. A new type of recurrent network architecture, named Oscillatory Fourier Neural Network, based on the proposed neuron is presented and applied to various types of sequential tasks. We demonstrate that recurrent neural network with the proposed neuron model is mathematically equivalent to a simplified form of discrete Fourier transform applied onto periodical activation. In particular, the computationally intensive back-propagation through time in training is eliminated, leading to faster training while achieving the state of the art inference accuracy in a diverse group of sequential tasks. For instance, applying the proposed model to sentiment analysis on IMDB review dataset reaches 89.4% test accuracy within 5 epochs, accompanied by over 35x reduction in the model size compared to LSTM. The proposed novel RNN architecture is well poised for intelligent sequential processing in resource constrained hardware.","Title: Automatic classification method of thyroid pathological images using multiple magnification factors\nYear: 2021\n\nAbstract: Thyroid cancer is the most common form of endocrine malignancy, and the informative pathological images are critical for thyroid cancer risk stratification, prognosis and treatment guidance. Deep learning methods have achieved promising results on pathology image classification benchmarks. However, due to the complexity of thyroid carcinoma pathological images and the lack of labeled data, there are few researches study on the auto-classification of thyroid cancer. Inspired by the diagnostic process of pathologists, this paper proposes an active classification method for papillary thyroid carcinoma (PTC) pathological images classification to divide thyroid pathological images into PTC and normal thyroid pathological images. We employ the attention mechanism to combine pathological images with different magnification factors, which imitates the diagnosis procedures of thyroid cancer under the microscope. At the same time, we utilize the uncertainty and representative information provided by the convolutional neural network to determine the most valuable samples for annotation that can reduce the labeling cost. Besides, a pathological image dataset named VIP-TCHis is conducted from thyroid tissues of 55 real cases. The experimental results show that our method can obtain good performance of PTC recognition on the VIP-TCHis dataset.","Title: Site Selection Of Fire Stations In Large Cities Based On Actual Spatiotemporal Demands: A Case Study Of Nanjing City\nYear: 2021\n\nAbstract: The rapid expansion of cities brings in new challenges for the urban firefighting security, while the increasing fire frequency poses serious threats to the life, property, and safety of individuals living in cities. Firefighting in cities is a challenging task, and the optimal spatial arrangement of fire stations is critical to firefighting security. However, existing researches lack any consideration of the negative effects of the spatial randomness of fire outbreaks and delayed response time due to traffic jams upon the site selection. Based on the set cover location model integrated with the spatiotemporal big data, this paper combines the fire outbreak point with the traffic situation. The presented site selection strategy manages to ensure the arrival of the firefighting task force at random simulated fire outbreak points within the required time, under the constraints of the actual city planning and traffic situation. Taking Nanjing city as an example, this paper collects multi-source big data for the comprehensive analysis, including the full data of the fire outbreak history from June 2014 to June 2018, the traffic jam data based on the Amap, and the investigation data of the firefighting facilities in Nanjing. The regularity behind fire outbreaks is analyzed, the factors related to fire risks are identified, and the risk score is calculated. The previous fire outbreak points are put through the clustering analysis, the spatial distribution probability at points in each cluster is calculated according to the clustering score, and the random fire outbreak points are generated via the Monte Carlo simulation. Meanwhile, the objective emergency response time is set as five minutes. The average vehicle speed for each road in the urban area is calculated, and the actual traffic network model is built to compute the travel time from massive randomly-distributed simulated fire points. The problem is solved by making the travel time for all simulated demand points below five minutes. At last, the site selection result based on our model is adjusted and validated, according to the planned land use. The presented method incorporates the view of the spatiotemporal big data and provides a new idea and technical method for the modification and efficiency improvement of the fire station site selection model, contributing to a service cover ratio increase from 58% to 90%.","Title: Different Prevention And Treatment Strategies For Knee Osteoarthritis (Koa) With Various Lower Limb Exoskeletons - A Comprehensive Review\nYear: 2021\n\nAbstract: It was reported that about 10% of people suffer from painful knee arthritis, and a quarter of them were severely disabled. The core activities of daily living were severely limited by knee osteoarthritis (KOA). In order to reduce knee pain and prolong the life of the knee joint, there has been an increasing demand on the development of exoskeletons, for prevention and treatment. The course of KOA was closely related to the biomechanics of knee joint, and the pathogenesis was summarized based on the biomechanics of knee joint. For the prevention and clinical treatment, exoskeletons are classified into three categories: prevention, treatment, and rehabilitation after the operation. Furthermore, the design concepts, actuators, sensors, control strategies, and evaluation criteria were presented. Finally, the shortcomings and limitations were summarized. It is useful for researchers to develop suitable exoskeletons in the future.","Title: Distance Constraint Between Features For Unsupervised Domain Adaptive Person Re-Identification\nYear: 2021\n\nAbstract: Many superior person re-identification (re-ID) approaches face a challenge: their performance show disastrous when all supervised models are generalized to a new domain. Some researchers have recently proposed domain adaptive person re-ID methods to address this difficulty, whereas they directly leveraged the target-domain data that would generate lots of noisy pseudo labels. Hence, this paper proposes a distance constraint between features (DCF) method, which clusters the feature distribution fitted the real target-domain data. We assemble different parts of one person for multi-scale self-supervised learning. After introducing domain invariance and designing the inter-image and inter-class distance constraint to regulate distances between target samples, the feature distribution extracted from the encoder trained by the source data can fit the real target data distribution, which leads our domain adaptive model to enjoy the more reliable clustering results and thus obtain a great identification performance in target domain. Extensive experiments demonstrate our approach outperforms state-of-the-art methods on three large-scale released datasets. (c) 2021 Elsevier B.V. All rights reserved.","Title: Identity-Preserving Face Anonymization via Adaptively Facial Attributes Obfuscation\nYear: 2021\n\nAbstract: ABSTRACTWith the popularity of using computer vision technology in monitoring system, there is an increasing societal concern on intruding people's privacy as the captured images/videos may contain identity-related information e.g. people's face. Existing methods on protecting such privacy focus on removing the identity-related information from faces. However, this would weaken the utility of current monitoring system. In this paper, we develop a face anonymization framework that could obfuscate visual appearance while preserving the identity discriminability. The framework is composed of two parts: an identity-aware region discovery module and an identity-aware face confusion module. The former adaptively locates the identity-independent attributes on human faces, and the latter generates the privacy-preserving faces using original faces and discovered facial attributes. To optimize the face generator, we employ a multi-task based loss function, which consists of discriminator loss, identify preserving loss, and reconstruction loss functions. Our model can achieve a balance between recognition utility and appearance anonymizing by modifying different numbers of facial attributes according to pratical demands, and provide a variety of results. Extensive experiments conducted on two public benchmarks Celeb-A and VGG-Face2 demonstrate the effectiveness of our model under distinct face recognition scenarios.","Title: Underwater Target Recognition Based on Multi-Decision LOFAR Spectrum Enhancement: A Deep-Learning Approach\nYear: 2021\n\nAbstract: Underwater target recognition is an important supporting technology for the development of marine resources, which is mainly limited by the purity of feature extraction and the universality of recognition schemes. The low-frequency analysis and recording (LOFAR) spectrum is one of the key features of the underwater target, which can be used for feature extraction. However, the complex underwater environment noise and the extremely low signal-to-noise ratio of the target signal lead to breakpoints in the LOFAR spectrum, which seriously hinders the underwater target recognition. To overcome this issue and to further improve the recognition performance, we adopted a deep-learning approach for underwater target recognition, and a novel LOFAR spectrum enhancement (LSE)-based underwater target-recognition scheme was proposed, which consists of preprocessing, offline training, and online testing. In preprocessing, we specifically design a LOFAR spectrum enhancement based on multi-step decision algorithm to recover the breakpoints in LOFAR spectrum. In offline training, the enhanced LOFAR spectrum is adopted as the input of convolutional neural network (CNN) and a LOFAR-based CNN (LOFAR-CNN) for online recognition is developed. Taking advantage of the powerful capability of CNN in feature extraction, the recognition accuracy can be further improved by the proposed LOFAR-CNN. Finally, extensive simulation results demonstrate that the LOFAR-CNN network can achieve a recognition accuracy of 95.22%, which outperforms the state-of-the-art methods.","Title: A framework for semi-automatically identifying fully occluded objects in 3D models: Towards comprehensive construction design review in virtual reality\nYear: 2021\n\nAbstract: •Point cloud-based algorithms for automatically identifying occluded objects in 3D building information models.•Compatible input and output files with current industry practices.•Achieved over 90% recall rate and over 75% precision rate in classifying the visibility status of objects.•Validated using building information models of an academic building and an industrial facility.","Title: A High-Efficient Triboelectric-Electromagnetic Hybrid Nanogenerator For Vibration Energy Harvesting And Wireless Monitoring\nYear: 2022\n\nAbstract: Scavenging mechanical vibration energy by combining triboelectric nanogenerators (TENGs) and electromagnetic generators (EMGs) is a renewable and low-carbon way to generate electric power. In this paper, we fabricate a cylindrical structure hybrid nanogenerator based on spring structure to effectively scavenge vibration energy. The introduction of spring structure not only enhances the space utilization, but also supports the moving magnet. Owing to its innovative structure, the instantaneous output power of TENG and EMG are 0.88 mW at a load of 10 M omega and 216.7 mW at a load of 60 omega, respectively. With the aids of a power management circuit, the hybrid nanogenerator can provide continuous power for two hygrothermographs connected in series. In addition, a self-powered wireless acceleration sensing monitoring system (SWAM) is developed to measure the acceleration signals of vibration sources. This study provides a novel guideline for harvesting mechanical vibration energy.","Title: A Moving Ship Detection and Tracking Method Based on Optical Remote Sensing Images from the Geostationary Satellite\nYear: 2021\n\nAbstract: Geostationary optical remote sensing satellites, such as the GF-4, have a high temporal resolution and wide coverage, which enables the continuous tracking and observation of ship targets over a large range. However, the ship targets in the images are usually small and dim and the images are easily affected by clouds, islands and other factors, which make it difficult to detect the ship targets. This paper proposes a new method for detecting ships moving on the sea surface using GF-4 satellite images. First, the adaptive nonlinear gray stretch (ANGS) method was used to enhance the image and highlight small and dim ship targets. Second, a multi-scale dual-neighbor difference contrast measure (MDDCM) method was designed to enable detection of the position of the candidate ship target. The shape characteristics of each candidate area were analyzed to remove false ship targets. Finally, the joint probability data association (JPDA) method was used for multi-frame data association and tracking. Our results suggest that the proposed method can effectively detect and track moving ship targets in GF-4 satellite optical remote sensing images, with better detection performance than other classical methods.","Title: A Novel Framework of Integrating UV and NIR Atmospheric Correction Algorithms for Coastal Ocean Color Remote Sensing\nYear: 2021\n\nAbstract: Atmospheric correction is a fundamental process of ocean color remote sensing to remove the atmospheric effect from the top-of-atmosphere. Generally, Near Infrared (NIR) based algorithms perform well for clear waters, while Ultraviolet (UV) based algorithms can obtain good results for turbid waters. However, the latter tends to produce noisy patterns for clear waters. An ideal and practical solution to deal with such a dilemma is to apply NIR- and UV-based algorithms for clear and turbid waters, respectively. We propose a novel atmospheric correction method that integrates the advantages of UV- and NIR-based atmospheric correction (AC) algorithms for coastal ocean color remote sensing. The new approach is called UV-NIR combined AC algorithm. The performance of the new algorithm is evaluated based on match-ups between GOCI images and the AERONET-OC dataset. The results show that the values of retrieved Rrs (Remote Sensing Reflectance) at visible bands agreed well with the in-situ observations. Compared with the SeaDAS (SeaWiFS Data Analysis System) standard NIR algorithm, the new AC algorithm can achieve better precision and provide more available data.","Title: Multi-layer VI-GNSS Global Positioning Framework with Numerical Solution aided MAP Initialization\nYear: 2021\n\nAbstract: Motivated by the goal of achieving long-term drift-free camera pose estimation in complex scenarios, we propose a global positioning framework fusing visual, inertial and Global Navigation Satellite System (GNSS) measurements in multiple layers. Different from previous loosely- and tightly-coupled methods, the proposed multi-layer fusion allows us to delicately correct the drift of visual odometry and keep reliable positioning while GNSS degrades. In particular, local motion estimation is conducted in the inner-layer, solving the problem of scale drift and inaccurate bias estimation in visual odometry by fusing the velocity of GNSS, pre-integration of Inertial Measurement Unit (IMU) and camera measurement in a tightly-coupled way. The global localization is achieved in the outer-layer, where the local motion is further fused with GNSS position and course in a long-term period in a loosely-coupled way. Furthermore, a dedicated initialization method is proposed to guarantee fast and accurate estimation for all state variables and parameters. We give exhaustive tests of the proposed framework on indoor and outdoor public datasets. The mean localization error is reduced up to 63%, with a promotion of 69% in initialization accuracy compared with state-of-the-art works. We have applied the algorithm to Augmented Reality (AR) navigation, crowd sourcing high-precision map update and other large-scale applications.","Title: A Novel Adaptive Cluster Based Routing Protocol for Energy-Harvesting Wireless Sensor Networks\nYear: 2022\n\nAbstract: With the various applications of the Internet of Things, research into wireless sensor networks (WSNs) has become increasingly important. However, because of their limited energy, the communication abilities of the wireless nodes distributed in the WSN are limited. The main task of WSNs is to collect more data from targets in an energy-efficient way, because the battery replacement of large amounts of nodes is a labor-consuming work. Although the life of WSNs can be prolonged through energy-harvesting (EH) technology, it is necessary to design an energy-efficient routing protocol for the energy harvesting-based wireless sensor networks (EH-WSNs) as the nodes would be unavailable in the energy harvesting phase. A certain number of unavailable nodes would cause a coverage hole, thereby affecting the WSN's monitoring function of the target environment. In this paper, an adaptive hierarchical-clustering-based routing protocol for EH-WSNs (HCEH-UC) is proposed to achieve uninterrupted coverage of the target region through the distributed adjustment of the data transmission. Firstly, a hierarchical-clustering-based routing protocol is proposed to balance the energy consumption of nodes. Then, a distributed alternation of working modes is proposed to adaptively control the number of nodes in the energy-harvesting mode, which could lead to uninterrupted target coverage. The simulation experimental results verify that the proposed HCEH-UC protocol can prolong the maximal lifetime coverage of WSNs compared with the conventional routing protocol and achieve uninterrupted target coverage using energy-harvesting technology.","Title: PU-GACNet: Graph Attention Convolution Network for Point Cloud Upsampling\nYear: 2022\n\nAbstract: Real-scanned point clouds are often sparse and non-uniform. To conquer the problem, researchers propose point cloud upsampling techniques, whose efficiency and effectiveness heavily rely on their feature extractors and feature expanders used therein. Therefore, in this paper, to capture the global and local structured features of point clouds, we first design a Graph Attention Convolution (GAC) module as a feature extractor by assigning different attentional weights to combine spatial positions and feature attributes dynamically. Furthermore, we propose an Edge-aware NodeShuffle (ENS) module as a feature expander to upsampling point features smoothly, in an effort to better preserve local geometric details and emphasize local edges. Finally, we combine GAC module with ENS module into a novel point cloud upsampling pipeline, named as PU-GACNet. Extensive experiments as well as theoretical analysis demonstrate this pipeline significantly outperforms previous methods in network performance for 3D point cloud upsampling, to obtain more efficient inference with much fewer parameters.","Title: AdaMixer: A Fast-Converging Query-Based Object Detector\nYear: 2022\n\nAbstract: Traditional object detectors employ the dense paradigm of scanning over locations and scales in an image. The recent query-based object detectors break this convention by decoding image features with a set of learnable queries. However, this paradigm still suffers from slow convergence, limited performance, and design complexity of extra networks between backbone and decoder. In this paper, we find that the key to these issues is the adaptability of decoders for casting queries to varying objects. Accordingly, we propose a fast-converging query-based detector, named AdaMixer, by improving the adaptability of query-based decoding processes in two aspects. First, each query adaptively samples features over space and scales based on estimated offsets, which allows AdaMixer to efficiently attend to the coherent regions of objects. Then, we dynamically decode these sampled features with an adaptive MLP-Mixer under the guidance of each query. Thanks to these two critical designs, AdaMixer enjoys architectural simplicity without requiring dense attentional encoders or explicit pyramid networks. On the challenging MS COCO benchmark, AdaMixer with ResNet-50 as the backbone, with 12 training epochs, reaches up to 45.0 AP on the validation set along with 27.9 APs in detecting small objects. With the longer training scheme, AdaMixer with ResNeXt-101-DCN and Swin-S reaches 49.5 and 51.3 AP. Our work sheds light on a simple, accurate, and fast converging architecture for query-based object detectors. The code is made available at https://github.com/MCG-NJU/AdaMixer.","Title: How does (im)balanced acceptance of robots between customers and frontline employees affect hotels’ service quality?\nYear: 2022\n\nAbstract: With the increasingly prevalent of service robots in tourism and hospitality sectors, service success relies heavily on robot acceptance. However, most of tourism practitioners are challenged by a dilemma of focusing on customer or employee acceptance of robots. Borrowing the lenses of resource-based view and information processing theory, this study explores the differential effectiveness of imbalanced robotic strategy (i.e., higher/lower customer acceptance of robots than employee acceptance) implemented by hotels in driving service quality under customer-side (i.e., customer demandingness) and employee-side (i.e., frontline task ambidexterity) demand uncertainties. Polynomial regression analysis on a dataset of 1066 matched customer-employee dyads reveals that, an imbalanced robotic strategy is superior than a balanced one for service quality. In addition, when customer demandingness is high, a customer-focused robotic strategy (i.e., higher customer acceptance of robots than employee acceptance) is the optimal choice to improve service quality. However, when frontline task ambidexterity is high, the positive effects of imbalanced robotic strategy on service quality diminish. This study contributes to the literature on and practices of robot acceptance from a customer-robot-employee triadic perspective.","Title: Parameterized Soil Recognition Using Normal Similarity Measures on Dynamic Neutrosophic Cubic Sets\nYear: 2022\n\nAbstract: As a kind of newly proposed fuzzy set, dynamic neutrosophic cubic set (DNCS) has varying information on elements of truth, indeterminacy, and falsity in different time intervals, and has been widely used. Considering that normal distribution is the most widely used distribution in real world, this study proposes a kind of parameterized normal similarity measures (NSM) on DNCSs to depict the similarity characteristic between different elements in DNCS. Moreover, this study also proposes a kind of soil recognition model. The main innovation of this study is as follows. Firstly, this study presents NSMs on fuzzy sets, especially on DNCSs for the first time. Secondly, this study proposes a kind of hesitant neutrosophic cubic set with probability vector. Thirdly, a parameter in the proposed parameterized NSMs has ability to adjust weight indirectly and adaptively. Fourthly, this study introduces a soil recognition model using NSMs on DNCSs. After that, a soil recognition example illustrates the reliability of the proposed parameterized NSMs and the soil recognition model.","Title: Patterns of Nighttime Crowd Flows in Tourism Cities Based on Taxi Data-Take Haikou Prefecture as an Example\nYear: 2022\n\nAbstract: The study of patterns of crowd flows represents an emerging and expanding research field. The most straightforward and efficient approach to investigate the patterns of crowd flows is to concentrate on traffic flow. However, assessments of simple point-to-point movement frequently lack universal validity, and little research has been conducted on the regularity of nighttime movement. Due to the suspension of public transportation at night, taxi orders are critical in capturing the features of nighttime crowd flows in a tourism city. Using Haikou as an example, this paper proposes a mixed Geogrid Spatio-temporal model (MG-STM) for the tourism city in order to address the challenges. Firstly, by collecting the pick-up/drop-off/in-out flow of crowds, this research uses DCNMF dimensionality reduction to extract semi-supervised spatio-temporal variation features and the K-Means clustering method to determine the cluster types of nighttime crowd flows' changes in each geogrid. Secondly, by constructing a mixed-evaluation model based on LJ1-01 nighttime light data, crowd flows' clusters, and land use data in geogrid-based regions, the pattern of nighttime crowd flows in urban land use areas is successfully determined. The results suggest that MG-STM can estimate changes in the number of collective flows in various regions of Haikou effectively and appropriately. Moreover, population density of land use areas shows a high positive correlation with the lag of crowd flows. Each 5% increase in population density results in a 30-min delay in the peak of crowd flows. The MG-STM will be extremely beneficial in developing and implementing systems for criminal tracking and pandemic prevention.","Title: Influence of Residual Amplitude and Phase Error for GF-3 Quad-Polarization SAR on Wind Vector Retrieval\nYear: 2022\n\nAbstract: High-resolution wind vector is important to investigate local winds' variability over the global ocean. Quad-polarization Synthetic Aperture Radar (SAR) can provide wind vector independently without any external wind direction inputs. Although quad-polarization SAR wind retrieval algorithms have been widely studied, improvements are still required. The amplitude and phase imbalance of polarization channel cannot be neglected for improving the wind vector retrieval precision. In this study, rainforest was performed to remove the amplitude and phase imbalance of polarization channel of GF-3 SAR. To explore the applicability of this method for sea surface measurement, the influence of residual amplitude and phase error for GF-3 quad-polarization SAR on wind vector retrieval was assessed. Variation of amplitude and phase imbalance of sea surface for transmit and receive channel were assessed against collocated wind speed and incidence angle. Considering the polarization difference of VV channel relative to HH channel, the residual amplitude and phase error was found to be closely related to wind speed and polarization isolation. Correction of residual amplitude and phase error were employed to improve the retrieval precision of wind vector. It is revealed that the wind speed retrieval precision of VV polarization improved with correction of residual amplitude error. In addition, the influence of residual amplitude and phase error on wind direction retrieval can be neglected. Thus, it is concluded that correction of amplitude and phase error has the potential to improve wind vector retrievals from GF-3 quad-polarization SAR.","Title: Mixed logit model based on nonlinear random utility functions: a transfer passenger demand prediction method on overnight D-trains\nYear: 2022\n\nAbstract: In recent years, with the development of high-speed railway in China, the operating mileage and passenger transport capacity have increased rapidly in transportation industry. Due to the high density of trains in the daytime, we usually set up skylights at night (0:00–6:00 am) on high-speed railway for comprehensive maintenance. However, this arrangement contradicts with the operation demand of D-series overnight high-speed trains (overnight D-trains for short). In order to adjust the operation plan of overnight D-trains with skylights coordinately, it is necessary to predict the passenger demand of newly added overnight D-trains. Therefore, in this paper, a mixed logit model based on nonlinear random utility functions for different transport modes is proposed, in order to predict transfer passenger demand. According to Maximum Simulated Likelihood Method, the likelihood function of this mixed logit model is proposed to maximize the overall utility value of different passenger groups while Metropolis–Hastings algorithm is adopted to iteratively solve the probabilities of discrete random variables in utility functions. After that, the unknown distributions of parameters are estimated and the optimal solution of this model is provided by traditional algorithms, basic heuristic algorithms and improved heuristic algorithms including improved fireworks-simulated annealing algorithm proposed in this paper, respectively. Finally, a real-world instance with related data of Beijing–Shanghai corridor is implemented to demonstrate the performance and effectiveness of the proposed approaches.","Title: First Assessment of GF3-02 SAR Ocean Wind Retrieval\nYear: 2022\n\nAbstract: On 23 November 2021, the Gaofen-3-02 (GF3-02) satellite was successfully launched in the Jiuquan Satellite Launch Center of China. The primary payload is C-band Synthetic Aperture Radar (SAR), with a maximum resolution of 1 m, and includes 12 imaging modes such as Spotlight, Strip, and TOPSAR, which will play an essential role in marine environment monitoring. As an important marine environmental parameter, the wind speed accuracy retrieved by GF3-02 SAR directly reflects its performance and effectiveness as an operational product. Therefore, based on the wind data of buoys of the National Data Buoy Center (NDBC), ECMWF reanalysis V5 (ERA5), and HY-2B Scatterometer (SCA), a preliminary accuracy assessment of the wind speed retrieved by GF3-02 SAR is carried out in this paper. The wind speed retrieval accuracy of GF3-02 SAR in the co-polarization (HH+VV) data under different Geophysical Model Functions (GMFs) is discussed by using 478 level-1A Single Look Complex (SLC) ocean products acquired in Quad-Polarization Strip I (QPSI) and produced by the National Satellite Ocean Application Service (NSOAS) from January to March 2022. The results show that the optimal root mean square errors (RMSE) are 1.40 m/s, 1.18 m/s, and 1.24 m/s for the VV polarization and 1.39 m/s, 1.19 m/s, and 1.52 m/s for the HH polarization compared to the NDBC wind speed, the ERA5 wind speed, and the HY-2B SCA wind speed, respectively. The preliminary results show that GF3-02 SAR has good wind speed retrieval ability and can meet the needs of operational products.","Title: Disturbance observer-based fixed-time robust control for constrained air-breathing hypersonic vehicle\nYear: 2022\n\nAbstract: This article studies the fixed-time robust control problem for the longitudinal dynamics of hypersonic vehicles in the presence of parametric uncertainties, external disturbances and input constraints. First, the dynamic model is transformed into two fourth-order integral chain subsystems by feedback linearization technology. Four novel fast integrating sliding surfaces are designed for each subsystem to guarantee the fixed time convergence of the errors and the derivatives. The double power reaching law is investigated to accelerate the convergence of sliding surfaces. Furthermore, the fixed-time disturbance observer technique is applied to estimate the lumped disturbance precisely. A novel fixed-time anti-saturation auxiliary system is designed to tackle the saturation caused by constraints of actuators. Then the semi-global uniform boundedness of the closed-loop system in a fixed time is proved by Lyapunov's stability theory. Finally, comparison simulation experiments with the existing higher order sliding mode control method are carried out to verify the proposed method's effectiveness and superiority.","Title: Auto Complementary Exposure Control for High Dynamic Range Video Capturing\nYear: 2021\n\nAbstract: Recent trends in consumer electronics have led to a proliferation of high dynamic range (HDR) video acquisition and transmission studies. High-end video capturing systems increase the bit depth of each pixel to adapt to the high-contrast ambient light. However, the extended bit depth is pathetically meager compared with the far wider dynamic range of the incident light. What's more, incompatibility issues for the subsequent compression and transmission system may be arisen along with the increased bit depth. In this paper, we propose an auto complementary exposure (ACE) algorithm for optimal exposure parameter estimation under Siamese Trigger (ST) mode to maximize the entropy of the output HDR image. With the proposed algorithm, neither the sensor nor other related hardware needs to be modified. Only the auto-exposure algorithm in the firmware needs to be updated. In the proposed ACE algorithm, every two consecutive images are treated as Siamese twins, and the exposure parameters for the twin images are calculated simultaneously. The target of the ACE algorithm is not to maximize the entropy of each image but to maximize that of the fused image. Compared to existing auto exposure or bracketed fusing algorithms, the proposed algorithm has three significant advantages. Firstly, no extra bit-depth is needed to embrace the details of the scene. Thus the image transmission system is not affected. Secondly, only two raw images are necessary for one output fused HDR image. The computation load is relatively low. Thirdly, the proposed method can fit video recording, which has a broader application prospect than HDR image capturing. Experiment results on a self-built database and publicly accessible database demonstrate that the algorithm works well under various types of scenarios. Furthermore, the experiment results on the real-time system show that the captured video image reveals many details under high dynamic scenes.","Title: Cross-Architecture Self-supervised Video Representation Learning.\nYear: 2022\n\nAbstract: In this paper, we present a new cross-architecture contrastive learning (CACL) framework for self-supervised video representation learning. CACL consists of a 3D CNN and a video transformer which are used in parallel to generate diverse positive pairs for contrastive learning. This allows the model to learn strong representations from such diverse yet meaningful pairs. Furthermore, we introduce a temporal self-supervised learning module able to predict an Edit distance explicitly between two video sequences in the temporal order. This enables the model to learn a rich temporal representation that compensates strongly to the video-level representation learned by the CACL. We evaluate our method on the tasks of video retrieval and action recognition on UCF101 and HMDB51 datasets, where our method achieves excellent performance, surpassing the state-of-the-art methods such as VideoMoCo and MoCo+BE by a large margin. The code is made available at https://github.com/guoshengcv/CACL.","Title: Real-Trading-Oriented Price Prediction With Explainable Multiobjective Optimization in Quantitative Trading\nYear: 2022\n\nAbstract: Price prediction using machine learning is a heated topic and an effective task in quantitative trading. However, the objectives that existing researches focus on are typically naive and can hardly express the diverse properties in real trading, by using which the results of price prediction cannot precisely reflect the returns in real trading. To alleviate this problem, we first formulate the characteristics of transactions and then propose a multi-objective method under a real-trading-oriented perspective, where a tree model inducing explainable trading characteristics is proposed. With lots of considerations of real trading characteristics, the results of price prediction can better obtain the properties in trading, and especially perform quite well in real trading. Multiple experiments on the Chinese Index Future Market display the effectiveness of the proposed model, where the performance of real trading has reached the industry-leading level. In particular, the difference between model prediction and returns in real trading can be diminished.","Title: Grid-optimized UAV indoor path planning algorithms in a complex environment\nYear: 2022\n\nAbstract: •A set of grid-optimized UAV indoor path planning algorithms was proposed.•GO-IAM algorithm established an enriched indoor 3D model of the airspace based on GeoSOT-3D model.•GO-APP algorithm represented the SOTA path planning in regular indoor scenarios.•GO-LBPP algorithm solved the indoor “dead zone” issue in complex multiobstacle airspace.","Title: Self-Supervised Speaker Verification Using Dynamic Loss-Gate and Label Correction\nYear: 2022\n\nAbstract: For self-supervised speaker verification, the quality of pseudo labels decides the upper bound of the system due to the massive unreliable labels. In this work, we propose dynamic loss-gate and label correction (DLG-LC) to alleviate the performance degradation caused by unreliable estimated labels. In DLG, we adopt Gaussian Mixture Model (GMM) to dynamically model the loss distribution and use the estimated GMM to distinguish the reliable and unreliable labels automatically. Besides, to better utilize the unreliable data instead of dropping them directly, we correct the unreliable label with model predictions. Moreover, we apply the negative-pairs-free DINO framework in our experiments for further improvement. Compared to the best-known speaker verification system with self-supervised learning, our proposed DLG-LC converges faster and achieves 11.45%, 18.35% and 15.16% relative improvement on Vox-O, Vox-E and Vox-H trials of Voxceleb1 evaluation dataset.","Title: The SJTU System for Short-Duration Speaker Verification Challenge 2021.\nYear: 2021\n\nAbstract: This paper presents the SJTU system for both text-dependent and text-independent tasks in short-duration speaker verification (SdSV) challenge 2021. In this challenge, we explored different strong embedding extractors to extract robust speaker embedding. For text-independent task, language-dependent adaptive snorm is explored to improve the system performance under the cross-lingual verification condition. For text-dependent task, we mainly focus on the in-domain fine-tuning strategies based on the model pre-trained on large-scale out-of-domain data. In order to improve the distinction between different speakers uttering the same phrase, we proposed several novel phrase-aware fine-tuning strategies and phrase-aware neural PLDA. With such strategies, the system performance is further improved. Finally, we fused the scores of different systems, and our fusion systems achieved 0.0473 in Task1 (rank 3) and 0.0581 in Task2 (rank 8) on the primary evaluation metric.","Title: A Smart Trader for Portfolio Management based on Normalizing Flows.\nYear: 2022\n\nAbstract: In this paper, we study a new kind of portfolio problem, named trading point aware portfolio optimization (TPPO), which aims to obtain excess intraday profit by deciding the portfolio weights and their trading points simultaneously based on microscopic information. However, a strategy for the TPPO problem faces two challenging problems, i.e., modeling the ever-changing and irregular microscopic stock price time series and deciding the scattering candidate trading points. To address these problems, we propose a novel TPPO strategy named STrader based on normalizing flows. STrader is not only promising in reversibly transforming the geometric Brownian motion process to the unobservable and complicated stochastic process of the microscopic stock price time series for modeling such series, but also has the ability to earn excess intraday profit by capturing the appropriate trading points of the portfolio. Extensive experiments conducted on three public datasets demonstrate STrader's superiority over the state-of-the-art portfolio strategies.","Title: Distributed Fault Estimation and Fixed-Time Fault-Tolerant Formation Control for Multi-UAVs subject to Sensor Faults\nYear: 2022\n\nAbstract: This paper develops a fault estimation-based fixed-time fault-tolerant formation control strategy for multiple unmanned aerial vehicles under the case of sensor faults and directed connective communication topology. Four common types of sensor faults on position and velocity measurement channels are considered simultaneously, which are only assumed to be bounded. For monitoring the occurrence of sensor faults, a fault detection observer is developed using \n \n \n \n $$H_\\infty$$\n \n approach. Then, the amplitude of the sensor fault is reconstructed by the modified adaptive radial basis function neural network-based cooperative fault estimation observer, which is triggered by the fault alarm from fault detection observer. Furthermore, a novel sliding mode-based distributed fault-tolerant formation controller is designed to compensate the effects of faults using the estimated fault information, where the unknown bounded fault compensation error and lumped disturbances are explicitly taken into consideration. Additionally, the fixed-time stability of the whole formation system is analyzed based on Lyapunov approach. Finally, a comparative simulation is performed to illustrate the performance and superiority of the proposed scheme.","Title: A Novel Approach to Processing Very-High-Resolution Spaceborne SAR Data With Severe Spatial Dependence\nYear: 2022\n\nAbstract: An innovative approach, mainly featured by progressive iteration of partitioning and focusing, to processing very-high-resolution spaceborne synthetic aperture radar (SAR) data is presented in this article. Due to the long integration time endured during data acquisition as well as large scene extensions being common to the advanced SAR systems, spatial dependence of range histories may be severe and must be properly dealt with. In this article, after deriving the exact two dimensional spectrum for an curved satellite orbit, we focus the entire echo data through several iterations of partitioning and focusing. By partitioning, each partitioned data block can be better focused by the following focusing step, and meanwhile, focusing with better quality enables the following partitioning with both higher granularity and less overlapped data region. Besides, a new technique aiming at eliminating spatial dependence inside a data block is also presented and deployed in the processing procedure. Owing to the feature of high granularity partitioning, the spatial dependence of many factors can be accommodated finer and easier compared with other conventional algorithms. What is more, the artifacts caused by subaperture recombination are fundamentally avoided by the fact that the partitioned blocks are not recombined until being processed into fully focused image blocks. Finally, the precision and the efficiency of the proposed methodology are validated by results on a simulated and a real SAR data.","Title: Multi-objective optimal trajectory planning of customized industrial robot based on reliable dynamic identification for improving control accuracy\nYear: 2022\n\nAbstract: Purpose The purpose of this paper is to enhance control accuracy, energy efficiency and productivity of customized industrial robots by the proposed multi-objective trajectory optimization approach. To obtain accurate dynamic matching torques of the robot joints with optimal motion, an improved dynamic model built by a novel parameter identification method has been proposed. Design/methodology/approach This paper proposes a novel multi-objective optimal approach to minimize the time and energy consumption of robot trajectory. First, the authors develop a reliable dynamic parameters identification method to obtain joint torques for formulating the normalized energy optimization function and dynamic constraints. Then, optimal trajectory variables are solved by converting the objective function into relaxation constraints based on second-order cone programming and Runge-Kutta discrete method to reduce the solving complexity. Findings Extensive experiments via simulation and in real customized robots are conducted. The results of this paper illustrate that the accuracy of joint torque predicted by the proposed model increases by 28.79% to 79.05% over the simplified models used in existing optimization studies. Meanwhile, under the same solving efficiency, the proposed optimization trajectory consumes a shorter time and less energy compared with the existing optimization ones and the polynomial trajectory. Originality/value A novel time-energy consumption optimal trajectory planning method based on dynamic identification is proposed. Most existing optimization methods neglect the effect of dynamic model reliability on energy efficiency optimization. A novel parameter identification approach and a complete dynamic torque model are proposed. Experimental results of dynamic matching torques verify that the control accuracy of optimal robot motion can be significantly improved by the proposed model.","Title: A novel scheme for processing FMCW-ladar data acquired with low sampling rate\nYear: 2022\n\nAbstract: The frequency-modulated continuous wave (FMCW) ladar is a useful sensor for remote sensing applications and has been widely used in both the military and civilian fields for precise geospatial data acquisition. Typically, the FMCW-ladar signal is collected by use of the heterodyne detection through the dechirping so as to decrease the sampling rate commensurate with the bandwidth. In this manuscript, we proposed a novel scheme for processing the FMCW-ladar signal acquired through the simplified heterodyne detection, and the sampling rate was the same as that in the dechirp detection. Based on the time-frequency relation of the FMCW-ladar signal, two algorithms were proposed to post-process the echo acquired by the simplified heterodyne detection with the sub-Nyquist sampling rate. For the FMCW-ladar echo data acquired by the simplified heterodyne detection with the same sampling rate as in the traditional dechirp detection, the algorithms can achieve the unambiguous range image retaining the range resolution commensurate with the transmitted chirp bandwidth. The effectiveness of the scheme was validated by simulation and real data processing experiments. The capability of the proposed scheme provides an alternative for the FMCW-ladar system without use of the dechirp detection, which can benefit the future FMCW-ladar and microwave photonics radar applications in the remote sensing.","Title: Simulation Study of Wireless Coverage in Straight Long Corridors on Container Ship Deck\nYear: 2022\n\nAbstract: A wireless coverage simulation based on the ray tracing method is constructed to realize the wireless coverage prediction of the straight long corridor on the container ship deck. The ray reflection model based on the reverse algorithm of the ray tracing method is established to realize the simulation calculation of the point-to-point propagation path. Referring to the recommendations given by the International Telecommunication Union, the Fresnel loss of the straight long corridors and the first Fresnel loss point are calculated, and the ray reflection model is effectively corrected. The least square method is used to fit the double slope of the simulation curve, and the loss formula of the straight long corridors on the deck is obtained. Through experimental simulation, the path loss factors of the fully enclosed corridor are 1.248 and 3.245, and the path loss factors of the semi-closed corridor are 1.251 and 3.444. The simulation results agree with the general conclusions of the ITU recommendation. The simulation can provide the prediction of wireless coverage effect in the design of container ships and provide guidance for the layout design and optimization of base stations of the straight long corridor on container ship deck with different scales.","Title: Development of equipment production information monitoring system based on electric current signal\nYear: 2022\n\nAbstract: Traditional machining equipment typically does not provide online production information such as part production quantities, efficiency and abnormal operating conditions. In order to solve this problem, a real-time production information monitoring system for traditional machining equipment based on electric current signal has been development. Firstly, the data acquisition hardware system using current sensors is designed to collect the electric current signal of the equipment being monitored. Next, the current data is processed by calibration algorithm to obtain production process feature vectors. Finally, a feature matching algorithm is used to identify the operating status. Based on the above algorithms, a monitoring software system is realized by C++ programming language on Qt platform. The monitoring experiment was carried out with automobile transmission shaft parts. The experimental results show that the machining start time and end time of each machined part are correctly and timely identified, and the abnormal state of the equipment could be accurately identified. The developed system is suitable for real-time monitoring of the traditional machining equipment.","Title: A Novel Distributed Data-Driven Strategy for Fault Detection of Multi-Source Dynamic Systems\nYear: 2022\n\nAbstract: In this brief, a distributed data-driven fault detection method based on sparse integrated dynamic principal component analysis is designed to enhance the fault detection performance of multi-source dynamic systems. First, by dividing the complex system into multiple sub-sources according to the structural principle, the fusion and complementation of process mechanism knowledge and data-driven are realized. Second, by introducing the Kronecker product covariance structure, the individual modes within each sub-source as well as joint modes shared between sub-sources are captured. Notably, by developing a truncated contribution power method, key variables are selected and the interpretability of principal components is improved. Finally, experimental results demonstrate that the proposed strategy can significantly improve the detection performance compared with the existing PCA-based methods.","Title: Carbon-reducing Train Rescheduling Method for Urban Railway Systems considering the Grid with Wind Power Supply\nYear: 2022\n\nAbstract: Nowadays, carbon emission reduction has been given the priority in most of the countries around the world to save the mankind and our planet, while transport sector and energy sector are both still with high carbon emission. With the development of the intelligent transportation systems, smart grid technologies and renewable energy integration, the opportunity for the coordination of systems can be achieved. This paper proposes a linear programming model to reschedule the train timetable, where the urban railway system and power system with wind power supply are coordinated to minimize the carbon emission. In the paper, a general railway system with IEEE 30-bus system is adopted in the case studies. The running time of the train for each inter-station section can be rescheduled, and the optimal output of each power plants can also be given after the optimization. The optimal results show that by coordinating the railway system and power system, the energy consumption and carbon emission of the train operation can be reduced by 12.32% and 12.65% respectively when compared with the original solution."],"string":"[\n \"Title: Application Of Artificial Intelligence In Autonomous English Learning Among College Students\\nYear: 2019\\n\\nAbstract: To improve the English proficiency of college students, this paper attempts to promote autonomous English learning among these students using artificial intelligence technology. Specifically, the principles, features and application fields of artificial intelligence were introduced in details, and the requirements, structure, database and processes of autonomous English learning systems were discussed at length. On this basis, the backpropagation (BP) neural network, a typical artificial intelligence technique, was selected to design the modules of the autonomous English learning system for college students. The proposed system was then implemented in integrated open environment of Visual Studio, the architecture of the browser/server system and the SQL Server database on the Windows platform. The results show that the proposed system provides college students with intelligent and open self-evaluation tests and effective learning feedbacks. The research findings shed new light on improving the English learning of college students and reliving the pressure on college English teachers.\",\n \"Title: Universal learning network and its application for nonlinear system with long time delay\\nYear: 2006\\n\\nAbstract: This paper discusses universal learning network (ULN) and its application to control a class of nonlinear systems with long time delay. Two control architectures, model predictive control based on ULN model and single neuron PID (SN-PID) controller based on ULN predictor, are designed to control pH neutralization process, respectively. In addition, to verify the performance of ULN, two comparisons are also made. One is the generalization ability between ULN and back-propagation (BP) network, the other comparison is ULN predictor and Smith predictor, in which the same controller is used. Simulation results prove the applicability and effectiveness of the ULN model. The special architecture and its learning algorithm give ULN more representing abilities to model and control complicated nonlinear systems with long time delay.\",\n \"Title: An iterative joint channel estimation and symbol detection algorithm applied in OFDM system with high data to pilot power ratio\\nYear: 2003\\n\\nAbstract: For orthogonal frequency-division multiplexing (OFDM) systems with pilot-symbol-aided channel estimation, a straightforward way to overcome system throughput deterioration is by decreasing the power and reducing the number of pilots, which unfortunately, leads directly to the degradation of BER performance. For the purpose of improving the BER performance, an iterative joint channel estimation and symbol detection algorithm with low complexity and fast convergence is proposed. Both theoretical analysis and computer simulation show that this algorithm gives better BER performance and saves the overhead of OFDM systems.\",\n \"Title: A Compensation Method With a Standard Square Wave for Precise DC Measurement of Mutual Inductance for Joule Balance\\nYear: 2012\\n\\nAbstract: The precise dc-based measurement of the mutual inductance is one of the key issues in the Joule Balance method used to determine Planck's constant h. Here, a new compensation method with a standard square wave is provided. The dc value of the mutual inductance is obtained by measuring the integral area of the induced voltage. To increase the accuracy of this integral method, we just accurately measure the residual area observed between the induced voltage of the mutual inductor and a synchronously generated standard square wave. Experimental results indicate that this approach is consistent with the previous frequency extrapolation method within 1 ppm. The measurement uncertainty is estimated to be about 0.22 μH/H for the 340-mH mutual inductor at room temperature. A better result of 0.1 ppm could be obtained when the superconducting coil is used.\",\n \"Title: Joint channel estimation and symbol detection for SFBC-OFDM systems via the EM algorithm\\nYear: 2004\\n\\nAbstract: Joint channel estimation and symbol detection for space-frequency block coded OFDM (SFBC-OFDM) systems is considered. The expectation-maximization (EM) algorithm is employed to devise both soft-in-hard-out (SIHO) maximum likelihood (ML) iterative receivers and soft-in-soft-out (SISO) maximum a posteriori (MAP) iterative receivers for SFBC-OFDM systems. Besides the low computational complexity and fast convergence, the EM-based iterative receivers also have the ability of tracing the variation of fast fading channels, and thus provide quite satisfying performance and bandwidth efficiency even in high-vehicular-speed environment. Numerical results are provided to corroborate the theoretical designs.\",\n \"Title: Mutual Inductance Measurement of the Superconducting Coil for the Joule Balance\\nYear: 2013\\n\\nAbstract: The joule balance, an approach for precisely measuring the Planck constant at the National Institute of Metrology (NIM), China, is currently limited by the self-heating of the coil when the measuring current flows. To solve this heating problem, a superconducting coil has been built to replace the existing fixed coil wound with enameled copper wire. The movable coil set is formed by two enameled coils connected oppositely for easy alignment in air. However, the large self-inductance of the superconducting coil brings new challenges for the a mutual inductance measurement. Mutual inductance measurement of superconducting coil based on standard square wave compensation method is presented and discussed in this paper.\",\n \"Title: Social Networks, Information Acquisition, and Asset Prices.\\nYear: 2013\\n\\nAbstract: We analyze a rational expectations equilibrium model to explore the implications of information networks for the financial market. When information is exogenous, social communication improves market efficiency. However, social communication crowds out information production due to traders' incentives to \\\"free ride\\\" on informed friends and on a more informative price system. Overall, social communication hurts market efficiency when information is endogenous. The network effects on the cost of capital, liquidity, trading volume, and welfare are also sensitive to whether information is endogenous. Our analysis highlights the importance of information acquisition in examining the implications of information networks for financial markets.\",\n \"Title: A Bernstein-type estimator for decreasing density with application to p-value adjustments\\nYear: 2012\\n\\nAbstract: The nonparametric maximum likelihood estimator (NPMLE) is a popular approach to estimating decreasing densities, i.e., f(s)=f(t),s@?t. A less ideal feature of NPMLE is its step-function form. In this paper, we propose two nonparametric density estimators based on the Bernstein-type polynomials of the NPMLE. The proposed estimators have relatively simple forms and easy implementation. They have satisfactory smoothness as well as estimation efficiency. Numerical examples demonstrate the superior performance of the proposed estimators compared to existing methods. Decreasing densities have been applied in simultaneous inference to estimate the proportion of true null hypotheses and the local false discovery rate. We applied the proposed estimators to conduct simultaneous tests for a gene expression data set.\",\n \"Title: Hard multidimensional multiple choice knapsack problems, an empirical study\\nYear: 2010\\n\\nAbstract: Recent advances in algorithms for the multidimensional multiple choice knapsack problems have enabled us to solve rather large problem instances. However, these algorithms are evaluated with very limited benchmark instances. In this study, we propose new methods to systematically generate comprehensive benchmark instances. Some instances with special correlation properties between parameters are found to be several orders of magnitude harder than those currently used for benchmarking the algorithms. Experiments on an existing exact algorithm and two generic solvers show that instances whose weights are uncorrelated with the profits are easier compared with weakly or strongly correlated cases. Instances with classes containing similar set of profits for items and with weights strongly correlated to the profits are the hardest among all instance groups investigated. These hard instances deserve further study and understanding their properties may shed light to better algorithms.\",\n \"Title: Sweep-Line analysis of TCP connection management\\nYear: 2005\\n\\nAbstract: Despite the widespread use of the Transmission Control Protocol (TCP) as the main transport protocol in the Internet, the procedures for connection establishment and release are still not fully understood. This paper extends the analysis of a Coloured Petri net model of TCP’s Connection Management procedures by applying the state explosion alleviation technique known as the sweep-line method. The protocol is assumed to be operating over a reordering lossless channel. Termination and absence of deadlock properties are investigated for many scenarios, including client-server and simultaneous connection establishment, orderly release and abortion. The sweep-line method provides a reduction in memory usage of around a factor of 10 and allows investigation of many scenarios that were previously out of the reach of conventional methods.\",\n \"Title: Neuromorphic models on a GPGPU cluster\\nYear: 2010\\n\\nAbstract: There is currently a strong push in the research community to develop biological scale implementations of neuron based vision models. Systems at this scale are computationally demanding and have generally utilized more accurate neuron models, such as the Izhikevich and Hodgkin- Huxley models, in favor of the integrate and fire model. This paper examines the feasibility of using a cluster of NVIDIA General Purpose Graphics Processing Units (GPGPUs) for accelerating a spiking neural network based character recognition network based on the Izhikevich and Hodgkin-Huxley models to enable such large scale systems. We utilized a 32 node cluster at NCSA containing an NVIDIA Tesla S1070 GPGPU on each node. Based on a thorough review of the literature, this is the first study examining the use of a cluster of GPGPUs for accelerating neuromorphic models. Our results show that the GPGPU can provide speedups of 24.6 and 177.0 times over a dual core 2.4 GHz AMD Opteron processor for the Izhikevich and Hodgkin-Huxley models respectively. Additionally, the MPI implementations of the models scaled almost linearly, with 16 GPGPUs providing throughputs of 14.1 and 15.9 times that of a single GPGPU for the Izhikevich and Hodgkin-Huxley models respectively. This indicates that clusters of GPGPUs are well suited for this application domain.\",\n \"Title: An Automatic MSRM Method with a Feedback Based on Shape Information for Auroral Oval Segmentation.\\nYear: 2013\\n\\nAbstract: Auroral oval segmentation is of great significance to the study of auroral activities. In this paper, we propose an automatic maximal similarity based region merging (MSRM) method with a feedback based on shape information. Firstly, K-means method is employed to mark auroral oval points and background points, thus guiding the process of MSRM to obtain the initial segmentation result. Then the direct least-square ellipse fitting method is used to fit an ellipse on the initial boundary and points in the fitted ellipse are set as adjusted markers of auroral oval. Finally, the MSRM mechanism is used again to get the final segmentation result. Experimental results show that the proposed algorithm obtains a good performance. s © 2013 Springer-Verlag Berlin Heidelberg.\",\n \"Title: Comparing quantitative trait Loci and gene expression data.\\nYear: 2008\\n\\nAbstract: We develop methods to compare the positions of quantitative trait loci (QTL) with a set of genes selected by other methods, such as microarray experiments, from a sequenced genome. We apply our methods to QTL for addictive behavior in mouse, and a set of genes upregulated in a region of the brain associated with addictive behavior, the nucleus accumbens (NA). The association between the QTL and NA genes is not significantly stronger than expected by chance. However, chromosomes 2 and 16 do show strong associations suggesting that genes on these chromosomes might be associated with addictive behavior. The statistical methodology developed for this study can be applied to similar studies to assess the mutual information in microarray and QTL analyses.\",\n \"Title: Medium-Earth-orbit SAR imaging based on keystone transform and azimuth perturbation\\nYear: 2012\\n\\nAbstract: Due to the significant azimuth variance property in medium-Earth-orbit (MEO) synthetic aperture radar (SAR) echo, it is difficult for the conventional SAR algorithms to achieve a good compromise between accuracy and efficiency. A novel algorithm based on Keystone transform (KT) and azimuth perturbation (AP) is introduced in this paper to handle this problem. The function of KT is to correct the range walk and thus to mitigate the azimuth variance effect on range processing. The function of AP is to equalize the Doppler histories in each range gate and thus to mitigate the azimuth variance effect on azimuth compressing. Simulation results of an L-band MEO SAR with 5 m resolution at 10,000 km altitude demonstrate the capability of our algorithm.\",\n \"Title: Predictive control method of improved double-controller scheme based on neural networks\\nYear: 2006\\n\\nAbstract: This paper considers the problem of stabilizing a black-box plant with time delay using an improved double controller scheme. The PID parameters of the load controller of the double-controller scheme are obtained by a neural network controller with back propagation algorithm. Based on the adaptive algorithm of Universal Learning Network (ULN), ULN is adopted for modeling the plant and being a predictor of the control system. Simulation results prove the applicability and effectiveness of the improved double-controller scheme. ULN and the neural network controller give the double-controller scheme more representing abilities and robust ability.\",\n \"Title: Dayside Corona Aurora Classification Based On X-Grey Level Aura Matrices And Feature Selection\\nYear: 2011\\n\\nAbstract: Dayside corona aurora is the main form of aurora at magnetic noon, which is generated by the dynamics process of the interaction of the sun and earth's magnetosphere. Hence the study of dayside corona aurora is of great importance to the analysis of ionosphere and its dynamic feature. This paper proposes a novel aurora texture extraction method based static image classification of dayside aurora, in which X-grey level aura matrices are designed to extract the features of the original aurora images. Besides, a dayside aurora classification algorithm for dayside aurora based on feature selection is proposed to handle the large quantities of aurora samples. To eliminate the effect of noise and solve the problem of high feature dimensionality, the ReliefF is adopted to select the effective feature vector. For classification, texture models are learned by using the support vector machine, then a given texture of aurora image can be classified into one of the pre-learned classes. All of the aurora images in the experiment are obtained from the all-sky aurora image system in the Chinese Arctic Yellow River Station. The experimental results illustrate the high effectiveness of the proposed dayside aurora classification algorithm.\",\n \"Title: Termination properties of TCP's connection management procedures\\nYear: 2005\\n\\nAbstract: The Transmission Control Protocol (TCP) is the most widely used transport protocol in the Internet, providing a reliable data transfer service to many applications. This paper analyses TCP's Connection Management procedures for correct termination and absence of deadlocks. The protocol is assumed to be operating over a reordering lossless channel and is modelled using Coloured Petri nets. The following connection management scenarios are examined using state space analysis: client-server and simultaneous opening; orderly release; and abortion. The results demonstrate that TCP terminates correctly for client-server and simultaneous connection establishment, orderly release after the connection is established and aborting of connections. However, we discover a deadlock when connection release is initiated before the connection has been fully established when operating over a reordering lossless channel.\",\n \"Title: A novel feature weighted clustering algorithm based on rough sets for shot boundary detection\\nYear: 2006\\n\\nAbstract: Shot boundary detection as the crucial step attracts much more research interests in recent years. To partition news video into shots, many metrics were constructed to measure the similarity among video frames based on all the available video features. However, too many features will reduce the efficiency of the shot boundary detection. Therefore, it is necessary to perform feature reduction before shot boundary detection. For this purpose, the classification method based on clustering algorithm of Variable Precision Rough-Fuzzy Sets and Variable Precision Rough Sets for feature reduction and feature weighting is proposed. According to the particularity of news scenes, shot transition can be divided into three types: cut transition, gradual transition and no transition. The efficiency of the proposed method is extensively tested on UCI data sets and more than 3 h of news programs and 96.2% recall with 96.3% precision have been achieved.\",\n \"Title: A novel clustering algorithm based on variable precision rough-fuzzy sets\\nYear: 2006\\n\\nAbstract: In the field of cluster analysis and data mining, fuzzy c-means algorithm is one of effective methods, which has widely used in unsupervised pattern classification. However, the above algorithm assumes that each feature of the samples plays a uniform contribution for cluster analysis. To consider the different contribution of each dimensional feature of the given samples to be classified, this paper presents a novel fuzzy c-means clustering algorithm based on feature weighted, in which the Variable Precision Rough-Fuzzy Sets is used to assign the weights to each feature. Due to the advantages of Rough Sets for feature reduction, we can obtain the better results than the traditional one, which enriches the theory of FCM-type algorithms. Then, we apply the proposed method into video data to detect shot boundary in video indexing and browsing. The test experiment with UCI data and the video data from CCTV demonstrate the effectiveness of the novel algorithm.\",\n \"Title: Taking the road less traveled by: Does conversation eradicate pernicious cascades?\\nYear: 2011\\n\\nAbstract: We offer a model in which sequences of individuals often converge upon poor decisions and are prone to fads, despite communication of the payoff outcomes from past choices. This reflects both direct and indirect action-based information externalities. In contrast with previous cascades literature, cascades here are spontaneously dislodged and in general have a probability less than one of lasting forever. Furthermore, the ability of individuals to communicate can reduce average decision accuracy and welfare.\",\n \"Title: A Markov blanket-based method for detecting causal SNPs in GWAS.\\nYear: 2010\\n\\nAbstract: Detecting epistatic interactions associated with complex and common diseases can help to improve prevention, diagnosis and treatment of these diseases. With the development of genome-wide association studies (GWAS), designing powerful and robust computational method for identifying epistatic interactions associated with common diseases becomes a great challenge to bioinformatics society, because the study of epistatic interactions often deals with the large size of the genotyped data and the huge amount of combinations of all the possible genetic factors. Most existing computational detection methods are based on the classification capacity of SNP sets, which may fail to identify SNP sets that are strongly associated with the diseases and introduce a lot of false positives. In addition, most methods are not suitable for genome-wide scale studies due to their computational complexity.We propose a new Markov Blanket-based method, DASSO-MB (Detection of ASSOciations using Markov Blanket) to detect epistatic interactions in case-control GWAS. Markov blanket of a target variable T can completely shield T from all other variables. Thus, we can guarantee that the SNP set detected by DASSO-MB has a strong association with diseases and contains fewest false positives. Furthermore, DASSO-MB uses a heuristic search strategy by calculating the association between variables to avoid the time-consuming training process as in other machine-learning methods. We apply our algorithm to simulated datasets and a real case-control dataset. We compare DASSO-MB to other commonly-used methods and show that our method significantly outperforms other methods and is capable of finding SNPs strongly associated with diseases.Our study shows that DASSO-MB can identify a minimal set of causal SNPs associated with diseases, which contains less false positives compared to other existing methods. Given the huge size of genomic dataset produced by GWAS, this is critical in saving the potential costs of biological experiments and being an efficient guideline for pathogenesis research.\",\n \"Title: A method of airborne InSAR DEM reconstruction in layover areas\\nYear: 2012\\n\\nAbstract: The layover phenomenon of SAR imaging arises when different height contributions collapse in the same range-azimuth resolution cell, due to the presence of strong terrain slopes or discontinuities in the scenarios. Because of the phase discontinuities, for single baseline interferometric SAR, it is difficult to recover the accurate unwrapped phase in layover areas by traditional phase unwrapping methods. In this paper, according to the phase characteristic of layover areas, we propose a new method to retrieve unwrapped phase based on local frequency estimate. It can avoid the unwrapping error resulted from the phase jump at the edge of layover areas. As a result, relative accurate DEM of layover areas can be reconstructed, which is beneficial to afterward geocoding in InSAR topographic mapping.\",\n \"Title: A saliency map method with cortex-like mechanisms and sparse representation\\nYear: 2010\\n\\nAbstract: In the field of visual attention, bottom-up or saliency-based visual attention allows primates to detect non-specific conspicuous objects or targets in cluttered scenes. Simple multi-scale \\\"feature maps\\\" detect local spatial discontinuities in intensity, color, orientation, and are combined into a \\\"saliency\\\" map. HMAX is a feature extraction method and this method is motivated by a quantitative model of visual cortex. In this paper, we introduce the Saliency Criteria to measure the perspective fields. This model is based on cortex-like mechanisms and sparse representation, Saliency Criteria is obtained from Shannon's self-information and entropy. We demonstrate that the proposed model achieves superior accuracy with the comparison to classical approach in static saliency map generation on real data of natural scenes and psychology stimuli patterns.\",\n \"Title: FEPI-MB: identifying SNPs-disease association using a Markov Blanket-based approach.\\nYear: 2011\\n\\nAbstract: The interactions among genetic factors related to diseases are called epistasis. With the availability of genotyped data from genome-wide association studies, it is now possible to computationally unravel epistasis related to the susceptibility to common complex human diseases such as asthma, diabetes, and hypertension. However, the difficulties of detecting epistatic interaction arose from the large number of genetic factors and the enormous size of possible combinations of genetic factors. Most computational methods to detect epistatic interactions are predictor-based methods and can not find true causal factor elements. Moreover, they are both time-consuming and sample-consuming.We propose a new and fast Markov Blanket-based method, FEPI-MB (Fast EPistatic Interactions detection using Markov Blanket), for epistatic interactions detection. The Markov Blanket is a minimal set of variables that can completely shield the target variable from all other variables. Learning of Markov blankets can be used to detect epistatic interactions by a heuristic search for a minimal set of SNPs, which may cause the disease. Experimental results on both simulated data sets and a real data set demonstrate that FEPI-MB significantly outperforms other existing methods and is capable of finding SNPs that have a strong association with common diseases.FEPI-MB algorithm outperforms other computational methods for detection of epistatic interactions in terms of both the power and sample-efficiency. Moreover, compared to other Markov Blanket learning methods, FEPI-MB is more time-efficient and achieves a better performance.\",\n \"Title: Saliency Based On Cortex-Like Mechanisms\\nYear: 2011\\n\\nAbstract: A visual attention system should preferentially locate the most informative spots in complex environments. Feature-integration theory of attention plays an important role in bottom-up computational model for visual attention. This point extremely decreases the detection accuracy and also impacts the performance of the automatic visual attention model. To improve the accuracy of saliency detection in human visual attention, we propose a model based on cortex-like mechanisms. Saliency Criteria are obtained from two pathways: Shannon's entropy and sparse representation. And our model not only substantiates the bottom-up model proposed by Itti and HMAX model by Paggio, but also enriches the theory of saliency detection. We demonstrate that the proposed model achieves superior accuracy in comparison to the classical approach in static saliency map generation on real data of natural scenes and psychology stimuli patterns.\",\n \"Title: Large-scale Protein-Protein Interaction prediction using novel kernel methods.\\nYear: 2008\\n\\nAbstract: Knowledge of Protein-Protein Interactions (PPIs) can give us new insights into molecular mechanisms and properties of the cell. In this paper, we propose a novel domain-based kernel method to predict PPIs. A new kernel that measures the similarity between protein pairs based on a new feature representation is developed and applied to a large scale PPI database. Experimental results demonstrate its effectiveness. Furthermore, we evaluate the problem of cross-species PPI prediction and the effect of the number of negative samples on the performance of PPI predictions, which are two fundamental problems in most in silico PPI methods.\",\n \"Title: A Quantum Potential Strategy by Quantum Genetic Algorithm.\\nYear: 2012\\n\\nAbstract: In this papers, The potential of a quantum bit is defined and a novel quantum evolutionary algorithm based on potential of the quantum bit is proposed. The character of quantum entangled interference based on potential of the quantum bit is introduced. And convergence analysis and rationality analysis are implemented.\",\n \"Title: Genetic studies of complex human diseases: Characterizing SNP-disease associations using Bayesian networks.\\nYear: 2012\\n\\nAbstract: Detecting epistatic interactions plays a significant role in improving pathogenesis, prevention, diagnosis, and treatment of complex human diseases. Applying machine learning or statistical methods to epistatic interaction detection will encounter some common problems, e.g., very limited number of samples, an extremely high search space, a large number of false positives, and ways to measure the association between disease markers and the phenotype.To address the problems of computational methods in epistatic interaction detection, we propose a score-based Bayesian network structure learning method, EpiBN, to detect epistatic interactions. We apply the proposed method to both simulated datasets and three real disease datasets. Experimental results on simulation data show that our method outperforms some other commonly-used methods in terms of power and sample-efficiency, and is especially suitable for detecting epistatic interactions with weak or no marginal effects. Furthermore, our method is scalable to real disease data.We propose a Bayesian network-based method, EpiBN, to detect epistatic interactions. In EpiBN, we develop a new scoring function, which can reflect higher-order epistatic interactions by estimating the model complexity from data, and apply a fast Branch-and-Bound algorithm to learn the structure of a two-layer Bayesian network containing only one target node. To make our method scalable to real data, we propose the use of a Markov chain Monte Carlo (MCMC) method to perform the screening process. Applications of the proposed method to some real GWAS (genome-wide association studies) datasets may provide helpful insights into understanding the genetic basis of Age-related Macular Degeneration, late-onset Alzheimer's disease, and autism.\",\n \"Title: Closed form expressions for the state space of TCP's Data Transfer Service operating over unbounded channels\\nYear: 2004\\n\\nAbstract: The Internet is a very complex system, comprising a dynamically changing network of networks. The Internet's original designers created the Transmission Control Protocol (TCP) to provide a reliable end to end data service to its users, that would operate correctly in the face of failures in the network infrastructure. TCP handles the vast majority of the ever increasing traffic over the Internet and it is therefore of utmost importance that it operates correctly as the Internet grows and as speeds on the Internet increase. This paper provides a step towards its formal verification by providing a general formalisation of TCP requirements in the knowledge that it operates over a medium of very large and unknown capacity. These requirements are expressed in what is termed a service specification. Once the intent of the service that TCP is to provide to its users is defined in the service specification, TCP can be verified against this intent. A central part of the service specification is the definition of the sequences of user observable events (known as service primitives) that can occur at the user/TCP boundary. This is known as the service language. An important verification task is then to prove that TCP complies with this service language. The formal verification of a protocol as complex as TCP is an ambitious undertaking. To simplify the task, we consider the connection management and data transfer parts of the protocol and service separately. In this paper, we are concerned with the data transfer part of the service. Unfortunately the TCP Data Transfer Service language grows exponentially with the size of the medium over which it operates, which in general is unbounded. This implies that the automaton that represents the Data Transfer Service language is infinite. To tackle this problem, we parameterise the Data Transfer Service by the size of the medium. We provide a Coloured Petri Net model of the Data Transfer Service based on previous work and establish closed form expressions for its state space parameterised by the medium capacity. The state space is the automaton that represents the Data Transfer Service language. These expressions eliminate the need for reachability analysis and determine a parameterised automaton that embodies the Data Transfer Service language for arbitrary medium capacity.\",\n \"Title: The evolution and developing tendency of DAC design methods\\nYear: 2012\\n\\nAbstract: There are many different methods in DAC (digital-analog-convertor) design to meet the fact requirements. The methods of DAC design can be divided into several classifications. From the papers published before, the history of the evolution and limitations of each method can be summarized. The real requirements drive the innovation and change of DAC development. Base on the discourse above, it is possible to make a good optimization and find developing tendency of DAC design methods.\",\n \"Title: Query range problem in wireless sensor networks\\nYear: 2009\\n\\nAbstract: Wireless sensor networks with multiple users extracting data directly from nearby sensors have many potential applications. An important problem in such a network is how to allocate the multi-hop query range for each user such that a certain global optimality is achieved. We introduce this problem and show it is NP-complete in its generic form. Distributed heuristic is proposed and evaluated with simulations. Interesting behaviors of the network when optimized with different global objectives are observed from the simulation results.\",\n \"Title: Robust detection of sources based on clustering in spatially correlated noise fields\\nYear: 2006\\n\\nAbstract: The research of sources number detection is still open and challenging issue in array signal processing. The accurate estimation may be very essential to those high resolution direction finding algorithms. Three crucial issues are discussed for the application of cluster method to the source-number detection. A detection method based on Fuzzy-c-Means clustering algorithm has been proposed, which uses canonical correlation coefficients of the joint covariance matrix as the feature to be classified. Compared with the classical methods, our algorithm has better performance in low SNR and angular resolution. In the spatially correlated noise fields, the simulation results demonstrate the effectiveness and robustness of the proposed scheme.\",\n \"Title: DASSO-MB: Detection of Epistatic Interactions in Genome-Wide Association Studies Using Markov Blankets\\nYear: 2009\\n\\nAbstract: With the development of genome-wide association studies (GWAS), computationally identifying the epistatic interactions associated with common diseases presents a significant challenge to bioinformatics society. Most existing computational detection methods are based on the classification capacity of SNP sets, which may fail to identify SNP sets that are strongly associated with the diseases. In addition, most methods are not suitable for genome-wide scale studies due to their computational complexity. To address these issues, we propose the use of a Markov Blanket-based method, DASSO-MB, for epistatic interaction detection. We apply our method to both simulated data sets and a real data set, and demonstrate that DASSO-MB significantly outperforms other existing methods and is capable of finding SNPs that have a strong association with common diseases\",\n \"Title: A case study on REST-style architecture for cyber-physical systems: Restful smart gateway.\\nYear: 2011\\n\\nAbstract: Due to several key factors, Cyber-physical systems (CPS) pose great challenges in software system design, which are dynamic composition, heterogeneous, adaptation and uncertain in environmental factors. In this paper we present our research on the development of REST-style architecture for CPS. We propose a path towards solving requirements of CPS architecture through Restful principles. By using this architectural style, we have built a prototyping system called the restful smart gateway, which seamlessly integrates conceptual and physical resources into the Web and scale better. Some experiments on the smart gateway are given to illustrate its performance.\",\n \"Title: Formalising TCP's Data Transfer Service Language: A Symbolic Automaton and its Properties\\nYear: 2007\\n\\nAbstract: The TCP/IP protocol suite defines the procedures governing the movement of data within the Internet. A major component of the suite is the Transmission Control Protocol (TCP), which ensures data flows reliably between Internet applications. We use Coloured Petri Nets (CPNs) to model the application's view of the service provided by TCP for the transfer of data. This service depends on the storage capacity available in the Internet. We firstly establish exact expressions for the size of the CPN model's reachability graph (or state space) in terms of the storage capacity. They indicate that both the nodes and arcs of the state space grow exponentially in the storage capacity. Secondly,we derive a symbolic state space which represents an infinite family of state spaces, one for each value of the capacity. We prove that each member of this family is a strongly connected graph and that its associated finite state automaton is deterministic and minimum. Finally, we formulate the TCP data transfer service language for arbitrary capacity, from the symbolic representation of the family of automata. This service language is the yardstick against which the TCP data transfer protocol can be verified with respect to user observable sequences of events.\",\n \"Title: Frequency synchronization for TDS-OFDM system with transmit diversity\\nYear: 2009\\n\\nAbstract: This paper proposes a frequency synchronization algorithm for time domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) system with multiple antenna transmit diversity. The training sequences used in the transmit diversity TDS-OFDM system are orthogonal and have near-optimal autocorrelation property, and the carrier frequency offset (CFO) estimation algorithm is based on these properties. From computer simulations, the proposed frequency synchronization method performs better when increasing the number of the transmit antenna in AWGN environment, and the estimated CFO can make the carrier-to-interference ratio be greater than 20 dB even over time-varying multipath channels with SNR=10 dB.\",\n \"Title: Forecast Accuracy Uncertainty and Momentum\\nYear: 2009\\n\\nAbstract: We demonstrate that stock price momentum and earnings momentum can result from uncertainty surrounding the accuracy of cash flow forecasts. Our model has multiple information sources issuing cash flow forecasts for a stock. The investor combines these forecasts into an aggregate cash flow estimate that has minimal mean-squared forecast error. This aggregate estimate weights each cash flow forecast by the estimated accuracy of its issuer, which is obtained from their past forecast errors. Momentum arises from the investor gradually learning about the relative accuracy of the information sources and updating their weights. Empirical tests validate the model's prediction of stronger momentum in stocks with large information weight fluctuations and high forecast dispersion. We also identify return predictability attributable to changes in the information weights.\",\n \"Title: A Novel Feature Combination Methods for Saliency-Based Visual Attention\\nYear: 2009\\n\\nAbstract: In the field of visual attention, bottom-up or saliency-based visual attention allows primates to detect non-specific conspicuous objects or targets in cluttered scenes. Simple multi-scale “feature maps” detect local spatial discontinuities in intensity, color, orientation, and are combined into a “saliency” map. In this paper, we propose a Saliency Map based on feature weighted, in which the Rough Sets is used to assign the weighting for every feature. This method measures the contribution of each conspicuity map obtained from the feature maps to saliency map. And it also carries out a dynamic weighting of individual conspicuity maps. We obtain results, which enrich the theory of Saliency detection. We use the real data of natural scenes to demonstrate the effectiveness of the algorithm.\",\n \"Title: Applications of FBG and ZigBee in telemetering of vortex-induced vibration for pipelines\\nYear: 2013\\n\\nAbstract: Under the influence of sustained flood scouring, buried pipelines are often exposed out of earth surface and this will result in vortex-induced vibration of suspended pipeline, which is one of the main reasons of fatigue failure of pipelines. In order to realize the remote monitoring of pipelines threatened by floods, we have designed a new kind of telemetering system based on fiber Bragg grating and wireless network technologies, which can satisfy the requirements automatic and real-time monitoring of vortex-induced vibration of suspended pipelines subjected to flood impacting. The telemetering system has been put into practical application in a monitoring project, the measurement results indicate that it is of such advantages as real-time capability, high accuracy, unattended operation, and it can well meet the present requirements of safety monitoring of oil and gas pipelines.\",\n \"Title: PPP: prefix-based popularity prediction for effective caching in content-centric networking\\nYear: 2014\\n\\nAbstract: In the Content-Centric Networking (CCN) architecture, popular content can be cached in some intermediate network devices while being delivered, and the following requests for the cached content can be efficiently handled by the caches. Thus, how to design in-network caching is important for reducing both the traffic load and the delivery delay. In this paper, we propose a caching framework of Prefix-based Popularity Prediction (PPP) for efficient caching in CCN. PPP assigns a lifetime (in a cache) to the prefix of a name (of each cached object) based on its access history (or popularity), which is represented as a Prefix-Tree (PT). We demonstrate PPP's predictability of content popularity in CCN by both traces and simulations. The evaluation results show that PPP can achieve higher cache hits and less traffic load than traditional caching algorithms (i.e., LRU and LFU). Also, its performance gain increases with users of high mobility.\",\n \"Title: Multiple Hierarchical Image Transmission Over Rayleigh Fading Channels\\nYear: 2000\\n\\nAbstract: In this paper, Ne present a multiple hierarchical protection scheme applied in for Rayleigh fading channels using multilevel coding (MLC) system in which both capacity rule and block partitioning (BP) have been taken into account. As a result, the non-uniform set-partitioning design trades the performance of the less important data for better performance of the most important ones, and thus improves the multiple hierarchical protection ability of MLC schemes.\",\n \"Title: Image representation by compressive sensing for visual sensor networks\\nYear: 2010\\n\\nAbstract: This paper addresses the image representation problem in visual sensor networks. We propose a new image representation method for visual sensor networks based on compressive sensing (CS). CS is a new sampling method for sparse signals, which is able to compress the input data in the sampling process. Combining both signal sampling and data compression, CS is more capable of image representation for reducing the computation complexity in image/video encoder in visual sensor networks where computation resource is extremely limited. Since CS is more efficient for sparse signals, in our scheme, the input image is firstly decomposed into two components, i.e., dense and sparse components; then the dense component is encoded by the traditional approach (JPEG or JPEG 2000) while the sparse component is encoded by a CS technique. In order to improve the rate distortion performance, we leverage the strong correlation between dense and sparse components by using a piecewise autoregressive model to construct a prediction of the sparse component from the corresponding dense component. Given the measurements and the prediction of the sparse component as initial guess, we use projection onto convex set (POCS) to reconstruct the sparse component. Our method considerably reduces the number of random measurements needed for CS reconstruction and the decoding computational complexity, compared to the existing CS methods. In addition, our experimental results show that our method may achieves up to 2dB gain in PSNR over the existing CS based schemes, for the same number of measurements.\",\n \"Title: Design and Implementation of University Focused Crawler Based on BP Network Classifier\\nYear: 2009\\n\\nAbstract: The rapid growth of the World-Wide web poses unprecedented scaling challenges for general-purpose crawlers and search engines. Crawling the Web quickly and entirely is an expensive, unrealistic goal because of the required hardware and network resources. A focused crawler is an agent that targets a particular topic and visits and gathers only a relevant, narrow web segment while trying not to waste resources on irrelevant material. It can be used to build domain-specific web search portals and online personalized search tools. In this paper, we describe the design and implementation of a university focused crawler that runs on BP network classifier for prediction of the links leading to relevant pages. We present the flow of the system, discuss the performance, report the experimental results based on it. Our experiments show that the BP classifier performs very well in obtaining accurate relevant university Web resources.\",\n \"Title: A Compensation Method to Measure the Mutual Inductance at Low Frequency\\nYear: 2011\\n\\nAbstract: Traditionally, the most precise way to get the mutual inductance value is by a Campbell calculable mutual inductor. Here, an approach of measuring the mutual inductance at low frequency with compensation method is described. A multi-channel direct digital frequency synthesis source is developed to provide stable and precise phase. The mutual inductance is traced to the value of the frequency and resistance directly. The measurement uncertainty is 0.43 ppm at present and could be improved further.\",\n \"Title: On defining the service provided by TCP\\nYear: 2003\\n\\nAbstract: The Transmission Control Protocol (TCP) is an important transport layer protocol providing a reliable data transfer service to support many applications running over the Internet such as the World Wide Web. It is therefore important to define the intent of TCP. This is achieved by describing the services it is intended to provide in an abstract way, known as a service definition. Several attempts have been made to define the services provided by TCP, however, they are all inadequate in various ways. In particular, most of them follow the applications interface defined in the TCP Request for Comments (RFC 793) too closely, making them rather implementation specific. The aim of our work is to provide an appropriately abstract definition of the TCP service, following the guidance of the Open Systems Interconnection service conventions, and to make it as general and as complete as possible. We define the service in terms of a set of 15 service primitives and their sequences, for TCP's main features of connection establishment, data transfer, urgent data transfer, orderly connection release, and aborting connections. The choice of primitives is discussed in terms of their relationship with the Interface Definition of RFC 793. We formalise the TCP service using Coloured Petri Nets, and from it generate the global sequences of service primitives for TCP's Connection Management services (including aborts). We believe this is the first time this has been achieved.\",\n \"Title: A Quantum Genetic Algorithm to Solve the Problem of Multivariate.\\nYear: 2011\\n\\nAbstract: In order to apply quantum genetic algorithm to solve multi-peak continuous function optimal problem, a novel quantum genetic algorithm referring to multi-variable problem is proposed. It operates variables upon a universal quantum chromosome collectively. Thus it can adjust evolutionary intensity dynamically and consider evolutionary orientation depending on generated function of rotation angle. Theoretical and experimental results show that, this quantum genetic algorithm can obtain better evolutionary speed and better performance of convergence on multi-variable problem.\",\n \"Title: 3D dense reconstruction from 2D video sequence via 3D geometric segmentation\\nYear: 2011\\n\\nAbstract: 3D reconstruction is a major problem in computer vision. This paper considers the problem of reconstructing 3D structures, given a 2D video sequence. This problem is challenging since it is difficult to identify the trajectory of each object point/pixel over time. Traditional stereo 3D reconstruction methods and volumetric 3D reconstruction methods suffer from the blank wall problem, and the estimated dense depth map is not smooth, resulting in loss of actual geometric structures such as planes. To retain geometric structures embedded in the 3D scene, this paper proposes a novel surface fitting approach for 3D dense reconstruction. Specifically, we develop an expanded deterministic annealing algorithm to decompose 3D point cloud to multiple geometric structures, and estimate the parameters of each geometric structure. In this paper, we only consider plane structure, but our methodology can be extended to other parametric geometric structures such as spheres, cylinders, and cones. The experimental results show that the new approach is able to segment 3D point cloud into appropriate geometric structures and generate accurate 3D dense depth map.\",\n \"Title: Recent Development on the Joule Balance at NIM.\\nYear: 2011\\n\\nAbstract: The precise measurement of Planck constant h to establish a kind of quantum mass standard is an important but very difficult work. The Joule Balance method, for the same purpose but different from Watt Balance method, has been going on at the National Institute of Metrology for three years. All the measurements were carried out in the atmosphere of an air-conditioned room. The uncertainty is sever...\",\n \"Title: A measurement method of the DTMB modulator\\nYear: 2011\\n\\nAbstract: In this paper, a measurement method for DTMB modulator which performs the estimation of timing and frequency offsets, channel impulse response (CIR), as well as modulation error ratio (MER) by using only two consecutive frames in an iterative manner is proposed to precisely measure the modulator performance in short time. Both simulation and lab testing results show that the measurement method could reflect the performance of DTMB modulator with high accuracy.\",\n \"Title: A Remark on Distance Regular Graph with k = 10, a 1 = 1.\\nYear: 2012\\n\\nAbstract: Distance-regular graph is a kind of highly regular graph. It is studied actively in recent years. The classification is one of the important contents in the study of Distance-regular graph. In this thesis we discuss the distance-regular graph with k = 10,a(1) = 1 by mean of intersection diagrams, circuit chasing techniques and properties of distance-regular graphs. We prove the following conclusion: Let Gamma be a distance-regular graph of k = 10,a(1) = 1, if c(r+2) = 4 and a(r+2) = 5, then c(d) = 10. It is useful to a classification of distance-regular graph of k = 10,a(1) =1\",\n \"Title: A Revised Research Approach to the Defining of DEILPHI Weight Value Based on Objective Predilections\\nYear: 2009\\n\\nAbstract: This paper analyzes the characteristics of the traditional DEILPHI method, pointing out that the method has the shortcomings of strong subjectivity, complex data acquisition, and lack of real-time circumstance-specific solutions. It proposes an improved DEILPHI method based on objective preferences. According to the characteristics of evaluation indicators and the specific situation of actual evaluation work, this method takes different preference data to build preference matrix and amend the original DEILPHI judging matrix. In addition, based on this improved method, the article also designs the experimental environment to authenticate the example.\",\n \"Title: On compatibility of uncertain additive linguistic preference relations and its application in the group decision making\\nYear: 2011\\n\\nAbstract: We develop a new compatibility for the uncertain additive linguistic preference relations and utilize it to determine the optimal weights of experts in the group decision making (GDM). Based on some operational laws for the uncertain additive linguistic preference labels, we propose some new concepts of the compatibility degree and acceptable compatibility index for the two uncertain additive linguistic preference relations. We also prove the properties that the synthetic preference relation is also of acceptable compatibility under the condition that additive linguistic preference relations provided by experts are all of acceptable compatibility with the specific linguistic preference relation, which provides a theoretic basis for the application of the uncertain additive linguistic preference relations in the GDM. Furthermore, we establish a mathematical model to obtain the weights of experts based on the criterion of minimizing the compatibility in the GDM, and we discuss the solution to the model. Finally, we give a numerical example to make comparative analysis on compatibility index using the optimal experts' weights approach and the equal experts' weights approach, which indicates that the model is feasible and effective.\",\n \"Title: A shot boundary detection method for news video based on rough sets and fuzzy clustering\\nYear: 2005\\n\\nAbstract: With the rapid growing amount of multimedia, content-based infomation retrieval has become more and more important. As a crucial step in content-based news video indexing and retrieval system, shot boundary detection attracts much more research interests in recent years. To partition news video into shots, many metrics were constructed to measure the similarity among video frames based on all the available video features. However, too many features will reduce the efficiency of the shot boundary detection. Therefore, it is necessary to perform feature reduction for every decision of shot boundary. For this purpose, the classification method based on rough sets and fuzzy c-means clustering for feature reduction and rule generation is proposed. According to the particularity of news scenes, shot transition can be divided into three types: cut transition, gradual transition and no transition. The efficacy of the proposed method is extensively tested on more than 2 h of news programs and 98.0% recall with 96.6% precision have been achieved.\",\n \"Title: A Coloured Petri Net Approach to Protocol Verification\\nYear: 2003\\n\\nAbstract: The correct operation of communication and co-operation protocols, including signalling systems in various networks, is essential for the reliability of the many distributed systems that facilitate our global economy. This paper presents a methodology for the formal specification, analysis and verification of protocols based on the use of Coloured Petri nets and automata theory. The methodology is illustrated using two case studies. The first belongs to the category of data transfer protocols, called Stop-and-Wait Protocols, while the second investigates the connection management part of the Internet's Transmission Control Protocol (TCP). Stop-and-Wait protocols (SWP) incorporate retransmission strategies to recover from data transmission errors that occur on noisy transmission media. Although relatively simple, their basic mechanisms are important for practical protocols such as the data transfer procedures of TCP. The SWP case study is quite detailed. It considers a class of protocols characterized by two parameters: the maximum sequence number (MaxSeqNo) and the maximum number of retransmissions (MaxRetrans). We investigate the operation of the protocol over (lossy) in-sequence (FIFO) channels, and then over (lossy) re-ordering media, such as that provided by the Internet Protocol. Four properties are considered: the bound on the number of messages that can be in the communication channels; whether or not the protocol provides the expected service of alternating sends and receives; (unknowing) loss of messages (i.e. data sent but not received, and not detected as lost by the protocol); and the acceptance of duplicates as new messages. The model is analysed using a combination of hand proofs and automatic techniques. A new result for the bound of the channels (2MaxRetrans+1) is proved for FIFO channels. It is further shown that for re-ordering channels, the channels are unbounded, loss and duplication can occur, and that the SWP does not provide the expected service. We discuss the relevance of these results to the Transmission Control Protocol and indicate the limitations of our approach and the need for further work. The second case study (TCP) illustrates the use of hierarchies to provide a compact and readable CPN model for a complex protocol. We advocate an incremental approach to both modelling and analysis. The importance of stating the assumptions involved is emphasised and we illustrate how they affect the abstractions that can be made to simplify the model. The incremental approach to analysis allows us to validate the model against the TCP definition and to show how errors in the connection establishment procedures can be found. Finally we provide some observations and tips on the how the methodology can be used based on many years of experience. The emphasis of the paper is on providing a tutorial style introduction to the methodology, examining case studies in depth, rather than breadth, and giving some insight into the process while noting its limitations.\",\n \"Title: Generalized intuitionistic fuzzy geometric interaction operators and their application to decision making\\nYear: 2014\\n\\nAbstract: Considering that there may exist some interactions between membership function and non-membership function of different intuitionistic fuzzy sets, we present some new operational laws from the probability point of view and give a geometric interpretation of the new operations. Based on which, a new class of generalized intuitionistic fuzzy aggregation operators are developed, including the generalized intuitionistic fuzzy weighted geometric interaction averaging (GIFWGIA) operator, the generalized intuitionistic fuzzy ordered weighted geometric interaction averaging (GIFOWGIA) operator and the generalized intuitionistic fuzzy hybrid geometric interaction averaging (GIFHGIA) operator. The properties of these new generalized aggregation operators are investigated. Moreover, approaches to multiple attributes decision making are given based on the generalized aggregation operators under intuitionistic fuzzy environment, and an example is illustrated to show the validity and feasibility of new approach. Finally, we give a systematic comparison between the work of this paper and that of other papers.\",\n \"Title: An adaptive dynamic evolution feedforward neural network on modified particle swarm optimization\\nYear: 2009\\n\\nAbstract: In order to improve the generalization capacity of neural networks for poorly known nonlinear dynamic system with long time-delay, a novel adaptive dynamic feedforward neural network on modified Particle Swarm Optimization (PSO) algorithm is proposed. The adaptive time delay operator is adopted between input layer and the first hidden layer, and also the last hidden layer and output layer. Utilizing these dynamic time delay parameters, the proposed structure can adequately identify different classes of nonlinear systems expressed in the input-output representation form and pure time delay. Otherwise, to overcome the particles' premature convergence, the white noise and Logistic mapping are used to enhance the particles' search performance. Furthermore, the parameters in the dynamic feedforward neural network are trained by the modified PSO method. The proposed neural network shows a satisfactory global search and quick convergence capability, avoiding the complexity of gradient calculation. Simulation results demonstrate that the proposed algorithm is effective and accurate in identifying long-time delay nonlinear systems through the comparison with other methods.\",\n \"Title: Modelling and analysing the functional behaviour of TCP’s connection management procedures\\nYear: 2007\\n\\nAbstract: The transmission control protocol (TCP) is the most widely used transport protocol in the Internet. It provides a reliable data transfer service to many applications. In this paper, Coloured Petri Nets are used to model TCP’s connection management procedures. The model is created to verify TCP’s functional correctness (e.g. the absence of deadlocks and livelocks). We discuss different modelling approaches to motivate the approach taken. The paper defines the termination property of TCP’s connection management procedures, including the notions of desired and acceptable terminal states. Finally, we analyse TCP’s connection management procedures operating over re-ordering non-lossy and lossy channels. This is done incrementally and considers 11 different configurations. The analysis provides some insights into TCP’s behaviour where in certain circumstances the protocol fails to establish or terminate successfully.\",\n \"Title: GReplica: A Web-based Data Grid Replica Management System\\nYear: 2005\\n\\nAbstract: In wide-area distributed computing systems, it is often desirable to create remote copies (replicas) of files. File replication can be used to reduce access latency, improve data locality, and/or increase robustness, scalability and performance for distributed applications. In this paper, we describe a Web-based data grid replica management system (we call it GReplica) developed in Globus Toolkit.\",\n \"Title: An Adaptive Location Service for Wireless Sensor Networks with Mobile Sinks\\nYear: 2006\\n\\nAbstract: How to provide the sensor nodes with the locations of mobile sinks with minimum energy consumption is a key issue to the performance of the geographic location based routing protocols. In this paper, we investigate three location update methods: Neighbour update (NUPD), Flooding update (FUPD) and Unicast update (UUPD). We get the analytical expression of energy consumption of each method. Based on the comparison results of the performance of these methods under various network conditions, we propose an adaptive location service for mobile sinks in WSN, which enables the mobile sinks to send their current location to the sensor nodes efficiently according to various data traffic characteristics, network size and node density. We evaluate the performance of our location service by comprehensive simulations and compared it with the TTDD protocol. The results show that while keeping the packet delay below an upper bound, our location update algorithm can effectively decrease the energy consumption thus prolong the network lifetime.\",\n \"Title: Optimizing Multi-hop Queries in ZigBee Based Multi-sink Sensor Networks\\nYear: 2009\\n\\nAbstract: Wireless sensor networks with multiple users collecting data directly from the sensors have many potential applications. An important problem is to allocate for each user a query range to achieve certain global optimality while avoid congesting the sensors in the meanwhile. We study this problem for a ZigBee cluster tree by formulating it into a multi-dimensional multi-choice knapsack problem. Maximum overall query range and max-min fair query range objectives are investigated. Distributed algorithms are proposed which exploit the ZigBee cluster tree structure to keep the computation local. Extensive simulations show that the proposed methods achieve good approximation to the optimal solution with little overhead and improve the network performance.\",\n \"Title: Automatic news audio classification based on selective ensemble SVMs\\nYear: 2005\\n\\nAbstract: With the rapid growing amount of multimedia, content-based information retrieval has become more and more important. As a significant clue for video indexing and retrieval, audio detection and classification attracts much more attention and becomes a hot topic. On the basis of the priori model of news video structure, a selective ensemble support vector machines (SE-SVMs) is proposed to detect and classify the news audio into 4 types, i.e., silence, music, speech, and speech with music background. Experiments with news audio clips of 8514 seconds in total length illustrate that the average accuracy rate of the proposed audio classification method reaches to 98.9%, which is much better than that of the available SVM-based or traditional threshold-based method.\",\n \"Title: Colorimetric characterisation of low-end digital camera and its application for on-screen texture visualisation\\nYear: 2000\\n\\nAbstract: Along with the industrial success of precise colour communication using calibrated computer screens, images captured by digital cameras are widely used for realistic product simulation and true colour communication through the computer network. The need to simulate texture, through its precise micro-level colour variation characteristics, as opposed to simulating and presenting an overall pleasing picture of an object, cannot be over-stressed. This study compares the performance of a high-end and a low-end digital camera in terms of accuracy of colorimetric characterisation and WYSIWYG (what you see is what you get) colour texture simulation\",\n \"Title: FPGA implementation of QR decomposition for MIMO-OFDM using four CORDIC cores\\nYear: 2013\\n\\nAbstract: A low-resource implementation of QR decomposition (QRD) is proposed for MIMO-OFDM systems that employ turbo MMSE-IC (Minimum mean square error interference cancellation) detector. The proposed implementation uses only four CORDIC cores for QR decomposition of complex-valued matrices while existing systolic array architecture requires 15 to 38 CORDIC cores to achieve similar throughput. For 4-by-4 MIMO with 1024 subcarrier OFDM, the turbo MMSE-IC requires 1024 QRD of 4-by-4 complex matrices followed by back substitution. Each matrix QRD is accompanied by rotations of four vectors for the second and subsequent turbo iterations. The proposed architecture implements the multiple vector rotation by multipliers rather than CORDIC cores, thus saving resources. When combined with a 2-dimensional array of back substitution, the proposed QRD architecture can achieve 64-matrix pipelining and a throughput of seven clocks per matrix for MMSE-IC calculation. The proposed MMSE-IC architecture has been implemented in Altera Stratix IV FPGA with LDPC decoding and other baseband receiver components.\",\n \"Title: Circle-Based Improvement Strategy of Simulated Annealing Genetic Algorithm.\\nYear: 2012\\n\\nAbstract: Circular regulation is an important law of bionomics. Simulated annealing genetic algorithm is an effective method of improving genetic algorithm. Combining circular strategy with simulated annealing genetic algorithm efficiently, a novel simulated annealing genetic algorithm applying circular strategy is proposed. And it is justified according to schema evolution analysis and convergence analysis. It can not only assure the capability of global convergence, but also accelerate the evolution of colony and acquire the satisfactory global optimal solution.\",\n \"Title: A biological inspired features based saliency map\\nYear: 2012\\n\\nAbstract: A visual attention mechanism is believed to be responsible for the most informative spots in complex scenes. We proposed a novel biologically inspired attention model based on Cortex-like mechanisms and sparse representation. Biological Inspired model, HMAX model, is a feature extraction method and this method is motivated by a quantitative model of visual cortex. This biological inspired feature will be used to build the Saliency Criteria to measure the perspective fields. Saliency Criteria is obtained from Shannon's information entropy and sparse representation. We demonstrate that the proposed model achieves superior accuracy with the comparison to classical approach in static saliency map generation on real data of natural scenes and psychology stimuli patterns.\",\n \"Title: A software to generate finite element meshes for patient-specific denture\\nYear: 2012\\n\\nAbstract: Finite element modeling has been widely used in theoretical study of orthodontic biomechanics. However, the dental structure is complicate and it is time-consuming for traditional finite element meshes method. This restricts the application of finite element analysis of individual orthodontic treatment in clinic. In this paper, a novel software is developed, which includes the functions of semi-automatic teeth segmentation of patient-specific denture, 3D reconstruction and visualization of dental structure, 3D measurement of geometrical characteristics, simulation of tooth movement, and automatic generation of finite element meshes. This software may be used for patient-specific biomechanical analysis in orthodontics.\",\n \"Title: Medium-Earth-Orbit SAR Focusing Using Range Doppler Algorithm With Integrated Two-Step Azimuth Perturbation\\nYear: 2015\\n\\nAbstract: Existing low-Earth-orbit synthetic aperture radar (SAR) algorithms generally assume that the data are azimuth invariant. However, this assumption does not hold for the medium-Earth-orbit (MEO) SAR systems due to the significantly longer azimuth integration time and complex imaging geometries. As a result, the MEO SAR data cannot be processed accurately and efficiently using the existing algorithms. To solve this problem, this letter proposes a two-step azimuth perturbation (AP) method that uses the first-step AP to remove the bulk azimuth variance at the range processing stage and the second-step AP to remove the residual variance at the azimuth processing stage. As an example, an improved range Doppler algorithm with the integrated two-step AP is discussed in this letter. Simulations of an L-band MEO SAR with 5-m resolution at 10 000-km orbit height are used to demonstrate the validity and accuracy of this algorithm.\",\n \"Title: Shape-Constrained Sparse and Low-Rank Decomposition for Auroral Substorm Detection.\\nYear: 2016\\n\\nAbstract: An auroral substorm is an important geophysical phenomenon that reflects the interaction between the solar wind and the Earth's magnetosphere. Detecting substorms is of practical significance in order to prevent disruption to communication and global positioning systems. However, existing detection methods can be inaccurate or require time-consuming manual analysis and are therefore impractical for large-scale data sets. In this paper, we propose an automatic auroral substorm detection method based on a shape-constrained sparse and low-rank decomposition (SCSLD) framework. Our method automatically detects real substorm onsets in large-scale aurora sequences, which overcomes the limitations of manual detection. To reduce noise interference inherent in current SLD methods, we introduce a shape constraint to force the noise to be assigned to the low-rank part (stationary background), thus ensuring the accuracy of the sparse part (moving object) and improving the performance. Experiments conducted on aurora sequences in solar cycle 23 (1996-2008) show that the proposed SCSLD method achieves good performance for motion analysis of aurora sequences. Moreover, the obtained results are highly consistent with manual analysis, suggesting that the proposed automatic method is useful and effective in practice.\",\n \"Title: Construction, Measurement, Shimming, and Performance of the NIST-4 Magnet System\\nYear: 2014\\n\\nAbstract: The magnet system is one of the key elements of a watt balance. For the new watt balance currently under construction at the National Institute of Standards and Technology, a permanent magnet system was chosen. We describe the detailed construction of the magnet system, first measurements of the field profile, and shimming techniques that were used to achieve a flat field profile. The relative change of the radial magnetic flux density is <; 10-4 over a range of 5 cm. We further characterize the most important aspects of the magnet and give order of magnitude estimates for several systematic effects that originate from the magnet system.\",\n \"Title: A shape-initialized and intensity-adaptive level set method for auroral oval segmentation.\\nYear: 2014\\n\\nAbstract: •An intensity-adaptive level set is proposed to obtain the auroral oval boundary.•A saliency morphological map is constructed to initialize the level set.•The proposed method can segment not only full oval images, but also the gap oval ones.•The segmentation result is a complete boundary without any leak.\",\n \"Title: An Iterative Algorithm To Process The Top-K Query For The Wireless Sensor Networks\\nYear: 2015\\n\\nAbstract: The top-k query is an important type of query for the wireless sensor network. In this paper, we present an iterative algorithm to process the top-k query, which is a distributed algorithm combining the in-network aggregation and the trace back techniques. By using the in-network aggregation technique, the iterative algorithm calculates the current maximum value in the network. By using the trace back technology, the maximum value just calculated is removed from the network. The two steps are repeated k times. As the current maximum value in the network is selected per round, the answer to a top-k query can be obtained after k repetitions. Experimental results show that the iterative algorithm can reduce the number of messages transmitted during the procedure of the top-k query processing.\",\n \"Title: Dayside aurora classification via BIFs-based sparse representation using manifold learning\\nYear: 2014\\n\\nAbstract: Aurora is the typical ionosphere track generated by the interaction of solar wind and magnetosphere, whose modality and variation are significant to the study of space weather activity. A new aurora classification algorithm based on biologically inspired features (BIFs) and discriminative locality alignment (DLA) is proposed in this paper. First, an aurora image is represented by the BIFs, which combines the C1 units from the hierarchical model of object recognition in cortex and the gist features from the saliency map; then, the manifold learning method called DLA is used to obtain the effective sparse representation for auroras based on BIFs; finally, classification results using support vector machine and nearest neighbour with three sets of features: the C1 unit features, the gist features and the BIFs illustrate the effectiveness and robustness of our method on the real aurora image database from Chinese Arctic Yellow River Station.\",\n \"Title: Improving scenario discovery using orthogonal rotations\\nYear: 2013\\n\\nAbstract: Scenario discovery offers a new means to characterize and communicate the information in computer simulation models under conditions of deep uncertainty. The approach first defines scenarios as the future states of world where a proposed policy fails to meet its goals and then uses statistical algorithms to find concise descriptions of such regions in large databases of simulation model results. Current scenario discovery applications rely on the Patient Rule Induction Method (PRIM), a user-interactive bump-hunting algorithm that identifies hyper-rectangular regions in the input space of the simulation model. While often successful, scenario discovery applications have been limited because in general a policy's vulnerabilities are not well described by the PRIM's hyper-rectangular regions. This study proposes and evaluates improved scenario discovery algorithms that address this challenge with a Principal Component Analysis (PCA)-based preprocessing step that transforms the original model input parameters so that PRIM can then identify high quality hyper-rectangular scenarios in the new rotated coordination system. We explore two versions. PCA-PRIM allows rotations among all uncertain model input parameters and CPCA-PRIM (for constrained PCA-PRIM) only allows rotations among parameters within user-specified domains. The latter may provide more useful information to users, who may find scenario axes described by linear combinations of related domain parameters more interpretable than combinations of dissimilar parameters. We run two sets of tests on the PCA-PRIM and CPCA-PRIM algorithms, the first using simulated test date and the second results from a model used in a previous RAND study of the cost-effectiveness of renewable energy portfolio standards. We find that the new algorithms produce higher quality scenarios than PRIM alone as evaluated by several important measures of merit. In the test data, PCA-PRIM produces improvements averaging 37 percent, and CPCA-PRIM averaging 14 percent, over PRIM alone. In the renewable energy policy case study, PCA-PRIM and CPCA-PRIM exhibit similar improvements of about 16 percent over PRIM, and CPCA-PRIM generates scenarios interpretable by, and that provide useful information to, decision makers.\",\n \"Title: Efficient implementation of iterative multi-input–multi-output orthogonal frequency-division multiplexing receiver using minimum-mean-square error interference cancellation\\nYear: 2014\\n\\nAbstract: An efficient hardware implementation scheme is proposed for iterative multi-input-multi-output orthogonal frequency-division multiplexing receiver which includes an MMSE-IC (minimum-mean-square error interference cancellation) detector, a channel estimator, a low-density parity-check (LDPC) decoder and other supporting modules. The proposed implementation uses the QR decomposition (QRD) of the complex-valued matrices with four coordinate rotation digital computer (CORDIC) cores and a back substitution to solve the MMSE-IC equations while the existing systolic array architectures require 15-38 CORDIC cores to achieve a similar throughput. The proposed 4-CORDIC QRD architecture can be configured as a 16-matrix or a 64-matrix pipelining by using a different number of multipliers combined with one-dimensional (1D) or 2D arrays of the back substitution, respectively. The channel estimator implements a commonly-used frequency domain least squares channel estimation with the canonic-signed-digits method, thanks to the character of the Zadroff-Chu sequence used as the pilot. In the LDPC decoder, the min-sum algorithm is implemented for the quasicyclic LDPC decoding. The two schemes for the MMSE-IC detector with different throughput and resource usages have been implemented in a Field Programmable Gate Array for a complete baseband turbo receiver. Their resource usages, throughputs and latencies are compared with the classic systolic array architectures, which demonstrate that the proposed receiver architecture achieves the best tradeoff between the throughput and the resource usage.\",\n \"Title: Anatomical Structure Sketcher For Cephalograms By Bimodal Deep Learning\\nYear: 2013\\n\\nAbstract: The lateral cephalogram is a commonly used medium to acquire patient-specific morphology for diagnose and treatment planning in clinical dentistry. The robust anatomical structure detection and accurate annotation remain challenging considering the personal skeletal variations and image blurs caused by device-specific projection magnification, together with structure overlapping in the lateral cephalograms. We propose a novel cephalogram sketcher system, where the contour extraction of anatomical structures is formulated as a cross-modal morphology transfer from regular image patches to arbitrary curves. Specifically, the image patches of structures of interest are located by a hierarchical pictorial model. The automatic contour sketcher converts the image patch to a morphable boundary curve via a bimodal deep Boltzmann machine. The deep machine learns a joint representation of patch textures and contours, and forms a path from one modality (patches) to the other (contours). Thus, the sketcher can infer the contours by alternating Gibbs sampling along the path in a manner similar to the data completion. The proposed method is robust not only to structure detection, but also tends to produce accurate structure shapes and landmarks even in blurry X-ray images. The experiments performed on clinically captured cephalograms demonstrate the effectiveness of our method.\",\n \"Title: Investigation of Degradation Factors for Adaptive Modulation and Coding in OFDM-MIMO Multiplexing\\nYear: 2014\\n\\nAbstract: This paper investigates the throughput performance using adaptive modulation and coding (AMC) focusing on the causes of impairment to Orthogonal Frequency Division Multiplexing (OFDM) - Multiple-Input Multiple-Output (MIMO) multiplexing. We first investigate the influence of a limited number of modulation and coding schemes (MCSs) and channel estimation error for MCS selection based on mutual information (MI). Second, we clarify the effect of MCS selection error on an increasing maximum Doppler frequency due to the round trip delay time (RTT) in comparison to the throughput upper bound that is computed from the MCS combination providing the maximum throughput considering the block error. Third, we clarify the effect of channel estimation error of maximum likelihood detection (MLD) when using reference signal (RS) based channel estimation in comparison to ideal channel estimation. Through the evaluations, we clarify the impairment factors that degrade the achievable throughput for OFDM-MIMO multiplexing using AMC in an actual multipath fading channel.\",\n \"Title: Effect of joint modulation and coding scheme selection on turbo SIC for OFDM-MIMO multiplexing\\nYear: 2013\\n\\nAbstract: This paper presents the effect of joint modulation and coding scheme (MCS) selection on a turbo soft interference canceller (SIC) for OFDM-MIMO multiplexing using adaptive modulation and coding (AMC). We use a turbo SIC to detect signals by performing iterative interference cancellation using soft-symbol estimates based on the a posteriori log-likelihood ratio (LLR) at the Max-Log-MAP decoder output and linear minimum mean-square error (LMMSE)-based signal detection. Joint MCS section selects the best MCS combinations of all transmission streams so that the total throughput is maximized considering the residual interference from other streams for the turbo SIC. Computer simulations show that the joint MCS selection mitigates the decrease in throughput due to the large difference in the peak data rate between consecutive MCSs compared to independent MCS selection. Hence, we show that by using joint MCS selection, the total throughput with the number of MCSs of NMCS = 8 becomes almost identical to that for NMCS = 12 for 4-by-4 MIMO multiplexing using the turbo SIC.\",\n \"Title: Evaluation Of Texture Mapping Algorithms\\nYear: 2004\\n\\nAbstract: Five texture mapping algorithms were developed for simulating textures of physical objects on a CRT. Twenty-one thread winding cards with different colours were captured by a digital camera for generating texture profiles which were used to describe the statistical colour distributions of images. A pair comparison method was used to assess the performance of various texture mapping algorithms. A total of 42000 judgements were made by a panel of 10 normal colour vision observers. The results showed that realistic appearance can be achieved by mapping a single set of tristimulus values onto a texture profile specified by the colour distribution only in luminance factor channel.\",\n \"Title: Higher Rank Principal Kronecker Model for Triply Selective Fading Channels With Experimental Validation\\nYear: 2015\\n\\nAbstract: This paper proposes a higher rank principal Kronecker model (PKM) for simulating triply selective fading channels. To construct the PKM, the channel correlation matrices are decomposed using the higher order singular value decomposition (HOSVD) method. The proposed PKM-HOSVD model improves upon the original Kronecker model by using higher rank approximation of the channel correlation matrices rather than the rank-1 approximation. The proposed PKM-HOSVD model was validated by extensive field experiments conducted for 4 × 4 multiple-input-multiple-output (MIMO) systems in both indoor and outdoor environments. The carrier frequencies used included 800 MHz, 2.2 GHz, and 5.2 GHz. The channel correlation matrices calculated from the measured channel coefficients were then decomposed via the proposed PKM-HOSVD method. The quality of the decomposition was evaluated by not only the mean square error but also the correlation matrix distance. These results indicate that many practical channels must use higher rank approximation rather than the commonly used rank-1 approximation (or the Kermoal method) to achieve satisfactory decomposition accuracy. In addition, the predicted channel capacity by the proposed channel simulation model achieves better accuracy than the original rank-1 channel simulation model.\",\n \"Title: Coils and the Electromagnet Used in the Joule Balance at the NIM\\nYear: 2015\\n\\nAbstract: In the joule balance developed at National Institute of Metrology, the dynamic phase of a watt balance is replaced by the mutual inductance measurement in an attempt to provide an alternative method for the kilogram redefinition. However, for this method a rather large current in the exciting coil, is needed to offer the necessary magnetic field in the force weighing phase, and the coil heating becomes an important uncertainty source. To reduce coil heating, a new coil system, in which a ferromagnetic material is used to increase the magnetic field was designed recently. However, adopting the ferromagnetic material brings the difficulty from the nonlinear characteristic of material. This problem can be removed by measuring the magnetic flux linkage difference of the suspended coil at two vertical positions directly to replace the mutual inductance parameter. Some systematic effects of this magnet are discussed.\",\n \"Title: Throughput Analyses Based On Practical Upper Bound For Adaptive Modulation And Coding In Ofdm Mimo Multiplexing\\nYear: 2016\\n\\nAbstract: This paper proposes a practical throughput upper bound that considers physical layer techniques using adaptive modulation and coding (AMC) for orthogonal frequency division multiplexing (OFDM) multiple-input multiple-output (MIMO) multiplexing. The proposed upper bound is computed from the modulation and coding scheme (MCS) that provides the maximum throughput considering the required block error rate (BLER) at the respective received signal-to-noise power ratios as a constraint. Then, based on the practical throughput upper bound, we present the causes of impairment for selecting the best MCS based on the computed mutual information for OFDM MIMO multiplexing. More specifically, through the evaluations, we investigate the effect of MCS selection error on an increasing maximum Doppler frequency due to the round trip delay time and the effect of channel estimation error of maximum likelihood detection associated with reference signal based channel estimation.\",\n \"Title: Mathematical model of precipitation of magnesium carbonate with carbon dioxide from the magnesium hydroxide slurry.\\nYear: 2016\\n\\nAbstract: •A novel model for magnesium carbonation in heterogeneous reactor is included.•The model combining dissolution, absorption and precipitation is solved.•Nernst-Planck equation is used to maintain mass balance and electroneutrality.•The effects of size of dissolving particles and enhancement factor are included.•The interaction between the complex physical processes is analyzed.\",\n \"Title: Monitoring the Weibull Shape Parameter with Type II Censored Data\\nYear: 2015\\n\\nAbstract: In this article, we introduce a method for monitoring the Weibull shape parameter with type II (failure) censored data. The control limits depend on the sample size, the number of censored observations, the target average run length, and the stable value of . The method assumes that the scale parameter is constant during each sampling period, which is true under rational subgrouping. The proposed method utilizes the relationship between Weibull and smallest extreme value distribution. We propose an unbiased estimator of sigma=1/ as the monitoring statistic. We derive the control limits for one-sided and two-sided charts for several stable process average run lengths. We discuss two schemes, namely, the control-limits-only scheme and the control-limits-with-warning-lines scheme. The stable process average run length performance of the proposed charts is studied and compared with those of other charts for monitoring under similar assumptions. Copyright (c) 2014 John Wiley & Sons, Ltd.\",\n \"Title: 2DPCANet: Dayside Aurora Classification Based on Deep Learning.\\nYear: 2015\\n\\nAbstract: The mysterious and beautiful aurora represents various physical meaning, thus the classification of aurora images have significant scientific value to human beings. Principal component analysis network (PCANet) has achieved good results in classification. But when using PCANet to extract the image features, it transform original image into a vector, so that the structure information of the image are missing. Compared with PCA, 2DPCA is based on 2D image matrices rather than 1D vectors so that 2DPCA can use the structure information of original image more efficiently and reduce the computational complexity. Based on PCANet, we develop a classification method of aurora images, 2-dimension PCANet (2DPCANet). To evaluate 2DPCANet performance, a series of experiments were performed on two different aurora databases. The classification rate across all experiments was higher using 2DPCANet than PCANet. The experiment results also indicated that the classification time is shorter using 2DPCANet than PCANet.\",\n \"Title: Auroral Oval Boundary Modeling Based on Deep Learning Method.\\nYear: 2015\\n\\nAbstract: Research on the location of the auroral oval is important to understand the coupling processes of the Sun-Earth system. The equatorward boundary and poleward boundary of the auroral oval are significant parameters of the auroral oval location. Thus auroral oval boundary modeling is an efficient way to study the location of auroral oval. As the location of the auroral oval boundary is subject to a variety of geomagnetic factors, there are some limitations on traditional methods, which express the auroral oval boundary as a function of only one or several geomagnetic activity index. Deep learning method is used in this paper to learn the essential features of the inputs, which are a large number of geomagnetic parameters and the former locations of aurora boundary. Furthermore, a model is established to forecast the location of the auroral oval boundary. The experiment results show that our method can model and forecast the boundary of aurora oval efficiently on the data set obtained from Ultraviolet Imager UVI on Polar satellite and OMNI database on NASA.\",\n \"Title: Self-Protecting JavaScript Based on Hierarchical Control\\nYear: 2014\\n\\nAbstract: Self-protecting JavaScript can prevent malicious code execution by wrapping security relevant API calls and embedding security policy into the code. This mechanism is easy to use but hold high run-time overhead. This paper introduces hierarchical policy for self-protecting JavaScript. We make the division in accordance with the frequency and control mode of policy and mainly reduce the number of times policies were called. The new approach is able to ensure safety and decrease run-time overhead significantly.\",\n \"Title: Public information and uninformed trading: Implications for market liquidity and price efficiency.\\nYear: 2016\\n\\nAbstract: We develop a rational expectations equilibrium model in which noise trading comes from discretionary liquidity traders. The equilibrium quantity of aggregate noise trading is endogenously determined by the population size of liquidity traders active in the financial market. By improving market liquidity, public information reduces the expected trading loss of liquidity traders and thus attracts more such traders to the market, which negatively affects information aggregation. Analyzing an alternative setting that models noise trading as coming from hedgers yields similar insights. In a setting with endogenous information, public information can harm information aggregation both through crowding out private information and through attracting noise trading.\",\n \"Title: Probabilistic Deep Spiking Neural Systems Enabled by Magnetic Tunnel Junction.\\nYear: 2016\\n\\nAbstract: Deep spiking neural networks are becoming increasingly powerful tools for cognitive computing platforms. However, most of the existing studies on such computing models are developed with limited insights on the underlying hardware implementation, resulting in area and power expensive designs. Although several neuromimetic devices emulating neural operations have been proposed recently, their funct...\",\n \"Title: BDSCyto: An Automated Approach for Identifying Cytokines Based on Best Dimension Searching.\\nYear: 2016\\n\\nAbstract: We proposed an automated method for distinguishing cytokines from other proteins according to their primary sequences. Two strategies were employed to extract features from protein sequences. The first one is a single method, which includes autocorrelation and pseudo amino acid composition extracted feature methods based on composition and physical-chemical properties of proteins; while the second one is an optimal dimension searching method. Moreover, we developed BDSCyto as a web server to help researchers in classifying protein sequences efficiently and accurately. BDSCyto reduces the processing time and offers high accuracy by a series of efficient methods and multithreading technology based on Spark for large-scale data. Currently, numerous methods exceed 90 % accuracy in cytokine protein prediction, which is better than the existing single methods. BDSCyto is an open-source project and can be freely accessed by the public at http://bdscyto.sinaapp.com/\",\n \"Title: An Approach To Linguistic Group Decision Making Based On Unbalanced Linguistic Generalized Power Aggregation Operator\\nYear: 2016\\n\\nAbstract: Linguistic group decision making (GDM) always focuses on balanced linguistic terms sets, which are uniform and symmetrical. However, in real life there exist GDM problems that need to be assessed by linguistic terms sets with non-symmetrical and non-uniform distributions. The purpose of this paper is to present an unbalanced linguistic generalized power ordered weighted averaging (ULGPOWA) operator for aggregating unbalanced linguistic variables in GDM. The main characteristic of ULGPOWA operator is that the relationship among linguistic variables being aggregated is taken into account by using a support measure for each argument from others. Then properties of new operator are developed and an illustrative example is given to demonstrate the detailed process of proposed method, and the validity is verified by sensitivity analysis for the new approach.\",\n \"Title: On Equivalence Of Two Approaches To Group Decision Making With Interval Multiplicative Preference Relation Based On Cowg Operator\\nYear: 2016\\n\\nAbstract: We propose two new approaches to rank all the alternatives in the group decision making (GDM) with interval multiplicative preference relation (IMPR) and point out their equivalence on solutions, based on the continuous ordered weighted geometric (COWG) operator. First we introduced the COWG operator and used it to aggregate the individual IMPR into crisp one, which can be obtained the priority vector by eigenvector method. Then we develop two new approaches to derive the group priority vector. One is the weighted geometric mean method (WGMM) of all individual priority vectors under the condition of alpha - acceptable consistency of multiplicative preference relation. The other is the relative entropy method (REM), in which an optimization model is constructed to minimize the difference between the group priority vector and all individual priority vectors based on the conception of relative entropy. We find that the REM has the same solution with WGMM, which provides the optimal theory basis of WGMM. Finally, a numerical example is illustrated to rank the given alternatives in the GDM with IMPR and show that the REM is feasible.\",\n \"Title: On the energy benefits of spiking deep neural networks: A case study.\\nYear: 2016\\n\\nAbstract: Deep learning neural networks have achieved success in a large number of visual processing tasks and are currently utilized for many real-world applications like image search and speech recognition among others. However, in spite of achieving high accuracy in such classification problems, they involve significant computational resources. Over the past few years, artificial neural network models have evolved into the biologically realistic and event-driven spiking neural networks. Recent research efforts have been directed at developing mechanisms to convert traditional deep artificial nets to spiking nets where the neurons communicate by means of spikes. However, there have been limited studies providing insights on the specific power, area and energy benefits offered by deep spiking neural nets in comparison to their non-spiking counterparts. In this paper, we perform a case study for a hardware implementation of a spiking/non-spiking deep net on the MNIST dataset and clearly outline the design prospects involved in implementing neural computing platforms in the spiking mode of operation.\",\n \"Title: Digitalizing Seismograms Using a Neighborhood Backtracking Method.\\nYear: 2016\\n\\nAbstract: In this paper, we present a new algorithm for tracing waves on seismograms and digitalising the waves into single vectors in the form of a time series data. The algorithm consists of two main components that will be used to handle the smooth and complicate cases, respectively. The underlying feature of the algorithm lies in a novel searching process based on examining the context of pixels to ascertain how the tracing moves forward. The algorithm has been evaluated on a limited number of samples and the result demonstrates its competence. The work presented can be regarded as an effort of developing a uniform earthquake archive covering both the historical and the digital device periods for future reassessment of the seismic hazard cross the world. The archive developed will serve as an effective means for discovering precursor of earthquakes by characterising the spectral seismograms and the source parameters of less active sources, whereby permitting comparative studies on earthquakes and development of prediction models.\",\n \"Title: A superpixel-based CRF saliency detection approach.\\nYear: 2017\\n\\nAbstract: Salient object detection, experienced several decades, has been an active and popular topic in computer vision. Although a large amount of detection algorithms have been proposed, the obtained saliency maps are still not satisfying enough. To this end, we proposed a simple and novel supervised algorithm to detect a pure background saliency map using conditional random fields (CRF) and saliency cues. Most existing CRF approaches set up the probabilistic graphical models with pixel-wise eight neighborhood grid-shaped graph, while our superpixel level graph handling can not only simplify the model but also promote the performance due to the superpixel level two-ring with pseudo-background neighborhood system. It is intuitive and easy to interpret. As a result, the saliency maps generated by the proposed model have relatively pure background regions. Extensive experimental evaluations on six benchmark datasets with pixel-wise ground truths validated the robustness and effectiveness of the proposed saliency model.\",\n \"Title: Enhanced Successive Cancellation List Decoding of Polar Codes.\\nYear: 2017\\n\\nAbstract: Polar codes can be efficiently decoded by the successive cancellation list (SCL) algorithm in which the sorter plays an important role. The sorter, however, is time-consuming. We significantly improve this decoding scheme by developing a new non-sorting direct selection method for the SCL algorithm. In the non-sorting strategy, we design a new selector for choosing the L best surviving paths, which is achieved by exploiting the bitwise comparison of the candidate path metrics. The analysis and simulation results show that, for M-bit length path metrics, our direct selection method with time complexity O(ML) is superior to the latest simplified bubble sorter and the quick sorting strategy whose time complexity is O(ML 2 ) and O(ML log L), respectively. In the scenario of short codeword length and large list size, our selector can remarkably reduce the latency and the hardware resource consumption while not assuming that the metrics in the decoding are sorted.\",\n \"Title: Development of a Semi-Analytical Algorithm for the Retrieval of Suspended Particulate Matter from Remote Sensing over Clear to Very Turbid Waters.\\nYear: 2016\\n\\nAbstract: Remote sensing of suspended particulate matter, SPM, from space has long been used to assess its spatio-temporal variability in various coastal areas. The associated algorithms were generally site specific or developed over a relatively narrow range of concentration, which make them inappropriate for global applications (or at least over broad SPM range). In the frame of the GlobCoast project, a large in situ data set of SPM and remote sensing reflectance, R-rs(lambda), has been built gathering together measurements from various coastal areas around Europe, French Guiana, North Canada, Vietnam, and China. This data set covers various contrasting coastal environments diversely affected by different biogeochemical and physical processes such as sediment resuspension, phytoplankton bloom events, and rivers discharges (Amazon, Mekong, Yellow river, MacKenzie, etc.). The SPM concentration spans about four orders of magnitude, from 0.15 to 2626 g center dot m(-3). Different empirical and semi-analytical approaches developed to assess SPM from R-rs(lambda) were tested over this in situ data set. As none of them provides satisfactory results over the whole SPM range, a generic semi-analytical approach has been developed. This algorithm is based on two standard semi-analytical equations calibrated for low-to-medium and highly turbid waters, respectively. A mixing law has also been developed for intermediate environments. Sources of uncertainties in SPM retrieval such as the bio-optical variability, atmospheric correction errors, and spectral bandwidth have been evaluated. The coefficients involved in these different algorithms have been calculated for ocean color (SeaWiFS, MODIS-A/T, MERIS/OLCI, VIIRS) and high spatial resolution (LandSat8-OLI, and Sentinel2-MSI) sensors. The performance of the proposed algorithm varies only slightly from one sensor to another demonstrating the great potential applicability of the proposed approach over global and contrasting coastal waters.\",\n \"Title: The Influence of Different Spatial Resolutions on the Retrieval Accuracy of Sea Surface Wind Speed With C-2PO Models Using Full Polarization C-Band SAR.\\nYear: 2017\\n\\nAbstract: This paper presents a comparison strategy for investigating the influence of spatial resolutions on sea surface wind speed retrieval accuracy with cross-polarized synthetic aperture radar images. First, for wind speeds retrieved from vertical transmitting-vertical receiving (VV)-polarized images, the optimal geophysical C-band model (CMOD) function was selected among four CMOD functions. Second, t...\",\n \"Title: Joint Monitoring of Pipeline-Flood Based on FBG and Zigbee Technologies.\\nYear: 2017\\n\\nAbstract: A joint monitoring system of pipeline-flood on the basis of FBG and ZigBee is designed in this article by applying fiber Bragg grating sensing into hydrologic observation and mechanical vibration measurement field, which can realize multipoint and online monitoring of rainfall, river level, flow velocity and vibration frequency of pipeline. The influence of temperature on measurement results can be eliminated by using the dual-fiber pattern. In order to achieve the real-time and effective performances of data collection, the monitoring system is built on the basis of ZigBee wireless network system, which consists of the sensor nodes equipped with the FBG ombrometer, FBG fluviograph, FBG flowmeter and FBG vibrometer in the monitoring area, and they are connected to a network coordinator in a star topology structure. The communication between the ZigBee system and remote monitoring terminal has been realized by GPRS modules. This monitoring system has been put into practical application on a gas pipeline laid in a river valley. The experiment results have proved that the system has the characteristics of flexible and fast networking, real-time capability, low power consumption, high reliability and unattended operation, which can meet the requirements of safety monitoring of oil and gas pipelines subjected to flood impacting.\",\n \"Title: Transformation Of Xml Data Sources For Sequential Path Mining\\nYear: 2016\\n\\nAbstract: In recent years XML has become one of the most promising ways to define semi-structured data. Data mining techniques devised for detecting interesting patterns from semi-structure data have also grown in popularity, but carrying out such techniques on XML data can be problematic due to its hierarchical structure. Therefore, it has become necessary to transform XML into flattened, path data, so as to enable data mining to be carried out efficiently. However, problems may arise when the XML tree needs to be reconstructed from the traversal path. There are currently many transformation techniques for XML data, many of which take advantage of its tree-like hierarchical structure; but most of these approaches do not allow the XML tree to be reconstructed from the traversal path. In this paper we propose a new approach to the transformation of XML data into path data. The new approach employs a 5 step transformation process along with a new 'Postorder Sequencing' method of traversing the XML tree. The proposed method, on the one hand, can be seen an efficient and effective way of transforming XML data into collections of paths, and on the other hand enables XML trees to be generated from the traversal paths.\",\n \"Title: A Multi-size Kernels CNN with Eye Movement Guided Task-Specific Initialization for Aurora Image Classification.\\nYear: 2017\\n\\nAbstract: Aurora is the ionosphere track generated by the interaction of solar wind and magnetosphere. Different aurora correspond to different dynamics process in the magnetosphere. So aurora image classification is the basis for aurora analysis. Deep networks available that pretrained with large scale natural image dataset are not suitable for aurora classification due to the characteristics of aurora images. In this paper, a novel multi-size kernels CNN (MSKCNN) with eye movement guided task-specific initialization is proposed to classify aurora images. First, according to the human visual and cognitive mechanism, the patches are extracted guided by eye movement information of space physics. Then, a task-specific initialization scheme is design by using the features learned from aurora image patches to initialize the first layer of our proposed network. In addition, the network contains three parallel streams to learn features under different scale. The comparison experimental results illustrate the effectiveness of the proposed method.\",\n \"Title: On the Processing of Very High Resolution Spaceborne SAR Data: A Chirp-Modulated Back Projection Approach.\\nYear: 2018\\n\\nAbstract: A new image formation algorithm is proposed for processing very high resolution spaceborne sliding-spotlight synthetic aperture radar (SAR) data. Because of along-track antenna steering, the Doppler bandwidth of the received SAR data is expanded significantly beyond one pulse repetition frequency interval. Furthermore, the range histories become spatially dependent in both dimensions and cannot be...\",\n \"Title: Three-stage electric vehicle scheduling considering stakeholders economic inconsistency and battery degradation.\\nYear: 2017\\n\\nAbstract: This study proposes an electric vehicle (EV) aggregator operation mechanism in a residential community. The EV charging and discharging operation behaviours are scheduled to maximise the EV aggregator revenue, while EV aggregator provides reserve service for the grid. This study not only considers the energy and information interactions between three stakeholders: EV aggregator, EV owners, and pow...\",\n \"Title: A new version of distance and similarity measures for hesitant fuzzy linguistic term sets and its application.\\nYear: 2018\\n\\nAbstract: In this paper, a new version of distance measure for hesitant fuzzy linguistic term sets is developed. The new version of distance measure provides an objective way to handle the diverse dimensions of two HFLTSs, i.e., without shortening the HFLTS to be a linguistic interval or extending the shorter one by adding linguistic terms. The Hamming distance, the Euclidean distance and the corresponding forms are put forward. By the relationship between the distance measure and the similarity measure, some parallel similarity measures are also investigated. As applications of the proposed measures, the issues of pattern recognition, clustering analysis and multi-criteria decision making are considered.\",\n \"Title: A Quality Assessment Method Based on Common Distributed Targets for GF-3 Polarimetric SAR Data.\\nYear: 2018\\n\\nAbstract: The GaoFen-3 (GF-3) satellite, launched on 10 August 2016, is the first C-band polarimetric synthetic aperture radar (PolSAR) satellite in China. The PolSAR system of GF-3 can collect a significant wealth of information for geophysical research and applications. Being used for related applications, GF-3 PolSAR images must be of good quality. It is necessary to evaluate the quality of polarimetric data and achieve the normalized quality monitoring during 8-year designed life of GF-3. In this study, a new quality assessment method of PolSAR data based on common distributed targets is proposed, and the performance of the method is analyzed by simulations and GF-3 experiments. We evaluate the quality of GF-3 PolSAR data by this method. Results suggest that GF-3 antenna is highly isolated, and the quality of calibrated data satisfies the requests of quantitative applications.\",\n \"Title: The GF-3 SAR Data Processor.\\nYear: 2018\\n\\nAbstract: The Gaofen-3 (GF-3) data processor was developed as a workstation-based GF-3 synthetic aperture radar (SAR) data processing system. The processor consists of two vital subsystems of the GF-3 ground segment, which are referred to as data ingesting subsystem (DIS) and product generation subsystem (PGS). The primary purpose of DIS is to record and catalogue GF-3 raw data with a transferring format, and PGS is to produce slant range or geocoded imagery from the signal data. This paper presents a brief introduction of the GF-3 data processor, including descriptions of the system architecture, the processing algorithms and its output format.\",\n \"Title: Fault Diagnosis Method of Diesel Engine Based on Improved Structure Preserving and K-NN Algorithm.\\nYear: 2018\\n\\nAbstract: The diesel engine fault data is nonlinear and it's difficult to extract the characteristic information. Kernel Principal Component Analysis (KPCA) is used to extract features of nonlinear data, only considering global structure. Kernel Locality Preserving Projection (KLPP) considers the local feature structure. So an improved algorithm for global and local structure preserving is proposed to extract the feature of data. The improved feature extraction algorithm combining KPCA and KLPP, avoids the loss of information considering the global and local feature structure and then uses the modified K-NN algorithm for fault classification. In this paper, the software AVL BOOST is used to simulate the faults of diesel engine. The simulation experiments indicate the proposed method can extract the feature vectors effectively, and shows good classification performance.\",\n \"Title: Active fault tolerant control design approach for the flexible spacecraft with sensor faults.\\nYear: 2017\\n\\nAbstract: In this paper, the problem of active fault tolerant control (FTC) is studied for a class of flexible spacecraft attitude systems with Lipschitz nonlinearity and sensor fault. Firstly, a functional observer is designed for the attitude systems of flexible spacecraft in order to detect the time of unknown sensor fault occurred. Next, the sensor fault estimation is performed by filtering the output estimation error, as usually done in the residual generation framework. Then, a dynamic output feedback-based FTC approach is proposed to the flexible spacecraft in sensor faulty case, it not only attenuates flexible appendage disturbance with a given level γ, but also tolerates the effect of unknown sensor fault. Finally, the effectiveness of the proposed FTC method is demonstrated in the attitude systems of flexible spacecraft subject to a time-varying sensor fault.\",\n \"Title: A High-Resolution SAR Focusing Experiment Based on GF-3 Staring Data.\\nYear: 2018\\n\\nAbstract: Spotlight synthetic aperture radar (SAR) is a proven technique, which can provide high-resolution images as compared to those produced by traditional stripmap SAR. This paper addresses a high-resolution SAR focusing experiment based on Gaofen-3 satellite (GF-3) staring data with about 55 cm azimuth resolution and 240 MHz range bandwidth. In staring spotlight (ST) mode, the antenna always illuminates the same scene on the ground, which can extend the synthetic aperture. Based on a two-step processing algorithm, some special aspects such as curved-orbit model error correction, stop-and-go correction, and antenna pattern demodulation must be considered in image focusing. We provide detailed descriptions of all these aspects and put forward corresponding solutions. Using these suggested methods directly in an imaging module without any modification for other data processing software can make the most of the existing ground data processor. Finally, actual data acquired in GF-3 ST mode is used to validate these methodologies, and a well-focused, high-resolution image is obtained as a result of this focusing experiment.\",\n \"Title: A Range Ambiguity Suppression Processing Method for Spaceborne SAR with Up and Down Chirp Modulation.\\nYear: 2018\\n\\nAbstract: Range ambiguity is one of the factors which affect the SAR image quality. Alternately transmitting up and down chirp modulation pulses is one of the methods used to suppress the range ambiguity. However, the defocusing range ambiguous signal can still hold the stronger backscattering intensity than the mainlobe imaging area in some case, which has a severe impact on visual effects and subsequent applications. In this paper, a novel hybrid range ambiguity suppression method for up and down chirp modulation is proposed. The method can obtain the ambiguity area image and reduce the ambiguity signal power appropriately, by applying pulse compression using a contrary modulation rate and CFAR detecting method. The effectiveness and correctness of the approach is demonstrated by processing the archive images acquired by Chinese Gaofen-3 SAR sensor in full-polarization mode.\",\n \"Title: A Preliminary Analysis of Wind Retrieval, Based on GF-3 Wave Mode Data.\\nYear: 2018\\n\\nAbstract: This paper presents an analysis of measurements of the normalized radar cross-(NRCS) in Wave Mode for Chinese C-band Gaofen-3(GF-3) synthetic aperture radar (SAR). Based on 2779 images from GF-3 quad-polarization SAR in Wave Mode and collocated wind vectors from ERA-Interim, this experiment verifies the feasibility of using ocean surface wind fields and VV-polarized NRCS to perform normalized calibration. The method uses well-validated empirical C-band geophysical model function (CMOD4) to estimate the calibration constant for each beam. In addition, the relationship between cross-pol NRCS and wind vectors is discussed. The cross-pol NRCS increases linearly with wind speed and it is obviously modulated by the wind direction when the wind speed is greater than 8 m/s. Furthermore, the properties of the polarization ratio, denoted PR, are also investigated. The PR is dependent on incidence angle and azimuth angle. Two empirical models of the PR are fitted, one as a function of incidence angle only, the other with additional dependence on azimuth angle. Assessments show that the sigma(0)(VV) retrieved from new PR models as well as sigma(0)(HH) is in good agreement with sigma(0)(VV) extracted from SAR images directly.\",\n \"Title: Eye Fixation Assisted Video Saliency Detection via Total Variation-Based Pairwise Interaction.\\nYear: 2018\\n\\nAbstract: As human visual attention is naturally biased toward foreground objects in a scene, it can be used to extract salient objects in video clips. In this paper, we proposed a weakly supervised learning-based video saliency detection algorithm utilizing eye fixations information from multiple subjects. Our main idea is to extend eye fixations to saliency regions step by step. First, visual seeds are co...\",\n \"Title: Xcel-RAM: Accelerating Binary Neural Networks in High-Throughput SRAM Compute Arrays.\\nYear: 2018\\n\\nAbstract: Deep neural networks are a biologically-inspired class of algorithms that have recently demonstrated state-of-the-art accuracies involving large-scale classification and recognition tasks. Indeed, a major landmark that enables efficient hardware accelerators for deep networks is the recent advances from the machine learning community that have demonstrated aggressively scaled deep binary networks with state-of-the-art accuracies. In this paper, we demonstrate how deep binary networks can be accelerated in modified von-Neumann machines by enabling binary convolutions within the SRAM array. In general, binary convolutions consist of bit-wise XNOR followed by a population-count (popcount). We present a charge sharing XNOR and popcount operation in 10 transistor SRAM cells. We have employed multiple circuit techniques including dual-read-worldines (Dual-RWL) along with a dual-stage ADC that overcomes the inaccuracies of a low precision ADC, to achieve a fairly accurate popcount. In addition, a key highlight of the present work is the fact that we propose sectioning of the SRAM array by adding switches onto the read-bitlines, thereby achieving improved parallelism. This is beneficial for deep networks, where the kernel size grows and requires to be stored in multiple sub-banks. As such, one needs to evaluate the partial popcount from multiple sub-banks and sum them up for achieving the final popcount. For n-sections per sub-array, we can perform n convolutions within one particular sub-bank, thereby improving overall system throughput as well as the energy efficiency. Our results at the array level show that the energy consumption and delay per-operation was 1.914pJ and 45ns, respectively. Moreover, an energy improvement of 2.5x, and a performance improvement of 4x was achieved by using the proposed sectioned-SRAM, compared to a non-sectioned SRAM design.\",\n \"Title: An Approach to Linguistic Multiple Attribute Decision-Making Based on Unbalanced Linguistic Generalized Heronian Mean Aggregation Operator\\nYear: 2018\\n\\nAbstract: AbstractThis paper proposes an approach to linguistic multiple attribute decision-making problems with interactive unbalanced linguistic assessment information by unbalanced linguistic generalized Heronian mean aggregation operators. First, some generalized Heronian mean aggregation operators with unbalanced linguistic information are proposed, involving the unbalanced linguistic generalized arithmetic Heronian mean operator and the unbalanced linguistic generalized geometric Heronian mean operator. For the situation that the input arguments have different degrees of importance, the unbalanced linguistic generalized weighted arithmetic Heronian mean operator and the unbalanced linguistic generalized weighted geometric Heronian mean operator are developed. Then we investigate their properties and some particular cases. Finally, the effectiveness and universality of the developed approach are illustrated by a low-carbon tourist instance and comparison analysis. A sensitivity analysis is performed as well to test the robustness of proposed methods.\",\n \"Title: Toward a spintronic deep learning spiking neural processor.\\nYear: 2016\\n\\nAbstract: Deep Spiking neural architectures are becoming increasingly popular tools in complex pattern recognition tasks. However, implementation of such algorithms in conventional CMOS hardware entails huge area and power consumption due to the significant mismatch between the computational units and the corresponding CMOS devices. In this paper, we explore the design of an All-Spin Deep Spiking Neural Network where we demonstrate the mapping of synaptic and neuronal functionalities to domain wall dynamics in ferromagnets. We evaluate the potential advantages offered by such spintronic devices by performing micromagnetic simulations calibrated to experimental results. In order to investigate the benefits of such a spintronic design for large-scale neuromorphic systems, we perform device-circuit-algorithm co-design for a standard digit recognition problem on the MNIST dataset. Results indicate 250x improvement in energy consumption and 56x improvement in EDP of the spintronic deep network over a baseline CMOS implementation in commercial 45nm technology.\",\n \"Title: Spectral Tailoring of Random Fiber Laser Based on the Multimode Interference Filter.\\nYear: 2018\\n\\nAbstract: In this paper, we propose and experimentally demonstrate a novel scheme to tailor the spectrum of random fiber laser (RFL) based on the multimode interference effect. A fiber loop mirror (FLM) incorporating multimode interference filter (MMIF) with single mode fiber (SMF)-MMF-SMF structure acts as a wavelength-dependent point reflector in half-opened RFL. By varying the length of MMF, either single wavelength random lasing with specific wavelength or multiwavelength random lasing with selective wavelength spacing can be generated in MMIF-FLM-based RFL. Due to the wide wavelength range of the MMIF, cascaded multiwavelength random lasing can also be realized. The spectrum adjustable all fiberized RFL based on the MMIF shows distinct advantages, such as easy fabrication, wide operating wavelength range, and low-cost. Besides, the MMIF-FLM can has good power handling ability, by combining the MMIF-FLM and the short-length cavity, the proposed MMIF-based forward-pumping RFL also has a great potential to generate high power, high efficiency random lasing with the adjustable spectral property.\",\n \"Title: On Intuitionistic Fuzzy Copula Aggregation Operators in Multiple- Attribute Decision Making.\\nYear: 2018\\n\\nAbstract: Operations of intuitionistic fuzzy values have been widely studied and have attracted significant interest. In this paper, some other operations on intuitionistic fuzzy values on the basis of Archimedean copulas and corresponding co-copulas are introduced. Such novel operations can show the relevance between intuitionistic fuzzy values. A family of weighted aggregation operators are developed according to the proposed operations, i.e., the intuitionistic fuzzy copula aggregation operator. The properties of the novel operations and the weighted aggregation operators are also considered. In the end, we provide a modified maximizing deviation decision procedure for multiple attributes decision making under intuitionistic fuzzy environment, and show a case study to illustrate the application of the proposed approach.\",\n \"Title: Interval-Valued 2-Tuple Linguistic Induced Continuous Ordered Weighted Distance Measure And Its Application To Multiple Attribute Group Decision Making\\nYear: 2018\\n\\nAbstract: This paper aims to propose a new distance measure, the interval-valued 2-tuple linguistic induced continuous ordered weighted distance (IT-ICOWD) measure, which consists of the intervalvalued 2-tuple linguistic induced continuous ordered weighted averaging (IT-ICOWA) operator and the ordered weighted distance (OWD) measure. In these operators, we consider the risk attitude of decision maker. Furthermore, we discuss some desired properties and various special cases of the IT-ICOWD measure. Additionally, a method of multiple attribute group decision making (MAGDM) in interval-valued 2-tuple linguistic environment is developed on the basis of the IT-ICOWD measure. Through this method, we obtain three simple and exact formulae to determine the order-inducing variables of the IT-ICOWD measure, the weighting vector of decision makers and the weighting vector of attributes, respectively. At last, a numerical example is presented to illustrate the practicability and feasibility of proposed method.\",\n \"Title: Screening of Port Enterprise Value Chain Routines Based on Evolution Equilibrium.\\nYear: 2018\\n\\nAbstract: In order to find the screening mechanism of port enterprises value chain routines, based on describing the selection of port enterprises value chain routines, an evolutionary game model is presented and constructed. Using this model, we analyze the strategy of port enterprises value chain routines when they achieve the stability of the evolution. The results show that port enterprise value chain routine selection is a dynamic and repeated game. The expected revenue and convention cost of routines taking part in the game playing directly correlate with the evolutionary stable strategy and the selection of port enterprises value chain routines tend to be conservative strategy. Introducing evolutionary game theory provides a new perspective for the study on the formation, search and selection of port enterprises value chain routines and provides favorable theoretical support for further research in related fields.\",\n \"Title: Predicting pathological response to neoadjuvant chemotherapy in breast cancer patients based on imbalanced clinical data.\\nYear: 2018\\n\\nAbstract: Neoadjuvant chemotherapy (NAC) may help some breast cancer patients with subsequent surgery or radiotherapy. However, there are certain risks associated with NAC. To lower the risks, machine-learning methods can be used to assist the diagnosis of breast tumors based on clinical data. This study investigated the use of ensemble machine-learning models in the prediction of pathological response to NAC for breast cancer patients with actual clinical data. The ensemble k-nearest neighbor (EKNN) model was determined to predict pathological responses. The imbalanced clinical data of patients with NAC were reviewed retrospectively, and 11 clinicopathological variables were selected from all features to establish succinct EKNN model. A total of 259 patients’ clinical data was included in the model. The training and testing set for each single k-nearest neighbor (KNN) contained 27 and 9 patients, respectively. A total of 259 breast cancer patients in the database included 36 cases of pathological complete response, 157 cases of partial response, and 66 cases of stable disease. To solve the imbalanced clinical data problem, an ensemble-learning EKNN was designed, where the number of samples for each class in a base learner is set to equal to the minimum number 36. It showed that the classification accuracy of pathological response for breast cancer patients after NAC was 81.48% by EKNN model and the Kappa coefficient was 0.72, indicating that the robustness and generalization were better than the average prediction ability of single KNN model (average accuracy of single KNN model was 62.22% and Kappa coefficient was 0.43). Based on actual clinical data, important clinicopathological variables are selected, and the imbalanced problem are well solved by the ensemble EKNN model. The model improved the robustness and generalization for predicting the pathological response with imbalanced clinical data. It suggested that ensemble machine learning has possible practical applications for assisting cancer stage diagnoses and precision medicine.\",\n \"Title: The Novel Computational Model Of Unbalanced Linguistic Variables Based On Archimedean Copula\\nYear: 2018\\n\\nAbstract: We develop a novel computation model of unbalanced linguistic variables on the basis of Archimedean copulas and corresponding co-copulas, which provides a new tool to aggregate unbalanced linguistic information. The properties of the proposed computational model are also studied. We present the concepts of weighted unbalanced Archimedean copula arithmetic aggregation operators and weighted unbalanced Archimedean copula geometric aggregation operators. The properties of these aggregation operators are further investigated. Finally, a group decision making of sensory evaluation is introduced to illustrate the feasibility and validity of our proposed computational model.\",\n \"Title: iQIYI-VID: A Large Dataset for Multi-modal Person Identification.\\nYear: 2018\\n\\nAbstract: Person identification in the wild is very challenging due to great variation in poses, face quality, clothes, makeup and so on. Traditional research, such as face recognition, person re-identification, and speaker recognition, often focuses on a single modal of information, which is inadequate to handle all the situations in practice. Multi-modal person identification is a more promising way that we can jointly utilize face, head, body, audio features, and so on. In this paper, we introduce iQIYI-VID, the largest video dataset for multi-modal person identification. It is composed of 600K video clips of 5,000 celebrities. These video clips are extracted from 400K hours of online videos of various types, ranging from movies, variety shows, TV series, to news broadcasting. All video clips pass through a careful human annotation process, and the error rate of labels is lower than 0.2%. We evaluated the state-of-art models of face recognition, person re-identification, and speaker recognition on the iQIYI-VID dataset. Experimental results show that these models are still far from being perfect for task of person identification in the wild. We further demonstrate that a simple fusion of multi-modal features can improve person identification considerably. We have released the dataset online to promote multi-modal person identification research.\",\n \"Title: Interval Type-2 Fuzzy Neural Networks for Chaotic Time Series Prediction: A Concise Overview.\\nYear: 2019\\n\\nAbstract: Chaotic time series widely exists in nature and society (e.g., meteorology, physics, economics, etc.), which usually exhibits seemingly unpredictable features due to its inherent nonstationary and high complexity. Thankfully, multifarious advanced approaches have been developed to tackle the prediction issues, such as statistical methods, artificial neural networks (ANNs), and support vector machines. Among them, the interval type-2 fuzzy neural network (IT2FNN), which is a synergistic integration of fuzzy logic systems and ANNs, has received wide attention in the field of chaotic time series prediction. This paper begins with the structural features and superiorities of IT2FNN. Moreover, chaotic characters identification and phase-space reconstruction matters for prediction are presented. In addition, we also offer a comprehensive review of state-of-the-art applications of IT2FNN, with an emphasis on chaotic time series prediction and summarize their main contributions as well as some hardware implementations for computation speedup. Finally, this paper trends and extensions of this field, along with an outlook of future challenges are revealed. The primary objective of this paper is to serve as a tutorial or referee for interested researchers to have an overall picture on the current developments and identify their potential research direction to further investigation.\",\n \"Title: Energy Efficient Neural Computing: A Study of Cross-Layer Approximations.\\nYear: 2018\\n\\nAbstract: Deep neural networks (DNNs) have emerged as the state-of-the-art technique in a wide range of machine learning tasks for analytics and computer vision in the next generation of embedded (mobile, IoT, and wearable) devices. Despite their success, they suffer from high energy requirements. In recent years, the inherent error resiliency of DNNs has been exploited by introducing approximations at eith...\",\n \"Title: Boundary constraint factor embedded localizing active contour model for medical image segmentation\\nYear: 2019\\n\\nAbstract: Medical image segmentation has been widely used in clinical practice. It is an important basis for medical experts to diagnose the disease. However, weak edges and intensity inhomogeneity (Niu et al. in 2nd IEEE international conference on computational intelligence and applications (ICCIA). https://doi.org/10.1109/ciapp.2017.816722, 2017) in medical images may hinder the accuracy of any traditional active contour segmentation methods. In this paper, we propose an improved active contour method by embedding a boundary constraint factor and adding the local information of images to the energy function of Chan–Vese model. Then graph cuts method is used to optimize the new energy function of the improved active contour method in this paper. There are several salient features: (1) we can obtain more accurate boundaries by embedding a constraint factor and adding local information of images. (2) The energy function is not easily to fall into local optimum. (3) Finally, our method only need to adjust one parameter. The evaluation results on Magnetic Resonance Imaging and Computed Tomography, blood vessel images, and mammograms masses show that the proposed method leads to more accurate boundary detection results than the state-of-the-art edge-based and region-based active contour segmentation methods.\",\n \"Title: Structural Reweight Sparse Subspace Clustering\\nYear: 2019\\n\\nAbstract: Subspace clustering aims to segment a group of data points into a union of subspaces. Reweight sparse subspace clustering is one of the state-of-the-art algorithms which proposed an iterative weighted subspace clustering. The reweight matrix helps to improve the performance of the affinity matrix construction process but it easily falls into a local minimization. In this paper, we propose a structural reweight sparse subspace clustering algorithm which introduces the structural information into reweight subspace clustering. The structural information achieved in spectral clustering process is useful for the subsequent iterative optimization process which helps to obtain a better local minimization. The experimental results on the Extended Yale B, Hopkins 155, and COIL 20 datasets demonstrate that our algorithm achieves a better performance on subspace clustering problem.\",\n \"Title: An Improved Imaging Algorithm for High-Resolution Spotlight SAR with Continuous PRI Variation Based on Modified Sinc Interpolation.\\nYear: 2019\\n\\nAbstract: This paper focuses on an improved imaging algorithm for spotlight synthetic aperture radar (SAR) with continuous Pulse Repetition Interval (PRI) variation in extremely high-resolution. Conventional SAR systems are limited in that a wide swath cannot be achieved with a high azimuth resolution in the meantime. This limitation can be overcome by Pulse Repetition Frequency (PRF) variation in a SAR system. However, there are problems such as the ambiguities of point targets or extended targets caused by nonuniform sampling. A reconstructive method, Nonuniform Discrete Fourier Transform (NUDFT) has been presented in the current literature, but it is rather computationally expensive. In this paper, a modified sinc interpolation based on NUDFT is proposed, which is used to reconstruct the uniformly sampled echo in time domain. Since the interpolation kernel length is relatively short, it is more computationally efficient. Then, the two-step processing approach combined with the modified sinc interpolation is further presented, which has much better accuracy than that combined with the conventional sinc interpolation. Both the simulated data and the extracted GF-3 data experiment demonstrate the validity and accuracy of the proposed approach.\",\n \"Title: Cross-Layer Design Exploration for Energy-Quality Tradeoffs in Spiking and Non-Spiking Deep Artificial Neural Networks.\\nYear: 2018\\n\\nAbstract: Deep learning convolutional artificial neural networks have achieved success in a large number of visual processing tasks and are currently utilized for many real-world applications like image search and speech recognition among others. However, despite achieving high accuracy in such classification problems, they involve significant computational resources. Over the past few years, non-spiking de...\",\n \"Title: DeltaFrame-BP: An Algorithm Using Frame Difference for Deep Convolutional Neural Networks Training and Inference on Video Data.\\nYear: 2018\\n\\nAbstract: Inspired by the success of deep convolutional neural networks (CNNs) with back-propagation (BP) training on large-scale image recognition tasks, recent research efforts concentrated on expending deep CNNs toward more challenging automatized video analysis, such as video classification, object tracking, action recognition and optical flow detection. Video comprises a sequence of images (frames) cap...\",\n \"Title: A Bi-Level Approach for Determining Optimal Dynamic Retail Electricity Pricing of Large Industrial Customers\\nYear: 2019\\n\\nAbstract: In this paper, a bi-level optimization model is proposed for determining the optimal dynamic retail electricity price, which balances the monetary benefits between utility companies and large industrial customers. That is, the utility company at the upper level devotes to maximize the profit of electricity sale by tuning dynamic retail electricity prices, while large industrial customers at the lower level minimize their power consumption costs by optimally scheduling tasks and generation outputs of self-provided power plants. Specifically, the task scheduling problem of large industrial customers is formulated as a modified continuous-time mixed-integer linear programing (MILP) problem, for effectively handling the exact start time and cancellation of tasks as well as optimally deploying and executing tasks of large industrial customers. A hybrid optimization algorithm by integrating genetic algorithm (GA) and MILP is proposed for addressing computational complexity of the proposed bi-level optimization problem. That is, GA is used to solve the upper level problem, while the lower level scheduling problem is solved by a commercial MILP optimizer. Numerical case results show that the proposed method can effectively increase the profit of the utility company, while improving electricity consumption patterns and reducing average power consumption costs of large industrial customers.\",\n \"Title: Structural and Hierarchical Partitioning of Virtual Microgrids in Power Distribution Network\\nYear: 2019\\n\\nAbstract: Nowadays, the infrastructure and technologies in conventional distribution networks (CDNs) limit its further development to smart distribution networks. One way to overcome this is to develop CDNs using interconnected virtual microgrids (VMs); however, there is no consensus for an explicit definition of VMs. Therefore, in this paper, a comprehensive discussion on VMs is conducted, and a general framework for developing VMs is proposed. The core issue for developing VMs is to identify boundaries of VMs, and most of the suggested methods are based on analyzing operating states. In this paper, a different partitioning method, which is based on the analysis of structural characteristics, is proposed. The electrical coupling strength is defined as a composite weight coefficient for the analysis of electrical connection, and the electrical modularity is used to assess the quality of the partition so as to identify boundaries of VMs. This partitioning method is applied to the IEEE 33-bus distribution network and the PG&E 69-bus distribution network, and it can quickly evaluate the partitioning quality and effectively identify the boundaries of VMs.\",\n \"Title: Low Phase Noise Co-Mb-Ofdm Optical Burst Transmitter For Time And Spectral Optical Aggregation\\nYear: 2017\\n\\nAbstract: We demonstrate experimentally the feasibility of a low phase noise CO-MB-OFDM burst transmitter for time and spectral optical aggregation based on our proposition of a 100 kHz linewidth and 100 ns switching time laser source.\",\n \"Title: Method of Tikhonov regularization for weighted frequency-difference electrical impedance tomography\\nYear: 2017\\n\\nAbstract: Frequency-difference electrical impedance tomography (fdEIT) has attracted considerable attention, which can visualize anomalies without requiring any time-reference data obtained in the absence of anomalies and alleviate undesirable effects of modeling errors. Simple frequency-difference (SFD) method and weighted frequency-difference (WFD) method are two existing algorithms. Besides, WFD perform better due to its high resolution in the condition where the conductivity of background changes much. However, truncated singular value decomposition (TSVD) regularization adopted in WFD have several problems such as difficulties to choose the accurate truncation parameter, unsatisfactory image quality even in low noise condition and overwhelming computational tasks for large-scale problems. In this paper, an improved WFD algorithm using Tikhonov regularization (WFDTR) method as well as the selection method of regularization parameter is proposed. This method can figure out the appropriate regularization parameter more easily through L curve method and acquire reconstructed distribution images of good quality. What's more, the second corner of L curve is investigated. Finally, the proposed method has been compared with the conventional WFD method through numerical simulations. The proposed method works better than WFD in relatively low noise cases according to localization error, shape error as well as image noise indexes. In relatively high noise condition, they perform approximately equally.\",\n \"Title: Low Phase Noise Optical Burst Transmitter For Time And Spectral Optical Aggregation Solution\\nYear: 2017\\n\\nAbstract: Sub-Lambda Photonically Switched Network (SLPSN) solutions are able to improve the use of capacity in data networks while limiting the growth of energy consumption. Recently, we have proposed the TIme and Spectral optical Aggregation (TISA) concept in order to respond to operator's needs of flexibility in the allocation of network resources. Additionally, the TISA solution makes a better use of already deployed network resources. In TISA, each source sends data in the form of bursts that only occupies a spectral sub-band, thus offering purely optical aggregation and disaggregation in both time and spectral domains with the finest possible granularity. Wavelength-based routing is then performed passively in the network. A fast tunable laser with low phase noise is necessary to implement the TISA transmitter but, current existing commercial lasers can't meet these two specifications. In this paper we present a transmitter solution fulfilling the switching time and low phase noise requirements. We give experimental results of a CO-MB-OFDM transmitter realized by combining optical gates and low phase noise lasers and report its performance in a back-to-back configuration.\",\n \"Title: The probabilistic ordered weighted continuous OWA operator and its application in group decision making\\nYear: 2019\\n\\nAbstract: In this paper, the aim is to propose a new aggregation operator called the probabilistic ordered weighted continuous OWA (POW-COWA) operator to aggregate the continuous interval number. The primary character of the POW-COWA operator is that it unifies the probability and the continuous OWA (COWA) operator in the same formulation through considering various importance degrees of different concepts in the aggregation process. Simultaneously, the new operator can be employed to diminish uncertainty and lower the complexity of data processing. Furthermore, we discuss some desired properties and different special cases of the developed operator. Additionally, a method of group decision making (GDM) under interval number environment is proposed based on the POW-COWA operator. At last, a numerical example is presented to illustrate the proposed method.\",\n \"Title: 100km Quasi-Lossless Fiber-Optic Transmission With A Novel Cascaded Random Raman Fiber Laser\\nYear: 2018\\n\\nAbstract: Ultra-long distance (100km) quasi-lossless fiber-optic transmission is experimentally performed by using the 3rd-order Raman amplification pumped by a novel 1280nm cascaded random Raman fiber laser, which is the longest quasi-lossless transmission system reported to date.\",\n \"Title: SLAM estimation method for uncertain model noise parameters\\nYear: 2019\\n\\nAbstract: It is difficult to accurately obtain the statistical parameters of mobile robots motion parameters for its mobility. And it is also the same in observation system because of the environmental variability, which would cause the SLAM system noise statistical parameters uncertainty, and decrease the filtering performance. A SLAM adaptive filtering algorithm with noise statistical characteristics estimation is proposed, when the noise statistical parameters are constant but unknown, three additional maximum posteriori estimators are constructed, and adaptive CKF SLAM algorithm are designed. Compared with the standard CKF SLAM algorithm, the results show that under the uncertainty noise parameters, if the noise variance is Gaussian white noise distribution, the designed adaptive CKF SLAM with three maximum posterior can well estimate the state, track noise variance, and handle the problem of nonlinear systems. The simulation results verify the effectiveness of the proposed SLAM estimation method with the uncertainty of the model noise parameters.\",\n \"Title: A Novel Ofdm Training Sequence Strategy Based On A Sliding Window For Optical Burst Traffic\\nYear: 2018\\n\\nAbstract: In order to mitigate the transients occurring in the coherent receivers facing an absence of burst, a novel channel estimator, using a sliding window, is proposed here for optical DP-16QAM- OFDM burst traffic. Our experimental results show a significant performance gain of this new estimator resulting in an error threshold cancellation.\",\n \"Title: Error Source Analysis and Correction of GF-3 Polarimetric Data.\\nYear: 2018\\n\\nAbstract: The GaoFen-3 (GF-3) satellite is the first polarimetric synthetic aperture radar (PolSAR) satellite in China. With a designed in-orbit life of 8 years, it will provide large amounts of PolSAR data for ocean monitoring, disaster reduction, and many other applications. The polarimetric data quality is essential for all these applications, so the analysis and calibration of the polarimetric error sources are very important for GF-3. In this study, we established a full-link error model for GF-3 PolSAR system. Based on this model, we comprehensively analyzed the quantitative effects of the main error sources including the composition, figured out characteristics of the phase imbalance introduced by the antenna, and pointed out the error sources which have to be corrected. Furthermore, the polarimetric correction method for GF-3 PolSAR system is proposed. Finally, assisted by several external calibration experiments, polarimetric errors of GF-3 data are efficiently corrected during in-orbit-test phase.\",\n \"Title: Who Owns Internet Of Thing Devices?\\nYear: 2018\\n\\nAbstract: Although Internet of Things (IoT) has been recently receiving attention from the research community, undoubtedly, there still exists several privacy concerns about those devices. In particular, IoT devices in the cyberspace are reachable and visible through IP addresses. This article uniquely exploits to qualify the distribution of owner information of IoT devices based on the observation; consumers may write relevant details into the application-layer service on the IoT devices, such as company or usernames. We propose to automatically extract owner annotation by utilizing a set of techniques (network scanning, machine learning, and natural language processing). We use the probing and classifier to determine whether the response data come from an IoT device. The natural language-processing technique is used to extract owner information from IoT devices. We have conducted real-world experiments to evaluate our integrated approach empirically. The results show that the precision is 97% and the coverage is 96%. Furthermore, our approach is running on a more larger unlabeled dataset consisting of 93 million response packets from the whole IPv4 space. Our analysis has drawn upon nearly 4.3 million IoT devices exposed to the public, and it is a typical trail effect of the owner information distribution.\",\n \"Title: R - Tree Index Construction of Dynamic K-means Algorithm.\\nYear: 2018\\n\\nAbstract: In order to better construct the R - tree index structure and improve the efficiency of R - tree construction and retrieval, the R - tree construction algorithm is optimized from the following three aspects: the selection and determination of the initial center point, the weighted distance between data combined with the actual situation, and the division of redundant data by combining the theories of nearest neighbor, information entropy and probability statistics. The feasibility of the proposed algorithm is verified by comparing the objective function values, iteration times and time consumption under different cluster numbers. Furthermore, experimental results show that the index structure constructed by this method has smaller overlapping area and more efficient query efficiency.\",\n \"Title: Power flow optimization for DC distribution grid with distributed energy access based on Newton–Raphson method through upper level control\\nYear: 2019\\n\\nAbstract: DC distribution grids with distributed energy integration have been gaining a continually increasing interest in recent years both in academia and industry, power flow of the grids is a key issue to make use of these energy effectively and reliably, paper aims at optimizing the power flow of DC distribution through the upper level control, by considering the factors of distributed energy permeability and DC grid topology, without hardware investment increase nor using complex algorithm. firstly, the change rule of the network loss and voltage distribution are studied under different distributed energy permeability values and DC grid topology, Secondly, one OPF mathematical model of DC distribution network is established taking the index of network loss and voltage imbalance degree as objective function. According to the change of system flow, the optimized instruction value is provided by the system through calculation for each AC/DC, DC/DC converter; an optimization method of DC power flow is presented by controlling converter instruction. At last, two typical cases are demonstrated by simulation of PSCAD/EMTDC, indexes of the network loss and voltage imbalance degree of the DC distribution network are compared before and after the optimization, Simulation results show that the presented method is feasible to promote DC Distribution Grid.\",\n \"Title: Optimization method for reducing network loss of dc distribution system with distributed resource\\nYear: 2019\\n\\nAbstract: With mature of the direct current (dc) distribution grid technology, the distributed energy is embraced as much as possible, and reducing network loss becomes an effective way to increase energy efficiency and system stability. The conventional reducing loss approaches include complex algorithms, loss modeling in power devices and adding hardware, which lead to costs and system response time increase and system stability reduce. Considering the Intermittent characteristics of distributed energy, for reducing network loss, the effective option is to control error and response speed directly through the upper control, when distributed energy accesses on dc distribution network, which is no need to change grid topologies, run cumbersome power flow algorithms, nor add additional equipment. A reducing network loss method is proposed based on this idea. The details are as follows: Firstly, the network loss formula is derived based on power flow calculation, and the network loss change rules are analyzed. Secondly, one optimal power flow mathematical model of dc distribution network is established, which takes the minimum network loss as the objective functions and conforms to constraints of system security and components operating limit. The tide optimization is solved by using the artificial bees (ABC) algorithm. Thirdly, the network loss reduction method is proposed in the dc distribution network by using master–slave control through real-time control instruction optimization. The node voltage, branch current and main voltage source converter power are precisely regulated to control the power flow; thus, the network loss of multiple distributed energy access dc distribution network can be reduced. Fourthly, when wind and solar energy is connected to the dc distribution network, a typical IEEE16 node case is demonstrated to verify the feasibility of the proposed method using software MATLAB/SIMULINK. Fifthly, evaluation and prospect are made for the research.\",\n \"Title: The region based MMTD energy function for image segmentation\\nYear: 2019\\n\\nAbstract: This paper presents a region-based model based on measure of medium truth degree for image segmentation. Firstly, a new energy function based on measure of medium truth degree is constructed. To enhance the robustness against noise, a noise penalty term which is built by spatial distance measure is embedded to the conventional active contour energy function. Then local information is added to the internal and external energy term of the conventional active contour energy function to deal with intensity inhomogeneity images. Finally, to obtain more accurate and smoother boundary, a new stop function is introduced into the boundary smooth term of the conventional active contour energy function. Experiments results demonstrate that relatively complete and accurate boundaries can be obtained by the proposed model compared with the state-of-art methods on aurora images, images with intensity inhomogeneity, images with multi-objects, natural images, medical images.\",\n \"Title: Safety Control Method of Robot-Assisted Cataract Surgery with Virtual Fixture and Virtual Force Feedback\\nYear: 2020\\n\\nAbstract: Surgery is an effective means of treating cataracts and restoring vision. However, cataract surgery rate (CSR) in developing countries and regions is relatively low due to the lack of experienced high-level surgeons. In this paper, to reduce the reliance of surgery on physician experience and thereby increase CSR, a master-slave robotic system and safety control strategies with a virtual fixture and virtual force feedback are proposed to assist cataract surgery. First, the surgery is divided into four different stages with different robot control modes. Secondly, the virtual constraint area with virtual spring model in the operating stage is established, so that the doctor can distinguish the operation area where the end of the surgical instrument is located by feedback force. Thirdly, safety control algorithm guarantees that the surgical instrument strictly moves around the surgical incision point, which is regarded as a remote centre of motion, so that the cornea outside the incision point is not injured. Finally, the experimental results show that the proposed safety control strategy allows the robotic system to perform the procedure safely.\",\n \"Title: Moving Target Detection with Modified Logarithm Background Subtraction and Its Application to the GF-3 Spotlight Mode.\\nYear: 2019\\n\\nAbstract: Spaceborne spotlight SAR mode has drawn attention due to its high-resolution capability, however, the studies about moving target detection with this mode are less. The paper proposes an image sequence-based method entitled modified logarithm background subtraction to detect ground moving targets with Gaofen-3 Single Look Complex (SLC) spotlight SAR images. The original logarithm background subtraction method is designed by our team for airborne SAR. It uses the subaperture image sequence to generate a background image, then detects moving targets by using image sequence to subtract background. When we apply the original algorithm to the spaceborne spotlight SAR data, a high false alarm problem occurs. To tackle the high false alarm problem due to the target's low signal-to-noise-ratio (SNR) in spaceborne cases, several improvements are made. First, to preserve most of the moving target signatures, a low threshold CFAR (constant false alarm rate) detector is used to get the coarse detection. Second, because the moving target signatures have higher density than false detections in the coarse detection, a modified DBSCAN (density-based spatial-clustering-of-applications-with-noise) clustering method is then adopted to reduce false alarms. Third, the Kalman tracker is used to exclude the residual false detections, due to the real moving target signature having dynamic behavior. The proposed method is validated by real data, the shown results also prove the feasibility of the proposed method for both Gaofen-3 and other spaceborne systems.\",\n \"Title: Fault Diagnosis of Complex Processes Using Sparse Kernel Local Fisher Discriminant Analysis.\\nYear: 2020\\n\\nAbstract: As an outstanding discriminant analysis technique, Fisher discriminant analysis (FDA) gained extensive attention in supervised dimensionality reduction and fault diagnosis fields. However, it typically ignores the multimodality within the measured data, which may cause infeasibility in practice. In addition, it generally incorporates all process variables without emphasizing the key faulty ones when modeling the complex process, thus leading to degraded fault classification capability and poor model interpretability. To ease the above two drawbacks of conventional FDA, this brief presents an advantageously sparse local FDA (SLFDA) model, it first preserves the within-class multimodality by introducing local weighting factors into scatter matrix. Then, the responsible faulty variables are identified automatically through the elastic net algorithm, and the current optimization problem is subsequently settled through the feasible gradient direction method. Since then, the local data structure characteristics are exploited from both the sample dimension and variable dimension so that the fault diagnosis performance and model interpretability are significantly enhanced. In addition, we naturally extend SLFDA model to nonlinear variant (i.e., sparse kernel local FDA) by the kernel trick, which is substantially more resistant to strong nonlinearity. The simulation studies on Tennessee Eastman (TE) benchmark process and real-world diesel engine working process both validate that the novel diagnosis strategy is more accurate and reliable than the existing state-of-the-art methods.\",\n \"Title: Research On Feature Extraction Method Of Indoor Visual Positioning Image Based On Area Division Of Foreground And Background\\nYear: 2021\\n\\nAbstract: In the process of indoor visual positioning and navigation, difficult points often exist in corridors, stairwells, and other scenes that contain large areas of white walls, strong consistent background, and sparse feature points. Aiming at the problem of positioning and navigation in the real physical world where the walls with sparse feature points are difficult to be filled with pictures, this paper designs a feature extraction method, ARAC (Adaptive Region Adjustment based on Consistency) using Free and Open-Source Software and tools. It divides the image into foreground and background and extracts their features respectively, to achieve not only retain positioning information but also focus more energy on the foreground area which is favourable for navigation. In the test phase, under the combined conditions of illumination, scale and affine changes, the feature matching maps by the feature extraction algorithm proposed in this paper are compared with those by SIFT and SURF. Experiments show that the number of correctly matched feature pairs obtained by ARAC is better than SIFT and SURF, and whose time of feature extraction and matching is comparable to SURF, which verifies the accuracy and efficiency of the ARAC feature extraction method.\",\n \"Title: Modeling And Performance Optimization Of Unmanned Aerial Vehicle Channels In Urban Emergency Management\\nYear: 2021\\n\\nAbstract: With the development of smart cities, the use of unmanned aerial vehicles (UAVs) for interactive information exchange between air and ground can provide effective support for the deployment of emergency work. However, the existing UAV air-to-ground channels often use a single channel model. Considering that the density and distribution of obstructions on information transmission paths at different heights are different, only using a single channel model greatly affects the reliability of communications. Aiming at addressing the different channel characteristics of air-to-ground channels at different heights, a height-based adaptive SUUL-SULA channel model is proposed in this paper. Firstly, in the ultra-low altitude environment, the influence of large-scale fading and small-scale fading on the envelope of the received signal is discussed based on the classic LOO model, and the probability density function and bit error rate model of the received signal are derived. Secondly, a SULA channel model based on Jakes' model is proposed in the low-altitude environment. The uniform circular array beamforming technology is adopted to realize the design of the Doppler frequency shift compensation algorithm. Finally, the simulation results show that the SUUL-SULA model effectively reduces the bit error rate of the system and improves the reliability of communication. Therefore, this model can provide effective physical support for the application of UAV in smart city emergency management.\",\n \"Title: Fighting Fake News: Two Stream Network for Deepfake Detection via Learnable SRM\\nYear: 2021\\n\\nAbstract: Benefitting from the development of deep generative networks, modern fake news generation methods called Deepfake rapidly go viral over the Internet, calling for efficient detection methods. Existing Deepfake detection methods basically use binary classification networks trained on frame-level inputs and lack leveraging temporal information in videos. Besides, the accuracy of these methods will ra...\",\n \"Title: SYNAUG: SYNTHESIS-BASED DATA AUGMENTATION FOR TEXT-DEPENDENT SPEAKER VERIFICATION\\nYear: 2021\\n\\nAbstract: Text-dependent speaker verification systems trained on large amount of labelled data exhibit remarkable performance. However, collecting the speech from a lot of speakers with target transcript is a lengthy and expensive process. In this work, we propose a synthesis based data augmentation method (SynAug) to expand the training set with more speakers and text-controlled synthesized speech. The performance of SynAug is evaluated on the RSR2015 dataset. Experimental results show that for i-vector framework, the proposed methods can boost the system performance significantly, especially for the low-resource condition where the amount of genuine speech is extremely limited. Moreover, combined with traditional data augmentation methods such as adding noises and reverberation, the systems could be further strengthened in extremely limited resource situation.\",\n \"Title: Distributed Fault-Tolerant Formation Control For Multiple Unmanned Aerial Vehicles Under Actuator Fault And Intermittent Communication Interrupt\\nYear: 2021\\n\\nAbstract: This article develops a distributed adaptive fault-tolerant formation control scheme for the multiple unmanned aerial vehicles to counteract actuator faults and intermittent communication interrupt, where the issues on control input saturation and mismatched uncertainties are also addressed. The discontinuous communication protocol technique is exploited to achieve the stability of the formation system, if the conditions of dwell time and the rate of communication are satisfied. On the basis of the local information of neighboring unmanned aerial vehicles, a novel distributed adaptive mechanism is designed to estimate the bounds of actuator faults and uncertainties, where the input saturation is explicitly taken into consideration. The stability of the whole formation system under the designed fault-tolerant formation control strategy is analyzed using the Lyapunov approach. Finally, simulation results are presented to illustrate the effectiveness of the proposed scheme.\",\n \"Title: Oscillatory Fourier Neural Network: A Compact and Efficient Architecture for Sequential Processing.\\nYear: 2022\\n\\nAbstract: Tremendous progress has been made in sequential processing with the recent advances in recurrent neural networks. However, recurrent architectures face the challenge of exploding/vanishing gradients during training, and require significant computational resources to execute back-propagation through time. Moreover, large models are typically needed for executing complex sequential tasks. To address these challenges, we propose a novel neuron model that has cosine activation with a time varying component for sequential processing. The proposed neuron provides an efficient building block for projecting sequential inputs into spectral domain, which helps to retain long-term dependencies with minimal extra model parameters and computation. A new type of recurrent network architecture, named Oscillatory Fourier Neural Network, based on the proposed neuron is presented and applied to various types of sequential tasks. We demonstrate that recurrent neural network with the proposed neuron model is mathematically equivalent to a simplified form of discrete Fourier transform applied onto periodical activation. In particular, the computationally intensive back-propagation through time in training is eliminated, leading to faster training while achieving the state of the art inference accuracy in a diverse group of sequential tasks. For instance, applying the proposed model to sentiment analysis on IMDB review dataset reaches 89.4% test accuracy within 5 epochs, accompanied by over 35x reduction in the model size compared to LSTM. The proposed novel RNN architecture is well poised for intelligent sequential processing in resource constrained hardware.\",\n \"Title: Automatic classification method of thyroid pathological images using multiple magnification factors\\nYear: 2021\\n\\nAbstract: Thyroid cancer is the most common form of endocrine malignancy, and the informative pathological images are critical for thyroid cancer risk stratification, prognosis and treatment guidance. Deep learning methods have achieved promising results on pathology image classification benchmarks. However, due to the complexity of thyroid carcinoma pathological images and the lack of labeled data, there are few researches study on the auto-classification of thyroid cancer. Inspired by the diagnostic process of pathologists, this paper proposes an active classification method for papillary thyroid carcinoma (PTC) pathological images classification to divide thyroid pathological images into PTC and normal thyroid pathological images. We employ the attention mechanism to combine pathological images with different magnification factors, which imitates the diagnosis procedures of thyroid cancer under the microscope. At the same time, we utilize the uncertainty and representative information provided by the convolutional neural network to determine the most valuable samples for annotation that can reduce the labeling cost. Besides, a pathological image dataset named VIP-TCHis is conducted from thyroid tissues of 55 real cases. The experimental results show that our method can obtain good performance of PTC recognition on the VIP-TCHis dataset.\",\n \"Title: Site Selection Of Fire Stations In Large Cities Based On Actual Spatiotemporal Demands: A Case Study Of Nanjing City\\nYear: 2021\\n\\nAbstract: The rapid expansion of cities brings in new challenges for the urban firefighting security, while the increasing fire frequency poses serious threats to the life, property, and safety of individuals living in cities. Firefighting in cities is a challenging task, and the optimal spatial arrangement of fire stations is critical to firefighting security. However, existing researches lack any consideration of the negative effects of the spatial randomness of fire outbreaks and delayed response time due to traffic jams upon the site selection. Based on the set cover location model integrated with the spatiotemporal big data, this paper combines the fire outbreak point with the traffic situation. The presented site selection strategy manages to ensure the arrival of the firefighting task force at random simulated fire outbreak points within the required time, under the constraints of the actual city planning and traffic situation. Taking Nanjing city as an example, this paper collects multi-source big data for the comprehensive analysis, including the full data of the fire outbreak history from June 2014 to June 2018, the traffic jam data based on the Amap, and the investigation data of the firefighting facilities in Nanjing. The regularity behind fire outbreaks is analyzed, the factors related to fire risks are identified, and the risk score is calculated. The previous fire outbreak points are put through the clustering analysis, the spatial distribution probability at points in each cluster is calculated according to the clustering score, and the random fire outbreak points are generated via the Monte Carlo simulation. Meanwhile, the objective emergency response time is set as five minutes. The average vehicle speed for each road in the urban area is calculated, and the actual traffic network model is built to compute the travel time from massive randomly-distributed simulated fire points. The problem is solved by making the travel time for all simulated demand points below five minutes. At last, the site selection result based on our model is adjusted and validated, according to the planned land use. The presented method incorporates the view of the spatiotemporal big data and provides a new idea and technical method for the modification and efficiency improvement of the fire station site selection model, contributing to a service cover ratio increase from 58% to 90%.\",\n \"Title: Different Prevention And Treatment Strategies For Knee Osteoarthritis (Koa) With Various Lower Limb Exoskeletons - A Comprehensive Review\\nYear: 2021\\n\\nAbstract: It was reported that about 10% of people suffer from painful knee arthritis, and a quarter of them were severely disabled. The core activities of daily living were severely limited by knee osteoarthritis (KOA). In order to reduce knee pain and prolong the life of the knee joint, there has been an increasing demand on the development of exoskeletons, for prevention and treatment. The course of KOA was closely related to the biomechanics of knee joint, and the pathogenesis was summarized based on the biomechanics of knee joint. For the prevention and clinical treatment, exoskeletons are classified into three categories: prevention, treatment, and rehabilitation after the operation. Furthermore, the design concepts, actuators, sensors, control strategies, and evaluation criteria were presented. Finally, the shortcomings and limitations were summarized. It is useful for researchers to develop suitable exoskeletons in the future.\",\n \"Title: Distance Constraint Between Features For Unsupervised Domain Adaptive Person Re-Identification\\nYear: 2021\\n\\nAbstract: Many superior person re-identification (re-ID) approaches face a challenge: their performance show disastrous when all supervised models are generalized to a new domain. Some researchers have recently proposed domain adaptive person re-ID methods to address this difficulty, whereas they directly leveraged the target-domain data that would generate lots of noisy pseudo labels. Hence, this paper proposes a distance constraint between features (DCF) method, which clusters the feature distribution fitted the real target-domain data. We assemble different parts of one person for multi-scale self-supervised learning. After introducing domain invariance and designing the inter-image and inter-class distance constraint to regulate distances between target samples, the feature distribution extracted from the encoder trained by the source data can fit the real target data distribution, which leads our domain adaptive model to enjoy the more reliable clustering results and thus obtain a great identification performance in target domain. Extensive experiments demonstrate our approach outperforms state-of-the-art methods on three large-scale released datasets. (c) 2021 Elsevier B.V. All rights reserved.\",\n \"Title: Identity-Preserving Face Anonymization via Adaptively Facial Attributes Obfuscation\\nYear: 2021\\n\\nAbstract: ABSTRACTWith the popularity of using computer vision technology in monitoring system, there is an increasing societal concern on intruding people's privacy as the captured images/videos may contain identity-related information e.g. people's face. Existing methods on protecting such privacy focus on removing the identity-related information from faces. However, this would weaken the utility of current monitoring system. In this paper, we develop a face anonymization framework that could obfuscate visual appearance while preserving the identity discriminability. The framework is composed of two parts: an identity-aware region discovery module and an identity-aware face confusion module. The former adaptively locates the identity-independent attributes on human faces, and the latter generates the privacy-preserving faces using original faces and discovered facial attributes. To optimize the face generator, we employ a multi-task based loss function, which consists of discriminator loss, identify preserving loss, and reconstruction loss functions. Our model can achieve a balance between recognition utility and appearance anonymizing by modifying different numbers of facial attributes according to pratical demands, and provide a variety of results. Extensive experiments conducted on two public benchmarks Celeb-A and VGG-Face2 demonstrate the effectiveness of our model under distinct face recognition scenarios.\",\n \"Title: Underwater Target Recognition Based on Multi-Decision LOFAR Spectrum Enhancement: A Deep-Learning Approach\\nYear: 2021\\n\\nAbstract: Underwater target recognition is an important supporting technology for the development of marine resources, which is mainly limited by the purity of feature extraction and the universality of recognition schemes. The low-frequency analysis and recording (LOFAR) spectrum is one of the key features of the underwater target, which can be used for feature extraction. However, the complex underwater environment noise and the extremely low signal-to-noise ratio of the target signal lead to breakpoints in the LOFAR spectrum, which seriously hinders the underwater target recognition. To overcome this issue and to further improve the recognition performance, we adopted a deep-learning approach for underwater target recognition, and a novel LOFAR spectrum enhancement (LSE)-based underwater target-recognition scheme was proposed, which consists of preprocessing, offline training, and online testing. In preprocessing, we specifically design a LOFAR spectrum enhancement based on multi-step decision algorithm to recover the breakpoints in LOFAR spectrum. In offline training, the enhanced LOFAR spectrum is adopted as the input of convolutional neural network (CNN) and a LOFAR-based CNN (LOFAR-CNN) for online recognition is developed. Taking advantage of the powerful capability of CNN in feature extraction, the recognition accuracy can be further improved by the proposed LOFAR-CNN. Finally, extensive simulation results demonstrate that the LOFAR-CNN network can achieve a recognition accuracy of 95.22%, which outperforms the state-of-the-art methods.\",\n \"Title: A framework for semi-automatically identifying fully occluded objects in 3D models: Towards comprehensive construction design review in virtual reality\\nYear: 2021\\n\\nAbstract: •Point cloud-based algorithms for automatically identifying occluded objects in 3D building information models.•Compatible input and output files with current industry practices.•Achieved over 90% recall rate and over 75% precision rate in classifying the visibility status of objects.•Validated using building information models of an academic building and an industrial facility.\",\n \"Title: A High-Efficient Triboelectric-Electromagnetic Hybrid Nanogenerator For Vibration Energy Harvesting And Wireless Monitoring\\nYear: 2022\\n\\nAbstract: Scavenging mechanical vibration energy by combining triboelectric nanogenerators (TENGs) and electromagnetic generators (EMGs) is a renewable and low-carbon way to generate electric power. In this paper, we fabricate a cylindrical structure hybrid nanogenerator based on spring structure to effectively scavenge vibration energy. The introduction of spring structure not only enhances the space utilization, but also supports the moving magnet. Owing to its innovative structure, the instantaneous output power of TENG and EMG are 0.88 mW at a load of 10 M omega and 216.7 mW at a load of 60 omega, respectively. With the aids of a power management circuit, the hybrid nanogenerator can provide continuous power for two hygrothermographs connected in series. In addition, a self-powered wireless acceleration sensing monitoring system (SWAM) is developed to measure the acceleration signals of vibration sources. This study provides a novel guideline for harvesting mechanical vibration energy.\",\n \"Title: A Moving Ship Detection and Tracking Method Based on Optical Remote Sensing Images from the Geostationary Satellite\\nYear: 2021\\n\\nAbstract: Geostationary optical remote sensing satellites, such as the GF-4, have a high temporal resolution and wide coverage, which enables the continuous tracking and observation of ship targets over a large range. However, the ship targets in the images are usually small and dim and the images are easily affected by clouds, islands and other factors, which make it difficult to detect the ship targets. This paper proposes a new method for detecting ships moving on the sea surface using GF-4 satellite images. First, the adaptive nonlinear gray stretch (ANGS) method was used to enhance the image and highlight small and dim ship targets. Second, a multi-scale dual-neighbor difference contrast measure (MDDCM) method was designed to enable detection of the position of the candidate ship target. The shape characteristics of each candidate area were analyzed to remove false ship targets. Finally, the joint probability data association (JPDA) method was used for multi-frame data association and tracking. Our results suggest that the proposed method can effectively detect and track moving ship targets in GF-4 satellite optical remote sensing images, with better detection performance than other classical methods.\",\n \"Title: A Novel Framework of Integrating UV and NIR Atmospheric Correction Algorithms for Coastal Ocean Color Remote Sensing\\nYear: 2021\\n\\nAbstract: Atmospheric correction is a fundamental process of ocean color remote sensing to remove the atmospheric effect from the top-of-atmosphere. Generally, Near Infrared (NIR) based algorithms perform well for clear waters, while Ultraviolet (UV) based algorithms can obtain good results for turbid waters. However, the latter tends to produce noisy patterns for clear waters. An ideal and practical solution to deal with such a dilemma is to apply NIR- and UV-based algorithms for clear and turbid waters, respectively. We propose a novel atmospheric correction method that integrates the advantages of UV- and NIR-based atmospheric correction (AC) algorithms for coastal ocean color remote sensing. The new approach is called UV-NIR combined AC algorithm. The performance of the new algorithm is evaluated based on match-ups between GOCI images and the AERONET-OC dataset. The results show that the values of retrieved Rrs (Remote Sensing Reflectance) at visible bands agreed well with the in-situ observations. Compared with the SeaDAS (SeaWiFS Data Analysis System) standard NIR algorithm, the new AC algorithm can achieve better precision and provide more available data.\",\n \"Title: Multi-layer VI-GNSS Global Positioning Framework with Numerical Solution aided MAP Initialization\\nYear: 2021\\n\\nAbstract: Motivated by the goal of achieving long-term drift-free camera pose estimation in complex scenarios, we propose a global positioning framework fusing visual, inertial and Global Navigation Satellite System (GNSS) measurements in multiple layers. Different from previous loosely- and tightly-coupled methods, the proposed multi-layer fusion allows us to delicately correct the drift of visual odometry and keep reliable positioning while GNSS degrades. In particular, local motion estimation is conducted in the inner-layer, solving the problem of scale drift and inaccurate bias estimation in visual odometry by fusing the velocity of GNSS, pre-integration of Inertial Measurement Unit (IMU) and camera measurement in a tightly-coupled way. The global localization is achieved in the outer-layer, where the local motion is further fused with GNSS position and course in a long-term period in a loosely-coupled way. Furthermore, a dedicated initialization method is proposed to guarantee fast and accurate estimation for all state variables and parameters. We give exhaustive tests of the proposed framework on indoor and outdoor public datasets. The mean localization error is reduced up to 63%, with a promotion of 69% in initialization accuracy compared with state-of-the-art works. We have applied the algorithm to Augmented Reality (AR) navigation, crowd sourcing high-precision map update and other large-scale applications.\",\n \"Title: A Novel Adaptive Cluster Based Routing Protocol for Energy-Harvesting Wireless Sensor Networks\\nYear: 2022\\n\\nAbstract: With the various applications of the Internet of Things, research into wireless sensor networks (WSNs) has become increasingly important. However, because of their limited energy, the communication abilities of the wireless nodes distributed in the WSN are limited. The main task of WSNs is to collect more data from targets in an energy-efficient way, because the battery replacement of large amounts of nodes is a labor-consuming work. Although the life of WSNs can be prolonged through energy-harvesting (EH) technology, it is necessary to design an energy-efficient routing protocol for the energy harvesting-based wireless sensor networks (EH-WSNs) as the nodes would be unavailable in the energy harvesting phase. A certain number of unavailable nodes would cause a coverage hole, thereby affecting the WSN's monitoring function of the target environment. In this paper, an adaptive hierarchical-clustering-based routing protocol for EH-WSNs (HCEH-UC) is proposed to achieve uninterrupted coverage of the target region through the distributed adjustment of the data transmission. Firstly, a hierarchical-clustering-based routing protocol is proposed to balance the energy consumption of nodes. Then, a distributed alternation of working modes is proposed to adaptively control the number of nodes in the energy-harvesting mode, which could lead to uninterrupted target coverage. The simulation experimental results verify that the proposed HCEH-UC protocol can prolong the maximal lifetime coverage of WSNs compared with the conventional routing protocol and achieve uninterrupted target coverage using energy-harvesting technology.\",\n \"Title: PU-GACNet: Graph Attention Convolution Network for Point Cloud Upsampling\\nYear: 2022\\n\\nAbstract: Real-scanned point clouds are often sparse and non-uniform. To conquer the problem, researchers propose point cloud upsampling techniques, whose efficiency and effectiveness heavily rely on their feature extractors and feature expanders used therein. Therefore, in this paper, to capture the global and local structured features of point clouds, we first design a Graph Attention Convolution (GAC) module as a feature extractor by assigning different attentional weights to combine spatial positions and feature attributes dynamically. Furthermore, we propose an Edge-aware NodeShuffle (ENS) module as a feature expander to upsampling point features smoothly, in an effort to better preserve local geometric details and emphasize local edges. Finally, we combine GAC module with ENS module into a novel point cloud upsampling pipeline, named as PU-GACNet. Extensive experiments as well as theoretical analysis demonstrate this pipeline significantly outperforms previous methods in network performance for 3D point cloud upsampling, to obtain more efficient inference with much fewer parameters.\",\n \"Title: AdaMixer: A Fast-Converging Query-Based Object Detector\\nYear: 2022\\n\\nAbstract: Traditional object detectors employ the dense paradigm of scanning over locations and scales in an image. The recent query-based object detectors break this convention by decoding image features with a set of learnable queries. However, this paradigm still suffers from slow convergence, limited performance, and design complexity of extra networks between backbone and decoder. In this paper, we find that the key to these issues is the adaptability of decoders for casting queries to varying objects. Accordingly, we propose a fast-converging query-based detector, named AdaMixer, by improving the adaptability of query-based decoding processes in two aspects. First, each query adaptively samples features over space and scales based on estimated offsets, which allows AdaMixer to efficiently attend to the coherent regions of objects. Then, we dynamically decode these sampled features with an adaptive MLP-Mixer under the guidance of each query. Thanks to these two critical designs, AdaMixer enjoys architectural simplicity without requiring dense attentional encoders or explicit pyramid networks. On the challenging MS COCO benchmark, AdaMixer with ResNet-50 as the backbone, with 12 training epochs, reaches up to 45.0 AP on the validation set along with 27.9 APs in detecting small objects. With the longer training scheme, AdaMixer with ResNeXt-101-DCN and Swin-S reaches 49.5 and 51.3 AP. Our work sheds light on a simple, accurate, and fast converging architecture for query-based object detectors. The code is made available at https://github.com/MCG-NJU/AdaMixer.\",\n \"Title: How does (im)balanced acceptance of robots between customers and frontline employees affect hotels’ service quality?\\nYear: 2022\\n\\nAbstract: With the increasingly prevalent of service robots in tourism and hospitality sectors, service success relies heavily on robot acceptance. However, most of tourism practitioners are challenged by a dilemma of focusing on customer or employee acceptance of robots. Borrowing the lenses of resource-based view and information processing theory, this study explores the differential effectiveness of imbalanced robotic strategy (i.e., higher/lower customer acceptance of robots than employee acceptance) implemented by hotels in driving service quality under customer-side (i.e., customer demandingness) and employee-side (i.e., frontline task ambidexterity) demand uncertainties. Polynomial regression analysis on a dataset of 1066 matched customer-employee dyads reveals that, an imbalanced robotic strategy is superior than a balanced one for service quality. In addition, when customer demandingness is high, a customer-focused robotic strategy (i.e., higher customer acceptance of robots than employee acceptance) is the optimal choice to improve service quality. However, when frontline task ambidexterity is high, the positive effects of imbalanced robotic strategy on service quality diminish. This study contributes to the literature on and practices of robot acceptance from a customer-robot-employee triadic perspective.\",\n \"Title: Parameterized Soil Recognition Using Normal Similarity Measures on Dynamic Neutrosophic Cubic Sets\\nYear: 2022\\n\\nAbstract: As a kind of newly proposed fuzzy set, dynamic neutrosophic cubic set (DNCS) has varying information on elements of truth, indeterminacy, and falsity in different time intervals, and has been widely used. Considering that normal distribution is the most widely used distribution in real world, this study proposes a kind of parameterized normal similarity measures (NSM) on DNCSs to depict the similarity characteristic between different elements in DNCS. Moreover, this study also proposes a kind of soil recognition model. The main innovation of this study is as follows. Firstly, this study presents NSMs on fuzzy sets, especially on DNCSs for the first time. Secondly, this study proposes a kind of hesitant neutrosophic cubic set with probability vector. Thirdly, a parameter in the proposed parameterized NSMs has ability to adjust weight indirectly and adaptively. Fourthly, this study introduces a soil recognition model using NSMs on DNCSs. After that, a soil recognition example illustrates the reliability of the proposed parameterized NSMs and the soil recognition model.\",\n \"Title: Patterns of Nighttime Crowd Flows in Tourism Cities Based on Taxi Data-Take Haikou Prefecture as an Example\\nYear: 2022\\n\\nAbstract: The study of patterns of crowd flows represents an emerging and expanding research field. The most straightforward and efficient approach to investigate the patterns of crowd flows is to concentrate on traffic flow. However, assessments of simple point-to-point movement frequently lack universal validity, and little research has been conducted on the regularity of nighttime movement. Due to the suspension of public transportation at night, taxi orders are critical in capturing the features of nighttime crowd flows in a tourism city. Using Haikou as an example, this paper proposes a mixed Geogrid Spatio-temporal model (MG-STM) for the tourism city in order to address the challenges. Firstly, by collecting the pick-up/drop-off/in-out flow of crowds, this research uses DCNMF dimensionality reduction to extract semi-supervised spatio-temporal variation features and the K-Means clustering method to determine the cluster types of nighttime crowd flows' changes in each geogrid. Secondly, by constructing a mixed-evaluation model based on LJ1-01 nighttime light data, crowd flows' clusters, and land use data in geogrid-based regions, the pattern of nighttime crowd flows in urban land use areas is successfully determined. The results suggest that MG-STM can estimate changes in the number of collective flows in various regions of Haikou effectively and appropriately. Moreover, population density of land use areas shows a high positive correlation with the lag of crowd flows. Each 5% increase in population density results in a 30-min delay in the peak of crowd flows. The MG-STM will be extremely beneficial in developing and implementing systems for criminal tracking and pandemic prevention.\",\n \"Title: Influence of Residual Amplitude and Phase Error for GF-3 Quad-Polarization SAR on Wind Vector Retrieval\\nYear: 2022\\n\\nAbstract: High-resolution wind vector is important to investigate local winds' variability over the global ocean. Quad-polarization Synthetic Aperture Radar (SAR) can provide wind vector independently without any external wind direction inputs. Although quad-polarization SAR wind retrieval algorithms have been widely studied, improvements are still required. The amplitude and phase imbalance of polarization channel cannot be neglected for improving the wind vector retrieval precision. In this study, rainforest was performed to remove the amplitude and phase imbalance of polarization channel of GF-3 SAR. To explore the applicability of this method for sea surface measurement, the influence of residual amplitude and phase error for GF-3 quad-polarization SAR on wind vector retrieval was assessed. Variation of amplitude and phase imbalance of sea surface for transmit and receive channel were assessed against collocated wind speed and incidence angle. Considering the polarization difference of VV channel relative to HH channel, the residual amplitude and phase error was found to be closely related to wind speed and polarization isolation. Correction of residual amplitude and phase error were employed to improve the retrieval precision of wind vector. It is revealed that the wind speed retrieval precision of VV polarization improved with correction of residual amplitude error. In addition, the influence of residual amplitude and phase error on wind direction retrieval can be neglected. Thus, it is concluded that correction of amplitude and phase error has the potential to improve wind vector retrievals from GF-3 quad-polarization SAR.\",\n \"Title: Mixed logit model based on nonlinear random utility functions: a transfer passenger demand prediction method on overnight D-trains\\nYear: 2022\\n\\nAbstract: In recent years, with the development of high-speed railway in China, the operating mileage and passenger transport capacity have increased rapidly in transportation industry. Due to the high density of trains in the daytime, we usually set up skylights at night (0:00–6:00 am) on high-speed railway for comprehensive maintenance. However, this arrangement contradicts with the operation demand of D-series overnight high-speed trains (overnight D-trains for short). In order to adjust the operation plan of overnight D-trains with skylights coordinately, it is necessary to predict the passenger demand of newly added overnight D-trains. Therefore, in this paper, a mixed logit model based on nonlinear random utility functions for different transport modes is proposed, in order to predict transfer passenger demand. According to Maximum Simulated Likelihood Method, the likelihood function of this mixed logit model is proposed to maximize the overall utility value of different passenger groups while Metropolis–Hastings algorithm is adopted to iteratively solve the probabilities of discrete random variables in utility functions. After that, the unknown distributions of parameters are estimated and the optimal solution of this model is provided by traditional algorithms, basic heuristic algorithms and improved heuristic algorithms including improved fireworks-simulated annealing algorithm proposed in this paper, respectively. Finally, a real-world instance with related data of Beijing–Shanghai corridor is implemented to demonstrate the performance and effectiveness of the proposed approaches.\",\n \"Title: First Assessment of GF3-02 SAR Ocean Wind Retrieval\\nYear: 2022\\n\\nAbstract: On 23 November 2021, the Gaofen-3-02 (GF3-02) satellite was successfully launched in the Jiuquan Satellite Launch Center of China. The primary payload is C-band Synthetic Aperture Radar (SAR), with a maximum resolution of 1 m, and includes 12 imaging modes such as Spotlight, Strip, and TOPSAR, which will play an essential role in marine environment monitoring. As an important marine environmental parameter, the wind speed accuracy retrieved by GF3-02 SAR directly reflects its performance and effectiveness as an operational product. Therefore, based on the wind data of buoys of the National Data Buoy Center (NDBC), ECMWF reanalysis V5 (ERA5), and HY-2B Scatterometer (SCA), a preliminary accuracy assessment of the wind speed retrieved by GF3-02 SAR is carried out in this paper. The wind speed retrieval accuracy of GF3-02 SAR in the co-polarization (HH+VV) data under different Geophysical Model Functions (GMFs) is discussed by using 478 level-1A Single Look Complex (SLC) ocean products acquired in Quad-Polarization Strip I (QPSI) and produced by the National Satellite Ocean Application Service (NSOAS) from January to March 2022. The results show that the optimal root mean square errors (RMSE) are 1.40 m/s, 1.18 m/s, and 1.24 m/s for the VV polarization and 1.39 m/s, 1.19 m/s, and 1.52 m/s for the HH polarization compared to the NDBC wind speed, the ERA5 wind speed, and the HY-2B SCA wind speed, respectively. The preliminary results show that GF3-02 SAR has good wind speed retrieval ability and can meet the needs of operational products.\",\n \"Title: Disturbance observer-based fixed-time robust control for constrained air-breathing hypersonic vehicle\\nYear: 2022\\n\\nAbstract: This article studies the fixed-time robust control problem for the longitudinal dynamics of hypersonic vehicles in the presence of parametric uncertainties, external disturbances and input constraints. First, the dynamic model is transformed into two fourth-order integral chain subsystems by feedback linearization technology. Four novel fast integrating sliding surfaces are designed for each subsystem to guarantee the fixed time convergence of the errors and the derivatives. The double power reaching law is investigated to accelerate the convergence of sliding surfaces. Furthermore, the fixed-time disturbance observer technique is applied to estimate the lumped disturbance precisely. A novel fixed-time anti-saturation auxiliary system is designed to tackle the saturation caused by constraints of actuators. Then the semi-global uniform boundedness of the closed-loop system in a fixed time is proved by Lyapunov's stability theory. Finally, comparison simulation experiments with the existing higher order sliding mode control method are carried out to verify the proposed method's effectiveness and superiority.\",\n \"Title: Auto Complementary Exposure Control for High Dynamic Range Video Capturing\\nYear: 2021\\n\\nAbstract: Recent trends in consumer electronics have led to a proliferation of high dynamic range (HDR) video acquisition and transmission studies. High-end video capturing systems increase the bit depth of each pixel to adapt to the high-contrast ambient light. However, the extended bit depth is pathetically meager compared with the far wider dynamic range of the incident light. What's more, incompatibility issues for the subsequent compression and transmission system may be arisen along with the increased bit depth. In this paper, we propose an auto complementary exposure (ACE) algorithm for optimal exposure parameter estimation under Siamese Trigger (ST) mode to maximize the entropy of the output HDR image. With the proposed algorithm, neither the sensor nor other related hardware needs to be modified. Only the auto-exposure algorithm in the firmware needs to be updated. In the proposed ACE algorithm, every two consecutive images are treated as Siamese twins, and the exposure parameters for the twin images are calculated simultaneously. The target of the ACE algorithm is not to maximize the entropy of each image but to maximize that of the fused image. Compared to existing auto exposure or bracketed fusing algorithms, the proposed algorithm has three significant advantages. Firstly, no extra bit-depth is needed to embrace the details of the scene. Thus the image transmission system is not affected. Secondly, only two raw images are necessary for one output fused HDR image. The computation load is relatively low. Thirdly, the proposed method can fit video recording, which has a broader application prospect than HDR image capturing. Experiment results on a self-built database and publicly accessible database demonstrate that the algorithm works well under various types of scenarios. Furthermore, the experiment results on the real-time system show that the captured video image reveals many details under high dynamic scenes.\",\n \"Title: Cross-Architecture Self-supervised Video Representation Learning.\\nYear: 2022\\n\\nAbstract: In this paper, we present a new cross-architecture contrastive learning (CACL) framework for self-supervised video representation learning. CACL consists of a 3D CNN and a video transformer which are used in parallel to generate diverse positive pairs for contrastive learning. This allows the model to learn strong representations from such diverse yet meaningful pairs. Furthermore, we introduce a temporal self-supervised learning module able to predict an Edit distance explicitly between two video sequences in the temporal order. This enables the model to learn a rich temporal representation that compensates strongly to the video-level representation learned by the CACL. We evaluate our method on the tasks of video retrieval and action recognition on UCF101 and HMDB51 datasets, where our method achieves excellent performance, surpassing the state-of-the-art methods such as VideoMoCo and MoCo+BE by a large margin. The code is made available at https://github.com/guoshengcv/CACL.\",\n \"Title: Real-Trading-Oriented Price Prediction With Explainable Multiobjective Optimization in Quantitative Trading\\nYear: 2022\\n\\nAbstract: Price prediction using machine learning is a heated topic and an effective task in quantitative trading. However, the objectives that existing researches focus on are typically naive and can hardly express the diverse properties in real trading, by using which the results of price prediction cannot precisely reflect the returns in real trading. To alleviate this problem, we first formulate the characteristics of transactions and then propose a multi-objective method under a real-trading-oriented perspective, where a tree model inducing explainable trading characteristics is proposed. With lots of considerations of real trading characteristics, the results of price prediction can better obtain the properties in trading, and especially perform quite well in real trading. Multiple experiments on the Chinese Index Future Market display the effectiveness of the proposed model, where the performance of real trading has reached the industry-leading level. In particular, the difference between model prediction and returns in real trading can be diminished.\",\n \"Title: Grid-optimized UAV indoor path planning algorithms in a complex environment\\nYear: 2022\\n\\nAbstract: •A set of grid-optimized UAV indoor path planning algorithms was proposed.•GO-IAM algorithm established an enriched indoor 3D model of the airspace based on GeoSOT-3D model.•GO-APP algorithm represented the SOTA path planning in regular indoor scenarios.•GO-LBPP algorithm solved the indoor “dead zone” issue in complex multiobstacle airspace.\",\n \"Title: Self-Supervised Speaker Verification Using Dynamic Loss-Gate and Label Correction\\nYear: 2022\\n\\nAbstract: For self-supervised speaker verification, the quality of pseudo labels decides the upper bound of the system due to the massive unreliable labels. In this work, we propose dynamic loss-gate and label correction (DLG-LC) to alleviate the performance degradation caused by unreliable estimated labels. In DLG, we adopt Gaussian Mixture Model (GMM) to dynamically model the loss distribution and use the estimated GMM to distinguish the reliable and unreliable labels automatically. Besides, to better utilize the unreliable data instead of dropping them directly, we correct the unreliable label with model predictions. Moreover, we apply the negative-pairs-free DINO framework in our experiments for further improvement. Compared to the best-known speaker verification system with self-supervised learning, our proposed DLG-LC converges faster and achieves 11.45%, 18.35% and 15.16% relative improvement on Vox-O, Vox-E and Vox-H trials of Voxceleb1 evaluation dataset.\",\n \"Title: The SJTU System for Short-Duration Speaker Verification Challenge 2021.\\nYear: 2021\\n\\nAbstract: This paper presents the SJTU system for both text-dependent and text-independent tasks in short-duration speaker verification (SdSV) challenge 2021. In this challenge, we explored different strong embedding extractors to extract robust speaker embedding. For text-independent task, language-dependent adaptive snorm is explored to improve the system performance under the cross-lingual verification condition. For text-dependent task, we mainly focus on the in-domain fine-tuning strategies based on the model pre-trained on large-scale out-of-domain data. In order to improve the distinction between different speakers uttering the same phrase, we proposed several novel phrase-aware fine-tuning strategies and phrase-aware neural PLDA. With such strategies, the system performance is further improved. Finally, we fused the scores of different systems, and our fusion systems achieved 0.0473 in Task1 (rank 3) and 0.0581 in Task2 (rank 8) on the primary evaluation metric.\",\n \"Title: A Smart Trader for Portfolio Management based on Normalizing Flows.\\nYear: 2022\\n\\nAbstract: In this paper, we study a new kind of portfolio problem, named trading point aware portfolio optimization (TPPO), which aims to obtain excess intraday profit by deciding the portfolio weights and their trading points simultaneously based on microscopic information. However, a strategy for the TPPO problem faces two challenging problems, i.e., modeling the ever-changing and irregular microscopic stock price time series and deciding the scattering candidate trading points. To address these problems, we propose a novel TPPO strategy named STrader based on normalizing flows. STrader is not only promising in reversibly transforming the geometric Brownian motion process to the unobservable and complicated stochastic process of the microscopic stock price time series for modeling such series, but also has the ability to earn excess intraday profit by capturing the appropriate trading points of the portfolio. Extensive experiments conducted on three public datasets demonstrate STrader's superiority over the state-of-the-art portfolio strategies.\",\n \"Title: Distributed Fault Estimation and Fixed-Time Fault-Tolerant Formation Control for Multi-UAVs subject to Sensor Faults\\nYear: 2022\\n\\nAbstract: This paper develops a fault estimation-based fixed-time fault-tolerant formation control strategy for multiple unmanned aerial vehicles under the case of sensor faults and directed connective communication topology. Four common types of sensor faults on position and velocity measurement channels are considered simultaneously, which are only assumed to be bounded. For monitoring the occurrence of sensor faults, a fault detection observer is developed using \\n \\n \\n \\n $$H_\\\\infty$$\\n \\n approach. Then, the amplitude of the sensor fault is reconstructed by the modified adaptive radial basis function neural network-based cooperative fault estimation observer, which is triggered by the fault alarm from fault detection observer. Furthermore, a novel sliding mode-based distributed fault-tolerant formation controller is designed to compensate the effects of faults using the estimated fault information, where the unknown bounded fault compensation error and lumped disturbances are explicitly taken into consideration. Additionally, the fixed-time stability of the whole formation system is analyzed based on Lyapunov approach. Finally, a comparative simulation is performed to illustrate the performance and superiority of the proposed scheme.\",\n \"Title: A Novel Approach to Processing Very-High-Resolution Spaceborne SAR Data With Severe Spatial Dependence\\nYear: 2022\\n\\nAbstract: An innovative approach, mainly featured by progressive iteration of partitioning and focusing, to processing very-high-resolution spaceborne synthetic aperture radar (SAR) data is presented in this article. Due to the long integration time endured during data acquisition as well as large scene extensions being common to the advanced SAR systems, spatial dependence of range histories may be severe and must be properly dealt with. In this article, after deriving the exact two dimensional spectrum for an curved satellite orbit, we focus the entire echo data through several iterations of partitioning and focusing. By partitioning, each partitioned data block can be better focused by the following focusing step, and meanwhile, focusing with better quality enables the following partitioning with both higher granularity and less overlapped data region. Besides, a new technique aiming at eliminating spatial dependence inside a data block is also presented and deployed in the processing procedure. Owing to the feature of high granularity partitioning, the spatial dependence of many factors can be accommodated finer and easier compared with other conventional algorithms. What is more, the artifacts caused by subaperture recombination are fundamentally avoided by the fact that the partitioned blocks are not recombined until being processed into fully focused image blocks. Finally, the precision and the efficiency of the proposed methodology are validated by results on a simulated and a real SAR data.\",\n \"Title: Multi-objective optimal trajectory planning of customized industrial robot based on reliable dynamic identification for improving control accuracy\\nYear: 2022\\n\\nAbstract: Purpose The purpose of this paper is to enhance control accuracy, energy efficiency and productivity of customized industrial robots by the proposed multi-objective trajectory optimization approach. To obtain accurate dynamic matching torques of the robot joints with optimal motion, an improved dynamic model built by a novel parameter identification method has been proposed. Design/methodology/approach This paper proposes a novel multi-objective optimal approach to minimize the time and energy consumption of robot trajectory. First, the authors develop a reliable dynamic parameters identification method to obtain joint torques for formulating the normalized energy optimization function and dynamic constraints. Then, optimal trajectory variables are solved by converting the objective function into relaxation constraints based on second-order cone programming and Runge-Kutta discrete method to reduce the solving complexity. Findings Extensive experiments via simulation and in real customized robots are conducted. The results of this paper illustrate that the accuracy of joint torque predicted by the proposed model increases by 28.79% to 79.05% over the simplified models used in existing optimization studies. Meanwhile, under the same solving efficiency, the proposed optimization trajectory consumes a shorter time and less energy compared with the existing optimization ones and the polynomial trajectory. Originality/value A novel time-energy consumption optimal trajectory planning method based on dynamic identification is proposed. Most existing optimization methods neglect the effect of dynamic model reliability on energy efficiency optimization. A novel parameter identification approach and a complete dynamic torque model are proposed. Experimental results of dynamic matching torques verify that the control accuracy of optimal robot motion can be significantly improved by the proposed model.\",\n \"Title: A novel scheme for processing FMCW-ladar data acquired with low sampling rate\\nYear: 2022\\n\\nAbstract: The frequency-modulated continuous wave (FMCW) ladar is a useful sensor for remote sensing applications and has been widely used in both the military and civilian fields for precise geospatial data acquisition. Typically, the FMCW-ladar signal is collected by use of the heterodyne detection through the dechirping so as to decrease the sampling rate commensurate with the bandwidth. In this manuscript, we proposed a novel scheme for processing the FMCW-ladar signal acquired through the simplified heterodyne detection, and the sampling rate was the same as that in the dechirp detection. Based on the time-frequency relation of the FMCW-ladar signal, two algorithms were proposed to post-process the echo acquired by the simplified heterodyne detection with the sub-Nyquist sampling rate. For the FMCW-ladar echo data acquired by the simplified heterodyne detection with the same sampling rate as in the traditional dechirp detection, the algorithms can achieve the unambiguous range image retaining the range resolution commensurate with the transmitted chirp bandwidth. The effectiveness of the scheme was validated by simulation and real data processing experiments. The capability of the proposed scheme provides an alternative for the FMCW-ladar system without use of the dechirp detection, which can benefit the future FMCW-ladar and microwave photonics radar applications in the remote sensing.\",\n \"Title: Simulation Study of Wireless Coverage in Straight Long Corridors on Container Ship Deck\\nYear: 2022\\n\\nAbstract: A wireless coverage simulation based on the ray tracing method is constructed to realize the wireless coverage prediction of the straight long corridor on the container ship deck. The ray reflection model based on the reverse algorithm of the ray tracing method is established to realize the simulation calculation of the point-to-point propagation path. Referring to the recommendations given by the International Telecommunication Union, the Fresnel loss of the straight long corridors and the first Fresnel loss point are calculated, and the ray reflection model is effectively corrected. The least square method is used to fit the double slope of the simulation curve, and the loss formula of the straight long corridors on the deck is obtained. Through experimental simulation, the path loss factors of the fully enclosed corridor are 1.248 and 3.245, and the path loss factors of the semi-closed corridor are 1.251 and 3.444. The simulation results agree with the general conclusions of the ITU recommendation. The simulation can provide the prediction of wireless coverage effect in the design of container ships and provide guidance for the layout design and optimization of base stations of the straight long corridor on container ship deck with different scales.\",\n \"Title: Development of equipment production information monitoring system based on electric current signal\\nYear: 2022\\n\\nAbstract: Traditional machining equipment typically does not provide online production information such as part production quantities, efficiency and abnormal operating conditions. In order to solve this problem, a real-time production information monitoring system for traditional machining equipment based on electric current signal has been development. Firstly, the data acquisition hardware system using current sensors is designed to collect the electric current signal of the equipment being monitored. Next, the current data is processed by calibration algorithm to obtain production process feature vectors. Finally, a feature matching algorithm is used to identify the operating status. Based on the above algorithms, a monitoring software system is realized by C++ programming language on Qt platform. The monitoring experiment was carried out with automobile transmission shaft parts. The experimental results show that the machining start time and end time of each machined part are correctly and timely identified, and the abnormal state of the equipment could be accurately identified. The developed system is suitable for real-time monitoring of the traditional machining equipment.\",\n \"Title: A Novel Distributed Data-Driven Strategy for Fault Detection of Multi-Source Dynamic Systems\\nYear: 2022\\n\\nAbstract: In this brief, a distributed data-driven fault detection method based on sparse integrated dynamic principal component analysis is designed to enhance the fault detection performance of multi-source dynamic systems. First, by dividing the complex system into multiple sub-sources according to the structural principle, the fusion and complementation of process mechanism knowledge and data-driven are realized. Second, by introducing the Kronecker product covariance structure, the individual modes within each sub-source as well as joint modes shared between sub-sources are captured. Notably, by developing a truncated contribution power method, key variables are selected and the interpretability of principal components is improved. Finally, experimental results demonstrate that the proposed strategy can significantly improve the detection performance compared with the existing PCA-based methods.\",\n \"Title: Carbon-reducing Train Rescheduling Method for Urban Railway Systems considering the Grid with Wind Power Supply\\nYear: 2022\\n\\nAbstract: Nowadays, carbon emission reduction has been given the priority in most of the countries around the world to save the mankind and our planet, while transport sector and energy sector are both still with high carbon emission. With the development of the intelligent transportation systems, smart grid technologies and renewable energy integration, the opportunity for the coordination of systems can be achieved. This paper proposes a linear programming model to reschedule the train timetable, where the urban railway system and power system with wind power supply are coordinated to minimize the carbon emission. In the paper, a general railway system with IEEE 30-bus system is adopted in the case studies. The running time of the train for each inter-station section can be rescheduled, and the optimal output of each power plants can also be given after the optimization. The optimal results show that by coordinating the railway system and power system, the energy consumption and carbon emission of the train operation can be reduced by 12.32% and 12.65% respectively when compared with the original solution.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":188,"string":"188"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13661,"string":"13,661"}}},{"rowIdx":13662,"cells":{"question_id":{"kind":"string","value":"Vasco Furtado"},"question":{"kind":"string","value":"Generate an abstract for the title \"Performance evaluation of a Fog-assisted IoT solution for e-Health applications\" using the following items: 1. Cloud Computing\n2. IoT Solutions\n3. Fog Computing \n4. Healthcare \n5. Performance Evaluation"},"answer":{"kind":"string","value":"Cloud Computing has been a predominant approach for the development of Internet of Things (IoT) solutions. However, to meet the requirements of real-time and latency-sensitive applications in healthcare, a new computing paradigm that follows a Cloud computing approach, called Fog Computing, demonstrates to be an effective tool by extending the Cloud resources to the edge of the network. This work studies the contribution of the Fog Computing paradigm applied to healthcare, highlighting its main benefits regarding latency, network usage, and power consumption. Based on these parameters, a Fog-assisted health monitoring system is proposed and its performance evaluation and demonstration is carried out. The results demonstrates the potential enhancement of this approach to minimise data traffic in the core of the network because data is analysed locally and, also, enhancing security on health information that can be kept locally, enhancing data security and providing better insights of patient’s health status."},"correct_answer":{"kind":"string","value":"Cloud Computing has been a predominant approach for the development of Internet of Things (IoT) solutions. However, to meet the requirements of real-time and latency-sensitive applications in healthcare, a new computing paradigm that follows a Cloud computing approach, called Fog Computing, demonstrates to be an effective tool by extending the Cloud resources to the edge of the network. This work studies the contribution of the Fog Computing paradigm applied to healthcare, highlighting its main benefits regarding latency, network usage, and power consumption. Based on these parameters, a Fog-assisted health monitoring system is proposed and its performance evaluation and demonstration is carried out. The results demonstrates the potential enhancement of this approach to minimise data traffic in the core of the network because data is analysed locally and, also, enhancing security on health information that can be kept locally, enhancing data security and providing better insights of patient’s health status."},"documents":{"kind":"list like","value":["Title: Knowledge-intensive word disambiguation via common-sense and wikipedia\nYear: 2012\n\nAbstract: A promising approach to cope with the challenges that Word Sense Disambiguation brings is to use knowledge-intensive methods. Typically they rely on Wikipedia for supporting automatic concept identification. The exclusive use of Wikipedia as a knowledge base for word disambiguation and therefore the general identification of topics, however, have low accuracy vis-à-vis texts with diverse topics, as can be the case with blogs. This motivated us to propose a method for word disambiguation that, in addition to the use of Wikipedia, uses a common sense database. Use of this base enriches the definition of the concepts previously identified with the help of Wikipedia, and permits the definition of a similarity measure between concepts, which is characterized by verifying the similarity of two concepts from the viewpoint of conceptual proximity in the Wikipedia hierarchy, in addition to the proximity between such concepts in terms of the inferences that they can make. We show that by doing this, we improved the accuracy of automatic disambiguation of words compared with methods that do not use a common sense base.","Title: Learning universal access guidelines by solving HCI problems\nYear: 2001\n\nAbstract: This work addresses the problem of learning guidelines to both (i) design user interfaces and (ii) evaluates the quality of interfaces, for universal access systems. We have developed a collaborative environment for helping the designer of user interfaces in solving problems of accessibility, usability and acceptability. The designer accesses the environment as a tool for assistance, aiming at solving difficulties he encounters when the user is not satisfied with the system with which the latter is interacting. During the problem solving process, the environment provides the designer with assistance by showing graphic scenarios, texts and by retrieving similar past solution cases. Moreover, the designer has a collaborative support that allows him to interact, in a synchronous or asynchronous way, with other designers.","Title: Information Extraction from Text Based on Semantic Inferentialism\nYear: 2009\n\nAbstract: One of the growing needs of information extraction (IE) from text is that the IE system must be able to perform enriched inferences in order to discover and extract information. We argue that one reason for the current limitation of the approaches that use semantics for that is that they are based on ontologies that express the characteristics of things represented by names, and seek to draw inferences and to extract information based on such characteristics, disregarding the linguistic praxis (i.e. the uses of the natural language). In this paper, we describe a generic architecture for IE systems based on Semantic Inferentialism. We propose a model that seeks to express the inferential power of concepts and how these concepts, combined in sentence structures, contribute to the inferential power of sentences. We demonstrate the validity of the approach and evaluate it by deploying an application for extracting information about crime reported in on line newspapers.","Title: Self-organized and social models of criminal activity in urban environments\nYear: 2006\n\nAbstract: Multi-Agent Systems (MAS) are extensively used as a tool for simulation of dynamic systems. Geosimulation is an urban phenomena approach that uses the multi-agent methodology to simulate discrete, dynamic, and event-oriented systems. Our focus in this paper is to use self-organization, specially strategies inspired by solutions from Swarm Intelligence, as well as the idea of social networks, and demonstrate their effect on learning in geosimulation agents.","Title: An architecture for interactive knowledge based systems\nYear: 2003\n\nAbstract: Knowledge-based systems (KBS) have been developed to help people to execute knowledge-intensive tasks. Some shortcomings are identified in the use of KBS, since they don't take into account both the user's profile and the organization's constraints that limit his/her actions, and to the lack of integration of the aspects of human-computer interaction in KBS's development process. This work presents a proposal of architecture for KBSi which are systems that interact with users and that adapt to their characteristics and environment during its use. The main feature of the architecture is the derivation of dialog patterns that facilitates the development of adaptive KBSi, from the specification of the expert reasoning process represented by means of PSM (Problem-Solving Methods).","Title: A Heuristic Method for Balanced Graph Partitioning: An Application for the Demarcation of Preventive Police Patrol Areas\nYear: 2008\n\nAbstract: In this paper we describe a heuristic method, based on graph-partitioning algorithms, with the purpose of improving the demarcation of areas for police patrolling. This demarcation seeks to homogenize the number of crimes among the patrol regions. If the map of a particular region is taken as a graph, we can say that the problem faced by police forces (typically preventive police) is similar to the problem of finding balanced connected q partitions of graphs (BCPq). Since this is a problem belonging to the NP-Hard class, approximate algorithms are the most suitable for solving such a problem. The method described in this article obtains results nearest those considered optimal for the more general case of BCPq, for q≥2.","Title: A bio-inspired crime simulation model\nYear: 2009\n\nAbstract: In this paper we describe a multiagent crime simulation model that resorts to concepts of self-organizing bio-inspired systems, in particular, of the Ant Colony Optimization algorithm. As the matching between simulated and real crime data distributions depends upon the tuning of some control parameters of the simulation model (in particular, of the initial places where criminals start out), we have modeled the calibration of the simulation as an optimization problem. The solution for the allocation of criminals into gateways is also undertaken by a bio-inspired method, namely, a customized Genetic Algorithm. We show that this approach allows for the automatic discovery of gateway configurations that, when employed in the simulation, produce crime distributions that are statistically close to those observed in real data.","Title: Using a Problem Solving Method for Dynamic Configuration of Data Mining Applications on the Grid\nYear: 2005\n\nAbstract: In this paper, we describe how SMARTBASEG, a knowledge-based architecture, can optimize dynamically data mining (DM) applications for the Grid. SMARTBASEG can be seen as a layer that intercepts the submission of a DM application to the Grid, identifies the procedures to be parallelized and optimizes the composition of the job to be submitted to the Grid. The job composition process is done by means of a problem solving method (PSM) of configuration that adopts a propose-and-revise strategy. We present our approach by describing formally the implementation of the PSM in SMARTBASEG and by exemplifying the representation of heuristics for DM application optimization by means of constraints and repairs. Finally, we show the preliminary results we have obtained in some experiments.","Title: Promoting performance and separation of concerns for data mining applications on the grid\nYear: 2007\n\nAbstract: Grid Computing brought the promise of making high-performance computing cheaper and more easily available than traditional supercomputing platforms. Such a promise was very well received by the data mining (DM) community, as DM applications typically process very large datasets and are thus very resource intensive. However, since the Grid is very dynamic and parallel data mining is prone to load unbalancing, obtaining good data mining performance on the Grid is hard. It typically requires the scheduler to understand the inner workings of the application, bringing two related problems. First, good Grid schedulers tend to be very specialized in the application they target. Second, changing the application may require changing the scheduler, which may be especially challenging when there is no clear separation between the application and the scheduler code. We here propose and evaluate a knowledge-based approach that provides abstractions to the DM developer and optimizes at runtime the DM application on the Grid.","Title: Uma estratégia para análise da adoção de sistemas colaborativos baseada nas relações entre experiências de usuário, tecnologia e marketing\nYear: 2011\n\nAbstract: This paper is the result of a research Project that discusses the application of a strategy for evaluating of factors that affect the adoption of collaborative software by users. The authors discuss TI adoption, and present a literature review of this topic. The user behavior model that was used in the evaluation process is then shown. The main objective of the strategy is to investigate the main questions of an entrepreneur about her product in beta version, trying to understand which factors are the most relevant to her postrelease actions. In the study case, data was collected and there were evidences that the relations among user experiences, marketing and technology affected user behaviors. It was given some recommendation to orient the entrepreneur in her post-release actions.","Title: A Unified Architecture to Develop Interactive Knowledge Based Systems\nYear: 2004\n\nAbstract: A growing need related to the use of knowledge-based systems (KBSs) is that these systems provide ways of adaptive interaction with the user. A comparative analysis of approaches to develop KBSs allowed us to identify a high functional quality level and a lack of integration of human factors in their frameworks. In this article, we propose an approach to develop adaptive and interactive KBSs that integrate works from the Knowledge Engineering and HCI areas, through the definition of a unified software architecture. A contribution of this work is the use of interaction patterns in order to define the interaction flow according to the user profile. These interaction patterns are defined for different kinds of interaction, such as, explanation, cooperation, argumentation or criticism. The reusable architecture components were implemented using Java and Protege-2000, and they were used in a KBS for assessment of installments of tax debts.","Title: Geosimulation in Education: A System for Teaching Police Resource Allocation\nYear: 2007\n\nAbstract: This article describes the ExpertCop tutorial system. ExpertCop is a geosimulator of criminal dynamics in urban environments that aims to train police officers in the activity of preventive policing allocation. ExpertCop intends to i) help students comprehend the task of resources allocation and identify the variables involved; ii) induce students to reflect about their actions related to resources allocation; and iii) help students understand the relation between preventive policing and crime. In ExpertCop, the students (police officers) configure and allocate an available police force according to a selected geographic region, and then follow the simulation process. The students interpret the results with the aid of an intelligent tutor, the Pedagogical Agent, by observing how crime behaves in the presence of the allocated preventive policing. The interaction between domain agents representing social entities such as criminals and police teams allocated by the students drives the simulation. Assisting the user, the Pedagogical Agent aims to define interaction strategies between the student and the geosimulator in order to make simulated phenomena better understood. The innovative character of the work is in the strategy adopted by the Pedagogical Agent in order to assist the user in understanding the simulated model. It seeks to explain the model at the macro-level (global or emergent behavior) and micro-level (behavior of the agents individually). The evaluation of the system in training held with students of the domain has shown that the system attains its intended pedagogical goals.","Title: A conceptual framework for the design and evaluation of affective usability in educational geosimulation systems\nYear: 2007\n\nAbstract: In this article we propose a conceptual framework for associating the concepts of usability, computer education and affective quality. We analyze the interaction student-teacher under the light of learning strategies used in educational geosimulators for defining the main emotional constructs that are involved in this process. We elaborate this initial analysis by identifying which interactive objects should be associated with the identified emotional constructs. We associate these objects with an architecture that defines the basics components of an educational geosimulation system as well as the learning strategies used in this context. We illustrate the utility of the framework with an evaluation of an education geosimulator for police training as well as an evaluation of the student's satisfaction during the interaction in different scenarios.","Title: Maps-TV: a software architecture to support development of collaborative maps in DTVi\nYear: 2012\n\nAbstract: This paper proposes a software architecture for the development of collaborative maps applied to the model of the Brazilian Digital Television System (SBTVD), called MAPS-TV. We describe the modules of MAPS-TV, how they interact, and how we extend the content representation model used by TV-Anytime to allow this interaction. For the purpose of validating our proposal, we also demonstrate how MAPS-TV facilitated the creation of collaborative maps -- related to urban problems -- to be used in the SBTVD, with no need for participation of software developers in the process.","Title: Open Government and Citizen Participation in Law Enforcement via Crowd Mapping\nYear: 2012\n\nAbstract: The authors describe WikiCrimes, a project founded on a website of the same name that aims to offer a common interaction space for the general public where they can note criminal activity and track the lo-cations where such crimes occur. The goal is to encourage collaborative participation that generates useful information for everyone. The authors describe their experience with WikiCrimes, emphasizing the services it provides to citizens and underlining some of the problems they've overcome to date. Their ultimate goal is to help establish a research agenda about the topic by inviting the academic community to embrace WikiCrimes as a platform that bridges citizen participation and open government.","Title: Using Self-Organization in an Agent Framework to Model Criminal Activity in Response to Police Patrol Routes\nYear: 2006\n\nAbstract: The organizational structure of the police is character- ized by the existence of a centralized command with the task of distributing and redistributing the police force in a region according to an analysis of crime and the fac- tors that lead to it. On the other hand, criminals are char- acterized as a decentralized system in which individual agents have autonomy and act primarily based on accu- mulated experience they gain from their life in crime. Simulation of different strategies of physical reorgani- zation is a first step to better understand the influence that specific police patrol routes have on the reduction of crime rates and how such decentralization can be ef- ficiently combated. In this article we describe a tool for assisting the investigation of different strategies of agent physical reorganizations where criminal agents demon- strate emergent characteristics. We believe our simula- tion tool is an effective way to train the police in the effectiveness of their patrolling.","Title: Analyzing police patrol routes by simulating the physical reorganization of agents\nYear: 2005\n\nAbstract: In this article we describe a tool for assisting the investigation of different strategies for the physical reorganization of agents. We show how the tool was used in the public safety domain to help in the study of strategies of preventive policing. A society of agents that simulates criminal and police behavior in a geographical region was constructed. In this society, artificial agents representing the police are responsible for preventing crimes. The organizational structure of the police is characterized by the existence of a centralized command that has the task of distributing and redistributing the police force in a region according to an analysis on crime and the factors that influence it. The simulation of different strategies of physical reorganization is a first step to better understand the influence that different police patrol routes have on the reduction of crime rates.","Title: KnowiXML: a knowledge-based system generating multiple abstract user interfaces in USIXML\nYear: 2004\n\nAbstract: This research presents a multidisciplinary approach aimed at generating multiple Abstract User Interfaces (AUIs), which are adaptable for different kinds of users, performing different tasks, using specific devices in various physical environments. The UI generation framework, called IKnowU, is based on a unified process for interactive system design, which integrates Software Engineering (SE), and Human-Computer Interaction (HCI) best practices. This framework is supported by KnowiXML, a Knowledge-Based System (KBS) that facilitates the application of models and the allocation of appropriate visual elements during the generation of AUIs. These AUIs are generated by using problem solving methods studied in Artificial Intelligence (AI). Design knowledge encoded in KnowiXML uniformly manipulates models and UI specifications through the use of an User Interface Description Language (UIDL).","Title: A semi-automated method for acquisition of common-sense and inferentialist knowledge.\nYear: 2013\n\nAbstract: This paper presents a semi-automated method for the acquisition of common-sense and inferentialist concepts in Portuguese. Its innovative feature is a module of reasoning over the pre-existing knowledge that aims to offer original content to the user, helping in the expression of semantic relationships and validation of new concepts. This reasoning process is based on heuristics and syntactic analysis of noun phrases. A qualitative evaluation with users who interacted with the system built on the proposed method showed that the interactions made in the process of knowledge acquisition are more productive since the user is reminded about semantic relations and common-sense knowledge about the new concepts.","Title: GAPatrol: an evolutionary multiagent approach for the automatic definition of hotspots and patrol routes\nYear: 2006\n\nAbstract: In this work, we present a novel evolutionary multiagent-based simulation tool, named as GAPatrol. Such system is devoted to the specification of effective police patrol route strategies for coping with criminal activities happening in a given artificial urban environment, which, in turn, mimics a real demographic region of interest. The approach underlying GAPatrol allows for the automatic uncovering of hotspots and routes of surveillance, which, in real life, are usually discovered by hand with the help of statistical and/or specialized mapping techniques. The qualitative/quantitative results achieved by GAPatrol in two scenarios of study are discussed here, evidencing the potentialities of the novel approach as a promising decision-support tool for police patrolling.","Title: A Method for Identifying Malicious Activity in Collaborative Systems with Maps\nYear: 2009\n\nAbstract: Blogs are popular communication instruments in today's web and altogether, they form the so-called blogosphere. This blogosphere has repeatedly been subject to structural analyses, and one of the findings has been the discovery of the A-List phenomenon, ...","Title: A crime simulation model based on social networks and swarm intelligence\nYear: 2007\n\nAbstract: Experience in the domain of criminology has shown that spatial data distribution of crime in urban centers follows a Zipf law in which few places concentrate most of the crimes while several other places have few crimes. In order to reproduce and better understand the nuances of such a crime distribution profile, we introduce in this paper a novel multi-agent-based crime simulation model that is directly inspired by the swarm intelligence paradigm. In this model, criminals are regarded as distributed entities endowed with the capability to pursue self-organizing behavior by considering their individual (local) activities as well as the influence of other criminals. Through controlled experiments with the simulation model, we could indeed observe that self-organization phenomena (i.e. criminal behavior toward crime) emerge as the result of both individual and social learning factors. At the same time, our experiments reveal that the spatial distribution of crime achieved by experimenting with the simulation model closely follows the real crime data distribution as expected.","Title: An integrated method for designing user interfaces based on tests\nYear: 2005\n\nAbstract: We present artifacts and techniques used for User Interface (UI) evaluation, performed by professionals from the Human-Computer Interaction (HCI) area of study, covering Usability and Semiotic Engineering, which can assist Software Engineering (SE) to perform usability tests earlier. The study of various interaction alternatives, made possible by these artifacts, verifies if they are in accordance with users' preferences and constraints, and usability patterns, and can enhance the probability of achieving a more usable and reliable product.","Title: A process for separation of crosscutting grid concerns\nYear: 2006\n\nAbstract: This paper describes how to explicitly separate crosscutting Grid concerns in a parallel Java application. This process, named GridAspecting, uses a restricted subset of the Java threads model for application decomposition, and aspect-oriented programming for allowing parallel execution of the application's threads as Grid tasks. As a result of the process, all Grid-related code is encapsulated in aspects, thus improving the application's modularity. In addition, by relying on Java's native concurrency abstractions the process simplifies the Grid programming model and makes it possible to test a Grid application even without the Grid.","Title: Towards optimal police patrol routes with genetic algorithms\nYear: 2006\n\nAbstract: It is quite consensual that police patrolling can be regarded as one of the best well-known practices for implementing public-safety preventive policies towards the combat of an assortment of urban crimes. However, the specification of successful police patrol routes is by no means a trivial task to pursue, mainly when one considers large demographic areas. In this work, we present the first results achieved with GAPatrol, a novel evolutionary multiagent-based simulation tool devised to assist police managers in the design of effective police patrol route strategies. One particular aspect investigated here relates to the GAPatrol’s facility to automatically discover crime hotspots, that is, high-crime-density regions (or targets) that deserve to be better covered by routine patrol surveillance. In order to testify the potentialities of the novel approach in such regard, simulation results related to two scenarios of study over the same artificial urban territory are presented and discussed here.","Title: A Multi-Disciplinary Strategy For Identifying Affective Usability Aspects In Educational Geosimulation Systems\nYear: 2008\n\nAbstract: In this article we propose a multi-disciplinary strategy for identifying affective usability design aspects in educational geosimulation systems. It is based on the association of these aspects with an architecture that defines the basic components of a geosimulation system as well as the learning strategies used in this context. Our goal is to provide design strategies that might elicit positive emotional responses from the students in learning experiences. We illustrate how these strategies have been used in a learning system by evaluating the students' emotional responses evoked during their interaction with the system.","Title: Business Modeling for Digital TV Services: An Approach Focused on the Analysis of User Experience\nYear: 2009\n\nAbstract: This article presents a strategy to support the definition of a business model for Digital TV that prioritizes the valorization of knowledge of the professionals working in the areas of HCI and business, leading them to share knowledge. The authors advocate a novel approach to the design of the Digital TV business model that considers it essential to understand the contextual and human factors of the target population. The application of the strategy in a scenario of Digital TV implementation in a Brazilian town is described.","Title: A Multiagent Simulator for Teaching Police Allocation\nYear: 2006\n\nAbstract: This article describes the,ExpertCop tutorial system, a simulator of crime in an urban region. In ExpertCop, the students (police officers) configure and allocate an available police force according to a selected geographic region and then interact with the simulation. The student interprets the results with the help of an intelligent tutor, the pedagogical agent, observing how crime behaves in the presence of the allocated preventive policing. The interaction between domain agents representing social entities as criminals and police teams drives the simulation. ExpertCop induces students to reflect on resource allocation. The pedagogical agent implements interaction strategies between the student and the geosimblator, designed to make simulated phenomena better understood. In particular, the agent uses a machine-learning algorithm to identify patterns in simulation data and to formulate questions to the student about these patterns.","Title: Using Explanations of Agents to Increase Understanding of Simulations for Tutoring Police Allocation\nYear: 2005\n\nAbstract: This article describes the ExpertCop tutorial system, a simulator of crime in an urban region. In ExpertCop, the students (police officers) configure and allocate an available police force according to a selected geographic region and then interact with the simulation. The student interprets the simulation results with the help of an intelligent tutor, the Pedagogical Agent, observing how crime behaves in the presence of the allocated preventive policing. The pedagogical agent implements a strategy of explanation of the agentýs individual behavior to make simulated phenomena better understood.","Title: A service-oriented architecture for assisting the authoring of semantic crowd maps\nYear: 2012\n\nAbstract: Although there are increasingly more initiatives for the generation of semantic knowledge based on user participation, there is still a shortage of platforms for regular users to create applications on which semantic data can be exploited and generated automatically. We propose an architecture, called Semantic Maps (SeMaps), for assisting the authoring and hosting of applications in which the maps combine the aggregation of a Geographic Information System and crowd-generated content (called here crowd maps). In these systems, the digital map works as a blackboard for accommodating stories told by people about events they want to share with others typically participating in their social networks. SeMaps offers an environment for the creation and maintenance of sites based on crowd maps with the possibility for the user to characterize semantically that which s/he intends to mark on the map. The designer of a crowd map, by informing a linguistic expression that designates what has to be marked on the maps, is guided in a process that aims to associate a concept from a common-sense base to this linguistic expression. Thus, the crowd maps start to have dominion over common-sense inferential relations that define the meaning of the marker, and are able to make inferences about the network of linked data. This makes it possible to generate maps that have the power to perform inferences and access external sources (such as DBpedia) that constitute information that is useful and appropriate to the context of the map. In this paper we describe the architecture of SeMaps and how it was applied in a crowd map authoring tool.","Title: Towards the generation of explanations for semantic web services in OWL-S\nYear: 2008\n\nAbstract: In this article we define a system, called XplainS that automatically generates an infrastructure for providing explanations of semantic web services described in OWL-S. XplainS has a strategy for generating production rules from a flow where several levels of distribution of the services exist.","Title: InferenceNet.Br: expression of inferentialist semantic content of the Portuguese language\nYear: 2010\n\nAbstract: Often, the information necessary for a complete understanding of texts is implicit, which requires drawing inferences from the use of concepts in the linguistic praxis. We consider that the usual semantic reasoners of natural language systems face difficulties in capturing this knowledge, due mainly to the lack of linguistic-semantic resources that support reasoning of this nature. This paper presents a new linguistic resource that expresses semantic-inferentialist knowledge for the Portuguese language – InferenceNet.Br – containing a base of concepts and a base of sentence patterns. These bases provide content for a top layer of semantic reasoning in natural language systems, where semantic relations are considered according to their roles in inferences, as premises or conclusions. This linguistic resource was used in a system for extracting information about crime, and the results of this proof of concept are discussed.","Title: Towards a common sense base in portuguese for the linked open data cloud\nYear: 2012\n\nAbstract: The Linked Open Data (LOD) cloud is a promising reality since the major content producers are offering their data on an open and linked network, through RDF (Resource Description Framework), with the aim of providing Semantic Web applications with a single global database for retrieval of related content and to perform inferences over the network. However, bases with Portuguese-language content are still incipient. In this paper we present the process of inclusion of the InferenceNet --- the first resource with common sense and inferentialist knowledge in Portuguese language --- on the LOD. Our main goal is to leverage the use and development of Semantic Web applications by content producers in Portuguese language. We develop and evaluated a platform, called SemWidgets, for the creation and execution of widgets able to access and reason over InferenceNet and the open linked data, like DBPedia, Yago, and Article Search API of the New York Times.","Title: Interface design through knowledge-based systems: an approach centered on explanations from problem-solving models\nYear: 2005\n\nAbstract: The process of generating user interfaces is complex and demands a great deal of effort from the specialist, because there are several possible combinations and uncertainties regarding any one option. We believe that the modeling of HCI concepts and of the knowledge of all parties involved is of great importance, as well as the reuse of this modeling to automate and optimize such process. Therefore, we are proposing a KBS (Knowledge-Based System) that represents the knowledge of the interface designers. This System possesses the feature of interactivity, so that the user can have an explanation of the results produced, thus improving the understanding and acceptance of the proposed interface. Furthermore, the system allows for its knowledge base to be modified. We applied the proposed method to generate an abstract user interface for a system of email control and for a system of simulation of criminal activity.","Title: A User Interface to Support Dialogue and Negotiation in Participatory Simulations\nYear: 2008\n\nAbstract: In this paper, we discuss the process of analysis and design of a user interface to support dialogue and negotiation between players of participatory simulations. The underlying context is an interdisciplinary project, named SimParc [8], about participatory management of protected areas for biodiversity conservation and social inclusion. This project is inspired by the ComMod MAS/RPG approach [6] and by recent proposals for software support for distributed role playing games, such as those by Guyot [14] and by Adamatti [1]. In this paper, we focus on the analysis and design of a user interface to ease and structure dialogue and negotiation between players, using a methodology based on semiotic engineering. Our main objective is to try to find a good balance between the necessary structuring and the spontaneity of dialog and negotiation.","Title: Collective intelligence in law enforcement - The WikiCrimes system\nYear: 2010\n\nAbstract: Collaboration is on the rise, primordially leveraged by the Web 2.0 in which the difference between information producer and consumer decreases significantly. In this paper we describe WikiCrimes, an example of a Web 2.0 application that offers a collaborative environment based on the use and direct manipulation of maps, in order to register and research criminal events. WikiCrimes is driven by three goals: (i) to give more transparency and publicity to criminal information, (ii) to provide means for citizen prevention, and (iii) to reduce the phenomenon of under-reporting (crimes that are not notified to authorities). In the paper we pay particular attention to the fact that in this context, finding the equilibrium between people's participation and information credibility is crucial. Anonymous mass collaboration is the easiest way to receive information; however, the credibility of the received information is depreciated, because the source of information is unknown. We particularly concentrate on the ways we have created to keep the ''participation vs. reliability of information'' trade-off at good level. The reputation model is the main component created with this purpose. The experiences and the obstacles we are facing in the implementation of the project are the subjects of discussion and analyses.","Title: WebExplain: A UPML Extension to Support the Development of Explanations on the Web for Knowledge-Based Systems\nYear: 2006\n\nAbstract: Knowledge-based systems (KBS) should be able to explain their results to improve the understanding and credibility of their answers by users. However, most KBS explanation components cannot be easily reused by other applications, thus increasing the effort of implementing KBSs with explanation capabilities. In this paper we present WebExplain, an extension to Unified Problem-Solving Method Description Language (UPML), a KBS development framework. WebExplain is integrated with UPML generic components and can be easily reused during the development of other problem-solving methods and KBSs. WebExplain uses the Inference Web for enabling proof and explanation interoperability between distributed applications. We exemplify our approach by describing WebExplain's use in the development of a problem-solving method and a KBS with explanation capabilities.","Title: A multi-agent simulator for teaching police allocation\nYear: 2005\n\nAbstract: This article describes the ExpertCop tutorial system, a simulator of the crime in an urban region. In ExpertCop, the students (police officers) configure and allocate an available police force according to a selected geographic region and then interact with the simulation. The student interprets the results with the help of an intelligent tutor, the Pedagogical Agent, observing how the crime behaves in the presence of the allocated preventive policing. The interaction between domain agents representing social entities as criminals and police teams drives the simulation. ExpertCop induces students to reflect on resource allocation. The pedagogical agent implents interaction strategies between the student and the geosimulator, designed to make simulated phenomena better understood. In particular, the agent uses a machine learning algorithm to identify patterns on simulation data and to formulate questions to the student about these patterns. Moreover, it explores the reasoning process of the domain agents by providing explanations that help the student to understand simulation events.","Title: Geração de interfaces através de sistemas baseados em conhecimento: uma abordagem centrada em explicações de modelos de resolução de problemas\nYear: 2005\n\nAbstract: O processo de geração de interface de usuário é complexo e exige um grande esforço do especialista porque existem várias combinações possíveis e incertezas sobre a mesma opção. Acreditamos que é importante a modelagem dos conceitos de IHC e dos conhecimentos de todos os envolvidos e um reuso dessa modelagem para automatizar e otimizar tal processo. Portanto propomos um SBC (Sistema Baseado em Conhecimento) que represente o conhecimento dos projetistas de interface. Esse Sistema possui a característica interativa, de maneira que o usuário pode ter uma explicação do resultado produzido melhorando o entendimento e a aceitação da interface proposta. Aplicamos o método proposto para gerar protótipos da interface do usuário de um sistema de simulação da criminalidade.","Title: Using Color to Help in the Interactive Concept Formation\nYear: 2004\n\nAbstract: This article describes a technique that aims at qualifying a concept hierarchy with colors, in such a way that it can be feasible to promote the interactivity between the user and an incremental probabilistic concept formation algorithm. The main idea behind this technique is to use colors to map the concept properties being generated, to combine them, and to provide a resulting color that will represent a specific concept. The intention is to assign similar colors to similar concepts, thereby making it possible for the user to interact with the algorithm and to intervene in the concept formation process by identifying which approximate concepts are being separately formed. An operator for interactive merge has been used to allow the user to combine concepts he/she considers similar. Preliminary evaluation on concepts generated after interaction has demonstrated improved accuracy.","Title: SIM: um modelo semântico-inferencialista para sistemas de linguagem natural\nYear: 2008\n\nAbstract: One of the growing needs related to systems of Natural Language Processing (NLP) is that such systems must be able to perform enriched textual inferences. We argue that one reason for the current limitation of the inferences generated by these systems is that---for the most part---they are based on the characteristics of the things represented by names, and seek to draw inferences based on such characteristics. In this work, we propose the Semantic Inferentialism Model (SIM), which follows a natural path and represents a new paradigm: it seeks to express the inferential capacity of concepts and how these concepts, combined in sentence structures, contribute to the inferential power of sentences. We present a SIM-based Information Extraction System and a pre-evaluation of the results.","Title: Abstracting Web Agent Proofs into Human-Level Justifications\nYear: 2007\n\nAbstract: Information supporting answer explanations are derived from proofs. One of the difficulties for humans to understand web agent proofs is that the proofs are typically described at the machine-level. In this paper, we introduce a novel and generic approach for abstracting machine-level portable proofs into human-level justifications. This abstraction facilitates generating explanations from proofs on the web. Our approach consists of creating a repository of proof templates, called abstraction patterns, describing how machine-level inference rules and axioms in proofs can be replaced by rules that are more meaningful for humans. Intermediate results supporting machine- level proofs may also be dropped during the abstraction process. The Inference Web Abstractor algorithm has been developed with the goal of matching the abstraction patterns in the repository against the original proof and applying a set of strategies to abstract the proof thereby simplifying its presentation. The tools used for creating and applying abstraction patterns are shown along with an intelligence analysis example.1","Title: Semantic Textual Similarity Of Portuguese-Language Texts: An Approach Based On The Semantic Inferentialism Model\nYear: 2014\n\nAbstract: The Semantic Textual Similarity (STS) task aims capturing a bidirectional-graded equivalence between the pair of short texts. This work proposes a STS measure for the Portuguese Language based on Semantic Inferentialism Model (SIM) and InferenceNet.BR. We argue that the expression of inferential, causal, motivational and encyclopedic content of InferenceNet enables a more robust and efficient model for the STS task. An extrinsic evaluation in a Portuguese-language processing application - a Case-Based Reasoning system for Requirements Engineering - provided real scenario to assess how the proposed STS measure contributes to the effectiveness of NLP applications.","Title: Multiagent models for police resource allocation and dispatch\nYear: 2014\n\nAbstract: In this article we investigate Multi-agent simulation as a way for modeling strategies for resource allocation problems in Public Safety. The goal is to show how simulation can be used to help law enforcement authorities to evaluate, in a controlled environment, different strategies for allocating and dispatching resources, aiming at reducing both response time and the number of unattended calls. We created a Multiagent model to represent police cars that answer to emergency calls. In particular, the environment in which the agents live is a grid in which emergency occurrences appear. Random and Zipfian distributions drive the occurrence of calls. A comparison of the strategies for resource allocation in this environment shows that serving first those calls with low estimated attendance times delivers the best overall performance. However this is practically impossible since prioritization of certain crime types is necessary leading to the increase of the waiting time in the queue. Such degradation may be assimilated in real life scenarios because high priority calls are served first. Also, it is important to implement a policy of aging to avoid the cost of great degradation for low priority crimes.","Title: Open Collaborative Systems as Institutions of Agents\nYear: 2008\n\nAbstract: A wiki is a typical Web 2.0 system where people from various backgrounds interoperate and/or cooperate, sharing information and, at the same time, building a knowledge base on different areas of interest. Its characteristics are very similar to the ones owned by the open multi-agent systems modeled as electronic institutional environments. This paper proposes an approach to develop open collaborative systems as institutions of agents and demonstrates that suitability through the adaptation of a typical wiki application, known as WikiCrimes, to an institutional environment.","Title: Natural Language Processing based on Semantic inferentialism for extracting crime information from text\nYear: 2010\n\nAbstract: This article describes an architecture for Information Extraction systems on the web, based on Natural Language Processing (NLP) and especially geared toward the exploration of information about crime. The main feature of the architecture is its NLP module, which is based on the Semantic Inferential Model. We demonstrate the feasibility of the architecture through the implementation thereof to provide input for a collaborative web-based system of registering crimes called WikiCrimes.","Title: WikiCrimes as an Open Institutional Environment: A Study Case\nYear: 2013\n\nAbstract: WikiCrimes is a collaborative system where users can store information about crime occurrences in a georeferenced database. Anyone can access WikiCrimes to consult or add information to the database. In this paper we describe the further work done to extend the model and implementation of the WikiCrimes collaborative system as an Open Institutional Environment. The prototype implements an environment where commitments are used to define norms, and together with reputation bring social order to the system.","Title: Collaboration Networks From A Large Cv Database: Dynamics, Topology And Bonus Impact\nYear: 2013\n\nAbstract: Understanding the dynamics of research production and collaboration may reveal better strategies for scientific careers, academic institutions, and funding agencies. Here we propose the use of a large and multidisciplinary database of scientific curricula in Brazil, namely, the Lattes Platform, to study patterns of scientific production and collaboration. Detailed information about publications and researchers is available in this database. Individual curricula are submitted by the researchers themselves so that coauthorship is unambiguous. Researchers can be evaluated by scientific productivity, geographical location and field of expertise. Our results show that the collaboration network is growing exponentially for the last three decades, with a distribution of number of collaborators per researcher that approaches a power-law as the network gets older. Moreover, both the distributions of number of collaborators and production per researcher obey power-law behaviors, regardless of the geographical location or field, suggesting that the same universal mechanism might be responsible for network growth and productivity. We also show that the collaboration network under investigation displays a typical assortative mixing behavior, where teeming researchers (i.e., with high degree) tend to collaborate with others alike.","Title: Does content categorization lead to knowledge building? An experiment in a social bookmarking service.\nYear: 2015\n\nAbstract: •We aimed to verify whether content categorization leads to knowledge building (KB).•We ran an experiment in the social bookmarking service Uxmarks.com.•We measured the perception of KB×user participation (lurking or posting behavior).•There was no correlation between KB perception and user participation.•In other words, and lurkers and posters could equally benefit from the service.","Title: Ranking Keyphrases from Semantic and Syntactic Features of Textual Terms.\nYear: 2015\n\nAbstract: Two important lines of research in key phrase extraction from text are methods that use machine learning to discover rules based on statistics of terms, and knowledge-intensive methods that seek to understand the semantics of the text with the help of conceptual bases like Wikipedia. Our argument is that the task of key phrase extraction for different domains requires defining ranking functions that take into account the advantages and shortcomings of each approach to the specific problem. To determine the best ranking function, arrangements of weights are generated, which in turn weight each of the attributes used in both the statistic and semantic functions. The arrangement of weights that presents better average performance sets the weights of the attributes of the ranking function. We show comparative tests conducted with current approaches that use only syntactic or semantic features with a hybrid ranking approach. The later outperformed the state of the art.","Title: Using Markov Models to Learn the Sentiment of Soccer Fans from Bets and the Result of Matches\nYear: 2015\n\nAbstract: In this paper we investigate variations of Hidden Markov Models (HMM) as a viable tool for predicting the sentiment of soccer fans based on information regarding the result of matches. The models were constructed from data collected from a social network where fans of a soccer team periodically express feelings towards their team. Our claim is that the change in a fan's sentiment is analogous to a Markovian process of change of state through time. A comparative evaluation performed between variations of the proposed models showed that a second order HMM, considering the match results and fan's gambling information, is the most accurate model.","Title: Using Non-textual Terms for Boosting Document Keyphrase Assignment.\nYear: 2015\n\nAbstract: Machine-learning state-of-the-art keyphrase extraction systems do not take into consideration the fact that part of these keyphrases may not be found in the text. Therefore these systems typically use a training set restricted to textual terms, reducing the learning capabilities of any inductive algorithm. Our research investigates ways to improve the accuracy of these systems by allowing classification algorithms to learn from non-textual terms as well. The basic assumption we have followed is that non-textual terms can be included into the training set by inference from an eventual semantic relationship with textual terms. In order to discover the latent relationship between non-textual and textual terms, we propose deductive strategies to be applied in common sense bases such as Wikipedia. We show that algorithms that follow our approach outperform others that do not use the same methods introduced here.","Title: Learning the Sentiment of Soccer Fans from Data on Bets and Social Nets.\nYear: 2017\n\nAbstract: In this paper we propose a Hidden Markov Model in order to predict the sentiment of soccer fans based on information regarding the result of matches. The model was constructed by data collected from a social network where fans of a soccer team periodically expressed feelings towards their team. We show that the choice of a HMM is justified due to the fact that the change in a fan's sentiment is analogous to a Markovian process of change of state through time. A comparative evaluation will be performed between variations of the proposed models and also between the most accurate of them and classification algorithms. Second order HMM, considering the match results and fan's gambling information, is the most accurate model.","Title: Investigating Usability and User Experience from the User Postings in Social Systems\nYear: 2015\n\nAbstract: Social Systems (SS) are dynamic systems, with features such as: frequent exchange of messages and expression of feelings spontaneously. The postings of users on SS reveal their opinions on various issues, including on what they think of the system. In this study, we classified postings of users from two SS of different contexts: a popular SS (entertainment) - Twitter and an academic SS of a university - forums, to investigate information related to usability and user experience (UX) of such systems. Results showed they are useful to (i) obtain user's sentiments about the system; (ii) to identify possible problems during their experience and (iii) to perceive from the set of classified postings the context of use of the system.","Title: Multi-objective evolutionary algorithms and multiagent models for optimizing police dispatch\nYear: 2015\n\nAbstract: In this article we investigate Multi-agent simulation and Multi-objective Evolutionary Algorithms for optimizing resource allocation in Public Safety. We describe a tool that helps Law Enforcement authorities to evaluate, in a controlled environment, different strategies for allocating and dispatching resources, aiming at reducing conflicting goals such as response time, the number of unattended calls and cost of displacement of police cars. This tool is a multi-agent model to represent police cars that lives in a grid in which emergency occurrences appear. A comparison of the strategies for resource dispatch in this environment shows that serving first those calls with low estimated attendance times delivers the best overall performance in terms of waiting time. However this is practically impossible since prioritization of certain crime types is necessary leading to the increase of the waiting time in the queue. Instead of manually trying to identify the best allocation strategy to apply, we have coupled a multi-objective evolutionary algorithm to the simulation model in order to uncover automatically a function to rank the calls in the best order for attendance satisfying multiple and sometimes conflicting goals.","Title: Mining the Networks of Supply and Demand of Public Transport for Overload and Waste of Resources.\nYear: 2016\n\nAbstract: We propose here a methodology to help to understand the shortcomings of public transportation in a city via the mining of complex networks representing the supply and demand of public transport. We show how to build these networks based upon data on smart card use in buses via the application of algorithms that estimate an OD and reconstruct the complete itinerary of the passengers. The overlapping of the two networks sheds light in potential overload and waste in the offer of resources that can be mitigated with strategies for balancing supply and demand.","Title: Micro-interventions in urban transportation from pattern discovery on the flow of passengers and on the bus network\nYear: 2016\n\nAbstract: In this paper, we describe a case study in a large metropolis in which, based on data collected by digital sensors, we sought to understand mobility patterns of people using buses and how this can generate knowledge to suggest interventions to be applied incrementally to the transportation network in use. We first estimated an Origin-Destination matrix of bus users based on datasets about ticket validation and GPS positioning of buses. Then we represented the supply of buses with their routes through bus stops as a complex network, which allowed us to understand the bottlenecks of the current scenario and, in particular, apply community discovery techniques in order to identify clusters in the service supply infrastructure. Finally, by superimposing the flow of people represented in the Origin-Destination matrix in the supply network, we exemplify how micro-interventions can be prospected, by means of an example of introducing express routes.","Title: A Framework To Evaluate User Empowerment In Decision-Making Experiences With Participatory Gis\nYear: 2016\n\nAbstract: This paper describes a framework to guide designers on how to investigate UX empowerment by using PGIS to support the decision making process. Investigative questions are proposed and aligned with UX elements in order to facilitate the access to information and its discussion within a community, as well as empower users (decision makers) to act not just as consumers of information and systems, but also as creators of new knowledge. The goal was to guarantee an appropriate gathering and observation of the questions in order to evaluate how particular users tasks can be accomplished in a PGIS while complying with requisites that promote empowerment.","Title: Towards Keyphrase Assignment For Texts In Portuguese Language\nYear: 2016\n\nAbstract: Keyphrase assignment has often been confounded with keyphrase extraction, since the basic hypothesis is that a keyphrase of a text must be extracted from this text. Typically, keyphrase extraction approaches use a training set restricted to textual terms, reducing the learning capabilities of any inductive algorithm. Our research investigates ways to improve the accuracy of the keyphrase assignment systems for texts in Portuguese language by allowing classification algorithms to learn from non-textual terms as well. The basic assumption we have followed is that non-textual terms can be included into the training set by inference from an eventual semantic relationship with textual terms. In order to discover the latent relationship between non-textual and textual terms, we use deductive strategies to be applied in Portuguese common sense bases such as Wikipedia and InferenceNet. We show that algorithms that follow our approach outperform others that do not use the same methods introduced here.","Title: Human Mobility In Large Cities As A Proxy For Crime\nYear: 2016\n\nAbstract: We investigate at the subscale of the neighborhoods of a highly populated city the incidence of property crimes in terms of both the resident and the floating population. Our results show that a relevant allometric relation could only be observed between property crimes and floating population. More precisely, the evidence of a superlinear behavior indicates that a disproportional number of property crimes occurs in regions where an increased flow of people takes place in the city. For comparison, we also found that the number of crimes of peace disturbance only correlates well, and in a superlinear fashion too, with the resident population. Our study raises the interesting possibility that the super-linearity observed in previous studies [Bettencourt et al., Proc. Natl. Acad. Sci. USA 104, 7301 (2007) and Melo et al., Sci. Rep. 4, 6239 (2014)] for homicides versus population at the city scale could have its origin in the fact that the floating population, and not the resident one, should be taken as the relevant variable determining the intrinsic microdynamical behavior of the system.","Title: Análise comparativa de pesquisas de origens e destinos: uma abordagem baseada em Redes Complexas.\nYear: 2017\n\nAbstract: In this paper, a comparative study was conducted between complex networks representing origin and destination survey data. Similarities were found between the characteristics of the networks of Brazilian cities with networks of foreign cities. Power laws were found in the distributions of edge weights and this scale - free behavior can occur due to the economic characteristics of the cities.","Title: Contextual Data Collection for Smart Cities.\nYear: 2015\n\nAbstract: As part of Smart Cities initiatives, national, regional and local governments all over the globe are under the mandate of being more open regarding how they share their data. Under this mandate, many of these governments are publishing data under the umbrella of open government data, which includes measurement data from city-wide sensor networks. Furthermore, many of these data are published in so-called data portals as documents that may be spreadsheets, comma-separated value (CSV) data files, or plain documents in PDF or Word documents. The sharing of these documents may be a convenient way for the data provider to convey and publish data but it is not the ideal way for data consumers to reuse the data. For example, the problems of reusing the data may range from difficulty opening a document that is provided in any format that is not plain text, to the actual problem of understanding the meaning of each piece of knowledge inside of the document. Our proposal tackles those challenges by identifying metadata that has been regarded to be relevant for measurement data and providing a schema for this metadata. We further leverage the Human-Aware Sensor Network Ontology (HASNetO) to build an architecture for data collected in urban environments. We discuss the use of HASNetO and the supporting infrastructure to manage both data and metadata in support of the City of Fortaleza, a large metropolitan area in Brazil.","Title: Geracao Automatica de Paineis de Controle para Analise de Mobilidade Urbana Utilizando Redes Complexas.\nYear: 2017\n\nAbstract: In this paper we describe an automatic generator to support the data scientist to construct, in a user-friendly way, dashboards from data represented as networks. The generator called SBINet (Semantic for Business Intelligence from Networks) has a semantic layer that, through ontologies, describes the data that represents a network as well as the possible metrics to be calculated in the network. Thus, with SBINet, the stages of the dashboard constructing process that uses complex network metrics are facilitated and can be done by users who do not necessarily know about complex networks.","Title: Increasing the Likelihood of Finding Public Transport Riders that Face Problems Through a Data-Driven approach.\nYear: 2017\n\nAbstract: The maintenance of big cities public transport service quality requires constant monitoring, which may become an expensive and time-consuming practice. The perception of quality, from the users point of view is an important aspect of quality monitoring. In this sense, we proposed a methodology for data analysis and visualization, supported by software, which allows for the structuring of estimates and assumptions of where and who seems to be having unsatisfactory experiences while making use of the public transportation in populous metropolitan areas. Moreover, it provides support in setting up a plan for on-site quality surveys. The proposed methodology increases the likelihood that, with the on-site visits, the interviewer finds users who suffer inconveniences, which influence their behavior. Simulation comparison and a small-scale pilot survey stand for the validity of the proposed method.","Title: From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Dashboards.\nYear: 2017\n\nAbstract: In the context of Smart Cities, indicator definitions have been used to calculate values that enable the comparison among different cities. The calculation of an indicator values has challenges as the calculation may need to combine some aspects of quality while addressing different levels of abstraction. Knowledge graphs (KGs) have been used successfully to support flexible representation, which can support improved understanding and data analysis in similar settings. This paper presents an operational description for a city KG, an indicator ontology that support indicator discovery and data visualization and an application capable of performing metadata analysis to automatically build and display dashboards according to discovered indicators. We describe our implementation in an urban mobility setting.","Title: Towards Understanding the Impact of Human Mobility on Police Allocation.\nYear: 2017\n\nAbstract: Motivated by recent findings that human mobility is proxy for crime behavior in big cities and that there is a superlinear relationship between the peopleu0027s movement and crime, this article aims to evaluate the impact of how these findings influence police allocation. More precisely, we shed light on the differences between an allocation strategy, in which the resources are distributed by clusters of floating population, and conventional allocation strategies, in which the police resources are distributed by an Administrative Area (typically based on resident population). We observed a substantial difference in the distributions of police resources allocated following these strategies, what evidences the imprecision of conventional police allocation methods.","Title: An environment to support the design, implementation and evaluation of gamification in social systems.\nYear: 2015\n\nAbstract: Gamification has become a popular strategy to increase the engagement and participation of users in social systems. Despite the popularity that now reigns, we identified that gamification can not be treated simply as award points and provide badgets for users. Care must be taken, since the choice of the game mechanics must consider the profile of the user as a player. If such considerations are not made properly, the desired goals by the developer cannot be obtained. In other words, one needs to understand the relationship between the developeru0027s goals, the game mechanics and the profile of users (players). The lack of computational tools to support the developer to consider how these three aspects relate to and impact, led us to propose GameWork. It is a Service-oriented Architecture to support gamification in general, especially to help monitoring the system and the players behaviors with respect to the incentives provided. This article describes GameWork and an example of its implementation in a commercial social network.","Title: Traveling heterogeneity in public transportation.\nYear: 2018\n\nAbstract: It is well reported that long commutes have a large detrimental effect on people’s health and on the economy of cities. Interestingly, despite the strong impact on our daily lives, a simple way to measure the quality of urban transportation is still unknown. We performed data analysis on the transportation network of two large cities (Fortaleza and Dublin). By dividing each bus trajectory into equal pieces of space, we determine the distribution of time intervals for each trip, and we propose that the heterogeneity of the time distribution can be used to characterize the quality of that trip. Inspired by the use of the Gini coefficient to quantify the inequality level of income distribution, we used the Gini in order to characterize the heterogeneity level of the time distribution. We demonstrated that Gini coefficients are strongly correlated with peak usage of the mobility system, as well as the schedule delays in the system. Finally, our method can be used to find highly heterogeneous trips which have a large negative effect on the urban mobility and can help find new directions for new public planning strategies.","Title: Graph mining for the detection of overcrowding and waste of resources in public transport.\nYear: 2018\n\nAbstract: The imbalance between the quantity of supply and demand in public transport systems causes a series of disruptions in large metropolises. While extremely crowded vehicles are uncomfortable for passengers, virtually empty vehicles generate economic losses for system managers, and this usually comes back to passengers in the form of fare increases. In this article a new data processing methodology will be presented for the evaluation of collective transportation systems. It proposes the construction and mining of graphs that represent complex networks of supply and demand of the system to find such imbalances. In a case study with the bus system of a large Brazilian metropolis, it was shown that the methodology in question is capable of identifying global imbalances in the system based on an evaluation of the weight distributions of the edges of the supply and demand networks. It has also been shown that even in a scenario where information about the demand is incomplete, using community detection techniques it is possible to identify the stretches of the network that are potentially causing these imbalances on a global scale.","Title: LOADng-IoT: An Enhanced Routing Protocol for Internet of Things Applications over Low Power Networks.\nYear: 2019\n\nAbstract: The Internet of Things (IoT) is an emerging paradigm that proposes the connection of objects to exchange information in order to reach a common objective. In IoT networks, it is expected that the nodes will exchange data between each other and with external Internet services. However, due to deployment costs, not all the network devices are able to communicate with the Internet directly. Thus, other network nodes should use Internet-connected nodes as a gateway to forward messages to Internet services. Considering the fact that main routing protocols for low-power networks are not able to reach suitable performance in the displayed IoT environment, this work presents an enhancement to the Lightweight On-demand Ad hoc Distance-vector Routing Protocol-Next Generation (LOADng) for IoT scenarios. The proposal, named LOADng-IoT, is based on three improvements that will allow the nodes to find Internet-connected nodes autonomously and dynamically, decreasing the control message overhead required for the route construction, and reducing the loss of data messages directed to the Internet. Based on the performed assessment study, which considered several number of nodes in dense, sparse, and mobility scenarios, the proposed approach is able to present significant results in metrics related to quality-of-service, reliability, and energy efficiency.","Title: An IoT-Based Smart Solution for Preventing Domestic CO and LPG Gas Accidents\nYear: 2018\n\nAbstract: Gas monitoring systems have turned into a key feature in many applications in different fields, such as medicine, industry and environmental monitoring. Accidents involving gas leakage generate massive costs and deaths, where carbon monoxide (CO) and liquefied petroleum gas (LPG) are the main causes of domestic accidents. They could be avoidable with smart gas sensors integrated with other smart devices through the Internet of Things (IoT) paradigm. This paper focuses on the design and construction of a smart multi-gas plug-and-play sensor to avoid accidents in smart environments involving CO and LPG. This device is ready to connect on an IoT solution. It uses the standard IEEE 802.15.4 to communicate with a border router responsible to make the data available to other devices. The data will be sent to a middleware granting that other smart devices on the same context can access the information and make the correct decisions. The created prototype was evaluated, demonstrated, and validated, and it is ready for use.","Title: Railways Networks - Challenges for IoT Underground Wireless Communications\nYear: 2018\n\nAbstract: The use of wireless network technologies for the deployment of data communications systems in railway transportation infrastructure and services is essential. In fact, wireless solution for railways, especially for underground, is a big challenge to operators concerning this environment particularities. Many technologies were pointed as promising candidates for the future railway mobile telecommunications but, in reality, no current technology features overall appropriated. This paper discusses a Practical Guidance based on challenges for IoT wireless networks, based on a conventional Wi-Fi network case study. Moreover, it also highlights topics where new intelligent networking technologies, emerging from Internet of Things (IoT) paradigm, can contribute significantly to the improvement of railway networks.","Title: A Fan-Based Smart Selective Trap for Flying Insects\nYear: 2018\n\nAbstract: For several years, insect-borne diseases have been a major global concern. Countries with warmer climates are these disease vectors primary victims. However, due to rising global temperatures, countries with no history of insect-borne diseases have presented alarming statistics on the mosquitoes population lack control. Due to the importance of this subject, this paper proposes a smart and selective fan-based trap to capture the Aedes aegypti mosquito, main vector of diseases such as Yellow Fever, Dengue Fever, Zika, and Chikungunya. The central purpose of this study is the development of an electronic trap integrable to variable approaches of flying insects sensors. The trap is made up of a microcontroller that operates two DC fans arranged at the ends of an aerodynamic structure. The construction of a prototype validates the project through results evaluation and discussion.","Title: Management Platforms and Protocols for Internet of Things: A Survey.\nYear: 2019\n\nAbstract: Internet of Things (IoT) management systems require scalability, standardized communication, and context-awareness to achieve the management of connected devices with security and accuracy in real environments. Interoperability and heterogeneity between hardware and application layers are also critical issues. To attend to the network requirements and different functionalities, a dynamic and context-sensitive configuration management system is required. Thus, reference architectures (RAs) represent a basic architecture and the definition of key characteristics for the construction of IoT environments. Therefore, choosing the best technologies of the IoT management platforms and protocols through comparison and evaluation is a hard task, since they are difficult to compare due to their lack of standardization. However, in the literature, there are no management platforms focused on addressing all IoT issues. For this purpose, this paper surveys the available policies and solutions for IoT Network Management and devices. Among the available technologies, an evaluation was performed using features such as heterogeneity, scalability, supported technologies, and security. Based on this evaluation, the most promising technologies were chosen for a detailed performance evaluation study (through simulation and deployment in real environments). In terms of contributions, these protocols and platforms were studied in detail, the main features of each approach are highlighted and discussed, open research issues are identified as well as the lessons learned on the topic.","Title: Averaged one-dependence estimators on edge devices for smart pregnancy data analysis\nYear: 2019\n\nAbstract: •Review of edge network architectures, applications, and trends in IoT-based healthcare solutions.•Study of algorithms based on machine learning (ML) techniques and their use in conjunction with edge devices for high-risk pregnancy monitoring.•Classification mechanism to predict childbirth outcome in pregnancy: Averaged one-dependence estimators.•Performance evaluation using a real dataset from the Maternity School Assis Chateaubriand through a 10-fold cross-validation method.","Title: Pareto set as a model for dispatching resources in emergency Centres\nYear: 2019\n\nAbstract: The article investigates policies for helping emergency-centre authorities for dispatching resources aimed at reducing goals such as response time, the number of unattended calls, the attending of priority calls, and the cost of displacement of vehicles. Pareto Set is shown to be the appropriated way to support the representation of policies of dispatch since it naturally fits the challenges of multi-objective optimization. Understanding which are the best policies of dispatching as well as workload balancing are very important in the context FOG computing, which aims to distribute in part workload and services on fog devices (such as hardened routers, switches, IP video cameras, etc.). Fog-computing systems produce an ever-increasing service request impacting, for instance, to the power consumption on cloud servers. On the other hand, it is equally crucial to guarantee the quality of service (e.g., latency requirements) of end users. By means of the concept of Pareto dominance a set with objectives may be ordered in a way that guides the dispatch of resources. Instead of manually trying to identify the best dispatching strategy, a multi-objective evolutionary algorithm coupled with an Emergency Call Simulator uncovers automatically the best approximation of the optimal Pareto Set that would be the responsible for indicating the importance of each objective and consequently the order of attendance of the calls. The scenario of validation is a big metropolis in Brazil using one-year of real data from 911 calls. Comparisons with traditional policies proposed in the literature are done as well as other innovative policies inspired from different domains as computer science and operational research. The results show that strategy of ranking the calls from a Pareto Set discovered by the evolutionary method is a good option because it has the second best (lowest) waiting time, serves almost 100% of priority calls, is the second most economical, and is the second in attendance of calls. That is to say, it is a strategy in which the four dimensions are considered without major impairment to any of them.","Title: IoT-Based Solid Waste Management Solutions: A Survey.\nYear: 2019\n\nAbstract: With the increase of population density and the rural exodus to cities, urbanization is assuming extreme proportions and presents a tremendous urban problem related to waste generation. The increase of waste generation has been considered a significant challenge to large urban centers worldwide and represents a critical issue for countries with accelerated population growth in cities. The Internet of Things (IoT) and cloud computing offer an automation possibility through cyberphysical systems that will change the way solid waste management is performed. Considering IoT requirements, a review analysis of waste management models available in the literature is performed in detail in this paper. Then, a deep review is undertaken of the related literature based on IoT infrastructure for efficient handling of waste generated in urban scenarios, focusing on the interaction among concessionaires and waste generators (citizens) from the perspective of a shorter collection time with reduced costs, as well as citizenship promotion. An IoT-based reference model is described, and a comparison analysis of the available solutions is presented, with the goal to highlight the most relevant approaches and identify open research issues on the topic.","Title: Empirical Analysis of Bias in Voice-based Personal Assistants\nYear: 2019\n\nAbstract: Voice-based assistants are becoming increasingly widespread all over the world. However, the performance of these assistants in the interaction with users of languages and accents of developing countries is not clear yet. Eventual bias against specific language or accent of different groups of people in developing countries is maybe a factor to increase the digital gap in these countries. Our research aims at analysing the presence of bias in the interaction via audio. We carried out experiments to verify the quality of the recognition of phrases spoken by different groups of people. We evaluated the behaviour of Google Assistant and Siri for groups of people formed according to gender and regions that have different accents. Preliminary results indicate that accent and mispronunciation due to regional differences are not being properly considered by the assistants we have analyzed.","Title: Efficient artificial fish swarm based clustering approach on mobility aware energy‐efficient for MANET\nYear: 2019\n\nAbstract: AbstractAbstractMobile ad hoc network (MANET) is a gathering of self‐ruling mobile nodes in which ad hoc network framed without settled foundation. Dynamic topology property of MANET may debase the execution of the network, mobility attentiveness, and energy effectiveness because of an ideal cluster head (CH). The low energy adaptive clustering hierarchy protocol is used in the MANETs for enhancement of network life time by proficiently utilizing of the accessible constrained energy. Artificial fish swarm optimization is used to select the CH in MANET communication process. The election of CH takes care of mobility and remaining energy as well as the degree of connectivity for selecting nodes to serve as cluster for longer duration of time. From the implementation results, it is observed that the proposed method provides the maximum network life time as 80 to 140 hours and minimum energy consumption as 9 to 130 J, compared with existing protocol and optimization technique. View Figure Model for proposed work. AFS, artificial fish swarm; CH, cluster head; LEACH, low energy adaptive clustering hierarchy.The LEACH protocol is used in the MANETs for enhancement of network lifetime by proficiently utilizing of the accessible constrained energy. Artificial Fish Swarm (AFS) optimization is used to select the CH in MANET communication process. The election of CH takes care of mobility and remaining energy as well as the degree of connectivity for selecting nodes to serve as a cluster for the longer duration of time. The energy consumption of the suggested routing protocol is very low comparing to the existing energy routing protocols.","Title: Assessment of urban cultural-heritage protection zones using a co-visibility-analysis tool\nYear: 2019\n\nAbstract: •Anthropic changes in urban settings threaten the visibility of heritage buildings;•Protection zones are ineffective to prevent heritage negative visual intrusions;•Vantage points and co-visibility are key aspects for assessing visual intrusions;•3D analysis tool allows identifying both co-visibility and positive vantage points;•Dependable tool to identify visual intrusions benefits heritage policy making;","Title: Learning the Sentiment of Soccer Fans from Data on Bets and Social Nets\nYear: 2015\n\nAbstract: In this paper we propose a Hidden Markov Model in order to predict the sentiment of soccer fans based on information regarding the result of matches. The model was constructed by data collected from a social network where fans of a soccer team periodically expressed feelings towards their team. We show that the choice of a HMM is justified due to the fact that the change in a fan's sentiment is analogous to a Markovian process of change of state through time. A comparative evaluation will be performed between variations of the proposed models and also between the most accurate of them and classification algorithms. Second order HMM, considering the match results and fan's gambling information, is the most accurate model.","Title: A Data-Driven Approach To Help Understanding The Preferences Of Public Transport Users\nYear: 2017\n\nAbstract: The maintenance of the quality of the public transport service in big cities requires constant monitoring, which may become an expensive and time-consuming practice. The perception of quality, from the users point of view is an important aspect of quality monitoring. In this sense, we proposed a methodology based on big data analysis and visualization, which allows for the structuring of estimates and assumptions of where and who seems to be having unsatisfactory experiences while making use of the public transportation in metropolitan areas. Moreover, it provides support in setting up a plan for on-site quality surveys. The proposed methodology increases the likelihood that, with the on-site visits, the interviewer finds users who suffer inconveniences, which influence their behavior. Simulation comparison and a small-scale pilot survey helped validate the proposed method.","Title: Using Topic Modeling in Classification of Brazilian Lawsuits\nYear: 2022\n\nAbstract: Legal text processing is a challenging task for modeling approaches due to the peculiarities inherent to its features, such as long texts and their technical vocabulary. Topic modeling consists of discovering a semantic structure in the text. This paper investigates the application of topic modeling and the use of information about the legislation cited in identifying the subject of legal documents and evaluating its applicability in the classification of Brazilian lawsuits. The models were trained with a Golden Collection of 16 thousand initial petitions and indictments from the Court of Justice of the State of Ceara, in Brazil, whose lawsuits were classified in the five more representative National Council of Justice (CNJ) of Brazil classes - Common Civil Procedure, Execution of Extrajudicial Title, Criminal Action - Ordinary Procedure, Special Civil Court Procedure, and Tax Enforcement. The results obtained outperform the baseline, achieving 0.89 of F1 score (macro). Our interpretation is that the representation of the document through contextual embeddings generated by BERT, as well as the architecture of the model with bidirectional contexts, makes it possible to capture the specific context of the domain of legal documents. Thus, the use of the legislation mentioned in the representation of documents can improve the accuracy of the classification task."],"string":"[\n \"Title: Knowledge-intensive word disambiguation via common-sense and wikipedia\\nYear: 2012\\n\\nAbstract: A promising approach to cope with the challenges that Word Sense Disambiguation brings is to use knowledge-intensive methods. Typically they rely on Wikipedia for supporting automatic concept identification. The exclusive use of Wikipedia as a knowledge base for word disambiguation and therefore the general identification of topics, however, have low accuracy vis-à-vis texts with diverse topics, as can be the case with blogs. This motivated us to propose a method for word disambiguation that, in addition to the use of Wikipedia, uses a common sense database. Use of this base enriches the definition of the concepts previously identified with the help of Wikipedia, and permits the definition of a similarity measure between concepts, which is characterized by verifying the similarity of two concepts from the viewpoint of conceptual proximity in the Wikipedia hierarchy, in addition to the proximity between such concepts in terms of the inferences that they can make. We show that by doing this, we improved the accuracy of automatic disambiguation of words compared with methods that do not use a common sense base.\",\n \"Title: Learning universal access guidelines by solving HCI problems\\nYear: 2001\\n\\nAbstract: This work addresses the problem of learning guidelines to both (i) design user interfaces and (ii) evaluates the quality of interfaces, for universal access systems. We have developed a collaborative environment for helping the designer of user interfaces in solving problems of accessibility, usability and acceptability. The designer accesses the environment as a tool for assistance, aiming at solving difficulties he encounters when the user is not satisfied with the system with which the latter is interacting. During the problem solving process, the environment provides the designer with assistance by showing graphic scenarios, texts and by retrieving similar past solution cases. Moreover, the designer has a collaborative support that allows him to interact, in a synchronous or asynchronous way, with other designers.\",\n \"Title: Information Extraction from Text Based on Semantic Inferentialism\\nYear: 2009\\n\\nAbstract: One of the growing needs of information extraction (IE) from text is that the IE system must be able to perform enriched inferences in order to discover and extract information. We argue that one reason for the current limitation of the approaches that use semantics for that is that they are based on ontologies that express the characteristics of things represented by names, and seek to draw inferences and to extract information based on such characteristics, disregarding the linguistic praxis (i.e. the uses of the natural language). In this paper, we describe a generic architecture for IE systems based on Semantic Inferentialism. We propose a model that seeks to express the inferential power of concepts and how these concepts, combined in sentence structures, contribute to the inferential power of sentences. We demonstrate the validity of the approach and evaluate it by deploying an application for extracting information about crime reported in on line newspapers.\",\n \"Title: Self-organized and social models of criminal activity in urban environments\\nYear: 2006\\n\\nAbstract: Multi-Agent Systems (MAS) are extensively used as a tool for simulation of dynamic systems. Geosimulation is an urban phenomena approach that uses the multi-agent methodology to simulate discrete, dynamic, and event-oriented systems. Our focus in this paper is to use self-organization, specially strategies inspired by solutions from Swarm Intelligence, as well as the idea of social networks, and demonstrate their effect on learning in geosimulation agents.\",\n \"Title: An architecture for interactive knowledge based systems\\nYear: 2003\\n\\nAbstract: Knowledge-based systems (KBS) have been developed to help people to execute knowledge-intensive tasks. Some shortcomings are identified in the use of KBS, since they don't take into account both the user's profile and the organization's constraints that limit his/her actions, and to the lack of integration of the aspects of human-computer interaction in KBS's development process. This work presents a proposal of architecture for KBSi which are systems that interact with users and that adapt to their characteristics and environment during its use. The main feature of the architecture is the derivation of dialog patterns that facilitates the development of adaptive KBSi, from the specification of the expert reasoning process represented by means of PSM (Problem-Solving Methods).\",\n \"Title: A Heuristic Method for Balanced Graph Partitioning: An Application for the Demarcation of Preventive Police Patrol Areas\\nYear: 2008\\n\\nAbstract: In this paper we describe a heuristic method, based on graph-partitioning algorithms, with the purpose of improving the demarcation of areas for police patrolling. This demarcation seeks to homogenize the number of crimes among the patrol regions. If the map of a particular region is taken as a graph, we can say that the problem faced by police forces (typically preventive police) is similar to the problem of finding balanced connected q partitions of graphs (BCPq). Since this is a problem belonging to the NP-Hard class, approximate algorithms are the most suitable for solving such a problem. The method described in this article obtains results nearest those considered optimal for the more general case of BCPq, for q≥2.\",\n \"Title: A bio-inspired crime simulation model\\nYear: 2009\\n\\nAbstract: In this paper we describe a multiagent crime simulation model that resorts to concepts of self-organizing bio-inspired systems, in particular, of the Ant Colony Optimization algorithm. As the matching between simulated and real crime data distributions depends upon the tuning of some control parameters of the simulation model (in particular, of the initial places where criminals start out), we have modeled the calibration of the simulation as an optimization problem. The solution for the allocation of criminals into gateways is also undertaken by a bio-inspired method, namely, a customized Genetic Algorithm. We show that this approach allows for the automatic discovery of gateway configurations that, when employed in the simulation, produce crime distributions that are statistically close to those observed in real data.\",\n \"Title: Using a Problem Solving Method for Dynamic Configuration of Data Mining Applications on the Grid\\nYear: 2005\\n\\nAbstract: In this paper, we describe how SMARTBASEG, a knowledge-based architecture, can optimize dynamically data mining (DM) applications for the Grid. SMARTBASEG can be seen as a layer that intercepts the submission of a DM application to the Grid, identifies the procedures to be parallelized and optimizes the composition of the job to be submitted to the Grid. The job composition process is done by means of a problem solving method (PSM) of configuration that adopts a propose-and-revise strategy. We present our approach by describing formally the implementation of the PSM in SMARTBASEG and by exemplifying the representation of heuristics for DM application optimization by means of constraints and repairs. Finally, we show the preliminary results we have obtained in some experiments.\",\n \"Title: Promoting performance and separation of concerns for data mining applications on the grid\\nYear: 2007\\n\\nAbstract: Grid Computing brought the promise of making high-performance computing cheaper and more easily available than traditional supercomputing platforms. Such a promise was very well received by the data mining (DM) community, as DM applications typically process very large datasets and are thus very resource intensive. However, since the Grid is very dynamic and parallel data mining is prone to load unbalancing, obtaining good data mining performance on the Grid is hard. It typically requires the scheduler to understand the inner workings of the application, bringing two related problems. First, good Grid schedulers tend to be very specialized in the application they target. Second, changing the application may require changing the scheduler, which may be especially challenging when there is no clear separation between the application and the scheduler code. We here propose and evaluate a knowledge-based approach that provides abstractions to the DM developer and optimizes at runtime the DM application on the Grid.\",\n \"Title: Uma estratégia para análise da adoção de sistemas colaborativos baseada nas relações entre experiências de usuário, tecnologia e marketing\\nYear: 2011\\n\\nAbstract: This paper is the result of a research Project that discusses the application of a strategy for evaluating of factors that affect the adoption of collaborative software by users. The authors discuss TI adoption, and present a literature review of this topic. The user behavior model that was used in the evaluation process is then shown. The main objective of the strategy is to investigate the main questions of an entrepreneur about her product in beta version, trying to understand which factors are the most relevant to her postrelease actions. In the study case, data was collected and there were evidences that the relations among user experiences, marketing and technology affected user behaviors. It was given some recommendation to orient the entrepreneur in her post-release actions.\",\n \"Title: A Unified Architecture to Develop Interactive Knowledge Based Systems\\nYear: 2004\\n\\nAbstract: A growing need related to the use of knowledge-based systems (KBSs) is that these systems provide ways of adaptive interaction with the user. A comparative analysis of approaches to develop KBSs allowed us to identify a high functional quality level and a lack of integration of human factors in their frameworks. In this article, we propose an approach to develop adaptive and interactive KBSs that integrate works from the Knowledge Engineering and HCI areas, through the definition of a unified software architecture. A contribution of this work is the use of interaction patterns in order to define the interaction flow according to the user profile. These interaction patterns are defined for different kinds of interaction, such as, explanation, cooperation, argumentation or criticism. The reusable architecture components were implemented using Java and Protege-2000, and they were used in a KBS for assessment of installments of tax debts.\",\n \"Title: Geosimulation in Education: A System for Teaching Police Resource Allocation\\nYear: 2007\\n\\nAbstract: This article describes the ExpertCop tutorial system. ExpertCop is a geosimulator of criminal dynamics in urban environments that aims to train police officers in the activity of preventive policing allocation. ExpertCop intends to i) help students comprehend the task of resources allocation and identify the variables involved; ii) induce students to reflect about their actions related to resources allocation; and iii) help students understand the relation between preventive policing and crime. In ExpertCop, the students (police officers) configure and allocate an available police force according to a selected geographic region, and then follow the simulation process. The students interpret the results with the aid of an intelligent tutor, the Pedagogical Agent, by observing how crime behaves in the presence of the allocated preventive policing. The interaction between domain agents representing social entities such as criminals and police teams allocated by the students drives the simulation. Assisting the user, the Pedagogical Agent aims to define interaction strategies between the student and the geosimulator in order to make simulated phenomena better understood. The innovative character of the work is in the strategy adopted by the Pedagogical Agent in order to assist the user in understanding the simulated model. It seeks to explain the model at the macro-level (global or emergent behavior) and micro-level (behavior of the agents individually). The evaluation of the system in training held with students of the domain has shown that the system attains its intended pedagogical goals.\",\n \"Title: A conceptual framework for the design and evaluation of affective usability in educational geosimulation systems\\nYear: 2007\\n\\nAbstract: In this article we propose a conceptual framework for associating the concepts of usability, computer education and affective quality. We analyze the interaction student-teacher under the light of learning strategies used in educational geosimulators for defining the main emotional constructs that are involved in this process. We elaborate this initial analysis by identifying which interactive objects should be associated with the identified emotional constructs. We associate these objects with an architecture that defines the basics components of an educational geosimulation system as well as the learning strategies used in this context. We illustrate the utility of the framework with an evaluation of an education geosimulator for police training as well as an evaluation of the student's satisfaction during the interaction in different scenarios.\",\n \"Title: Maps-TV: a software architecture to support development of collaborative maps in DTVi\\nYear: 2012\\n\\nAbstract: This paper proposes a software architecture for the development of collaborative maps applied to the model of the Brazilian Digital Television System (SBTVD), called MAPS-TV. We describe the modules of MAPS-TV, how they interact, and how we extend the content representation model used by TV-Anytime to allow this interaction. For the purpose of validating our proposal, we also demonstrate how MAPS-TV facilitated the creation of collaborative maps -- related to urban problems -- to be used in the SBTVD, with no need for participation of software developers in the process.\",\n \"Title: Open Government and Citizen Participation in Law Enforcement via Crowd Mapping\\nYear: 2012\\n\\nAbstract: The authors describe WikiCrimes, a project founded on a website of the same name that aims to offer a common interaction space for the general public where they can note criminal activity and track the lo-cations where such crimes occur. The goal is to encourage collaborative participation that generates useful information for everyone. The authors describe their experience with WikiCrimes, emphasizing the services it provides to citizens and underlining some of the problems they've overcome to date. Their ultimate goal is to help establish a research agenda about the topic by inviting the academic community to embrace WikiCrimes as a platform that bridges citizen participation and open government.\",\n \"Title: Using Self-Organization in an Agent Framework to Model Criminal Activity in Response to Police Patrol Routes\\nYear: 2006\\n\\nAbstract: The organizational structure of the police is character- ized by the existence of a centralized command with the task of distributing and redistributing the police force in a region according to an analysis of crime and the fac- tors that lead to it. On the other hand, criminals are char- acterized as a decentralized system in which individual agents have autonomy and act primarily based on accu- mulated experience they gain from their life in crime. Simulation of different strategies of physical reorgani- zation is a first step to better understand the influence that specific police patrol routes have on the reduction of crime rates and how such decentralization can be ef- ficiently combated. In this article we describe a tool for assisting the investigation of different strategies of agent physical reorganizations where criminal agents demon- strate emergent characteristics. We believe our simula- tion tool is an effective way to train the police in the effectiveness of their patrolling.\",\n \"Title: Analyzing police patrol routes by simulating the physical reorganization of agents\\nYear: 2005\\n\\nAbstract: In this article we describe a tool for assisting the investigation of different strategies for the physical reorganization of agents. We show how the tool was used in the public safety domain to help in the study of strategies of preventive policing. A society of agents that simulates criminal and police behavior in a geographical region was constructed. In this society, artificial agents representing the police are responsible for preventing crimes. The organizational structure of the police is characterized by the existence of a centralized command that has the task of distributing and redistributing the police force in a region according to an analysis on crime and the factors that influence it. The simulation of different strategies of physical reorganization is a first step to better understand the influence that different police patrol routes have on the reduction of crime rates.\",\n \"Title: KnowiXML: a knowledge-based system generating multiple abstract user interfaces in USIXML\\nYear: 2004\\n\\nAbstract: This research presents a multidisciplinary approach aimed at generating multiple Abstract User Interfaces (AUIs), which are adaptable for different kinds of users, performing different tasks, using specific devices in various physical environments. The UI generation framework, called IKnowU, is based on a unified process for interactive system design, which integrates Software Engineering (SE), and Human-Computer Interaction (HCI) best practices. This framework is supported by KnowiXML, a Knowledge-Based System (KBS) that facilitates the application of models and the allocation of appropriate visual elements during the generation of AUIs. These AUIs are generated by using problem solving methods studied in Artificial Intelligence (AI). Design knowledge encoded in KnowiXML uniformly manipulates models and UI specifications through the use of an User Interface Description Language (UIDL).\",\n \"Title: A semi-automated method for acquisition of common-sense and inferentialist knowledge.\\nYear: 2013\\n\\nAbstract: This paper presents a semi-automated method for the acquisition of common-sense and inferentialist concepts in Portuguese. Its innovative feature is a module of reasoning over the pre-existing knowledge that aims to offer original content to the user, helping in the expression of semantic relationships and validation of new concepts. This reasoning process is based on heuristics and syntactic analysis of noun phrases. A qualitative evaluation with users who interacted with the system built on the proposed method showed that the interactions made in the process of knowledge acquisition are more productive since the user is reminded about semantic relations and common-sense knowledge about the new concepts.\",\n \"Title: GAPatrol: an evolutionary multiagent approach for the automatic definition of hotspots and patrol routes\\nYear: 2006\\n\\nAbstract: In this work, we present a novel evolutionary multiagent-based simulation tool, named as GAPatrol. Such system is devoted to the specification of effective police patrol route strategies for coping with criminal activities happening in a given artificial urban environment, which, in turn, mimics a real demographic region of interest. The approach underlying GAPatrol allows for the automatic uncovering of hotspots and routes of surveillance, which, in real life, are usually discovered by hand with the help of statistical and/or specialized mapping techniques. The qualitative/quantitative results achieved by GAPatrol in two scenarios of study are discussed here, evidencing the potentialities of the novel approach as a promising decision-support tool for police patrolling.\",\n \"Title: A Method for Identifying Malicious Activity in Collaborative Systems with Maps\\nYear: 2009\\n\\nAbstract: Blogs are popular communication instruments in today's web and altogether, they form the so-called blogosphere. This blogosphere has repeatedly been subject to structural analyses, and one of the findings has been the discovery of the A-List phenomenon, ...\",\n \"Title: A crime simulation model based on social networks and swarm intelligence\\nYear: 2007\\n\\nAbstract: Experience in the domain of criminology has shown that spatial data distribution of crime in urban centers follows a Zipf law in which few places concentrate most of the crimes while several other places have few crimes. In order to reproduce and better understand the nuances of such a crime distribution profile, we introduce in this paper a novel multi-agent-based crime simulation model that is directly inspired by the swarm intelligence paradigm. In this model, criminals are regarded as distributed entities endowed with the capability to pursue self-organizing behavior by considering their individual (local) activities as well as the influence of other criminals. Through controlled experiments with the simulation model, we could indeed observe that self-organization phenomena (i.e. criminal behavior toward crime) emerge as the result of both individual and social learning factors. At the same time, our experiments reveal that the spatial distribution of crime achieved by experimenting with the simulation model closely follows the real crime data distribution as expected.\",\n \"Title: An integrated method for designing user interfaces based on tests\\nYear: 2005\\n\\nAbstract: We present artifacts and techniques used for User Interface (UI) evaluation, performed by professionals from the Human-Computer Interaction (HCI) area of study, covering Usability and Semiotic Engineering, which can assist Software Engineering (SE) to perform usability tests earlier. The study of various interaction alternatives, made possible by these artifacts, verifies if they are in accordance with users' preferences and constraints, and usability patterns, and can enhance the probability of achieving a more usable and reliable product.\",\n \"Title: A process for separation of crosscutting grid concerns\\nYear: 2006\\n\\nAbstract: This paper describes how to explicitly separate crosscutting Grid concerns in a parallel Java application. This process, named GridAspecting, uses a restricted subset of the Java threads model for application decomposition, and aspect-oriented programming for allowing parallel execution of the application's threads as Grid tasks. As a result of the process, all Grid-related code is encapsulated in aspects, thus improving the application's modularity. In addition, by relying on Java's native concurrency abstractions the process simplifies the Grid programming model and makes it possible to test a Grid application even without the Grid.\",\n \"Title: Towards optimal police patrol routes with genetic algorithms\\nYear: 2006\\n\\nAbstract: It is quite consensual that police patrolling can be regarded as one of the best well-known practices for implementing public-safety preventive policies towards the combat of an assortment of urban crimes. However, the specification of successful police patrol routes is by no means a trivial task to pursue, mainly when one considers large demographic areas. In this work, we present the first results achieved with GAPatrol, a novel evolutionary multiagent-based simulation tool devised to assist police managers in the design of effective police patrol route strategies. One particular aspect investigated here relates to the GAPatrol’s facility to automatically discover crime hotspots, that is, high-crime-density regions (or targets) that deserve to be better covered by routine patrol surveillance. In order to testify the potentialities of the novel approach in such regard, simulation results related to two scenarios of study over the same artificial urban territory are presented and discussed here.\",\n \"Title: A Multi-Disciplinary Strategy For Identifying Affective Usability Aspects In Educational Geosimulation Systems\\nYear: 2008\\n\\nAbstract: In this article we propose a multi-disciplinary strategy for identifying affective usability design aspects in educational geosimulation systems. It is based on the association of these aspects with an architecture that defines the basic components of a geosimulation system as well as the learning strategies used in this context. Our goal is to provide design strategies that might elicit positive emotional responses from the students in learning experiences. We illustrate how these strategies have been used in a learning system by evaluating the students' emotional responses evoked during their interaction with the system.\",\n \"Title: Business Modeling for Digital TV Services: An Approach Focused on the Analysis of User Experience\\nYear: 2009\\n\\nAbstract: This article presents a strategy to support the definition of a business model for Digital TV that prioritizes the valorization of knowledge of the professionals working in the areas of HCI and business, leading them to share knowledge. The authors advocate a novel approach to the design of the Digital TV business model that considers it essential to understand the contextual and human factors of the target population. The application of the strategy in a scenario of Digital TV implementation in a Brazilian town is described.\",\n \"Title: A Multiagent Simulator for Teaching Police Allocation\\nYear: 2006\\n\\nAbstract: This article describes the,ExpertCop tutorial system, a simulator of crime in an urban region. In ExpertCop, the students (police officers) configure and allocate an available police force according to a selected geographic region and then interact with the simulation. The student interprets the results with the help of an intelligent tutor, the pedagogical agent, observing how crime behaves in the presence of the allocated preventive policing. The interaction between domain agents representing social entities as criminals and police teams drives the simulation. ExpertCop induces students to reflect on resource allocation. The pedagogical agent implements interaction strategies between the student and the geosimblator, designed to make simulated phenomena better understood. In particular, the agent uses a machine-learning algorithm to identify patterns in simulation data and to formulate questions to the student about these patterns.\",\n \"Title: Using Explanations of Agents to Increase Understanding of Simulations for Tutoring Police Allocation\\nYear: 2005\\n\\nAbstract: This article describes the ExpertCop tutorial system, a simulator of crime in an urban region. In ExpertCop, the students (police officers) configure and allocate an available police force according to a selected geographic region and then interact with the simulation. The student interprets the simulation results with the help of an intelligent tutor, the Pedagogical Agent, observing how crime behaves in the presence of the allocated preventive policing. The pedagogical agent implements a strategy of explanation of the agentýs individual behavior to make simulated phenomena better understood.\",\n \"Title: A service-oriented architecture for assisting the authoring of semantic crowd maps\\nYear: 2012\\n\\nAbstract: Although there are increasingly more initiatives for the generation of semantic knowledge based on user participation, there is still a shortage of platforms for regular users to create applications on which semantic data can be exploited and generated automatically. We propose an architecture, called Semantic Maps (SeMaps), for assisting the authoring and hosting of applications in which the maps combine the aggregation of a Geographic Information System and crowd-generated content (called here crowd maps). In these systems, the digital map works as a blackboard for accommodating stories told by people about events they want to share with others typically participating in their social networks. SeMaps offers an environment for the creation and maintenance of sites based on crowd maps with the possibility for the user to characterize semantically that which s/he intends to mark on the map. The designer of a crowd map, by informing a linguistic expression that designates what has to be marked on the maps, is guided in a process that aims to associate a concept from a common-sense base to this linguistic expression. Thus, the crowd maps start to have dominion over common-sense inferential relations that define the meaning of the marker, and are able to make inferences about the network of linked data. This makes it possible to generate maps that have the power to perform inferences and access external sources (such as DBpedia) that constitute information that is useful and appropriate to the context of the map. In this paper we describe the architecture of SeMaps and how it was applied in a crowd map authoring tool.\",\n \"Title: Towards the generation of explanations for semantic web services in OWL-S\\nYear: 2008\\n\\nAbstract: In this article we define a system, called XplainS that automatically generates an infrastructure for providing explanations of semantic web services described in OWL-S. XplainS has a strategy for generating production rules from a flow where several levels of distribution of the services exist.\",\n \"Title: InferenceNet.Br: expression of inferentialist semantic content of the Portuguese language\\nYear: 2010\\n\\nAbstract: Often, the information necessary for a complete understanding of texts is implicit, which requires drawing inferences from the use of concepts in the linguistic praxis. We consider that the usual semantic reasoners of natural language systems face difficulties in capturing this knowledge, due mainly to the lack of linguistic-semantic resources that support reasoning of this nature. This paper presents a new linguistic resource that expresses semantic-inferentialist knowledge for the Portuguese language – InferenceNet.Br – containing a base of concepts and a base of sentence patterns. These bases provide content for a top layer of semantic reasoning in natural language systems, where semantic relations are considered according to their roles in inferences, as premises or conclusions. This linguistic resource was used in a system for extracting information about crime, and the results of this proof of concept are discussed.\",\n \"Title: Towards a common sense base in portuguese for the linked open data cloud\\nYear: 2012\\n\\nAbstract: The Linked Open Data (LOD) cloud is a promising reality since the major content producers are offering their data on an open and linked network, through RDF (Resource Description Framework), with the aim of providing Semantic Web applications with a single global database for retrieval of related content and to perform inferences over the network. However, bases with Portuguese-language content are still incipient. In this paper we present the process of inclusion of the InferenceNet --- the first resource with common sense and inferentialist knowledge in Portuguese language --- on the LOD. Our main goal is to leverage the use and development of Semantic Web applications by content producers in Portuguese language. We develop and evaluated a platform, called SemWidgets, for the creation and execution of widgets able to access and reason over InferenceNet and the open linked data, like DBPedia, Yago, and Article Search API of the New York Times.\",\n \"Title: Interface design through knowledge-based systems: an approach centered on explanations from problem-solving models\\nYear: 2005\\n\\nAbstract: The process of generating user interfaces is complex and demands a great deal of effort from the specialist, because there are several possible combinations and uncertainties regarding any one option. We believe that the modeling of HCI concepts and of the knowledge of all parties involved is of great importance, as well as the reuse of this modeling to automate and optimize such process. Therefore, we are proposing a KBS (Knowledge-Based System) that represents the knowledge of the interface designers. This System possesses the feature of interactivity, so that the user can have an explanation of the results produced, thus improving the understanding and acceptance of the proposed interface. Furthermore, the system allows for its knowledge base to be modified. We applied the proposed method to generate an abstract user interface for a system of email control and for a system of simulation of criminal activity.\",\n \"Title: A User Interface to Support Dialogue and Negotiation in Participatory Simulations\\nYear: 2008\\n\\nAbstract: In this paper, we discuss the process of analysis and design of a user interface to support dialogue and negotiation between players of participatory simulations. The underlying context is an interdisciplinary project, named SimParc [8], about participatory management of protected areas for biodiversity conservation and social inclusion. This project is inspired by the ComMod MAS/RPG approach [6] and by recent proposals for software support for distributed role playing games, such as those by Guyot [14] and by Adamatti [1]. In this paper, we focus on the analysis and design of a user interface to ease and structure dialogue and negotiation between players, using a methodology based on semiotic engineering. Our main objective is to try to find a good balance between the necessary structuring and the spontaneity of dialog and negotiation.\",\n \"Title: Collective intelligence in law enforcement - The WikiCrimes system\\nYear: 2010\\n\\nAbstract: Collaboration is on the rise, primordially leveraged by the Web 2.0 in which the difference between information producer and consumer decreases significantly. In this paper we describe WikiCrimes, an example of a Web 2.0 application that offers a collaborative environment based on the use and direct manipulation of maps, in order to register and research criminal events. WikiCrimes is driven by three goals: (i) to give more transparency and publicity to criminal information, (ii) to provide means for citizen prevention, and (iii) to reduce the phenomenon of under-reporting (crimes that are not notified to authorities). In the paper we pay particular attention to the fact that in this context, finding the equilibrium between people's participation and information credibility is crucial. Anonymous mass collaboration is the easiest way to receive information; however, the credibility of the received information is depreciated, because the source of information is unknown. We particularly concentrate on the ways we have created to keep the ''participation vs. reliability of information'' trade-off at good level. The reputation model is the main component created with this purpose. The experiences and the obstacles we are facing in the implementation of the project are the subjects of discussion and analyses.\",\n \"Title: WebExplain: A UPML Extension to Support the Development of Explanations on the Web for Knowledge-Based Systems\\nYear: 2006\\n\\nAbstract: Knowledge-based systems (KBS) should be able to explain their results to improve the understanding and credibility of their answers by users. However, most KBS explanation components cannot be easily reused by other applications, thus increasing the effort of implementing KBSs with explanation capabilities. In this paper we present WebExplain, an extension to Unified Problem-Solving Method Description Language (UPML), a KBS development framework. WebExplain is integrated with UPML generic components and can be easily reused during the development of other problem-solving methods and KBSs. WebExplain uses the Inference Web for enabling proof and explanation interoperability between distributed applications. We exemplify our approach by describing WebExplain's use in the development of a problem-solving method and a KBS with explanation capabilities.\",\n \"Title: A multi-agent simulator for teaching police allocation\\nYear: 2005\\n\\nAbstract: This article describes the ExpertCop tutorial system, a simulator of the crime in an urban region. In ExpertCop, the students (police officers) configure and allocate an available police force according to a selected geographic region and then interact with the simulation. The student interprets the results with the help of an intelligent tutor, the Pedagogical Agent, observing how the crime behaves in the presence of the allocated preventive policing. The interaction between domain agents representing social entities as criminals and police teams drives the simulation. ExpertCop induces students to reflect on resource allocation. The pedagogical agent implents interaction strategies between the student and the geosimulator, designed to make simulated phenomena better understood. In particular, the agent uses a machine learning algorithm to identify patterns on simulation data and to formulate questions to the student about these patterns. Moreover, it explores the reasoning process of the domain agents by providing explanations that help the student to understand simulation events.\",\n \"Title: Geração de interfaces através de sistemas baseados em conhecimento: uma abordagem centrada em explicações de modelos de resolução de problemas\\nYear: 2005\\n\\nAbstract: O processo de geração de interface de usuário é complexo e exige um grande esforço do especialista porque existem várias combinações possíveis e incertezas sobre a mesma opção. Acreditamos que é importante a modelagem dos conceitos de IHC e dos conhecimentos de todos os envolvidos e um reuso dessa modelagem para automatizar e otimizar tal processo. Portanto propomos um SBC (Sistema Baseado em Conhecimento) que represente o conhecimento dos projetistas de interface. Esse Sistema possui a característica interativa, de maneira que o usuário pode ter uma explicação do resultado produzido melhorando o entendimento e a aceitação da interface proposta. Aplicamos o método proposto para gerar protótipos da interface do usuário de um sistema de simulação da criminalidade.\",\n \"Title: Using Color to Help in the Interactive Concept Formation\\nYear: 2004\\n\\nAbstract: This article describes a technique that aims at qualifying a concept hierarchy with colors, in such a way that it can be feasible to promote the interactivity between the user and an incremental probabilistic concept formation algorithm. The main idea behind this technique is to use colors to map the concept properties being generated, to combine them, and to provide a resulting color that will represent a specific concept. The intention is to assign similar colors to similar concepts, thereby making it possible for the user to interact with the algorithm and to intervene in the concept formation process by identifying which approximate concepts are being separately formed. An operator for interactive merge has been used to allow the user to combine concepts he/she considers similar. Preliminary evaluation on concepts generated after interaction has demonstrated improved accuracy.\",\n \"Title: SIM: um modelo semântico-inferencialista para sistemas de linguagem natural\\nYear: 2008\\n\\nAbstract: One of the growing needs related to systems of Natural Language Processing (NLP) is that such systems must be able to perform enriched textual inferences. We argue that one reason for the current limitation of the inferences generated by these systems is that---for the most part---they are based on the characteristics of the things represented by names, and seek to draw inferences based on such characteristics. In this work, we propose the Semantic Inferentialism Model (SIM), which follows a natural path and represents a new paradigm: it seeks to express the inferential capacity of concepts and how these concepts, combined in sentence structures, contribute to the inferential power of sentences. We present a SIM-based Information Extraction System and a pre-evaluation of the results.\",\n \"Title: Abstracting Web Agent Proofs into Human-Level Justifications\\nYear: 2007\\n\\nAbstract: Information supporting answer explanations are derived from proofs. One of the difficulties for humans to understand web agent proofs is that the proofs are typically described at the machine-level. In this paper, we introduce a novel and generic approach for abstracting machine-level portable proofs into human-level justifications. This abstraction facilitates generating explanations from proofs on the web. Our approach consists of creating a repository of proof templates, called abstraction patterns, describing how machine-level inference rules and axioms in proofs can be replaced by rules that are more meaningful for humans. Intermediate results supporting machine- level proofs may also be dropped during the abstraction process. The Inference Web Abstractor algorithm has been developed with the goal of matching the abstraction patterns in the repository against the original proof and applying a set of strategies to abstract the proof thereby simplifying its presentation. The tools used for creating and applying abstraction patterns are shown along with an intelligence analysis example.1\",\n \"Title: Semantic Textual Similarity Of Portuguese-Language Texts: An Approach Based On The Semantic Inferentialism Model\\nYear: 2014\\n\\nAbstract: The Semantic Textual Similarity (STS) task aims capturing a bidirectional-graded equivalence between the pair of short texts. This work proposes a STS measure for the Portuguese Language based on Semantic Inferentialism Model (SIM) and InferenceNet.BR. We argue that the expression of inferential, causal, motivational and encyclopedic content of InferenceNet enables a more robust and efficient model for the STS task. An extrinsic evaluation in a Portuguese-language processing application - a Case-Based Reasoning system for Requirements Engineering - provided real scenario to assess how the proposed STS measure contributes to the effectiveness of NLP applications.\",\n \"Title: Multiagent models for police resource allocation and dispatch\\nYear: 2014\\n\\nAbstract: In this article we investigate Multi-agent simulation as a way for modeling strategies for resource allocation problems in Public Safety. The goal is to show how simulation can be used to help law enforcement authorities to evaluate, in a controlled environment, different strategies for allocating and dispatching resources, aiming at reducing both response time and the number of unattended calls. We created a Multiagent model to represent police cars that answer to emergency calls. In particular, the environment in which the agents live is a grid in which emergency occurrences appear. Random and Zipfian distributions drive the occurrence of calls. A comparison of the strategies for resource allocation in this environment shows that serving first those calls with low estimated attendance times delivers the best overall performance. However this is practically impossible since prioritization of certain crime types is necessary leading to the increase of the waiting time in the queue. Such degradation may be assimilated in real life scenarios because high priority calls are served first. Also, it is important to implement a policy of aging to avoid the cost of great degradation for low priority crimes.\",\n \"Title: Open Collaborative Systems as Institutions of Agents\\nYear: 2008\\n\\nAbstract: A wiki is a typical Web 2.0 system where people from various backgrounds interoperate and/or cooperate, sharing information and, at the same time, building a knowledge base on different areas of interest. Its characteristics are very similar to the ones owned by the open multi-agent systems modeled as electronic institutional environments. This paper proposes an approach to develop open collaborative systems as institutions of agents and demonstrates that suitability through the adaptation of a typical wiki application, known as WikiCrimes, to an institutional environment.\",\n \"Title: Natural Language Processing based on Semantic inferentialism for extracting crime information from text\\nYear: 2010\\n\\nAbstract: This article describes an architecture for Information Extraction systems on the web, based on Natural Language Processing (NLP) and especially geared toward the exploration of information about crime. The main feature of the architecture is its NLP module, which is based on the Semantic Inferential Model. We demonstrate the feasibility of the architecture through the implementation thereof to provide input for a collaborative web-based system of registering crimes called WikiCrimes.\",\n \"Title: WikiCrimes as an Open Institutional Environment: A Study Case\\nYear: 2013\\n\\nAbstract: WikiCrimes is a collaborative system where users can store information about crime occurrences in a georeferenced database. Anyone can access WikiCrimes to consult or add information to the database. In this paper we describe the further work done to extend the model and implementation of the WikiCrimes collaborative system as an Open Institutional Environment. The prototype implements an environment where commitments are used to define norms, and together with reputation bring social order to the system.\",\n \"Title: Collaboration Networks From A Large Cv Database: Dynamics, Topology And Bonus Impact\\nYear: 2013\\n\\nAbstract: Understanding the dynamics of research production and collaboration may reveal better strategies for scientific careers, academic institutions, and funding agencies. Here we propose the use of a large and multidisciplinary database of scientific curricula in Brazil, namely, the Lattes Platform, to study patterns of scientific production and collaboration. Detailed information about publications and researchers is available in this database. Individual curricula are submitted by the researchers themselves so that coauthorship is unambiguous. Researchers can be evaluated by scientific productivity, geographical location and field of expertise. Our results show that the collaboration network is growing exponentially for the last three decades, with a distribution of number of collaborators per researcher that approaches a power-law as the network gets older. Moreover, both the distributions of number of collaborators and production per researcher obey power-law behaviors, regardless of the geographical location or field, suggesting that the same universal mechanism might be responsible for network growth and productivity. We also show that the collaboration network under investigation displays a typical assortative mixing behavior, where teeming researchers (i.e., with high degree) tend to collaborate with others alike.\",\n \"Title: Does content categorization lead to knowledge building? An experiment in a social bookmarking service.\\nYear: 2015\\n\\nAbstract: •We aimed to verify whether content categorization leads to knowledge building (KB).•We ran an experiment in the social bookmarking service Uxmarks.com.•We measured the perception of KB×user participation (lurking or posting behavior).•There was no correlation between KB perception and user participation.•In other words, and lurkers and posters could equally benefit from the service.\",\n \"Title: Ranking Keyphrases from Semantic and Syntactic Features of Textual Terms.\\nYear: 2015\\n\\nAbstract: Two important lines of research in key phrase extraction from text are methods that use machine learning to discover rules based on statistics of terms, and knowledge-intensive methods that seek to understand the semantics of the text with the help of conceptual bases like Wikipedia. Our argument is that the task of key phrase extraction for different domains requires defining ranking functions that take into account the advantages and shortcomings of each approach to the specific problem. To determine the best ranking function, arrangements of weights are generated, which in turn weight each of the attributes used in both the statistic and semantic functions. The arrangement of weights that presents better average performance sets the weights of the attributes of the ranking function. We show comparative tests conducted with current approaches that use only syntactic or semantic features with a hybrid ranking approach. The later outperformed the state of the art.\",\n \"Title: Using Markov Models to Learn the Sentiment of Soccer Fans from Bets and the Result of Matches\\nYear: 2015\\n\\nAbstract: In this paper we investigate variations of Hidden Markov Models (HMM) as a viable tool for predicting the sentiment of soccer fans based on information regarding the result of matches. The models were constructed from data collected from a social network where fans of a soccer team periodically express feelings towards their team. Our claim is that the change in a fan's sentiment is analogous to a Markovian process of change of state through time. A comparative evaluation performed between variations of the proposed models showed that a second order HMM, considering the match results and fan's gambling information, is the most accurate model.\",\n \"Title: Using Non-textual Terms for Boosting Document Keyphrase Assignment.\\nYear: 2015\\n\\nAbstract: Machine-learning state-of-the-art keyphrase extraction systems do not take into consideration the fact that part of these keyphrases may not be found in the text. Therefore these systems typically use a training set restricted to textual terms, reducing the learning capabilities of any inductive algorithm. Our research investigates ways to improve the accuracy of these systems by allowing classification algorithms to learn from non-textual terms as well. The basic assumption we have followed is that non-textual terms can be included into the training set by inference from an eventual semantic relationship with textual terms. In order to discover the latent relationship between non-textual and textual terms, we propose deductive strategies to be applied in common sense bases such as Wikipedia. We show that algorithms that follow our approach outperform others that do not use the same methods introduced here.\",\n \"Title: Learning the Sentiment of Soccer Fans from Data on Bets and Social Nets.\\nYear: 2017\\n\\nAbstract: In this paper we propose a Hidden Markov Model in order to predict the sentiment of soccer fans based on information regarding the result of matches. The model was constructed by data collected from a social network where fans of a soccer team periodically expressed feelings towards their team. We show that the choice of a HMM is justified due to the fact that the change in a fan's sentiment is analogous to a Markovian process of change of state through time. A comparative evaluation will be performed between variations of the proposed models and also between the most accurate of them and classification algorithms. Second order HMM, considering the match results and fan's gambling information, is the most accurate model.\",\n \"Title: Investigating Usability and User Experience from the User Postings in Social Systems\\nYear: 2015\\n\\nAbstract: Social Systems (SS) are dynamic systems, with features such as: frequent exchange of messages and expression of feelings spontaneously. The postings of users on SS reveal their opinions on various issues, including on what they think of the system. In this study, we classified postings of users from two SS of different contexts: a popular SS (entertainment) - Twitter and an academic SS of a university - forums, to investigate information related to usability and user experience (UX) of such systems. Results showed they are useful to (i) obtain user's sentiments about the system; (ii) to identify possible problems during their experience and (iii) to perceive from the set of classified postings the context of use of the system.\",\n \"Title: Multi-objective evolutionary algorithms and multiagent models for optimizing police dispatch\\nYear: 2015\\n\\nAbstract: In this article we investigate Multi-agent simulation and Multi-objective Evolutionary Algorithms for optimizing resource allocation in Public Safety. We describe a tool that helps Law Enforcement authorities to evaluate, in a controlled environment, different strategies for allocating and dispatching resources, aiming at reducing conflicting goals such as response time, the number of unattended calls and cost of displacement of police cars. This tool is a multi-agent model to represent police cars that lives in a grid in which emergency occurrences appear. A comparison of the strategies for resource dispatch in this environment shows that serving first those calls with low estimated attendance times delivers the best overall performance in terms of waiting time. However this is practically impossible since prioritization of certain crime types is necessary leading to the increase of the waiting time in the queue. Instead of manually trying to identify the best allocation strategy to apply, we have coupled a multi-objective evolutionary algorithm to the simulation model in order to uncover automatically a function to rank the calls in the best order for attendance satisfying multiple and sometimes conflicting goals.\",\n \"Title: Mining the Networks of Supply and Demand of Public Transport for Overload and Waste of Resources.\\nYear: 2016\\n\\nAbstract: We propose here a methodology to help to understand the shortcomings of public transportation in a city via the mining of complex networks representing the supply and demand of public transport. We show how to build these networks based upon data on smart card use in buses via the application of algorithms that estimate an OD and reconstruct the complete itinerary of the passengers. The overlapping of the two networks sheds light in potential overload and waste in the offer of resources that can be mitigated with strategies for balancing supply and demand.\",\n \"Title: Micro-interventions in urban transportation from pattern discovery on the flow of passengers and on the bus network\\nYear: 2016\\n\\nAbstract: In this paper, we describe a case study in a large metropolis in which, based on data collected by digital sensors, we sought to understand mobility patterns of people using buses and how this can generate knowledge to suggest interventions to be applied incrementally to the transportation network in use. We first estimated an Origin-Destination matrix of bus users based on datasets about ticket validation and GPS positioning of buses. Then we represented the supply of buses with their routes through bus stops as a complex network, which allowed us to understand the bottlenecks of the current scenario and, in particular, apply community discovery techniques in order to identify clusters in the service supply infrastructure. Finally, by superimposing the flow of people represented in the Origin-Destination matrix in the supply network, we exemplify how micro-interventions can be prospected, by means of an example of introducing express routes.\",\n \"Title: A Framework To Evaluate User Empowerment In Decision-Making Experiences With Participatory Gis\\nYear: 2016\\n\\nAbstract: This paper describes a framework to guide designers on how to investigate UX empowerment by using PGIS to support the decision making process. Investigative questions are proposed and aligned with UX elements in order to facilitate the access to information and its discussion within a community, as well as empower users (decision makers) to act not just as consumers of information and systems, but also as creators of new knowledge. The goal was to guarantee an appropriate gathering and observation of the questions in order to evaluate how particular users tasks can be accomplished in a PGIS while complying with requisites that promote empowerment.\",\n \"Title: Towards Keyphrase Assignment For Texts In Portuguese Language\\nYear: 2016\\n\\nAbstract: Keyphrase assignment has often been confounded with keyphrase extraction, since the basic hypothesis is that a keyphrase of a text must be extracted from this text. Typically, keyphrase extraction approaches use a training set restricted to textual terms, reducing the learning capabilities of any inductive algorithm. Our research investigates ways to improve the accuracy of the keyphrase assignment systems for texts in Portuguese language by allowing classification algorithms to learn from non-textual terms as well. The basic assumption we have followed is that non-textual terms can be included into the training set by inference from an eventual semantic relationship with textual terms. In order to discover the latent relationship between non-textual and textual terms, we use deductive strategies to be applied in Portuguese common sense bases such as Wikipedia and InferenceNet. We show that algorithms that follow our approach outperform others that do not use the same methods introduced here.\",\n \"Title: Human Mobility In Large Cities As A Proxy For Crime\\nYear: 2016\\n\\nAbstract: We investigate at the subscale of the neighborhoods of a highly populated city the incidence of property crimes in terms of both the resident and the floating population. Our results show that a relevant allometric relation could only be observed between property crimes and floating population. More precisely, the evidence of a superlinear behavior indicates that a disproportional number of property crimes occurs in regions where an increased flow of people takes place in the city. For comparison, we also found that the number of crimes of peace disturbance only correlates well, and in a superlinear fashion too, with the resident population. Our study raises the interesting possibility that the super-linearity observed in previous studies [Bettencourt et al., Proc. Natl. Acad. Sci. USA 104, 7301 (2007) and Melo et al., Sci. Rep. 4, 6239 (2014)] for homicides versus population at the city scale could have its origin in the fact that the floating population, and not the resident one, should be taken as the relevant variable determining the intrinsic microdynamical behavior of the system.\",\n \"Title: Análise comparativa de pesquisas de origens e destinos: uma abordagem baseada em Redes Complexas.\\nYear: 2017\\n\\nAbstract: In this paper, a comparative study was conducted between complex networks representing origin and destination survey data. Similarities were found between the characteristics of the networks of Brazilian cities with networks of foreign cities. Power laws were found in the distributions of edge weights and this scale - free behavior can occur due to the economic characteristics of the cities.\",\n \"Title: Contextual Data Collection for Smart Cities.\\nYear: 2015\\n\\nAbstract: As part of Smart Cities initiatives, national, regional and local governments all over the globe are under the mandate of being more open regarding how they share their data. Under this mandate, many of these governments are publishing data under the umbrella of open government data, which includes measurement data from city-wide sensor networks. Furthermore, many of these data are published in so-called data portals as documents that may be spreadsheets, comma-separated value (CSV) data files, or plain documents in PDF or Word documents. The sharing of these documents may be a convenient way for the data provider to convey and publish data but it is not the ideal way for data consumers to reuse the data. For example, the problems of reusing the data may range from difficulty opening a document that is provided in any format that is not plain text, to the actual problem of understanding the meaning of each piece of knowledge inside of the document. Our proposal tackles those challenges by identifying metadata that has been regarded to be relevant for measurement data and providing a schema for this metadata. We further leverage the Human-Aware Sensor Network Ontology (HASNetO) to build an architecture for data collected in urban environments. We discuss the use of HASNetO and the supporting infrastructure to manage both data and metadata in support of the City of Fortaleza, a large metropolitan area in Brazil.\",\n \"Title: Geracao Automatica de Paineis de Controle para Analise de Mobilidade Urbana Utilizando Redes Complexas.\\nYear: 2017\\n\\nAbstract: In this paper we describe an automatic generator to support the data scientist to construct, in a user-friendly way, dashboards from data represented as networks. The generator called SBINet (Semantic for Business Intelligence from Networks) has a semantic layer that, through ontologies, describes the data that represents a network as well as the possible metrics to be calculated in the network. Thus, with SBINet, the stages of the dashboard constructing process that uses complex network metrics are facilitated and can be done by users who do not necessarily know about complex networks.\",\n \"Title: Increasing the Likelihood of Finding Public Transport Riders that Face Problems Through a Data-Driven approach.\\nYear: 2017\\n\\nAbstract: The maintenance of big cities public transport service quality requires constant monitoring, which may become an expensive and time-consuming practice. The perception of quality, from the users point of view is an important aspect of quality monitoring. In this sense, we proposed a methodology for data analysis and visualization, supported by software, which allows for the structuring of estimates and assumptions of where and who seems to be having unsatisfactory experiences while making use of the public transportation in populous metropolitan areas. Moreover, it provides support in setting up a plan for on-site quality surveys. The proposed methodology increases the likelihood that, with the on-site visits, the interviewer finds users who suffer inconveniences, which influence their behavior. Simulation comparison and a small-scale pilot survey stand for the validity of the proposed method.\",\n \"Title: From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Dashboards.\\nYear: 2017\\n\\nAbstract: In the context of Smart Cities, indicator definitions have been used to calculate values that enable the comparison among different cities. The calculation of an indicator values has challenges as the calculation may need to combine some aspects of quality while addressing different levels of abstraction. Knowledge graphs (KGs) have been used successfully to support flexible representation, which can support improved understanding and data analysis in similar settings. This paper presents an operational description for a city KG, an indicator ontology that support indicator discovery and data visualization and an application capable of performing metadata analysis to automatically build and display dashboards according to discovered indicators. We describe our implementation in an urban mobility setting.\",\n \"Title: Towards Understanding the Impact of Human Mobility on Police Allocation.\\nYear: 2017\\n\\nAbstract: Motivated by recent findings that human mobility is proxy for crime behavior in big cities and that there is a superlinear relationship between the peopleu0027s movement and crime, this article aims to evaluate the impact of how these findings influence police allocation. More precisely, we shed light on the differences between an allocation strategy, in which the resources are distributed by clusters of floating population, and conventional allocation strategies, in which the police resources are distributed by an Administrative Area (typically based on resident population). We observed a substantial difference in the distributions of police resources allocated following these strategies, what evidences the imprecision of conventional police allocation methods.\",\n \"Title: An environment to support the design, implementation and evaluation of gamification in social systems.\\nYear: 2015\\n\\nAbstract: Gamification has become a popular strategy to increase the engagement and participation of users in social systems. Despite the popularity that now reigns, we identified that gamification can not be treated simply as award points and provide badgets for users. Care must be taken, since the choice of the game mechanics must consider the profile of the user as a player. If such considerations are not made properly, the desired goals by the developer cannot be obtained. In other words, one needs to understand the relationship between the developeru0027s goals, the game mechanics and the profile of users (players). The lack of computational tools to support the developer to consider how these three aspects relate to and impact, led us to propose GameWork. It is a Service-oriented Architecture to support gamification in general, especially to help monitoring the system and the players behaviors with respect to the incentives provided. This article describes GameWork and an example of its implementation in a commercial social network.\",\n \"Title: Traveling heterogeneity in public transportation.\\nYear: 2018\\n\\nAbstract: It is well reported that long commutes have a large detrimental effect on people’s health and on the economy of cities. Interestingly, despite the strong impact on our daily lives, a simple way to measure the quality of urban transportation is still unknown. We performed data analysis on the transportation network of two large cities (Fortaleza and Dublin). By dividing each bus trajectory into equal pieces of space, we determine the distribution of time intervals for each trip, and we propose that the heterogeneity of the time distribution can be used to characterize the quality of that trip. Inspired by the use of the Gini coefficient to quantify the inequality level of income distribution, we used the Gini in order to characterize the heterogeneity level of the time distribution. We demonstrated that Gini coefficients are strongly correlated with peak usage of the mobility system, as well as the schedule delays in the system. Finally, our method can be used to find highly heterogeneous trips which have a large negative effect on the urban mobility and can help find new directions for new public planning strategies.\",\n \"Title: Graph mining for the detection of overcrowding and waste of resources in public transport.\\nYear: 2018\\n\\nAbstract: The imbalance between the quantity of supply and demand in public transport systems causes a series of disruptions in large metropolises. While extremely crowded vehicles are uncomfortable for passengers, virtually empty vehicles generate economic losses for system managers, and this usually comes back to passengers in the form of fare increases. In this article a new data processing methodology will be presented for the evaluation of collective transportation systems. It proposes the construction and mining of graphs that represent complex networks of supply and demand of the system to find such imbalances. In a case study with the bus system of a large Brazilian metropolis, it was shown that the methodology in question is capable of identifying global imbalances in the system based on an evaluation of the weight distributions of the edges of the supply and demand networks. It has also been shown that even in a scenario where information about the demand is incomplete, using community detection techniques it is possible to identify the stretches of the network that are potentially causing these imbalances on a global scale.\",\n \"Title: LOADng-IoT: An Enhanced Routing Protocol for Internet of Things Applications over Low Power Networks.\\nYear: 2019\\n\\nAbstract: The Internet of Things (IoT) is an emerging paradigm that proposes the connection of objects to exchange information in order to reach a common objective. In IoT networks, it is expected that the nodes will exchange data between each other and with external Internet services. However, due to deployment costs, not all the network devices are able to communicate with the Internet directly. Thus, other network nodes should use Internet-connected nodes as a gateway to forward messages to Internet services. Considering the fact that main routing protocols for low-power networks are not able to reach suitable performance in the displayed IoT environment, this work presents an enhancement to the Lightweight On-demand Ad hoc Distance-vector Routing Protocol-Next Generation (LOADng) for IoT scenarios. The proposal, named LOADng-IoT, is based on three improvements that will allow the nodes to find Internet-connected nodes autonomously and dynamically, decreasing the control message overhead required for the route construction, and reducing the loss of data messages directed to the Internet. Based on the performed assessment study, which considered several number of nodes in dense, sparse, and mobility scenarios, the proposed approach is able to present significant results in metrics related to quality-of-service, reliability, and energy efficiency.\",\n \"Title: An IoT-Based Smart Solution for Preventing Domestic CO and LPG Gas Accidents\\nYear: 2018\\n\\nAbstract: Gas monitoring systems have turned into a key feature in many applications in different fields, such as medicine, industry and environmental monitoring. Accidents involving gas leakage generate massive costs and deaths, where carbon monoxide (CO) and liquefied petroleum gas (LPG) are the main causes of domestic accidents. They could be avoidable with smart gas sensors integrated with other smart devices through the Internet of Things (IoT) paradigm. This paper focuses on the design and construction of a smart multi-gas plug-and-play sensor to avoid accidents in smart environments involving CO and LPG. This device is ready to connect on an IoT solution. It uses the standard IEEE 802.15.4 to communicate with a border router responsible to make the data available to other devices. The data will be sent to a middleware granting that other smart devices on the same context can access the information and make the correct decisions. The created prototype was evaluated, demonstrated, and validated, and it is ready for use.\",\n \"Title: Railways Networks - Challenges for IoT Underground Wireless Communications\\nYear: 2018\\n\\nAbstract: The use of wireless network technologies for the deployment of data communications systems in railway transportation infrastructure and services is essential. In fact, wireless solution for railways, especially for underground, is a big challenge to operators concerning this environment particularities. Many technologies were pointed as promising candidates for the future railway mobile telecommunications but, in reality, no current technology features overall appropriated. This paper discusses a Practical Guidance based on challenges for IoT wireless networks, based on a conventional Wi-Fi network case study. Moreover, it also highlights topics where new intelligent networking technologies, emerging from Internet of Things (IoT) paradigm, can contribute significantly to the improvement of railway networks.\",\n \"Title: A Fan-Based Smart Selective Trap for Flying Insects\\nYear: 2018\\n\\nAbstract: For several years, insect-borne diseases have been a major global concern. Countries with warmer climates are these disease vectors primary victims. However, due to rising global temperatures, countries with no history of insect-borne diseases have presented alarming statistics on the mosquitoes population lack control. Due to the importance of this subject, this paper proposes a smart and selective fan-based trap to capture the Aedes aegypti mosquito, main vector of diseases such as Yellow Fever, Dengue Fever, Zika, and Chikungunya. The central purpose of this study is the development of an electronic trap integrable to variable approaches of flying insects sensors. The trap is made up of a microcontroller that operates two DC fans arranged at the ends of an aerodynamic structure. The construction of a prototype validates the project through results evaluation and discussion.\",\n \"Title: Management Platforms and Protocols for Internet of Things: A Survey.\\nYear: 2019\\n\\nAbstract: Internet of Things (IoT) management systems require scalability, standardized communication, and context-awareness to achieve the management of connected devices with security and accuracy in real environments. Interoperability and heterogeneity between hardware and application layers are also critical issues. To attend to the network requirements and different functionalities, a dynamic and context-sensitive configuration management system is required. Thus, reference architectures (RAs) represent a basic architecture and the definition of key characteristics for the construction of IoT environments. Therefore, choosing the best technologies of the IoT management platforms and protocols through comparison and evaluation is a hard task, since they are difficult to compare due to their lack of standardization. However, in the literature, there are no management platforms focused on addressing all IoT issues. For this purpose, this paper surveys the available policies and solutions for IoT Network Management and devices. Among the available technologies, an evaluation was performed using features such as heterogeneity, scalability, supported technologies, and security. Based on this evaluation, the most promising technologies were chosen for a detailed performance evaluation study (through simulation and deployment in real environments). In terms of contributions, these protocols and platforms were studied in detail, the main features of each approach are highlighted and discussed, open research issues are identified as well as the lessons learned on the topic.\",\n \"Title: Averaged one-dependence estimators on edge devices for smart pregnancy data analysis\\nYear: 2019\\n\\nAbstract: •Review of edge network architectures, applications, and trends in IoT-based healthcare solutions.•Study of algorithms based on machine learning (ML) techniques and their use in conjunction with edge devices for high-risk pregnancy monitoring.•Classification mechanism to predict childbirth outcome in pregnancy: Averaged one-dependence estimators.•Performance evaluation using a real dataset from the Maternity School Assis Chateaubriand through a 10-fold cross-validation method.\",\n \"Title: Pareto set as a model for dispatching resources in emergency Centres\\nYear: 2019\\n\\nAbstract: The article investigates policies for helping emergency-centre authorities for dispatching resources aimed at reducing goals such as response time, the number of unattended calls, the attending of priority calls, and the cost of displacement of vehicles. Pareto Set is shown to be the appropriated way to support the representation of policies of dispatch since it naturally fits the challenges of multi-objective optimization. Understanding which are the best policies of dispatching as well as workload balancing are very important in the context FOG computing, which aims to distribute in part workload and services on fog devices (such as hardened routers, switches, IP video cameras, etc.). Fog-computing systems produce an ever-increasing service request impacting, for instance, to the power consumption on cloud servers. On the other hand, it is equally crucial to guarantee the quality of service (e.g., latency requirements) of end users. By means of the concept of Pareto dominance a set with objectives may be ordered in a way that guides the dispatch of resources. Instead of manually trying to identify the best dispatching strategy, a multi-objective evolutionary algorithm coupled with an Emergency Call Simulator uncovers automatically the best approximation of the optimal Pareto Set that would be the responsible for indicating the importance of each objective and consequently the order of attendance of the calls. The scenario of validation is a big metropolis in Brazil using one-year of real data from 911 calls. Comparisons with traditional policies proposed in the literature are done as well as other innovative policies inspired from different domains as computer science and operational research. The results show that strategy of ranking the calls from a Pareto Set discovered by the evolutionary method is a good option because it has the second best (lowest) waiting time, serves almost 100% of priority calls, is the second most economical, and is the second in attendance of calls. That is to say, it is a strategy in which the four dimensions are considered without major impairment to any of them.\",\n \"Title: IoT-Based Solid Waste Management Solutions: A Survey.\\nYear: 2019\\n\\nAbstract: With the increase of population density and the rural exodus to cities, urbanization is assuming extreme proportions and presents a tremendous urban problem related to waste generation. The increase of waste generation has been considered a significant challenge to large urban centers worldwide and represents a critical issue for countries with accelerated population growth in cities. The Internet of Things (IoT) and cloud computing offer an automation possibility through cyberphysical systems that will change the way solid waste management is performed. Considering IoT requirements, a review analysis of waste management models available in the literature is performed in detail in this paper. Then, a deep review is undertaken of the related literature based on IoT infrastructure for efficient handling of waste generated in urban scenarios, focusing on the interaction among concessionaires and waste generators (citizens) from the perspective of a shorter collection time with reduced costs, as well as citizenship promotion. An IoT-based reference model is described, and a comparison analysis of the available solutions is presented, with the goal to highlight the most relevant approaches and identify open research issues on the topic.\",\n \"Title: Empirical Analysis of Bias in Voice-based Personal Assistants\\nYear: 2019\\n\\nAbstract: Voice-based assistants are becoming increasingly widespread all over the world. However, the performance of these assistants in the interaction with users of languages and accents of developing countries is not clear yet. Eventual bias against specific language or accent of different groups of people in developing countries is maybe a factor to increase the digital gap in these countries. Our research aims at analysing the presence of bias in the interaction via audio. We carried out experiments to verify the quality of the recognition of phrases spoken by different groups of people. We evaluated the behaviour of Google Assistant and Siri for groups of people formed according to gender and regions that have different accents. Preliminary results indicate that accent and mispronunciation due to regional differences are not being properly considered by the assistants we have analyzed.\",\n \"Title: Efficient artificial fish swarm based clustering approach on mobility aware energy‐efficient for MANET\\nYear: 2019\\n\\nAbstract: AbstractAbstractMobile ad hoc network (MANET) is a gathering of self‐ruling mobile nodes in which ad hoc network framed without settled foundation. Dynamic topology property of MANET may debase the execution of the network, mobility attentiveness, and energy effectiveness because of an ideal cluster head (CH). The low energy adaptive clustering hierarchy protocol is used in the MANETs for enhancement of network life time by proficiently utilizing of the accessible constrained energy. Artificial fish swarm optimization is used to select the CH in MANET communication process. The election of CH takes care of mobility and remaining energy as well as the degree of connectivity for selecting nodes to serve as cluster for longer duration of time. From the implementation results, it is observed that the proposed method provides the maximum network life time as 80 to 140 hours and minimum energy consumption as 9 to 130 J, compared with existing protocol and optimization technique. View Figure Model for proposed work. AFS, artificial fish swarm; CH, cluster head; LEACH, low energy adaptive clustering hierarchy.The LEACH protocol is used in the MANETs for enhancement of network lifetime by proficiently utilizing of the accessible constrained energy. Artificial Fish Swarm (AFS) optimization is used to select the CH in MANET communication process. The election of CH takes care of mobility and remaining energy as well as the degree of connectivity for selecting nodes to serve as a cluster for the longer duration of time. The energy consumption of the suggested routing protocol is very low comparing to the existing energy routing protocols.\",\n \"Title: Assessment of urban cultural-heritage protection zones using a co-visibility-analysis tool\\nYear: 2019\\n\\nAbstract: •Anthropic changes in urban settings threaten the visibility of heritage buildings;•Protection zones are ineffective to prevent heritage negative visual intrusions;•Vantage points and co-visibility are key aspects for assessing visual intrusions;•3D analysis tool allows identifying both co-visibility and positive vantage points;•Dependable tool to identify visual intrusions benefits heritage policy making;\",\n \"Title: Learning the Sentiment of Soccer Fans from Data on Bets and Social Nets\\nYear: 2015\\n\\nAbstract: In this paper we propose a Hidden Markov Model in order to predict the sentiment of soccer fans based on information regarding the result of matches. The model was constructed by data collected from a social network where fans of a soccer team periodically expressed feelings towards their team. We show that the choice of a HMM is justified due to the fact that the change in a fan's sentiment is analogous to a Markovian process of change of state through time. A comparative evaluation will be performed between variations of the proposed models and also between the most accurate of them and classification algorithms. Second order HMM, considering the match results and fan's gambling information, is the most accurate model.\",\n \"Title: A Data-Driven Approach To Help Understanding The Preferences Of Public Transport Users\\nYear: 2017\\n\\nAbstract: The maintenance of the quality of the public transport service in big cities requires constant monitoring, which may become an expensive and time-consuming practice. The perception of quality, from the users point of view is an important aspect of quality monitoring. In this sense, we proposed a methodology based on big data analysis and visualization, which allows for the structuring of estimates and assumptions of where and who seems to be having unsatisfactory experiences while making use of the public transportation in metropolitan areas. Moreover, it provides support in setting up a plan for on-site quality surveys. The proposed methodology increases the likelihood that, with the on-site visits, the interviewer finds users who suffer inconveniences, which influence their behavior. Simulation comparison and a small-scale pilot survey helped validate the proposed method.\",\n \"Title: Using Topic Modeling in Classification of Brazilian Lawsuits\\nYear: 2022\\n\\nAbstract: Legal text processing is a challenging task for modeling approaches due to the peculiarities inherent to its features, such as long texts and their technical vocabulary. Topic modeling consists of discovering a semantic structure in the text. This paper investigates the application of topic modeling and the use of information about the legislation cited in identifying the subject of legal documents and evaluating its applicability in the classification of Brazilian lawsuits. The models were trained with a Golden Collection of 16 thousand initial petitions and indictments from the Court of Justice of the State of Ceara, in Brazil, whose lawsuits were classified in the five more representative National Council of Justice (CNJ) of Brazil classes - Common Civil Procedure, Execution of Extrajudicial Title, Criminal Action - Ordinary Procedure, Special Civil Court Procedure, and Tax Enforcement. The results obtained outperform the baseline, achieving 0.89 of F1 score (macro). Our interpretation is that the representation of the document through contextual embeddings generated by BERT, as well as the architecture of the model with bidirectional contexts, makes it possible to capture the specific context of the domain of legal documents. Thus, the use of the legislation mentioned in the representation of documents can improve the accuracy of the classification task.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":83,"string":"83"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13662,"string":"13,662"}}},{"rowIdx":13663,"cells":{"question_id":{"kind":"string","value":"Peer Timo Bremer"},"question":{"kind":"string","value":"Generate an abstract for the title \"Suppressing simulation bias in multi-modal data using transfer learning\" using the following items: sparse data, simulated data, multi-modal calibration, transfer learning, deep learning"},"answer":{"kind":"string","value":"Many problems in science and engineering require making predictions based on few observations. To build a robust predictive model, these sparse data may need to be augmented with simulated data, especially when the design space is multi-dimensional. Simulations, however, often suffer from an inherent bias. Estimation of this bias may be poorly constrained not only because of data sparsity, but also because traditional predictive models fit only one type of observed outputs, such as scalars or images, instead of all available output data modalities, which might have been acquired and simulated at great cost. To break this limitation and open up the path for multi-modal calibration, we propose to combine a novel, transfer learning technique for suppressing the bias with recent developments in deep learning, which allow building predictive models with multi-modal outputs. First, we train an initial neural network model on simulated data to learn important correlations between different output modalities and between simulation inputs and outputs. Then, the model is partially retrained, or transfer learned, to fit the experiments; a method that has never been implemented in this type of architecture. Using fewer than 10 inertial confinement fusion experiments for training, transfer learning systematically improves the simulation predictions while a simple output calibration, which we design as a baseline, makes the predictions worse. We also offer extensive cross-validation with real and carefully designed synthetic data. The method described in this paper can be applied to a wide range of problems that require transferring knowledge from simulations to the domain of experiments."},"correct_answer":{"kind":"string","value":"Many problems in science and engineering require making predictions based on few observations. To build a robust predictive model, these sparse data may need to be augmented with simulated data, especially when the design space is multi-dimensional. Simulations, however, often suffer from an inherent bias. Estimation of this bias may be poorly constrained not only because of data sparsity, but also because traditional predictive models fit only one type of observed outputs, such as scalars or images, instead of all available output data modalities, which might have been acquired and simulated at great cost. To break this limitation and open up the path for multi-modal calibration, we propose to combine a novel, transfer learning technique for suppressing the bias with recent developments in deep learning, which allow building predictive models with multi-modal outputs. First, we train an initial neural network model on simulated data to learn important correlations between different output modalities and between simulation inputs and outputs. Then, the model is partially retrained, or transfer learned, to fit the experiments; a method that has never been implemented in this type of architecture. Using fewer than 10 inertial confinement fusion experiments for training, transfer learning systematically improves the simulation predictions while a simple output calibration, which we design as a baseline, makes the predictions worse. We also offer extensive cross-validation with real and carefully designed synthetic data. The method described in this paper can be applied to a wide range of problems that require transferring knowledge from simulations to the domain of experiments."},"documents":{"kind":"list like","value":["Title: Segmenting molecular surfaces\nYear: 2006\n\nAbstract: This paper presents a new method for segmentation of molecular surfaces. Topological analysis of a scalar function defined on the surface and its associated gradient field reveals the relationship between the features of interest and critical points of the scalar function. The segmentation is obtained by associating segments with local minima/maxima. Controlled simplification of the function merges segments resulting in a hierarchical segmentation of the molecular surface. This segmentation is used to identify rigid components of protein molecules and to study the role of cavities and protrusions in protein-protein interactions.","Title: Spectral surface quadrangulation\nYear: 2006\n\nAbstract: Resampling raw surface meshes is one of the most fundamental operations used by nearly all digital geometry processing systems. The vast majority of this work has focused on triangular remeshing, yet quadrilateral meshes are preferred for many surface PDE problems, especially fluid dynamics, and are best suited for defining Catmull-Clark subdivision surfaces. We describe a fundamentally new approach to the quadrangulation of manifold polygon meshes using Laplacian eigenfunctions, the natural harmonics of the surface. These surface functions distribute their extrema evenly across a mesh, which connect via gradient flow into a quadrangular base mesh. An iterative relaxation algorithm simultaneously refines this initial complex to produce a globally smooth parameterization of the surface. From this, we can construct a well-shaped quadrilateral mesh with very few extraordinary vertices. The quality of this mesh relies on the initial choice of eigenfunction, for which we describe algorithms and hueristics to efficiently and effectively select the harmonic most appropriate for the intended application.","Title: Interactive editing of massive imagery made simple: Turning Atlanta into Atlantis\nYear: 2011\n\nAbstract: This article presents a simple framework for progressive processing of high-resolution images with minimal resources. We demonstrate this framework's effectiveness by implementing an adaptive, multi-resolution solver for gradient-based image processing that, for the first time, is capable of handling gigapixel imagery in real time. With our system, artists can use commodity hardware to interactively edit massive imagery and apply complex operators, such as seamless cloning, panorama stitching, and tone mapping. We introduce a progressive Poisson solver that processes images in a purely coarse-to-fine manner, providing near instantaneous global approximations for interactive display (see Figure 1). We also allow for data-driven adaptive refinements to locally emulate the effects of a global solution. These techniques, combined with a fast, cache-friendly data access mechanism, allow the user to interactively explore and edit massive imagery, with the illusion of having a full solution at hand. In particular, we demonstrate the interactive modification of gigapixel panoramas that previously required extensive offline processing. Even with massive satellite images surpassing a hundred gigapixels in size, we enable repeated interactive editing in a dynamically changing environment. Images at these scales are significantly beyond the purview of previous methods yet are processed interactively using our techniques. Finally our system provides a robust and scalable out-of-core solver that consistently offers high-quality solutions while maintaining strict control over system resources.","Title: Novel views of performance data to analyze large-scale adaptive applications\nYear: 2012\n\nAbstract: Performance analysis of parallel scientific codes is becoming increasingly difficult due to the rapidly growing complexity of applications and architectures. Existing tools fall short in providing intuitive views that facilitate the process of performance debugging and tuning. In this paper, we extend recent ideas of projecting and visualizing performance data for faster, more intuitive analysis of applications. We collect detailed per-level and per-phase measurements for a dynamically load-balanced, structured AMR library and project per-core data collected in the hardware domain on to the application's communication topology. We show how our projections and visualizations lead to a rapid diagnosis of and mitigation strategy for a previously elusive scaling bottleneck in the library that is hard to detect using conventional tools. Our new insights have resulted in a 22% performance improvement for a 65,536-core run of the AMR library on an IBM Blue Gene/P system.","Title: Performance Analysis Techniques for the Exascale Co-Design Process.\nYear: 2013\n\nAbstract: Efficient and effective performance analysis techniques are critical for the development of future generation systems. They are the drivers behind the required co-design process that helps establish the principles needed for their design. In this paper, we will highlight two such approaches: PAVE, a project that investigates mapping of performance data to more intuitive domains and uses advanced visualization techniques to expose problems, and GREMLIN, a system evaluation environment capable of emulating expected properties of exascale architectures on petascale machines. Combined with other approaches in system modeling and simulation, these projects enable us to provide a meaningful introspection into a target application's characteristics as well as its expected behavior and, more importantly, likely bottlenecks on future generation machines.","Title: Abstract: Exploring Performance Data with Boxfish\nYear: 2012\n\nAbstract: The growth in size and complexity of scaling applications and the systems on which they run pose challenges in analyzing and improving their overall performance. With metrics coming from thousands or millions of processes, visualization techniques are necessary to make sense of the increasing amount of data. To aid the process of exploration and understanding, we announce the initial release of Boxfish, an extensible tool for manipulating and visualizing data pertaining to application behavior. Combining and visually presenting data and knowledge from multiple domains, such as the application's communication patterns and the hardware's network configuration and routing policies, can yield the insight necessary to discover the underlying causes of observed behavior. Boxfish allows users to query, filter and project data across these domains to create interactive, linked visualizations.","Title: Lessons learned from moving earth system grid data sets over a 20 Gbps wide-area network\nYear: 2010\n\nAbstract: In preparation for the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report, the climate community will run the Coupled Model Intercomparison Project phase 5 (CMIP-5) experiments, which are designed to answer crucial questions about future regional climate change and the results of carbon feedback for different mitigation scenarios. The CMIP-5 experiments will generate petabytes of data that must be replicated seamlessly, reliably, and quickly to hundreds of research teams around the globe. As an end-to-end test of the technologies that will be used to perform this task, a multi-disciplinary team of researchers moved a small portion (10 TB) of the multimodel Coupled Model Intercomparison Project, Phase 3 data set used in the IPCC Fourth Assessment Report from three sources---the Argonne Leadership Computing Facility (ALCF), Lawrence Livermore National Laboratory (LLNL) and National Energy Research Scientific Computing Center (NERSC)---to the 2009 Supercomputing conference (SC09) show floor in Portland, Oregon, over circuits provided by DOE's ESnet. The team achieved a sustained data rate of 15 Gb/s on a 20 Gb/s network. More important, this effort provided critical feedback on how to deploy, tune, and monitor the middleware that will be used to replicate the upcoming petascale climate datasets. We report on obstacles overcome and the key lessons learned from this successful bandwidth challenge effort.","Title: Fast Multiresolution Reads of Massive Simulation Datasets\nYear: 2014\n\nAbstract: Today's massively parallel simulation codes can produce output ranging up to many terabytes of data. Utilizing this data to support scientific inquiry requires analysis and visualization, yet the sheer size of the data makes it cumbersome or impossible to read without computational resources similar to the original simulation. We identify two broad classes of problems for reading data and present effective solutions for both. The first class of data reads depends on user requirements and available resources. Tasks such as visualization and user-guided analysis may be accomplished using only a subset of variables with a restricted spatial extent at a reduced resolution. The other class of reads requires full resolution multivariate data to be loaded, for example to restart a simulation. We show that utilizing the hierarchical multiresolution IDX data format enables scalable and efficient serial and parallel read access on a variety of hardware from supercomputers down to portable devices. We demonstrate interactive view-dependent visualization and analysis of massive scientific datasets using low-power commodity hardware, and we compare read performance with other parallel file formats for both full and partial resolution data.","Title: Flow Visualization with Quantified Spatial and Temporal Errors using Edge Maps.\nYear: 2012\n\nAbstract: Robust analysis of vector fields has been established as an important tool for deriving insights from the complex systems these fields model. Traditional analysis and visualization techniques rely primarily on computing streamlines through numerical integration. The inherent numerical errors of such approaches are usually ignored, leading to inconsistencies that cause unreliable visualizations and can ultimately prevent in-depth analysis. We propose a new representation for vector fields on surfaces that replaces numerical integration through triangles with maps from the triangle boundaries to themselves. This representation, called edge maps, permits a concise description of flow behaviors and is equivalent to computing all possible streamlines at a user defined error threshold. Independent of this error all streamlines computed using edge maps are guaranteed to be consistent up to floating point precision, enabling the stable extraction of features such as the topological skeleton. Furthermore, our representation explicitly stores the spatial and temporal errors which we use to produce more informative visualizations. This work describes the construction of edge maps, the error quantification, and a refinement procedure to adhere to a user defined error bound. Finally, we introduce new visualizations using the additional information provided by the edge maps to indicate the uncertainty involved in computing streamlines and topological structures.","Title: Interactive exploration of large-scale time-varying data using dynamic tracking graphs\nYear: 2012\n\nAbstract: Exploring and analyzing the temporal evolution of features in large-scale time-varying datasets is a common problem in many areas of science and engineering. One natural representation of such data is tracking graphs, i.e., constrained graph layouts that use one spatial dimension to indicate time and show the “tracks” of each feature as it evolves, merges or disappears. However, for practical data sets creating the corresponding optimal graph layouts that minimize the number of intersections can take hours to compute with existing techniques. Furthermore, the resulting graphs are often unmanageably large and complex even with an ideal layout. Finally, due to the cost of the layout, changing the feature definition, e.g. by changing an iso-value, or analyzing properly adjusted sub-graphs is infeasible. To address these challenges, this paper presents a new framework that couples hierarchical feature definitions with progressive graph layout algorithms to provide an interactive exploration of dynamically constructed tracking graphs. Our system enables users to change feature definitions on-the-fly and filter features using arbitrary attributes while providing an interactive view of the resulting tracking graphs. Furthermore, the graph display is integrated into a linked view system that provides a traditional 3D view of the current set of features and allows a cross-linked selection to enable a fully flexible spatio-temporal exploration of data. We demonstrate the utility of our approach with several large-scale scientific simulations from combustion science.","Title: Ensemble-Vis: A Framework for the Statistical Visualization of Ensemble Data\nYear: 2009\n\nAbstract: Scientists increasingly use ensemble data sets to explore relationships present in dynamic systems. Ensemble data sets combine spatio-temporal simulation results generated using multiple numerical models, sampled input conditions and perturbed parameters. While ensemble data sets are a powerful tool for mitigating uncertainty, they pose significant visualization and analysis challenges due to their complexity. In this article, we present Ensemble-Vis, a framework consisting of a collection of overview and statistical displays linked through a high level of interactivity. Ensemble-Vis allows scientists to gain key scientific insight into the distribution of simulation results as well as the uncertainty associated with the scientific data. In contrast to methods that present large amounts of diverse information in a single display, we argue that combining multiple linked displays yields a clearer presentation of the data and facilitates a greater level of visual data analysis. We demonstrate our framework using driving problems from climate modeling and meteorology and discuss generalizations to other fields.","Title: Robust computation of Morse-Smale complexes of bilinear functions.\nYear: 2013\n\nAbstract: The Morse–Smale (MS) complex has proven to be a useful tool in extracting and visualizing features from scalar-valued data. However, existing algorithms to compute the MS complex are restricted to either piecewise linear or discrete scalar fields. This paper presents a new combinatorial algorithm to compute MS complexes for two-dimensional piecewise bilinear functions defined on quadrilateral meshes. We derive a new invariant of the gradient flow within a bilinear cell and use it to develop a provably correct computation, unaffected by numerical instabilities. This includes a combinatorial algorithm to detect and classify critical points as well as a way to determine the asymptotes of cell-based saddles and their intersection with cell edges. Finally, we introduce a simple data structure to compute and store integral lines on quadrilateral meshes which by construction prevents intersections and allows to enforce constraints on the gradient flow that preserve known invariants.","Title: Maximizing Adaptivity in Hierarchical Topological Models\nYear: 2005\n\nAbstract: We present a novel algorithm based on least-squares minimization to approximate point cloud data in 2D plane with a smooth B-spline curve. The point cloud data may represent an open curve with self intersection and sharp corner. Unlike other existing ...","Title: A Topological Framework for the Interactive Exploration of Large Scale Turbulent Combustion\nYear: 2009\n\nAbstract: The advent of highly accurate, large scale volumetric simulations has made data analysis and visualization techniques an integral part of the modern scientific process. To develop new insights from raw data, scientists need the ability to define features of interest in a flexible manner and to understand how changes in the feature definition impact the subsequent analysis of the data. Therefore, simply exploring the raw data is not sufficient. This paper presents a new topological framework for the analysis of large scale, time-varying, turbulent combustion simulations. It allows the scientists to interactively explore the complete parameter space of fuel consumption thresholds for an entire time-dependent combustion simulation. By computing augmented merge trees and their corresponding data segmentations, the system allows the user complete flexibility to segment, select, and track burning cells through time thanks to a linked view interface. We developed this technique in the context of low-swirl turbulent pre-mixed flame simulation analysis, where the topological abstractions enable an efficient tracking through time of the burning cells and provide new qualitative and quantitative insights into the dynamics of the combustion process.","Title: Hybrid CPU-GPU Solver for Gradient Domain Processing of Massive Images\nYear: 2011\n\nAbstract: Gradient domain processing is a computationally expensive image processing technique. Its use for processing massive images, giga or terapixels in size, can take several hours with serial techniques. To address this challenge, parallel algorithms are being developed to make this class of techniques applicable to the largest images available with running times that are more acceptable to the users. To this end we target the most ubiquitous form of computing power available today, which is small or medium scale clusters of commodity hardware. Such clusters are continuously increasing in scale, not only in the number of nodes, but also in the amount of parallelism available within each node in the form of multicore CPUs and GPUs. In this paper we present a hybrid parallel implementation of gradient domain processing for seamless stitching of gigapixel panoramas that utilizes MPI, threading and a CUDA based GPU component. We demonstrate the performance and scalability of our implementation by presenting results from two GPU clusters processing two large data sets.","Title: Understanding the Structure of the Turbulent Mixing Layer in Hydrodynamic Instabilities\nYear: 2006\n\nAbstract: When a heavy fluid is placed above a light fluid, tiny vertical perturbations in the interface create a characteristic structure of rising bubbles and falling spikes known as Rayleigh-Taylor instability. Rayleigh-Taylor instabilities have received much attention over the past half-century because of their importance in understanding many natural and man-made phenomena, ranging from the rate of formation of heavy elements in supernovae to the design of capsules for Inertial Confinement Fusion. We present a new approach to analyze Rayleigh-Taylor instabilities in which we extract a hierarchical segmentation of the mixing envelope surface to identify bubbles and analyze analogous segmentations of fields on the original interface plane. We compute meaningful statistical information that reveals the evolution of topological features and corroborates the observations made by scientists. We also use geometric tracking to follow the evolution of single bubbles and highlight merge/split events leading to the formation of the large and complex structures characteristic of the later stages. In particular we (i) Provide a formal definition of a bubble; (ii) Segment the envelope surface to identify bubbles; (iii) Provide a multi-scale analysis technique to produce statistical measures of bubble growth; (iv) Correlate bubble measurements with analysis of fields on the interface plane; (v) Track the evolution of individual bubbles over time. Our approach is based on the rigorous mathematical foundations of Morse theory and can be applied to a more general class of applications.","Title: Extracting logical structure and identifying stragglers in parallel execution traces\nYear: 2014\n\nAbstract: We introduce a new approach to automatically extract an idealized logical structure from a parallel execution trace. We use this structure to define intuitive metrics such as the lateness of a process involved in a parallel execution. By analyzing and illustrating traces in terms of logical steps, we leverage a developer's understanding of the happened-before relations in a parallel program. This technique can uncover dependency chains, elucidate communication patterns, and highlight sources and propagation of delays, all of which may be obscured in a traditional trace visualization.","Title: Visual exploration of high dimensional scalar functions.\nYear: 2010\n\nAbstract: An important goal of scientific data analysis is to understand the behavior of a system or process based on a sample of the system. In many instances it is possible to observe both input parameters and system outputs, and characterize the system as a high-dimensional function. Such data sets arise, for instance, in large numerical simulations, as energy landscapes in optimization problems, or in the analysis of image data relating to biological or medical parameters. This paper proposes an approach to analyze and visualizing such data sets. The proposed method combines topological and geometric techniques to provide interactive visualizations of discretely sampled high-dimensional scalar fields. The method relies on a segmentation of the parameter space using an approximate Morse-Smale complex on the cloud of point samples. For each crystal of the Morse-Smale complex, a regression of the system parameters with respect to the output yields a curve in the parameter space. The result is a simplified geometric representation of the Morse-Smale complex in the high dimensional input domain. Finally, the geometric representation is embedded in 2D, using dimension reduction, to provide a visualization platform. The geometric properties of the regression curves enable the visualization of additional information about each crystal such as local and global shape, width, length, and sampling densities. The method is illustrated on several synthetic examples of two dimensional functions. Two use cases, using data sets from the UCI machine learning repository, demonstrate the utility of the proposed approach on real data. Finally, in collaboration with domain experts the proposed method is applied to two scientific challenges. The analysis of parameters of climate simulations and their relationship to predicted global energy flux and the concentrations of chemical species in a combustion simulation and their integration with temperature.","Title: On the use of graph search techniques for the analysis of extreme-scale combustion simulation data\nYear: 2012\n\nAbstract: With the continuous increase in high performance computing capabilities, simulations are becoming ever larger and more complex, using bigger domains, tracking more variables, and producing more time steps. This increase in the ranges of spatial and temporal simulation scales results in data that presents significant challenges to as well as new opportunities for the visualization and data analysis community. For example, highly-localized, intermittent events (such as the formation of ignition kernels in turbulent combustion) may be caused by interactions between multiple variables across a series of time steps, making both their definition and their extraction difficult, particularly at scale. This paper introduces an intuitive framework to support the identification, characterization, and tracking of such complex, multivariate, temporally evolving events in large-scale simulations. In a pre-processing step, we use topological techniques to create a hierarchical family of feature definitions for each variable of interest. Subsequently, we select a particular set of features for analysis and, using overlap-based metrics, we generate an attributed relational graph (ARG) capturing the relationships between different variables both within one and across multiple time steps. Finally, we leverage subgraph-isomorphism search heuristics to identify patterns in the ARG that characterize interesting events. We demonstrate the power of this approach by analyzing a large-scale turbulent combustion simulation.","Title: A topological approach to simplification of three-dimensional scalar functions.\nYear: 2006\n\nAbstract: This paper describes an efficient combinatorial method for simplification of topological features in a 3D scalar function. The Morse-Smale complex, which provides a succinct representation of a function's associated gradient flow field, is used to identify topological features and their significance. The simplification process, guided by the Morse-Smale complex, proceeds by repeatedly applying two atomic operations that each remove a pair of critical points from the complex. Efficient storage of the complex results in execution of these atomic operations at interactive rates. Visualization of the simplified complex shows that the simplification preserves significant topological features while removing small features and noise.","Title: Combining in-situ and in-transit processing to enable extreme-scale scientific analysis\nYear: 2012\n\nAbstract: With the onset of extreme-scale computing, I/O constraints make it increasingly difficult for scientists to save a sufficient amount of raw simulation data to persistent storage. One potential solution is to change the data analysis pipeline from a post-process centric to a concurrent approach based on either in-situ or in-transit processing. In this context computations are considered in-situ if they utilize the primary compute resources, while in-transit processing refers to offloading computations to a set of secondary resources using asynchronous data transfers. In this paper we explore the design and implementation of three common analysis techniques typically performed on large-scale scientific simulations: topological analysis, descriptive statistics, and visualization. We summarize algorithmic developments, describe a resource scheduling system to coordinate the execution of various analysis workflows, and discuss our implementation using the DataSpaces and ADIOS frameworks that support efficient data movement between in-situ and in-transit computations. We demonstrate the efficiency of our lightweight, flexible framework by deploying it on the Jaguar XK6 to analyze data generated by S3D, a massively parallel turbulent combustion code. Our framework allows scientists dealing with the data deluge at extreme scale to perform analyses at increased temporal resolutions, mitigate I/O costs, and significantly improve the time to insight.","Title: Computing Morse-Smale Complexes with Accurate Geometry\nYear: 2012\n\nAbstract: Topological techniques have proven highly successful in analyzing and visualizing scientific data. As a result, significant efforts have been made to compute structures like the Morse-Smale complex as robustly and efficiently as possible. However, the resulting algorithms, while topologically consistent, often produce incorrect connectivity as well as poor geometry. These problems may compromise or even invalidate any subsequent analysis. Moreover, such techniques may fail to improve even when the resolution of the domain mesh is increased, thus producing potentially incorrect results even for highly resolved functions. To address these problems we introduce two new algorithms: (i) a randomized algorithm to compute the discrete gradient of a scalar field that converges under refinement; and (ii) a deterministic variant which directly computes accurate geometry and thus correct connectivity of the MS complex. The first algorithm converges in the sense that on average it produces the correct result and its standard deviation approaches zero with increasing mesh resolution. The second algorithm uses two ordered traversals of the function to integrate the probabilities of the first to extract correct (near optimal) geometry and connectivity. We present an extensive empirical study using both synthetic and real-world data and demonstrates the advantages of our algorithms in comparison with several popular approaches.","Title: Ultrascale Visualization of Climate Data\nYear: 2013\n\nAbstract: Collaboration across research, government, academic, and private sectors is integrating more than 70 scientific computing libraries and applications through a tailorable provenance framework, empowering scientists to exchange and examine data in novel ways.","Title: Comments on the \"Meshless Helmholtz-Hodge Decomposition\"\nYear: 2013\n\nAbstract: The Helmholtz-Hodge decomposition (HHD) is one of the fundamental theorems of fluids describing the decomposition of a flow field into its divergence-free, curl-free, and harmonic components. Solving for the HHD is intimately connected to the choice of boundary conditions which determine the uniqueness and orthogonality of the decomposition. This article points out that one of the boundary conditions used in a recent paper “Meshless Helmholtz-Hodge Decomposition” [5] is, in general, invalid and provides an analytical example demonstrating the problem. We hope that this clarification on the theory will foster further research in this area and prevent undue problems in applying and extending the original approach.","Title: Topological Spines: A Structure-preserving Visual Representation of Scalar Fields\nYear: 2011\n\nAbstract: We present topological spines−a new visual representation that preserves the topological and geometric structure of a scalar field. This representation encodes the spatial relationships of the extrema of a scalar field together with the local volume and nesting structure of the surrounding contours. Unlike other topological representations, such as contour trees, our approach preserves the local geometric structure of the scalar field, including structural cycles that are useful for exposing symmetries in the data. To obtain this representation, we describe a novel mechanism based on the extraction of extremum graphs−sparse subsets of the Morse-Smale complex that retain the important structural information without the clutter and occlusion problems that arise from visualizing the entire complex directly. Extremum graphs form a natural multiresolution structure that allows the user to suppress noise and enhance topological features via the specification of a persistence range. Applications of our approach include the visualization of 3D scalar fields without occlusion artifacts, and the exploratory analysis of high-dimensional functions.","Title: Visualizing Network Traffic to Understand the Performance of Massively Parallel Simulations\nYear: 2012\n\nAbstract: The performance of massively parallel applications is often heavily impacted by the cost of communication among compute nodes. However, determining how to best use the network is a formidable task, made challenging by the ever increasing size and complexity of modern supercomputers. This paper applies visualization techniques to aid parallel application developers in understanding the network activity by enabling a detailed exploration of the flow of packets through the hardware interconnect. In order to visualize this large and complex data, we employ two linked views of the hardware network. The first is a 2D view, that represents the network structure as one of several simplified planar projections. This view is designed to allow a user to easily identify trends and patterns in the network traffic. The second is a 3D view that augments the 2D view by preserving the physical network topology and providing a context that is familiar to the application developers. Using the massively parallel multi-physics code pF3D as a case study, we demonstrate that our tool provides valuable insight that we use to explain and optimize pF3D’s performance on an IBM Blue Gene/P system.","Title: A topological hierarchy for functions on triangulated surfaces.\nYear: 2004\n\nAbstract: We combine topological and geometric methods to construct a multiresolution representation for a function over a two-dimensional domain. In a preprocessing stage, we create the Morse-Smale complex of the function and progressively simplify its topology by cancelling pairs of critical points. Based on a simple notion of dependency among these cancellations, we construct a hierarchical data structure supporting traversal and reconstruction operations similarly to traditional geometry-based representations. We use this data structure to extract topologically valid approximations that satisfy error bounds provided at runtime.","Title: Interactive Exploration and Analysis of Large Scale Simulations Using Topology-based Data Segmentation.\nYear: 2011\n\nAbstract: Large-scale simulations are increasingly being used to study complex scientific and engineering phenomena. As a result, advanced visualization and data analysis are also becoming an integral part of the scientific process. Often, a key step in extracting insight from these large simulations involves the definition, extraction, and evaluation of features in the space and time coordinates of the solution. However, in many applications these features involve a range of parameters and decisions that will affect the quality and direction of the analysis. Examples include particular level sets of a specific scalar field, or local inequalities between derived quantities. A critical step in the analysis is to understand how these arbitrary parameters/decisions impact the statistical properties of the features, since such a characterization will help to evaluate the conclusions of the analysis as a whole.","Title: Interpreting Performance Data across Intuitive Domains\nYear: 2011\n\nAbstract: To exploit the capabilities of current and future systems, developers must understand the interplay between on-node performance, domain decomposition, and an application's intrinsic communication patterns. While tools exist to gather and analyze data for each of these components individually, the resulting information is generally processed in isolation and presented in an abstract, categorical fashion unintuitive to most users. In this paper we present the HAC model, in which we identify the three domains of performance data most familiar to the user: (i)the application domain containing the application's working set, (ii) the hardware domain of the compute and network devices, and (iii) the communication domain of logical data transfers. We show that taking data from each of these domains and projecting, visualizing, and correlating it to the other domains can give valuable insights into the behavior of parallel application codes. The HAC abstraction opens the door for a new generation of tools that can help users more easily and intuitively associate performance data with root causes in the hardware system, the application's structure, and in its communication behavior, and by doing so leads to an improved understanding of the performance of their codes.","Title: Local, smooth, and consistent Jacobi set simplification.\nYear: 2013\n\nAbstract: The relation between two Morse functions defined on a smooth, compact, and orientable 2-manifold can be studied in terms of their Jacobi set. The Jacobi set contains points in the domain where the gradients of the two functions are aligned. Both the Jacobi set itself as well as the segmentation of the domain it induces, have shown to be useful in various applications. In practice, unfortunately, functions often contain noise and discretization artifacts, causing their Jacobi set to become unmanageably large and complex. Although there exist techniques to simplify Jacobi sets, they are unsuitable for most applications as they lack fine-grained control over the process, and heavily restrict the type of simplifications possible.This paper introduces the theoretical foundations of a new simplification framework for Jacobi sets. We present a new interpretation of Jacobi set simplification based on the perspective of domain segmentation. Generalizing the cancellation of critical points from scalar functions to Jacobi sets, we focus on simplifications that can be realized by smooth approximations of the corresponding functions, and show how these cancellations imply simultaneous simplification of contiguous subsets of the Jacobi set. Using these extended cancellations as atomic operations, we introduce an algorithm to successively cancel subsets of the Jacobi set with minimal modifications to some user-defined metric. We show that for simply connected domains, our algorithm reduces a given Jacobi set to its minimal configuration, that is, one with no birth-death points (a birth-death point is a specific type of singularity within the Jacobi set where the level sets of the two functions and the Jacobi set have a common normal direction).","Title: Ridge-Valley graphs: Combinatorial ridge detection using Jacobi sets.\nYear: 2013\n\nAbstract: Ridges are one of the key features of interest in areas such as computer vision and image processing. Even though a significant amount of research has been directed to defining and extracting ridges some fundamental challenges remain. For example, the most popular ridge definition (height ridge) is not invariant under monotonic transformations and its global structure is typically ignored during numerical computations. Furthermore, many existing algorithms are based on numerical heuristics and are rarely guaranteed to produce consistent results. This paper reexamines a slightly different ridge definition that is consistent with all desired invariants. Nevertheless, we show that this definition results in similar structures compared to height ridges and that both formulations are equivalent for quadratic functions. Furthermore, this definition can be cast in the form of a degenerate Jacobi set, which allows insights into the global structure of ridges. In particular, we introduce the Ridge–Valley graph as the complete description of all ridges in an image. Finally, using the connection to Jacobi sets we describe a new combinatorial algorithm to extract the Ridge–Valley graph from sampled images guaranteed to produce a valid structure.","Title: Adaptive extraction and quantification of geophysical vortices.\nYear: 2011\n\nAbstract: We consider the problem of extracting discrete two-dimensional vortices from a turbulent flow. In our approach we use a reference model describing the expected physics and geometry of an idealized vortex. The model allows us to derive a novel correlation between the size of the vortex and its strength, measured as the square of its strain minus the square of its vorticity. For vortex detection in real models we use the strength parameter to locate potential vortex cores, then measure the similarity of our ideal analytical vortex and the real vortex core for different strength thresholds. This approach provides a metric for how well a vortex core is modeled by an ideal vortex. Moreover, this provides insight into the problem of choosing the thresholds that identify a vortex. By selecting a target coefficient of determination (i.e., statistical confidence), we determine on a per-vortex basis what threshold of the strength parameter would be required to extract that vortex at the chosen confidence. We validate our approach on real data from a global ocean simulation and derive from it a map of expected vortex strengths over the global ocean.","Title: A Sampling Theorem for MLS Surfaces\nYear: 2005\n\nAbstract: Recently, point set surfaces have been the focus of a large number of research efforts. Several different methods have been proposed to define surfaces from points and have been used in a variety of applications. However, so far little is know about the mathematical properties of the resulting surface. A central assumption for most algorithms is that the surface construction is well defined within a neighborhood of the samples. However, it is not clear that given an irregular sampling of a surface this is the case. The fundamental problem is that point based methods often use a weighted least squares fit of a plane to approximate a surface normal. If this minimization problem is ill-defined so is the surface construction. In this paper, we provide a proof that given reasonable sampling conditions the normal approximations are well defined within a neighborhood of the samples. Similar to methods in surface reconstruction, our sampling conditions are based on the local feature size and thus allow the sampling density to vary according to geometric complexity.","Title: Edge maps: Representing flow with bounded error\nYear: 2011\n\nAbstract: Robust analysis of vector fields has been established as an important tool for deriving insights from the complex systems these fields model. Many analysis techniques rely on computing streamlines, a task often hampered by numerical instabilities. Approaches that ignore the resulting errors can lead to inconsistencies that may produce unreliable visualizations and ultimately prevent in-depth analysis. We propose a new representation for vector fields on surfaces that replaces numerical integration through triangles with linear maps defined on its boundary. This representation, called edge maps, is equivalent to computing all possible streamlines at a user defined error threshold. In spite of this error, all the streamlines computed using edge maps will be pairwise disjoint. Furthermore, our representation stores the error explicitly, and thus can be used to produce more informative visualizations. Given a piecewise-linear interpolated vector field, a recent result [15] shows that there are only 23 possible map classes for a triangle, permitting a concise description of flow behaviors. This work describes the details of computing edge maps, provides techniques to quantify and refine edge map error, and gives qualitative and visual comparisons to more traditional techniques.","Title: Robust on-line computation of Reeb graphs: simplicity and speed\nYear: 2007\n\nAbstract: Reeb graphs are a fundamental data structure for understanding and representing the topology of shapes. They are used in computer graphics, solid modeling, and visualization for applications ranging from the computation of similarities and finding defects in complex models to the automatic selection of visualization parameters. We introduce an on-line algorithm that reads a stream of elements (vertices, triangles, tetrahedra, etc.) and continuously maintains the Reeb graph of all elements already reed. The algorithm is robust in handling non-manifold meshes and general in its applicability to input models of any dimension. Optionally, we construct a skeleton-like embedding of the Reeb graph, and/or remove topological noise to reduce the output size. For interactive multi-resolution navigation we also build a hierarchical data structure which allows real-time extraction of approximated Reeb graphs containing all topological features above a given error threshold. Our extensive experiments show both high performance and practical linear scalability for meshes ranging from thousands to hundreds of millions of triangles. We apply our algorithm to the largest, most general, triangulated surfaces available to us, including 3D, 4D and 5D simplicial meshes. To demonstrate one important application we use Reeb graphs to find and highlight topological defects in meshes, including some widely believed to be \"clean.\"","Title: Feature-Based Statistical Analysis of Combustion Simulation Data\nYear: 2011\n\nAbstract: We present a new framework for feature-based statistical analysis of large-scale scientific data and demonstrate its effectiveness by analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion. Turbulent flows are ubiquitous and account for transport and mixing processes in combustion, astrophysics, fusion, and climate modeling among other disciplines. They are also characterized by coherent structure or organized motion, i.e. nonlocal entities whose geometrical features can directly impact molecular mixing and reactive processes. While traditional multi-point statistics provide correlative information, they lack nonlocal structural information, and hence, fail to provide mechanistic causality information between organized fluid motion and mixing and reactive processes. Hence, it is of great interest to capture and track flow features and their statistics together with their correlation with relevant scalar quantities, e.g. temperature or species concentrations. In our approach we encode the set of all possible flow features by pre-computing merge trees augmented with attributes, such as statistical moments of various scalar fields, e.g. temperature, as well as length-scales computed via spectral analysis. The computation is performed in an efficient streaming manner in a pre-processing step and results in a collection of meta-data that is orders of magnitude smaller than the original simulation data. This meta-data is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. We combine the analysis with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze the equivalent of one terabyte of simulation data. We highlight the utility of this new framework for combustion science; however, it is applicable to many other science domains.","Title: Topological landscapes: a terrain metaphor for scientific data.\nYear: 2007\n\nAbstract: Scientific visualization and illustration tools are designed to help people understand the structure and complexity of scientific data with images that are as informative and intuitive as possible. In this context the use of metaphors plays an important role since they make complex information easily accessible by using commonly known concepts. In this paper we propose a new metaphor, called \"Topological Landscapes,\" which facilitates understanding the topological structure of scalar functions. The basic idea is to construct a terrain with the same topology as a given dataset and to display the terrain as an easily understood representation of the actual input data. In this projection from an $n$-dimensional scalar function to a two-dimensional (2D) model we preserve function values of critical points, the persistence (function span) of topological features, and one possible additional metric property (in our examples volume). By displaying this topologically equivalent landscape together with the original data we harness the natural human proficiency in understanding terrain topography and make complex topological information easily accessible.","Title: Gaussian mixture model based volume visualization\nYear: 2012\n\nAbstract: Representing uncertainty when creating visualizations is becoming more indispensable to understand and analyze scientific data. Uncertainty may come from different sources, such as, ensembles of experiments or unavoidable information loss when performing data reduction. One natural model to represent uncertainty is to assume that each position in space instead of a single value may take on a distribution of values. In this paper we present a new volume rendering method using per voxel Gaussian mixture models (GMMs) as the input data representation. GMMs are an elegant and compact way to drastically reduce the amount of data stored while still enabling realtime data access and rendering on the GPU. Our renderer offers efficient sampling of the data distribution, generating renderings of the data that flicker at each frame to indicate high variance. We can accumulate samples as well to generate still frames of the data, which preserve additional details in the data as compared to either traditional scalar indicators (such as a mean or a single nearest neighbor down sample) or to fitting the data with only a single Gaussian per voxel. We demonstrate the effectiveness of our method using ensembles of climate simulations and MRI scans as well as the down sampling of large scalar fields as examples.","Title: Ordering Traces Logically to Identify Lateness in Message Passing Programs\nYear: 2016\n\nAbstract: Event traces are valuable for understanding the behavior of parallel programs. However, automatically analyzing a large parallel trace is difficult, especially without a specific objective. We aid this endeavor by extracting a trace’s logical structure, an ordering of trace events derived from happened-before relationships, while taking into account developer intent. Using this structure, we can calculate an operation’s delay relative to its peers on other processes. The logical structure also serves as a platform for comparing and clustering processes as well as highlighting communication patterns in a trace visualization. We present an algorithm for determining this idealized logical structure from traces of message passing programs, and we develop metrics to quantify delays and differences among processes. We implement our techniques in Ravel, a parallel trace visualization tool that displays both logical and physical timelines. Rather than showing the duration of each operation, we display where delays begin and end, and how they propagate. We apply our approach to the traces of several message passing applications, demonstrating the accuracy of our extracted structure and its utility in analyzing these codes.","Title: Combing the Communication Hairball: Visualizing Parallel Execution Traces using Logical Time\nYear: 2014\n\nAbstract: With the continuous rise in complexity of modern supercomputers, optimizing the performance of large-scale parallel programs is becoming increasingly challenging. Simultaneously, the growth in scale magnifies the impact of even minor inefficiencies - potentially millions of compute hours and megawatts in power consumption can be wasted on avoidable mistakes or sub-optimal algorithms. This makes performance analysis and optimization critical elements in the software development process. One of the most common forms of performance analysis is to study execution traces, which record a history of per-process events and interprocess messages in a parallel application. Trace visualizations allow users to browse this event history and search for insights into the observed performance behavior. However, current visualizations are difficult to understand even for small process counts and do not scale gracefully beyond a few hundred processes. Organizing events in time leads to a virtually unintelligible conglomerate of interleaved events and moderately high process counts overtax even the largest display. As an alternative, we present a new trace visualization approach based on transforming the event history into logical time inferred directly from happened-before relationships. This emphasizes the code's structural behavior, which is much more familiar to the application developer. The original timing data, or other information, is then encoded through color, leading to a more intuitive visualization. Furthermore, we use the discrete nature of logical timelines to cluster processes according to their local behavior leading to a scalable visualization of even long traces on large process counts. We demonstrate our system using two case studies on large-scale parallel codes.","Title: Distributed Seams for Gigapixel Panoramas\nYear: 2015\n\nAbstract: Gigapixel panoramas are an increasingly popular digital image application. They are often created as a mosaic of many smaller images. The mosaic acquisition can take many hours causing the individual images to differ in exposure and lighting conditions. A blending operation is often necessary to give the appearance of a seamless image. The blending quality depends on the magnitude of discontinuity along the image boundaries. Often, new boundaries, or seams, are first computed that minimize this transition. Current techniques based on multi-labeling Graph Cuts are too slow and memory intensive for gigapixel sized panoramas. In this paper, we present a parallel, out-of-core seam computing technique that is fast, has small memory footprint, and is capable of running efficiently on different types of parallel systems. Its maximum memory usage is configurable, in the form of a cache, which can improve performance by reducing redundant disk I/O and computations. It shows near-perfect scaling on symmetric multiprocessing systems and good scaling on clusters and distributed shared memory systems. Our technique improves the time required to compute seams for gigapixel imagery from many hours (or even days) to just a few minutes, while still producing boundaries with energy that is on-par with Graph Cuts.","Title: The Natural Helmholtz-Hodge Decomposition for Open-Boundary Flow Analysis.\nYear: 2014\n\nAbstract: The Helmholtz-Hodge decomposition (HHD), which describes a flow as the sum of an incompressible, an irrotational, and a harmonic flow, is a fundamental tool for simulation and analysis. Unfortunately, for bounded domains, the HHD is not uniquely defined, traditionally, boundary conditions are imposed to obtain a unique solution. However, in general, the boundary conditions used during the simulati...","Title: Extracting Features from Time-Dependent Vector Fields Using Internal Reference Frames\nYear: 2014\n\nAbstract: Extracting features from complex, time-dependent flow fields remains a significant challenge despite substantial research efforts, especially because most flow features of interest are defined with respect to a given reference frame. Pathline-based techniques, such as the FTLE field, are complex to implement and resource intensive, whereas scalar transforms, such as λ2, often produce artifacts and require somewhat arbitrary thresholds. Both approaches aim to analyze the flow in a more suitable frame, yet neither technique explicitly constructs one.","Title: Distortion-Guided Structure-Driven Interactive Exploration of High-Dimensional Data\nYear: 2014\n\nAbstract: Dimension reduction techniques are essential for feature selection and feature extraction of complex high-dimensional data. These techniques, which construct low-dimensional representations of data, are typically geometrically motivated, computationally efficient and approximately preserve certain structural properties of the data. However, they are often used as black box solutions in data exploration and their results can be difficult to interpret. To assess the quality of these results, quality measures, such as co-ranking [LV09], have been proposed to quantify structural distortions that occur between high-dimensional and low-dimensional data representations. Such measures could be evaluated and visualized point-wise to further highlight erroneous regions [MLGH13]. In this work, we provide an interactive visualization framework for exploring high-dimensional data via its two-dimensional embeddings obtained from dimension reduction, using a rich set of user interactions. We ask the following question: what new insights do we obtain regarding the structure of the data, with interactive manipulations of its embeddings in the visual space? We augment the two-dimensional embeddings with structural abstractions obtained from hierarchical clusterings, to help users navigate and manipulate subsets of the data. We use point-wise distortion measures to highlight interesting regions in the domain, and further to guide our selection of the appropriate level of clusterings that are aligned with the regions of interest. Under the static setting, point-wise distortions indicate the level of structural uncertainty within the embeddings. Under the dynamic setting, on-the-fly updates of point-wise distortions due to data movement and data deletion reflect structural relations among different parts of the data, which may lead to new and valuable insights.","Title: Data-parallel halo finding with variable linking lengths\nYear: 2014\n\nAbstract: State-of-the-art cosmological simulations regularly contain billions of particles, providing scientists the opportunity to study the evolution of the Universe in great detail. However, the rate at which these simulations generate data severely taxes existing analysis techniques. Therefore, developing new scalable alternatives is essential for continued scientific progress. Here, we present a dataparallel, friends-of-friends halo finding algorithm that provides unprecedented flexibility in the analysis by extracting multiple linking lengths. Even for a single linking length, it is as fast as the existing techniques, and is portable to multi-threaded many-core systems as well as co-processing resources. Our system is implemented using PISTON and is coupled to an interactive analysis environment used to study halos at different linking lengths and track their evolution over time.","Title: Multivariate volume visualization through dynamic projections\nYear: 2014\n\nAbstract: We propose a multivariate volume visualization framework that tightly couples dynamic projections with a high-dimensional transfer function design for interactive volume visualization. We assume that the complex, high-dimensional data in the attribute space can be well-represented through a collection of low-dimensional linear subspaces, and embed the data points in a variety of 2D views created as projections onto these subspaces. Through dynamic projections, we present animated transitions between different views to help the user navigate and explore the attribute space for effective transfer function design. Our framework not only provides a more intuitive understanding of the attribute space but also allows the design of the transfer function under multiple dynamic views, which is more flexible than being restricted to a single static view of the data. For large volumetric datasets, we maintain interactivity during the transfer function design via intelligent sampling and scalable clustering. Using examples in combustion and climate simulations, we demonstrate how our framework can be used to visualize interesting structures in the volumetric space.","Title: Visualizing the five-dimensional torus network of the IBM blue gene/Q\nYear: 2014\n\nAbstract: Understanding the interactions between a parallel application and the interconnection network over which it exchanges data is critical to optimizing performance in modern supercomputers. However, recent supercomputing architectures use networks that do not have natural low-dimensional representations, making them difficult to comprehend or visualize. In particular, high-dimensional torus networks are common and are used in four of the top ten supercomputers and eight of the top ten on the Graph500 list. We present a new visualization of five-dimensional torus networks. We use four connected views depicting the network at different levels of detail, allowing analysts to observe general large-scale traffic patterns while simultaneously viewing individual links or outliers in any specific section of the network. We demonstrate this approach by analyzing network traffic for a pF3D simulation running on the IBM Blue Gene/Q architecture, and show how it is both intuitive and effective for understanding and optimizing parallel application behavior.","Title: In-situ feature extraction of large scale combustion simulations using segmented merge trees\nYear: 2014\n\nAbstract: The ever increasing amount of data generated by scientific simulations coupled with system I/O constraints are fueling a need for in-situ analysis techniques. Of particular interest are approaches that produce reduced data representations while maintaining the ability to redefine, extract, and study features in a post-process to obtain scientific insights. This paper presents two variants of in-situ feature extraction techniques using segmented merge trees, which encode a wide range of threshold based features. The first approach is a fast, low communication cost technique that generates an exact solution but has limited scalability. The second is a scalable, local approximation that nevertheless is guaranteed to correctly extract all features up to a predefined size. We demonstrate both variants using some of the largest combustion simulations available on leadership class supercomputers. Our approach allows state-of-the-art, feature-based analysis to be performed in-situ at significantly higher frequency than currently possible and with negligible impact on the overall simulation runtime.","Title: Image segmentation using consensus from hierarchical segmentation ensembles\nYear: 2014\n\nAbstract: Unsupervised, automatic image segmentation without contextual knowledge, or user intervention is a challenging problem. The key to robust segmentation is an appropriate selection of local features and metrics. However, a single aggregation of the local features using a greedy merging order often results in incorrect segmentation. This paper presents an unsupervised approach, which uses the consensus inferred from hierarchical segmentation ensembles, for partitioning images into foreground and background regions. By exploring an expanded set of possible aggregations of the local features, the proposed method generates meaningful segmentations that are not often revealed when only the optimal hierarchy is considered. A graph cuts-based approach is employed to combine the consensus along with a foreground-background model estimate, obtained using the ensemble, for effective segmentation. Experiments with a standard dataset show promising results when compared to several existing methods including the state-of-the-art weak supervised techniques that use co-segmentation.","Title: Automatic image annotation using inverse maps from semantic embeddings\nYear: 2014\n\nAbstract: Human annotation in large scale image databases is time-consuming and error-prone. Since it is very hard to mine image databases using just visual features or textual descriptors, it is common to transform the image features into a semantically meaningful space. In this paper, we propose to perform image annotation in a semantic space inferred based on sparse representations. By constructing a semantic embedding for the visual features, that is constrained to be close to the tag embedding, we show that a robust inverse map can be used to predict the tags. Experiments using standard datasets show the effectiveness of the proposed approach in automatic image annotation when compared to existing methods.","Title: Multiple kernel interpolation for inverting non-linear dimensionality reduction and dimension estimation\nYear: 2014\n\nAbstract: The problem of stably inverting a non-linear dimensionality reduction map has applications in data visualization and machine learning, besides being of theoretical interest. In this paper, we propose a meshfree interpolation method for obtaining such inverse maps using a non-negative linear combination of multiple interpolants. We show that the proposed scheme can improve upon the approximation power of its individual constituent kernels, and discuss the conditions under which its parameters can be uniquely estimated. We also provide an approach for estimating the intrinsic dimensionality (ID) of manifolds using the proposed inverse map. Experiments using multiple kernel interpolation for reconstruction of novel test data and ID estimation show an improved or similar performance compared to existing techniques.","Title: A Practical Approach to Morse-Smale Complex Computation: Scalability and Generality\nYear: 2008\n\nAbstract: The Morse-Smale (MS) complex has proven to be a useful tool in extracting and visualizing features from scalar-valued data. However, efficient computation of the MS complex for large scale data remains a challenging problem. We describe a new algorithm and easily extensible framework for computing MS complexes for large scale data of any dimension where scalar values are given at the vertices of a closure-finite and weak topology (CW) complex, therefore enabling computation on a wide variety of meshes such as regular grids, simplicial meshes, and adaptive multiresolution (AMR) meshes. A new divide-and-conquer strategy allows for memory-efficient computation of the MS complex and simplification on-the-fly to control the size of the output. In addition to being able to handle various data formats, the framework supports implementation-specific optimizations, for example, for regular data. We present the complete characterization of critical point cancellations in all dimensions. This technique enables the topology based analysis of large data on off-the-shelf computers. In particular we demonstrate the first full computation of the MS complex for a 1 billion/1024(3) node grid on a laptop computer with 2Gb memory.","Title: Analyzing and Tracking Burning Structures in Lean Premixed Hydrogen Flames\nYear: 2010\n\nAbstract: This paper presents topology-based methods to robustly extract, analyze, and track features defined as subsets of isosurfaces. First, we demonstrate how features identified by thresholding isosurfaces can be defined in terms of the Morse complex. Second, we present a specialized hierarchy that encodes the feature segmentation independent of the threshold while still providing a flexible multiresolution representation. Third, for a given parameter selection, we create detailed tracking graphs representing the complete evolution of all features in a combustion simulation over several hundred time steps. Finally, we discuss a user interface that correlates the tracking information with interactive rendering of the segmented isosurfaces enabling an in-depth analysis of the temporal behavior. We demonstrate our approach by analyzing three numerical simulations of lean hydrogen flames subject to different levels of turbulence. Due to their unstable nature, lean flames burn in cells separated by locally extinguished regions. The number, area, and evolution over time of these cells provide important insights into the impact of turbulence on the combustion process. Utilizing the hierarchy, we can perform an extensive parameter study without reprocessing the data for each set of parameters. The resulting statistics enable scientists to select appropriate parameters and provide insight into the sensitivity of the results with respect to the choice of parameters. Our method allows for the first time to quantitatively correlate the turbulence of the burning process with the distribution of burning regions, properly segmented and selected. In particular, our analysis shows that counterintuitively stronger turbulence leads to larger cell structures, which burn more intensely than expected. This behavior suggests that flames could be stabilized under much leaner conditions than previously anticipated.","Title: The Helmholtz-Hodge Decomposition—A Survey\nYear: 2013\n\nAbstract: The Helmholtz-Hodge Decomposition (HHD) describes the decomposition of a flow field into its divergence-free and curl-free components. Many researchers in various communities like weather modeling, oceanology, geophysics, and computer graphics are interested in understanding the properties of flow representing physical phenomena such as incompressibility and vorticity. The HHD has proven to be an important tool in the analysis of fluids, making it one of the fundamental theorems in fluid dynamics. The recent advances in the area of flow analysis have led to the application of the HHD in a number of research communities such as flow visualization, topological analysis, imaging, and robotics. However, because the initial body of work, primarily in the physics communities, research on the topic has become fragmented with different communities working largely in isolation often repeating and sometimes contradicting each others results. Additionally, different nomenclature has evolved which further obscures the fundamental connections between fields making the transfer of knowledge difficult. This survey attempts to address these problems by collecting a comprehensive list of relevant references and examining them using a common terminology. A particular focus is the discussion of boundary conditions when computing the HHD. The goal is to promote further research in the field by creating a common repository of techniques to compute the HHD as well as a large collection of example applications in a broad range of areas.","Title: Connecting Performance Analysis and Visualization (Dagstuhl Perspectives Workshop 14022).\nYear: 2015\n\nAbstract: In the first week of January 2014 Schloss Dagstuhl hosted a Perspectives Workshop on “Connecting Performance Analysis and Visualization to Advance Extreme Scale Computing”. The workshop brought together two previously separate communities – from Visualization and Performance Analysis for High Performance Computing – to discuss a long term joint research agenda. The goal was to identify and address the challenges in using visual representations to understand and optimize the performance of extreme-scale applications running on todayu0027s most powerful\r\ncomputing systems like climate modeling, combustion, material science or astro-physics simulations.","Title: Interstitial And Interlayer Ion Diffusion Geometry Extraction In Graphitic Nanosphere Battery Materials\nYear: 2016\n\nAbstract: Large-scale molecular dynamics (MD) simulations are commonly used for simulating the synthesis and ion diffusion of battery materials. A good battery anode material is determined by its capacity to store ion or other diffusers. However, modeling of ion diffusion dynamics and transport properties at large length and long time scales would be impossible with current MD codes. To analyze the fundamental properties of these materials, therefore, we turn to geometric and topological analysis of their structure. In this paper, we apply a novel technique inspired by discrete Morse theory to the Delaunay triangulation of the simulated geometry of a thermally annealed carbon nanosphere. We utilize our computed structures to drive further geometric analysis to extract the interstitial diffusion structure as a single mesh. Our results provide a new approach to analyze the geometry of the simulated carbon nanosphere, and new insights into the role of carbon defect size and distribution in determining the charge capacity and charge dynamics of these carbon based battery materials.","Title: Analyzing simulation-based PRA data through traditional and topological clustering: A BWR station blackout case study.\nYear: 2016\n\nAbstract: Dynamic probabilistic risk assessment (DPRA) methodologies couple system simulator codes (e.g., RELAP and MELCOR) with simulation controller codes (e.g., RAVEN and ADAPT). Whereas system simulator codes model system dynamics deterministically, simulation controller codes introduce both deterministic (e.g., system control logic and operating procedures) and stochastic (e.g., component failures and parameter uncertainties) elements into the simulation. Typically, a DPRA is performed by sampling values of a set of parameters and simulating the system behavior for that specific set of parameter values. For complex systems, a major challenge in using DPRA methodologies is to analyze the large number of scenarios generated, where clustering techniques are typically employed to better organize and interpret the data. In this paper, we focus on the analysis of two nuclear simulation datasets that are part of the risk-informed safety margin characterization (RISMC) boiling water reactor (BWR) station blackout (SBO) case study. We provide the domain experts a software tool that encodes traditional and topological clustering techniques within an interactive analysis and visualization environment, for understanding the structures of such high-dimensional nuclear simulation datasets. We demonstrate through our case study that both types of clustering techniques complement each other for enhanced structural understanding of the data.","Title: Visual Exploration of High‐Dimensional Data through Subspace Analysis and Dynamic Projections\nYear: 2015\n\nAbstract: We introduce a novel interactive framework for visualizing and exploring high-dimensional datasets based on subspace analysis and dynamic projections. We assume the high-dimensional dataset can be represented by a mixture of low-dimensional linear subspaces with mixed dimensions, and provide a method to reliably estimate the intrinsic dimension and linear basis of each subspace extracted from the subspace clustering. Subsequently, we use these bases to define unique 2D linear projections as viewpoints from which to visualize the data. To understand the relationships among the different projections and to discover hidden patterns, we connect these projections through dynamic projections that create smooth animated transitions between pairs of projections. We introduce the view transition graph, which provides flexible navigation among these projections to facilitate an intuitive exploration. Finally, we provide detailed comparisons with related systems, and use real-world examples to demonstrate the novelty and usability of our proposed framework.","Title: Tracking features in embedded surfaces: Understanding extinction in turbulent combustion\nYear: 2015\n\nAbstract: Understanding the temporal evolution of features of interest requires the ability to: (i) extract features from each snapshot; (ii) correlate them over time; and (iii) understand the resulting tracking graph. This paper provides new solutions for the last two challenges in the context of large-scale turbulent combustion simulations. In particular, we present a simple and general algorithm to correlate hierarchical features, embedded in time-dependent surfaces. This, for the first time, provides a parameter independent approach to track embedded features. Furthermore, we provide a new technique to adaptively change feature parameters over time to both: alleviate artifacts due to insufficient temporal resolution as well as to simplify the resulting tracking graphs to promote new scientific insights. Our solutions are integrated into a general and flexible analysis environment that allows users to interactively explore the spatio-temporal behavior of large-scale simulations. We demonstrate the results using the analysis of extinction holes in turbulent combustion as primary case study and a number of other applications to illustrate the generality of the approach.","Title: A Randomized Ensemble Approach to Industrial CT Segmentation\nYear: 2015\n\nAbstract: Tuning the models and parameters of common segmentation approaches is challenging especially in the presence of noise and artifacts. Ensemble-based techniques attempt to compensate by randomly varying models and/or parameters to create a diverse set of hypotheses, which are subsequently ranked to arrive at the best solution. However, these methods have been restricted to cases where the underlying models are well-established, e.g. natural images. In practice, it is difficult to determine a suitable base-model and the amount of randomization required. Furthermore, for multi-object scenes no single hypothesis may perform well for all objects, reducing the overall quality of the results. This paper presents a new ensemble-based segmentation framework for industrial CT images demonstrating that comparatively simple models and randomization strategies can significantly improve the result over existing techniques. Furthermore, we introduce a per-object based ranking, followed by a consensus inference that can outperform even the best case scenario of existing hypothesis ranking approaches. We demonstrate the effectiveness of our approach using a set of noise and artifact rich CT images from baggage security and show that it significantly outperforms existing solutions in this area.","Title: Recovering logical structure from Charm++ event traces\nYear: 2015\n\nAbstract: Asynchrony and non-determinism in Charm++ programs present a significant challenge in analyzing their event traces. We present a new framework to organize event traces of parallel programs written in Charm++. Our reorganization allows one to more easily explore and analyze such traces by providing context through logical structure. We describe several heuristics to compensate for missing dependencies between events that currently cannot be easily recorded. We introduce a new task ordering that recovers logical structure from the non-deterministic execution order. Using the logical structure, we define several metrics to help guide developers to performance problems. We demonstrate our approach through two proxy applications written in Charm++. Finally, we discuss the applicability of this framework to other task-based runtimes and provide guidelines for tracing to support this form of analysis.","Title: Relating memory performance data to application domain data using an integration API.\nYear: 2015\n\nAbstract: Understanding performance data, and more specifically memory access pattern is essential in optimizing scientific applications. Among the various factors affecting performance, such as the hardware architecture, the algorithms, or the system software stack, performance is also often related to the applications' physics. While there exists a number of techniques to collect relevant performance metrics, such as number of cache misses, traditional tools almost exclusively present this data relative to the code or as abstract tuples. This can obscure the data dependent nature of performance bottlenecks and make root-cause analysis difficult. Here we take advantage of the fact that a large class of applications are defined over some domain discretized by a mesh. By projecting the performance data directly onto these meshes, we enable developers to explore the performance data in the context of their application resulting in more intuitive visualizations. We introduce a lightweight, general interface to couple a performance visualization tool, MemAxes, to an external visualization tool, VisIt. This allows us to harness the advanced analytic capabilities of MemAxes to drive the exploration while exploiting the capabilities of VisIt to visualize both application and performance data in the application domain.","Title: Identifying the Culprits Behind Network Congestion\nYear: 2015\n\nAbstract: Network congestion is one of the primary causes of performance degradation, performance variability and poor scaling in communication-heavy parallel applications. However, the causes and mechanisms of network congestion on modern interconnection networks are not well understood. We need new approaches to analyze, model and predict this critical behaviour in order to improve the performance of large-scale parallel applications. This paper applies supervised learning algorithms, such as forests of extremely randomized trees and gradient boosted regression trees, to perform regression analysis on communication data and application execution time. Using data derived from multiple executions, we create models to predict the execution time of communication-heavy parallel applications. This analysis also identifies the features and associated hardware components that have the most impact on network congestion and intern, on execution time. The ideas presented in this paper have wide applicability: predicting the execution time on a different number of nodes, or different input datasets, or even for an unknown code, identifying the best configuration parameters for an application, and finding the root causes of network congestion on different architectures.","Title: The Grassmannian Atlas: A General Framework for Exploring Linear Projections of High-Dimensional Data.\nYear: 2016\n\nAbstract: Linear projections are one of the most common approaches to visualize high-dimensional data. Since the space of possible projections is large, existing systems usually select a small set of interesting projections by ranking a large set of candidate projections based on a chosen quality measure. However, while highly ranked projections can be informative, some lower ranked ones could offer important complementary information. Therefore, selection based on ranking may miss projections that are important to provide a global picture of the data. The proposed work fills this gap by presenting the Grassmannian Atlas, a framework that captures the global structures of quality measures in the space of all projections, which enables a systematic exploration of many complementary projections and provides new insights into the properties of existing quality measures.","Title: Evaluation of In-Situ Analysis Strategies at Scale for Power Efficiency and Scalability\nYear: 2016\n\nAbstract: The increasing gap between available compute power and I/O capabilities is resulting in simulation pipelines running on leadership computing facilities being reformulated. In particular, in-situ processing is complementing conventional post-process analysis, however, it can be performed by using the same compute resources as the simulation or using secondary dedicated resources. In this paper, we focus on three different in-situ analysis strategies, which use the same compute resources as the ongoing simulation but different data movement strategies. We evaluate the costs incurred by these strategies in terms of run time, scalability and power/energy consumption. Furthermore, we extrapolate power behavior to peta-scale and investigate different design choices through projections. Experimental evaluation at full machine scale on Titan supports that using fewer cores per node for in-situ analysis is the optimum choice in terms of scalability. Hence, further research effort should be devoted towards developing in-situ analysis techniques following this strategy in future high-end systems.","Title: Analyzing Network Health and Congestion in Dragonfly-Based Supercomputers\nYear: 2016\n\nAbstract: The dragonfly topology is a popular choice for building high-radix, low-diameter, hierarchical networks with high-bandwidth links. On Cray installations of the dragonfly network, job placement policies and routing inefficiencies can lead to significant network congestion for a single job and multi-job workloads. In this paper, we explore the effects of job placement, parallel workloads and network configurations on network health to develop a better understanding of inter-job interference. We have developed a functional network simulator, Damselfly, to model the network behavior of Cray Cascade, and a visual analytics tool, DragonView, to analyze the simulation output. We simulate several parallel workloads based on five representative communication patterns on up to 131,072 cores. Our simulations and visualizations provide unique insight into the buildup of network congestion and present a trade-off between deployment dollar costs and performance of the network.","Title: Interactive exploration of atomic trajectories through relative-angle distribution and associated uncertainties\nYear: 2016\n\nAbstract: Exploration of atomic trajectories is fundamental to understanding and characterizing complex chemical systems important in many applications. For instance, any new insight into the mechanisms of ionic migration in catalytic materials could lead to a substantial increase in battery performance. A new statistical measure, called the relative-angle distribution, has been proposed to understand complex motion - whether Brownian, ballistic, or diffusive. The relative-angle distribution can be represented as a collection of 1D histograms, but is currently created in a slow, offline process, making any parameter exploration a tedious and time-consuming task. Furthermore, the resulting plot can hide uncertainty in both the data and the visualization. As a result, once rastered or printed at a fixed resolution, these histograms can be misleading. We present a new analysis tool for the exploration of atomic trajectories that combines an interactive histogram visualization with uncertainty information for both data and plotting errors, and is also linked to an interactive 3D display of trajectories. Our tool enables a holistic exploration of trajectories previously not feasible, with the potential for significant scientific impact. In collaboration with domain experts, we have deployed our tool ta analyze molecular dynamics simulations of lithium-ion diffusion. Users have found that the tool significantly accelerates the exploration process and have used it to validate a number of previously unconfirmed hypotheses.","Title: Scalable Visualization and Interactive Analysis Using Massive Data Streams.\nYear: 2012\n\nAbstract: Historically, data creation and storage has always outpaced the infrastructure for its movement and utilization. This trend is increasing now more than ever, with the ever growing size of scientific simulations, increased resolution of sensors, and large mosaic images. Effective exploration of massive scientific models demands the combination of data management, analysis, and visualization techniques, working together in an interactive setting. The ViSUS application framework has been designed as an environment that allows the interactive exploration and analysis of massive scientific models in a cache-oblivious, hardware-agnostic manner, enabling processing and visualization of possibly geographically distributed data using many kinds of devices and platforms. For general purpose feature segmentation and exploration we discuss a new paradigm based on topological analysis. This approach enables the extraction of summaries of features present in the data through abstract models that are orders of magnitude smaller than the raw data, providing enough information to support general queries and perform a wide range of analyses without access to the original data.","Title: Stair blue noise sampling.\nYear: 2016\n\nAbstract: A common solution to reducing visible aliasing artifacts in image reconstruction is to employ sampling patterns with a blue noise power spectrum. These sampling patterns can prevent discernible artifacts by replacing them with incoherent noise. Here, we propose a new family of blue noise distributions, Stair blue noise, which is mathematically tractable and enables parameter optimization to obtain the optimal sampling distribution. Furthermore, for a given sample budget, the proposed blue noise distribution achieves a significantly larger alias-free low-frequency region compared to existing approaches, without introducing visible artifacts in the mid-frequencies. We also develop a new sample synthesis algorithm that benefits from the use of an unbiased spatial statistics estimator and efficient optimization strategies.","Title: In-Staging Data Placement for Asynchronous Coupling of Task-Based Scientific Workflows.\nYear: 2016\n\nAbstract: Coupled application workflows composed of applications implemented using task-based models present new coupling and data exchange challenges, due to the asynchronous interaction and coupling behaviors between tasks of the component applications. In this paper, we present an adaptive data placement approach that addresses these challenges by dynamically adjusting to the asynchronous coupling patterns. Specifically, it places data across a set of staging cores/nodeswith an awareness of the application-specific data locality requirements and the runtime task executions at these staging cores/nodes, with the goal of reducing end-to-end execution time and data movement overhead of the workflow. We experimentally demonstrate the effectiveness of our approach on the Titan Cray XK7 system using representative data coupling patterns derived from current scientific workflows. The evaluation demonstrates that our approach efficiently improves performance by reducing the time-to-solution and increasing the quality of insights for scientific discovery.","Title: MemAxes: Visualization and Analytics for Characterizing Complex Memory Performance Behaviors.\nYear: 2018\n\nAbstract: Memory performance is often a major bottleneck for high-performance computing (HPC) applications. Deepening memory hierarchies, complex memory management, and non-uniform access times have made memory performance behavior difficult to characterize, and users require novel, sophisticated tools to analyze and optimize this aspect of their codes. Existing tools target only specific factors of memory ...","Title: Visualizing High-Dimensional Data: Advances in the Past Decade.\nYear: 2015\n\nAbstract: Massive simulations and arrays of sensing devices, in combination with increasing computing resources, have generated large, complex, high-dimensional datasets used to study phenomena across numerous fields of study. Visualization plays an important role in exploring such datasets. We provide a comprehensive survey of advances in high-dimensional data visualization that focuses on the past decade. We aim at providing guidance for data practitioners to navigate through a modular view of the recent advances, inspiring the creation of new visualizations along the enriched visualization pipeline, and identifying future opportunities for visualization research.","Title: Two-dimensional shape retrieval using the distribution of extrema of Laplacian eigenfunctions.\nYear: 2017\n\nAbstract: We propose a new method using the distribution of extrema of Laplacian eigenfunctions for two-dimensional (2D) shape description and matching. We construct a weighted directed graph, which we call signed natural neighbor graph, to represent a Laplacian eigenfunction of a shape. The nodes of this sparse graph are the extrema of the corresponding eigenfunction, and the edge weights are defined by signed natural neighbor coordinates derived from the local spatial arrangement of extrema. We construct the signed natural neighbor graphs defined by a small number of low-frequency Laplacian eigenfunctions of a shape to describe it. This shape descriptor is invariant under rigid transformations and uniform scaling, and is also insensitive to minor boundary deformations. When using our shape descriptor for matching two shapes, we determine their similarity by comparing the graphs induced by corresponding Laplacian eigenfunctions of the two shapes. Our experimental shape-matching results demonstrate that our method is effective for 2D shape retrieval.","Title: Embedded domain-specific language and runtime system for progressive spatiotemporal data analysis and visualization\nYear: 2016\n\nAbstract: As our ability to generate large and complex datasets grows, accessing and processing these massive data collections is increasingly the primary bottleneck in scientific analysis. Challenges include retrieving, converting, resampling, and combining remote and often disparately located data ensembles with only limited support from existing tools. In particular, existing solutions rely predominantly on extensive data transfers or large-scale remote computing resources, both of which are inherently offline processes with long delays and substantial repercussions for any mistakes. Such workflows severely limit the flexible exploration and rapid evaluation of new hypotheses that are crucial to the scientific process and thereby impede scientific discovery. Here we present an embedded domain-specific language (EDSL) specifically designed for the interactive exploration of large-scale, remote data. Our EDSL allows users to express a wide range of data analysis operations in a simple and abstract manner. The underlying runtime system transparently resolves issues such as remote data access and resampling while at the same time maintaining interactivity through progressive and interruptible computation. This system enables, for the first time, interactive remote exploration of massive datasets such as the 7km NASA GEOS-5 Nature Run simulation, which previously have been analyzed only offline or at reduced resolution.","Title: Visual Exploration of Semantic Relationships in Neural Word Embeddings.\nYear: 2018\n\nAbstract: Constructing distributed representations for words through neural language models and using the resulting vector spaces for analysis has become a crucial component of natural language processing (NLP). However, despite their widespread application, little is known about the structure and properties of these spaces. To gain insights into the relationship between words, the NLP community has begun t...","Title: Poisson Disk Sampling on the Grassmannnian: Applications in Subspace Optimization\nYear: 2017\n\nAbstract: To develop accurate inference algorithms on embedded manifolds such as the Grassmannian, we often employ several optimization tools and incorporate the characteristics of known manifolds as additional constraints. However, a direct analysis of the nature of functions on manifolds is rarely performed. In this paper, we propose an alternative approach to this inference by adopting a statistical pipeline that first generates an initial sampling of the manifold, and then performs subsequent analysis based on these samples. First, we introduce a better sampling technique based on dart throwing (called the Poisson disk sampling (PDS)) to effectively sample the Grassmannian. Next, using Grassmannian sparse coding, we demonstrate the improved coverage achieved by PDS. Finally, we develop a consensus approach, with Grassmann samples, to infer the optimal embeddings for linear dimensionality reduction, and show that the resulting solutions are nearly optimal.","Title: ISAVS: interactive scalable analysis and visualization system.\nYear: 2017\n\nAbstract: Modern science is inundated with ever increasing data sizes as computational capabilities and image acquisition techniques continue to improve. For example, simulations are tackling ever larger domains with higher fidelity, and high-throughput microscopy techniques generate larger data that are fundamental to gather biologically and medically relevant insights. As the image sizes exceed memory, and even sometimes local disk space, each step in a scientific workflow is impacted. Current software solutions enable data exploration with limited interactivity for visualization and analytic tasks. Furthermore analysis on HPC systems often require complex hand-written parallel implementations of algorithms that suffer from poor portability and maintainability.\n\nWe present a software infrastructure that simplifies end-to-end visualization and analysis of massive data. First, a hierarchical streaming data access layer enables interactive exploration of remote data, with fast data fetching to test analytics on subsets of the data. Second, a library simplifies the process of developing new analytics algorithms, allowing users to rapidly prototype new approaches and deploy them in an HPC setting. Third, a scalable runtime system automates mapping analysis algorithms to whatever computational hardware is available, reducing the complexity of developing scaling algorithms. We demonstrate the usability and performance of our system using a use case from neuroscience: filtering, registration, and visualization of tera-scale microscopy data. We evaluate the performance of our system using a leadership-class supercomputer, Shaheen II.","Title: Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion.\nYear: 2018\n\nAbstract: Computed Tomography (CT) reconstruction is a fundamental component to a wide variety of applications ranging from security, to healthcare. The classical techniques require measuring projections, called sinograms, from a full 180° view of the object. However, obtaining a full-view is not always feasible, such as when scanning irregular objects that limit flexibility of scanner rotation. The resulting limited angle sinograms are known to produce highly artifact-laden reconstructions with existing techniques. In this paper, we propose to address this problem using CTNet - a system of 1D and 2D convolutional neural networks, that operates directly on a limited angle sinogram to predict the reconstruction. We use the x-ray transform on this prediction to obtain a completed sinogram, as if it came from a full 180°view. We feed this to standard analytical and iterative reconstruction techniques to obtain the final reconstruction. We show with extensive experimentation on a challenging real world dataset that this combined strategy outperforms many competitive baselines. We also propose a measure of confidence for the reconstruction that enables a practitioner to gauge the reliability of a prediction made by CTNet. We show that this measure is a strong indicator of quality as measured by the PSNR, while not requiring ground truth at test time. Finally, using a segmentation experiment, we show that our reconstruction also preserves the 3D structure of objects better than existing solutions.","Title: Interactive Visualization and Exploration of Patient Progression in a Hospital Setting.\nYear: 2017\n\nAbstract: As medical organizations increasingly adopt the use of electronic health records (EHRs), large volumes of clinical data are being captured on a daily basis. These data provide comprehensive information about patients and have the potential to improve a wide range of application domains in healthcare. Physicians and clinical researchers are interested in finding effective ways to understand this abundance of data. Use of visual analytics to explore healthcare data is one such research direction. Here, we present a visualization and analysis environment to understand patient progression over time. Through the use of optimized data structures and progressive visualization techniques, we allow users to interactively explore how patients and their progression change over time. Compared to existing techniques, our work provides additional flexibility in analyzing patient data and has the potential to be used in a real-time hospital setting. Finally, we demonstrate the utility of our approach using a publicly available intensive care unit (ICU) database.","Title: A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms.\nYear: 2018\n\nAbstract: This paper proposes a new approach to construct high quality space-filling sample designs. First, we propose a novel technique to quantify the space-filling property and optimally trade-off uniformity and randomness in sample designs in arbitrary dimensions. Second, we connect the proposed metric (defined in the spatial domain) to the quality metric of the design performance (defined in the spectral domain). This connection serves as an analytic framework for evaluating the qualitative properties of space-filling designs in general. Using the theoretical insights provided by this spatial-spectral analysis, we derive the notion of optimal space-filling designs, which we refer to as space-filling spectral designs. Third, we propose an efficient estimator to evaluate the space-filling properties of sample designs in arbitrary dimensions and use it to develop an optimization framework for generating high quality space-filling designs. Finally, we carry out a detailed performance comparison on two different applications in varying dimensions: a) image reconstruction and b) surrogate modeling for several benchmark optimization functions and a physics simulation code for inertial confinement fusion (ICF). Our results clearly evidence the superiority of the proposed space-filling designs over existing approaches, particularly in high dimensions.","Title: TopoMS: Comprehensive topological exploration for molecular and condensed-matter systems.\nYear: 2018\n\nAbstract: We introduce TopoMS, a computational tool enabling detailed topological analysis of molecular and condensed-matter systems, including the computation of atomic volumes and charges through the quantum theory of atoms in molecules, as well as the complete molecular graph. With roots in techniques from computational topology, and using a shared-memory parallel approach, TopoMS provides scalable, numerically robust, and topologically consistent analysis. TopoMS can be used as a command-line tool or with a GUI (graphical user interface), where the latter also enables an interactive exploration of the molecular graph. This paper presents algorithmic details of TopoMS and compares it with state-of-the-art tools: Bader charge analysis v1.0 (Arnaldsson et al., 01/11/17) and molecular graph extraction using Critic2 (Otero-de-la-Roza et al., Comput. Phys. Commun. 2014, 185, 1007). TopoMS not only combines the functionality of these individual codes but also demonstrates up to 4x performance gain on a standard laptop, faster convergence to fine-grid solution, robustness against lattice bias, and topological consistency. TopoMS is released publicly under BSD License. (c) 2018 Wiley Periodicals, Inc.","Title: Rendering and Extracting Extremal Features in 3D Fields.\nYear: 2018\n\nAbstract: Visualizing and extracting three-dimensional features is important for many computational science applications, each with their own feature definitions and data types. While some are simple to state and implement (e.g. isosurfaces), others require more complicated mathematics (e.g. multiple derivatives, curvature, eigenvectors, etc.). Correctly implementing mathematical definitions is difficult, so experimenting with new features requires substantial investments. Furthermore, traditional interpolants rarely support the necessary derivatives, and approximations can reduce numerical stability. Our new approach directly translates mathematical notation into practical visualization and feature extraction, with minimal mental and implementation overhead. Using a mathematically expressive domain-specific language, Diderot, we compute direct volume renderings and particle-based feature samplings for a range of mathematical features. Non-expert users can experiment with feature definitions without any exposure to meshes, interpolants, derivative computation, etc. We demonstrate high-quality results on notoriously difficult features, such as ridges and vortex cores, using working code simple enough to be presented in its entirety.","Title: BabelFlow: An Embedded Domain Specific Language for Parallel Analysis and Visualization\nYear: 2018\n\nAbstract: The rapid growth in simulation data requires large-scale parallel implementations of scientific analysis and visualization algorithms, both to produce results within an acceptable timeframe and to enable in situ deployment. However, efficient and scalable implementations, especially of more complex analysis approaches, require not only advanced algorithms, but also an in-depth knowledge of the underlying runtime. Furthermore, different machine configurations and different applications may favor different runtimes, i.e., MPI vs Charm++ vs Legion, etc., and different hardware architectures. This diversity makes developing and maintaining a broadly applicable analysis software infrastructure challenging. We address some of these problems by explicitly separating the implementation of individual tasks of an algorithm from the dataflow connecting these tasks. In particular, we present an embedded domain specific language (EDSL) to describe algorithms using a new task graph abstraction. This task graph is then executed on top of one of several available runtimes (MPI, Charm++, Legion) using a thin layer of library calls. We demonstrate the flexibility and performance of this approach using three different large scale analysis and visualization use cases, i.e., topological analysis, rendering and compositing dataflow, and image registration of large microscopy scans. Despite the unavoidable overheads of a generic solution, our approach demonstrates performance portability at scale, and, in some cases, outperforms hand-optimized implementations.","Title: Controlled Random Search Improves Sample Mining and Hyper-Parameter Optimization.\nYear: 2018\n\nAbstract: A common challenge in machine learning and related fields is the need to efficiently explore high dimensional parameter spaces using small numbers of samples. Typical examples are hyper-parameter optimization in deep learning and sample mining in predictive modeling tasks. All such problems trade-off exploration, which samples the space without knowledge of the target function, and exploitation where information from previous evaluations is used in an adaptive feedback loop. Much of the recent focus has been on the exploitation while exploration is done with simple designs such as Latin hypercube or even uniform random sampling. In this paper, we introduce optimal space-filling sample designs for effective exploration of high dimensional spaces. Specifically, we propose a new parameterized family of sample designs called space-filling spectral designs, and introduce a framework to choose optimal designs for a given sample size and dimension. Furthermore, we present an efficient algorithm to synthesize a given spectral design. Finally, we evaluate the performance of spectral designs in both data space and model space applications. The data space exploration is targeted at recovering complex regression functions in high dimensional spaces. The model space exploration focuses on selecting hyper-parameters for a given neural network architecture. Our empirical studies demonstrate that the proposed approach consistently outperforms state-of-the-art techniques, particularly with smaller design sizes.","Title: Understanding Deep Neural Networks Through Input Uncertainties\nYear: 2018\n\nAbstract: Techniques for understanding the functioning of complex machine learning models are becoming increasingly popular, not only to improve the validation process, but also to extract new insights about the data via exploratory analysis. Though a large class of such tools currently exists, most assume that predictions are point estimates and use a sensitivity analysis of these estimates to interpret the model. Using lightweight probabilistic networks we show how including prediction uncertainties in the sensitivity analysis leads to: (i) more robust and generalizable models; and (ii) a new approach for model interpretation through uncertainty decomposition. In particular, we introduce a new regularization that takes both the mean and variance of a prediction into account and demonstrate that the resulting networks provide improved generalization to unseen data. Furthermore, we propose a new technique to explain prediction uncertainties through uncertainties in the input domain, thus providing new ways to validate and interpret deep learning models.","Title: Unsupervised Dimension Selection using a Blue Noise Spectrum.\nYear: 2018\n\nAbstract: Unsupervised dimension selection is an important problem that seeks to reduce dimensionality of data, while preserving the most useful characteristics. While dimensionality reduction is commonly utilized to construct low-dimensional embeddings, they produce feature spaces that are hard to interpret. Further, in applications such as sensor design, one needs to perform reduction directly in the input domain, instead of constructing transformed spaces. Consequently, dimension selection (DS) aims to solve the combinatorial problem of identifying the top-$k$ dimensions, which is required for effective experiment design, reducing data while keeping it interpretable, and designing better sensing mechanisms. In this paper, we develop a novel approach for DS based on graph signal analysis to measure feature influence. By analyzing synthetic graph signals with a blue noise spectrum, we show that we can measure the importance of each dimension. Using experiments in supervised learning and image masking, we demonstrate the superiority of the proposed approach over existing techniques in capturing crucial characteristics of high dimensional spaces, using only a small subset of the original features.","Title: Shared-Memory Parallel Computation Of Morse-Smale Complexes With Improved Accuracy\nYear: 2019\n\nAbstract: Topological techniques have proven to be a powerful tool in the analysis and visualization of large-scale scientific data. In particular, the Morse-Smale complex and its various components provide a rich framework for robust feature definition and computation. Consequently, there now exist a number of approaches to compute Morse-Smale complexes for large-scale data in parallel. However, existing techniques are based on discrete concepts which produce the correct topological structure but are known to introduce grid artifacts in the resulting geometry. Here, we present a new approach that combines parallel streamline computation with combinatorial methods to construct a high-quality discrete Morse-Smale complex. In addition to being invariant to the orientation of the underlying grid, this algorithm allows users to selectively build a subset of features using high-quality geometry. In particular, a user may specifically select which ascending/descending manifolds are reconstructed with improved accuracy, focusing computational effort where it matters for subsequent analysis. This approach computes Morse-Smale complexes for larger data than previously feasible with significant speedups. We demonstrate and validate our approach using several examples from a variety of different scientific domains, and evaluate the performance of our method.","Title: A Study Of The Trade-Off Between Reducing Precision And Reducing Resolution For Data Analysis And Visualization\nYear: 2019\n\nAbstract: There currently exist two dominant strategies to reduce data sizes in analysis and visualization: reducing the precision of the data, e.g.. through quantization, or reducing its resolution, e.g., by subsampling. Both have advantages and disadvantages and both face fundamental limits at which the reduced information ceases to be useful. The paper explores the additional gains that could be achieved by combining both strategies. In particular, we present a common framework that allows us to study the trade-off in reducing precision and/or resolution in a principled manner. We represent data reduction schemes as progressive streams of bits and study how various bit orderings such as by resolution, by precision, etc., impact the resulting approximation error across a variety of data sets as well as analysis tasks. Furthermore. we compute streams that are optimized for different tasks to serve as lower bounds on the achievable error. Scientific data management systems can use the results presented in this paper as guidance on how to store and stream data to make efficient use of the limited storage and bandwidth in practice.","Title: NLIZE: A Perturbation-Driven Visual Interrogation Tool for Analyzing and Interpreting Natural Language Inference Models.\nYear: 2019\n\nAbstract: With the recent advances in deep learning, neural network models have obtained state-of-the-art performances for many linguistic tasks in natural language processing. However, this rapid progress also brings enormous challenges. The opaque nature of a neural network model leads to hard-to-debug-systems and difficult-to-interpret mechanisms. Here, we introduce a visualization system that, through a...","Title: Stability of Dissipation Elements: A Case Study in Combustion\nYear: 2014\n\nAbstract: Recently, dissipation elements have been gaining popularity as a mechanism for measurement of fundamental properties of turbulent flow, such as turbulence length scales and zonal partitioning. Dissipation elements segment a domain according to the source and destination of streamlines in the gradient flow field of a scalar function f : ﾿ ℝ. They have traditionally been computed by numerically integrating streamlines from the center of each voxel in the positive and negative gradient directions, and grouping those voxels whose streamlines terminate at the same extremal pair. We show that the same structures map well to combinatorial topology concepts developed recently in the visualization community. Namely, dissipation elements correspond to sets of cells of the Morse-Smale complex. The topology-based formulation enables a more exploratory analysis of the nature of dissipation elements, in particular, in understanding their stability with respect to small scale variations. We present two examples from combustion science that raise significant questions about the role of small scale perturbation and indeed the definition of dissipation elements themselves.","Title: Interactive Investigation of Traffic Congestion on Fat-Tree Networks Using TreeScope.\nYear: 2018\n\nAbstract: Parallel simulation codes often suffer from performance bottlenecks due to network congestion, leaving millions of dollars of investments underutilized. Given a network topology, it is critical to understand how different applications, job placements, routing schemes, etc., are affected by and contribute to network congestion, especially for large and complex networks. Understanding and optimizing communication on large-scale networks is an active area of research. Domain experts often use exploratory tools to develop both intuitive and formal metrics for network health and performance. This paper presents TreeScope, an interactive, web-based visualization tool for exploring network traffic on large-scale fat-tree networks. TreeScope encodes the network topology using a tailored matrix-based representation and provides detailed visualization of all traffic in the network. We report on the design process of TreeScope, which has been received positively by network researchers as well as system administrators. Through case studies of real and simulated data, we demonstrate how TreeScope's visual design and interactive support for complex queries on network traffic can provide experts with new insights into the occurrences and causes of congestion in the network.","Title: A Look at the Effect of Sample Design on Generalization through the Lens of Spectral Analysis.\nYear: 2019\n\nAbstract: This paper provides a general framework to study the effect of sampling properties of training data on the generalization error of the learned machine learning (ML) models. Specifically, we propose a new spectral analysis of the generalization error, expressed in terms of the power spectra of the sampling pattern and the function involved. The framework is build in the Euclidean space using Fourier analysis and establishes a connection between some high dimensional geometric objects and optimal spectral form of different state-of-the-art sampling patterns. Subsequently, we estimate the expected error bounds and convergence rate of different state-of-the-art sampling patterns, as the number of samples and dimensions increase. We make several observations about generalization error which are valid irrespective of the approximation scheme (or learning architecture) and training (or optimization) algorithms. Our result also sheds light on ways to formulate design principles for constructing optimal sampling methods for particular problems.","Title: Unsupervised Dimension Selection Using A Blue Noise Graph Spectrum\nYear: 2019\n\nAbstract: Unsupervised dimension selection is an important problem that seeks to reduce dimensionality of data, while preserving the most useful characteristics. While dimensionality reduction is commonly utilized to construct low-dimensional embeddings, they produce feature spaces that are hard to interpret. Further, in applications such as sensor design, one needs to perform reduction directly in the input domain, instead of constructing transformed spaces. Consequently, dimension selection (DS) aims to solve the combinatorial problem of identifying the top-k dimensions, which is required for effective experiment design, reducing data while keeping it interpretable, and designing better sensing mechanisms. In this paper, we develop a novel approach for DS based on graph signal analysis to measure feature influence. By analyzing synthetic graph signals with a blue noise spectrum, we show that we can measure the importance of each dimension. Using experiments in supervised learning and image masking, we demonstrate the superiority of the proposed approach over existing techniques in capturing crucial characteristics of high dimensional spaces, using only a small subset of the original features.","Title: Distributed merge forest: a new fast and scalable approach for topological analysis at scale\nYear: 2021\n\nAbstract: ABSTRACTTopological analysis is used in several domains to identify and characterize important features in scientific data, and is now one of the established classes of techniques of proven practical use in scientific computing. The growth in parallelism and problem size tackled by modern simulations poses a particular challenge for these approaches. Fundamentally, the global encoding of topological features necessitates interprocess communication that limits their scaling. In this paper, we extend a new topological paradigm to the case of distributed computing, where the construction of a global merge tree is replaced by a distributed data structure, the merge forest, trading slower individual queries on the structure for faster end-to-end performance and scaling. Empirically, the queries that are most negatively affected also tend to have limited practical use. Our experimental results demonstrate the scalability of both the merge forest construction and the parallel queries needed in scientific workflows, and contrast this scalability with the two established alternatives that construct variations of a global tree.","Title: Leveraging Topological Events In Tracking Graphs For Understanding Particle Diffusion\nYear: 2021\n\nAbstract: Single particle tracking (SPT) of fluorescent molecules provides significant insights into the diffusion and relative motion of tagged proteins and other structures of interest in biology. However, despite the latest advances in high-resolution microscopy, individual particles are typically not distinguished from clusters of particles. This lack of resolution obscures potential evidence for how merging and splitting of particles affect their diffusion and any implications on the biological environment. The particle tracks are typically decomposed into individual segments at observed merge and split events, and analysis is performed without knowing the true count of particles in the resulting segments. Here, we address the challenges in analyzing particle tracks in the context of cancer biology. In particular, we study the tracks of KRAS protein, which is implicated in nearly 20% of all human cancers, and whose clustering and aggregation have been linked to the signaling pathway leading to uncontrolled cell growth. We present a new analysis approach for particle tracks by representing them as tracking graphs and using topological events - merging and splitting, to disambiguate the tracks. Using this analysis, we infer a lower bound on the count of particles as they cluster and create conditional distributions of diffusion speeds before and after merge and split events. Using thousands of time-steps of simulated and in-vitro SPT data, we demonstrate the efficacy of our method, as it offers the biologists a new, detailed look into the relationship between KRAS clustering and diffusion speeds.","Title: Towards replacing physical testing of granular materials with a Topology-based Model\nYear: 2022\n\nAbstract: In the study of packed granular materials, the performance of a sample (e.g., the detonation of a high-energy explosive) often correlates to measurements of a fluid flowing through it. The “effective surface area,” the surface area accessible to the airflow, is typically measured using a permeametry apparatus that relates the flow conductance to the permeable surface area via the Carman-Kozeny equ...","Title: Generalizable coordination of large multiscale workflows: challenges and learnings at scale\nYear: 2021\n\nAbstract: ABSTRACTThe advancement of machine learning techniques and the heterogeneous architectures of most current supercomputers are propelling the demand for large multiscale simulations that can automatically and autonomously couple diverse components and map them to relevant resources to solve complex problems at multiple scales. Nevertheless, despite the recent progress in workflow technologies, current capabilities are limited to coupling two scales. In the first-ever demonstration of using three scales of resolution, we present a scalable and generalizable framework that couples pairs of models using machine learning and in situ feedback. We expand upon the massively parallel Multiscale Machine-Learned Modeling Infrastructure (MuMMI), a recent, award-winning workflow, and generalize the framework beyond its original design. We discuss the challenges and learnings in executing a massive multiscale simulation campaign that utilized over 600,000 node hours on Summit and achieved more than 98% GPU occupancy for more than 83% of the time. We present innovations to enable several orders of magnitude scaling, including simultaneously coordinating 24,000 jobs, and managing several TBs of new data per day and over a billion files in total. Finally, we describe the generalizability of our framework and, with an upcoming open-source release, discuss how the presented framework may be used for new applications.","Title: Data-Driven Estimation of Temporal-Sampling Errors in Unsteady Flows\nYear: 2021\n\nAbstract: While computer simulations typically store data at the highest available spatial resolution, it is often infeasible to do so for the temporal dimension. Instead, the common practice is to store data at regular intervals, the frequency of which is strictly limited by the available storage and I/O bandwidth. However, this manner of temporal subsampling can cause significant errors in subsequent analysis steps. More importantly, since the intermediate data is lost, there is no direct way of measuring this error after the fact. One particularly important use case that is affected is the analysis of unsteady flows using pathlines, as it depends on an accurate interpolation across time. Although the potential problem with temporal undersampling is widely acknowledged, there currently does not exist a practical way to estimate the potential impact. This paper presents a simple-to-implement yet powerful technique to estimate the error in pathlines due to temporal subsampling. Given an unsteady flow, we compute pathlines at the given temporal resolution as well as subsamples thereof. We then compute the error induced due to various levels of subsampling and use it to estimate the error between the given resolution and the unknown ground truth. Using two turbulent flows, we demonstrate that our approach, for the first time, provides an accurate, a posteriori error estimate for pathline computations. This estimate will enable scientists to better understand the uncertainties involved in pathline-based analysis techniques and can lead to new uncertainty visualization approaches using the predicted errors.","Title: Portable and Composable Flow Graphs for In Situ Analytics\nYear: 2021\n\nAbstract: Existing data analysis and visualization algorithms are used in a wide range of simulations that strive to support an increasing number of runtime systems. The BabelFlow framework has been designed to address this situation by providing users with a simple interface to implement analysis algorithms as dataflow graphs portable across different runtimes. The limitation in BabelFlow, however, is that...","Title: AMM: Adaptive Multilinear Meshes\nYear: 2022\n\nAbstract: Adaptive representations are increasingly indispensable for reducing the in-memory and on-disk footprints of large-scale data. Usual solutions are designed broadly along two themes: reducing data precision, e.g., through compression, or adapting data resolution, e.g., using spatial hierarchies. Recent research suggests that combining the two approaches, i.e., adapting both res...","Title: Enabling machine learning-ready HPC ensembles with Merlin\nYear: 2022\n\nAbstract: With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data. With complexities such as multi-component workflows, heterogeneous machine architectures, parallel file systems, and batch scheduling, care must be taken to facilitate this analysis in a high performance computing (HPC) environment. In this paper, we present Merlin, a workflow framework to enable large ML-friendly ensembles of scientific HPC simulations. By augmenting traditional HPC with distributed compute technologies, Merlin aims to lower the barrier for scientific subject matter experts to incorporate ML into their analysis. As a producer–consumer workflow model, Merlin enables multi-machine, cross-batch job, dynamically allocated yet persistent workflows capable of utilizing surge-compute resources. Key features of Merlin are a flexible HPC-centric interface, low per-task overhead, multi-tiered fault recovery, and a hierarchical sampling algorithm that allows for O(N) task execution and O(NlnN) task queuing to ensembles of millions of tasks. In addition to Merlin’s design, we test the algorithm’s performance in an HPC center and demonstrate the ability to enqueue 40 million simulations in 100 s, with a 30 millisecond per-task overhead that is independent of ensemble size. Finally, we describe some example applications that Merlin has enabled on leadership-class HPC resources, such as the ML-augmented optimization of nuclear fusion experiments and the calibration of infectious disease models to study the progression of and possible mitigation strategies for COVID-19.","Title: The Case for Optimized Edge-Centric Tractography at Scale\nYear: 2022\n\nAbstract: The anatomic validity of structural connectomes remains a significant uncertainty in neuroimaging. Edge-centric tractography reconstructs streamlines in bundles between each pair of cortical or subcortical regions. Although edge bundles provides a stronger anatomic embedding than traditional connectomes, calculating them for each region-pair requires exponentially greater computation. We observe that major speedup can be achieved by reducing the number of streamlines used by probabilistic tractography algorithms. To ensure this does not degrade connectome quality, we calculate the identifiability of edge-centric connectomes between test and re-test sessions as a proxy for information content. We find that running PROBTRACKX2 with as few as 1 streamline per voxel per region-pair has no significant impact on identifiability. Variation in identifiability caused by streamline count is overshadowed by variation due to subject demographics. This finding even holds true in an entirely different tractography algorithm using MRTrix. Incidentally, we observe that Jaccard similarity is more effective than Pearson correlation in calculating identifiability for our subject population.","Title: Virtual Inspection of Additively Manufactured Parts\nYear: 2022\n\nAbstract: Advanced manufacturing techniques, such as additive manufacturing, enable the design of increasingly complex components for a wide range of industrial applications. However, this complexity makes qualification of the parts, determining whether a part is within some margin of error from the initial design, difficult. To inspect and qualify complex internal geometries that are not accessible with an external probe, parts are typically scanned with computed tomography (CT), and manually compared to the computer-aided design (CAD) model using visual inspections. Matching the CAD model to the 3D reconstructed object is challenging in a traditional desktop environment due to the lack of depth perception and 3D interaction. An additional challenge comes from the geometric complexity of CAD meshes and large-scale CT scans. We present a virtual reality (VR) system for manual qualification, providing a novel defect visualization method. First, we describe a semiautomatic CAD-to-Scan Registration approach in VR using a finite element mesh. Second, we introduce the Defect Box, which enables full-resolution inspection for massive scans and CAD-CT comparison of local defect regions. Finally, our system includes intuitive 3D Metrology methods that enable natural interactions for the measurement of features and defects in VR. We demonstrate our approach on both real and synthetic data and discuss feedback from four expert users in nondestructive qualification.","Title: A Study of the Locality of Persistence-Based Queries and Its Implications for the Efficiency of Localized Data Structures\nYear: 2022\n\nAbstract: Scientific datasets are often analyzed and visualized using isosurfaces. The connected components at or above the isovalue defining these isosurfaces are called superlevel-set components. The vertex set of these superlevel-set components can be used to compute local statistics, such as mean temperature or histogram per component, or to segment the data. However, in datasets produced by acquisition devices or simulations, noise induces many spurious components that clutter the visualization and analysis results. Many of these spurious components would disappear if the data values were slightly adjusted. The notion of persistence captures the stability of a component with respect to function value changes, and so we are interested in computing persistence quickly. Locality of computation is critical for parallel scalability, minimization of communication in a distributed environment, or an out-of-core processing. The recently introduced merge forest attained high performance by exploiting locality, thereby avoiding communication until needed to resolve a feature query. We extend the merge forest to support persistence-based queries and study the locality of these queries by evaluating the traversals of regions of data during a query. We confirm that the majority of evaluated datasets have the property that the noise is mostly local, and thus can be efficiently eliminated without performing a global analysis. Finally, we compare the query running times with those of a triplet merge tree because a triplet merge tree answers all proposed queries in constant time and can be constructed from a merge tree in linear time."],"string":"[\n \"Title: Segmenting molecular surfaces\\nYear: 2006\\n\\nAbstract: This paper presents a new method for segmentation of molecular surfaces. Topological analysis of a scalar function defined on the surface and its associated gradient field reveals the relationship between the features of interest and critical points of the scalar function. The segmentation is obtained by associating segments with local minima/maxima. Controlled simplification of the function merges segments resulting in a hierarchical segmentation of the molecular surface. This segmentation is used to identify rigid components of protein molecules and to study the role of cavities and protrusions in protein-protein interactions.\",\n \"Title: Spectral surface quadrangulation\\nYear: 2006\\n\\nAbstract: Resampling raw surface meshes is one of the most fundamental operations used by nearly all digital geometry processing systems. The vast majority of this work has focused on triangular remeshing, yet quadrilateral meshes are preferred for many surface PDE problems, especially fluid dynamics, and are best suited for defining Catmull-Clark subdivision surfaces. We describe a fundamentally new approach to the quadrangulation of manifold polygon meshes using Laplacian eigenfunctions, the natural harmonics of the surface. These surface functions distribute their extrema evenly across a mesh, which connect via gradient flow into a quadrangular base mesh. An iterative relaxation algorithm simultaneously refines this initial complex to produce a globally smooth parameterization of the surface. From this, we can construct a well-shaped quadrilateral mesh with very few extraordinary vertices. The quality of this mesh relies on the initial choice of eigenfunction, for which we describe algorithms and hueristics to efficiently and effectively select the harmonic most appropriate for the intended application.\",\n \"Title: Interactive editing of massive imagery made simple: Turning Atlanta into Atlantis\\nYear: 2011\\n\\nAbstract: This article presents a simple framework for progressive processing of high-resolution images with minimal resources. We demonstrate this framework's effectiveness by implementing an adaptive, multi-resolution solver for gradient-based image processing that, for the first time, is capable of handling gigapixel imagery in real time. With our system, artists can use commodity hardware to interactively edit massive imagery and apply complex operators, such as seamless cloning, panorama stitching, and tone mapping. We introduce a progressive Poisson solver that processes images in a purely coarse-to-fine manner, providing near instantaneous global approximations for interactive display (see Figure 1). We also allow for data-driven adaptive refinements to locally emulate the effects of a global solution. These techniques, combined with a fast, cache-friendly data access mechanism, allow the user to interactively explore and edit massive imagery, with the illusion of having a full solution at hand. In particular, we demonstrate the interactive modification of gigapixel panoramas that previously required extensive offline processing. Even with massive satellite images surpassing a hundred gigapixels in size, we enable repeated interactive editing in a dynamically changing environment. Images at these scales are significantly beyond the purview of previous methods yet are processed interactively using our techniques. Finally our system provides a robust and scalable out-of-core solver that consistently offers high-quality solutions while maintaining strict control over system resources.\",\n \"Title: Novel views of performance data to analyze large-scale adaptive applications\\nYear: 2012\\n\\nAbstract: Performance analysis of parallel scientific codes is becoming increasingly difficult due to the rapidly growing complexity of applications and architectures. Existing tools fall short in providing intuitive views that facilitate the process of performance debugging and tuning. In this paper, we extend recent ideas of projecting and visualizing performance data for faster, more intuitive analysis of applications. We collect detailed per-level and per-phase measurements for a dynamically load-balanced, structured AMR library and project per-core data collected in the hardware domain on to the application's communication topology. We show how our projections and visualizations lead to a rapid diagnosis of and mitigation strategy for a previously elusive scaling bottleneck in the library that is hard to detect using conventional tools. Our new insights have resulted in a 22% performance improvement for a 65,536-core run of the AMR library on an IBM Blue Gene/P system.\",\n \"Title: Performance Analysis Techniques for the Exascale Co-Design Process.\\nYear: 2013\\n\\nAbstract: Efficient and effective performance analysis techniques are critical for the development of future generation systems. They are the drivers behind the required co-design process that helps establish the principles needed for their design. In this paper, we will highlight two such approaches: PAVE, a project that investigates mapping of performance data to more intuitive domains and uses advanced visualization techniques to expose problems, and GREMLIN, a system evaluation environment capable of emulating expected properties of exascale architectures on petascale machines. Combined with other approaches in system modeling and simulation, these projects enable us to provide a meaningful introspection into a target application's characteristics as well as its expected behavior and, more importantly, likely bottlenecks on future generation machines.\",\n \"Title: Abstract: Exploring Performance Data with Boxfish\\nYear: 2012\\n\\nAbstract: The growth in size and complexity of scaling applications and the systems on which they run pose challenges in analyzing and improving their overall performance. With metrics coming from thousands or millions of processes, visualization techniques are necessary to make sense of the increasing amount of data. To aid the process of exploration and understanding, we announce the initial release of Boxfish, an extensible tool for manipulating and visualizing data pertaining to application behavior. Combining and visually presenting data and knowledge from multiple domains, such as the application's communication patterns and the hardware's network configuration and routing policies, can yield the insight necessary to discover the underlying causes of observed behavior. Boxfish allows users to query, filter and project data across these domains to create interactive, linked visualizations.\",\n \"Title: Lessons learned from moving earth system grid data sets over a 20 Gbps wide-area network\\nYear: 2010\\n\\nAbstract: In preparation for the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report, the climate community will run the Coupled Model Intercomparison Project phase 5 (CMIP-5) experiments, which are designed to answer crucial questions about future regional climate change and the results of carbon feedback for different mitigation scenarios. The CMIP-5 experiments will generate petabytes of data that must be replicated seamlessly, reliably, and quickly to hundreds of research teams around the globe. As an end-to-end test of the technologies that will be used to perform this task, a multi-disciplinary team of researchers moved a small portion (10 TB) of the multimodel Coupled Model Intercomparison Project, Phase 3 data set used in the IPCC Fourth Assessment Report from three sources---the Argonne Leadership Computing Facility (ALCF), Lawrence Livermore National Laboratory (LLNL) and National Energy Research Scientific Computing Center (NERSC)---to the 2009 Supercomputing conference (SC09) show floor in Portland, Oregon, over circuits provided by DOE's ESnet. The team achieved a sustained data rate of 15 Gb/s on a 20 Gb/s network. More important, this effort provided critical feedback on how to deploy, tune, and monitor the middleware that will be used to replicate the upcoming petascale climate datasets. We report on obstacles overcome and the key lessons learned from this successful bandwidth challenge effort.\",\n \"Title: Fast Multiresolution Reads of Massive Simulation Datasets\\nYear: 2014\\n\\nAbstract: Today's massively parallel simulation codes can produce output ranging up to many terabytes of data. Utilizing this data to support scientific inquiry requires analysis and visualization, yet the sheer size of the data makes it cumbersome or impossible to read without computational resources similar to the original simulation. We identify two broad classes of problems for reading data and present effective solutions for both. The first class of data reads depends on user requirements and available resources. Tasks such as visualization and user-guided analysis may be accomplished using only a subset of variables with a restricted spatial extent at a reduced resolution. The other class of reads requires full resolution multivariate data to be loaded, for example to restart a simulation. We show that utilizing the hierarchical multiresolution IDX data format enables scalable and efficient serial and parallel read access on a variety of hardware from supercomputers down to portable devices. We demonstrate interactive view-dependent visualization and analysis of massive scientific datasets using low-power commodity hardware, and we compare read performance with other parallel file formats for both full and partial resolution data.\",\n \"Title: Flow Visualization with Quantified Spatial and Temporal Errors using Edge Maps.\\nYear: 2012\\n\\nAbstract: Robust analysis of vector fields has been established as an important tool for deriving insights from the complex systems these fields model. Traditional analysis and visualization techniques rely primarily on computing streamlines through numerical integration. The inherent numerical errors of such approaches are usually ignored, leading to inconsistencies that cause unreliable visualizations and can ultimately prevent in-depth analysis. We propose a new representation for vector fields on surfaces that replaces numerical integration through triangles with maps from the triangle boundaries to themselves. This representation, called edge maps, permits a concise description of flow behaviors and is equivalent to computing all possible streamlines at a user defined error threshold. Independent of this error all streamlines computed using edge maps are guaranteed to be consistent up to floating point precision, enabling the stable extraction of features such as the topological skeleton. Furthermore, our representation explicitly stores the spatial and temporal errors which we use to produce more informative visualizations. This work describes the construction of edge maps, the error quantification, and a refinement procedure to adhere to a user defined error bound. Finally, we introduce new visualizations using the additional information provided by the edge maps to indicate the uncertainty involved in computing streamlines and topological structures.\",\n \"Title: Interactive exploration of large-scale time-varying data using dynamic tracking graphs\\nYear: 2012\\n\\nAbstract: Exploring and analyzing the temporal evolution of features in large-scale time-varying datasets is a common problem in many areas of science and engineering. One natural representation of such data is tracking graphs, i.e., constrained graph layouts that use one spatial dimension to indicate time and show the “tracks” of each feature as it evolves, merges or disappears. However, for practical data sets creating the corresponding optimal graph layouts that minimize the number of intersections can take hours to compute with existing techniques. Furthermore, the resulting graphs are often unmanageably large and complex even with an ideal layout. Finally, due to the cost of the layout, changing the feature definition, e.g. by changing an iso-value, or analyzing properly adjusted sub-graphs is infeasible. To address these challenges, this paper presents a new framework that couples hierarchical feature definitions with progressive graph layout algorithms to provide an interactive exploration of dynamically constructed tracking graphs. Our system enables users to change feature definitions on-the-fly and filter features using arbitrary attributes while providing an interactive view of the resulting tracking graphs. Furthermore, the graph display is integrated into a linked view system that provides a traditional 3D view of the current set of features and allows a cross-linked selection to enable a fully flexible spatio-temporal exploration of data. We demonstrate the utility of our approach with several large-scale scientific simulations from combustion science.\",\n \"Title: Ensemble-Vis: A Framework for the Statistical Visualization of Ensemble Data\\nYear: 2009\\n\\nAbstract: Scientists increasingly use ensemble data sets to explore relationships present in dynamic systems. Ensemble data sets combine spatio-temporal simulation results generated using multiple numerical models, sampled input conditions and perturbed parameters. While ensemble data sets are a powerful tool for mitigating uncertainty, they pose significant visualization and analysis challenges due to their complexity. In this article, we present Ensemble-Vis, a framework consisting of a collection of overview and statistical displays linked through a high level of interactivity. Ensemble-Vis allows scientists to gain key scientific insight into the distribution of simulation results as well as the uncertainty associated with the scientific data. In contrast to methods that present large amounts of diverse information in a single display, we argue that combining multiple linked displays yields a clearer presentation of the data and facilitates a greater level of visual data analysis. We demonstrate our framework using driving problems from climate modeling and meteorology and discuss generalizations to other fields.\",\n \"Title: Robust computation of Morse-Smale complexes of bilinear functions.\\nYear: 2013\\n\\nAbstract: The Morse–Smale (MS) complex has proven to be a useful tool in extracting and visualizing features from scalar-valued data. However, existing algorithms to compute the MS complex are restricted to either piecewise linear or discrete scalar fields. This paper presents a new combinatorial algorithm to compute MS complexes for two-dimensional piecewise bilinear functions defined on quadrilateral meshes. We derive a new invariant of the gradient flow within a bilinear cell and use it to develop a provably correct computation, unaffected by numerical instabilities. This includes a combinatorial algorithm to detect and classify critical points as well as a way to determine the asymptotes of cell-based saddles and their intersection with cell edges. Finally, we introduce a simple data structure to compute and store integral lines on quadrilateral meshes which by construction prevents intersections and allows to enforce constraints on the gradient flow that preserve known invariants.\",\n \"Title: Maximizing Adaptivity in Hierarchical Topological Models\\nYear: 2005\\n\\nAbstract: We present a novel algorithm based on least-squares minimization to approximate point cloud data in 2D plane with a smooth B-spline curve. The point cloud data may represent an open curve with self intersection and sharp corner. Unlike other existing ...\",\n \"Title: A Topological Framework for the Interactive Exploration of Large Scale Turbulent Combustion\\nYear: 2009\\n\\nAbstract: The advent of highly accurate, large scale volumetric simulations has made data analysis and visualization techniques an integral part of the modern scientific process. To develop new insights from raw data, scientists need the ability to define features of interest in a flexible manner and to understand how changes in the feature definition impact the subsequent analysis of the data. Therefore, simply exploring the raw data is not sufficient. This paper presents a new topological framework for the analysis of large scale, time-varying, turbulent combustion simulations. It allows the scientists to interactively explore the complete parameter space of fuel consumption thresholds for an entire time-dependent combustion simulation. By computing augmented merge trees and their corresponding data segmentations, the system allows the user complete flexibility to segment, select, and track burning cells through time thanks to a linked view interface. We developed this technique in the context of low-swirl turbulent pre-mixed flame simulation analysis, where the topological abstractions enable an efficient tracking through time of the burning cells and provide new qualitative and quantitative insights into the dynamics of the combustion process.\",\n \"Title: Hybrid CPU-GPU Solver for Gradient Domain Processing of Massive Images\\nYear: 2011\\n\\nAbstract: Gradient domain processing is a computationally expensive image processing technique. Its use for processing massive images, giga or terapixels in size, can take several hours with serial techniques. To address this challenge, parallel algorithms are being developed to make this class of techniques applicable to the largest images available with running times that are more acceptable to the users. To this end we target the most ubiquitous form of computing power available today, which is small or medium scale clusters of commodity hardware. Such clusters are continuously increasing in scale, not only in the number of nodes, but also in the amount of parallelism available within each node in the form of multicore CPUs and GPUs. In this paper we present a hybrid parallel implementation of gradient domain processing for seamless stitching of gigapixel panoramas that utilizes MPI, threading and a CUDA based GPU component. We demonstrate the performance and scalability of our implementation by presenting results from two GPU clusters processing two large data sets.\",\n \"Title: Understanding the Structure of the Turbulent Mixing Layer in Hydrodynamic Instabilities\\nYear: 2006\\n\\nAbstract: When a heavy fluid is placed above a light fluid, tiny vertical perturbations in the interface create a characteristic structure of rising bubbles and falling spikes known as Rayleigh-Taylor instability. Rayleigh-Taylor instabilities have received much attention over the past half-century because of their importance in understanding many natural and man-made phenomena, ranging from the rate of formation of heavy elements in supernovae to the design of capsules for Inertial Confinement Fusion. We present a new approach to analyze Rayleigh-Taylor instabilities in which we extract a hierarchical segmentation of the mixing envelope surface to identify bubbles and analyze analogous segmentations of fields on the original interface plane. We compute meaningful statistical information that reveals the evolution of topological features and corroborates the observations made by scientists. We also use geometric tracking to follow the evolution of single bubbles and highlight merge/split events leading to the formation of the large and complex structures characteristic of the later stages. In particular we (i) Provide a formal definition of a bubble; (ii) Segment the envelope surface to identify bubbles; (iii) Provide a multi-scale analysis technique to produce statistical measures of bubble growth; (iv) Correlate bubble measurements with analysis of fields on the interface plane; (v) Track the evolution of individual bubbles over time. Our approach is based on the rigorous mathematical foundations of Morse theory and can be applied to a more general class of applications.\",\n \"Title: Extracting logical structure and identifying stragglers in parallel execution traces\\nYear: 2014\\n\\nAbstract: We introduce a new approach to automatically extract an idealized logical structure from a parallel execution trace. We use this structure to define intuitive metrics such as the lateness of a process involved in a parallel execution. By analyzing and illustrating traces in terms of logical steps, we leverage a developer's understanding of the happened-before relations in a parallel program. This technique can uncover dependency chains, elucidate communication patterns, and highlight sources and propagation of delays, all of which may be obscured in a traditional trace visualization.\",\n \"Title: Visual exploration of high dimensional scalar functions.\\nYear: 2010\\n\\nAbstract: An important goal of scientific data analysis is to understand the behavior of a system or process based on a sample of the system. In many instances it is possible to observe both input parameters and system outputs, and characterize the system as a high-dimensional function. Such data sets arise, for instance, in large numerical simulations, as energy landscapes in optimization problems, or in the analysis of image data relating to biological or medical parameters. This paper proposes an approach to analyze and visualizing such data sets. The proposed method combines topological and geometric techniques to provide interactive visualizations of discretely sampled high-dimensional scalar fields. The method relies on a segmentation of the parameter space using an approximate Morse-Smale complex on the cloud of point samples. For each crystal of the Morse-Smale complex, a regression of the system parameters with respect to the output yields a curve in the parameter space. The result is a simplified geometric representation of the Morse-Smale complex in the high dimensional input domain. Finally, the geometric representation is embedded in 2D, using dimension reduction, to provide a visualization platform. The geometric properties of the regression curves enable the visualization of additional information about each crystal such as local and global shape, width, length, and sampling densities. The method is illustrated on several synthetic examples of two dimensional functions. Two use cases, using data sets from the UCI machine learning repository, demonstrate the utility of the proposed approach on real data. Finally, in collaboration with domain experts the proposed method is applied to two scientific challenges. The analysis of parameters of climate simulations and their relationship to predicted global energy flux and the concentrations of chemical species in a combustion simulation and their integration with temperature.\",\n \"Title: On the use of graph search techniques for the analysis of extreme-scale combustion simulation data\\nYear: 2012\\n\\nAbstract: With the continuous increase in high performance computing capabilities, simulations are becoming ever larger and more complex, using bigger domains, tracking more variables, and producing more time steps. This increase in the ranges of spatial and temporal simulation scales results in data that presents significant challenges to as well as new opportunities for the visualization and data analysis community. For example, highly-localized, intermittent events (such as the formation of ignition kernels in turbulent combustion) may be caused by interactions between multiple variables across a series of time steps, making both their definition and their extraction difficult, particularly at scale. This paper introduces an intuitive framework to support the identification, characterization, and tracking of such complex, multivariate, temporally evolving events in large-scale simulations. In a pre-processing step, we use topological techniques to create a hierarchical family of feature definitions for each variable of interest. Subsequently, we select a particular set of features for analysis and, using overlap-based metrics, we generate an attributed relational graph (ARG) capturing the relationships between different variables both within one and across multiple time steps. Finally, we leverage subgraph-isomorphism search heuristics to identify patterns in the ARG that characterize interesting events. We demonstrate the power of this approach by analyzing a large-scale turbulent combustion simulation.\",\n \"Title: A topological approach to simplification of three-dimensional scalar functions.\\nYear: 2006\\n\\nAbstract: This paper describes an efficient combinatorial method for simplification of topological features in a 3D scalar function. The Morse-Smale complex, which provides a succinct representation of a function's associated gradient flow field, is used to identify topological features and their significance. The simplification process, guided by the Morse-Smale complex, proceeds by repeatedly applying two atomic operations that each remove a pair of critical points from the complex. Efficient storage of the complex results in execution of these atomic operations at interactive rates. Visualization of the simplified complex shows that the simplification preserves significant topological features while removing small features and noise.\",\n \"Title: Combining in-situ and in-transit processing to enable extreme-scale scientific analysis\\nYear: 2012\\n\\nAbstract: With the onset of extreme-scale computing, I/O constraints make it increasingly difficult for scientists to save a sufficient amount of raw simulation data to persistent storage. One potential solution is to change the data analysis pipeline from a post-process centric to a concurrent approach based on either in-situ or in-transit processing. In this context computations are considered in-situ if they utilize the primary compute resources, while in-transit processing refers to offloading computations to a set of secondary resources using asynchronous data transfers. In this paper we explore the design and implementation of three common analysis techniques typically performed on large-scale scientific simulations: topological analysis, descriptive statistics, and visualization. We summarize algorithmic developments, describe a resource scheduling system to coordinate the execution of various analysis workflows, and discuss our implementation using the DataSpaces and ADIOS frameworks that support efficient data movement between in-situ and in-transit computations. We demonstrate the efficiency of our lightweight, flexible framework by deploying it on the Jaguar XK6 to analyze data generated by S3D, a massively parallel turbulent combustion code. Our framework allows scientists dealing with the data deluge at extreme scale to perform analyses at increased temporal resolutions, mitigate I/O costs, and significantly improve the time to insight.\",\n \"Title: Computing Morse-Smale Complexes with Accurate Geometry\\nYear: 2012\\n\\nAbstract: Topological techniques have proven highly successful in analyzing and visualizing scientific data. As a result, significant efforts have been made to compute structures like the Morse-Smale complex as robustly and efficiently as possible. However, the resulting algorithms, while topologically consistent, often produce incorrect connectivity as well as poor geometry. These problems may compromise or even invalidate any subsequent analysis. Moreover, such techniques may fail to improve even when the resolution of the domain mesh is increased, thus producing potentially incorrect results even for highly resolved functions. To address these problems we introduce two new algorithms: (i) a randomized algorithm to compute the discrete gradient of a scalar field that converges under refinement; and (ii) a deterministic variant which directly computes accurate geometry and thus correct connectivity of the MS complex. The first algorithm converges in the sense that on average it produces the correct result and its standard deviation approaches zero with increasing mesh resolution. The second algorithm uses two ordered traversals of the function to integrate the probabilities of the first to extract correct (near optimal) geometry and connectivity. We present an extensive empirical study using both synthetic and real-world data and demonstrates the advantages of our algorithms in comparison with several popular approaches.\",\n \"Title: Ultrascale Visualization of Climate Data\\nYear: 2013\\n\\nAbstract: Collaboration across research, government, academic, and private sectors is integrating more than 70 scientific computing libraries and applications through a tailorable provenance framework, empowering scientists to exchange and examine data in novel ways.\",\n \"Title: Comments on the \\\"Meshless Helmholtz-Hodge Decomposition\\\"\\nYear: 2013\\n\\nAbstract: The Helmholtz-Hodge decomposition (HHD) is one of the fundamental theorems of fluids describing the decomposition of a flow field into its divergence-free, curl-free, and harmonic components. Solving for the HHD is intimately connected to the choice of boundary conditions which determine the uniqueness and orthogonality of the decomposition. This article points out that one of the boundary conditions used in a recent paper “Meshless Helmholtz-Hodge Decomposition” [5] is, in general, invalid and provides an analytical example demonstrating the problem. We hope that this clarification on the theory will foster further research in this area and prevent undue problems in applying and extending the original approach.\",\n \"Title: Topological Spines: A Structure-preserving Visual Representation of Scalar Fields\\nYear: 2011\\n\\nAbstract: We present topological spines−a new visual representation that preserves the topological and geometric structure of a scalar field. This representation encodes the spatial relationships of the extrema of a scalar field together with the local volume and nesting structure of the surrounding contours. Unlike other topological representations, such as contour trees, our approach preserves the local geometric structure of the scalar field, including structural cycles that are useful for exposing symmetries in the data. To obtain this representation, we describe a novel mechanism based on the extraction of extremum graphs−sparse subsets of the Morse-Smale complex that retain the important structural information without the clutter and occlusion problems that arise from visualizing the entire complex directly. Extremum graphs form a natural multiresolution structure that allows the user to suppress noise and enhance topological features via the specification of a persistence range. Applications of our approach include the visualization of 3D scalar fields without occlusion artifacts, and the exploratory analysis of high-dimensional functions.\",\n \"Title: Visualizing Network Traffic to Understand the Performance of Massively Parallel Simulations\\nYear: 2012\\n\\nAbstract: The performance of massively parallel applications is often heavily impacted by the cost of communication among compute nodes. However, determining how to best use the network is a formidable task, made challenging by the ever increasing size and complexity of modern supercomputers. This paper applies visualization techniques to aid parallel application developers in understanding the network activity by enabling a detailed exploration of the flow of packets through the hardware interconnect. In order to visualize this large and complex data, we employ two linked views of the hardware network. The first is a 2D view, that represents the network structure as one of several simplified planar projections. This view is designed to allow a user to easily identify trends and patterns in the network traffic. The second is a 3D view that augments the 2D view by preserving the physical network topology and providing a context that is familiar to the application developers. Using the massively parallel multi-physics code pF3D as a case study, we demonstrate that our tool provides valuable insight that we use to explain and optimize pF3D’s performance on an IBM Blue Gene/P system.\",\n \"Title: A topological hierarchy for functions on triangulated surfaces.\\nYear: 2004\\n\\nAbstract: We combine topological and geometric methods to construct a multiresolution representation for a function over a two-dimensional domain. In a preprocessing stage, we create the Morse-Smale complex of the function and progressively simplify its topology by cancelling pairs of critical points. Based on a simple notion of dependency among these cancellations, we construct a hierarchical data structure supporting traversal and reconstruction operations similarly to traditional geometry-based representations. We use this data structure to extract topologically valid approximations that satisfy error bounds provided at runtime.\",\n \"Title: Interactive Exploration and Analysis of Large Scale Simulations Using Topology-based Data Segmentation.\\nYear: 2011\\n\\nAbstract: Large-scale simulations are increasingly being used to study complex scientific and engineering phenomena. As a result, advanced visualization and data analysis are also becoming an integral part of the scientific process. Often, a key step in extracting insight from these large simulations involves the definition, extraction, and evaluation of features in the space and time coordinates of the solution. However, in many applications these features involve a range of parameters and decisions that will affect the quality and direction of the analysis. Examples include particular level sets of a specific scalar field, or local inequalities between derived quantities. A critical step in the analysis is to understand how these arbitrary parameters/decisions impact the statistical properties of the features, since such a characterization will help to evaluate the conclusions of the analysis as a whole.\",\n \"Title: Interpreting Performance Data across Intuitive Domains\\nYear: 2011\\n\\nAbstract: To exploit the capabilities of current and future systems, developers must understand the interplay between on-node performance, domain decomposition, and an application's intrinsic communication patterns. While tools exist to gather and analyze data for each of these components individually, the resulting information is generally processed in isolation and presented in an abstract, categorical fashion unintuitive to most users. In this paper we present the HAC model, in which we identify the three domains of performance data most familiar to the user: (i)the application domain containing the application's working set, (ii) the hardware domain of the compute and network devices, and (iii) the communication domain of logical data transfers. We show that taking data from each of these domains and projecting, visualizing, and correlating it to the other domains can give valuable insights into the behavior of parallel application codes. The HAC abstraction opens the door for a new generation of tools that can help users more easily and intuitively associate performance data with root causes in the hardware system, the application's structure, and in its communication behavior, and by doing so leads to an improved understanding of the performance of their codes.\",\n \"Title: Local, smooth, and consistent Jacobi set simplification.\\nYear: 2013\\n\\nAbstract: The relation between two Morse functions defined on a smooth, compact, and orientable 2-manifold can be studied in terms of their Jacobi set. The Jacobi set contains points in the domain where the gradients of the two functions are aligned. Both the Jacobi set itself as well as the segmentation of the domain it induces, have shown to be useful in various applications. In practice, unfortunately, functions often contain noise and discretization artifacts, causing their Jacobi set to become unmanageably large and complex. Although there exist techniques to simplify Jacobi sets, they are unsuitable for most applications as they lack fine-grained control over the process, and heavily restrict the type of simplifications possible.This paper introduces the theoretical foundations of a new simplification framework for Jacobi sets. We present a new interpretation of Jacobi set simplification based on the perspective of domain segmentation. Generalizing the cancellation of critical points from scalar functions to Jacobi sets, we focus on simplifications that can be realized by smooth approximations of the corresponding functions, and show how these cancellations imply simultaneous simplification of contiguous subsets of the Jacobi set. Using these extended cancellations as atomic operations, we introduce an algorithm to successively cancel subsets of the Jacobi set with minimal modifications to some user-defined metric. We show that for simply connected domains, our algorithm reduces a given Jacobi set to its minimal configuration, that is, one with no birth-death points (a birth-death point is a specific type of singularity within the Jacobi set where the level sets of the two functions and the Jacobi set have a common normal direction).\",\n \"Title: Ridge-Valley graphs: Combinatorial ridge detection using Jacobi sets.\\nYear: 2013\\n\\nAbstract: Ridges are one of the key features of interest in areas such as computer vision and image processing. Even though a significant amount of research has been directed to defining and extracting ridges some fundamental challenges remain. For example, the most popular ridge definition (height ridge) is not invariant under monotonic transformations and its global structure is typically ignored during numerical computations. Furthermore, many existing algorithms are based on numerical heuristics and are rarely guaranteed to produce consistent results. This paper reexamines a slightly different ridge definition that is consistent with all desired invariants. Nevertheless, we show that this definition results in similar structures compared to height ridges and that both formulations are equivalent for quadratic functions. Furthermore, this definition can be cast in the form of a degenerate Jacobi set, which allows insights into the global structure of ridges. In particular, we introduce the Ridge–Valley graph as the complete description of all ridges in an image. Finally, using the connection to Jacobi sets we describe a new combinatorial algorithm to extract the Ridge–Valley graph from sampled images guaranteed to produce a valid structure.\",\n \"Title: Adaptive extraction and quantification of geophysical vortices.\\nYear: 2011\\n\\nAbstract: We consider the problem of extracting discrete two-dimensional vortices from a turbulent flow. In our approach we use a reference model describing the expected physics and geometry of an idealized vortex. The model allows us to derive a novel correlation between the size of the vortex and its strength, measured as the square of its strain minus the square of its vorticity. For vortex detection in real models we use the strength parameter to locate potential vortex cores, then measure the similarity of our ideal analytical vortex and the real vortex core for different strength thresholds. This approach provides a metric for how well a vortex core is modeled by an ideal vortex. Moreover, this provides insight into the problem of choosing the thresholds that identify a vortex. By selecting a target coefficient of determination (i.e., statistical confidence), we determine on a per-vortex basis what threshold of the strength parameter would be required to extract that vortex at the chosen confidence. We validate our approach on real data from a global ocean simulation and derive from it a map of expected vortex strengths over the global ocean.\",\n \"Title: A Sampling Theorem for MLS Surfaces\\nYear: 2005\\n\\nAbstract: Recently, point set surfaces have been the focus of a large number of research efforts. Several different methods have been proposed to define surfaces from points and have been used in a variety of applications. However, so far little is know about the mathematical properties of the resulting surface. A central assumption for most algorithms is that the surface construction is well defined within a neighborhood of the samples. However, it is not clear that given an irregular sampling of a surface this is the case. The fundamental problem is that point based methods often use a weighted least squares fit of a plane to approximate a surface normal. If this minimization problem is ill-defined so is the surface construction. In this paper, we provide a proof that given reasonable sampling conditions the normal approximations are well defined within a neighborhood of the samples. Similar to methods in surface reconstruction, our sampling conditions are based on the local feature size and thus allow the sampling density to vary according to geometric complexity.\",\n \"Title: Edge maps: Representing flow with bounded error\\nYear: 2011\\n\\nAbstract: Robust analysis of vector fields has been established as an important tool for deriving insights from the complex systems these fields model. Many analysis techniques rely on computing streamlines, a task often hampered by numerical instabilities. Approaches that ignore the resulting errors can lead to inconsistencies that may produce unreliable visualizations and ultimately prevent in-depth analysis. We propose a new representation for vector fields on surfaces that replaces numerical integration through triangles with linear maps defined on its boundary. This representation, called edge maps, is equivalent to computing all possible streamlines at a user defined error threshold. In spite of this error, all the streamlines computed using edge maps will be pairwise disjoint. Furthermore, our representation stores the error explicitly, and thus can be used to produce more informative visualizations. Given a piecewise-linear interpolated vector field, a recent result [15] shows that there are only 23 possible map classes for a triangle, permitting a concise description of flow behaviors. This work describes the details of computing edge maps, provides techniques to quantify and refine edge map error, and gives qualitative and visual comparisons to more traditional techniques.\",\n \"Title: Robust on-line computation of Reeb graphs: simplicity and speed\\nYear: 2007\\n\\nAbstract: Reeb graphs are a fundamental data structure for understanding and representing the topology of shapes. They are used in computer graphics, solid modeling, and visualization for applications ranging from the computation of similarities and finding defects in complex models to the automatic selection of visualization parameters. We introduce an on-line algorithm that reads a stream of elements (vertices, triangles, tetrahedra, etc.) and continuously maintains the Reeb graph of all elements already reed. The algorithm is robust in handling non-manifold meshes and general in its applicability to input models of any dimension. Optionally, we construct a skeleton-like embedding of the Reeb graph, and/or remove topological noise to reduce the output size. For interactive multi-resolution navigation we also build a hierarchical data structure which allows real-time extraction of approximated Reeb graphs containing all topological features above a given error threshold. Our extensive experiments show both high performance and practical linear scalability for meshes ranging from thousands to hundreds of millions of triangles. We apply our algorithm to the largest, most general, triangulated surfaces available to us, including 3D, 4D and 5D simplicial meshes. To demonstrate one important application we use Reeb graphs to find and highlight topological defects in meshes, including some widely believed to be \\\"clean.\\\"\",\n \"Title: Feature-Based Statistical Analysis of Combustion Simulation Data\\nYear: 2011\\n\\nAbstract: We present a new framework for feature-based statistical analysis of large-scale scientific data and demonstrate its effectiveness by analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion. Turbulent flows are ubiquitous and account for transport and mixing processes in combustion, astrophysics, fusion, and climate modeling among other disciplines. They are also characterized by coherent structure or organized motion, i.e. nonlocal entities whose geometrical features can directly impact molecular mixing and reactive processes. While traditional multi-point statistics provide correlative information, they lack nonlocal structural information, and hence, fail to provide mechanistic causality information between organized fluid motion and mixing and reactive processes. Hence, it is of great interest to capture and track flow features and their statistics together with their correlation with relevant scalar quantities, e.g. temperature or species concentrations. In our approach we encode the set of all possible flow features by pre-computing merge trees augmented with attributes, such as statistical moments of various scalar fields, e.g. temperature, as well as length-scales computed via spectral analysis. The computation is performed in an efficient streaming manner in a pre-processing step and results in a collection of meta-data that is orders of magnitude smaller than the original simulation data. This meta-data is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. We combine the analysis with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze the equivalent of one terabyte of simulation data. We highlight the utility of this new framework for combustion science; however, it is applicable to many other science domains.\",\n \"Title: Topological landscapes: a terrain metaphor for scientific data.\\nYear: 2007\\n\\nAbstract: Scientific visualization and illustration tools are designed to help people understand the structure and complexity of scientific data with images that are as informative and intuitive as possible. In this context the use of metaphors plays an important role since they make complex information easily accessible by using commonly known concepts. In this paper we propose a new metaphor, called \\\"Topological Landscapes,\\\" which facilitates understanding the topological structure of scalar functions. The basic idea is to construct a terrain with the same topology as a given dataset and to display the terrain as an easily understood representation of the actual input data. In this projection from an $n$-dimensional scalar function to a two-dimensional (2D) model we preserve function values of critical points, the persistence (function span) of topological features, and one possible additional metric property (in our examples volume). By displaying this topologically equivalent landscape together with the original data we harness the natural human proficiency in understanding terrain topography and make complex topological information easily accessible.\",\n \"Title: Gaussian mixture model based volume visualization\\nYear: 2012\\n\\nAbstract: Representing uncertainty when creating visualizations is becoming more indispensable to understand and analyze scientific data. Uncertainty may come from different sources, such as, ensembles of experiments or unavoidable information loss when performing data reduction. One natural model to represent uncertainty is to assume that each position in space instead of a single value may take on a distribution of values. In this paper we present a new volume rendering method using per voxel Gaussian mixture models (GMMs) as the input data representation. GMMs are an elegant and compact way to drastically reduce the amount of data stored while still enabling realtime data access and rendering on the GPU. Our renderer offers efficient sampling of the data distribution, generating renderings of the data that flicker at each frame to indicate high variance. We can accumulate samples as well to generate still frames of the data, which preserve additional details in the data as compared to either traditional scalar indicators (such as a mean or a single nearest neighbor down sample) or to fitting the data with only a single Gaussian per voxel. We demonstrate the effectiveness of our method using ensembles of climate simulations and MRI scans as well as the down sampling of large scalar fields as examples.\",\n \"Title: Ordering Traces Logically to Identify Lateness in Message Passing Programs\\nYear: 2016\\n\\nAbstract: Event traces are valuable for understanding the behavior of parallel programs. However, automatically analyzing a large parallel trace is difficult, especially without a specific objective. We aid this endeavor by extracting a trace’s logical structure, an ordering of trace events derived from happened-before relationships, while taking into account developer intent. Using this structure, we can calculate an operation’s delay relative to its peers on other processes. The logical structure also serves as a platform for comparing and clustering processes as well as highlighting communication patterns in a trace visualization. We present an algorithm for determining this idealized logical structure from traces of message passing programs, and we develop metrics to quantify delays and differences among processes. We implement our techniques in Ravel, a parallel trace visualization tool that displays both logical and physical timelines. Rather than showing the duration of each operation, we display where delays begin and end, and how they propagate. We apply our approach to the traces of several message passing applications, demonstrating the accuracy of our extracted structure and its utility in analyzing these codes.\",\n \"Title: Combing the Communication Hairball: Visualizing Parallel Execution Traces using Logical Time\\nYear: 2014\\n\\nAbstract: With the continuous rise in complexity of modern supercomputers, optimizing the performance of large-scale parallel programs is becoming increasingly challenging. Simultaneously, the growth in scale magnifies the impact of even minor inefficiencies - potentially millions of compute hours and megawatts in power consumption can be wasted on avoidable mistakes or sub-optimal algorithms. This makes performance analysis and optimization critical elements in the software development process. One of the most common forms of performance analysis is to study execution traces, which record a history of per-process events and interprocess messages in a parallel application. Trace visualizations allow users to browse this event history and search for insights into the observed performance behavior. However, current visualizations are difficult to understand even for small process counts and do not scale gracefully beyond a few hundred processes. Organizing events in time leads to a virtually unintelligible conglomerate of interleaved events and moderately high process counts overtax even the largest display. As an alternative, we present a new trace visualization approach based on transforming the event history into logical time inferred directly from happened-before relationships. This emphasizes the code's structural behavior, which is much more familiar to the application developer. The original timing data, or other information, is then encoded through color, leading to a more intuitive visualization. Furthermore, we use the discrete nature of logical timelines to cluster processes according to their local behavior leading to a scalable visualization of even long traces on large process counts. We demonstrate our system using two case studies on large-scale parallel codes.\",\n \"Title: Distributed Seams for Gigapixel Panoramas\\nYear: 2015\\n\\nAbstract: Gigapixel panoramas are an increasingly popular digital image application. They are often created as a mosaic of many smaller images. The mosaic acquisition can take many hours causing the individual images to differ in exposure and lighting conditions. A blending operation is often necessary to give the appearance of a seamless image. The blending quality depends on the magnitude of discontinuity along the image boundaries. Often, new boundaries, or seams, are first computed that minimize this transition. Current techniques based on multi-labeling Graph Cuts are too slow and memory intensive for gigapixel sized panoramas. In this paper, we present a parallel, out-of-core seam computing technique that is fast, has small memory footprint, and is capable of running efficiently on different types of parallel systems. Its maximum memory usage is configurable, in the form of a cache, which can improve performance by reducing redundant disk I/O and computations. It shows near-perfect scaling on symmetric multiprocessing systems and good scaling on clusters and distributed shared memory systems. Our technique improves the time required to compute seams for gigapixel imagery from many hours (or even days) to just a few minutes, while still producing boundaries with energy that is on-par with Graph Cuts.\",\n \"Title: The Natural Helmholtz-Hodge Decomposition for Open-Boundary Flow Analysis.\\nYear: 2014\\n\\nAbstract: The Helmholtz-Hodge decomposition (HHD), which describes a flow as the sum of an incompressible, an irrotational, and a harmonic flow, is a fundamental tool for simulation and analysis. Unfortunately, for bounded domains, the HHD is not uniquely defined, traditionally, boundary conditions are imposed to obtain a unique solution. However, in general, the boundary conditions used during the simulati...\",\n \"Title: Extracting Features from Time-Dependent Vector Fields Using Internal Reference Frames\\nYear: 2014\\n\\nAbstract: Extracting features from complex, time-dependent flow fields remains a significant challenge despite substantial research efforts, especially because most flow features of interest are defined with respect to a given reference frame. Pathline-based techniques, such as the FTLE field, are complex to implement and resource intensive, whereas scalar transforms, such as λ2, often produce artifacts and require somewhat arbitrary thresholds. Both approaches aim to analyze the flow in a more suitable frame, yet neither technique explicitly constructs one.\",\n \"Title: Distortion-Guided Structure-Driven Interactive Exploration of High-Dimensional Data\\nYear: 2014\\n\\nAbstract: Dimension reduction techniques are essential for feature selection and feature extraction of complex high-dimensional data. These techniques, which construct low-dimensional representations of data, are typically geometrically motivated, computationally efficient and approximately preserve certain structural properties of the data. However, they are often used as black box solutions in data exploration and their results can be difficult to interpret. To assess the quality of these results, quality measures, such as co-ranking [LV09], have been proposed to quantify structural distortions that occur between high-dimensional and low-dimensional data representations. Such measures could be evaluated and visualized point-wise to further highlight erroneous regions [MLGH13]. In this work, we provide an interactive visualization framework for exploring high-dimensional data via its two-dimensional embeddings obtained from dimension reduction, using a rich set of user interactions. We ask the following question: what new insights do we obtain regarding the structure of the data, with interactive manipulations of its embeddings in the visual space? We augment the two-dimensional embeddings with structural abstractions obtained from hierarchical clusterings, to help users navigate and manipulate subsets of the data. We use point-wise distortion measures to highlight interesting regions in the domain, and further to guide our selection of the appropriate level of clusterings that are aligned with the regions of interest. Under the static setting, point-wise distortions indicate the level of structural uncertainty within the embeddings. Under the dynamic setting, on-the-fly updates of point-wise distortions due to data movement and data deletion reflect structural relations among different parts of the data, which may lead to new and valuable insights.\",\n \"Title: Data-parallel halo finding with variable linking lengths\\nYear: 2014\\n\\nAbstract: State-of-the-art cosmological simulations regularly contain billions of particles, providing scientists the opportunity to study the evolution of the Universe in great detail. However, the rate at which these simulations generate data severely taxes existing analysis techniques. Therefore, developing new scalable alternatives is essential for continued scientific progress. Here, we present a dataparallel, friends-of-friends halo finding algorithm that provides unprecedented flexibility in the analysis by extracting multiple linking lengths. Even for a single linking length, it is as fast as the existing techniques, and is portable to multi-threaded many-core systems as well as co-processing resources. Our system is implemented using PISTON and is coupled to an interactive analysis environment used to study halos at different linking lengths and track their evolution over time.\",\n \"Title: Multivariate volume visualization through dynamic projections\\nYear: 2014\\n\\nAbstract: We propose a multivariate volume visualization framework that tightly couples dynamic projections with a high-dimensional transfer function design for interactive volume visualization. We assume that the complex, high-dimensional data in the attribute space can be well-represented through a collection of low-dimensional linear subspaces, and embed the data points in a variety of 2D views created as projections onto these subspaces. Through dynamic projections, we present animated transitions between different views to help the user navigate and explore the attribute space for effective transfer function design. Our framework not only provides a more intuitive understanding of the attribute space but also allows the design of the transfer function under multiple dynamic views, which is more flexible than being restricted to a single static view of the data. For large volumetric datasets, we maintain interactivity during the transfer function design via intelligent sampling and scalable clustering. Using examples in combustion and climate simulations, we demonstrate how our framework can be used to visualize interesting structures in the volumetric space.\",\n \"Title: Visualizing the five-dimensional torus network of the IBM blue gene/Q\\nYear: 2014\\n\\nAbstract: Understanding the interactions between a parallel application and the interconnection network over which it exchanges data is critical to optimizing performance in modern supercomputers. However, recent supercomputing architectures use networks that do not have natural low-dimensional representations, making them difficult to comprehend or visualize. In particular, high-dimensional torus networks are common and are used in four of the top ten supercomputers and eight of the top ten on the Graph500 list. We present a new visualization of five-dimensional torus networks. We use four connected views depicting the network at different levels of detail, allowing analysts to observe general large-scale traffic patterns while simultaneously viewing individual links or outliers in any specific section of the network. We demonstrate this approach by analyzing network traffic for a pF3D simulation running on the IBM Blue Gene/Q architecture, and show how it is both intuitive and effective for understanding and optimizing parallel application behavior.\",\n \"Title: In-situ feature extraction of large scale combustion simulations using segmented merge trees\\nYear: 2014\\n\\nAbstract: The ever increasing amount of data generated by scientific simulations coupled with system I/O constraints are fueling a need for in-situ analysis techniques. Of particular interest are approaches that produce reduced data representations while maintaining the ability to redefine, extract, and study features in a post-process to obtain scientific insights. This paper presents two variants of in-situ feature extraction techniques using segmented merge trees, which encode a wide range of threshold based features. The first approach is a fast, low communication cost technique that generates an exact solution but has limited scalability. The second is a scalable, local approximation that nevertheless is guaranteed to correctly extract all features up to a predefined size. We demonstrate both variants using some of the largest combustion simulations available on leadership class supercomputers. Our approach allows state-of-the-art, feature-based analysis to be performed in-situ at significantly higher frequency than currently possible and with negligible impact on the overall simulation runtime.\",\n \"Title: Image segmentation using consensus from hierarchical segmentation ensembles\\nYear: 2014\\n\\nAbstract: Unsupervised, automatic image segmentation without contextual knowledge, or user intervention is a challenging problem. The key to robust segmentation is an appropriate selection of local features and metrics. However, a single aggregation of the local features using a greedy merging order often results in incorrect segmentation. This paper presents an unsupervised approach, which uses the consensus inferred from hierarchical segmentation ensembles, for partitioning images into foreground and background regions. By exploring an expanded set of possible aggregations of the local features, the proposed method generates meaningful segmentations that are not often revealed when only the optimal hierarchy is considered. A graph cuts-based approach is employed to combine the consensus along with a foreground-background model estimate, obtained using the ensemble, for effective segmentation. Experiments with a standard dataset show promising results when compared to several existing methods including the state-of-the-art weak supervised techniques that use co-segmentation.\",\n \"Title: Automatic image annotation using inverse maps from semantic embeddings\\nYear: 2014\\n\\nAbstract: Human annotation in large scale image databases is time-consuming and error-prone. Since it is very hard to mine image databases using just visual features or textual descriptors, it is common to transform the image features into a semantically meaningful space. In this paper, we propose to perform image annotation in a semantic space inferred based on sparse representations. By constructing a semantic embedding for the visual features, that is constrained to be close to the tag embedding, we show that a robust inverse map can be used to predict the tags. Experiments using standard datasets show the effectiveness of the proposed approach in automatic image annotation when compared to existing methods.\",\n \"Title: Multiple kernel interpolation for inverting non-linear dimensionality reduction and dimension estimation\\nYear: 2014\\n\\nAbstract: The problem of stably inverting a non-linear dimensionality reduction map has applications in data visualization and machine learning, besides being of theoretical interest. In this paper, we propose a meshfree interpolation method for obtaining such inverse maps using a non-negative linear combination of multiple interpolants. We show that the proposed scheme can improve upon the approximation power of its individual constituent kernels, and discuss the conditions under which its parameters can be uniquely estimated. We also provide an approach for estimating the intrinsic dimensionality (ID) of manifolds using the proposed inverse map. Experiments using multiple kernel interpolation for reconstruction of novel test data and ID estimation show an improved or similar performance compared to existing techniques.\",\n \"Title: A Practical Approach to Morse-Smale Complex Computation: Scalability and Generality\\nYear: 2008\\n\\nAbstract: The Morse-Smale (MS) complex has proven to be a useful tool in extracting and visualizing features from scalar-valued data. However, efficient computation of the MS complex for large scale data remains a challenging problem. We describe a new algorithm and easily extensible framework for computing MS complexes for large scale data of any dimension where scalar values are given at the vertices of a closure-finite and weak topology (CW) complex, therefore enabling computation on a wide variety of meshes such as regular grids, simplicial meshes, and adaptive multiresolution (AMR) meshes. A new divide-and-conquer strategy allows for memory-efficient computation of the MS complex and simplification on-the-fly to control the size of the output. In addition to being able to handle various data formats, the framework supports implementation-specific optimizations, for example, for regular data. We present the complete characterization of critical point cancellations in all dimensions. This technique enables the topology based analysis of large data on off-the-shelf computers. In particular we demonstrate the first full computation of the MS complex for a 1 billion/1024(3) node grid on a laptop computer with 2Gb memory.\",\n \"Title: Analyzing and Tracking Burning Structures in Lean Premixed Hydrogen Flames\\nYear: 2010\\n\\nAbstract: This paper presents topology-based methods to robustly extract, analyze, and track features defined as subsets of isosurfaces. First, we demonstrate how features identified by thresholding isosurfaces can be defined in terms of the Morse complex. Second, we present a specialized hierarchy that encodes the feature segmentation independent of the threshold while still providing a flexible multiresolution representation. Third, for a given parameter selection, we create detailed tracking graphs representing the complete evolution of all features in a combustion simulation over several hundred time steps. Finally, we discuss a user interface that correlates the tracking information with interactive rendering of the segmented isosurfaces enabling an in-depth analysis of the temporal behavior. We demonstrate our approach by analyzing three numerical simulations of lean hydrogen flames subject to different levels of turbulence. Due to their unstable nature, lean flames burn in cells separated by locally extinguished regions. The number, area, and evolution over time of these cells provide important insights into the impact of turbulence on the combustion process. Utilizing the hierarchy, we can perform an extensive parameter study without reprocessing the data for each set of parameters. The resulting statistics enable scientists to select appropriate parameters and provide insight into the sensitivity of the results with respect to the choice of parameters. Our method allows for the first time to quantitatively correlate the turbulence of the burning process with the distribution of burning regions, properly segmented and selected. In particular, our analysis shows that counterintuitively stronger turbulence leads to larger cell structures, which burn more intensely than expected. This behavior suggests that flames could be stabilized under much leaner conditions than previously anticipated.\",\n \"Title: The Helmholtz-Hodge Decomposition—A Survey\\nYear: 2013\\n\\nAbstract: The Helmholtz-Hodge Decomposition (HHD) describes the decomposition of a flow field into its divergence-free and curl-free components. Many researchers in various communities like weather modeling, oceanology, geophysics, and computer graphics are interested in understanding the properties of flow representing physical phenomena such as incompressibility and vorticity. The HHD has proven to be an important tool in the analysis of fluids, making it one of the fundamental theorems in fluid dynamics. The recent advances in the area of flow analysis have led to the application of the HHD in a number of research communities such as flow visualization, topological analysis, imaging, and robotics. However, because the initial body of work, primarily in the physics communities, research on the topic has become fragmented with different communities working largely in isolation often repeating and sometimes contradicting each others results. Additionally, different nomenclature has evolved which further obscures the fundamental connections between fields making the transfer of knowledge difficult. This survey attempts to address these problems by collecting a comprehensive list of relevant references and examining them using a common terminology. A particular focus is the discussion of boundary conditions when computing the HHD. The goal is to promote further research in the field by creating a common repository of techniques to compute the HHD as well as a large collection of example applications in a broad range of areas.\",\n \"Title: Connecting Performance Analysis and Visualization (Dagstuhl Perspectives Workshop 14022).\\nYear: 2015\\n\\nAbstract: In the first week of January 2014 Schloss Dagstuhl hosted a Perspectives Workshop on “Connecting Performance Analysis and Visualization to Advance Extreme Scale Computing”. The workshop brought together two previously separate communities – from Visualization and Performance Analysis for High Performance Computing – to discuss a long term joint research agenda. The goal was to identify and address the challenges in using visual representations to understand and optimize the performance of extreme-scale applications running on todayu0027s most powerful\\r\\ncomputing systems like climate modeling, combustion, material science or astro-physics simulations.\",\n \"Title: Interstitial And Interlayer Ion Diffusion Geometry Extraction In Graphitic Nanosphere Battery Materials\\nYear: 2016\\n\\nAbstract: Large-scale molecular dynamics (MD) simulations are commonly used for simulating the synthesis and ion diffusion of battery materials. A good battery anode material is determined by its capacity to store ion or other diffusers. However, modeling of ion diffusion dynamics and transport properties at large length and long time scales would be impossible with current MD codes. To analyze the fundamental properties of these materials, therefore, we turn to geometric and topological analysis of their structure. In this paper, we apply a novel technique inspired by discrete Morse theory to the Delaunay triangulation of the simulated geometry of a thermally annealed carbon nanosphere. We utilize our computed structures to drive further geometric analysis to extract the interstitial diffusion structure as a single mesh. Our results provide a new approach to analyze the geometry of the simulated carbon nanosphere, and new insights into the role of carbon defect size and distribution in determining the charge capacity and charge dynamics of these carbon based battery materials.\",\n \"Title: Analyzing simulation-based PRA data through traditional and topological clustering: A BWR station blackout case study.\\nYear: 2016\\n\\nAbstract: Dynamic probabilistic risk assessment (DPRA) methodologies couple system simulator codes (e.g., RELAP and MELCOR) with simulation controller codes (e.g., RAVEN and ADAPT). Whereas system simulator codes model system dynamics deterministically, simulation controller codes introduce both deterministic (e.g., system control logic and operating procedures) and stochastic (e.g., component failures and parameter uncertainties) elements into the simulation. Typically, a DPRA is performed by sampling values of a set of parameters and simulating the system behavior for that specific set of parameter values. For complex systems, a major challenge in using DPRA methodologies is to analyze the large number of scenarios generated, where clustering techniques are typically employed to better organize and interpret the data. In this paper, we focus on the analysis of two nuclear simulation datasets that are part of the risk-informed safety margin characterization (RISMC) boiling water reactor (BWR) station blackout (SBO) case study. We provide the domain experts a software tool that encodes traditional and topological clustering techniques within an interactive analysis and visualization environment, for understanding the structures of such high-dimensional nuclear simulation datasets. We demonstrate through our case study that both types of clustering techniques complement each other for enhanced structural understanding of the data.\",\n \"Title: Visual Exploration of High‐Dimensional Data through Subspace Analysis and Dynamic Projections\\nYear: 2015\\n\\nAbstract: We introduce a novel interactive framework for visualizing and exploring high-dimensional datasets based on subspace analysis and dynamic projections. We assume the high-dimensional dataset can be represented by a mixture of low-dimensional linear subspaces with mixed dimensions, and provide a method to reliably estimate the intrinsic dimension and linear basis of each subspace extracted from the subspace clustering. Subsequently, we use these bases to define unique 2D linear projections as viewpoints from which to visualize the data. To understand the relationships among the different projections and to discover hidden patterns, we connect these projections through dynamic projections that create smooth animated transitions between pairs of projections. We introduce the view transition graph, which provides flexible navigation among these projections to facilitate an intuitive exploration. Finally, we provide detailed comparisons with related systems, and use real-world examples to demonstrate the novelty and usability of our proposed framework.\",\n \"Title: Tracking features in embedded surfaces: Understanding extinction in turbulent combustion\\nYear: 2015\\n\\nAbstract: Understanding the temporal evolution of features of interest requires the ability to: (i) extract features from each snapshot; (ii) correlate them over time; and (iii) understand the resulting tracking graph. This paper provides new solutions for the last two challenges in the context of large-scale turbulent combustion simulations. In particular, we present a simple and general algorithm to correlate hierarchical features, embedded in time-dependent surfaces. This, for the first time, provides a parameter independent approach to track embedded features. Furthermore, we provide a new technique to adaptively change feature parameters over time to both: alleviate artifacts due to insufficient temporal resolution as well as to simplify the resulting tracking graphs to promote new scientific insights. Our solutions are integrated into a general and flexible analysis environment that allows users to interactively explore the spatio-temporal behavior of large-scale simulations. We demonstrate the results using the analysis of extinction holes in turbulent combustion as primary case study and a number of other applications to illustrate the generality of the approach.\",\n \"Title: A Randomized Ensemble Approach to Industrial CT Segmentation\\nYear: 2015\\n\\nAbstract: Tuning the models and parameters of common segmentation approaches is challenging especially in the presence of noise and artifacts. Ensemble-based techniques attempt to compensate by randomly varying models and/or parameters to create a diverse set of hypotheses, which are subsequently ranked to arrive at the best solution. However, these methods have been restricted to cases where the underlying models are well-established, e.g. natural images. In practice, it is difficult to determine a suitable base-model and the amount of randomization required. Furthermore, for multi-object scenes no single hypothesis may perform well for all objects, reducing the overall quality of the results. This paper presents a new ensemble-based segmentation framework for industrial CT images demonstrating that comparatively simple models and randomization strategies can significantly improve the result over existing techniques. Furthermore, we introduce a per-object based ranking, followed by a consensus inference that can outperform even the best case scenario of existing hypothesis ranking approaches. We demonstrate the effectiveness of our approach using a set of noise and artifact rich CT images from baggage security and show that it significantly outperforms existing solutions in this area.\",\n \"Title: Recovering logical structure from Charm++ event traces\\nYear: 2015\\n\\nAbstract: Asynchrony and non-determinism in Charm++ programs present a significant challenge in analyzing their event traces. We present a new framework to organize event traces of parallel programs written in Charm++. Our reorganization allows one to more easily explore and analyze such traces by providing context through logical structure. We describe several heuristics to compensate for missing dependencies between events that currently cannot be easily recorded. We introduce a new task ordering that recovers logical structure from the non-deterministic execution order. Using the logical structure, we define several metrics to help guide developers to performance problems. We demonstrate our approach through two proxy applications written in Charm++. Finally, we discuss the applicability of this framework to other task-based runtimes and provide guidelines for tracing to support this form of analysis.\",\n \"Title: Relating memory performance data to application domain data using an integration API.\\nYear: 2015\\n\\nAbstract: Understanding performance data, and more specifically memory access pattern is essential in optimizing scientific applications. Among the various factors affecting performance, such as the hardware architecture, the algorithms, or the system software stack, performance is also often related to the applications' physics. While there exists a number of techniques to collect relevant performance metrics, such as number of cache misses, traditional tools almost exclusively present this data relative to the code or as abstract tuples. This can obscure the data dependent nature of performance bottlenecks and make root-cause analysis difficult. Here we take advantage of the fact that a large class of applications are defined over some domain discretized by a mesh. By projecting the performance data directly onto these meshes, we enable developers to explore the performance data in the context of their application resulting in more intuitive visualizations. We introduce a lightweight, general interface to couple a performance visualization tool, MemAxes, to an external visualization tool, VisIt. This allows us to harness the advanced analytic capabilities of MemAxes to drive the exploration while exploiting the capabilities of VisIt to visualize both application and performance data in the application domain.\",\n \"Title: Identifying the Culprits Behind Network Congestion\\nYear: 2015\\n\\nAbstract: Network congestion is one of the primary causes of performance degradation, performance variability and poor scaling in communication-heavy parallel applications. However, the causes and mechanisms of network congestion on modern interconnection networks are not well understood. We need new approaches to analyze, model and predict this critical behaviour in order to improve the performance of large-scale parallel applications. This paper applies supervised learning algorithms, such as forests of extremely randomized trees and gradient boosted regression trees, to perform regression analysis on communication data and application execution time. Using data derived from multiple executions, we create models to predict the execution time of communication-heavy parallel applications. This analysis also identifies the features and associated hardware components that have the most impact on network congestion and intern, on execution time. The ideas presented in this paper have wide applicability: predicting the execution time on a different number of nodes, or different input datasets, or even for an unknown code, identifying the best configuration parameters for an application, and finding the root causes of network congestion on different architectures.\",\n \"Title: The Grassmannian Atlas: A General Framework for Exploring Linear Projections of High-Dimensional Data.\\nYear: 2016\\n\\nAbstract: Linear projections are one of the most common approaches to visualize high-dimensional data. Since the space of possible projections is large, existing systems usually select a small set of interesting projections by ranking a large set of candidate projections based on a chosen quality measure. However, while highly ranked projections can be informative, some lower ranked ones could offer important complementary information. Therefore, selection based on ranking may miss projections that are important to provide a global picture of the data. The proposed work fills this gap by presenting the Grassmannian Atlas, a framework that captures the global structures of quality measures in the space of all projections, which enables a systematic exploration of many complementary projections and provides new insights into the properties of existing quality measures.\",\n \"Title: Evaluation of In-Situ Analysis Strategies at Scale for Power Efficiency and Scalability\\nYear: 2016\\n\\nAbstract: The increasing gap between available compute power and I/O capabilities is resulting in simulation pipelines running on leadership computing facilities being reformulated. In particular, in-situ processing is complementing conventional post-process analysis, however, it can be performed by using the same compute resources as the simulation or using secondary dedicated resources. In this paper, we focus on three different in-situ analysis strategies, which use the same compute resources as the ongoing simulation but different data movement strategies. We evaluate the costs incurred by these strategies in terms of run time, scalability and power/energy consumption. Furthermore, we extrapolate power behavior to peta-scale and investigate different design choices through projections. Experimental evaluation at full machine scale on Titan supports that using fewer cores per node for in-situ analysis is the optimum choice in terms of scalability. Hence, further research effort should be devoted towards developing in-situ analysis techniques following this strategy in future high-end systems.\",\n \"Title: Analyzing Network Health and Congestion in Dragonfly-Based Supercomputers\\nYear: 2016\\n\\nAbstract: The dragonfly topology is a popular choice for building high-radix, low-diameter, hierarchical networks with high-bandwidth links. On Cray installations of the dragonfly network, job placement policies and routing inefficiencies can lead to significant network congestion for a single job and multi-job workloads. In this paper, we explore the effects of job placement, parallel workloads and network configurations on network health to develop a better understanding of inter-job interference. We have developed a functional network simulator, Damselfly, to model the network behavior of Cray Cascade, and a visual analytics tool, DragonView, to analyze the simulation output. We simulate several parallel workloads based on five representative communication patterns on up to 131,072 cores. Our simulations and visualizations provide unique insight into the buildup of network congestion and present a trade-off between deployment dollar costs and performance of the network.\",\n \"Title: Interactive exploration of atomic trajectories through relative-angle distribution and associated uncertainties\\nYear: 2016\\n\\nAbstract: Exploration of atomic trajectories is fundamental to understanding and characterizing complex chemical systems important in many applications. For instance, any new insight into the mechanisms of ionic migration in catalytic materials could lead to a substantial increase in battery performance. A new statistical measure, called the relative-angle distribution, has been proposed to understand complex motion - whether Brownian, ballistic, or diffusive. The relative-angle distribution can be represented as a collection of 1D histograms, but is currently created in a slow, offline process, making any parameter exploration a tedious and time-consuming task. Furthermore, the resulting plot can hide uncertainty in both the data and the visualization. As a result, once rastered or printed at a fixed resolution, these histograms can be misleading. We present a new analysis tool for the exploration of atomic trajectories that combines an interactive histogram visualization with uncertainty information for both data and plotting errors, and is also linked to an interactive 3D display of trajectories. Our tool enables a holistic exploration of trajectories previously not feasible, with the potential for significant scientific impact. In collaboration with domain experts, we have deployed our tool ta analyze molecular dynamics simulations of lithium-ion diffusion. Users have found that the tool significantly accelerates the exploration process and have used it to validate a number of previously unconfirmed hypotheses.\",\n \"Title: Scalable Visualization and Interactive Analysis Using Massive Data Streams.\\nYear: 2012\\n\\nAbstract: Historically, data creation and storage has always outpaced the infrastructure for its movement and utilization. This trend is increasing now more than ever, with the ever growing size of scientific simulations, increased resolution of sensors, and large mosaic images. Effective exploration of massive scientific models demands the combination of data management, analysis, and visualization techniques, working together in an interactive setting. The ViSUS application framework has been designed as an environment that allows the interactive exploration and analysis of massive scientific models in a cache-oblivious, hardware-agnostic manner, enabling processing and visualization of possibly geographically distributed data using many kinds of devices and platforms. For general purpose feature segmentation and exploration we discuss a new paradigm based on topological analysis. This approach enables the extraction of summaries of features present in the data through abstract models that are orders of magnitude smaller than the raw data, providing enough information to support general queries and perform a wide range of analyses without access to the original data.\",\n \"Title: Stair blue noise sampling.\\nYear: 2016\\n\\nAbstract: A common solution to reducing visible aliasing artifacts in image reconstruction is to employ sampling patterns with a blue noise power spectrum. These sampling patterns can prevent discernible artifacts by replacing them with incoherent noise. Here, we propose a new family of blue noise distributions, Stair blue noise, which is mathematically tractable and enables parameter optimization to obtain the optimal sampling distribution. Furthermore, for a given sample budget, the proposed blue noise distribution achieves a significantly larger alias-free low-frequency region compared to existing approaches, without introducing visible artifacts in the mid-frequencies. We also develop a new sample synthesis algorithm that benefits from the use of an unbiased spatial statistics estimator and efficient optimization strategies.\",\n \"Title: In-Staging Data Placement for Asynchronous Coupling of Task-Based Scientific Workflows.\\nYear: 2016\\n\\nAbstract: Coupled application workflows composed of applications implemented using task-based models present new coupling and data exchange challenges, due to the asynchronous interaction and coupling behaviors between tasks of the component applications. In this paper, we present an adaptive data placement approach that addresses these challenges by dynamically adjusting to the asynchronous coupling patterns. Specifically, it places data across a set of staging cores/nodeswith an awareness of the application-specific data locality requirements and the runtime task executions at these staging cores/nodes, with the goal of reducing end-to-end execution time and data movement overhead of the workflow. We experimentally demonstrate the effectiveness of our approach on the Titan Cray XK7 system using representative data coupling patterns derived from current scientific workflows. The evaluation demonstrates that our approach efficiently improves performance by reducing the time-to-solution and increasing the quality of insights for scientific discovery.\",\n \"Title: MemAxes: Visualization and Analytics for Characterizing Complex Memory Performance Behaviors.\\nYear: 2018\\n\\nAbstract: Memory performance is often a major bottleneck for high-performance computing (HPC) applications. Deepening memory hierarchies, complex memory management, and non-uniform access times have made memory performance behavior difficult to characterize, and users require novel, sophisticated tools to analyze and optimize this aspect of their codes. Existing tools target only specific factors of memory ...\",\n \"Title: Visualizing High-Dimensional Data: Advances in the Past Decade.\\nYear: 2015\\n\\nAbstract: Massive simulations and arrays of sensing devices, in combination with increasing computing resources, have generated large, complex, high-dimensional datasets used to study phenomena across numerous fields of study. Visualization plays an important role in exploring such datasets. We provide a comprehensive survey of advances in high-dimensional data visualization that focuses on the past decade. We aim at providing guidance for data practitioners to navigate through a modular view of the recent advances, inspiring the creation of new visualizations along the enriched visualization pipeline, and identifying future opportunities for visualization research.\",\n \"Title: Two-dimensional shape retrieval using the distribution of extrema of Laplacian eigenfunctions.\\nYear: 2017\\n\\nAbstract: We propose a new method using the distribution of extrema of Laplacian eigenfunctions for two-dimensional (2D) shape description and matching. We construct a weighted directed graph, which we call signed natural neighbor graph, to represent a Laplacian eigenfunction of a shape. The nodes of this sparse graph are the extrema of the corresponding eigenfunction, and the edge weights are defined by signed natural neighbor coordinates derived from the local spatial arrangement of extrema. We construct the signed natural neighbor graphs defined by a small number of low-frequency Laplacian eigenfunctions of a shape to describe it. This shape descriptor is invariant under rigid transformations and uniform scaling, and is also insensitive to minor boundary deformations. When using our shape descriptor for matching two shapes, we determine their similarity by comparing the graphs induced by corresponding Laplacian eigenfunctions of the two shapes. Our experimental shape-matching results demonstrate that our method is effective for 2D shape retrieval.\",\n \"Title: Embedded domain-specific language and runtime system for progressive spatiotemporal data analysis and visualization\\nYear: 2016\\n\\nAbstract: As our ability to generate large and complex datasets grows, accessing and processing these massive data collections is increasingly the primary bottleneck in scientific analysis. Challenges include retrieving, converting, resampling, and combining remote and often disparately located data ensembles with only limited support from existing tools. In particular, existing solutions rely predominantly on extensive data transfers or large-scale remote computing resources, both of which are inherently offline processes with long delays and substantial repercussions for any mistakes. Such workflows severely limit the flexible exploration and rapid evaluation of new hypotheses that are crucial to the scientific process and thereby impede scientific discovery. Here we present an embedded domain-specific language (EDSL) specifically designed for the interactive exploration of large-scale, remote data. Our EDSL allows users to express a wide range of data analysis operations in a simple and abstract manner. The underlying runtime system transparently resolves issues such as remote data access and resampling while at the same time maintaining interactivity through progressive and interruptible computation. This system enables, for the first time, interactive remote exploration of massive datasets such as the 7km NASA GEOS-5 Nature Run simulation, which previously have been analyzed only offline or at reduced resolution.\",\n \"Title: Visual Exploration of Semantic Relationships in Neural Word Embeddings.\\nYear: 2018\\n\\nAbstract: Constructing distributed representations for words through neural language models and using the resulting vector spaces for analysis has become a crucial component of natural language processing (NLP). However, despite their widespread application, little is known about the structure and properties of these spaces. To gain insights into the relationship between words, the NLP community has begun t...\",\n \"Title: Poisson Disk Sampling on the Grassmannnian: Applications in Subspace Optimization\\nYear: 2017\\n\\nAbstract: To develop accurate inference algorithms on embedded manifolds such as the Grassmannian, we often employ several optimization tools and incorporate the characteristics of known manifolds as additional constraints. However, a direct analysis of the nature of functions on manifolds is rarely performed. In this paper, we propose an alternative approach to this inference by adopting a statistical pipeline that first generates an initial sampling of the manifold, and then performs subsequent analysis based on these samples. First, we introduce a better sampling technique based on dart throwing (called the Poisson disk sampling (PDS)) to effectively sample the Grassmannian. Next, using Grassmannian sparse coding, we demonstrate the improved coverage achieved by PDS. Finally, we develop a consensus approach, with Grassmann samples, to infer the optimal embeddings for linear dimensionality reduction, and show that the resulting solutions are nearly optimal.\",\n \"Title: ISAVS: interactive scalable analysis and visualization system.\\nYear: 2017\\n\\nAbstract: Modern science is inundated with ever increasing data sizes as computational capabilities and image acquisition techniques continue to improve. For example, simulations are tackling ever larger domains with higher fidelity, and high-throughput microscopy techniques generate larger data that are fundamental to gather biologically and medically relevant insights. As the image sizes exceed memory, and even sometimes local disk space, each step in a scientific workflow is impacted. Current software solutions enable data exploration with limited interactivity for visualization and analytic tasks. Furthermore analysis on HPC systems often require complex hand-written parallel implementations of algorithms that suffer from poor portability and maintainability.\\n\\nWe present a software infrastructure that simplifies end-to-end visualization and analysis of massive data. First, a hierarchical streaming data access layer enables interactive exploration of remote data, with fast data fetching to test analytics on subsets of the data. Second, a library simplifies the process of developing new analytics algorithms, allowing users to rapidly prototype new approaches and deploy them in an HPC setting. Third, a scalable runtime system automates mapping analysis algorithms to whatever computational hardware is available, reducing the complexity of developing scaling algorithms. We demonstrate the usability and performance of our system using a use case from neuroscience: filtering, registration, and visualization of tera-scale microscopy data. We evaluate the performance of our system using a leadership-class supercomputer, Shaheen II.\",\n \"Title: Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion.\\nYear: 2018\\n\\nAbstract: Computed Tomography (CT) reconstruction is a fundamental component to a wide variety of applications ranging from security, to healthcare. The classical techniques require measuring projections, called sinograms, from a full 180° view of the object. However, obtaining a full-view is not always feasible, such as when scanning irregular objects that limit flexibility of scanner rotation. The resulting limited angle sinograms are known to produce highly artifact-laden reconstructions with existing techniques. In this paper, we propose to address this problem using CTNet - a system of 1D and 2D convolutional neural networks, that operates directly on a limited angle sinogram to predict the reconstruction. We use the x-ray transform on this prediction to obtain a completed sinogram, as if it came from a full 180°view. We feed this to standard analytical and iterative reconstruction techniques to obtain the final reconstruction. We show with extensive experimentation on a challenging real world dataset that this combined strategy outperforms many competitive baselines. We also propose a measure of confidence for the reconstruction that enables a practitioner to gauge the reliability of a prediction made by CTNet. We show that this measure is a strong indicator of quality as measured by the PSNR, while not requiring ground truth at test time. Finally, using a segmentation experiment, we show that our reconstruction also preserves the 3D structure of objects better than existing solutions.\",\n \"Title: Interactive Visualization and Exploration of Patient Progression in a Hospital Setting.\\nYear: 2017\\n\\nAbstract: As medical organizations increasingly adopt the use of electronic health records (EHRs), large volumes of clinical data are being captured on a daily basis. These data provide comprehensive information about patients and have the potential to improve a wide range of application domains in healthcare. Physicians and clinical researchers are interested in finding effective ways to understand this abundance of data. Use of visual analytics to explore healthcare data is one such research direction. Here, we present a visualization and analysis environment to understand patient progression over time. Through the use of optimized data structures and progressive visualization techniques, we allow users to interactively explore how patients and their progression change over time. Compared to existing techniques, our work provides additional flexibility in analyzing patient data and has the potential to be used in a real-time hospital setting. Finally, we demonstrate the utility of our approach using a publicly available intensive care unit (ICU) database.\",\n \"Title: A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms.\\nYear: 2018\\n\\nAbstract: This paper proposes a new approach to construct high quality space-filling sample designs. First, we propose a novel technique to quantify the space-filling property and optimally trade-off uniformity and randomness in sample designs in arbitrary dimensions. Second, we connect the proposed metric (defined in the spatial domain) to the quality metric of the design performance (defined in the spectral domain). This connection serves as an analytic framework for evaluating the qualitative properties of space-filling designs in general. Using the theoretical insights provided by this spatial-spectral analysis, we derive the notion of optimal space-filling designs, which we refer to as space-filling spectral designs. Third, we propose an efficient estimator to evaluate the space-filling properties of sample designs in arbitrary dimensions and use it to develop an optimization framework for generating high quality space-filling designs. Finally, we carry out a detailed performance comparison on two different applications in varying dimensions: a) image reconstruction and b) surrogate modeling for several benchmark optimization functions and a physics simulation code for inertial confinement fusion (ICF). Our results clearly evidence the superiority of the proposed space-filling designs over existing approaches, particularly in high dimensions.\",\n \"Title: TopoMS: Comprehensive topological exploration for molecular and condensed-matter systems.\\nYear: 2018\\n\\nAbstract: We introduce TopoMS, a computational tool enabling detailed topological analysis of molecular and condensed-matter systems, including the computation of atomic volumes and charges through the quantum theory of atoms in molecules, as well as the complete molecular graph. With roots in techniques from computational topology, and using a shared-memory parallel approach, TopoMS provides scalable, numerically robust, and topologically consistent analysis. TopoMS can be used as a command-line tool or with a GUI (graphical user interface), where the latter also enables an interactive exploration of the molecular graph. This paper presents algorithmic details of TopoMS and compares it with state-of-the-art tools: Bader charge analysis v1.0 (Arnaldsson et al., 01/11/17) and molecular graph extraction using Critic2 (Otero-de-la-Roza et al., Comput. Phys. Commun. 2014, 185, 1007). TopoMS not only combines the functionality of these individual codes but also demonstrates up to 4x performance gain on a standard laptop, faster convergence to fine-grid solution, robustness against lattice bias, and topological consistency. TopoMS is released publicly under BSD License. (c) 2018 Wiley Periodicals, Inc.\",\n \"Title: Rendering and Extracting Extremal Features in 3D Fields.\\nYear: 2018\\n\\nAbstract: Visualizing and extracting three-dimensional features is important for many computational science applications, each with their own feature definitions and data types. While some are simple to state and implement (e.g. isosurfaces), others require more complicated mathematics (e.g. multiple derivatives, curvature, eigenvectors, etc.). Correctly implementing mathematical definitions is difficult, so experimenting with new features requires substantial investments. Furthermore, traditional interpolants rarely support the necessary derivatives, and approximations can reduce numerical stability. Our new approach directly translates mathematical notation into practical visualization and feature extraction, with minimal mental and implementation overhead. Using a mathematically expressive domain-specific language, Diderot, we compute direct volume renderings and particle-based feature samplings for a range of mathematical features. Non-expert users can experiment with feature definitions without any exposure to meshes, interpolants, derivative computation, etc. We demonstrate high-quality results on notoriously difficult features, such as ridges and vortex cores, using working code simple enough to be presented in its entirety.\",\n \"Title: BabelFlow: An Embedded Domain Specific Language for Parallel Analysis and Visualization\\nYear: 2018\\n\\nAbstract: The rapid growth in simulation data requires large-scale parallel implementations of scientific analysis and visualization algorithms, both to produce results within an acceptable timeframe and to enable in situ deployment. However, efficient and scalable implementations, especially of more complex analysis approaches, require not only advanced algorithms, but also an in-depth knowledge of the underlying runtime. Furthermore, different machine configurations and different applications may favor different runtimes, i.e., MPI vs Charm++ vs Legion, etc., and different hardware architectures. This diversity makes developing and maintaining a broadly applicable analysis software infrastructure challenging. We address some of these problems by explicitly separating the implementation of individual tasks of an algorithm from the dataflow connecting these tasks. In particular, we present an embedded domain specific language (EDSL) to describe algorithms using a new task graph abstraction. This task graph is then executed on top of one of several available runtimes (MPI, Charm++, Legion) using a thin layer of library calls. We demonstrate the flexibility and performance of this approach using three different large scale analysis and visualization use cases, i.e., topological analysis, rendering and compositing dataflow, and image registration of large microscopy scans. Despite the unavoidable overheads of a generic solution, our approach demonstrates performance portability at scale, and, in some cases, outperforms hand-optimized implementations.\",\n \"Title: Controlled Random Search Improves Sample Mining and Hyper-Parameter Optimization.\\nYear: 2018\\n\\nAbstract: A common challenge in machine learning and related fields is the need to efficiently explore high dimensional parameter spaces using small numbers of samples. Typical examples are hyper-parameter optimization in deep learning and sample mining in predictive modeling tasks. All such problems trade-off exploration, which samples the space without knowledge of the target function, and exploitation where information from previous evaluations is used in an adaptive feedback loop. Much of the recent focus has been on the exploitation while exploration is done with simple designs such as Latin hypercube or even uniform random sampling. In this paper, we introduce optimal space-filling sample designs for effective exploration of high dimensional spaces. Specifically, we propose a new parameterized family of sample designs called space-filling spectral designs, and introduce a framework to choose optimal designs for a given sample size and dimension. Furthermore, we present an efficient algorithm to synthesize a given spectral design. Finally, we evaluate the performance of spectral designs in both data space and model space applications. The data space exploration is targeted at recovering complex regression functions in high dimensional spaces. The model space exploration focuses on selecting hyper-parameters for a given neural network architecture. Our empirical studies demonstrate that the proposed approach consistently outperforms state-of-the-art techniques, particularly with smaller design sizes.\",\n \"Title: Understanding Deep Neural Networks Through Input Uncertainties\\nYear: 2018\\n\\nAbstract: Techniques for understanding the functioning of complex machine learning models are becoming increasingly popular, not only to improve the validation process, but also to extract new insights about the data via exploratory analysis. Though a large class of such tools currently exists, most assume that predictions are point estimates and use a sensitivity analysis of these estimates to interpret the model. Using lightweight probabilistic networks we show how including prediction uncertainties in the sensitivity analysis leads to: (i) more robust and generalizable models; and (ii) a new approach for model interpretation through uncertainty decomposition. In particular, we introduce a new regularization that takes both the mean and variance of a prediction into account and demonstrate that the resulting networks provide improved generalization to unseen data. Furthermore, we propose a new technique to explain prediction uncertainties through uncertainties in the input domain, thus providing new ways to validate and interpret deep learning models.\",\n \"Title: Unsupervised Dimension Selection using a Blue Noise Spectrum.\\nYear: 2018\\n\\nAbstract: Unsupervised dimension selection is an important problem that seeks to reduce dimensionality of data, while preserving the most useful characteristics. While dimensionality reduction is commonly utilized to construct low-dimensional embeddings, they produce feature spaces that are hard to interpret. Further, in applications such as sensor design, one needs to perform reduction directly in the input domain, instead of constructing transformed spaces. Consequently, dimension selection (DS) aims to solve the combinatorial problem of identifying the top-$k$ dimensions, which is required for effective experiment design, reducing data while keeping it interpretable, and designing better sensing mechanisms. In this paper, we develop a novel approach for DS based on graph signal analysis to measure feature influence. By analyzing synthetic graph signals with a blue noise spectrum, we show that we can measure the importance of each dimension. Using experiments in supervised learning and image masking, we demonstrate the superiority of the proposed approach over existing techniques in capturing crucial characteristics of high dimensional spaces, using only a small subset of the original features.\",\n \"Title: Shared-Memory Parallel Computation Of Morse-Smale Complexes With Improved Accuracy\\nYear: 2019\\n\\nAbstract: Topological techniques have proven to be a powerful tool in the analysis and visualization of large-scale scientific data. In particular, the Morse-Smale complex and its various components provide a rich framework for robust feature definition and computation. Consequently, there now exist a number of approaches to compute Morse-Smale complexes for large-scale data in parallel. However, existing techniques are based on discrete concepts which produce the correct topological structure but are known to introduce grid artifacts in the resulting geometry. Here, we present a new approach that combines parallel streamline computation with combinatorial methods to construct a high-quality discrete Morse-Smale complex. In addition to being invariant to the orientation of the underlying grid, this algorithm allows users to selectively build a subset of features using high-quality geometry. In particular, a user may specifically select which ascending/descending manifolds are reconstructed with improved accuracy, focusing computational effort where it matters for subsequent analysis. This approach computes Morse-Smale complexes for larger data than previously feasible with significant speedups. We demonstrate and validate our approach using several examples from a variety of different scientific domains, and evaluate the performance of our method.\",\n \"Title: A Study Of The Trade-Off Between Reducing Precision And Reducing Resolution For Data Analysis And Visualization\\nYear: 2019\\n\\nAbstract: There currently exist two dominant strategies to reduce data sizes in analysis and visualization: reducing the precision of the data, e.g.. through quantization, or reducing its resolution, e.g., by subsampling. Both have advantages and disadvantages and both face fundamental limits at which the reduced information ceases to be useful. The paper explores the additional gains that could be achieved by combining both strategies. In particular, we present a common framework that allows us to study the trade-off in reducing precision and/or resolution in a principled manner. We represent data reduction schemes as progressive streams of bits and study how various bit orderings such as by resolution, by precision, etc., impact the resulting approximation error across a variety of data sets as well as analysis tasks. Furthermore. we compute streams that are optimized for different tasks to serve as lower bounds on the achievable error. Scientific data management systems can use the results presented in this paper as guidance on how to store and stream data to make efficient use of the limited storage and bandwidth in practice.\",\n \"Title: NLIZE: A Perturbation-Driven Visual Interrogation Tool for Analyzing and Interpreting Natural Language Inference Models.\\nYear: 2019\\n\\nAbstract: With the recent advances in deep learning, neural network models have obtained state-of-the-art performances for many linguistic tasks in natural language processing. However, this rapid progress also brings enormous challenges. The opaque nature of a neural network model leads to hard-to-debug-systems and difficult-to-interpret mechanisms. Here, we introduce a visualization system that, through a...\",\n \"Title: Stability of Dissipation Elements: A Case Study in Combustion\\nYear: 2014\\n\\nAbstract: Recently, dissipation elements have been gaining popularity as a mechanism for measurement of fundamental properties of turbulent flow, such as turbulence length scales and zonal partitioning. Dissipation elements segment a domain according to the source and destination of streamlines in the gradient flow field of a scalar function f : ﾿ ℝ. They have traditionally been computed by numerically integrating streamlines from the center of each voxel in the positive and negative gradient directions, and grouping those voxels whose streamlines terminate at the same extremal pair. We show that the same structures map well to combinatorial topology concepts developed recently in the visualization community. Namely, dissipation elements correspond to sets of cells of the Morse-Smale complex. The topology-based formulation enables a more exploratory analysis of the nature of dissipation elements, in particular, in understanding their stability with respect to small scale variations. We present two examples from combustion science that raise significant questions about the role of small scale perturbation and indeed the definition of dissipation elements themselves.\",\n \"Title: Interactive Investigation of Traffic Congestion on Fat-Tree Networks Using TreeScope.\\nYear: 2018\\n\\nAbstract: Parallel simulation codes often suffer from performance bottlenecks due to network congestion, leaving millions of dollars of investments underutilized. Given a network topology, it is critical to understand how different applications, job placements, routing schemes, etc., are affected by and contribute to network congestion, especially for large and complex networks. Understanding and optimizing communication on large-scale networks is an active area of research. Domain experts often use exploratory tools to develop both intuitive and formal metrics for network health and performance. This paper presents TreeScope, an interactive, web-based visualization tool for exploring network traffic on large-scale fat-tree networks. TreeScope encodes the network topology using a tailored matrix-based representation and provides detailed visualization of all traffic in the network. We report on the design process of TreeScope, which has been received positively by network researchers as well as system administrators. Through case studies of real and simulated data, we demonstrate how TreeScope's visual design and interactive support for complex queries on network traffic can provide experts with new insights into the occurrences and causes of congestion in the network.\",\n \"Title: A Look at the Effect of Sample Design on Generalization through the Lens of Spectral Analysis.\\nYear: 2019\\n\\nAbstract: This paper provides a general framework to study the effect of sampling properties of training data on the generalization error of the learned machine learning (ML) models. Specifically, we propose a new spectral analysis of the generalization error, expressed in terms of the power spectra of the sampling pattern and the function involved. The framework is build in the Euclidean space using Fourier analysis and establishes a connection between some high dimensional geometric objects and optimal spectral form of different state-of-the-art sampling patterns. Subsequently, we estimate the expected error bounds and convergence rate of different state-of-the-art sampling patterns, as the number of samples and dimensions increase. We make several observations about generalization error which are valid irrespective of the approximation scheme (or learning architecture) and training (or optimization) algorithms. Our result also sheds light on ways to formulate design principles for constructing optimal sampling methods for particular problems.\",\n \"Title: Unsupervised Dimension Selection Using A Blue Noise Graph Spectrum\\nYear: 2019\\n\\nAbstract: Unsupervised dimension selection is an important problem that seeks to reduce dimensionality of data, while preserving the most useful characteristics. While dimensionality reduction is commonly utilized to construct low-dimensional embeddings, they produce feature spaces that are hard to interpret. Further, in applications such as sensor design, one needs to perform reduction directly in the input domain, instead of constructing transformed spaces. Consequently, dimension selection (DS) aims to solve the combinatorial problem of identifying the top-k dimensions, which is required for effective experiment design, reducing data while keeping it interpretable, and designing better sensing mechanisms. In this paper, we develop a novel approach for DS based on graph signal analysis to measure feature influence. By analyzing synthetic graph signals with a blue noise spectrum, we show that we can measure the importance of each dimension. Using experiments in supervised learning and image masking, we demonstrate the superiority of the proposed approach over existing techniques in capturing crucial characteristics of high dimensional spaces, using only a small subset of the original features.\",\n \"Title: Distributed merge forest: a new fast and scalable approach for topological analysis at scale\\nYear: 2021\\n\\nAbstract: ABSTRACTTopological analysis is used in several domains to identify and characterize important features in scientific data, and is now one of the established classes of techniques of proven practical use in scientific computing. The growth in parallelism and problem size tackled by modern simulations poses a particular challenge for these approaches. Fundamentally, the global encoding of topological features necessitates interprocess communication that limits their scaling. In this paper, we extend a new topological paradigm to the case of distributed computing, where the construction of a global merge tree is replaced by a distributed data structure, the merge forest, trading slower individual queries on the structure for faster end-to-end performance and scaling. Empirically, the queries that are most negatively affected also tend to have limited practical use. Our experimental results demonstrate the scalability of both the merge forest construction and the parallel queries needed in scientific workflows, and contrast this scalability with the two established alternatives that construct variations of a global tree.\",\n \"Title: Leveraging Topological Events In Tracking Graphs For Understanding Particle Diffusion\\nYear: 2021\\n\\nAbstract: Single particle tracking (SPT) of fluorescent molecules provides significant insights into the diffusion and relative motion of tagged proteins and other structures of interest in biology. However, despite the latest advances in high-resolution microscopy, individual particles are typically not distinguished from clusters of particles. This lack of resolution obscures potential evidence for how merging and splitting of particles affect their diffusion and any implications on the biological environment. The particle tracks are typically decomposed into individual segments at observed merge and split events, and analysis is performed without knowing the true count of particles in the resulting segments. Here, we address the challenges in analyzing particle tracks in the context of cancer biology. In particular, we study the tracks of KRAS protein, which is implicated in nearly 20% of all human cancers, and whose clustering and aggregation have been linked to the signaling pathway leading to uncontrolled cell growth. We present a new analysis approach for particle tracks by representing them as tracking graphs and using topological events - merging and splitting, to disambiguate the tracks. Using this analysis, we infer a lower bound on the count of particles as they cluster and create conditional distributions of diffusion speeds before and after merge and split events. Using thousands of time-steps of simulated and in-vitro SPT data, we demonstrate the efficacy of our method, as it offers the biologists a new, detailed look into the relationship between KRAS clustering and diffusion speeds.\",\n \"Title: Towards replacing physical testing of granular materials with a Topology-based Model\\nYear: 2022\\n\\nAbstract: In the study of packed granular materials, the performance of a sample (e.g., the detonation of a high-energy explosive) often correlates to measurements of a fluid flowing through it. The “effective surface area,” the surface area accessible to the airflow, is typically measured using a permeametry apparatus that relates the flow conductance to the permeable surface area via the Carman-Kozeny equ...\",\n \"Title: Generalizable coordination of large multiscale workflows: challenges and learnings at scale\\nYear: 2021\\n\\nAbstract: ABSTRACTThe advancement of machine learning techniques and the heterogeneous architectures of most current supercomputers are propelling the demand for large multiscale simulations that can automatically and autonomously couple diverse components and map them to relevant resources to solve complex problems at multiple scales. Nevertheless, despite the recent progress in workflow technologies, current capabilities are limited to coupling two scales. In the first-ever demonstration of using three scales of resolution, we present a scalable and generalizable framework that couples pairs of models using machine learning and in situ feedback. We expand upon the massively parallel Multiscale Machine-Learned Modeling Infrastructure (MuMMI), a recent, award-winning workflow, and generalize the framework beyond its original design. We discuss the challenges and learnings in executing a massive multiscale simulation campaign that utilized over 600,000 node hours on Summit and achieved more than 98% GPU occupancy for more than 83% of the time. We present innovations to enable several orders of magnitude scaling, including simultaneously coordinating 24,000 jobs, and managing several TBs of new data per day and over a billion files in total. Finally, we describe the generalizability of our framework and, with an upcoming open-source release, discuss how the presented framework may be used for new applications.\",\n \"Title: Data-Driven Estimation of Temporal-Sampling Errors in Unsteady Flows\\nYear: 2021\\n\\nAbstract: While computer simulations typically store data at the highest available spatial resolution, it is often infeasible to do so for the temporal dimension. Instead, the common practice is to store data at regular intervals, the frequency of which is strictly limited by the available storage and I/O bandwidth. However, this manner of temporal subsampling can cause significant errors in subsequent analysis steps. More importantly, since the intermediate data is lost, there is no direct way of measuring this error after the fact. One particularly important use case that is affected is the analysis of unsteady flows using pathlines, as it depends on an accurate interpolation across time. Although the potential problem with temporal undersampling is widely acknowledged, there currently does not exist a practical way to estimate the potential impact. This paper presents a simple-to-implement yet powerful technique to estimate the error in pathlines due to temporal subsampling. Given an unsteady flow, we compute pathlines at the given temporal resolution as well as subsamples thereof. We then compute the error induced due to various levels of subsampling and use it to estimate the error between the given resolution and the unknown ground truth. Using two turbulent flows, we demonstrate that our approach, for the first time, provides an accurate, a posteriori error estimate for pathline computations. This estimate will enable scientists to better understand the uncertainties involved in pathline-based analysis techniques and can lead to new uncertainty visualization approaches using the predicted errors.\",\n \"Title: Portable and Composable Flow Graphs for In Situ Analytics\\nYear: 2021\\n\\nAbstract: Existing data analysis and visualization algorithms are used in a wide range of simulations that strive to support an increasing number of runtime systems. The BabelFlow framework has been designed to address this situation by providing users with a simple interface to implement analysis algorithms as dataflow graphs portable across different runtimes. The limitation in BabelFlow, however, is that...\",\n \"Title: AMM: Adaptive Multilinear Meshes\\nYear: 2022\\n\\nAbstract: Adaptive representations are increasingly indispensable for reducing the in-memory and on-disk footprints of large-scale data. Usual solutions are designed broadly along two themes: reducing data precision, e.g., through compression, or adapting data resolution, e.g., using spatial hierarchies. Recent research suggests that combining the two approaches, i.e., adapting both res...\",\n \"Title: Enabling machine learning-ready HPC ensembles with Merlin\\nYear: 2022\\n\\nAbstract: With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data. With complexities such as multi-component workflows, heterogeneous machine architectures, parallel file systems, and batch scheduling, care must be taken to facilitate this analysis in a high performance computing (HPC) environment. In this paper, we present Merlin, a workflow framework to enable large ML-friendly ensembles of scientific HPC simulations. By augmenting traditional HPC with distributed compute technologies, Merlin aims to lower the barrier for scientific subject matter experts to incorporate ML into their analysis. As a producer–consumer workflow model, Merlin enables multi-machine, cross-batch job, dynamically allocated yet persistent workflows capable of utilizing surge-compute resources. Key features of Merlin are a flexible HPC-centric interface, low per-task overhead, multi-tiered fault recovery, and a hierarchical sampling algorithm that allows for O(N) task execution and O(NlnN) task queuing to ensembles of millions of tasks. In addition to Merlin’s design, we test the algorithm’s performance in an HPC center and demonstrate the ability to enqueue 40 million simulations in 100 s, with a 30 millisecond per-task overhead that is independent of ensemble size. Finally, we describe some example applications that Merlin has enabled on leadership-class HPC resources, such as the ML-augmented optimization of nuclear fusion experiments and the calibration of infectious disease models to study the progression of and possible mitigation strategies for COVID-19.\",\n \"Title: The Case for Optimized Edge-Centric Tractography at Scale\\nYear: 2022\\n\\nAbstract: The anatomic validity of structural connectomes remains a significant uncertainty in neuroimaging. Edge-centric tractography reconstructs streamlines in bundles between each pair of cortical or subcortical regions. Although edge bundles provides a stronger anatomic embedding than traditional connectomes, calculating them for each region-pair requires exponentially greater computation. We observe that major speedup can be achieved by reducing the number of streamlines used by probabilistic tractography algorithms. To ensure this does not degrade connectome quality, we calculate the identifiability of edge-centric connectomes between test and re-test sessions as a proxy for information content. We find that running PROBTRACKX2 with as few as 1 streamline per voxel per region-pair has no significant impact on identifiability. Variation in identifiability caused by streamline count is overshadowed by variation due to subject demographics. This finding even holds true in an entirely different tractography algorithm using MRTrix. Incidentally, we observe that Jaccard similarity is more effective than Pearson correlation in calculating identifiability for our subject population.\",\n \"Title: Virtual Inspection of Additively Manufactured Parts\\nYear: 2022\\n\\nAbstract: Advanced manufacturing techniques, such as additive manufacturing, enable the design of increasingly complex components for a wide range of industrial applications. However, this complexity makes qualification of the parts, determining whether a part is within some margin of error from the initial design, difficult. To inspect and qualify complex internal geometries that are not accessible with an external probe, parts are typically scanned with computed tomography (CT), and manually compared to the computer-aided design (CAD) model using visual inspections. Matching the CAD model to the 3D reconstructed object is challenging in a traditional desktop environment due to the lack of depth perception and 3D interaction. An additional challenge comes from the geometric complexity of CAD meshes and large-scale CT scans. We present a virtual reality (VR) system for manual qualification, providing a novel defect visualization method. First, we describe a semiautomatic CAD-to-Scan Registration approach in VR using a finite element mesh. Second, we introduce the Defect Box, which enables full-resolution inspection for massive scans and CAD-CT comparison of local defect regions. Finally, our system includes intuitive 3D Metrology methods that enable natural interactions for the measurement of features and defects in VR. We demonstrate our approach on both real and synthetic data and discuss feedback from four expert users in nondestructive qualification.\",\n \"Title: A Study of the Locality of Persistence-Based Queries and Its Implications for the Efficiency of Localized Data Structures\\nYear: 2022\\n\\nAbstract: Scientific datasets are often analyzed and visualized using isosurfaces. The connected components at or above the isovalue defining these isosurfaces are called superlevel-set components. The vertex set of these superlevel-set components can be used to compute local statistics, such as mean temperature or histogram per component, or to segment the data. However, in datasets produced by acquisition devices or simulations, noise induces many spurious components that clutter the visualization and analysis results. Many of these spurious components would disappear if the data values were slightly adjusted. The notion of persistence captures the stability of a component with respect to function value changes, and so we are interested in computing persistence quickly. Locality of computation is critical for parallel scalability, minimization of communication in a distributed environment, or an out-of-core processing. The recently introduced merge forest attained high performance by exploiting locality, thereby avoiding communication until needed to resolve a feature query. We extend the merge forest to support persistence-based queries and study the locality of these queries by evaluating the traversals of regions of data during a query. We confirm that the majority of evaluated datasets have the property that the noise is mostly local, and thus can be efficiently eliminated without performing a global analysis. Finally, we compare the query running times with those of a triplet merge tree because a triplet merge tree answers all proposed queries in constant time and can be constructed from a merge tree in linear time.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":104,"string":"104"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13663,"string":"13,663"}}},{"rowIdx":13664,"cells":{"question_id":{"kind":"string","value":"Yu Guan"},"question":{"kind":"string","value":"Generate an abstract for the title \"Ubi-SleepNet: Advanced Multimodal Fusion Techniques for Three-stage Sleep Classification Using Ubiquitous Sensing.\" using the following items: 1. Sleep monitoring \n2. Polysomnography \n3. Deep learning \n4. Sleep classification \n5. Ubiquitous sensing"},"answer":{"kind":"string","value":"Sleep is a fundamental physiological process that is essential for sustaining a healthy body and mind. The gold standard for clinical sleep monitoring is polysomnography(PSG), based on which sleep can be categorized into five stages, including wake/rapid eye movement sleep (REM sleep)/Non-REM sleep 1 (N1)/Non-REM sleep 2 (N2)/Non-REM sleep 3 (N3). However, PSG is expensive, burdensome, and not suitable for daily use. For long-term sleep monitoring, ubiquitous sensing may be a solution. Most recently, cardiac and movement sensing has become popular in classifying three-stage sleep, since both modalities can be easily acquired from research-grade or consumer-grade devices (e.g., Apple Watch). However, how best to fuse the data for the greatest accuracy remains an open question. In this work, we comprehensively studied deep learning (DL)-based advanced fusion techniques consisting of three fusion strategies alongside three fusion methods for three-stage sleep classification based on two publicly available datasets. Experimental results demonstrate important evidence that three-stage sleep can be reliably classified by fusing cardiac/movement sensing modalities, which may potentially become a practical tool to conduct large-scale sleep stage assessment studies or long-term self-tracking on sleep. To accelerate the progression of sleep research in the ubiquitous/wearable computing community, we made this project open source, and the code can be found at: https://github.com/bzhai/Ubi-SleepNet."},"correct_answer":{"kind":"string","value":"Sleep is a fundamental physiological process that is essential for sustaining a healthy body and mind. The gold standard for clinical sleep monitoring is polysomnography(PSG), based on which sleep can be categorized into five stages, including wake/rapid eye movement sleep (REM sleep)/Non-REM sleep 1 (N1)/Non-REM sleep 2 (N2)/Non-REM sleep 3 (N3). However, PSG is expensive, burdensome, and not suitable for daily use. For long-term sleep monitoring, ubiquitous sensing may be a solution. Most recently, cardiac and movement sensing has become popular in classifying three-stage sleep, since both modalities can be easily acquired from research-grade or consumer-grade devices (e.g., Apple Watch). However, how best to fuse the data for the greatest accuracy remains an open question. In this work, we comprehensively studied deep learning (DL)-based advanced fusion techniques consisting of three fusion strategies alongside three fusion methods for three-stage sleep classification based on two publicly available datasets. Experimental results demonstrate important evidence that three-stage sleep can be reliably classified by fusing cardiac/movement sensing modalities, which may potentially become a practical tool to conduct large-scale sleep stage assessment studies or long-term self-tracking on sleep. To accelerate the progression of sleep research in the ubiquitous/wearable computing community, we made this project open source, and the code can be found at: https://github.com/bzhai/Ubi-SleepNet."},"documents":{"kind":"list like","value":["Title: Random Subspace Method for Gait Recognition\nYear: 2012\n\nAbstract: Over fitting is a common problem for gait recognition algorithms when gait sequences in gallery for training are acquired under a single walking condition. In this paper, we propose an approach based on the random subspace method (RSM) to address such over learning problems. Initially, two-dimensional Principle Component Analysis (2DPCA) is adopted to obtain the full hypothesis space (i.e., eigen space). Multiple inductive biases (i.e., subspaces) are constructed, each with the corresponding basis vectors randomly chosen from the initial eigen space. This procedure can not only largely avoid over adaptation but also facilitate dimension reduction. The final classification is achieved by the decision committee which follows a majority voting criterion from the labeling results of all the subspaces. Experimental results on the benchmark USF Human ID gait database show that the proposed method is a feasible framework for gait recognition under unknown walking conditions.","Title: Interactive simulation of large-scale forests in virtual reality applications\nYear: 2006\n\nAbstract: We propose a hybrid geometry-image representation of trees, which consists of three hierarchies. In the lowest hierarchy, a collection of triangles are used to represent the trunk and salient branches. The finer hierarchy for the small branches is represented by Frond Map, a special type of textures. As the finest hierarchy, the leaves are clustered first and represented as a set of billboards. For each hierarchy, we generate a sequence of LODs (Level of Details). The appropriate level is selected on-the-fly. Smooth transitions between successive levels are accomplished using an image-space voting technique. Our representation facilitates the rapid modeling of trees, the dynamic simulation of trees swaying under wind as well as realistic simulation of large-scale forest scenes. Experimental results demonstrate the efficiency and high quality of our approaches.","Title: Modeling And Rendering Of Realistic Waterfall Scenes With Dynamic Texture Sprites\nYear: 2006\n\nAbstract: We propose an efficient approach for authoring dynamic and realistic waterfall scenes based on an acquired video sequence. Traditional video based techniques generate new images by synthesizing 2D samples, i.e., texture sprites chosen from a video sequence. However, they are limited to one fixed viewpoint and cannot provide arbitrary walkthrough into 3D scenes. Our approach extends this scheme by synthesizing dynamic 2D texture sprites and projecting them into 3D space, We first generate a set of basis texture sprites, which capture the representative appearance and motions of waterfall scenes contained in the video sequence. To model the shape and motion Of a new waterfall scene, we interactively construct a set of flow lines taking account of physical principles. Along each flow line, the basis texture sprites are manipulated and animated dynamically, yielding a sequence of dynamic texture sprites in 3D space. These texture sprites are displayed using the point splatting technique, which can be accelerated efficiently by graphics hardware. By choosing varied basis texture sprites, waterfall scenes with different appearance and shapes can be conveniently simulated. The experimental results demonstrate that our approach achieves realistic effects and real-time frame rates on consumer PC platforms. Copyright (c) 2006 John Wiley & Sons, Ltd.","Title: Parallel genetic algorithms with schema migration\nYear: 2002\n\nAbstract: Genetic algorithms (GAs) are efficient non-gradient stochastic search methods. Parallel GAs are proposed to overcome the deficiencies of sequential GAs, such as low speed and aptness to locally converge. However the tremendous communication cost incurred offsets the advantages of parallel GAs. Hence reducing communication cost is the key issue of this problem. Instead of reducing the communication cost simply by compressing the size of the messages, we tackle the problem by improving the effectiveness of the schema to be disseminated. We propose a new schema migration scheme (SMS). This SMS consists of a schema extracting mechanism and a schema disseminating mechanism. This SMS is valid and requires less communication cost.","Title: Algebraic Connectivity Of Weighed Graphs Under Shifting Components\nYear: 2010\n\nAbstract: Integrating quadratic form of algebraic connectivity and Perron value of bottleneck matrices, we investigate how the algebraic connectivity of a connected weighted graph behaves under shifting components. Generally speaking, when shift components not containing characteristic vertex from less positive (zlarger negative) valuation vertices to larger positive (zless negative) valuation vertices, or reduce weights of some edges, or add some new blocks, its algebraic connectivity is nonincreasing; when shift components along paths from blocks to other block closer to characteristic block (zvertex), or increase weights of some edges, or delete some blocks, its algebraic connectivity is nondecreasing. Therefore, algebraic connectivity could be regarded as a measure of central tendency about blocks of a connected weighted graph with characteristic block (zvertex) as its center.","Title: Robust Clothing-Invariant Gait Recognition\nYear: 2012\n\nAbstract: Robust gait recognition is a challenging problem, due to the large intra-subject variations and small inter-subject variations. Out of the covariate factors like shoe type, carrying condition, elapsed time, it has been demonstrated that clothing is the most challenging covariate factor for appearance-based gait recognition. For example, long coat may cover a significant amount of gait features and make it difficult for individual recognition. In this paper, we proposed a random subspace method (RSM) framework for clothing-invariant gait recognition by combining multiple inductive biases for classification. Even for small size training set, this method can achieve promising performance. Experiments are conducted on the OU-ISIR Treadmill dataset B which includes 32 combinations of clothing types, and the average recognition accuracy is more than 80%, which indicates the effectiveness of our proposed method.","Title: A robust speed-invariant gait recognition system for walker and runner identification\nYear: 2013\n\nAbstract: In real-world scenarios, walking/running speed is one of the most common covariate factors that can affect the performance of gait recognition systems. By assuming the effect caused by the speed changes (from the query walker-s/runners) are intra-class variations that the training data (i.e., gallery) fails to capture, overfitting to the less representative training data may be the main problem that degrades the performance. In this work, we employ a general model based on random subspace method to solve this problem. More specifically, for query gaits in unknown speeds, we try to reduce the generalization errors by combining a large number of weak classifiers. We evaluate our method on two benchmark databases, i.e., Infrared CASIA-C dataset and Treadmill OU-ISIR-A dataset. For the cross-speed walking/running gait recognition experiments, nearly perfect results are achieved, significantly higher than other state-of-the-art algorithms. We also study the unknown- speed nrunner identification solely using the walking gait gallery, and the encouraging experimental results suggest the effectiveness of our method in such cross-mode gait recognition tasks.","Title: An adaptive system for gait recognition in multi-view environments.\nYear: 2012\n\nAbstract: Gait recognition systems often suffer from the challenges when query gaits are under the coupled effects of unknown view angles and large intra-class variations (e.g., wearing a coat). In this paper, we deem it as a two-stage classification problem, namely, view detection and fixed-view gait recognition. First, we propose two simple yet effective feature types (i.e., global features and local features) for view detection. By using the detected view information, the corresponding gallery (i.e., enrolled gait) for the detected view can be adaptively selected to perform the fixed-view gait recognition. For fixed-view gait recognition, since the inter-class variations for training are normally small, whereas the query gait usually has large intra-class variations, random subspace method are adopted. We evaluate our approach on the largest multi-view gait database CASIA-B dataset. The avoidance of searching whole multi-view database as well as the competitive performance indicate that our proposed method is practical for gait recognition in real world surveillance scenarios.","Title: Dynamic Modeling And Rendering Of Grass Wagging In Wind\nYear: 2005\n\nAbstract: Simulation of dynamic natural scene is one of the most challenging tasks in computer graphics. In this paper, we propose a new approach to dynamic modeling and rendering of grasses wagging in wind. Through length preserving free-form deformation of the 3D skeleton lines of each grass blade and using the alpha test to implement transparent texture mapping, we successfully model grasses of different shapes with rich details. To simulate the real time waggle of grasses, the grasses of a meadow are represented in LOD, while their skeleton lines are dynamically deformed according to some physical models. Simplification technique is also employed to accelerate the collision detection between neighboring grasses. Experiments show that our method can realistically render the animated grass scenes under wind of different speeds and types in real time. Copyright (c) 2005 John Wiley & Sons, Ltd.","Title: On the Generalization Power of Face and Gait in Gender Recognition\nYear: 2014\n\nAbstract: Human face/gait-based gender recognition has been intensively studied in the previous literatures, yet most of them are based on the same database. Although nearly perfect gender recognition rates can be achieved in the same face/gait dataset, they assume a closed-world and neglect the problems caused by dataset bias. Real-world human gender recognition system should be dataset-independent, i.e., it can be trained on one face/gait dataset and tested on another. In this paper, the authors test several popular face/gait-based gender recognition algorithms in a cross-dataset manner. The recognition rates decrease significantly and some of them are only slightly better than random guess. These observations suggest that the generalization power of conventional algorithms is less satisfied, and highlight the need for further research on face/gait-based gender recognition for real-world applications.","Title: People Identification and Tracking Through Fusion of Facial and Gait Features.\nYear: 2014\n\nAbstract: This paper reviews the contemporary (face, gait, and fusion) computational approaches for automatic human identification at a distance. For remote identification, there may exist large intra-class variations that can affect the performance of face/gait systems substantially. First, we review the face recognition algorithms in light of factors, such as illumination, resolution, blur, occlusion, and pose. Then we introduce several popular gait feature templates, and the algorithms against factors such as shoe, carrying condition, camera view, walking surface, elapsed time, and clothing. The motivation of fusing face and gait, is that, gait is less sensitive to the factors that may affect face (e.g., low resolution, illumination, facial occlusion, etc.), while face is robust to the factors that may affect gait (walking surface, clothing, etc.). We review several most recent face and gait fusion methods with different strategies, and the significant performance gains suggest these two modality are complementary for human identification at a distance.","Title: Combining gait and face for tackling the elapsed time challenges\nYear: 2013\n\nAbstract: Random Subspace Method (RSM) has been demonstrated as an effective framework for gait recognition. Through combining a large number of weak classifiers, the generalization errors can be greatly reduced. Although RSM-based gait recognition system is robust to a large number of covariate factors, it is, in essence an unimodal biometric system and has the limitations when facing extremely large intra-class variations. One of the major challenges is the elapsed time covariate, which may affect the human walking style in an unpredictable manner. To tackle this challenge, in this paper we propose a multimodal-RSM framework, and side face is used to strengthen the weak classifiers without compromising the generalization power of the whole system. We evaluate our method on the TUM-GAID dataset, and it significantly outperforms other multimodal methods. Specifically, our method achieves very competitive results for tackling the most challenging elapsed time covariate, which potentially also includes the changes in shoe, carrying status, clothing, lighting condition, etc.","Title: Human gait identification from extremely low-quality videos: an enhanced classifier ensemble method\nYear: 2014\n\nAbstract: Nowadays, surveillance cameras are widely installed in public places for security and law enforcement, but the video quality may be low because of the limited transmission bandwidth and storage capacity. In this study, the authors proposed a gait recognition method for extremely low-quality videos, which have a frame-rate at one frame per second (1 fps) and resolution of 32 × 22 pixels. Different from popular temporal reconstruction-based methods, the proposed method uses the average gait image (AGI) over the whole sequence as the appearance-based feature description. Based on the AGI description, the authors employed a large number of weak classifiers to reduce the generalisation errors. The performance can be further improved by incorporating the model-based information into the classifier ensemble. The authors found that the performance improvement is directly proportional to the average disagreement level of weak classifiers (i.e. diversity), which can be increased by using the model-based information. The authors evaluated the proposed method on both indoor and outdoor databases (i.e. the low-quality versions of OU-ISIR-D and USF databases), and the results suggest that our method is more general and effective than other state-of-the-art algorithms.","Title: Image Restoration Based on 3-D Autoregressive Model via Low-Rank Minimization\nYear: 2015\n\nAbstract: Due to all kinds of need of customers and the complicated transmitting environment of digital image and video resources, numerous practical applications emerge, e.g. Image in painting, interpolation, super-resolution and the removal of salt and pepper noise. One thing these cases all have in common is that there are plenty of missing pixels randomly distributed in an image. Existing image restoration methods aiming at solving this problem include kernel regression [1], matrix completion [2] and total variation (TV) model [3]. The 3-D AR model has also been proposed to detect and interpolate the missing data in video sequences. However, the missing rate or missing region in these papers is usually small. With the missing rate increasing, known pixels in a local neighborhood are not going to be enough to form a solvable linear system. Thus, generally speaking, AR model is not suitable for image restoration from high missing rates. Nevertheless, with proper preliminary processing as proposed in this paper, AR models can be well utilized and present good results even in high missing rates. In this paper, we propose a novel method for image restoration. For the first time, the 3-D AR model is utilized in a single image to simultaneously measure correlation within and between similar patches. 2-D AR model combining with a multiscale structure reconstruct the image using its low-resolution versions to preserve important perceptual statistics such as edges. After obtaining the preliminary reconstruction of the reconstructed full size image, similar patches are collected and the 3-D AR model is applied to form a more local-consistent patch set. Then, an iterative singular value thresholding (SVT) method is utilized to solve the low-rank minimization problem. Instead of aggregating all the overlapped patches after each patch set is processed, we perform SVT for each patch set and aggregate all the overlapped patches into an intermediate image, then the iterative regularization is carried out on the image to produce the newly output for next iteration. Experimental results demonstrate that the proposed method achieves higher PSNR and SSIM than state-of-the-art methods [1-3] and the processed images possess a better visual quality especially in edge structures and texture regions.","Title: PCA-based denoising of Sensor Pattern Noise for source camera identification\nYear: 2014\n\nAbstract: Sensor Pattern Noise (SPN) has been proved to be an inherent fingerprint of the imaging device for source identification. However, SPN extracted from digital images can be severely contaminated by scene details. Moreover, SPN with high dimensionality may cause excessive time cost on calculating correlation between SPNs, which will limit its applicability to the source camera identification or image classification with a large dataset. In this work, an effective scheme based on principal component analysis (PCA) is proposed to address these two problems. By transforming SPN into eigenspace spanned by the principal components, the scene details and trivial information can be significantly suppressed. In addition, due to the dimensionality reduction property of PCA, the size of SPN is greatly reduced, consequently reducing the time cost of calculating similarity between SPNs. Our experiments are conducted on the Dresden database, and results demonstrate that the proposed method outperforms could achieve the state-of-art performance in terms of the Receiver Operating Characteristic (ROC) curves while reducing the dimensionality of SPN.","Title: A compact representation of sensor fingerprint for camera identification and fingerprint matching\nYear: 2015\n\nAbstract: Sensor Pattern Noise (SPN) has been proved as an effective fingerprint of imaging devices to link pictures to the cameras that acquired them. In practice, forensic investigators usually extract this camera fingerprint from large image block to improve the matching accuracy because large image blocks tend to contain more SPN information. As a result, camera fingerprints usually have a very high dimensionality. However, the high dimensionality of fingerprint will incur a costly computation in the matching phase, thus hindering many interesting applications which require an efficient real-time camera matching. To solve this problem, an effective feature extraction method based on PCA and LDA is proposed in this work to compress the dimensionality of camera fingerprint. Our experimental results show that the proposed feature extraction algorithm could greatly reduce the size of fingerprint and enhance the performance in term of Receiver Operating Characteristic (ROC) curve of several existing methods.","Title: Enhanced SVD for Collaborative Filtering.\nYear: 2016\n\nAbstract: Matrix factorization is one of the most popular techniques for prediction problems in the fields of intelligent systems and data mining. It has shown its effectiveness in many real-world applications such as recommender systems. As a collaborative filtering method, it gives users recommendations based on their previous preferences (or ratings). Due to the extreme sparseness of the ratings matrix, active learning is used for eliciting ratings for a user to get better recommendations. In this paper, we propose a new matrix factorization model called Enhanced SVD (ESVD) which combines the classic matrix factorization method with a specific rating elicitation strategy. We evaluate the proposed ESVD method on the Movielens data set, and the experimental results suggest its effectiveness in terms of both accuracy and efficiency, when compared with traditional matrix factorization methods and active learning methods.","Title: Robust gait recognition from extremely low frame-rate videos\nYear: 2013\n\nAbstract: In this paper, we propose a gait recognition method for extremely low frame-rate videos. Different from the popular temporal reconstruction-based methods, the proposed method uses the average gait over the whole sequence as input feature template. Assuming the effect caused by extremely low frame-rate or large gait fluctuations are intra-class variations that the gallery data fails to capture, we build a general model based on random subspace method. More specifically, a number of weak classifiers are combined to reduce the generalization errors. We evaluate our method on the OU-ISIR-D dataset with large/small gait fluctuations, and very competitive results are achieved when both the probe and gallery are extremely low frame-rate gait sequences (e.g., 1 fps).","Title: Random subspace method for source camera identification\nYear: 2015\n\nAbstract: Sensor pattern noise is an inherent fingerprint of imaging devices, which has been widely used for source camera identification, image classification, and forgery detection. In a previous work, we proposed a feature extraction method based on the principal component analysis denoising concept, which can enhance the performance of conventional SPN extraction methods. However, this method is vulnerable, because the training samples are seriously affected by the image content. Accordingly, it is difficult to train a reliable feature extractor by using such a training set. To address this problem, a camera identification framework based on the random subspace method and majority voting is proposed in this work. The experimental results show that the proposed solution can suppress the interference from scene details and enhance the performance in terms of the receiver operating characteristic curve.","Title: Directed-Propagation Link-State Routing Protocol for Information-Centric Networks.\nYear: 2016\n\nAbstract: This paper presents the design of Directed-Propagation Link-State Routing Protocol (DPLSR), which tries to address the issue of prefix flooding storm in NDN Link-state Routing Protocol. Since the number of data object is several order of magnitude of that of routers, legacy Link State Advertisements (LSAs) flooding model is no more appropriate in NDN. This paper proposes a LSA directed-propagation scheme that LSA messages are only forwarded towards Root anchor, not flooding. Upon requesting a data object, a Look-up message is sent towards Root anchor. When it encounters a router with accroding anchor and prefix information, an anchor list is returned. Users are able to access the nearest copy or build one or multi-path connections to the anchors. In DPLSR, messages are propagated along some paths, and only parts of router store prefix and anchor information. Thus, the communication overhead and prefix storage are reduced significantly compared with the existing NDN routing protocols. Simulated experiments also verifies the performance gain of DPLSR.","Title: Ensembles of Deep LSTM Learners for Activity Recognition using Wearables.\nYear: 2017\n\nAbstract: Recently, deep learning (DL) methods have been introduced very successfully into human activity recognition (HAR) scenarios in ubiquitous and wearable computing. Especially the prospect of overcoming the need for manual feature design combined with superior classification capabilities render deep neural networks very attractive for real-life HAR applications. Even though DL-based approaches now outperform the state-of-the-art in a number of recognition tasks, still substantial challenges remain. Most prominently, issues with real-life datasets, typically including imbalanced datasets and problematic data quality, still limit the effectiveness of activity recognition using wearables. In this paper we tackle such challenges through Ensembles of deep Long Short Term Memory (LSTM) networks. LSTM networks currently represent the state-of-the-art with superior classification performance on relevant HAR benchmark datasets. We have developed modified training procedures for LSTM networks and combine sets of diverse LSTM learners into classifier collectives. We demonstrate that Ensembles of deep LSTM learners outperform individual LSTM networks and thus push the state-of-the-art in human activity recognition using wearables. Through an extensive experimental evaluation on three standard benchmarks (Opportunity, PAMAP2, Skoda) we demonstrate the excellent recognition capabilities of our approach and its potential for real-life applications of human activity recognition.","Title: Gait recognition based on the golden ratio.\nYear: 2016\n\nAbstract: Gait is known to be an effective behavioral biometric trait for the identification of individuals. However, clothing has a dramatic influence on the recognition rate. Researchers have attempted to deal with this issue of clothing by segmenting parts of the gait images based on anatomical proportions. However, the clothing proportion is not the same as the anatomical proportion, as clothing is designed according to the golden ratio to enhance its look. Hence, methods for eliminating the influence of clothing should be based on the proportions of clothing. In this paper, we propose the golden ratio-based segmentation method to reduce the influence of clothing. Experiments are conducted on the CASIA-B dataset, and experimental results show that the proposed method outperforms other approaches, achieving a 94.76 % recognition rate in various clothing conditions and a rate of 91.53 % when bags are being carried.","Title: A Hybrid Classification Approach Based On Support Vector Machine And K-Nearest Neighbor For Remote Sensing Data\nYear: 2017\n\nAbstract: Analysis and classification for remote sensing landscape based on remote sensing imagery is a popular research topic. In this paper, we propose a new remote sensing data classifier by incorporating the support vector machine (SVM) learning information into the K-nearest neighbor (KNN) classifier. The SVM is well known for its extraordinary generalization capability even with limited learning samples, and it is very useful for remote sensing applications as data samples are usually limited. The KNN has been widely used in data classification due to its simplicity and effectiveness. However, the KNN is instance-based and needs to keep all the training samples for classification, which could cause not only high computation complexity but also overfitting problems. Meanwhile, the performance of the KNN classifier is sensitive to the neighborhood size K and how to select the value of the parameter K relies heavily on practice and experience. Based on the observations that the SVM can contribute to the KNN on the problems of smaller training samples size as well as the selection of the parameter K, we propose a support vector nearest neighbor (abbreviated as SV-NN) hybrid classification approach which can simplify the parameter selection while maintaining classification accuracy. The proposed approach is consist of two stages. In the first stage, the SVM is performed on the training samples to obtain the reduced support vectors (SVs) for each of the sample categories. In the second stage, a nearest neighbor classifier (NNC) is used to classify a testing sample, i.e. the average Euclidean distance between the testing data point to each set of SVs from different categories is calculated and the NNC identifies the category with minimum distance. To evaluate the effectiveness of the proposed approach,firstly experiments of classification for samples from remote sensing data are evaluated, and then experiments of identifying different land covers regions in the remote sensing images are evaluated. Experimental results show that the SV-NN approach maintains good classification accuracy while reduces the training samples compared with the conventional SVM and KNN classification model.","Title: Inference of a compact representation of sensor fingerprint for source camera identification.\nYear: 2018\n\nAbstract: •An effective framework for de-noising and compressing sensor pattern noise (a form of device fingerprint) is proposed.•A compact and discriminative representation can be obtained directly from a high-dimensional SPN.•A novel training set construction method is designed to minimize the impact of various interfering artifacts in training samples.•The proposed methods can be used as a post-processing framework applied after any SPN extraction methods for effective source camera identification.","Title: Matrix Factorization With Rating Completion: An Enhanced SVD Model for Collaborative Filtering Recommender Systems.\nYear: 2017\n\nAbstract: Collaborative filtering algorithms, such as matrix factorization techniques, are recently gaining momentum due to their promising performance on recommender systems. However, most collaborative filtering algorithms suffer from data sparsity. Active learning algorithms are effective in reducing the sparsity problem for recommender systems by requesting users to give ratings to some items when they enter the systems. In this paper, a new matrix factorization model, called Enhanced SVD (ESVD) is proposed, which incorporates the classic matrix factorization algorithms with ratings completion inspired by active learning. In addition, the connection between the prediction accuracy and the density of matrix is built to further explore its potentials. We also propose the Multi-layer ESVD, which learns the model iteratively to further improve the prediction accuracy. To handle the imbalanced data sets that contain far more users than items or more items than users, the Item-wise ESVD and User-wise ESVD are presented, respectively. The proposed methods are evaluated on the famous Netflix and Movielens data sets. Experimental results validate their effectiveness in terms of both accuracy and efficiency when compared with traditional matrix factorization methods and active learning methods.","Title: An improved optimum-path forest clustering algorithm for remote sensing image segmentation.\nYear: 2018\n\nAbstract: Remote sensing image segmentation is a key technology for processing remote sensing images. The image segmentation results can be used for feature extraction, target identification and object description. Thus, image segmentation directly affects the subsequent processing results. This paper proposes a novel Optimum-Path Forest (OPF) clustering algorithm that can be used for remote sensing segmentation. The method utilizes the principle that the cluster centres are characterized based on their densities and the distances between the centres and samples with higher densities. A new OPF clustering algorithm probability density function is defined based on this principle and applied to remote sensing image segmentation. Experiments are conducted using five remote sensing land cover images. The experimental results illustrate that the proposed method can outperform the original OPF approach.","Title: A New Technique For Remote Sensing Image Classification Based On Combinatorial Algorithm Of Svm And Knn\nYear: 2018\n\nAbstract: In remote sensing image classification, distance measurements and classification criteria are equally important; and less accuracy of either would affect classification accuracy. Remote sensing image classification was performed by combining support vector machine (SVM) and k-nearest neighbor (KNN). This was based on the separability of classes using SVM and the spatial and spectral characteristics of remote sensing data. Moreover, a distance formula is proposed as the measure criterion that considers both luminance and direction of the vectors. First, the SVM is trained and the support vectors (SVs) are obtained for each class. In the testing phase, newly tested samples were entered, and average distance between the test samples and SVs for each class are calculated using the distance formula. Finally, decisions are made to classify the test samples into the class with minimal average distance. This procedure is repeated until all test samples are classified. In the combinatorial algorithm, the nearest neighbor classifier is used to classify a testing sample, i.e. the average Euclidean distance between the testing data point to each set of SVs from different categories is calculated and the nearest neighbor classifier identifies the category with minimum distance. The proposed combinatorial algorithm has advantages over the conventional KNN for eliminating the k parameter selection problem and reducing heavy learning time. A comparison is carried out with SVM, KNN and Spectral Angle Mapper (SAM) classification using ALOS/PALSAR and PSM images, and the effectiveness of the proposed algorithm is demonstrated.","Title: A Fast and Easily-Realized Concentration Sensor for Binary Gas Mixtures and Its Design Analysis.\nYear: 2018\n\nAbstract: A low-cost and easily-realized sensing device used for the detection of gas mixtures at different concentrations is presented. Its sensing part includes a small critical nozzle, a laminar structure, and a differential pressure sensor. When gas flows through the laminar structure, there is a pressure drop between both ends of it, and for different components of gas, the pressure drop is different. Based on this feature, the concentration detection is achieved. Concentration tests for two types of fire extinguishing agents CBrF3 and C3HF7 are presented. The results show the characteristics of fast response/recovery time, high accuracy, and good repeatability. Based on the theoretical analysis, the effects of the design parameters on the sensing performance to concentration detection are discussed in detail.","Title: Deep Learning for Human Activity Recognition in Mobile Computing.\nYear: 2018\n\nAbstract: By leveraging advances in deep learning, challenging pattern recognition problems have been solved in computer vision, speech recognition, natural language processing, and more. Mobile computing has also adopted these powerful modeling approaches, delivering astonishing success in the field's core application domains, including the ongoing transformation of human activity recognition technology th...","Title: Active learning in multi-domain collaborative filtering recommender systems.\nYear: 2018\n\nAbstract: The lack of information is an acute challenge in most recommender systems, especially for the collaborative filtering algorithms which utilize user-item rating matrix as the only source of information. Active learning can be used to remedy this problem by querying users to give ratings to some items. Apart from the active learning algorithms, cross-domain recommender system techniques try to alleviate the sparsity problem by exploiting knowledge from auxiliary (source) domains. A special case of cross-domain recommendation is multi-domain recommendation that utilizes the shared knowledge across multiple domains to alleviate the data sparsity in all domains. In this paper, we propose a novel multi-domain active learning framework by incorporating active learning techniques with cross-domain collaborative filtering algorithms in the multi-domain scenarios. Specifically, our proposed active learning elicits all the ratings simultaneously based on the criteria with regard to both items and users, for the purpose of improving the performance of the whole system. We evaluate a variety of active learning strategies in the proposed framework on different multi-domain recommendation tasks based on three popular datasets: Movielens, Netflix and Book-Crossing. The results show that the system performance can be improved further when combining cross-domain collaborative filtering with active learning algorithms.","Title: Triple Verification Network for Generalized Zero-Shot Learning.\nYear: 2019\n\nAbstract: Conventional zero-shot learning approaches often suffer from severe performance degradation in the generalized zero-shot learning (GZSL) scenario, i.e., to recognize test images that are from both seen and unseen classes. This paper studies the Class-level Over-fitting (CO) and empirically shows its effects to GZSL. We then address ZSL as a triple verification problem and propose a unified optimiz...","Title: Mobile Based Continuous Authentication Using Deep Features.\nYear: 2018\n\nAbstract: Continuous authentication is a promising approach to validate the user's identity during a work session, e.g., for mobile banking applications. Recently, it has been demonstrated that changes in the motion patterns of the user may help to note the unauthorised use of mobile devices. Several approaches have been proposed in this area but with relatively weak performance results. In this work, we propose an approach which uses a Siamese convolutional neural network to learn the signatures of the motion patterns from users and achieve a competitive verification accuracy up to 97.8%. We also find our algorithm is not very sensitive to sampling frequency and the length of the sequence.","Title: Robust Cross-View Gait Identification with Evidence: A Discriminant Gait GAN (DiGGAN) Approach on 10000 People.\nYear: 2018\n\nAbstract: Gait is an important biometric trait for surveillance and forensic applications, which can be used to identify individuals at a large distance through CCTV cameras. However, it is very difficult to develop robust automated gait recognition systems, since gait may be affected by many covariate factors such as clothing, walking surface, walking speed, camera view angle, etc. Out of them, large view angle was deemed as the most challenging factor since it may alter the overall gait appearance substantially. Recently, some deep learning approaches (such as CNNs) have been employed to extract view-invariant features, and achieved encouraging results on small datasets. However, they do not scale well to large dataset, and the performance decreases significantly w.r.t. number of subjects, which is impractical to large-scale surveillance applications. To address this issue, in this work we propose a Discriminant Gait Generative Adversarial Network (DiGGAN) framework, which not only can learn view-invariant gait features for cross-view gait recognition tasks, but also can be used to reconstruct the gait templates in all views --- serving as important evidences for forensic applications. We evaluated our DiGGAN framework on the worldu0027s largest multi-view OU-MVLP dataset (which includes more than 10000 subjects), and our method outperforms state-of-the-art algorithms significantly on various cross-view gait identification scenarios (e.g., cooperative/uncooperative mode). Our DiGGAN framework also has the best results on the popular CASIA-B dataset, and it shows great generalisation capability across different datasets.","Title: UtilCache: Effectively and Practicably Reducing Link Cost in Information-Centric Network\nYear: 2018\n\nAbstract: Minimizing total link cost in Information-Centric Network (ICN) by optimizing content placement is challenging in both effectiveness and practicality. To attain better performance, upstream link cost caused by a cache miss should be considered in addition to content popularity. To make it more practicable, a content placement strategy is supposed to be distributed, adaptive, with low coordination overhead as well as low computational complexity. In this paper, we present such a content placement strategy, UtilCache, that is both effective and practicable. UtilCache is compatible with any cache replacement policy. When the cache replacement policy tends to maintain popular contents, UtilCache attains low link cost. In terms of practicality, UtilCache introduces little coordination overhead because of piggybacked collaborative messages, and its computational complexity depends mainly on content replacement policy, which means it can be O(1) vthen working with LRU. Evaluations prove the effectiveness of UtilCache, as it saves nearly 40% link cost more than current ICN design.","Title: Remote Cloud-Based Automated Stroke Rehabilitation Assessment Using Wearables\nYear: 2018\n\nAbstract: We outline a system enabling accurate remote assessment of stroke rehabilitation levels using wrist worn accelerometer time series data. The system is built based on features generated from clustering models across sliding windows in the data and makes use of computation in the cloud. Predictive models are built using advanced machine learning techniques.","Title: Generic compact representation through visual-semantic ambiguity removal.\nYear: 2019\n\nAbstract: •A novel VSAR algorithm that can simultaneously remove the ambiguity between visual and semantic information.•Extensive evaluations suggest the important role of visual-semantic ambiguity to the performance improvement.•A unified framework that can deal with both category-level and instance-level zero-shot learning tasks.","Title: Automatic Cataract Diagnosis By Image-Based Interpretability\nYear: 2018\n\nAbstract: Cataract is defined as a lenticular opacity presenting usually with poor visual acuity. It is considered the most common cause of blindness. Early diagnosis and treatment can reduce the suffering of patients and prevent visual impairment from turning into blindness. Recently, cataract diagnosis applying pattern recognition is in a rising period. For retinal fundus images, the task is usually cataract classification. However, it needs complex manual processing, which demands dexterous people and time taking exertion. Besides, it faces the challenge of effective interpretability and dependability. In this paper, we develop a deep-learning algorithm to intuitively identify cataract attributes to solve these limitations. Our model, is a 18(50)-layer convolutional neural network that inputs retinal fundus images in G channel and outputs the prediction with heatmap. The heatmap localizes the areas where most indicative of different levels of cataract is. Furthermore, we extend the training strategy for the corresponding task, which aims at improving the performance of the network. Comparing with other methods in cataract classification, we succeeded to achieve state of the art accuracy of proposed method on detection and grading task. Most importantly, our model provides a compelling reason via localizing the areas revealing cataract in the image.","Title: SVD-Based Algorithms for the Best Rank-1 Approximation of a Symmetric Tensor\nYear: 2018\n\nAbstract: This paper revisits the problem of finding the best rank-1 approximation to a symmetric tensor and makes three contributions. First, in contrast to the many long and lingering arguments in the literature, it offers a straightforward justification that generically the best rank-1 approximation to a symmetric tensor is symmetric. Second, in contrast to the typical workhorse in the practice for the low-rank tensor approximation, namely, the alternating least squares (ALS) technique which improves one factor a time, this paper proposes three alternative algorithms based on the singular value decomposition (SVD) that modifies two factors a time. One step of SVD-based iteration is superior to two steps of ALS iterations. Third, it is not only proved that the generalized Rayleigh quotients generated from the three SVD-based algorithms enjoy monotone convergence, but also that the iterates themselves converge.","Title: Co-sleep: Design for workplace based wellness program to raise awareness of sleep deprivation.\nYear: 2018\n\nAbstract: Sleep deprivation is a public health issue. A lack of sleep not only harms our body immune systems but also degrades their capacity to maintain cognitive skills. Awareness of sleep deprivation has not been widely investigated in work-based wellness programmes. In the study, the project carried out with nine participants from a local manufacture company to raise that awareness. The common causes of sleep deprivation have been identified the through the deployment of probes and the interviews. The research generated design concepts of smart IoT workplace to track and share daytime sleep-related activities. Through the bottom up and co-design methods, participants give the points of considering the use of sleep data from different power relationship perspective, includes the unexpected use of sleep for fatigue risk management and evaluation of employee performance.","Title: Towards Reliable, Automated General Movement Assessment for Perinatal Stroke Screening in Infants Using Wearable Accelerometers\nYear: 2019\n\nAbstract: Perinatal stroke (PS) is a serious condition that, if undetected and thus untreated, often leads to life-long disability, in particular Cerebral Palsy (CP). In clinical settings, Prechtl's General Movement Assessment (GMA) can be used to classify infant movements using a Gestalt approach, identifying infants at high risk of developing PS. Training and maintenance of assessment skills are essential and expensive for the correct use of GMA, yet many practitioners lack these skills, preventing larger-scale screening and leading to significant risks of missing opportunities for early detection and intervention for affected infants. We present an automated approach to GMA, based on body-worn accelerometers and a novel sensor data analysis method--Discriminative Pattern Discovery (DPD)--that is designed to cope with scenarios where only coarse annotations of data are available for model training. We demonstrate the effectiveness of our approach in a study with 34 newborns (21 typically developing infants and 13 PS infants with abnormal movements). Our method is able to correctly recognise the trials with abnormal movements with at least the accuracy that is required by newly trained human annotators (75%), which is encouraging towards our ultimate goal of an automated PS screening system that can be used population-wide.","Title: A hybrid global-local representation CNN model for automatic cataract grading.\nYear: 2020\n\nAbstract: Cataract is one of the most serious eye diseases leading to blindness. Early detection and treatment can reduce the rate of blindness in cataract patients. However, the professional knowledge of ophthalmologists is necessary for the clinical cataract detection. Therefore, the potential costs may make it difficult for the widespread use of cataract detection to prevent blindness. Artificial intelligence assisted diagnosis based on medical images has attracted more and more attention of researchers. Many studies have focused on the use of pre-defined feature sets for cataract classification, but the predefined feature sets may be incomplete or redundant. On account of the aforementioned issues, some studies have proposed deep learning methods to automatically extract image features, but all based on global features and none has analyzed the layer-by-layer transformation process of the middle-tier features. This paper uses convolutional neural networks (CNN) to learn useful features directly from input data, and deconvolution network method is employed to investigate how CNN characterizes cataract layer-by-layer. We found that compared to the global feature set, the detail vascular information, which is lost after multi-layer convolution calculation also plays an important role in cataract grading task. And this finding fits with the morphological definition of fundus image. Through the finding, we gained insights into the design of hybrid global-local feature representation model to improve the recognition performance of automatic cataract grading.","Title: Consistent Dynamic Server Assignment in Content Delivery Network.\nYear: 2019\n\nAbstract: Server assignment is an essential part in Content Delivery Network (CDN), responsible for choosing a surrogate server to provide the best Quality of Service (QoS) for clients and balancing the server load. But it faces two critical challenges in practical: 1) the constraint of DNS schedule granularity makes it difficult to distribute client demands in arbitrary proportion. 2) dynamic server assignment may bring about a huge amount of cached content migration among servers. To address these issues, we work with one of the largest CDN operators in the world, and design a persistent dynamic server assignment algorithm, DynDNS. It gives a more sticky solution that can be easily implemented in DNS-based load balancing while keeping the overall QoS and server load satisfying. To the best of our knowledge, this is the first quantitative model for the dynamic DNS server assignment problem. Theoretical analysis proves that the proposed greedy algorithm has considerable optimality guarantees, and evaluation shows that DynDNS can avoid about 50% unnecessary shift in demand allocation and yield more stable cache hit ratio.","Title: Orchestrating the Development Lifecycle of Machine Learning-based IoT Applications: A Taxonomy and Survey\nYear: 2020\n\nAbstract: AbstractMachine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock the potential of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services. Hence, orchestrating ML pipelines that encompass model training and implication involved in the holistic development lifecycle of an IoT application often leads to complex system integration. This article provides a comprehensive and systematic survey of the development lifecycle of ML-based IoT applications. We outline the core roadmap and taxonomy and subsequently assess and compare existing standard techniques used at individual stages.","Title: Discriminative Latent Semantic Graph for Video Captioning\nYear: 2021\n\nAbstract: ABSTRACTVideo captioning aims to automatically generate natural language sentences that can describe the visual contents of a given video. Existing generative models like encoder-decoder frameworks cannot explicitly explore the object-level interactions and frame-level information from complex spatio-temporal data to generate semantic-rich captions. Our main contribution is to identify three key problems in a joint framework for future video summarization tasks. 1) Enhanced Object Proposal: we propose a novel Conditional Graph that can fuse spatio-temporal information into latent object proposal. 2) Visual Knowledge: Latent Proposal Aggregation is proposed to dynamically extract visual words with higher semantic levels. 3) Sentence Validation: A novel Discriminative Language Validator is proposed to verify generated captions so that key semantic concepts can be effectively preserved. Our experiments on two public datasets (MVSD and MSR-VTT) manifest significant improvements over state-of-the-art approaches on all metrics, especially for BLEU-4 and CIDEr. Our code is available at https://github.com/baiyang4/D-LSG-Video-Caption.","Title: Towards Automated Fatigue Assessment using Wearable Sensing and Mixed-Effects Models\nYear: 2021\n\nAbstract: ABSTRACT Fatigue is a broad, multifactorial concept that includes the subjective perception of reduced physical and mental energy levels. It is also one of the key factors that strongly affect patients’ health-related quality of life. To date, most fatigue assessment methods were based on self-reporting, which may suffer from many factors such as recall bias. To address this issue, in this work, we recorded multi-modal physiological data (including ECG, accelerometer, skin temperature and respiratory rate, as well as demographic information such as age, BMI) in free-living environments, and developed automated fatigue assessment models. Specifically, we extracted features from each modality, and employed the random forest-based mixed-effects models, which can take advantage of the demographic information for improved performance. We conducted experiments on our collected dataset, and very promising preliminary results were achieved. Our results suggested ECG played an important role in the fatigue assessment tasks.","Title: Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action Recognition\nYear: 2021\n\nAbstract: ABSTRACTThe task of skeleton-based action recognition remains a core challenge in human-centred scene understanding due to the multiple granularities and large variation in human motion. Existing approaches typically employ a single neural representation for different motion patterns, which has difficulty in capturing fine-grained action classes given limited training data. To address the aforementioned problems, we propose a novel multi-granular spatio-temporal graph network for skeleton-based action classification that jointly models the coarse- and fine-grained skeleton motion patterns. To this end, we develop a dual-head graph network consisting of two interleaved branches, which enables us to extract features at two spatio-temporal resolutions in an effective and efficient manner. Moreover, our network utilises a cross-head communication strategy to mutually enhance the representations of both heads. We conducted extensive experiments on three large-scale datasets, namely NTU RGB+D 60, NTU RGB+D 120, and Kinetics-Skeleton, and achieves the state-of-the-art performance on all the benchmarks, which validates the effectiveness of our method1.","Title: Automatic Cataract Grading with Visual-semantic Interpretability\nYear: 2021\n\nAbstract: Cataract is a chronic eye disease that causes irreversible vision loss. Automatic cataract detection can help people prevent visual impairment and decrease the possibility of blindness. To date, many studies utilize deep learning methods to grade cataract severity on fundus images. However, they mainly focus on the classification performance and ignore the model interpretability, which may lead to a semantic gap between networks and users. In this paper, we present a deep learning network to improve the model interpretability, which consists three main modules: deep feature extraction, visual saliency module and semantic description module. Visual and semantic interpretation jointly employed to provide cataract-grade oriented interpretation for the overall model. Experimental results on real clinical data set show that our method improves the interpretability for cataract grading while ensuring the high classification performance.","Title: Participatory Action Research and Open Source Hardware Appropriation for Large Scale In-The-Wild Studies\nYear: 2021\n\nAbstract: ABSTRACT With the rise of ubiquitous computing, the use of research in-the-wild approach in both Ubicomp and HCI context has become more viable, but not without its challenges. End-users and communities can be great beneficiaries of such an approach if actively involved in the process from an early stage and throughout the whole process. We start by presenting a future case study, the Cue Band study, which revolves around the creation of a wristband cueing device for people with Parkinson’s that experience drooling. We present an approach for research in-the-wild, which draws on participatory action research theory, that places the end-user at the centre of the process, aiming to first and for most to create a workable product for the end-user, before engaging in a formal study. In the last section, we explore the appropriation of existing open-source hardware for in-the-wild research, by describing problems and solutions associated with developing Ubicomp technologies for large-scale studies.","Title: LSTA-Net: Long short-term Spatio-Temporal Aggregation Network for Skeleton-based Action Recognition.\nYear: 2021\n\nAbstract: Modelling various spatio-temporal dependencies is the key to recognising human actions in skeleton sequences. Most existing methods excessively relied on the design of traversal rules or graph topologies to draw the dependencies of the dynamic joints, which is inadequate to reflect the relationships of the distant yet important joints. Furthermore, due to the locally adopted operations, the important long-range temporal information is therefore not well explored in existing works. To address this issue, in this work we propose LSTA-Net: a novel Long short-term Spatio-Temporal Aggregation Network, which can effectively capture the long/short-range dependencies in a spatio-temporal manner. We devise our model into a pure factorised architecture which can alternately perform spatial feature aggregation and temporal feature aggregation. To improve the feature aggregation effect, a channel-wise attention mechanism is also designed and employed. Extensive experiments were conducted on three public benchmark datasets, and the results suggest that our approach can capture both long-and-short range dependencies in the space and time domain, yielding higher results than other state-of-the-art methods. Code available at https://github.com/tailin1009/LSTA-Net.","Title: Multi-task Learning Based on Multi-type Dataset for Retinal Abnormality Detection\nYear: 2021\n\nAbstract: The number of people suffering from ophthalmic diseases is increasing with the population aging. Many studies have been proposed to automatically identify diseases to reduce the risks of further retinal damage. However, most of existing methods mainly used a single type of dataset to solve the specific medical task, which is not clinically practical in the realworld scenarios. In this paper, we pr...","Title: GLA-Net: A global-local attention network for automatic cataract classification\nYear: 2021\n\nAbstract: •A multi-level attention network is proposed for cataract classification task.•Global-level subnet aims to learn global structure features.•Local-level subnet is used to extract local detailed features.•Both are jointly adopted to improve the hierarchical representation learning.•Extensive Experiments are conducted to prove the effectiveness of our method.","Title: Multi-Wavelet Guided Deep Mean-Shift Prior For Image Restoration\nYear: 2021\n\nAbstract: Image restoration is essentially recognized as an ill-posed problem. A promising solution in recent years is incorporating deep network-driven priors into the iterative restoration procedure as constrained conditions. Among them, deep mean-shift prior utilizes the denoising autoencoder to play the role of prior updating. In this study, we present multiple wavelets guided deep mean-shift prior, which integrates the advantages of structural representation in the wavelet transform and learning ability in deep network. Specifically, by re-arranging the multi-view and multi-resolution features generated by multiple wavelet transforms as the input of denoising autoencoder, a more powerful prior information is learned. It benefits from the recurrent structure-preserving and multi-view complementary aggregation properties. We embed the learned prior information into the iterative recovery process and adopt proximal gradient descent to tackle it. Extensive experiments on image deblurring and compressed sensing tasks demonstrated significantly improved performances both visually and quantitatively.","Title: A-PSPNet: A novel segmentation method of renal ultrasound image\nYear: 2021\n\nAbstract: Hydronephrosis is a common renal disease in children which can lead to a series of complications, and ultrasonography is a basic examination usually performed on suspected hydronephrosis patients. If we can use deep learning approaches to judge and grade the disease in the ultrasonic examination stage, we can save a lot of manpower, medical resources, money, and help the suffered patients. For the...","Title: Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition\nYear: 2022\n\nAbstract: AbstractIn wearable-based human activity recognition (HAR) research, one of the major challenges is the large intra-class variability problem. The collected activity signal is often, if not always, coupled with noises or bias caused by personal, environmental, or other factors, making it difficult to learn effective features for HAR tasks, especially when with inadequate data. To address this issue, in this work, we proposed a Behaviour Pattern Disentanglement (BPD) framework, which can disentangle the behavior patterns from the irrelevant noises such as personal styles or environmental noises, etc. Based on a disentanglement network, we designed several loss functions and used an adversarial training strategy for optimization, which can disentangle activity signals from the irrelevant noises with the least dependency (between them) in the feature space. Our BPD framework is flexible, and it can be used on top of existing deep learning (DL) approaches for feature refinement. Extensive experiments were conducted on four public HAR datasets, and the promising results of our proposed BPD scheme suggest its flexibility and effectiveness. This is an open-source project, and the code can be found at http://github.com/Jie-su/BPD","Title: Research of the Response Rate Variation for the Extinguishant Detection Technique Based on the Differential Pressure Principle\nYear: 2022\n\nAbstract: The intent of this work is to explore the variation mechanism of the response rate for the extinguishant detection technique based on the differential pressure principle, which is an aspirating concentration detection technique and quite suitable for extinguishant detection. The effects of the sampling pipe, sensing structure, and operating temperature on the response rate were analyzed, and the theoretical model was established by analyzing the gas dispersion behavior. It is found that the response/recovery time is greatly affected by the above factors, which can change from 0.068/0.088 to 0.960/1.01 s. Results show that with the increase of the length and inner diameter of the sampling pipe, the response/recovery time rapidly increases. For the sensing structure, the increase of the external diameter of the vascular bundles will increase the response/recovery time, and the increase of the throat diameter of the critical flow nozzle will greatly decrease the response/recovery time. The increase of the operating temperature can decrease the response/recovery time with limited improving effect. The research results in this article can provide suggestions for the design of sensor.","Title: Action Quality Assessment with Temporal Parsing Transformer.\nYear: 2022\n\nAbstract: Action Quality Assessment(AQA) is important for action understanding and resolving the task poses unique challenges due to subtle visual differences. Existing state-of-the-art methods typically rely on the holistic video representations for score regression or ranking, which limits the generalization to capture fine-grained intra-class variation. To overcome the above limitation, we propose a temporal parsing transformer to decompose the holistic feature into temporal part-level representations. Specifically, we utilize a set of learnable queries to represent the atomic temporal patterns for a specific action. Our decoding process converts the frame representations to a fixed number of temporally ordered part representations. To obtain the quality score, we adopt the state-of-the-art contrastive regression based on the part representations. Since existing AQA datasets do not provide temporal part-level labels or partitions, we propose two novel loss functions on the cross attention responses of the decoder: a ranking loss to ensure the learnable queries to satisfy the temporal order in cross attention and a sparsity loss to encourage the part representations to be more discriminative. Extensive experiments show that our proposed method outperforms prior work on three public AQA benchmarks by a considerable margin.","Title: Cross-project defect prediction based on G-LSTM model\nYear: 2022\n\nAbstract: •To create your highlights, please type over the instructions in the template box below:•Pre-processing and feature extraction techniques from NLP domain to CPDP domain.•The feature extractor combines BLSTM with an attention mechanism to enhance the effect.•An adversarial structure is used to solve the distribution differences problem.","Title: High Myopia Detection Method On Fundus Images Based On Curriculum Learning\nYear: 2022\n\nAbstract: Myopia has become a major public health problem affecting the eye health of our citizens, especially teenagers. Fundus images can be obtained non-invasively and can be used to monitor and follow up on the progress in high myopia. However, with the development of artificial intelligence, it is still difficult to establish a computer-aided diagnosis model for high myopia with young children as research objects, mainly because 1) it is very difficult to collect labeled fundus images, and there is no large amount of such data that can be freely accessed; 2) hard samples which have clinical significance for population screening and diagnosis are rare and indistinguishable. To solve these problems, we propose a high myopia detection model on fundus images based on curriculum learning. We design a dual-curriculum generation module, which aims to use the expert model to endow the curriculum with new training indicators so that the student model can gradually and robustly identify hard samples. Compared with the baseline, our framework significantly improves the convergence speed of the training process and achieves the best performance during testing. Experimental results on a high myopia fundus images dataset show that our framework provides efficient and accurate detection and outperforms other methods.","Title: Joint optimisation for dynamic flexible job-shop scheduling problem with transportation time and resource constraints\nYear: 2022\n\nAbstract: Dynamic flexible job-shop scheduling is traditionally a challenge in real-world manufacturing systems, especially considering the constraints of transportation resources and transportation time. To address the dynamic optimisation problem in flexible manufacturing systems, this paper proposes a novel proactive-reactive methodology to adapt to the dynamic changes in working environments and addresses the joint scheduling problem for machine tools and transportation resources. The joint optimisation model is first formulated as a mixed-integer programming model considering production efficiency and transportation constraints. The flowchart of the dynamic scheduling system is then designed for dynamic decision-making, and a novel particle swarm optimisation algorithm integrated with genetic operators is developed to respond to dynamic events and generate the reschedule plan in time. Finally, several numerical experiments and case studies in reality are applied to verify the efficiency of the developed methodology. Common dispatching rules and heuristic methods are also applied to test and evaluate the efficiency of the developed algorithm. Computational results demonstrate that the developed methods and decision models are efficient for dynamic job-shop scheduling problems in flexible manufacturing systems, which can acquire rather a good effect in practical production."],"string":"[\n \"Title: Random Subspace Method for Gait Recognition\\nYear: 2012\\n\\nAbstract: Over fitting is a common problem for gait recognition algorithms when gait sequences in gallery for training are acquired under a single walking condition. In this paper, we propose an approach based on the random subspace method (RSM) to address such over learning problems. Initially, two-dimensional Principle Component Analysis (2DPCA) is adopted to obtain the full hypothesis space (i.e., eigen space). Multiple inductive biases (i.e., subspaces) are constructed, each with the corresponding basis vectors randomly chosen from the initial eigen space. This procedure can not only largely avoid over adaptation but also facilitate dimension reduction. The final classification is achieved by the decision committee which follows a majority voting criterion from the labeling results of all the subspaces. Experimental results on the benchmark USF Human ID gait database show that the proposed method is a feasible framework for gait recognition under unknown walking conditions.\",\n \"Title: Interactive simulation of large-scale forests in virtual reality applications\\nYear: 2006\\n\\nAbstract: We propose a hybrid geometry-image representation of trees, which consists of three hierarchies. In the lowest hierarchy, a collection of triangles are used to represent the trunk and salient branches. The finer hierarchy for the small branches is represented by Frond Map, a special type of textures. As the finest hierarchy, the leaves are clustered first and represented as a set of billboards. For each hierarchy, we generate a sequence of LODs (Level of Details). The appropriate level is selected on-the-fly. Smooth transitions between successive levels are accomplished using an image-space voting technique. Our representation facilitates the rapid modeling of trees, the dynamic simulation of trees swaying under wind as well as realistic simulation of large-scale forest scenes. Experimental results demonstrate the efficiency and high quality of our approaches.\",\n \"Title: Modeling And Rendering Of Realistic Waterfall Scenes With Dynamic Texture Sprites\\nYear: 2006\\n\\nAbstract: We propose an efficient approach for authoring dynamic and realistic waterfall scenes based on an acquired video sequence. Traditional video based techniques generate new images by synthesizing 2D samples, i.e., texture sprites chosen from a video sequence. However, they are limited to one fixed viewpoint and cannot provide arbitrary walkthrough into 3D scenes. Our approach extends this scheme by synthesizing dynamic 2D texture sprites and projecting them into 3D space, We first generate a set of basis texture sprites, which capture the representative appearance and motions of waterfall scenes contained in the video sequence. To model the shape and motion Of a new waterfall scene, we interactively construct a set of flow lines taking account of physical principles. Along each flow line, the basis texture sprites are manipulated and animated dynamically, yielding a sequence of dynamic texture sprites in 3D space. These texture sprites are displayed using the point splatting technique, which can be accelerated efficiently by graphics hardware. By choosing varied basis texture sprites, waterfall scenes with different appearance and shapes can be conveniently simulated. The experimental results demonstrate that our approach achieves realistic effects and real-time frame rates on consumer PC platforms. Copyright (c) 2006 John Wiley & Sons, Ltd.\",\n \"Title: Parallel genetic algorithms with schema migration\\nYear: 2002\\n\\nAbstract: Genetic algorithms (GAs) are efficient non-gradient stochastic search methods. Parallel GAs are proposed to overcome the deficiencies of sequential GAs, such as low speed and aptness to locally converge. However the tremendous communication cost incurred offsets the advantages of parallel GAs. Hence reducing communication cost is the key issue of this problem. Instead of reducing the communication cost simply by compressing the size of the messages, we tackle the problem by improving the effectiveness of the schema to be disseminated. We propose a new schema migration scheme (SMS). This SMS consists of a schema extracting mechanism and a schema disseminating mechanism. This SMS is valid and requires less communication cost.\",\n \"Title: Algebraic Connectivity Of Weighed Graphs Under Shifting Components\\nYear: 2010\\n\\nAbstract: Integrating quadratic form of algebraic connectivity and Perron value of bottleneck matrices, we investigate how the algebraic connectivity of a connected weighted graph behaves under shifting components. Generally speaking, when shift components not containing characteristic vertex from less positive (zlarger negative) valuation vertices to larger positive (zless negative) valuation vertices, or reduce weights of some edges, or add some new blocks, its algebraic connectivity is nonincreasing; when shift components along paths from blocks to other block closer to characteristic block (zvertex), or increase weights of some edges, or delete some blocks, its algebraic connectivity is nondecreasing. Therefore, algebraic connectivity could be regarded as a measure of central tendency about blocks of a connected weighted graph with characteristic block (zvertex) as its center.\",\n \"Title: Robust Clothing-Invariant Gait Recognition\\nYear: 2012\\n\\nAbstract: Robust gait recognition is a challenging problem, due to the large intra-subject variations and small inter-subject variations. Out of the covariate factors like shoe type, carrying condition, elapsed time, it has been demonstrated that clothing is the most challenging covariate factor for appearance-based gait recognition. For example, long coat may cover a significant amount of gait features and make it difficult for individual recognition. In this paper, we proposed a random subspace method (RSM) framework for clothing-invariant gait recognition by combining multiple inductive biases for classification. Even for small size training set, this method can achieve promising performance. Experiments are conducted on the OU-ISIR Treadmill dataset B which includes 32 combinations of clothing types, and the average recognition accuracy is more than 80%, which indicates the effectiveness of our proposed method.\",\n \"Title: A robust speed-invariant gait recognition system for walker and runner identification\\nYear: 2013\\n\\nAbstract: In real-world scenarios, walking/running speed is one of the most common covariate factors that can affect the performance of gait recognition systems. By assuming the effect caused by the speed changes (from the query walker-s/runners) are intra-class variations that the training data (i.e., gallery) fails to capture, overfitting to the less representative training data may be the main problem that degrades the performance. In this work, we employ a general model based on random subspace method to solve this problem. More specifically, for query gaits in unknown speeds, we try to reduce the generalization errors by combining a large number of weak classifiers. We evaluate our method on two benchmark databases, i.e., Infrared CASIA-C dataset and Treadmill OU-ISIR-A dataset. For the cross-speed walking/running gait recognition experiments, nearly perfect results are achieved, significantly higher than other state-of-the-art algorithms. We also study the unknown- speed nrunner identification solely using the walking gait gallery, and the encouraging experimental results suggest the effectiveness of our method in such cross-mode gait recognition tasks.\",\n \"Title: An adaptive system for gait recognition in multi-view environments.\\nYear: 2012\\n\\nAbstract: Gait recognition systems often suffer from the challenges when query gaits are under the coupled effects of unknown view angles and large intra-class variations (e.g., wearing a coat). In this paper, we deem it as a two-stage classification problem, namely, view detection and fixed-view gait recognition. First, we propose two simple yet effective feature types (i.e., global features and local features) for view detection. By using the detected view information, the corresponding gallery (i.e., enrolled gait) for the detected view can be adaptively selected to perform the fixed-view gait recognition. For fixed-view gait recognition, since the inter-class variations for training are normally small, whereas the query gait usually has large intra-class variations, random subspace method are adopted. We evaluate our approach on the largest multi-view gait database CASIA-B dataset. The avoidance of searching whole multi-view database as well as the competitive performance indicate that our proposed method is practical for gait recognition in real world surveillance scenarios.\",\n \"Title: Dynamic Modeling And Rendering Of Grass Wagging In Wind\\nYear: 2005\\n\\nAbstract: Simulation of dynamic natural scene is one of the most challenging tasks in computer graphics. In this paper, we propose a new approach to dynamic modeling and rendering of grasses wagging in wind. Through length preserving free-form deformation of the 3D skeleton lines of each grass blade and using the alpha test to implement transparent texture mapping, we successfully model grasses of different shapes with rich details. To simulate the real time waggle of grasses, the grasses of a meadow are represented in LOD, while their skeleton lines are dynamically deformed according to some physical models. Simplification technique is also employed to accelerate the collision detection between neighboring grasses. Experiments show that our method can realistically render the animated grass scenes under wind of different speeds and types in real time. Copyright (c) 2005 John Wiley & Sons, Ltd.\",\n \"Title: On the Generalization Power of Face and Gait in Gender Recognition\\nYear: 2014\\n\\nAbstract: Human face/gait-based gender recognition has been intensively studied in the previous literatures, yet most of them are based on the same database. Although nearly perfect gender recognition rates can be achieved in the same face/gait dataset, they assume a closed-world and neglect the problems caused by dataset bias. Real-world human gender recognition system should be dataset-independent, i.e., it can be trained on one face/gait dataset and tested on another. In this paper, the authors test several popular face/gait-based gender recognition algorithms in a cross-dataset manner. The recognition rates decrease significantly and some of them are only slightly better than random guess. These observations suggest that the generalization power of conventional algorithms is less satisfied, and highlight the need for further research on face/gait-based gender recognition for real-world applications.\",\n \"Title: People Identification and Tracking Through Fusion of Facial and Gait Features.\\nYear: 2014\\n\\nAbstract: This paper reviews the contemporary (face, gait, and fusion) computational approaches for automatic human identification at a distance. For remote identification, there may exist large intra-class variations that can affect the performance of face/gait systems substantially. First, we review the face recognition algorithms in light of factors, such as illumination, resolution, blur, occlusion, and pose. Then we introduce several popular gait feature templates, and the algorithms against factors such as shoe, carrying condition, camera view, walking surface, elapsed time, and clothing. The motivation of fusing face and gait, is that, gait is less sensitive to the factors that may affect face (e.g., low resolution, illumination, facial occlusion, etc.), while face is robust to the factors that may affect gait (walking surface, clothing, etc.). We review several most recent face and gait fusion methods with different strategies, and the significant performance gains suggest these two modality are complementary for human identification at a distance.\",\n \"Title: Combining gait and face for tackling the elapsed time challenges\\nYear: 2013\\n\\nAbstract: Random Subspace Method (RSM) has been demonstrated as an effective framework for gait recognition. Through combining a large number of weak classifiers, the generalization errors can be greatly reduced. Although RSM-based gait recognition system is robust to a large number of covariate factors, it is, in essence an unimodal biometric system and has the limitations when facing extremely large intra-class variations. One of the major challenges is the elapsed time covariate, which may affect the human walking style in an unpredictable manner. To tackle this challenge, in this paper we propose a multimodal-RSM framework, and side face is used to strengthen the weak classifiers without compromising the generalization power of the whole system. We evaluate our method on the TUM-GAID dataset, and it significantly outperforms other multimodal methods. Specifically, our method achieves very competitive results for tackling the most challenging elapsed time covariate, which potentially also includes the changes in shoe, carrying status, clothing, lighting condition, etc.\",\n \"Title: Human gait identification from extremely low-quality videos: an enhanced classifier ensemble method\\nYear: 2014\\n\\nAbstract: Nowadays, surveillance cameras are widely installed in public places for security and law enforcement, but the video quality may be low because of the limited transmission bandwidth and storage capacity. In this study, the authors proposed a gait recognition method for extremely low-quality videos, which have a frame-rate at one frame per second (1 fps) and resolution of 32 × 22 pixels. Different from popular temporal reconstruction-based methods, the proposed method uses the average gait image (AGI) over the whole sequence as the appearance-based feature description. Based on the AGI description, the authors employed a large number of weak classifiers to reduce the generalisation errors. The performance can be further improved by incorporating the model-based information into the classifier ensemble. The authors found that the performance improvement is directly proportional to the average disagreement level of weak classifiers (i.e. diversity), which can be increased by using the model-based information. The authors evaluated the proposed method on both indoor and outdoor databases (i.e. the low-quality versions of OU-ISIR-D and USF databases), and the results suggest that our method is more general and effective than other state-of-the-art algorithms.\",\n \"Title: Image Restoration Based on 3-D Autoregressive Model via Low-Rank Minimization\\nYear: 2015\\n\\nAbstract: Due to all kinds of need of customers and the complicated transmitting environment of digital image and video resources, numerous practical applications emerge, e.g. Image in painting, interpolation, super-resolution and the removal of salt and pepper noise. One thing these cases all have in common is that there are plenty of missing pixels randomly distributed in an image. Existing image restoration methods aiming at solving this problem include kernel regression [1], matrix completion [2] and total variation (TV) model [3]. The 3-D AR model has also been proposed to detect and interpolate the missing data in video sequences. However, the missing rate or missing region in these papers is usually small. With the missing rate increasing, known pixels in a local neighborhood are not going to be enough to form a solvable linear system. Thus, generally speaking, AR model is not suitable for image restoration from high missing rates. Nevertheless, with proper preliminary processing as proposed in this paper, AR models can be well utilized and present good results even in high missing rates. In this paper, we propose a novel method for image restoration. For the first time, the 3-D AR model is utilized in a single image to simultaneously measure correlation within and between similar patches. 2-D AR model combining with a multiscale structure reconstruct the image using its low-resolution versions to preserve important perceptual statistics such as edges. After obtaining the preliminary reconstruction of the reconstructed full size image, similar patches are collected and the 3-D AR model is applied to form a more local-consistent patch set. Then, an iterative singular value thresholding (SVT) method is utilized to solve the low-rank minimization problem. Instead of aggregating all the overlapped patches after each patch set is processed, we perform SVT for each patch set and aggregate all the overlapped patches into an intermediate image, then the iterative regularization is carried out on the image to produce the newly output for next iteration. Experimental results demonstrate that the proposed method achieves higher PSNR and SSIM than state-of-the-art methods [1-3] and the processed images possess a better visual quality especially in edge structures and texture regions.\",\n \"Title: PCA-based denoising of Sensor Pattern Noise for source camera identification\\nYear: 2014\\n\\nAbstract: Sensor Pattern Noise (SPN) has been proved to be an inherent fingerprint of the imaging device for source identification. However, SPN extracted from digital images can be severely contaminated by scene details. Moreover, SPN with high dimensionality may cause excessive time cost on calculating correlation between SPNs, which will limit its applicability to the source camera identification or image classification with a large dataset. In this work, an effective scheme based on principal component analysis (PCA) is proposed to address these two problems. By transforming SPN into eigenspace spanned by the principal components, the scene details and trivial information can be significantly suppressed. In addition, due to the dimensionality reduction property of PCA, the size of SPN is greatly reduced, consequently reducing the time cost of calculating similarity between SPNs. Our experiments are conducted on the Dresden database, and results demonstrate that the proposed method outperforms could achieve the state-of-art performance in terms of the Receiver Operating Characteristic (ROC) curves while reducing the dimensionality of SPN.\",\n \"Title: A compact representation of sensor fingerprint for camera identification and fingerprint matching\\nYear: 2015\\n\\nAbstract: Sensor Pattern Noise (SPN) has been proved as an effective fingerprint of imaging devices to link pictures to the cameras that acquired them. In practice, forensic investigators usually extract this camera fingerprint from large image block to improve the matching accuracy because large image blocks tend to contain more SPN information. As a result, camera fingerprints usually have a very high dimensionality. However, the high dimensionality of fingerprint will incur a costly computation in the matching phase, thus hindering many interesting applications which require an efficient real-time camera matching. To solve this problem, an effective feature extraction method based on PCA and LDA is proposed in this work to compress the dimensionality of camera fingerprint. Our experimental results show that the proposed feature extraction algorithm could greatly reduce the size of fingerprint and enhance the performance in term of Receiver Operating Characteristic (ROC) curve of several existing methods.\",\n \"Title: Enhanced SVD for Collaborative Filtering.\\nYear: 2016\\n\\nAbstract: Matrix factorization is one of the most popular techniques for prediction problems in the fields of intelligent systems and data mining. It has shown its effectiveness in many real-world applications such as recommender systems. As a collaborative filtering method, it gives users recommendations based on their previous preferences (or ratings). Due to the extreme sparseness of the ratings matrix, active learning is used for eliciting ratings for a user to get better recommendations. In this paper, we propose a new matrix factorization model called Enhanced SVD (ESVD) which combines the classic matrix factorization method with a specific rating elicitation strategy. We evaluate the proposed ESVD method on the Movielens data set, and the experimental results suggest its effectiveness in terms of both accuracy and efficiency, when compared with traditional matrix factorization methods and active learning methods.\",\n \"Title: Robust gait recognition from extremely low frame-rate videos\\nYear: 2013\\n\\nAbstract: In this paper, we propose a gait recognition method for extremely low frame-rate videos. Different from the popular temporal reconstruction-based methods, the proposed method uses the average gait over the whole sequence as input feature template. Assuming the effect caused by extremely low frame-rate or large gait fluctuations are intra-class variations that the gallery data fails to capture, we build a general model based on random subspace method. More specifically, a number of weak classifiers are combined to reduce the generalization errors. We evaluate our method on the OU-ISIR-D dataset with large/small gait fluctuations, and very competitive results are achieved when both the probe and gallery are extremely low frame-rate gait sequences (e.g., 1 fps).\",\n \"Title: Random subspace method for source camera identification\\nYear: 2015\\n\\nAbstract: Sensor pattern noise is an inherent fingerprint of imaging devices, which has been widely used for source camera identification, image classification, and forgery detection. In a previous work, we proposed a feature extraction method based on the principal component analysis denoising concept, which can enhance the performance of conventional SPN extraction methods. However, this method is vulnerable, because the training samples are seriously affected by the image content. Accordingly, it is difficult to train a reliable feature extractor by using such a training set. To address this problem, a camera identification framework based on the random subspace method and majority voting is proposed in this work. The experimental results show that the proposed solution can suppress the interference from scene details and enhance the performance in terms of the receiver operating characteristic curve.\",\n \"Title: Directed-Propagation Link-State Routing Protocol for Information-Centric Networks.\\nYear: 2016\\n\\nAbstract: This paper presents the design of Directed-Propagation Link-State Routing Protocol (DPLSR), which tries to address the issue of prefix flooding storm in NDN Link-state Routing Protocol. Since the number of data object is several order of magnitude of that of routers, legacy Link State Advertisements (LSAs) flooding model is no more appropriate in NDN. This paper proposes a LSA directed-propagation scheme that LSA messages are only forwarded towards Root anchor, not flooding. Upon requesting a data object, a Look-up message is sent towards Root anchor. When it encounters a router with accroding anchor and prefix information, an anchor list is returned. Users are able to access the nearest copy or build one or multi-path connections to the anchors. In DPLSR, messages are propagated along some paths, and only parts of router store prefix and anchor information. Thus, the communication overhead and prefix storage are reduced significantly compared with the existing NDN routing protocols. Simulated experiments also verifies the performance gain of DPLSR.\",\n \"Title: Ensembles of Deep LSTM Learners for Activity Recognition using Wearables.\\nYear: 2017\\n\\nAbstract: Recently, deep learning (DL) methods have been introduced very successfully into human activity recognition (HAR) scenarios in ubiquitous and wearable computing. Especially the prospect of overcoming the need for manual feature design combined with superior classification capabilities render deep neural networks very attractive for real-life HAR applications. Even though DL-based approaches now outperform the state-of-the-art in a number of recognition tasks, still substantial challenges remain. Most prominently, issues with real-life datasets, typically including imbalanced datasets and problematic data quality, still limit the effectiveness of activity recognition using wearables. In this paper we tackle such challenges through Ensembles of deep Long Short Term Memory (LSTM) networks. LSTM networks currently represent the state-of-the-art with superior classification performance on relevant HAR benchmark datasets. We have developed modified training procedures for LSTM networks and combine sets of diverse LSTM learners into classifier collectives. We demonstrate that Ensembles of deep LSTM learners outperform individual LSTM networks and thus push the state-of-the-art in human activity recognition using wearables. Through an extensive experimental evaluation on three standard benchmarks (Opportunity, PAMAP2, Skoda) we demonstrate the excellent recognition capabilities of our approach and its potential for real-life applications of human activity recognition.\",\n \"Title: Gait recognition based on the golden ratio.\\nYear: 2016\\n\\nAbstract: Gait is known to be an effective behavioral biometric trait for the identification of individuals. However, clothing has a dramatic influence on the recognition rate. Researchers have attempted to deal with this issue of clothing by segmenting parts of the gait images based on anatomical proportions. However, the clothing proportion is not the same as the anatomical proportion, as clothing is designed according to the golden ratio to enhance its look. Hence, methods for eliminating the influence of clothing should be based on the proportions of clothing. In this paper, we propose the golden ratio-based segmentation method to reduce the influence of clothing. Experiments are conducted on the CASIA-B dataset, and experimental results show that the proposed method outperforms other approaches, achieving a 94.76 % recognition rate in various clothing conditions and a rate of 91.53 % when bags are being carried.\",\n \"Title: A Hybrid Classification Approach Based On Support Vector Machine And K-Nearest Neighbor For Remote Sensing Data\\nYear: 2017\\n\\nAbstract: Analysis and classification for remote sensing landscape based on remote sensing imagery is a popular research topic. In this paper, we propose a new remote sensing data classifier by incorporating the support vector machine (SVM) learning information into the K-nearest neighbor (KNN) classifier. The SVM is well known for its extraordinary generalization capability even with limited learning samples, and it is very useful for remote sensing applications as data samples are usually limited. The KNN has been widely used in data classification due to its simplicity and effectiveness. However, the KNN is instance-based and needs to keep all the training samples for classification, which could cause not only high computation complexity but also overfitting problems. Meanwhile, the performance of the KNN classifier is sensitive to the neighborhood size K and how to select the value of the parameter K relies heavily on practice and experience. Based on the observations that the SVM can contribute to the KNN on the problems of smaller training samples size as well as the selection of the parameter K, we propose a support vector nearest neighbor (abbreviated as SV-NN) hybrid classification approach which can simplify the parameter selection while maintaining classification accuracy. The proposed approach is consist of two stages. In the first stage, the SVM is performed on the training samples to obtain the reduced support vectors (SVs) for each of the sample categories. In the second stage, a nearest neighbor classifier (NNC) is used to classify a testing sample, i.e. the average Euclidean distance between the testing data point to each set of SVs from different categories is calculated and the NNC identifies the category with minimum distance. To evaluate the effectiveness of the proposed approach,firstly experiments of classification for samples from remote sensing data are evaluated, and then experiments of identifying different land covers regions in the remote sensing images are evaluated. Experimental results show that the SV-NN approach maintains good classification accuracy while reduces the training samples compared with the conventional SVM and KNN classification model.\",\n \"Title: Inference of a compact representation of sensor fingerprint for source camera identification.\\nYear: 2018\\n\\nAbstract: •An effective framework for de-noising and compressing sensor pattern noise (a form of device fingerprint) is proposed.•A compact and discriminative representation can be obtained directly from a high-dimensional SPN.•A novel training set construction method is designed to minimize the impact of various interfering artifacts in training samples.•The proposed methods can be used as a post-processing framework applied after any SPN extraction methods for effective source camera identification.\",\n \"Title: Matrix Factorization With Rating Completion: An Enhanced SVD Model for Collaborative Filtering Recommender Systems.\\nYear: 2017\\n\\nAbstract: Collaborative filtering algorithms, such as matrix factorization techniques, are recently gaining momentum due to their promising performance on recommender systems. However, most collaborative filtering algorithms suffer from data sparsity. Active learning algorithms are effective in reducing the sparsity problem for recommender systems by requesting users to give ratings to some items when they enter the systems. In this paper, a new matrix factorization model, called Enhanced SVD (ESVD) is proposed, which incorporates the classic matrix factorization algorithms with ratings completion inspired by active learning. In addition, the connection between the prediction accuracy and the density of matrix is built to further explore its potentials. We also propose the Multi-layer ESVD, which learns the model iteratively to further improve the prediction accuracy. To handle the imbalanced data sets that contain far more users than items or more items than users, the Item-wise ESVD and User-wise ESVD are presented, respectively. The proposed methods are evaluated on the famous Netflix and Movielens data sets. Experimental results validate their effectiveness in terms of both accuracy and efficiency when compared with traditional matrix factorization methods and active learning methods.\",\n \"Title: An improved optimum-path forest clustering algorithm for remote sensing image segmentation.\\nYear: 2018\\n\\nAbstract: Remote sensing image segmentation is a key technology for processing remote sensing images. The image segmentation results can be used for feature extraction, target identification and object description. Thus, image segmentation directly affects the subsequent processing results. This paper proposes a novel Optimum-Path Forest (OPF) clustering algorithm that can be used for remote sensing segmentation. The method utilizes the principle that the cluster centres are characterized based on their densities and the distances between the centres and samples with higher densities. A new OPF clustering algorithm probability density function is defined based on this principle and applied to remote sensing image segmentation. Experiments are conducted using five remote sensing land cover images. The experimental results illustrate that the proposed method can outperform the original OPF approach.\",\n \"Title: A New Technique For Remote Sensing Image Classification Based On Combinatorial Algorithm Of Svm And Knn\\nYear: 2018\\n\\nAbstract: In remote sensing image classification, distance measurements and classification criteria are equally important; and less accuracy of either would affect classification accuracy. Remote sensing image classification was performed by combining support vector machine (SVM) and k-nearest neighbor (KNN). This was based on the separability of classes using SVM and the spatial and spectral characteristics of remote sensing data. Moreover, a distance formula is proposed as the measure criterion that considers both luminance and direction of the vectors. First, the SVM is trained and the support vectors (SVs) are obtained for each class. In the testing phase, newly tested samples were entered, and average distance between the test samples and SVs for each class are calculated using the distance formula. Finally, decisions are made to classify the test samples into the class with minimal average distance. This procedure is repeated until all test samples are classified. In the combinatorial algorithm, the nearest neighbor classifier is used to classify a testing sample, i.e. the average Euclidean distance between the testing data point to each set of SVs from different categories is calculated and the nearest neighbor classifier identifies the category with minimum distance. The proposed combinatorial algorithm has advantages over the conventional KNN for eliminating the k parameter selection problem and reducing heavy learning time. A comparison is carried out with SVM, KNN and Spectral Angle Mapper (SAM) classification using ALOS/PALSAR and PSM images, and the effectiveness of the proposed algorithm is demonstrated.\",\n \"Title: A Fast and Easily-Realized Concentration Sensor for Binary Gas Mixtures and Its Design Analysis.\\nYear: 2018\\n\\nAbstract: A low-cost and easily-realized sensing device used for the detection of gas mixtures at different concentrations is presented. Its sensing part includes a small critical nozzle, a laminar structure, and a differential pressure sensor. When gas flows through the laminar structure, there is a pressure drop between both ends of it, and for different components of gas, the pressure drop is different. Based on this feature, the concentration detection is achieved. Concentration tests for two types of fire extinguishing agents CBrF3 and C3HF7 are presented. The results show the characteristics of fast response/recovery time, high accuracy, and good repeatability. Based on the theoretical analysis, the effects of the design parameters on the sensing performance to concentration detection are discussed in detail.\",\n \"Title: Deep Learning for Human Activity Recognition in Mobile Computing.\\nYear: 2018\\n\\nAbstract: By leveraging advances in deep learning, challenging pattern recognition problems have been solved in computer vision, speech recognition, natural language processing, and more. Mobile computing has also adopted these powerful modeling approaches, delivering astonishing success in the field's core application domains, including the ongoing transformation of human activity recognition technology th...\",\n \"Title: Active learning in multi-domain collaborative filtering recommender systems.\\nYear: 2018\\n\\nAbstract: The lack of information is an acute challenge in most recommender systems, especially for the collaborative filtering algorithms which utilize user-item rating matrix as the only source of information. Active learning can be used to remedy this problem by querying users to give ratings to some items. Apart from the active learning algorithms, cross-domain recommender system techniques try to alleviate the sparsity problem by exploiting knowledge from auxiliary (source) domains. A special case of cross-domain recommendation is multi-domain recommendation that utilizes the shared knowledge across multiple domains to alleviate the data sparsity in all domains. In this paper, we propose a novel multi-domain active learning framework by incorporating active learning techniques with cross-domain collaborative filtering algorithms in the multi-domain scenarios. Specifically, our proposed active learning elicits all the ratings simultaneously based on the criteria with regard to both items and users, for the purpose of improving the performance of the whole system. We evaluate a variety of active learning strategies in the proposed framework on different multi-domain recommendation tasks based on three popular datasets: Movielens, Netflix and Book-Crossing. The results show that the system performance can be improved further when combining cross-domain collaborative filtering with active learning algorithms.\",\n \"Title: Triple Verification Network for Generalized Zero-Shot Learning.\\nYear: 2019\\n\\nAbstract: Conventional zero-shot learning approaches often suffer from severe performance degradation in the generalized zero-shot learning (GZSL) scenario, i.e., to recognize test images that are from both seen and unseen classes. This paper studies the Class-level Over-fitting (CO) and empirically shows its effects to GZSL. We then address ZSL as a triple verification problem and propose a unified optimiz...\",\n \"Title: Mobile Based Continuous Authentication Using Deep Features.\\nYear: 2018\\n\\nAbstract: Continuous authentication is a promising approach to validate the user's identity during a work session, e.g., for mobile banking applications. Recently, it has been demonstrated that changes in the motion patterns of the user may help to note the unauthorised use of mobile devices. Several approaches have been proposed in this area but with relatively weak performance results. In this work, we propose an approach which uses a Siamese convolutional neural network to learn the signatures of the motion patterns from users and achieve a competitive verification accuracy up to 97.8%. We also find our algorithm is not very sensitive to sampling frequency and the length of the sequence.\",\n \"Title: Robust Cross-View Gait Identification with Evidence: A Discriminant Gait GAN (DiGGAN) Approach on 10000 People.\\nYear: 2018\\n\\nAbstract: Gait is an important biometric trait for surveillance and forensic applications, which can be used to identify individuals at a large distance through CCTV cameras. However, it is very difficult to develop robust automated gait recognition systems, since gait may be affected by many covariate factors such as clothing, walking surface, walking speed, camera view angle, etc. Out of them, large view angle was deemed as the most challenging factor since it may alter the overall gait appearance substantially. Recently, some deep learning approaches (such as CNNs) have been employed to extract view-invariant features, and achieved encouraging results on small datasets. However, they do not scale well to large dataset, and the performance decreases significantly w.r.t. number of subjects, which is impractical to large-scale surveillance applications. To address this issue, in this work we propose a Discriminant Gait Generative Adversarial Network (DiGGAN) framework, which not only can learn view-invariant gait features for cross-view gait recognition tasks, but also can be used to reconstruct the gait templates in all views --- serving as important evidences for forensic applications. We evaluated our DiGGAN framework on the worldu0027s largest multi-view OU-MVLP dataset (which includes more than 10000 subjects), and our method outperforms state-of-the-art algorithms significantly on various cross-view gait identification scenarios (e.g., cooperative/uncooperative mode). Our DiGGAN framework also has the best results on the popular CASIA-B dataset, and it shows great generalisation capability across different datasets.\",\n \"Title: UtilCache: Effectively and Practicably Reducing Link Cost in Information-Centric Network\\nYear: 2018\\n\\nAbstract: Minimizing total link cost in Information-Centric Network (ICN) by optimizing content placement is challenging in both effectiveness and practicality. To attain better performance, upstream link cost caused by a cache miss should be considered in addition to content popularity. To make it more practicable, a content placement strategy is supposed to be distributed, adaptive, with low coordination overhead as well as low computational complexity. In this paper, we present such a content placement strategy, UtilCache, that is both effective and practicable. UtilCache is compatible with any cache replacement policy. When the cache replacement policy tends to maintain popular contents, UtilCache attains low link cost. In terms of practicality, UtilCache introduces little coordination overhead because of piggybacked collaborative messages, and its computational complexity depends mainly on content replacement policy, which means it can be O(1) vthen working with LRU. Evaluations prove the effectiveness of UtilCache, as it saves nearly 40% link cost more than current ICN design.\",\n \"Title: Remote Cloud-Based Automated Stroke Rehabilitation Assessment Using Wearables\\nYear: 2018\\n\\nAbstract: We outline a system enabling accurate remote assessment of stroke rehabilitation levels using wrist worn accelerometer time series data. The system is built based on features generated from clustering models across sliding windows in the data and makes use of computation in the cloud. Predictive models are built using advanced machine learning techniques.\",\n \"Title: Generic compact representation through visual-semantic ambiguity removal.\\nYear: 2019\\n\\nAbstract: •A novel VSAR algorithm that can simultaneously remove the ambiguity between visual and semantic information.•Extensive evaluations suggest the important role of visual-semantic ambiguity to the performance improvement.•A unified framework that can deal with both category-level and instance-level zero-shot learning tasks.\",\n \"Title: Automatic Cataract Diagnosis By Image-Based Interpretability\\nYear: 2018\\n\\nAbstract: Cataract is defined as a lenticular opacity presenting usually with poor visual acuity. It is considered the most common cause of blindness. Early diagnosis and treatment can reduce the suffering of patients and prevent visual impairment from turning into blindness. Recently, cataract diagnosis applying pattern recognition is in a rising period. For retinal fundus images, the task is usually cataract classification. However, it needs complex manual processing, which demands dexterous people and time taking exertion. Besides, it faces the challenge of effective interpretability and dependability. In this paper, we develop a deep-learning algorithm to intuitively identify cataract attributes to solve these limitations. Our model, is a 18(50)-layer convolutional neural network that inputs retinal fundus images in G channel and outputs the prediction with heatmap. The heatmap localizes the areas where most indicative of different levels of cataract is. Furthermore, we extend the training strategy for the corresponding task, which aims at improving the performance of the network. Comparing with other methods in cataract classification, we succeeded to achieve state of the art accuracy of proposed method on detection and grading task. Most importantly, our model provides a compelling reason via localizing the areas revealing cataract in the image.\",\n \"Title: SVD-Based Algorithms for the Best Rank-1 Approximation of a Symmetric Tensor\\nYear: 2018\\n\\nAbstract: This paper revisits the problem of finding the best rank-1 approximation to a symmetric tensor and makes three contributions. First, in contrast to the many long and lingering arguments in the literature, it offers a straightforward justification that generically the best rank-1 approximation to a symmetric tensor is symmetric. Second, in contrast to the typical workhorse in the practice for the low-rank tensor approximation, namely, the alternating least squares (ALS) technique which improves one factor a time, this paper proposes three alternative algorithms based on the singular value decomposition (SVD) that modifies two factors a time. One step of SVD-based iteration is superior to two steps of ALS iterations. Third, it is not only proved that the generalized Rayleigh quotients generated from the three SVD-based algorithms enjoy monotone convergence, but also that the iterates themselves converge.\",\n \"Title: Co-sleep: Design for workplace based wellness program to raise awareness of sleep deprivation.\\nYear: 2018\\n\\nAbstract: Sleep deprivation is a public health issue. A lack of sleep not only harms our body immune systems but also degrades their capacity to maintain cognitive skills. Awareness of sleep deprivation has not been widely investigated in work-based wellness programmes. In the study, the project carried out with nine participants from a local manufacture company to raise that awareness. The common causes of sleep deprivation have been identified the through the deployment of probes and the interviews. The research generated design concepts of smart IoT workplace to track and share daytime sleep-related activities. Through the bottom up and co-design methods, participants give the points of considering the use of sleep data from different power relationship perspective, includes the unexpected use of sleep for fatigue risk management and evaluation of employee performance.\",\n \"Title: Towards Reliable, Automated General Movement Assessment for Perinatal Stroke Screening in Infants Using Wearable Accelerometers\\nYear: 2019\\n\\nAbstract: Perinatal stroke (PS) is a serious condition that, if undetected and thus untreated, often leads to life-long disability, in particular Cerebral Palsy (CP). In clinical settings, Prechtl's General Movement Assessment (GMA) can be used to classify infant movements using a Gestalt approach, identifying infants at high risk of developing PS. Training and maintenance of assessment skills are essential and expensive for the correct use of GMA, yet many practitioners lack these skills, preventing larger-scale screening and leading to significant risks of missing opportunities for early detection and intervention for affected infants. We present an automated approach to GMA, based on body-worn accelerometers and a novel sensor data analysis method--Discriminative Pattern Discovery (DPD)--that is designed to cope with scenarios where only coarse annotations of data are available for model training. We demonstrate the effectiveness of our approach in a study with 34 newborns (21 typically developing infants and 13 PS infants with abnormal movements). Our method is able to correctly recognise the trials with abnormal movements with at least the accuracy that is required by newly trained human annotators (75%), which is encouraging towards our ultimate goal of an automated PS screening system that can be used population-wide.\",\n \"Title: A hybrid global-local representation CNN model for automatic cataract grading.\\nYear: 2020\\n\\nAbstract: Cataract is one of the most serious eye diseases leading to blindness. Early detection and treatment can reduce the rate of blindness in cataract patients. However, the professional knowledge of ophthalmologists is necessary for the clinical cataract detection. Therefore, the potential costs may make it difficult for the widespread use of cataract detection to prevent blindness. Artificial intelligence assisted diagnosis based on medical images has attracted more and more attention of researchers. Many studies have focused on the use of pre-defined feature sets for cataract classification, but the predefined feature sets may be incomplete or redundant. On account of the aforementioned issues, some studies have proposed deep learning methods to automatically extract image features, but all based on global features and none has analyzed the layer-by-layer transformation process of the middle-tier features. This paper uses convolutional neural networks (CNN) to learn useful features directly from input data, and deconvolution network method is employed to investigate how CNN characterizes cataract layer-by-layer. We found that compared to the global feature set, the detail vascular information, which is lost after multi-layer convolution calculation also plays an important role in cataract grading task. And this finding fits with the morphological definition of fundus image. Through the finding, we gained insights into the design of hybrid global-local feature representation model to improve the recognition performance of automatic cataract grading.\",\n \"Title: Consistent Dynamic Server Assignment in Content Delivery Network.\\nYear: 2019\\n\\nAbstract: Server assignment is an essential part in Content Delivery Network (CDN), responsible for choosing a surrogate server to provide the best Quality of Service (QoS) for clients and balancing the server load. But it faces two critical challenges in practical: 1) the constraint of DNS schedule granularity makes it difficult to distribute client demands in arbitrary proportion. 2) dynamic server assignment may bring about a huge amount of cached content migration among servers. To address these issues, we work with one of the largest CDN operators in the world, and design a persistent dynamic server assignment algorithm, DynDNS. It gives a more sticky solution that can be easily implemented in DNS-based load balancing while keeping the overall QoS and server load satisfying. To the best of our knowledge, this is the first quantitative model for the dynamic DNS server assignment problem. Theoretical analysis proves that the proposed greedy algorithm has considerable optimality guarantees, and evaluation shows that DynDNS can avoid about 50% unnecessary shift in demand allocation and yield more stable cache hit ratio.\",\n \"Title: Orchestrating the Development Lifecycle of Machine Learning-based IoT Applications: A Taxonomy and Survey\\nYear: 2020\\n\\nAbstract: AbstractMachine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock the potential of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services. Hence, orchestrating ML pipelines that encompass model training and implication involved in the holistic development lifecycle of an IoT application often leads to complex system integration. This article provides a comprehensive and systematic survey of the development lifecycle of ML-based IoT applications. We outline the core roadmap and taxonomy and subsequently assess and compare existing standard techniques used at individual stages.\",\n \"Title: Discriminative Latent Semantic Graph for Video Captioning\\nYear: 2021\\n\\nAbstract: ABSTRACTVideo captioning aims to automatically generate natural language sentences that can describe the visual contents of a given video. Existing generative models like encoder-decoder frameworks cannot explicitly explore the object-level interactions and frame-level information from complex spatio-temporal data to generate semantic-rich captions. Our main contribution is to identify three key problems in a joint framework for future video summarization tasks. 1) Enhanced Object Proposal: we propose a novel Conditional Graph that can fuse spatio-temporal information into latent object proposal. 2) Visual Knowledge: Latent Proposal Aggregation is proposed to dynamically extract visual words with higher semantic levels. 3) Sentence Validation: A novel Discriminative Language Validator is proposed to verify generated captions so that key semantic concepts can be effectively preserved. Our experiments on two public datasets (MVSD and MSR-VTT) manifest significant improvements over state-of-the-art approaches on all metrics, especially for BLEU-4 and CIDEr. Our code is available at https://github.com/baiyang4/D-LSG-Video-Caption.\",\n \"Title: Towards Automated Fatigue Assessment using Wearable Sensing and Mixed-Effects Models\\nYear: 2021\\n\\nAbstract: ABSTRACT Fatigue is a broad, multifactorial concept that includes the subjective perception of reduced physical and mental energy levels. It is also one of the key factors that strongly affect patients’ health-related quality of life. To date, most fatigue assessment methods were based on self-reporting, which may suffer from many factors such as recall bias. To address this issue, in this work, we recorded multi-modal physiological data (including ECG, accelerometer, skin temperature and respiratory rate, as well as demographic information such as age, BMI) in free-living environments, and developed automated fatigue assessment models. Specifically, we extracted features from each modality, and employed the random forest-based mixed-effects models, which can take advantage of the demographic information for improved performance. We conducted experiments on our collected dataset, and very promising preliminary results were achieved. Our results suggested ECG played an important role in the fatigue assessment tasks.\",\n \"Title: Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action Recognition\\nYear: 2021\\n\\nAbstract: ABSTRACTThe task of skeleton-based action recognition remains a core challenge in human-centred scene understanding due to the multiple granularities and large variation in human motion. Existing approaches typically employ a single neural representation for different motion patterns, which has difficulty in capturing fine-grained action classes given limited training data. To address the aforementioned problems, we propose a novel multi-granular spatio-temporal graph network for skeleton-based action classification that jointly models the coarse- and fine-grained skeleton motion patterns. To this end, we develop a dual-head graph network consisting of two interleaved branches, which enables us to extract features at two spatio-temporal resolutions in an effective and efficient manner. Moreover, our network utilises a cross-head communication strategy to mutually enhance the representations of both heads. We conducted extensive experiments on three large-scale datasets, namely NTU RGB+D 60, NTU RGB+D 120, and Kinetics-Skeleton, and achieves the state-of-the-art performance on all the benchmarks, which validates the effectiveness of our method1.\",\n \"Title: Automatic Cataract Grading with Visual-semantic Interpretability\\nYear: 2021\\n\\nAbstract: Cataract is a chronic eye disease that causes irreversible vision loss. Automatic cataract detection can help people prevent visual impairment and decrease the possibility of blindness. To date, many studies utilize deep learning methods to grade cataract severity on fundus images. However, they mainly focus on the classification performance and ignore the model interpretability, which may lead to a semantic gap between networks and users. In this paper, we present a deep learning network to improve the model interpretability, which consists three main modules: deep feature extraction, visual saliency module and semantic description module. Visual and semantic interpretation jointly employed to provide cataract-grade oriented interpretation for the overall model. Experimental results on real clinical data set show that our method improves the interpretability for cataract grading while ensuring the high classification performance.\",\n \"Title: Participatory Action Research and Open Source Hardware Appropriation for Large Scale In-The-Wild Studies\\nYear: 2021\\n\\nAbstract: ABSTRACT With the rise of ubiquitous computing, the use of research in-the-wild approach in both Ubicomp and HCI context has become more viable, but not without its challenges. End-users and communities can be great beneficiaries of such an approach if actively involved in the process from an early stage and throughout the whole process. We start by presenting a future case study, the Cue Band study, which revolves around the creation of a wristband cueing device for people with Parkinson’s that experience drooling. We present an approach for research in-the-wild, which draws on participatory action research theory, that places the end-user at the centre of the process, aiming to first and for most to create a workable product for the end-user, before engaging in a formal study. In the last section, we explore the appropriation of existing open-source hardware for in-the-wild research, by describing problems and solutions associated with developing Ubicomp technologies for large-scale studies.\",\n \"Title: LSTA-Net: Long short-term Spatio-Temporal Aggregation Network for Skeleton-based Action Recognition.\\nYear: 2021\\n\\nAbstract: Modelling various spatio-temporal dependencies is the key to recognising human actions in skeleton sequences. Most existing methods excessively relied on the design of traversal rules or graph topologies to draw the dependencies of the dynamic joints, which is inadequate to reflect the relationships of the distant yet important joints. Furthermore, due to the locally adopted operations, the important long-range temporal information is therefore not well explored in existing works. To address this issue, in this work we propose LSTA-Net: a novel Long short-term Spatio-Temporal Aggregation Network, which can effectively capture the long/short-range dependencies in a spatio-temporal manner. We devise our model into a pure factorised architecture which can alternately perform spatial feature aggregation and temporal feature aggregation. To improve the feature aggregation effect, a channel-wise attention mechanism is also designed and employed. Extensive experiments were conducted on three public benchmark datasets, and the results suggest that our approach can capture both long-and-short range dependencies in the space and time domain, yielding higher results than other state-of-the-art methods. Code available at https://github.com/tailin1009/LSTA-Net.\",\n \"Title: Multi-task Learning Based on Multi-type Dataset for Retinal Abnormality Detection\\nYear: 2021\\n\\nAbstract: The number of people suffering from ophthalmic diseases is increasing with the population aging. Many studies have been proposed to automatically identify diseases to reduce the risks of further retinal damage. However, most of existing methods mainly used a single type of dataset to solve the specific medical task, which is not clinically practical in the realworld scenarios. In this paper, we pr...\",\n \"Title: GLA-Net: A global-local attention network for automatic cataract classification\\nYear: 2021\\n\\nAbstract: •A multi-level attention network is proposed for cataract classification task.•Global-level subnet aims to learn global structure features.•Local-level subnet is used to extract local detailed features.•Both are jointly adopted to improve the hierarchical representation learning.•Extensive Experiments are conducted to prove the effectiveness of our method.\",\n \"Title: Multi-Wavelet Guided Deep Mean-Shift Prior For Image Restoration\\nYear: 2021\\n\\nAbstract: Image restoration is essentially recognized as an ill-posed problem. A promising solution in recent years is incorporating deep network-driven priors into the iterative restoration procedure as constrained conditions. Among them, deep mean-shift prior utilizes the denoising autoencoder to play the role of prior updating. In this study, we present multiple wavelets guided deep mean-shift prior, which integrates the advantages of structural representation in the wavelet transform and learning ability in deep network. Specifically, by re-arranging the multi-view and multi-resolution features generated by multiple wavelet transforms as the input of denoising autoencoder, a more powerful prior information is learned. It benefits from the recurrent structure-preserving and multi-view complementary aggregation properties. We embed the learned prior information into the iterative recovery process and adopt proximal gradient descent to tackle it. Extensive experiments on image deblurring and compressed sensing tasks demonstrated significantly improved performances both visually and quantitatively.\",\n \"Title: A-PSPNet: A novel segmentation method of renal ultrasound image\\nYear: 2021\\n\\nAbstract: Hydronephrosis is a common renal disease in children which can lead to a series of complications, and ultrasonography is a basic examination usually performed on suspected hydronephrosis patients. If we can use deep learning approaches to judge and grade the disease in the ultrasonic examination stage, we can save a lot of manpower, medical resources, money, and help the suffered patients. For the...\",\n \"Title: Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition\\nYear: 2022\\n\\nAbstract: AbstractIn wearable-based human activity recognition (HAR) research, one of the major challenges is the large intra-class variability problem. The collected activity signal is often, if not always, coupled with noises or bias caused by personal, environmental, or other factors, making it difficult to learn effective features for HAR tasks, especially when with inadequate data. To address this issue, in this work, we proposed a Behaviour Pattern Disentanglement (BPD) framework, which can disentangle the behavior patterns from the irrelevant noises such as personal styles or environmental noises, etc. Based on a disentanglement network, we designed several loss functions and used an adversarial training strategy for optimization, which can disentangle activity signals from the irrelevant noises with the least dependency (between them) in the feature space. Our BPD framework is flexible, and it can be used on top of existing deep learning (DL) approaches for feature refinement. Extensive experiments were conducted on four public HAR datasets, and the promising results of our proposed BPD scheme suggest its flexibility and effectiveness. This is an open-source project, and the code can be found at http://github.com/Jie-su/BPD\",\n \"Title: Research of the Response Rate Variation for the Extinguishant Detection Technique Based on the Differential Pressure Principle\\nYear: 2022\\n\\nAbstract: The intent of this work is to explore the variation mechanism of the response rate for the extinguishant detection technique based on the differential pressure principle, which is an aspirating concentration detection technique and quite suitable for extinguishant detection. The effects of the sampling pipe, sensing structure, and operating temperature on the response rate were analyzed, and the theoretical model was established by analyzing the gas dispersion behavior. It is found that the response/recovery time is greatly affected by the above factors, which can change from 0.068/0.088 to 0.960/1.01 s. Results show that with the increase of the length and inner diameter of the sampling pipe, the response/recovery time rapidly increases. For the sensing structure, the increase of the external diameter of the vascular bundles will increase the response/recovery time, and the increase of the throat diameter of the critical flow nozzle will greatly decrease the response/recovery time. The increase of the operating temperature can decrease the response/recovery time with limited improving effect. The research results in this article can provide suggestions for the design of sensor.\",\n \"Title: Action Quality Assessment with Temporal Parsing Transformer.\\nYear: 2022\\n\\nAbstract: Action Quality Assessment(AQA) is important for action understanding and resolving the task poses unique challenges due to subtle visual differences. Existing state-of-the-art methods typically rely on the holistic video representations for score regression or ranking, which limits the generalization to capture fine-grained intra-class variation. To overcome the above limitation, we propose a temporal parsing transformer to decompose the holistic feature into temporal part-level representations. Specifically, we utilize a set of learnable queries to represent the atomic temporal patterns for a specific action. Our decoding process converts the frame representations to a fixed number of temporally ordered part representations. To obtain the quality score, we adopt the state-of-the-art contrastive regression based on the part representations. Since existing AQA datasets do not provide temporal part-level labels or partitions, we propose two novel loss functions on the cross attention responses of the decoder: a ranking loss to ensure the learnable queries to satisfy the temporal order in cross attention and a sparsity loss to encourage the part representations to be more discriminative. Extensive experiments show that our proposed method outperforms prior work on three public AQA benchmarks by a considerable margin.\",\n \"Title: Cross-project defect prediction based on G-LSTM model\\nYear: 2022\\n\\nAbstract: •To create your highlights, please type over the instructions in the template box below:•Pre-processing and feature extraction techniques from NLP domain to CPDP domain.•The feature extractor combines BLSTM with an attention mechanism to enhance the effect.•An adversarial structure is used to solve the distribution differences problem.\",\n \"Title: High Myopia Detection Method On Fundus Images Based On Curriculum Learning\\nYear: 2022\\n\\nAbstract: Myopia has become a major public health problem affecting the eye health of our citizens, especially teenagers. Fundus images can be obtained non-invasively and can be used to monitor and follow up on the progress in high myopia. However, with the development of artificial intelligence, it is still difficult to establish a computer-aided diagnosis model for high myopia with young children as research objects, mainly because 1) it is very difficult to collect labeled fundus images, and there is no large amount of such data that can be freely accessed; 2) hard samples which have clinical significance for population screening and diagnosis are rare and indistinguishable. To solve these problems, we propose a high myopia detection model on fundus images based on curriculum learning. We design a dual-curriculum generation module, which aims to use the expert model to endow the curriculum with new training indicators so that the student model can gradually and robustly identify hard samples. Compared with the baseline, our framework significantly improves the convergence speed of the training process and achieves the best performance during testing. Experimental results on a high myopia fundus images dataset show that our framework provides efficient and accurate detection and outperforms other methods.\",\n \"Title: Joint optimisation for dynamic flexible job-shop scheduling problem with transportation time and resource constraints\\nYear: 2022\\n\\nAbstract: Dynamic flexible job-shop scheduling is traditionally a challenge in real-world manufacturing systems, especially considering the constraints of transportation resources and transportation time. To address the dynamic optimisation problem in flexible manufacturing systems, this paper proposes a novel proactive-reactive methodology to adapt to the dynamic changes in working environments and addresses the joint scheduling problem for machine tools and transportation resources. The joint optimisation model is first formulated as a mixed-integer programming model considering production efficiency and transportation constraints. The flowchart of the dynamic scheduling system is then designed for dynamic decision-making, and a novel particle swarm optimisation algorithm integrated with genetic operators is developed to respond to dynamic events and generate the reschedule plan in time. Finally, several numerical experiments and case studies in reality are applied to verify the efficiency of the developed methodology. Common dispatching rules and heuristic methods are also applied to test and evaluate the efficiency of the developed algorithm. Computational results demonstrate that the developed methods and decision models are efficient for dynamic job-shop scheduling problems in flexible manufacturing systems, which can acquire rather a good effect in practical production.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":59,"string":"59"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13664,"string":"13,664"}}},{"rowIdx":13665,"cells":{"question_id":{"kind":"string","value":"Hongbo Li"},"question":{"kind":"string","value":"Generate an abstract for the title \"Adaptive filtering under maximum mutual information criterion.\" using the following items: 1. MaxMI \n2. Adaptive filtering \n3. FIR filter \n4. SMIG algorithm \n5. Robustness"},"answer":{"kind":"string","value":"The maximum mutual information (MaxMI) criterion is used as the adaptation cost for the adaptive filtering. This criterion is robust to measure distortions, and has strong connection with traditional mean-square error (MSE) criterion. Under Gaussian assumption, the closed-form solution of the finite impulse response (FIR) filter is obtained. Further, based on the kernel density estimation, the stochastic mutual information gradient (SMIG) algorithm is derived. Simulation results emphasize the robustness of this new algorithm."},"correct_answer":{"kind":"string","value":"The maximum mutual information (MaxMI) criterion is used as the adaptation cost for the adaptive filtering. This criterion is robust to measure distortions, and has strong connection with traditional mean-square error (MSE) criterion. Under Gaussian assumption, the closed-form solution of the finite impulse response (FIR) filter is obtained. Further, based on the kernel density estimation, the stochastic mutual information gradient (SMIG) algorithm is derived. Simulation results emphasize the robustness of this new algorithm."},"documents":{"kind":"list like","value":["Title: Gain Scheduling Control of Delta Operator System Using Network-Based Measurements\nYear: 2014\n\nAbstract: This paper addresses the state feedback stabilization problem for a class of network-based delta operator control systems. To compensate time delays, a gain scheduling controller based on one-way delay measurement is constructed, and the resulting closed-loop system is transformed into a switched delta operator system. The stability condition is derived for the closed-loop system, and the corresponding stabilizing and optimal controller design techniques are also developed. The simulation and experimental results are given to demonstrate the effectiveness and applicability of the proposed approach.","Title: Some special types of solutions of a class of the (N+1)-dimensional nonlinear wave equations\nYear: 2008\n\nAbstract: Some special types of solutions with arbitrary functions for the (N + 1)-dimensional sine-cosine-Gordon equation, the (N + 1)-dimensional double sinh-Gordon equation, and the (N + 1)-dimensional sinh-cosinh-Gordon equations are constructed by means of the separation transformation approach. As an illustrative sample, the properties of some solutions are shown by their figures. © 2008 Elsevier Ltd. All rights reserved.","Title: Use Closure of Relevant Sets of Attributes to Efficiently Find Candidate Keys\nYear: 2008\n\nAbstract: Based on canonical cover or minimum cover for functional dependency set F, a data structure MAC is constructed, which facilitates to compute closures of all attributes sets. Using MAC, a fast algorithm of computing closure of an individual set of attributes is improved to efficiently find candidate keys. The improved algorithm of finding candidate keys neednpsilat compute closures of the total subsets of R, but compute closures of the subsets which are closely relevant to candidate keys, namely, which are candidate keys or one part of candidate keys. Based on the improved algorithm for fast computing closures of the sets of attributes in left set and theorem of reference, an efficient algorithm of finding the total candidate keys are gained, whose time complexity is O(nmiddot|R|middot|Fc|2).","Title: nD object representation and detection from Single 2D line drawing\nYear: 2004\n\nAbstract: In this paper, we propose the wireframe representation of nD object, which is a single 2D line drawing. A wireframe model of an n D object is composed of a set of edges connecting a set of vertices, a subset of closed r -chains called boundary r -chains which surround the (r +1)D pieces of the object, and a set of filling patterns for the boundary r -chains for 0 r n. The wireframe representation is the perspective projection of the wireframe model of the object from its surrounding space to the image plane. Combining the projective geometric constraints of the wireframe representation with the idea of local construction and deletion, we propose an algorithm for high dimensional object detection from single 2D line drawing, under the most general assumption that neither the dimension of the object nor the dimension of the surrounding space is known, two neighboring faces can be coplanar, and whether or not the object is a manifold is unknown. Our algorithm outperforms any other algorithm in 2D face identification in that it generally does not generate redundant cycles that are not assigned as faces, and can handle 3D solids of over 10,000 faces.","Title: Reducing consistency checks in generating corrective explanations for interactive constraint satisfaction\nYear: 2013\n\nAbstract: Constraint satisfaction problem has many applications in Artificial Intelligence. Its interactive applications usually require advice from a system to help a user solve the problem. Based on maximal relaxations, the CorrectiveExp algorithm is a representative method to compute explanations. However, we found that the CorrectiveRelax algorithm, used by the CorrectiveExp algorithm to compute maximal relaxations, has a defect that it executes more consistency checks than necessary. It is very important to avoid these unnecessary consistency checks because in general each consistency check needs to resort to backtrack search. To tackle this problem, this paper proposes two improved algorithms to compute maximal relaxations, called CorrectiveRelaxReduced and CorrectiveRelaxDC respectively. The former utilizes the existing results of consistency checks to shrink the search scope for some inconsistent user constraints. Furthermore, we have proved that the number of consistency checks executed by the CorrectiveRelaxReduced algorithm is always less than or equals to that of the CorrectiveRelax algorithm. The latter uses a divide-and-conquer approach to avoid unnecessary consistency checks. Our experimental results show that the two improved algorithms execute less consistency checks than CorrectiveExp while computing maximal relaxations.","Title: Feature Weighting Random Forest for Detection of Hidden Web Search Interfaces\nYear: 2008\n\nAbstract: Search interface detection is an essential task for extracting information from the hidden Web. The challenge for this task is that search interface data is represented in high-dimensional and sparse features with many missing values. This paper presents a new multi-classifier ensemble approach to solving this problem. In this approach, we have extended the random forest algorithm with a weighted feature selection method to build the individual classifiers. With this improved random forest algorithm (IRFA), each classifier can be learned from a weighted subset of the feature space so that the ensemble of decision trees can fully exploit the useful features of search interface patterns. We have compared our ensemble approach with other well-known classification algorithms, such as SVM, C4.5, Naïve Bayes, and original random forest algorithm (RFA). The experimental results have shown that our method is more effective in detecting search interfaces of the hidden Web.","Title: Some Applications of Clifford Algebra to Geometries\nYear: 1998\n\nAbstract: This paper focuses on a Clifford algebra model for geometric computations in 2D and 3D geometries. The model integrates symbolic representation of geometric entities, such as points, lines, planes, circles and spheres, with that of geometric constraints such as angles and distances, and is appropriate for both symbolic and numeric computations. Details on how to apply this model are provided and examples are given to illustrate the application.","Title: On the mixed cayley-sylvester resultant matrix\nYear: 2006\n\nAbstract: For a generic n-degree polynomial system which contains n+1 polynomials in n variables, there are two classical resultant matrices, Sylvester resultant matrix and Cayley resultant matrix, lie at the two ends of a gamut of n+1 resultant matrices. This paper gives the construction of the n–1 resultant matrices which lie between the two pure resultant matrices by the combined method of Sylvester dialytic and Cayley quotient. Since the construction involves two steps, Cayley quotient and Sylvester dialytic, the block structure of these mixed resultant matrices are similar to that of Sylvester resultant matrix in large scale, and the detailed submatrices are similar to Dixon resultant matrix.","Title: The Lie Model for Euclidean Geometry\nYear: 2000\n\nAbstract: Abstract. In this paper we investigate the Lie model of Lie sphere geometry using Clif- ford algebra. We employ it to Euclidean geometric problems involving oriented contact to simplify algebraic description and computation.","Title: Modeling Government Credit Information Systems Diffusion in China: A System Dynamics Approach.\nYear: 2010\n\nAbstract: This paper examines the usage and diffusion of Government Credit Information Systems (GCIS) in district-level governments in shanghai. The diffusion of GCIS was studied from a process-oriented perspective. A System Dynamics (SD) model is developed to simulate the relationships of technological, organizational and environmental, and institutional factors on GCIS diffusion under different management policies. A holistic view on the feedback loops, the consequently nonlinear behavior pattern of GCIS usage, and its diffusion in Shanghai government agencies is examined. Our research model and results suggest that workload faced by GCIS users and the tolerable maximum workload have a high impact on GCIS usage, task volume brought by GCIS, work pressure and the perception of technological factors. Different combination of work intensity and the tolerable maximum work intensity significantly influence the system usage. The contribution of our study lies in revealing that the diffusion of GCIS requires a systematic consideration of the business development plan, the GCIS user's workload and the organization's business environment. Flexible managerial incentive strategy will enhance user's work efficiency, thus lead to effective diffusion of GCIS in organizations. The theoretical and practical implications of this study are discussed.","Title: Optimal Controller Design for a class of Networked Control Systems\nYear: 2005\n\nAbstract: This paper is concerned with the optimal controller design of networked control systems (NCSs). Aiming at a class of NCSs, in which network-induced delay and data packet dropout exist simultaneously from sensor to controller, this paper proposes a novel optimal controller based on a new adaptive genetic algorithm and simulated anneal algorithm (AGASA), guaranteed cost controller (GCC) and state feedback controller (SFC). The proposed controller is less conservative than GCC and SFC, and can be applied to both linear and nonlinear NCSs. Two examples are presented to illustrate the proposed controller.","Title: A SPA-based K-means clustering algorithm for the remote sensing information extraction\nYear: 2012\n\nAbstract: Set Pair Analysis (SPA) is a new methodology to describe and process uncertainty system, which has been applied in many fields recently. In this paper, a new approach to remote sensing information extraction, the SPA-based k-means clustering algorithm (SPAKM), has been proposed based on the principle of SPA. The basic ideals and steps of SPAKM are discussed. The proposed algorithm can overcome the limitation of K-means clustering algorithm to certain extent. Finally, cluster analysis experiments of LANDSAT TM image have been made. The results show that the improved K-means clustering algorithm is superior to K-means in classification accuracy of land cover classes of mixed pixels.","Title: Fuzzy dynamic characteristic modeling and adaptive control of nonlinear systems and its application to hypersonic vehicles.\nYear: 2011\n\nAbstract: In this paper, a fuzzy dynamic characteristic modeling and adaptive control method is proposed for a class of nonlinear systems.\n By employing fuzzy dynamic characteristic model, the controlled plant is described as a slowly time-varying fuzzy system,\n wherein the parameters are estimated online by using recursive Least-Squares algorithm. Under this framework, a fuzzy adaptive\n controller is constructed, and the stability condition of the closed-loop system is also derived. The main advantage of the\n proposed method lies in no requirement for the prior knowledge of system model and less parameters to tune, which allows engineers\n to operate it in a simple, straightforward manner. The proposed method is applied to the control of hypersonic vehicle, and\n simulation results are given to demonstrate the effectiveness of the obtained results.","Title: Approximate rational solutions torational ODEs defined on discrete differentiable curves\nYear: 2011\n\nAbstract: In this paper, a new concept is proposed for discrete differential geometry: discrete n-differentiable curve, which is a tangent n-jet on a sequence of space points. A complete method is proposed to solve ODEs of the form n(m)=F(r, r', ..., r(n), n, n', ..., n(m-1), u)/G(r, r', ..., r(n), n, n', ..., n(m-1), u), where F, G are respectively vector-valued and scalar-valued polynomials, where r is a discrete curve obtained by sampling along an unknown smooth curve parametrized by u, and where n is the vector field to be computed along the curve. Our Maple-13 program outputs an approximate rational solution with the highest order of approximation for given data and neighborhood size. The method is used to compute rotation minimizing frames of space curves in CAGD. For one-step backward-forward chasing, a 6th-order approximate rational solution is found, and 6 is guaranteed to be the highest order of approximation by rational functions. The theoretical order of approximation is also supported by numerical experiments.","Title: A recipe for symbolic geometric computing: long geometric product, BREEFS and Clifford factorization\nYear: 2007\n\nAbstract: In symbolic computing, a major bottleneck is middle expression swell. Symbolic geometric computing based on invariant algebras can alleviate this difficulty. For example, the size of projective geometric computing based on bracket algebra can often be restrained to two terms, using final polynomials, area method, Cayley expansion,etc. This is the \"binomial \"feature of projective geometric computing in the language of bracket algebra. In this paper we report a stunning discovery in Euclidean geometric computing: the term preservation phenomenon. Input an expression in the language of Null Bracket Algebra (NBA), by the recipe we are to propose in this paper, the computing procedure can often be controlled to within the same number of terms as the input, through to the end. In particular, the conclusions of most Euclidean geometric theorems can be expressed by monomials in NBA, and the expression size in the proving procedure can often be controlled to within one term! Euclidean geometric computing can now be announced as having a \"monomial \"feature in the language of NBA. The recipe is composed of three parts: use long geometric product to represent and compute multiplicatively, use \"BREEFS \" to control the expression size locally, and use Clifford factorization for term reduction and transition from algebra to geometry. By the time this paper is being written, the recipe has been tested by 70+ examples from [1], among which 30+ have monomial proofs. Among those outside the scope, the famous Miquel's five circle theorem [2 ], whose analytic proof is straightforward but very difficult for symbolic computing, is discovered to have a 3-termed elegant proof with the recipe.","Title: Trifocal Tensors with Grassmann-Cayley Algebra\nYear: 2001\n\nAbstract: In this paper we study trifocal tensors with Grassmann-Cayley algebra. We propose a new method to derive relations among epipoles, fundamental tensors and trifocal tensors of three pinhole cameras. By this method we can find some new constraints satisfied by trifocal tensors.","Title: Dynamic Fault-Tolerant Routing Based on FSA for LEO Satellite Networks\nYear: 2013\n\nAbstract: We formalize the construction of fault blocks by a state transition model based on finite state automata. Based on the model, a boundary diffusion method is presented for the rectilinear-monotone orthogonal convex fault model such as the rectangular fault model and minimal-connected-component (MCC) faulty model, whereby an adaptive fault-tolerant routing algorithm, called X-Y boundary routing algorithm (X-YBRA), is presented for deadlock-free fault-tolerant adaptive routing outside the fault blocks. To improve the network resources utilization, we put forward a routing diffusion method in the fault block, which completely solves the routing problem in the fault block. The experiment result shows that the diffusion overhead of our method is far lower than that of the traditional routing algorithms such as distance vector and link state routing algorithms with the light loss in convergence time. For the occurrence and recovery of random faults, the expansion and shrinkage of the fault block are also discussed. Accordingly, the dynamic boundary and routing updating methods are put forward to respond to these cases. Based on these methods, we develop low earth orbit satellite networks into an adaptive fault-tolerant system in routing. Our works can be also applied to other 2D mesh networks such as the interconnect multiprocessor computer systems.","Title: Assessing Performance Risk for Complex Product Development: A Simulation-Based Model.\nYear: 2013\n\nAbstract: Assessing and managing performance risk is important for complex product development projects. The performance risk of complex product development can be defined as the probability failing to achieve desired performance level subject to cost and time constraints. This article presents a simulation model based on the graphical evaluation and review technique (GERT) and analyzes the complex product development project using simulation data. The model computes the probability distribution of project duration and cost in a GERT network with multifeedback branches considering activities rework that is caused by probabilistic failure to meet the planned design objective. A simulation strategy is designed to assess the performance risk in both subsystems and system development. With the performance risk assessment methodology, project managers can make a better decision and then minimize the performance risk of product development project. Finally, an obstacle clearance armored vehicle development project is used to illustrate the effectiveness of the proposed model. Copyright (c) 2012 John Wiley & Sons, Ltd.","Title: Optimal State Feedback Integral Control Using Network-Based Measurements.\nYear: 2012\n\nAbstract: This paper is concerned with the stabilization problem of a class of networked control systems, where the plant measurements and the control signals are transmitted over a network and encounter both time delays and packet losses. To deal with the nonzero disturbance rejection issue, two kinds of networked optimal state feedback integral control (SFIC) methods are proposed in this paper: One is a n...","Title: Polyhedral scene analysis combining parametric propagation with calotte analysis\nYear: 2004\n\nAbstract: Polyhedral scene analysis studies whether a 2D line drawing of a 3D polyhedron is realizable in the space, and if so, parameterizing the space of all possible realizations. For generic 2D data, symbolic computation with Grassmann-Cayley algebra is necessary. In this paper we propose a general method, called parametric calotte propagation, to solve the realization and parameterization problems in polyhedral scene analysis at the same time. Starting with the fundamental equations of Sugihara in the form of bivector equations, we can parameterize all the bivectors by introducing new parameters. The realization conditions are implied in the scalar equations satisfied by the new parameters, and can be derived by further analysis of the propagation result. The propagation procedure generally does not bifurcate, and the result often contains equations in factored form, thus makes further algebraic manipulation easier. In application, the method can be used to find linear construction sequences for non-spherical polyhedra.","Title: Intrinsic differential geometry with geometric calculus\nYear: 2004\n\nAbstract: Setting up a symbolic algebraic system is the first step in mathematics mechanization of any branch of mathematics. In this paper, we establish a compact symbolic algebraic framework for local geometric computing in intrinsic differential geometry, by choosing only the Lie derivative and the covariant derivative as basic local differential operators. In this framework, not only geometric entities such as the curvature and torsion of an affine connection have elegant representations, but their involved local geometric computing can be simplified.","Title: Normalization of Polynomials in Algebraic Invariants of Three-Dimensional Orthogonal Geometry\nYear: 2013\n\nAbstract: In classical invariant theory, the Gr\\\"obner base of the ideal of syzygies and the normal forms of polynomials of invariants are two core contents. To improve the performance of invariant theory in symbolic computing of classical geometry, advanced invariants are introduced via Clifford product. This paper addresses and solves the two key problems in advanced invariant theory: the Gr\\\"obner base of the ideal of syzygies among advanced invariants, and the normal forms of polynomials of advanced invariants. These results beautifully extend the straightening of Young tableaux to advanced invariants.","Title: On miquel's five-circle theorem\nYear: 2004\n\nAbstract: Miquel's Five-Circle Theorem is difficult to prove algebraically. In this paper, the details of the first algebraic proof of this theorem is provided. The proof is based on conformal geometric algebra and its accompanying invariant algebra called null bracket algebra, and is the outcome of the powerful computational techniques of null bracket algebra embodying the novel idea breefs.","Title: Partial Max-restricted Path Consistency\nYear: 2012\n\nAbstract: Filtering techniques are essential in the search algorithms solving constraint satisfaction problems (CSPs). Arc consistency (AC) is the most often used filtering technique because it cheaply removes some values that cannot belong to any solutions. Comparing with AC, max-Restricted Path Consistency (maxRPC) has a stronger pruning power while it is not suited for use during search because of the prohibitive time cost. Thus, light maxRPC which is the light version of maxRPC was proposed. Comparing with maxRPC, it has a lower time cost and the search algorithm maintaining light maxRPC (MlmaxRPC) can outperform the search algorithm maintaining AC (MAC) on some problems. However, MlmaxRPC suffers from the time waste in the cases that applying a stricter checking standard does not intrigue any value deletion. In order to avoid the time waste in MlmaxRPC, in this paper, partial maxRPC which is a new approximation of maxRPC is proposed. It only applies the stricter checking standard when the value deletion is of high possibility. MpmaxRPC which is the search algorithm maintaining partial maxRPC has a better average performance than MAC and MlmaxRPC.","Title: Arbitrary shape reconstruction from NC sectional data and applications in space cutter compensation and interference detection\nYear: 2009\n\nAbstract: This paper proposes an efficient shape reconstruction method from sectional data in 3-axis Numerical Control (NC) machining. A merge-divide algorithm is proposed to construct triangle meshes for machined surfaces with arbitrary shape and genus. The algorithm is of linear complexity due to the special property of the NC data. The method is used to interference detection and space cutter compensation in 3-axis NC machining. Experimental results verify that our method gives efficient and valid solutions to these important problems in NC machining.","Title: Automated Theorem Proving in Incidence Geometry - A Bracket Algebra Based Elimination Method\nYear: 2000\n\nAbstract: In this paper we propose a bracket algebra based elimination method for automated generation of readable proofs for theorems in incidence geometry. This method features three techniques, the first being heuristic automated reordering of geometric constructions for the purpose of producing shorter proofs, the second being some heuristic elimination rules which improve the performance of the area method of Zhang and others without introducing signed length ratios, the third being a simplification technique called contraction, which reduces the size of bracket polynomials. More than twenty theorems in incidence geometry have been proved, for which short proofs can be produced very fast, together with the corresponding nondegeneracy conditions. An interesting phenomenon is that a proof composed of polynomials of at most two terms can always be found for any of these theorems, similar to that by the biquadratic final polynomial method of Richter-Gebert.","Title: Tunneling blasting simulation for digital mine\nYear: 2012\n\nAbstract: Tunneling blasting is a major component of digital mine simulation, with the high cost, dangerous and irreversible features of blasting, it is difficult to design tunneling blasting scheme in real world. The paper simulates tunneling blasting by combining blasting animation of computer graphics with mining empirical formulas. A connected voxel model is used to represent rocks and their failure mechanism. This simulation helps engineers test and adjust blasting scheme before real operation.","Title: Complex brackets, balanced complex differences, and applications in symbolic geometric computing\nYear: 2008\n\nAbstract: In advanced invariant algebras such as null bracket algebra (NBA), symmetries of algebraic operators are the most important devices of encoding and employing syzygies of advanced geometric invariants. The larger the symmetry group, the more powerful the computing devices. In this paper, the largest symmetry group of the two kinds of bracket operators in the NBA of plane geometry is found. An algorithm of complexity O(N log N) is proposed to reduce a bracket of length N to its normal form, and then decide the congruence of two brackets of length N. By writing the two bracket operators as the real and pure imaginary parts of a complex bracket operator, their normal forms can be translated into a class of complex polynomials whose variables are first-order differences, called balanced complex difference (BCD) polynomials. BCD polynomials provide a complex-numbers-based invariant language for advanced algebraic manipulations of geometric problems. A simplification algorithm is proposed for making symbolic geometric computing with NBA and BCD polynomials, with the unique feature of controlling the expression size by avoiding multilinear expansions of the first-order difference variables of complex polynomials.","Title: Cooperative control for a class of large-scale linear system via delta operator approach\nYear: 2013\n\nAbstract: In this paper, the problem of cooperative control for a class of large-scale linear system is investigated via delta operator approach. Based on Lyapunov-Krasovskii function in delta domain, a state feedback controller is presented in terms of linear matrix inequalities. It is shown that the large-scale system can be cooperatively stabilized even if the subsystems has the bad stability. The proposed method can unify some previous related continuous and discrete systems into the delta operator systems framework. Numerical examples are given to illustrate the effectiveness of the results.","Title: Automated short proof generation for projective geometric theorems with Cayley and bracket algebras: II. conic geometry\nYear: 2003\n\nAbstract: In this paper we study plane conic geometry, particularly different representations of geometric constructions and relations in plane conic geometry, with Cayley and bracket algebras. We propose three powerful simplification techniques for bracket computation involving conic points, and an algorithm for rational Cayley factorization in conic geometry. The factorization algorithm is not a general one, but works for all the examples tried so far. We establish a series of elimination rules for various geometric constructions based on the idea of bracket-oriented representation and elimination, and an algorithm for optimal representation of the conclusion in theorem proving. These techniques can be used in any applications involving brackets and conics. In theorem proving, our algorithm based on these techniques can produce extremely short proofs for difficult theorems in conic geometry.","Title: Automated short proof generation for projective geometric theorems with Cayley and bracket algebras: I. incidence geometry\nYear: 2003\n\nAbstract: In this paper we establish the Cayley expansion theory on factored and shortest expansions of typical Cayley expressions in two- and three-dimensional projective geometry. We set up a group of Cayley factorization formulae based on the classification of Cayley expansions. We continue to establish three powerful simplification techniques in bracket computation. On top of the Cayley expansions and simplifications, together with a set of elimination rules, we design an algorithm that can produce extremely short proofs in two- and three-dimensional projective geometry. The techniques developed here can be immediately applied to other symbolic computation tasks involving brackets.","Title: A network-bound-dependent stabilization method of networked control systems.\nYear: 2013\n\nAbstract: This paper presents a new stabilization method for networked control systems (NCSs). An improved stability criterion dependent on both time delay bound and packet loss bound is derived and the corresponding stabilizing controller design technique is also provided. The merit of the proposed method lies in its much less conservatism, which is achieved by explicitly considering NCSs physical properties and by guaranteeing the deceasement of Lyapunov functional at each control signal updating step rather than at each sampling step, which is largely ignored in the existing literature. Illustrative examples are provided to show the advantage and effectiveness of the developed results.","Title: Optimal Stabilizing Gain Selection for Networked Control Systems With Time Delays and Packet Losses\nYear: 2009\n\nAbstract: This brief addresses the optimal-stabilization problem for networked control systems (NCSs) with time delays and packet losses. The closed-loop NCS is modeled as a discrete-time switched system, and the stability conditions are derived in terms of linear matrix inequality. A controller design method with both system stability and control performance taken into account is proposed, and estimation of distribution algorithm is used to select the optimal stabilizing gain. The proposed method can be easily implemented to various applications, since it has simple structure and has no assumptions on time-delay and packet-loss models. Simulation and experimental results are given to demonstrate the effectiveness of the proposed approach.","Title: EDA-Based Speed Control of a Networked DC Motor System With Time Delays and Packet Losses\nYear: 2009\n\nAbstract: This paper presents a new controller design method for networked DC motor system in the presence of time delays and packet losses. The sufficient condition under which the closed-loop system is asymptotically stable and the necessary condition under which the networked DC motor has zero steady-state tracking error are derived. Based on the obtained conditions, an output tracking controller design ...","Title: Vectorial Equations Solving for Mechanical Geometry Theorem Proving\nYear: 2000\n\nAbstract: In this paper a new method is proposed for mechanical geometry theorem proving. It combines vectorial equations solving in Clifford algebra formalism with Wu\"s method. The proofs produced have significantly enhanced geometric meaning and fewer nongeometric nondegeneracy conditions.","Title: Symbolic computation in the homogeneous geometric model with clifford algebra\nYear: 2004\n\nAbstract: Clifford algebra provides nice algebraic representations for Euclidean geometry via the homogeneous model, and is suitable for doing geometric reasoning through symbolic computation. In this paper, we propose various symbolic computation techniques in Clifford algebra. The content includes representation, elimination, expansion and simplification. Simplification includes contraction, combination and factorization. We apply the techniques to automated geometric deduction, and derive the conclusion in completely factored form in which every factor is a basic invariant. The efficiency of Clifford algebra in doing geometric reasoning is reflected in the short and readable procedure of deriving it sincere geometric factorization.","Title: Two Proofs on Max-Min-Max Principle of Jerk Control in Time-Optimal Rectilinear Motion.\nYear: 2013\n\nAbstract: This paper provides two proofs to the well-known principle of max-min-max jerk control in time-optimal jerk-controlled rectilinear motion, whose proof seems to be missing in the literature. One proof is constructive in that it computes and finds out the solution; the other is a disproof in that it shows all other control schemes lead to longer motion time. The ideas employed in the proofs may be used to solve the same kind of problems in a more general setting. © 2012 Springer Basel.","Title: Power Series Solution for Isoscallop Tool Path Generation on Free-form Surface with Ball-end Cutter.\nYear: 2012\n\nAbstract: In this paper we propose a method to compute symbolically a curve of constant scallop height on the surface swept by a ball-end cutter when it traces a free-form cutter-contact curve on a free-form surface. We give explicit formulas for power series solutions to the curve of constant scallop height and the next cutter-contact curve, when the initial cutter-contact curve is given. To make the symbolic results useful in practice, we propose a method to approximate the solutions unilaterally in the parametric plane by piecewise Chebyshev polynomials and exponential functions. © 2012 Springer Basel AG.","Title: Discrete Interpolation of G01 Codes in 2D Machining under Bounded Accelerations.\nYear: 2012\n\nAbstract: In this paper, a new algorithm is proposed to interpolate G01 codes in 2D milling. Without resorting to smooth curve approximation, the algorithm makes full use of the acceleration bounds to change directions with optimal velocities at the cutter location points designated by the G01 codes, and then makes global lookahead and optimization to generate time-optimal interpolation of the G01 codes. The algorithm is realized and tested on NC wood carving machine, showing that when compared with the classical equal-velocity corner-turning method, the current method can reduce the machining time by 14-115 %, while maintaining better details than the classical method. © 2012 Springer Basel AG.","Title: Clifford Algebraic Reduction Method for Automated Theorem Proving in Differential Geometry\nYear: 1998\n\nAbstract: In this paper a new method is proposed for mechanically proving theoremsin the local theory of space curves. The method is based on Ritt-Wu’swell-ordering principle of ordinary differential polynomials, Cliffordalgebraic representation of Euclidean space and equation set solving inClifford algebra formalism. It has been tested by various theorems and seemsto be efficient.","Title: B-spline patches and transfinite interpolation method for PDE controlled simulation.\nYear: 2012\n\nAbstract: This paper is to discuss an approach which combines B-spline patches and transfinite interpolation to establish a linear algebraic system for solving partial differential equations and modify the WEB-spline method developed by Klaus Hollig to derive this new idea.First of all,the authors replace the R-function method with transfinite interpolation to build a function which vanishes on boundaries. Secondly,the authors simulate the partial differential equation by directly applying differential operators to basis functions,which is similar to the RBF method rather than Hollig's method.These new strategies then make the constructing of bases and the linear system much more straightforward.And as the interpolation is brought in,the design of schemes for solving practical PDEs can be more flexible. This new method is easy to carry out and suitable for simulations in the fields such as graphics to achieve rapid rendering.Especially when the specified precision is not very high,this method performs much faster than WEB-spline method.","Title: Theoretical investigation of an energetic fullerene derivative.\nYear: 2010\n\nAbstract: A self-consistent estimation method for the thermochemical properties of N-methyl-3-(2',4',6'-trinitrobenzene)-fulleropyrrolidine (MTNBFP) is presented. This method is based on enthalpy of formation (Delta H-f(m)circle minus) and enthalpy of combustion obtained from BLYP/DNP calculations of the total energies and frequencies for MTNBFP. The enthalpy of formation was calculated by an optimized set of isodesmic reactions given the available experimental Delta H-f(m)circle minus of relative compounds. MTNBFP has a high enthalpy of formation, 2782.2 kJ/mol. Detonation velocity and detonation pressure were also presented in terms of Kamlet and Jacobs equations. Drop hammer impact sensitivity tests and blasting point per 5 s tests indicate MTNBFP may be a potential candidate primary explosive. To understand the test results well, we proposed a series of chemical reaction mechanisms and interpreted the relationship between impact sensitivity and electronic structures from the viewpoint of nitro group charge, electrostatic potential, and vibrational modes. (C) 2010 Wiley Periodicals, Inc. J Comput Chem 31: 2233-2237, 2010","Title: Algebraic Representation, Elimination and Expansion in Automated Geometric Theorem Proving\nYear: 2002\n\nAbstract: Cayley algebra and bracket algebra are important approaches to invariant computing in projective and affine geometries, but there are some difficulties in doing algebraic computation. In this paper we show how the principle \"breefs\" - bracket-oriented representation, elimination and expansion for factored and shortest results, can significantly simplify algebraic computations. We present several typical examples on automated theorem proving in conics and make detailed discussions on the procedure of applying the principle to automated geometric theorem proving.","Title: A symbolic approach to polyhedral scene analysis by parametric calotte propagation*\nYear: 2008\n\nAbstract: Polyhedral scene analysis studies whether a 2D line drawing of a 3D polyhedron is realizable in space, and if so, it gives the results of parameterizing the space of all possible realizations. For generic 2D data, symbolic computation with Grassmann–Cayley algebra is needed in the analysis. In this paper, we propose a method called parametric calotte propagation to solve the realization and parameterization problems for general polyhedral scenes at the same time. In algebraic manipulation, parametric propagation is more efficient than elimination. In applications, it can lead to linear construction sequences for nonspherical polyhedra whose resolvable sequences do not exist.","Title: A dynamic interaction mass transfer model for simulating the mass transfer process in extraction columns\nYear: 2006\n\nAbstract: In this study, the population balance model was used to simulate the steady axial profile of the drop size distribution along the extraction column. Based on the population balance model, a dynamic interaction mass transfer model for simulation of the mass transfer process within extraction columns was developed with only one parameter that needs to be optimized. In comparison with conventional mass transfer models, the dynamic interaction mass transfer model has a theoretical basis and is less empirical because the influence of drop breakage and drop coalescence on the drop size distribution and the mass transfer process were carefully considered.","Title: Efficient Singleton Consistency by Combining Forward Checking and Bound Consistency\nYear: 2012\n\nAbstract: Maintaining local consistencies can improve the efficiencies of the search algorithms solving constraint satisfaction problems (CSPs). Comparing with arc consistency which is the most widely used local consistency, stronger local consistencies can make the search space smaller while they require higher computational cost. In this paper, we make an attempt on the compromise between the pruning ability and the computational cost. A new local consistency called singleton strong bound consistency (SSBC) and its light version, light SSBC, are proposed. The search algorithm maintaining light SSBC can outperform MAC on a considerable number of problems.","Title: A modified single-phase h-bridge PWM rectifier with power decoupling\nYear: 2012\n\nAbstract: It is well known that single-phase PWM rectifiers suffer from second-order ripple power and subsequent dc-link voltage pulsation. To reduce this low frequency fluctuation of dc-link voltage as well as to obtain the sinusoidal grid current with unity power factor, a modified single-phase H-bridge PWM rectifier consisting of a conventional H-bridge rectifier and a pure passive auxiliary circuit is presented. Through utilization of the extra degree of freedom of the H-bridge, the proposed circuit can achieve power decoupling as well as maintaining basic function of a conventional PWM rectifier without additional power devices, which decreases both the product volume and the cost. The corresponding dc-link ripple reduction control method, with the instantaneous power feedforward loop and the dc-link ripple voltage feedback loop, is also provided. The experimental results based on a 1.3kW prototype verified the feasibility of the proposed method.","Title: Geometric error control in the parabola-blending linear interpolator.\nYear: 2013\n\nAbstract: This paper considers geometric error control in the parabola-blending linear interpolation method (Zhang, et al., 2011). Classical model of chord error by approximation with contact circle on the parabolas leads to incorrect result. By computing the geometric error directly without accumulating the approximation error and chord error, the authors realize correct geometric error control by establishing inequality constraints on the accelerations of the motion. © 2013 Institute of Systems Science, Academy of Mathematics and Systems Science, CAS and Springer-Verlag Berlin Heidelberg.","Title: Network soft partition based on topological potential\nYear: 2011\n\nAbstract: Partitioning of complex networks, esp. of social networks, has been a hotly debated topic in academic circles in recent years. Since actual networks usually contain some boundary nodes that are difficult to assign to a certain community, soft partitioning is under great demand in practical applications. However, at present network partitioning is done mainly by hard partition, soft partition methods are not common. In this context, a soft partition method is proposed hereby based on topological potential and specific algorithms are also provided. This method not only considers the spread of the uncertainty of community-identity of the boundary nodes in the network, but also realizes a quantified representation of the community-identity of the boundary nodes. Experiments show that this method yields results that are consistent with those by classic methods and is more reasonable.","Title: Community identification based on clustering coefficient\nYear: 2011\n\nAbstract: Researches show that numerous complex networks have clustering effect. It is an indispensable step to identify node clusters in network, namely community, in which nodes are closely related, in many applications such as identification of ringleaders in anti-criminal and anti-terrorist network, efficient storage of data in Wireless Sensor Network (WSN). At present, most of community identification methods still require the specifications of the number or the scale of community by user and still can't handle boundary nodes. In an attempt to solve these problems, a network community identification method based on clustering coefficient is proposed. This method makes use of individual-centered theory for reference and can automatically determine the number of communities. It is shown through contrastive experiments that communities identified by this method have more reasonable size and closer structure than those obtained by other methods which are also based on the individual-centered theory. Finally, a research direction is proposed of network community identification method based on the individual-centered theory.","Title: An Improved Tcp Friendly Rate Control Algorithm For Wireless Networks\nYear: 2011\n\nAbstract: TCP Friendly Rate Control (TFRC) has been widely used in the Internet multimedia streaming applications. However, performance of traditional TFRC algorithm degrades significantly when deployed over wireless networks. Although numerous TFRC variants have been proposed to improve the performance of TFRC over wireless networks, designing a TFRC algorithm with graceful performance both in throughput and fairness still remains a great challenge. In this paper, a novel TFRC algorithm, named TFRC-FIT, is proposed to improve the performance of TFRC over wireless environments. In the proposed approach, the behavior of multiple TFRC flows is simulated in single connection, while the number of simulated flows is adjusted by the network queuing delay. Through this mechanism, TFRC-FIT can fully utilize the capacity of wireless networks, while maintaining good fairness and TCP friendliness. Both theoretical analysis and extensive experiments over hardware network emulator, Planetlab test bed as well as commercial 3G wireless networks are carried out to characterize and validate the performance of our proposed approach.","Title: H ∞ stabilisation of networked control systems with time delays and packet losses.\nYear: 2014\n\nAbstract: This paper addresses the H-infinity state feedback stabilisation problem for networked control systems (NCSs) in the presence of time delays and packet losses. By introducing the concept of an effective sensor packet, the NCS is transformed into a new discrete-time switched model, where the parameters have a clear physical meaning and can be easily determined. In this framework, we derive the stability conditions of the closed-loop system in the H-infinity sense, and also provide the corresponding H-infinity stabilising controller design method. Finally, we give simulation and experimental results to demonstrate the effectiveness of the proposed approaches.","Title: A survivable routing protocol for two-layered LEO/MEO satellite networks\nYear: 2014\n\nAbstract: Due to the rapid development of space communication, satellite networks will be confronted with more complex space environment in future, which poses the important demand on the design of the survivable and efficient routing protocols. Among satellite networks, two-layered Low Earth Orbit (LEO)/Medium Earth Orbit (MEO) satellite networks (LMSNs) have become an attractive architecture for their better communication service than single-layered satellite networks. To determine the topological dynamics of LMSN, the satellite group and group manager (SGGM) method is a prevalent strategy. However, it can not precisely capture the topological dynamics of the LEO layer, which may result in the unreliability of data transmission. Besides, most existing routing protocols based on the SGGM method will collapse once any top satellite fails. To overcome both limitations, this paper proposes a new topology control strategy for LMSNs. The proposed strategy determines the snapshot in terms of the topological change of the LEO layer, which ensures the topological consistency of routing calculation. Moreover, a new survivable routing protocol (SRP) is presented for LMSNs by combining both centralized and distributed routing strategies. The SRP can provide strong survivability under the LEO or MEO satellite failure. Besides, it can also achieve the minimum delay routing provided the MEO layer can effectively work. The performance of SRP is also evaluated by simulation and analysis.","Title: Spectral Characterizations of the Corona of a Cycle and Two Isolated Vertices\nYear: 2014\n\nAbstract: For a cycle C n , let C n 2 K 1 be the graph obtained from C n by attaching two pendant edges to each vertex of C n . In this paper, we prove that C n 2 K 1 is determined by its signless Laplacian spectrum when n 32, 64. We also show that C n 2 K 1 is determined by its Laplacian spectrum.","Title: Analyzing Integrated Cost-Schedule Risk for Complex Product Systems R&D Projects.\nYear: 2014\n\nAbstract: The vast majority of the research efforts in project risk management tend to assess cost risk and schedule risk independently. However, project cost and time are related in reality and the relationship between them should be analyzed directly. We propose an integrated cost and schedule risk assessment model for complex product systems R&D projects. Graphical evaluation review technique (GERT), Monte Carlo simulation, and probability distribution theory are utilized to establish the model. In addition, statistical analysis and regression analysis techniques are employed to analyze simulation outputs. Finally, a complex product systems R&D project as an example is modeled by the proposed approach and the simulation outputs are analyzed to illustrate the effectiveness of the risk assessment model. It seems that integrating cost and schedule risk assessment can provide more reliable risk estimation results.","Title: Sliding-Mode Predictive Control of Networked Control Systems Under a Multiple-Packet Transmission Policy\nYear: 2014\n\nAbstract: This paper addresses the output feedback stabilization problem for a class of networked control systems (NCSs) under a multiple-packet transmission policy. To compensate the time delays and packet losses, a sliding-mode predictive controller is constructed by taking full advantage of network transmission capacity. A Kalman predictor is used to estimate the current and predict the future plant states, while sliding-mode control (SMC) is employed to compute the control sequences. The estimated state errors are transformed into a system disturbance, and the stability of the closed-loop NCSs is guaranteed by constructing the robust stabilizing SMC controller. Simulation and experimental results are given to demonstrate the effectiveness of the proposed approach.","Title: An improved algorithm of mining preferred browsing paths\nYear: 2014\n\nAbstract: Existing algorithms of mining preferred browsing paths just consider the influence of user visiting times, but ignore the accuracy influenced by other factors. In order to solve the problem, an improved algorithm which imports page similarity and support-preference concepts is proposed. Firstly a Web-log-based user access matrix is set up. Then by calculating the angel cosine similarity and support-preference, the 2-items preferred browsing sub-path set is obtained. Finally all the sub-paths are combined. Experiments show that the algorithm is more accurate and efficient.","Title: Reduction among bracket polynomials\nYear: 2014\n\nAbstract: In this paper, we propose an SL(n)-invariant division of SL(n)-invariant polynomials by establishing an admissible order among the invariant polynomials in normal form. The invariant division leads to an invariant Gröbner basis theory. The invariant division is closely related to multivariate coordinate polynomial division. This feature leads to a proof of the result that if f1,..., fk are SL(n,K)-invariant where K is an arbitrary field, possibly of positive characteristic, then the invariant ideal generated by them is the intersection of the ideal generated by the fi in the polynomial ring of coordinates with the algebra of invariants.","Title: Observer-based cluster consensus control of high-order multi-agent systems\nYear: 2015\n\nAbstract: The cluster consensus problem of high-order multi-agent systems is considered in this note with an observer-based control scheme. Sufficient condition for cluster consensus is presented in terms of easily checkable algebraic topology criterion. It is found that intra-cluster balanced topologies with antisymmetric (i.e., bidirectional and opposite signed) inter-cluster links promote cluster consensus, irrelevant of the magnitudes of intra-cluster coupling weights. The effectiveness of the theoretical results is illustrated by a numerical example.","Title: Low-Load Survivable Routing Protocol for LEO Satellite Networks\nYear: 2015\n\nAbstract: In this paper, we put forward the methods of footprint shift and separation for the virtual topology model of LEO satellite networks, which can reduce the effects of the Polar Regions and cross-seam inter-satellite-links (ISLs) on the virtual topology and improve the utilization of ISLs. Furthermore, a low-load survivable routing protocol called Virtual Snapshot Routing Protocol is proposed for LEO satellite networks. The proposed protocol not only reduces the routing computation and storage overheads, but also provides good routing survivability under random satellite failure. The experimental results confirm our conclusions.","Title: Improving degree-based variable ordering heuristics for solving constraint satisfaction problems.\nYear: 2016\n\nAbstract: In this paper, we improved two classical degree-based variable ordering heuristics, $$\\\\frac{\\\\textit{Dom}}{\\\\textit{Ddeg}}$$DomDdeg and $$\\\\frac{\\\\textit{Dom}}{\\\\textit{Wdeg}}$$DomWdeg. We propose a method using the summation of constraint tightness in degree-based heuristics. We also propose two methods to calculate dynamic constraint tightness for binary extensional constraints and non-binary intensional constraints respectively. Our work shows how constraint tightness can be practically used to guide search. We performed a number of experiments on some benchmark instances. The results have shown that, the new heuristics improve the classical ones by both computational time and search tree nodes and they are more efficient than some other successful heuristics on the instances where the classical heuristics work well.","Title: Stabilization and Separation Principle of Networked Control Systems Using the T–S Fuzzy Model Approach\nYear: 2015\n\nAbstract: This paper is concerned with the stabilization problem for a class of discrete-time networked control systems (NCSs) with bounded time delays and packet losses. The controlled plant is represented by a Takagi-Sugeno fuzzy model, and both the state feedback control and output feedback control cases are considered. By guaranteeing the decrement of Lyapunov functional at each control signal updating ...","Title: 3D Moth-inspired chemical plume tracking and adaptive step control strategy.\nYear: 2016\n\nAbstract: This paper presents an adaptive-step-control ASC strategy for autonomous chemical plume tracking CPT in both 2D and 3D spaces. The aim of CPT is tracking a target chemical flow back to its source and declaring its location. While the majority of related works are based on wheeled robots in 2D, the rapid development of rotorcrafts makes 3D plume tracking possible. Hence, in this paper, we first discuss the generalized form of moth-inspired CPT work flow, then extend the orthodox moth-inspired CPT from 2D to 3D. More importantly, we also propose an ASC strategy to deal with the ?passing-by? problem and to gain better control performance. Simulation results are given to demonstrate that ASC can solve the inflexibility caused by ?hard? step length in diffusive airflow, because it can autonomously adjust the step lengths of the robot according to the status of the environment, enabling our robot to stride confidently in the low chemical concentration areas and focus on highly suspicious areas.","Title: Detection And Imaging Of Moving Objects With Multichannel Sar System\nYear: 2015\n\nAbstract: It presents an approach of moving target detection by multi-channel synthetic aperture radar (SAR), which the receivers displaced in heterogeneous arrays. The approach achieves the maximal unaliased band of radial velocities and Minimum detectable velocity, retains full resolution SAR images, the multiple measurement vectors were employed for improvement of the target focusing, and requires no increase in receiver samples. The proposed multi-channel synthetic aperture radar system and the associated signal processing are detailed, and the approach is numerically demonstrated via simulation and raw data experiments.","Title: 3d Moth-Inspired Chemical Plume Tracking\nYear: 2015\n\nAbstract: This paper analyzes the conventional moth inspired chemical plume tracking, and presents a 3D moth-inspired CPT using multi-sensors. The aim of CPT is tracking a target chemical flow back to its source and declaring its location. While the moths acturally perform their CPT in 3D space, the majority of related works are based on wheels robots in 2D. Nowadays the rapid development of rotorcrafts makes the 3D plume tracking possible. Hence in this paper, we first present a detailed analysis of moth-inspired CPT work flow, then extend the orthodox moth-inspired CPT from 2D to 3D. Our simulation results demonstrate that our the multi-sensor CPT strategy proposed is a feasible and efficient method to locate odour source.","Title: Symbolic Geometric Reasoning with Advanced Invariant Algebras.\nYear: 2015\n\nAbstract: In symbolic geometric reasoning, the output of an algebraic method is expected to be geometrically interpretable, and the size of the middle steps is expected to be sufficiently small for computational efficiency. Invariant algebras often perform well in meeting the two expectations for relatively simple geometric problems. For example in classical geometry, symbolic manipulations based on basic invariants such as squared distances, areas and volumes often have great performance in generating readable proofs. For more complicated geometric problems, the basic invariants are still insufficient and may not generate geometrically meaningful results. An advanced invariant is a monomial in an \\\"advanced algebra\\\", and can be expanded into a polynomial of basic invariants that are also included in the algebra. In projective incidence geometry, Grassmann-Cayley algebra and Cayley bracket algebra are an advanced algebra in which the basic invariants are determinants of homogeneous coordinates of points, and the advanced invariants are Cayley brackets. In Euclidean conformal geometry, Conformal Geometric Algebra and null bracket algebra are an advanced algebra where the basic invariants are squared distances between points and and signed volumes of simplexes, and the advanced invariants are Clifford brackets. This paper introduces the above advanced invariant algebras together with their applications in automated geometric theorem proving. These algebras are capable of generating extremely short and readable proofs. For projective incidence theorems, the proofs generated are usually two-termed in that the conclusion expression maintains two-termed during symbolic manipulations. For Euclidean geometry, the proofs generated are mostly one-termed or two-termed.","Title: EDA-based output tracking control for networked control systems with time delays and packet losses\nYear: 2011\n\nAbstract: This paper is concerned with the output tracking problem of a class of networked control systems (NCSs) in the presence of time delays and packet losses. The motivation comes from that existing networked output tracking methods can not work very well if there is modeling error or non-zero disturbance in NCSs. To deal with the aforementioned issues, an Estimation of Distribution Algorithm (EDA) based output tracking control approach, which is more robust to modeling error and has better disturbance rejection capabilities, is proposed in this paper. Simulation and experimental results are given to demonstrate the effectiveness of the proposed approach. © 2011 IEEE.","Title: Efficient Spatio-Temporal Tactile Object Recognition with Randomized Tiling Convolutional Networks in a Hierarchical Fusion Strategy.\nYear: 2016\n\nAbstract: Robotic tactile recognition aims at identifying target objects or environments from tactile sensory readings. The advancement of unsupervised feature learning and biological tactile sensing inspire us proposing the model of 3T-RTCN that performs spatio-temporal feature representation and fusion for tactile recognition. It decomposes tactile data into spatial and temporal threads, and incorporates the strength of randomized tiling convolutional networks. Experimental evaluations show that it outperforms some state-of-the-art methods with a large margin regarding recognition accuracy, robustness, and fault-tolerance; we also achieve an order-of-magnitude speedup over equivalent networks with pretraining and fine-tuning. Practical suggestions and hints are summarized in the end for effectively handling the tactile data.","Title: Scaled Cluster Consensus Of Discrete-Time Multi-Agent Systems With General Directed Topologies\nYear: 2016\n\nAbstract: This note proposes a notion of scaled cluster consensus, wherein the final consensus states within different clusters converge to prescribed ratios. Unlike most results in existing literature on cluster consensus, no constraints are imposed on the system topologies under the designed protocol, i.e. the agents are not required to possess any cluster affiliation information of others. For the delay-free case, an explicit scaled cluster consensus function is provided by exploring the characteristics of stochastic matrices. Diverse input delays and asymmetric communication delays are both considered, and sufficient condition for scaled cluster consensus is derived based on frequency domain analysis. Finally, numerical examples are given to illustrate the effectiveness of the presented results.","Title: Road Extraction Base On Zernike Algorithm On Sar Image\nYear: 2016\n\nAbstract: In the SAR image, the road recognition is conducive to the SAR image interpretation using in traffic monitoring, GIS information, and Geographical mapping, but the road is difficult to extract for blurring by speckles. Based on mathematical morphology, a new road extraction method is proposed in this paper. After the pre-processing to reduce the influence of the speckles, the binary image information is gained by the Otsu method. Then, the information without the road information is removed by the mathematical morphological opening operation. In addition, after being corroded and reconstructed, the complete road boundary is detected by edge detection operations, and the result is relocated on the original image. Compared with the morphological method, road extraction is done by applying the Zernike moments. Orthogonal property of Zernike moment basis functions guarantees the statistically independence of coefficients in extracted feature vectors. The proposed method is evaluated on the SAR Image data. Results show that presented method outperforms recently presented works due to its high performance.","Title: GreenLA: green linear algebra software for GPU-accelerated heterogeneous computing.\nYear: 2016\n\nAbstract: While many linear algebra libraries have been developed to optimize their performance, no linear algebra library considers their energy efficiency at the library design time. In this paper, we present GreenLA - an energy efficient linear algebra software package that leverages linear algebra algorithmic characteristics to maximize energy savings with negligible overhead. GreenLA is (1) energy efficient: it saves up to several times more energy than the best existing energy saving approaches that do not modify library source codes; (2) high performance: its performance is comparable to the highly optimized linear algebra library MAGMA; and (3) transparent to applications: with the same programming interface, existing MAGMA users do not need to modify their source codes to benefit from GreenLA. Experimental results demonstrate that GreenLA is able to save up to three times more energy than the best existing energy saving approaches while delivering similar performance compared to the state-of-the-art linear algebra library MAGMA.","Title: Riemann Tensor Polynomial Canonicalization by Graph Algebra Extension.\nYear: 2017\n\nAbstract: Tensor expression simplification is an \\\"ancient\\\" topic in computer algebra, a representative of which is the canonicalization of Riemann tensor polynomials. Practically fast algorithms exist for monoterm canonicalization, but not for multiterm canonicalization. Targeting the multiterm difficulty, in this paper we establish the extension theory of graph algebra, and propose a canonicalization algorithm for Riemann tensor polynomials based on this theory.","Title: Advancing the incremental fusion of robotic sensory features using online multi-kernel extreme learning machine.\nYear: 2017\n\nAbstract: Robot recognition tasks usually require multiple homogeneous or heterogeneous sensors which intrinsically generate sequential, redundant, and storage demanding data with various noise pollution. Thus, online machine learning algorithms performing efficient sensory feature fusion have become a hot topic in robot recognition domain. This paper proposes an online multi-kernel extreme learning machine (OM-ELM) which assembles multiple ELM classifiers and optimizes the kernel weights with a p-norm formulation of multi-kernel learning (MKL) problem. It can be applied in feature fusion applications that require incremental learning over multiple sequential sensory readings. The performance of OM-ELM is tested towards four different robot recognition tasks. By comparing to several state-of-the-art online models for multi-kernel learning, we claim that our method achieves a superior or equivalent training accuracy and generalization ability with less training time. Practical suggestions are also given to aid effective online fusion of robot sensory features.","Title: Neural-network-based sliding-mode control for multiple rigid-body attitude tracking with inertial information completely unknown.\nYear: 2017\n\nAbstract: This paper addresses the multiple rigid bodies attitude tracking control problem in presence of inertial information completely unknown. In order to attenuate the effect of unknown parameters, the neural networks technology is employed to approximate the unknown nonlinear terms derived from the controller design procedure. Based on lumped tracking errors between neighbors, a novel sliding-mode control protocol is proposed and an approximate expression of inverse matrix is also applied to avoid possible singular phenomenon. The Lyapunov theory is applied to guarantee that all signals in the closed-loop system are uniformly ultimately bounded and that all rigid-body attitude synchronize to the desired trajectory with bounded residual errors. Compared with prior works, the dynamics of each rigid-body discussed here is more general and does not require the assumption linearity in the unknown parameters or the matching condition. Moreover, the bounded residual errors can be reduced as small as desired by selected suitable parameters. Simulation results demonstrate the effectiveness of the proposed method.","Title: An efficient public-key searchable encryption scheme secure against inside keyword guessing attacks.\nYear: 2018\n\nAbstract: How to efficiently search over encrypted data is an important and interesting problem in the cloud era. To solve it, Boneh etal.introduced the notion of public key encryption with keyword search (PEKS), in 2004. However, in almost all the PEKS schemes an inside adversary may recover the keyword from a given trapdoor by exhaustively guessing the keywords offline. How to resist the inside keyword guessing attack in PEKS remains a hard problem.In this paper we propose introduce the notion of Public-key Authenticated Encryption with Keyword Search (PAEKS) to solve the problem, in which the data sender not only encrypts a keyword, but also authenticates it, so that a verifier would be convinced that the encrypted keyword can only be generated by the sender. We propose a concrete and efficient construction of PAEKS, and prove its security based on simple and static assumptions in the random oracle model under the given security models. Experimental results show that our scheme enjoys a comparable efficiency with Boneh etal.s scheme.","Title: Narrowing Support Searching Range in Maintaining Arc Consistency for Solving Constraint Satisfaction Problems.\nYear: 2017\n\nAbstract: Arc consistency is the most popular filtering technique for solving constraint satisfaction problems. Constraint check plays a central role in establishing arc consistency. In this paper, we propose a method to save constraint checks in maintaining coarse-grained arc consistency during backtracking search for solving the constraint satisfaction problems. We reduce the support searching range by utilizing the information generated by an AC3.1 algorithm at preprocessing step. Compared with the existing maintaining arc consistency (MAC) algorithms, the proposed MAC3(be) algorithm saves constraint checks without maintaining additional data structures at each search tree node. Our experimental results show that MAC3(be) saves both constraint checks and CPU time while solving some benchmark problems.","Title: FESR: A Framework for Eliciting Security Requirements Based on Integration of Common Criteria and Weakness Detection Formal Model\nYear: 2017\n\nAbstract: It is critical and foremost to come up with the corresponding security requirements first which the following implementations are based on. However, previous security requirement elicitation work based on Common Criteria (CC) rarely addresses the detailed elicitation process of threats from specific functional requirements, which thus results in the widen gap between specific functional requirements and their corresponding threats. To this end, this paper proposes a framework for eliciting corresponding security requirements of specific functional requirements from the requirements specification. A formal model is built in the framework to assist requirement analysts in half-automatic collecting threats. To enhance the framework's automaticity and reusability, a security property base is constructed based on authoritative sources of security properties to support the framework. A practical information system is applied to verify the framework's practicability. Finally the framework's advantages and limitations are discussed thoroughly compared with previous approaches and useful insights are revealed.","Title: Correcting soft errors online in fast fourier transform\nYear: 2017\n\nAbstract: While many algorithm-based fault tolerance (ABFT) schemes have been proposed to detect soft errors offline in the fast Fourier transform (FFT) after computation finishes, none of the existing ABFT schemes detect soft errors online before the computation finishes. This paper presents an online ABFT scheme for FFT so that soft errors can be detected online and the corrupted computation can be terminated in a much more timely manner. We also extend our scheme to tolerate both arithmetic errors and memory errors, develop strategies to reduce its fault tolerance overhead and improve its numerical stability and fault coverage, and finally incorporate it into the widely used FFTW library - one of the today's fastest FFT software implementations. Experimental results demonstrate that: (1) the proposed online ABFT scheme introduces much lower overhead than the existing offline ABFT schemes; (2) it detects errors in a much more timely manner; and (3) it also has higher numerical stability and better fault coverage.","Title: Parastack: efficient hang detection for MPI programs at large scale\nYear: 2017\n\nAbstract: While program hangs on large parallel systems can be detected via the widely used timeout mechanism, it is difficult for the users to set the timeout - too small a timeout leads to high false alarm rates and too large a timeout wastes a vast amount of valuable computing resources. To address the above problems with hang detection, this paper presents ParaStack, an extremely lightweight tool to detect hangs in a timely manner with high accuracy, negligible overhead with great scalability, and without requiring the user to select a timeout value. For a detected hang, it provides direction for further analysis by telling users whether the hang is the result of an error in the computation phase or the communication phase. For a computation-error induced hang, our tool pinpoints the faulty process by excluding hundreds and thousands of other processes. We have adapted ParaStack to work with the Torque and Slurm parallel batch schedulers and validated its functionality and performance on Tianhe-2 and Stampede that are respectively the world's current 2nd and 12th fastest supercomputers. Experimental results demonstrate that ParaStack detects hangs in a timely manner at negligible overhead with over 99% accuracy. No false alarm is observed in correct runs taking 66 hours at scale of 256 processes and 39.7 hours at scale of 1024 processes. ParaStack accurately reports the faulty process for computation-error induced hangs.","Title: Test sequencing for sequential system diagnosis with precedence constraints and imperfect tests.\nYear: 2017\n\nAbstract: We study sequential system testing with the objective of minimizing the total expected testing costs. The goal is to discover the state of a system that consists of a set of independent components. The state of the system depends on the states of the individual components and is classified as working if at least a pre-specified number of components are working, otherwise it is said to be down. During the diagnostic testing procedure, components are tested one by one, in a pre-specified order. The resulting test sequencing problem is NP-hard with general precedence constraints even when the tests are perfect, in which case a component test always reports the correct state of the component. In this work, we will also consider the additional complication that tests can be imperfect, meaning that a test can report a component to be working when it is actually down, and vice versa. We develop a tabu search algorithm together with a simulation-based evaluation technique that incorporates importance sampling to find high-quality solutions within limited runtimes.","Title: Multi-mode resource leveling in projects with mode-dependent generalized precedence relations.\nYear: 2018\n\nAbstract: •We study the multi-mode project resource leveling problem.•Mode-dependent generalized precedence relations are considered.•We propose two solution approaches: decomposition and integration.•We devise a hybrid estimation of distribution algorithm and two descent algorithms.•Computational results show that our algorithms outperform the baseline algorithms.","Title: Adaptive Fuzzy Control for Pure-Feedback Nonlinear Systems With Nonaffine Functions Being Semibounded and Indifferentiable.\nYear: 2018\n\nAbstract: An adaptive fuzzy control approach is proposed for nonaffine pure-feedback systems with nonaffine functions being semibounded and possibly indifferentiable. A semibounded and continuous condition for nonaffine function is presented to guarantee the controllability of system. To overcome the difficulty from this mild condition, a compact set is introduced, and the nonaffine nonlinear functions are ...","Title: Fast Straightening Algorithm For Bracket Polynomials Based On Tableau Manipulations\nYear: 2018\n\nAbstract: Straightening is the most fundamental symbolic manipulation in bracket algebra. Young's classical algorithm and White's more recent algorithm have poor performance in straightening bracket polynomials of degree >4. Rota's straightening algorithm based on Capelli operator is generally superior to the former two in speed, but still performs badly when the degree reaches 5.In this paper, a new operator is defined in bracket algebra based on tableau manipulations, and is simpler than Capelli operator. A new straightening algorithm is then proposed, and is superior to the above three algorithms by a speedup of two order of magnitude on average by testing over 500 examples in the past two years.","Title: COMPI: Concolic Testing for MPI Applications\nYear: 2018\n\nAbstract: MPI is widely used as the bedrock of HPC applications, but there are no effective systematic software testing techniques for MPI programs. In this paper we develop COMPI, the first practical concolic testing tool for MPI applications. COMPI tackles two major challenges. First, it provides an automated testing tool for MPI programs - it performs concolic execution on a single process and records branch coverage across all. Infusing MPI semantics such as MPI rank and MPI_COMM_WORLD into COMPI enables it to automatically direct testing with various processes' executions as well as automatically determine the total number of processes used in the testing. Second, COMPI employs three techniques to effectively control the cost of testing as too high a cost may prevent its adoption. By capping input values, COMPI is made practical as too large an input can make the testing extremely slow and sometimes even fail as memory needed could exceed the computing platform's memory limit. With two-way instrumentation, we reduce the unnecessary memory and I/O overhead of COMPI and the target program. With constraint set reduction, COMPI keeps significantly fewer constraints by removing redundant ones in the presence of loops so as to avoid redundant tests against these branches. Our evaluation of COMPI uncovered four new bugs in a complex application and achieved 69-86% branch coverage which far exceeds the 1.8-38% coverage achieved via random testing.","Title: Non-intrusively Avoiding Scaling Problems in and out of MPI Collectives\nYear: 2018\n\nAbstract: It has been observed that scaling problems are highly likely to manifest when MPI applications are launched at a large scale where the scale is characterized by the degree of parallelism and the problem size. As the complexity of MPI collectives is directly impacted by both parallelism scale and problem size, their use often triggers scaling problems. Scaling problems' root cause can be outside of MPI libraries and these can be easily exposed via the dynamic interaction between user code and MPI library as the scale goes up. Specifically, irregular collectives suffer the most as the C int displacement array can easily be corrupted with integer overflow. Scaling problems can also result from a bug inside the released MPI libraries due to the lack of a systematic testing of MPI libraries as well as the platform or environment dependency of some scaling problems. Hence it is important for library users to perform testing on their platform to expose potential scaling problems. Fixing a scaling problem is challenging, and thus it usually takes much time for users to wait for an official fix, which sometimes is not even possible due to the difficulty of bug reproduction, root-cause identification, and fix development. To improve users' productivity, we establish the necessity of user side testing and provide a protection layer to avoid scaling problems non-intrusively, i.e., without requiring any changes to the MPI library or user programs. This provides an immediate remedy when an official fix is not readily available.","Title: A Novel Strategy of Combining Variable Ordering Heuristics for Constraint Satisfaction Problems.\nYear: 2018\n\nAbstract: Variable ordering heuristic plays a central role in solving constraint satisfaction problems. Many heuristics have been proposed and well-studied. In order to take advantage of the fact that many generic variable ordering heuristics work well for different problems, we propose a novel method in this paper, namely ParetoHeu, to combine variable ordering heuristics. At each node of the search tree, a set of candidate variables is generated by a new strategy based on Pareto optimality and a variable is selected from the set randomly. The method is easy to be implemented in constraint solvers. The experiments on various benchmark problems show that ParetoHeu is more efficient than both the participant heuristics which are popular in constraint solvers. It is also more robust than some classical strategies which have been used to combine variable ordering heuristics.","Title: End-to-End ConvNet for Tactile Recognition Using Residual Orthogonal Tiling and Pyramid Convolution Ensemble.\nYear: 2018\n\nAbstract: Tactile recognition enables robots identify target objects or environments from tactile sensory readings. The recent advancement of deep learning and biological tactile sensing inspire us proposing an end-to-end architecture ROTConvPCE-mv that performs tactile recognition using residual orthogonal tiling and pyramid convolution ensemble. Our approach uses stacks of raw frames and tactile flow as dual input, and incorporates the strength of multi-layer OTConvs (orthogonal tiling convolutions) organized in a residual learning paradigm. We empirically demonstrate that OTConvs have adjustable invariance capability to different input transformations such as translation, rotation, and scaling. To effectively capture multi-scale global context, a pyramid convolution structure is attached to the concatenated output of two residual OTConv pathways. The extensive experimental evaluations show that ROTConvPCE-mv outperforms several state-of-the-art methods with a large margin regarding recognition accuracy, robustness, and fault-tolerance. Practical suggestions and hints are summarized throughout this paper to facilitate the effective recognition using tactile sensory data.","Title: Fault tolerant one-sided matrix decompositions on heterogeneous systems with GPUs.\nYear: 2018\n\nAbstract: Current algorithm-based fault tolerance (ABFT) approach for one-sided matrix decomposition on heterogeneous systems with GPUs have following limitations: (1) they do not provide sufficient protection as most of them only maintain checksum in one dimension; (2) their checking scheme is not efficient due to redundant checksum verifications; (3) they fail to protect PCIe communication; and (4) the checksum calculation based on a special type of matrix multiplication is far from efficient. By overcoming the above limitations, we design an efficient ABFT approach providing stronger protection for one-sided matrix decomposition methods on heterogeneous systems. First, we provide full matrix protection by using checksums in two dimensions. Second, our checking scheme is more efficient by prioritizing the checksum verification according to the sensitivity of matrix operations to soft errors. Third, we protect PCIe communication by reordering checksum verifications and decomposition steps. Fourth, we accelerate the checksum calculation by 1.7x via better utilizing GPUs.","Title: Ciphertext-Policy Attribute-Based Encrypted Data Equality Test and Classification.\nYear: 2018\n\nAbstract: Thanks to the ease of access and low expenses, it is now popular for people to store data in cloud servers. To protect sensitive data from being leaked to the outside, people usually encrypt the data in the cloud. However, management of these encrypted data becomes a challenging problem, e.g. data classification. Besides, how to selectively share data with other users is also an important and interesting problem in cloud storage. In this paper, we focus on ciphertext-policy attribute based encryption with equality test (CP-ABEET). People can use CP-ABEET to implement not only flexible authorization for the access to encrypted data, but also efficient data label classification, i.e. test of whether two encrypted data contain the same message. We construct an efficient CP-ABEET scheme, and prove its security based on a reasonable number-theoretic assumption. Compared with the only existing CP-ABEET scheme, our construction is more efficient in key generation, and has shorter attribute-related secret keys and better security.","Title: EFTG: Efficient and Flexible Top-K Geo-textual Publish/Subscribe.\nYear: 2018\n\nAbstract: With the popularity of mobile networks and smartphones, geo-textual publish/subscribe messaging has attracted wide attention. Different from the traditional publish/subscribe format, geo-textual data is published and subscribed in the form of dynamic data flow in the mobile network. The difference creates more requirements for efficiency and flexibility. However, most of the existing Top-k geo-textual publish/subscribe schemes have the following deficiencies: (1) All publications have to be scored for each subscription, which is not efficient enough. (2) A user should take time to set a threshold for each subscription, which is not flexible enough. Therefore, we propose an efficient and flexible Top-k geo-textual publish/subscribe scheme. First, our scheme groups publish and subscribe based on text classification. Thus, only a few parts of related publications should be scored for each subscription, which significantly enhances efficiency. Second, our scheme proposes an adaptive publish/subscribe matching algorithm. The algorithm does not require the user to set a threshold. It can adaptively return Top-k results to the user for each subscription, which significantly enhances flexibility. Finally, theoretical analysis and experimental evaluation verify the efficiency and effectiveness of our scheme.","Title: Telepresence control of humanoid robot via high-frequency phase-tagged SSVEP stimuli\nYear: 2016\n\nAbstract: This paper presents a high-frequency steady-state visual evoked potential-based model for a brain-controlled humanoid robot. An advantage of this model is to reduce subjects' fatigue by using visual stimuli with a frequency of 30Hz. This study optimizes the stimulus patterns to increase the brain signals and applies a fuzzy-based classification approach to identify human mental activities and convert them into control commands. Seven subjects successfully navigated a NAO humanoid robot to walk through a map with obstacle avoidance based on live video feedback. The on-line robot navigation experiment reached the average control success rate of 94.26% and an average collision of 1.8 times during a mission.","Title: Efficient concolic testing of MPI applications\nYear: 2019\n\nAbstract: Software testing is widely used in industry, but its application in the High Performance Computing area has been scarce. Concolic testing, that automates testing via generation of inputs, has been highly successful for desktop applications and thus recent work on the COMPI tool has extended it to MPI programs. However, COMPI has two limitations. First, it requires the user to specify an upper limit on input size – if the chosen limit is too big, considerable time is wasted and if the chosen limit is too small, the branch coverage achieved is limited. Second, COMPI does not support floating point arithmetic that is common in HPC applications. \n\nIn this paper, we overcome the above limitations as follows. We propose input tuning that eliminates the need for users to set hard limits and generates inputs such that the testing achieves high coverage while avoiding waste of testing time by selecting suitable input sizes. Moreover, we enable handling of floating point data types and operations and demonstrate that the efficiency of constraint solving can be improved if we rely on the use of reals instead of floating point values. Our evaluation demonstrates that with input tuning the coverage we achieve in 10 minutes is typically higher than the coverage achieved in 1 hour when input tuning is not used. Without input tuning, 9.6-57.1% loss in coverage occurs for a real-world physics simulation program. For the physics simulation program, using our floating-point extension that uses reals covers 46 more branches than without using the extension. Also, we cover 122 more branches when solving floating-point constraints using reals rather than directly using floating-point numbers.","Title: Automated Theorem Proving Practice with Null Geometric Algebra.\nYear: 2019\n\nAbstract: This paper presents the practice of automated theorem proving in Euclidean geometry with null geometric algebra, a combination of Conformal Geometric Algebra and Grassmann-Cayley algebra. This algebra helps generating extremely short and readable proofs: The proofs generated are mostly one-termed or two-termed. Besides, the theorems are naturally extended from qualitative description to quantitative characterization by removing one or more geometric constraints from the hypotheses.","Title: Quantum Algorithm Design: Techniques and Applications.\nYear: 2019\n\nAbstract: In recent years, rapid developments of quantum computer are witnessed in both the hardware and the algorithm domains, making it necessary to have an updated review of some major techniques and applications in quantum algorithm design.","Title: Designated-server identity-based authenticated encryption with keyword search for encrypted emails.\nYear: 2019\n\nAbstract: In encrypted email system, how to search over encrypted cloud emails without decryption is an important and practical problem. Public key encryption with keyword search (PEKS) is an efficient solution to it. However, PEKS suffers from the complex key management problem in the public key infrastructure. Its variant in the identity-based setting addresses the drawback, however, almost all the schemes does not resist against offline keyword guessing attacks (KGA) by inside adversaries. In this work we introduce the notion of designated-server identity-based authenticated encryption with keyword search (dIBAEKS), in which the email sender authenticates the message while encrypting so that no adversary including the server can launch offline KGA. Furthermore, we strengthen the security requirement so that only the designated server has the capability to search over encrypted emails for receivers. We formally define dIBAEKS and its security models, and propose two dIBAEKS constructions using Type-I and Type-III bilinear pairing, respectively. We compare our schemes with some related IBEKS schemes in the literature, and do experiments to demonstrate its efficiency. Although they are slightly less computationally efficient than but still comparable with the related schemes, our schemes provide stronger security guarantee and better protect users’ privacy.","Title: nD polyhedral scene reconstruction from single 2D line drawing by local propagation\nYear: 2004\n\nAbstract: In this paper, we study the problem of reconstructing the polyhedral structures and geometric positions of a general nD polyhedral scene from a single 2D line drawing. With the idea of local construction and propagation, we propose several powerful techniques for structural reconstruction (i.e. face identification) and geometric reconstruction (i.e. realizability and parametrization). Our structural reconstruction algorithm can handle 3D solids of over 10,000 faces efficiently, outperforming any other existing method. Our geometric reconstruction algorithm can lead to amazing simplification in symbolic manipulation of the geometric data, and can be used to find linear construction sequences for non-spherical polyhedra.","Title: Saving constraint checks in maintaining coarse-grained generalized arc consistency\nYear: 2019\n\nAbstract: Constraint check plays a central role in establishing generalized arc consistency which is widely used to solve constraint satisfaction problems. In this paper, we propose a new generalized arc consistency algorithm, called GTR, which ensures that the tuples that have been checked to be allowed by a constraint will never be checked again. For each constraint, GTR maintains a dynamic list of the tuples that were checked to be allowed by this constraint and check their validities to identify some values with supports. It is equipped with a mechanism avoiding redundant validity checks. The basic GAC3 algorithm is employed to find a support for the rest values and to add new tuples to the dynamic list. The experiments show that maintaining GTR during search saves a number of constraint checks. It also brings some improvements over cpu time while solving some CSPs with tight constraints.","Title: An Effective Genetic Algorithm For The Resource Levelling Problem With Generalised Precedence Relations\nYear: 2018\n\nAbstract: Resource levelling aims to obtain a feasible schedule to minimise the resource usage fluctuations during project execution. It is of crucial importance in project scheduling to ensure the effective use of scarce and expensive renewable resources, and has been successfully applied to production environments, such as make-to-order and engineering-to-order systems. In real-life projects, general temporal relationships are often needed to model complex time-dependencies among activities. We develop a novel genetic algorithm (GA) for the resource levelling problem with generalised precedence relations. Our design and implementation of GA features an efficient schedule generation scheme, built upon a new encoding mechanism that combines the random key representation and the shift vector representation. A two-pass local search-based improvement procedure is devised and integrated into the GA to enhance the algorithmic performance. Our GA is able to obtain near optimal solutions with less than 2% optimality gap for small instances in fractions of a second. It outperforms or is competitive with the state-of-the-art algorithms for large benchmark instances with size up to 1000 activities.","Title: Does online media sequence matter in product marketing?\nYear: 2018\n\nAbstract: •Interactions between online media sequences and product types affect the persuasion of consumers.•For search goods, OBM-OIM yields higher message consumer acceptance and message responses.•For experience goods, OIM-OBM yields higher consumer message acceptance and message responses.•Message acceptance mediates the effect of online media sequences on message responses.","Title: High-Frequency Spectrum Analysis and Channel Availability Decision on Sea Surface Environment\nYear: 2017\n\nAbstract: This paper measured entire spectrum in the HF environment of two places by an electromagnetic spectrum monitor system. Based on the above measured data, the HF spectrum characteristics of 2-30 MHz band were analyzed: power spectral density (PSD), Time-frequency distribution, The probability distribution of 3D map, etc. The paper analyzed high frequency bands occupancy one the sea with setting different latitude, season and time as control groups. Hence, we confirm the source of interference noise is broadcasting, and the silent frequency band is obtained. Further analyze has proven it has short-time stationary property. At last, the paper defines the channel availability function for evaluating availability of the channel.","Title: A New Shadow Tracking Method to Locate the Moving Target in SAR Imagery Based on KCF\nYear: 2017\n\nAbstract: Shadow detection and tracking (SDT) is the effective method to locate moving targets, which always be shifted or smeared outside the scene, in different images sequence by Synthetic Aperture Radar (SAR). In this process, the detection of shadow is obtained on the first frame image, and the Kernelized Correlation Filter is used to track target among the following SAR images, using the changes between the image sequence. By the experiments and performance analysis, the validity of the proposed algorithm can be demonstrated.","Title: A Transfer Learning Method For Ship Recognition In Multi-Optical Remote Sensing Satellites\nYear: 2018\n\nAbstract: In this paper, a transfer learning method of ship target recognition is proposed. This method aims at identifying unlabeled ships in high-resolution, based on the theory of transfer learning, assisting with a number of ship samples with different resolutions from different satellites. In the traditional machine learning method, training data and test data are assumed to have the same distribution. However, because of the different distributions and spaces in most cases, such as images from different satellites, the accuracy rate will decline. In this paper, we proposed a method that aligns the distributions as well as the subspace bases to solve this problem. This paper first proposed Adaptation Local Linear Embedding (ALLE) algorithm to achieve space alignment and then aligned both marginal distribution and conditional distribution by Joint Distribution Adaptation (JDA). This paper focuses on the identification of three types of ships which are destroyers, cruisers, and aircraft carriers basing on the method proposed (ALLE-JDA), using the knowledge of source samples low-resolution labeled ships to help identify the unlabeled high-resolution samples. The experimental results show that this method is better than several state-of-the-art methods.","Title: Challenging Theorem Provers with Mathematical Olympiad Problems in Solid Geometry.\nYear: 2016\n\nAbstract: Mathematical Olympiad problems can be used to evaluate the performance of theorem provers. In the encyclopedia (Wu et al. in The Dictionary of International Mathematical Olympiads, Volume of Geometry. Hebei Children Press, Shijiazhuang, 2012) there are 207 Mathematical Olympiad contest problems in solid geometry collected around the world and ranging over one century, among which 97 problems can be used to test algebraic provers of equality type. Three general-purpose theorem proving methods are used in the test: the characteristic set method, the Grobner basis method, and the vector algebra method. 91 out of the 97 problems are proved by the provers, and some contest problems are found to need additional specification to be correct. The proving efficiency and geometric interpretability of the additional non-degeneracy conditions for the 91 problems by the three provers are compared.","Title: Customized Filesystem with Dynamic Stripe Strategies on Lustre-Based Hadoop.\nYear: 2017\n\nAbstract: With large-scale data exploding so quickly that the traditional big data processing framework Hadoop has met its bottleneck on data storing layer. Running Hadoop on modern HPC clusters has attracted much attention due to its unique data processing and analyzing capabilities. Lustre file system is a promising parallel storage file system occupied HPC file system market for many years. Thus, Lustre-based Hadoop platform will pose many new opportunities and challenges on today's data era. In this paper, we customized our LustreFileSystem class which inherits from FileSystem class (inner Hadoop source code) to build our Lustre-based Hadoop. And to make full use of the high-performance in Lustre file system, we propose a novel dynamic stripe strategy to optimize stripe size during writing data to Lustre file system. Our results indicate that, we can improve the performance obviously in throughput (mb/sec) about 3x in writing and 11x in reading, and average IO rate (mb/sec) at least 3 times at the same time when compared with initial Hadoop. Besides, our dynamic stripe strategy can smooth the reading operation and give a slight improvement on writing procedure when compared with existing Lustre-based Hadoop.","Title: Authorized Equality Test on Identity-Based Ciphertexts for Secret Data Sharing via Cloud Storage.\nYear: 2019\n\nAbstract: With the higher rate of using cloud storage, protecting data privacy becomes an important issue. The most effective solution is to encrypt data before uploading to the cloud. However, how to efficiently search over data encrypted with different keys is still an open problem. To address this problem, we introduce a new notion of the identity-based encryption with equality test supporting flexible authorization (IBEETFA). It supports the test of whether two ciphertexts encrypted under the different keys encapsulate the same message, and in the meanwhile supports fine-grained authorization of the test. Based on the equality test on ciphertexts, there is a direct way to support an authorized user to search over ciphertexts of different users, which accelerates secret data sharing among a group of users. Besides, IBEET-FA does not suffer from the complex key management problem of its counterpart in the traditional public key infrastructure. We propose a concrete construction of IBEET-FA and prove it to be securely based on simple mathematical assumptions. The experimental results show that our IBEET-FA scheme is efficient and can satisfy various types of search over encrypted data.","Title: Design and Implementation of Lead-Carbon Battery Storage System.\nYear: 2019\n\nAbstract: In this paper, we described a design scheme for a lead-carbon battery energy storage system (BESS). A two-stage topology of lead-carbon battery energy storage system was adopted. The number and connection structure of battery cells were designed based on the actual demand. The main circuit parameters of the BESS were determined according to the power transfer capability, harmonic suppression, and dynamic response capability. A state feedback linearization method in a nonlinear differential geometry theory was used for dq-axis current decoupling based on the mathematical model used in the dq coordinate system of the BESS. A control strategy based on filter capacitor current inner loop, grid current middle loop, and dc voltage outer loop was adopted to suppress the resonance peak and achieve the independent regulation of active power and reactive power. The PSCAD/EMTDC simulation results and physical prototype experiments showed that the lead-carbon BESS had a good dynamic and steady-state performance.","Title: Windowed Fourier transform and general wavelet algorithms in quantum computation.\nYear: 2019\n\nAbstract: In this paper, we define the quantum windowed Fourier transform and study some of its properties, then we develop two useful operations called quantum convolution and 'integral'. Quantum 'integral' allows us to implement the integral transforms quantum-mechanically with a certain probability, including general wavelet kernel transforms. Furthermore, we propose an improved wavelet kernel transform for quantum computation.","Title: A Secure Cloud Data Sharing Protocol for Enterprise Supporting Hierarchical Keyword Search\nYear: 2022\n\nAbstract: Cloud storage becomes the priority for storing and sharing data for enterprise users. Encrypting prior to uploading data to the cloud is the best way to protect business secrets, however, it hinders the convenient operations on plaintexts, such as searching over the cloud data. In addition, employees in an enterprise have multiple layer structures and a higher layer employee should have the privil...","Title: Public Key Encryption with Fuzzy Matching\nYear: 2021\n\nAbstract: The rise of cloud computing is driving the development in various fields and becoming one of the hot topics in recent years. To solve the problem of comparing ciphertexts between different users in cloud storage, Public Key Encryption with Equality Test (PKEET) was proposed. In PKEET, a tester can determine whether two ciphertexts encrypted with different public keys contain the same message without decrypting the ciphertexts. However, PKEET only supports exact matching, which may not be practical when two messages have misspellings, formatting differences, or differences in the data itself. Therefore, to support fuzzy matching, in this paper we propose the concept of Public Key Encryption with Fuzzy Matching (PKEFM), which allows to determine whether the edit distance between two encrypted messages is less than a threshold value. PKEFM can be well applied to support fuzzy data comparison in encrypted e-mail systems or encrypted gene testing. We then modify the scheme to provide the decryption function, and support (encrypted) wildcards in the matching, in order to further improve the generality of PKEFM at the cost of a small amount of extra computation.","Title: Private Set Intersection With Authorization Over Outsourced Encrypted Datasets\nYear: 2021\n\nAbstract: Thanks to its convenience and cost-savings feature, cloud computing ushers a new era. Yet its security and privacy issues must not be neglected. Private set intersection (PSI) is useful and important in many cloud computing applications, such as document similarity, genetic paternity and data mining. The cloud server performs intersection operations on two outsourced encrypted datasets of data owners. In the existing protocols, however, data owners cannot decide whether to use all or part of their encrypted data to compute the intersection, nor can they specify whom to compare with. In this paper, we introduce an enhanced notion of outsourced PSI, called authorized PSI (APSI), which supports flexible authorization and cross-type authorized comparison of datasets. To demonstrate this notion, we propose a concrete APSI protocol, and prove it to be secure in the random oracle model based on simple number-theoretic assumptions. Experimental results show that our APSI protocol has performance comparable with existing related outsourced PSI protocols.","Title: Better than humans: a method for inferring consumer shopping intentions by reading facial expressions\nYear: 2021\n\nAbstract: How to make robots more human-like has been a hot topic of interest for researchers in the field of robot interaction. Empowering robots in retail services with empathy to read minds is still a major challenge. To address this problem, we proposed a deep learning-based method for programs to infer consumers' shopping interest levels, which we confirmed effectiveness has met or exceeded that of hum...","Title: A high precision intrusion detection system for network security communication based on multi-scale convolutional neural network\nYear: 2022\n\nAbstract: The openness of network data makes it vulnerable to hackers, viruses and other attacks, which seriously threatens the privacy and property security of users. In order to improve the accuracy of the intrusion detection for network security communication, based on the traditional intrusion detection system, combining with the deep learning theory and shortcomings, this paper proposed an intrusion detection system for network security communication based on multi-scale convolutional neural network, and conducted the corresponding experiments on public data sets. The experimental results perform that compared to the intrusion detection system based on Adaboost model and Recurrent Neural Network model, the convergence speed of multi-scale convolutional neural network system is faster, the average error detection rate is reduced by 4.02%, and the average accuracy is improved by 4.37%. The results prove that the intrusion detection system based on multi-scale convolution neural network has a high detection accuracy.","Title: Nonlinear ESO-based tracking control for warehouse mobile robots with detachable loads\nYear: 2022\n\nAbstract: It is an essential task to guarantee satisfactory tracking performance for a warehouse mobile robot with a detachable load. To this end, a nonlinear extended state observer (ESO)-based tracking control is investigated via a double closed-loop framework in this paper. A kinematics controller is designed in an outer loop to generate desired velocities for the warehouse mobile robot. A nonlinear ESO with an improved error function is proposed in an inner loop to estimate load variations and internal unmodeled dynamics. Then a nonlinear error feedback controller based on estimation values is given to track the desired velocities from the outer loop. Simulation and experiment results illustrate the effectiveness and superiority of the proposed control strategy.","Title: Adaptive Task Planning for Large-Scale Robotized Warehouses\nYear: 2022\n\nAbstract: Robotized warehouses are deployed to automatically distribute millions of items brought by the massive logistic orders from e-commerce. A key to automated item distribution is to plan paths for robots, also known as task planning, where each task is to deliver racks with items to pickers for processing and then return the rack back. Prior solutions are unfit for large-scale robotized warehouses due to the inflexibility to time-varying item arrivals and the low efficiency for high throughput. In this paper, we propose a new task planning problem called TPRW, which aims to minimize the end-to-end makespan that incorporates the entire item distribution pipeline, known as a fulfilment cycle. Direct extensions from state-of-the-art path finding methods are ineffective to solve the TPRW problem because they fail to adapt to the bottleneck variations of fulfillment cycles. In response, we propose Efficient Adaptive Task Planning, a framework for large-scale robotized warehouses with time-varying item arrivals. It adaptively selects racks to fulfill at each timestamp via reinforcement learning, accounting for the time-varying bottleneck of the fulfillment cycles. Then it finds paths for robots to transport the selected racks. The framework adopts a series of efficient optimizations on both time and memory to handle large-scale item throughput. Evaluations on both synthesized and real data show an improvement of 37.1% in effectiveness and 75.5% in efficiency over the state-of-the-arts.","Title: A Decentrlized Trading Model Based on Public Blockchain with Regulatable Bi-Tierred Identities\nYear: 2021\n\nAbstract: Traditional network trading platforms process transactions and store data on their centralized servers. Usually, users deem them as the third parties worth to be trusted, while exploitations and leaks of user privacy never cease. Emerging cryptocurrency systems based on blockchains provide a P2P trading model in a decentralized way, avoiding many problems inherent in traditional centralized platforms. However, the intrinsic anonymity makes it hard to trace identities and regulate the blockchain system when something goes wrong with the transactions, always causing financial losses to users. In this paper, we propose a blockchain trading prototype based on a traceable two-tier identity structure, which combines a personal CA-based identity with anonymous addresses to enable appealings without centrally keeping transaction data, hence protecting privacy and regulating transactions simultaneously. We develop an off-chain transaction confirmation protocol and build an on-chain regulatory agency based on smart contracts. The separation of the on-chain part from the off-chain part makes it suitable to be deployed in many cryptocurrency system that supports smart contracts. Experiments show that all the transaction data can be stored by users themselves and the regulatory agency still works independently. A security analysis on the supervision smart contract is also made. We recommend this model to be an alternate to the traditional centralized electronic business, if privacy preserving and regulations are both considered.","Title: Meta-Learner-Based Stacking Network on Space Target Recognition for ISAR Images\nYear: 2021\n\nAbstract: Recently, the deep learning models have achieved great success in the recognition of inverse synthetic aperture radar (ISAR) images. However, most of the deep learning models fail to obtain satisfactory results under the condition of small samples due to the contradiction between the large parameter space of the deep learning models and the insufficient labeled samples of space target imaging by ISAR. In this article, a method of meta-learner-based stacking network (MSN) is proposed, which can realize the high-precision classification of space target by ISAR images under the condition of small sample. Innovatively, a rotation-invariant attention mechanism (RAM) module is added into Resnet50 network to magnify the difference of embedded features of target and background. Complementarily, the deep relationship between the features of fine-grained ISAR image is extracted by using graph convolutional network and relation network. Finally, an innovative adaptive weighted XGBoost algorithm is used to integrate the prediction results of the base learners. The main contributions of this article include proposing a RAM module and using an innovative adaptive weighted XGBoost algorithm to realize ensemble learning. The experiment results show that the RAM module effectively concentrates the network's attention on the recognized target, and the recognition rate of MSN is about 5% higher than that of a single base learner under different data volume conditions, which proves that MSN achieves competitive accuracy against other state-of-the-art approaches.","Title: Attribute-Based Hierarchical Access Control With Extendable Policy\nYear: 2022\n\nAbstract: Attribute-based encryption scheme is a promising mechanism to realize one-to-many fine-grained access control which strengthens the security in cloud computing. However, massive amounts of data and various data sharing requirements bring great challenges to the complex but isolated and fixed access structures in most of the existing attribute-based encryption schemes. In this paper, we propose an attribute-based hierarchical encryption scheme with extendable policy, called Extendable Hierarchical Ciphertext-Policy Attribute-Based Encryption (EH-CP-ABE), to improve the data sharing efficiency and security simultaneously. The scheme realizes the function of hierarchical encryption, in which, data with hierarchical access control relationships could be encrypted together flexibly to improve the efficiency. The scheme also achieves external and internal extension of the access structure to further encrypt newly added hierarchical data without updating the original ciphertexts or with only a minor update depending on the data sharing requirements, which simplifies the encryption process and greatly reduces the computation overhead. We formally prove the security of the scheme is IND-CCA secure in the random oracle model based on bilinear Diffie-Hellman assumption, and we also implement our scheme to demonstrate its efficiency and practicality.","Title: Image Reconstruction for Low-Oversampled Staggered SAR Based on Sparsity Bayesian Learning in the Presence of a Nonlinear PRI Variation Strategy\nYear: 2022\n\nAbstract: As an innovative concept of high-resolution and wide-swath system, the low-oversampled staggered synthetic aperture radar (SAR) can not only deal with the blind ranges but also suppress range ambiguities and reduce the data volume. Due to the variation of pulse repetition interval (PRI), there will be echo pulse loss and nonuniform sampling in azimuth. Generally, the existing reconstruction algorithms mostly resample the nonuniformly sampled signal into a uniform grid and then perform conventional focused processing. However, the accuracy of the resampling is limited, and the advantage of the nonuniformity over uniform sampling in terms of reconstruction performance is ignored, especially with low oversampling factors. In this article, a reconstruction algorithm for low-oversampled staggered SAR is proposed based on the sparsity Bayesian learning (SBL) in the presence of a nonlinear PRI variation strategy. To ensure that the blind range distribution, which depends on the PRI variation strategy, brings a superior reconstruction performance, we define a novel objective function and optimize a sequence of nonlinear PRI variation with genetic algorithm (GA). On the basis of the optimized sequence, the proposed reconstruction algorithm performs the second-order keystone transform to achieve range curvature correction for nonuniformly sampled data in azimuth. Then, a nonuniform observation model is established. The SBL using a hierarchical form of the Laplace prior is applied to reconstruct the focused images directly with the nonuniform sampling. Simulations and experiments on raw data generated in the staggered SAR mode with low oversampling factors are performed to verify the effectiveness of the proposed method.","Title: Refocusing on SAR Ship Targets With Three-Dimensional Rotating Based on Complex-Valued Convolutional Gated Recurrent Unit\nYear: 2022\n\nAbstract: This letter proposes a complex-valued convolutional gated recurrent unit (CV-ConvGRU) network for the 3-D rotation refocusing task of a synthetic aperture radar (SAR) ship target. To take advantage of the amplitude and phase information of complex SAR images, all the elements of CV-ConvGRU, including the convolutional layer, activation function, update gate, and reset gate, are extended to the complex domain. Based on CV-ConvGRU, a complex-valued SAR ship refocusing network (CV-SSRN) architecture is designed for refocusing experiments. To verify the robustness of the proposed CV-ConvGRU over ConvGRU on information perception, this letter also raises a real-valued SAR ship refocusing network (RV-SSRN), which has the same degree of freedom as CV-SSRN. Finally, experiments are carried out, and all the results show the superiority of the proposed method on refocusing accuracy.","Title: A novel identity-based multi-signature scheme over NTRU lattices\nYear: 2022\n\nAbstract: Multi-signature is an important technology to compress multiple signatures on the common message into a compact one, thereby reducing the consumption of storage space and transmission bandwidth. Such a cryptographic primitive is widely used in financial applications such as blockchain which requires multiple keys to authorize a transaction. With the advent of quantum computers, traditional multi-signature schemes may no longer be secure as their underlying security assumptions (e.g. RSA or discrete logarithm problems) may not hold anymore. In this paper, we propose a novel identity-based multi-signature (IBMS) scheme over NTRU lattices, which is secure against the attacks of quantum computers. To the best of our knowledge, it is the first lattice-based IBMS scheme. We show that our scheme is provably secure in the random oracle model based on the ring version of the short integer solution assumption (Ring-SIS). Compared with the closely related works in the literature, our scheme enables the signer to select its system identity (such as email address, physical IP address, and etc.) as the public key, which effectively alleviates the certificate management problem in the PKI setting, and takes the advantage of discrete Gaussian distribution instead of uniform distribution to generate secret signing keys and multi-signatures. Besides, our scheme does not require all the system users to setup a trusted common string, which further simplifies the deployment of our scheme in practice.","Title: PRO-Face: A Generic Framework for Privacy-preserving Recognizable Obfuscation of Face Images\nYear: 2022\n\nAbstract: ABSTRACTA number of applications (e.g., video surveillance and authentication) rely on automated face recognition to guarantee functioning of secure services, and meanwhile, have to take into account the privacy of individuals exposed under camera systems. This is the so-called Privacy-Utility trade-off. However, most existing approaches to facial privacy protection focus on removing identifiable visual information from images, leaving protected face unrecognizable to machine, which sacrifice utility for privacy. To tackle the privacy-utility challenge, we propose a novel, generic, effective, yet lightweight framework for Privacy-preserving Recognizable Obfuscation of Face images (named as PRO-Face). The framework allows one to first process a face image using any preferred obfuscation, such as image blur, pixelate and face morphing. It then leverages a Siamese network to fuse the original image with its obfuscated form, generating the final protected image visually similar to the obfuscated one from human perception (for privacy) but still recognized as the original identity by machine (for utility). The framework supports various obfuscations for facial anonymization. The face recognition can be performed accurately not only across anonymized images but also between plain and anonymized ones, based on only pre-trained recognizers. Those feature the \"generic\" merit of the proposed framework. In-depth objective and subjective evaluations demonstrate the effectiveness of the proposed framework in both privacy protection and utility preservation under distinct scenarios. Our source code, models and any supplementary materials are made publicly available.","Title: Complex-Valued Graph Neural Network on Space Target Classification for Defocused ISAR Images\nYear: 2022\n\nAbstract: Recently, research on the classification for inverse synthetic aperture radar (ISAR) images continues to deepen. However, the maneuvering and attitude adjustment of space targets will bring high-order terms to received echoes, which causes defocus on ISAR images and affects the classification. The current classification models ignore the information of high-order terms containing in the relationship of real parts and imaginary parts of data. To this end, this letter proposes an end-to-end framework, called complex-valued (CV)-graph neural network (GNN), specifically for the classification of defocused ISAR images under the few-shot condition. It models the features of real parts and imaginary parts of CV images as graph information reasoning. Specifically, the deep relationship between them is mined to contribute to classification by CV graph convolution. Moreover, the backpropagation process is derived in detail for updating the weights and bias of the network. The proposed method is then experimented with a mixed few-shot dataset of real and simulated data. Compared with the state-of-the-art methods, CV-GNN performs well in defocused image classification for each class of targets, and ablation studies verify the effectiveness of CV network and GNN. The code and dataset will be available online (https://github.com/yhx-hit/cv_gnn).","Title: High-Resolution Refocusing for Defocused ISAR Images by Complex-Valued Pix2pixHD Network\nYear: 2022\n\nAbstract: Inverse synthetic aperture radar (ISAR) is an effective detection method for targets. However, for the maneuvering targets, the Doppler frequency induced by an arbitrary scatterer on the target is time-varying, which will cause defocus on ISAR images and bring difficulties for the further recognition process. It is hard for traditional methods to well refocus all positions on the target well. In recent years, generative adversarial networks (GANs) achieve great success in image translation. However, the current refocusing models ignore the information of high-order terms containing in the relationship between real and imaginary parts of the data. To this end, an end-to-end refocusing network, named complex-valued pix2pixHD (CVPHD), is proposed to learn the mapping from defocus to focus, which utilizes complex-valued (CV) ISAR images as an input. A CV instance normalization layer is applied to mine the deep relationship between the complex parts by calculating the covariance of them and accelerate the training. Subsequently, an innovative adaptively weighted loss function is put forward to improve the overall refocusing effect. Finally, the proposed CVPHD is tested with the simulated and real dataset, and both can get well-refocused results. The results of comparative experiments show that the refocusing error can be reduced if extending the pix2pixHD network to the CV domain and the performance of CVPHD surpasses other autofocus methods in refocusing effects. The code and dataset have been available online (https://github.com/yhx-hit/CVPHD)."],"string":"[\n \"Title: Gain Scheduling Control of Delta Operator System Using Network-Based Measurements\\nYear: 2014\\n\\nAbstract: This paper addresses the state feedback stabilization problem for a class of network-based delta operator control systems. To compensate time delays, a gain scheduling controller based on one-way delay measurement is constructed, and the resulting closed-loop system is transformed into a switched delta operator system. The stability condition is derived for the closed-loop system, and the corresponding stabilizing and optimal controller design techniques are also developed. The simulation and experimental results are given to demonstrate the effectiveness and applicability of the proposed approach.\",\n \"Title: Some special types of solutions of a class of the (N+1)-dimensional nonlinear wave equations\\nYear: 2008\\n\\nAbstract: Some special types of solutions with arbitrary functions for the (N + 1)-dimensional sine-cosine-Gordon equation, the (N + 1)-dimensional double sinh-Gordon equation, and the (N + 1)-dimensional sinh-cosinh-Gordon equations are constructed by means of the separation transformation approach. As an illustrative sample, the properties of some solutions are shown by their figures. © 2008 Elsevier Ltd. All rights reserved.\",\n \"Title: Use Closure of Relevant Sets of Attributes to Efficiently Find Candidate Keys\\nYear: 2008\\n\\nAbstract: Based on canonical cover or minimum cover for functional dependency set F, a data structure MAC is constructed, which facilitates to compute closures of all attributes sets. Using MAC, a fast algorithm of computing closure of an individual set of attributes is improved to efficiently find candidate keys. The improved algorithm of finding candidate keys neednpsilat compute closures of the total subsets of R, but compute closures of the subsets which are closely relevant to candidate keys, namely, which are candidate keys or one part of candidate keys. Based on the improved algorithm for fast computing closures of the sets of attributes in left set and theorem of reference, an efficient algorithm of finding the total candidate keys are gained, whose time complexity is O(nmiddot|R|middot|Fc|2).\",\n \"Title: nD object representation and detection from Single 2D line drawing\\nYear: 2004\\n\\nAbstract: In this paper, we propose the wireframe representation of nD object, which is a single 2D line drawing. A wireframe model of an n D object is composed of a set of edges connecting a set of vertices, a subset of closed r -chains called boundary r -chains which surround the (r +1)D pieces of the object, and a set of filling patterns for the boundary r -chains for 0 r n. The wireframe representation is the perspective projection of the wireframe model of the object from its surrounding space to the image plane. Combining the projective geometric constraints of the wireframe representation with the idea of local construction and deletion, we propose an algorithm for high dimensional object detection from single 2D line drawing, under the most general assumption that neither the dimension of the object nor the dimension of the surrounding space is known, two neighboring faces can be coplanar, and whether or not the object is a manifold is unknown. Our algorithm outperforms any other algorithm in 2D face identification in that it generally does not generate redundant cycles that are not assigned as faces, and can handle 3D solids of over 10,000 faces.\",\n \"Title: Reducing consistency checks in generating corrective explanations for interactive constraint satisfaction\\nYear: 2013\\n\\nAbstract: Constraint satisfaction problem has many applications in Artificial Intelligence. Its interactive applications usually require advice from a system to help a user solve the problem. Based on maximal relaxations, the CorrectiveExp algorithm is a representative method to compute explanations. However, we found that the CorrectiveRelax algorithm, used by the CorrectiveExp algorithm to compute maximal relaxations, has a defect that it executes more consistency checks than necessary. It is very important to avoid these unnecessary consistency checks because in general each consistency check needs to resort to backtrack search. To tackle this problem, this paper proposes two improved algorithms to compute maximal relaxations, called CorrectiveRelaxReduced and CorrectiveRelaxDC respectively. The former utilizes the existing results of consistency checks to shrink the search scope for some inconsistent user constraints. Furthermore, we have proved that the number of consistency checks executed by the CorrectiveRelaxReduced algorithm is always less than or equals to that of the CorrectiveRelax algorithm. The latter uses a divide-and-conquer approach to avoid unnecessary consistency checks. Our experimental results show that the two improved algorithms execute less consistency checks than CorrectiveExp while computing maximal relaxations.\",\n \"Title: Feature Weighting Random Forest for Detection of Hidden Web Search Interfaces\\nYear: 2008\\n\\nAbstract: Search interface detection is an essential task for extracting information from the hidden Web. The challenge for this task is that search interface data is represented in high-dimensional and sparse features with many missing values. This paper presents a new multi-classifier ensemble approach to solving this problem. In this approach, we have extended the random forest algorithm with a weighted feature selection method to build the individual classifiers. With this improved random forest algorithm (IRFA), each classifier can be learned from a weighted subset of the feature space so that the ensemble of decision trees can fully exploit the useful features of search interface patterns. We have compared our ensemble approach with other well-known classification algorithms, such as SVM, C4.5, Naïve Bayes, and original random forest algorithm (RFA). The experimental results have shown that our method is more effective in detecting search interfaces of the hidden Web.\",\n \"Title: Some Applications of Clifford Algebra to Geometries\\nYear: 1998\\n\\nAbstract: This paper focuses on a Clifford algebra model for geometric computations in 2D and 3D geometries. The model integrates symbolic representation of geometric entities, such as points, lines, planes, circles and spheres, with that of geometric constraints such as angles and distances, and is appropriate for both symbolic and numeric computations. Details on how to apply this model are provided and examples are given to illustrate the application.\",\n \"Title: On the mixed cayley-sylvester resultant matrix\\nYear: 2006\\n\\nAbstract: For a generic n-degree polynomial system which contains n+1 polynomials in n variables, there are two classical resultant matrices, Sylvester resultant matrix and Cayley resultant matrix, lie at the two ends of a gamut of n+1 resultant matrices. This paper gives the construction of the n–1 resultant matrices which lie between the two pure resultant matrices by the combined method of Sylvester dialytic and Cayley quotient. Since the construction involves two steps, Cayley quotient and Sylvester dialytic, the block structure of these mixed resultant matrices are similar to that of Sylvester resultant matrix in large scale, and the detailed submatrices are similar to Dixon resultant matrix.\",\n \"Title: The Lie Model for Euclidean Geometry\\nYear: 2000\\n\\nAbstract: Abstract. In this paper we investigate the Lie model of Lie sphere geometry using Clif- ford algebra. We employ it to Euclidean geometric problems involving oriented contact to simplify algebraic description and computation.\",\n \"Title: Modeling Government Credit Information Systems Diffusion in China: A System Dynamics Approach.\\nYear: 2010\\n\\nAbstract: This paper examines the usage and diffusion of Government Credit Information Systems (GCIS) in district-level governments in shanghai. The diffusion of GCIS was studied from a process-oriented perspective. A System Dynamics (SD) model is developed to simulate the relationships of technological, organizational and environmental, and institutional factors on GCIS diffusion under different management policies. A holistic view on the feedback loops, the consequently nonlinear behavior pattern of GCIS usage, and its diffusion in Shanghai government agencies is examined. Our research model and results suggest that workload faced by GCIS users and the tolerable maximum workload have a high impact on GCIS usage, task volume brought by GCIS, work pressure and the perception of technological factors. Different combination of work intensity and the tolerable maximum work intensity significantly influence the system usage. The contribution of our study lies in revealing that the diffusion of GCIS requires a systematic consideration of the business development plan, the GCIS user's workload and the organization's business environment. Flexible managerial incentive strategy will enhance user's work efficiency, thus lead to effective diffusion of GCIS in organizations. The theoretical and practical implications of this study are discussed.\",\n \"Title: Optimal Controller Design for a class of Networked Control Systems\\nYear: 2005\\n\\nAbstract: This paper is concerned with the optimal controller design of networked control systems (NCSs). Aiming at a class of NCSs, in which network-induced delay and data packet dropout exist simultaneously from sensor to controller, this paper proposes a novel optimal controller based on a new adaptive genetic algorithm and simulated anneal algorithm (AGASA), guaranteed cost controller (GCC) and state feedback controller (SFC). The proposed controller is less conservative than GCC and SFC, and can be applied to both linear and nonlinear NCSs. Two examples are presented to illustrate the proposed controller.\",\n \"Title: A SPA-based K-means clustering algorithm for the remote sensing information extraction\\nYear: 2012\\n\\nAbstract: Set Pair Analysis (SPA) is a new methodology to describe and process uncertainty system, which has been applied in many fields recently. In this paper, a new approach to remote sensing information extraction, the SPA-based k-means clustering algorithm (SPAKM), has been proposed based on the principle of SPA. The basic ideals and steps of SPAKM are discussed. The proposed algorithm can overcome the limitation of K-means clustering algorithm to certain extent. Finally, cluster analysis experiments of LANDSAT TM image have been made. The results show that the improved K-means clustering algorithm is superior to K-means in classification accuracy of land cover classes of mixed pixels.\",\n \"Title: Fuzzy dynamic characteristic modeling and adaptive control of nonlinear systems and its application to hypersonic vehicles.\\nYear: 2011\\n\\nAbstract: In this paper, a fuzzy dynamic characteristic modeling and adaptive control method is proposed for a class of nonlinear systems.\\n By employing fuzzy dynamic characteristic model, the controlled plant is described as a slowly time-varying fuzzy system,\\n wherein the parameters are estimated online by using recursive Least-Squares algorithm. Under this framework, a fuzzy adaptive\\n controller is constructed, and the stability condition of the closed-loop system is also derived. The main advantage of the\\n proposed method lies in no requirement for the prior knowledge of system model and less parameters to tune, which allows engineers\\n to operate it in a simple, straightforward manner. The proposed method is applied to the control of hypersonic vehicle, and\\n simulation results are given to demonstrate the effectiveness of the obtained results.\",\n \"Title: Approximate rational solutions torational ODEs defined on discrete differentiable curves\\nYear: 2011\\n\\nAbstract: In this paper, a new concept is proposed for discrete differential geometry: discrete n-differentiable curve, which is a tangent n-jet on a sequence of space points. A complete method is proposed to solve ODEs of the form n(m)=F(r, r', ..., r(n), n, n', ..., n(m-1), u)/G(r, r', ..., r(n), n, n', ..., n(m-1), u), where F, G are respectively vector-valued and scalar-valued polynomials, where r is a discrete curve obtained by sampling along an unknown smooth curve parametrized by u, and where n is the vector field to be computed along the curve. Our Maple-13 program outputs an approximate rational solution with the highest order of approximation for given data and neighborhood size. The method is used to compute rotation minimizing frames of space curves in CAGD. For one-step backward-forward chasing, a 6th-order approximate rational solution is found, and 6 is guaranteed to be the highest order of approximation by rational functions. The theoretical order of approximation is also supported by numerical experiments.\",\n \"Title: A recipe for symbolic geometric computing: long geometric product, BREEFS and Clifford factorization\\nYear: 2007\\n\\nAbstract: In symbolic computing, a major bottleneck is middle expression swell. Symbolic geometric computing based on invariant algebras can alleviate this difficulty. For example, the size of projective geometric computing based on bracket algebra can often be restrained to two terms, using final polynomials, area method, Cayley expansion,etc. This is the \\\"binomial \\\"feature of projective geometric computing in the language of bracket algebra. In this paper we report a stunning discovery in Euclidean geometric computing: the term preservation phenomenon. Input an expression in the language of Null Bracket Algebra (NBA), by the recipe we are to propose in this paper, the computing procedure can often be controlled to within the same number of terms as the input, through to the end. In particular, the conclusions of most Euclidean geometric theorems can be expressed by monomials in NBA, and the expression size in the proving procedure can often be controlled to within one term! Euclidean geometric computing can now be announced as having a \\\"monomial \\\"feature in the language of NBA. The recipe is composed of three parts: use long geometric product to represent and compute multiplicatively, use \\\"BREEFS \\\" to control the expression size locally, and use Clifford factorization for term reduction and transition from algebra to geometry. By the time this paper is being written, the recipe has been tested by 70+ examples from [1], among which 30+ have monomial proofs. Among those outside the scope, the famous Miquel's five circle theorem [2 ], whose analytic proof is straightforward but very difficult for symbolic computing, is discovered to have a 3-termed elegant proof with the recipe.\",\n \"Title: Trifocal Tensors with Grassmann-Cayley Algebra\\nYear: 2001\\n\\nAbstract: In this paper we study trifocal tensors with Grassmann-Cayley algebra. We propose a new method to derive relations among epipoles, fundamental tensors and trifocal tensors of three pinhole cameras. By this method we can find some new constraints satisfied by trifocal tensors.\",\n \"Title: Dynamic Fault-Tolerant Routing Based on FSA for LEO Satellite Networks\\nYear: 2013\\n\\nAbstract: We formalize the construction of fault blocks by a state transition model based on finite state automata. Based on the model, a boundary diffusion method is presented for the rectilinear-monotone orthogonal convex fault model such as the rectangular fault model and minimal-connected-component (MCC) faulty model, whereby an adaptive fault-tolerant routing algorithm, called X-Y boundary routing algorithm (X-YBRA), is presented for deadlock-free fault-tolerant adaptive routing outside the fault blocks. To improve the network resources utilization, we put forward a routing diffusion method in the fault block, which completely solves the routing problem in the fault block. The experiment result shows that the diffusion overhead of our method is far lower than that of the traditional routing algorithms such as distance vector and link state routing algorithms with the light loss in convergence time. For the occurrence and recovery of random faults, the expansion and shrinkage of the fault block are also discussed. Accordingly, the dynamic boundary and routing updating methods are put forward to respond to these cases. Based on these methods, we develop low earth orbit satellite networks into an adaptive fault-tolerant system in routing. Our works can be also applied to other 2D mesh networks such as the interconnect multiprocessor computer systems.\",\n \"Title: Assessing Performance Risk for Complex Product Development: A Simulation-Based Model.\\nYear: 2013\\n\\nAbstract: Assessing and managing performance risk is important for complex product development projects. The performance risk of complex product development can be defined as the probability failing to achieve desired performance level subject to cost and time constraints. This article presents a simulation model based on the graphical evaluation and review technique (GERT) and analyzes the complex product development project using simulation data. The model computes the probability distribution of project duration and cost in a GERT network with multifeedback branches considering activities rework that is caused by probabilistic failure to meet the planned design objective. A simulation strategy is designed to assess the performance risk in both subsystems and system development. With the performance risk assessment methodology, project managers can make a better decision and then minimize the performance risk of product development project. Finally, an obstacle clearance armored vehicle development project is used to illustrate the effectiveness of the proposed model. Copyright (c) 2012 John Wiley & Sons, Ltd.\",\n \"Title: Optimal State Feedback Integral Control Using Network-Based Measurements.\\nYear: 2012\\n\\nAbstract: This paper is concerned with the stabilization problem of a class of networked control systems, where the plant measurements and the control signals are transmitted over a network and encounter both time delays and packet losses. To deal with the nonzero disturbance rejection issue, two kinds of networked optimal state feedback integral control (SFIC) methods are proposed in this paper: One is a n...\",\n \"Title: Polyhedral scene analysis combining parametric propagation with calotte analysis\\nYear: 2004\\n\\nAbstract: Polyhedral scene analysis studies whether a 2D line drawing of a 3D polyhedron is realizable in the space, and if so, parameterizing the space of all possible realizations. For generic 2D data, symbolic computation with Grassmann-Cayley algebra is necessary. In this paper we propose a general method, called parametric calotte propagation, to solve the realization and parameterization problems in polyhedral scene analysis at the same time. Starting with the fundamental equations of Sugihara in the form of bivector equations, we can parameterize all the bivectors by introducing new parameters. The realization conditions are implied in the scalar equations satisfied by the new parameters, and can be derived by further analysis of the propagation result. The propagation procedure generally does not bifurcate, and the result often contains equations in factored form, thus makes further algebraic manipulation easier. In application, the method can be used to find linear construction sequences for non-spherical polyhedra.\",\n \"Title: Intrinsic differential geometry with geometric calculus\\nYear: 2004\\n\\nAbstract: Setting up a symbolic algebraic system is the first step in mathematics mechanization of any branch of mathematics. In this paper, we establish a compact symbolic algebraic framework for local geometric computing in intrinsic differential geometry, by choosing only the Lie derivative and the covariant derivative as basic local differential operators. In this framework, not only geometric entities such as the curvature and torsion of an affine connection have elegant representations, but their involved local geometric computing can be simplified.\",\n \"Title: Normalization of Polynomials in Algebraic Invariants of Three-Dimensional Orthogonal Geometry\\nYear: 2013\\n\\nAbstract: In classical invariant theory, the Gr\\\\\\\"obner base of the ideal of syzygies and the normal forms of polynomials of invariants are two core contents. To improve the performance of invariant theory in symbolic computing of classical geometry, advanced invariants are introduced via Clifford product. This paper addresses and solves the two key problems in advanced invariant theory: the Gr\\\\\\\"obner base of the ideal of syzygies among advanced invariants, and the normal forms of polynomials of advanced invariants. These results beautifully extend the straightening of Young tableaux to advanced invariants.\",\n \"Title: On miquel's five-circle theorem\\nYear: 2004\\n\\nAbstract: Miquel's Five-Circle Theorem is difficult to prove algebraically. In this paper, the details of the first algebraic proof of this theorem is provided. The proof is based on conformal geometric algebra and its accompanying invariant algebra called null bracket algebra, and is the outcome of the powerful computational techniques of null bracket algebra embodying the novel idea breefs.\",\n \"Title: Partial Max-restricted Path Consistency\\nYear: 2012\\n\\nAbstract: Filtering techniques are essential in the search algorithms solving constraint satisfaction problems (CSPs). Arc consistency (AC) is the most often used filtering technique because it cheaply removes some values that cannot belong to any solutions. Comparing with AC, max-Restricted Path Consistency (maxRPC) has a stronger pruning power while it is not suited for use during search because of the prohibitive time cost. Thus, light maxRPC which is the light version of maxRPC was proposed. Comparing with maxRPC, it has a lower time cost and the search algorithm maintaining light maxRPC (MlmaxRPC) can outperform the search algorithm maintaining AC (MAC) on some problems. However, MlmaxRPC suffers from the time waste in the cases that applying a stricter checking standard does not intrigue any value deletion. In order to avoid the time waste in MlmaxRPC, in this paper, partial maxRPC which is a new approximation of maxRPC is proposed. It only applies the stricter checking standard when the value deletion is of high possibility. MpmaxRPC which is the search algorithm maintaining partial maxRPC has a better average performance than MAC and MlmaxRPC.\",\n \"Title: Arbitrary shape reconstruction from NC sectional data and applications in space cutter compensation and interference detection\\nYear: 2009\\n\\nAbstract: This paper proposes an efficient shape reconstruction method from sectional data in 3-axis Numerical Control (NC) machining. A merge-divide algorithm is proposed to construct triangle meshes for machined surfaces with arbitrary shape and genus. The algorithm is of linear complexity due to the special property of the NC data. The method is used to interference detection and space cutter compensation in 3-axis NC machining. Experimental results verify that our method gives efficient and valid solutions to these important problems in NC machining.\",\n \"Title: Automated Theorem Proving in Incidence Geometry - A Bracket Algebra Based Elimination Method\\nYear: 2000\\n\\nAbstract: In this paper we propose a bracket algebra based elimination method for automated generation of readable proofs for theorems in incidence geometry. This method features three techniques, the first being heuristic automated reordering of geometric constructions for the purpose of producing shorter proofs, the second being some heuristic elimination rules which improve the performance of the area method of Zhang and others without introducing signed length ratios, the third being a simplification technique called contraction, which reduces the size of bracket polynomials. More than twenty theorems in incidence geometry have been proved, for which short proofs can be produced very fast, together with the corresponding nondegeneracy conditions. An interesting phenomenon is that a proof composed of polynomials of at most two terms can always be found for any of these theorems, similar to that by the biquadratic final polynomial method of Richter-Gebert.\",\n \"Title: Tunneling blasting simulation for digital mine\\nYear: 2012\\n\\nAbstract: Tunneling blasting is a major component of digital mine simulation, with the high cost, dangerous and irreversible features of blasting, it is difficult to design tunneling blasting scheme in real world. The paper simulates tunneling blasting by combining blasting animation of computer graphics with mining empirical formulas. A connected voxel model is used to represent rocks and their failure mechanism. This simulation helps engineers test and adjust blasting scheme before real operation.\",\n \"Title: Complex brackets, balanced complex differences, and applications in symbolic geometric computing\\nYear: 2008\\n\\nAbstract: In advanced invariant algebras such as null bracket algebra (NBA), symmetries of algebraic operators are the most important devices of encoding and employing syzygies of advanced geometric invariants. The larger the symmetry group, the more powerful the computing devices. In this paper, the largest symmetry group of the two kinds of bracket operators in the NBA of plane geometry is found. An algorithm of complexity O(N log N) is proposed to reduce a bracket of length N to its normal form, and then decide the congruence of two brackets of length N. By writing the two bracket operators as the real and pure imaginary parts of a complex bracket operator, their normal forms can be translated into a class of complex polynomials whose variables are first-order differences, called balanced complex difference (BCD) polynomials. BCD polynomials provide a complex-numbers-based invariant language for advanced algebraic manipulations of geometric problems. A simplification algorithm is proposed for making symbolic geometric computing with NBA and BCD polynomials, with the unique feature of controlling the expression size by avoiding multilinear expansions of the first-order difference variables of complex polynomials.\",\n \"Title: Cooperative control for a class of large-scale linear system via delta operator approach\\nYear: 2013\\n\\nAbstract: In this paper, the problem of cooperative control for a class of large-scale linear system is investigated via delta operator approach. Based on Lyapunov-Krasovskii function in delta domain, a state feedback controller is presented in terms of linear matrix inequalities. It is shown that the large-scale system can be cooperatively stabilized even if the subsystems has the bad stability. The proposed method can unify some previous related continuous and discrete systems into the delta operator systems framework. Numerical examples are given to illustrate the effectiveness of the results.\",\n \"Title: Automated short proof generation for projective geometric theorems with Cayley and bracket algebras: II. conic geometry\\nYear: 2003\\n\\nAbstract: In this paper we study plane conic geometry, particularly different representations of geometric constructions and relations in plane conic geometry, with Cayley and bracket algebras. We propose three powerful simplification techniques for bracket computation involving conic points, and an algorithm for rational Cayley factorization in conic geometry. The factorization algorithm is not a general one, but works for all the examples tried so far. We establish a series of elimination rules for various geometric constructions based on the idea of bracket-oriented representation and elimination, and an algorithm for optimal representation of the conclusion in theorem proving. These techniques can be used in any applications involving brackets and conics. In theorem proving, our algorithm based on these techniques can produce extremely short proofs for difficult theorems in conic geometry.\",\n \"Title: Automated short proof generation for projective geometric theorems with Cayley and bracket algebras: I. incidence geometry\\nYear: 2003\\n\\nAbstract: In this paper we establish the Cayley expansion theory on factored and shortest expansions of typical Cayley expressions in two- and three-dimensional projective geometry. We set up a group of Cayley factorization formulae based on the classification of Cayley expansions. We continue to establish three powerful simplification techniques in bracket computation. On top of the Cayley expansions and simplifications, together with a set of elimination rules, we design an algorithm that can produce extremely short proofs in two- and three-dimensional projective geometry. The techniques developed here can be immediately applied to other symbolic computation tasks involving brackets.\",\n \"Title: A network-bound-dependent stabilization method of networked control systems.\\nYear: 2013\\n\\nAbstract: This paper presents a new stabilization method for networked control systems (NCSs). An improved stability criterion dependent on both time delay bound and packet loss bound is derived and the corresponding stabilizing controller design technique is also provided. The merit of the proposed method lies in its much less conservatism, which is achieved by explicitly considering NCSs physical properties and by guaranteeing the deceasement of Lyapunov functional at each control signal updating step rather than at each sampling step, which is largely ignored in the existing literature. Illustrative examples are provided to show the advantage and effectiveness of the developed results.\",\n \"Title: Optimal Stabilizing Gain Selection for Networked Control Systems With Time Delays and Packet Losses\\nYear: 2009\\n\\nAbstract: This brief addresses the optimal-stabilization problem for networked control systems (NCSs) with time delays and packet losses. The closed-loop NCS is modeled as a discrete-time switched system, and the stability conditions are derived in terms of linear matrix inequality. A controller design method with both system stability and control performance taken into account is proposed, and estimation of distribution algorithm is used to select the optimal stabilizing gain. The proposed method can be easily implemented to various applications, since it has simple structure and has no assumptions on time-delay and packet-loss models. Simulation and experimental results are given to demonstrate the effectiveness of the proposed approach.\",\n \"Title: EDA-Based Speed Control of a Networked DC Motor System With Time Delays and Packet Losses\\nYear: 2009\\n\\nAbstract: This paper presents a new controller design method for networked DC motor system in the presence of time delays and packet losses. The sufficient condition under which the closed-loop system is asymptotically stable and the necessary condition under which the networked DC motor has zero steady-state tracking error are derived. Based on the obtained conditions, an output tracking controller design ...\",\n \"Title: Vectorial Equations Solving for Mechanical Geometry Theorem Proving\\nYear: 2000\\n\\nAbstract: In this paper a new method is proposed for mechanical geometry theorem proving. It combines vectorial equations solving in Clifford algebra formalism with Wu\\\"s method. The proofs produced have significantly enhanced geometric meaning and fewer nongeometric nondegeneracy conditions.\",\n \"Title: Symbolic computation in the homogeneous geometric model with clifford algebra\\nYear: 2004\\n\\nAbstract: Clifford algebra provides nice algebraic representations for Euclidean geometry via the homogeneous model, and is suitable for doing geometric reasoning through symbolic computation. In this paper, we propose various symbolic computation techniques in Clifford algebra. The content includes representation, elimination, expansion and simplification. Simplification includes contraction, combination and factorization. We apply the techniques to automated geometric deduction, and derive the conclusion in completely factored form in which every factor is a basic invariant. The efficiency of Clifford algebra in doing geometric reasoning is reflected in the short and readable procedure of deriving it sincere geometric factorization.\",\n \"Title: Two Proofs on Max-Min-Max Principle of Jerk Control in Time-Optimal Rectilinear Motion.\\nYear: 2013\\n\\nAbstract: This paper provides two proofs to the well-known principle of max-min-max jerk control in time-optimal jerk-controlled rectilinear motion, whose proof seems to be missing in the literature. One proof is constructive in that it computes and finds out the solution; the other is a disproof in that it shows all other control schemes lead to longer motion time. The ideas employed in the proofs may be used to solve the same kind of problems in a more general setting. © 2012 Springer Basel.\",\n \"Title: Power Series Solution for Isoscallop Tool Path Generation on Free-form Surface with Ball-end Cutter.\\nYear: 2012\\n\\nAbstract: In this paper we propose a method to compute symbolically a curve of constant scallop height on the surface swept by a ball-end cutter when it traces a free-form cutter-contact curve on a free-form surface. We give explicit formulas for power series solutions to the curve of constant scallop height and the next cutter-contact curve, when the initial cutter-contact curve is given. To make the symbolic results useful in practice, we propose a method to approximate the solutions unilaterally in the parametric plane by piecewise Chebyshev polynomials and exponential functions. © 2012 Springer Basel AG.\",\n \"Title: Discrete Interpolation of G01 Codes in 2D Machining under Bounded Accelerations.\\nYear: 2012\\n\\nAbstract: In this paper, a new algorithm is proposed to interpolate G01 codes in 2D milling. Without resorting to smooth curve approximation, the algorithm makes full use of the acceleration bounds to change directions with optimal velocities at the cutter location points designated by the G01 codes, and then makes global lookahead and optimization to generate time-optimal interpolation of the G01 codes. The algorithm is realized and tested on NC wood carving machine, showing that when compared with the classical equal-velocity corner-turning method, the current method can reduce the machining time by 14-115 %, while maintaining better details than the classical method. © 2012 Springer Basel AG.\",\n \"Title: Clifford Algebraic Reduction Method for Automated Theorem Proving in Differential Geometry\\nYear: 1998\\n\\nAbstract: In this paper a new method is proposed for mechanically proving theoremsin the local theory of space curves. The method is based on Ritt-Wu’swell-ordering principle of ordinary differential polynomials, Cliffordalgebraic representation of Euclidean space and equation set solving inClifford algebra formalism. It has been tested by various theorems and seemsto be efficient.\",\n \"Title: B-spline patches and transfinite interpolation method for PDE controlled simulation.\\nYear: 2012\\n\\nAbstract: This paper is to discuss an approach which combines B-spline patches and transfinite interpolation to establish a linear algebraic system for solving partial differential equations and modify the WEB-spline method developed by Klaus Hollig to derive this new idea.First of all,the authors replace the R-function method with transfinite interpolation to build a function which vanishes on boundaries. Secondly,the authors simulate the partial differential equation by directly applying differential operators to basis functions,which is similar to the RBF method rather than Hollig's method.These new strategies then make the constructing of bases and the linear system much more straightforward.And as the interpolation is brought in,the design of schemes for solving practical PDEs can be more flexible. This new method is easy to carry out and suitable for simulations in the fields such as graphics to achieve rapid rendering.Especially when the specified precision is not very high,this method performs much faster than WEB-spline method.\",\n \"Title: Theoretical investigation of an energetic fullerene derivative.\\nYear: 2010\\n\\nAbstract: A self-consistent estimation method for the thermochemical properties of N-methyl-3-(2',4',6'-trinitrobenzene)-fulleropyrrolidine (MTNBFP) is presented. This method is based on enthalpy of formation (Delta H-f(m)circle minus) and enthalpy of combustion obtained from BLYP/DNP calculations of the total energies and frequencies for MTNBFP. The enthalpy of formation was calculated by an optimized set of isodesmic reactions given the available experimental Delta H-f(m)circle minus of relative compounds. MTNBFP has a high enthalpy of formation, 2782.2 kJ/mol. Detonation velocity and detonation pressure were also presented in terms of Kamlet and Jacobs equations. Drop hammer impact sensitivity tests and blasting point per 5 s tests indicate MTNBFP may be a potential candidate primary explosive. To understand the test results well, we proposed a series of chemical reaction mechanisms and interpreted the relationship between impact sensitivity and electronic structures from the viewpoint of nitro group charge, electrostatic potential, and vibrational modes. (C) 2010 Wiley Periodicals, Inc. J Comput Chem 31: 2233-2237, 2010\",\n \"Title: Algebraic Representation, Elimination and Expansion in Automated Geometric Theorem Proving\\nYear: 2002\\n\\nAbstract: Cayley algebra and bracket algebra are important approaches to invariant computing in projective and affine geometries, but there are some difficulties in doing algebraic computation. In this paper we show how the principle \\\"breefs\\\" - bracket-oriented representation, elimination and expansion for factored and shortest results, can significantly simplify algebraic computations. We present several typical examples on automated theorem proving in conics and make detailed discussions on the procedure of applying the principle to automated geometric theorem proving.\",\n \"Title: A symbolic approach to polyhedral scene analysis by parametric calotte propagation*\\nYear: 2008\\n\\nAbstract: Polyhedral scene analysis studies whether a 2D line drawing of a 3D polyhedron is realizable in space, and if so, it gives the results of parameterizing the space of all possible realizations. For generic 2D data, symbolic computation with Grassmann–Cayley algebra is needed in the analysis. In this paper, we propose a method called parametric calotte propagation to solve the realization and parameterization problems for general polyhedral scenes at the same time. In algebraic manipulation, parametric propagation is more efficient than elimination. In applications, it can lead to linear construction sequences for nonspherical polyhedra whose resolvable sequences do not exist.\",\n \"Title: A dynamic interaction mass transfer model for simulating the mass transfer process in extraction columns\\nYear: 2006\\n\\nAbstract: In this study, the population balance model was used to simulate the steady axial profile of the drop size distribution along the extraction column. Based on the population balance model, a dynamic interaction mass transfer model for simulation of the mass transfer process within extraction columns was developed with only one parameter that needs to be optimized. In comparison with conventional mass transfer models, the dynamic interaction mass transfer model has a theoretical basis and is less empirical because the influence of drop breakage and drop coalescence on the drop size distribution and the mass transfer process were carefully considered.\",\n \"Title: Efficient Singleton Consistency by Combining Forward Checking and Bound Consistency\\nYear: 2012\\n\\nAbstract: Maintaining local consistencies can improve the efficiencies of the search algorithms solving constraint satisfaction problems (CSPs). Comparing with arc consistency which is the most widely used local consistency, stronger local consistencies can make the search space smaller while they require higher computational cost. In this paper, we make an attempt on the compromise between the pruning ability and the computational cost. A new local consistency called singleton strong bound consistency (SSBC) and its light version, light SSBC, are proposed. The search algorithm maintaining light SSBC can outperform MAC on a considerable number of problems.\",\n \"Title: A modified single-phase h-bridge PWM rectifier with power decoupling\\nYear: 2012\\n\\nAbstract: It is well known that single-phase PWM rectifiers suffer from second-order ripple power and subsequent dc-link voltage pulsation. To reduce this low frequency fluctuation of dc-link voltage as well as to obtain the sinusoidal grid current with unity power factor, a modified single-phase H-bridge PWM rectifier consisting of a conventional H-bridge rectifier and a pure passive auxiliary circuit is presented. Through utilization of the extra degree of freedom of the H-bridge, the proposed circuit can achieve power decoupling as well as maintaining basic function of a conventional PWM rectifier without additional power devices, which decreases both the product volume and the cost. The corresponding dc-link ripple reduction control method, with the instantaneous power feedforward loop and the dc-link ripple voltage feedback loop, is also provided. The experimental results based on a 1.3kW prototype verified the feasibility of the proposed method.\",\n \"Title: Geometric error control in the parabola-blending linear interpolator.\\nYear: 2013\\n\\nAbstract: This paper considers geometric error control in the parabola-blending linear interpolation method (Zhang, et al., 2011). Classical model of chord error by approximation with contact circle on the parabolas leads to incorrect result. By computing the geometric error directly without accumulating the approximation error and chord error, the authors realize correct geometric error control by establishing inequality constraints on the accelerations of the motion. © 2013 Institute of Systems Science, Academy of Mathematics and Systems Science, CAS and Springer-Verlag Berlin Heidelberg.\",\n \"Title: Network soft partition based on topological potential\\nYear: 2011\\n\\nAbstract: Partitioning of complex networks, esp. of social networks, has been a hotly debated topic in academic circles in recent years. Since actual networks usually contain some boundary nodes that are difficult to assign to a certain community, soft partitioning is under great demand in practical applications. However, at present network partitioning is done mainly by hard partition, soft partition methods are not common. In this context, a soft partition method is proposed hereby based on topological potential and specific algorithms are also provided. This method not only considers the spread of the uncertainty of community-identity of the boundary nodes in the network, but also realizes a quantified representation of the community-identity of the boundary nodes. Experiments show that this method yields results that are consistent with those by classic methods and is more reasonable.\",\n \"Title: Community identification based on clustering coefficient\\nYear: 2011\\n\\nAbstract: Researches show that numerous complex networks have clustering effect. It is an indispensable step to identify node clusters in network, namely community, in which nodes are closely related, in many applications such as identification of ringleaders in anti-criminal and anti-terrorist network, efficient storage of data in Wireless Sensor Network (WSN). At present, most of community identification methods still require the specifications of the number or the scale of community by user and still can't handle boundary nodes. In an attempt to solve these problems, a network community identification method based on clustering coefficient is proposed. This method makes use of individual-centered theory for reference and can automatically determine the number of communities. It is shown through contrastive experiments that communities identified by this method have more reasonable size and closer structure than those obtained by other methods which are also based on the individual-centered theory. Finally, a research direction is proposed of network community identification method based on the individual-centered theory.\",\n \"Title: An Improved Tcp Friendly Rate Control Algorithm For Wireless Networks\\nYear: 2011\\n\\nAbstract: TCP Friendly Rate Control (TFRC) has been widely used in the Internet multimedia streaming applications. However, performance of traditional TFRC algorithm degrades significantly when deployed over wireless networks. Although numerous TFRC variants have been proposed to improve the performance of TFRC over wireless networks, designing a TFRC algorithm with graceful performance both in throughput and fairness still remains a great challenge. In this paper, a novel TFRC algorithm, named TFRC-FIT, is proposed to improve the performance of TFRC over wireless environments. In the proposed approach, the behavior of multiple TFRC flows is simulated in single connection, while the number of simulated flows is adjusted by the network queuing delay. Through this mechanism, TFRC-FIT can fully utilize the capacity of wireless networks, while maintaining good fairness and TCP friendliness. Both theoretical analysis and extensive experiments over hardware network emulator, Planetlab test bed as well as commercial 3G wireless networks are carried out to characterize and validate the performance of our proposed approach.\",\n \"Title: H ∞ stabilisation of networked control systems with time delays and packet losses.\\nYear: 2014\\n\\nAbstract: This paper addresses the H-infinity state feedback stabilisation problem for networked control systems (NCSs) in the presence of time delays and packet losses. By introducing the concept of an effective sensor packet, the NCS is transformed into a new discrete-time switched model, where the parameters have a clear physical meaning and can be easily determined. In this framework, we derive the stability conditions of the closed-loop system in the H-infinity sense, and also provide the corresponding H-infinity stabilising controller design method. Finally, we give simulation and experimental results to demonstrate the effectiveness of the proposed approaches.\",\n \"Title: A survivable routing protocol for two-layered LEO/MEO satellite networks\\nYear: 2014\\n\\nAbstract: Due to the rapid development of space communication, satellite networks will be confronted with more complex space environment in future, which poses the important demand on the design of the survivable and efficient routing protocols. Among satellite networks, two-layered Low Earth Orbit (LEO)/Medium Earth Orbit (MEO) satellite networks (LMSNs) have become an attractive architecture for their better communication service than single-layered satellite networks. To determine the topological dynamics of LMSN, the satellite group and group manager (SGGM) method is a prevalent strategy. However, it can not precisely capture the topological dynamics of the LEO layer, which may result in the unreliability of data transmission. Besides, most existing routing protocols based on the SGGM method will collapse once any top satellite fails. To overcome both limitations, this paper proposes a new topology control strategy for LMSNs. The proposed strategy determines the snapshot in terms of the topological change of the LEO layer, which ensures the topological consistency of routing calculation. Moreover, a new survivable routing protocol (SRP) is presented for LMSNs by combining both centralized and distributed routing strategies. The SRP can provide strong survivability under the LEO or MEO satellite failure. Besides, it can also achieve the minimum delay routing provided the MEO layer can effectively work. The performance of SRP is also evaluated by simulation and analysis.\",\n \"Title: Spectral Characterizations of the Corona of a Cycle and Two Isolated Vertices\\nYear: 2014\\n\\nAbstract: For a cycle C n , let C n 2 K 1 be the graph obtained from C n by attaching two pendant edges to each vertex of C n . In this paper, we prove that C n 2 K 1 is determined by its signless Laplacian spectrum when n 32, 64. We also show that C n 2 K 1 is determined by its Laplacian spectrum.\",\n \"Title: Analyzing Integrated Cost-Schedule Risk for Complex Product Systems R&D Projects.\\nYear: 2014\\n\\nAbstract: The vast majority of the research efforts in project risk management tend to assess cost risk and schedule risk independently. However, project cost and time are related in reality and the relationship between them should be analyzed directly. We propose an integrated cost and schedule risk assessment model for complex product systems R&D projects. Graphical evaluation review technique (GERT), Monte Carlo simulation, and probability distribution theory are utilized to establish the model. In addition, statistical analysis and regression analysis techniques are employed to analyze simulation outputs. Finally, a complex product systems R&D project as an example is modeled by the proposed approach and the simulation outputs are analyzed to illustrate the effectiveness of the risk assessment model. It seems that integrating cost and schedule risk assessment can provide more reliable risk estimation results.\",\n \"Title: Sliding-Mode Predictive Control of Networked Control Systems Under a Multiple-Packet Transmission Policy\\nYear: 2014\\n\\nAbstract: This paper addresses the output feedback stabilization problem for a class of networked control systems (NCSs) under a multiple-packet transmission policy. To compensate the time delays and packet losses, a sliding-mode predictive controller is constructed by taking full advantage of network transmission capacity. A Kalman predictor is used to estimate the current and predict the future plant states, while sliding-mode control (SMC) is employed to compute the control sequences. The estimated state errors are transformed into a system disturbance, and the stability of the closed-loop NCSs is guaranteed by constructing the robust stabilizing SMC controller. Simulation and experimental results are given to demonstrate the effectiveness of the proposed approach.\",\n \"Title: An improved algorithm of mining preferred browsing paths\\nYear: 2014\\n\\nAbstract: Existing algorithms of mining preferred browsing paths just consider the influence of user visiting times, but ignore the accuracy influenced by other factors. In order to solve the problem, an improved algorithm which imports page similarity and support-preference concepts is proposed. Firstly a Web-log-based user access matrix is set up. Then by calculating the angel cosine similarity and support-preference, the 2-items preferred browsing sub-path set is obtained. Finally all the sub-paths are combined. Experiments show that the algorithm is more accurate and efficient.\",\n \"Title: Reduction among bracket polynomials\\nYear: 2014\\n\\nAbstract: In this paper, we propose an SL(n)-invariant division of SL(n)-invariant polynomials by establishing an admissible order among the invariant polynomials in normal form. The invariant division leads to an invariant Gröbner basis theory. The invariant division is closely related to multivariate coordinate polynomial division. This feature leads to a proof of the result that if f1,..., fk are SL(n,K)-invariant where K is an arbitrary field, possibly of positive characteristic, then the invariant ideal generated by them is the intersection of the ideal generated by the fi in the polynomial ring of coordinates with the algebra of invariants.\",\n \"Title: Observer-based cluster consensus control of high-order multi-agent systems\\nYear: 2015\\n\\nAbstract: The cluster consensus problem of high-order multi-agent systems is considered in this note with an observer-based control scheme. Sufficient condition for cluster consensus is presented in terms of easily checkable algebraic topology criterion. It is found that intra-cluster balanced topologies with antisymmetric (i.e., bidirectional and opposite signed) inter-cluster links promote cluster consensus, irrelevant of the magnitudes of intra-cluster coupling weights. The effectiveness of the theoretical results is illustrated by a numerical example.\",\n \"Title: Low-Load Survivable Routing Protocol for LEO Satellite Networks\\nYear: 2015\\n\\nAbstract: In this paper, we put forward the methods of footprint shift and separation for the virtual topology model of LEO satellite networks, which can reduce the effects of the Polar Regions and cross-seam inter-satellite-links (ISLs) on the virtual topology and improve the utilization of ISLs. Furthermore, a low-load survivable routing protocol called Virtual Snapshot Routing Protocol is proposed for LEO satellite networks. The proposed protocol not only reduces the routing computation and storage overheads, but also provides good routing survivability under random satellite failure. The experimental results confirm our conclusions.\",\n \"Title: Improving degree-based variable ordering heuristics for solving constraint satisfaction problems.\\nYear: 2016\\n\\nAbstract: In this paper, we improved two classical degree-based variable ordering heuristics, $$\\\\\\\\frac{\\\\\\\\textit{Dom}}{\\\\\\\\textit{Ddeg}}$$DomDdeg and $$\\\\\\\\frac{\\\\\\\\textit{Dom}}{\\\\\\\\textit{Wdeg}}$$DomWdeg. We propose a method using the summation of constraint tightness in degree-based heuristics. We also propose two methods to calculate dynamic constraint tightness for binary extensional constraints and non-binary intensional constraints respectively. Our work shows how constraint tightness can be practically used to guide search. We performed a number of experiments on some benchmark instances. The results have shown that, the new heuristics improve the classical ones by both computational time and search tree nodes and they are more efficient than some other successful heuristics on the instances where the classical heuristics work well.\",\n \"Title: Stabilization and Separation Principle of Networked Control Systems Using the T–S Fuzzy Model Approach\\nYear: 2015\\n\\nAbstract: This paper is concerned with the stabilization problem for a class of discrete-time networked control systems (NCSs) with bounded time delays and packet losses. The controlled plant is represented by a Takagi-Sugeno fuzzy model, and both the state feedback control and output feedback control cases are considered. By guaranteeing the decrement of Lyapunov functional at each control signal updating ...\",\n \"Title: 3D Moth-inspired chemical plume tracking and adaptive step control strategy.\\nYear: 2016\\n\\nAbstract: This paper presents an adaptive-step-control ASC strategy for autonomous chemical plume tracking CPT in both 2D and 3D spaces. The aim of CPT is tracking a target chemical flow back to its source and declaring its location. While the majority of related works are based on wheeled robots in 2D, the rapid development of rotorcrafts makes 3D plume tracking possible. Hence, in this paper, we first discuss the generalized form of moth-inspired CPT work flow, then extend the orthodox moth-inspired CPT from 2D to 3D. More importantly, we also propose an ASC strategy to deal with the ?passing-by? problem and to gain better control performance. Simulation results are given to demonstrate that ASC can solve the inflexibility caused by ?hard? step length in diffusive airflow, because it can autonomously adjust the step lengths of the robot according to the status of the environment, enabling our robot to stride confidently in the low chemical concentration areas and focus on highly suspicious areas.\",\n \"Title: Detection And Imaging Of Moving Objects With Multichannel Sar System\\nYear: 2015\\n\\nAbstract: It presents an approach of moving target detection by multi-channel synthetic aperture radar (SAR), which the receivers displaced in heterogeneous arrays. The approach achieves the maximal unaliased band of radial velocities and Minimum detectable velocity, retains full resolution SAR images, the multiple measurement vectors were employed for improvement of the target focusing, and requires no increase in receiver samples. The proposed multi-channel synthetic aperture radar system and the associated signal processing are detailed, and the approach is numerically demonstrated via simulation and raw data experiments.\",\n \"Title: 3d Moth-Inspired Chemical Plume Tracking\\nYear: 2015\\n\\nAbstract: This paper analyzes the conventional moth inspired chemical plume tracking, and presents a 3D moth-inspired CPT using multi-sensors. The aim of CPT is tracking a target chemical flow back to its source and declaring its location. While the moths acturally perform their CPT in 3D space, the majority of related works are based on wheels robots in 2D. Nowadays the rapid development of rotorcrafts makes the 3D plume tracking possible. Hence in this paper, we first present a detailed analysis of moth-inspired CPT work flow, then extend the orthodox moth-inspired CPT from 2D to 3D. Our simulation results demonstrate that our the multi-sensor CPT strategy proposed is a feasible and efficient method to locate odour source.\",\n \"Title: Symbolic Geometric Reasoning with Advanced Invariant Algebras.\\nYear: 2015\\n\\nAbstract: In symbolic geometric reasoning, the output of an algebraic method is expected to be geometrically interpretable, and the size of the middle steps is expected to be sufficiently small for computational efficiency. Invariant algebras often perform well in meeting the two expectations for relatively simple geometric problems. For example in classical geometry, symbolic manipulations based on basic invariants such as squared distances, areas and volumes often have great performance in generating readable proofs. For more complicated geometric problems, the basic invariants are still insufficient and may not generate geometrically meaningful results. An advanced invariant is a monomial in an \\\\\\\"advanced algebra\\\\\\\", and can be expanded into a polynomial of basic invariants that are also included in the algebra. In projective incidence geometry, Grassmann-Cayley algebra and Cayley bracket algebra are an advanced algebra in which the basic invariants are determinants of homogeneous coordinates of points, and the advanced invariants are Cayley brackets. In Euclidean conformal geometry, Conformal Geometric Algebra and null bracket algebra are an advanced algebra where the basic invariants are squared distances between points and and signed volumes of simplexes, and the advanced invariants are Clifford brackets. This paper introduces the above advanced invariant algebras together with their applications in automated geometric theorem proving. These algebras are capable of generating extremely short and readable proofs. For projective incidence theorems, the proofs generated are usually two-termed in that the conclusion expression maintains two-termed during symbolic manipulations. For Euclidean geometry, the proofs generated are mostly one-termed or two-termed.\",\n \"Title: EDA-based output tracking control for networked control systems with time delays and packet losses\\nYear: 2011\\n\\nAbstract: This paper is concerned with the output tracking problem of a class of networked control systems (NCSs) in the presence of time delays and packet losses. The motivation comes from that existing networked output tracking methods can not work very well if there is modeling error or non-zero disturbance in NCSs. To deal with the aforementioned issues, an Estimation of Distribution Algorithm (EDA) based output tracking control approach, which is more robust to modeling error and has better disturbance rejection capabilities, is proposed in this paper. Simulation and experimental results are given to demonstrate the effectiveness of the proposed approach. © 2011 IEEE.\",\n \"Title: Efficient Spatio-Temporal Tactile Object Recognition with Randomized Tiling Convolutional Networks in a Hierarchical Fusion Strategy.\\nYear: 2016\\n\\nAbstract: Robotic tactile recognition aims at identifying target objects or environments from tactile sensory readings. The advancement of unsupervised feature learning and biological tactile sensing inspire us proposing the model of 3T-RTCN that performs spatio-temporal feature representation and fusion for tactile recognition. It decomposes tactile data into spatial and temporal threads, and incorporates the strength of randomized tiling convolutional networks. Experimental evaluations show that it outperforms some state-of-the-art methods with a large margin regarding recognition accuracy, robustness, and fault-tolerance; we also achieve an order-of-magnitude speedup over equivalent networks with pretraining and fine-tuning. Practical suggestions and hints are summarized in the end for effectively handling the tactile data.\",\n \"Title: Scaled Cluster Consensus Of Discrete-Time Multi-Agent Systems With General Directed Topologies\\nYear: 2016\\n\\nAbstract: This note proposes a notion of scaled cluster consensus, wherein the final consensus states within different clusters converge to prescribed ratios. Unlike most results in existing literature on cluster consensus, no constraints are imposed on the system topologies under the designed protocol, i.e. the agents are not required to possess any cluster affiliation information of others. For the delay-free case, an explicit scaled cluster consensus function is provided by exploring the characteristics of stochastic matrices. Diverse input delays and asymmetric communication delays are both considered, and sufficient condition for scaled cluster consensus is derived based on frequency domain analysis. Finally, numerical examples are given to illustrate the effectiveness of the presented results.\",\n \"Title: Road Extraction Base On Zernike Algorithm On Sar Image\\nYear: 2016\\n\\nAbstract: In the SAR image, the road recognition is conducive to the SAR image interpretation using in traffic monitoring, GIS information, and Geographical mapping, but the road is difficult to extract for blurring by speckles. Based on mathematical morphology, a new road extraction method is proposed in this paper. After the pre-processing to reduce the influence of the speckles, the binary image information is gained by the Otsu method. Then, the information without the road information is removed by the mathematical morphological opening operation. In addition, after being corroded and reconstructed, the complete road boundary is detected by edge detection operations, and the result is relocated on the original image. Compared with the morphological method, road extraction is done by applying the Zernike moments. Orthogonal property of Zernike moment basis functions guarantees the statistically independence of coefficients in extracted feature vectors. The proposed method is evaluated on the SAR Image data. Results show that presented method outperforms recently presented works due to its high performance.\",\n \"Title: GreenLA: green linear algebra software for GPU-accelerated heterogeneous computing.\\nYear: 2016\\n\\nAbstract: While many linear algebra libraries have been developed to optimize their performance, no linear algebra library considers their energy efficiency at the library design time. In this paper, we present GreenLA - an energy efficient linear algebra software package that leverages linear algebra algorithmic characteristics to maximize energy savings with negligible overhead. GreenLA is (1) energy efficient: it saves up to several times more energy than the best existing energy saving approaches that do not modify library source codes; (2) high performance: its performance is comparable to the highly optimized linear algebra library MAGMA; and (3) transparent to applications: with the same programming interface, existing MAGMA users do not need to modify their source codes to benefit from GreenLA. Experimental results demonstrate that GreenLA is able to save up to three times more energy than the best existing energy saving approaches while delivering similar performance compared to the state-of-the-art linear algebra library MAGMA.\",\n \"Title: Riemann Tensor Polynomial Canonicalization by Graph Algebra Extension.\\nYear: 2017\\n\\nAbstract: Tensor expression simplification is an \\\\\\\"ancient\\\\\\\" topic in computer algebra, a representative of which is the canonicalization of Riemann tensor polynomials. Practically fast algorithms exist for monoterm canonicalization, but not for multiterm canonicalization. Targeting the multiterm difficulty, in this paper we establish the extension theory of graph algebra, and propose a canonicalization algorithm for Riemann tensor polynomials based on this theory.\",\n \"Title: Advancing the incremental fusion of robotic sensory features using online multi-kernel extreme learning machine.\\nYear: 2017\\n\\nAbstract: Robot recognition tasks usually require multiple homogeneous or heterogeneous sensors which intrinsically generate sequential, redundant, and storage demanding data with various noise pollution. Thus, online machine learning algorithms performing efficient sensory feature fusion have become a hot topic in robot recognition domain. This paper proposes an online multi-kernel extreme learning machine (OM-ELM) which assembles multiple ELM classifiers and optimizes the kernel weights with a p-norm formulation of multi-kernel learning (MKL) problem. It can be applied in feature fusion applications that require incremental learning over multiple sequential sensory readings. The performance of OM-ELM is tested towards four different robot recognition tasks. By comparing to several state-of-the-art online models for multi-kernel learning, we claim that our method achieves a superior or equivalent training accuracy and generalization ability with less training time. Practical suggestions are also given to aid effective online fusion of robot sensory features.\",\n \"Title: Neural-network-based sliding-mode control for multiple rigid-body attitude tracking with inertial information completely unknown.\\nYear: 2017\\n\\nAbstract: This paper addresses the multiple rigid bodies attitude tracking control problem in presence of inertial information completely unknown. In order to attenuate the effect of unknown parameters, the neural networks technology is employed to approximate the unknown nonlinear terms derived from the controller design procedure. Based on lumped tracking errors between neighbors, a novel sliding-mode control protocol is proposed and an approximate expression of inverse matrix is also applied to avoid possible singular phenomenon. The Lyapunov theory is applied to guarantee that all signals in the closed-loop system are uniformly ultimately bounded and that all rigid-body attitude synchronize to the desired trajectory with bounded residual errors. Compared with prior works, the dynamics of each rigid-body discussed here is more general and does not require the assumption linearity in the unknown parameters or the matching condition. Moreover, the bounded residual errors can be reduced as small as desired by selected suitable parameters. Simulation results demonstrate the effectiveness of the proposed method.\",\n \"Title: An efficient public-key searchable encryption scheme secure against inside keyword guessing attacks.\\nYear: 2018\\n\\nAbstract: How to efficiently search over encrypted data is an important and interesting problem in the cloud era. To solve it, Boneh etal.introduced the notion of public key encryption with keyword search (PEKS), in 2004. However, in almost all the PEKS schemes an inside adversary may recover the keyword from a given trapdoor by exhaustively guessing the keywords offline. How to resist the inside keyword guessing attack in PEKS remains a hard problem.In this paper we propose introduce the notion of Public-key Authenticated Encryption with Keyword Search (PAEKS) to solve the problem, in which the data sender not only encrypts a keyword, but also authenticates it, so that a verifier would be convinced that the encrypted keyword can only be generated by the sender. We propose a concrete and efficient construction of PAEKS, and prove its security based on simple and static assumptions in the random oracle model under the given security models. Experimental results show that our scheme enjoys a comparable efficiency with Boneh etal.s scheme.\",\n \"Title: Narrowing Support Searching Range in Maintaining Arc Consistency for Solving Constraint Satisfaction Problems.\\nYear: 2017\\n\\nAbstract: Arc consistency is the most popular filtering technique for solving constraint satisfaction problems. Constraint check plays a central role in establishing arc consistency. In this paper, we propose a method to save constraint checks in maintaining coarse-grained arc consistency during backtracking search for solving the constraint satisfaction problems. We reduce the support searching range by utilizing the information generated by an AC3.1 algorithm at preprocessing step. Compared with the existing maintaining arc consistency (MAC) algorithms, the proposed MAC3(be) algorithm saves constraint checks without maintaining additional data structures at each search tree node. Our experimental results show that MAC3(be) saves both constraint checks and CPU time while solving some benchmark problems.\",\n \"Title: FESR: A Framework for Eliciting Security Requirements Based on Integration of Common Criteria and Weakness Detection Formal Model\\nYear: 2017\\n\\nAbstract: It is critical and foremost to come up with the corresponding security requirements first which the following implementations are based on. However, previous security requirement elicitation work based on Common Criteria (CC) rarely addresses the detailed elicitation process of threats from specific functional requirements, which thus results in the widen gap between specific functional requirements and their corresponding threats. To this end, this paper proposes a framework for eliciting corresponding security requirements of specific functional requirements from the requirements specification. A formal model is built in the framework to assist requirement analysts in half-automatic collecting threats. To enhance the framework's automaticity and reusability, a security property base is constructed based on authoritative sources of security properties to support the framework. A practical information system is applied to verify the framework's practicability. Finally the framework's advantages and limitations are discussed thoroughly compared with previous approaches and useful insights are revealed.\",\n \"Title: Correcting soft errors online in fast fourier transform\\nYear: 2017\\n\\nAbstract: While many algorithm-based fault tolerance (ABFT) schemes have been proposed to detect soft errors offline in the fast Fourier transform (FFT) after computation finishes, none of the existing ABFT schemes detect soft errors online before the computation finishes. This paper presents an online ABFT scheme for FFT so that soft errors can be detected online and the corrupted computation can be terminated in a much more timely manner. We also extend our scheme to tolerate both arithmetic errors and memory errors, develop strategies to reduce its fault tolerance overhead and improve its numerical stability and fault coverage, and finally incorporate it into the widely used FFTW library - one of the today's fastest FFT software implementations. Experimental results demonstrate that: (1) the proposed online ABFT scheme introduces much lower overhead than the existing offline ABFT schemes; (2) it detects errors in a much more timely manner; and (3) it also has higher numerical stability and better fault coverage.\",\n \"Title: Parastack: efficient hang detection for MPI programs at large scale\\nYear: 2017\\n\\nAbstract: While program hangs on large parallel systems can be detected via the widely used timeout mechanism, it is difficult for the users to set the timeout - too small a timeout leads to high false alarm rates and too large a timeout wastes a vast amount of valuable computing resources. To address the above problems with hang detection, this paper presents ParaStack, an extremely lightweight tool to detect hangs in a timely manner with high accuracy, negligible overhead with great scalability, and without requiring the user to select a timeout value. For a detected hang, it provides direction for further analysis by telling users whether the hang is the result of an error in the computation phase or the communication phase. For a computation-error induced hang, our tool pinpoints the faulty process by excluding hundreds and thousands of other processes. We have adapted ParaStack to work with the Torque and Slurm parallel batch schedulers and validated its functionality and performance on Tianhe-2 and Stampede that are respectively the world's current 2nd and 12th fastest supercomputers. Experimental results demonstrate that ParaStack detects hangs in a timely manner at negligible overhead with over 99% accuracy. No false alarm is observed in correct runs taking 66 hours at scale of 256 processes and 39.7 hours at scale of 1024 processes. ParaStack accurately reports the faulty process for computation-error induced hangs.\",\n \"Title: Test sequencing for sequential system diagnosis with precedence constraints and imperfect tests.\\nYear: 2017\\n\\nAbstract: We study sequential system testing with the objective of minimizing the total expected testing costs. The goal is to discover the state of a system that consists of a set of independent components. The state of the system depends on the states of the individual components and is classified as working if at least a pre-specified number of components are working, otherwise it is said to be down. During the diagnostic testing procedure, components are tested one by one, in a pre-specified order. The resulting test sequencing problem is NP-hard with general precedence constraints even when the tests are perfect, in which case a component test always reports the correct state of the component. In this work, we will also consider the additional complication that tests can be imperfect, meaning that a test can report a component to be working when it is actually down, and vice versa. We develop a tabu search algorithm together with a simulation-based evaluation technique that incorporates importance sampling to find high-quality solutions within limited runtimes.\",\n \"Title: Multi-mode resource leveling in projects with mode-dependent generalized precedence relations.\\nYear: 2018\\n\\nAbstract: •We study the multi-mode project resource leveling problem.•Mode-dependent generalized precedence relations are considered.•We propose two solution approaches: decomposition and integration.•We devise a hybrid estimation of distribution algorithm and two descent algorithms.•Computational results show that our algorithms outperform the baseline algorithms.\",\n \"Title: Adaptive Fuzzy Control for Pure-Feedback Nonlinear Systems With Nonaffine Functions Being Semibounded and Indifferentiable.\\nYear: 2018\\n\\nAbstract: An adaptive fuzzy control approach is proposed for nonaffine pure-feedback systems with nonaffine functions being semibounded and possibly indifferentiable. A semibounded and continuous condition for nonaffine function is presented to guarantee the controllability of system. To overcome the difficulty from this mild condition, a compact set is introduced, and the nonaffine nonlinear functions are ...\",\n \"Title: Fast Straightening Algorithm For Bracket Polynomials Based On Tableau Manipulations\\nYear: 2018\\n\\nAbstract: Straightening is the most fundamental symbolic manipulation in bracket algebra. Young's classical algorithm and White's more recent algorithm have poor performance in straightening bracket polynomials of degree >4. Rota's straightening algorithm based on Capelli operator is generally superior to the former two in speed, but still performs badly when the degree reaches 5.In this paper, a new operator is defined in bracket algebra based on tableau manipulations, and is simpler than Capelli operator. A new straightening algorithm is then proposed, and is superior to the above three algorithms by a speedup of two order of magnitude on average by testing over 500 examples in the past two years.\",\n \"Title: COMPI: Concolic Testing for MPI Applications\\nYear: 2018\\n\\nAbstract: MPI is widely used as the bedrock of HPC applications, but there are no effective systematic software testing techniques for MPI programs. In this paper we develop COMPI, the first practical concolic testing tool for MPI applications. COMPI tackles two major challenges. First, it provides an automated testing tool for MPI programs - it performs concolic execution on a single process and records branch coverage across all. Infusing MPI semantics such as MPI rank and MPI_COMM_WORLD into COMPI enables it to automatically direct testing with various processes' executions as well as automatically determine the total number of processes used in the testing. Second, COMPI employs three techniques to effectively control the cost of testing as too high a cost may prevent its adoption. By capping input values, COMPI is made practical as too large an input can make the testing extremely slow and sometimes even fail as memory needed could exceed the computing platform's memory limit. With two-way instrumentation, we reduce the unnecessary memory and I/O overhead of COMPI and the target program. With constraint set reduction, COMPI keeps significantly fewer constraints by removing redundant ones in the presence of loops so as to avoid redundant tests against these branches. Our evaluation of COMPI uncovered four new bugs in a complex application and achieved 69-86% branch coverage which far exceeds the 1.8-38% coverage achieved via random testing.\",\n \"Title: Non-intrusively Avoiding Scaling Problems in and out of MPI Collectives\\nYear: 2018\\n\\nAbstract: It has been observed that scaling problems are highly likely to manifest when MPI applications are launched at a large scale where the scale is characterized by the degree of parallelism and the problem size. As the complexity of MPI collectives is directly impacted by both parallelism scale and problem size, their use often triggers scaling problems. Scaling problems' root cause can be outside of MPI libraries and these can be easily exposed via the dynamic interaction between user code and MPI library as the scale goes up. Specifically, irregular collectives suffer the most as the C int displacement array can easily be corrupted with integer overflow. Scaling problems can also result from a bug inside the released MPI libraries due to the lack of a systematic testing of MPI libraries as well as the platform or environment dependency of some scaling problems. Hence it is important for library users to perform testing on their platform to expose potential scaling problems. Fixing a scaling problem is challenging, and thus it usually takes much time for users to wait for an official fix, which sometimes is not even possible due to the difficulty of bug reproduction, root-cause identification, and fix development. To improve users' productivity, we establish the necessity of user side testing and provide a protection layer to avoid scaling problems non-intrusively, i.e., without requiring any changes to the MPI library or user programs. This provides an immediate remedy when an official fix is not readily available.\",\n \"Title: A Novel Strategy of Combining Variable Ordering Heuristics for Constraint Satisfaction Problems.\\nYear: 2018\\n\\nAbstract: Variable ordering heuristic plays a central role in solving constraint satisfaction problems. Many heuristics have been proposed and well-studied. In order to take advantage of the fact that many generic variable ordering heuristics work well for different problems, we propose a novel method in this paper, namely ParetoHeu, to combine variable ordering heuristics. At each node of the search tree, a set of candidate variables is generated by a new strategy based on Pareto optimality and a variable is selected from the set randomly. The method is easy to be implemented in constraint solvers. The experiments on various benchmark problems show that ParetoHeu is more efficient than both the participant heuristics which are popular in constraint solvers. It is also more robust than some classical strategies which have been used to combine variable ordering heuristics.\",\n \"Title: End-to-End ConvNet for Tactile Recognition Using Residual Orthogonal Tiling and Pyramid Convolution Ensemble.\\nYear: 2018\\n\\nAbstract: Tactile recognition enables robots identify target objects or environments from tactile sensory readings. The recent advancement of deep learning and biological tactile sensing inspire us proposing an end-to-end architecture ROTConvPCE-mv that performs tactile recognition using residual orthogonal tiling and pyramid convolution ensemble. Our approach uses stacks of raw frames and tactile flow as dual input, and incorporates the strength of multi-layer OTConvs (orthogonal tiling convolutions) organized in a residual learning paradigm. We empirically demonstrate that OTConvs have adjustable invariance capability to different input transformations such as translation, rotation, and scaling. To effectively capture multi-scale global context, a pyramid convolution structure is attached to the concatenated output of two residual OTConv pathways. The extensive experimental evaluations show that ROTConvPCE-mv outperforms several state-of-the-art methods with a large margin regarding recognition accuracy, robustness, and fault-tolerance. Practical suggestions and hints are summarized throughout this paper to facilitate the effective recognition using tactile sensory data.\",\n \"Title: Fault tolerant one-sided matrix decompositions on heterogeneous systems with GPUs.\\nYear: 2018\\n\\nAbstract: Current algorithm-based fault tolerance (ABFT) approach for one-sided matrix decomposition on heterogeneous systems with GPUs have following limitations: (1) they do not provide sufficient protection as most of them only maintain checksum in one dimension; (2) their checking scheme is not efficient due to redundant checksum verifications; (3) they fail to protect PCIe communication; and (4) the checksum calculation based on a special type of matrix multiplication is far from efficient. By overcoming the above limitations, we design an efficient ABFT approach providing stronger protection for one-sided matrix decomposition methods on heterogeneous systems. First, we provide full matrix protection by using checksums in two dimensions. Second, our checking scheme is more efficient by prioritizing the checksum verification according to the sensitivity of matrix operations to soft errors. Third, we protect PCIe communication by reordering checksum verifications and decomposition steps. Fourth, we accelerate the checksum calculation by 1.7x via better utilizing GPUs.\",\n \"Title: Ciphertext-Policy Attribute-Based Encrypted Data Equality Test and Classification.\\nYear: 2018\\n\\nAbstract: Thanks to the ease of access and low expenses, it is now popular for people to store data in cloud servers. To protect sensitive data from being leaked to the outside, people usually encrypt the data in the cloud. However, management of these encrypted data becomes a challenging problem, e.g. data classification. Besides, how to selectively share data with other users is also an important and interesting problem in cloud storage. In this paper, we focus on ciphertext-policy attribute based encryption with equality test (CP-ABEET). People can use CP-ABEET to implement not only flexible authorization for the access to encrypted data, but also efficient data label classification, i.e. test of whether two encrypted data contain the same message. We construct an efficient CP-ABEET scheme, and prove its security based on a reasonable number-theoretic assumption. Compared with the only existing CP-ABEET scheme, our construction is more efficient in key generation, and has shorter attribute-related secret keys and better security.\",\n \"Title: EFTG: Efficient and Flexible Top-K Geo-textual Publish/Subscribe.\\nYear: 2018\\n\\nAbstract: With the popularity of mobile networks and smartphones, geo-textual publish/subscribe messaging has attracted wide attention. Different from the traditional publish/subscribe format, geo-textual data is published and subscribed in the form of dynamic data flow in the mobile network. The difference creates more requirements for efficiency and flexibility. However, most of the existing Top-k geo-textual publish/subscribe schemes have the following deficiencies: (1) All publications have to be scored for each subscription, which is not efficient enough. (2) A user should take time to set a threshold for each subscription, which is not flexible enough. Therefore, we propose an efficient and flexible Top-k geo-textual publish/subscribe scheme. First, our scheme groups publish and subscribe based on text classification. Thus, only a few parts of related publications should be scored for each subscription, which significantly enhances efficiency. Second, our scheme proposes an adaptive publish/subscribe matching algorithm. The algorithm does not require the user to set a threshold. It can adaptively return Top-k results to the user for each subscription, which significantly enhances flexibility. Finally, theoretical analysis and experimental evaluation verify the efficiency and effectiveness of our scheme.\",\n \"Title: Telepresence control of humanoid robot via high-frequency phase-tagged SSVEP stimuli\\nYear: 2016\\n\\nAbstract: This paper presents a high-frequency steady-state visual evoked potential-based model for a brain-controlled humanoid robot. An advantage of this model is to reduce subjects' fatigue by using visual stimuli with a frequency of 30Hz. This study optimizes the stimulus patterns to increase the brain signals and applies a fuzzy-based classification approach to identify human mental activities and convert them into control commands. Seven subjects successfully navigated a NAO humanoid robot to walk through a map with obstacle avoidance based on live video feedback. The on-line robot navigation experiment reached the average control success rate of 94.26% and an average collision of 1.8 times during a mission.\",\n \"Title: Efficient concolic testing of MPI applications\\nYear: 2019\\n\\nAbstract: Software testing is widely used in industry, but its application in the High Performance Computing area has been scarce. Concolic testing, that automates testing via generation of inputs, has been highly successful for desktop applications and thus recent work on the COMPI tool has extended it to MPI programs. However, COMPI has two limitations. First, it requires the user to specify an upper limit on input size – if the chosen limit is too big, considerable time is wasted and if the chosen limit is too small, the branch coverage achieved is limited. Second, COMPI does not support floating point arithmetic that is common in HPC applications. \\n\\nIn this paper, we overcome the above limitations as follows. We propose input tuning that eliminates the need for users to set hard limits and generates inputs such that the testing achieves high coverage while avoiding waste of testing time by selecting suitable input sizes. Moreover, we enable handling of floating point data types and operations and demonstrate that the efficiency of constraint solving can be improved if we rely on the use of reals instead of floating point values. Our evaluation demonstrates that with input tuning the coverage we achieve in 10 minutes is typically higher than the coverage achieved in 1 hour when input tuning is not used. Without input tuning, 9.6-57.1% loss in coverage occurs for a real-world physics simulation program. For the physics simulation program, using our floating-point extension that uses reals covers 46 more branches than without using the extension. Also, we cover 122 more branches when solving floating-point constraints using reals rather than directly using floating-point numbers.\",\n \"Title: Automated Theorem Proving Practice with Null Geometric Algebra.\\nYear: 2019\\n\\nAbstract: This paper presents the practice of automated theorem proving in Euclidean geometry with null geometric algebra, a combination of Conformal Geometric Algebra and Grassmann-Cayley algebra. This algebra helps generating extremely short and readable proofs: The proofs generated are mostly one-termed or two-termed. Besides, the theorems are naturally extended from qualitative description to quantitative characterization by removing one or more geometric constraints from the hypotheses.\",\n \"Title: Quantum Algorithm Design: Techniques and Applications.\\nYear: 2019\\n\\nAbstract: In recent years, rapid developments of quantum computer are witnessed in both the hardware and the algorithm domains, making it necessary to have an updated review of some major techniques and applications in quantum algorithm design.\",\n \"Title: Designated-server identity-based authenticated encryption with keyword search for encrypted emails.\\nYear: 2019\\n\\nAbstract: In encrypted email system, how to search over encrypted cloud emails without decryption is an important and practical problem. Public key encryption with keyword search (PEKS) is an efficient solution to it. However, PEKS suffers from the complex key management problem in the public key infrastructure. Its variant in the identity-based setting addresses the drawback, however, almost all the schemes does not resist against offline keyword guessing attacks (KGA) by inside adversaries. In this work we introduce the notion of designated-server identity-based authenticated encryption with keyword search (dIBAEKS), in which the email sender authenticates the message while encrypting so that no adversary including the server can launch offline KGA. Furthermore, we strengthen the security requirement so that only the designated server has the capability to search over encrypted emails for receivers. We formally define dIBAEKS and its security models, and propose two dIBAEKS constructions using Type-I and Type-III bilinear pairing, respectively. We compare our schemes with some related IBEKS schemes in the literature, and do experiments to demonstrate its efficiency. Although they are slightly less computationally efficient than but still comparable with the related schemes, our schemes provide stronger security guarantee and better protect users’ privacy.\",\n \"Title: nD polyhedral scene reconstruction from single 2D line drawing by local propagation\\nYear: 2004\\n\\nAbstract: In this paper, we study the problem of reconstructing the polyhedral structures and geometric positions of a general nD polyhedral scene from a single 2D line drawing. With the idea of local construction and propagation, we propose several powerful techniques for structural reconstruction (i.e. face identification) and geometric reconstruction (i.e. realizability and parametrization). Our structural reconstruction algorithm can handle 3D solids of over 10,000 faces efficiently, outperforming any other existing method. Our geometric reconstruction algorithm can lead to amazing simplification in symbolic manipulation of the geometric data, and can be used to find linear construction sequences for non-spherical polyhedra.\",\n \"Title: Saving constraint checks in maintaining coarse-grained generalized arc consistency\\nYear: 2019\\n\\nAbstract: Constraint check plays a central role in establishing generalized arc consistency which is widely used to solve constraint satisfaction problems. In this paper, we propose a new generalized arc consistency algorithm, called GTR, which ensures that the tuples that have been checked to be allowed by a constraint will never be checked again. For each constraint, GTR maintains a dynamic list of the tuples that were checked to be allowed by this constraint and check their validities to identify some values with supports. It is equipped with a mechanism avoiding redundant validity checks. The basic GAC3 algorithm is employed to find a support for the rest values and to add new tuples to the dynamic list. The experiments show that maintaining GTR during search saves a number of constraint checks. It also brings some improvements over cpu time while solving some CSPs with tight constraints.\",\n \"Title: An Effective Genetic Algorithm For The Resource Levelling Problem With Generalised Precedence Relations\\nYear: 2018\\n\\nAbstract: Resource levelling aims to obtain a feasible schedule to minimise the resource usage fluctuations during project execution. It is of crucial importance in project scheduling to ensure the effective use of scarce and expensive renewable resources, and has been successfully applied to production environments, such as make-to-order and engineering-to-order systems. In real-life projects, general temporal relationships are often needed to model complex time-dependencies among activities. We develop a novel genetic algorithm (GA) for the resource levelling problem with generalised precedence relations. Our design and implementation of GA features an efficient schedule generation scheme, built upon a new encoding mechanism that combines the random key representation and the shift vector representation. A two-pass local search-based improvement procedure is devised and integrated into the GA to enhance the algorithmic performance. Our GA is able to obtain near optimal solutions with less than 2% optimality gap for small instances in fractions of a second. It outperforms or is competitive with the state-of-the-art algorithms for large benchmark instances with size up to 1000 activities.\",\n \"Title: Does online media sequence matter in product marketing?\\nYear: 2018\\n\\nAbstract: •Interactions between online media sequences and product types affect the persuasion of consumers.•For search goods, OBM-OIM yields higher message consumer acceptance and message responses.•For experience goods, OIM-OBM yields higher consumer message acceptance and message responses.•Message acceptance mediates the effect of online media sequences on message responses.\",\n \"Title: High-Frequency Spectrum Analysis and Channel Availability Decision on Sea Surface Environment\\nYear: 2017\\n\\nAbstract: This paper measured entire spectrum in the HF environment of two places by an electromagnetic spectrum monitor system. Based on the above measured data, the HF spectrum characteristics of 2-30 MHz band were analyzed: power spectral density (PSD), Time-frequency distribution, The probability distribution of 3D map, etc. The paper analyzed high frequency bands occupancy one the sea with setting different latitude, season and time as control groups. Hence, we confirm the source of interference noise is broadcasting, and the silent frequency band is obtained. Further analyze has proven it has short-time stationary property. At last, the paper defines the channel availability function for evaluating availability of the channel.\",\n \"Title: A New Shadow Tracking Method to Locate the Moving Target in SAR Imagery Based on KCF\\nYear: 2017\\n\\nAbstract: Shadow detection and tracking (SDT) is the effective method to locate moving targets, which always be shifted or smeared outside the scene, in different images sequence by Synthetic Aperture Radar (SAR). In this process, the detection of shadow is obtained on the first frame image, and the Kernelized Correlation Filter is used to track target among the following SAR images, using the changes between the image sequence. By the experiments and performance analysis, the validity of the proposed algorithm can be demonstrated.\",\n \"Title: A Transfer Learning Method For Ship Recognition In Multi-Optical Remote Sensing Satellites\\nYear: 2018\\n\\nAbstract: In this paper, a transfer learning method of ship target recognition is proposed. This method aims at identifying unlabeled ships in high-resolution, based on the theory of transfer learning, assisting with a number of ship samples with different resolutions from different satellites. In the traditional machine learning method, training data and test data are assumed to have the same distribution. However, because of the different distributions and spaces in most cases, such as images from different satellites, the accuracy rate will decline. In this paper, we proposed a method that aligns the distributions as well as the subspace bases to solve this problem. This paper first proposed Adaptation Local Linear Embedding (ALLE) algorithm to achieve space alignment and then aligned both marginal distribution and conditional distribution by Joint Distribution Adaptation (JDA). This paper focuses on the identification of three types of ships which are destroyers, cruisers, and aircraft carriers basing on the method proposed (ALLE-JDA), using the knowledge of source samples low-resolution labeled ships to help identify the unlabeled high-resolution samples. The experimental results show that this method is better than several state-of-the-art methods.\",\n \"Title: Challenging Theorem Provers with Mathematical Olympiad Problems in Solid Geometry.\\nYear: 2016\\n\\nAbstract: Mathematical Olympiad problems can be used to evaluate the performance of theorem provers. In the encyclopedia (Wu et al. in The Dictionary of International Mathematical Olympiads, Volume of Geometry. Hebei Children Press, Shijiazhuang, 2012) there are 207 Mathematical Olympiad contest problems in solid geometry collected around the world and ranging over one century, among which 97 problems can be used to test algebraic provers of equality type. Three general-purpose theorem proving methods are used in the test: the characteristic set method, the Grobner basis method, and the vector algebra method. 91 out of the 97 problems are proved by the provers, and some contest problems are found to need additional specification to be correct. The proving efficiency and geometric interpretability of the additional non-degeneracy conditions for the 91 problems by the three provers are compared.\",\n \"Title: Customized Filesystem with Dynamic Stripe Strategies on Lustre-Based Hadoop.\\nYear: 2017\\n\\nAbstract: With large-scale data exploding so quickly that the traditional big data processing framework Hadoop has met its bottleneck on data storing layer. Running Hadoop on modern HPC clusters has attracted much attention due to its unique data processing and analyzing capabilities. Lustre file system is a promising parallel storage file system occupied HPC file system market for many years. Thus, Lustre-based Hadoop platform will pose many new opportunities and challenges on today's data era. In this paper, we customized our LustreFileSystem class which inherits from FileSystem class (inner Hadoop source code) to build our Lustre-based Hadoop. And to make full use of the high-performance in Lustre file system, we propose a novel dynamic stripe strategy to optimize stripe size during writing data to Lustre file system. Our results indicate that, we can improve the performance obviously in throughput (mb/sec) about 3x in writing and 11x in reading, and average IO rate (mb/sec) at least 3 times at the same time when compared with initial Hadoop. Besides, our dynamic stripe strategy can smooth the reading operation and give a slight improvement on writing procedure when compared with existing Lustre-based Hadoop.\",\n \"Title: Authorized Equality Test on Identity-Based Ciphertexts for Secret Data Sharing via Cloud Storage.\\nYear: 2019\\n\\nAbstract: With the higher rate of using cloud storage, protecting data privacy becomes an important issue. The most effective solution is to encrypt data before uploading to the cloud. However, how to efficiently search over data encrypted with different keys is still an open problem. To address this problem, we introduce a new notion of the identity-based encryption with equality test supporting flexible authorization (IBEETFA). It supports the test of whether two ciphertexts encrypted under the different keys encapsulate the same message, and in the meanwhile supports fine-grained authorization of the test. Based on the equality test on ciphertexts, there is a direct way to support an authorized user to search over ciphertexts of different users, which accelerates secret data sharing among a group of users. Besides, IBEET-FA does not suffer from the complex key management problem of its counterpart in the traditional public key infrastructure. We propose a concrete construction of IBEET-FA and prove it to be securely based on simple mathematical assumptions. The experimental results show that our IBEET-FA scheme is efficient and can satisfy various types of search over encrypted data.\",\n \"Title: Design and Implementation of Lead-Carbon Battery Storage System.\\nYear: 2019\\n\\nAbstract: In this paper, we described a design scheme for a lead-carbon battery energy storage system (BESS). A two-stage topology of lead-carbon battery energy storage system was adopted. The number and connection structure of battery cells were designed based on the actual demand. The main circuit parameters of the BESS were determined according to the power transfer capability, harmonic suppression, and dynamic response capability. A state feedback linearization method in a nonlinear differential geometry theory was used for dq-axis current decoupling based on the mathematical model used in the dq coordinate system of the BESS. A control strategy based on filter capacitor current inner loop, grid current middle loop, and dc voltage outer loop was adopted to suppress the resonance peak and achieve the independent regulation of active power and reactive power. The PSCAD/EMTDC simulation results and physical prototype experiments showed that the lead-carbon BESS had a good dynamic and steady-state performance.\",\n \"Title: Windowed Fourier transform and general wavelet algorithms in quantum computation.\\nYear: 2019\\n\\nAbstract: In this paper, we define the quantum windowed Fourier transform and study some of its properties, then we develop two useful operations called quantum convolution and 'integral'. Quantum 'integral' allows us to implement the integral transforms quantum-mechanically with a certain probability, including general wavelet kernel transforms. Furthermore, we propose an improved wavelet kernel transform for quantum computation.\",\n \"Title: A Secure Cloud Data Sharing Protocol for Enterprise Supporting Hierarchical Keyword Search\\nYear: 2022\\n\\nAbstract: Cloud storage becomes the priority for storing and sharing data for enterprise users. Encrypting prior to uploading data to the cloud is the best way to protect business secrets, however, it hinders the convenient operations on plaintexts, such as searching over the cloud data. In addition, employees in an enterprise have multiple layer structures and a higher layer employee should have the privil...\",\n \"Title: Public Key Encryption with Fuzzy Matching\\nYear: 2021\\n\\nAbstract: The rise of cloud computing is driving the development in various fields and becoming one of the hot topics in recent years. To solve the problem of comparing ciphertexts between different users in cloud storage, Public Key Encryption with Equality Test (PKEET) was proposed. In PKEET, a tester can determine whether two ciphertexts encrypted with different public keys contain the same message without decrypting the ciphertexts. However, PKEET only supports exact matching, which may not be practical when two messages have misspellings, formatting differences, or differences in the data itself. Therefore, to support fuzzy matching, in this paper we propose the concept of Public Key Encryption with Fuzzy Matching (PKEFM), which allows to determine whether the edit distance between two encrypted messages is less than a threshold value. PKEFM can be well applied to support fuzzy data comparison in encrypted e-mail systems or encrypted gene testing. We then modify the scheme to provide the decryption function, and support (encrypted) wildcards in the matching, in order to further improve the generality of PKEFM at the cost of a small amount of extra computation.\",\n \"Title: Private Set Intersection With Authorization Over Outsourced Encrypted Datasets\\nYear: 2021\\n\\nAbstract: Thanks to its convenience and cost-savings feature, cloud computing ushers a new era. Yet its security and privacy issues must not be neglected. Private set intersection (PSI) is useful and important in many cloud computing applications, such as document similarity, genetic paternity and data mining. The cloud server performs intersection operations on two outsourced encrypted datasets of data owners. In the existing protocols, however, data owners cannot decide whether to use all or part of their encrypted data to compute the intersection, nor can they specify whom to compare with. In this paper, we introduce an enhanced notion of outsourced PSI, called authorized PSI (APSI), which supports flexible authorization and cross-type authorized comparison of datasets. To demonstrate this notion, we propose a concrete APSI protocol, and prove it to be secure in the random oracle model based on simple number-theoretic assumptions. Experimental results show that our APSI protocol has performance comparable with existing related outsourced PSI protocols.\",\n \"Title: Better than humans: a method for inferring consumer shopping intentions by reading facial expressions\\nYear: 2021\\n\\nAbstract: How to make robots more human-like has been a hot topic of interest for researchers in the field of robot interaction. Empowering robots in retail services with empathy to read minds is still a major challenge. To address this problem, we proposed a deep learning-based method for programs to infer consumers' shopping interest levels, which we confirmed effectiveness has met or exceeded that of hum...\",\n \"Title: A high precision intrusion detection system for network security communication based on multi-scale convolutional neural network\\nYear: 2022\\n\\nAbstract: The openness of network data makes it vulnerable to hackers, viruses and other attacks, which seriously threatens the privacy and property security of users. In order to improve the accuracy of the intrusion detection for network security communication, based on the traditional intrusion detection system, combining with the deep learning theory and shortcomings, this paper proposed an intrusion detection system for network security communication based on multi-scale convolutional neural network, and conducted the corresponding experiments on public data sets. The experimental results perform that compared to the intrusion detection system based on Adaboost model and Recurrent Neural Network model, the convergence speed of multi-scale convolutional neural network system is faster, the average error detection rate is reduced by 4.02%, and the average accuracy is improved by 4.37%. The results prove that the intrusion detection system based on multi-scale convolution neural network has a high detection accuracy.\",\n \"Title: Nonlinear ESO-based tracking control for warehouse mobile robots with detachable loads\\nYear: 2022\\n\\nAbstract: It is an essential task to guarantee satisfactory tracking performance for a warehouse mobile robot with a detachable load. To this end, a nonlinear extended state observer (ESO)-based tracking control is investigated via a double closed-loop framework in this paper. A kinematics controller is designed in an outer loop to generate desired velocities for the warehouse mobile robot. A nonlinear ESO with an improved error function is proposed in an inner loop to estimate load variations and internal unmodeled dynamics. Then a nonlinear error feedback controller based on estimation values is given to track the desired velocities from the outer loop. Simulation and experiment results illustrate the effectiveness and superiority of the proposed control strategy.\",\n \"Title: Adaptive Task Planning for Large-Scale Robotized Warehouses\\nYear: 2022\\n\\nAbstract: Robotized warehouses are deployed to automatically distribute millions of items brought by the massive logistic orders from e-commerce. A key to automated item distribution is to plan paths for robots, also known as task planning, where each task is to deliver racks with items to pickers for processing and then return the rack back. Prior solutions are unfit for large-scale robotized warehouses due to the inflexibility to time-varying item arrivals and the low efficiency for high throughput. In this paper, we propose a new task planning problem called TPRW, which aims to minimize the end-to-end makespan that incorporates the entire item distribution pipeline, known as a fulfilment cycle. Direct extensions from state-of-the-art path finding methods are ineffective to solve the TPRW problem because they fail to adapt to the bottleneck variations of fulfillment cycles. In response, we propose Efficient Adaptive Task Planning, a framework for large-scale robotized warehouses with time-varying item arrivals. It adaptively selects racks to fulfill at each timestamp via reinforcement learning, accounting for the time-varying bottleneck of the fulfillment cycles. Then it finds paths for robots to transport the selected racks. The framework adopts a series of efficient optimizations on both time and memory to handle large-scale item throughput. Evaluations on both synthesized and real data show an improvement of 37.1% in effectiveness and 75.5% in efficiency over the state-of-the-arts.\",\n \"Title: A Decentrlized Trading Model Based on Public Blockchain with Regulatable Bi-Tierred Identities\\nYear: 2021\\n\\nAbstract: Traditional network trading platforms process transactions and store data on their centralized servers. Usually, users deem them as the third parties worth to be trusted, while exploitations and leaks of user privacy never cease. Emerging cryptocurrency systems based on blockchains provide a P2P trading model in a decentralized way, avoiding many problems inherent in traditional centralized platforms. However, the intrinsic anonymity makes it hard to trace identities and regulate the blockchain system when something goes wrong with the transactions, always causing financial losses to users. In this paper, we propose a blockchain trading prototype based on a traceable two-tier identity structure, which combines a personal CA-based identity with anonymous addresses to enable appealings without centrally keeping transaction data, hence protecting privacy and regulating transactions simultaneously. We develop an off-chain transaction confirmation protocol and build an on-chain regulatory agency based on smart contracts. The separation of the on-chain part from the off-chain part makes it suitable to be deployed in many cryptocurrency system that supports smart contracts. Experiments show that all the transaction data can be stored by users themselves and the regulatory agency still works independently. A security analysis on the supervision smart contract is also made. We recommend this model to be an alternate to the traditional centralized electronic business, if privacy preserving and regulations are both considered.\",\n \"Title: Meta-Learner-Based Stacking Network on Space Target Recognition for ISAR Images\\nYear: 2021\\n\\nAbstract: Recently, the deep learning models have achieved great success in the recognition of inverse synthetic aperture radar (ISAR) images. However, most of the deep learning models fail to obtain satisfactory results under the condition of small samples due to the contradiction between the large parameter space of the deep learning models and the insufficient labeled samples of space target imaging by ISAR. In this article, a method of meta-learner-based stacking network (MSN) is proposed, which can realize the high-precision classification of space target by ISAR images under the condition of small sample. Innovatively, a rotation-invariant attention mechanism (RAM) module is added into Resnet50 network to magnify the difference of embedded features of target and background. Complementarily, the deep relationship between the features of fine-grained ISAR image is extracted by using graph convolutional network and relation network. Finally, an innovative adaptive weighted XGBoost algorithm is used to integrate the prediction results of the base learners. The main contributions of this article include proposing a RAM module and using an innovative adaptive weighted XGBoost algorithm to realize ensemble learning. The experiment results show that the RAM module effectively concentrates the network's attention on the recognized target, and the recognition rate of MSN is about 5% higher than that of a single base learner under different data volume conditions, which proves that MSN achieves competitive accuracy against other state-of-the-art approaches.\",\n \"Title: Attribute-Based Hierarchical Access Control With Extendable Policy\\nYear: 2022\\n\\nAbstract: Attribute-based encryption scheme is a promising mechanism to realize one-to-many fine-grained access control which strengthens the security in cloud computing. However, massive amounts of data and various data sharing requirements bring great challenges to the complex but isolated and fixed access structures in most of the existing attribute-based encryption schemes. In this paper, we propose an attribute-based hierarchical encryption scheme with extendable policy, called Extendable Hierarchical Ciphertext-Policy Attribute-Based Encryption (EH-CP-ABE), to improve the data sharing efficiency and security simultaneously. The scheme realizes the function of hierarchical encryption, in which, data with hierarchical access control relationships could be encrypted together flexibly to improve the efficiency. The scheme also achieves external and internal extension of the access structure to further encrypt newly added hierarchical data without updating the original ciphertexts or with only a minor update depending on the data sharing requirements, which simplifies the encryption process and greatly reduces the computation overhead. We formally prove the security of the scheme is IND-CCA secure in the random oracle model based on bilinear Diffie-Hellman assumption, and we also implement our scheme to demonstrate its efficiency and practicality.\",\n \"Title: Image Reconstruction for Low-Oversampled Staggered SAR Based on Sparsity Bayesian Learning in the Presence of a Nonlinear PRI Variation Strategy\\nYear: 2022\\n\\nAbstract: As an innovative concept of high-resolution and wide-swath system, the low-oversampled staggered synthetic aperture radar (SAR) can not only deal with the blind ranges but also suppress range ambiguities and reduce the data volume. Due to the variation of pulse repetition interval (PRI), there will be echo pulse loss and nonuniform sampling in azimuth. Generally, the existing reconstruction algorithms mostly resample the nonuniformly sampled signal into a uniform grid and then perform conventional focused processing. However, the accuracy of the resampling is limited, and the advantage of the nonuniformity over uniform sampling in terms of reconstruction performance is ignored, especially with low oversampling factors. In this article, a reconstruction algorithm for low-oversampled staggered SAR is proposed based on the sparsity Bayesian learning (SBL) in the presence of a nonlinear PRI variation strategy. To ensure that the blind range distribution, which depends on the PRI variation strategy, brings a superior reconstruction performance, we define a novel objective function and optimize a sequence of nonlinear PRI variation with genetic algorithm (GA). On the basis of the optimized sequence, the proposed reconstruction algorithm performs the second-order keystone transform to achieve range curvature correction for nonuniformly sampled data in azimuth. Then, a nonuniform observation model is established. The SBL using a hierarchical form of the Laplace prior is applied to reconstruct the focused images directly with the nonuniform sampling. Simulations and experiments on raw data generated in the staggered SAR mode with low oversampling factors are performed to verify the effectiveness of the proposed method.\",\n \"Title: Refocusing on SAR Ship Targets With Three-Dimensional Rotating Based on Complex-Valued Convolutional Gated Recurrent Unit\\nYear: 2022\\n\\nAbstract: This letter proposes a complex-valued convolutional gated recurrent unit (CV-ConvGRU) network for the 3-D rotation refocusing task of a synthetic aperture radar (SAR) ship target. To take advantage of the amplitude and phase information of complex SAR images, all the elements of CV-ConvGRU, including the convolutional layer, activation function, update gate, and reset gate, are extended to the complex domain. Based on CV-ConvGRU, a complex-valued SAR ship refocusing network (CV-SSRN) architecture is designed for refocusing experiments. To verify the robustness of the proposed CV-ConvGRU over ConvGRU on information perception, this letter also raises a real-valued SAR ship refocusing network (RV-SSRN), which has the same degree of freedom as CV-SSRN. Finally, experiments are carried out, and all the results show the superiority of the proposed method on refocusing accuracy.\",\n \"Title: A novel identity-based multi-signature scheme over NTRU lattices\\nYear: 2022\\n\\nAbstract: Multi-signature is an important technology to compress multiple signatures on the common message into a compact one, thereby reducing the consumption of storage space and transmission bandwidth. Such a cryptographic primitive is widely used in financial applications such as blockchain which requires multiple keys to authorize a transaction. With the advent of quantum computers, traditional multi-signature schemes may no longer be secure as their underlying security assumptions (e.g. RSA or discrete logarithm problems) may not hold anymore. In this paper, we propose a novel identity-based multi-signature (IBMS) scheme over NTRU lattices, which is secure against the attacks of quantum computers. To the best of our knowledge, it is the first lattice-based IBMS scheme. We show that our scheme is provably secure in the random oracle model based on the ring version of the short integer solution assumption (Ring-SIS). Compared with the closely related works in the literature, our scheme enables the signer to select its system identity (such as email address, physical IP address, and etc.) as the public key, which effectively alleviates the certificate management problem in the PKI setting, and takes the advantage of discrete Gaussian distribution instead of uniform distribution to generate secret signing keys and multi-signatures. Besides, our scheme does not require all the system users to setup a trusted common string, which further simplifies the deployment of our scheme in practice.\",\n \"Title: PRO-Face: A Generic Framework for Privacy-preserving Recognizable Obfuscation of Face Images\\nYear: 2022\\n\\nAbstract: ABSTRACTA number of applications (e.g., video surveillance and authentication) rely on automated face recognition to guarantee functioning of secure services, and meanwhile, have to take into account the privacy of individuals exposed under camera systems. This is the so-called Privacy-Utility trade-off. However, most existing approaches to facial privacy protection focus on removing identifiable visual information from images, leaving protected face unrecognizable to machine, which sacrifice utility for privacy. To tackle the privacy-utility challenge, we propose a novel, generic, effective, yet lightweight framework for Privacy-preserving Recognizable Obfuscation of Face images (named as PRO-Face). The framework allows one to first process a face image using any preferred obfuscation, such as image blur, pixelate and face morphing. It then leverages a Siamese network to fuse the original image with its obfuscated form, generating the final protected image visually similar to the obfuscated one from human perception (for privacy) but still recognized as the original identity by machine (for utility). The framework supports various obfuscations for facial anonymization. The face recognition can be performed accurately not only across anonymized images but also between plain and anonymized ones, based on only pre-trained recognizers. Those feature the \\\"generic\\\" merit of the proposed framework. In-depth objective and subjective evaluations demonstrate the effectiveness of the proposed framework in both privacy protection and utility preservation under distinct scenarios. Our source code, models and any supplementary materials are made publicly available.\",\n \"Title: Complex-Valued Graph Neural Network on Space Target Classification for Defocused ISAR Images\\nYear: 2022\\n\\nAbstract: Recently, research on the classification for inverse synthetic aperture radar (ISAR) images continues to deepen. However, the maneuvering and attitude adjustment of space targets will bring high-order terms to received echoes, which causes defocus on ISAR images and affects the classification. The current classification models ignore the information of high-order terms containing in the relationship of real parts and imaginary parts of data. To this end, this letter proposes an end-to-end framework, called complex-valued (CV)-graph neural network (GNN), specifically for the classification of defocused ISAR images under the few-shot condition. It models the features of real parts and imaginary parts of CV images as graph information reasoning. Specifically, the deep relationship between them is mined to contribute to classification by CV graph convolution. Moreover, the backpropagation process is derived in detail for updating the weights and bias of the network. The proposed method is then experimented with a mixed few-shot dataset of real and simulated data. Compared with the state-of-the-art methods, CV-GNN performs well in defocused image classification for each class of targets, and ablation studies verify the effectiveness of CV network and GNN. The code and dataset will be available online (https://github.com/yhx-hit/cv_gnn).\",\n \"Title: High-Resolution Refocusing for Defocused ISAR Images by Complex-Valued Pix2pixHD Network\\nYear: 2022\\n\\nAbstract: Inverse synthetic aperture radar (ISAR) is an effective detection method for targets. However, for the maneuvering targets, the Doppler frequency induced by an arbitrary scatterer on the target is time-varying, which will cause defocus on ISAR images and bring difficulties for the further recognition process. It is hard for traditional methods to well refocus all positions on the target well. In recent years, generative adversarial networks (GANs) achieve great success in image translation. However, the current refocusing models ignore the information of high-order terms containing in the relationship between real and imaginary parts of the data. To this end, an end-to-end refocusing network, named complex-valued pix2pixHD (CVPHD), is proposed to learn the mapping from defocus to focus, which utilizes complex-valued (CV) ISAR images as an input. A CV instance normalization layer is applied to mine the deep relationship between the complex parts by calculating the covariance of them and accelerate the training. Subsequently, an innovative adaptively weighted loss function is put forward to improve the overall refocusing effect. Finally, the proposed CVPHD is tested with the simulated and real dataset, and both can get well-refocused results. The results of comparative experiments show that the refocusing error can be reduced if extending the pix2pixHD network to the CV domain and the performance of CVPHD surpasses other autofocus methods in refocusing effects. The code and dataset have been available online (https://github.com/yhx-hit/CVPHD).\"\n]","isConversational":false},"profile_size":{"kind":"number","value":123,"string":"123"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13665,"string":"13,665"}}},{"rowIdx":13666,"cells":{"question_id":{"kind":"string","value":"Ning Yu"},"question":{"kind":"string","value":"Generate an abstract for the title \"A Novel Deep Learning Network Architecture with Cross-Layer Neurons\" using the following items: 1. Very Deep Convolutional Networks\n2. Cross-Layer Architecture\n3. Training Deep Networks\n4. Classification Performance\n5. Improved Accuracy on CIFAR-10 and CIFAR-"},"answer":{"kind":"string","value":"Very deep convolutional networks have recently demonstrated impressive classification performance on competitive benchmarks such as the ImageNet or COCO tasks. However, training such deep convolutional networks becomes more difficult. In this paper, we propose a novel deep network structure called cross-layer architecture to make the best use of information learned from all the lower-level layers. It utilizes cross-layer neurons to control all the lower-level layers to gather and send information to the upper layers. It shows that our new architecture can be applied in training deeper networks. The classification results demonstrate that our new architecture can effectively improve the performance by using cross-layer neurons. Particularly, our new architecture can achieve up to 8.39% and 6.53% relative accuracy improvement compared to the state-of-the-art methods on CIFAR-10 and CIFAR-100 datasets, respectively."},"correct_answer":{"kind":"string","value":"Very deep convolutional networks have recently demonstrated impressive classification performance on competitive benchmarks such as the ImageNet or COCO tasks. However, training such deep convolutional networks becomes more difficult. In this paper, we propose a novel deep network structure called cross-layer architecture to make the best use of information learned from all the lower-level layers. It utilizes cross-layer neurons to control all the lower-level layers to gather and send information to the upper layers. It shows that our new architecture can be applied in training deeper networks. The classification results demonstrate that our new architecture can effectively improve the performance by using cross-layer neurons. Particularly, our new architecture can achieve up to 8.39% and 6.53% relative accuracy improvement compared to the state-of-the-art methods on CIFAR-10 and CIFAR-100 datasets, respectively."},"documents":{"kind":"list like","value":["Title: Localization refinement for wireless sensor networks\nYear: 2009\n\nAbstract: Being a key supporting technology, localization is of significance to the research and implement of other attracting applications in wireless sensor networks. The positioning accuracy and energy consumption are two major indicators of localization performance. In this paper, we are concerned with the problem of optimization for coordinates calculation in localization process, regardless of the specific underlying localization mechanism. In order to improve accuracy and avoid extra energy consumption, we develop three schemes based on least squares and multilateration: Taylor-LS, WLS and CTLS. Moreover, we develop a generalized Cramer-Rao lower bound on the localization errors in multihop scenario to help us theoretically analyze the performance of our localization refinement methods. We evaluate the proposed methods via simulations under various environmental settings. Overall, the simulation results validate the practicality of our proposed localization refinement methods, and show that they outperform multilateration at a certain extent under a wide range of conditions and have no extra communication overhead.","Title: A Hybrid Computational Approach for the Prediction of Small Non-coding RNAs from Genome Sequences\nYear: 2009\n\nAbstract: Researching the bacterial gene expression is a meaningful way to control and prevent the diseases which caused by bacteria. Recent researches indicate non-coding RNAs (ncRNA / sRNA) perform a variety of critical regulatory functions in bacteria. Since sRNAs have the consistent sequence characteristics, the genome-wide searching for sRNAs, especially the computational method, have become an effective way to predict the non-coding RNAs. This article proposes a hybrid computational approach for prediction of small non-coding RNAs which integrates three critical techniques, secondary structural algorithm, thermo-dynamic stability analysis and sequence conservation prediction. Relying on these computational techniques, our approach was used to search for sRNAs in Streptococcus pyogenes which is one of the most important bacteria for human health. This search led five candidates of sRNA to be predicted as the key components of known regulatory pathways in S. pyogens.","Title: An in-memory relevance feedback technique for high-performance image retrieval systems\nYear: 2007\n\nAbstract: Content-based image retrieval with relevant feedback has been widely adopted as the query model of choice for improved effectiveness in image retrieval. The effectiveness of this solution, however, depends on the efficiency of the feedback mechanism. Current methods rely on searching the database, stored on disks, in each round of relevance feedback. This strategy incurs long delay making relevance feedback less friendly to the user, especially for very large databases. Thus, scalability is a limitation of existing solutions. In this paper, we propose an in-memory relevance feedback technique to substantially reduce the delay associated with feedback processing, and therefore improve system usability. Our new data-independent dimensionality-reduction technique is used to compress the metadata to build a small in-memory database to support relevance feedback operations with minimal disk accesses. We compare the performance of this approach with conventional relevance feedback techniques in terms of computation efficiency and retrieval accuracy. The results indicate that the new technique substantially reduces response time for user feedback while maintaining the quality of the retrieval.","Title: Efficiently support concurrent queries in multiuser CBIR systems\nYear: 2009\n\nAbstract: Various techniques have been developed for different query types in content-based image retrieval systems such as sampling queries, constrained sampling queries, multiple constrained sampling queries, k-NN queries, constrained k-NN queries, and multiple localized k-NN queries. In this paper, we propose a generalized query model suitable for expressing queries of different types, and investigate efficient processing techniques for this new framework. We exploit sequential access and data sharing by developing new storage and query processing techniques to leverage inter-query concurrency. Our experimental results, based on the Corel dataset, indicate that the proposed optimization can significantly reduce average response time in a multiuser environment, and achieve better retrieval precision and recall compared to two recent techniques.","Title: Dynamic simulation based localization for mobile sensor networks\nYear: 2007\n\nAbstract: In mobile wireless sensor networks, sensors can move randomly or keep static temporarily. Mobility makes the sensor networks better acquire information, but also makes accurate localization more difficult since the network environment changes continually. In this paper, an energy-efficient dynamic simulation based localization (DSL) algorithm is introduced that uses range measurement information to restrict sample region and establishes a dynamic filtering mechanism to improve the localization performance and efficiency. Analytical and simulation results are provided to study the localization cost and location accuracy in different mobility models and various environmental settings. The results indicate that our algorithm outperforms the best known simulation based localization schemes under a wide range of conditions, with localization accuracy improved by an average of 24% and computation cost reduced significantly for a similar high localization accuracy.","Title: Efficient exploration of large combinatorial chemistry spaces by monomer-based similarity searching.\nYear: 2009\n\nAbstract: In modern drug discovery, 2-D similarity searching is widely employed as a cost-effective way to screen large compound collections and select subsets of molecules that may have interesting biological activity prior to experimental screening. Nowadays, there is a growing interest in applying the existing 2-D similarity searching methods to combinatorial chemistry libraries to search for novel hits or to evolve lead series. A dilemma thus arises when many identical substructures recur in library products and they have to be considered repeatedly in descriptor calculations. The dilemma is exacerbated by the astronomical number of combinatorial products. This problem imposes a major barrier to similarity searching of large combinatorial chemistry spaces. An efficient approach, termed Monomer-based Similarity Searching (MoBSS), is proposed to remedy the problem. MoBSS calculates atom pair (AP) descriptors based on interatomic topological distances, which lend themselves to pair additivity. A fast algorithm is employed in MoBSS to rapidly compute product atom pairs from those of the constituent fragments. The details of the algorithm are presented along with a series of proof-of-concept studies, which demonstrate the speed, accuracy, and utility of the MoBSS approach.","Title: Robust Multihop Localization For Wireless Sensor Networks With Unreliable Beacons\nYear: 2012\n\nAbstract: Multihop localization is a popular approach for determining the positions of normal nodes in large-scale wireless sensor networks. However, most existing multihop localization studies assume that the declared positions of beacon nodes are always reliable or even free of errors, which is not a valid assumption in practice. In this paper, we propose a robust multihop localization algorithm (RMLA) based on trust evaluation for diminishing the effect of unreliable beacons on the accuracy of node localization. Firstly, the trust evaluation framework is established on the basis of evidence theory. According to the multihop geometric relationship among nodes, every beacon evaluates the reliability of other beacons' declared positions. Then, the normal nodes integrate the evaluation results to obtain the total trust degrees of their multihop communication beacons, by use of an average method or an enhanced D-S evidence combination rule. Finally, the normal nodes employ the weighted Taylor-series least squares solver to estimate the optimal values of their coordinates. Extensive simulation results in isotropic and anisotropic networks show the robustness and effectiveness of our algorithm.","Title: Brs-Based Robust Secure Localization Algorithm For Wireless Sensor Networks\nYear: 2013\n\nAbstract: Localization is the key supporting technology for wireless sensor networks (WSNs). Security and accuracy are the premise of the localization application. Real-world applications of wireless sensor networks are often subject to a variety of adverse circumstances interference, and the localization performance is seriously affected. In this paper, we propose a BRS-based robust secure localization (BRSL) algorithm in order to reduce the impact of the malicious attackers in WSNs. The BRSL method includes two phases. In the first stage, the trust evaluation framework is established on the basis of beta reputation system. In the second phase, we employ the weighted Taylor-series least squares method to estimate the coordinates of sensor nodes. Simulation results demonstrate that the proposed algorithm is robust and effective.","Title: Conversations in the Blogosphere: An Analysis \"From the Bottom Up\"\nYear: 2005\n\nAbstract: The \"blogosphere\" has been claimed to be a densely interconnected conversation, with bloggers linking to other bloggers, referring to them in their entries, and posting comments on each other's blogs. Most such characterizations have privileged a subset of popular blogs, known as the 'A-list.' This study empirically investigates the extent to which, and in what patterns, blogs are interconnected, taking as its point of departure randomly-selected blogs. Quantitative social network analysis, visualization of link patterns, and qualitative analysis of references and comments in pairs of reciprocally-linked blogs show that A-list blogs are overrepresented and central in the network, although other groupings of blogs are more densely interconnected. At the same time, a majority of blogs link sparsely or not at all to other blogs in the sample, suggesting that the blogosphere is partially interconnected and sporadically conversational.","Title: A Credible Routing Based On A Novel Trust Mechanism In Ad Hoc Networks\nYear: 2013\n\nAbstract: Many existing routing protocols in Mobile Ad hoc Networks (MANETs) focus on finding paths in dynamic networks without considering security. In this paper, we propose a trust model which evaluates neighbours' direct trust by factors of encounter time, mobility, and successful cooperation frequency. The revised D-S evidence theory is used to combine multiple recommended pieces of evidence and obtain the recommended trust value. Then based on the novel trust mechanism, we propose a trusted routing protocol named TDS-AODV protocol by extending the AODV protocol. In this protocol, a node makes a routing decision according to the trust values of its neighbour nodes. Finally, two routes are built: the main route with highest route trust value in the candidate routes and a backup route. Simulation results reveal that TDS-AODV can eliminate malicious nodes effectively when building the route; furthermore, it also achieves better performance than TAODV and AODV in terms of throughput, packet delivery ratio, and average end to end delay.","Title: Dynamic Directional Navigation in Content-Based Image Retrieval\nYear: 2008\n\nAbstract: Nowadayspsila image retrieval techniques focus on modifying a query contour based on the relative correlations of relevant images. The spatial relationship between the relevant images and all the other images in database (i.e. the directional information) is not explored and utilized well. From the aspect of feature space, when a user selects relevant images, the direction to the potential relevant images is implicitly expressed. In this paper, we propose a Dynamic Directional Navigation (DDN) system to explore the directional information and navigate in the data space to find similar images. Multiple queries will be used when the relevant images point to different directions. The experiment results indicate that our technique can address the semantic gap well in Content-Based Image Retrieval (CBIR) and achieves better precision and recall than the previous techniques.","Title: An Evolutionary Game-Based Trust Cooperative Stimulation Model For Large Scale Manets\nYear: 2013\n\nAbstract: In order to realize a methodical, effective cooperative stimulation for MANETs and search dynamic trust cooperative stimulation scheme in environment under a high malicious ratio, we have proposed an evolutionary game-based trust cooperative stimulation model for large scale MANETs in this paper. First, the system members' pluralistic behavior for MANETs has been covered by means of constructing the complete multirisk level strategy space. Then a trust-preferential strategy has been built through trust numerical value mapping technology, which achieves the aim that the malicious action is effectively constrained to avoid a low trust level. Furthermore, the mobility probable parameters and information propagation error matrix are introduced into game model, and the convergence condition between optimum strategy which represents payoff maximization principle and trust-preferential strategy is deduced through evolutionary analyzing finally. Both theoretical analysis and simulation experiments have demonstrated that our model can effectively stimulate cooperation among members and meanwhile be robust under the condition where the environment is harsh under a high original malicious ratio in large scale MANETs.","Title: Fast query point movement techniques with relevance feedback for content-based image retrieval\nYear: 2006\n\nAbstract: Target search in content-based image retrieval (CBIR) systems refers to finding a specific (target) image such as a particular registered logo or a specific historical photograph. Existing techniques were designed around query refinement based on relevance feedback, suffer from slow convergence, and do not even guarantee to find intended targets. To address those limitations, we propose several efficient query point movement methods. We theoretically prove that our approach is able to reach any given target image with fewer iterations in the worst and average cases. Extensive experiments in simulated and realistic environments show that our approach significantly reduces the number of iterations and improves overall retrieval performance. The experiments also confirm that our approach can always retrieve intended targets even with poor selection of initial query points and can be employed to improve the effectiveness and efficiency of existing CBIR systems.","Title: Trust Management Scheme Based On D-S Evidence Theory For Wireless Sensor Networks\nYear: 2013\n\nAbstract: Trust management scheme has been regarded as a powerful tool to defend against the wide set of security attacks and identify malicious nodes. In this paper, we propose a trust management scheme based on revised Dempster-Shafer (D-S) evidence theory. D-S theory is preponderant in tackling both random and subjective uncertainty in the trust mechanism. A trust propagation mechanism including conditional trust transitivity and dynamic recommendation aggregation is developed for obtaining the recommended trust values from third part nodes. We adopt a flexible synthesis method that uses recommended trust only when no direct trust exists to keep a good trust-energy consumption balance. We also consider on-off attack and bad mouthing attack in our simulation. The simulation results and analysis show that the proposed method has excellent ability to deal with typical network attacks, better security, and longer network lifetime.","Title: Client-Side Relevance Feedback Approach for Image Retrieval in Mobile Environment\nYear: 2007\n\nAbstract: With the proliferation of handheld devices, the demand of multimedia information retrieval on mobile devices has attracted more attention. A relevance feedback information retrieval process usually includes several rounds of query refinement. Each round incurs exchange of tens of images between the mobile device and the server. With limited wireless bandwidth, this process can incur substantial delay making the system unfriendly to use. We address this issue by considering a client-side relevance feedback (CRF) technique. Since relevance feedback is done on client side, system response is instantaneous significantly enhancing system usability. Furthermore, since the server is not involved in relevance feedback processing, it is able to support thousands more users simultaneously. Our experimental results indicate that this scheme also effectively address the semantic gap in content-based image retrieval.","Title: Client-Side Relevance Feedback Approach for Image Retrieval in Mobile Environment\nYear: 2011\n\nAbstract: During the last decade, high quality i.e. over 1 megapixel built-in cameras have become standard features of handheld devices. Users can take high-resolution pictures and share with friends via the internet. At the same time, the demand of multimedia information retrieval using those pictures on mobile devices has become an urgent problem to solve, and therefore attracts attention. A relevance feedback information retrieval process includes several rounds of query refinement, which incurs exchange of images between the mobile device and the server. With limited wireless bandwidth, this process can incur substantial delay, making the system unfriendly to use. This issue is addressed by considering a Client-side Relevance Feedback CRF technique. In the CRF system, Relevance Feedback RF is done on client side along. Mobile devices' battery power is saved from exchanging images between server and client and system response is instantaneous, which significantly enhances system usability. Furthermore, because the server is not involved in RF processing, it is able to support more users simultaneously. The experiment indicates that the system outperforms the traditional server-client relevance feedback systems on the aspects of system response time, mobile battery power saving, and retrieval result.","Title: Fusion approach to finding opinionated blogs\nYear: 2007\n\nAbstract: In this paper, we describe a fusion approach to finding opinion about a given target in blog postings. We tackled the opinion blog retrieval task by breaking it down to two sequential subtasks: on- topic retrieval followed by opinion classification. Our opinion retrieval approach was to first apply traditional IR methods to retrieve on-topic blogs, and then boost the ranks of opinionated blogs using combined opinion scores generated by four opinion assessment methods. Our opinion module consists of Opinion Term Module, which identify opinions based on the frequency of opinion terms (i.e., terms that only occur frequently in opinion blogs), Rare Term Module, which uses uncommon/rare terms (e.g., \"sooo good\") for opinion classification, IU Module, which uses IU (I and you) collocations, and Adjective-Verb Module, which uses computational linguistics' distribution similarity approach to learn the subjective language from training data.","Title: Efficient target search with relevance feedback for large CBIR systems\nYear: 2006\n\nAbstract: Recent content-based image retrieval (CBIR) techniques were designed around query refinement based on relevance feedback. They suffer from slow convergence, high disk I/O, and do not even guarantee to find intended targets. In this paper, we identify the cause of these problems and propose several efficient target search methods to address these drawbacks. Our complexity analysis shows that our approach is able to reach any given target image with fewer iterations in the worst and average cases. We evaluated our techniques on large datasets in simulated and realistic environments. The results show that our approach significantly reduces the number of iterations and improves overall retrieval performance. The experiments also confirm that our approach can always retrieve intended targets even with poor selection of initial query points and can be used to improve the effectiveness of existing CBIR systems with relevance feedback.","Title: A Multi-Directional Search technique for image annotation propagation\nYear: 2012\n\nAbstract: Image annotation has attracted lots of attention due to its importance in image understanding and search areas. In this paper, we propose a novel Multi-Directional Search framework for semi-automatic annotation propagation. In this system, the user interacts with the system to provide example images and the corresponding annotations during the annotation propagation process. In each iteration, the example images are clustered and the corresponding annotations are propagated separately to each cluster: images in the local neighborhood are annotated. Furthermore, some of those images are returned to the user for further annotation. As the user marks more images, the annotation process goes into multiple directions in the feature space. The query movements can be treated as multiple path navigation. Each path could be further split based on the user's input. In this manner, the system provides accurate annotation assistance to the user - images with the same semantic meaning but different visual characteristics can be handled effectively. From comprehensive experiments on Corel and U. of Washington image databases, the proposed technique shows accuracy and efficiency on annotating image databases.","Title: An extensible cheat-proofing multi-secret sharing scheme with low computation complexity\nYear: 2014\n\nAbstract: AbstractSecret sharing schemes are widely applied in various areas, and there are series of achievements, among which the verifiable multi-secret schemes are the most representative ones. A few verifiable multi-secret schemes own the following drawbacks: insensitive to the differences of security levels, lack of expandability, high computation complexity of verification process, and low efficiency of execution. Addressing the issues, an extensible cheat-proofing multi-secret sharing scheme with low computation complexity is proposed. With the security of RSA public key system and discrete logarithm puzzle, the proposed scheme can realize the following functions: first, the secret dealer can set relatively threshold values according to different security levels; second, the participants and secret combiner can verify the secret shares, which can guard against cheating problem, resist various kinds of attacks and improve the success rate of secret reconstruction; in addition, the secret shares are reusable in the situations of new group secret and new participant joining the group, which brings about good expandability. Moreover, compared with the previous schemes, the proposed scheme reduces the information content published by secret dealer and brings down the computation complexity of verification, which makes the efficiency of the scheme greatly improved. Copyright © 2013 John Wiley & Sons, Ltd.","Title: Fast Query Point Movement Techniques for Large CBIR Systems\nYear: 2009\n\nAbstract: Target search in content-based image retrieval (CBIR) systems refers to finding a specific (target) image such as a particular registered logo or a specific historical photograph. Existing techniques, designed around query refinement based on relevance feedback, suffer from slow convergence, and do not guarantee to find intended targets. To address these limitations, we propose several efficient query point movement methods. We prove that our approach is able to reach any given target image with fewer iterations in the worst and average cases. We propose a new index structure and query processing technique to improve retrieval effectiveness and efficiency. We also consider strategies to minimize the effects of users' inaccurate relevance feedback. Extensive experiments in simulated and realistic environments show that our approach significantly reduces the number of required iterations and improves overall retrieval performance. The experimental results also confirm that our approach can always retrieve intended targets even with poor selection of initial query points.","Title: A RSSI Localization Algorithm Based on Interval Analysis for Indoor Wireless Sensor Networks\nYear: 2013\n\nAbstract: RSSI-based localization is popular for its low cost and low complexity. Aiming at the noisy indoor environment that affects the accuracy of RSSI-based localization, this paper proposes a novel algorithm based on interval analysis to reduce the localization error and improve its robustness. In the ranging phase, we utilize Bootstrap resample to construct the confidence interval of measured distance, which lays the foundation for interval analysis. In the localization phase, we cast all the environment noise into unknown but bounded (UBB) noise and use interval analysis to compute the coordinate in set-membership framework. The simulation results show that our method has higher localization accuracy and localization coverage than two other popular localization schemes. Meanwhile, our method can accommodate different noise interference better.","Title: Boost image clustering with user query log\nYear: 2008\n\nAbstract: Image clustering is to derive a salient grouping of images such that similar ones are placed in the same cluster, which is useful in many applications. In this paper, we propose a constrained clustering algorithm, which leverages the collected user query log to guide the clustering process. Our method models a set of images as a graph and randomly contracts two vertices into a meta vertex iteratively with regarding to their similarity until the desired number of image groups has been reached. The experimental results demonstrate the superiority of our proposal.","Title: WIDIT: fusion-based approach to web search optimization\nYear: 2005\n\nAbstract: To facilitate both the understanding and the discovery of information, we need to utilize multiple sources of evidence, integrate a variety of methodologies, and combine human capabilities with those of the machine. The Web Information Discovery Integrated Tool (WIDIT) Laboratory at the School of Library and Information Science, Indiana University-Bloomington, houses several projects that employ this idea of multi-level fusion in the areas of information retrieval and knowledge discovery. This paper describes a Web search optimization study by the TREC research group of WIDIT, who explores a fusion-based approach to enhancing retrieval performance on the Web. In the study, we employed both static and dynamic tuning methods to optimize the fusion formula that combines multiple sources of evidence. By static tuning, we refer to the typical stepwise tuning of system parameters based on training data. “Dynamic tuning”, the key idea of which is to combine the human intelligence, especially pattern recognition ability, with the computational power of the machine, involves an interactive system tuning process that facilitates fine-tuning of the system parameters based on the cognitive analysis of immediate system feedback. The rest of the paper is organized as follows. The next section discusses related work in Web information retrieval (IR). Section 3 details the WIDIT approach to Web IR, followed by the description of our experiment using the TREC .gov data in section 4 and the discussion of results in section 5.","Title: Nonparametric Bootstrap-Based Multihop Localization Algorithm For Large-Scale Wireless Sensor Networks In Complex Environments\nYear: 2013\n\nAbstract: This paper presents a nonparametric bootstrap multihop localization algorithm for large-scale wireless sensor networks (WSNs) in complex environments. Unlike most of the existing schemes, this work is based on the consideration that it is not feasible to obtain a lot of available distance measurements sample for estimation and to get exact noise distributions or enough prior information for conventional statistical methods, which is a situation commonly encountered in complex environments practically. For the first time, we introduce a nonparametric bootstrap method into multihop localization to build confidence intervals for multihop distance estimation, which can eliminate the risk of small sample size and unknown distribution. On this basis, we integrate the interval analysis method with bootstrap approach for ordinary nodes localization. To reduce the computational complexity, boxes approach is utilized to approximate the irregular intersections. Simulation results show that our proposed scheme is less affected by the variation of unknown distributions and indicate that our method can achieve high localization coverage with relatively small average localization error in large-scale WSNs, especially in sparse and complex network with smaller connectivity and anchor percentage.","Title: WIDIT in TREC 2005 HARD, Robust, and SPAM Tracks\nYear: 2005\n\nAbstract: In this year's HARD track, we explored the possibility of improving retrieval performance by taking a fusion approach that combines a variety of techniques as well as different data sources. In order to deal effectively with the \"difficult\" topics, we employed a variety of query expansion strategies, the results of which were combined via an automatic fusion optimization process. We hypothesized that the \"difficulty\" of topics is often due to the lack of appropriate query terms and/or misguided emphasis on non-pivotal query terms by the system. Thus, our first-tier solution was to devise a wide range of query expansion methods that can not only enrich the query with useful term additions but also identify important query terms. Our automatic query expansion included such techniques as noun phrase extraction, acronym expansion, synonym identification, definition term extraction, keyword extraction by overlapping sliding window, and Web query expansion. For synonym identification, we integrated a sense disambiguation module into WIDIT's synset identification module so that best synonym set can be selected according to the term context. To reduce the noise from word definitions, we applied the overlapping sliding window (OSW) method to multiple definitions harvested from web and extracted the overlapping terms. To extract noun phrase, we combined the results of multiple NLP taggers as well as applying the OSW method. OSW method was also applied to topic fields to identify important topic terms. The Web query expansion method was a slight modification of the PIRC approach. To produce the optimum baseline results, we merged various combinations of query formulation results, which involved combinations of topic fields (title, description, narrative) and stemming (simple plural stemmer, combination stemmer), and the query expansion results using a weighted sum fusion formula. The fusion weights were determined using last year's Robust data to train the system via an automatic fusion optimization process. The fusion optimization involved iterations of fusion runs (i.e., result merging), where best performing systems in selected categories (e.g., short query, top 10 systems, etc.) were combined using average precision as fusion weights until the performance gain fell below a threshold. We viewed the clarification form (CF) as both manual query expansion and relevance feedback mechanism. Our clarification form included query term synonyms, noun phrase, and best sentences from top documents of the baseline result. Since difficult topics tend to produce few relevant documents in top ranks, we clustered the results and selected the best sentence from each cluster to include in the CF. In addition to expanding the query with user-selected terms","Title: An automatic feature generation approach to multiple instance learning and its applications to image databases\nYear: 2010\n\nAbstract: Automatic content-based image categorization is a challenging research topic and has many practical applications. Images are usually represented as bags of feature vectors, and the categorization problem is studied in the Multiple-Instance Learning (MIL) framework. In this paper, we propose a novel learning technique which transforms the MIL problem into a standard supervised learning problem by defining a feature vector for each image bag. Specifically, the feature vectors of the image bags are grouped into clusters and each cluster is given a label. Using these labels, each instance of an image bag can be replaced by a corresponding label to obtain a bag of cluster labels. Data mining can then be employed to uncover common label patterns for each image category. These label patterns are converted into bags of feature vectors; and they are used to transform each image bag in the data set into a feature vector such that each vector element is the distance of the image bag to a distinct pattern bag. With this new image representation, standard supervised learning algorithms can be applied to classify the images into the pre-defined categories. Our experimental results demonstrate the superiority of the proposed technique in categorization accuracy as compared to state-of-the-art methods.","Title: Research on Service Oriented and Middleware Based Active QoS Infrastructure of Wireless Sensor Networks\nYear: 2009\n\nAbstract: This paper presents an active QoS infrastructure of wireless sensor networks (WSN), named QISM, which is based on middleware and service-oriented. The design, structure, important modules and working processes are discussed. The important characteristics of QISM include the active regulation mechanism based on feedback and negotiation between applications and network, the architecture based on middleware and service publishing and subscribing, the expressing method based on Topic and functional domain. Through the active regulation for applications and network, QISM makes the network support applications and makes the applications adapt the network furthest. Therefore, the quality of application accomplishment is better guaranteed, and the lifetime of network is prolonged as well. Proved by simulation of a typical scenario, QISM has good QoS control ability and flexibility, can support complex applications, and is independent of network architectures.","Title: PARED: A testbed with parallel reprogramming and multi-channel debugging for WSNs\nYear: 2013\n\nAbstract: Testbed plays an invaluable role in facilitating research and development of wireless sensor networks (WSNs). A WSN testbed involves three fundamental tasks, which are reprogramming the WSN nodes, debugging the WSN applications and observing the status of the WSN nodes. In this paper, we propose a testbed with parallel reprogramming and multi-channel debugging capabilities (PARED) to meet the challenge behind those tasks. A parallel reprogramming mechanism is properly designed, which can reprogram all the nodes simultaneously and significantly diminishes the time wasted in WSN application deployment. PARED provides multichannel debugging support allowing users to set the debugging channel. PARED is also capable of observing the energy consumption of the sensor node along with its status information. We have deployed PARED in our laboratory at the scale of 50 nodes and a series of experiments have been conducted which proved that it is a reliable and convenient infrastructure for WSN research.","Title: Filling the Gap: Semi-Supervised Learning for Opinion Detection Across Domains.\nYear: 2011\n\nAbstract: We investigate the use of Semi-Supervised Learning (SSL) in opinion detection both in sparse data situations and for domain adaptation. We show that co-training reaches the best results in an in-domain setting with small labeled data sets, with a maximum absolute gain of 33.5%. For domain transfer, we show that self-training gains an absolute improvement in labeling accuracy for blog data of 16% over the supervised approach with target domain training data.","Title: Organizing the Web: Semi-Automatic Construction of a Faceted Scheme\nYear: 2004\n\nAbstract: In this study, we explore the construction of a faceted vocabulary that can be used as a mechanism for organizing Web-based resources. After analyzing the manual process of faceted vocabulary construction using an existing organizational structure to identify heuristics for automating the construction process, we modeled a hybrid, semi-automatic approach to facet generation that integrates the strengths of manual and automatic methods.","Title: Query Decomposition: A Multiple Neighborhood Approach to Relevance Feedback Processing in Content-based Image Retrieval\nYear: 2006\n\nAbstract: Todays Content-Based Image Retrieval (CBIR) techniques are based on the \"k-nearest neighbors\" (k- NN) model. They retrieve images from a single neighborhood using low-level visual features. In this model, semantically similar images are assumed to be clustered in the high-dimensional feature space. Unfortunately, no visual-based feature vector is sufficient to facilitate perfect semantic clustering; and semantically similar images with different appearances are always clustered into distinct neighborhoods in the feature space. Confinement of the search results to a single neighborhood is an inherent limitation of the k-NN techniques. In this paper we consider a new image retrieval paradigm — the Query Decomposition model - that facilitates retrieval of semantically similar images from multiple neighborhoods in the feature space. The retrieval results are the k most similar images from different relevant clusters. We introduce a prototype, and present experimental results to illustrate the effectiveness and efficiency of this new approach to content-based image retrieval.","Title: Set-Membership Adaptive Localization Algorithm With Time-Varying Error Bounds For Underwater Wireless Sensor Networks\nYear: 2013\n\nAbstract: This paper presents a set-membership adaptive localization algorithm with time-varying error bounds for underwater wireless sensor networks (UWSNs). In large-scale UWSNs, the nonstationary underwater environments, the insufficient prior information of hybrid noise, the small sample size of available distance measurements, and the node mobility all pose severe challenges for localization, and most current schemes are not applicable. Unlike most of the existing approaches, we tackle the multihop localization problem in a set-membership framework based on the consideration that the distance measurement uncertainty can be cast into an unknown but bounded (UBB) context. The principle of our scheme is firstly to use the bootstrap method to build confidence intervals and error bounds from a small sample set of distance measurements and then to determine the positions by a low-complexity interval analysis method as well as an adaptive localization update specification with time-varying error bounds. Simulation results show that our proposed scheme is an effective and efficient localization approach in large-scale UWSNs.","Title: A hybrid approach to faceted classification based on analysis of descriptor suffixes\nYear: 2005\n\nAbstract: This study explores a hybrid approach to the construction of faceted vocabularies that can be used for organizing Web-based resources. After analyzing the manual process of faceted vocabulary construction using existing organizational structures to identify heuristics for automating the construction process, we modeled a semi-automatic approach to facet generation that integrates the strengths of manual and automatic methods. One strategy in this hybrid approach utilizes the organization of terms according to the meanings of their suffixes. Although this heuristic could not effectively organize all of the terms of the lexicon bases to which it was applied, it nevertheless shows promise as a component of hybrid classification.","Title: Enable Web-Based Interactive Applications of High-Resolution 3D Medical Image Data\nYear: 2007\n\nAbstract: High-resolution 3D medical image data are typically huge in volume, varying from several hundred megabytes to several dozen gigabytes. Since such large-size data sets severely stress storage systems and networks, most existing Web-based interactive 3D medical imaging applications, such as 3D slicer and region-of-interest viewing, deal with only low-or medium-resolution images. In this paper, we introduce a single instance storage design, a client sharing technique, and a corresponding query processing method to enhance our existing remote medical image viewing system. We give performance results to show that the new design can significantly reduce storage and communication bandwidth requirements. Furthermore, it is more scalable to support a larger number of concurrent users.","Title: Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering.\nYear: 2014\n\nAbstract: Taking the advantage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unravelling complex relationships between genotype and phenotype. At present, traditional single-locus-based methods are insufficient to detect interactions consisting of multiple-locus, which are broadly existing in complex traits. In addition, statistic tests for high order epistatic interactions with more than 2 SNPs propose computational and analytical challenges because the computation increases exponentially as the cardinality of SNPs combinations gets larger.In this paper, we provide a simple, fast and powerful method using dynamic clustering and cloud computing to detect genome-wide multi-locus epistatic interactions. We have constructed systematic experiments to compare powers performance against some recently proposed algorithms, including TEAM, SNPRuler, EDCF and BOOST. Furthermore, we have applied our method on two real GWAS datasets, Age-related macular degeneration (AMD) and Rheumatoid arthritis (RA) datasets, where we find some novel potential disease-related genetic factors which are not shown up in detections of 2-loci epistatic interactions.Experimental results on simulated data demonstrate that our method is more powerful than some recently proposed methods on both two- and three-locus disease models. Our method has discovered many novel high-order associations that are significantly enriched in cases from two real GWAS datasets. Moreover, the running time of the cloud implementation for our method on AMD dataset and RA dataset are roughly 2 hours and 50 hours on a cluster with forty small virtual machines for detecting two-locus interactions, respectively. Therefore, we believe that our method is suitable and effective for the full-scale analysis of multiple-locus epistatic interactions in GWAS.","Title: Scalable Loop Self-Scheduling Schemes for Heterogeneous Clusters\nYear: 2005\n\nAbstract: Distributed systems (e.g. a LAN of computers) can be used for concurrent processing for some applications. However, a serious difficulty in concurrent programming of a distributed system is how to deal with scheduling and load balancing of such a system which may consist of heterogeneous computers. Distributed scheduling schemes suitable for parallel loops with independent iterations on heterogeneous computer clusters have been proposed and analyzed in the past. Here, we implement the previous schemes in the CORBA (Orbix). We also present an extension of these schemes implemented in a hierarchical master-slave architecture. We present experimental results and comparisons.","Title: Semi-supervised Learning for Opinion Detection\nYear: 2010\n\nAbstract: Research on opinion detection has shown that a large number of opinion-labeled data are necessary for capturing subtle opinions. However, opinion-labeled data, especially at the sub-document level, are often limited. This paper describes the application of Semi-Supervised Learning (SSL) to automatically produce more labeled data and explores the potential of SSL to improve transfer of labeled data to new domains. Preliminary results show that SSL performance is very close to a supervised system trained on the full data set and improves performance on out-of-domain data.","Title: One Optimization Method on the Navigation Performance Reliability of Planing Craft\nYear: 2011\n\nAbstract: In this paper, a comprehensive optimization mathematical model of the planing craft navigational performance reliability was established. A hierarchical parallel chaos-genetic algorithm, which is called P-CX-GA, is proposed based on parallel thinking, genetic algorithm and new chaos algorithm. The conclusion based on quantities of computation results shows that P-CX-GA is reliable and efficient. It proposed a solid foundation for hull form optimization design and evaluation analysis of the high-speed ships.","Title: Hierarchical leak detection and localization method in natural gas pipeline monitoring sensor networks.\nYear: 2012\n\nAbstract: In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point's position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate.","Title: Self-adaption link-quality detection algorithm for data collecting in OSN\nYear: 2010\n\nAbstract: A Self-adaption Link-quality Detection Algorithm(SLDA) is proposed to implement the Data Collecting in Opportunistic Sensor Network. The new scheme adopts Self adaptive Link-quality Detection strategy to measure the real time link quality weight factor (LQWF), and combines energy consumption model of mobile nodes to predict optimal transmission path for message forwarding by means of the Unscented Kalman Filter (UKF). On the other hand, SLDAuses a new message queue management that analyzes the lifetime of every message, so all messages are graded by stepping factor which reflects importance degree of messages. Simulation results show that SLDA enhances the predicted accuracy of link decision. It also increases the average delivery ratio and reduces the average transmission delay. Comparing with other typical algorithms, SLDA performs best in OSN, especially in the situation of sparse deployment of mobile nodes.","Title: Multi-hop localization algorithm based on grid-scanning for wireless sensor networks.\nYear: 2011\n\nAbstract: For large-scale wireless sensor networks (WSNs) with a minority of anchor nodes, multi-hop localization is a popular scheme for determining the geographical positions of the normal nodes. However, in practice existing multi-hop localization methods suffer from various kinds of problems, such as poor adaptability to irregular topology, high computational complexity, low positioning accuracy, etc. To address these issues in this paper, we propose a novel Multi-hop Localization algorithm based on Grid-Scanning (MLGS). First, the factors that influence the multi-hop distance estimation are studied and a more realistic multi-hop localization model is constructed. Then, the feasible regions of the normal nodes are determined according to the intersection of bounding square rings. Finally, a verifiably good approximation scheme based on grid-scanning is developed to estimate the coordinates of the normal nodes. Additionally, the positioning accuracy of the normal nodes can be improved through neighbors' collaboration. Extensive simulations are performed in isotropic and anisotropic networks. The comparisons with some typical algorithms of node localization confirm the effectiveness and efficiency of our algorithm.","Title: A RSSI Localization Algorithm and Implementation for Indoor Wireless Sensor Networks.\nYear: 2014\n\nAbstract: Taking the low-cost into consideration, Received Signal Strength Indicator (RSSI) technology is often used in positioning system. This paper proposes a RSSI localization algorithm which could improve the localization accuracy, coverage and robustness. First, a training process is conducted to build the log-distance path loss model. Then we utilize Bootstrap method to construct the Feasible Solution Set (FSS) for unknown node's coordinate. Finally the estimated coordinate of unknown node is figured out with interval analysis. Furthermore, the algorithm is applied to an indoor positioning system (IPS) developed by ourselves to evaluate its performance. The simulation results show that our algorithm achieves better accuracy, coverage and robustness than other two localization algorithms. And the experimental results show that compared with other four famous IPSs, the IPS using our localization algorithm obtain a better trade-off between accuracy and cost.","Title: Repeated Game Based Localization Algorithm for Wireless Sensor Networks with Malicious Beacon Nodes.\nYear: 2015\n\nAbstract: Localization is the key supporting technology for wireless sensor networks. Security and accuracy are the premise of the localization application. Real-world applications of wireless sensor networks are often subject to a variety of adverse circumstances interference and the localization performance is seriously affected. In this paper, we focus on the problem of node localization with malicious beacon nodes and solve it based on a repeated game method. Trust evaluation method is introduced to distinguish malicious beacon nodes. A repeated game framework is established and the strategies and payoffs are analyzed. Both malicious and normal nodes are driven by profits to a cooperation state and the whole localization performance is improved. Extensive simulation results show normal nodes can obtain higher location precision based on the repeated game. Malicious beacon nodes prefer to take the long-term cooperation attitude. The total losses to the network are less than the total profits caused by malicious nodes with game control.","Title: Orthogonal Regression Based Multihop Localization Algorithm For Large-Scale Underwater Wireless Sensor Networks\nYear: 2014\n\nAbstract: For large-scale underwater wireless sensor networks (UWSNs) with a minority of anchor nodes, multihop localization is a popular scheme for determining the geographical positions of normal nodes. However, existing multihop localization studies have considered the anchor positions to be free of errors, which is not a valid assumption in practice. In this paper, the problems existing in nonlinear least square-based node self-localization schemes are analyzed, and the biased distribution characteristic of multihop distance estimation errors is pointed out. Then, the orthogonal regression method is employed for the localization of normal nodes in the presence of anchor position errors. In particular, the influences of errors in independent variables and biases in dependent variables on node coordinate estimation are taken into account simultaneously. Extensive simulation results illustrate the robustness and effectiveness of our method.","Title: A novel D-S based secure localization algorithm for wireless sensor networks\nYear: 2014\n\nAbstract: AbstractRecent advances in wireless sensor networks WSNs technologies have brought significant convenience for location-based services. And the problem of enabling nodes of WSNs to determine their locations in a hostile environment has become more and more important in WSNs applications. In this article, we propose a novel Dempster-Shafer based secure localization algorithm to diminish the effect of the unreliable beacons that provide false localization information in the WSNs. First, the framework of trust evaluation is established on the basis of evidence theory. According to the characteristics of time and space of beacons, we set up the basic probability assignment BPA values and BPA evidences of beacons. Then, an improved Dempster-Shafer method is utilized to resolve the conflicts among the BPA evidences and to integrate the trust values of beacons. The max_mass function is used to decide which beacon is attacked, and then, we eliminate the attacked beacons before the localization stage. Finally, we employ the weighted Taylor-series least squares method to estimate the normal nodes coordinates. Simulation results demonstrate that our proposed algorithm is robust and effective. Copyright © 2013 John Wiley & Sons, Ltd.","Title: DIME: a novel framework for de novo metagenomic sequence assembly.\nYear: 2015\n\nAbstract: The recently developed next generation sequencing platforms not only decrease the cost for metagenomics data analysis, but also greatly enlarge the size of metagenomic sequence datasets. A common bottleneck of available assemblers is that the trade-off between the noise of the resulting contigs and the gain in sequence length for better annotation has not been attended enough for large-scale sequencing projects, especially for the datasets with low coverage and a large number of nonoverlapping contigs. To address this limitation and promote both accuracy and efficiency, we develop a novel metagenomic sequence assembly framework, DIME, by taking the DIvide, conquer, and MErge strategies. In addition, we give two MapReduce implementations of DIME, DIME-cap3 and DIME-genovo, on Apache Hadoop platform. For a systematic comparison of the performance of the assembly tasks, we tested DIME and five other popular short read assembly programs, Cap3, Genovo, MetaVelvet, SOAPdenovo, and SPAdes on four synthetic and three real metagenomic sequence datasets with various reads from fifty thousand to a couple million in size. The experimental results demonstrate that our method not only partitions the sequence reads with an extremely high accuracy, but also reconstructs more bases, generates higher quality assembled consensus, and yields higher assembly scores, including corrected N50 and BLAST-score-per-base, than other tools with a nearly theoretical speed-up. Results indicate that DIME offers great improvement in assembly across a range of sequence abundances and thus is robust to decreasing coverage.","Title: Exploring Co-training strategies for opinion detection\nYear: 2014\n\nAbstract: AbstractFor the last decade or so, sentiment analysis, which aims to automatically identify opinions, polarities, or emotions from user-generated content e.g., blogs, tweets, has attracted interest from both academic and industrial communities. Most sentiment analysis strategies fall into 2 categories: lexicon-based and corpus-based approaches. While the latter often requires sentiment-labeled data to build a machine learning model, both approaches need sentiment-labeled data for evaluation. Unfortunately, most data domains lack sufficient quantities of labeled data, especially at the subdocument level. Semisupervised learning SSL, a machine learning technique that requires only a few labeled examples and can automatically label unlabeled data, is a promising strategy to deal with the issue of insufficient labeled data. Although previous studies have shown promising results of applying various SSL algorithms to solve a sentiment-analysis problem, co-training, an SSL algorithm, has not attracted much attention for sentiment analysis largely due to its restricted assumptions. Therefore, this study focuses on revisiting co-training in depth and discusses several co-training strategies for sentiment analysis following a loose assumption. Results suggest that co-training can be more effective than can other currently adopted SSL methods for sentiment analysis.","Title: Bubble-based assembly of micro-tube with coordinated multiple manipulators\nYear: 2014\n\nAbstract: Assembly of a micro-tube with small diameter attracts great interest due to its promising contribution on vascular network to deliver oxygen and nutrition in thick biological substitutes. This paper presents a dexterous robotic system with three manipulators to reconstruct the interested micro-tube based on the interaction between bubbles and 2D hydrogel micro-units. Each manipulator has 3 degrees of freedom (DOFs), but they are equipped with different end-effectors for unique tasks under an inverted optical microscope (OM). Strategy of their coordinated operation is integrated under visual feedback. To verify the effectiveness of the proposed system, we conduct experiments on 2D hydrogel micro-units to achieve assembly of a 3D micro-channel for tissue engineering. Preliminary experimental results show that our assembly method is flexible and efficient.","Title: DNA AS X: An Information-Coding-Based Model to Improve the Sensitivity in Comparative Gene Analysis.\nYear: 2015\n\nAbstract: In recent studies [1-3], lots of hidden homology in DNA genome are not found by current comparative tools despite decades of research. Many scholars modeled the genome as a monotonous string, which limits and probably obstructs the discovery of some significant patterns. We propose an information-coding-based model called DNA As X (DAX) to improve the sensitivity in comparative genomic studies by integrating the principles and concepts of other disciplines including information coding theory and signal processing into genome analysis. The proposed DNA As X model uses character-analysis-free (CAF) techniques, where X is the intermediate for analysis that can be digit, code, signal, vector, tree, graph network and so on. It provides novel and comprehensive perspectives to further analyze and recognize the critical patterns hidden in DNA genomes. Comparing with traditional character-analysis-based (CAB) methods, DAX not only enriches the tools and the knowledge library of computational biology but also extends the domain from 1-D character string analysis to 2-D spatial/temporal domain. Furthermore, by applying the DAX model to the issue of exon prediction as an evaluation, we illustrate the insights behind this model. The experimental results show that the DAX methodology can improve the sensitivity in genome analysis by using the novel information-coding techniques.","Title: Analysis of Overall E-Business Solution on Personalized Medical Care - - Taking Private Hospitals for Example.\nYear: 2012\n\nAbstract: The patient is the lifeblood of the hospital and only win customers can win the future. When choosing a hospital, in addition to the strength of the medical treatment of the hospital itself, patients considering most are the services of the hospital which is the very factor that can influence customer making decision. Clearly, the needs of patients can't be achieved in overcrowded public hospitals. This paper starts from the content of E-Business, analyzes the current situation and problems of private hospital development, points out that development of E-Business to enhance customer relationship management is the only way to solve the practical problems of private hospitals, gives personal attention of private hospitals overall E-Business solutions and development strategies.","Title: J2M: a Java to MapReduce translator for cloud computing.\nYear: 2016\n\nAbstract: Cloud computing has gradually evolved into an infrastructural tool for a variety of scientific research and computing. It has become a trend that lots of products have been migrated from local servers to cloud by many institutions and organizations. One of the challenges in cloud computing now is how to run software efficiently on cloud platforms since lots of original codes are not capable of being executed in parallel on cloud contexts, resulting in that the power of clouds cannot be exerted well. It is costly to redesign and convert current sequential codes into cloud platform. Thus, automatic translation from sequential code to cloud code is one of the directions that could be taken to resolve the problem of code migration in cloud infrastructure. In this paper, a new Java to MapReduce (J2M) translator is developed to achieve the automatic translation from sequential Java to cloud for specific data-parallel code with large loops. This paper will provide details about the design of our translator and evaluate our performance through experiments. The experimental results not only indicate that the translator can precisely translate the sequential Java into cloud codes, but also show that it can achieve very good speedup in performance, and we expect that an almost linear speedup is possible if larger enough data is processed. It is believed that the J2M translator is an ideal stereotype for code migration and will play an important role in the transition era of cloud computing.","Title: GaussianCpG: A Gaussian model for detection of human CpG island\nYear: 2015\n\nAbstract: CpG contents in human DNA genome are generally suppressed to only around 1% comparing with other combinations [1]. The scarcity of CpG dinucleotide can be explained by bio-chemical binding force and energy [2]. CpG islands (CGI), where high profiles of CpG sites are densely contained in some certain genome regions, play important roles in DNA methylation, gene regulation, epigenetic inheritance, gene mutation, chromosome inactivation and nuclesome retention [3]. In vertebrate, DNA methylation usually occurs in CpG islands and adds an additional methyl to cytosine such that the gene silencing may be caused by the additional methyl. This subtle process can further give rise to different gene regulation and diverse epigenetic issues. Conventional bisulfite modification-based methods to determine the CpG island are time-consuming [4]. Although new sequencing techniques are developed for whole genome assays, it is reported to be too costly [5]. Thus, computational investigations to CpG islands is efficient and fundamental for many biological studies.","Title: Measurement Of Mwcnt/Tungsten Contact Resistance By Bridging Cnt On Two Tungsten Electrodes With Two Manipulators\nYear: 2015\n\nAbstract: Semiconductor nano-devices based on carbon nanotubes (CNTs) have attracted great interests recently such as field effect transistors (CNTFETs). One of the challenges is to reduce the contact resistance between CNT and electrodes. This paper presents a robotic system with two nano-manipulators for two-lead contact resistance measurement. Electron beam induced deposition (EBID) technique was applied to decrease the contact resistance between multi-walled CNTs (MWCNTs) and Tungsten deposition fabricated by focused ion beam (FIB). A Tungsten (W) probe fixed on one manipulator is used to pick up a CNT selectively from a CNT bundle. The picked CNT was positioned to approach a W deposition on an atomic force microscopy (AFM) cantilever. The cantilever mounted on another manipulator was coordinated by positioning for well contact between the W deposition and CNT. To strengthen this contact, four W points are deposited on the contact area of CNT for totally 9min by EBID. A resistance between W probe and cantilever was measured before and after EBID. Based on these measured resistance, contact resistance was calculated by a theoretical model. It showed the contact resistance of MWCNT/Tungsten deposition was reduced by 98.8% by the EBID deposition.","Title: Reliable routing in wireless sensor networks based on coalitional game theory.\nYear: 2016\n\nAbstract: Nodes in wireless sensor networks (WSNs) have the potential to be selfish without transmitting packets in routing. This study mainly focuses on the problem of reliable delivery mechanism in WSNs, and the authors' objective is to ensure stable cooperation among nodes for packets delivery and minimum routing cost at the same time. Based on the performance metrics in terms of the rate of packets forw...","Title: A Radio-Map Automatic Construction Algorithm Based on Crowdsourcing.\nYear: 2016\n\nAbstract: Traditional radio-map-based localization methods need to sample a large number of location fingerprints offline, which requires huge amount of human and material resources. To solve the high sampling cost problem, an automatic radio-map construction algorithm based on crowdsourcing is proposed. The algorithm employs the crowd-sourced information provided by a large number of users when they are walking in the buildings as the source of location fingerprint data. Through the variation characteristics of users' smartphone sensors, the indoor anchors (doors) are identified and their locations are regarded as reference positions of the whole radio-map. The AP-Cluster method is used to cluster the crowdsourced fingerprints to acquire the representative fingerprints. According to the reference positions and the similarity between fingerprints, the representative fingerprints are linked to their corresponding physical locations and the radio-map is generated. Experimental results demonstrate that the proposed algorithm reduces the cost of fingerprint sampling and radio-map construction and guarantees the localization accuracy. The proposed method does not require users' explicit participation, which effectively solves the resource-consumption problem when a location fingerprint database is established.","Title: A Cloud-Assisted Application over Apache Spark for Investigating Epigenetic Markers on DNA Genome Sequences\nYear: 2016\n\nAbstract: As an important epigenetic marker in identifying biological elements and processes in mammalian genomes, CpG islands (CGI) play significant roles in DNA methylation, gene regulation, epigenetic inheritance, gene mutation, chromosome inactivation and nuclesome retention. Investigating the CpG islands and their structures is a rigorous task because of unknown structures and the exponential number of possible patterns. In this paper, we design and implement an ad hoc application by combining the merits of Apache Spark platform and Spark programming paradigm with the particular properties of DNA genome sequences for CpG island investigation. A novel CpG box model and a Markov model are developed primarily for redefining and investigating the CpG island. Meanwhile, these models can easily fit to Spark-based cloud platforms that can greatly accelerate the analytic procedure. Two types of evaluations are successfully performed: one is accuracy-related and another is computing performance test. This paper is meant to describe this particular application on assisting the processing and the genomic analysis in epigenetic studies.","Title: Bayesian Networks-Based Selection Algorithm for Virtual Machine to Be Migrated\nYear: 2016\n\nAbstract: In cloud data centers, virtual machine (VM) consolidation is one of the challenge topics. In which, the selection of VMs to be migrated is one of the key issues in the process of VM consolidation. In this paper, under consideration of the dynamical uncertain environment, a Bayesian networks-based estimation model was constructed. Because excessive VM migrations influence the Quality of Service (QoS) of data center, the model aims at estimating the migration probability of VMs and calculating the potential total number of migrations occurred in physical hosts. Based on the proposed model, a Bayesian networks-based selection algorithm (BN-SA) for VMs to be migrated was proposed. The BN-SA adaptively adjusts the overloaded threshold and selects VMs which have relatively short migration time and big impact on potential migrations of host in the phase of reallocating VMs. The experimental results show that BN-SA algorithm has a promising performance.","Title: Supervoxel-Based Hierarchical Markov Random Field Framework For Multi-Atlas Segmentation\nYear: 2016\n\nAbstract: Multi-atlas segmentation serves as an important technique for quantitative analysis of medical images. In many applications, top performing techniques rely on computationally expensive deformable registration to transfer labels from atlas images to the target image. We propose a more computationally efficient label transfer strategy that uses supervoxel matching regularized by Markov random field (MRF), followed by regional voxel-wise joint label fusion and a second MRF. We evaluate this hierarchical MRF framework for multi-label diencephalon segmentation from the MICCAI 2013 SATA Challenge. Our segmentation results are comparable to the top-tier one obtained by deformable registration, but with much lower computational complexity.","Title: Bayesian network-based Virtual Machines consolidation method.\nYear: 2017\n\nAbstract: Efficient Virtual Machines (VMs) consolidation, as one of the primary methods for balancing between guaranteeing Quality of Service (QoS) and saving energy, is critical for data centers. Most existing VMs consolidation methods reallocate physical resources by adopting live VM migration. Therefore, VMs consolidation can be cast into estimating the physical resource utilization in Physical Machines (PMs) and predicting the migration probability of VMs. In this paper, we develop a Bayesian network-based estimation model (BNEM) for live VM migration, allowing a comprehensive treatment of nine actual factors in real data centers. A selection criterion of VMs to be migrated and a VM placement criterion are presented. By combining three algorithms corresponding to different phases in VMs consolidation, a hybrid Bayesian network-based VMs consolidation (BN-VMC) method is proposed. We have validated our approach by conducting a performance evaluation study using CloudSim toolkit, and the trace-driven comparison experiments are also performed. The simulation results show that the method can significantly degrade energy consumption, avoid inefficient VM migrations, and improve QoS.","Title: A Comprehensive Review Of Emerging Computational Methods For Gene Identification\nYear: 2016\n\nAbstract: Gene identification is at the center of genomic studies. Although the first phase of the Encyclopedia of DNA Elements (ENCODE) project has been claimed to be complete, the annotation of the functional elements is far from being so. Computational methods in gene identification continue to play important roles in this area and other relevant issues. So far, a lot of work has been performed on this area, and a plethora of computational methods and avenues have been developed. Many review papers have summarized these methods and other related work. However, most of them focus on the methodologies from a particular aspect or perspective. Different from these existing bodies of research, this paper aims to comprehensively summarize the mainstream computational methods in gene identification and tries to provide a short but concise technical reference for future studies. Moreover, this review sheds light on the emerging trends and cutting-edge techniques that are believed to be capable of leading the research on this field in the future.","Title: Deep Learning In Genomic And Medical Image Data Analysis: Challenges And Approaches\nYear: 2017\n\nAbstract: Artificial intelligence, especially deep learning technology, is penetrating the majority of research areas, including the field of bioinformatics. However, deep learning has some limitations, such as the complexity of parameter tuning, architecture design, and so forth. In this study, we analyze these issues and challenges in regards to its applications in bioinformatics, particularly genomic analysis and medical image analytics, and give the corresponding approaches and solutions. Although these solutions are mostly rule of thumb, they can effectively handle the issues connected to training learning machines. As such, we explore the tendency of deep learning technology by examining several directions, such as automation, scalability, individuality, mobility, integration, and intelligence warehousing.","Title: Microbubbles for High-Speed Assembly of Cell-Laden Vascular-Like Microtube.\nYear: 2016\n\nAbstract: Vascular-like microtube takes an important role in delivering oxygen and nutrient to keep cell alive in the generated tissue. In this paper, we present an automated micromanipulation system to assemble 2-D gel micro-rings to vascular-like microtubes by means of generating and controlling microbubbles. The detailed automated assembly process is described. To enable the automated assembly, real-time...","Title: Evaluating the Impact of Encoding Schemes on Deep Auto-Encoders for DNA Annotation.\nYear: 2017\n\nAbstract: Deep Neural Networks show their promise over traditional neural network on DNA genomic analysis. However, due to the uncertainty of DNA sequence data, it performs differently in various encoding schemes. In this article we focus on the comparison of different schemes on various auto-encoder algorithms in DNA annotation and analyze their impacts on deep learning. We also aim to find the best encoding schemes used on deep auto-encoder algorithms for DNA annotation.","Title: A deep learning method for lincRNA detection using auto-encoder algorithm.\nYear: 2017\n\nAbstract: RNA sequencing technique (RNA-seq) enables scientists to develop novel data-driven methods for discovering more unidentified lincRNAs. Meantime, knowledge-based technologies are experiencing a potential revolution ignited by the new deep learning methods. By scanning the newly found data set from RNA-seq, scientists have found that: (1) the expression of lincRNAs appears to be regulated, that is, the relevance exists along the DNA sequences; (2) lincRNAs contain some conversed patterns/motifs tethered together by non-conserved regions. The two evidences give the reasoning for adopting knowledge-based deep learning methods in lincRNA detection. Similar to coding region transcription, non-coding regions are split at transcriptional sites. However, regulatory RNAs rather than message RNAs are generated. That is, the transcribed RNAs participate the biological process as regulatory units instead of generating proteins. Identifying these transcriptional regions from non-coding regions is the first step towards lincRNA recognition.The auto-encoder method achieves 100% and 92.4% prediction accuracy on transcription sites over the putative data sets. The experimental results also show the excellent performance of predictive deep neural network on the lincRNA data sets compared with support vector machine and traditional neural network. In addition, it is validated through the newly discovered lincRNA data set and one unreported transcription site is found by feeding the whole annotated sequences through the deep learning machine, which indicates that deep learning method has the extensive ability for lincRNA prediction.The transcriptional sequences of lincRNAs are collected from the annotated human DNA genome data. Subsequently, a two-layer deep neural network is developed for the lincRNA detection, which adopts the auto-encoder algorithm and utilizes different encoding schemes to obtain the best performance over intergenic DNA sequence data. Driven by those newly annotated lincRNA data, deep learning methods based on auto-encoder algorithm can exert their capability in knowledge learning in order to capture the useful features and the information correlation along DNA genome sequences for lincRNA detection. As our knowledge, this is the first application to adopt the deep learning techniques for identifying lincRNA transcription sequences.","Title: Topology control for minimizing interference with delay constraints in an ad hoc network.\nYear: 2018\n\nAbstract: Most of the existing work on topology control is to minimize the interference or delay separately. However, minimizing the interference and reducing the delay are two conflicting goals; therefore, considering a trade-off between them is necessary. In this paper, our goal is to minimize the interference while guaranteeing the end-to-end delay under a constraint. We take into account three optimization objectives, including the maximum interference, average interference and average path interference. We propose a centralized algorithm with a greedy strategy that can minimize the maximum interference while satisfying the delay constraint. A distributed algorithm (LDMST) is proposed to minimize the average interference. In LDMST, each node builds a delay-constrained minimum spanning tree (DMST). To minimize the path interference, a localized delay-constrained Bellman–Ford (LDBF) algorithm is proposed. LDBF employs an improved Bellman–Ford algorithm to find the optimal path, which has the minimum interference and satisfies the delay constraint. The final topology is composed of these optimal paths. The simulation results illustrate that the proposed topology control algorithms exhibit good performance in terms of the interference objectives and can also guarantee the end-to-end delay under the constraint.","Title: Three-Stream Convolutional Networks for Video-based Person Re-Identification\nYear: 2017\n\nAbstract: This paper aims to develop a new architecture that can make full use of the feature maps of convolutional networks. To this end, we study a number of methods for video-based person re-identification and make the following findings: 1) Maxpooling only focuses on the maximum value of a receptive field, wasting a lot of information. 2) Networks with different streams even including the one with the worst performance work better than networks with same streams, where each one has the best performance alone. 3) A fully connection layer at the end of convolutional networks is not necessary. Based on these studies, we propose a new convolutional architecture termed Three-Stream Convolutional Networks (TSCN). It first uses different streams to learn different aspects of feature maps for attentive spatio-temporal fusion of video, and then merges them together to study some union features. To further utilize the feature maps, two architectures are designed by using the strategies of multi-scale and upsampling. Comparative experiments on iLIDS-VID, PRID-2011 and MARS datasets illustrate that the proposed architectures are significantly better for feature extraction than the state-of-the-art models.","Title: Locality-Based Visual Outlier Detection Algorithm for Time Series\nYear: 2017\n\nAbstract: AbstractPhysiological theories indicate that the deepest impression for time series data with respect to the human visual system is its extreme value. Based on this principle, by researching the strategies of extreme-point-based hierarchy segmentation, the hierarchy-segmentation-based data extraction method for time series, and the ideas of locality outlier, a novel outlier detection model and method for time series are proposed. The presented algorithm intuitively labels an outlier factor to each subsequence in time series such that the visual outlier detection gets relatively direct. The experimental results demonstrate the average advantage of the developed method over the compared methods and the efficient data reduction capability for time series, which indicates the promising performance of the proposed method and its practical application value.","Title: A Precise Dead Reckoning Algorithm Based on Bluetooth and Multiple Sensors.\nYear: 2018\n\nAbstract: More and more applications of location-based services lead to the development of indoor positioning technology. As a part of the Internet of Things ecosystem, low-power Bluetooth technology provides a new direction for indoor positioning. Most existing indoor positioning algorithms are applied to specific situations. Thus, they are difficult to adapt to actually complex environments and different ...","Title: Convolutional networks with cross-layer neurons for image recognition.\nYear: 2018\n\nAbstract: Very deep convolutional networks have recently achieved a series of breakthroughs on several challenging tasks such as the ImageNet or COCO competitions. However, it is difficult to train such deep neural networks. In this paper, we present a novel structure called cross-layer neurons architecture, which has the capability to train effective deeper neural networks. It utilizes cross-layer neurons to synthesize the information (features) learned from all the lower-level layers and send them to the higher-level layers through the cross-layer. Based on this novel architecture, we propose a new deep neural model termed Cross-Layer Neurons Networks (CLNN). It is shown that CLNN can relieve the problem of vanishing gradient. It is also shown that CLNN has the capability of improving the convergence rate of classification. Comparative experiments on several benchmark datasets (MNIST, CIFAR-10, CIFAR-100, SVHN and STL-10) clearly demonstrate that our proposed model is suitable for training deeper networks and can effectively improve the performance by utilizing cross-layer neurons.","Title: Adaptive Compressed Sensing For Acceleration Data Transmission In Human Motion Capture\nYear: 2017\n\nAbstract: In a human motion capture system, massive acceleration data will cost much transmission time. In order to reduce the acceleration data transmission volume while guaranteeing measurement accuracy, this paper presents an adaptive compressed sensing algorithm for acceleration data transmission in human motion capture. An innovative feature of our approach is to associate the sparsity and best Compression Ratio of acceleration data in a learning phase. Based on this correlation, the sensor node uses sparsity to describe the structural characteristics of acceleration data, and it adjusts best Compression Ratios of data according to the characteristics of the real-time acceleration data. The results of experiment show that algorithm can adjust the best Compression Ratios of original acceleration data at different parts of body in several typical motions, while ensuring Reconstruction Accuracy. For example, in running, the approach can achieve that the Compression Ratios at arms and legs separately reach to approximately 80% and 60%, while the compression data can achieve the same Reconstruction Accuracy. What's more, the algorithm can increase the Compression Ratio of capture system by about 20% compared with the current BSBL-BO algorithm, while achieving the same minimum Reconstruction Accuracy of capture system.","Title: Learning To Detect Multiple Photographic Defects\nYear: 2018\n\nAbstract: In this paper, we introduce the problem of simultaneously detecting multiple photographic defects. We aim at detecting the existence, severity, and potential locations of common photographic defects related to color, noise, blur and composition. The automatic detection of such defects could be used to provide users with suggestions for how to improve photos without the need to laboriously try various correction methods. Defect detection could also help users select photos of higher quality while filtering out those with severe defects in photo curation and summarization.To investigate this problem, we collected a large-scale dataset of user annotations on seven common photographic defects, which allows us to evaluate algorithms by measuring their consistency with human judgments. Our new dataset enables us to formulate the problem as a multi-task learning problem and train a multi-column deep convolutional neural network (CNN) to simultaneously predict the severity of all the defects. Unlike some existing single-defect estimation methods that rely on low-level statistics and may fail in many cases on natural photographs, our model is able to understand image contents and quality at a higher level. As a result, in our experiments, we show that our model has predictions with much higher consistency with human judgments than low-level methods as well as several baseline CNN models. Our model also performs better than an average human from our user study.","Title: Attributing Fake Images to GANs: Analyzing Fingerprints in Generated Images.\nYear: 2018\n\nAbstract: Research in computer graphics has been in pursuit of realistic image generation for a long time. Recent advances in machine learning with deep generative models have shown increasing success of closing the realism gap by using data-driven and learned components. There is an increasing concern that real and fake images will become more and more difficult to tell apart. We take a first step towards this larger research challenge by asking the question if and to what extend a generated fake image can be attribute to a particular Generative Adversarial Networks (GANs) of a certain architecture and trained with particular data and random seed. Our analysis shows single samples from GANs carry highly characteristic fingerprints which make attribution of images to GANs possible. Surprisingly, this is even possible for GANs with same architecture and same training that only differ by the training seed.","Title: Analysis of Public Opinion Heat Trend in Universities on The Basis of Markov Chain\nYear: 2018\n\nAbstract: With the development of the information age, Micro-blog, as the main carrier and platform of public opinion in universities, has gradually increased its influence. Focusing on the high fluctuation of public opinion in universities and other certain characters, this paper proposes a predictive model of public opinion heat trend based on Markov chain. In this model, it collects some index data and gives out time series of the public opinion heat value; then classifies the state space of public opinion heat trend in universities; thirdly, constructs state transition matrix of public opinion heat; and lastly, forecasts the trend variation interval of public opinion heat. Finally, the experimental results show that such model can effectively predict the trend of public opinion in universities and guide and manage them, providing a theoretical model basis for effect appraisal of public opinion in universities.","Title: Texture Mixer: A Network For Controllable Synthesis And Interpolation Of Texture\nYear: 2019\n\nAbstract: This paper addresses the problem of interpolating visual textures. We formulate this problem by requiring (1) by example controllability and (2) realistic and smooth interpolation among an arbitrary number of texture samples. To solve it we propose a neural network trained simultaneously on a reconstruction task and a generation task, which can project texture examples onto a latent space where they can be linearly interpolated and projected back onto the image domain, thus ensuring both intuitive control and realistic results. We show our method outperforms a number of base-lines according to a comprehensive suite of metrics as well as a user study. We further show several applications based on our technique, which include texture brush, texture dis-solve, and animal hybridization.","Title: A deep learning method for lincRNA identification using auto-encoder algorithm\nYear: 2016\n\nAbstract: LincRNAs are four times more than coding RNA sequences. However, currently only 21 thousand lincRNAs are computationally discovered [1]. Although this was one of the most important findings in lincRNA identification, identification of lincRNAs is far from being complete and those predicted lincRNAs are not validated yet. Currently new identified lincRNAs are most from the computational analysis of RNA-seq transcript data while deep learning based methods are barely seen in detecting and validating lincRNAs.","Title: Automatic Image Defect Diagnosis.\nYear: 2016\n\nAbstract: Although problems relating to specific image correction have been explored intensively, the problem of simultaneous diagnosis for multiple photographic defects remains relatively untouched. Solutions to this problem attempt to predict the existence, severity, and locations of common defects. This paper proposes a first attempt at a solution to the general defect diagnosis problem based on our novel dataset. We formulate the defect diagnosis problem as a multi-task prediction problem and utilize multi-column deep neural networks (DNN) to approach the problem. We propose DNN models with holistic and multi-patch inputs and combine their predicted scores to integrate multi-scale information. During experiments, we validate the complementarity of both kinds of inputs. We also validate that our combined predictions have a more consistent ranking correlation with our ground truth than the average of individual usersu0027 judgments. Furthermore, we apply the fully convolutional version of our trained model to visualize defect severity heat maps, which can effectively identify defective regions of input images. We propose that our work will provide casual photographers with better experiences when using image editing software to improve image quality. Another promising avenue for future application involves the equipping of photo summarization systems with defect cues to focus more on defect-free photos.","Title: WIDIT: integrated approach to HARD topic search\nYear: 2006\n\nAbstract: Web Information Discovery Tool (WIDIT) Laboratory at the Indiana University School of Library, whose basic approach to combine multiple methods as well as to leverage multiple sources of evidence, participated in 2005 Text Retrieval Conference’s Hard track (HARD-2005) to investigate methods of effectively dealing with HARD topics by exploring a variety of query expansion strategies, the results of which were combined via an automatic fusion optimization process. We hypothesized that the “difficulty” of topics is often due to the lack of appropriate query terms and/or misguided emphasis on non-pivotal query terms by the system. Thus, our first-tier solution was to devise a wide range of query expansion methods that can not only enrich the query with useful term additions but also identify important query terms. Our automatic query expansion included such techniques as noun phrase extraction, synonym identification, definition term extraction, keyword extraction by overlapping sliding window, and Web query expansion. The results of automatic expansion were used in soliciting user feedback, which was utilized in a post-retrieval reranking process. The paper describes our participation in HARD-2005 and is organized as follows. Section 2 gives an overview of HARD track, section 3 describes the WIDIT approach to HARD-2005, and section 4 discusses the results and implications, followed by the concluding remarks in section 5.","Title: Reconstruction of Hidden Representation for Robust Feature Extraction.\nYear: 2017\n\nAbstract: This article aims to develop a new and robust approach to feature representation. Motivated by the success of Auto-Encoders, we first theoretically analyze and summarize the general properties of all algorithms that are based on traditional Auto-Encoders: (1) The reconstruction error of the input cannot be lower than a lower bound, which can be viewed as a guiding principle for reconstructing the input. Additionally, when the input is corrupted with noises, the reconstruction error of the corrupted input also cannot be lower than a lower bound. (2) The reconstruction of a hidden representation achieving its ideal situation is the necessary condition for the reconstruction of the input to reach the ideal state. (3) Minimizing the Frobenius norm of the Jacobian matrix of the hidden representation has a deficiency and may result in a much worse local optimum value. We believe that minimizing the reconstruction error of the hidden representation is more robust than minimizing the Frobenius norm of the Jacobian matrix of the hidden representation. Based on the above analysis, we propose a new model termed Double Denoising Auto-Encoders (DDAEs), which uses corruption and reconstruction on both the input and the hidden representation. We demonstrate that the proposed model is highly flexible and extensible and has a potentially better capability to learn invariant and robust feature representations. We also show that our model is more robust than Denoising Auto-Encoders (DAEs) for dealing with noises or inessential features. Furthermore, we detail how to train DDAEs with two different pretraining methods by optimizing the objective function in a combined and separate manner, respectively. Comparative experiments illustrate that the proposed model is significantly better for representation learning than the state-of-the-art models.","Title: Improving Cube-to-ERP Conversion Performance with Geometry Features of 360 Video Structure\nYear: 2019\n\nAbstract: 360 videos provide an omnidirectional view of the scene with extremely large data. Therefore, representing 360 videos with less data has become more and more important. Cube format is such a popular representation of 360 videos. However, we have to convert cube to Equirectangula(ERP) for displaying convenience. In this paper, we enhance Cube-to-ERP conversion performance by joint using Convolutional Neural Network(CNN) and classical interpolation method. The optimal threshold of boundary is derived according to geometry features of the cube-to-ERP format. This threshold is the guidance of how to combine CNN and classical interpolation method. Our experiment results prove that the derived threshold has a certain degree of guiding significance. Furthermore, we propose a new evaluation criterion with the help of Marsaglia model. It is much easier and more accurate to evaluate geometry conversion process.","Title: Bagging Machine Learning Algorithms: A Generic Computing Framework Based On Machine-Learning Methods For Regional Rainfall Forecasting In Upstate New York\nYear: 2021\n\nAbstract: Regional rainfall forecasting is an important issue in hydrology and meteorology. Machine learning algorithms especially deep learning methods have emerged as a part of prediction tools for regional rainfall forecasting. This paper aims to design and implement a generic computing framework that can assemble a variety of machine learning algorithms as computational engines for regional rainfall forecasting in Upstate New York. The algorithms that have been bagged in the computing framework include the classical algorithms and the state-of-the-art deep learning algorithms, such as K-Nearest Neighbors, Support Vector Machine, Deep Neural Network, Wide Neural Network, Deep and Wide Neural Network, Reservoir Computing, and Long Short Term Memory methods. Through the experimental results and the performance comparisons of these various engines, we have observed that the SVM- and KNN-based method are outstanding models over other models in classification while DWNN- and KNN-based methods outstrip other models in regression, particularly those prevailing deep-learning-based methods, for handling uncertain and complex climatic data for precipitation forecasting. Meanwhile, the normalization methods such as Z-score and Minmax are also integrated into the generic computing framework for the investigation and evaluation of their impacts on machine learning models.","Title: Ufacenet: Research On Multi-Task Face Recognition Algorithm Based On Cnn\nYear: 2021\n\nAbstract: With the application of deep convolutional neural networks, the performance of computer vision tasks has been improved to a new level. The construction of a deeper and more complex network allows the face recognition algorithm to obtain a higher accuracy, However, the disadvantages of large computation and storage costs of neural networks limit the further popularization of the algorithm. To solve this problem, we have studied the unified and efficient neural network face recognition algorithm under the condition of a single camera; we propose that the complete face recognition process consists of four tasks: face detection, in vivo detection, keypoint detection, and face verification; combining the key algorithms of these four tasks, we propose a unified network model based on a deep separable convolutional structure-UFaceNet. The model uses multisource data to carry out multitask joint training and uses the keypoint detection results to aid the learning of other tasks. It further introduces the attention mechanism through feature level clipping and alignment to ensure the accuracy of the model, using the shared convolutional layer network among tasks to reduce model calculations amount and realize network acceleration. The learning goal of multi-tasking implicitly increases the amount of training data and different data distribution, making it easier to learn the characteristics with generalization. The experimental results show that the UFaceNet model is better than other models in terms of calculation amount and number of parameters with higher efficiency, and some potential areas to be used.","Title: Artificial Fingerprinting for Generative Models - Rooting Deepfake Attribution in Training Data.\nYear: 2021\n\nAbstract: Photorealistic image generation has reached a new level of quality due to the breakthroughs of generative adversarial networks (GANs). Yet, the dark side of such deepfakes, the malicious use of generated media, raises concerns about visual misinformation. While existing research work on deepfake detection demonstrates high accuracy, it is subject to advances in generation techniques and adversarial iterations on detection countermeasure techniques. Thus, we seek a proactive and sustainable solution on deepfake detection, that is agnostic to the evolution of generative models, by introducing artificial fingerprints into the models. Our approach is simple and effective. We first embed artificial fingerprints into training data, then validate a surprising discovery on the transferability of such fingerprints from training data to generative models, which in turn appears in the generated deepfakes. Experiments show that our fingerprinting solution (1) holds for a variety of cutting-edge generative models, (2) leads to a negligible side effect on generation quality, (3) stays robust against image-level and model-level perturbations, (4) stays hard to be detected by adversaries, and (5) converts deepfake detection and attribution into trivial tasks and outperforms the recent state-of-the-art baselines. Our solution closes the responsibility loop between publishing pre-trained generative model inventions and their possible misuses, which makes it independent of the current arms race.","Title: RelaxLoss: Defending Membership Inference Attacks without Losing Utility\nYear: 2022\n\nAbstract: As a long-term threat to the privacy of training data, membership inference attacks (MIAs) emerge ubiquitously in machine learning models.\nExisting works evidence strong connection between the distinguishability of the training and testing loss distributions and the model's vulnerability to MIAs. Motivated by existing results, we propose a novel training framework based on a relaxed loss ($\\textbf{RelaxLoss}$) with a more achievable learning target, which leads to narrowed generalization gap and reduced privacy leakage. RelaxLoss is applicable to any classification model with added benefits of easy implementation and negligible overhead. Through extensive evaluations on five datasets with diverse modalities (images, medical data, transaction records), our approach consistently outperforms state-of-the-art defense mechanisms in terms of resilience against MIAs as well as model utility. Our defense is the first that can withstand a wide range of attacks while preserving (or even improving) the target model's utility.","Title: A Calibration Scheme With Combination of the Optical Shaft Encoder and Laser Triangulation Sensor for Low-Frequency Angular Acceleration Rotary Table\nYear: 2021\n\nAbstract: A low-frequency angular acceleration rotary table (LFAART) is capable of sinusoidal motion around a fixed position and essential to verify dynamic characteristics of a gyroscope device. The table's performance at different swinging frequencies and amplitudes needs to be calibrated to realize traceability. However, there has been little research on the LFAART calibration. Besides, the existing measurement methods of sinusoidal angular motion parameters are not adequate for the needs of the LFAART calibration in terms of measurement range, precision, and feasibility. This article presents a calibration scheme combining an optical shaft encoder and a laser triangulation sensor. When the rotation amplitude covers sufficient slots of the scale grating, the optical shaft encoder is applied, while the laser triangulation sensor is used for a tiny amplitude motion. An algorithm is proposed to accurately calculate the angular acceleration amplitude and frequency of the rotation utilizing sinusoidal phase-modulated signals of the optical shaft encoder output. Singular zero-crossings of the series are discarded, and intervals of reversing moments are recognized according to the signal derivative's sign at remaining zero-crossings. Then intervals are divided into subintervals to obtain accurate reversing moments. Finally, angular displacements between two adjacent reversing moments are estimated based on the fringe counting method, and the amplitude and frequency are calculated. The measurement uncertainty analysis and experiment demonstrate that the calibration scheme, including the algorithm, possesses high accuracy and resolution, wide range, and good operability, which perfectly solves the rotation's angular acceleration amplitude and frequency.","Title: Multipolicy Robot-Following Model Based on Reinforcement Learning\nYear: 2021\n\nAbstract: We propose in this paper a new approach to solve the decision problem of robot-following. Different from the existing single policy model, we propose a multipolicy model, which can change the following policy in time according to the scene. The value of this paper is to obtain a multipolicy robot-following model by the self-learning method, which is used to improve the safety, efficiency, and stability of robot-following in the complex environments. Empirical investigation on a number of datasets reveals that overall, the proposed approach tends to have superior out-of-sample performance when compared to alternative robotfollowing decision methods. The performance of the model has been improved by about 2 times in situations where there are few obstacles and about 6 times in situations where there are lots of obstacles.","Title: A Supervised Progressive Growing Generative Adversarial Network for Remote Sensing Image Scene Classification\nYear: 2022\n\nAbstract: Remote sensing image scene classification is a challenging task. With the development of deep learning, methods based on convolutional neural networks (CNNs) have made great achievements in remote sensing image scene classification. Since the training of a CNN requires a large number of labeled samples, a generative adversarial network (GAN) for sample generation represents a new opportunity to solve the problem of the limited samples. However, most of the existing GAN-based sample generation methods can only generate unlabeled samples, instead of samples labeled with the corresponding scene category. In this article, to solve the problem, a supervised progressive growing generative adversarial network (SPG-GAN) is proposed for remote sensing image scene classification. The proposed method can generate labeled samples for the remote sensing image scene classification, significantly improving the classification accuracy in the case of limited samples. The SPG-GAN method has two main improvements. First, a conditional generative framework for labeled samples is proposed, in which the label information is added in the channel dimension as the input. By considering the constraints of the label information in the loss function, the network can be trained in the direction of a specific category. As a result, the network can generate remote sensing image scene classification samples with label categories. Second, a progressive growing sample generation method is introduced. In order to ensure that the generated samples have more spatial details, they are generated by progressively adding modules to the generator and discriminator, thereby ensuring that the generated sample is of better quality. After testing on two benchmark datasets and carrying out a large-scale experiment in the central area of the city of Wuhan in China, it was found that the proposed method can obtain a superior scene classification accuracy in the case of limited samples.","Title: RepMix: Representation Mixing for Robust Attribution of Synthesized Images.\nYear: 2022\n\nAbstract: Rapid advances in Generative Adversarial Networks (GANs) raise new challenges for image attribution; detecting whether an image is synthetic and, if so, determining which GAN architecture created it. Uniquely, we present a solution to this task capable of 1) matching images invariant to their semantic content; 2) robust to benign transformations (changes in quality, resolution, shape, etc.) commonly encountered as images are re-shared online. In order to formalize our research, a challenging benchmark, Attribution88, is collected for robust and practical image attribution. We then propose RepMix, our GAN fingerprinting technique based on representation mixing and a novel loss. We validate its capability of tracing the provenance of GAN-generated images invariant to the semantic content of the image and also robust to perturbations. We show our approach improves significantly from existing GAN fingerprinting works on both semantic generalization and robustness. Data and code are available at https://github.com/TuBui/image_attribution.","Title: Robot hand-eye cooperation based on improved inverse reinforcement learning\nYear: 2022\n\nAbstract: Purpose How to make accurate action decisions based on visual information is one of the important research directions of industrial robots. The purpose of this paper is to design a highly optimized hand-eye coordination model of the robot to improve the robots' on-site decision-making ability. Design/methodology/approach The combination of inverse reinforcement learning (IRL) algorithm and generative adversarial network can effectively reduce the dependence on expert samples and robots can obtain the decision-making performance that the degree of optimization is not lower than or even higher than that of expert samples. Findings The performance of the proposed model is verified in the simulation environment and real scene. By monitoring the reward distribution of the reward function and the trajectory of the robot, the proposed model is compared with other existing methods. The experimental results show that the proposed model has better decision-making performance in the case of less expert data. Originality/value A robot hand-eye cooperation model based on improved IRL is proposed and verified. Empirical investigations on real experiments reveal that overall, the proposed approach tends to improve the real efficiency by more than 10% when compared to alternative hand-eye cooperation methods.","Title: Predicting cancer drug response using parallel heterogeneous graph convolutional networks with neighborhood interactions\nYear: 2022\n\nAbstract: Motivation: Due to cancer heterogeneity, the therapeutic effect may not be the same when a cohort of patients of the same cancer type receive the same treatment. The anticancer drug response prediction may help develop personalized therapy regimens to increase survival and reduce patients' expenses. Recently, graph neural network-based methods have aroused widespread interest and achieved impressive results on the drug response prediction task. However, most of them apply graph convolution to process cell line-drug bipartite graphs while ignoring the intrinsic differences between cell lines and drug nodes. Moreover, most of these methods aggregate node-wise neighbor features but fail to consider the element-wise interaction between cell lines and drugs. Results: This work proposes a neighborhood interaction (NI)-based heterogeneous graph convolution network method, namely NIHGCN, for anticancer drug response prediction in an end-to-end way. Firstly, it constructs a heterogeneous network consisting of drugs, cell lines and the known drug response information. Cell line gene expression and drug molecular fingerprints are linearly transformed and input as node attributes into an interaction model. The interaction module consists of a parallel graph convolution network layer and a NI layer, which aggregates node-level features from their neighbors through graph convolution operation and considers the element-level of interactions with their neighbors in the NI layer. Finally, the drug response predictions are made by calculating the linear correlation coefficients of feature representations of cell lines and drugs. We have conducted extensive experiments to assess the effectiveness of our model on Cancer Drug Sensitivity Data (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets. It has achieved the best performance compared with the state-of-the-art algorithms, especially in predicting drug responses for new cell lines, new drugs and targeted drugs. Furthermore, our model that was well trained on the GDSC dataset can be successfully applied to predict samples of PDX and TCGA, which verified the transferability of our model from cell line in vitro to the datasets in vivo. Availability and implementation: The source code can be obtained from. Supplementary information: Supplementary data are available at Bioinformatics online.","Title: Auditing Membership Leakages of Multi-Exit Networks\nYear: 2022\n\nAbstract: ABSTRACTRelying on the truth that not all inputs require the same level of computational cost to produce reliable predictions, multi-exit networks are gaining attention as a prominent approach for pushing the limits of efficient deployment. Multi-exit networks endow a backbone model with early exits, allowing predictions at intermediate layers of the model and thus saving computation time and energy. However, various current designs of multi-exit networks are only considered to achieve the best trade-off between resource usage efficiency and prediction accuracy, the privacy risks stemming from them have never been explored. This prompts the need for a comprehensive investigation of privacy risks in multi-exit networks. In this paper, we perform the first privacy analysis of multi-exit networks through the lens of membership leakages. In particular, we first leverage the existing attack methodologies to quantify the multi-exit networks' vulnerability to membership leakages. Our experimental results show that multi-exit networks are less vulnerable to membership leakages, and the exit (number and depth) attached to the backbone model is highly correlated with the attack performance. Furthermore, we propose a hybrid attack that exploits the exit information to improve the performance of existing attacks. We evaluate membership leakage threat caused by our hybrid attack under three different adversarial setups, ultimately arriving at a model-free and data-free adversary. These results clearly demonstrate that our hybrid attacks are very broadly applicable, thereby the corresponding risks are much more severe than shown by existing membership inference attacks. We further present a defense mechanism called TimeGuard specifically for multi-exit networks and show that TimeGuard mitigates the newly proposed attacks perfectly.","Title: Cell Classification Based on Stacked Autoencoder for Single-Cell RNA Sequencing\nYear: 2022\n\nAbstract: Single-cell transcriptome sequencing (scRNA-seq) allows researchers to investigate cellular heterogeneity in gene expression profiles, identify cell types and predict cell fate at the single-cell level. Analysis of scRNA-seq data can effectively extract unknown heterogeneity and functional diversity of cell populations. Cell classification is one of the most important tasks in scRNA-seq data analysis, which contains cell clustering and classification of cell subtypes. Before assigning a cell type to each cluster, the unsupervised clustering methods look for marker genes for each cluster. These approaches are susceptible to a number of drawbacks in terms of sources of variation, technology, etc. Meanwhile, as more cell subtypes are gradually discovered, cluster-based cell type identification methods have been gradually leaning towards classification-based cell type identification. In this paper, we proposed a new cell classification method based on stacked autoencoder for representation learning (scSAERLs), which enhances the accuracy of classification by learning the feature representation of the data through deep network models. The stacked autoencoder-based classification model employed an unsupervised greedy pre-training learning procedure, which was followed by supervised label-based fine-tuning of the entire classification model. We tested the model in the intra and inter datasets, evaluated its performance with a standard classification metric. Experimental results showed that scSAERLs outperformed other commonly used classification methods in terms of classification accuracy and F1-score."],"string":"[\n \"Title: Localization refinement for wireless sensor networks\\nYear: 2009\\n\\nAbstract: Being a key supporting technology, localization is of significance to the research and implement of other attracting applications in wireless sensor networks. The positioning accuracy and energy consumption are two major indicators of localization performance. In this paper, we are concerned with the problem of optimization for coordinates calculation in localization process, regardless of the specific underlying localization mechanism. In order to improve accuracy and avoid extra energy consumption, we develop three schemes based on least squares and multilateration: Taylor-LS, WLS and CTLS. Moreover, we develop a generalized Cramer-Rao lower bound on the localization errors in multihop scenario to help us theoretically analyze the performance of our localization refinement methods. We evaluate the proposed methods via simulations under various environmental settings. Overall, the simulation results validate the practicality of our proposed localization refinement methods, and show that they outperform multilateration at a certain extent under a wide range of conditions and have no extra communication overhead.\",\n \"Title: A Hybrid Computational Approach for the Prediction of Small Non-coding RNAs from Genome Sequences\\nYear: 2009\\n\\nAbstract: Researching the bacterial gene expression is a meaningful way to control and prevent the diseases which caused by bacteria. Recent researches indicate non-coding RNAs (ncRNA / sRNA) perform a variety of critical regulatory functions in bacteria. Since sRNAs have the consistent sequence characteristics, the genome-wide searching for sRNAs, especially the computational method, have become an effective way to predict the non-coding RNAs. This article proposes a hybrid computational approach for prediction of small non-coding RNAs which integrates three critical techniques, secondary structural algorithm, thermo-dynamic stability analysis and sequence conservation prediction. Relying on these computational techniques, our approach was used to search for sRNAs in Streptococcus pyogenes which is one of the most important bacteria for human health. This search led five candidates of sRNA to be predicted as the key components of known regulatory pathways in S. pyogens.\",\n \"Title: An in-memory relevance feedback technique for high-performance image retrieval systems\\nYear: 2007\\n\\nAbstract: Content-based image retrieval with relevant feedback has been widely adopted as the query model of choice for improved effectiveness in image retrieval. The effectiveness of this solution, however, depends on the efficiency of the feedback mechanism. Current methods rely on searching the database, stored on disks, in each round of relevance feedback. This strategy incurs long delay making relevance feedback less friendly to the user, especially for very large databases. Thus, scalability is a limitation of existing solutions. In this paper, we propose an in-memory relevance feedback technique to substantially reduce the delay associated with feedback processing, and therefore improve system usability. Our new data-independent dimensionality-reduction technique is used to compress the metadata to build a small in-memory database to support relevance feedback operations with minimal disk accesses. We compare the performance of this approach with conventional relevance feedback techniques in terms of computation efficiency and retrieval accuracy. The results indicate that the new technique substantially reduces response time for user feedback while maintaining the quality of the retrieval.\",\n \"Title: Efficiently support concurrent queries in multiuser CBIR systems\\nYear: 2009\\n\\nAbstract: Various techniques have been developed for different query types in content-based image retrieval systems such as sampling queries, constrained sampling queries, multiple constrained sampling queries, k-NN queries, constrained k-NN queries, and multiple localized k-NN queries. In this paper, we propose a generalized query model suitable for expressing queries of different types, and investigate efficient processing techniques for this new framework. We exploit sequential access and data sharing by developing new storage and query processing techniques to leverage inter-query concurrency. Our experimental results, based on the Corel dataset, indicate that the proposed optimization can significantly reduce average response time in a multiuser environment, and achieve better retrieval precision and recall compared to two recent techniques.\",\n \"Title: Dynamic simulation based localization for mobile sensor networks\\nYear: 2007\\n\\nAbstract: In mobile wireless sensor networks, sensors can move randomly or keep static temporarily. Mobility makes the sensor networks better acquire information, but also makes accurate localization more difficult since the network environment changes continually. In this paper, an energy-efficient dynamic simulation based localization (DSL) algorithm is introduced that uses range measurement information to restrict sample region and establishes a dynamic filtering mechanism to improve the localization performance and efficiency. Analytical and simulation results are provided to study the localization cost and location accuracy in different mobility models and various environmental settings. The results indicate that our algorithm outperforms the best known simulation based localization schemes under a wide range of conditions, with localization accuracy improved by an average of 24% and computation cost reduced significantly for a similar high localization accuracy.\",\n \"Title: Efficient exploration of large combinatorial chemistry spaces by monomer-based similarity searching.\\nYear: 2009\\n\\nAbstract: In modern drug discovery, 2-D similarity searching is widely employed as a cost-effective way to screen large compound collections and select subsets of molecules that may have interesting biological activity prior to experimental screening. Nowadays, there is a growing interest in applying the existing 2-D similarity searching methods to combinatorial chemistry libraries to search for novel hits or to evolve lead series. A dilemma thus arises when many identical substructures recur in library products and they have to be considered repeatedly in descriptor calculations. The dilemma is exacerbated by the astronomical number of combinatorial products. This problem imposes a major barrier to similarity searching of large combinatorial chemistry spaces. An efficient approach, termed Monomer-based Similarity Searching (MoBSS), is proposed to remedy the problem. MoBSS calculates atom pair (AP) descriptors based on interatomic topological distances, which lend themselves to pair additivity. A fast algorithm is employed in MoBSS to rapidly compute product atom pairs from those of the constituent fragments. The details of the algorithm are presented along with a series of proof-of-concept studies, which demonstrate the speed, accuracy, and utility of the MoBSS approach.\",\n \"Title: Robust Multihop Localization For Wireless Sensor Networks With Unreliable Beacons\\nYear: 2012\\n\\nAbstract: Multihop localization is a popular approach for determining the positions of normal nodes in large-scale wireless sensor networks. However, most existing multihop localization studies assume that the declared positions of beacon nodes are always reliable or even free of errors, which is not a valid assumption in practice. In this paper, we propose a robust multihop localization algorithm (RMLA) based on trust evaluation for diminishing the effect of unreliable beacons on the accuracy of node localization. Firstly, the trust evaluation framework is established on the basis of evidence theory. According to the multihop geometric relationship among nodes, every beacon evaluates the reliability of other beacons' declared positions. Then, the normal nodes integrate the evaluation results to obtain the total trust degrees of their multihop communication beacons, by use of an average method or an enhanced D-S evidence combination rule. Finally, the normal nodes employ the weighted Taylor-series least squares solver to estimate the optimal values of their coordinates. Extensive simulation results in isotropic and anisotropic networks show the robustness and effectiveness of our algorithm.\",\n \"Title: Brs-Based Robust Secure Localization Algorithm For Wireless Sensor Networks\\nYear: 2013\\n\\nAbstract: Localization is the key supporting technology for wireless sensor networks (WSNs). Security and accuracy are the premise of the localization application. Real-world applications of wireless sensor networks are often subject to a variety of adverse circumstances interference, and the localization performance is seriously affected. In this paper, we propose a BRS-based robust secure localization (BRSL) algorithm in order to reduce the impact of the malicious attackers in WSNs. The BRSL method includes two phases. In the first stage, the trust evaluation framework is established on the basis of beta reputation system. In the second phase, we employ the weighted Taylor-series least squares method to estimate the coordinates of sensor nodes. Simulation results demonstrate that the proposed algorithm is robust and effective.\",\n \"Title: Conversations in the Blogosphere: An Analysis \\\"From the Bottom Up\\\"\\nYear: 2005\\n\\nAbstract: The \\\"blogosphere\\\" has been claimed to be a densely interconnected conversation, with bloggers linking to other bloggers, referring to them in their entries, and posting comments on each other's blogs. Most such characterizations have privileged a subset of popular blogs, known as the 'A-list.' This study empirically investigates the extent to which, and in what patterns, blogs are interconnected, taking as its point of departure randomly-selected blogs. Quantitative social network analysis, visualization of link patterns, and qualitative analysis of references and comments in pairs of reciprocally-linked blogs show that A-list blogs are overrepresented and central in the network, although other groupings of blogs are more densely interconnected. At the same time, a majority of blogs link sparsely or not at all to other blogs in the sample, suggesting that the blogosphere is partially interconnected and sporadically conversational.\",\n \"Title: A Credible Routing Based On A Novel Trust Mechanism In Ad Hoc Networks\\nYear: 2013\\n\\nAbstract: Many existing routing protocols in Mobile Ad hoc Networks (MANETs) focus on finding paths in dynamic networks without considering security. In this paper, we propose a trust model which evaluates neighbours' direct trust by factors of encounter time, mobility, and successful cooperation frequency. The revised D-S evidence theory is used to combine multiple recommended pieces of evidence and obtain the recommended trust value. Then based on the novel trust mechanism, we propose a trusted routing protocol named TDS-AODV protocol by extending the AODV protocol. In this protocol, a node makes a routing decision according to the trust values of its neighbour nodes. Finally, two routes are built: the main route with highest route trust value in the candidate routes and a backup route. Simulation results reveal that TDS-AODV can eliminate malicious nodes effectively when building the route; furthermore, it also achieves better performance than TAODV and AODV in terms of throughput, packet delivery ratio, and average end to end delay.\",\n \"Title: Dynamic Directional Navigation in Content-Based Image Retrieval\\nYear: 2008\\n\\nAbstract: Nowadayspsila image retrieval techniques focus on modifying a query contour based on the relative correlations of relevant images. The spatial relationship between the relevant images and all the other images in database (i.e. the directional information) is not explored and utilized well. From the aspect of feature space, when a user selects relevant images, the direction to the potential relevant images is implicitly expressed. In this paper, we propose a Dynamic Directional Navigation (DDN) system to explore the directional information and navigate in the data space to find similar images. Multiple queries will be used when the relevant images point to different directions. The experiment results indicate that our technique can address the semantic gap well in Content-Based Image Retrieval (CBIR) and achieves better precision and recall than the previous techniques.\",\n \"Title: An Evolutionary Game-Based Trust Cooperative Stimulation Model For Large Scale Manets\\nYear: 2013\\n\\nAbstract: In order to realize a methodical, effective cooperative stimulation for MANETs and search dynamic trust cooperative stimulation scheme in environment under a high malicious ratio, we have proposed an evolutionary game-based trust cooperative stimulation model for large scale MANETs in this paper. First, the system members' pluralistic behavior for MANETs has been covered by means of constructing the complete multirisk level strategy space. Then a trust-preferential strategy has been built through trust numerical value mapping technology, which achieves the aim that the malicious action is effectively constrained to avoid a low trust level. Furthermore, the mobility probable parameters and information propagation error matrix are introduced into game model, and the convergence condition between optimum strategy which represents payoff maximization principle and trust-preferential strategy is deduced through evolutionary analyzing finally. Both theoretical analysis and simulation experiments have demonstrated that our model can effectively stimulate cooperation among members and meanwhile be robust under the condition where the environment is harsh under a high original malicious ratio in large scale MANETs.\",\n \"Title: Fast query point movement techniques with relevance feedback for content-based image retrieval\\nYear: 2006\\n\\nAbstract: Target search in content-based image retrieval (CBIR) systems refers to finding a specific (target) image such as a particular registered logo or a specific historical photograph. Existing techniques were designed around query refinement based on relevance feedback, suffer from slow convergence, and do not even guarantee to find intended targets. To address those limitations, we propose several efficient query point movement methods. We theoretically prove that our approach is able to reach any given target image with fewer iterations in the worst and average cases. Extensive experiments in simulated and realistic environments show that our approach significantly reduces the number of iterations and improves overall retrieval performance. The experiments also confirm that our approach can always retrieve intended targets even with poor selection of initial query points and can be employed to improve the effectiveness and efficiency of existing CBIR systems.\",\n \"Title: Trust Management Scheme Based On D-S Evidence Theory For Wireless Sensor Networks\\nYear: 2013\\n\\nAbstract: Trust management scheme has been regarded as a powerful tool to defend against the wide set of security attacks and identify malicious nodes. In this paper, we propose a trust management scheme based on revised Dempster-Shafer (D-S) evidence theory. D-S theory is preponderant in tackling both random and subjective uncertainty in the trust mechanism. A trust propagation mechanism including conditional trust transitivity and dynamic recommendation aggregation is developed for obtaining the recommended trust values from third part nodes. We adopt a flexible synthesis method that uses recommended trust only when no direct trust exists to keep a good trust-energy consumption balance. We also consider on-off attack and bad mouthing attack in our simulation. The simulation results and analysis show that the proposed method has excellent ability to deal with typical network attacks, better security, and longer network lifetime.\",\n \"Title: Client-Side Relevance Feedback Approach for Image Retrieval in Mobile Environment\\nYear: 2007\\n\\nAbstract: With the proliferation of handheld devices, the demand of multimedia information retrieval on mobile devices has attracted more attention. A relevance feedback information retrieval process usually includes several rounds of query refinement. Each round incurs exchange of tens of images between the mobile device and the server. With limited wireless bandwidth, this process can incur substantial delay making the system unfriendly to use. We address this issue by considering a client-side relevance feedback (CRF) technique. Since relevance feedback is done on client side, system response is instantaneous significantly enhancing system usability. Furthermore, since the server is not involved in relevance feedback processing, it is able to support thousands more users simultaneously. Our experimental results indicate that this scheme also effectively address the semantic gap in content-based image retrieval.\",\n \"Title: Client-Side Relevance Feedback Approach for Image Retrieval in Mobile Environment\\nYear: 2011\\n\\nAbstract: During the last decade, high quality i.e. over 1 megapixel built-in cameras have become standard features of handheld devices. Users can take high-resolution pictures and share with friends via the internet. At the same time, the demand of multimedia information retrieval using those pictures on mobile devices has become an urgent problem to solve, and therefore attracts attention. A relevance feedback information retrieval process includes several rounds of query refinement, which incurs exchange of images between the mobile device and the server. With limited wireless bandwidth, this process can incur substantial delay, making the system unfriendly to use. This issue is addressed by considering a Client-side Relevance Feedback CRF technique. In the CRF system, Relevance Feedback RF is done on client side along. Mobile devices' battery power is saved from exchanging images between server and client and system response is instantaneous, which significantly enhances system usability. Furthermore, because the server is not involved in RF processing, it is able to support more users simultaneously. The experiment indicates that the system outperforms the traditional server-client relevance feedback systems on the aspects of system response time, mobile battery power saving, and retrieval result.\",\n \"Title: Fusion approach to finding opinionated blogs\\nYear: 2007\\n\\nAbstract: In this paper, we describe a fusion approach to finding opinion about a given target in blog postings. We tackled the opinion blog retrieval task by breaking it down to two sequential subtasks: on- topic retrieval followed by opinion classification. Our opinion retrieval approach was to first apply traditional IR methods to retrieve on-topic blogs, and then boost the ranks of opinionated blogs using combined opinion scores generated by four opinion assessment methods. Our opinion module consists of Opinion Term Module, which identify opinions based on the frequency of opinion terms (i.e., terms that only occur frequently in opinion blogs), Rare Term Module, which uses uncommon/rare terms (e.g., \\\"sooo good\\\") for opinion classification, IU Module, which uses IU (I and you) collocations, and Adjective-Verb Module, which uses computational linguistics' distribution similarity approach to learn the subjective language from training data.\",\n \"Title: Efficient target search with relevance feedback for large CBIR systems\\nYear: 2006\\n\\nAbstract: Recent content-based image retrieval (CBIR) techniques were designed around query refinement based on relevance feedback. They suffer from slow convergence, high disk I/O, and do not even guarantee to find intended targets. In this paper, we identify the cause of these problems and propose several efficient target search methods to address these drawbacks. Our complexity analysis shows that our approach is able to reach any given target image with fewer iterations in the worst and average cases. We evaluated our techniques on large datasets in simulated and realistic environments. The results show that our approach significantly reduces the number of iterations and improves overall retrieval performance. The experiments also confirm that our approach can always retrieve intended targets even with poor selection of initial query points and can be used to improve the effectiveness of existing CBIR systems with relevance feedback.\",\n \"Title: A Multi-Directional Search technique for image annotation propagation\\nYear: 2012\\n\\nAbstract: Image annotation has attracted lots of attention due to its importance in image understanding and search areas. In this paper, we propose a novel Multi-Directional Search framework for semi-automatic annotation propagation. In this system, the user interacts with the system to provide example images and the corresponding annotations during the annotation propagation process. In each iteration, the example images are clustered and the corresponding annotations are propagated separately to each cluster: images in the local neighborhood are annotated. Furthermore, some of those images are returned to the user for further annotation. As the user marks more images, the annotation process goes into multiple directions in the feature space. The query movements can be treated as multiple path navigation. Each path could be further split based on the user's input. In this manner, the system provides accurate annotation assistance to the user - images with the same semantic meaning but different visual characteristics can be handled effectively. From comprehensive experiments on Corel and U. of Washington image databases, the proposed technique shows accuracy and efficiency on annotating image databases.\",\n \"Title: An extensible cheat-proofing multi-secret sharing scheme with low computation complexity\\nYear: 2014\\n\\nAbstract: AbstractSecret sharing schemes are widely applied in various areas, and there are series of achievements, among which the verifiable multi-secret schemes are the most representative ones. A few verifiable multi-secret schemes own the following drawbacks: insensitive to the differences of security levels, lack of expandability, high computation complexity of verification process, and low efficiency of execution. Addressing the issues, an extensible cheat-proofing multi-secret sharing scheme with low computation complexity is proposed. With the security of RSA public key system and discrete logarithm puzzle, the proposed scheme can realize the following functions: first, the secret dealer can set relatively threshold values according to different security levels; second, the participants and secret combiner can verify the secret shares, which can guard against cheating problem, resist various kinds of attacks and improve the success rate of secret reconstruction; in addition, the secret shares are reusable in the situations of new group secret and new participant joining the group, which brings about good expandability. Moreover, compared with the previous schemes, the proposed scheme reduces the information content published by secret dealer and brings down the computation complexity of verification, which makes the efficiency of the scheme greatly improved. Copyright © 2013 John Wiley & Sons, Ltd.\",\n \"Title: Fast Query Point Movement Techniques for Large CBIR Systems\\nYear: 2009\\n\\nAbstract: Target search in content-based image retrieval (CBIR) systems refers to finding a specific (target) image such as a particular registered logo or a specific historical photograph. Existing techniques, designed around query refinement based on relevance feedback, suffer from slow convergence, and do not guarantee to find intended targets. To address these limitations, we propose several efficient query point movement methods. We prove that our approach is able to reach any given target image with fewer iterations in the worst and average cases. We propose a new index structure and query processing technique to improve retrieval effectiveness and efficiency. We also consider strategies to minimize the effects of users' inaccurate relevance feedback. Extensive experiments in simulated and realistic environments show that our approach significantly reduces the number of required iterations and improves overall retrieval performance. The experimental results also confirm that our approach can always retrieve intended targets even with poor selection of initial query points.\",\n \"Title: A RSSI Localization Algorithm Based on Interval Analysis for Indoor Wireless Sensor Networks\\nYear: 2013\\n\\nAbstract: RSSI-based localization is popular for its low cost and low complexity. Aiming at the noisy indoor environment that affects the accuracy of RSSI-based localization, this paper proposes a novel algorithm based on interval analysis to reduce the localization error and improve its robustness. In the ranging phase, we utilize Bootstrap resample to construct the confidence interval of measured distance, which lays the foundation for interval analysis. In the localization phase, we cast all the environment noise into unknown but bounded (UBB) noise and use interval analysis to compute the coordinate in set-membership framework. The simulation results show that our method has higher localization accuracy and localization coverage than two other popular localization schemes. Meanwhile, our method can accommodate different noise interference better.\",\n \"Title: Boost image clustering with user query log\\nYear: 2008\\n\\nAbstract: Image clustering is to derive a salient grouping of images such that similar ones are placed in the same cluster, which is useful in many applications. In this paper, we propose a constrained clustering algorithm, which leverages the collected user query log to guide the clustering process. Our method models a set of images as a graph and randomly contracts two vertices into a meta vertex iteratively with regarding to their similarity until the desired number of image groups has been reached. The experimental results demonstrate the superiority of our proposal.\",\n \"Title: WIDIT: fusion-based approach to web search optimization\\nYear: 2005\\n\\nAbstract: To facilitate both the understanding and the discovery of information, we need to utilize multiple sources of evidence, integrate a variety of methodologies, and combine human capabilities with those of the machine. The Web Information Discovery Integrated Tool (WIDIT) Laboratory at the School of Library and Information Science, Indiana University-Bloomington, houses several projects that employ this idea of multi-level fusion in the areas of information retrieval and knowledge discovery. This paper describes a Web search optimization study by the TREC research group of WIDIT, who explores a fusion-based approach to enhancing retrieval performance on the Web. In the study, we employed both static and dynamic tuning methods to optimize the fusion formula that combines multiple sources of evidence. By static tuning, we refer to the typical stepwise tuning of system parameters based on training data. “Dynamic tuning”, the key idea of which is to combine the human intelligence, especially pattern recognition ability, with the computational power of the machine, involves an interactive system tuning process that facilitates fine-tuning of the system parameters based on the cognitive analysis of immediate system feedback. The rest of the paper is organized as follows. The next section discusses related work in Web information retrieval (IR). Section 3 details the WIDIT approach to Web IR, followed by the description of our experiment using the TREC .gov data in section 4 and the discussion of results in section 5.\",\n \"Title: Nonparametric Bootstrap-Based Multihop Localization Algorithm For Large-Scale Wireless Sensor Networks In Complex Environments\\nYear: 2013\\n\\nAbstract: This paper presents a nonparametric bootstrap multihop localization algorithm for large-scale wireless sensor networks (WSNs) in complex environments. Unlike most of the existing schemes, this work is based on the consideration that it is not feasible to obtain a lot of available distance measurements sample for estimation and to get exact noise distributions or enough prior information for conventional statistical methods, which is a situation commonly encountered in complex environments practically. For the first time, we introduce a nonparametric bootstrap method into multihop localization to build confidence intervals for multihop distance estimation, which can eliminate the risk of small sample size and unknown distribution. On this basis, we integrate the interval analysis method with bootstrap approach for ordinary nodes localization. To reduce the computational complexity, boxes approach is utilized to approximate the irregular intersections. Simulation results show that our proposed scheme is less affected by the variation of unknown distributions and indicate that our method can achieve high localization coverage with relatively small average localization error in large-scale WSNs, especially in sparse and complex network with smaller connectivity and anchor percentage.\",\n \"Title: WIDIT in TREC 2005 HARD, Robust, and SPAM Tracks\\nYear: 2005\\n\\nAbstract: In this year's HARD track, we explored the possibility of improving retrieval performance by taking a fusion approach that combines a variety of techniques as well as different data sources. In order to deal effectively with the \\\"difficult\\\" topics, we employed a variety of query expansion strategies, the results of which were combined via an automatic fusion optimization process. We hypothesized that the \\\"difficulty\\\" of topics is often due to the lack of appropriate query terms and/or misguided emphasis on non-pivotal query terms by the system. Thus, our first-tier solution was to devise a wide range of query expansion methods that can not only enrich the query with useful term additions but also identify important query terms. Our automatic query expansion included such techniques as noun phrase extraction, acronym expansion, synonym identification, definition term extraction, keyword extraction by overlapping sliding window, and Web query expansion. For synonym identification, we integrated a sense disambiguation module into WIDIT's synset identification module so that best synonym set can be selected according to the term context. To reduce the noise from word definitions, we applied the overlapping sliding window (OSW) method to multiple definitions harvested from web and extracted the overlapping terms. To extract noun phrase, we combined the results of multiple NLP taggers as well as applying the OSW method. OSW method was also applied to topic fields to identify important topic terms. The Web query expansion method was a slight modification of the PIRC approach. To produce the optimum baseline results, we merged various combinations of query formulation results, which involved combinations of topic fields (title, description, narrative) and stemming (simple plural stemmer, combination stemmer), and the query expansion results using a weighted sum fusion formula. The fusion weights were determined using last year's Robust data to train the system via an automatic fusion optimization process. The fusion optimization involved iterations of fusion runs (i.e., result merging), where best performing systems in selected categories (e.g., short query, top 10 systems, etc.) were combined using average precision as fusion weights until the performance gain fell below a threshold. We viewed the clarification form (CF) as both manual query expansion and relevance feedback mechanism. Our clarification form included query term synonyms, noun phrase, and best sentences from top documents of the baseline result. Since difficult topics tend to produce few relevant documents in top ranks, we clustered the results and selected the best sentence from each cluster to include in the CF. In addition to expanding the query with user-selected terms\",\n \"Title: An automatic feature generation approach to multiple instance learning and its applications to image databases\\nYear: 2010\\n\\nAbstract: Automatic content-based image categorization is a challenging research topic and has many practical applications. Images are usually represented as bags of feature vectors, and the categorization problem is studied in the Multiple-Instance Learning (MIL) framework. In this paper, we propose a novel learning technique which transforms the MIL problem into a standard supervised learning problem by defining a feature vector for each image bag. Specifically, the feature vectors of the image bags are grouped into clusters and each cluster is given a label. Using these labels, each instance of an image bag can be replaced by a corresponding label to obtain a bag of cluster labels. Data mining can then be employed to uncover common label patterns for each image category. These label patterns are converted into bags of feature vectors; and they are used to transform each image bag in the data set into a feature vector such that each vector element is the distance of the image bag to a distinct pattern bag. With this new image representation, standard supervised learning algorithms can be applied to classify the images into the pre-defined categories. Our experimental results demonstrate the superiority of the proposed technique in categorization accuracy as compared to state-of-the-art methods.\",\n \"Title: Research on Service Oriented and Middleware Based Active QoS Infrastructure of Wireless Sensor Networks\\nYear: 2009\\n\\nAbstract: This paper presents an active QoS infrastructure of wireless sensor networks (WSN), named QISM, which is based on middleware and service-oriented. The design, structure, important modules and working processes are discussed. The important characteristics of QISM include the active regulation mechanism based on feedback and negotiation between applications and network, the architecture based on middleware and service publishing and subscribing, the expressing method based on Topic and functional domain. Through the active regulation for applications and network, QISM makes the network support applications and makes the applications adapt the network furthest. Therefore, the quality of application accomplishment is better guaranteed, and the lifetime of network is prolonged as well. Proved by simulation of a typical scenario, QISM has good QoS control ability and flexibility, can support complex applications, and is independent of network architectures.\",\n \"Title: PARED: A testbed with parallel reprogramming and multi-channel debugging for WSNs\\nYear: 2013\\n\\nAbstract: Testbed plays an invaluable role in facilitating research and development of wireless sensor networks (WSNs). A WSN testbed involves three fundamental tasks, which are reprogramming the WSN nodes, debugging the WSN applications and observing the status of the WSN nodes. In this paper, we propose a testbed with parallel reprogramming and multi-channel debugging capabilities (PARED) to meet the challenge behind those tasks. A parallel reprogramming mechanism is properly designed, which can reprogram all the nodes simultaneously and significantly diminishes the time wasted in WSN application deployment. PARED provides multichannel debugging support allowing users to set the debugging channel. PARED is also capable of observing the energy consumption of the sensor node along with its status information. We have deployed PARED in our laboratory at the scale of 50 nodes and a series of experiments have been conducted which proved that it is a reliable and convenient infrastructure for WSN research.\",\n \"Title: Filling the Gap: Semi-Supervised Learning for Opinion Detection Across Domains.\\nYear: 2011\\n\\nAbstract: We investigate the use of Semi-Supervised Learning (SSL) in opinion detection both in sparse data situations and for domain adaptation. We show that co-training reaches the best results in an in-domain setting with small labeled data sets, with a maximum absolute gain of 33.5%. For domain transfer, we show that self-training gains an absolute improvement in labeling accuracy for blog data of 16% over the supervised approach with target domain training data.\",\n \"Title: Organizing the Web: Semi-Automatic Construction of a Faceted Scheme\\nYear: 2004\\n\\nAbstract: In this study, we explore the construction of a faceted vocabulary that can be used as a mechanism for organizing Web-based resources. After analyzing the manual process of faceted vocabulary construction using an existing organizational structure to identify heuristics for automating the construction process, we modeled a hybrid, semi-automatic approach to facet generation that integrates the strengths of manual and automatic methods.\",\n \"Title: Query Decomposition: A Multiple Neighborhood Approach to Relevance Feedback Processing in Content-based Image Retrieval\\nYear: 2006\\n\\nAbstract: Todays Content-Based Image Retrieval (CBIR) techniques are based on the \\\"k-nearest neighbors\\\" (k- NN) model. They retrieve images from a single neighborhood using low-level visual features. In this model, semantically similar images are assumed to be clustered in the high-dimensional feature space. Unfortunately, no visual-based feature vector is sufficient to facilitate perfect semantic clustering; and semantically similar images with different appearances are always clustered into distinct neighborhoods in the feature space. Confinement of the search results to a single neighborhood is an inherent limitation of the k-NN techniques. In this paper we consider a new image retrieval paradigm — the Query Decomposition model - that facilitates retrieval of semantically similar images from multiple neighborhoods in the feature space. The retrieval results are the k most similar images from different relevant clusters. We introduce a prototype, and present experimental results to illustrate the effectiveness and efficiency of this new approach to content-based image retrieval.\",\n \"Title: Set-Membership Adaptive Localization Algorithm With Time-Varying Error Bounds For Underwater Wireless Sensor Networks\\nYear: 2013\\n\\nAbstract: This paper presents a set-membership adaptive localization algorithm with time-varying error bounds for underwater wireless sensor networks (UWSNs). In large-scale UWSNs, the nonstationary underwater environments, the insufficient prior information of hybrid noise, the small sample size of available distance measurements, and the node mobility all pose severe challenges for localization, and most current schemes are not applicable. Unlike most of the existing approaches, we tackle the multihop localization problem in a set-membership framework based on the consideration that the distance measurement uncertainty can be cast into an unknown but bounded (UBB) context. The principle of our scheme is firstly to use the bootstrap method to build confidence intervals and error bounds from a small sample set of distance measurements and then to determine the positions by a low-complexity interval analysis method as well as an adaptive localization update specification with time-varying error bounds. Simulation results show that our proposed scheme is an effective and efficient localization approach in large-scale UWSNs.\",\n \"Title: A hybrid approach to faceted classification based on analysis of descriptor suffixes\\nYear: 2005\\n\\nAbstract: This study explores a hybrid approach to the construction of faceted vocabularies that can be used for organizing Web-based resources. After analyzing the manual process of faceted vocabulary construction using existing organizational structures to identify heuristics for automating the construction process, we modeled a semi-automatic approach to facet generation that integrates the strengths of manual and automatic methods. One strategy in this hybrid approach utilizes the organization of terms according to the meanings of their suffixes. Although this heuristic could not effectively organize all of the terms of the lexicon bases to which it was applied, it nevertheless shows promise as a component of hybrid classification.\",\n \"Title: Enable Web-Based Interactive Applications of High-Resolution 3D Medical Image Data\\nYear: 2007\\n\\nAbstract: High-resolution 3D medical image data are typically huge in volume, varying from several hundred megabytes to several dozen gigabytes. Since such large-size data sets severely stress storage systems and networks, most existing Web-based interactive 3D medical imaging applications, such as 3D slicer and region-of-interest viewing, deal with only low-or medium-resolution images. In this paper, we introduce a single instance storage design, a client sharing technique, and a corresponding query processing method to enhance our existing remote medical image viewing system. We give performance results to show that the new design can significantly reduce storage and communication bandwidth requirements. Furthermore, it is more scalable to support a larger number of concurrent users.\",\n \"Title: Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering.\\nYear: 2014\\n\\nAbstract: Taking the advantage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unravelling complex relationships between genotype and phenotype. At present, traditional single-locus-based methods are insufficient to detect interactions consisting of multiple-locus, which are broadly existing in complex traits. In addition, statistic tests for high order epistatic interactions with more than 2 SNPs propose computational and analytical challenges because the computation increases exponentially as the cardinality of SNPs combinations gets larger.In this paper, we provide a simple, fast and powerful method using dynamic clustering and cloud computing to detect genome-wide multi-locus epistatic interactions. We have constructed systematic experiments to compare powers performance against some recently proposed algorithms, including TEAM, SNPRuler, EDCF and BOOST. Furthermore, we have applied our method on two real GWAS datasets, Age-related macular degeneration (AMD) and Rheumatoid arthritis (RA) datasets, where we find some novel potential disease-related genetic factors which are not shown up in detections of 2-loci epistatic interactions.Experimental results on simulated data demonstrate that our method is more powerful than some recently proposed methods on both two- and three-locus disease models. Our method has discovered many novel high-order associations that are significantly enriched in cases from two real GWAS datasets. Moreover, the running time of the cloud implementation for our method on AMD dataset and RA dataset are roughly 2 hours and 50 hours on a cluster with forty small virtual machines for detecting two-locus interactions, respectively. Therefore, we believe that our method is suitable and effective for the full-scale analysis of multiple-locus epistatic interactions in GWAS.\",\n \"Title: Scalable Loop Self-Scheduling Schemes for Heterogeneous Clusters\\nYear: 2005\\n\\nAbstract: Distributed systems (e.g. a LAN of computers) can be used for concurrent processing for some applications. However, a serious difficulty in concurrent programming of a distributed system is how to deal with scheduling and load balancing of such a system which may consist of heterogeneous computers. Distributed scheduling schemes suitable for parallel loops with independent iterations on heterogeneous computer clusters have been proposed and analyzed in the past. Here, we implement the previous schemes in the CORBA (Orbix). We also present an extension of these schemes implemented in a hierarchical master-slave architecture. We present experimental results and comparisons.\",\n \"Title: Semi-supervised Learning for Opinion Detection\\nYear: 2010\\n\\nAbstract: Research on opinion detection has shown that a large number of opinion-labeled data are necessary for capturing subtle opinions. However, opinion-labeled data, especially at the sub-document level, are often limited. This paper describes the application of Semi-Supervised Learning (SSL) to automatically produce more labeled data and explores the potential of SSL to improve transfer of labeled data to new domains. Preliminary results show that SSL performance is very close to a supervised system trained on the full data set and improves performance on out-of-domain data.\",\n \"Title: One Optimization Method on the Navigation Performance Reliability of Planing Craft\\nYear: 2011\\n\\nAbstract: In this paper, a comprehensive optimization mathematical model of the planing craft navigational performance reliability was established. A hierarchical parallel chaos-genetic algorithm, which is called P-CX-GA, is proposed based on parallel thinking, genetic algorithm and new chaos algorithm. The conclusion based on quantities of computation results shows that P-CX-GA is reliable and efficient. It proposed a solid foundation for hull form optimization design and evaluation analysis of the high-speed ships.\",\n \"Title: Hierarchical leak detection and localization method in natural gas pipeline monitoring sensor networks.\\nYear: 2012\\n\\nAbstract: In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point's position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate.\",\n \"Title: Self-adaption link-quality detection algorithm for data collecting in OSN\\nYear: 2010\\n\\nAbstract: A Self-adaption Link-quality Detection Algorithm(SLDA) is proposed to implement the Data Collecting in Opportunistic Sensor Network. The new scheme adopts Self adaptive Link-quality Detection strategy to measure the real time link quality weight factor (LQWF), and combines energy consumption model of mobile nodes to predict optimal transmission path for message forwarding by means of the Unscented Kalman Filter (UKF). On the other hand, SLDAuses a new message queue management that analyzes the lifetime of every message, so all messages are graded by stepping factor which reflects importance degree of messages. Simulation results show that SLDA enhances the predicted accuracy of link decision. It also increases the average delivery ratio and reduces the average transmission delay. Comparing with other typical algorithms, SLDA performs best in OSN, especially in the situation of sparse deployment of mobile nodes.\",\n \"Title: Multi-hop localization algorithm based on grid-scanning for wireless sensor networks.\\nYear: 2011\\n\\nAbstract: For large-scale wireless sensor networks (WSNs) with a minority of anchor nodes, multi-hop localization is a popular scheme for determining the geographical positions of the normal nodes. However, in practice existing multi-hop localization methods suffer from various kinds of problems, such as poor adaptability to irregular topology, high computational complexity, low positioning accuracy, etc. To address these issues in this paper, we propose a novel Multi-hop Localization algorithm based on Grid-Scanning (MLGS). First, the factors that influence the multi-hop distance estimation are studied and a more realistic multi-hop localization model is constructed. Then, the feasible regions of the normal nodes are determined according to the intersection of bounding square rings. Finally, a verifiably good approximation scheme based on grid-scanning is developed to estimate the coordinates of the normal nodes. Additionally, the positioning accuracy of the normal nodes can be improved through neighbors' collaboration. Extensive simulations are performed in isotropic and anisotropic networks. The comparisons with some typical algorithms of node localization confirm the effectiveness and efficiency of our algorithm.\",\n \"Title: A RSSI Localization Algorithm and Implementation for Indoor Wireless Sensor Networks.\\nYear: 2014\\n\\nAbstract: Taking the low-cost into consideration, Received Signal Strength Indicator (RSSI) technology is often used in positioning system. This paper proposes a RSSI localization algorithm which could improve the localization accuracy, coverage and robustness. First, a training process is conducted to build the log-distance path loss model. Then we utilize Bootstrap method to construct the Feasible Solution Set (FSS) for unknown node's coordinate. Finally the estimated coordinate of unknown node is figured out with interval analysis. Furthermore, the algorithm is applied to an indoor positioning system (IPS) developed by ourselves to evaluate its performance. The simulation results show that our algorithm achieves better accuracy, coverage and robustness than other two localization algorithms. And the experimental results show that compared with other four famous IPSs, the IPS using our localization algorithm obtain a better trade-off between accuracy and cost.\",\n \"Title: Repeated Game Based Localization Algorithm for Wireless Sensor Networks with Malicious Beacon Nodes.\\nYear: 2015\\n\\nAbstract: Localization is the key supporting technology for wireless sensor networks. Security and accuracy are the premise of the localization application. Real-world applications of wireless sensor networks are often subject to a variety of adverse circumstances interference and the localization performance is seriously affected. In this paper, we focus on the problem of node localization with malicious beacon nodes and solve it based on a repeated game method. Trust evaluation method is introduced to distinguish malicious beacon nodes. A repeated game framework is established and the strategies and payoffs are analyzed. Both malicious and normal nodes are driven by profits to a cooperation state and the whole localization performance is improved. Extensive simulation results show normal nodes can obtain higher location precision based on the repeated game. Malicious beacon nodes prefer to take the long-term cooperation attitude. The total losses to the network are less than the total profits caused by malicious nodes with game control.\",\n \"Title: Orthogonal Regression Based Multihop Localization Algorithm For Large-Scale Underwater Wireless Sensor Networks\\nYear: 2014\\n\\nAbstract: For large-scale underwater wireless sensor networks (UWSNs) with a minority of anchor nodes, multihop localization is a popular scheme for determining the geographical positions of normal nodes. However, existing multihop localization studies have considered the anchor positions to be free of errors, which is not a valid assumption in practice. In this paper, the problems existing in nonlinear least square-based node self-localization schemes are analyzed, and the biased distribution characteristic of multihop distance estimation errors is pointed out. Then, the orthogonal regression method is employed for the localization of normal nodes in the presence of anchor position errors. In particular, the influences of errors in independent variables and biases in dependent variables on node coordinate estimation are taken into account simultaneously. Extensive simulation results illustrate the robustness and effectiveness of our method.\",\n \"Title: A novel D-S based secure localization algorithm for wireless sensor networks\\nYear: 2014\\n\\nAbstract: AbstractRecent advances in wireless sensor networks WSNs technologies have brought significant convenience for location-based services. And the problem of enabling nodes of WSNs to determine their locations in a hostile environment has become more and more important in WSNs applications. In this article, we propose a novel Dempster-Shafer based secure localization algorithm to diminish the effect of the unreliable beacons that provide false localization information in the WSNs. First, the framework of trust evaluation is established on the basis of evidence theory. According to the characteristics of time and space of beacons, we set up the basic probability assignment BPA values and BPA evidences of beacons. Then, an improved Dempster-Shafer method is utilized to resolve the conflicts among the BPA evidences and to integrate the trust values of beacons. The max_mass function is used to decide which beacon is attacked, and then, we eliminate the attacked beacons before the localization stage. Finally, we employ the weighted Taylor-series least squares method to estimate the normal nodes coordinates. Simulation results demonstrate that our proposed algorithm is robust and effective. Copyright © 2013 John Wiley & Sons, Ltd.\",\n \"Title: DIME: a novel framework for de novo metagenomic sequence assembly.\\nYear: 2015\\n\\nAbstract: The recently developed next generation sequencing platforms not only decrease the cost for metagenomics data analysis, but also greatly enlarge the size of metagenomic sequence datasets. A common bottleneck of available assemblers is that the trade-off between the noise of the resulting contigs and the gain in sequence length for better annotation has not been attended enough for large-scale sequencing projects, especially for the datasets with low coverage and a large number of nonoverlapping contigs. To address this limitation and promote both accuracy and efficiency, we develop a novel metagenomic sequence assembly framework, DIME, by taking the DIvide, conquer, and MErge strategies. In addition, we give two MapReduce implementations of DIME, DIME-cap3 and DIME-genovo, on Apache Hadoop platform. For a systematic comparison of the performance of the assembly tasks, we tested DIME and five other popular short read assembly programs, Cap3, Genovo, MetaVelvet, SOAPdenovo, and SPAdes on four synthetic and three real metagenomic sequence datasets with various reads from fifty thousand to a couple million in size. The experimental results demonstrate that our method not only partitions the sequence reads with an extremely high accuracy, but also reconstructs more bases, generates higher quality assembled consensus, and yields higher assembly scores, including corrected N50 and BLAST-score-per-base, than other tools with a nearly theoretical speed-up. Results indicate that DIME offers great improvement in assembly across a range of sequence abundances and thus is robust to decreasing coverage.\",\n \"Title: Exploring Co-training strategies for opinion detection\\nYear: 2014\\n\\nAbstract: AbstractFor the last decade or so, sentiment analysis, which aims to automatically identify opinions, polarities, or emotions from user-generated content e.g., blogs, tweets, has attracted interest from both academic and industrial communities. Most sentiment analysis strategies fall into 2 categories: lexicon-based and corpus-based approaches. While the latter often requires sentiment-labeled data to build a machine learning model, both approaches need sentiment-labeled data for evaluation. Unfortunately, most data domains lack sufficient quantities of labeled data, especially at the subdocument level. Semisupervised learning SSL, a machine learning technique that requires only a few labeled examples and can automatically label unlabeled data, is a promising strategy to deal with the issue of insufficient labeled data. Although previous studies have shown promising results of applying various SSL algorithms to solve a sentiment-analysis problem, co-training, an SSL algorithm, has not attracted much attention for sentiment analysis largely due to its restricted assumptions. Therefore, this study focuses on revisiting co-training in depth and discusses several co-training strategies for sentiment analysis following a loose assumption. Results suggest that co-training can be more effective than can other currently adopted SSL methods for sentiment analysis.\",\n \"Title: Bubble-based assembly of micro-tube with coordinated multiple manipulators\\nYear: 2014\\n\\nAbstract: Assembly of a micro-tube with small diameter attracts great interest due to its promising contribution on vascular network to deliver oxygen and nutrition in thick biological substitutes. This paper presents a dexterous robotic system with three manipulators to reconstruct the interested micro-tube based on the interaction between bubbles and 2D hydrogel micro-units. Each manipulator has 3 degrees of freedom (DOFs), but they are equipped with different end-effectors for unique tasks under an inverted optical microscope (OM). Strategy of their coordinated operation is integrated under visual feedback. To verify the effectiveness of the proposed system, we conduct experiments on 2D hydrogel micro-units to achieve assembly of a 3D micro-channel for tissue engineering. Preliminary experimental results show that our assembly method is flexible and efficient.\",\n \"Title: DNA AS X: An Information-Coding-Based Model to Improve the Sensitivity in Comparative Gene Analysis.\\nYear: 2015\\n\\nAbstract: In recent studies [1-3], lots of hidden homology in DNA genome are not found by current comparative tools despite decades of research. Many scholars modeled the genome as a monotonous string, which limits and probably obstructs the discovery of some significant patterns. We propose an information-coding-based model called DNA As X (DAX) to improve the sensitivity in comparative genomic studies by integrating the principles and concepts of other disciplines including information coding theory and signal processing into genome analysis. The proposed DNA As X model uses character-analysis-free (CAF) techniques, where X is the intermediate for analysis that can be digit, code, signal, vector, tree, graph network and so on. It provides novel and comprehensive perspectives to further analyze and recognize the critical patterns hidden in DNA genomes. Comparing with traditional character-analysis-based (CAB) methods, DAX not only enriches the tools and the knowledge library of computational biology but also extends the domain from 1-D character string analysis to 2-D spatial/temporal domain. Furthermore, by applying the DAX model to the issue of exon prediction as an evaluation, we illustrate the insights behind this model. The experimental results show that the DAX methodology can improve the sensitivity in genome analysis by using the novel information-coding techniques.\",\n \"Title: Analysis of Overall E-Business Solution on Personalized Medical Care - - Taking Private Hospitals for Example.\\nYear: 2012\\n\\nAbstract: The patient is the lifeblood of the hospital and only win customers can win the future. When choosing a hospital, in addition to the strength of the medical treatment of the hospital itself, patients considering most are the services of the hospital which is the very factor that can influence customer making decision. Clearly, the needs of patients can't be achieved in overcrowded public hospitals. This paper starts from the content of E-Business, analyzes the current situation and problems of private hospital development, points out that development of E-Business to enhance customer relationship management is the only way to solve the practical problems of private hospitals, gives personal attention of private hospitals overall E-Business solutions and development strategies.\",\n \"Title: J2M: a Java to MapReduce translator for cloud computing.\\nYear: 2016\\n\\nAbstract: Cloud computing has gradually evolved into an infrastructural tool for a variety of scientific research and computing. It has become a trend that lots of products have been migrated from local servers to cloud by many institutions and organizations. One of the challenges in cloud computing now is how to run software efficiently on cloud platforms since lots of original codes are not capable of being executed in parallel on cloud contexts, resulting in that the power of clouds cannot be exerted well. It is costly to redesign and convert current sequential codes into cloud platform. Thus, automatic translation from sequential code to cloud code is one of the directions that could be taken to resolve the problem of code migration in cloud infrastructure. In this paper, a new Java to MapReduce (J2M) translator is developed to achieve the automatic translation from sequential Java to cloud for specific data-parallel code with large loops. This paper will provide details about the design of our translator and evaluate our performance through experiments. The experimental results not only indicate that the translator can precisely translate the sequential Java into cloud codes, but also show that it can achieve very good speedup in performance, and we expect that an almost linear speedup is possible if larger enough data is processed. It is believed that the J2M translator is an ideal stereotype for code migration and will play an important role in the transition era of cloud computing.\",\n \"Title: GaussianCpG: A Gaussian model for detection of human CpG island\\nYear: 2015\\n\\nAbstract: CpG contents in human DNA genome are generally suppressed to only around 1% comparing with other combinations [1]. The scarcity of CpG dinucleotide can be explained by bio-chemical binding force and energy [2]. CpG islands (CGI), where high profiles of CpG sites are densely contained in some certain genome regions, play important roles in DNA methylation, gene regulation, epigenetic inheritance, gene mutation, chromosome inactivation and nuclesome retention [3]. In vertebrate, DNA methylation usually occurs in CpG islands and adds an additional methyl to cytosine such that the gene silencing may be caused by the additional methyl. This subtle process can further give rise to different gene regulation and diverse epigenetic issues. Conventional bisulfite modification-based methods to determine the CpG island are time-consuming [4]. Although new sequencing techniques are developed for whole genome assays, it is reported to be too costly [5]. Thus, computational investigations to CpG islands is efficient and fundamental for many biological studies.\",\n \"Title: Measurement Of Mwcnt/Tungsten Contact Resistance By Bridging Cnt On Two Tungsten Electrodes With Two Manipulators\\nYear: 2015\\n\\nAbstract: Semiconductor nano-devices based on carbon nanotubes (CNTs) have attracted great interests recently such as field effect transistors (CNTFETs). One of the challenges is to reduce the contact resistance between CNT and electrodes. This paper presents a robotic system with two nano-manipulators for two-lead contact resistance measurement. Electron beam induced deposition (EBID) technique was applied to decrease the contact resistance between multi-walled CNTs (MWCNTs) and Tungsten deposition fabricated by focused ion beam (FIB). A Tungsten (W) probe fixed on one manipulator is used to pick up a CNT selectively from a CNT bundle. The picked CNT was positioned to approach a W deposition on an atomic force microscopy (AFM) cantilever. The cantilever mounted on another manipulator was coordinated by positioning for well contact between the W deposition and CNT. To strengthen this contact, four W points are deposited on the contact area of CNT for totally 9min by EBID. A resistance between W probe and cantilever was measured before and after EBID. Based on these measured resistance, contact resistance was calculated by a theoretical model. It showed the contact resistance of MWCNT/Tungsten deposition was reduced by 98.8% by the EBID deposition.\",\n \"Title: Reliable routing in wireless sensor networks based on coalitional game theory.\\nYear: 2016\\n\\nAbstract: Nodes in wireless sensor networks (WSNs) have the potential to be selfish without transmitting packets in routing. This study mainly focuses on the problem of reliable delivery mechanism in WSNs, and the authors' objective is to ensure stable cooperation among nodes for packets delivery and minimum routing cost at the same time. Based on the performance metrics in terms of the rate of packets forw...\",\n \"Title: A Radio-Map Automatic Construction Algorithm Based on Crowdsourcing.\\nYear: 2016\\n\\nAbstract: Traditional radio-map-based localization methods need to sample a large number of location fingerprints offline, which requires huge amount of human and material resources. To solve the high sampling cost problem, an automatic radio-map construction algorithm based on crowdsourcing is proposed. The algorithm employs the crowd-sourced information provided by a large number of users when they are walking in the buildings as the source of location fingerprint data. Through the variation characteristics of users' smartphone sensors, the indoor anchors (doors) are identified and their locations are regarded as reference positions of the whole radio-map. The AP-Cluster method is used to cluster the crowdsourced fingerprints to acquire the representative fingerprints. According to the reference positions and the similarity between fingerprints, the representative fingerprints are linked to their corresponding physical locations and the radio-map is generated. Experimental results demonstrate that the proposed algorithm reduces the cost of fingerprint sampling and radio-map construction and guarantees the localization accuracy. The proposed method does not require users' explicit participation, which effectively solves the resource-consumption problem when a location fingerprint database is established.\",\n \"Title: A Cloud-Assisted Application over Apache Spark for Investigating Epigenetic Markers on DNA Genome Sequences\\nYear: 2016\\n\\nAbstract: As an important epigenetic marker in identifying biological elements and processes in mammalian genomes, CpG islands (CGI) play significant roles in DNA methylation, gene regulation, epigenetic inheritance, gene mutation, chromosome inactivation and nuclesome retention. Investigating the CpG islands and their structures is a rigorous task because of unknown structures and the exponential number of possible patterns. In this paper, we design and implement an ad hoc application by combining the merits of Apache Spark platform and Spark programming paradigm with the particular properties of DNA genome sequences for CpG island investigation. A novel CpG box model and a Markov model are developed primarily for redefining and investigating the CpG island. Meanwhile, these models can easily fit to Spark-based cloud platforms that can greatly accelerate the analytic procedure. Two types of evaluations are successfully performed: one is accuracy-related and another is computing performance test. This paper is meant to describe this particular application on assisting the processing and the genomic analysis in epigenetic studies.\",\n \"Title: Bayesian Networks-Based Selection Algorithm for Virtual Machine to Be Migrated\\nYear: 2016\\n\\nAbstract: In cloud data centers, virtual machine (VM) consolidation is one of the challenge topics. In which, the selection of VMs to be migrated is one of the key issues in the process of VM consolidation. In this paper, under consideration of the dynamical uncertain environment, a Bayesian networks-based estimation model was constructed. Because excessive VM migrations influence the Quality of Service (QoS) of data center, the model aims at estimating the migration probability of VMs and calculating the potential total number of migrations occurred in physical hosts. Based on the proposed model, a Bayesian networks-based selection algorithm (BN-SA) for VMs to be migrated was proposed. The BN-SA adaptively adjusts the overloaded threshold and selects VMs which have relatively short migration time and big impact on potential migrations of host in the phase of reallocating VMs. The experimental results show that BN-SA algorithm has a promising performance.\",\n \"Title: Supervoxel-Based Hierarchical Markov Random Field Framework For Multi-Atlas Segmentation\\nYear: 2016\\n\\nAbstract: Multi-atlas segmentation serves as an important technique for quantitative analysis of medical images. In many applications, top performing techniques rely on computationally expensive deformable registration to transfer labels from atlas images to the target image. We propose a more computationally efficient label transfer strategy that uses supervoxel matching regularized by Markov random field (MRF), followed by regional voxel-wise joint label fusion and a second MRF. We evaluate this hierarchical MRF framework for multi-label diencephalon segmentation from the MICCAI 2013 SATA Challenge. Our segmentation results are comparable to the top-tier one obtained by deformable registration, but with much lower computational complexity.\",\n \"Title: Bayesian network-based Virtual Machines consolidation method.\\nYear: 2017\\n\\nAbstract: Efficient Virtual Machines (VMs) consolidation, as one of the primary methods for balancing between guaranteeing Quality of Service (QoS) and saving energy, is critical for data centers. Most existing VMs consolidation methods reallocate physical resources by adopting live VM migration. Therefore, VMs consolidation can be cast into estimating the physical resource utilization in Physical Machines (PMs) and predicting the migration probability of VMs. In this paper, we develop a Bayesian network-based estimation model (BNEM) for live VM migration, allowing a comprehensive treatment of nine actual factors in real data centers. A selection criterion of VMs to be migrated and a VM placement criterion are presented. By combining three algorithms corresponding to different phases in VMs consolidation, a hybrid Bayesian network-based VMs consolidation (BN-VMC) method is proposed. We have validated our approach by conducting a performance evaluation study using CloudSim toolkit, and the trace-driven comparison experiments are also performed. The simulation results show that the method can significantly degrade energy consumption, avoid inefficient VM migrations, and improve QoS.\",\n \"Title: A Comprehensive Review Of Emerging Computational Methods For Gene Identification\\nYear: 2016\\n\\nAbstract: Gene identification is at the center of genomic studies. Although the first phase of the Encyclopedia of DNA Elements (ENCODE) project has been claimed to be complete, the annotation of the functional elements is far from being so. Computational methods in gene identification continue to play important roles in this area and other relevant issues. So far, a lot of work has been performed on this area, and a plethora of computational methods and avenues have been developed. Many review papers have summarized these methods and other related work. However, most of them focus on the methodologies from a particular aspect or perspective. Different from these existing bodies of research, this paper aims to comprehensively summarize the mainstream computational methods in gene identification and tries to provide a short but concise technical reference for future studies. Moreover, this review sheds light on the emerging trends and cutting-edge techniques that are believed to be capable of leading the research on this field in the future.\",\n \"Title: Deep Learning In Genomic And Medical Image Data Analysis: Challenges And Approaches\\nYear: 2017\\n\\nAbstract: Artificial intelligence, especially deep learning technology, is penetrating the majority of research areas, including the field of bioinformatics. However, deep learning has some limitations, such as the complexity of parameter tuning, architecture design, and so forth. In this study, we analyze these issues and challenges in regards to its applications in bioinformatics, particularly genomic analysis and medical image analytics, and give the corresponding approaches and solutions. Although these solutions are mostly rule of thumb, they can effectively handle the issues connected to training learning machines. As such, we explore the tendency of deep learning technology by examining several directions, such as automation, scalability, individuality, mobility, integration, and intelligence warehousing.\",\n \"Title: Microbubbles for High-Speed Assembly of Cell-Laden Vascular-Like Microtube.\\nYear: 2016\\n\\nAbstract: Vascular-like microtube takes an important role in delivering oxygen and nutrient to keep cell alive in the generated tissue. In this paper, we present an automated micromanipulation system to assemble 2-D gel micro-rings to vascular-like microtubes by means of generating and controlling microbubbles. The detailed automated assembly process is described. To enable the automated assembly, real-time...\",\n \"Title: Evaluating the Impact of Encoding Schemes on Deep Auto-Encoders for DNA Annotation.\\nYear: 2017\\n\\nAbstract: Deep Neural Networks show their promise over traditional neural network on DNA genomic analysis. However, due to the uncertainty of DNA sequence data, it performs differently in various encoding schemes. In this article we focus on the comparison of different schemes on various auto-encoder algorithms in DNA annotation and analyze their impacts on deep learning. We also aim to find the best encoding schemes used on deep auto-encoder algorithms for DNA annotation.\",\n \"Title: A deep learning method for lincRNA detection using auto-encoder algorithm.\\nYear: 2017\\n\\nAbstract: RNA sequencing technique (RNA-seq) enables scientists to develop novel data-driven methods for discovering more unidentified lincRNAs. Meantime, knowledge-based technologies are experiencing a potential revolution ignited by the new deep learning methods. By scanning the newly found data set from RNA-seq, scientists have found that: (1) the expression of lincRNAs appears to be regulated, that is, the relevance exists along the DNA sequences; (2) lincRNAs contain some conversed patterns/motifs tethered together by non-conserved regions. The two evidences give the reasoning for adopting knowledge-based deep learning methods in lincRNA detection. Similar to coding region transcription, non-coding regions are split at transcriptional sites. However, regulatory RNAs rather than message RNAs are generated. That is, the transcribed RNAs participate the biological process as regulatory units instead of generating proteins. Identifying these transcriptional regions from non-coding regions is the first step towards lincRNA recognition.The auto-encoder method achieves 100% and 92.4% prediction accuracy on transcription sites over the putative data sets. The experimental results also show the excellent performance of predictive deep neural network on the lincRNA data sets compared with support vector machine and traditional neural network. In addition, it is validated through the newly discovered lincRNA data set and one unreported transcription site is found by feeding the whole annotated sequences through the deep learning machine, which indicates that deep learning method has the extensive ability for lincRNA prediction.The transcriptional sequences of lincRNAs are collected from the annotated human DNA genome data. Subsequently, a two-layer deep neural network is developed for the lincRNA detection, which adopts the auto-encoder algorithm and utilizes different encoding schemes to obtain the best performance over intergenic DNA sequence data. Driven by those newly annotated lincRNA data, deep learning methods based on auto-encoder algorithm can exert their capability in knowledge learning in order to capture the useful features and the information correlation along DNA genome sequences for lincRNA detection. As our knowledge, this is the first application to adopt the deep learning techniques for identifying lincRNA transcription sequences.\",\n \"Title: Topology control for minimizing interference with delay constraints in an ad hoc network.\\nYear: 2018\\n\\nAbstract: Most of the existing work on topology control is to minimize the interference or delay separately. However, minimizing the interference and reducing the delay are two conflicting goals; therefore, considering a trade-off between them is necessary. In this paper, our goal is to minimize the interference while guaranteeing the end-to-end delay under a constraint. We take into account three optimization objectives, including the maximum interference, average interference and average path interference. We propose a centralized algorithm with a greedy strategy that can minimize the maximum interference while satisfying the delay constraint. A distributed algorithm (LDMST) is proposed to minimize the average interference. In LDMST, each node builds a delay-constrained minimum spanning tree (DMST). To minimize the path interference, a localized delay-constrained Bellman–Ford (LDBF) algorithm is proposed. LDBF employs an improved Bellman–Ford algorithm to find the optimal path, which has the minimum interference and satisfies the delay constraint. The final topology is composed of these optimal paths. The simulation results illustrate that the proposed topology control algorithms exhibit good performance in terms of the interference objectives and can also guarantee the end-to-end delay under the constraint.\",\n \"Title: Three-Stream Convolutional Networks for Video-based Person Re-Identification\\nYear: 2017\\n\\nAbstract: This paper aims to develop a new architecture that can make full use of the feature maps of convolutional networks. To this end, we study a number of methods for video-based person re-identification and make the following findings: 1) Maxpooling only focuses on the maximum value of a receptive field, wasting a lot of information. 2) Networks with different streams even including the one with the worst performance work better than networks with same streams, where each one has the best performance alone. 3) A fully connection layer at the end of convolutional networks is not necessary. Based on these studies, we propose a new convolutional architecture termed Three-Stream Convolutional Networks (TSCN). It first uses different streams to learn different aspects of feature maps for attentive spatio-temporal fusion of video, and then merges them together to study some union features. To further utilize the feature maps, two architectures are designed by using the strategies of multi-scale and upsampling. Comparative experiments on iLIDS-VID, PRID-2011 and MARS datasets illustrate that the proposed architectures are significantly better for feature extraction than the state-of-the-art models.\",\n \"Title: Locality-Based Visual Outlier Detection Algorithm for Time Series\\nYear: 2017\\n\\nAbstract: AbstractPhysiological theories indicate that the deepest impression for time series data with respect to the human visual system is its extreme value. Based on this principle, by researching the strategies of extreme-point-based hierarchy segmentation, the hierarchy-segmentation-based data extraction method for time series, and the ideas of locality outlier, a novel outlier detection model and method for time series are proposed. The presented algorithm intuitively labels an outlier factor to each subsequence in time series such that the visual outlier detection gets relatively direct. The experimental results demonstrate the average advantage of the developed method over the compared methods and the efficient data reduction capability for time series, which indicates the promising performance of the proposed method and its practical application value.\",\n \"Title: A Precise Dead Reckoning Algorithm Based on Bluetooth and Multiple Sensors.\\nYear: 2018\\n\\nAbstract: More and more applications of location-based services lead to the development of indoor positioning technology. As a part of the Internet of Things ecosystem, low-power Bluetooth technology provides a new direction for indoor positioning. Most existing indoor positioning algorithms are applied to specific situations. Thus, they are difficult to adapt to actually complex environments and different ...\",\n \"Title: Convolutional networks with cross-layer neurons for image recognition.\\nYear: 2018\\n\\nAbstract: Very deep convolutional networks have recently achieved a series of breakthroughs on several challenging tasks such as the ImageNet or COCO competitions. However, it is difficult to train such deep neural networks. In this paper, we present a novel structure called cross-layer neurons architecture, which has the capability to train effective deeper neural networks. It utilizes cross-layer neurons to synthesize the information (features) learned from all the lower-level layers and send them to the higher-level layers through the cross-layer. Based on this novel architecture, we propose a new deep neural model termed Cross-Layer Neurons Networks (CLNN). It is shown that CLNN can relieve the problem of vanishing gradient. It is also shown that CLNN has the capability of improving the convergence rate of classification. Comparative experiments on several benchmark datasets (MNIST, CIFAR-10, CIFAR-100, SVHN and STL-10) clearly demonstrate that our proposed model is suitable for training deeper networks and can effectively improve the performance by utilizing cross-layer neurons.\",\n \"Title: Adaptive Compressed Sensing For Acceleration Data Transmission In Human Motion Capture\\nYear: 2017\\n\\nAbstract: In a human motion capture system, massive acceleration data will cost much transmission time. In order to reduce the acceleration data transmission volume while guaranteeing measurement accuracy, this paper presents an adaptive compressed sensing algorithm for acceleration data transmission in human motion capture. An innovative feature of our approach is to associate the sparsity and best Compression Ratio of acceleration data in a learning phase. Based on this correlation, the sensor node uses sparsity to describe the structural characteristics of acceleration data, and it adjusts best Compression Ratios of data according to the characteristics of the real-time acceleration data. The results of experiment show that algorithm can adjust the best Compression Ratios of original acceleration data at different parts of body in several typical motions, while ensuring Reconstruction Accuracy. For example, in running, the approach can achieve that the Compression Ratios at arms and legs separately reach to approximately 80% and 60%, while the compression data can achieve the same Reconstruction Accuracy. What's more, the algorithm can increase the Compression Ratio of capture system by about 20% compared with the current BSBL-BO algorithm, while achieving the same minimum Reconstruction Accuracy of capture system.\",\n \"Title: Learning To Detect Multiple Photographic Defects\\nYear: 2018\\n\\nAbstract: In this paper, we introduce the problem of simultaneously detecting multiple photographic defects. We aim at detecting the existence, severity, and potential locations of common photographic defects related to color, noise, blur and composition. The automatic detection of such defects could be used to provide users with suggestions for how to improve photos without the need to laboriously try various correction methods. Defect detection could also help users select photos of higher quality while filtering out those with severe defects in photo curation and summarization.To investigate this problem, we collected a large-scale dataset of user annotations on seven common photographic defects, which allows us to evaluate algorithms by measuring their consistency with human judgments. Our new dataset enables us to formulate the problem as a multi-task learning problem and train a multi-column deep convolutional neural network (CNN) to simultaneously predict the severity of all the defects. Unlike some existing single-defect estimation methods that rely on low-level statistics and may fail in many cases on natural photographs, our model is able to understand image contents and quality at a higher level. As a result, in our experiments, we show that our model has predictions with much higher consistency with human judgments than low-level methods as well as several baseline CNN models. Our model also performs better than an average human from our user study.\",\n \"Title: Attributing Fake Images to GANs: Analyzing Fingerprints in Generated Images.\\nYear: 2018\\n\\nAbstract: Research in computer graphics has been in pursuit of realistic image generation for a long time. Recent advances in machine learning with deep generative models have shown increasing success of closing the realism gap by using data-driven and learned components. There is an increasing concern that real and fake images will become more and more difficult to tell apart. We take a first step towards this larger research challenge by asking the question if and to what extend a generated fake image can be attribute to a particular Generative Adversarial Networks (GANs) of a certain architecture and trained with particular data and random seed. Our analysis shows single samples from GANs carry highly characteristic fingerprints which make attribution of images to GANs possible. Surprisingly, this is even possible for GANs with same architecture and same training that only differ by the training seed.\",\n \"Title: Analysis of Public Opinion Heat Trend in Universities on The Basis of Markov Chain\\nYear: 2018\\n\\nAbstract: With the development of the information age, Micro-blog, as the main carrier and platform of public opinion in universities, has gradually increased its influence. Focusing on the high fluctuation of public opinion in universities and other certain characters, this paper proposes a predictive model of public opinion heat trend based on Markov chain. In this model, it collects some index data and gives out time series of the public opinion heat value; then classifies the state space of public opinion heat trend in universities; thirdly, constructs state transition matrix of public opinion heat; and lastly, forecasts the trend variation interval of public opinion heat. Finally, the experimental results show that such model can effectively predict the trend of public opinion in universities and guide and manage them, providing a theoretical model basis for effect appraisal of public opinion in universities.\",\n \"Title: Texture Mixer: A Network For Controllable Synthesis And Interpolation Of Texture\\nYear: 2019\\n\\nAbstract: This paper addresses the problem of interpolating visual textures. We formulate this problem by requiring (1) by example controllability and (2) realistic and smooth interpolation among an arbitrary number of texture samples. To solve it we propose a neural network trained simultaneously on a reconstruction task and a generation task, which can project texture examples onto a latent space where they can be linearly interpolated and projected back onto the image domain, thus ensuring both intuitive control and realistic results. We show our method outperforms a number of base-lines according to a comprehensive suite of metrics as well as a user study. We further show several applications based on our technique, which include texture brush, texture dis-solve, and animal hybridization.\",\n \"Title: A deep learning method for lincRNA identification using auto-encoder algorithm\\nYear: 2016\\n\\nAbstract: LincRNAs are four times more than coding RNA sequences. However, currently only 21 thousand lincRNAs are computationally discovered [1]. Although this was one of the most important findings in lincRNA identification, identification of lincRNAs is far from being complete and those predicted lincRNAs are not validated yet. Currently new identified lincRNAs are most from the computational analysis of RNA-seq transcript data while deep learning based methods are barely seen in detecting and validating lincRNAs.\",\n \"Title: Automatic Image Defect Diagnosis.\\nYear: 2016\\n\\nAbstract: Although problems relating to specific image correction have been explored intensively, the problem of simultaneous diagnosis for multiple photographic defects remains relatively untouched. Solutions to this problem attempt to predict the existence, severity, and locations of common defects. This paper proposes a first attempt at a solution to the general defect diagnosis problem based on our novel dataset. We formulate the defect diagnosis problem as a multi-task prediction problem and utilize multi-column deep neural networks (DNN) to approach the problem. We propose DNN models with holistic and multi-patch inputs and combine their predicted scores to integrate multi-scale information. During experiments, we validate the complementarity of both kinds of inputs. We also validate that our combined predictions have a more consistent ranking correlation with our ground truth than the average of individual usersu0027 judgments. Furthermore, we apply the fully convolutional version of our trained model to visualize defect severity heat maps, which can effectively identify defective regions of input images. We propose that our work will provide casual photographers with better experiences when using image editing software to improve image quality. Another promising avenue for future application involves the equipping of photo summarization systems with defect cues to focus more on defect-free photos.\",\n \"Title: WIDIT: integrated approach to HARD topic search\\nYear: 2006\\n\\nAbstract: Web Information Discovery Tool (WIDIT) Laboratory at the Indiana University School of Library, whose basic approach to combine multiple methods as well as to leverage multiple sources of evidence, participated in 2005 Text Retrieval Conference’s Hard track (HARD-2005) to investigate methods of effectively dealing with HARD topics by exploring a variety of query expansion strategies, the results of which were combined via an automatic fusion optimization process. We hypothesized that the “difficulty” of topics is often due to the lack of appropriate query terms and/or misguided emphasis on non-pivotal query terms by the system. Thus, our first-tier solution was to devise a wide range of query expansion methods that can not only enrich the query with useful term additions but also identify important query terms. Our automatic query expansion included such techniques as noun phrase extraction, synonym identification, definition term extraction, keyword extraction by overlapping sliding window, and Web query expansion. The results of automatic expansion were used in soliciting user feedback, which was utilized in a post-retrieval reranking process. The paper describes our participation in HARD-2005 and is organized as follows. Section 2 gives an overview of HARD track, section 3 describes the WIDIT approach to HARD-2005, and section 4 discusses the results and implications, followed by the concluding remarks in section 5.\",\n \"Title: Reconstruction of Hidden Representation for Robust Feature Extraction.\\nYear: 2017\\n\\nAbstract: This article aims to develop a new and robust approach to feature representation. Motivated by the success of Auto-Encoders, we first theoretically analyze and summarize the general properties of all algorithms that are based on traditional Auto-Encoders: (1) The reconstruction error of the input cannot be lower than a lower bound, which can be viewed as a guiding principle for reconstructing the input. Additionally, when the input is corrupted with noises, the reconstruction error of the corrupted input also cannot be lower than a lower bound. (2) The reconstruction of a hidden representation achieving its ideal situation is the necessary condition for the reconstruction of the input to reach the ideal state. (3) Minimizing the Frobenius norm of the Jacobian matrix of the hidden representation has a deficiency and may result in a much worse local optimum value. We believe that minimizing the reconstruction error of the hidden representation is more robust than minimizing the Frobenius norm of the Jacobian matrix of the hidden representation. Based on the above analysis, we propose a new model termed Double Denoising Auto-Encoders (DDAEs), which uses corruption and reconstruction on both the input and the hidden representation. We demonstrate that the proposed model is highly flexible and extensible and has a potentially better capability to learn invariant and robust feature representations. We also show that our model is more robust than Denoising Auto-Encoders (DAEs) for dealing with noises or inessential features. Furthermore, we detail how to train DDAEs with two different pretraining methods by optimizing the objective function in a combined and separate manner, respectively. Comparative experiments illustrate that the proposed model is significantly better for representation learning than the state-of-the-art models.\",\n \"Title: Improving Cube-to-ERP Conversion Performance with Geometry Features of 360 Video Structure\\nYear: 2019\\n\\nAbstract: 360 videos provide an omnidirectional view of the scene with extremely large data. Therefore, representing 360 videos with less data has become more and more important. Cube format is such a popular representation of 360 videos. However, we have to convert cube to Equirectangula(ERP) for displaying convenience. In this paper, we enhance Cube-to-ERP conversion performance by joint using Convolutional Neural Network(CNN) and classical interpolation method. The optimal threshold of boundary is derived according to geometry features of the cube-to-ERP format. This threshold is the guidance of how to combine CNN and classical interpolation method. Our experiment results prove that the derived threshold has a certain degree of guiding significance. Furthermore, we propose a new evaluation criterion with the help of Marsaglia model. It is much easier and more accurate to evaluate geometry conversion process.\",\n \"Title: Bagging Machine Learning Algorithms: A Generic Computing Framework Based On Machine-Learning Methods For Regional Rainfall Forecasting In Upstate New York\\nYear: 2021\\n\\nAbstract: Regional rainfall forecasting is an important issue in hydrology and meteorology. Machine learning algorithms especially deep learning methods have emerged as a part of prediction tools for regional rainfall forecasting. This paper aims to design and implement a generic computing framework that can assemble a variety of machine learning algorithms as computational engines for regional rainfall forecasting in Upstate New York. The algorithms that have been bagged in the computing framework include the classical algorithms and the state-of-the-art deep learning algorithms, such as K-Nearest Neighbors, Support Vector Machine, Deep Neural Network, Wide Neural Network, Deep and Wide Neural Network, Reservoir Computing, and Long Short Term Memory methods. Through the experimental results and the performance comparisons of these various engines, we have observed that the SVM- and KNN-based method are outstanding models over other models in classification while DWNN- and KNN-based methods outstrip other models in regression, particularly those prevailing deep-learning-based methods, for handling uncertain and complex climatic data for precipitation forecasting. Meanwhile, the normalization methods such as Z-score and Minmax are also integrated into the generic computing framework for the investigation and evaluation of their impacts on machine learning models.\",\n \"Title: Ufacenet: Research On Multi-Task Face Recognition Algorithm Based On Cnn\\nYear: 2021\\n\\nAbstract: With the application of deep convolutional neural networks, the performance of computer vision tasks has been improved to a new level. The construction of a deeper and more complex network allows the face recognition algorithm to obtain a higher accuracy, However, the disadvantages of large computation and storage costs of neural networks limit the further popularization of the algorithm. To solve this problem, we have studied the unified and efficient neural network face recognition algorithm under the condition of a single camera; we propose that the complete face recognition process consists of four tasks: face detection, in vivo detection, keypoint detection, and face verification; combining the key algorithms of these four tasks, we propose a unified network model based on a deep separable convolutional structure-UFaceNet. The model uses multisource data to carry out multitask joint training and uses the keypoint detection results to aid the learning of other tasks. It further introduces the attention mechanism through feature level clipping and alignment to ensure the accuracy of the model, using the shared convolutional layer network among tasks to reduce model calculations amount and realize network acceleration. The learning goal of multi-tasking implicitly increases the amount of training data and different data distribution, making it easier to learn the characteristics with generalization. The experimental results show that the UFaceNet model is better than other models in terms of calculation amount and number of parameters with higher efficiency, and some potential areas to be used.\",\n \"Title: Artificial Fingerprinting for Generative Models - Rooting Deepfake Attribution in Training Data.\\nYear: 2021\\n\\nAbstract: Photorealistic image generation has reached a new level of quality due to the breakthroughs of generative adversarial networks (GANs). Yet, the dark side of such deepfakes, the malicious use of generated media, raises concerns about visual misinformation. While existing research work on deepfake detection demonstrates high accuracy, it is subject to advances in generation techniques and adversarial iterations on detection countermeasure techniques. Thus, we seek a proactive and sustainable solution on deepfake detection, that is agnostic to the evolution of generative models, by introducing artificial fingerprints into the models. Our approach is simple and effective. We first embed artificial fingerprints into training data, then validate a surprising discovery on the transferability of such fingerprints from training data to generative models, which in turn appears in the generated deepfakes. Experiments show that our fingerprinting solution (1) holds for a variety of cutting-edge generative models, (2) leads to a negligible side effect on generation quality, (3) stays robust against image-level and model-level perturbations, (4) stays hard to be detected by adversaries, and (5) converts deepfake detection and attribution into trivial tasks and outperforms the recent state-of-the-art baselines. Our solution closes the responsibility loop between publishing pre-trained generative model inventions and their possible misuses, which makes it independent of the current arms race.\",\n \"Title: RelaxLoss: Defending Membership Inference Attacks without Losing Utility\\nYear: 2022\\n\\nAbstract: As a long-term threat to the privacy of training data, membership inference attacks (MIAs) emerge ubiquitously in machine learning models.\\nExisting works evidence strong connection between the distinguishability of the training and testing loss distributions and the model's vulnerability to MIAs. Motivated by existing results, we propose a novel training framework based on a relaxed loss ($\\\\textbf{RelaxLoss}$) with a more achievable learning target, which leads to narrowed generalization gap and reduced privacy leakage. RelaxLoss is applicable to any classification model with added benefits of easy implementation and negligible overhead. Through extensive evaluations on five datasets with diverse modalities (images, medical data, transaction records), our approach consistently outperforms state-of-the-art defense mechanisms in terms of resilience against MIAs as well as model utility. Our defense is the first that can withstand a wide range of attacks while preserving (or even improving) the target model's utility.\",\n \"Title: A Calibration Scheme With Combination of the Optical Shaft Encoder and Laser Triangulation Sensor for Low-Frequency Angular Acceleration Rotary Table\\nYear: 2021\\n\\nAbstract: A low-frequency angular acceleration rotary table (LFAART) is capable of sinusoidal motion around a fixed position and essential to verify dynamic characteristics of a gyroscope device. The table's performance at different swinging frequencies and amplitudes needs to be calibrated to realize traceability. However, there has been little research on the LFAART calibration. Besides, the existing measurement methods of sinusoidal angular motion parameters are not adequate for the needs of the LFAART calibration in terms of measurement range, precision, and feasibility. This article presents a calibration scheme combining an optical shaft encoder and a laser triangulation sensor. When the rotation amplitude covers sufficient slots of the scale grating, the optical shaft encoder is applied, while the laser triangulation sensor is used for a tiny amplitude motion. An algorithm is proposed to accurately calculate the angular acceleration amplitude and frequency of the rotation utilizing sinusoidal phase-modulated signals of the optical shaft encoder output. Singular zero-crossings of the series are discarded, and intervals of reversing moments are recognized according to the signal derivative's sign at remaining zero-crossings. Then intervals are divided into subintervals to obtain accurate reversing moments. Finally, angular displacements between two adjacent reversing moments are estimated based on the fringe counting method, and the amplitude and frequency are calculated. The measurement uncertainty analysis and experiment demonstrate that the calibration scheme, including the algorithm, possesses high accuracy and resolution, wide range, and good operability, which perfectly solves the rotation's angular acceleration amplitude and frequency.\",\n \"Title: Multipolicy Robot-Following Model Based on Reinforcement Learning\\nYear: 2021\\n\\nAbstract: We propose in this paper a new approach to solve the decision problem of robot-following. Different from the existing single policy model, we propose a multipolicy model, which can change the following policy in time according to the scene. The value of this paper is to obtain a multipolicy robot-following model by the self-learning method, which is used to improve the safety, efficiency, and stability of robot-following in the complex environments. Empirical investigation on a number of datasets reveals that overall, the proposed approach tends to have superior out-of-sample performance when compared to alternative robotfollowing decision methods. The performance of the model has been improved by about 2 times in situations where there are few obstacles and about 6 times in situations where there are lots of obstacles.\",\n \"Title: A Supervised Progressive Growing Generative Adversarial Network for Remote Sensing Image Scene Classification\\nYear: 2022\\n\\nAbstract: Remote sensing image scene classification is a challenging task. With the development of deep learning, methods based on convolutional neural networks (CNNs) have made great achievements in remote sensing image scene classification. Since the training of a CNN requires a large number of labeled samples, a generative adversarial network (GAN) for sample generation represents a new opportunity to solve the problem of the limited samples. However, most of the existing GAN-based sample generation methods can only generate unlabeled samples, instead of samples labeled with the corresponding scene category. In this article, to solve the problem, a supervised progressive growing generative adversarial network (SPG-GAN) is proposed for remote sensing image scene classification. The proposed method can generate labeled samples for the remote sensing image scene classification, significantly improving the classification accuracy in the case of limited samples. The SPG-GAN method has two main improvements. First, a conditional generative framework for labeled samples is proposed, in which the label information is added in the channel dimension as the input. By considering the constraints of the label information in the loss function, the network can be trained in the direction of a specific category. As a result, the network can generate remote sensing image scene classification samples with label categories. Second, a progressive growing sample generation method is introduced. In order to ensure that the generated samples have more spatial details, they are generated by progressively adding modules to the generator and discriminator, thereby ensuring that the generated sample is of better quality. After testing on two benchmark datasets and carrying out a large-scale experiment in the central area of the city of Wuhan in China, it was found that the proposed method can obtain a superior scene classification accuracy in the case of limited samples.\",\n \"Title: RepMix: Representation Mixing for Robust Attribution of Synthesized Images.\\nYear: 2022\\n\\nAbstract: Rapid advances in Generative Adversarial Networks (GANs) raise new challenges for image attribution; detecting whether an image is synthetic and, if so, determining which GAN architecture created it. Uniquely, we present a solution to this task capable of 1) matching images invariant to their semantic content; 2) robust to benign transformations (changes in quality, resolution, shape, etc.) commonly encountered as images are re-shared online. In order to formalize our research, a challenging benchmark, Attribution88, is collected for robust and practical image attribution. We then propose RepMix, our GAN fingerprinting technique based on representation mixing and a novel loss. We validate its capability of tracing the provenance of GAN-generated images invariant to the semantic content of the image and also robust to perturbations. We show our approach improves significantly from existing GAN fingerprinting works on both semantic generalization and robustness. Data and code are available at https://github.com/TuBui/image_attribution.\",\n \"Title: Robot hand-eye cooperation based on improved inverse reinforcement learning\\nYear: 2022\\n\\nAbstract: Purpose How to make accurate action decisions based on visual information is one of the important research directions of industrial robots. The purpose of this paper is to design a highly optimized hand-eye coordination model of the robot to improve the robots' on-site decision-making ability. Design/methodology/approach The combination of inverse reinforcement learning (IRL) algorithm and generative adversarial network can effectively reduce the dependence on expert samples and robots can obtain the decision-making performance that the degree of optimization is not lower than or even higher than that of expert samples. Findings The performance of the proposed model is verified in the simulation environment and real scene. By monitoring the reward distribution of the reward function and the trajectory of the robot, the proposed model is compared with other existing methods. The experimental results show that the proposed model has better decision-making performance in the case of less expert data. Originality/value A robot hand-eye cooperation model based on improved IRL is proposed and verified. Empirical investigations on real experiments reveal that overall, the proposed approach tends to improve the real efficiency by more than 10% when compared to alternative hand-eye cooperation methods.\",\n \"Title: Predicting cancer drug response using parallel heterogeneous graph convolutional networks with neighborhood interactions\\nYear: 2022\\n\\nAbstract: Motivation: Due to cancer heterogeneity, the therapeutic effect may not be the same when a cohort of patients of the same cancer type receive the same treatment. The anticancer drug response prediction may help develop personalized therapy regimens to increase survival and reduce patients' expenses. Recently, graph neural network-based methods have aroused widespread interest and achieved impressive results on the drug response prediction task. However, most of them apply graph convolution to process cell line-drug bipartite graphs while ignoring the intrinsic differences between cell lines and drug nodes. Moreover, most of these methods aggregate node-wise neighbor features but fail to consider the element-wise interaction between cell lines and drugs. Results: This work proposes a neighborhood interaction (NI)-based heterogeneous graph convolution network method, namely NIHGCN, for anticancer drug response prediction in an end-to-end way. Firstly, it constructs a heterogeneous network consisting of drugs, cell lines and the known drug response information. Cell line gene expression and drug molecular fingerprints are linearly transformed and input as node attributes into an interaction model. The interaction module consists of a parallel graph convolution network layer and a NI layer, which aggregates node-level features from their neighbors through graph convolution operation and considers the element-level of interactions with their neighbors in the NI layer. Finally, the drug response predictions are made by calculating the linear correlation coefficients of feature representations of cell lines and drugs. We have conducted extensive experiments to assess the effectiveness of our model on Cancer Drug Sensitivity Data (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets. It has achieved the best performance compared with the state-of-the-art algorithms, especially in predicting drug responses for new cell lines, new drugs and targeted drugs. Furthermore, our model that was well trained on the GDSC dataset can be successfully applied to predict samples of PDX and TCGA, which verified the transferability of our model from cell line in vitro to the datasets in vivo. Availability and implementation: The source code can be obtained from. Supplementary information: Supplementary data are available at Bioinformatics online.\",\n \"Title: Auditing Membership Leakages of Multi-Exit Networks\\nYear: 2022\\n\\nAbstract: ABSTRACTRelying on the truth that not all inputs require the same level of computational cost to produce reliable predictions, multi-exit networks are gaining attention as a prominent approach for pushing the limits of efficient deployment. Multi-exit networks endow a backbone model with early exits, allowing predictions at intermediate layers of the model and thus saving computation time and energy. However, various current designs of multi-exit networks are only considered to achieve the best trade-off between resource usage efficiency and prediction accuracy, the privacy risks stemming from them have never been explored. This prompts the need for a comprehensive investigation of privacy risks in multi-exit networks. In this paper, we perform the first privacy analysis of multi-exit networks through the lens of membership leakages. In particular, we first leverage the existing attack methodologies to quantify the multi-exit networks' vulnerability to membership leakages. Our experimental results show that multi-exit networks are less vulnerable to membership leakages, and the exit (number and depth) attached to the backbone model is highly correlated with the attack performance. Furthermore, we propose a hybrid attack that exploits the exit information to improve the performance of existing attacks. We evaluate membership leakage threat caused by our hybrid attack under three different adversarial setups, ultimately arriving at a model-free and data-free adversary. These results clearly demonstrate that our hybrid attacks are very broadly applicable, thereby the corresponding risks are much more severe than shown by existing membership inference attacks. We further present a defense mechanism called TimeGuard specifically for multi-exit networks and show that TimeGuard mitigates the newly proposed attacks perfectly.\",\n \"Title: Cell Classification Based on Stacked Autoencoder for Single-Cell RNA Sequencing\\nYear: 2022\\n\\nAbstract: Single-cell transcriptome sequencing (scRNA-seq) allows researchers to investigate cellular heterogeneity in gene expression profiles, identify cell types and predict cell fate at the single-cell level. Analysis of scRNA-seq data can effectively extract unknown heterogeneity and functional diversity of cell populations. Cell classification is one of the most important tasks in scRNA-seq data analysis, which contains cell clustering and classification of cell subtypes. Before assigning a cell type to each cluster, the unsupervised clustering methods look for marker genes for each cluster. These approaches are susceptible to a number of drawbacks in terms of sources of variation, technology, etc. Meanwhile, as more cell subtypes are gradually discovered, cluster-based cell type identification methods have been gradually leaning towards classification-based cell type identification. In this paper, we proposed a new cell classification method based on stacked autoencoder for representation learning (scSAERLs), which enhances the accuracy of classification by learning the feature representation of the data through deep network models. The stacked autoencoder-based classification model employed an unsupervised greedy pre-training learning procedure, which was followed by supervised label-based fine-tuning of the entire classification model. We tested the model in the intra and inter datasets, evaluated its performance with a standard classification metric. Experimental results showed that scSAERLs outperformed other commonly used classification methods in terms of classification accuracy and F1-score.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":92,"string":"92"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13666,"string":"13,666"}}},{"rowIdx":13667,"cells":{"question_id":{"kind":"string","value":"Johannes Sch ning"},"question":{"kind":"string","value":"Generate an abstract for the title \"The office smartwatch: development and design of a smartwatch app to digitally augment interactions in an office environment\" using the following items: Smartwatches, Digital Augmentation, Interaction, Office Environment, Design and Development."},"answer":{"kind":"string","value":"In office spaces, knowledge workers interact both with each other and with various analog and digital devices in the office. We think that the office environment opens up an interesting space to utilize smartwatches to support and digitally augment interactions. In this paper, we describe the design and development of a smartwatch application to digitally augment interactions that are commonly performed in an office environment. For example, our application allows one to physically and virtually lock and unlock doors, to acquire room information and to send virtual knocks with an app running on the watch."},"correct_answer":{"kind":"string","value":"In office spaces, knowledge workers interact both with each other and with various analog and digital devices in the office. We think that the office environment opens up an interesting space to utilize smartwatches to support and digitally augment interactions. In this paper, we describe the design and development of a smartwatch application to digitally augment interactions that are commonly performed in an office environment. For example, our application allows one to physically and virtually lock and unlock doors, to acquire room information and to send virtual knocks with an app running on the watch."},"documents":{"kind":"list like","value":["Title: Mobile product customization\nYear: 2010\n\nAbstract: Many companies are using the web to enable customers to individually customize their products that range from automobiles and bicycles to CDs, cosmetics and shirts. In this paper we present a mobile application for product customization and production within a smart factory. This allows the ad hoc configuration of products at the point of sale (POS). We investigate human factors when customizing products while interacting with them. We focus on the concept of the mobile client that enables this ad hoc modification, but also present the production chain behind our product. We believe that this particular 3D interaction with a product and a mobile device help to improve the customer satisfaction as it allows for customizing a product in an easy and intuitive way. From a CHI perspective an important aspect is that our mobile augmented reality interface can help to match the costumer's expectations with the final modified product and allows the most natural and intuitive interaction. As a use case of the system, we present the modification of a soap dispenser.","Title: Whole Body Interaction with Geospatial Data\nYear: 2009\n\nAbstract: Common Geographic Information Systems (GIS) require a high degree of expertise from its users, making them difficult to be operated by laymen. This paper describes novel approaches to easily perform typical basic spatial tasks within a GIS: e.g. pan-, zoom- and selection-operations by using multi-touch gestures in combination with foot gestures. We are interested in understanding how non-expert users interact with such multi-touch surfaces. We provide a categorization and a framework of multi-touch hand gestures for interacting with a GIS. This framework is based on an initial evaluation. We present results of a more detailed in situ-study mainly focusing on multi-user multi-touch interaction with geospatial data. Furthermore we extend our framework using a combination of multi-touch gestures with a small set of foot gestures to solve geospatial tasks.","Title: Informing intelligent user interfaces by inferring affective states from body postures in ubiquitous computing environments\nYear: 2013\n\nAbstract: Intelligent User Interfaces can benefit from having knowledge on the user's emotion. However, current implementations to detect affective states, are often constraining the user's freedom of movement by instrumenting her with sensors. This prevents affective computing from being deployed in naturalistic and ubiquitous computing contexts. In this paper, we present a novel system called mASqUE, which uses a set of association rules to infer someone's affective state from their body postures. This is done without any user instrumentation and using off-the-shelf and non-expensive commodity hardware: a depth camera tracks the body posture of the users and their postures are also used as an indicator of their openness. By combining the posture information with physiological sensors measurements we were able to mine a set of association rules relating postures to affective states. We demonstrate the possibility of inferring affective states from body postures in ubiquitous computing environments and our study also provides insights how this opens up new possibilities for IUI to access the affective states of users from body postures in a nonintrusive way.","Title: Explanatory semantic relatedness and explicit spatialization for exploratory search\nYear: 2012\n\nAbstract: Exploratory search, in which a user investigates complex concepts, is cumbersome with today's search engines. We present a new exploratory search approach that generates interactive visualizations of query concepts using thematic cartography (e.g. choropleth maps, heat maps). We show how the approach can be applied broadly across both geographic and non-geographic contexts through explicit spatialization, a novel method that leverages any figure or diagram -- from a periodic table, to a parliamentary seating chart, to a world map -- as a spatial search environment. We enable this capability by introducing explanatory semantic relatedness measures. These measures extend frequently-used semantic relatedness measures to not only estimate the degree of relatedness between two concepts, but also generate human-readable explanations for their estimates by mining Wikipedia's text, hyperlinks, and category structure. We implement our approach in a system called Atlasify, evaluate its key components, and present several use cases.","Title: Carpus: a non-intrusive user identification technique for interactive surfaces\nYear: 2012\n\nAbstract: Interactive surfaces have great potential for co-located collaboration because of their ability to track multiple inputs simultaneously. However, the multi-user experience on these devices could be enriched significantly if touch points could be associated with a particular user. Existing approaches to user identification are intrusive, require users to stay in a fixed position, or suffer from poor accuracy. We present a non-intrusive, high-accuracy technique for mapping touches to their corresponding user in a collaborative environment. By mounting a high-resolution camera above the interactive surface, we are able to identify touches reliably without any extra instrumentation, and users are able to move around the surface at will. Our technique, which leverages the back of users' hands as identifiers, supports walk-up-and-use situations in which multiple people interact on a shared surface.","Title: Gestural interaction on the steering wheel: reducing the visual demand\nYear: 2011\n\nAbstract: Cars offer an increasing number of infotainment systems as well as comfort functions that can be controlled by the driver. In our research, we investigate new interaction techniques that aim to make it easier to interact with these systems while driving. We suggest utilizing the steering wheel as an additional interaction surface. In this paper, we present two user studies conducted with a working prototype of a multi-touch steering wheel. In the first, we developed a user-defined steering wheel gesture set, and in the second, we applied the identified gestures and compared their application to conventional user interaction with infotainment systems in terms of driver distraction. The main outcome was that driver's visual demand is reduced significantly by using gestural interaction on the multi-touch steering wheel.","Title: Geographic human-computer interaction\nYear: 2013\n\nAbstract: Geography is playing an increasingly important role in areas of HCI ranging from social computing to natural user interfaces. At the same time, research in geography has focused more and more on technology-mediated interaction with spatiotemporal phenomena. Despite the growing popularity of this geographic human-computer interaction (GeoHCI) in both fields, there have been few opportunities for GeoHCI knowledge sharing, knowledge creation or community building in either discipline, let alone between them. The goal of this workshop is thus two-fold. First, we will seek to sum up the state of GeoHCI knowledge and address GeoHCI core issues by inviting prominent researchers in the space to share and discuss the most important high-level findings from their work. Second, through our interdisciplinary organizing committee, we will recruit participants from both fields, with the goal of laying the groundwork for a community that works across intra- and interdisciplinary boundaries.","Title: Improving the Communication of Spatial Information in Crisis Response by Combining Paper Maps and Mobile Devices\nYear: 2008\n\nAbstract: Efficient and effective communication between mobile units and the central emergency operation center is a key factor to respond successfully to the challenges of emergency management. Nowadays, the only ubiquitously available modality is a voice channel through mobile phones or radio transceivers. This makes it often very difficult to convey exact geographic locations and can lead to misconceptions with severe consequences, such as a fire brigade heading to the right street address in the wrong city. In this paper we describe a handheld augmented reality approach to support the communication of spatial information in a crisis response scenario. The approach combines mobile camera devices with paper maps to ensure a quick and reliable exchange of spatial information.","Title: Using hands and feet to navigate and manipulate spatial data\nYear: 2009\n\nAbstract: We demonstrate how multi-touch hand gestures in combination with foot gestures can be used to perform navigation tasks in interactive systems. The geospatial domain is an interesting example to show the advantages of the combination of both modalities because the complex user interfaces of common Geographic Information System (GIS) requires a high degree of expertise from its users. Recent developments in interactive surfaces that enable the construction of low cost multi-touch displays and relatively cheap sensor technology to detect foot gestures allow the deep exploration of these input modalities for GIS users with medium or low expertise. In this paper, we provide a categorization of multitouch hand and foot gestures for the interaction with spatial data on a large-scale interactive wall. In addition we show with an initial evaluation how these gestures can improve the overall interaction with spatial information.","Title: Investigating the effects of using biofeedback as visual stress indicator during video-mediated collaboration\nYear: 2014\n\nAbstract: During remote video-mediated assistance, instructors often guide workers through problems and instruct them to perform unfamiliar or complex operations. However, the workers' performance might deteriorate due to stress. We argue that informing biofeedback to the instructor, can improve communication and lead to lower stress. This paper presents a thorough investigation on mental workload and stress perceived by twenty participants, paired up in an instructor-worker scenario, performing remote video-mediated tasks. The interface conditions differ in task, facial and biofeedback communication. Two self-report measures are used to assess mental workload and stress. Results show that pairs reported lower mental workload and stress when instructors are using the biofeedback as compared to using interfaces with facial view. Significant correlations were found on task performance with reducing stress (i.e. increased task engagement and decreased worry) for instructors and declining mental workload (i.e. increased performance) for workers. Our findings provide insights to advance video-mediated interfaces for remote collaborative work.","Title: The hybrid shopping list: bridging the gap between physical and digital shopping lists\nYear: 2011\n\nAbstract: Shopping is one of the most frequently occuring tasks in our daily lives, and creation and management of shopping lists is an important aspect of this task. Given the recent adoption of mobile devices, the process of writing lists is not only limited to the use of pen and paper, as a good number of digital tools and applications are available. The goal of this paper is to study and understand the transition between paper-based and digital shopping lists. We analyze how people interact with paper-based shopping lists and derive design implications for our own hybrid shopping support application, which combines paper-based lists with a mobile application. We contribute the study and the design and implementation of a hybrid (pen-and-paper-based UI and mobile GUI) application for the creation of shopping lists.","Title: Child computer interaction: workshop on UI technologies and educational pedagogy\nYear: 2011\n\nAbstract: Given the growth of Child Computer Interaction research, next generation HCI technologies play an important role in the future of education. Educators rely on technology to improve and adapt learning to the pedagogical needs of learners. Hence, this community needs to understand how current technology concepts match with current pedagogical paradigms. The classroom is a high stakes environment for experimentation, thus new interaction techniques need to be validated to prove their pedagogical value in the educational setting. This workshop provides a forum to discuss key HCI issues facing next generation education. With a particular focus on child computer interaction, these issues comprise inter alia the interaction with whole class interactive whiteboards, small group interactive multi-touch tables, and individual personal response systems (e.g. mobile devices) in the classroom.","Title: PINwI: pedestrian indoor navigation without infrastructure\nYear: 2010\n\nAbstract: Navigation in larger unfamiliar buildings like town halls, airports, shopping malls or other public indoor locations is often difficult for humans. Due to the high amount of infrastructure needed for indoor positioning, just a few navigation services for indoor environments exist. Therefore in many of these buildings 'YOU-ARE-HERE' (YAH) maps are provided, often located at the entrance or other key places, to facilitate orientation and navigation within the building, but they have the disadvantages of being stationary. In this paper, we try to overcome these problems by presenting PINwI (Pedestrian Indoor Navigation without Infrastructure), an application that allows the user of a mobile camera device with integrated compass and accelerometer to utilize a photo of such an indoor YAH-map to navigate through the corresponding building. Using a dead reckoning approach, we enrich stationary analog YAH-maps with basic location functionality and turn them into a digital and dynamic medium that can help decision making while taking turns or estimating distances.","Title: The path is the reward: considering social networks to contribute to the pleasure of urban strolling\nYear: 2013\n\nAbstract: Most (mobile) online map services focus on providing their users the most efficient route to their target location. In this paper we investigate the relationship between the physical and digital urban navigation to improve wayfinding for pedestrians by enhancing their experiences when strolling through a city. With our application \"Space Recommender System\" we describe a new wayfinding approach by implementing common digital online methods of commenting and recommender systems into the physical world, using voting data from social network services. Initial findings highlight the general importance of the walking experience to the public and suggest that implementing social media based recommendations in route finding algorithms enhance the pleasure of urban strolling. The initial user tests of the system in a real world context together with collected feedback and the observations throughout the design process stimulate the discussions of wider issues and highlight its potential for future novel wayfinding applications.","Title: Impact of item density on magic lens interactions\nYear: 2009\n\nAbstract: We conducted a user study to investigate the effect of visual context in handheld augmented reality interfaces. A dynamic peephole interface (without visual context beyond the device display) was compared to a magic lens interface (with video see-through augmentation of external visual context). The task was to explore objects on a map and look for a specific attribute shown on the display. We tested different sizes of visual context as well as different numbers of items per area, i.e. different item densities. We found that visual context is most effective for sparse item distributions and the performance benefit decreases with increasing density. User performance in the magic lens case approaches the performance of the dynamic peephole case the more densely spaced the items are. In all conditions, subjective feedback indicates that participants generally prefer visual context over the lack thereof. The insights gained from this study are relevant for designers of mobile AR and dynamic peephole interfaces by suggesting when external visual context is most beneficial.","Title: Next generation of HCI and education: workshop on UI technologies and educational pedagogy\nYear: 2010\n\nAbstract: Given the exponential growth of interactive whiteboards in classrooms around the world, and the recent emergence of multi-touch tables, tangible computing devices and mobile devices, there has been a need to explore how next generation HCI will impact education in the future. Educators are depending on the interaction communities to deliver technologies that will improve/adapt learning to an ever-changing world. In addition to novel UI concepts, the HCI community needs to examine how these concepts can be matched to contemporary paradigms in Educational pedagogy. The classroom is a challenging environment for evaluation, thus new interaction techniques need to be established to prove the value of new HCI interactions in the educational space. This workshop provides a forum to discuss key HCI issues facing next generation education ranging from whole class interactive whiteboards, small group interactive multi-touch tables, and individual personal response systems in the classroom.","Title: Photomap: Snap, Grab and Walk away with a \"You Are Here\" Map\nYear: 2008\n\nAbstract: One compelling scenario for the use of GPS enabled phones is support for navigation, e.g. enabling a user to glance down at the screen of her mobile phone in order to be reassured that she is indeed located where she thinks she is. While service based approaches to support such navigation tasks are becoming increasingly available { whereby a user down- loads (for a fee) a relevant map of her current area onto her GPS enabled phone, the approach is often far from ideal. Typically, the user is unsure as to the cost of downloading the map (especially when she is in a foreign country) and such maps are highly generalised and may not match the user's current activity and needs. For example, rather than requiring a standard map on a mobile device of the area, the user may simply require a map of a university campus with all departments or a map showing footpaths around the area in which she is currently trekking. Indeed, one will often see such specialised maps on public signs situated where they may be required (in a just{in{time sense) and it is inter- esting to consider how one might enable users to walk up to such situated signs and use their mobile phone to 'take away' the map presented in order to use it to assist their on- going navigation activity. In this paper, we are interested in a subset of this problem space in which the user 'grabs' a map shown on a public display by taking a photograph of it and using it as a digital map on her mobile phone. We present two dierent scenarios for our new application called PhotoMaps: In the","Title: Second International Workshop on Location and the Web (LocWeb 2009)\nYear: 2009\n\nAbstract: Location-Based Services are becoming increasingly Web-based, as a result of the availability of networked mobile devices and mobile Internet access. The \"Location and the Web (LocWeb)\" workshop targets the capabilities and constraints of Web-based location-based services, which can be implemented as browser-based applications, or as native applications using Web services. The focus of this CHI workshop is on developing approaches to handle the complexity of location-based services, specifically looking at location abstractions, location sharing, context-relevant information, privacy issues, and interface design. The goal of this workshop is to serve as a starting point for better understanding how the Web has to change to embrace location as a first-level concept, and how these changes are reflected in applications and user interfaces to transform the Web into a platform for location-based services.","Title: Improving interaction with virtual globes through spatial thinking: helping users ask \"why?\"\nYear: 2008\n\nAbstract: Virtual globes have progressed from little-known technology to broadly popular software in a mere few years. We investigated this phenomenon through a survey and discovered that, while virtual globes are en vogue, their use is restricted to a small set of tasks so simple that they do not involve any spatial thinking. Spatial thinking requires that users ask \"what is where\" and \"why\"; the most common virtual globe tasks only include the \"what\". Based on the results of this survey, we have developed a multi-touch virtual globe derived from an adapted virtual globe paradigm designed to widen the potential uses of the technology by helping its users to inquire about both the \"what is where\" and \"why\" of spatial distribution. We do not seek to provide users with full GIS (geographic information system) functionality, but rather we aim to facilitate the asking and answering of simple \"why\" questions about general topics that appeal to a wide virtual globe user base.","Title: Impact of item density on the utility of visual context in magic lens interactions\nYear: 2009\n\nAbstract: This article reports on two user studies investigating the effect of visual context in handheld augmented reality interfaces. A dynamic peephole interface (without visual context beyond the device display) was compared to a magic lens interface (with video see-through augmentation of external visual context). The task was to explore items on a map and look for a specific attribute. We tested different sizes of visual context as well as different numbers of items per area, i.e. different item densities. Hand motion patterns and eye movements were recorded. We found that visual context is most effective for sparsely distributed items and gets less helpful with increasing item density. User performance in the magic lens case is generally better than in the dynamic peephole case, but approaches the performance of the latter the more densely the items are spaced. In all conditions, subjective feedback indicates that participants generally prefer visual context over the lack thereof. The insights gained from this study are relevant for designers of mobile AR and dynamic peephole interfaces, involving spatially tracked personal displays or combined personal and public displays, by suggesting when to use visual context.","Title: Projector Phones: A New Class of Interfaces for Augmented Reality\nYear: 2010\n\nAbstract: With the miniaturization of projection technology, the integration of tiny projection units into mobile devices is no longer fiction; therefore, such integrated projectors in mobile devices could make mobile projection ubiquitous. These phones will have the ability to project large-scale information onto any surfaces in the real world, and by doing so, the interaction space of the mobile device can be considerably expanded. In addition, physical objects in the environment can be augmented with additional information, which can support interaction concepts that are not even possible on modern desktop computers today. The authors believe that mobile camera-projector units can form a promising interface type for mobile Augmented Reality AR applications, thus, this paper identifies different application classes of such interfaces. In addition, different spatial setups of camera and projector units will have an effect on the possible applications and the interaction space with the focus on the augmentation of real word objects in the environment. This paper presents two examples of applications for mobile camera-projector units and different hardware prototypes that allow augmentation of real world objects.","Title: CHI 2039: speculative research visions\nYear: 2014\n\nAbstract: This paper presents a curated collection of fictional abstracts for papers that could appear in the proceedings of the 2039 CHI Conference. It provides an opportunity to consider the various visions guiding work in HCI, the futures toward which we (believe we) are working, and how research in the field might relate with broader social, political, and cultural changes over the next quarter century.","Title: Falling asleep with Angry Birds, Facebook and Kindle: a large scale study on mobile application usage\nYear: 2011\n\nAbstract: While applications for mobile devices have become extremely important in the last few years, little public information exists on mobile application usage behavior. We describe a large-scale deployment-based research study that logged detailed application usage information from over 4,100 users of Android-powered mobile devices. We present two types of results from analyzing this data: basic descriptive statistics and contextual descriptive statistics. In the case of the former, we find that the average session with an application lasts less than a minute, even though users spend almost an hour a day using their phones. Our contextual findings include those related to time of day and location. For instance, we show that news applications are most popular in the morning and games are at night, but communication applications dominate through most of the day. We also find that despite the variety of apps available, communication applications are almost always the first used upon a device's waking from sleep. In addition, we discuss the notion of a virtual application sensor, which we used to collect the data.","Title: Evaluating automatically generated location-based stories for tourists\nYear: 2008\n\nAbstract: Tourism provides over six percent of the world's gross domestic product. As a result, there have been many efforts to use technology to improve the tourist's experience via mobile tour guide systems. One key bottleneck in such location-based systems is content development; existing systems either provide trivial information at a global scale or present quality narratives but at an extremely local scale. The primary reason for this dichotomy is that, although good narrative content is more educationally effective (and more entertaining) than a stream of simple, disconnected facts, it is time-intensive and expensive to develop. However, the WikEar system uses narrative theory-informed data mining methodologies in an effort to produce high-quality narrative content for any location on Earth. It allows tourists to interact with these narratives using their camera-enabled cell phones and an innovative interface designed around a magic lens and paper map metaphor. In this paper, we describe a first evaluation of these narratives and the WikEar interface, which reported promising, but not conclusive, results. We also present ideas for future work that will use this feedback to improve the narratives.","Title: LocWeb 2010: Third International Workshop on Location and the Web\nYear: 2010\n\nAbstract: The Third International Workshop on Location and the Web (LocWeb 2010) focuses on research and development that targets the intersection of Internet-enabled location-aware and/or located devices, and services based on Web technologies and Web architecture. The rapid rise of multi-sensory mobile devices and Internet-enabled \"things\" equipped with sensors and ubiquitous connectivity opens new possibilities and provides the foundations to capture, share and use Web services and applications in ways which go beyond the traditional scenarios of stationary or even mobile computer-like devices. Increasingly, applications will have to bridge the physical world and the Web space, and location is one of the major connecting links. When Web services will \"surround\" users, designers have to address the challenges of scalability and interoperability on the Web, and designers also have to look at policy, regulatory, and legislative responses to the privacy and security challenges created by something as sensitive as location information.","Title: Map navigation with mobile devices: virtual versus physical movement with and without visual context\nYear: 2007\n\nAbstract: A user study was conducted to compare the performance of three methods for map navigation with mobile devices. These methods are joystick navigation, the dynamic peephole method without visual context, and the magic lens paradigm using external visual context. The joystick method is the familiar scrolling and panning of a virtual map keeping the device itself static. In the dynamic peephole method the device is moved and the map is fixed with respect to an external frame of reference, but no visual information is present outside the device's display. The magic lens method augments an external content with graphical overlays, hence providing visual context outside the device display. Here too motion of the device serves to steer navigation. We compare these methods in a study measuring user performance, motion patterns, and subjective preference via questionnaires. The study demonstrates the advantage of dynamic peephole and magic lens interaction over joystick interaction in terms of search time and degree of exploration of the search space.","Title: iBookmark: locative texts and place-based authoring\nYear: 2009\n\nAbstract: With the recent developments in ePaper technology, consumer eBook readers have display qualities and form factors that are approaching that of traditional books. These eBook readers are already replacing paper in some commercial domains, but the potential of eBooks to extend forms of writing and storytelling has not been significantly explored. Using the digital and dynamic characteristics afforded by eBook readers, we are developing iBookmark, a GPS-enabled eBook reader. In iBookmark, writers can create stories that change in response to the location of the eBook itself. By setting context variables based on current and past locations of the eBook reader and using these in the rule-based generation of text and illustrations. We are developing new rhetorical device for writers that extend the expressive range of eBook delivered stories.","Title: PhotoMap: using spontaneously taken images of public maps for pedestrian navigation tasks on mobile devices\nYear: 2009\n\nAbstract: In many mid- to large-sized cities public maps are ubiquitous. One can also find a great number of maps in parks or near hiking trails. Public maps help to facilitate orientation and provide special information to not only tourists but also to locals who just want to look up an unfamiliar place while on the go. These maps offer many advantages compared to mobile maps from services like Google Maps Mobile or Nokia Maps. They often show local landmarks and sights that are not shown on standard digital maps. Often these 'You are here' (YAH) maps are adapted to a special use case, e.g. a zoo map or a hiking map of a certain area. Being designed for a fashioned purpose these maps are often aesthetically well designed and their usage is therefore more pleasant. In this paper we present a novel technique and application called PhotoMap that uses images of 'You are here' maps taken with a GPS-enhanced mobile camera phone as background maps for on-the-fly navigation tasks. We discuss different implementations of the main challenge, namely helping the user to properly georeference the taken image with sufficient accuracy to support pedestrian navigation tasks. We present a study that discusses the suitability of various public maps for this task and we evaluate if these georeferenced photos can be used for navigation on GPS-enabled devices.","Title: The role of physiological cues during remote collaboration\nYear: 2014\n\nAbstract: Empathic communication allows individuals to perceive and understand the feeling and emotion of the person with whom they are interacting. This could be particularly important during remote collaboration such as remote assistance or distance learning to enhance the social and emotional understanding of geographically distributed partners. However, supporting awareness in remote collaboration is very challenging especially when the interaction with the remote parties results in less information that can be communicated than in a physical interaction. We explore the effect of visualization using physiological cues that allow users to interpret emotional behaviors of remote parties with whom they are interacting in real time. The proposed visual representation allows users to infer emotional patterns from physiological cues that can potentially influence their communication approach toward a more aggressive style or maintain passive and peaceful interaction. We conducted a study involving participants who were paired up for a collaborative assessment task, interacting via voice only, videoconference, or a visual representation of the physiological measurements. Participants perceived the usage of our visual representation with higher group cohesiveness than using voice-only interaction. Further analysis shows that the visual representation significantly increases the positive affect score i.e., participants are perceived to be more alert and demonstrate less distress during remote collaboration. We discuss the possibilities of the proposed visual representation to support empathic communication during remote collaboration, and the benefits to the remote partners of having positive affect and group cohesiveness.","Title: Marauders light: replacing the wand with a mobile camera projector unit\nYear: 2009\n\nAbstract: Classic paper-based maps provide high-resolution, large-scale information and are ubiquitous in larger cities and even outdoors. In this paper we present a combination of their advantages with a mobile camera projector unit to create a new mobile and intuitive buddy finder interface. Current friend or buddy finder systems on mobile phones such as Google Latitude suffer from the small screen estate and display size of mobile devices. To overcome this problem we use a lightweight mobile camera projector unit to augment the paper map with a projected overlay of the buddies' positions. By using digital and geo-referenced representations of public maps no extra preperation for the tracking is needed. The idea is presented by enhancing Google Latitude, which allows users to browse the positions of their friends, to project the positions directly on the paper map without the cumbersome panning and zooming of a digital map on a small display.","Title: A multi-touch enabled steering wheel: exploring the design space\nYear: 2010\n\nAbstract: Cars offer an increasing number of infotainment systems as well as comfort functions that can be controlled by the driver. With our research we investigate new interaction techniques that aim to make it easier to interact with these systems while driving. In contrast to the standard approach of combining all functions into hierarchical menus controlled by a multifunctional controller or a touch screen we suggest to utilize the space on the steering wheel as additional interaction surface. In this paper we show the design challenges that arise for multi-touch interaction on a steering wheel. In particular we investigate how to deal with input and output while driving and hence rotating the wheel. We describe the details of a functional prototype of a multi-touch steering wheel that is based on FTIR and a projector, which was built to explore experimentally the user experience created. In an initial study with 12 participants we show that the approach has a general utility and that people can use gestures for controlling applications intuitively but have difficulties to imagine gestures to select applications.","Title: Multi-user interaction on media facades through live video on mobile devices\nYear: 2011\n\nAbstract: The increasing number of media facades in urban spaces offers great potential for new forms of interaction especially for collaborative multi-user scenarios. In this paper, we present a way to directly interact with them through live video on mobile devices. We extend the Touch Projector interface to accommodate multiple users by showing individual content on the mobile display that would otherwise clutter the facade's canvas or distract other users. To demonstrate our concept, we built two collaborative multi-user applications: (1) painting on the facade and (2) solving a 15-puzzle. We gathered informal feedback during the ARS Electronica Festival in Linz, Austria and found that our interaction technique is (1) considered easy-to-learn, but (2) may leave users unaware of the actions of others.","Title: Bro-cam: improving game experience with empathic feedback using posture tracking\nYear: 2013\n\nAbstract: In todays videogames user feedback is often provided through raw statistics and scoreboards. We envision that incorporating empathic feedback matching the player's current mood will improve the overall gaming experience. In this paper we present Bro-cam, a novel system that provides empathic feedback to the player based on their body postures. Different body postures of the players are used as an indicator for their openness. From their level of openness, Bro-cam profiles the players into different personality types ranging from introvert to extrovert. Empathic feedback is then automatically generated and matched to their preferences for certain humoristic feedback statements. We use a depth camera to track the player's body postures and movements during the game and analyze these to provide customized feedback. We conducted a user study involving 32 players to investigate their subjective assessment on the empathic game feedback. Semi-structured interviews reveal that participants were positive about the empathic feedback and Bro-cam significantly improves their game experience.","Title: GlobalData: multi-user interaction with geographic information systems on interactive surfaces\nYear: 2010\n\nAbstract: The geographical domain was often used as a showcase to show the possibilities of multi-touch interaction. Nonetheless, researchers have rarely investigated multi-user interaction with GIS - in fact, most of the geographical tabletop applications are not suited to multi-user interaction. Our multitouch application, GlobalData, allows multiple people to interact and collaborate in examining global, geolocated data. In idle mode, the device simply shows a stylized map of the earth. Users can open circular GeoLenses. These circles show the same map segment as the underlying base map and superimpose different data layers on it.","Title: Image geo-mashups: the example of an augmented reality weather camera\nYear: 2008\n\nAbstract: This paper presents the general idea of image geo-mashups, which combines concepts from web mashups and augmented reality by adding geo-referenced data to a perspective image. The paper shows how to design and implement an augmented reality weather cam, that combines data from a steerable weather cam with additional sensor information retrieved from the web.","Title: IRL SmartCart - a user-adaptive context-aware interface for shopping assistance\nYear: 2011\n\nAbstract: The electronic market has rapidly grown in the last few years. However, despite this success, consumers still enjoy visiting a \"real store with real products\". Therefore various common technologies have been installed in supermarkets to support the customer's shopping process and experience. In this paper, we introduce the IRL SmartCart - an instrumented shopping cart that acts as a user interface to support the shopping process. We show how RFID technology enables recognizing products that are put in the cart's basket. We are also able to determine the cart's position in an instrumented shopping environment. User input and visual output are possible by means of a touch screen, which is fitted in the IRL SmartCart's handle to support different tasks involved in the shopping process. We present and discuss different location- and context-based services that run on the cart interface system, e.g. a personalized shopping list sorted corresponding to the products in the user's vicinity or a navigation service to different products that the customer is searching for.","Title: Investigating mobile stereoscopic 3D touchscreen interaction\nYear: 2013\n\nAbstract: 3D output is no longer limited to large screens in cinemas or living rooms. Nowadays more and more mobile devices are equipped with autostereoscopic 3D (S3D) touchscreens. As a consequence interaction with 3D content now also happens whilst users are on the move. In this paper we carried out a user study with 27 participants to assess how mobile interaction, i.e. whilst walking, with mobile S3D devices, differs from interaction with 2D mobile touchscreens. We investigate the difference in touch accuracy between 2D touchscreens and mobile S3D touchscreens and evaluate the minimum touch target size for mobile S3D touchscreens. The contributions of this paper are twofold: Firstly, we found the increase in minimum touch target size caused by walking was larger for a mobile S3D UI than for a 2D UI. Secondly, we present touch target sizes and aspect ratios required for reliable user interaction in each case. Additionally we examined differences in the angle at which users held the mobile S3D device compared to a 2D mobile device. We found that mobile S3D caused users to hold the device at a different angle when walking, compared to the 2D case. This first study of its kind provides valuable information to developers of the next generation of UIs and applications for mobile S3D displays and devices.","Title: Paddle: highly deformable mobile devices with physical controls\nYear: 2014\n\nAbstract: We present the concept of highly deformable mobile devices that can be transformed into various special-purpose controls in order to bring physical controls to mobile devices. Physical controls have the advantage of exploiting people's innate abilities for manipulating physical objects in the real world. We designed and implemented a prototype, called Paddle, to demonstrate our concept. Additionally, we explore the interaction techniques enabled by this concept and conduct an in-depth study to evaluate our transformable physical controls. Our findings show that these physical controls provide several benefits over traditional touch interaction techniques commonly used on mobile devices.","Title: iPiccer: automatically retrieving and inferring tagged location information from web repositories\nYear: 2009\n\nAbstract: We present iPiccer, a system that allows mobile camera phones users to interact with meta-tagged image material available from web repositories, such as flickr in two novel ways. Users of the system are able to infer photo tags from their location and orientation, but are also able to infer their location and orientation from their spatial photo tags. An implementation of iPiccerTaker is outlined, and the potential of this new form of interaction with web repository data is discussed.","Title: Map torchlight: a mobile augmented reality camera projector unit\nYear: 2009\n\nAbstract: The advantages of paper-based maps have been utilized in the field of mobile Augmented Reality (AR) in the last few years. Traditional paper-based maps provide high-resolution, large-scale information with zero power consumption. There are numerous implementations of magic lens interfaces that combine high-resolution paper maps with dynamic handheld displays. From an HCI perspective, the main challenge of magic lens interfaces is that users have to switch their attention between the magic lens and the information in the background. In this paper, we attempt to overcome this problem by using a lightweight mobile camera projector unit to augment the paper map directly with additional information. The \"Map Torchlight\" is tracked over a paper map and can precisely highlight points of interest, streets, and areas to give directions or other guidance for interacting with the map.","Title: How to nudge in Situ: designing lambent devices to deliver salient information in supermarkets\nYear: 2011\n\nAbstract: There are a number of mobile shopping aids and recommender systems available, but none can be easily used for a weekly shop at a local supermarket. We present a minimal, mobile and fully functional lambent display that clips onto any shopping trolley handle, intended to nudge people when choosing what to buy. It provides salient information about the food miles for various scanned food items represented by varying lengths of lit LEDs on the handle and a changing emoticon comparing the average miles of all the products in the trolley against a social norm. When evaluated in situ, the lambent handle display nudged people to choose products with fewer food miles than the items they selected using their ordinary shopping strategies. People also felt guilty when the average mileage of the contents of their entire shopping trolley was above the social norm. The findings are discussed in terms of how to provide different kinds of product information that people care about, using simple lambent displays.","Title: Interaction With Geospatial Data\nYear: 2015\n\nAbstract: My research interest lies at the interaction between human-computer interaction (HCI) and geoinformatics. I am interested in developing new methods and novel user interfaces to navigate through spatial information. This article will give a brief overview on my past and current research topics and streams. Generally speaking, geography is playing an increasingly important role in computer science and also in the field of HCI ranging from social computing to natural user interfaces (NUIs). At the same time, research in geography has focused more and more on technology-mediated interaction with spatiotemporal phenomena. By bridging the two fields, my aimis to exploit this fruitful intersection between those two and develop, design and evaluate user interfaces that help people to solve their daily tasks more enjoyable and effectively.","Title: A large-scale quantitative survey of the german geocaching community in 2007\nYear: 2014\n\nAbstract: We present a large-scale quantitative contextual survey of the geocaching community in Germany, one of the world's largest geocaching communities. We investigate the features, attitudes, interests, and motivations that characterise the German geocachers. Two anonymous surveys have been carried out on this issue in the year 2007. We conducted a large-scale qualitative general study based on web questionnaires and a more targeted study, which aimed at a comprehensive amount of revealed geocaches of a certain region. With sample sizes of n = 1982 (study 1: general study) and n = 310 (study 2: regional study) we provide a representative basis to ground previous qualitative research in this domain. In addition, we investigated the usage of technology in combination with traditional paper-based media by the geocachers. This knowledge can be used to reflect on past and future trends within the geocaching community.","Title: SubwayPS: towards smartphone positioning in underground public transportation systems\nYear: 2014\n\nAbstract: Thanks to rapid advances in technologies like GPS and Wi-Fi positioning, smartphone users are able to determine their location almost everywhere they go. This is not true, however, of people who are traveling in underground public transportation networks, one of the few types of high-traffic areas where smartphones do not have access to accurate position information. In this paper, we introduce the problem of underground transport positioning on smartphones and present SubwayPS, an accelerometer-based positioning technique that allows smartphones to determine their location substantially better than baseline approaches, even deep beneath city streets. We highlight several immediate applications of positioning in subway networks in domains ranging from mobile advertising to mobile maps and present MetroNavigator, a proof-of-concept smartphone and smartwatch app that notifies users of upcoming points-of-interest and alerts them when it is time to get ready to exit the train.","Title: Captchat: A Messaging Tool to Frustrate Ubiquitous Surveillance\nYear: 2015\n\nAbstract: There is currently a widespread uncertainty regarding the ability of citizens to control privacy online in the face of ubiquitous surveillance. This is a huge and complex societal problem. Despite the multi-faceted nature of the problem, we propose that HCI researchers can still make a positive contribution in this space through the design of technologies that support citizens to engage with issues of surveillance. In this paper we describe the design of a messaging application called Captchat. Captchat enables people to send everyday messages embedded into images, with the added ability to apply visual distortions to the message to resemble an online CAPTCHA. We propose the chief benefit would be that Captchat messages (with potentially \\\"one-time\\\" distortions) can increase the difficulty for algorithms to index private messages and necessitate the involvement of much more costly human labor in the surveillance process. We developed a prototype and conducted a user study; the results suggest that people were likely to create Captchat messages that were difficult to index for an OCR package but still easy to understand by humans, even without explicit instructions to interact 'securely' with the application. While more work is still required to understand the limitations of Captchat, we hope it can open discussion on how HCI researchers can respond to the challenges faced from ubiquitous surveillance.","Title: WatchMe: A Novel Input Method Combining a Smartwatch and Bimanual Interaction\nYear: 2015\n\nAbstract: Smartwatches can facilitate several tasks that are performed on mobile devices. However, due to their limited size, touch interaction can be cumbersome. Although alternative input modalities such as speech input could be used, these can also introduce other issues regarding privacy or ease of use. Consequently, HCI researchers have been exploring novel input techniques for smartwatches. In this paper, we introduce WatchMe, a smartwatch application that uses the smartwatch camera and image processing techniques to allow for providing input on a drawing canvas composed of everyday objects and surfaces. We rely on a cloud OCR engine to retrieve text from captured images. Combining these characteristics, we illustrate some scenarios in which WatchMe could be used, such as a novel method for two-factor authentication.","Title: You Can't Smoke Here: Towards Support for Space Usage Rules in Location-aware Technologies\nYear: 2015\n\nAbstract: Recent work has identified the lack of space usage rule (SUR) data -- e.g. \\\"no smoking\\\", \\\"no campfires\\\" -- as an important limitation of online/mobile maps that presents risks to user safety and the environment. In order to address this limitation, a large-scale means of mapping SURs must be developed. In this paper, we introduce and motivate the problem of mapping space usage rules and take the first steps towards identifying solutions. We show how computer vision can be employed to identify SUR indicators in the environment (e.g. \\\"No Smoking\\\" signs) with reasonable accuracy and describe techniques that can assign each rule to the appropriate geographic feature.","Title: A User Interface for Encoding Space Usage Rules Expressed in Natural Language\nYear: 2015\n\nAbstract: Our interactions with the spaces around us are frequently defined by space usage rules (SURs) like \\\"no smoking\\\", \\\"no dogs allowed\\\", and \\\"stay on the trail\\\". These rules are important public health tools and help protect the environment, among other applications. However, despite their importance, no large-scale database of SURs currently exists. This prevents online/mobile maps from presenting these rules to users, as their traditional paper counterparts have been shown to do regularly. The lack of a SUR database also prevents developers from building novel SUR-based applications, e.g. mobile apps that inform users where they can smoke or walk their dog. In this paper, we present an in-development user interface to support the semi-automatic encoding of SURs expressed in natural language in documents such as laws, city ordinances, park rules, and institution FAQ pages. Our system will allow an untrained user to easily enter complex rules into a spatial data format compatible with OpenStreetMap.","Title: Augmenting Social Interactions: Realtime Behavioural Feedback using Social Signal Processing Techniques\nYear: 2015\n\nAbstract: Nonverbal and unconscious behaviour is an important component of daily human-human interaction. This is especially true in situations such as public speaking, job interviews or information sensitive conversations, where researchers have shown that an increased awareness of one's behaviour can improve the outcome of the interaction. With wearable technology, such as Google Glass, we now have the opportunity to augment social interactions and provide realtime feedback on one's behaviour in an unobtrusive way. In this paper we present Logue, a system that provides realtime feedback on the presenters' openness, body energy and speech rate during public speaking. The system analyses the user's nonverbal behaviour using social signal processing techniques and gives visual feedback on a head-mounted display. We conducted two user studies with a staged and a real presentation scenario which yielded that Logue's feedback was perceived helpful and had a positive impact on the speaker's performance.","Title: Informing online and mobile map design with the collective wisdom of cartographers\nYear: 2014\n\nAbstract: Despite the large and growing prominence of online and mobile maps, they have not been broadly and systematically examined with a lens informed by traditional cartography. Using an approach rooted in cartographic theory and a unique dataset of 382 publicly-displayed local maps, we identify the collective wisdom of hundreds of cartographers with respect to a number of cartographic design decisions. We compare our findings to the approaches taken in popular online and mobile map platforms and develop suggestions for incorporating the collective wisdom of cartographers into these systems. Our suggestions include the adoption of location-aware cartography, in which cartographic approaches are intelligently varied based on the type of location being viewed. We provide mockup designs of online and mobile maps that implement our suggestions and discuss means by which the surprising gap between online and mobile maps and traditional cartography may be bridged.","Title: Unexpected journeys with the HOBBIT: the design and evaluation of an asocial hiking app\nYear: 2014\n\nAbstract: In the age of mobile communications and social media, users are connected to interact with other people, and often obliged to be socially active as technology drives to connect us. In this paper, we harness the technology for the opposite use: helping people to avoid company instead of encouraging interaction. We have developed the concept of an asocial hiking application (app), in which users can generate routes that avoid meeting other people. We developed the concept based on user feedback data derived from an online survey (n=157) and two focus groups, and created a tool that generates solitary hiking routes based on OpenStreetMap data and additional information from the web. In addition, to make the application react to dynamic changes in the environment, we developed a mobile application prototype that scans Wi-Fi signals to detect other hikers nearby and warn of their approach. The prototype was tested and evaluated with 8 hikers in-the-wild. In addition to the concept design and the functional prototype, we present findings on people's, especially hikers, need for solitude, and introduce user feedback from each stage of the prototype design process as well as design recommendations for an asocial navigation application.","Title: Attention as an input modality for Post-WIMP interfaces using the viGaze eye tracking framework\nYear: 2016\n\nAbstract: Abstract Eye tracking is one of the most prominent modalities to track user attention while interacting with computational devices. Today, most of the current eye tracking frameworks focus on tracking the user gaze during website browsing or while performing other tasks and interactions with a digital device. Most frameworks have in common that they do not exploit gaze as an input modality. In this paper we describe the realization of a framework named viGaze. Its main goal is to provide an easy to use framework to exploit the use of eye gaze as an input modality in various contexts. Therefore it provides features to explore explicit and implicit interactions in complex virtual environments by using the eye gaze of a user for various interactions. The viGaze framework is flexible and can be easily extended to incorporate other input modalities typically used in Post-WIMP interfaces such as gesture or foot input. In this paper we describe the key components of our viGaze framework and additionally describe a user study that was conducted to test the framework. The user study took place in a virtual retail environment, which provides a challenging pervasive environment and contains complex interactions that can be supported by gaze. The participants performed two gaze-based interactions with products on virtual shelves and started an interaction cycle between the products and an advertisement monitor placed on the shelf. We demonstrate how gaze can be used in Post-WIMP interfaces to steer the attention of users to certain components of the system. We conclude by discussing the advantages provided through the viGaze framework and highlighting the potentials of gaze-based interaction.","Title: No more Autobahn!: Scenic Route Generation Using Googles Street View.\nYear: 2016\n\nAbstract: Navigation systems allow drivers to find the shortest or fastest path between two or multiple locations mostly using time or distance as input parameters. Various researchers extended traditional route planning approaches by taking into account the user's preferences, such as enjoying a coastal view or alpine landscapes during a drive. Current approaches mainly rely on volunteered geographic information (VGI), such as point of interest (POI) data from OpenStreetMap, or social media data, such as geotagged photos from Flickr, to generate scenic routes. While these approaches use proximity, distribution or other spatial relationships of the data sets, they do not take into account the actual view on specific route segments. In this paper, we propose Autobahn: a system for generating scenic routes using Google Street View images to classify route segments based on their visual characteristics enhancing the driving experience. We show that this vision-based approach can complement other approaches for scenic route planning and introduce a personalized scenic route by aligning the characteristics of the route to the preferences of the user.","Title: Combining Direct and Indirect Touch Input for Interactive Workspaces using Gaze Input\nYear: 2015\n\nAbstract: Interactive workspaces combine horizontal and vertical touch surfaces into a single digital workspace. During an exploration of these systems, it was shown that direct interaction on the vertical surface is cumbersome and more inaccurate than on the horizontal one. To overcome these problems, indirect touch systems turn the horizontal touch surface into the input which allows manipulation of objects on the vertical display. If the horizontal touch surface also acts as a display, however, it becomes necessary to notify the system which screen is currently in use by providing a switching mode. We investigate the use of gaze tracking to perform these mode switches. In three user studies, we compare absolute and relative gaze augmented selection techniques with the traditional direct-touch approach. Our results show that our relative gaze augmented selection technique outperforms the other techniques for simple tapping tasks alternating between horizontal and vertical surfaces, and for dragging on the vertical surface. However, when tasks involve dragging across surfaces, the findings are more complex. We provide a detailed description of the proposed interaction techniques, a statistical analysis of these interaction techniques, and how they can be applied to systems that involve a combination of multiple horizontal and vertical touch surfaces.","Title: Cross-Surface: Workshop on Interacting with Multi-Device Ecologies in the Wild\nYear: 2015\n\nAbstract: In this workshop, we will review and discuss opportunities, technical challenges and problems with cross-device interactions in interactive multi-surface and multi-device ecologies. We aim to bring together researchers and practitioners currently working on novel techniques for cross-surface interactions, focusing both on technical as well as interaction challenges for introducing these technologies into the wild, and highlighting opportunities for further research. The workshop will help to facilitate knowledge exchange on the inherent challenges of building robust and intuitive cross-surface interactions, identify application domains and enabling technologies for cross-surface interactions in the wild, and establish a research community to develop effective strategies for successful design of cross-device interactions.","Title: StripeMaps: Improving Map-based Pedestrian Navigation for Smartwatches\nYear: 2015\n\nAbstract: Map applications for smartwatches present new challenges in cartography, a domain in which large display sizes have significant advantages. In this paper, we introduce StripeMaps, a system that adapts the mobile web design technique of linearization for displaying maps on the small screens of smartwatches. Just as web designers simplify multiple column desktop websites into a single column for easier navigation on mobile devices, StripeMaps transforms any two-dimensional route map into a one-dimensional \\\"stripe\\\". Through a user study, we show that this simplification allows StripeMaps to outperform both traditional mobile map interfaces and turn-by-turn directions for pedestrian navigation using smartwatches. In addition to introducing StripeMaps, this paper also has a secondary contribution. It contains the first empirical comparison of different approaches for pedestrian smartwatch navigation and illuminates their pros and cons.","Title: The Geography and Importance of Localness in Geotagged Social Media.\nYear: 2016\n\nAbstract: Geotagged tweets and other forms of social media volunteered geographic information (VGI) are becoming increasingly critical to many applications and scientific studies. An important assumption underlying much of this research is that social media VGI is \\\"local\\\", or that its geotags correspond closely with the general home locations of its contributors. We demonstrate through a study on three separate social media communities (Twitter, Flickr, Swarm) that this localness assumption holds in only about 75% of cases. In addition, we show that the geographic contours of localness follow important sociodemographic trends, with social media in, for instance, rural areas and older areas, being substantially less local in character (when controlling for other demographics). We demonstrate through a case study that failure to account for non-local social media VGI can lead to misrepresentative results in social media VGI-based studies. Finally, we compare the methods for determining localness, finding substantial disagreement in certain cases, and highlight new best practices for social media VGI-based studies and systems.","Title: You Can Touch This: Eleven Years and 258218 Images of Objects.\nYear: 2016\n\nAbstract: Touch has become a central input modality for a wide variety of interactive devices, most of our mobile devices are operated using touch. In addition to interacting with digital artifacts, people touch and interact with many other objects in their daily lives. We provide a unique photo dataset containing all touched objects over the last 11 years. All photos were contributed by Alberto Frigo, who was involved early on in the \"Quantified Self\" movement. He takes photos of every object he touches with his dominant hand. We analyzed the 258,218 images with respect to the types objects, their distribution, and related activities.","Title: NatureCHI: Unobtrusive User Experiences with Technology in Nature.\nYear: 2016\n\nAbstract: Being in nature is typically regarded to be calming, relaxing and purifying. When in nature, people often seek physical activity like hiking, or meditative, mindful or inspiring experiences remote from the urban everyday life. However, the modern lifestyle easily extends technology use to all sectors of our everyday life, and e.g. the rise of sports tracking technologies, mobile phone integrated cameras and omnipresent social media access have contributed to technologies also arriving into the use context of nature. Also maps and tourist guides are increasingly smart phone or tablet based services. This workshop addresses the challenges that are related to interacting with technology in nature. The viewpoints cover, but are not limited to interaction design and prototyping, social and cultural issues, user experiences that aim for unobtrusive interactions with the technology with nature as the use context","Title: Cross-Surface: Challenges and Opportunities for 'bring your own device' in the wild.\nYear: 2016\n\nAbstract: In this workshop, we will review and discuss challenges and opportunities for HCI in relation to cross-surface interaction in the wild based on the bring-your-own-device (BYOD) practice. We aim to bring together researchers and practitioners working on technical infra-structures for cross-surface computing, studies of cross-surface computing in particular domains as well as interaction challenges for introducing cross-surface computing in the wild, all with a particular focus on BYOD. Examples of application domains are: cultural institutions, work places, public libraries, schools and education.","Title: Helping Computers Understand Geographically-Bound Activity Restrictions.\nYear: 2016\n\nAbstract: The lack of certain types of geographic data prevents the development of location-aware technologies in a number of important domains. One such type of \"unmapped\" geographic data is space usage rules (SURs), which are defined as geographically-bound activity restrictions (e.g. \"no dogs\", \"no smoking\", \"no fishing\", \"no skateboarding\"). Researchers in the area of human-computer interaction have recently begun to develop techniques for the automated mapping of SURs with the aim of supporting activity planning systems (e.g. one-touch \"Can I Smoke Here?\" apps, SUR-aware vacation planning tools). In this paper, we present a novel SUR mapping technique -- SPtP -- that outperforms state-of-the-art approaches by 30% for one of the most important components of the SUR mapping pipeline: associating a point observation of a SUR (e.g. a 'no smoking' sign) with the corresponding polygon in which the SUR applies (e.g. the nearby park or the entire campus on which the sign is located). This paper also contributes a series of new SUR benchmark datasets to help further research in this area.","Title: Not at Home on the Range: Peer Production and the Urban/Rural Divide.\nYear: 2016\n\nAbstract: Wikipedia articles about places, OpenStreetMap features, and other forms of peer-produced content have become critical sources of geographic knowledge for humans and intelligent technologies. In this paper, we explore the effectiveness of the peer production model across the rural/urban divide, a divide that has been shown to be an important factor in many online social systems. We find that in both Wikipedia and OpenStreetMap, peer-produced content about rural areas is of systematically lower quality, is less likely to have been produced by contributors who focus on the local area, and is more likely to have been generated by automated software agents (i.e. \"bots\"). We then codify the systemic challenges inherent to characterizing rural phenomena through peer production and discuss potential solutions.","Title: Heartefacts: Augmenting Mobile Video Sharing Using Wrist-Worn Heart Rate Sensors.\nYear: 2016\n\nAbstract: An increasing share of our daily interactions with others is mediated through mobile communication technologies. People communicate via text, emoticons, emojis and rich media such as video. We explore the design of Heartefacts, short video clips composed of highlights determined by heart rate changes while watching videos. Our survey investigated video sharing behaviour, and our feasibility study examined the possibility of detecting highlights in videos by monitoring people's heart rates measured with off-the-shelf wrist-worn sensors. Our results show that people do indeed have measurable responses with respect to their heartbeat patterns to six different emotions elicited by video clips. We compare video highlights verbally identified by our participants to physiological highlights as indicated by their heart rate data and also discuss and compare the automatically generated Heartefacts with video highlights created by an expert in video art. We close with design considerations for Heartefacts in mobile technology.","Title: Who are the people in my life? towards tangible eco-maps.\nYear: 2016\n\nAbstract: Eco-Maps, diagrammatic assessments of family relationships, are used by social workers in individual and family contact to understand the subjective experiences of both supportive and disruptive family, friendship, and community relationships. In this paper we present the first phase of the development of the People in my Life app. We have developed a tangible version of Eco-Maps to unlock the potential of eco-maps in the digital domain and to enhance social work practice with children and young people. The central idea is to develop an improved, technology-based means to assess relationships that supports and enhances social work practice and foregrounds children and young people's experiences. In particular, the project has three aims: (a) to collaboratively develop with social workers an application for creating eco-maps using touch-screens with tangible objects, (b) to test its usability with children, in the first instance, and (c) to provide a first exploration of the psychometric properties of parameters captured by the app.","Title: StrapMaps: bringing map-based navigation to the straps of bags.\nYear: 2016\n\nAbstract: Most outdoor activities and outdoor sports include a navigation component. For example, hiking, biking, mountaineering and climbing activities require orientation and wayfinding at certain points to certain degrees. With the rise of personal GPS devices, numerous navigation apps now exist that are tailored to the specific needs of outdoor enthusiasts. Nevertheless, they all have the common problem, that they require the users to \\\"store\\\" the mobile device in their pockets to have the \\\"hands free\\\" for interaction. While wearable devices such as smartwatches overcome this problem, they have the disadvantage that they often offer very small screen space. In this paper we present StrapMaps, a concept to bring interactive e-ink displays to straps of backpacks or harnesses to present map information while on-the-go in nature.","Title: Cross-Surface: Challenges and Opportunities of Spatial and Proxemic Interaction.\nYear: 2016\n\nAbstract: In this workshop, we will review and discuss open issues, technical challenges and conceptual models for multi-device spatial or proxemic interaction. We aim to bring together researchers, students and practitioners working on technical infrastructures, studies and designs of spatial in-terfaces, or domain specific multi-device applications that use space as a unit of analysis. We focus specifically on analysing how such interfaces, tools and tracking technolo-gy can be deployed \\\"in the wild\\\". The workshop will facili-tate knowledge exchange about the current state of spatial and proxemic interactive systems, identify application do-mains and enabling technologies for cross-surface interac-tions in the wild, and establish a research community to develop effective strategies for successful design of cross-device interactions.","Title: The aesthetics of StripeMaps: being small and beautiful.\nYear: 2016\n\nAbstract: Maps are a very powerful form of information visualization. Beside their main purpose to communicate often very complex relationships between elements of some space, \\\"good\\\" maps are often perceived as aesthetically pleasing and beautiful. Maps are collected for those reasons; since centuries and ages they are \\\"beautiful\\\" --over all trends. Cartography, the study and practice of making maps, has long tradition. Even today, most of the maps, e.g., road maps and public you-are-here maps, are still crafted by professional cartographers. However, maps do require space to be displayed while mobile and wearable devices are getting smaller and smaller. This presents new challenges in cartography, as designing aesthetic user interfaces is an important aspect in the area of HCI. With StripeMaps we present a technique to bring maps to very small displays without destroying their utility and beauty. In this workshop paper we discuss our design considerations in depth and open up the discussion for alternative approaches.","Title: Changing the camera-to-screen angle to improve AR browser usage.\nYear: 2016\n\nAbstract: Mobile devices are currently the most commonly used platform to experience Augmented Reality (AR). Nevertheless, they typically provide a less than ideal ergonomic experience, requiring the user to operate them with arms raised. In this paper we evaluate how to improve the ergonomics of AR experiences by modifying the angle between the mobile device's camera and its display. Whereas current mobile device cameras point out vertically from the back cover, we modify the camera angle to be 0, 45 and 90 degrees. In addition, we also investigate the use of the smartwatch as an AR browser form factor. Key findings are, that whilst the current approximately see-through configuration provides the fastest task completion times, a camera offset angle of 45° provides reduced task load and was preferred by users. When comparing different form factors and screen sizes, the smartwatch format was found to be unsuitable for AR browsing use.","Title: ScrollingHome: bringing image-based indoor navigation to smartwatches.\nYear: 2016\n\nAbstract: Providing pedestrian navigation instructions on small screens is a challenging task due to limited screen space. As image-based approaches for navigation have been successfully proven to outperform map-based navigation on mobile devices, we propose to bring image-based navigation to smartwatches. We contribute a straightforward pipeline to easily create image-based indoor navigation instructions that allow users to freely navigate in indoor environments without any localization infrastructure and with minimal user input on the smartwatch. In a user study, we show that our approach outperforms the current state-of-the art application in terms of task completion time, perceived task load and perceived usability. In addition, we did not find an indication that there is a need to provide explicit directional instructions for image-based navigation on small screens.","Title: Crouch, Hold and Engage: Spatial Aspects of Augmented Reality Browsing.\nYear: 2016\n\nAbstract: Displays that use an AR browser to augment a printed poster are nowadays commonplace in many cities. Whilst much research exists on the technical issues and interaction techniques related to such AR displays, currently there has been little research on how these applications are actually used. In this paper, we present the first study reporting on usage comfort and spatial aspects when using an AR browsing app on to browse augmented content on a physical poster. Key findings are that the mean distance users preferred to stand from the poster content was 129 cm (SD = 34 cm) and that standing further from the poster enabled a browsing task to be completed more quickly. Based on our findings we make recommendations on the content and spatial location of AR poster installations.","Title: Beautiful...but at What Cost?: An Examination of Externalities in Geographic Vehicle Routing.\nYear: 2017\n\nAbstract: Millions of people use platforms such as Google Maps to search for routes to their desired destinations. Recently, researchers and mapping platforms have shown growing interest in optimizing routes for criteria other than travel time, e.g. simplicity, safety, and beauty. However, despite the ubiquity of algorithmic routing and its potential to define how millions of people move around the world, very little is known about the externalities that arise when adopting these new optimization criteria, e.g. potential redistribution of traffic to certain neighborhoods and increased route complexity (with its associated risks). In this paper, we undertake the first controlled examination of these externalities, doing so across multiple mapping platforms, alternative optimizations, and cities. We find, for example, that scenic routing (i.e. “beauty”-optimized routing) would remove vehicles from highways, greatly increase traffic around parks, and, in certain cases, do the same for high-income areas. Our results also highlight that the interaction between routing criteria and urban structure is complex and effects vary from city to city, an important consideration for the growing literature on alternative routing strategies. Finally, to address the lack of open implementations of alternative routing algorithms and controlled routing evaluation frameworks, we are releasing our alternative routing and evaluation platform with this paper.","Title: Controllability matters: The user experience of adaptive maps.\nYear: 2017\n\nAbstract: Adaptive map interfaces have the potential of increasing usability by providing more task dependent and personalized support. It is unclear, however, how map adaptation must be designed to avoid a loss of control, transparency, and predictability. This article investigates the user experience of adaptive map interfaces in the context of gaze-based activity recognition. In a Wizard of Oz experiment we study two adaptive map interfaces differing in the degree of controllability and compare them to a non-adaptive map interface. Adaptive interfaces were found to cause higher user experience and lower perceived cognitive workload than the non-adaptive interface. Among the adaptive interfaces, users clearly preferred the condition with higher controllability. Results from structured interviews reveal that participants dislike being interrupted in their spatial cognitive processes by a sudden adaptation of the map content. Our results suggest that adaptive map interfaces should provide their users with control at what time an adaptation will be performed.","Title: The Geography of Pokémon GO: Beneficial and Problematic Effects on Places and Movement.\nYear: 2017\n\nAbstract: The widespread popularity of Pokémon GO presents the first opportunity to observe the geographic effects of location-based gaming at scale. This paper reports the results of a mixed methods study of the geography of Pokémon GO that includes a five-country field survey of 375 Pokémon GO players and a large scale geostatistical analysis of game elements. Focusing on the key geographic themes of places and movement, we find that the design of Pokémon GO reinforces existing geographically-linked biases (e.g. the game advantages urban areas and neighborhoods with smaller minority populations), that Pokémon GO may have instigated a relatively rare large-scale shift in global human mobility patterns, and that Pokémon GO has geographically-linked safety risks, but not those typically emphasized by the media. Our results point to geographic design implications for future systems in this space such as a means through which the geographic biases present in Pokémon GO may be counteracted.","Title: HCI, Solidarity Movements and the Solidarity Economy.\nYear: 2017\n\nAbstract: The financial crisis and austerity politics in Europe has had a devastating impact on public services, social security and vulnerable populations. Greek civil society responded quickly by establishing solidarity structures aimed at helping vulnerable citizens to meet their basic needs and empower them to co-create an anti-austerity movement. While digital technology and social media played an important role in the initiation of the movement, it has a negligible role in the movement's on-going practices. Through embedded work with several solidarity structures in Greece, we have begun to understand the \\\"solidarity economy\\\" (SE) as an experiment in direct democracy and self-organization. Working with a range of solidarity structures we are developing a vision for a \\\"Solidarity HCI\\\" committed to designing to support personal, social and institutional transformation through processes of agonistic pluralism and contestation, where the aims and objectives of the SE are continuously re-formulated and put into practice.","Title: Grouve: Spontaneous Proximal Group Formation with Ultrasonic Sound Waves\nYear: 2016\n\nAbstract: In daily life, we use computers and smartphones to organize a groups activities and share documents with its members. However, creating that group in the digital domain is often performed manually. Solutions to automate the process of forming a group of physically proximate members, so far, mostly relied on Bluetooth or WiFi. In contrast to these solutions we present Grouve, an automatic ad-hoc based group formation technique running based on ultrasonic waves. As Grouve uses inaudible sounds for communication, it has the natural advantage that the sound signal stays inside a limited space, which matches most of the application contexts. In this paper we report on the design process and implementation of Grouve.","Title: It's All Around You: Exploring 360° Video Viewing Experiences on Mobile Devices.\nYear: 2017\n\nAbstract: 360° videos are a new kind of medium that gives the viewers a sense of real immersion as they glimpse the action from all angles and directions. Naturally, professional and amateur film-makers are actively adopting this new medium for transformative storytelling. Despite this phenomenal progress in 360° video creation, current understanding on users' viewing experience of these videos is limited. In this paper, we present the first comparative study on the user experience with 360° videos on mobile devices using different interaction techniques. We observed 18 participants' interaction with six 360°videos with different viewport characteristics (static or moving) on a smartphone, a tablet and a head mounted display (HMD) respectively and measured how they interact with the content. We then conducted semi-structured interviews with the participants in which they explained their interaction with and viewing experience of 360° videos across three devices. Our findings show that 360° videos with moving viewports elicit higher engagement from the viewers, and offer superior viewing experience. However, these videos are cognitively demanding and require constant user attention. Our participants preferred the condition with dynamic peephole interaction on a smartphone for watching 360° videos due to the simplicity in exploration and familiarity with navigation controls. Many participants reported that the HMD offers the most immersive experience however it comes at the expense of higher cognitive burden, motion sickness and physical discomfort.","Title: Pharos: Improving Navigation Instructions On Smartwatches By Including Global Landmarks\nYear: 2017\n\nAbstract: Landmark-based navigation systems have proven benefits relative to traditional turn-by-turn systems that use street names and distances. However, one obstacle to the implementation of landmark-based navigation systems is the complex challenge of selecting salient local landmarks at each decision point for each user. In this paper, we present Pharos, a novel system that extends turn-by-turn navigation instructions using a single global landmark (e.g. the Eiffel Tower, the Burj Khalifa, municipal TV towers) rather than multiple, hard-to-select local landmarks. We first show that our approach is feasible in a large number of cities around the world through the use of computer vision to select global landmarks. We then present the results of a study demonstrating that by including global landmarks in navigation instructions, users navigate more confidently and build a more accurate mental map of the navigated area than using turn-by-turn instructions.","Title: \"In Search Of Light\": Enhancing Touristic Recommender Services With Local Weather Data\nYear: 2017\n\nAbstract: Many destinations' economies strongly rely on tourism. Therefore, it is crucial to meet tourists' expectations, so they will return to the destination. The geographical formation of certain touristic islands often leads to local climates where it can be rainy and windy on one side of the island, whereas the other part is sunny. In this paper, we present a novel use for a network of sensors, LightBeam, a mobile location-based application aiming to improve the tourists' experience. The application focuses on providing real-time guidance for tourists seeking sunlight to maximize their holiday experience by suggesting the closest points of interest (POIs) to the user with the \"best sunlight\". To achieve this, we implemented and installed a network of geospatial sensors. The data from the sensor network is combined with the current location of the users to provide recommendations. We report on the initial design and prototype of LightBeam.","Title: Augmented invaders: a mixed reality multiplayer outdoor game\nYear: 2017\n\nAbstract: Many virtual and mixed reality games focus on single player experiences. In this paper, we describe the concept and prototype implementation of a mixed reality multiplayer game that can be played with a smartphone and an HMD in outdoor environments. Players can team up to fight against attacking alien drones. The relative positions between the players are tracked using GPS, and the rear camera of the smartphone is used to augment the environment and teammates with virtual objects. The combination of multiplayer, mixed reality, the use of geographical location and outdoor action together with affordable, mobile equipment enables a novel strategic and social game experience.","Title: SubwayAPPS: Using Smartphone Barometers for Positioning in Underground Transportation Environments.\nYear: 2016\n\nAbstract: Location information that is crucial for all location-based services is almost always available due to a number of different positioning techniques and technologies such as GPS and WiFi positioning. However, positioning technologies cannot provide sufficient position information when a user is underground, e. g. travelling with a car through a tunnel or on subways on an underground public transportation network. While there have been a number of attempts to utilize expensive infrastructure and smartphone sensors to address this situation, all of these techniques are either limited in scope, very expensive, or somewhat limited in accuracy. In this paper, we present a novel smartphone-based approach called SubwayAPPS (Subway Air Pressure Positioning System) that for the first time utilizes relative air pressure changes as detected by smartphone barometers to position a user. We first demonstrate the feasibility of this approach by comparing the depth characteristics of five major underground transportation networks across the globe and show that our novel approach is feasible for positioning users while they are underground in these networks. Second, we showwith two user tests in Brussels and London that our lightweight approach works well as other more complex techniques, e. g. techniques that rely on pattern matching using the build-in accelerometers or gyroscopes, under realistic conditions.","Title: NavigaTone: Seamlessly Embedding Navigation Cues in Mobile Music Listening\nYear: 2018\n\nAbstract: ABSTRACTAs humans, we have the natural capability of localizing the origin of sounds. Spatial audio rendering leverages this skill by applying special filters to recorded audio to create the impression that a sound emanates from a certain position in the physical space. A main application for spatial audio on mobile devices is to provide non-visual navigation cues. Current systems require users to either listen to artificial beacon sounds, or the entire audio source (e.g., a song) is repositioned in space, which impacts the listening experience. We present NavigaTone, a system that takes advantage of multi-track recordings and provides directional cues by moving a single track in the auditory space. While minimizing the impact of the navigation component on the listening experience, a user study showed that participants could localize sources as good as with stereo panning while the listening experience was rated to be closer to common music listening.","Title: Defining and Predicting the Localness of Volunteered Geographic Information using Ground Truth Data\nYear: 2018\n\nAbstract: ABSTRACTMany applications of geotagged content are predicated on the concept of localness (e.g., local restaurant recommendation, mining social media for local perspectives on an issue). However, definitions of who is a \"local\" in a given area are typically informal and ad-hoc and, as a result, approaches for localness assessment that have been used in the past have not been formally validated. In this paper, we begin the process of addressing these gaps in the literature. Specifically, we (1) formalize definitions of \"local\" using themes identified in a 30-paper literature review, (2) develop the first ground truth localness dataset consisting of 132 Twitter users and 58,945 place-tagged tweets, and (3) use this dataset to evaluate existing localness assessment approaches. Our results provide important methodological guidance to the large body of research and practice that depends on the concept of localness and suggest means by which localness assessment can be improved.","Title: SIG: Making Maps Accessible and Putting Accessibility in Maps.\nYear: 2018\n\nAbstract: Digital maps represent an incredible HCI success-they have transformed the way people navigate in and access information about the world. While these platforms contain terabytes of data about road networks and points of interest (POIs), their information about physical accessibility is commensurately poor. Moreover, because of their highly graphical nature and reliance on gesture and mouse input, digital maps can be inaccessible to some user groups (e.g., those with visual or motor impairments). While there is active HCI work towards addressing both concerns, to our knowledge, there has been no direct effort to unite this research community. The goal of this SIG is threefold: first, to bring together and network scholars and practitioners who are broadly working in the area of accessible maps; second, to identify grand challenges and open problems; third, to help better establish accessible maps as a valuable topic with important HCI-related research problems.","Title: Reflections on the NatureCHI Workshop Series: Unobtrusive User Experiences with Technology in Nature.\nYear: 2018\n\nAbstract: Being in nature is often regarded to be calming, relaxing and purifying. While technology has the potential to support engagement with nature, developing systems that provide support in an unobtrusive manner holds many challenges for interaction design. In this article, we describe our reflections around the NatureCHI workshop series. Our aim with the workshops has been to help foster a research community interested in the design of Unobtrusive User Experiences with Technology in Nature. The first of two workshops ran as part of CHI 2016 in San Jose, California, while the second workshop took place alongside MobileHCI 2017 in Vienna, Austria. With 25 papers presented in total, the workshops demonstrate a rising interest in the areas where nature and interactive technologies meet.","Title: Where2: Exploring prior visitor journeys to enrich upcoming visitor's experience.\nYear: 2018\n\nAbstract: In recent years, the growth of the travel industry has been astounding. Yet, it is still not easy for an individual to have a meaningful travel experience even, so vast amounts of information are available on the web. As an attempt to support travel decisionmaking and improve visitoru0027s experience, we designed Where2. Where2 is a location-based mobile app that highlights Points of Interest (POI) customized to touristu0027s travel- related attributes (e.g. the length of their stay). POIs are in-situ recommendations proposed as Ghost Paths journeys - the idea that you can follow a prior tourist shoes to explore the city. We generate Ghost Paths journeys by using a non-intrusive positioning system that captures prior touristsu0027 Wi-Fi signal and detect their journeys across the city. With this system our goal is to support visitors with their travel decisions and nudge them towards optimal journey choices that maximize their utilities.","Title: Foldwatch: using origami-inspired paper prototypes to explore the extension of output space in smartwatches.\nYear: 2018\n\nAbstract: Smartwatches are highly portable, ubiquitous devices, allowing rich interaction at a small scale. However, the display size can hinder user engagement, limit information display, and presentation style. Most research focuses on exploring ways in which the interaction area of smartwatches can be extended, although this mainly entails simple fold-out displays or additional screens. Conversely, added weight and size can hinder the wearable experience. In response, we took inspiration from origami and explored the design space for new types of lightweight, highly foldable smartwatch, by developing complex paper-prototypes which demonstrate novel ways of extending screen space. We collected data on potential input and output interaction with complex folded smartwatch displays during workshops with expert and non-expert users, discovering application ideas and additional input/output functionality. These insights were used to produce and evaluate a concept video for the FoldWatch prototype.","Title: Integrating Physical Activity and Health Aspects in Everyday Mobility.\nYear: 2018\n\nAbstract: Everyday mobility encompasses different forms of public and private transportation. However, everyday mobility often does not involve substantial levels of physical activity. The goal of this workshop is to investigate ways of integrating physical activity into everyday mobility in accordance with widely accepted health recommendations. We aim to explore wearable and ambient systems that sense and support active navigation as well as conceptual aspects from a variety of perspectives, such as persuasive technologies. Researchers from different disciplines are invited to contribute their point of view by means of position papers, posters, and demonstrations. One planned outcome of this workshop is a set of design guidelines for navigation systems that explicitly consider health aspects. The workshop explores requirements and design challenges in a creative setting.","Title: Tactile hand motion and pose guidance for 3D interaction\nYear: 2018\n\nAbstract: We present a novel forearm-and-glove tactile interface that can enhance 3D interaction by guiding hand motor planning and coordination. In particular, we aim to improve hand motion and pose actions related to selection and manipulation tasks. Through our user studies, we illustrate how tactile patterns can guide the user, by triggering hand pose and motion changes, for example to grasp (select) and manipulate (move) an object. We discuss the potential and limitations of the interface, and outline future work.","Title: Using smartphones to take eye images for disease diagnosis in developing countries.\nYear: 2018\n\nAbstract: Mobile health applications can increase access to healthcare in low and middle-income countries, where the penetration of smartphones is rapidly increasing. However, despite a large amount of clinical trials of mobile health applications, there is little proof of beneficial clinical outcomes. Recent studies suggest that one of the reasons is user experience issues. The aim of our work is to improve the user experience of acquiring medical eye images with a smartphone camera. We present EyeGuide, a mobile application that assists people in taking eye images. Possible application areas are the algorithmic detection of diseases and the support of clinicians during tele-medicine examinations.","Title: Grand challenges in accessible maps\nYear: 2019\n\nAbstract: In this forum we celebrate research that helps to successfully bring the benefits of computing technologies to children, older adults, people with disabilities, and other populations that are often ignored in the design of mass-marketed products. --- Juan Pablo Hourcade, Editor","Title: Bimanual Interaction with Interscopic Multi-Touch Surfaces\nYear: 2009\n\nAbstract: Multi-touch interaction has received considerable attention in the last few years, in particular for natural two-dimensional (2D) interaction. However, many application areas deal with three-dimensional (3D) data and require intuitive 3D interaction techniques therefore. Indeed, virtual reality (VR) systems provide sophisticated 3D user interface, but then lack efficient 2D interaction, and are therefore rarely adopted by ordinary users or even by experts. Since multi-touch interfaces represent a good trade-off between intuitive, constrained interaction on a touch surface providing tangible feedback, and unrestricted natural interaction without any instrumentation, they have the potential to form the foundation of the next generation user interface for 2D as well as 3D interaction. In particular, stereoscopic display of 3D data provides an additional depth cue, but until now the challenges and limitations for multi-touch interaction in this context have not been considered. In this paper we present new multi-touch paradigms and interactions that combine both traditional 2D interaction and novel 3D interaction on a touch surface to form a new class of multi-touch systems, which we refer to as interscopic multi-touch surfaces (iMUTS). We discuss iMUTS-based user interfaces that support interaction with 2D content displayed in monoscopic mode and 3D content usually displayed stereoscopically. In order to underline the potential of the proposed iMUTS setup, we have developed and evaluated two example interaction metaphors for different domains. First, we present intuitive navigation techniques for virtual 3D city models, and then we describe a natural metaphor for deforming volumetric datasets in a medical context.","Title: WatchThru: Expanding Smartwatch Displays with Mid-air Visuals and Wrist-worn Augmented Reality.\nYear: 2017\n\nAbstract: We introduce WatchThru, an interactive method for extended wrist-worn display on commercially-available smartwatches. To address the limited visual and interaction space, WatchThru expands the device into 3D through a transparent display. This enables novel interactions that leverage and extend smartwatch glanceability. We describe three novel interaction techniques, Pop-up Visuals, Second Perspective and Peek-through, and discuss how they can complement interaction on current devices. We also describe two types of prototypes that helped us to explore standalone interactions, as well as, proof-of-concept AR interfaces using our platform.","Title: The Role of Physical Props in VR Climbing Environments.\nYear: 2019\n\nAbstract: Dealing with fear of falling is a challenge in sport climbing. Virtual reality (VR) research suggests that using physical and reality-based interaction increases the presence in VR. In this paper, we present a study that investigates the influence of physical props on presence, stress and anxiety in a VR climbing environment involving whole body movement. To help climbers overcoming fear of falling, we compared three different conditions: Climbing in reality at 10 m height, physical climbing in VR (with props attached to the climbing wall) and virtual climbing in VR using game controllers. From subjective reports and biosignals, our results show that climbing with props in VR increases the anxiety and sense of realism in VR for sport climbing. This suggests that VR in combination with physical props are an effective simulation setup to induce the sense of height.","Title: Understanding Users' Knowledge About The Privacy And Security Of Browser Extensions\nYear: 2021\n\nAbstract: Browser extensions enrich users' browsing experience, e.g., by blocking unwanted advertisements on websites. To perform these functions, users must grant certain permissions during the installation process. These permissions, however, give very limited information about the fact that they allow the extension to access user's personal data and browsing behaviour, posing security and privacy risks. To understand users' awareness of these privileges and the associated threats, we conducted an online survey with 353 participants, focusing on users' attitude, knowledge, and preference towards extensions' permission requests. We found that users report interest in seeking information, trust the developers but do little to protect their data. They have limited knowledge about the technical abilities of browser extensions and prefer permission statements that evoke a clear mental model. Based on our findings we derive recommendations for the improvement of browser extension permission dialogues through clear language, technical improvements and distinct responsibilities.","Title: VeloCity: Using Voice Assistants for Cyclists to Provide Traffic Reports\nYear: 2021\n\nAbstract: ABSTRACTCycling is on the rise as a relevant alternative to car-based mobility and even though there are mobile applications specifically designed for cyclists to support this development, many still face unresolved challenges in terms of safe user interaction with complex data while riding. We present the design, development, and evaluation of VeloCity - an application for reporting traffic incidents and structures relevant to cyclists. In a case study, we compared its' three input methods (touch, in-app speech recognition, the voice assistant of the operating system) to evaluate which attributes make for safe interaction while cycling. We found that participants prefer to use the voice assistant over the other modalities as it was least distracting due to its hands- and eyes-free interaction design. Furthermore, they chose short commands over conversational phrases. Based on our results, we present five guidelines for designing voice user interfaces for cyclists and argue for moving away from touch-based interfaces in this domain, which still make up most of the applied interaction techniques today.","Title: Different Length, Different Needs: Qualitative Analysis of Threads in Online Health Communities\nYear: 2022\n\nAbstract: Online health communities provide a knowledge exchange platform for a wide range of diseases and health conditions. Informational and emotional support helps forum participants orient around health issues beyond in-person doctor visits. So far, little is known about the relation between the level of participation and participants' contributions in online health communities. To gain insights on the issue, we analyzed 456 posts in 56 threads from the Dermatology sub-forum of an online health community. While low participation threads ('short threads') revolved around solving an individual's health issue through diagnosis suggestions and medical advice, participants in high participation threads ('long threads') built collective knowledge and a sense of community, typically discussing chronic and rare conditions that medical professionals were unfamiliar with or could not treat effectively. Our results suggest that in short threads an individual's health issue is addressed, while in long threads, sub-communities about specific rare and chronic diseases emerge. This has implications for the user interface design of health forums, which could be developed to better support community building elements, even in short threads."],"string":"[\n \"Title: Mobile product customization\\nYear: 2010\\n\\nAbstract: Many companies are using the web to enable customers to individually customize their products that range from automobiles and bicycles to CDs, cosmetics and shirts. In this paper we present a mobile application for product customization and production within a smart factory. This allows the ad hoc configuration of products at the point of sale (POS). We investigate human factors when customizing products while interacting with them. We focus on the concept of the mobile client that enables this ad hoc modification, but also present the production chain behind our product. We believe that this particular 3D interaction with a product and a mobile device help to improve the customer satisfaction as it allows for customizing a product in an easy and intuitive way. From a CHI perspective an important aspect is that our mobile augmented reality interface can help to match the costumer's expectations with the final modified product and allows the most natural and intuitive interaction. As a use case of the system, we present the modification of a soap dispenser.\",\n \"Title: Whole Body Interaction with Geospatial Data\\nYear: 2009\\n\\nAbstract: Common Geographic Information Systems (GIS) require a high degree of expertise from its users, making them difficult to be operated by laymen. This paper describes novel approaches to easily perform typical basic spatial tasks within a GIS: e.g. pan-, zoom- and selection-operations by using multi-touch gestures in combination with foot gestures. We are interested in understanding how non-expert users interact with such multi-touch surfaces. We provide a categorization and a framework of multi-touch hand gestures for interacting with a GIS. This framework is based on an initial evaluation. We present results of a more detailed in situ-study mainly focusing on multi-user multi-touch interaction with geospatial data. Furthermore we extend our framework using a combination of multi-touch gestures with a small set of foot gestures to solve geospatial tasks.\",\n \"Title: Informing intelligent user interfaces by inferring affective states from body postures in ubiquitous computing environments\\nYear: 2013\\n\\nAbstract: Intelligent User Interfaces can benefit from having knowledge on the user's emotion. However, current implementations to detect affective states, are often constraining the user's freedom of movement by instrumenting her with sensors. This prevents affective computing from being deployed in naturalistic and ubiquitous computing contexts. In this paper, we present a novel system called mASqUE, which uses a set of association rules to infer someone's affective state from their body postures. This is done without any user instrumentation and using off-the-shelf and non-expensive commodity hardware: a depth camera tracks the body posture of the users and their postures are also used as an indicator of their openness. By combining the posture information with physiological sensors measurements we were able to mine a set of association rules relating postures to affective states. We demonstrate the possibility of inferring affective states from body postures in ubiquitous computing environments and our study also provides insights how this opens up new possibilities for IUI to access the affective states of users from body postures in a nonintrusive way.\",\n \"Title: Explanatory semantic relatedness and explicit spatialization for exploratory search\\nYear: 2012\\n\\nAbstract: Exploratory search, in which a user investigates complex concepts, is cumbersome with today's search engines. We present a new exploratory search approach that generates interactive visualizations of query concepts using thematic cartography (e.g. choropleth maps, heat maps). We show how the approach can be applied broadly across both geographic and non-geographic contexts through explicit spatialization, a novel method that leverages any figure or diagram -- from a periodic table, to a parliamentary seating chart, to a world map -- as a spatial search environment. We enable this capability by introducing explanatory semantic relatedness measures. These measures extend frequently-used semantic relatedness measures to not only estimate the degree of relatedness between two concepts, but also generate human-readable explanations for their estimates by mining Wikipedia's text, hyperlinks, and category structure. We implement our approach in a system called Atlasify, evaluate its key components, and present several use cases.\",\n \"Title: Carpus: a non-intrusive user identification technique for interactive surfaces\\nYear: 2012\\n\\nAbstract: Interactive surfaces have great potential for co-located collaboration because of their ability to track multiple inputs simultaneously. However, the multi-user experience on these devices could be enriched significantly if touch points could be associated with a particular user. Existing approaches to user identification are intrusive, require users to stay in a fixed position, or suffer from poor accuracy. We present a non-intrusive, high-accuracy technique for mapping touches to their corresponding user in a collaborative environment. By mounting a high-resolution camera above the interactive surface, we are able to identify touches reliably without any extra instrumentation, and users are able to move around the surface at will. Our technique, which leverages the back of users' hands as identifiers, supports walk-up-and-use situations in which multiple people interact on a shared surface.\",\n \"Title: Gestural interaction on the steering wheel: reducing the visual demand\\nYear: 2011\\n\\nAbstract: Cars offer an increasing number of infotainment systems as well as comfort functions that can be controlled by the driver. In our research, we investigate new interaction techniques that aim to make it easier to interact with these systems while driving. We suggest utilizing the steering wheel as an additional interaction surface. In this paper, we present two user studies conducted with a working prototype of a multi-touch steering wheel. In the first, we developed a user-defined steering wheel gesture set, and in the second, we applied the identified gestures and compared their application to conventional user interaction with infotainment systems in terms of driver distraction. The main outcome was that driver's visual demand is reduced significantly by using gestural interaction on the multi-touch steering wheel.\",\n \"Title: Geographic human-computer interaction\\nYear: 2013\\n\\nAbstract: Geography is playing an increasingly important role in areas of HCI ranging from social computing to natural user interfaces. At the same time, research in geography has focused more and more on technology-mediated interaction with spatiotemporal phenomena. Despite the growing popularity of this geographic human-computer interaction (GeoHCI) in both fields, there have been few opportunities for GeoHCI knowledge sharing, knowledge creation or community building in either discipline, let alone between them. The goal of this workshop is thus two-fold. First, we will seek to sum up the state of GeoHCI knowledge and address GeoHCI core issues by inviting prominent researchers in the space to share and discuss the most important high-level findings from their work. Second, through our interdisciplinary organizing committee, we will recruit participants from both fields, with the goal of laying the groundwork for a community that works across intra- and interdisciplinary boundaries.\",\n \"Title: Improving the Communication of Spatial Information in Crisis Response by Combining Paper Maps and Mobile Devices\\nYear: 2008\\n\\nAbstract: Efficient and effective communication between mobile units and the central emergency operation center is a key factor to respond successfully to the challenges of emergency management. Nowadays, the only ubiquitously available modality is a voice channel through mobile phones or radio transceivers. This makes it often very difficult to convey exact geographic locations and can lead to misconceptions with severe consequences, such as a fire brigade heading to the right street address in the wrong city. In this paper we describe a handheld augmented reality approach to support the communication of spatial information in a crisis response scenario. The approach combines mobile camera devices with paper maps to ensure a quick and reliable exchange of spatial information.\",\n \"Title: Using hands and feet to navigate and manipulate spatial data\\nYear: 2009\\n\\nAbstract: We demonstrate how multi-touch hand gestures in combination with foot gestures can be used to perform navigation tasks in interactive systems. The geospatial domain is an interesting example to show the advantages of the combination of both modalities because the complex user interfaces of common Geographic Information System (GIS) requires a high degree of expertise from its users. Recent developments in interactive surfaces that enable the construction of low cost multi-touch displays and relatively cheap sensor technology to detect foot gestures allow the deep exploration of these input modalities for GIS users with medium or low expertise. In this paper, we provide a categorization of multitouch hand and foot gestures for the interaction with spatial data on a large-scale interactive wall. In addition we show with an initial evaluation how these gestures can improve the overall interaction with spatial information.\",\n \"Title: Investigating the effects of using biofeedback as visual stress indicator during video-mediated collaboration\\nYear: 2014\\n\\nAbstract: During remote video-mediated assistance, instructors often guide workers through problems and instruct them to perform unfamiliar or complex operations. However, the workers' performance might deteriorate due to stress. We argue that informing biofeedback to the instructor, can improve communication and lead to lower stress. This paper presents a thorough investigation on mental workload and stress perceived by twenty participants, paired up in an instructor-worker scenario, performing remote video-mediated tasks. The interface conditions differ in task, facial and biofeedback communication. Two self-report measures are used to assess mental workload and stress. Results show that pairs reported lower mental workload and stress when instructors are using the biofeedback as compared to using interfaces with facial view. Significant correlations were found on task performance with reducing stress (i.e. increased task engagement and decreased worry) for instructors and declining mental workload (i.e. increased performance) for workers. Our findings provide insights to advance video-mediated interfaces for remote collaborative work.\",\n \"Title: The hybrid shopping list: bridging the gap between physical and digital shopping lists\\nYear: 2011\\n\\nAbstract: Shopping is one of the most frequently occuring tasks in our daily lives, and creation and management of shopping lists is an important aspect of this task. Given the recent adoption of mobile devices, the process of writing lists is not only limited to the use of pen and paper, as a good number of digital tools and applications are available. The goal of this paper is to study and understand the transition between paper-based and digital shopping lists. We analyze how people interact with paper-based shopping lists and derive design implications for our own hybrid shopping support application, which combines paper-based lists with a mobile application. We contribute the study and the design and implementation of a hybrid (pen-and-paper-based UI and mobile GUI) application for the creation of shopping lists.\",\n \"Title: Child computer interaction: workshop on UI technologies and educational pedagogy\\nYear: 2011\\n\\nAbstract: Given the growth of Child Computer Interaction research, next generation HCI technologies play an important role in the future of education. Educators rely on technology to improve and adapt learning to the pedagogical needs of learners. Hence, this community needs to understand how current technology concepts match with current pedagogical paradigms. The classroom is a high stakes environment for experimentation, thus new interaction techniques need to be validated to prove their pedagogical value in the educational setting. This workshop provides a forum to discuss key HCI issues facing next generation education. With a particular focus on child computer interaction, these issues comprise inter alia the interaction with whole class interactive whiteboards, small group interactive multi-touch tables, and individual personal response systems (e.g. mobile devices) in the classroom.\",\n \"Title: PINwI: pedestrian indoor navigation without infrastructure\\nYear: 2010\\n\\nAbstract: Navigation in larger unfamiliar buildings like town halls, airports, shopping malls or other public indoor locations is often difficult for humans. Due to the high amount of infrastructure needed for indoor positioning, just a few navigation services for indoor environments exist. Therefore in many of these buildings 'YOU-ARE-HERE' (YAH) maps are provided, often located at the entrance or other key places, to facilitate orientation and navigation within the building, but they have the disadvantages of being stationary. In this paper, we try to overcome these problems by presenting PINwI (Pedestrian Indoor Navigation without Infrastructure), an application that allows the user of a mobile camera device with integrated compass and accelerometer to utilize a photo of such an indoor YAH-map to navigate through the corresponding building. Using a dead reckoning approach, we enrich stationary analog YAH-maps with basic location functionality and turn them into a digital and dynamic medium that can help decision making while taking turns or estimating distances.\",\n \"Title: The path is the reward: considering social networks to contribute to the pleasure of urban strolling\\nYear: 2013\\n\\nAbstract: Most (mobile) online map services focus on providing their users the most efficient route to their target location. In this paper we investigate the relationship between the physical and digital urban navigation to improve wayfinding for pedestrians by enhancing their experiences when strolling through a city. With our application \\\"Space Recommender System\\\" we describe a new wayfinding approach by implementing common digital online methods of commenting and recommender systems into the physical world, using voting data from social network services. Initial findings highlight the general importance of the walking experience to the public and suggest that implementing social media based recommendations in route finding algorithms enhance the pleasure of urban strolling. The initial user tests of the system in a real world context together with collected feedback and the observations throughout the design process stimulate the discussions of wider issues and highlight its potential for future novel wayfinding applications.\",\n \"Title: Impact of item density on magic lens interactions\\nYear: 2009\\n\\nAbstract: We conducted a user study to investigate the effect of visual context in handheld augmented reality interfaces. A dynamic peephole interface (without visual context beyond the device display) was compared to a magic lens interface (with video see-through augmentation of external visual context). The task was to explore objects on a map and look for a specific attribute shown on the display. We tested different sizes of visual context as well as different numbers of items per area, i.e. different item densities. We found that visual context is most effective for sparse item distributions and the performance benefit decreases with increasing density. User performance in the magic lens case approaches the performance of the dynamic peephole case the more densely spaced the items are. In all conditions, subjective feedback indicates that participants generally prefer visual context over the lack thereof. The insights gained from this study are relevant for designers of mobile AR and dynamic peephole interfaces by suggesting when external visual context is most beneficial.\",\n \"Title: Next generation of HCI and education: workshop on UI technologies and educational pedagogy\\nYear: 2010\\n\\nAbstract: Given the exponential growth of interactive whiteboards in classrooms around the world, and the recent emergence of multi-touch tables, tangible computing devices and mobile devices, there has been a need to explore how next generation HCI will impact education in the future. Educators are depending on the interaction communities to deliver technologies that will improve/adapt learning to an ever-changing world. In addition to novel UI concepts, the HCI community needs to examine how these concepts can be matched to contemporary paradigms in Educational pedagogy. The classroom is a challenging environment for evaluation, thus new interaction techniques need to be established to prove the value of new HCI interactions in the educational space. This workshop provides a forum to discuss key HCI issues facing next generation education ranging from whole class interactive whiteboards, small group interactive multi-touch tables, and individual personal response systems in the classroom.\",\n \"Title: Photomap: Snap, Grab and Walk away with a \\\"You Are Here\\\" Map\\nYear: 2008\\n\\nAbstract: One compelling scenario for the use of GPS enabled phones is support for navigation, e.g. enabling a user to glance down at the screen of her mobile phone in order to be reassured that she is indeed located where she thinks she is. While service based approaches to support such navigation tasks are becoming increasingly available { whereby a user down- loads (for a fee) a relevant map of her current area onto her GPS enabled phone, the approach is often far from ideal. Typically, the user is unsure as to the cost of downloading the map (especially when she is in a foreign country) and such maps are highly generalised and may not match the user's current activity and needs. For example, rather than requiring a standard map on a mobile device of the area, the user may simply require a map of a university campus with all departments or a map showing footpaths around the area in which she is currently trekking. Indeed, one will often see such specialised maps on public signs situated where they may be required (in a just{in{time sense) and it is inter- esting to consider how one might enable users to walk up to such situated signs and use their mobile phone to 'take away' the map presented in order to use it to assist their on- going navigation activity. In this paper, we are interested in a subset of this problem space in which the user 'grabs' a map shown on a public display by taking a photograph of it and using it as a digital map on her mobile phone. We present two dierent scenarios for our new application called PhotoMaps: In the\",\n \"Title: Second International Workshop on Location and the Web (LocWeb 2009)\\nYear: 2009\\n\\nAbstract: Location-Based Services are becoming increasingly Web-based, as a result of the availability of networked mobile devices and mobile Internet access. The \\\"Location and the Web (LocWeb)\\\" workshop targets the capabilities and constraints of Web-based location-based services, which can be implemented as browser-based applications, or as native applications using Web services. The focus of this CHI workshop is on developing approaches to handle the complexity of location-based services, specifically looking at location abstractions, location sharing, context-relevant information, privacy issues, and interface design. The goal of this workshop is to serve as a starting point for better understanding how the Web has to change to embrace location as a first-level concept, and how these changes are reflected in applications and user interfaces to transform the Web into a platform for location-based services.\",\n \"Title: Improving interaction with virtual globes through spatial thinking: helping users ask \\\"why?\\\"\\nYear: 2008\\n\\nAbstract: Virtual globes have progressed from little-known technology to broadly popular software in a mere few years. We investigated this phenomenon through a survey and discovered that, while virtual globes are en vogue, their use is restricted to a small set of tasks so simple that they do not involve any spatial thinking. Spatial thinking requires that users ask \\\"what is where\\\" and \\\"why\\\"; the most common virtual globe tasks only include the \\\"what\\\". Based on the results of this survey, we have developed a multi-touch virtual globe derived from an adapted virtual globe paradigm designed to widen the potential uses of the technology by helping its users to inquire about both the \\\"what is where\\\" and \\\"why\\\" of spatial distribution. We do not seek to provide users with full GIS (geographic information system) functionality, but rather we aim to facilitate the asking and answering of simple \\\"why\\\" questions about general topics that appeal to a wide virtual globe user base.\",\n \"Title: Impact of item density on the utility of visual context in magic lens interactions\\nYear: 2009\\n\\nAbstract: This article reports on two user studies investigating the effect of visual context in handheld augmented reality interfaces. A dynamic peephole interface (without visual context beyond the device display) was compared to a magic lens interface (with video see-through augmentation of external visual context). The task was to explore items on a map and look for a specific attribute. We tested different sizes of visual context as well as different numbers of items per area, i.e. different item densities. Hand motion patterns and eye movements were recorded. We found that visual context is most effective for sparsely distributed items and gets less helpful with increasing item density. User performance in the magic lens case is generally better than in the dynamic peephole case, but approaches the performance of the latter the more densely the items are spaced. In all conditions, subjective feedback indicates that participants generally prefer visual context over the lack thereof. The insights gained from this study are relevant for designers of mobile AR and dynamic peephole interfaces, involving spatially tracked personal displays or combined personal and public displays, by suggesting when to use visual context.\",\n \"Title: Projector Phones: A New Class of Interfaces for Augmented Reality\\nYear: 2010\\n\\nAbstract: With the miniaturization of projection technology, the integration of tiny projection units into mobile devices is no longer fiction; therefore, such integrated projectors in mobile devices could make mobile projection ubiquitous. These phones will have the ability to project large-scale information onto any surfaces in the real world, and by doing so, the interaction space of the mobile device can be considerably expanded. In addition, physical objects in the environment can be augmented with additional information, which can support interaction concepts that are not even possible on modern desktop computers today. The authors believe that mobile camera-projector units can form a promising interface type for mobile Augmented Reality AR applications, thus, this paper identifies different application classes of such interfaces. In addition, different spatial setups of camera and projector units will have an effect on the possible applications and the interaction space with the focus on the augmentation of real word objects in the environment. This paper presents two examples of applications for mobile camera-projector units and different hardware prototypes that allow augmentation of real world objects.\",\n \"Title: CHI 2039: speculative research visions\\nYear: 2014\\n\\nAbstract: This paper presents a curated collection of fictional abstracts for papers that could appear in the proceedings of the 2039 CHI Conference. It provides an opportunity to consider the various visions guiding work in HCI, the futures toward which we (believe we) are working, and how research in the field might relate with broader social, political, and cultural changes over the next quarter century.\",\n \"Title: Falling asleep with Angry Birds, Facebook and Kindle: a large scale study on mobile application usage\\nYear: 2011\\n\\nAbstract: While applications for mobile devices have become extremely important in the last few years, little public information exists on mobile application usage behavior. We describe a large-scale deployment-based research study that logged detailed application usage information from over 4,100 users of Android-powered mobile devices. We present two types of results from analyzing this data: basic descriptive statistics and contextual descriptive statistics. In the case of the former, we find that the average session with an application lasts less than a minute, even though users spend almost an hour a day using their phones. Our contextual findings include those related to time of day and location. For instance, we show that news applications are most popular in the morning and games are at night, but communication applications dominate through most of the day. We also find that despite the variety of apps available, communication applications are almost always the first used upon a device's waking from sleep. In addition, we discuss the notion of a virtual application sensor, which we used to collect the data.\",\n \"Title: Evaluating automatically generated location-based stories for tourists\\nYear: 2008\\n\\nAbstract: Tourism provides over six percent of the world's gross domestic product. As a result, there have been many efforts to use technology to improve the tourist's experience via mobile tour guide systems. One key bottleneck in such location-based systems is content development; existing systems either provide trivial information at a global scale or present quality narratives but at an extremely local scale. The primary reason for this dichotomy is that, although good narrative content is more educationally effective (and more entertaining) than a stream of simple, disconnected facts, it is time-intensive and expensive to develop. However, the WikEar system uses narrative theory-informed data mining methodologies in an effort to produce high-quality narrative content for any location on Earth. It allows tourists to interact with these narratives using their camera-enabled cell phones and an innovative interface designed around a magic lens and paper map metaphor. In this paper, we describe a first evaluation of these narratives and the WikEar interface, which reported promising, but not conclusive, results. We also present ideas for future work that will use this feedback to improve the narratives.\",\n \"Title: LocWeb 2010: Third International Workshop on Location and the Web\\nYear: 2010\\n\\nAbstract: The Third International Workshop on Location and the Web (LocWeb 2010) focuses on research and development that targets the intersection of Internet-enabled location-aware and/or located devices, and services based on Web technologies and Web architecture. The rapid rise of multi-sensory mobile devices and Internet-enabled \\\"things\\\" equipped with sensors and ubiquitous connectivity opens new possibilities and provides the foundations to capture, share and use Web services and applications in ways which go beyond the traditional scenarios of stationary or even mobile computer-like devices. Increasingly, applications will have to bridge the physical world and the Web space, and location is one of the major connecting links. When Web services will \\\"surround\\\" users, designers have to address the challenges of scalability and interoperability on the Web, and designers also have to look at policy, regulatory, and legislative responses to the privacy and security challenges created by something as sensitive as location information.\",\n \"Title: Map navigation with mobile devices: virtual versus physical movement with and without visual context\\nYear: 2007\\n\\nAbstract: A user study was conducted to compare the performance of three methods for map navigation with mobile devices. These methods are joystick navigation, the dynamic peephole method without visual context, and the magic lens paradigm using external visual context. The joystick method is the familiar scrolling and panning of a virtual map keeping the device itself static. In the dynamic peephole method the device is moved and the map is fixed with respect to an external frame of reference, but no visual information is present outside the device's display. The magic lens method augments an external content with graphical overlays, hence providing visual context outside the device display. Here too motion of the device serves to steer navigation. We compare these methods in a study measuring user performance, motion patterns, and subjective preference via questionnaires. The study demonstrates the advantage of dynamic peephole and magic lens interaction over joystick interaction in terms of search time and degree of exploration of the search space.\",\n \"Title: iBookmark: locative texts and place-based authoring\\nYear: 2009\\n\\nAbstract: With the recent developments in ePaper technology, consumer eBook readers have display qualities and form factors that are approaching that of traditional books. These eBook readers are already replacing paper in some commercial domains, but the potential of eBooks to extend forms of writing and storytelling has not been significantly explored. Using the digital and dynamic characteristics afforded by eBook readers, we are developing iBookmark, a GPS-enabled eBook reader. In iBookmark, writers can create stories that change in response to the location of the eBook itself. By setting context variables based on current and past locations of the eBook reader and using these in the rule-based generation of text and illustrations. We are developing new rhetorical device for writers that extend the expressive range of eBook delivered stories.\",\n \"Title: PhotoMap: using spontaneously taken images of public maps for pedestrian navigation tasks on mobile devices\\nYear: 2009\\n\\nAbstract: In many mid- to large-sized cities public maps are ubiquitous. One can also find a great number of maps in parks or near hiking trails. Public maps help to facilitate orientation and provide special information to not only tourists but also to locals who just want to look up an unfamiliar place while on the go. These maps offer many advantages compared to mobile maps from services like Google Maps Mobile or Nokia Maps. They often show local landmarks and sights that are not shown on standard digital maps. Often these 'You are here' (YAH) maps are adapted to a special use case, e.g. a zoo map or a hiking map of a certain area. Being designed for a fashioned purpose these maps are often aesthetically well designed and their usage is therefore more pleasant. In this paper we present a novel technique and application called PhotoMap that uses images of 'You are here' maps taken with a GPS-enhanced mobile camera phone as background maps for on-the-fly navigation tasks. We discuss different implementations of the main challenge, namely helping the user to properly georeference the taken image with sufficient accuracy to support pedestrian navigation tasks. We present a study that discusses the suitability of various public maps for this task and we evaluate if these georeferenced photos can be used for navigation on GPS-enabled devices.\",\n \"Title: The role of physiological cues during remote collaboration\\nYear: 2014\\n\\nAbstract: Empathic communication allows individuals to perceive and understand the feeling and emotion of the person with whom they are interacting. This could be particularly important during remote collaboration such as remote assistance or distance learning to enhance the social and emotional understanding of geographically distributed partners. However, supporting awareness in remote collaboration is very challenging especially when the interaction with the remote parties results in less information that can be communicated than in a physical interaction. We explore the effect of visualization using physiological cues that allow users to interpret emotional behaviors of remote parties with whom they are interacting in real time. The proposed visual representation allows users to infer emotional patterns from physiological cues that can potentially influence their communication approach toward a more aggressive style or maintain passive and peaceful interaction. We conducted a study involving participants who were paired up for a collaborative assessment task, interacting via voice only, videoconference, or a visual representation of the physiological measurements. Participants perceived the usage of our visual representation with higher group cohesiveness than using voice-only interaction. Further analysis shows that the visual representation significantly increases the positive affect score i.e., participants are perceived to be more alert and demonstrate less distress during remote collaboration. We discuss the possibilities of the proposed visual representation to support empathic communication during remote collaboration, and the benefits to the remote partners of having positive affect and group cohesiveness.\",\n \"Title: Marauders light: replacing the wand with a mobile camera projector unit\\nYear: 2009\\n\\nAbstract: Classic paper-based maps provide high-resolution, large-scale information and are ubiquitous in larger cities and even outdoors. In this paper we present a combination of their advantages with a mobile camera projector unit to create a new mobile and intuitive buddy finder interface. Current friend or buddy finder systems on mobile phones such as Google Latitude suffer from the small screen estate and display size of mobile devices. To overcome this problem we use a lightweight mobile camera projector unit to augment the paper map with a projected overlay of the buddies' positions. By using digital and geo-referenced representations of public maps no extra preperation for the tracking is needed. The idea is presented by enhancing Google Latitude, which allows users to browse the positions of their friends, to project the positions directly on the paper map without the cumbersome panning and zooming of a digital map on a small display.\",\n \"Title: A multi-touch enabled steering wheel: exploring the design space\\nYear: 2010\\n\\nAbstract: Cars offer an increasing number of infotainment systems as well as comfort functions that can be controlled by the driver. With our research we investigate new interaction techniques that aim to make it easier to interact with these systems while driving. In contrast to the standard approach of combining all functions into hierarchical menus controlled by a multifunctional controller or a touch screen we suggest to utilize the space on the steering wheel as additional interaction surface. In this paper we show the design challenges that arise for multi-touch interaction on a steering wheel. In particular we investigate how to deal with input and output while driving and hence rotating the wheel. We describe the details of a functional prototype of a multi-touch steering wheel that is based on FTIR and a projector, which was built to explore experimentally the user experience created. In an initial study with 12 participants we show that the approach has a general utility and that people can use gestures for controlling applications intuitively but have difficulties to imagine gestures to select applications.\",\n \"Title: Multi-user interaction on media facades through live video on mobile devices\\nYear: 2011\\n\\nAbstract: The increasing number of media facades in urban spaces offers great potential for new forms of interaction especially for collaborative multi-user scenarios. In this paper, we present a way to directly interact with them through live video on mobile devices. We extend the Touch Projector interface to accommodate multiple users by showing individual content on the mobile display that would otherwise clutter the facade's canvas or distract other users. To demonstrate our concept, we built two collaborative multi-user applications: (1) painting on the facade and (2) solving a 15-puzzle. We gathered informal feedback during the ARS Electronica Festival in Linz, Austria and found that our interaction technique is (1) considered easy-to-learn, but (2) may leave users unaware of the actions of others.\",\n \"Title: Bro-cam: improving game experience with empathic feedback using posture tracking\\nYear: 2013\\n\\nAbstract: In todays videogames user feedback is often provided through raw statistics and scoreboards. We envision that incorporating empathic feedback matching the player's current mood will improve the overall gaming experience. In this paper we present Bro-cam, a novel system that provides empathic feedback to the player based on their body postures. Different body postures of the players are used as an indicator for their openness. From their level of openness, Bro-cam profiles the players into different personality types ranging from introvert to extrovert. Empathic feedback is then automatically generated and matched to their preferences for certain humoristic feedback statements. We use a depth camera to track the player's body postures and movements during the game and analyze these to provide customized feedback. We conducted a user study involving 32 players to investigate their subjective assessment on the empathic game feedback. Semi-structured interviews reveal that participants were positive about the empathic feedback and Bro-cam significantly improves their game experience.\",\n \"Title: GlobalData: multi-user interaction with geographic information systems on interactive surfaces\\nYear: 2010\\n\\nAbstract: The geographical domain was often used as a showcase to show the possibilities of multi-touch interaction. Nonetheless, researchers have rarely investigated multi-user interaction with GIS - in fact, most of the geographical tabletop applications are not suited to multi-user interaction. Our multitouch application, GlobalData, allows multiple people to interact and collaborate in examining global, geolocated data. In idle mode, the device simply shows a stylized map of the earth. Users can open circular GeoLenses. These circles show the same map segment as the underlying base map and superimpose different data layers on it.\",\n \"Title: Image geo-mashups: the example of an augmented reality weather camera\\nYear: 2008\\n\\nAbstract: This paper presents the general idea of image geo-mashups, which combines concepts from web mashups and augmented reality by adding geo-referenced data to a perspective image. The paper shows how to design and implement an augmented reality weather cam, that combines data from a steerable weather cam with additional sensor information retrieved from the web.\",\n \"Title: IRL SmartCart - a user-adaptive context-aware interface for shopping assistance\\nYear: 2011\\n\\nAbstract: The electronic market has rapidly grown in the last few years. However, despite this success, consumers still enjoy visiting a \\\"real store with real products\\\". Therefore various common technologies have been installed in supermarkets to support the customer's shopping process and experience. In this paper, we introduce the IRL SmartCart - an instrumented shopping cart that acts as a user interface to support the shopping process. We show how RFID technology enables recognizing products that are put in the cart's basket. We are also able to determine the cart's position in an instrumented shopping environment. User input and visual output are possible by means of a touch screen, which is fitted in the IRL SmartCart's handle to support different tasks involved in the shopping process. We present and discuss different location- and context-based services that run on the cart interface system, e.g. a personalized shopping list sorted corresponding to the products in the user's vicinity or a navigation service to different products that the customer is searching for.\",\n \"Title: Investigating mobile stereoscopic 3D touchscreen interaction\\nYear: 2013\\n\\nAbstract: 3D output is no longer limited to large screens in cinemas or living rooms. Nowadays more and more mobile devices are equipped with autostereoscopic 3D (S3D) touchscreens. As a consequence interaction with 3D content now also happens whilst users are on the move. In this paper we carried out a user study with 27 participants to assess how mobile interaction, i.e. whilst walking, with mobile S3D devices, differs from interaction with 2D mobile touchscreens. We investigate the difference in touch accuracy between 2D touchscreens and mobile S3D touchscreens and evaluate the minimum touch target size for mobile S3D touchscreens. The contributions of this paper are twofold: Firstly, we found the increase in minimum touch target size caused by walking was larger for a mobile S3D UI than for a 2D UI. Secondly, we present touch target sizes and aspect ratios required for reliable user interaction in each case. Additionally we examined differences in the angle at which users held the mobile S3D device compared to a 2D mobile device. We found that mobile S3D caused users to hold the device at a different angle when walking, compared to the 2D case. This first study of its kind provides valuable information to developers of the next generation of UIs and applications for mobile S3D displays and devices.\",\n \"Title: Paddle: highly deformable mobile devices with physical controls\\nYear: 2014\\n\\nAbstract: We present the concept of highly deformable mobile devices that can be transformed into various special-purpose controls in order to bring physical controls to mobile devices. Physical controls have the advantage of exploiting people's innate abilities for manipulating physical objects in the real world. We designed and implemented a prototype, called Paddle, to demonstrate our concept. Additionally, we explore the interaction techniques enabled by this concept and conduct an in-depth study to evaluate our transformable physical controls. Our findings show that these physical controls provide several benefits over traditional touch interaction techniques commonly used on mobile devices.\",\n \"Title: iPiccer: automatically retrieving and inferring tagged location information from web repositories\\nYear: 2009\\n\\nAbstract: We present iPiccer, a system that allows mobile camera phones users to interact with meta-tagged image material available from web repositories, such as flickr in two novel ways. Users of the system are able to infer photo tags from their location and orientation, but are also able to infer their location and orientation from their spatial photo tags. An implementation of iPiccerTaker is outlined, and the potential of this new form of interaction with web repository data is discussed.\",\n \"Title: Map torchlight: a mobile augmented reality camera projector unit\\nYear: 2009\\n\\nAbstract: The advantages of paper-based maps have been utilized in the field of mobile Augmented Reality (AR) in the last few years. Traditional paper-based maps provide high-resolution, large-scale information with zero power consumption. There are numerous implementations of magic lens interfaces that combine high-resolution paper maps with dynamic handheld displays. From an HCI perspective, the main challenge of magic lens interfaces is that users have to switch their attention between the magic lens and the information in the background. In this paper, we attempt to overcome this problem by using a lightweight mobile camera projector unit to augment the paper map directly with additional information. The \\\"Map Torchlight\\\" is tracked over a paper map and can precisely highlight points of interest, streets, and areas to give directions or other guidance for interacting with the map.\",\n \"Title: How to nudge in Situ: designing lambent devices to deliver salient information in supermarkets\\nYear: 2011\\n\\nAbstract: There are a number of mobile shopping aids and recommender systems available, but none can be easily used for a weekly shop at a local supermarket. We present a minimal, mobile and fully functional lambent display that clips onto any shopping trolley handle, intended to nudge people when choosing what to buy. It provides salient information about the food miles for various scanned food items represented by varying lengths of lit LEDs on the handle and a changing emoticon comparing the average miles of all the products in the trolley against a social norm. When evaluated in situ, the lambent handle display nudged people to choose products with fewer food miles than the items they selected using their ordinary shopping strategies. People also felt guilty when the average mileage of the contents of their entire shopping trolley was above the social norm. The findings are discussed in terms of how to provide different kinds of product information that people care about, using simple lambent displays.\",\n \"Title: Interaction With Geospatial Data\\nYear: 2015\\n\\nAbstract: My research interest lies at the interaction between human-computer interaction (HCI) and geoinformatics. I am interested in developing new methods and novel user interfaces to navigate through spatial information. This article will give a brief overview on my past and current research topics and streams. Generally speaking, geography is playing an increasingly important role in computer science and also in the field of HCI ranging from social computing to natural user interfaces (NUIs). At the same time, research in geography has focused more and more on technology-mediated interaction with spatiotemporal phenomena. By bridging the two fields, my aimis to exploit this fruitful intersection between those two and develop, design and evaluate user interfaces that help people to solve their daily tasks more enjoyable and effectively.\",\n \"Title: A large-scale quantitative survey of the german geocaching community in 2007\\nYear: 2014\\n\\nAbstract: We present a large-scale quantitative contextual survey of the geocaching community in Germany, one of the world's largest geocaching communities. We investigate the features, attitudes, interests, and motivations that characterise the German geocachers. Two anonymous surveys have been carried out on this issue in the year 2007. We conducted a large-scale qualitative general study based on web questionnaires and a more targeted study, which aimed at a comprehensive amount of revealed geocaches of a certain region. With sample sizes of n = 1982 (study 1: general study) and n = 310 (study 2: regional study) we provide a representative basis to ground previous qualitative research in this domain. In addition, we investigated the usage of technology in combination with traditional paper-based media by the geocachers. This knowledge can be used to reflect on past and future trends within the geocaching community.\",\n \"Title: SubwayPS: towards smartphone positioning in underground public transportation systems\\nYear: 2014\\n\\nAbstract: Thanks to rapid advances in technologies like GPS and Wi-Fi positioning, smartphone users are able to determine their location almost everywhere they go. This is not true, however, of people who are traveling in underground public transportation networks, one of the few types of high-traffic areas where smartphones do not have access to accurate position information. In this paper, we introduce the problem of underground transport positioning on smartphones and present SubwayPS, an accelerometer-based positioning technique that allows smartphones to determine their location substantially better than baseline approaches, even deep beneath city streets. We highlight several immediate applications of positioning in subway networks in domains ranging from mobile advertising to mobile maps and present MetroNavigator, a proof-of-concept smartphone and smartwatch app that notifies users of upcoming points-of-interest and alerts them when it is time to get ready to exit the train.\",\n \"Title: Captchat: A Messaging Tool to Frustrate Ubiquitous Surveillance\\nYear: 2015\\n\\nAbstract: There is currently a widespread uncertainty regarding the ability of citizens to control privacy online in the face of ubiquitous surveillance. This is a huge and complex societal problem. Despite the multi-faceted nature of the problem, we propose that HCI researchers can still make a positive contribution in this space through the design of technologies that support citizens to engage with issues of surveillance. In this paper we describe the design of a messaging application called Captchat. Captchat enables people to send everyday messages embedded into images, with the added ability to apply visual distortions to the message to resemble an online CAPTCHA. We propose the chief benefit would be that Captchat messages (with potentially \\\\\\\"one-time\\\\\\\" distortions) can increase the difficulty for algorithms to index private messages and necessitate the involvement of much more costly human labor in the surveillance process. We developed a prototype and conducted a user study; the results suggest that people were likely to create Captchat messages that were difficult to index for an OCR package but still easy to understand by humans, even without explicit instructions to interact 'securely' with the application. While more work is still required to understand the limitations of Captchat, we hope it can open discussion on how HCI researchers can respond to the challenges faced from ubiquitous surveillance.\",\n \"Title: WatchMe: A Novel Input Method Combining a Smartwatch and Bimanual Interaction\\nYear: 2015\\n\\nAbstract: Smartwatches can facilitate several tasks that are performed on mobile devices. However, due to their limited size, touch interaction can be cumbersome. Although alternative input modalities such as speech input could be used, these can also introduce other issues regarding privacy or ease of use. Consequently, HCI researchers have been exploring novel input techniques for smartwatches. In this paper, we introduce WatchMe, a smartwatch application that uses the smartwatch camera and image processing techniques to allow for providing input on a drawing canvas composed of everyday objects and surfaces. We rely on a cloud OCR engine to retrieve text from captured images. Combining these characteristics, we illustrate some scenarios in which WatchMe could be used, such as a novel method for two-factor authentication.\",\n \"Title: You Can't Smoke Here: Towards Support for Space Usage Rules in Location-aware Technologies\\nYear: 2015\\n\\nAbstract: Recent work has identified the lack of space usage rule (SUR) data -- e.g. \\\\\\\"no smoking\\\\\\\", \\\\\\\"no campfires\\\\\\\" -- as an important limitation of online/mobile maps that presents risks to user safety and the environment. In order to address this limitation, a large-scale means of mapping SURs must be developed. In this paper, we introduce and motivate the problem of mapping space usage rules and take the first steps towards identifying solutions. We show how computer vision can be employed to identify SUR indicators in the environment (e.g. \\\\\\\"No Smoking\\\\\\\" signs) with reasonable accuracy and describe techniques that can assign each rule to the appropriate geographic feature.\",\n \"Title: A User Interface for Encoding Space Usage Rules Expressed in Natural Language\\nYear: 2015\\n\\nAbstract: Our interactions with the spaces around us are frequently defined by space usage rules (SURs) like \\\\\\\"no smoking\\\\\\\", \\\\\\\"no dogs allowed\\\\\\\", and \\\\\\\"stay on the trail\\\\\\\". These rules are important public health tools and help protect the environment, among other applications. However, despite their importance, no large-scale database of SURs currently exists. This prevents online/mobile maps from presenting these rules to users, as their traditional paper counterparts have been shown to do regularly. The lack of a SUR database also prevents developers from building novel SUR-based applications, e.g. mobile apps that inform users where they can smoke or walk their dog. In this paper, we present an in-development user interface to support the semi-automatic encoding of SURs expressed in natural language in documents such as laws, city ordinances, park rules, and institution FAQ pages. Our system will allow an untrained user to easily enter complex rules into a spatial data format compatible with OpenStreetMap.\",\n \"Title: Augmenting Social Interactions: Realtime Behavioural Feedback using Social Signal Processing Techniques\\nYear: 2015\\n\\nAbstract: Nonverbal and unconscious behaviour is an important component of daily human-human interaction. This is especially true in situations such as public speaking, job interviews or information sensitive conversations, where researchers have shown that an increased awareness of one's behaviour can improve the outcome of the interaction. With wearable technology, such as Google Glass, we now have the opportunity to augment social interactions and provide realtime feedback on one's behaviour in an unobtrusive way. In this paper we present Logue, a system that provides realtime feedback on the presenters' openness, body energy and speech rate during public speaking. The system analyses the user's nonverbal behaviour using social signal processing techniques and gives visual feedback on a head-mounted display. We conducted two user studies with a staged and a real presentation scenario which yielded that Logue's feedback was perceived helpful and had a positive impact on the speaker's performance.\",\n \"Title: Informing online and mobile map design with the collective wisdom of cartographers\\nYear: 2014\\n\\nAbstract: Despite the large and growing prominence of online and mobile maps, they have not been broadly and systematically examined with a lens informed by traditional cartography. Using an approach rooted in cartographic theory and a unique dataset of 382 publicly-displayed local maps, we identify the collective wisdom of hundreds of cartographers with respect to a number of cartographic design decisions. We compare our findings to the approaches taken in popular online and mobile map platforms and develop suggestions for incorporating the collective wisdom of cartographers into these systems. Our suggestions include the adoption of location-aware cartography, in which cartographic approaches are intelligently varied based on the type of location being viewed. We provide mockup designs of online and mobile maps that implement our suggestions and discuss means by which the surprising gap between online and mobile maps and traditional cartography may be bridged.\",\n \"Title: Unexpected journeys with the HOBBIT: the design and evaluation of an asocial hiking app\\nYear: 2014\\n\\nAbstract: In the age of mobile communications and social media, users are connected to interact with other people, and often obliged to be socially active as technology drives to connect us. In this paper, we harness the technology for the opposite use: helping people to avoid company instead of encouraging interaction. We have developed the concept of an asocial hiking application (app), in which users can generate routes that avoid meeting other people. We developed the concept based on user feedback data derived from an online survey (n=157) and two focus groups, and created a tool that generates solitary hiking routes based on OpenStreetMap data and additional information from the web. In addition, to make the application react to dynamic changes in the environment, we developed a mobile application prototype that scans Wi-Fi signals to detect other hikers nearby and warn of their approach. The prototype was tested and evaluated with 8 hikers in-the-wild. In addition to the concept design and the functional prototype, we present findings on people's, especially hikers, need for solitude, and introduce user feedback from each stage of the prototype design process as well as design recommendations for an asocial navigation application.\",\n \"Title: Attention as an input modality for Post-WIMP interfaces using the viGaze eye tracking framework\\nYear: 2016\\n\\nAbstract: Abstract Eye tracking is one of the most prominent modalities to track user attention while interacting with computational devices. Today, most of the current eye tracking frameworks focus on tracking the user gaze during website browsing or while performing other tasks and interactions with a digital device. Most frameworks have in common that they do not exploit gaze as an input modality. In this paper we describe the realization of a framework named viGaze. Its main goal is to provide an easy to use framework to exploit the use of eye gaze as an input modality in various contexts. Therefore it provides features to explore explicit and implicit interactions in complex virtual environments by using the eye gaze of a user for various interactions. The viGaze framework is flexible and can be easily extended to incorporate other input modalities typically used in Post-WIMP interfaces such as gesture or foot input. In this paper we describe the key components of our viGaze framework and additionally describe a user study that was conducted to test the framework. The user study took place in a virtual retail environment, which provides a challenging pervasive environment and contains complex interactions that can be supported by gaze. The participants performed two gaze-based interactions with products on virtual shelves and started an interaction cycle between the products and an advertisement monitor placed on the shelf. We demonstrate how gaze can be used in Post-WIMP interfaces to steer the attention of users to certain components of the system. We conclude by discussing the advantages provided through the viGaze framework and highlighting the potentials of gaze-based interaction.\",\n \"Title: No more Autobahn!: Scenic Route Generation Using Googles Street View.\\nYear: 2016\\n\\nAbstract: Navigation systems allow drivers to find the shortest or fastest path between two or multiple locations mostly using time or distance as input parameters. Various researchers extended traditional route planning approaches by taking into account the user's preferences, such as enjoying a coastal view or alpine landscapes during a drive. Current approaches mainly rely on volunteered geographic information (VGI), such as point of interest (POI) data from OpenStreetMap, or social media data, such as geotagged photos from Flickr, to generate scenic routes. While these approaches use proximity, distribution or other spatial relationships of the data sets, they do not take into account the actual view on specific route segments. In this paper, we propose Autobahn: a system for generating scenic routes using Google Street View images to classify route segments based on their visual characteristics enhancing the driving experience. We show that this vision-based approach can complement other approaches for scenic route planning and introduce a personalized scenic route by aligning the characteristics of the route to the preferences of the user.\",\n \"Title: Combining Direct and Indirect Touch Input for Interactive Workspaces using Gaze Input\\nYear: 2015\\n\\nAbstract: Interactive workspaces combine horizontal and vertical touch surfaces into a single digital workspace. During an exploration of these systems, it was shown that direct interaction on the vertical surface is cumbersome and more inaccurate than on the horizontal one. To overcome these problems, indirect touch systems turn the horizontal touch surface into the input which allows manipulation of objects on the vertical display. If the horizontal touch surface also acts as a display, however, it becomes necessary to notify the system which screen is currently in use by providing a switching mode. We investigate the use of gaze tracking to perform these mode switches. In three user studies, we compare absolute and relative gaze augmented selection techniques with the traditional direct-touch approach. Our results show that our relative gaze augmented selection technique outperforms the other techniques for simple tapping tasks alternating between horizontal and vertical surfaces, and for dragging on the vertical surface. However, when tasks involve dragging across surfaces, the findings are more complex. We provide a detailed description of the proposed interaction techniques, a statistical analysis of these interaction techniques, and how they can be applied to systems that involve a combination of multiple horizontal and vertical touch surfaces.\",\n \"Title: Cross-Surface: Workshop on Interacting with Multi-Device Ecologies in the Wild\\nYear: 2015\\n\\nAbstract: In this workshop, we will review and discuss opportunities, technical challenges and problems with cross-device interactions in interactive multi-surface and multi-device ecologies. We aim to bring together researchers and practitioners currently working on novel techniques for cross-surface interactions, focusing both on technical as well as interaction challenges for introducing these technologies into the wild, and highlighting opportunities for further research. The workshop will help to facilitate knowledge exchange on the inherent challenges of building robust and intuitive cross-surface interactions, identify application domains and enabling technologies for cross-surface interactions in the wild, and establish a research community to develop effective strategies for successful design of cross-device interactions.\",\n \"Title: StripeMaps: Improving Map-based Pedestrian Navigation for Smartwatches\\nYear: 2015\\n\\nAbstract: Map applications for smartwatches present new challenges in cartography, a domain in which large display sizes have significant advantages. In this paper, we introduce StripeMaps, a system that adapts the mobile web design technique of linearization for displaying maps on the small screens of smartwatches. Just as web designers simplify multiple column desktop websites into a single column for easier navigation on mobile devices, StripeMaps transforms any two-dimensional route map into a one-dimensional \\\\\\\"stripe\\\\\\\". Through a user study, we show that this simplification allows StripeMaps to outperform both traditional mobile map interfaces and turn-by-turn directions for pedestrian navigation using smartwatches. In addition to introducing StripeMaps, this paper also has a secondary contribution. It contains the first empirical comparison of different approaches for pedestrian smartwatch navigation and illuminates their pros and cons.\",\n \"Title: The Geography and Importance of Localness in Geotagged Social Media.\\nYear: 2016\\n\\nAbstract: Geotagged tweets and other forms of social media volunteered geographic information (VGI) are becoming increasingly critical to many applications and scientific studies. An important assumption underlying much of this research is that social media VGI is \\\\\\\"local\\\\\\\", or that its geotags correspond closely with the general home locations of its contributors. We demonstrate through a study on three separate social media communities (Twitter, Flickr, Swarm) that this localness assumption holds in only about 75% of cases. In addition, we show that the geographic contours of localness follow important sociodemographic trends, with social media in, for instance, rural areas and older areas, being substantially less local in character (when controlling for other demographics). We demonstrate through a case study that failure to account for non-local social media VGI can lead to misrepresentative results in social media VGI-based studies. Finally, we compare the methods for determining localness, finding substantial disagreement in certain cases, and highlight new best practices for social media VGI-based studies and systems.\",\n \"Title: You Can Touch This: Eleven Years and 258218 Images of Objects.\\nYear: 2016\\n\\nAbstract: Touch has become a central input modality for a wide variety of interactive devices, most of our mobile devices are operated using touch. In addition to interacting with digital artifacts, people touch and interact with many other objects in their daily lives. We provide a unique photo dataset containing all touched objects over the last 11 years. All photos were contributed by Alberto Frigo, who was involved early on in the \\\"Quantified Self\\\" movement. He takes photos of every object he touches with his dominant hand. We analyzed the 258,218 images with respect to the types objects, their distribution, and related activities.\",\n \"Title: NatureCHI: Unobtrusive User Experiences with Technology in Nature.\\nYear: 2016\\n\\nAbstract: Being in nature is typically regarded to be calming, relaxing and purifying. When in nature, people often seek physical activity like hiking, or meditative, mindful or inspiring experiences remote from the urban everyday life. However, the modern lifestyle easily extends technology use to all sectors of our everyday life, and e.g. the rise of sports tracking technologies, mobile phone integrated cameras and omnipresent social media access have contributed to technologies also arriving into the use context of nature. Also maps and tourist guides are increasingly smart phone or tablet based services. This workshop addresses the challenges that are related to interacting with technology in nature. The viewpoints cover, but are not limited to interaction design and prototyping, social and cultural issues, user experiences that aim for unobtrusive interactions with the technology with nature as the use context\",\n \"Title: Cross-Surface: Challenges and Opportunities for 'bring your own device' in the wild.\\nYear: 2016\\n\\nAbstract: In this workshop, we will review and discuss challenges and opportunities for HCI in relation to cross-surface interaction in the wild based on the bring-your-own-device (BYOD) practice. We aim to bring together researchers and practitioners working on technical infra-structures for cross-surface computing, studies of cross-surface computing in particular domains as well as interaction challenges for introducing cross-surface computing in the wild, all with a particular focus on BYOD. Examples of application domains are: cultural institutions, work places, public libraries, schools and education.\",\n \"Title: Helping Computers Understand Geographically-Bound Activity Restrictions.\\nYear: 2016\\n\\nAbstract: The lack of certain types of geographic data prevents the development of location-aware technologies in a number of important domains. One such type of \\\"unmapped\\\" geographic data is space usage rules (SURs), which are defined as geographically-bound activity restrictions (e.g. \\\"no dogs\\\", \\\"no smoking\\\", \\\"no fishing\\\", \\\"no skateboarding\\\"). Researchers in the area of human-computer interaction have recently begun to develop techniques for the automated mapping of SURs with the aim of supporting activity planning systems (e.g. one-touch \\\"Can I Smoke Here?\\\" apps, SUR-aware vacation planning tools). In this paper, we present a novel SUR mapping technique -- SPtP -- that outperforms state-of-the-art approaches by 30% for one of the most important components of the SUR mapping pipeline: associating a point observation of a SUR (e.g. a 'no smoking' sign) with the corresponding polygon in which the SUR applies (e.g. the nearby park or the entire campus on which the sign is located). This paper also contributes a series of new SUR benchmark datasets to help further research in this area.\",\n \"Title: Not at Home on the Range: Peer Production and the Urban/Rural Divide.\\nYear: 2016\\n\\nAbstract: Wikipedia articles about places, OpenStreetMap features, and other forms of peer-produced content have become critical sources of geographic knowledge for humans and intelligent technologies. In this paper, we explore the effectiveness of the peer production model across the rural/urban divide, a divide that has been shown to be an important factor in many online social systems. We find that in both Wikipedia and OpenStreetMap, peer-produced content about rural areas is of systematically lower quality, is less likely to have been produced by contributors who focus on the local area, and is more likely to have been generated by automated software agents (i.e. \\\"bots\\\"). We then codify the systemic challenges inherent to characterizing rural phenomena through peer production and discuss potential solutions.\",\n \"Title: Heartefacts: Augmenting Mobile Video Sharing Using Wrist-Worn Heart Rate Sensors.\\nYear: 2016\\n\\nAbstract: An increasing share of our daily interactions with others is mediated through mobile communication technologies. People communicate via text, emoticons, emojis and rich media such as video. We explore the design of Heartefacts, short video clips composed of highlights determined by heart rate changes while watching videos. Our survey investigated video sharing behaviour, and our feasibility study examined the possibility of detecting highlights in videos by monitoring people's heart rates measured with off-the-shelf wrist-worn sensors. Our results show that people do indeed have measurable responses with respect to their heartbeat patterns to six different emotions elicited by video clips. We compare video highlights verbally identified by our participants to physiological highlights as indicated by their heart rate data and also discuss and compare the automatically generated Heartefacts with video highlights created by an expert in video art. We close with design considerations for Heartefacts in mobile technology.\",\n \"Title: Who are the people in my life? towards tangible eco-maps.\\nYear: 2016\\n\\nAbstract: Eco-Maps, diagrammatic assessments of family relationships, are used by social workers in individual and family contact to understand the subjective experiences of both supportive and disruptive family, friendship, and community relationships. In this paper we present the first phase of the development of the People in my Life app. We have developed a tangible version of Eco-Maps to unlock the potential of eco-maps in the digital domain and to enhance social work practice with children and young people. The central idea is to develop an improved, technology-based means to assess relationships that supports and enhances social work practice and foregrounds children and young people's experiences. In particular, the project has three aims: (a) to collaboratively develop with social workers an application for creating eco-maps using touch-screens with tangible objects, (b) to test its usability with children, in the first instance, and (c) to provide a first exploration of the psychometric properties of parameters captured by the app.\",\n \"Title: StrapMaps: bringing map-based navigation to the straps of bags.\\nYear: 2016\\n\\nAbstract: Most outdoor activities and outdoor sports include a navigation component. For example, hiking, biking, mountaineering and climbing activities require orientation and wayfinding at certain points to certain degrees. With the rise of personal GPS devices, numerous navigation apps now exist that are tailored to the specific needs of outdoor enthusiasts. Nevertheless, they all have the common problem, that they require the users to \\\\\\\"store\\\\\\\" the mobile device in their pockets to have the \\\\\\\"hands free\\\\\\\" for interaction. While wearable devices such as smartwatches overcome this problem, they have the disadvantage that they often offer very small screen space. In this paper we present StrapMaps, a concept to bring interactive e-ink displays to straps of backpacks or harnesses to present map information while on-the-go in nature.\",\n \"Title: Cross-Surface: Challenges and Opportunities of Spatial and Proxemic Interaction.\\nYear: 2016\\n\\nAbstract: In this workshop, we will review and discuss open issues, technical challenges and conceptual models for multi-device spatial or proxemic interaction. We aim to bring together researchers, students and practitioners working on technical infrastructures, studies and designs of spatial in-terfaces, or domain specific multi-device applications that use space as a unit of analysis. We focus specifically on analysing how such interfaces, tools and tracking technolo-gy can be deployed \\\\\\\"in the wild\\\\\\\". The workshop will facili-tate knowledge exchange about the current state of spatial and proxemic interactive systems, identify application do-mains and enabling technologies for cross-surface interac-tions in the wild, and establish a research community to develop effective strategies for successful design of cross-device interactions.\",\n \"Title: The aesthetics of StripeMaps: being small and beautiful.\\nYear: 2016\\n\\nAbstract: Maps are a very powerful form of information visualization. Beside their main purpose to communicate often very complex relationships between elements of some space, \\\\\\\"good\\\\\\\" maps are often perceived as aesthetically pleasing and beautiful. Maps are collected for those reasons; since centuries and ages they are \\\\\\\"beautiful\\\\\\\" --over all trends. Cartography, the study and practice of making maps, has long tradition. Even today, most of the maps, e.g., road maps and public you-are-here maps, are still crafted by professional cartographers. However, maps do require space to be displayed while mobile and wearable devices are getting smaller and smaller. This presents new challenges in cartography, as designing aesthetic user interfaces is an important aspect in the area of HCI. With StripeMaps we present a technique to bring maps to very small displays without destroying their utility and beauty. In this workshop paper we discuss our design considerations in depth and open up the discussion for alternative approaches.\",\n \"Title: Changing the camera-to-screen angle to improve AR browser usage.\\nYear: 2016\\n\\nAbstract: Mobile devices are currently the most commonly used platform to experience Augmented Reality (AR). Nevertheless, they typically provide a less than ideal ergonomic experience, requiring the user to operate them with arms raised. In this paper we evaluate how to improve the ergonomics of AR experiences by modifying the angle between the mobile device's camera and its display. Whereas current mobile device cameras point out vertically from the back cover, we modify the camera angle to be 0, 45 and 90 degrees. In addition, we also investigate the use of the smartwatch as an AR browser form factor. Key findings are, that whilst the current approximately see-through configuration provides the fastest task completion times, a camera offset angle of 45° provides reduced task load and was preferred by users. When comparing different form factors and screen sizes, the smartwatch format was found to be unsuitable for AR browsing use.\",\n \"Title: ScrollingHome: bringing image-based indoor navigation to smartwatches.\\nYear: 2016\\n\\nAbstract: Providing pedestrian navigation instructions on small screens is a challenging task due to limited screen space. As image-based approaches for navigation have been successfully proven to outperform map-based navigation on mobile devices, we propose to bring image-based navigation to smartwatches. We contribute a straightforward pipeline to easily create image-based indoor navigation instructions that allow users to freely navigate in indoor environments without any localization infrastructure and with minimal user input on the smartwatch. In a user study, we show that our approach outperforms the current state-of-the art application in terms of task completion time, perceived task load and perceived usability. In addition, we did not find an indication that there is a need to provide explicit directional instructions for image-based navigation on small screens.\",\n \"Title: Crouch, Hold and Engage: Spatial Aspects of Augmented Reality Browsing.\\nYear: 2016\\n\\nAbstract: Displays that use an AR browser to augment a printed poster are nowadays commonplace in many cities. Whilst much research exists on the technical issues and interaction techniques related to such AR displays, currently there has been little research on how these applications are actually used. In this paper, we present the first study reporting on usage comfort and spatial aspects when using an AR browsing app on to browse augmented content on a physical poster. Key findings are that the mean distance users preferred to stand from the poster content was 129 cm (SD = 34 cm) and that standing further from the poster enabled a browsing task to be completed more quickly. Based on our findings we make recommendations on the content and spatial location of AR poster installations.\",\n \"Title: Beautiful...but at What Cost?: An Examination of Externalities in Geographic Vehicle Routing.\\nYear: 2017\\n\\nAbstract: Millions of people use platforms such as Google Maps to search for routes to their desired destinations. Recently, researchers and mapping platforms have shown growing interest in optimizing routes for criteria other than travel time, e.g. simplicity, safety, and beauty. However, despite the ubiquity of algorithmic routing and its potential to define how millions of people move around the world, very little is known about the externalities that arise when adopting these new optimization criteria, e.g. potential redistribution of traffic to certain neighborhoods and increased route complexity (with its associated risks). In this paper, we undertake the first controlled examination of these externalities, doing so across multiple mapping platforms, alternative optimizations, and cities. We find, for example, that scenic routing (i.e. “beauty”-optimized routing) would remove vehicles from highways, greatly increase traffic around parks, and, in certain cases, do the same for high-income areas. Our results also highlight that the interaction between routing criteria and urban structure is complex and effects vary from city to city, an important consideration for the growing literature on alternative routing strategies. Finally, to address the lack of open implementations of alternative routing algorithms and controlled routing evaluation frameworks, we are releasing our alternative routing and evaluation platform with this paper.\",\n \"Title: Controllability matters: The user experience of adaptive maps.\\nYear: 2017\\n\\nAbstract: Adaptive map interfaces have the potential of increasing usability by providing more task dependent and personalized support. It is unclear, however, how map adaptation must be designed to avoid a loss of control, transparency, and predictability. This article investigates the user experience of adaptive map interfaces in the context of gaze-based activity recognition. In a Wizard of Oz experiment we study two adaptive map interfaces differing in the degree of controllability and compare them to a non-adaptive map interface. Adaptive interfaces were found to cause higher user experience and lower perceived cognitive workload than the non-adaptive interface. Among the adaptive interfaces, users clearly preferred the condition with higher controllability. Results from structured interviews reveal that participants dislike being interrupted in their spatial cognitive processes by a sudden adaptation of the map content. Our results suggest that adaptive map interfaces should provide their users with control at what time an adaptation will be performed.\",\n \"Title: The Geography of Pokémon GO: Beneficial and Problematic Effects on Places and Movement.\\nYear: 2017\\n\\nAbstract: The widespread popularity of Pokémon GO presents the first opportunity to observe the geographic effects of location-based gaming at scale. This paper reports the results of a mixed methods study of the geography of Pokémon GO that includes a five-country field survey of 375 Pokémon GO players and a large scale geostatistical analysis of game elements. Focusing on the key geographic themes of places and movement, we find that the design of Pokémon GO reinforces existing geographically-linked biases (e.g. the game advantages urban areas and neighborhoods with smaller minority populations), that Pokémon GO may have instigated a relatively rare large-scale shift in global human mobility patterns, and that Pokémon GO has geographically-linked safety risks, but not those typically emphasized by the media. Our results point to geographic design implications for future systems in this space such as a means through which the geographic biases present in Pokémon GO may be counteracted.\",\n \"Title: HCI, Solidarity Movements and the Solidarity Economy.\\nYear: 2017\\n\\nAbstract: The financial crisis and austerity politics in Europe has had a devastating impact on public services, social security and vulnerable populations. Greek civil society responded quickly by establishing solidarity structures aimed at helping vulnerable citizens to meet their basic needs and empower them to co-create an anti-austerity movement. While digital technology and social media played an important role in the initiation of the movement, it has a negligible role in the movement's on-going practices. Through embedded work with several solidarity structures in Greece, we have begun to understand the \\\\\\\"solidarity economy\\\\\\\" (SE) as an experiment in direct democracy and self-organization. Working with a range of solidarity structures we are developing a vision for a \\\\\\\"Solidarity HCI\\\\\\\" committed to designing to support personal, social and institutional transformation through processes of agonistic pluralism and contestation, where the aims and objectives of the SE are continuously re-formulated and put into practice.\",\n \"Title: Grouve: Spontaneous Proximal Group Formation with Ultrasonic Sound Waves\\nYear: 2016\\n\\nAbstract: In daily life, we use computers and smartphones to organize a groups activities and share documents with its members. However, creating that group in the digital domain is often performed manually. Solutions to automate the process of forming a group of physically proximate members, so far, mostly relied on Bluetooth or WiFi. In contrast to these solutions we present Grouve, an automatic ad-hoc based group formation technique running based on ultrasonic waves. As Grouve uses inaudible sounds for communication, it has the natural advantage that the sound signal stays inside a limited space, which matches most of the application contexts. In this paper we report on the design process and implementation of Grouve.\",\n \"Title: It's All Around You: Exploring 360° Video Viewing Experiences on Mobile Devices.\\nYear: 2017\\n\\nAbstract: 360° videos are a new kind of medium that gives the viewers a sense of real immersion as they glimpse the action from all angles and directions. Naturally, professional and amateur film-makers are actively adopting this new medium for transformative storytelling. Despite this phenomenal progress in 360° video creation, current understanding on users' viewing experience of these videos is limited. In this paper, we present the first comparative study on the user experience with 360° videos on mobile devices using different interaction techniques. We observed 18 participants' interaction with six 360°videos with different viewport characteristics (static or moving) on a smartphone, a tablet and a head mounted display (HMD) respectively and measured how they interact with the content. We then conducted semi-structured interviews with the participants in which they explained their interaction with and viewing experience of 360° videos across three devices. Our findings show that 360° videos with moving viewports elicit higher engagement from the viewers, and offer superior viewing experience. However, these videos are cognitively demanding and require constant user attention. Our participants preferred the condition with dynamic peephole interaction on a smartphone for watching 360° videos due to the simplicity in exploration and familiarity with navigation controls. Many participants reported that the HMD offers the most immersive experience however it comes at the expense of higher cognitive burden, motion sickness and physical discomfort.\",\n \"Title: Pharos: Improving Navigation Instructions On Smartwatches By Including Global Landmarks\\nYear: 2017\\n\\nAbstract: Landmark-based navigation systems have proven benefits relative to traditional turn-by-turn systems that use street names and distances. However, one obstacle to the implementation of landmark-based navigation systems is the complex challenge of selecting salient local landmarks at each decision point for each user. In this paper, we present Pharos, a novel system that extends turn-by-turn navigation instructions using a single global landmark (e.g. the Eiffel Tower, the Burj Khalifa, municipal TV towers) rather than multiple, hard-to-select local landmarks. We first show that our approach is feasible in a large number of cities around the world through the use of computer vision to select global landmarks. We then present the results of a study demonstrating that by including global landmarks in navigation instructions, users navigate more confidently and build a more accurate mental map of the navigated area than using turn-by-turn instructions.\",\n \"Title: \\\"In Search Of Light\\\": Enhancing Touristic Recommender Services With Local Weather Data\\nYear: 2017\\n\\nAbstract: Many destinations' economies strongly rely on tourism. Therefore, it is crucial to meet tourists' expectations, so they will return to the destination. The geographical formation of certain touristic islands often leads to local climates where it can be rainy and windy on one side of the island, whereas the other part is sunny. In this paper, we present a novel use for a network of sensors, LightBeam, a mobile location-based application aiming to improve the tourists' experience. The application focuses on providing real-time guidance for tourists seeking sunlight to maximize their holiday experience by suggesting the closest points of interest (POIs) to the user with the \\\"best sunlight\\\". To achieve this, we implemented and installed a network of geospatial sensors. The data from the sensor network is combined with the current location of the users to provide recommendations. We report on the initial design and prototype of LightBeam.\",\n \"Title: Augmented invaders: a mixed reality multiplayer outdoor game\\nYear: 2017\\n\\nAbstract: Many virtual and mixed reality games focus on single player experiences. In this paper, we describe the concept and prototype implementation of a mixed reality multiplayer game that can be played with a smartphone and an HMD in outdoor environments. Players can team up to fight against attacking alien drones. The relative positions between the players are tracked using GPS, and the rear camera of the smartphone is used to augment the environment and teammates with virtual objects. The combination of multiplayer, mixed reality, the use of geographical location and outdoor action together with affordable, mobile equipment enables a novel strategic and social game experience.\",\n \"Title: SubwayAPPS: Using Smartphone Barometers for Positioning in Underground Transportation Environments.\\nYear: 2016\\n\\nAbstract: Location information that is crucial for all location-based services is almost always available due to a number of different positioning techniques and technologies such as GPS and WiFi positioning. However, positioning technologies cannot provide sufficient position information when a user is underground, e. g. travelling with a car through a tunnel or on subways on an underground public transportation network. While there have been a number of attempts to utilize expensive infrastructure and smartphone sensors to address this situation, all of these techniques are either limited in scope, very expensive, or somewhat limited in accuracy. In this paper, we present a novel smartphone-based approach called SubwayAPPS (Subway Air Pressure Positioning System) that for the first time utilizes relative air pressure changes as detected by smartphone barometers to position a user. We first demonstrate the feasibility of this approach by comparing the depth characteristics of five major underground transportation networks across the globe and show that our novel approach is feasible for positioning users while they are underground in these networks. Second, we showwith two user tests in Brussels and London that our lightweight approach works well as other more complex techniques, e. g. techniques that rely on pattern matching using the build-in accelerometers or gyroscopes, under realistic conditions.\",\n \"Title: NavigaTone: Seamlessly Embedding Navigation Cues in Mobile Music Listening\\nYear: 2018\\n\\nAbstract: ABSTRACTAs humans, we have the natural capability of localizing the origin of sounds. Spatial audio rendering leverages this skill by applying special filters to recorded audio to create the impression that a sound emanates from a certain position in the physical space. A main application for spatial audio on mobile devices is to provide non-visual navigation cues. Current systems require users to either listen to artificial beacon sounds, or the entire audio source (e.g., a song) is repositioned in space, which impacts the listening experience. We present NavigaTone, a system that takes advantage of multi-track recordings and provides directional cues by moving a single track in the auditory space. While minimizing the impact of the navigation component on the listening experience, a user study showed that participants could localize sources as good as with stereo panning while the listening experience was rated to be closer to common music listening.\",\n \"Title: Defining and Predicting the Localness of Volunteered Geographic Information using Ground Truth Data\\nYear: 2018\\n\\nAbstract: ABSTRACTMany applications of geotagged content are predicated on the concept of localness (e.g., local restaurant recommendation, mining social media for local perspectives on an issue). However, definitions of who is a \\\"local\\\" in a given area are typically informal and ad-hoc and, as a result, approaches for localness assessment that have been used in the past have not been formally validated. In this paper, we begin the process of addressing these gaps in the literature. Specifically, we (1) formalize definitions of \\\"local\\\" using themes identified in a 30-paper literature review, (2) develop the first ground truth localness dataset consisting of 132 Twitter users and 58,945 place-tagged tweets, and (3) use this dataset to evaluate existing localness assessment approaches. Our results provide important methodological guidance to the large body of research and practice that depends on the concept of localness and suggest means by which localness assessment can be improved.\",\n \"Title: SIG: Making Maps Accessible and Putting Accessibility in Maps.\\nYear: 2018\\n\\nAbstract: Digital maps represent an incredible HCI success-they have transformed the way people navigate in and access information about the world. While these platforms contain terabytes of data about road networks and points of interest (POIs), their information about physical accessibility is commensurately poor. Moreover, because of their highly graphical nature and reliance on gesture and mouse input, digital maps can be inaccessible to some user groups (e.g., those with visual or motor impairments). While there is active HCI work towards addressing both concerns, to our knowledge, there has been no direct effort to unite this research community. The goal of this SIG is threefold: first, to bring together and network scholars and practitioners who are broadly working in the area of accessible maps; second, to identify grand challenges and open problems; third, to help better establish accessible maps as a valuable topic with important HCI-related research problems.\",\n \"Title: Reflections on the NatureCHI Workshop Series: Unobtrusive User Experiences with Technology in Nature.\\nYear: 2018\\n\\nAbstract: Being in nature is often regarded to be calming, relaxing and purifying. While technology has the potential to support engagement with nature, developing systems that provide support in an unobtrusive manner holds many challenges for interaction design. In this article, we describe our reflections around the NatureCHI workshop series. Our aim with the workshops has been to help foster a research community interested in the design of Unobtrusive User Experiences with Technology in Nature. The first of two workshops ran as part of CHI 2016 in San Jose, California, while the second workshop took place alongside MobileHCI 2017 in Vienna, Austria. With 25 papers presented in total, the workshops demonstrate a rising interest in the areas where nature and interactive technologies meet.\",\n \"Title: Where2: Exploring prior visitor journeys to enrich upcoming visitor's experience.\\nYear: 2018\\n\\nAbstract: In recent years, the growth of the travel industry has been astounding. Yet, it is still not easy for an individual to have a meaningful travel experience even, so vast amounts of information are available on the web. As an attempt to support travel decisionmaking and improve visitoru0027s experience, we designed Where2. Where2 is a location-based mobile app that highlights Points of Interest (POI) customized to touristu0027s travel- related attributes (e.g. the length of their stay). POIs are in-situ recommendations proposed as Ghost Paths journeys - the idea that you can follow a prior tourist shoes to explore the city. We generate Ghost Paths journeys by using a non-intrusive positioning system that captures prior touristsu0027 Wi-Fi signal and detect their journeys across the city. With this system our goal is to support visitors with their travel decisions and nudge them towards optimal journey choices that maximize their utilities.\",\n \"Title: Foldwatch: using origami-inspired paper prototypes to explore the extension of output space in smartwatches.\\nYear: 2018\\n\\nAbstract: Smartwatches are highly portable, ubiquitous devices, allowing rich interaction at a small scale. However, the display size can hinder user engagement, limit information display, and presentation style. Most research focuses on exploring ways in which the interaction area of smartwatches can be extended, although this mainly entails simple fold-out displays or additional screens. Conversely, added weight and size can hinder the wearable experience. In response, we took inspiration from origami and explored the design space for new types of lightweight, highly foldable smartwatch, by developing complex paper-prototypes which demonstrate novel ways of extending screen space. We collected data on potential input and output interaction with complex folded smartwatch displays during workshops with expert and non-expert users, discovering application ideas and additional input/output functionality. These insights were used to produce and evaluate a concept video for the FoldWatch prototype.\",\n \"Title: Integrating Physical Activity and Health Aspects in Everyday Mobility.\\nYear: 2018\\n\\nAbstract: Everyday mobility encompasses different forms of public and private transportation. However, everyday mobility often does not involve substantial levels of physical activity. The goal of this workshop is to investigate ways of integrating physical activity into everyday mobility in accordance with widely accepted health recommendations. We aim to explore wearable and ambient systems that sense and support active navigation as well as conceptual aspects from a variety of perspectives, such as persuasive technologies. Researchers from different disciplines are invited to contribute their point of view by means of position papers, posters, and demonstrations. One planned outcome of this workshop is a set of design guidelines for navigation systems that explicitly consider health aspects. The workshop explores requirements and design challenges in a creative setting.\",\n \"Title: Tactile hand motion and pose guidance for 3D interaction\\nYear: 2018\\n\\nAbstract: We present a novel forearm-and-glove tactile interface that can enhance 3D interaction by guiding hand motor planning and coordination. In particular, we aim to improve hand motion and pose actions related to selection and manipulation tasks. Through our user studies, we illustrate how tactile patterns can guide the user, by triggering hand pose and motion changes, for example to grasp (select) and manipulate (move) an object. We discuss the potential and limitations of the interface, and outline future work.\",\n \"Title: Using smartphones to take eye images for disease diagnosis in developing countries.\\nYear: 2018\\n\\nAbstract: Mobile health applications can increase access to healthcare in low and middle-income countries, where the penetration of smartphones is rapidly increasing. However, despite a large amount of clinical trials of mobile health applications, there is little proof of beneficial clinical outcomes. Recent studies suggest that one of the reasons is user experience issues. The aim of our work is to improve the user experience of acquiring medical eye images with a smartphone camera. We present EyeGuide, a mobile application that assists people in taking eye images. Possible application areas are the algorithmic detection of diseases and the support of clinicians during tele-medicine examinations.\",\n \"Title: Grand challenges in accessible maps\\nYear: 2019\\n\\nAbstract: In this forum we celebrate research that helps to successfully bring the benefits of computing technologies to children, older adults, people with disabilities, and other populations that are often ignored in the design of mass-marketed products. --- Juan Pablo Hourcade, Editor\",\n \"Title: Bimanual Interaction with Interscopic Multi-Touch Surfaces\\nYear: 2009\\n\\nAbstract: Multi-touch interaction has received considerable attention in the last few years, in particular for natural two-dimensional (2D) interaction. However, many application areas deal with three-dimensional (3D) data and require intuitive 3D interaction techniques therefore. Indeed, virtual reality (VR) systems provide sophisticated 3D user interface, but then lack efficient 2D interaction, and are therefore rarely adopted by ordinary users or even by experts. Since multi-touch interfaces represent a good trade-off between intuitive, constrained interaction on a touch surface providing tangible feedback, and unrestricted natural interaction without any instrumentation, they have the potential to form the foundation of the next generation user interface for 2D as well as 3D interaction. In particular, stereoscopic display of 3D data provides an additional depth cue, but until now the challenges and limitations for multi-touch interaction in this context have not been considered. In this paper we present new multi-touch paradigms and interactions that combine both traditional 2D interaction and novel 3D interaction on a touch surface to form a new class of multi-touch systems, which we refer to as interscopic multi-touch surfaces (iMUTS). We discuss iMUTS-based user interfaces that support interaction with 2D content displayed in monoscopic mode and 3D content usually displayed stereoscopically. In order to underline the potential of the proposed iMUTS setup, we have developed and evaluated two example interaction metaphors for different domains. First, we present intuitive navigation techniques for virtual 3D city models, and then we describe a natural metaphor for deforming volumetric datasets in a medical context.\",\n \"Title: WatchThru: Expanding Smartwatch Displays with Mid-air Visuals and Wrist-worn Augmented Reality.\\nYear: 2017\\n\\nAbstract: We introduce WatchThru, an interactive method for extended wrist-worn display on commercially-available smartwatches. To address the limited visual and interaction space, WatchThru expands the device into 3D through a transparent display. This enables novel interactions that leverage and extend smartwatch glanceability. We describe three novel interaction techniques, Pop-up Visuals, Second Perspective and Peek-through, and discuss how they can complement interaction on current devices. We also describe two types of prototypes that helped us to explore standalone interactions, as well as, proof-of-concept AR interfaces using our platform.\",\n \"Title: The Role of Physical Props in VR Climbing Environments.\\nYear: 2019\\n\\nAbstract: Dealing with fear of falling is a challenge in sport climbing. Virtual reality (VR) research suggests that using physical and reality-based interaction increases the presence in VR. In this paper, we present a study that investigates the influence of physical props on presence, stress and anxiety in a VR climbing environment involving whole body movement. To help climbers overcoming fear of falling, we compared three different conditions: Climbing in reality at 10 m height, physical climbing in VR (with props attached to the climbing wall) and virtual climbing in VR using game controllers. From subjective reports and biosignals, our results show that climbing with props in VR increases the anxiety and sense of realism in VR for sport climbing. This suggests that VR in combination with physical props are an effective simulation setup to induce the sense of height.\",\n \"Title: Understanding Users' Knowledge About The Privacy And Security Of Browser Extensions\\nYear: 2021\\n\\nAbstract: Browser extensions enrich users' browsing experience, e.g., by blocking unwanted advertisements on websites. To perform these functions, users must grant certain permissions during the installation process. These permissions, however, give very limited information about the fact that they allow the extension to access user's personal data and browsing behaviour, posing security and privacy risks. To understand users' awareness of these privileges and the associated threats, we conducted an online survey with 353 participants, focusing on users' attitude, knowledge, and preference towards extensions' permission requests. We found that users report interest in seeking information, trust the developers but do little to protect their data. They have limited knowledge about the technical abilities of browser extensions and prefer permission statements that evoke a clear mental model. Based on our findings we derive recommendations for the improvement of browser extension permission dialogues through clear language, technical improvements and distinct responsibilities.\",\n \"Title: VeloCity: Using Voice Assistants for Cyclists to Provide Traffic Reports\\nYear: 2021\\n\\nAbstract: ABSTRACTCycling is on the rise as a relevant alternative to car-based mobility and even though there are mobile applications specifically designed for cyclists to support this development, many still face unresolved challenges in terms of safe user interaction with complex data while riding. We present the design, development, and evaluation of VeloCity - an application for reporting traffic incidents and structures relevant to cyclists. In a case study, we compared its' three input methods (touch, in-app speech recognition, the voice assistant of the operating system) to evaluate which attributes make for safe interaction while cycling. We found that participants prefer to use the voice assistant over the other modalities as it was least distracting due to its hands- and eyes-free interaction design. Furthermore, they chose short commands over conversational phrases. Based on our results, we present five guidelines for designing voice user interfaces for cyclists and argue for moving away from touch-based interfaces in this domain, which still make up most of the applied interaction techniques today.\",\n \"Title: Different Length, Different Needs: Qualitative Analysis of Threads in Online Health Communities\\nYear: 2022\\n\\nAbstract: Online health communities provide a knowledge exchange platform for a wide range of diseases and health conditions. Informational and emotional support helps forum participants orient around health issues beyond in-person doctor visits. So far, little is known about the relation between the level of participation and participants' contributions in online health communities. To gain insights on the issue, we analyzed 456 posts in 56 threads from the Dermatology sub-forum of an online health community. While low participation threads ('short threads') revolved around solving an individual's health issue through diagnosis suggestions and medical advice, participants in high participation threads ('long threads') built collective knowledge and a sense of community, typically discussing chronic and rare conditions that medical professionals were unfamiliar with or could not treat effectively. Our results suggest that in short threads an individual's health issue is addressed, while in long threads, sub-communities about specific rare and chronic diseases emerge. This has implications for the user interface design of health forums, which could be developed to better support community building elements, even in short threads.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":96,"string":"96"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13667,"string":"13,667"}}},{"rowIdx":13668,"cells":{"question_id":{"kind":"string","value":"Baogang Wei"},"question":{"kind":"string","value":"Generate an abstract for the title \"Evaluating Collinearity Constraint for Automatic Range Image Registration\" using the following items: range image registration, collinearity error, closest point criterion, false matches, motion parameters."},"answer":{"kind":"string","value":"While most of the existing range image registration algorithms either have to extract and match structural (geometric or optical) features or have to estimate the motion parameters of interest from outliers corrupted point correspondence data for the elimination of false matches in the process of image registration, the registration error and the collinearity error derived directly from the traditional closest point criterion are also capable of doing the same job. However, the latter has an advantage of easy implementation. The purpose of this paper is to investigate which definition of collinearity is more accurate and stable in eliminating false matches inevitably introduced by the closest point criterion. The experiments based on real images show the advantages and disadvantages of different definitions of collinearity."},"correct_answer":{"kind":"string","value":"While most of the existing range image registration algorithms either have to extract and match structural (geometric or optical) features or have to estimate the motion parameters of interest from outliers corrupted point correspondence data for the elimination of false matches in the process of image registration, the registration error and the collinearity error derived directly from the traditional closest point criterion are also capable of doing the same job. However, the latter has an advantage of easy implementation. The purpose of this paper is to investigate which definition of collinearity is more accurate and stable in eliminating false matches inevitably introduced by the closest point criterion. The experiments based on real images show the advantages and disadvantages of different definitions of collinearity."},"documents":{"kind":"list like","value":["Title: Cross-Reading by Leveraging a Hybrid Index of Heterogeneous Information.\nYear: 2013\n\nAbstract: In this paper, we present a novel application named Cross-reading, which is derived from user's reading process. Cross-reading is essentially a searching by document task from large-scale text corpus. The state-of-the-art approaches utilize similarity hashing to address this issue by modeling it as a high-dimensional data similarity search problem. However, most approaches only consider document's lexical information while ignoring documents semantic information and metadata. Moreover, searching similar hash codes from massive hash codes quickly is still a major bottleneck. To address those problems, we propose a Fast Searching By Document approach, which considers the Cross-reading from the perspective of semantic similarity and time efficiency. ? Springer Science+Business Media Singapore 2014.","Title: A new video text detection method\nYear: 2011\n\nAbstract: Nowadays, digital libraries contain more and more videos in them, and how to organize and retrieve those videos effectively has become very urgent. Text in videos is a very meaningful clue for video semantic understanding, and it can be used for video organization and retrieval. However, existing text recognizing methods can not deal with multilingual texts or texts embedded in a complex background very well. In this paper, we propose a novel video text detection method. Edge detection and candidate region extraction are firstly used to obtain all rough candidate text regions, and then region refinement is used to obtain the accurate location of each region. Based on our observation that a real text region has a uniform distribution with its non-zero pixels in its binary image, an entropy filter is used to remove non-text regions. Experiments on various videos show that our method is effective and robust against different languages, background complexities and font styles.","Title: In-depth utilization of Chinese ancient maps: a hybrid approach to digitizing map resources in CADAL\nYear: 2010\n\nAbstract: Digital map is getting increasingly popular as an intuitive and interactive platform for data presentation recently. Thus applications integrated with digital map have attracted much attention. But no offtheshelf systems or services could we use if the time span of maps be extended to historical ones. There are a large number of valuable ancient atlases in CADAL digital library. However, they are seldom made use of because the ones which are in image format are not convenient for users to read or search. In this paper, we propose a novel hybrid approach to utilizing these atlases directly and constructing some applications based on ancient maps. We call it CAMAME which means Chinese Ancient Maps Automatic Marking and Extraction. We create a gazetteer to store the geographic information of sites which will be project on the map, then use kernel method to do the regression and correct the estimated results with image processing and local regression methods. The empirical results show that CAMAME is effective and efficient, by which most valuable data in the map images is marked and identified. Some Chinese literary chronicle applications that exhibit ancient literary and related historical information over those digitized atlas resources in CADAL digital library were developed.","Title: Topic Discovery based on LDA_col Model and Topic Significance Re-ranking.\nYear: 2011\n\nAbstract: This paper presents a method to find the topics efficiently by the combination of topic discovery and topic re-ranking. Most topic models rely on the bag-ofwords(BOW) assumption. Our approach allows an extension of LDA model-Latent Dirichlet Allocation_Collocation (LDA_col) to work in corpus such that the word order can be taken into consideration for phrase discovery, and slightly modify the modal for modal consistency and effectiveness. However, LDA_col results may not be ideal for user's understanding. In order to improve the topic modeling results, two topic significance re-ranking methods (Topic Coverage(TC) and Topic Similarity(TS)) are proposed. We conduct our method on both English and Chinese corpus, the experimental results show that the modified LDA_col discovers more meaningful phrases and more understandable topics than LDA and LDA_col. Meanwhile, topic re-ranking method based on TC performs better than TS, and has the ability of re-ranking the \"significant\" topics higher than \"insignificant\" ones. © 2011 ACADEMY PUBLISHER.","Title: A Scalable, Adaptive, Self-management and Fault-Tolerant Architecture for Digital Library.\nYear: 2012\n\nAbstract: With the rapid growth of data, two major challenges are taking place in digital library technologies. One is the transition from database to Data Ocean; the other is the transition from digital library to Smart Library. In order to address these challenges, we propose a scalable, adaptive, self-management, fault-tolerant architecture for various services in the digital library. Users can deploy services quite easily on the architecture, and it also enables the deployed services load balancing, scalable, adaptive and fault-tolerant. The practice in CADAL demonstrates the feasibility and efficiency of our architecture. © 2012 Springer-Verlag.","Title: 3D shape matching using collinearity constraint\nYear: 2004\n\nAbstract: In this paper, a novel algorithm is proposed to carry out automatic 3D shape matching with 3D shapes represented as sets of points. After the possible matches between the 3D shapes have been determined by the tradition ICP criterion, the novel approach employs the collinearity constraint to eliminate false matches based on a statistical model. A comparative study based on real range images has shown that the proposed algorithm is accurate, robust and efficient for the automatic matching of overlapping 3D shapes.","Title: 3D Free Form Surface Matching Based on Orientation Difference Length Distribution\nYear: 2005\n\nAbstract: In this paper, we propose using orientation difference length distribution (ODLD) to represent a 3D view of each object with free form surface. ODLD has a number of advantages over the shape distribution. A comparative study of ODLD and the shape distribution has shown that the former is significantly more accurate for 3D free form surface matching than the latter. © 2005 IEEE.","Title: CMSOF: a structured data organization framework for scanned Chinese medicine books in digital libraries.\nYear: 2010\n\nAbstract: Organizing unstructured information from books into a well-defined structure is a significant challenge in digital libraries.\n Most digital libraries can provide only search services at the granularity of books and few libraries allow books to be accessed\n at the granularity of chapters, as manually constructing directory information for books is time-consuming. Extracting structured\n data from scanned books thus remains an urgent and important work. In this paper, we propose a novel structured data organization\n framework called CMSOF to organize scanned data automatically, and apply it to a Chinese medicine digital library. In the\n framework, image blocks and text blocks on the scanned page of books are separated based on the gray histogram projection\n method or a hybrid method of region growth and the Ada-Boosting classifier at first, and then the text structure is obtained\n from text blocks by text size and font type recognition. Finally, image blocks and structured OCRed text are correlated at\n the semantic level. By integrating the structured data into a Chinese medicine information system (CMIS), we can organize\n the Chinese medicine books well and users can access the books with flexibility, which indicates that CMSOF is an efficient\n framework to organize books mixed with images and text.","Title: When personalization meets socialization: an iCADAL approach\nYear: 2011\n\nAbstract: CADAL has been a large-scale non-profit digital library. Besides various search facilities in the CADAL portal, we designed and implemented the iCADAL system for providing the user-oriented micro-content services on one million books in CADAL. Users of iCADAL can receive the stream of short messages of lending, annotation and the other reading activities shared by their followees in a twitter-like way, which combines socialization with personalization. Our implementation makes extensive use of open source softwares, i.e. Pylons, Cassandra, in order to support the high-traffic online micro-content services.","Title: Projecting registration error for accurate registration of overlapping range images\nYear: 2006\n\nAbstract: In this paper, we propose a novel algorithm for the automatic registration of two overlapping range images. Since it is relatively difficult to compare the registration errors of different point matches, we project them onto a virtual image plane for more accurate comparison using the classical pin-hole perspective projection camera model. While the traditional ICP algorithm is more interested in the points in the second image close to the sphere centred at the transformed point, the novel algorithm is more interested in the points in the second image as collinear as possible to the transformed point. The novel algorithm then extracts useful information from both the registration error and projected error histograms for the elimination of false matches without any feature extraction, image segmentation or the requirement of motion estimation from outliers corrupted data and, thus, has an advantage of easy implementation. A comparative study based on real images captured under typical imaging conditions has shown that the novel algorithm produces good registration results.","Title: Free form surface matching using motion consistency\nYear: 2003\n\nAbstract: In this paper, we propose a novel algorithm to evaluate the possible point matches established by the traditional ICP algorithm in the process of matching two overlapping free from surfaces represented as two sets of unorganised points. While the existing methods mainly use feature matching to establish or evaluate possible correspondences between the surfaces to be matched, our novel approach applies motion consistency to evaluate the possible correspondences. In particular, while the existing methods assume that satisfying local structural constraints is a necessary condition for a pair of points to represent a real correspondence, our novel approach proves that satisfying global rigid motion constraints is a sufficient condition. A comparative study based on real images has shown that the proposed algorithm is accurate and robust for the matching of overlapping free form surfaces.","Title: Transactional Multi-row Access Guarantee in the Key-Value Store\nYear: 2012\n\nAbstract: The emergence of Cloud Computing and Big Data drives the development of novel data stores named NoSQL. A mass of data stores are developed and the most are key-value stores, where the stores are partitioned with keys and a key can identify a row uniquely. However, the requirement for efficiency and scalability makes them only provide the single-row atomic access. But in the Big Data era, more and more applications built on the key-value stores need transactional functionality across multiple rows. So, it is natural to implement a multi-row transaction management for key-value stores. In this paper, we implement a transaction processing system (TrasPS) which guarantees the transactional multi-row access from the application client to the key-value store in our unstructured data management system (UDMS). We also provide fault tolerance and recovery for the transactions. The implementation and experiments in our UDMS show that TrasPS can provide scalable multi-row access functionality at a very low overhead.","Title: Using hybrid knowledge engineering and image processing in color virtual restoration of ancient murals\nYear: 2003\n\nAbstract: This paper proposes a novel scheme to virtually restore the colors of ancient murals. Our approach integrates artificial intelligence techniques with digital image processing methods. The knowledge related to the mural colors is first categorized into four types. A hybrid frame and rule-based approach is then developed to represent knowledge and to inter colors. An algorithm that takes into account color similarity and spatial proximity is developed to segment mural images. A novel color transformation method based on color histograms is finally proposed to restore the colors of murals. A number of experiments based on real images have demonstrated the validity of the proposed scheme for color restoration.","Title: Developing structural constraints for accurate registration of overlapping range images\nYear: 2004\n\nAbstract: Automatic image registration is an attractive and unresolved problem in the machine vision literature. This paper presents two novel structural constraints, namely proximity and closeness constraints, that improve both the accuracy and robustness of an existing motion consistency based algorithm (GICP) for automatic image registration. It also defines the conditions when such constraints are fired at specific points of the registration of two overlapping range images. The proximity constraint says that neighbouring points should be neighbouring before and after a rigid motion. The closeness constraint implements a local closest point search from the second image to the first image. While the GICP algorithm uses an exhaustive search for the closest points, in this paper, we employ the optimised k–d tree data structure to accelerate the closest point search. A large number of experiments based on real range images demonstrate that the combination of rigid motion constraints with these novel proximity and closeness constraints leads to a more accurate and robust evaluation of possible correspondences leading, in turn, to more accurate, robust and efficient automatic image registration results.","Title: Cubic algebraic curves based on geometric constraints\nYear: 2001\n\nAbstract: Methods for curve modeling with cubic algebraic curves based on geometric constraints are introduced in this paper. A 1-parameter family of cubic curves with four given points as well as two tangent lines at the endpoints is constructed in the first part of the paper. A 1-parameter family and 2-parameter family of cubic curve constructions are presented for interpolating two given endpoints and two given tangent lines as well as two given curvatures at the endpoints in the last part of the paper.","Title: Improving multi-faceted book search by incorporating sparse latent semantic analysis of click-through logs\nYear: 2012\n\nAbstract: Multi-faceted book search engine presents diverse category-style options to allow users to refine search results without re-entering a query. In this paper, we propose a novel multi-faceted book search engine that utilizes users' query-related latent intents mined from click-through logs as multiple facets for books. The latent query intents can be effectively and efficiently discovered by applying the Sparse Latent Semantic Analysis (LSA) model to users' query and clicking behaviors in the click-through logs. This paper presents the details to improve the multi-faceted book search by incorporating the compact representation of query-intent-book relationships generated by Sparse LSA into the off-line and online processing procedures. The specificity of latent query intents can be flexibly changed by adjusting the sparsity level of projection matrix in the Sparse LSA model. We evaluated our approach on CADAL click-through logs containing 45,892 queries and 164,822 books. The experimental results show the Sparse LSA model with more sparse projection matrix tends to discover the more specific latent query intents. The latent query intents suggested by our approach usually gain the high user satisfaction ratio.","Title: Discontinuity-Based Simplification of Free Form Surface From a Range Image\nYear: 2006\n\nAbstract: This paper improves an existing method for more accurate simplification of free form surface represented as dense points. The main improvement lies in two aspects: one is the introduction of a novel transformation of curvatures, the other is to propose sampling points using both curvature information and curvature variation information. The experimental results based on real images show that the original object surface has been better approximated and the majority of the surface discontinuities have been better preserved with considerably few sampled (5%) data points.","Title: HAaaS: Towards Highly Available Distributed Systems\nYear: 2012\n\nAbstract: High availability is a valuable property in distributed systems. The master-slave model is used wildly in data management systems for high performance. However, many master-slave systems still have SPOF (Single Point of Failure) for the single master node. We exploit a generalized solution to meet several common use cases for different master-slave systems. The solution makes the high availability as a service (HAaaS), which uses a shared storage infrastructure to make the master stateless and provides an automatic fail over of high-availability service. We deploy the HAaaS in many master-slave subsystems in our unstructured data management system (UDMS) to make the UDMS highly available. The experiments demonstrate the feasibility and efficiency of our solution.","Title: A digital library architecture supporting massive small files and efficient replica maintenance\nYear: 2010\n\nAbstract: In this paper, we presented a service infrastructure based on distributed file system for massive storage in digital library. In addition, we addressed the small-file problem by merging small files into big ones, and proposed a novel dynamic replica number adjustment scheme to ensure the maximal availability and reliability in a limited storage space.","Title: A Cluster-Based Incremental Recommendation Algorithm on Stream Processing Architecture\nYear: 2013\n\nAbstract: By helping users discover books they may be interested in, recommender systems fully exploit the resources of digital libraries and better facilitate users' reading demands. Traditional memory-based collaborative filtering (CF) methods are effective and easy to interpret. However, when datasets become larger, the traditional way turns to be infeasible in both time and space. In order to address this challenge, we propose an incremental, cluster-based algorithm on Stream Processing Architecture, which is scalable and suitable to real-time environment. Our experimental results on MovieLens datasets and CADAL user-chapter logs show our algorithm is efficient, while still maintains comparable accuracy and interpretability.","Title: Digital Library Engine: Adapting Digital Library for Cloud Computing\nYear: 2013\n\nAbstract: With the rapid growth of digital libraries, more data and smart services are involved. People come to recognize the importance of digital libraries and the convenience they might bring to the society. However, the cost of owning a digital library is quite high, and many institutions do not have the ability to run and maintain a digital library by themselves, especially for massive data and complex services which require lots of storage and computing resources. In this paper, we proposed the Digital Library Engine, which aims to provide a new Platform as a Service for fast developing and deploying digital libraries in cloud. To the best of our knowledge, it is the first work to create a PaaS system for digital libraries. With the help of Digital Library Engine, institutions only need to develop some service bundles, which can be deployed in the engine, and then their own digital libraries could be running well with features of scalability, reliability, security, extensibility, availability and manageability. The practice in CADAL and the experiments demonstrate the feasibility and efficiency of our engine.","Title: CARES: a ranking-oriented CADAL recommender system\nYear: 2009\n\nAbstract: A recommender system is useful for a digital library to suggest the books that are likely preferred by a user. Most recommender systems using collaborative filtering approaches leverage the explicit user ratings to make personalized recommendations. However, many users are reluctant to provide explicit ratings, so ratings-oriented recommender systems do not work well. In this paper, we present a recommender system for CADAL digital library, namely CARES, which makes recommendations using a ranking-oriented collaborative filtering approach based on users' access logs, avoiding the problem of the lack of user ratings. Our approach employs mean AP correlation coefficients for computing similarities among users' implicit preference models and a random walk based algorithm for generating a book ranking personalized for the individual. Experimental results on real access logs from the CADAL web site show the effectiveness of our system and the impact of different values of parameters on the recommendation performance.","Title: Range image registration using hierarchical segmentation and clustering\nYear: 2009\n\nAbstract: An accurate, robust, and automatic registration of overlapping range images is usually a pre-requisite step for range image analysis and applications. While accurate depiction of object geometry requires the increase of the resolutions of images and thus, the amount of data to process, an efficient processing of such data then usually becomes an issue. In this paper, we first employ the efficient tensor analysis and k means clustering methods to hierarchically segment and cluster the original range images into a small number of planar patches represented as the closest points in the original images to their centroids. Then an advanced ICP variant is adopted to register such closest points. Finally, another ICP variant is used to refine the registration results obtained over all the points in the images. The experimental results based on real range images show that the proposed technique significantly outperforms the selected two state of the art ones for accurate and efficient registration of overlapping range images.","Title: A Clustering Approach to Free Form Surface Reconstruction from Multi-view Range Images\nYear: 2009\n\nAbstract: 3D modelling finds a wide range of applications in robot vision and reverse engineering. However due to the presence of surface scanning noise, accumulative registration error, and improper data fusion, the reconstructed surfaces from multiple registered range images are often non-smooth and distorted with thick patches, false connections and blurred features. These shortcomings will limit the wide applications of 3D modelling using the latest laser scanning systems. In this paper, we first employ the principal component analysis and hierarchical segmentation to classify surface patches into featured and non-features areas, the k-means clustering and fuzzy c means clustering approaches from the pattern recognition literature are then employed to integrate these areas respectively. Finally the fused point set is triangulated using the improved Delaunay method, guaranteeing a watertight surface. The new method is theoretically guaranteed to converge and combine the advantage of exclusive k means clustering and overlapping fuzzy c means clustering methods. A comparative study based on real images shows that the proposed algorithm desirably retains geometric details and produces smooth surface.","Title: Accurate Range Image Registration: Eliminating or Modelling Outliers\nYear: 2007\n\nAbstract: Automatic and accurate range image registration is often a prerequisite step for range image analysis and interpretation. Due to occlusion, appearance and disappearance of points in different images, outliers inevitably occur. In this case, various techniques to eliminate and model outliers have been proposed for accurate range image registration. The objective of this paper is to experimentally investigate which of the outlier elimination and modelling is more effective for the evaluation of possible correspondences established, so that a deep insight into how advanced range image registration algorithms will be developed can be obtained. The experimental results based on both synthetic data and real images show that the outlier modelling often outperforms the outlier elimination in the sense of producing more accurate and robust range image registration results.","Title: Enhanced Micro-structure Descriptor based Image Retrieval.\nYear: 2013\n\nAbstract: We present a new image retrieval method based on enhanced micro-structure descriptor in this paper. Traditional micro-structure based descriptors such as multi-texton histogram (MTH) and micro-structure descriptor (MSD) can integrate local texture, shape and color feature from the image into a feature vector, but their description abilities for natural images are not sufficient. In this paper we firstly use a new local pattern map to create filter map, and the enhanced MSD descriptor (EMSD) is extracted based on the color co-occurrence relationship on the filtered and quantized image. We then normalize the distance between images to range [0, 1]. The proposed method is extensively tested on Corel-5000 and Corel-10000 image sets and experimental results show that our method can achieve a higher retrieval precision than MSD and other representative methods.","Title: A social network-aware top-N recommender system using GPU\nYear: 2011\n\nAbstract: A book recommender system is very useful for a digital library. Good book recommender systems can effectively help users find interesting and relevant books from the massive resources, by providing individual recommendation book list for each end-user. By now, a variety of collaborative filtering algorithms have been invented, which are the cores of most recommender systems. However, because of the explosion of information, especially in the Internet, the improvement of the efficiency of the collaborative filting (CF) algorithm becomes more and more important. In this paper, we first propose a parallel Top-N recommendation algorithm in CUDA (Compute Unified Device Architecture) which combines the collaborative filtering and trust-based approach to deal with the cold-start user problem. Then based on this algorithm, we present a parallel book recommender system on a GPU (Graphics Processor unit) for CADAL digital library platform. Our experimental results show our algorithm is very efficient to process the large-scale datasets with good accuracy, and we report the impact of different values of parameters on the recommendation performance.","Title: Accurate overlap area detection using a histogram and multiple closest points\nYear: 2010\n\nAbstract: In this paper, we propose a novel ICP variant that uses a histogram in conjunction with multiple closest points to detect the overlap area between range images being registered. Tentative correspondences sharing similar distances are normally all within, or all outside, the overlap area. Thus, the overlap area can be detected in a bin by bin batch manner using a histogram. Using multiple closest points is likely to enlarge the distance difference for tentative correspondences in the histogram, and pull together the images being registered, facilitating the overlap area detection. Our experimental results based on real range images show that the performance of our proposed algorithm enhances the state of the art.","Title: Nose detection based feature extraction on both normal and abnormal 3D faces\nYear: 2008\n\nAbstract: This paper presents a feature extraction method, which does not require a frontal model and is applicable on both normal and some abnormal faces (abnormality caused by some genetic syndromes). The algorithm starts by a nose detection step. A geometry property match is employed to get possible candidates and then a symmetry calculation is carried out to select out the nose tip. Once the nose tip is decided, the 3D model is adjusted and normalized. After that, region segmentation is performed efficiently benefiting from the nose tip hint acquired in previous step. With the feature regions classified, feature points can be extracted easily with a set of profiles. Experiment results show that the overall performance is over 90% for both normal and abnormal models.","Title: Evaluating Structural Constraints for Accurate Range Image Registration\nYear: 2003\n\nAbstract: New technological developments in optics and electronics are rendering laser scanning systems cheaper and more accurate. Such systems can directly capture depth information from objects simplifying range image analysis and enlarging their applications scope. Accurate image registration algorithms represent a pivotal aspect of range image analysis which still require substantial improvements. While a large number of algorithms have been proposed, it is very important to compare the performance of different registration algorithms so that good algorithms can be selected for real applications. Unfortunately, most of these algorithms are iterative with a number of parameters initialised by users. The main disadvantages of these methods are that these parameters are often context dependent. Thus, while aiming to develop automatic registration algorithms instead of comparing the registration algorithms themselves, we compare the structural constraints based on a single registration algorithm(GICP). A large number of experiments using real range images demonstrate their effectiveness on improving the GICP algorithm.","Title: A method for text line detection in natural images\nYear: 2015\n\nAbstract: Text information in natural images is very important to cross-media retrieval, index and understanding. However, its detection is challenging due to varying backgrounds, low contrast between text and non-text regions, perspective distortion and other disturbing factors. In this paper, we propose a novel text line detection method which can detect text line aligned with a straight line in any direction. It is mainly composed of three steps. In the first step, we use the maximal stable extremal region detector with dam line constraint to detect candidate text regions, we then define a similarity measurement between two regions which combines sizes, absolute distance, relative distance, contextual information and color histograms. In the second step, we propose a text line identification algorithm based on the defined similarity measurement. The algorithm firstly searches three regions as the seeds of a line, and then expands to obtain all regions in the line. In the last step, we develop a filter to remove non-text lines. The filter uses a sparse classifier based on two dictionaries which are learned from feature vectors extracted from morphological skeletons of those candidate text lines. A comparative study using two datasets shows the excellent performance of the proposed method for accurate text line detection with horizontal or arbitrary consistent orientation.","Title: Amplifying scientific paper's abstract by leveraging data-weighted reconstruction\nYear: 2014\n\nAbstract: This paper considers the problem of amplifying scientific paper's abstract by using its citation sentences. While scientific paper's abstract is concise and subjective, its citation sentences, which can be regarded as one kind of comment, are redundant and objective. A summary combining the merits of those two resource is helpful to researchers. A data-weighted reconstruction approach is proposed to generate this summary and sentence's weight is learned through a ranking algorithm over hypergraph of bibliographic network. The experimental results show that the proposed approach can achieve significantly better than several document summarization's techniques.","Title: A GPU-accelerated non-negative sparse latent semantic analysis algorithm for social tagging data\nYear: 2014\n\nAbstract: Nowadays large-scale social tagging data have become very valuable in organizing and indexing multimedia resources. In this paper, we apply Non-negative Sparse Latent Semantic Analysis (NN-Sparse LSA) to discover the latent semantic space behind associations between multimedia resources and tagging data. Based on the traditional coordinate-descent algorithm, column-orthogonality and non-negative constraints, we derive a much faster optimization algorithm in theory for solving the NN-Sparse LSA model. Furthermore, we implement the parallel version of our fast NN-Sparse LSA algorithm using the NVIDIA CUDA (Compute Unified Device Architecture) parallel programming framework and a data partitioning scheme that effectively reduces the memory traffic between the global memory of the Graphic Processing Unit (GPU) and the host memory. The experimental results on image classification and tag recommendation tasks on MIRFLICKR and NUS-WIDE datasets show that our parallelized fast optimization algorithm can achieve comparable or even better performance than the other examined methods, while speeds up the original optimization algorithm 20-110 times.","Title: Structured Sparse Linear Model For Social Trust Prediction\nYear: 2014\n\nAbstract: Social trust prediction aims at predicting the missing trust relations between online users. In this paper, we propose a novel and scalable structured sparse linear model for social trust prediction from a global neighborhood-based collaborative filtering perspective. We formulate the prediction problem as a set of independent linear regression problems regularized by pairwise elastic net, to automatically learn correlation coefficients between a user and its most similar neighbors. In order to deal with large-scale sparse social trust data, we utilize efficient hashing techniques and stochastic coordinate descent algorithm to cut down the computational cost of training model. The experimental results on three real-world data sets show that our approach can significantly outperform the other tested methods in terms of prediction quality and efficiency.","Title: Fast image-based Chinese calligraphic character retrieval on large scale data\nYear: 2014\n\nAbstract: Chinese calligraphy is the art of handwriting, it draws a lot of attention for its beauty and elegance. In CADAL1, a Calligraphic Character Dictionary (CCD) which contains hundreds of thousands of character images labeled with semantic meaning has been constructed and provided online to common users. It is a great challenge to perform quick and accurate image-based calligraphic character retrieval on CCD. In this paper, a novel shape descriptor, Oriented Shape Context (OSC) is proposed basing on the tranditional Shape Context (SC) to perform similarity searching. Together with GIST, GIST-OSC descriptor is proposed to represent calligraphic character image for efficient and effective retrieval. In addition, an effective retrieval schema is proposed. The retrieval schema works in two steps. Firstly approximate nearest neighbors of the query image are found quickly using GIST and then one-to-one fine matching between approximate nearest neighbors and the query image is performed using OSC. Our experiments show that the GIST-OSC descriptor and the retrieval schema are efficient and effective for Chinese calligraphic character retrieval on large scale data.","Title: Discovering treatment pattern in traditional Chinese medicine clinical cases using topic model and domain knowledge\nYear: 2014\n\nAbstract: In Traditional Chinese Medicine (TCM), the prescription is the crystallization of clinical experience of doctors, which is the main way to cure diseases in China for thousands of years. Clinical cases, on the other hand, describe how doctors diagnose and prescribe a prescription. In this paper, we propose a framework which mines the treatment pattern in TCM clinical cases by using probabilistic topic model and TCM domain knowledge. The framework can reflect principle rules in TCM and improve function prediction of a new prescription. We evaluate our model on real world TCM clinical cases. The experiment validates the effectiveness of our method.","Title: Discovering Treatment Pattern in Traditional Chinese Medicine Clinical Cases by Exploiting Supervised Topic Model and Domain Knowledge.\nYear: 2015\n\nAbstract: In Traditional Chinese Medicine (TCM), the prescription is the crystallization of clinical experience of doctors, which is the main way to cure diseases in China for thousands of years. Clinical cases, on the other hand, describe how doctors diagnose and prescribe. In this paper, we propose a framework which mines treatment patterns in TCM clinical cases by exploiting supervised topic model and TCM domain knowledge. The framework can reflect principle rules in TCM and improve function prediction of a new prescription. We evaluate our method on 3,090 real world TCM clinical cases. The experiment validates the effectiveness of our method.","Title: D-Ocean: an unstructured data management system for data ocean environment.\nYear: 2016\n\nAbstract: Together with the big datamovement,many organizations collect their own big data and build distinctive applications. In order to provide smart services upon big data, massive variable data should be well linked and organized to form Data Ocean, which specially emphasizes the deep exploration of the relationships among unstructured data to support smart services. Currently, almost all of these applications have to deal with unstructured data by integrating various analysis and search techniques upon massive storage and processing infrastructure at the application level, which greatly increase the difficulty and cost of application development.","Title: Regularization Based Iterative Point Match Weighting for Accurate Rigid Transformation Estimation\nYear: 2015\n\nAbstract: Feature extraction and matching (FEM) for 3D shapes finds numerous applications in computer graphics and vision for object modeling, retrieval, morphing, and recognition. However, unavoidable incorrect matches lead to inaccurate estimation of the transformation relating different datasets. Inspired by AdaBoost, this paper proposes a novel iterative re-weighting method to tackle the challenging problem of evaluating point matches established by typical FEM methods. Weights are used to indicate the degree of belief that each point match is correct. Our method has three key steps: (i) estimation of the underlying transformation using weighted least squares, (ii) penalty parameter estimation via minimization of the weighted variance of the matching errors, and (iii) weight reestimation taking into account both matching errors and information learnt in previous iterations. A comparative study, based on real shapes captured by two laser scanners, shows that the proposed method outperforms four other state-of-the-art methods in terms of evaluating point matches between overlapping shapes established by two typical FEM methods, resulting in more accurate estimates of the underlying transformation. This improved transformation can be used to better initialize the iterative closest point algorithm and its variants, making 3D shape registration more likely to succeed.","Title: Improving MapReduce Performance with Partial Speculative Execution\nYear: 2015\n\nAbstract: Abstract The MapReduce framework has become the de facto standard for big data processing due to its attractive features and abilities. One is that it automatically parallelizes a job into multiple tasks and transparently handles task execution on a large cluster of commodity machines. The increasing heterogeneity of distributed environments may result in a few straggling tasks, which prolong job completion. Speculative execution is proposed to mitigate stragglers. However, the existing speculative execution mechanism could not work efficiently as many speculative tasks are still slower than their original tasks. In this paper, we explore an approach to increase the efficiency of speculative execution, and further improve MapReduce performance. We propose the Partial Speculative Execution (PSE) strategy to make speculative tasks start from the checkpoint. By leveraging the checkpoint of original tasks, PSE can eliminate the costs of re-reading, re-copying, and re-computing the processed data. We implement PSE in Hadoop, and evaluate its performance in terms of job completion time and the efficiency of speculative execution under several kinds of classical workloads. Experimental results show that, in heterogeneous environments with stragglers, PSE completes jobs 56 % faster than that with no speculation and 12 % faster than that with LATE, an improved speculative execution algorithm. In addition, on average PSE can improve the efficiency of speculative execution by 24 % compared to LATE.","Title: Taxonomy Induction from Chinese Encyclopedias by Combinatorial Optimization.\nYear: 2015\n\nAbstract: Taxonomy is an important component in knowledge bases, and it is an urgent, meaningful but challenging task for Chinese taxonomy construction. In this paper, we propose a taxonomy induction approach from a Chinese encyclopedia by using combinatorial optimizations. At first, subclass-of relations are derived by validating the relation between two categories. Then, integer programming optimizations are applied to find out instance-of relations from encyclopedia articles by considering the constrains among categories. The experimental results show that our approach can construct a practicable taxonomy from Chinese encyclopedias.","Title: Mining RDF from Tables in Chinese Encyclopedias\nYear: 2015\n\nAbstract: Web tables understanding has recently attracted a number of studies. However, many works focus on the tables in English, because they usually need the help of knowledge bases, while the existing knowledge bases such as DBpedia, YAGO, Freebase and Probase mainly contain knowledge in English. In this paper, we focus on the RDF triples extraction from tables in Chinese encyclopedias. Firstly, we constructed a Chinese knowledge base through taxonomy mining and class attribute mining. Then, with the help of our knowledge base, we extracted triples from tables through column scoring, table classification and RDF extraction. In our experiments, we practically implemented our approach in 6,618,544 articles from Hudong Baike with 764,292 tables, and extracted about 1,053,407 unique and new RDF triples with an estimated accuracy of $$90.2\\\\%$$, which outperforms other similar works.","Title: Incorporating Probabilistic Knowledge into Topic Models.\nYear: 2015\n\nAbstract: Probabilistic Topic Models could be used to extract low-dimension aspects from document collections. However, such models without any human knowledge often produce aspects that are not interpretable. In recent years, a number of knowledge-based models have been proposed, which allow the user to input prior knowledge of the domain to produce more coherent and meaningful topics. In this paper, we incorporate human knowledge in the form of probabilistic knowledge base into topic models. By combining latent Dirichlet allocation, a widely used topic model with Probase, a large-scale probabilistic knowledge base, we improve the semantic coherence significantly. Our evaluation results will demonstrate the effectiveness of our method.","Title: Short Text Understanding by Leveraging Knowledge into Topic Model.\nYear: 2015\n\nAbstract: In this paper, we investigate the challenging task of understanding short text (STU task) by jointly considering topic modeling and knowledge incorporation. Knowledge incorporation can solve the content sparsity problem effectively for topic modeling. Specifically, the phrase topic model is proposed to leverage the auto-mined knowledge, i.e., the phrases, to guide the generative process of short text. Experimental results illustrate the effectiveness of the mechanism that utilizes knowledge to improve topic modeling’s performance.","Title: A question-answering system over Traditional Chinese Medicine\nYear: 2015\n\nAbstract: Traditional Chinese Medicine (TCM) has been around for thousands of years and it's a significant part of Chinese cultural heritage. The theoretical framework of TCM is unique and with rich of content, which contains the complex relationships between disease and medicine. Research on question-answering (QA) over TCM is significant for Chinese NLP and representative, because the resources of TCM are mostly Chinese-based. In this paper we present a QA system over TCM, which transforms user supplied questions into conjunctive query sentences (i.e. SQL) and retrieves the answer from both the built-up dataset and online encyclopedia. The contribution of this paper is threefold: Firstly, we introduce a novel approach for word segmentation over Chinese questions. We employ a TF-IDF model on the dataset to generate domain-specific dictionary with weight factor and tags, which are computed to select the best result of segmentation. Secondly, we present a novel method for constructing queries to retrieve answers. We compute the entity-attribute distance over a set of tagged words to construct incomplete ontology instances, which are used as the intermediary to generate queries. Lastly, we propose a method to integrate web data extraction with question answering, which allows us to extract answers from online encyclopedia website (i.e. Wikipedia). The results of our evaluation with 50 benchmark queries demonstrate the value of our approach.","Title: Region-based saliency estimation for 3D shape analysis and understanding.\nYear: 2016\n\nAbstract: The detection of salient regions is an important pre-processing step for many 3D shape analysis and understanding tasks. This paper proposes a novel method for saliency detection in 3D free form shapes. Firstly, we smooth the surface normals by a bilateral filter. Such a method is capable of smoothing the surfaces and retaining the local details. Secondly, a novel method is proposed for the estimation of the saliency value of each vertex. To this end, two new features are defined: Retinex-based Importance Feature (RIF) and Relative Normal Distance (RND). They are based on the human visual perception characteristics and surface geometry respectively. Since the vertex based method cannot guarantee that the detected salient regions are semantically continuous and complete, we propose to refine such values based on surface patches. The detected saliency is finally used to guide the existing techniques for mesh simplification, interest point detection, and overlapping point cloud registration. The comparative studies based on real data from three publicly accessible databases show that the proposed method usually outperforms five selected state of the art ones both qualitatively and quantitatively for saliency detection and 3D shape analysis and understanding.","Title: Query Subtopic Mining via Subtractive Initialization of Non-negative Sparse Latent Semantic Analysis.\nYear: 2016\n\nAbstract: Ambiguous and multifaceted queries widely exist in academic and commercial search engines. Identifying the popular subtopics of queries is an important issue for search engines. In this paper, we propose a novel method to discover the popular subtopics for a given query. Our method first constructs a search behavior tripartite graph based on the search log data. Then, we utilize a subtractive initialized Non-negative Sparse LSA model to mine subtopics from the tripartite graph. The experimental results on two real-world Chinese search logs (i.e., the CADAL search log and the Sogou search log) show that our proposed method can significantly outperform the other comparison methods in term of MAP, alpha-nDCG and S-recall. We also applied our method into the CADAL digital library to provide a novel faceted book search service which can reduce the users' efforts in finding books.","Title: A Joint Model For Question-Answering Over Traditional Chinese Medicine\nYear: 2016\n\nAbstract: Traditional Chinese Medicine (TCM) has been around for over 2000 years and it's a significant part of Chinese cultural heritage. The theoretical framework of TCM is unique and with rich of content, which contains the complex relationships between disease and medicine and has formed a unique system to diagnose and cure illness. Research on question-answering (QA) over TCM is significant for Chinese NLP and representative, because the resources of TCM are mostly Chinese-based. The general strategies for QA are pipeline, includes Natural Language Processing (NLP) for original questions and structural query process. However, noises are easily introduced and information contained in the original questions may loss during these two separate procedures. In this paper we present a novel joint model for QA over TCM, which unites these two procedures into a uniform framework, together with Wikipedia alignment. The results of our evaluation with 100 benchmark queries demonstrate the value of our approach.","Title: Traditional Chinese Medicine Clinical Records Classification Using Knowledge-Powered Document Embedding\nYear: 2016\n\nAbstract: Text classification is one of the fundamental tasks in text mining. In the medical domain, there have been a number of studies on text classification in modern medicine clinical notes written in English. However, very limited text classification research has been conducted on clinical notes written in Chinese, especially traditional Chinese medicine (TCM) clinical records. The goal of this study was to investigate features and machine learning classification algorithms for TCM clinical text classification. We collected 7,037 TCM clinical records of famous TCM doctors as our dataset, and investigated the effects of different types of features and classification algorithms. Additionally, we proposed a novel method to combine deep learning text representation with TCM domain knowledge, which results in the best classification performance.","Title: KeyphraseDS: Automatic generation of survey by exploiting keyphrase information.\nYear: 2017\n\nAbstract: Abstract In this paper, we present a novel document summarization mechanism called KeyphraseDS that can organize the scientific articles into multi-aspect and informative scientific survey by exploiting keyphrases. Keyphrases describe textu0027s salience and central focus, which can serve as the component of aspects under specific topic. KeyphraseDS consists of three steps: keyphrase graph construction, semantic aspect generation and content selection. Keyprhases are firstly extracted through CRF-based model exploiting various features, such as syntactic features, correlation features, etc. Spectral clustering is then performed on keyphrase graph to generate different aspects, where the semantic relatedness between keyphrases is computed through knowledge-based similarity and topic-based similarity. The proposed semantic relatedness can not only utilize the statistical text signals efficiently but also overcome the data sparsity problem. Significant sentences are then selected with respect to the generated aspects through integer linear programming (ILP), which takes semantic relevance, semantic diversity, and keyphrase salience into consideration. Extensive experiments, measured by automatic evaluation and human evaluation, demonstrate the effectiveness of our mechanism for generating scientific survey.","Title: Chinese calligraphic style representation for recognition.\nYear: 2017\n\nAbstract: Chinese calligraphy draws a lot of attention for its beauty and elegance. The various styles of calligraphic characters make calligraphy even more charming. But it is not always easy to recognize the calligraphic style correctly, especially for beginners. In this paper, an automatic character styles representation for recognition method is proposed. Three kinds of features are extracted to represent the calligraphic characters. Two of them are typical hand-designed features: the global feature, GIST and the local feature, scale invariant feature transform. The left one is deep feature which is extracted by a deep convolutional neural network (CNN). The state-of-the-art classifier modified quadratic discriminant function was employed to perform recognition. We evaluated our method on two calligraphic character datasets, the unconstraint real-world calligraphic character dataset (CCD) and SCL (the standard calligraphic character library). And we also compare MQDF with other two classifiers, support vector machine and neural network, to perform recognition. In our experiments, all three kinds of feature are evaluated with all three classifiers, respectively, finding that deep feature is the best feature for calligraphic style recognition. We also fine-tune the deep CNN (alex-net) in Krizhevsky et al. (Advances in Neural Information Processing Systems, pp. 1097---1105, 2012) to perform calligraphic style recognition. It turns out our method achieves about equal accuracy comparing with the fine-tuned alex-net but with much less training time. Furthermore, the algorithm style discrimination evaluation is developed to evaluate the discriminative style quantitatively.","Title: An easy-to-use evaluation framework for benchmarking entity recognition and disambiguation systems.\nYear: 2017\n\nAbstract: Entity recognition and disambiguation (ERD) is a crucial technique for knowledge base population and information extraction. In recent years, numerous papers have been published on this subject, and various ERD systems have been developed. However, there are still some confusions over the ERD field for a fair and complete comparison of these systems. Therefore, it is of emerging interest to develop a unified evaluation framework. In this paper, we present an easy-to-use evaluation framework (EUEF), which aims at facilitating the evaluation process and giving a fair comparison of ERD systems. EUEF is well designed and released to the public as an open source, and thus could be easily extended with novel ERD systems, datasets, and evaluation metrics. It is easy to discover the advantages and disadvantages of a specific ERD system and its components based on EUEF. We perform a comparison of several popular and publicly available ERD systems by using EUEF, and draw some interesting conclusions after a detailed analysis.","Title: Incorporating Knowledge Graph Embeddings into Topic Modeling.\nYear: 2017\n\nAbstract: Probabilistic topic models could be used to extract low dimension topics from document collections. However, such models without any human knowledge often produce topics that are not interpretable. In recent years, a number of knowledge-based topic models have been proposed, but they could not process fact-oriented triple knowledge in knowledge graphs. Knowledge graph embeddings, on the other hand, automatically capture relations between entities in knowledge graphs. In this paper, we propose a novel knowledge-based topic model by incorporating knowledge graph embeddings into topic modeling. By combining latent Dirichlet allocation, a widely used topic model with knowledge encoded by entity vectors, we improve the semantic coherence significantly and capture a better representation of a document in the topic space. Our evaluation results will demonstrate the effectiveness of our method.","Title: Hybrid storage architecture and efficient MapReduce processing for unstructured data.\nYear: 2017\n\nAbstract: •We present a hybrid storage architecture which integrates various kinds of data stores.•We propose three partitioning strategies to execute MapReduce-based batch-processing jobs.•Our hybrid solution shows 10% to 8.6 times faster performance than the Hadoop solution.•We demonstrate that it is possible to build a smart and efficient system by utilizing the characteristics of different data stores.","Title: Mining coherent topics in documents using word embeddings and large-scale text data.\nYear: 2017\n\nAbstract: Probabilistic topic models have been extensively used to extract low-dimension aspects from document collections. However, such models without any human knowledge often generate topics that are not interpretable. Recently, a number of knowledge-based topic models have been proposed, which enable users to input prior domain knowledge to produce more meaningful and coherent topics. Word embeddings, on the other hand, can automatically capture both semantic and syntactic information of words from a large amount of documents, and can be used to measure word similarities. In this paper, we incorporate word embeddings obtained from a large number of domains into topic modeling. By combining Latent Dirichlet Allocation, a widely used topic model with Skip-Gram, a well-known framework for learning word vectors, we improve the semantic coherence significantly. Our evaluation results using product review documents from 100 domains will demonstrate the effectiveness of our method.","Title: Cross-Lingual Entity Matching for Heterogeneous Online Wikis.\nYear: 2017\n\nAbstract: Knowledge bases play an increasing important role in many applications. However, many knowledge bases mainly focus on English knowledge, and have only a few knowledge for low-resource languages (LLs). If we can map the entities in LLs to these in high-resource languages (HLs), many knowledge such as relation between entities can be transferred from HLs to LLs. In this paper, we propose an efficient and effective Cross-Lingual Entity Matching approach (CL-EM) to enrich the existing cross-lingual links by learning to rank framework with the learned language-independent features, including cross-lingual topic features and document embedding features. In the experiments, we verified our approach on the existing cross-lingual links between Chinese Wikipedia and English Wikipedia by comparing it with other state-of-art approaches. In addition, we also discovered 141,754 new cross-lingual links between Baidu Baike and English Wikipedia, which almost doubles the number of the existing cross-lingual links.","Title: Boosting Collective Entity Linking via Type-Guided Semantic Embedding.\nYear: 2017\n\nAbstract: Entity Linking (EL) is the task of mapping mentions in natural-language text to their corresponding entities in a knowledge base (KB). Type modeling for mention and entity could be beneficial for entity linking. In this paper, we propose a type-guided semantic embedding approach to boost collective entity linking. We use Bidirectional Long Short-Term Memory (BiLSTM) and dynamic convolutional neural network (DCNN) to model the mention and the entity respectively. Then, we build a graph with the semantic relatedness of mentions and entities for the collective entity linking. Finally, we evaluate our approach by comparing the state-of-the-art entity linking approaches over a wide range of very different data sets, such as TAC-KBP from 2009 to 2013, AIDA, DBPediaSpotlight, N3-Reuters-128, and N3-RSS-500. Besides, we also evaluate our approach with a Chinese Corpora. The experiments reveal that the modeling for entity type can be very beneficial to the entity linking.","Title: Incorporating knowledge into neural network for text representation.\nYear: 2018\n\nAbstract: •A framework which incorporates knowledge into neural network is proposed.•The framework is a kind of architecture to produce knowledge based text features.•A method for transforming a raw text into a conceptualized text is proposed.•The framework is tested on the sentence level task and the document level task.","Title: Bilinear joint learning of word and entity embeddings for Entity Linking.\nYear: 2018\n\nAbstract: Entity Linking (EL) is the task of resolving mentions to referential entities in a knowledge base, which facilitates applications such as information retrieval, question answering, and knowledge base population. In this paper, we propose a novel embedding method specifically designed for EL. The proposed model jointly learns word and entity embeddings which are located in different distributed spaces, and a bilinear model is introduced to simulate the interaction between words and entities. We treat EL as a ranking problem, and utilize a pairwise learning-to-rank framework with features constructed with learned embeddings as well as conventional EL features. Experimental results show the proposed model produces effective embeddings which improve the performance of our EL algorithm. Our method yields the state-of-the-art performances on two benchmark datasets CoNLL and TAC-KBP 2010.","Title: A Topic Modeling Approach for Traditional Chinese Medicine Prescriptions.\nYear: 2018\n\nAbstract: In traditional Chinese medicine (TCM), prescriptions are the daughters of doctors' clinical experiences, which have been the main way to cure diseases in China for several thousand years. In the long Chinese history, a large number of prescriptions have been invented based on TCM theories. Regularities in the prescriptions are important for both clinical practice and novel prescription development...","Title: A method for finding groups of related herbs in traditional chinese medicine\nYear: 2011\n\nAbstract: As a complementary system to Western medicine, Traditional Chinese Medicine (TCM) provides a unique theoretical and practical approach of treatment to diseases over thousands of years. Accompanying with the increasing number of TCM digital books in digital library, there is an urgent need to explore these resources by the techniques of knowledge discovery. We present a method for creating a network of herbs and partitioning it into groups of related herbs. The method extracts structured information from several TCM digital books, then a new method named Support and Dependency Evaluation (SDE) is presented for herbal combinational rule mining. The herbal network is created from the extracted dataset of paired herbs. The partitioning procedure is designed to extend FEC algorithm to deal with the weighted herbal network. Experiments demonstrate that the method proposed has the capability of discovering groups of related herbs.","Title: Knowledge representation learning with entity descriptions, hierarchical types, and textual relations.\nYear: 2019\n\nAbstract: Knowledge representation learning methods usually only utilize triple facts, or just consider one kind of extra information. In this paper, we propose a multi-source knowledge representation learning (MKRL) model, which can combine entity descriptions, hierarchical types, and textual relations with triple facts. Specifically, for entity descriptions, a convolutional neural network is used to get representations. For hierarchical type, weighted hierarchy encoders are used to construct the projection matrixes of hierarchical types, and the projection matrix of an entity combines all hierarchical type projection matrixes of the entity with the relation-specific type constrains. For textual relations, a sentence-level attention mechanism is employed to get representations. We evaluate MKRL model on knowledge graph completion task with dataset FB15k-237, and experimental results demonstrate that our model outperforms the state-of-the-art methods, which indicates the effectiveness of multi-source information for knowledge representation.","Title: Metro maps for efficient knowledge learning by summarizing massive electronic textbooks\nYear: 2019\n\nAbstract: As the number of textbooks soars, people may be stuck into thousands of books when learning knowledge. In order to provide a concise yet comprehensive picture for learning, we propose a novel framework, called MM4Books, to automatically build metro maps for efficient knowledge learning by summarizing massive electronic textbooks. We represent each book in digital libraries as a sequence of chapters, and then obtain learning objects by clustering the semantically similar chapters via an unsupervised clustering method to create a learning graph, and then build the metro map by applying an integer linear programming-based technique to select a collection of high informative and fluent but low redundant learning paths from the learning graph. To the best of our knowledge, it is the first work to address this task. Experiments show that our proposed approach outperforms all the state-of-the-art baseline approaches, and we also implemented a practical MM4Books system to prove that users can really benefit from the proposed approach for knowledge learning.","Title: Part-of-Speech-Based Long Short-Term Memory Network for Learning Sentence Representations\nYear: 2019\n\nAbstract: Sentence representations play an important role in the field of natural language processing. While word representation has been applied to many natural language processing tasks, sentence representation has not been applied as widely due to the more complex structure and richer syntactic information of sentences. To learn sentence representations with structural and syntactic information, we propose a new model called the part-of-speech-based Long Short-Term Memory network (pos-LSTM) model. The pos-LSTM model generates a structural representation using the standard LSTM model and a syntactic representation using the part of speech; then, the pos-LSTM model obtains the final sentence representation by combining these two representations. The experimental results from 20 sentence similarity tasks and an entailment classification task show that the pos-LSTM model can better capture the syntactic information of sentences and generate higher quality sentence representations than traditional models."],"string":"[\n \"Title: Cross-Reading by Leveraging a Hybrid Index of Heterogeneous Information.\\nYear: 2013\\n\\nAbstract: In this paper, we present a novel application named Cross-reading, which is derived from user's reading process. Cross-reading is essentially a searching by document task from large-scale text corpus. The state-of-the-art approaches utilize similarity hashing to address this issue by modeling it as a high-dimensional data similarity search problem. However, most approaches only consider document's lexical information while ignoring documents semantic information and metadata. Moreover, searching similar hash codes from massive hash codes quickly is still a major bottleneck. To address those problems, we propose a Fast Searching By Document approach, which considers the Cross-reading from the perspective of semantic similarity and time efficiency. ? Springer Science+Business Media Singapore 2014.\",\n \"Title: A new video text detection method\\nYear: 2011\\n\\nAbstract: Nowadays, digital libraries contain more and more videos in them, and how to organize and retrieve those videos effectively has become very urgent. Text in videos is a very meaningful clue for video semantic understanding, and it can be used for video organization and retrieval. However, existing text recognizing methods can not deal with multilingual texts or texts embedded in a complex background very well. In this paper, we propose a novel video text detection method. Edge detection and candidate region extraction are firstly used to obtain all rough candidate text regions, and then region refinement is used to obtain the accurate location of each region. Based on our observation that a real text region has a uniform distribution with its non-zero pixels in its binary image, an entropy filter is used to remove non-text regions. Experiments on various videos show that our method is effective and robust against different languages, background complexities and font styles.\",\n \"Title: In-depth utilization of Chinese ancient maps: a hybrid approach to digitizing map resources in CADAL\\nYear: 2010\\n\\nAbstract: Digital map is getting increasingly popular as an intuitive and interactive platform for data presentation recently. Thus applications integrated with digital map have attracted much attention. But no offtheshelf systems or services could we use if the time span of maps be extended to historical ones. There are a large number of valuable ancient atlases in CADAL digital library. However, they are seldom made use of because the ones which are in image format are not convenient for users to read or search. In this paper, we propose a novel hybrid approach to utilizing these atlases directly and constructing some applications based on ancient maps. We call it CAMAME which means Chinese Ancient Maps Automatic Marking and Extraction. We create a gazetteer to store the geographic information of sites which will be project on the map, then use kernel method to do the regression and correct the estimated results with image processing and local regression methods. The empirical results show that CAMAME is effective and efficient, by which most valuable data in the map images is marked and identified. Some Chinese literary chronicle applications that exhibit ancient literary and related historical information over those digitized atlas resources in CADAL digital library were developed.\",\n \"Title: Topic Discovery based on LDA_col Model and Topic Significance Re-ranking.\\nYear: 2011\\n\\nAbstract: This paper presents a method to find the topics efficiently by the combination of topic discovery and topic re-ranking. Most topic models rely on the bag-ofwords(BOW) assumption. Our approach allows an extension of LDA model-Latent Dirichlet Allocation_Collocation (LDA_col) to work in corpus such that the word order can be taken into consideration for phrase discovery, and slightly modify the modal for modal consistency and effectiveness. However, LDA_col results may not be ideal for user's understanding. In order to improve the topic modeling results, two topic significance re-ranking methods (Topic Coverage(TC) and Topic Similarity(TS)) are proposed. We conduct our method on both English and Chinese corpus, the experimental results show that the modified LDA_col discovers more meaningful phrases and more understandable topics than LDA and LDA_col. Meanwhile, topic re-ranking method based on TC performs better than TS, and has the ability of re-ranking the \\\"significant\\\" topics higher than \\\"insignificant\\\" ones. © 2011 ACADEMY PUBLISHER.\",\n \"Title: A Scalable, Adaptive, Self-management and Fault-Tolerant Architecture for Digital Library.\\nYear: 2012\\n\\nAbstract: With the rapid growth of data, two major challenges are taking place in digital library technologies. One is the transition from database to Data Ocean; the other is the transition from digital library to Smart Library. In order to address these challenges, we propose a scalable, adaptive, self-management, fault-tolerant architecture for various services in the digital library. Users can deploy services quite easily on the architecture, and it also enables the deployed services load balancing, scalable, adaptive and fault-tolerant. The practice in CADAL demonstrates the feasibility and efficiency of our architecture. © 2012 Springer-Verlag.\",\n \"Title: 3D shape matching using collinearity constraint\\nYear: 2004\\n\\nAbstract: In this paper, a novel algorithm is proposed to carry out automatic 3D shape matching with 3D shapes represented as sets of points. After the possible matches between the 3D shapes have been determined by the tradition ICP criterion, the novel approach employs the collinearity constraint to eliminate false matches based on a statistical model. A comparative study based on real range images has shown that the proposed algorithm is accurate, robust and efficient for the automatic matching of overlapping 3D shapes.\",\n \"Title: 3D Free Form Surface Matching Based on Orientation Difference Length Distribution\\nYear: 2005\\n\\nAbstract: In this paper, we propose using orientation difference length distribution (ODLD) to represent a 3D view of each object with free form surface. ODLD has a number of advantages over the shape distribution. A comparative study of ODLD and the shape distribution has shown that the former is significantly more accurate for 3D free form surface matching than the latter. © 2005 IEEE.\",\n \"Title: CMSOF: a structured data organization framework for scanned Chinese medicine books in digital libraries.\\nYear: 2010\\n\\nAbstract: Organizing unstructured information from books into a well-defined structure is a significant challenge in digital libraries.\\n Most digital libraries can provide only search services at the granularity of books and few libraries allow books to be accessed\\n at the granularity of chapters, as manually constructing directory information for books is time-consuming. Extracting structured\\n data from scanned books thus remains an urgent and important work. In this paper, we propose a novel structured data organization\\n framework called CMSOF to organize scanned data automatically, and apply it to a Chinese medicine digital library. In the\\n framework, image blocks and text blocks on the scanned page of books are separated based on the gray histogram projection\\n method or a hybrid method of region growth and the Ada-Boosting classifier at first, and then the text structure is obtained\\n from text blocks by text size and font type recognition. Finally, image blocks and structured OCRed text are correlated at\\n the semantic level. By integrating the structured data into a Chinese medicine information system (CMIS), we can organize\\n the Chinese medicine books well and users can access the books with flexibility, which indicates that CMSOF is an efficient\\n framework to organize books mixed with images and text.\",\n \"Title: When personalization meets socialization: an iCADAL approach\\nYear: 2011\\n\\nAbstract: CADAL has been a large-scale non-profit digital library. Besides various search facilities in the CADAL portal, we designed and implemented the iCADAL system for providing the user-oriented micro-content services on one million books in CADAL. Users of iCADAL can receive the stream of short messages of lending, annotation and the other reading activities shared by their followees in a twitter-like way, which combines socialization with personalization. Our implementation makes extensive use of open source softwares, i.e. Pylons, Cassandra, in order to support the high-traffic online micro-content services.\",\n \"Title: Projecting registration error for accurate registration of overlapping range images\\nYear: 2006\\n\\nAbstract: In this paper, we propose a novel algorithm for the automatic registration of two overlapping range images. Since it is relatively difficult to compare the registration errors of different point matches, we project them onto a virtual image plane for more accurate comparison using the classical pin-hole perspective projection camera model. While the traditional ICP algorithm is more interested in the points in the second image close to the sphere centred at the transformed point, the novel algorithm is more interested in the points in the second image as collinear as possible to the transformed point. The novel algorithm then extracts useful information from both the registration error and projected error histograms for the elimination of false matches without any feature extraction, image segmentation or the requirement of motion estimation from outliers corrupted data and, thus, has an advantage of easy implementation. A comparative study based on real images captured under typical imaging conditions has shown that the novel algorithm produces good registration results.\",\n \"Title: Free form surface matching using motion consistency\\nYear: 2003\\n\\nAbstract: In this paper, we propose a novel algorithm to evaluate the possible point matches established by the traditional ICP algorithm in the process of matching two overlapping free from surfaces represented as two sets of unorganised points. While the existing methods mainly use feature matching to establish or evaluate possible correspondences between the surfaces to be matched, our novel approach applies motion consistency to evaluate the possible correspondences. In particular, while the existing methods assume that satisfying local structural constraints is a necessary condition for a pair of points to represent a real correspondence, our novel approach proves that satisfying global rigid motion constraints is a sufficient condition. A comparative study based on real images has shown that the proposed algorithm is accurate and robust for the matching of overlapping free form surfaces.\",\n \"Title: Transactional Multi-row Access Guarantee in the Key-Value Store\\nYear: 2012\\n\\nAbstract: The emergence of Cloud Computing and Big Data drives the development of novel data stores named NoSQL. A mass of data stores are developed and the most are key-value stores, where the stores are partitioned with keys and a key can identify a row uniquely. However, the requirement for efficiency and scalability makes them only provide the single-row atomic access. But in the Big Data era, more and more applications built on the key-value stores need transactional functionality across multiple rows. So, it is natural to implement a multi-row transaction management for key-value stores. In this paper, we implement a transaction processing system (TrasPS) which guarantees the transactional multi-row access from the application client to the key-value store in our unstructured data management system (UDMS). We also provide fault tolerance and recovery for the transactions. The implementation and experiments in our UDMS show that TrasPS can provide scalable multi-row access functionality at a very low overhead.\",\n \"Title: Using hybrid knowledge engineering and image processing in color virtual restoration of ancient murals\\nYear: 2003\\n\\nAbstract: This paper proposes a novel scheme to virtually restore the colors of ancient murals. Our approach integrates artificial intelligence techniques with digital image processing methods. The knowledge related to the mural colors is first categorized into four types. A hybrid frame and rule-based approach is then developed to represent knowledge and to inter colors. An algorithm that takes into account color similarity and spatial proximity is developed to segment mural images. A novel color transformation method based on color histograms is finally proposed to restore the colors of murals. A number of experiments based on real images have demonstrated the validity of the proposed scheme for color restoration.\",\n \"Title: Developing structural constraints for accurate registration of overlapping range images\\nYear: 2004\\n\\nAbstract: Automatic image registration is an attractive and unresolved problem in the machine vision literature. This paper presents two novel structural constraints, namely proximity and closeness constraints, that improve both the accuracy and robustness of an existing motion consistency based algorithm (GICP) for automatic image registration. It also defines the conditions when such constraints are fired at specific points of the registration of two overlapping range images. The proximity constraint says that neighbouring points should be neighbouring before and after a rigid motion. The closeness constraint implements a local closest point search from the second image to the first image. While the GICP algorithm uses an exhaustive search for the closest points, in this paper, we employ the optimised k–d tree data structure to accelerate the closest point search. A large number of experiments based on real range images demonstrate that the combination of rigid motion constraints with these novel proximity and closeness constraints leads to a more accurate and robust evaluation of possible correspondences leading, in turn, to more accurate, robust and efficient automatic image registration results.\",\n \"Title: Cubic algebraic curves based on geometric constraints\\nYear: 2001\\n\\nAbstract: Methods for curve modeling with cubic algebraic curves based on geometric constraints are introduced in this paper. A 1-parameter family of cubic curves with four given points as well as two tangent lines at the endpoints is constructed in the first part of the paper. A 1-parameter family and 2-parameter family of cubic curve constructions are presented for interpolating two given endpoints and two given tangent lines as well as two given curvatures at the endpoints in the last part of the paper.\",\n \"Title: Improving multi-faceted book search by incorporating sparse latent semantic analysis of click-through logs\\nYear: 2012\\n\\nAbstract: Multi-faceted book search engine presents diverse category-style options to allow users to refine search results without re-entering a query. In this paper, we propose a novel multi-faceted book search engine that utilizes users' query-related latent intents mined from click-through logs as multiple facets for books. The latent query intents can be effectively and efficiently discovered by applying the Sparse Latent Semantic Analysis (LSA) model to users' query and clicking behaviors in the click-through logs. This paper presents the details to improve the multi-faceted book search by incorporating the compact representation of query-intent-book relationships generated by Sparse LSA into the off-line and online processing procedures. The specificity of latent query intents can be flexibly changed by adjusting the sparsity level of projection matrix in the Sparse LSA model. We evaluated our approach on CADAL click-through logs containing 45,892 queries and 164,822 books. The experimental results show the Sparse LSA model with more sparse projection matrix tends to discover the more specific latent query intents. The latent query intents suggested by our approach usually gain the high user satisfaction ratio.\",\n \"Title: Discontinuity-Based Simplification of Free Form Surface From a Range Image\\nYear: 2006\\n\\nAbstract: This paper improves an existing method for more accurate simplification of free form surface represented as dense points. The main improvement lies in two aspects: one is the introduction of a novel transformation of curvatures, the other is to propose sampling points using both curvature information and curvature variation information. The experimental results based on real images show that the original object surface has been better approximated and the majority of the surface discontinuities have been better preserved with considerably few sampled (5%) data points.\",\n \"Title: HAaaS: Towards Highly Available Distributed Systems\\nYear: 2012\\n\\nAbstract: High availability is a valuable property in distributed systems. The master-slave model is used wildly in data management systems for high performance. However, many master-slave systems still have SPOF (Single Point of Failure) for the single master node. We exploit a generalized solution to meet several common use cases for different master-slave systems. The solution makes the high availability as a service (HAaaS), which uses a shared storage infrastructure to make the master stateless and provides an automatic fail over of high-availability service. We deploy the HAaaS in many master-slave subsystems in our unstructured data management system (UDMS) to make the UDMS highly available. The experiments demonstrate the feasibility and efficiency of our solution.\",\n \"Title: A digital library architecture supporting massive small files and efficient replica maintenance\\nYear: 2010\\n\\nAbstract: In this paper, we presented a service infrastructure based on distributed file system for massive storage in digital library. In addition, we addressed the small-file problem by merging small files into big ones, and proposed a novel dynamic replica number adjustment scheme to ensure the maximal availability and reliability in a limited storage space.\",\n \"Title: A Cluster-Based Incremental Recommendation Algorithm on Stream Processing Architecture\\nYear: 2013\\n\\nAbstract: By helping users discover books they may be interested in, recommender systems fully exploit the resources of digital libraries and better facilitate users' reading demands. Traditional memory-based collaborative filtering (CF) methods are effective and easy to interpret. However, when datasets become larger, the traditional way turns to be infeasible in both time and space. In order to address this challenge, we propose an incremental, cluster-based algorithm on Stream Processing Architecture, which is scalable and suitable to real-time environment. Our experimental results on MovieLens datasets and CADAL user-chapter logs show our algorithm is efficient, while still maintains comparable accuracy and interpretability.\",\n \"Title: Digital Library Engine: Adapting Digital Library for Cloud Computing\\nYear: 2013\\n\\nAbstract: With the rapid growth of digital libraries, more data and smart services are involved. People come to recognize the importance of digital libraries and the convenience they might bring to the society. However, the cost of owning a digital library is quite high, and many institutions do not have the ability to run and maintain a digital library by themselves, especially for massive data and complex services which require lots of storage and computing resources. In this paper, we proposed the Digital Library Engine, which aims to provide a new Platform as a Service for fast developing and deploying digital libraries in cloud. To the best of our knowledge, it is the first work to create a PaaS system for digital libraries. With the help of Digital Library Engine, institutions only need to develop some service bundles, which can be deployed in the engine, and then their own digital libraries could be running well with features of scalability, reliability, security, extensibility, availability and manageability. The practice in CADAL and the experiments demonstrate the feasibility and efficiency of our engine.\",\n \"Title: CARES: a ranking-oriented CADAL recommender system\\nYear: 2009\\n\\nAbstract: A recommender system is useful for a digital library to suggest the books that are likely preferred by a user. Most recommender systems using collaborative filtering approaches leverage the explicit user ratings to make personalized recommendations. However, many users are reluctant to provide explicit ratings, so ratings-oriented recommender systems do not work well. In this paper, we present a recommender system for CADAL digital library, namely CARES, which makes recommendations using a ranking-oriented collaborative filtering approach based on users' access logs, avoiding the problem of the lack of user ratings. Our approach employs mean AP correlation coefficients for computing similarities among users' implicit preference models and a random walk based algorithm for generating a book ranking personalized for the individual. Experimental results on real access logs from the CADAL web site show the effectiveness of our system and the impact of different values of parameters on the recommendation performance.\",\n \"Title: Range image registration using hierarchical segmentation and clustering\\nYear: 2009\\n\\nAbstract: An accurate, robust, and automatic registration of overlapping range images is usually a pre-requisite step for range image analysis and applications. While accurate depiction of object geometry requires the increase of the resolutions of images and thus, the amount of data to process, an efficient processing of such data then usually becomes an issue. In this paper, we first employ the efficient tensor analysis and k means clustering methods to hierarchically segment and cluster the original range images into a small number of planar patches represented as the closest points in the original images to their centroids. Then an advanced ICP variant is adopted to register such closest points. Finally, another ICP variant is used to refine the registration results obtained over all the points in the images. The experimental results based on real range images show that the proposed technique significantly outperforms the selected two state of the art ones for accurate and efficient registration of overlapping range images.\",\n \"Title: A Clustering Approach to Free Form Surface Reconstruction from Multi-view Range Images\\nYear: 2009\\n\\nAbstract: 3D modelling finds a wide range of applications in robot vision and reverse engineering. However due to the presence of surface scanning noise, accumulative registration error, and improper data fusion, the reconstructed surfaces from multiple registered range images are often non-smooth and distorted with thick patches, false connections and blurred features. These shortcomings will limit the wide applications of 3D modelling using the latest laser scanning systems. In this paper, we first employ the principal component analysis and hierarchical segmentation to classify surface patches into featured and non-features areas, the k-means clustering and fuzzy c means clustering approaches from the pattern recognition literature are then employed to integrate these areas respectively. Finally the fused point set is triangulated using the improved Delaunay method, guaranteeing a watertight surface. The new method is theoretically guaranteed to converge and combine the advantage of exclusive k means clustering and overlapping fuzzy c means clustering methods. A comparative study based on real images shows that the proposed algorithm desirably retains geometric details and produces smooth surface.\",\n \"Title: Accurate Range Image Registration: Eliminating or Modelling Outliers\\nYear: 2007\\n\\nAbstract: Automatic and accurate range image registration is often a prerequisite step for range image analysis and interpretation. Due to occlusion, appearance and disappearance of points in different images, outliers inevitably occur. In this case, various techniques to eliminate and model outliers have been proposed for accurate range image registration. The objective of this paper is to experimentally investigate which of the outlier elimination and modelling is more effective for the evaluation of possible correspondences established, so that a deep insight into how advanced range image registration algorithms will be developed can be obtained. The experimental results based on both synthetic data and real images show that the outlier modelling often outperforms the outlier elimination in the sense of producing more accurate and robust range image registration results.\",\n \"Title: Enhanced Micro-structure Descriptor based Image Retrieval.\\nYear: 2013\\n\\nAbstract: We present a new image retrieval method based on enhanced micro-structure descriptor in this paper. Traditional micro-structure based descriptors such as multi-texton histogram (MTH) and micro-structure descriptor (MSD) can integrate local texture, shape and color feature from the image into a feature vector, but their description abilities for natural images are not sufficient. In this paper we firstly use a new local pattern map to create filter map, and the enhanced MSD descriptor (EMSD) is extracted based on the color co-occurrence relationship on the filtered and quantized image. We then normalize the distance between images to range [0, 1]. The proposed method is extensively tested on Corel-5000 and Corel-10000 image sets and experimental results show that our method can achieve a higher retrieval precision than MSD and other representative methods.\",\n \"Title: A social network-aware top-N recommender system using GPU\\nYear: 2011\\n\\nAbstract: A book recommender system is very useful for a digital library. Good book recommender systems can effectively help users find interesting and relevant books from the massive resources, by providing individual recommendation book list for each end-user. By now, a variety of collaborative filtering algorithms have been invented, which are the cores of most recommender systems. However, because of the explosion of information, especially in the Internet, the improvement of the efficiency of the collaborative filting (CF) algorithm becomes more and more important. In this paper, we first propose a parallel Top-N recommendation algorithm in CUDA (Compute Unified Device Architecture) which combines the collaborative filtering and trust-based approach to deal with the cold-start user problem. Then based on this algorithm, we present a parallel book recommender system on a GPU (Graphics Processor unit) for CADAL digital library platform. Our experimental results show our algorithm is very efficient to process the large-scale datasets with good accuracy, and we report the impact of different values of parameters on the recommendation performance.\",\n \"Title: Accurate overlap area detection using a histogram and multiple closest points\\nYear: 2010\\n\\nAbstract: In this paper, we propose a novel ICP variant that uses a histogram in conjunction with multiple closest points to detect the overlap area between range images being registered. Tentative correspondences sharing similar distances are normally all within, or all outside, the overlap area. Thus, the overlap area can be detected in a bin by bin batch manner using a histogram. Using multiple closest points is likely to enlarge the distance difference for tentative correspondences in the histogram, and pull together the images being registered, facilitating the overlap area detection. Our experimental results based on real range images show that the performance of our proposed algorithm enhances the state of the art.\",\n \"Title: Nose detection based feature extraction on both normal and abnormal 3D faces\\nYear: 2008\\n\\nAbstract: This paper presents a feature extraction method, which does not require a frontal model and is applicable on both normal and some abnormal faces (abnormality caused by some genetic syndromes). The algorithm starts by a nose detection step. A geometry property match is employed to get possible candidates and then a symmetry calculation is carried out to select out the nose tip. Once the nose tip is decided, the 3D model is adjusted and normalized. After that, region segmentation is performed efficiently benefiting from the nose tip hint acquired in previous step. With the feature regions classified, feature points can be extracted easily with a set of profiles. Experiment results show that the overall performance is over 90% for both normal and abnormal models.\",\n \"Title: Evaluating Structural Constraints for Accurate Range Image Registration\\nYear: 2003\\n\\nAbstract: New technological developments in optics and electronics are rendering laser scanning systems cheaper and more accurate. Such systems can directly capture depth information from objects simplifying range image analysis and enlarging their applications scope. Accurate image registration algorithms represent a pivotal aspect of range image analysis which still require substantial improvements. While a large number of algorithms have been proposed, it is very important to compare the performance of different registration algorithms so that good algorithms can be selected for real applications. Unfortunately, most of these algorithms are iterative with a number of parameters initialised by users. The main disadvantages of these methods are that these parameters are often context dependent. Thus, while aiming to develop automatic registration algorithms instead of comparing the registration algorithms themselves, we compare the structural constraints based on a single registration algorithm(GICP). A large number of experiments using real range images demonstrate their effectiveness on improving the GICP algorithm.\",\n \"Title: A method for text line detection in natural images\\nYear: 2015\\n\\nAbstract: Text information in natural images is very important to cross-media retrieval, index and understanding. However, its detection is challenging due to varying backgrounds, low contrast between text and non-text regions, perspective distortion and other disturbing factors. In this paper, we propose a novel text line detection method which can detect text line aligned with a straight line in any direction. It is mainly composed of three steps. In the first step, we use the maximal stable extremal region detector with dam line constraint to detect candidate text regions, we then define a similarity measurement between two regions which combines sizes, absolute distance, relative distance, contextual information and color histograms. In the second step, we propose a text line identification algorithm based on the defined similarity measurement. The algorithm firstly searches three regions as the seeds of a line, and then expands to obtain all regions in the line. In the last step, we develop a filter to remove non-text lines. The filter uses a sparse classifier based on two dictionaries which are learned from feature vectors extracted from morphological skeletons of those candidate text lines. A comparative study using two datasets shows the excellent performance of the proposed method for accurate text line detection with horizontal or arbitrary consistent orientation.\",\n \"Title: Amplifying scientific paper's abstract by leveraging data-weighted reconstruction\\nYear: 2014\\n\\nAbstract: This paper considers the problem of amplifying scientific paper's abstract by using its citation sentences. While scientific paper's abstract is concise and subjective, its citation sentences, which can be regarded as one kind of comment, are redundant and objective. A summary combining the merits of those two resource is helpful to researchers. A data-weighted reconstruction approach is proposed to generate this summary and sentence's weight is learned through a ranking algorithm over hypergraph of bibliographic network. The experimental results show that the proposed approach can achieve significantly better than several document summarization's techniques.\",\n \"Title: A GPU-accelerated non-negative sparse latent semantic analysis algorithm for social tagging data\\nYear: 2014\\n\\nAbstract: Nowadays large-scale social tagging data have become very valuable in organizing and indexing multimedia resources. In this paper, we apply Non-negative Sparse Latent Semantic Analysis (NN-Sparse LSA) to discover the latent semantic space behind associations between multimedia resources and tagging data. Based on the traditional coordinate-descent algorithm, column-orthogonality and non-negative constraints, we derive a much faster optimization algorithm in theory for solving the NN-Sparse LSA model. Furthermore, we implement the parallel version of our fast NN-Sparse LSA algorithm using the NVIDIA CUDA (Compute Unified Device Architecture) parallel programming framework and a data partitioning scheme that effectively reduces the memory traffic between the global memory of the Graphic Processing Unit (GPU) and the host memory. The experimental results on image classification and tag recommendation tasks on MIRFLICKR and NUS-WIDE datasets show that our parallelized fast optimization algorithm can achieve comparable or even better performance than the other examined methods, while speeds up the original optimization algorithm 20-110 times.\",\n \"Title: Structured Sparse Linear Model For Social Trust Prediction\\nYear: 2014\\n\\nAbstract: Social trust prediction aims at predicting the missing trust relations between online users. In this paper, we propose a novel and scalable structured sparse linear model for social trust prediction from a global neighborhood-based collaborative filtering perspective. We formulate the prediction problem as a set of independent linear regression problems regularized by pairwise elastic net, to automatically learn correlation coefficients between a user and its most similar neighbors. In order to deal with large-scale sparse social trust data, we utilize efficient hashing techniques and stochastic coordinate descent algorithm to cut down the computational cost of training model. The experimental results on three real-world data sets show that our approach can significantly outperform the other tested methods in terms of prediction quality and efficiency.\",\n \"Title: Fast image-based Chinese calligraphic character retrieval on large scale data\\nYear: 2014\\n\\nAbstract: Chinese calligraphy is the art of handwriting, it draws a lot of attention for its beauty and elegance. In CADAL1, a Calligraphic Character Dictionary (CCD) which contains hundreds of thousands of character images labeled with semantic meaning has been constructed and provided online to common users. It is a great challenge to perform quick and accurate image-based calligraphic character retrieval on CCD. In this paper, a novel shape descriptor, Oriented Shape Context (OSC) is proposed basing on the tranditional Shape Context (SC) to perform similarity searching. Together with GIST, GIST-OSC descriptor is proposed to represent calligraphic character image for efficient and effective retrieval. In addition, an effective retrieval schema is proposed. The retrieval schema works in two steps. Firstly approximate nearest neighbors of the query image are found quickly using GIST and then one-to-one fine matching between approximate nearest neighbors and the query image is performed using OSC. Our experiments show that the GIST-OSC descriptor and the retrieval schema are efficient and effective for Chinese calligraphic character retrieval on large scale data.\",\n \"Title: Discovering treatment pattern in traditional Chinese medicine clinical cases using topic model and domain knowledge\\nYear: 2014\\n\\nAbstract: In Traditional Chinese Medicine (TCM), the prescription is the crystallization of clinical experience of doctors, which is the main way to cure diseases in China for thousands of years. Clinical cases, on the other hand, describe how doctors diagnose and prescribe a prescription. In this paper, we propose a framework which mines the treatment pattern in TCM clinical cases by using probabilistic topic model and TCM domain knowledge. The framework can reflect principle rules in TCM and improve function prediction of a new prescription. We evaluate our model on real world TCM clinical cases. The experiment validates the effectiveness of our method.\",\n \"Title: Discovering Treatment Pattern in Traditional Chinese Medicine Clinical Cases by Exploiting Supervised Topic Model and Domain Knowledge.\\nYear: 2015\\n\\nAbstract: In Traditional Chinese Medicine (TCM), the prescription is the crystallization of clinical experience of doctors, which is the main way to cure diseases in China for thousands of years. Clinical cases, on the other hand, describe how doctors diagnose and prescribe. In this paper, we propose a framework which mines treatment patterns in TCM clinical cases by exploiting supervised topic model and TCM domain knowledge. The framework can reflect principle rules in TCM and improve function prediction of a new prescription. We evaluate our method on 3,090 real world TCM clinical cases. The experiment validates the effectiveness of our method.\",\n \"Title: D-Ocean: an unstructured data management system for data ocean environment.\\nYear: 2016\\n\\nAbstract: Together with the big datamovement,many organizations collect their own big data and build distinctive applications. In order to provide smart services upon big data, massive variable data should be well linked and organized to form Data Ocean, which specially emphasizes the deep exploration of the relationships among unstructured data to support smart services. Currently, almost all of these applications have to deal with unstructured data by integrating various analysis and search techniques upon massive storage and processing infrastructure at the application level, which greatly increase the difficulty and cost of application development.\",\n \"Title: Regularization Based Iterative Point Match Weighting for Accurate Rigid Transformation Estimation\\nYear: 2015\\n\\nAbstract: Feature extraction and matching (FEM) for 3D shapes finds numerous applications in computer graphics and vision for object modeling, retrieval, morphing, and recognition. However, unavoidable incorrect matches lead to inaccurate estimation of the transformation relating different datasets. Inspired by AdaBoost, this paper proposes a novel iterative re-weighting method to tackle the challenging problem of evaluating point matches established by typical FEM methods. Weights are used to indicate the degree of belief that each point match is correct. Our method has three key steps: (i) estimation of the underlying transformation using weighted least squares, (ii) penalty parameter estimation via minimization of the weighted variance of the matching errors, and (iii) weight reestimation taking into account both matching errors and information learnt in previous iterations. A comparative study, based on real shapes captured by two laser scanners, shows that the proposed method outperforms four other state-of-the-art methods in terms of evaluating point matches between overlapping shapes established by two typical FEM methods, resulting in more accurate estimates of the underlying transformation. This improved transformation can be used to better initialize the iterative closest point algorithm and its variants, making 3D shape registration more likely to succeed.\",\n \"Title: Improving MapReduce Performance with Partial Speculative Execution\\nYear: 2015\\n\\nAbstract: Abstract The MapReduce framework has become the de facto standard for big data processing due to its attractive features and abilities. One is that it automatically parallelizes a job into multiple tasks and transparently handles task execution on a large cluster of commodity machines. The increasing heterogeneity of distributed environments may result in a few straggling tasks, which prolong job completion. Speculative execution is proposed to mitigate stragglers. However, the existing speculative execution mechanism could not work efficiently as many speculative tasks are still slower than their original tasks. In this paper, we explore an approach to increase the efficiency of speculative execution, and further improve MapReduce performance. We propose the Partial Speculative Execution (PSE) strategy to make speculative tasks start from the checkpoint. By leveraging the checkpoint of original tasks, PSE can eliminate the costs of re-reading, re-copying, and re-computing the processed data. We implement PSE in Hadoop, and evaluate its performance in terms of job completion time and the efficiency of speculative execution under several kinds of classical workloads. Experimental results show that, in heterogeneous environments with stragglers, PSE completes jobs 56 % faster than that with no speculation and 12 % faster than that with LATE, an improved speculative execution algorithm. In addition, on average PSE can improve the efficiency of speculative execution by 24 % compared to LATE.\",\n \"Title: Taxonomy Induction from Chinese Encyclopedias by Combinatorial Optimization.\\nYear: 2015\\n\\nAbstract: Taxonomy is an important component in knowledge bases, and it is an urgent, meaningful but challenging task for Chinese taxonomy construction. In this paper, we propose a taxonomy induction approach from a Chinese encyclopedia by using combinatorial optimizations. At first, subclass-of relations are derived by validating the relation between two categories. Then, integer programming optimizations are applied to find out instance-of relations from encyclopedia articles by considering the constrains among categories. The experimental results show that our approach can construct a practicable taxonomy from Chinese encyclopedias.\",\n \"Title: Mining RDF from Tables in Chinese Encyclopedias\\nYear: 2015\\n\\nAbstract: Web tables understanding has recently attracted a number of studies. However, many works focus on the tables in English, because they usually need the help of knowledge bases, while the existing knowledge bases such as DBpedia, YAGO, Freebase and Probase mainly contain knowledge in English. In this paper, we focus on the RDF triples extraction from tables in Chinese encyclopedias. Firstly, we constructed a Chinese knowledge base through taxonomy mining and class attribute mining. Then, with the help of our knowledge base, we extracted triples from tables through column scoring, table classification and RDF extraction. In our experiments, we practically implemented our approach in 6,618,544 articles from Hudong Baike with 764,292 tables, and extracted about 1,053,407 unique and new RDF triples with an estimated accuracy of $$90.2\\\\\\\\%$$, which outperforms other similar works.\",\n \"Title: Incorporating Probabilistic Knowledge into Topic Models.\\nYear: 2015\\n\\nAbstract: Probabilistic Topic Models could be used to extract low-dimension aspects from document collections. However, such models without any human knowledge often produce aspects that are not interpretable. In recent years, a number of knowledge-based models have been proposed, which allow the user to input prior knowledge of the domain to produce more coherent and meaningful topics. In this paper, we incorporate human knowledge in the form of probabilistic knowledge base into topic models. By combining latent Dirichlet allocation, a widely used topic model with Probase, a large-scale probabilistic knowledge base, we improve the semantic coherence significantly. Our evaluation results will demonstrate the effectiveness of our method.\",\n \"Title: Short Text Understanding by Leveraging Knowledge into Topic Model.\\nYear: 2015\\n\\nAbstract: In this paper, we investigate the challenging task of understanding short text (STU task) by jointly considering topic modeling and knowledge incorporation. Knowledge incorporation can solve the content sparsity problem effectively for topic modeling. Specifically, the phrase topic model is proposed to leverage the auto-mined knowledge, i.e., the phrases, to guide the generative process of short text. Experimental results illustrate the effectiveness of the mechanism that utilizes knowledge to improve topic modeling’s performance.\",\n \"Title: A question-answering system over Traditional Chinese Medicine\\nYear: 2015\\n\\nAbstract: Traditional Chinese Medicine (TCM) has been around for thousands of years and it's a significant part of Chinese cultural heritage. The theoretical framework of TCM is unique and with rich of content, which contains the complex relationships between disease and medicine. Research on question-answering (QA) over TCM is significant for Chinese NLP and representative, because the resources of TCM are mostly Chinese-based. In this paper we present a QA system over TCM, which transforms user supplied questions into conjunctive query sentences (i.e. SQL) and retrieves the answer from both the built-up dataset and online encyclopedia. The contribution of this paper is threefold: Firstly, we introduce a novel approach for word segmentation over Chinese questions. We employ a TF-IDF model on the dataset to generate domain-specific dictionary with weight factor and tags, which are computed to select the best result of segmentation. Secondly, we present a novel method for constructing queries to retrieve answers. We compute the entity-attribute distance over a set of tagged words to construct incomplete ontology instances, which are used as the intermediary to generate queries. Lastly, we propose a method to integrate web data extraction with question answering, which allows us to extract answers from online encyclopedia website (i.e. Wikipedia). The results of our evaluation with 50 benchmark queries demonstrate the value of our approach.\",\n \"Title: Region-based saliency estimation for 3D shape analysis and understanding.\\nYear: 2016\\n\\nAbstract: The detection of salient regions is an important pre-processing step for many 3D shape analysis and understanding tasks. This paper proposes a novel method for saliency detection in 3D free form shapes. Firstly, we smooth the surface normals by a bilateral filter. Such a method is capable of smoothing the surfaces and retaining the local details. Secondly, a novel method is proposed for the estimation of the saliency value of each vertex. To this end, two new features are defined: Retinex-based Importance Feature (RIF) and Relative Normal Distance (RND). They are based on the human visual perception characteristics and surface geometry respectively. Since the vertex based method cannot guarantee that the detected salient regions are semantically continuous and complete, we propose to refine such values based on surface patches. The detected saliency is finally used to guide the existing techniques for mesh simplification, interest point detection, and overlapping point cloud registration. The comparative studies based on real data from three publicly accessible databases show that the proposed method usually outperforms five selected state of the art ones both qualitatively and quantitatively for saliency detection and 3D shape analysis and understanding.\",\n \"Title: Query Subtopic Mining via Subtractive Initialization of Non-negative Sparse Latent Semantic Analysis.\\nYear: 2016\\n\\nAbstract: Ambiguous and multifaceted queries widely exist in academic and commercial search engines. Identifying the popular subtopics of queries is an important issue for search engines. In this paper, we propose a novel method to discover the popular subtopics for a given query. Our method first constructs a search behavior tripartite graph based on the search log data. Then, we utilize a subtractive initialized Non-negative Sparse LSA model to mine subtopics from the tripartite graph. The experimental results on two real-world Chinese search logs (i.e., the CADAL search log and the Sogou search log) show that our proposed method can significantly outperform the other comparison methods in term of MAP, alpha-nDCG and S-recall. We also applied our method into the CADAL digital library to provide a novel faceted book search service which can reduce the users' efforts in finding books.\",\n \"Title: A Joint Model For Question-Answering Over Traditional Chinese Medicine\\nYear: 2016\\n\\nAbstract: Traditional Chinese Medicine (TCM) has been around for over 2000 years and it's a significant part of Chinese cultural heritage. The theoretical framework of TCM is unique and with rich of content, which contains the complex relationships between disease and medicine and has formed a unique system to diagnose and cure illness. Research on question-answering (QA) over TCM is significant for Chinese NLP and representative, because the resources of TCM are mostly Chinese-based. The general strategies for QA are pipeline, includes Natural Language Processing (NLP) for original questions and structural query process. However, noises are easily introduced and information contained in the original questions may loss during these two separate procedures. In this paper we present a novel joint model for QA over TCM, which unites these two procedures into a uniform framework, together with Wikipedia alignment. The results of our evaluation with 100 benchmark queries demonstrate the value of our approach.\",\n \"Title: Traditional Chinese Medicine Clinical Records Classification Using Knowledge-Powered Document Embedding\\nYear: 2016\\n\\nAbstract: Text classification is one of the fundamental tasks in text mining. In the medical domain, there have been a number of studies on text classification in modern medicine clinical notes written in English. However, very limited text classification research has been conducted on clinical notes written in Chinese, especially traditional Chinese medicine (TCM) clinical records. The goal of this study was to investigate features and machine learning classification algorithms for TCM clinical text classification. We collected 7,037 TCM clinical records of famous TCM doctors as our dataset, and investigated the effects of different types of features and classification algorithms. Additionally, we proposed a novel method to combine deep learning text representation with TCM domain knowledge, which results in the best classification performance.\",\n \"Title: KeyphraseDS: Automatic generation of survey by exploiting keyphrase information.\\nYear: 2017\\n\\nAbstract: Abstract In this paper, we present a novel document summarization mechanism called KeyphraseDS that can organize the scientific articles into multi-aspect and informative scientific survey by exploiting keyphrases. Keyphrases describe textu0027s salience and central focus, which can serve as the component of aspects under specific topic. KeyphraseDS consists of three steps: keyphrase graph construction, semantic aspect generation and content selection. Keyprhases are firstly extracted through CRF-based model exploiting various features, such as syntactic features, correlation features, etc. Spectral clustering is then performed on keyphrase graph to generate different aspects, where the semantic relatedness between keyphrases is computed through knowledge-based similarity and topic-based similarity. The proposed semantic relatedness can not only utilize the statistical text signals efficiently but also overcome the data sparsity problem. Significant sentences are then selected with respect to the generated aspects through integer linear programming (ILP), which takes semantic relevance, semantic diversity, and keyphrase salience into consideration. Extensive experiments, measured by automatic evaluation and human evaluation, demonstrate the effectiveness of our mechanism for generating scientific survey.\",\n \"Title: Chinese calligraphic style representation for recognition.\\nYear: 2017\\n\\nAbstract: Chinese calligraphy draws a lot of attention for its beauty and elegance. The various styles of calligraphic characters make calligraphy even more charming. But it is not always easy to recognize the calligraphic style correctly, especially for beginners. In this paper, an automatic character styles representation for recognition method is proposed. Three kinds of features are extracted to represent the calligraphic characters. Two of them are typical hand-designed features: the global feature, GIST and the local feature, scale invariant feature transform. The left one is deep feature which is extracted by a deep convolutional neural network (CNN). The state-of-the-art classifier modified quadratic discriminant function was employed to perform recognition. We evaluated our method on two calligraphic character datasets, the unconstraint real-world calligraphic character dataset (CCD) and SCL (the standard calligraphic character library). And we also compare MQDF with other two classifiers, support vector machine and neural network, to perform recognition. In our experiments, all three kinds of feature are evaluated with all three classifiers, respectively, finding that deep feature is the best feature for calligraphic style recognition. We also fine-tune the deep CNN (alex-net) in Krizhevsky et al. (Advances in Neural Information Processing Systems, pp. 1097---1105, 2012) to perform calligraphic style recognition. It turns out our method achieves about equal accuracy comparing with the fine-tuned alex-net but with much less training time. Furthermore, the algorithm style discrimination evaluation is developed to evaluate the discriminative style quantitatively.\",\n \"Title: An easy-to-use evaluation framework for benchmarking entity recognition and disambiguation systems.\\nYear: 2017\\n\\nAbstract: Entity recognition and disambiguation (ERD) is a crucial technique for knowledge base population and information extraction. In recent years, numerous papers have been published on this subject, and various ERD systems have been developed. However, there are still some confusions over the ERD field for a fair and complete comparison of these systems. Therefore, it is of emerging interest to develop a unified evaluation framework. In this paper, we present an easy-to-use evaluation framework (EUEF), which aims at facilitating the evaluation process and giving a fair comparison of ERD systems. EUEF is well designed and released to the public as an open source, and thus could be easily extended with novel ERD systems, datasets, and evaluation metrics. It is easy to discover the advantages and disadvantages of a specific ERD system and its components based on EUEF. We perform a comparison of several popular and publicly available ERD systems by using EUEF, and draw some interesting conclusions after a detailed analysis.\",\n \"Title: Incorporating Knowledge Graph Embeddings into Topic Modeling.\\nYear: 2017\\n\\nAbstract: Probabilistic topic models could be used to extract low dimension topics from document collections. However, such models without any human knowledge often produce topics that are not interpretable. In recent years, a number of knowledge-based topic models have been proposed, but they could not process fact-oriented triple knowledge in knowledge graphs. Knowledge graph embeddings, on the other hand, automatically capture relations between entities in knowledge graphs. In this paper, we propose a novel knowledge-based topic model by incorporating knowledge graph embeddings into topic modeling. By combining latent Dirichlet allocation, a widely used topic model with knowledge encoded by entity vectors, we improve the semantic coherence significantly and capture a better representation of a document in the topic space. Our evaluation results will demonstrate the effectiveness of our method.\",\n \"Title: Hybrid storage architecture and efficient MapReduce processing for unstructured data.\\nYear: 2017\\n\\nAbstract: •We present a hybrid storage architecture which integrates various kinds of data stores.•We propose three partitioning strategies to execute MapReduce-based batch-processing jobs.•Our hybrid solution shows 10% to 8.6 times faster performance than the Hadoop solution.•We demonstrate that it is possible to build a smart and efficient system by utilizing the characteristics of different data stores.\",\n \"Title: Mining coherent topics in documents using word embeddings and large-scale text data.\\nYear: 2017\\n\\nAbstract: Probabilistic topic models have been extensively used to extract low-dimension aspects from document collections. However, such models without any human knowledge often generate topics that are not interpretable. Recently, a number of knowledge-based topic models have been proposed, which enable users to input prior domain knowledge to produce more meaningful and coherent topics. Word embeddings, on the other hand, can automatically capture both semantic and syntactic information of words from a large amount of documents, and can be used to measure word similarities. In this paper, we incorporate word embeddings obtained from a large number of domains into topic modeling. By combining Latent Dirichlet Allocation, a widely used topic model with Skip-Gram, a well-known framework for learning word vectors, we improve the semantic coherence significantly. Our evaluation results using product review documents from 100 domains will demonstrate the effectiveness of our method.\",\n \"Title: Cross-Lingual Entity Matching for Heterogeneous Online Wikis.\\nYear: 2017\\n\\nAbstract: Knowledge bases play an increasing important role in many applications. However, many knowledge bases mainly focus on English knowledge, and have only a few knowledge for low-resource languages (LLs). If we can map the entities in LLs to these in high-resource languages (HLs), many knowledge such as relation between entities can be transferred from HLs to LLs. In this paper, we propose an efficient and effective Cross-Lingual Entity Matching approach (CL-EM) to enrich the existing cross-lingual links by learning to rank framework with the learned language-independent features, including cross-lingual topic features and document embedding features. In the experiments, we verified our approach on the existing cross-lingual links between Chinese Wikipedia and English Wikipedia by comparing it with other state-of-art approaches. In addition, we also discovered 141,754 new cross-lingual links between Baidu Baike and English Wikipedia, which almost doubles the number of the existing cross-lingual links.\",\n \"Title: Boosting Collective Entity Linking via Type-Guided Semantic Embedding.\\nYear: 2017\\n\\nAbstract: Entity Linking (EL) is the task of mapping mentions in natural-language text to their corresponding entities in a knowledge base (KB). Type modeling for mention and entity could be beneficial for entity linking. In this paper, we propose a type-guided semantic embedding approach to boost collective entity linking. We use Bidirectional Long Short-Term Memory (BiLSTM) and dynamic convolutional neural network (DCNN) to model the mention and the entity respectively. Then, we build a graph with the semantic relatedness of mentions and entities for the collective entity linking. Finally, we evaluate our approach by comparing the state-of-the-art entity linking approaches over a wide range of very different data sets, such as TAC-KBP from 2009 to 2013, AIDA, DBPediaSpotlight, N3-Reuters-128, and N3-RSS-500. Besides, we also evaluate our approach with a Chinese Corpora. The experiments reveal that the modeling for entity type can be very beneficial to the entity linking.\",\n \"Title: Incorporating knowledge into neural network for text representation.\\nYear: 2018\\n\\nAbstract: •A framework which incorporates knowledge into neural network is proposed.•The framework is a kind of architecture to produce knowledge based text features.•A method for transforming a raw text into a conceptualized text is proposed.•The framework is tested on the sentence level task and the document level task.\",\n \"Title: Bilinear joint learning of word and entity embeddings for Entity Linking.\\nYear: 2018\\n\\nAbstract: Entity Linking (EL) is the task of resolving mentions to referential entities in a knowledge base, which facilitates applications such as information retrieval, question answering, and knowledge base population. In this paper, we propose a novel embedding method specifically designed for EL. The proposed model jointly learns word and entity embeddings which are located in different distributed spaces, and a bilinear model is introduced to simulate the interaction between words and entities. We treat EL as a ranking problem, and utilize a pairwise learning-to-rank framework with features constructed with learned embeddings as well as conventional EL features. Experimental results show the proposed model produces effective embeddings which improve the performance of our EL algorithm. Our method yields the state-of-the-art performances on two benchmark datasets CoNLL and TAC-KBP 2010.\",\n \"Title: A Topic Modeling Approach for Traditional Chinese Medicine Prescriptions.\\nYear: 2018\\n\\nAbstract: In traditional Chinese medicine (TCM), prescriptions are the daughters of doctors' clinical experiences, which have been the main way to cure diseases in China for several thousand years. In the long Chinese history, a large number of prescriptions have been invented based on TCM theories. Regularities in the prescriptions are important for both clinical practice and novel prescription development...\",\n \"Title: A method for finding groups of related herbs in traditional chinese medicine\\nYear: 2011\\n\\nAbstract: As a complementary system to Western medicine, Traditional Chinese Medicine (TCM) provides a unique theoretical and practical approach of treatment to diseases over thousands of years. Accompanying with the increasing number of TCM digital books in digital library, there is an urgent need to explore these resources by the techniques of knowledge discovery. We present a method for creating a network of herbs and partitioning it into groups of related herbs. The method extracts structured information from several TCM digital books, then a new method named Support and Dependency Evaluation (SDE) is presented for herbal combinational rule mining. The herbal network is created from the extracted dataset of paired herbs. The partitioning procedure is designed to extend FEC algorithm to deal with the weighted herbal network. Experiments demonstrate that the method proposed has the capability of discovering groups of related herbs.\",\n \"Title: Knowledge representation learning with entity descriptions, hierarchical types, and textual relations.\\nYear: 2019\\n\\nAbstract: Knowledge representation learning methods usually only utilize triple facts, or just consider one kind of extra information. In this paper, we propose a multi-source knowledge representation learning (MKRL) model, which can combine entity descriptions, hierarchical types, and textual relations with triple facts. Specifically, for entity descriptions, a convolutional neural network is used to get representations. For hierarchical type, weighted hierarchy encoders are used to construct the projection matrixes of hierarchical types, and the projection matrix of an entity combines all hierarchical type projection matrixes of the entity with the relation-specific type constrains. For textual relations, a sentence-level attention mechanism is employed to get representations. We evaluate MKRL model on knowledge graph completion task with dataset FB15k-237, and experimental results demonstrate that our model outperforms the state-of-the-art methods, which indicates the effectiveness of multi-source information for knowledge representation.\",\n \"Title: Metro maps for efficient knowledge learning by summarizing massive electronic textbooks\\nYear: 2019\\n\\nAbstract: As the number of textbooks soars, people may be stuck into thousands of books when learning knowledge. In order to provide a concise yet comprehensive picture for learning, we propose a novel framework, called MM4Books, to automatically build metro maps for efficient knowledge learning by summarizing massive electronic textbooks. We represent each book in digital libraries as a sequence of chapters, and then obtain learning objects by clustering the semantically similar chapters via an unsupervised clustering method to create a learning graph, and then build the metro map by applying an integer linear programming-based technique to select a collection of high informative and fluent but low redundant learning paths from the learning graph. To the best of our knowledge, it is the first work to address this task. Experiments show that our proposed approach outperforms all the state-of-the-art baseline approaches, and we also implemented a practical MM4Books system to prove that users can really benefit from the proposed approach for knowledge learning.\",\n \"Title: Part-of-Speech-Based Long Short-Term Memory Network for Learning Sentence Representations\\nYear: 2019\\n\\nAbstract: Sentence representations play an important role in the field of natural language processing. While word representation has been applied to many natural language processing tasks, sentence representation has not been applied as widely due to the more complex structure and richer syntactic information of sentences. To learn sentence representations with structural and syntactic information, we propose a new model called the part-of-speech-based Long Short-Term Memory network (pos-LSTM) model. The pos-LSTM model generates a structural representation using the standard LSTM model and a syntactic representation using the part of speech; then, the pos-LSTM model obtains the final sentence representation by combining these two representations. The experimental results from 20 sentence similarity tasks and an entailment classification task show that the pos-LSTM model can better capture the syntactic information of sentences and generate higher quality sentence representations than traditional models.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":64,"string":"64"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13668,"string":"13,668"}}},{"rowIdx":13669,"cells":{"question_id":{"kind":"string","value":"Marco Zaffalon"},"question":{"kind":"string","value":"Generate an abstract for the title \"Axiomatising Incomplete Preferences through Sets of Desirable Gambles.\" using the following items: 1. Incomplete preferences \n2. Coherent sets of desirable gambles \n3. State independence \n4. Archimedean condition \n5. Desirability and decision theory"},"answer":{"kind":"string","value":"We establish the equivalence of two very general theories: the first is the decision theoretic formalisation of incomplete preferences based on the mixture independence axiom; the second is the theory of coherent sets of desirable gambles (bounded variables) developed in the context of imprecise probability and extended here to vector-valued gambles. Such an equivalence allows us to analyse the theory of incomplete preferences from the point of view of desirability. Among other things, this leads us to uncover an unexpected and clarifying relation: that the notion of state independence-the traditional assumption that we can have separate models for beliefs (probabilities) and values (utilities)-coincides with that of strong independence in imprecise probability; this connection leads us also to propose much weaker, and arguably more realistic, notions of state independence. Then we simplify the treatment of complete beliefs and values by putting them on a more equal footing. We study the role of the Archimedean condition-which allows us to actually talk of expected utility-, identify some weaknesses and propose alternatives that solve these. More generally speaking, we show that desirability is a valuable alternative foundation to preferences for decision theory that streamlines and unifies a number of concepts while preserving great generality. In addition, the mentioned equivalence shows for the first time how to extend the theory of desirability to imprecise non-linear utility, thus enabling us to formulate one of the most powerful self-consistent theories of reasoning and decision-making available today."},"correct_answer":{"kind":"string","value":"We establish the equivalence of two very general theories: the first is the decision theoretic formalisation of incomplete preferences based on the mixture independence axiom; the second is the theory of coherent sets of desirable gambles (bounded variables) developed in the context of imprecise probability and extended here to vector-valued gambles. Such an equivalence allows us to analyse the theory of incomplete preferences from the point of view of desirability. Among other things, this leads us to uncover an unexpected and clarifying relation: that the notion of state independence-the traditional assumption that we can have separate models for beliefs (probabilities) and values (utilities)-coincides with that of strong independence in imprecise probability; this connection leads us also to propose much weaker, and arguably more realistic, notions of state independence. Then we simplify the treatment of complete beliefs and values by putting them on a more equal footing. We study the role of the Archimedean condition-which allows us to actually talk of expected utility-, identify some weaknesses and propose alternatives that solve these. More generally speaking, we show that desirability is a valuable alternative foundation to preferences for decision theory that streamlines and unifies a number of concepts while preserving great generality. In addition, the mentioned equivalence shows for the first time how to extend the theory of desirability to imprecise non-linear utility, thus enabling us to formulate one of the most powerful self-consistent theories of reasoning and decision-making available today."},"documents":{"kind":"list like","value":["Title: Coherence graphs\nYear: 2009\n\nAbstract: We study the consistency of a number of probability distributions, which are allowed to be imprecise. To make the treatment as general as possible, we represent those probabilistic assessments as a collection of conditional lower previsions. The problem then becomes proving Walley's (strong) coherence of the assessments. In order to maintain generality in the analysis, we assume to be given nearly no information about the numbers that make up the lower previsions in the collection. Under this condition, we investigate the extent to which the above global task can be decomposed into simpler and more local ones. This is done by introducing a graphical representation of the conditional lower previsions that we call the coherence graph: we show that the coherence graph allows one to isolate some subsets of the collection whose coherence is sufficient for the coherence of all the assessments; and we provide a polynomial-time algorithm that finds the subsets efficiently. We show some of the implications of our results by focusing on three models and problems: Bayesian and credal networks, of which we prove coherence; the compatibility problem, for which we provide an optimal graphical decomposition; probabilistic satisfiability, of which we show that some intractable instances can instead be solved efficiently by exploiting coherence graphs.","Title: Conglomerable coherence\nYear: 2013\n\nAbstract: We contrast Williams@? and Walley@?s theories of coherent lower previsions in the light of conglomerability. These are two of the most credited approaches to a behavioural theory of imprecise probability. Conglomerability is the notion that distinguishes them most: Williams@? theory does not consider it, while Walley aims at embedding it in his theory. This question is important, as conglomerability is a major point of disagreement at the foundations of probability, since it was first defined by de Finetti in 1930. We show that Walley@?s notion of joint coherence (which is the single axiom of his theory) for conditional lower previsions does not take all the implications of conglomerability into account. Considering also some previous results in the literature, we deduce that Williams@? theory should be the one to use when conglomerability is not required; for the opposite case, we define the new theory of conglomerably coherent lower previsions, which is arguably the one to use, and of which Walley@?s theory can be understood as an approximation. We show that this approximation is exact in two important cases: when all conditioning events have positive lower probability, and when conditioning partitions are nested.","Title: Independent natural extension\nYear: 2010\n\nAbstract: We introduce a general definition for the independence of a number of finite-valued variables, based on coherent lower previsions. Our definition has an epistemic flavour: it arises from personal judgements that a number of variables are irrelevant to one another. We show that a number of already existing notions, such as strong independence, satisfy our definition. Moreover, there always is a least-committal independent model, for which we provide an explicit formula: the independent natural extension. Our central result is that the independent natural extension satisfies so-called marginalisation, associativity and strong factorisation properties. These allow us to relate our research to more traditional ways of defining independence based on factorisation.","Title: Independent Natural Extension\nYear: 2011\n\nAbstract: We introduce a general definition for the independence of a number of finite-valued variables, based on coherent lower previsions.\n Our definition has an epistemic flavour: it arises from personal judgements that a number of variables are irrelevant to one\n another. We show that a number of already existing notions, such as strong independence, satisfy our definition. Moreover,\n there always is a least-committal independent model, for which we provide an explicit formula: the independent natural extension. Our central result is that the independent natural extension satisfies so-called marginalisation, associativity and strong factorisation properties. These allow us to relate our research to more traditional ways of defining independence based on factorisation.","Title: Conglomerable natural extension\nYear: 2012\n\nAbstract: At the foundations of probability theory lies a question that has been open since de Finetti framed it in 1930: whether or not an uncertainty model should be required to be conglomerable. Conglomerability is related to accepting infinitely many conditional bets. Walley is one of the authors who have argued in favor of conglomerability, while de Finetti rejected the idea. In this paper we study the extension of the conglomerability condition to two types of uncertainty models that are more general than the ones envisaged by de Finetti: sets of desirable gambles and coherent lower previsions. We focus in particular on the weakest (i.e., the least-committal) of those extensions, which we call the conglomerable natural extension. The weakest extension that does not take conglomerability into account is simply called the natural extension. We show that taking the natural extension of assessments after imposing conglomerability-the procedure adopted in Walley's theory-does not yield, in general, the conglomerable natural extension (but it does so in the case of the marginal extension). Iterating this process of imposing conglomerability and taking the natural extension produces a sequence of models that approach the conglomerable natural extension, although it is not known, at this point, whether this sequence converges to it. We give sufficient conditions for this to happen in some special cases, and study the differences between working with coherent sets of desirable gambles and coherent lower previsions. Our results indicate that it is necessary to rethink the foundations of Walley's theory of coherent lower previsions for infinite partitions of conditioning events.","Title: Probability and time\nYear: 2013\n\nAbstract: Probabilistic reasoning is often attributed a temporal meaning, in which conditioning is regarded as a normative rule to compute future beliefs out of current beliefs and observations. However, the well-established 'updating interpretation' of conditioning is not concerned with beliefs that evolve in time, and in particular with future beliefs. On the other hand, a temporal justification of conditioning was proposed already by De Moivre and Bayes, by requiring that current and future beliefs be consistent. We reconsider the latter approach while dealing with a generalised version of the problem, using a behavioural theory of imprecise probability in the form of coherent lower previsions as well as of coherent sets of desirable gambles, and letting the possibility space be finite or infinite. We obtain that using conditioning is normative, in the imprecise case, only if one establishes future behavioural commitments at the same time of current beliefs. In this case it is also normative that present beliefs be conglomerable, which is a result that touches on a long-term controversy at the foundations of probability. In the remaining case, where one commits to some future behaviour after establishing present beliefs, we characterise the several possibilities to define consistent future assessments; this shows in particular that temporal consistency does not preclude changes of mind. And yet, our analysis does not support that rationality requires consistency in general, even though pursuing consistency makes sense and is useful, at least as a way to guide and evaluate the assessment process. These considerations narrow down in the special case of precise probability, because this formalism cannot distinguish the two different situations illustrated above: it turns out that the only consistent rule is conditioning and moreover that it is not rational to be willing to stick to precise probability while using a rule different from conditioning to compute future beliefs; rationality requires in addition the disintegrability of the present-time probability.","Title: CREDO: A military decision-support system based on credal networks\nYear: 2013\n\nAbstract: A software tool especially designed for military domains to create and query decision-support systems is presented. Credal networks, which are Bayesian networks whose parameters have the freedom to vary in convex sets, are used to model the relations among the system variables. A novel elicitation procedure of these sets, which allows the military experts to report their knowledge by purely qualitative judgements, is proposed. Two high-level fusion procedures to cope with multiple experts in this framework are also derived. All these features are supported by the software and demonstrated in an application to space security tested during the last NATO multinational experiment.","Title: Conglomerable Coherent Lower Previsions.\nYear: 2012\n\nAbstract: Walley's theory of coherent lower previsions builds upon the former theory by Williams with the explicit aim to make it deal with conglomerability. We show that such a construction has been only partly successful because Walley's founding axiom of joint coherence does not entirely capture the implications of conglomerability. As a way to fully achieve Walley's original aim, we propose then the new theory of conglomerable coherent lower previsions. We show that Walley's theory coincides with ours when all conditioning events have positive lower probability, or when conditioning partitions are nested.","Title: Lazy naive credal classifier\nYear: 2009\n\nAbstract: We propose a local (or lazy) version of the naive credal classifier. The latter is an extension of naive Bayes to imprecise probability developed to issue reliable classifications despite small amounts of data, which may then be carrying highly uncertain information about a domain. Reliability is maintained because credal classifiers can issue set-valued classifications on instances that are particularly difficult to classify. We show by extensive experiments that the local classifier outperforms the original one, both in terms of accuracy of classification and because it leads to stronger conclusions (i.e., set-valued classifications made by fewer classes). By comparing the local credal classifier with a local version of naive Bayes, we also show that the former reliably deals with instances which are difficult to classify, unlike the local naive Bayes which leads to fragile classifications.","Title: Locally specified credal networks\nYear: 2006\n\nAbstract: Credal networks are models that extend Bayesian nets to deal with imprecision in probability, and can actually be regarded as sets of Bayesian nets. Evidence suggests that credal nets are a powerful means to represent and deal with many important and challenging problems in uncertain reasoning. We give examples to show that some of these problems can only be modelled by credal nets called non-separately specie d. These, however, are still missing a graphical representation language and solution algorithms. The situation is quite the opposite with separately specied credal nets, which have been the subject of much study and algorithmic development. This paper gives two major contributions. First, it delivers a new graphical language to formulate any type of credal network, both separately and non-separately specied. Second, it shows that any non-separately specied net represented with the new language can be easily transformed into an equivalent separately specied net, dened over a larger domain. This result opens up a number of new perspectives and concrete outcomes: rst of all, it immediately enables the existing algorithms for separately specied credal nets to be applied to non-separately specied ones.","Title: Updating with incomplete observations.\nYear: 2003\n\nAbstract: Currently, there is renewed interest in the problem, raised by Shafer in 1985, of updating probabilities when observations are incomplete (or setvalued). This is a fundamental problem, and of articular interest for Bayesian networks. Recently, Griinwald and Halpern have shown that commonly used updating strategies fail here, except under very special assumptions. We propose a new rule for updating probabilities with incomplete observations. Our approach is deliberately conservative: we make no or weak assumptions about the so-called incompleteness mechanism that produces incomplete observations. We model our ignorance about this mechanism by a vacuous lower prevision, a tool from the theory of imprecise probabilities, and we derive a new updating rule using coherence arguments. In general, our rule produces lower posterior probabilities, as well as partially determinate decisions. This is a logical consequence of the ignorance about the incompleteness mechanism. We show how the new rule can properly address the apparent paradox in the 'Monty Hall' puzzle. In addition, we apply it to the classification of new evidence in Bayesian networks constructed using expert knowledge. We provide an exact algorithm for this task with linear-time complexity, also for multiply connected nets.","Title: Robust inference of trees\nYear: 2005\n\nAbstract: This paper is concerned with the reliable inference of optimal tree-approximations to the dependency structure of an unknown distribution generating data. The traditional approach to the problem measures the dependency strength between random variables by the index called mutual information. In this paper reliability is achieved by Walley's imprecise Dirichlet model, which generalizes Bayesian learning with Dirichlet priors. Adopting the imprecise Dirichlet model results in posterior interval expectation for mutual information, and in a set of plausible trees consistent with the data. Reliable inference about the actual tree is achieved by focusing on the substructure common to all the plausible trees. We develop an exact algorithm that infers the substructure in time O(m 4), m being the number of random variables. The new algorithm is applied to a set of data sampled from a known distribution. The method is shown to reliably infer edges of the actual tree even when the data are very scarce, unlike the traditional approach. Finally, we provide lower and upper credibility limits for mutual information under the imprecise Dirichlet model. These enable the previous developments to be extended to a full inferential method for trees.","Title: Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2\nYear: 2008\n\nAbstract: In this paper, the naive credal classifier, which is a set-valued counterpart of naive Bayes, is extended to a general and flexible treatment of incomplete data, yielding a new classifier called naive credal classifier 2 (NCC2). The new classifier delivers classifications that are reliable even in the presence of small sample sizes and missing values. Extensive empirical evaluations show that, by issuing set-valued classifications, NCC2 is able to isolate and properly deal with instances that are hard to classify (on which naive Bayes accuracy drops considerably), and to perform as well as naive Bayes on the other instances. The experiments point to a general problem: they show that with missing values, empirical evaluations may not reliably estimate the accuracy of a traditional classifier, such as naive Bayes. This phenomenon adds even more value to the robust approach to classification implemented by NCC2.","Title: Generalized loopy 2U: A new algorithm for approximate inference in credal networks\nYear: 2010\n\nAbstract: Credal networks generalize Bayesian networks by relaxing the requirement of precision of probabilities. Credal networks are considerably more expressive than Bayesian networks, but this makes belief updating NP-hard even on polytrees. We develop a new efficient algorithm for approximate belief updating in credal networks. The algorithm is based on an important representation result we prove for general credal networks: that any credal network can be equivalently reformulated as a credal network with binary variables; moreover, the transformation, which is considerably more complex than in the Bayesian case, can be implemented in polynomial time. The equivalent binary credal network is then updated by L2U, a loopy approximate algorithm for binary credal networks. Overall, we generalize L2U to non-binary credal networks, obtaining a scalable algorithm for the general case, which is approximate only because of its loopy nature. The accuracy of the inferences with respect to other state-of-the-art algorithms is evaluated by extensive numerical tests.","Title: Credal networks for military identification problems\nYear: 2009\n\nAbstract: Credal networks are imprecise probabilistic graphical models generalizing Bayesian networks to convex sets of probability mass functions. This makes credal networks particularly suited to model expert knowledge under very general conditions, including states of qualitative and incomplete knowledge. In this paper, we present a credal network for risk evaluation in case of intrusion of civil aircrafts into a restricted flight area. The different factors relevant for this evaluation, together with an independence structure over them, are initially identified. These factors are observed by sensors, whose reliabilities can be affected by variable external factors, and even by the behaviour of the intruder. A model of these observation processes, and the necessary fusion scheme for the information returned by the sensors measuring the same factor, are both completely embedded into the structure of the credal network. A pool of experts, facilitated in their task by specific techniques to convert qualitative judgements into imprecise probabilistic assessments, has made possible the quantification of the network. We show the capabilities of the proposed model by means of some preliminary tests referred to simulated scenarios. Overall, we can regard this application as a useful tool to support military experts in their decision, but also as a quite general imprecise-probability paradigm for information fusion.","Title: The Complexity of Approximately Solving Influence Diagrams\nYear: 2012\n\nAbstract: Influence diagrams allow for intuitive and yet precise description of complex situations involving decision making under uncertainty. Unfortunately, most of the problems described by influence diagrams are hard to solve. In this paper we discuss the complexity of approximately solving influence diagrams. We do not assume no-forgetting or regularity, which makes the class of problems we address very broad. Remarkably, we show that when both the tree-width and the cardinality of the variables are bounded the problem admits a fully polynomial-time approximation scheme.","Title: Approximating credal network inferences by linear programming\nYear: 2013\n\nAbstract: An algorithm for approximate credal network updating is presented. The problem in its general formulation is a multilinear optimization task, which can be linearized by an appropriate rule for fixing all the local models apart from those of a single variable. This simple idea can be iterated and quickly leads to very accurate inferences. The approach can also be specialized to classification with credal networks based on the maximality criterion. A complexity analysis for both the problem and the algorithm is reported together with numerical experiments, which confirm the good performance of the method. While the inner approximation produced by the algorithm gives rise to a classifier which might return a subset of the optimal class set, preliminary empirical results suggest that the accuracy of the optimal class set is seldom affected by the approximate probabilities.","Title: Reliable hidden Markov model filtering through coherent lower previsions.\nYear: 2009\n\nAbstract: We extend Hidden Markov Models for continuous variables taking into account imprecision in our knowledge about the probabilistic relationships involved. To achieve that, we consider sets of probabilities, also, called coherent lower previsions. In addition to the general formulation, we study in detail a particular case of interest: linear-vacuous mixtures. We also show, in a practical case, that our extension outperforms the Kalman filter when modelling errors are present in the system.","Title: Epistemic irrelevance in credal nets: The case of imprecise Markov trees\nYear: 2010\n\nAbstract: We focus on credal nets, which are graphical models that generalise Bayesian nets to imprecise probability. We replace the notion of strong independence commonly used in credal nets with the weaker notion of epistemic irrelevance, which is arguably more suited for a behavioural theory of probability. Focusing on directed trees, we show how to combine the given local uncertainty models in the nodes of the graph into a global model, and we use this to construct and justify an exact message-passing algorithm that computes updated beliefs for a variable in the tree. The algorithm, which is linear in the number of nodes, is formulated entirely in terms of coherent lower previsions, and is shown to satisfy a number of rationality requirements. We supply examples of the algorithm's operation, and report an application to on-line character recognition that illustrates the advantages of our approach for prediction. We comment on the perspectives, opened by the availability, for the first time, of a truly efficient algorithm based on epistemic irrelevance.","Title: Inference and risk measurement with the pari-mutuel model\nYear: 2010\n\nAbstract: We explore generalizations of the pari-mutuel model (PMM), a formalization of an intuitive way of assessing an upper probability from a precise one. We discuss a naive extension of the PMM considered in insurance, compare the PMM with a related model, the Total Variation Model, and generalize the natural extension of the PMM introduced by P. Walley and other pertained formulae. The results are subsequently given a risk measurement interpretation: in particular it is shown that a known risk measure, Tail Value at Risk (TVaR), is derived from the PMM, and a coherent risk measure more general than TVaR from its imprecise version. We analyze further the conditions for coherence of a related risk measure, Conditional Tail Expectation. Conditioning with the PMM is investigated too, computing its natural extension, characterising its dilation and studying the weaker concept of imprecision increase.","Title: Fast algorithms for robust classification with Bayesian nets\nYear: 2005\n\nAbstract: We focus on a well-known classification task with expert systems based on Bayesian networks: predicting the state of a target variable given an incomplete observation of the other variables in the network, i.e., an observation of a subset of all the possible variables. To provide conclusions robust to near-ignorance about the process that prevents some of the variables from being observed, it has recently been derived a new rule, called conservative updating. With this paper we address the problem to efficiently compute the conservative updating rule for robust classification with Bayesian networks. We show first that the general problem is NP-hard, thus establishing a fundamental limit to the possibility to do robust classification efficiently. Then we define a wide subclass of Bayesian networks that does admit efficient computation. We show this by developing a new classification algorithm for such a class, which extends substantially the limits of efficient computation with respect to the previously existing algorithm. The algorithm is formulated as a variable elimination procedure, whose computation time is linear in the input size.","Title: Notes on desirability and conditional lower previsions\nYear: 2010\n\nAbstract: We detail the relationship between sets of desirable gambles and conditional lower previsions. The former is one the most general models of uncertainty. The latter corresponds to Walley's celebrated theory of imprecise probability. We consider two avenues: when a collection of conditional lower previsions is derived from a set of desirable gambles, and its converse. In either case, we relate the properties of the derived model with those of the originating one. Our results constitute basic tools to move from one formalism to the other, and thus to take advantage of work done in the two fronts.","Title: Conservative Rules For Predictive Inference With Incomplete Data\nYear: 2005\n\nAbstract: This paper addresses the following question: how should we update our beliefs after observing some incomplete data, in order to make credible predictions about new, and possibly incomplete, data? There may be several answers to this question according to the model of the process that creates the incompleteness. This paper develops a rigorous modelling framework that makes it clear the conditions that justify the different answers; and, on this basis, it derives a new conditioning rule for predictive inference to be used in a wide range of states of knowledge about the incompleteness process, including near-ignorance, which, surprisingly, does not seem to have received attention so far. Such a case is instead particularly important, as modelling incompleteness processes can be highly impractical, and because there are limitations to statistical inference with incomplete data: it is generally not possible to learn how incompleteness processes work by using the available data; and it may not be possible, as the paper shows, to measure empirically the quality of the predictions. Yet, these depend heavily on the assumptions made.","Title: Simulation and planning of an intermodal container terminal\nYear: 1998\n\nAbstract: A decision support system for the management of an intermodal container terminal is presented. Among the problems to be solved, there are the spatial allocation of containers on the terminal yard, the allocation of resources and the scheduling of operations in order to maximise a performance function based on some economic indicators. These problems are solved using techniques from optimisation, like job-shop scheduling, genetic algorithms or mixed-integer linear programming. At the terminal, the same problems are usually solved by the terminal manager, only using his/her experience. The manager can trust computer generated solutions only by validating them by means of a simulation model of the terminal. Thus, the simulation tool also becomes a means to introduce new approaches into traditional settings. In the present paper we focus on the resource allocation problem. We describe our modules for the optimisation of the allocation process and for the simulation of the terminal. The former is based on integer linear program- ming; the latter is a discrete event simulation tool, based on the process- oriented paradigm. The simulator provides a test bed for checking the validity and the robustness of the policy computed by the optimisation module. The case study of the Contship La Spezia Container Terminal, located in the Mediterranean Sea in Italy, is examined.","Title: Limits Of Learning From Imperfect Observations Under Prior Ignorance: The Case Of The Imprecise Dirichlet Model\nYear: 2005\n\nAbstract: Consider a relaxed multinomial setup, in which there may be mistakes in observing the outcomes of the process-this is often the case in real applications. What can we say about the next outcome if we start learning about the process in conditions of prior ignorance? To answer this question we extend the imprecise Dirichlet model to the case of imperfect observations and we focus on posterior predictive probabilities for the next outcome. The results are very surprising: the posterior predictive probabilities are vacuous, irrespectively of the amount of observations we do, and however small is the probability of doing mistakes. In other words, the imprecise Dirichlet model cannot help us to learn from data when the observational mechanism is imperfect. This result seems to rise a serious question about the use of the imprecise Dirichlet model for practical applications, and, more generally, about the possibility to learn from imperfect observations under prior ignorance.","Title: Bayesian Treatment of Incomplete Discrete Data applied to Mutual Information and Feature Selection\nYear: 2003\n\nAbstract: Given the joint chances of a pair of random variables one can compute quantities of interest, like. the mutual information. The Bayesian treatment of unknown chances involves computing, from a second order prior distribution and the data likelihood, a posterior distribution of the chances. A common treatment of incomplete data is to assume ignorability and determine the chances by the expectation maximization (EM) algorithm. The two different methods above are well established but typically separated. This paper joins the two approaches in the case of Dirichlet priors, and derives efficient approximations for the mean, mode and the (co)variance of the chances and the mutual information. Furthermore, we prove the unimodality of the posterior distribution, whence the important property of convergence of EM to the global maximum in the chosen framework. These results are applied to the problem of selecting features for incremental learning and naive Bayes classification. A fast filter based on the distribution of mutual information is shown to outperform the traditional filter based on empirical mutual information on a number of incomplete real data sets.","Title: Decision-theoretic specification of credal networks: A unified language for uncertain modeling with sets of Bayesian networks\nYear: 2008\n\nAbstract: Credal networks are models that extend Bayesian nets to deal with imprecision in probability, and can actually be regarded as sets of Bayesian nets. Credal nets appear to be powerful means to represent and deal with many important and challenging problems in uncertain reasoning. We give examples to show that some of these problems can only be modeled by credal nets called non-separately specified. These, however, are still missing a graphical representation language and updating algorithms. The situation is quite the opposite with separately specified credal nets, which have been the subject of much study and algorithmic development. This paper gives two major contributions. First, it delivers a new graphical language to formulate any type of credal network, both separately and non-separately specified. Second, it shows that any non-separately specified net represented with the new language can be easily transformed into an equivalent separately specified net, defined over a larger domain. This result opens up a number of new outlooks and concrete outcomes: first of all, it immediately enables the existing algorithms for separately specified credal nets to be applied to non-separately specified ones. We explore this possibility for the 2U algorithm: an algorithm for exact updating of singly connected credal nets, which is extended by our results to a class of non-separately specified models. We also consider the problem of inference on Bayesian networks, when the reason that prevents some of the variables from being observed is unknown. The problem is first reformulated in the new graphical language, and then mapped into an equivalent problem on a separately specified net. This provides a first algorithmic approach to this kind of inference, which is also proved to be NP-hard by similar transformations based on our formalism.","Title: Credible classification for environmental problems\nYear: 2005\n\nAbstract: Classifiers that aim at doing credible predictions should rely on carefully elicited prior knowledge. Often this is not available so they should start learning from data in condition of near-ignorance. This paper shows empirically, on an agricultural data set, that established methods of classification do not always adhere to this principle. Traditional ways to represent prior ignorance are shown to have an overwhelming weight compared to the information in the data, producing overconfident predictions. This point is crucial for problems, such as environmental ones, where prior knowledge is often scarce and even the data may not be known precisely. Credal classification, and in particular the naive credal classifier, is proposed as more faithful ways to cope with the ignorance problem. With credal classification, conditions of ignorance may limit the power of the inferences, not the credibility of the predictions.","Title: Credal Classification for Dementia Screening\nYear: 2001\n\nAbstract: Dementia is a very serious personal, medical and social problem. Early and accurate diagnoses seem to be the key to effectively\n cope with it. This paper presents a diagnostic tool that couples the most widely used computerized system of cognitive tests\n in dementia research, the Cognitive Drug Research system, with the naive credal classifier. Although the classifier is trained\n on an incomplete database, it provides unmatched predictive performance and reliability. The tool also proves to be very effective\n in discriminating between Alzheimer’s disease and dementia with Lewy bodies, which is a problem on the frontier of research\n on dementia.","Title: Conservative inference rule for uncertain reasoning under incompleteness\nYear: 2014\n\nAbstract: In this paper we formulate the problem of inference under incomplete information in very general terms. This includes modelling the process responsible for the incompleteness, which we call the incompleteness process. We allow the process' behaviour to be partly unknown. Then we use Walley's theory of coherent lower previsions, a generalisation of the Bayesian theory to imprecision, to derive the rule to update beliefs under incompleteness that logically follows from our assumptions, and that we call conservative inference rule. This rule has some remarkable properties: it is an abstract rule to update beliefs that can be applied in any situation or domain; it gives us the opportunity to be neither too optimistic nor too pessimistic about the incompleteness process, which is a necessary condition to draw reliable while strong enough conclusions; and it is a coherent rule, in the sense that it cannot lead to inconsistencies. We give examples to show how the new rule can be applied in expert systems, in parametric statistical inference, and in pattern classification, and discuss more generally the view of incompleteness processes defended here as well as some of its consequences.","Title: Reproducing human decisions in reservoir management: the case of lake Lugano\nYear: 2009\n\nAbstract: The objective of this study is to identify a model able to represent the behavior of the historical decision maker (DM) in the management of lake Lugano, during the period 1982-2002. The DM decides every day how much water to release from the lake. We combine hydrological knowledge and machine learning techniques to property develop the model. As a predictive tool we use lazy learning, namely local linear regression. We setup a daily predictor, which achieves good accuracy, with a mean absolute percentage error around 8.5%. Yet, the behavior of the model is not fully satisfactory during the floods. In fact, from an interview with a domain expert, it appears that the DM can even update the release decision every 6 hours during emergencies. We have therefore developed a refined version of the model, which works with a variable time step: it updates the release decision once a day in normal conditions, and every 6 hours during emergencies. This turns out to be a sensible choice, as the error during emergencies (which represent about 5% of the data set) decreases from 9 to 3 m(3)/sec.","Title: Binarization Algorithms for Approximate Updating in Credal Nets\nYear: 2006\n\nAbstract: Credal networks generalize Bayesian networks relaxing numerical parameters. This considerably expands expressivity, but makes belief updating a hard task even on polytrees. Nevertheless, if all the variables are binary, polytree-shaped credal networks can be efficiently updated by the 2U algorithm. In this paper we present a binarization algorithm, that makes it possible to approximate an updating problem in a credal net by a corresponding problem in a credal net over binary variables. The procedure leads to outer bounds for the original problem. The binarized nets are in general multiply connected, but can be updated by the loopy variant of 2U. The quality of the overall approximation is investigated by promising numerical experiments.","Title: On the complexity of solving polytree-shaped limited memory influence diagrams with binary variables\nYear: 2013\n\nAbstract: Influence diagrams are intuitive and concise representations of structured decision problems. When the problem is non-Markovian, an optimal strategy can be exponentially large in the size of the diagram. We can avoid the inherent intractability by constraining the size of admissible strategies, giving rise to limited memory influence diagrams. A valuable question is then how small do strategies need to be to enable efficient optimal planning. Arguably, the smallest strategies one can conceive simply prescribe an action for each time step, without considering past decisions or observations. Previous work has shown that finding such optimal strategies even for polytree-shaped diagrams with ternary variables and a single value node is NP-hard, but the case of binary variables was left open. In this paper we address such a case, by first noting that optimal strategies can be obtained in polynomial time for polytree-shaped diagrams with binary variables and a single value node. We then show that the same problem is NP-hard if the diagram has multiple value nodes. These two results close the fixed-parameter complexity analysis of optimal strategy selection in influence diagrams parametrized by the shape of the diagram, the number of value nodes and the maximum variable cardinality.","Title: Factorisation Properties of the Strong Product\nYear: 2010\n\nAbstract: We investigate a number of factorisation conditions in the framework of sets of probability measures, or coherent lower previsions, with finite referential spaces. We show that the so-called strong product constitutes one way to combine a number of marginal coherent lower previsions into an independent joint lower prevision, and we prove that under some conditions it is the only independent product that satisfies the factorisation conditions.","Title: Multiple model tracking by imprecise markov trees.\nYear: 2009\n\nAbstract: We present a new procedure for tracking manoeuvring objects by hidden Markov chains. It leads to more reliable modelling of the transitions between hidden states compared to similar approaches proposed within the Bayesian framework: we adopt convex sets of probability mass functions rather than single 'precise probability' specifications, in order to provide a more realistic and cautious model of the manoeuvre dynamics. In general, the downside of such increased freedom in the modelling phase is a higher inferential complexity. However, the simple topology, of hidden Markov chains allows for efficient tracking of the object through a recently developed belief propagation algorithm. Furthermore, the imprecise specification of the transitions can produce so-called indecision, meaning that more than one model may be suggested by our method as a possible explanation of the target kinematics. In summary, our approach leads to a multiple-model estimator who se performance, investigated through extensive numerical tests, turns out to be more accurate and robust than that of Bayesian ones.","Title: Equivalence Between Bayesian and Credal Nets on an Updating Problem\nYear: 2006\n\nAbstract: We establish an intimate connection between Bayesian and credal nets. Bayesian nets are precise graphical models, credal nets extend Bayesian nets to imprecise probability. We focus on traditional belief updating with credal nets, and on the kind of belief updating that arises with Bayesian nets when the reason for the missingness of some of the unobserved variables in the net is unknown. We show that the two updating problems are formally the same.","Title: Evaluating credal classifiers by utility-discounted predictive accuracy\nYear: 2012\n\nAbstract: Predictions made by imprecise-probability models are often indeterminate (that is, set-valued). Measuring the quality of an indeterminate prediction by a single number is important to fairly compare different models, but a principled approach to this problem is currently missing. In this paper we derive, from a set of assumptions, a metric to evaluate the predictions of credal classifiers. These are supervised learning models that issue set-valued predictions. The metric turns out to be made of an objective component, and another that is related to the decision-maker's degree of risk aversion to the variability of predictions. We discuss when the measure can be rendered independent of such a degree, and provide insights as to how the comparison of classifiers based on the new measure changes with the number of predictions to be made. Finally, we make extensive empirical tests of credal, as well as precise, classifiers by using the new metric. This shows the practical usefulness of the metric, while yielding a first insightful and extensive comparison of credal classifiers.","Title: Tree-Based Credal Networks for Classification\nYear: 2003\n\nAbstract: Bayesian networks are models for uncertain reasoning which are achiev- ing a growing importance also for the data mining task of classification. Credal networks extend Bayesian nets to sets of distributions, or credal sets. This paper extends a state-of-the-art Bayesian net for classification, called tree-augmented naive Bayes classifier, to credal sets originated from probability intervals. This extension is a basis to address the fundamental problem of prior ignorance about the distribution that generates the data, which is a commonplace in data mining applications. This issue is often neglected, but addressing it properly is a key to ultimately draw reliable conclusions from the inferred models. In this paper we formalize the new model, develop an exact linear-time classification algorithm, and evaluate the credal net- based classifier on a number of real data sets. The empirical analysis shows that the new classifier is good and reliable, and raises a problem of excessive caution that is discussed in the paper. Overall, given the favorable trade-off between expressiveness and efficient computation, the newly proposed classifier appears to be a good can- didate for the wide-scale application of reliable classifiers based on credal networks, to real and complex tasks.","Title: Solving limited memory influence diagrams\nYear: 2012\n\nAbstract: We present a new algorithm for exactly solving decision making problems represented as influence diagrams. We do not require the usual assumptions of no forgetting and regularity; this allows us to solve problems with simultaneous decisions and limited information. The algorithm is empirically shown to outperform a state-of-the-art algorithm on randomly generated problems of up to 150 variables and 1064 solutions. We show that these problems are NP-hard even if the underlying graph structure of the problem has low treewidth and the variables take on a bounded number of states, and that they admit no provably good approximation if variables can take on an arbitrary number of states.","Title: Updating beliefs with incomplete observations\nYear: 2003\n\nAbstract: Currently, there is renewed interest in the problem, raised by Shafer in 1985, of updating probabilities when observations are incomplete (or set-valued). This is a fundamental problem in general, and of particular interest for Bayesian networks. Recently, Grünwald and Halpern have shown that commonly used updating strategies fail in this case, except under very special assumptions. In this paper we propose a new method for updating probabilities with incomplete observations. Our approach is deliberately conservative: we make no assumptions about the so-called incompleteness mechanism that associates complete with incomplete observations. We model our ignorance about this mechanism by a vacuous lower prevision, a tool from the theory of imprecise probabilities, and we use only coherence arguments to turn prior into posterior (updated) probabilities. In general, this new approach to updating produces lower and upper posterior probabilities and previsions (expectations), as well as partially determinate decisions. This is a logical consequence of the existing ignorance about the incompleteness mechanism. As an example, we use the new updating method to properly address the apparent paradox in the 'Monty Hall' puzzle. More importantly, we apply it to the problem of classification of new evidence in probabilistic expert systems, where it leads to a new, so-called conservative updating rule. In the special case of Bayesian networks constructed using expert knowledge, we provide an exact algorithm to compare classes based on our updating rule, which has linear-time complexity for a class of networks wider than polytrees. This result is then extended to the more general framework of credal networks, where computations are often much harder than with Bayesian nets. Using an example, we show that our rule appears to provide a solid basis for reliable updating with incomplete observations, when no strong assumptions about the incompleteness mechanism are justified.","Title: Updating credal networks is approximable in polynomial time\nYear: 2012\n\nAbstract: Credal networks relax the precise probability requirement of Bayesian networks, enabling a richer representation of uncertainty in the form of closed convex sets of probability measures. The increase in expressiveness comes at the expense of higher computational costs. In this paper, we present a new variable elimination algorithm for exactly computing posterior inferences in extensively specified credal networks, which is empirically shown to outperform a state-of-the-art algorithm. The algorithm is then turned into a provably good approximation scheme, that is, a procedure that for any input is guaranteed to return a solution not worse than the optimum by a given factor. Remarkably, we show that when the networks have bounded treewidth and bounded number of states per variable the approximation algorithm runs in time polynomial in the input size and in the inverse of the error factor, thus being the first known fully polynomial-time approximation scheme for inference in credal networks.","Title: 2U: an exact interval propagation algorithm for polytrees with binary variables\nYear: 1998\n\nAbstract: This paper addresses the problem of computing posterior in a discrete Bayesian network where the conditional distributions of the model belong to convex sets. The computation on a general Bayesian network with convex sets of conditional distributions is formalized as a global optimization problem. It is shown that such a problem can be reduced to a combinatorial problem, suitable to exact algorithmic solutions. An exact propagation algorithm for the updating of a polytree with binary variables is derived. The overall complexity is linear to the size of the network, when the maximum number of parents is fixed. (C) 1998 Elsevier Science B.V. All rights reserved.","Title: Naive credal classifier 2: an extension of naive Bayes for delivering robust classifications\nYear: 2008\n\nAbstract: Naive credal classifier 2 (NCC2) extends naive Bayes in order to deliver more robust classifications. NCC2 is based on a set of prior densities rather than on a single prior; as a consequence, when faced with instances whose classification is prior-dependent (and therefore might not be reliable), it returns a set of classes (we call this an indeterminate classi- fication) instead of a single class. Moreover, NCC2 introduces a very general and flexible treatment of missing data, which, under certain circumstances, can also lead to indeterminate classifications. In this case, indeterminacy can be regarded as a way to preserve reliability despite the information hidden by missing values. We call hard-to-classify the instances classified indeterminately by NCC2. Extensive empirical evaluations show that naive Bayes' accuracy drops considerably on the hard-to- classify instances identified by NCC2, and that on the other hand, NCC2 has high set-accuracy (the proportion of times that the actual class is contained in the set of returned classes) when it is indeterminate.","Title: Distribution of mutual information from complete and incomplete data\nYear: 2005\n\nAbstract: Mutual information is widely used, in a descriptive way, to measure the stochastic dependence of categorical random variables. In order to address questions such as the reliability of the descriptive value, one must consider sample-to-population inferential approaches. This paper deals with the posterior distribution of mutual information, as obtained in a Bayesian framework by a second-order Dirichlet prior distribution. The exact analytical expression for the mean, and analytical approximations for the variance, skewness and kurtosis are derived. These approximations have a guaranteed accuracy level of the order O(n−3), where n is the sample size. Leading order approximations for the mean and the variance are derived in the case of incomplete samples. The derived analytical expressions allow the distribution of mutual information to be approximated reliably and quickly. In fact, the derived expressions can be computed with the same order of complexity needed for descriptive mutual information. This makes the distribution of mutual information become a concrete alternative to descriptive mutual information in many applications which would benefit from moving to the inductive side. Some of these prospective applications are discussed, and one of them, namely feature selection, is shown to perform significantly better when inductive mutual information is used.","Title: JNCC2: An extension of naive Bayes classifier suited for small and incomplete data sets\nYear: 2008\n\nAbstract: JNCC2 implements the Naive Credal Classifier 2 (NCC2), i.e., an extension of naive Bayes to imprecise probabilities, designed to return robust classification even on small and/or incomplete data sets, which is often the case in environmental case studies.","Title: Robust Feature Selection by Mutual Information Distributions.\nYear: 2002\n\nAbstract: Mutual information is widely used in artificial intelligence, in a descriptive way, to measure the stochastic dependence of discrete random variables. In order to address questions such as the reliability of the empirical value, one must consider sample-to-population inferential approaches. This paper deals with the distribution of mutual information, as obtained in a Bayesian framework by a second-order Dirichlet prior distribution. The exact analytical expression for the mean and an analytical approximation of the variance are reported. Asymptotic approximations of the distribution are proposed. The results are applied to the problem of selecting features for incremental learning and classification of the naive Bayes classifier. A fast, newly defined method is shown to outperform the traditional approach based on empirical mutual information on a number of real data sets. Finally, a theoretical development is reported that allows one to efficiently extend the above methods to incomplete samples in an easy and effective way.","Title: Notes on “Notes on conditional previsions”\nYear: 2007\n\nAbstract: These notes comment on Williams' fundamental essay Notes on Conditional Previsions, written as a research report in 1975 and published in the present issue. Basic aspects of that work are discussed, including historical background and relevance to the foundations of probability; examples are supplied to help understanding.","Title: Reliable diagnoses of dementia by the naive credal classifier inferred from incomplete cognitive data.\nYear: 2003\n\nAbstract: Dementia is a serious personal, medical and social problem. Recent research indicates early and accurate diagnoses as the key to effectively cope with it. No definitive cure is available but in some cases when the impairment is still mild the disease can be contained. This paper describes a diagnostic tool that jointly uses the naive credal classifier and the most widely used computerized system of cognitive tests in dementia research, the Cognitive Drug Research system. The naive credal classifier extends the discrete naive Bayes classifier to imprecise probabilities. The naive credal classifier models both prior ignorance and ignorance about the likelihood by sets of probability distributions. This is a new way to deal with small and incomplete datasets that departs significantly from most established classification methods. In the empirical study presented here, the naive credal classifier provides reliability and unmatched predictive performance. It delivers up to 95% correct predictions while being very robust with respect to the partial ignorance due to the largely incomplete data. The diagnostic tool also proves to be very effective in discriminating between Alzheimer's disease and dementia with Lewy bodies.","Title: Constraint Logic Programming and Integer Programming Approaches and Their Collaboration in Solving an Assignment Scheduling Problem\nYear: 1997\n\nAbstract: Generalised Assignment Problems (GAP), traditionally solved by Integer Programmingtechniques, are addressed in the light of current Constraint Programming methods. Ascheduling application from manufacturing, based on a modified GAP, is used to examinethe performance of each technique under a variety of problem characteristics.Experimental evidence showed that, for a set of assignment problems, Constraint LogicProgramming (CLP) performed consistently better than Integer Programming (IP)....","Title: Credal Model Averaging: An Extension of Bayesian Model Averaging to Imprecise Probabilities\nYear: 2008\n\nAbstract: We deal with the arbitrariness in the choice of the prior over the models in Bayesian model averaging(BMA), by modelling prior knowledge by a set of priors (i.e., a prior credal set). We consider Dash and Cooper's BMA applied to naive Bayesian networks, replacing the single prior over the naive models by a credal set; this models a condition close to prior ignorance about the models, which leads to credal model averaging(CMA). CMA returns an indeterminateclassification, i.e., multiple classes, on the instances for which the learning set is not informative enough to smooth the effect of the choice of the prior. We give an algorithm to compute exact credal model averaging for naive networks. Extensive experiments show that indeterminate classifications preserve the reliability of CMA on the instances which are classified in a prior-dependent way by BMA.","Title: Credal Networks for Operational Risk Measurement and Management\nYear: 2007\n\nAbstract: According to widely accepted guidelines for self-regulation, the capital requirements of a bank should relate to the level of risk with respect to three different categories. Among them, operational riskis the more difficult to assess, as it requires merging expert judgments and quantitative information about the functional structure of the bank. A number of approaches to the evaluation of operational risk based on Bayesian networks have been recently considered. In this paper, we propose credal networks, which are a generalization of Bayesian networks to imprecise probabilities, as a more appropriate framework for the measurementand managementof operational risk. The reason is the higher flexibility provided by credal networks compared to Bayesian networks in the quantification of the probabilities underlying the model: this makes it possible to represent human expertise required for these evaluations in a credible and robust way. We use a real-world application to demonstrate these features and to show how to measure operational risk by means of algorithms for inference over credal nets. This is shown to be possible, also in the case when the observation of some factor is vague.","Title: Limits of learning about a categorical latent variable under prior near-ignorance\nYear: 2009\n\nAbstract: In this paper, we consider the coherent theory of (epistemic) uncertainty of Walley, in which beliefs are represented through sets of probability distributions, and we focus on the problem of modeling prior ignorance about a categorical random variable. In this setting, it is a known result that a state of prior ignorance is not compatible with learning. To overcome this problem, another state of beliefs, called near-ignorance, has been proposed. Near-ignorance resembles ignorance very closely, by satisfying some principles that can arguably be regarded as necessary in a state of ignorance, and allows learning to take place. What this paper does, is to provide new and substantial evidence that also near-ignorance cannot be really regarded as a way out of the problem of starting statistical inference in conditions of very weak beliefs. The key to this result is focusing on a setting characterized by a variable of interest that is latent. We argue that such a setting is by far the most common case in practice, and we provide, for the case of categorical latent variables (and general manifest variables) a condition that, if satisfied, prevents learning to take place under prior near-ignorance. This condition is shown to be easily satisfied even in the most common statistical problems. We regard these results as a strong form of evidence against the possibility to adopt a condition of prior near-ignorance in real statistical problems.","Title: On the problem of computing the conglomerable natural extension.\nYear: 2015\n\nAbstract: Embedding conglomerability as a rationality requirement in probability was among the aims of Walley's behavioural theory of coherent lower previsions. However, recent work has shown that this attempt has only been partly successful. If we focus in particular on the extension of given assessments to a rational and conglomerable model (in the least-committal way), we have that the procedure used in Walley's theory, the natural extension, provides only an approximation to the model that is actually sought for: the so-called conglomerable natural extension. In this paper we consider probabilistic assessments in the form of a coherent lower prevision P̲, which is another name for a lower expectation functional, and make an in-depth mathematical study of the problem of computing the conglomerable natural extension for this case: that is, where it is defined as the smallest coherent lower prevision F̲≥P̲ that is conglomerable, in case it exists. Past work has shown that F̲ can be approximated by an increasing sequence (E̲n)n∈N of coherent lower previsions. We solve an open problem by showing that this sequence can consist of infinitely many distinct elements. Moreover, we give sufficient conditions, of quite broad applicability, to make sure that the point-wise limit of the sequence is F̲ in case P̲ is the lower envelope of finitely many linear previsions. In addition, we study the question of the existence of F̲ and its relationship with the notion of marginal extension.","Title: Comments on “Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks” by Andrés R. Masegosa and Serafín Moral\nYear: 2014\n\nAbstract: We briefly overview the problem of learning probabilities from data using imprecise probability models that express very weak prior beliefs. Then we comment on the new contributions to this question given in the paper by Masegosa and Moral and provide some insights about the performance of their models in data mining experiments of classification.","Title: Approximate credal network updating by linear programming with applications to decision making\nYear: 2015\n\nAbstract: Credal nets are probabilistic graphical models which extend Bayesian nets to cope with sets of distributions. An algorithm for approximate credal network updating is presented. The problem in its general formulation is a multilinear optimization task, which can be linearized by an appropriate rule for fixing all the local models apart from those of a single variable. This simple idea can be iterated and quickly leads to accurate inferences. A transformation is also derived to reduce decision making in credal networks based on the maximality criterion to updating. The decision task is proved to have the same complexity of standard inference, being NPPP-complete for general credal nets and NP-complete for polytrees. Similar results are derived for the E-admissibility criterion. Numerical experiments confirm a good performance of the method.","Title: A Bayesian Wilcoxon signed-rank test based on the Dirichlet process.\nYear: 2014\n\nAbstract: Bayesian methods are ubiquitous in machine learning. Nevertheless, the analysis of empirical results is typically performed by frequentist tests. This implies dealing with null hypothesis significance tests and p-values, even though the shortcomings of such methods are well known. We propose a nonparametric Bayesian version of the Wilcoxon signed-rank test using a Dirichlet process (DP) based prior. We address in two different ways the problem of how to choose the infinite dimensional parameter that characterizes the DP. The proposed test has all the traditional strengths of the Bayesian approach; for instance, unlike the frequentist tests, it allows verifying the null hypothesis, not only rejecting it, and taking decision which minimize the expected loss. Moreover, one of the solutions proposed to model the infinitedimensional parameter of the DP, allows isolating instances in which the traditional frequentist test is guessing at random. We show results dealing with the comparison of two classifiers using real and simulated data.","Title: Extended Tree Augmented Naive Classifier\nYear: 2014\n\nAbstract: This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds' algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). A range of experiments show that we obtain models with better accuracy than TAN and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator.","Title: Min-Bdeu And Max-Bdeu Scores For Learning Bayesian Networks\nYear: 2014\n\nAbstract: This work presents two new score functions based on the Bayesian Dirichlet equivalent uniform (BDeu) score for learning Bayesian network structures. They consider the sensitivity of BDeu to varying parameters of the Dirichlet prior. The scores take on the most adversary and the most beneficial priors among those within a contamination set around the symmetric one. We build these scores in such way that they are decomposable and can be computed efficiently. Because of that, they can be integrated into any state-of-the-art structure learning method that explores the space of directed acyclic graphs and allows decomposable scores. Empirical results suggest that our scores outperform the standard BDeu score in terms of the likelihood of unseen data and in terms of edge discovery with respect to the true network, at least when the training sample size is small. We discuss the relation between these new scores and the accuracy of inferred models. Moreover, our new criteria can be used to identify the amount of data after which learning is saturated, that is, additional data are of little help to improve the resulting model.","Title: Robust Filtering Through Coherent Lower Previsions\nYear: 2011\n\nAbstract: The classical filtering problem is re-examined to take into account imprecision in the knowledge about the probabilistic relationships involved. Imprecision is modeled in this paper by closed convex sets of probabilities. We derive a solution of the state estimation problem under such a framework that is very general: it can deal with any closed convex set of probability distributions used to characterize uncertainty in the prior, likelihood, and state transition models. This is made possible by formulating the theory directly in terms of coherent lower previsions, that is, of the lower envelopes of the expectations obtained from the set of distributions. The general solution is specialized to two particular classes of coherent lower previsions. The first consists of a family of Gaussian distributions whose means are only known to belong to an interval. The second is the so-called linear-vacuous mixture model, which is a family made of convex combinations of a known nominal distribution (e.g., a Gaussian) with arbitrary distributions. For the latter case, we empirically compare the proposed estimator with the Kalman filter. This shows that our solution is more robust to the presence of modelling errors in the system and that, hence, appears to be a more realistic approach than the Kalman filter in such a case.","Title: Bayesian network data imputation with application to survival tree analysis\nYear: 2016\n\nAbstract: Retrospective clinical datasets are often characterized by a relatively small sample size and many missing data. In this case, a common way for handling the missingness consists in discarding from the analysis patients with missing covariates, further reducing the sample size. Alternatively, if the mechanism that generated the missing allows, incomplete data can be imputed on the basis of the observed data, avoiding the reduction of the sample size and allowing methods to deal with complete data later on. Moreover, methodologies for data imputation might depend on the particular purpose and might achieve better results by considering specific characteristics of the domain. The problem of missing data treatment is studied in the context of survival tree analysis for the estimation of a prognostic patient stratification. Survival tree methods usually address this problem by using surrogate splits, that is, splitting rules that use other variables yielding similar results to the original ones. Instead, our methodology consists in modeling the dependencies among the clinical variables with a Bayesian network, which is then used to perform data imputation, thus allowing the survival tree to be applied on the completed dataset. The Bayesian network is directly learned from the incomplete data using a structural expectation-maximization (EM) procedure in which the maximization step is performed with an exact anytime method, so that the only source of approximation is due to the EM formulation itself. On both simulated and real data, our proposed methodology usually outperformed several existing methods for data imputation and the imputation so obtained improved the stratification estimated by the survival tree (especially with respect to using surrogate splits). Retrospective clinical datasets have often small sample size and many missing data.We use Bayesian networks to impute missing data enhancing survival tree analysis.The Bayesian network is learned from incomplete data and used for the imputation.Our method generally achieved more accurate predictions than widely used approaches.","Title: Desirability and the birth of incomplete preferences.\nYear: 2015\n\nAbstract: We establish an equivalence between two seemingly different theories: one is the traditional axiomatisation of incomplete preferences on horse lotteries based on the mixture independence axiom; the other is the theory of desirable gambles developed in the context of imprecise probability. The equivalence allows us to revisit incomplete preferences from the viewpoint of desirability and through the derived notion of coherent lower previsions. On this basis, we obtain new results and insights: in particular, we show that the theory of incomplete preferences can be developed assuming only the existence of a worst act---no best act is needed---, and that a weakened Archimedean axiom suffices too; this axiom allows us also to address some controversy about the regularity assumption (that probabilities should be positive---they need not), which enables us also to deal with uncountable possibility spaces; we show that it is always possible to extend in a minimal way a preference relation to one with a worst act, and yet the resulting relation is never Archimedean, except in a trivial case; we show that the traditional notion of state independence coincides with the notion called strong independence in imprecise probability---this leads us to give much a weaker definition of state independence than the traditional one; we rework and uniform the notions of complete preferences, beliefs, values; we argue that Archimedeanity does not capture all the problems that can be modelled with sets of expected utilities and we provide a new notion that does precisely that. Perhaps most importantly, we argue throughout that desirability is a powerful and natural setting to model, and work with, incomplete preferences, even in case of non-Archimedean problems. This leads us to suggest that desirability, rather than preference, should be the primitive notion at the basis of decision-theoretic axiomatisations.","Title: Learning extended tree augmented naive structures.\nYear: 2016\n\nAbstract: This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds' algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). We enhance our procedure with a new score function that only takes into account arcs that are relevant to predict the class, as well as an optimization over the equivalent sample size during learning. These ideas may be useful for structure learning of Bayesian networks in general. A range of experiments shows that we obtain models with better prediction accuracy than naive Bayes and TAN, and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator (AODE). We release our implementation of ETAN so that it can be easily installed and run within Weka.","Title: A Bayesian nonparametric procedure for comparing algorithms\nYear: 2015\n\nAbstract: A fundamental task in machine learning is to compare the performance of multiple algorithms. This is usually performed by the frequentist Friedman test followed by multiple comparisons. This implies dealing with the well-known shortcomings of null hypothesis significance tests. We propose a Bayesian approach to overcome these problems. We provide three main contributions. First, we propose a nonparametric Bayesian version of the Friedman test using a Dirichlet process (DP) based prior. We show that, from a Bayesian perspective, the Friedman test is an inference for a multivariate mean based on an ellipsoid inclusion test. Second, we derive a joint procedure for the multiple comparisons which accounts for their dependencies and which is based on the posterior probability computed through the DP. The proposed approach allows verifying the null hypothesis, not only rejecting it. Third, as a practical application we show the results in our algorithm for racing, i.e. identifying the best algorithm among a large set of candidates sequentially assessed. Our approach consistently outperforms its frequentist counterpart.","Title: Bayesian Hypothesis Testing in Machine Learning\nYear: 2015\n\nAbstract: Most hypothesis testing in machine learning is done using the frequentist null-hypothesis significance test, which has severe drawbacks. We review recent Bayesian tests which overcome the drawbacks of the frequentist ones.","Title: Learning Bayesian Networks with Thousands of Variables\nYear: 2015\n\nAbstract: We present a method for learning Bayesian networks from data sets containing thousands of variables without the need for structure constraints. Our approach is made of two parts. The first is a novel algorithm that effectively explores the space of possible parent sets of a node. It guides the exploration towards the most promising parent sets on the basis of an approximated score function that is computed in constant time. The second part is an improvement of an existing ordering-based algorithm for structure optimization. The new algorithm provably achieves a higher score compared to its original formulation. Our novel approach consistently outperforms the state of the art on very large data sets.","Title: Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis.\nYear: 2017\n\nAbstract: The machine learning community adopted the use of null hypothesis significance testing (NHST) in order to ensure the statistical validity of results. Many scientific fields however realized the shortcomings of frequentist reasoning and in the most radical cases even banned its use in publications. We should do the same: just as we have embraced the Bayesian paradigm in the development of new machine learning methods, so we should also use it in the analysis of our own results. We argue for abandonment of NHST by exposing its fallacies and, more importantly, offer better more sound and useful alternatives for it.","Title: Learning Bounded Treewidth Bayesian Networks with Thousands of Variables.\nYear: 2016\n\nAbstract: We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables. Bounding the treewidth of a Bayesian greatly reduces the complexity of inferences. Yet, being a global property of the graph, it considerably increases the difficulty of the learning process. We propose a novel algorithm for this task, able to scale to large domains and large treewidths. Our novel approach consistently outperforms the state of the art on data sets with up to ten thousand variables.","Title: Statistical comparison of classifiers through Bayesian hierarchical modelling.\nYear: 2017\n\nAbstract: Usually one compares the accuracy of two competing classifiers using null hypothesis significance tests. Yet such tests suffer from important shortcomings, which can be overcome by switching to Bayesian hypothesis testing. We propose a Bayesian hierarchical model that jointly analyzes the cross-validation results obtained by two classifiers on multiple data sets. The model estimates more accurately the difference between classifiers on the individual data sets than the traditional approach of averaging, independently on each data set, the cross-validation results. It does so by jointly analyzing the results obtained on all data sets, and applying shrinkage to the estimates. The model eventually returns the posterior probability of the accuracies of the two classifiers being practically equivalent or significantly different.","Title: Conformity and independence with coherent lower previsions.\nYear: 2016\n\nAbstract: We define the conformity of marginal and conditional models with a joint model within Walley's theory of coherent lower previsions. Loosely speaking, conformity means that the joint can reproduce the marginal and conditional models we started from. By studying conformity with and without additional assumptions of epistemic irrelevance and independence, we establish connections with a number of prominent models in Walley's theory: the marginal extension, the irrelevant natural extension, the independent natural extension and the strong product.","Title: SOS for Bounded Rationality.\nYear: 2017\n\nAbstract: In the gambling foundation of probability theory, rationality requires that a subject should always (never) find desirable all nonnegative (negative) gambles, because no matter the result of the experiment the subject never (always) decreases her money. Evaluating the nonnegativity of a gamble in infinite spaces is a difficult task. In fact, even if we restrict the gambles to be polynomials in R^n , the problem of determining nonnegativity is NP-hard. The aim of this paper is to develop a computable theory of desirable gambles. Instead of requiring the subject to accept all nonnegative gambles, we only require her to accept gambles for which she can efficiently determine the nonnegativity (in particular SOS polynomials). We refer to this new criterion as bounded rationality.","Title: A Polarity Theory for Sets of Desirable Gambles.\nYear: 2017\n\nAbstract: J. Vicente-Perez was partially supported by MINECO of Spain and ERDF of EU, Grants MTM2014-59179-C2-1-P and ECO2016-77200-P.","Title: Entropy-based Pruning for Learning Bayesian Networks using BIC.\nYear: 2018\n\nAbstract: For decomposable score-based structure learning of Bayesian networks, existing approaches first compute a collection of candidate parent sets for each variable and then optimize over this collection by choosing one parent set for each variable without creating directed cycles while maximizing the total score. We target the task of constructing the collection of candidate parent sets when the score of choice is the Bayesian Information Criterion (BIC). We provide new non-trivial results that can be used to prune the search space of candidate parent sets of each node. We analyze how these new results relate to previous ideas in the literature both theoretically and empirically. We show in experiments with UCI data sets that gains can be significant. Since the new pruning rules are easy to implement and have low computational costs, they can be promptly integrated into all state-of-the-art methods for structure learning of Bayesian networks.","Title: Hierarchical Multinomial-Dirichlet Model for the Estimation of Conditional Probability Tables\nYear: 2017\n\nAbstract: We present a novel approach for estimating conditional probability tables, based on a joint, rather than independent, estimate of the conditional distributions belonging to the same table. We derive exact analytical expressions for the estimators and we analyse their properties both analytically and via simulation. We then apply this method to the estimation of parameters in a Bayesian network. Given the structure of the network, the proposed approach better estimates the joint distribution and significantly improves the classification performance with respect to traditional approaches.","Title: Efficient Learning of Bounded-Treewidth Bayesian Networks from Complete and Incomplete Data Sets.\nYear: 2018\n\nAbstract: Learning a Bayesian networks with bounded treewidth is important for reducing the complexity of the inferences. We present a novel anytime algorithm (k-MAX) method for this task, which scales up to thousands of variables. Through extensive experiments we show that it consistently yields higher-scoring structures than its competitors on complete data sets. We then consider the problem of structure learning from incomplete data sets. This can be addressed by structural EM, which however is computationally very demanding. We thus adopt the novel k-MAX algorithm in the maximization step of structural EM, obtaining an efficient computation of the expected sufficient statistics. We test the resulting structural EM method on the task of imputing missing data, comparing it against the state-of-the-art approach based on random forests. Our approach achieves the same imputation accuracy of the competitors, but in about one tenth of the time. Furthermore we show that it has worst-case complexity linear in the input size, and that it is easily parallelizable.","Title: Quantum Rational Preferences and Desirability.\nYear: 2016\n\nAbstract: We develop a theory of quantum rational decision making in the tradition of Anscombe and Aumannu0027s axiomatisation of preferences on horse lotteries. It is essentially the Bayesian decision theory generalised to the space of Hermitian matrices. Among other things, this leads us to give a representation theorem showing that quantum complete rational preferences are obtained by means of expected utility considerations.","Title: Approximate structure learning for large Bayesian networks\nYear: 2018\n\nAbstract: We present approximate structure learning algorithms for Bayesian networks. We discuss the two main phases of the task: the preparation of the cache of the scores and structure optimization, both with bounded and unbounded treewidth. We improve on state-of-the-art methods that rely on an ordering-based search by sampling more effectively the space of the orders. This allows for a remarkable improvement in learning Bayesian networks from thousands of variables. We also present a thorough study of the accuracy and the running time of inference, comparing bounded-treewidth and unbounded-treewidth models.","Title: Full Conglomerability, Continuity and Marginal Extension.\nYear: 2016\n\nAbstract: We investigate fully conglomerable coherent lower previsions in the sense of Walley, and some particular cases of interest: envelopes of fully conglomerable linear previsions, envelopes of countably additive linear previsions and fully disintegrable linear previsions. We study the connections with continuity and countable super-additivity, and show that full conglomerability can be characterised in terms of a supremum of marginal extension models.","Title: Compatibility, Coherence and the RIP.\nYear: 2018\n\nAbstract: We generalise the classical result on the compatibility of marginal, possible non-disjoint, assessments in terms of the running intersection property to the imprecise case, where our beliefs are modelled in terms of sets of desirable gambles. We consider the case where we have unconditional and conditional assessments, and show that the problem can be simplified via a tree decomposition.","Title: Sum-of-squares for bounded rationality.\nYear: 2019\n\nAbstract: In the gambling foundation of probability theory, rationality requires that a subject should always (never) find desirable all nonnegative (negative) gambles, because no matter the result of the experiment the subject never (always) decreases her money. Evaluating the nonnegativity of a gamble in infinite spaces is a difficult task. In fact, even if we restrict the gambles to be polynomials in Rn, the problem of determining nonnegativity is NP-hard. The aim of this paper is to develop a computable theory of desirable gambles. Instead of requiring the subject to desire all nonnegative gambles, we only require her to desire gambles for which she can efficiently determine the nonnegativity (in particular sum-of-squares polynomials). We refer to this new criterion as bounded rationality.","Title: Classification of dementia types from cognitive profiles data\nYear: 2006\n\nAbstract: The Cognitive Drug Research (CDR) system is specifically validated for dementia assessment; it consists of a series of computerized tests, which assess the cognitive faculties of the patient to derive a cognitive profile. We use six different classification algorithms to classify clinically diagnosed diseases from their cognitive profiles. Good accuracy was obtained in separating patients affected by Parkinson's disease from demented patients, and in discriminating between Alzheimer's disease and Vascular Dementia. However, in discriminating between Parkinson disease with dementia (PDD) and dementia with Lewy bodies (DLB), the accuracy was only slightly superior to chance; the existence of a significant difference in the cognitive profiles of DLB and PDD is indeed questioned in the medical literature.","Title: Desirability foundations of robust rational decision making\nYear: 2021\n\nAbstract: Recent work has formally linked the traditional axiomatisation of incomplete preferences à la Anscombe-Aumann with the theory of desirability developed in the context of imprecise probability, by showing in particular that they are the very same theory. The equivalence has been established under the constraint that the set of possible prizes is finite. In this paper, we relax such a constraint, thus de facto creating one of the most general theories of rationality and decision making available today. We provide the theory with a sound interpretation and with basic notions, and results, for the separation of beliefs and values, and for the case of complete preferences. Moreover, we discuss the role of conglomerability for the presented theory, arguing that it should be a rationality requirement under very broad conditions.","Title: Computational Complexity and the Nature of Quantum Mechanics.\nYear: 2019\n\nAbstract: Quantum theory (QT) has been confirmed by numerous experiments, yet we still cannot fully grasp the meaning of the theory. As a consequence, the quantum world appears to us paradoxical. Here we shed new light on QT by having it follow from two main postulates (i) the theory should be logically consistent; (ii) inferences in the theory should be computable in polynomial time. The first postulate is what we require to each well-founded mathematical theory. The computation postulate defines the physical component of the theory. We show that the computation postulate is the only true divide between QT, seen as a generalised theory of probability, and classical probability. All quantum paradoxes, and entanglement in particular, arise from the clash of trying to reconcile a computationally intractable, somewhat idealised, theory (classical physics) with a computationally tractable theory (QT) or, in other words, from regarding physics as fundamental rather than computation.","Title: Computational Complexity and the Nature of Quantum Mechanics (Extended version).\nYear: 2019\n\nAbstract: Quantum theory (QT) has been confirmed by numerous experiments, yet we still cannot fully grasp the meaning of the theory. As a consequence, the quantum world appears to us paradoxical. Here we shed new light on QT by having it follow from two main postulates (i) the theory should be logically consistent; (ii) inferences in the theory should be computable in polynomial time. The first postulate is what we require to each well-founded mathematical theory. The computation postulate defines the physical component of the theory. We show that the computation postulate is the only true divide between QT, seen as a generalised theory of probability, and classical probability. All quantum paradoxes, and entanglement in particular, arise from the clash of trying to reconcile a computationally intractable, somewhat idealised, theory (classical physics) with a computationally tractable theory (QT) or, in other words, from regarding physics as fundamental rather than computation.","Title: Hierarchical estimation of parameters in Bayesian networks\nYear: 2019\n\nAbstract: A novel approach for parameter estimation in Bayesian networks is presented. The main idea is to introduce a hyper-prior in the Multinomial–Dirichletmodel, traditionally used for conditional distribution estimation in Bayesian networks. The resulting hierarchical model jointly estimates different conditional distributions belonging to the same conditional probability table, thus borrowing statistical strength from each other. An analytical study of the dependence structure a priori induced by the hierarchical model is performed and an ad hoc variational algorithm for fast and accurate inference is derived. The proposed hierarchical model yields a major performance improvement in classification with Bayesian networks compared to traditional models. The proposed variational algorithm reduces by two orders of magnitude the computational time, with the same accuracy in parameter estimation, compared to traditional MCMC methods. Moreover, motivated by a real case study, the hierarchical model is applied to the estimation of Bayesian networks parameters by borrowing strength from related domains.","Title: Recursive Estimation for Sparse Gaussian Process Regression.\nYear: 2019\n\nAbstract: Gaussian Processes (GPs) are powerful kernelized methods for non-parametric regression used in many applications. However, their use is limited to a few thousand of training samples due to their cubic time complexity. In order to scale GPs to larger datasets, several sparse approximations based on so-called inducing points have been proposed in the literature. In this work we investigate the connection between a general class of sparse inducing point GP regression methods and Bayesian recursive estimation which enables Kalman Filter like updating for online learning. The majority of previous work has focused on the batch setting, in particular for learning the model parameters and the position of the inducing points, here instead we focus on training with mini-batches. By exploiting the Kalman filter formulation, we propose a novel approach that estimates such parameters by recursively propagating the analytical gradients of the posterior over mini-batches of the data. Compared to state of the art methods, our method keeps analytic updates for the mean and covariance of the posterior, thus reducing drastically the size of the optimization problem. We show that our method achieves faster convergence and superior performance compared to state of the art sequential Gaussian Process regression on synthetic GP as well as real-world data with up to a million of data samples.","Title: Time Series Forecasting with Gaussian Processes Needs Priors\nYear: 2021\n\nAbstract: Automatic forecasting is the task of receiving a time series and returning a forecast for the next time steps without any human intervention. Gaussian Processes (GPs) are a powerful tool for modeling time series, but so far there are no competitive approaches for automatic forecasting based on GPs. We propose practical solutions to two problems: automatic selection of the optimal kernel and reliable estimation of the hyperparameters. We propose a fixed composition of kernels, which contains the components needed to model most time series: linear trend, periodic patterns, and other flexible kernel for modeling the non-linear trend. Not all components are necessary to model each time series; during training the unnecessary components are automatically made irrelevant via automatic relevance determination (ARD). We moreover assign priors to the hyperparameters, in order to keep the inference within a plausible range; we design such priors through an empirical Bayes approach. We present results on many time series of different types; our GP model is more accurate than state-of-the-art time series models. Thanks to the priors, a single restart is enough the estimate the hyperparameters; hence the model is also fast to train.","Title: Nonlinear Desirability as a Linear Classification Problem\nYear: 2021\n\nAbstract: The present paper proposes a generalization of linearity axioms of coherence through a geometrical approach, which leads to an alternative interpretation of desirability as a classification problem. In particular, we analyze different sets of rationality axioms and, for each one of them, we show that proving that a subject, who provides finite accept and reject statements, respects these axioms, corresponds to solving a binary classification task using, each time, a different (usually nonlinear) family of classifiers. Moreover, by borrowing ideas from machine learning, we show the possibility to define a feature mapping allowing us to reformulate the above nonlinear classification problems as linear ones in a higher-dimensional space. This allows us to interpret gambles directly as payoffs vectors of monetary lotteries, as well as to reduce the task of proving the rationality of a subject to a linear classification task.","Title: The Sure Thing\nYear: 2021\n\nAbstract: If we prefer action a to b both under an event and under its complement, then we should just prefer a to b. This is Savage's sure-thing principle. In spite of its intuitive- and simple-looking nature, for which it gets almost immediate acceptance, the sure thing is not a logical principle. So where does it get its support from? In fact, the sure thing may actually fail. This is related to a variety of deep and foundational concepts in causality, decision theory, and probability, as well as to Simpsons' paradox and Blyth's game. In this paper we try to systematically clarify such a network of relations. Then we propose a general desirability theory for nonlinear utility scales. We use that to show that the sure thing is primitive to many of the previous concepts: In non-causal settings, the sure thing follows from considerations of temporal coherence and coincides with conglomerability; it can be understood as a rationality axiom to enable well-behaved conditioning in logic. In causal settings, it can be derived using only coherence and a causal independence condition.","Title: Algebras of Sets and Coherent Sets of Gambles\nYear: 2021\n\nAbstract: In a recent work we have shown how to construct an information algebra of coherent sets of gambles defined on general possibility spaces. Here we analyze the connection of such an algebra with the set algebra of sets of its atoms and the set algebra of subsets of the possibility space on which gambles are defined. Set algebras are particularly important information algebras since they are their prototypical structures. Furthermore, they are the algebraic counterparts of classical propositional logic. As a consequence, this paper also details how propositional logic is naturally embedded into the theory of imprecise probabilities.","Title: Information algebras in the theory of imprecise probabilities\nYear: 2022\n\nAbstract: In this paper we create a bridge between desirability and information algebras: we show how coherent sets of gambles, as well as coherent lower previsions, induce such structures. This allows us to enforce the view of such imprecise-probability objects as algebraic and logical structures; moreover, it enforces the interpretation of probability as information, and gives tools to manipulate them as such.","Title: Bounding Counterfactuals under Selection Bias.\nYear: 2022\n\nAbstract: Causal analysis may be affected by selection bias, which is defined as the systematic exclusion of data from a certain subpopulation. Previous work in this area focused on the derivation of identifiability conditions. We propose instead a first algorithm to address both identifiable and unidentifiable queries. We prove that, in spite of the missingness induced by the selection bias, the likelihood of the available data is unimodal. This enables us to use the causal expectation-maximisation scheme to obtain the values of causal queries in the identifiable case, and to compute bounds otherwise. Experiments demonstrate the approach to be practically viable. Theoretical convergence characterisations are provided.","Title: Information algebras in the theory of imprecise probabilities, an extension\nYear: 2022\n\nAbstract: In recent works, we have shown how to construct an information algebra of coherent sets of gambles, considering firstly a particular model to represent questions, called the multivariate model, and then generalizing it. Here we further extend the construction made to the highest level of generality, setting up an associated information algebra of coherent lower previsions, analyzing the connection of both the information algebras constructed with an instance of set algebras and, finally, establishing and inspecting a version of the marginal problem in this framework."],"string":"[\n \"Title: Coherence graphs\\nYear: 2009\\n\\nAbstract: We study the consistency of a number of probability distributions, which are allowed to be imprecise. To make the treatment as general as possible, we represent those probabilistic assessments as a collection of conditional lower previsions. The problem then becomes proving Walley's (strong) coherence of the assessments. In order to maintain generality in the analysis, we assume to be given nearly no information about the numbers that make up the lower previsions in the collection. Under this condition, we investigate the extent to which the above global task can be decomposed into simpler and more local ones. This is done by introducing a graphical representation of the conditional lower previsions that we call the coherence graph: we show that the coherence graph allows one to isolate some subsets of the collection whose coherence is sufficient for the coherence of all the assessments; and we provide a polynomial-time algorithm that finds the subsets efficiently. We show some of the implications of our results by focusing on three models and problems: Bayesian and credal networks, of which we prove coherence; the compatibility problem, for which we provide an optimal graphical decomposition; probabilistic satisfiability, of which we show that some intractable instances can instead be solved efficiently by exploiting coherence graphs.\",\n \"Title: Conglomerable coherence\\nYear: 2013\\n\\nAbstract: We contrast Williams@? and Walley@?s theories of coherent lower previsions in the light of conglomerability. These are two of the most credited approaches to a behavioural theory of imprecise probability. Conglomerability is the notion that distinguishes them most: Williams@? theory does not consider it, while Walley aims at embedding it in his theory. This question is important, as conglomerability is a major point of disagreement at the foundations of probability, since it was first defined by de Finetti in 1930. We show that Walley@?s notion of joint coherence (which is the single axiom of his theory) for conditional lower previsions does not take all the implications of conglomerability into account. Considering also some previous results in the literature, we deduce that Williams@? theory should be the one to use when conglomerability is not required; for the opposite case, we define the new theory of conglomerably coherent lower previsions, which is arguably the one to use, and of which Walley@?s theory can be understood as an approximation. We show that this approximation is exact in two important cases: when all conditioning events have positive lower probability, and when conditioning partitions are nested.\",\n \"Title: Independent natural extension\\nYear: 2010\\n\\nAbstract: We introduce a general definition for the independence of a number of finite-valued variables, based on coherent lower previsions. Our definition has an epistemic flavour: it arises from personal judgements that a number of variables are irrelevant to one another. We show that a number of already existing notions, such as strong independence, satisfy our definition. Moreover, there always is a least-committal independent model, for which we provide an explicit formula: the independent natural extension. Our central result is that the independent natural extension satisfies so-called marginalisation, associativity and strong factorisation properties. These allow us to relate our research to more traditional ways of defining independence based on factorisation.\",\n \"Title: Independent Natural Extension\\nYear: 2011\\n\\nAbstract: We introduce a general definition for the independence of a number of finite-valued variables, based on coherent lower previsions.\\n Our definition has an epistemic flavour: it arises from personal judgements that a number of variables are irrelevant to one\\n another. We show that a number of already existing notions, such as strong independence, satisfy our definition. Moreover,\\n there always is a least-committal independent model, for which we provide an explicit formula: the independent natural extension. Our central result is that the independent natural extension satisfies so-called marginalisation, associativity and strong factorisation properties. These allow us to relate our research to more traditional ways of defining independence based on factorisation.\",\n \"Title: Conglomerable natural extension\\nYear: 2012\\n\\nAbstract: At the foundations of probability theory lies a question that has been open since de Finetti framed it in 1930: whether or not an uncertainty model should be required to be conglomerable. Conglomerability is related to accepting infinitely many conditional bets. Walley is one of the authors who have argued in favor of conglomerability, while de Finetti rejected the idea. In this paper we study the extension of the conglomerability condition to two types of uncertainty models that are more general than the ones envisaged by de Finetti: sets of desirable gambles and coherent lower previsions. We focus in particular on the weakest (i.e., the least-committal) of those extensions, which we call the conglomerable natural extension. The weakest extension that does not take conglomerability into account is simply called the natural extension. We show that taking the natural extension of assessments after imposing conglomerability-the procedure adopted in Walley's theory-does not yield, in general, the conglomerable natural extension (but it does so in the case of the marginal extension). Iterating this process of imposing conglomerability and taking the natural extension produces a sequence of models that approach the conglomerable natural extension, although it is not known, at this point, whether this sequence converges to it. We give sufficient conditions for this to happen in some special cases, and study the differences between working with coherent sets of desirable gambles and coherent lower previsions. Our results indicate that it is necessary to rethink the foundations of Walley's theory of coherent lower previsions for infinite partitions of conditioning events.\",\n \"Title: Probability and time\\nYear: 2013\\n\\nAbstract: Probabilistic reasoning is often attributed a temporal meaning, in which conditioning is regarded as a normative rule to compute future beliefs out of current beliefs and observations. However, the well-established 'updating interpretation' of conditioning is not concerned with beliefs that evolve in time, and in particular with future beliefs. On the other hand, a temporal justification of conditioning was proposed already by De Moivre and Bayes, by requiring that current and future beliefs be consistent. We reconsider the latter approach while dealing with a generalised version of the problem, using a behavioural theory of imprecise probability in the form of coherent lower previsions as well as of coherent sets of desirable gambles, and letting the possibility space be finite or infinite. We obtain that using conditioning is normative, in the imprecise case, only if one establishes future behavioural commitments at the same time of current beliefs. In this case it is also normative that present beliefs be conglomerable, which is a result that touches on a long-term controversy at the foundations of probability. In the remaining case, where one commits to some future behaviour after establishing present beliefs, we characterise the several possibilities to define consistent future assessments; this shows in particular that temporal consistency does not preclude changes of mind. And yet, our analysis does not support that rationality requires consistency in general, even though pursuing consistency makes sense and is useful, at least as a way to guide and evaluate the assessment process. These considerations narrow down in the special case of precise probability, because this formalism cannot distinguish the two different situations illustrated above: it turns out that the only consistent rule is conditioning and moreover that it is not rational to be willing to stick to precise probability while using a rule different from conditioning to compute future beliefs; rationality requires in addition the disintegrability of the present-time probability.\",\n \"Title: CREDO: A military decision-support system based on credal networks\\nYear: 2013\\n\\nAbstract: A software tool especially designed for military domains to create and query decision-support systems is presented. Credal networks, which are Bayesian networks whose parameters have the freedom to vary in convex sets, are used to model the relations among the system variables. A novel elicitation procedure of these sets, which allows the military experts to report their knowledge by purely qualitative judgements, is proposed. Two high-level fusion procedures to cope with multiple experts in this framework are also derived. All these features are supported by the software and demonstrated in an application to space security tested during the last NATO multinational experiment.\",\n \"Title: Conglomerable Coherent Lower Previsions.\\nYear: 2012\\n\\nAbstract: Walley's theory of coherent lower previsions builds upon the former theory by Williams with the explicit aim to make it deal with conglomerability. We show that such a construction has been only partly successful because Walley's founding axiom of joint coherence does not entirely capture the implications of conglomerability. As a way to fully achieve Walley's original aim, we propose then the new theory of conglomerable coherent lower previsions. We show that Walley's theory coincides with ours when all conditioning events have positive lower probability, or when conditioning partitions are nested.\",\n \"Title: Lazy naive credal classifier\\nYear: 2009\\n\\nAbstract: We propose a local (or lazy) version of the naive credal classifier. The latter is an extension of naive Bayes to imprecise probability developed to issue reliable classifications despite small amounts of data, which may then be carrying highly uncertain information about a domain. Reliability is maintained because credal classifiers can issue set-valued classifications on instances that are particularly difficult to classify. We show by extensive experiments that the local classifier outperforms the original one, both in terms of accuracy of classification and because it leads to stronger conclusions (i.e., set-valued classifications made by fewer classes). By comparing the local credal classifier with a local version of naive Bayes, we also show that the former reliably deals with instances which are difficult to classify, unlike the local naive Bayes which leads to fragile classifications.\",\n \"Title: Locally specified credal networks\\nYear: 2006\\n\\nAbstract: Credal networks are models that extend Bayesian nets to deal with imprecision in probability, and can actually be regarded as sets of Bayesian nets. Evidence suggests that credal nets are a powerful means to represent and deal with many important and challenging problems in uncertain reasoning. We give examples to show that some of these problems can only be modelled by credal nets called non-separately specie d. These, however, are still missing a graphical representation language and solution algorithms. The situation is quite the opposite with separately specied credal nets, which have been the subject of much study and algorithmic development. This paper gives two major contributions. First, it delivers a new graphical language to formulate any type of credal network, both separately and non-separately specied. Second, it shows that any non-separately specied net represented with the new language can be easily transformed into an equivalent separately specied net, dened over a larger domain. This result opens up a number of new perspectives and concrete outcomes: rst of all, it immediately enables the existing algorithms for separately specied credal nets to be applied to non-separately specied ones.\",\n \"Title: Updating with incomplete observations.\\nYear: 2003\\n\\nAbstract: Currently, there is renewed interest in the problem, raised by Shafer in 1985, of updating probabilities when observations are incomplete (or setvalued). This is a fundamental problem, and of articular interest for Bayesian networks. Recently, Griinwald and Halpern have shown that commonly used updating strategies fail here, except under very special assumptions. We propose a new rule for updating probabilities with incomplete observations. Our approach is deliberately conservative: we make no or weak assumptions about the so-called incompleteness mechanism that produces incomplete observations. We model our ignorance about this mechanism by a vacuous lower prevision, a tool from the theory of imprecise probabilities, and we derive a new updating rule using coherence arguments. In general, our rule produces lower posterior probabilities, as well as partially determinate decisions. This is a logical consequence of the ignorance about the incompleteness mechanism. We show how the new rule can properly address the apparent paradox in the 'Monty Hall' puzzle. In addition, we apply it to the classification of new evidence in Bayesian networks constructed using expert knowledge. We provide an exact algorithm for this task with linear-time complexity, also for multiply connected nets.\",\n \"Title: Robust inference of trees\\nYear: 2005\\n\\nAbstract: This paper is concerned with the reliable inference of optimal tree-approximations to the dependency structure of an unknown distribution generating data. The traditional approach to the problem measures the dependency strength between random variables by the index called mutual information. In this paper reliability is achieved by Walley's imprecise Dirichlet model, which generalizes Bayesian learning with Dirichlet priors. Adopting the imprecise Dirichlet model results in posterior interval expectation for mutual information, and in a set of plausible trees consistent with the data. Reliable inference about the actual tree is achieved by focusing on the substructure common to all the plausible trees. We develop an exact algorithm that infers the substructure in time O(m 4), m being the number of random variables. The new algorithm is applied to a set of data sampled from a known distribution. The method is shown to reliably infer edges of the actual tree even when the data are very scarce, unlike the traditional approach. Finally, we provide lower and upper credibility limits for mutual information under the imprecise Dirichlet model. These enable the previous developments to be extended to a full inferential method for trees.\",\n \"Title: Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2\\nYear: 2008\\n\\nAbstract: In this paper, the naive credal classifier, which is a set-valued counterpart of naive Bayes, is extended to a general and flexible treatment of incomplete data, yielding a new classifier called naive credal classifier 2 (NCC2). The new classifier delivers classifications that are reliable even in the presence of small sample sizes and missing values. Extensive empirical evaluations show that, by issuing set-valued classifications, NCC2 is able to isolate and properly deal with instances that are hard to classify (on which naive Bayes accuracy drops considerably), and to perform as well as naive Bayes on the other instances. The experiments point to a general problem: they show that with missing values, empirical evaluations may not reliably estimate the accuracy of a traditional classifier, such as naive Bayes. This phenomenon adds even more value to the robust approach to classification implemented by NCC2.\",\n \"Title: Generalized loopy 2U: A new algorithm for approximate inference in credal networks\\nYear: 2010\\n\\nAbstract: Credal networks generalize Bayesian networks by relaxing the requirement of precision of probabilities. Credal networks are considerably more expressive than Bayesian networks, but this makes belief updating NP-hard even on polytrees. We develop a new efficient algorithm for approximate belief updating in credal networks. The algorithm is based on an important representation result we prove for general credal networks: that any credal network can be equivalently reformulated as a credal network with binary variables; moreover, the transformation, which is considerably more complex than in the Bayesian case, can be implemented in polynomial time. The equivalent binary credal network is then updated by L2U, a loopy approximate algorithm for binary credal networks. Overall, we generalize L2U to non-binary credal networks, obtaining a scalable algorithm for the general case, which is approximate only because of its loopy nature. The accuracy of the inferences with respect to other state-of-the-art algorithms is evaluated by extensive numerical tests.\",\n \"Title: Credal networks for military identification problems\\nYear: 2009\\n\\nAbstract: Credal networks are imprecise probabilistic graphical models generalizing Bayesian networks to convex sets of probability mass functions. This makes credal networks particularly suited to model expert knowledge under very general conditions, including states of qualitative and incomplete knowledge. In this paper, we present a credal network for risk evaluation in case of intrusion of civil aircrafts into a restricted flight area. The different factors relevant for this evaluation, together with an independence structure over them, are initially identified. These factors are observed by sensors, whose reliabilities can be affected by variable external factors, and even by the behaviour of the intruder. A model of these observation processes, and the necessary fusion scheme for the information returned by the sensors measuring the same factor, are both completely embedded into the structure of the credal network. A pool of experts, facilitated in their task by specific techniques to convert qualitative judgements into imprecise probabilistic assessments, has made possible the quantification of the network. We show the capabilities of the proposed model by means of some preliminary tests referred to simulated scenarios. Overall, we can regard this application as a useful tool to support military experts in their decision, but also as a quite general imprecise-probability paradigm for information fusion.\",\n \"Title: The Complexity of Approximately Solving Influence Diagrams\\nYear: 2012\\n\\nAbstract: Influence diagrams allow for intuitive and yet precise description of complex situations involving decision making under uncertainty. Unfortunately, most of the problems described by influence diagrams are hard to solve. In this paper we discuss the complexity of approximately solving influence diagrams. We do not assume no-forgetting or regularity, which makes the class of problems we address very broad. Remarkably, we show that when both the tree-width and the cardinality of the variables are bounded the problem admits a fully polynomial-time approximation scheme.\",\n \"Title: Approximating credal network inferences by linear programming\\nYear: 2013\\n\\nAbstract: An algorithm for approximate credal network updating is presented. The problem in its general formulation is a multilinear optimization task, which can be linearized by an appropriate rule for fixing all the local models apart from those of a single variable. This simple idea can be iterated and quickly leads to very accurate inferences. The approach can also be specialized to classification with credal networks based on the maximality criterion. A complexity analysis for both the problem and the algorithm is reported together with numerical experiments, which confirm the good performance of the method. While the inner approximation produced by the algorithm gives rise to a classifier which might return a subset of the optimal class set, preliminary empirical results suggest that the accuracy of the optimal class set is seldom affected by the approximate probabilities.\",\n \"Title: Reliable hidden Markov model filtering through coherent lower previsions.\\nYear: 2009\\n\\nAbstract: We extend Hidden Markov Models for continuous variables taking into account imprecision in our knowledge about the probabilistic relationships involved. To achieve that, we consider sets of probabilities, also, called coherent lower previsions. In addition to the general formulation, we study in detail a particular case of interest: linear-vacuous mixtures. We also show, in a practical case, that our extension outperforms the Kalman filter when modelling errors are present in the system.\",\n \"Title: Epistemic irrelevance in credal nets: The case of imprecise Markov trees\\nYear: 2010\\n\\nAbstract: We focus on credal nets, which are graphical models that generalise Bayesian nets to imprecise probability. We replace the notion of strong independence commonly used in credal nets with the weaker notion of epistemic irrelevance, which is arguably more suited for a behavioural theory of probability. Focusing on directed trees, we show how to combine the given local uncertainty models in the nodes of the graph into a global model, and we use this to construct and justify an exact message-passing algorithm that computes updated beliefs for a variable in the tree. The algorithm, which is linear in the number of nodes, is formulated entirely in terms of coherent lower previsions, and is shown to satisfy a number of rationality requirements. We supply examples of the algorithm's operation, and report an application to on-line character recognition that illustrates the advantages of our approach for prediction. We comment on the perspectives, opened by the availability, for the first time, of a truly efficient algorithm based on epistemic irrelevance.\",\n \"Title: Inference and risk measurement with the pari-mutuel model\\nYear: 2010\\n\\nAbstract: We explore generalizations of the pari-mutuel model (PMM), a formalization of an intuitive way of assessing an upper probability from a precise one. We discuss a naive extension of the PMM considered in insurance, compare the PMM with a related model, the Total Variation Model, and generalize the natural extension of the PMM introduced by P. Walley and other pertained formulae. The results are subsequently given a risk measurement interpretation: in particular it is shown that a known risk measure, Tail Value at Risk (TVaR), is derived from the PMM, and a coherent risk measure more general than TVaR from its imprecise version. We analyze further the conditions for coherence of a related risk measure, Conditional Tail Expectation. Conditioning with the PMM is investigated too, computing its natural extension, characterising its dilation and studying the weaker concept of imprecision increase.\",\n \"Title: Fast algorithms for robust classification with Bayesian nets\\nYear: 2005\\n\\nAbstract: We focus on a well-known classification task with expert systems based on Bayesian networks: predicting the state of a target variable given an incomplete observation of the other variables in the network, i.e., an observation of a subset of all the possible variables. To provide conclusions robust to near-ignorance about the process that prevents some of the variables from being observed, it has recently been derived a new rule, called conservative updating. With this paper we address the problem to efficiently compute the conservative updating rule for robust classification with Bayesian networks. We show first that the general problem is NP-hard, thus establishing a fundamental limit to the possibility to do robust classification efficiently. Then we define a wide subclass of Bayesian networks that does admit efficient computation. We show this by developing a new classification algorithm for such a class, which extends substantially the limits of efficient computation with respect to the previously existing algorithm. The algorithm is formulated as a variable elimination procedure, whose computation time is linear in the input size.\",\n \"Title: Notes on desirability and conditional lower previsions\\nYear: 2010\\n\\nAbstract: We detail the relationship between sets of desirable gambles and conditional lower previsions. The former is one the most general models of uncertainty. The latter corresponds to Walley's celebrated theory of imprecise probability. We consider two avenues: when a collection of conditional lower previsions is derived from a set of desirable gambles, and its converse. In either case, we relate the properties of the derived model with those of the originating one. Our results constitute basic tools to move from one formalism to the other, and thus to take advantage of work done in the two fronts.\",\n \"Title: Conservative Rules For Predictive Inference With Incomplete Data\\nYear: 2005\\n\\nAbstract: This paper addresses the following question: how should we update our beliefs after observing some incomplete data, in order to make credible predictions about new, and possibly incomplete, data? There may be several answers to this question according to the model of the process that creates the incompleteness. This paper develops a rigorous modelling framework that makes it clear the conditions that justify the different answers; and, on this basis, it derives a new conditioning rule for predictive inference to be used in a wide range of states of knowledge about the incompleteness process, including near-ignorance, which, surprisingly, does not seem to have received attention so far. Such a case is instead particularly important, as modelling incompleteness processes can be highly impractical, and because there are limitations to statistical inference with incomplete data: it is generally not possible to learn how incompleteness processes work by using the available data; and it may not be possible, as the paper shows, to measure empirically the quality of the predictions. Yet, these depend heavily on the assumptions made.\",\n \"Title: Simulation and planning of an intermodal container terminal\\nYear: 1998\\n\\nAbstract: A decision support system for the management of an intermodal container terminal is presented. Among the problems to be solved, there are the spatial allocation of containers on the terminal yard, the allocation of resources and the scheduling of operations in order to maximise a performance function based on some economic indicators. These problems are solved using techniques from optimisation, like job-shop scheduling, genetic algorithms or mixed-integer linear programming. At the terminal, the same problems are usually solved by the terminal manager, only using his/her experience. The manager can trust computer generated solutions only by validating them by means of a simulation model of the terminal. Thus, the simulation tool also becomes a means to introduce new approaches into traditional settings. In the present paper we focus on the resource allocation problem. We describe our modules for the optimisation of the allocation process and for the simulation of the terminal. The former is based on integer linear program- ming; the latter is a discrete event simulation tool, based on the process- oriented paradigm. The simulator provides a test bed for checking the validity and the robustness of the policy computed by the optimisation module. The case study of the Contship La Spezia Container Terminal, located in the Mediterranean Sea in Italy, is examined.\",\n \"Title: Limits Of Learning From Imperfect Observations Under Prior Ignorance: The Case Of The Imprecise Dirichlet Model\\nYear: 2005\\n\\nAbstract: Consider a relaxed multinomial setup, in which there may be mistakes in observing the outcomes of the process-this is often the case in real applications. What can we say about the next outcome if we start learning about the process in conditions of prior ignorance? To answer this question we extend the imprecise Dirichlet model to the case of imperfect observations and we focus on posterior predictive probabilities for the next outcome. The results are very surprising: the posterior predictive probabilities are vacuous, irrespectively of the amount of observations we do, and however small is the probability of doing mistakes. In other words, the imprecise Dirichlet model cannot help us to learn from data when the observational mechanism is imperfect. This result seems to rise a serious question about the use of the imprecise Dirichlet model for practical applications, and, more generally, about the possibility to learn from imperfect observations under prior ignorance.\",\n \"Title: Bayesian Treatment of Incomplete Discrete Data applied to Mutual Information and Feature Selection\\nYear: 2003\\n\\nAbstract: Given the joint chances of a pair of random variables one can compute quantities of interest, like. the mutual information. The Bayesian treatment of unknown chances involves computing, from a second order prior distribution and the data likelihood, a posterior distribution of the chances. A common treatment of incomplete data is to assume ignorability and determine the chances by the expectation maximization (EM) algorithm. The two different methods above are well established but typically separated. This paper joins the two approaches in the case of Dirichlet priors, and derives efficient approximations for the mean, mode and the (co)variance of the chances and the mutual information. Furthermore, we prove the unimodality of the posterior distribution, whence the important property of convergence of EM to the global maximum in the chosen framework. These results are applied to the problem of selecting features for incremental learning and naive Bayes classification. A fast filter based on the distribution of mutual information is shown to outperform the traditional filter based on empirical mutual information on a number of incomplete real data sets.\",\n \"Title: Decision-theoretic specification of credal networks: A unified language for uncertain modeling with sets of Bayesian networks\\nYear: 2008\\n\\nAbstract: Credal networks are models that extend Bayesian nets to deal with imprecision in probability, and can actually be regarded as sets of Bayesian nets. Credal nets appear to be powerful means to represent and deal with many important and challenging problems in uncertain reasoning. We give examples to show that some of these problems can only be modeled by credal nets called non-separately specified. These, however, are still missing a graphical representation language and updating algorithms. The situation is quite the opposite with separately specified credal nets, which have been the subject of much study and algorithmic development. This paper gives two major contributions. First, it delivers a new graphical language to formulate any type of credal network, both separately and non-separately specified. Second, it shows that any non-separately specified net represented with the new language can be easily transformed into an equivalent separately specified net, defined over a larger domain. This result opens up a number of new outlooks and concrete outcomes: first of all, it immediately enables the existing algorithms for separately specified credal nets to be applied to non-separately specified ones. We explore this possibility for the 2U algorithm: an algorithm for exact updating of singly connected credal nets, which is extended by our results to a class of non-separately specified models. We also consider the problem of inference on Bayesian networks, when the reason that prevents some of the variables from being observed is unknown. The problem is first reformulated in the new graphical language, and then mapped into an equivalent problem on a separately specified net. This provides a first algorithmic approach to this kind of inference, which is also proved to be NP-hard by similar transformations based on our formalism.\",\n \"Title: Credible classification for environmental problems\\nYear: 2005\\n\\nAbstract: Classifiers that aim at doing credible predictions should rely on carefully elicited prior knowledge. Often this is not available so they should start learning from data in condition of near-ignorance. This paper shows empirically, on an agricultural data set, that established methods of classification do not always adhere to this principle. Traditional ways to represent prior ignorance are shown to have an overwhelming weight compared to the information in the data, producing overconfident predictions. This point is crucial for problems, such as environmental ones, where prior knowledge is often scarce and even the data may not be known precisely. Credal classification, and in particular the naive credal classifier, is proposed as more faithful ways to cope with the ignorance problem. With credal classification, conditions of ignorance may limit the power of the inferences, not the credibility of the predictions.\",\n \"Title: Credal Classification for Dementia Screening\\nYear: 2001\\n\\nAbstract: Dementia is a very serious personal, medical and social problem. Early and accurate diagnoses seem to be the key to effectively\\n cope with it. This paper presents a diagnostic tool that couples the most widely used computerized system of cognitive tests\\n in dementia research, the Cognitive Drug Research system, with the naive credal classifier. Although the classifier is trained\\n on an incomplete database, it provides unmatched predictive performance and reliability. The tool also proves to be very effective\\n in discriminating between Alzheimer’s disease and dementia with Lewy bodies, which is a problem on the frontier of research\\n on dementia.\",\n \"Title: Conservative inference rule for uncertain reasoning under incompleteness\\nYear: 2014\\n\\nAbstract: In this paper we formulate the problem of inference under incomplete information in very general terms. This includes modelling the process responsible for the incompleteness, which we call the incompleteness process. We allow the process' behaviour to be partly unknown. Then we use Walley's theory of coherent lower previsions, a generalisation of the Bayesian theory to imprecision, to derive the rule to update beliefs under incompleteness that logically follows from our assumptions, and that we call conservative inference rule. This rule has some remarkable properties: it is an abstract rule to update beliefs that can be applied in any situation or domain; it gives us the opportunity to be neither too optimistic nor too pessimistic about the incompleteness process, which is a necessary condition to draw reliable while strong enough conclusions; and it is a coherent rule, in the sense that it cannot lead to inconsistencies. We give examples to show how the new rule can be applied in expert systems, in parametric statistical inference, and in pattern classification, and discuss more generally the view of incompleteness processes defended here as well as some of its consequences.\",\n \"Title: Reproducing human decisions in reservoir management: the case of lake Lugano\\nYear: 2009\\n\\nAbstract: The objective of this study is to identify a model able to represent the behavior of the historical decision maker (DM) in the management of lake Lugano, during the period 1982-2002. The DM decides every day how much water to release from the lake. We combine hydrological knowledge and machine learning techniques to property develop the model. As a predictive tool we use lazy learning, namely local linear regression. We setup a daily predictor, which achieves good accuracy, with a mean absolute percentage error around 8.5%. Yet, the behavior of the model is not fully satisfactory during the floods. In fact, from an interview with a domain expert, it appears that the DM can even update the release decision every 6 hours during emergencies. We have therefore developed a refined version of the model, which works with a variable time step: it updates the release decision once a day in normal conditions, and every 6 hours during emergencies. This turns out to be a sensible choice, as the error during emergencies (which represent about 5% of the data set) decreases from 9 to 3 m(3)/sec.\",\n \"Title: Binarization Algorithms for Approximate Updating in Credal Nets\\nYear: 2006\\n\\nAbstract: Credal networks generalize Bayesian networks relaxing numerical parameters. This considerably expands expressivity, but makes belief updating a hard task even on polytrees. Nevertheless, if all the variables are binary, polytree-shaped credal networks can be efficiently updated by the 2U algorithm. In this paper we present a binarization algorithm, that makes it possible to approximate an updating problem in a credal net by a corresponding problem in a credal net over binary variables. The procedure leads to outer bounds for the original problem. The binarized nets are in general multiply connected, but can be updated by the loopy variant of 2U. The quality of the overall approximation is investigated by promising numerical experiments.\",\n \"Title: On the complexity of solving polytree-shaped limited memory influence diagrams with binary variables\\nYear: 2013\\n\\nAbstract: Influence diagrams are intuitive and concise representations of structured decision problems. When the problem is non-Markovian, an optimal strategy can be exponentially large in the size of the diagram. We can avoid the inherent intractability by constraining the size of admissible strategies, giving rise to limited memory influence diagrams. A valuable question is then how small do strategies need to be to enable efficient optimal planning. Arguably, the smallest strategies one can conceive simply prescribe an action for each time step, without considering past decisions or observations. Previous work has shown that finding such optimal strategies even for polytree-shaped diagrams with ternary variables and a single value node is NP-hard, but the case of binary variables was left open. In this paper we address such a case, by first noting that optimal strategies can be obtained in polynomial time for polytree-shaped diagrams with binary variables and a single value node. We then show that the same problem is NP-hard if the diagram has multiple value nodes. These two results close the fixed-parameter complexity analysis of optimal strategy selection in influence diagrams parametrized by the shape of the diagram, the number of value nodes and the maximum variable cardinality.\",\n \"Title: Factorisation Properties of the Strong Product\\nYear: 2010\\n\\nAbstract: We investigate a number of factorisation conditions in the framework of sets of probability measures, or coherent lower previsions, with finite referential spaces. We show that the so-called strong product constitutes one way to combine a number of marginal coherent lower previsions into an independent joint lower prevision, and we prove that under some conditions it is the only independent product that satisfies the factorisation conditions.\",\n \"Title: Multiple model tracking by imprecise markov trees.\\nYear: 2009\\n\\nAbstract: We present a new procedure for tracking manoeuvring objects by hidden Markov chains. It leads to more reliable modelling of the transitions between hidden states compared to similar approaches proposed within the Bayesian framework: we adopt convex sets of probability mass functions rather than single 'precise probability' specifications, in order to provide a more realistic and cautious model of the manoeuvre dynamics. In general, the downside of such increased freedom in the modelling phase is a higher inferential complexity. However, the simple topology, of hidden Markov chains allows for efficient tracking of the object through a recently developed belief propagation algorithm. Furthermore, the imprecise specification of the transitions can produce so-called indecision, meaning that more than one model may be suggested by our method as a possible explanation of the target kinematics. In summary, our approach leads to a multiple-model estimator who se performance, investigated through extensive numerical tests, turns out to be more accurate and robust than that of Bayesian ones.\",\n \"Title: Equivalence Between Bayesian and Credal Nets on an Updating Problem\\nYear: 2006\\n\\nAbstract: We establish an intimate connection between Bayesian and credal nets. Bayesian nets are precise graphical models, credal nets extend Bayesian nets to imprecise probability. We focus on traditional belief updating with credal nets, and on the kind of belief updating that arises with Bayesian nets when the reason for the missingness of some of the unobserved variables in the net is unknown. We show that the two updating problems are formally the same.\",\n \"Title: Evaluating credal classifiers by utility-discounted predictive accuracy\\nYear: 2012\\n\\nAbstract: Predictions made by imprecise-probability models are often indeterminate (that is, set-valued). Measuring the quality of an indeterminate prediction by a single number is important to fairly compare different models, but a principled approach to this problem is currently missing. In this paper we derive, from a set of assumptions, a metric to evaluate the predictions of credal classifiers. These are supervised learning models that issue set-valued predictions. The metric turns out to be made of an objective component, and another that is related to the decision-maker's degree of risk aversion to the variability of predictions. We discuss when the measure can be rendered independent of such a degree, and provide insights as to how the comparison of classifiers based on the new measure changes with the number of predictions to be made. Finally, we make extensive empirical tests of credal, as well as precise, classifiers by using the new metric. This shows the practical usefulness of the metric, while yielding a first insightful and extensive comparison of credal classifiers.\",\n \"Title: Tree-Based Credal Networks for Classification\\nYear: 2003\\n\\nAbstract: Bayesian networks are models for uncertain reasoning which are achiev- ing a growing importance also for the data mining task of classification. Credal networks extend Bayesian nets to sets of distributions, or credal sets. This paper extends a state-of-the-art Bayesian net for classification, called tree-augmented naive Bayes classifier, to credal sets originated from probability intervals. This extension is a basis to address the fundamental problem of prior ignorance about the distribution that generates the data, which is a commonplace in data mining applications. This issue is often neglected, but addressing it properly is a key to ultimately draw reliable conclusions from the inferred models. In this paper we formalize the new model, develop an exact linear-time classification algorithm, and evaluate the credal net- based classifier on a number of real data sets. The empirical analysis shows that the new classifier is good and reliable, and raises a problem of excessive caution that is discussed in the paper. Overall, given the favorable trade-off between expressiveness and efficient computation, the newly proposed classifier appears to be a good can- didate for the wide-scale application of reliable classifiers based on credal networks, to real and complex tasks.\",\n \"Title: Solving limited memory influence diagrams\\nYear: 2012\\n\\nAbstract: We present a new algorithm for exactly solving decision making problems represented as influence diagrams. We do not require the usual assumptions of no forgetting and regularity; this allows us to solve problems with simultaneous decisions and limited information. The algorithm is empirically shown to outperform a state-of-the-art algorithm on randomly generated problems of up to 150 variables and 1064 solutions. We show that these problems are NP-hard even if the underlying graph structure of the problem has low treewidth and the variables take on a bounded number of states, and that they admit no provably good approximation if variables can take on an arbitrary number of states.\",\n \"Title: Updating beliefs with incomplete observations\\nYear: 2003\\n\\nAbstract: Currently, there is renewed interest in the problem, raised by Shafer in 1985, of updating probabilities when observations are incomplete (or set-valued). This is a fundamental problem in general, and of particular interest for Bayesian networks. Recently, Grünwald and Halpern have shown that commonly used updating strategies fail in this case, except under very special assumptions. In this paper we propose a new method for updating probabilities with incomplete observations. Our approach is deliberately conservative: we make no assumptions about the so-called incompleteness mechanism that associates complete with incomplete observations. We model our ignorance about this mechanism by a vacuous lower prevision, a tool from the theory of imprecise probabilities, and we use only coherence arguments to turn prior into posterior (updated) probabilities. In general, this new approach to updating produces lower and upper posterior probabilities and previsions (expectations), as well as partially determinate decisions. This is a logical consequence of the existing ignorance about the incompleteness mechanism. As an example, we use the new updating method to properly address the apparent paradox in the 'Monty Hall' puzzle. More importantly, we apply it to the problem of classification of new evidence in probabilistic expert systems, where it leads to a new, so-called conservative updating rule. In the special case of Bayesian networks constructed using expert knowledge, we provide an exact algorithm to compare classes based on our updating rule, which has linear-time complexity for a class of networks wider than polytrees. This result is then extended to the more general framework of credal networks, where computations are often much harder than with Bayesian nets. Using an example, we show that our rule appears to provide a solid basis for reliable updating with incomplete observations, when no strong assumptions about the incompleteness mechanism are justified.\",\n \"Title: Updating credal networks is approximable in polynomial time\\nYear: 2012\\n\\nAbstract: Credal networks relax the precise probability requirement of Bayesian networks, enabling a richer representation of uncertainty in the form of closed convex sets of probability measures. The increase in expressiveness comes at the expense of higher computational costs. In this paper, we present a new variable elimination algorithm for exactly computing posterior inferences in extensively specified credal networks, which is empirically shown to outperform a state-of-the-art algorithm. The algorithm is then turned into a provably good approximation scheme, that is, a procedure that for any input is guaranteed to return a solution not worse than the optimum by a given factor. Remarkably, we show that when the networks have bounded treewidth and bounded number of states per variable the approximation algorithm runs in time polynomial in the input size and in the inverse of the error factor, thus being the first known fully polynomial-time approximation scheme for inference in credal networks.\",\n \"Title: 2U: an exact interval propagation algorithm for polytrees with binary variables\\nYear: 1998\\n\\nAbstract: This paper addresses the problem of computing posterior in a discrete Bayesian network where the conditional distributions of the model belong to convex sets. The computation on a general Bayesian network with convex sets of conditional distributions is formalized as a global optimization problem. It is shown that such a problem can be reduced to a combinatorial problem, suitable to exact algorithmic solutions. An exact propagation algorithm for the updating of a polytree with binary variables is derived. The overall complexity is linear to the size of the network, when the maximum number of parents is fixed. (C) 1998 Elsevier Science B.V. All rights reserved.\",\n \"Title: Naive credal classifier 2: an extension of naive Bayes for delivering robust classifications\\nYear: 2008\\n\\nAbstract: Naive credal classifier 2 (NCC2) extends naive Bayes in order to deliver more robust classifications. NCC2 is based on a set of prior densities rather than on a single prior; as a consequence, when faced with instances whose classification is prior-dependent (and therefore might not be reliable), it returns a set of classes (we call this an indeterminate classi- fication) instead of a single class. Moreover, NCC2 introduces a very general and flexible treatment of missing data, which, under certain circumstances, can also lead to indeterminate classifications. In this case, indeterminacy can be regarded as a way to preserve reliability despite the information hidden by missing values. We call hard-to-classify the instances classified indeterminately by NCC2. Extensive empirical evaluations show that naive Bayes' accuracy drops considerably on the hard-to- classify instances identified by NCC2, and that on the other hand, NCC2 has high set-accuracy (the proportion of times that the actual class is contained in the set of returned classes) when it is indeterminate.\",\n \"Title: Distribution of mutual information from complete and incomplete data\\nYear: 2005\\n\\nAbstract: Mutual information is widely used, in a descriptive way, to measure the stochastic dependence of categorical random variables. In order to address questions such as the reliability of the descriptive value, one must consider sample-to-population inferential approaches. This paper deals with the posterior distribution of mutual information, as obtained in a Bayesian framework by a second-order Dirichlet prior distribution. The exact analytical expression for the mean, and analytical approximations for the variance, skewness and kurtosis are derived. These approximations have a guaranteed accuracy level of the order O(n−3), where n is the sample size. Leading order approximations for the mean and the variance are derived in the case of incomplete samples. The derived analytical expressions allow the distribution of mutual information to be approximated reliably and quickly. In fact, the derived expressions can be computed with the same order of complexity needed for descriptive mutual information. This makes the distribution of mutual information become a concrete alternative to descriptive mutual information in many applications which would benefit from moving to the inductive side. Some of these prospective applications are discussed, and one of them, namely feature selection, is shown to perform significantly better when inductive mutual information is used.\",\n \"Title: JNCC2: An extension of naive Bayes classifier suited for small and incomplete data sets\\nYear: 2008\\n\\nAbstract: JNCC2 implements the Naive Credal Classifier 2 (NCC2), i.e., an extension of naive Bayes to imprecise probabilities, designed to return robust classification even on small and/or incomplete data sets, which is often the case in environmental case studies.\",\n \"Title: Robust Feature Selection by Mutual Information Distributions.\\nYear: 2002\\n\\nAbstract: Mutual information is widely used in artificial intelligence, in a descriptive way, to measure the stochastic dependence of discrete random variables. In order to address questions such as the reliability of the empirical value, one must consider sample-to-population inferential approaches. This paper deals with the distribution of mutual information, as obtained in a Bayesian framework by a second-order Dirichlet prior distribution. The exact analytical expression for the mean and an analytical approximation of the variance are reported. Asymptotic approximations of the distribution are proposed. The results are applied to the problem of selecting features for incremental learning and classification of the naive Bayes classifier. A fast, newly defined method is shown to outperform the traditional approach based on empirical mutual information on a number of real data sets. Finally, a theoretical development is reported that allows one to efficiently extend the above methods to incomplete samples in an easy and effective way.\",\n \"Title: Notes on “Notes on conditional previsions”\\nYear: 2007\\n\\nAbstract: These notes comment on Williams' fundamental essay Notes on Conditional Previsions, written as a research report in 1975 and published in the present issue. Basic aspects of that work are discussed, including historical background and relevance to the foundations of probability; examples are supplied to help understanding.\",\n \"Title: Reliable diagnoses of dementia by the naive credal classifier inferred from incomplete cognitive data.\\nYear: 2003\\n\\nAbstract: Dementia is a serious personal, medical and social problem. Recent research indicates early and accurate diagnoses as the key to effectively cope with it. No definitive cure is available but in some cases when the impairment is still mild the disease can be contained. This paper describes a diagnostic tool that jointly uses the naive credal classifier and the most widely used computerized system of cognitive tests in dementia research, the Cognitive Drug Research system. The naive credal classifier extends the discrete naive Bayes classifier to imprecise probabilities. The naive credal classifier models both prior ignorance and ignorance about the likelihood by sets of probability distributions. This is a new way to deal with small and incomplete datasets that departs significantly from most established classification methods. In the empirical study presented here, the naive credal classifier provides reliability and unmatched predictive performance. It delivers up to 95% correct predictions while being very robust with respect to the partial ignorance due to the largely incomplete data. The diagnostic tool also proves to be very effective in discriminating between Alzheimer's disease and dementia with Lewy bodies.\",\n \"Title: Constraint Logic Programming and Integer Programming Approaches and Their Collaboration in Solving an Assignment Scheduling Problem\\nYear: 1997\\n\\nAbstract: Generalised Assignment Problems (GAP), traditionally solved by Integer Programmingtechniques, are addressed in the light of current Constraint Programming methods. Ascheduling application from manufacturing, based on a modified GAP, is used to examinethe performance of each technique under a variety of problem characteristics.Experimental evidence showed that, for a set of assignment problems, Constraint LogicProgramming (CLP) performed consistently better than Integer Programming (IP)....\",\n \"Title: Credal Model Averaging: An Extension of Bayesian Model Averaging to Imprecise Probabilities\\nYear: 2008\\n\\nAbstract: We deal with the arbitrariness in the choice of the prior over the models in Bayesian model averaging(BMA), by modelling prior knowledge by a set of priors (i.e., a prior credal set). We consider Dash and Cooper's BMA applied to naive Bayesian networks, replacing the single prior over the naive models by a credal set; this models a condition close to prior ignorance about the models, which leads to credal model averaging(CMA). CMA returns an indeterminateclassification, i.e., multiple classes, on the instances for which the learning set is not informative enough to smooth the effect of the choice of the prior. We give an algorithm to compute exact credal model averaging for naive networks. Extensive experiments show that indeterminate classifications preserve the reliability of CMA on the instances which are classified in a prior-dependent way by BMA.\",\n \"Title: Credal Networks for Operational Risk Measurement and Management\\nYear: 2007\\n\\nAbstract: According to widely accepted guidelines for self-regulation, the capital requirements of a bank should relate to the level of risk with respect to three different categories. Among them, operational riskis the more difficult to assess, as it requires merging expert judgments and quantitative information about the functional structure of the bank. A number of approaches to the evaluation of operational risk based on Bayesian networks have been recently considered. In this paper, we propose credal networks, which are a generalization of Bayesian networks to imprecise probabilities, as a more appropriate framework for the measurementand managementof operational risk. The reason is the higher flexibility provided by credal networks compared to Bayesian networks in the quantification of the probabilities underlying the model: this makes it possible to represent human expertise required for these evaluations in a credible and robust way. We use a real-world application to demonstrate these features and to show how to measure operational risk by means of algorithms for inference over credal nets. This is shown to be possible, also in the case when the observation of some factor is vague.\",\n \"Title: Limits of learning about a categorical latent variable under prior near-ignorance\\nYear: 2009\\n\\nAbstract: In this paper, we consider the coherent theory of (epistemic) uncertainty of Walley, in which beliefs are represented through sets of probability distributions, and we focus on the problem of modeling prior ignorance about a categorical random variable. In this setting, it is a known result that a state of prior ignorance is not compatible with learning. To overcome this problem, another state of beliefs, called near-ignorance, has been proposed. Near-ignorance resembles ignorance very closely, by satisfying some principles that can arguably be regarded as necessary in a state of ignorance, and allows learning to take place. What this paper does, is to provide new and substantial evidence that also near-ignorance cannot be really regarded as a way out of the problem of starting statistical inference in conditions of very weak beliefs. The key to this result is focusing on a setting characterized by a variable of interest that is latent. We argue that such a setting is by far the most common case in practice, and we provide, for the case of categorical latent variables (and general manifest variables) a condition that, if satisfied, prevents learning to take place under prior near-ignorance. This condition is shown to be easily satisfied even in the most common statistical problems. We regard these results as a strong form of evidence against the possibility to adopt a condition of prior near-ignorance in real statistical problems.\",\n \"Title: On the problem of computing the conglomerable natural extension.\\nYear: 2015\\n\\nAbstract: Embedding conglomerability as a rationality requirement in probability was among the aims of Walley's behavioural theory of coherent lower previsions. However, recent work has shown that this attempt has only been partly successful. If we focus in particular on the extension of given assessments to a rational and conglomerable model (in the least-committal way), we have that the procedure used in Walley's theory, the natural extension, provides only an approximation to the model that is actually sought for: the so-called conglomerable natural extension. In this paper we consider probabilistic assessments in the form of a coherent lower prevision P̲, which is another name for a lower expectation functional, and make an in-depth mathematical study of the problem of computing the conglomerable natural extension for this case: that is, where it is defined as the smallest coherent lower prevision F̲≥P̲ that is conglomerable, in case it exists. Past work has shown that F̲ can be approximated by an increasing sequence (E̲n)n∈N of coherent lower previsions. We solve an open problem by showing that this sequence can consist of infinitely many distinct elements. Moreover, we give sufficient conditions, of quite broad applicability, to make sure that the point-wise limit of the sequence is F̲ in case P̲ is the lower envelope of finitely many linear previsions. In addition, we study the question of the existence of F̲ and its relationship with the notion of marginal extension.\",\n \"Title: Comments on “Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks” by Andrés R. Masegosa and Serafín Moral\\nYear: 2014\\n\\nAbstract: We briefly overview the problem of learning probabilities from data using imprecise probability models that express very weak prior beliefs. Then we comment on the new contributions to this question given in the paper by Masegosa and Moral and provide some insights about the performance of their models in data mining experiments of classification.\",\n \"Title: Approximate credal network updating by linear programming with applications to decision making\\nYear: 2015\\n\\nAbstract: Credal nets are probabilistic graphical models which extend Bayesian nets to cope with sets of distributions. An algorithm for approximate credal network updating is presented. The problem in its general formulation is a multilinear optimization task, which can be linearized by an appropriate rule for fixing all the local models apart from those of a single variable. This simple idea can be iterated and quickly leads to accurate inferences. A transformation is also derived to reduce decision making in credal networks based on the maximality criterion to updating. The decision task is proved to have the same complexity of standard inference, being NPPP-complete for general credal nets and NP-complete for polytrees. Similar results are derived for the E-admissibility criterion. Numerical experiments confirm a good performance of the method.\",\n \"Title: A Bayesian Wilcoxon signed-rank test based on the Dirichlet process.\\nYear: 2014\\n\\nAbstract: Bayesian methods are ubiquitous in machine learning. Nevertheless, the analysis of empirical results is typically performed by frequentist tests. This implies dealing with null hypothesis significance tests and p-values, even though the shortcomings of such methods are well known. We propose a nonparametric Bayesian version of the Wilcoxon signed-rank test using a Dirichlet process (DP) based prior. We address in two different ways the problem of how to choose the infinite dimensional parameter that characterizes the DP. The proposed test has all the traditional strengths of the Bayesian approach; for instance, unlike the frequentist tests, it allows verifying the null hypothesis, not only rejecting it, and taking decision which minimize the expected loss. Moreover, one of the solutions proposed to model the infinitedimensional parameter of the DP, allows isolating instances in which the traditional frequentist test is guessing at random. We show results dealing with the comparison of two classifiers using real and simulated data.\",\n \"Title: Extended Tree Augmented Naive Classifier\\nYear: 2014\\n\\nAbstract: This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds' algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). A range of experiments show that we obtain models with better accuracy than TAN and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator.\",\n \"Title: Min-Bdeu And Max-Bdeu Scores For Learning Bayesian Networks\\nYear: 2014\\n\\nAbstract: This work presents two new score functions based on the Bayesian Dirichlet equivalent uniform (BDeu) score for learning Bayesian network structures. They consider the sensitivity of BDeu to varying parameters of the Dirichlet prior. The scores take on the most adversary and the most beneficial priors among those within a contamination set around the symmetric one. We build these scores in such way that they are decomposable and can be computed efficiently. Because of that, they can be integrated into any state-of-the-art structure learning method that explores the space of directed acyclic graphs and allows decomposable scores. Empirical results suggest that our scores outperform the standard BDeu score in terms of the likelihood of unseen data and in terms of edge discovery with respect to the true network, at least when the training sample size is small. We discuss the relation between these new scores and the accuracy of inferred models. Moreover, our new criteria can be used to identify the amount of data after which learning is saturated, that is, additional data are of little help to improve the resulting model.\",\n \"Title: Robust Filtering Through Coherent Lower Previsions\\nYear: 2011\\n\\nAbstract: The classical filtering problem is re-examined to take into account imprecision in the knowledge about the probabilistic relationships involved. Imprecision is modeled in this paper by closed convex sets of probabilities. We derive a solution of the state estimation problem under such a framework that is very general: it can deal with any closed convex set of probability distributions used to characterize uncertainty in the prior, likelihood, and state transition models. This is made possible by formulating the theory directly in terms of coherent lower previsions, that is, of the lower envelopes of the expectations obtained from the set of distributions. The general solution is specialized to two particular classes of coherent lower previsions. The first consists of a family of Gaussian distributions whose means are only known to belong to an interval. The second is the so-called linear-vacuous mixture model, which is a family made of convex combinations of a known nominal distribution (e.g., a Gaussian) with arbitrary distributions. For the latter case, we empirically compare the proposed estimator with the Kalman filter. This shows that our solution is more robust to the presence of modelling errors in the system and that, hence, appears to be a more realistic approach than the Kalman filter in such a case.\",\n \"Title: Bayesian network data imputation with application to survival tree analysis\\nYear: 2016\\n\\nAbstract: Retrospective clinical datasets are often characterized by a relatively small sample size and many missing data. In this case, a common way for handling the missingness consists in discarding from the analysis patients with missing covariates, further reducing the sample size. Alternatively, if the mechanism that generated the missing allows, incomplete data can be imputed on the basis of the observed data, avoiding the reduction of the sample size and allowing methods to deal with complete data later on. Moreover, methodologies for data imputation might depend on the particular purpose and might achieve better results by considering specific characteristics of the domain. The problem of missing data treatment is studied in the context of survival tree analysis for the estimation of a prognostic patient stratification. Survival tree methods usually address this problem by using surrogate splits, that is, splitting rules that use other variables yielding similar results to the original ones. Instead, our methodology consists in modeling the dependencies among the clinical variables with a Bayesian network, which is then used to perform data imputation, thus allowing the survival tree to be applied on the completed dataset. The Bayesian network is directly learned from the incomplete data using a structural expectation-maximization (EM) procedure in which the maximization step is performed with an exact anytime method, so that the only source of approximation is due to the EM formulation itself. On both simulated and real data, our proposed methodology usually outperformed several existing methods for data imputation and the imputation so obtained improved the stratification estimated by the survival tree (especially with respect to using surrogate splits). Retrospective clinical datasets have often small sample size and many missing data.We use Bayesian networks to impute missing data enhancing survival tree analysis.The Bayesian network is learned from incomplete data and used for the imputation.Our method generally achieved more accurate predictions than widely used approaches.\",\n \"Title: Desirability and the birth of incomplete preferences.\\nYear: 2015\\n\\nAbstract: We establish an equivalence between two seemingly different theories: one is the traditional axiomatisation of incomplete preferences on horse lotteries based on the mixture independence axiom; the other is the theory of desirable gambles developed in the context of imprecise probability. The equivalence allows us to revisit incomplete preferences from the viewpoint of desirability and through the derived notion of coherent lower previsions. On this basis, we obtain new results and insights: in particular, we show that the theory of incomplete preferences can be developed assuming only the existence of a worst act---no best act is needed---, and that a weakened Archimedean axiom suffices too; this axiom allows us also to address some controversy about the regularity assumption (that probabilities should be positive---they need not), which enables us also to deal with uncountable possibility spaces; we show that it is always possible to extend in a minimal way a preference relation to one with a worst act, and yet the resulting relation is never Archimedean, except in a trivial case; we show that the traditional notion of state independence coincides with the notion called strong independence in imprecise probability---this leads us to give much a weaker definition of state independence than the traditional one; we rework and uniform the notions of complete preferences, beliefs, values; we argue that Archimedeanity does not capture all the problems that can be modelled with sets of expected utilities and we provide a new notion that does precisely that. Perhaps most importantly, we argue throughout that desirability is a powerful and natural setting to model, and work with, incomplete preferences, even in case of non-Archimedean problems. This leads us to suggest that desirability, rather than preference, should be the primitive notion at the basis of decision-theoretic axiomatisations.\",\n \"Title: Learning extended tree augmented naive structures.\\nYear: 2016\\n\\nAbstract: This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds' algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). We enhance our procedure with a new score function that only takes into account arcs that are relevant to predict the class, as well as an optimization over the equivalent sample size during learning. These ideas may be useful for structure learning of Bayesian networks in general. A range of experiments shows that we obtain models with better prediction accuracy than naive Bayes and TAN, and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator (AODE). We release our implementation of ETAN so that it can be easily installed and run within Weka.\",\n \"Title: A Bayesian nonparametric procedure for comparing algorithms\\nYear: 2015\\n\\nAbstract: A fundamental task in machine learning is to compare the performance of multiple algorithms. This is usually performed by the frequentist Friedman test followed by multiple comparisons. This implies dealing with the well-known shortcomings of null hypothesis significance tests. We propose a Bayesian approach to overcome these problems. We provide three main contributions. First, we propose a nonparametric Bayesian version of the Friedman test using a Dirichlet process (DP) based prior. We show that, from a Bayesian perspective, the Friedman test is an inference for a multivariate mean based on an ellipsoid inclusion test. Second, we derive a joint procedure for the multiple comparisons which accounts for their dependencies and which is based on the posterior probability computed through the DP. The proposed approach allows verifying the null hypothesis, not only rejecting it. Third, as a practical application we show the results in our algorithm for racing, i.e. identifying the best algorithm among a large set of candidates sequentially assessed. Our approach consistently outperforms its frequentist counterpart.\",\n \"Title: Bayesian Hypothesis Testing in Machine Learning\\nYear: 2015\\n\\nAbstract: Most hypothesis testing in machine learning is done using the frequentist null-hypothesis significance test, which has severe drawbacks. We review recent Bayesian tests which overcome the drawbacks of the frequentist ones.\",\n \"Title: Learning Bayesian Networks with Thousands of Variables\\nYear: 2015\\n\\nAbstract: We present a method for learning Bayesian networks from data sets containing thousands of variables without the need for structure constraints. Our approach is made of two parts. The first is a novel algorithm that effectively explores the space of possible parent sets of a node. It guides the exploration towards the most promising parent sets on the basis of an approximated score function that is computed in constant time. The second part is an improvement of an existing ordering-based algorithm for structure optimization. The new algorithm provably achieves a higher score compared to its original formulation. Our novel approach consistently outperforms the state of the art on very large data sets.\",\n \"Title: Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis.\\nYear: 2017\\n\\nAbstract: The machine learning community adopted the use of null hypothesis significance testing (NHST) in order to ensure the statistical validity of results. Many scientific fields however realized the shortcomings of frequentist reasoning and in the most radical cases even banned its use in publications. We should do the same: just as we have embraced the Bayesian paradigm in the development of new machine learning methods, so we should also use it in the analysis of our own results. We argue for abandonment of NHST by exposing its fallacies and, more importantly, offer better more sound and useful alternatives for it.\",\n \"Title: Learning Bounded Treewidth Bayesian Networks with Thousands of Variables.\\nYear: 2016\\n\\nAbstract: We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables. Bounding the treewidth of a Bayesian greatly reduces the complexity of inferences. Yet, being a global property of the graph, it considerably increases the difficulty of the learning process. We propose a novel algorithm for this task, able to scale to large domains and large treewidths. Our novel approach consistently outperforms the state of the art on data sets with up to ten thousand variables.\",\n \"Title: Statistical comparison of classifiers through Bayesian hierarchical modelling.\\nYear: 2017\\n\\nAbstract: Usually one compares the accuracy of two competing classifiers using null hypothesis significance tests. Yet such tests suffer from important shortcomings, which can be overcome by switching to Bayesian hypothesis testing. We propose a Bayesian hierarchical model that jointly analyzes the cross-validation results obtained by two classifiers on multiple data sets. The model estimates more accurately the difference between classifiers on the individual data sets than the traditional approach of averaging, independently on each data set, the cross-validation results. It does so by jointly analyzing the results obtained on all data sets, and applying shrinkage to the estimates. The model eventually returns the posterior probability of the accuracies of the two classifiers being practically equivalent or significantly different.\",\n \"Title: Conformity and independence with coherent lower previsions.\\nYear: 2016\\n\\nAbstract: We define the conformity of marginal and conditional models with a joint model within Walley's theory of coherent lower previsions. Loosely speaking, conformity means that the joint can reproduce the marginal and conditional models we started from. By studying conformity with and without additional assumptions of epistemic irrelevance and independence, we establish connections with a number of prominent models in Walley's theory: the marginal extension, the irrelevant natural extension, the independent natural extension and the strong product.\",\n \"Title: SOS for Bounded Rationality.\\nYear: 2017\\n\\nAbstract: In the gambling foundation of probability theory, rationality requires that a subject should always (never) find desirable all nonnegative (negative) gambles, because no matter the result of the experiment the subject never (always) decreases her money. Evaluating the nonnegativity of a gamble in infinite spaces is a difficult task. In fact, even if we restrict the gambles to be polynomials in R^n , the problem of determining nonnegativity is NP-hard. The aim of this paper is to develop a computable theory of desirable gambles. Instead of requiring the subject to accept all nonnegative gambles, we only require her to accept gambles for which she can efficiently determine the nonnegativity (in particular SOS polynomials). We refer to this new criterion as bounded rationality.\",\n \"Title: A Polarity Theory for Sets of Desirable Gambles.\\nYear: 2017\\n\\nAbstract: J. Vicente-Perez was partially supported by MINECO of Spain and ERDF of EU, Grants MTM2014-59179-C2-1-P and ECO2016-77200-P.\",\n \"Title: Entropy-based Pruning for Learning Bayesian Networks using BIC.\\nYear: 2018\\n\\nAbstract: For decomposable score-based structure learning of Bayesian networks, existing approaches first compute a collection of candidate parent sets for each variable and then optimize over this collection by choosing one parent set for each variable without creating directed cycles while maximizing the total score. We target the task of constructing the collection of candidate parent sets when the score of choice is the Bayesian Information Criterion (BIC). We provide new non-trivial results that can be used to prune the search space of candidate parent sets of each node. We analyze how these new results relate to previous ideas in the literature both theoretically and empirically. We show in experiments with UCI data sets that gains can be significant. Since the new pruning rules are easy to implement and have low computational costs, they can be promptly integrated into all state-of-the-art methods for structure learning of Bayesian networks.\",\n \"Title: Hierarchical Multinomial-Dirichlet Model for the Estimation of Conditional Probability Tables\\nYear: 2017\\n\\nAbstract: We present a novel approach for estimating conditional probability tables, based on a joint, rather than independent, estimate of the conditional distributions belonging to the same table. We derive exact analytical expressions for the estimators and we analyse their properties both analytically and via simulation. We then apply this method to the estimation of parameters in a Bayesian network. Given the structure of the network, the proposed approach better estimates the joint distribution and significantly improves the classification performance with respect to traditional approaches.\",\n \"Title: Efficient Learning of Bounded-Treewidth Bayesian Networks from Complete and Incomplete Data Sets.\\nYear: 2018\\n\\nAbstract: Learning a Bayesian networks with bounded treewidth is important for reducing the complexity of the inferences. We present a novel anytime algorithm (k-MAX) method for this task, which scales up to thousands of variables. Through extensive experiments we show that it consistently yields higher-scoring structures than its competitors on complete data sets. We then consider the problem of structure learning from incomplete data sets. This can be addressed by structural EM, which however is computationally very demanding. We thus adopt the novel k-MAX algorithm in the maximization step of structural EM, obtaining an efficient computation of the expected sufficient statistics. We test the resulting structural EM method on the task of imputing missing data, comparing it against the state-of-the-art approach based on random forests. Our approach achieves the same imputation accuracy of the competitors, but in about one tenth of the time. Furthermore we show that it has worst-case complexity linear in the input size, and that it is easily parallelizable.\",\n \"Title: Quantum Rational Preferences and Desirability.\\nYear: 2016\\n\\nAbstract: We develop a theory of quantum rational decision making in the tradition of Anscombe and Aumannu0027s axiomatisation of preferences on horse lotteries. It is essentially the Bayesian decision theory generalised to the space of Hermitian matrices. Among other things, this leads us to give a representation theorem showing that quantum complete rational preferences are obtained by means of expected utility considerations.\",\n \"Title: Approximate structure learning for large Bayesian networks\\nYear: 2018\\n\\nAbstract: We present approximate structure learning algorithms for Bayesian networks. We discuss the two main phases of the task: the preparation of the cache of the scores and structure optimization, both with bounded and unbounded treewidth. We improve on state-of-the-art methods that rely on an ordering-based search by sampling more effectively the space of the orders. This allows for a remarkable improvement in learning Bayesian networks from thousands of variables. We also present a thorough study of the accuracy and the running time of inference, comparing bounded-treewidth and unbounded-treewidth models.\",\n \"Title: Full Conglomerability, Continuity and Marginal Extension.\\nYear: 2016\\n\\nAbstract: We investigate fully conglomerable coherent lower previsions in the sense of Walley, and some particular cases of interest: envelopes of fully conglomerable linear previsions, envelopes of countably additive linear previsions and fully disintegrable linear previsions. We study the connections with continuity and countable super-additivity, and show that full conglomerability can be characterised in terms of a supremum of marginal extension models.\",\n \"Title: Compatibility, Coherence and the RIP.\\nYear: 2018\\n\\nAbstract: We generalise the classical result on the compatibility of marginal, possible non-disjoint, assessments in terms of the running intersection property to the imprecise case, where our beliefs are modelled in terms of sets of desirable gambles. We consider the case where we have unconditional and conditional assessments, and show that the problem can be simplified via a tree decomposition.\",\n \"Title: Sum-of-squares for bounded rationality.\\nYear: 2019\\n\\nAbstract: In the gambling foundation of probability theory, rationality requires that a subject should always (never) find desirable all nonnegative (negative) gambles, because no matter the result of the experiment the subject never (always) decreases her money. Evaluating the nonnegativity of a gamble in infinite spaces is a difficult task. In fact, even if we restrict the gambles to be polynomials in Rn, the problem of determining nonnegativity is NP-hard. The aim of this paper is to develop a computable theory of desirable gambles. Instead of requiring the subject to desire all nonnegative gambles, we only require her to desire gambles for which she can efficiently determine the nonnegativity (in particular sum-of-squares polynomials). We refer to this new criterion as bounded rationality.\",\n \"Title: Classification of dementia types from cognitive profiles data\\nYear: 2006\\n\\nAbstract: The Cognitive Drug Research (CDR) system is specifically validated for dementia assessment; it consists of a series of computerized tests, which assess the cognitive faculties of the patient to derive a cognitive profile. We use six different classification algorithms to classify clinically diagnosed diseases from their cognitive profiles. Good accuracy was obtained in separating patients affected by Parkinson's disease from demented patients, and in discriminating between Alzheimer's disease and Vascular Dementia. However, in discriminating between Parkinson disease with dementia (PDD) and dementia with Lewy bodies (DLB), the accuracy was only slightly superior to chance; the existence of a significant difference in the cognitive profiles of DLB and PDD is indeed questioned in the medical literature.\",\n \"Title: Desirability foundations of robust rational decision making\\nYear: 2021\\n\\nAbstract: Recent work has formally linked the traditional axiomatisation of incomplete preferences à la Anscombe-Aumann with the theory of desirability developed in the context of imprecise probability, by showing in particular that they are the very same theory. The equivalence has been established under the constraint that the set of possible prizes is finite. In this paper, we relax such a constraint, thus de facto creating one of the most general theories of rationality and decision making available today. We provide the theory with a sound interpretation and with basic notions, and results, for the separation of beliefs and values, and for the case of complete preferences. Moreover, we discuss the role of conglomerability for the presented theory, arguing that it should be a rationality requirement under very broad conditions.\",\n \"Title: Computational Complexity and the Nature of Quantum Mechanics.\\nYear: 2019\\n\\nAbstract: Quantum theory (QT) has been confirmed by numerous experiments, yet we still cannot fully grasp the meaning of the theory. As a consequence, the quantum world appears to us paradoxical. Here we shed new light on QT by having it follow from two main postulates (i) the theory should be logically consistent; (ii) inferences in the theory should be computable in polynomial time. The first postulate is what we require to each well-founded mathematical theory. The computation postulate defines the physical component of the theory. We show that the computation postulate is the only true divide between QT, seen as a generalised theory of probability, and classical probability. All quantum paradoxes, and entanglement in particular, arise from the clash of trying to reconcile a computationally intractable, somewhat idealised, theory (classical physics) with a computationally tractable theory (QT) or, in other words, from regarding physics as fundamental rather than computation.\",\n \"Title: Computational Complexity and the Nature of Quantum Mechanics (Extended version).\\nYear: 2019\\n\\nAbstract: Quantum theory (QT) has been confirmed by numerous experiments, yet we still cannot fully grasp the meaning of the theory. As a consequence, the quantum world appears to us paradoxical. Here we shed new light on QT by having it follow from two main postulates (i) the theory should be logically consistent; (ii) inferences in the theory should be computable in polynomial time. The first postulate is what we require to each well-founded mathematical theory. The computation postulate defines the physical component of the theory. We show that the computation postulate is the only true divide between QT, seen as a generalised theory of probability, and classical probability. All quantum paradoxes, and entanglement in particular, arise from the clash of trying to reconcile a computationally intractable, somewhat idealised, theory (classical physics) with a computationally tractable theory (QT) or, in other words, from regarding physics as fundamental rather than computation.\",\n \"Title: Hierarchical estimation of parameters in Bayesian networks\\nYear: 2019\\n\\nAbstract: A novel approach for parameter estimation in Bayesian networks is presented. The main idea is to introduce a hyper-prior in the Multinomial–Dirichletmodel, traditionally used for conditional distribution estimation in Bayesian networks. The resulting hierarchical model jointly estimates different conditional distributions belonging to the same conditional probability table, thus borrowing statistical strength from each other. An analytical study of the dependence structure a priori induced by the hierarchical model is performed and an ad hoc variational algorithm for fast and accurate inference is derived. The proposed hierarchical model yields a major performance improvement in classification with Bayesian networks compared to traditional models. The proposed variational algorithm reduces by two orders of magnitude the computational time, with the same accuracy in parameter estimation, compared to traditional MCMC methods. Moreover, motivated by a real case study, the hierarchical model is applied to the estimation of Bayesian networks parameters by borrowing strength from related domains.\",\n \"Title: Recursive Estimation for Sparse Gaussian Process Regression.\\nYear: 2019\\n\\nAbstract: Gaussian Processes (GPs) are powerful kernelized methods for non-parametric regression used in many applications. However, their use is limited to a few thousand of training samples due to their cubic time complexity. In order to scale GPs to larger datasets, several sparse approximations based on so-called inducing points have been proposed in the literature. In this work we investigate the connection between a general class of sparse inducing point GP regression methods and Bayesian recursive estimation which enables Kalman Filter like updating for online learning. The majority of previous work has focused on the batch setting, in particular for learning the model parameters and the position of the inducing points, here instead we focus on training with mini-batches. By exploiting the Kalman filter formulation, we propose a novel approach that estimates such parameters by recursively propagating the analytical gradients of the posterior over mini-batches of the data. Compared to state of the art methods, our method keeps analytic updates for the mean and covariance of the posterior, thus reducing drastically the size of the optimization problem. We show that our method achieves faster convergence and superior performance compared to state of the art sequential Gaussian Process regression on synthetic GP as well as real-world data with up to a million of data samples.\",\n \"Title: Time Series Forecasting with Gaussian Processes Needs Priors\\nYear: 2021\\n\\nAbstract: Automatic forecasting is the task of receiving a time series and returning a forecast for the next time steps without any human intervention. Gaussian Processes (GPs) are a powerful tool for modeling time series, but so far there are no competitive approaches for automatic forecasting based on GPs. We propose practical solutions to two problems: automatic selection of the optimal kernel and reliable estimation of the hyperparameters. We propose a fixed composition of kernels, which contains the components needed to model most time series: linear trend, periodic patterns, and other flexible kernel for modeling the non-linear trend. Not all components are necessary to model each time series; during training the unnecessary components are automatically made irrelevant via automatic relevance determination (ARD). We moreover assign priors to the hyperparameters, in order to keep the inference within a plausible range; we design such priors through an empirical Bayes approach. We present results on many time series of different types; our GP model is more accurate than state-of-the-art time series models. Thanks to the priors, a single restart is enough the estimate the hyperparameters; hence the model is also fast to train.\",\n \"Title: Nonlinear Desirability as a Linear Classification Problem\\nYear: 2021\\n\\nAbstract: The present paper proposes a generalization of linearity axioms of coherence through a geometrical approach, which leads to an alternative interpretation of desirability as a classification problem. In particular, we analyze different sets of rationality axioms and, for each one of them, we show that proving that a subject, who provides finite accept and reject statements, respects these axioms, corresponds to solving a binary classification task using, each time, a different (usually nonlinear) family of classifiers. Moreover, by borrowing ideas from machine learning, we show the possibility to define a feature mapping allowing us to reformulate the above nonlinear classification problems as linear ones in a higher-dimensional space. This allows us to interpret gambles directly as payoffs vectors of monetary lotteries, as well as to reduce the task of proving the rationality of a subject to a linear classification task.\",\n \"Title: The Sure Thing\\nYear: 2021\\n\\nAbstract: If we prefer action a to b both under an event and under its complement, then we should just prefer a to b. This is Savage's sure-thing principle. In spite of its intuitive- and simple-looking nature, for which it gets almost immediate acceptance, the sure thing is not a logical principle. So where does it get its support from? In fact, the sure thing may actually fail. This is related to a variety of deep and foundational concepts in causality, decision theory, and probability, as well as to Simpsons' paradox and Blyth's game. In this paper we try to systematically clarify such a network of relations. Then we propose a general desirability theory for nonlinear utility scales. We use that to show that the sure thing is primitive to many of the previous concepts: In non-causal settings, the sure thing follows from considerations of temporal coherence and coincides with conglomerability; it can be understood as a rationality axiom to enable well-behaved conditioning in logic. In causal settings, it can be derived using only coherence and a causal independence condition.\",\n \"Title: Algebras of Sets and Coherent Sets of Gambles\\nYear: 2021\\n\\nAbstract: In a recent work we have shown how to construct an information algebra of coherent sets of gambles defined on general possibility spaces. Here we analyze the connection of such an algebra with the set algebra of sets of its atoms and the set algebra of subsets of the possibility space on which gambles are defined. Set algebras are particularly important information algebras since they are their prototypical structures. Furthermore, they are the algebraic counterparts of classical propositional logic. As a consequence, this paper also details how propositional logic is naturally embedded into the theory of imprecise probabilities.\",\n \"Title: Information algebras in the theory of imprecise probabilities\\nYear: 2022\\n\\nAbstract: In this paper we create a bridge between desirability and information algebras: we show how coherent sets of gambles, as well as coherent lower previsions, induce such structures. This allows us to enforce the view of such imprecise-probability objects as algebraic and logical structures; moreover, it enforces the interpretation of probability as information, and gives tools to manipulate them as such.\",\n \"Title: Bounding Counterfactuals under Selection Bias.\\nYear: 2022\\n\\nAbstract: Causal analysis may be affected by selection bias, which is defined as the systematic exclusion of data from a certain subpopulation. Previous work in this area focused on the derivation of identifiability conditions. We propose instead a first algorithm to address both identifiable and unidentifiable queries. We prove that, in spite of the missingness induced by the selection bias, the likelihood of the available data is unimodal. This enables us to use the causal expectation-maximisation scheme to obtain the values of causal queries in the identifiable case, and to compute bounds otherwise. Experiments demonstrate the approach to be practically viable. Theoretical convergence characterisations are provided.\",\n \"Title: Information algebras in the theory of imprecise probabilities, an extension\\nYear: 2022\\n\\nAbstract: In recent works, we have shown how to construct an information algebra of coherent sets of gambles, considering firstly a particular model to represent questions, called the multivariate model, and then generalizing it. Here we further extend the construction made to the highest level of generality, setting up an associated information algebra of coherent lower previsions, analyzing the connection of both the information algebras constructed with an instance of set algebras and, finally, establishing and inspecting a version of the marginal problem in this framework.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":92,"string":"92"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13669,"string":"13,669"}}},{"rowIdx":13670,"cells":{"question_id":{"kind":"string","value":"David Camacho"},"question":{"kind":"string","value":"Generate an abstract for the title \"Analyzing Planning and Monitoring Skills of Users in a Multi-UAV Simulation Environment.\" using the following items: Unmanned Aerial Vehicles, Multi-UAVs, Training, Metrics, User profiling."},"answer":{"kind":"string","value":"The study of Unmanned Aerial Vehicles UAVs is currently a growing area. The accelerated expansion of these technologies is demanding the work of more and more qualified operators, able to supervise and control multiple UAVs at the same time. Unfortunately, the training process for this type of systems is still unstructured, and it is needed to define methods to assess and classify operators in the context of a specific skill, for both novice users and experts. This work is focused on analyzing the planning and monitoring skills of inexperienced users in a multi-UAV simulation environment, through the use of a set of metrics capturing the performance of a user and defining its profile. The user profiles will be clustered to extract shared behavioral patterns that help us to decide the planning and monitoring level for each group of users, and to select potential operators."},"correct_answer":{"kind":"string","value":"The study of Unmanned Aerial Vehicles UAVs is currently a growing area. The accelerated expansion of these technologies is demanding the work of more and more qualified operators, able to supervise and control multiple UAVs at the same time. Unfortunately, the training process for this type of systems is still unstructured, and it is needed to define methods to assess and classify operators in the context of a specific skill, for both novice users and experts. This work is focused on analyzing the planning and monitoring skills of inexperienced users in a multi-UAV simulation environment, through the use of a set of metrics capturing the performance of a user and defining its profile. The user profiles will be clustered to extract shared behavioral patterns that help us to decide the planning and monitoring level for each group of users, and to select potential operators."},"documents":{"kind":"list like","value":["Title: A simple CSP-based model for Unmanned Air Vehicle Mission Planning\nYear: 2014\n\nAbstract: The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAV) can be formulated as a Temporal Constraint Satisfaction Problem (TCSP). It consists on a set of locations that should visit in different time windows, and the actions that the vehicle can perform based on its features such as the payload, speed or fuel capacity. In this paper, a temporal constraint model is implemented and tested by performing Backtracking search in several missions where its complexity has been incrementally modified. The experimental phase consists on two different phases. On the one hand, several mission simulations containing (n) UAVs using different sensors and characteristics located in different waypoints, and (m) requested tasks varying mission priorities have been carried out. On the other hand, the second experimental phase uses a backtracking algorithm to look through the whole solutions space to measure the scalability of the problem. This scalability has been measured as a relation between the number of tasks to be performed in the mission and the number of UAVs needed to perform it.","Title: Adapting Searchy to extract data using evolved wrappers\nYear: 2012\n\nAbstract: Organizations need diverse information systems to deal with the increasing requirements in information storage and processing, yielding the creation of information islands and therefore an intrinsic difficulty to obtain a global view. Being able to provide such an unified view of the -likely heterogeneous-information available in an organization is a goal that provides added-value to the information systems and has been subject of intense research. In this paper we present an extension of a solution named Searchy, an agent-based mediator system specialized in data extraction and Integration. Through the use of a set of wrappers, it integrates information from arbitrary sources and semantically translates them according to a mediated scheme. Searchy is actually a domain-independent wrapper container that ease wrapper development, providing, for example, semantic mapping. The extension of Searchy proposed in this paper introduces an evolutionary wrapper that is able to evolve wrappers using regular expressions. To achieve this, a Genetic Algorithm (GA) is used to learn a regex able to extract a set of positive samples while rejects a set of negative samples.","Title: The Effects Of Transfer Of Global Improvements In Genetic Programming\nYear: 2004\n\nAbstract: Koza has shown how Automatically Defined Functions (ADFs) can reduce computational effort in the genetic programming paradigm. In Koza's Automatically Defined Functions, as well as in standard genetic programming, an improvement in a part of a program (an ADF or a main body) can only be transferred to other individuals in the population via crossover. In this article, we consider whether it is a good idea to transfer immediately improvements found by a single individual to other individuals in the population. A system that implements this idea has been proposed and tested for the even-5-parity, even-6-parity, and even10-parity problems. Results are very encouraging: computational effort is reduced (compared to Koza's ADFs) and the system seems to be less prone to early stagnation. Also, as evolution occurs in separate populations, our approach permits to parallelize genetic programming in another different way.","Title: Using the clustering coefficient to guide a genetic-based communities finding algorithm\nYear: 2011\n\nAbstract: Finding communities in networks is a hot topic in several research areas like social network, graph theory or sociology among others. This work considers the community finding problem as a clustering problem where an evolutionary approach can provide a new method to find overlapping and stable communities in a graph. We apply some clustering concepts to search for new solutions that use new simple fitness functions which combine network properties with the clustering coefficient of the graph. Finally, our approach has been applied to the Eurovision contest dataset, a well-known social-based data network, to show how communities can be found using our method.","Title: A genetic graph-based clustering approach to biomedical summarization\nYear: 2013\n\nAbstract: Summarization techniques have become increasingly important over the last few years, specially in biomedical research, where information overload is major problem. Researchers of this area need a shorter version of the texts which contains all the important information while discarding irrelevant one. There are several applications which deal with this problem, however, these applications are sometimes less informative than the user needs. This work deals with this problem trying to improve a summarization graph-based process using genetic clustering techniques. Our automatic summaries are compared to those produced by several commercial and research summarizers, and demonstrate the appropriateness of using genetic techniques in automatic summarization.","Title: Analysis of grammatical evolutionary approaches to regular expression induction.\nYear: 2011\n\nAbstract: Regular expressions, or regexes, have been used traditionally as a pattern matching tool to search for structures in a set of objects, like files, text documents or folders. Pattern matching can be used to look for files whose name contains a given string, to search files that contain a specific pattern within them, or simply to extract text in a set of documents. It is very popular to apply regexes to detect and extract patterns that represent phone numbers, URLs, email addresses, etc. These kind of information can be characterized because it has a well defined structure. Nevertheless, regexes are not very frequently used because its high complexity in both, syntax and grammatical rules, makes regexes difficult to understand. For this reason, the development of programs able to automatically generate, and evaluate, regexes has become a valuable task. This work analyzes the performance of different grammatical evolutionary approaches in the generation of regexes able to extract URL patterns. Four different types of grammars have been evaluated: a context-free grammar, a context-free grammar with a penalized fitness function, an extensible context-free grammar, and a Christiansen grammar. For the considered problem, the experimental results show that the best performance of the system, measured as cumulative success rate, is achieved using Christiansen grammars.","Title: Game-like language learning in 3-D virtual environments\nYear: 2013\n\nAbstract: This paper presents our recent experiences with the design of game-like applications in 3-D virtual environments as well as its impact on student motivation and learning. Therefore our paper starts with a brief analysis of the motivational aspects of videogames and virtual worlds (VWs). We then go on to explore the possible benefits of both in the area of foreign language learning. For our research study we have designed a VW-platform, called VirtUAM. This permits us to store and record data related to users' behaviour within the VW. Furthermore the platform has been employed to build several islands (virtual spaces), which implement different game levels. The virtual spaces themselves are used to give students a basic training in different language skills related to the German language. In order to obtain data regarding the game's impact on student learning and motivation, we designed several tests, which were completed both before and after the student participants played the game. Additionally we gave them a general questionnaire, which was only filled out after the game and which aimed at getting personal feedback from the learners. Quantitative and qualitative results shown in this work are part of a larger project which intends to study the impact of game-like applications within virtual environments and with regard to teaching and learning processes in general.","Title: Effects Of The Lack Of Selective Pressure On The Expected Run-Time Distribution In Genetic Programming\nYear: 2013\n\nAbstract: Run-time analysis is a powerful tool to analyze algorithms. It is focused on studying the time required by an algorithm to find a solution, the expected run-time, which is one of the most relevant algorithm attributes. Previous research has associated the expected run-time in GP with the lognormal distribution. In this paper we provide additional evidence in that regard and show how the algorithm parametrization may change the resulting run-time distribution. In particular, we explore the influence of the selective pressure on the run-time distribution in tree-based GP, finding that, at least in two problem instances, the lack of selective pressure generates an expected run-time distribution well described by the Weibull probability distribution.","Title: A multi-agent architecture for intelligent gathering systems\nYear: 2005\n\nAbstract: This paper presents a model to define heterogeneous agents that solve problems by sharing the knowledge retrieved from the WEB, and cooperating among them. The control structure of those agents is based on a general purpose Multi-Agent architecture (SKELETONAGENT) based on a deliberative approach. Any agent in the architecture is built by means of several interrelated modules: control module, language and communication module, skills modules, knowledge base, yellow pages, etc. The control module uses an agenda to activate and coordinate the agent skills. This agenda handles actions from both the internal goals of the agent and from other agents in the environment. In the paper, we show a high level agent model, which is later instantiated to build a set of heterogeneous specialized agents. The paper describes how SKELETONAGENT has been used to implement different kinds of agents and a specialized Multi-Agent System (MAS). The implemented MAS, MAPWEB-ETOURISM, is the specific implementation of a general WEB gathering architecture, named MAPWEB, which extends SKELETONAGENT. MAPWEB has been designed to solve problems in WEB domain through the integration of information gathering and planning techniques. The MAPWEB-ETOURISM system has been applied to a specific WEB domain (e-tourism) which uses information gathered directly from several WEB sources (plane, train, and hotel companies) to solve travel problems. This paper shows how the proposed architecture allows to integrate the different agents tasks with AI techniques like planning to build a MAS which is able to gather and integrate information retrieved from the WEB to solve problems.","Title: Contextual information retrieval based on algorithmic information theory and statistical outlier detection\nYear: 2007\n\nAbstract: This work presents an Information Retrieval technique based on algorithmic information theory (using the normalized compression distance), statistical data outlier detection, and a novel database structure. The paper shows how they all can be integrated to retrieve information from generic databases using long text-based queries. Two important problems are addressed. On the one hand, we analyze and tyr to solve the detection of a particular case of false positives: when the distance among two documents is outlyingly low but there is not actual similarity. On the other hand, we propose a way to structure the database such that the similarity distance estimation scales well with the length of the size of the query. All design choices are justified with an experimental evaluation.","Title: Is the contextual information relevant in text clustering by compression?\nYear: 2012\n\nAbstract: Usually, when analyzing data that have not been processed or filtered yet, it can be observed that not all the data have equal importance. Thus, it is common to find relevant data surrounded by non relevant one. This occurs when analyzing textual information due to its intrinsic nature: texts contain words that provide a lot of information about the subject matter, whereas they contain other words with a little meaning or relevance. We believe that although in principle the non-relevant words are not as important as the relevant ones, the former constitute the substrate that supports the last. Since this substrate is the context that surrounds the relevant information, we call it the contextual information. In this paper, we analyze the relevance that the contextual information has in textual data, in a clustering by compression scenario. We generate the contextual information applying a distortion technique previously developed by the authors. One of the main characteristics of this technique is that it maintains the contextual information. In this paper we compare this technique with three new distortion techniques that destroy the contextual information in different ways. The experimental results support our hypothesis that the contextual information is relevant at least in the area of text clustering by compression.","Title: Human Drivers Knowledge Integration in a Logistics Decision Support Tool.\nYear: 2011\n\nAbstract: The logistics sector is a very dynamic and complex domain with large impact in our day-to-day life. It is also very sensitive to the market changes, so tools that can improve their processes are needed. This paper describes a decision support tool for a Spanish Logistics Company. The tool takes the knowlege from the human drivers, i.e. the address used to deliver a package, integrates and exploits it into the decision process. However, this is precisely the data that is often wrongly introduced by the drivers. The tool analyses and detects different mistakes (i.e. misspellings) in the addresses introduced, groups by zones the packages that have to be delivered, and proposes the routes that the drivers should follow. To achieve this, the tool combines three different techniques from Artificial Intelligence. First, Data Mining techniques are used to detect and correct addresses inconsistencies. Second, Case-Based Reasoning techniques that are employed to separate and learn the most frequent areas or zones that the experienced drivers do. And finally, we use Evolutionary Computation (EC) to plan optimal routes (using the human drivers past experiences) from the learned areas, and evaluate those plans against the original route executed by the human driver. Results show that the tool can automatically correct most of the misspelling in the data.","Title: Relevance of contextual information in compression-based text clustering\nYear: 2010\n\nAbstract: In this paper we take a step towards understanding compression distances by analyzing the relevance of contextual information in compression-based text clustering. In order to do so, two kinds of word removal are explored, one that maintains part of the contextual information despite the removal, and one that does not maintain it. We show how removing words in such a way that the contextual information is maintained despite the word removal helps the compression-based text clustering and improves its accuracy, while on the contrary, removing words losing that contextual information makes the clustering results worse.","Title: Intelligent Travel Planning: A MultiAgent Planning System to Solve Web Problems in the e-Tourism Domain\nYear: 2001\n\nAbstract: This paper presents Intelligent Travel Planning (ITP), a multiagent planning system to solve Web electronic problems in the Web, whose main goal is to search for useful solutions in the electronic-Tourism domain to system users. The system uses different types of intelligent autonomous agents whose main characteristics are cooperation, negotiation, learning, planning and knowledge sharing. Obviously the information used by the intelligent agents is heterogeneous and geographically distributed, since the main information source of the system is Internet. Other information sources are agent knowledge bases in the distributed system. The process to obtain, filter, and store the information is performed automatically by agents. This information is translated into a homogeneous format for high-level reasoning in order to obtain different partial solutions. Partial solutions are reconstructed into a general solution (or solutions) to be presented to the user. The system will show a set of solutions to the users that can be evaluated by them.","Title: Personalized Handling of Semantic Data with MIG\nYear: 2009\n\nAbstract: This paper shows a semantically-enabled web application named MIG used to create user profiles which enhances users accessibility by allowing the creation of an user interface adapted to the user needs, the device used, and its preferences. This approach exploits the Semantic Web technologies and the infrastructure and applications created in previous work.","Title: Allocating Educational Resources Through Happiness Maximization And Traditional Csp Approach\nYear: 2009\n\nAbstract: An instance of an Educational Resources Allocation (ERA) problem is the distribution of a set of students in different laboratories. This can be a complex and dynamic problem if non-quantitative considerations (i.e. how close the final allocation is to the student preferences or desires) are involved in the decision process. Traditionally, different approaches based on Constraint-Satisfaction techniques and Multi-agent negotiation have been applied to the general problem of Resource Allocation. This paper shows how a Multi-agent approach can be used to model and simulate the assignment of sets of students to several predefined laboratories, by using their preferences to guide the allocation process. This approach aims at finding new solutions that try to satisfy individual student needs with no knowledge about the general allocation problem. The paper shows some experimental results and a comparison, between a CSP-based solution modeled in CHOCO, a CSP Java-based library, and a Multi-agent model implemented using MASON, a multi-agent simulation platform.","Title: A study on the impact of crowd-based voting schemes in the 'Eurovision' European contest\nYear: 2011\n\nAbstract: The Eurovision contest has been the reference on european song contests for the past 50 years. Countries in the European Union can shows the rest of the participants their current music tendencies. This phenomena has been studied in domains like physic and social sciences to find correlations between contests and current political and socio-economy trends in EU. The inclusion of web and social technologies some years ago, have caused a disruption in the traditional voting system whereby the audience is encouraged to participate by casting votes for their favorite song. As a result, this system yields new, relevant information that may be extrapolated to social and political tendencies in Europe with a higher degree accuracy than by data collected using the previous jury-based system. This paper provides an initial data analysis in crowd behavior to assess the impact of the televote system, in the Eurovision voting dynamic, by focusing on two distinct five years periods that can successfully contrast each voting scheme. Analyzing these periods separately, we can observe results from the televoting contests and then compare to the jury to see if there is a change in voting patterns. Finally, we study the underlying community structure of the voting network using the Cluster Percolation Method and Edge Betweenness to discover stable core communities spanning a number of years in the contest. The clusters obtained using these algorithms are then used to compare how these stable communities have evolving during the considered periods.","Title: Multi-agent plan based information gathering\nYear: 2006\n\nAbstract: The evolution of the Web has encouraged the development of new Information Gathering techniques. Artificial Intelligence techniques, such as Planning, have also been used for Information Gathering in order to go beyond merely retrieving Web data. Planning has been used traditionally to generate a sequence of actions that specify how information sources should be accessed. In this paper, planning is used mainly for integrating information found in heterogeneous sources. For instance, two different Web sources about flight and train travels, can be represented by two different planning operators, which will be subsequently combined and integrated by a single plan. We have found that a Multi-Agent framework is very appropriate to implement our technique. In order to evaluate our approach empirically, it has been applied to a tourism domain (MAPWEB-ETOURISM), whose purpose is to help a customer to plan his/her trips. In this domain, several specialized Web agents have been used to query travel Web sources, whose results are subsequently integrated by a planning agent to build complete travel solutions. Experimental results show that, by means of integration, more solutions can be found than by using single information sources or even travel meta-searchers. Also, (MAPWEB-ETOURISM) can find new types of solutions by integrating information gathered from heterogeneous Web sources (i.e. flights and trains).","Title: A new CSP graph-based representation for Ant Colony Optimization\nYear: 2013\n\nAbstract: Constraint Satisfaction Problems (CSP) have been widely studied in several research areas like Artificial Intelligence or Operational Research due their complexity and industrial interest. From previous research areas, heuristic (informed) search methods have been particularly active looking for feasible approaches. One of the critical problems to work with CSP is related to the exponential growth of computational resources needed to solve even the simplest problems. This paper presents a new efficient CSP graph-based representation to solve CSP by using Ant Colony Optimization (ACO) algorithms. This paper presents also a new heuristic (called Oblivion Rate), that have been designed to improve the current state-of-the-art in the application of ACO algorithms on these domains. The presented graph construction provides a strong reduction in both, the number of connections and the number of nodes needed to model the CSP. Also, the new heuristic is used to reduce the number of pheromones in the system (allowing to solve problems with an increasing complexity). This new approach has been tested, as case study, using the classical N-Queens Problem. Experimental results show how the new approach works in both, reducing the complexity of the resulting CSP graph and solving problems with increasing complexity through the utilization of the Oblivion Rate.","Title: SHAMASH: An AI Tool for Modelling and Optimizing Business Processes\nYear: 2001\n\nAbstract: In this paper we describe SHAMASH, a tool for modeling and automatically optimizing Business Processes. The main features that differentiate it from most current related tools are its ability to define and use organisation standards, and functional structure, and make automatic model simulation and optimisation of them. SHAMASH is a knowledge based system, and we include a discussion on how knowledge acquisition did take place. Furthemore, we introduce a high level description of the architecture, the conceptual model, and other importants modules of the system.","Title: Confidence intervals of success rates in evolutionary computation\nYear: 2010\n\nAbstract: Success Rate (SR) is a statistic straightforward to use and interpret, however a number of non-trivial statistical issues arises when it is examinated in detail. We address some of those issues, providing evidence that suggests that SR follows a binomial density function, therefore its statistical properties are independent of the flavour of the Evolutionary Algorithm (EA) and its domain. It is fully described by the SR and the number of runs. Moreover, the binomial distribution is a well known statistical distribution with a large corpus of tools available that can be used in the context of EC research. One of those tools, confidence intervals (CIs), is studied.","Title: Statistical distribution of generation-to-success in GP: application to model accumulated success probability\nYear: 2011\n\nAbstract: Many different metrics have been defined in Genetic Programming. Depending on the experiment requirements and objectives, a collection of measures are selected in order to achieve an understanding of the algorithm behaviour. One of the most common metrics is the accumulated success probability, which evaluates the probability of an algorithm to achieve a solution in a certain generation. We propose a model of accumulated success probability composed by two parts, a binomial distribution that models the total number of success, and a lognormal approximation to the generation-to-success, that models the variation of the success probability with the generation.","Title: Predicting Performance In Team Games The Automatic Coach\nYear: 2011\n\nAbstract: A wide range of modern videogames involves a number of players collaborating to obtain a common goal. The way the players are teamed up is usually based on a measure of performance that makes players with a similar level of performance play together. We propose a novel technique based on clustering over observed behaviour in the game that seeks to exploit the particular way of playing of every player to find other players with a gameplay such that in combination will constitute a good team, in a similar way to a human coach. This paper describes the preliminary results using these techniques for the characterization of player and team behaviours. Experiments are performed in the domain of Soccerbots.","Title: Experiences using social networks in Spanish public administrations\nYear: 2011\n\nAbstract: Public administrations are developing several mechanisms to increase the reception level or the impact of the information that they communicate (i.e public policy-making or public service delivery) using for this, marketing techniques. Therefore, we may consider the concept of marketing in the public sector as the process of identifying the needs of a target audience to try to meet them, taking into account at the same time the objectives of the organization and the society. This paper aims to address this reality, regarding to the new opportunities resulting in the use of the Internet. Therefore, the purpose of this paper is to analyze how the public administrations are using Internet and, above all, the tools associated with social networks, to carry out the marketing process of their policies and public services. This study shows several experiencies to integrate the citizenry within a more collaborative information process.","Title: Using preferences to solve student-class allocation problem\nYear: 2009\n\nAbstract: Resource allocation is a form of Constraint Satisfaction Problem (CSP) in which a set of resources must be assigned to a set of agents. Multiagent Resource Allocation (MARA) makes possible solving CSP using qualitative parameters as, for example, reduce idle time in scheduling or preferences over the set of resources to be allocated. In this paper, we are going to use a MARA approach to find a solution to the student-class allocation problem, where each class has a number of seats, each student has his preferences over the schedule and the institution requests a uniform distribution of the students in classes and some other rules.","Title: Using Hierarchical Knowledge Structures to Implement Dynamic FAQ Systems\nYear: 2004\n\nAbstract: This paper presents a knowledge representation that allows building hierarchical structures that can be used to build dynamically solutions to solve a particular user question. The goal of this work is to show how it is possible to define (and handle) a new knowledge representation that allows the integration of Case-based Knowledge into a graph-based representation that can be easily learned and managed. The combination of both, Case-based Knowledge and graphs allows to implement a flexible hierarchical structures (or learning graphs) that have been applied to implement a new kind of Frequently Asked Questions Systems. In these systems the output is dynamically built from the user query, using as basis structures the knowledge retrieved from a Case Base. The paper shows how the management of these cases allow enriching the knowledge base. Finally, the paper shows a specific application of this technique, in particular a Web system named DynJAQ (Dynamic Java Asked Questions). DynJAQ is a FAQ system that is able to generate dynamically several HTML guides that can be used to answer any possible question about a particular programming language (Java).","Title: Solving Travel Problems by Integrating WEB Information with Planning\nYear: 2002\n\nAbstract: The evolution of the WEB has encouraged the development of new information gathering techniques. In order to retrieve WEB information, it is necessary to integrate different sources. Planning techniques have been used for this purpose in the field of information gathering. A plan for information gathering is the sequence of actions that specify what information sources should be accessed so that some characteristicts, like access efficiency, are optimised. MAPWEB is a multiagent framework that integrates Planning Agents and WEB Information Retrieval Agents. In MAPWEB, planning is not only used to integrate and to select information sources, but also to solve actual planning problems with information gathered from the WEB. For instance, in an travel assistant domain, plans represent the sequence of actions an user has to follow to perform his/her trip. But also, each step in the plan informs the WebAgents which information sources should be accessed. In this paper we describe MAPWEB and study experimentally two information retrieval characteristics: the average number of solutions retrieved depending on the WebAgents used and the allocated time limit, and the number of problems solved (those travel assistant problems for which at least one solution was retrieved).","Title: CAMOU: A Simple Integrated eLearning and Planning Techniques Tool\nYear: 2007\n\nAbstract: In this paper we present an educational tool which has been designed to manage (learning) knowledge acquired from the interactions with the students, and to automatically aids educators in the complex process of course design and analysis. In the tool, only some essential learning knowledge will be translated (mapped) and provided to an au- tomatic reasoning system, named ipss. This system, which integrates Ar- tificial Intelligence Planning and Scheduling, analyzes and detects prob- lems in the current tested course, providing new solutions in form of new learning designs that can be approved (or rejected) by educators.","Title: A genetic tango attack against the David-Prasad RFID ultra-lightweight authentication protocol\nYear: 2014\n\nAbstract: AbstractRadio frequency identification RFID is a powerful technology that enables wireless information storage and control in an economical way. These properties have generated a wide range of applications in different areas. Due to economic and technological constrains, RFID devices are seriously limited, having small or even tiny computational capabilities. This issue is particularly challenging from the security point of view. Security protocols in RFID environments have to deal with strong computational limitations, and classical protocols cannot be used in this context. There have been several attempts to overcome these limitations in the form of new lightweight security protocols designed to be used in very constrained sometimes called ultra-lightweight RFID environments. One of these proposals is the David-Prasad ultra-lightweight authentication protocol. This protocol was successfully attacked using a cryptanalysis technique named Tango attack. The capacity of the attack depends on a set of boolean approximations. In this paper, we present an enhanced version of the Tango attack, named Genetic Tango attack, that uses Genetic Programming to design those approximations, easing the generation of automatic cryptanalysis and improving its power compared to a manually designed attack. Experimental results are given to illustrate the effectiveness of this new attack.","Title: A knowledge-based approach for business process reengineering, SHAMASH\nYear: 2002\n\nAbstract: In this paper we present an overview of SHAMASH, a process modelling tool for business process reengineering. The main features that differentiate it from most current related tools are its ability to define and use organisation standards, and functional structure, and make automatic model simulation and optimisation of them. SHAMASH is a knowledge-based system, and we include a discussion on how knowledge acquisition did take place. Furthermore, we introduce a high level description of the architecture, the conceptual model, and other important modules of the system.","Title: A Case Study on Grammatical-Based Representation for Regular Expression Evolution\nYear: 2010\n\nAbstract: Regular expressions, or simply regex, have been widely used as a powerful pattern matching and text extractor tool through decades. Although they provide a powerful and flexible notation to define and retrieve patterns from text, the syntax and the grammatical rules of these regex notations are not easy to use, and even to understand. Any regex can be represented as a Deterministic or Non-Deterministic Finite Automata; so it is possible to design a representation to automatically build a regex, and a optimization algorithm able to find the best regex in terms of complexity. This paper introduces both, a graph-based representation for regex, and a particular heuristic-based evolutionary computing algorithm based on grammatical features from this language in a particular data extraction problem.","Title: Educational Resource Scheduling Based on Socio-inspired Agents\nYear: 2009\n\nAbstract: Scheduling a set of constrained resources is a difficult task, specially when there is no clear definition of 'optimal'. When the constraints depend not only on physical or temporal issues but also in human desires or preferences the task gets harder. This is the case of educational resources, for example when a set of students must be distributed into a limited set of laboratories to attend to periodical practical sessions, in this case weekly. The preferences of the students may vary during the process for reasons such as the number of people already in that group. This paper presents a socio inspired solution implemented as a multiagent system. The agents enroll themselves in the lab sessions based on their preferences and negotiate with other agents, using the resources they already have, to obtain desired groups that were already full.","Title: A Framework for Agent-Based Evaluation of Genetic Algorithms\nYear: 2009\n\nAbstract: Genetic Algorithms (GA) are a set of algorithms that use biological evolution as inspiration to solve search problems. One of the difficulties found when working with GA are the several parameters that have to be set and the many details that can be tunned in the GA. Usually it leads to the execution of several experiments in order to study how the GA behaves under different circumstances. In general it requires several computational resources and time to code the same algorithm with slight differences several times. In this paper we propose a framework based on agent technology able to parallelize the experiment and to split it into several components. It is complemented with a description of how this framework can be used in the evolution of regular expressions.","Title: Maximal Component Detection in Graphs Using Swarm-Based and Genetic Algorithms.\nYear: 2012\n\nAbstract: Nowadays, there is an increasing interest in the application of Collective Intelligence and Evolutive optimization algorithms for solving NP-complete problems. This is because the solution or optimization process of these type of problems requires a huge amount of resources (such as computational effort or time). Some examples of these types of problems are scheduling problems, constrained satisfaction problems, or routing problems. Collective strategies are heuristics that allow to look for new solutions in real complex problems using concepts extracted from a metaphor of social behavior of ants, bees, bacteria, flocks of birds and/or schools of fish. In this paper we propose a practical comparison between a classical Genetic-based approach and a Swarm-based strategy applied to the detection of maximal component in graphs. This work describes how these two different optimization strategies can be adapted and used to extract the different sub-graphs that contains the maximum number of nodes. Experimental results show the best results are obtained using ACO algorithm, but new strategies must be taken into account in order to improve the results.","Title: Extracting Collective Trends from Twitter Using Social-Based Data Mining.\nYear: 2013\n\nAbstract: Social Networks have become an important environment for Collective Trends extraction. The interactions amongst users provide information of their preferences and relationships. This information can be used to measure the influence of ideas, or opinions, and how they are spread within the Network. Currently, one of the most relevant and popular Social Network is Twitter. This Social Network was created to share comments and opinions. The information provided by users is specially useful in different fields and research areas such as marketing. This data is presented as short text strings containing different ideas expressed by real people. With this representation, different Data Mining and Text Mining techniques (such as classification and clustering) might be used for knowledge extraction trying to distinguish the meaning of the opinions. This work is focused on the analysis about how these techniques can interpret these opinions within the Social Network using information related to IKEA® company.","Title: Dynjaq: An Adaptive And Flexible Dynamic Faq System\nYear: 2007\n\nAbstract: This article presents a new type of Frequently Asked Questions (FAQ) System, called DynJAQ (Dynamic Java Asked Questions) that has been designed with the purpose of making learning more appealing to beginner students of engineering disciplines and overcome the inconvenience of these systems. DynJAQ is able to generate dynamically several HTML guides that can be used to answer any possible question about a particular programming language (Java), although it can be easily extended to any other topic. DynJAQ integrates case-based knowledge into a graph-based representation that can be easily learned and managed. The combination of both case-based knowledge and graphs allows it to implement a flexible hierarchical structures (or learning graphs) that have been applied to implement a new kind of Frequently Asked Questions Systems. In these systems the output is dynamically built from the user query, using as basis structures the knowledge retrieved from a Case Base. The management of these cases allows enriching the knowledge base. (c) 2007 Wiley Periodicals, Inc.","Title: A Multi-agent Traffic Simulation Framework for Evaluating the Impact of Traffic Lights.\nYear: 2011\n\nAbstract: The growing of the number of vehicles cause serious strains on road infrastructures. Traffic jams inevitably occur, wasting time and money for both cities and their drivers. To mitigate this problem, traffic simulation tools based on multiagent techniques can be used to quickly prototype potentially problematic scenarios to better understand their inherent causes. This work centers around the effects of traffic light configuration on the flow of vehicles in a road network. To do so, a Multi-Agent Traffic Simulation Framework based on Particle Swarm Optimization techniques has been designed and implemented. Experimental results from this framework show an improvement in the average speed obtained by traffic controlled by adaptive over static traffic lights.","Title: Clustering avatars behaviours from virtual worlds interactions\nYear: 2012\n\nAbstract: Virtual Worlds (VWs) platforms and applications provide a practical implementation of the Metaverse concept. These applications, as highly inmersive and interactive 3D environments, have become very popular in social networks and games domains. The existence of a set of open platforms like OpenSim or OpenCobalt have played a major role in the popularization of this technology and they open new exciting research areas. One of these areas is behaviour analysis. In virtual world, the user (or avatar) can move and interact within an artificial world with a high degree of freedom. The movements and iterations of the avatar can be monitorized, and hence this information can be analysed to obtain interesting behavioural patterns. Usually, only the information related to the avatars conversations (textual chat logs) are directly available for processing. However, these open platforms allow to capture other kind of information like the exact position of an avatar in the VW, what they are looking at (eye-gazing) or which actions they perform inside these worlds. This paper studies how this information, can be extracted, processed and later used by clustering methods to detect behaviour or group formations in the world. To detect the behavioural patterns of the avatars considered, clustering techniques have been used. These techniques, using the correct data preprocessing and modelling, can be used to automatically detect hidden patterns from data.","Title: An empirical study on the accuracy of computational effort in Genetic Programming\nYear: 2011\n\nAbstract: Some commonly used performance measures in Genetic Programming are those defined by John Koza in his first book. These measures, mainly computational effort and number of individuals to be processed, estimate the performance of the algorithm as well as the difficulty of a problem. Although Koza's performance measures have been widely used in the literature, their behaviour is not well known. In this paper we study the accuracy of these measures and advance in the understanding of the factors that influence them. In order to achieve this goal, we report an empirical study that attempts to systematically measure the effects of two variability sources in the estimation of the number of individuals to be processed and the computational effort. The results obtained in those experiments suggests that these measures, in common experimental setups, and under certain circumstances, might have a high relative error.","Title: Environmental Influence in Bio-inspired Game Level Solver Algorithms.\nYear: 2013\n\nAbstract: Bio-inspired algorithms have been widely used to solve problems in areas like heuristic search, classical optimization, or optimum configuration in complex systems. This paper studies how Genetic Algorithms (GA) and Ant Colony Optimization (ACO) algorithms can be applied to automatically solve levels in the well known Lemmings Game. The main goal of this work is to study the influence that the environment exerts over these algorithms, specially when the goal of the selected game is to save an individual (lemming) that should take into account their environment to improve their possibilities of survival. The experimental evaluations carried out reveals that the performance of the algorithm (i.e. number of paths found) is improve when the algorithm uses a small quantity of information about the environment.","Title: Variable Length-Based Genetic Representation to Automatically Evolve Wrappers\nYear: 2010\n\nAbstract: The Web has been the star service on the Internet, however the outsized information available and its decentralized nature has originated an intrinsic difficulty to locate, extract and compose information. An automatic approach is required to handle with this huge amount of data. In this paper we present a machine learning algorithm based on Genetic Algorithms which generates a set of complex wrappers, able to extract information from the Web. The paper presents the experimental evaluation of these wrappers over a set of basic data sets.","Title: Roboskeleton: An architecture for coordinating robot soccer agents\nYear: 2006\n\nAbstract: SkeletonAgent is an agent framework whose main feature is to integrate different artificial intelligent skills, like planning or learning, to obtain new behaviours in a multi-agent environment. This framework has been previously instantiated in a deliberative domain (electronic tourism), where planning was used to integrate Web information in a tourist plan. RoboSkeleton results from the instantiation of the same framework, SkeletonAgent, in a very different domain, the robot soccer. This paper shows how this architecture is used to obtain collaborative behaviours in a reactive domain. The paper describes how the different modules of the architecture for the robot soccer agents are designed, directly showing the flexibility of our framework.","Title: A Swarm Simulation Platform for Agent-Based Social Simulations.\nYear: 2011\n\nAbstract: The social sciences have long employed simulations and statistical models in their research methodology as testing and validation tools. Recent advances in complex networks have produced a set of new graph-based tools with which to study and characterize larges-scale, complex social data. We propose a swarm simulation platform that allows quick prototyping of agent-based social models, using an iterative simulation development cycle. This platform is designed to provide meaningful graph-based metrics of the social ties formed. A description of the system is given, as well as experimental results obtained from sample models.","Title: Optimal Message Interchange in a Self-organizing Multi-agent System.\nYear: 2010\n\nAbstract: Over the last decade there has been a growing interest on Intelligent Agents and Multi-Agent Systems (MAS) in several fields such as Artificial Intelligence (Al), Software Engineering, Psychology, etc... Different problems can be solved in these fields by creating societies of agents that communicate with each other. Nevertheless, when the number of agents is large and the connectivity is extensive, the system suffers from overhead in the communication among agents due to the large number messages exchanged. This work addresses the search for an optimal communication topology to avoid these situations. This optimal topology is characterized by the use of a redirecting probability in the communication. The redirection of a communication is performed before the execution of the MAS. Once agents start the execution, the topology is fixed and remains unchanged. This characteristic is useful in those systems where a given topology can not be changed as, for example, in wired networks. On the other hand, in the proposed solution agents contain a local message discrimination process as a function of the sender of the message. Experiments show an important improvement in terms of a reduction in the number of iterations needed to solve the problem and also in the number of messages exchanged.","Title: Comparative study of text clustering techniques in virtual worlds\nYear: 2013\n\nAbstract: Virt-UAM (Virtual Worlds at Universidad Autónoma de Madrid) platform allows to design and implement virtual spaces where a set of avatars can be intensively monitored using a set of tools which can be managed by an administrator. In a virtual world, the users can move and interact between them with a high degree of freedom. The movements, interactions and any other information related to the avatars conversations can be stored. Hence this data is available for processing and analysing to obtain the user behavioural patterns. Document clustering techniques have been intensively applied to automatically organize a document corpus into clusters or similar groups. The topic detection problem can be considered as a special case of document clustering, therefore, these techniques can be used over textual chat to detect clusters from the data, and then extract the conversation topics. Mahout(TM) machine learning library is an Apache(TM) project whose main goal is to build scalable machine learning libraries. This library provides a set of algorithms for data mining and for information retrieval ready to use. This paper shows a practical application of some of these available clustering mahout algorithms, in a virtual world-based scenario. These algorithms have been applied to extract the topics based on clusters obtained from the text messages. Finally, a comparative study of these document clustering algorithms used is presented.","Title: Challenges and issues of web intelligence research\nYear: 2013\n\nAbstract: The Web has an enormous influence on our everyday life. Thus, more efficient intelligent approaches and technologies are needed to make the most of the Web's unlimited potential. Intelligence can be achieved by making the Web knowing its own structures and content and by applying intelligent techniques to effectively access the resources. Semantic web was one of the significant steps towards Web Intelligence. In this editorial, we discuss some interesting challenges and issues in Web intelligence research. Also the WIMS conference's objective and activities are presented.","Title: A Decision Support System for Logistics Operations\nYear: 2010\n\nAbstract: This paper describes an Artificial Intelligence based application for a logistic company that solves the problem of grouping by zones the packages that have to be delivered and propose the routes that the drivers should follow. The tool combines from the one hand, Case-Based Reasoning techniques to separate and learn the most frequent areas or zones that the experienced logistic operators do. These techniques allow the company to separate the daily incidents that generate noise in the routes, from the decision made based on the knowledge of the route. From the other hand, we have used Evolutionary Computation to plan optimal routes from the learning areas and evaluate those routes. The application allows the users to decide under what parameters (i.e. distance, time, etc) the route should be optimized.","Title: Behaviour-based identification of student communities in virtual worlds.\nYear: 2014\n\nAbstract: Virtual Worlds (VW) have gained popularity in the last years in domains like training or education mainly due to their highly immersive and interactive 3D characteristics. In these platforms, the user (represented by an avatar) can move and interact in an artificial world with a high degree of freedom. They can talk, chat, build and design objects, program and compile their own developed programs, or move (flying, teleporting, walking or running) to different parts of the world. Although these environments provide an interesting working place for students and educators, VW platforms (such as Open Cobalt or OpenSim amongst others) rarely provide mechanisms to facilitate the automatic (or semi-automatic) behaviour analysis of users interactions. Using a VW platform called VirtUAM, the information extracted from different experiments are used to analyse and define students communities based on their behaviour. To define the individual student behaviour, different characteristics are extracted from the system, such as the avatar position (in form of GPS coordinates) and the set of actions (interactions) performed by students within the VW. Later this information is used to automatically detect behavioural patterns. This paper shows how this information can be used to group students in different communities based on their behaviour. Experimental results show how community identification can be successfully perform using K-Means algorithm and Normalized Compression Distance. Resulting communities contains users working in near places or with similar behaviours inside the virtual world.","Title: A contribution-based framework for the creation of semantically-enabled web applications\nYear: 2010\n\nAbstract: We present Fortunata, a wiki-based framework designed to simplify the creation of seman- tically-enabled web applications. This framework facilitates the management and publica- tion of semantic data in web-based applications, to the extent that application developers do not need to be skilled in client-side technologies, and promotes application reuse by fos- tering collaboration among developers by means of wiki plugins. We illustrate the use of this framework with two Fortunata-based applications named OMEMO and VPOET, and we eval- uate it with two experiments performed with usability evaluators and application develop- ers respectively. These experiments show a good balance between the usability of the applications created with this framework and the effort and skills required by developers.","Title: A Genetic Graph-Based Approach For Partitional Clustering\nYear: 2014\n\nAbstract: Clustering is one of the most versatile tools for data analysis. In the recent years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the spectral clustering (SC) algorithm, which is based on graph cut: It initially generates a similarity graph using a distance measure and then studies its graph spectrum to find the best cut. This approach is sensitive to the parameters of the metric, and a correct parameter choice is critical to the quality of the cluster. This work proposes a new algorithm, inspired by SC, that reduces the parameter dependency while maintaining the quality of the solution. The new algorithm, named genetic graph-based clustering (GGC), takes an evolutionary approach introducing a genetic algorithm (GA) to cluster the similarity graph. The experimental validation shows that GGC increases robustness of SC and has competitive performance in comparison with classical clustering methods, at least, in the synthetic and real dataset used in the experiments.","Title: Communication by identity discrimination in bio-inspired multi-agent systems\nYear: 2012\n\nAbstract: Network communications have been widely studied in the last decades in different research fields: artificial intelligence, computer science, biology, medicine and psychology among others. Some important efforts have been carried out to analyse communication features such as overhead, connectivity or communication protocols in these areas from their own perspectives. When this problem is restricted to intelligent agents or multi-agent systems, networks are built by a set of interconnected agents that can be software or hardware. In multi-agent systems, communication optimization is used to improve the overall performance of the system by reducing the information sharing (i.e. number of messages or message size) between the agents. This paper analyses a scale-free network topology of agents to solve a multi-sorting problem. The agents use their local information as well as a bio-inspired identity discrimination process to select only those messages that are relevant for each agent to solve jigsaw puzzles. We provide a comprehensive study on the influence of some essential parameters (memory information size and reconnection probability) in an agent network, and how they can be set to obtain a better performance in the system. The experiments show that this strategy contributes to reduce the number of iterations needed to solve the problem. Copyright © 2011 John Wiley & Sons, Ltd.","Title: Distributed parameter tuning for genetic algorithms\nYear: 2010\n\nAbstract: Genetic Algorithms (GA) is a family of search algorithms based on the mechanics of natural selection and biological evolution. They are able to efficiently exploit historical information in the evolution process to look for optimal solutions or approximate them for a given problem, achieving excellent performance in optimization problems that involve a large set of dependent variables. Despite the excellent results of GAs, their use may generate new problems. One of them is how to provide a good fitting in the usually large number of parameters that must be tuned to allow a good performance. This paper describes a new platform that is able to extract the Regular Expression that matches a set of examples, using a supervised learning and agent-based framework. In order to do that, GA-based agents decompose the GA execution in a distributed sequence of operations performed by them. The platform has been applied to Language induction problem, for that reason the experiments are focused on the extraction of the regular expression that matches a set of examples. Finally, the paper shows the efficiency of the proposed platform (in terms of fitness value) applied to three case studies: emails, phone numbers and URLs. Moreover, it is described how the codification of the alphabet affects to the performance of the platform.","Title: Influence Of Music Representation On Compression-Based Clustering\nYear: 2010\n\nAbstract: Multimedia Information Retrieval is currently a hot research topic due the popularity of the World Wide Web and the huge amount of multimedia data available. There exists an increasing interest to design and develop new methods and techniques to represent and classify this kind of information. Among the different sources of multimedia information currently available, we have decided to work with music audio files. Three different music representations (binary code, wave information, and SAX) have been used to study how the selection of a particular representation could affect a clustering process based on a set of similarity clusters. Two different algorithms (a hierarchical clustering method based on the quartet tree method and a genetic algorithm) have been applied to automatically perform the clustering. A compression distance, the Normalized Compression Distance (NCD), has been used to generate the similarities among the music files. This distance is parameter-free and widely applicable so we can use it directly with different formats and representations. The paper shows some experimental results using these representations and compares the behavior of both clustering methods.","Title: Reducing the Loss of Information through Annealing Text Distortion\nYear: 2011\n\nAbstract: Compression distances have been widely used in knowledge discovery and data mining. They are parameter-free, widely applicable, and very effective in several domains. However, little has been done to interpret their results or to explain their behavior. In this paper, we take a step toward understanding compression distances by performing an experimental evaluation of the impact of several kinds of information distortion on compression-based text clustering. We show how progressively removing words in such a way that the complexity of a document is slowly reduced helps the compression-based text clustering and improves its accuracy. In fact, we show how the nondistorted text clustering can be improved by means of annealing text distortion. The experimental results shown in this paper are consistent using different data sets, and different compression algorithms belonging to the most important compression families: Lempel-Ziv, Statistical and Block-Sorting.","Title: Features selection from high-dimensional web data using clustering analysis\nYear: 2012\n\nAbstract: The features selection methodologies have become an important field of the data preprocessing techniques. These methods are applied to reduced the dimension of the attributes of different datasets to simplify their analysis. Some of the classical techniques used are wrapper approaches, heuristic functions and filters. The main problem of these approaches is that they usually are black box and computationally expensive algorithms. This work presents a new straightforward strategy to reduce the dimension of the attributes. This new methodology cares about the variables distribution and has been oriented to clustering analysis. It provides an easier human interpretation of the attributes selection strategy and the resulting clusters. Finally, this new approach has been experimentally tested using the FIFA World Cup web dataset, a well-known social-based statistical data with a high number of variables, to show how the features selection strategy find the most relevant variables.","Title: Towards an automatic monitoring for higher education Learning Design\nYear: 2007\n\nAbstract: The development of new Information Technologies (IT) has originated new possibilities to design pedagogical methodologies that provide the necessary knowledge and skills in the higher education. This paper presents a metadata-based model representation that is used to represent, detect, and even automatically correct possible pitfalls in the schedule process of a Learning Design (LD) in e-learning environments. This metadata-based model is combined with Artificial Intelligence techniques, such as, planning and scheduling to monitor how is evolving a particular LD, and to propose solutions in those modules of the design that learning problems among the students have been found.","Title: Software and Performance Measures for Evaluating Multi-Agent Frameworks\nYear: 2005\n\nAbstract: Nowadays, multi-agent frameworks allow to implement very complex systems by means of agent technology. However, this complexity makes it more difficult to evaluate software and runtime characteristics of multiagent systems (MAS). Our aim is to define and study some quantitative measures to measure MAS aspects like development time, reusability, scalability, etc. These measures could be used by engineers to guide the selection among several MAS frameworks. Our study has been carried out in several MAS frameworks like JADE, JATLITE, SKELETONAGENT, and ZEUS, which have been used to build a MAS application for news retrieval.","Title: VPOET: Using a Distributed Collaborative Platform for Semantic Web Applications\nYear: 2008\n\nAbstract: This paper describes a distributed collaborative wiki-based platform that has been designed to facilitate the development of Semantic Web applications. The applications designed using this platform are able to build semantic data through the cooperation of different developers and to exploit that semantic data. The paper shows a practical case study on the application VPOET, and how an application based on Google Gadgets has been designed to test VPOET and let human users exploit the semantic data created. This practical example can be used to show how different Semantic Web technologies can be integrated into a particular Web application, and how the knowledge can be cooperatively improved.","Title: A Tool Suite to Enable Web Designers, Web Application Developers and End-users to Handle Semantic Data\nYear: 2010\n\nAbstract: Current web application development requires highly qualified staff, dealing with an extensive number of architectures and technologies. When these applications incorporate semantic data, the list of skill requirements becomes even larger, leading to a high adoption barrier for the development of semantically enabled Web applications. This paper describes VPOET, a tool focused mainly on two types of users: web designers and web application developers. By using this tool, web designers do not need specific skills in semantic web technologies to create web templates to handle semantic data. Web application developers incorporate those templates into their web applications, by means of a simple mechanism based in HTTP messages. End-users can use these templates through a Google Gadget. As web designers play a key role in the system, an experimental evaluation has been conducted, showing that VPOET provides good usability features for a representative group of web designers in a wide range of competencies in client-side technologies, ranging from amateur HTML developers to professional web designers.","Title: A plan-based tool for automatic eLearning courses redesign\nYear: 2008\n\nAbstract: In this paper we present an educational tool, named CAMOU, which has been designed to both, ac- quire (learning) knowledge from the (educational) interactions among students and educators, and aid educators in the process of course design and analysis. CAMOU integrates a system, named IPSS, which uses planning and scheduling modules as main reasoning module. IPSS is used to automatically analyse, detect and propose new solutions (as new learning designs or courses) for those problems that currently are handled by our tool. This paper shows how to deal with these knowledge using only some statistical and educational interactions, and how these knowledge can be adequately mapped in the correct repre- sentation used by IPSS. This paper shows both, the currently deployed functionalities of our eLearning tool (used mainly to acquire these information), and how it have been integrated with a planning/scheduling system to develop a complete aid-educational environment.","Title: Programming Robosoccer agents by modeling human behavior\nYear: 2009\n\nAbstract: The Robosoccer simulator is a challenging environment for artificial intelligence, where a human has to program a team of agents and introduce it into a soccer virtual environment. Most usually, Robosoccer agents are programmed by hand. In some cases, agents make use of Machine learning (ML) to adapt and predict the behavior of the opposite team, but the bulk of the agent has been preprogrammed. The main aim of this paper is to transform Robosoccer into an interactive game and let a human control a Robosoccer agent. Then ML techniques can be used to model his/her behavior from training instances generated during the play. This model will be used later to control a Robosoccer agent, thus imitating the human behavior. We have focused our research on low-level behavior, like looking for the ball, conducting the ball towards the goal, or scoring in the presence of opponent players. Results have shown that indeed, Robosoccer agents can be controlled by programs that model human play.","Title: Adaptive K-Means Algorithm For Overlapped Graph Clustering\nYear: 2012\n\nAbstract: The graph clustering problem has become highly relevant due to the growing interest of several research communities in social networks and their possible applications. Overlapped graph clustering algorithms try to find subsets of nodes that can belong to different clusters. In social network-based applications it is quite usual for a node of the network to belong to different groups, or communities, in the graph. Therefore, algorithms trying to discover, or analyze, the behavior of these networks needed to handle this feature, detecting and identifying the overlapped nodes. This paper shows a soft clustering approach based on a genetic algorithm where a new encoding is designed to achieve two main goals: first, the automatic adaptation of the number of communities that can be detected and second, the definition of several fitness functions that guide the searching process using some measures extracted from graph theory. Finally, our approach has been experimentally tested using the Eurovision contest dataset, a well-known social-based data network, to show how overlapped communities can be found using our method.","Title: Improving NCD accuracy by combining document segmentation and document distortion\nYear: 2014\n\nAbstract: Compression distances have been applied to a broad range of domains because of their parameter-free nature, wide applicability and leading efficacy. However, they have a characteristic that can be a drawback when applied under particular circumstances. Said drawback is that when they are used to compare two very different-sized objects, they do not consider them to be similar even if they are related by a substring relationship. This work focuses on addressing this issue when compression distances are used to calculate similarities between documents. The approach proposed in this paper consists of combining document segmentation and document distortion. On the one hand, it is proposed to use document segmentation to tackle the above mentioned drawback. On the other hand, it is proposed to use document distortion to help compression distances to obtain more reliable similarities. The results show that combining both techniques provides better results than not applying them or applying them separately. The said results are consistent across datasets of diverse nature.","Title: Acquisition of business intelligence from human experience in route planning\nYear: 2015\n\nAbstract: The logistic sector raises a number of highly challenging problems. Probably one of the most important ones is the shipping planning, i.e. plan the routes that the shippers have to follow to deliver the goods. In this article, we present an artificial intelligence-based solution that has been designed to help a logistic company to improve its routes planning process. In order to achieve this goal, the solution uses the knowledge acquired by the company drivers to propose optimised routes. Hence, the proposed solution gathers the experience of the drivers, processes it and optimises the delivery process. The solution uses data mining to extract knowledge from the company information systems and prepares it for analysis with a case-based reasoning (CBR) algorithm. The CBR obtains critical business intelligence knowledge from the drivers experience that is needed by the planner. The design of the routes is done by a genetic algorithm that, given the processed information, optimises the routes following several objectives, such as minimise the distance or time. Experimentation shows that the proposed approach is able to find routes that improve, on average, the routes made by the human experts.","Title: SACOC: A Spectral-Based ACO Clustering Algorithm.\nYear: 2014\n\nAbstract: The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning have been focused on clustering, where ACO-based techniques have showed a great potential. At the same time, new clustering techniques that seek the continuity of data, specially focused on spectral-based approaches in opposition to classical centroid-based approaches, have attracted an increasing research interest-an area still under study by ACO clustering techniques. This work presents a hybrid spectral-based ACO clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach combines ACOC with the spectral Laplacian to generate a new search space for the algorithm in order to obtain more promising solutions. The new algorithm, called SACOC, has been compared against well-known algorithms (K-means and Spectral Clustering) and with ACOC. The experiments measure the accuracy of the algorithm for both synthetic datasets and real-world datasets extracted from the UCI Machine Learning Repository.","Title: Online Gamers Classification Using K-means.\nYear: 2014\n\nAbstract: In order to achieve flow and increase player retention, it is important that games difficulty matches player skills. Being able to evaluate how people play a game is a crucial component for detecting gamers strategies in video-games. One of the main problems in player strategy detection is whether attributes selected to define strategies correctly detect the actions of the player. In this paper, we will study a Real Time Strategy (RTS) game. In RTS the participants make use of units and structures to secure areas of a map and/or destroy the opponents resources. We will extract real-time information about the players strategies at several gameplays through a Web Platform. After gathering enough information, the model will be evaluated in terms of unsupervised learning (concretely, K-Means). Finally, we will study the similitude between several gameplays where players use different strategies.","Title: Mixed Clustering Methods to Forecast Baseball Trends.\nYear: 2014\n\nAbstract: Sport betting has become one of the most profitable business around the world. This business generates millions of dollars every year. One of the most influenced games is Baseball. Baseball has suffered an important change after the introduction of statistical methods to tune up the team strategy. This effect, called Moneyball, started in 2002 when the team Oaklans Atletics began to choose players according to their statistics. After this successful approach, several teams decided to continue with this strategy, generating strong statistical teams. The statistical information about players and matches have acquired highly importance, creating different datasets, such as Retrosheet which collects detailed information about players, teams and matches since 1956 until today. This work pretends to generate a forecasting model for Baseball focused on the result prediction of new matches using statistical previous information. We combine time-series and clustering algorithms to generate a model which learns about the teams and matches evolution and tries to predict the final results. Even whether this model is not complete accurated, it becomes a good starting point for future models.","Title: A Survey of Social Web Mining Applications for Disease Outbreak Detection.\nYear: 2014\n\nAbstract: Social Web Media is one of the most important sources of big data to extract and acquire new knowledge. Social Networks have become an important environment where users provide information of their preferences and relationships. This information can be used to measure the influence of ideas and the society opinions in real time, being very useful on several fields and research areas such as marketing campaigns, financial prediction or public healthcare among others. Recently, the research on artificial intelligence techniques applied to develop technologies allowing monitoring web data sources for detecting public health events has emerged as a new relevant discipline called Epidemic Intelligence. Epidemic Intelligence Systems are nowadays widely used by public health organizations like monitoring mechanisms for early detection of disease outbreaks to reduce the impact of epidemics. This paper presents a survey on current data mining applications and web systems based on web data for public healthcare over the last years. It tries to take special attention to machine learning and data mining techniques and how they have been applied to these web data to extract collective knowledge from Twitter","Title: Tweetsemminer: A Meta-Topic Identification Model For Twitter Using Semantic Analysis\nYear: 2014\n\nAbstract: The information contained in Social Networks has become increasingly important over the last few years. Inside this field, Twitter is one of the main current information sources, produced by the comments and contents that their users interchange. This information is usually noisy, however, there are some hidden patterns that can be extracted such as trends, opinions, sentiments, etc. These patterns are useful to generate users communities, which can be focused, for example, on marketing campaigns. Nevertheless, the identification process is usually blind, difficulting this information extaction. Based on this idea, this work pretends to extract relevant data from Twitter. In order to achieve this goal, we have desgined a system, called TweetSemMiner, to classify user comments (or tweets) using general topics (or meta-topics). There are several works devoted to analize social networks, however, only Topic Detection techniques have been applied in this context. This paper provides a new approach to the problem of classification using semantic analysis. The system has been developed focused on the detection of a single meta-topic and uses techniques such as Latent Semantic Analysis (LSA) combined with semantic queries in DBpedia, in order to obtain some results which can be used to analyze the effectiveness of the model. We have tested the model using real users, whose comments were subsequently evaluated to check the effectiveness of this approach.","Title: Branching To Find Feasible Solutions In Unmanned Air Vehicle Mission Planning\nYear: 2014\n\nAbstract: Mission Planning is a classical problem that has been traditionally studied in several cases from Robotics to Space missions. This kind of problems can be extremely difficult in real and dynamic scenarios. This paper provides a first analysis for mission planning to Unmanned Air Vehicles (UAVs), where sensors and other equipment of UAVs to perform a task are modelled based on Temporal Constraint Satisfaction Problems (TCSPs). In this model, a set of resources and temporal constraints are designed to represent the main characteristics (task time, fuel consumption, ... ) of this kind of aircrafts. Using this simplified TCSP model, and a Branch and Bound (B&B) search algorithm, a set of feasible solutions will be found trying to minimize the fuel cost, flight time spent and the number of UAVs used in the mission. Finally, some experiments will be carried out to validate both the quality of the solutions found and the spent runtime to found them.","Title: On Interlinking Linked Data Sources By Using Ontology Matching Techniques And The Map-Reduce Framework\nYear: 2014\n\nAbstract: Interlinking different data sources has become a crucial task due to the explosion of diverse, heterogeneous information repositories in the so-called Web of Data. In this paper an approach to extract relationships between entities existing in huge Linked Data sources is presented. Our approach hinges on the Map-Reduce processing framework and context-based ontology matching techniques so as to discover the maximum number of possible relationships between entities within different data sources in an computationally efficient fashion. To this end the processing flow is composed by three Map-Reduce jobs in charge for 1) the collection of linksets between datasets; 2) context generation; and 3) construction of entity pairs and similarity computation. In order to assess the performance of the proposed scheme an exemplifying prototype is implemented between DBpedia and LinkedMDB datasets. The obtained results are promising and pave the way towards benchmarking the proposed interlinking procedure with other ontology matching systems.","Title: MACOC: A Medoid-Based ACO Clustering Algorithm.\nYear: 2014\n\nAbstract: The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning have been focused on clustering, showing great potential of ACO-based techniques. This work presents an ACO-based clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach restructures ACOC from a centroid-based technique to a medoid-based technique, where the properties of the search space are not necessarily known. Instead, it only relies on the information about the distances amongst data. The new algorithm, called MACOC, has been compared against well-known algorithms (K-means and Partition Around Medoids) and with ACOC. The experiments measure the accuracy of the algorithm for both synthetic datasets and real-world datasets extracted from the UCI Machine Learning Repository.","Title: Micro and Macro Lemmings Simulations Based on Ants Colonies.\nYear: 2014\n\nAbstract: Ant Colony Optimization (ACO) has been successfully applied to a wide number of complex and real domains. From classical optimization problems to video games, these kind of swarm-based approaches have been adapted, to be later used, to search for new metaheuristic based solutions. This paper presents a simple ACO algorithm that uses a specifically designed heuristic, called common-sense, which has been applied in the classical video game Lemmings. In this game a set of lemmings must reach the exit point of each level, using a subset of finite number of skills, taking into account the contextual information given from the level. The paper describes both the graph model and the context-based heuristic, designed to implement our ACO approach. Afterwards, two different kind of simulations have been carried out to analyse the behaviour of the ACO algorithm. On the one hand, a micro simulation, where each ant is used to model a lemming, and a macro simulation where a swarm of lemmings is represented using only one ant. Using both kind of simulations, a complete experimental comparison based on the number and quality of solutions found and the levels solved, is carried out to study the behaviour of the algorithm under different game configurations.","Title: Combining Time Series And Clustering To Extract Gamer Profile Evolution\nYear: 2014\n\nAbstract: Video-games industry is specially focused on user entertainment. It is really important for these companies to develop interactive and usable games in order to satisfy their client preferences. The main problem for the game developers is to get information about the user behaviour during the game-play. This information is important, specially nowadays, because gamers can buy new extra levels, or new games, interactively using their own consoles. Developers can use the gamer profile extracted from the game-play to create new levels, adapt the game to different user, recommend new video games and also match up users. This work tries to deal with this problem. Here, we present a new game, called \"Dream\", whose philosophy is based on the information extraction process focused on the player game-play profile and its evolution. We also present a methodology based on time series clustering to group users according to their profile evolution. This methodology has been tested with real users which have played Dream during several rounds.","Title: A new CSP graph-based representation to resource-constrained project scheduling problem\nYear: 2014\n\nAbstract: Resource-Constrained Project Scheduling Problem (RCPSP) is a NP-hard combinatorial problem that consists in scheduling different activities in such a way the resource, precedence, and temporal constraints are satisfied. The main problem when dealing with NP-hard problems is the exponential growth of the computational resources needed to solve the problems. This work is an extension of a previous one, where a new CSP graph-based representation to solve Constraint Satisfaction Problems (CSP) by using Ant Colony Optimization (ACO) were proposed. This paper studies the behaviour of the CSP graph-based representation when it is applied to a real-world complex problem, in this case the RCPSP. The dataset used in this work has been extracted from Project Scheduling Problem Library (PSPLIB). Experimental results show that the proposed approach provides excellent results, closer to the optimum values published in the PSPLIB repository. Also, it has been analysed how the number of jobs and the number of different execution modes affect the performance of the algorithm.","Title: Evolutionary clustering algorithm for community detection using graph-based information\nYear: 2014\n\nAbstract: The problem of community detection has become highly relevant due to the growing interest in social networks. The information contained in a social network is often represented as a graph. The idea of graph partitioning of graph theory can be apply to split a graph into node groups based on its topology information. In this paper the problem of detecting communities within a social network is handled applying graph clustering algorithms based on this idea. The new approach proposed is based on a genetic algorithm. A new fitness function has been designed to guide the clustering process combining different measures of network topology (Density, Centralization, Heterogeneity, Neighbourhood, Clustering Coefficient). These different network measures have been experimentally tested using a real-world social network. Experimental results show that the proposed approach is able to detect communities and the results obtained in previous work have been improved.","Title: Combining graph connectivity and genetic clustering to improve biomedical summarization\nYear: 2014\n\nAbstract: Automatic summarization is emerging as a feasible instrument to help biomedical researchers to access online literature and face information overload. The Natural Language Processing community is actively working toward the development of effective summarization applications; however, automatic summaries are sometimes less informative than the user needs. In this work, our aim is to improve a summarization graph-based process combining genetic clustering with graph connectivity information. In this way, while genetic clustering allows us to identify the different topics that are dealt with in a document, connectivity information (in particular, degree centrality) allows us to asses and exploit the relevance of the different topics. Our automatic summaries are compared with others produced by commercial and research applications, to demonstrate the appropriateness of using this combination of techniques for automatic summarization.","Title: A Co-Evolutionary Multi-Objective approach for a K-adaptive graph-based clustering algorithm\nYear: 2014\n\nAbstract: Clustering is a field of Data Mining that deals with the problem of extract knowledge from data blindly. Basically, clustering identifies similar data in a dataset and groups them in sets named clusters. The high number of clustering practical applications has made it a fertile research topic with several approaches. One recent method that is gaining popularity in the research community is Spectral Clustering (SC). It is a clustering method that builds a similarity graph and applies spectral analysis to preserve the data continuity in the cluster. This work presents a new algorithm inspired by SC algorithm, the Co-Evolutionary Multi-Objective Genetic Graph-based Clustering (CEMOG) algorithm, which is based on the Multi-Objective Genetic Graph-based Clustering (MOGGC) algorithm and extends it by introducing an adaptative number of clusters. CEMOG takes an island-model approach where each island keeps a population of candidate solutions for ki clusters. Individuals in the islands can migrate to encourage genetic diversity and the propagation of individuals around promising search regions. This new approach shows its competitive performance, compared to several classical clustering algorithms (EM, SC and K-means), through a set of experiments involving synthetic and real datasets.","Title: A Multi-Objective Genetic Graph-Based Clustering algorithm with memory optimization\nYear: 2013\n\nAbstract: Clustering is one of the most versatile tools for data analysis. Over the last few years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the Spectral Clustering algorithm, which is based on graph cut: it initially generates a Similarity Graph using a distance measure and then uses its Graph Spectrum to find the best cut. Memory consuption is a serious limitation in that algorithm: The Similarity Graph representation usually requires a very large matrix with a high memory cost. This work proposes a new algorithm, based on a previous implementation named Genetic Graph-based Clustering (GGC), that improves the memory usage while maintaining the quality of the solution. The new algorithm, called Multi-Objective Genetic Graph-based Clustering (MOGGC), uses an evolutionary approach introducing a Multi-Objective Genetic Algorithm to manage a reduced version of the Similarity Graph. The experimental validation shows that MOGGC increases the memory efficiency, maintaining and improving the GGC results in the synthetic and real datasets used in the experiments. An experimental comparison with several classical clustering methods (EM, SC and K-means) has been included to show the efficiency of the proposed algorithm.","Title: Automatic profile generation for UAV operators using a simulation-based training environment.\nYear: 2016\n\nAbstract: Unmanned aerial vehicles (UAVs) are becoming a hot topic in the last few years for several research areas, such as aeronautics or computer science. Big companies such as Airbus or Amazon aim to incorporate this technology to their current systems to improve the quality of their services while reducing the human costs. However, current UAV technology requires a strong human supervisory control and this supposes an important potential risk. Therefore, it is critical to keep track of the pilot behavior to be able to determine whether he is ready or not to operate with this technology. To deal with this problem, we have developed a methodology based on different performance metrics to automatically evaluate planning and monitoring skills of new users trained in a multi-UAV simulation environment. This methodology, based on unsupervised learning and fuzzy logic, can automatically generate a profile of future operators and use it to assess their skills.","Title: On the statistical distribution of the expected run-time in population-based search algorithms\nYear: 2015\n\nAbstract: Run-time analysis is a method that characterizes the run-time behaviour of an algorithm. It has been successfully used to analyse metaheuristics and stochastic local search algorithms. Some studies on genetic programming and related stochastic search algorithms suggested the existence of a rationale behind the run-time behaviour which could be exploited to better understand the algorithm looking at its run-time response. Under that hypothesis, this paper presents empirical evidence suggesting common statistical properties in the run-time of several types of population-based search algorithms, including genetic algorithms, genetic programming, grammatical evolution, differential evolution or particle swarm optimization. In this analysis, only the run-time of runs that were able to find a solution are considered; unsuccessful runs are not included in the analysis. The run-time to find a solution, measured by the number of evaluations, of some well-known evolutionary algorithms is empirically obtained, finding that it usually can be approximated using a lognormal distribution, with the exception of some difficult problems, which are better approximated by an exponential. We also show that the algorithm parameter settings might influence the yielding run-time statistical distribution; in particular, when there is no selective pressure, the run-time, measured as the number of evaluations to find a solution, follows a Weibull distribution instead of a lognormal one. Finally, we outline a framework using a simple theoretical discrete-time model, showing that the geometrically distributed run-time is a consequence of the lack of memory in the algorithm.","Title: An Empirical Study on Collective Intelligence Algorithms for Video Games Problem-Solving\nYear: 2015\n\nAbstract: Computational intelligence (CI), such as evolutionary computation or swarm intelligence methods, is a set of bio-inspired algorithms that have been widely used to solve problems in areas like planning, scheduling or constraint satisfaction problems. Constrained satisfaction problems (CSP) have taken an important attention from the research community due to their applicability to real problems. Any CSP problem is usually modelled as a constrained graph where the edges represent a set of restrictions that must be verified by the variables (represented as nodes in the graph) which will define the solution of the problem. This paper studies the performance of two particular CI algorithms, ant colony optimization (ACO) and genetic algorithms (GA), when dealing with graph-constrained models in video games problems. As an application domain, the \"Lemmings\" video game has been selected, where a set of lemmings must reach the exit point of each level. In order to do that, each level is represented as a graph where the edges store the allowed movements inside the world. The goal of the algorithms is to assign the best skills in each position on a particular level, to guide the lemmings to reach the exit. The paper describes how the ACO and GA algorithms have been modelled and applied to the selected video game. Finally, a complete experimental comparison between both algorithms, based on the number of solutions found and the levels solved, is analysed to study the behaviour of those algorithms in the proposed domain.","Title: Intelligent Distributed Processing Methods for Big Data.\nYear: 2015\n\nAbstract: Motivation Today, “Big Data” is a new information overloading problem in many different areas. Such areas include health cares (e.g., medical records, bioinformatics), e-sciences (e.g., physics, chemistry, and geology), and social sciences (e.g., politics). Thus, as we have various types of feasible data from a number of available sources, it is becoming increasingly more difficult to efficiently process such Big Data.","Title: Social big data: Recent achievements and new challenges\nYear: 2016\n\nAbstract: •The paper presents the methodologies on information fusion for social media.•The methodologies, frameworks, and software used to work with big data are given.•The state of the art in the data analytic techniques on social big data is provided.•Social big data applications for various domains are described and analyzed.","Title: Design and development of a lightweight multi-UAV simulator\nYear: 2015\n\nAbstract: UAVs have become enormously popular over the last few years. These new systems could make a remarkable improvement in public safety and have many applications in a variety of fields such as agriculture or postage of packages. In order to test UAV capacities and train UAV mission operators, several simulators are used, and the use of them usually entails high costs. This work presents a low-cost and easily distributable simulator, focused on simulating missions carried out by multiple UAVs and extracting data from them. To evaluate its effectiveness, it has been subjected to stress testing with thousands of virtual users, proving to have a potentially competitive response.","Title: Bio-inspired clustering: Basic features and future trends in the era of Big Data\nYear: 2015\n\nAbstract: Clustering is perhaps one of the most popular approaches used in unsupervised machine learning. There's a huge number of different methods and algorithms that have been designed in the last decades related to this “blind pattern search”, some of these approaches are based on bio-inspired methods such as Evolutionary Computation, Swarm Intelligence or Neural Networks among others. In the last years, and due to the fast growing of Big Data problems, some interesting advances and new approaches are currently being developed in this area, new algorithms like online clustering and streaming clustering are appearing. These new algorithms try to solve classical problems in Clustering and deal with the new features of these new kind of problems. This keynote lecture will provide some basics on both, Clustering methods and bio-inspired computation, and how they have been combined to improve the quality of these algorithms, to later show the main features that Big Data needs to obtain reliable clustering approaches. Finally, some practical examples and applications will be described to show how these new algorithms are evolving to be used in the near future in complex and dynamic environments.","Title: Optimizing Satisfaction in a Multi-courses Allocation Problem.\nYear: 2015\n\nAbstract: The resource allocation problem is a traditional kind of NP-hard problem. One of its application domains is the allocation of educational resources. In most universities, students select some courses they would like to attend by ranking the proposed courses. However, to ensure the quality of a course, the number of seats is limited, so not all students can enroll in their preferred courses. Therefore, the school administration needs some mechanism to assign the available resources. In this paper, the course allocation problem has been modeled as a Constraint Satisfaction Optimization Problem (CSOP) and two metrics have been defined to quantify the satisfaction of students. The problem is solved with Gecode and a greedy-based algorithm showing how the CSOP approach is able to allocate resources optimizing the students' satisfaction. Another contribution of this work is the allocation of several courses simultaneously, generating feasible solutions in a short time. The allocation procedures are based on preferences for courses defined by students, and on the administration's constraints at Ecole Hoteliere de Lausanne. Ten data sets have been generated using the distribution of students' preferences for courses, and have been used to carry out a complete experimental analysis.","Title: GAMPP: Genetic Algorithm for UAV Mission Planning Problems.\nYear: 2015\n\nAbstract: Due to the rapid development of the UAVs capabilities, these are being incorporated into many fields to perform increasingly complex tasks. Some of these tasks are becoming very important because they involve a high risk to the vehicle driver, such as detecting forest fires or rescue tasks, while using UAVs avoids risking human lives. Recent researches on artificial intelligence techniques applied to these systems provide a new degree of high-level autonomy of them. Mission planning for teams of UAVs can be defined as the planning process of locations to visit (way-points) and the vehicle actions to do (loading/dropping a load, taking videos/pictures, acquiring information), typically over a time period. Currently, UAVs are controlled remotely by human operators from ground control stations, or use rudimentary systems. This paper presents a new Genetic Algorithm for solving Mission Planning Problems (GAMPP) using a cooperative team of UAVs. The fitness function has been designed combining several measures to look for optimal solutions minimizing the fuel consumption and the mission time (or makespan). The algorithm has been experimentally tested through several missions where its complexity is incrementally modified to measure the scalability of the problem. Experimental results show that the new algorithm is able to obtain good solutions improving the runtime of a previous approach based on CSPs.","Title: Constraint-based model design for timetabling problems in secondary schools\nYear: 2015\n\nAbstract: In this paper, we propose a constraint-based model for the combination of a course timetabling problem and a course allocation problem for secondary schools in Switzerland. Course timetabling has been widely studied for Universities, but for schools, solutions have only been proposed for specific countries or even for specific schools. In fact, timetabling problems presents the difficulty to be case-specific through specific constraints satisfaction. In addition, due to a recent reform of the education system in Switzerland, the timetabling problem is combined with a course allocation problem that has an important impact on timetables. Indeed, some topics are defined by a curriculum but each student may be assigned to up to five different options depending on past grades and students choices. Both problems, timetabling and allocation must be solved simultaneously and educational objectives are related to both composition of classes and timetables of students. A description of the problem and the educational objectives to consider are presented. Finally, a complete constraint-based model based on hard and soft constraints has been designed to solve the combined problem, both types of constraints are described in detail and a qualitative complexity analysis of this model is given.","Title: A Hybrid MOGA-CSP for Multi-UAV Mission Planning\nYear: 2015\n\nAbstract: Mission Planning Problem for a large number of Unmanned Air Vehicles (UAV) consists of a set of locations to visit in different time windows, and the actions that the vehicle can perform based on its features such as the sensors, speed or fuel capacity. After formulating this problem as a Constraint Satisfaction Problem (CSP), we try to search the set of Non dominated solutions which minimize the fuel consumption and the makespan of the mission. To solve it, we will use a Multi-Objective Genetic Algorithm (MOGA), that will match the model constraints and use a multi-objective function in order to optimize these objective variables.","Title: Ephemeral Computing and Bioinspired Optimization.\nYear: 2015\n\nAbstract: Computational devices with significant computing power are pervasive yet often under-exploited since they are frequently idle or performing non-demanding tasks. Exploiting this power can be a cost-effective solution for solving complex computational tasks. Device-wise, this computational power can some times comprise a stable, long-lasting availability windows but it will more frequently take the form of brief, ephemeral bursts, mainly in the presence of devices âlentâ voluntarily by their users. A highly dynamic and volatile computational landscape emerges from the collective contribution of numerous such devices. Algorithms consciously running on these environments require specific properties in terms of flexibility, plasticity and robustness. Bioinspired algorithms are particularly well suited to this endeavor, thanks to their intrinsic features: decentralized functioning, intrinsic parallelism, resilience, and adaptiveness. The latter is essential to exert advanced self-control on the functioning and/or structure of the algorithm. Much has been done in providing self-adaptation capabilities to these techniques, yet the science of self-? bionspired algorithms is still nascent, in particular regarding to higher-level self-adaptation, and self-management in the context of large scale optimization problems and distributed ephemeral computing technologies. Deploying bioinspired techniques on this scenario will also pave the way for the application of other techniques on this computational domain.","Title: A Multi-Uav Mission Planning Videogame-Based Framework For Player Analysis\nYear: 2015\n\nAbstract: The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAVs) comprises a set of locations to visit in different time windows, and the actions that the vehicle can perform based on its features, such as sensors, speed or fuel consumption. Although this problem is increasingly more supported by Artificial Intelligence systems, nowadays human factors are still critical to guarantee the success of the designed plan. Studying and analyzing how humans solve this problem is sometimes difficult due to the complexity of the problem and the lack of data available. To overcome this problem, we have developed an analysis framework for Multi-UAV Cooperative Mission Planning Problem (MCMPP) based on a videogame that gamifies the problem and allows a player to design plans for multiple UAVs intuitively. On the other hand, we have also developed a mission planner algorithm based on Constraint Satisfaction Problems (CSPs) and solved with a Multi-Objective Branch & Bound (MOBB) method which optimizes the objective variables of the problem and gets the best solutions in the Pareto Optimal Frontier (POF). To prove the environment potential, we have performed a comparative study between the plans generated by a heterogenous group of human players and the solutions obtained by this planner.","Title: GANY: A genetic spectral-based clustering algorithm for Large Data Analysis\nYear: 2015\n\nAbstract: Recently, Data analysis is one of the most growing fields. The big amounts of data are making their analysis a really challenging area. The most relevant techniques are mainly divided in two sub-domains: Classification and Clustering. Even though Classification is currently growing and evolving, one of the promising techniques to deal with the Large Data Analysis is Clustering, because Classification needs human supervision, which makes the analysis more expensive. Clustering is a blind process used to group data by similarity. Currently, the most relevant methods are those based on manifold identification. The main idea behind these techniques is to group data using the form they define in the space. In order to achieve this goal, there are several techniques based on Spectral Analysis which deal with this problem. However, these techniques are not suitable for Large Data, due to they require a lot of memory to determine the groups. Besides, there are some problems of local minima convergence in these techniques which are common in statistical methodologies. This work is focused on combining Genetic Algorithms with spectral-based methodologies to deal with the Large Data Analysis problem. Here, we will combine the Nyström method with the Spectrum to generate an approximation of the problem to an accurate summary of the search space. Also a genetic algorithm is used to reduce the local minimum convergence problem in the new search space. The performance of this methodology has been evaluated using the accuracy with both, synthetic and real-world datasets extracted from the literature.","Title: On the Applicability of Ant Colony Optimization to Non-Intrusive Load Monitoring in Smart Grids.\nYear: 2015\n\nAbstract: Along with the proliferation of the Smart Grid, power load disaggregation is a research area that is lately gaining a lot of popularity due to the interest of energy distribution companies and customers in identifying consumption patterns towards improving the way the energy is produced and consumed via e.g. demand side management strategies. Such data can be extracted by using smart meters, but the expensive cost of incorporating a monitoring device for each appliance jeopardizes significantly the massive implementation of any straightforward approach. When resorting to a single meter to monitor the global consumption of the house at hand, the identification of the different appliances giving rise to the recorded consumption profile renders a particular instance of the so-called source separation problem, for which a number of algorithmic proposals have been reported in the literature. This paper gravitates on the applicability of the Ant Colony Optimization ACO algorithm to perform this power disaggregation treating the problem as a Constraint Satisfaction Problem CSP. The discussed experimental results utilize data contained in the REDD dataset, which corresponds to real power consumption traces of different households. Although the experiments carried out in this work reveal that the ACO solver can be successfully applied to the Non-Intrusive Load Monitoring problems, further work is needed towards assessing its performance when tackling more diverse appliance models and noisy power load traces.","Title: Medoid-based clustering using ant colony optimization.\nYear: 2016\n\nAbstract: The application of ACO-based algorithms in data mining has been growing over the last few years, and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works about unsupervised learning have focused on clustering, showing the potential of ACO-based techniques. However, there are still clustering areas that are almost unexplored using these techniques, such as medoid-based clustering. Medoid-based clustering methods are helpful—compared to classical centroid-based techniques—when centroids cannot be easily defined. This paper proposes two medoid-based ACO clustering algorithms, where the only information needed is the distance between data: one algorithm that uses an ACO procedure to determine an optimal medoid set (METACOC algorithm) and another algorithm that uses an automatic selection of the number of clusters (METACOC-K algorithm). The proposed algorithms are compared against classical clustering approaches using synthetic and real-world datasets.","Title: Dynamic Traffic Light Control System Based on Process Synchronization Among Connected Vehicles.\nYear: 2016\n\nAbstract: Vehicular traffic is tremendously increasing around the world, especially in large urban areas. The resulting congestion has become a key issue and emerging research topic to transportation specialist and decision makers. In this study, inspired by recent advanced vehicle technologies, we take into account in improving traffic flow in real-time problem. In order to solve the problem, we propose a new approach to manage traffic flow at the intersection in real-time via controlling by traffic light scheduling. In particular, the proposed method is based on process synchronization theory and connected vehicle technology where each vehicle is able to communicate with others. The traffic deadlock is also taken into consideration in case of high traffic volume. The simulation shows the potential results comparing with the existing traffic management system.","Title: Application Areas of Ephemeral Computing: A Survey.\nYear: 2016\n\nAbstract: It is increasingly common that computational devices with significant computing power are underexploited. Some of the reasons for that are due to frequent idle-time or to the low computational demand of the tasks they perform, either sporadically or in their regular duty. The exploitation of this otherwise-wasted computational power is a cost-effective solution for solving complex computational tasks. Individually device-wise, this computational power can sometimes comprise a stable, long-lasting availability window but it will more frequently take the form of brief, ephemeral bursts. Then, in this context a highly dynamic and volatile computational landscape emerges from the collective contribution of such numerous devices. Algorithms consciously running on this kind of environment require specific properties in terms of flexibility, plasticity and robustness. Bioinspired algorithms are particularly well suited to this endeavor, thanks to some of the features they inherit from their biological sources of inspiration, namely decentralized functioning, intrinsic parallelism, resilience, and adaptiveness. Deploying bioinspired techniques on this scenario, and conducting analysis and modelling of the underlying Ephemeral Computing environment will also pave the way for the application of other non-bioinspired techniques on this computational domain. Computational creativity and content generation in video games are applications areas of the foremost economical interest and are well suited to Ephemeral Computing due to their intrinsic ephemeral nature and the widespread abundance of gaming applications in all kinds of devices. In this paper, we will explain why and how they can be adapted to this new environment.","Title: Detecting discussion communities on vaccination in twitter.\nYear: 2017\n\nAbstract: Vaccines have contributed to dramatically decrease mortality from infectious diseases in the 20th century. However, several social discussion groups related to vaccines have emerged, influencing the opinion of the population about vaccination for the past 20 years. These communities discussing on vaccines have taken advantage of social media to effectively disseminate their theories. Nowadays, recent outbreaks of preventable diseases such as measles, polio, or influenza, have shown the effect of a decrease in vaccination rates. Social Networks are one of the most important sources of Big Data. Specifically, Twitter generates over 400 million tweets every day. Data mining provides the necessary algorithms and techniques to analyse massive data and to discover new knowledge. This work proposes the use of these techniques to detect and track discussion communities on vaccination arising from Social Networks. Firstly, a preliminary analysis using data from Twitter and official vaccination coverage rates is performed, showing how vaccine opinions of Twitter users can influence over vaccination decision-making. Then, algorithms for community detection are applied to discover user groups opining about vaccines. The experimental results show that these techniques can be used to discover social discussion communities providing useful information to improve immunization strategies. Public Healthcare Organizations may try to use the detection and tracking of these social communities to avoid or mitigate new outbreaks of eradicated diseases.","Title: ACO-based clustering for Ego Network analysis.\nYear: 2017\n\nAbstract: The unstoppable growth of Social Networks (SNs), and the huge number of connected users, have become these networks as one of the most popular and successful domains for a large number of research areas. The different possibilities, volume and variety that these SNs offer, has become them an essential tool for every-day working and social relationships. One of the basic features that any SN provides is to allow users to group, organize and classify their connections into different groups, or “circles”. These circles can be defined using different characteristics as roommates, workmates, hobbies, professional skills, etc. The problem of finding these circles taking into account the variety, volume and dynamics of these SNs has become an important challenge for a wide number of Computer Science areas, as Big Data, Data Mining or Machine Learning among others. Problems related to pre-processing, fusion and knowledge discovering of information from these sources are still an open question. This paper presents a new Bio-inspired method, based on Ant Colony Optimization (ACO) algorithms, that has been designed to find and analyze these circles. Given any user in a network, the new method is able to automatically determine the different users that compose his/her groups or circles of interest, so the network will be clustered into different components based on the users profiles and their dynamics. This algorithm has been applied to Ego Networks where the node centering the network (called “Ego”) represents the user being studied. In this work two different ACO algorithms, that differ in the source of information used to perform the community finding tasks, have been designed. The first ACO algorithm uses the information extracted from the topology of the network, whereas the second one uses the profile information provided by users. The proposed algorithms are able to detect the different circles in three popular Social Networks: Facebook, Twitter and Google+. Finally, and using several databases from previous SNs, an experimental evaluation of our methods has been carried out to show how the algorithms are currently working.","Title: String-based Malware Detection for Android Environments.\nYear: 2016\n\nAbstract: Android platforms are known as the less security smartphone devices. The increasing number of malicious apps published on Android markets suppose an important threat to users sensitive data, compromising more devices everyday. The commercial solutions that aims to :fight against this malware are based on signature methodologies whose detection ratio is low. Furthermore, these engines can be easily defeated by obfuscation techniques, which are extremely common in app plagiarism. This work aims to improve malware detection using only the binary information and the permissions that are normally used by the anti-virus engines, in order to provide a scalable solution based on machine learning. In order to evaluate the performance of this approach, we carry out our experiments using 5000 malware and 5000 benign-ware, and compare the results with 56 Anti-Virus Engines from VirusTotal.","Title: Studying the Influence of Static API Calls for Hiding Malware.\nYear: 2016\n\nAbstract: Malware detection has become a challenging task over the last few years. Different concealment strategies such as packing compression, polymorphic encryption and metamorphic obfuscation have produced that malware Analysts need to find more original techniques to discriminate whether a file is malware or not. One of the current benchmark techniques is static analysis of API Calls. This technique aims to detect malware using the API Calls information extracted from the malware files. In this work, we aim to show a complete study of this technique using a behavioural model, built through an evolutionary process, in order to define possible limitations. For this analysis we will use a benchmark dataset to study the discrimination between malware and benignware and evaluate how malware writers are trying to imitate benign behaviour in order to defeat this technique.","Title: A study on performance metrics and clustering methods for analyzing behavior in UAV operations.\nYear: 2017\n\nAbstract: Unmanned Aerial Vehicles (UAVs) are starting to provide new possibilities to human societies and their demand is growing according to the new industrial application fields for these revolutionary tools. The current systems are still evolving, specially from an Artificial Intelligence perspective, which is increasing the different tasks that UAVs can perform. However, the current state still requires a strong human supervision. As a consequence, a good preparation forUAVoperators is mandatory due to some of their applications might affect human safety. During the training process, it is important to measure the performance of these operators according to different factors that can help to decide what operators are more suitable for different kinds of missions creating operator profiles. Having this goal in mind, this work aims to present an extensive and robust methodology to automatically extract different performance profiles from the training process of operators in an UAV simulation environment. Our method combines the definition of a set of performance metrics with clustering techniques to define operators profiles, ensuring that the behavior discrimination is suitable and consistent.","Title: Measuring the Radicalisation Risk in Social Networks.\nYear: 2017\n\nAbstract: Social networks (SNs) have become a powerful tool for the jihadism as they serve as recruitment assets, live forums, psychological warfare, as well as sharing platforms. SNs enable vulnerable individuals to reach radicalized people, hence triggering their own radicalization process. There are many vulnerability factors linked to socio-economic and demographic conditions that make jihadist militants suitable targets for their radicalization. We focus on these vulnerability factors, studying, understanding, and identifying them on the Internet. Here, we present a set of radicalization indicators and a model to assess them using a data set of tweets published by several Islamic State of Iraq and Sham sympathizers. Results show that there is a strong correlation between the values assigned by the model to the indicators.","Title: Game theoretic approach on Real-time decision making for IoT-based traffic light control.\nYear: 2017\n\nAbstract: Smart traffic light control at intersections is 1 of the major issues in Intelligent Transportation System. In this paper, on the basis of the new emerging technologies of Internet of Things, we introduce a new approach for smart traffic light control at intersection. In particular, we firstly propose a connected intersection system where every objects such as vehicles, sensors, and traffic lights will be connected and sharing information to one another. By this way, the controller is able to collect effectively and mobility traffic flow at intersection in real-time. Secondly, we propose the optimization algorithms for traffic lights by applying algorithmic game theory. Specially, 2 game models (which are Cournot Model and Stackelberg Model) are proposed to deal with difference scenarios of traffic flow. In this regard, based on the density of vehicles, controller will make real-time decisions for the time durations of traffic lights to optimize traffic flow. To evaluate our approach, we have used Netlogo simulator, an agent-based modeling environment for designing and implementing a simple working traffic. The simulation results shows that our approach achieves potential performance with various situations of traffic flow.","Title: Improving experimental methods on success rates in evolutionary computation.\nYear: 2017\n\nAbstract: Due to the complexity of theoretical approaches in evolutionary computation (EC), research has being largely performed on experimental basis. One popular measure used by the EC community is the success rate (SR), which is used alone or as part of more complex measures such as Koza's computational effort in genetic programming. A common practice in EC is to report just a punctual estimation of the SR, without additional information about its associated uncertainty. We aim to motivate EC researchers to adopt more rigorous practices when working with SRs. In particular, we introduce the importance of correctly reporting this measure and highlight its binomial nature. Unfortunately, this fact is usually overlooked in the literature. Considering the binomiality of the SR opens the whole corpus of binomial statistics to EA research and practice. In particular, we focus on studying several methods to compute SR confidence intervals, the factors that determine their quality in terms of coverage probability and interval length. Due to its practical interest, we also briefly discuss the number of required runs to build confidence intervals with a certain quality, providing a sound method to set the number of runs, one of the most important experimental settings in EC. Evidence suggests that Wilson is, on average, a reliable and simple method to bound an estimation of SR with confidence intervals, while the standard method, which is quite popular because of its conceptual simplicity, should be avoided in any case. However, other methods can also be of interest under certain circumstances. We encourage to report the number of trials and successes, as well as the interval, to ease further comparability of the results.","Title: Identifying and ranking cultural heritage resources on geotagged social media for smart cultural tourism services.\nYear: 2017\n\nAbstract: With a large amount of data (e.g., ratings and feedbacks) obtained from social media (e.g., TripAdvisor), smart tourism applications and services have been studied to understand the contexts of users. More particularly, in this work, we have been focusing on \\\"cultural tourism\\\" service by automatically identifying and ranking cultural things (e.g., historical places). The main aims of this research are (1) to identify useful cultural heritage resources from geotagged social media (more precisely, spatial folksonomy), and (2) to rank them, with respect to the user context (e.g., location). Thus, the smart cultural tourism service can deliver smart interactions between the visitors of smart tourism environments by collecting and analyzing geotagged multimedia data (e.g., photos, tags, and comments) from available social media. In order to evaluate the proposed service, the system has been implemented with the real-world datasets related to cultural heritage sites (e.g., Hue, Hoi An, My Son in Vietnam, and Gyeongbokgung, Changdeokgung, Gyeongju in Korea).","Title: GRSAT: A Novel Method on Group Recommendation by Social Affinity and Trustworthiness.\nYear: 2017\n\nAbstract: Existing group recommender systems generate a consensus function to aggregate individual preference into group preference. However, the systems encounter difficulty in gathering rating-scores and validating their reliability, since the aggregation strategy requires user rating-scores. To solve these problems, we propose Group Recommendation based on Social Affinity and Trustworthiness GRSAT based on social affinity and trustworthiness, which is obtained from the user’s watching-history and content features, without rating-score. Our experiment proves that GRSAT has outstanding performance for group recommendation compared with the other consensus functions, in terms of the number of the movies and users, on both biased and unbiased groups.","Title: A knee point based evolutionary multi-objective optimization for mission planning problems.\nYear: 2017\n\nAbstract: The current boom of Unmanned Aerial Vehicles (UAVs) is increasing the number of potential industrial and research applications. One of the most demanded topics in this area is related to the automated planning of a UAVs swarm, controlled by one or several Ground Control Stations (GCSs). In this context, there are several variables that influence the selection of the most appropriate plan, such as the makespan, the cost or the risk of the mission. This problem can be seen as a Multi-Objective Optimization Problem (MOP). On previous approaches, the problem was modelled as a Constraint Satisfaction Problem (CSP) and solved using a Multi-Objective Genetic Algorithm (MOGA), so a Pareto Optimal Frontier (POF) was obtained. The main problem with this approach is based on the large number of obtained solutions, which hinders the selection of the best solution. This paper presents a new algorithm that has been designed to obtain the most significant solutions in the POF. This approach is based on Knee Points applied to MOGA. The new algorithm has been proved in a real scenario with different number of optimization variables, the experimental results show a significant improvement of the algorithm performance.","Title: Handling swarm of UAVs based on evolutionary multi-objective optimization.\nYear: 2017\n\nAbstract: The fast technological improvements in unmanned aerial vehicles (UAVs) has created new scenarios where a swarm of UAVs could operate in a distributed way. This swarm of vehicles needs to be controlled from a set of ground control stations, and new reliable mission planning systems, which should be able to handle the large amount of variables and constraints. This paper presents a new approach where this complex problem has been modelled as a constraint satisfaction problem (CSP), and is solved using a multi-objective genetic algorithm (MOGA). The algorithm has been designed to minimize several variables of the mission, such as the fuel consumption or the makespan among others. The designed fitness function, used by the algorithm, takes into consideration, as a weighted penalty function, the number of constraints fulfilled for each solution. Therefore, the MOGA algorithm is able to manage the number of constraints fulfilled by the selected plan, so it is possible to maximize in the elitism phase of the MOGA the quality of the solutions found. This approach allows to alleviate the computational effort carried out by the CSP solver, finding new solutions from the Pareto front, and therefore reducing the execution time to obtain a solution. In order to test the performance of this new approach 16 different mission scenarios have been designed. The experimental results show that the approach outperforms the convergence of the algorithm in terms of number of generations and runtime.","Title: Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms.\nYear: 2017\n\nAbstract: Due to recent booming of unmanned air vehicles (UAVs) technologies, these are being used in many fields involving complex tasks. Some of them involve a high risk to the vehicle driver, such as fire monitoring and rescue tasks, which make UAVs excellent for avoiding human risks. Mission planning for UAVs is the process of planning the locations and actions (loading/dropping a load, taking videos/pictures, acquiring information) for the vehicles, typically over a time period. These vehicles are controlled from ground control stations (GCSs) where human operators use rudimentary systems. This paper presents a new multi-objective genetic algorithm for solving complex mission planning problems involving a team of UAVs and a set of GCSs. A hybrid fitness function has been designed using a constraint satisfaction problem to check whether solutions are valid and Pareto-based measures to look for optimal solutions. The algorithm has been tested on several datasets, optimizing different variables of the mission, such as the makespan, the fuel consumption, and distance. Experimental results show that the new algorithm is able to obtain good solutions; however, as the problem becomes more complex, the optimal solutions also become harder to find.","Title: Real-Time Traffic Flow Management Based on Inter-Object Communication: a Case Study at Intersection.\nYear: 2017\n\nAbstract: Intersections become very congested when traffic volumes are high, creating inefficiency that results in user delay and frustration. There have been many approaches which focus on optimization signal of Traffic Light System and Vehicle Trajectory Analysis to improve traffic flow at intersection. However, to implement those approaches into reality become a challenges since real-time problem. In this study, inspired by recent advanced vehicle technologies, we propose an approach for traffic flow management at intersection. In particular, with the exploding at an enormous rate of Internet of Things (IoT), the connected object has been the most visible and familiar application. By this way, based on connected object, we design a model which communicating among objects to improve traffic flow at intersection with real time problem. Moreover, traffic congestion is also taken into consideration in case of high traffic volume. The simulation shows the potential results comparing with the existing traffic management system.","Title: Extracting Radicalisation Behavioural Patterns from Social Network Data\nYear: 2017\n\nAbstract: Social networks (SNs) have become essential communication tools in recent years, generating a large amount of information about its users that can be analysed with data processing algorithms. Recently, a new type of SN user has emerged: jihadists that use SNs as a tool to recruit new militants and share their propaganda. In this paper, we study a set of indicators to assess the risk of radicalisation of a social network user. These radicalisation indicators help law-enforcement agencies, prosecutors and organizations devoted to fight terrorism to detect vulnerable targets even before the radicalisation process is completed. Moreover, these indicators are the first steps towards a software tool to gather, represent, pre-process and analyse behavioural indicators of radicalisation in terrorism.","Title: An Initial Study on Radicalization Risk Factors: Towards an Assessment Software Tool\nYear: 2017\n\nAbstract: Radicalization has increasingly become a transnational risk as cyber-technologies and social networks have steadily improved in the past years. DAESH has utilized the benefits of these new technologies to radicalize and recruit home-grown fighters, inviting them to join their ranks in the Islamic State territories, or inciting them to attack in their Western countries. However, this process of radicalization is not a simple task. Jihadist recruiters take advantage of certain vulnerable individuals who are better targets for radicalization. Academics have subsequently attempted to identify and examine these characteristics, which can be useful in identifying people who may be vulnerable to jihadist rhetoric. Violence risk assessment is a method which has been used by psychologists, Law Enforcement Agencies, prosecutors and other relevant actors, in order to assess the risk of an individual to commit violent acts. As terrorist or political violence is not comparable with other types of violence, specialized tools are needed to calculate the risk of radicalization or attack threat. This paper tries to gather the risk factors associated with violent extremism and jihadist radicalization from the literature and the risk assessment tools already available or under development. This paper presents some details related to the RiskTrack software tool, which is currently under development by behavioral researchers and computer engineers. The RiskTrack software tool aims to automatically assess an individual's risk of becoming radicalized. This is done by analyzing social media profiles and testing for specific factors that have been found to be associated with radicalization.","Title: Comparative study of pheromone control heuristics in ACO algorithms for solving RCPSP problems.\nYear: 2017\n\nAbstract: •A thorough analysis and description of both our model and the ACO algorithm.•The Dynamic Oblivion Rate determines the percentage of pheromones to be removed.•A new Oblivion Rate heuristic based on the bio-inspired Coral Reef Optimization.•A complete analysis of different Oblivion Rates using PSPLIB dataset.","Title: Extending The Sacoc Algorithm Through The Nystrom Method For Dense Manifold Data Analysis\nYear: 2017\n\nAbstract: The growing amount of data demands new analytical methodologies to extract relevant knowledge. Clustering is one of the most competitive techniques in this context. Using a dataset as a starting point, clustering techniques blindly group the data by similarity. Among the different areas, manifold identification is currently gaining importance. Spectral-based methods, which are one of the main used methodologies, are sensitive to metric parameters and noise. In order to solve these problems, new bio-inspired techniques have been combined with different heuristics to perform the cluster selection, in particular for dense datasets, featured by areas of higher density. This paper extends a previous algorithm named spectral-based ant colony optimisation clustering (SACOC), used for manifold identification. We focus on improving it through the Nystrom extension for dealing with dense data problems. We evaluated the new approach, called SACON, comparing it against online clustering algorithms and the Nystrm extension of spectral clustering.","Title: Solving Strategy Board Games Using A Csp-Based Aco Approach\nYear: 2017\n\nAbstract: In the last years, there have been a huge increase in the number of research contributions that use games and video-games as an application domain for testing different artificial intelligence algorithms. Some of these problems can be represented as a constraint satisfaction problem (CSP), and heuristics algorithms (such as ant colony optimisation) can be used due to the complexity of the modelled problems. This paper presents a comparative study of the performance of a novel ACO model for CSP-based board games. In this work, two different oblivion rate meta-heuristics for controlling the number of pheromones created in the model have been created. Experimental results reveal that both meta-heuristics reduce considerably the number of pheromones produced in the system without affecting the quality of the solutions in terms of average optimality.","Title: Modelling Behaviour in UAV Operations Using Higher Order Double Chain Markov Models.\nYear: 2017\n\nAbstract: Creating behavioural models of human operators engaged in supervisory control tasks with UAVs is of great value due to the high cost of operator failures. Recent works in the field advocate the use of Hidden Markov Models (HMMs) and derivatives to model the operator behaviour, since they offer interpretable patterns for a domain expert and, at the same time, provide valuable predictions which can ...","Title: MOCDroid: multi-objective evolutionary classifier for Android malware detection.\nYear: 2017\n\nAbstract: Malware threats are growing, while at the same time, concealment strategies are being used to make them undetectable for current commercial antivirus. Android is one of the target architectures where these problems are specially alarming due to the wide extension of the platform in different everyday devices. The detection is specially relevant for Android markets in order to ensure that all the software they offer is clean. However, obfuscation has proven to be effective at evading the detection process. In this paper, we leverage third-party calls to bypass the effects of these concealment strategies, since they cannot be obfuscated. We combine clustering and multi-objective optimisation to generate a classifier based on specific behaviours defined by third-party call groups. The optimiser ensures that these groups are related to malicious or benign behaviours cleaning any non-discriminative pattern. This tool, named MOCDroid, achieves an accuracy of 95.15 % in test with 1.69 % of false positives with real apps extracted from the wild, overcoming all commercial antivirus engines from VirusTotal.","Title: Can an Automatic Tool Assess Risk of Radicalization Online? A Case Study on Facebook\nYear: 2017\n\nAbstract: Can risk of radicalization be measured analyzing users' information on the Internet? Following the background from recent studies [1], [2], [3], [4], this work aims to test the viability of detecting and measuring risk factors of jihadist radicalization with a Facebook case. The work has been performed under the frame of RiskTrack project, which aim is to develop an automatic risk assessment tool for jihadist radicalization, using data from online social networks profiles.","Title: Wind Power Production Forecasting Using Ant Colony Optimization and Extreme Learning Machines.\nYear: 2017\n\nAbstract: Nowadays the energy generation strategy of almost every nation around the world relies on a strong contribution from renewable energy sources. In certain countries the relevance taken by wind energy is particularly high within its national production share, mainly due to its large-scale wind flow patterns. This noted potentiality of wind energy has so far attracted public and private funds to support the development of advanced wind energy technologies. However, the proliferation of wind farms makes it challenging to achieve a proper electricity balance of the grid, a problem that becomes further involved due to the fluctuations of wind generation that occur at different time scales. Therefore, acquiring a predictive insight on the variability of this renewable energy source becomes essential in order to optimally inject the produced wind energy into the electricity grid. To this end the present work elaborates on a hybrid predictive model for wind power production forecasting based on meteorological data collected at different locations over the area where a wind farm is located. The proposed method hybridizes Extreme Learning Machines with a feature selection wrapper that models the discovery of the optimum subset of predictors as a metric-based search for the optimum path through a solution graph efficiently tackled via Ant Colony Optimization. Results obtained by our approach for two real wind farms in Zamora and Galicia (Spain) are presented and discussed, from which we conclude that the proposed hybrid model is able to efficiently reduce the number of input features and enhance the overall model performance.","Title: Multiple classifiers in biometrics. part 1: Fundamentals and review.\nYear: 2018\n\nAbstract: •Fundamentals of Multiple Classifier Systems with nomenclature, methods and theory.•Review of MCS applied to multimodal biometrics for person recognition.•Generality enables the export of MCS methods from biometrics to other areas.","Title: Multiple classifiers in biometrics. Part 2: Trends and challenges.\nYear: 2018\n\nAbstract: •Recent trends in Multiple Classifier Systems from the field of biometrics.•Information fusion methods incorporating context information in an adaptive way.•Information from context: quality measures and user-specific behaviors.•Challenges in biometrics: nature, robustness, integration, usability, and security.•Role of Multiple Classifier Systems in biometrics challenges.","Title: EvoDeep: A new evolutionary approach for automatic Deep Neural Networks parametrisation.\nYear: 2018\n\nAbstract: Deep Neural Networks (DNN) have become a powerful, and extremely popular mechanism, which has been widely used to solve problems of varied complexity, due to their ability to make models fitted to non-linear complex problems. Despite its well-known benefits, DNNs are complex learning models whose parametrisationand architecture are made usually by hand. This paper proposes a new Evolutionary Algorithm, named EvoDeep, devoted to evolve the parameters and the architecture of a DNN in order to maximise its classification accuracy, as well as maintaining a valid sequence of layers. This model is tested against a widely used dataset of handwritten digits images. The experiments performed using this dataset show that the Evolutionary Algorithm is able to select the parameters and the DNN architecture appropriately, achieving a 98.93% accuracy in the best run.","Title: An Ontology-Based Approach for Mining Radicalization Indicators from Online Messages\nYear: 2018\n\nAbstract: Detecting radicalization on social networks is crucial to the fight against violent extremism and terrorism. In most cases, online radicalization has clear warning indicators that can be detected at the early stages of the radicalization process. In this paper, we focus on mining radicalization indicators from online messages by exploiting structured domain knowledge. More precisely, we propose an approach to automatically annotate social messages with concepts from a domain ontology. Annotations are then exploited within an inference phase to identify the messages exhibiting a radicalization indicator. We conducted a set of experiments on a sample extracted from a public dataset that contains radicalized individuals along with their social messages (i.e. Tweets). Obtained results show the effectiveness of our approach compared to a baseline method.","Title: From ephemeral computing to deep bioinspired algorithms: New trends and applications.\nYear: 2018\n\nAbstract: Ephemeral computing is a term that describes computing systems whose nodes or their connectivity have an ephemeral, heterogeneous and possibly also unpredictable nature. These properties will affect the functioning of distributed versions of computer algorithms. Such algorithms, which are usually straightforward extensions of sequential algorithms, will have to be redesigned and, in many cases, rethought from the ground up, to be able to use all ephemerally available resources. Porting algorithms to an inherently ephemeral, unreliable and massively heterogeneous computing substrate is thus one of the main challenges in the ephemeral computing field. Algorithms adapted so that they can be consciously running on this kind of environments require specific properties in terms of flexibility, plasticity and robustness. Bioinspired algorithms are particularly well suited to this endeavor, thanks to their decentralized functioning, intrinsic parallelism, resilience, adaptiveness, and amenability for being endowed with algorithmic components dealing with both the massive complexity of the computational substrate and that of the problem being tackled. Arranging these components and functionalities in a collection of algorithmic strata results in deep architectures, whereby different layers of optimization are organized into loosely-coupled hierarchies that not only are able to use ephemeral computing environments, but also profit from them by making adaptivity and diversity maintenance features of the algorithm. Moreover, the synergies that arise when these massively heterogeneous computing resources are made available to deep versions of bioinspired algorithms may enable hard real-world problems and applications to be successfully faced in the Big Data context (including but not limited to social data analysis) as well as problems in the areas of computational creativity or computer gaming.","Title: CANDYMAN: Classifying Android malware families by modelling dynamic traces with Markov chains.\nYear: 2018\n\nAbstract: Malware writers are usually focused on those platforms which are most used among common users, with the aim of attacking as many devices as possible. Due to this reason, Android has been heavily attacked for years. Efforts dedicated to combat Android malware are mainly concentrated on detection, in order to prevent malicious software to be installed in a target device. However, it is equally important to put effort into an automatic classification of the type, or family, of a malware sample, in order to establish which actions are necessary to mitigate the damage caused. In this paper, we present CANDYMAN, a tool that classifies Android malware families by combining dynamic analysis and Markov chains. A dynamic analysis process allows to extract representative information of a malware sample, in form of a sequence of states, while a Markov chain allows to model the transition probabilities between the states of the sequence, which will be used as features in the classification process. The space of features built is used to train classical Machine Learning, including methods for imbalanced learning, and Deep Learning algorithms, over a dataset of malware samples from different families, in order to evaluate the proposed method. Using a collection of 5,560 malware samples grouped into 179 different families (extracted from the Drebin dataset), and once made a selection based on a minimum number of relevant and valid samples, a final set of 4,442 samples grouped into 24 different malware families was used. The experimental results indicate a precision performance of 81.8% over this dataset.","Title: A Multi-Objective Genetic Algorithm for overlapping community detection based on edge encoding.\nYear: 2018\n\nAbstract: The Community Detection Problem (CDP) in Social Networks has been widely studied from different areas such as Data Mining, Graph Theory Physics, or Social Network Analysis, among others. This problem tries to divide a graph into different groups of nodes (communities), according to the graph topology. A partition is a division of the graph where each node belongs to only one community. However, a common feature observed in real-world networks is the existence of overlapping communities, where a given node can belong to more than one community. This paper presents a new Multi-Objective Genetic Algorithm (MOGA-OCD) designed to detect overlapping communities, by using measures related to the network connectivity. For this purpose, the proposed algorithm uses a phenotype-type encoding based on the edge information, and a new fitness function focused on optimizing two classical objectives in CDP: the first one is used to maximize the internal connectivity of the communities, whereas the second one is used to minimize the external connections to the rest of the graph. To select the most appropriate metrics for these objectives, a comparative assessment of several connectivity metrics has been carried out using real-world networks. Finally, the algorithm has been evaluated against other well-known algorithms from the state of the art in CDP. The experimental results show that the proposed approach improves overall the accuracy and quality of alternative methods in CDP, showing its effectiveness as a new powerful algorithm for detecting structured overlapping communities.","Title: Consensual Negotiation-Based Decision Making for Connected Appliances in Smart Home Management Systems.\nYear: 2018\n\nAbstract: Recently, the concept of Internet of Agent has been introduced as a potential technology that pushes intelligence, data processing, analytics and communication capabilities down to the point where the data originates. In this paper, we introduce a novel approach for a Decentralized Home Energy Management System by applying the Internet of Agent concept. In particular, we first present an Internet of Agent framework in terms of sensing, communicating and collaborating among connected appliances. Then, the decentralized management based on consensual negotiation mechanism with several intelligent techniques are proposed for dynamic scheduling connected appliance. Specifically, by applying the Internet of Agent framework, connected appliances are regarded as smart agents that are able to make individual decisions by reaching agreement over the exchange of operations on competitive resources. Furthermore, in this study, the load balancing problem in which load shifting is able to reduce the electricity demand during peak hours is taken into account in order to emphasize the effectiveness of our approach. For the experiment, we develop a simulation of smart home environment to evaluate our approach using NetLogo, a tool which provides real-time analysis in the modeling and simulation domain of complex systems.","Title: Extending QGroundControl for Automated Mission Planning of UAVs.\nYear: 2018\n\nAbstract: Unmanned Aerial Vehicles (UAVs) have become very popular in the last decade due to some advantages such as strong terrain adaptation, low cost, zero casualties, and so on. One of the most interesting advances in this field is the automation of mission planning (task allocation) and real-time replanning, which are highly useful to increase the autonomy of the vehicle and reduce the operator workload. These automated mission planning and replanning systems require a Human Computer Interface (HCI) that facilitates the visualization and selection of plans that will be executed by the vehicles. In addition, most missions should be assessed before their real-life execution. This paper extends QGroundControl, an open-source simulation environment for flight control of multiple vehicles, by adding a mission designer that permits the operator to build complex missions with tasks and other scenario items; an interface for automated mission planning and replanning, which works as a test bed for different algorithms, and a Decision Support System (DSS) that helps the operator in the selection of the plan. In this work, a complete guide of these systems and some practical use cases are provided.","Title: A Genetic Algorithm with Local Search Based on Label Propagation for Detecting Dynamic Communities.\nYear: 2018\n\nAbstract: The interest in community detection problems on networks that evolves over time have experienced an increasing attention over the last years. Genetic Algorithms, and other bio-inspired methods, have been successfully applied to tackle the community finding problem in static networks. However, few research works have been done related to the improvement of these algorithms for temporal domains. This paper is focused on the design, implementation, and empirical analysis of a new Genetic Algorithm pair with a local search operator based on Label Propagation to identify communities on dynamic networks.","Title: A discrete water cycle algorithm for solving the symmetric and asymmetric traveling salesman problem.\nYear: 2018\n\nAbstract: •An improved discrete water cycle algorithm is presented for the TSP and ATSP.•This version includes inclination feature, enhancing exploration and exploitation.•33 datasets of the TSP/ATSP have been used for the experimentation.•Results have been compared with six different techniques.•Friedman's and Holm's post hoc statistical tests have been conducted.","Title: An In-Depth Study Of The Jisut Family Of Android Ransomware\nYear: 2018\n\nAbstract: Android malware is increasing in spread and complexity. Advanced obfuscation, emulation detection, delayed payload activation or dynamic code loading are some of the techniques employed by the current malware to hinder the use of reverse engineering techniques and anti-malware tools. This growing complexity is particularly noticeable in the evolution of different strands of the same malware family. Over the years, these families mature to become more effective by incorporating new and enhanced techniques. In this paper, we focus on a particular Android ransomware family named Jisut, and perform a thorough technical analysis. We also provide a detailed overall perspective, which will hopefully help to create new tools and techniques to tackle more effectively the threat posed by ransomware.","Title: A taxonomy and state of the art revision on affective games.\nYear: 2019\n\nAbstract: Affective games are a sub-field of affective computing that tries to study how to design videogames that are able to react to the emotions expressed by the player, as well as provoking desired emotions to them. To achieve those goals it is necessary to research on how to measure and detect human emotions using a computer, and how to adapt videogames to the perceived emotions to finally provoke them to the players. This work presents a taxonomy for research on affective games centring on the aforementioned issues. Here we devise as well a revision of the most relevant published works known to the authors on this area. Finally, we analyse and discuss which important research problem are yet open and might be tackled by future investigations in the area of affective games.","Title: Constrained multi-objective optimization for multi-UAV planning\nYear: 2019\n\nAbstract: Over the last decade, developments in \nunmanned aerial vehicles (UAVs) has greatly increased, and they are being used in many fields including surveillance, crisis management or automated mission planning. This last field implies the search of plans for missions with multiple tasks, UAVs and ground control stations; and the optimization of several objectives, including makespan, fuel consumption or cost, among others. In this work, this problem has been solved using a multi-objective evolutionary algorithm combined with a constraint satisfaction problem model, which is used in the fitness function of the algorithm. The algorithm has been tested on several missions of increasing complexity, and the computational complexity of the different element considered in the missions has been studied.","Title: Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning.\nYear: 2019\n\nAbstract: Management and mission planning over a swarm of unmanned aerial vehicle (UAV) remains to date as a challenging research trend in what regards to this particular type of aircrafts. These vehicles are controlled by a number of ground control station (GCS), from which they are commanded to cooperatively perform different tasks in specific geographic areas of interest. Mathematically the problem of coordinating and assigning tasks to a swarm of UAV can be modeled as a constraint satisfaction problem, whose complexity and multiple conflicting criteria has hitherto motivated the adoption of multi-objective solvers such as multi-objective evolutionary algorithm (MOEA). The encoding approach consists of different alleles representing the decision variables, whereas the fitness function checks that all constraints are fulfilled, minimizing the optimization criteria of the problem. In problems of high complexity involving several tasks, UAV and GCS, where the space of search is huge compared to the space of valid solutions, the convergence rate of the algorithm increases significantly. To overcome this issue, this work proposes a weighted random generator for the creation and mutation of new individuals. The main objective of this work is to reduce the convergence rate of the MOEA solver for multi-UAV mission planning using weighted random strategies that focus the search on potentially better regions of the solution space. Extensive experimental results over a diverse range of scenarios evince the benefits of the proposed approach, which notably improves this convergence rate with respect to a naïve MOEA approach.","Title: Statistical analysis of risk assessment factors and metrics to evaluate radicalisation in Twitter.\nYear: 2019\n\nAbstract: Nowadays, Social Networks have become an essential communication tools producing a large amount of information about their users and their interactions, which can be analysed with Data Mining methods. In the last years, Social Networks are being used to radicalise people. In this paper, we study the performance of a set of indicators and their respective metrics, devoted to assess the risk of radicalisation of a precise individual on three different datasets. Keyword-based metrics, even though depending on the written language, performs well when measuring frustration, perception of discrimination as well as declaration of negative and positive ideas about Western society and Jihadism, respectively. However, metrics based on frequent habits such as writing ellipses are not well enough to characterise a user in risk of radicalisation. The paper presents a detailed description of both, the set of indicators used to assess the radicalisation in Social Networks and the set of datasets used to evaluate them. Finally, an experimental study over these datasets are carried out to evaluate the performance of the metrics considered.","Title: A genetic graph-based clustering algorithm\nYear: 2012\n\nAbstract: The interest in the analysis and study of clustering techniques have grown since the introduction of new algorithms based on the continuity of the data, where problems related to image segmentation and tracking, amongst others, makes difficult the correct classification of data into their appropriate groups, or clusters. Some new techniques, such as Spectral Clustering (SC), uses graph theory to generate the clusters through the spectrum of the graph created by a similarity function applied to the elements of the database. The approach taken by SC allows to handle the problem of data continuity though the graph representation. Based on this idea, this study uses genetic algorithms to select the groups using the same similarity graph built by the Spectral Clustering method. The main contribution is to create a new algorithm which improves the robustness of the Spectral Clustering algorithm reducing the dependency of the similarity metric parameters that currently affects to the performance of SC approaches. This algorithm, named Genetic Graph-based Clustering (GGC), has been tested with different synthetic and real-world datasets, the experimental results have been compared against classical clustering algorithms like K-Means, EM and SC.","Title: Optimizing satisfaction in a multi-courses allocation problem combined with a timetabling problem.\nYear: 2017\n\nAbstract: The resource allocation problem and the timetabling problem are traditional kinds of NP-hard problems. Both problems can be found in universities where students can select courses they would like to attend before or after the timetabling is done. When demand exceeds capacity, the universities may allocate the available seats independently from the timetabling, but students may have then to decide which courses they are going to attend because of clashes in their timetable. To avoid this situation, some universities prepare their timetable considering students selection. In addition to that, students may submit preferences over courses, and the school administration has to assign seats and do the timetable considering both preferences and clashes. In this paper, both problems, seats allocation and timetabling, have been modeled separately and combined as constraint satisfaction optimization problems (CSOP). Two algorithms have been designed and implemented to find a solution to both problems simultaneously maximizing the satisfaction of students using a CSOP solver and an Ant colony optimization algorithm for the timetabling problem. The results of both algorithms are then compared. The allocation and timetabling procedures are based on preferences for courses defined by students, and on the administration’s constraints at Ecole Hôtelière de Lausanne. Three real data sets have been used to carry out a complete experimental analysis. High-quality solutions are obtained in a few minutes with both approaches; those solutions are currently used at Ecole Hôtelière de Lausanne.","Title: Analysing temporal performance profiles of UAV operators using time series clustering.\nYear: 2017\n\nAbstract: •A methodology for analysing UAV operators based on their performance evolution.•Time series clustering to extract the most representative operator temporal profiles.•Quantitative evaluation using human judgement-based ground truth data.•Experimentation carried out in a lightweight multi-UAV simulation environment.•The resulting profiles contain distinct and informative behavioural patterns.","Title: Automatic Procedure Following Evaluation Using Petri Net-Based Workflows.\nYear: 2018\n\nAbstract: An operating procedure (OP), also known as checklist or action plan, is a list of actions or criteria arranged in a systematic way, commonly used in areas such as aviation or healthcare to ensure the success of critical tasks and to help decrease human errors. In these areas, operators are hardly trained to follow the OP carefully, but the evaluation of how they are following, it is usually perfor...","Title: Bioinspired Algorithms in Complex Ephemeral Environments.\nYear: 2018\n\nAbstract: The concept of Ephemeral Computing is an emergent topic that is currently consolidating among the research community. It includes computing systems where the nodes or the connectivity have an ephemeral and thus unpredictable nature. Although the capacity and computer power of small and medium devices (as smartphones or tablets) are increasing swiftly, their computing capacities are usually underexploited. The availability of highly-volatile heterogeneous computer resources capable of running software agents requires suitable algorithms to make a proper use of the available resources while circumventing the potential problems that may produce such non-reliable systems. Due to the non-reliable nature of the system where the algorithms under consideration should run, they have to be ephemerality-aware, having the self-capability for understanding this kind of environments and adapt to them by means of exibility, plasticity and robustness. Because of their decentralized functioning, intrinsic parallelism, resilience, and adaptiveness, bioinspired algorithms suit well to this endeavour.","Title: Design Of A Software System To Support Value Education In Sports Through Gamification Techniques\nYear: 2019\n\nAbstract: Nowadays, it is quite common to find violent acts in grassroot sports, such as football. Almost every week, it is possible to find news about team supporters fighting against each other, or football players arguing aggressively to the referee. And the worst part in this story is that most of these acts are watched by children. In order to alleviate this situation and create awareness of the necessity to create educational programs to prevent violence in sports, the European Union has funded several projects focused on this area. One of this project is called SAVEit project, and its goal is to create and develop innovative educational tools to promote values in grassroot sports. This paper presents the software architecture designed in SAVEit project to achieve this goal. This architecture is mainly composed of a Learning Management System, where coaches will learn about the values; a Team Management Site, where coaches can evaluate the values acquired by the children of the teams; and finally, a Video Game that using gamification techniques will keep the motivation of children during the learning process.","Title: Deep learning for EEG data analytics: A survey: Deep learning for EEG data analytics: A survey\nYear: 2020\n\nAbstract: In this work, we conducted a literature review about deep learning (DNN, RNN, CNN, and so on) for analyzing EEG data for decoding the activity of human's brain and diagnosing disease and explained details about various architectures for understanding the details of CNN and RNN. It has analyzed a word, which presented a model based on CNN and LSTM methods, and how these methods can be used to both optimize and set up the hyper parameters of deep learning architecture. Later, it is studied how semi-supervised learning on EEG data analytics can be applied. We review some studies about different methods of semi-supervised learning on EEG data analytics and discussing the importance of semi-supervised learning for analyzing EEG data. In this paper, we also discuss the most common applications for human EEG research and review some papers about the application of EEG data analytics such as Neuromarketing, human factors, social interaction, and BCI. Finally, some future trends of development and research in this area, according to the theoretical background on deep learning, are given.","Title: Bio-inspired computation: Where we stand and what's next\nYear: 2019\n\nAbstract: In recent years, the research community has witnessed an explosion of literature dealing with the mimicking of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques.","Title: Android malware detection through hybrid features fusion and ensemble classifiers: The AndroPyTool framework and the OmniDroid dataset\nYear: 2019\n\nAbstract: •An automated framework for dynamic and static analysis of Android applications.•A large dataset of static and dynamic features extracted from Android applications.•Evaluation of the dataset through a wide set of ensemble classifiers.•Malware detection using fusion of features and a voting based classifier.•Results show feasibility and the potential use of the proposed tool and dataset.","Title: Combining bio-inspired meta-heuristics and novelty search for community detection over evolving graph streams.\nYear: 2019\n\nAbstract: Finding communities of interrelated nodes is a learning task that often holds in problems that can be modeled as a graph. In any case, detecting an optimal partition in a graph is highly time-consuming and complex. For this reason, the implementation of search-based metaheuristics arises as an alternative for addressing these problems. This manuscript focuses on optimally partitioning dynamic network instances, in which the connections between vertices change dynamically along time. Specifically, the application of Novelty Search mechanism for solving the problem of finding communities in dynamic networks is studied in this paper. For this goal, this procedure has been embedded in the search process undertaken by three different bio-inspired meta-heuristic schemes: Bat Algorithm, Firefly Algorithm and Particle Swarm Optimization. All these methods have been properly adapted for dealing with this discrete and dynamic problem, using a reformulated expression of the modularity coefficient as its fitness function. A thorough experimentation has been conducted using a benchmark composed by 12 synthetically created instances, with the main objective of analyzing the performance of the proposed Novelty Search mechanism when facing this problem. In light of the outperforming behavior of our approach and its relevance dictated by two different statistical tests, we conclude that Novelty Search is a promising procedure for finding communities in evolving graph data.","Title: FacTeR-Check: Semi-automated fact-checking through semantic similarity and natural language inference\nYear: 2022\n\nAbstract: Our society produces and shares overwhelming amounts of information through Online Social Networks (OSNs). Within this environment, misinformation and disinformation have proliferated, becoming a public safety concern in most countries. Allowing the public and professionals to efficiently find reliable evidence about the factual veracity of a claim is a crucial step to mitigate this harmful spread. To this end, we propose FacTeR-Check, a multilingual architecture for semi-automated fact-checking and hoaxes propagation analysis that can be used to implement applications designed for both the general public and for fact-checking organisations. FacTeR-Check implements three different modules relying on the XLM-RoBERTa Transformer architecture to evaluate semantic similarity, to calculate natural language inference and to build search queries through automatic keywords extraction and Named-Entity Recognition. The three modules have been validated using state-of-the-art benchmark datasets, exhibiting good performance in all of them. Besides, FacTeR-Check is employed to collect and label a dataset, called NLI19-SP, composed of more than 40,000 tweets supporting or denying 60 hoaxes related to COVID-19, released publicly. Finally, an analysis of the data collected in this dataset is provided, which allows to obtain a deep insight of how disinformation operated during the COVID-19 pandemic in Spanish-speaking countries.","Title: Memetic Coral Reefs Optimization Algorithms For Optimal Geometrical Design Of Submerged Arches\nYear: 2021\n\nAbstract: This paper deals with the geometrically nonlinear analysis of submerged arches by means of memetic Coral Reefs Optimization algorithms. The classic design of submerged arches is only focused on calculating the bend-ing stress-less shape (funicular shape) of the structure. Nevertheless, recent works show that this funicular shape can be approached by using a parametric family curve, which also allows a multi-variable optimization of the arch's geometry. Using this novel parametric set of curves, we propose a new Coral Reefs Optimization (CRO) algorithm based on a memetic approach to tackle the geometrically nonlinear design of submerged arches. Specif-ically, the proposed CRO approaches have been tested with different search procedures as exploration operators, and we also test a multi-method version of the algorithm, the Coral Reefs Optimization with Substrate Layers (CRO-SL), which considers several search procedures within the same evolutionary population. A local search to improve the solutions has been considered in all cases, to obtain powerful memetic operators for this problem. It is also shown how the different memetic versions of the CRO (specially those involving multi-methods and Dif-ferential Evolution search procedures), together with the parametric encoding, are able to obtain nearly-optimal geometries for underwater installations. The performance of the proposed algorithm has been compared with state-of-the-art algorithms for optimization: L-SHADE and HCLPSO. Statistical tests have carried out with the aim of comparing the results. It is shown that there is not significant differences between the proposed results by the three algorithms.","Title: An Effective Approach for Rumor Detection of Arabic Tweets Using eXtreme Gradient Boosting Method\nYear: 2022\n\nAbstract: AbstractTwitter is currently one of the most popular microblogging platforms allowing people to post short messages, news, thoughts, and so on. The Twitter user community is growing very fast. It has an average of 328 million active accounts today, making it one of the most common media for getting information during any influential or important event. Because it is freely used by the public, some credibility checking is required, especially when it comes to events of high importance. Automatic rumor detection in Arabic tweets is a challenging task due to the changes in the structural and morphological nature of the Arabic language, which makes the detection of rumors more difficult than in other languages. In this article, we proposed an effective approach for rumor detection of Arabic tweets using an eXtreme gradient boosting (XGBoost) classifier. We conducted a set of experiments on a public dataset that contained a large number of rumor and non-rumor tweets. The model uses a comprehensive set of features, including content-based, user-based, and topic-based features, allowing one to look at credibility from different angles. The experimental results demonstrated that the proposed XGBoost-based approach achieves 97.18% accuracy on 60% of the dataset as a training set, which is the highest accuracy rate compared with the other methods used in recent related work.","Title: YoungRes: A Serious Game-Based Intervention to Increase Youngsters Resilience Against Extremist Ideologies\nYear: 2022\n\nAbstract: Extremist ideologies are proliferating nowadays in both political and social levels. Considering that youngsters are in a development stage where they are still conforming their own social identity, they become especially vulnerable to these ideologies' influence. Therefore, it becomes critical to provide them with the psychological skills to rationalize and resist those influences. Video games, which are already a technology commonly consumed by these generations, provide a way to motivate and engage youngsters. Therefore, implementing these video games in interventions to increase psychological resilience represents an opportunity to create an innovative learning approach. Following this motivation, this paper has three main objectives: adapting a traditional emotional intelligence training program to a novel serious game based intervention, called YoungRes; providing a metric to measure the student' evolution based on in-game behavioural patterns, instead of indirect measures; and evaluating the impact of the intervention itself after its implementation. To do so, an 11 sessions intervention was applied to 36 students from two primary schools in Spain. Quantitative and qualitative data was extracted from the experience, consisting on data extracted from the player's behaviour and a final survey. A detailed statistical analysis carried out showed two main outcomes: first, the serious game based intervention was very appreciated by the students, specially by those who frequently play video games; second, the intervention allowed to improve several emotional intelligence competences, such as active listening and controlled breathing, as well as to promote knowledge about the Islamic culture. Finally, the authors discussed about how the game could be improved for future applications in schools.","Title: Addressing Evolutionary-Based Dynamic Problems: A New Methodology for Evaluating Immigrants Strategies in MOGAs\nYear: 2022\n\nAbstract: Multi-Objective Genetic Algorithms (MOGAs) have been successfully used to address dynamic problems in a wide variety of domains. In these domains, data changes over time, so a non-static analysis is required to obtain feasible solutions. In this type of environments, MOGAs are often time-consuming and require special adaptation to work properly. A number of different techniques have been proposed to adapt MOGAs to dynamic environments for tackling the previous problems such as hypermutation, memory and immigrant schemes or multi-population methods, among others. In particular, immigrant strategies are one of the most commonly used methods, for that reason, this work proposes a new methodology that allows to make a detailed evaluation of their performance when these strategies are used. The proposed methodology works on two levels, a coarse-grain one and a fine-grain one. In the former, an overall evaluation of the different immigrant strategies is made based on three different dimensions: Quality, Stability and Speed. In the latter, a detailed study of the status of the immigrant individuals during the evolution of the algorithm is carried out. This is a very relevant aspect to take into account in order to evaluate whether an immigrant strategy is working properly or not. To deploy this methodology, a new visualization technique for population mixing analysis is proposed in this work. In order to validate the proposed methodology, a test case in the context of the Dynamic Community Detection problem (DCD) has been selected using a MOGA that applies several different immigrant schemes, showing both how the methodology works and how it could be employed in a particular dynamic problem.","Title: Recent advances on effective and efficient deep learning-based solutions\nYear: 2022\n\nAbstract: This editorial briefly analyses, describes, and provides a short summary of a set of selected papers published in a special issue focused on deep learning methods and architectures and their application to several domains and research areas. The set of selected and published articles covers several aspects related to two basic aspects in deep learning (DL) methods, efficiency of the models and effectiveness of the architectures These papers revolve around different interesting application domains such as health (e.g. cancer, polyps, melanoma, mental health), wearable technologies solar irradiance, social networks, cloud computing, wind turbines, object detection, music, and electricity, among others. This editorial provides a short description of each published article and a brief analysis of their main contributions.","Title: SILT: Efficient transformer training for inter-lingual inference\nYear: 2022\n\nAbstract: The ability of transformers to perform precision tasks such as question answering, Natural Language Inference (NLI) or summarizing, has enabled them to be ranked as one of the best paradigms to address Natural Language Processing (NLP) tasks. NLI is one of the best scenarios to test these architectures, due to the knowledge required to understand complex sentences and established relationships between a hypothesis and a premise. Nevertheless, these models suffer from the incapacity to generalize to other domains or from difficulties to face multilingual and interlingual scenarios. The leading pathway in the literature to address these issues involve designing and training extremely large architectures, but this causes unpredictable behaviors and establishes barriers which impede broad access and fine tuning. In this paper, we propose a new architecture called Siamese Inter-Lingual Transformer (SILT). This architecture is able to efficiently align multilingual embeddings for Natural Language Inference, allowing for unmatched language pairs to be processed. SILT leverages siamese pre-trained multi-lingual transformers with frozen weights where the two input sentences attend to each other to later be combined through a matrix alignment method. The experimental results carried out in this paper evidence that SILT allows to reduce drastically the number of trainable parameters while allowing for inter-lingual NLI and achieving state-of-the-art performance on common benchmarks.","Title: IoHT-based deep learning controlled robot vehicle for paralyzed patients of smart cities\nYear: 2022\n\nAbstract: Paralysis caused by physical trauma is a common disease today, with approximately 30% of paralysis caused by this trauma. The disease in question both physically restricts mobility and brings along psychological problems. Especially in advanced ages, paralysis becomes much more difficult and requires serious care since it causes many effects in elderly people’s daily routines, required specific healthcare services, costs…etc. Therefore, in this study, it was aimed to design an Internet of Health Things (IoHT)-based unmanned robot vehicle for paralyzed patients, by using deep learning for control of the system and creating a connected healthcare synergy. In this context, the electronic part of the robot was designed first and then a deep learning-based model was created. Before creating the model, the data obtained from the sensors were adequately pre-processed, and the related problem formation was shaped for a Deep Learning process. In order to compare the Deep Learning model with other machine learning techniques from the state of the art, different models based on Random Forest (RF), Decision Trees (DT), Naive Bayes (NB), Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Logistic Regression (LR) and Artificial Neural Network (ANN) were generated using the same data. The performances of these models were compared and the obtained results were analyzed. According to the experimental results, the Deep Learning model has the highest performance with 99.5% success rate and 0.5% loss rate. This work presents some relevant results in which using deep learning techniques point excellent performances in the area of IoHT applications. A second contribution of this paper is related to the evaluation of the communication flow of the systems, to analyze if it was good enough to cover a smart environment. The findings in this manner were positive in terms of technical analysis and the patient feedback taken from a total of 10 paralyzed patients.","Title: A new intrusion detection system based on Moth–Flame Optimizer algorithm\nYear: 2022\n\nAbstract: This study relies on using a Moth–Flame Optimization (MFO) method as a search algorithm and a Decision Tree (DT) as an evaluation algorithm to generate an efficient feature subset for intrusion detection systems (IDS). The target is to find a feature subset using the minimum number of traffic network features that later obtains the maximum performance by the machine algorithms used in the classification task. This depends on enhancing the MFO by adopting new operators besides the embedded spiral operator to balance the exploration and exploitation alleviating the local minima problem. The main contribution of this work is the adoption of the cosine similarity measure to binarize the continuous MFO into a binary problem. Cosine similarity overcomes the limitations of the commonly used sigmoid function that depends on using a threshold value for conversion. However, cosine similarity computes the similarity ratio between the current solution and the optimal solution. The augmented MFO wrapper framework is applied as an IDS to detect anomalous traffic in the network. The proposed method is compared against several well-known state-of-the-art algorithms on three network datasets (KDDCUPP9, NSL-KDD, and UNSW-NB15), using IDSACC, IDSTPR, IDSFPR, IDSF-score, and convergence evaluation measures to assess the performance of the proposed method. The experimental results show the superiority of the proposed cosine similarity method compared to other algorithms with an accuracy of 97.8%, F-score of 99%, TPR of 99.6%, and FPR of 8.1% using only five selected features from the KDDCUPP99 dataset. It achieved the accuracy of 89.7%, TPR of 89.1%, FPR of 2.9%, when four selected features from the NSL-KDD dataset are used. And finally, it achieved an accuracy of 92.4%, TPR of 92.3%, FPR of 3%, and F-score 94.2% when the UNSW-NB15 dataset is used.","Title: Predicting the effects of kinetic impactors on asteroid deflection using end-to-end deep learning\nYear: 2022\n\nAbstract: One possible approach to deflect the trajectory of an asteroid on a collision course with the Earth, and prevent a potentially devastating impact, is the use of a kinetic impactor. The upcoming NASA DART and ESA Hera space missions will be the first to study and demonstrate this technique, by driving a spacecraft into the moon of a binary asteroid system with the aim of altering its momentum, and knocking it off course. In this work, we seek to predict critical parameters associated with such an impact, namely the momentum transfer efficiency and axial ratio of the target body, based on light curve data observed from ground before and after the impact in order to give insights into the real effect of the deflection effort. We present here our approach to this problem, which we address from a purely data-driven perspective based on simulated data provided as a part of the Andrea Milani Planetary Defence Challenge, organised by the EU H2020 Stardust-R research network in conjunction with ESA. Formulating the problem as a time series regression task, we develop an end-to-end deep learning pipeline in which we apply the latest advances in deep learning for time series, such as the use of the Transformer architecture as well as ensembling and self-supervised learning techniques. Exploiting these techniques for the challenge, we achieved second place out of the student teams, and fifth place overall without relying on any a priori knowledge of the physics of the asteroid system.","Title: Cooperative Negotiation in Connected Vehicles for Mitigating Traffic Congestion\nYear: 2021\n\nAbstract: Traffic congestion has an impact on traffic efficiency and the quality of life. To address this issue, this paper proposes a distributed, cooperative negotiation method for connected vehicles in traffic flow optimization. In particular, when the connected vehicles obtain the traffic congestion alerts from the roadside units, they exchange their routing information and distribute the traffic flows across the roads by using a collective learning algorithm that does not rely on a centralized controller. Results exported from Simulation of Urban Mobility show that the proposed method outperforms traditional routing methods. In a high traffic demand scenario, the average travel time of the proposed method decreases by 35% and 12% compared with the shortest path routing and the dynamic traffic routing methods, respectively."],"string":"[\n \"Title: A simple CSP-based model for Unmanned Air Vehicle Mission Planning\\nYear: 2014\\n\\nAbstract: The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAV) can be formulated as a Temporal Constraint Satisfaction Problem (TCSP). It consists on a set of locations that should visit in different time windows, and the actions that the vehicle can perform based on its features such as the payload, speed or fuel capacity. In this paper, a temporal constraint model is implemented and tested by performing Backtracking search in several missions where its complexity has been incrementally modified. The experimental phase consists on two different phases. On the one hand, several mission simulations containing (n) UAVs using different sensors and characteristics located in different waypoints, and (m) requested tasks varying mission priorities have been carried out. On the other hand, the second experimental phase uses a backtracking algorithm to look through the whole solutions space to measure the scalability of the problem. This scalability has been measured as a relation between the number of tasks to be performed in the mission and the number of UAVs needed to perform it.\",\n \"Title: Adapting Searchy to extract data using evolved wrappers\\nYear: 2012\\n\\nAbstract: Organizations need diverse information systems to deal with the increasing requirements in information storage and processing, yielding the creation of information islands and therefore an intrinsic difficulty to obtain a global view. Being able to provide such an unified view of the -likely heterogeneous-information available in an organization is a goal that provides added-value to the information systems and has been subject of intense research. In this paper we present an extension of a solution named Searchy, an agent-based mediator system specialized in data extraction and Integration. Through the use of a set of wrappers, it integrates information from arbitrary sources and semantically translates them according to a mediated scheme. Searchy is actually a domain-independent wrapper container that ease wrapper development, providing, for example, semantic mapping. The extension of Searchy proposed in this paper introduces an evolutionary wrapper that is able to evolve wrappers using regular expressions. To achieve this, a Genetic Algorithm (GA) is used to learn a regex able to extract a set of positive samples while rejects a set of negative samples.\",\n \"Title: The Effects Of Transfer Of Global Improvements In Genetic Programming\\nYear: 2004\\n\\nAbstract: Koza has shown how Automatically Defined Functions (ADFs) can reduce computational effort in the genetic programming paradigm. In Koza's Automatically Defined Functions, as well as in standard genetic programming, an improvement in a part of a program (an ADF or a main body) can only be transferred to other individuals in the population via crossover. In this article, we consider whether it is a good idea to transfer immediately improvements found by a single individual to other individuals in the population. A system that implements this idea has been proposed and tested for the even-5-parity, even-6-parity, and even10-parity problems. Results are very encouraging: computational effort is reduced (compared to Koza's ADFs) and the system seems to be less prone to early stagnation. Also, as evolution occurs in separate populations, our approach permits to parallelize genetic programming in another different way.\",\n \"Title: Using the clustering coefficient to guide a genetic-based communities finding algorithm\\nYear: 2011\\n\\nAbstract: Finding communities in networks is a hot topic in several research areas like social network, graph theory or sociology among others. This work considers the community finding problem as a clustering problem where an evolutionary approach can provide a new method to find overlapping and stable communities in a graph. We apply some clustering concepts to search for new solutions that use new simple fitness functions which combine network properties with the clustering coefficient of the graph. Finally, our approach has been applied to the Eurovision contest dataset, a well-known social-based data network, to show how communities can be found using our method.\",\n \"Title: A genetic graph-based clustering approach to biomedical summarization\\nYear: 2013\\n\\nAbstract: Summarization techniques have become increasingly important over the last few years, specially in biomedical research, where information overload is major problem. Researchers of this area need a shorter version of the texts which contains all the important information while discarding irrelevant one. There are several applications which deal with this problem, however, these applications are sometimes less informative than the user needs. This work deals with this problem trying to improve a summarization graph-based process using genetic clustering techniques. Our automatic summaries are compared to those produced by several commercial and research summarizers, and demonstrate the appropriateness of using genetic techniques in automatic summarization.\",\n \"Title: Analysis of grammatical evolutionary approaches to regular expression induction.\\nYear: 2011\\n\\nAbstract: Regular expressions, or regexes, have been used traditionally as a pattern matching tool to search for structures in a set of objects, like files, text documents or folders. Pattern matching can be used to look for files whose name contains a given string, to search files that contain a specific pattern within them, or simply to extract text in a set of documents. It is very popular to apply regexes to detect and extract patterns that represent phone numbers, URLs, email addresses, etc. These kind of information can be characterized because it has a well defined structure. Nevertheless, regexes are not very frequently used because its high complexity in both, syntax and grammatical rules, makes regexes difficult to understand. For this reason, the development of programs able to automatically generate, and evaluate, regexes has become a valuable task. This work analyzes the performance of different grammatical evolutionary approaches in the generation of regexes able to extract URL patterns. Four different types of grammars have been evaluated: a context-free grammar, a context-free grammar with a penalized fitness function, an extensible context-free grammar, and a Christiansen grammar. For the considered problem, the experimental results show that the best performance of the system, measured as cumulative success rate, is achieved using Christiansen grammars.\",\n \"Title: Game-like language learning in 3-D virtual environments\\nYear: 2013\\n\\nAbstract: This paper presents our recent experiences with the design of game-like applications in 3-D virtual environments as well as its impact on student motivation and learning. Therefore our paper starts with a brief analysis of the motivational aspects of videogames and virtual worlds (VWs). We then go on to explore the possible benefits of both in the area of foreign language learning. For our research study we have designed a VW-platform, called VirtUAM. This permits us to store and record data related to users' behaviour within the VW. Furthermore the platform has been employed to build several islands (virtual spaces), which implement different game levels. The virtual spaces themselves are used to give students a basic training in different language skills related to the German language. In order to obtain data regarding the game's impact on student learning and motivation, we designed several tests, which were completed both before and after the student participants played the game. Additionally we gave them a general questionnaire, which was only filled out after the game and which aimed at getting personal feedback from the learners. Quantitative and qualitative results shown in this work are part of a larger project which intends to study the impact of game-like applications within virtual environments and with regard to teaching and learning processes in general.\",\n \"Title: Effects Of The Lack Of Selective Pressure On The Expected Run-Time Distribution In Genetic Programming\\nYear: 2013\\n\\nAbstract: Run-time analysis is a powerful tool to analyze algorithms. It is focused on studying the time required by an algorithm to find a solution, the expected run-time, which is one of the most relevant algorithm attributes. Previous research has associated the expected run-time in GP with the lognormal distribution. In this paper we provide additional evidence in that regard and show how the algorithm parametrization may change the resulting run-time distribution. In particular, we explore the influence of the selective pressure on the run-time distribution in tree-based GP, finding that, at least in two problem instances, the lack of selective pressure generates an expected run-time distribution well described by the Weibull probability distribution.\",\n \"Title: A multi-agent architecture for intelligent gathering systems\\nYear: 2005\\n\\nAbstract: This paper presents a model to define heterogeneous agents that solve problems by sharing the knowledge retrieved from the WEB, and cooperating among them. The control structure of those agents is based on a general purpose Multi-Agent architecture (SKELETONAGENT) based on a deliberative approach. Any agent in the architecture is built by means of several interrelated modules: control module, language and communication module, skills modules, knowledge base, yellow pages, etc. The control module uses an agenda to activate and coordinate the agent skills. This agenda handles actions from both the internal goals of the agent and from other agents in the environment. In the paper, we show a high level agent model, which is later instantiated to build a set of heterogeneous specialized agents. The paper describes how SKELETONAGENT has been used to implement different kinds of agents and a specialized Multi-Agent System (MAS). The implemented MAS, MAPWEB-ETOURISM, is the specific implementation of a general WEB gathering architecture, named MAPWEB, which extends SKELETONAGENT. MAPWEB has been designed to solve problems in WEB domain through the integration of information gathering and planning techniques. The MAPWEB-ETOURISM system has been applied to a specific WEB domain (e-tourism) which uses information gathered directly from several WEB sources (plane, train, and hotel companies) to solve travel problems. This paper shows how the proposed architecture allows to integrate the different agents tasks with AI techniques like planning to build a MAS which is able to gather and integrate information retrieved from the WEB to solve problems.\",\n \"Title: Contextual information retrieval based on algorithmic information theory and statistical outlier detection\\nYear: 2007\\n\\nAbstract: This work presents an Information Retrieval technique based on algorithmic information theory (using the normalized compression distance), statistical data outlier detection, and a novel database structure. The paper shows how they all can be integrated to retrieve information from generic databases using long text-based queries. Two important problems are addressed. On the one hand, we analyze and tyr to solve the detection of a particular case of false positives: when the distance among two documents is outlyingly low but there is not actual similarity. On the other hand, we propose a way to structure the database such that the similarity distance estimation scales well with the length of the size of the query. All design choices are justified with an experimental evaluation.\",\n \"Title: Is the contextual information relevant in text clustering by compression?\\nYear: 2012\\n\\nAbstract: Usually, when analyzing data that have not been processed or filtered yet, it can be observed that not all the data have equal importance. Thus, it is common to find relevant data surrounded by non relevant one. This occurs when analyzing textual information due to its intrinsic nature: texts contain words that provide a lot of information about the subject matter, whereas they contain other words with a little meaning or relevance. We believe that although in principle the non-relevant words are not as important as the relevant ones, the former constitute the substrate that supports the last. Since this substrate is the context that surrounds the relevant information, we call it the contextual information. In this paper, we analyze the relevance that the contextual information has in textual data, in a clustering by compression scenario. We generate the contextual information applying a distortion technique previously developed by the authors. One of the main characteristics of this technique is that it maintains the contextual information. In this paper we compare this technique with three new distortion techniques that destroy the contextual information in different ways. The experimental results support our hypothesis that the contextual information is relevant at least in the area of text clustering by compression.\",\n \"Title: Human Drivers Knowledge Integration in a Logistics Decision Support Tool.\\nYear: 2011\\n\\nAbstract: The logistics sector is a very dynamic and complex domain with large impact in our day-to-day life. It is also very sensitive to the market changes, so tools that can improve their processes are needed. This paper describes a decision support tool for a Spanish Logistics Company. The tool takes the knowlege from the human drivers, i.e. the address used to deliver a package, integrates and exploits it into the decision process. However, this is precisely the data that is often wrongly introduced by the drivers. The tool analyses and detects different mistakes (i.e. misspellings) in the addresses introduced, groups by zones the packages that have to be delivered, and proposes the routes that the drivers should follow. To achieve this, the tool combines three different techniques from Artificial Intelligence. First, Data Mining techniques are used to detect and correct addresses inconsistencies. Second, Case-Based Reasoning techniques that are employed to separate and learn the most frequent areas or zones that the experienced drivers do. And finally, we use Evolutionary Computation (EC) to plan optimal routes (using the human drivers past experiences) from the learned areas, and evaluate those plans against the original route executed by the human driver. Results show that the tool can automatically correct most of the misspelling in the data.\",\n \"Title: Relevance of contextual information in compression-based text clustering\\nYear: 2010\\n\\nAbstract: In this paper we take a step towards understanding compression distances by analyzing the relevance of contextual information in compression-based text clustering. In order to do so, two kinds of word removal are explored, one that maintains part of the contextual information despite the removal, and one that does not maintain it. We show how removing words in such a way that the contextual information is maintained despite the word removal helps the compression-based text clustering and improves its accuracy, while on the contrary, removing words losing that contextual information makes the clustering results worse.\",\n \"Title: Intelligent Travel Planning: A MultiAgent Planning System to Solve Web Problems in the e-Tourism Domain\\nYear: 2001\\n\\nAbstract: This paper presents Intelligent Travel Planning (ITP), a multiagent planning system to solve Web electronic problems in the Web, whose main goal is to search for useful solutions in the electronic-Tourism domain to system users. The system uses different types of intelligent autonomous agents whose main characteristics are cooperation, negotiation, learning, planning and knowledge sharing. Obviously the information used by the intelligent agents is heterogeneous and geographically distributed, since the main information source of the system is Internet. Other information sources are agent knowledge bases in the distributed system. The process to obtain, filter, and store the information is performed automatically by agents. This information is translated into a homogeneous format for high-level reasoning in order to obtain different partial solutions. Partial solutions are reconstructed into a general solution (or solutions) to be presented to the user. The system will show a set of solutions to the users that can be evaluated by them.\",\n \"Title: Personalized Handling of Semantic Data with MIG\\nYear: 2009\\n\\nAbstract: This paper shows a semantically-enabled web application named MIG used to create user profiles which enhances users accessibility by allowing the creation of an user interface adapted to the user needs, the device used, and its preferences. This approach exploits the Semantic Web technologies and the infrastructure and applications created in previous work.\",\n \"Title: Allocating Educational Resources Through Happiness Maximization And Traditional Csp Approach\\nYear: 2009\\n\\nAbstract: An instance of an Educational Resources Allocation (ERA) problem is the distribution of a set of students in different laboratories. This can be a complex and dynamic problem if non-quantitative considerations (i.e. how close the final allocation is to the student preferences or desires) are involved in the decision process. Traditionally, different approaches based on Constraint-Satisfaction techniques and Multi-agent negotiation have been applied to the general problem of Resource Allocation. This paper shows how a Multi-agent approach can be used to model and simulate the assignment of sets of students to several predefined laboratories, by using their preferences to guide the allocation process. This approach aims at finding new solutions that try to satisfy individual student needs with no knowledge about the general allocation problem. The paper shows some experimental results and a comparison, between a CSP-based solution modeled in CHOCO, a CSP Java-based library, and a Multi-agent model implemented using MASON, a multi-agent simulation platform.\",\n \"Title: A study on the impact of crowd-based voting schemes in the 'Eurovision' European contest\\nYear: 2011\\n\\nAbstract: The Eurovision contest has been the reference on european song contests for the past 50 years. Countries in the European Union can shows the rest of the participants their current music tendencies. This phenomena has been studied in domains like physic and social sciences to find correlations between contests and current political and socio-economy trends in EU. The inclusion of web and social technologies some years ago, have caused a disruption in the traditional voting system whereby the audience is encouraged to participate by casting votes for their favorite song. As a result, this system yields new, relevant information that may be extrapolated to social and political tendencies in Europe with a higher degree accuracy than by data collected using the previous jury-based system. This paper provides an initial data analysis in crowd behavior to assess the impact of the televote system, in the Eurovision voting dynamic, by focusing on two distinct five years periods that can successfully contrast each voting scheme. Analyzing these periods separately, we can observe results from the televoting contests and then compare to the jury to see if there is a change in voting patterns. Finally, we study the underlying community structure of the voting network using the Cluster Percolation Method and Edge Betweenness to discover stable core communities spanning a number of years in the contest. The clusters obtained using these algorithms are then used to compare how these stable communities have evolving during the considered periods.\",\n \"Title: Multi-agent plan based information gathering\\nYear: 2006\\n\\nAbstract: The evolution of the Web has encouraged the development of new Information Gathering techniques. Artificial Intelligence techniques, such as Planning, have also been used for Information Gathering in order to go beyond merely retrieving Web data. Planning has been used traditionally to generate a sequence of actions that specify how information sources should be accessed. In this paper, planning is used mainly for integrating information found in heterogeneous sources. For instance, two different Web sources about flight and train travels, can be represented by two different planning operators, which will be subsequently combined and integrated by a single plan. We have found that a Multi-Agent framework is very appropriate to implement our technique. In order to evaluate our approach empirically, it has been applied to a tourism domain (MAPWEB-ETOURISM), whose purpose is to help a customer to plan his/her trips. In this domain, several specialized Web agents have been used to query travel Web sources, whose results are subsequently integrated by a planning agent to build complete travel solutions. Experimental results show that, by means of integration, more solutions can be found than by using single information sources or even travel meta-searchers. Also, (MAPWEB-ETOURISM) can find new types of solutions by integrating information gathered from heterogeneous Web sources (i.e. flights and trains).\",\n \"Title: A new CSP graph-based representation for Ant Colony Optimization\\nYear: 2013\\n\\nAbstract: Constraint Satisfaction Problems (CSP) have been widely studied in several research areas like Artificial Intelligence or Operational Research due their complexity and industrial interest. From previous research areas, heuristic (informed) search methods have been particularly active looking for feasible approaches. One of the critical problems to work with CSP is related to the exponential growth of computational resources needed to solve even the simplest problems. This paper presents a new efficient CSP graph-based representation to solve CSP by using Ant Colony Optimization (ACO) algorithms. This paper presents also a new heuristic (called Oblivion Rate), that have been designed to improve the current state-of-the-art in the application of ACO algorithms on these domains. The presented graph construction provides a strong reduction in both, the number of connections and the number of nodes needed to model the CSP. Also, the new heuristic is used to reduce the number of pheromones in the system (allowing to solve problems with an increasing complexity). This new approach has been tested, as case study, using the classical N-Queens Problem. Experimental results show how the new approach works in both, reducing the complexity of the resulting CSP graph and solving problems with increasing complexity through the utilization of the Oblivion Rate.\",\n \"Title: SHAMASH: An AI Tool for Modelling and Optimizing Business Processes\\nYear: 2001\\n\\nAbstract: In this paper we describe SHAMASH, a tool for modeling and automatically optimizing Business Processes. The main features that differentiate it from most current related tools are its ability to define and use organisation standards, and functional structure, and make automatic model simulation and optimisation of them. SHAMASH is a knowledge based system, and we include a discussion on how knowledge acquisition did take place. Furthemore, we introduce a high level description of the architecture, the conceptual model, and other importants modules of the system.\",\n \"Title: Confidence intervals of success rates in evolutionary computation\\nYear: 2010\\n\\nAbstract: Success Rate (SR) is a statistic straightforward to use and interpret, however a number of non-trivial statistical issues arises when it is examinated in detail. We address some of those issues, providing evidence that suggests that SR follows a binomial density function, therefore its statistical properties are independent of the flavour of the Evolutionary Algorithm (EA) and its domain. It is fully described by the SR and the number of runs. Moreover, the binomial distribution is a well known statistical distribution with a large corpus of tools available that can be used in the context of EC research. One of those tools, confidence intervals (CIs), is studied.\",\n \"Title: Statistical distribution of generation-to-success in GP: application to model accumulated success probability\\nYear: 2011\\n\\nAbstract: Many different metrics have been defined in Genetic Programming. Depending on the experiment requirements and objectives, a collection of measures are selected in order to achieve an understanding of the algorithm behaviour. One of the most common metrics is the accumulated success probability, which evaluates the probability of an algorithm to achieve a solution in a certain generation. We propose a model of accumulated success probability composed by two parts, a binomial distribution that models the total number of success, and a lognormal approximation to the generation-to-success, that models the variation of the success probability with the generation.\",\n \"Title: Predicting Performance In Team Games The Automatic Coach\\nYear: 2011\\n\\nAbstract: A wide range of modern videogames involves a number of players collaborating to obtain a common goal. The way the players are teamed up is usually based on a measure of performance that makes players with a similar level of performance play together. We propose a novel technique based on clustering over observed behaviour in the game that seeks to exploit the particular way of playing of every player to find other players with a gameplay such that in combination will constitute a good team, in a similar way to a human coach. This paper describes the preliminary results using these techniques for the characterization of player and team behaviours. Experiments are performed in the domain of Soccerbots.\",\n \"Title: Experiences using social networks in Spanish public administrations\\nYear: 2011\\n\\nAbstract: Public administrations are developing several mechanisms to increase the reception level or the impact of the information that they communicate (i.e public policy-making or public service delivery) using for this, marketing techniques. Therefore, we may consider the concept of marketing in the public sector as the process of identifying the needs of a target audience to try to meet them, taking into account at the same time the objectives of the organization and the society. This paper aims to address this reality, regarding to the new opportunities resulting in the use of the Internet. Therefore, the purpose of this paper is to analyze how the public administrations are using Internet and, above all, the tools associated with social networks, to carry out the marketing process of their policies and public services. This study shows several experiencies to integrate the citizenry within a more collaborative information process.\",\n \"Title: Using preferences to solve student-class allocation problem\\nYear: 2009\\n\\nAbstract: Resource allocation is a form of Constraint Satisfaction Problem (CSP) in which a set of resources must be assigned to a set of agents. Multiagent Resource Allocation (MARA) makes possible solving CSP using qualitative parameters as, for example, reduce idle time in scheduling or preferences over the set of resources to be allocated. In this paper, we are going to use a MARA approach to find a solution to the student-class allocation problem, where each class has a number of seats, each student has his preferences over the schedule and the institution requests a uniform distribution of the students in classes and some other rules.\",\n \"Title: Using Hierarchical Knowledge Structures to Implement Dynamic FAQ Systems\\nYear: 2004\\n\\nAbstract: This paper presents a knowledge representation that allows building hierarchical structures that can be used to build dynamically solutions to solve a particular user question. The goal of this work is to show how it is possible to define (and handle) a new knowledge representation that allows the integration of Case-based Knowledge into a graph-based representation that can be easily learned and managed. The combination of both, Case-based Knowledge and graphs allows to implement a flexible hierarchical structures (or learning graphs) that have been applied to implement a new kind of Frequently Asked Questions Systems. In these systems the output is dynamically built from the user query, using as basis structures the knowledge retrieved from a Case Base. The paper shows how the management of these cases allow enriching the knowledge base. Finally, the paper shows a specific application of this technique, in particular a Web system named DynJAQ (Dynamic Java Asked Questions). DynJAQ is a FAQ system that is able to generate dynamically several HTML guides that can be used to answer any possible question about a particular programming language (Java).\",\n \"Title: Solving Travel Problems by Integrating WEB Information with Planning\\nYear: 2002\\n\\nAbstract: The evolution of the WEB has encouraged the development of new information gathering techniques. In order to retrieve WEB information, it is necessary to integrate different sources. Planning techniques have been used for this purpose in the field of information gathering. A plan for information gathering is the sequence of actions that specify what information sources should be accessed so that some characteristicts, like access efficiency, are optimised. MAPWEB is a multiagent framework that integrates Planning Agents and WEB Information Retrieval Agents. In MAPWEB, planning is not only used to integrate and to select information sources, but also to solve actual planning problems with information gathered from the WEB. For instance, in an travel assistant domain, plans represent the sequence of actions an user has to follow to perform his/her trip. But also, each step in the plan informs the WebAgents which information sources should be accessed. In this paper we describe MAPWEB and study experimentally two information retrieval characteristics: the average number of solutions retrieved depending on the WebAgents used and the allocated time limit, and the number of problems solved (those travel assistant problems for which at least one solution was retrieved).\",\n \"Title: CAMOU: A Simple Integrated eLearning and Planning Techniques Tool\\nYear: 2007\\n\\nAbstract: In this paper we present an educational tool which has been designed to manage (learning) knowledge acquired from the interactions with the students, and to automatically aids educators in the complex process of course design and analysis. In the tool, only some essential learning knowledge will be translated (mapped) and provided to an au- tomatic reasoning system, named ipss. This system, which integrates Ar- tificial Intelligence Planning and Scheduling, analyzes and detects prob- lems in the current tested course, providing new solutions in form of new learning designs that can be approved (or rejected) by educators.\",\n \"Title: A genetic tango attack against the David-Prasad RFID ultra-lightweight authentication protocol\\nYear: 2014\\n\\nAbstract: AbstractRadio frequency identification RFID is a powerful technology that enables wireless information storage and control in an economical way. These properties have generated a wide range of applications in different areas. Due to economic and technological constrains, RFID devices are seriously limited, having small or even tiny computational capabilities. This issue is particularly challenging from the security point of view. Security protocols in RFID environments have to deal with strong computational limitations, and classical protocols cannot be used in this context. There have been several attempts to overcome these limitations in the form of new lightweight security protocols designed to be used in very constrained sometimes called ultra-lightweight RFID environments. One of these proposals is the David-Prasad ultra-lightweight authentication protocol. This protocol was successfully attacked using a cryptanalysis technique named Tango attack. The capacity of the attack depends on a set of boolean approximations. In this paper, we present an enhanced version of the Tango attack, named Genetic Tango attack, that uses Genetic Programming to design those approximations, easing the generation of automatic cryptanalysis and improving its power compared to a manually designed attack. Experimental results are given to illustrate the effectiveness of this new attack.\",\n \"Title: A knowledge-based approach for business process reengineering, SHAMASH\\nYear: 2002\\n\\nAbstract: In this paper we present an overview of SHAMASH, a process modelling tool for business process reengineering. The main features that differentiate it from most current related tools are its ability to define and use organisation standards, and functional structure, and make automatic model simulation and optimisation of them. SHAMASH is a knowledge-based system, and we include a discussion on how knowledge acquisition did take place. Furthermore, we introduce a high level description of the architecture, the conceptual model, and other important modules of the system.\",\n \"Title: A Case Study on Grammatical-Based Representation for Regular Expression Evolution\\nYear: 2010\\n\\nAbstract: Regular expressions, or simply regex, have been widely used as a powerful pattern matching and text extractor tool through decades. Although they provide a powerful and flexible notation to define and retrieve patterns from text, the syntax and the grammatical rules of these regex notations are not easy to use, and even to understand. Any regex can be represented as a Deterministic or Non-Deterministic Finite Automata; so it is possible to design a representation to automatically build a regex, and a optimization algorithm able to find the best regex in terms of complexity. This paper introduces both, a graph-based representation for regex, and a particular heuristic-based evolutionary computing algorithm based on grammatical features from this language in a particular data extraction problem.\",\n \"Title: Educational Resource Scheduling Based on Socio-inspired Agents\\nYear: 2009\\n\\nAbstract: Scheduling a set of constrained resources is a difficult task, specially when there is no clear definition of 'optimal'. When the constraints depend not only on physical or temporal issues but also in human desires or preferences the task gets harder. This is the case of educational resources, for example when a set of students must be distributed into a limited set of laboratories to attend to periodical practical sessions, in this case weekly. The preferences of the students may vary during the process for reasons such as the number of people already in that group. This paper presents a socio inspired solution implemented as a multiagent system. The agents enroll themselves in the lab sessions based on their preferences and negotiate with other agents, using the resources they already have, to obtain desired groups that were already full.\",\n \"Title: A Framework for Agent-Based Evaluation of Genetic Algorithms\\nYear: 2009\\n\\nAbstract: Genetic Algorithms (GA) are a set of algorithms that use biological evolution as inspiration to solve search problems. One of the difficulties found when working with GA are the several parameters that have to be set and the many details that can be tunned in the GA. Usually it leads to the execution of several experiments in order to study how the GA behaves under different circumstances. In general it requires several computational resources and time to code the same algorithm with slight differences several times. In this paper we propose a framework based on agent technology able to parallelize the experiment and to split it into several components. It is complemented with a description of how this framework can be used in the evolution of regular expressions.\",\n \"Title: Maximal Component Detection in Graphs Using Swarm-Based and Genetic Algorithms.\\nYear: 2012\\n\\nAbstract: Nowadays, there is an increasing interest in the application of Collective Intelligence and Evolutive optimization algorithms for solving NP-complete problems. This is because the solution or optimization process of these type of problems requires a huge amount of resources (such as computational effort or time). Some examples of these types of problems are scheduling problems, constrained satisfaction problems, or routing problems. Collective strategies are heuristics that allow to look for new solutions in real complex problems using concepts extracted from a metaphor of social behavior of ants, bees, bacteria, flocks of birds and/or schools of fish. In this paper we propose a practical comparison between a classical Genetic-based approach and a Swarm-based strategy applied to the detection of maximal component in graphs. This work describes how these two different optimization strategies can be adapted and used to extract the different sub-graphs that contains the maximum number of nodes. Experimental results show the best results are obtained using ACO algorithm, but new strategies must be taken into account in order to improve the results.\",\n \"Title: Extracting Collective Trends from Twitter Using Social-Based Data Mining.\\nYear: 2013\\n\\nAbstract: Social Networks have become an important environment for Collective Trends extraction. The interactions amongst users provide information of their preferences and relationships. This information can be used to measure the influence of ideas, or opinions, and how they are spread within the Network. Currently, one of the most relevant and popular Social Network is Twitter. This Social Network was created to share comments and opinions. The information provided by users is specially useful in different fields and research areas such as marketing. This data is presented as short text strings containing different ideas expressed by real people. With this representation, different Data Mining and Text Mining techniques (such as classification and clustering) might be used for knowledge extraction trying to distinguish the meaning of the opinions. This work is focused on the analysis about how these techniques can interpret these opinions within the Social Network using information related to IKEA® company.\",\n \"Title: Dynjaq: An Adaptive And Flexible Dynamic Faq System\\nYear: 2007\\n\\nAbstract: This article presents a new type of Frequently Asked Questions (FAQ) System, called DynJAQ (Dynamic Java Asked Questions) that has been designed with the purpose of making learning more appealing to beginner students of engineering disciplines and overcome the inconvenience of these systems. DynJAQ is able to generate dynamically several HTML guides that can be used to answer any possible question about a particular programming language (Java), although it can be easily extended to any other topic. DynJAQ integrates case-based knowledge into a graph-based representation that can be easily learned and managed. The combination of both case-based knowledge and graphs allows it to implement a flexible hierarchical structures (or learning graphs) that have been applied to implement a new kind of Frequently Asked Questions Systems. In these systems the output is dynamically built from the user query, using as basis structures the knowledge retrieved from a Case Base. The management of these cases allows enriching the knowledge base. (c) 2007 Wiley Periodicals, Inc.\",\n \"Title: A Multi-agent Traffic Simulation Framework for Evaluating the Impact of Traffic Lights.\\nYear: 2011\\n\\nAbstract: The growing of the number of vehicles cause serious strains on road infrastructures. Traffic jams inevitably occur, wasting time and money for both cities and their drivers. To mitigate this problem, traffic simulation tools based on multiagent techniques can be used to quickly prototype potentially problematic scenarios to better understand their inherent causes. This work centers around the effects of traffic light configuration on the flow of vehicles in a road network. To do so, a Multi-Agent Traffic Simulation Framework based on Particle Swarm Optimization techniques has been designed and implemented. Experimental results from this framework show an improvement in the average speed obtained by traffic controlled by adaptive over static traffic lights.\",\n \"Title: Clustering avatars behaviours from virtual worlds interactions\\nYear: 2012\\n\\nAbstract: Virtual Worlds (VWs) platforms and applications provide a practical implementation of the Metaverse concept. These applications, as highly inmersive and interactive 3D environments, have become very popular in social networks and games domains. The existence of a set of open platforms like OpenSim or OpenCobalt have played a major role in the popularization of this technology and they open new exciting research areas. One of these areas is behaviour analysis. In virtual world, the user (or avatar) can move and interact within an artificial world with a high degree of freedom. The movements and iterations of the avatar can be monitorized, and hence this information can be analysed to obtain interesting behavioural patterns. Usually, only the information related to the avatars conversations (textual chat logs) are directly available for processing. However, these open platforms allow to capture other kind of information like the exact position of an avatar in the VW, what they are looking at (eye-gazing) or which actions they perform inside these worlds. This paper studies how this information, can be extracted, processed and later used by clustering methods to detect behaviour or group formations in the world. To detect the behavioural patterns of the avatars considered, clustering techniques have been used. These techniques, using the correct data preprocessing and modelling, can be used to automatically detect hidden patterns from data.\",\n \"Title: An empirical study on the accuracy of computational effort in Genetic Programming\\nYear: 2011\\n\\nAbstract: Some commonly used performance measures in Genetic Programming are those defined by John Koza in his first book. These measures, mainly computational effort and number of individuals to be processed, estimate the performance of the algorithm as well as the difficulty of a problem. Although Koza's performance measures have been widely used in the literature, their behaviour is not well known. In this paper we study the accuracy of these measures and advance in the understanding of the factors that influence them. In order to achieve this goal, we report an empirical study that attempts to systematically measure the effects of two variability sources in the estimation of the number of individuals to be processed and the computational effort. The results obtained in those experiments suggests that these measures, in common experimental setups, and under certain circumstances, might have a high relative error.\",\n \"Title: Environmental Influence in Bio-inspired Game Level Solver Algorithms.\\nYear: 2013\\n\\nAbstract: Bio-inspired algorithms have been widely used to solve problems in areas like heuristic search, classical optimization, or optimum configuration in complex systems. This paper studies how Genetic Algorithms (GA) and Ant Colony Optimization (ACO) algorithms can be applied to automatically solve levels in the well known Lemmings Game. The main goal of this work is to study the influence that the environment exerts over these algorithms, specially when the goal of the selected game is to save an individual (lemming) that should take into account their environment to improve their possibilities of survival. The experimental evaluations carried out reveals that the performance of the algorithm (i.e. number of paths found) is improve when the algorithm uses a small quantity of information about the environment.\",\n \"Title: Variable Length-Based Genetic Representation to Automatically Evolve Wrappers\\nYear: 2010\\n\\nAbstract: The Web has been the star service on the Internet, however the outsized information available and its decentralized nature has originated an intrinsic difficulty to locate, extract and compose information. An automatic approach is required to handle with this huge amount of data. In this paper we present a machine learning algorithm based on Genetic Algorithms which generates a set of complex wrappers, able to extract information from the Web. The paper presents the experimental evaluation of these wrappers over a set of basic data sets.\",\n \"Title: Roboskeleton: An architecture for coordinating robot soccer agents\\nYear: 2006\\n\\nAbstract: SkeletonAgent is an agent framework whose main feature is to integrate different artificial intelligent skills, like planning or learning, to obtain new behaviours in a multi-agent environment. This framework has been previously instantiated in a deliberative domain (electronic tourism), where planning was used to integrate Web information in a tourist plan. RoboSkeleton results from the instantiation of the same framework, SkeletonAgent, in a very different domain, the robot soccer. This paper shows how this architecture is used to obtain collaborative behaviours in a reactive domain. The paper describes how the different modules of the architecture for the robot soccer agents are designed, directly showing the flexibility of our framework.\",\n \"Title: A Swarm Simulation Platform for Agent-Based Social Simulations.\\nYear: 2011\\n\\nAbstract: The social sciences have long employed simulations and statistical models in their research methodology as testing and validation tools. Recent advances in complex networks have produced a set of new graph-based tools with which to study and characterize larges-scale, complex social data. We propose a swarm simulation platform that allows quick prototyping of agent-based social models, using an iterative simulation development cycle. This platform is designed to provide meaningful graph-based metrics of the social ties formed. A description of the system is given, as well as experimental results obtained from sample models.\",\n \"Title: Optimal Message Interchange in a Self-organizing Multi-agent System.\\nYear: 2010\\n\\nAbstract: Over the last decade there has been a growing interest on Intelligent Agents and Multi-Agent Systems (MAS) in several fields such as Artificial Intelligence (Al), Software Engineering, Psychology, etc... Different problems can be solved in these fields by creating societies of agents that communicate with each other. Nevertheless, when the number of agents is large and the connectivity is extensive, the system suffers from overhead in the communication among agents due to the large number messages exchanged. This work addresses the search for an optimal communication topology to avoid these situations. This optimal topology is characterized by the use of a redirecting probability in the communication. The redirection of a communication is performed before the execution of the MAS. Once agents start the execution, the topology is fixed and remains unchanged. This characteristic is useful in those systems where a given topology can not be changed as, for example, in wired networks. On the other hand, in the proposed solution agents contain a local message discrimination process as a function of the sender of the message. Experiments show an important improvement in terms of a reduction in the number of iterations needed to solve the problem and also in the number of messages exchanged.\",\n \"Title: Comparative study of text clustering techniques in virtual worlds\\nYear: 2013\\n\\nAbstract: Virt-UAM (Virtual Worlds at Universidad Autónoma de Madrid) platform allows to design and implement virtual spaces where a set of avatars can be intensively monitored using a set of tools which can be managed by an administrator. In a virtual world, the users can move and interact between them with a high degree of freedom. The movements, interactions and any other information related to the avatars conversations can be stored. Hence this data is available for processing and analysing to obtain the user behavioural patterns. Document clustering techniques have been intensively applied to automatically organize a document corpus into clusters or similar groups. The topic detection problem can be considered as a special case of document clustering, therefore, these techniques can be used over textual chat to detect clusters from the data, and then extract the conversation topics. Mahout(TM) machine learning library is an Apache(TM) project whose main goal is to build scalable machine learning libraries. This library provides a set of algorithms for data mining and for information retrieval ready to use. This paper shows a practical application of some of these available clustering mahout algorithms, in a virtual world-based scenario. These algorithms have been applied to extract the topics based on clusters obtained from the text messages. Finally, a comparative study of these document clustering algorithms used is presented.\",\n \"Title: Challenges and issues of web intelligence research\\nYear: 2013\\n\\nAbstract: The Web has an enormous influence on our everyday life. Thus, more efficient intelligent approaches and technologies are needed to make the most of the Web's unlimited potential. Intelligence can be achieved by making the Web knowing its own structures and content and by applying intelligent techniques to effectively access the resources. Semantic web was one of the significant steps towards Web Intelligence. In this editorial, we discuss some interesting challenges and issues in Web intelligence research. Also the WIMS conference's objective and activities are presented.\",\n \"Title: A Decision Support System for Logistics Operations\\nYear: 2010\\n\\nAbstract: This paper describes an Artificial Intelligence based application for a logistic company that solves the problem of grouping by zones the packages that have to be delivered and propose the routes that the drivers should follow. The tool combines from the one hand, Case-Based Reasoning techniques to separate and learn the most frequent areas or zones that the experienced logistic operators do. These techniques allow the company to separate the daily incidents that generate noise in the routes, from the decision made based on the knowledge of the route. From the other hand, we have used Evolutionary Computation to plan optimal routes from the learning areas and evaluate those routes. The application allows the users to decide under what parameters (i.e. distance, time, etc) the route should be optimized.\",\n \"Title: Behaviour-based identification of student communities in virtual worlds.\\nYear: 2014\\n\\nAbstract: Virtual Worlds (VW) have gained popularity in the last years in domains like training or education mainly due to their highly immersive and interactive 3D characteristics. In these platforms, the user (represented by an avatar) can move and interact in an artificial world with a high degree of freedom. They can talk, chat, build and design objects, program and compile their own developed programs, or move (flying, teleporting, walking or running) to different parts of the world. Although these environments provide an interesting working place for students and educators, VW platforms (such as Open Cobalt or OpenSim amongst others) rarely provide mechanisms to facilitate the automatic (or semi-automatic) behaviour analysis of users interactions. Using a VW platform called VirtUAM, the information extracted from different experiments are used to analyse and define students communities based on their behaviour. To define the individual student behaviour, different characteristics are extracted from the system, such as the avatar position (in form of GPS coordinates) and the set of actions (interactions) performed by students within the VW. Later this information is used to automatically detect behavioural patterns. This paper shows how this information can be used to group students in different communities based on their behaviour. Experimental results show how community identification can be successfully perform using K-Means algorithm and Normalized Compression Distance. Resulting communities contains users working in near places or with similar behaviours inside the virtual world.\",\n \"Title: A contribution-based framework for the creation of semantically-enabled web applications\\nYear: 2010\\n\\nAbstract: We present Fortunata, a wiki-based framework designed to simplify the creation of seman- tically-enabled web applications. This framework facilitates the management and publica- tion of semantic data in web-based applications, to the extent that application developers do not need to be skilled in client-side technologies, and promotes application reuse by fos- tering collaboration among developers by means of wiki plugins. We illustrate the use of this framework with two Fortunata-based applications named OMEMO and VPOET, and we eval- uate it with two experiments performed with usability evaluators and application develop- ers respectively. These experiments show a good balance between the usability of the applications created with this framework and the effort and skills required by developers.\",\n \"Title: A Genetic Graph-Based Approach For Partitional Clustering\\nYear: 2014\\n\\nAbstract: Clustering is one of the most versatile tools for data analysis. In the recent years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the spectral clustering (SC) algorithm, which is based on graph cut: It initially generates a similarity graph using a distance measure and then studies its graph spectrum to find the best cut. This approach is sensitive to the parameters of the metric, and a correct parameter choice is critical to the quality of the cluster. This work proposes a new algorithm, inspired by SC, that reduces the parameter dependency while maintaining the quality of the solution. The new algorithm, named genetic graph-based clustering (GGC), takes an evolutionary approach introducing a genetic algorithm (GA) to cluster the similarity graph. The experimental validation shows that GGC increases robustness of SC and has competitive performance in comparison with classical clustering methods, at least, in the synthetic and real dataset used in the experiments.\",\n \"Title: Communication by identity discrimination in bio-inspired multi-agent systems\\nYear: 2012\\n\\nAbstract: Network communications have been widely studied in the last decades in different research fields: artificial intelligence, computer science, biology, medicine and psychology among others. Some important efforts have been carried out to analyse communication features such as overhead, connectivity or communication protocols in these areas from their own perspectives. When this problem is restricted to intelligent agents or multi-agent systems, networks are built by a set of interconnected agents that can be software or hardware. In multi-agent systems, communication optimization is used to improve the overall performance of the system by reducing the information sharing (i.e. number of messages or message size) between the agents. This paper analyses a scale-free network topology of agents to solve a multi-sorting problem. The agents use their local information as well as a bio-inspired identity discrimination process to select only those messages that are relevant for each agent to solve jigsaw puzzles. We provide a comprehensive study on the influence of some essential parameters (memory information size and reconnection probability) in an agent network, and how they can be set to obtain a better performance in the system. The experiments show that this strategy contributes to reduce the number of iterations needed to solve the problem. Copyright © 2011 John Wiley & Sons, Ltd.\",\n \"Title: Distributed parameter tuning for genetic algorithms\\nYear: 2010\\n\\nAbstract: Genetic Algorithms (GA) is a family of search algorithms based on the mechanics of natural selection and biological evolution. They are able to efficiently exploit historical information in the evolution process to look for optimal solutions or approximate them for a given problem, achieving excellent performance in optimization problems that involve a large set of dependent variables. Despite the excellent results of GAs, their use may generate new problems. One of them is how to provide a good fitting in the usually large number of parameters that must be tuned to allow a good performance. This paper describes a new platform that is able to extract the Regular Expression that matches a set of examples, using a supervised learning and agent-based framework. In order to do that, GA-based agents decompose the GA execution in a distributed sequence of operations performed by them. The platform has been applied to Language induction problem, for that reason the experiments are focused on the extraction of the regular expression that matches a set of examples. Finally, the paper shows the efficiency of the proposed platform (in terms of fitness value) applied to three case studies: emails, phone numbers and URLs. Moreover, it is described how the codification of the alphabet affects to the performance of the platform.\",\n \"Title: Influence Of Music Representation On Compression-Based Clustering\\nYear: 2010\\n\\nAbstract: Multimedia Information Retrieval is currently a hot research topic due the popularity of the World Wide Web and the huge amount of multimedia data available. There exists an increasing interest to design and develop new methods and techniques to represent and classify this kind of information. Among the different sources of multimedia information currently available, we have decided to work with music audio files. Three different music representations (binary code, wave information, and SAX) have been used to study how the selection of a particular representation could affect a clustering process based on a set of similarity clusters. Two different algorithms (a hierarchical clustering method based on the quartet tree method and a genetic algorithm) have been applied to automatically perform the clustering. A compression distance, the Normalized Compression Distance (NCD), has been used to generate the similarities among the music files. This distance is parameter-free and widely applicable so we can use it directly with different formats and representations. The paper shows some experimental results using these representations and compares the behavior of both clustering methods.\",\n \"Title: Reducing the Loss of Information through Annealing Text Distortion\\nYear: 2011\\n\\nAbstract: Compression distances have been widely used in knowledge discovery and data mining. They are parameter-free, widely applicable, and very effective in several domains. However, little has been done to interpret their results or to explain their behavior. In this paper, we take a step toward understanding compression distances by performing an experimental evaluation of the impact of several kinds of information distortion on compression-based text clustering. We show how progressively removing words in such a way that the complexity of a document is slowly reduced helps the compression-based text clustering and improves its accuracy. In fact, we show how the nondistorted text clustering can be improved by means of annealing text distortion. The experimental results shown in this paper are consistent using different data sets, and different compression algorithms belonging to the most important compression families: Lempel-Ziv, Statistical and Block-Sorting.\",\n \"Title: Features selection from high-dimensional web data using clustering analysis\\nYear: 2012\\n\\nAbstract: The features selection methodologies have become an important field of the data preprocessing techniques. These methods are applied to reduced the dimension of the attributes of different datasets to simplify their analysis. Some of the classical techniques used are wrapper approaches, heuristic functions and filters. The main problem of these approaches is that they usually are black box and computationally expensive algorithms. This work presents a new straightforward strategy to reduce the dimension of the attributes. This new methodology cares about the variables distribution and has been oriented to clustering analysis. It provides an easier human interpretation of the attributes selection strategy and the resulting clusters. Finally, this new approach has been experimentally tested using the FIFA World Cup web dataset, a well-known social-based statistical data with a high number of variables, to show how the features selection strategy find the most relevant variables.\",\n \"Title: Towards an automatic monitoring for higher education Learning Design\\nYear: 2007\\n\\nAbstract: The development of new Information Technologies (IT) has originated new possibilities to design pedagogical methodologies that provide the necessary knowledge and skills in the higher education. This paper presents a metadata-based model representation that is used to represent, detect, and even automatically correct possible pitfalls in the schedule process of a Learning Design (LD) in e-learning environments. This metadata-based model is combined with Artificial Intelligence techniques, such as, planning and scheduling to monitor how is evolving a particular LD, and to propose solutions in those modules of the design that learning problems among the students have been found.\",\n \"Title: Software and Performance Measures for Evaluating Multi-Agent Frameworks\\nYear: 2005\\n\\nAbstract: Nowadays, multi-agent frameworks allow to implement very complex systems by means of agent technology. However, this complexity makes it more difficult to evaluate software and runtime characteristics of multiagent systems (MAS). Our aim is to define and study some quantitative measures to measure MAS aspects like development time, reusability, scalability, etc. These measures could be used by engineers to guide the selection among several MAS frameworks. Our study has been carried out in several MAS frameworks like JADE, JATLITE, SKELETONAGENT, and ZEUS, which have been used to build a MAS application for news retrieval.\",\n \"Title: VPOET: Using a Distributed Collaborative Platform for Semantic Web Applications\\nYear: 2008\\n\\nAbstract: This paper describes a distributed collaborative wiki-based platform that has been designed to facilitate the development of Semantic Web applications. The applications designed using this platform are able to build semantic data through the cooperation of different developers and to exploit that semantic data. The paper shows a practical case study on the application VPOET, and how an application based on Google Gadgets has been designed to test VPOET and let human users exploit the semantic data created. This practical example can be used to show how different Semantic Web technologies can be integrated into a particular Web application, and how the knowledge can be cooperatively improved.\",\n \"Title: A Tool Suite to Enable Web Designers, Web Application Developers and End-users to Handle Semantic Data\\nYear: 2010\\n\\nAbstract: Current web application development requires highly qualified staff, dealing with an extensive number of architectures and technologies. When these applications incorporate semantic data, the list of skill requirements becomes even larger, leading to a high adoption barrier for the development of semantically enabled Web applications. This paper describes VPOET, a tool focused mainly on two types of users: web designers and web application developers. By using this tool, web designers do not need specific skills in semantic web technologies to create web templates to handle semantic data. Web application developers incorporate those templates into their web applications, by means of a simple mechanism based in HTTP messages. End-users can use these templates through a Google Gadget. As web designers play a key role in the system, an experimental evaluation has been conducted, showing that VPOET provides good usability features for a representative group of web designers in a wide range of competencies in client-side technologies, ranging from amateur HTML developers to professional web designers.\",\n \"Title: A plan-based tool for automatic eLearning courses redesign\\nYear: 2008\\n\\nAbstract: In this paper we present an educational tool, named CAMOU, which has been designed to both, ac- quire (learning) knowledge from the (educational) interactions among students and educators, and aid educators in the process of course design and analysis. CAMOU integrates a system, named IPSS, which uses planning and scheduling modules as main reasoning module. IPSS is used to automatically analyse, detect and propose new solutions (as new learning designs or courses) for those problems that currently are handled by our tool. This paper shows how to deal with these knowledge using only some statistical and educational interactions, and how these knowledge can be adequately mapped in the correct repre- sentation used by IPSS. This paper shows both, the currently deployed functionalities of our eLearning tool (used mainly to acquire these information), and how it have been integrated with a planning/scheduling system to develop a complete aid-educational environment.\",\n \"Title: Programming Robosoccer agents by modeling human behavior\\nYear: 2009\\n\\nAbstract: The Robosoccer simulator is a challenging environment for artificial intelligence, where a human has to program a team of agents and introduce it into a soccer virtual environment. Most usually, Robosoccer agents are programmed by hand. In some cases, agents make use of Machine learning (ML) to adapt and predict the behavior of the opposite team, but the bulk of the agent has been preprogrammed. The main aim of this paper is to transform Robosoccer into an interactive game and let a human control a Robosoccer agent. Then ML techniques can be used to model his/her behavior from training instances generated during the play. This model will be used later to control a Robosoccer agent, thus imitating the human behavior. We have focused our research on low-level behavior, like looking for the ball, conducting the ball towards the goal, or scoring in the presence of opponent players. Results have shown that indeed, Robosoccer agents can be controlled by programs that model human play.\",\n \"Title: Adaptive K-Means Algorithm For Overlapped Graph Clustering\\nYear: 2012\\n\\nAbstract: The graph clustering problem has become highly relevant due to the growing interest of several research communities in social networks and their possible applications. Overlapped graph clustering algorithms try to find subsets of nodes that can belong to different clusters. In social network-based applications it is quite usual for a node of the network to belong to different groups, or communities, in the graph. Therefore, algorithms trying to discover, or analyze, the behavior of these networks needed to handle this feature, detecting and identifying the overlapped nodes. This paper shows a soft clustering approach based on a genetic algorithm where a new encoding is designed to achieve two main goals: first, the automatic adaptation of the number of communities that can be detected and second, the definition of several fitness functions that guide the searching process using some measures extracted from graph theory. Finally, our approach has been experimentally tested using the Eurovision contest dataset, a well-known social-based data network, to show how overlapped communities can be found using our method.\",\n \"Title: Improving NCD accuracy by combining document segmentation and document distortion\\nYear: 2014\\n\\nAbstract: Compression distances have been applied to a broad range of domains because of their parameter-free nature, wide applicability and leading efficacy. However, they have a characteristic that can be a drawback when applied under particular circumstances. Said drawback is that when they are used to compare two very different-sized objects, they do not consider them to be similar even if they are related by a substring relationship. This work focuses on addressing this issue when compression distances are used to calculate similarities between documents. The approach proposed in this paper consists of combining document segmentation and document distortion. On the one hand, it is proposed to use document segmentation to tackle the above mentioned drawback. On the other hand, it is proposed to use document distortion to help compression distances to obtain more reliable similarities. The results show that combining both techniques provides better results than not applying them or applying them separately. The said results are consistent across datasets of diverse nature.\",\n \"Title: Acquisition of business intelligence from human experience in route planning\\nYear: 2015\\n\\nAbstract: The logistic sector raises a number of highly challenging problems. Probably one of the most important ones is the shipping planning, i.e. plan the routes that the shippers have to follow to deliver the goods. In this article, we present an artificial intelligence-based solution that has been designed to help a logistic company to improve its routes planning process. In order to achieve this goal, the solution uses the knowledge acquired by the company drivers to propose optimised routes. Hence, the proposed solution gathers the experience of the drivers, processes it and optimises the delivery process. The solution uses data mining to extract knowledge from the company information systems and prepares it for analysis with a case-based reasoning (CBR) algorithm. The CBR obtains critical business intelligence knowledge from the drivers experience that is needed by the planner. The design of the routes is done by a genetic algorithm that, given the processed information, optimises the routes following several objectives, such as minimise the distance or time. Experimentation shows that the proposed approach is able to find routes that improve, on average, the routes made by the human experts.\",\n \"Title: SACOC: A Spectral-Based ACO Clustering Algorithm.\\nYear: 2014\\n\\nAbstract: The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning have been focused on clustering, where ACO-based techniques have showed a great potential. At the same time, new clustering techniques that seek the continuity of data, specially focused on spectral-based approaches in opposition to classical centroid-based approaches, have attracted an increasing research interest-an area still under study by ACO clustering techniques. This work presents a hybrid spectral-based ACO clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach combines ACOC with the spectral Laplacian to generate a new search space for the algorithm in order to obtain more promising solutions. The new algorithm, called SACOC, has been compared against well-known algorithms (K-means and Spectral Clustering) and with ACOC. The experiments measure the accuracy of the algorithm for both synthetic datasets and real-world datasets extracted from the UCI Machine Learning Repository.\",\n \"Title: Online Gamers Classification Using K-means.\\nYear: 2014\\n\\nAbstract: In order to achieve flow and increase player retention, it is important that games difficulty matches player skills. Being able to evaluate how people play a game is a crucial component for detecting gamers strategies in video-games. One of the main problems in player strategy detection is whether attributes selected to define strategies correctly detect the actions of the player. In this paper, we will study a Real Time Strategy (RTS) game. In RTS the participants make use of units and structures to secure areas of a map and/or destroy the opponents resources. We will extract real-time information about the players strategies at several gameplays through a Web Platform. After gathering enough information, the model will be evaluated in terms of unsupervised learning (concretely, K-Means). Finally, we will study the similitude between several gameplays where players use different strategies.\",\n \"Title: Mixed Clustering Methods to Forecast Baseball Trends.\\nYear: 2014\\n\\nAbstract: Sport betting has become one of the most profitable business around the world. This business generates millions of dollars every year. One of the most influenced games is Baseball. Baseball has suffered an important change after the introduction of statistical methods to tune up the team strategy. This effect, called Moneyball, started in 2002 when the team Oaklans Atletics began to choose players according to their statistics. After this successful approach, several teams decided to continue with this strategy, generating strong statistical teams. The statistical information about players and matches have acquired highly importance, creating different datasets, such as Retrosheet which collects detailed information about players, teams and matches since 1956 until today. This work pretends to generate a forecasting model for Baseball focused on the result prediction of new matches using statistical previous information. We combine time-series and clustering algorithms to generate a model which learns about the teams and matches evolution and tries to predict the final results. Even whether this model is not complete accurated, it becomes a good starting point for future models.\",\n \"Title: A Survey of Social Web Mining Applications for Disease Outbreak Detection.\\nYear: 2014\\n\\nAbstract: Social Web Media is one of the most important sources of big data to extract and acquire new knowledge. Social Networks have become an important environment where users provide information of their preferences and relationships. This information can be used to measure the influence of ideas and the society opinions in real time, being very useful on several fields and research areas such as marketing campaigns, financial prediction or public healthcare among others. Recently, the research on artificial intelligence techniques applied to develop technologies allowing monitoring web data sources for detecting public health events has emerged as a new relevant discipline called Epidemic Intelligence. Epidemic Intelligence Systems are nowadays widely used by public health organizations like monitoring mechanisms for early detection of disease outbreaks to reduce the impact of epidemics. This paper presents a survey on current data mining applications and web systems based on web data for public healthcare over the last years. It tries to take special attention to machine learning and data mining techniques and how they have been applied to these web data to extract collective knowledge from Twitter\",\n \"Title: Tweetsemminer: A Meta-Topic Identification Model For Twitter Using Semantic Analysis\\nYear: 2014\\n\\nAbstract: The information contained in Social Networks has become increasingly important over the last few years. Inside this field, Twitter is one of the main current information sources, produced by the comments and contents that their users interchange. This information is usually noisy, however, there are some hidden patterns that can be extracted such as trends, opinions, sentiments, etc. These patterns are useful to generate users communities, which can be focused, for example, on marketing campaigns. Nevertheless, the identification process is usually blind, difficulting this information extaction. Based on this idea, this work pretends to extract relevant data from Twitter. In order to achieve this goal, we have desgined a system, called TweetSemMiner, to classify user comments (or tweets) using general topics (or meta-topics). There are several works devoted to analize social networks, however, only Topic Detection techniques have been applied in this context. This paper provides a new approach to the problem of classification using semantic analysis. The system has been developed focused on the detection of a single meta-topic and uses techniques such as Latent Semantic Analysis (LSA) combined with semantic queries in DBpedia, in order to obtain some results which can be used to analyze the effectiveness of the model. We have tested the model using real users, whose comments were subsequently evaluated to check the effectiveness of this approach.\",\n \"Title: Branching To Find Feasible Solutions In Unmanned Air Vehicle Mission Planning\\nYear: 2014\\n\\nAbstract: Mission Planning is a classical problem that has been traditionally studied in several cases from Robotics to Space missions. This kind of problems can be extremely difficult in real and dynamic scenarios. This paper provides a first analysis for mission planning to Unmanned Air Vehicles (UAVs), where sensors and other equipment of UAVs to perform a task are modelled based on Temporal Constraint Satisfaction Problems (TCSPs). In this model, a set of resources and temporal constraints are designed to represent the main characteristics (task time, fuel consumption, ... ) of this kind of aircrafts. Using this simplified TCSP model, and a Branch and Bound (B&B) search algorithm, a set of feasible solutions will be found trying to minimize the fuel cost, flight time spent and the number of UAVs used in the mission. Finally, some experiments will be carried out to validate both the quality of the solutions found and the spent runtime to found them.\",\n \"Title: On Interlinking Linked Data Sources By Using Ontology Matching Techniques And The Map-Reduce Framework\\nYear: 2014\\n\\nAbstract: Interlinking different data sources has become a crucial task due to the explosion of diverse, heterogeneous information repositories in the so-called Web of Data. In this paper an approach to extract relationships between entities existing in huge Linked Data sources is presented. Our approach hinges on the Map-Reduce processing framework and context-based ontology matching techniques so as to discover the maximum number of possible relationships between entities within different data sources in an computationally efficient fashion. To this end the processing flow is composed by three Map-Reduce jobs in charge for 1) the collection of linksets between datasets; 2) context generation; and 3) construction of entity pairs and similarity computation. In order to assess the performance of the proposed scheme an exemplifying prototype is implemented between DBpedia and LinkedMDB datasets. The obtained results are promising and pave the way towards benchmarking the proposed interlinking procedure with other ontology matching systems.\",\n \"Title: MACOC: A Medoid-Based ACO Clustering Algorithm.\\nYear: 2014\\n\\nAbstract: The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning have been focused on clustering, showing great potential of ACO-based techniques. This work presents an ACO-based clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach restructures ACOC from a centroid-based technique to a medoid-based technique, where the properties of the search space are not necessarily known. Instead, it only relies on the information about the distances amongst data. The new algorithm, called MACOC, has been compared against well-known algorithms (K-means and Partition Around Medoids) and with ACOC. The experiments measure the accuracy of the algorithm for both synthetic datasets and real-world datasets extracted from the UCI Machine Learning Repository.\",\n \"Title: Micro and Macro Lemmings Simulations Based on Ants Colonies.\\nYear: 2014\\n\\nAbstract: Ant Colony Optimization (ACO) has been successfully applied to a wide number of complex and real domains. From classical optimization problems to video games, these kind of swarm-based approaches have been adapted, to be later used, to search for new metaheuristic based solutions. This paper presents a simple ACO algorithm that uses a specifically designed heuristic, called common-sense, which has been applied in the classical video game Lemmings. In this game a set of lemmings must reach the exit point of each level, using a subset of finite number of skills, taking into account the contextual information given from the level. The paper describes both the graph model and the context-based heuristic, designed to implement our ACO approach. Afterwards, two different kind of simulations have been carried out to analyse the behaviour of the ACO algorithm. On the one hand, a micro simulation, where each ant is used to model a lemming, and a macro simulation where a swarm of lemmings is represented using only one ant. Using both kind of simulations, a complete experimental comparison based on the number and quality of solutions found and the levels solved, is carried out to study the behaviour of the algorithm under different game configurations.\",\n \"Title: Combining Time Series And Clustering To Extract Gamer Profile Evolution\\nYear: 2014\\n\\nAbstract: Video-games industry is specially focused on user entertainment. It is really important for these companies to develop interactive and usable games in order to satisfy their client preferences. The main problem for the game developers is to get information about the user behaviour during the game-play. This information is important, specially nowadays, because gamers can buy new extra levels, or new games, interactively using their own consoles. Developers can use the gamer profile extracted from the game-play to create new levels, adapt the game to different user, recommend new video games and also match up users. This work tries to deal with this problem. Here, we present a new game, called \\\"Dream\\\", whose philosophy is based on the information extraction process focused on the player game-play profile and its evolution. We also present a methodology based on time series clustering to group users according to their profile evolution. This methodology has been tested with real users which have played Dream during several rounds.\",\n \"Title: A new CSP graph-based representation to resource-constrained project scheduling problem\\nYear: 2014\\n\\nAbstract: Resource-Constrained Project Scheduling Problem (RCPSP) is a NP-hard combinatorial problem that consists in scheduling different activities in such a way the resource, precedence, and temporal constraints are satisfied. The main problem when dealing with NP-hard problems is the exponential growth of the computational resources needed to solve the problems. This work is an extension of a previous one, where a new CSP graph-based representation to solve Constraint Satisfaction Problems (CSP) by using Ant Colony Optimization (ACO) were proposed. This paper studies the behaviour of the CSP graph-based representation when it is applied to a real-world complex problem, in this case the RCPSP. The dataset used in this work has been extracted from Project Scheduling Problem Library (PSPLIB). Experimental results show that the proposed approach provides excellent results, closer to the optimum values published in the PSPLIB repository. Also, it has been analysed how the number of jobs and the number of different execution modes affect the performance of the algorithm.\",\n \"Title: Evolutionary clustering algorithm for community detection using graph-based information\\nYear: 2014\\n\\nAbstract: The problem of community detection has become highly relevant due to the growing interest in social networks. The information contained in a social network is often represented as a graph. The idea of graph partitioning of graph theory can be apply to split a graph into node groups based on its topology information. In this paper the problem of detecting communities within a social network is handled applying graph clustering algorithms based on this idea. The new approach proposed is based on a genetic algorithm. A new fitness function has been designed to guide the clustering process combining different measures of network topology (Density, Centralization, Heterogeneity, Neighbourhood, Clustering Coefficient). These different network measures have been experimentally tested using a real-world social network. Experimental results show that the proposed approach is able to detect communities and the results obtained in previous work have been improved.\",\n \"Title: Combining graph connectivity and genetic clustering to improve biomedical summarization\\nYear: 2014\\n\\nAbstract: Automatic summarization is emerging as a feasible instrument to help biomedical researchers to access online literature and face information overload. The Natural Language Processing community is actively working toward the development of effective summarization applications; however, automatic summaries are sometimes less informative than the user needs. In this work, our aim is to improve a summarization graph-based process combining genetic clustering with graph connectivity information. In this way, while genetic clustering allows us to identify the different topics that are dealt with in a document, connectivity information (in particular, degree centrality) allows us to asses and exploit the relevance of the different topics. Our automatic summaries are compared with others produced by commercial and research applications, to demonstrate the appropriateness of using this combination of techniques for automatic summarization.\",\n \"Title: A Co-Evolutionary Multi-Objective approach for a K-adaptive graph-based clustering algorithm\\nYear: 2014\\n\\nAbstract: Clustering is a field of Data Mining that deals with the problem of extract knowledge from data blindly. Basically, clustering identifies similar data in a dataset and groups them in sets named clusters. The high number of clustering practical applications has made it a fertile research topic with several approaches. One recent method that is gaining popularity in the research community is Spectral Clustering (SC). It is a clustering method that builds a similarity graph and applies spectral analysis to preserve the data continuity in the cluster. This work presents a new algorithm inspired by SC algorithm, the Co-Evolutionary Multi-Objective Genetic Graph-based Clustering (CEMOG) algorithm, which is based on the Multi-Objective Genetic Graph-based Clustering (MOGGC) algorithm and extends it by introducing an adaptative number of clusters. CEMOG takes an island-model approach where each island keeps a population of candidate solutions for ki clusters. Individuals in the islands can migrate to encourage genetic diversity and the propagation of individuals around promising search regions. This new approach shows its competitive performance, compared to several classical clustering algorithms (EM, SC and K-means), through a set of experiments involving synthetic and real datasets.\",\n \"Title: A Multi-Objective Genetic Graph-Based Clustering algorithm with memory optimization\\nYear: 2013\\n\\nAbstract: Clustering is one of the most versatile tools for data analysis. Over the last few years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the Spectral Clustering algorithm, which is based on graph cut: it initially generates a Similarity Graph using a distance measure and then uses its Graph Spectrum to find the best cut. Memory consuption is a serious limitation in that algorithm: The Similarity Graph representation usually requires a very large matrix with a high memory cost. This work proposes a new algorithm, based on a previous implementation named Genetic Graph-based Clustering (GGC), that improves the memory usage while maintaining the quality of the solution. The new algorithm, called Multi-Objective Genetic Graph-based Clustering (MOGGC), uses an evolutionary approach introducing a Multi-Objective Genetic Algorithm to manage a reduced version of the Similarity Graph. The experimental validation shows that MOGGC increases the memory efficiency, maintaining and improving the GGC results in the synthetic and real datasets used in the experiments. An experimental comparison with several classical clustering methods (EM, SC and K-means) has been included to show the efficiency of the proposed algorithm.\",\n \"Title: Automatic profile generation for UAV operators using a simulation-based training environment.\\nYear: 2016\\n\\nAbstract: Unmanned aerial vehicles (UAVs) are becoming a hot topic in the last few years for several research areas, such as aeronautics or computer science. Big companies such as Airbus or Amazon aim to incorporate this technology to their current systems to improve the quality of their services while reducing the human costs. However, current UAV technology requires a strong human supervisory control and this supposes an important potential risk. Therefore, it is critical to keep track of the pilot behavior to be able to determine whether he is ready or not to operate with this technology. To deal with this problem, we have developed a methodology based on different performance metrics to automatically evaluate planning and monitoring skills of new users trained in a multi-UAV simulation environment. This methodology, based on unsupervised learning and fuzzy logic, can automatically generate a profile of future operators and use it to assess their skills.\",\n \"Title: On the statistical distribution of the expected run-time in population-based search algorithms\\nYear: 2015\\n\\nAbstract: Run-time analysis is a method that characterizes the run-time behaviour of an algorithm. It has been successfully used to analyse metaheuristics and stochastic local search algorithms. Some studies on genetic programming and related stochastic search algorithms suggested the existence of a rationale behind the run-time behaviour which could be exploited to better understand the algorithm looking at its run-time response. Under that hypothesis, this paper presents empirical evidence suggesting common statistical properties in the run-time of several types of population-based search algorithms, including genetic algorithms, genetic programming, grammatical evolution, differential evolution or particle swarm optimization. In this analysis, only the run-time of runs that were able to find a solution are considered; unsuccessful runs are not included in the analysis. The run-time to find a solution, measured by the number of evaluations, of some well-known evolutionary algorithms is empirically obtained, finding that it usually can be approximated using a lognormal distribution, with the exception of some difficult problems, which are better approximated by an exponential. We also show that the algorithm parameter settings might influence the yielding run-time statistical distribution; in particular, when there is no selective pressure, the run-time, measured as the number of evaluations to find a solution, follows a Weibull distribution instead of a lognormal one. Finally, we outline a framework using a simple theoretical discrete-time model, showing that the geometrically distributed run-time is a consequence of the lack of memory in the algorithm.\",\n \"Title: An Empirical Study on Collective Intelligence Algorithms for Video Games Problem-Solving\\nYear: 2015\\n\\nAbstract: Computational intelligence (CI), such as evolutionary computation or swarm intelligence methods, is a set of bio-inspired algorithms that have been widely used to solve problems in areas like planning, scheduling or constraint satisfaction problems. Constrained satisfaction problems (CSP) have taken an important attention from the research community due to their applicability to real problems. Any CSP problem is usually modelled as a constrained graph where the edges represent a set of restrictions that must be verified by the variables (represented as nodes in the graph) which will define the solution of the problem. This paper studies the performance of two particular CI algorithms, ant colony optimization (ACO) and genetic algorithms (GA), when dealing with graph-constrained models in video games problems. As an application domain, the \\\"Lemmings\\\" video game has been selected, where a set of lemmings must reach the exit point of each level. In order to do that, each level is represented as a graph where the edges store the allowed movements inside the world. The goal of the algorithms is to assign the best skills in each position on a particular level, to guide the lemmings to reach the exit. The paper describes how the ACO and GA algorithms have been modelled and applied to the selected video game. Finally, a complete experimental comparison between both algorithms, based on the number of solutions found and the levels solved, is analysed to study the behaviour of those algorithms in the proposed domain.\",\n \"Title: Intelligent Distributed Processing Methods for Big Data.\\nYear: 2015\\n\\nAbstract: Motivation Today, “Big Data” is a new information overloading problem in many different areas. Such areas include health cares (e.g., medical records, bioinformatics), e-sciences (e.g., physics, chemistry, and geology), and social sciences (e.g., politics). Thus, as we have various types of feasible data from a number of available sources, it is becoming increasingly more difficult to efficiently process such Big Data.\",\n \"Title: Social big data: Recent achievements and new challenges\\nYear: 2016\\n\\nAbstract: •The paper presents the methodologies on information fusion for social media.•The methodologies, frameworks, and software used to work with big data are given.•The state of the art in the data analytic techniques on social big data is provided.•Social big data applications for various domains are described and analyzed.\",\n \"Title: Design and development of a lightweight multi-UAV simulator\\nYear: 2015\\n\\nAbstract: UAVs have become enormously popular over the last few years. These new systems could make a remarkable improvement in public safety and have many applications in a variety of fields such as agriculture or postage of packages. In order to test UAV capacities and train UAV mission operators, several simulators are used, and the use of them usually entails high costs. This work presents a low-cost and easily distributable simulator, focused on simulating missions carried out by multiple UAVs and extracting data from them. To evaluate its effectiveness, it has been subjected to stress testing with thousands of virtual users, proving to have a potentially competitive response.\",\n \"Title: Bio-inspired clustering: Basic features and future trends in the era of Big Data\\nYear: 2015\\n\\nAbstract: Clustering is perhaps one of the most popular approaches used in unsupervised machine learning. There's a huge number of different methods and algorithms that have been designed in the last decades related to this “blind pattern search”, some of these approaches are based on bio-inspired methods such as Evolutionary Computation, Swarm Intelligence or Neural Networks among others. In the last years, and due to the fast growing of Big Data problems, some interesting advances and new approaches are currently being developed in this area, new algorithms like online clustering and streaming clustering are appearing. These new algorithms try to solve classical problems in Clustering and deal with the new features of these new kind of problems. This keynote lecture will provide some basics on both, Clustering methods and bio-inspired computation, and how they have been combined to improve the quality of these algorithms, to later show the main features that Big Data needs to obtain reliable clustering approaches. Finally, some practical examples and applications will be described to show how these new algorithms are evolving to be used in the near future in complex and dynamic environments.\",\n \"Title: Optimizing Satisfaction in a Multi-courses Allocation Problem.\\nYear: 2015\\n\\nAbstract: The resource allocation problem is a traditional kind of NP-hard problem. One of its application domains is the allocation of educational resources. In most universities, students select some courses they would like to attend by ranking the proposed courses. However, to ensure the quality of a course, the number of seats is limited, so not all students can enroll in their preferred courses. Therefore, the school administration needs some mechanism to assign the available resources. In this paper, the course allocation problem has been modeled as a Constraint Satisfaction Optimization Problem (CSOP) and two metrics have been defined to quantify the satisfaction of students. The problem is solved with Gecode and a greedy-based algorithm showing how the CSOP approach is able to allocate resources optimizing the students' satisfaction. Another contribution of this work is the allocation of several courses simultaneously, generating feasible solutions in a short time. The allocation procedures are based on preferences for courses defined by students, and on the administration's constraints at Ecole Hoteliere de Lausanne. Ten data sets have been generated using the distribution of students' preferences for courses, and have been used to carry out a complete experimental analysis.\",\n \"Title: GAMPP: Genetic Algorithm for UAV Mission Planning Problems.\\nYear: 2015\\n\\nAbstract: Due to the rapid development of the UAVs capabilities, these are being incorporated into many fields to perform increasingly complex tasks. Some of these tasks are becoming very important because they involve a high risk to the vehicle driver, such as detecting forest fires or rescue tasks, while using UAVs avoids risking human lives. Recent researches on artificial intelligence techniques applied to these systems provide a new degree of high-level autonomy of them. Mission planning for teams of UAVs can be defined as the planning process of locations to visit (way-points) and the vehicle actions to do (loading/dropping a load, taking videos/pictures, acquiring information), typically over a time period. Currently, UAVs are controlled remotely by human operators from ground control stations, or use rudimentary systems. This paper presents a new Genetic Algorithm for solving Mission Planning Problems (GAMPP) using a cooperative team of UAVs. The fitness function has been designed combining several measures to look for optimal solutions minimizing the fuel consumption and the mission time (or makespan). The algorithm has been experimentally tested through several missions where its complexity is incrementally modified to measure the scalability of the problem. Experimental results show that the new algorithm is able to obtain good solutions improving the runtime of a previous approach based on CSPs.\",\n \"Title: Constraint-based model design for timetabling problems in secondary schools\\nYear: 2015\\n\\nAbstract: In this paper, we propose a constraint-based model for the combination of a course timetabling problem and a course allocation problem for secondary schools in Switzerland. Course timetabling has been widely studied for Universities, but for schools, solutions have only been proposed for specific countries or even for specific schools. In fact, timetabling problems presents the difficulty to be case-specific through specific constraints satisfaction. In addition, due to a recent reform of the education system in Switzerland, the timetabling problem is combined with a course allocation problem that has an important impact on timetables. Indeed, some topics are defined by a curriculum but each student may be assigned to up to five different options depending on past grades and students choices. Both problems, timetabling and allocation must be solved simultaneously and educational objectives are related to both composition of classes and timetables of students. A description of the problem and the educational objectives to consider are presented. Finally, a complete constraint-based model based on hard and soft constraints has been designed to solve the combined problem, both types of constraints are described in detail and a qualitative complexity analysis of this model is given.\",\n \"Title: A Hybrid MOGA-CSP for Multi-UAV Mission Planning\\nYear: 2015\\n\\nAbstract: Mission Planning Problem for a large number of Unmanned Air Vehicles (UAV) consists of a set of locations to visit in different time windows, and the actions that the vehicle can perform based on its features such as the sensors, speed or fuel capacity. After formulating this problem as a Constraint Satisfaction Problem (CSP), we try to search the set of Non dominated solutions which minimize the fuel consumption and the makespan of the mission. To solve it, we will use a Multi-Objective Genetic Algorithm (MOGA), that will match the model constraints and use a multi-objective function in order to optimize these objective variables.\",\n \"Title: Ephemeral Computing and Bioinspired Optimization.\\nYear: 2015\\n\\nAbstract: Computational devices with significant computing power are pervasive yet often under-exploited since they are frequently idle or performing non-demanding tasks. Exploiting this power can be a cost-effective solution for solving complex computational tasks. Device-wise, this computational power can some times comprise a stable, long-lasting availability windows but it will more frequently take the form of brief, ephemeral bursts, mainly in the presence of devices âlentâ voluntarily by their users. A highly dynamic and volatile computational landscape emerges from the collective contribution of numerous such devices. Algorithms consciously running on these environments require specific properties in terms of flexibility, plasticity and robustness. Bioinspired algorithms are particularly well suited to this endeavor, thanks to their intrinsic features: decentralized functioning, intrinsic parallelism, resilience, and adaptiveness. The latter is essential to exert advanced self-control on the functioning and/or structure of the algorithm. Much has been done in providing self-adaptation capabilities to these techniques, yet the science of self-? bionspired algorithms is still nascent, in particular regarding to higher-level self-adaptation, and self-management in the context of large scale optimization problems and distributed ephemeral computing technologies. Deploying bioinspired techniques on this scenario will also pave the way for the application of other techniques on this computational domain.\",\n \"Title: A Multi-Uav Mission Planning Videogame-Based Framework For Player Analysis\\nYear: 2015\\n\\nAbstract: The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAVs) comprises a set of locations to visit in different time windows, and the actions that the vehicle can perform based on its features, such as sensors, speed or fuel consumption. Although this problem is increasingly more supported by Artificial Intelligence systems, nowadays human factors are still critical to guarantee the success of the designed plan. Studying and analyzing how humans solve this problem is sometimes difficult due to the complexity of the problem and the lack of data available. To overcome this problem, we have developed an analysis framework for Multi-UAV Cooperative Mission Planning Problem (MCMPP) based on a videogame that gamifies the problem and allows a player to design plans for multiple UAVs intuitively. On the other hand, we have also developed a mission planner algorithm based on Constraint Satisfaction Problems (CSPs) and solved with a Multi-Objective Branch & Bound (MOBB) method which optimizes the objective variables of the problem and gets the best solutions in the Pareto Optimal Frontier (POF). To prove the environment potential, we have performed a comparative study between the plans generated by a heterogenous group of human players and the solutions obtained by this planner.\",\n \"Title: GANY: A genetic spectral-based clustering algorithm for Large Data Analysis\\nYear: 2015\\n\\nAbstract: Recently, Data analysis is one of the most growing fields. The big amounts of data are making their analysis a really challenging area. The most relevant techniques are mainly divided in two sub-domains: Classification and Clustering. Even though Classification is currently growing and evolving, one of the promising techniques to deal with the Large Data Analysis is Clustering, because Classification needs human supervision, which makes the analysis more expensive. Clustering is a blind process used to group data by similarity. Currently, the most relevant methods are those based on manifold identification. The main idea behind these techniques is to group data using the form they define in the space. In order to achieve this goal, there are several techniques based on Spectral Analysis which deal with this problem. However, these techniques are not suitable for Large Data, due to they require a lot of memory to determine the groups. Besides, there are some problems of local minima convergence in these techniques which are common in statistical methodologies. This work is focused on combining Genetic Algorithms with spectral-based methodologies to deal with the Large Data Analysis problem. Here, we will combine the Nyström method with the Spectrum to generate an approximation of the problem to an accurate summary of the search space. Also a genetic algorithm is used to reduce the local minimum convergence problem in the new search space. The performance of this methodology has been evaluated using the accuracy with both, synthetic and real-world datasets extracted from the literature.\",\n \"Title: On the Applicability of Ant Colony Optimization to Non-Intrusive Load Monitoring in Smart Grids.\\nYear: 2015\\n\\nAbstract: Along with the proliferation of the Smart Grid, power load disaggregation is a research area that is lately gaining a lot of popularity due to the interest of energy distribution companies and customers in identifying consumption patterns towards improving the way the energy is produced and consumed via e.g. demand side management strategies. Such data can be extracted by using smart meters, but the expensive cost of incorporating a monitoring device for each appliance jeopardizes significantly the massive implementation of any straightforward approach. When resorting to a single meter to monitor the global consumption of the house at hand, the identification of the different appliances giving rise to the recorded consumption profile renders a particular instance of the so-called source separation problem, for which a number of algorithmic proposals have been reported in the literature. This paper gravitates on the applicability of the Ant Colony Optimization ACO algorithm to perform this power disaggregation treating the problem as a Constraint Satisfaction Problem CSP. The discussed experimental results utilize data contained in the REDD dataset, which corresponds to real power consumption traces of different households. Although the experiments carried out in this work reveal that the ACO solver can be successfully applied to the Non-Intrusive Load Monitoring problems, further work is needed towards assessing its performance when tackling more diverse appliance models and noisy power load traces.\",\n \"Title: Medoid-based clustering using ant colony optimization.\\nYear: 2016\\n\\nAbstract: The application of ACO-based algorithms in data mining has been growing over the last few years, and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works about unsupervised learning have focused on clustering, showing the potential of ACO-based techniques. However, there are still clustering areas that are almost unexplored using these techniques, such as medoid-based clustering. Medoid-based clustering methods are helpful—compared to classical centroid-based techniques—when centroids cannot be easily defined. This paper proposes two medoid-based ACO clustering algorithms, where the only information needed is the distance between data: one algorithm that uses an ACO procedure to determine an optimal medoid set (METACOC algorithm) and another algorithm that uses an automatic selection of the number of clusters (METACOC-K algorithm). The proposed algorithms are compared against classical clustering approaches using synthetic and real-world datasets.\",\n \"Title: Dynamic Traffic Light Control System Based on Process Synchronization Among Connected Vehicles.\\nYear: 2016\\n\\nAbstract: Vehicular traffic is tremendously increasing around the world, especially in large urban areas. The resulting congestion has become a key issue and emerging research topic to transportation specialist and decision makers. In this study, inspired by recent advanced vehicle technologies, we take into account in improving traffic flow in real-time problem. In order to solve the problem, we propose a new approach to manage traffic flow at the intersection in real-time via controlling by traffic light scheduling. In particular, the proposed method is based on process synchronization theory and connected vehicle technology where each vehicle is able to communicate with others. The traffic deadlock is also taken into consideration in case of high traffic volume. The simulation shows the potential results comparing with the existing traffic management system.\",\n \"Title: Application Areas of Ephemeral Computing: A Survey.\\nYear: 2016\\n\\nAbstract: It is increasingly common that computational devices with significant computing power are underexploited. Some of the reasons for that are due to frequent idle-time or to the low computational demand of the tasks they perform, either sporadically or in their regular duty. The exploitation of this otherwise-wasted computational power is a cost-effective solution for solving complex computational tasks. Individually device-wise, this computational power can sometimes comprise a stable, long-lasting availability window but it will more frequently take the form of brief, ephemeral bursts. Then, in this context a highly dynamic and volatile computational landscape emerges from the collective contribution of such numerous devices. Algorithms consciously running on this kind of environment require specific properties in terms of flexibility, plasticity and robustness. Bioinspired algorithms are particularly well suited to this endeavor, thanks to some of the features they inherit from their biological sources of inspiration, namely decentralized functioning, intrinsic parallelism, resilience, and adaptiveness. Deploying bioinspired techniques on this scenario, and conducting analysis and modelling of the underlying Ephemeral Computing environment will also pave the way for the application of other non-bioinspired techniques on this computational domain. Computational creativity and content generation in video games are applications areas of the foremost economical interest and are well suited to Ephemeral Computing due to their intrinsic ephemeral nature and the widespread abundance of gaming applications in all kinds of devices. In this paper, we will explain why and how they can be adapted to this new environment.\",\n \"Title: Detecting discussion communities on vaccination in twitter.\\nYear: 2017\\n\\nAbstract: Vaccines have contributed to dramatically decrease mortality from infectious diseases in the 20th century. However, several social discussion groups related to vaccines have emerged, influencing the opinion of the population about vaccination for the past 20 years. These communities discussing on vaccines have taken advantage of social media to effectively disseminate their theories. Nowadays, recent outbreaks of preventable diseases such as measles, polio, or influenza, have shown the effect of a decrease in vaccination rates. Social Networks are one of the most important sources of Big Data. Specifically, Twitter generates over 400 million tweets every day. Data mining provides the necessary algorithms and techniques to analyse massive data and to discover new knowledge. This work proposes the use of these techniques to detect and track discussion communities on vaccination arising from Social Networks. Firstly, a preliminary analysis using data from Twitter and official vaccination coverage rates is performed, showing how vaccine opinions of Twitter users can influence over vaccination decision-making. Then, algorithms for community detection are applied to discover user groups opining about vaccines. The experimental results show that these techniques can be used to discover social discussion communities providing useful information to improve immunization strategies. Public Healthcare Organizations may try to use the detection and tracking of these social communities to avoid or mitigate new outbreaks of eradicated diseases.\",\n \"Title: ACO-based clustering for Ego Network analysis.\\nYear: 2017\\n\\nAbstract: The unstoppable growth of Social Networks (SNs), and the huge number of connected users, have become these networks as one of the most popular and successful domains for a large number of research areas. The different possibilities, volume and variety that these SNs offer, has become them an essential tool for every-day working and social relationships. One of the basic features that any SN provides is to allow users to group, organize and classify their connections into different groups, or “circles”. These circles can be defined using different characteristics as roommates, workmates, hobbies, professional skills, etc. The problem of finding these circles taking into account the variety, volume and dynamics of these SNs has become an important challenge for a wide number of Computer Science areas, as Big Data, Data Mining or Machine Learning among others. Problems related to pre-processing, fusion and knowledge discovering of information from these sources are still an open question. This paper presents a new Bio-inspired method, based on Ant Colony Optimization (ACO) algorithms, that has been designed to find and analyze these circles. Given any user in a network, the new method is able to automatically determine the different users that compose his/her groups or circles of interest, so the network will be clustered into different components based on the users profiles and their dynamics. This algorithm has been applied to Ego Networks where the node centering the network (called “Ego”) represents the user being studied. In this work two different ACO algorithms, that differ in the source of information used to perform the community finding tasks, have been designed. The first ACO algorithm uses the information extracted from the topology of the network, whereas the second one uses the profile information provided by users. The proposed algorithms are able to detect the different circles in three popular Social Networks: Facebook, Twitter and Google+. Finally, and using several databases from previous SNs, an experimental evaluation of our methods has been carried out to show how the algorithms are currently working.\",\n \"Title: String-based Malware Detection for Android Environments.\\nYear: 2016\\n\\nAbstract: Android platforms are known as the less security smartphone devices. The increasing number of malicious apps published on Android markets suppose an important threat to users sensitive data, compromising more devices everyday. The commercial solutions that aims to :fight against this malware are based on signature methodologies whose detection ratio is low. Furthermore, these engines can be easily defeated by obfuscation techniques, which are extremely common in app plagiarism. This work aims to improve malware detection using only the binary information and the permissions that are normally used by the anti-virus engines, in order to provide a scalable solution based on machine learning. In order to evaluate the performance of this approach, we carry out our experiments using 5000 malware and 5000 benign-ware, and compare the results with 56 Anti-Virus Engines from VirusTotal.\",\n \"Title: Studying the Influence of Static API Calls for Hiding Malware.\\nYear: 2016\\n\\nAbstract: Malware detection has become a challenging task over the last few years. Different concealment strategies such as packing compression, polymorphic encryption and metamorphic obfuscation have produced that malware Analysts need to find more original techniques to discriminate whether a file is malware or not. One of the current benchmark techniques is static analysis of API Calls. This technique aims to detect malware using the API Calls information extracted from the malware files. In this work, we aim to show a complete study of this technique using a behavioural model, built through an evolutionary process, in order to define possible limitations. For this analysis we will use a benchmark dataset to study the discrimination between malware and benignware and evaluate how malware writers are trying to imitate benign behaviour in order to defeat this technique.\",\n \"Title: A study on performance metrics and clustering methods for analyzing behavior in UAV operations.\\nYear: 2017\\n\\nAbstract: Unmanned Aerial Vehicles (UAVs) are starting to provide new possibilities to human societies and their demand is growing according to the new industrial application fields for these revolutionary tools. The current systems are still evolving, specially from an Artificial Intelligence perspective, which is increasing the different tasks that UAVs can perform. However, the current state still requires a strong human supervision. As a consequence, a good preparation forUAVoperators is mandatory due to some of their applications might affect human safety. During the training process, it is important to measure the performance of these operators according to different factors that can help to decide what operators are more suitable for different kinds of missions creating operator profiles. Having this goal in mind, this work aims to present an extensive and robust methodology to automatically extract different performance profiles from the training process of operators in an UAV simulation environment. Our method combines the definition of a set of performance metrics with clustering techniques to define operators profiles, ensuring that the behavior discrimination is suitable and consistent.\",\n \"Title: Measuring the Radicalisation Risk in Social Networks.\\nYear: 2017\\n\\nAbstract: Social networks (SNs) have become a powerful tool for the jihadism as they serve as recruitment assets, live forums, psychological warfare, as well as sharing platforms. SNs enable vulnerable individuals to reach radicalized people, hence triggering their own radicalization process. There are many vulnerability factors linked to socio-economic and demographic conditions that make jihadist militants suitable targets for their radicalization. We focus on these vulnerability factors, studying, understanding, and identifying them on the Internet. Here, we present a set of radicalization indicators and a model to assess them using a data set of tweets published by several Islamic State of Iraq and Sham sympathizers. Results show that there is a strong correlation between the values assigned by the model to the indicators.\",\n \"Title: Game theoretic approach on Real-time decision making for IoT-based traffic light control.\\nYear: 2017\\n\\nAbstract: Smart traffic light control at intersections is 1 of the major issues in Intelligent Transportation System. In this paper, on the basis of the new emerging technologies of Internet of Things, we introduce a new approach for smart traffic light control at intersection. In particular, we firstly propose a connected intersection system where every objects such as vehicles, sensors, and traffic lights will be connected and sharing information to one another. By this way, the controller is able to collect effectively and mobility traffic flow at intersection in real-time. Secondly, we propose the optimization algorithms for traffic lights by applying algorithmic game theory. Specially, 2 game models (which are Cournot Model and Stackelberg Model) are proposed to deal with difference scenarios of traffic flow. In this regard, based on the density of vehicles, controller will make real-time decisions for the time durations of traffic lights to optimize traffic flow. To evaluate our approach, we have used Netlogo simulator, an agent-based modeling environment for designing and implementing a simple working traffic. The simulation results shows that our approach achieves potential performance with various situations of traffic flow.\",\n \"Title: Improving experimental methods on success rates in evolutionary computation.\\nYear: 2017\\n\\nAbstract: Due to the complexity of theoretical approaches in evolutionary computation (EC), research has being largely performed on experimental basis. One popular measure used by the EC community is the success rate (SR), which is used alone or as part of more complex measures such as Koza's computational effort in genetic programming. A common practice in EC is to report just a punctual estimation of the SR, without additional information about its associated uncertainty. We aim to motivate EC researchers to adopt more rigorous practices when working with SRs. In particular, we introduce the importance of correctly reporting this measure and highlight its binomial nature. Unfortunately, this fact is usually overlooked in the literature. Considering the binomiality of the SR opens the whole corpus of binomial statistics to EA research and practice. In particular, we focus on studying several methods to compute SR confidence intervals, the factors that determine their quality in terms of coverage probability and interval length. Due to its practical interest, we also briefly discuss the number of required runs to build confidence intervals with a certain quality, providing a sound method to set the number of runs, one of the most important experimental settings in EC. Evidence suggests that Wilson is, on average, a reliable and simple method to bound an estimation of SR with confidence intervals, while the standard method, which is quite popular because of its conceptual simplicity, should be avoided in any case. However, other methods can also be of interest under certain circumstances. We encourage to report the number of trials and successes, as well as the interval, to ease further comparability of the results.\",\n \"Title: Identifying and ranking cultural heritage resources on geotagged social media for smart cultural tourism services.\\nYear: 2017\\n\\nAbstract: With a large amount of data (e.g., ratings and feedbacks) obtained from social media (e.g., TripAdvisor), smart tourism applications and services have been studied to understand the contexts of users. More particularly, in this work, we have been focusing on \\\\\\\"cultural tourism\\\\\\\" service by automatically identifying and ranking cultural things (e.g., historical places). The main aims of this research are (1) to identify useful cultural heritage resources from geotagged social media (more precisely, spatial folksonomy), and (2) to rank them, with respect to the user context (e.g., location). Thus, the smart cultural tourism service can deliver smart interactions between the visitors of smart tourism environments by collecting and analyzing geotagged multimedia data (e.g., photos, tags, and comments) from available social media. In order to evaluate the proposed service, the system has been implemented with the real-world datasets related to cultural heritage sites (e.g., Hue, Hoi An, My Son in Vietnam, and Gyeongbokgung, Changdeokgung, Gyeongju in Korea).\",\n \"Title: GRSAT: A Novel Method on Group Recommendation by Social Affinity and Trustworthiness.\\nYear: 2017\\n\\nAbstract: Existing group recommender systems generate a consensus function to aggregate individual preference into group preference. However, the systems encounter difficulty in gathering rating-scores and validating their reliability, since the aggregation strategy requires user rating-scores. To solve these problems, we propose Group Recommendation based on Social Affinity and Trustworthiness GRSAT based on social affinity and trustworthiness, which is obtained from the user’s watching-history and content features, without rating-score. Our experiment proves that GRSAT has outstanding performance for group recommendation compared with the other consensus functions, in terms of the number of the movies and users, on both biased and unbiased groups.\",\n \"Title: A knee point based evolutionary multi-objective optimization for mission planning problems.\\nYear: 2017\\n\\nAbstract: The current boom of Unmanned Aerial Vehicles (UAVs) is increasing the number of potential industrial and research applications. One of the most demanded topics in this area is related to the automated planning of a UAVs swarm, controlled by one or several Ground Control Stations (GCSs). In this context, there are several variables that influence the selection of the most appropriate plan, such as the makespan, the cost or the risk of the mission. This problem can be seen as a Multi-Objective Optimization Problem (MOP). On previous approaches, the problem was modelled as a Constraint Satisfaction Problem (CSP) and solved using a Multi-Objective Genetic Algorithm (MOGA), so a Pareto Optimal Frontier (POF) was obtained. The main problem with this approach is based on the large number of obtained solutions, which hinders the selection of the best solution. This paper presents a new algorithm that has been designed to obtain the most significant solutions in the POF. This approach is based on Knee Points applied to MOGA. The new algorithm has been proved in a real scenario with different number of optimization variables, the experimental results show a significant improvement of the algorithm performance.\",\n \"Title: Handling swarm of UAVs based on evolutionary multi-objective optimization.\\nYear: 2017\\n\\nAbstract: The fast technological improvements in unmanned aerial vehicles (UAVs) has created new scenarios where a swarm of UAVs could operate in a distributed way. This swarm of vehicles needs to be controlled from a set of ground control stations, and new reliable mission planning systems, which should be able to handle the large amount of variables and constraints. This paper presents a new approach where this complex problem has been modelled as a constraint satisfaction problem (CSP), and is solved using a multi-objective genetic algorithm (MOGA). The algorithm has been designed to minimize several variables of the mission, such as the fuel consumption or the makespan among others. The designed fitness function, used by the algorithm, takes into consideration, as a weighted penalty function, the number of constraints fulfilled for each solution. Therefore, the MOGA algorithm is able to manage the number of constraints fulfilled by the selected plan, so it is possible to maximize in the elitism phase of the MOGA the quality of the solutions found. This approach allows to alleviate the computational effort carried out by the CSP solver, finding new solutions from the Pareto front, and therefore reducing the execution time to obtain a solution. In order to test the performance of this new approach 16 different mission scenarios have been designed. The experimental results show that the approach outperforms the convergence of the algorithm in terms of number of generations and runtime.\",\n \"Title: Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms.\\nYear: 2017\\n\\nAbstract: Due to recent booming of unmanned air vehicles (UAVs) technologies, these are being used in many fields involving complex tasks. Some of them involve a high risk to the vehicle driver, such as fire monitoring and rescue tasks, which make UAVs excellent for avoiding human risks. Mission planning for UAVs is the process of planning the locations and actions (loading/dropping a load, taking videos/pictures, acquiring information) for the vehicles, typically over a time period. These vehicles are controlled from ground control stations (GCSs) where human operators use rudimentary systems. This paper presents a new multi-objective genetic algorithm for solving complex mission planning problems involving a team of UAVs and a set of GCSs. A hybrid fitness function has been designed using a constraint satisfaction problem to check whether solutions are valid and Pareto-based measures to look for optimal solutions. The algorithm has been tested on several datasets, optimizing different variables of the mission, such as the makespan, the fuel consumption, and distance. Experimental results show that the new algorithm is able to obtain good solutions; however, as the problem becomes more complex, the optimal solutions also become harder to find.\",\n \"Title: Real-Time Traffic Flow Management Based on Inter-Object Communication: a Case Study at Intersection.\\nYear: 2017\\n\\nAbstract: Intersections become very congested when traffic volumes are high, creating inefficiency that results in user delay and frustration. There have been many approaches which focus on optimization signal of Traffic Light System and Vehicle Trajectory Analysis to improve traffic flow at intersection. However, to implement those approaches into reality become a challenges since real-time problem. In this study, inspired by recent advanced vehicle technologies, we propose an approach for traffic flow management at intersection. In particular, with the exploding at an enormous rate of Internet of Things (IoT), the connected object has been the most visible and familiar application. By this way, based on connected object, we design a model which communicating among objects to improve traffic flow at intersection with real time problem. Moreover, traffic congestion is also taken into consideration in case of high traffic volume. The simulation shows the potential results comparing with the existing traffic management system.\",\n \"Title: Extracting Radicalisation Behavioural Patterns from Social Network Data\\nYear: 2017\\n\\nAbstract: Social networks (SNs) have become essential communication tools in recent years, generating a large amount of information about its users that can be analysed with data processing algorithms. Recently, a new type of SN user has emerged: jihadists that use SNs as a tool to recruit new militants and share their propaganda. In this paper, we study a set of indicators to assess the risk of radicalisation of a social network user. These radicalisation indicators help law-enforcement agencies, prosecutors and organizations devoted to fight terrorism to detect vulnerable targets even before the radicalisation process is completed. Moreover, these indicators are the first steps towards a software tool to gather, represent, pre-process and analyse behavioural indicators of radicalisation in terrorism.\",\n \"Title: An Initial Study on Radicalization Risk Factors: Towards an Assessment Software Tool\\nYear: 2017\\n\\nAbstract: Radicalization has increasingly become a transnational risk as cyber-technologies and social networks have steadily improved in the past years. DAESH has utilized the benefits of these new technologies to radicalize and recruit home-grown fighters, inviting them to join their ranks in the Islamic State territories, or inciting them to attack in their Western countries. However, this process of radicalization is not a simple task. Jihadist recruiters take advantage of certain vulnerable individuals who are better targets for radicalization. Academics have subsequently attempted to identify and examine these characteristics, which can be useful in identifying people who may be vulnerable to jihadist rhetoric. Violence risk assessment is a method which has been used by psychologists, Law Enforcement Agencies, prosecutors and other relevant actors, in order to assess the risk of an individual to commit violent acts. As terrorist or political violence is not comparable with other types of violence, specialized tools are needed to calculate the risk of radicalization or attack threat. This paper tries to gather the risk factors associated with violent extremism and jihadist radicalization from the literature and the risk assessment tools already available or under development. This paper presents some details related to the RiskTrack software tool, which is currently under development by behavioral researchers and computer engineers. The RiskTrack software tool aims to automatically assess an individual's risk of becoming radicalized. This is done by analyzing social media profiles and testing for specific factors that have been found to be associated with radicalization.\",\n \"Title: Comparative study of pheromone control heuristics in ACO algorithms for solving RCPSP problems.\\nYear: 2017\\n\\nAbstract: •A thorough analysis and description of both our model and the ACO algorithm.•The Dynamic Oblivion Rate determines the percentage of pheromones to be removed.•A new Oblivion Rate heuristic based on the bio-inspired Coral Reef Optimization.•A complete analysis of different Oblivion Rates using PSPLIB dataset.\",\n \"Title: Extending The Sacoc Algorithm Through The Nystrom Method For Dense Manifold Data Analysis\\nYear: 2017\\n\\nAbstract: The growing amount of data demands new analytical methodologies to extract relevant knowledge. Clustering is one of the most competitive techniques in this context. Using a dataset as a starting point, clustering techniques blindly group the data by similarity. Among the different areas, manifold identification is currently gaining importance. Spectral-based methods, which are one of the main used methodologies, are sensitive to metric parameters and noise. In order to solve these problems, new bio-inspired techniques have been combined with different heuristics to perform the cluster selection, in particular for dense datasets, featured by areas of higher density. This paper extends a previous algorithm named spectral-based ant colony optimisation clustering (SACOC), used for manifold identification. We focus on improving it through the Nystrom extension for dealing with dense data problems. We evaluated the new approach, called SACON, comparing it against online clustering algorithms and the Nystrm extension of spectral clustering.\",\n \"Title: Solving Strategy Board Games Using A Csp-Based Aco Approach\\nYear: 2017\\n\\nAbstract: In the last years, there have been a huge increase in the number of research contributions that use games and video-games as an application domain for testing different artificial intelligence algorithms. Some of these problems can be represented as a constraint satisfaction problem (CSP), and heuristics algorithms (such as ant colony optimisation) can be used due to the complexity of the modelled problems. This paper presents a comparative study of the performance of a novel ACO model for CSP-based board games. In this work, two different oblivion rate meta-heuristics for controlling the number of pheromones created in the model have been created. Experimental results reveal that both meta-heuristics reduce considerably the number of pheromones produced in the system without affecting the quality of the solutions in terms of average optimality.\",\n \"Title: Modelling Behaviour in UAV Operations Using Higher Order Double Chain Markov Models.\\nYear: 2017\\n\\nAbstract: Creating behavioural models of human operators engaged in supervisory control tasks with UAVs is of great value due to the high cost of operator failures. Recent works in the field advocate the use of Hidden Markov Models (HMMs) and derivatives to model the operator behaviour, since they offer interpretable patterns for a domain expert and, at the same time, provide valuable predictions which can ...\",\n \"Title: MOCDroid: multi-objective evolutionary classifier for Android malware detection.\\nYear: 2017\\n\\nAbstract: Malware threats are growing, while at the same time, concealment strategies are being used to make them undetectable for current commercial antivirus. Android is one of the target architectures where these problems are specially alarming due to the wide extension of the platform in different everyday devices. The detection is specially relevant for Android markets in order to ensure that all the software they offer is clean. However, obfuscation has proven to be effective at evading the detection process. In this paper, we leverage third-party calls to bypass the effects of these concealment strategies, since they cannot be obfuscated. We combine clustering and multi-objective optimisation to generate a classifier based on specific behaviours defined by third-party call groups. The optimiser ensures that these groups are related to malicious or benign behaviours cleaning any non-discriminative pattern. This tool, named MOCDroid, achieves an accuracy of 95.15 % in test with 1.69 % of false positives with real apps extracted from the wild, overcoming all commercial antivirus engines from VirusTotal.\",\n \"Title: Can an Automatic Tool Assess Risk of Radicalization Online? A Case Study on Facebook\\nYear: 2017\\n\\nAbstract: Can risk of radicalization be measured analyzing users' information on the Internet? Following the background from recent studies [1], [2], [3], [4], this work aims to test the viability of detecting and measuring risk factors of jihadist radicalization with a Facebook case. The work has been performed under the frame of RiskTrack project, which aim is to develop an automatic risk assessment tool for jihadist radicalization, using data from online social networks profiles.\",\n \"Title: Wind Power Production Forecasting Using Ant Colony Optimization and Extreme Learning Machines.\\nYear: 2017\\n\\nAbstract: Nowadays the energy generation strategy of almost every nation around the world relies on a strong contribution from renewable energy sources. In certain countries the relevance taken by wind energy is particularly high within its national production share, mainly due to its large-scale wind flow patterns. This noted potentiality of wind energy has so far attracted public and private funds to support the development of advanced wind energy technologies. However, the proliferation of wind farms makes it challenging to achieve a proper electricity balance of the grid, a problem that becomes further involved due to the fluctuations of wind generation that occur at different time scales. Therefore, acquiring a predictive insight on the variability of this renewable energy source becomes essential in order to optimally inject the produced wind energy into the electricity grid. To this end the present work elaborates on a hybrid predictive model for wind power production forecasting based on meteorological data collected at different locations over the area where a wind farm is located. The proposed method hybridizes Extreme Learning Machines with a feature selection wrapper that models the discovery of the optimum subset of predictors as a metric-based search for the optimum path through a solution graph efficiently tackled via Ant Colony Optimization. Results obtained by our approach for two real wind farms in Zamora and Galicia (Spain) are presented and discussed, from which we conclude that the proposed hybrid model is able to efficiently reduce the number of input features and enhance the overall model performance.\",\n \"Title: Multiple classifiers in biometrics. part 1: Fundamentals and review.\\nYear: 2018\\n\\nAbstract: •Fundamentals of Multiple Classifier Systems with nomenclature, methods and theory.•Review of MCS applied to multimodal biometrics for person recognition.•Generality enables the export of MCS methods from biometrics to other areas.\",\n \"Title: Multiple classifiers in biometrics. Part 2: Trends and challenges.\\nYear: 2018\\n\\nAbstract: •Recent trends in Multiple Classifier Systems from the field of biometrics.•Information fusion methods incorporating context information in an adaptive way.•Information from context: quality measures and user-specific behaviors.•Challenges in biometrics: nature, robustness, integration, usability, and security.•Role of Multiple Classifier Systems in biometrics challenges.\",\n \"Title: EvoDeep: A new evolutionary approach for automatic Deep Neural Networks parametrisation.\\nYear: 2018\\n\\nAbstract: Deep Neural Networks (DNN) have become a powerful, and extremely popular mechanism, which has been widely used to solve problems of varied complexity, due to their ability to make models fitted to non-linear complex problems. Despite its well-known benefits, DNNs are complex learning models whose parametrisationand architecture are made usually by hand. This paper proposes a new Evolutionary Algorithm, named EvoDeep, devoted to evolve the parameters and the architecture of a DNN in order to maximise its classification accuracy, as well as maintaining a valid sequence of layers. This model is tested against a widely used dataset of handwritten digits images. The experiments performed using this dataset show that the Evolutionary Algorithm is able to select the parameters and the DNN architecture appropriately, achieving a 98.93% accuracy in the best run.\",\n \"Title: An Ontology-Based Approach for Mining Radicalization Indicators from Online Messages\\nYear: 2018\\n\\nAbstract: Detecting radicalization on social networks is crucial to the fight against violent extremism and terrorism. In most cases, online radicalization has clear warning indicators that can be detected at the early stages of the radicalization process. In this paper, we focus on mining radicalization indicators from online messages by exploiting structured domain knowledge. More precisely, we propose an approach to automatically annotate social messages with concepts from a domain ontology. Annotations are then exploited within an inference phase to identify the messages exhibiting a radicalization indicator. We conducted a set of experiments on a sample extracted from a public dataset that contains radicalized individuals along with their social messages (i.e. Tweets). Obtained results show the effectiveness of our approach compared to a baseline method.\",\n \"Title: From ephemeral computing to deep bioinspired algorithms: New trends and applications.\\nYear: 2018\\n\\nAbstract: Ephemeral computing is a term that describes computing systems whose nodes or their connectivity have an ephemeral, heterogeneous and possibly also unpredictable nature. These properties will affect the functioning of distributed versions of computer algorithms. Such algorithms, which are usually straightforward extensions of sequential algorithms, will have to be redesigned and, in many cases, rethought from the ground up, to be able to use all ephemerally available resources. Porting algorithms to an inherently ephemeral, unreliable and massively heterogeneous computing substrate is thus one of the main challenges in the ephemeral computing field. Algorithms adapted so that they can be consciously running on this kind of environments require specific properties in terms of flexibility, plasticity and robustness. Bioinspired algorithms are particularly well suited to this endeavor, thanks to their decentralized functioning, intrinsic parallelism, resilience, adaptiveness, and amenability for being endowed with algorithmic components dealing with both the massive complexity of the computational substrate and that of the problem being tackled. Arranging these components and functionalities in a collection of algorithmic strata results in deep architectures, whereby different layers of optimization are organized into loosely-coupled hierarchies that not only are able to use ephemeral computing environments, but also profit from them by making adaptivity and diversity maintenance features of the algorithm. Moreover, the synergies that arise when these massively heterogeneous computing resources are made available to deep versions of bioinspired algorithms may enable hard real-world problems and applications to be successfully faced in the Big Data context (including but not limited to social data analysis) as well as problems in the areas of computational creativity or computer gaming.\",\n \"Title: CANDYMAN: Classifying Android malware families by modelling dynamic traces with Markov chains.\\nYear: 2018\\n\\nAbstract: Malware writers are usually focused on those platforms which are most used among common users, with the aim of attacking as many devices as possible. Due to this reason, Android has been heavily attacked for years. Efforts dedicated to combat Android malware are mainly concentrated on detection, in order to prevent malicious software to be installed in a target device. However, it is equally important to put effort into an automatic classification of the type, or family, of a malware sample, in order to establish which actions are necessary to mitigate the damage caused. In this paper, we present CANDYMAN, a tool that classifies Android malware families by combining dynamic analysis and Markov chains. A dynamic analysis process allows to extract representative information of a malware sample, in form of a sequence of states, while a Markov chain allows to model the transition probabilities between the states of the sequence, which will be used as features in the classification process. The space of features built is used to train classical Machine Learning, including methods for imbalanced learning, and Deep Learning algorithms, over a dataset of malware samples from different families, in order to evaluate the proposed method. Using a collection of 5,560 malware samples grouped into 179 different families (extracted from the Drebin dataset), and once made a selection based on a minimum number of relevant and valid samples, a final set of 4,442 samples grouped into 24 different malware families was used. The experimental results indicate a precision performance of 81.8% over this dataset.\",\n \"Title: A Multi-Objective Genetic Algorithm for overlapping community detection based on edge encoding.\\nYear: 2018\\n\\nAbstract: The Community Detection Problem (CDP) in Social Networks has been widely studied from different areas such as Data Mining, Graph Theory Physics, or Social Network Analysis, among others. This problem tries to divide a graph into different groups of nodes (communities), according to the graph topology. A partition is a division of the graph where each node belongs to only one community. However, a common feature observed in real-world networks is the existence of overlapping communities, where a given node can belong to more than one community. This paper presents a new Multi-Objective Genetic Algorithm (MOGA-OCD) designed to detect overlapping communities, by using measures related to the network connectivity. For this purpose, the proposed algorithm uses a phenotype-type encoding based on the edge information, and a new fitness function focused on optimizing two classical objectives in CDP: the first one is used to maximize the internal connectivity of the communities, whereas the second one is used to minimize the external connections to the rest of the graph. To select the most appropriate metrics for these objectives, a comparative assessment of several connectivity metrics has been carried out using real-world networks. Finally, the algorithm has been evaluated against other well-known algorithms from the state of the art in CDP. The experimental results show that the proposed approach improves overall the accuracy and quality of alternative methods in CDP, showing its effectiveness as a new powerful algorithm for detecting structured overlapping communities.\",\n \"Title: Consensual Negotiation-Based Decision Making for Connected Appliances in Smart Home Management Systems.\\nYear: 2018\\n\\nAbstract: Recently, the concept of Internet of Agent has been introduced as a potential technology that pushes intelligence, data processing, analytics and communication capabilities down to the point where the data originates. In this paper, we introduce a novel approach for a Decentralized Home Energy Management System by applying the Internet of Agent concept. In particular, we first present an Internet of Agent framework in terms of sensing, communicating and collaborating among connected appliances. Then, the decentralized management based on consensual negotiation mechanism with several intelligent techniques are proposed for dynamic scheduling connected appliance. Specifically, by applying the Internet of Agent framework, connected appliances are regarded as smart agents that are able to make individual decisions by reaching agreement over the exchange of operations on competitive resources. Furthermore, in this study, the load balancing problem in which load shifting is able to reduce the electricity demand during peak hours is taken into account in order to emphasize the effectiveness of our approach. For the experiment, we develop a simulation of smart home environment to evaluate our approach using NetLogo, a tool which provides real-time analysis in the modeling and simulation domain of complex systems.\",\n \"Title: Extending QGroundControl for Automated Mission Planning of UAVs.\\nYear: 2018\\n\\nAbstract: Unmanned Aerial Vehicles (UAVs) have become very popular in the last decade due to some advantages such as strong terrain adaptation, low cost, zero casualties, and so on. One of the most interesting advances in this field is the automation of mission planning (task allocation) and real-time replanning, which are highly useful to increase the autonomy of the vehicle and reduce the operator workload. These automated mission planning and replanning systems require a Human Computer Interface (HCI) that facilitates the visualization and selection of plans that will be executed by the vehicles. In addition, most missions should be assessed before their real-life execution. This paper extends QGroundControl, an open-source simulation environment for flight control of multiple vehicles, by adding a mission designer that permits the operator to build complex missions with tasks and other scenario items; an interface for automated mission planning and replanning, which works as a test bed for different algorithms, and a Decision Support System (DSS) that helps the operator in the selection of the plan. In this work, a complete guide of these systems and some practical use cases are provided.\",\n \"Title: A Genetic Algorithm with Local Search Based on Label Propagation for Detecting Dynamic Communities.\\nYear: 2018\\n\\nAbstract: The interest in community detection problems on networks that evolves over time have experienced an increasing attention over the last years. Genetic Algorithms, and other bio-inspired methods, have been successfully applied to tackle the community finding problem in static networks. However, few research works have been done related to the improvement of these algorithms for temporal domains. This paper is focused on the design, implementation, and empirical analysis of a new Genetic Algorithm pair with a local search operator based on Label Propagation to identify communities on dynamic networks.\",\n \"Title: A discrete water cycle algorithm for solving the symmetric and asymmetric traveling salesman problem.\\nYear: 2018\\n\\nAbstract: •An improved discrete water cycle algorithm is presented for the TSP and ATSP.•This version includes inclination feature, enhancing exploration and exploitation.•33 datasets of the TSP/ATSP have been used for the experimentation.•Results have been compared with six different techniques.•Friedman's and Holm's post hoc statistical tests have been conducted.\",\n \"Title: An In-Depth Study Of The Jisut Family Of Android Ransomware\\nYear: 2018\\n\\nAbstract: Android malware is increasing in spread and complexity. Advanced obfuscation, emulation detection, delayed payload activation or dynamic code loading are some of the techniques employed by the current malware to hinder the use of reverse engineering techniques and anti-malware tools. This growing complexity is particularly noticeable in the evolution of different strands of the same malware family. Over the years, these families mature to become more effective by incorporating new and enhanced techniques. In this paper, we focus on a particular Android ransomware family named Jisut, and perform a thorough technical analysis. We also provide a detailed overall perspective, which will hopefully help to create new tools and techniques to tackle more effectively the threat posed by ransomware.\",\n \"Title: A taxonomy and state of the art revision on affective games.\\nYear: 2019\\n\\nAbstract: Affective games are a sub-field of affective computing that tries to study how to design videogames that are able to react to the emotions expressed by the player, as well as provoking desired emotions to them. To achieve those goals it is necessary to research on how to measure and detect human emotions using a computer, and how to adapt videogames to the perceived emotions to finally provoke them to the players. This work presents a taxonomy for research on affective games centring on the aforementioned issues. Here we devise as well a revision of the most relevant published works known to the authors on this area. Finally, we analyse and discuss which important research problem are yet open and might be tackled by future investigations in the area of affective games.\",\n \"Title: Constrained multi-objective optimization for multi-UAV planning\\nYear: 2019\\n\\nAbstract: Over the last decade, developments in \\nunmanned aerial vehicles (UAVs) has greatly increased, and they are being used in many fields including surveillance, crisis management or automated mission planning. This last field implies the search of plans for missions with multiple tasks, UAVs and ground control stations; and the optimization of several objectives, including makespan, fuel consumption or cost, among others. In this work, this problem has been solved using a multi-objective evolutionary algorithm combined with a constraint satisfaction problem model, which is used in the fitness function of the algorithm. The algorithm has been tested on several missions of increasing complexity, and the computational complexity of the different element considered in the missions has been studied.\",\n \"Title: Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning.\\nYear: 2019\\n\\nAbstract: Management and mission planning over a swarm of unmanned aerial vehicle (UAV) remains to date as a challenging research trend in what regards to this particular type of aircrafts. These vehicles are controlled by a number of ground control station (GCS), from which they are commanded to cooperatively perform different tasks in specific geographic areas of interest. Mathematically the problem of coordinating and assigning tasks to a swarm of UAV can be modeled as a constraint satisfaction problem, whose complexity and multiple conflicting criteria has hitherto motivated the adoption of multi-objective solvers such as multi-objective evolutionary algorithm (MOEA). The encoding approach consists of different alleles representing the decision variables, whereas the fitness function checks that all constraints are fulfilled, minimizing the optimization criteria of the problem. In problems of high complexity involving several tasks, UAV and GCS, where the space of search is huge compared to the space of valid solutions, the convergence rate of the algorithm increases significantly. To overcome this issue, this work proposes a weighted random generator for the creation and mutation of new individuals. The main objective of this work is to reduce the convergence rate of the MOEA solver for multi-UAV mission planning using weighted random strategies that focus the search on potentially better regions of the solution space. Extensive experimental results over a diverse range of scenarios evince the benefits of the proposed approach, which notably improves this convergence rate with respect to a naïve MOEA approach.\",\n \"Title: Statistical analysis of risk assessment factors and metrics to evaluate radicalisation in Twitter.\\nYear: 2019\\n\\nAbstract: Nowadays, Social Networks have become an essential communication tools producing a large amount of information about their users and their interactions, which can be analysed with Data Mining methods. In the last years, Social Networks are being used to radicalise people. In this paper, we study the performance of a set of indicators and their respective metrics, devoted to assess the risk of radicalisation of a precise individual on three different datasets. Keyword-based metrics, even though depending on the written language, performs well when measuring frustration, perception of discrimination as well as declaration of negative and positive ideas about Western society and Jihadism, respectively. However, metrics based on frequent habits such as writing ellipses are not well enough to characterise a user in risk of radicalisation. The paper presents a detailed description of both, the set of indicators used to assess the radicalisation in Social Networks and the set of datasets used to evaluate them. Finally, an experimental study over these datasets are carried out to evaluate the performance of the metrics considered.\",\n \"Title: A genetic graph-based clustering algorithm\\nYear: 2012\\n\\nAbstract: The interest in the analysis and study of clustering techniques have grown since the introduction of new algorithms based on the continuity of the data, where problems related to image segmentation and tracking, amongst others, makes difficult the correct classification of data into their appropriate groups, or clusters. Some new techniques, such as Spectral Clustering (SC), uses graph theory to generate the clusters through the spectrum of the graph created by a similarity function applied to the elements of the database. The approach taken by SC allows to handle the problem of data continuity though the graph representation. Based on this idea, this study uses genetic algorithms to select the groups using the same similarity graph built by the Spectral Clustering method. The main contribution is to create a new algorithm which improves the robustness of the Spectral Clustering algorithm reducing the dependency of the similarity metric parameters that currently affects to the performance of SC approaches. This algorithm, named Genetic Graph-based Clustering (GGC), has been tested with different synthetic and real-world datasets, the experimental results have been compared against classical clustering algorithms like K-Means, EM and SC.\",\n \"Title: Optimizing satisfaction in a multi-courses allocation problem combined with a timetabling problem.\\nYear: 2017\\n\\nAbstract: The resource allocation problem and the timetabling problem are traditional kinds of NP-hard problems. Both problems can be found in universities where students can select courses they would like to attend before or after the timetabling is done. When demand exceeds capacity, the universities may allocate the available seats independently from the timetabling, but students may have then to decide which courses they are going to attend because of clashes in their timetable. To avoid this situation, some universities prepare their timetable considering students selection. In addition to that, students may submit preferences over courses, and the school administration has to assign seats and do the timetable considering both preferences and clashes. In this paper, both problems, seats allocation and timetabling, have been modeled separately and combined as constraint satisfaction optimization problems (CSOP). Two algorithms have been designed and implemented to find a solution to both problems simultaneously maximizing the satisfaction of students using a CSOP solver and an Ant colony optimization algorithm for the timetabling problem. The results of both algorithms are then compared. The allocation and timetabling procedures are based on preferences for courses defined by students, and on the administration’s constraints at Ecole Hôtelière de Lausanne. Three real data sets have been used to carry out a complete experimental analysis. High-quality solutions are obtained in a few minutes with both approaches; those solutions are currently used at Ecole Hôtelière de Lausanne.\",\n \"Title: Analysing temporal performance profiles of UAV operators using time series clustering.\\nYear: 2017\\n\\nAbstract: •A methodology for analysing UAV operators based on their performance evolution.•Time series clustering to extract the most representative operator temporal profiles.•Quantitative evaluation using human judgement-based ground truth data.•Experimentation carried out in a lightweight multi-UAV simulation environment.•The resulting profiles contain distinct and informative behavioural patterns.\",\n \"Title: Automatic Procedure Following Evaluation Using Petri Net-Based Workflows.\\nYear: 2018\\n\\nAbstract: An operating procedure (OP), also known as checklist or action plan, is a list of actions or criteria arranged in a systematic way, commonly used in areas such as aviation or healthcare to ensure the success of critical tasks and to help decrease human errors. In these areas, operators are hardly trained to follow the OP carefully, but the evaluation of how they are following, it is usually perfor...\",\n \"Title: Bioinspired Algorithms in Complex Ephemeral Environments.\\nYear: 2018\\n\\nAbstract: The concept of Ephemeral Computing is an emergent topic that is currently consolidating among the research community. It includes computing systems where the nodes or the connectivity have an ephemeral and thus unpredictable nature. Although the capacity and computer power of small and medium devices (as smartphones or tablets) are increasing swiftly, their computing capacities are usually underexploited. The availability of highly-volatile heterogeneous computer resources capable of running software agents requires suitable algorithms to make a proper use of the available resources while circumventing the potential problems that may produce such non-reliable systems. Due to the non-reliable nature of the system where the algorithms under consideration should run, they have to be ephemerality-aware, having the self-capability for understanding this kind of environments and adapt to them by means of exibility, plasticity and robustness. Because of their decentralized functioning, intrinsic parallelism, resilience, and adaptiveness, bioinspired algorithms suit well to this endeavour.\",\n \"Title: Design Of A Software System To Support Value Education In Sports Through Gamification Techniques\\nYear: 2019\\n\\nAbstract: Nowadays, it is quite common to find violent acts in grassroot sports, such as football. Almost every week, it is possible to find news about team supporters fighting against each other, or football players arguing aggressively to the referee. And the worst part in this story is that most of these acts are watched by children. In order to alleviate this situation and create awareness of the necessity to create educational programs to prevent violence in sports, the European Union has funded several projects focused on this area. One of this project is called SAVEit project, and its goal is to create and develop innovative educational tools to promote values in grassroot sports. This paper presents the software architecture designed in SAVEit project to achieve this goal. This architecture is mainly composed of a Learning Management System, where coaches will learn about the values; a Team Management Site, where coaches can evaluate the values acquired by the children of the teams; and finally, a Video Game that using gamification techniques will keep the motivation of children during the learning process.\",\n \"Title: Deep learning for EEG data analytics: A survey: Deep learning for EEG data analytics: A survey\\nYear: 2020\\n\\nAbstract: In this work, we conducted a literature review about deep learning (DNN, RNN, CNN, and so on) for analyzing EEG data for decoding the activity of human's brain and diagnosing disease and explained details about various architectures for understanding the details of CNN and RNN. It has analyzed a word, which presented a model based on CNN and LSTM methods, and how these methods can be used to both optimize and set up the hyper parameters of deep learning architecture. Later, it is studied how semi-supervised learning on EEG data analytics can be applied. We review some studies about different methods of semi-supervised learning on EEG data analytics and discussing the importance of semi-supervised learning for analyzing EEG data. In this paper, we also discuss the most common applications for human EEG research and review some papers about the application of EEG data analytics such as Neuromarketing, human factors, social interaction, and BCI. Finally, some future trends of development and research in this area, according to the theoretical background on deep learning, are given.\",\n \"Title: Bio-inspired computation: Where we stand and what's next\\nYear: 2019\\n\\nAbstract: In recent years, the research community has witnessed an explosion of literature dealing with the mimicking of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques.\",\n \"Title: Android malware detection through hybrid features fusion and ensemble classifiers: The AndroPyTool framework and the OmniDroid dataset\\nYear: 2019\\n\\nAbstract: •An automated framework for dynamic and static analysis of Android applications.•A large dataset of static and dynamic features extracted from Android applications.•Evaluation of the dataset through a wide set of ensemble classifiers.•Malware detection using fusion of features and a voting based classifier.•Results show feasibility and the potential use of the proposed tool and dataset.\",\n \"Title: Combining bio-inspired meta-heuristics and novelty search for community detection over evolving graph streams.\\nYear: 2019\\n\\nAbstract: Finding communities of interrelated nodes is a learning task that often holds in problems that can be modeled as a graph. In any case, detecting an optimal partition in a graph is highly time-consuming and complex. For this reason, the implementation of search-based metaheuristics arises as an alternative for addressing these problems. This manuscript focuses on optimally partitioning dynamic network instances, in which the connections between vertices change dynamically along time. Specifically, the application of Novelty Search mechanism for solving the problem of finding communities in dynamic networks is studied in this paper. For this goal, this procedure has been embedded in the search process undertaken by three different bio-inspired meta-heuristic schemes: Bat Algorithm, Firefly Algorithm and Particle Swarm Optimization. All these methods have been properly adapted for dealing with this discrete and dynamic problem, using a reformulated expression of the modularity coefficient as its fitness function. A thorough experimentation has been conducted using a benchmark composed by 12 synthetically created instances, with the main objective of analyzing the performance of the proposed Novelty Search mechanism when facing this problem. In light of the outperforming behavior of our approach and its relevance dictated by two different statistical tests, we conclude that Novelty Search is a promising procedure for finding communities in evolving graph data.\",\n \"Title: FacTeR-Check: Semi-automated fact-checking through semantic similarity and natural language inference\\nYear: 2022\\n\\nAbstract: Our society produces and shares overwhelming amounts of information through Online Social Networks (OSNs). Within this environment, misinformation and disinformation have proliferated, becoming a public safety concern in most countries. Allowing the public and professionals to efficiently find reliable evidence about the factual veracity of a claim is a crucial step to mitigate this harmful spread. To this end, we propose FacTeR-Check, a multilingual architecture for semi-automated fact-checking and hoaxes propagation analysis that can be used to implement applications designed for both the general public and for fact-checking organisations. FacTeR-Check implements three different modules relying on the XLM-RoBERTa Transformer architecture to evaluate semantic similarity, to calculate natural language inference and to build search queries through automatic keywords extraction and Named-Entity Recognition. The three modules have been validated using state-of-the-art benchmark datasets, exhibiting good performance in all of them. Besides, FacTeR-Check is employed to collect and label a dataset, called NLI19-SP, composed of more than 40,000 tweets supporting or denying 60 hoaxes related to COVID-19, released publicly. Finally, an analysis of the data collected in this dataset is provided, which allows to obtain a deep insight of how disinformation operated during the COVID-19 pandemic in Spanish-speaking countries.\",\n \"Title: Memetic Coral Reefs Optimization Algorithms For Optimal Geometrical Design Of Submerged Arches\\nYear: 2021\\n\\nAbstract: This paper deals with the geometrically nonlinear analysis of submerged arches by means of memetic Coral Reefs Optimization algorithms. The classic design of submerged arches is only focused on calculating the bend-ing stress-less shape (funicular shape) of the structure. Nevertheless, recent works show that this funicular shape can be approached by using a parametric family curve, which also allows a multi-variable optimization of the arch's geometry. Using this novel parametric set of curves, we propose a new Coral Reefs Optimization (CRO) algorithm based on a memetic approach to tackle the geometrically nonlinear design of submerged arches. Specif-ically, the proposed CRO approaches have been tested with different search procedures as exploration operators, and we also test a multi-method version of the algorithm, the Coral Reefs Optimization with Substrate Layers (CRO-SL), which considers several search procedures within the same evolutionary population. A local search to improve the solutions has been considered in all cases, to obtain powerful memetic operators for this problem. It is also shown how the different memetic versions of the CRO (specially those involving multi-methods and Dif-ferential Evolution search procedures), together with the parametric encoding, are able to obtain nearly-optimal geometries for underwater installations. The performance of the proposed algorithm has been compared with state-of-the-art algorithms for optimization: L-SHADE and HCLPSO. Statistical tests have carried out with the aim of comparing the results. It is shown that there is not significant differences between the proposed results by the three algorithms.\",\n \"Title: An Effective Approach for Rumor Detection of Arabic Tweets Using eXtreme Gradient Boosting Method\\nYear: 2022\\n\\nAbstract: AbstractTwitter is currently one of the most popular microblogging platforms allowing people to post short messages, news, thoughts, and so on. The Twitter user community is growing very fast. It has an average of 328 million active accounts today, making it one of the most common media for getting information during any influential or important event. Because it is freely used by the public, some credibility checking is required, especially when it comes to events of high importance. Automatic rumor detection in Arabic tweets is a challenging task due to the changes in the structural and morphological nature of the Arabic language, which makes the detection of rumors more difficult than in other languages. In this article, we proposed an effective approach for rumor detection of Arabic tweets using an eXtreme gradient boosting (XGBoost) classifier. We conducted a set of experiments on a public dataset that contained a large number of rumor and non-rumor tweets. The model uses a comprehensive set of features, including content-based, user-based, and topic-based features, allowing one to look at credibility from different angles. The experimental results demonstrated that the proposed XGBoost-based approach achieves 97.18% accuracy on 60% of the dataset as a training set, which is the highest accuracy rate compared with the other methods used in recent related work.\",\n \"Title: YoungRes: A Serious Game-Based Intervention to Increase Youngsters Resilience Against Extremist Ideologies\\nYear: 2022\\n\\nAbstract: Extremist ideologies are proliferating nowadays in both political and social levels. Considering that youngsters are in a development stage where they are still conforming their own social identity, they become especially vulnerable to these ideologies' influence. Therefore, it becomes critical to provide them with the psychological skills to rationalize and resist those influences. Video games, which are already a technology commonly consumed by these generations, provide a way to motivate and engage youngsters. Therefore, implementing these video games in interventions to increase psychological resilience represents an opportunity to create an innovative learning approach. Following this motivation, this paper has three main objectives: adapting a traditional emotional intelligence training program to a novel serious game based intervention, called YoungRes; providing a metric to measure the student' evolution based on in-game behavioural patterns, instead of indirect measures; and evaluating the impact of the intervention itself after its implementation. To do so, an 11 sessions intervention was applied to 36 students from two primary schools in Spain. Quantitative and qualitative data was extracted from the experience, consisting on data extracted from the player's behaviour and a final survey. A detailed statistical analysis carried out showed two main outcomes: first, the serious game based intervention was very appreciated by the students, specially by those who frequently play video games; second, the intervention allowed to improve several emotional intelligence competences, such as active listening and controlled breathing, as well as to promote knowledge about the Islamic culture. Finally, the authors discussed about how the game could be improved for future applications in schools.\",\n \"Title: Addressing Evolutionary-Based Dynamic Problems: A New Methodology for Evaluating Immigrants Strategies in MOGAs\\nYear: 2022\\n\\nAbstract: Multi-Objective Genetic Algorithms (MOGAs) have been successfully used to address dynamic problems in a wide variety of domains. In these domains, data changes over time, so a non-static analysis is required to obtain feasible solutions. In this type of environments, MOGAs are often time-consuming and require special adaptation to work properly. A number of different techniques have been proposed to adapt MOGAs to dynamic environments for tackling the previous problems such as hypermutation, memory and immigrant schemes or multi-population methods, among others. In particular, immigrant strategies are one of the most commonly used methods, for that reason, this work proposes a new methodology that allows to make a detailed evaluation of their performance when these strategies are used. The proposed methodology works on two levels, a coarse-grain one and a fine-grain one. In the former, an overall evaluation of the different immigrant strategies is made based on three different dimensions: Quality, Stability and Speed. In the latter, a detailed study of the status of the immigrant individuals during the evolution of the algorithm is carried out. This is a very relevant aspect to take into account in order to evaluate whether an immigrant strategy is working properly or not. To deploy this methodology, a new visualization technique for population mixing analysis is proposed in this work. In order to validate the proposed methodology, a test case in the context of the Dynamic Community Detection problem (DCD) has been selected using a MOGA that applies several different immigrant schemes, showing both how the methodology works and how it could be employed in a particular dynamic problem.\",\n \"Title: Recent advances on effective and efficient deep learning-based solutions\\nYear: 2022\\n\\nAbstract: This editorial briefly analyses, describes, and provides a short summary of a set of selected papers published in a special issue focused on deep learning methods and architectures and their application to several domains and research areas. The set of selected and published articles covers several aspects related to two basic aspects in deep learning (DL) methods, efficiency of the models and effectiveness of the architectures These papers revolve around different interesting application domains such as health (e.g. cancer, polyps, melanoma, mental health), wearable technologies solar irradiance, social networks, cloud computing, wind turbines, object detection, music, and electricity, among others. This editorial provides a short description of each published article and a brief analysis of their main contributions.\",\n \"Title: SILT: Efficient transformer training for inter-lingual inference\\nYear: 2022\\n\\nAbstract: The ability of transformers to perform precision tasks such as question answering, Natural Language Inference (NLI) or summarizing, has enabled them to be ranked as one of the best paradigms to address Natural Language Processing (NLP) tasks. NLI is one of the best scenarios to test these architectures, due to the knowledge required to understand complex sentences and established relationships between a hypothesis and a premise. Nevertheless, these models suffer from the incapacity to generalize to other domains or from difficulties to face multilingual and interlingual scenarios. The leading pathway in the literature to address these issues involve designing and training extremely large architectures, but this causes unpredictable behaviors and establishes barriers which impede broad access and fine tuning. In this paper, we propose a new architecture called Siamese Inter-Lingual Transformer (SILT). This architecture is able to efficiently align multilingual embeddings for Natural Language Inference, allowing for unmatched language pairs to be processed. SILT leverages siamese pre-trained multi-lingual transformers with frozen weights where the two input sentences attend to each other to later be combined through a matrix alignment method. The experimental results carried out in this paper evidence that SILT allows to reduce drastically the number of trainable parameters while allowing for inter-lingual NLI and achieving state-of-the-art performance on common benchmarks.\",\n \"Title: IoHT-based deep learning controlled robot vehicle for paralyzed patients of smart cities\\nYear: 2022\\n\\nAbstract: Paralysis caused by physical trauma is a common disease today, with approximately 30% of paralysis caused by this trauma. The disease in question both physically restricts mobility and brings along psychological problems. Especially in advanced ages, paralysis becomes much more difficult and requires serious care since it causes many effects in elderly people’s daily routines, required specific healthcare services, costs…etc. Therefore, in this study, it was aimed to design an Internet of Health Things (IoHT)-based unmanned robot vehicle for paralyzed patients, by using deep learning for control of the system and creating a connected healthcare synergy. In this context, the electronic part of the robot was designed first and then a deep learning-based model was created. Before creating the model, the data obtained from the sensors were adequately pre-processed, and the related problem formation was shaped for a Deep Learning process. In order to compare the Deep Learning model with other machine learning techniques from the state of the art, different models based on Random Forest (RF), Decision Trees (DT), Naive Bayes (NB), Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Logistic Regression (LR) and Artificial Neural Network (ANN) were generated using the same data. The performances of these models were compared and the obtained results were analyzed. According to the experimental results, the Deep Learning model has the highest performance with 99.5% success rate and 0.5% loss rate. This work presents some relevant results in which using deep learning techniques point excellent performances in the area of IoHT applications. A second contribution of this paper is related to the evaluation of the communication flow of the systems, to analyze if it was good enough to cover a smart environment. The findings in this manner were positive in terms of technical analysis and the patient feedback taken from a total of 10 paralyzed patients.\",\n \"Title: A new intrusion detection system based on Moth–Flame Optimizer algorithm\\nYear: 2022\\n\\nAbstract: This study relies on using a Moth–Flame Optimization (MFO) method as a search algorithm and a Decision Tree (DT) as an evaluation algorithm to generate an efficient feature subset for intrusion detection systems (IDS). The target is to find a feature subset using the minimum number of traffic network features that later obtains the maximum performance by the machine algorithms used in the classification task. This depends on enhancing the MFO by adopting new operators besides the embedded spiral operator to balance the exploration and exploitation alleviating the local minima problem. The main contribution of this work is the adoption of the cosine similarity measure to binarize the continuous MFO into a binary problem. Cosine similarity overcomes the limitations of the commonly used sigmoid function that depends on using a threshold value for conversion. However, cosine similarity computes the similarity ratio between the current solution and the optimal solution. The augmented MFO wrapper framework is applied as an IDS to detect anomalous traffic in the network. The proposed method is compared against several well-known state-of-the-art algorithms on three network datasets (KDDCUPP9, NSL-KDD, and UNSW-NB15), using IDSACC, IDSTPR, IDSFPR, IDSF-score, and convergence evaluation measures to assess the performance of the proposed method. The experimental results show the superiority of the proposed cosine similarity method compared to other algorithms with an accuracy of 97.8%, F-score of 99%, TPR of 99.6%, and FPR of 8.1% using only five selected features from the KDDCUPP99 dataset. It achieved the accuracy of 89.7%, TPR of 89.1%, FPR of 2.9%, when four selected features from the NSL-KDD dataset are used. And finally, it achieved an accuracy of 92.4%, TPR of 92.3%, FPR of 3%, and F-score 94.2% when the UNSW-NB15 dataset is used.\",\n \"Title: Predicting the effects of kinetic impactors on asteroid deflection using end-to-end deep learning\\nYear: 2022\\n\\nAbstract: One possible approach to deflect the trajectory of an asteroid on a collision course with the Earth, and prevent a potentially devastating impact, is the use of a kinetic impactor. The upcoming NASA DART and ESA Hera space missions will be the first to study and demonstrate this technique, by driving a spacecraft into the moon of a binary asteroid system with the aim of altering its momentum, and knocking it off course. In this work, we seek to predict critical parameters associated with such an impact, namely the momentum transfer efficiency and axial ratio of the target body, based on light curve data observed from ground before and after the impact in order to give insights into the real effect of the deflection effort. We present here our approach to this problem, which we address from a purely data-driven perspective based on simulated data provided as a part of the Andrea Milani Planetary Defence Challenge, organised by the EU H2020 Stardust-R research network in conjunction with ESA. Formulating the problem as a time series regression task, we develop an end-to-end deep learning pipeline in which we apply the latest advances in deep learning for time series, such as the use of the Transformer architecture as well as ensembling and self-supervised learning techniques. Exploiting these techniques for the challenge, we achieved second place out of the student teams, and fifth place overall without relying on any a priori knowledge of the physics of the asteroid system.\",\n \"Title: Cooperative Negotiation in Connected Vehicles for Mitigating Traffic Congestion\\nYear: 2021\\n\\nAbstract: Traffic congestion has an impact on traffic efficiency and the quality of life. To address this issue, this paper proposes a distributed, cooperative negotiation method for connected vehicles in traffic flow optimization. In particular, when the connected vehicles obtain the traffic congestion alerts from the roadside units, they exchange their routing information and distribute the traffic flows across the roads by using a collective learning algorithm that does not rely on a centralized controller. Results exported from Simulation of Urban Mobility show that the proposed method outperforms traditional routing methods. In a high traffic demand scenario, the average travel time of the proposed method decreases by 35% and 12% compared with the shortest path routing and the dynamic traffic routing methods, respectively.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":157,"string":"157"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13670,"string":"13,670"}}},{"rowIdx":13671,"cells":{"question_id":{"kind":"string","value":"Troels B S rensen"},"question":{"kind":"string","value":"Generate an abstract for the title \"Performance Of Mimo With Frequency Domain Packet Scheduling In Utran Lte Downlink\" using the following items: SDM, MIMO, FDPS, LTE, throughput."},"answer":{"kind":"string","value":"This paper addresses the performance of spatial division multiplexing (SDM) multiple-input multiple-output (MIMO) techniques together with frequency domain packet scheduling (FDPS) based on UTRAN Long Term Evolution (LTE) downlink. Two SDM concepts currently considered in 3GPP are the single-user (SU-) and the multi-user (MU-) MIMO. For MU-MIMO, multiple users can be scheduled on different streams on the same time-frequency resource, while SU-MIMO restricts one time-frequency resource to a single user. Both concepts are analyzed against the 1x2 maximal ratio combining (MRC) case, using the proportional fair (PF) criterion extended to frequency and spatial domains. The realistic finite buffer best effort traffic model is used in the study. Results show that in the macro-cell scenario, the combination of FDPS and SDM (SDM-FDPS) without precoding achieves only marginal gain in cell throughput, due to limited SINR dynamic range. The gain is increased to around 20% when precoding is applied. In the micro-cell scenario, the gain of SDM-FDPS over the reference scheme is increased up to 35% with precoding."},"correct_answer":{"kind":"string","value":"This paper addresses the performance of spatial division multiplexing (SDM) multiple-input multiple-output (MIMO) techniques together with frequency domain packet scheduling (FDPS) based on UTRAN Long Term Evolution (LTE) downlink. Two SDM concepts currently considered in 3GPP are the single-user (SU-) and the multi-user (MU-) MIMO. For MU-MIMO, multiple users can be scheduled on different streams on the same time-frequency resource, while SU-MIMO restricts one time-frequency resource to a single user. Both concepts are analyzed against the 1x2 maximal ratio combining (MRC) case, using the proportional fair (PF) criterion extended to frequency and spatial domains. The realistic finite buffer best effort traffic model is used in the study. Results show that in the macro-cell scenario, the combination of FDPS and SDM (SDM-FDPS) without precoding achieves only marginal gain in cell throughput, due to limited SINR dynamic range. The gain is increased to around 20% when precoding is applied. In the micro-cell scenario, the gain of SDM-FDPS over the reference scheme is increased up to 35% with precoding."},"documents":{"kind":"list like","value":["Title: Experimental Validation of a Distributed Algorithm for Dynamic Spectrum Access in Local Area Networks.\nYear: 2013\n\nAbstract: Next generation wireless networks aim at a significant improvement of the spectral efficiency in order to meet the dramatic increase in data service demand. In local area scenarios user-deployed base stations are expected to take place, thus making the centralized planning of frequency resources among the cells, a non-viable solution. Cognitive Radio (CR) and Dynamic Spectrum Access (DSA) are the research paradigms which are expected to provide the network nodes the capabilities for an autonomous and efficient selection of the spectrum resources. In this paper we present the first experimental activities with the Autonomous Component Carrier Selection (ACCS) algorithm, a distributed solution for interference management among small neighboring cells. A preliminary evaluation of the algorithm performance is provided considering its live execution on a software defined radio network testbed. The obtained experimental results confirm the performance trends obtained from prior simulation studies. The analysis in dynamic environment conditions also allowed identifying the utilization of static thresholds in the decision making process, as a critical aspect for the optimization of network capacity.","Title: System Level Analysis of ACK/NACK Bundling for Multi-Component Carrier LTE-Advanced\nYear: 2011\n\nAbstract: This paper focuses on reducing the uplink overhead caused by Acknowledgement (ACK)/Non-acknowledgement (NACK) signaling in an Long Term Evolution (LTE)-Advanced system. Such a system typically aggregates multiple Component Carrier (CC)s to obtain a wide bandwidth. Usually it has much larger feedback overhead than the single-CC system, and hence reducing the overhead is of special importance. A technique called ACK/NACK bundling has been used for the overhead reduction of the ACK/NACK signaling. Along with the overhead reduction, the downlink performance is also degraded. The tradeoff between the downlink performance and the overhead is analyzed under various load conditions and antenna configurations. The presented results can be used for selecting the proper bundling scheme based on specific system setting.","Title: Utility Maximization in LTE-Advanced Systems with Carrier Aggregation\nYear: 2011\n\nAbstract: Long Term Evolution (LTE)-Advanced is expected to aggregate multiple Component Carrier (CC)s to fulfil the high data rate requirement. It may serve users with different capabilities in accessing these CCs, e.g., some can access all CCs, whereas some may operate on only one CC. This gives challenges to the packet scheduler to maximize the system performance over all CCs. In this paper we provide a mathematical model of the log-measure utility in an LTE-Advanced system, and give proof that our previously developed cross-CC Proportional Fair (PF) packet scheduler maximizes this utility. System level simulations are performed, which confirm that cross-CC PF scheduling offers much higher utility than independent PF and channel blind schedulers. This scheduler is then generalized to adjust the resource sharing among users. It can trade off between average cell throughput and cell edge user throughput. However, any adjustment in the resource sharing leads to a loss in utility.","Title: Transmission over multiple component carriers in LTE-A uplink.\nYear: 2011\n\nAbstract: Long Term Evolution - Advanced systems are currently being standardized by 3GPP and aim at very high peak data rates of 1 Gb/s in the downlink and 500 Mb/s in the uplink. Those ambitious targets can only be achieved by using advanced MIMO antenna techniques as well as wide spectrum allocation, up to 100 MHz. A multiple component carrier structure has been agreed on in the 3GPP Work Item as a solution to extend the 18 MHz bandwidth of the previous LTE Release 8 up to 100 MHz. The multiple access schemes on both uplink and downlink now have to be adapted to the new spectrum configuration. Furthermore, in the link adaptation design the transmission over multiple CCs would reasonably lead to an increase of the feedback overhead. Bundling of the spatial or frequency parameters can keep the overhead low at the cost of lower throughput. In this article, we consider as a study case the LTE-A uplink, where NxDFT-spread-OFDM has been selected as the multiple access scheme. The validity of this scheme for the uplink is evaluated in terms of cubic metric, which is an indicator of the power de-rating needed at the transmitter to avoid intermodulation distortion. Furthermore, the impact of bundling the link adaptation parameters on the link performance is discussed considering both linear and turbo successive interference cancellation (SIC) receivers. Two codeword mixing stategies in the frequency and spatial domains are also proposed to boost the performance when the bundling is made per antenna or per CC, respectively. Results show that when a linear receiver is used in the base station, mixing techniques can increase spectral efficiency, thus reducing the performance gap to the no bundling case, which is the most expensive solution in terms of feedback signaling. However, when a turbo SIC receiver is used, only mixing over CCs is a valid option to achieve link performance gain.","Title: A Site-Specific Study of In-Building Wireless Solutions\nYear: 2010\n\nAbstract: This paper studies the different ways of extending wireless coverage for high data rates and improving the data capacity in a building. The solutions considered include deployments of single or multiple small base stations, i.e. Picocell or Femtocell, and distributed antenna system, operated on the same frequency as the macro cellular network. We look at the radio performance of each solution in a LTE downlink context with in-building path loss values retrieved from real-life measurements. The performance is compared by means of maximum supportable user numbers and average system throughput. Potential gains by hard frequency reuse in the building and QoS based packet scheduling are examined. Simulation results show the robustness of the proposed distributed antenna system and the conditions for multi-Femtocell deployment.","Title: Channel estimation based on divergence minimization for OFDM sytems with co-channel interference\nYear: 2009\n\nAbstract: In this paper, we present a novel approach for pilot-aided channel estimation in OFDM systems with synchronous co-channel interference. The estimator is derived based on the Kullback-Leibler divergence minimization framework. The obtained solution iteratively updates both the desired user's and the interferer's channels, using a combination of linear minimum mean squared-error (LMMSE) filtering and interference cancellation, avoiding the complex matrix inversions involved in the full LMMSE channel estimation approach. Estimation of the noise variance is also included in the iterative algorithm, accounting for Gaussian noise and residual interference after each iteration. The estimates of both channels are used at the equalizer to reject the interfering signal, thus mitigating the degradation due to co-channel interference. Simulation results show that the receiver using the proposed estimator performs as good as the one employing the full LMMSE estimator and very closely to a receiver having perfect knowledge of the channel coefficients.","Title: Downlink Transmission in Multi-Carrier Systems with Reduced Feedback\nYear: 2010\n\nAbstract: in this paper we address the problem of reducing the feedback for the downlink transmission in multi-carrier systems. In these systems multiple Component Carriers (CCs) are aggregated together to form a wide spectrum. Consequently, a large feedback overhead is required to report the channel quality information over such a wide bandwidth. We first generalize two existing feedback reduction techniques, and then propose a new one. These techniques use different feedback schemes across the CCs, or allow some CCs to be un-reported, for the purpose of reducing the amount of feedback. Performance for the investigated techniques is evaluated in a realistic system setting with different traffic conditions and terminal categories. Based on the obtained results, the selection of proper feedback reduction technique under different load conditions can be made. The findings are useful for reducing the feedback overhead in future generation wireless communication systems that operate over multiple CCs.","Title: SVD-Based vs. Release 8 Codebooks for Single User MIMO LTE-A Uplink.\nYear: 2010\n\nAbstract: The ambitious data rate target of the Long Term Evolution - Advanced (LTE-A) systems, which are currently being standardized by the 3rd Generation Partnership Project (3GPP), can only be achieved by using advanced Multiple Input Multiple Output (MIMO) antenna techniques on both uplink and downlink. In this paper, the focus is on LTE-A uplink and we discuss the suitability of two options for the closed loop precoded MIMO transmission. The first option is to use the same codebook of precoding matrices defined for LTE downlink, while the second option exploits the Singular Value Decomposition (SVD) of the channel matrix, which could theoretically achieve the MIMO capacity. Qualitative benefits of both solutions are discussed. Link level simulation results show that, the SVD-based approach only lead to a poor performance gain in terms of spectral efficiency over the LTE downlink codebook because of the losses due to the quantization of the right singular vectors of the channel matrix. Since SVD-based solution does not even exploit some useful properties (e. g., power balance over the antennas) which LTE Release 8 codebook offers, the latter results to be a more suitable candidate for the closed loop MIMO transmission in LTE-A uplink.","Title: Assessing And Modelling The Effect Of Rf Impairments On Utra Lte Uplink Performance\nYear: 2007\n\nAbstract: In this paper we investigate the effect of modelled RF transmitter impairments on UTRA Long Term Evolution (LTE) uplink link performance. Nonlinear power amplifier, gain imbalance, phase imbalance, and phase noise are considered. The main contributions of this work is assessment of the relation between Error Vector Magnitude (EVM) and link performance loss due to each impairment and the investigation of a white Gaussian noise source in the transmitter as an equivalent model of the impairment. The results obtained are based on the UTRA LTE uplink parameter settings. Results show that the EVM is a good measure to estimate the link level performance loss for numerous impairments. The performance degradation in AWGN and a frequency selective fading when using a white noise source as an equivalent transmitter RF impairment model, fits well with the other impairments in the anticipated range of EVM. An analytical formulation describing the effect of the white noise source in the SNR loss fits similarly with the simulation results. With 16QAM rate 3/4, an EVM value of 8% corresponds to an SNR loss of approximately 0.5 dB.","Title: Combined Time And Code Division Scheduling For Enhanced Uplink Packet Access In Wcdma\nYear: 2004\n\nAbstract: This paper contains an evaluation of three different time and code division schedulers for data transmission on enhanced dedicated channel (E-DCH) in WCDMA uplink. Time scheduling on E-DCH is possible thanks to fast Node B scheduling operation on a Transmission Time Interval (TTI) basis. Time scheduling allows for allocating users for transmission with instantaneously high data rates, and possibly when they experience favourable channel conditions. The system capacity can therefore be increased, also because interference generated to other cells is reduced. We apply a combined time and code division scheduling strategy in three different cases: a \"blind fair throughput\" scheduler (BFT), a \"maximise transmit power efficiency\" scheduler (MTPE) and a \"channel-state aware fair throughput\" scheduler (CSAFT). The two latter schedulers utilise an appositely defined Uplink Channel Quality Indicator (UCQI) to exploit channel-state information in the packet scheduling procedure. While the MTPE scheduler maximises cell throughput at the cost of a low degree of fairness between users, the CSAFT algorithm is shown to offer an appealing trade-off between users' fairness and throughput maximisation. Under a specific outage constraint for the packet call throughput, the capacity gain of the CSAFT scheduler over a Node B code division scheduler is 17% and 30% for Vehicular A and Pedestrian A channel profile at 3 km/h, respectively. The estimated capacity increase compared to a reference RNC-based scheduler is estimated between 50% and 80%, also depending on the propagation scenario.","Title: On The Design Of A Mimo-Sic Receiver For Lte Downlink\nYear: 2008\n\nAbstract: In this paper, we investigate different multiple-input multiple-output (MIMO) receiver structures based on MMSE filtering and sequential interference cancellation (SIC) for the downlink of the 3GPP long term evolution (LTE) system. We divide them into two approaches: symbol-SIC receivers, in which the detection and interference cancellation is done independently for each subcarrier, and codeword-SIC structures, in which the processing is carried out for each independently-coded stream by including the turbo-decoder inside the feedback loop. The results show that symbol-SIC receivers need to take into account the propagation of errors in the interference cancellation to provide the turbo decoder with reliable soft bit values. However, these are clearly outperformed by codeword-SIC schemes, due to the error correction capabilities of the turbo-decoder inside the feedback loop. We show that the best tradeoff between computational complexity and receiver performance is achieved by only cancelling the interference of a codeword when this has been successfully decoded.","Title: On the Potential of Interference Rejection Combining in B4G Networks\nYear: 2013\n\nAbstract: Beyond 4th Generation (B4G) local area networks will be characterized by the dense uncoordinated deployment of small cells. This paper shows that inter-cell interference, which is a main limiting factor in such networks, can be effectively contained using Interference Rejection Combining (IRC). By simulation we investigate two significantly different interference scenarios with dense small cell deployment. The results show that IRC brings considerable improvement in outage as well as in peak and median throughputs in both scenarios, and thus has a big potential as a capacity and coverage enhancing technique for B4G. The IRC gain mechanism depends strongly on the interference scenario and to some extent on the use of frequency reuse. These results are achieved with no coordination between cells and suggests that MIMO rank adaptation and IRC can be done independently.","Title: An Interference Aware Dynamic Spectrum Sharing Algorithm for Local Area LTE-Advanced Networks\nYear: 2009\n\nAbstract: This paper presents a dynamic spectrum sharing algorithm to minimize the inter-cell interference, so as to achieve a high system performance. This algorithm operates in a self-organized manner without the need of any centralized control, thereby is especially useful in local area (LA) where the enhanced-NodeBs (eNB) are close and coordination among them is hard or expensive to achieve. Results show that with very limited signaling between the eNBs, the proposed algorithm can effectively improve the system performance, and it can even overcome the performance with fixed frequency plan.","Title: Subjective evaluation of packet service performance in UMTS and heterogeneous networks\nYear: 2006\n\nAbstract: Quality of Service in mobile telecommunication systems is usually identified by some basic performance metrics such as delay, throughput and jitter. However, the main impact of service quality is on the end user, and as such a detailed study of service performance should involve the end user. In this paper, such an approach is taken where subjective performance evaluation is undertaken for web browsing and video streaming services in Universal Mobile Telecommunication System (UMTS) and a heterogeneous network comprised of UMTS and Wireless LAN (WLAN)Using a real-time network emulation testbed, users rated the perceived quality of the services under different network conditions. The users' ratings are analyzed and the results showed clear trends with very few outliers. The objective and subjective measures also were found to be in line except for one case where a higher layer effect influenced the users' perception.","Title: Interference cancellation based on divergence minimization for MIMO-OFDM receivers\nYear: 2009\n\nAbstract: In this paper, we present a novel iterative receiver for MIMO-OFDM systems with synchronous interferers. The receiver is derived based on the Kullback-Leibler divergence minimization framework, and combines channel estimation, interference cancellation and residual noise estimation in an iterative manner. By using both the pilot and data symbols, the channel estimator improves the accuracy of the estimates in each iteration, which leads to a more effective interference cancellation and data detection process. A performance evaluation based on Monte-Carlo simulations shows that the proposed scheme can effectively mitigate the effect of interferers, and operates very close to the single-user performance even in severe interference scenarios.","Title: Channelization Issues With Fairness Considerations For Mu-Mimo Precoding Based Utra-Lte/Tdd Systems\nYear: 2008\n\nAbstract: In a pre-coded Multi User Multiple Input Multiple Output (MU-MIMO) system, the channelization can be done either by using any of the two basic access techniques, namely Orthogonal Frequency Division Multiple Access (OFDMA) and Space Division Multiple Access (SDMA), or by combining them. From resource allocation point of view, choice of any technique will require different fairness conditions among users. In this paper, we have studied these different fairness conditions when combined with basic or joint access schemes mentioned above, while applied in a MU-MIMO based UTRA-LTE system. We have evaluated the resource allocation fairness issue when two well-known linear MU-MIMO precoding is used on a UTRA-LTE system. User grouping issue is dealt with when SDMA component is considered in the system. The results in this work provides an indicative analysis of the usability of different channelization techniques for considered system.","Title: Uplink overhead analysis and outage protection for multi-carrier LTE-Advanced systems\nYear: 2009\n\nAbstract: In a multi-component carrier LTE-Advanced system, a user can simultaneously be scheduled on all component carriers (CCs). For the base station to perform radio channel aware packet scheduling, each user ideally has to feedback information corresponding to all CCs. This results in a potentially high amount of uplink overhead. Furthermore, a user in a power limited situation may experience outage because of insufficient power for the required feedback. In this paper we first propose several techniques at different layers for the overhead reduction, as well as protecting the users from being in the outage situation. Afterwards, we show how these techniques can be integrated in a LTE-Advanced system. A weighted proportional fair scheduler is also proposed to maintain good performance with a reduced amount of overhead. Extensive simulation results are presented in the end for analyzing the proper trade-off between performance and overhead reduction and outage protection.","Title: Site-Specific Study Of In-Building Wireless Solutions With Poisson Traffic\nYear: 2011\n\nAbstract: Due to the poor outdoor-to-indoor propagation into buildings, dedicated wireless systems installed inside the buildings are often preferred for providing indoor users with high-data-rate services. We study the typical in-building wireless solutions - distributed antenna system and small cell Femto system - together with another multi-cell system using our proposed centralized scheduling scheme. In our previous work, their performance is evaluated and compared in the LTE downlink context with full buffer traffic. Compared to real mobile networks, the full buffer traffic model is usually a worst-case estimation of traffic load which causes severe interference conditions. Especially for Femto cells with universal frequency reuse it degrades system performance and may lead to biased conclusions on the relative performance of the different inbuilding solutions. In this study, we use a more realistic traffic model with fixed buffer size and Poisson arrival. Our new results show better performance for Femto cells with frequency reuse 1 at light to medium load, although the intelligent distributed system still obtains considerable better cell edge user throughput for the same number of access points.","Title: Improving SC-FDMA Performance by Turbo Equalization in UTRA LTE Uplink.\nYear: 2008\n\nAbstract: Turbo equalization is known as an advanced iterative equalization and decoding technique that allows to enhance the performance of the data transmission over a frequency selective fading channel. The turbo equalizer will result in extra receiver complexity, but isolated to the base-station, which does not have stringent power constraints. In this paper, a turbo equalization technique to improve Single Carrier Frequency Division Multiple Access (SC-FDMA) performance is proposed. A new adaptive coefficients solution for frequency domain equalization is considered. The work is in the context of UTRA Long Term Evolution (LTE) Uplink. The performance is evaluated for 1x2 Single Input Multiple Output (SIMO) antenna configuration in a 6 paths Typical Urban (TU-06) channel profile. For assessment purpose, the results are compared with SC-FDMA MMSE and OFDMA schemes. Simulation results show that the turbo equalizer can improve the BLER performance around I dB with only a few iterations, and improve the SC-FDMA performance over OFDM, especially at high coding rate.","Title: Communication Theory Spatial frequency scheduling for uplink SC-FDMA based linearly precoded multiuser MIMO systems\nYear: 2010\n\nAbstract: This paper investigates the performance of the uplink single carrier (SC) frequency division multiple access (FDMA) based linearly precoded multiuser multiple input multiple output (MIMO) systems with frequency domain packet scheduling. A mathematical expression of the received signal to interference plus noise ratio (SINR) for the studied systems is derived and a utility function based spatial frequency packet scheduling algorithms is investigated. The schedulers are shown to be able to exploit the available multiuser diversity in time, frequency and spatial domains. Copyright (C) 2009 John Wiley & Sons, Ltd.","Title: Carrier load balancing and packet scheduling for multi-carrier systems\nYear: 2010\n\nAbstract: Abstract-In this paper we focus on resource allocation for next generation wireless communication systems with aggregation of multiple Component Carriers (CCs), i.e., how to assign the CCs to each user, and how to multiplex multiple users in each CC. We first investigate two carrier load balancing methods for allocating the CCs to the users- Round Robin (RR) and Mobile Hashing (MH) balancing by means of a simple theoretical formulation, as well as system level simulations. At Layer-2 we propose a simple cross-CC packet scheduling algorithm that improves the coverage performance and the resource allocation fairness among users, as compared to independent scheduling per CC. The Long Term Evolution (LTE)-Advanced is selected for the case study of a multi-carrier system. In such a system, RR provides better performance than MH balancing, and the proposed simple scheduling algorithm is shown to be effective in providing up to 90% coverage gain with no loss of the overall cell throughput, as compared to independent scheduling per CC.","Title: Carrier load balancing methods with bursty traffic for LTE-Advanced systems\nYear: 2009\n\nAbstract: In this paper we focus on LTE-Advanced performance under bursty traffic conditions, and devote our effort to the different methods for balancing the load across multiple carriers. These carriers are the component carriers (CCs) that belong to the same carrier frequency. They are bonded together in order to fulfill the requirement of wide spectrum in LTE-Advanced. We first derive the analytical model for a bursty birth-death traffic model with fixed payload size for OFDMA with different frequency domain packet schedulers. Applying this model for a multi-carrier system, we compute the performance for different system setups. The obtained analytical results are verified using extensive system level simulations. Based on the analytical and simulation results, it is suggested to assign the users on all CCs if a cell is not heavily loaded. Otherwise, assign each user with only one CC using the load balancing method of Round Robin is preferable, in the sense that it maintains good performance with low uplink overhead.","Title: Mitigating Signaling Requirements For Mimo With Frequency Domain Packet Scheduling\nYear: 2007\n\nAbstract: MIMO techniques in the spatial division multiplexing (SDM) mode have demonstrated tremendous performance potential when a single user link is considered. Recent studies have shown that frequency domain packet scheduling (FDPS)PS) can provide a significant gain when combined with SDM (SDM-FDPS) by exploiting the multi-user diversity gain over frequency as well as the spatial domains. However, the increased signaling requirements in both uplink and downlink may prevent its practical use. In this paper several effective approaches are identified for reducing the signaling requirements for SDM-FDPS while still maintaining acceptable performance. Results show that the performance loss can be kept within 10% for a uplink signaling reduction of 88% and a downlink signaling reduction of 30%.","Title: Resource allocation considerations for multi-carrier LTE-Advanced systems operating in backward compatible mode\nYear: 2009\n\nAbstract: In this paper we focus on LTE-Advanced with backward compatibility, which serves a mixture of LTE-Advanced and LTE-Rel'8 users. Aggregation of multiple component carriers (CCs) to form a wide spectrum is assumed in order to fulfill the bandwidth requirement for the next generation systems, thereby leading to a multi-carrier system. Although a LTE-Advanced user can simultaneously access all the CCs, a LTE-Rel'8 user is restricted to operate on a single CC at a time. Different methods for balancing the load across these CCs will affect the system performance. This is investigated through both analytical methods and system level simulations. Bearing in mind that the LTE-Advanced users are scheduled on more CCs than the LTE-Rel'8 users, we propose a simple cross CC packet scheduling algorithm that improves the resource allocation fairness among the two categories of users. This simple scheduling algorithm is shown to be effective in providing better coverage performance with no loss of the overall cell throughput, as compared to independent scheduling per CC.","Title: Iterative Channel Estimation with Robust Wiener Filtering in LTE Downlink.\nYear: 2008\n\nAbstract: In this paper, an iterative enhancement of the robust Wiener filter (RWF) estimator is presented for a turbo-coded orthogonal frequency division multiplexing (OFDM) system under the umbrella of the 3GPP Long Term Evolution. The proposed scheme can operate with uncoded or coded feedback, and outperforms the conventional linear RWF in the whole signal-to-noise ratio (SNR) range with both approaches. Results show that most of the gain is obtained in the first iteration of the algorithm, and better performance is achieved with the coded feedback scheme. A good tradeoff between accuracy and complexity is achieved by selecting a low number of turbo coding iterations (TCI) in the iterative loop and concentrating most of them at the final decoding stage. Following this design, cell spectral efficiency gains of around 2.7 % and 6.5 % can be obtained with respect to linear RWF for micro-and macro-cell scenarios respectively.","Title: Economical Comparison of Enterprise In-Building Wireless Solutions Using DAS and Femto\nYear: 2012\n\nAbstract: As small cell solutions for efficient in-building wireless deployment, the distributed antenna system (DAS) and Femtocells constitute two major options. In this work, we study the Total Cost of Ownership (TCO) in combination with system performance in order to reach technology-economical conclusions regarding of these deployment options. Our aim is to identify the most cost economical solution for indoor wireless deployment which meets the indoor capacity need and provides ubiquitous indoor coverage. In the analysis we focus on LTE enhancement in the enterprise buildings. We discuss the unique cost features of DAS and Femto systems and highlight the importance of backhaul visibility in the cost analysis. For enterprise building solutions, we show the cost advantage of low-cost Femtocells over DAS in all scenarios regardless of coverage building size. We also suggest when DAS becomes more economical for large buildings with low data traffic need.","Title: Precoded multirank transmission with linear receivers for LTE-A Uplink.\nYear: 2009\n\nAbstract: The 3rd Generation Partnership Project (3GPP) defined the requirements for the upcoming Long Term Evolution - Advanced (LTE-A) systems, aiming at peak spectral efficiencies of 30 bit/s/Hz in the downlink and 15 bits/s/Hz in the uplink. These ambitious targets can be accomplished only by using advanced Multiple Input Multiple Output (MIMO) antenna techniques. In this paper, we evaluate closed loop precoded multirank transmission for Single Carrier Frequency Division Multiplexing (SC-FDM) as selected uplink modulation scheme by using the precoding codebook defined for LTE downlink. Its performance is compared with Orthogonal Frequency Division Multiplexing (OFDM). Results show that OFDM outperforms SC-FDM for medium-high ranks, but the gap can be reduced by adding further diversity branches. While narrowband precoding shows considerable gain over open loop transmission for all ranks, wideband precoding works with a very small feedback overhead but results to be a valid option mainly for low-rank transmission, where the low Peak-to-Average Power Ratio (PAPR) of SC-FDM signals is also preserved.","Title: Allocation Fairness for MIMO Precoded UTRA-LTE TDD System\nYear: 2008\n\nAbstract: In future time division duplex (TDD)-based broadband wireless systems, it will be possible to exploit the channel reciprocity to implement channel state information (CSI)-based multi user multiple input multiple output (MU-MIMO) techniques, which will ensure highly efficient spectrum usage. To increase the cell coverage while ensuring the quality of service (QoS) for all UEs across the cell area, fairness should be maximized as much as possible. This paper presents a novel way to help improving fairness performance in the physical layer, via fair power allocation together with resource allocation, in MU-MIMO precoding scenarios where the common approach of guaranteeing fairness at MAC layer is not feasible. The results presented in this paper show that the proposed algorithm is able to reduce the system outage event to a large extent, thus increases fairness.","Title: Turbo Receivers For Single User Mimo Lte-A Uplink\nYear: 2009\n\nAbstract: The paper deals with turbo detection techniques for Single User Multiple-Input-Multiple-Output (SU MIMO) antenna schemes. The context is on the uplink of the upcoming Long Term Evolution - Advanced (LTE-A) systems. Iterative approaches based on Parallel Interference Cancellation (PIC) and Successive Interference Cancellation (SIC) are investigated, and a low-complexity solution allowing to combine interstream interference cancellation and noise enhancement reduction is proposed. Performance is evaluated for Orthogonal Frequency Division Multiplexing (OFDM) and Single Carrier Frequency Division Multiplexing (SC-FDM) as candidate uplink modulation schemes for LTE-A. Simulation results show that, in a 2x2 antenna configuration, the turbo processing allows a consistent improvement of the link performance, being SC-FDM the one having higher relative gain with respect to linear detection. The turbo receiver's impact is however much reduced for both modulation schemes in a 2x4 configuration, due to the higher diversity gain provided by the additional receive antennas.","Title: DAS, Uncoordinated Femto and Joint Scheduling Systems for In-Building Wireless Solutions\nYear: 2011\n\nAbstract: Small cells are popular deployment options for coverage holes and capacity hotspot areas. Due to the poor outdoor-to-indoor propagation property of in-building environment, a dedicated wireless system installed inside the building is often preferred for providing indoor users high-data-rate services. Distributed antenna systems and Femto cells are cost-efficient techniques for this application. In this paper, their performance is evaluated in an LTE downlink context along with a proposed joint scheduling system, which maximizes the supported number of users under a QoS constraint. The selection of the enterprise building model includes a general office building model described in the WINNER II project and a site-specific office building with large scale path-loss values retrieved from measurements. Results show superior performance of the Femto system compared to DAS in providing high-data-rate services in most cases with a quality-guaranteed scheduler, while the centralized joint scheduling system gives the best performance. The centralized scheme can also help improve the system robustness in obtaining high performance even in the situation where access points are placed non-optimally.","Title: On the Feasibility of Precoded Single User MIMO for LTE-A Uplink\nYear: 2009\n\nAbstract: Abstract—The,3rd,Generation,Partnership,Project (3GPP) is currently specifying the system,requirements,for Long Term Evolution - Advanced (LTE-A), having as a tar- get peak data rates of 1 Gbit/s in local areas and 100 Mbit/s in wide,areas. To meet,these ambitious,requirements,for the uplink, multiple-input-multiple-output (MIMO) antenna techniques are expected to be deployed. In this paper, several channel-aware,MIMO precoding,techniques,are presented. Specifically, precoded single user spatial multiplexing for both Orthogonal,Frequency,Division Multiplexing (OFDM) and,Single Carrier Frequency,Division Multiplexing,(SC- FDM) is studied, and its feasibility in a LTE-A uplink system is discussed. Particular emphasis,is given,to the limited feedback precoding, where a codebook generation method based,on the Lloyd algorithm,is proposed. Results show that, when full channel knowledge is available at both the transmitter and the receiver, precoding leads to a spectral efficiency gain up,to 4 dB over,open,loop transmission; furthermore, OFDM slightly outperforms SC-FDM because of its higher,robustness,to the,noise. Limited,feedback precoding,has been,shown,to be effective and,consistently robust to the subcarrier grouping,in a urban,micro scenario. However, the performance is severely degraded in the blind precoding case, where transmitter and receiver compute the precoding matrix independently, due to the high sensitivity to the delay. Finally, the precoding operation over the SC- FDM signal is shown,to increase its Peak to Average Power Ratio (PAPR), thus reducing its advantage with respect to","Title: Initial Performance Evaluation of DFT-Spread OFDM Based SC-FDMA for UTRA LTE Uplink\nYear: 2007\n\nAbstract: In this paper we present an initial performance evaluation of the 3GPP UTRA long term evolution (UTRA LTE) uplink with baseline settings. The performance results are obtained from a detailed UTRA LTE uplink link level simulator supporting OFDMA and SC-FDMA schemes. The basic transmission scheme for uplink direction is based on single-carrier transmission in the form of DFT spread OFDM with an MMSE receiver. Two antenna configurations, SISO and 1times2 SIMO are considered in the analysis of spectral efficiency in addition to adaptive modulation and coding (AMC) and L1-HARQ. For assessment purposes, the performance results of SC-FDMA are compared with OFDMA. It is shown that 1times2 SIMO greatly increases the spectral efficiency of SC-FDMA making it comparable to OFDMA, especially for high coding rate. Furthermore, SC-FDMA has a flexibility to increase BLER performance by exploiting frequency diversity.","Title: Network Time-Synchronization In Tdd Based Lte-Advanced Systems\nYear: 2009\n\nAbstract: In Frequency Division Duplex (FDD) systems or Time Division Duplex (TDD) systems with perfect Uplink (UL) /Downlink (DL) alignment, all UL transmissions receive interference only from other UL links and vice versa. However, take into consideration one of the most important benefits that TDD offers, i.e., dynamic UL/DL switching points, the UL and DL transmission can be misaligned. This means UL might be corrupted by DL, and vice versa. The loss of network time-synchronization among different cells makes things even more complicated for TDD systems. Many techniques are developed to achieve network synchronization, e.g., GPS based or IEEE 1588 based techniques. In this paper, the effect that different synchronization and UL/DL switching points introduce to the system performance is studied. The purpose is to find out the benefit that synchronization can offer. Based on the observations, it is suggested to achieve at least loose network synchronization.","Title: An SDR architecture for OFDM transmission over USRP2 boards.\nYear: 2011\n\nAbstract: The Universal Software Radio Peripheral (USRP) developed by Ettus research is emerging as one of the most promising hardware solution for building a Software Defined Radio (SDR) platform. Originally designed for supporting GNU radio, it can also be interfaced to customized C++ code, thus allowing a higher degree of flexibility in the design of the transceiver chain. In this paper we describe the implementation of a coded Orthogonal Frequency Division Multiplexing (OFDM) transceiver running over USRP2 boards. The baseband processing and the radio-frequency settings are designed for coping with a local area scenario as well as with the physical capabilities of the USRP2 boards. Moreover, a simple subcarrier blinding algorithm is proposed with the aim of compensating the common phase error in the symbol constellation due to the limited nominal accuracy of the local oscillators. Performance results show the effectiveness of the proposed architecture and settings for achieving Block Error Rate (BLER) results below 1% at 12 dB of Signal-to-Noise Ratio (SNR) without requiring a high precision reference clock.","Title: Radio Propagation into Modern Buildings: Attenuation Measurements in the Range from 800 MHz to 18 GHz\nYear: 2014\n\nAbstract: Energy-efficient buildings are gaining momentum in order to comply with the new energy regulations. Especially in northern cold countries, thick reinforced walls and energy-efficient windows composed of several layers of glass plus metal coating are becoming the de facto elements in modern building constructions, and it has been noticed that they can impact heavily on radio signal propagation. This paper presents a measurement-based analysis of the outdoor-to-indoor attenuation experienced in several modern constructions compared to an old building. The measurements are performed for frequencies from 800 MHz to 18 GHz with the aim of identifying the frequency dependence and the impact of the new materials on not only the cellular frequency bands used today (mainly below 3 GHz), but also the potential future bands (above 3 GHz). The results show a material dependent and a frequency dependent attenuation, with an average increase of 20-25 dB in modern constructions compared to the old construction, which presents a low and almost constant attenuation below 10 dB. The different measurement results and observations presented along the paper are useful for future radio network planning considerations.","Title: Distributed Synchronization of a testbed network with USRP N200 radio boards\nYear: 2014\n\nAbstract: Network synchronization is a fundamental enabler of interference mitigation techniques that are required in ultradense deployment of small cells as targeted by upcoming 5th Generation (5G) wireless system. In this paper, we experimentally evaluate the possibility of acheiving in a distributed manner (i.e. without any external high precision reference clocks) a tight time alignment in a network of small cells. We are considering a software defined radio (SDR) network of 8 nodes, where each node adopts the Universal Software Radio Peripheral N200 (USRP N200) radio boards by Ettus Research, and the ASGARD software platform. Experimental results demonstrate the possibility of synchronizing the timing of the multiple nodes with a few μs precision, despite of the occurrencies of beacon losses.","Title: Implementation of LTE SC-FDMA on the USRP2 software defined radio platform\nYear: 2011\n\nAbstract: In this paper we discuss the implementation of a Single Carrier Frequency Division Multiple Access (SC-FDMA) transceiver running over the Universal Software Radio Peripheral 2 (USRP2). SC-FDMA is the air interface which has been selected for the uplink in the latest Long Term Evolution (LTE) standard. In this paper we derive an AWGN channel model for SC-FDMA transmission, which is useful for benchmarking experimental results. In our implementation, we deal with signal scaling, equalization and partial synchronization to realize SC-FDMA transmission over a noisy channel at rates up to 5.184 Mbit/s. Experimental results on the Bit Error Rate (BER) versus Signal-to-Noise Ratio (SNR) are presented and compared to theoretical and simulated performance.","Title: Distributed Synchronization For Beyond 4g Indoor Femtocells\nYear: 2013\n\nAbstract: The exponential growth in indoor data traffic necessitates a massive deployment of small cells, and emphasizes the importance of interference coordination and suppression to realize the full potential of this densification. To be effective however, interference coordination and suppression requires strict time synchronization between the cells. This paper deals with distributed runtime synchronization for Beyond 4G femtocells. A simple random scheduling solution for the clock distribution messages is proposed, as well as different clock update mechanisms. Simulation results for a dense cell scenario with two stripes of apartments show that a 'multiplicative clock update' exhibits an initial large time divergence among neighbor cells, but is able to achieve a lower long-term error floor than 'additive clock update'. Practical implications of the residual time misalignment on the Beyond 4G system design are also addressed.","Title: Ensuring Energy Efficient 5G User Equipment by Technology Evolution and Reuse\nYear: 2014\n\nAbstract: Research on fifth generation (5G) radio access technology (RAT) is ramping up, with the goal of significantly improving user data rates and latency compared to previous RAT generations. While energy efficiency (EE) of the user equipment (UE) was not a key optimization parameter for the current wireless standards, it is anticipated to become a distinguishing factor for 5G. In this paper, we analyze established and emerging technological solutions for features such as waveform, frame structure, duplexing and multiple antenna transmission from an EE perspective. Our contribution is to identify and discuss the features' pros and cons in achieving high performance in terms of data rate and/or latency while limiting their effect on the UE power consumption. Based on the discussion we give general recommendations for an energy efficient 5G design in the context of a previously proposed RAT concept.","Title: Base Station Antenna Pattern Distortion in Practical Urban Deployment Scenarios\nYear: 2014\n\nAbstract: In real urban deployments, base station antennas are typically not placed in free space conditions. Therefore, the radiation pattern can be affected by mounting structures and nearby obstacles located in the proximity of the antenna (near-field), which are often not taken into consideration. Also the intrinsic propagation mechanisms of the urban environment (far-field) can contribute to the distortion of the radiation pattern observed in a practical deployment scenario, especially when comparing it to the antenna pattern provided by the manufacturer and typically measured in free space. This paper presents a combination of near-field and far-field simulations aimed to provide an overview of the distortion experienced by the base station antenna pattern in two different urban deployment scenarios: rooftop and telecommunications tower. The study illustrates how, in comparison with the near-field effects, the urban propagation becomes the main contributor to the total distortion experienced. For the considered scenarios, the simulation results show a front-to-back-ratio reduction of approximately 10-15 dB in the effective antenna pattern compared to the free space reference. The near-field distortion has been evaluated by means of finite integration technique (FIT) simulations, while the far-field effects have been analyzed through intelligent ray tracing (IRT).","Title: Distributed Initial Synchronization for 5G Small Cells\nYear: 2014\n\nAbstract: Time synchronization in a large network of small cells enables efficient interference management as well as advanced transmission techniques which can boost the network throughput. In this paper, we focus on the distributed initial synchronization problem and propose different solutions aiming at reducing the residual asynchronous interference while limiting the number of large timing reconfigurations at each node. Simulation results show the effectiveness of an ID-based approach in limiting the asynchronous interference, however at the expense of a potential high number of timing reconfigurations. A pathloss-based solution limits such reconfigurations but also leads to a higher residual interference than the ID-based solution. An hybrid solution is shown to be an effective trade-off between the two former approaches.","Title: On the Potential of Zero-Tail DFT-Spread-OFDM in 5G Networks\nYear: 2014\n\nAbstract: Zero-tail Discrete Fourier Transform -spread OFDM (ZT DFT-s-OFDM) modulation allows to dynamically cope with the delay spread of the multipath channel, thus avoiding the limitations of hard-coded Cyclic Prefix (CP). In this paper, we discuss the potential of ZT DFT-s-OFDM modulation for the envisioned 5th generation (5G) radio access technology, characterized by an ultra-dense deployment of small cells and the support of novel paradigms such as Device-to-Device (D2D). We address the benefits of ZT DFT-s-OFDM in terms of coexistence among devices operating over adjacent frequency chunks, possibility of adopting unified radio numerology among different cells, reduced latency and support of agile link direction switching. The robustness of ZT DFT-s-OFDM towards non-idealities such as phase noise and non-linear power amplifier is also discussed.","Title: Inter-cell interference management using maximum rank planning in 5G small cell networks\nYear: 2014\n\nAbstract: In this paper, we propose Maximum Rank Planning (MRP) as a novel inter-cell interference management technique for the ultra-dense uncoordinated deployment of small cells targeted by 5th Generation (5G) networks. Rather than operating in the frequency domain as the conventional Frequency Reuse Planning (FRP) technique, MRP acts by reducing the number of Multiple Input Multiple Output (MIMO) spatial multiplexing streams. This reduction leads to an increased probability that the Interference Rejection Combining (IRC) receiver will have sufficient degrees-of-freedom to reject the strongest interferers. System-level simulation results show that MRP outperforms FRP in both low and high traffic load conditions, with 44% and 49% outage throughput gains in each case, respectively.","Title: Effect of Phase Noise on Spectral Efficiency for Utra Long Term Evolution\nYear: 2006\n\nAbstract: In this paper, the effects of phase noise on the spectral efficiency of the next generation of OFDM based mobile systems with channel estimation is investigated. The simulation context and parameter settings are taken from the 3GPP Evolved UTRA (E-UTRA) study item, focusing on an OFDM downlink single antenna system in 20 MHz bandwidth. Phase noise is modeled as a Wiener-Levy process and several phase noise powers are evaluated. The OFDM coherent detection method is based on pilot assisted channel estimation (PACE) with Wiener based frequency domain interpolation and second order Gaussian interpolation for the time domain interpolation. The cell level spectral efficiency is also evaluated for micro and macro-cell scenarios. The simulation results indicate that the phase noise effect in E-UTRA downlink can be reduced by using high performance local oscillator or by placing pilots in every OFDM symbols","Title: Evaluation of Potential Relay Locations in a Urban Macro-Cell Scenario with Applicability to LTE-A\nYear: 2012\n\nAbstract: Relay base stations are expected to play an important role in extending coverage for beyond 3G networks, such as LTE-A. However, the signal quality experienced on the backhaul link between the macro-cell and the relay node has a major impact on the performance of the multi-hop transmission. This paper presents a measurement-based study focusing on the performance evaluation of the relay backhaul link for different potential relay locations and antenna configurations in a real urban macro-cell scenario. Based on the assumption that a similar network deployment would apply for LTE-A, a fully operational 3G network has been used for measuring both received signal strength and Signal-to-Interference-Ratio (SIR). Furthermore, the results have been used to estimate the performance of the multi-hop transmission under simplifying assumptions. The experimental results show that by increasing the relay height from 2.4 m to 5 m, the received signal strength improves on average by 1.7 dB for omnidirectional antennas. When a directional antenna is mounted at 5 m and pointed towards the serving macro cell, significant SIR gains (around 5.9 dB) and throughput improvements are achieved at the locations where the relay node and the macro-cell are in Line-of-Sight (LOS).","Title: Path loss validation for urban micro cell scenarios at 3.5 GHz compared to 1.9 GHz\nYear: 2013\n\nAbstract: The 3.5 GHz band is a strong candidate for future urban micro cell deployment with base station antennas located below rooftop. Compared to other frequency bands, propagation in the 3.5 GHz band is relatively unexplored for the micro cell deployment. This paper presents a measurement-based analysis of outdoor and outdoor-to-indoor propagation at 3.5 GHz in comparison to the more well-known frequency of 1.9 GHz. A simple two-slope line-of-sight/non-line-of-sight outdoor path loss model is proposed and compared to different existing path loss models. The outdoor path loss is found to be approximately 5 dB higher for 3.5 GHz compared to 1.9 GHz. The outdoor-to-indoor propagation is investigated for two office buildings and different street shops. For the different presented scenarios, penetration loss increases with frequency and is found to be up to 5 dB higher for 3.5 GHz compared with 1.9 GHz. Although some existing models predict the observations with good accuracy, we propose a model based on line-of-sight probability that is simpler and easier to apply.","Title: On the Impact of Receiver Imperfections on the MMSE-IRC Receiver Performance in 5G Networks\nYear: 2014\n\nAbstract: The usage of Minimum Mean Square Error - Interference Rejection Combining (MMSE-IRC) receivers is expected to be a significant performance booster in the ultra-dense deployment of small cells envisioned by an upcoming 5th generation (5G) Radio Access Technology (RAT). However, hardware limitations of the radio- frequency front-end and poor covariance matrix estimation may severely compromise its ideal gains. In this paper, we evaluate the network performance of MMSE-IRC receivers by including the effects of the receiver imperfections as well as realistic covariance matrix estimates. System level simulation results confirm that a realistic MMSE-IRC receiver can achieve throughput gains close to ideal, provided a reasonably high resolution Analog-to-Digital Converter (ADC) as well as a supportive radio frame format design are used.","Title: Interference-Robust Air Interface for 5G Ultra-dense Small Cells\nYear: 2016\n\nAbstract: An ultra-dense deployment of small cells is foreseen as the solution to cope with the exponential increase of the data rate demand targeted by the 5th Generation (5G) radio access technology. In this article, we propose an interference-robust air interface built upon the usage of advanced receivers as main interference mitigation technique. Both Interference Rejection Combining (IRC) and Successive Interference Cancellation (SIC) principles are considered. An efficient usage of such receivers is ensured by a proper frame structure design and system assumptions. Different approaches for the rank adaptation are also investigated. Simulation results show that proposed air interface built upon advanced receiver can be a feasible alternative to traditional solutions based on frequency reuse planning. Open issues and further challenges are also addressed.","Title: Mission-critical mobile broadband communications in open-pit mines.\nYear: 2016\n\nAbstract: The need for continuous safety improvements and increased operational efficiency is driving the mining industry through a transition toward automated operations. From a communications perspective, this transition introduces a new set of high-bandwidth business-critical and mission- critical applications that need to be met by the wireless network. This article introduces fundamental concepts behin...","Title: Experimental evaluation of interference rejection combining for 5G small cells\nYear: 2015\n\nAbstract: The Interference Rejection Combining (IRC) receiver can significantly boost the network throughput in scenarios characterized by dense uncoordinated deployment of small cells, as targeted by future 5th generation (5G) radio access technology. This paper presents an experimental study on the potential benefit of IRC receiver in real deployment scenarios. The study is carried out using a software defined radio (SDR) testbed network with four cells, each featuring one Access Point (AP) and one User Equipment (UE) with two antennas. The testbed network was placed in an indoor office and open hall scenarios, respectively. In each scenario, the cells were arranged to characterize the propagation in different spatial configurations. Using the obtained propagation data, we analysed the cases of closed and open subscriber group for the respective scenarios, to compare the achievable throughput with IRC and Maximum Ratio Combining (MRC) receivers. Different frequency reuse schemes were also considered. The throughput results confirm the effectiveness of the IRC receiver in improving the network throughput with respect to the MRC receiver, under the assumption of single stream (rank 1) transmission. Results show average gains up to around 40% and outage gains up to 70% over the MRC receiver. The combination of the IRC receiver and frequency reuse achieves a favourable trade-off between the network throughput and fairness. Overall, due to the direct propagation, the open hall open subscriber group scenario is benefiting the most from the ability of the IRC receiver to cancel a strong dominant interferer.","Title: Experimental Evaluation Of Interference Suppression Receivers And Rank Adaptation In 5g Small Cells\nYear: 2015\n\nAbstract: Advanced receivers are a key component of the 5th Generation (5G) ultra-dense small cells concept given their capability of efficiently dealing with the ever-increasing problem of inter-cell interference. In this paper, we evaluate the potential of interference suppression receivers in real network scenarios using a software defined radio (SDR) testbed. The experimentation is carried out in an indoor office and open hall scenarios. In particular, we study to what extent the interference suppression receiver is an alternative to traditional frequency reuse techniques. To this end we evaluate the Interference Rejection Combining (IRC) and Successive Interference Cancellation (SIC) receivers and different rank adaptation approaches. Each node in our software defined radio (SDR) testbed features a 2x2 MIMO transceiver built with the USRP N200 hardware by Ettus Research. Our experimental results confirm that interference suppression receivers can be a valid alternative to frequency reuse, by achieving nearly the same outage and higher peak throughput performance.","Title: Estimation Of A 10 Gb/S 5g Receiver'S Performance And Power Evolution Towards 2030\nYear: 2015\n\nAbstract: The 5G vision of 10 Gb/s leads to performance and power consumption challenges in the mobile terminal receiver because 10 Gb/s requires 400 MHz bandwidth, faster baseband processing, and an increased number of component carriers and MIMO streams. The contribution of this paper is to estimate the impact from these physical layer requirements to the power consumption of main receiver components, including LNA, ADC and baseband processor with Turbo decoding, in a Direct Conversion Architecture. The estimate of power consumption results from a comprehensive survey of component performance evolution, and extrapolation hereof to estimate trends, and main challenges, towards 2030. According to our results, in 2020 the receiver power consumption exceeds 3 W, and is thus a challenge to be addressed. Assuming the performance evolution continues as observed in this work, the 5G receiver will be on par with the current LTE implementations in 2027 and thus consume less than 0.8 W.","Title: Rlc Sdu Loss And Arrival Delay In Multi-Sim Ues\nYear: 2015\n\nAbstract: The number of User Equipments (UEs) with multiple active subscriber identities (Multi-SIM) will increase throughout the coming years. The requirement in such UEs for each of the Subscriber Identity Modules (SIMs) to have parallel network access, invoke access conflicts on baseband/RF level. These conflicts ends up in one or more of the inserted SIMs being denied access for brief period of time, usually on the scale of tenths of milliseconds repeated every half to two minutes. These Multi-SIM conflicts are analysed using a combination of link level, and frame based simulation. The simulation clarifies the impact of Global System for Mobile (GSM)-like idle mode operations conflicting with an active Long Term Evolution (LTE) connection. The presented results show the effect Multi-SIM has on Radio Link Control (RLC) Unacknowledged Mode (UM) Service Data Unit (SDU) drop rate, and Acknowledge Mode (AM) SDU arrival time. It is shown that the impact of Multi-SIM on the drop rate and arrival time of the SDUs is based not only on the amount of conflicts between the SIMs, but also on the time alignment between the basestations retransmission timing strategy and the placement of the conflicts in time. The results may prove as base for discussions on some requirements to the basestations network configuration and performance.","Title: Experimental Verification Of Interference Mitigation Techniques For 5g Small Cells\nYear: 2015\n\nAbstract: Inter-cell interference is the main performance limiting factor in the dense deployment of small cells targeted by the upcoming 5th Generation (5G) radio access technology. In this paper, we present an experimental evaluation of inter-cell interference mitigation techniques in a real indoor office deployment with four cells, where each cell features one Access Point (AP) and one User Equipment (UE). In particular, we compare traditional Frequency Reuse Planning (FRP) with the recently proposed Maximum Rank Planning (MRP) technique, which relies on the degrees of freedom offered by the multi-antenna transceivers for suppressing a number of interfering streams. Different receiver types are also considered, namely Interference Rejection Combining (IRC) and the interference unaware Maximum Ratio Combining (MRC). Each node in our software defined radio (SDR) testbed features a 2 x 2 MIMO transceiver built with the USRP N200 hardware by Ettus Research. The experimental results in a fully loaded network reveal the capability of the MRP technique to achieve higher throughput performance than FRP for 90% of the cases when IRC receivers are used. Lower network loads lead to further performance improvements for MRP.","Title: Open loop transmit diversity solutions for LTE-A uplink\nYear: 2010\n\nAbstract: Transmit diversity (TD) techniques are expected to be included in the uplink of the upcoming Long Term Evolution - Advanced (LTE-A) systems to boost the user performance in low Signal-to-Noise Ratio (SNR) conditions. In this paper, several open loop TD solutions based on both space frequency coding (SFC) and space time coding (STC) are evaluated in a Single Carrier Frequency Division Multiplexing (SC-FDM) system with the aim of discussing their suitability for the upcoming standard. Traditional SFC is shown to increase the Peak-to-Average Power Ratio (PAPR) of the SC-FDM signal but it also outperforms STC for high speed and high order modulation and coding schemes (MCSs). Moreover, STC suffers from reduced flexibility in the time domain encoding. Starting from the single carrier sequences in the time domain, a SFC solution keeping low PAPR is derived; it is shown to be a valid option for low delay spread channels and small amount of data to be transmitted.","Title: Software architecture design for a Dynamic Spectrum Allocation-enabled Cognitive Radio testbed\nYear: 2011\n\nAbstract: The evolution of wireless communications is bringing into reality the dense deployment of femto and local area cells, which represents a challenging scenario for proving the effectiveness of Cognitive Radio (CR) frameworks. In particular the Dynamic Spectrum Allocation (DSA) paradigm aims at solving the resource allocation problem in a fully autonomous way. While in both CR and standardization lot of effort has been spent in developing efficient DSA algorithms, their research-oriented software radio implementation is still disregarded. In this paper, the design approach used for the definition of a CR-based, local area-oriented testbed platform is introduced. In particular, the testbed design in relation to the selected scenario and the architecture of the developed software framework are presented. An analysis of the performance of key software functionalities is also provided: preliminary results show a computational load compatible with nowadays Commercial-Off-The-Shelf computers.","Title: A study on the radio coverage in underground stations of the new copenhagen metro system\nYear: 2013\n\nAbstract: In connection with the extension of the Copenhagen Metro system, architects and wireless operators met early in the design phase to plan the radio coverage inside the public areas of the metro transport system. Based on common best practice, an initial design for the antenna installations, and hence radio coverage, was proposed for a distributed antenna system in each of two distinctly different types of underground stations. In this paper, we describe the considerations for the design, and specifically the modelling and analysis of the underground stations by way of a commercial ray-tracing tool. Radio coverage results are given for different designs, including different number and types of antennas, their configuration and placement, as well as the dependency on frequency and construction materials and presence of trains on the station platforms. In a practical case like this, compromises between stakeholders impact to the design and in some cases rule out options with better, more uniform, radio coverage. Also, given the constraints of the problem, common best practice leads to a conservative design. It shows a good margin for uncertainties and unaccounted propagation phenomena by an analytical comparison.","Title: Generalized DFT-Spread-OFDM as 5G Waveform.\nYear: 2016\n\nAbstract: This article introduces G-DFT-s-OFDM as a potential 5G waveform candidate. G-DFT-sOFDM replaces the CP with a sequence having a tunable length; this sequence is part of the IFFT output rather than being appended to it. Benefits in terms of flexibility, spectral containment, low latency, and robustness to Doppler spread and phase noise are discussed in the article.","Title: An Experimental Study of Advanced Receivers in a Practical Dense Small Cells Network.\nYear: 2016\n\nAbstract: 5G is targeting a peak data rate in the order of 10 Gb/s and at least 100 Mb/s data rate is generally expected to be available everywhere. For fulfilling such 5G broadband targets, massive deployment of small cells is considered as one of the promising solutions. However, intercell interference leads to significant limitations on the network throughput in such deployments. In addition, network densification introduces difficulty in network deployment. This paper presents a study on the benefits of advanced receiver in a practical uncoordinated dense small cells deployment. Our aim is to show that advanced receivers can alleviate the need for detailed cell planning. To this end we adopt a hybrid simulation evaluation approach where propagation data are obtained from experimental analysis, and by which we analyse how MIMO constellation and network size impacts to the aim. The experimental data have been obtained using a software defined radio (SDR) testbed network with 12 testbed nodes, configured as either access point or user equipment. Each node features a 4 x 4 or a 2 x 2 MIMO configuration. The results demonstrate that advanced receivers with a larger MIMO antenna configuration significantly improves the throughput performance in a practical dense small cells network due to the interference suppression capability. In addition, the results prove that the operators can rely on uncoordinated deployment of small cells, since the resulting interference can be suppressed by the advanced receiver processing with sufficiently capable MIMO antenna configuration.","Title: From LTE to 5G for Connected Mobility.\nYear: 2017\n\nAbstract: Long Term Evolution, the fourth generation of mobile communication technology, has been commercially deployed for about five years. Even though it is continuously updated through new releases, and with LTE Advanced Pro Release 13 being the latest one, the development of the fifth generation has been initiated. In this article, we measure how current LTE network implementations perform in comparison with the initial LTE requirements. The target is to identify certain key performance indicators that have suboptimal implementations and therefore lend themselves to careful consideration when designing and standardizing next generation wireless technology. Specifically, we analyze user and control plane latency, handover execution time, and coverage, which are critical parameters for connected mobility use cases such as road vehicle safety and efficiency. We study the latency, handover execution time, and coverage of four operational LTE networks based on 19,000 km of drive tests covering a mixture of rural, suburban, and urban environments. The measurements have been collected using commercial radio network scanners and measurement smartphones. Even though LTE has low air interface delays, the measurements reveal that core network delays compromise the overall round-trip time design requirement. LTE's breakbefore- make handover implementation causes a data interruption at each handover of 40 ms at the median level. While this is in compliance with the LTE requirements, and lower values are certainly possible, it is also clear that break-before-make will not be sufficient for connected mobility use cases such as road vehicle safety. Furthermore, the measurements reveal that LTE can provide coverage for 99 percent of the outdoor and road users, but the LTE-M or NarrowBand-IoT upgrades, as of LTE Release 13, are required in combination with other measures to allow for additional penetration losses, such as those experienced in underground parking lots. Based on the observed discrepancies between measured and standardized LTE performance, in terms of latency, handover execution time, and coverage, we conclude the article with a discussion of techniques that need careful consideration for connected mobility in fifth generation mobile communication technology.","Title: Dynamic Multi-Sim Gap Creating Procedure\nYear: 2016\n\nAbstract: User Equipments (UEs) supporting insertion of Multiple Subscriber Identity Modules (Multi-SIM) have been becoming increasingly popular within the last half decade. The end-user requirements for low cost is achieved by implementing a minimum of extra hardware resources, compared to a single SIM UE. The sparse amount of added hardware give rise to access conflicts between the multiple protocol stacks, and the available hardware, ending up in lost data on at least one of the connections. This paper suggests a framework using a dynamic weighting system based on the data characteristics, to resolve such conflicts. The goal of the weighting is to handle conflicts optimally; such that each protocols procedure has minimal impact on the others. We present the framework, as well as the dynamic weighting routine. Furthermore, performance results are presented, showing that the proposed method significantly reduces the latency experienced by the active data connection, when under the influence of Multi-SIM conflicts. The results are obtained using a Long Term Evolution (LTE) downlink simulator combined with a Global System for Mobile (GSM) control channel simulator. Furthermore, we also analyse the impact on the conflicting idle mode Subscriber Identity Module (SIM).","Title: SINR based capacity performance analysis of hovering Ad-Hoc network\nYear: 2016\n\nAbstract: The use of small, low-altitude Unmanned Aerial Vehicles (UAVs) have been emerging as suitable solutions in the cellular network context to provide radio coverage in disaster recovery regions as well as in hotspot areas. However, when such hotspot groups are in movement, they create a demand for coverage and capacity at locations of their traversal, congesting the nearby ground base stations. In our previous work, we investigated this phenomenon of congestion and defined it as the Place Time Capacity (PTC) problem. Further, we proposed a deployable architecture of the Hovering Ad-Hoc Network (HANET) to assist the ground infrastructure in serving the PTC. In this paper, we present a simulation-based SINR and capacity performance analysis of a LTE-A HetNet based network scenario with HANET members complementing the ground Base Stations (BS) to handle the PTC problem. The network simulation model is based on the ITU-R M.2135-1 radio interface guidelines for IMT-Advanced standard.","Title: How to Ensure Reliable Connectivity for Aerial Vehicles Over Cellular Networks.\nYear: 2018\n\nAbstract: Widely deployed cellular networks are an attractive solution to provide large scale radio connectivity to unmanned aerial vehicles. One main prerequisite is that co-existence and optimal performance for both aerial and terrestrial users can be provided. Today's cellular networks are, however, not designed for aerial coverage, and deployments are primarily optimized to provide good service for terrestrial users. These considerations, in combination with the strict regulatory requirements, lead to extensive research and standardization efforts to ensure that the current cellular networks can enable reliable operation of aerial vehicles in various deployment scenarios. In this paper, we investigate the performance of aerial radio connectivity in a typical rural area network deployment using extensive channel measurements and system simulations. First, we highlight that downlink and uplink radio interference play a key role, and yield relatively poor performance for the aerial traffic, when load is high in the network. Second, we analyze two potential terminal side interference mitigation solutions: interference cancellation and antenna beam selection. We show that each of these can improve the overall, aerial and terrestrial, system performance to a certain degree, with up to 30% throughput gain, and an increase in the reliability of the aerial radio connectivity to over 99%. Further, we introduce and evaluate a novel downlink inter-cell interference coordination mechanism applied to the aerial command and control traffic. Our proposed coordination mechanism is shown to provide the required aerial downlink performance at the cost of 10% capacity degradation in the serving and interfering cells.","Title: Radio Propagation Analysis of Industrial Scenarios within the Context of Ultra-Reliable Communication\nYear: 2018\n\nAbstract: One of the 5G use cases, known as ultra- reliable communication (URC), is expected to support very low packet error rate on the order of $10^{-5}$ with a 1 ms latency. In an industrial scenario, this would make possible replacing wired connections with wireless for controlling critical processes. Industrial environments with large metallic machinery and concrete structures can lead to deep shadowing and severe fading in the radio propagation channel, and thus pose a challenge for achieving the outage levels in connection with URC. In this paper, we present and analyze the large-scale propagation characteristics of two different industrial environments - open production space and dense factory clutter - based on measurements conducted at 2.3 and 5.7 GHz. By including a large number of spatially distributed samples, as per our experimental approach, we show the importance of properly characterizing the large-scale fading outage for URC. For instance, we show that based on a simple one-slope distance dependent path loss model, the conventional log-normal model for large-scale shadow fading is by far too simple for this environment. Our results show that at the 10^{-4} percentile, the tail of the shadow fading distribution can deviate by up to 10-20 dB from the log-normal model with respect to the average NLOS values (around 6 dB and 8 dB at 2.3 and 5.7 GHz, respectively). The simplicity of the one-slope path loss model, and its ability as we show, to express the trends with respect to scenarios, frequencies, and antenna heights, makes it an attractable option. However, there is a need for further experimental insight, possibly in combination with deterministic analysis, to get a better understanding of the large-scale fading for the study of URC in industrial environments.","Title: An Empirical Study of Propagation Models for Wireless Communications in Open-Pit Mines\nYear: 2018\n\nAbstract: In this paper, we investigate the suitability of the propagation models ITU-R 526, Okumura Hata, COST Hata models and Standard Propagation Model (SPM) to predict the path loss in open-pit mines. The models are evaluated by comparing the predicted data with measurements obtained in two operational iron-ore mining complexes in Brazil. Additionally, a simple deterministic model, based on the inclusion of an effective antenna height term to the ITU-R 526, is proposed and compared to the other methods. The results show that the proposed model results in root-mean-square error (RMSE) values between 5.5 dB and 9.2 dB, and it is capable of providing a close approximation of the best predictions (i.e. those with lowest RMSE) as provided by the SPM. The proposed model, however, reduces the calibration complexity considerably.","Title: Measured Uplink Interference Caused by Aerial Vehicles in LTE Cellular Networks.\nYear: 2018\n\nAbstract: Aerial users, such as unmanned aerial vehicles (UAVs), experience different radio propagation conditions than users on the ground. This is a concern regarding the integration of such users into cellular networks in the near future. This letter investigates the impact of uplink transmissions from an aerial user equipment. Full buffer transmissions were performed by a device at ground level and also...","Title: Emulation based performance investigation of FTP file downloads over UMTS dedicated channels\nYear: 2005\n\nAbstract: The Radio Link Control (RLC) protocol of Universal Mobile Telecommunication System (UMTS) provides link layer reliability that could mitigate the effects of the hostile radio propagation channel on packet data transmission. In this paper, the impact of some of the RLC reliability mechanisms on the performance of File Transport Protocol (FTP) is investigated. The investigations are carried out using a real time emulation platform, which makes the results from this study more realistic than simulation or simplified analytical studies as the overall End-2-End performance is analyzed involving real world protocol implementations.","Title: Experimental Evaluation of Data-driven Signal Level Estimation in Cellular Networks\nYear: 2021\n\nAbstract: Estimating accurately the signal levels that a user equipment experiences along a movement route is a key step in the process of providing and guaranteeing the required service quality. Obtaining accurate location-specific estimations of the signal level is challenging due to its random variations. In this paper we investigate the use of aggregated measurements from multiple User Equipments (UE) t...","Title: Decentralized Cooperative Resource Allocation with Reliability at Four Nines\nYear: 2021\n\nAbstract: Decentralized cooperative resource allocation schemes for robotic swarms represents an alternative to infrastructure-based communications across different commercial, industrial and environmental protection use cases. The cooperative communication schemes, device sequential and group scheduling in [1], have shown superior performance in comparison to 5G NR sidelink mode 2, but have also shown performance issues due to signaling overhead and signaling induced failures. In this paper we introduce different techniques that reduce the failure probability of data packet transmissions and the packet inter-reception (PIR) time. We evaluate two techniques, respectively, of incremental redundancy using hybrid automatic repeat request and link adaptation by aggregation, as well as their combination for our decentralized cooperative resource allocation schemes and sidelink mode 2. Our results show that the introduced enhancements, allow to double the amount of supported swarm members while achieving four nines reliability when compared to the case where the same enhancements are applied to the sidelink mode 2."],"string":"[\n \"Title: Experimental Validation of a Distributed Algorithm for Dynamic Spectrum Access in Local Area Networks.\\nYear: 2013\\n\\nAbstract: Next generation wireless networks aim at a significant improvement of the spectral efficiency in order to meet the dramatic increase in data service demand. In local area scenarios user-deployed base stations are expected to take place, thus making the centralized planning of frequency resources among the cells, a non-viable solution. Cognitive Radio (CR) and Dynamic Spectrum Access (DSA) are the research paradigms which are expected to provide the network nodes the capabilities for an autonomous and efficient selection of the spectrum resources. In this paper we present the first experimental activities with the Autonomous Component Carrier Selection (ACCS) algorithm, a distributed solution for interference management among small neighboring cells. A preliminary evaluation of the algorithm performance is provided considering its live execution on a software defined radio network testbed. The obtained experimental results confirm the performance trends obtained from prior simulation studies. The analysis in dynamic environment conditions also allowed identifying the utilization of static thresholds in the decision making process, as a critical aspect for the optimization of network capacity.\",\n \"Title: System Level Analysis of ACK/NACK Bundling for Multi-Component Carrier LTE-Advanced\\nYear: 2011\\n\\nAbstract: This paper focuses on reducing the uplink overhead caused by Acknowledgement (ACK)/Non-acknowledgement (NACK) signaling in an Long Term Evolution (LTE)-Advanced system. Such a system typically aggregates multiple Component Carrier (CC)s to obtain a wide bandwidth. Usually it has much larger feedback overhead than the single-CC system, and hence reducing the overhead is of special importance. A technique called ACK/NACK bundling has been used for the overhead reduction of the ACK/NACK signaling. Along with the overhead reduction, the downlink performance is also degraded. The tradeoff between the downlink performance and the overhead is analyzed under various load conditions and antenna configurations. The presented results can be used for selecting the proper bundling scheme based on specific system setting.\",\n \"Title: Utility Maximization in LTE-Advanced Systems with Carrier Aggregation\\nYear: 2011\\n\\nAbstract: Long Term Evolution (LTE)-Advanced is expected to aggregate multiple Component Carrier (CC)s to fulfil the high data rate requirement. It may serve users with different capabilities in accessing these CCs, e.g., some can access all CCs, whereas some may operate on only one CC. This gives challenges to the packet scheduler to maximize the system performance over all CCs. In this paper we provide a mathematical model of the log-measure utility in an LTE-Advanced system, and give proof that our previously developed cross-CC Proportional Fair (PF) packet scheduler maximizes this utility. System level simulations are performed, which confirm that cross-CC PF scheduling offers much higher utility than independent PF and channel blind schedulers. This scheduler is then generalized to adjust the resource sharing among users. It can trade off between average cell throughput and cell edge user throughput. However, any adjustment in the resource sharing leads to a loss in utility.\",\n \"Title: Transmission over multiple component carriers in LTE-A uplink.\\nYear: 2011\\n\\nAbstract: Long Term Evolution - Advanced systems are currently being standardized by 3GPP and aim at very high peak data rates of 1 Gb/s in the downlink and 500 Mb/s in the uplink. Those ambitious targets can only be achieved by using advanced MIMO antenna techniques as well as wide spectrum allocation, up to 100 MHz. A multiple component carrier structure has been agreed on in the 3GPP Work Item as a solution to extend the 18 MHz bandwidth of the previous LTE Release 8 up to 100 MHz. The multiple access schemes on both uplink and downlink now have to be adapted to the new spectrum configuration. Furthermore, in the link adaptation design the transmission over multiple CCs would reasonably lead to an increase of the feedback overhead. Bundling of the spatial or frequency parameters can keep the overhead low at the cost of lower throughput. In this article, we consider as a study case the LTE-A uplink, where NxDFT-spread-OFDM has been selected as the multiple access scheme. The validity of this scheme for the uplink is evaluated in terms of cubic metric, which is an indicator of the power de-rating needed at the transmitter to avoid intermodulation distortion. Furthermore, the impact of bundling the link adaptation parameters on the link performance is discussed considering both linear and turbo successive interference cancellation (SIC) receivers. Two codeword mixing stategies in the frequency and spatial domains are also proposed to boost the performance when the bundling is made per antenna or per CC, respectively. Results show that when a linear receiver is used in the base station, mixing techniques can increase spectral efficiency, thus reducing the performance gap to the no bundling case, which is the most expensive solution in terms of feedback signaling. However, when a turbo SIC receiver is used, only mixing over CCs is a valid option to achieve link performance gain.\",\n \"Title: A Site-Specific Study of In-Building Wireless Solutions\\nYear: 2010\\n\\nAbstract: This paper studies the different ways of extending wireless coverage for high data rates and improving the data capacity in a building. The solutions considered include deployments of single or multiple small base stations, i.e. Picocell or Femtocell, and distributed antenna system, operated on the same frequency as the macro cellular network. We look at the radio performance of each solution in a LTE downlink context with in-building path loss values retrieved from real-life measurements. The performance is compared by means of maximum supportable user numbers and average system throughput. Potential gains by hard frequency reuse in the building and QoS based packet scheduling are examined. Simulation results show the robustness of the proposed distributed antenna system and the conditions for multi-Femtocell deployment.\",\n \"Title: Channel estimation based on divergence minimization for OFDM sytems with co-channel interference\\nYear: 2009\\n\\nAbstract: In this paper, we present a novel approach for pilot-aided channel estimation in OFDM systems with synchronous co-channel interference. The estimator is derived based on the Kullback-Leibler divergence minimization framework. The obtained solution iteratively updates both the desired user's and the interferer's channels, using a combination of linear minimum mean squared-error (LMMSE) filtering and interference cancellation, avoiding the complex matrix inversions involved in the full LMMSE channel estimation approach. Estimation of the noise variance is also included in the iterative algorithm, accounting for Gaussian noise and residual interference after each iteration. The estimates of both channels are used at the equalizer to reject the interfering signal, thus mitigating the degradation due to co-channel interference. Simulation results show that the receiver using the proposed estimator performs as good as the one employing the full LMMSE estimator and very closely to a receiver having perfect knowledge of the channel coefficients.\",\n \"Title: Downlink Transmission in Multi-Carrier Systems with Reduced Feedback\\nYear: 2010\\n\\nAbstract: in this paper we address the problem of reducing the feedback for the downlink transmission in multi-carrier systems. In these systems multiple Component Carriers (CCs) are aggregated together to form a wide spectrum. Consequently, a large feedback overhead is required to report the channel quality information over such a wide bandwidth. We first generalize two existing feedback reduction techniques, and then propose a new one. These techniques use different feedback schemes across the CCs, or allow some CCs to be un-reported, for the purpose of reducing the amount of feedback. Performance for the investigated techniques is evaluated in a realistic system setting with different traffic conditions and terminal categories. Based on the obtained results, the selection of proper feedback reduction technique under different load conditions can be made. The findings are useful for reducing the feedback overhead in future generation wireless communication systems that operate over multiple CCs.\",\n \"Title: SVD-Based vs. Release 8 Codebooks for Single User MIMO LTE-A Uplink.\\nYear: 2010\\n\\nAbstract: The ambitious data rate target of the Long Term Evolution - Advanced (LTE-A) systems, which are currently being standardized by the 3rd Generation Partnership Project (3GPP), can only be achieved by using advanced Multiple Input Multiple Output (MIMO) antenna techniques on both uplink and downlink. In this paper, the focus is on LTE-A uplink and we discuss the suitability of two options for the closed loop precoded MIMO transmission. The first option is to use the same codebook of precoding matrices defined for LTE downlink, while the second option exploits the Singular Value Decomposition (SVD) of the channel matrix, which could theoretically achieve the MIMO capacity. Qualitative benefits of both solutions are discussed. Link level simulation results show that, the SVD-based approach only lead to a poor performance gain in terms of spectral efficiency over the LTE downlink codebook because of the losses due to the quantization of the right singular vectors of the channel matrix. Since SVD-based solution does not even exploit some useful properties (e. g., power balance over the antennas) which LTE Release 8 codebook offers, the latter results to be a more suitable candidate for the closed loop MIMO transmission in LTE-A uplink.\",\n \"Title: Assessing And Modelling The Effect Of Rf Impairments On Utra Lte Uplink Performance\\nYear: 2007\\n\\nAbstract: In this paper we investigate the effect of modelled RF transmitter impairments on UTRA Long Term Evolution (LTE) uplink link performance. Nonlinear power amplifier, gain imbalance, phase imbalance, and phase noise are considered. The main contributions of this work is assessment of the relation between Error Vector Magnitude (EVM) and link performance loss due to each impairment and the investigation of a white Gaussian noise source in the transmitter as an equivalent model of the impairment. The results obtained are based on the UTRA LTE uplink parameter settings. Results show that the EVM is a good measure to estimate the link level performance loss for numerous impairments. The performance degradation in AWGN and a frequency selective fading when using a white noise source as an equivalent transmitter RF impairment model, fits well with the other impairments in the anticipated range of EVM. An analytical formulation describing the effect of the white noise source in the SNR loss fits similarly with the simulation results. With 16QAM rate 3/4, an EVM value of 8% corresponds to an SNR loss of approximately 0.5 dB.\",\n \"Title: Combined Time And Code Division Scheduling For Enhanced Uplink Packet Access In Wcdma\\nYear: 2004\\n\\nAbstract: This paper contains an evaluation of three different time and code division schedulers for data transmission on enhanced dedicated channel (E-DCH) in WCDMA uplink. Time scheduling on E-DCH is possible thanks to fast Node B scheduling operation on a Transmission Time Interval (TTI) basis. Time scheduling allows for allocating users for transmission with instantaneously high data rates, and possibly when they experience favourable channel conditions. The system capacity can therefore be increased, also because interference generated to other cells is reduced. We apply a combined time and code division scheduling strategy in three different cases: a \\\"blind fair throughput\\\" scheduler (BFT), a \\\"maximise transmit power efficiency\\\" scheduler (MTPE) and a \\\"channel-state aware fair throughput\\\" scheduler (CSAFT). The two latter schedulers utilise an appositely defined Uplink Channel Quality Indicator (UCQI) to exploit channel-state information in the packet scheduling procedure. While the MTPE scheduler maximises cell throughput at the cost of a low degree of fairness between users, the CSAFT algorithm is shown to offer an appealing trade-off between users' fairness and throughput maximisation. Under a specific outage constraint for the packet call throughput, the capacity gain of the CSAFT scheduler over a Node B code division scheduler is 17% and 30% for Vehicular A and Pedestrian A channel profile at 3 km/h, respectively. The estimated capacity increase compared to a reference RNC-based scheduler is estimated between 50% and 80%, also depending on the propagation scenario.\",\n \"Title: On The Design Of A Mimo-Sic Receiver For Lte Downlink\\nYear: 2008\\n\\nAbstract: In this paper, we investigate different multiple-input multiple-output (MIMO) receiver structures based on MMSE filtering and sequential interference cancellation (SIC) for the downlink of the 3GPP long term evolution (LTE) system. We divide them into two approaches: symbol-SIC receivers, in which the detection and interference cancellation is done independently for each subcarrier, and codeword-SIC structures, in which the processing is carried out for each independently-coded stream by including the turbo-decoder inside the feedback loop. The results show that symbol-SIC receivers need to take into account the propagation of errors in the interference cancellation to provide the turbo decoder with reliable soft bit values. However, these are clearly outperformed by codeword-SIC schemes, due to the error correction capabilities of the turbo-decoder inside the feedback loop. We show that the best tradeoff between computational complexity and receiver performance is achieved by only cancelling the interference of a codeword when this has been successfully decoded.\",\n \"Title: On the Potential of Interference Rejection Combining in B4G Networks\\nYear: 2013\\n\\nAbstract: Beyond 4th Generation (B4G) local area networks will be characterized by the dense uncoordinated deployment of small cells. This paper shows that inter-cell interference, which is a main limiting factor in such networks, can be effectively contained using Interference Rejection Combining (IRC). By simulation we investigate two significantly different interference scenarios with dense small cell deployment. The results show that IRC brings considerable improvement in outage as well as in peak and median throughputs in both scenarios, and thus has a big potential as a capacity and coverage enhancing technique for B4G. The IRC gain mechanism depends strongly on the interference scenario and to some extent on the use of frequency reuse. These results are achieved with no coordination between cells and suggests that MIMO rank adaptation and IRC can be done independently.\",\n \"Title: An Interference Aware Dynamic Spectrum Sharing Algorithm for Local Area LTE-Advanced Networks\\nYear: 2009\\n\\nAbstract: This paper presents a dynamic spectrum sharing algorithm to minimize the inter-cell interference, so as to achieve a high system performance. This algorithm operates in a self-organized manner without the need of any centralized control, thereby is especially useful in local area (LA) where the enhanced-NodeBs (eNB) are close and coordination among them is hard or expensive to achieve. Results show that with very limited signaling between the eNBs, the proposed algorithm can effectively improve the system performance, and it can even overcome the performance with fixed frequency plan.\",\n \"Title: Subjective evaluation of packet service performance in UMTS and heterogeneous networks\\nYear: 2006\\n\\nAbstract: Quality of Service in mobile telecommunication systems is usually identified by some basic performance metrics such as delay, throughput and jitter. However, the main impact of service quality is on the end user, and as such a detailed study of service performance should involve the end user. In this paper, such an approach is taken where subjective performance evaluation is undertaken for web browsing and video streaming services in Universal Mobile Telecommunication System (UMTS) and a heterogeneous network comprised of UMTS and Wireless LAN (WLAN)Using a real-time network emulation testbed, users rated the perceived quality of the services under different network conditions. The users' ratings are analyzed and the results showed clear trends with very few outliers. The objective and subjective measures also were found to be in line except for one case where a higher layer effect influenced the users' perception.\",\n \"Title: Interference cancellation based on divergence minimization for MIMO-OFDM receivers\\nYear: 2009\\n\\nAbstract: In this paper, we present a novel iterative receiver for MIMO-OFDM systems with synchronous interferers. The receiver is derived based on the Kullback-Leibler divergence minimization framework, and combines channel estimation, interference cancellation and residual noise estimation in an iterative manner. By using both the pilot and data symbols, the channel estimator improves the accuracy of the estimates in each iteration, which leads to a more effective interference cancellation and data detection process. A performance evaluation based on Monte-Carlo simulations shows that the proposed scheme can effectively mitigate the effect of interferers, and operates very close to the single-user performance even in severe interference scenarios.\",\n \"Title: Channelization Issues With Fairness Considerations For Mu-Mimo Precoding Based Utra-Lte/Tdd Systems\\nYear: 2008\\n\\nAbstract: In a pre-coded Multi User Multiple Input Multiple Output (MU-MIMO) system, the channelization can be done either by using any of the two basic access techniques, namely Orthogonal Frequency Division Multiple Access (OFDMA) and Space Division Multiple Access (SDMA), or by combining them. From resource allocation point of view, choice of any technique will require different fairness conditions among users. In this paper, we have studied these different fairness conditions when combined with basic or joint access schemes mentioned above, while applied in a MU-MIMO based UTRA-LTE system. We have evaluated the resource allocation fairness issue when two well-known linear MU-MIMO precoding is used on a UTRA-LTE system. User grouping issue is dealt with when SDMA component is considered in the system. The results in this work provides an indicative analysis of the usability of different channelization techniques for considered system.\",\n \"Title: Uplink overhead analysis and outage protection for multi-carrier LTE-Advanced systems\\nYear: 2009\\n\\nAbstract: In a multi-component carrier LTE-Advanced system, a user can simultaneously be scheduled on all component carriers (CCs). For the base station to perform radio channel aware packet scheduling, each user ideally has to feedback information corresponding to all CCs. This results in a potentially high amount of uplink overhead. Furthermore, a user in a power limited situation may experience outage because of insufficient power for the required feedback. In this paper we first propose several techniques at different layers for the overhead reduction, as well as protecting the users from being in the outage situation. Afterwards, we show how these techniques can be integrated in a LTE-Advanced system. A weighted proportional fair scheduler is also proposed to maintain good performance with a reduced amount of overhead. Extensive simulation results are presented in the end for analyzing the proper trade-off between performance and overhead reduction and outage protection.\",\n \"Title: Site-Specific Study Of In-Building Wireless Solutions With Poisson Traffic\\nYear: 2011\\n\\nAbstract: Due to the poor outdoor-to-indoor propagation into buildings, dedicated wireless systems installed inside the buildings are often preferred for providing indoor users with high-data-rate services. We study the typical in-building wireless solutions - distributed antenna system and small cell Femto system - together with another multi-cell system using our proposed centralized scheduling scheme. In our previous work, their performance is evaluated and compared in the LTE downlink context with full buffer traffic. Compared to real mobile networks, the full buffer traffic model is usually a worst-case estimation of traffic load which causes severe interference conditions. Especially for Femto cells with universal frequency reuse it degrades system performance and may lead to biased conclusions on the relative performance of the different inbuilding solutions. In this study, we use a more realistic traffic model with fixed buffer size and Poisson arrival. Our new results show better performance for Femto cells with frequency reuse 1 at light to medium load, although the intelligent distributed system still obtains considerable better cell edge user throughput for the same number of access points.\",\n \"Title: Improving SC-FDMA Performance by Turbo Equalization in UTRA LTE Uplink.\\nYear: 2008\\n\\nAbstract: Turbo equalization is known as an advanced iterative equalization and decoding technique that allows to enhance the performance of the data transmission over a frequency selective fading channel. The turbo equalizer will result in extra receiver complexity, but isolated to the base-station, which does not have stringent power constraints. In this paper, a turbo equalization technique to improve Single Carrier Frequency Division Multiple Access (SC-FDMA) performance is proposed. A new adaptive coefficients solution for frequency domain equalization is considered. The work is in the context of UTRA Long Term Evolution (LTE) Uplink. The performance is evaluated for 1x2 Single Input Multiple Output (SIMO) antenna configuration in a 6 paths Typical Urban (TU-06) channel profile. For assessment purpose, the results are compared with SC-FDMA MMSE and OFDMA schemes. Simulation results show that the turbo equalizer can improve the BLER performance around I dB with only a few iterations, and improve the SC-FDMA performance over OFDM, especially at high coding rate.\",\n \"Title: Communication Theory Spatial frequency scheduling for uplink SC-FDMA based linearly precoded multiuser MIMO systems\\nYear: 2010\\n\\nAbstract: This paper investigates the performance of the uplink single carrier (SC) frequency division multiple access (FDMA) based linearly precoded multiuser multiple input multiple output (MIMO) systems with frequency domain packet scheduling. A mathematical expression of the received signal to interference plus noise ratio (SINR) for the studied systems is derived and a utility function based spatial frequency packet scheduling algorithms is investigated. The schedulers are shown to be able to exploit the available multiuser diversity in time, frequency and spatial domains. Copyright (C) 2009 John Wiley & Sons, Ltd.\",\n \"Title: Carrier load balancing and packet scheduling for multi-carrier systems\\nYear: 2010\\n\\nAbstract: Abstract-In this paper we focus on resource allocation for next generation wireless communication systems with aggregation of multiple Component Carriers (CCs), i.e., how to assign the CCs to each user, and how to multiplex multiple users in each CC. We first investigate two carrier load balancing methods for allocating the CCs to the users- Round Robin (RR) and Mobile Hashing (MH) balancing by means of a simple theoretical formulation, as well as system level simulations. At Layer-2 we propose a simple cross-CC packet scheduling algorithm that improves the coverage performance and the resource allocation fairness among users, as compared to independent scheduling per CC. The Long Term Evolution (LTE)-Advanced is selected for the case study of a multi-carrier system. In such a system, RR provides better performance than MH balancing, and the proposed simple scheduling algorithm is shown to be effective in providing up to 90% coverage gain with no loss of the overall cell throughput, as compared to independent scheduling per CC.\",\n \"Title: Carrier load balancing methods with bursty traffic for LTE-Advanced systems\\nYear: 2009\\n\\nAbstract: In this paper we focus on LTE-Advanced performance under bursty traffic conditions, and devote our effort to the different methods for balancing the load across multiple carriers. These carriers are the component carriers (CCs) that belong to the same carrier frequency. They are bonded together in order to fulfill the requirement of wide spectrum in LTE-Advanced. We first derive the analytical model for a bursty birth-death traffic model with fixed payload size for OFDMA with different frequency domain packet schedulers. Applying this model for a multi-carrier system, we compute the performance for different system setups. The obtained analytical results are verified using extensive system level simulations. Based on the analytical and simulation results, it is suggested to assign the users on all CCs if a cell is not heavily loaded. Otherwise, assign each user with only one CC using the load balancing method of Round Robin is preferable, in the sense that it maintains good performance with low uplink overhead.\",\n \"Title: Mitigating Signaling Requirements For Mimo With Frequency Domain Packet Scheduling\\nYear: 2007\\n\\nAbstract: MIMO techniques in the spatial division multiplexing (SDM) mode have demonstrated tremendous performance potential when a single user link is considered. Recent studies have shown that frequency domain packet scheduling (FDPS)PS) can provide a significant gain when combined with SDM (SDM-FDPS) by exploiting the multi-user diversity gain over frequency as well as the spatial domains. However, the increased signaling requirements in both uplink and downlink may prevent its practical use. In this paper several effective approaches are identified for reducing the signaling requirements for SDM-FDPS while still maintaining acceptable performance. Results show that the performance loss can be kept within 10% for a uplink signaling reduction of 88% and a downlink signaling reduction of 30%.\",\n \"Title: Resource allocation considerations for multi-carrier LTE-Advanced systems operating in backward compatible mode\\nYear: 2009\\n\\nAbstract: In this paper we focus on LTE-Advanced with backward compatibility, which serves a mixture of LTE-Advanced and LTE-Rel'8 users. Aggregation of multiple component carriers (CCs) to form a wide spectrum is assumed in order to fulfill the bandwidth requirement for the next generation systems, thereby leading to a multi-carrier system. Although a LTE-Advanced user can simultaneously access all the CCs, a LTE-Rel'8 user is restricted to operate on a single CC at a time. Different methods for balancing the load across these CCs will affect the system performance. This is investigated through both analytical methods and system level simulations. Bearing in mind that the LTE-Advanced users are scheduled on more CCs than the LTE-Rel'8 users, we propose a simple cross CC packet scheduling algorithm that improves the resource allocation fairness among the two categories of users. This simple scheduling algorithm is shown to be effective in providing better coverage performance with no loss of the overall cell throughput, as compared to independent scheduling per CC.\",\n \"Title: Iterative Channel Estimation with Robust Wiener Filtering in LTE Downlink.\\nYear: 2008\\n\\nAbstract: In this paper, an iterative enhancement of the robust Wiener filter (RWF) estimator is presented for a turbo-coded orthogonal frequency division multiplexing (OFDM) system under the umbrella of the 3GPP Long Term Evolution. The proposed scheme can operate with uncoded or coded feedback, and outperforms the conventional linear RWF in the whole signal-to-noise ratio (SNR) range with both approaches. Results show that most of the gain is obtained in the first iteration of the algorithm, and better performance is achieved with the coded feedback scheme. A good tradeoff between accuracy and complexity is achieved by selecting a low number of turbo coding iterations (TCI) in the iterative loop and concentrating most of them at the final decoding stage. Following this design, cell spectral efficiency gains of around 2.7 % and 6.5 % can be obtained with respect to linear RWF for micro-and macro-cell scenarios respectively.\",\n \"Title: Economical Comparison of Enterprise In-Building Wireless Solutions Using DAS and Femto\\nYear: 2012\\n\\nAbstract: As small cell solutions for efficient in-building wireless deployment, the distributed antenna system (DAS) and Femtocells constitute two major options. In this work, we study the Total Cost of Ownership (TCO) in combination with system performance in order to reach technology-economical conclusions regarding of these deployment options. Our aim is to identify the most cost economical solution for indoor wireless deployment which meets the indoor capacity need and provides ubiquitous indoor coverage. In the analysis we focus on LTE enhancement in the enterprise buildings. We discuss the unique cost features of DAS and Femto systems and highlight the importance of backhaul visibility in the cost analysis. For enterprise building solutions, we show the cost advantage of low-cost Femtocells over DAS in all scenarios regardless of coverage building size. We also suggest when DAS becomes more economical for large buildings with low data traffic need.\",\n \"Title: Precoded multirank transmission with linear receivers for LTE-A Uplink.\\nYear: 2009\\n\\nAbstract: The 3rd Generation Partnership Project (3GPP) defined the requirements for the upcoming Long Term Evolution - Advanced (LTE-A) systems, aiming at peak spectral efficiencies of 30 bit/s/Hz in the downlink and 15 bits/s/Hz in the uplink. These ambitious targets can be accomplished only by using advanced Multiple Input Multiple Output (MIMO) antenna techniques. In this paper, we evaluate closed loop precoded multirank transmission for Single Carrier Frequency Division Multiplexing (SC-FDM) as selected uplink modulation scheme by using the precoding codebook defined for LTE downlink. Its performance is compared with Orthogonal Frequency Division Multiplexing (OFDM). Results show that OFDM outperforms SC-FDM for medium-high ranks, but the gap can be reduced by adding further diversity branches. While narrowband precoding shows considerable gain over open loop transmission for all ranks, wideband precoding works with a very small feedback overhead but results to be a valid option mainly for low-rank transmission, where the low Peak-to-Average Power Ratio (PAPR) of SC-FDM signals is also preserved.\",\n \"Title: Allocation Fairness for MIMO Precoded UTRA-LTE TDD System\\nYear: 2008\\n\\nAbstract: In future time division duplex (TDD)-based broadband wireless systems, it will be possible to exploit the channel reciprocity to implement channel state information (CSI)-based multi user multiple input multiple output (MU-MIMO) techniques, which will ensure highly efficient spectrum usage. To increase the cell coverage while ensuring the quality of service (QoS) for all UEs across the cell area, fairness should be maximized as much as possible. This paper presents a novel way to help improving fairness performance in the physical layer, via fair power allocation together with resource allocation, in MU-MIMO precoding scenarios where the common approach of guaranteeing fairness at MAC layer is not feasible. The results presented in this paper show that the proposed algorithm is able to reduce the system outage event to a large extent, thus increases fairness.\",\n \"Title: Turbo Receivers For Single User Mimo Lte-A Uplink\\nYear: 2009\\n\\nAbstract: The paper deals with turbo detection techniques for Single User Multiple-Input-Multiple-Output (SU MIMO) antenna schemes. The context is on the uplink of the upcoming Long Term Evolution - Advanced (LTE-A) systems. Iterative approaches based on Parallel Interference Cancellation (PIC) and Successive Interference Cancellation (SIC) are investigated, and a low-complexity solution allowing to combine interstream interference cancellation and noise enhancement reduction is proposed. Performance is evaluated for Orthogonal Frequency Division Multiplexing (OFDM) and Single Carrier Frequency Division Multiplexing (SC-FDM) as candidate uplink modulation schemes for LTE-A. Simulation results show that, in a 2x2 antenna configuration, the turbo processing allows a consistent improvement of the link performance, being SC-FDM the one having higher relative gain with respect to linear detection. The turbo receiver's impact is however much reduced for both modulation schemes in a 2x4 configuration, due to the higher diversity gain provided by the additional receive antennas.\",\n \"Title: DAS, Uncoordinated Femto and Joint Scheduling Systems for In-Building Wireless Solutions\\nYear: 2011\\n\\nAbstract: Small cells are popular deployment options for coverage holes and capacity hotspot areas. Due to the poor outdoor-to-indoor propagation property of in-building environment, a dedicated wireless system installed inside the building is often preferred for providing indoor users high-data-rate services. Distributed antenna systems and Femto cells are cost-efficient techniques for this application. In this paper, their performance is evaluated in an LTE downlink context along with a proposed joint scheduling system, which maximizes the supported number of users under a QoS constraint. The selection of the enterprise building model includes a general office building model described in the WINNER II project and a site-specific office building with large scale path-loss values retrieved from measurements. Results show superior performance of the Femto system compared to DAS in providing high-data-rate services in most cases with a quality-guaranteed scheduler, while the centralized joint scheduling system gives the best performance. The centralized scheme can also help improve the system robustness in obtaining high performance even in the situation where access points are placed non-optimally.\",\n \"Title: On the Feasibility of Precoded Single User MIMO for LTE-A Uplink\\nYear: 2009\\n\\nAbstract: Abstract—The,3rd,Generation,Partnership,Project (3GPP) is currently specifying the system,requirements,for Long Term Evolution - Advanced (LTE-A), having as a tar- get peak data rates of 1 Gbit/s in local areas and 100 Mbit/s in wide,areas. To meet,these ambitious,requirements,for the uplink, multiple-input-multiple-output (MIMO) antenna techniques are expected to be deployed. In this paper, several channel-aware,MIMO precoding,techniques,are presented. Specifically, precoded single user spatial multiplexing for both Orthogonal,Frequency,Division Multiplexing (OFDM) and,Single Carrier Frequency,Division Multiplexing,(SC- FDM) is studied, and its feasibility in a LTE-A uplink system is discussed. Particular emphasis,is given,to the limited feedback precoding, where a codebook generation method based,on the Lloyd algorithm,is proposed. Results show that, when full channel knowledge is available at both the transmitter and the receiver, precoding leads to a spectral efficiency gain up,to 4 dB over,open,loop transmission; furthermore, OFDM slightly outperforms SC-FDM because of its higher,robustness,to the,noise. Limited,feedback precoding,has been,shown,to be effective and,consistently robust to the subcarrier grouping,in a urban,micro scenario. However, the performance is severely degraded in the blind precoding case, where transmitter and receiver compute the precoding matrix independently, due to the high sensitivity to the delay. Finally, the precoding operation over the SC- FDM signal is shown,to increase its Peak to Average Power Ratio (PAPR), thus reducing its advantage with respect to\",\n \"Title: Initial Performance Evaluation of DFT-Spread OFDM Based SC-FDMA for UTRA LTE Uplink\\nYear: 2007\\n\\nAbstract: In this paper we present an initial performance evaluation of the 3GPP UTRA long term evolution (UTRA LTE) uplink with baseline settings. The performance results are obtained from a detailed UTRA LTE uplink link level simulator supporting OFDMA and SC-FDMA schemes. The basic transmission scheme for uplink direction is based on single-carrier transmission in the form of DFT spread OFDM with an MMSE receiver. Two antenna configurations, SISO and 1times2 SIMO are considered in the analysis of spectral efficiency in addition to adaptive modulation and coding (AMC) and L1-HARQ. For assessment purposes, the performance results of SC-FDMA are compared with OFDMA. It is shown that 1times2 SIMO greatly increases the spectral efficiency of SC-FDMA making it comparable to OFDMA, especially for high coding rate. Furthermore, SC-FDMA has a flexibility to increase BLER performance by exploiting frequency diversity.\",\n \"Title: Network Time-Synchronization In Tdd Based Lte-Advanced Systems\\nYear: 2009\\n\\nAbstract: In Frequency Division Duplex (FDD) systems or Time Division Duplex (TDD) systems with perfect Uplink (UL) /Downlink (DL) alignment, all UL transmissions receive interference only from other UL links and vice versa. However, take into consideration one of the most important benefits that TDD offers, i.e., dynamic UL/DL switching points, the UL and DL transmission can be misaligned. This means UL might be corrupted by DL, and vice versa. The loss of network time-synchronization among different cells makes things even more complicated for TDD systems. Many techniques are developed to achieve network synchronization, e.g., GPS based or IEEE 1588 based techniques. In this paper, the effect that different synchronization and UL/DL switching points introduce to the system performance is studied. The purpose is to find out the benefit that synchronization can offer. Based on the observations, it is suggested to achieve at least loose network synchronization.\",\n \"Title: An SDR architecture for OFDM transmission over USRP2 boards.\\nYear: 2011\\n\\nAbstract: The Universal Software Radio Peripheral (USRP) developed by Ettus research is emerging as one of the most promising hardware solution for building a Software Defined Radio (SDR) platform. Originally designed for supporting GNU radio, it can also be interfaced to customized C++ code, thus allowing a higher degree of flexibility in the design of the transceiver chain. In this paper we describe the implementation of a coded Orthogonal Frequency Division Multiplexing (OFDM) transceiver running over USRP2 boards. The baseband processing and the radio-frequency settings are designed for coping with a local area scenario as well as with the physical capabilities of the USRP2 boards. Moreover, a simple subcarrier blinding algorithm is proposed with the aim of compensating the common phase error in the symbol constellation due to the limited nominal accuracy of the local oscillators. Performance results show the effectiveness of the proposed architecture and settings for achieving Block Error Rate (BLER) results below 1% at 12 dB of Signal-to-Noise Ratio (SNR) without requiring a high precision reference clock.\",\n \"Title: Radio Propagation into Modern Buildings: Attenuation Measurements in the Range from 800 MHz to 18 GHz\\nYear: 2014\\n\\nAbstract: Energy-efficient buildings are gaining momentum in order to comply with the new energy regulations. Especially in northern cold countries, thick reinforced walls and energy-efficient windows composed of several layers of glass plus metal coating are becoming the de facto elements in modern building constructions, and it has been noticed that they can impact heavily on radio signal propagation. This paper presents a measurement-based analysis of the outdoor-to-indoor attenuation experienced in several modern constructions compared to an old building. The measurements are performed for frequencies from 800 MHz to 18 GHz with the aim of identifying the frequency dependence and the impact of the new materials on not only the cellular frequency bands used today (mainly below 3 GHz), but also the potential future bands (above 3 GHz). The results show a material dependent and a frequency dependent attenuation, with an average increase of 20-25 dB in modern constructions compared to the old construction, which presents a low and almost constant attenuation below 10 dB. The different measurement results and observations presented along the paper are useful for future radio network planning considerations.\",\n \"Title: Distributed Synchronization of a testbed network with USRP N200 radio boards\\nYear: 2014\\n\\nAbstract: Network synchronization is a fundamental enabler of interference mitigation techniques that are required in ultradense deployment of small cells as targeted by upcoming 5th Generation (5G) wireless system. In this paper, we experimentally evaluate the possibility of acheiving in a distributed manner (i.e. without any external high precision reference clocks) a tight time alignment in a network of small cells. We are considering a software defined radio (SDR) network of 8 nodes, where each node adopts the Universal Software Radio Peripheral N200 (USRP N200) radio boards by Ettus Research, and the ASGARD software platform. Experimental results demonstrate the possibility of synchronizing the timing of the multiple nodes with a few μs precision, despite of the occurrencies of beacon losses.\",\n \"Title: Implementation of LTE SC-FDMA on the USRP2 software defined radio platform\\nYear: 2011\\n\\nAbstract: In this paper we discuss the implementation of a Single Carrier Frequency Division Multiple Access (SC-FDMA) transceiver running over the Universal Software Radio Peripheral 2 (USRP2). SC-FDMA is the air interface which has been selected for the uplink in the latest Long Term Evolution (LTE) standard. In this paper we derive an AWGN channel model for SC-FDMA transmission, which is useful for benchmarking experimental results. In our implementation, we deal with signal scaling, equalization and partial synchronization to realize SC-FDMA transmission over a noisy channel at rates up to 5.184 Mbit/s. Experimental results on the Bit Error Rate (BER) versus Signal-to-Noise Ratio (SNR) are presented and compared to theoretical and simulated performance.\",\n \"Title: Distributed Synchronization For Beyond 4g Indoor Femtocells\\nYear: 2013\\n\\nAbstract: The exponential growth in indoor data traffic necessitates a massive deployment of small cells, and emphasizes the importance of interference coordination and suppression to realize the full potential of this densification. To be effective however, interference coordination and suppression requires strict time synchronization between the cells. This paper deals with distributed runtime synchronization for Beyond 4G femtocells. A simple random scheduling solution for the clock distribution messages is proposed, as well as different clock update mechanisms. Simulation results for a dense cell scenario with two stripes of apartments show that a 'multiplicative clock update' exhibits an initial large time divergence among neighbor cells, but is able to achieve a lower long-term error floor than 'additive clock update'. Practical implications of the residual time misalignment on the Beyond 4G system design are also addressed.\",\n \"Title: Ensuring Energy Efficient 5G User Equipment by Technology Evolution and Reuse\\nYear: 2014\\n\\nAbstract: Research on fifth generation (5G) radio access technology (RAT) is ramping up, with the goal of significantly improving user data rates and latency compared to previous RAT generations. While energy efficiency (EE) of the user equipment (UE) was not a key optimization parameter for the current wireless standards, it is anticipated to become a distinguishing factor for 5G. In this paper, we analyze established and emerging technological solutions for features such as waveform, frame structure, duplexing and multiple antenna transmission from an EE perspective. Our contribution is to identify and discuss the features' pros and cons in achieving high performance in terms of data rate and/or latency while limiting their effect on the UE power consumption. Based on the discussion we give general recommendations for an energy efficient 5G design in the context of a previously proposed RAT concept.\",\n \"Title: Base Station Antenna Pattern Distortion in Practical Urban Deployment Scenarios\\nYear: 2014\\n\\nAbstract: In real urban deployments, base station antennas are typically not placed in free space conditions. Therefore, the radiation pattern can be affected by mounting structures and nearby obstacles located in the proximity of the antenna (near-field), which are often not taken into consideration. Also the intrinsic propagation mechanisms of the urban environment (far-field) can contribute to the distortion of the radiation pattern observed in a practical deployment scenario, especially when comparing it to the antenna pattern provided by the manufacturer and typically measured in free space. This paper presents a combination of near-field and far-field simulations aimed to provide an overview of the distortion experienced by the base station antenna pattern in two different urban deployment scenarios: rooftop and telecommunications tower. The study illustrates how, in comparison with the near-field effects, the urban propagation becomes the main contributor to the total distortion experienced. For the considered scenarios, the simulation results show a front-to-back-ratio reduction of approximately 10-15 dB in the effective antenna pattern compared to the free space reference. The near-field distortion has been evaluated by means of finite integration technique (FIT) simulations, while the far-field effects have been analyzed through intelligent ray tracing (IRT).\",\n \"Title: Distributed Initial Synchronization for 5G Small Cells\\nYear: 2014\\n\\nAbstract: Time synchronization in a large network of small cells enables efficient interference management as well as advanced transmission techniques which can boost the network throughput. In this paper, we focus on the distributed initial synchronization problem and propose different solutions aiming at reducing the residual asynchronous interference while limiting the number of large timing reconfigurations at each node. Simulation results show the effectiveness of an ID-based approach in limiting the asynchronous interference, however at the expense of a potential high number of timing reconfigurations. A pathloss-based solution limits such reconfigurations but also leads to a higher residual interference than the ID-based solution. An hybrid solution is shown to be an effective trade-off between the two former approaches.\",\n \"Title: On the Potential of Zero-Tail DFT-Spread-OFDM in 5G Networks\\nYear: 2014\\n\\nAbstract: Zero-tail Discrete Fourier Transform -spread OFDM (ZT DFT-s-OFDM) modulation allows to dynamically cope with the delay spread of the multipath channel, thus avoiding the limitations of hard-coded Cyclic Prefix (CP). In this paper, we discuss the potential of ZT DFT-s-OFDM modulation for the envisioned 5th generation (5G) radio access technology, characterized by an ultra-dense deployment of small cells and the support of novel paradigms such as Device-to-Device (D2D). We address the benefits of ZT DFT-s-OFDM in terms of coexistence among devices operating over adjacent frequency chunks, possibility of adopting unified radio numerology among different cells, reduced latency and support of agile link direction switching. The robustness of ZT DFT-s-OFDM towards non-idealities such as phase noise and non-linear power amplifier is also discussed.\",\n \"Title: Inter-cell interference management using maximum rank planning in 5G small cell networks\\nYear: 2014\\n\\nAbstract: In this paper, we propose Maximum Rank Planning (MRP) as a novel inter-cell interference management technique for the ultra-dense uncoordinated deployment of small cells targeted by 5th Generation (5G) networks. Rather than operating in the frequency domain as the conventional Frequency Reuse Planning (FRP) technique, MRP acts by reducing the number of Multiple Input Multiple Output (MIMO) spatial multiplexing streams. This reduction leads to an increased probability that the Interference Rejection Combining (IRC) receiver will have sufficient degrees-of-freedom to reject the strongest interferers. System-level simulation results show that MRP outperforms FRP in both low and high traffic load conditions, with 44% and 49% outage throughput gains in each case, respectively.\",\n \"Title: Effect of Phase Noise on Spectral Efficiency for Utra Long Term Evolution\\nYear: 2006\\n\\nAbstract: In this paper, the effects of phase noise on the spectral efficiency of the next generation of OFDM based mobile systems with channel estimation is investigated. The simulation context and parameter settings are taken from the 3GPP Evolved UTRA (E-UTRA) study item, focusing on an OFDM downlink single antenna system in 20 MHz bandwidth. Phase noise is modeled as a Wiener-Levy process and several phase noise powers are evaluated. The OFDM coherent detection method is based on pilot assisted channel estimation (PACE) with Wiener based frequency domain interpolation and second order Gaussian interpolation for the time domain interpolation. The cell level spectral efficiency is also evaluated for micro and macro-cell scenarios. The simulation results indicate that the phase noise effect in E-UTRA downlink can be reduced by using high performance local oscillator or by placing pilots in every OFDM symbols\",\n \"Title: Evaluation of Potential Relay Locations in a Urban Macro-Cell Scenario with Applicability to LTE-A\\nYear: 2012\\n\\nAbstract: Relay base stations are expected to play an important role in extending coverage for beyond 3G networks, such as LTE-A. However, the signal quality experienced on the backhaul link between the macro-cell and the relay node has a major impact on the performance of the multi-hop transmission. This paper presents a measurement-based study focusing on the performance evaluation of the relay backhaul link for different potential relay locations and antenna configurations in a real urban macro-cell scenario. Based on the assumption that a similar network deployment would apply for LTE-A, a fully operational 3G network has been used for measuring both received signal strength and Signal-to-Interference-Ratio (SIR). Furthermore, the results have been used to estimate the performance of the multi-hop transmission under simplifying assumptions. The experimental results show that by increasing the relay height from 2.4 m to 5 m, the received signal strength improves on average by 1.7 dB for omnidirectional antennas. When a directional antenna is mounted at 5 m and pointed towards the serving macro cell, significant SIR gains (around 5.9 dB) and throughput improvements are achieved at the locations where the relay node and the macro-cell are in Line-of-Sight (LOS).\",\n \"Title: Path loss validation for urban micro cell scenarios at 3.5 GHz compared to 1.9 GHz\\nYear: 2013\\n\\nAbstract: The 3.5 GHz band is a strong candidate for future urban micro cell deployment with base station antennas located below rooftop. Compared to other frequency bands, propagation in the 3.5 GHz band is relatively unexplored for the micro cell deployment. This paper presents a measurement-based analysis of outdoor and outdoor-to-indoor propagation at 3.5 GHz in comparison to the more well-known frequency of 1.9 GHz. A simple two-slope line-of-sight/non-line-of-sight outdoor path loss model is proposed and compared to different existing path loss models. The outdoor path loss is found to be approximately 5 dB higher for 3.5 GHz compared to 1.9 GHz. The outdoor-to-indoor propagation is investigated for two office buildings and different street shops. For the different presented scenarios, penetration loss increases with frequency and is found to be up to 5 dB higher for 3.5 GHz compared with 1.9 GHz. Although some existing models predict the observations with good accuracy, we propose a model based on line-of-sight probability that is simpler and easier to apply.\",\n \"Title: On the Impact of Receiver Imperfections on the MMSE-IRC Receiver Performance in 5G Networks\\nYear: 2014\\n\\nAbstract: The usage of Minimum Mean Square Error - Interference Rejection Combining (MMSE-IRC) receivers is expected to be a significant performance booster in the ultra-dense deployment of small cells envisioned by an upcoming 5th generation (5G) Radio Access Technology (RAT). However, hardware limitations of the radio- frequency front-end and poor covariance matrix estimation may severely compromise its ideal gains. In this paper, we evaluate the network performance of MMSE-IRC receivers by including the effects of the receiver imperfections as well as realistic covariance matrix estimates. System level simulation results confirm that a realistic MMSE-IRC receiver can achieve throughput gains close to ideal, provided a reasonably high resolution Analog-to-Digital Converter (ADC) as well as a supportive radio frame format design are used.\",\n \"Title: Interference-Robust Air Interface for 5G Ultra-dense Small Cells\\nYear: 2016\\n\\nAbstract: An ultra-dense deployment of small cells is foreseen as the solution to cope with the exponential increase of the data rate demand targeted by the 5th Generation (5G) radio access technology. In this article, we propose an interference-robust air interface built upon the usage of advanced receivers as main interference mitigation technique. Both Interference Rejection Combining (IRC) and Successive Interference Cancellation (SIC) principles are considered. An efficient usage of such receivers is ensured by a proper frame structure design and system assumptions. Different approaches for the rank adaptation are also investigated. Simulation results show that proposed air interface built upon advanced receiver can be a feasible alternative to traditional solutions based on frequency reuse planning. Open issues and further challenges are also addressed.\",\n \"Title: Mission-critical mobile broadband communications in open-pit mines.\\nYear: 2016\\n\\nAbstract: The need for continuous safety improvements and increased operational efficiency is driving the mining industry through a transition toward automated operations. From a communications perspective, this transition introduces a new set of high-bandwidth business-critical and mission- critical applications that need to be met by the wireless network. This article introduces fundamental concepts behin...\",\n \"Title: Experimental evaluation of interference rejection combining for 5G small cells\\nYear: 2015\\n\\nAbstract: The Interference Rejection Combining (IRC) receiver can significantly boost the network throughput in scenarios characterized by dense uncoordinated deployment of small cells, as targeted by future 5th generation (5G) radio access technology. This paper presents an experimental study on the potential benefit of IRC receiver in real deployment scenarios. The study is carried out using a software defined radio (SDR) testbed network with four cells, each featuring one Access Point (AP) and one User Equipment (UE) with two antennas. The testbed network was placed in an indoor office and open hall scenarios, respectively. In each scenario, the cells were arranged to characterize the propagation in different spatial configurations. Using the obtained propagation data, we analysed the cases of closed and open subscriber group for the respective scenarios, to compare the achievable throughput with IRC and Maximum Ratio Combining (MRC) receivers. Different frequency reuse schemes were also considered. The throughput results confirm the effectiveness of the IRC receiver in improving the network throughput with respect to the MRC receiver, under the assumption of single stream (rank 1) transmission. Results show average gains up to around 40% and outage gains up to 70% over the MRC receiver. The combination of the IRC receiver and frequency reuse achieves a favourable trade-off between the network throughput and fairness. Overall, due to the direct propagation, the open hall open subscriber group scenario is benefiting the most from the ability of the IRC receiver to cancel a strong dominant interferer.\",\n \"Title: Experimental Evaluation Of Interference Suppression Receivers And Rank Adaptation In 5g Small Cells\\nYear: 2015\\n\\nAbstract: Advanced receivers are a key component of the 5th Generation (5G) ultra-dense small cells concept given their capability of efficiently dealing with the ever-increasing problem of inter-cell interference. In this paper, we evaluate the potential of interference suppression receivers in real network scenarios using a software defined radio (SDR) testbed. The experimentation is carried out in an indoor office and open hall scenarios. In particular, we study to what extent the interference suppression receiver is an alternative to traditional frequency reuse techniques. To this end we evaluate the Interference Rejection Combining (IRC) and Successive Interference Cancellation (SIC) receivers and different rank adaptation approaches. Each node in our software defined radio (SDR) testbed features a 2x2 MIMO transceiver built with the USRP N200 hardware by Ettus Research. Our experimental results confirm that interference suppression receivers can be a valid alternative to frequency reuse, by achieving nearly the same outage and higher peak throughput performance.\",\n \"Title: Estimation Of A 10 Gb/S 5g Receiver'S Performance And Power Evolution Towards 2030\\nYear: 2015\\n\\nAbstract: The 5G vision of 10 Gb/s leads to performance and power consumption challenges in the mobile terminal receiver because 10 Gb/s requires 400 MHz bandwidth, faster baseband processing, and an increased number of component carriers and MIMO streams. The contribution of this paper is to estimate the impact from these physical layer requirements to the power consumption of main receiver components, including LNA, ADC and baseband processor with Turbo decoding, in a Direct Conversion Architecture. The estimate of power consumption results from a comprehensive survey of component performance evolution, and extrapolation hereof to estimate trends, and main challenges, towards 2030. According to our results, in 2020 the receiver power consumption exceeds 3 W, and is thus a challenge to be addressed. Assuming the performance evolution continues as observed in this work, the 5G receiver will be on par with the current LTE implementations in 2027 and thus consume less than 0.8 W.\",\n \"Title: Rlc Sdu Loss And Arrival Delay In Multi-Sim Ues\\nYear: 2015\\n\\nAbstract: The number of User Equipments (UEs) with multiple active subscriber identities (Multi-SIM) will increase throughout the coming years. The requirement in such UEs for each of the Subscriber Identity Modules (SIMs) to have parallel network access, invoke access conflicts on baseband/RF level. These conflicts ends up in one or more of the inserted SIMs being denied access for brief period of time, usually on the scale of tenths of milliseconds repeated every half to two minutes. These Multi-SIM conflicts are analysed using a combination of link level, and frame based simulation. The simulation clarifies the impact of Global System for Mobile (GSM)-like idle mode operations conflicting with an active Long Term Evolution (LTE) connection. The presented results show the effect Multi-SIM has on Radio Link Control (RLC) Unacknowledged Mode (UM) Service Data Unit (SDU) drop rate, and Acknowledge Mode (AM) SDU arrival time. It is shown that the impact of Multi-SIM on the drop rate and arrival time of the SDUs is based not only on the amount of conflicts between the SIMs, but also on the time alignment between the basestations retransmission timing strategy and the placement of the conflicts in time. The results may prove as base for discussions on some requirements to the basestations network configuration and performance.\",\n \"Title: Experimental Verification Of Interference Mitigation Techniques For 5g Small Cells\\nYear: 2015\\n\\nAbstract: Inter-cell interference is the main performance limiting factor in the dense deployment of small cells targeted by the upcoming 5th Generation (5G) radio access technology. In this paper, we present an experimental evaluation of inter-cell interference mitigation techniques in a real indoor office deployment with four cells, where each cell features one Access Point (AP) and one User Equipment (UE). In particular, we compare traditional Frequency Reuse Planning (FRP) with the recently proposed Maximum Rank Planning (MRP) technique, which relies on the degrees of freedom offered by the multi-antenna transceivers for suppressing a number of interfering streams. Different receiver types are also considered, namely Interference Rejection Combining (IRC) and the interference unaware Maximum Ratio Combining (MRC). Each node in our software defined radio (SDR) testbed features a 2 x 2 MIMO transceiver built with the USRP N200 hardware by Ettus Research. The experimental results in a fully loaded network reveal the capability of the MRP technique to achieve higher throughput performance than FRP for 90% of the cases when IRC receivers are used. Lower network loads lead to further performance improvements for MRP.\",\n \"Title: Open loop transmit diversity solutions for LTE-A uplink\\nYear: 2010\\n\\nAbstract: Transmit diversity (TD) techniques are expected to be included in the uplink of the upcoming Long Term Evolution - Advanced (LTE-A) systems to boost the user performance in low Signal-to-Noise Ratio (SNR) conditions. In this paper, several open loop TD solutions based on both space frequency coding (SFC) and space time coding (STC) are evaluated in a Single Carrier Frequency Division Multiplexing (SC-FDM) system with the aim of discussing their suitability for the upcoming standard. Traditional SFC is shown to increase the Peak-to-Average Power Ratio (PAPR) of the SC-FDM signal but it also outperforms STC for high speed and high order modulation and coding schemes (MCSs). Moreover, STC suffers from reduced flexibility in the time domain encoding. Starting from the single carrier sequences in the time domain, a SFC solution keeping low PAPR is derived; it is shown to be a valid option for low delay spread channels and small amount of data to be transmitted.\",\n \"Title: Software architecture design for a Dynamic Spectrum Allocation-enabled Cognitive Radio testbed\\nYear: 2011\\n\\nAbstract: The evolution of wireless communications is bringing into reality the dense deployment of femto and local area cells, which represents a challenging scenario for proving the effectiveness of Cognitive Radio (CR) frameworks. In particular the Dynamic Spectrum Allocation (DSA) paradigm aims at solving the resource allocation problem in a fully autonomous way. While in both CR and standardization lot of effort has been spent in developing efficient DSA algorithms, their research-oriented software radio implementation is still disregarded. In this paper, the design approach used for the definition of a CR-based, local area-oriented testbed platform is introduced. In particular, the testbed design in relation to the selected scenario and the architecture of the developed software framework are presented. An analysis of the performance of key software functionalities is also provided: preliminary results show a computational load compatible with nowadays Commercial-Off-The-Shelf computers.\",\n \"Title: A study on the radio coverage in underground stations of the new copenhagen metro system\\nYear: 2013\\n\\nAbstract: In connection with the extension of the Copenhagen Metro system, architects and wireless operators met early in the design phase to plan the radio coverage inside the public areas of the metro transport system. Based on common best practice, an initial design for the antenna installations, and hence radio coverage, was proposed for a distributed antenna system in each of two distinctly different types of underground stations. In this paper, we describe the considerations for the design, and specifically the modelling and analysis of the underground stations by way of a commercial ray-tracing tool. Radio coverage results are given for different designs, including different number and types of antennas, their configuration and placement, as well as the dependency on frequency and construction materials and presence of trains on the station platforms. In a practical case like this, compromises between stakeholders impact to the design and in some cases rule out options with better, more uniform, radio coverage. Also, given the constraints of the problem, common best practice leads to a conservative design. It shows a good margin for uncertainties and unaccounted propagation phenomena by an analytical comparison.\",\n \"Title: Generalized DFT-Spread-OFDM as 5G Waveform.\\nYear: 2016\\n\\nAbstract: This article introduces G-DFT-s-OFDM as a potential 5G waveform candidate. G-DFT-sOFDM replaces the CP with a sequence having a tunable length; this sequence is part of the IFFT output rather than being appended to it. Benefits in terms of flexibility, spectral containment, low latency, and robustness to Doppler spread and phase noise are discussed in the article.\",\n \"Title: An Experimental Study of Advanced Receivers in a Practical Dense Small Cells Network.\\nYear: 2016\\n\\nAbstract: 5G is targeting a peak data rate in the order of 10 Gb/s and at least 100 Mb/s data rate is generally expected to be available everywhere. For fulfilling such 5G broadband targets, massive deployment of small cells is considered as one of the promising solutions. However, intercell interference leads to significant limitations on the network throughput in such deployments. In addition, network densification introduces difficulty in network deployment. This paper presents a study on the benefits of advanced receiver in a practical uncoordinated dense small cells deployment. Our aim is to show that advanced receivers can alleviate the need for detailed cell planning. To this end we adopt a hybrid simulation evaluation approach where propagation data are obtained from experimental analysis, and by which we analyse how MIMO constellation and network size impacts to the aim. The experimental data have been obtained using a software defined radio (SDR) testbed network with 12 testbed nodes, configured as either access point or user equipment. Each node features a 4 x 4 or a 2 x 2 MIMO configuration. The results demonstrate that advanced receivers with a larger MIMO antenna configuration significantly improves the throughput performance in a practical dense small cells network due to the interference suppression capability. In addition, the results prove that the operators can rely on uncoordinated deployment of small cells, since the resulting interference can be suppressed by the advanced receiver processing with sufficiently capable MIMO antenna configuration.\",\n \"Title: From LTE to 5G for Connected Mobility.\\nYear: 2017\\n\\nAbstract: Long Term Evolution, the fourth generation of mobile communication technology, has been commercially deployed for about five years. Even though it is continuously updated through new releases, and with LTE Advanced Pro Release 13 being the latest one, the development of the fifth generation has been initiated. In this article, we measure how current LTE network implementations perform in comparison with the initial LTE requirements. The target is to identify certain key performance indicators that have suboptimal implementations and therefore lend themselves to careful consideration when designing and standardizing next generation wireless technology. Specifically, we analyze user and control plane latency, handover execution time, and coverage, which are critical parameters for connected mobility use cases such as road vehicle safety and efficiency. We study the latency, handover execution time, and coverage of four operational LTE networks based on 19,000 km of drive tests covering a mixture of rural, suburban, and urban environments. The measurements have been collected using commercial radio network scanners and measurement smartphones. Even though LTE has low air interface delays, the measurements reveal that core network delays compromise the overall round-trip time design requirement. LTE's breakbefore- make handover implementation causes a data interruption at each handover of 40 ms at the median level. While this is in compliance with the LTE requirements, and lower values are certainly possible, it is also clear that break-before-make will not be sufficient for connected mobility use cases such as road vehicle safety. Furthermore, the measurements reveal that LTE can provide coverage for 99 percent of the outdoor and road users, but the LTE-M or NarrowBand-IoT upgrades, as of LTE Release 13, are required in combination with other measures to allow for additional penetration losses, such as those experienced in underground parking lots. Based on the observed discrepancies between measured and standardized LTE performance, in terms of latency, handover execution time, and coverage, we conclude the article with a discussion of techniques that need careful consideration for connected mobility in fifth generation mobile communication technology.\",\n \"Title: Dynamic Multi-Sim Gap Creating Procedure\\nYear: 2016\\n\\nAbstract: User Equipments (UEs) supporting insertion of Multiple Subscriber Identity Modules (Multi-SIM) have been becoming increasingly popular within the last half decade. The end-user requirements for low cost is achieved by implementing a minimum of extra hardware resources, compared to a single SIM UE. The sparse amount of added hardware give rise to access conflicts between the multiple protocol stacks, and the available hardware, ending up in lost data on at least one of the connections. This paper suggests a framework using a dynamic weighting system based on the data characteristics, to resolve such conflicts. The goal of the weighting is to handle conflicts optimally; such that each protocols procedure has minimal impact on the others. We present the framework, as well as the dynamic weighting routine. Furthermore, performance results are presented, showing that the proposed method significantly reduces the latency experienced by the active data connection, when under the influence of Multi-SIM conflicts. The results are obtained using a Long Term Evolution (LTE) downlink simulator combined with a Global System for Mobile (GSM) control channel simulator. Furthermore, we also analyse the impact on the conflicting idle mode Subscriber Identity Module (SIM).\",\n \"Title: SINR based capacity performance analysis of hovering Ad-Hoc network\\nYear: 2016\\n\\nAbstract: The use of small, low-altitude Unmanned Aerial Vehicles (UAVs) have been emerging as suitable solutions in the cellular network context to provide radio coverage in disaster recovery regions as well as in hotspot areas. However, when such hotspot groups are in movement, they create a demand for coverage and capacity at locations of their traversal, congesting the nearby ground base stations. In our previous work, we investigated this phenomenon of congestion and defined it as the Place Time Capacity (PTC) problem. Further, we proposed a deployable architecture of the Hovering Ad-Hoc Network (HANET) to assist the ground infrastructure in serving the PTC. In this paper, we present a simulation-based SINR and capacity performance analysis of a LTE-A HetNet based network scenario with HANET members complementing the ground Base Stations (BS) to handle the PTC problem. The network simulation model is based on the ITU-R M.2135-1 radio interface guidelines for IMT-Advanced standard.\",\n \"Title: How to Ensure Reliable Connectivity for Aerial Vehicles Over Cellular Networks.\\nYear: 2018\\n\\nAbstract: Widely deployed cellular networks are an attractive solution to provide large scale radio connectivity to unmanned aerial vehicles. One main prerequisite is that co-existence and optimal performance for both aerial and terrestrial users can be provided. Today's cellular networks are, however, not designed for aerial coverage, and deployments are primarily optimized to provide good service for terrestrial users. These considerations, in combination with the strict regulatory requirements, lead to extensive research and standardization efforts to ensure that the current cellular networks can enable reliable operation of aerial vehicles in various deployment scenarios. In this paper, we investigate the performance of aerial radio connectivity in a typical rural area network deployment using extensive channel measurements and system simulations. First, we highlight that downlink and uplink radio interference play a key role, and yield relatively poor performance for the aerial traffic, when load is high in the network. Second, we analyze two potential terminal side interference mitigation solutions: interference cancellation and antenna beam selection. We show that each of these can improve the overall, aerial and terrestrial, system performance to a certain degree, with up to 30% throughput gain, and an increase in the reliability of the aerial radio connectivity to over 99%. Further, we introduce and evaluate a novel downlink inter-cell interference coordination mechanism applied to the aerial command and control traffic. Our proposed coordination mechanism is shown to provide the required aerial downlink performance at the cost of 10% capacity degradation in the serving and interfering cells.\",\n \"Title: Radio Propagation Analysis of Industrial Scenarios within the Context of Ultra-Reliable Communication\\nYear: 2018\\n\\nAbstract: One of the 5G use cases, known as ultra- reliable communication (URC), is expected to support very low packet error rate on the order of $10^{-5}$ with a 1 ms latency. In an industrial scenario, this would make possible replacing wired connections with wireless for controlling critical processes. Industrial environments with large metallic machinery and concrete structures can lead to deep shadowing and severe fading in the radio propagation channel, and thus pose a challenge for achieving the outage levels in connection with URC. In this paper, we present and analyze the large-scale propagation characteristics of two different industrial environments - open production space and dense factory clutter - based on measurements conducted at 2.3 and 5.7 GHz. By including a large number of spatially distributed samples, as per our experimental approach, we show the importance of properly characterizing the large-scale fading outage for URC. For instance, we show that based on a simple one-slope distance dependent path loss model, the conventional log-normal model for large-scale shadow fading is by far too simple for this environment. Our results show that at the 10^{-4} percentile, the tail of the shadow fading distribution can deviate by up to 10-20 dB from the log-normal model with respect to the average NLOS values (around 6 dB and 8 dB at 2.3 and 5.7 GHz, respectively). The simplicity of the one-slope path loss model, and its ability as we show, to express the trends with respect to scenarios, frequencies, and antenna heights, makes it an attractable option. However, there is a need for further experimental insight, possibly in combination with deterministic analysis, to get a better understanding of the large-scale fading for the study of URC in industrial environments.\",\n \"Title: An Empirical Study of Propagation Models for Wireless Communications in Open-Pit Mines\\nYear: 2018\\n\\nAbstract: In this paper, we investigate the suitability of the propagation models ITU-R 526, Okumura Hata, COST Hata models and Standard Propagation Model (SPM) to predict the path loss in open-pit mines. The models are evaluated by comparing the predicted data with measurements obtained in two operational iron-ore mining complexes in Brazil. Additionally, a simple deterministic model, based on the inclusion of an effective antenna height term to the ITU-R 526, is proposed and compared to the other methods. The results show that the proposed model results in root-mean-square error (RMSE) values between 5.5 dB and 9.2 dB, and it is capable of providing a close approximation of the best predictions (i.e. those with lowest RMSE) as provided by the SPM. The proposed model, however, reduces the calibration complexity considerably.\",\n \"Title: Measured Uplink Interference Caused by Aerial Vehicles in LTE Cellular Networks.\\nYear: 2018\\n\\nAbstract: Aerial users, such as unmanned aerial vehicles (UAVs), experience different radio propagation conditions than users on the ground. This is a concern regarding the integration of such users into cellular networks in the near future. This letter investigates the impact of uplink transmissions from an aerial user equipment. Full buffer transmissions were performed by a device at ground level and also...\",\n \"Title: Emulation based performance investigation of FTP file downloads over UMTS dedicated channels\\nYear: 2005\\n\\nAbstract: The Radio Link Control (RLC) protocol of Universal Mobile Telecommunication System (UMTS) provides link layer reliability that could mitigate the effects of the hostile radio propagation channel on packet data transmission. In this paper, the impact of some of the RLC reliability mechanisms on the performance of File Transport Protocol (FTP) is investigated. The investigations are carried out using a real time emulation platform, which makes the results from this study more realistic than simulation or simplified analytical studies as the overall End-2-End performance is analyzed involving real world protocol implementations.\",\n \"Title: Experimental Evaluation of Data-driven Signal Level Estimation in Cellular Networks\\nYear: 2021\\n\\nAbstract: Estimating accurately the signal levels that a user equipment experiences along a movement route is a key step in the process of providing and guaranteeing the required service quality. Obtaining accurate location-specific estimations of the signal level is challenging due to its random variations. In this paper we investigate the use of aggregated measurements from multiple User Equipments (UE) t...\",\n \"Title: Decentralized Cooperative Resource Allocation with Reliability at Four Nines\\nYear: 2021\\n\\nAbstract: Decentralized cooperative resource allocation schemes for robotic swarms represents an alternative to infrastructure-based communications across different commercial, industrial and environmental protection use cases. The cooperative communication schemes, device sequential and group scheduling in [1], have shown superior performance in comparison to 5G NR sidelink mode 2, but have also shown performance issues due to signaling overhead and signaling induced failures. In this paper we introduce different techniques that reduce the failure probability of data packet transmissions and the packet inter-reception (PIR) time. We evaluate two techniques, respectively, of incremental redundancy using hybrid automatic repeat request and link adaptation by aggregation, as well as their combination for our decentralized cooperative resource allocation schemes and sidelink mode 2. Our results show that the introduced enhancements, allow to double the amount of supported swarm members while achieving four nines reliability when compared to the case where the same enhancements are applied to the sidelink mode 2.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":69,"string":"69"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13671,"string":"13,671"}}},{"rowIdx":13672,"cells":{"question_id":{"kind":"string","value":"Jingyu Hua"},"question":{"kind":"string","value":"Generate an abstract for the title \"A SMS-based mobile Botnet using flooding algorithm\" using the following items: 1. Mobile phones \n2. Malware \n3. Botnet design \n4. SMS-based C&C \n5. Defense strategies"},"answer":{"kind":"string","value":"As a lot of sophisticated duties are being migrated to mobile phones, they are gradually becoming hot targets of hackers. Actually, during the past few years, It has appeared many malware targeting mobile phones and the situation is getting worse. Under this circumstance, we may ask a serious question: whether can those infected phones be organized to a botnet? In this paper, we present a design of such a botnet using Short Message Service (SMS) as its Command and Control (C&C) medium. We cover all the aspects of the botnet design including the stealthiness protection, the topology selecting and the botnet maintaining. Our simulations show that in our proposed SMS-based botnet a newly issued C&C message can be covertly propagated to over 90% of the total 20000 bots within 20 minutes based on a simple flooding algorithm. Moreover, in this process each bot sends no more than four SMS messages and the botnet is robust to both random and selective node failures. Thereby, we demonstrate that the proposed mobile botnet is indeed a serious threat on the security of the mobile computing environment. For this reason, we further explore several effective defense strategies against such a botnet. In doing so, we hope to be one step ahead of the hackers to discover and prevent this upcoming threat."},"correct_answer":{"kind":"string","value":"As a lot of sophisticated duties are being migrated to mobile phones, they are gradually becoming hot targets of hackers. Actually, during the past few years, It has appeared many malware targeting mobile phones and the situation is getting worse. Under this circumstance, we may ask a serious question: whether can those infected phones be organized to a botnet? In this paper, we present a design of such a botnet using Short Message Service (SMS) as its Command and Control (C&C) medium. We cover all the aspects of the botnet design including the stealthiness protection, the topology selecting and the botnet maintaining. Our simulations show that in our proposed SMS-based botnet a newly issued C&C message can be covertly propagated to over 90% of the total 20000 bots within 20 minutes based on a simple flooding algorithm. Moreover, in this process each bot sends no more than four SMS messages and the botnet is robust to both random and selective node failures. Thereby, we demonstrate that the proposed mobile botnet is indeed a serious threat on the security of the mobile computing environment. For this reason, we further explore several effective defense strategies against such a botnet. In doing so, we hope to be one step ahead of the hackers to discover and prevent this upcoming threat."},"documents":{"kind":"list like","value":["Title: Efficient Intrusion Detection Based on Static Analysis and Stack Walks\nYear: 2009\n\nAbstract: Some intrusion detection models such as the VPStatic first construct a behavior model for a program via static analysis, and then perform intrusion detection by monitoring whether its execution is consistent with this behavior model. These models usually share the highly desirable feature that they do not produce false alarms but they face the conflict between precision and efficiency. The high precision of the VPStatic is at the cost of high space complexity. In this paper, we propose a new context-sensitive intrusion detection model based on static analysis and stack walks, which is similar to VPStatic but much more efficient, especially in memory use. We replace the automaton in the VPStatic with a state transition table (STT) and all redundant states and transitions in VPStatic are eliminated. We prove that our STT model is a deterministic pushdown automaton (DPDA) and the precision is the same as the VPStatic. Experiments also demonstrate that our STT model reduces both time and memory costs comparing with the VPStatic, in particular, memory overheads are less than half of the VPStatic's. Thereby, we alleviate the conflict between precision and efficiency.","Title: Research on Qos Protocol Based on Quantum State Algorithm for MANET.\nYear: 2010\n\nAbstract: With the increase in size and application are of the communication network, more attention has been paid to the invulnerability of mobile ad hoc network. By analyzing the working mechanism and key techniques of the Ad Hoc Qos routing models, the searching for the most optimal routing is NP-hard problem. There is only sub-optimal routing available because of the rather high complexity of the algorithm. This paper analysis the Qos parameters firstly, then build the quantum multiprogramming model considering the link distance, node power and time delay to choose the optimal route to transmit the data effetely. Simulations and comparisons with some typical route algorithms show that our algorithm is robust and effective. © 2010 ACADEMY PUBLISHER.","Title: An Accurate Scheme For Channel Parameter Estimation In Mobile Propagations\nYear: 2009\n\nAbstract: In this letter, we first investigate the bias of Doppler shift estimator based on autocorrelation Junction (ACF). Then we derive a signal-to-noise ratio (SNR) independent condition for Doppler shift estimation and achieve this condition by a adaptive process. Moreover, we present theoretical analysis about the convergency of our adaptive Doppler shift estimator, and derive a close-form expression for its mean square error (MSE). We verify the proposed estimator by computer simulation, the results of which are in agreement with the analysis, i.e., the proposed method achieves a good SNR-independent performance in a wide range of velocities and SNRs.","Title: An anti-interference cooperative spectrum sharing strategy with full-duplex\nYear: 2013\n\nAbstract: In this paper, we propose an anti-interference cooperative spectrum sharing strategy in cognitive two-phase full-duplex relaying networks, in which the secondary system acts as a decode-and-forward relay to assist the primary system achieve the target transmission rate. As a reward, the secondary system can gain access to the licensed primary spectrum to transmit its own signal in both two phases. Since the two systems use different bandwidth to transmit their signals, there will be no interference effect between primary and secondary systems. We study the joint optimization of the two-phase bandwidth allocation to maximize the received rate of the secondary system. Simulation results are given to verify our analysis and illustrate the advantages of the proposed strategy.","Title: Botnet command and control based on Short Message Service and human mobility\nYear: 2013\n\nAbstract: Many serious threats for PCs are spreading to the mobile environment. A mobile botnet, which is a collection of hijacked smartphones under the control of hackers, is one of them. With the quick development of the computing and communication abilities of smartphones, many command and control (C&C) techniques in PC botnets can be easily reused in mobile botnets. However, some particular functions and characteristics of smartphones may provide botmasters with additional means to control their mobile botnets. This paper presents two special C&C mechanisms that leverage Short Message Service and human mobility, respectively. The first one designs a SMS-based flooding algorithm to propagate commands. We theoretically prove that the uniform random graph is the optimal topology for this botnet, and demonstrate its high efficiency and stealth with various simulations. The second one utilizes Bluetooth to transmit botnet commands when hijacked smartphones encounter each other while in motion. We study its performance in a 100mx100m square area with NS-2 simulations, and show that human-mobility characteristics facilitate the command propagation. Even if the infection rate is low, the command can still be effectively propagated provided that the mobility of devices is high. In the end, we propose effective defense strategies against these two special C&C mechanisms.","Title: A Joint Estimator of SNR and Mobile Speed in Wireless Communications\nYear: 2007\n\nAbstract: Recently, signal-to-noise ratio (SNR) and mobile speed estimations have drawn a great amount of attention due to their comprehensive use in modern adaptive signal processing. Hence after investigating a double sampling rate (DSR) signal processing technique, we propose a joint estimator to estimate these two parameters, where we utilize the fact that the autocorrelation function (ACF) of time-varying wireless channel is related to the SNR and Doppler Shift, the latter of which is also related to mobile speed. We verify the proposed algorithm by computer simulation, and the results have showed a good SNR-independent peformance in a wide range of velocities and SNRs.","Title: A Dynamic Clustering-Based Algorithm for Wireless Sensor Networks\nYear: 2008\n\nAbstract: Clinical pathways are widely adopted by many large hospitals around the world in order to provide high-quality patient treatment and reduce the length and cost of hospital stay. However, nowadays most of them are static and non-personalized. Our objective ...","Title: An improved orthogonal digital filter structure\nYear: 2010\n\nAbstract: A novel structure is derived for digital filter implementation. This structure is actually an improved version of an existing one in terms of implementation efficiency and reducing finite word length (FWL) effects. Expression of roundoff noise gain is obtained for the proposed structure. Design examples are given to demonstrate the performance of this structure and to compare it with the existing one and the classical minimum roundoff state-space realizations. Numerical examples show that the proposed structure outperforms the others in terms of minimizing roundoff noise as well as implementation efficiency.","Title: Impact Of Channel Prediction On Adaptive Modulation Performance In V-Blast Systems\nYear: 2006\n\nAbstract: Since the accuracy of channel prediction influences the performance of adaptive multiple-input-multiple-output (MIMO) systems in Rayleigh fading channels, we analyze the performance of adaptive V-BLAST systems with minimum-mean-square-error (MMSE) channel predictor in this paper. Considering the channel prediction error, we obtain the closed-form expressions for bit-error-rate (BER) and throughput of adaptive V-BLAST systems, and maximize the system transmission rate by choosing optimum block length under the BER constraint. The numerical results reveal the critical value of channel prediction error below which the systems can achieve the similar transmission rate to that of the systems with perfect channel prediction.","Title: Model-Based Intrusion Detection by Abstract Interpretation\nYear: 2010\n\nAbstract: Model-based intrusion detection works by comparing a process's runtime behavior with a pre-computed normal program model. This paper studies this technology from the viewpoint of abstract interpretation theory. We regard different program behavior models used to perform intrusion detection as different abstractions of the concrete trace semantics of programs. Based on this point, we formally define model-based intrusion detection and present a generic generation algorithm for program models on a provided abstraction domain. Eventually, we discuss how to use this mechanism to implement a real intrusion detection model proposed by us before.","Title: Modeling and containment of search worms targeting web applications\nYear: 2010\n\nAbstract: Many web applications leak sensitive pages (we name them eigenpages) that can disclose their vulnerabilities. As a result, some worms like Santy locate their targets by searching specific eigenpages in search engines with well-crafted keywords. Such worms are so called search worms. In this paper, we focus on the modeling and containment of these search worms. We first study the influence of the eigenpage distribution on their spreading by introducing two propagation models: U-Model assuming eigenpages uniformly distributed on servers and PL-Model assuming the distribution follows a power law. We show that the uniform distribution maximizes the spreading speed of the search worm. Then we study the influence of the page ranking and introduce another propagation model: PR-Model. In this model, search results are ranked based on their PageRank values and the relative importance of their resident servers. Finally, we propose a containment system for search worms based on honey-page insertion: a small number of fake pages which will induce visitors to pre-established honeypots are randomly inserted into search results, and then infectious can be detected and reported to search engines when their malicious scans hit honeypots. We study the relationship between the containment effectiveness and the honey-page insert rate with our propagation models and find that the Santy worm can be almost completely stopped at its early age by inserting no more than 2 honey pages in every 100 search results, which is extremely effective.","Title: An adaptive signal-to-noise ratio estimator in mobile communication channels\nYear: 2010\n\nAbstract: Representing the reception condition directly, signal-to-noise ratio (SNR) is an important parameter in mobile propagation channels, and therefore is widely used in system performance evaluations and adaptive applications. Hence, this paper puts forward a frequency domain SNR estimator in mobile communications, where we exploit the signal model with the band-limited fading channel and the additive white Gaussian noise. With the above model, the noise power spectrum density can be estimated from the periodogram of channel-plus-noise signals, subsequently leading to our SNR estimation. Moreover, in order to degrade the intrinsic spectrum leakage of fast Fourier transform in periodogram calculation, the leakage expression is derived analytically and then an adaptive process is proposed to make a tradeoff between leakage reduction and noise smoothing. We verify our algorithm by simulations and observe high accuracy in a wide range of velocities and SNRs. Additionally, unlike the conventional work, the proposed estimator is not strictly based on the assumption of specific Doppler spectral shapes except for the requirement of the band-limited channel, hence it is robust to general mobile channels.","Title: Image denoising using 2-D FIR filters designed with DEPSO\nYear: 2014\n\nAbstract: Digital images are often corrupted by additive noises during transmission. Thus, how to alleviate noise as much as possible has received concerns for decades. In this paper, we present a simple denoising method based on two dimensional (2-D) finite impulse response (FIR) filtering, where by differential evolution particle swarm optimization (DEPSO) algorithm, five two dimensional finite impulse response filters are designed to filter different kinds of pixels. Comprised by differential evolution algorithm and particle swarm optimization algorithm, differential evolution particle swarm optimization algorithm is effective and robust, which helps to yield better denoise performance. And computer simulation demonstrates that the proposed method is superior to the conventional lowpass filtering method, as well as the modern bilateral filtering and stochastic denoising method.","Title: Pseudo-Gray Coding for Beamforming Systems\nYear: 2008\n\nAbstract: In a multiple-input multiple-output (MIMO) beam- forming system with finite rate feedback, the receiver sends back quantized channel information to the transmitter via a feedback channel. The overall system performance is degraded due to feedback errors. In this paper, we treat the feedback of the beamforming vector as a generalized vector quantizer (VQ), and adopt index assignment (IA) technique to cope with feedback errors. A lower bound to the average symbol error rate (SER) is derived, and an IA design criterion is proposed to minimize this bound, which is in accord with the pseudo-Gray coding principle in conventional IA design literature. Numerical results show that well-designed IA schemes improve the SER considerably.","Title: An anti-interference cooperative spectrum sharing strategy with joint optimization of time and bandwidth\nYear: 2014\n\nAbstract: In this paper, we propose an anti-interference cooperative spectrum sharing strategy for cognitive system, in which a secondary system can operate on the same spectrum of a primary system. Specifically, the primary system leases a fraction of its transmission time to the secondary system in exchange for cooperation to achieve the target rate. To gain access to the spectrum of the primary system, the secondary system needs to allocate a fraction of bandwidth to help forward the primary signal. As a reward, the secondary system can use the remaining bandwidth to transmit its own signal. The secondary system uses different bandwidth to transmit the primary and its own signal. Thus, there will be no interference felt at primary and secondary systems. We study the joint optimization of time and bandwidth allocation such that the transmission rate of the secondary system is maximized, while guaranteeing the primary system, as a higher priority, to achieve its target transmission rate. Numerical results show that the secondary system can gain significant improvement with the proposed strategy.","Title: Efficient Context-Sensitive Intrusion Detection Based On State Transition Table\nYear: 2011\n\nAbstract: Those host-based intrusion detection models like VPStatic first construct a model of acceptable behaviors for each monitored program via static analysis, and then perform intrusion detection by comparing them with programs' runtime behaviors. These models usually share the highly desirable feature that they do not produce false alarms but face the conflicts between accuracy and efficiency. For instance, the high accuracy of the VPStatic model is at the cost of high space complexity. In this paper, we use a statically-constructed state transition table (STT), which records expected transitions among system calls as well as their stack states (return address lists), as a behavior model to perform context-sensitive intrusion detection. According to our analysis, our STT model improves the space efficiency of the VPStatic model without decreasing its high precision and time efficiency. Experiments show that for three test programs, memory uses of our STT models are all much less than half of the VPStatic models'. Thereby, we alleviate the conflicts between the accuracy and the efficiency.","Title: A novel digital filter structure with minimum roundoff noise\nYear: 2010\n\nAbstract: In this paper, a novel structure is derived for efficient implementation of digital filters as well as minimizing the finite word length (FWL) errors. Such a structure is actually an improved version of that reported previously. The performance of this new structure and the famous normalized lattice structure are analyzed by deriving the corresponding expression for the roundoff noise gain. Design examples are presented to illustrate the behavior of the proposed structure and to compare it with some existing ones. It is shown that the proposed structure outperforms the others in terms of minimizing roundoff noise as well as implementation efficiency.","Title: Novel Scheme for Joint Estimation of SNR, Doppler, and Carrier Frequency Offset in Double-Selective Wireless Channels\nYear: 2009\n\nAbstract: As receiver performance will be degraded by carrier frequency offset (CFO), Doppler shift, and low signal-to-noise ratio (SNR), a novel estimator that jointly considers CFO, Doppler shift, and SNR is proposed in this paper. The proposed algorithm uses the Fourier transform (FT) to calculate the power spectral density of time-varying channels through channel estimates. Then, a new periodogram technique is utilized to estimate CFO, Doppler shift, and SNR together. Unlike conventional methods in sinusoid estimation, which rely on the peak-value search of a periodogram, this paper exploits the hypothesis test to address the random frequency modulation of time-varying channels. Furthermore, to optimize estimation performance, a theoretical analysis is also provided on the influences of some key parameters, e.g., the length of the signal processed with fast FT , the amplitude threshold value, the SNR dynamic range, and the velocity dynamic range. Correspondingly, the appropriate key parameters are chosen according to this analysis and are validated by simulations. The results are consistent with our analysis and present high accuracy over a wide range of velocities and SNRs.","Title: An Improved Scheme For Channel Parameter Estimation In Mobile Communication Systems\nYear: 2005\n\nAbstract: In [1], an algorithm based on phase variations,of received pilot symbols was proposed to estimate one of the most important channel parameters, maximum Doppler shift, fd. However, AWGN (Additive white gauss noise) will cause large estimation error in some cases. In order to analyze the influence of noise, we extended the phase probability density function (pdf) in [1] to the scenario with both fading and AWGN, then the estimation error is characterized in closed-form expression. By this error expression, we found that power control will affect the estimator of [1] and we proposed a modification method based on SNR estimation to obtain accurate Doppler shift estimation in moderate low SNRs (signal-to-noise ratio). Simulation results show high accuracy in wide range of velocities and SNRs.","Title: Research on Qos Routing Protocol Based on Quantum State Algorithm for MANET\nYear: 2009\n\nAbstract: With the increase in size and application are of the communication network, more attention has been paid to the invulnerability of mobile ad hoc network. By analyzing the working mechanism and key techniques of the Ad Hoc Qos routing models, the searching for the most optimal routing is NP-hard problem. There is only sub-optimal routing available because of the rather high complexity of the algorithm. This paper analysis the Qos parameters firstly, then build the quantum multi-programming model considering the link distance, node power and time delay to choose the optimal route to transmit the data effetely. Simulations and comparisons with some typical route algorithms show that our algorithm is robust and effective.","Title: Comment and further results on paper \"Carrier Frequency Recovery Technique in OFDM systems\"\nYear: 2007\n\nAbstract: This comment points out some inaccurate formulae, and shows some irrelevant comparison of different estimators. Moreover, the correct formulae are provided and the modification to the fractional frequency offset (FFO) estimator is indicated. On the other hand, Our analysis have shown that the integer frequency offset (IFO) estimator of Ai is of serious theoretic problem.","Title: Low complexity soft decision equalization for block transmission systems\nYear: 2005\n\nAbstract: This paper addresses the problem of the soft decision equalization. We show the relation between the iterative soft decision interference cancellation (ISDIC) and probabilistic data association (PDA) multiuser detector (PDA-MUD). We prove that ISDIC is equivalent to PDA-MUD, and give the block-wise implementation of them. We also present a soft interference cancellation algorithm for the polynomial expansion linear detector. With its low complexity, simple implementations, and impressive performance offered by iterative soft-decision processing, it is an attractive candidate to deliver efficient reception solutions to practical wireless transmission systems. Simulation comparisons of the new method with ISDIC are presented under the frequency selective channels.","Title: Frequency sampling design of 2-D FIR filters based on DEPSO\nYear: 2012\n\nAbstract: Frequency sampling is a classical method for 2-D FIR filters design, which can be subdivided into the uniform and non-uniform approaches. However, previous work mainly focused on reducing the algorithm complexity or seeking the alternative implementation approaches rather than optimizing the samples. In this paper, a method to refine the conventional frequency sampling method is proposed, where samples equally distributed on the Cartesian grid are adjusted by utilizing DEPSO algorithm so as to achieve the nearly optimum minimax design. Simulation results demonstrated that this new method outperforms the conventional methods on minimax sense. And the generated filters can give better image denoising performance than the median filter.","Title: Barrier: a lightweight hypervisor for protecting kernel integrity via memory isolation\nYear: 2012\n\nAbstract: In the present operating systems such as Linux, all the kernel modules, including unknown extensions, run in the same address space. They are granted the highest privilege and can access arbitrary memory without any limitation. This is at the root of kernel rootkits, which are malware seriously threatening the kernel integrity. In this paper, we present Barrier, a lightweight hypervisor designed for enhancing the kernel integrity of personal computers by isolating the kernel modules. Since this hypervisor is designed for the OS protection on PCs, it does not implement unnecessary virtualization features that are commonly found on the general-purpose hypervisors to support running multiple OS instances concurrently on the same server. As a result, it is much smaller and also much easier to use, especially for unprofessional users. Barrier leverages the hardware-supported memory virtualization to isolate the kernel modules into different address spaces. All the interactions across address spaces have to go through a strict mediation based on some predefined MAC rules. This greatly increases the attacker's hardness to compromise the kernel integrity. We have implemented a prototype of Barrier. The evaluation results show that Barrier can well protect the kernel integrity without bringing unaffordable performance overheads.","Title: A Snr Independent Doppler Shift Estimator Based On Iterative Process In Mobile Communication Systems\nYear: 2006\n\nAbstract: In this paper, we propose a Doppler shift estimator based on the Autocorrelation Function (ACF) of the time-varying channel. In order to eliminate the effect of additive noise, we analyze the conditions for the estimator independent of the signal-to- noise ratio (SNR) and implement the conditions with a simple iterative process named as adaptive lag. The proposed method achieves good SNR-independent performance in wide range of velocities and SNRs.","Title: A Low Papr Multiple Access Scheme For Lte-A\nYear: 2011\n\nAbstract: In this letter, we propose two antenna grouping schemes for uplink Nx SC-FDMA MIMO systems, where the multiple component carriers can be divided into several groups which are handled by different antennas, thus the number of component carriers on each antenna will be reduced by the group method. As a result, the peak-to-average power ratio (PAPR) of each antenna has been reduced. To further enhance the performance, an interleaving method is proposed to achieve better diversity gain due to the channel varying in the spatial domain and the frequency domain during one turbo coded stream. Our simulation figures clearly demonstrate that in all examples, the proposed schemes are shown to be effective in improving the Block Error Rate (BLER) performance while reducing the PAPR.","Title: Wireless localization based on the time sum of arrival and Taylor expansion\nYear: 2013\n\nAbstract: The localization of a wireless device is important for current wireless networks, therefore this paper investigates the time-sum-of-arrival (TSOA) based localization method, where the Taylor Series algorithm is analytically derived according to the elliptical geometry, i.e., TSOA based Taylor Series method, and then the conventional time-difference-of-arrival (TDOA) based algorithm is provided as a comparison. The simulation shows that the TSOA based algorithm has a better performance than the latter.","Title: A novel structure for 2nd order digital filters.\nYear: 2008\n\nAbstract: In this paper, based on a new parametrization of the input-balanced realizations a novel structure is proposed for implementation of 2nd order digital filters. Like the normalized lattice structure, it contains a Givens rotation. The expression of roundoff noise gain for this structure is derived. This proposed structure is simpler than the normalized structure. Though having the same number of structure parameters as that of the classical optimal state-space roundoff realizations, it can be implemented much more efficiently using the CORDIC techniques. Numerical examples show that the proposed structure also outperforms the normalized structure and the classical optimal state-space realizations in terms of minimizing roundoff noise. © 2008 IEEE.","Title: Turbo detection and decoding for single-carrier block transmission systems\nYear: 2004\n\nAbstract: We study the low complexity turbo detector for cyclic prefix based single-carrier block transmission (CP-SCBT) systems with multiple receive antennas. It has the following characteristics: firstly, it can be implemented by FFT/IFFT with low complexity; secondly, along with SISO decoder, turbo detection can be applied. Simulation results show that the iterative receiver provides much better performance than the conventional CP-SCBT system. It also outperforms its coded OFDM counterpart. The most attractive advantage is that it can be implemented by FFT/IFFT without significantly increasing the complexity of the system.","Title: Low complexity iterative receiver for multiuser STBC block transmission systems\nYear: 2004\n\nAbstract: In this paper, a low complexity space-frequency iterative detection and decoding scheme is developed for multiuser space-time block-coded (STBC) block transmission systems, such as the cyclic prefix based single-carrier block transmission (CP-SCBT) system and OFDM system. Using the algebraic properties of such systems, the MMSE-based turbo detection algorithm can be implemented in the frequency domain and then the complexity of the matrix inversion can be reduced greatly. The performance of the iterative receiver for multiuser STBC block transmission systems with bit-interleaved coded modulation (BICM) is evaluated in mobile multipath frequency selective channels through computer simulations. It has been shown that the proposed receiver significantly outperforms the conventional noniterative receiver. Moreover, at high signal-to-noise ratio, the detrimental effects of multiple-access interference (MAI) and intersymbol interference (ISI) in the channel can almost be completely overcome by the turbo processing, and the performance is very close to that of BICM in a Gaussian channel.","Title: How Accurate Does Channel Prediction Need To Be In Adaptive V-Blast Systems?\nYear: 2007\n\nAbstract: Since the accuracy of channel prediction influences the performance of adaptive multiple-input-multiple-output (MIMO) systems in Rayleigh fading channels, we analyze die performance of adaptive V-BLAST systems with a minimum-mean-square-error (MMSE) channel predictor in this paper. Considering the channel prediction error, we obtain the closed-form expressions for the bit-error-rate (BER) and throughput of adaptive V-BLAST systems, and maximize system transmission rate by choosing optimum block length under BER. constraint. The numerical results reveal the critical value of channel prediction error below which systems can match the transmission rate of the systems with perfect channel prediction.","Title: Comments and Further Results on `Performance Evaluation of a New BPSK-OFDM Timing Estimation Algorithm'\nYear: 2009\n\nAbstract: This comment first points out some inaccurate formulae and irrelevant comparisons between Wen’s algorithm and Beek’s one.\n Then, the correct formulae are provided, the limitations of these two estimators are discussed and some investigations on\n the local maximum in timing estimation are also presented. Both the simulation and the analysis demonstrate that the timing\n precision of Wen’s algorithm highly depends on the length of cyclic prefix (CP) and the channel fading rate. On the other hand, Beek’s algorithm will work better under a large CP length and in fast\n fading channels.","Title: Design of Universal Wireless Channel Generator Accounting for the 3-D Scatter Distribution and Hardware Output\nYear: 2015\n\nAbstract: Hardware to generate the wireless channel is indispensable to the tests of wireless equipment. Previous literature always focused on the pure-hardware design, which is difficult or expensive for the small- and medium-sized enterprises. Hence, this paper presents a simple strategy to develop a flexible and low-cost channel generator, where the theoretical derivation and hardware realization are integrated together to save the test-cost and reduce the realization difficulty. In the proposed method, the fading channel is first modeled in the Agilent advanced design systems (ADS) software, and then a conventional signal generator hardware is connected and controlled to output the physical fading signal to wireless equipment waiting for measurements. Meanwhile, to cover a widerange of fading scenarios, we exploit 3-D geometry-based stochastic channel model as well as the autoregressive (AR) modeling technique. In general, for a given nonisotropic 3-D scattering environment, the angle of arrival, autocorrelation function, and level crossing rate are derived first, and then the AR coefficients are obtained by solving the Yule-Walker equation. Finally, the AR coefficients are fed to the ADS software to control the hardware generating channels. In addition, the channel model and the radio frequency parameters can be controlled by the ADS software, yielding a great flexibility. We verify the proposed method via both the computer simulation and the hardware measurement, and the simulation agrees with the theoretical result well. In addition, the physical measurements also demonstrate that the physical fading channels are consistent with the design specifications.","Title: Accurate and Simple Wireless Localizations Based on Time Product of Arrival in the DDM-NLOS Propagation Environment\nYear: 2015\n\nAbstract: Wireless localization has become an important issue in internet of things. However, its performance degrades significantly when there are only non-line-of-sight (NLOS) propagation paths instead of line-of-sight (LOS) propagation paths. Therefore, this paper proposes a novel localization algorithm to improve the localization accuracy, where we assume that the NLOS error satisfies the requirement of distance dependent model (DDM). In order to obtain a stronger NLOS tolerance than conventional methods, the proposed algorithm analyzes the localization geometry and employs a new localization parameter, namely the time product of arrival (TPOA), to construct the linear localization equation group (LLEQ). Meanwhile, a simple least-squares (LS) principle is applied to solve this LLEQ, resulting in a low complexity. We verify our algorithm by computer simulations, and the result demonstrates that the proposed algorithm yields a better performance than traditional algorithms in the NLOS-DDM environment.","Title: Power optimization for dynamic spectrum access with convex optimization and intelligent algorithm\nYear: 2015\n\nAbstract: In dynamic spectrum access networks, relay transmission has been considered as a potential method to help cognitive users communicate by reducing their interferences to primary networks especially in the underlay mode. In this paper, we propose a power optimization algorithm for both regenerative and non-regenerative relay systems in condition of Rayleigh fading channels. Based on analyzing the features of dynamic spectrum access networks, a relevant interference model is first built. Then, we propose a combined power allocation strategy in order to minimize the outage probabilities in the cooperative transmission. For regenerative system, we give a closed-form expression for the power allocation by taking into account the characteristics of the fading channels. For non-regenerative system, we utilize pattern search algorithm to solve the optimization problem since the objective function is complex and uneasy to be figured out directly. A competitive strategy for initial point selection is also designed to guarantee a global optimal outcome for the proposed pattern search algorithm. Numerical results show that the system performances with optimum power allocation outperform those with uniform power allocation whereas lower outage probabilities can be obtained.","Title: Generation of 3-D physical fading channel with non-isotropic scattering\nYear: 2013\n\nAbstract: This paper presents the application of autoregressive (AR) methods in 3-D fading channel modeling and generation. It employs the AR method to yield the filter coefficient in the 3D and non-isotropic scattering environment, and derives the channel statistics from the geometry-based stochastic channel model (GSCM). Simulations show that the statistical variable of simulated channel agrees with the referenced model. Moreover, by exploiting the Agilent advanced design systems (ADS) software and the Agilent E4432B vector signal generator, the above AR model can be realized in ADS, and then the physical fading channel can be generated through the E4432B instrument. The tests of frequency analyzer demonstrate that the proposed scheme can generate the physical fading channel precisely by the design requirement.","Title: Comparative study of heuristic search algorithm in digital FIR filter design\nYear: 2012\n\nAbstract: The group intelligent algorithm is random optimization tool following the group behavior in the nature biological structure, which is a powerful tool for many optimization issues. This paper first models the digital filter design a constrained optimization problem with non-uniform frequency sampling, and then contrasts three kinds of intelligent algorithms to solve the problem. The computer simulation and the statistical analysis of the results suggest that digital filter designed by the artificial bee colony algorithm is better than those of genetic algorithm and the particle swarm optimization algorithm in terms of the convergence and the iterative times and filter.","Title: An efficient and robust orthogonal structure for linear discrete-time systems\nYear: 2010\n\nAbstract: An orthogonal structure is derived for system implementation. The expression of roundoff noise propagation gain for this structure is obtained. The proposed structure is an improved version of that reported in the paper by G. Li, M. Gevers, and Y.X. Sun (Performance analysis of a new structure for digital filter implementation, IEEE Trans. Circuits Syst. I 47:474-482, 2000), but is more efficient and robust against the quantization errors. For comparison, an alternative expression of roundoff noise gain for the normalized lattice structure is derived, based on which it is shown that the roundoff noise gain of an Nth order all-pass system, when implemented using the normalized lattice structure, is 4N and that an Nth order system implemented with the structure proposed in the paper by P.P. Vaidyanathan, S.K. Mitra, and Y. Neuvo (A new approach to the realization of low sensitivity IIR digital filters, IEEE Trans. Acoust. Speech Signal Process. ASSP-34(2):350-361, 1986), yields a roundoff noise gain of N +1, smaller than that of the classical optimal roundoff noise state-space realizations. Design examples are presented to illustrate the behavior of the proposed structure and to compare it with a class of existing orthogonal structures and the classical optimal roundoff noise realizations. It is shown that the proposed structure outperforms the others in terms of minimization of roundoff noise as well as implementation efficiency. © Springer Science+Business Media, LLC 2010.","Title: Minimum-error-based approximation model for symmetric alpha stable distribution\nYear: 2012\n\nAbstract: In many communication channels the impulsive noise is usually assumed to be of a symmetric alpha stable (SαS) distribution. Unfortunately, except for the Gaussian, Cauchy, and Lévy laws, the analytical expressions for the probability density functions (PDF) of alpha stable distributions are unknown, resulting in very limited application of this distribution. In a practical system, the bi-parameter Cauchy-Gaussian mixture (BCGM) distribution is used to approximate the PDF of the SαS distribution to tackle this difficulty. In this paper, we derive the optimal mixture ratio of the BCGM model based on the minimum square error criterion and furthermore propose a simplified and robust version of BCGM for the SαS distribution. Numerical simulations show that our proposed model achieves better performance and is more robust than the conventional models, without incurring additional complexity. © Springer Science+Business Media, LLC 2012.","Title: EV-Linker: Mapping eavesdropped Wi-Fi packets to individuals via electronic and visual signal matching\nYear: 2016\n\nAbstract: •We propose a new attack EV-Linker to map eavesdropped Wi-Fi packets to individuals.•We solve the challenges caused by the devices, environment and people's movements.•EV-Linker considers both moving and stationary people.•Experiments demonstrate the effectiveness of EV-Linker.","Title: Differentially Private Matrix Factorization.\nYear: 2015\n\nAbstract: Matrix factorization (MF) is a prevailing collaborative filtering method for building recommender systems. It requires users to upload their personal preferences to the recommender for performing MF, which raises serious privacy concerns. This paper proposes a differentially private MF mechanism that can prevent an untrusted recommender from learning any users' ratings or profiles. Our design decouples computations upon users' private data from the recommender to users, and makes the recommender aggregate local results in a privacy-preserving way. It uses the objective perturbation to make sure that the final item profiles satisfy differential privacy and solves the challenge to decompose the noise component for objective perturbation into small pieces that can be determined locally and independently by users. We also propose a third-party based mechanism to reduce noises added in each iteration and adapt our online algorithm to the dynamic setting that allows users to leave and join. The experiments show that our proposal is efficient and introduces acceptable side effects on the precision of results.","Title: Doppler shift estimation exploiting iterative processing in mobile communications\nYear: 2014\n\nAbstract: The Doppler shift, or equivalently the vehicular speed, represents the fading rate of mobile channels, thus its estimation must be important in the mobile communication system. This paper investigates the estimation of Doppler shift by taking into consideration the rate of minimum (ROM) of envelope, where both the iterative processing and polynomial fitting are employed to improve the estimating performance. We verify the proposed scheme by numerical simulations, and see the satisfying performance for variable speeds and SNRs.","Title: Differentially private publication of general time-serial trajectory data\nYear: 2015\n\nAbstract: Trajectory data, i.e., human mobility traces, is extremely valuable for a wide range of mobile applications. However, publishing raw trajectories without special sanitization poses serious threats to individual privacy. Recently, researchers begin to leverage differential privacy to solve this challenge. Nevertheless, existing mechanisms make an implicit assumption that the trajectories contain a lot of identical prefixes or n-grams, which is not true in many applications. This paper aims to remove this assumption and propose a differentially private publishing mechanism for more general time-series trajectories. One natural solution is to generalize the trajectories, i.e., merge the locations at the same time. However, trivial merging schemes may breach differential privacy. We, thus, propose the first differentially-private generalization algorithm for trajectories, which leverage a carefully-designed exponential mechanism to probabilistically merge nodes based on trajectory distances. Afterwards, we propose another efficient algorithm to release trajectories after generalization in a differential private manner. Our experiments with real-life trajectory data show that the proposed mechanism maintains high data utility and is scalable to large trajectory datasets.","Title: Advertiser and Publisher-centric Privacy Aware Online Behavioral Advertising\nYear: 2015\n\nAbstract: Online behavioral advertising (OBA) has become one of the most successful advertising models on the Internet. Nevertheless, all existing OBA systems are broker-centric in the billing phase, which means it is the broker who exclusively determines advertisers' expenses and publishers' revenues. Consequently, a malicious broker may cheat in their tallying of ad clicks to overcharge advertisers or underpay publishers. Furthermore, as the broker cannot justify the bills, malicious advertisers may deny actual clicks to ask for refunds, and malicious publishers may claim non-existing clicks to demand extra revenue shares. This paper solves these problems by reversing the priority between the broker and the advertisers and publishers. Specifically, when users click on ads, it makes corresponding advertisers and publishers forward click reports of clients to the broker after checking, anonymizing and signing them. The broker then settles accounts with advertisers and publishers fully based on these reports. To guarantee the interests of the broker after the priority reversal, we further propose effective mechanisms for detecting underreporting advertisers and over reporting publishers, respectively.","Title: Pricing-Based Spectrum Leasing For Cognitive Networks With Channel Quality Diversity\nYear: 2015\n\nAbstract: In cognitive networks, unused spectrum should be carefully priced to maximize primary system's profit and satisfy secondary user's diverse requirements. In this paper, we investigate a pricing-based spectrum leasing framework where a primary system and multiple cognitive users involved in. In the proposed model, unused spectrum is classified into two types of channels with different spectrum quality. Secondary user chooses the channel according to its own selection preference which is relevant to the channel quality and spectrum evaluation. On the other hand, a spectrum pricing mechanism for the primary user is proposed based on the distribution characteristics of the secondary preference. We cast the optimization problem into the Hotelling game model where the pricing game is carried out with distinct product diversity. By solving the Nash equilibrium of the game model, an iterative algorithm for spectrum pricing is obtained. We further prove the existence and uniqueness of the equilibrium as well as the convergence characteristic of the iterative algorithm. Numerical results are presented to show how the settings of selection preference and spectrum pricing affect the spectrum owner's profits.","Title: Towards Attack-Resistant Peer-Assisted Indoor Localization\nYear: 2015\n\nAbstract: Peer-assisted smartphone localization, which leverages pairwise acoustic ranging among nearby peer phones to refine location estimation, significantly pushes the accuracy limit of WiFi-based indoor localization. Unfortunately, this technique is designed for non-adversarial settings. Dishonest peers may cheat in their distance measurements. Outside attackers may interfere with the acoustic ranging by continually broadcasting interference signals. In this paper, we propose counter-measures against each of these attacks. We first present an algorithm that can identify peers that are not cheating in the current localization, by searching for devices that can be embedded into the same plane according to their pairwise distances. We also design a robust acoustic ranging method exploiting signal modulation, which can defend effectively against intentional interference of outside attackers. Experimental results demonstrate that our countermeasures can greatly improve the robustness of peer-assisted localization.","Title: Privacy-Preserving Utility Verification of the Data Published by Non-Interactive Differentially Private Mechanisms.\nYear: 2016\n\nAbstract: In the problem of privacy-preserving collaborative data publishing, a central data publisher is responsible for aggregating sensitive data from multiple parties and then anonymizing it before publishing for data mining. In such scenarios, the data users may have a strong demand to measure the utility of the published data, since most anonymization techniques have side effects on data utility. Neve...","Title: Foum: A Flow-Ordered Consistent Update Mechanism For Software-Defined Networking In Adversarial Settings\nYear: 2016\n\nAbstract: Due to the asynchronous and distributed nature of the data plane, consistent configuration updating across multiple switches is a challenging issue in Software-Defined Networking (SDN). The existing version-stamping-based mechanism (VSM) could guarantee per-packet consistency, but this mechanism is designed for non-adversarial settings and can be compromised easily by a malicious attacker. In this paper, we propose an efficient flow-ordered update mechanism that aims to provide per-packet consistency in adversarial settings. Our proposal does not need to stamp data packets with the configuration version, and is robust against both the packet-tampering and packet-dropping attacks. It outperforms a naive mechanism that simply patches VSM using digital signatures in three aspects: First, the switches in this mechanism only need to sign and verify a single control packet, which significantly improves the packet processing time. Second, it avoids keeping both old and new policies on switches during the update, and thus achieves better space efficiency. Third, it reduces the time delay for new policies to come into force. We evaluate our mechanism on a self-constructed SDN testbed and the results demonstrate high efficiency.","Title: Transmit-Time Allocation In Wireless Sensor Relay Transmission With Energy Fairness\nYear: 2015\n\nAbstract: Cooperative transmission is an efficient method for wireless sensor networks (WSN) to decrease the power consumption of sensor transmitter and counter the detrimental effect of signal fading inherent to wireless channels. During the course, a key issue is how the sensor relay should handle the balance of assisting other node's communications and accomplishing its own tasks and what kind of situation the sensor transmitter needs to ask for the cooperation rather than direct communication. In this paper, we investigate the potential bargaining for resource allocation in WSN over Rayleigh fading environments to address the problems by considering the energy fairness for both the participants of the cooperative transmission. Based on Rayleigh fading channels' characteristics, a relevant system model is first built. Then, after analyzing the system capacity in given outage probability over the fading channels, a system utility function with regard to the transmit-time allocation addressing both participants' energy concern is proposed. An iterative algorithm is subsequently achieved by solving the Nash equilibrium. Furthermore, essential analyses and discussions on the equilibrium existence and convergence conditions of the iterative algorithm are provided.","Title: Primary and secondary QoS guranteed spectrum sharing protocol with optimal time allocation\nYear: 2015\n\nAbstract: In this paper, we propose a collaborative spectrum sharing protocol which aims to maximize the sum transmission rate while the quality-of-service (QoS) of both primary and secondary systems' can be supported. Specifically, the primary system authorizes the secondary system to access its spectrum band with a fraction of transmission time only when it can help the primary system achieve the target rate. During the accessing time, the secondary system assigns half power to help primary system reach the target rate and utilizes the remaining half power to transmit its own signal. We determine an optimal time allocation by analyzing the sum transmission rate of primary and secondary systems with both of their QoS can be ensured. Simulation results show that the efficiency of the proposed spectrum sharing protocol and both primary and secondary systems can benefit from it.","Title: An Anti-Interference Spectrum Sharing Protocol With Secondary User Selection\nYear: 2015\n\nAbstract: In this paper, we proposed an anti-interference cooperation spectrum sharing protocol with secondary user selection. Specifically, one secondary user is selected to assist the primary system to achieve the target rate by acting as a decode-and-forward (DF) relay, which uses a fraction of time and bandwidth to forward the primary signal. As a reward, the selected secondary user can gain spectrum access by using the remaining bandwidth to transmit its own signal. The secondary system use different bandwidth to transmit the primary and its own signal, thus there will be no interference exists between the primary and secondary systems. We study the joint optimization of time and bandwidth as well as secondary user selection such that the secondary transmission rate is maximized, while the primary system can achieve its target rate. Simulation results demonstrate that both primary and secondary systems benefit from the proposed spectrum sharing protocol.","Title: Performance Analysis Of Stochastic Process Modulation System Based On Autoregressive Model Under Low Snr\nYear: 2015\n\nAbstract: A novel stochastic process shift keying (SPSK) to achieve a secure communication system in the presence of heavy noise is presented. The Gaussian noise sequence from the output of autoregressive (AR) filter carries the binary information. At the receiver, a damped sinusoidal model of autocorrelation function is employed to identify the two different AR filters from the noisy observations to estimate the transmitted binary message. Due to its excellent identification performance, the proposed communication system can be effective even under very low signal-to-noise ratio.","Title: Power Optimization In Cognitive Networks With Hybrid Intelligent Algorithms\nYear: 2015\n\nAbstract: In this paper, we introduce a hybrid strategy containing pattern search (PS) optimization and genetic algorithm (GA) to resolve the problem of power allocation in cognitive radio networks. Considering the fluctuant interference thresholds in cognitive networks, an approach for solving the problem of coexistence between licensed users and cognitive users is proposed first. Then, based on the analyses of outage probability for cognitive users, a corresponding objective function with regard to the power allocation over Rayleigh fading channels is obtained. Due to the nonlinearity and complexity of this power optimization problem, it is uneasy to figure it out directly by using traditional methods, such as common mathematical deduction or linear programming. Inspired by the concept of intelligent algorithms, we try to employ the scheme of combining PS optimization and GA method, which are both efficient intelligent algorithms to resolve this difficulty. Furthermore, the numerical results are encouraging and show that the proposed approach is worthy of consideration in solving the problem of optimal power allocation in cognitive networks.","Title: Simultaneous Wireless Information and Power Transfer Based on Joint Subcarrier and Power Allocation in OFDM Systems.\nYear: 2017\n\nAbstract: Energy harvesting (EH) is a prominent method to extend the operation time of energy-limited wireless networks. By integrating EH into wireless communications, the same spectrum is able to be used by simultaneous wireless information and power transfer (SWIPT) without affecting the quality of service. In this paper, we propose a joint subcarrier and power allocation-based SWIPT scheme in orthogonal frequency division multiplexing (OFDM) systems. Specifically, the received OFDM subcarriers are partitioned into two groups. A fraction of the received subcarriers are allocated to form one group, which is used for information decoding (ID), and the remaining subcarriers form the other group, which is used for energy harvesting. Thus, no splitter is needed at the receiver. A joint subcarrier and power allocation problem is formulated to maximize the harvested energy, subject to the ID constraint. By using the dual decomposition method, an efficient algorithm is proposed to solve this joint resource allocation problem.","Title: Cooperative Spectrum Sharing in OFDM Two-Way Relay Systems With Bidirectional Transmissions.\nYear: 2017\n\nAbstract: In this letter, we propose a cooperative spectrum sharing protocol, where both primary users (PUs) and secondary users (SUs) achieve bidirectional transmission. In particular, the SUs help PUs to achieve their bidirectional target rates through orthogonal frequency division multiplexing two-way relaying. In return, the PUs allow SUs to communicate bidirectionally on a subset of subcarriers. Subcarrier and power allocation are jointly optimized for both directions to maximize the SU data rate, given that the PUs' target rate is satisfied. Simulation results demonstrate a significant enhancement in spectrum efficiency compared with several benchmark schemes.","Title: A Jointly Differentially Private Scheduling Protocol for Ridesharing Services.\nYear: 2017\n\nAbstract: Ridesharing services have gained tremendous popularity in recent years, benefiting the traffic and environment of cities to a large extent. However, with the demand of ridesharing services increasing sharply, serious privacy concerns (e.g., users' mobility patterns) of ridesharing have become a major barrier against its further development. In this paper, we study the privacy protection of users' ...","Title: Covert digital communication systems based on joint normal distribution.\nYear: 2017\n\nAbstract: The correlation coefficient of two consecutive Gaussian sequences is modulated by a binary message bit to achieve a secure communication system. The receiver of the proposed random communication system demodulates the received signal by estimating the correlation coefficient of the transmitted two consecutive sequences. Theoretical bit error rate (BER) expressions in frequency-flat/-selective fadi...","Title: Secure Keyboards Against Motion Based Keystroke Inference Attack.\nYear: 2016\n\nAbstract: Nowadays, attackers seek various covert channels to access the users’ privacy on the mobile devices. Recent research has demonstrated that the built-in motion sensors can be exploited to monitor the users’ screen taps and infer what they have typed. This paper presents several practical and convenient countermeasures against this attack in terms of the soft keyboard. We find that this attack is sensitive to the motion noise of the mobile device and the layout variation of the soft keyboard. We, thus, present two kinds of countermeasures against this attack by introducing vibration noise in sensor readings and dynamics in the keyboard layout, respectively. We implement these countermeasures on Android platform and recruit 20 volunteers to evaluate these countermeasures’ effectiveness and usability on both the smartphones and tablets. The results show that the proposed countermeasures can effectively reduce the attackers’ keystroke inference accuracy without significantly hurting the typing efficiency.","Title: Cooperative TDD reciprocity calibration with diversity combining for distributed large-scale MIMO systems.\nYear: 2017\n\nAbstract: Thanks to channel reciprocity, the time-division duplexing operation is typically used for distributed large-scale multiple-input multiple-output systems. With the knowledge of uplink channel state, APs (Access Points) perform the downlink precoding to cooperatively communicate with multiple UEs (User Equipments). Unfortunately, the channel reciprocity is often jeopardized by the non-symmetric transceiver RF (Radio Frequency) circuits at both sides of the link. By excluding UEs from the calibration procedure, the TLS (Total Least Squares) method is extended to the case of partial calibration, where only the APs are involved to exchange calibration signals to compensate for RF mismatches. Nevertheless, channel fluctuations between APs significantly degrade system performance. Therefore, we propose a scheme to obtain the diversity gain, by performing a calibration with the combination of the calibration signals of different channel coherent times. Simulation results confirm the effective performance and robustness of our scheme.","Title: Spectrum Sharing in OFDM Two-Way Relaying Systems with Joint Optimal Subcarrier and Power Allocation.\nYear: 2017\n\nAbstract: In this paper, we propose a cooperative spectrum sharing protocol based on OFDM two-way relaying with joint optimal subcarrier and power allocation. Specifically, the secondary system helps the primary system achieve their target rates through OFDM two-way relaying, where the secondary system forwards the primary signal by using a fraction of subcarriers and power. In return, the secondary system can gain spectrum access by using the remaining subcarriers and power to transmit its own signal. Joint optimal subcarrier and power allocation is derived aiming to maximize secondary transmission rate with primary transmission rate constraint. Simulation results demonstrate a significant enhancement in spectrum efficiency compared with several benchmark schemes.","Title: A Geo-Indistinguishable Location Perturbation Mechanism for Location-Based Services Supporting Frequent Queries.\nYear: 2018\n\nAbstract: As location-based services (LBSs) on smartphones become increasingly popular, such services are causing serious privacy concerns, because many users are unwilling to see their location information leaked to service providers. Recently, in order to protect users' location privacy, researchers have introduced geo-indistinguishability, the first specialized privacy model for LBSs that can provide pro...","Title: Spectrum Allocation With Asymmetric Monopoly Model for Multibeam-Based Cognitive Satellite Networks.\nYear: 2018\n\nAbstract: With the rapid development of modern satellite communications, broadband satellite services are experiencing a period of remarkable growth in both user population and available bandwidth. Efficient spectrum management is required in order to meet the ever-increasing demand for broadband spectrum. In this paper, a spectrum allocation scheme for cognitive satellite networks is proposed to improve spectrum efficiency by addressing the situation that scarce spectrum resource is under-utilized while the overall demands of cognitive satellite users are not satisfied. The proposed scheme is devised to optimize bandwidth efficiency by achieving the Bayesian equilibrium through spectrum competition among cognitive satellite users. Due to the scarcity of spectrum resource, satellite systems have to eliminate some of the cognitive users’ transmission information types in order to fulfill the spectrum needs of priority users. After declining one or several kinds of modulation modes, cognitive users can update their spectrum lists. With the increase in number of cognitive users and their corresponding transmission information types, the complexity of the optimization problem increases significantly and becomes a computation burden to cognitive satellite systems. Therefore, in this paper, we propose a simplified solution for spectrum resource optimization in a computation-efficient manner. Through rounds of eliminating operations, the satellite systems can identify the final Bayesian equilibrium as an optimal spectrum allocation strategy. We also provide proofs for the existence of Bayesian equilibrium. Numerical results are given to evaluate the performance of our proposed method from diverse perspectives.","Title: A novel energy-balanced routing algorithm in energy harvesting sensor networks.\nYear: 2018\n\nAbstract: In energy harvesting wireless sensor networks, despite the network lifetime can be prolonged through node’s energy collection, how to enhance energy efficiency and optimize routing protocol are still critical and challenging due to volatility and uncertainty of energy harvesting. In this paper, we propose a novel routing method in energy harvesting sensor networks by taking into account the outage probability of relay transmission and node’s residual energy. Our goal is to achieve a balanced-based routing algorithm with improved transmission performance. We design an optimal power control strategy to optimize the outage probability for bidirectional sub-channels in energy harvesting sensor networks. Then, we raise a rational objective function to address the concerns of high efficient and balanced energy consumption in searching router. The main routine of the proposed routing algorithm is also given. Numerical results are provided to testify the performances of our proposed method from the perspectives of network lifetime and outage probability.","Title: Joint resource optimization for DF relaying SWIPT based cognitive sensor networks.\nYear: 2018\n\nAbstract: Spectrum and energy are two important resources for wireless sensor networks (WSNs). Cognitive Radio (CR) can improve the efficiency of spectrum utilization by allowing secondary users (SUs) access to the licensed bands as long as they do not significantly affect the performance of primary users (PUs). Simultaneous wireless information and power transfer (SWIPT) can harvest energy and decode information simultaneously from radio-frequency (RF) signal, which can prolong the life time of energy-constraint networks (e.g., WSNs). In this paper, we propose a joint resource optimization scheme in respect of transmission power and power splitting ratio for decode-and-forward (DF) relaying SWIPT based cognitive sensor networks (CSNs), where relay sensor node (RSN) harvests energy from the signal of source sensor node (SSN) by using power splitting (PS) receiver architecture and utilize the harvested energy to help forward SSNs information. We maximize the throughput of SUs while guaranteeing the interference to PUs caused by SUs is smaller than the interference threshold. Simulation results demonstrate the performance of the joint resource optimization scheme.","Title: Topology-Preserving Traffic Engineering for Hierarchical Multi-Domain SDN.\nYear: 2018\n\nAbstract: Decoupling of control and data plane in Software Define Networks (SDN) creates significant flexibility in network management. As networks are evolving into a complex multi-domain multi-layer architecture, traffic engineering across multiple domains and layers entails significant challenges for the control plane, especially when each separate administrative domain is not willing to disclose its network topology and resource information. In this paper, we present the first traffic engineering scheme for a multi-domain SDN by using a hierarchical control plane, in which each domain is managed by a local controller, and they are further controlled by a centralized root controller. To preserve the local topology of each domain, we extend the Network Information Base (NIB) into a hierarchical architecture as well, and the root controller maintains only an abstracted view of the lower-layer networks in its NIB. Then, we devise a communication protocol, which enables controllers at different layers and domains to work collaboratively on bandwidth allocation by reading to the hierarchical NIB. We also present an improved traffic engineering algorithm by considering bandwidth and delay simultaneously, to maximize the network utilization while respecting max-min fairness. Experiments on a 719-switches 5-domain network topology demonstrate that our proposal could drive the link utilization ratio to more than 85%.","Title: A Novel Physical Layer Encryption Algorithm Based on Statistical Characteristics of Time-Selective Channels.\nYear: 2018\n\nAbstract: Currently, the security of information in the physical layer has attracted much attentions. However, such a physical layer security of conventional study depends on the assumption of reciprocity between the uplink and downlink channels. This assumption cannot be ensured in the time-selective channel of mobile communications. Hence, this paper proposes an encryption algorithm in mobile communications. First, the statistics of time-selective channel are extracted to act as the seeds for multiple chaos generator. Second, the produced chaos sequences are combined to produce the secret code. Finally, the physical signal is encrypted by the simple scrambling operation. We verify the proposed algorithm by computer simulations, and the results demonstrate that the encrypted data can be successfully recovered by the legal receiver, while the sniffer produces poor and flat bit-error-rate performance in a wide range of signal-to-noise ratios.","Title: Design of Waveform Shaping Filter in the UFMC System.\nYear: 2018\n\nAbstract: The universal filtered multi-carrier (UFMC) has been taken as a promising candidate for future wireless communication, where a finite impulse response filter is employed to shape the waveform and enhance its resistance to inter-carrier interference. In previous studies, the Dolph-Chebyshev filter had been used due to its low-sidelobe levels, but at the cost of low flexibility to filter the performance control. Hence, this paper puts forward an effective scheme to design an anti-interference filter for UFMC system, where the Nyquist condition (or sampling inter-symbol interference equivalently), the in-band distortion, and the out-of-band emission are taken into consideration. First, this paper models the filter design as a constrained minimax optimization concerning above-mentioned filter performance indexes. Then, the original nonconvex constraints on Nyquist condition are approximately transformed to a linear matrix inequation and two linear inequations. Finally, the optimization problem is solved by semidefinite programming The numerical examples explicitly demonstrate the flexible performance tradeoff of the proposed method, in which the included filter performance indexes can be effectively controlled. Moreover, the bit-error-rate (BER) tests of the UFMC system confirms the effectiveness of our study, where the designed filters show BER advantages over filters of the previous literature. Therefore, the proposed waveform shaping filters can benefit variant applications of UFMC for its flexible performance tradeoffs as well as the improved anti-interference performance.","Title: NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks.\nYear: 2018\n\nAbstract: The problem of target localization in WSN (wireless sensor network) has received much attention in recent years. However, the performance of traditional localization algorithms will drastically degrade in the non-line of sight (NLOS) environment. Moreover, variable methods have been presented to address this issue, such as the optimization-based method and the NLOS modeling method. The former produces a higher complexity and the latter is sensitive to the propagating environment. Therefore, this paper puts forward a simple NLOS identification and localization algorithm based on the residual analysis, where at least two line-of-sight (LOS) propagating anchor nodes (AN) are required. First, all ANs are grouped into several subgroups, and each subgroup can get intermediate position estimates of target node through traditional localization algorithms. Then, the AN with an NLOS propagation, namely NLOS-AN, can be identified by the threshold based hypothesis test, where the test variable, i.e., the localization residual, is computed according to the intermediate position estimations. Finally, the position of target node can be estimated by only using ANs under line of sight (LOS) propagations. Simulation results show that the proposed algorithm can successfully identify the NLOS-AN, by which the following localization produces high accuracy so long as there are no less than two LOS-ANs.","Title: Preference-Based Spectrum Pricing in Dynamic Spectrum Access Networks.\nYear: 2018\n\nAbstract: With market-driven secondary spectrum trading, licensed users can receive benefits in terms of monetary rewards or various transmission services, thus setting a fair pricing structure by suitably defining spectrum quality characteristics and accurately addressing participant's requirement is a key issue. In this paper, we investigate the pricing-based spectrum access by casting the problem of spec...","Title: Securing peer-assisted indoor localization leveraging acoustic ranging.\nYear: 2019\n\nAbstract: •We carefully survey the attacks to the peer-assisted indoor localization.•We propose the defenses to deal with attacks launched by the dishonest peers.•We propose the defense against the intentional interference of outside attackers.•We give the detailed theoretical proofs about our proposed countermeasures.","Title: A Non-line-of-Sight Localization Method Based on the Algorithm Residual Error Minimization\nYear: 2017\n\nAbstract: Wireless localization has become a key technology location based services, and the non-line-of-sight (NLOS) propagation is one of the most important error source in the localization. Therefore, this paper defines a novel algorithm residual error (ARE) in NOLS environment, and estimates the position of mobile station (MS) by minimizing this ARE, where the quadratic programming is employed to solve the minimization problem. The simulation results show that the proposed algorithm produces significant performance improvements in NLOS environments.","Title: Two-Phase Prototype Filter Design for FBMC Systems\nYear: 2017\n\nAbstract: FBMC has been taken as a candidate waveform for the next enhanced 5th generation (5G). To further improve its advantages over OFDM as well as to promote its application in burst transmission, a two-phase method is applied to design its prototype filter, i.e. a square-root Nyquist filter. In this method, the autocorrelation-based technique and a spectral factorization aimed at minimum stopband energy are successively exploited to acquire the final prototype filter. Through the relaxation of Nyquist condition and benefited from the nonlinear-phase, the frequency selectivity of our designed filters can be greatly improved. Furthermore, the performances of the proposed prototype filter brings a better BER in simulations, which demonstrates the effectiveness of our square-root Nyquist filter design for FBMC systems.","Title: Accurate and Efficient Wireless Device Fingerprinting Using Channel State Information.\nYear: 2018\n\nAbstract: Due to the loose authentication requirement between access points (APs) and clients, it is notoriously known that WLANs face long-standing threats such as rogue APs and network freeloading. Take the rogue AP problem as an example, unfortunately encryption alone does not provide authentication. APs need to be equipped with certificates that are trusted by clients ahead of time. This requires either the presence of PKI for APs or other forms of pre-established trust (e.g., distributing the certificates offline), none of which is widely used. Before any strong security solution is deployed, we still need a practical solution that can mitigate the problem. In this paper, we explore a non-cryptographic solution that is readily deployable today on end hosts (e.g., smartphones and laptops) without requiring any changes to the APs or the network infrastructure. The solution infers the Carrier Frequency Offsets (CFOs) of wireless devices from Channel State Information (CSI) as their hardware fingerprints without any special hardware requirement. CFO is attributed to the oscillator drift, which is a fundamental physical property that cannot he manipulated easily and remains fairly consistent over time but varies significantly across devices. The real experiments on 23 smartphones and 34 APs (with both identical and different brands) in different scenarios demonstrate that the detection rate could exceed 94%.","Title: Optimal Power Splitting of Cognitive Radio Networks with SWIPT-Enabled Relay\nYear: 2018\n\nAbstract: Cognitive radio (CR), as an intelligent spectrum sharing technology, can improve utilization of spectrum by sharing the licensed spectrum bands with secondary users (SUs) as long as do not have harmful effect on primary users (PUs). Simultaneous wireless information and power transfer (SWIPT) combines wireless information transmission (WIT) technology and wireless power transfer (WPT) technology, which harvesting energy from ambient RF signals. In this paper, we consider amplify-and-forward (AF) cognitive radio networks (CRNs) with SWIPT-enabled secondary relay node. We aim to maximize the throughput of secondary network in considering the interference caused by the transmitted signal of secondary relay node to PUs, and derived the closed-form expression of the optimal power splitting ratio. Simulation results demonstrate the performance of the optimal power splitting ratio.","Title: The nonlinear-phase design of FBMC prototype filter based on filter coefficient symmetry characteristic\nYear: 2020\n\nAbstract: To promote filter bank multicarrier (FBMC) in future communication, a novel design method of nonlinear phase FIR filter (NLPFF) is proposed under the principle of constrained minimax (CMM), by which the designed filter will act as the FBMC prototype filter. In this method, the filter coefficient symmetry characteristic (FCSC) is exploited in replace of the conventional phase response error (PRE), which simplifies the establishment of phase constraint and benefits the construction of direct magnitude constraint. Consequently, the proposed constraints related to FCSC outperform the conventional constraints addressing complex frequency response errors, since the former yields a wider range of controllable PRE as well as the dramatically reduced constraint scale. Moreover, in order to restrict the sampling inter-symbol interference, the Nyquist condition (NYQ) is constrained in the frequency domain, which circumvents the initial nonconvex problem. Simulations confirm the effectiveness of the proposed method, where the FCSC can be sufficiently relaxed to improve the stopband attenuation, making this nonlinear-phase design more flexible than previous designs of FBMC prototype filter. Besides, the bit error rate test demonstrates that the designed NLPFFs are superior to the conventional PHYDYAS filter, therefore, the proposed design can certainly produce high quality prototype filter for FBMC system.","Title: Resource Allocation Based Simultaneous Wireless Information and Power Transfer for Multiuser OFDM Systems.\nYear: 2018\n\nAbstract: In this paper, we mainly study simultaneous wireless information and power transfer (SWIPT) for the multiuser resource allocation. All subcarriers are divided into two parts, part of which are for information decoding and another part are for energy harvesting. We optimize the subcarrier allocation and power allocation to maximize the energy that collected by all users under the target rate constraint. The original optimal problem is complicated, so it is hard to find the optimal solution directly. By transforming the primal problem, we finally solved the original problem by using the Lagrange dual method.","Title: A Method of Balanced Sleep Scheduling in Renewable Wireless Sensor Networks.\nYear: 2018\n\nAbstract: Energy harvesting from its environmental sources becomes an integral part of green cities. This paper considers a low-energy consumption Wireless Sensor Networks to improve energy utilization in green cities. By this approach, a wireless node can directly harvest energy from its ambient by introducing an energy-harvesting layer on the top of traditional WSN layer. The energy harvesting layer composed of charging points (CPs) that it can harvest energy from ambient renewable energy sources (solar, vibration, light, and electromagnetic wave, etc.) transfer the harvested energy to the underlying WSN layer by wireless energy transfer. Furthermore, in order to conserve battery power in very dense sensor networks, some sensor nodes may be put into the sleep state while other sensor nodes remain active for the sensing and communication tasks. The proposed scheme applies energy informatics to increase the energy efficiency by optimizing energy harvesting time interval and energy consumption of the node for uniform data gathering over the network.","Title: OFDM Based SWIPT in a Two-Way Relaying Network.\nYear: 2018\n\nAbstract: We consider a simultaneous wireless transfer of information and power in a two-way relaying network, a decode-and-forward protocol and OFDM modulation is employed. Subcarriers are divided into a couple of groups, one is for information decoding, and another is for energy harvesting. With total rate maximized, subcarrier grouping optimization is performed. And the power of subcarriers is optimized according to channel state. The performance of the optimal design is performed on different transmit conditions.","Title: An Improved Constrained Least Squares Localization Algorithm in NLOS Propagating Environment.\nYear: 2018\n\nAbstract: The non-line-of-sight (NLOS) error is a major error source in wireless localization. Therefore, an improved constrained least-squares (CLS) algorithm is put forward to tackle this issue, where the positioning problem is formulated as a mathematical programming problem. And then, the cost function of the optimization is studied and a new one is proposed. Finally, through the presented optimization, we try to minimize the positioning influence of NLOS errors. Moreover, the studied method does not depend on a particular distribution of the NLOS error. Simulation results show that the positioning accuracy is significantly improved over traditional CLS algorithms, even under highly NLOS conditions.","Title: Improving Spectrum Management for Satellite Communication Systems with Hunger Marketing\nYear: 2019\n\nAbstract: Efficient spectrum management is an important issue in satellite communications due to the pervasive growth in wireless communications and the ever-increasing demand by bandwidth-hungry mobile applications. While various kinds of static and dynamic spectrum allocation schemes have been studied by satellite researchers, the issue of unpredictable user behavior, hence demand for bandwidth, remains o...","Title: Interference Analysis in the Asynchronous f-OFDM Systems\nYear: 2019\n\nAbstract: By supporting asynchronous transmission and flexible subband (SB) setting, filtered orthogonal frequency division multiplexing (f-OFDM) has been identified as one of the most promising waveforms for future wireless communications, which makes the deep investigation on the f-OFDM an urgent mission. Therefore, this paper investigates the downlink interference of asynchronous f-OFDM systems under dif...","Title: A Fast Realization Of New Mersenne Number Transformation And Its Applications\nYear: 2019\n\nAbstract: The new Mersenne number transform (NMNT) can be realized by the fast Fourier transform (FFT) with power-of-two length, which results in great flexibility in real-world fixed-point computations, such as the convolution-based signal processing in the embedded device. Yet the FFT realization exists the truncation errors and in order to further reduce the computations, this paper puts forward a novel realization structure for the NMNT, where the Walsh-Hadamard transform (WHT) is employed to accelerate the NMNT computation. Moreover, we also propose the refined computing structure using the matrix decomposition and multiple constant multiplication (MCM), and then all multiplications can be replaced by the shift-addition operations without precision loss. Besides, the use of lookup table (LUT) to reduce the complexity is also discussed in our study. A typical convolution application is tested by computer simulations, while the result demonstrates that the proposed scheme produces precise computing results with reduced complexity.","Title: A Complexity-reduced Block Encryption Algorithm Suitable for Internet of Things\nYear: 2019\n\nAbstract: The Internet of Things (IoT) poses stringent requirements on service quality and security strength. A highly reliable encryption algorithm with low complexity is required to ensure information security. Therefore, this paper proposes a complexity-reduced secure and fast encryption routine (SAFER)-Fermat block encryption method that accounts for the confusion and diffusion principles. In particular, a novel diffusion layer that exploiting the Fermat number theory transform is proposed, while the confusion layer remains unchanged as the SAFER algorithm. We also propose two data record structures to accommodate the two layers and deal with the overflow issue. The proposed scheme only considers the integer operation, the inclusion of multipliers in our study is no longer necessary, thereby resulting in low complexity and lightweight encryption method. Therefore, the proposed encryption method is advantageous for power-limited IoT devices. The simulations demonstrate that the proposed encryption method maintains the security of the SAFER++ method but requires fewer computations.","Title: Energy Efficient Swipt Based Mobile Edge Computing Framework For Wsn-Assisted Iot\nYear: 2021\n\nAbstract: With the increasing deployment of IoT devices and applications, a large number of devices that can sense and monitor the environment in IoT network are needed. This trend also brings great challenges, such as data explosion and energy insufficiency. This paper proposes a system that integrates mobile edge computing (MEC) technology and simultaneous wireless information and power transfer (SWIPT) technology to improve the service supply capability of WSN-assisted IoT applications. A novel optimization problem is formulated to minimize the total system energy consumption under the constraints of data transmission rate and transmitting power requirements by jointly considering power allocation, CPU frequency, offloading weight factor and energy harvest weight factor. Since the problem is non-convex, we propose a novel alternate group iteration optimization (AGIO) algorithm, which decomposes the original problem into three subproblems, and alternately optimizes each subproblem using the group interior point iterative algorithm. Numerical simulations validate that the energy consumption of our proposed design is much lower than the two benchmark algorithms. The relationship between system variables and energy consumption of the system is also discussed.","Title: Practical Location Privacy Attacks and Defense on Point-of-interest Aggregates\nYear: 2021\n\nAbstract: Location-based services have significantly affected mobile users' everyday life, and location privacy is also an essential issue in these services. In some applications (e.g., location-based recommendation, mobility analytic), the raw data is not required, and the service providers adopt aggregation to protect users' location traces. However, some works show that even these aggregation data may di...","Title: Optimization of ACF-DSR-based joint Doppler shift and SNR estimator for Internet of Vehicle system\nYear: 2022\n\nAbstract: Accurate vehicle speed estimation is required for intelligent internet of vehicles, and it can be realized by Doppler shift estimation in mobile communication. In this paper, the ACF-DSR (autocorrelation function-double sampling rate) method for joint Doppler shift and SNR estimation is further investigated. Based on the analysis of ACF, an improved algorithm model taking account of estimation deviation is formulated. Then, the effects of sampling intervals in DSR are figured out by mean square error analysis of estimation, and two better choices are obtained. By Monte Carlo simulations, it is demonstrated that the optimized ACF-DSR method with better choice of sampling intervals can achieve better estimation and outperform previous methods.","Title: Optimization of the energy efficiency in Smart Internet of Vehicles assisted by MEC\nYear: 2022\n\nAbstract: Smart Internet of Vehicles (IoV) as a promising application in Internet of Things emerges with the development of the fifth generation mobile communication (5G). Nevertheless, the heterogeneous requirements of sufficient battery capacity, powerful computing ability and energy efficiency for electric vehicles face great challenges due to the explosive data growth in 5G and the sixth generation of mobile communication (6G) networks. In order to alleviate the deficiencies mentioned above, this paper proposes a mobile edge computing (MEC) enabled IoV system, in which electric vehicle nodes upload and download data through an anchor node which is integrated with a MEC server. Meanwhile, the anchor node transmitters radio signal to electric vehicles with simultaneous wireless information and power transfer technology so as to compensate the battery limitation of eletric vehicles. Moreover, the spectrum efficiency is further improved by multi-input and multi-output and full-duplex technologies which is equipped at the anchor node. In consideration of the issues above, we maximize the average energy efficiency of electric vehicles by jointly optimize the CPU frequency, vehicle transmitting power, computing tasks and uplink rate. Since the problem is nonconvex, we propose a novel alternate interior-point iterative scheme (AIIS) under the constraints of computing tasks, energy consumption and time latency. Results and discussion section verifies the effectiveness of the proposed AIIS scheme comparing with the benchmark schemes."],"string":"[\n \"Title: Efficient Intrusion Detection Based on Static Analysis and Stack Walks\\nYear: 2009\\n\\nAbstract: Some intrusion detection models such as the VPStatic first construct a behavior model for a program via static analysis, and then perform intrusion detection by monitoring whether its execution is consistent with this behavior model. These models usually share the highly desirable feature that they do not produce false alarms but they face the conflict between precision and efficiency. The high precision of the VPStatic is at the cost of high space complexity. In this paper, we propose a new context-sensitive intrusion detection model based on static analysis and stack walks, which is similar to VPStatic but much more efficient, especially in memory use. We replace the automaton in the VPStatic with a state transition table (STT) and all redundant states and transitions in VPStatic are eliminated. We prove that our STT model is a deterministic pushdown automaton (DPDA) and the precision is the same as the VPStatic. Experiments also demonstrate that our STT model reduces both time and memory costs comparing with the VPStatic, in particular, memory overheads are less than half of the VPStatic's. Thereby, we alleviate the conflict between precision and efficiency.\",\n \"Title: Research on Qos Protocol Based on Quantum State Algorithm for MANET.\\nYear: 2010\\n\\nAbstract: With the increase in size and application are of the communication network, more attention has been paid to the invulnerability of mobile ad hoc network. By analyzing the working mechanism and key techniques of the Ad Hoc Qos routing models, the searching for the most optimal routing is NP-hard problem. There is only sub-optimal routing available because of the rather high complexity of the algorithm. This paper analysis the Qos parameters firstly, then build the quantum multiprogramming model considering the link distance, node power and time delay to choose the optimal route to transmit the data effetely. Simulations and comparisons with some typical route algorithms show that our algorithm is robust and effective. © 2010 ACADEMY PUBLISHER.\",\n \"Title: An Accurate Scheme For Channel Parameter Estimation In Mobile Propagations\\nYear: 2009\\n\\nAbstract: In this letter, we first investigate the bias of Doppler shift estimator based on autocorrelation Junction (ACF). Then we derive a signal-to-noise ratio (SNR) independent condition for Doppler shift estimation and achieve this condition by a adaptive process. Moreover, we present theoretical analysis about the convergency of our adaptive Doppler shift estimator, and derive a close-form expression for its mean square error (MSE). We verify the proposed estimator by computer simulation, the results of which are in agreement with the analysis, i.e., the proposed method achieves a good SNR-independent performance in a wide range of velocities and SNRs.\",\n \"Title: An anti-interference cooperative spectrum sharing strategy with full-duplex\\nYear: 2013\\n\\nAbstract: In this paper, we propose an anti-interference cooperative spectrum sharing strategy in cognitive two-phase full-duplex relaying networks, in which the secondary system acts as a decode-and-forward relay to assist the primary system achieve the target transmission rate. As a reward, the secondary system can gain access to the licensed primary spectrum to transmit its own signal in both two phases. Since the two systems use different bandwidth to transmit their signals, there will be no interference effect between primary and secondary systems. We study the joint optimization of the two-phase bandwidth allocation to maximize the received rate of the secondary system. Simulation results are given to verify our analysis and illustrate the advantages of the proposed strategy.\",\n \"Title: Botnet command and control based on Short Message Service and human mobility\\nYear: 2013\\n\\nAbstract: Many serious threats for PCs are spreading to the mobile environment. A mobile botnet, which is a collection of hijacked smartphones under the control of hackers, is one of them. With the quick development of the computing and communication abilities of smartphones, many command and control (C&C) techniques in PC botnets can be easily reused in mobile botnets. However, some particular functions and characteristics of smartphones may provide botmasters with additional means to control their mobile botnets. This paper presents two special C&C mechanisms that leverage Short Message Service and human mobility, respectively. The first one designs a SMS-based flooding algorithm to propagate commands. We theoretically prove that the uniform random graph is the optimal topology for this botnet, and demonstrate its high efficiency and stealth with various simulations. The second one utilizes Bluetooth to transmit botnet commands when hijacked smartphones encounter each other while in motion. We study its performance in a 100mx100m square area with NS-2 simulations, and show that human-mobility characteristics facilitate the command propagation. Even if the infection rate is low, the command can still be effectively propagated provided that the mobility of devices is high. In the end, we propose effective defense strategies against these two special C&C mechanisms.\",\n \"Title: A Joint Estimator of SNR and Mobile Speed in Wireless Communications\\nYear: 2007\\n\\nAbstract: Recently, signal-to-noise ratio (SNR) and mobile speed estimations have drawn a great amount of attention due to their comprehensive use in modern adaptive signal processing. Hence after investigating a double sampling rate (DSR) signal processing technique, we propose a joint estimator to estimate these two parameters, where we utilize the fact that the autocorrelation function (ACF) of time-varying wireless channel is related to the SNR and Doppler Shift, the latter of which is also related to mobile speed. We verify the proposed algorithm by computer simulation, and the results have showed a good SNR-independent peformance in a wide range of velocities and SNRs.\",\n \"Title: A Dynamic Clustering-Based Algorithm for Wireless Sensor Networks\\nYear: 2008\\n\\nAbstract: Clinical pathways are widely adopted by many large hospitals around the world in order to provide high-quality patient treatment and reduce the length and cost of hospital stay. However, nowadays most of them are static and non-personalized. Our objective ...\",\n \"Title: An improved orthogonal digital filter structure\\nYear: 2010\\n\\nAbstract: A novel structure is derived for digital filter implementation. This structure is actually an improved version of an existing one in terms of implementation efficiency and reducing finite word length (FWL) effects. Expression of roundoff noise gain is obtained for the proposed structure. Design examples are given to demonstrate the performance of this structure and to compare it with the existing one and the classical minimum roundoff state-space realizations. Numerical examples show that the proposed structure outperforms the others in terms of minimizing roundoff noise as well as implementation efficiency.\",\n \"Title: Impact Of Channel Prediction On Adaptive Modulation Performance In V-Blast Systems\\nYear: 2006\\n\\nAbstract: Since the accuracy of channel prediction influences the performance of adaptive multiple-input-multiple-output (MIMO) systems in Rayleigh fading channels, we analyze the performance of adaptive V-BLAST systems with minimum-mean-square-error (MMSE) channel predictor in this paper. Considering the channel prediction error, we obtain the closed-form expressions for bit-error-rate (BER) and throughput of adaptive V-BLAST systems, and maximize the system transmission rate by choosing optimum block length under the BER constraint. The numerical results reveal the critical value of channel prediction error below which the systems can achieve the similar transmission rate to that of the systems with perfect channel prediction.\",\n \"Title: Model-Based Intrusion Detection by Abstract Interpretation\\nYear: 2010\\n\\nAbstract: Model-based intrusion detection works by comparing a process's runtime behavior with a pre-computed normal program model. This paper studies this technology from the viewpoint of abstract interpretation theory. We regard different program behavior models used to perform intrusion detection as different abstractions of the concrete trace semantics of programs. Based on this point, we formally define model-based intrusion detection and present a generic generation algorithm for program models on a provided abstraction domain. Eventually, we discuss how to use this mechanism to implement a real intrusion detection model proposed by us before.\",\n \"Title: Modeling and containment of search worms targeting web applications\\nYear: 2010\\n\\nAbstract: Many web applications leak sensitive pages (we name them eigenpages) that can disclose their vulnerabilities. As a result, some worms like Santy locate their targets by searching specific eigenpages in search engines with well-crafted keywords. Such worms are so called search worms. In this paper, we focus on the modeling and containment of these search worms. We first study the influence of the eigenpage distribution on their spreading by introducing two propagation models: U-Model assuming eigenpages uniformly distributed on servers and PL-Model assuming the distribution follows a power law. We show that the uniform distribution maximizes the spreading speed of the search worm. Then we study the influence of the page ranking and introduce another propagation model: PR-Model. In this model, search results are ranked based on their PageRank values and the relative importance of their resident servers. Finally, we propose a containment system for search worms based on honey-page insertion: a small number of fake pages which will induce visitors to pre-established honeypots are randomly inserted into search results, and then infectious can be detected and reported to search engines when their malicious scans hit honeypots. We study the relationship between the containment effectiveness and the honey-page insert rate with our propagation models and find that the Santy worm can be almost completely stopped at its early age by inserting no more than 2 honey pages in every 100 search results, which is extremely effective.\",\n \"Title: An adaptive signal-to-noise ratio estimator in mobile communication channels\\nYear: 2010\\n\\nAbstract: Representing the reception condition directly, signal-to-noise ratio (SNR) is an important parameter in mobile propagation channels, and therefore is widely used in system performance evaluations and adaptive applications. Hence, this paper puts forward a frequency domain SNR estimator in mobile communications, where we exploit the signal model with the band-limited fading channel and the additive white Gaussian noise. With the above model, the noise power spectrum density can be estimated from the periodogram of channel-plus-noise signals, subsequently leading to our SNR estimation. Moreover, in order to degrade the intrinsic spectrum leakage of fast Fourier transform in periodogram calculation, the leakage expression is derived analytically and then an adaptive process is proposed to make a tradeoff between leakage reduction and noise smoothing. We verify our algorithm by simulations and observe high accuracy in a wide range of velocities and SNRs. Additionally, unlike the conventional work, the proposed estimator is not strictly based on the assumption of specific Doppler spectral shapes except for the requirement of the band-limited channel, hence it is robust to general mobile channels.\",\n \"Title: Image denoising using 2-D FIR filters designed with DEPSO\\nYear: 2014\\n\\nAbstract: Digital images are often corrupted by additive noises during transmission. Thus, how to alleviate noise as much as possible has received concerns for decades. In this paper, we present a simple denoising method based on two dimensional (2-D) finite impulse response (FIR) filtering, where by differential evolution particle swarm optimization (DEPSO) algorithm, five two dimensional finite impulse response filters are designed to filter different kinds of pixels. Comprised by differential evolution algorithm and particle swarm optimization algorithm, differential evolution particle swarm optimization algorithm is effective and robust, which helps to yield better denoise performance. And computer simulation demonstrates that the proposed method is superior to the conventional lowpass filtering method, as well as the modern bilateral filtering and stochastic denoising method.\",\n \"Title: Pseudo-Gray Coding for Beamforming Systems\\nYear: 2008\\n\\nAbstract: In a multiple-input multiple-output (MIMO) beam- forming system with finite rate feedback, the receiver sends back quantized channel information to the transmitter via a feedback channel. The overall system performance is degraded due to feedback errors. In this paper, we treat the feedback of the beamforming vector as a generalized vector quantizer (VQ), and adopt index assignment (IA) technique to cope with feedback errors. A lower bound to the average symbol error rate (SER) is derived, and an IA design criterion is proposed to minimize this bound, which is in accord with the pseudo-Gray coding principle in conventional IA design literature. Numerical results show that well-designed IA schemes improve the SER considerably.\",\n \"Title: An anti-interference cooperative spectrum sharing strategy with joint optimization of time and bandwidth\\nYear: 2014\\n\\nAbstract: In this paper, we propose an anti-interference cooperative spectrum sharing strategy for cognitive system, in which a secondary system can operate on the same spectrum of a primary system. Specifically, the primary system leases a fraction of its transmission time to the secondary system in exchange for cooperation to achieve the target rate. To gain access to the spectrum of the primary system, the secondary system needs to allocate a fraction of bandwidth to help forward the primary signal. As a reward, the secondary system can use the remaining bandwidth to transmit its own signal. The secondary system uses different bandwidth to transmit the primary and its own signal. Thus, there will be no interference felt at primary and secondary systems. We study the joint optimization of time and bandwidth allocation such that the transmission rate of the secondary system is maximized, while guaranteeing the primary system, as a higher priority, to achieve its target transmission rate. Numerical results show that the secondary system can gain significant improvement with the proposed strategy.\",\n \"Title: Efficient Context-Sensitive Intrusion Detection Based On State Transition Table\\nYear: 2011\\n\\nAbstract: Those host-based intrusion detection models like VPStatic first construct a model of acceptable behaviors for each monitored program via static analysis, and then perform intrusion detection by comparing them with programs' runtime behaviors. These models usually share the highly desirable feature that they do not produce false alarms but face the conflicts between accuracy and efficiency. For instance, the high accuracy of the VPStatic model is at the cost of high space complexity. In this paper, we use a statically-constructed state transition table (STT), which records expected transitions among system calls as well as their stack states (return address lists), as a behavior model to perform context-sensitive intrusion detection. According to our analysis, our STT model improves the space efficiency of the VPStatic model without decreasing its high precision and time efficiency. Experiments show that for three test programs, memory uses of our STT models are all much less than half of the VPStatic models'. Thereby, we alleviate the conflicts between the accuracy and the efficiency.\",\n \"Title: A novel digital filter structure with minimum roundoff noise\\nYear: 2010\\n\\nAbstract: In this paper, a novel structure is derived for efficient implementation of digital filters as well as minimizing the finite word length (FWL) errors. Such a structure is actually an improved version of that reported previously. The performance of this new structure and the famous normalized lattice structure are analyzed by deriving the corresponding expression for the roundoff noise gain. Design examples are presented to illustrate the behavior of the proposed structure and to compare it with some existing ones. It is shown that the proposed structure outperforms the others in terms of minimizing roundoff noise as well as implementation efficiency.\",\n \"Title: Novel Scheme for Joint Estimation of SNR, Doppler, and Carrier Frequency Offset in Double-Selective Wireless Channels\\nYear: 2009\\n\\nAbstract: As receiver performance will be degraded by carrier frequency offset (CFO), Doppler shift, and low signal-to-noise ratio (SNR), a novel estimator that jointly considers CFO, Doppler shift, and SNR is proposed in this paper. The proposed algorithm uses the Fourier transform (FT) to calculate the power spectral density of time-varying channels through channel estimates. Then, a new periodogram technique is utilized to estimate CFO, Doppler shift, and SNR together. Unlike conventional methods in sinusoid estimation, which rely on the peak-value search of a periodogram, this paper exploits the hypothesis test to address the random frequency modulation of time-varying channels. Furthermore, to optimize estimation performance, a theoretical analysis is also provided on the influences of some key parameters, e.g., the length of the signal processed with fast FT , the amplitude threshold value, the SNR dynamic range, and the velocity dynamic range. Correspondingly, the appropriate key parameters are chosen according to this analysis and are validated by simulations. The results are consistent with our analysis and present high accuracy over a wide range of velocities and SNRs.\",\n \"Title: An Improved Scheme For Channel Parameter Estimation In Mobile Communication Systems\\nYear: 2005\\n\\nAbstract: In [1], an algorithm based on phase variations,of received pilot symbols was proposed to estimate one of the most important channel parameters, maximum Doppler shift, fd. However, AWGN (Additive white gauss noise) will cause large estimation error in some cases. In order to analyze the influence of noise, we extended the phase probability density function (pdf) in [1] to the scenario with both fading and AWGN, then the estimation error is characterized in closed-form expression. By this error expression, we found that power control will affect the estimator of [1] and we proposed a modification method based on SNR estimation to obtain accurate Doppler shift estimation in moderate low SNRs (signal-to-noise ratio). Simulation results show high accuracy in wide range of velocities and SNRs.\",\n \"Title: Research on Qos Routing Protocol Based on Quantum State Algorithm for MANET\\nYear: 2009\\n\\nAbstract: With the increase in size and application are of the communication network, more attention has been paid to the invulnerability of mobile ad hoc network. By analyzing the working mechanism and key techniques of the Ad Hoc Qos routing models, the searching for the most optimal routing is NP-hard problem. There is only sub-optimal routing available because of the rather high complexity of the algorithm. This paper analysis the Qos parameters firstly, then build the quantum multi-programming model considering the link distance, node power and time delay to choose the optimal route to transmit the data effetely. Simulations and comparisons with some typical route algorithms show that our algorithm is robust and effective.\",\n \"Title: Comment and further results on paper \\\"Carrier Frequency Recovery Technique in OFDM systems\\\"\\nYear: 2007\\n\\nAbstract: This comment points out some inaccurate formulae, and shows some irrelevant comparison of different estimators. Moreover, the correct formulae are provided and the modification to the fractional frequency offset (FFO) estimator is indicated. On the other hand, Our analysis have shown that the integer frequency offset (IFO) estimator of Ai is of serious theoretic problem.\",\n \"Title: Low complexity soft decision equalization for block transmission systems\\nYear: 2005\\n\\nAbstract: This paper addresses the problem of the soft decision equalization. We show the relation between the iterative soft decision interference cancellation (ISDIC) and probabilistic data association (PDA) multiuser detector (PDA-MUD). We prove that ISDIC is equivalent to PDA-MUD, and give the block-wise implementation of them. We also present a soft interference cancellation algorithm for the polynomial expansion linear detector. With its low complexity, simple implementations, and impressive performance offered by iterative soft-decision processing, it is an attractive candidate to deliver efficient reception solutions to practical wireless transmission systems. Simulation comparisons of the new method with ISDIC are presented under the frequency selective channels.\",\n \"Title: Frequency sampling design of 2-D FIR filters based on DEPSO\\nYear: 2012\\n\\nAbstract: Frequency sampling is a classical method for 2-D FIR filters design, which can be subdivided into the uniform and non-uniform approaches. However, previous work mainly focused on reducing the algorithm complexity or seeking the alternative implementation approaches rather than optimizing the samples. In this paper, a method to refine the conventional frequency sampling method is proposed, where samples equally distributed on the Cartesian grid are adjusted by utilizing DEPSO algorithm so as to achieve the nearly optimum minimax design. Simulation results demonstrated that this new method outperforms the conventional methods on minimax sense. And the generated filters can give better image denoising performance than the median filter.\",\n \"Title: Barrier: a lightweight hypervisor for protecting kernel integrity via memory isolation\\nYear: 2012\\n\\nAbstract: In the present operating systems such as Linux, all the kernel modules, including unknown extensions, run in the same address space. They are granted the highest privilege and can access arbitrary memory without any limitation. This is at the root of kernel rootkits, which are malware seriously threatening the kernel integrity. In this paper, we present Barrier, a lightweight hypervisor designed for enhancing the kernel integrity of personal computers by isolating the kernel modules. Since this hypervisor is designed for the OS protection on PCs, it does not implement unnecessary virtualization features that are commonly found on the general-purpose hypervisors to support running multiple OS instances concurrently on the same server. As a result, it is much smaller and also much easier to use, especially for unprofessional users. Barrier leverages the hardware-supported memory virtualization to isolate the kernel modules into different address spaces. All the interactions across address spaces have to go through a strict mediation based on some predefined MAC rules. This greatly increases the attacker's hardness to compromise the kernel integrity. We have implemented a prototype of Barrier. The evaluation results show that Barrier can well protect the kernel integrity without bringing unaffordable performance overheads.\",\n \"Title: A Snr Independent Doppler Shift Estimator Based On Iterative Process In Mobile Communication Systems\\nYear: 2006\\n\\nAbstract: In this paper, we propose a Doppler shift estimator based on the Autocorrelation Function (ACF) of the time-varying channel. In order to eliminate the effect of additive noise, we analyze the conditions for the estimator independent of the signal-to- noise ratio (SNR) and implement the conditions with a simple iterative process named as adaptive lag. The proposed method achieves good SNR-independent performance in wide range of velocities and SNRs.\",\n \"Title: A Low Papr Multiple Access Scheme For Lte-A\\nYear: 2011\\n\\nAbstract: In this letter, we propose two antenna grouping schemes for uplink Nx SC-FDMA MIMO systems, where the multiple component carriers can be divided into several groups which are handled by different antennas, thus the number of component carriers on each antenna will be reduced by the group method. As a result, the peak-to-average power ratio (PAPR) of each antenna has been reduced. To further enhance the performance, an interleaving method is proposed to achieve better diversity gain due to the channel varying in the spatial domain and the frequency domain during one turbo coded stream. Our simulation figures clearly demonstrate that in all examples, the proposed schemes are shown to be effective in improving the Block Error Rate (BLER) performance while reducing the PAPR.\",\n \"Title: Wireless localization based on the time sum of arrival and Taylor expansion\\nYear: 2013\\n\\nAbstract: The localization of a wireless device is important for current wireless networks, therefore this paper investigates the time-sum-of-arrival (TSOA) based localization method, where the Taylor Series algorithm is analytically derived according to the elliptical geometry, i.e., TSOA based Taylor Series method, and then the conventional time-difference-of-arrival (TDOA) based algorithm is provided as a comparison. The simulation shows that the TSOA based algorithm has a better performance than the latter.\",\n \"Title: A novel structure for 2nd order digital filters.\\nYear: 2008\\n\\nAbstract: In this paper, based on a new parametrization of the input-balanced realizations a novel structure is proposed for implementation of 2nd order digital filters. Like the normalized lattice structure, it contains a Givens rotation. The expression of roundoff noise gain for this structure is derived. This proposed structure is simpler than the normalized structure. Though having the same number of structure parameters as that of the classical optimal state-space roundoff realizations, it can be implemented much more efficiently using the CORDIC techniques. Numerical examples show that the proposed structure also outperforms the normalized structure and the classical optimal state-space realizations in terms of minimizing roundoff noise. © 2008 IEEE.\",\n \"Title: Turbo detection and decoding for single-carrier block transmission systems\\nYear: 2004\\n\\nAbstract: We study the low complexity turbo detector for cyclic prefix based single-carrier block transmission (CP-SCBT) systems with multiple receive antennas. It has the following characteristics: firstly, it can be implemented by FFT/IFFT with low complexity; secondly, along with SISO decoder, turbo detection can be applied. Simulation results show that the iterative receiver provides much better performance than the conventional CP-SCBT system. It also outperforms its coded OFDM counterpart. The most attractive advantage is that it can be implemented by FFT/IFFT without significantly increasing the complexity of the system.\",\n \"Title: Low complexity iterative receiver for multiuser STBC block transmission systems\\nYear: 2004\\n\\nAbstract: In this paper, a low complexity space-frequency iterative detection and decoding scheme is developed for multiuser space-time block-coded (STBC) block transmission systems, such as the cyclic prefix based single-carrier block transmission (CP-SCBT) system and OFDM system. Using the algebraic properties of such systems, the MMSE-based turbo detection algorithm can be implemented in the frequency domain and then the complexity of the matrix inversion can be reduced greatly. The performance of the iterative receiver for multiuser STBC block transmission systems with bit-interleaved coded modulation (BICM) is evaluated in mobile multipath frequency selective channels through computer simulations. It has been shown that the proposed receiver significantly outperforms the conventional noniterative receiver. Moreover, at high signal-to-noise ratio, the detrimental effects of multiple-access interference (MAI) and intersymbol interference (ISI) in the channel can almost be completely overcome by the turbo processing, and the performance is very close to that of BICM in a Gaussian channel.\",\n \"Title: How Accurate Does Channel Prediction Need To Be In Adaptive V-Blast Systems?\\nYear: 2007\\n\\nAbstract: Since the accuracy of channel prediction influences the performance of adaptive multiple-input-multiple-output (MIMO) systems in Rayleigh fading channels, we analyze die performance of adaptive V-BLAST systems with a minimum-mean-square-error (MMSE) channel predictor in this paper. Considering the channel prediction error, we obtain the closed-form expressions for the bit-error-rate (BER) and throughput of adaptive V-BLAST systems, and maximize system transmission rate by choosing optimum block length under BER. constraint. The numerical results reveal the critical value of channel prediction error below which systems can match the transmission rate of the systems with perfect channel prediction.\",\n \"Title: Comments and Further Results on `Performance Evaluation of a New BPSK-OFDM Timing Estimation Algorithm'\\nYear: 2009\\n\\nAbstract: This comment first points out some inaccurate formulae and irrelevant comparisons between Wen’s algorithm and Beek’s one.\\n Then, the correct formulae are provided, the limitations of these two estimators are discussed and some investigations on\\n the local maximum in timing estimation are also presented. Both the simulation and the analysis demonstrate that the timing\\n precision of Wen’s algorithm highly depends on the length of cyclic prefix (CP) and the channel fading rate. On the other hand, Beek’s algorithm will work better under a large CP length and in fast\\n fading channels.\",\n \"Title: Design of Universal Wireless Channel Generator Accounting for the 3-D Scatter Distribution and Hardware Output\\nYear: 2015\\n\\nAbstract: Hardware to generate the wireless channel is indispensable to the tests of wireless equipment. Previous literature always focused on the pure-hardware design, which is difficult or expensive for the small- and medium-sized enterprises. Hence, this paper presents a simple strategy to develop a flexible and low-cost channel generator, where the theoretical derivation and hardware realization are integrated together to save the test-cost and reduce the realization difficulty. In the proposed method, the fading channel is first modeled in the Agilent advanced design systems (ADS) software, and then a conventional signal generator hardware is connected and controlled to output the physical fading signal to wireless equipment waiting for measurements. Meanwhile, to cover a widerange of fading scenarios, we exploit 3-D geometry-based stochastic channel model as well as the autoregressive (AR) modeling technique. In general, for a given nonisotropic 3-D scattering environment, the angle of arrival, autocorrelation function, and level crossing rate are derived first, and then the AR coefficients are obtained by solving the Yule-Walker equation. Finally, the AR coefficients are fed to the ADS software to control the hardware generating channels. In addition, the channel model and the radio frequency parameters can be controlled by the ADS software, yielding a great flexibility. We verify the proposed method via both the computer simulation and the hardware measurement, and the simulation agrees with the theoretical result well. In addition, the physical measurements also demonstrate that the physical fading channels are consistent with the design specifications.\",\n \"Title: Accurate and Simple Wireless Localizations Based on Time Product of Arrival in the DDM-NLOS Propagation Environment\\nYear: 2015\\n\\nAbstract: Wireless localization has become an important issue in internet of things. However, its performance degrades significantly when there are only non-line-of-sight (NLOS) propagation paths instead of line-of-sight (LOS) propagation paths. Therefore, this paper proposes a novel localization algorithm to improve the localization accuracy, where we assume that the NLOS error satisfies the requirement of distance dependent model (DDM). In order to obtain a stronger NLOS tolerance than conventional methods, the proposed algorithm analyzes the localization geometry and employs a new localization parameter, namely the time product of arrival (TPOA), to construct the linear localization equation group (LLEQ). Meanwhile, a simple least-squares (LS) principle is applied to solve this LLEQ, resulting in a low complexity. We verify our algorithm by computer simulations, and the result demonstrates that the proposed algorithm yields a better performance than traditional algorithms in the NLOS-DDM environment.\",\n \"Title: Power optimization for dynamic spectrum access with convex optimization and intelligent algorithm\\nYear: 2015\\n\\nAbstract: In dynamic spectrum access networks, relay transmission has been considered as a potential method to help cognitive users communicate by reducing their interferences to primary networks especially in the underlay mode. In this paper, we propose a power optimization algorithm for both regenerative and non-regenerative relay systems in condition of Rayleigh fading channels. Based on analyzing the features of dynamic spectrum access networks, a relevant interference model is first built. Then, we propose a combined power allocation strategy in order to minimize the outage probabilities in the cooperative transmission. For regenerative system, we give a closed-form expression for the power allocation by taking into account the characteristics of the fading channels. For non-regenerative system, we utilize pattern search algorithm to solve the optimization problem since the objective function is complex and uneasy to be figured out directly. A competitive strategy for initial point selection is also designed to guarantee a global optimal outcome for the proposed pattern search algorithm. Numerical results show that the system performances with optimum power allocation outperform those with uniform power allocation whereas lower outage probabilities can be obtained.\",\n \"Title: Generation of 3-D physical fading channel with non-isotropic scattering\\nYear: 2013\\n\\nAbstract: This paper presents the application of autoregressive (AR) methods in 3-D fading channel modeling and generation. It employs the AR method to yield the filter coefficient in the 3D and non-isotropic scattering environment, and derives the channel statistics from the geometry-based stochastic channel model (GSCM). Simulations show that the statistical variable of simulated channel agrees with the referenced model. Moreover, by exploiting the Agilent advanced design systems (ADS) software and the Agilent E4432B vector signal generator, the above AR model can be realized in ADS, and then the physical fading channel can be generated through the E4432B instrument. The tests of frequency analyzer demonstrate that the proposed scheme can generate the physical fading channel precisely by the design requirement.\",\n \"Title: Comparative study of heuristic search algorithm in digital FIR filter design\\nYear: 2012\\n\\nAbstract: The group intelligent algorithm is random optimization tool following the group behavior in the nature biological structure, which is a powerful tool for many optimization issues. This paper first models the digital filter design a constrained optimization problem with non-uniform frequency sampling, and then contrasts three kinds of intelligent algorithms to solve the problem. The computer simulation and the statistical analysis of the results suggest that digital filter designed by the artificial bee colony algorithm is better than those of genetic algorithm and the particle swarm optimization algorithm in terms of the convergence and the iterative times and filter.\",\n \"Title: An efficient and robust orthogonal structure for linear discrete-time systems\\nYear: 2010\\n\\nAbstract: An orthogonal structure is derived for system implementation. The expression of roundoff noise propagation gain for this structure is obtained. The proposed structure is an improved version of that reported in the paper by G. Li, M. Gevers, and Y.X. Sun (Performance analysis of a new structure for digital filter implementation, IEEE Trans. Circuits Syst. I 47:474-482, 2000), but is more efficient and robust against the quantization errors. For comparison, an alternative expression of roundoff noise gain for the normalized lattice structure is derived, based on which it is shown that the roundoff noise gain of an Nth order all-pass system, when implemented using the normalized lattice structure, is 4N and that an Nth order system implemented with the structure proposed in the paper by P.P. Vaidyanathan, S.K. Mitra, and Y. Neuvo (A new approach to the realization of low sensitivity IIR digital filters, IEEE Trans. Acoust. Speech Signal Process. ASSP-34(2):350-361, 1986), yields a roundoff noise gain of N +1, smaller than that of the classical optimal roundoff noise state-space realizations. Design examples are presented to illustrate the behavior of the proposed structure and to compare it with a class of existing orthogonal structures and the classical optimal roundoff noise realizations. It is shown that the proposed structure outperforms the others in terms of minimization of roundoff noise as well as implementation efficiency. © Springer Science+Business Media, LLC 2010.\",\n \"Title: Minimum-error-based approximation model for symmetric alpha stable distribution\\nYear: 2012\\n\\nAbstract: In many communication channels the impulsive noise is usually assumed to be of a symmetric alpha stable (SαS) distribution. Unfortunately, except for the Gaussian, Cauchy, and Lévy laws, the analytical expressions for the probability density functions (PDF) of alpha stable distributions are unknown, resulting in very limited application of this distribution. In a practical system, the bi-parameter Cauchy-Gaussian mixture (BCGM) distribution is used to approximate the PDF of the SαS distribution to tackle this difficulty. In this paper, we derive the optimal mixture ratio of the BCGM model based on the minimum square error criterion and furthermore propose a simplified and robust version of BCGM for the SαS distribution. Numerical simulations show that our proposed model achieves better performance and is more robust than the conventional models, without incurring additional complexity. © Springer Science+Business Media, LLC 2012.\",\n \"Title: EV-Linker: Mapping eavesdropped Wi-Fi packets to individuals via electronic and visual signal matching\\nYear: 2016\\n\\nAbstract: •We propose a new attack EV-Linker to map eavesdropped Wi-Fi packets to individuals.•We solve the challenges caused by the devices, environment and people's movements.•EV-Linker considers both moving and stationary people.•Experiments demonstrate the effectiveness of EV-Linker.\",\n \"Title: Differentially Private Matrix Factorization.\\nYear: 2015\\n\\nAbstract: Matrix factorization (MF) is a prevailing collaborative filtering method for building recommender systems. It requires users to upload their personal preferences to the recommender for performing MF, which raises serious privacy concerns. This paper proposes a differentially private MF mechanism that can prevent an untrusted recommender from learning any users' ratings or profiles. Our design decouples computations upon users' private data from the recommender to users, and makes the recommender aggregate local results in a privacy-preserving way. It uses the objective perturbation to make sure that the final item profiles satisfy differential privacy and solves the challenge to decompose the noise component for objective perturbation into small pieces that can be determined locally and independently by users. We also propose a third-party based mechanism to reduce noises added in each iteration and adapt our online algorithm to the dynamic setting that allows users to leave and join. The experiments show that our proposal is efficient and introduces acceptable side effects on the precision of results.\",\n \"Title: Doppler shift estimation exploiting iterative processing in mobile communications\\nYear: 2014\\n\\nAbstract: The Doppler shift, or equivalently the vehicular speed, represents the fading rate of mobile channels, thus its estimation must be important in the mobile communication system. This paper investigates the estimation of Doppler shift by taking into consideration the rate of minimum (ROM) of envelope, where both the iterative processing and polynomial fitting are employed to improve the estimating performance. We verify the proposed scheme by numerical simulations, and see the satisfying performance for variable speeds and SNRs.\",\n \"Title: Differentially private publication of general time-serial trajectory data\\nYear: 2015\\n\\nAbstract: Trajectory data, i.e., human mobility traces, is extremely valuable for a wide range of mobile applications. However, publishing raw trajectories without special sanitization poses serious threats to individual privacy. Recently, researchers begin to leverage differential privacy to solve this challenge. Nevertheless, existing mechanisms make an implicit assumption that the trajectories contain a lot of identical prefixes or n-grams, which is not true in many applications. This paper aims to remove this assumption and propose a differentially private publishing mechanism for more general time-series trajectories. One natural solution is to generalize the trajectories, i.e., merge the locations at the same time. However, trivial merging schemes may breach differential privacy. We, thus, propose the first differentially-private generalization algorithm for trajectories, which leverage a carefully-designed exponential mechanism to probabilistically merge nodes based on trajectory distances. Afterwards, we propose another efficient algorithm to release trajectories after generalization in a differential private manner. Our experiments with real-life trajectory data show that the proposed mechanism maintains high data utility and is scalable to large trajectory datasets.\",\n \"Title: Advertiser and Publisher-centric Privacy Aware Online Behavioral Advertising\\nYear: 2015\\n\\nAbstract: Online behavioral advertising (OBA) has become one of the most successful advertising models on the Internet. Nevertheless, all existing OBA systems are broker-centric in the billing phase, which means it is the broker who exclusively determines advertisers' expenses and publishers' revenues. Consequently, a malicious broker may cheat in their tallying of ad clicks to overcharge advertisers or underpay publishers. Furthermore, as the broker cannot justify the bills, malicious advertisers may deny actual clicks to ask for refunds, and malicious publishers may claim non-existing clicks to demand extra revenue shares. This paper solves these problems by reversing the priority between the broker and the advertisers and publishers. Specifically, when users click on ads, it makes corresponding advertisers and publishers forward click reports of clients to the broker after checking, anonymizing and signing them. The broker then settles accounts with advertisers and publishers fully based on these reports. To guarantee the interests of the broker after the priority reversal, we further propose effective mechanisms for detecting underreporting advertisers and over reporting publishers, respectively.\",\n \"Title: Pricing-Based Spectrum Leasing For Cognitive Networks With Channel Quality Diversity\\nYear: 2015\\n\\nAbstract: In cognitive networks, unused spectrum should be carefully priced to maximize primary system's profit and satisfy secondary user's diverse requirements. In this paper, we investigate a pricing-based spectrum leasing framework where a primary system and multiple cognitive users involved in. In the proposed model, unused spectrum is classified into two types of channels with different spectrum quality. Secondary user chooses the channel according to its own selection preference which is relevant to the channel quality and spectrum evaluation. On the other hand, a spectrum pricing mechanism for the primary user is proposed based on the distribution characteristics of the secondary preference. We cast the optimization problem into the Hotelling game model where the pricing game is carried out with distinct product diversity. By solving the Nash equilibrium of the game model, an iterative algorithm for spectrum pricing is obtained. We further prove the existence and uniqueness of the equilibrium as well as the convergence characteristic of the iterative algorithm. Numerical results are presented to show how the settings of selection preference and spectrum pricing affect the spectrum owner's profits.\",\n \"Title: Towards Attack-Resistant Peer-Assisted Indoor Localization\\nYear: 2015\\n\\nAbstract: Peer-assisted smartphone localization, which leverages pairwise acoustic ranging among nearby peer phones to refine location estimation, significantly pushes the accuracy limit of WiFi-based indoor localization. Unfortunately, this technique is designed for non-adversarial settings. Dishonest peers may cheat in their distance measurements. Outside attackers may interfere with the acoustic ranging by continually broadcasting interference signals. In this paper, we propose counter-measures against each of these attacks. We first present an algorithm that can identify peers that are not cheating in the current localization, by searching for devices that can be embedded into the same plane according to their pairwise distances. We also design a robust acoustic ranging method exploiting signal modulation, which can defend effectively against intentional interference of outside attackers. Experimental results demonstrate that our countermeasures can greatly improve the robustness of peer-assisted localization.\",\n \"Title: Privacy-Preserving Utility Verification of the Data Published by Non-Interactive Differentially Private Mechanisms.\\nYear: 2016\\n\\nAbstract: In the problem of privacy-preserving collaborative data publishing, a central data publisher is responsible for aggregating sensitive data from multiple parties and then anonymizing it before publishing for data mining. In such scenarios, the data users may have a strong demand to measure the utility of the published data, since most anonymization techniques have side effects on data utility. Neve...\",\n \"Title: Foum: A Flow-Ordered Consistent Update Mechanism For Software-Defined Networking In Adversarial Settings\\nYear: 2016\\n\\nAbstract: Due to the asynchronous and distributed nature of the data plane, consistent configuration updating across multiple switches is a challenging issue in Software-Defined Networking (SDN). The existing version-stamping-based mechanism (VSM) could guarantee per-packet consistency, but this mechanism is designed for non-adversarial settings and can be compromised easily by a malicious attacker. In this paper, we propose an efficient flow-ordered update mechanism that aims to provide per-packet consistency in adversarial settings. Our proposal does not need to stamp data packets with the configuration version, and is robust against both the packet-tampering and packet-dropping attacks. It outperforms a naive mechanism that simply patches VSM using digital signatures in three aspects: First, the switches in this mechanism only need to sign and verify a single control packet, which significantly improves the packet processing time. Second, it avoids keeping both old and new policies on switches during the update, and thus achieves better space efficiency. Third, it reduces the time delay for new policies to come into force. We evaluate our mechanism on a self-constructed SDN testbed and the results demonstrate high efficiency.\",\n \"Title: Transmit-Time Allocation In Wireless Sensor Relay Transmission With Energy Fairness\\nYear: 2015\\n\\nAbstract: Cooperative transmission is an efficient method for wireless sensor networks (WSN) to decrease the power consumption of sensor transmitter and counter the detrimental effect of signal fading inherent to wireless channels. During the course, a key issue is how the sensor relay should handle the balance of assisting other node's communications and accomplishing its own tasks and what kind of situation the sensor transmitter needs to ask for the cooperation rather than direct communication. In this paper, we investigate the potential bargaining for resource allocation in WSN over Rayleigh fading environments to address the problems by considering the energy fairness for both the participants of the cooperative transmission. Based on Rayleigh fading channels' characteristics, a relevant system model is first built. Then, after analyzing the system capacity in given outage probability over the fading channels, a system utility function with regard to the transmit-time allocation addressing both participants' energy concern is proposed. An iterative algorithm is subsequently achieved by solving the Nash equilibrium. Furthermore, essential analyses and discussions on the equilibrium existence and convergence conditions of the iterative algorithm are provided.\",\n \"Title: Primary and secondary QoS guranteed spectrum sharing protocol with optimal time allocation\\nYear: 2015\\n\\nAbstract: In this paper, we propose a collaborative spectrum sharing protocol which aims to maximize the sum transmission rate while the quality-of-service (QoS) of both primary and secondary systems' can be supported. Specifically, the primary system authorizes the secondary system to access its spectrum band with a fraction of transmission time only when it can help the primary system achieve the target rate. During the accessing time, the secondary system assigns half power to help primary system reach the target rate and utilizes the remaining half power to transmit its own signal. We determine an optimal time allocation by analyzing the sum transmission rate of primary and secondary systems with both of their QoS can be ensured. Simulation results show that the efficiency of the proposed spectrum sharing protocol and both primary and secondary systems can benefit from it.\",\n \"Title: An Anti-Interference Spectrum Sharing Protocol With Secondary User Selection\\nYear: 2015\\n\\nAbstract: In this paper, we proposed an anti-interference cooperation spectrum sharing protocol with secondary user selection. Specifically, one secondary user is selected to assist the primary system to achieve the target rate by acting as a decode-and-forward (DF) relay, which uses a fraction of time and bandwidth to forward the primary signal. As a reward, the selected secondary user can gain spectrum access by using the remaining bandwidth to transmit its own signal. The secondary system use different bandwidth to transmit the primary and its own signal, thus there will be no interference exists between the primary and secondary systems. We study the joint optimization of time and bandwidth as well as secondary user selection such that the secondary transmission rate is maximized, while the primary system can achieve its target rate. Simulation results demonstrate that both primary and secondary systems benefit from the proposed spectrum sharing protocol.\",\n \"Title: Performance Analysis Of Stochastic Process Modulation System Based On Autoregressive Model Under Low Snr\\nYear: 2015\\n\\nAbstract: A novel stochastic process shift keying (SPSK) to achieve a secure communication system in the presence of heavy noise is presented. The Gaussian noise sequence from the output of autoregressive (AR) filter carries the binary information. At the receiver, a damped sinusoidal model of autocorrelation function is employed to identify the two different AR filters from the noisy observations to estimate the transmitted binary message. Due to its excellent identification performance, the proposed communication system can be effective even under very low signal-to-noise ratio.\",\n \"Title: Power Optimization In Cognitive Networks With Hybrid Intelligent Algorithms\\nYear: 2015\\n\\nAbstract: In this paper, we introduce a hybrid strategy containing pattern search (PS) optimization and genetic algorithm (GA) to resolve the problem of power allocation in cognitive radio networks. Considering the fluctuant interference thresholds in cognitive networks, an approach for solving the problem of coexistence between licensed users and cognitive users is proposed first. Then, based on the analyses of outage probability for cognitive users, a corresponding objective function with regard to the power allocation over Rayleigh fading channels is obtained. Due to the nonlinearity and complexity of this power optimization problem, it is uneasy to figure it out directly by using traditional methods, such as common mathematical deduction or linear programming. Inspired by the concept of intelligent algorithms, we try to employ the scheme of combining PS optimization and GA method, which are both efficient intelligent algorithms to resolve this difficulty. Furthermore, the numerical results are encouraging and show that the proposed approach is worthy of consideration in solving the problem of optimal power allocation in cognitive networks.\",\n \"Title: Simultaneous Wireless Information and Power Transfer Based on Joint Subcarrier and Power Allocation in OFDM Systems.\\nYear: 2017\\n\\nAbstract: Energy harvesting (EH) is a prominent method to extend the operation time of energy-limited wireless networks. By integrating EH into wireless communications, the same spectrum is able to be used by simultaneous wireless information and power transfer (SWIPT) without affecting the quality of service. In this paper, we propose a joint subcarrier and power allocation-based SWIPT scheme in orthogonal frequency division multiplexing (OFDM) systems. Specifically, the received OFDM subcarriers are partitioned into two groups. A fraction of the received subcarriers are allocated to form one group, which is used for information decoding (ID), and the remaining subcarriers form the other group, which is used for energy harvesting. Thus, no splitter is needed at the receiver. A joint subcarrier and power allocation problem is formulated to maximize the harvested energy, subject to the ID constraint. By using the dual decomposition method, an efficient algorithm is proposed to solve this joint resource allocation problem.\",\n \"Title: Cooperative Spectrum Sharing in OFDM Two-Way Relay Systems With Bidirectional Transmissions.\\nYear: 2017\\n\\nAbstract: In this letter, we propose a cooperative spectrum sharing protocol, where both primary users (PUs) and secondary users (SUs) achieve bidirectional transmission. In particular, the SUs help PUs to achieve their bidirectional target rates through orthogonal frequency division multiplexing two-way relaying. In return, the PUs allow SUs to communicate bidirectionally on a subset of subcarriers. Subcarrier and power allocation are jointly optimized for both directions to maximize the SU data rate, given that the PUs' target rate is satisfied. Simulation results demonstrate a significant enhancement in spectrum efficiency compared with several benchmark schemes.\",\n \"Title: A Jointly Differentially Private Scheduling Protocol for Ridesharing Services.\\nYear: 2017\\n\\nAbstract: Ridesharing services have gained tremendous popularity in recent years, benefiting the traffic and environment of cities to a large extent. However, with the demand of ridesharing services increasing sharply, serious privacy concerns (e.g., users' mobility patterns) of ridesharing have become a major barrier against its further development. In this paper, we study the privacy protection of users' ...\",\n \"Title: Covert digital communication systems based on joint normal distribution.\\nYear: 2017\\n\\nAbstract: The correlation coefficient of two consecutive Gaussian sequences is modulated by a binary message bit to achieve a secure communication system. The receiver of the proposed random communication system demodulates the received signal by estimating the correlation coefficient of the transmitted two consecutive sequences. Theoretical bit error rate (BER) expressions in frequency-flat/-selective fadi...\",\n \"Title: Secure Keyboards Against Motion Based Keystroke Inference Attack.\\nYear: 2016\\n\\nAbstract: Nowadays, attackers seek various covert channels to access the users’ privacy on the mobile devices. Recent research has demonstrated that the built-in motion sensors can be exploited to monitor the users’ screen taps and infer what they have typed. This paper presents several practical and convenient countermeasures against this attack in terms of the soft keyboard. We find that this attack is sensitive to the motion noise of the mobile device and the layout variation of the soft keyboard. We, thus, present two kinds of countermeasures against this attack by introducing vibration noise in sensor readings and dynamics in the keyboard layout, respectively. We implement these countermeasures on Android platform and recruit 20 volunteers to evaluate these countermeasures’ effectiveness and usability on both the smartphones and tablets. The results show that the proposed countermeasures can effectively reduce the attackers’ keystroke inference accuracy without significantly hurting the typing efficiency.\",\n \"Title: Cooperative TDD reciprocity calibration with diversity combining for distributed large-scale MIMO systems.\\nYear: 2017\\n\\nAbstract: Thanks to channel reciprocity, the time-division duplexing operation is typically used for distributed large-scale multiple-input multiple-output systems. With the knowledge of uplink channel state, APs (Access Points) perform the downlink precoding to cooperatively communicate with multiple UEs (User Equipments). Unfortunately, the channel reciprocity is often jeopardized by the non-symmetric transceiver RF (Radio Frequency) circuits at both sides of the link. By excluding UEs from the calibration procedure, the TLS (Total Least Squares) method is extended to the case of partial calibration, where only the APs are involved to exchange calibration signals to compensate for RF mismatches. Nevertheless, channel fluctuations between APs significantly degrade system performance. Therefore, we propose a scheme to obtain the diversity gain, by performing a calibration with the combination of the calibration signals of different channel coherent times. Simulation results confirm the effective performance and robustness of our scheme.\",\n \"Title: Spectrum Sharing in OFDM Two-Way Relaying Systems with Joint Optimal Subcarrier and Power Allocation.\\nYear: 2017\\n\\nAbstract: In this paper, we propose a cooperative spectrum sharing protocol based on OFDM two-way relaying with joint optimal subcarrier and power allocation. Specifically, the secondary system helps the primary system achieve their target rates through OFDM two-way relaying, where the secondary system forwards the primary signal by using a fraction of subcarriers and power. In return, the secondary system can gain spectrum access by using the remaining subcarriers and power to transmit its own signal. Joint optimal subcarrier and power allocation is derived aiming to maximize secondary transmission rate with primary transmission rate constraint. Simulation results demonstrate a significant enhancement in spectrum efficiency compared with several benchmark schemes.\",\n \"Title: A Geo-Indistinguishable Location Perturbation Mechanism for Location-Based Services Supporting Frequent Queries.\\nYear: 2018\\n\\nAbstract: As location-based services (LBSs) on smartphones become increasingly popular, such services are causing serious privacy concerns, because many users are unwilling to see their location information leaked to service providers. Recently, in order to protect users' location privacy, researchers have introduced geo-indistinguishability, the first specialized privacy model for LBSs that can provide pro...\",\n \"Title: Spectrum Allocation With Asymmetric Monopoly Model for Multibeam-Based Cognitive Satellite Networks.\\nYear: 2018\\n\\nAbstract: With the rapid development of modern satellite communications, broadband satellite services are experiencing a period of remarkable growth in both user population and available bandwidth. Efficient spectrum management is required in order to meet the ever-increasing demand for broadband spectrum. In this paper, a spectrum allocation scheme for cognitive satellite networks is proposed to improve spectrum efficiency by addressing the situation that scarce spectrum resource is under-utilized while the overall demands of cognitive satellite users are not satisfied. The proposed scheme is devised to optimize bandwidth efficiency by achieving the Bayesian equilibrium through spectrum competition among cognitive satellite users. Due to the scarcity of spectrum resource, satellite systems have to eliminate some of the cognitive users’ transmission information types in order to fulfill the spectrum needs of priority users. After declining one or several kinds of modulation modes, cognitive users can update their spectrum lists. With the increase in number of cognitive users and their corresponding transmission information types, the complexity of the optimization problem increases significantly and becomes a computation burden to cognitive satellite systems. Therefore, in this paper, we propose a simplified solution for spectrum resource optimization in a computation-efficient manner. Through rounds of eliminating operations, the satellite systems can identify the final Bayesian equilibrium as an optimal spectrum allocation strategy. We also provide proofs for the existence of Bayesian equilibrium. Numerical results are given to evaluate the performance of our proposed method from diverse perspectives.\",\n \"Title: A novel energy-balanced routing algorithm in energy harvesting sensor networks.\\nYear: 2018\\n\\nAbstract: In energy harvesting wireless sensor networks, despite the network lifetime can be prolonged through node’s energy collection, how to enhance energy efficiency and optimize routing protocol are still critical and challenging due to volatility and uncertainty of energy harvesting. In this paper, we propose a novel routing method in energy harvesting sensor networks by taking into account the outage probability of relay transmission and node’s residual energy. Our goal is to achieve a balanced-based routing algorithm with improved transmission performance. We design an optimal power control strategy to optimize the outage probability for bidirectional sub-channels in energy harvesting sensor networks. Then, we raise a rational objective function to address the concerns of high efficient and balanced energy consumption in searching router. The main routine of the proposed routing algorithm is also given. Numerical results are provided to testify the performances of our proposed method from the perspectives of network lifetime and outage probability.\",\n \"Title: Joint resource optimization for DF relaying SWIPT based cognitive sensor networks.\\nYear: 2018\\n\\nAbstract: Spectrum and energy are two important resources for wireless sensor networks (WSNs). Cognitive Radio (CR) can improve the efficiency of spectrum utilization by allowing secondary users (SUs) access to the licensed bands as long as they do not significantly affect the performance of primary users (PUs). Simultaneous wireless information and power transfer (SWIPT) can harvest energy and decode information simultaneously from radio-frequency (RF) signal, which can prolong the life time of energy-constraint networks (e.g., WSNs). In this paper, we propose a joint resource optimization scheme in respect of transmission power and power splitting ratio for decode-and-forward (DF) relaying SWIPT based cognitive sensor networks (CSNs), where relay sensor node (RSN) harvests energy from the signal of source sensor node (SSN) by using power splitting (PS) receiver architecture and utilize the harvested energy to help forward SSNs information. We maximize the throughput of SUs while guaranteeing the interference to PUs caused by SUs is smaller than the interference threshold. Simulation results demonstrate the performance of the joint resource optimization scheme.\",\n \"Title: Topology-Preserving Traffic Engineering for Hierarchical Multi-Domain SDN.\\nYear: 2018\\n\\nAbstract: Decoupling of control and data plane in Software Define Networks (SDN) creates significant flexibility in network management. As networks are evolving into a complex multi-domain multi-layer architecture, traffic engineering across multiple domains and layers entails significant challenges for the control plane, especially when each separate administrative domain is not willing to disclose its network topology and resource information. In this paper, we present the first traffic engineering scheme for a multi-domain SDN by using a hierarchical control plane, in which each domain is managed by a local controller, and they are further controlled by a centralized root controller. To preserve the local topology of each domain, we extend the Network Information Base (NIB) into a hierarchical architecture as well, and the root controller maintains only an abstracted view of the lower-layer networks in its NIB. Then, we devise a communication protocol, which enables controllers at different layers and domains to work collaboratively on bandwidth allocation by reading to the hierarchical NIB. We also present an improved traffic engineering algorithm by considering bandwidth and delay simultaneously, to maximize the network utilization while respecting max-min fairness. Experiments on a 719-switches 5-domain network topology demonstrate that our proposal could drive the link utilization ratio to more than 85%.\",\n \"Title: A Novel Physical Layer Encryption Algorithm Based on Statistical Characteristics of Time-Selective Channels.\\nYear: 2018\\n\\nAbstract: Currently, the security of information in the physical layer has attracted much attentions. However, such a physical layer security of conventional study depends on the assumption of reciprocity between the uplink and downlink channels. This assumption cannot be ensured in the time-selective channel of mobile communications. Hence, this paper proposes an encryption algorithm in mobile communications. First, the statistics of time-selective channel are extracted to act as the seeds for multiple chaos generator. Second, the produced chaos sequences are combined to produce the secret code. Finally, the physical signal is encrypted by the simple scrambling operation. We verify the proposed algorithm by computer simulations, and the results demonstrate that the encrypted data can be successfully recovered by the legal receiver, while the sniffer produces poor and flat bit-error-rate performance in a wide range of signal-to-noise ratios.\",\n \"Title: Design of Waveform Shaping Filter in the UFMC System.\\nYear: 2018\\n\\nAbstract: The universal filtered multi-carrier (UFMC) has been taken as a promising candidate for future wireless communication, where a finite impulse response filter is employed to shape the waveform and enhance its resistance to inter-carrier interference. In previous studies, the Dolph-Chebyshev filter had been used due to its low-sidelobe levels, but at the cost of low flexibility to filter the performance control. Hence, this paper puts forward an effective scheme to design an anti-interference filter for UFMC system, where the Nyquist condition (or sampling inter-symbol interference equivalently), the in-band distortion, and the out-of-band emission are taken into consideration. First, this paper models the filter design as a constrained minimax optimization concerning above-mentioned filter performance indexes. Then, the original nonconvex constraints on Nyquist condition are approximately transformed to a linear matrix inequation and two linear inequations. Finally, the optimization problem is solved by semidefinite programming The numerical examples explicitly demonstrate the flexible performance tradeoff of the proposed method, in which the included filter performance indexes can be effectively controlled. Moreover, the bit-error-rate (BER) tests of the UFMC system confirms the effectiveness of our study, where the designed filters show BER advantages over filters of the previous literature. Therefore, the proposed waveform shaping filters can benefit variant applications of UFMC for its flexible performance tradeoffs as well as the improved anti-interference performance.\",\n \"Title: NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks.\\nYear: 2018\\n\\nAbstract: The problem of target localization in WSN (wireless sensor network) has received much attention in recent years. However, the performance of traditional localization algorithms will drastically degrade in the non-line of sight (NLOS) environment. Moreover, variable methods have been presented to address this issue, such as the optimization-based method and the NLOS modeling method. The former produces a higher complexity and the latter is sensitive to the propagating environment. Therefore, this paper puts forward a simple NLOS identification and localization algorithm based on the residual analysis, where at least two line-of-sight (LOS) propagating anchor nodes (AN) are required. First, all ANs are grouped into several subgroups, and each subgroup can get intermediate position estimates of target node through traditional localization algorithms. Then, the AN with an NLOS propagation, namely NLOS-AN, can be identified by the threshold based hypothesis test, where the test variable, i.e., the localization residual, is computed according to the intermediate position estimations. Finally, the position of target node can be estimated by only using ANs under line of sight (LOS) propagations. Simulation results show that the proposed algorithm can successfully identify the NLOS-AN, by which the following localization produces high accuracy so long as there are no less than two LOS-ANs.\",\n \"Title: Preference-Based Spectrum Pricing in Dynamic Spectrum Access Networks.\\nYear: 2018\\n\\nAbstract: With market-driven secondary spectrum trading, licensed users can receive benefits in terms of monetary rewards or various transmission services, thus setting a fair pricing structure by suitably defining spectrum quality characteristics and accurately addressing participant's requirement is a key issue. In this paper, we investigate the pricing-based spectrum access by casting the problem of spec...\",\n \"Title: Securing peer-assisted indoor localization leveraging acoustic ranging.\\nYear: 2019\\n\\nAbstract: •We carefully survey the attacks to the peer-assisted indoor localization.•We propose the defenses to deal with attacks launched by the dishonest peers.•We propose the defense against the intentional interference of outside attackers.•We give the detailed theoretical proofs about our proposed countermeasures.\",\n \"Title: A Non-line-of-Sight Localization Method Based on the Algorithm Residual Error Minimization\\nYear: 2017\\n\\nAbstract: Wireless localization has become a key technology location based services, and the non-line-of-sight (NLOS) propagation is one of the most important error source in the localization. Therefore, this paper defines a novel algorithm residual error (ARE) in NOLS environment, and estimates the position of mobile station (MS) by minimizing this ARE, where the quadratic programming is employed to solve the minimization problem. The simulation results show that the proposed algorithm produces significant performance improvements in NLOS environments.\",\n \"Title: Two-Phase Prototype Filter Design for FBMC Systems\\nYear: 2017\\n\\nAbstract: FBMC has been taken as a candidate waveform for the next enhanced 5th generation (5G). To further improve its advantages over OFDM as well as to promote its application in burst transmission, a two-phase method is applied to design its prototype filter, i.e. a square-root Nyquist filter. In this method, the autocorrelation-based technique and a spectral factorization aimed at minimum stopband energy are successively exploited to acquire the final prototype filter. Through the relaxation of Nyquist condition and benefited from the nonlinear-phase, the frequency selectivity of our designed filters can be greatly improved. Furthermore, the performances of the proposed prototype filter brings a better BER in simulations, which demonstrates the effectiveness of our square-root Nyquist filter design for FBMC systems.\",\n \"Title: Accurate and Efficient Wireless Device Fingerprinting Using Channel State Information.\\nYear: 2018\\n\\nAbstract: Due to the loose authentication requirement between access points (APs) and clients, it is notoriously known that WLANs face long-standing threats such as rogue APs and network freeloading. Take the rogue AP problem as an example, unfortunately encryption alone does not provide authentication. APs need to be equipped with certificates that are trusted by clients ahead of time. This requires either the presence of PKI for APs or other forms of pre-established trust (e.g., distributing the certificates offline), none of which is widely used. Before any strong security solution is deployed, we still need a practical solution that can mitigate the problem. In this paper, we explore a non-cryptographic solution that is readily deployable today on end hosts (e.g., smartphones and laptops) without requiring any changes to the APs or the network infrastructure. The solution infers the Carrier Frequency Offsets (CFOs) of wireless devices from Channel State Information (CSI) as their hardware fingerprints without any special hardware requirement. CFO is attributed to the oscillator drift, which is a fundamental physical property that cannot he manipulated easily and remains fairly consistent over time but varies significantly across devices. The real experiments on 23 smartphones and 34 APs (with both identical and different brands) in different scenarios demonstrate that the detection rate could exceed 94%.\",\n \"Title: Optimal Power Splitting of Cognitive Radio Networks with SWIPT-Enabled Relay\\nYear: 2018\\n\\nAbstract: Cognitive radio (CR), as an intelligent spectrum sharing technology, can improve utilization of spectrum by sharing the licensed spectrum bands with secondary users (SUs) as long as do not have harmful effect on primary users (PUs). Simultaneous wireless information and power transfer (SWIPT) combines wireless information transmission (WIT) technology and wireless power transfer (WPT) technology, which harvesting energy from ambient RF signals. In this paper, we consider amplify-and-forward (AF) cognitive radio networks (CRNs) with SWIPT-enabled secondary relay node. We aim to maximize the throughput of secondary network in considering the interference caused by the transmitted signal of secondary relay node to PUs, and derived the closed-form expression of the optimal power splitting ratio. Simulation results demonstrate the performance of the optimal power splitting ratio.\",\n \"Title: The nonlinear-phase design of FBMC prototype filter based on filter coefficient symmetry characteristic\\nYear: 2020\\n\\nAbstract: To promote filter bank multicarrier (FBMC) in future communication, a novel design method of nonlinear phase FIR filter (NLPFF) is proposed under the principle of constrained minimax (CMM), by which the designed filter will act as the FBMC prototype filter. In this method, the filter coefficient symmetry characteristic (FCSC) is exploited in replace of the conventional phase response error (PRE), which simplifies the establishment of phase constraint and benefits the construction of direct magnitude constraint. Consequently, the proposed constraints related to FCSC outperform the conventional constraints addressing complex frequency response errors, since the former yields a wider range of controllable PRE as well as the dramatically reduced constraint scale. Moreover, in order to restrict the sampling inter-symbol interference, the Nyquist condition (NYQ) is constrained in the frequency domain, which circumvents the initial nonconvex problem. Simulations confirm the effectiveness of the proposed method, where the FCSC can be sufficiently relaxed to improve the stopband attenuation, making this nonlinear-phase design more flexible than previous designs of FBMC prototype filter. Besides, the bit error rate test demonstrates that the designed NLPFFs are superior to the conventional PHYDYAS filter, therefore, the proposed design can certainly produce high quality prototype filter for FBMC system.\",\n \"Title: Resource Allocation Based Simultaneous Wireless Information and Power Transfer for Multiuser OFDM Systems.\\nYear: 2018\\n\\nAbstract: In this paper, we mainly study simultaneous wireless information and power transfer (SWIPT) for the multiuser resource allocation. All subcarriers are divided into two parts, part of which are for information decoding and another part are for energy harvesting. We optimize the subcarrier allocation and power allocation to maximize the energy that collected by all users under the target rate constraint. The original optimal problem is complicated, so it is hard to find the optimal solution directly. By transforming the primal problem, we finally solved the original problem by using the Lagrange dual method.\",\n \"Title: A Method of Balanced Sleep Scheduling in Renewable Wireless Sensor Networks.\\nYear: 2018\\n\\nAbstract: Energy harvesting from its environmental sources becomes an integral part of green cities. This paper considers a low-energy consumption Wireless Sensor Networks to improve energy utilization in green cities. By this approach, a wireless node can directly harvest energy from its ambient by introducing an energy-harvesting layer on the top of traditional WSN layer. The energy harvesting layer composed of charging points (CPs) that it can harvest energy from ambient renewable energy sources (solar, vibration, light, and electromagnetic wave, etc.) transfer the harvested energy to the underlying WSN layer by wireless energy transfer. Furthermore, in order to conserve battery power in very dense sensor networks, some sensor nodes may be put into the sleep state while other sensor nodes remain active for the sensing and communication tasks. The proposed scheme applies energy informatics to increase the energy efficiency by optimizing energy harvesting time interval and energy consumption of the node for uniform data gathering over the network.\",\n \"Title: OFDM Based SWIPT in a Two-Way Relaying Network.\\nYear: 2018\\n\\nAbstract: We consider a simultaneous wireless transfer of information and power in a two-way relaying network, a decode-and-forward protocol and OFDM modulation is employed. Subcarriers are divided into a couple of groups, one is for information decoding, and another is for energy harvesting. With total rate maximized, subcarrier grouping optimization is performed. And the power of subcarriers is optimized according to channel state. The performance of the optimal design is performed on different transmit conditions.\",\n \"Title: An Improved Constrained Least Squares Localization Algorithm in NLOS Propagating Environment.\\nYear: 2018\\n\\nAbstract: The non-line-of-sight (NLOS) error is a major error source in wireless localization. Therefore, an improved constrained least-squares (CLS) algorithm is put forward to tackle this issue, where the positioning problem is formulated as a mathematical programming problem. And then, the cost function of the optimization is studied and a new one is proposed. Finally, through the presented optimization, we try to minimize the positioning influence of NLOS errors. Moreover, the studied method does not depend on a particular distribution of the NLOS error. Simulation results show that the positioning accuracy is significantly improved over traditional CLS algorithms, even under highly NLOS conditions.\",\n \"Title: Improving Spectrum Management for Satellite Communication Systems with Hunger Marketing\\nYear: 2019\\n\\nAbstract: Efficient spectrum management is an important issue in satellite communications due to the pervasive growth in wireless communications and the ever-increasing demand by bandwidth-hungry mobile applications. While various kinds of static and dynamic spectrum allocation schemes have been studied by satellite researchers, the issue of unpredictable user behavior, hence demand for bandwidth, remains o...\",\n \"Title: Interference Analysis in the Asynchronous f-OFDM Systems\\nYear: 2019\\n\\nAbstract: By supporting asynchronous transmission and flexible subband (SB) setting, filtered orthogonal frequency division multiplexing (f-OFDM) has been identified as one of the most promising waveforms for future wireless communications, which makes the deep investigation on the f-OFDM an urgent mission. Therefore, this paper investigates the downlink interference of asynchronous f-OFDM systems under dif...\",\n \"Title: A Fast Realization Of New Mersenne Number Transformation And Its Applications\\nYear: 2019\\n\\nAbstract: The new Mersenne number transform (NMNT) can be realized by the fast Fourier transform (FFT) with power-of-two length, which results in great flexibility in real-world fixed-point computations, such as the convolution-based signal processing in the embedded device. Yet the FFT realization exists the truncation errors and in order to further reduce the computations, this paper puts forward a novel realization structure for the NMNT, where the Walsh-Hadamard transform (WHT) is employed to accelerate the NMNT computation. Moreover, we also propose the refined computing structure using the matrix decomposition and multiple constant multiplication (MCM), and then all multiplications can be replaced by the shift-addition operations without precision loss. Besides, the use of lookup table (LUT) to reduce the complexity is also discussed in our study. A typical convolution application is tested by computer simulations, while the result demonstrates that the proposed scheme produces precise computing results with reduced complexity.\",\n \"Title: A Complexity-reduced Block Encryption Algorithm Suitable for Internet of Things\\nYear: 2019\\n\\nAbstract: The Internet of Things (IoT) poses stringent requirements on service quality and security strength. A highly reliable encryption algorithm with low complexity is required to ensure information security. Therefore, this paper proposes a complexity-reduced secure and fast encryption routine (SAFER)-Fermat block encryption method that accounts for the confusion and diffusion principles. In particular, a novel diffusion layer that exploiting the Fermat number theory transform is proposed, while the confusion layer remains unchanged as the SAFER algorithm. We also propose two data record structures to accommodate the two layers and deal with the overflow issue. The proposed scheme only considers the integer operation, the inclusion of multipliers in our study is no longer necessary, thereby resulting in low complexity and lightweight encryption method. Therefore, the proposed encryption method is advantageous for power-limited IoT devices. The simulations demonstrate that the proposed encryption method maintains the security of the SAFER++ method but requires fewer computations.\",\n \"Title: Energy Efficient Swipt Based Mobile Edge Computing Framework For Wsn-Assisted Iot\\nYear: 2021\\n\\nAbstract: With the increasing deployment of IoT devices and applications, a large number of devices that can sense and monitor the environment in IoT network are needed. This trend also brings great challenges, such as data explosion and energy insufficiency. This paper proposes a system that integrates mobile edge computing (MEC) technology and simultaneous wireless information and power transfer (SWIPT) technology to improve the service supply capability of WSN-assisted IoT applications. A novel optimization problem is formulated to minimize the total system energy consumption under the constraints of data transmission rate and transmitting power requirements by jointly considering power allocation, CPU frequency, offloading weight factor and energy harvest weight factor. Since the problem is non-convex, we propose a novel alternate group iteration optimization (AGIO) algorithm, which decomposes the original problem into three subproblems, and alternately optimizes each subproblem using the group interior point iterative algorithm. Numerical simulations validate that the energy consumption of our proposed design is much lower than the two benchmark algorithms. The relationship between system variables and energy consumption of the system is also discussed.\",\n \"Title: Practical Location Privacy Attacks and Defense on Point-of-interest Aggregates\\nYear: 2021\\n\\nAbstract: Location-based services have significantly affected mobile users' everyday life, and location privacy is also an essential issue in these services. In some applications (e.g., location-based recommendation, mobility analytic), the raw data is not required, and the service providers adopt aggregation to protect users' location traces. However, some works show that even these aggregation data may di...\",\n \"Title: Optimization of ACF-DSR-based joint Doppler shift and SNR estimator for Internet of Vehicle system\\nYear: 2022\\n\\nAbstract: Accurate vehicle speed estimation is required for intelligent internet of vehicles, and it can be realized by Doppler shift estimation in mobile communication. In this paper, the ACF-DSR (autocorrelation function-double sampling rate) method for joint Doppler shift and SNR estimation is further investigated. Based on the analysis of ACF, an improved algorithm model taking account of estimation deviation is formulated. Then, the effects of sampling intervals in DSR are figured out by mean square error analysis of estimation, and two better choices are obtained. By Monte Carlo simulations, it is demonstrated that the optimized ACF-DSR method with better choice of sampling intervals can achieve better estimation and outperform previous methods.\",\n \"Title: Optimization of the energy efficiency in Smart Internet of Vehicles assisted by MEC\\nYear: 2022\\n\\nAbstract: Smart Internet of Vehicles (IoV) as a promising application in Internet of Things emerges with the development of the fifth generation mobile communication (5G). Nevertheless, the heterogeneous requirements of sufficient battery capacity, powerful computing ability and energy efficiency for electric vehicles face great challenges due to the explosive data growth in 5G and the sixth generation of mobile communication (6G) networks. In order to alleviate the deficiencies mentioned above, this paper proposes a mobile edge computing (MEC) enabled IoV system, in which electric vehicle nodes upload and download data through an anchor node which is integrated with a MEC server. Meanwhile, the anchor node transmitters radio signal to electric vehicles with simultaneous wireless information and power transfer technology so as to compensate the battery limitation of eletric vehicles. Moreover, the spectrum efficiency is further improved by multi-input and multi-output and full-duplex technologies which is equipped at the anchor node. In consideration of the issues above, we maximize the average energy efficiency of electric vehicles by jointly optimize the CPU frequency, vehicle transmitting power, computing tasks and uplink rate. Since the problem is nonconvex, we propose a novel alternate interior-point iterative scheme (AIIS) under the constraints of computing tasks, energy consumption and time latency. Results and discussion section verifies the effectiveness of the proposed AIIS scheme comparing with the benchmark schemes.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":87,"string":"87"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13672,"string":"13,672"}}},{"rowIdx":13673,"cells":{"question_id":{"kind":"string","value":"Wei Zhou"},"question":{"kind":"string","value":"Generate an abstract for the title \"Mixed-Phoneme BERT: Improving BERT with Mixed Phoneme and Sup-Phoneme Representations for Text to Speech.\" using the following items: BERT, TTS, Phoneme encoder, MixedPhoneme BERT, Inference speedup."},"answer":{"kind":"string","value":"Recently, leveraging BERT pre-training to improve the phoneme encoder in text to speech (TTS) has drawn increasing attention. However, the works apply pre-training with character-based units to enhance the TTS phoneme encoder, which is inconsistent with the TTS fine-tuning that takes phonemes as input. Pre-training only with phonemes as input can alleviate the input mismatch but lack the ability to model rich representations and semantic information due to limited phoneme vocabulary. In this paper, we propose MixedPhoneme BERT, a novel variant of the BERT model that uses mixed phoneme and sup-phoneme representations to enhance the learning capability. Specifically, we merge the adjacent phonemes into sup-phonemes and combine the phoneme sequence and the merged sup-phoneme sequence as the model input, which can enhance the model capacity to learn rich contextual representations. Experiment results demonstrate that our proposed Mixed-Phoneme BERT significantly improves the TTS performance with 0.30 CMOS gain compared with the FastSpeech 2 baseline. The Mixed-Phoneme BERT achieves 3x inference speedup and similar voice quality to the previous TTS pre-trained model PnG BERT"},"correct_answer":{"kind":"string","value":"Recently, leveraging BERT pre-training to improve the phoneme encoder in text to speech (TTS) has drawn increasing attention. However, the works apply pre-training with character-based units to enhance the TTS phoneme encoder, which is inconsistent with the TTS fine-tuning that takes phonemes as input. Pre-training only with phonemes as input can alleviate the input mismatch but lack the ability to model rich representations and semantic information due to limited phoneme vocabulary. In this paper, we propose MixedPhoneme BERT, a novel variant of the BERT model that uses mixed phoneme and sup-phoneme representations to enhance the learning capability. Specifically, we merge the adjacent phonemes into sup-phonemes and combine the phoneme sequence and the merged sup-phoneme sequence as the model input, which can enhance the model capacity to learn rich contextual representations. Experiment results demonstrate that our proposed Mixed-Phoneme BERT significantly improves the TTS performance with 0.30 CMOS gain compared with the FastSpeech 2 baseline. The Mixed-Phoneme BERT achieves 3x inference speedup and similar voice quality to the previous TTS pre-trained model PnG BERT"},"documents":{"kind":"list like","value":["Title: On-Line Temperature Estimation for Noisy Thermal Sensors Using a Smoothing Filter-Based Kalman Predictor.\nYear: 2018\n\nAbstract: Dynamic thermal management (DTM) mechanisms utilize embedded thermal sensors to collect fine-grained temperature information for monitoring the real-time thermal behavior of multi-core processors. However, embedded thermal sensors are very susceptible to a variety of sources of noise, including environmental uncertainty and process variation. This causes the discrepancies between actual temperatures and those observed by on-chip thermal sensors, which seriously affect the efficiency of DTM. In this paper, a smoothing filter-based Kalman prediction technique is proposed to accurately estimate the temperatures from noisy sensor readings. For the multi-sensor estimation scenario, the spatial correlations among different sensor locations are exploited. On this basis, a multi-sensor synergistic calibration algorithm (known as MSSCA) is proposed to improve the simultaneous prediction accuracy of multiple sensors. Moreover, an infrared imaging-based temperature measurement technique is also proposed to capture the thermal traces of an advanced micro devices (AMD) quad-core processor in real time. The acquired real temperature data are used to evaluate our prediction performance. Simulation shows that the proposed synergistic calibration scheme can reduce the root-mean-square error (RMSE) by 1.2 degrees C and increase the signal-to-noise ratio (SNR) by 15.8 dB (with a very small average runtime overhead) compared with assuming the thermal sensor readings to be ideal. Additionally, the average false alarm rate (FAR) of the corrected sensor temperature readings can be reduced by 28.6%. These results clearly demonstrate that if our approach is used to perform temperature estimation, the response mechanisms of DTM can be triggered to adjust the voltages, frequencies, and cooling fan speeds at more appropriate times.","Title: Sensitivity-based acceleration and compression algorithm for convolution neural network.\nYear: 2017\n\nAbstract: Convolution neural networks(CNNs) have an impressive performance in image processing and other machine learning tasks, meanwhile, colossal computation and memory requirements existing in most classical models restrict the deployment in portable and power-limited devices. The efficient approaches to circumventing the above hindrances stem from shrinking the network scale, which can be divided into two categories, i.e., the network pruning and low-rank approximation of kernel matrices. As compared to the pruning scheme, the low-rank approximation method has lower compression ratio, but is much friendlier to parallelism. In this paper, by analyzing the sensitivity of the rank of each layer to the network accuracy, we proposed a sensitivity-based layer-wise low-rank approximation algorithm. As compared with the traditional rank reduce methods, the acceleration ratio of our proposal is improved by 20%. When deploying our method on VGGNet-16 model, 2:7X compression/acceleration ratio on convolution layers and 10:9X compression/acceleration ratio on FC layers are achieved with 0.05% top-1 accuracy loss and 0.01% top-5 accuracy loss.","Title: RoboCloud: augmenting robotic visions for open environment modeling using Internet knowledge.\nYear: 2018\n\nAbstract: Modeling an open environment that contains unpredictable objects is a challenging problem in the field of robotics. In traditional approaches, when a robot encounters an unknown object, a mistake will inevitably be added to the robot’s environmental model, severely constraining the robot’s autonomy, and possibly leading to disastrous consequences in certain settings. The abundant knowledge accumulated on the Internet has the potential to remedy the uncertainties that result from encountering with unknown objects. However, robotic applications generally pay considerable attention to quality of service (QoS). For this reason, directly accessing the Internet, which can be unpredictable, is generally not acceptable. RoboCloud is proposed as a novel approach to environment modeling that takes advantage of the Internet without sacrificing the critical properties of QoS. RoboCloud is a “mission cloud–public cloud” layered cloud organization model in which the mission cloud provides QoS-available environment modeling capability with built-in prior knowledge while the public cloud is the existing services provided by the Internet. The “cloud phase transition” mechanism seeks help from the public cloud only when a request is outside the knowledge of the mission cloud and the QoS cost is acceptable. We have adopted semantic mapping, a typical robotic environment modeling task, to illustrate and substantiate our approach and key mechanism. Experiments using open 2D and 3D datasets with real robots have demonstrated that RoboCloud is able to augment robotic visions for open environment modeling.","Title: 3D Surface velocity retrieval of mountain glacier using an offset tracking technique applied to ascending and descending SAR constellation data: a case study of the Yiga Glacier\nYear: 2019\n\nAbstract: COSMO-SkyMed is a constellation of four X-band high-resolution radar satellites with a minimum revisit period of 12 hours. These satellites can obtain ascending and descending synthetic aperture radar (SAR) images with very similar periods for use in the three-dimensional (3D) inversion of glacier velocities. In this paper, based on ascending and descending COSMO-SkyMed data acquired at nearly the same time, the surface velocity of the Yiga Glacier, located in the Jiali County, Tibet, China, is estimated in four directions using an offset tracking technique during the periods of 16 January to 3 February 2017 and 1 February to 19 February 2017. Through the geometrical relationships between the measurements and the SAR images, the least square method is used to retrieve the 3D components of the glacier surface velocity in the eastward, northward and upward directions. The results show that applying the offset tracking technique to COSMO-SkyMed images can be used to derive the true 3D velocity of a glacier's surface. During the two periods, the Yiga Glacier had a stable velocity, and the maximum surface velocity, 2.4 m/d, was observed in the middle portion of the glacier, which corresponds to the location of the steepest slope.","Title: Application Of Powell Fruit Fly Optimization Algorithm To Solutions Of Nonlinear Equations\nYear: 2017\n\nAbstract: Solution to nonlinear equations is with extensive practical meanings in engineering. The convergence of classic numerical solutions is dependent on the initial value which is actually hard to be determined in actual computation. Thus, a new solution to nonlinear equations called PFOA is put forward to turn the complicated solution to nonlinear equations to function optimization. Group search and global convergence of the fruit fly optimization algorithm (FOA) can be fully utilized via PFOA, thereby effectively overcoming the initial point sensitivity of Powell. However, positive local optimal solution can be realized by Powell, so the two algorithms are improved effectively along with integration and complementation in this paper. Five typical nonlinear equations are selected, and the calculation results show the designed PFOA is superior to the basic FOA and is considered a successful solution to nonlinear equations.","Title: Automatic Life Cycle Management of Network Configurations.\nYear: 2018\n\nAbstract: Managing the life cycle of network configurations, including the generation, update, transition and diagnosis of the configurations, is the primary task of network operators and a critical process for the reliability and efficiency of the networks. This paper presents NetCraft, a framework which automates the life cycle management of network configurations with a unified network model. Designed for life cycle automation, NetCraft's network model can expressively encode all parts and protocols in the network; It can be converted to or constructed from configurations with interoperability; It is able to perform fine-grained configurations with flexibility to deactivate or undo any configurations for safe configuration updates; And it can work without cooperations from device vendors. We have built and deployed an initial version of NetCraft in Alibaba's global WAN. Evaluations in real environments show that NetCraft can reduce the network incidents caused by configurations by 95% and cut the average time to plan and execute a network update by up to 93%.","Title: A Time-Domain Preconditioned Truncated Newton Approach to Visco-acoustic Multiparameter Full Waveform Inversion\nYear: 2018\n\nAbstract: A truncated Newton (TRN) method for time-domain full waveform inversion (FWI) in a visco-acoustic medium has been developed based on the 2nd-order adjoint state formulation. Time-domain gradient estimation and Hessian-vector product are managed by recomputing, without numerical instabilities, the incident wavefield at the same time as the adjoint wavefield for mitigating memory issues. Generic algorithm workflow has been proposed to switch between parameterizations, thanks to the chain rule. An efficient preconditioner adapted to the multiparameter configuration is developed to enhance the convergence rate of the inner conjugate gradient iterations. An additional user-defined scaling is introduced in the preconditioner to mitigate the weak sensitivity of specific parameters to waveform variations. The importance of the inverse Hessian for mitigating interparameter trade-off is validated on a toy example. Through a realistic 2D synthetic case based on a North Sea real data application, encouraging numerical results under (V-p, rho, Q(-1)) parametrization demonstrate that considering the Hessian influence significantly improves the multiparameter reconstruction, mitigating the coupling between parameters, for a reasonable increase of the computational cost compared to standard quasi-Newton optimization strategies.","Title: Design and Application of an Attractiveness Index for Urban Hotspots Based on GPS Trajectory Data.\nYear: 2018\n\nAbstract: Urban hotspots refer to regions where flourishing shopping centers are located, the travel volume is very large, and there is high traffic. The formation of hotspots is strongly correlated with many features, i.e., time, space, and the distribution of points of interest; however, most studies have used qualitative analyses to describe the relevance of these features, and there is a lack of quantitative analyses. Therefore, we propose the concept and a model of the attractiveness index of a hotspot that is used to quantify the spatio-temporal distribution of the hotspots and determine the degree of attractiveness to the residents. In addition, a novel algorithm of hotspot similarity is designed and implemented to improve the efficiency of summarizing the hotspot data, which is usually performed manually. We mine the data and determine different hotspots using a one-week GPS trajectory dataset collected from 6599 taxis in Kunming. Furthermore, we calculate the attractiveness index of hotspots and visualize their characteristics. The research results provide a scientific basis and reference for public infrastructure planning, land-value evaluation, store location planning, consumption recommendations, and other applications.","Title: Dynamic Target Template Matching For Railway Catenary Suspension Motion Detection In Wind Area\nYear: 2018\n\nAbstract: A dynamic target template matching method was proposed to identify railway catenary suspension movements of wind-induced vibration in wind area. Catenary positioning point was taken as the target template, which was compared with equal-sized image sequentially using the proposed matching difference. And, three-dimensional contour map of matching difference value at each sub-area was obtained, where the target pixel coordinates were determined by the minimum matching difference value. Considering the complex imaging condition, the target template was updated by the detected target image to sense the gradual change of illumination conditions like brightness and contrast. Furthermore, to eliminate detecting errors due to wind-induced camera vibration, both static and moving target templates were identified for acquiring the absolute motion of the moving target. Finally, validation test was performed with animation in PowerPoint. The calculated target displacement agrees well with theoretical motion with maximum relative error of 1.8%. And experiment application was conducted at site by analyzing the relationship between detecting displacement and wind speed. Results indicate that the proposed dynamic target template matching method can meet required engineering precision and provide an effective way for wind-vibration safety research of railway catenary system in wind area.","Title: An Online Sockpuppet Detection Method Based on Subgraph Similarity Matching\nYear: 2018\n\nAbstract: Multiple identity deception (sockpuppetry) has become an increasingly important issue in the social media environment, because they are engaged in undesired behaviors such as spreading rumors, posting hate speeches. The case of blocked users initiating new accounts, called sockpuppets, is widely known and past efforts, which have attempted to detect such users, have been primarily based on verbal features and non-verbal behaviors like posting habits. Although these methods have achieved a certain degree of success, it is easy for smart sockpuppets to forge verbal and behavioral features to circumvent management, making it difficult to guarantee the performance of these detection methods. In this paper, we observe that the social network plays a critical role in sockpuppets' influence, because recovering similar social structures can a sockpuppet guarantee the similar propagation impact. With this observation, a structure-based online method is proposed, which turns the sockpuppet detection problem into a subgraph similarity matching problem. The experiment on two real-world datasets of Sina Weibo demonstrates that our method obtains excellent detection performance, significantly outperforming previous methods.","Title: Temporal-spatial Analysis & Visualization of Passenger Flow after Opening New Railway Lines in Shenzhen Metro.\nYear: 2018\n\nAbstract: Opening new lines will bring the structural change of passenger flow characteristics in network operation of Urban Rail Transit. Based on the smart card data from Shenzhen Metro, temporal-spatial characteristics was discussed comprehensively from different statistics dimensions and analysis angles. At the same time, passenger flow in 28 transfer stations was analyzed by clustering algorithm and the land use nature was attained. Consequently, the passenger flow law in Shenzhen Metro will be understood better combined with visualization. Especially, when facing the risk of large passenger flow, targeted measures can be taken in advance to improve the safety and resilience of Shenzhen Metro.","Title: Monitoring and Analyzing Mountain Glacier Surface Movement Using SAR Data and a Terrestrial Laser Scanner: A Case Study of the Himalayas North Slope Glacier Area.\nYear: 2019\n\nAbstract: The offset tracking technique based on synthetic aperture radar (SAR) image intensity information can estimate glacier displacement even when glacier velocities are high and the time interval between images is long, allowing for the broad use of this technique in glacier velocity monitoring. Terrestrial laser scanners, a non-contact measuring system, can measure the velocity of a glacier even if there are no control points arranged on a glacier. In this study, six COSMO-SkyMed images acquired between 31 July and 22 December 2016 were used to obtain the glacial movements of five glaciers on the northern slope of the central Himalayas using the offset tracking approach. During the period of image acquirement, a terrestrial laser scanner was used, and point clouds of two periods in a small area at the terminus of the Pingcuoliesa Glacier were obtained. By selecting three fixed areas of the point clouds that have similar shapes across two periods, the displacements of the centers of gravity of the selected areas were calculated by using contrast analyses of feature points. Although the overall low-density point clouds data indicate that the glacial surfaces have low albedos relative to the wavelength of the terrestrial laser scanner and the effect of its application is therefore influenced in this research, the registration accuracy of 0.0023 m/d in the non-glacial areas of the scanner's measurements is acceptable, considering the magnitude of 0.072 m/d of the minimum glacial velocity measured by the scanner. The displacements from the point clouds broadly agree with the results of the offset tracking technique in the same area, which provides further evidence of the reliability of the measurements of the SAR data in addition to the analyses of the root mean squared error of the velocity residuals in non-glacial areas. The analysis of the movement of five glaciers in the study area revealed the dynamic behavior of these glacial surfaces across five periods. G089972E28213N Glacier, Pingcuoliesa Glacier and Shimo Glacier show increasing surface movement velocities from the terminus end to the upper part with elevations of 1500 m, 4500 m, and 6400 m, respectively. The maximum velocities on the glacial surface profiles were 31.69 cm/d, 62.40 cm/d, and 42.00 cm/d, respectively. In contrast, the maximum velocity of Shie Glacier, 50.60 cm/d, was observed at the glacier's terminus. For each period, G090138E28210N Glacier exhibited similar velocity values across the surface profile, with a maximum velocity of 39.70 cm/d. The maximum velocities of G089972E28213N Glacier, Pingcuoliesa Glacier, and Shie Glacier occur in the areas where the topography is steepest. In general, glacial surface velocities are higher in the summer than in the winter in this region. With the assistance of a terrestrial laser scanner with optimized wavelengths or other proper ground-based remote sensing instruments, the offset tracking technique based on high-resolution satellite SAR data should provide more reliable and detailed information for local and even single glacial surface displacement monitoring.","Title: Cross perishable effect on optimal inventory preservation control.\nYear: 2019\n\nAbstract: •We introduce a novel concept of cross-perishability in literature.•We introduce a novel control variable of preservation effort in modelling.•We prove the upper and lower bound conditions for the decision variables.•We use the boundary conditions to facilitate the searching algorithm.•We analyze this novel model and investigate the optimal policy & mechanism.","Title: A New Denoising Method for UHF PD Signals Using Adaptive VMD and SSA-Based Shrinkage Method.\nYear: 2019\n\nAbstract: Noise suppression is one of the key issues for the partial discharge (PD) ultra-high frequency (UHF) method to detect and diagnose the insulation defect of high voltage electrical equipment. However, most existing denoising algorithms are unable to reduce various noises simultaneously. Meanwhile, these methods pay little attention to the feature preservation. To solve this problem, a new denoising method for UHF PD signals is proposed. Firstly, an automatic selection method of mode number for the variational mode decomposition (VMD) is designed to decompose the original signal into a series of band limited intrinsic mode functions (BLIMFs). Then, a kurtosis-based judgement rule is employed to select the effective BLIMFs (eBLIMFs). Next, a singular spectrum analysis (SSA)-based thresholding technique is presented to suppress the residual white noise in each eBLIMF, and the final denoised signal is synthesized by these denoised eBLIMFs. To verify the performance of our method, UHF PD data are collected from the computer simulation, laboratory experiment and a field test, respectively. Particularly, two new evaluation indices are designed for the laboratorial and field data, which consider both the noise suppression and feature preservation. The effectiveness of the proposed approach and its superiority over some traditional methods is demonstrated through these case studies.","Title: Improving Short-Length LDPC Codes with a CRC and Iterative Ordered Statistic Decoding : (Invited Paper)\nYear: 2019\n\nAbstract: We present a CRC-aided LDPC coding scheme that can outperform the underlying LDPC code under ordered statistic decoding (OSD). In this scheme, the CRC is used jointly with the LDPC code to construct a candidate list, instead of conventionally being regarded as a detection code to prune the list generated by the LDPC code alone. As an example we consider a (128,64)5 G LDPC code with BP decoding, which we can outperform by 2 dB using a (128,72) LDPC code in combination with a 8-bit CRC under OSD of order t=3. The proposed decoding scheme for CRC-aided LDPC codes also achieves a better performance than the conventional scheme where the CRC is used to prune the list. A manageable complexity can be achieved with iterative reliability based OSD, which is demonstrated to perform well with a small OSD order.","Title: Enhancing Efficient Link Performance in ZigBee Under Cross-Technology Interference\nYear: 2020\n\nAbstract: The coexistence of heterogenous wireless networks has long been a problem especially with the explosive development in recent years. These heterogenous, such as WiFi, ZigBee and Bluetooth operating in the crowded ISM band, have to compete with each other for scarce spectrum resources, generating cross-technology interference (CTI). Since CTI may lead to severe performance degradation of ZigBee networks, especially under high density WiFi interference. In this paper, we propose a novel adaptive packet delivery (APD) algorithm that enables ZigBee links to achieve enhanced performance under the presence of heavy WiFi traffic. First, we pre-analyze the coexistence of co-located networks, we find the WiFi frame cluster well follow the power law distribution model with burst WiFi traffic, then the PER analysis is also determined to help obtain the probability of delivering packets successfully. Second, we propose a new metric called channel idle state indicator (CISI) to quantify current channel quality. Third, based on CISI and the pre-analysis, we build APD to instruct ZigBee nodes to transmit, besides, we consider the occasion where the packet contains an emergency message. Extensive experimental results show that, under the coverage of different WiFi traffic, our lightweight mechanism can achieve 4x and 1.5x performance gains over WISE and CII. Particularly, when WiFi traffic is 4 Mbps, we can still obtain over 90%. Furthermore, the energy consumption efficiency can be improved markedly with compared protocols.","Title: LaSa: Location Aware Wireless Security Access Control for IoT Systems\nYear: 2019\n\nAbstract: IoT (Internet of Things) security has become a severe yet not well solved problem attracting increasing research attention as well as industrial concerns. Location-based access control approaches, such as Wi-Fi geo-fencing, promise to fulfill the needs of preventing unauthorized access to IoT systems. We propose a crowdsourcing method for location aware security access control, namely LaSa, which is able to confine wireless network access inside certain physical areas only using a single commercial Access Point (AP). Specifically, LaSa detects whether a user enters or exits a room by discovering and recognizing the unique signal patterns. It combines the Received Signal Strength (RSS), Channel State Information (CSI), and coarse Angle of Arrival (AoA) data to improve the accuracy of user classification for accessing the wireless network. Real-world experimental results show that LaSa can achieve a 97.0% accuracy of identification of unauthorized users while maintaining a low false blocking rate of authorized users as low as 3.3%. LaSa is designed to be straightforward for integration with commercial APs and deployment to home and business Wi-Fi environments.","Title: A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG.\nYear: 2019\n\nAbstract: Reconstruction of a 12-lead electrocardiogram (ECG) from a serial 3-lead ECG has been researched in the past to satisfy the need for more wearing comfort and ambulatory situations. The accuracy and real-time performance of traditional methods need to be improved. In this study, we present a novel method based on convolutional neural networks (CNNs) for the synthesis of missing precordial leads. The results show that the proposed method receives better similarity and consumes less time using the PTB database. Particularly, the presented method shows outstanding performance in reconstructing the pathological ECG signal, which is crucial for cardiac diagnosis. Our CNN-based method is shown to be more accurate and time-saving for deployment in non-hospital situations to synthesize a standard 12-lead ECG from a reduced lead-set ECG recording. This is promising for real cardiac care.","Title: Joint graph optimization and projection learning for dimensionality reduction.\nYear: 2019\n\nAbstract: •A novel framework termed joint graph optimization and projection learning (JGOPL) is proposed for graph-based dimensionality reduction.•The l21-norm based distance measurement is adopted in the loss function of our JGOPL so that its robustness to the negative influence caused by the outliers or variations of data can be improved.•In order to well exploit and preserve the local structure information of high-dimensional data, a locality constraint is introduced into the proposed JGOPL to discourage a sample from connecting with the distant samples during graph optimization.•The locality constraint and graph optimization strategy proposed is not only limited to dimensionality reduction, but also can be incorporated into other relevant graph-based tasks.","Title: Performance and power consumption tradeoff in multimedia cloud\nYear: 2020\n\nAbstract: With the rapidly growing adoption of mobile devices, such as smartphones and tablets, the Internet multimedia traffic generated by mobile users has increased. An important problem involves providing users with satisfactory multimedia services. Multimedia cloud is emerging as a specific computing paradigm to resolve the aforementioned issue by providing quality of service (QoS) provisioning for multimedia services. Given the emergence of multimedia cloud services, studies of multi-platform multimedia information analysis are significantly supported. However, because the multimedia cloud includes limited resources, the allocation of resources to satisfy user requirements with respect to QoS guaranteed multimedia services is a challenging task. Additionally, the rapidly growing demand for multimedia services motivates cloud service providers (CSPs) to deploy a great number of servers in their data centers. However, the power consumption of the servers is a matter of increasing concern for CSPs. In this study, we investigate the problem of optimal resource allocation in a multimedia cloud by considering two server models, namely the constant-speed model and idle-speed model. Our objective involves achieving the tradeoff between response time and power consumption in the two server models. With respect to each server model, we formulate an optimization problem and solve it by employing the Lagrangian multiplier method. We present numerical results to verify the effectiveness of the proposed method. The numerical results indicate that our proposed resource allocation scheme achieves both minimal response time and power consumption when compared with the existing allocation schemes.","Title: The low-rank decomposition of correlation-enhanced superpixels for video segmentation\nYear: 2019\n\nAbstract: Low-rank decomposition (LRD) is an effective scheme to explore the affinity among superpixels in the image and video segmentation. However, the superpixel feature collected based on colour, shape, and texture may be rough, incompatible, and even conflicting if multiple features extracted in various manners are vectored and stacked straight together. It poses poor correlation, inconsistence on intra-category superpixels, and similarities on inter-category superpixels. This paper proposes a correlation-enhanced superpixel for video segmentation in the framework of LRD. Our algorithm mainly consists of two steps, feature analysis to establish the initial affinity among superpixels, followed by construction of a correlation-enhanced superpixel. This work is very helpful to perform LRD effectively and find the affinity accurately and quickly. Experiments conducted on datasets validate the proposed method. Comparisons with the state-of-the-art algorithms show higher speed and more precise in video segmentation.","Title: A High-Precision Coarse-Fine Time-To-Digital Converter With The Analog-Digital Hybrid Interpolation\nYear: 2019\n\nAbstract: An innovative high-precision coarse-fine time-to-digital converter (TDC) is presented. The TDC architecture mainly consists of a coarse counter and a delay-line-based analog-digital hybrid interpolator. In order to improve the precision, the key timing control of the interpolator is provided by a novel customized synchronizer. Comparing to the traditional delay-line-based interpolator, the proposed analog-digital hybrid interpolator using the multi-voltage sampling technique improves the resolution significantly. A prototype chip has been fabricated in a 0.35 mu m CMOS process. After the calibration and calculation based on fitting algorithms, the TDC prototype reaches a dynamic range of 512 ns and a single-channel single-shot precision of 6 ps using the external look-up table (LUT) for the interpolator.","Title: Automatic optic disc detection using low-rank representation based semi-supervised extreme learning machine\nYear: 2020\n\nAbstract: Optic disc detection plays an important role in developing automatic screening systems for diabetic retinopathy. Several supervised learning-based approaches have been proposed for optic disc detection. However, these approaches demand that the input training examples are completely labelled. Essentially, in medical image analysis, it is difficult to prepare several training samples which were given reliable class labels due to the fact that manually labelling data is very expensive. Moreover, retinal images such as complex vessels structures in the optic disc constituting nonlinear relationships in high-dimensional observation space, which cannot work well by traditional linear classifiers. In this study, a novel approach named low-rank representation based semi-supervised extreme learning machine (LRR-SSELM) is proposed for automated optic disc detection. Our model has the following advantages. First, it detects the optic disc from the viewpoint of semi-supervised learning and overcomes the problem there are small portion of labelled samples. Second, a nonlinear classifier is introduced into our model to fully explore the nonlinear data. Third, the local and global structures of original data can be greatly persevered by low-rank representation (LRR). The performance of the proposed method is validated on three publicly available databases, DIARETDB0, DIARETDB1 and Messidor. The experimental results indicate the advantages and effectiveness of the proposed approach.","Title: Bug Localization for Version Issues With Defect Patterns.\nYear: 2019\n\nAbstract: Version issues are becoming more and more prominent with the continuous development of software. Bug localization for version issues is time-consuming and labor-intensive. Although some bug localization techniques, such as those based on information retrieval (IR), have been proposed, they cannot handle these bugs very well as the version-related bugs have their own defect patterns. However, few existing works have focused on revealing these defect patterns and utilizing them for localization of bugs. To fill this gap, we propose a new approach by leveraging the version-related defect patterns to localize the version-related issues integrated with the IR technique. First, we extract version-related bugs from bug repositories and build a version-related bug repository. Given a new version-related bug report, we identify the defect patterns of corresponding similar historical bug reports from the version-related bug repository. Then, we combine these defect patterns with the IR technique to rank the candidate code snippets as suspicious code for developers to fix. The evaluation demonstrates that our approach is more effective to identify the faulty code related to version issues than the existing IR-based bug localization technique.","Title: Sensitivity-Oriented Layer-Wise Acceleration and Compression for Convolutional Neural Network.\nYear: 2019\n\nAbstract: Convolutional neural networks (CNNs) have achieved excellent performance improvement in image processing and other machine learning tasks. However, tremendous computation and memory consumption for most classical CNN models pose a great challenge to the deployment in portable and power-limited devices. In this paper, by analyzing the sensitivity of the rank in each layer of the network accuracy, we propose a sensitivity-oriented layer-wise low-rank approximation algorithm. With specific compression and acceleration requirement, the convolutional layer with higher sensitivity keeps more kernels than that with lower sensitivity. In addition, we also demonstrated that global optimization can obtain a better classification performance than layer-wise fine-tuning. The experimental results show that the proposed method can achieve 20% acceleration ratio gaining compared with the traditional rank-reducing methods. When deployed on the VGGNet-16 model, the proposed method can achieve 2.7 x compression/acceleration ratio on convolutional layers and 10.9x compression/acceleration ratio on fully connected (FC) layers with 0.05% top-1 accuracy loss and 0.01% top-5 accuracy loss.","Title: Feature-Critic Networks for Heterogeneous Domain Generalization.\nYear: 2019\n\nAbstract: The well known domain shift issue causes model performance to degrade when deployed to a new target domain with different statistics to training. Domain adaptation techniques alleviate this, but need some instances from the target domain to drive adaptation. Domain generalization is the recently topical problem of learning a model that generalizes to unseen domains out of the box, without accessing any target data. Various domain generalization approaches aim to train a domain-invariant feature extractor, typically by adding some manually designed losses. In this work, we propose a learning to learn approach, where the auxiliary loss that helps generalization is itself learned. This approach is conceptually simple and flexible, and leads to considerable improvement in robustness to domain shift. Beyond conventional domain generalization, we consider a more challenging setting of heterogeneous domain generalization, where the unseen domains do not share label space with the seen ones, and the goal is to train a feature which is useful off-the-shelf for novel data and novel categories. Experimental evaluation demonstrates that our method outperforms state-of-the-art solutions in both settings.","Title: Hierarchical Reinforcement Learning for Multi-agent MOBA Game.\nYear: 2019\n\nAbstract: Although deep reinforcement learning has achieved great success recently, there are still challenges in Real Time Strategy (RTS) games. Due to its large state and action space, as well as hidden information, RTS games require macro strategies as well as micro level manipulation to obtain satisfactory performance. In this paper, we present a novel hierarchical reinforcement learning model for mastering Multiplayer Online Battle Arena (MOBA) games, a sub-genre of RTS games. In this hierarchical framework, agents make macro strategies by imitation learning and do micromanipulations through reinforcement learning. Moreover, we propose a simple self-learning method to get better sample efficiency for reinforcement part and extract some global features by multi-target detection method in the absence of game engine or API. In 1v1 mode, our agent successfully learns to combat and defeat built-in AI with 100% win rate, and experiments show that our method can create a competitive multi-agent for a kind of mobile MOBA game King of Glory (KOG) in 5v5 mode.","Title: Gain-Flattened C+L Band Distributed Raman Amplification for MDM and WDM Transmission of Orbital Angular Momentum (OAM) Modes Over 100-km Large-Core Fiber\nYear: 2018\n\nAbstract: We experimentally demonstrate the first record C+L ultra-wideband orbital angular momentum (OAM) mode multiplexing and wavelength multiplexing transmission over 100-km large-core fiber (LCF) assisted by all-fiber OAM (de)multiplexer. Distributed Raman amplification with four pumps provides the C+L band flat gain.","Title: First Demonstration of Ultra-Low-Noise Long-Haul 110-km MDM+WDM Transmission of Orbital Angular Momentum (OAM) Modes with 1st / 2nd-Order Distributed Raman Amplifier\nYear: 2018\n\nAbstract: We experimentally demonstrate the first record long-single-span MDM+WDM transmission of 3 orbital angular momentum (OAM) modes and 22 wavelength channels in 110-km large-core fiber without using MIMO-DSP assisted by homemade all-fiber OAM (de)multiplexer and 1st-/2nd-order distributed Raman amplification.","Title: Dynamic liquid level detection method based on resonant frequency difference for oil wells\nYear: 2018\n\nAbstract: The dynamic liquid level of an oil well can be used to determine the oil production strategies and analyze the reservoir performance. Therefore, it is important to measure the dynamic liquid level in an oil field. This paper proposes a novel dynamic liquid level measurement method for oil wells, where the resonant frequency difference (RFD) of the resonant acoustic signal in annular is used to calculate the dynamic liquid level. To solve the noise interference problem in the resonant acoustic signal, a spectral fast Fourier transform (FFT) method based on Welch power spectrum is proposed to obtain the RFD. First, the Welch power spectrum approach is employed to process the resonant acoustic signal, and a high-pass filter is designed to filter the inherent envelope of low frequency in the power spectrum. In particular, a clear and smooth power spectrum can be obtained by choosing a suitable window and a sectional length. Furthermore, the short-time Fourier transform method is used to extract the strongest energy spectrum of the power spectrum. Finally, the RFD of two adjacent resonant harmonics can be accurately obtained by using FFT for the strongest energy spectrum. Experimental results show the effectiveness of the proposed approach.","Title: Halodpc: An Improved Recognition Method On Halo Node For Density Peak Clustering Algorithm\nYear: 2019\n\nAbstract: The density peaks clustering (DPC) is known as an excellent approach to detect some complicated-shaped clusters with high-dimensionality. However, it is not able to detect outliers, hub nodes and boundary nodes, or form low-density clusters. Therefore, halo is adopted to improve the performance of DPC in processing low-density nodes. This paper explores the potential reasons for adopting halos instead of low-density nodes, and proposes an improved recognition method on Halo node for Density Peak Clustering algorithm (HaloDPC). The proposed HaloDPC has improved the ability to deal with varying densities, irregular shapes, the number of clusters, outlier and hub node detection. This paper presents the advantages of the HaloDPC algorithm on several test cases.","Title: Velocity Prediction of Intelligent and Connected Vehicles for a Traffic Light Distance on the Urban Road\nYear: 2019\n\nAbstract: Accurate vehicle velocity prediction has important theoretical value and widespread applications in many areas, such as optimal control of vehicle propulsion system, eco-driving, and advanced driver assistance systems. However, for dynamic changes of traffic condition caused by traffic lights, intersections, and other factors, it is hard to predict the vehicle velocity accurately on the urban road. In this paper, we present a novel vehicle velocity prediction algorithm for intelligent and connected vehicles based on the historical driving data of the preceding vehicle and traffic light information. First, the basic driving rules on the urban road are studied in two different driving scenarios. Then, a vehicle trajectory generation algorithm (VTGA) is proposed to generate the vehiclesu0027 trajectories according to the basic driving rules. To identify vehiclesu0027 quantity and the global positioning system information of each vehicle in the unknown area, an identification algorithm (IA) is designed based on the VTGA and genetic algorithm. Finally, a vehicle velocity prediction algorithm is applied to predict the velocity of the target vehicle based on the VTGA and the results of IA. To verify the method proposed in this paper, the next generation simulation database is utilized. The results demonstrate that the accuracy of the vehicle velocity prediction has a significant improvement in the urban network, and the root-mean-square error reduces from 0.50~4.78 m/s (5~20 s) to 0.7594~0.9166 m/s (9.3~43.8 s), when compared with methods of other studies.","Title: NURBS-enhanced line integration boundary element method for 2D elasticity problems with body forces\nYear: 2019\n\nAbstract: A NURBS-enhanced boundary element method for 2D elasticity problems with body forces is proposed in this paper. The non-uniform rational B-spline (NURBS) basis functions are applied to construct the geometry and the model can be reproduced exactly at all stages since the refinement will not change the shape of the boundary. Both open curves and closed curves are considered. The fields are approximated by the traditional Lagrangian basis functions in parameter space, rather than by the same NURBS basis functions for geometry approximation. The parametric boundary elements and collocation nodes are defined from the knot vector of the curve and the refinement of the NURBS curve is easy. Boundary conditions can be imposed directly since the Lagrangian basis functions have the property of delta function. In addition, most methods for the treatment of singular integrals in traditional boundary element method can be applied in the proposed method. To overcome the difficulty for evaluation of the domain integrals in problems with body forces, a line integration method is further applied in this paper to compute the domain integrals without additional volume discretizations. Numerical examples have shown the accuracy of the proposed method.","Title: Modeling and analysis for multilayered complex communication network of satellite navigation system\nYear: 2019\n\nAbstract: AbstractSummaryThis study is extended to construct the network model, the node model, and the link model of complex communication network for satellite navigation system (CCN‐SNS) based on the hierarchical architecture. Firstly, a method called snapshots was proposed to describe the dynamic topology for CCN‐SNS; secondly, another method was put forward to model the different nodes of the CCN‐SNS; thirdly, the different links between every two different nodes were modeled. Therefore, based on the OPNET tools, a simulation for the CCN‐SNS, which contains the models that proposed earlier used to analyze the navigation accuracy and network transmission performance, was performed.This study is extended to construct the network model, the node model, and the link model of complex communication network for satellite navigation system (CCN‐SNS) based on the hierarchical architecture, and then a method called snapshots was proposed to describe the dynamic topology for CCN‐SNS. Finally, a simulation for the CCN‐SNS, which contains the models that proposed used to analyze the navigation accuracy and network transmission performance, was performed. View Figure","Title: Advanced community question answering by leveraging external knowledge and multi-task learning\nYear: 2019\n\nAbstract: Community question answering (CQA) is an important but challenging task. Meantime, as the theory of deep learning develops, remarkable progress has been made by deep neural networks. This paper studies an advanced deep neural network that not only uses external knowledge to learn better representations of questions and answers but also improves representation learning by considering question categorization as an auxiliary task. Specifically, we propose a novel Multi-task and Knowledge enhanced Multi-head Interactive Attention network for Community Question Answering (MKMIA-CQA). It contains a document modeling module responsible for utilizing external commonsense knowledge to help identify background information (entity mentions and their relations) and filter out noise information from the long text which has complicated semantic and syntactic structures. Moreover, the model is trained in a multi-task manner. It regards community question answering as the primary task and question categorization as the auxiliary task, which aims to learn a category-aware encoder and improve the quality of locating the salient information of a long question. The experimental results on three widely used CQA datasets demonstrate that our model achieves impressive results compared to other strong competitors.","Title: Multiple attribute group decision making using J-divergence and evidential reasoning theory under intuitionistic fuzzy environment\nYear: 2020\n\nAbstract: The theory of intuitionistic fuzzy sets has been proved to be an effective and convenient tool in the construction of fuzzy multiple attribute group decision-making models to deal with the uncertainty in developing complex decision support systems. Concerning this topic, the current studies mainly focus on their attention on two aspects including aggregation operators on intuitionistic fuzzy sets and determining the weights of both decision makers and attributes. However, some challenges have not been fully considered including existing aggregation operators which may induce unreasonable results in some situations and how to objectively determine the weights of both attributes and decision makers to meet different decision-making demands. To overcome the challenges of existing decision-making models and to satisfy much more decision-making situations, a novel intuitionistic fuzzy multiple attribute group decision-making method via J-divergence and evidential reasoning theory is proposed in this paper as a supplement of conventional models. On the one hand, a weighted J-divergence of intuitionistic fuzzy sets and a J-divergence between two intuitionistic fuzzy matrices are introduced. Following the two concepts, two consensus-based approaches are proposed to determine the weights of both decision makers and attributes. The weights obtained from the proposed method can more accurately reflect the importance levels of both attributes and decision makers from the perspective of consensus by comparison with existing models. On the other hand, an evidential reasoning theory-based operator is established to replace conventional operators for aggregating intuitionistic fuzzy information. The fusion result via this operator is consistent with most of intuitionistic fuzzy numbers. With these works, the proposed method can provide more accurate and reasonable decision results than existing algorithms.","Title: Fashion Recommendations through Cross-media Information Retrieval\nYear: 2019\n\nAbstract: •A novel fashion product recommendation method using multimodal information.•Integrating both text and image mining techniques.•Facilitating similar and mix-and-match fashion product recommendations.•Leveraging text-based product attributes and image features in the recommendations.","Title: The Modeling Of Interval-Valued Time Series: A Method Based On Fuzzy Set Theory And Artificial Neural Networks\nYear: 2019\n\nAbstract: Interval-valued time series (ITS) are interval-valued data that are collected in chronological order. The modeling of ITS is an ongoing issue in domain of time series analysis. This paper presents a new modeling method of ITS based on the synergy of fuzzy set theory and artificial neural networks. The proposed method involves the construction of collection of fuzzy sets describing characteristics of amplitude of ITS, the expression and reconstruction mechanism of ITS and the emergence of model of ITS based on artificial neural network (ANN). The resulting model of ITS not only supports the linguistic output but also the numeric output in interval format. A series of experimental studies is reported for two publicly available financial datasets showing different dynamic characteristics. Experimental results clearly show that the constructed ITS model has the better performance on the linguistic and numeric level.","Title: Consensus Graph Learning for Incomplete Multi-view Clustering\nYear: 2019\n\nAbstract: Multi-view data clustering is a fundamental task in current machine learning, known as multi-view clustering. Existing multi-view clustering methods mostly assume that each data instance is sampled in all views. However, in real-world applications, it is common that certain views miss number of data instances, resulting in incomplete multi-view data. This paper concerns the task of clustering of incomplete multi-view data. We propose a novel Graph-based Incomplete Multi-view Clustering (GIMC) to perform this task. GIMC can effectively construct a complete graph for each view with the help of other view(s), and automatically weight each constructed graph to learn a consensus graph, which gives the final clusters. An alternating iterative optimization algorithm is proposed to optimize the objective function. Experimental results on real-world datasets show that the proposed method outperforms state-of-the-art baseline methods markedly.","Title: Deep Ensemble Detection of Congestive Heart Failure using Short-term RR Intervals\nYear: 2019\n\nAbstract: Heart rate variability (HRV) is an effective predictor of congestive heart failure (CHF). However, important challenges exist regarding the effective temporal feature extraction and efficient classification using high-dimensional HRV representations. To solve these challenges, an ensemble method for CHF detection using short-term HRV data and deep neural networks was proposed. In this paper, five opensource databases, the BIDMC CHF database (BIDMC-CHF), CHF RR interval database (CHF-RR), MITBIH normal sinus rhythm (NSR) database, fantasia database (ED), and NSR RR interval database (NSR-RR), were used. Additionally, three RR segment length types (N = 500, 1000, and 2000) were used to evaluate the proposed method. First, we extracted the expert features of RR intervals (RRIs) and then built a long short-term memory-convolutional neural network-based network to extract deep-learning (DL) features automatically. Finally, an ensemble classifier was used for CHF detection using the above features. With blindfold validation (three CHF subjects and three normal subjects), the proposed method achieved 99.85%, 99.41%, and 99.17% accuracy on N = 500, 1000, and 2000 length RRIs, respectively, using the BIDMC-CHF, NSR, and FD databases. With blindfold validation (six CHF subjects and six normal subjects), the proposed method achieved 83.84%, 87.54%, and 85.71% accuracy on N = 500, 1000, and 2000 length RRIs, respectively, using the NSR-RR and CHF-RR ndatabases. Based on feature ranking, the significant effectiveness provided by the DL features has been proven. The results have shown that the deep ensemble method can achieve reliable CHF detection using short-term heart rate signals and enable CHF detection through intelligent hardware.","Title: Hierarchical human-like strategy for aspect-level sentiment classification with sentiment linguistic knowledge and reinforcement learning.\nYear: 2019\n\nAbstract: Aspect-level sentiment analysis is a crucial problem in fine-grained sentiment analysis, which aims to automatically predict the sentiment polarity of the specific aspect in its context. Although remarkable progress has been made by deep learning based methods, aspect-level sentiment classification in real-world remains a challenging task. The human reading cognition is rarely explored in sentiment classification, which however is able to improve the effectiveness of the sentiment classification by considering the process of reading comprehension and logical thinking. Motivated by the process of the human reading cognition that follows a hierarchical routine, we propose a novel Hierarchical Human-like strategy for Aspect-level Sentiment classification (HHAS). The model contains three major components, a sentiment-aware mutual attention module, an aspect-specific knowledge distillation module, and a reinforcement learning based re-reading module, which are consistent with the stages of the human reading cognitive process (i.e., pre-reading, active reading, and post-reading). To measure the effectiveness of HHAS, extensive experiments are conducted on three widely used datasets. Experimental results demonstrate that HHAS achieves impressive results and yields state-of-the-art results on the three datasets.","Title: Personalized Web Service Recommendation Based on QoS Prediction and Hierarchical Tensor Decomposition.\nYear: 2019\n\nAbstract: Web service recommendation based on the quality of service (QoS) is important for users to find the exact Web service among many functionally similar Web services. Although service recommendations have been recently studied, the performance of the existing ones is unsatisfactory because: 1) the current QoS predicting algorithms still experience data sparsity and cannot predict the QoS values accurately and 2) the previous approaches fail to consider the QoS variance according to the users and services' locations carefully. A Web service recommendation method based on the QoS prediction and hierarchical tensor decomposition is proposed in this paper. The method is called QoSHTD that is based on location clustering and hierarchical tensor decomposition. First, the users and services of the QoSHTD cluster into several local groups based on their location and models local and global triadic tensors for the user-service-time relationship. The hierarchical tensor decomposition is then performed on the local and global triadic tensors. Finally, the predicted QoS value through local and global tensor decomposition is combined as the missing QoS values. The comprehensive experiment shows that the proposed method achieves a high prediction accuracy and recommending quality of Web service, and can partially address data sparsity.","Title: Spectral Perturbation Meets Incomplete Multi-view Data.\nYear: 2019\n\nAbstract: Beyond existing multi-view clustering, this paper studies a more realistic clustering scenario, referred to as incomplete multi-view clustering, where a number of data instances are missing in certain views. To tackle this problem, we explore spectral perturbation theory. In this work, we show a strong link between perturbation risk bounds and incomplete multi-view clustering. That is, as the similarity matrix fed into spectral clustering is a quantity bounded in magnitude O(1), we transfer the missing problem from data to similarity and tailor a matrix completion method for incomplete similarity matrix. Moreover, we show that the minimization of perturbation risk bounds among different views maximizes the final fusion result across all views. This provides a solid fusion criteria for multi-view data. We motivate and propose a Perturbation-oriented Incomplete multi-view Clustering (PIC) method. Experimental results demonstrate the effectiveness of the proposed method.","Title: High-Performance FPGA-Based CNN Accelerator With Block-Floating-Point Arithmetic\nYear: 2019\n\nAbstract: Convolutional neural networks (CNNs) are widely used and have achieved great success in computer vision and speech processing applications. However, deploying the large-scale CNN model in the embedded system is subject to the constraints of computation and memory. An optimized block-floating-point (BFP) arithmetic is adopted in our accelerator for efficient inference of deep neural networks in this paper. The feature maps and model parameters are represented in 16-bit and 8-bit formats, respectively, in the off-chip memory, which can reduce memory and off-chip bandwidth requirements by 50% and 75% compared to the 32-bit FP counterpart. The proposed 8-bit BFP arithmetic with optimized rounding and shifting-operation-based quantization schemes improves the energy and hardware efficiency by three times. One CNN model can be deployed in our accelerator without retraining at the cost of an accuracy loss of not more than 0.12%. The proposed reconfigurable accelerator with three parallelism dimensions, ping-pong off-chip DDR3 memory access, and an optimized on-chip buffer group is implemented on the Xilinx VC709 evaluation board. Our accelerator achieves a performance of 760.83 GOP/s and 82.88 GOP/s/W under a 200-MHz working frequency, significantly outperforming previous accelerators.","Title: Ensemble learning regression for estimating unconfined compressive strength of cemented paste backfill\nYear: 2019\n\nAbstract: Though machine learning (ML) approaches have proliferated in the mechanical properties prediction of cemented paste backfill (CPB), their applications have not reached the peak potential due to the lack of more robust techniques. In the present contribution, the state-of-the-art ensemble learning method was employed for improved estimation of the unconfined compressive strength (UCS) of CPB. 126 UCS tests were conducted on two new tailings to provide an enlarged dataset. Tree-based ML approaches, namely, regression tree (RT), random forest (RF), and gradient boosting regression tree (GBRT), were chosen to be individual ML approaches. The ensemble learning framework was used to combine the optimum individual regressors by means of GBRT. 5-fold cross-validation was used as the validation method and the performance was evaluated using correlation coefficient (R). Hyper-parameters tuning was conducted using particle swarm optimization (PSO). The results show that the best training set size was 70%. PSO was robust in the hyper-parameters tuning since the R value between experimental and predicted UCS on the training set was progressively increased. The ensemble learning can be used to improve the UCS prediction of CPB. The R values between experimental and predicted UCS obtained by RT, RF, GBRT, the ensemble GBRT regressors were 0.9442, 0.9507, 0.9832, and 0.9837, respectively. The method presented in this study extends recent efforts for UCS prediction of CPB and can significantly accelerate the CPB design.","Title: Hybrid Multi-Kernel Construction of Polar Codes\nYear: 2019\n\nAbstract: A new family of multi-kernel polar codes are proposed. Instead of using identical matrices in each stage, assigning different constituent matrices to different virtual channels based on their properties will further improve polarization performance and error rate performance. Matrices with the same block-length can be classified based on Bhattacharyya parameters and a measure M (G) is proposed to compare their polarization performance. Construction framework and graph representation of hybrid multi-kernels are explicitly given. Numerical illustrations show that hybrid multi-kernels have better performance than previous multi-kernels.","Title: Construction of Replacement Set in the GC-LDPC Codes Based on BIBDs\nYear: 2019\n\nAbstract: In the GC-LDPC code, the replacement set plays an important role in the construction of the parity-check matrix H. Considering BIBDs with λ = 1, a replacement set can be structured which satisfies RC-constraint. The parity-check matrix H of the GC-LDPC code can be straightforwardly constructed from the replacement set. Simulation results show that the performance of BIBDs based CN-GC-LDPC code surpasses finite field based CN-GC-LDPC code.","Title: Mlp-Ia: Multi-Label User Profile Based On Implicit Association Labels\nYear: 2019\n\nAbstract: Multi-Label user profile is widely used and have made great contributions in the field of recommendation systems, personalized searches, etc. Current researches on multi-label user profile either ignore the associations among labels or only consider the explicit associations among them, which are not sufficient to take full advantage of the internal associations. In this paper, a new insight is presented to mine the internal correlation among implicit association labels. To take advantage of this insight, a multi-label propagation method with implicit associations (MLP-IA) is proposed to get user profile. A probability matrix is first designed to record the implicit associations and then combine the multi-label propagation method with this probability matrix to get more accurate user profile. Finally, this method proves to be convergent and faster than traditional label propagation algorithm. Experiments on six real-world datasets in Weibo show that, compared with state-of-the-art methods, our approach can accelerate the convergence and its performance is significantly better than the previous ones.","Title: Sockpuppet Detection In Social Network Via Propagation Tree\nYear: 2019\n\nAbstract: Sockpuppet detection is a valuable and challenging issue in social network. Current works are continually making efforts to detect sockpuppet based on verbal, non-verbal or network-structure features. However, they do not consider the propagation characteristic and propagation structure of sockpuppet. With our observation, the propagation trees of sockpuppet and ordinary account are different. Sockpuppet' propagation tree is evidently wider and deeper than that of the ordinary one. Based on these observations, we propose a propagation-structure based method to tackle sockpuppet detection problem. The experiment on two real-world datasets of Sina Weibo demonstrates that our method is more robust and outperforms previous methods.","Title: Improving Short Job Latency Performance in Hybrid Job Schedulers with Dice\nYear: 2019\n\nAbstract: It is common to find a mixture of both long batch jobs and latency-sensitive short jobs in enterprise data centers. Recently hybrid job schedulers emerge as attractive alternatives of conventional centralized job schedulers.\n\nIn this paper, we conduct trace-driven experiments to study the job-completion-delay performance of two representative hybrid job schedulers (Hawk and Eagle), and find that short jobs still encounter long latency issues due to fluctuating bursty nature of workloads. To this end, we propose Dice, a general performance optimization framework for hybrid job schedulers, to alleviate the high job-completion-delay problem of short jobs. Dice is composed of two simple yet effective techniques: Elastic Sizing and Opportunistic Preemption. Both Elastic Sizing and Opportunistic Preemption keep track of the task waiting times of short jobs. When the mean task waiting time of short jobs is high, Elastic Sizing dynamically and adaptively increases the short partition size to prioritize short jobs over long jobs. On the other hand, Opportunistic Preemption preempts resources from long tasks running in the general partition on demand, so as to mitigate the \"head-of-line\" blocking problem of short jobs.\n\nWe enhance the two schedulers with Dice and evaluate Dice performance improvement in our prototype implementation. Experiment results show that Dice achieves 50.9%, 54.5%, and 43.5% improvement on 50th-percentile, 75th-percentile, and 90th-percentile job completion delays of short jobs in Hawk respectively, as well as 33.2%, 74.1%, and 85.3% improvement on those in Eagle respectively under the Google trace, at low performance costs to long jobs.","Title: Eirene: Improving Short Job Latency Performance with Coordinated Cold Data Migration and Scheduler-Aware Task Cloning\nYear: 2019\n\nAbstract: In large-scale enterprise data centers for big data analytics, long batched jobs and short interactive jobs are usually mixed. Hybrid job schedulers, consisting of one centralized scheduler for long jobs and multiple distributed schedulers for short jobs, have become a promising alternative because they can significantly shorten latencies of short jobs via independent and parallelized assignment of short tasks by distributed schedulers and lower chances of head-of-line blocking via a number of performance optimization techniques.However, short jobs are still facing long job latencies under hybrid job schedulers due to workload fluctuation and straggler task problem. In this paper, we propose Eirene to optimize the latency performance of short jobs via two schemes tightly coupled into the general architecture of hybrid job schedulers. Coordinated Cold Data Migration leverages high task waiting time of short jobs under heavily-loaded periods and migrates cold data from disks to local memory for the initial phase of reading input so as to shorten task runtime and queueing time. On the other hand, Scheduler-Aware Task Cloning exploits spare computing resources under lightly-loaded periods and performs proactive task cloning for short jobs to mitigate the straggler problem.We implement a prototype of Eirene based on Eagle, a state-of-the-art hybrid job scheduler. Experimental results show that, under heavy loads, Eirene is able to improve 50-percentile (P50), 75-percentile (P75), 90-percentile (P90) latency performance of short jobs by up to 44.4%, 80.3%, 84.1% respectively compared with Eagle under the Facebook trace with a cluster of 50000 nodes.","Title: Yet another approach to understanding news event evolution\nYear: 2020\n\nAbstract: With information explosion on the Internet, only returning ranked documents by search engines cannot satisfy people’s requirements on news events understanding. A more intelligent news events search engine should not only retrieve all related documents about a specific event, but also provide a global view about how the event originates and evolves. In order to solve this challenge, two tasks, event news retrieval and eventline generation should be processed. For event news retrieval, existing approaches mainly focus on the document-level similarity to retrieve related news documents, while external knowledge is not effectively taken into consideration. To this end, we propose a similarity model named Event-Oriented Similarity combining the document-level with the knowledge-level similarity to retrieve news documents related to the specific event. For eventline generation, in order to outline the event structure more accurately, we construct an Event-Oriented Similarity Graph to represent the relationship among retrieved event news documents and develop a community detection algorithm to segment sub-events which are consequently chained into a cohesive eventline. Experimental results on real-world datasets demonstrate that the proposed approach outperforms existing methods.","Title: Challenging distributional models with a conceptual network of philosophical terms\nYear: 2021\n\nAbstract: Computational linguistic research on language change through distributional semantic (DS) models has inspired researchers from fields such as philosophy and literary studies, who use these methods for the exploration and comparison of comparatively small datasets traditionally analyzed by close reading. Research on methods for small data is still in early stages and it is not clear which methods achieve the best results. We investigate the possibilities and limitations of using distributional semantic models for analyzing philosophical data by means of a realistic use-case. We provide a ground truth for evaluation created by philosophy experts and a blueprint for using DS models in a sound methodological setup. We compare three methods for creating specialized models from small datasets. Though the models do not perform well enough to directly support philosophers yet, we find that models designed for small data yield promising directions for future work.","Title: Server Configuration Optimization In Mobile Edge Computing: A Cost-Performance Tradeoff Perspective\nYear: 2021\n\nAbstract: Before service providers build up an mobile edge computing (MEC) platform, an important issue that needs to be considered is the configuration of computing resources on edge servers. Since the computing resources on an edge server are limited compared with a cloud server and the service provider's deployment budget is limited, it would be unrealistic to equip all edge servers with abundant computing resources. In addition, the edge servers have different computation demands due to their different geographies. Therefore, this article investigates the problem of server configuration optimization in an MEC environment based on a given computation demand statistics of the selected deployment locations. Our strategy is to treat each edge server as an M/M/m queueing model, and then establish the performance and cost models for the system. Two optimization problems, including cost constrained performance optimization, and performance constrained cost optimization are formulated based on our models and solved by a series of fast numerical algorithms. We also conduct extensive numerical simulation examples to show the effectiveness of the proposed algorithms. MEC service providers can use our strategy to get the appropriate type of processor and obtain the optimal processor number for each edge server to achieve two different goals: (1) deliver the highest-quality services with a given cost constraint; (2) minimize the investment cost with a service-quality guarantee. Our research is of great significance for service providers to control the tradeoff between investment cost and service quality.","Title: Bregman linearized reweighted alternating minimization for robust sparse recovery\nYear: 2021\n\nAbstract: AbstractHighlights •We propose a new algorithm dubbed Bregman linearized reweighted alternating minimization with continuation (BLRAMC) to solve a class of nonconvex and nonsmooth optimization problems for robust sparse recovery.•By leveraging the linearized techniques, all subproblems of alternating minimization formulated can be solved in closed-form. The convergence of the proposed algorithm is further improved by using Bregman distance and continuation technique.•With general assumptions of the cost function, the convergence analysis of the proposed algorithm is provided, which indicates that a stationary point of the problem can be sought out. AbstractIn this paper, we consider a class of nonconvex and nonsmooth minimization problems with linear constraints in the area of compressed sensing (CS). The classical alternating minimization (AM) had difficulties in solving formulated nonconvex or nonsmooth subproblems. In view of this problem, we propose a Bregman linearized reweighted alternating minimization with continuation (BLRAMC). In this algorithm, all subproblems are convex and can be thus solved efficiently. Moreover, with the introduced Kurdyka-Łojasiewicz property, it is proved that the proposed algorithm converges to a stationary point of the objective function. Numerical experiments for the robust sparse recovery with different impulsive noise are presented to substantiate the effectiveness of the proposed algorithm.","Title: A Novel Adaptive Density-Based Spatial Clustering Of Application With Noise Based On Bird Swarm Optimization Algorithm\nYear: 2021\n\nAbstract: The commonly used density-based spatial clustering method (DBSCAN) connects contiguous regions with sufficiently large densities when processing datasets to efficiently discover clusters of different shapes and densities and outliers. However, the algorithm has the problem that radius of neighborhood (Eps) argument requires to be selected manually. For datasets with higher dimensionality and larger data volume, the selection of Eps parameters can be difficult thus leading to poor clustering quality. To solve the above problem, we propose a novel adaptive density-based spatial clustering of application with noise based on bird swarm optimization algorithm (BSA-DBSCAN). We use the global search capability of the bird swarm method to select the best Eps parameter neighborhood values. We can avoid manual intervention and realize adaptive parameter optimization in the clustering process. To further explore the clustering performance of BSA-DBSCAN method, we test the synthetic datasets and the real-world datasets respectively and perform images analysis on the clustering evaluation index values. The simulation experiments show that the improved method in this paper can reasonably search the Eps parameter value and can obtain the higher accuracy of clustering.","Title: Model For Snow-Fighting Vehicle Route Planning Considering Deadheading Restriction\nYear: 2018\n\nAbstract: Snow(1) removal is an important job for urban traffic authority to keep urban traffic smooth and safe, particularly for cities in cold regions. Snow-fighting vehicle routing problem, for mechanically removing measure, is an arc routing problem. However, the deadheading problem, in which vehicles may travel on a road covered by snow without serving it, is unreasonable and increasing the cost. Therefore, an optimization model is constructed to restrict the deadheading problem, which allows deadheading only after the road has been serviced. Also, a two-stage serial algorithm, which is first-clustering-then-routing, is proposed to solve the optimization model. A numerical example is conducted subsequently. Results show that the efficiency of snow removal drops as demand rises and most of the snow is removed in the front of the whole time. Besides, three alternative objective functions are analyzed and the outcome indicates taking minimal makespan as the objective function is better in most instances.","Title: GGP: A Graph-based Grouping Planner for Explicit Control of Long Text Generation\nYear: 2021\n\nAbstract: BSTRACTExisting data-driven methods can well handle short text generation. However, when applied to the long-text generation scenarios such as story generation or advertising text generation in the commercial scenario, these methods may generate illogical and uncontrollable texts. To address these aforementioned issues, we propose a graph-based grouping planner~(GGP) following the idea of first-plan-then-generate. Specifically, given a collection of key phrases, GGP firstly encodes these phrases into a instance-level sequential representation and a corpus-level graph-based representation separately. With these two synergic representations, we then regroup these phrases into a fine-grained plan, based on which we generate the final long text. We conduct our experiments on three long text generation datasets and the experimental results reveal that GGP significantly outperforms baselines, which proves that GGP can control the long text generation with knowing how to say and in what order.","Title: Safety Helmet Detection Algorithm based on Color and HOG Features\nYear: 2020\n\nAbstract: There are many unstable factors in the working environment of electrical workers, which threaten their safety. Therefore, how to protect electrical workers is a problem worth thinking about. Safety helmet protects worker’s head from injury when they fall. This paper presents a method to detect whether electrical workers wear a safety helmet or not. This method is based on Support Vector Machine (S...","Title: Pen4rec: Preference Evolution Networks For Session-Based Recommendation\nYear: 2021\n\nAbstract: Session-based recommendation aims to predict user the next action based on historical behaviors in an anonymous session. For better recommendations, it is vital to capture user preferences as well as their dynamics. Besides, user preferences evolve over time dynamically and each preference has its own evolving track. However, most previous works neglect the evolving trend of preferences and can be easily disturbed by the effect of preference drifting. In this paper, we propose a novel Preference Evolution Networks for session-based Recommendation (PEN4Rec) to model preference evolving process by a two-stage retrieval from historical contexts. Specifically, the first-stage process integrates relevant behaviors according to recent items. Then, the second-stage process models the preference evolving trajectory over time dynamically and infer rich preferences. The process can strengthen the effect of relevant sequential behaviors during the preference evolution and weaken the disturbance from preference drifting. Extensive experiments on three public datasets demonstrate the effectiveness and superiority of the proposed model.","Title: Ensemblefool: A Method To Generate Adversarial Examples Based On Model Fusion Strategy\nYear: 2021\n\nAbstract: Deep neural networks have been shown vulnerable to adversarial attacks launched by adversarial examples. These examples' transferability makes an attack in the real-world feasible, which poses a security threat to deep learning. Considering the limited representation capacity of a single deep model, the transferability of an adversarial example generated by a single attack model would cause the failure of attacking other different models. In this paper, we propose a new adversarial attack method, named EnsembleFool, which flexibly integrates multiple models to enhance adversarial examples' transferability. Specifically, the model confidence concerning an input example reveals the risk of a successful attack. In an iterative attacking case, the result of a previous attack could guide us to enforce a new attack that possesses a higher probability of success. Regarding this, we design a series of integration strategies to improve the adversarial examples in each iteration. Extensive experiments on ImageNet indicate that the proposed method has superior attack performance and transferability than state-of-the-art methods. (c) 2021 Elsevier Ltd. All rights reserved.","Title: MeLL: Large-scale Extensible User Intent Classification for Dialogue Systems with Meta Lifelong Learning\nYear: 2021\n\nAbstract: ABSTRACTUser intent detection is vital for understanding their demands in dialogue systems. Although the User Intent Classification (UIC) task has been widely studied, for large-scale industrial applications, the task is still challenging. This is because user inputs in distinct domains may have different text distributions and target intent sets. When the underlying application evolves, new UIC tasks continuously emerge in a large quantity. Hence, it is crucial to develop a framework for large-scale extensible UIC that continuously fits new tasks and avoids catastrophic forgetting with an acceptable parameter growth rate. In this paper, we introduce the Meta Lifelong Learning (MeLL) framework to address this task. In MeLL, a BERT-based text encoder is employed to learn robust text representations across tasks, which is slowly updated for lifelong learning. We design global and local memory networks to capture the cross-task prototype representations of different classes, treated as the meta-learner quickly adapted to different tasks. Additionally, the Least Recently Used replacement policy is applied to manage the global memory such that the model size does not explode through time. Finally, each UIC task has its own task-specific output layer, with the attentive summarization of various features. We have conducted extensive experiments on both open-source and real industry datasets. Results show that MeLL improves the performance compared with strong baselines and also reduces the number of total parameters. We have also deployed MeLL on a real-world e-commerce dialogue system AliMe and observed significant improvements in terms of both F1 and the resources usage.","Title: Effect Of Scan Line Spacing On Laser-Reduced Graphene Oxide Based Temperature Sensing\nYear: 2021\n\nAbstract: This paper systematically studies the effect of scan line spacing on the sensitivity of laser-reduced graphene oxide based temperature sensor. As for the fabrication, we firstly sputter and pattern a pair of 80 nm thick gold electrodes on PET film, and then coat a layer of graphene oxide solution in the middle of the electrode. Second, graphene oxide is reduced by UV laser scanning to bridge the electrodes with the laser-reduced graphene oxide, which is a temperature sensitive material with temperature-dependent resistivity. A group of rGO temperature sensors are fabricated in this way with different scan line spacings ranging from 0.02 to 0.12 mm with a step of 0.02 mm. Raman spectroscopy analysis shows that the defects of graphene first decrease and then increase with increasing scanning spacing. The temperature calibration results show that defects of graphene generally enhance the sensitivity of rGO temperature sensor, and the larger sensitivity is achieved with a scan line spacing of 0.02 mm and 0.12 mm.","Title: A Robotic Dynamic Tactile Sensing System Based On Electronic Skin\nYear: 2021\n\nAbstract: Robot tactile is getting more and more practical with the rapid development of electronic skin (e-skin). In this work, we propose a dynamic tactile sensing system based on e-skin, a high-density array tactile sensor. The system is comprised of three algorithms to track motion and detect slip. With real-time slip detecting, an adaptive controller that can rapidly tune applied grasping force without having a priori knowledge of the objects was designed and experimentally validated.","Title: Exploring Misclassification Information For Fine-Grained Image Classification\nYear: 2021\n\nAbstract: Fine-grained image classification is a hot topic that has been widely studied recently. Many fine-grained image classification methods ignore misclassification information, which is important to improve classification accuracy. To make use of misclassification information, in this paper, we propose a novel fine-grained image classification method by exploring the misclassification information (FGMI) of prelearned models. For each class, we harvest the confusion information from several prelearned fine-grained image classification models. For one particular class, we select a number of classes which are likely to be misclassified with this class. The images of selected classes are then used to train classifiers. In this way, we can reduce the influence of irrelevant images to some extent. We use the misclassification information for all the classes by training a number of confusion classifiers. The outputs of these trained classifiers are combined to represent images and produce classifications. To evaluate the effectiveness of the proposed FGMI method, we conduct fine-grained classification experiments on several public image datasets. Experimental results prove the usefulness of the proposed method.","Title: TOPIC SEQUENCE EMBEDDING FOR USER IDENTITY LINKAGE FROM HETEROGENEOUS BEHAVIOR DATA\nYear: 2021\n\nAbstract: In social media, user identity linkage is a vital information security issue of identifying users' private information across multiple online social networks. With the popularity of behavior-rich social services, existing methods attempt to align users through encoding behaviors. However, most of the efforts suffer from the high variety and heterogeneity of behavior data across social networks, resulting in a limitation of modeling user intrinsic characteristics. To address the above issues, we focus on keyword-based topics to formulate user's variety behaviors for user identity linkage. In this paper, a novel Topic Sequence Embedding (TSeqE) method is proposed to embed contextual information of topics to represent users' intrinsic characteristics for identity linkage. Furthermore, we introduce a domain-adversarial training strategy to tackle the behavior heterogeneity problem. Our experiments on two real-world datasets demonstrate that TSeqE produces a significant improvement compared with several strong baselines.","Title: Weighted Likelihood Cfar Detection For Weibull Background\nYear: 2021\n\nAbstract: In modern radar detection systems, constant false alarm rate (CFAR) control is a key technique for automatic target detection in clutter level unknown environments. The maximum likelihood criterion is often used to design the CFAR detector for which it has the best detection performance with the expected probability of false alarm. However, the heterogeneous environments, viz., multiple-target scenario and clutter power transition, will deteriorate the performance of the optimal CFAR detector. In this paper, we propose a novel CFAR detector for Weibull background with known shape parameter. The new CFAR detector is based on robust weighted likelihood estimator, with robustness to interferences. In addition, to prove the CFAR property of it, we introduce the invariant theory. Computation analysis and simulation results verify the effectiveness and superiority of our proposed CFAR detector. (C) 2021 Elsevier Inc. All rights reserved.","Title: Online Koopman Operator Learning to Identify Cross-Coupling Effect of Piezoelectric Tube Scanners in Atomic Force Microscopes\nYear: 2022\n\nAbstract: As one of the most significant applications of nanopositioning technology, the tremendous development of atomic force microscopes (AFMs) has been witnessed these years. Essentially, the scanning motions of AFMs are generally driven by piezoelectric tube scanners (PTSs), whose cross-coupling effect hinders their high speed and high-precision positioning. Therefore, it becomes an urgent yet challeng...","Title: Complex Dynamical Behavior And Numerical Simulation Of A Cournot-Bertrand Duopoly Game With Heterogeneous Players\nYear: 2021\n\nAbstract: In this paper, we consider the Cournot-Bertrand duopoly mixed competition model, which is characterized by different decision-making variables and different objective functions of the two enterprises. This form of competition is more consistent with the complex actual economic market. The general stability conditions of the four equilibria are given and analyzed with eigenvalues and Jury criterion, so that enterprise decision makers could choose appropriate parameters to determine the development of the enterprise. Under the specific parameter conditions, the stability conditions are analyzed and demonstrated in detail by using two-dimensional bifurcation diagrams, stable region diagrams and bifurcation curves. Many fractal Arnold's tongues are found in two-dimensional bifurcation diagrams. These tongues are associated with the Neimark-Sacker bifurcation of the fixed point and are arranged in accordance with the organization law of the periodic tree of the SternBrocot tree, which is helpful for us to analyze the transitions of system between period and chaos. With the help of the basin of attraction, the coexistence of attractors is analyzed, on which the decision maker can choose the initial conditions that are beneficial to the development of the enterprise. We also analyze the topology structure of basin of attraction and the formation mechanism of holes in the basin of attraction by using the theory of critical curve and noninvertible map. (C) 2021 Elsevier B.V. All rights reserved.","Title: Msset: A High-Performance Time-Frequency Analysis Method For Sparse-Spectrum Biomedical Signal\nYear: 2021\n\nAbstract: When processing sparse-spectrum biomedical signals, traditional time-frequency (TF) analysis methods are faced with the defects of blurry energy concentration and low TF resolution caused by the Heisenberg uncertainty principle. The synchrosqueezing-based methods have demonstrated advanced TF performances in recent studies. However, these methods contain at least three drawbacks: (1) existence of non-reassigned points (NRPs), (2) low noise robustness, and (3) low amplitude accuracy. In this study, the novel TF method, termed multisynchrosqueezing extracting transform (MSSET), is proposed to address these limitations. The proposed MSSET is divided into three steps. First, multisynchrosqueezing transform (MSST) is performed with specific iterations. Second, a synch-extracting is applied to retain the TF distribution of MSST results that relate most to time-varying information of the raw signal; meanwhile, the other smeared TF energy is discarded. Finally, the MSSET result is obtained by rounding the adjacent results at the frequency plane. Numerical verification results show that the proposed MSSET method can effectively solve the NRPs problem and enhance noise robustness. Furthermore, while retaining superior energy concentration and signal reconstruction capability, the MSSET's amplitude accuracy reaches about 90%, significantly higher than other methods. In the same conditions, the MSSET even consumes less time than MSST and IMSST. It also achieves the best composite performance with the least amplitude accuracy-time cost ratio (ATCR) index. Actual application examples in the Bat signal and the electrocardiograph (ECG) signal also validate the excellent performances of our method. To conclude, our proposed MSSET is superior to state-of-the-art methods and is expected to be widely used in the sparse-spectrum biomedical signal.","Title: Sparse Bayesian Learning Based on Collaborative Neurodynamic Optimization\nYear: 2022\n\nAbstract: Regression in a sparse Bayesian learning (SBL) framework is usually formulated as a global optimization problem with a nonconvex objective function and solved in a majorization–minimization framework where the solution quality and consistency depend heavily on the initial values of the used algorithm. In view of the shortcomings, this article presents an SBL algorithm based on collaborative neurodynamic optimization (CNO) for searching global optimal solutions to the global optimization problem. The CNO system consists of a population of recurrent neural networks (RNNs) where each RNN is convergent to a local optimum to the global optimization problem. Reinitialized repetitively via particle swarm optimization with exchanged local optima information, the RNNs iteratively improve their searching performance until reaching global convergence. The proposed CNO-based SBL algorithm is almost surely convergent to a global optimal solution to the formulated global optimization problem. Two applications with experimental results on sparse signal reconstruction and partial differential equation identification are elaborated to substantiate the superiority and efficacy of the proposed method in terms of solution optimality and consistency.","Title: A Sparse Dimensionality Reduction Approach Based on False Nearest Neighbors for Nonlinear Fault Detection\nYear: 2021\n\nAbstract: As a newly emerging multivariate statistical process monitoring method, non-negative matrix factorization (NMF) and its variants avoid the positive and negative cancellations between the extracted features because of the purely additive combination of non-negative components. Thus, they make a good match with this reality that the negative values of both observations and decomposed components are ...","Title: Bifurcation Analysis Of A Bounded Rational Duopoly Game With Consumer Surplus\nYear: 2021\n\nAbstract: The nonlinear dynamical behaviors of economic models have been extensively examined and still represented a great challenge for economists in recent and future years. A proposed boundedly rational game incorporating consumer surplus is introduced. This paper aims at studying stability and bifurcation types of the presented model. The flip and Neimark-Sacker bifurcations are analyzed via applying the normal form theory and the center manifold theorem. This study helps determine an appropriate choice of decision parameters which have significant influences on the behavior of the game. The duopoly game that is formed by considering bounded rationality and consumer surplus is more realistic than the ordinary duopoly game which only has profit maximization. And then, some numerical simulations are provided to verify the theoretical analysis. Finally, we compare the dynamical behaviors of the built model with that of Bischi-Naimzada model so as to better understand the performance of the duopoly game with consumer surplus.","Title: Research On Design, Calibration And Real-Time Image Expansion Technology Of Unmanned System Variable-Scale Panoramic Vision System\nYear: 2021\n\nAbstract: This paper summarized the research status, imaging model, systems calibration, distortion correction, and panoramic expansion of panoramic vision systems, pointed out the existing problems and put forward the prospect of future research. According to the research status of panoramic vision systems, a panoramic vision system with single viewpoint of refraction and reflection is designed. The systems had the characteristics of fast acquisition, low manufacturing cost, fixed single-view imaging, integrated imaging, and automatic switching depth of field. Based on these systems, an improved nonlinear optimization polynomial fitting method is proposed to calibrate the monocular HOVS, and the binocular HOVS is calibrated with the Aruco label. This method not only improves the robustness of the calibration results, but also simplifies the calibration process. Finally, a real-time method of panoramic map of multi-function vehicle based on vcam is proposed.","Title: Pooling Attention-based Encoder–Decoder Network for semantic segmentation\nYear: 2021\n\nAbstract: AbstractAbstractAiming to the challenge of poor pixel-consistency in inter-category and pixel-similarity in inter-category, in this paper, we propose an Encoder–Decoder network for image semantic segmentation using pooling SE-ResNet attention module, called PAEDN. It is an effective of attention mechanism to get aggregated information. According to the principle of SE-ResNet, a collection of Average, Maximum and Stochastic global pooling, which concentrate on contoured, detailed, and generalized information in a certain semantic segmentation, form attention modules. Channel Pooling Attention Module (CPAM) and Position Pooling Attention Module (PPAM) are designed and integrated into the Encoder to extract discriminative features from input images, and the Decoder is developed through SE-ResNet attention module to fuse the feature map in high-resolution with that in low-resolution. Experimental evaluations performed on the data sets PASCAL and Cityscapes, show the proposed Encoder–Decoder with pooling attention module produces good pixel-consistency semantic label, achieves 15.1% improvement to FCN.Graphical abstractDisplay OmittedHighlights •The encoder and decoder work together to enhance the consistency of pixels.•The work uses maximum, average and stochastic pooling to gain contoured, detailed and generalized information.•Two attention modules use pooling operations to integrate discriminative feature information.","Title: Social Network And Bibliometric Analysis Of Unmanned Aerial Vehicle Remote Sensing Applications From 2010 To 2021\nYear: 2021\n\nAbstract: Unmanned Aerial Vehicle (UAV) Remote sensing (RS) has unique advantages over traditional satellite RS, including convenience, high resolution, affordability and fast acquisition speed, making it widely used in many fields. To provide an overview of the development of UAV RS applications during the past decade, we screened related publications from the Web of Science core database from 2010 to 2021, built co-author networks, a discipline interaction network, a keywords timeline view, a co-citation cluster, and detected burst citations using bibliometrics and social network analysis. Our results show that: (1) The number of UAV RS publications had an increasing trend, with explosive growth in the past five years. The number of papers published by China and the United States (US) is far ahead in this field; (2) The US has currently the greatest influence in this field through the largest number of international cooperations. Cooperation is mainly concentrated in countries and institutions with a large number of publications but is not widely distributed. (3) The application of UAV RS involves multiple interdisciplinary subjects, among which \"Environmental Science and Ecology\" ranks first; (4) Future research trends of UAV RS are expected to be related to artificial intelligence (e.g., artificial neural networks-based research). This paper provides a scientific basis and guidance for future developments of UAV RS applications, which can help the research community to better grasp the developments of this field.","Title: Multi-Metric Fusion Network for Image Quality Assessment\nYear: 2021\n\nAbstract: With the fast proliferation of multimedia applications, the reliable prediction of image/video quality is urgently needed. Many quality assessment metrics have been proposed in the past decades with various complexity and consistency with human ratings. The metrics are designed from different aspects, e.g., pixel level fidelity, structural similarity, information theory and data-driven. In this paper, we design a Multi-Metric Fusion Network (MMFN) for aggregating the quality scores predicted by diverse metrics to generate more accurate results. To be specific, we utilize the image features extracted from the pretrained network to adaptively rescale the predicted quality from different metrics, and leverage the fully-connected layers to regress a single scalar as the final score. Pairwise images can be further integrated into the training procedure by adding a Score2Prob layer. Experimental results on the validation and test sets demonstrate that our proposed MMFN achieves better prediction accuracy compared with other metrics.","Title: AliMe MKG: A Multi-modal Knowledge Graph for Live-streaming E-commerce\nYear: 2021\n\nAbstract: ABSTRACTLive streaming is becoming an increasingly popular trend of sales in E-commerce. The core of live-streaming sales is to encourage customers to purchase in an online broadcasting room. To enable customers to better understand a product without jumping out, we propose AliMe MKG, a multi-modal knowledge graph that aims at providing a cognitive profile for products, through which customers are able to seek information about and understand a product. Based on the MKG, we build an online live assistant that highlights product search, product exhibition and question answering, allowing customers to skim over item list, view item details, and ask item-related questions. Our system has been launched online in the Taobao app, and currently serves hundreds of thousands of customers per day.","Title: Dynamic Weighted \"Small-World\" Graphical Network Establishment For Fnirs Time-Varying Brain Function Analysis\nYear: 2021\n\nAbstract: With the increasing application of functional near-infrared spectroscopy (fNIRS) technology, topological brain network analysis has recently been developed and successfully applied in fNIRS-based brain research. However, the current network information is analyzed through the binary network, and it lacks a dynamic estimation. In this study, we proposed a novel dynamic weighted \"small-world\" topology network and applied it to fNIRS analysis. Firstly, we introduced a novel flat-top (FT) window to estimate sliding-window correlation (SWC), improving the spectrum performances. And then, we applied the least absolute shrinkage and selection operator (LASSO) algorithm to the time-varying correlation matrix and obtained corresponding sparse matrix results. Finally, we established a dynamic weighted topological graph and calculated \"small-world\" network parameters to analyze the brain network dynamics. Simulation results showed that our proposed FT-based SWC method realized wider bandwidth, better spectrum performance, and more accurate dynamic tracking capability with the minimum mean square errors(MSEs) results in each iterative simulation. The fNIRS results showed that the average node degree (DE) and global efficiency (GE) reached a peak in the middle and late periods of the task. The network parameters showed similar changes in 2-back and 3-back tasks, which differ from the 0-back task. Statistical analysis revealed the DE and GE of the subnetwork decreased as the task became more difficult. These results revealed the typical time-varying characteristics of the brain network dynamics during the working memory process, which were expected to unveil the brain mechanisms underlying the cognitive neural activity in greater depth.","Title: Assessment And Selection Of Smart Agriculture Solutions Using An Information Error-Based Pythagorean Fuzzy Cloud Algorithm\nYear: 2021\n\nAbstract: Smart agriculture can enhance agricultural production efficiency, improve the ecological environment, and realize the sustainable development of agriculture. Many countries and companies are working hard to develop or introduce smart agricultural solutions. Because of the shackles of traditional agricultural management methods and fierce competition with a variety of different solutions, it is a difficult task for enterprises to select and implement smart agricultural solutions smoothly. Hence, enterprises must assess alternative solutions and select a feasible solution in advance. This study drew a novel assessment and selection for smart agriculture solutions using an information error-based Pythagorean fuzzy cloud algorithm. First, an evaluation index system built on smart agriculture solutions was constructed from four aspects. Then, a new concept of Pythagorean fuzzy clouds was defined to express the evaluation information for each indicator. Simultaneously, the Pythagorean fuzzy cloud weighted Bonferroni mean (PFCWBM) operator was developed to aggregate the assessment information of multiple indicators. Next, an assessment and selection decision framework for smart agriculture solutions based on the PFCWBM operator was presented. In addition, an example was given to illustrate the effectiveness of the proposed algorithm. Finally, a discussion was conducted to verify the superiority of our approach. The results showed that our algorithm can characterize and evaluate complex information and has high sensitivity and environmental adaptability.","Title: Spatial-Temporal Dynamic Graph Convolution Neural Network for Air Quality Prediction\nYear: 2021\n\nAbstract: Air quality prediction has received widespread attention from both the governments and citizens due to its close relation to our lives. Analyzing the spatial relations and temporal trends in air quality data is essential for air quality prediction task. However, most existing approaches require a pre-defined graph structure to capture the spatial dependencies of air quality data, and thus they can not be applied when a well-defined graph structure is unavailable. Besides, those methods do not give sufficient consideration to the latent relationships among entities of the graph over time. To overcome the above limitations, we propose a Spatial-Temporal Dynamic Graph Convolution Neural Network (ST-DGCN) in this paper. Our approach develops a dynamic adjacency matrix into graph convolution layer, which extracts the potential and time-varying spatial dependencies. To jointly model the spatial and temporal correlations, we combine dynamic graph convolution with gated recurrent unit and propose a unified DGC-GRU block. Next, a residual operation is further introduced into the DGC-GRU to simultaneously handle the information from different particles. Experimental results demonstrate that the proposed method outperforms the state-of-art baselines on two real-world air quality datasets.","Title: Hybrid-TC: A Hybrid Teaching-Learning-Based Optimization Algorithm for Service Composition in Cloud Manufacturing\nYear: 2021\n\nAbstract: During the process of cloud manufacturing, service composition is an essential technology to ensure the smooth execution of the task. An important challenge for cloud manufacturing is how to implement effective and accurate service composition strategy, which are to execute the task effectively while also satisfy the requirements of user by maximizing the overall quality of service (QoS). However, the current cloud manufacturing service composition methods generally have the problem of poor solution quality in the large scale environment. To solve this problem, we propose a hybrid optimization algorithm, named hybrid-TC, which is a hybrid of the teaching-learningbased optimization algorithm (TLBO) and the crisscross optimization algorithm (CSO). First, we added Skyline query in the initialization phase to improve the convergence speed and the quality of the solution. Second, the horizontal crossover of CSO is used in the teaching phase of original TLBO, thereby some dimensions in the population that trapped in the local optimum have the chance to jump out of the iteration. Finally, the offspring individuals learned in the teaching-phase will continue to learn to improve the quality of the solution. Experiments show that our proposed method is effective for improving the solution quality of large-scale environmental service composition.","Title: On Language Model Integration for RNN Transducer Based Speech Recognition.\nYear: 2022\n\nAbstract: The mismatch between an external language model (LM) and the implicitly learned internal LM (ILM) of RNN-Transducer (RNN-T) can limit the performance of LM integration such as simple shallow fusion. A Bayesian interpretation suggests to remove this sequence prior as ILM correction. In this work, we study various ILM correction-based LM integration methods formulated in a common RNN-T framework. We provide a decoding interpretation on two major reasons for performance improvement with ILM correction, which is further experimentally verified with detailed analysis. We also propose an exact-ILM training framework by extending the proof given in the hybrid autoregressive transducer, which enables a theoretical justification for other ILM approaches. Systematic comparison is conducted for both in-domain and cross-domain evaluation on the Librispeech and TED-LIUM Release 2 corpora, respectively. Our proposed exact-ILM training can further improve the best ILM method.","Title: Research On Integral Separation Control Of Warp Tension Based On Fuzzy Parameter Optimization\nYear: 2021\n\nAbstract: In textile machines, the stability of warp tension is one of the decisive factors for the reliability, stability and product quality of weaving process. In order to meet the improving requirement for weaving efficiency and fabric quality, it is proposed that a fuzzy optimization integral separation PID warp tension control scheme based on process sampling to improve the warp tension control level of loom Aiming at the problems of time-varying, nonlinear and variable coupling in the warp tension control system of loom, the forming mechanism of warp tension is modeled and analyzed, and the sampling scheme of warp tension based on process is proposed. Based on the periodic change of warp tension at macro level and continuous fluctuation at micro level, the integral separation control and fuzzy optimization theory are introduced to optimize the control effect of the control system on the basis of classical PID control algorithm. Finally, the simulation and experiment show that the scheme can improve the tension controls performance and effectively reduce the tension error fluctuation.","Title: Deep Progressive Attention For Person Re-Identification\nYear: 2021\n\nAbstract: Person re-identification (Re-ID) aims to retrieve specific individuals across non overlapping camera views. In recent years, attention-based models contribute to many computer vision tasks due to their great ability for learning discriminative features. We propose the deep progressive attention (DPA) in a more natural manner for person Re-ID. Similar to human visual attention mechanism, the proposed DPA progressively selects the most discriminative parts of a specific individual and formulates feature representation for comparison purpose. Concretely, on the one hand, the proposed DPA uses a long-term reward to optimize the discriminative feature selection. On the other hand, a deep convolutional architecture is integrated into a recurrent model for feature representation learning. Extensive experiments on three person Re-ID benchmarks Market-1501, DukeMTMC-reID, and CUHK03-NP demonstrate the proposed DPA is on par with the state-of-the-art. Moreover, the experiments on partial person Re-ID datasets indicate the proposed DPA is competitive with the specially designed partial person Re-ID methods. (c) 2021 SPIE and IS&T [DOI: 10.1117/1.JEI.30.4.043028]","Title: Research Overview on Carbon Emission Models\nYear: 2021\n\nAbstract: Based on the literatures of current research on carbon emission models in China and abroad, this paper uses the bibliometrics statistical method to investigate the data driven carbon emission research literatures published in CNKI and SCI from 2011 to 2020. In this research, we systematically analyzed the work done by predecessors from the aspects of industries, data models and data sets. This pap...","Title: Research on Prediction and Source Analysis of Ozone Pollution\nYear: 2021\n\nAbstract: For researchers and practitioners in the environmental field, accurate ozone pollution prediction and source analysis play an important role in smart air monitoring systems. However, there are some challenges in predicting and analyzing the source of ozone pollution in the air. Ozone data are spatio-temporal data, so when analyzing spatio-temporal data, the spatio-temporal correlation of the data ...","Title: Intuitionistic Fuzzy Three-Factor Ratio Models And Multi-Preference Fusion\nYear: 2021\n\nAbstract: Generally, decision-makers (DMs) can provide subjective opinions based on their risk attitude and intuitionistic preference for specific alternatives and attributes, which can be taken as cognitive information. For instance, alternative A is better than alternatives B and C, or attribute 3 is the most important. These multi-dimensional preferences and social cognitive behavior described by DMs based on their subjective evaluation should be taken into account when making a decision. How to fuse multiple, uncertain, imprecise but important preferences and cognitive information and then make a decision in an intuitionistic fuzzy environment is becoming a practical issue. Therefore, this paper defines a three-factor ratio of the intuitionistic fuzzy number and then proposes a basic intuitionistic fuzzy three-factor ratio (IFTR) model. To present DMs' risk preferences, this paper constructs two extreme IFTR models and describes risk preferences with a risk appetite parameter. For DMs' alternative preferences, this paper develops a continuous IFTR model in which alternative preferences are fused to calculate the optimal risk appetite parameter. To fully consider DMs' risk, alternative, and attribute preferences, this paper further proposes a generalized IFTR model. Thus, risk, alternative, and attribute preferences, which can be viewed as the social cognitive information, can be fused in an intuitionistic fuzzy decision-making and group decision-making process simultaneously. An illustrative example to address the problem of demolishing old urban villages is provided to show the effectiveness of the proposed models.","Title: Perceptual Quality Assessment of Internet Videos\nYear: 2021\n\nAbstract: ABSTRACTWith the fast proliferation of online video sites and social media platforms, user, professionally and occupationally generated content (UGC, PGC, OGC) videos are streamed and explosively shared over the Internet. Consequently, it is urgent to monitor the content quality of these Internet videos to guarantee the user experience. However, most existing modern video quality assessment (VQA) databases only include UGC videos and cannot meet the demands for other kinds of Internet videos with real-world distortions. To this end, we collect 1,072 videos from Youku, a leading Chinese video hosting service platform, to establish the Internet video quality assessment database (Youku-V1K). A special sampling method based on several quality indicators is adopted to maximize the content and distortion diversities within a limited database, and a probabilistic graphical model is applied to recover reliable labels from noisy crowdsourcing annotations. Based on the properties of Internet videos originated from Youku, we propose a spatio-temporal distortion-aware model (STDAM). First, the model works blindly which means the pristine video is unnecessary. Second, the model is familiar with diverse contents by pre-training on the large-scale image quality assessment databases. Third, to measure spatial and temporal distortions, we introduce the graph convolution and attention module to extract and enhance the features of the input video. Besides, we leverage the motion information and integrate the frame-level features into video-level features via a bi-directional long short-term memory network. Experimental results on the self-built database and the public VQA databases demonstrate that our model outperforms the state-of-the-art methods and exhibits promising generalization ability.","Title: Chaotic Particle Swarm Optimization Algorithm With Constraint Handling And Its Application In Combined Bidding Model\nYear: 2021\n\nAbstract: Particle swarm optimization (PSO) is a widely used intelligent optimization algorithm. The premature convergence and the constraint handling, however, remain problems for the global optimization searching. In this paper, a dual fitness value chaotic PSO algorithm with equality constraint handling (EDFC-PSO) is proposed. The particle mutation process based on the chaotic Cat mapping is introduced to increase the diversity of particles. Equality constraints are solved by parametric equations, and inequality constraints are considered using the dual fitness value. The proposed EDFC-PSO algorithm is tested by several test functions and then compared with other algorithms. Further, the performance of the proposed algorithm is validated by a combined bidding model of a power system. Our EDFC-PSO algorithm has equality constraints satisfied and enhances the global searching capability in both the test functions and the engineering model.","Title: A Method For Identifying Marine Targets Based On Mining Of Multi-Characteristic Movement Patterns\nYear: 2021\n\nAbstract: Currently, marine ship targets are often identified directly through sensing targets. As a result, the potential value of massive spatiotemporal data has not been fully utilized. Because relevant maritime authorities have new requirements for understanding the situation of naval traffic flows and target identity behavior, obtaining necessary information about maritime targets at the data level and further understanding the ship movement patterns at the characteristic level are the focus of the current study. From the perspective of system design, this study proposes a method for obtaining a number of feature sets by mining the ship trajectory data and then identifying unknown targets. In the process of constructing the multi-characteristic sets, this study focuses on the importance of the distribution characteristics of ship channels for target identification. This study proposes an improved similarity measure-a trajectory rasterization model method. Experimental verification based on the measured data set shows that the system design can successfully fulfill the requirements for the identification of unknown targets and has a certain universality.","Title: Improve Semi-Supervised Learning With Metric Learning Clusters And Auxiliary Fake Samples\nYear: 2021\n\nAbstract: Because it is very expensive to collect a large number of labeled samples to train deep neural networks in certain fields, semi-supervised learning (SSL) researcher has become increasingly important in recent years. There are many consistency regularization-based methods for solving SSL tasks, such as the Pi model and mean teacher. In this paper, we first show through an experiment that the traditional consistency-based methods exist the following two problems: (1) as the size of unlabeled samples increases, the accuracy of these methods increases very slowly, which means they cannot make full use of unlabeled samples. (2) When the number of labeled samples is vary small, the performance of these methods will be very low. Based on these two findings, we propose two methods, metric learning clustering (MLC) and auxiliary fake samples, to alleviate these problems. The proposed methods achieve state-of-the-art results on SSL benchmarks. The error rates are 10.20%, 38.44% and 4.24% for CIFAR-10 with 4000 labels, CIFAR-100 with 10,000 labels and SVHN with 1000 labels by using MLC. For MNIST, the auxiliary fake samples method shows great results in cases with the very few labels.","Title: Fpanet: Feature-Enhanced Position Attention Network For Semantic Segmentation\nYear: 2021\n\nAbstract: Attention mechanism is beneficial to capture the contextual information in visual task. This paper proposes a feature-enhanced position attention network (FPANet) for semantic segmentation based on framework of FCN. On the top of dilated FCN, we design a feature integration module, which aggregates the context over local features by expanding the receptive field and multiscale representation, to promote a position attention module, which models spatial interdependencies over features, so as to form a feature-enhanced position attention module to enhance the discrimination of features for better semantic segmentation. Experimental comparisons show that our proposed FPANet is superior to other state-of-the-art models in the performance of segmentation accuracy on datasets PASCAL VOC 2012 and Cityscapes.","Title: Deterministic Communications for In-vehicle Network: Overview and Challenges\nYear: 2021\n\nAbstract: In recent years, intelligence has become an inevitable trend in the development of automobile industry, and advanced assistant driving or autonomous driving are important features of automobile intelligence. The increasing level of self-driving puts forward higher demands for in-vehicle communication. And traditional in-vehicle networks are difficult to cover a series of complex deterministic communication requirements including high-bandwidth, low latency, low jitter and zero congestion. Therefore, Automotive Ethernet technology came into being. IEEE developed a series of Time Sensitive Networks (TSN) standard protocols based on traditional Ethernet. TSN added a series of key mechanisms that provide realtime and reliability guarantees for data transmission. In this paper, we reviewed the evolution of in-vehicle networks and outlined the development of Time Triggered Ethernet (TTE) and TSN researches in recent years. We also discussed the importance of deterministic researches on in-vehicle networks and the challenges in the development of the Internet of Things, E/E (Electrical/Electronic) architectures, intelligent driving, and new energy vehicles.","Title: Entity and Relation Matching Consensus for Entity Alignment\nYear: 2021\n\nAbstract: ABSTRACTEntity alignment aims to match synonymous entities across different knowledge graphs, which is a fundamental task for knowledge integration. Recently, researchers have devoted to leveraging rich information within relations to enhance entity alignment. They explicitly incorporate relations in entity representation and alignment, demonstrating remarkable results. However, affected by the semantic assumptions from early works, these works represent a relation by combining all the entities it connects, ignoring the semantic independence between entity and relation. Moreover, since these works perform alignment by comparing embedding similarity, they fail to consider a graph level alignment and tend to find local false correspondences. In this paper, we propose Entity and Relation Matching Consensus (ERMC), a two-stage matching schema based on graph matching consensus that jointly models and aligns entities and relations and retains their semantic independence at the same time. In the first stage, we design a bidirectional relation-aware graph convolutional network to jointly learn entity and relation embeddings based on the triadic graph by a novel message passing mechanism. Then, we jointly align the entities and relations by computing a graph-level matching consensus. In the second stage, we introduce a refinement strategy to detect and correct false alignments in the first stage. Experimental results on three real-world multilingual datasets demonstrate that ERMC outperforms some state-of-the-art models on both entity alignment and relation alignment tasks.","Title: DLB: Deep Learning Based Load Balancing\nYear: 2021\n\nAbstract: In this paper, we introduce DLB, a Deep Learning based load Balancing mechanism, to effectively address the data skew problem. The key idea of DLB is to replace hash functions in the load balancing mechanisms with deep learning models, which are trained to be able to map different distributions of workloads and data to the servers in a uniformed manner. We implemented DLB and deployed it on a prac...","Title: Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic\nYear: 2022\n\nAbstract: Autonomous driving has attracted significant research interests in the past two decades as it offers many potential benefits, including releasing drivers from exhausting driving and mitigating traffic congestion, among others. Despite promising progress, lane-changing remains a great challenge for autonomous vehicles (AV), especially in mixed and dynamic traffic scenarios. Recently, reinforcement learning (RL) has been widely explored for lane-changing decision makings in AVs with encouraging results demonstrated. However, the majority of those studies are focused on a single-vehicle setting, and lane-changing in the context of multiple AVs coexisting with human-driven vehicles (HDVs) have received scarce attention. In this paper, we formulate the lane-changing decision-making of multiple AVs in a mixed-traffic highway environment as a multi-agent reinforcement learning (MARL) problem, where each AV makes lane-changing decisions based on the motions of both neighboring AVs and HDVs. Specifically, a multi-agent advantage actor-critic (MA2C) method is proposed with a novel local reward design and a parameter sharing scheme. In particular, a multi-objective reward function is designed to incorporate fuel efficiency, driving comfort, and the safety of autonomous driving. A comprehensive experimental study is made that our proposed MARL framework consistently outperforms several state-of-the-art benchmarks in terms of efficiency, safety, and driver comfort.","Title: Modeling Multi-Attribute and Implicit Relationship Factors for Collaborative Filtering Recommender System\nYear: 2021\n\nAbstract: Interactions of users and items can be naturally modeled as a user-item bipartite graph in recommender systems, and emerging research is devoted to exploring user-item graphs for collaborative filtering methods. In reality, user-item interaction usually stems from more complex underlying factors, such as the users’ specific preferences. A user-item bipartite graph could be used to understand the d...","Title: An integrated fuzzy decision support system for analyzing challenges and pathways to promote green and climate smart mining\nYear: 2022\n\nAbstract: •Green and climate smart mining is an innovative mining mode.•Implementation of green and climate smart mining is hampered by challenges.•This study proposes an integrated fuzzy-based decision support system.•Nine pathways to overcome 24 challenges (grouped into 5 categories) are considered.•The proposed system help implement and promote sustainable mining.","Title: image2emmet: Automatic code generation from web user interface image\nYear: 2021\n\nAbstract: AbstractAbstractWeb development usually follows with analyzing the functionality, designing the user interface (UI) prototype, implementing the UI by front‐end (FE) developers and implementing the REpresentational State Transfer (RESTful) application programming interface (API) by back‐end (BE) programmers. Unfortunately, web development is a tedious, cumbersome, and time‐consuming task, which makes it a challenge for the FE programmers to work in an efficient way. In this paper, we propose an approach, image2emmet, to assist FE programmers in implementing the UI. First, we collect HyperText Markup Language, Cascading Style Sheets (HTML‐CSS) dataset in an automatic and efficient way. The HTML‐CSS dataset used for model training consists of HTML‐CSS code and its display images. Second, the faster region‐based convolutional neural network (CNN) (R‐CNN) is utilized to detect the UI component. Finally, we build a model combining CNN and long short‐term memory (LSTM) to transform the UI component into the HTML‐CSS code. The empirical study demonstrates that image2emmet can achieve a precision of 80% on the UI component detection and 60% on the transformation of UI component into HTML‐CSS code.We collect HTML‐CSS dataset both with HTML‐CSS code and its display images in an automatic and efficient way. We utilize the deep learning object detection technique in computer vision (CV) to detect UI components from UI images, and the experiment shows that the UI components can be correctly detected at a precision of 80%. We directly transform UI components into HTML‐CSS code, and the experiment shows that the accuracy of code generation can achieve 60%. View Figure","Title: Analyzing Midair Object Pointing Mappings for Smart Display Input\nYear: 2021\n\nAbstract: AbstractOne common task when controlling smart displays is the manipulation of menu items. Given current examples of smart displays that support distant bare hand control, in this paper we explore menu item selection tasks with three different mappings of barehand movement to target selection. Through a series of experiments, we demonstrate that Positional mapping is faster than other mappings when the target is visible but requires many clutches in large targeting spaces. Rate-based mapping is, in contrast, preferred by participants due to its perceived lower effort, despite being slightly harder to learn initially. Tradeoffs in the design of target selection in smart tv displays are discussed.","Title: Combinational Optimization Power (COP) - A Practical Power Budgeting Method for Many-cores\nYear: 2021\n\nAbstract: In order to improve the performance of many-core systems, it is necessary to adopt dynamic power constraints for different core mapping decisions. However, existing power budgeting methods are generally over pessimistic, e.g. Thermal Safe Power (TSP), or over optimistic, e.g. Greedy based Dynamic Power (GDP). This paper proposes a practical power budgeting method, called Combinational Optimization Power (COP). Different from existing methods, which ignore some actual factors, such as communication overhead and lifetime reliability, COP formulates the power budgeting problem as a thermal-constrained combinational optimization power problem. For the steady-state case, COP achieves the target fusion of optimized temperature and communication energy consumption by applying task priority ranking and task-to-core mapping. For the transient case, COP uses the rainflow counting algorithm to construct the reliability framework based on the thermal cycling failure mechanism, and then establishes a linear time-invariant transient thermal model to obtain the core mapping selection and the corresponding dynamic power budget. Experimental results show that COP is capable of providing an optimized core mapping decision, which can maximize power budget while ensuring the system performance.","Title: Feasibility Analysis of Machine Learning Optimization on GPU-based Low-cost Edges\nYear: 2021\n\nAbstract: Many AI algorithms have been deployed on edge devices as edge computing has the advantages of reducing latency, saving network bandwidth, and protecting data privacy. Whether edge devices can run AI algorithms is an important challenge due to the low-power and low-cost characteristics of edge devices. Therefore, this paper analyzed the performance of optimization techniques by running YOLOv3 on a ...","Title: Multi-ColorGAN: Few-shot vehicle recoloring via memory-augmented networks\nYear: 2021\n\nAbstract: Despite the notable successes of Generative adversarial networks (GANs) achieved to date, applying them to real-world problems still poses significant challenges. In real traffic surveillance scenarios, for the task of generating images of multiple color of truck heads and cars without changing textures and license plates, conditional image generation hardly manipulate the generated images by the color attribute. Image style transfer methods inevitably produce color smearing. Even state-of-the-art methods of disentangled representation learning (e.g. MixNMatch) cannot disentangle colors individually, ensuring that irrelevant factors, such as texture remain the same. To solve this problem, we present an approach called Multi-ColorGAN based on memory-augmented networks for multi-color real vehicle coloring/generation with limited data. In particular, our model could filter out unwanted color changes in specific areas with a simple but effective method called Fusion Module, and generate more natural color images. Experiments on three vehicle image benchmarks and a new truck image dataset are conducted to evaluate the proposed Multi-ColorGAN compared to state-of-the-art.","Title: Evaluation of Topographic Correction Models Based on 3-D Radiative Transfer Simulation\nYear: 2022\n\nAbstract: The timely and accurate assessment of changes in mountain vegetation biomass and other parameters is of great importance to mountain ecosystem conservation. With the rapid development of remote sensing technology, hyperspectral remote sensing images have facilitated the large-scale and long-time series monitoring of environmental changes in mountainous areas. However, topographic effects cause remote sensing images of mountainous areas to be prone to spectral variations within the same land cover and spectral confusion among different land covers. This phenomenon seriously affects the accuracy of remote sensing inversions and hinders the development and application of remote sensing in mountainous areas. Numerous scholars have established various topographic correction models (TCMs) to eliminate the influence of topographic effects. Comparative evaluation of the performance of different TCMs allows us to better understand their characteristics. Most previous evaluation studies have directly applied in remote sensing images, which were limited by the changing conditions of the study area. Therefore, this letter used computer simulations to controllably evaluate six popular TCMs on hyperspectral images. The results showed that their performance varied with the spectral band, and overall, the best performance was achieved by the C correction model, followed by the sun-canopy-sensor (SCS) + C model. This letter provides a basis for the optimal selection of TCMs in complex terrains.","Title: Deformation monitoring and thematic mapping of the Badaling Great Wall using very high-resolution interferometric synthetic aperture radar data\nYear: 2021\n\nAbstract: •Badaling Great Wall was monitored using VHR InSAR and environment social data.•An adaptable InSAR approach with the coarse–fine search algorithm was proposed.•Motion indicators are informative to characterize the condition of monuments.•An understandable thematic map was provided for the site sustainability.•The predicted tourist capacity of the site was 1.0 million per month.","Title: Resource Allocation in IoT Edge Computing via Concurrent Federated Reinforcement Learning\nYear: 2022\n\nAbstract: Resource allocation is a fundamental research issue in IoT edge computing, and reinforcement learning is fast becoming a common solution. The majority of the current techniques involve decision makers who determine how and where resources should be distributed. In a standard cloud system, this decision maker is a central server. In an edge system, the decision maker is an edge host. Both approache...","Title: Real-time Multiple-Channel Shoulder EMG Processing for a Rehabilitative Upper-limb Exoskeleton Motion Control Using ANN Machine Learning\nYear: 2021\n\nAbstract: Machine learning (ML) has been proposed for surface electromyography (sEMG) signal processing to control assistive robots. However, most of the studies using ML focused on recording sEMG for off-line data analysis and pattern recognition. Only a few studies reported online ML EMG processing for one DoF hand motion pattern recognition and instant robot control. Using ML for multiple channels of sho...","Title: Perceptual Evaluation of Pre-processing for Video Transcoding\nYear: 2021\n\nAbstract: Recently, the pre-processed video transcoding has attracted wide attention and has been increasingly used in practical applications for improving the perceptual experience and saving transmission resources. However, very few works have been conducted to evaluate the performance of pre-processing methods. In this paper, we select the source (SRC) videos and various pre-processing approaches to cons...","Title: Effect of Autoimmune Cell Therapy on Immune Cell Content in Patients with COPD: A Randomized Controlled Trial\nYear: 2022\n\nAbstract: Objective. To explore the effect of autoimmune cell therapy on immune cells in patients with chronic obstructive pulmonary disease (COPD) and to provide a reference for clinical treatment of COPD. Methods. Sixty patients with stable COPD were randomly divided into control group and treatment group (n=30). The control group was given conventional treatment, and the treatment group was given one autoimmune cell therapy on the basis of conventional treatment. The serum levels of CD3+ T cells, CD4+ T cells, CD8+ cells, B cells, and NK cells in the peripheral blood were detected by flow cytometry. Possible adverse reactions were detected at any time during treatment. Results. There were no significant differences in the contents of CD3+ T cells, CD4+ T cells, CD8+ cells, B cells, and NK cells in the serum of the control group (P > 0.05). Compared with before treatment, the contents of CD3+ T cells, CD4+ T cells, CD8+ cells, B cells, and NK cells in the serum of the treatment group were significantly increased (P < 0.05). The ratio of CD4+/CD8+ T cells in both control and treatment groups did not change significantly during treatment (P > 0.05). There were no significant differences in serum CD3+ T cells, CD4+ T cells, CD8+ cells, B cells, and NK cells in the treatment group at 30 days and 90 days after treatment (P > 0.05), but they were significantly higher than those in the control group (P < 0.05). Conclusion. Autoimmune cell therapy can significantly increase the level of immune cells in the body and can be maintained for a long period of time, which has certain clinical benefits for recurrent respiratory tract infections and acute exacerbation in patients with COPD.","Title: A Service Migration Method for Resource Competition in Mobile Edge Computing\nYear: 2021\n\nAbstract: In the mobile edge computing (MEC) environment, tasks can be offloaded to edge servers for better service. However, the locations of users are not fixed. As users move, tasks need to be migrated to the vicinity of users to ensure quality of service (QoS). At present, most research considers the problem of service migration from the perspective of a single user. The single user migration scheme is ...","Title: Multi-GNSS Combined Orbit and Clock Solutions at iGMAS\nYear: 2022\n\nAbstract: Global navigation services from the quad-constellation of GPS, GLONASS, BDS, and Galileo are now available. The international GNSS monitoring and assessment system (iGMAS) aims to evaluate the navigation performance of the current quad systems under a unified framework. In order to assess impact of orbit and clock errors on the positioning accuracy, the user range error (URE) is always taken as a metric by comparison with the precise products. Compared with the solutions from a single analysis center, the combined solutions derived from multiple analysis centers are characterized with robustness and reliability and preferred to be used as references to assess the performance of broadcast ephemerides. In this paper, the combination method of iGMAS orbit and clock products is described, and the performance of the combined solutions is evaluated by various means. There are different internal precisions of the combined orbit and clock for different constellations, which indicates that consistent weights should be assigned for individual constellations and analysis centers included in the combination. For BDS-3, Galileo, and GLONASS combined orbits of iGMAS, the root-mean-square error (RMSE) of 5 cm is achieved by satellite laser ranging (SLR) observations. Meanwhile, the SLR residuals are characterized with a linear pattern with respect to the position of the sun, which indicates that the solar radiation pressure (SRP) model adopted in precise orbit determination needs further improvement. The consistency between combined orbit and clock of quad-constellation is validated by precise point positioning (PPP), and the accuracies of simulated kinematic tests are 1.4, 1.2, and 2.9 cm for east, north, and up components, respectively.","Title: A New Mapping Function for Spaceborne TEC Conversion Based on the Plasmaspheric Scale Height\nYear: 2021\n\nAbstract: The mapping function is crucial for the conversion of slant total electron content (TEC) to vertical TEC for low Earth orbit (LEO) satellite-based observations. Instead of collapsing the ionosphere into one single shell in commonly used mapping models, we defined a new mapping function assuming the vertical ionospheric distribution as an exponential profiler with one simple parameter: the plasmaspheric scale height in the zenith direction of LEO satellites. The scale height obtained by an empirical model introduces spatial and temporal variances into the mapping function. The performance of the new method is compared with the mapping function F&K by simulating experiments based on the global core plasma model (GCPM), and it is discussed along with the latitude, seasons, local time, as well as solar activity conditions and varying LEO orbit altitudes. The assessment indicates that the new mapping function has a comparable or better performance than the F&K mapping model, especially on the TEC conversion of low elevation angles.","Title: Health Monitoring Framework for Weather Radar Based on Long Short-Term Memory Network with a Real Case Study\nYear: 2022\n\nAbstract: The health management of weather radar plays a key role in achieving timely and accurate weather forecasting. The current practice mainly exploits a fixed threshold prespecified for some monitoring parameters for fault detection. This causes abundant false alarms due to the evolving working environments, increasing complexity of the modern weather radar, and the ignorance of the dependencies among monitoring parameters. To address the above issues, we propose a deep learning-based health monitoring framework for weather radar. First, we develop a two-stage approach for problem formulation that address issues of fault scarcity and abundant false fault alarms in processing the databases of monitoring data, fault alarm record, and maintenance records. The temporal evolution of weather radar under healthy conditions is represented by a long short-term memory network (LSTM) model. As such, any anomaly can be identified according to the deviation between the LSTM-based prediction and the actual measurement. Then, construct a health indicator based on the portion of the occurrence of deviation beyond a user-specified threshold within a time window. The proposed framework is demonstrated by a real case study for the Chinese S-band weather radar (CINRAD-SA). The results validate the effectiveness of the proposed framework in providing early fault warnings.","Title: Research on low-quality waste heat recovery system based on fuzzy proportion integration differentiation control\nYear: 2021\n\nAbstract: In the current industrial production process, waste heat of low quality is seriously wasted. In order to effectively recover low-quality waste heat, the research group developed a small energy conversion device - Roots power machine. On this basis, the research group designed a low-quality waste heat efficient utilization system with the equipment as the core and successfully applied it to low-quality waste heat recovery. However, in the actual operation process, the system can not run stably due to the occasional fluctuation of the input gas source. In view of this, after the study of waste heat recovery system, the fluctuation of gas source can be controlled by different grades according to the degree of change. Fuzzy rules also divide variables into different grades to solve problems, and fuzzy control can convert continuous changes of airflow into discrete changes, which greatly reduces the complexity of the control system. Therefore, the research group proposed a control strategy based on fuzzy PID. The simulation results show that the adjustment time of fuzzy PID is within 7 s, and the adjustment effect is obviously better than that of traditional PID. The experimental results show that the speed deviation under the condition of air source fluctuation is within the speed fluctuation rate (+/- 5%), and the speed deviation under the condition of sudden disturbance load is within the steady speed adjustment rate (+/- 3.5%), both of which meet the requirements of indexes. Therefore, the fuzzy PID control strategy can further improve the stability of output speed, reduce airflow pulsation, and provide the possibility for grid-connected power generation.","Title: Classification method of marine target motion pattern based on spatial-temporal trajectories\nYear: 2021\n\nAbstract: Classifying the motion pattern of marine targets is of important significance to promote target surveillance and management efficiency of marine area and to guarantee sea route safety. This paper proposes a moving target classification algorithm model based on channel extraction-segmentation-LCSCA-l(p) norm minimization The algorithm firstly analyzes the entire distribution of channels in specific region, and defines the categories of potential ship motion patterns; on this basis, through secondary segmentation processing method, it obtains several line segment trajectories as training sample sets, to improve the accuracy of classification algorithm; then, it further uses the Leastsquares Cubic Spline Curves Approximation (LCSCA) technology to represent the training sample sets, and builds a motion pattern classification sample dictionary; finally, it uses l(p) norm minimized sparse representation classification model to realize the classification of motion patterns. The verification experiment based on real spatial-temporal trajectory dataset indicates that, this method can effectively realize the motion pattern classification of marine targets, and shows better time performance and classification accuracy than other representative classification methods.","Title: An adaptive phase-field model based on bilinear elements for tensile-compressive-shear fracture\nYear: 2022\n\nAbstract: An efficient adaptive phase-field method based on bilinear elements for tensile-compressive-shear fracture is developed. On the one hand, refined meshes are needed near the crack path to obtain accurate results, and the computational efficiency is very low if a prior mesh refinement is applied, especially when the crack paths are unknown. On the other hand, conventional phase-field models are not applicable to tensile-compressive shear fracture under complex stress states at present. In this paper, an adaptive scheme is proposed to improve the computational efficiency, in which a bilinear multi-node element is adopted to avoid using high order quadrature and shape functions when new nodes are inserted in elements, and a new multi-node triangular element is outlined to expand the scope of application of the proposed adaptive method. Another important aspect of the contribution is the development of the phase-field model for tensile-compressive-shear fracture under complex stress states, in which a universal fracture criterion is embedded. To determine the optimal parameters in the adaptive scheme and demonstrate the advantages of the proposed bilinear adaptive method, a series of numerical examples are performed for sensitivity analysis. Comparison with experimental results is also conducted to validate the proposed phase-field model for tensile-compressive-shear fracture under complex stress states.","Title: Fingerprinting Mainstream IoT Platforms Using Traffic Analysis\nYear: 2022\n\nAbstract: The Internet of Things (IoT) platforms have been widely used in many application scenarios, especially for the smart home. Under the management of the IoT platform, a massive amount of IoT devices have been connected between remote cloud servers and users’ mobile terminals. While bringing unprecedented convenience for device manufacturers and smart home users, the existence of mainstream IoT platf...","Title: A deep learning method for video-based action recognition\nYear: 2021\n\nAbstract: In this paper, a deep learning method for video-based action recognition is proposed. On the one hand, boundary compensation on the basis of a deep neural network is performed to achieve action proposal. Boundary compensation considering non-maximum suppression according to sliding window priority is applied to remove redundant windows. To accurately detect boundaries, a boundary compensation network is established with multiple networks to process different numbers of segments. On the other hand, action recognition based on the resultant action proposals is performed. To further utilise boundary compensation, three methods are introduced for key frame selection. Optical flow and RGB features are combined via a channel fusion to realise feature representation. A two-stream network with a spatiotemporal structure is adopted for action recognition. The proposed method is evaluated on three public datasets. The experimental results demonstrate that the proposed method achieves a superior performance to that of state-of-the-art methods.","Title: Depth-Keeping Control for a Deep-Sea Self-Holding Intelligent Buoy System Based on Inversion Time Constraint Stability Strategy Optimization\nYear: 2022\n\nAbstract: Based on the nonlinear disturbance observer (NDO), the inversion time-constraint stability strategy (ITCS) is designed to make the deep-sea self-holding intelligent buoy (DSIB) system hovered at an appointed depth within a specified time limit. However, it is very challenging to determine the optimal parameters of an ITCS depth controller. Firstly, a genetic algorithm based on quantum theory (QGA) is proposed to obtain the optimal parameter combination by using the individual expression form of quantum bit and the adjustment strategy of quantum rotary gate. To improve the speed and accuracy of global search in the QGA optimization process, taking the number of odd and even evolutions as the best combination point of the genetic and chaos particle swarm algorithm (GACPSO), an ITCS depth controller based on GACPSO strategy is proposed. Besides, the simulations and hardware-in-the-loop system experiments are conducted to examine the effectiveness and feasibility of the proposed QGA-ITCS and GACPSO-ITCS depth controller. The results show that the proposed GACPSO-ITCS depth controller provides higher stability with smaller steady-state error and less settling time in the depth-control process. The research of the proposed method can provide a stable operation condition for the marine sensors carried by the DSIB.","Title: Dynamic competitive game study of a green supply chain with R&D level\nYear: 2022\n\nAbstract: Green and low carbon is an important way to coordinate economic and social development, which is also true for enterprises. Hence, the essay mainly builds up a green supply chain consisting of one supplier and two manufacturers. Gradient adjustment mechanism is used to establish a dynamic green competition model based on bounded rationality in this paper. Subsequently, the stability of four equilibrium points is researched through different methods. Dynamic behaviors are analyzed by 1-D bifurcation diagrams, the largest Lyapunov exponent, as well as 2-D bifurcation diagrams. We have found that the system goes from a steady state to chaos mainly via flip bifurcation. The chaotic state of system can be alleviated as the increase of adjustment speed in a certain range. Furthermore, invariant sets and natural transverse Lyapunov exponent are used to study synchronization behavior. Basins of attraction under the coexistence of attractors are simulated numerically. The results indicate that the system will have two and four groups of coexisting attractors, where the structure and size of the attractor and its basin will change as the speed of adjustment increases. When the attractor contacts with the boundary of its basin of attraction, the global bifurcation occurs.","Title: Color-in-fist: a metaphor for color selection with mid-air interaction\nYear: 2022\n\nAbstract: Color picker is a necessary interactive component in visualization system. Current color pickers are based on the WIMP paradigm and are mainly designed for mouse, touch and ray interactions. In the context of immersive and remote interaction, mid-air interaction is an important method. However, common solution of color selection using hands is simulating cursors or rays with fingers, which does not take full advantage of mid-air interaction-intuition, naturalness and remote interaction. This results in fatigue and long selection time. In this paper, we proposed a mid-air interaction metaphor for color selection which enabled users to select hue, saturation and lightness simultaneously using mutually independent and intuitive hand motions. We conducted a user study to evaluate the performance of the interaction technique. Results showed that the metaphor improved the efficiency of color selection with mid-air interaction. Questionnaire results showed that the metaphor enhanced users' perception of color space; the semantics of hand motions were consistent with users' intention of color selection; the metaphor was easy to learn and memorize. Graphic Abstract","Title: Rank-Sensitive Computation of the Rank Profile of a Polynomial Matrix.\nYear: 2022\n\nAbstract: Consider a matrix $\\mathbf{F} \\in \\mathbb{K}^{m \\times n}$ of univariate polynomials over a field~$\\mathbb{K}$. We study the problem of computing the column rank profile of $\\mathbf{F}$. To this end we first give an algorithm which improves the minimal kernel basis algorithm of Zhou, Labahn, and Storjohann (Proceedings ISSAC 2012). We then provide a second algorithm which computes the column rank profile of $\\mathbf{F}$ with a rank-sensitive complexity of $O\\tilde{~}(r^{\\omega-2} n (m+D))$ operations in $\\mathbb{K}$. Here, $D$ is the sum of row degrees of $\\mathbf{F}$, $\\omega$ is the exponent of matrix multiplication, and $O\\tilde{~}(\\cdot)$ hides logarithmic factors.","Title: SocialLGN: Light graph convolution network for social recommendation\nYear: 2022\n\nAbstract: •We propose a SocialLGN system based on GCN for social recommendation problems.•We design a customized graph fusion component to fuse the user representations.•Empirical experiments verify the effectiveness of the proposed system.","Title: A <tex>$1.2\\ \\mu \\mathrm{W},\\ 1.7\\ \\mu V_{rms}$</tex> ECG Acquisition IC with Low-Noise Amplifier and Full-Digital Heartbeat Detector\nYear: 2021\n\nAbstract: In this paper, a full-digital heartbeat (HB) detection system-on-chip (SoC) with low-noise analog front end (AFE) is presented. With the use of transconductance bootstrap and source degradation, the noise of AFE can be greatly reduced. The SoC is implemented in a \n<tex xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">$0.18\\mu \\mathrm{m}$</tex>\n CMOS process and consumes \n<tex xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">$1.2\\mu \\mathrm{W}$</tex>\n from a 1.2V supply, of which AFE and heart rate detector consume \n<tex xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">$0.8\\ \\mu \\mathrm{W}$</tex>\n and \n<tex xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">$0.4\\ \\mu \\mathrm{W}$</tex>\n, respectively. Besides, the noise from 1Hz to 1kHz is only \n<tex xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">$1.7\\ \\mu V_{rms}$</tex>\n. The gain of instrument amplifier (IA) is 33dB, and the cutoff frequency of high and low frequency are 0.4Hz and 40kHz respectively.","Title: A brief survey on adaptive video streaming quality assessment\nYear: 2022\n\nAbstract: Quality of experience (QoE) assessment for adaptive video streaming plays a significant role in advanced network management systems. It is especially challenging in case of dynamic adaptive streaming schemes over HTTP (DASH) which has increasingly complex characteristics including additional playback issues. In this paper, we provide a brief overview of adaptive video streaming quality assessment. Upon our review of related works, we analyze and compare different variations of objective QoE assessment models with or without using machine learning techniques for adaptive video streaming. Through the performance analysis, we observe that hybrid models perform better than both quality-of-service (QoS) driven QoE approaches and signal fidelity measurement. Moreover, the machine learning-based model slightly outperforms the model without using machine learning for the same setting. In addition, we find that existing video streaming QoE assessment models still have limited performance, which makes it difficult to be applied in practical communication systems. Therefore, based on the success of deep learned feature representations for traditional video quality prediction, we also apply the off-the-shelf deep convolutional neural network (DCNN) to evaluate the perceptual quality of streaming videos, where the spatio-temporal properties of streaming videos are taken into consideration. Experiments demonstrate its superiority, which sheds light on the future development of specifically designed deep learning frameworks for adaptive video streaming quality assessment. We believe this survey can serve as a guideline for QoE assessment of adaptive video streaming.","Title: BDS-3 Integrity Risk Modeling and Probability Evaluation\nYear: 2022\n\nAbstract: Focusing on the global integrity monitoring performance of the BDS-3, the integrity failure model and effect analysis (IFMEA) of the system space segment and the ground control segment were carried out, and the system integrity risk tree model was established according to the IFMEA results. To evaluate the system's integrity risk probability more reasonably, this paper establishes an abnormal event prediction model with a Bayesian method, based on actual operation data, under a situation in which the BDS-3 just opened service. With statistical analysis results of system anomalies since 27 December 2018-which is the date the BDS-3 began providing basic service-according to the anomaly prediction model, the system's integrity risk probability under the 95% confidence limit was calculated to be approximately 0.9e-7/h, meeting the design index requirements of 1e-7/h. The analysis results also show that the main risk factors affecting the integrity of BDS-3 are 'pseudorange measurement anomaly' and 'miss-detection of satellite autonomous integrity monitoring'. The results are important references with practical engineering significance for improving the integrity performance of BDS-3.","Title: Reducing the Residual Topography Phase for the Robust Landscape Deformation Monitoring of Architectural Heritage Sites in Mountain Areas: The Pseudo-Combination SBAS Method\nYear: 2022\n\nAbstract: Monitoring deformation of architectural heritage sites is important for the quantitative evaluation of their stability. However, deformation monitoring of sites in mountainous areas remains challenging whether utilizing global navigation satellite system (GNSS) or interferometric synthetic aperture radar (InSAR) techniques. In this study, we improved the small baseline subset (SBAS) approach by introducing the pseudo-baseline combination strategy to avoid the errors caused by inaccurate external DEM, resulting in robust deformation estimations in mountainous areas where the architectural heritage site of the Great Wall is located. First, a simulated dataset and a real dataset were used to verify the reliability and effectiveness of the algorithm, respectively. Subsequently, the algorithm was applied in the landscape deformation monitoring of the Shanhaiguan section of the Great Wall using 51 Sentinel-1 scenes acquired from 2016 to 2018. A thematic stability map of the Shanhaiguan Great Wall corridor was generated, revealing that the landscape was generally stable save for local instabilities due to to unstable rocks and wall monuments. This study demonstrated the capabilities of adaptive multitemporal InSAR (MTInSAR) approaches in the preventive landscape deformation monitoring of large-scale architectural heritage sites in complex terrain.","Title: Ada: Adversarial learning based data augmentation for malicious users detection\nYear: 2022\n\nAbstract: Malicious user detection in the recommender systems has attracted much attention in the last two decades because malicious users can seriously affect the recommendation results and user experience. The up-to-date detection models usually concentrate on distinguishing users according to their latent features represented in user embeddings. These models can improve detection performance; however, they are usually not fully up to expectations, especially in the scenarios with unbalanced use samples. From these models that concentrate on user embedding representations, we can summarize the following difficulties: (1) the cost of manual labeling malicious causes the lack of labeled malicious users in training data, which leads to imprecise representations of users; (2) current augmentation methods that aim at mitigating the lack of labeled malicious users are hard to simulate the distribution of malicious users. In this paper, we propose a detection model, using adversarial learning based data augmentation (a.k.a. Ada) to alleviate these problems. Concretely, to get precise representations of users, the model integrates potential user relations and structural similarities into user embeddings. After obtaining precise user representation, it presents a novel data augmentation based on the deep convolutional generative adversarial networks (DCGAN) to simulate the distribution of malicious user embeddings and generate additional fake user embeddings. Experiments on public datasets show our model outperforms state-of-the-art detection models with sparse labeled malicious users, and the ablation study confirms the importance and effectiveness of each component of the model.(C) 2022 Elsevier B.V. All rights reserved.","Title: Synchronization and Global Dynamics of a Cournot Model with Nonlinear Demand and R&D Spillovers\nYear: 2021\n\nAbstract: In this paper, a dynamical Cournot model with nonlinear demand and R&D spillovers is established. The system is symmetric when the duopoly firms have same economic environments, and it is proved that both the diagonal and the coordinate axes are the one-dimensional invariant manifolds of system. The results show that Milnor attractor of system can be found through calculating the transverse Lyapunov exponents. The synchronization phenomenon is verified through basins of attraction. The effects of adjusting speed and R&D spillovers on the dynamical behaviors of the system are discussed. The topological structures of basins of attraction are analyzed through critical curves, and the evolution process of \"holes\" in the feasible region is numerically simulated. In addition, various global bifurcation behaviors, such as two kinds of contact bifurcation and the blowout bifurcation, are shown.","Title: Computational Identification of Functional Centers in Complex Proteins: A Step-by-Step Guide With Examples.\nYear: 2021\n\nAbstract: In proteins, functional centers consist of the key amino acids required to perform molecular functions such as catalysis, ligand-binding, hormone- and gas-sensing. These centers are often embedded within complex multi-domain proteins and can perform important cellular signaling functions that enable fine-tuning of temporal and spatial regulation of signaling molecules and networks. To discover hidden functional centers, we have developed a protocol that consists of the following sequential steps. The first is the assembly of a search motif based on the key amino acids in the functional center followed by querying proteomes of interest with the assembled motif. The second consists of a structural assessment of proteins that harbor the motif. This approach, that relies on the application of computational tools for the analysis of data in public repositories and the biological interpretation of the search results, has to-date uncovered several novel functional centers in complex proteins. Here, we use recent examples to describe a step-by-step guide that details the workflow of this approach and supplement with notes, recommendations and cautions to make this protocol robust and widely applicable for the discovery of hidden functional centers.","Title: Efficient Training of Neural Transducer for Speech Recognition.\nYear: 2022\n\nAbstract: As one of the most popular sequence-to-sequence modeling approaches for speech recognition, the RNN-Transducer has achieved evolving performance with more and more sophisticated neural network models of growing size and increasing training epochs. While strong computation resources seem to be the prerequisite of training superior models, we try to overcome it by carefully designing a more efficient training pipeline. In this work, we propose an efficient 3-stage progressive training pipeline to build highly-performing neural transducer models from scratch with very limited computation resources in a reasonable short time period. The effectiveness of each stage is experimentally verified on both Librispeech and Switchboard corpora. The proposed pipeline is able to train transducer models approaching state-of-the-art performance with a single GPU in just 2-3 weeks. Our best conformer transducer achieves 4.1% WER on Librispeech test-other with only 35 epochs of training.","Title: Multi-Granularity Semantic Aware Graph Model for Reducing Position Bias in Emotion-Cause Pair Extraction\nYear: 2022\n\nAbstract: The Emotion-Cause Pair Extraction (ECPE) task aims to extract emotions and causes as pairs from documents. We observe that the relative distance distribution of emotions and causes is extremely imbalanced in the typical ECPE dataset. Existing methods have set a fixed size window to capture relations between neighboring clauses. However, they neglect the effective semantic connections between distant clauses, leading to poor generalization ability towards position-insensitive data. To alleviate the problem, we propose a novel Multi-Granularity Semantic Aware Graph model (MGSAG) to incorporate fine-grained and coarse-grained semantic features jointly, without regard to distance limitation. In particular, we first explore semantic dependencies between clauses and keywords extracted from the document that convey fine-grained semantic features, obtaining keywords enhanced clause representations. Besides, a clause graph is also established to model coarse-grained semantic relations between clauses. Experimental results indicate that MGSAG surpasses the existing state-of-the-art ECPE models. Especially, MGSAG outperforms other models significantly in the condition of position-insensitive data.","Title: Design of a High-Speed Rapier Loom Control System Based on a Mixed Current Attenuation Algorithm\nYear: 2021\n\nAbstract: At present, the weft selection and selvedge control systems available on the loom do not support precise control of the weft selection and selvedge devices, which will reduce the dynamic performance and production efficiency of the loom. The low-frequency oscillation and back Electromotive force (EMF) of the stepping motor are the main factors that affect the stability of the control system. This paper proposes an optimized mixed current attenuation algorithm that is dedicated to improving the performance of the system. The contribution and innovation of this paper is that the mixed current attenuation algorithm is proposed to improve the low frequency oscillation and back-EMF of the stepping motor and applied to the actual loom production. In this paper, the current subdivision drive is used to improve the low-frequency oscillation of the stepper motor of the weft selection and twisting device, and the back-EMF is reduced by the mixed current attenuation algorithm. First, to verify the effectiveness of the mixed current attenuation algorithm, this paper conducts a motor winding current waveform test. Then, by comparing the acceleration and deceleration control effects in the current self-decay mode, it is verified that the mixed current attenuation algorithm can effectively reduce the low-frequency oscillation and back-EMF of the stepper motor, and improve the dynamic performance and production efficiency of the stepper motor. Finally, the algorithm was debugged and verified on the high-speed rapier loom test platform. By recording and comparing the number of stops of the loom in a certain period of time, it can be concluded that the number of stops caused by the process error of weft selection and selvedge is greatly reduced. Experiments show that the weft selection and selvedge control system using the mixed current attenuation algorithm can achieve precise control of the weft selection and selvedge device, which can effectively improve the productivity and dynamic performance of the loom while reducing the low-frequency oscillation and back-EMF of the stepping motor. Experiments show that the mixed current attenuation algorithm meets the technological requirements of weft selection and selvedge and has a good application prospect.","Title: A cloud service recommendation method based on extended multi-source information fusion\nYear: 2022\n\nAbstract: With the rapid development of information technology, the problem of \"information overload\" emerges when users choose cloud services. How to integrate multi-source information to achieve accurate service recommendation is an urgent problem to be solved by current recommendation systems. This article proposes a cloud service recommendation method based on extended multi-source information fusion. First, we propose a score prediction based on matrix decomposition and topic matrix, and we fully mine existing explicit data and feedback data, such as user ratings, social trust information, reviews, and user personalized preferences and so on. Second, in order to solve the problems of data sparseness and cold start of the system, we integrate the score, social trust information and review into a comprehensive model through collaborative filtering (CF), and propose a multi-source information fusion recommendation method. The CF fusion method mainly combines two parts: social matrix decomposition and topic matrix decomposition. Finally, in order to further improve the accuracy and scalability, the implicit feature matrix is integrated into the user rating matrix, and the original CF enhancement based on scoring matrix decomposition is a matrix decomposition method that can learn implicit features. Experimental results show that compared with other recommendation algorithms, the cloud service recommendation method proposed in this article can improve the recommendation accuracy and allow users to choose satisfactory cloud services.","Title: Simulation of Soil Organic Carbon Content Based on Laboratory Spectrum in the Three-Rivers Source Region of China\nYear: 2022\n\nAbstract: Soil organic carbon (SOC) changes affect the land carbon cycle and are also closely related to climate change. Visible-near infrared spectroscopy (Vis-NIRS) has proven to be an effective tool in predicting soil properties. Spectral transformations are necessary to reduce noise and ensemble learning methods can improve the estimation accuracy of SOC. Yet, it is still unclear which is the optimal ensemble learning method exploiting the results of spectral transformations to accurately simulate SOC content changes in the Three-Rivers Source Region of China. In this study, 272 soil samples were collected and used to build the Vis-NIRS simulation models for SOC content. The ensemble learning was conducted by the building of stack models. Sixteen combinations were produced by eight spectral transformations (S-G, LR, MSC, CR, FD, LRFD, MSCFD and CRFD) and two machine learning models of RF and XGBoost. Then, the prediction results of these 16 combinations were used to build the first-step stack models (Stack1, Stack2, Stack3). The next-step stack models (Stack4, Stack5, Stack6) were then made after the input variables were optimized based on the threshold of the feature importance of the first-step stack models (importance > 0.05). The results in this study showed that the stack models method obtained higher accuracy than the single model and transformations method. Among the six stack models, Stack 6 (5 selected combinations + XGBoost) showed the best simulation performance (RMSE = 7.3511, R-2 = 0.8963, and RPD = 3.0139, RPIQ = 3.339), and obtained higher accuracy than Stack3 (16 combinations + XGBoost). Overall, our results suggested that the ensemble learning of spectral transformations and simulation models can improve the estimation accuracy of the SOC content. This study can provide useful suggestions for the high-precision estimation of SOC in the alpine ecosystem.","Title: Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images\nYear: 2022\n\nAbstract: As a classic deep learning target detection algorithm, Faster R-CNN (region convolutional neural network) has been widely used in high-resolution synthetic aperture radar (SAR) and inverse SAR (ISAR) image detection. However, for most common low-resolution radar plane position indicator (PPI) images, it is difficult to achieve good performance. In this paper, taking navigation radar PPI images as an example, a marine target detection method based on the Marine-Faster R-CNN algorithm is proposed in the case of complex background (e.g., sea clutter) and target characteristics. The method performs feature extraction and target recognition on PPI images generated by radar echoes with the convolutional neural network (CNN). First, to improve the accuracy of detecting marine targets and reduce the false alarm rate, Faster R-CNN was optimized as the Marine-Faster R-CNN in five respects: new backbone network, anchor size, dense target detection, data sample balance, and scale normalization. Then, JRC (Japan Radio Co., Ltd.) navigation radar was used to collect echo data under different conditions to build a marine target dataset. Finally, comparisons with the classic Faster R-CNN method and the constant false alarm rate (CFAR) algorithm proved that the proposed method is more accurate and robust, has stronger generalization ability, and can be applied to the detection of marine targets for navigation radar. Its performance was tested with datasets from different observation conditions (sea states, radar parameters, and different targets).","Title: Quality assessment of multi-exposure image fusion by synthesizing local and global intermediate references\nYear: 2022\n\nAbstract: •Inspired by the global-to-local perception mechanism of the HVS, we propose a FR-IQA method for MEF images by generating Local and Global Intermediate References (LGIR) from the input multiple reference images.•We build proper intermediate reference information in gradient domain, structure tensor domain, and global perception domain, respectively, to characterize the global-to-local perception mechanism of the HVS.•We also apply LGIR to automatic parameter tuning of an existing MEF algorithm. Numerical experiments indicate that LGIR provides a useful tool to exploit the parameter space and to pick the optimal parameter set that produces MEF image with the best visual quality.","Title: Exploring complementary information of self-supervised pretext tasks for unsupervised video pre-training\nYear: 2022\n\nAbstract: This study addresses the problem of the unsupervised pre-training of video representation learning. The authors' focus is on two common approaches: knowledge distillation and self-supervised learning. The insight provided is that knowledge distillation and the rapidly advancing self-supervised technique can be mutually beneficial. Combining these two approaches, a unified framework of self-supervised learning and image-based distillation (SSID) for unsupervised video pre-training is proposed. The effectiveness of SSID in comparison to both image-based distillation methods and the existing self-supervised pre-training baseline is demonstrated. In particular, the authors' model leverages three signals from the unlabelled data. First, the authors distil from the classifier of a 2D pre-trained model as a soft label. To regularize the training process, the authors then build a novel positive pair of contrastive learning on the representation of the 2D/3D model. Finally, a self-supervised pretext task is introduced to enhance the authors' model to become aware of the temporal evolution. The authors' experiment results showed that the learnt features achieved the best performance when transferred to action recognition tasks on UCF101 and HMDB51, reaching increases of 2.4% and 1.9% compared to the existing unsupervised pre-training model, respectively.","Title: Research on Wireless Sensor Network Access Control and Load Balancing in the Industrial Digital Twin Scenario\nYear: 2022\n\nAbstract: Wireless sensor networks which are based on distributed information processing technology are taking an increasingly key role in industrial digital twin scenarios. There are many important issues in the access of networks. One of the most important issues is how to improve network access control and the effectiveness of load balancing. Based on the industrial digital twin technology, this article first introduces several typical network access and network loads and performs tree-structured processing on the outliers generated during the chain formation process to reduce the length of the data transmission path, optimize the main chain head and subchain chain head selection strategy and chaining rules, and perform nonchain operations on common nodes and chain heads near sink to reduce data inverse transfer. The experimental results show that this paper uses the digital twin calculation formula to accurately and objectively determine the remaining cluster head and the distance head and the base station, so that when the node distance is limited, the network energy consumption can be balanced as much as possible, and the network load is promoted.","Title: A Deep Multitask Convolutional Neural Network for Remote Sensing Image Super-Resolution and Colorization\nYear: 2022\n\nAbstract: Remote sensing data have become increasingly vital in target detection, disaster monitoring, and military surveillance. Abundant pan-sharpening and super-resolution (SR) methods based on deep learning have been proposed and have achieved remarkable performance. However, pan-sharpening requires paired panchromatic (PAN) and multispectral (MS) images, and SR cannot increase the spectral resolution of PAN. Thus, we introduce a computational imaging-based method to recover or produce the incomplete data of single PAN or MS. This work also explores the integration of multiple tasks by a single neural network. We start with SR and colorization, study the feasibility of simultaneously finishing SR colorization, and use a model trained in SR colorization to finish pan-sharpening without MS. A generic neural network, remote sensing image improvement network (RSI-Net), is designed for remote sensing image SR, colorization, simultaneous SR colorization, and pan-sharpening. To verify its performance, RSI-Net is compared with the state-of-the-art SR and colorization methods. Experiments show that RSI-Net can be competitive in visual effects and evaluation indexes, and it performs well at simultaneous SR colorization, and RSI-Net finishes pan-sharpening and only needs to input PAN. Our experiments confirm the effect of integrating multiple tasks.","Title: PQROM: To optimize software defined network QoS-aware routing with proximal policy optimization\nYear: 2022\n\nAbstract: Currently, Quality-of-Service (QoS)-aware routing is one of the crucial challenges in Software Defined Network (SDN). The QoS performances, e.g. latency, packet loss ratio and throughput, must be optimized to improve the performance of network. Traditional static routing algorithms based on Open Shortest Path First (OSPF) could not adapt to traffic fluctuation, which may cause severe network congestion and service degradation. Central intelligence of SDN controller and recent breakthroughs of Deep Reinforcement Learning (DRL) pose a promising solution to tackle this challenge. Thus, we propose an on-policy DRL mechanism, namely the PPO-based (Proximal Policy Optimization) QoS -aware Routing Optimization Mechanism (PQROM), to achieve a general and re-customizable routing optimization. PQROM can dynamically update the routing calculation by adjusting the reward function according to different optimization objectives, and it is independent of any specific network pattern. Additionally, as a black-box one-step optimization, PQROM is qualified for both continuous and discrete action space with high-dimensional input and output. The OMNeT ++ simulation experiment results show that PQROM not only has good convergence, but also has better stability compared with OSPF, less training time and simpler hyper-parameters adjustment than Deep Deterministic Policy Gradient (DDPG) and less hardware consumption than Asynchronous Advantage Actor-Critic (A3C).","Title: Impulsive Noise Excision Using Robust Smoothing\nYear: 2022\n\nAbstract: The impulsive noise originated from regional lightning discharges can dramatically degrade the performance of skywave over-the-horizon radar (OTHR) target detection with generally 10-20-dB increase of noise level in the Doppler domain. As a result, it is necessary to excise impulsive noise in corrupted data while preserving the useful echo signal. Traditional techniques first locate the impulsive noise in slow-time data with a high-pass finite-duration impulse response (FIR) notch filter and then restore the contaminated samples by neighboring good data. However, the effect of impulsive noise is propagated into the subsequent filtered residuals, which will mislead the impulsive noise detection. Instead of the conventional detection-interpolation methods, we propose a novel impulsive noise excision approach based on robust smoothing. The proposed method can robustly smooth the corrupted data and determine the position of impulsive noise with a preset threshold against the residuals of corrupted data subtracting the smoothed signal. The contaminated data at last are replaced by the smoothed signal. Experimental results have demonstrated the effectiveness of our proposed method.","Title: Remote sensing-based deformation monitoring of pagodas at the Bagan cultural heritage site, Myanmar\nYear: 2022\n\nAbstract: As a World Cultural Heritage site with sacred landscape featuring an exceptional range of Buddhist art and architecture, much attention has been focused on the sustainable development of Bagan (Myanmar). Particularly, the monitoring of landscape surface subsidence and monument instability is of great importance to the protection and development planning of the Bagan heritage site. In this study, we applied high resolution TerraSAR-X imagery acquired from 2019 to 2020 for deformation monitoring based on the small baseline subset (SBAS) and persistent scatterer synthetic aperture radar interferometry (PSInSAR) approaches. We identified several hotspots and pagodas with displacement anomalies linked to land cover change and previous earthquakes. The cross comparison between SBAS and PSInSAR and the precision of height estimates derived by PSInSAR indicated a millimetric precision of the derived deformation products. The combination of the two multi-temporal SAR interferometry approaches satisfies the two-scale monitoring requirements from landscapes to monuments, particularly for large-scale World Heritage sites. The non-contact monitoring method has potential when traditional methods using field accessibility and surveillance are constrained.","Title: Braking angle control of loom spindle based on neuron-adaptive PID algorithm\nYear: 2022\n\nAbstract: The intelligent control strategy of electromagnetic clutch actuator is analyzed in detail in this paper. The start - stop control of the loom is realized by an electromagnetic clutch. The existing control method of electromagnetic clutch of loom is high and low pressure control strategy. The operator sets the braking advance angle according to experience, to realize the accurate braking of the spindle, but it is difficult to realize the fast and accurate control. In order to achieve good performance, it is very important to develop a fast and accurate loom braking system. Aiming at the fabric defects caused by the elongation of the warp when the loom is stopped, a method of stabilizing the excitation current of the electromagnetic clutch by using the neural adaptive PID (proportional integral differential) controller is proposed to improve the control precision of the actuator. The experimental results show that the proposed control algorithm is feasible and can effectively realize the adaptive control of the spindle braking Angle within the allowable error range.","Title: High-speed loom brake system controlled by fuzzy PID algorithm based on cloud platform\nYear: 2022\n\nAbstract: When a high-speed loom is suddenly stopped or is driven again in a faulty state, it is easy to cause defects such as brake marks and driving marks on the fabric. This is because the brake system of the loom has insufficient control of the accuracy of the parking angle. To improve the control accuracy of the braking system, this paper analyzes the dynamic process of the braking system when braking, builds a mathematical model of the electromagnetic clutch of the braking system, and proposes to regulate the excitation current of the system by fuzzy PID algorithm. The rule table of this PID controller is established by expert experience, and the center of gravity method is used for defuzzification. In addition, this article combines internet communication and storage technology to build a cloud platform for the brake system, and realizes the monitoring, storage and diagnosis of the brake system data. Finally, hardware testing and on-site braking experiments (including parking angle data acquisition experiment and spindle speed change experiment during braking) verify that the design can achieve high-precision parking angle positioning at different speeds, and has a fast dynamic response and a smooth braking process.","Title: A new method of conflicting evidence management based on non-extensive entropy and Lance distance in uncertain scenarios\nYear: 2022\n\nAbstract: Handing uncertain information is one of the research focuses currently. For the sake of great ability of handing uncertain information, Dempster-Shafer evidence theory (D-S theory) has been widely used in various fields of uncertain information processing. However, when highly contradictory evidence appears, the results of the classical Dempster combination rules (DCR) can be counterintuitive. Aiming at this defect, by considering the relationship between the evidence and its own characteristics, the proposed method is a new method of conflicting evidence management based on non-extensive entropy and Lance distance in uncertain scenarios. Firstly, the Lance distance function is used to measure the degree of discrepancy and conflict between evidences, and the credibility of evidence is expressed by matrix. Introducing non-extensive entropy to measure the amount of information about evidence and express the uncertainty of evidence. Secondly, the discount coefficient of the final fusion evidence is measured by considering the credibility and uncertainty of the evidence, and the original evidence is modified by the discount coefficient. Then, the final result is obtained by evidence fusion with DCR. Finally, two numerical examples are provided to illustrate the efficiency of the proposed method, and the utility of our work is demonstrated through an application of the active lane change to avoid obstacles to the autonomous driving of new energy vehicles. The proposed method has a better identification accuracy, reaching 0.9811.","Title: A Cost-Effective Implementation of Independent Data and Power Transmission Channels in Wireless Power Transfer Systems\nYear: 2022\n\nAbstract: Communication between the primary and secondary sides is of critical significance in wireless power transfer (WPT) systems. In this brief, a novel cost-effective implementation of independent data and power transmission channels is proposed. Based on the typical WPT systems structure, the capacitance between metal shield plates provides naturally an independent capacitive channel for data transmission. Considering the parasitic capacitance between coil and plate, a concentric design for metal plate division is used to form a six-plate capacitive coupling mechanism with symmetrical misalignment tolerance ability, and meanwhile, the magnetic shielding function of the metal plate is maintained. Furthermore, there is no direct line connection between power and data transmission channels, and the parasitic capacitances between coil and plate sections on the same side are almost equal, resulting in virtually no interference between the power and data transmission. Compared with conventional simultaneous power and data transmission methods, additional wave trappers and coupling structures are not needed, which significantly reduces the overall cost and size of WPT systems. Finally, a 50W experimental prototype, with closed-loop output voltage control using 9600bps data transfer rate, validates the feasibility of the proposed method.","Title: Deciphering the Spatial Modular Patterns of Tissues by Integrating Spatial and Single-Cell Transcriptomic Data.\nYear: 2022\n\nAbstract: Single-cell RNA sequencing (scRNA-seq) provides a powerful tool to analyze the expression level of tissues at a cellular resolution. However, it could not capture the spatial organization of cells in a tissue. The spatially resolved transcriptomics technologies (ST) have been developed to address this issue. However, the emerging STs are still inefficient at single-cell resolution and/or fail to capture the sufficient reads. To this end, we adopted a partial least squares-based method (spatial modular patterns [SpaMOD]) to simultaneously integrate the two data modalities, as well as the networks related to cells and spots, to identify the cell-spot comodules for deciphering the SpaMOD of tissues. We applied SpaMOD to three paired scRNA-seq and ST datasets, derived from the mouse brain, granuloma, and pancreatic ductal adenocarcinoma, respectively. The identified cell-spot comodules provide detailed biological insights into the spatial relationships between cell populations and their spatial locations in the tissue.","Title: Review of fuzzy investment research considering modelling environment and element fusion\nYear: 2022\n\nAbstract: Reasonable investment strategies are of great importance for investors, companies, or even countries. Therefore, many investment methods have been proposed to assist people with their investment decision-makings. In this paper, we focus on qualitative investment methods, namely fuzzy investment theory, and provide an overview of the related studies via numerical analysis and literature review. As for the numerical analysis, the status quo and evolution track of fuzzy investment research are presented from static and dynamic perspectives. Specifically, the main publish stages, the whole development trend and the theory diffusion paths of this field are provided in detail. Regarding the literature review, we summaries the fuzzy investment models with consideration of diverse fuzzy environments such as the interval-valued fuzzy, intuitionistic fuzzy, and hesitant fuzzy environments. Various elements fused in the fuzzy investment models such as risk factor, decision-making technique, and portfolio construction are introduced and overviewed as well. Furthermore, we demonstrate the corresponding practical applications of the fuzzy investment methods and clarify their application processes. This study would be helpful for readers, investors, and researchers to comprehensively learn and apply the fuzzy investment theory, which is an effective tool to fuse their subjective opinions into real investment decision-makings.","Title: The Dual-Fuzzy Convolutional Neural Network to Deal With Handwritten Image Recognition\nYear: 2022\n\nAbstract: Subjective evaluation is a commonly used method in the real recognition process. Generally, two fuzziness can be found in evaluation information, namely what values should be given to fully describe the information and how to distinguish different values. The recently developed probabilistic hesitant fuzzy set could perfectly address these issues. In this article, we propose a dual-fuzzy convolutional neural network (DF-CNN) by fusing the hot neural network algorithm into the probabilistic hesitant fuzzy environment and then using it in a practical handwritten image recognition process. For this new DF-CNN, we provide the whole calculation process including the forward propagation, backward propagation, and parameter updating calculations. Also, the optimization algorithm of the DF-CNN is given to derive its optimal results. Finally, we apply the DF-CNN and its optimization algorithm to deal with a real issue, namely the handwritten numeral image recognition. The calculation process and the comparison fully demonstrate the feasibility and effectiveness of the proposed new model and algorithm.","Title: Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation.\nYear: 2022\n\nAbstract: The emotion recognition in conversation (ERC) task aims to predict the emotion label of an utterance in a conversation. Since the dependencies between speakers are complex and dynamic, which consist of intra- and inter-speaker dependencies, the modeling of speaker-specific information is a vital role in ERC. Although existing researchers have proposed various methods of speaker interaction modeling, they cannot explore dynamic intra- and inter-speaker dependencies jointly, leading to the insufficient comprehension of context and further hindering emotion prediction. To this end, we design a novel speaker modeling scheme that explores intra- and inter-speaker dependencies jointly in a dynamic manner. Besides, we propose a Speaker-Guided Encoder-Decoder (SGED) framework for ERC, which fully exploits speaker information for the decoding of emotion. We use different existing methods as the conversational context encoder of our framework, showing the high scalability and flexibility of the proposed framework. Experimental results demonstrate the superiority and effectiveness of SGED.","Title: Event-Triggered Optimal Control for the Continuous Stirred Tank Reactor System\nYear: 2022\n\nAbstract: Continuous stirred tank reactor (CSTR)is essential equipment found in chemical processing industries. Control of the CSTR process has long been a challenging issue due to the high complexity and strong nonlinearity of the chemical process. In this article, a novel event-triggered (ET) adaptive dynamic programming (ADP) optimal control algorithm is developed for the CSTR system. To reduce the compu...","Title: Manipulability and Robustness Optimization of the Cable-Driven Redundant Soft Manipulator\nYear: 2021\n\nAbstract: Redundancy makes soft manipulators manage to complete a variety of tasks in complex environments. However, a pseudo-inverse kinematics controller for redundant soft manipulators simply maximizes the manipulability while ignoring some material constraints, e.g., actuation saturation and control noise. As manipulability is the indicator for the dexterity of motion in all directions of the redundant ...","Title: The Trustworthiness Measurement Model of Component-based Software Based on the Subjective and Objective Weight Allocation Method\nYear: 2020\n\nAbstract: Software trustworthiness includes many attributes. Reasonable weight allocation of trustworthy attributes plays a key role in the software trustworthiness measurement. In practical application, attribute weight usually comes from experts' evaluation to attributes and hidden information derived from attributes. Therefore, when the weight of attributes is researched, it is necessary to consider weig...","Title: Multi-View Spatial-Temporal Adaptive Graph Convolutional Networks for Traffic Forecasting\nYear: 2021\n\nAbstract: Traffic flow prediction is vital to traffic management and public safety, and it plays a very important role in a smart transportation system. Traffic flow data contains complex temporal relation, spatial relation, and spatial-temporal relation. In this paper, we propose a novel Multi-View Spatial-Temporal Adaptive Graph Convolutional Network (MVST-AGCN) for traffic flow prediction. MVST-AGCN is m...","Title: Multi-interaction fusion collaborative filtering for social recommendation\nYear: 2022\n\nAbstract: GNNs(Graph Neural Networks) use graph structure to make recommendations, receiving more and more attention. Firstly, existing work focuses on aggregating social interaction information, ignoring users who rate on the same items. Secondly, the existing recommendation methods cannot dynamically reflect changes in user interests. Thirdly, existing methods do not take into account the interaction of subgraphs in GNNs and interactions between user and item factors. In this paper, a graph social fusion recommendation (GSFR) method is proposed. GSFR captures multiple social information simultaneously, based on which it can dynamically adjust user interest weight. Specifically, GSFR captures the interactions in subgraphs with a dynamic attention mechanism, which can represent changes in user interests in heterogeneous networks. A mutualistic mechanism is combined to simulate the mutually reinforcing relationship between social behavior and virtual behavior. User and item latent factors are obtained based on space vectors from the aggregation part. Recommendations are made from inherent characteristics of space vectors interaction behaviors. Comprehensive experimental results on three public datasets show the effectiveness of the proposed model.","Title: RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment\nYear: 2022\n\nAbstract: Coronary CT Angiography (CCTA) is susceptible to various distortions (e.g., artifacts and noise), which severely compromise the exact diagnosis of cardiovascular diseases. The appropriate CCTA Vessel-level Image Quality Assessment (CCTA VIQA) algorithm can be used to reduce the risk of error diagnosis. The primary challenges of CCTA VIQA are that the local part of coronary that determines final quality is hard to locate. To tackle the challenge, we formulate CCTA VIQA as a multiple-instance learning (MIL) problem, and exploit Transformer-based MIL module (termed as T-MIL) to aggregate the multiple instances along the coronary centerline into the final quality. However, not all instances are informative for final quality. There are some quality-irrelevant/negative instances intervening the exact quality assessment(e.g., instances covering only background or the coronary in instances is not identifiable). Therefore, we propose a Progressive Reinforcement learning based Instance Discarding module (termed as PRID) to progressively remove quality-irrelevant/negative instances for CCTA VIQA. Based on the above two modules, we propose a Reinforced Transformer Network (RTN) for automatic CCTA VIQA based on endto-end optimization. The experimental results demonstrate that our proposed method achieves the state-of-the-art performance on the real-world CCTA dataset, exceeding previous MIL methods by a large margin.","Title: A Compact-Size Multi-Winding Transformer-Based Discharge Equalizer for Electric Two-Wheelers and Three-Wheelers Vehicles Power Battery\nYear: 2022\n\nAbstract: Due to the complexity of driving conditions and the increase of the driving range of electric two-wheelers and three-wheelers vehicles, the power battery string is in a long-period unbalanced discharge state, which affects the power output and even causes certain safety risks. A compact-size multi-winding transformer-based discharge equalizer is proposed to solve these problems and minimize the impact on the system volume by sharing the primary inductor with a boost discharger. When the boost converter is powering a load, the multi-winding transformer equalizer is driven by utilizing the square wave voltage generated on the inductor in the boost converter and produces multiple identical voltage levels to achieve voltage balance. Thus, the automatic balance can be achieved with only one winding and one diode for each module. An experimental prototype containing four batteries was established to verify the feasibility of the proposed method. Moreover, a quantitative comparison shows the proposed method obtain a more compact-size equalizer because of no need for a separate transformer, compared with the traditional equalization method based on the transformer.","Title: A Multifocus Image Fusion Scheme Based on Similarity Measure of Transformed Isosceles Triangles Between Intuitionistic Fuzzy Sets\nYear: 2022\n\nAbstract: Intuitionistic fuzzy set (IFS) theory is widely used to solve imprecise and uncertain problems, and the similarity measure between IFSs can describe the similarity or dissimilarity degree of objects in the real word. In image fusion, it is a key issue to measure and extract the features of multifocus images to fuse them. This research proposes a similarity measure technique of IFSs, which is introduced into image fusion to measure the feature similarity of adjacent pixels. In this work, we present an available similarity measure method between IFSs and propose an image fusion method based on this measure and also design an edge reserved method based on the Canny edge detection to reinforce the image fusion effect. First, the proposed similarity measure is introduced and proved. Second, a focus detection method that can detect the focused regions of an image is designed based on the proposed measure method of IFSs to obtain a preliminary decision map. Third, morphological operations are employed to generate the secondary decision map. Fourth, a Canny edge model-based technique is designed to preserve the edges of the source images. Extensive experiments show that the presented similarity measure method of IFSs is reliable, and the fuzzy measure-based image fusion scheme is effective compared with current methods.","Title: A Bayesian Deep Image Prior Downscaling Approach for High-Resolution Soil Moisture Estimation\nYear: 2022\n\nAbstract: Soil moisture (SM) estimation is a critical part of environmental and agricultural monitoring, with satellite-based microwave remote sensing being the main SM source. However, the limited spatial resolution of most current remote sensing SM products reduces their utility for many applications, such as evapotranspiration modeling and agriculture management. In this article, to address this issue, we propose a Bayesian deep image prior (BDIP) downscaling approach to estimate the high-resolution SM from satellite products. More specifically, the high-resolution SM estimation problem is formulated as a maximum a posteriori problem, and solved via a neural network comprising of a deep fully convolutional neural network (FCNN) for modeling the prior spatial correlation distribution of the underlying high-resolution SM variables, and a forward model characterizing the SM map degeneration process for modeling the data likelihood. As such, the proposed BDIP approach provides a statistical framework that integrates deep learning with forward modeling in a coherent manner for combining different sources of information, i.e., the knowledge in the forward model, the spatial correlation prior in FCNN architecture, and the remote sensing data and products. Experiments on the downscaling of SM active passive SM products using the moderate resolution imaging spectroradiometer products show that SM maps estimated using the proposed method provide greater spatial detail information than other downscaling methods, with the SM estimates very close to in situ measurements.","Title: Dynamic Development Analysis of Complex Network Research: A Bibliometric Analysis\nYear: 2022\n\nAbstract: In recent years, the method of the complex network has been applied to various fields. Dynamics research in complex networks is also an important branch. There are many types of research into dynamic complex network, but few scholars use bibliometrics to study it. Therefore, this paper adopts the method of bibliometrics to analyze the development history and status quo of dynamic complex network, providing a summary description of this research field. We used CiteSpace and Pajek to analyze 2936 relevant papers on the website of Web of Science and further interpreted and summarized the analysis results. To fully show the bibliometric results, we further derive the productive countries, institutions, sources, and authors and some main research directions in the dynamic complex network research. The research results show that the dynamic complex network research method was applied to various fields after the dynamic research method had been integrated into the complex network. According to the subject analysis of the cited papers, we find that the dynamic complex network method has been widely used in various subjects. Through emergent analysis, we found that the latest hot research trends are the study of infectious diseases and applications in neural networks. At the same time, through the main path analysis, we find the essential literature to elaborate on the development context of a dynamic complex network at different time points. This paper gives a comprehensive analysis of dynamic complex network research and provides some enlightenment for the future research direction.","Title: Dynamic behaviors in a Cournot duopoly model with knowledge spillover effect based on constant conjectural variation\nYear: 2022\n\nAbstract: On the basis of constant conjectural variation, a static Cournot duopoly model is built first in this paper, where the effects of knowledge spillover and product differentiation have been parameterized. Through the first order conditions, we can obtain the static conjectural variation equilibrium. By using the gradient adjustment mechanism, a dynamic conjectural variation model, represented by a two-dimensional difference equation, is established, in which two firms have bounded rationality. Then, the local stability of the Nash equilibrium is analyzed. From further studies on the effects of conjectural variation to the dynamics of the established model, we conclude that the more intense the market competition, the bigger the size of the stability region. In general case, the two axes are invariant sets, while the diagonal line is also an invariant set in symmetric case. Through the method of numerical simulation, we can deeply understand the complex characteristics of dynamic behaviors and the means for economic activities. Finally, synchronized behaviors are also studied by the method of critical curves and basins of attraction.","Title: ISANET: Non-small cell lung cancer classification and detection based on CNN and attention mechanism\nYear: 2022\n\nAbstract: Lung cancer is one of the malignant tumors with high morbidity and mortality worldwide. Among them, non small cell lung cancer accounts for about 85% of all lung cancers. In the existing chest CT plain scan or enhanced scan, non-small cell lung cancer images may overlap significantly in imaging features, leading to misdiagnosis and the inability to give an accurate histological classification. Among the existing relevant non small cell lung cancer classification models, few studies on the precise classification of non-small cell lung cancer types. A multi-category classification model based on CNN is proposed to tackle these problems. The proposed model ISANET embeds channel attention and spatial attention mechanisms to focus on pathological areas based on InceptionV3. The three-category lung cancer dataset provided by the Affiliated Hospital of Hebei University is used, including lung squamous cell carcinoma, lung adenocarcinoma, and normal. Comparative experiments are done between ISANET and the traditional models AlexNet, VGG16, InceptionV3, MobilenetV2, and ResNet18. Results of experiments on two public datasets verify the effectiveness of ISANET, reaching 95.24% and 98.14% respectively, which indicates that ISANET has obtained superior accuracy in classifying non-small cell lung cancer.","Title: Displacement Back Analysis of Reservoir Landslide Based on Multi-Source Monitoring Data: A Case Study of the Cheyiping Landslide in the Lancang River Basin, China\nYear: 2022\n\nAbstract: Landslides that occur in the littoral zone of a reservoir can directly damage the hydraulic structures and threaten the lives and property around the reservoir. Due to the spatial variability and heterogeneities of rock mass, a limited amount of data obtained from laboratory and in situ tests cannot comprehensively characterize the mechanical properties of rock and soil masses. Therefore, displacement back analysis is often performed to determine the mechanical parameters of rock and soil masses. The spaceborne Interferometric synthetic aperture radar (InSAR) has proved to be a powerful tool for geodesy in the measurement of landslide movement. However, InSAR can only measure the surface motion of the landslide without the subsurface information. This study uses multi-source monitoring data in the landslide displacement back analysis, including surface InSAR and an internal borehole inclinometer. The identified material parameters and finite element simulation are used to predict the landslide deformation. The case study of the Cheyiping landslide located in the Lancang River basin demonstrates the necessity and feasibility of using multi-source monitoring data in landslide displacement back analysis. The Cheyiping landslide is currently in the creep deformation stage. The decrease in shear strength of rock masses due to the rheological deformation and the change in reservoir water level are the internal and external factors leading to excessive landslide deformation. The numerical modeling can accurately simulate the landslide movement using the identified material parameters. By combing multi-source monitoring data and numerical modeling, the reservoir landslide deformation analysis can help evaluate the landslide deformation state and stability, which is vital for reservoir risk mitigation and the sustainable development of hydropower resources.","Title: Decentralized Self-Organizing Networks for Poor Reconfigurability of Loom Networks\nYear: 2022\n\nAbstract: The intelligent weaving of looms requires networking and intelligent industrial production systems that use industrial equipment with network communication and data computing, creating a \"smart factory\". However, limited by the development of industrial technology, existing industrial loom equipment does not have sufficient communication or computing capabilities: communications primarily rely on bus and Ethernet communication, which is limited to wired connections; and computations primarily rely on the host computers and cloud computers that only provide weak real-time computing. Currently, the remote management system used in the loom industry can be constructed using a cloud platform, but machine-machine interconnections between the loom control terminals are ignored. To solve the problem of poor reconfigurability in loom networks that rely on cloud servers, this study analyzes the networking requirements of a loom network and uses mesh network technology to build a wireless self-organizing loom network. A mesh network can be formed with clouds, loom equipment, and other equipment. By constructing an experimental platform with a decentralized self-organizing network to build a remote loom monitoring system, an anti-destruction experiment of the developed self-organizing loom network is performed. After the self-organizing network is constructed, key nodes in the network are disconnected, and the network is automatically reconfigured to resume normal work. The reconfigurability and stability of the network are demonstrated via destructive experiments. Results show that the self-organizing network construction method developed in this study can build a loom self-organizing network, which is anti-destructive and mitigate the poor reconfigurability of existing loom networks. Compared with the wireless communication network of the previous loom remote monitoring system, the loom interconnection self-organizing network is more reliable and anti-interference, which can better meet the network demand of intelligent manufacturing for loom equipment. This study provides an effective example of loom networking technology for the industry and is important when promoting the construction of smart loom factories.","Title: Path extension similarity link prediction method based on matrix algebra in directed networks\nYear: 2022\n\nAbstract: Traditional link prediction methods are generally only calculated for the neighbor information of nodes, and the network path between nodes has not been fully utilized. Therefore, this paper proposes a directed network link prediction method based on path extension similarity to improve the prediction accuracy of potential edges of network nodes. Firstly, the mathematical definition of each local index is expressed in matrix form through matrix algebra; secondly, according to the algorithm principle of global and quasi-local indices, the extension form of local indices is clarified; and the path extension of each local index is carried out respectively; finally, multiple real data sets are used to analyze the benchmark indices and extended indices. The results of the AUC and Precision evaluation metrics show that the path extension similarity proposed in this paper has higher accuracy and stronger robustness than the benchmark indices.","Title: Spatiotemporal Filtering for Continuous GPS Coordinate Time Series in Mainland China by Using Independent Component Analysis\nYear: 2022\n\nAbstract: Continuous Global Positioning Systems (GPS) coordinate time series with a high spatiotemporal resolution, and provide a great opportunity to study their noise models and common mode errors (CMEs), thus making it possible to detect and analyse spatiotemporal characteristics of tectonic and non-tectonic signals in time series, and further to estimate a reliable and accurate velocity field of crustal movement in a region by removing CMEs and using the optimal noise model. In this paper, we used GPS coordinate time series from the Crustal Movement Observation Network of China (CMONOC) with an approximate decadal period from 2010 to 2020, to construct optimal noise models by fitting them with several noise combinations according to the Akaike information criterion (AIC). We further adopted independent component analysis (ICA) to extract CMEs and analysed their spatiotemporal characteristics, and then evaluated their effects on noise models and velocity uncertainties, and finally estimated a decennial velocity field of crustal movement with a higher signal-to-noise ratio (SNR) by applying the CME filtering and considering the optimal noise model in Mainland China. Our results show that optimal noise models are dominated by white noise (WN) plus flicker noise (FN) for both east and north components, and WN plus power law noise (PN) with spectral index close to -1 for up component, respectively. ICA filtering can remove the highly spatially correlated CMEs and decrease the mean RMSEs of the residual time series by about 40-60%, providing a more accurate velocity field with a higher SNR in Mainland China, accordingly.","Title: Stochastic configuration broad learning system and its approximation capability analysis\nYear: 2022\n\nAbstract: In this paper, a kind of stochastic configuration broad learning system (SCBLS) is proposed for data modeling. The proposed SCBLS is established in the form of a flat network and its architecture is determined by a constructive learning approach. The input parameters of feature nodes and enhancement nodes of SCBLS are randomly assigned in the light of a supervisory mechanism. Inequality constraints are used to randomly assign the hidden parameters and adaptively select the scopes of random parameters. The output parameters of SCBLS are determined either by a constructive manner or by solving a global least squares problem. It is proved that the proposed SCBLS possesses universal approximation properties. The performances of the proposed SCBLS are evaluated by function approximation, benchmark datasets and time series prediction. Numerical examples show that SCBLS can achieve satisfactory approximation accuracy.","Title: Quality Assessment of Image Super-Resolution: Balancing Deterministic and Statistical Fidelity\nYear: 2022\n\nAbstract: ABSTRACTThere has been a growing interest in developing image super-resolution (SR) algorithms that convert low-resolution (LR) to higher resolution images, but automatically evaluating the visual quality of super-resolved images remains a challenging problem. Here we look at the problem of SR image quality assessment (SR IQA) in a two-dimensional (2D) space of deterministic fidelity (DF) versus statistical fidelity (SF). This allows us to better understand the advantages and disadvantages of existing SR algorithms, which produce images at different clusters in the 2D space of (DF, SF). Specifically, we observe an interesting trend from more traditional SR algorithms that are typically inclined to optimize for DF while losing SF, to more recent generative adversarial network (GAN) based approaches that by contrast exhibit strong advantages in achieving high SF but sometimes appear weak at maintaining DF. Furthermore, we propose an uncertainty weighting scheme based on content-dependent sharpness and texture assessment that merges the two fidelity measures into an overall quality prediction named the Super Resolution Image Fidelity (SRIF) index, which demonstrates superior performance against state-of-the-art IQA models when tested on subject-rated datasets.","Title: Network analysis for elucidating the mechanisms of Shenfu injection in preventing and treating COVID-19 combined with heart failure\nYear: 2022\n\nAbstract: •A systematic framework is developed to clarify the pharmacological effects of SFI.•The ADME filtering and target fishing identify the active substances and targets.•The C-T-D network dissects the therapeutic effect of SFI against COVID-19 combined with HF.•The PPI network is proposed to obtain hub targets of SFI, COVID-19 and HF.•The pathway analysis reveals the pharmacological mechanisms of SFI at the pathway level.","Title: Research on the Rotational Correction of Distributed Autonomous Orbit Determination in the Satellite Navigation Constellation\nYear: 2022\n\nAbstract: The autonomous orbit determination of the navigation constellation uses only bidirectional ranging data of the inter-satellite link for data processing. The lack of space-time benchmark information related to the Earth inevitably causes overall rotational uncertainty in the constellation, leading to a decrease in orbit accuracy and affecting user positioning accuracy. This study (1) introduces a method for rotation correction in distributed autonomous orbit determination based on inter-satellite bidirectional ranging; (2) conducts constellation autonomous orbit determination and time synchronization processing experiments based on inter-satellite ranging data for the 24 medium Earth orbit (MEO) satellites in the Beidou-3 global satellite navigation system (BDS-3); and (3) makes comparative analyses on the accuracy of autonomous orbit determination based on three rotation correction cases, including a no-rotation-correction case, independent satellite constraints case, and global satellite constraints case. The experimental results are described as follows. For the no-rotationcorrection case, the prediction error of the orbital inclination angle (iot, i) for the entire constellation on the 30th day was 2.11 x 10(-7)/rad, the prediction error of the right ascension of the ascending point (Omega, Omega) was 2.25 x 10(-7)/rad, and the average root mean square (RMS) of the user range error (URE) for the entire constellation orbit was 1.41 m. In the autonomous orbit determination experiment with independent constraints on satellites, the prediction error of i for the entire constellation on the 30th day was 5.43 x 10(-7)/rad, the prediction error of Omega was 2.03 x 10(-7)/rad, and the average RMS of the orbital URE for the entire constellation was 1.09 m. In the autonomous orbit determination experiment with global satellite constraints, the prediction error of i for the entire constellation on the 30th day was 5.31 x 10(-7)/rad, the prediction error of Omega was 1.95 x 10(-7)/rad, and the RMS of the orbital URE for the entire constellation was 0.94 m. According to the analysis of the above experimental results, compared with the autonomous orbit determination under the no-rotation-correction case, the adoption of an algorithm for independent satellite constraints to correct the overall constellation rotation weakens the constellation rotation influence; however, it may destroy the overall constellation configuration, which affects the stability of autonomous orbit determination. Finally, the algorithm based on global satellite constraints both impairs the influence of constellation rotation and maintains the overall constellation configuration.","Title: Design and Research of M2M Message Transfer Mechanism of Looms for Information Transmission\nYear: 2022\n\nAbstract: Machine-to-machine (M2M) communication is a key implementation technology for future Industrial Internet of Things (IIoT) applications. The requirements for flexibility, efficiency, and cross-platform compatibility in intermodule communication between the connected machines raise challenges for the M2M messaging mechanism. For the loom message transmission mechanism, it is necessary to have certain flexibility and meet certain M2M communication requirements to understand each other. In this paper, we analyze and study the data model, data exchange language, MQTT protocol and logo resolution technology of looms to address the current problems of low data transmission efficiency, poor flexibility and lack of flexibility in the network. By establishing the data model of looms and classifying them by topic, selecting JSON format as the transmission format of data, and using MQTT protocol to realize data transmission between loom networks, an MQTT-based messaging mechanism is established. The data involved in the communication of the loom equipment are grouped according to the communication requirements, and only the loom data required by the application are subscribed. At the same time, the hardware and software parts of the communication module are designed with ESP32 as the core, so that it can complete the functions of loom network communication, data analysis, format conversion and so on. In addition, the real-time requirements of the loom network communication and the network requirements for future intelligent manufacturing must be met. This paper uses the BP neural network to intelligently optimize the loom network, and the existing problems related to performance indices are analyzed. The original loom network is optimized by integrating the RSSI, QoS requirements, transmission priority, real-time communication and data transmission volume of nodes. In the meantime, the particle swarm optimization algorithm is introduced to optimize the BP neural network to avoid the problem that it easily can fall into the local optimal solution and effectively improving the network optimization efficiency. Finally, the feasibility of this method is proven by using the experimental platform, which can solve the problems of low network transmission efficiency and difficult network optimization of looms and provide an effective technical example of loom information transmission and network optimization for industry.","Title: BMAM: complete the missing POI in the incomplete trajectory via mask and bidirectional attention model\nYear: 2022\n\nAbstract: Studies on the checked-in point-of-interests have become an important means to learn user’s behavior. Nevertheless, users do not sign in to all visited locations. There are unobserved check-in locations in the generated POI trajectory. Such the trajectory is called an incomplete trajectory, and unobserved point is called missing point. However, incomplete trajectory has a negative impact on downstream tasks such as personalized recommendation system, criminal identification and next location prediction. It is a challenge to use the forward sequence and backward sequence information of the missing point to complete the missing POI. Therefore, we propose a bidirectional model based on mask and attention mechanism (BMAM) to solve the problem of missing POI completion in user’s incomplete trajectory. The context information of trajectory checked in by user can be mined to connect the missing POI with the forward sequence and backward sequence information. Therefore, the model learns the order dependence between each location according to the user trajectory sequence and obtain the user’s dynamic preference to identify the missing POI in the sequence. Besides, the attention mechanism is used to improve the user's representation feature, that is, the preference for POI categories. The experimental results demonstrate that our BMAM outperforms the state-of-the-art models for completion on missing POI of user’s incomplete sequence.","Title: A Block PatchMatch-Based Energy-Resource Efficient Stereo Matching Processor on FPGA\nYear: 2022\n\nAbstract: This paper presents a field programmable gate array (FPGA)-based, high-performance and energy-resource efficient stereo matching processor. The proposed processor executes block-level PatchMatch-based stereo matching algorithm with a random search strategy to avoid estimation of all disparity levels. To take advantages of different block scales, a coarse-to-fine multi-scale propagation (MSP) scheme is proposed for label update. Based on that, a dedicated hardware architecture is further proposed to explore the benefit of the algorithm. Experimental results show that the proposed FPGA-based processor, running at 350MHz, achieves a peak performance of \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$1920\\times 1080.165$ </tex-math></inline-formula>\n.7 frame per second (FPS) at 128 disparity levels with 3.35W power dissipation. The energy and resource efficiency of the proposed design outperforms state-of-the-art FPGA-based stereo matching processors. When disparity level increases to 256, the computing resource increment of the proposed design is much less than existing designs because random search instead of winner-takes-all (WTA) is utilized. Moreover, unlike existing dedicated stereo matching processors which output only disparity information, the proposed design is also capable of deriving plane slant. This information can be beneficial for follow-up tasks like 3D reconstruction.","Title: Proprioceptive feedback design for gait synchronization in collective undulatory robots\nYear: 2022\n\nAbstract: The collective movement of animals has long been a source of inspiration for multi-agent swarm robotics. One of the fundamental goals for swarm robotics study is to understand how effective and robust collective behaviors can emerge from simple interaction principles. When animal or robot collectives are in high-density configurations the ability for visual or auditory sensing is diminished and the opportunities for interacting through mechanical contact are enhanced. In this paper, we study how robots that move through lateral body undulation in close proximity are capable of synchronizing their oscillatory gaits through contact interactions between adjacent robots. Critically, gait phase synchronization occurs without the requirement for robot-robot communication, and instead can be engineered as an emergent property of the robot control system. We present a proprioceptive feedback control system that generates collective gait phase synchronization of undulatory robots in experiment and simulation. We first validate this control system using a simple one-dimensional toy model to demonstrate how proprioceptive feedback governs phase synchronization. Simulations and experiments with undulatory three-link robots further demonstrate how phase synchronization can be controlled. Lastly, we demonstrate that robot pairs moving in a confined tunnel can synchronize their movements which leads to faster group locomotion through confined spaces.","Title: ProbResist: a database for drug-resistant probiotic bacteria\nYear: 2022\n\nAbstract: Drug resistance remains a global threat, and the rising trend of consuming probiotic-containing foods, many of which harbor antibiotic resistant determinants, has raised serious health concerns. Currently, the lack of accessibility to location-, drug- and species-specific information of drug-resistant probiotics has hampered efforts to combat the global spread of drug resistance. Here, we describe the development of ProbResist, which is a manually curated online database that catalogs reports of probiotic bacteria that have been experimentally proven to be resistant to antibiotics. ProbResist allows users to search for information of drug resistance in probiotics by querying with the names of the bacteria, antibiotic or location. Retrieved results are presented in a downloadable table format containing the names of the antibiotic, probiotic species, resistant determinants, region where the study was conducted and digital article identifiers (PubMed Identifier and Digital Object Identifier) hyperlinked to the original sources. The webserver also presents a simple analysis of information stored in the database. Given the increasing reports of drug-resistant probiotics, an exclusive database is necessary to catalog them in one platform. It will enable medical practitioners and experts involved in policy making to access this information quickly and conveniently, thus contributing toward the broader goal of combating drug resistance.","Title: Ultrasonographic features of cervical lymph node metastases from medullary thyroid cancer: a retrospective study\nYear: 2022\n\nAbstract: To investigate sonographic features of cervical lymph node metastases from medullary thyroid cancer (LNM-MTC), as compared with lymph node metastases from papillary thyroid cancer (LNM-PTC). A total of 42 MTC patients with 52 metastatic LNs and 222 PTC patients with 234 metastatic LNs who were confirmed by fine needle aspiration and post-operative pathology, were enrolled in this study. The clinical characteristics and sonographic features of LNs were compared between the two groups. Univariate analysis and multivariate logistic regression analysis were performed on the sonographic features of LNs, including short and long-axis diameter, long-axis diameter/short-axis, shape, border, hilum, echogenicity, calcifications, cystic change and vascularity pattern. The discriminating performance was assessed with the area under the receiver operating characteristic curve (AUC). The mean age of patients with LNM-MTC was older than that of patients with LNM-PTC (46.81 ± 13.05 vs 39.09 ± 12.05, P < 0.001). No differences were observed in gender, location, long-axis diameter/short-axis, shape, border, echogenicity, cystic change and vascularity pattern between LNM-MTC and LNM-PTC groups (P > 0.05, for all). However, long-axis and short-axis diameter, hilum and calcifications were statistically different between these two groups (P < 0.05, for all). The AUC of discriminate value between LNM-MTC and LNM-PTC was 0.808 (95% confidence interval 0.739–0.877). Compared with LNM-PTC, LNM-MTC tended to have the sonographic characteristics of larger size, absence of hilum, and less calcifications, and awareness of these features might be helpful to in the diagnosis of LNM-MTC.","Title: Monitoring Extractive Activity-Induced Surface Subsidence in Highland and Alpine Opencast Coal Mining Areas with Multi-Source Data\nYear: 2022\n\nAbstract: Mining developments in alpine coal mining areas result in slow or rapid ground subsidence, which can lead to melting and collapse of permafrost. This paper integrated unmanned aerial vehicle (UAV) images and satellite-based SAR interferometry images to monitor intensive surface mining subsidence during reclamation. Digital Surface Model (DSM) acquired from UAV images was first used to evaluate the changes of the reclamation scheme on the microtopography carried out by slope and the Digital Elevation Model (DEM) of difference (DoD). The monitoring results showed that the slope had been reduced from over 30 degrees to under 15 degrees after the terrain had been reshaped. The DoD map revealed the distribution of main extraction areas and landfill areas. To further monitor the surface subsidence after local terrain adjustment, the Permanent Scatterer Interferometry (PS-InSAR) method was used to reveal the surface subsidence characteristics of the mine site before and after reclamation. The maximum cumulative subsidence ranged from -772.3 to 1183 mm based on 21 Sentinel-1A images in three years. Within a year of terrain reshaping, uplift and subsidence still occurred at hills and pit side slopes, following the nearly equal subsidence rate. The experimental results showed that the slope reshaping and vegetation recovery had a limited impact on the reduction of the ground subsidence in a short period. Therefore, on this basis, a combination of UAV and PS-InSAR methods can be used to continue monitoring time series subsidence in alpine mines.","Title: Deep sparse autoencoder integrated with three-stage framework for glaucoma diagnosis\nYear: 2022\n\nAbstract: Recently, end-to-end deep neural networks-based glaucoma diagnosis approaches have been gaining much attention. However, the feature extractor and classier in these approaches are trained together, which is known as coadaptation. Therefore, the feature distribution in them should adapt to particular decision boundaries. To learn generic data representations and improve the generalization ability of the model, this paper designs a three-stage framework for glaucoma diagnosis. In the first stage, preprocessing is utilized to extract the Region of Interesting around the Optic Disc to reduce the computational cost and nonobjective interference. In the second stage, Deep Sparse Autoencoder is designed to learn hybrid features between the deep features and the original features, which could improve the effectiveness of final high-level feature expression. Meanwhile, L1 regularization is introduced and applied on the hybrid features to obtain deep features with high complementarity under small sample problem. In the third stage, the obtained generic feature representations are fed into different classifiers, in which Support Vector Machine classifier achieves the best diagnosis performance. The proposed approach is evaluated on two publicly available databases. Extensive experimental results indicate that our approach outperforms the state-of-the-art approaches with the accuracy of 96.00%, 97.00% and Area Under Curve of 96.94%, 98.28% for REFUGE and Drishti-GS1 databases, respectively.","Title: Stability and global dynamics of a quantum Cournot duopoly game with isoelastic demand\nYear: 2022\n\nAbstract: In this paper, a statics quantum Cournot duopoly model with isoelastic demand is firstly set up considering quantum entanglement in decision making between firms. On this basis, a dynamic duopoly game with bounded rationality is established by dynamic adjustment mechanism, and then, local stability of the Nash equilibrium is studied by Jacobian matrix and Jury criterion. In addition, local bifurcation, the multi-stability motion and contact bifurcation of system are analyzed in detail through numerical simulation. It is found that the system will gradually enter chaos as the speed of adjustment increases. Furthermore, the marginal cost can change the stability region and the bifurcation types of the system. The results of quantum entanglement show that the squeezing parameter affects the global dynamical behaviors of the system and the equilibrium profits of firms. Finally, we compared the dynamical behaviors of the established model with those of the linear demand model proposed by Yang and Gong.","Title: Effect of Stochastic Modeling for Inter-Frequency Biases of Receiver on BDS-3 Five-Frequency Undifferenced and Uncombined Precise Point Positioning\nYear: 2022\n\nAbstract: The third generation of the Beidou navigation satellite system (BDS-3) broadcasts navigation signals of five frequencies. Focusing on the deep integration of five-frequency signals, we applied the joint BDS-3 five-frequency undifferenced and uncombined precise point positioning (UC-PPP) to analyze the receiver inter-frequency biases (IFB). Firstly, 12 Multi-GNSS Experiment tracking (MGEX) stations are selected to investigate the time-varying characteristics of receiver IFB and, according to random characteristics, three random modeling schemes are proposed. Secondly, the effects of three stochastic modeling methods on zenith tropospheric delay, ionospheric delay, floating ambiguity, and quality control are analyzed. Finally, the effects of three IFB stochastic modeling methods on positioning performance are evaluated. The results showed that the amplitude in the IFB for B2b is 5.139 m, B2a is 1.964 m, and B1C is 0.950 m by measuring one week's observation data. The IFB stochastic modeling method based on random walks can shorten the PPP convergence time by 4 similar to 12%, diminish the false alarm of quality control, and improve the positioning accuracy. The random walk model is recommended to simulate the variation of IFB, which can not only overcome the disadvantage of the time constant model being unable to accurately describe the time-varying characteristics of the IFB, but also avoid reducing the strength of the kinematic PPP positioning model due to the large process noise of the white noise model.","Title: Phenological Responses to Snow Seasonality in the Qilian Mountains Is a Function of Both Elevation and Vegetation Types\nYear: 2022\n\nAbstract: In high-elevation mountains, seasonal snow cover affects land surface phenology and the functioning of the ecosystem. However, studies regarding the long-term effects of snow cover on phenological changes for high mountains are still limited. Our study is based on MODIS data from 2003 to 2021. First, the NDPI was calculated, time series were reconstructed, and an SG filter was used. Land surface phenology metrics were estimated based on the dynamic thresholding method. Then, snow seasonality metrics were also estimated based on snow seasonality extraction rules. Finally, correlation and significance between snow seasonality and land surface phenology metrics were tested. Changes were analyzed across elevation and vegetation types. Results showed that (1) the asymmetry in the significant correlation between the snow seasonality and land surface phenology metrics suggests that a more snow-prone non-growing season (earlier first snow, later snowmelt, longer snow season and more snow cover days) benefits a more flourishing vegetation growing season in the following year (earlier start and later end of growing season, longer growing season). (2) Vegetation phenology metrics above 3500 m is sensitive to the length of the snow season and the number of snow cover days. The effect of first snow day on vegetation phenology shifts around 3300 m. The later snowmelt favors earlier and longer vegetation growing season regardless of the elevation. (3) The sensitivity of land surface phenology metrics to snow seasonality varied among vegetation types. Grass and shrub are sensitive to last snow day, alpine vegetation to snow season length, desert to number of snow cover days, and forest to first snow day. In this study, we used a more reliable NDPI at high elevations and confirmed the past conclusions about the impact of snow seasonality metrics. We also described in detail the curves of snow seasonal metrics effects with elevation change. This study reveals the relationship between land surface phenology and snow seasonality in the Qilian Mountains and has important implications for quantifying the impact of climate change on ecosystems.","Title: A vision-based abnormal trajectory detection framework for online traffic incident alert on freeways\nYear: 2022\n\nAbstract: Abnormal trajectory detection from surveillance cameras is a highly desirable but challenging task, especially for online traffic incident alert on freeways. Existing methods are mainly customized for offline alert and easily suffer from false alerts when applying them to online alert. To fill this gap, an anomaly trajectory detection framework is proposed for online traffic incident alert on freeways. Based on a LSTM autoencoder, this framework introduces an adversarial learner (AL) for offline training and an abnormal trajectory discriminator (ATD) for online alert. The adversarial learner uses an additional adversarial loss to enable the autoencoder to learn a better normal trajectory pattern that is beneficial for reducing false alerts, while an abnormal trajectory discriminator is established and trained to detect small mean shift and filter out instantaneous false alerts. The experimental results show that our proposed framework effectively filters out false alerts and obtains a state-of-art performance (AUC = 0.97) compared to existing methods. Moreover, our framework could timely alert traffic incidents within 0.25 s, which is significant for timely preventing the occurrence of traffic crashes and improving the response speed of incident management on freeways.","Title: Evolutionary Search With Multiview Prediction for Dynamic Multiobjective Optimization\nYear: 2022\n\nAbstract: Dynamic multiobjective optimization problem (DMOP) denotes the multiobjective optimization problem which varies over time. As changes in DMOP may exist some patterns that are predictable, to solve DMOP, a number of research efforts have been made to develop evolutionary search with prediction approaches to estimate the changes of the problem. A common practice of existing prediction approaches is to predict the change of Pareto-optimal solutions (POS) based on the historical solutions obtained in the decision space. However, the change of a DMOP may occur in both decision and objective spaces. Prediction only in the decision space thus may not be able to give the proper estimation of the problem change. Taking this cue, in this article, we propose an evolutionary search with multiview prediction for solving DMOP. In contrast to existing prediction methods, the proposed approach conducts prediction from the views of both decision and objective spaces. To estimate dynamic changes in DMOP, a kernelized autoencoding model is derived to perform the multiview prediction in a reproducing kernel Hilbert space (RKHS), which holds a closed-form solution. To examine the performance of the proposed method, comprehensive empirical studies on the commonly used DMOP benchmarks, as well as a real-world case study on the movie recommendation problem, are presented. The obtained experimental results verified the efficacy of the proposed method for solving both benchmark and real-world DMOPs.","Title: Integrating the Pre-trained Item Representations with Reformed Self-attention Network for Sequential Recommendation\nYear: 2022\n\nAbstract: The conventional sequential recommendation tends to recommend an item to a sequence due to the concurrence of this item in other similar sequences. It is a typical collaborative filtering recommendation strategy that suffers from the data sparsity problem. Integrating the item-side information into the recommendation model can alleviate the data sparsity problem and avoid the privacy issue. However, roughly fusing item-side information in item embeddings to optimize for final recommendation accuracy might lose the objective associations among items. Thus, this paper proposes a self-supervised learning task on item-side information to generate the item representation space in advance of the recommendation task. The space can gather similar items and separate the different items. Then we reform a recently successful sequential recommendation method, self-attention network, by integrating an untrainable mechanism to fully take advantage of the pre-trained item representation space. The two procedures comprise a hybrid recommendation framework to achieve collaborative filtering and content-based recommendation strategies. Extensive experiments conducted on two benchmark datasets demonstrate the superiority of our proposed methods over other baseline methods.","Title: LCAN: Light Cross-Attention Network for Collaborative Filtering Recommendation\nYear: 2022\n\nAbstract: In recent years, an emerging research work in recommendation systems aimed at exploring users' potential interaction preferences. However, most existing methods can only capture information about the user's purchase (or click) history. To estimate users' potential interaction preferences more accurately, it is necessary to consider auxiliary information when modeling user-item interactions. In this paper, a Light Cross-Attention Network (LCAN) is proposed. LCAN makes full use of existing information in three parts: 1) User-Item interaction graph. The interaction history is an important signal, and the user's interaction preferences can be obtained directly from the interaction history. 2) User-User and Item-Item relationships. The user-user and item-item graphs are additionally constructed based on the similarity between users and items to alleviate data sparseness. 3) Complementarity between graphs. Information between different graphs is interrelated, and a graph-level cross-attention network is used to capture the complementarity between graphs. Extensive experiments have been conducted by comparing state-of-the-art methods, and it shows that our LCAN model can outperform the most advanced recommendation methods.","Title: Towards Robust Uncertainty Estimation in the Presence of Noisy Labels\nYear: 2022\n\nAbstract: In security-critical applications, it is essential to know how confident the model is in its predictions. Many uncertainty estimation methods have been proposed recently, and these methods are reliable when the training data do not contain labeling errors. However, we find that the quality of these uncertainty estimation methods decreases dramatically when noisy labels are present in the training data. In some datasets, the uncertainty estimates would become completely absurd, even though these labeling noises barely affect the test accuracy. We further analyze the impact of existing label noise handling methods on the reliability of uncertainty estimates, although most of these methods focus only on improving the accuracy of the models. We identify that the data cleaning-based approach can alleviate the influence of label noise on uncertainty estimates to some extent, but there are still some drawbacks. Finally, we propose a robust uncertainty estimation method under label noise. Compared with other algorithms, our approach achieves a more reliable uncertainty estimates in the presence of noisy labels, especially when there are large-scale labeling errors in the training data.","Title: Uncertainty-aware Propagation Structure Reconstruction for Fake News Detection.\nYear: 2022\n\nAbstract: The widespread of fake news has detrimental societal effects. Recent works model information propagation as graph structure and aggregate structural features from user interactions for fake news detection. However, they usually neglect a broader propagation uncertainty issue, caused by some missing and unreliable interactions during actual spreading, and suffer from learning accurate and diverse structural properties. In this paper, we propose a novel dual graph-based model, Uncertainty-aware Propagation Structure Reconstruction (UPSR) for improving fake news detection. Specifically, after the original propagation modeling, we introduce propagation structure reconstruction to fully explore latent interactions in the actual propagation. We design a novel Gaussian Propagation Estimation to refine the original deterministic node representation by multiple Gaussian distributions and arise latent interactions with KL divergence between distributions in a multi-facet manner. Extensive experiments on two real-world datasets demonstrate the effectiveness and superiority of our model.","Title: A Unified Propagation Forest-based Framework for Fake News Detection.\nYear: 2022\n\nAbstract: Fake news’s quick propagation on social media brings severe social ramifications and economic damage. Previous fake news detection usually learn semantic and structural patterns within a single target propagation tree. However, they are usually limited in narrow signals since they do not consider latent information cross other propagation trees. Motivated by a common phenomenon that most fake news is published around a specific hot event/topic, this paper develops a new concept of propagation forest to naturally combine propagation trees in a semantic-aware clustering. We propose a novel Unified Propagation Forest-based framework (UniPF) to fully explore latent correlations between propagation trees to improve fake news detection. Besides, we design a root-induced training strategy, which encourages representations of propagation trees to be closer to their prototypical root nodes. Extensive experiments on four benchmarks consistently suggest the effectiveness and scalability of UniPF.","Title: Time-Delay Memristive Recurrent Neural Network and Its Complex Dynamics\nYear: 2022\n\nAbstract: This paper focuses on the hybrid effects of memristor characteristics, coupling coefficient and time-delay on the recurrent neural network. We construct a novel time-delay recurrent neural network based on a passive hyperbolic tangent memristor, whose instability condition at equilibrium points is judged according to the Routh criterion. Under the changes of the memristor internal parameter, coupling coefficient and time-delay, some complex dynamic behaviors, including chaos and various coexisting attractors, of the time-delay memristive recurrent neural network are observed in detail through the bifurcation diagram, the largest Lyapunov exponent, and the basin of attraction. Finally, the theoretical results are verified by the circuit simulation through MULTISIM.","Title: Triangular FM-OFDM Waveform Design for Integrated Sensing and Communication\nYear: 2022\n\nAbstract: The integrated sensing and communication (ISAC) technology will support the development of emerging services such as machine-type communication and immersive services toward the future 5th generation advanced (5G-A) and 6th generation (6G) eras. To satisfy the high-precision sensing requirements inspired by 6G emerging services and increase the communication rate, we exploit the advantages of large time-bandwidth product of linear frequency modulation (LFM) and high spectral efficiency of orthogonal frequency division multiplexing (OFDM), proposing an ISAC waveform combining triangular frequency modulation (FM) and OFDM. The delayDoppler decoupling radar signal processing scheme is proposed, which obtains better sensing performance than the OFDM based ISAC scheme. Simulation results show that the proposed scheme can achieve high accuracy ranging estimation, and the resolution of velocity estimation is doubly improved compared with the OFDM based ISAC scheme.","Title: A 39pJ/label 1920x1080 165.7 FPS Block PatchMatch Based Stereo Matching Processor on FPGA\nYear: 2022\n\nAbstract: Depth is a fundamental information for lots of computer vision applications. Stereo matching is a commonly used depth estimation method that mimics the human binocular vision system. Most stereo matching systems suffer when attempt to strike a balance between accuracy and computational complexity because of two reasons: 1) It is usually assumed that all the surfaces are fronto-parallel, meaning that neighbors share the same disparity. But a lot of real-world situations are slant surfaces like roads and walls. 2) Conventional methods usually utilize winner takes all (WTA) strategy [1]–[4], where aggregated costs in all disparity levels (usually 128 or 256) must be calculated. But there is only one true guess for each pixel position, such that most of the calculated costs are meaningless. To solve the above issues, in this work, a block PatchMatch-based FPGA accelerator for stereo matching is proposed. PatchMatch introduces random search strategy and slant label, where rectangle superpixels called blocks are used as the basic computing element. Main improvements of this work are: 1) Utilized random search strategy and block level computation can save massive computation. 2) Closer-to-reality slant label improves accuracy. Moreover, plane slant is also helpful for following tasks like 3D reconstruction [5], but none of the existing hardware accelerators can provide this information. 3) Algorithm-hardware co-optimized 6-points Census feature and multi-scale propagation (MSP) are proposed. 4) Based on the testing results on industrial-level KITTI dataset, the real-time performance and energy efficiency of the proposed design outperform state-of-the-art FPGA and ASIC designs, with comparable accuracy.","Title: Linearity Analysis of Phase-Domain Delta-Sigma Modulator\nYear: 2022\n\nAbstract: This paper presents a study on the input-output linearity of Phase-Domain Delta-Sigma Modulator (PDDSM). This study identifies that, besides the selection of phase references in the PDDSM feedback path which has been reported to affect linearity in prior art, there are additional design parameters which have significant impact on linearity, namely the quantizer sampling frequency (f\n<inf xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">s</inf>\n) and the input driving frequency ($f_{\\text{drive}}$). Comprehensive theoretical analysis of their impact on PDDSM’s linearity has been performed through analytical derivation and validated through behavioral model simulation in MATLAB Simulink™, where results are in good agreement with each other. This analysis shows that to achieve high linearity in PDDSM design, f\n<inf xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">s</inf>\n should equal to $2\\cdot f_{\\text{drive}}/\\mathbf{m}$ where m is a positive integer, and $f_{\\text{drive}}$ should be large enough as compared to the rate of change of input phase, e.g. about $ 10^{4}\\times$.","Title: Text Similarity Measurement Method and Application of Online Medical Community Based on Density Peak Clustering\nYear: 2022\n\nAbstract: Text similarity measurement is a link between basic research such as text modeling and upper-level application research of text potential information. In order to improve the accuracy of text similarity measurement, this paper proposes a semantic similarity calculation method integrating word2vec model and TF-IDF and applies it to the density peak clustering of Chinese text data consulted by patients in the online medical community. Experimental results show that the proposed similarity measurement method is superior to the traditional method. Furthermore, the study is among the first to apply the density peak clustering algorithm to the online medical community, which offers a reference for how to find user demands from medical text data in the big data environment.","Title: A Random Forest Model for Drought: Monitoring and Validation for Grassland Drought Based on Multi-Source Remote Sensing Data\nYear: 2022\n\nAbstract: The accuracy of drought monitoring models is crucial for drought monitoring and early warning. Random forest (RF) is being used widely in the field of artificial intelligence. Nonetheless, the application of a random forest model in grassland drought monitoring research is yet to be further explored. In this study, various drought hazard factors were integrated based on remote sensing data, including from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Precipitation Measurement (GPM), as multisource remote sensing data. Based on the RF, a comprehensive grassland drought monitoring model was constructed and tested in Inner Mongolia, China, as an example. The critical issue addressed is the construction of a grassland drought disaster monitoring model based on meteorological data and multisource remote sensing data by using an RF model, and the verification of the accuracy and reliability of its monitoring results. The results show that the grassland drought monitoring model could quantitatively monitor the drought situation in Inner Mongolia grasslands. There was a significantly positive correlation between the drought indicators output by the model and the standardized precipitation evapotranspiration index (SPEI) measured in the field. The correlation coefficients (R) between the drought degree were 0.9706 and 0.6387 for the training set and test set, respectively. The consistent rate between the model drought index and the SPEI reached 87.90%. Drought events in Inner Mongolia were monitored from April to September in wet years, normal years, and dry years using the constructed model. The monitoring results of the model constructed in this study were in accordance with the actual drought conditions, reflecting the development and spatial evolution of drought conditions. This study provides a new application method for the comprehensive assessment of grassland drought.","Title: Fault diagnosis of rod pumping system based on deep conditional domain adaption network\nYear: 2021\n\nAbstract: The generalization ability of traditional fault diagnosis methods is insufficient. This paper presents a fault diagnosis method of sucker rod pumping system based on deep condition domain adaption network (DCDAN). Firstly, the convolution neural network is used for feature extraction. Then, the weighted maximum mean discrepancy (WMMD) is used to adjust the characteristic distribution of relevant subclasses in different domains to realize the fine-grained domain adaptation of subclasses. At the same time, the fault classification ability of the model is guaranteed by optimizing the classification loss. The results show that this method can improve the generalization performance of the model in the target domain.","Title: A Bayesian Approach for Joint Discriminative Dictionary and Classifier Learning\nYear: 2022\n\nAbstract: Sparse representation has been widely applied to image classification, where the key issue is to extract a suitable discriminative dictionary. To this end, we propose a joint dictionary and classifier learning algorithm based on a parameterized Bayesian model. Therein, the Gaussian priors of a dictionary endow it with the capability of discrimination and representation. Moreover, we introduce a multivariate Gaussian prior for the sparse codes to achieve group sparsity, thereby substantially improving the classification performance. Furthermore, the sparse codes are estimated by a group-sparse Bayesian learning (GSBL) method, and the dictionary atoms are updated sequentially by maximizing a posterior. Moreover, to avoid manual parameter adjustment, the hyperparameters are optimized by an evidence maximization method. Accordingly, we develop a classification scheme via GSBL. Finally, extensive experiments are conducted on six benchmark datasets of face classification, object recognition, handwritten recognition, and scene categorization to substantiate the effectiveness and superiority of the proposed method.","Title: Intergroup Cascade Broad Learning System With Optimized Parameters for Chaotic Time Series Prediction\nYear: 2022\n\nAbstract: The prediction of chaotic time series can well describe the physical properties and influencing factors of a sequence of numerical data points in successive order and has played an increasingly crucial role in various scientific and engineering communities. These time series data require the prediction model to have strong dynamic feature representation ability, which often leads to excessive calculation burden. As an emerging and effective learning method, a broad learning system (BLS) can be trained quickly by incremental learning, and the system can be reconstructed without requiring a lengthy retraining process, which has been shown to facilitate superior time-saving performance. However, the historical information of time series data is often ignored, resulting in the lack of dynamic feature representation ability of the original BLS algorithm. In this article, a novel intergroup cascade BLS (ICBLS) method and its variant with optimized parameters, termed MOICBLS, are proposed, which can combine the previous knowledge with current information to determine the output results by constructing a novel intergroup cascade structure for the enhancement nodes. The new algorithms try to retain the time-saving advantage of BLS and greatly enhance the extraction ability of dynamic features of chaotic time series data. In addition, it cannot be ignored that the network structure parameters have an unquestionable impact on the performance of a BLS. In response, two conflicting goals of prediction accuracy and diversity are formulated as multiobjective optimization functions in the training phase to generate the optimized network structure parameters needed by multiple candidate models. The experimental results on two benchmark chaotic time series dataset, the Lorenz system and Rossler system, and a geomagnetic disturbance storm time chaotic time series dataset demonstrate that our proposed algorithms are effective for prediction of chaotic time series, and the prediction accuracy is better than some existing approaches.","Title: Mining Weak Relations Between Reviews for Opinion Spam Detection\nYear: 2023\n\nAbstract: Online reviews play a significant role in purchase decisions of consumers by providing feedback information from buyers of products. In order to mislead consumers, opinion spammers are hired to write fake reviews to promote or demote specific products for illegitimate benefits. Existing methods for spam review detection mainly focused on designing manual features, which highly rely on expert knowledge. Although recent works utilized deep learning methods to automatically learn the semantics of reviews through the inherent user-review-product strong relation, they fail to capture the weak relations between reviews at the content, sentiment and temporal levels, which provides various semantic information to expose fake reviews. Moreover, the imbalanced class distribution in spam detection issues makes this work even more challenging. To address the above problems, we propose a novel Weak-Strong Unified Network (WSUN) for opinion spam detection. Multi-level weak relation graphs are constructed to reveal the abnormal behavioral patterns of spammers, which aggregates the semantics of strong relations by graph convolutional networks and extracts comprehensive review representation by utilizing relation-level attention mechanism. In addition, a graph-based over-sampling method is devised to mitigate the impact of imbalanced class distribution. Extensive experimental results on real-world datasets show that our model is more effective than the state-of-the-art methods."],"string":"[\n \"Title: On-Line Temperature Estimation for Noisy Thermal Sensors Using a Smoothing Filter-Based Kalman Predictor.\\nYear: 2018\\n\\nAbstract: Dynamic thermal management (DTM) mechanisms utilize embedded thermal sensors to collect fine-grained temperature information for monitoring the real-time thermal behavior of multi-core processors. However, embedded thermal sensors are very susceptible to a variety of sources of noise, including environmental uncertainty and process variation. This causes the discrepancies between actual temperatures and those observed by on-chip thermal sensors, which seriously affect the efficiency of DTM. In this paper, a smoothing filter-based Kalman prediction technique is proposed to accurately estimate the temperatures from noisy sensor readings. For the multi-sensor estimation scenario, the spatial correlations among different sensor locations are exploited. On this basis, a multi-sensor synergistic calibration algorithm (known as MSSCA) is proposed to improve the simultaneous prediction accuracy of multiple sensors. Moreover, an infrared imaging-based temperature measurement technique is also proposed to capture the thermal traces of an advanced micro devices (AMD) quad-core processor in real time. The acquired real temperature data are used to evaluate our prediction performance. Simulation shows that the proposed synergistic calibration scheme can reduce the root-mean-square error (RMSE) by 1.2 degrees C and increase the signal-to-noise ratio (SNR) by 15.8 dB (with a very small average runtime overhead) compared with assuming the thermal sensor readings to be ideal. Additionally, the average false alarm rate (FAR) of the corrected sensor temperature readings can be reduced by 28.6%. These results clearly demonstrate that if our approach is used to perform temperature estimation, the response mechanisms of DTM can be triggered to adjust the voltages, frequencies, and cooling fan speeds at more appropriate times.\",\n \"Title: Sensitivity-based acceleration and compression algorithm for convolution neural network.\\nYear: 2017\\n\\nAbstract: Convolution neural networks(CNNs) have an impressive performance in image processing and other machine learning tasks, meanwhile, colossal computation and memory requirements existing in most classical models restrict the deployment in portable and power-limited devices. The efficient approaches to circumventing the above hindrances stem from shrinking the network scale, which can be divided into two categories, i.e., the network pruning and low-rank approximation of kernel matrices. As compared to the pruning scheme, the low-rank approximation method has lower compression ratio, but is much friendlier to parallelism. In this paper, by analyzing the sensitivity of the rank of each layer to the network accuracy, we proposed a sensitivity-based layer-wise low-rank approximation algorithm. As compared with the traditional rank reduce methods, the acceleration ratio of our proposal is improved by 20%. When deploying our method on VGGNet-16 model, 2:7X compression/acceleration ratio on convolution layers and 10:9X compression/acceleration ratio on FC layers are achieved with 0.05% top-1 accuracy loss and 0.01% top-5 accuracy loss.\",\n \"Title: RoboCloud: augmenting robotic visions for open environment modeling using Internet knowledge.\\nYear: 2018\\n\\nAbstract: Modeling an open environment that contains unpredictable objects is a challenging problem in the field of robotics. In traditional approaches, when a robot encounters an unknown object, a mistake will inevitably be added to the robot’s environmental model, severely constraining the robot’s autonomy, and possibly leading to disastrous consequences in certain settings. The abundant knowledge accumulated on the Internet has the potential to remedy the uncertainties that result from encountering with unknown objects. However, robotic applications generally pay considerable attention to quality of service (QoS). For this reason, directly accessing the Internet, which can be unpredictable, is generally not acceptable. RoboCloud is proposed as a novel approach to environment modeling that takes advantage of the Internet without sacrificing the critical properties of QoS. RoboCloud is a “mission cloud–public cloud” layered cloud organization model in which the mission cloud provides QoS-available environment modeling capability with built-in prior knowledge while the public cloud is the existing services provided by the Internet. The “cloud phase transition” mechanism seeks help from the public cloud only when a request is outside the knowledge of the mission cloud and the QoS cost is acceptable. We have adopted semantic mapping, a typical robotic environment modeling task, to illustrate and substantiate our approach and key mechanism. Experiments using open 2D and 3D datasets with real robots have demonstrated that RoboCloud is able to augment robotic visions for open environment modeling.\",\n \"Title: 3D Surface velocity retrieval of mountain glacier using an offset tracking technique applied to ascending and descending SAR constellation data: a case study of the Yiga Glacier\\nYear: 2019\\n\\nAbstract: COSMO-SkyMed is a constellation of four X-band high-resolution radar satellites with a minimum revisit period of 12 hours. These satellites can obtain ascending and descending synthetic aperture radar (SAR) images with very similar periods for use in the three-dimensional (3D) inversion of glacier velocities. In this paper, based on ascending and descending COSMO-SkyMed data acquired at nearly the same time, the surface velocity of the Yiga Glacier, located in the Jiali County, Tibet, China, is estimated in four directions using an offset tracking technique during the periods of 16 January to 3 February 2017 and 1 February to 19 February 2017. Through the geometrical relationships between the measurements and the SAR images, the least square method is used to retrieve the 3D components of the glacier surface velocity in the eastward, northward and upward directions. The results show that applying the offset tracking technique to COSMO-SkyMed images can be used to derive the true 3D velocity of a glacier's surface. During the two periods, the Yiga Glacier had a stable velocity, and the maximum surface velocity, 2.4 m/d, was observed in the middle portion of the glacier, which corresponds to the location of the steepest slope.\",\n \"Title: Application Of Powell Fruit Fly Optimization Algorithm To Solutions Of Nonlinear Equations\\nYear: 2017\\n\\nAbstract: Solution to nonlinear equations is with extensive practical meanings in engineering. The convergence of classic numerical solutions is dependent on the initial value which is actually hard to be determined in actual computation. Thus, a new solution to nonlinear equations called PFOA is put forward to turn the complicated solution to nonlinear equations to function optimization. Group search and global convergence of the fruit fly optimization algorithm (FOA) can be fully utilized via PFOA, thereby effectively overcoming the initial point sensitivity of Powell. However, positive local optimal solution can be realized by Powell, so the two algorithms are improved effectively along with integration and complementation in this paper. Five typical nonlinear equations are selected, and the calculation results show the designed PFOA is superior to the basic FOA and is considered a successful solution to nonlinear equations.\",\n \"Title: Automatic Life Cycle Management of Network Configurations.\\nYear: 2018\\n\\nAbstract: Managing the life cycle of network configurations, including the generation, update, transition and diagnosis of the configurations, is the primary task of network operators and a critical process for the reliability and efficiency of the networks. This paper presents NetCraft, a framework which automates the life cycle management of network configurations with a unified network model. Designed for life cycle automation, NetCraft's network model can expressively encode all parts and protocols in the network; It can be converted to or constructed from configurations with interoperability; It is able to perform fine-grained configurations with flexibility to deactivate or undo any configurations for safe configuration updates; And it can work without cooperations from device vendors. We have built and deployed an initial version of NetCraft in Alibaba's global WAN. Evaluations in real environments show that NetCraft can reduce the network incidents caused by configurations by 95% and cut the average time to plan and execute a network update by up to 93%.\",\n \"Title: A Time-Domain Preconditioned Truncated Newton Approach to Visco-acoustic Multiparameter Full Waveform Inversion\\nYear: 2018\\n\\nAbstract: A truncated Newton (TRN) method for time-domain full waveform inversion (FWI) in a visco-acoustic medium has been developed based on the 2nd-order adjoint state formulation. Time-domain gradient estimation and Hessian-vector product are managed by recomputing, without numerical instabilities, the incident wavefield at the same time as the adjoint wavefield for mitigating memory issues. Generic algorithm workflow has been proposed to switch between parameterizations, thanks to the chain rule. An efficient preconditioner adapted to the multiparameter configuration is developed to enhance the convergence rate of the inner conjugate gradient iterations. An additional user-defined scaling is introduced in the preconditioner to mitigate the weak sensitivity of specific parameters to waveform variations. The importance of the inverse Hessian for mitigating interparameter trade-off is validated on a toy example. Through a realistic 2D synthetic case based on a North Sea real data application, encouraging numerical results under (V-p, rho, Q(-1)) parametrization demonstrate that considering the Hessian influence significantly improves the multiparameter reconstruction, mitigating the coupling between parameters, for a reasonable increase of the computational cost compared to standard quasi-Newton optimization strategies.\",\n \"Title: Design and Application of an Attractiveness Index for Urban Hotspots Based on GPS Trajectory Data.\\nYear: 2018\\n\\nAbstract: Urban hotspots refer to regions where flourishing shopping centers are located, the travel volume is very large, and there is high traffic. The formation of hotspots is strongly correlated with many features, i.e., time, space, and the distribution of points of interest; however, most studies have used qualitative analyses to describe the relevance of these features, and there is a lack of quantitative analyses. Therefore, we propose the concept and a model of the attractiveness index of a hotspot that is used to quantify the spatio-temporal distribution of the hotspots and determine the degree of attractiveness to the residents. In addition, a novel algorithm of hotspot similarity is designed and implemented to improve the efficiency of summarizing the hotspot data, which is usually performed manually. We mine the data and determine different hotspots using a one-week GPS trajectory dataset collected from 6599 taxis in Kunming. Furthermore, we calculate the attractiveness index of hotspots and visualize their characteristics. The research results provide a scientific basis and reference for public infrastructure planning, land-value evaluation, store location planning, consumption recommendations, and other applications.\",\n \"Title: Dynamic Target Template Matching For Railway Catenary Suspension Motion Detection In Wind Area\\nYear: 2018\\n\\nAbstract: A dynamic target template matching method was proposed to identify railway catenary suspension movements of wind-induced vibration in wind area. Catenary positioning point was taken as the target template, which was compared with equal-sized image sequentially using the proposed matching difference. And, three-dimensional contour map of matching difference value at each sub-area was obtained, where the target pixel coordinates were determined by the minimum matching difference value. Considering the complex imaging condition, the target template was updated by the detected target image to sense the gradual change of illumination conditions like brightness and contrast. Furthermore, to eliminate detecting errors due to wind-induced camera vibration, both static and moving target templates were identified for acquiring the absolute motion of the moving target. Finally, validation test was performed with animation in PowerPoint. The calculated target displacement agrees well with theoretical motion with maximum relative error of 1.8%. And experiment application was conducted at site by analyzing the relationship between detecting displacement and wind speed. Results indicate that the proposed dynamic target template matching method can meet required engineering precision and provide an effective way for wind-vibration safety research of railway catenary system in wind area.\",\n \"Title: An Online Sockpuppet Detection Method Based on Subgraph Similarity Matching\\nYear: 2018\\n\\nAbstract: Multiple identity deception (sockpuppetry) has become an increasingly important issue in the social media environment, because they are engaged in undesired behaviors such as spreading rumors, posting hate speeches. The case of blocked users initiating new accounts, called sockpuppets, is widely known and past efforts, which have attempted to detect such users, have been primarily based on verbal features and non-verbal behaviors like posting habits. Although these methods have achieved a certain degree of success, it is easy for smart sockpuppets to forge verbal and behavioral features to circumvent management, making it difficult to guarantee the performance of these detection methods. In this paper, we observe that the social network plays a critical role in sockpuppets' influence, because recovering similar social structures can a sockpuppet guarantee the similar propagation impact. With this observation, a structure-based online method is proposed, which turns the sockpuppet detection problem into a subgraph similarity matching problem. The experiment on two real-world datasets of Sina Weibo demonstrates that our method obtains excellent detection performance, significantly outperforming previous methods.\",\n \"Title: Temporal-spatial Analysis & Visualization of Passenger Flow after Opening New Railway Lines in Shenzhen Metro.\\nYear: 2018\\n\\nAbstract: Opening new lines will bring the structural change of passenger flow characteristics in network operation of Urban Rail Transit. Based on the smart card data from Shenzhen Metro, temporal-spatial characteristics was discussed comprehensively from different statistics dimensions and analysis angles. At the same time, passenger flow in 28 transfer stations was analyzed by clustering algorithm and the land use nature was attained. Consequently, the passenger flow law in Shenzhen Metro will be understood better combined with visualization. Especially, when facing the risk of large passenger flow, targeted measures can be taken in advance to improve the safety and resilience of Shenzhen Metro.\",\n \"Title: Monitoring and Analyzing Mountain Glacier Surface Movement Using SAR Data and a Terrestrial Laser Scanner: A Case Study of the Himalayas North Slope Glacier Area.\\nYear: 2019\\n\\nAbstract: The offset tracking technique based on synthetic aperture radar (SAR) image intensity information can estimate glacier displacement even when glacier velocities are high and the time interval between images is long, allowing for the broad use of this technique in glacier velocity monitoring. Terrestrial laser scanners, a non-contact measuring system, can measure the velocity of a glacier even if there are no control points arranged on a glacier. In this study, six COSMO-SkyMed images acquired between 31 July and 22 December 2016 were used to obtain the glacial movements of five glaciers on the northern slope of the central Himalayas using the offset tracking approach. During the period of image acquirement, a terrestrial laser scanner was used, and point clouds of two periods in a small area at the terminus of the Pingcuoliesa Glacier were obtained. By selecting three fixed areas of the point clouds that have similar shapes across two periods, the displacements of the centers of gravity of the selected areas were calculated by using contrast analyses of feature points. Although the overall low-density point clouds data indicate that the glacial surfaces have low albedos relative to the wavelength of the terrestrial laser scanner and the effect of its application is therefore influenced in this research, the registration accuracy of 0.0023 m/d in the non-glacial areas of the scanner's measurements is acceptable, considering the magnitude of 0.072 m/d of the minimum glacial velocity measured by the scanner. The displacements from the point clouds broadly agree with the results of the offset tracking technique in the same area, which provides further evidence of the reliability of the measurements of the SAR data in addition to the analyses of the root mean squared error of the velocity residuals in non-glacial areas. The analysis of the movement of five glaciers in the study area revealed the dynamic behavior of these glacial surfaces across five periods. G089972E28213N Glacier, Pingcuoliesa Glacier and Shimo Glacier show increasing surface movement velocities from the terminus end to the upper part with elevations of 1500 m, 4500 m, and 6400 m, respectively. The maximum velocities on the glacial surface profiles were 31.69 cm/d, 62.40 cm/d, and 42.00 cm/d, respectively. In contrast, the maximum velocity of Shie Glacier, 50.60 cm/d, was observed at the glacier's terminus. For each period, G090138E28210N Glacier exhibited similar velocity values across the surface profile, with a maximum velocity of 39.70 cm/d. The maximum velocities of G089972E28213N Glacier, Pingcuoliesa Glacier, and Shie Glacier occur in the areas where the topography is steepest. In general, glacial surface velocities are higher in the summer than in the winter in this region. With the assistance of a terrestrial laser scanner with optimized wavelengths or other proper ground-based remote sensing instruments, the offset tracking technique based on high-resolution satellite SAR data should provide more reliable and detailed information for local and even single glacial surface displacement monitoring.\",\n \"Title: Cross perishable effect on optimal inventory preservation control.\\nYear: 2019\\n\\nAbstract: •We introduce a novel concept of cross-perishability in literature.•We introduce a novel control variable of preservation effort in modelling.•We prove the upper and lower bound conditions for the decision variables.•We use the boundary conditions to facilitate the searching algorithm.•We analyze this novel model and investigate the optimal policy & mechanism.\",\n \"Title: A New Denoising Method for UHF PD Signals Using Adaptive VMD and SSA-Based Shrinkage Method.\\nYear: 2019\\n\\nAbstract: Noise suppression is one of the key issues for the partial discharge (PD) ultra-high frequency (UHF) method to detect and diagnose the insulation defect of high voltage electrical equipment. However, most existing denoising algorithms are unable to reduce various noises simultaneously. Meanwhile, these methods pay little attention to the feature preservation. To solve this problem, a new denoising method for UHF PD signals is proposed. Firstly, an automatic selection method of mode number for the variational mode decomposition (VMD) is designed to decompose the original signal into a series of band limited intrinsic mode functions (BLIMFs). Then, a kurtosis-based judgement rule is employed to select the effective BLIMFs (eBLIMFs). Next, a singular spectrum analysis (SSA)-based thresholding technique is presented to suppress the residual white noise in each eBLIMF, and the final denoised signal is synthesized by these denoised eBLIMFs. To verify the performance of our method, UHF PD data are collected from the computer simulation, laboratory experiment and a field test, respectively. Particularly, two new evaluation indices are designed for the laboratorial and field data, which consider both the noise suppression and feature preservation. The effectiveness of the proposed approach and its superiority over some traditional methods is demonstrated through these case studies.\",\n \"Title: Improving Short-Length LDPC Codes with a CRC and Iterative Ordered Statistic Decoding : (Invited Paper)\\nYear: 2019\\n\\nAbstract: We present a CRC-aided LDPC coding scheme that can outperform the underlying LDPC code under ordered statistic decoding (OSD). In this scheme, the CRC is used jointly with the LDPC code to construct a candidate list, instead of conventionally being regarded as a detection code to prune the list generated by the LDPC code alone. As an example we consider a (128,64)5 G LDPC code with BP decoding, which we can outperform by 2 dB using a (128,72) LDPC code in combination with a 8-bit CRC under OSD of order t=3. The proposed decoding scheme for CRC-aided LDPC codes also achieves a better performance than the conventional scheme where the CRC is used to prune the list. A manageable complexity can be achieved with iterative reliability based OSD, which is demonstrated to perform well with a small OSD order.\",\n \"Title: Enhancing Efficient Link Performance in ZigBee Under Cross-Technology Interference\\nYear: 2020\\n\\nAbstract: The coexistence of heterogenous wireless networks has long been a problem especially with the explosive development in recent years. These heterogenous, such as WiFi, ZigBee and Bluetooth operating in the crowded ISM band, have to compete with each other for scarce spectrum resources, generating cross-technology interference (CTI). Since CTI may lead to severe performance degradation of ZigBee networks, especially under high density WiFi interference. In this paper, we propose a novel adaptive packet delivery (APD) algorithm that enables ZigBee links to achieve enhanced performance under the presence of heavy WiFi traffic. First, we pre-analyze the coexistence of co-located networks, we find the WiFi frame cluster well follow the power law distribution model with burst WiFi traffic, then the PER analysis is also determined to help obtain the probability of delivering packets successfully. Second, we propose a new metric called channel idle state indicator (CISI) to quantify current channel quality. Third, based on CISI and the pre-analysis, we build APD to instruct ZigBee nodes to transmit, besides, we consider the occasion where the packet contains an emergency message. Extensive experimental results show that, under the coverage of different WiFi traffic, our lightweight mechanism can achieve 4x and 1.5x performance gains over WISE and CII. Particularly, when WiFi traffic is 4 Mbps, we can still obtain over 90%. Furthermore, the energy consumption efficiency can be improved markedly with compared protocols.\",\n \"Title: LaSa: Location Aware Wireless Security Access Control for IoT Systems\\nYear: 2019\\n\\nAbstract: IoT (Internet of Things) security has become a severe yet not well solved problem attracting increasing research attention as well as industrial concerns. Location-based access control approaches, such as Wi-Fi geo-fencing, promise to fulfill the needs of preventing unauthorized access to IoT systems. We propose a crowdsourcing method for location aware security access control, namely LaSa, which is able to confine wireless network access inside certain physical areas only using a single commercial Access Point (AP). Specifically, LaSa detects whether a user enters or exits a room by discovering and recognizing the unique signal patterns. It combines the Received Signal Strength (RSS), Channel State Information (CSI), and coarse Angle of Arrival (AoA) data to improve the accuracy of user classification for accessing the wireless network. Real-world experimental results show that LaSa can achieve a 97.0% accuracy of identification of unauthorized users while maintaining a low false blocking rate of authorized users as low as 3.3%. LaSa is designed to be straightforward for integration with commercial APs and deployment to home and business Wi-Fi environments.\",\n \"Title: A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG.\\nYear: 2019\\n\\nAbstract: Reconstruction of a 12-lead electrocardiogram (ECG) from a serial 3-lead ECG has been researched in the past to satisfy the need for more wearing comfort and ambulatory situations. The accuracy and real-time performance of traditional methods need to be improved. In this study, we present a novel method based on convolutional neural networks (CNNs) for the synthesis of missing precordial leads. The results show that the proposed method receives better similarity and consumes less time using the PTB database. Particularly, the presented method shows outstanding performance in reconstructing the pathological ECG signal, which is crucial for cardiac diagnosis. Our CNN-based method is shown to be more accurate and time-saving for deployment in non-hospital situations to synthesize a standard 12-lead ECG from a reduced lead-set ECG recording. This is promising for real cardiac care.\",\n \"Title: Joint graph optimization and projection learning for dimensionality reduction.\\nYear: 2019\\n\\nAbstract: •A novel framework termed joint graph optimization and projection learning (JGOPL) is proposed for graph-based dimensionality reduction.•The l21-norm based distance measurement is adopted in the loss function of our JGOPL so that its robustness to the negative influence caused by the outliers or variations of data can be improved.•In order to well exploit and preserve the local structure information of high-dimensional data, a locality constraint is introduced into the proposed JGOPL to discourage a sample from connecting with the distant samples during graph optimization.•The locality constraint and graph optimization strategy proposed is not only limited to dimensionality reduction, but also can be incorporated into other relevant graph-based tasks.\",\n \"Title: Performance and power consumption tradeoff in multimedia cloud\\nYear: 2020\\n\\nAbstract: With the rapidly growing adoption of mobile devices, such as smartphones and tablets, the Internet multimedia traffic generated by mobile users has increased. An important problem involves providing users with satisfactory multimedia services. Multimedia cloud is emerging as a specific computing paradigm to resolve the aforementioned issue by providing quality of service (QoS) provisioning for multimedia services. Given the emergence of multimedia cloud services, studies of multi-platform multimedia information analysis are significantly supported. However, because the multimedia cloud includes limited resources, the allocation of resources to satisfy user requirements with respect to QoS guaranteed multimedia services is a challenging task. Additionally, the rapidly growing demand for multimedia services motivates cloud service providers (CSPs) to deploy a great number of servers in their data centers. However, the power consumption of the servers is a matter of increasing concern for CSPs. In this study, we investigate the problem of optimal resource allocation in a multimedia cloud by considering two server models, namely the constant-speed model and idle-speed model. Our objective involves achieving the tradeoff between response time and power consumption in the two server models. With respect to each server model, we formulate an optimization problem and solve it by employing the Lagrangian multiplier method. We present numerical results to verify the effectiveness of the proposed method. The numerical results indicate that our proposed resource allocation scheme achieves both minimal response time and power consumption when compared with the existing allocation schemes.\",\n \"Title: The low-rank decomposition of correlation-enhanced superpixels for video segmentation\\nYear: 2019\\n\\nAbstract: Low-rank decomposition (LRD) is an effective scheme to explore the affinity among superpixels in the image and video segmentation. However, the superpixel feature collected based on colour, shape, and texture may be rough, incompatible, and even conflicting if multiple features extracted in various manners are vectored and stacked straight together. It poses poor correlation, inconsistence on intra-category superpixels, and similarities on inter-category superpixels. This paper proposes a correlation-enhanced superpixel for video segmentation in the framework of LRD. Our algorithm mainly consists of two steps, feature analysis to establish the initial affinity among superpixels, followed by construction of a correlation-enhanced superpixel. This work is very helpful to perform LRD effectively and find the affinity accurately and quickly. Experiments conducted on datasets validate the proposed method. Comparisons with the state-of-the-art algorithms show higher speed and more precise in video segmentation.\",\n \"Title: A High-Precision Coarse-Fine Time-To-Digital Converter With The Analog-Digital Hybrid Interpolation\\nYear: 2019\\n\\nAbstract: An innovative high-precision coarse-fine time-to-digital converter (TDC) is presented. The TDC architecture mainly consists of a coarse counter and a delay-line-based analog-digital hybrid interpolator. In order to improve the precision, the key timing control of the interpolator is provided by a novel customized synchronizer. Comparing to the traditional delay-line-based interpolator, the proposed analog-digital hybrid interpolator using the multi-voltage sampling technique improves the resolution significantly. A prototype chip has been fabricated in a 0.35 mu m CMOS process. After the calibration and calculation based on fitting algorithms, the TDC prototype reaches a dynamic range of 512 ns and a single-channel single-shot precision of 6 ps using the external look-up table (LUT) for the interpolator.\",\n \"Title: Automatic optic disc detection using low-rank representation based semi-supervised extreme learning machine\\nYear: 2020\\n\\nAbstract: Optic disc detection plays an important role in developing automatic screening systems for diabetic retinopathy. Several supervised learning-based approaches have been proposed for optic disc detection. However, these approaches demand that the input training examples are completely labelled. Essentially, in medical image analysis, it is difficult to prepare several training samples which were given reliable class labels due to the fact that manually labelling data is very expensive. Moreover, retinal images such as complex vessels structures in the optic disc constituting nonlinear relationships in high-dimensional observation space, which cannot work well by traditional linear classifiers. In this study, a novel approach named low-rank representation based semi-supervised extreme learning machine (LRR-SSELM) is proposed for automated optic disc detection. Our model has the following advantages. First, it detects the optic disc from the viewpoint of semi-supervised learning and overcomes the problem there are small portion of labelled samples. Second, a nonlinear classifier is introduced into our model to fully explore the nonlinear data. Third, the local and global structures of original data can be greatly persevered by low-rank representation (LRR). The performance of the proposed method is validated on three publicly available databases, DIARETDB0, DIARETDB1 and Messidor. The experimental results indicate the advantages and effectiveness of the proposed approach.\",\n \"Title: Bug Localization for Version Issues With Defect Patterns.\\nYear: 2019\\n\\nAbstract: Version issues are becoming more and more prominent with the continuous development of software. Bug localization for version issues is time-consuming and labor-intensive. Although some bug localization techniques, such as those based on information retrieval (IR), have been proposed, they cannot handle these bugs very well as the version-related bugs have their own defect patterns. However, few existing works have focused on revealing these defect patterns and utilizing them for localization of bugs. To fill this gap, we propose a new approach by leveraging the version-related defect patterns to localize the version-related issues integrated with the IR technique. First, we extract version-related bugs from bug repositories and build a version-related bug repository. Given a new version-related bug report, we identify the defect patterns of corresponding similar historical bug reports from the version-related bug repository. Then, we combine these defect patterns with the IR technique to rank the candidate code snippets as suspicious code for developers to fix. The evaluation demonstrates that our approach is more effective to identify the faulty code related to version issues than the existing IR-based bug localization technique.\",\n \"Title: Sensitivity-Oriented Layer-Wise Acceleration and Compression for Convolutional Neural Network.\\nYear: 2019\\n\\nAbstract: Convolutional neural networks (CNNs) have achieved excellent performance improvement in image processing and other machine learning tasks. However, tremendous computation and memory consumption for most classical CNN models pose a great challenge to the deployment in portable and power-limited devices. In this paper, by analyzing the sensitivity of the rank in each layer of the network accuracy, we propose a sensitivity-oriented layer-wise low-rank approximation algorithm. With specific compression and acceleration requirement, the convolutional layer with higher sensitivity keeps more kernels than that with lower sensitivity. In addition, we also demonstrated that global optimization can obtain a better classification performance than layer-wise fine-tuning. The experimental results show that the proposed method can achieve 20% acceleration ratio gaining compared with the traditional rank-reducing methods. When deployed on the VGGNet-16 model, the proposed method can achieve 2.7 x compression/acceleration ratio on convolutional layers and 10.9x compression/acceleration ratio on fully connected (FC) layers with 0.05% top-1 accuracy loss and 0.01% top-5 accuracy loss.\",\n \"Title: Feature-Critic Networks for Heterogeneous Domain Generalization.\\nYear: 2019\\n\\nAbstract: The well known domain shift issue causes model performance to degrade when deployed to a new target domain with different statistics to training. Domain adaptation techniques alleviate this, but need some instances from the target domain to drive adaptation. Domain generalization is the recently topical problem of learning a model that generalizes to unseen domains out of the box, without accessing any target data. Various domain generalization approaches aim to train a domain-invariant feature extractor, typically by adding some manually designed losses. In this work, we propose a learning to learn approach, where the auxiliary loss that helps generalization is itself learned. This approach is conceptually simple and flexible, and leads to considerable improvement in robustness to domain shift. Beyond conventional domain generalization, we consider a more challenging setting of heterogeneous domain generalization, where the unseen domains do not share label space with the seen ones, and the goal is to train a feature which is useful off-the-shelf for novel data and novel categories. Experimental evaluation demonstrates that our method outperforms state-of-the-art solutions in both settings.\",\n \"Title: Hierarchical Reinforcement Learning for Multi-agent MOBA Game.\\nYear: 2019\\n\\nAbstract: Although deep reinforcement learning has achieved great success recently, there are still challenges in Real Time Strategy (RTS) games. Due to its large state and action space, as well as hidden information, RTS games require macro strategies as well as micro level manipulation to obtain satisfactory performance. In this paper, we present a novel hierarchical reinforcement learning model for mastering Multiplayer Online Battle Arena (MOBA) games, a sub-genre of RTS games. In this hierarchical framework, agents make macro strategies by imitation learning and do micromanipulations through reinforcement learning. Moreover, we propose a simple self-learning method to get better sample efficiency for reinforcement part and extract some global features by multi-target detection method in the absence of game engine or API. In 1v1 mode, our agent successfully learns to combat and defeat built-in AI with 100% win rate, and experiments show that our method can create a competitive multi-agent for a kind of mobile MOBA game King of Glory (KOG) in 5v5 mode.\",\n \"Title: Gain-Flattened C+L Band Distributed Raman Amplification for MDM and WDM Transmission of Orbital Angular Momentum (OAM) Modes Over 100-km Large-Core Fiber\\nYear: 2018\\n\\nAbstract: We experimentally demonstrate the first record C+L ultra-wideband orbital angular momentum (OAM) mode multiplexing and wavelength multiplexing transmission over 100-km large-core fiber (LCF) assisted by all-fiber OAM (de)multiplexer. Distributed Raman amplification with four pumps provides the C+L band flat gain.\",\n \"Title: First Demonstration of Ultra-Low-Noise Long-Haul 110-km MDM+WDM Transmission of Orbital Angular Momentum (OAM) Modes with 1st / 2nd-Order Distributed Raman Amplifier\\nYear: 2018\\n\\nAbstract: We experimentally demonstrate the first record long-single-span MDM+WDM transmission of 3 orbital angular momentum (OAM) modes and 22 wavelength channels in 110-km large-core fiber without using MIMO-DSP assisted by homemade all-fiber OAM (de)multiplexer and 1st-/2nd-order distributed Raman amplification.\",\n \"Title: Dynamic liquid level detection method based on resonant frequency difference for oil wells\\nYear: 2018\\n\\nAbstract: The dynamic liquid level of an oil well can be used to determine the oil production strategies and analyze the reservoir performance. Therefore, it is important to measure the dynamic liquid level in an oil field. This paper proposes a novel dynamic liquid level measurement method for oil wells, where the resonant frequency difference (RFD) of the resonant acoustic signal in annular is used to calculate the dynamic liquid level. To solve the noise interference problem in the resonant acoustic signal, a spectral fast Fourier transform (FFT) method based on Welch power spectrum is proposed to obtain the RFD. First, the Welch power spectrum approach is employed to process the resonant acoustic signal, and a high-pass filter is designed to filter the inherent envelope of low frequency in the power spectrum. In particular, a clear and smooth power spectrum can be obtained by choosing a suitable window and a sectional length. Furthermore, the short-time Fourier transform method is used to extract the strongest energy spectrum of the power spectrum. Finally, the RFD of two adjacent resonant harmonics can be accurately obtained by using FFT for the strongest energy spectrum. Experimental results show the effectiveness of the proposed approach.\",\n \"Title: Halodpc: An Improved Recognition Method On Halo Node For Density Peak Clustering Algorithm\\nYear: 2019\\n\\nAbstract: The density peaks clustering (DPC) is known as an excellent approach to detect some complicated-shaped clusters with high-dimensionality. However, it is not able to detect outliers, hub nodes and boundary nodes, or form low-density clusters. Therefore, halo is adopted to improve the performance of DPC in processing low-density nodes. This paper explores the potential reasons for adopting halos instead of low-density nodes, and proposes an improved recognition method on Halo node for Density Peak Clustering algorithm (HaloDPC). The proposed HaloDPC has improved the ability to deal with varying densities, irregular shapes, the number of clusters, outlier and hub node detection. This paper presents the advantages of the HaloDPC algorithm on several test cases.\",\n \"Title: Velocity Prediction of Intelligent and Connected Vehicles for a Traffic Light Distance on the Urban Road\\nYear: 2019\\n\\nAbstract: Accurate vehicle velocity prediction has important theoretical value and widespread applications in many areas, such as optimal control of vehicle propulsion system, eco-driving, and advanced driver assistance systems. However, for dynamic changes of traffic condition caused by traffic lights, intersections, and other factors, it is hard to predict the vehicle velocity accurately on the urban road. In this paper, we present a novel vehicle velocity prediction algorithm for intelligent and connected vehicles based on the historical driving data of the preceding vehicle and traffic light information. First, the basic driving rules on the urban road are studied in two different driving scenarios. Then, a vehicle trajectory generation algorithm (VTGA) is proposed to generate the vehiclesu0027 trajectories according to the basic driving rules. To identify vehiclesu0027 quantity and the global positioning system information of each vehicle in the unknown area, an identification algorithm (IA) is designed based on the VTGA and genetic algorithm. Finally, a vehicle velocity prediction algorithm is applied to predict the velocity of the target vehicle based on the VTGA and the results of IA. To verify the method proposed in this paper, the next generation simulation database is utilized. The results demonstrate that the accuracy of the vehicle velocity prediction has a significant improvement in the urban network, and the root-mean-square error reduces from 0.50~4.78 m/s (5~20 s) to 0.7594~0.9166 m/s (9.3~43.8 s), when compared with methods of other studies.\",\n \"Title: NURBS-enhanced line integration boundary element method for 2D elasticity problems with body forces\\nYear: 2019\\n\\nAbstract: A NURBS-enhanced boundary element method for 2D elasticity problems with body forces is proposed in this paper. The non-uniform rational B-spline (NURBS) basis functions are applied to construct the geometry and the model can be reproduced exactly at all stages since the refinement will not change the shape of the boundary. Both open curves and closed curves are considered. The fields are approximated by the traditional Lagrangian basis functions in parameter space, rather than by the same NURBS basis functions for geometry approximation. The parametric boundary elements and collocation nodes are defined from the knot vector of the curve and the refinement of the NURBS curve is easy. Boundary conditions can be imposed directly since the Lagrangian basis functions have the property of delta function. In addition, most methods for the treatment of singular integrals in traditional boundary element method can be applied in the proposed method. To overcome the difficulty for evaluation of the domain integrals in problems with body forces, a line integration method is further applied in this paper to compute the domain integrals without additional volume discretizations. Numerical examples have shown the accuracy of the proposed method.\",\n \"Title: Modeling and analysis for multilayered complex communication network of satellite navigation system\\nYear: 2019\\n\\nAbstract: AbstractSummaryThis study is extended to construct the network model, the node model, and the link model of complex communication network for satellite navigation system (CCN‐SNS) based on the hierarchical architecture. Firstly, a method called snapshots was proposed to describe the dynamic topology for CCN‐SNS; secondly, another method was put forward to model the different nodes of the CCN‐SNS; thirdly, the different links between every two different nodes were modeled. Therefore, based on the OPNET tools, a simulation for the CCN‐SNS, which contains the models that proposed earlier used to analyze the navigation accuracy and network transmission performance, was performed.This study is extended to construct the network model, the node model, and the link model of complex communication network for satellite navigation system (CCN‐SNS) based on the hierarchical architecture, and then a method called snapshots was proposed to describe the dynamic topology for CCN‐SNS. Finally, a simulation for the CCN‐SNS, which contains the models that proposed used to analyze the navigation accuracy and network transmission performance, was performed. View Figure\",\n \"Title: Advanced community question answering by leveraging external knowledge and multi-task learning\\nYear: 2019\\n\\nAbstract: Community question answering (CQA) is an important but challenging task. Meantime, as the theory of deep learning develops, remarkable progress has been made by deep neural networks. This paper studies an advanced deep neural network that not only uses external knowledge to learn better representations of questions and answers but also improves representation learning by considering question categorization as an auxiliary task. Specifically, we propose a novel Multi-task and Knowledge enhanced Multi-head Interactive Attention network for Community Question Answering (MKMIA-CQA). It contains a document modeling module responsible for utilizing external commonsense knowledge to help identify background information (entity mentions and their relations) and filter out noise information from the long text which has complicated semantic and syntactic structures. Moreover, the model is trained in a multi-task manner. It regards community question answering as the primary task and question categorization as the auxiliary task, which aims to learn a category-aware encoder and improve the quality of locating the salient information of a long question. The experimental results on three widely used CQA datasets demonstrate that our model achieves impressive results compared to other strong competitors.\",\n \"Title: Multiple attribute group decision making using J-divergence and evidential reasoning theory under intuitionistic fuzzy environment\\nYear: 2020\\n\\nAbstract: The theory of intuitionistic fuzzy sets has been proved to be an effective and convenient tool in the construction of fuzzy multiple attribute group decision-making models to deal with the uncertainty in developing complex decision support systems. Concerning this topic, the current studies mainly focus on their attention on two aspects including aggregation operators on intuitionistic fuzzy sets and determining the weights of both decision makers and attributes. However, some challenges have not been fully considered including existing aggregation operators which may induce unreasonable results in some situations and how to objectively determine the weights of both attributes and decision makers to meet different decision-making demands. To overcome the challenges of existing decision-making models and to satisfy much more decision-making situations, a novel intuitionistic fuzzy multiple attribute group decision-making method via J-divergence and evidential reasoning theory is proposed in this paper as a supplement of conventional models. On the one hand, a weighted J-divergence of intuitionistic fuzzy sets and a J-divergence between two intuitionistic fuzzy matrices are introduced. Following the two concepts, two consensus-based approaches are proposed to determine the weights of both decision makers and attributes. The weights obtained from the proposed method can more accurately reflect the importance levels of both attributes and decision makers from the perspective of consensus by comparison with existing models. On the other hand, an evidential reasoning theory-based operator is established to replace conventional operators for aggregating intuitionistic fuzzy information. The fusion result via this operator is consistent with most of intuitionistic fuzzy numbers. With these works, the proposed method can provide more accurate and reasonable decision results than existing algorithms.\",\n \"Title: Fashion Recommendations through Cross-media Information Retrieval\\nYear: 2019\\n\\nAbstract: •A novel fashion product recommendation method using multimodal information.•Integrating both text and image mining techniques.•Facilitating similar and mix-and-match fashion product recommendations.•Leveraging text-based product attributes and image features in the recommendations.\",\n \"Title: The Modeling Of Interval-Valued Time Series: A Method Based On Fuzzy Set Theory And Artificial Neural Networks\\nYear: 2019\\n\\nAbstract: Interval-valued time series (ITS) are interval-valued data that are collected in chronological order. The modeling of ITS is an ongoing issue in domain of time series analysis. This paper presents a new modeling method of ITS based on the synergy of fuzzy set theory and artificial neural networks. The proposed method involves the construction of collection of fuzzy sets describing characteristics of amplitude of ITS, the expression and reconstruction mechanism of ITS and the emergence of model of ITS based on artificial neural network (ANN). The resulting model of ITS not only supports the linguistic output but also the numeric output in interval format. A series of experimental studies is reported for two publicly available financial datasets showing different dynamic characteristics. Experimental results clearly show that the constructed ITS model has the better performance on the linguistic and numeric level.\",\n \"Title: Consensus Graph Learning for Incomplete Multi-view Clustering\\nYear: 2019\\n\\nAbstract: Multi-view data clustering is a fundamental task in current machine learning, known as multi-view clustering. Existing multi-view clustering methods mostly assume that each data instance is sampled in all views. However, in real-world applications, it is common that certain views miss number of data instances, resulting in incomplete multi-view data. This paper concerns the task of clustering of incomplete multi-view data. We propose a novel Graph-based Incomplete Multi-view Clustering (GIMC) to perform this task. GIMC can effectively construct a complete graph for each view with the help of other view(s), and automatically weight each constructed graph to learn a consensus graph, which gives the final clusters. An alternating iterative optimization algorithm is proposed to optimize the objective function. Experimental results on real-world datasets show that the proposed method outperforms state-of-the-art baseline methods markedly.\",\n \"Title: Deep Ensemble Detection of Congestive Heart Failure using Short-term RR Intervals\\nYear: 2019\\n\\nAbstract: Heart rate variability (HRV) is an effective predictor of congestive heart failure (CHF). However, important challenges exist regarding the effective temporal feature extraction and efficient classification using high-dimensional HRV representations. To solve these challenges, an ensemble method for CHF detection using short-term HRV data and deep neural networks was proposed. In this paper, five opensource databases, the BIDMC CHF database (BIDMC-CHF), CHF RR interval database (CHF-RR), MITBIH normal sinus rhythm (NSR) database, fantasia database (ED), and NSR RR interval database (NSR-RR), were used. Additionally, three RR segment length types (N = 500, 1000, and 2000) were used to evaluate the proposed method. First, we extracted the expert features of RR intervals (RRIs) and then built a long short-term memory-convolutional neural network-based network to extract deep-learning (DL) features automatically. Finally, an ensemble classifier was used for CHF detection using the above features. With blindfold validation (three CHF subjects and three normal subjects), the proposed method achieved 99.85%, 99.41%, and 99.17% accuracy on N = 500, 1000, and 2000 length RRIs, respectively, using the BIDMC-CHF, NSR, and FD databases. With blindfold validation (six CHF subjects and six normal subjects), the proposed method achieved 83.84%, 87.54%, and 85.71% accuracy on N = 500, 1000, and 2000 length RRIs, respectively, using the NSR-RR and CHF-RR ndatabases. Based on feature ranking, the significant effectiveness provided by the DL features has been proven. The results have shown that the deep ensemble method can achieve reliable CHF detection using short-term heart rate signals and enable CHF detection through intelligent hardware.\",\n \"Title: Hierarchical human-like strategy for aspect-level sentiment classification with sentiment linguistic knowledge and reinforcement learning.\\nYear: 2019\\n\\nAbstract: Aspect-level sentiment analysis is a crucial problem in fine-grained sentiment analysis, which aims to automatically predict the sentiment polarity of the specific aspect in its context. Although remarkable progress has been made by deep learning based methods, aspect-level sentiment classification in real-world remains a challenging task. The human reading cognition is rarely explored in sentiment classification, which however is able to improve the effectiveness of the sentiment classification by considering the process of reading comprehension and logical thinking. Motivated by the process of the human reading cognition that follows a hierarchical routine, we propose a novel Hierarchical Human-like strategy for Aspect-level Sentiment classification (HHAS). The model contains three major components, a sentiment-aware mutual attention module, an aspect-specific knowledge distillation module, and a reinforcement learning based re-reading module, which are consistent with the stages of the human reading cognitive process (i.e., pre-reading, active reading, and post-reading). To measure the effectiveness of HHAS, extensive experiments are conducted on three widely used datasets. Experimental results demonstrate that HHAS achieves impressive results and yields state-of-the-art results on the three datasets.\",\n \"Title: Personalized Web Service Recommendation Based on QoS Prediction and Hierarchical Tensor Decomposition.\\nYear: 2019\\n\\nAbstract: Web service recommendation based on the quality of service (QoS) is important for users to find the exact Web service among many functionally similar Web services. Although service recommendations have been recently studied, the performance of the existing ones is unsatisfactory because: 1) the current QoS predicting algorithms still experience data sparsity and cannot predict the QoS values accurately and 2) the previous approaches fail to consider the QoS variance according to the users and services' locations carefully. A Web service recommendation method based on the QoS prediction and hierarchical tensor decomposition is proposed in this paper. The method is called QoSHTD that is based on location clustering and hierarchical tensor decomposition. First, the users and services of the QoSHTD cluster into several local groups based on their location and models local and global triadic tensors for the user-service-time relationship. The hierarchical tensor decomposition is then performed on the local and global triadic tensors. Finally, the predicted QoS value through local and global tensor decomposition is combined as the missing QoS values. The comprehensive experiment shows that the proposed method achieves a high prediction accuracy and recommending quality of Web service, and can partially address data sparsity.\",\n \"Title: Spectral Perturbation Meets Incomplete Multi-view Data.\\nYear: 2019\\n\\nAbstract: Beyond existing multi-view clustering, this paper studies a more realistic clustering scenario, referred to as incomplete multi-view clustering, where a number of data instances are missing in certain views. To tackle this problem, we explore spectral perturbation theory. In this work, we show a strong link between perturbation risk bounds and incomplete multi-view clustering. That is, as the similarity matrix fed into spectral clustering is a quantity bounded in magnitude O(1), we transfer the missing problem from data to similarity and tailor a matrix completion method for incomplete similarity matrix. Moreover, we show that the minimization of perturbation risk bounds among different views maximizes the final fusion result across all views. This provides a solid fusion criteria for multi-view data. We motivate and propose a Perturbation-oriented Incomplete multi-view Clustering (PIC) method. Experimental results demonstrate the effectiveness of the proposed method.\",\n \"Title: High-Performance FPGA-Based CNN Accelerator With Block-Floating-Point Arithmetic\\nYear: 2019\\n\\nAbstract: Convolutional neural networks (CNNs) are widely used and have achieved great success in computer vision and speech processing applications. However, deploying the large-scale CNN model in the embedded system is subject to the constraints of computation and memory. An optimized block-floating-point (BFP) arithmetic is adopted in our accelerator for efficient inference of deep neural networks in this paper. The feature maps and model parameters are represented in 16-bit and 8-bit formats, respectively, in the off-chip memory, which can reduce memory and off-chip bandwidth requirements by 50% and 75% compared to the 32-bit FP counterpart. The proposed 8-bit BFP arithmetic with optimized rounding and shifting-operation-based quantization schemes improves the energy and hardware efficiency by three times. One CNN model can be deployed in our accelerator without retraining at the cost of an accuracy loss of not more than 0.12%. The proposed reconfigurable accelerator with three parallelism dimensions, ping-pong off-chip DDR3 memory access, and an optimized on-chip buffer group is implemented on the Xilinx VC709 evaluation board. Our accelerator achieves a performance of 760.83 GOP/s and 82.88 GOP/s/W under a 200-MHz working frequency, significantly outperforming previous accelerators.\",\n \"Title: Ensemble learning regression for estimating unconfined compressive strength of cemented paste backfill\\nYear: 2019\\n\\nAbstract: Though machine learning (ML) approaches have proliferated in the mechanical properties prediction of cemented paste backfill (CPB), their applications have not reached the peak potential due to the lack of more robust techniques. In the present contribution, the state-of-the-art ensemble learning method was employed for improved estimation of the unconfined compressive strength (UCS) of CPB. 126 UCS tests were conducted on two new tailings to provide an enlarged dataset. Tree-based ML approaches, namely, regression tree (RT), random forest (RF), and gradient boosting regression tree (GBRT), were chosen to be individual ML approaches. The ensemble learning framework was used to combine the optimum individual regressors by means of GBRT. 5-fold cross-validation was used as the validation method and the performance was evaluated using correlation coefficient (R). Hyper-parameters tuning was conducted using particle swarm optimization (PSO). The results show that the best training set size was 70%. PSO was robust in the hyper-parameters tuning since the R value between experimental and predicted UCS on the training set was progressively increased. The ensemble learning can be used to improve the UCS prediction of CPB. The R values between experimental and predicted UCS obtained by RT, RF, GBRT, the ensemble GBRT regressors were 0.9442, 0.9507, 0.9832, and 0.9837, respectively. The method presented in this study extends recent efforts for UCS prediction of CPB and can significantly accelerate the CPB design.\",\n \"Title: Hybrid Multi-Kernel Construction of Polar Codes\\nYear: 2019\\n\\nAbstract: A new family of multi-kernel polar codes are proposed. Instead of using identical matrices in each stage, assigning different constituent matrices to different virtual channels based on their properties will further improve polarization performance and error rate performance. Matrices with the same block-length can be classified based on Bhattacharyya parameters and a measure M (G) is proposed to compare their polarization performance. Construction framework and graph representation of hybrid multi-kernels are explicitly given. Numerical illustrations show that hybrid multi-kernels have better performance than previous multi-kernels.\",\n \"Title: Construction of Replacement Set in the GC-LDPC Codes Based on BIBDs\\nYear: 2019\\n\\nAbstract: In the GC-LDPC code, the replacement set plays an important role in the construction of the parity-check matrix H. Considering BIBDs with λ = 1, a replacement set can be structured which satisfies RC-constraint. The parity-check matrix H of the GC-LDPC code can be straightforwardly constructed from the replacement set. Simulation results show that the performance of BIBDs based CN-GC-LDPC code surpasses finite field based CN-GC-LDPC code.\",\n \"Title: Mlp-Ia: Multi-Label User Profile Based On Implicit Association Labels\\nYear: 2019\\n\\nAbstract: Multi-Label user profile is widely used and have made great contributions in the field of recommendation systems, personalized searches, etc. Current researches on multi-label user profile either ignore the associations among labels or only consider the explicit associations among them, which are not sufficient to take full advantage of the internal associations. In this paper, a new insight is presented to mine the internal correlation among implicit association labels. To take advantage of this insight, a multi-label propagation method with implicit associations (MLP-IA) is proposed to get user profile. A probability matrix is first designed to record the implicit associations and then combine the multi-label propagation method with this probability matrix to get more accurate user profile. Finally, this method proves to be convergent and faster than traditional label propagation algorithm. Experiments on six real-world datasets in Weibo show that, compared with state-of-the-art methods, our approach can accelerate the convergence and its performance is significantly better than the previous ones.\",\n \"Title: Sockpuppet Detection In Social Network Via Propagation Tree\\nYear: 2019\\n\\nAbstract: Sockpuppet detection is a valuable and challenging issue in social network. Current works are continually making efforts to detect sockpuppet based on verbal, non-verbal or network-structure features. However, they do not consider the propagation characteristic and propagation structure of sockpuppet. With our observation, the propagation trees of sockpuppet and ordinary account are different. Sockpuppet' propagation tree is evidently wider and deeper than that of the ordinary one. Based on these observations, we propose a propagation-structure based method to tackle sockpuppet detection problem. The experiment on two real-world datasets of Sina Weibo demonstrates that our method is more robust and outperforms previous methods.\",\n \"Title: Improving Short Job Latency Performance in Hybrid Job Schedulers with Dice\\nYear: 2019\\n\\nAbstract: It is common to find a mixture of both long batch jobs and latency-sensitive short jobs in enterprise data centers. Recently hybrid job schedulers emerge as attractive alternatives of conventional centralized job schedulers.\\n\\nIn this paper, we conduct trace-driven experiments to study the job-completion-delay performance of two representative hybrid job schedulers (Hawk and Eagle), and find that short jobs still encounter long latency issues due to fluctuating bursty nature of workloads. To this end, we propose Dice, a general performance optimization framework for hybrid job schedulers, to alleviate the high job-completion-delay problem of short jobs. Dice is composed of two simple yet effective techniques: Elastic Sizing and Opportunistic Preemption. Both Elastic Sizing and Opportunistic Preemption keep track of the task waiting times of short jobs. When the mean task waiting time of short jobs is high, Elastic Sizing dynamically and adaptively increases the short partition size to prioritize short jobs over long jobs. On the other hand, Opportunistic Preemption preempts resources from long tasks running in the general partition on demand, so as to mitigate the \\\"head-of-line\\\" blocking problem of short jobs.\\n\\nWe enhance the two schedulers with Dice and evaluate Dice performance improvement in our prototype implementation. Experiment results show that Dice achieves 50.9%, 54.5%, and 43.5% improvement on 50th-percentile, 75th-percentile, and 90th-percentile job completion delays of short jobs in Hawk respectively, as well as 33.2%, 74.1%, and 85.3% improvement on those in Eagle respectively under the Google trace, at low performance costs to long jobs.\",\n \"Title: Eirene: Improving Short Job Latency Performance with Coordinated Cold Data Migration and Scheduler-Aware Task Cloning\\nYear: 2019\\n\\nAbstract: In large-scale enterprise data centers for big data analytics, long batched jobs and short interactive jobs are usually mixed. Hybrid job schedulers, consisting of one centralized scheduler for long jobs and multiple distributed schedulers for short jobs, have become a promising alternative because they can significantly shorten latencies of short jobs via independent and parallelized assignment of short tasks by distributed schedulers and lower chances of head-of-line blocking via a number of performance optimization techniques.However, short jobs are still facing long job latencies under hybrid job schedulers due to workload fluctuation and straggler task problem. In this paper, we propose Eirene to optimize the latency performance of short jobs via two schemes tightly coupled into the general architecture of hybrid job schedulers. Coordinated Cold Data Migration leverages high task waiting time of short jobs under heavily-loaded periods and migrates cold data from disks to local memory for the initial phase of reading input so as to shorten task runtime and queueing time. On the other hand, Scheduler-Aware Task Cloning exploits spare computing resources under lightly-loaded periods and performs proactive task cloning for short jobs to mitigate the straggler problem.We implement a prototype of Eirene based on Eagle, a state-of-the-art hybrid job scheduler. Experimental results show that, under heavy loads, Eirene is able to improve 50-percentile (P50), 75-percentile (P75), 90-percentile (P90) latency performance of short jobs by up to 44.4%, 80.3%, 84.1% respectively compared with Eagle under the Facebook trace with a cluster of 50000 nodes.\",\n \"Title: Yet another approach to understanding news event evolution\\nYear: 2020\\n\\nAbstract: With information explosion on the Internet, only returning ranked documents by search engines cannot satisfy people’s requirements on news events understanding. A more intelligent news events search engine should not only retrieve all related documents about a specific event, but also provide a global view about how the event originates and evolves. In order to solve this challenge, two tasks, event news retrieval and eventline generation should be processed. For event news retrieval, existing approaches mainly focus on the document-level similarity to retrieve related news documents, while external knowledge is not effectively taken into consideration. To this end, we propose a similarity model named Event-Oriented Similarity combining the document-level with the knowledge-level similarity to retrieve news documents related to the specific event. For eventline generation, in order to outline the event structure more accurately, we construct an Event-Oriented Similarity Graph to represent the relationship among retrieved event news documents and develop a community detection algorithm to segment sub-events which are consequently chained into a cohesive eventline. Experimental results on real-world datasets demonstrate that the proposed approach outperforms existing methods.\",\n \"Title: Challenging distributional models with a conceptual network of philosophical terms\\nYear: 2021\\n\\nAbstract: Computational linguistic research on language change through distributional semantic (DS) models has inspired researchers from fields such as philosophy and literary studies, who use these methods for the exploration and comparison of comparatively small datasets traditionally analyzed by close reading. Research on methods for small data is still in early stages and it is not clear which methods achieve the best results. We investigate the possibilities and limitations of using distributional semantic models for analyzing philosophical data by means of a realistic use-case. We provide a ground truth for evaluation created by philosophy experts and a blueprint for using DS models in a sound methodological setup. We compare three methods for creating specialized models from small datasets. Though the models do not perform well enough to directly support philosophers yet, we find that models designed for small data yield promising directions for future work.\",\n \"Title: Server Configuration Optimization In Mobile Edge Computing: A Cost-Performance Tradeoff Perspective\\nYear: 2021\\n\\nAbstract: Before service providers build up an mobile edge computing (MEC) platform, an important issue that needs to be considered is the configuration of computing resources on edge servers. Since the computing resources on an edge server are limited compared with a cloud server and the service provider's deployment budget is limited, it would be unrealistic to equip all edge servers with abundant computing resources. In addition, the edge servers have different computation demands due to their different geographies. Therefore, this article investigates the problem of server configuration optimization in an MEC environment based on a given computation demand statistics of the selected deployment locations. Our strategy is to treat each edge server as an M/M/m queueing model, and then establish the performance and cost models for the system. Two optimization problems, including cost constrained performance optimization, and performance constrained cost optimization are formulated based on our models and solved by a series of fast numerical algorithms. We also conduct extensive numerical simulation examples to show the effectiveness of the proposed algorithms. MEC service providers can use our strategy to get the appropriate type of processor and obtain the optimal processor number for each edge server to achieve two different goals: (1) deliver the highest-quality services with a given cost constraint; (2) minimize the investment cost with a service-quality guarantee. Our research is of great significance for service providers to control the tradeoff between investment cost and service quality.\",\n \"Title: Bregman linearized reweighted alternating minimization for robust sparse recovery\\nYear: 2021\\n\\nAbstract: AbstractHighlights •We propose a new algorithm dubbed Bregman linearized reweighted alternating minimization with continuation (BLRAMC) to solve a class of nonconvex and nonsmooth optimization problems for robust sparse recovery.•By leveraging the linearized techniques, all subproblems of alternating minimization formulated can be solved in closed-form. The convergence of the proposed algorithm is further improved by using Bregman distance and continuation technique.•With general assumptions of the cost function, the convergence analysis of the proposed algorithm is provided, which indicates that a stationary point of the problem can be sought out. AbstractIn this paper, we consider a class of nonconvex and nonsmooth minimization problems with linear constraints in the area of compressed sensing (CS). The classical alternating minimization (AM) had difficulties in solving formulated nonconvex or nonsmooth subproblems. In view of this problem, we propose a Bregman linearized reweighted alternating minimization with continuation (BLRAMC). In this algorithm, all subproblems are convex and can be thus solved efficiently. Moreover, with the introduced Kurdyka-Łojasiewicz property, it is proved that the proposed algorithm converges to a stationary point of the objective function. Numerical experiments for the robust sparse recovery with different impulsive noise are presented to substantiate the effectiveness of the proposed algorithm.\",\n \"Title: A Novel Adaptive Density-Based Spatial Clustering Of Application With Noise Based On Bird Swarm Optimization Algorithm\\nYear: 2021\\n\\nAbstract: The commonly used density-based spatial clustering method (DBSCAN) connects contiguous regions with sufficiently large densities when processing datasets to efficiently discover clusters of different shapes and densities and outliers. However, the algorithm has the problem that radius of neighborhood (Eps) argument requires to be selected manually. For datasets with higher dimensionality and larger data volume, the selection of Eps parameters can be difficult thus leading to poor clustering quality. To solve the above problem, we propose a novel adaptive density-based spatial clustering of application with noise based on bird swarm optimization algorithm (BSA-DBSCAN). We use the global search capability of the bird swarm method to select the best Eps parameter neighborhood values. We can avoid manual intervention and realize adaptive parameter optimization in the clustering process. To further explore the clustering performance of BSA-DBSCAN method, we test the synthetic datasets and the real-world datasets respectively and perform images analysis on the clustering evaluation index values. The simulation experiments show that the improved method in this paper can reasonably search the Eps parameter value and can obtain the higher accuracy of clustering.\",\n \"Title: Model For Snow-Fighting Vehicle Route Planning Considering Deadheading Restriction\\nYear: 2018\\n\\nAbstract: Snow(1) removal is an important job for urban traffic authority to keep urban traffic smooth and safe, particularly for cities in cold regions. Snow-fighting vehicle routing problem, for mechanically removing measure, is an arc routing problem. However, the deadheading problem, in which vehicles may travel on a road covered by snow without serving it, is unreasonable and increasing the cost. Therefore, an optimization model is constructed to restrict the deadheading problem, which allows deadheading only after the road has been serviced. Also, a two-stage serial algorithm, which is first-clustering-then-routing, is proposed to solve the optimization model. A numerical example is conducted subsequently. Results show that the efficiency of snow removal drops as demand rises and most of the snow is removed in the front of the whole time. Besides, three alternative objective functions are analyzed and the outcome indicates taking minimal makespan as the objective function is better in most instances.\",\n \"Title: GGP: A Graph-based Grouping Planner for Explicit Control of Long Text Generation\\nYear: 2021\\n\\nAbstract: BSTRACTExisting data-driven methods can well handle short text generation. However, when applied to the long-text generation scenarios such as story generation or advertising text generation in the commercial scenario, these methods may generate illogical and uncontrollable texts. To address these aforementioned issues, we propose a graph-based grouping planner~(GGP) following the idea of first-plan-then-generate. Specifically, given a collection of key phrases, GGP firstly encodes these phrases into a instance-level sequential representation and a corpus-level graph-based representation separately. With these two synergic representations, we then regroup these phrases into a fine-grained plan, based on which we generate the final long text. We conduct our experiments on three long text generation datasets and the experimental results reveal that GGP significantly outperforms baselines, which proves that GGP can control the long text generation with knowing how to say and in what order.\",\n \"Title: Safety Helmet Detection Algorithm based on Color and HOG Features\\nYear: 2020\\n\\nAbstract: There are many unstable factors in the working environment of electrical workers, which threaten their safety. Therefore, how to protect electrical workers is a problem worth thinking about. Safety helmet protects worker’s head from injury when they fall. This paper presents a method to detect whether electrical workers wear a safety helmet or not. This method is based on Support Vector Machine (S...\",\n \"Title: Pen4rec: Preference Evolution Networks For Session-Based Recommendation\\nYear: 2021\\n\\nAbstract: Session-based recommendation aims to predict user the next action based on historical behaviors in an anonymous session. For better recommendations, it is vital to capture user preferences as well as their dynamics. Besides, user preferences evolve over time dynamically and each preference has its own evolving track. However, most previous works neglect the evolving trend of preferences and can be easily disturbed by the effect of preference drifting. In this paper, we propose a novel Preference Evolution Networks for session-based Recommendation (PEN4Rec) to model preference evolving process by a two-stage retrieval from historical contexts. Specifically, the first-stage process integrates relevant behaviors according to recent items. Then, the second-stage process models the preference evolving trajectory over time dynamically and infer rich preferences. The process can strengthen the effect of relevant sequential behaviors during the preference evolution and weaken the disturbance from preference drifting. Extensive experiments on three public datasets demonstrate the effectiveness and superiority of the proposed model.\",\n \"Title: Ensemblefool: A Method To Generate Adversarial Examples Based On Model Fusion Strategy\\nYear: 2021\\n\\nAbstract: Deep neural networks have been shown vulnerable to adversarial attacks launched by adversarial examples. These examples' transferability makes an attack in the real-world feasible, which poses a security threat to deep learning. Considering the limited representation capacity of a single deep model, the transferability of an adversarial example generated by a single attack model would cause the failure of attacking other different models. In this paper, we propose a new adversarial attack method, named EnsembleFool, which flexibly integrates multiple models to enhance adversarial examples' transferability. Specifically, the model confidence concerning an input example reveals the risk of a successful attack. In an iterative attacking case, the result of a previous attack could guide us to enforce a new attack that possesses a higher probability of success. Regarding this, we design a series of integration strategies to improve the adversarial examples in each iteration. Extensive experiments on ImageNet indicate that the proposed method has superior attack performance and transferability than state-of-the-art methods. (c) 2021 Elsevier Ltd. All rights reserved.\",\n \"Title: MeLL: Large-scale Extensible User Intent Classification for Dialogue Systems with Meta Lifelong Learning\\nYear: 2021\\n\\nAbstract: ABSTRACTUser intent detection is vital for understanding their demands in dialogue systems. Although the User Intent Classification (UIC) task has been widely studied, for large-scale industrial applications, the task is still challenging. This is because user inputs in distinct domains may have different text distributions and target intent sets. When the underlying application evolves, new UIC tasks continuously emerge in a large quantity. Hence, it is crucial to develop a framework for large-scale extensible UIC that continuously fits new tasks and avoids catastrophic forgetting with an acceptable parameter growth rate. In this paper, we introduce the Meta Lifelong Learning (MeLL) framework to address this task. In MeLL, a BERT-based text encoder is employed to learn robust text representations across tasks, which is slowly updated for lifelong learning. We design global and local memory networks to capture the cross-task prototype representations of different classes, treated as the meta-learner quickly adapted to different tasks. Additionally, the Least Recently Used replacement policy is applied to manage the global memory such that the model size does not explode through time. Finally, each UIC task has its own task-specific output layer, with the attentive summarization of various features. We have conducted extensive experiments on both open-source and real industry datasets. Results show that MeLL improves the performance compared with strong baselines and also reduces the number of total parameters. We have also deployed MeLL on a real-world e-commerce dialogue system AliMe and observed significant improvements in terms of both F1 and the resources usage.\",\n \"Title: Effect Of Scan Line Spacing On Laser-Reduced Graphene Oxide Based Temperature Sensing\\nYear: 2021\\n\\nAbstract: This paper systematically studies the effect of scan line spacing on the sensitivity of laser-reduced graphene oxide based temperature sensor. As for the fabrication, we firstly sputter and pattern a pair of 80 nm thick gold electrodes on PET film, and then coat a layer of graphene oxide solution in the middle of the electrode. Second, graphene oxide is reduced by UV laser scanning to bridge the electrodes with the laser-reduced graphene oxide, which is a temperature sensitive material with temperature-dependent resistivity. A group of rGO temperature sensors are fabricated in this way with different scan line spacings ranging from 0.02 to 0.12 mm with a step of 0.02 mm. Raman spectroscopy analysis shows that the defects of graphene first decrease and then increase with increasing scanning spacing. The temperature calibration results show that defects of graphene generally enhance the sensitivity of rGO temperature sensor, and the larger sensitivity is achieved with a scan line spacing of 0.02 mm and 0.12 mm.\",\n \"Title: A Robotic Dynamic Tactile Sensing System Based On Electronic Skin\\nYear: 2021\\n\\nAbstract: Robot tactile is getting more and more practical with the rapid development of electronic skin (e-skin). In this work, we propose a dynamic tactile sensing system based on e-skin, a high-density array tactile sensor. The system is comprised of three algorithms to track motion and detect slip. With real-time slip detecting, an adaptive controller that can rapidly tune applied grasping force without having a priori knowledge of the objects was designed and experimentally validated.\",\n \"Title: Exploring Misclassification Information For Fine-Grained Image Classification\\nYear: 2021\\n\\nAbstract: Fine-grained image classification is a hot topic that has been widely studied recently. Many fine-grained image classification methods ignore misclassification information, which is important to improve classification accuracy. To make use of misclassification information, in this paper, we propose a novel fine-grained image classification method by exploring the misclassification information (FGMI) of prelearned models. For each class, we harvest the confusion information from several prelearned fine-grained image classification models. For one particular class, we select a number of classes which are likely to be misclassified with this class. The images of selected classes are then used to train classifiers. In this way, we can reduce the influence of irrelevant images to some extent. We use the misclassification information for all the classes by training a number of confusion classifiers. The outputs of these trained classifiers are combined to represent images and produce classifications. To evaluate the effectiveness of the proposed FGMI method, we conduct fine-grained classification experiments on several public image datasets. Experimental results prove the usefulness of the proposed method.\",\n \"Title: TOPIC SEQUENCE EMBEDDING FOR USER IDENTITY LINKAGE FROM HETEROGENEOUS BEHAVIOR DATA\\nYear: 2021\\n\\nAbstract: In social media, user identity linkage is a vital information security issue of identifying users' private information across multiple online social networks. With the popularity of behavior-rich social services, existing methods attempt to align users through encoding behaviors. However, most of the efforts suffer from the high variety and heterogeneity of behavior data across social networks, resulting in a limitation of modeling user intrinsic characteristics. To address the above issues, we focus on keyword-based topics to formulate user's variety behaviors for user identity linkage. In this paper, a novel Topic Sequence Embedding (TSeqE) method is proposed to embed contextual information of topics to represent users' intrinsic characteristics for identity linkage. Furthermore, we introduce a domain-adversarial training strategy to tackle the behavior heterogeneity problem. Our experiments on two real-world datasets demonstrate that TSeqE produces a significant improvement compared with several strong baselines.\",\n \"Title: Weighted Likelihood Cfar Detection For Weibull Background\\nYear: 2021\\n\\nAbstract: In modern radar detection systems, constant false alarm rate (CFAR) control is a key technique for automatic target detection in clutter level unknown environments. The maximum likelihood criterion is often used to design the CFAR detector for which it has the best detection performance with the expected probability of false alarm. However, the heterogeneous environments, viz., multiple-target scenario and clutter power transition, will deteriorate the performance of the optimal CFAR detector. In this paper, we propose a novel CFAR detector for Weibull background with known shape parameter. The new CFAR detector is based on robust weighted likelihood estimator, with robustness to interferences. In addition, to prove the CFAR property of it, we introduce the invariant theory. Computation analysis and simulation results verify the effectiveness and superiority of our proposed CFAR detector. (C) 2021 Elsevier Inc. All rights reserved.\",\n \"Title: Online Koopman Operator Learning to Identify Cross-Coupling Effect of Piezoelectric Tube Scanners in Atomic Force Microscopes\\nYear: 2022\\n\\nAbstract: As one of the most significant applications of nanopositioning technology, the tremendous development of atomic force microscopes (AFMs) has been witnessed these years. Essentially, the scanning motions of AFMs are generally driven by piezoelectric tube scanners (PTSs), whose cross-coupling effect hinders their high speed and high-precision positioning. Therefore, it becomes an urgent yet challeng...\",\n \"Title: Complex Dynamical Behavior And Numerical Simulation Of A Cournot-Bertrand Duopoly Game With Heterogeneous Players\\nYear: 2021\\n\\nAbstract: In this paper, we consider the Cournot-Bertrand duopoly mixed competition model, which is characterized by different decision-making variables and different objective functions of the two enterprises. This form of competition is more consistent with the complex actual economic market. The general stability conditions of the four equilibria are given and analyzed with eigenvalues and Jury criterion, so that enterprise decision makers could choose appropriate parameters to determine the development of the enterprise. Under the specific parameter conditions, the stability conditions are analyzed and demonstrated in detail by using two-dimensional bifurcation diagrams, stable region diagrams and bifurcation curves. Many fractal Arnold's tongues are found in two-dimensional bifurcation diagrams. These tongues are associated with the Neimark-Sacker bifurcation of the fixed point and are arranged in accordance with the organization law of the periodic tree of the SternBrocot tree, which is helpful for us to analyze the transitions of system between period and chaos. With the help of the basin of attraction, the coexistence of attractors is analyzed, on which the decision maker can choose the initial conditions that are beneficial to the development of the enterprise. We also analyze the topology structure of basin of attraction and the formation mechanism of holes in the basin of attraction by using the theory of critical curve and noninvertible map. (C) 2021 Elsevier B.V. All rights reserved.\",\n \"Title: Msset: A High-Performance Time-Frequency Analysis Method For Sparse-Spectrum Biomedical Signal\\nYear: 2021\\n\\nAbstract: When processing sparse-spectrum biomedical signals, traditional time-frequency (TF) analysis methods are faced with the defects of blurry energy concentration and low TF resolution caused by the Heisenberg uncertainty principle. The synchrosqueezing-based methods have demonstrated advanced TF performances in recent studies. However, these methods contain at least three drawbacks: (1) existence of non-reassigned points (NRPs), (2) low noise robustness, and (3) low amplitude accuracy. In this study, the novel TF method, termed multisynchrosqueezing extracting transform (MSSET), is proposed to address these limitations. The proposed MSSET is divided into three steps. First, multisynchrosqueezing transform (MSST) is performed with specific iterations. Second, a synch-extracting is applied to retain the TF distribution of MSST results that relate most to time-varying information of the raw signal; meanwhile, the other smeared TF energy is discarded. Finally, the MSSET result is obtained by rounding the adjacent results at the frequency plane. Numerical verification results show that the proposed MSSET method can effectively solve the NRPs problem and enhance noise robustness. Furthermore, while retaining superior energy concentration and signal reconstruction capability, the MSSET's amplitude accuracy reaches about 90%, significantly higher than other methods. In the same conditions, the MSSET even consumes less time than MSST and IMSST. It also achieves the best composite performance with the least amplitude accuracy-time cost ratio (ATCR) index. Actual application examples in the Bat signal and the electrocardiograph (ECG) signal also validate the excellent performances of our method. To conclude, our proposed MSSET is superior to state-of-the-art methods and is expected to be widely used in the sparse-spectrum biomedical signal.\",\n \"Title: Sparse Bayesian Learning Based on Collaborative Neurodynamic Optimization\\nYear: 2022\\n\\nAbstract: Regression in a sparse Bayesian learning (SBL) framework is usually formulated as a global optimization problem with a nonconvex objective function and solved in a majorization–minimization framework where the solution quality and consistency depend heavily on the initial values of the used algorithm. In view of the shortcomings, this article presents an SBL algorithm based on collaborative neurodynamic optimization (CNO) for searching global optimal solutions to the global optimization problem. The CNO system consists of a population of recurrent neural networks (RNNs) where each RNN is convergent to a local optimum to the global optimization problem. Reinitialized repetitively via particle swarm optimization with exchanged local optima information, the RNNs iteratively improve their searching performance until reaching global convergence. The proposed CNO-based SBL algorithm is almost surely convergent to a global optimal solution to the formulated global optimization problem. Two applications with experimental results on sparse signal reconstruction and partial differential equation identification are elaborated to substantiate the superiority and efficacy of the proposed method in terms of solution optimality and consistency.\",\n \"Title: A Sparse Dimensionality Reduction Approach Based on False Nearest Neighbors for Nonlinear Fault Detection\\nYear: 2021\\n\\nAbstract: As a newly emerging multivariate statistical process monitoring method, non-negative matrix factorization (NMF) and its variants avoid the positive and negative cancellations between the extracted features because of the purely additive combination of non-negative components. Thus, they make a good match with this reality that the negative values of both observations and decomposed components are ...\",\n \"Title: Bifurcation Analysis Of A Bounded Rational Duopoly Game With Consumer Surplus\\nYear: 2021\\n\\nAbstract: The nonlinear dynamical behaviors of economic models have been extensively examined and still represented a great challenge for economists in recent and future years. A proposed boundedly rational game incorporating consumer surplus is introduced. This paper aims at studying stability and bifurcation types of the presented model. The flip and Neimark-Sacker bifurcations are analyzed via applying the normal form theory and the center manifold theorem. This study helps determine an appropriate choice of decision parameters which have significant influences on the behavior of the game. The duopoly game that is formed by considering bounded rationality and consumer surplus is more realistic than the ordinary duopoly game which only has profit maximization. And then, some numerical simulations are provided to verify the theoretical analysis. Finally, we compare the dynamical behaviors of the built model with that of Bischi-Naimzada model so as to better understand the performance of the duopoly game with consumer surplus.\",\n \"Title: Research On Design, Calibration And Real-Time Image Expansion Technology Of Unmanned System Variable-Scale Panoramic Vision System\\nYear: 2021\\n\\nAbstract: This paper summarized the research status, imaging model, systems calibration, distortion correction, and panoramic expansion of panoramic vision systems, pointed out the existing problems and put forward the prospect of future research. According to the research status of panoramic vision systems, a panoramic vision system with single viewpoint of refraction and reflection is designed. The systems had the characteristics of fast acquisition, low manufacturing cost, fixed single-view imaging, integrated imaging, and automatic switching depth of field. Based on these systems, an improved nonlinear optimization polynomial fitting method is proposed to calibrate the monocular HOVS, and the binocular HOVS is calibrated with the Aruco label. This method not only improves the robustness of the calibration results, but also simplifies the calibration process. Finally, a real-time method of panoramic map of multi-function vehicle based on vcam is proposed.\",\n \"Title: Pooling Attention-based Encoder–Decoder Network for semantic segmentation\\nYear: 2021\\n\\nAbstract: AbstractAbstractAiming to the challenge of poor pixel-consistency in inter-category and pixel-similarity in inter-category, in this paper, we propose an Encoder–Decoder network for image semantic segmentation using pooling SE-ResNet attention module, called PAEDN. It is an effective of attention mechanism to get aggregated information. According to the principle of SE-ResNet, a collection of Average, Maximum and Stochastic global pooling, which concentrate on contoured, detailed, and generalized information in a certain semantic segmentation, form attention modules. Channel Pooling Attention Module (CPAM) and Position Pooling Attention Module (PPAM) are designed and integrated into the Encoder to extract discriminative features from input images, and the Decoder is developed through SE-ResNet attention module to fuse the feature map in high-resolution with that in low-resolution. Experimental evaluations performed on the data sets PASCAL and Cityscapes, show the proposed Encoder–Decoder with pooling attention module produces good pixel-consistency semantic label, achieves 15.1% improvement to FCN.Graphical abstractDisplay OmittedHighlights •The encoder and decoder work together to enhance the consistency of pixels.•The work uses maximum, average and stochastic pooling to gain contoured, detailed and generalized information.•Two attention modules use pooling operations to integrate discriminative feature information.\",\n \"Title: Social Network And Bibliometric Analysis Of Unmanned Aerial Vehicle Remote Sensing Applications From 2010 To 2021\\nYear: 2021\\n\\nAbstract: Unmanned Aerial Vehicle (UAV) Remote sensing (RS) has unique advantages over traditional satellite RS, including convenience, high resolution, affordability and fast acquisition speed, making it widely used in many fields. To provide an overview of the development of UAV RS applications during the past decade, we screened related publications from the Web of Science core database from 2010 to 2021, built co-author networks, a discipline interaction network, a keywords timeline view, a co-citation cluster, and detected burst citations using bibliometrics and social network analysis. Our results show that: (1) The number of UAV RS publications had an increasing trend, with explosive growth in the past five years. The number of papers published by China and the United States (US) is far ahead in this field; (2) The US has currently the greatest influence in this field through the largest number of international cooperations. Cooperation is mainly concentrated in countries and institutions with a large number of publications but is not widely distributed. (3) The application of UAV RS involves multiple interdisciplinary subjects, among which \\\"Environmental Science and Ecology\\\" ranks first; (4) Future research trends of UAV RS are expected to be related to artificial intelligence (e.g., artificial neural networks-based research). This paper provides a scientific basis and guidance for future developments of UAV RS applications, which can help the research community to better grasp the developments of this field.\",\n \"Title: Multi-Metric Fusion Network for Image Quality Assessment\\nYear: 2021\\n\\nAbstract: With the fast proliferation of multimedia applications, the reliable prediction of image/video quality is urgently needed. Many quality assessment metrics have been proposed in the past decades with various complexity and consistency with human ratings. The metrics are designed from different aspects, e.g., pixel level fidelity, structural similarity, information theory and data-driven. In this paper, we design a Multi-Metric Fusion Network (MMFN) for aggregating the quality scores predicted by diverse metrics to generate more accurate results. To be specific, we utilize the image features extracted from the pretrained network to adaptively rescale the predicted quality from different metrics, and leverage the fully-connected layers to regress a single scalar as the final score. Pairwise images can be further integrated into the training procedure by adding a Score2Prob layer. Experimental results on the validation and test sets demonstrate that our proposed MMFN achieves better prediction accuracy compared with other metrics.\",\n \"Title: AliMe MKG: A Multi-modal Knowledge Graph for Live-streaming E-commerce\\nYear: 2021\\n\\nAbstract: ABSTRACTLive streaming is becoming an increasingly popular trend of sales in E-commerce. The core of live-streaming sales is to encourage customers to purchase in an online broadcasting room. To enable customers to better understand a product without jumping out, we propose AliMe MKG, a multi-modal knowledge graph that aims at providing a cognitive profile for products, through which customers are able to seek information about and understand a product. Based on the MKG, we build an online live assistant that highlights product search, product exhibition and question answering, allowing customers to skim over item list, view item details, and ask item-related questions. Our system has been launched online in the Taobao app, and currently serves hundreds of thousands of customers per day.\",\n \"Title: Dynamic Weighted \\\"Small-World\\\" Graphical Network Establishment For Fnirs Time-Varying Brain Function Analysis\\nYear: 2021\\n\\nAbstract: With the increasing application of functional near-infrared spectroscopy (fNIRS) technology, topological brain network analysis has recently been developed and successfully applied in fNIRS-based brain research. However, the current network information is analyzed through the binary network, and it lacks a dynamic estimation. In this study, we proposed a novel dynamic weighted \\\"small-world\\\" topology network and applied it to fNIRS analysis. Firstly, we introduced a novel flat-top (FT) window to estimate sliding-window correlation (SWC), improving the spectrum performances. And then, we applied the least absolute shrinkage and selection operator (LASSO) algorithm to the time-varying correlation matrix and obtained corresponding sparse matrix results. Finally, we established a dynamic weighted topological graph and calculated \\\"small-world\\\" network parameters to analyze the brain network dynamics. Simulation results showed that our proposed FT-based SWC method realized wider bandwidth, better spectrum performance, and more accurate dynamic tracking capability with the minimum mean square errors(MSEs) results in each iterative simulation. The fNIRS results showed that the average node degree (DE) and global efficiency (GE) reached a peak in the middle and late periods of the task. The network parameters showed similar changes in 2-back and 3-back tasks, which differ from the 0-back task. Statistical analysis revealed the DE and GE of the subnetwork decreased as the task became more difficult. These results revealed the typical time-varying characteristics of the brain network dynamics during the working memory process, which were expected to unveil the brain mechanisms underlying the cognitive neural activity in greater depth.\",\n \"Title: Assessment And Selection Of Smart Agriculture Solutions Using An Information Error-Based Pythagorean Fuzzy Cloud Algorithm\\nYear: 2021\\n\\nAbstract: Smart agriculture can enhance agricultural production efficiency, improve the ecological environment, and realize the sustainable development of agriculture. Many countries and companies are working hard to develop or introduce smart agricultural solutions. Because of the shackles of traditional agricultural management methods and fierce competition with a variety of different solutions, it is a difficult task for enterprises to select and implement smart agricultural solutions smoothly. Hence, enterprises must assess alternative solutions and select a feasible solution in advance. This study drew a novel assessment and selection for smart agriculture solutions using an information error-based Pythagorean fuzzy cloud algorithm. First, an evaluation index system built on smart agriculture solutions was constructed from four aspects. Then, a new concept of Pythagorean fuzzy clouds was defined to express the evaluation information for each indicator. Simultaneously, the Pythagorean fuzzy cloud weighted Bonferroni mean (PFCWBM) operator was developed to aggregate the assessment information of multiple indicators. Next, an assessment and selection decision framework for smart agriculture solutions based on the PFCWBM operator was presented. In addition, an example was given to illustrate the effectiveness of the proposed algorithm. Finally, a discussion was conducted to verify the superiority of our approach. The results showed that our algorithm can characterize and evaluate complex information and has high sensitivity and environmental adaptability.\",\n \"Title: Spatial-Temporal Dynamic Graph Convolution Neural Network for Air Quality Prediction\\nYear: 2021\\n\\nAbstract: Air quality prediction has received widespread attention from both the governments and citizens due to its close relation to our lives. Analyzing the spatial relations and temporal trends in air quality data is essential for air quality prediction task. However, most existing approaches require a pre-defined graph structure to capture the spatial dependencies of air quality data, and thus they can not be applied when a well-defined graph structure is unavailable. Besides, those methods do not give sufficient consideration to the latent relationships among entities of the graph over time. To overcome the above limitations, we propose a Spatial-Temporal Dynamic Graph Convolution Neural Network (ST-DGCN) in this paper. Our approach develops a dynamic adjacency matrix into graph convolution layer, which extracts the potential and time-varying spatial dependencies. To jointly model the spatial and temporal correlations, we combine dynamic graph convolution with gated recurrent unit and propose a unified DGC-GRU block. Next, a residual operation is further introduced into the DGC-GRU to simultaneously handle the information from different particles. Experimental results demonstrate that the proposed method outperforms the state-of-art baselines on two real-world air quality datasets.\",\n \"Title: Hybrid-TC: A Hybrid Teaching-Learning-Based Optimization Algorithm for Service Composition in Cloud Manufacturing\\nYear: 2021\\n\\nAbstract: During the process of cloud manufacturing, service composition is an essential technology to ensure the smooth execution of the task. An important challenge for cloud manufacturing is how to implement effective and accurate service composition strategy, which are to execute the task effectively while also satisfy the requirements of user by maximizing the overall quality of service (QoS). However, the current cloud manufacturing service composition methods generally have the problem of poor solution quality in the large scale environment. To solve this problem, we propose a hybrid optimization algorithm, named hybrid-TC, which is a hybrid of the teaching-learningbased optimization algorithm (TLBO) and the crisscross optimization algorithm (CSO). First, we added Skyline query in the initialization phase to improve the convergence speed and the quality of the solution. Second, the horizontal crossover of CSO is used in the teaching phase of original TLBO, thereby some dimensions in the population that trapped in the local optimum have the chance to jump out of the iteration. Finally, the offspring individuals learned in the teaching-phase will continue to learn to improve the quality of the solution. Experiments show that our proposed method is effective for improving the solution quality of large-scale environmental service composition.\",\n \"Title: On Language Model Integration for RNN Transducer Based Speech Recognition.\\nYear: 2022\\n\\nAbstract: The mismatch between an external language model (LM) and the implicitly learned internal LM (ILM) of RNN-Transducer (RNN-T) can limit the performance of LM integration such as simple shallow fusion. A Bayesian interpretation suggests to remove this sequence prior as ILM correction. In this work, we study various ILM correction-based LM integration methods formulated in a common RNN-T framework. We provide a decoding interpretation on two major reasons for performance improvement with ILM correction, which is further experimentally verified with detailed analysis. We also propose an exact-ILM training framework by extending the proof given in the hybrid autoregressive transducer, which enables a theoretical justification for other ILM approaches. Systematic comparison is conducted for both in-domain and cross-domain evaluation on the Librispeech and TED-LIUM Release 2 corpora, respectively. Our proposed exact-ILM training can further improve the best ILM method.\",\n \"Title: Research On Integral Separation Control Of Warp Tension Based On Fuzzy Parameter Optimization\\nYear: 2021\\n\\nAbstract: In textile machines, the stability of warp tension is one of the decisive factors for the reliability, stability and product quality of weaving process. In order to meet the improving requirement for weaving efficiency and fabric quality, it is proposed that a fuzzy optimization integral separation PID warp tension control scheme based on process sampling to improve the warp tension control level of loom Aiming at the problems of time-varying, nonlinear and variable coupling in the warp tension control system of loom, the forming mechanism of warp tension is modeled and analyzed, and the sampling scheme of warp tension based on process is proposed. Based on the periodic change of warp tension at macro level and continuous fluctuation at micro level, the integral separation control and fuzzy optimization theory are introduced to optimize the control effect of the control system on the basis of classical PID control algorithm. Finally, the simulation and experiment show that the scheme can improve the tension controls performance and effectively reduce the tension error fluctuation.\",\n \"Title: Deep Progressive Attention For Person Re-Identification\\nYear: 2021\\n\\nAbstract: Person re-identification (Re-ID) aims to retrieve specific individuals across non overlapping camera views. In recent years, attention-based models contribute to many computer vision tasks due to their great ability for learning discriminative features. We propose the deep progressive attention (DPA) in a more natural manner for person Re-ID. Similar to human visual attention mechanism, the proposed DPA progressively selects the most discriminative parts of a specific individual and formulates feature representation for comparison purpose. Concretely, on the one hand, the proposed DPA uses a long-term reward to optimize the discriminative feature selection. On the other hand, a deep convolutional architecture is integrated into a recurrent model for feature representation learning. Extensive experiments on three person Re-ID benchmarks Market-1501, DukeMTMC-reID, and CUHK03-NP demonstrate the proposed DPA is on par with the state-of-the-art. Moreover, the experiments on partial person Re-ID datasets indicate the proposed DPA is competitive with the specially designed partial person Re-ID methods. (c) 2021 SPIE and IS&T [DOI: 10.1117/1.JEI.30.4.043028]\",\n \"Title: Research Overview on Carbon Emission Models\\nYear: 2021\\n\\nAbstract: Based on the literatures of current research on carbon emission models in China and abroad, this paper uses the bibliometrics statistical method to investigate the data driven carbon emission research literatures published in CNKI and SCI from 2011 to 2020. In this research, we systematically analyzed the work done by predecessors from the aspects of industries, data models and data sets. This pap...\",\n \"Title: Research on Prediction and Source Analysis of Ozone Pollution\\nYear: 2021\\n\\nAbstract: For researchers and practitioners in the environmental field, accurate ozone pollution prediction and source analysis play an important role in smart air monitoring systems. However, there are some challenges in predicting and analyzing the source of ozone pollution in the air. Ozone data are spatio-temporal data, so when analyzing spatio-temporal data, the spatio-temporal correlation of the data ...\",\n \"Title: Intuitionistic Fuzzy Three-Factor Ratio Models And Multi-Preference Fusion\\nYear: 2021\\n\\nAbstract: Generally, decision-makers (DMs) can provide subjective opinions based on their risk attitude and intuitionistic preference for specific alternatives and attributes, which can be taken as cognitive information. For instance, alternative A is better than alternatives B and C, or attribute 3 is the most important. These multi-dimensional preferences and social cognitive behavior described by DMs based on their subjective evaluation should be taken into account when making a decision. How to fuse multiple, uncertain, imprecise but important preferences and cognitive information and then make a decision in an intuitionistic fuzzy environment is becoming a practical issue. Therefore, this paper defines a three-factor ratio of the intuitionistic fuzzy number and then proposes a basic intuitionistic fuzzy three-factor ratio (IFTR) model. To present DMs' risk preferences, this paper constructs two extreme IFTR models and describes risk preferences with a risk appetite parameter. For DMs' alternative preferences, this paper develops a continuous IFTR model in which alternative preferences are fused to calculate the optimal risk appetite parameter. To fully consider DMs' risk, alternative, and attribute preferences, this paper further proposes a generalized IFTR model. Thus, risk, alternative, and attribute preferences, which can be viewed as the social cognitive information, can be fused in an intuitionistic fuzzy decision-making and group decision-making process simultaneously. An illustrative example to address the problem of demolishing old urban villages is provided to show the effectiveness of the proposed models.\",\n \"Title: Perceptual Quality Assessment of Internet Videos\\nYear: 2021\\n\\nAbstract: ABSTRACTWith the fast proliferation of online video sites and social media platforms, user, professionally and occupationally generated content (UGC, PGC, OGC) videos are streamed and explosively shared over the Internet. Consequently, it is urgent to monitor the content quality of these Internet videos to guarantee the user experience. However, most existing modern video quality assessment (VQA) databases only include UGC videos and cannot meet the demands for other kinds of Internet videos with real-world distortions. To this end, we collect 1,072 videos from Youku, a leading Chinese video hosting service platform, to establish the Internet video quality assessment database (Youku-V1K). A special sampling method based on several quality indicators is adopted to maximize the content and distortion diversities within a limited database, and a probabilistic graphical model is applied to recover reliable labels from noisy crowdsourcing annotations. Based on the properties of Internet videos originated from Youku, we propose a spatio-temporal distortion-aware model (STDAM). First, the model works blindly which means the pristine video is unnecessary. Second, the model is familiar with diverse contents by pre-training on the large-scale image quality assessment databases. Third, to measure spatial and temporal distortions, we introduce the graph convolution and attention module to extract and enhance the features of the input video. Besides, we leverage the motion information and integrate the frame-level features into video-level features via a bi-directional long short-term memory network. Experimental results on the self-built database and the public VQA databases demonstrate that our model outperforms the state-of-the-art methods and exhibits promising generalization ability.\",\n \"Title: Chaotic Particle Swarm Optimization Algorithm With Constraint Handling And Its Application In Combined Bidding Model\\nYear: 2021\\n\\nAbstract: Particle swarm optimization (PSO) is a widely used intelligent optimization algorithm. The premature convergence and the constraint handling, however, remain problems for the global optimization searching. In this paper, a dual fitness value chaotic PSO algorithm with equality constraint handling (EDFC-PSO) is proposed. The particle mutation process based on the chaotic Cat mapping is introduced to increase the diversity of particles. Equality constraints are solved by parametric equations, and inequality constraints are considered using the dual fitness value. The proposed EDFC-PSO algorithm is tested by several test functions and then compared with other algorithms. Further, the performance of the proposed algorithm is validated by a combined bidding model of a power system. Our EDFC-PSO algorithm has equality constraints satisfied and enhances the global searching capability in both the test functions and the engineering model.\",\n \"Title: A Method For Identifying Marine Targets Based On Mining Of Multi-Characteristic Movement Patterns\\nYear: 2021\\n\\nAbstract: Currently, marine ship targets are often identified directly through sensing targets. As a result, the potential value of massive spatiotemporal data has not been fully utilized. Because relevant maritime authorities have new requirements for understanding the situation of naval traffic flows and target identity behavior, obtaining necessary information about maritime targets at the data level and further understanding the ship movement patterns at the characteristic level are the focus of the current study. From the perspective of system design, this study proposes a method for obtaining a number of feature sets by mining the ship trajectory data and then identifying unknown targets. In the process of constructing the multi-characteristic sets, this study focuses on the importance of the distribution characteristics of ship channels for target identification. This study proposes an improved similarity measure-a trajectory rasterization model method. Experimental verification based on the measured data set shows that the system design can successfully fulfill the requirements for the identification of unknown targets and has a certain universality.\",\n \"Title: Improve Semi-Supervised Learning With Metric Learning Clusters And Auxiliary Fake Samples\\nYear: 2021\\n\\nAbstract: Because it is very expensive to collect a large number of labeled samples to train deep neural networks in certain fields, semi-supervised learning (SSL) researcher has become increasingly important in recent years. There are many consistency regularization-based methods for solving SSL tasks, such as the Pi model and mean teacher. In this paper, we first show through an experiment that the traditional consistency-based methods exist the following two problems: (1) as the size of unlabeled samples increases, the accuracy of these methods increases very slowly, which means they cannot make full use of unlabeled samples. (2) When the number of labeled samples is vary small, the performance of these methods will be very low. Based on these two findings, we propose two methods, metric learning clustering (MLC) and auxiliary fake samples, to alleviate these problems. The proposed methods achieve state-of-the-art results on SSL benchmarks. The error rates are 10.20%, 38.44% and 4.24% for CIFAR-10 with 4000 labels, CIFAR-100 with 10,000 labels and SVHN with 1000 labels by using MLC. For MNIST, the auxiliary fake samples method shows great results in cases with the very few labels.\",\n \"Title: Fpanet: Feature-Enhanced Position Attention Network For Semantic Segmentation\\nYear: 2021\\n\\nAbstract: Attention mechanism is beneficial to capture the contextual information in visual task. This paper proposes a feature-enhanced position attention network (FPANet) for semantic segmentation based on framework of FCN. On the top of dilated FCN, we design a feature integration module, which aggregates the context over local features by expanding the receptive field and multiscale representation, to promote a position attention module, which models spatial interdependencies over features, so as to form a feature-enhanced position attention module to enhance the discrimination of features for better semantic segmentation. Experimental comparisons show that our proposed FPANet is superior to other state-of-the-art models in the performance of segmentation accuracy on datasets PASCAL VOC 2012 and Cityscapes.\",\n \"Title: Deterministic Communications for In-vehicle Network: Overview and Challenges\\nYear: 2021\\n\\nAbstract: In recent years, intelligence has become an inevitable trend in the development of automobile industry, and advanced assistant driving or autonomous driving are important features of automobile intelligence. The increasing level of self-driving puts forward higher demands for in-vehicle communication. And traditional in-vehicle networks are difficult to cover a series of complex deterministic communication requirements including high-bandwidth, low latency, low jitter and zero congestion. Therefore, Automotive Ethernet technology came into being. IEEE developed a series of Time Sensitive Networks (TSN) standard protocols based on traditional Ethernet. TSN added a series of key mechanisms that provide realtime and reliability guarantees for data transmission. In this paper, we reviewed the evolution of in-vehicle networks and outlined the development of Time Triggered Ethernet (TTE) and TSN researches in recent years. We also discussed the importance of deterministic researches on in-vehicle networks and the challenges in the development of the Internet of Things, E/E (Electrical/Electronic) architectures, intelligent driving, and new energy vehicles.\",\n \"Title: Entity and Relation Matching Consensus for Entity Alignment\\nYear: 2021\\n\\nAbstract: ABSTRACTEntity alignment aims to match synonymous entities across different knowledge graphs, which is a fundamental task for knowledge integration. Recently, researchers have devoted to leveraging rich information within relations to enhance entity alignment. They explicitly incorporate relations in entity representation and alignment, demonstrating remarkable results. However, affected by the semantic assumptions from early works, these works represent a relation by combining all the entities it connects, ignoring the semantic independence between entity and relation. Moreover, since these works perform alignment by comparing embedding similarity, they fail to consider a graph level alignment and tend to find local false correspondences. In this paper, we propose Entity and Relation Matching Consensus (ERMC), a two-stage matching schema based on graph matching consensus that jointly models and aligns entities and relations and retains their semantic independence at the same time. In the first stage, we design a bidirectional relation-aware graph convolutional network to jointly learn entity and relation embeddings based on the triadic graph by a novel message passing mechanism. Then, we jointly align the entities and relations by computing a graph-level matching consensus. In the second stage, we introduce a refinement strategy to detect and correct false alignments in the first stage. Experimental results on three real-world multilingual datasets demonstrate that ERMC outperforms some state-of-the-art models on both entity alignment and relation alignment tasks.\",\n \"Title: DLB: Deep Learning Based Load Balancing\\nYear: 2021\\n\\nAbstract: In this paper, we introduce DLB, a Deep Learning based load Balancing mechanism, to effectively address the data skew problem. The key idea of DLB is to replace hash functions in the load balancing mechanisms with deep learning models, which are trained to be able to map different distributions of workloads and data to the servers in a uniformed manner. We implemented DLB and deployed it on a prac...\",\n \"Title: Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic\\nYear: 2022\\n\\nAbstract: Autonomous driving has attracted significant research interests in the past two decades as it offers many potential benefits, including releasing drivers from exhausting driving and mitigating traffic congestion, among others. Despite promising progress, lane-changing remains a great challenge for autonomous vehicles (AV), especially in mixed and dynamic traffic scenarios. Recently, reinforcement learning (RL) has been widely explored for lane-changing decision makings in AVs with encouraging results demonstrated. However, the majority of those studies are focused on a single-vehicle setting, and lane-changing in the context of multiple AVs coexisting with human-driven vehicles (HDVs) have received scarce attention. In this paper, we formulate the lane-changing decision-making of multiple AVs in a mixed-traffic highway environment as a multi-agent reinforcement learning (MARL) problem, where each AV makes lane-changing decisions based on the motions of both neighboring AVs and HDVs. Specifically, a multi-agent advantage actor-critic (MA2C) method is proposed with a novel local reward design and a parameter sharing scheme. In particular, a multi-objective reward function is designed to incorporate fuel efficiency, driving comfort, and the safety of autonomous driving. A comprehensive experimental study is made that our proposed MARL framework consistently outperforms several state-of-the-art benchmarks in terms of efficiency, safety, and driver comfort.\",\n \"Title: Modeling Multi-Attribute and Implicit Relationship Factors for Collaborative Filtering Recommender System\\nYear: 2021\\n\\nAbstract: Interactions of users and items can be naturally modeled as a user-item bipartite graph in recommender systems, and emerging research is devoted to exploring user-item graphs for collaborative filtering methods. In reality, user-item interaction usually stems from more complex underlying factors, such as the users’ specific preferences. A user-item bipartite graph could be used to understand the d...\",\n \"Title: An integrated fuzzy decision support system for analyzing challenges and pathways to promote green and climate smart mining\\nYear: 2022\\n\\nAbstract: •Green and climate smart mining is an innovative mining mode.•Implementation of green and climate smart mining is hampered by challenges.•This study proposes an integrated fuzzy-based decision support system.•Nine pathways to overcome 24 challenges (grouped into 5 categories) are considered.•The proposed system help implement and promote sustainable mining.\",\n \"Title: image2emmet: Automatic code generation from web user interface image\\nYear: 2021\\n\\nAbstract: AbstractAbstractWeb development usually follows with analyzing the functionality, designing the user interface (UI) prototype, implementing the UI by front‐end (FE) developers and implementing the REpresentational State Transfer (RESTful) application programming interface (API) by back‐end (BE) programmers. Unfortunately, web development is a tedious, cumbersome, and time‐consuming task, which makes it a challenge for the FE programmers to work in an efficient way. In this paper, we propose an approach, image2emmet, to assist FE programmers in implementing the UI. First, we collect HyperText Markup Language, Cascading Style Sheets (HTML‐CSS) dataset in an automatic and efficient way. The HTML‐CSS dataset used for model training consists of HTML‐CSS code and its display images. Second, the faster region‐based convolutional neural network (CNN) (R‐CNN) is utilized to detect the UI component. Finally, we build a model combining CNN and long short‐term memory (LSTM) to transform the UI component into the HTML‐CSS code. The empirical study demonstrates that image2emmet can achieve a precision of 80% on the UI component detection and 60% on the transformation of UI component into HTML‐CSS code.We collect HTML‐CSS dataset both with HTML‐CSS code and its display images in an automatic and efficient way. We utilize the deep learning object detection technique in computer vision (CV) to detect UI components from UI images, and the experiment shows that the UI components can be correctly detected at a precision of 80%. We directly transform UI components into HTML‐CSS code, and the experiment shows that the accuracy of code generation can achieve 60%. View Figure\",\n \"Title: Analyzing Midair Object Pointing Mappings for Smart Display Input\\nYear: 2021\\n\\nAbstract: AbstractOne common task when controlling smart displays is the manipulation of menu items. Given current examples of smart displays that support distant bare hand control, in this paper we explore menu item selection tasks with three different mappings of barehand movement to target selection. Through a series of experiments, we demonstrate that Positional mapping is faster than other mappings when the target is visible but requires many clutches in large targeting spaces. Rate-based mapping is, in contrast, preferred by participants due to its perceived lower effort, despite being slightly harder to learn initially. Tradeoffs in the design of target selection in smart tv displays are discussed.\",\n \"Title: Combinational Optimization Power (COP) - A Practical Power Budgeting Method for Many-cores\\nYear: 2021\\n\\nAbstract: In order to improve the performance of many-core systems, it is necessary to adopt dynamic power constraints for different core mapping decisions. However, existing power budgeting methods are generally over pessimistic, e.g. Thermal Safe Power (TSP), or over optimistic, e.g. Greedy based Dynamic Power (GDP). This paper proposes a practical power budgeting method, called Combinational Optimization Power (COP). Different from existing methods, which ignore some actual factors, such as communication overhead and lifetime reliability, COP formulates the power budgeting problem as a thermal-constrained combinational optimization power problem. For the steady-state case, COP achieves the target fusion of optimized temperature and communication energy consumption by applying task priority ranking and task-to-core mapping. For the transient case, COP uses the rainflow counting algorithm to construct the reliability framework based on the thermal cycling failure mechanism, and then establishes a linear time-invariant transient thermal model to obtain the core mapping selection and the corresponding dynamic power budget. Experimental results show that COP is capable of providing an optimized core mapping decision, which can maximize power budget while ensuring the system performance.\",\n \"Title: Feasibility Analysis of Machine Learning Optimization on GPU-based Low-cost Edges\\nYear: 2021\\n\\nAbstract: Many AI algorithms have been deployed on edge devices as edge computing has the advantages of reducing latency, saving network bandwidth, and protecting data privacy. Whether edge devices can run AI algorithms is an important challenge due to the low-power and low-cost characteristics of edge devices. Therefore, this paper analyzed the performance of optimization techniques by running YOLOv3 on a ...\",\n \"Title: Multi-ColorGAN: Few-shot vehicle recoloring via memory-augmented networks\\nYear: 2021\\n\\nAbstract: Despite the notable successes of Generative adversarial networks (GANs) achieved to date, applying them to real-world problems still poses significant challenges. In real traffic surveillance scenarios, for the task of generating images of multiple color of truck heads and cars without changing textures and license plates, conditional image generation hardly manipulate the generated images by the color attribute. Image style transfer methods inevitably produce color smearing. Even state-of-the-art methods of disentangled representation learning (e.g. MixNMatch) cannot disentangle colors individually, ensuring that irrelevant factors, such as texture remain the same. To solve this problem, we present an approach called Multi-ColorGAN based on memory-augmented networks for multi-color real vehicle coloring/generation with limited data. In particular, our model could filter out unwanted color changes in specific areas with a simple but effective method called Fusion Module, and generate more natural color images. Experiments on three vehicle image benchmarks and a new truck image dataset are conducted to evaluate the proposed Multi-ColorGAN compared to state-of-the-art.\",\n \"Title: Evaluation of Topographic Correction Models Based on 3-D Radiative Transfer Simulation\\nYear: 2022\\n\\nAbstract: The timely and accurate assessment of changes in mountain vegetation biomass and other parameters is of great importance to mountain ecosystem conservation. With the rapid development of remote sensing technology, hyperspectral remote sensing images have facilitated the large-scale and long-time series monitoring of environmental changes in mountainous areas. However, topographic effects cause remote sensing images of mountainous areas to be prone to spectral variations within the same land cover and spectral confusion among different land covers. This phenomenon seriously affects the accuracy of remote sensing inversions and hinders the development and application of remote sensing in mountainous areas. Numerous scholars have established various topographic correction models (TCMs) to eliminate the influence of topographic effects. Comparative evaluation of the performance of different TCMs allows us to better understand their characteristics. Most previous evaluation studies have directly applied in remote sensing images, which were limited by the changing conditions of the study area. Therefore, this letter used computer simulations to controllably evaluate six popular TCMs on hyperspectral images. The results showed that their performance varied with the spectral band, and overall, the best performance was achieved by the C correction model, followed by the sun-canopy-sensor (SCS) + C model. This letter provides a basis for the optimal selection of TCMs in complex terrains.\",\n \"Title: Deformation monitoring and thematic mapping of the Badaling Great Wall using very high-resolution interferometric synthetic aperture radar data\\nYear: 2021\\n\\nAbstract: •Badaling Great Wall was monitored using VHR InSAR and environment social data.•An adaptable InSAR approach with the coarse–fine search algorithm was proposed.•Motion indicators are informative to characterize the condition of monuments.•An understandable thematic map was provided for the site sustainability.•The predicted tourist capacity of the site was 1.0 million per month.\",\n \"Title: Resource Allocation in IoT Edge Computing via Concurrent Federated Reinforcement Learning\\nYear: 2022\\n\\nAbstract: Resource allocation is a fundamental research issue in IoT edge computing, and reinforcement learning is fast becoming a common solution. The majority of the current techniques involve decision makers who determine how and where resources should be distributed. In a standard cloud system, this decision maker is a central server. In an edge system, the decision maker is an edge host. Both approache...\",\n \"Title: Real-time Multiple-Channel Shoulder EMG Processing for a Rehabilitative Upper-limb Exoskeleton Motion Control Using ANN Machine Learning\\nYear: 2021\\n\\nAbstract: Machine learning (ML) has been proposed for surface electromyography (sEMG) signal processing to control assistive robots. However, most of the studies using ML focused on recording sEMG for off-line data analysis and pattern recognition. Only a few studies reported online ML EMG processing for one DoF hand motion pattern recognition and instant robot control. Using ML for multiple channels of sho...\",\n \"Title: Perceptual Evaluation of Pre-processing for Video Transcoding\\nYear: 2021\\n\\nAbstract: Recently, the pre-processed video transcoding has attracted wide attention and has been increasingly used in practical applications for improving the perceptual experience and saving transmission resources. However, very few works have been conducted to evaluate the performance of pre-processing methods. In this paper, we select the source (SRC) videos and various pre-processing approaches to cons...\",\n \"Title: Effect of Autoimmune Cell Therapy on Immune Cell Content in Patients with COPD: A Randomized Controlled Trial\\nYear: 2022\\n\\nAbstract: Objective. To explore the effect of autoimmune cell therapy on immune cells in patients with chronic obstructive pulmonary disease (COPD) and to provide a reference for clinical treatment of COPD. Methods. Sixty patients with stable COPD were randomly divided into control group and treatment group (n=30). The control group was given conventional treatment, and the treatment group was given one autoimmune cell therapy on the basis of conventional treatment. The serum levels of CD3+ T cells, CD4+ T cells, CD8+ cells, B cells, and NK cells in the peripheral blood were detected by flow cytometry. Possible adverse reactions were detected at any time during treatment. Results. There were no significant differences in the contents of CD3+ T cells, CD4+ T cells, CD8+ cells, B cells, and NK cells in the serum of the control group (P > 0.05). Compared with before treatment, the contents of CD3+ T cells, CD4+ T cells, CD8+ cells, B cells, and NK cells in the serum of the treatment group were significantly increased (P < 0.05). The ratio of CD4+/CD8+ T cells in both control and treatment groups did not change significantly during treatment (P > 0.05). There were no significant differences in serum CD3+ T cells, CD4+ T cells, CD8+ cells, B cells, and NK cells in the treatment group at 30 days and 90 days after treatment (P > 0.05), but they were significantly higher than those in the control group (P < 0.05). Conclusion. Autoimmune cell therapy can significantly increase the level of immune cells in the body and can be maintained for a long period of time, which has certain clinical benefits for recurrent respiratory tract infections and acute exacerbation in patients with COPD.\",\n \"Title: A Service Migration Method for Resource Competition in Mobile Edge Computing\\nYear: 2021\\n\\nAbstract: In the mobile edge computing (MEC) environment, tasks can be offloaded to edge servers for better service. However, the locations of users are not fixed. As users move, tasks need to be migrated to the vicinity of users to ensure quality of service (QoS). At present, most research considers the problem of service migration from the perspective of a single user. The single user migration scheme is ...\",\n \"Title: Multi-GNSS Combined Orbit and Clock Solutions at iGMAS\\nYear: 2022\\n\\nAbstract: Global navigation services from the quad-constellation of GPS, GLONASS, BDS, and Galileo are now available. The international GNSS monitoring and assessment system (iGMAS) aims to evaluate the navigation performance of the current quad systems under a unified framework. In order to assess impact of orbit and clock errors on the positioning accuracy, the user range error (URE) is always taken as a metric by comparison with the precise products. Compared with the solutions from a single analysis center, the combined solutions derived from multiple analysis centers are characterized with robustness and reliability and preferred to be used as references to assess the performance of broadcast ephemerides. In this paper, the combination method of iGMAS orbit and clock products is described, and the performance of the combined solutions is evaluated by various means. There are different internal precisions of the combined orbit and clock for different constellations, which indicates that consistent weights should be assigned for individual constellations and analysis centers included in the combination. For BDS-3, Galileo, and GLONASS combined orbits of iGMAS, the root-mean-square error (RMSE) of 5 cm is achieved by satellite laser ranging (SLR) observations. Meanwhile, the SLR residuals are characterized with a linear pattern with respect to the position of the sun, which indicates that the solar radiation pressure (SRP) model adopted in precise orbit determination needs further improvement. The consistency between combined orbit and clock of quad-constellation is validated by precise point positioning (PPP), and the accuracies of simulated kinematic tests are 1.4, 1.2, and 2.9 cm for east, north, and up components, respectively.\",\n \"Title: A New Mapping Function for Spaceborne TEC Conversion Based on the Plasmaspheric Scale Height\\nYear: 2021\\n\\nAbstract: The mapping function is crucial for the conversion of slant total electron content (TEC) to vertical TEC for low Earth orbit (LEO) satellite-based observations. Instead of collapsing the ionosphere into one single shell in commonly used mapping models, we defined a new mapping function assuming the vertical ionospheric distribution as an exponential profiler with one simple parameter: the plasmaspheric scale height in the zenith direction of LEO satellites. The scale height obtained by an empirical model introduces spatial and temporal variances into the mapping function. The performance of the new method is compared with the mapping function F&K by simulating experiments based on the global core plasma model (GCPM), and it is discussed along with the latitude, seasons, local time, as well as solar activity conditions and varying LEO orbit altitudes. The assessment indicates that the new mapping function has a comparable or better performance than the F&K mapping model, especially on the TEC conversion of low elevation angles.\",\n \"Title: Health Monitoring Framework for Weather Radar Based on Long Short-Term Memory Network with a Real Case Study\\nYear: 2022\\n\\nAbstract: The health management of weather radar plays a key role in achieving timely and accurate weather forecasting. The current practice mainly exploits a fixed threshold prespecified for some monitoring parameters for fault detection. This causes abundant false alarms due to the evolving working environments, increasing complexity of the modern weather radar, and the ignorance of the dependencies among monitoring parameters. To address the above issues, we propose a deep learning-based health monitoring framework for weather radar. First, we develop a two-stage approach for problem formulation that address issues of fault scarcity and abundant false fault alarms in processing the databases of monitoring data, fault alarm record, and maintenance records. The temporal evolution of weather radar under healthy conditions is represented by a long short-term memory network (LSTM) model. As such, any anomaly can be identified according to the deviation between the LSTM-based prediction and the actual measurement. Then, construct a health indicator based on the portion of the occurrence of deviation beyond a user-specified threshold within a time window. The proposed framework is demonstrated by a real case study for the Chinese S-band weather radar (CINRAD-SA). The results validate the effectiveness of the proposed framework in providing early fault warnings.\",\n \"Title: Research on low-quality waste heat recovery system based on fuzzy proportion integration differentiation control\\nYear: 2021\\n\\nAbstract: In the current industrial production process, waste heat of low quality is seriously wasted. In order to effectively recover low-quality waste heat, the research group developed a small energy conversion device - Roots power machine. On this basis, the research group designed a low-quality waste heat efficient utilization system with the equipment as the core and successfully applied it to low-quality waste heat recovery. However, in the actual operation process, the system can not run stably due to the occasional fluctuation of the input gas source. In view of this, after the study of waste heat recovery system, the fluctuation of gas source can be controlled by different grades according to the degree of change. Fuzzy rules also divide variables into different grades to solve problems, and fuzzy control can convert continuous changes of airflow into discrete changes, which greatly reduces the complexity of the control system. Therefore, the research group proposed a control strategy based on fuzzy PID. The simulation results show that the adjustment time of fuzzy PID is within 7 s, and the adjustment effect is obviously better than that of traditional PID. The experimental results show that the speed deviation under the condition of air source fluctuation is within the speed fluctuation rate (+/- 5%), and the speed deviation under the condition of sudden disturbance load is within the steady speed adjustment rate (+/- 3.5%), both of which meet the requirements of indexes. Therefore, the fuzzy PID control strategy can further improve the stability of output speed, reduce airflow pulsation, and provide the possibility for grid-connected power generation.\",\n \"Title: Classification method of marine target motion pattern based on spatial-temporal trajectories\\nYear: 2021\\n\\nAbstract: Classifying the motion pattern of marine targets is of important significance to promote target surveillance and management efficiency of marine area and to guarantee sea route safety. This paper proposes a moving target classification algorithm model based on channel extraction-segmentation-LCSCA-l(p) norm minimization The algorithm firstly analyzes the entire distribution of channels in specific region, and defines the categories of potential ship motion patterns; on this basis, through secondary segmentation processing method, it obtains several line segment trajectories as training sample sets, to improve the accuracy of classification algorithm; then, it further uses the Leastsquares Cubic Spline Curves Approximation (LCSCA) technology to represent the training sample sets, and builds a motion pattern classification sample dictionary; finally, it uses l(p) norm minimized sparse representation classification model to realize the classification of motion patterns. The verification experiment based on real spatial-temporal trajectory dataset indicates that, this method can effectively realize the motion pattern classification of marine targets, and shows better time performance and classification accuracy than other representative classification methods.\",\n \"Title: An adaptive phase-field model based on bilinear elements for tensile-compressive-shear fracture\\nYear: 2022\\n\\nAbstract: An efficient adaptive phase-field method based on bilinear elements for tensile-compressive-shear fracture is developed. On the one hand, refined meshes are needed near the crack path to obtain accurate results, and the computational efficiency is very low if a prior mesh refinement is applied, especially when the crack paths are unknown. On the other hand, conventional phase-field models are not applicable to tensile-compressive shear fracture under complex stress states at present. In this paper, an adaptive scheme is proposed to improve the computational efficiency, in which a bilinear multi-node element is adopted to avoid using high order quadrature and shape functions when new nodes are inserted in elements, and a new multi-node triangular element is outlined to expand the scope of application of the proposed adaptive method. Another important aspect of the contribution is the development of the phase-field model for tensile-compressive-shear fracture under complex stress states, in which a universal fracture criterion is embedded. To determine the optimal parameters in the adaptive scheme and demonstrate the advantages of the proposed bilinear adaptive method, a series of numerical examples are performed for sensitivity analysis. Comparison with experimental results is also conducted to validate the proposed phase-field model for tensile-compressive-shear fracture under complex stress states.\",\n \"Title: Fingerprinting Mainstream IoT Platforms Using Traffic Analysis\\nYear: 2022\\n\\nAbstract: The Internet of Things (IoT) platforms have been widely used in many application scenarios, especially for the smart home. Under the management of the IoT platform, a massive amount of IoT devices have been connected between remote cloud servers and users’ mobile terminals. While bringing unprecedented convenience for device manufacturers and smart home users, the existence of mainstream IoT platf...\",\n \"Title: A deep learning method for video-based action recognition\\nYear: 2021\\n\\nAbstract: In this paper, a deep learning method for video-based action recognition is proposed. On the one hand, boundary compensation on the basis of a deep neural network is performed to achieve action proposal. Boundary compensation considering non-maximum suppression according to sliding window priority is applied to remove redundant windows. To accurately detect boundaries, a boundary compensation network is established with multiple networks to process different numbers of segments. On the other hand, action recognition based on the resultant action proposals is performed. To further utilise boundary compensation, three methods are introduced for key frame selection. Optical flow and RGB features are combined via a channel fusion to realise feature representation. A two-stream network with a spatiotemporal structure is adopted for action recognition. The proposed method is evaluated on three public datasets. The experimental results demonstrate that the proposed method achieves a superior performance to that of state-of-the-art methods.\",\n \"Title: Depth-Keeping Control for a Deep-Sea Self-Holding Intelligent Buoy System Based on Inversion Time Constraint Stability Strategy Optimization\\nYear: 2022\\n\\nAbstract: Based on the nonlinear disturbance observer (NDO), the inversion time-constraint stability strategy (ITCS) is designed to make the deep-sea self-holding intelligent buoy (DSIB) system hovered at an appointed depth within a specified time limit. However, it is very challenging to determine the optimal parameters of an ITCS depth controller. Firstly, a genetic algorithm based on quantum theory (QGA) is proposed to obtain the optimal parameter combination by using the individual expression form of quantum bit and the adjustment strategy of quantum rotary gate. To improve the speed and accuracy of global search in the QGA optimization process, taking the number of odd and even evolutions as the best combination point of the genetic and chaos particle swarm algorithm (GACPSO), an ITCS depth controller based on GACPSO strategy is proposed. Besides, the simulations and hardware-in-the-loop system experiments are conducted to examine the effectiveness and feasibility of the proposed QGA-ITCS and GACPSO-ITCS depth controller. The results show that the proposed GACPSO-ITCS depth controller provides higher stability with smaller steady-state error and less settling time in the depth-control process. The research of the proposed method can provide a stable operation condition for the marine sensors carried by the DSIB.\",\n \"Title: Dynamic competitive game study of a green supply chain with R&D level\\nYear: 2022\\n\\nAbstract: Green and low carbon is an important way to coordinate economic and social development, which is also true for enterprises. Hence, the essay mainly builds up a green supply chain consisting of one supplier and two manufacturers. Gradient adjustment mechanism is used to establish a dynamic green competition model based on bounded rationality in this paper. Subsequently, the stability of four equilibrium points is researched through different methods. Dynamic behaviors are analyzed by 1-D bifurcation diagrams, the largest Lyapunov exponent, as well as 2-D bifurcation diagrams. We have found that the system goes from a steady state to chaos mainly via flip bifurcation. The chaotic state of system can be alleviated as the increase of adjustment speed in a certain range. Furthermore, invariant sets and natural transverse Lyapunov exponent are used to study synchronization behavior. Basins of attraction under the coexistence of attractors are simulated numerically. The results indicate that the system will have two and four groups of coexisting attractors, where the structure and size of the attractor and its basin will change as the speed of adjustment increases. When the attractor contacts with the boundary of its basin of attraction, the global bifurcation occurs.\",\n \"Title: Color-in-fist: a metaphor for color selection with mid-air interaction\\nYear: 2022\\n\\nAbstract: Color picker is a necessary interactive component in visualization system. Current color pickers are based on the WIMP paradigm and are mainly designed for mouse, touch and ray interactions. In the context of immersive and remote interaction, mid-air interaction is an important method. However, common solution of color selection using hands is simulating cursors or rays with fingers, which does not take full advantage of mid-air interaction-intuition, naturalness and remote interaction. This results in fatigue and long selection time. In this paper, we proposed a mid-air interaction metaphor for color selection which enabled users to select hue, saturation and lightness simultaneously using mutually independent and intuitive hand motions. We conducted a user study to evaluate the performance of the interaction technique. Results showed that the metaphor improved the efficiency of color selection with mid-air interaction. Questionnaire results showed that the metaphor enhanced users' perception of color space; the semantics of hand motions were consistent with users' intention of color selection; the metaphor was easy to learn and memorize. Graphic Abstract\",\n \"Title: Rank-Sensitive Computation of the Rank Profile of a Polynomial Matrix.\\nYear: 2022\\n\\nAbstract: Consider a matrix $\\\\mathbf{F} \\\\in \\\\mathbb{K}^{m \\\\times n}$ of univariate polynomials over a field~$\\\\mathbb{K}$. We study the problem of computing the column rank profile of $\\\\mathbf{F}$. To this end we first give an algorithm which improves the minimal kernel basis algorithm of Zhou, Labahn, and Storjohann (Proceedings ISSAC 2012). We then provide a second algorithm which computes the column rank profile of $\\\\mathbf{F}$ with a rank-sensitive complexity of $O\\\\tilde{~}(r^{\\\\omega-2} n (m+D))$ operations in $\\\\mathbb{K}$. Here, $D$ is the sum of row degrees of $\\\\mathbf{F}$, $\\\\omega$ is the exponent of matrix multiplication, and $O\\\\tilde{~}(\\\\cdot)$ hides logarithmic factors.\",\n \"Title: SocialLGN: Light graph convolution network for social recommendation\\nYear: 2022\\n\\nAbstract: •We propose a SocialLGN system based on GCN for social recommendation problems.•We design a customized graph fusion component to fuse the user representations.•Empirical experiments verify the effectiveness of the proposed system.\",\n \"Title: A <tex>$1.2\\\\ \\\\mu \\\\mathrm{W},\\\\ 1.7\\\\ \\\\mu V_{rms}$</tex> ECG Acquisition IC with Low-Noise Amplifier and Full-Digital Heartbeat Detector\\nYear: 2021\\n\\nAbstract: In this paper, a full-digital heartbeat (HB) detection system-on-chip (SoC) with low-noise analog front end (AFE) is presented. With the use of transconductance bootstrap and source degradation, the noise of AFE can be greatly reduced. The SoC is implemented in a \\n<tex xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">$0.18\\\\mu \\\\mathrm{m}$</tex>\\n CMOS process and consumes \\n<tex xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">$1.2\\\\mu \\\\mathrm{W}$</tex>\\n from a 1.2V supply, of which AFE and heart rate detector consume \\n<tex xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">$0.8\\\\ \\\\mu \\\\mathrm{W}$</tex>\\n and \\n<tex xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">$0.4\\\\ \\\\mu \\\\mathrm{W}$</tex>\\n, respectively. Besides, the noise from 1Hz to 1kHz is only \\n<tex xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">$1.7\\\\ \\\\mu V_{rms}$</tex>\\n. The gain of instrument amplifier (IA) is 33dB, and the cutoff frequency of high and low frequency are 0.4Hz and 40kHz respectively.\",\n \"Title: A brief survey on adaptive video streaming quality assessment\\nYear: 2022\\n\\nAbstract: Quality of experience (QoE) assessment for adaptive video streaming plays a significant role in advanced network management systems. It is especially challenging in case of dynamic adaptive streaming schemes over HTTP (DASH) which has increasingly complex characteristics including additional playback issues. In this paper, we provide a brief overview of adaptive video streaming quality assessment. Upon our review of related works, we analyze and compare different variations of objective QoE assessment models with or without using machine learning techniques for adaptive video streaming. Through the performance analysis, we observe that hybrid models perform better than both quality-of-service (QoS) driven QoE approaches and signal fidelity measurement. Moreover, the machine learning-based model slightly outperforms the model without using machine learning for the same setting. In addition, we find that existing video streaming QoE assessment models still have limited performance, which makes it difficult to be applied in practical communication systems. Therefore, based on the success of deep learned feature representations for traditional video quality prediction, we also apply the off-the-shelf deep convolutional neural network (DCNN) to evaluate the perceptual quality of streaming videos, where the spatio-temporal properties of streaming videos are taken into consideration. Experiments demonstrate its superiority, which sheds light on the future development of specifically designed deep learning frameworks for adaptive video streaming quality assessment. We believe this survey can serve as a guideline for QoE assessment of adaptive video streaming.\",\n \"Title: BDS-3 Integrity Risk Modeling and Probability Evaluation\\nYear: 2022\\n\\nAbstract: Focusing on the global integrity monitoring performance of the BDS-3, the integrity failure model and effect analysis (IFMEA) of the system space segment and the ground control segment were carried out, and the system integrity risk tree model was established according to the IFMEA results. To evaluate the system's integrity risk probability more reasonably, this paper establishes an abnormal event prediction model with a Bayesian method, based on actual operation data, under a situation in which the BDS-3 just opened service. With statistical analysis results of system anomalies since 27 December 2018-which is the date the BDS-3 began providing basic service-according to the anomaly prediction model, the system's integrity risk probability under the 95% confidence limit was calculated to be approximately 0.9e-7/h, meeting the design index requirements of 1e-7/h. The analysis results also show that the main risk factors affecting the integrity of BDS-3 are 'pseudorange measurement anomaly' and 'miss-detection of satellite autonomous integrity monitoring'. The results are important references with practical engineering significance for improving the integrity performance of BDS-3.\",\n \"Title: Reducing the Residual Topography Phase for the Robust Landscape Deformation Monitoring of Architectural Heritage Sites in Mountain Areas: The Pseudo-Combination SBAS Method\\nYear: 2022\\n\\nAbstract: Monitoring deformation of architectural heritage sites is important for the quantitative evaluation of their stability. However, deformation monitoring of sites in mountainous areas remains challenging whether utilizing global navigation satellite system (GNSS) or interferometric synthetic aperture radar (InSAR) techniques. In this study, we improved the small baseline subset (SBAS) approach by introducing the pseudo-baseline combination strategy to avoid the errors caused by inaccurate external DEM, resulting in robust deformation estimations in mountainous areas where the architectural heritage site of the Great Wall is located. First, a simulated dataset and a real dataset were used to verify the reliability and effectiveness of the algorithm, respectively. Subsequently, the algorithm was applied in the landscape deformation monitoring of the Shanhaiguan section of the Great Wall using 51 Sentinel-1 scenes acquired from 2016 to 2018. A thematic stability map of the Shanhaiguan Great Wall corridor was generated, revealing that the landscape was generally stable save for local instabilities due to to unstable rocks and wall monuments. This study demonstrated the capabilities of adaptive multitemporal InSAR (MTInSAR) approaches in the preventive landscape deformation monitoring of large-scale architectural heritage sites in complex terrain.\",\n \"Title: Ada: Adversarial learning based data augmentation for malicious users detection\\nYear: 2022\\n\\nAbstract: Malicious user detection in the recommender systems has attracted much attention in the last two decades because malicious users can seriously affect the recommendation results and user experience. The up-to-date detection models usually concentrate on distinguishing users according to their latent features represented in user embeddings. These models can improve detection performance; however, they are usually not fully up to expectations, especially in the scenarios with unbalanced use samples. From these models that concentrate on user embedding representations, we can summarize the following difficulties: (1) the cost of manual labeling malicious causes the lack of labeled malicious users in training data, which leads to imprecise representations of users; (2) current augmentation methods that aim at mitigating the lack of labeled malicious users are hard to simulate the distribution of malicious users. In this paper, we propose a detection model, using adversarial learning based data augmentation (a.k.a. Ada) to alleviate these problems. Concretely, to get precise representations of users, the model integrates potential user relations and structural similarities into user embeddings. After obtaining precise user representation, it presents a novel data augmentation based on the deep convolutional generative adversarial networks (DCGAN) to simulate the distribution of malicious user embeddings and generate additional fake user embeddings. Experiments on public datasets show our model outperforms state-of-the-art detection models with sparse labeled malicious users, and the ablation study confirms the importance and effectiveness of each component of the model.(C) 2022 Elsevier B.V. All rights reserved.\",\n \"Title: Synchronization and Global Dynamics of a Cournot Model with Nonlinear Demand and R&D Spillovers\\nYear: 2021\\n\\nAbstract: In this paper, a dynamical Cournot model with nonlinear demand and R&D spillovers is established. The system is symmetric when the duopoly firms have same economic environments, and it is proved that both the diagonal and the coordinate axes are the one-dimensional invariant manifolds of system. The results show that Milnor attractor of system can be found through calculating the transverse Lyapunov exponents. The synchronization phenomenon is verified through basins of attraction. The effects of adjusting speed and R&D spillovers on the dynamical behaviors of the system are discussed. The topological structures of basins of attraction are analyzed through critical curves, and the evolution process of \\\"holes\\\" in the feasible region is numerically simulated. In addition, various global bifurcation behaviors, such as two kinds of contact bifurcation and the blowout bifurcation, are shown.\",\n \"Title: Computational Identification of Functional Centers in Complex Proteins: A Step-by-Step Guide With Examples.\\nYear: 2021\\n\\nAbstract: In proteins, functional centers consist of the key amino acids required to perform molecular functions such as catalysis, ligand-binding, hormone- and gas-sensing. These centers are often embedded within complex multi-domain proteins and can perform important cellular signaling functions that enable fine-tuning of temporal and spatial regulation of signaling molecules and networks. To discover hidden functional centers, we have developed a protocol that consists of the following sequential steps. The first is the assembly of a search motif based on the key amino acids in the functional center followed by querying proteomes of interest with the assembled motif. The second consists of a structural assessment of proteins that harbor the motif. This approach, that relies on the application of computational tools for the analysis of data in public repositories and the biological interpretation of the search results, has to-date uncovered several novel functional centers in complex proteins. Here, we use recent examples to describe a step-by-step guide that details the workflow of this approach and supplement with notes, recommendations and cautions to make this protocol robust and widely applicable for the discovery of hidden functional centers.\",\n \"Title: Efficient Training of Neural Transducer for Speech Recognition.\\nYear: 2022\\n\\nAbstract: As one of the most popular sequence-to-sequence modeling approaches for speech recognition, the RNN-Transducer has achieved evolving performance with more and more sophisticated neural network models of growing size and increasing training epochs. While strong computation resources seem to be the prerequisite of training superior models, we try to overcome it by carefully designing a more efficient training pipeline. In this work, we propose an efficient 3-stage progressive training pipeline to build highly-performing neural transducer models from scratch with very limited computation resources in a reasonable short time period. The effectiveness of each stage is experimentally verified on both Librispeech and Switchboard corpora. The proposed pipeline is able to train transducer models approaching state-of-the-art performance with a single GPU in just 2-3 weeks. Our best conformer transducer achieves 4.1% WER on Librispeech test-other with only 35 epochs of training.\",\n \"Title: Multi-Granularity Semantic Aware Graph Model for Reducing Position Bias in Emotion-Cause Pair Extraction\\nYear: 2022\\n\\nAbstract: The Emotion-Cause Pair Extraction (ECPE) task aims to extract emotions and causes as pairs from documents. We observe that the relative distance distribution of emotions and causes is extremely imbalanced in the typical ECPE dataset. Existing methods have set a fixed size window to capture relations between neighboring clauses. However, they neglect the effective semantic connections between distant clauses, leading to poor generalization ability towards position-insensitive data. To alleviate the problem, we propose a novel Multi-Granularity Semantic Aware Graph model (MGSAG) to incorporate fine-grained and coarse-grained semantic features jointly, without regard to distance limitation. In particular, we first explore semantic dependencies between clauses and keywords extracted from the document that convey fine-grained semantic features, obtaining keywords enhanced clause representations. Besides, a clause graph is also established to model coarse-grained semantic relations between clauses. Experimental results indicate that MGSAG surpasses the existing state-of-the-art ECPE models. Especially, MGSAG outperforms other models significantly in the condition of position-insensitive data.\",\n \"Title: Design of a High-Speed Rapier Loom Control System Based on a Mixed Current Attenuation Algorithm\\nYear: 2021\\n\\nAbstract: At present, the weft selection and selvedge control systems available on the loom do not support precise control of the weft selection and selvedge devices, which will reduce the dynamic performance and production efficiency of the loom. The low-frequency oscillation and back Electromotive force (EMF) of the stepping motor are the main factors that affect the stability of the control system. This paper proposes an optimized mixed current attenuation algorithm that is dedicated to improving the performance of the system. The contribution and innovation of this paper is that the mixed current attenuation algorithm is proposed to improve the low frequency oscillation and back-EMF of the stepping motor and applied to the actual loom production. In this paper, the current subdivision drive is used to improve the low-frequency oscillation of the stepper motor of the weft selection and twisting device, and the back-EMF is reduced by the mixed current attenuation algorithm. First, to verify the effectiveness of the mixed current attenuation algorithm, this paper conducts a motor winding current waveform test. Then, by comparing the acceleration and deceleration control effects in the current self-decay mode, it is verified that the mixed current attenuation algorithm can effectively reduce the low-frequency oscillation and back-EMF of the stepper motor, and improve the dynamic performance and production efficiency of the stepper motor. Finally, the algorithm was debugged and verified on the high-speed rapier loom test platform. By recording and comparing the number of stops of the loom in a certain period of time, it can be concluded that the number of stops caused by the process error of weft selection and selvedge is greatly reduced. Experiments show that the weft selection and selvedge control system using the mixed current attenuation algorithm can achieve precise control of the weft selection and selvedge device, which can effectively improve the productivity and dynamic performance of the loom while reducing the low-frequency oscillation and back-EMF of the stepping motor. Experiments show that the mixed current attenuation algorithm meets the technological requirements of weft selection and selvedge and has a good application prospect.\",\n \"Title: A cloud service recommendation method based on extended multi-source information fusion\\nYear: 2022\\n\\nAbstract: With the rapid development of information technology, the problem of \\\"information overload\\\" emerges when users choose cloud services. How to integrate multi-source information to achieve accurate service recommendation is an urgent problem to be solved by current recommendation systems. This article proposes a cloud service recommendation method based on extended multi-source information fusion. First, we propose a score prediction based on matrix decomposition and topic matrix, and we fully mine existing explicit data and feedback data, such as user ratings, social trust information, reviews, and user personalized preferences and so on. Second, in order to solve the problems of data sparseness and cold start of the system, we integrate the score, social trust information and review into a comprehensive model through collaborative filtering (CF), and propose a multi-source information fusion recommendation method. The CF fusion method mainly combines two parts: social matrix decomposition and topic matrix decomposition. Finally, in order to further improve the accuracy and scalability, the implicit feature matrix is integrated into the user rating matrix, and the original CF enhancement based on scoring matrix decomposition is a matrix decomposition method that can learn implicit features. Experimental results show that compared with other recommendation algorithms, the cloud service recommendation method proposed in this article can improve the recommendation accuracy and allow users to choose satisfactory cloud services.\",\n \"Title: Simulation of Soil Organic Carbon Content Based on Laboratory Spectrum in the Three-Rivers Source Region of China\\nYear: 2022\\n\\nAbstract: Soil organic carbon (SOC) changes affect the land carbon cycle and are also closely related to climate change. Visible-near infrared spectroscopy (Vis-NIRS) has proven to be an effective tool in predicting soil properties. Spectral transformations are necessary to reduce noise and ensemble learning methods can improve the estimation accuracy of SOC. Yet, it is still unclear which is the optimal ensemble learning method exploiting the results of spectral transformations to accurately simulate SOC content changes in the Three-Rivers Source Region of China. In this study, 272 soil samples were collected and used to build the Vis-NIRS simulation models for SOC content. The ensemble learning was conducted by the building of stack models. Sixteen combinations were produced by eight spectral transformations (S-G, LR, MSC, CR, FD, LRFD, MSCFD and CRFD) and two machine learning models of RF and XGBoost. Then, the prediction results of these 16 combinations were used to build the first-step stack models (Stack1, Stack2, Stack3). The next-step stack models (Stack4, Stack5, Stack6) were then made after the input variables were optimized based on the threshold of the feature importance of the first-step stack models (importance > 0.05). The results in this study showed that the stack models method obtained higher accuracy than the single model and transformations method. Among the six stack models, Stack 6 (5 selected combinations + XGBoost) showed the best simulation performance (RMSE = 7.3511, R-2 = 0.8963, and RPD = 3.0139, RPIQ = 3.339), and obtained higher accuracy than Stack3 (16 combinations + XGBoost). Overall, our results suggested that the ensemble learning of spectral transformations and simulation models can improve the estimation accuracy of the SOC content. This study can provide useful suggestions for the high-precision estimation of SOC in the alpine ecosystem.\",\n \"Title: Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images\\nYear: 2022\\n\\nAbstract: As a classic deep learning target detection algorithm, Faster R-CNN (region convolutional neural network) has been widely used in high-resolution synthetic aperture radar (SAR) and inverse SAR (ISAR) image detection. However, for most common low-resolution radar plane position indicator (PPI) images, it is difficult to achieve good performance. In this paper, taking navigation radar PPI images as an example, a marine target detection method based on the Marine-Faster R-CNN algorithm is proposed in the case of complex background (e.g., sea clutter) and target characteristics. The method performs feature extraction and target recognition on PPI images generated by radar echoes with the convolutional neural network (CNN). First, to improve the accuracy of detecting marine targets and reduce the false alarm rate, Faster R-CNN was optimized as the Marine-Faster R-CNN in five respects: new backbone network, anchor size, dense target detection, data sample balance, and scale normalization. Then, JRC (Japan Radio Co., Ltd.) navigation radar was used to collect echo data under different conditions to build a marine target dataset. Finally, comparisons with the classic Faster R-CNN method and the constant false alarm rate (CFAR) algorithm proved that the proposed method is more accurate and robust, has stronger generalization ability, and can be applied to the detection of marine targets for navigation radar. Its performance was tested with datasets from different observation conditions (sea states, radar parameters, and different targets).\",\n \"Title: Quality assessment of multi-exposure image fusion by synthesizing local and global intermediate references\\nYear: 2022\\n\\nAbstract: •Inspired by the global-to-local perception mechanism of the HVS, we propose a FR-IQA method for MEF images by generating Local and Global Intermediate References (LGIR) from the input multiple reference images.•We build proper intermediate reference information in gradient domain, structure tensor domain, and global perception domain, respectively, to characterize the global-to-local perception mechanism of the HVS.•We also apply LGIR to automatic parameter tuning of an existing MEF algorithm. Numerical experiments indicate that LGIR provides a useful tool to exploit the parameter space and to pick the optimal parameter set that produces MEF image with the best visual quality.\",\n \"Title: Exploring complementary information of self-supervised pretext tasks for unsupervised video pre-training\\nYear: 2022\\n\\nAbstract: This study addresses the problem of the unsupervised pre-training of video representation learning. The authors' focus is on two common approaches: knowledge distillation and self-supervised learning. The insight provided is that knowledge distillation and the rapidly advancing self-supervised technique can be mutually beneficial. Combining these two approaches, a unified framework of self-supervised learning and image-based distillation (SSID) for unsupervised video pre-training is proposed. The effectiveness of SSID in comparison to both image-based distillation methods and the existing self-supervised pre-training baseline is demonstrated. In particular, the authors' model leverages three signals from the unlabelled data. First, the authors distil from the classifier of a 2D pre-trained model as a soft label. To regularize the training process, the authors then build a novel positive pair of contrastive learning on the representation of the 2D/3D model. Finally, a self-supervised pretext task is introduced to enhance the authors' model to become aware of the temporal evolution. The authors' experiment results showed that the learnt features achieved the best performance when transferred to action recognition tasks on UCF101 and HMDB51, reaching increases of 2.4% and 1.9% compared to the existing unsupervised pre-training model, respectively.\",\n \"Title: Research on Wireless Sensor Network Access Control and Load Balancing in the Industrial Digital Twin Scenario\\nYear: 2022\\n\\nAbstract: Wireless sensor networks which are based on distributed information processing technology are taking an increasingly key role in industrial digital twin scenarios. There are many important issues in the access of networks. One of the most important issues is how to improve network access control and the effectiveness of load balancing. Based on the industrial digital twin technology, this article first introduces several typical network access and network loads and performs tree-structured processing on the outliers generated during the chain formation process to reduce the length of the data transmission path, optimize the main chain head and subchain chain head selection strategy and chaining rules, and perform nonchain operations on common nodes and chain heads near sink to reduce data inverse transfer. The experimental results show that this paper uses the digital twin calculation formula to accurately and objectively determine the remaining cluster head and the distance head and the base station, so that when the node distance is limited, the network energy consumption can be balanced as much as possible, and the network load is promoted.\",\n \"Title: A Deep Multitask Convolutional Neural Network for Remote Sensing Image Super-Resolution and Colorization\\nYear: 2022\\n\\nAbstract: Remote sensing data have become increasingly vital in target detection, disaster monitoring, and military surveillance. Abundant pan-sharpening and super-resolution (SR) methods based on deep learning have been proposed and have achieved remarkable performance. However, pan-sharpening requires paired panchromatic (PAN) and multispectral (MS) images, and SR cannot increase the spectral resolution of PAN. Thus, we introduce a computational imaging-based method to recover or produce the incomplete data of single PAN or MS. This work also explores the integration of multiple tasks by a single neural network. We start with SR and colorization, study the feasibility of simultaneously finishing SR colorization, and use a model trained in SR colorization to finish pan-sharpening without MS. A generic neural network, remote sensing image improvement network (RSI-Net), is designed for remote sensing image SR, colorization, simultaneous SR colorization, and pan-sharpening. To verify its performance, RSI-Net is compared with the state-of-the-art SR and colorization methods. Experiments show that RSI-Net can be competitive in visual effects and evaluation indexes, and it performs well at simultaneous SR colorization, and RSI-Net finishes pan-sharpening and only needs to input PAN. Our experiments confirm the effect of integrating multiple tasks.\",\n \"Title: PQROM: To optimize software defined network QoS-aware routing with proximal policy optimization\\nYear: 2022\\n\\nAbstract: Currently, Quality-of-Service (QoS)-aware routing is one of the crucial challenges in Software Defined Network (SDN). The QoS performances, e.g. latency, packet loss ratio and throughput, must be optimized to improve the performance of network. Traditional static routing algorithms based on Open Shortest Path First (OSPF) could not adapt to traffic fluctuation, which may cause severe network congestion and service degradation. Central intelligence of SDN controller and recent breakthroughs of Deep Reinforcement Learning (DRL) pose a promising solution to tackle this challenge. Thus, we propose an on-policy DRL mechanism, namely the PPO-based (Proximal Policy Optimization) QoS -aware Routing Optimization Mechanism (PQROM), to achieve a general and re-customizable routing optimization. PQROM can dynamically update the routing calculation by adjusting the reward function according to different optimization objectives, and it is independent of any specific network pattern. Additionally, as a black-box one-step optimization, PQROM is qualified for both continuous and discrete action space with high-dimensional input and output. The OMNeT ++ simulation experiment results show that PQROM not only has good convergence, but also has better stability compared with OSPF, less training time and simpler hyper-parameters adjustment than Deep Deterministic Policy Gradient (DDPG) and less hardware consumption than Asynchronous Advantage Actor-Critic (A3C).\",\n \"Title: Impulsive Noise Excision Using Robust Smoothing\\nYear: 2022\\n\\nAbstract: The impulsive noise originated from regional lightning discharges can dramatically degrade the performance of skywave over-the-horizon radar (OTHR) target detection with generally 10-20-dB increase of noise level in the Doppler domain. As a result, it is necessary to excise impulsive noise in corrupted data while preserving the useful echo signal. Traditional techniques first locate the impulsive noise in slow-time data with a high-pass finite-duration impulse response (FIR) notch filter and then restore the contaminated samples by neighboring good data. However, the effect of impulsive noise is propagated into the subsequent filtered residuals, which will mislead the impulsive noise detection. Instead of the conventional detection-interpolation methods, we propose a novel impulsive noise excision approach based on robust smoothing. The proposed method can robustly smooth the corrupted data and determine the position of impulsive noise with a preset threshold against the residuals of corrupted data subtracting the smoothed signal. The contaminated data at last are replaced by the smoothed signal. Experimental results have demonstrated the effectiveness of our proposed method.\",\n \"Title: Remote sensing-based deformation monitoring of pagodas at the Bagan cultural heritage site, Myanmar\\nYear: 2022\\n\\nAbstract: As a World Cultural Heritage site with sacred landscape featuring an exceptional range of Buddhist art and architecture, much attention has been focused on the sustainable development of Bagan (Myanmar). Particularly, the monitoring of landscape surface subsidence and monument instability is of great importance to the protection and development planning of the Bagan heritage site. In this study, we applied high resolution TerraSAR-X imagery acquired from 2019 to 2020 for deformation monitoring based on the small baseline subset (SBAS) and persistent scatterer synthetic aperture radar interferometry (PSInSAR) approaches. We identified several hotspots and pagodas with displacement anomalies linked to land cover change and previous earthquakes. The cross comparison between SBAS and PSInSAR and the precision of height estimates derived by PSInSAR indicated a millimetric precision of the derived deformation products. The combination of the two multi-temporal SAR interferometry approaches satisfies the two-scale monitoring requirements from landscapes to monuments, particularly for large-scale World Heritage sites. The non-contact monitoring method has potential when traditional methods using field accessibility and surveillance are constrained.\",\n \"Title: Braking angle control of loom spindle based on neuron-adaptive PID algorithm\\nYear: 2022\\n\\nAbstract: The intelligent control strategy of electromagnetic clutch actuator is analyzed in detail in this paper. The start - stop control of the loom is realized by an electromagnetic clutch. The existing control method of electromagnetic clutch of loom is high and low pressure control strategy. The operator sets the braking advance angle according to experience, to realize the accurate braking of the spindle, but it is difficult to realize the fast and accurate control. In order to achieve good performance, it is very important to develop a fast and accurate loom braking system. Aiming at the fabric defects caused by the elongation of the warp when the loom is stopped, a method of stabilizing the excitation current of the electromagnetic clutch by using the neural adaptive PID (proportional integral differential) controller is proposed to improve the control precision of the actuator. The experimental results show that the proposed control algorithm is feasible and can effectively realize the adaptive control of the spindle braking Angle within the allowable error range.\",\n \"Title: High-speed loom brake system controlled by fuzzy PID algorithm based on cloud platform\\nYear: 2022\\n\\nAbstract: When a high-speed loom is suddenly stopped or is driven again in a faulty state, it is easy to cause defects such as brake marks and driving marks on the fabric. This is because the brake system of the loom has insufficient control of the accuracy of the parking angle. To improve the control accuracy of the braking system, this paper analyzes the dynamic process of the braking system when braking, builds a mathematical model of the electromagnetic clutch of the braking system, and proposes to regulate the excitation current of the system by fuzzy PID algorithm. The rule table of this PID controller is established by expert experience, and the center of gravity method is used for defuzzification. In addition, this article combines internet communication and storage technology to build a cloud platform for the brake system, and realizes the monitoring, storage and diagnosis of the brake system data. Finally, hardware testing and on-site braking experiments (including parking angle data acquisition experiment and spindle speed change experiment during braking) verify that the design can achieve high-precision parking angle positioning at different speeds, and has a fast dynamic response and a smooth braking process.\",\n \"Title: A new method of conflicting evidence management based on non-extensive entropy and Lance distance in uncertain scenarios\\nYear: 2022\\n\\nAbstract: Handing uncertain information is one of the research focuses currently. For the sake of great ability of handing uncertain information, Dempster-Shafer evidence theory (D-S theory) has been widely used in various fields of uncertain information processing. However, when highly contradictory evidence appears, the results of the classical Dempster combination rules (DCR) can be counterintuitive. Aiming at this defect, by considering the relationship between the evidence and its own characteristics, the proposed method is a new method of conflicting evidence management based on non-extensive entropy and Lance distance in uncertain scenarios. Firstly, the Lance distance function is used to measure the degree of discrepancy and conflict between evidences, and the credibility of evidence is expressed by matrix. Introducing non-extensive entropy to measure the amount of information about evidence and express the uncertainty of evidence. Secondly, the discount coefficient of the final fusion evidence is measured by considering the credibility and uncertainty of the evidence, and the original evidence is modified by the discount coefficient. Then, the final result is obtained by evidence fusion with DCR. Finally, two numerical examples are provided to illustrate the efficiency of the proposed method, and the utility of our work is demonstrated through an application of the active lane change to avoid obstacles to the autonomous driving of new energy vehicles. The proposed method has a better identification accuracy, reaching 0.9811.\",\n \"Title: A Cost-Effective Implementation of Independent Data and Power Transmission Channels in Wireless Power Transfer Systems\\nYear: 2022\\n\\nAbstract: Communication between the primary and secondary sides is of critical significance in wireless power transfer (WPT) systems. In this brief, a novel cost-effective implementation of independent data and power transmission channels is proposed. Based on the typical WPT systems structure, the capacitance between metal shield plates provides naturally an independent capacitive channel for data transmission. Considering the parasitic capacitance between coil and plate, a concentric design for metal plate division is used to form a six-plate capacitive coupling mechanism with symmetrical misalignment tolerance ability, and meanwhile, the magnetic shielding function of the metal plate is maintained. Furthermore, there is no direct line connection between power and data transmission channels, and the parasitic capacitances between coil and plate sections on the same side are almost equal, resulting in virtually no interference between the power and data transmission. Compared with conventional simultaneous power and data transmission methods, additional wave trappers and coupling structures are not needed, which significantly reduces the overall cost and size of WPT systems. Finally, a 50W experimental prototype, with closed-loop output voltage control using 9600bps data transfer rate, validates the feasibility of the proposed method.\",\n \"Title: Deciphering the Spatial Modular Patterns of Tissues by Integrating Spatial and Single-Cell Transcriptomic Data.\\nYear: 2022\\n\\nAbstract: Single-cell RNA sequencing (scRNA-seq) provides a powerful tool to analyze the expression level of tissues at a cellular resolution. However, it could not capture the spatial organization of cells in a tissue. The spatially resolved transcriptomics technologies (ST) have been developed to address this issue. However, the emerging STs are still inefficient at single-cell resolution and/or fail to capture the sufficient reads. To this end, we adopted a partial least squares-based method (spatial modular patterns [SpaMOD]) to simultaneously integrate the two data modalities, as well as the networks related to cells and spots, to identify the cell-spot comodules for deciphering the SpaMOD of tissues. We applied SpaMOD to three paired scRNA-seq and ST datasets, derived from the mouse brain, granuloma, and pancreatic ductal adenocarcinoma, respectively. The identified cell-spot comodules provide detailed biological insights into the spatial relationships between cell populations and their spatial locations in the tissue.\",\n \"Title: Review of fuzzy investment research considering modelling environment and element fusion\\nYear: 2022\\n\\nAbstract: Reasonable investment strategies are of great importance for investors, companies, or even countries. Therefore, many investment methods have been proposed to assist people with their investment decision-makings. In this paper, we focus on qualitative investment methods, namely fuzzy investment theory, and provide an overview of the related studies via numerical analysis and literature review. As for the numerical analysis, the status quo and evolution track of fuzzy investment research are presented from static and dynamic perspectives. Specifically, the main publish stages, the whole development trend and the theory diffusion paths of this field are provided in detail. Regarding the literature review, we summaries the fuzzy investment models with consideration of diverse fuzzy environments such as the interval-valued fuzzy, intuitionistic fuzzy, and hesitant fuzzy environments. Various elements fused in the fuzzy investment models such as risk factor, decision-making technique, and portfolio construction are introduced and overviewed as well. Furthermore, we demonstrate the corresponding practical applications of the fuzzy investment methods and clarify their application processes. This study would be helpful for readers, investors, and researchers to comprehensively learn and apply the fuzzy investment theory, which is an effective tool to fuse their subjective opinions into real investment decision-makings.\",\n \"Title: The Dual-Fuzzy Convolutional Neural Network to Deal With Handwritten Image Recognition\\nYear: 2022\\n\\nAbstract: Subjective evaluation is a commonly used method in the real recognition process. Generally, two fuzziness can be found in evaluation information, namely what values should be given to fully describe the information and how to distinguish different values. The recently developed probabilistic hesitant fuzzy set could perfectly address these issues. In this article, we propose a dual-fuzzy convolutional neural network (DF-CNN) by fusing the hot neural network algorithm into the probabilistic hesitant fuzzy environment and then using it in a practical handwritten image recognition process. For this new DF-CNN, we provide the whole calculation process including the forward propagation, backward propagation, and parameter updating calculations. Also, the optimization algorithm of the DF-CNN is given to derive its optimal results. Finally, we apply the DF-CNN and its optimization algorithm to deal with a real issue, namely the handwritten numeral image recognition. The calculation process and the comparison fully demonstrate the feasibility and effectiveness of the proposed new model and algorithm.\",\n \"Title: Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation.\\nYear: 2022\\n\\nAbstract: The emotion recognition in conversation (ERC) task aims to predict the emotion label of an utterance in a conversation. Since the dependencies between speakers are complex and dynamic, which consist of intra- and inter-speaker dependencies, the modeling of speaker-specific information is a vital role in ERC. Although existing researchers have proposed various methods of speaker interaction modeling, they cannot explore dynamic intra- and inter-speaker dependencies jointly, leading to the insufficient comprehension of context and further hindering emotion prediction. To this end, we design a novel speaker modeling scheme that explores intra- and inter-speaker dependencies jointly in a dynamic manner. Besides, we propose a Speaker-Guided Encoder-Decoder (SGED) framework for ERC, which fully exploits speaker information for the decoding of emotion. We use different existing methods as the conversational context encoder of our framework, showing the high scalability and flexibility of the proposed framework. Experimental results demonstrate the superiority and effectiveness of SGED.\",\n \"Title: Event-Triggered Optimal Control for the Continuous Stirred Tank Reactor System\\nYear: 2022\\n\\nAbstract: Continuous stirred tank reactor (CSTR)is essential equipment found in chemical processing industries. Control of the CSTR process has long been a challenging issue due to the high complexity and strong nonlinearity of the chemical process. In this article, a novel event-triggered (ET) adaptive dynamic programming (ADP) optimal control algorithm is developed for the CSTR system. To reduce the compu...\",\n \"Title: Manipulability and Robustness Optimization of the Cable-Driven Redundant Soft Manipulator\\nYear: 2021\\n\\nAbstract: Redundancy makes soft manipulators manage to complete a variety of tasks in complex environments. However, a pseudo-inverse kinematics controller for redundant soft manipulators simply maximizes the manipulability while ignoring some material constraints, e.g., actuation saturation and control noise. As manipulability is the indicator for the dexterity of motion in all directions of the redundant ...\",\n \"Title: The Trustworthiness Measurement Model of Component-based Software Based on the Subjective and Objective Weight Allocation Method\\nYear: 2020\\n\\nAbstract: Software trustworthiness includes many attributes. Reasonable weight allocation of trustworthy attributes plays a key role in the software trustworthiness measurement. In practical application, attribute weight usually comes from experts' evaluation to attributes and hidden information derived from attributes. Therefore, when the weight of attributes is researched, it is necessary to consider weig...\",\n \"Title: Multi-View Spatial-Temporal Adaptive Graph Convolutional Networks for Traffic Forecasting\\nYear: 2021\\n\\nAbstract: Traffic flow prediction is vital to traffic management and public safety, and it plays a very important role in a smart transportation system. Traffic flow data contains complex temporal relation, spatial relation, and spatial-temporal relation. In this paper, we propose a novel Multi-View Spatial-Temporal Adaptive Graph Convolutional Network (MVST-AGCN) for traffic flow prediction. MVST-AGCN is m...\",\n \"Title: Multi-interaction fusion collaborative filtering for social recommendation\\nYear: 2022\\n\\nAbstract: GNNs(Graph Neural Networks) use graph structure to make recommendations, receiving more and more attention. Firstly, existing work focuses on aggregating social interaction information, ignoring users who rate on the same items. Secondly, the existing recommendation methods cannot dynamically reflect changes in user interests. Thirdly, existing methods do not take into account the interaction of subgraphs in GNNs and interactions between user and item factors. In this paper, a graph social fusion recommendation (GSFR) method is proposed. GSFR captures multiple social information simultaneously, based on which it can dynamically adjust user interest weight. Specifically, GSFR captures the interactions in subgraphs with a dynamic attention mechanism, which can represent changes in user interests in heterogeneous networks. A mutualistic mechanism is combined to simulate the mutually reinforcing relationship between social behavior and virtual behavior. User and item latent factors are obtained based on space vectors from the aggregation part. Recommendations are made from inherent characteristics of space vectors interaction behaviors. Comprehensive experimental results on three public datasets show the effectiveness of the proposed model.\",\n \"Title: RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment\\nYear: 2022\\n\\nAbstract: Coronary CT Angiography (CCTA) is susceptible to various distortions (e.g., artifacts and noise), which severely compromise the exact diagnosis of cardiovascular diseases. The appropriate CCTA Vessel-level Image Quality Assessment (CCTA VIQA) algorithm can be used to reduce the risk of error diagnosis. The primary challenges of CCTA VIQA are that the local part of coronary that determines final quality is hard to locate. To tackle the challenge, we formulate CCTA VIQA as a multiple-instance learning (MIL) problem, and exploit Transformer-based MIL module (termed as T-MIL) to aggregate the multiple instances along the coronary centerline into the final quality. However, not all instances are informative for final quality. There are some quality-irrelevant/negative instances intervening the exact quality assessment(e.g., instances covering only background or the coronary in instances is not identifiable). Therefore, we propose a Progressive Reinforcement learning based Instance Discarding module (termed as PRID) to progressively remove quality-irrelevant/negative instances for CCTA VIQA. Based on the above two modules, we propose a Reinforced Transformer Network (RTN) for automatic CCTA VIQA based on endto-end optimization. The experimental results demonstrate that our proposed method achieves the state-of-the-art performance on the real-world CCTA dataset, exceeding previous MIL methods by a large margin.\",\n \"Title: A Compact-Size Multi-Winding Transformer-Based Discharge Equalizer for Electric Two-Wheelers and Three-Wheelers Vehicles Power Battery\\nYear: 2022\\n\\nAbstract: Due to the complexity of driving conditions and the increase of the driving range of electric two-wheelers and three-wheelers vehicles, the power battery string is in a long-period unbalanced discharge state, which affects the power output and even causes certain safety risks. A compact-size multi-winding transformer-based discharge equalizer is proposed to solve these problems and minimize the impact on the system volume by sharing the primary inductor with a boost discharger. When the boost converter is powering a load, the multi-winding transformer equalizer is driven by utilizing the square wave voltage generated on the inductor in the boost converter and produces multiple identical voltage levels to achieve voltage balance. Thus, the automatic balance can be achieved with only one winding and one diode for each module. An experimental prototype containing four batteries was established to verify the feasibility of the proposed method. Moreover, a quantitative comparison shows the proposed method obtain a more compact-size equalizer because of no need for a separate transformer, compared with the traditional equalization method based on the transformer.\",\n \"Title: A Multifocus Image Fusion Scheme Based on Similarity Measure of Transformed Isosceles Triangles Between Intuitionistic Fuzzy Sets\\nYear: 2022\\n\\nAbstract: Intuitionistic fuzzy set (IFS) theory is widely used to solve imprecise and uncertain problems, and the similarity measure between IFSs can describe the similarity or dissimilarity degree of objects in the real word. In image fusion, it is a key issue to measure and extract the features of multifocus images to fuse them. This research proposes a similarity measure technique of IFSs, which is introduced into image fusion to measure the feature similarity of adjacent pixels. In this work, we present an available similarity measure method between IFSs and propose an image fusion method based on this measure and also design an edge reserved method based on the Canny edge detection to reinforce the image fusion effect. First, the proposed similarity measure is introduced and proved. Second, a focus detection method that can detect the focused regions of an image is designed based on the proposed measure method of IFSs to obtain a preliminary decision map. Third, morphological operations are employed to generate the secondary decision map. Fourth, a Canny edge model-based technique is designed to preserve the edges of the source images. Extensive experiments show that the presented similarity measure method of IFSs is reliable, and the fuzzy measure-based image fusion scheme is effective compared with current methods.\",\n \"Title: A Bayesian Deep Image Prior Downscaling Approach for High-Resolution Soil Moisture Estimation\\nYear: 2022\\n\\nAbstract: Soil moisture (SM) estimation is a critical part of environmental and agricultural monitoring, with satellite-based microwave remote sensing being the main SM source. However, the limited spatial resolution of most current remote sensing SM products reduces their utility for many applications, such as evapotranspiration modeling and agriculture management. In this article, to address this issue, we propose a Bayesian deep image prior (BDIP) downscaling approach to estimate the high-resolution SM from satellite products. More specifically, the high-resolution SM estimation problem is formulated as a maximum a posteriori problem, and solved via a neural network comprising of a deep fully convolutional neural network (FCNN) for modeling the prior spatial correlation distribution of the underlying high-resolution SM variables, and a forward model characterizing the SM map degeneration process for modeling the data likelihood. As such, the proposed BDIP approach provides a statistical framework that integrates deep learning with forward modeling in a coherent manner for combining different sources of information, i.e., the knowledge in the forward model, the spatial correlation prior in FCNN architecture, and the remote sensing data and products. Experiments on the downscaling of SM active passive SM products using the moderate resolution imaging spectroradiometer products show that SM maps estimated using the proposed method provide greater spatial detail information than other downscaling methods, with the SM estimates very close to in situ measurements.\",\n \"Title: Dynamic Development Analysis of Complex Network Research: A Bibliometric Analysis\\nYear: 2022\\n\\nAbstract: In recent years, the method of the complex network has been applied to various fields. Dynamics research in complex networks is also an important branch. There are many types of research into dynamic complex network, but few scholars use bibliometrics to study it. Therefore, this paper adopts the method of bibliometrics to analyze the development history and status quo of dynamic complex network, providing a summary description of this research field. We used CiteSpace and Pajek to analyze 2936 relevant papers on the website of Web of Science and further interpreted and summarized the analysis results. To fully show the bibliometric results, we further derive the productive countries, institutions, sources, and authors and some main research directions in the dynamic complex network research. The research results show that the dynamic complex network research method was applied to various fields after the dynamic research method had been integrated into the complex network. According to the subject analysis of the cited papers, we find that the dynamic complex network method has been widely used in various subjects. Through emergent analysis, we found that the latest hot research trends are the study of infectious diseases and applications in neural networks. At the same time, through the main path analysis, we find the essential literature to elaborate on the development context of a dynamic complex network at different time points. This paper gives a comprehensive analysis of dynamic complex network research and provides some enlightenment for the future research direction.\",\n \"Title: Dynamic behaviors in a Cournot duopoly model with knowledge spillover effect based on constant conjectural variation\\nYear: 2022\\n\\nAbstract: On the basis of constant conjectural variation, a static Cournot duopoly model is built first in this paper, where the effects of knowledge spillover and product differentiation have been parameterized. Through the first order conditions, we can obtain the static conjectural variation equilibrium. By using the gradient adjustment mechanism, a dynamic conjectural variation model, represented by a two-dimensional difference equation, is established, in which two firms have bounded rationality. Then, the local stability of the Nash equilibrium is analyzed. From further studies on the effects of conjectural variation to the dynamics of the established model, we conclude that the more intense the market competition, the bigger the size of the stability region. In general case, the two axes are invariant sets, while the diagonal line is also an invariant set in symmetric case. Through the method of numerical simulation, we can deeply understand the complex characteristics of dynamic behaviors and the means for economic activities. Finally, synchronized behaviors are also studied by the method of critical curves and basins of attraction.\",\n \"Title: ISANET: Non-small cell lung cancer classification and detection based on CNN and attention mechanism\\nYear: 2022\\n\\nAbstract: Lung cancer is one of the malignant tumors with high morbidity and mortality worldwide. Among them, non small cell lung cancer accounts for about 85% of all lung cancers. In the existing chest CT plain scan or enhanced scan, non-small cell lung cancer images may overlap significantly in imaging features, leading to misdiagnosis and the inability to give an accurate histological classification. Among the existing relevant non small cell lung cancer classification models, few studies on the precise classification of non-small cell lung cancer types. A multi-category classification model based on CNN is proposed to tackle these problems. The proposed model ISANET embeds channel attention and spatial attention mechanisms to focus on pathological areas based on InceptionV3. The three-category lung cancer dataset provided by the Affiliated Hospital of Hebei University is used, including lung squamous cell carcinoma, lung adenocarcinoma, and normal. Comparative experiments are done between ISANET and the traditional models AlexNet, VGG16, InceptionV3, MobilenetV2, and ResNet18. Results of experiments on two public datasets verify the effectiveness of ISANET, reaching 95.24% and 98.14% respectively, which indicates that ISANET has obtained superior accuracy in classifying non-small cell lung cancer.\",\n \"Title: Displacement Back Analysis of Reservoir Landslide Based on Multi-Source Monitoring Data: A Case Study of the Cheyiping Landslide in the Lancang River Basin, China\\nYear: 2022\\n\\nAbstract: Landslides that occur in the littoral zone of a reservoir can directly damage the hydraulic structures and threaten the lives and property around the reservoir. Due to the spatial variability and heterogeneities of rock mass, a limited amount of data obtained from laboratory and in situ tests cannot comprehensively characterize the mechanical properties of rock and soil masses. Therefore, displacement back analysis is often performed to determine the mechanical parameters of rock and soil masses. The spaceborne Interferometric synthetic aperture radar (InSAR) has proved to be a powerful tool for geodesy in the measurement of landslide movement. However, InSAR can only measure the surface motion of the landslide without the subsurface information. This study uses multi-source monitoring data in the landslide displacement back analysis, including surface InSAR and an internal borehole inclinometer. The identified material parameters and finite element simulation are used to predict the landslide deformation. The case study of the Cheyiping landslide located in the Lancang River basin demonstrates the necessity and feasibility of using multi-source monitoring data in landslide displacement back analysis. The Cheyiping landslide is currently in the creep deformation stage. The decrease in shear strength of rock masses due to the rheological deformation and the change in reservoir water level are the internal and external factors leading to excessive landslide deformation. The numerical modeling can accurately simulate the landslide movement using the identified material parameters. By combing multi-source monitoring data and numerical modeling, the reservoir landslide deformation analysis can help evaluate the landslide deformation state and stability, which is vital for reservoir risk mitigation and the sustainable development of hydropower resources.\",\n \"Title: Decentralized Self-Organizing Networks for Poor Reconfigurability of Loom Networks\\nYear: 2022\\n\\nAbstract: The intelligent weaving of looms requires networking and intelligent industrial production systems that use industrial equipment with network communication and data computing, creating a \\\"smart factory\\\". However, limited by the development of industrial technology, existing industrial loom equipment does not have sufficient communication or computing capabilities: communications primarily rely on bus and Ethernet communication, which is limited to wired connections; and computations primarily rely on the host computers and cloud computers that only provide weak real-time computing. Currently, the remote management system used in the loom industry can be constructed using a cloud platform, but machine-machine interconnections between the loom control terminals are ignored. To solve the problem of poor reconfigurability in loom networks that rely on cloud servers, this study analyzes the networking requirements of a loom network and uses mesh network technology to build a wireless self-organizing loom network. A mesh network can be formed with clouds, loom equipment, and other equipment. By constructing an experimental platform with a decentralized self-organizing network to build a remote loom monitoring system, an anti-destruction experiment of the developed self-organizing loom network is performed. After the self-organizing network is constructed, key nodes in the network are disconnected, and the network is automatically reconfigured to resume normal work. The reconfigurability and stability of the network are demonstrated via destructive experiments. Results show that the self-organizing network construction method developed in this study can build a loom self-organizing network, which is anti-destructive and mitigate the poor reconfigurability of existing loom networks. Compared with the wireless communication network of the previous loom remote monitoring system, the loom interconnection self-organizing network is more reliable and anti-interference, which can better meet the network demand of intelligent manufacturing for loom equipment. This study provides an effective example of loom networking technology for the industry and is important when promoting the construction of smart loom factories.\",\n \"Title: Path extension similarity link prediction method based on matrix algebra in directed networks\\nYear: 2022\\n\\nAbstract: Traditional link prediction methods are generally only calculated for the neighbor information of nodes, and the network path between nodes has not been fully utilized. Therefore, this paper proposes a directed network link prediction method based on path extension similarity to improve the prediction accuracy of potential edges of network nodes. Firstly, the mathematical definition of each local index is expressed in matrix form through matrix algebra; secondly, according to the algorithm principle of global and quasi-local indices, the extension form of local indices is clarified; and the path extension of each local index is carried out respectively; finally, multiple real data sets are used to analyze the benchmark indices and extended indices. The results of the AUC and Precision evaluation metrics show that the path extension similarity proposed in this paper has higher accuracy and stronger robustness than the benchmark indices.\",\n \"Title: Spatiotemporal Filtering for Continuous GPS Coordinate Time Series in Mainland China by Using Independent Component Analysis\\nYear: 2022\\n\\nAbstract: Continuous Global Positioning Systems (GPS) coordinate time series with a high spatiotemporal resolution, and provide a great opportunity to study their noise models and common mode errors (CMEs), thus making it possible to detect and analyse spatiotemporal characteristics of tectonic and non-tectonic signals in time series, and further to estimate a reliable and accurate velocity field of crustal movement in a region by removing CMEs and using the optimal noise model. In this paper, we used GPS coordinate time series from the Crustal Movement Observation Network of China (CMONOC) with an approximate decadal period from 2010 to 2020, to construct optimal noise models by fitting them with several noise combinations according to the Akaike information criterion (AIC). We further adopted independent component analysis (ICA) to extract CMEs and analysed their spatiotemporal characteristics, and then evaluated their effects on noise models and velocity uncertainties, and finally estimated a decennial velocity field of crustal movement with a higher signal-to-noise ratio (SNR) by applying the CME filtering and considering the optimal noise model in Mainland China. Our results show that optimal noise models are dominated by white noise (WN) plus flicker noise (FN) for both east and north components, and WN plus power law noise (PN) with spectral index close to -1 for up component, respectively. ICA filtering can remove the highly spatially correlated CMEs and decrease the mean RMSEs of the residual time series by about 40-60%, providing a more accurate velocity field with a higher SNR in Mainland China, accordingly.\",\n \"Title: Stochastic configuration broad learning system and its approximation capability analysis\\nYear: 2022\\n\\nAbstract: In this paper, a kind of stochastic configuration broad learning system (SCBLS) is proposed for data modeling. The proposed SCBLS is established in the form of a flat network and its architecture is determined by a constructive learning approach. The input parameters of feature nodes and enhancement nodes of SCBLS are randomly assigned in the light of a supervisory mechanism. Inequality constraints are used to randomly assign the hidden parameters and adaptively select the scopes of random parameters. The output parameters of SCBLS are determined either by a constructive manner or by solving a global least squares problem. It is proved that the proposed SCBLS possesses universal approximation properties. The performances of the proposed SCBLS are evaluated by function approximation, benchmark datasets and time series prediction. Numerical examples show that SCBLS can achieve satisfactory approximation accuracy.\",\n \"Title: Quality Assessment of Image Super-Resolution: Balancing Deterministic and Statistical Fidelity\\nYear: 2022\\n\\nAbstract: ABSTRACTThere has been a growing interest in developing image super-resolution (SR) algorithms that convert low-resolution (LR) to higher resolution images, but automatically evaluating the visual quality of super-resolved images remains a challenging problem. Here we look at the problem of SR image quality assessment (SR IQA) in a two-dimensional (2D) space of deterministic fidelity (DF) versus statistical fidelity (SF). This allows us to better understand the advantages and disadvantages of existing SR algorithms, which produce images at different clusters in the 2D space of (DF, SF). Specifically, we observe an interesting trend from more traditional SR algorithms that are typically inclined to optimize for DF while losing SF, to more recent generative adversarial network (GAN) based approaches that by contrast exhibit strong advantages in achieving high SF but sometimes appear weak at maintaining DF. Furthermore, we propose an uncertainty weighting scheme based on content-dependent sharpness and texture assessment that merges the two fidelity measures into an overall quality prediction named the Super Resolution Image Fidelity (SRIF) index, which demonstrates superior performance against state-of-the-art IQA models when tested on subject-rated datasets.\",\n \"Title: Network analysis for elucidating the mechanisms of Shenfu injection in preventing and treating COVID-19 combined with heart failure\\nYear: 2022\\n\\nAbstract: •A systematic framework is developed to clarify the pharmacological effects of SFI.•The ADME filtering and target fishing identify the active substances and targets.•The C-T-D network dissects the therapeutic effect of SFI against COVID-19 combined with HF.•The PPI network is proposed to obtain hub targets of SFI, COVID-19 and HF.•The pathway analysis reveals the pharmacological mechanisms of SFI at the pathway level.\",\n \"Title: Research on the Rotational Correction of Distributed Autonomous Orbit Determination in the Satellite Navigation Constellation\\nYear: 2022\\n\\nAbstract: The autonomous orbit determination of the navigation constellation uses only bidirectional ranging data of the inter-satellite link for data processing. The lack of space-time benchmark information related to the Earth inevitably causes overall rotational uncertainty in the constellation, leading to a decrease in orbit accuracy and affecting user positioning accuracy. This study (1) introduces a method for rotation correction in distributed autonomous orbit determination based on inter-satellite bidirectional ranging; (2) conducts constellation autonomous orbit determination and time synchronization processing experiments based on inter-satellite ranging data for the 24 medium Earth orbit (MEO) satellites in the Beidou-3 global satellite navigation system (BDS-3); and (3) makes comparative analyses on the accuracy of autonomous orbit determination based on three rotation correction cases, including a no-rotation-correction case, independent satellite constraints case, and global satellite constraints case. The experimental results are described as follows. For the no-rotationcorrection case, the prediction error of the orbital inclination angle (iot, i) for the entire constellation on the 30th day was 2.11 x 10(-7)/rad, the prediction error of the right ascension of the ascending point (Omega, Omega) was 2.25 x 10(-7)/rad, and the average root mean square (RMS) of the user range error (URE) for the entire constellation orbit was 1.41 m. In the autonomous orbit determination experiment with independent constraints on satellites, the prediction error of i for the entire constellation on the 30th day was 5.43 x 10(-7)/rad, the prediction error of Omega was 2.03 x 10(-7)/rad, and the average RMS of the orbital URE for the entire constellation was 1.09 m. In the autonomous orbit determination experiment with global satellite constraints, the prediction error of i for the entire constellation on the 30th day was 5.31 x 10(-7)/rad, the prediction error of Omega was 1.95 x 10(-7)/rad, and the RMS of the orbital URE for the entire constellation was 0.94 m. According to the analysis of the above experimental results, compared with the autonomous orbit determination under the no-rotation-correction case, the adoption of an algorithm for independent satellite constraints to correct the overall constellation rotation weakens the constellation rotation influence; however, it may destroy the overall constellation configuration, which affects the stability of autonomous orbit determination. Finally, the algorithm based on global satellite constraints both impairs the influence of constellation rotation and maintains the overall constellation configuration.\",\n \"Title: Design and Research of M2M Message Transfer Mechanism of Looms for Information Transmission\\nYear: 2022\\n\\nAbstract: Machine-to-machine (M2M) communication is a key implementation technology for future Industrial Internet of Things (IIoT) applications. The requirements for flexibility, efficiency, and cross-platform compatibility in intermodule communication between the connected machines raise challenges for the M2M messaging mechanism. For the loom message transmission mechanism, it is necessary to have certain flexibility and meet certain M2M communication requirements to understand each other. In this paper, we analyze and study the data model, data exchange language, MQTT protocol and logo resolution technology of looms to address the current problems of low data transmission efficiency, poor flexibility and lack of flexibility in the network. By establishing the data model of looms and classifying them by topic, selecting JSON format as the transmission format of data, and using MQTT protocol to realize data transmission between loom networks, an MQTT-based messaging mechanism is established. The data involved in the communication of the loom equipment are grouped according to the communication requirements, and only the loom data required by the application are subscribed. At the same time, the hardware and software parts of the communication module are designed with ESP32 as the core, so that it can complete the functions of loom network communication, data analysis, format conversion and so on. In addition, the real-time requirements of the loom network communication and the network requirements for future intelligent manufacturing must be met. This paper uses the BP neural network to intelligently optimize the loom network, and the existing problems related to performance indices are analyzed. The original loom network is optimized by integrating the RSSI, QoS requirements, transmission priority, real-time communication and data transmission volume of nodes. In the meantime, the particle swarm optimization algorithm is introduced to optimize the BP neural network to avoid the problem that it easily can fall into the local optimal solution and effectively improving the network optimization efficiency. Finally, the feasibility of this method is proven by using the experimental platform, which can solve the problems of low network transmission efficiency and difficult network optimization of looms and provide an effective technical example of loom information transmission and network optimization for industry.\",\n \"Title: BMAM: complete the missing POI in the incomplete trajectory via mask and bidirectional attention model\\nYear: 2022\\n\\nAbstract: Studies on the checked-in point-of-interests have become an important means to learn user’s behavior. Nevertheless, users do not sign in to all visited locations. There are unobserved check-in locations in the generated POI trajectory. Such the trajectory is called an incomplete trajectory, and unobserved point is called missing point. However, incomplete trajectory has a negative impact on downstream tasks such as personalized recommendation system, criminal identification and next location prediction. It is a challenge to use the forward sequence and backward sequence information of the missing point to complete the missing POI. Therefore, we propose a bidirectional model based on mask and attention mechanism (BMAM) to solve the problem of missing POI completion in user’s incomplete trajectory. The context information of trajectory checked in by user can be mined to connect the missing POI with the forward sequence and backward sequence information. Therefore, the model learns the order dependence between each location according to the user trajectory sequence and obtain the user’s dynamic preference to identify the missing POI in the sequence. Besides, the attention mechanism is used to improve the user's representation feature, that is, the preference for POI categories. The experimental results demonstrate that our BMAM outperforms the state-of-the-art models for completion on missing POI of user’s incomplete sequence.\",\n \"Title: A Block PatchMatch-Based Energy-Resource Efficient Stereo Matching Processor on FPGA\\nYear: 2022\\n\\nAbstract: This paper presents a field programmable gate array (FPGA)-based, high-performance and energy-resource efficient stereo matching processor. The proposed processor executes block-level PatchMatch-based stereo matching algorithm with a random search strategy to avoid estimation of all disparity levels. To take advantages of different block scales, a coarse-to-fine multi-scale propagation (MSP) scheme is proposed for label update. Based on that, a dedicated hardware architecture is further proposed to explore the benefit of the algorithm. Experimental results show that the proposed FPGA-based processor, running at 350MHz, achieves a peak performance of \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"> <tex-math notation=\\\"LaTeX\\\">$1920\\\\times 1080.165$ </tex-math></inline-formula>\\n.7 frame per second (FPS) at 128 disparity levels with 3.35W power dissipation. The energy and resource efficiency of the proposed design outperforms state-of-the-art FPGA-based stereo matching processors. When disparity level increases to 256, the computing resource increment of the proposed design is much less than existing designs because random search instead of winner-takes-all (WTA) is utilized. Moreover, unlike existing dedicated stereo matching processors which output only disparity information, the proposed design is also capable of deriving plane slant. This information can be beneficial for follow-up tasks like 3D reconstruction.\",\n \"Title: Proprioceptive feedback design for gait synchronization in collective undulatory robots\\nYear: 2022\\n\\nAbstract: The collective movement of animals has long been a source of inspiration for multi-agent swarm robotics. One of the fundamental goals for swarm robotics study is to understand how effective and robust collective behaviors can emerge from simple interaction principles. When animal or robot collectives are in high-density configurations the ability for visual or auditory sensing is diminished and the opportunities for interacting through mechanical contact are enhanced. In this paper, we study how robots that move through lateral body undulation in close proximity are capable of synchronizing their oscillatory gaits through contact interactions between adjacent robots. Critically, gait phase synchronization occurs without the requirement for robot-robot communication, and instead can be engineered as an emergent property of the robot control system. We present a proprioceptive feedback control system that generates collective gait phase synchronization of undulatory robots in experiment and simulation. We first validate this control system using a simple one-dimensional toy model to demonstrate how proprioceptive feedback governs phase synchronization. Simulations and experiments with undulatory three-link robots further demonstrate how phase synchronization can be controlled. Lastly, we demonstrate that robot pairs moving in a confined tunnel can synchronize their movements which leads to faster group locomotion through confined spaces.\",\n \"Title: ProbResist: a database for drug-resistant probiotic bacteria\\nYear: 2022\\n\\nAbstract: Drug resistance remains a global threat, and the rising trend of consuming probiotic-containing foods, many of which harbor antibiotic resistant determinants, has raised serious health concerns. Currently, the lack of accessibility to location-, drug- and species-specific information of drug-resistant probiotics has hampered efforts to combat the global spread of drug resistance. Here, we describe the development of ProbResist, which is a manually curated online database that catalogs reports of probiotic bacteria that have been experimentally proven to be resistant to antibiotics. ProbResist allows users to search for information of drug resistance in probiotics by querying with the names of the bacteria, antibiotic or location. Retrieved results are presented in a downloadable table format containing the names of the antibiotic, probiotic species, resistant determinants, region where the study was conducted and digital article identifiers (PubMed Identifier and Digital Object Identifier) hyperlinked to the original sources. The webserver also presents a simple analysis of information stored in the database. Given the increasing reports of drug-resistant probiotics, an exclusive database is necessary to catalog them in one platform. It will enable medical practitioners and experts involved in policy making to access this information quickly and conveniently, thus contributing toward the broader goal of combating drug resistance.\",\n \"Title: Ultrasonographic features of cervical lymph node metastases from medullary thyroid cancer: a retrospective study\\nYear: 2022\\n\\nAbstract: To investigate sonographic features of cervical lymph node metastases from medullary thyroid cancer (LNM-MTC), as compared with lymph node metastases from papillary thyroid cancer (LNM-PTC). A total of 42 MTC patients with 52 metastatic LNs and 222 PTC patients with 234 metastatic LNs who were confirmed by fine needle aspiration and post-operative pathology, were enrolled in this study. The clinical characteristics and sonographic features of LNs were compared between the two groups. Univariate analysis and multivariate logistic regression analysis were performed on the sonographic features of LNs, including short and long-axis diameter, long-axis diameter/short-axis, shape, border, hilum, echogenicity, calcifications, cystic change and vascularity pattern. The discriminating performance was assessed with the area under the receiver operating characteristic curve (AUC). The mean age of patients with LNM-MTC was older than that of patients with LNM-PTC (46.81 ± 13.05 vs 39.09 ± 12.05, P < 0.001). No differences were observed in gender, location, long-axis diameter/short-axis, shape, border, echogenicity, cystic change and vascularity pattern between LNM-MTC and LNM-PTC groups (P > 0.05, for all). However, long-axis and short-axis diameter, hilum and calcifications were statistically different between these two groups (P < 0.05, for all). The AUC of discriminate value between LNM-MTC and LNM-PTC was 0.808 (95% confidence interval 0.739–0.877). Compared with LNM-PTC, LNM-MTC tended to have the sonographic characteristics of larger size, absence of hilum, and less calcifications, and awareness of these features might be helpful to in the diagnosis of LNM-MTC.\",\n \"Title: Monitoring Extractive Activity-Induced Surface Subsidence in Highland and Alpine Opencast Coal Mining Areas with Multi-Source Data\\nYear: 2022\\n\\nAbstract: Mining developments in alpine coal mining areas result in slow or rapid ground subsidence, which can lead to melting and collapse of permafrost. This paper integrated unmanned aerial vehicle (UAV) images and satellite-based SAR interferometry images to monitor intensive surface mining subsidence during reclamation. Digital Surface Model (DSM) acquired from UAV images was first used to evaluate the changes of the reclamation scheme on the microtopography carried out by slope and the Digital Elevation Model (DEM) of difference (DoD). The monitoring results showed that the slope had been reduced from over 30 degrees to under 15 degrees after the terrain had been reshaped. The DoD map revealed the distribution of main extraction areas and landfill areas. To further monitor the surface subsidence after local terrain adjustment, the Permanent Scatterer Interferometry (PS-InSAR) method was used to reveal the surface subsidence characteristics of the mine site before and after reclamation. The maximum cumulative subsidence ranged from -772.3 to 1183 mm based on 21 Sentinel-1A images in three years. Within a year of terrain reshaping, uplift and subsidence still occurred at hills and pit side slopes, following the nearly equal subsidence rate. The experimental results showed that the slope reshaping and vegetation recovery had a limited impact on the reduction of the ground subsidence in a short period. Therefore, on this basis, a combination of UAV and PS-InSAR methods can be used to continue monitoring time series subsidence in alpine mines.\",\n \"Title: Deep sparse autoencoder integrated with three-stage framework for glaucoma diagnosis\\nYear: 2022\\n\\nAbstract: Recently, end-to-end deep neural networks-based glaucoma diagnosis approaches have been gaining much attention. However, the feature extractor and classier in these approaches are trained together, which is known as coadaptation. Therefore, the feature distribution in them should adapt to particular decision boundaries. To learn generic data representations and improve the generalization ability of the model, this paper designs a three-stage framework for glaucoma diagnosis. In the first stage, preprocessing is utilized to extract the Region of Interesting around the Optic Disc to reduce the computational cost and nonobjective interference. In the second stage, Deep Sparse Autoencoder is designed to learn hybrid features between the deep features and the original features, which could improve the effectiveness of final high-level feature expression. Meanwhile, L1 regularization is introduced and applied on the hybrid features to obtain deep features with high complementarity under small sample problem. In the third stage, the obtained generic feature representations are fed into different classifiers, in which Support Vector Machine classifier achieves the best diagnosis performance. The proposed approach is evaluated on two publicly available databases. Extensive experimental results indicate that our approach outperforms the state-of-the-art approaches with the accuracy of 96.00%, 97.00% and Area Under Curve of 96.94%, 98.28% for REFUGE and Drishti-GS1 databases, respectively.\",\n \"Title: Stability and global dynamics of a quantum Cournot duopoly game with isoelastic demand\\nYear: 2022\\n\\nAbstract: In this paper, a statics quantum Cournot duopoly model with isoelastic demand is firstly set up considering quantum entanglement in decision making between firms. On this basis, a dynamic duopoly game with bounded rationality is established by dynamic adjustment mechanism, and then, local stability of the Nash equilibrium is studied by Jacobian matrix and Jury criterion. In addition, local bifurcation, the multi-stability motion and contact bifurcation of system are analyzed in detail through numerical simulation. It is found that the system will gradually enter chaos as the speed of adjustment increases. Furthermore, the marginal cost can change the stability region and the bifurcation types of the system. The results of quantum entanglement show that the squeezing parameter affects the global dynamical behaviors of the system and the equilibrium profits of firms. Finally, we compared the dynamical behaviors of the established model with those of the linear demand model proposed by Yang and Gong.\",\n \"Title: Effect of Stochastic Modeling for Inter-Frequency Biases of Receiver on BDS-3 Five-Frequency Undifferenced and Uncombined Precise Point Positioning\\nYear: 2022\\n\\nAbstract: The third generation of the Beidou navigation satellite system (BDS-3) broadcasts navigation signals of five frequencies. Focusing on the deep integration of five-frequency signals, we applied the joint BDS-3 five-frequency undifferenced and uncombined precise point positioning (UC-PPP) to analyze the receiver inter-frequency biases (IFB). Firstly, 12 Multi-GNSS Experiment tracking (MGEX) stations are selected to investigate the time-varying characteristics of receiver IFB and, according to random characteristics, three random modeling schemes are proposed. Secondly, the effects of three stochastic modeling methods on zenith tropospheric delay, ionospheric delay, floating ambiguity, and quality control are analyzed. Finally, the effects of three IFB stochastic modeling methods on positioning performance are evaluated. The results showed that the amplitude in the IFB for B2b is 5.139 m, B2a is 1.964 m, and B1C is 0.950 m by measuring one week's observation data. The IFB stochastic modeling method based on random walks can shorten the PPP convergence time by 4 similar to 12%, diminish the false alarm of quality control, and improve the positioning accuracy. The random walk model is recommended to simulate the variation of IFB, which can not only overcome the disadvantage of the time constant model being unable to accurately describe the time-varying characteristics of the IFB, but also avoid reducing the strength of the kinematic PPP positioning model due to the large process noise of the white noise model.\",\n \"Title: Phenological Responses to Snow Seasonality in the Qilian Mountains Is a Function of Both Elevation and Vegetation Types\\nYear: 2022\\n\\nAbstract: In high-elevation mountains, seasonal snow cover affects land surface phenology and the functioning of the ecosystem. However, studies regarding the long-term effects of snow cover on phenological changes for high mountains are still limited. Our study is based on MODIS data from 2003 to 2021. First, the NDPI was calculated, time series were reconstructed, and an SG filter was used. Land surface phenology metrics were estimated based on the dynamic thresholding method. Then, snow seasonality metrics were also estimated based on snow seasonality extraction rules. Finally, correlation and significance between snow seasonality and land surface phenology metrics were tested. Changes were analyzed across elevation and vegetation types. Results showed that (1) the asymmetry in the significant correlation between the snow seasonality and land surface phenology metrics suggests that a more snow-prone non-growing season (earlier first snow, later snowmelt, longer snow season and more snow cover days) benefits a more flourishing vegetation growing season in the following year (earlier start and later end of growing season, longer growing season). (2) Vegetation phenology metrics above 3500 m is sensitive to the length of the snow season and the number of snow cover days. The effect of first snow day on vegetation phenology shifts around 3300 m. The later snowmelt favors earlier and longer vegetation growing season regardless of the elevation. (3) The sensitivity of land surface phenology metrics to snow seasonality varied among vegetation types. Grass and shrub are sensitive to last snow day, alpine vegetation to snow season length, desert to number of snow cover days, and forest to first snow day. In this study, we used a more reliable NDPI at high elevations and confirmed the past conclusions about the impact of snow seasonality metrics. We also described in detail the curves of snow seasonal metrics effects with elevation change. This study reveals the relationship between land surface phenology and snow seasonality in the Qilian Mountains and has important implications for quantifying the impact of climate change on ecosystems.\",\n \"Title: A vision-based abnormal trajectory detection framework for online traffic incident alert on freeways\\nYear: 2022\\n\\nAbstract: Abnormal trajectory detection from surveillance cameras is a highly desirable but challenging task, especially for online traffic incident alert on freeways. Existing methods are mainly customized for offline alert and easily suffer from false alerts when applying them to online alert. To fill this gap, an anomaly trajectory detection framework is proposed for online traffic incident alert on freeways. Based on a LSTM autoencoder, this framework introduces an adversarial learner (AL) for offline training and an abnormal trajectory discriminator (ATD) for online alert. The adversarial learner uses an additional adversarial loss to enable the autoencoder to learn a better normal trajectory pattern that is beneficial for reducing false alerts, while an abnormal trajectory discriminator is established and trained to detect small mean shift and filter out instantaneous false alerts. The experimental results show that our proposed framework effectively filters out false alerts and obtains a state-of-art performance (AUC = 0.97) compared to existing methods. Moreover, our framework could timely alert traffic incidents within 0.25 s, which is significant for timely preventing the occurrence of traffic crashes and improving the response speed of incident management on freeways.\",\n \"Title: Evolutionary Search With Multiview Prediction for Dynamic Multiobjective Optimization\\nYear: 2022\\n\\nAbstract: Dynamic multiobjective optimization problem (DMOP) denotes the multiobjective optimization problem which varies over time. As changes in DMOP may exist some patterns that are predictable, to solve DMOP, a number of research efforts have been made to develop evolutionary search with prediction approaches to estimate the changes of the problem. A common practice of existing prediction approaches is to predict the change of Pareto-optimal solutions (POS) based on the historical solutions obtained in the decision space. However, the change of a DMOP may occur in both decision and objective spaces. Prediction only in the decision space thus may not be able to give the proper estimation of the problem change. Taking this cue, in this article, we propose an evolutionary search with multiview prediction for solving DMOP. In contrast to existing prediction methods, the proposed approach conducts prediction from the views of both decision and objective spaces. To estimate dynamic changes in DMOP, a kernelized autoencoding model is derived to perform the multiview prediction in a reproducing kernel Hilbert space (RKHS), which holds a closed-form solution. To examine the performance of the proposed method, comprehensive empirical studies on the commonly used DMOP benchmarks, as well as a real-world case study on the movie recommendation problem, are presented. The obtained experimental results verified the efficacy of the proposed method for solving both benchmark and real-world DMOPs.\",\n \"Title: Integrating the Pre-trained Item Representations with Reformed Self-attention Network for Sequential Recommendation\\nYear: 2022\\n\\nAbstract: The conventional sequential recommendation tends to recommend an item to a sequence due to the concurrence of this item in other similar sequences. It is a typical collaborative filtering recommendation strategy that suffers from the data sparsity problem. Integrating the item-side information into the recommendation model can alleviate the data sparsity problem and avoid the privacy issue. However, roughly fusing item-side information in item embeddings to optimize for final recommendation accuracy might lose the objective associations among items. Thus, this paper proposes a self-supervised learning task on item-side information to generate the item representation space in advance of the recommendation task. The space can gather similar items and separate the different items. Then we reform a recently successful sequential recommendation method, self-attention network, by integrating an untrainable mechanism to fully take advantage of the pre-trained item representation space. The two procedures comprise a hybrid recommendation framework to achieve collaborative filtering and content-based recommendation strategies. Extensive experiments conducted on two benchmark datasets demonstrate the superiority of our proposed methods over other baseline methods.\",\n \"Title: LCAN: Light Cross-Attention Network for Collaborative Filtering Recommendation\\nYear: 2022\\n\\nAbstract: In recent years, an emerging research work in recommendation systems aimed at exploring users' potential interaction preferences. However, most existing methods can only capture information about the user's purchase (or click) history. To estimate users' potential interaction preferences more accurately, it is necessary to consider auxiliary information when modeling user-item interactions. In this paper, a Light Cross-Attention Network (LCAN) is proposed. LCAN makes full use of existing information in three parts: 1) User-Item interaction graph. The interaction history is an important signal, and the user's interaction preferences can be obtained directly from the interaction history. 2) User-User and Item-Item relationships. The user-user and item-item graphs are additionally constructed based on the similarity between users and items to alleviate data sparseness. 3) Complementarity between graphs. Information between different graphs is interrelated, and a graph-level cross-attention network is used to capture the complementarity between graphs. Extensive experiments have been conducted by comparing state-of-the-art methods, and it shows that our LCAN model can outperform the most advanced recommendation methods.\",\n \"Title: Towards Robust Uncertainty Estimation in the Presence of Noisy Labels\\nYear: 2022\\n\\nAbstract: In security-critical applications, it is essential to know how confident the model is in its predictions. Many uncertainty estimation methods have been proposed recently, and these methods are reliable when the training data do not contain labeling errors. However, we find that the quality of these uncertainty estimation methods decreases dramatically when noisy labels are present in the training data. In some datasets, the uncertainty estimates would become completely absurd, even though these labeling noises barely affect the test accuracy. We further analyze the impact of existing label noise handling methods on the reliability of uncertainty estimates, although most of these methods focus only on improving the accuracy of the models. We identify that the data cleaning-based approach can alleviate the influence of label noise on uncertainty estimates to some extent, but there are still some drawbacks. Finally, we propose a robust uncertainty estimation method under label noise. Compared with other algorithms, our approach achieves a more reliable uncertainty estimates in the presence of noisy labels, especially when there are large-scale labeling errors in the training data.\",\n \"Title: Uncertainty-aware Propagation Structure Reconstruction for Fake News Detection.\\nYear: 2022\\n\\nAbstract: The widespread of fake news has detrimental societal effects. Recent works model information propagation as graph structure and aggregate structural features from user interactions for fake news detection. However, they usually neglect a broader propagation uncertainty issue, caused by some missing and unreliable interactions during actual spreading, and suffer from learning accurate and diverse structural properties. In this paper, we propose a novel dual graph-based model, Uncertainty-aware Propagation Structure Reconstruction (UPSR) for improving fake news detection. Specifically, after the original propagation modeling, we introduce propagation structure reconstruction to fully explore latent interactions in the actual propagation. We design a novel Gaussian Propagation Estimation to refine the original deterministic node representation by multiple Gaussian distributions and arise latent interactions with KL divergence between distributions in a multi-facet manner. Extensive experiments on two real-world datasets demonstrate the effectiveness and superiority of our model.\",\n \"Title: A Unified Propagation Forest-based Framework for Fake News Detection.\\nYear: 2022\\n\\nAbstract: Fake news’s quick propagation on social media brings severe social ramifications and economic damage. Previous fake news detection usually learn semantic and structural patterns within a single target propagation tree. However, they are usually limited in narrow signals since they do not consider latent information cross other propagation trees. Motivated by a common phenomenon that most fake news is published around a specific hot event/topic, this paper develops a new concept of propagation forest to naturally combine propagation trees in a semantic-aware clustering. We propose a novel Unified Propagation Forest-based framework (UniPF) to fully explore latent correlations between propagation trees to improve fake news detection. Besides, we design a root-induced training strategy, which encourages representations of propagation trees to be closer to their prototypical root nodes. Extensive experiments on four benchmarks consistently suggest the effectiveness and scalability of UniPF.\",\n \"Title: Time-Delay Memristive Recurrent Neural Network and Its Complex Dynamics\\nYear: 2022\\n\\nAbstract: This paper focuses on the hybrid effects of memristor characteristics, coupling coefficient and time-delay on the recurrent neural network. We construct a novel time-delay recurrent neural network based on a passive hyperbolic tangent memristor, whose instability condition at equilibrium points is judged according to the Routh criterion. Under the changes of the memristor internal parameter, coupling coefficient and time-delay, some complex dynamic behaviors, including chaos and various coexisting attractors, of the time-delay memristive recurrent neural network are observed in detail through the bifurcation diagram, the largest Lyapunov exponent, and the basin of attraction. Finally, the theoretical results are verified by the circuit simulation through MULTISIM.\",\n \"Title: Triangular FM-OFDM Waveform Design for Integrated Sensing and Communication\\nYear: 2022\\n\\nAbstract: The integrated sensing and communication (ISAC) technology will support the development of emerging services such as machine-type communication and immersive services toward the future 5th generation advanced (5G-A) and 6th generation (6G) eras. To satisfy the high-precision sensing requirements inspired by 6G emerging services and increase the communication rate, we exploit the advantages of large time-bandwidth product of linear frequency modulation (LFM) and high spectral efficiency of orthogonal frequency division multiplexing (OFDM), proposing an ISAC waveform combining triangular frequency modulation (FM) and OFDM. The delayDoppler decoupling radar signal processing scheme is proposed, which obtains better sensing performance than the OFDM based ISAC scheme. Simulation results show that the proposed scheme can achieve high accuracy ranging estimation, and the resolution of velocity estimation is doubly improved compared with the OFDM based ISAC scheme.\",\n \"Title: A 39pJ/label 1920x1080 165.7 FPS Block PatchMatch Based Stereo Matching Processor on FPGA\\nYear: 2022\\n\\nAbstract: Depth is a fundamental information for lots of computer vision applications. Stereo matching is a commonly used depth estimation method that mimics the human binocular vision system. Most stereo matching systems suffer when attempt to strike a balance between accuracy and computational complexity because of two reasons: 1) It is usually assumed that all the surfaces are fronto-parallel, meaning that neighbors share the same disparity. But a lot of real-world situations are slant surfaces like roads and walls. 2) Conventional methods usually utilize winner takes all (WTA) strategy [1]–[4], where aggregated costs in all disparity levels (usually 128 or 256) must be calculated. But there is only one true guess for each pixel position, such that most of the calculated costs are meaningless. To solve the above issues, in this work, a block PatchMatch-based FPGA accelerator for stereo matching is proposed. PatchMatch introduces random search strategy and slant label, where rectangle superpixels called blocks are used as the basic computing element. Main improvements of this work are: 1) Utilized random search strategy and block level computation can save massive computation. 2) Closer-to-reality slant label improves accuracy. Moreover, plane slant is also helpful for following tasks like 3D reconstruction [5], but none of the existing hardware accelerators can provide this information. 3) Algorithm-hardware co-optimized 6-points Census feature and multi-scale propagation (MSP) are proposed. 4) Based on the testing results on industrial-level KITTI dataset, the real-time performance and energy efficiency of the proposed design outperform state-of-the-art FPGA and ASIC designs, with comparable accuracy.\",\n \"Title: Linearity Analysis of Phase-Domain Delta-Sigma Modulator\\nYear: 2022\\n\\nAbstract: This paper presents a study on the input-output linearity of Phase-Domain Delta-Sigma Modulator (PDDSM). This study identifies that, besides the selection of phase references in the PDDSM feedback path which has been reported to affect linearity in prior art, there are additional design parameters which have significant impact on linearity, namely the quantizer sampling frequency (f\\n<inf xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">s</inf>\\n) and the input driving frequency ($f_{\\\\text{drive}}$). Comprehensive theoretical analysis of their impact on PDDSM’s linearity has been performed through analytical derivation and validated through behavioral model simulation in MATLAB Simulink™, where results are in good agreement with each other. This analysis shows that to achieve high linearity in PDDSM design, f\\n<inf xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">s</inf>\\n should equal to $2\\\\cdot f_{\\\\text{drive}}/\\\\mathbf{m}$ where m is a positive integer, and $f_{\\\\text{drive}}$ should be large enough as compared to the rate of change of input phase, e.g. about $ 10^{4}\\\\times$.\",\n \"Title: Text Similarity Measurement Method and Application of Online Medical Community Based on Density Peak Clustering\\nYear: 2022\\n\\nAbstract: Text similarity measurement is a link between basic research such as text modeling and upper-level application research of text potential information. In order to improve the accuracy of text similarity measurement, this paper proposes a semantic similarity calculation method integrating word2vec model and TF-IDF and applies it to the density peak clustering of Chinese text data consulted by patients in the online medical community. Experimental results show that the proposed similarity measurement method is superior to the traditional method. Furthermore, the study is among the first to apply the density peak clustering algorithm to the online medical community, which offers a reference for how to find user demands from medical text data in the big data environment.\",\n \"Title: A Random Forest Model for Drought: Monitoring and Validation for Grassland Drought Based on Multi-Source Remote Sensing Data\\nYear: 2022\\n\\nAbstract: The accuracy of drought monitoring models is crucial for drought monitoring and early warning. Random forest (RF) is being used widely in the field of artificial intelligence. Nonetheless, the application of a random forest model in grassland drought monitoring research is yet to be further explored. In this study, various drought hazard factors were integrated based on remote sensing data, including from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Precipitation Measurement (GPM), as multisource remote sensing data. Based on the RF, a comprehensive grassland drought monitoring model was constructed and tested in Inner Mongolia, China, as an example. The critical issue addressed is the construction of a grassland drought disaster monitoring model based on meteorological data and multisource remote sensing data by using an RF model, and the verification of the accuracy and reliability of its monitoring results. The results show that the grassland drought monitoring model could quantitatively monitor the drought situation in Inner Mongolia grasslands. There was a significantly positive correlation between the drought indicators output by the model and the standardized precipitation evapotranspiration index (SPEI) measured in the field. The correlation coefficients (R) between the drought degree were 0.9706 and 0.6387 for the training set and test set, respectively. The consistent rate between the model drought index and the SPEI reached 87.90%. Drought events in Inner Mongolia were monitored from April to September in wet years, normal years, and dry years using the constructed model. The monitoring results of the model constructed in this study were in accordance with the actual drought conditions, reflecting the development and spatial evolution of drought conditions. This study provides a new application method for the comprehensive assessment of grassland drought.\",\n \"Title: Fault diagnosis of rod pumping system based on deep conditional domain adaption network\\nYear: 2021\\n\\nAbstract: The generalization ability of traditional fault diagnosis methods is insufficient. This paper presents a fault diagnosis method of sucker rod pumping system based on deep condition domain adaption network (DCDAN). Firstly, the convolution neural network is used for feature extraction. Then, the weighted maximum mean discrepancy (WMMD) is used to adjust the characteristic distribution of relevant subclasses in different domains to realize the fine-grained domain adaptation of subclasses. At the same time, the fault classification ability of the model is guaranteed by optimizing the classification loss. The results show that this method can improve the generalization performance of the model in the target domain.\",\n \"Title: A Bayesian Approach for Joint Discriminative Dictionary and Classifier Learning\\nYear: 2022\\n\\nAbstract: Sparse representation has been widely applied to image classification, where the key issue is to extract a suitable discriminative dictionary. To this end, we propose a joint dictionary and classifier learning algorithm based on a parameterized Bayesian model. Therein, the Gaussian priors of a dictionary endow it with the capability of discrimination and representation. Moreover, we introduce a multivariate Gaussian prior for the sparse codes to achieve group sparsity, thereby substantially improving the classification performance. Furthermore, the sparse codes are estimated by a group-sparse Bayesian learning (GSBL) method, and the dictionary atoms are updated sequentially by maximizing a posterior. Moreover, to avoid manual parameter adjustment, the hyperparameters are optimized by an evidence maximization method. Accordingly, we develop a classification scheme via GSBL. Finally, extensive experiments are conducted on six benchmark datasets of face classification, object recognition, handwritten recognition, and scene categorization to substantiate the effectiveness and superiority of the proposed method.\",\n \"Title: Intergroup Cascade Broad Learning System With Optimized Parameters for Chaotic Time Series Prediction\\nYear: 2022\\n\\nAbstract: The prediction of chaotic time series can well describe the physical properties and influencing factors of a sequence of numerical data points in successive order and has played an increasingly crucial role in various scientific and engineering communities. These time series data require the prediction model to have strong dynamic feature representation ability, which often leads to excessive calculation burden. As an emerging and effective learning method, a broad learning system (BLS) can be trained quickly by incremental learning, and the system can be reconstructed without requiring a lengthy retraining process, which has been shown to facilitate superior time-saving performance. However, the historical information of time series data is often ignored, resulting in the lack of dynamic feature representation ability of the original BLS algorithm. In this article, a novel intergroup cascade BLS (ICBLS) method and its variant with optimized parameters, termed MOICBLS, are proposed, which can combine the previous knowledge with current information to determine the output results by constructing a novel intergroup cascade structure for the enhancement nodes. The new algorithms try to retain the time-saving advantage of BLS and greatly enhance the extraction ability of dynamic features of chaotic time series data. In addition, it cannot be ignored that the network structure parameters have an unquestionable impact on the performance of a BLS. In response, two conflicting goals of prediction accuracy and diversity are formulated as multiobjective optimization functions in the training phase to generate the optimized network structure parameters needed by multiple candidate models. The experimental results on two benchmark chaotic time series dataset, the Lorenz system and Rossler system, and a geomagnetic disturbance storm time chaotic time series dataset demonstrate that our proposed algorithms are effective for prediction of chaotic time series, and the prediction accuracy is better than some existing approaches.\",\n \"Title: Mining Weak Relations Between Reviews for Opinion Spam Detection\\nYear: 2023\\n\\nAbstract: Online reviews play a significant role in purchase decisions of consumers by providing feedback information from buyers of products. In order to mislead consumers, opinion spammers are hired to write fake reviews to promote or demote specific products for illegitimate benefits. Existing methods for spam review detection mainly focused on designing manual features, which highly rely on expert knowledge. Although recent works utilized deep learning methods to automatically learn the semantics of reviews through the inherent user-review-product strong relation, they fail to capture the weak relations between reviews at the content, sentiment and temporal levels, which provides various semantic information to expose fake reviews. Moreover, the imbalanced class distribution in spam detection issues makes this work even more challenging. To address the above problems, we propose a novel Weak-Strong Unified Network (WSUN) for opinion spam detection. Multi-level weak relation graphs are constructed to reveal the abnormal behavioral patterns of spammers, which aggregates the semantics of strong relations by graph convolutional networks and extracts comprehensive review representation by utilizing relation-level attention mechanism. In addition, a graph-based over-sampling method is devised to mitigate the impact of imbalanced class distribution. Extensive experimental results on real-world datasets show that our model is more effective than the state-of-the-art methods.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":200,"string":"200"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13673,"string":"13,673"}}},{"rowIdx":13674,"cells":{"question_id":{"kind":"string","value":"Yuichi Tanaka"},"question":{"kind":"string","value":"Generate an abstract for the title \"A simplified lattice structure of first-order linear-phase filter banks\" using the following items: 1. Simplified lattice structure \n2. First-order linear-phase filter banks (FOLPFBs) \n3. Reduced redundant parameters \n4. Regularity for improved perceptual quality \n5"},"answer":{"kind":"string","value":"A simplified lattice structure for first-order linear-phase filter banks (FOLPFBs) is presented in this paper. A FOLPFB is a gen- eralized version of biorthogonal linear-phase filter banks regarding their synthesis filter lengths. FOLPFBs' structure is more compli- cated and has more parameters than that in other FBs. We propose a method to reduce their redundant parameters without losing their properties. Moreover, regularity can be imposed which reduces the design freedom as well as improves the perceptual quality in image coding."},"correct_answer":{"kind":"string","value":"A simplified lattice structure for first-order linear-phase filter banks (FOLPFBs) is presented in this paper. A FOLPFB is a gen- eralized version of biorthogonal linear-phase filter banks regarding their synthesis filter lengths. FOLPFBs' structure is more compli- cated and has more parameters than that in other FBs. We propose a method to reduce their redundant parameters without losing their properties. Moreover, regularity can be imposed which reduces the design freedom as well as improves the perceptual quality in image coding."},"documents":{"kind":"list like","value":["Title: Flexible Combination Of Time-Domain Lapped Transforms With Various Downsampling Factors\nYear: 2012\n\nAbstract: In this paper, we present a method to combine lapped transforms with various downsampling factors. The factor is changed depending on a local feature of a given signal, and it can be realized by using time-domain lapped transforms. In image coding application, our method maintains good image coding performance for a wide range of bitrates and fills the gap between undersampled and critically-/oversampled systems.","Title: Higher-order feasible building blocks for lattice structure of oversampled linear-phase perfect reconstruction filter banks\nYear: 2009\n\nAbstract: This paper proposes new building blocks for the lattice structure of oversampled linear-phase perfect reconstruction filter banks (OLPPRFBs). The structure is an extended version of higher-order feasible building blocks for critically sampled LPPRFBs. It uses fewer number of building blocks and design parameters than those of traditional OLPPRFBs, whereas frequency characteristics of the new OLPPRFBs are comparable to those of traditional one. Furthermore, the building block structures for arbitrary number of channels and downsampling factors are presented.","Title: Mixed-Resolution Wyner-Ziv Video Coding Based On Selective Data Pruning\nYear: 2011\n\nAbstract: In current distributed video coding (DVC), interpolation is performed at the decoder and the interpolated pixels are reconstructed by using error-correcting codes, such as Turbo codes and LDPC. There are two possibilities for downsampling video sequences at the encoder: temporally or spatially. Traditionally temporal downsampling, i.e., frame dropping, is used for DVC. Furthermore, those with spatial downsampling (scaling) have been investigated. Unfortunately, most of them are based on uniform downsampling. Due to this, details in video sequences are often discarded. For example, edges and textured regions are difficult to interpolate, and thus require many parity bits to restore the interpolated portions for the spatial domain DVC. In this paper, we propose a new spatial domain DVC based on adaptive line dropping so-called selective data pruning (SDP). SDP is a simple nonuniform downsampling method. The pruned lines are determined to avoid cutting across edges and textures. Experimental results show the proposed method outperforms a conventional DVC for sequences with a large amount of motions.","Title: Design of M-Channel Perfect Reconstruction Filter Banks with IIR-FIR Hybrid Building Blocks\nYear: 2007\n\nAbstract: This paper discusses a new structure of M-channel IIR perfect reconstruction filter banks. A novel building block defined as a cascade connection of some IIR building blocks and FIR building blocks is presented. An IIR building block is written by state space representation, where we easily obtain a stable filter bank by setting eigenvalues of the state transition matrix into the unit circle. Due to cascade connection of building blocks, we are able to design a system with a larger number of free parameters while keeping the stability. We introduce the condition which obtains the new building block without increasing of the filter order in spite of cascade connection. Additionally, by showing the simulation results, we show that this implementation has a better stopband attenuation than conventional methods.","Title: Adaptive color-to-gray image mapping using directional transform.\nYear: 2012\n\nAbstract: Several color-to-gray mapping methods were proposed to restore a color image from its corresponding grayscale image. These methods are based on embedding chrominance signals into high-frequency subbands of the luminance signal. In the conventional methods, only separable filters are used for the subband decomposition. In addition, subbands at fixed positions are always used to embed chrominance signals. However, in many images, diagonal components are included as well as horizontal/vertical ones. Therefore, qualities of chrominance-embedded grayscale and restored color images are sometimes degraded especially for images with rich textures. In this paper, we propose an improved color restoration method using a directional transform named hybrid wavelets and directional filter banks which considers directionality in images. Experimental results show our method improves the image qualities of the restored color images as well as the chrominance-embedded grayscale images.","Title: DESIGN OF UNEQUAL-LENGTH LINEAR-PHASE FILTER BANKS WITHOUT REDUNDANCY\nYear: 2005\n\nAbstract: In this paper,we presenta new structure for linear-phasefilter banks without redundancy, which have unequal-length filters at each sub- band. First, we extend the simplified lattice structure of the linear- phase filter bank to the unequal-length linear-phase paraunitary fil- ter bank. In general, the unequal-length linear-phase paraunitary filter bank has equal-length filters at both the analysis and the syn- thesis bank because the synthesis bank is a transposed version of the analysis one, while the biorthogonal one does not. So, we dis- cussthe conditions that thelinear-phasebiorthogonalfilter bankhas unequal-length filters at 1) the analysis bank, 2) the synthesis bank, and 3) both the analysis and the synthesis banks. Finally, several design and image coding examples are shown.","Title: A Bottom-Up Design Approach Of Critically Sampled Contourlet Transform For Efficient Image Representation\nYear: 2009\n\nAbstract: In this paper. a novel design method of critically sampled contourlet transform (CSCT) is proposed. The original CT which consists of Laplacian pyramid and directional filter bank provides efficient frequency plane partition for image representation. However its overcompleteness is not suitable for sonic applications such as image coding. its critical sampling version has been Studied recently. Although several types of the CSCT have been proposed. they have problems on their realization or unnatural frequency plane partition which is different front the original CT. In contrast to the way in conventional design methods based on a \"top-down\" approach. the proposed method is based on a \"bottom-up\" one. That is, the proposed CSCT decomposes the frequency plane into small directional subbands, and then synthesizes them up to a target frequency plane partition, while the conventional ones decompose into it directly, By this way, the proposed CSCT can design an efficient frequency division which is the same as the original CT tor image representation can be realized. In this paper. its effectiveness is verified by non-linear approximation simulation.","Title: Image Coding Using Concentration And Dilution Based On Seam Carving With Hierarchical Search\nYear: 2010\n\nAbstract: Image concentration is an image resizing technique which shrinks an image size but does not change important region(s) in the original image, whereas image dilution is the reverse process to image concentration. In this paper, we incorporate image concentration/dilution process in image coding. An image retargeting method called seam carving is used for image concentration. Furthermore, it is modified to yield rate-distortion optimized seams by a hierarchical search process. Our method can be incorporated into any image encoder since the image concentration (dilution) is a pre(post)processing of image encoder (decoder). In the experimental results, JPEG/SPIHT with image concentration/ dilution presents significant bitrate savings compared with the original JPEG/SPIHT alone and reconstructed image qualities are very similar to each other.","Title: Highpass-filtering based adaptive directional wavelet transform.\nYear: 2009\n\nAbstract: In this paper, a calculation method of transform directions for adaptive directional wavelet transform (AD WT) is proposed. It uses 2-D highpass filters as a preprocessing of the conventional calculation stage. The filters can reduce the number of direction candidates effectively. In image coding, our proposed framework shows very comparable results to the conventional AD WT and outperforms the traditional separable WT.","Title: An Adaptive Extension Of Combined 2d And 1d-Directional Filter Banks\nYear: 2009\n\nAbstract: In this paper, we propose an extension of combined 2D and 1D-directional filter banks (TODFBs) with adaptive approach in their 1D-directional stages. TODFBs show better performance in image coding and denoising compared to the traditional wavelet transform (WT), however, they still have possibilities to improve their performance in image coding by adopting the adaptive directional WTs for their 1D-directional stages. An efficient method to determine the transform directions is also proposed. In image coding results, our proposed filter banks gain PSNR and visual quality improvements compared with the WTs and the non-adaptive TODFBs.","Title: Selective Data Pruning Based Distributed Video Coding With Modified High-Order Edge-Directed Interpolation\nYear: 2013\n\nAbstract: In distributed video coding (DVC), a low resolution video sequence is usually generated with spatial and/or temporal downsampling at an encoder. At a decoder side, interpolation is performed and the interpolated pixels are further refined by using an error-correcting code such as Turbo codes or LDPC. In our previous work, we proposed a spatial domain DVC which uses a line-based downsampling method called selective data pruning (SDP). SDP is used for reducing the spatial frame size before compression. After decoding, received frames are decoded and interpolated back to their original size by high-order edge-directed interpolation (HEDI). In this paper, we propose a modified high-order edge-directed interpolation (M-HEDI) to avoid interpolation artifacts. Experimental results show that the interpolated frames by using M-HEDI have higher quality than those using HEDI. The proposed method outperforms conventional DVCs and the video coding standard H.264/AVC in majority of all the test video sequences.","Title: Multiplierless Lifting Based Fft Via Fast Hartley Transform\nYear: 2013\n\nAbstract: The multiplierless fast Fourier transform (FFT) with dyadic-valued (rational) coefficients is important for many signal processing tools. The proposed lifting based FFT (L-FFT) based on fast Hartley transform (FHT) has a simpler structure than existing ones because fewer lifting steps need to be approximated. In addition, it has a structure of real-valued calculation followed by complex-valued parts, thereby it requires fewer memories for the internal implementation than the conventional FFTs.","Title: Oversampled linear-phase perfect reconstruction filter banks with higher-order feasible building blocks: Structure and parameterization\nYear: 2008\n\nAbstract: This paper proposes new building blocks for the lattice structure of oversampled linear-phase perfect reconstruction filter banks (OLPPRFBs). The structure is an extended version of higher-order feasible building blocks for critically-sampled LPPRFBs. It uses fewer number of building blocks and design parameters than those of tra- ditional OLPPRFBs, whereas the frequency characteristic of the new OLPPRFB is comparable to that of traditional one. I. INTRODUCTION Oversampled filter bank (OFB) is one of the most developing issues in the field of FB theory (1)-(3). OFB is a superclass of critically- sampled FBs since its polyphase matrix has the size P ×M ( P> M) which covers entire M × M classes for critically-sampled ones. They have more design freedom than the critically-sampled FBs, thus they are expected to have good frequency characteristics and/or robustness against errors. Additionally, linear-phase (LP) and perfect reconstruction (PR) properties are usually highly desired in practical signal processing framework (4), (5). In this paper, we consider to derive a new structure of OLPPRFBs. Gan et al. investigated lattice structures for OLPPRFBs and proved the necessary conditions which are regarded as the extended version of those for critically-sampled LPPRFBs (2). However, their structure has a P × M rectangular starting lattice block. Following the block, critically-sampled P -channel building blocks are cascaded to yield desired filter length L = KM in spite of the downsampling factor M. Hence, the calculation cost is much higher than the critically- sampled M -channel ones. Liang et al. proposed a subset of Gan's structure which is pre- and postprocessing system of a P × M oversampling matrix (6). The alternative structure helps to decrease the calculation cost for long filters. However, most of the previous works on the lattice structure of OLPPRFBs are based on cascading order-1 building blocks (2), (6). It is effective in the viewpoint of achieving any-order FBs. Unfortunately, the high-order FB requires many order-1 building blocks which increase the implementation cost. The authors proposed a lattice component of critically-sampled biorthogonal (BO) LPFBs (7) called higher-order feasible (HOF) building block. It realizes an order-N (N ≥ 2) LPPRFB with one HOF building block. Additionally, it is also regarded as an extended class of traditional (critically-sampled) order-1 building blocks. The advantage of HOF building blocks against cascaded order-1 ones is its ability to realize comparable performance with fewer number of building blocks and design parameters for the same filter lengths. We extend the HOF building block from critically-sampled to oversampled LPPRFBs. It inherits all the merits on critically-sampled HOF structure, furthermore, it has an attractive feature of a P × M rectangular oversampling matrix which exists particularly on the HOF structure. The parameterization which is a specific method for the HOF building blocks is described in the paper. In the design example, our OLPPRFB based on HOF buiding blocks shows the comparable frequency characteristic to that based on cascaded order-1 structure in spite of fewer number of design parameters. Remaining parts of this paper are organized as follows: Section II reviews traditional structures of OLPPRFBs and critically-sampled HOF building blocks. Section III describes the oversampled HOF structure. Section IV validates our proposed structure by comparing with the traditional one. Finally, the paper concludes in Section V. Notations: �·� and �·� define the functions which return ceiling and floor of (·), respectively. We also define the special matrices of size S ×S; IS is the identity matrix, JS is the reversal identity matrix and 0S is the null matrix. The sizes will be omitted if they are obvious.","Title: Theory And Design Of Two-Channel Complex Linear-Phase Pseudo-Orthogonal Filterbanks\nYear: 2006\n\nAbstract: In recent years, two-channel complex-valued filterbanks have been studied and found theirs several important applications by many reseachers, such as complex signal and image processing. One of those important results is that there is no twochannel complex-valued linear-phase paraunitary filterbank (CLPPUFB), except for its filter lengths of 2. In this paper, we introduce a class of special complex-valued filterbanks which is a subclass of biorthogonal filterbanks: Those filterbanks are called complex pseudo-orthogonal filterbanks (CPOFB), based on the concept of pseudo-orthogonality (PO). This kind of orthogonality, we propose, is different from the conventional one essentially. This paper also shows possibility to design a two-channel complex linear-phase filterbank (CLPFB) with its filter lengths are more than 2, based on PO. Finally, we show a design example of a complex linear-phase pseudo-orthogonal filterbank (CLPPOFB). Moreover, such CPOFB can satisfy the linear-phase condition simultaneously with filter lengths are more than 2. This paper shows a theory, a design method and an example of CLPPOFB.","Title: Multiresolution image representation using combined 2-D and 1-D directional filter banks.\nYear: 2009\n\nAbstract: In this paper, effective multiresolution image representations using a combination of 2-D filter bank (FB) and directional wavelet transform (WT) are presented. The proposed methods yield simple implementation and low computation costs compared to previous 1-D and 2-D FB combinations or adaptive directional WT methods. Furthermore, they are nonredundant transforms and realize quad-tree like multiresolution representations. In applications on nonlinear approximation, image coding, and denoising, the proposed filter banks show visual quality improvements and have higher PSNR than the conventional separable WT or the contourlet.","Title: General Factorization of Conjugate-Symmetric Hadamard Transforms\nYear: 2014\n\nAbstract: Complex-valued conjugate-symmetric Hadamard transforms ( $\\BBC $-CSHT) are variants of complex Hadamard transforms and found applications in signal processing. In addition, their real-valued transform counterparts ($\\BBR $-CSHTs) perform comparably with Hadamard transforms (HTs) despite their lower computational complexity. Closed-form factorizations of $\\BBC $-CSHTs and $\\BBR $-CSHTs have recently been proposed to make calculations more efficient. However, there is still room to find effective and general factorizations. This paper presents a simple closed-form complete factorization of $\\BBC $-CSHTs based on that of $\\BBR $ -CSHTs. The proposed factorization can be applied to both $\\BBC $- and $\\BBR $-CSHTs with one factorization and it provides several benefits: 1) It can save total implementation costs for both $\\BBC $ -CSHTs and $\\BBR $-CSHTs; 2) the generalized $\\BBR $-CSHT factorization significantly reduces its computational cost; 3) memory-saved local orientation detection of images can be achieved; 4) a fast direction-aware transform can be attained; 5) it clarifies that $\\BBC $- and $\\BBR $-CSHTs are closely related to common block transforms, such as the discrete Fourier transform (DFT), binDCT, and HT; and 6) it achieves a new integer complex-valued transform, which can approximate the DFT better than the original $\\BBC $-CSHT. The image orientation estimation and performance in image coding of our $\\BBR $-CSHTs were evaluated through examples of practical applications based on the proposed factorization.","Title: Smoothing of spatial filter by graph Fourier transform for EEG signals\nYear: 2014\n\nAbstract: Spatial filtering is useful for extracting features from multichannel EEG signals. In order to enhance robustness of the spatial filter against low SNR and small samples, we propose a smoothing method for the spatial filter using spectral graph theory. This method is based on an assumption that the electrodes installed in nearby locations observe the electrical activities of the same source. Therefore the spatial filter's coefficients corresponding to the nearby electrodes are supposed to be taken similar values, that is, the coefficients should be spatially smooth. To introduce the smoothness, we define a graph whose edge weights represent the physical distances between the electrodes. The spatial filter spatially smoothed is found out in the subspace that is spanned by the smooth basis of the graph Fourier transform. We evaluate the method with artificial signals and a dataset of motor imagery brain computer interface. The smoothness of the spatial filter given by the method provides robustness of the spatial filter in the condition that the small amount of the samples is available.","Title: Trilateral filter on graph spectral domain\nYear: 2014\n\nAbstract: This paper presents the trilateral filter (TF) in the perspective of graph signal processing. The TF is a single-pass nonlocal filter for edge-preserving smoothing. To smooth an image, it does not require many iterations compared to conventional smoothing methods, e.g., the bilateral filter. Additionally, one parameter is only required for filtering. Since the TF coefficients depend on original image data, it is not possible to provide a frequency domain representation using regular signal processing. To overcome this problem, we firstly show the TF as a vertex domain transform on a graph and then define it on graph spectral domain. In the experimental results, the proposed method presents better denoising performances than conventional methods.","Title: M-channel oversampled perfect reconstruction filter banks for graph signals\nYear: 2014\n\nAbstract: This paper proposes M-channel oversampled filter banks for graph signals. The filter set satisfies the perfect reconstruction condition. A method of designing oversampled graph filter banks is presented which allows us to design filters with arbitrary parameters, unlike the conventional critically-sampled graph filter banks. The practical performance of the proposed filter banks is validated through graph signal denoising experiments.","Title: Oversampled graph laplacian matrix for graph signals\nYear: 2014\n\nAbstract: In this paper, we propose oversampling of graph signals by using oversampled graph Laplacian matrix. The conventional critically sampled graph filter banks have to decompose an original graph into bipartite subgraphs, and the transform has to be performed on each subgraph due to the spectral folding phenomenon caused by downsampling of graph signals. Therefore, they cannot always utilize all edges of the original graph for the one-stage transformation. Our proposed method is based on oversampling of the underlying graph itself, and it can append nodes and edges to the graph somewhat arbitrarily. We use this approach to make one oversampled bipartite graph that includes all edges of the original non-bipartite graph. We apply the oversampled graph with the critically sampled filter bank for decomposing graph signals, and show the performance of graph signal denoising.","Title: Iterative edge-directed image upscaling with high directionality\nYear: 2014\n\nAbstract: Variety of image upscaling methods has been proposed and used in computer tools due to the increasing number of practical applications. The essential objectives of image upscaling are: it should be fast, natural to human perception and does not yield annoying artifacts. This paper proposes an edge-directed iterative image interpolation based on the second-order derivatives along multiple directions. Our method uses the assumption that the second-order derivatives of the image intensities are continuous along the interpolation directions. We first initialize the pixel values based on a grid filling and iteratively modify the interpolated pixels by minimizing an objective function depending on the second-order directional derivatives. Especially our method provides better results in edge regions while the computation time is reduced compared to other existing upscaling methods. Image coding with the proposed interpolation method is also performed as a possible application.","Title: Depth map denoising using collaborative graph wavelet shrinkage on connected image patches\nYear: 2014\n\nAbstract: In this paper, we propose a new patch-based image denoising algorithm using graph signal processing. The concept of this algorithm is to take advantage of the redundancy of the BM3D transform and the edge preservation property of graph-based image processing. More specifically, we collect similar patches in the image, and construct a graph by connecting obtained patches. Then we apply a graph wavelet filter bank on graph signals to attenuate additive white gaussian noise by shrinking derived coefficients. We apply our proposed algorithm to depth map denoising. The experimental results demonstrate significant performance gains for the edge preservation and the noise reduction.","Title: Edge-aware image graph expansion methods for oversampled graph Laplacian matrix\nYear: 2014\n\nAbstract: Graph signals can represent high-dimensional data effectively and images can also be viewed as signals on weighted graphs by connecting the pixels with their neighboring ones. Recently, we proposed the graph oversampling methods for signal processing on graphs that appends nodes and links to the original graph to obtain an oversampled graph Laplacian matrix. In this paper, we consider new over-sampling methods of image graphs. By using the graph oversampling, we can make a bipartite graph that considers rectangular and diagonal connections simultaneously, while it cannot be realized by conventional critically sampled bipartite graphs. Furthermore, expanding the graphs according to the edge information enables us to decompose the image with the edge-preserving property. We perform the critically sampled graph filter bank on the oversampled graph and show that the proposed method outperforms other transforms, including the critically sampled graph filter banks and the graph Laplacian pyramid, in non-linear approximation and denoising experiments.","Title: Multidimensional linear-phase perfect reconstruction filter banks with higher order feasible building blocks\nYear: 2014\n\nAbstract: In this paper, we propose multidimensional (MD) linear-phase perfect reconstruction filter banks (LPPRFBs) with higher order feasible (HOF) building blocks. Since existing HOF building blocks are only applicable for one-dimensional LPPRFBs, we generalize them to MD LPPRFBs. The generalized structure can design MD LP-PRFBs with HOF building blocks for both even and odd number of channels. The proposed MD-LPPRFBs realize large filter sizes with fewer number of building blocks than the traditional order-1 structures.","Title: Wireless optical transmissions with white colored LED for wireless home links\nYear: 2000\n\nAbstract: We propose a wireless optical communication system with white colored LEDs for a wireless home link (WHL). The white colored LEDs have a high power output and are regarded as lamps for the next generation. In the proposed system, this device is used for a wireless home link. The proposed system is suitable for private networks such as consumer communication networks. From numerical and simulation results, it is confirmed that the proposed system is available and the problems to be solved are made clear","Title: A lattice structure of biorthogonal linear-phase filter banks with higher-order feasible building blocks\nYear: 2008\n\nAbstract: This paper proposes a new structure of biorthogonal linear-phase filter banks (BOLPFBs) by using building blocks which can be re- alized long filters with fewer number of building blocks than con- ventional ones. The structure is derived from a generalization of the building blocks of first-order LPFBs. The resulting FBs have good performance in stopband attenuation and low implementation costs.","Title: A novel design of criticially sampled contourlet transform and its application to image coding\nYear: 2008\n\nAbstract: In this paper, a novel design method of critically sampled contourlet transform (CSCT) is proposed. Although, several types of CSCT have been proposed, they have some problems on efficiency and flexibility of their frequency plane parti- tion patterns. In contrast to the way in conventional design methods based on a \"top-down\" approach, the proposed one is based on a \"bottom-up\" one. That is, the proposed CSCT decomposes the frequency plane into small directional sub- bands, and then synthesizes them up to a target frequency plane partition, while the conventional ones decompose into it directly. By this way, the proposed CSCT can provide an efficient and flexible frequency plane partition for image cod- ing.","Title: Generalized selective data pruning for video sequence\nYear: 2011\n\nAbstract: In this paper, we extend a rate-dependent content-aware image retargeting method, generalized selective data pruning, to video sequence. It is regarded as a spatial-domain video retargeting, i.e., reduction of the spatial video size, with a rate-dependent property for side information. It is realized by the piecewise linear approximation of seam carving. The side information bitrate for video retargeting can vary depending on the control parameter and is negligible to transmit with the encoded video bitstream while the method maintains video retargeting performance.","Title: A structural similarity assessment for generating hybrid images\nYear: 2011\n\nAbstract: We discuss a structural similarity evaluation method in order to generate hybrid images for graphical password. The hybrid image consists of two images. One is edge information of a key image and the other is coarse features of a decoy image. Visibility of the hybrid image depends on the viewing distance and the structural similarity of the images. This property of the hybrid image is useful for preventing shoulder-surfing of graphical password. However, an evaluation method for checking whether the decoy image is appropriate to overlay the key image has not been established. To address this issue, we utilize Speeded Up Robust Features (SURF) to evaluate structural similarity between two images. Simulation results show that our similarity measure mostly coincides with the visibility of a key image. We also compare our objective metric with the structural similarity (SSIM) index and subjective scores obtained from a user test based on Scheffe's method of paired comparisons.","Title: On factorizations of conjugate symmetric Hadamard transform and its relationship with DCT\nYear: 2012\n\nAbstract: Complex-valued conjugate symmetric Hadamard transform (C-CSHT) is a variant of complex Hadamard transform and effective for some signal processing and communication applications. Its closed-form factorization of the general N-channel (N = 2m) case was recently proposed, however, there still exist a room to find an effective factorization especially for unified factorization of C-CSHT and its real-valued transform counterpart (R-CSHT). In this paper, we present another simple closed-form factorization of C-CSHT based on that of R-CSHT. The proposed factorization is applicable for both complex- and real-valued CSHTs with one factorization. Furthermore, the relationship with the common block transform, DCT, is revealed.","Title: Multiplierless fast algorithm for DCT via fast Hartley transform\nYear: 2012\n\nAbstract: Discrete cosine transform (DCT) is known as efficient frequency transform, and when it is implemented on software/hardware, multiplier is undesirable for faster implementation. This paper presents a realization of multiplierless fast DCT for lossy image/video coding on arbitrary devices. First, the proposed DCT is constructed by using fast Hartley transform (FHT). Next, the redundancy of the structure is eliminated by using several characteristics of rotation matrix. Then, multiplierless DCT is obtained by approximating rotation matrices to multiplierless lifting structures with adders and bit-shifters. Finally, the proposed DCT is validated by comparing with the conventional DCTs in image coding.","Title: Improved adaptive color embedding and recovery using DC level shifting\nYear: 2013\n\nAbstract: Several color-to-gray image mapping (CGIM) methods were proposed so far to restore a color image from its corresponding printed grayscale image. They are based on embedding chrominance values into high-frequency subbands of the luminance signal. In the conventional methods, textures often become noticeable in the chrominance-embedded gray image since subsampled chrominance values are embedded directly. As a result, qualities of the chrominance-embedded grayscale images are degraded especially for images with rich textures. In this paper, we propose an improved CGIM method using DC level shifting and directional transform. In the experimental results, our method greatly improves the qualities of both the chrominance-embedded grayscale and the restored color images in comparison with conventional methods.","Title: Improved image concentration for artifact-free image dilution and its application to image coding\nYear: 2010\n\nAbstract: In this paper, an improved method of image concentration for image coding with concentration and dilution is presented. Image concentration is an image resizing technique which shrinks an image size but does not change important region(s) in the original image, whereas image dilution is the reverse process to image concentration. The authors have proposed an image coding approach based on the image concentration and dilution. This paper focuses on an improvement of image concentration to enhance quality of the reconstructed (diluted) image. In the experimental results, the improved method obtains bitrate savings compared with image encoders alone, and the reconstructed images do not have perceptually noticeable artifacts.","Title: VQ based data hiding method for still images by tree-structured links\nYear: 2010\n\nAbstract: In this paper, we propose a data embedding method into still images based on Vector Quantization (VQ). In recent years, several VQ-based data embedding methods have been proposed. For example, `Mean Gray-Level Embedding method (MGLE)' are `Pair wise Nearest-Neighbor Embedding method (PNNE)' are simple, but not sufficiently effective. Meanwhile, an efficient adaptive data hiding method called `Adaptive Clustering Embedding method (ACE)' was proposed, but is somewhat complicated because the VQ indices have to be adaptively clustered in the embedding process. In our proposed method, output vectors are considered as nodes, and nodes are linked as a tree structure and information is embedded by using some of linked vectors. The simulation results show that our proposed method indicates higher SNR than the conventional methods under the same amounts of embedded data.","Title: A two-dimensional non-separable implementation of dyadic-valued cosine-sine modulated filter banks for low computational complexity\nYear: 2013\n\nAbstract: This paper aims to design a new two-dimensional (2D) non-separable implementation of dyadic-valued cosine-sine modulated filter banks (2D D-CSMFBs) for low computational complexity. CSMFBs satisfy rich directional selectivity (DS) and shift-invariance (SI), and they can be easily designed by the modulation of a prototype filter. In addition, our previous work introduced a one-dimensional (1D) D-CSMFBs (1D D-CSMFBs). By restricting real-valued filter coefficients to rational-valued ones, they can save computational cost while keeping DS and SI. The proposed 2D implementation in this paper can further reduce the computational cost by unifying the conventional 2D separable structure into a 2D non-separable one directly. Furthermore, experimental results of image non-linear approximation show that the proposed 2D D-CSMFBs are comparable or even better than the 1D D-CSMFBs.","Title: Design of optimized prefilters for time-domain lapped transforms with various downsampling factors\nYear: 2014\n\nAbstract: In image compression, decimation and interpolation techniques as pre-/postprocessing of a compression framework offer excellent energy compaction capability for low bit rate image coding. However, an interpolation filter cannot be defined as the inverse matrix of the given decimation filter since the former is a wide matrix and the latter is a tall one. Even without any compression, there will be some distortions in the reconstructed image. To tackle the problem, interpolation-dependent image downsampling (IDID) method produces optimized downsampled images, which leads to the optimized prefilter of a given postfilter. In this paper, we propose to integrate IDID with time-domain lapped transforms (TDLTs) to improve image coding performances.","Title: Retargeting pyramid using direct decimation\nYear: 2014\n\nAbstract: The retargeting pyramid (RP) is a multiscale image pyramid using content-aware image resizing. In the previous implementation of the RP, a two-step interpolation is adopted to obtain the desired resolution. However, this interpolation leads to performance loss for image processing. In this paper, we improve the performance of the RP by replacing the two-step interpolation with a single interpolation using a matrix representation of the bilateral filter and Tikhonov regularization.","Title: Seam carving with rate-dependent seam path information\nYear: 2011\n\nAbstract: In this paper, we present a seam carving, which is a well-known content-aware image resizing method, with a constraint on bitrates for seam path information (SPI). The SPI corresponds to pixel positions to be pruned and it generally requires high bitrate when we store or transmit it. However, the SPI should be transmitted to re ceivers in the case that the receivers are devices with low computing power, such as cell phones and PDAs since seam carving at the receivers is a computationally-demanding process. We resolve the problem by applying piecewise linear approximation to the seam paths. In the experimental results, the retargeted images yielded by the original seam carving and the proposed one are very similar, whereas required bitrate for the SPI in the proposed method is 34-97 % less than the original one.","Title: Oversampled Graph Laplacian Matrix for Graph Filter Banks\nYear: 2014\n\nAbstract: We describe a method of oversampling signals defined on a weighted graph by using an oversampled graph Laplacian matrix. The conventional method of using critically sampled graph filter banks has to decompose the original graph into bipartite subgraphs, and a transform has to be performed on each subgraph because of the spectral folding phenomenon caused by downsampling of graph signals. Therefore, the conventional method cannot always utilize all edges of the original graph in a single stage transformation. Our method is based on oversampling of the underlying graph itself, and it can append nodes and edges to the graph somewhat arbitrarily. We use this approach to make one oversampled bipartite graph that includes all edges of the original non-bipartite graph. We apply the oversampled graph with the critically sampled graph filter bank or the oversampled one for decomposing graph signals and show the performances on some experiments.","Title: Lifting-based paraunitary filterbanks for lossy/lossless image coding\nYear: 2006\n\nAbstract: This paper introduces one of the image transform methods using M-channel paraunitary filterbanks (PUFBs) based on Householder matirx. First, redundant parameters of PUFB are eliminated by using the fact that they can be factorized into Givens rotation matices. Next, we propose an eliminating redundant parameters method based on Householder matrix using relationship between Givens rotation and Householder matrices. In addition, PUFBs are factorized into the lifting structure for lossless image coding, and we call them as lifting-based PUFBs (LBPUFBs). LBPUFBs based on Householder matrix have less number of rounding operators than Givens rotation matrix version, since proposed structure is efficiency for lossless image coding. Finally, we show some exsamples to validate our method in lossy/lossless image coding.","Title: A novel image coding scheme by using two-channel complex-valued filter banks\nYear: 2007\n\nAbstract: A novel image coding scheme by using two-channel complex-valued filter banks (CFBs) is proposed in this paper. Up to now, most of the image coding schemes have used real-valued filter banks (RFBs). In other words, there are no efficient image coding framework using CFBs since the amount of samples transformed by CFBs is twice as much as that of RFBs. However, CFBs are expected to have better frequency characteristics than RFBs, since CFBs have more design parameters than that of RFBs. Thus, if the problem is solved, CFBs could be efficient for image coding. In this paper, a new algorithm which can preserve the amount of information when CFBs are applied to image coding is introduced. It is shown that the proposed image coding scheme using CFBs is superior to conventional real-valued wavelet-based image coding.","Title: Design of M-channel perfect reconstruction filterbanks with IIR-FIR hybrid building blocks\nYear: 2006\n\nAbstract: This paper discusses a new structure of M-channel IIR perfect reconstruction filterbanks. A novel design implementation for the new building block defined as a product of an IIR building block and an FIR building block is presented. The IIR building blocks are written by state space representation, in which we can easily obtain a stability of the filterbank by placing its eigen values inside the unit circle. Due to cascading of building blocks, we can get more free parameters. We introduce the condition how we obtain the new building blocks without increasing of the filter order. Additionally, by showing the simulation results, we show that our proposed FBs have better stopband attenuation than a conventional method.","Title: A fast binary arithmetic coding using probability table of expanded symbols\nYear: 2012\n\nAbstract: The arithmetic coding is known as a highly efficient entropy coding. However, its encoding and decoding are time-consuming due to the symbol-by-symbol arithmetic operations. Several approaches have been proposed to reduce the computation time by replacing the multiplication with the addition, subtraction or shift operation. However, these approximations cause a loss of the coding efficiency. In this paper, we propose a fast binary arithmetic coding by using a precomputed probability table of expanded symbols. Simulation results show that the codec speed, especially the encoding speed, is improved by our method while the coding efficiency is almost same as that of the conventional method.","Title: A directional and shift-invariant transform based on M-channel rational-valued cosine-sine modulated filter banks\nYear: 2012\n\nAbstract: This paper proposes a directional and shift-invariant transform based on M-channel rational-valued cosine-sine modulated filter banks (R-CSMFBs) for the practical implementation on hardware devices. M-channel CSMFBs can be easily designed by the modulation of a prototype filter and achieve a good stopband attenuation. In addition, in our previous work, the directionality and the shift-invariance of CSMFBs have been theoretically clarified. Thus, they can be an alternative choice of the dual-tree complex wavelet transform (DTCWT) which is one of the most popular directional and shift-invariant transforms. In this paper, it is shown that the proposed lifting-based structure of the R-CSMFB can also achieve rich directional selectivity and the shift-invariance even if the lifting coefficients are rounded to rational values. Finally, the R-CSMFB can provide better stopband attenuation and image denoising performance than that of the conventional M-channel rational-valued DTCWT in the simulation.","Title: Directional image decomposition using retargeting pyramid\nYear: 2012\n\nAbstract: Retargeting pyramid (RP) is an alternative method for multiscale image decomposition to the well-known Laplacian pyramid (LP). RP can be obtained by replacing the low-pass filtering process in LP with content-aware image resizing (a.k.a. retargeting), which is a developing technique for computer vision researches. Furthermore, we use RP for contourlet-based directional image decomposition. In experimental results, the proposed decomposition outperforms the LP-based contourlet transform for image denoising.","Title: Digital image watermarking method using between-class variance\nYear: 2012\n\nAbstract: We propose a low-complexity, pixel-based watermarking method utilizing the edge areas in an image. In this method, an image is divided into blocks and the blocks are classified as edge blocks and non-edge blocks. The histogram of pixels in an edge block is a bi-modal distribution which has two peaks. Therefore, these pixels are further classified into two classes named “high peak class” and “low peak class”. Then, a bit of the watermark is embedded by controlling the between-class variance by changing the pixel values in each class of the edge block. We utilized the discriminant analysis method for deciding a threshold and controlling the between-class variance. The proposed method can improve the image quality while maintaining robustness against compression and smoothing. In this paper, we evaluated the image quality of watermarked images using PSNR and SSIM. We also evaluated robustness against JPEG compression and Gaussian filtering.","Title: Scalable image representation using improved retargeting pyramid\nYear: 2013\n\nAbstract: The retargeting pyramid (RP) method is a good alternative to the well-known Laplacian pyramid (LP) approach for multiscale image decomposition. RP can be obtained by replacing the low-pass filtering and downsampling processes in LP with content-aware image resizing (a.k.a. retargeting), which is a technique being developed in computer vision research. In this paper, we improve RP so that it obtains good scalable image representation. The improved RP is then integrated with a well-known multiscale-multidirection (MSMD) transform, contourlet transform, to construct a saliency-oriented MSMD image representation. In the experiment, our decomposition outperforms the conventional pyramid structures.","Title: Three-Dimensional Reference Interaction Site Model Self-Consistent Field Analysis Of Solvent And Substituent Effects On The Absorption Spectra Of Brooker'S Merocyanine\nYear: 2015\n\nAbstract: Solvent and substituent effects on the absorption spectra of Brooker's merocyanine (BM) are investigated using the three-dimensional reference interaction site model self-consistent field method and time-dependent density functional theory. The -* excitation energies are computed for BM and its derivative 2,6-di-tert-butyl (di-t-Bu) BM. The behaviors of the computed excitation energies with increasing solvent polarity are in good agreement with those of the corresponding experimental measurements. In addition, analysis of the solute-solvent interaction energies and spatial distribution functions reveals that the effects of the solvent on the absorption spectra are reduced by the steric hindrance of the t-Bu groups. Furthermore, from the difference in the solute-solvent interaction energies of BM and di-t-Bu BM, it is shown that the effect of the t-Bu substituents on the absorption spectrum is greater in high-polarity solvents. (c) 2015 Wiley Periodicals, Inc.","Title: Near Orthogonal Oversampled Graph Filter Banks\nYear: 2016\n\nAbstract: A framework for oversampled filter banks for graph signals was recently proposed by Tanaka and Sakiyama (2014). It was shown that M-channel oversampled filter bank which is exactly orthogonal is not possible with polynomial spectral filter functions. The result is an extension of the critically sampled framework of Narang and Ortega (2012). In this work we present a solution that is nearly orthogonal which has the advantage of giving a nearly tight transform, i.e. nearly equal frame bounds. A family of target functions which satisfy the orthogonality condition is constructed using Bernstein polynomials. The design of the spectral filters is easily achieved through an approximation of the target functions. Filters with good spectral characteristics that are nearly orthogonal can be easily obtained using the proposed method.","Title: Oversampled bipartite graphs with controlled redundancy\nYear: 2015\n\nAbstract: This paper extends our previous work on graph oversampling for graph signal processing. In the graph oversampling method, nodes are duplicated and edges are appended to construct oversampled graph Laplacian matrix. It can convert an arbitrary K-colorable graph into one bipartite graph which includes all edges of the original graph. Since it uses a coloring-based algorithm, performance of graph signal processing depends on the coloring results. In this paper, we present graph oversampling based on a few different graph bipartition methods which use maximum spanning tree and eigendecomposition. Furthennore, we consider the effective selection method of duplicated nodes. The performance of the oversampled graphs is compared through an experiment on graph signal denoising.","Title: Critically Sampled Graph Wavelets Converted From Linear-Phase Biorthogonal Wavelets\nYear: 2015\n\nAbstract: This paper presents a design method of critically sampled graph wavelet transforms (CSGWTs) utilizing real-valued biorthogonal linear-phase wavelets for regular signals. Their filter characteristics are equivalent to those of biorthogonal linear-phase wavelets and can be expressed by real-valued closed-form with the sum of sinusoidal waves in the graph spectral domain. The proposed CSGWTs satisfy the perfect reconstruction condition for graph signals. Since the proposed filters are smooth functions, they are well-behaved even if we use a lower-order polynomial approximation. The performance of the proposed CSGWTs is evaluated by comparison with existing CSGWTs.","Title: Globalized BM3D using fast eigenvalue filtering\nYear: 2015\n\nAbstract: In this paper, we propose a progressive image denoising method using iterative filtering with Chebyshev polynomial approximation (CPA). It is known that a non-local/local image denoising method can be represented as matrix notation, and its denoising performance is improved by filtering the eigenvalues of the filter matrix. However, the eigenvalue filtering requires much computation time for eigendecomposition. To filter eigenvalues effectively, we proposed a fast eigenvalue filtering method using CPA [1]. The method drastically reduces the computation time but it still requires to construct a large sparse matrix. It often leads to much computational complexity. To overcome the problem, we propose an eigenvalue filtering method which does not construct a filter matrix by using the characteristic of the CPA. Experimental results show that our method is fast and applicable to large-size images. Additionally, the denoising performance of our method is almost better than those of the previous methods both in visual qualities and objective measures.","Title: Edge preserving multiscale image decomposition with customized domain transform filters\nYear: 2015\n\nAbstract: In this paper, a multiscale image decomposition method based on domain transform is proposed. The domain transform is a high speed edge preserving smoothing method and can be used to many image processing applications. However, it is highly sensitive to noise. The proposed method is based on filters used in the domain transform but is designed to be robust to noise by employing a multiscale method. An optimization problem is formulated to obtain desired domain-transformed output. As expected, the method can be used to many applications as the domain transform. We implement edge preserving smoothing for noise-free/noisy images and detail enhancement as possible applications. The results are satisfactory even in the noisy environment.","Title: Subspace-constrained multilinear discriminant analysis for ERP-based brain computer interface classification.\nYear: 2015\n\nAbstract: Classification in brain computer interfaces (BCIs) frequently suffers from small sample problem which leads an ill-posed problem and overfitting/overtraining. To avoid and reduce the problems, we propose a multilinear formed linear discriminant analysis with constraints which are derived from other datasets. The proposed method prevents the ill-posed problem by reducing variables and improves robustness by transferring information from other datasets. The experimental results show that the proposed method improves classification accuracy in event-related potential-based BCIs.","Title: Graph Signal Denoising via Trilateral Filter on Graph Spectral Domain.\nYear: 2016\n\nAbstract: This paper presents a graph signal denoising method with the trilateral filter defined in the graph spectral domain. The original trilateral filter (TF) is a data-dependent filter that is widely used as an edge-preserving smoothing method for image processing. However, because of the data-dependency, one cannot provide its frequency domain representation. To overcome this problem, we establish the...","Title: Spectral Graph Wavelets and Filter Banks With Low Approximation Error.\nYear: 2016\n\nAbstract: We propose filter banks in the graph spectral domain, where each filter is defined by a sum of sinusoidal waves. The main advantages of these filter banks are that (a) they have low approximation errors even if a lower-order shifted Chebyshev polynomial approximation is used, (b) the upper bound of the error after the pth order Chebyshev polynomial approximation can be calculated rigorously withou...","Title: Efficient sensor position selection using graph signal sampling theory.\nYear: 2016\n\nAbstract: We consider the problem of selecting optimal sensor placements. The proposed approach is based on the sampling theorem of graph signals. We choose sensors that maximize the graph cut-off frequency, i.e., the most informative sensors for predicting the values on unselected sensors. We study the existing methods in the context of graph signal processing and clarify the relationship between these methods and the proposed approach. The effectiveness of our approach is verified through numerical experiments, showing advantages in prediction error and execution time.","Title: Dimensionality reduction of sample covariance matrices by graph fourier transform for motor imagery brain-machine interface\nYear: 2016\n\nAbstract: An efficient method for dimensionality reduction in classification of multi-class electroencephalogram (EEG) during motor imagery (MI) aiming at brain-machine interfacing is proposed. In this method, the reduction of dimensions is achieved by spectral decomposition of a given graph, which is defined by a geometrical distribution of electrodes on the head surface. The resulting subspace reduces the dimension of EEG signals, and therefore, the size of the sample co-variance matrix (SCM) of EEG can also be reduced. The reduction method is combined with a differential geometry-based approach called tangent space mapping (TSM) that can map a SCM in a Riemannian manifold onto an element in an Euclidean space called a tangent space. Thus, any machine learning algorithm that works in the Euclidean space can be applied. Results of two-class and four-class classification of EEG during MI show that the proposed method of dimensionality reduction increases the recognition accuracy even in the case of a training dataset having a small number of elements.","Title: Critically sampled graph filter banks with polynomial filters from regular domain filter banks.\nYear: 2017\n\nAbstract: Graph signal processing deals with the processing of signals defined on irregular domains and is an emerging area of research. Graph filter banks allow the wavelet transform to be extended for processing graph signals. Sakiyama and Tanaka (2015) 22 recently proposed a technique to convert linear-phase biorthogonal filter banks for regular domain signals to biorthogonal graph filter banks. Perfect reconstruction is preserved using the technique but the resulting spectral filter functions are transcendental and not polynomial. Polynomial function filters are desired for the localization property and implementation efficiency. In this work we present alternative techniques to perform the conversion. Perfect reconstruction is preserved with the proposed techniques and the resulting spectral filters are polynomial functions. HighlightsWe design filter banks for signal defined over bipartite graphs.Spectral filters are polynomials resulting in localization and efficient implementation.Technique is based on converting 1D regular domain filter banks.Perfect reconstruction is exactly preserved and spectral response shape is almost preserved.","Title: Deforming Pyramid: Multiscale Image Representation Using Pixel Deformation And Filters For Non-Equispaced Signals\nYear: 2016\n\nAbstract: We propose an edge-preserving multiscale image decomposition method using filters for non-equispaced signals. It is inspired by the domain transform, which is a high-speed edge-preserving smoothing method, and it can be used in many image processing applications. One of the disadvantages of the domain transform is sensitivity to noise. Even though the proposed method is based on non-equispaced filters similar to the domain transform, it is robust to noise since it employs a multiscale decomposition. It uses the Laplacian pyramid scheme to decompose an input signal into the piecewise-smooth components and detail components. We design the filters by using an optimization based on edge-preserving smoothing with a conversion of signal distances and filters taking into account the distances between signal intervals. In addition, we also propose construction methods of filters for non-equispaced signals by using arbitrary continuous filters or graph spectral filters in order that various filters can be accommodated by the proposed method. As expected, we find that, similar to state-of-the-art edge-preserving smoothing techniques, including the domain transform, our approach can be used in many applications. We evaluated its effectiveness in edge-preserving smoothing of noise-free and noisy images, detail enhancement, pencil drawing, and stylization.","Title: Multilinear Discriminant Analysis With Subspace Constraints for Single-Trial Classification of Event-Related Potentials.\nYear: 2016\n\nAbstract: The classification accuracy of a brain-computer interface (BCI) frequently suffers from ill-posed and overfitting problems. To avoid and alleviate these problems, we propose a method of a multilinear discriminant analysis with constraints to augment parameter reduction, regularization, and additional prior information for event-related potential (ERP)-based BCIs. The method reduces the number of p...","Title: Smoothing of xDAWN spatial filters for robust extraction of event-related potentials.\nYear: 2016\n\nAbstract: The xDAWN algorithm is well-known as a method for designing spatial filters to improve signal-to-noise ratio and to reduce the dimension of observed EEG signals. This paper proposes a method for spatially smoothing xDAWN spatial filters to give a robustness against small sample problem. The proposed method gives a subspace constraint to the parameter space of the spatial filters. This subspace is given as a basis of the graph Fourier transform on a graph representing geometric structure of the electrodes. The spatial filters found in the subspace are smooth in the spatial domain. The proposed method is evaluated by experiments with artificial signals and BCI datasets.","Title: Mesh-based image retargeting with spectral graph filtering.\nYear: 2016\n\nAbstract: We propose a calculation method of deformed image pixel positions for mesh-based image retargeting. Image retargeting is a sophisticated image resizing method which yields resized images with acceptable quality even if we resize the image into different aspect ratio from the original one. It often employs a mesh-based approach, where pixels are nodes of a graph and relationships between pixels are represented as its edges. In this paper, we reformulate a pixel position deformation of image retargeting as a spectral graph filtering with a graph signal processing-based approach. We validate our method through some image retargeting examples with an appropriately designed filter kernels in the graph spectral domain.","Title: Solvatochromism And Preferential Solvation Of Brooker'S Merocyanine In Water-Methanol Mixtures\nYear: 2017\n\nAbstract: The excitation energy of Brooker's merocyanine in water-methanol mixtures shows nonlinear behavior with respect to the mole fraction of methanol, and it was suggested that this behavior is related to preferential solvation by methanol. We investigated the origin of this behavior and its relation to preferential solvation using the three-dimensional reference interaction site model self-consistent field method and time-dependent density functional theory. The calculated excitation energies were in good agreement with the experimental behavior. Analysis of the coordination numbers revealed preferential solvation by methanol. The free energy component analysis implied that solvent reorganization and solvation entropy drive the preferential solvation by methanol, while the direct solute-solvent interaction promotes solvation by water. The difference in the preferential solvation effect on the ground and excited states causes the nonlinear excitation energy shift. (c) 2017 Wiley Periodicals, Inc.","Title: Spectral Domain Sampling of Graph Signals.\nYear: 2018\n\nAbstract: Sampling methods for graph signals in the graph spectral domain are presented. Though the conventional sampling of graph signals can be regarded as sampling in the graph vertex domain, it does not have the desired characteristics in regard to the graph spectral domain. With the proposed methods, the down- and upsampled graph signals inherit the frequency-domain characteristics of the sampled signa...","Title: Construction of undersampled graph filter banks via row subset selection\nYear: 2016\n\nAbstract: This paper introduces a construction method of M-channel under-sampled spectral graph filter banks. They can be applied to any kind of undirected graphs, use arbitrary critically sampled or oversampled analysis filters, and obtain low redundancy, which is less than 1, regardless of the number of the analysis filters. We formulate the construction problem as a row subset selection method of the transform matrix of the prototype (critically sampled or oversampled) filter banks. In the experiment, a graph signal on Minnesota Traffic Graph is decomposed to examine the performance of our spectral graph filter banks.","Title: Non-Blind Deconvolution Of Point Cloud Attributes In Graph Spectral Domain\nYear: 2017\n\nAbstract: We propose a non-blind deconvolution algorithm of point cloud attributes inspired by multi-Wiener SURE-LET deconvolution for images. The image reconstructed by the SURE-LET approach is expressed as a linear combination of multiple filtered images where the filters are defined on the frequency domain. The coefficients of the linear combination are calculated so that the estimate of mean squared error between the original and restored images is minimized. Although the approach is very effective, it is only applicable to images. Recently we have to handle signals on irregular grids, e.g., texture data on 3D models, which are often blurred due to diffusion or motions of objects. However, we cannot utilize image processing-based approaches straightforwardly since these high-dimensional signals cannot be transformed into their frequency domain. To overcome the problem, we use graph signal processing (GSP) for deblurring the complex-structured data. That is, the SURE-LET approach is redefined on GSP, where the Wiener-like filtering is followed by the subband decomposition with an analysis graph filter bank, and then thresholding for each subband is performed. In the experiments, the proposed method is applied to blurred textures on 3D models and synthetic sparse data. The experimental results show clearly deblurred signals with SNR improvements.","Title: Graph reduction method using localization operator and its application to pyramid transform.\nYear: 2017\n\nAbstract: This paper proposes a graph reduction method by using the localization operator for signals defined on graphs. In the previous work, we proposed a method for sensor position selection considering signal localizations in graph vertex and spectral domains. This paper extends the selection method to graph reduction. It selects downsampled nodes according to the graph cut-off frequencies so that the downsampled signal preserves the much information for predicting the values on removed nodes. Furthermore, it also considers the localization in the graph vertex domain. We design the multiscale analysis with the graph reduction and apply it to graph signal denoising.","Title: Design of polynomial approximated filters for signals on directed graphs.\nYear: 2017\n\nAbstract: This paper presents a polynomial approximation method for filtering of signals defined on directed graphs. For spectral graph filtering, the polynomial approximation is often used to avoid full eigendecomposition of variation operators and to achieve the localization both in the vertex and spectral domains. However, the conventional approximation methods are not effective for signal processing on directed graphs. This is because they assume that the graph frequencies, i.e., eigenvalues of the variation operator like adjacency matrix, are real values despite those in directed cases often include complex values. To solve this problem, we approximate filters with a constraint on the magnitude of the polynomial coefficients. The approximated filters obtained by the proposed method have much smaller errors than the conventional polynomial approximation approaches. We show its validity through the experiments on the filter design and graph signal decomposition.","Title: Graph Spectral Image Processing.\nYear: 2018\n\nAbstract: Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2-D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structu...","Title: A Topic Extraction Method on the Flow of Conversation in Meetings\nYear: 2017\n\nAbstract: In this paper, we present a new topic extraction method for meetings according to the flow of conversation. Our method extracts appropriate topic words according to their importance in the conversation in a time series using text data taken from meetings. Since meetings take up a great deal of time, one of the most important issues for organizations and companies is to improve meeting efficiency. Therefore, we should analyze the contents of the meetings, but in order to do that, it is important to be able to automatically extract the most important topics made during each meeting. The changes in the importance of a topic can be seen in a time series, so it is necessary to utilize topic extraction according to its importance in time series variation during a meeting. We can then find the topic words in a meeting according to their importance in the time series variation by using our method.","Title: Multiplierless lifting-based fast X transforms derived from fast Hartley transform factorization\nYear: 2018\n\nAbstract: This paper presents -channel (, , ) multiplierless lifting-based (ML-) fast X transforms (FXTs), where X F (Fourier), C (cosine), S (sine), and H (Hartley), i.e., FFT, FCT, FST, and FHT, derived from FHT factorization as way of lowering the cost of signal (image) processing. The basic forms of ML-FXTs are described. Then, they are customized for efficient image processing. The customized ML-FFT has a real-valued calculation followed by a complex-valued one. The ML-FCT customization for a block size of 8, which is a typical size for image coding, further reduces computational costs. We produce two customized ML-FCTs for lossy and lossless image coding. Numerical simulations show that ML-FFT and ML-FCTs perform comparably to the conventional methods in spite of having fewer operations.","Title: Dynamic Color Lines\nYear: 2018\n\nAbstract: This paper proposes a new color model of videos, Dynamic Color Lines, as an extension of Color Lines Model for images. With our model, videos are assumed to be represented as dynamically moving elongated clusters in a 3-D RGB space. This can be done by assigning each pixel to the nearest cluster and by updating cluster directions and locations alternatively. The dynamic color lines model provides a heuristic approach for a new sparse and robust representation of videos. In numerical experiments, segmentation of surveillance videos and compression are performed.","Title: Design Of Sampling Matrices In Graph Frequency Domain For Graph Signal Processing\nYear: 2018\n\nAbstract: We propose a novel sampling method of graph signals that simultaneously inherits the characteristics of the sampled signals in the vertex and graph frequency domains. In the conventional sampling of graph signals, the sampled signal can retain the characteristics only in one (vertex or graph frequency) domain, but it is not suitable for another domain. The proposed method forms an optimization problem to design appropriate sampling matrices in the graph frequency domain for inheriting the characteristics of the sampled signals in the vertex domain. We perform downsampling of graph signals based on the proposed approach and apply it to denoising with graph Laplacian pyramid along with the comparison to the existing methods.","Title: A Time Series Structure Analysis Method of a Meeting Using Text Data and a Visualization Method of State Transitions.\nYear: 2019\n\nAbstract: In this paper, we present a time series structure analysis method of a meeting using text data and a method for the visualization of state transitions. Our method evaluates and visualizes the convergence/divergence of the meeting in a time series using text data from the meeting. It is important to facilitate and review meetings for improving efficiency. Therefore, it is important not only to review the final agreement and conclusion in the dialogue during the meeting but also to understand the dialogue process. We introduce two indicators: freshness and representativeness. Our system expresses the status of the meeting in four quadrants (“stagnation”, “exploration”, “deepening”, and “consensus building”) corresponding to the combination of degrees of freshness and representativeness. By conducting an analysis using these indicators, we can objectively find which parts of the dialogue stagnated or advanced the discussion. In addition, it is possible to clarify the meeting process as a structure for review and facilitation, thereby improving efficiency of the meeting. Thus, we implemented a system that realizes this method. Furthermore, we applied this system to real data gathered from meetings consisting of actual multi-company members and verified its effectiveness.","Title: Svd For Very Large Matrices: An Approach With Polar Decomposition And Polynomial Approximation\nYear: 2018\n\nAbstract: We propose a fast approximation method for singular value decomposition (SVD) based on polar decomposition (PD), which utilizes Chebyshev polynomial approximation (CPA). SVD is widely used in various applications in data mining, machine learning, and signal/image processing. However, very large computation times are usually required when large matrices need to be decomposed. SVD based on PD (SVDPD) is computationally efficient compared to the traditional SVD approach, as the eigenvalue decomposition in its algorithm can be easily parallelized. However, the PD part of the algorithm still needs to be accelerated due to the requirement of QR decomposition. This computational burden poses a problem both for the traditional SVD and SVDPD. In this paper, we propose an approximation method for PD using CPA. It can bypass QR decomposition, thus leading to the acceleration of SVDPD. First, we show that PD can be performed by \"filtering\" the eigenvalues of (DD)-D-inverted perpendicular, where D is the target matrix to be decomposed. Then, the actual algorithm and approximate error bound are shown. The proposed approximate SVD can be implemented only by multiplying the matrices; that is, QR decomposition is no longer required. As a result, the approximation of PD with the proposed method significantly reduces the computation cost and can be easily parallelized. Experimental results show that the proposed CPA-based SVDPD can decompose a matrix faster than the conventional approach, and the application of the proposed method does not affect performance.","Title: Low-rank Sparse Decomposition of Graph Adjacency Matrices for Extracting Clean Clusters.\nYear: 2018\n\nAbstract: In this paper, we propose a refining method of graphs having clusters. Clean graphs, i.e., those represent relationships between data clearly, are important for various applications. There have been many graph construction or learning methods, however, graphs obtained from the conventional approaches are not specifically designed to yield clean graphs that have dense connections within clusters whereas sparse ones between them. In this paper, we focus on making dense edges denser and sparse edges sparser for refining graphs. In order to make it possible, we propose a low-rank sparse decomposition of an adjacency matrix. We apply the methodology of robust PCA to the adjacency matrix for the decomposition. To obtain a valid adjacency matrix, we further formulate it in a form applicable to ADMM with proper constraints. In the experiments using synthetic data, we validate that the proposed method effectively refines graphs.","Title: Fast Singular Value Shrinkage with Chebyshev Polynomial Approximation Based on Signal Sparsity.\nYear: 2017\n\nAbstract: We propose an approximation method for thresholding of singular values using Chebyshev polynomial approximation (CPA). Many signal processing problems require iterative application of singular value decomposition (SVD) for minimizing the rank of a given data matrix with other cost functions and/or constraints, which is called matrix rank minimization. In matrix rank minimization, singular values o...","Title: Almost Tight Spectral Graph Wavelets With Polynomial Filters.\nYear: 2017\n\nAbstract: The construction of spectral filters for graph wavelet transforms is addressed in this paper. Both the undecimated and decimated cases will be considered. The filter functions are polynomials and can be implemented efficiently without the need for any eigendecomposition, which is computationally expensive for large graphs. Polynomial filters also have the advantage of the vertex localization prope...","Title: Two-Channel Critically Sampled Graph Filter Banks With Spectral Domain Sampling.\nYear: 2019\n\nAbstract: We propose two-channel critically-sampled filter banks for signals on undirected graphs that utilize spectral domain sampling. Unlike conventional approaches based on vertex domain sampling, our transforms have the following desirable properties: first, perfect reconstruction regardless of the characteristics of the underlying graphs and graph variation operators; and second, a symmetric structure...","Title: $M$-Channel Critically Sampled Spectral Graph Filter Banks With Symmetric Structure.\nYear: 2019\n\nAbstract: This letter proposes a class of M-channel spectral graph filter banks with a symmetric structure, that is, the transform has sampling operations and spectral graph filters on both the analysis and synthesis sides. The filter banks achieve maximum decimation, perfect recovery, and orthogonality. The proposed transform uses sampling in the graph frequency domain. This enables us to use any variation...","Title: Eigendecomposition-Free Sampling Set Selection for Graph Signals.\nYear: 2019\n\nAbstract: This paper addresses the problem of selecting an optimal sampling set for signals on graphs. The proposed sampling set selection (SSS) is based on a localization operator that can consider both vertex domain and spectral domain localizations. We clarify the relationships among the proposed method, sensor position selection methods in machine learning, and existing SSS methods based on graph freque...","Title: Time-Varying Graph Learning Based On Sparseness Of Temporal Variation\nYear: 2019\n\nAbstract: We propose a method for graph learning from spatiotemporal measurements. We aim at inferring time-varying graphs under the assumption that changes in graph topology and weights are sparse in time. The problem is formulated as a convex optimization problem to impose a constraint on the temporal relation of the time-varying graph. Experimental results with synthetic data show the effectiveness of our proposed method.","Title: Interpolation And Denoising Of Graph Signals Using Plug-And-Play Admm\nYear: 2019\n\nAbstract: Signals defined on a network or a graph are often prone to errors due to missing data and noise. In order to restore the graph signal, interpolation and denoising are two necessary steps along with other graph signal processing procedures. However, existing graph signal interpolation and denoising methods are largely decoupled due to the opposite objectives of the two tasks and the inherent high computational complexity. The goal of this paper is to integrate graph interpolation and denoising using the Plug-and-Play (PnP) ADMM, a recently developed technique in image processing. When using the subsampling process as the forward model and graph filter as the denoiser, we show that PnP ADMM is equivalent to interpolating a bandlimited signal. Preliminary results are demonstrated via experiments, where the proposed method shows significantly better performance over existing methods.","Title: Sampling Signals on Graphs: From Theory to Applications\nYear: 2020\n\nAbstract: The study of sampling signals on graphs, with the goal of building an analog of sampling for standard signals in the time and spatial domains, has attracted considerable attention recently. Beyond adding to the growing theory on graph signal processing (GSP), sampling on graphs has various promising applications. In this article, we review the current progress on sampling over graphs, focusing on theory and potential applications.","Title: Graph Signal Restoration Using Nested Deep Algorithm Unrolling\nYear: 2022\n\nAbstract: Graph signal processing is a ubiquitous task in many applications such as sensor, social, transportation and brain networks, point cloud processing, and graph neural networks. Often, graph signals are corrupted in the sensing process, thus requiring restoration. In this paper, we propose two graph signal restoration methods based on deep algorithm unrolling (DAU). First, we present a graph signal denoiser by unrolling iterations of the alternating direction method of multiplier (ADMM). We then suggest a general restoration method for linear degradation by unrolling iterations of Plug-and-Play ADMM (PnP-ADMM). In the second approach, the unrolled ADMM-based denoiser is incorporated as a submodule, leading to a nested DAU structure. The parameters in the proposed denoising/restoration methods are trainable in an end-to-end manner. Our approach is interpretable and keeps the number of parameters small since we only tune graph-independent regularization parameters. We overcome two main challenges in existing graph signal restoration methods: 1) limited performance of convex optimization algorithms due to fixed parameters which are often determined manually. 2) large number of parameters of graph neural networks that result in difficulty of training. Several experiments for graph signal denoising and interpolation are performed on synthetic and real-world data. The proposed methods show performance improvements over several existing techniques in terms of root mean squared error in both tasks.","Title: GRAPH SIGNAL DENOISING USING NESTED-STRUCTURED DEEP ALGORITHM UNROLLING\nYear: 2021\n\nAbstract: In this paper, we propose a deep algorithm unrolling (DAU) based on a variant of the alternating direction method of multiplier (ADMM) called Plug-and-Play ADMM(PnP-ADMM) for denoising of signals on graphs. DAU is a trainable deep architecture realized by unrolling iterations of an existing optimization algorithm which contains trainable parameters at each layer. We also propose a nested-structured DAU: Its submodules in the unrolled iterations are also designed by DAU. Several experiments for graph signal denoising are performed on synthetic signals on a community graph and U.S. temperature data to validate the proposed approach. Our proposed method outperforms alternative optimization- and deep learning-based approaches.","Title: DESIGN OF GRAPH SIGNAL SAMPLING MATRICES FOR ARBITRARY SIGNAL SUBSPACES\nYear: 2021\n\nAbstract: We propose a design method of sampling matrices for graph signals that guarantees perfect recovery for arbitrary graph signal subspaces. When the signal subspace is known, perfect reconstruction is always possible from the samples with an appropriately designed sampling matrix. However, most graph signal sampling methods so far design sampling matrices based on the bandlimited assumption and sometimes violates the perfect reconstruction condition for the other signal models. In this paper, we formulate an optimization problem for the design of the sampling matrix that guarantees perfect recovery, thanks to a generalized sampling framework for standard signals. In experiments with various signal models, our sampling matrix presents better reconstruction accuracy both for noiseless and noisy situations.","Title: Structure-Aware Multi-Hop Graph Convolution for Graph Neural Networks\nYear: 2022\n\nAbstract: We present a spatial graph convolution (GC) to classify signals on a graph. Existing GC methods are limited in using the structural information in the feature space. Furthermore, GCs only aggregate features from one-hop neighboring nodes to the target node in their single step. In this paper, we propose two methods to improve the performance of GCs: 1) Utilizing structural information in the feature space, and 2) exploiting the multi-hop information in one GC step. In the first method, we define three structural features in the feature space: feature angle, feature distance, and relational embedding. The second method aggregates the node-wise features of multi-hop neighbors in a GC. Both methods can be simultaneously used. We also propose graph neural networks (GNNs) integrating the proposed GC for classifying nodes in 3D point clouds and citation networks. In experiments, the proposed GNNs exhibited a higher classification accuracy than existing methods.","Title: Directional Analytic Discrete Cosine Frames\nYear: 2022\n\nAbstract: Block frames called directional analytic discrete cosine frames (DADCFs) are proposed for sparse image representation. In contrast to conventional overlapped frames, the proposed DADCFs require a reduced amount of 1) computational complexity, 2) memory usage, and 3) global memory access. These characteristics are strongly required for current high-resolution image processing. Specifically, we propose two DADCFs based on discrete cosine transform (DCT) and discrete sine transform (DST). The first DADCF is constructed from parallel separable transforms of DCT and DST, where the DST is permuted by row. The second DADCF is also designed based on DCT and DST, while the DST is customized to have no DC leakage property which is a desirable property for image processing. Both DADCFs have rich directional selectivity with slightly different characteristics each other and they can be implemented as non-overlapping block-based transforms, especially they form Parseval block frames with low transform redundancy. We perform experiments to evaluate our DADCFs and compare them with conventional directional block transforms in image recovery.","Title: Node Clustering of Time-Varying Graphs Based on Temporal Label Smoothness\nYear: 2021\n\nAbstract: We propose a node clustering method for time-varying graphs based on the assumption that the cluster labels are changed smoothly over time. Clustering is one of the fundamental tasks in machine learning, data mining, and signal processing. Although most existing studies focus on the clustering of nodes in static graphs, we often encounter time-varying graphs for time-series data, e.g., social networks, brain functional connectivity, and point clouds. In this paper, we formulate a clustering of nodes in time-varying graphs as an optimization problem based on spectral clustering, with a smoothness constraint of the node labels. Experiments on synthetic and real-world time-varying graphs are conducted to validate the effectiveness of the proposed approach.","Title: Regularity-Constrained Fast Sine Transforms\nYear: 2022\n\nAbstract: This letter proposes a fast implementation of the regularity-constrained discrete sine transform (R-DST). The original DST leaks the lowest frequency (DC: direct current) components of signals into high frequency (AC: alternating current) subbands. This property is not desired in many applications, particularly image processing, since most of the frequency components in natural images concentrate in DC subband. The characteristic of filter hanks whereby they do not leak DC components into the AC subbands is called regularity. While an R-DST has been proposed, it has no fast implementation because of the singular value decomposition (SVD) in its internal algorithm. In contrast, the proposed regularity-constrained fast sine transform (R-FST) is obtained by just appending a regularity constraint matrix as a postprocessing of the original DST. When the DST size is M x M (M = 2(l), l is an element of N->= 1), the regularity constraint matrix is constructed from only M/2 - 1 rotation matrices with the angles derived from the output of the DST for the constant-valued signal (i.e., the DC signal). Since it does not require SVD, the computation is simpler and faster than the R-DST while keeping all of its beneficial properties. An image processing example shows that the R-FST has fine frequency selectivity with no DC leakage and higher coding gain than the original DST. Also, in the case of M = 8, the R-FST saved approximately 0.126 seconds in a 2-D transformation of 512 x 512 signals compared with the R-DST because of fewer extra operations."],"string":"[\n \"Title: Flexible Combination Of Time-Domain Lapped Transforms With Various Downsampling Factors\\nYear: 2012\\n\\nAbstract: In this paper, we present a method to combine lapped transforms with various downsampling factors. The factor is changed depending on a local feature of a given signal, and it can be realized by using time-domain lapped transforms. In image coding application, our method maintains good image coding performance for a wide range of bitrates and fills the gap between undersampled and critically-/oversampled systems.\",\n \"Title: Higher-order feasible building blocks for lattice structure of oversampled linear-phase perfect reconstruction filter banks\\nYear: 2009\\n\\nAbstract: This paper proposes new building blocks for the lattice structure of oversampled linear-phase perfect reconstruction filter banks (OLPPRFBs). The structure is an extended version of higher-order feasible building blocks for critically sampled LPPRFBs. It uses fewer number of building blocks and design parameters than those of traditional OLPPRFBs, whereas frequency characteristics of the new OLPPRFBs are comparable to those of traditional one. Furthermore, the building block structures for arbitrary number of channels and downsampling factors are presented.\",\n \"Title: Mixed-Resolution Wyner-Ziv Video Coding Based On Selective Data Pruning\\nYear: 2011\\n\\nAbstract: In current distributed video coding (DVC), interpolation is performed at the decoder and the interpolated pixels are reconstructed by using error-correcting codes, such as Turbo codes and LDPC. There are two possibilities for downsampling video sequences at the encoder: temporally or spatially. Traditionally temporal downsampling, i.e., frame dropping, is used for DVC. Furthermore, those with spatial downsampling (scaling) have been investigated. Unfortunately, most of them are based on uniform downsampling. Due to this, details in video sequences are often discarded. For example, edges and textured regions are difficult to interpolate, and thus require many parity bits to restore the interpolated portions for the spatial domain DVC. In this paper, we propose a new spatial domain DVC based on adaptive line dropping so-called selective data pruning (SDP). SDP is a simple nonuniform downsampling method. The pruned lines are determined to avoid cutting across edges and textures. Experimental results show the proposed method outperforms a conventional DVC for sequences with a large amount of motions.\",\n \"Title: Design of M-Channel Perfect Reconstruction Filter Banks with IIR-FIR Hybrid Building Blocks\\nYear: 2007\\n\\nAbstract: This paper discusses a new structure of M-channel IIR perfect reconstruction filter banks. A novel building block defined as a cascade connection of some IIR building blocks and FIR building blocks is presented. An IIR building block is written by state space representation, where we easily obtain a stable filter bank by setting eigenvalues of the state transition matrix into the unit circle. Due to cascade connection of building blocks, we are able to design a system with a larger number of free parameters while keeping the stability. We introduce the condition which obtains the new building block without increasing of the filter order in spite of cascade connection. Additionally, by showing the simulation results, we show that this implementation has a better stopband attenuation than conventional methods.\",\n \"Title: Adaptive color-to-gray image mapping using directional transform.\\nYear: 2012\\n\\nAbstract: Several color-to-gray mapping methods were proposed to restore a color image from its corresponding grayscale image. These methods are based on embedding chrominance signals into high-frequency subbands of the luminance signal. In the conventional methods, only separable filters are used for the subband decomposition. In addition, subbands at fixed positions are always used to embed chrominance signals. However, in many images, diagonal components are included as well as horizontal/vertical ones. Therefore, qualities of chrominance-embedded grayscale and restored color images are sometimes degraded especially for images with rich textures. In this paper, we propose an improved color restoration method using a directional transform named hybrid wavelets and directional filter banks which considers directionality in images. Experimental results show our method improves the image qualities of the restored color images as well as the chrominance-embedded grayscale images.\",\n \"Title: DESIGN OF UNEQUAL-LENGTH LINEAR-PHASE FILTER BANKS WITHOUT REDUNDANCY\\nYear: 2005\\n\\nAbstract: In this paper,we presenta new structure for linear-phasefilter banks without redundancy, which have unequal-length filters at each sub- band. First, we extend the simplified lattice structure of the linear- phase filter bank to the unequal-length linear-phase paraunitary fil- ter bank. In general, the unequal-length linear-phase paraunitary filter bank has equal-length filters at both the analysis and the syn- thesis bank because the synthesis bank is a transposed version of the analysis one, while the biorthogonal one does not. So, we dis- cussthe conditions that thelinear-phasebiorthogonalfilter bankhas unequal-length filters at 1) the analysis bank, 2) the synthesis bank, and 3) both the analysis and the synthesis banks. Finally, several design and image coding examples are shown.\",\n \"Title: A Bottom-Up Design Approach Of Critically Sampled Contourlet Transform For Efficient Image Representation\\nYear: 2009\\n\\nAbstract: In this paper. a novel design method of critically sampled contourlet transform (CSCT) is proposed. The original CT which consists of Laplacian pyramid and directional filter bank provides efficient frequency plane partition for image representation. However its overcompleteness is not suitable for sonic applications such as image coding. its critical sampling version has been Studied recently. Although several types of the CSCT have been proposed. they have problems on their realization or unnatural frequency plane partition which is different front the original CT. In contrast to the way in conventional design methods based on a \\\"top-down\\\" approach. the proposed method is based on a \\\"bottom-up\\\" one. That is, the proposed CSCT decomposes the frequency plane into small directional subbands, and then synthesizes them up to a target frequency plane partition, while the conventional ones decompose into it directly, By this way, the proposed CSCT can design an efficient frequency division which is the same as the original CT tor image representation can be realized. In this paper. its effectiveness is verified by non-linear approximation simulation.\",\n \"Title: Image Coding Using Concentration And Dilution Based On Seam Carving With Hierarchical Search\\nYear: 2010\\n\\nAbstract: Image concentration is an image resizing technique which shrinks an image size but does not change important region(s) in the original image, whereas image dilution is the reverse process to image concentration. In this paper, we incorporate image concentration/dilution process in image coding. An image retargeting method called seam carving is used for image concentration. Furthermore, it is modified to yield rate-distortion optimized seams by a hierarchical search process. Our method can be incorporated into any image encoder since the image concentration (dilution) is a pre(post)processing of image encoder (decoder). In the experimental results, JPEG/SPIHT with image concentration/ dilution presents significant bitrate savings compared with the original JPEG/SPIHT alone and reconstructed image qualities are very similar to each other.\",\n \"Title: Highpass-filtering based adaptive directional wavelet transform.\\nYear: 2009\\n\\nAbstract: In this paper, a calculation method of transform directions for adaptive directional wavelet transform (AD WT) is proposed. It uses 2-D highpass filters as a preprocessing of the conventional calculation stage. The filters can reduce the number of direction candidates effectively. In image coding, our proposed framework shows very comparable results to the conventional AD WT and outperforms the traditional separable WT.\",\n \"Title: An Adaptive Extension Of Combined 2d And 1d-Directional Filter Banks\\nYear: 2009\\n\\nAbstract: In this paper, we propose an extension of combined 2D and 1D-directional filter banks (TODFBs) with adaptive approach in their 1D-directional stages. TODFBs show better performance in image coding and denoising compared to the traditional wavelet transform (WT), however, they still have possibilities to improve their performance in image coding by adopting the adaptive directional WTs for their 1D-directional stages. An efficient method to determine the transform directions is also proposed. In image coding results, our proposed filter banks gain PSNR and visual quality improvements compared with the WTs and the non-adaptive TODFBs.\",\n \"Title: Selective Data Pruning Based Distributed Video Coding With Modified High-Order Edge-Directed Interpolation\\nYear: 2013\\n\\nAbstract: In distributed video coding (DVC), a low resolution video sequence is usually generated with spatial and/or temporal downsampling at an encoder. At a decoder side, interpolation is performed and the interpolated pixels are further refined by using an error-correcting code such as Turbo codes or LDPC. In our previous work, we proposed a spatial domain DVC which uses a line-based downsampling method called selective data pruning (SDP). SDP is used for reducing the spatial frame size before compression. After decoding, received frames are decoded and interpolated back to their original size by high-order edge-directed interpolation (HEDI). In this paper, we propose a modified high-order edge-directed interpolation (M-HEDI) to avoid interpolation artifacts. Experimental results show that the interpolated frames by using M-HEDI have higher quality than those using HEDI. The proposed method outperforms conventional DVCs and the video coding standard H.264/AVC in majority of all the test video sequences.\",\n \"Title: Multiplierless Lifting Based Fft Via Fast Hartley Transform\\nYear: 2013\\n\\nAbstract: The multiplierless fast Fourier transform (FFT) with dyadic-valued (rational) coefficients is important for many signal processing tools. The proposed lifting based FFT (L-FFT) based on fast Hartley transform (FHT) has a simpler structure than existing ones because fewer lifting steps need to be approximated. In addition, it has a structure of real-valued calculation followed by complex-valued parts, thereby it requires fewer memories for the internal implementation than the conventional FFTs.\",\n \"Title: Oversampled linear-phase perfect reconstruction filter banks with higher-order feasible building blocks: Structure and parameterization\\nYear: 2008\\n\\nAbstract: This paper proposes new building blocks for the lattice structure of oversampled linear-phase perfect reconstruction filter banks (OLPPRFBs). The structure is an extended version of higher-order feasible building blocks for critically-sampled LPPRFBs. It uses fewer number of building blocks and design parameters than those of tra- ditional OLPPRFBs, whereas the frequency characteristic of the new OLPPRFB is comparable to that of traditional one. I. INTRODUCTION Oversampled filter bank (OFB) is one of the most developing issues in the field of FB theory (1)-(3). OFB is a superclass of critically- sampled FBs since its polyphase matrix has the size P ×M ( P> M) which covers entire M × M classes for critically-sampled ones. They have more design freedom than the critically-sampled FBs, thus they are expected to have good frequency characteristics and/or robustness against errors. Additionally, linear-phase (LP) and perfect reconstruction (PR) properties are usually highly desired in practical signal processing framework (4), (5). In this paper, we consider to derive a new structure of OLPPRFBs. Gan et al. investigated lattice structures for OLPPRFBs and proved the necessary conditions which are regarded as the extended version of those for critically-sampled LPPRFBs (2). However, their structure has a P × M rectangular starting lattice block. Following the block, critically-sampled P -channel building blocks are cascaded to yield desired filter length L = KM in spite of the downsampling factor M. Hence, the calculation cost is much higher than the critically- sampled M -channel ones. Liang et al. proposed a subset of Gan's structure which is pre- and postprocessing system of a P × M oversampling matrix (6). The alternative structure helps to decrease the calculation cost for long filters. However, most of the previous works on the lattice structure of OLPPRFBs are based on cascading order-1 building blocks (2), (6). It is effective in the viewpoint of achieving any-order FBs. Unfortunately, the high-order FB requires many order-1 building blocks which increase the implementation cost. The authors proposed a lattice component of critically-sampled biorthogonal (BO) LPFBs (7) called higher-order feasible (HOF) building block. It realizes an order-N (N ≥ 2) LPPRFB with one HOF building block. Additionally, it is also regarded as an extended class of traditional (critically-sampled) order-1 building blocks. The advantage of HOF building blocks against cascaded order-1 ones is its ability to realize comparable performance with fewer number of building blocks and design parameters for the same filter lengths. We extend the HOF building block from critically-sampled to oversampled LPPRFBs. It inherits all the merits on critically-sampled HOF structure, furthermore, it has an attractive feature of a P × M rectangular oversampling matrix which exists particularly on the HOF structure. The parameterization which is a specific method for the HOF building blocks is described in the paper. In the design example, our OLPPRFB based on HOF buiding blocks shows the comparable frequency characteristic to that based on cascaded order-1 structure in spite of fewer number of design parameters. Remaining parts of this paper are organized as follows: Section II reviews traditional structures of OLPPRFBs and critically-sampled HOF building blocks. Section III describes the oversampled HOF structure. Section IV validates our proposed structure by comparing with the traditional one. Finally, the paper concludes in Section V. Notations: �·� and �·� define the functions which return ceiling and floor of (·), respectively. We also define the special matrices of size S ×S; IS is the identity matrix, JS is the reversal identity matrix and 0S is the null matrix. The sizes will be omitted if they are obvious.\",\n \"Title: Theory And Design Of Two-Channel Complex Linear-Phase Pseudo-Orthogonal Filterbanks\\nYear: 2006\\n\\nAbstract: In recent years, two-channel complex-valued filterbanks have been studied and found theirs several important applications by many reseachers, such as complex signal and image processing. One of those important results is that there is no twochannel complex-valued linear-phase paraunitary filterbank (CLPPUFB), except for its filter lengths of 2. In this paper, we introduce a class of special complex-valued filterbanks which is a subclass of biorthogonal filterbanks: Those filterbanks are called complex pseudo-orthogonal filterbanks (CPOFB), based on the concept of pseudo-orthogonality (PO). This kind of orthogonality, we propose, is different from the conventional one essentially. This paper also shows possibility to design a two-channel complex linear-phase filterbank (CLPFB) with its filter lengths are more than 2, based on PO. Finally, we show a design example of a complex linear-phase pseudo-orthogonal filterbank (CLPPOFB). Moreover, such CPOFB can satisfy the linear-phase condition simultaneously with filter lengths are more than 2. This paper shows a theory, a design method and an example of CLPPOFB.\",\n \"Title: Multiresolution image representation using combined 2-D and 1-D directional filter banks.\\nYear: 2009\\n\\nAbstract: In this paper, effective multiresolution image representations using a combination of 2-D filter bank (FB) and directional wavelet transform (WT) are presented. The proposed methods yield simple implementation and low computation costs compared to previous 1-D and 2-D FB combinations or adaptive directional WT methods. Furthermore, they are nonredundant transforms and realize quad-tree like multiresolution representations. In applications on nonlinear approximation, image coding, and denoising, the proposed filter banks show visual quality improvements and have higher PSNR than the conventional separable WT or the contourlet.\",\n \"Title: General Factorization of Conjugate-Symmetric Hadamard Transforms\\nYear: 2014\\n\\nAbstract: Complex-valued conjugate-symmetric Hadamard transforms ( $\\\\BBC $-CSHT) are variants of complex Hadamard transforms and found applications in signal processing. In addition, their real-valued transform counterparts ($\\\\BBR $-CSHTs) perform comparably with Hadamard transforms (HTs) despite their lower computational complexity. Closed-form factorizations of $\\\\BBC $-CSHTs and $\\\\BBR $-CSHTs have recently been proposed to make calculations more efficient. However, there is still room to find effective and general factorizations. This paper presents a simple closed-form complete factorization of $\\\\BBC $-CSHTs based on that of $\\\\BBR $ -CSHTs. The proposed factorization can be applied to both $\\\\BBC $- and $\\\\BBR $-CSHTs with one factorization and it provides several benefits: 1) It can save total implementation costs for both $\\\\BBC $ -CSHTs and $\\\\BBR $-CSHTs; 2) the generalized $\\\\BBR $-CSHT factorization significantly reduces its computational cost; 3) memory-saved local orientation detection of images can be achieved; 4) a fast direction-aware transform can be attained; 5) it clarifies that $\\\\BBC $- and $\\\\BBR $-CSHTs are closely related to common block transforms, such as the discrete Fourier transform (DFT), binDCT, and HT; and 6) it achieves a new integer complex-valued transform, which can approximate the DFT better than the original $\\\\BBC $-CSHT. The image orientation estimation and performance in image coding of our $\\\\BBR $-CSHTs were evaluated through examples of practical applications based on the proposed factorization.\",\n \"Title: Smoothing of spatial filter by graph Fourier transform for EEG signals\\nYear: 2014\\n\\nAbstract: Spatial filtering is useful for extracting features from multichannel EEG signals. In order to enhance robustness of the spatial filter against low SNR and small samples, we propose a smoothing method for the spatial filter using spectral graph theory. This method is based on an assumption that the electrodes installed in nearby locations observe the electrical activities of the same source. Therefore the spatial filter's coefficients corresponding to the nearby electrodes are supposed to be taken similar values, that is, the coefficients should be spatially smooth. To introduce the smoothness, we define a graph whose edge weights represent the physical distances between the electrodes. The spatial filter spatially smoothed is found out in the subspace that is spanned by the smooth basis of the graph Fourier transform. We evaluate the method with artificial signals and a dataset of motor imagery brain computer interface. The smoothness of the spatial filter given by the method provides robustness of the spatial filter in the condition that the small amount of the samples is available.\",\n \"Title: Trilateral filter on graph spectral domain\\nYear: 2014\\n\\nAbstract: This paper presents the trilateral filter (TF) in the perspective of graph signal processing. The TF is a single-pass nonlocal filter for edge-preserving smoothing. To smooth an image, it does not require many iterations compared to conventional smoothing methods, e.g., the bilateral filter. Additionally, one parameter is only required for filtering. Since the TF coefficients depend on original image data, it is not possible to provide a frequency domain representation using regular signal processing. To overcome this problem, we firstly show the TF as a vertex domain transform on a graph and then define it on graph spectral domain. In the experimental results, the proposed method presents better denoising performances than conventional methods.\",\n \"Title: M-channel oversampled perfect reconstruction filter banks for graph signals\\nYear: 2014\\n\\nAbstract: This paper proposes M-channel oversampled filter banks for graph signals. The filter set satisfies the perfect reconstruction condition. A method of designing oversampled graph filter banks is presented which allows us to design filters with arbitrary parameters, unlike the conventional critically-sampled graph filter banks. The practical performance of the proposed filter banks is validated through graph signal denoising experiments.\",\n \"Title: Oversampled graph laplacian matrix for graph signals\\nYear: 2014\\n\\nAbstract: In this paper, we propose oversampling of graph signals by using oversampled graph Laplacian matrix. The conventional critically sampled graph filter banks have to decompose an original graph into bipartite subgraphs, and the transform has to be performed on each subgraph due to the spectral folding phenomenon caused by downsampling of graph signals. Therefore, they cannot always utilize all edges of the original graph for the one-stage transformation. Our proposed method is based on oversampling of the underlying graph itself, and it can append nodes and edges to the graph somewhat arbitrarily. We use this approach to make one oversampled bipartite graph that includes all edges of the original non-bipartite graph. We apply the oversampled graph with the critically sampled filter bank for decomposing graph signals, and show the performance of graph signal denoising.\",\n \"Title: Iterative edge-directed image upscaling with high directionality\\nYear: 2014\\n\\nAbstract: Variety of image upscaling methods has been proposed and used in computer tools due to the increasing number of practical applications. The essential objectives of image upscaling are: it should be fast, natural to human perception and does not yield annoying artifacts. This paper proposes an edge-directed iterative image interpolation based on the second-order derivatives along multiple directions. Our method uses the assumption that the second-order derivatives of the image intensities are continuous along the interpolation directions. We first initialize the pixel values based on a grid filling and iteratively modify the interpolated pixels by minimizing an objective function depending on the second-order directional derivatives. Especially our method provides better results in edge regions while the computation time is reduced compared to other existing upscaling methods. Image coding with the proposed interpolation method is also performed as a possible application.\",\n \"Title: Depth map denoising using collaborative graph wavelet shrinkage on connected image patches\\nYear: 2014\\n\\nAbstract: In this paper, we propose a new patch-based image denoising algorithm using graph signal processing. The concept of this algorithm is to take advantage of the redundancy of the BM3D transform and the edge preservation property of graph-based image processing. More specifically, we collect similar patches in the image, and construct a graph by connecting obtained patches. Then we apply a graph wavelet filter bank on graph signals to attenuate additive white gaussian noise by shrinking derived coefficients. We apply our proposed algorithm to depth map denoising. The experimental results demonstrate significant performance gains for the edge preservation and the noise reduction.\",\n \"Title: Edge-aware image graph expansion methods for oversampled graph Laplacian matrix\\nYear: 2014\\n\\nAbstract: Graph signals can represent high-dimensional data effectively and images can also be viewed as signals on weighted graphs by connecting the pixels with their neighboring ones. Recently, we proposed the graph oversampling methods for signal processing on graphs that appends nodes and links to the original graph to obtain an oversampled graph Laplacian matrix. In this paper, we consider new over-sampling methods of image graphs. By using the graph oversampling, we can make a bipartite graph that considers rectangular and diagonal connections simultaneously, while it cannot be realized by conventional critically sampled bipartite graphs. Furthermore, expanding the graphs according to the edge information enables us to decompose the image with the edge-preserving property. We perform the critically sampled graph filter bank on the oversampled graph and show that the proposed method outperforms other transforms, including the critically sampled graph filter banks and the graph Laplacian pyramid, in non-linear approximation and denoising experiments.\",\n \"Title: Multidimensional linear-phase perfect reconstruction filter banks with higher order feasible building blocks\\nYear: 2014\\n\\nAbstract: In this paper, we propose multidimensional (MD) linear-phase perfect reconstruction filter banks (LPPRFBs) with higher order feasible (HOF) building blocks. Since existing HOF building blocks are only applicable for one-dimensional LPPRFBs, we generalize them to MD LPPRFBs. The generalized structure can design MD LP-PRFBs with HOF building blocks for both even and odd number of channels. The proposed MD-LPPRFBs realize large filter sizes with fewer number of building blocks than the traditional order-1 structures.\",\n \"Title: Wireless optical transmissions with white colored LED for wireless home links\\nYear: 2000\\n\\nAbstract: We propose a wireless optical communication system with white colored LEDs for a wireless home link (WHL). The white colored LEDs have a high power output and are regarded as lamps for the next generation. In the proposed system, this device is used for a wireless home link. The proposed system is suitable for private networks such as consumer communication networks. From numerical and simulation results, it is confirmed that the proposed system is available and the problems to be solved are made clear\",\n \"Title: A lattice structure of biorthogonal linear-phase filter banks with higher-order feasible building blocks\\nYear: 2008\\n\\nAbstract: This paper proposes a new structure of biorthogonal linear-phase filter banks (BOLPFBs) by using building blocks which can be re- alized long filters with fewer number of building blocks than con- ventional ones. The structure is derived from a generalization of the building blocks of first-order LPFBs. The resulting FBs have good performance in stopband attenuation and low implementation costs.\",\n \"Title: A novel design of criticially sampled contourlet transform and its application to image coding\\nYear: 2008\\n\\nAbstract: In this paper, a novel design method of critically sampled contourlet transform (CSCT) is proposed. Although, several types of CSCT have been proposed, they have some problems on efficiency and flexibility of their frequency plane parti- tion patterns. In contrast to the way in conventional design methods based on a \\\"top-down\\\" approach, the proposed one is based on a \\\"bottom-up\\\" one. That is, the proposed CSCT decomposes the frequency plane into small directional sub- bands, and then synthesizes them up to a target frequency plane partition, while the conventional ones decompose into it directly. By this way, the proposed CSCT can provide an efficient and flexible frequency plane partition for image cod- ing.\",\n \"Title: Generalized selective data pruning for video sequence\\nYear: 2011\\n\\nAbstract: In this paper, we extend a rate-dependent content-aware image retargeting method, generalized selective data pruning, to video sequence. It is regarded as a spatial-domain video retargeting, i.e., reduction of the spatial video size, with a rate-dependent property for side information. It is realized by the piecewise linear approximation of seam carving. The side information bitrate for video retargeting can vary depending on the control parameter and is negligible to transmit with the encoded video bitstream while the method maintains video retargeting performance.\",\n \"Title: A structural similarity assessment for generating hybrid images\\nYear: 2011\\n\\nAbstract: We discuss a structural similarity evaluation method in order to generate hybrid images for graphical password. The hybrid image consists of two images. One is edge information of a key image and the other is coarse features of a decoy image. Visibility of the hybrid image depends on the viewing distance and the structural similarity of the images. This property of the hybrid image is useful for preventing shoulder-surfing of graphical password. However, an evaluation method for checking whether the decoy image is appropriate to overlay the key image has not been established. To address this issue, we utilize Speeded Up Robust Features (SURF) to evaluate structural similarity between two images. Simulation results show that our similarity measure mostly coincides with the visibility of a key image. We also compare our objective metric with the structural similarity (SSIM) index and subjective scores obtained from a user test based on Scheffe's method of paired comparisons.\",\n \"Title: On factorizations of conjugate symmetric Hadamard transform and its relationship with DCT\\nYear: 2012\\n\\nAbstract: Complex-valued conjugate symmetric Hadamard transform (C-CSHT) is a variant of complex Hadamard transform and effective for some signal processing and communication applications. Its closed-form factorization of the general N-channel (N = 2m) case was recently proposed, however, there still exist a room to find an effective factorization especially for unified factorization of C-CSHT and its real-valued transform counterpart (R-CSHT). In this paper, we present another simple closed-form factorization of C-CSHT based on that of R-CSHT. The proposed factorization is applicable for both complex- and real-valued CSHTs with one factorization. Furthermore, the relationship with the common block transform, DCT, is revealed.\",\n \"Title: Multiplierless fast algorithm for DCT via fast Hartley transform\\nYear: 2012\\n\\nAbstract: Discrete cosine transform (DCT) is known as efficient frequency transform, and when it is implemented on software/hardware, multiplier is undesirable for faster implementation. This paper presents a realization of multiplierless fast DCT for lossy image/video coding on arbitrary devices. First, the proposed DCT is constructed by using fast Hartley transform (FHT). Next, the redundancy of the structure is eliminated by using several characteristics of rotation matrix. Then, multiplierless DCT is obtained by approximating rotation matrices to multiplierless lifting structures with adders and bit-shifters. Finally, the proposed DCT is validated by comparing with the conventional DCTs in image coding.\",\n \"Title: Improved adaptive color embedding and recovery using DC level shifting\\nYear: 2013\\n\\nAbstract: Several color-to-gray image mapping (CGIM) methods were proposed so far to restore a color image from its corresponding printed grayscale image. They are based on embedding chrominance values into high-frequency subbands of the luminance signal. In the conventional methods, textures often become noticeable in the chrominance-embedded gray image since subsampled chrominance values are embedded directly. As a result, qualities of the chrominance-embedded grayscale images are degraded especially for images with rich textures. In this paper, we propose an improved CGIM method using DC level shifting and directional transform. In the experimental results, our method greatly improves the qualities of both the chrominance-embedded grayscale and the restored color images in comparison with conventional methods.\",\n \"Title: Improved image concentration for artifact-free image dilution and its application to image coding\\nYear: 2010\\n\\nAbstract: In this paper, an improved method of image concentration for image coding with concentration and dilution is presented. Image concentration is an image resizing technique which shrinks an image size but does not change important region(s) in the original image, whereas image dilution is the reverse process to image concentration. The authors have proposed an image coding approach based on the image concentration and dilution. This paper focuses on an improvement of image concentration to enhance quality of the reconstructed (diluted) image. In the experimental results, the improved method obtains bitrate savings compared with image encoders alone, and the reconstructed images do not have perceptually noticeable artifacts.\",\n \"Title: VQ based data hiding method for still images by tree-structured links\\nYear: 2010\\n\\nAbstract: In this paper, we propose a data embedding method into still images based on Vector Quantization (VQ). In recent years, several VQ-based data embedding methods have been proposed. For example, `Mean Gray-Level Embedding method (MGLE)' are `Pair wise Nearest-Neighbor Embedding method (PNNE)' are simple, but not sufficiently effective. Meanwhile, an efficient adaptive data hiding method called `Adaptive Clustering Embedding method (ACE)' was proposed, but is somewhat complicated because the VQ indices have to be adaptively clustered in the embedding process. In our proposed method, output vectors are considered as nodes, and nodes are linked as a tree structure and information is embedded by using some of linked vectors. The simulation results show that our proposed method indicates higher SNR than the conventional methods under the same amounts of embedded data.\",\n \"Title: A two-dimensional non-separable implementation of dyadic-valued cosine-sine modulated filter banks for low computational complexity\\nYear: 2013\\n\\nAbstract: This paper aims to design a new two-dimensional (2D) non-separable implementation of dyadic-valued cosine-sine modulated filter banks (2D D-CSMFBs) for low computational complexity. CSMFBs satisfy rich directional selectivity (DS) and shift-invariance (SI), and they can be easily designed by the modulation of a prototype filter. In addition, our previous work introduced a one-dimensional (1D) D-CSMFBs (1D D-CSMFBs). By restricting real-valued filter coefficients to rational-valued ones, they can save computational cost while keeping DS and SI. The proposed 2D implementation in this paper can further reduce the computational cost by unifying the conventional 2D separable structure into a 2D non-separable one directly. Furthermore, experimental results of image non-linear approximation show that the proposed 2D D-CSMFBs are comparable or even better than the 1D D-CSMFBs.\",\n \"Title: Design of optimized prefilters for time-domain lapped transforms with various downsampling factors\\nYear: 2014\\n\\nAbstract: In image compression, decimation and interpolation techniques as pre-/postprocessing of a compression framework offer excellent energy compaction capability for low bit rate image coding. However, an interpolation filter cannot be defined as the inverse matrix of the given decimation filter since the former is a wide matrix and the latter is a tall one. Even without any compression, there will be some distortions in the reconstructed image. To tackle the problem, interpolation-dependent image downsampling (IDID) method produces optimized downsampled images, which leads to the optimized prefilter of a given postfilter. In this paper, we propose to integrate IDID with time-domain lapped transforms (TDLTs) to improve image coding performances.\",\n \"Title: Retargeting pyramid using direct decimation\\nYear: 2014\\n\\nAbstract: The retargeting pyramid (RP) is a multiscale image pyramid using content-aware image resizing. In the previous implementation of the RP, a two-step interpolation is adopted to obtain the desired resolution. However, this interpolation leads to performance loss for image processing. In this paper, we improve the performance of the RP by replacing the two-step interpolation with a single interpolation using a matrix representation of the bilateral filter and Tikhonov regularization.\",\n \"Title: Seam carving with rate-dependent seam path information\\nYear: 2011\\n\\nAbstract: In this paper, we present a seam carving, which is a well-known content-aware image resizing method, with a constraint on bitrates for seam path information (SPI). The SPI corresponds to pixel positions to be pruned and it generally requires high bitrate when we store or transmit it. However, the SPI should be transmitted to re ceivers in the case that the receivers are devices with low computing power, such as cell phones and PDAs since seam carving at the receivers is a computationally-demanding process. We resolve the problem by applying piecewise linear approximation to the seam paths. In the experimental results, the retargeted images yielded by the original seam carving and the proposed one are very similar, whereas required bitrate for the SPI in the proposed method is 34-97 % less than the original one.\",\n \"Title: Oversampled Graph Laplacian Matrix for Graph Filter Banks\\nYear: 2014\\n\\nAbstract: We describe a method of oversampling signals defined on a weighted graph by using an oversampled graph Laplacian matrix. The conventional method of using critically sampled graph filter banks has to decompose the original graph into bipartite subgraphs, and a transform has to be performed on each subgraph because of the spectral folding phenomenon caused by downsampling of graph signals. Therefore, the conventional method cannot always utilize all edges of the original graph in a single stage transformation. Our method is based on oversampling of the underlying graph itself, and it can append nodes and edges to the graph somewhat arbitrarily. We use this approach to make one oversampled bipartite graph that includes all edges of the original non-bipartite graph. We apply the oversampled graph with the critically sampled graph filter bank or the oversampled one for decomposing graph signals and show the performances on some experiments.\",\n \"Title: Lifting-based paraunitary filterbanks for lossy/lossless image coding\\nYear: 2006\\n\\nAbstract: This paper introduces one of the image transform methods using M-channel paraunitary filterbanks (PUFBs) based on Householder matirx. First, redundant parameters of PUFB are eliminated by using the fact that they can be factorized into Givens rotation matices. Next, we propose an eliminating redundant parameters method based on Householder matrix using relationship between Givens rotation and Householder matrices. In addition, PUFBs are factorized into the lifting structure for lossless image coding, and we call them as lifting-based PUFBs (LBPUFBs). LBPUFBs based on Householder matrix have less number of rounding operators than Givens rotation matrix version, since proposed structure is efficiency for lossless image coding. Finally, we show some exsamples to validate our method in lossy/lossless image coding.\",\n \"Title: A novel image coding scheme by using two-channel complex-valued filter banks\\nYear: 2007\\n\\nAbstract: A novel image coding scheme by using two-channel complex-valued filter banks (CFBs) is proposed in this paper. Up to now, most of the image coding schemes have used real-valued filter banks (RFBs). In other words, there are no efficient image coding framework using CFBs since the amount of samples transformed by CFBs is twice as much as that of RFBs. However, CFBs are expected to have better frequency characteristics than RFBs, since CFBs have more design parameters than that of RFBs. Thus, if the problem is solved, CFBs could be efficient for image coding. In this paper, a new algorithm which can preserve the amount of information when CFBs are applied to image coding is introduced. It is shown that the proposed image coding scheme using CFBs is superior to conventional real-valued wavelet-based image coding.\",\n \"Title: Design of M-channel perfect reconstruction filterbanks with IIR-FIR hybrid building blocks\\nYear: 2006\\n\\nAbstract: This paper discusses a new structure of M-channel IIR perfect reconstruction filterbanks. A novel design implementation for the new building block defined as a product of an IIR building block and an FIR building block is presented. The IIR building blocks are written by state space representation, in which we can easily obtain a stability of the filterbank by placing its eigen values inside the unit circle. Due to cascading of building blocks, we can get more free parameters. We introduce the condition how we obtain the new building blocks without increasing of the filter order. Additionally, by showing the simulation results, we show that our proposed FBs have better stopband attenuation than a conventional method.\",\n \"Title: A fast binary arithmetic coding using probability table of expanded symbols\\nYear: 2012\\n\\nAbstract: The arithmetic coding is known as a highly efficient entropy coding. However, its encoding and decoding are time-consuming due to the symbol-by-symbol arithmetic operations. Several approaches have been proposed to reduce the computation time by replacing the multiplication with the addition, subtraction or shift operation. However, these approximations cause a loss of the coding efficiency. In this paper, we propose a fast binary arithmetic coding by using a precomputed probability table of expanded symbols. Simulation results show that the codec speed, especially the encoding speed, is improved by our method while the coding efficiency is almost same as that of the conventional method.\",\n \"Title: A directional and shift-invariant transform based on M-channel rational-valued cosine-sine modulated filter banks\\nYear: 2012\\n\\nAbstract: This paper proposes a directional and shift-invariant transform based on M-channel rational-valued cosine-sine modulated filter banks (R-CSMFBs) for the practical implementation on hardware devices. M-channel CSMFBs can be easily designed by the modulation of a prototype filter and achieve a good stopband attenuation. In addition, in our previous work, the directionality and the shift-invariance of CSMFBs have been theoretically clarified. Thus, they can be an alternative choice of the dual-tree complex wavelet transform (DTCWT) which is one of the most popular directional and shift-invariant transforms. In this paper, it is shown that the proposed lifting-based structure of the R-CSMFB can also achieve rich directional selectivity and the shift-invariance even if the lifting coefficients are rounded to rational values. Finally, the R-CSMFB can provide better stopband attenuation and image denoising performance than that of the conventional M-channel rational-valued DTCWT in the simulation.\",\n \"Title: Directional image decomposition using retargeting pyramid\\nYear: 2012\\n\\nAbstract: Retargeting pyramid (RP) is an alternative method for multiscale image decomposition to the well-known Laplacian pyramid (LP). RP can be obtained by replacing the low-pass filtering process in LP with content-aware image resizing (a.k.a. retargeting), which is a developing technique for computer vision researches. Furthermore, we use RP for contourlet-based directional image decomposition. In experimental results, the proposed decomposition outperforms the LP-based contourlet transform for image denoising.\",\n \"Title: Digital image watermarking method using between-class variance\\nYear: 2012\\n\\nAbstract: We propose a low-complexity, pixel-based watermarking method utilizing the edge areas in an image. In this method, an image is divided into blocks and the blocks are classified as edge blocks and non-edge blocks. The histogram of pixels in an edge block is a bi-modal distribution which has two peaks. Therefore, these pixels are further classified into two classes named “high peak class” and “low peak class”. Then, a bit of the watermark is embedded by controlling the between-class variance by changing the pixel values in each class of the edge block. We utilized the discriminant analysis method for deciding a threshold and controlling the between-class variance. The proposed method can improve the image quality while maintaining robustness against compression and smoothing. In this paper, we evaluated the image quality of watermarked images using PSNR and SSIM. We also evaluated robustness against JPEG compression and Gaussian filtering.\",\n \"Title: Scalable image representation using improved retargeting pyramid\\nYear: 2013\\n\\nAbstract: The retargeting pyramid (RP) method is a good alternative to the well-known Laplacian pyramid (LP) approach for multiscale image decomposition. RP can be obtained by replacing the low-pass filtering and downsampling processes in LP with content-aware image resizing (a.k.a. retargeting), which is a technique being developed in computer vision research. In this paper, we improve RP so that it obtains good scalable image representation. The improved RP is then integrated with a well-known multiscale-multidirection (MSMD) transform, contourlet transform, to construct a saliency-oriented MSMD image representation. In the experiment, our decomposition outperforms the conventional pyramid structures.\",\n \"Title: Three-Dimensional Reference Interaction Site Model Self-Consistent Field Analysis Of Solvent And Substituent Effects On The Absorption Spectra Of Brooker'S Merocyanine\\nYear: 2015\\n\\nAbstract: Solvent and substituent effects on the absorption spectra of Brooker's merocyanine (BM) are investigated using the three-dimensional reference interaction site model self-consistent field method and time-dependent density functional theory. The -* excitation energies are computed for BM and its derivative 2,6-di-tert-butyl (di-t-Bu) BM. The behaviors of the computed excitation energies with increasing solvent polarity are in good agreement with those of the corresponding experimental measurements. In addition, analysis of the solute-solvent interaction energies and spatial distribution functions reveals that the effects of the solvent on the absorption spectra are reduced by the steric hindrance of the t-Bu groups. Furthermore, from the difference in the solute-solvent interaction energies of BM and di-t-Bu BM, it is shown that the effect of the t-Bu substituents on the absorption spectrum is greater in high-polarity solvents. (c) 2015 Wiley Periodicals, Inc.\",\n \"Title: Near Orthogonal Oversampled Graph Filter Banks\\nYear: 2016\\n\\nAbstract: A framework for oversampled filter banks for graph signals was recently proposed by Tanaka and Sakiyama (2014). It was shown that M-channel oversampled filter bank which is exactly orthogonal is not possible with polynomial spectral filter functions. The result is an extension of the critically sampled framework of Narang and Ortega (2012). In this work we present a solution that is nearly orthogonal which has the advantage of giving a nearly tight transform, i.e. nearly equal frame bounds. A family of target functions which satisfy the orthogonality condition is constructed using Bernstein polynomials. The design of the spectral filters is easily achieved through an approximation of the target functions. Filters with good spectral characteristics that are nearly orthogonal can be easily obtained using the proposed method.\",\n \"Title: Oversampled bipartite graphs with controlled redundancy\\nYear: 2015\\n\\nAbstract: This paper extends our previous work on graph oversampling for graph signal processing. In the graph oversampling method, nodes are duplicated and edges are appended to construct oversampled graph Laplacian matrix. It can convert an arbitrary K-colorable graph into one bipartite graph which includes all edges of the original graph. Since it uses a coloring-based algorithm, performance of graph signal processing depends on the coloring results. In this paper, we present graph oversampling based on a few different graph bipartition methods which use maximum spanning tree and eigendecomposition. Furthennore, we consider the effective selection method of duplicated nodes. The performance of the oversampled graphs is compared through an experiment on graph signal denoising.\",\n \"Title: Critically Sampled Graph Wavelets Converted From Linear-Phase Biorthogonal Wavelets\\nYear: 2015\\n\\nAbstract: This paper presents a design method of critically sampled graph wavelet transforms (CSGWTs) utilizing real-valued biorthogonal linear-phase wavelets for regular signals. Their filter characteristics are equivalent to those of biorthogonal linear-phase wavelets and can be expressed by real-valued closed-form with the sum of sinusoidal waves in the graph spectral domain. The proposed CSGWTs satisfy the perfect reconstruction condition for graph signals. Since the proposed filters are smooth functions, they are well-behaved even if we use a lower-order polynomial approximation. The performance of the proposed CSGWTs is evaluated by comparison with existing CSGWTs.\",\n \"Title: Globalized BM3D using fast eigenvalue filtering\\nYear: 2015\\n\\nAbstract: In this paper, we propose a progressive image denoising method using iterative filtering with Chebyshev polynomial approximation (CPA). It is known that a non-local/local image denoising method can be represented as matrix notation, and its denoising performance is improved by filtering the eigenvalues of the filter matrix. However, the eigenvalue filtering requires much computation time for eigendecomposition. To filter eigenvalues effectively, we proposed a fast eigenvalue filtering method using CPA [1]. The method drastically reduces the computation time but it still requires to construct a large sparse matrix. It often leads to much computational complexity. To overcome the problem, we propose an eigenvalue filtering method which does not construct a filter matrix by using the characteristic of the CPA. Experimental results show that our method is fast and applicable to large-size images. Additionally, the denoising performance of our method is almost better than those of the previous methods both in visual qualities and objective measures.\",\n \"Title: Edge preserving multiscale image decomposition with customized domain transform filters\\nYear: 2015\\n\\nAbstract: In this paper, a multiscale image decomposition method based on domain transform is proposed. The domain transform is a high speed edge preserving smoothing method and can be used to many image processing applications. However, it is highly sensitive to noise. The proposed method is based on filters used in the domain transform but is designed to be robust to noise by employing a multiscale method. An optimization problem is formulated to obtain desired domain-transformed output. As expected, the method can be used to many applications as the domain transform. We implement edge preserving smoothing for noise-free/noisy images and detail enhancement as possible applications. The results are satisfactory even in the noisy environment.\",\n \"Title: Subspace-constrained multilinear discriminant analysis for ERP-based brain computer interface classification.\\nYear: 2015\\n\\nAbstract: Classification in brain computer interfaces (BCIs) frequently suffers from small sample problem which leads an ill-posed problem and overfitting/overtraining. To avoid and reduce the problems, we propose a multilinear formed linear discriminant analysis with constraints which are derived from other datasets. The proposed method prevents the ill-posed problem by reducing variables and improves robustness by transferring information from other datasets. The experimental results show that the proposed method improves classification accuracy in event-related potential-based BCIs.\",\n \"Title: Graph Signal Denoising via Trilateral Filter on Graph Spectral Domain.\\nYear: 2016\\n\\nAbstract: This paper presents a graph signal denoising method with the trilateral filter defined in the graph spectral domain. The original trilateral filter (TF) is a data-dependent filter that is widely used as an edge-preserving smoothing method for image processing. However, because of the data-dependency, one cannot provide its frequency domain representation. To overcome this problem, we establish the...\",\n \"Title: Spectral Graph Wavelets and Filter Banks With Low Approximation Error.\\nYear: 2016\\n\\nAbstract: We propose filter banks in the graph spectral domain, where each filter is defined by a sum of sinusoidal waves. The main advantages of these filter banks are that (a) they have low approximation errors even if a lower-order shifted Chebyshev polynomial approximation is used, (b) the upper bound of the error after the pth order Chebyshev polynomial approximation can be calculated rigorously withou...\",\n \"Title: Efficient sensor position selection using graph signal sampling theory.\\nYear: 2016\\n\\nAbstract: We consider the problem of selecting optimal sensor placements. The proposed approach is based on the sampling theorem of graph signals. We choose sensors that maximize the graph cut-off frequency, i.e., the most informative sensors for predicting the values on unselected sensors. We study the existing methods in the context of graph signal processing and clarify the relationship between these methods and the proposed approach. The effectiveness of our approach is verified through numerical experiments, showing advantages in prediction error and execution time.\",\n \"Title: Dimensionality reduction of sample covariance matrices by graph fourier transform for motor imagery brain-machine interface\\nYear: 2016\\n\\nAbstract: An efficient method for dimensionality reduction in classification of multi-class electroencephalogram (EEG) during motor imagery (MI) aiming at brain-machine interfacing is proposed. In this method, the reduction of dimensions is achieved by spectral decomposition of a given graph, which is defined by a geometrical distribution of electrodes on the head surface. The resulting subspace reduces the dimension of EEG signals, and therefore, the size of the sample co-variance matrix (SCM) of EEG can also be reduced. The reduction method is combined with a differential geometry-based approach called tangent space mapping (TSM) that can map a SCM in a Riemannian manifold onto an element in an Euclidean space called a tangent space. Thus, any machine learning algorithm that works in the Euclidean space can be applied. Results of two-class and four-class classification of EEG during MI show that the proposed method of dimensionality reduction increases the recognition accuracy even in the case of a training dataset having a small number of elements.\",\n \"Title: Critically sampled graph filter banks with polynomial filters from regular domain filter banks.\\nYear: 2017\\n\\nAbstract: Graph signal processing deals with the processing of signals defined on irregular domains and is an emerging area of research. Graph filter banks allow the wavelet transform to be extended for processing graph signals. Sakiyama and Tanaka (2015) 22 recently proposed a technique to convert linear-phase biorthogonal filter banks for regular domain signals to biorthogonal graph filter banks. Perfect reconstruction is preserved using the technique but the resulting spectral filter functions are transcendental and not polynomial. Polynomial function filters are desired for the localization property and implementation efficiency. In this work we present alternative techniques to perform the conversion. Perfect reconstruction is preserved with the proposed techniques and the resulting spectral filters are polynomial functions. HighlightsWe design filter banks for signal defined over bipartite graphs.Spectral filters are polynomials resulting in localization and efficient implementation.Technique is based on converting 1D regular domain filter banks.Perfect reconstruction is exactly preserved and spectral response shape is almost preserved.\",\n \"Title: Deforming Pyramid: Multiscale Image Representation Using Pixel Deformation And Filters For Non-Equispaced Signals\\nYear: 2016\\n\\nAbstract: We propose an edge-preserving multiscale image decomposition method using filters for non-equispaced signals. It is inspired by the domain transform, which is a high-speed edge-preserving smoothing method, and it can be used in many image processing applications. One of the disadvantages of the domain transform is sensitivity to noise. Even though the proposed method is based on non-equispaced filters similar to the domain transform, it is robust to noise since it employs a multiscale decomposition. It uses the Laplacian pyramid scheme to decompose an input signal into the piecewise-smooth components and detail components. We design the filters by using an optimization based on edge-preserving smoothing with a conversion of signal distances and filters taking into account the distances between signal intervals. In addition, we also propose construction methods of filters for non-equispaced signals by using arbitrary continuous filters or graph spectral filters in order that various filters can be accommodated by the proposed method. As expected, we find that, similar to state-of-the-art edge-preserving smoothing techniques, including the domain transform, our approach can be used in many applications. We evaluated its effectiveness in edge-preserving smoothing of noise-free and noisy images, detail enhancement, pencil drawing, and stylization.\",\n \"Title: Multilinear Discriminant Analysis With Subspace Constraints for Single-Trial Classification of Event-Related Potentials.\\nYear: 2016\\n\\nAbstract: The classification accuracy of a brain-computer interface (BCI) frequently suffers from ill-posed and overfitting problems. To avoid and alleviate these problems, we propose a method of a multilinear discriminant analysis with constraints to augment parameter reduction, regularization, and additional prior information for event-related potential (ERP)-based BCIs. The method reduces the number of p...\",\n \"Title: Smoothing of xDAWN spatial filters for robust extraction of event-related potentials.\\nYear: 2016\\n\\nAbstract: The xDAWN algorithm is well-known as a method for designing spatial filters to improve signal-to-noise ratio and to reduce the dimension of observed EEG signals. This paper proposes a method for spatially smoothing xDAWN spatial filters to give a robustness against small sample problem. The proposed method gives a subspace constraint to the parameter space of the spatial filters. This subspace is given as a basis of the graph Fourier transform on a graph representing geometric structure of the electrodes. The spatial filters found in the subspace are smooth in the spatial domain. The proposed method is evaluated by experiments with artificial signals and BCI datasets.\",\n \"Title: Mesh-based image retargeting with spectral graph filtering.\\nYear: 2016\\n\\nAbstract: We propose a calculation method of deformed image pixel positions for mesh-based image retargeting. Image retargeting is a sophisticated image resizing method which yields resized images with acceptable quality even if we resize the image into different aspect ratio from the original one. It often employs a mesh-based approach, where pixels are nodes of a graph and relationships between pixels are represented as its edges. In this paper, we reformulate a pixel position deformation of image retargeting as a spectral graph filtering with a graph signal processing-based approach. We validate our method through some image retargeting examples with an appropriately designed filter kernels in the graph spectral domain.\",\n \"Title: Solvatochromism And Preferential Solvation Of Brooker'S Merocyanine In Water-Methanol Mixtures\\nYear: 2017\\n\\nAbstract: The excitation energy of Brooker's merocyanine in water-methanol mixtures shows nonlinear behavior with respect to the mole fraction of methanol, and it was suggested that this behavior is related to preferential solvation by methanol. We investigated the origin of this behavior and its relation to preferential solvation using the three-dimensional reference interaction site model self-consistent field method and time-dependent density functional theory. The calculated excitation energies were in good agreement with the experimental behavior. Analysis of the coordination numbers revealed preferential solvation by methanol. The free energy component analysis implied that solvent reorganization and solvation entropy drive the preferential solvation by methanol, while the direct solute-solvent interaction promotes solvation by water. The difference in the preferential solvation effect on the ground and excited states causes the nonlinear excitation energy shift. (c) 2017 Wiley Periodicals, Inc.\",\n \"Title: Spectral Domain Sampling of Graph Signals.\\nYear: 2018\\n\\nAbstract: Sampling methods for graph signals in the graph spectral domain are presented. Though the conventional sampling of graph signals can be regarded as sampling in the graph vertex domain, it does not have the desired characteristics in regard to the graph spectral domain. With the proposed methods, the down- and upsampled graph signals inherit the frequency-domain characteristics of the sampled signa...\",\n \"Title: Construction of undersampled graph filter banks via row subset selection\\nYear: 2016\\n\\nAbstract: This paper introduces a construction method of M-channel under-sampled spectral graph filter banks. They can be applied to any kind of undirected graphs, use arbitrary critically sampled or oversampled analysis filters, and obtain low redundancy, which is less than 1, regardless of the number of the analysis filters. We formulate the construction problem as a row subset selection method of the transform matrix of the prototype (critically sampled or oversampled) filter banks. In the experiment, a graph signal on Minnesota Traffic Graph is decomposed to examine the performance of our spectral graph filter banks.\",\n \"Title: Non-Blind Deconvolution Of Point Cloud Attributes In Graph Spectral Domain\\nYear: 2017\\n\\nAbstract: We propose a non-blind deconvolution algorithm of point cloud attributes inspired by multi-Wiener SURE-LET deconvolution for images. The image reconstructed by the SURE-LET approach is expressed as a linear combination of multiple filtered images where the filters are defined on the frequency domain. The coefficients of the linear combination are calculated so that the estimate of mean squared error between the original and restored images is minimized. Although the approach is very effective, it is only applicable to images. Recently we have to handle signals on irregular grids, e.g., texture data on 3D models, which are often blurred due to diffusion or motions of objects. However, we cannot utilize image processing-based approaches straightforwardly since these high-dimensional signals cannot be transformed into their frequency domain. To overcome the problem, we use graph signal processing (GSP) for deblurring the complex-structured data. That is, the SURE-LET approach is redefined on GSP, where the Wiener-like filtering is followed by the subband decomposition with an analysis graph filter bank, and then thresholding for each subband is performed. In the experiments, the proposed method is applied to blurred textures on 3D models and synthetic sparse data. The experimental results show clearly deblurred signals with SNR improvements.\",\n \"Title: Graph reduction method using localization operator and its application to pyramid transform.\\nYear: 2017\\n\\nAbstract: This paper proposes a graph reduction method by using the localization operator for signals defined on graphs. In the previous work, we proposed a method for sensor position selection considering signal localizations in graph vertex and spectral domains. This paper extends the selection method to graph reduction. It selects downsampled nodes according to the graph cut-off frequencies so that the downsampled signal preserves the much information for predicting the values on removed nodes. Furthermore, it also considers the localization in the graph vertex domain. We design the multiscale analysis with the graph reduction and apply it to graph signal denoising.\",\n \"Title: Design of polynomial approximated filters for signals on directed graphs.\\nYear: 2017\\n\\nAbstract: This paper presents a polynomial approximation method for filtering of signals defined on directed graphs. For spectral graph filtering, the polynomial approximation is often used to avoid full eigendecomposition of variation operators and to achieve the localization both in the vertex and spectral domains. However, the conventional approximation methods are not effective for signal processing on directed graphs. This is because they assume that the graph frequencies, i.e., eigenvalues of the variation operator like adjacency matrix, are real values despite those in directed cases often include complex values. To solve this problem, we approximate filters with a constraint on the magnitude of the polynomial coefficients. The approximated filters obtained by the proposed method have much smaller errors than the conventional polynomial approximation approaches. We show its validity through the experiments on the filter design and graph signal decomposition.\",\n \"Title: Graph Spectral Image Processing.\\nYear: 2018\\n\\nAbstract: Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2-D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structu...\",\n \"Title: A Topic Extraction Method on the Flow of Conversation in Meetings\\nYear: 2017\\n\\nAbstract: In this paper, we present a new topic extraction method for meetings according to the flow of conversation. Our method extracts appropriate topic words according to their importance in the conversation in a time series using text data taken from meetings. Since meetings take up a great deal of time, one of the most important issues for organizations and companies is to improve meeting efficiency. Therefore, we should analyze the contents of the meetings, but in order to do that, it is important to be able to automatically extract the most important topics made during each meeting. The changes in the importance of a topic can be seen in a time series, so it is necessary to utilize topic extraction according to its importance in time series variation during a meeting. We can then find the topic words in a meeting according to their importance in the time series variation by using our method.\",\n \"Title: Multiplierless lifting-based fast X transforms derived from fast Hartley transform factorization\\nYear: 2018\\n\\nAbstract: This paper presents -channel (, , ) multiplierless lifting-based (ML-) fast X transforms (FXTs), where X F (Fourier), C (cosine), S (sine), and H (Hartley), i.e., FFT, FCT, FST, and FHT, derived from FHT factorization as way of lowering the cost of signal (image) processing. The basic forms of ML-FXTs are described. Then, they are customized for efficient image processing. The customized ML-FFT has a real-valued calculation followed by a complex-valued one. The ML-FCT customization for a block size of 8, which is a typical size for image coding, further reduces computational costs. We produce two customized ML-FCTs for lossy and lossless image coding. Numerical simulations show that ML-FFT and ML-FCTs perform comparably to the conventional methods in spite of having fewer operations.\",\n \"Title: Dynamic Color Lines\\nYear: 2018\\n\\nAbstract: This paper proposes a new color model of videos, Dynamic Color Lines, as an extension of Color Lines Model for images. With our model, videos are assumed to be represented as dynamically moving elongated clusters in a 3-D RGB space. This can be done by assigning each pixel to the nearest cluster and by updating cluster directions and locations alternatively. The dynamic color lines model provides a heuristic approach for a new sparse and robust representation of videos. In numerical experiments, segmentation of surveillance videos and compression are performed.\",\n \"Title: Design Of Sampling Matrices In Graph Frequency Domain For Graph Signal Processing\\nYear: 2018\\n\\nAbstract: We propose a novel sampling method of graph signals that simultaneously inherits the characteristics of the sampled signals in the vertex and graph frequency domains. In the conventional sampling of graph signals, the sampled signal can retain the characteristics only in one (vertex or graph frequency) domain, but it is not suitable for another domain. The proposed method forms an optimization problem to design appropriate sampling matrices in the graph frequency domain for inheriting the characteristics of the sampled signals in the vertex domain. We perform downsampling of graph signals based on the proposed approach and apply it to denoising with graph Laplacian pyramid along with the comparison to the existing methods.\",\n \"Title: A Time Series Structure Analysis Method of a Meeting Using Text Data and a Visualization Method of State Transitions.\\nYear: 2019\\n\\nAbstract: In this paper, we present a time series structure analysis method of a meeting using text data and a method for the visualization of state transitions. Our method evaluates and visualizes the convergence/divergence of the meeting in a time series using text data from the meeting. It is important to facilitate and review meetings for improving efficiency. Therefore, it is important not only to review the final agreement and conclusion in the dialogue during the meeting but also to understand the dialogue process. We introduce two indicators: freshness and representativeness. Our system expresses the status of the meeting in four quadrants (“stagnation”, “exploration”, “deepening”, and “consensus building”) corresponding to the combination of degrees of freshness and representativeness. By conducting an analysis using these indicators, we can objectively find which parts of the dialogue stagnated or advanced the discussion. In addition, it is possible to clarify the meeting process as a structure for review and facilitation, thereby improving efficiency of the meeting. Thus, we implemented a system that realizes this method. Furthermore, we applied this system to real data gathered from meetings consisting of actual multi-company members and verified its effectiveness.\",\n \"Title: Svd For Very Large Matrices: An Approach With Polar Decomposition And Polynomial Approximation\\nYear: 2018\\n\\nAbstract: We propose a fast approximation method for singular value decomposition (SVD) based on polar decomposition (PD), which utilizes Chebyshev polynomial approximation (CPA). SVD is widely used in various applications in data mining, machine learning, and signal/image processing. However, very large computation times are usually required when large matrices need to be decomposed. SVD based on PD (SVDPD) is computationally efficient compared to the traditional SVD approach, as the eigenvalue decomposition in its algorithm can be easily parallelized. However, the PD part of the algorithm still needs to be accelerated due to the requirement of QR decomposition. This computational burden poses a problem both for the traditional SVD and SVDPD. In this paper, we propose an approximation method for PD using CPA. It can bypass QR decomposition, thus leading to the acceleration of SVDPD. First, we show that PD can be performed by \\\"filtering\\\" the eigenvalues of (DD)-D-inverted perpendicular, where D is the target matrix to be decomposed. Then, the actual algorithm and approximate error bound are shown. The proposed approximate SVD can be implemented only by multiplying the matrices; that is, QR decomposition is no longer required. As a result, the approximation of PD with the proposed method significantly reduces the computation cost and can be easily parallelized. Experimental results show that the proposed CPA-based SVDPD can decompose a matrix faster than the conventional approach, and the application of the proposed method does not affect performance.\",\n \"Title: Low-rank Sparse Decomposition of Graph Adjacency Matrices for Extracting Clean Clusters.\\nYear: 2018\\n\\nAbstract: In this paper, we propose a refining method of graphs having clusters. Clean graphs, i.e., those represent relationships between data clearly, are important for various applications. There have been many graph construction or learning methods, however, graphs obtained from the conventional approaches are not specifically designed to yield clean graphs that have dense connections within clusters whereas sparse ones between them. In this paper, we focus on making dense edges denser and sparse edges sparser for refining graphs. In order to make it possible, we propose a low-rank sparse decomposition of an adjacency matrix. We apply the methodology of robust PCA to the adjacency matrix for the decomposition. To obtain a valid adjacency matrix, we further formulate it in a form applicable to ADMM with proper constraints. In the experiments using synthetic data, we validate that the proposed method effectively refines graphs.\",\n \"Title: Fast Singular Value Shrinkage with Chebyshev Polynomial Approximation Based on Signal Sparsity.\\nYear: 2017\\n\\nAbstract: We propose an approximation method for thresholding of singular values using Chebyshev polynomial approximation (CPA). Many signal processing problems require iterative application of singular value decomposition (SVD) for minimizing the rank of a given data matrix with other cost functions and/or constraints, which is called matrix rank minimization. In matrix rank minimization, singular values o...\",\n \"Title: Almost Tight Spectral Graph Wavelets With Polynomial Filters.\\nYear: 2017\\n\\nAbstract: The construction of spectral filters for graph wavelet transforms is addressed in this paper. Both the undecimated and decimated cases will be considered. The filter functions are polynomials and can be implemented efficiently without the need for any eigendecomposition, which is computationally expensive for large graphs. Polynomial filters also have the advantage of the vertex localization prope...\",\n \"Title: Two-Channel Critically Sampled Graph Filter Banks With Spectral Domain Sampling.\\nYear: 2019\\n\\nAbstract: We propose two-channel critically-sampled filter banks for signals on undirected graphs that utilize spectral domain sampling. Unlike conventional approaches based on vertex domain sampling, our transforms have the following desirable properties: first, perfect reconstruction regardless of the characteristics of the underlying graphs and graph variation operators; and second, a symmetric structure...\",\n \"Title: $M$-Channel Critically Sampled Spectral Graph Filter Banks With Symmetric Structure.\\nYear: 2019\\n\\nAbstract: This letter proposes a class of M-channel spectral graph filter banks with a symmetric structure, that is, the transform has sampling operations and spectral graph filters on both the analysis and synthesis sides. The filter banks achieve maximum decimation, perfect recovery, and orthogonality. The proposed transform uses sampling in the graph frequency domain. This enables us to use any variation...\",\n \"Title: Eigendecomposition-Free Sampling Set Selection for Graph Signals.\\nYear: 2019\\n\\nAbstract: This paper addresses the problem of selecting an optimal sampling set for signals on graphs. The proposed sampling set selection (SSS) is based on a localization operator that can consider both vertex domain and spectral domain localizations. We clarify the relationships among the proposed method, sensor position selection methods in machine learning, and existing SSS methods based on graph freque...\",\n \"Title: Time-Varying Graph Learning Based On Sparseness Of Temporal Variation\\nYear: 2019\\n\\nAbstract: We propose a method for graph learning from spatiotemporal measurements. We aim at inferring time-varying graphs under the assumption that changes in graph topology and weights are sparse in time. The problem is formulated as a convex optimization problem to impose a constraint on the temporal relation of the time-varying graph. Experimental results with synthetic data show the effectiveness of our proposed method.\",\n \"Title: Interpolation And Denoising Of Graph Signals Using Plug-And-Play Admm\\nYear: 2019\\n\\nAbstract: Signals defined on a network or a graph are often prone to errors due to missing data and noise. In order to restore the graph signal, interpolation and denoising are two necessary steps along with other graph signal processing procedures. However, existing graph signal interpolation and denoising methods are largely decoupled due to the opposite objectives of the two tasks and the inherent high computational complexity. The goal of this paper is to integrate graph interpolation and denoising using the Plug-and-Play (PnP) ADMM, a recently developed technique in image processing. When using the subsampling process as the forward model and graph filter as the denoiser, we show that PnP ADMM is equivalent to interpolating a bandlimited signal. Preliminary results are demonstrated via experiments, where the proposed method shows significantly better performance over existing methods.\",\n \"Title: Sampling Signals on Graphs: From Theory to Applications\\nYear: 2020\\n\\nAbstract: The study of sampling signals on graphs, with the goal of building an analog of sampling for standard signals in the time and spatial domains, has attracted considerable attention recently. Beyond adding to the growing theory on graph signal processing (GSP), sampling on graphs has various promising applications. In this article, we review the current progress on sampling over graphs, focusing on theory and potential applications.\",\n \"Title: Graph Signal Restoration Using Nested Deep Algorithm Unrolling\\nYear: 2022\\n\\nAbstract: Graph signal processing is a ubiquitous task in many applications such as sensor, social, transportation and brain networks, point cloud processing, and graph neural networks. Often, graph signals are corrupted in the sensing process, thus requiring restoration. In this paper, we propose two graph signal restoration methods based on deep algorithm unrolling (DAU). First, we present a graph signal denoiser by unrolling iterations of the alternating direction method of multiplier (ADMM). We then suggest a general restoration method for linear degradation by unrolling iterations of Plug-and-Play ADMM (PnP-ADMM). In the second approach, the unrolled ADMM-based denoiser is incorporated as a submodule, leading to a nested DAU structure. The parameters in the proposed denoising/restoration methods are trainable in an end-to-end manner. Our approach is interpretable and keeps the number of parameters small since we only tune graph-independent regularization parameters. We overcome two main challenges in existing graph signal restoration methods: 1) limited performance of convex optimization algorithms due to fixed parameters which are often determined manually. 2) large number of parameters of graph neural networks that result in difficulty of training. Several experiments for graph signal denoising and interpolation are performed on synthetic and real-world data. The proposed methods show performance improvements over several existing techniques in terms of root mean squared error in both tasks.\",\n \"Title: GRAPH SIGNAL DENOISING USING NESTED-STRUCTURED DEEP ALGORITHM UNROLLING\\nYear: 2021\\n\\nAbstract: In this paper, we propose a deep algorithm unrolling (DAU) based on a variant of the alternating direction method of multiplier (ADMM) called Plug-and-Play ADMM(PnP-ADMM) for denoising of signals on graphs. DAU is a trainable deep architecture realized by unrolling iterations of an existing optimization algorithm which contains trainable parameters at each layer. We also propose a nested-structured DAU: Its submodules in the unrolled iterations are also designed by DAU. Several experiments for graph signal denoising are performed on synthetic signals on a community graph and U.S. temperature data to validate the proposed approach. Our proposed method outperforms alternative optimization- and deep learning-based approaches.\",\n \"Title: DESIGN OF GRAPH SIGNAL SAMPLING MATRICES FOR ARBITRARY SIGNAL SUBSPACES\\nYear: 2021\\n\\nAbstract: We propose a design method of sampling matrices for graph signals that guarantees perfect recovery for arbitrary graph signal subspaces. When the signal subspace is known, perfect reconstruction is always possible from the samples with an appropriately designed sampling matrix. However, most graph signal sampling methods so far design sampling matrices based on the bandlimited assumption and sometimes violates the perfect reconstruction condition for the other signal models. In this paper, we formulate an optimization problem for the design of the sampling matrix that guarantees perfect recovery, thanks to a generalized sampling framework for standard signals. In experiments with various signal models, our sampling matrix presents better reconstruction accuracy both for noiseless and noisy situations.\",\n \"Title: Structure-Aware Multi-Hop Graph Convolution for Graph Neural Networks\\nYear: 2022\\n\\nAbstract: We present a spatial graph convolution (GC) to classify signals on a graph. Existing GC methods are limited in using the structural information in the feature space. Furthermore, GCs only aggregate features from one-hop neighboring nodes to the target node in their single step. In this paper, we propose two methods to improve the performance of GCs: 1) Utilizing structural information in the feature space, and 2) exploiting the multi-hop information in one GC step. In the first method, we define three structural features in the feature space: feature angle, feature distance, and relational embedding. The second method aggregates the node-wise features of multi-hop neighbors in a GC. Both methods can be simultaneously used. We also propose graph neural networks (GNNs) integrating the proposed GC for classifying nodes in 3D point clouds and citation networks. In experiments, the proposed GNNs exhibited a higher classification accuracy than existing methods.\",\n \"Title: Directional Analytic Discrete Cosine Frames\\nYear: 2022\\n\\nAbstract: Block frames called directional analytic discrete cosine frames (DADCFs) are proposed for sparse image representation. In contrast to conventional overlapped frames, the proposed DADCFs require a reduced amount of 1) computational complexity, 2) memory usage, and 3) global memory access. These characteristics are strongly required for current high-resolution image processing. Specifically, we propose two DADCFs based on discrete cosine transform (DCT) and discrete sine transform (DST). The first DADCF is constructed from parallel separable transforms of DCT and DST, where the DST is permuted by row. The second DADCF is also designed based on DCT and DST, while the DST is customized to have no DC leakage property which is a desirable property for image processing. Both DADCFs have rich directional selectivity with slightly different characteristics each other and they can be implemented as non-overlapping block-based transforms, especially they form Parseval block frames with low transform redundancy. We perform experiments to evaluate our DADCFs and compare them with conventional directional block transforms in image recovery.\",\n \"Title: Node Clustering of Time-Varying Graphs Based on Temporal Label Smoothness\\nYear: 2021\\n\\nAbstract: We propose a node clustering method for time-varying graphs based on the assumption that the cluster labels are changed smoothly over time. Clustering is one of the fundamental tasks in machine learning, data mining, and signal processing. Although most existing studies focus on the clustering of nodes in static graphs, we often encounter time-varying graphs for time-series data, e.g., social networks, brain functional connectivity, and point clouds. In this paper, we formulate a clustering of nodes in time-varying graphs as an optimization problem based on spectral clustering, with a smoothness constraint of the node labels. Experiments on synthetic and real-world time-varying graphs are conducted to validate the effectiveness of the proposed approach.\",\n \"Title: Regularity-Constrained Fast Sine Transforms\\nYear: 2022\\n\\nAbstract: This letter proposes a fast implementation of the regularity-constrained discrete sine transform (R-DST). The original DST leaks the lowest frequency (DC: direct current) components of signals into high frequency (AC: alternating current) subbands. This property is not desired in many applications, particularly image processing, since most of the frequency components in natural images concentrate in DC subband. The characteristic of filter hanks whereby they do not leak DC components into the AC subbands is called regularity. While an R-DST has been proposed, it has no fast implementation because of the singular value decomposition (SVD) in its internal algorithm. In contrast, the proposed regularity-constrained fast sine transform (R-FST) is obtained by just appending a regularity constraint matrix as a postprocessing of the original DST. When the DST size is M x M (M = 2(l), l is an element of N->= 1), the regularity constraint matrix is constructed from only M/2 - 1 rotation matrices with the angles derived from the output of the DST for the constant-valued signal (i.e., the DC signal). Since it does not require SVD, the computation is simpler and faster than the R-DST while keeping all of its beneficial properties. An image processing example shows that the R-FST has fine frequency selectivity with no DC leakage and higher coding gain than the original DST. Also, in the case of M = 8, the R-FST saved approximately 0.126 seconds in a 2-D transformation of 512 x 512 signals compared with the R-DST because of fewer extra operations.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":92,"string":"92"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13674,"string":"13,674"}}},{"rowIdx":13675,"cells":{"question_id":{"kind":"string","value":"Xubo Yang"},"question":{"kind":"string","value":"Generate an abstract for the title \"Add-on Occlusion: An External Module for Optical See-through Augmented Reality Displays to Support Mutual Occlusion\" using the following items: augmented reality, occlusion, optical see-through, polarization-based, prototype."},"answer":{"kind":"string","value":"The occlusion function benefits augmented reality (AR) in many aspects. However, existing occlusion-capable optical see-through augmented reality (OC-OST-AR) displays are designed by integrating virtual displays into a dedicated occlusion-capable architecture, hereby, we miss merits from emerging OST-AR displays. In this article, we propose an external occlusion module that can be added to common OST-AR displays. Per-pixel occlusion is supported with a small form-factor by using polarization-based optical path compression. The occlusion function can be switched on/off by controlling the incident light polarization. A prototype within a volume of 6 x 6 x 3cm is built. A preliminary experiment proves that occlusion is realized successfully."},"correct_answer":{"kind":"string","value":"The occlusion function benefits augmented reality (AR) in many aspects. However, existing occlusion-capable optical see-through augmented reality (OC-OST-AR) displays are designed by integrating virtual displays into a dedicated occlusion-capable architecture, hereby, we miss merits from emerging OST-AR displays. In this article, we propose an external occlusion module that can be added to common OST-AR displays. Per-pixel occlusion is supported with a small form-factor by using polarization-based optical path compression. The occlusion function can be switched on/off by controlling the incident light polarization. A prototype within a volume of 6 x 6 x 3cm is built. A preliminary experiment proves that occlusion is realized successfully."},"documents":{"kind":"list like","value":["Title: Real-time horizon-based reflection occlusion\nYear: 2009\n\nAbstract: Reflection occlusion(RO) method can yield the self-occluded effect when using environment maps to produce a reflection scene. In most modern renderers the common approaches to achieve RO are the ray-trace method and the static reflection maps method. We propose screen-space reflection occlusion(SSRO) method to compute RO in screen-space like screen-space ambient occlusion in [Bavoil et al. 2008] and screen-space directional occlusion in [Ritschel et al. 2009]. Our different strategies for both calculating the occlusion value and sampling in the depth image enable SSRO to calculate RO in real-time and to deal with dynamic scenes, variational lighting environments and changing views.","Title: Multisensory Musical Entertainment Systems\nYear: 2004\n\nAbstract: Two musical entertainment systems驴the Bubble Bumble and the Magic Music Desk驴let users create and play back music in 3D physical environments through augmented reality interfaces. During the process of creating and playing back music, users encounter visual驴speech, visual驴tactile, and tactile-auditory experiences.","Title: Relighting with real incident light source\nYear: 2008\n\nAbstract: This paper presents an approach that can relight captured image with real incident light source. We first captured basis images of objects illuminated with a projector as the basis light source. We then established a pixel level mapping relationship between a real incident light source and the basis light source. Finally we developed a relighting algorithm that can simulate the relit effects with the real light source. The techniques can be applied to mixed reality.","Title: Interactive Theatre Experience in Embodied + Wearable Mixed Reality Space\nYear: 2002\n\nAbstract: This paper presents an interactive theatre based on an embodied mixed reality space and wearable computers. Embodied computing mixed reality spaces integrate ubiquitous computing, tangible interaction and social computing within a mixed reality space, which enables intuitive interaction with physical world and virtual world. We believe it has potential advantages to support novel interactive theatre experiences. Therefore, we explored thenovel interactive theatre experience supported in the embodied mixed reality space, and implemented live 3D characters to interact with user in such a system.","Title: Human Pacman: a mobile, wide-area entertainment system based on physical, social, and ubiquitous computing\nYear: 2004\n\nAbstract: Human Pacman is a novel interactive entertainment system that ventures to embed the natural physical world seamlessly with a fantasy virtual playground by capitalizing on mobile computing, wireless LAN, ubiquitous computing, and motion-tracking technologies. Our human Pacman research is a physical role-playing augmented-reality computer fantasy together with real human–social and mobile gaming. It emphasizes collaboration and competition between players in a wide outdoor physical area which allows natural wide-area human–physical movements. Pacmen and Ghosts are now real human players in the real world, experiencing mixed computer graphics fantasy–reality provided by using the wearable computers. Virtual cookies and actual tangible physical objects are incorporated into the game play to provide novel experiences of seamless transitions between real and virtual worlds. We believe human Pacman is pioneering a new form of gaming that anchors on physicality, mobility, social interaction, and ubiquitous computing.","Title: Human Pacman: a mobile wide-area entertainment system based on physical, social, and ubiquitous computing\nYear: 2004\n\nAbstract: Human Pacman is a novel mixed reality interactive entertainment system that ventures to embed the natural physical world seamlessly with a fantasy virtual playground by capitalizing on infrastructure provided by wearable computer, mixed reality, and ubiquitous computing research. We have progressed from the old days of 2D arcade Pacman on screens, with incremental development, to the popular 3D game home console Pacman, and the recent mobile online Pacman. Finally with our research system Human Pacman, we have a physical role-playing computer fantasy together with real human-social and mobile-gaming that emphasizes on collaboration and competition between players in a wide outdoor area that allows natural wide-area human-physical movements. Pacmen and Ghosts are now human players in the real world experiencing computer graphics fantasy-reality by using the wearable computers on them. Virtual cookies and actual tangible physical objects are incorporated into the game play to provide unique experiences of seamless transitions between real and virtual worlds. We believe Human Pacman is pioneering a new form of gaming that anchors on physicality, mobility, social interaction, and ubiquitous computing.","Title: Robust and Fast Keypoint Recognition Based on SE-FAST\nYear: 2009\n\nAbstract: In this paper, we present a key point recognition scheme, which consists of a novel feature detector and an efficient descriptor. Inspired by FAST (features from accelerated segment test), our feature detector is easy to compute and has high repeatability. Scale-invariance and optimized robustness are gained by extending traditional FAST to scale space.We combine this detector with an adapted version of SURF (speed up robust features) descriptor, providing the system with all means to do feature matching and object detection. Experimental evaluation and comparison with standard SURF using Hessian matrix-based detector are included in this paper, showing improvement in speed with comparable robustness.","Title: Automatic detail analysis for fluid animation\nYear: 2011\n\nAbstract: We present an automatic fluid animation detail analysis method based on the wavelet theory. In our method, the fluid domain is divided into high frequency part and low frequency part through wavelet decomposition. Detail area can be detected automatically as the high frequency part. For this, we can use finer grid in the detail area to preserve detail. Also we provide a two-layer solver for fluid simulation. In this solver we solve low resolution grid in the common area and high resolution grid in the detail area. With this approach, we can get better visual result than the pure low resolution grid and be more efficient than the pure high resolution gird","Title: A Framework for Tangible User Interfaces within Projector-based Mixed Reality\nYear: 2007\n\nAbstract: This paper proposes a framework named TIPMR, for designing tangible user interfaces (TUIs) within projector-based mixed reality applications. The framework divides the target application into three parts: GUI-based application, TUI and an assistant. The assistant is employed as an adapter to translate between TUI operations and general GUI commands like mouse or keyboard events. This architecture makes it easier to focus on designing GUI-based applications and TUI separately. We built a tourist guidance system with two different tangible interaction modes based on TIPMR to demonstrate its usefulness and efficiency.","Title: A Real-Time Procam System For Interaction With Chinese Ink-And-Wash Cartoons\nYear: 2007\n\nAbstract: This poster describes our recently developed real-time projector-camera system for interaction with Chinese Inkand-Wash Cartoons. We implement a real-time interactive water simulation under the acceleration of GPUtogether with a sensing subsystem using computer vision techniques. Combined with Chinese stylized fish rendered in process, the system provides real-time interactions with traditional Chinese paintings. By stirring up the still water fish and other essential elements of Chinese paintings, we hope to present new interaction techniques and more lively Chinese painting sceneries compared to those in traditional static settings.","Title: Validation and prediction of traditional Chinese physical operation on spinal disease using multiple deformation models.\nYear: 2011\n\nAbstract: Purpose Traditional Chinese medical massage is a physical manipulation that achieves satisfactory results on spinal diseases, according\n to its advocates. However, the method relies on an expert’s experience. Accurate analysis and simulation of massage are essential\n for validation of traditional Chinese physical treatment. The objective of this study is to provide analysis and simulation\n that can reproducibly verify and predict treatment efficacy.\n \n \n \n \n Methods An improved physical multi-deformation model for simulating human cervical spine is proposed. First, the human spine, which\n includes muscle, vertebrae and inter- vertebral disks, are segmented and reconstructed from clinical CT and MR images. Homogeneous\n landmark registration is employed to align the spine models before and after the massage manipulation. Central line mass spring\n and contact FEM deformation models are used to individually evaluate spinal anatomy variations. The response of the human\n spine during the massage process is simulated based on specific clinical cases.\n \n \n \n \n Results Ten sets of patient data, including muscle–force relationships, displacement of vertebrae, strain and stress distribution\n on inter-vertebral disks were collected, including the pre-operation, post-operation and the 3-month follow-up. The simulation\n results demonstrate that traditional Chinese massage could significantly affect and treat most mild spinal disease.\n \n \n \n \n Conclusion A new method that simulates a traditional Chinese medical massage operation on the human spine may be a useful tool to scientifically\n validate and predict treatment efficacy.","Title: Interactive image based relighting with physical light acquisition\nYear: 2007\n\nAbstract: We present an interactive image-based technique to relight real scene with physical light sources acquisition. Firstly, basis images of the real scene are acquired from a fixed viewpoint. Secondly, light direction is estimated and physical light image is captured. Thirdly, measurement image between the reference light image and the novel light image is computed for relighting scene. We demonstrate the technique by interactively relighting a pre-captured real scene using a hand held spotlight projecting light on a white paper.","Title: Dynamic Environment Matting Using Radiance\nYear: 2006\n\nAbstract: Environment matting captures not only a foreground object and its opacity matte, but also a description of how that object refracts and reflects light. As radiance value is more accurate than color value when sampling a real scene and displaying it on digital devices, a method of dynamic environment matting in radiance space is presented to decrease the error caused by the nonlinearity of the optical system. The results of the approach can get more accurate and detailed realities compared to the composite images in color space. An image sequence is generated using two images taken with two different viewpoints. Then a simulation of the backdrop moving along the line which is parallel to the line connecting the two optical centers could be obtained as we composite all of these frames with the matte of the foreground object.","Title: Touch space: an embodied computing mixed reality game space\nYear: 2002\n\nAbstract: This paper presents a novel computer entertainment system which recaptures human touch and physical interaction with the real-world environment as essential elements of the game play, whilst also maintaining the exciting fantasy features of traditional computer entertainment. Our system called “Touch-Space” is an embodied (ubiquitous, tangible, and social) computing based mixed reality (MR) game space which regains the social aspects of traditional game plays. In this novel game space, the real-world environment is an essential and intrinsic game element and the human’s physical context influences the game play. It allows tangible interactions between players and virtual objects, and collaborations between players in different levels of reality. Thus, the system re-invigorates computer entertainment systems with social human-to-human and human-to-physical touch interactions.","Title: A Novel Projection Technique with Detail Capture and Shape Correction for Smoke Simulation.\nYear: 2013\n\nAbstract: Smoke simulation on a large grid is quite time consuming and most of the computation time is spent on the projection step. We present a novel projection method which produces quite similar visual results as those produced with the traditional projection method, but uses much less computation time. Our method includes two steps: detail-capture and shape-correction. The first step preserves most of the smoke details using an efficient DST (Discrete Sine Transformation) Poisson Solver with auxiliary boundary sweeping. The second step maintains the overall flow shape by solving a correcting Poisson equation on a coarse grid. Our algorithm is very fast and quite easy to implement. Experiments show that our projection is approximately 1030 times faster than the traditional projection with PCG(Preconditioned Conjugate Gradient), while convincingly preserving both the flow details and the overall shape of the smoke.","Title: A practical radiometric compensation method for projector-based augmentation\nYear: 2008\n\nAbstract: Radiometric compensation has made it possible for a projector to display on ordinary surface with colors and textures. For previous methods, it’s necessary to calibrate both the projector and camera at first. The calibration can be time-consuming and needs to be redone once the system settings change. We present a method that simplifies the calibration process. As a result, the system is more practicable for ad-hoc setups.","Title: Interactive smoke simulation and rendering on the GPU\nYear: 2013\n\nAbstract: We present a fluid dynamic system, which allows to moving objects and real-time simulations of large scale three dimensional smokes. Our system includes a GPU accelerated Eulerian fluid solver which is well suited for real-time application since it is unconditionally stable, takes constant calculation time per frame, and with high computing precision. We introduce a specialized geometry multigrid algorithm for solving the Poisson equation. To further accelerate the simulation, we design data structures and algorithms to exploit the computing power of modern GPUs. Our method can also handle moving solid objects as well as cells that are partially filled with solids. Moreover, we implement a GPU-accelerated volume rendering system that treats the particles as participating media and can render effects such as smoke, dust, or mist. This combination of related algorithms leads to real-time simulation system. We show the effectiveness of the proposed methods in several scenarios, all running at interactive rates on a modern GPU.","Title: GPU-based rendering and animation for Chinese painting cartoon\nYear: 2007\n\nAbstract: This paper presents a real-time rendering system for generating a Chinese ink-and-wash cartoon. The objective is to free the animators from laboriously designing traditional Chinese painting appearance. The system constitutes a morphing animation framework and a rendering process. The whole rendering process is based on Graphic Process Unit (GPU), including interior shading, silhouette extracting and background shading. Moreover, the morphing framework is created to automatically generate Chinese painting cartoon from a set of surface mesh models. These techniques can be applied to real-time Chinese style entertainment application.","Title: Multi-interfaces based refractive rendering\nYear: 2010\n\nAbstract: We presented an multi-interfaces image based method to simulate the refraction and related light effects in real time on a normal graphic card. The multi-interfaces based representation of the refractor is obtained by hiring depth peeling ideas. This leads to significantly better results than two interfaces refraction where only the front and back face of the object was captured.","Title: Visual Effects in Computer Games\nYear: 2009\n\nAbstract: Computer game players have already set the agenda for future visual effects and techniques: One group seeks to enjoy movie-quality photorealistic games, while another seeks maximum freedom of interaction in an open, self-evolving, persistent virtual world.","Title: An experimental study on the role of software synthesized 3D sound in augmented reality environments\nYear: 2004\n\nAbstract: Investigation of augmented reality (AR) environments has become a popular research topic for engineers, computer and cognitive scientists. Although application oriented studies focused on audio AR environments have been published, little work has been done to vigorously study and evaluate the important research questions of the effectiveness of 3D sound in the AR context, and to what extent the ad...","Title: A low-latency 3D teleconferencing system with image based approach\nYear: 2013\n\nAbstract: In this paper, we present a fully dynamic 3D one-to-one teleconferencing system that allows remote collaborators to see each other from different viewpoints. The local 3D scene including the participants is reconstructed with multiple calibrated color plus depth sensors and rendered under the tracked remote users viewpoint in real time. The rendering color and depth buffers are encoded as H.264 stream and transferred to the remote site. An image-based approach is applied to synthesize the desired viewing results in the remote site and reduce the interaction delay. Our system is featured with low latency, high frame rate and constant network flow.","Title: Image assisted remote visualization of volume data\nYear: 2004\n\nAbstract: Three-dimensional volume data as a new type of resources for digital library poses a challenge for user to visualize the data remotely with ordinary network and user terminal. This paper presents a decoupling architecture with a front-end viewer and a back-end server. Multiple reference images are produced by the server and warped and composed by the front-end. This approach enables remote visualization at interactive speed and also protects original data.","Title: Creating and Preserving Vortical Details in SPH Fluid\nYear: 2010\n\nAbstract: We present a new method to create and preserve the turbulent details generated around moving objects in SPH fluid. In our approach, a high-resolution overlapping grid is bounded to each object and translates with the object. The turbulence formation is modeled by resolving the local flow around objects using a hybrid SPH-FLIP method. Then these vortical details are carried on SPH particles flowing through the local region and preserved in the global field in a synthetic way. Our method provides a physically plausible way to model the turbulent details around both rigid and deformable objects in SPH fluid, and can efficiently produce animations of complex gaseous phenomena with rich visual details.","Title: An experimental study on the role of 3D sound in augmented reality environment\nYear: 2004\n\nAbstract: Investigation of augmented reality (AR) environments has become a popular research topic for engineers, computer and cognitive scientists. Although application oriented studies focused on audio AR environments have been published, little work has been done to vigorously study and evaluate the important research questions of the effectiveness of three-dimensional (3D) sound in the AR context, and t...","Title: Efficient visibility projection on spherical polar coordinates for shadow rendering using geometry shader.\nYear: 2008\n\nAbstract: Visibility precomputation has become essential for PRT-based rendering involving soft shadows. While graphic hardware rasterization has been increasingly used to compute visibility information for a mass of precomputation tasks, the linear feature of rasterization makes it unadaptive to sample visibility in a coordinates system, to which the Cartesian coordinates are non-linearly mapped. With the emergence of geometry shader in the framework of DirectX 10, this problem can be solved. In this paper, we present an efficient solution for visibility sampling on the spherical polar coordinates in the precomputation stage of PRT by means of the geometry shader within the DirectX 10 pipelines. In the mean time, a spherical reparameterization scheme is introduced for shadow rendering, under which spherical functions as lighting, BRDF and visibility are mapped onto the spherical polar coordinates.","Title: Real-time Human Pose and Shape Estimation for Virtual Try-On Using a Single Commodity Depth Camera.\nYear: 2014\n\nAbstract: We present a system that allows the user to virtually try on new clothes. It uses a single commodity depth camera to capture the user in 3D. Both the pose and the shape of the user are estimated with a novel real-time template-based approach that performs tracking and shape adaptation jointly. The result is then used to drive realistic cloth simulation, in which the synthesized clothes are overlayed on the input image. The main challenge is to handle missing data and pose ambiguities due to the monocular setup, which captures less than 50 percent of the full body. Our solution is to incorporate automatic shape adaptation and novel constraints in pose tracking. The effectiveness of our system is demonstrated with a number of examples.","Title: Human Pacman: a sensing-based mobile entertainment system with ubiquitous computing and tangible interaction\nYear: 2003\n\nAbstract: Human Pacman is an interactive ubiquitous and mobile entertainment system that is built upon position and perspective sensing via Global Positioning System and inertia sensors; and tangible human-computer interfacing with the use of Bluetooth and capacitive sensors. Although these sensing-based subsystems are weaved into the fabric of the game and are therefore translucent to players, they are nevertheless the technical enabling forces behind Human Pacman. The game strives to bring the computer gaming experience to a new level of emotional and sensory gratification by embedding the natural physical world ubiquitously and seamlessly with a fantasy virtual playground. We have progressed from the old days of 2D arcade Pacman on screens, with incremental development, to the popular 3D game console Pacman, and the recent mobile online Pacman. With our novel Human Pacman, we have a physical role-playing computer fantasy together with real human-social and mobile-gaming that emphasizes on collaboration and competition between players in a wide outdoor physical area that allows natural wide-area human-physical movements. Pacmen and Ghosts are now real human players in the real world experiencing mixed computer graphics fantasy-reality provided by using wearable computers that are equipped with GPS and inertia sensors for players' position and perspective tracking. Virtual cookies and actual tangible physical objects with Bluetooth devices and capacitive sensors are incorporated into the game play to provide novel experiences of seamless transitions between real and virtual worlds. In short, we believe Human Pacman is pioneering a new form of gaming that is based on sensing technology and anchored on physicality, mobility, social interaction, and ubiquitous computing.","Title: Human pacman: a wide area socio-physical interactive entertainment system in mixed reality\nYear: 2004\n\nAbstract: Human Pacman is a novel mixed reality interactive entertainment system that ventures to embed the natural physical world seamlessly with a fantasy virtual playground by capitalizing on infrastructure provided by wearable computer, mixed reality, and ubiquitous computing research. We have progressed from the old days of 2D arcade Pacman on screens, with incremental development, to the popular 3D game home console Pacman, and the recent mobile online Pacman. Finally with our research system Human Pacman, we have a physical role-playing computer fantasy together with real human-social and mobile-gaming that emphasizes on collaboration and competition between players in a wide outdoor area that allows natural wide-area human-physical movements. Pacmen and Ghosts are now human players in the real world experiencing computer graphics fantasy-reality by using the wearable computers on them. Virtual cookies and actual tangible physical objects are incorporated into the game play to provide unique experiences of seamless transitions between real and virtual worlds. We believe Human Pacman is pioneering a new form of gaming that anchors on physicality, mobility, social interaction,and ubiquitous computing.","Title: A practical framework for virtual viewing and relighting\nYear: 2007\n\nAbstract: Recently many practical applications have concerned with observing objects that have specular reflection properties. They intend to know how the specular reflections and other details vary according to different lighting conditions and view positions. In this paper, we propose an innovative framework combining the novel view synthesis algorithm and the relighting algorithm to fulfill these requirements. Another important feature of this framework is that all the algorithms are based on image without acquiring any 3D model which may contain some high confidential secrets. Meanwhile an image measurement criterion is proposed to verify the feasibility and effectiveness of the framework.","Title: GIVE: A General Interactive Visualization Environment\nYear: 1997\n\nAbstract: We present a data flow based software platform called GIVE (General Interactive Visualization Environment). GIVE owns a data flow kernel, a visual programming interface and an extensible module library. It greatly enhances the efficiency of building visualization programs. GIVE has the following distinctive features: it introduces control nodes and procedure nodes into traditional data flow architecture, so it can support complex ViSC application: it is a well opened environment, supported by two efficient interactive tools-GIVE/ME (Module Extender) and GIVE/DE (Data Extender)-which enables users to extend modules and data types into the system freely and conveniently; and it has a module library composed of varieties of modules. We first introduce several concepts of the data flow architecture, then describe the designation of GIVE, and finally discuss the implementation of GIVE","Title: Object creation based on free virtual grid with ARGlass\nYear: 2014\n\nAbstract: Object creation is popular in AR applications. People always load a model instead of creating models by themselves because loading a model is a simple replacement of creating people's own models if they lack the drawing skill or capability to use sophisticated modeling software proficiently. Our work provides people with an intuitive and convenient modeling method to create their own models.","Title: Position-based fluid control\nYear: 2015\n\nAbstract: We present a novel fluid control method that is capable of driving particle-based fluid simulation to match a rapidly changing target while keeping natural fluid-like motion. To achieve the desired behavior, we first generate control particles by sampling the target shape and then apply a non-linear constraint to each control particle, with its neighboring fluid particles keeping a constant fluid density within its influence region. This density constraint is highly in line with the incompressible nature of the fluid, which can drive the fluid to match the target shape in a natural way. In addition, to match a fast moving or deforming target, we add an adaptive spring for each fluid particle in the control region, connecting with its nearest control particle. The spring constraint takes effect only when the fluid particle is far from its corresponding control particle to avoid introducing artificial viscosity. Therefore, the fluid particles are well controlled even if the target shape changes rapidly. Furthermore, we integrate a velocity constraint to adjust the stiffness of the controlled fluid. All these three constraints are solved under position-based framework which enables our simulation fast, robust and well-suitable for interactive applications. We demonstrate the efficiency and effectiveness of our method in various scenarios in real time.","Title: Fluid simulation using overcomplete dictionaries based on sparse coding\nYear: 2014\n\nAbstract: This paper proposed a couple of overcomplete dictionaries method based on sparse coding, and applied it into fluid simulation in order to improve fine-scale details for fluid. We use the local fine structure of high-resolution fluid data to learn an overcomplete dictionary, which can match various complex local fine structures of high-resolution fluid data and be used to recover these local fine structures by combined with the sparse representation. We calculate a low-resolution dictionary by multiplying the learned high-resolution dictionary with a sampling matrix. To apply the coupled dictionaries into fluid simulation, we map the local fine structures of the high-resolution fluid data to their low-resolution version before the projection step, and the projection step is computed in a low-resolution grid, then we use the trained overcomplete dictionaries to up-sample and reconstruct the low-resolution fluid data to their high-resolution version. The reconstructed high-resolution fluid data will be used to compute the next frame. This framework avoids to compute the projection step on a high-resolution grid, which can accelerate the computation of fluid simulation. The result of this method will be shown in the Result Section, and proves that our method can recover the details of fluid to a certain extend.","Title: Hand-based interaction for object manipulation with augmented reality glasses\nYear: 2014\n\nAbstract: An increase number of gesture sensors and AR(augmented reality) displays are emerging these years. There are more and more study working on spatial interaction. In this work, we focus on evaluating the feasibility of hand-based interaction designed for AR glasses to do basic manipulation on 3D virtual objects in AR applications with current interaction technology. We design a user study with 10 volunteers to discuss whether and how to use natural user interfaces like gestures, with the help of 3D menus, to finish the tasks of manipulating AR scenes. The interface including 3D menus presents the advantages of usability and merits of intuition; However, it also exposes some issues like ergonomic discomfort and limitation like imprecise input. So our results indicate the kind of interactive device and technology are valuable in natural interaction without extra knowledge, though they are limited in serious applications now.","Title: A practical GPU accelerated surface reconstruction\nYear: 2014\n\nAbstract: This paper presents a novel liquid surface reconstruction framework based on GPU acceleration. The method can fast reconstruct triangle mesh from large set of particle data. This algorithm builds parallel spatial hash table and AABB structure, which improves the efficiency of surface reconstruction. In order to fast compute triangle mesh from implicit function on GPU, we also modified the traditional Histogram Pyramid based Marching Cubes algorithm on GPU. We compared the effect and efficiency with some other surface reconstruction methods, which proves that the method can improve the reconstruction efficiency and still maintain the detail. This paper builds some scenes and generates scene animation, which proves this method can be used in many common liquid animation scenes.","Title: The Role of 3-D Sound in Human Reaction and Performance in Augmented Reality Environments\nYear: 2007\n\nAbstract: Three-dimensional sound's effectiveness in virtual reality (VR) environments has been widely studied. However, due to the big differences between VR and augmented reality (AR) systems in registration, calibration, perceptual difference of immersiveness, navigation, and localization, it is important to develop new approaches to seamlessly register virtual 3-D sound in AR environments and conduct studies on 3-D sound's effectiveness in AR context. In this paper, we design two experimental AR environments to study the effectiveness of 3-D sound both quantitatively and qualitatively. Two different tracking methods are applied to retrieve the 3-D position of virtual sound sources in each experiment. We examine the impacts of 3-D sound on improving depth perception and shortening task completion time. We also investigate its impacts on immersive and realistic perception, different spatial objects identification, and subjective feeling of \"human presence and collaboration\". Our studies show that applying 3-D sound is an effective way to complement visual AR environments. It helps depth perception and task performance, and facilitates collaborations between users. Moreover, it enables a more realistic environment and more immersive feeling of being inside the AR environment by both visual and auditory means. In order to make full use of the intensity cues provided by 3-D sound, a process to scale the intensity difference of 3-D sound at different depths is designed to cater small AR environments. The user study results show that the scaled 3-D sound significantly increases the accuracy of depth judgments and shortens the searching task completion time. This method provides a necessary foundation for implementing 3-D sound in small AR environments. Our user study results also show that this process does not degrade the intuitiveness and realism of an augmented audio reality environment","Title: Data-driven projection method in fluid simulation.\nYear: 2016\n\nAbstract: Physically based fluid simulation requires much time in numerical calculation to solve Navier-Stokes equations. Especially in grid-based fluid simulation, because of iterative computation, the projection step is much more time-consuming than other steps. In this paper, we propose a novel data-driven projection method using an artificial neural network to avoid iterative computation. Once the grid resolution is decided, our data-driven method could obtain projection results in relatively constant time per grid cell, which is independent of scene complexity. Experimental results demonstrated that our data-driven method drastically speeded up the computation in the projection step. With the growth of grid resolution, the speed-up would increase strikingly. In addition, our method is still applicable in different fluid scenes with some alterations, when computational cost is more important than physical accuracy. Copyright © 2016 John Wiley & Sons, Ltd.","Title: An Interactive 2D-to-3D Cartoon Modeling System.\nYear: 2016\n\nAbstract: In this paper, we propose an interactive system that can quickly convert a 2D cartoon painting into a 3D textured cartoon model, enabling non-professional adults and children to easily create personalized 3D contents. Our system exploits a new approach based on solving Poisson equations to generate 3D models, which is free from the limitations of spherical topology in prior works. We also propose a novel method to generate whole textures for both sides of the models to deliver colorful appearances, making it possible to obtain stylized models rendered with cartoon textures. The results have shown that our method can greatly simplify the modeling process comparing with both traditional modeling softwares and prior sketch-based systems.","Title: Real-time high-quality surface rendering for large scale particle-based fluids.\nYear: 2017\n\nAbstract: Particle-based methods like Smoothed Particle Hydrodynamics (SPH) are increasingly adopted for large scale fluid simulation in interactive computer graphics. However, surface rendering for such dynamic particle sets is challenging: current methods either produce coarse results, or time consuming. We introduce a novel approach to render high-quality fluid surface in screen space by an efficient combination of particle splatting, ray-casting and surface normal estimation techniques. We apply particle splatting to accelerate ray-casting process, and estimate surface normal using Principal Component Analysis (PCA). We adopt GPU technique to further accelerate our method. Our method can produce high-quality smooth surface while preserving thin and sharp details of large scale fluids. The computation and memory cost of our rendering step only depend on the image resolution. These advantages make our method very suitable for previewing or rendering hundreds of millions particles interactively. We demonstrate the efficiency and effectiveness of our method by rendering various fluid scenarios with different-sized particle sets.","Title: Real-time full-body motion reconstruction and recognition for off-the-shelf VR devices.\nYear: 2016\n\nAbstract: Almost all VR multi-person applications have the requirement of reconstructing the whole-body motions in real-time in order to create deeper immersion. In fact, if we can ensure that the motion we reconstruct is natural, we can improve upon the comfort and number of wearing sensors at the cost of reconstruction precision because all users in VR are blindfolded. In this paper, we introduce a novel real-time motion reconstruction and recognition method in VR only using the positions and orientations of user's head and two hands. To reconstruct natural motions according to rare sensors, we divide the whole body into the upper body and the lower body. The upper body reconstruction algorithm based on inverse kinematics which is more accurate and the lower body based on animation blending which only needs a small number of prepared animations. Meanwhile, a natural action recognition algorithm based on neural network will run in need to detect the target motions we have trained. We show that our method can reconstruct various full-body motions, such as walking in any direction, jogging, jumping, crouching and turning. Our method has the ability to reconstruct natural human motions which can be used in almost all VR multi-person interactive applications. The configuration of sensors we attached on the head and hands is very popular in VR well-known devices, so the method can be easily integrated into off-the-shelf VR devices.","Title: Real-time head pose estimation for driver assistance system using low-cost on-board computer.\nYear: 2016\n\nAbstract: We propose a fast head pose estimation method using monocular video. It is highly optimized for on-board computers and for driving situations, which is applicable to existing low-cost on-board computer for cars and suitable for high real-time driver assistance systems. In our algorithm pipeline, the face detection step eliminates slow floating point computations using pixel intensity binary test, and reduce search scope effectively. In the face alignment step, we utilize the high performance of local binary feature and extend the single pose regression model to handle large rotations. The pose estimation step uses a mean rigid face model to calculate head pose fast by solving 2D-3D correspondence. To reduce computation further, we bypass or simplify pipeline steps using previous frame result, and redo the full pipeline adaptively. Experiments show our method is more efficient than existing approaches, which makes high real-time applications for on-board computers possible.","Title: A Schur Complement Preconditioner for Scalable Parallel Fluid Simulation\nYear: 2017\n\nAbstract: We present an algorithmically efficient and parallelized domain decomposition based approach to solving Poisson’s equation on irregular domains. Our technique employs the Schur complement method, which permits a high degree of parallel efficiency on multicore systems. We create a novel Schur complement preconditioner which achieves faster convergence, and requires less computation time and memory. This domain decomposition method allows us to apply different linear solvers for different regions of the flow. Subdomains with regular boundaries can be solved with an FFT-based Fast Poisson Solver. We can solve systems with 1,0243 degrees of freedom, and demonstrate its use for the pressure projection step of incompressible liquid and gas simulations. The results demonstrate considerable speedup over preconditioned conjugate gradient methods commonly employed to solve such problems, including a multigrid preconditioned conjugate gradient method.","Title: Proposal for Optimization of Spool Valve Flow Force Based on the MATLAB-AMESim-FLUENT Joint Simulation Method.\nYear: 2018\n\nAbstract: This paper focuses on the open-center multi-way valve used in loader buckets. To solve the problem of excessive flow force that leads to spool clamping in the reset process, joint simulations adopting MATLAB, AMESim, and FLUENT were carried out. Boundary conditions play a decisive role in the results of computational fluid dynamics (CFD) simulation. However, the boundary conditions of valve ports depend on the hydraulic system's working condition and are significantly impacted by the port area, which has always been neglected. This paper starts with the port area calculation method, then the port area curves are input into the simulation hydraulic system, obtaining the flow curves of valve port as output, which are then applied as the boundary conditions of the spool valve CH) simulation. Therefore, the steady-state flow force of the spool valve is accurately calculated, and the result verifies the hypothesis that excess flow force causes spool clamping. Based on this, four kinds of structures were introduced in an attempt to improve the situation, and simulating calculation and theoretical analysis were adopted to verify the effects of improvement. Results show that the four structures could reduce the peak value of flow force by 17.8%, 60.6%, 61.6%, and 55.7%, respectively. Of the four, structures II, III, and IV can reduce the peak value of flow force to below reset spring force value, thus successfully avoiding the spool clamping caused by flow force.","Title: PROME - projected measures model for fast human shape estimation.\nYear: 2018\n\nAbstract: Human parametric models can provide useful constraints for human shape estimation to produce more accurate results. However, the state-of-art models are computational expensive which limit their wide use in interactive graphics applications. We present PROME (PROjected MEasures) - a novel human parametric model which has high expressive power and low computational complexity. Projected measures are sets of 2D contour poly-lines that capture key measure features defined in anthropometry. The PROME model builds the relationship between 3D shape and pose parameters and 2D projected measures. We train the PROME model in two parts: the shape model formulates deformations of projected measures caused by shape variation, and the pose model formulates deformations of projected measures caused by pose variation. Based on the PROME model we further propose a fast shape estimation method which estimates the 3D shape parameters of a subject from a single image in nearly real-time. The method builds an optimize problem and solves it using gradient optimizing strategy. Experiment results show that the PROME model has well capability in representing human body in different shape and pose comparing to existing 3D human parametric models, such as SCAPE[Anguelov et al. 2005] and TenBo[Chen et al. 2013], yet keeps much lower computational complexity. Our shape estimation method can process an image in about one second, orders of magnitude faster than state-of-art methods, and the estimating result is very close to the ground truth. The proposed method can be widely used in interactive applications such as virtual try-on and virtual reality collaboration.","Title: ToonNet - a cartoon image dataset and a DNN-based semantic classification system.\nYear: 2018\n\nAbstract: Cartoon-style pictures can be seen almost everywhere in our daily life. Numerous applications try to deal with cartoon pictures, a dataset of cartoon pictures will be valuable for these applications. In this paper, we first present ToonNet: a cartoon-style image recognition dataset. We construct our benchmark set by 4000 images in 12 different classes collected from the Internet with little manual filtration. We extend the basal dataset to 10000 images by adopting several methods, including snapshots of rendered 3D models with a cartoon shader, a 2D-3D-2D converting procedure using a cartoon-modeling method and a hand-drawing stylization filter. Then, we describe how to build an effective neural network for image semantic classification based on ToonNet. We present three techniques for building the Deep Neural Network (DNN), namely, IUS: Inputs Unified Stylization, stylizing the inputs to reduce the complexity of hand-drawn cartoon images; FIN: Feature Inserted Network, inserting intuitionistic and valuable global features into the network; NPN: Network Plus Network, using multiple single networks as a new mixed network. We show the efficacy and generality of our network strategies in our experiments. By utilizing these techniques, the classification accuracy can reach 78% (top-1) and 93%(top-3), which has an improvement of about 5% (top-1) compared with classical DNNs.","Title: Touch-Space: Mixed Reality Game Space Based on Ubiquitous, Tangible, and Social Computing\nYear: 2002\n\nAbstract: This paper presents a novel computer entertainment system which recaptures human touch and physical interaction with the real-world environment as essential elements of the game play, whilst also maintaining the exciting fantasy features of traditional computer entertainment. Our system called ‘Touch-Space’ is an embodied (ubiquitous, tangible, and social) computing based Mixed Reality (MR) game space which regains the physical and social aspects of traditional game play. In this novel game space, the real-world environment is an essential and intrinsic game element, and the human’s physical context influences the game play. It also provides the full spectrum of game interaction experience ranging from the real physical environment (human to human and human to physical world interaction), to augmented reality, to the virtual environment. It allows tangible interactions between players and virtual objects, and collaborations between players in different levels of reality. Thus, the system re-invigorates computer entertainment systems with social human-to-human and human-to-physical touch interactions.","Title: Facial expressions recognition based on convolutional neural networks for mobile virtual reality.\nYear: 2016\n\nAbstract: We present a new system designed for enabling direct face-to-face interaction for users wearing a head-mounted displays (HMD) in virtual reality environment. Due to HMD's occlusion of a user's face, VR applications and games are mainly designed for single user. Even in some multi-player games, players can only communicate with each other using audio input devices or controllers. To address this problem, we develop a novel system that allows users to interact with each other using facial expressions in real-time. Our system consists of two major components: an automatic tracking and segmenting face processing component and a facial expressions recognizing component based on convolutional neural networks (CNN). First, our system tracks a specific marker on the front surface of the HMD and then uses the extracted spatial data to estimate face positions and rotations for mouth segmentation. At last, with the help of an adaptive approach for histogram based mouth segmentation [Panning et al. 2009], our system passes the processed lips pixels' information to CNN and get the facial expressions results in real-time. The results of our experiments show that our system can effectively recognize the basic expressions of users.","Title: A Novel CNN-based Poisson Solver for Fluid Simulation.\nYear: 2020\n\nAbstract: Solving a large-scale Poisson system is computationally expensive for most of the Eulerian fluid simulation applications. We propose a novel machine learning-based approach to accelerate this process. At the heart of our approach is a deep convolutional neural network (CNN), with the capability of predicting the solution (pressure) of a Poisson system given the discretization structure and the intermediate velocities as input. Our system consists of four main components, namely, a deep neural network to solve the large linear equations, a geometric structure to describe the spatial hierarchies of the input vector, a Principal Component Analysis (PCA) process to reduce the dimension of input in training, and a novel loss function to control the incompressibility constraint. We have demonstrated the efficacy of our approach by simulating a variety of high-resolution smoke and liquid phenomena. In particular, we have shown that our approach accelerates the projection step in a conventional Eulerian fluid simulator by two orders of magnitude. In addition, we have also demonstrated the generality of our approach by producing a diversity of animations deviating from the original datasets.","Title: A CNN-based Flow Correction Method for Fast Preview.\nYear: 2019\n\nAbstract: Eulerian-based smoke simulations are sensitive to the initial parameters and grid resolutions. Due to the numerical dissipation on different levels of the grid and the nonlinearity of the governing equations, the differences in simulation resolutions will result in different results. This makes it challenging for artists to preview the animation results based on low-resolution simulations. In this paper, we propose a learning-based flow correction method for fast previewing based on low-resolution smoke simulations. The main components of our approach lie in a deep convolutional neural network, a grid-layer feature vector and a special loss function. We provide a novel matching model to represent the relationship between low-resolution and high-resolution smoke simulations and correct the overall shape of a low-resolution simulation to closely follow the shape of a high-resolution down-sampled version. We introduce the grid-layer concept to effectively represent the 3D fluid shape, which can also reduce the input and output dimensions. We design a special loss function for the fluid divergence-free constraint in the neural network training process. We have demonstrated the efficacy and the generality of our approach by simulating a diversity of animations deviating from the original training set. In addition, we have integrated our approach into an existing fluid simulation framework to showcase its wide applications.","Title: Airplane detection based on fusion framework by combining saliency model with Deep Convolutional Neural Networks.\nYear: 2017\n\nAbstract: Aircraft detection from very high resolution remote sensing images, has gained more increasing interest in recent years due to the successful civil and military applications. However, several problems still exist: 1) how to extract the high-level features of aircraft; 2) locating objects within such a large image is difficult and time consuming; 3) A common problem of multiple resolutions of satellite images still exists. In this paper, inspirited by biological visual mechanism, the fusion detection framework is proposed, which fusing the top-down visual mechanism (deep CNN model) and bottom-up visual mechanism (GBVS) to detect aircraft. Besides, we use multi-scale training method for deep CNN model to solve the problem of multiple resolutions. Experimental results demonstrate that our method can achieve a better detection result than the other methods.","Title: Render-Based Factorization For Additive Light Field Display\nYear: 2021\n\nAbstract: Augmented Reality (AR) and Virtual Reality (VR) applications enable viewers to experience 3D graphics immersively. However, current hardware either fails to provide multiple viewpoints like projectors or cause conflicts between vergence and accommodation like consumable headsets. Prior researchers have designed multi-view displays to solve the problem by enabling view-dependent images and focus cues. Nevertheless, it requires minutes to calculate one frame, which is critical for real-time AR/VR applications. In this paper, we propose a new render-based factorization for additive light field display and further improve the performance by optimizing the initialization of layers. We next compare our work with the state of the art and the results show that our solution has competitive results while the calculation takes less than 20 ms for each frame.","Title: Towards Stereoscopic On-vehicle AR-HUD\nYear: 2021\n\nAbstract: On-vehicle AR-HUD (head-up display) is a driving assistant system, which can let drivers see navigation and warning information directly through the windshield with an easy-to-perceive augmented reality form. Traditional AR-HUD can only produce the content at a specific distance, which causes uncomfortable experiences such as frequent refocusing and unnatural floating of virtual objects. This paper proposed an innovative AR-HUD system, which can provide stereoscopic scenes to the driver. The system is composed of two traditional HUD displays and supports parallax by additive light field factorization. Optical paths and illumination of two displays are precisely calibrated for both views to ensure the combination of lights is as expected. The factorization algorithm is optimized for the system. With GPU acceleration, the system can run in real time. The system is cheap and simple to transform from a traditional AR-HUD system, which presents a feasible scheme to achieve a fusion-enhanced augmented reality assistant for driving.","Title: Real-time simulation of violent boiling in concentrated sulfuric acid dilution\nYear: 2021\n\nAbstract: Concentrated sulfuric acid dilution is an essential yet hazardous experiment in chemistry education. Incorrect operations can lead to intensive boiling and splattering or even endanger the experiment operator. This paper presents a novel approach to simulate the violent boiling phenomenon in real time and an efficient method to deal with particle penetration caused by fast motion. We introduce the anti-viscosity method to inject momentum into high-temperature particles and capture the chaotic dynamics in the boiling process with small computational overhead. To address the particle penetration issue, we propose a new constraint type called flask constraint which uses radius lookup tables to represent axisymmetric shapes efficiently and projects the particles back into their container when penetration occurs. These methods are integrated into a virtual reality application to simulate the concentrated sulfuric acid dilution experiment and demonstrate the efficiency and effectiveness of our method. Our work improves the capability and stability of particle-based fluid solvers and provides appealing solutions for integrating fluid simulation into interactive applications.","Title: Preliminary Study of Short-Term Visual Perceptual Training Based on Virtual Reality and Augmented Reality in Postoperative Strabismic Patients\nYear: 2022\n\nAbstract: The study aimed to explore the potential effect of short-term visual perceptual training based on virtual reality (VR) and augmented reality (AR) platforms in postoperative strabismic patients. We enrolled 236 postoperative strabismic patients, among whom 111 patients received VR-based training, and 125 patients received AR-based training. The stereoacuity of 1.5 m and dynamic stereopsis were improved by VR training; meanwhile, AR training exhibited more improvement in stereoacuity of 0.8 and 1.5 m, dynamic and coarse stereopsis. It was suggested that the visual perceptual training based on VR and AR technology can be potentially applied in postoperative strabismus treatment to promote the recovery of binocular vision.","Title: Rectangular Mapping-based Foveated Rendering\nYear: 2022\n\nAbstract: With the speedy increase of display resolution and the demand for interactive frame rate, rendering acceleration is becoming more critical for a wide range of virtual reality applications. Foveated rendering addresses this challenge by rendering with a non-uniform resolution for the display. Motivated by the non-linear optical lens equation, we present rectangular mapping-based foveated rendering ...","Title: Retargeting Destinations of Passive Props for Enhancing Haptic Feedback in Virtual Reality\nYear: 2022\n\nAbstract: Haptic retargeting is a commonly used technique to match passive props to virtual objects for adding tactile feedback in Virtual Reality (VR). However, researchers have mainly focused on single-hand retargeting and applied these techniques primarily for tasks of touching objects. In this work, we propose a novel retargeting solution for tasks of grabbing and placing objects in VR, called redirected placement (RP), which is applied when placing virtual objects. The key idea of this method is that when the user places the virtual object, it can enable the user to place the physical prop in a position that is easier to match with multiple other virtual objects, without being detected by the user.","Title: FoV-NeRF: Foveated Neural Radiance Fields for Virtual Reality\nYear: 2022\n\nAbstract: Virtual Reality (VR) is becoming ubiquitous with the rise of consumer displays and commercial VR platforms. Such displays require low latency and high quality rendering of synthetic imagery with reduced compute overheads. Recent advances in neural rendering showed promise of unlocking new possibilities in 3D computer graphics via image-based representations of virtual or physical environments. Specifically, the neural radiance fields (NeRF) demonstrated that photo-realistic quality and continuous view changes of 3D scenes can be achieved without loss of view-dependent effects. While NeRF can significantly benefit rendering for VR applications, it faces unique challenges posed by high field-of-view, high resolution, and stereoscopic/egocentric viewing, typically causing low quality and high latency of the rendered images. In VR, this not only harms the interaction experience but may also cause sickness. To tackle these problems toward six-degrees-of-freedom, egocentric, and stereo NeRF in VR, we present the first gaze-contingent 3D neural representation and view synthesis method. We incorporate the human psychophysics of visual- and stereo-acuity into an egocentric neural representation of 3D scenery. We then jointly optimize the latency/performance and visual quality while mutually bridging human perception and neural scene synthesis to achieve perceptually high-quality immersive interaction. We conducted both objective analysis and subjective studies to evaluate the effectiveness of our approach. We find that our method significantly reduces latency (up to 99% time reduction compared with NeRF) without loss of high-fidelity rendering (perceptually identical to full-resolution ground truth). The presented approach may serve as the first step toward future VR/AR systems that capture, teleport, and visualize remote environments in real-time.","Title: SREFBN: Enhanced feature block network for single-image super-resolution\nYear: 2022\n\nAbstract: Deep learning has assisted the field of single-image super-resolution (SR) in achieving new heights. However, the task of restoring a high-resolution (HR) image from a highly degraded low-resolution (LR) image is sophisticated due to poor image restoration quality. A novel and effective lightweight SR method is presented as super-resolution via an enhanced feature block network (SREFBN) that successfully reconstructs an HR image using a corresponding LR image with a purposed deep residual block. In addition, a novel shared parameters approach in the top-down pathway among low-level feature maps is introduced. The experimental results prove that SREFBN achieves remarkable performance. The presented framework requires lower computational cost and outperforms many state-of-the-art methods. It is also highly adaptable with low-end devices, requiring lower multiplication and adding operations. A trade-off comparison between the number of parameters, execution time, and accuracies is given while also showing different variations of our approach to prove the effectiveness and reliability of the shared parameters. Most importantly, the results indicate that our framework has gained state-of-the-art performance on larger scales 3 and 4. Code is available at ."],"string":"[\n \"Title: Real-time horizon-based reflection occlusion\\nYear: 2009\\n\\nAbstract: Reflection occlusion(RO) method can yield the self-occluded effect when using environment maps to produce a reflection scene. In most modern renderers the common approaches to achieve RO are the ray-trace method and the static reflection maps method. We propose screen-space reflection occlusion(SSRO) method to compute RO in screen-space like screen-space ambient occlusion in [Bavoil et al. 2008] and screen-space directional occlusion in [Ritschel et al. 2009]. Our different strategies for both calculating the occlusion value and sampling in the depth image enable SSRO to calculate RO in real-time and to deal with dynamic scenes, variational lighting environments and changing views.\",\n \"Title: Multisensory Musical Entertainment Systems\\nYear: 2004\\n\\nAbstract: Two musical entertainment systems驴the Bubble Bumble and the Magic Music Desk驴let users create and play back music in 3D physical environments through augmented reality interfaces. During the process of creating and playing back music, users encounter visual驴speech, visual驴tactile, and tactile-auditory experiences.\",\n \"Title: Relighting with real incident light source\\nYear: 2008\\n\\nAbstract: This paper presents an approach that can relight captured image with real incident light source. We first captured basis images of objects illuminated with a projector as the basis light source. We then established a pixel level mapping relationship between a real incident light source and the basis light source. Finally we developed a relighting algorithm that can simulate the relit effects with the real light source. The techniques can be applied to mixed reality.\",\n \"Title: Interactive Theatre Experience in Embodied + Wearable Mixed Reality Space\\nYear: 2002\\n\\nAbstract: This paper presents an interactive theatre based on an embodied mixed reality space and wearable computers. Embodied computing mixed reality spaces integrate ubiquitous computing, tangible interaction and social computing within a mixed reality space, which enables intuitive interaction with physical world and virtual world. We believe it has potential advantages to support novel interactive theatre experiences. Therefore, we explored thenovel interactive theatre experience supported in the embodied mixed reality space, and implemented live 3D characters to interact with user in such a system.\",\n \"Title: Human Pacman: a mobile, wide-area entertainment system based on physical, social, and ubiquitous computing\\nYear: 2004\\n\\nAbstract: Human Pacman is a novel interactive entertainment system that ventures to embed the natural physical world seamlessly with a fantasy virtual playground by capitalizing on mobile computing, wireless LAN, ubiquitous computing, and motion-tracking technologies. Our human Pacman research is a physical role-playing augmented-reality computer fantasy together with real human–social and mobile gaming. It emphasizes collaboration and competition between players in a wide outdoor physical area which allows natural wide-area human–physical movements. Pacmen and Ghosts are now real human players in the real world, experiencing mixed computer graphics fantasy–reality provided by using the wearable computers. Virtual cookies and actual tangible physical objects are incorporated into the game play to provide novel experiences of seamless transitions between real and virtual worlds. We believe human Pacman is pioneering a new form of gaming that anchors on physicality, mobility, social interaction, and ubiquitous computing.\",\n \"Title: Human Pacman: a mobile wide-area entertainment system based on physical, social, and ubiquitous computing\\nYear: 2004\\n\\nAbstract: Human Pacman is a novel mixed reality interactive entertainment system that ventures to embed the natural physical world seamlessly with a fantasy virtual playground by capitalizing on infrastructure provided by wearable computer, mixed reality, and ubiquitous computing research. We have progressed from the old days of 2D arcade Pacman on screens, with incremental development, to the popular 3D game home console Pacman, and the recent mobile online Pacman. Finally with our research system Human Pacman, we have a physical role-playing computer fantasy together with real human-social and mobile-gaming that emphasizes on collaboration and competition between players in a wide outdoor area that allows natural wide-area human-physical movements. Pacmen and Ghosts are now human players in the real world experiencing computer graphics fantasy-reality by using the wearable computers on them. Virtual cookies and actual tangible physical objects are incorporated into the game play to provide unique experiences of seamless transitions between real and virtual worlds. We believe Human Pacman is pioneering a new form of gaming that anchors on physicality, mobility, social interaction, and ubiquitous computing.\",\n \"Title: Robust and Fast Keypoint Recognition Based on SE-FAST\\nYear: 2009\\n\\nAbstract: In this paper, we present a key point recognition scheme, which consists of a novel feature detector and an efficient descriptor. Inspired by FAST (features from accelerated segment test), our feature detector is easy to compute and has high repeatability. Scale-invariance and optimized robustness are gained by extending traditional FAST to scale space.We combine this detector with an adapted version of SURF (speed up robust features) descriptor, providing the system with all means to do feature matching and object detection. Experimental evaluation and comparison with standard SURF using Hessian matrix-based detector are included in this paper, showing improvement in speed with comparable robustness.\",\n \"Title: Automatic detail analysis for fluid animation\\nYear: 2011\\n\\nAbstract: We present an automatic fluid animation detail analysis method based on the wavelet theory. In our method, the fluid domain is divided into high frequency part and low frequency part through wavelet decomposition. Detail area can be detected automatically as the high frequency part. For this, we can use finer grid in the detail area to preserve detail. Also we provide a two-layer solver for fluid simulation. In this solver we solve low resolution grid in the common area and high resolution grid in the detail area. With this approach, we can get better visual result than the pure low resolution grid and be more efficient than the pure high resolution gird\",\n \"Title: A Framework for Tangible User Interfaces within Projector-based Mixed Reality\\nYear: 2007\\n\\nAbstract: This paper proposes a framework named TIPMR, for designing tangible user interfaces (TUIs) within projector-based mixed reality applications. The framework divides the target application into three parts: GUI-based application, TUI and an assistant. The assistant is employed as an adapter to translate between TUI operations and general GUI commands like mouse or keyboard events. This architecture makes it easier to focus on designing GUI-based applications and TUI separately. We built a tourist guidance system with two different tangible interaction modes based on TIPMR to demonstrate its usefulness and efficiency.\",\n \"Title: A Real-Time Procam System For Interaction With Chinese Ink-And-Wash Cartoons\\nYear: 2007\\n\\nAbstract: This poster describes our recently developed real-time projector-camera system for interaction with Chinese Inkand-Wash Cartoons. We implement a real-time interactive water simulation under the acceleration of GPUtogether with a sensing subsystem using computer vision techniques. Combined with Chinese stylized fish rendered in process, the system provides real-time interactions with traditional Chinese paintings. By stirring up the still water fish and other essential elements of Chinese paintings, we hope to present new interaction techniques and more lively Chinese painting sceneries compared to those in traditional static settings.\",\n \"Title: Validation and prediction of traditional Chinese physical operation on spinal disease using multiple deformation models.\\nYear: 2011\\n\\nAbstract: Purpose Traditional Chinese medical massage is a physical manipulation that achieves satisfactory results on spinal diseases, according\\n to its advocates. However, the method relies on an expert’s experience. Accurate analysis and simulation of massage are essential\\n for validation of traditional Chinese physical treatment. The objective of this study is to provide analysis and simulation\\n that can reproducibly verify and predict treatment efficacy.\\n \\n \\n \\n \\n Methods An improved physical multi-deformation model for simulating human cervical spine is proposed. First, the human spine, which\\n includes muscle, vertebrae and inter- vertebral disks, are segmented and reconstructed from clinical CT and MR images. Homogeneous\\n landmark registration is employed to align the spine models before and after the massage manipulation. Central line mass spring\\n and contact FEM deformation models are used to individually evaluate spinal anatomy variations. The response of the human\\n spine during the massage process is simulated based on specific clinical cases.\\n \\n \\n \\n \\n Results Ten sets of patient data, including muscle–force relationships, displacement of vertebrae, strain and stress distribution\\n on inter-vertebral disks were collected, including the pre-operation, post-operation and the 3-month follow-up. The simulation\\n results demonstrate that traditional Chinese massage could significantly affect and treat most mild spinal disease.\\n \\n \\n \\n \\n Conclusion A new method that simulates a traditional Chinese medical massage operation on the human spine may be a useful tool to scientifically\\n validate and predict treatment efficacy.\",\n \"Title: Interactive image based relighting with physical light acquisition\\nYear: 2007\\n\\nAbstract: We present an interactive image-based technique to relight real scene with physical light sources acquisition. Firstly, basis images of the real scene are acquired from a fixed viewpoint. Secondly, light direction is estimated and physical light image is captured. Thirdly, measurement image between the reference light image and the novel light image is computed for relighting scene. We demonstrate the technique by interactively relighting a pre-captured real scene using a hand held spotlight projecting light on a white paper.\",\n \"Title: Dynamic Environment Matting Using Radiance\\nYear: 2006\\n\\nAbstract: Environment matting captures not only a foreground object and its opacity matte, but also a description of how that object refracts and reflects light. As radiance value is more accurate than color value when sampling a real scene and displaying it on digital devices, a method of dynamic environment matting in radiance space is presented to decrease the error caused by the nonlinearity of the optical system. The results of the approach can get more accurate and detailed realities compared to the composite images in color space. An image sequence is generated using two images taken with two different viewpoints. Then a simulation of the backdrop moving along the line which is parallel to the line connecting the two optical centers could be obtained as we composite all of these frames with the matte of the foreground object.\",\n \"Title: Touch space: an embodied computing mixed reality game space\\nYear: 2002\\n\\nAbstract: This paper presents a novel computer entertainment system which recaptures human touch and physical interaction with the real-world environment as essential elements of the game play, whilst also maintaining the exciting fantasy features of traditional computer entertainment. Our system called “Touch-Space” is an embodied (ubiquitous, tangible, and social) computing based mixed reality (MR) game space which regains the social aspects of traditional game plays. In this novel game space, the real-world environment is an essential and intrinsic game element and the human’s physical context influences the game play. It allows tangible interactions between players and virtual objects, and collaborations between players in different levels of reality. Thus, the system re-invigorates computer entertainment systems with social human-to-human and human-to-physical touch interactions.\",\n \"Title: A Novel Projection Technique with Detail Capture and Shape Correction for Smoke Simulation.\\nYear: 2013\\n\\nAbstract: Smoke simulation on a large grid is quite time consuming and most of the computation time is spent on the projection step. We present a novel projection method which produces quite similar visual results as those produced with the traditional projection method, but uses much less computation time. Our method includes two steps: detail-capture and shape-correction. The first step preserves most of the smoke details using an efficient DST (Discrete Sine Transformation) Poisson Solver with auxiliary boundary sweeping. The second step maintains the overall flow shape by solving a correcting Poisson equation on a coarse grid. Our algorithm is very fast and quite easy to implement. Experiments show that our projection is approximately 1030 times faster than the traditional projection with PCG(Preconditioned Conjugate Gradient), while convincingly preserving both the flow details and the overall shape of the smoke.\",\n \"Title: A practical radiometric compensation method for projector-based augmentation\\nYear: 2008\\n\\nAbstract: Radiometric compensation has made it possible for a projector to display on ordinary surface with colors and textures. For previous methods, it’s necessary to calibrate both the projector and camera at first. The calibration can be time-consuming and needs to be redone once the system settings change. We present a method that simplifies the calibration process. As a result, the system is more practicable for ad-hoc setups.\",\n \"Title: Interactive smoke simulation and rendering on the GPU\\nYear: 2013\\n\\nAbstract: We present a fluid dynamic system, which allows to moving objects and real-time simulations of large scale three dimensional smokes. Our system includes a GPU accelerated Eulerian fluid solver which is well suited for real-time application since it is unconditionally stable, takes constant calculation time per frame, and with high computing precision. We introduce a specialized geometry multigrid algorithm for solving the Poisson equation. To further accelerate the simulation, we design data structures and algorithms to exploit the computing power of modern GPUs. Our method can also handle moving solid objects as well as cells that are partially filled with solids. Moreover, we implement a GPU-accelerated volume rendering system that treats the particles as participating media and can render effects such as smoke, dust, or mist. This combination of related algorithms leads to real-time simulation system. We show the effectiveness of the proposed methods in several scenarios, all running at interactive rates on a modern GPU.\",\n \"Title: GPU-based rendering and animation for Chinese painting cartoon\\nYear: 2007\\n\\nAbstract: This paper presents a real-time rendering system for generating a Chinese ink-and-wash cartoon. The objective is to free the animators from laboriously designing traditional Chinese painting appearance. The system constitutes a morphing animation framework and a rendering process. The whole rendering process is based on Graphic Process Unit (GPU), including interior shading, silhouette extracting and background shading. Moreover, the morphing framework is created to automatically generate Chinese painting cartoon from a set of surface mesh models. These techniques can be applied to real-time Chinese style entertainment application.\",\n \"Title: Multi-interfaces based refractive rendering\\nYear: 2010\\n\\nAbstract: We presented an multi-interfaces image based method to simulate the refraction and related light effects in real time on a normal graphic card. The multi-interfaces based representation of the refractor is obtained by hiring depth peeling ideas. This leads to significantly better results than two interfaces refraction where only the front and back face of the object was captured.\",\n \"Title: Visual Effects in Computer Games\\nYear: 2009\\n\\nAbstract: Computer game players have already set the agenda for future visual effects and techniques: One group seeks to enjoy movie-quality photorealistic games, while another seeks maximum freedom of interaction in an open, self-evolving, persistent virtual world.\",\n \"Title: An experimental study on the role of software synthesized 3D sound in augmented reality environments\\nYear: 2004\\n\\nAbstract: Investigation of augmented reality (AR) environments has become a popular research topic for engineers, computer and cognitive scientists. Although application oriented studies focused on audio AR environments have been published, little work has been done to vigorously study and evaluate the important research questions of the effectiveness of 3D sound in the AR context, and to what extent the ad...\",\n \"Title: A low-latency 3D teleconferencing system with image based approach\\nYear: 2013\\n\\nAbstract: In this paper, we present a fully dynamic 3D one-to-one teleconferencing system that allows remote collaborators to see each other from different viewpoints. The local 3D scene including the participants is reconstructed with multiple calibrated color plus depth sensors and rendered under the tracked remote users viewpoint in real time. The rendering color and depth buffers are encoded as H.264 stream and transferred to the remote site. An image-based approach is applied to synthesize the desired viewing results in the remote site and reduce the interaction delay. Our system is featured with low latency, high frame rate and constant network flow.\",\n \"Title: Image assisted remote visualization of volume data\\nYear: 2004\\n\\nAbstract: Three-dimensional volume data as a new type of resources for digital library poses a challenge for user to visualize the data remotely with ordinary network and user terminal. This paper presents a decoupling architecture with a front-end viewer and a back-end server. Multiple reference images are produced by the server and warped and composed by the front-end. This approach enables remote visualization at interactive speed and also protects original data.\",\n \"Title: Creating and Preserving Vortical Details in SPH Fluid\\nYear: 2010\\n\\nAbstract: We present a new method to create and preserve the turbulent details generated around moving objects in SPH fluid. In our approach, a high-resolution overlapping grid is bounded to each object and translates with the object. The turbulence formation is modeled by resolving the local flow around objects using a hybrid SPH-FLIP method. Then these vortical details are carried on SPH particles flowing through the local region and preserved in the global field in a synthetic way. Our method provides a physically plausible way to model the turbulent details around both rigid and deformable objects in SPH fluid, and can efficiently produce animations of complex gaseous phenomena with rich visual details.\",\n \"Title: An experimental study on the role of 3D sound in augmented reality environment\\nYear: 2004\\n\\nAbstract: Investigation of augmented reality (AR) environments has become a popular research topic for engineers, computer and cognitive scientists. Although application oriented studies focused on audio AR environments have been published, little work has been done to vigorously study and evaluate the important research questions of the effectiveness of three-dimensional (3D) sound in the AR context, and t...\",\n \"Title: Efficient visibility projection on spherical polar coordinates for shadow rendering using geometry shader.\\nYear: 2008\\n\\nAbstract: Visibility precomputation has become essential for PRT-based rendering involving soft shadows. While graphic hardware rasterization has been increasingly used to compute visibility information for a mass of precomputation tasks, the linear feature of rasterization makes it unadaptive to sample visibility in a coordinates system, to which the Cartesian coordinates are non-linearly mapped. With the emergence of geometry shader in the framework of DirectX 10, this problem can be solved. In this paper, we present an efficient solution for visibility sampling on the spherical polar coordinates in the precomputation stage of PRT by means of the geometry shader within the DirectX 10 pipelines. In the mean time, a spherical reparameterization scheme is introduced for shadow rendering, under which spherical functions as lighting, BRDF and visibility are mapped onto the spherical polar coordinates.\",\n \"Title: Real-time Human Pose and Shape Estimation for Virtual Try-On Using a Single Commodity Depth Camera.\\nYear: 2014\\n\\nAbstract: We present a system that allows the user to virtually try on new clothes. It uses a single commodity depth camera to capture the user in 3D. Both the pose and the shape of the user are estimated with a novel real-time template-based approach that performs tracking and shape adaptation jointly. The result is then used to drive realistic cloth simulation, in which the synthesized clothes are overlayed on the input image. The main challenge is to handle missing data and pose ambiguities due to the monocular setup, which captures less than 50 percent of the full body. Our solution is to incorporate automatic shape adaptation and novel constraints in pose tracking. The effectiveness of our system is demonstrated with a number of examples.\",\n \"Title: Human Pacman: a sensing-based mobile entertainment system with ubiquitous computing and tangible interaction\\nYear: 2003\\n\\nAbstract: Human Pacman is an interactive ubiquitous and mobile entertainment system that is built upon position and perspective sensing via Global Positioning System and inertia sensors; and tangible human-computer interfacing with the use of Bluetooth and capacitive sensors. Although these sensing-based subsystems are weaved into the fabric of the game and are therefore translucent to players, they are nevertheless the technical enabling forces behind Human Pacman. The game strives to bring the computer gaming experience to a new level of emotional and sensory gratification by embedding the natural physical world ubiquitously and seamlessly with a fantasy virtual playground. We have progressed from the old days of 2D arcade Pacman on screens, with incremental development, to the popular 3D game console Pacman, and the recent mobile online Pacman. With our novel Human Pacman, we have a physical role-playing computer fantasy together with real human-social and mobile-gaming that emphasizes on collaboration and competition between players in a wide outdoor physical area that allows natural wide-area human-physical movements. Pacmen and Ghosts are now real human players in the real world experiencing mixed computer graphics fantasy-reality provided by using wearable computers that are equipped with GPS and inertia sensors for players' position and perspective tracking. Virtual cookies and actual tangible physical objects with Bluetooth devices and capacitive sensors are incorporated into the game play to provide novel experiences of seamless transitions between real and virtual worlds. In short, we believe Human Pacman is pioneering a new form of gaming that is based on sensing technology and anchored on physicality, mobility, social interaction, and ubiquitous computing.\",\n \"Title: Human pacman: a wide area socio-physical interactive entertainment system in mixed reality\\nYear: 2004\\n\\nAbstract: Human Pacman is a novel mixed reality interactive entertainment system that ventures to embed the natural physical world seamlessly with a fantasy virtual playground by capitalizing on infrastructure provided by wearable computer, mixed reality, and ubiquitous computing research. We have progressed from the old days of 2D arcade Pacman on screens, with incremental development, to the popular 3D game home console Pacman, and the recent mobile online Pacman. Finally with our research system Human Pacman, we have a physical role-playing computer fantasy together with real human-social and mobile-gaming that emphasizes on collaboration and competition between players in a wide outdoor area that allows natural wide-area human-physical movements. Pacmen and Ghosts are now human players in the real world experiencing computer graphics fantasy-reality by using the wearable computers on them. Virtual cookies and actual tangible physical objects are incorporated into the game play to provide unique experiences of seamless transitions between real and virtual worlds. We believe Human Pacman is pioneering a new form of gaming that anchors on physicality, mobility, social interaction,and ubiquitous computing.\",\n \"Title: A practical framework for virtual viewing and relighting\\nYear: 2007\\n\\nAbstract: Recently many practical applications have concerned with observing objects that have specular reflection properties. They intend to know how the specular reflections and other details vary according to different lighting conditions and view positions. In this paper, we propose an innovative framework combining the novel view synthesis algorithm and the relighting algorithm to fulfill these requirements. Another important feature of this framework is that all the algorithms are based on image without acquiring any 3D model which may contain some high confidential secrets. Meanwhile an image measurement criterion is proposed to verify the feasibility and effectiveness of the framework.\",\n \"Title: GIVE: A General Interactive Visualization Environment\\nYear: 1997\\n\\nAbstract: We present a data flow based software platform called GIVE (General Interactive Visualization Environment). GIVE owns a data flow kernel, a visual programming interface and an extensible module library. It greatly enhances the efficiency of building visualization programs. GIVE has the following distinctive features: it introduces control nodes and procedure nodes into traditional data flow architecture, so it can support complex ViSC application: it is a well opened environment, supported by two efficient interactive tools-GIVE/ME (Module Extender) and GIVE/DE (Data Extender)-which enables users to extend modules and data types into the system freely and conveniently; and it has a module library composed of varieties of modules. We first introduce several concepts of the data flow architecture, then describe the designation of GIVE, and finally discuss the implementation of GIVE\",\n \"Title: Object creation based on free virtual grid with ARGlass\\nYear: 2014\\n\\nAbstract: Object creation is popular in AR applications. People always load a model instead of creating models by themselves because loading a model is a simple replacement of creating people's own models if they lack the drawing skill or capability to use sophisticated modeling software proficiently. Our work provides people with an intuitive and convenient modeling method to create their own models.\",\n \"Title: Position-based fluid control\\nYear: 2015\\n\\nAbstract: We present a novel fluid control method that is capable of driving particle-based fluid simulation to match a rapidly changing target while keeping natural fluid-like motion. To achieve the desired behavior, we first generate control particles by sampling the target shape and then apply a non-linear constraint to each control particle, with its neighboring fluid particles keeping a constant fluid density within its influence region. This density constraint is highly in line with the incompressible nature of the fluid, which can drive the fluid to match the target shape in a natural way. In addition, to match a fast moving or deforming target, we add an adaptive spring for each fluid particle in the control region, connecting with its nearest control particle. The spring constraint takes effect only when the fluid particle is far from its corresponding control particle to avoid introducing artificial viscosity. Therefore, the fluid particles are well controlled even if the target shape changes rapidly. Furthermore, we integrate a velocity constraint to adjust the stiffness of the controlled fluid. All these three constraints are solved under position-based framework which enables our simulation fast, robust and well-suitable for interactive applications. We demonstrate the efficiency and effectiveness of our method in various scenarios in real time.\",\n \"Title: Fluid simulation using overcomplete dictionaries based on sparse coding\\nYear: 2014\\n\\nAbstract: This paper proposed a couple of overcomplete dictionaries method based on sparse coding, and applied it into fluid simulation in order to improve fine-scale details for fluid. We use the local fine structure of high-resolution fluid data to learn an overcomplete dictionary, which can match various complex local fine structures of high-resolution fluid data and be used to recover these local fine structures by combined with the sparse representation. We calculate a low-resolution dictionary by multiplying the learned high-resolution dictionary with a sampling matrix. To apply the coupled dictionaries into fluid simulation, we map the local fine structures of the high-resolution fluid data to their low-resolution version before the projection step, and the projection step is computed in a low-resolution grid, then we use the trained overcomplete dictionaries to up-sample and reconstruct the low-resolution fluid data to their high-resolution version. The reconstructed high-resolution fluid data will be used to compute the next frame. This framework avoids to compute the projection step on a high-resolution grid, which can accelerate the computation of fluid simulation. The result of this method will be shown in the Result Section, and proves that our method can recover the details of fluid to a certain extend.\",\n \"Title: Hand-based interaction for object manipulation with augmented reality glasses\\nYear: 2014\\n\\nAbstract: An increase number of gesture sensors and AR(augmented reality) displays are emerging these years. There are more and more study working on spatial interaction. In this work, we focus on evaluating the feasibility of hand-based interaction designed for AR glasses to do basic manipulation on 3D virtual objects in AR applications with current interaction technology. We design a user study with 10 volunteers to discuss whether and how to use natural user interfaces like gestures, with the help of 3D menus, to finish the tasks of manipulating AR scenes. The interface including 3D menus presents the advantages of usability and merits of intuition; However, it also exposes some issues like ergonomic discomfort and limitation like imprecise input. So our results indicate the kind of interactive device and technology are valuable in natural interaction without extra knowledge, though they are limited in serious applications now.\",\n \"Title: A practical GPU accelerated surface reconstruction\\nYear: 2014\\n\\nAbstract: This paper presents a novel liquid surface reconstruction framework based on GPU acceleration. The method can fast reconstruct triangle mesh from large set of particle data. This algorithm builds parallel spatial hash table and AABB structure, which improves the efficiency of surface reconstruction. In order to fast compute triangle mesh from implicit function on GPU, we also modified the traditional Histogram Pyramid based Marching Cubes algorithm on GPU. We compared the effect and efficiency with some other surface reconstruction methods, which proves that the method can improve the reconstruction efficiency and still maintain the detail. This paper builds some scenes and generates scene animation, which proves this method can be used in many common liquid animation scenes.\",\n \"Title: The Role of 3-D Sound in Human Reaction and Performance in Augmented Reality Environments\\nYear: 2007\\n\\nAbstract: Three-dimensional sound's effectiveness in virtual reality (VR) environments has been widely studied. However, due to the big differences between VR and augmented reality (AR) systems in registration, calibration, perceptual difference of immersiveness, navigation, and localization, it is important to develop new approaches to seamlessly register virtual 3-D sound in AR environments and conduct studies on 3-D sound's effectiveness in AR context. In this paper, we design two experimental AR environments to study the effectiveness of 3-D sound both quantitatively and qualitatively. Two different tracking methods are applied to retrieve the 3-D position of virtual sound sources in each experiment. We examine the impacts of 3-D sound on improving depth perception and shortening task completion time. We also investigate its impacts on immersive and realistic perception, different spatial objects identification, and subjective feeling of \\\"human presence and collaboration\\\". Our studies show that applying 3-D sound is an effective way to complement visual AR environments. It helps depth perception and task performance, and facilitates collaborations between users. Moreover, it enables a more realistic environment and more immersive feeling of being inside the AR environment by both visual and auditory means. In order to make full use of the intensity cues provided by 3-D sound, a process to scale the intensity difference of 3-D sound at different depths is designed to cater small AR environments. The user study results show that the scaled 3-D sound significantly increases the accuracy of depth judgments and shortens the searching task completion time. This method provides a necessary foundation for implementing 3-D sound in small AR environments. Our user study results also show that this process does not degrade the intuitiveness and realism of an augmented audio reality environment\",\n \"Title: Data-driven projection method in fluid simulation.\\nYear: 2016\\n\\nAbstract: Physically based fluid simulation requires much time in numerical calculation to solve Navier-Stokes equations. Especially in grid-based fluid simulation, because of iterative computation, the projection step is much more time-consuming than other steps. In this paper, we propose a novel data-driven projection method using an artificial neural network to avoid iterative computation. Once the grid resolution is decided, our data-driven method could obtain projection results in relatively constant time per grid cell, which is independent of scene complexity. Experimental results demonstrated that our data-driven method drastically speeded up the computation in the projection step. With the growth of grid resolution, the speed-up would increase strikingly. In addition, our method is still applicable in different fluid scenes with some alterations, when computational cost is more important than physical accuracy. Copyright © 2016 John Wiley & Sons, Ltd.\",\n \"Title: An Interactive 2D-to-3D Cartoon Modeling System.\\nYear: 2016\\n\\nAbstract: In this paper, we propose an interactive system that can quickly convert a 2D cartoon painting into a 3D textured cartoon model, enabling non-professional adults and children to easily create personalized 3D contents. Our system exploits a new approach based on solving Poisson equations to generate 3D models, which is free from the limitations of spherical topology in prior works. We also propose a novel method to generate whole textures for both sides of the models to deliver colorful appearances, making it possible to obtain stylized models rendered with cartoon textures. The results have shown that our method can greatly simplify the modeling process comparing with both traditional modeling softwares and prior sketch-based systems.\",\n \"Title: Real-time high-quality surface rendering for large scale particle-based fluids.\\nYear: 2017\\n\\nAbstract: Particle-based methods like Smoothed Particle Hydrodynamics (SPH) are increasingly adopted for large scale fluid simulation in interactive computer graphics. However, surface rendering for such dynamic particle sets is challenging: current methods either produce coarse results, or time consuming. We introduce a novel approach to render high-quality fluid surface in screen space by an efficient combination of particle splatting, ray-casting and surface normal estimation techniques. We apply particle splatting to accelerate ray-casting process, and estimate surface normal using Principal Component Analysis (PCA). We adopt GPU technique to further accelerate our method. Our method can produce high-quality smooth surface while preserving thin and sharp details of large scale fluids. The computation and memory cost of our rendering step only depend on the image resolution. These advantages make our method very suitable for previewing or rendering hundreds of millions particles interactively. We demonstrate the efficiency and effectiveness of our method by rendering various fluid scenarios with different-sized particle sets.\",\n \"Title: Real-time full-body motion reconstruction and recognition for off-the-shelf VR devices.\\nYear: 2016\\n\\nAbstract: Almost all VR multi-person applications have the requirement of reconstructing the whole-body motions in real-time in order to create deeper immersion. In fact, if we can ensure that the motion we reconstruct is natural, we can improve upon the comfort and number of wearing sensors at the cost of reconstruction precision because all users in VR are blindfolded. In this paper, we introduce a novel real-time motion reconstruction and recognition method in VR only using the positions and orientations of user's head and two hands. To reconstruct natural motions according to rare sensors, we divide the whole body into the upper body and the lower body. The upper body reconstruction algorithm based on inverse kinematics which is more accurate and the lower body based on animation blending which only needs a small number of prepared animations. Meanwhile, a natural action recognition algorithm based on neural network will run in need to detect the target motions we have trained. We show that our method can reconstruct various full-body motions, such as walking in any direction, jogging, jumping, crouching and turning. Our method has the ability to reconstruct natural human motions which can be used in almost all VR multi-person interactive applications. The configuration of sensors we attached on the head and hands is very popular in VR well-known devices, so the method can be easily integrated into off-the-shelf VR devices.\",\n \"Title: Real-time head pose estimation for driver assistance system using low-cost on-board computer.\\nYear: 2016\\n\\nAbstract: We propose a fast head pose estimation method using monocular video. It is highly optimized for on-board computers and for driving situations, which is applicable to existing low-cost on-board computer for cars and suitable for high real-time driver assistance systems. In our algorithm pipeline, the face detection step eliminates slow floating point computations using pixel intensity binary test, and reduce search scope effectively. In the face alignment step, we utilize the high performance of local binary feature and extend the single pose regression model to handle large rotations. The pose estimation step uses a mean rigid face model to calculate head pose fast by solving 2D-3D correspondence. To reduce computation further, we bypass or simplify pipeline steps using previous frame result, and redo the full pipeline adaptively. Experiments show our method is more efficient than existing approaches, which makes high real-time applications for on-board computers possible.\",\n \"Title: A Schur Complement Preconditioner for Scalable Parallel Fluid Simulation\\nYear: 2017\\n\\nAbstract: We present an algorithmically efficient and parallelized domain decomposition based approach to solving Poisson’s equation on irregular domains. Our technique employs the Schur complement method, which permits a high degree of parallel efficiency on multicore systems. We create a novel Schur complement preconditioner which achieves faster convergence, and requires less computation time and memory. This domain decomposition method allows us to apply different linear solvers for different regions of the flow. Subdomains with regular boundaries can be solved with an FFT-based Fast Poisson Solver. We can solve systems with 1,0243 degrees of freedom, and demonstrate its use for the pressure projection step of incompressible liquid and gas simulations. The results demonstrate considerable speedup over preconditioned conjugate gradient methods commonly employed to solve such problems, including a multigrid preconditioned conjugate gradient method.\",\n \"Title: Proposal for Optimization of Spool Valve Flow Force Based on the MATLAB-AMESim-FLUENT Joint Simulation Method.\\nYear: 2018\\n\\nAbstract: This paper focuses on the open-center multi-way valve used in loader buckets. To solve the problem of excessive flow force that leads to spool clamping in the reset process, joint simulations adopting MATLAB, AMESim, and FLUENT were carried out. Boundary conditions play a decisive role in the results of computational fluid dynamics (CFD) simulation. However, the boundary conditions of valve ports depend on the hydraulic system's working condition and are significantly impacted by the port area, which has always been neglected. This paper starts with the port area calculation method, then the port area curves are input into the simulation hydraulic system, obtaining the flow curves of valve port as output, which are then applied as the boundary conditions of the spool valve CH) simulation. Therefore, the steady-state flow force of the spool valve is accurately calculated, and the result verifies the hypothesis that excess flow force causes spool clamping. Based on this, four kinds of structures were introduced in an attempt to improve the situation, and simulating calculation and theoretical analysis were adopted to verify the effects of improvement. Results show that the four structures could reduce the peak value of flow force by 17.8%, 60.6%, 61.6%, and 55.7%, respectively. Of the four, structures II, III, and IV can reduce the peak value of flow force to below reset spring force value, thus successfully avoiding the spool clamping caused by flow force.\",\n \"Title: PROME - projected measures model for fast human shape estimation.\\nYear: 2018\\n\\nAbstract: Human parametric models can provide useful constraints for human shape estimation to produce more accurate results. However, the state-of-art models are computational expensive which limit their wide use in interactive graphics applications. We present PROME (PROjected MEasures) - a novel human parametric model which has high expressive power and low computational complexity. Projected measures are sets of 2D contour poly-lines that capture key measure features defined in anthropometry. The PROME model builds the relationship between 3D shape and pose parameters and 2D projected measures. We train the PROME model in two parts: the shape model formulates deformations of projected measures caused by shape variation, and the pose model formulates deformations of projected measures caused by pose variation. Based on the PROME model we further propose a fast shape estimation method which estimates the 3D shape parameters of a subject from a single image in nearly real-time. The method builds an optimize problem and solves it using gradient optimizing strategy. Experiment results show that the PROME model has well capability in representing human body in different shape and pose comparing to existing 3D human parametric models, such as SCAPE[Anguelov et al. 2005] and TenBo[Chen et al. 2013], yet keeps much lower computational complexity. Our shape estimation method can process an image in about one second, orders of magnitude faster than state-of-art methods, and the estimating result is very close to the ground truth. The proposed method can be widely used in interactive applications such as virtual try-on and virtual reality collaboration.\",\n \"Title: ToonNet - a cartoon image dataset and a DNN-based semantic classification system.\\nYear: 2018\\n\\nAbstract: Cartoon-style pictures can be seen almost everywhere in our daily life. Numerous applications try to deal with cartoon pictures, a dataset of cartoon pictures will be valuable for these applications. In this paper, we first present ToonNet: a cartoon-style image recognition dataset. We construct our benchmark set by 4000 images in 12 different classes collected from the Internet with little manual filtration. We extend the basal dataset to 10000 images by adopting several methods, including snapshots of rendered 3D models with a cartoon shader, a 2D-3D-2D converting procedure using a cartoon-modeling method and a hand-drawing stylization filter. Then, we describe how to build an effective neural network for image semantic classification based on ToonNet. We present three techniques for building the Deep Neural Network (DNN), namely, IUS: Inputs Unified Stylization, stylizing the inputs to reduce the complexity of hand-drawn cartoon images; FIN: Feature Inserted Network, inserting intuitionistic and valuable global features into the network; NPN: Network Plus Network, using multiple single networks as a new mixed network. We show the efficacy and generality of our network strategies in our experiments. By utilizing these techniques, the classification accuracy can reach 78% (top-1) and 93%(top-3), which has an improvement of about 5% (top-1) compared with classical DNNs.\",\n \"Title: Touch-Space: Mixed Reality Game Space Based on Ubiquitous, Tangible, and Social Computing\\nYear: 2002\\n\\nAbstract: This paper presents a novel computer entertainment system which recaptures human touch and physical interaction with the real-world environment as essential elements of the game play, whilst also maintaining the exciting fantasy features of traditional computer entertainment. Our system called ‘Touch-Space’ is an embodied (ubiquitous, tangible, and social) computing based Mixed Reality (MR) game space which regains the physical and social aspects of traditional game play. In this novel game space, the real-world environment is an essential and intrinsic game element, and the human’s physical context influences the game play. It also provides the full spectrum of game interaction experience ranging from the real physical environment (human to human and human to physical world interaction), to augmented reality, to the virtual environment. It allows tangible interactions between players and virtual objects, and collaborations between players in different levels of reality. Thus, the system re-invigorates computer entertainment systems with social human-to-human and human-to-physical touch interactions.\",\n \"Title: Facial expressions recognition based on convolutional neural networks for mobile virtual reality.\\nYear: 2016\\n\\nAbstract: We present a new system designed for enabling direct face-to-face interaction for users wearing a head-mounted displays (HMD) in virtual reality environment. Due to HMD's occlusion of a user's face, VR applications and games are mainly designed for single user. Even in some multi-player games, players can only communicate with each other using audio input devices or controllers. To address this problem, we develop a novel system that allows users to interact with each other using facial expressions in real-time. Our system consists of two major components: an automatic tracking and segmenting face processing component and a facial expressions recognizing component based on convolutional neural networks (CNN). First, our system tracks a specific marker on the front surface of the HMD and then uses the extracted spatial data to estimate face positions and rotations for mouth segmentation. At last, with the help of an adaptive approach for histogram based mouth segmentation [Panning et al. 2009], our system passes the processed lips pixels' information to CNN and get the facial expressions results in real-time. The results of our experiments show that our system can effectively recognize the basic expressions of users.\",\n \"Title: A Novel CNN-based Poisson Solver for Fluid Simulation.\\nYear: 2020\\n\\nAbstract: Solving a large-scale Poisson system is computationally expensive for most of the Eulerian fluid simulation applications. We propose a novel machine learning-based approach to accelerate this process. At the heart of our approach is a deep convolutional neural network (CNN), with the capability of predicting the solution (pressure) of a Poisson system given the discretization structure and the intermediate velocities as input. Our system consists of four main components, namely, a deep neural network to solve the large linear equations, a geometric structure to describe the spatial hierarchies of the input vector, a Principal Component Analysis (PCA) process to reduce the dimension of input in training, and a novel loss function to control the incompressibility constraint. We have demonstrated the efficacy of our approach by simulating a variety of high-resolution smoke and liquid phenomena. In particular, we have shown that our approach accelerates the projection step in a conventional Eulerian fluid simulator by two orders of magnitude. In addition, we have also demonstrated the generality of our approach by producing a diversity of animations deviating from the original datasets.\",\n \"Title: A CNN-based Flow Correction Method for Fast Preview.\\nYear: 2019\\n\\nAbstract: Eulerian-based smoke simulations are sensitive to the initial parameters and grid resolutions. Due to the numerical dissipation on different levels of the grid and the nonlinearity of the governing equations, the differences in simulation resolutions will result in different results. This makes it challenging for artists to preview the animation results based on low-resolution simulations. In this paper, we propose a learning-based flow correction method for fast previewing based on low-resolution smoke simulations. The main components of our approach lie in a deep convolutional neural network, a grid-layer feature vector and a special loss function. We provide a novel matching model to represent the relationship between low-resolution and high-resolution smoke simulations and correct the overall shape of a low-resolution simulation to closely follow the shape of a high-resolution down-sampled version. We introduce the grid-layer concept to effectively represent the 3D fluid shape, which can also reduce the input and output dimensions. We design a special loss function for the fluid divergence-free constraint in the neural network training process. We have demonstrated the efficacy and the generality of our approach by simulating a diversity of animations deviating from the original training set. In addition, we have integrated our approach into an existing fluid simulation framework to showcase its wide applications.\",\n \"Title: Airplane detection based on fusion framework by combining saliency model with Deep Convolutional Neural Networks.\\nYear: 2017\\n\\nAbstract: Aircraft detection from very high resolution remote sensing images, has gained more increasing interest in recent years due to the successful civil and military applications. However, several problems still exist: 1) how to extract the high-level features of aircraft; 2) locating objects within such a large image is difficult and time consuming; 3) A common problem of multiple resolutions of satellite images still exists. In this paper, inspirited by biological visual mechanism, the fusion detection framework is proposed, which fusing the top-down visual mechanism (deep CNN model) and bottom-up visual mechanism (GBVS) to detect aircraft. Besides, we use multi-scale training method for deep CNN model to solve the problem of multiple resolutions. Experimental results demonstrate that our method can achieve a better detection result than the other methods.\",\n \"Title: Render-Based Factorization For Additive Light Field Display\\nYear: 2021\\n\\nAbstract: Augmented Reality (AR) and Virtual Reality (VR) applications enable viewers to experience 3D graphics immersively. However, current hardware either fails to provide multiple viewpoints like projectors or cause conflicts between vergence and accommodation like consumable headsets. Prior researchers have designed multi-view displays to solve the problem by enabling view-dependent images and focus cues. Nevertheless, it requires minutes to calculate one frame, which is critical for real-time AR/VR applications. In this paper, we propose a new render-based factorization for additive light field display and further improve the performance by optimizing the initialization of layers. We next compare our work with the state of the art and the results show that our solution has competitive results while the calculation takes less than 20 ms for each frame.\",\n \"Title: Towards Stereoscopic On-vehicle AR-HUD\\nYear: 2021\\n\\nAbstract: On-vehicle AR-HUD (head-up display) is a driving assistant system, which can let drivers see navigation and warning information directly through the windshield with an easy-to-perceive augmented reality form. Traditional AR-HUD can only produce the content at a specific distance, which causes uncomfortable experiences such as frequent refocusing and unnatural floating of virtual objects. This paper proposed an innovative AR-HUD system, which can provide stereoscopic scenes to the driver. The system is composed of two traditional HUD displays and supports parallax by additive light field factorization. Optical paths and illumination of two displays are precisely calibrated for both views to ensure the combination of lights is as expected. The factorization algorithm is optimized for the system. With GPU acceleration, the system can run in real time. The system is cheap and simple to transform from a traditional AR-HUD system, which presents a feasible scheme to achieve a fusion-enhanced augmented reality assistant for driving.\",\n \"Title: Real-time simulation of violent boiling in concentrated sulfuric acid dilution\\nYear: 2021\\n\\nAbstract: Concentrated sulfuric acid dilution is an essential yet hazardous experiment in chemistry education. Incorrect operations can lead to intensive boiling and splattering or even endanger the experiment operator. This paper presents a novel approach to simulate the violent boiling phenomenon in real time and an efficient method to deal with particle penetration caused by fast motion. We introduce the anti-viscosity method to inject momentum into high-temperature particles and capture the chaotic dynamics in the boiling process with small computational overhead. To address the particle penetration issue, we propose a new constraint type called flask constraint which uses radius lookup tables to represent axisymmetric shapes efficiently and projects the particles back into their container when penetration occurs. These methods are integrated into a virtual reality application to simulate the concentrated sulfuric acid dilution experiment and demonstrate the efficiency and effectiveness of our method. Our work improves the capability and stability of particle-based fluid solvers and provides appealing solutions for integrating fluid simulation into interactive applications.\",\n \"Title: Preliminary Study of Short-Term Visual Perceptual Training Based on Virtual Reality and Augmented Reality in Postoperative Strabismic Patients\\nYear: 2022\\n\\nAbstract: The study aimed to explore the potential effect of short-term visual perceptual training based on virtual reality (VR) and augmented reality (AR) platforms in postoperative strabismic patients. We enrolled 236 postoperative strabismic patients, among whom 111 patients received VR-based training, and 125 patients received AR-based training. The stereoacuity of 1.5 m and dynamic stereopsis were improved by VR training; meanwhile, AR training exhibited more improvement in stereoacuity of 0.8 and 1.5 m, dynamic and coarse stereopsis. It was suggested that the visual perceptual training based on VR and AR technology can be potentially applied in postoperative strabismus treatment to promote the recovery of binocular vision.\",\n \"Title: Rectangular Mapping-based Foveated Rendering\\nYear: 2022\\n\\nAbstract: With the speedy increase of display resolution and the demand for interactive frame rate, rendering acceleration is becoming more critical for a wide range of virtual reality applications. Foveated rendering addresses this challenge by rendering with a non-uniform resolution for the display. Motivated by the non-linear optical lens equation, we present rectangular mapping-based foveated rendering ...\",\n \"Title: Retargeting Destinations of Passive Props for Enhancing Haptic Feedback in Virtual Reality\\nYear: 2022\\n\\nAbstract: Haptic retargeting is a commonly used technique to match passive props to virtual objects for adding tactile feedback in Virtual Reality (VR). However, researchers have mainly focused on single-hand retargeting and applied these techniques primarily for tasks of touching objects. In this work, we propose a novel retargeting solution for tasks of grabbing and placing objects in VR, called redirected placement (RP), which is applied when placing virtual objects. The key idea of this method is that when the user places the virtual object, it can enable the user to place the physical prop in a position that is easier to match with multiple other virtual objects, without being detected by the user.\",\n \"Title: FoV-NeRF: Foveated Neural Radiance Fields for Virtual Reality\\nYear: 2022\\n\\nAbstract: Virtual Reality (VR) is becoming ubiquitous with the rise of consumer displays and commercial VR platforms. Such displays require low latency and high quality rendering of synthetic imagery with reduced compute overheads. Recent advances in neural rendering showed promise of unlocking new possibilities in 3D computer graphics via image-based representations of virtual or physical environments. Specifically, the neural radiance fields (NeRF) demonstrated that photo-realistic quality and continuous view changes of 3D scenes can be achieved without loss of view-dependent effects. While NeRF can significantly benefit rendering for VR applications, it faces unique challenges posed by high field-of-view, high resolution, and stereoscopic/egocentric viewing, typically causing low quality and high latency of the rendered images. In VR, this not only harms the interaction experience but may also cause sickness. To tackle these problems toward six-degrees-of-freedom, egocentric, and stereo NeRF in VR, we present the first gaze-contingent 3D neural representation and view synthesis method. We incorporate the human psychophysics of visual- and stereo-acuity into an egocentric neural representation of 3D scenery. We then jointly optimize the latency/performance and visual quality while mutually bridging human perception and neural scene synthesis to achieve perceptually high-quality immersive interaction. We conducted both objective analysis and subjective studies to evaluate the effectiveness of our approach. We find that our method significantly reduces latency (up to 99% time reduction compared with NeRF) without loss of high-fidelity rendering (perceptually identical to full-resolution ground truth). The presented approach may serve as the first step toward future VR/AR systems that capture, teleport, and visualize remote environments in real-time.\",\n \"Title: SREFBN: Enhanced feature block network for single-image super-resolution\\nYear: 2022\\n\\nAbstract: Deep learning has assisted the field of single-image super-resolution (SR) in achieving new heights. However, the task of restoring a high-resolution (HR) image from a highly degraded low-resolution (LR) image is sophisticated due to poor image restoration quality. A novel and effective lightweight SR method is presented as super-resolution via an enhanced feature block network (SREFBN) that successfully reconstructs an HR image using a corresponding LR image with a purposed deep residual block. In addition, a novel shared parameters approach in the top-down pathway among low-level feature maps is introduced. The experimental results prove that SREFBN achieves remarkable performance. The presented framework requires lower computational cost and outperforms many state-of-the-art methods. It is also highly adaptable with low-end devices, requiring lower multiplication and adding operations. A trade-off comparison between the number of parameters, execution time, and accuracies is given while also showing different variations of our approach to prove the effectiveness and reliability of the shared parameters. Most importantly, the results indicate that our framework has gained state-of-the-art performance on larger scales 3 and 4. Code is available at .\"\n]","isConversational":false},"profile_size":{"kind":"number","value":59,"string":"59"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13675,"string":"13,675"}}},{"rowIdx":13676,"cells":{"question_id":{"kind":"string","value":"Juan Zhang"},"question":{"kind":"string","value":"Generate an abstract for the title \"Malaria Cell Counting Diagnosis within Large Field of View\" using the following items: 1. Malaria \n2. Diagnosis \n3. Image processing algorithms \n4. Large field of view \n5. Counting accuracy"},"answer":{"kind":"string","value":"Malaria is one of the most serious parasitic infections of human. The accurate and timely diagnosis of malaria infection is essential to control and cure the disease. Some image processing algorithms to automate the diagnosis of malaria on thin blood smears are developed, but the percentage of parasitaemia is often not as precise as manual count. One reason resulting in this error is ignoring the cells at the borders of images. In order to solve this problem, a kind of diagnosis scheme within large field of view (FOV) is proposed. It includes three steps. The first step is image mosaicing to obtain large FOV based on space-time manifolds. The second step is the segmentation of erythrocytes where an improved Hough Transform is used. The third step is the detection of nucleated components. At last, it is concluded that the counting accuracy of malaria infection within large FOV is finer than several regular FOVs."},"correct_answer":{"kind":"string","value":"Malaria is one of the most serious parasitic infections of human. The accurate and timely diagnosis of malaria infection is essential to control and cure the disease. Some image processing algorithms to automate the diagnosis of malaria on thin blood smears are developed, but the percentage of parasitaemia is often not as precise as manual count. One reason resulting in this error is ignoring the cells at the borders of images. In order to solve this problem, a kind of diagnosis scheme within large field of view (FOV) is proposed. It includes three steps. The first step is image mosaicing to obtain large FOV based on space-time manifolds. The second step is the segmentation of erythrocytes where an improved Hough Transform is used. The third step is the detection of nucleated components. At last, it is concluded that the counting accuracy of malaria infection within large FOV is finer than several regular FOVs."},"documents":{"kind":"list like","value":["Title: High positive correlation between soil temperature and NDVI from 1982 to 2006 in alpine meadow of the Three-River Source Region on the Qinghai-Tibetan Plateau\nYear: 2011\n\nAbstract: Using satellite-observed Normalized Difference Vegetation Index (NDVI) data and Rotated Empirical Orthogonal Function (REOF) method, we analyzed the spatio-temporal variation of vegetation during growing seasons from May to September in the Three-River Source Region, alpine meadow in the Qinghai-Tibetan Plateau from 1982 to 2006. We found that NDVI in the centre and east of the region, where the vegetation cover is low, showed a consistent but slight increase before 2003 and remarkable increase in 2004 and 2005. Impact factors analysis indicted that among air temperature, precipitation, humid index, soil surface temperature, and soil temperature at 10 cm and 20 cm depth, annual variation of NDVI was highly positive correlated with the soil surface temperature of the period from March to July. Further analysis revealed that the correlation between the vegetation and temperature was insignificant before 1995, but statistically significant from 1995. The study indicates that temperature is the major controlling factor of vegetation change in the Three-River Source Region, and the currently increase of temperature may increase vegetation coverage and/or density in the area. In addition, ecological restoration project started from 2005 in Three-River Source Region has a certain role in promoting the recovery of vegetation. Keywords NDVI (Normalized Differential Vegetation Index) Qinghai-Tibetan Plateau Temperature Vegetation","Title: Influence of removable devices on computer worms: Dynamic analysis and control strategies\nYear: 2011\n\nAbstract: Worms spreading via both Web-based scanning and removable devices account for a major part of threats on internet. However, their dynamical behavior and controlling methods remain unclear. As a result, we present a computer worm model incorporating specific features unique to those worms, in this paper. The threshold value R\"0 determining whether the worms die out is obtained. Theoretical analysis shows that if R\"0","Title: The numerical solution of fifth-order boundary value problems by the variational iteration method\nYear: 2009\n\nAbstract: This paper applies the variational iteration method to solve fifth-order boundary value problems; just one iteration results in highly accurate solutions. A comparison of the results with exact ones is made to confirm the validity and efficiency.","Title: Comparison Of Power Transfer Characteristics Between Cpt And Ipt System And Mutual Inductance Optimization For Ipt System\nYear: 2012\n\nAbstract: The capacitive power transfer (CPT) system and inductively power transfer (IPT) system are the two typical wireless power transfer systems. Based on the power transfer characteristics, the power transfer capacity of the two wireless power transfer systems were analyzed. Firstly, the maximum power transfer capacity and its existing condition of the two wireless power transfer systems were analyzed, and the choose gist of wireless power transfer system was presented according to the analysis result; then, the mutual inductance of 4 typical IPT systems was optimized for achieving maximum power transfer; finally, the theoretical research was justified via a simulation and experiment.","Title: Bounds for the eigenvalues of the continuous algebraic Riccati equation\nYear: 2011\n\nAbstract: By using singular value decomposition and majorisation inequalities, we propose new upper and lower bounds for summations of eigenvalues (including the trace) of the solution of the continuous algebraic Riccati equation. These bounds improve and extend some of the previous results. Finally, we give corresponding numerical examples to illustrate the effectiveness of our results.","Title: The dominant degree and disc theorem for the Schur complement of matrix\nYear: 2010\n\nAbstract: The theory of Schur complement plays an important role in many fields such as control theory and computational mathematics. In this paper, we obtain some estimates for the diagonally, @c-diagonally and product @c-diagonally dominant degree on the Schur complement of matrices, which improve some relative results. As application we present some bounds for the eigenvalues of Schur complement by the entries of the original matrix instead of those of the Schur complement. Particularly, we obtain that the eigenvalues of the Schur complements are located in the Gerschgorin Circles of the original matrices under certain conditions.","Title: Computer-aided diagnosis of breast DCE-MRI images using bilateral asymmetry of contrast enhancement between two breasts.\nYear: 2014\n\nAbstract: Dynamic contrast material-enhanced magnetic resonance imaging (DCE-MRI) of breasts is an important imaging modality in breast cancer diagnosis with higher sensitivity but relatively lower specificity. The objective of this study is to investigate a new approach to help improve diagnostic performance of DCE-MRI examinations based on the automated detection and analysis of bilateral asymmetry of characteristic kinetic features between the left and right breast. An image dataset involving 130 DCE-MRI examinations was assembled and used in which 80 were biopsy-proved malignant and 50 were benign. A computer-aided diagnosis (CAD) scheme was developed to segment breast areas depicted on each MR image, register images acquired from the sequential MR image scan series, compute average contrast enhancement of all pixels in one breast, and a set of kinetic features related to the difference of contrast enhancement between the left and right breast, and then use a multi-feature based Bayesian belief network to classify between malignant and benign cases. A leave-one-case-out validation method was applied to test CAD performance. The computed area under a receiver operating characteristic (ROC) curve is 0.78 ± 0.04. The positive and negative predictive values are 0.77 and 0.64, respectively. The study indicates that bilateral asymmetry of kinetic features between the left and right breasts is a potentially useful image biomarker to enhance the detection of angiogenesis associated with malignancy. It also demonstrates the feasibility of applying a simple CAD approach to classify between malignant and benign DCE-MRI examinations based on this new image biomarker.","Title: Using Genetic Algorithm to Improve Fuzzy k-NN\nYear: 2008\n\nAbstract: This paper presents a method of improving fuzzy k-nearest neighbor (k-NN) using genetic algorithm (GA). k-NN is an important classification algorithm, However, a major drawback of the method is that each of the patterns of known classification is considered equally important in the assignment of the pattern to be classified. This can cause difficulties in regions where pattern data overlap. To overcome this drawback, A fuzzy k-NN was proposed for increasing classification correct rate of overlapping pattern data . In this paper, Experiments are carried out and illustrate that both k-NN and fuzzy k-NN are still sensitive to the initial k and parameter m. GA is applied to calculate initial k and parameter m. Experimental results show that the our proposed approach has the higher classification correct rate than the fuzzy k-NN.","Title: Classification tree modeling to identify severe and moderate vehicular injuries in young and middle-aged adults\nYear: 2009\n\nAbstract: Objectives: Motor vehicle crashes are a leading cause of mortality and morbidity worldwide. Even though trauma centers provide the gold standard of care for motor vehicle crash patients with life- or limb-threatening injuries, many whose lives might be saved by trauma center care are treated instead at non-trauma center hospitals. Triage algorithms, designed to identify patients with life- or limb-threatening injuries who should be transported to a trauma center, lack appropriate sensitivity to many of these injuries. The challenge to the trauma community is differentiating patients with life- or limb-threatening injuries from those with less severe injuries at the crash scene so that the patients can be transported to the most appropriate level of care. The purpose of this study was to use crash scene data available to emergency responders to classify adults with moderate and severe injuries. These classifiers might be useful to guide triage decision making. Methods and material: Records of 74,626 adults, age 18-64 years, from the National Automotive Sampling System Crashworthiness Data Systems database were analyzed using classification and regression trees (CART) analysis. Both CART models (moderate injury and severe injury) included 13 predictor variables. The response variables were the targeted injury severity score cut points for moderate and severe injury. Two final classification trees were developed: one that classified occupants based on moderate injury and the other on severe injury. Misclassification costs were manipulated to achieve the best model fit for each tree. Results: The moderate injury classification tree had three splitters: police-estimated injury severity, restraint use, and number of persons injured. The severe injury classification tree had four splitters: police-estimated injury severity, manner of collision, number of persons injured in the crash, and age. Sensitivity and specificity of the classification trees were 93.70%, 77.53% (moderate) and 99.18%, 73.96% (severe), respectively. Conclusions: CART analysis can be used to classify injury severity using crash scene information that is available to emergency responders. This procedure offers an opportunity to examine alternative methods of identifying injury severity that might assist emergency responders to differentiate more accurately persons who should receive trauma center care from those who can be treated safely at a non-trauma center hospital.","Title: A CPN-based Software Testing Approach.\nYear: 2011\n\nAbstract: As a graphical and mathematical modeling tool, CPN (Colored Petri Net) is often used to describe the transition of states for an information system. The advantage of CPN model is that the model can be simulated dynamically. This paper presents an approach to generating test cases based on a transition graph of CPN model. The transition graph provides a solid basis for test cases generation in a form that can be easily manipulated. The case for vending machine illustrates the effectiveness of this method. © 2011 ACADEMY PUBLISHER.","Title: Improved mammographic mass retrieval performance using multi-view information\nYear: 2010\n\nAbstract: Breast cancer is the most common malignant disease in women. Mammographic mass retrieval system can help radiologists to improve the diagnostic accuracy by retrieving biopsy-proven masses which are similar with the diagnostic ones. However, although screening mammograms usually consists of two-view(MLO and CC) mammography of the same breast, most breast CAD systems incorporate with image retrieval techniques are based on a single-view principle where query ROI within a view is analyzed independently. In this paper, a mammographic mass retrieval approach based on multi-view information is proposed. In this work, the query example is a multi-view(MLO and CC) mass pair instead of the single view mass in the traditional image retrieval framework. In the experiments, several visual features are used for retrieval evaluation. Both distance similarity measures, such as Euclidean distance, and k-NN regression model based non-distance similarity measures are used for comparison. Experimental study was carried out on a database with 126 biopsy-proven masses(63 mass pairs). Preliminary results showed that multi-view based retrieval approach achieves better retrieval accuracy than single-view based one, especially for the k-NN regression model based similairy metric.","Title: Application of Fuzzy Mathematics Assessment to Groundwater Quality in Jining City\nYear: 2009\n\nAbstract: The water quality of groundwater in Jining has been assessed comprehensively based on the environmental investigation and monitoring in order to provide the scientific basis for the pollution treatment in the Jining city. This paper discussed the choice of the evaluated factors, the operations of the weight value, and compares with the status of the water quality in the course of the evaluation. The conclusions indicated that the result of the fuzzy mathematics evaluation represented the water quality of groundwater in Jining city. The evaluation result was compared with that of the traditional method and the reported results in Jining. It is indicated that the performance of the proposed model is practically feasible in the application of groundwater quality assessment and its application is simple.","Title: Modeling Software Testing Process Using HTCPN\nYear: 2009\n\nAbstract: Software testing process has being separated gradually from software development. As a relatively independent process, software testing process simulation and optimization has been also started to catch people's attention. It is particularly important to improve the reuse of software testing. This paper proposes a systematic way to model the software testing processes STP-NET based on HTCPN, in order to reuse them. The performance of the process, such as average time and average waiting time, is simulated and analyzed using CPN Tools. According to the results, the process configuration parameters, the license allocation model of automatic testing tool, can be optimized. The efficiency of the testing process can be improved by simulation and optimization using CPN Tools.","Title: Parallel algorithm design for remote sensing image processing in the PC cluster environment\nYear: 2010\n\nAbstract: In the PC cluster environment, parallel algorithms can significantly improve the efficiency of remote sensing image processing. The remote sensing dataset is the raster data stored by order of band, therefore, it is feasible to assign executable tasks to some computing nodes by band and complete the processing tasks together through communicating each other amount the computing nodes by MPI interface. In addition, the multi-thread processing algorithm is scheduled based on OpenMP library toward the single computing node with multi-core CPU. The experiment evaluates image radiation performance about the four-band HJ-1A CCD remote sensing image in the PC cluster environment which consists of 4 sets of dual-core computers. Its performance is increased by 6 times to 6.5 times comparing with a single PC. Through validating, the dual level design pattern is available based on integrating MPI and OpenMP to improve the efficiency of remote sensing image processing.","Title: Position/force hybrid control system for high precision aligning of small gripper to ring object\nYear: 2013\n\nAbstract: A position/force hybrid control system based on impedance control scheme is designed to align a small gripper to a special ring object. The vision information provided by microscope vision system is used as the feedback to indicate the position relationship between the gripper and the ring object. Multiple image features of the gripper and the ring object are extracted to estimate the relative positions between them. The end-effector of the gripper is tracked using the extracted features to keep the gripper moving in the field of view. The force information from the force sensor serves as the feedback to ensure that the contact force between the gripper and the ring object is limited in a small safe range. Experimental results verify the effectiveness of the proposed control strategy.","Title: New Matrix Bounds, An Existence Uniqueness And A Fixed-Point Iterative Algorithm For The Solution Of The Unified Coupled Algebraic Riccati Equation\nYear: 2012\n\nAbstract: In this paper, combining the equivalent form of the unified coupled algebraic Riccati equation (UCARE) with the eigenvalue inequalities of a matrix's sum and product, using the properties of an M-matrix and its inverse matrix, we offer new lower and upper matrix bounds for the solution of the UCARE. Furthermore, applying the derived lower and upper matrix bounds and a fixed-point theorem, an existence uniqueness condition of the solution of the UCARE is proposed. Then, we propose a new fixed-point iterative algorithm for the solution of the UCARE. Finally, we present a corresponding numerical example to demonstrate the effectiveness of our results.","Title: The application of nonaffine network structural model in sine pulsating flow field\nYear: 2004\n\nAbstract: In order to describe the rheological behavior of polymer melts in a practical vibration force field, the nonaffine network structural model is generalized from a simple oscillatory shear force field to a sine pulsating flow field. Through experiments and theoretic analyses, we prove that this model can be described preferably the viscous and elastic behavior of polymer melts in sine pulsating flow field, and the change amplitude of the shear stress increases with the vibration frequency or amplitude increasing.","Title: QP based framework for development and formal verification of flight control software of UAV\nYear: 2011\n\nAbstract: UAV is widely invested both in military and academic fields. The development and verification of UAV flight control software is an importance issues and hot topic. In this paper, a QP based method is present to develop and formally verify the UAV flight control software. The method combines the UML and OCL constraint language and output the UML file that constrained by OCL to XMI file. Then we input the XMI file to rule inference engine as facts. Aided by safety rules, the flight control software could be automatically verified. The method has many advantages and is hopeful to enhance the quality of UAV flight control software. It can also be used in similar scenarios.","Title: Pattern formation of a spatial predator-prey system.\nYear: 2012\n\nAbstract: There are random and directed movements of predator and prey populations in many natural systems which are strongly influenced and modified by spatial factors. To investigate how these migration (directed movement) and diffusion (random movement) affect predator–prey systems, we have studied the spatiotemporal complexity in a predator–prey system with Holling–Tanner form. A theoretical analysis of emerging spatial pattern is presented and wavelength and pattern speed are calculated. At the same time, we present the properties of pattern solutions. The results of numerical simulations show that migration has prominent effect on the pattern formation of the population, i.e., changing Turing pattern into traveling pattern. This study suggests that modelling by migration and diffusion in predator–prey systems can account for the dynamical complexity of ecosystems.","Title: Web Document Classification Based on Fuzzy k-NN Algorithm\nYear: 2009\n\nAbstract: Web document classification is an important technique of Web mining. Web pages classification has been studied extensively since the Internet has become a huge database of information. The k-NN is a simple classification algorithm that is used to assign patterns of unknown classification to the class of the majority of its k nearest neighbors of known classification according to the distance measure, but a main drawback of the method is that each of the patterns of known classification is considered equally important in the assignment of the pattern to be classified. Fuzzy k-nearest neighbor (fuzzy k-NN) is improving algorithm of k-NN, which is applied successfully in structural data classification. This paper presents the Web document classification based on fuzzy k-NN network, in the process of classification, TF/IDF (term frequency/inverse document frequency) is adopted for selecting features of document, to increase the accuracy and suit for real world, membership grade is used. Experimental results show that classification performance is better than both k-NN and support vector machine (SVM).","Title: Using Ontology to Build Information Recommendation System in e-Commerce.\nYear: 2011\n\nAbstract: The aim of ontologies is to formalize domain knowledge in a generic way and provide a common understanding of a domain, which may be used and shared by applications and groups. This study attempts to propose a framework to improve and upgrade the effectiveness of E-C recommendation by ontology building algorithms. As a result, a e-commerce system is built using the existing standards cited above by overcoming their limits and sharing their different advantages. This paper puts forward using ontology to build information recommendation system in e-commerce. The experimental results have shown our suggested information system will increase precision and performance.","Title: Scale-Free Networks with Different Types of Nodes\nYear: 2009\n\nAbstract: In many natural and social networks, nodes may play different roles or have different functions. In this paper, we propose a simple model with different types of nodes and deterministic selective linking rule. We investigate the structural properties by theoretical predictions. It is found that the given model exhibits a power-law distribution. In addition, we make the model become the weighted network by giving the links the weight and analyze the probability distribution of the node strength.","Title: A new multi-view identification scheme for breast masses in mammography using multi-agent algorithm\nYear: 2010\n\nAbstract: The classification of breast masses into benign and malignant categories plays an important role in the area of computer-aided diagnosis (CAD) of breast cancer. In this paper, in order to improve the accuracy and the robustness of the classification and reduce the false positive rates, we proposed one novel scheme that was based on information fusion in multi views. A series of contour and shape features of the masses were chosen, and new contour features were used. Then, a multi-classifier fusion based on multi-agent algorithm was introduced. And finally, a multi-view information fusion method was applied. Experimental results demonstrated that the proposed classification scheme achieved a higher accuracy than those schemes using the individual classifiers and the multi-classifier fusion technique in single-view.","Title: An efficient ensemble method for classifying skewed data streams\nYear: 2011\n\nAbstract: Class distributions of data streams in real application are usually unbalanced, they are hence called Skewed Data Streams (abbreviated as SDS). However, in the classification of SDS, it is a challenge for traditional methods because of the difficulty in the recognition of minority classes. Therefore, many approaches have been proposed to improve the recognition rate of minority classes, while they are time-consuming. Motivated by this, we propose an efficient Ensemble method for Classifying SDS called ECSDS. Our algorithm creates multiple classifiers based on C4.5, and adopts the threshold of F1-value to limit the updating frequency of classifiers. Meanwhile, it adds misclassified positive instances into the training data to guarantee the effectiveness of classifiers when updating. Experimental studies demonstrate that our proposed method enables reducing the time overhead and maintains a good performance on the classification accuracy.","Title: Dynamical analysis and perturbation solution of an SEIR epidemic model.\nYear: 2014\n\nAbstract: In this paper we present a kind of technique to obtain the asymptotical solution of an SEIR (where S is the susceptible population, E is the exposed population, I is the infectious population and R is the recovered population) epidemic model by employing the method of perturbation. At first, we investigate the epidemic model by analysing its dynamical behavior. Then, we use the method of perturbation to obtain the analytical solution of the model. We assign values for parameters and draw figures to observe the magnitude of error of the perturbation method in a macro view. Finally, we analyse macroscopically the two comparison chart on analytical solution and the exact solution, and know that it is feasible to analyse the solution of the epidemic model by using the perturbation method.","Title: Variational approach to solitary wave solution of the generalized Zakharov equation\nYear: 2007\n\nAbstract: He's semi-inverse method is applied to search for the solitary wave solution of the generalized Zakharov equation (GZE). The solution process reveals that the method is easy and straightforward.","Title: Clustering coefficients in a growing network model of polymer melts\nYear: 2011\n\nAbstract: In this paper, we apply the mean-field approach to analyze the clustering coefficient of the growing network model of polymer melts and conduct numerical simulations by computer. It reveals the rules of the changes of the clustering coefficients with various parameters, which provides an important guide for polymer melts research.","Title: A zone-diffusion based routing protocol for LEO satellite networks\nYear: 2011\n\nAbstract: A Zone-Diffusion based Routing Protocol (ZDRP) for LEO satellite networks is proposed in this paper. The proposed routing protocol aims at reducing the broadcast overhead, which is a big problem in LEO satellite networks because of the topological dynamics of LEO constellation. The protocol divides a satellite network into zones, and restricts the broadcasting to the domain of zone. ZDRP operates in an on-demand mode to set up paths between each source-destination pair, and consists of two routing mechanisms, i.e., Route Discovery and Route Maintenance. The two mechanisms work together to allow nodes to discover and maintain source routes to arbitrary destinations in the LEO network. Consequently, the broadcast overhead incurred by routing protocol is decreased greatly. Simulation results show that ZDRP has a good performance in terms of end-to-end delay. Moreover, the packet loss rate is decreased significantly when ZDRP is applied to LEO network.","Title: Design And Implementation Of Crop Recommendation Fertilization Decision System Based On Webgis At Village Scale\nYear: 2010\n\nAbstract: To study crop recommendation fertilization of the rural farmers as the main in our country, the paper took towns and villages of Hua County as the study area, took recommendation fertilization of wheat, maize and peanut as the study object, designed model components of crop balance fertilization by using Object-Oriented technique, and developed the decision-making system about crop recommendation fertilization based on ArcGIS Server at village scale. The decision-making system realized farmland nutrient management and fertilization recommendations decision-making according to soil output capacity, agricultural production level and crop target yield. It was successfully applied in crop production in Hua County. The research results show that the system has the characteristic of better expansibility than before, and it is significantly simple and practical to reduce crop production cost and increase agricultural production efficiency, which provides technical support for crop fertilization decision-making and is significant to improve agricultural ecological environment and increase the comprehensive production capacity of farmland.","Title: Hybridizing Differential Evolution and Particle Swarm Optimization to Design Powerful Optimizers: A Review and Taxonomy\nYear: 2012\n\nAbstract: Differential evolution (DE) and particle swarm optimization (PSO) are two formidable population-based optimizers (POs) that follow different philosophies and paradigms, which are successfully and widely applied in scientific and engineering research. The hybridization between DE and PSO represents a promising way to create more powerful optimizers, especially for specific problem solving. In the past decade, numerous hybrids of DE and PSO have emerged with diverse design ideas from many researchers. This paper attempts to comprehensively review the existing hybrids based on DE and PSO with the goal of collection of different ideas to build a systematic taxonomy of hybridization strategies. Taking into account five hybridization factors, i.e., the relationship between parent optimizers, hybridization level, operating order (OO), type of information transfer (TIT), and type of transferred information (TTI), we propose several classification mechanisms and a versatile taxonomy to differentiate and analyze various hybridization strategies. A large number of hybrids, which include the hybrids of DE and PSO and several other representative hybrids, are categorized according to the taxonomy. The taxonomy can be utilized not only as a tool to identify different hybridization strategies, but also as a reference to design hybrid optimizers. The tradeoff between exploration and exploitation regarding hybridization design is discussed and highlighted. Based on the taxonomy proposed, this paper also indicates several promising lines of research that are worthy of devotion in future.","Title: A complex network model of polymer melts without growth\nYear: 2011\n\nAbstract: In order to describe the entangled network structure in polymer melts visually, a complex network model without growth is proposed. The analysis shows that the exponential distribution can be exhibited from the statistic of the network after a long time accumulation, the average number of entanglements can be decreased if the probability of deleted edges is not less than that of created edges, and the simulation result and the theoretical one are the same, approximately.","Title: New solution bounds for the continuous algebraic Riccati equation\nYear: 2011\n\nAbstract: In this paper, by constructing the equivalent form of the continuous algebraic Riccati equation and utilizing the eigenvalue and singular value inequalities of matrix's sum and product, we propose new lower and upper matrix bounds for the solution of the continuous algebraic Riccati equation. Finally, we give corresponding numerical examples to illustrate the effectiveness of our results.","Title: Retrieving articulated 3-D models using medial surfaces\nYear: 2008\n\nAbstract: We consider the use of medial surfaces to represent symmetries of 3-D objects. This allows for a qualitative abstraction based on a directed acyclic graph of components and also a degree of invariance to a variety of transformations including the articulation of parts. We demonstrate the use of this representation for 3-D object model retrieval. Our formulation uses the geometric information associated with each node along with an eigenvalue labeling of the adjacency matrix of the subgraph rooted at that node. We present comparative retrieval results against the techniques of shape distributions (Osada et al.) and harmonic spheres (Kazhdan et al.) on 425 models from the McGill Shape Benchmark, representing 19 object classes. For objects with articulating parts, the precision vs recall curves using our method are consistently above and to the right of those of the other two techniques, demonstrating superior retrieval performance. For objects that are rigid, our method gives results that compare favorably with these methods.","Title: Passive data storage based housewares store management system\nYear: 2011\n\nAbstract: After referring several cases of management information system and RFID system, we design a novel and practical system for housewares store. We use the RFID tags in a different way - Passive Data Storage. In the paper, the structure and implementation of the system is described in detail. A case study is also given for illustration.","Title: The combining prediction of the RMB exchange rate series based on diverse architectural artificial neural network ensemble methodology.\nYear: 2010\n\nAbstract: Motivated by the neural network ensemble approach, this paper puts forward a diverse architectural artificial neural network (ANN) ensemble method to optimize the combining prediction of the RMB exchange rates. On the one hand, four types of architectures are adopted here including multilayer perceptron (MLP), recurrent neural networks (RNNs) to diversify the learning mechanism. On the other hand, the non parametric kernel smoothing technique is applied to make combining forecasts, which can overcome the drawbacks of traditional methods. The empirical results show that the proposed method has significantly improved the forecasting performance of the optimal single ANNs and random walk model, especially in RMB exchange rate series forecasting. © 2010 IEEE.","Title: Ant Colony Optimization Algorithm Based on Immunity Vaccine and Dynamic Pheromone Updating\nYear: 2010\n\nAbstract: To solve the problem of complexity and uncertainty in logistics and distribution system, a kind of Ant Colony Optimization algorithm based on immunity vaccine and dynamic pheromone updating is put forward in this paper. In this new Algorithm, initial antibody is vaccinated first to produce better solutions, and initial parameters are then set depending on these better solutions by the Ant Colony. Mechanism of dynamic adjustment and pheromone updating is also introduced into this algorithm to slow down the increment of the pheromone concentration difference between the paths, avoiding sectional solution of the algorithm. Experiment results show that DACOIV can effectively find optimal path for logistics and distribution system.","Title: Improving the prediction of chemotherapeutic sensitivity of tumors in breast cancer via optimizing the selection of candidate genes.\nYear: 2014\n\nAbstract: •The prediction of chemotherapeutic sensitivity is important in cancer treatment.•False positive genes seriously impact the performance of predictive models.•We proposed a combined strategy for optimizing the selection of candidate genes.•The performance of predictive models is improved when using the refined genes.•This research will benefit the guidance of the personalized chemotherapy.","Title: Professional tennis player ranking strategy based Monte Carlo feature selection\nYear: 2011\n\nAbstract: Extracting significant features from high-dimensional and small sample-size microarray data is a challenging problem. Other than wrapper or filter methods, we propose a novel feature selection algorithm which integrates the ideas of professional tennis players ranking, such as seed players and dynamic ranking with Monte Carlo simulation. Seed players make the 'game' more competitive and selective, hence improve the selection efficiency. Besides, the ranks of features are dynamically updated and this ensures that it is always the current best players to take part in each competitions. The proposed algorithm is tested on widely used public datasets. Results demonstrate that the proposed method comparatively converges faster, more stable and has good performance in classification and therefore is an efficient algorithm for feature selection.","Title: Identifying Ovarian Cancer Chemotherapy Response Relevant Gene Cliques\nYear: 2011\n\nAbstract: Operation with adjuvant chemotherapy is still the principal means to treat Ovarian cancer. Identifying Ovarian Cancer Chemotherapy Response (OCCR) relevant genes and describe their interactions is thus an important issue. However the problems of high dimensional micro array data and the scarcity of biological priors make building a complete OCCR biological network intractable. To this end, we combine liquid association (LA) algorithm with biological knowledgebase searching to identify OCCR relevant gene clique and describe their interactions. Rather than trying to build a gene network, our approach focus on identifying OCCR relevant gene cliques and then patching them up. Statistical analysis and biological validation show that the identified gene cliques play important roles in tumor genesis, immunity, cells proliferation and migration etc and significantly OCCR relevant. More importantly, the connection of independent gene cliques is established and the associations of genes are described. Methodologically, the proposed method avoids the problem of complex computation, relies only on available biological priors and provides a novel way to build gene network.","Title: Investigating the concordance of Gene Ontology terms reveals the intra- and inter-platform reproducibility of enrichment analysis.\nYear: 2013\n\nAbstract: Reliability and Reproducibility of differentially expressed genes (DEGs) are essential for the biological interpretation of microarray data. The microarray quality control (MAQC) project launched by US Food and Drug Administration (FDA) elucidated that the lists of DEGs generated by intra- and inter-platform comparisons can reach a high level of concordance, which mainly depended on the statistical criteria used for ranking and selecting DEGs. Generally, it will produce reproducible lists of DEGs when combining fold change ranking with a non-stringent p-value cutoff. For further interpretation of the gene expression data, statistical methods of gene enrichment analysis provide powerful tools for associating the DEGs with prior biological knowledge, e.g. Gene Ontology (GO) terms and pathways, and are widely used in genome-wide research. Although the DEG lists generated from the same compared conditions proved to be reliable, the reproducible enrichment results are still crucial to the discovery of the underlying molecular mechanism differentiating the two conditions. Therefore, it is important to know whether the enrichment results are still reproducible, when using the lists of DEGs generated by different statistic criteria from inter-laboratory and cross-platform comparisons. In our study, we used the MAQC data sets for systematically accessing the intra- and inter-platform concordance of GO terms enriched by Gene Set Enrichment Analysis (GSEA) and LRpath.In intra-platform comparisons, the overlapped percentage of enriched GO terms was as high as ~80% when the inputted lists of DEGs were generated by fold change ranking and Significance Analysis of Microarrays (SAM), whereas the percentages decreased about 20% when generating the lists of DEGs by using fold change ranking and t-test, or by using SAM and t-test. Similar results were found in inter-platform comparisons.Our results demonstrated that the lists of DEGs in a high level of concordance can ensure the high concordance of enrichment results. Importantly, based on the lists of DEGs generated by a straightforward method of combining fold change ranking with a non-stringent p-value cutoff, enrichment analysis will produce reproducible enriched GO terms for the biological interpretation.","Title: Evaluating Software Maintainability Using Fuzzy Entropy Theory\nYear: 2010\n\nAbstract: With the development of information technology, a large number of software products enter the maintenance phase. The maintainability of software products continues to attract people's attention. This paper proposes a new synthetic maintainability evaluation model basing on fuzzy entropy theory. In the model, the weight of different indexes is calculated by combining the entropy weight and expert weight. The entropy weight can be obtained through measuring the difference of membership degree of the index which reflects the consistency of the expert opinion. The evaluation result is more objective and reasonable. It provides a guideline to design a software product with high maintainability.","Title: Analysis of a periodic bacteria-immunity model with delayed quorum sensing\nYear: 2008\n\nAbstract: Based on [J. Zhang, M. Cerasuolo, P. Fergola, Z.E. Ma, On the influence of quorum sensing in the competition between bacteria and immune system (in press)], a bacteria-immunity model with periodically bacterial quorum sensing is formulated which describes the competition between bacteria and immune cells. A discrete delay is introduced to characterize the time in which bacteria receive signal molecules and combat with immune cells. In this paper, we focus on a subsystem of the bacteria-immunity model, investigate the existence and stability of the positively periodic solution, and discuss the existence of subharmonic bifurcation by the perturbation method.","Title: Optimal Contraction Theorem for Exploration–Exploitation Tradeoff in Search and Optimization\nYear: 2009\n\nAbstract: Global optimization process can often be divided into two subprocesses: exploration and exploitation. The tradeoff between exploration and exploitation (T:Er&Ei) is crucial in search and optimization, having a great effect on global optimization performance, e.g., accuracy and convergence speed of optimization algorithms. In this paper, definitions of exploration and exploitation are first given based on information correlation among samplings. Then, some general indicators of optimization hardness are presented to characterize problem difficulties. By analyzing a typical contraction-based three-stage optimization process, optimal contraction theorem is presented to show that T:Er&Ei depends on the optimization hardness of problems to be optimized. T:Er&Ei will gradually lean toward exploration as optimization hardness increases. In the case of great optimization hardness, exploration-dominated optimizers outperform exploitation-dominated optimizers. In particular, random sampling will become an outstanding optimizer when optimization hardness reaches a certain degree. Besides, the optimal number of contraction stages increases with optimization hardness. In an optimal contraction way, the whole sampling cost is evenly distributed in all contraction stages, and each contraction takes the same contracting ratio. Furthermore, the characterization of optimization hardness is discussed in detail. The experiments with several typical global optimization algorithms used to optimize three groups of test problems validate the correctness of the conclusions made by T:Er&Ei analysis.","Title: A compartmental model for the analysis of SARS transmission patterns and outbreak control measures in China\nYear: 2005\n\nAbstract: We propose a compartmental model BloComp(2,7) that mimics the SARS control strategies implemented by the Chinese government after the middle of April 2003: the division of the whole population into two parallel blocks corresponding to the so-called free environment and the isolated environment and the partition of these blocks further into the compartments of susceptible, exposed, infective, possible, diagnosed, removed and the health care workers. We introduce a novel approach to calculate the transfer rate from the free environment to the isolated environment, and we incorporate into the model the fact that many individuals were misdiagnosed as SARS suspected and hence were mistakenly put in the isolated environment due to lack of fast and effective SARS diagnostic tests. We develop some methods for the parameter identification using the daily reported data from the Ministry of Health of China. Simulations based on these parameters agree with the accural data well, thus provide additional validation of the model. We then vary some parameters to assess the effectiveness of different control measures: these new parameters correspond to the situation when the quarantine measures in the free-environment were prematurely relaxed (we thus observe the second outbreak with the maximal number of daily SARS patients much higher than the first outbreak) or when the quarantine time of SARS patients is postponed (we observe delayed peak time but with much higher number of SARS patients at the peak). We also calculate the basic reproductive number and the basic adequate contact rate.","Title: Test Case Reuse Based on Ontology\nYear: 2009\n\nAbstract: Test cases are one of the most important assets in the testing process. This paper presents the testing ontology based SWEBOK and software quality model. The management and retrieval of test cases will play a vital role in test cases reuse. The keyword-based, as well as facet-based retrieval cannot meet user's flexible query requirement because of lack of semantic information. SWEBOK provides a broad agreement on the content of the software engineering discipline. At last this paper discusses the management and retrieval of test cases based on the semantic similarity of two test concepts in two ontologies according to difference sets of superconcept, subconcept, extension, and intension.","Title: The Research of Construction Ontology-Based Material Management System in e-Commerce.\nYear: 2011\n\nAbstract: Ontologies are formal, explicit specifications of shared conceptuallizations of a given domain of discourse. The goal of materials management is to provide an unbroken chain of components for production to manufacture goods on time for the customer base. This paper attempts to propose a framework to improve and upgrade the effectiveness of E-C materials management by ontology construction algorithms. Our approach consists of a methodology that aids actors in understanding information generated from multiple construction participants. This paper presents using ontology to build materials management system in e-commerce. The experimental results have shown our suggested materials management system will increase precision and performance.","Title: Self-calibration method without joint iteration for distributed small satellite SAR systems.\nYear: 2013\n\nAbstract: The performance of distributed small satellite synthetic aperture radar systems degrades significantly due to the unavoidable array errors, including gain, phase, and position errors, in real operating scenarios. In the conventional method proposed in (IEEE T Aero. Elec. Sys. 42:436–451, 2006), the spectrum components within one Doppler bin are considered as calibration sources. However, it is found in this article that the gain error estimation and the position error estimation in the conventional method can interact with each other. The conventional method may converge to suboptimal solutions in large position errors since it requires the joint iteration between gain-phase error estimation and position error estimation. In addition, it is also found that phase errors can be estimated well regardless of position errors when the zero Doppler bin is chosen. In this article, we propose a method obtained by modifying the conventional one, based on these two observations. In this modified method, gain errors are firstly estimated and compensated, which eliminates the interaction between gain error estimation and position error estimation. Then, by using the zero Doppler bin data, the phase error estimation can be performed well independent of position errors. Finally, position errors are estimated based on the Taylor-series expansion. Meanwhile, the joint iteration between gain-phase error estimation and position error estimation is not required. Therefore, the problem of suboptimal convergence, which occurs in the conventional method, can be avoided with low computational method. The modified method has merits of faster convergence and lower estimation error compared to the conventional one. Theoretical analysis and computer simulation results verified the effectiveness of the modified method.","Title: Evolutionary decision-makings for the dynamic weapon-target assignment problem.\nYear: 2009\n\nAbstract: The dynamic weapon-target assignment (DWTA) problem is an important issue in the field of military command and control. An\n asset-based DWTA optimization model was proposed with four kinds of constraints considered, including capability constraints,\n strategy constraints, resource constraints and engagement feasibility constraints. A general “virtual” representation of decisions\n was presented to facilitate the generation of feasible decisions. The representation is in essence the permutation of all\n assignment pairs. A construction procedure converts the permutations into real feasible decisions. In order to solve this\n problem, three evolutionary decision-making algorithms, including a genetic algorithm and two memetic algorithms, were developed.\n Experimental results show that the memetic algorithm based on greedy local search can generate obviously better DWTA decisions,\n especially for large-scale problems, than the genetic algorithm and the memetic algorithm based on steepest local search.","Title: An Efficient Rule-Based Constructive Heuristic to Solve Dynamic Weapon-Target Assignment Problem\nYear: 2011\n\nAbstract: In this paper, we propose an efficient rule-based heuristic to solve asset-based dynamic weapon-target assignment (DWTA) problems. The main idea of the proposed heuristic is to utilize the domain knowledge of DWTA problems to directly achieve weapon assignment, without large number of function evaluations. We update the saturation states of constraints in the assignment process to guarantee the feasibility of generated solutions. For the purpose of testing the performance of the proposed heuristic, we build a general Monte Carlo simulation-based DWTA framework. For comparison, we also employ a Monte Carlo method (MCM) to make DWTA decisions in different defense scenarios. From simulations with DWTA instances under different scales, the heuristic has obvious advantages over the MCM with regard to solution quality and computation time. The proposed method can solve large-scale DWTA problems (e.g., those including 100 weapons, 100 targets, and four defense stages) within only a few seconds.","Title: Efficient Decision Makings for Dynamic Weapon-Target Assignment by Virtual Permutation and Tabu Search Heuristics\nYear: 2010\n\nAbstract: The dynamic weapon-target assignment (DWTA) problem is a typical constrained combinatorial optimization problem with the objective of maximizing the total value of surviving assets threatened by hostile targets through all defense stages. A generic asset-based DWTA model is established, especially for the warfare scenario of force coordination, to formulate this problem. Four categories of constraints, involving capability constraints, strategy constraints, resource constraints (i.e., ammunition constraints), and engagement feasibility constraints, are taken into account in the DWTA model. The concept of virtual permutation (VP) is proposed to facilitate the generation of feasible decisions. A construction procedure (CP) converts VPs into feasible DWTA decisions. With constraint satisfaction guaranteed by the synergy of VPs and the CP, an elaborate local search (LS) operator, namely move-to-head operator, is constructed to avoid repeatedly generating the same decisions. The operator is integrated into two tabu search (TS) algorithms to solve DWTA problems. Comparative experiments involving a random sampling method, an LS method, a hybrid genetic algorithm, a hybrid ant-colony optimization algorithm, and our TS algorithms show that the proposed TS heuristics for DWTA outperform their competitors in most test cases and they are competent for high-quality real-time DWTA decision makings.","Title: Intensity based image registration by minimizing exponential function weighted residual complexity.\nYear: 2013\n\nAbstract: In this paper, we propose a novel intensity-based similarity measure for medical image registration. Traditional intensity-based methods are sensitive to intensity distortions, contrast agent and noise. Although residual complexity can solve this problem in certain situations, relative modification of the parameter can generate dramatically different results. By introducing a specifically designed exponential weighting function to the residual term in residual complexity, the proposed similarity measure performed well due to automatically weighting the residual image between the reference image and the warped floating image. We utilized local variance of the reference image to model the exponential weighting function. The proposed technique was applied to brain magnetic resonance images, dynamic contrast enhanced magnetic resonance images (DCE-MRI) of breasts and contrast enhanced 3D CT liver images. The experimental results clearly indicated that the proposed approach has achieved more accurate and robust performance than mutual information, residual complexity and Jensen-Tsallis.","Title: Multi-instance learning for mass retrieval in digitized mammograms\nYear: 2012\n\nAbstract: Breast cancer is one of the most common malignant tumors in women. In mammogram retrieval system, the query mass is ambiguity and difficult to be described because in which the lesion and the normal tissue are physically adjacent. If the query mass can be processed as an image bag, then the ambiguity can be tackled by multi-instance learning (MIL) techniques. In this paper, we presented a preliminary study of MIL for mass retrieval in digitized mammograms, and proposed three image bag generators named J-Bag, A-Bag and K-Bag, respectively. Diverse Density (DD), EM-DD and BP-MIP were applied as MIL algorithms for mass retrieval. Experimental study was carried out on DDSM database and another database in which images were collected from the Zhejiang Cancer Hospital in China. Preliminary experiments showed that the MIL techniques can be applied to the problem of mass retrieval in digitized mammograms and the proposed bag generators A-Bag and K-Bag can achieve more efficient results than the existing bag generator SBN.","Title: Perceptual mass segmentation using eye-tracking and seed-growing\nYear: 2012\n\nAbstract: In the paper, we propose a novel scheme for breast mass segmentation in mammography, which is based on visual perception and consists of two steps. Firstly, radiologists' eye-gazing data is recorded by the eye-tracker during reading and then clustered with a density-based spatial clustering of applications with noise (DBSCAN) algorithm to achieve seeds locating radiologists' regions of interest (ROIs). The seeds-based region growing (SBRG) algorithm is applied to buckle ROIs containing suspicious lesions. Secondly, in order to achieve fine lesion contour as final result, the ROIs are segmented with a multi-scale mass segmentation approach using active contour models. The result of applying the proposed method to the mammograms from both DDSM and Zhejiang Cancer Hospital shows that the achieved average of overlap rate is 0.5915 and the achieved average of misclassification rate is 0.6342. The innovative point of the proposed approach is to introduce visual perception into breast mass segmentation, which makes the result of mass segmentation meet radiologists' subjective demand.","Title: Association between bilateral asymmetry of kinetic features computed from the DCE-MRI images and breast cancer\nYear: 2013\n\nAbstract: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of breast yields high sensitivity but relatively lower specificity. To improve diagnostic accuracy of DCE-MRI, we investigated the association between bilateral asymmetry of kinetic features computed from the left and right breasts and breast cancer detection with the hypothesis that due to the growth of angiogenesis associated with malignant lesions, the average dynamic contrast enhancement computed from the breasts depicting malignant lesions should be higher than negative or benign breasts. To test this hypothesis, we assembled a database involving 130 DCE-MRI examinations including 81 malignant and 49 benign cases. We developed a computerized scheme that automatically segments breast areas depicted on MR images and computes kinetic features related to the bilateral asymmetry of contrast enhancement ratio between two breasts. An artificial neural network (ANN) was then used to classify between malignant and benign cases. To identify the optimal approach to compute the bilateral kinetic feature asymmetry, we tested 4 different thresholds to select the enhanced pixels (voxels) from DCE-MRI images and compute the kinetic features. Using the optimal threshold, the ANN had a classification performance measured by the area under the ROC curve of AUC=0.79 +/- 0.04. The positive and negative predictive values were 0.75 and 0.67, respectively. The study suggested that the bilateral asymmetry of kinetic features or contrast enhancement of breast background tissue could provide valuable supplementary information to distinguish between the malignant and benign cases, which can be fused into existing computer-aided detection schemes to improve classification performance.","Title: Energy-Efficient Policy Based On Cross-Layer Cooperation In Wireless Communication\nYear: 2014\n\nAbstract: Cooperative communication has emerged as a new wireless network communication concept, in which parameter optimization such as cross-layer cooperation plays an important role. Heuristic evaluation postdecision state learning algorithm (HE-PDS) is proposed in cross-layer cooperation. The proposed algorithm exploits the determinate state information and jointly considers the transmitting power and channel state condition at the physical layer and the buffer congestion control at the media access control layer. The experimental results show that the cumulative average total costs of HE-PDS algorithm decrease about ten times and 8% under the maximum delay and throughput constraints and the power costs decrease about 50% and 28% under various delay limits and about 100% and 56% under the different throughput constraints than the traditional Q algorithm and PDS algorithm, demonstrating that the proposed algorithm has much better energy-efficient performance and faster convergence speed and outperforms the traditional Q learning algorithm and PDS learning algorithm.","Title: Transmission dynamics of a multi-group brucellosis model with mixed cross infection in public farm.\nYear: 2014\n\nAbstract: Brucellosis is one of the major public health problems in China, since there is a very large number of cattle and sheep. While the mixed feeding between cattle and sheep in public farm is one of the most typical characteristics in brucellosis outbreak regions, which has a large influence on the spreading of brucellosis. In this paper, we propose a multi-group SEIRV dynamical model with bidirectional mixed cross infection between cattle and sheep, and aim to investigate the influence of cross infection of mixed feeding on the brucellosis transmission. Firstly, we obtain the basic reproduction number R0, confirm the uniform existence and unique positive equilibrium of dynamic model when R0>1 and prove the global stability of endemic equilibrium when R0>1. In addition, sensitivity analysis of the infectious herds with different system in term of some parameter values are given. It is found that if there exists the bidirectional of mixed cross infection between cattle and sheep, the disease cannot disappear even though R01<1 and R02<1. These findings have important implication for brucellosis control: if we want to eliminate the brucellosis, firstly we should take measures to remove mixed cross infection, i.e., to prohibit mixed feeding.","Title: A two-phase approximation of cylindrical branching models\nYear: 2014\n\nAbstract: This paper presents a scheme of approximating cylindrical models using convolution surfaces and subdivision surfaces. Firstly, an inverse Catmul-Clark subdivision is utilized to approximate the cylindrical models. Then the subdivided mesh is evolved onto the target convolution surface-based shape to produce a natural blending at branches. The initial inverse subdivision reduce the evolution iterations efficiently. Results show that our method approximate the skeletal structures well.","Title: A subjective quality evaluation for 3D point cloud models\nYear: 2014\n\nAbstract: Research over the last decade has developed a wide application of 3D models. Much of this work, however, is not related to quality evaluation of 3D color models. This paper presents a novel subjective quality evaluation model that can be used to compute the difference between an original 3D color model and the other processed one. Our model jointly takes into account human visual perception of color and shape in 3D color models. As observed from the results of color and shape stimulations, our model can efficiently test the evaluation outcomes and realize a subjective metric of 3D color models. Experimental results show that our proposed evaluation model gives a reasonable correspondence with the human perception, and supports our relationship formulation between the changes in shape and color for the similarity measurements of 3D color models.","Title: Research on 3D laser scanning technology based on point cloud data acquisition\nYear: 2014\n\nAbstract: Three-dimensional terrestrial laser scanning technology is a high-tech and sophisticated development in recent years, so understanding how to use the laser scanner and knowing the acquisition process of point cloud data is very important. This paper focuses specifically on point cloud data acquisition process by using the FARO Focus3D 120 laser scanner. The paper explores whether light affects the accuracy of point cloud data scanned. A total of six tests were conducted in adequate light, low light and moderate light scan scenes in order to assess data accuracy associated with current scanning light environment.","Title: Bibliometric analysis for the literature of traditional Chinese medicine in PubMed\nYear: 2015\n\nAbstract: The aim of this study is to conduct a retrospective bibliometric analysis of articles about traditional Chinese medicine (TCM) research in PubMed and to learn about the development and perspective of TCM. A systematic bibliometric search was performed based on the PubMed database covering related publications between January 1, 1995, and December 31, 2014. Numbers and type of articles, countries and language of publications, as well as major journals were analyzed in accordance with bibliometrics methodologies. The retrieve results were analyzed and described in the form of texts, tables, and graphs. A total of 42,192 articles were identified from the PubMed database, among which 43.56 % were published as original articles. The articles were originated from 102 countries and territories. China was ranked first with 20,121 articles, followed by United States with 2207 articles. 57.74 % of the articles are published in English. 4364 articles were published by Zhongguo Zhong Yao Za Zhi. And complementary medicine was the most focused research are involving 30,544 articles. The publication activity of TCM literature increased rapidly in the past 20 years, indicating enhanced attention attracted to TCM and more research input. In view of its great advances achieved in scientific studies, TCM will continue to play an important role in medical research.","Title: Global dynamics of a predator-prey system modeling by metaphysiological approach.\nYear: 2016\n\nAbstract: We showed the detailed analysis for a predator-prey system.Transcritical and Hopf bifurcations are investigated.We get four types of bubbles.Our results link with biological systems in the real world. In this paper, a systematical analysis of a predator-prey system based on metaphysiological approach is proposed, which considers changes in the aggregate biomass density rather than changes in the demographics. Bifurcation behaviors including transcritical and Hopf bifurcation are observed in this type of system, and bubble phenomena can be seized by setting appropriate values for parameters. The obtained results provide some new insights in population dynamics, which play important roles in maintaining population diversity.","Title: The Existence Uniqueness And The Fixed Iterative Algorithm Of The Solution For The Discrete Coupled Algebraic Riccati Equation\nYear: 2011\n\nAbstract: In this article, applying the properties of M-matrix and non-negative matrix, utilising eigenvalue inequalities of matrix's sum and product, we firstly develop new upper and lower matrix bounds of the solution for discrete coupled algebraic Riccati equation (DCARE). Secondly, we discuss the solution existence uniqueness condition of the DCARE using the developed upper and lower matrix bounds and a fixed point theorem. Thirdly, a new fixed iterative algorithm of the solution for the DCARE is shown. Finally, the corresponding numerical examples are given to illustrate the effectiveness of the developed results.","Title: Circuit simulation on control and synchronization of fractional order switching chaotic system\nYear: 2015\n\nAbstract: In view of obtaining a larger key space and thus improve the level of security attainable in chaotic communication, this paper examines the dynamics of a switched, fractional-order chaotic system. Taking the fractional-order Chen system as a starting point, the dynamics of a modified version of that system is examined, and the composed system is hereafter realized by allowing switching between the two different subsystems. Our work first demonstrates how fractional-order chaotic systems can be realized physically. For the fractional-order switching chaotic system, a linear feedback controller and a number of synchronization schemes are designed. Our simulations show that control and synchronization of the individual subsystems can be achieved with the same feedback and synchronization controller.","Title: Upper Solution Bounds Of The Continuous Coupled Algebraic Riccati Matrix Equation\nYear: 2011\n\nAbstract: In this article, by using some matrix identities, we construct the equivalent form of the continuous coupled algebraic Riccati equation (CCARE). Further, with the aid of the eigenvalue inequalities of matrix's product, by solving the linear inequalities utilising the properties of M-matrix and its inverse matrix, new upper matrix bounds for the solutions of the CCARE are established, which improve and extend some of the recent results. Finally, a corresponding numerical example is proposed to illustrate the effectiveness of the derived results.","Title: Efficient privacy-preserving third-party auditing for ambient intelligence systems.\nYear: 2016\n\nAbstract: Due to limited resources of actors, third-party auditing has vital significance for ambient intelligence systems, which ensures the integrity of data stored in the information manager. However, Current public key based third-party auditing schemes are costly, while simple MAC based third-party auditing schemes can’t fulfill two security requirements, namely the privacy-preserving and the integrity-against-TPA requirements. Taking both security and efficiency into account, this paper proposes a novel simple MAC based third-party auditing scheme, which is much more efficient than current public key based schemes, while still fulfills the above two security requirements. Therefore, it is feasible to be deployed in ambient intelligence systems.","Title: PKDIP: Efficient Public-Key-Based Data Integrity Protection for Wireless Image Sensors\nYear: 2015\n\nAbstract: Due to limited energy of \"wireless image sensors (WISs),\" existing data integrity protection mechanisms typically employ a hash-function-based signing algorithm to generate \"message authentication codes (MACs)\" for long image frames. However, hash-function-based signing algorithm requires the WIS and the \"end user (EU)\" sharing a secret, which leads to a new security issue: Once the EU becomes malicious due to some reasons, it will be able to forge the WIS's data since it holds the shared secret. Therefore, public-key cryptography is desirable. Unfortunately, public-key cryptographic operations are quite time-consuming for energy-restrained WISs. Facing this dilemma, we present a novel data integrity protection protocol named PKDIP in this paper. Similar to the mechanisms of this field, PKDIP generates MACs for data integrity protection. However, different from other well-known approaches, PKDIP introduces the \"Montgomery Modular Multiplication (MontMM)\" technique to current public-key-based signing algorithms. Since MontMM is much more efficient than hash functions, PKDIP can reduce the signing cost significantly. Experimental results show PKDIP can even be more efficient than hash-function-based schemes.","Title: Formal Modelling And Analysis Of Hla Architectural Style\nYear: 2010\n\nAbstract: The high level architecture (HLA) was accepted as an IEEE standard in the year 2000 as reference architecture and common infrastructure for large scale distributed interactive simulation systems. But the problems caused by the informal description of the standard are widely recognised. The special requirements of HLA formalisation are analysed and considered as a new architectural style. Combined with the comparison of many architecture description languages (ADLs), the formal models of HLA style are introduced with a newly proposed formal language, p-ADL. These models are grouped as specific architectural concepts of composite, component and connector types. With respect to the different HLA concepts, the models are further described by internal behaviour and external interfaces. They are then tested and analysed with a simple HLA example using the p-ADL compiler p-ADLVM. An HLA internal deadlock reason is found automatically. With the formal specification, a new HLA system design environment is established, which improves the efficiency of the system development.","Title: Research on the Application of Face Symbol in Thematic Map Making\nYear: 2015\n\nAbstract: Currently, map symbols that represent multivariable data sets in thematic map are simple and not good at combined using, they can't satisfy the needs of displaying multiple-index data in mapping process. In this paper, face symbols are used to visualize multiple-variable data sets. Firstly, it analyzes the development of face symbol, then summarizes the steps and rules to design face symbol. At last, it compares the application effect of face symbol and conventional statistics symbol. The results show that face symbol represents multiple-variable data better in thematic map.","Title: Practical and Secure Outsourcing of Linear Algebra in the Cloud\nYear: 2013\n\nAbstract: Cloud computing provide an economically promising paradigm of outsourcing computation. Input/output privacy and verification are becoming the major security concern, and the efficiency of client and cloud side is becoming the main practice concern. Focusing on the existed linear algebra outsourcing schemes used fully homomorphic encryption scheme or somewhat homomorphic encryption scheme are far from practical goal. We use matrix fully homomorphic encryption scheme to construct an efficient linear algebra outsourcing scheme. The first basic property of the scheme is the protection of input/output privacy. The second basic property is the high efficiency of the client's encryption, decryption and verification algorithms and the cloud side computation.","Title: The a posteriori error estimates of Chebyshev-Petrov-Galerkin methods for second-order equations.\nYear: 2016\n\nAbstract: In this paper, the a posteriori error estimates of Chebyshev–Petrov–Galerkin approximations are investigated. For simplicity, we choose the Poisson equation with Dirichlet boundary conditions to discuss the a posteriori error estimators, and deduce their efficient and reliable properties. Some numerical experiments are performed to verify the theoretical analysis for the a posteriori error estimators.","Title: Galerkin spectral approximations for optimal control problems governed by the fourth order equation with an integral constraint on state.\nYear: 2016\n\nAbstract: An integral constraint on state optimal control problem governed by the first bi-harmonic equation is analyzed and its Legendre Galerkin spectral approximations are constructed. The optimality conditions of the continuous and the discretized optimal control problems are presented by the Kuhn–Tucker condition, respectively. The a priori error estimates of high accuracy are obtained. Furthermore, an efficient projection algorithm is proposed, and the convergent rate is discussed with precise details. Ample numerical examples are carried out to validate the theoretical results.","Title: Identity-based key management for wireless sensor networks using lagrange interpolation.\nYear: 2016\n\nAbstract: To simplify the certificate management in public key infrastructure, key management protocols for wireless sensor networks typically use identity-based cryptography for negotiating the shared key. However, current identity-based key management protocols are mainly based on the bilinear map technique, which requires high computation costs on sensor nodes, resulting in short lifetimes of sensor nodes. To address this issue, we present a novel identity-based key management protocol called Identity-Based Key Management using Lagrange Interpolation IBKML. Similar to the protocols of this field, IBKML can provide confidentiality and integrity protections for the negotiated key. However, compared to other well-known approaches, IBKML is based on Lagrange interpolation instead of bilinear map, which employs only a few modular multiplication operations. Because modular multiplication is much more efficient than bilinear pairing, IBKML can reduce the computation costs on sensor nodes significantly. Experimental results show that the proposed protocol is feasible for real applications. Copyright © 2016 John Wiley & Sons, Ltd.","Title: Echo Correlation Analysis Of Targets With Dominant Scatterers In Diversity Mimo Radar\nYear: 2016\n\nAbstract: A huge limitation of the correlation analysis in existing scatterer model is that the former model, only consisting of uniformly distributed scatterers, ignores the variability of complex scattering intensity in real situation. In fact, each part of the target has a different scattering architecture. Moreover, the scattering intensity of pinnacle or boundary scattering structure is an order of magnitude larger than the normal scatterers. To solve this problem, this paper studies the correlation coefficient for scattering model which has several dominant scatterers in spatial-frequency diversity MIMO radar. And the positions of the dominant scatterers assumed to be variables to make an equivalent of different real targets. The proposed analysis enhances the authenticity of scattering model and can simulate the real model accurately. Simulation results show the effectiveness of the proposed approach.","Title: An automatic positioning algorithm for archaeological fragments.\nYear: 2016\n\nAbstract: One of the main challenges on digital preservation of cultural heritage is to reassemble broken fragments. However, large amounts of fragments are mixed randomly when discovered, which brings terrific difficulties for their reassembly. This paper introduces an automatic approach for positioning large mixed archaeological fragments which come from a particular kind of artifact. The main idea is to position fragments based on partial matching between fragments and a complete artifact model using the multi-scale, informative and robust Heat Kernel Signature. The positioning pipeline contains four steps: feature points extraction for fragments and the complete artifact model based on Heat Kernel Signature; initial matching between feature points by comparing their Heat Kernel Signature curves; wrong matches removing using a basis points driven refinement procedure and rigid transformation generating by selecting three pairs of points among the correct matching results using farthest points sampling. After these steps, archaeological fragments can be positioned to different positions compared with the template model, which provides not only the classification information, but also the accurate relative position. The main contributions of this paper are using a novel feature extraction algorithm based on Heat Kernel Signature to assist partial matching, and a basis points driven refinement procedure to remove wrong matches. The proposed algorithm has been tested on the Terracotta Warriors fragments, and the results prove the effectiveness of the proposed positioning algorithm.","Title: A mesh-insensitive elastic model for simulation of solid bodies.\nYear: 2016\n\nAbstract: FEM-based elasticity models are popular in solid body simulation. To avoid its problems of mesh sensitivity and overly stiff, a novel smoothed pseudo-linear elasticity model is presented. First, the smoothed finite element method is employed to alleviate mesh distortion and overly stiff problems instead of the traditional spatial adaptive smoothing method. Then, we propose a smoothing domain-based stiffness warping technique to compensate the nonlinear errors introduced by linear elasticity models. With this approach, transient displacements are slightly affected by mesh distortion and total volumes are preserved under large rotations. It also shows apparently softening effects in the experiments. Simulation results are generated without adding significant complexity or computational cost to the standard corotational FEM.","Title: Comparative Analysis Of The Supply Situation Of Chinese Medicine Service System In Hubei Province\nYear: 2016\n\nAbstract: The policy of the traditional Chinese medicine was researched in this paper. Firstly, the stages of Chinese medicine policy since 1949 were introduced. Secondly, the characteristics and advantages of the traditional Chinese medicine in Hubei province were studied. Thirdly, the policy measures and economic indicators of Hubei province were compared with other provinces. Fourthly, the problems which Hubei confronted with to develop Chinese medicine were analyzed. Fifthly, the conclusions were drawn. According to this paper, in order to fully play the role of Chinese medicine for the public's health and promote the development of the traditional Chinese medicine in Hubei, national government should increase heavy investment for developing the Chinese medicine industry. And provincial health department should implement the government's preferential policies to the prosperity of the state hospital for Chinese medicine. Then the medical institutions should be encouraged to provide the service and technology of the traditional Chinese medicine. In addition, the service capabilities of Chinese medicine should be strengthened and improved.","Title: Error estimates of spectral element methods with generalized Jacobi polynomials on an interval.\nYear: 2017\n\nAbstract: Based on the generalized Jacobi polynomials with indices (−1,−1), a new upper bound for a posteriori error estimates is proposed, investigated and implemented for generalized Jacobi–Galerkin spectral element approximations. To simplify discussion for the error estimates, the second-order partial differential equation with homogeneous Dirichlet boundary conditions is considered on a unit interval.","Title: A 6gs/S 8-Bit Current Steering Dac With Optimized Current Switch Drive\nYear: 2017\n\nAbstract: A 6GS/s 8-bit current-steering DAC in 0.13 mu m CMOS technology is presented. The 5-31 binary to thermometer decoder and the switch drive signal are optimized, leading to obvious improvements in dynamic performance. A minimum spurious free dynamic range (SFDR) of 34 dB has been achieved over the full Nyquist bandwidth at 6GS/s. Total system power consumption is 95.44mW at 6GS/s with 2.98GHz input signal. Core area of DAC occupies 0.13mm2 without pads.","Title: The Improved Eigenvalue Bounds For The Solution Of The Discrete Algebraic Riccati Equation\nYear: 2017\n\nAbstract: In this paper, by using matrix eigenvalue inequalities and the properties of the positive definite solution for the discrete algebraic Riccati equation (DARE), we present upper and lower eigenvalue bounds for the solution of this equation. Moreover, applying majorization inequalities and eigenvalue summation (product) inequalities of special matrices, based on the derived results, we propose upper and lower bounds on eigenvalue summation and product for the solution of the DARE, which improve some of the recent results. The numerical examples show the effectiveness of the derived results.","Title: Lower Bounds On Eigenvalue Summation For The Solution Of The Lyapunov Matrix Differential Equation\nYear: 2017\n\nAbstract: The Lyapunov matrix differential equation has widely applications in control theory and linear system. In this paper, applying the properties of exponential matrices and integrable functions, we obtain lower bounds on eigenvalue summation for the solution of this equation using eigenvalue inequalities and majorization inequalities. Finally, we give a corresponding numerical example to show the effectiveness of the derived bounds.","Title: Adaptive detection with conic rejection to suppress deceptive jamming for frequency diverse MIMO radar.\nYear: 2017\n\nAbstract: With traditional radar systems, it is not easy to classify and suppress the intended false targets, especially in case of the mainlobe deceptive jamming. In this paper, the deceptive jamming suppression issue is formulated as a problem of detecting the presence of target signal while rejecting potential deceptive jamming under the multiple-input multiple-output (MIMO) radar with frequency diverse array (FDA) as the transmit array. The problem at hand is settled by resorting to the generalized likelihood ratio test (GLRT); at the design stage we define the sets where the false target and true target lie in. Since FDA-MIMO radar provides controllable degrees of freedom in both range and angle domains, the sets are range–angle related. This implies that both range and angle properties can be used to suppress the false targets. Based on the detector, a two-pulse scheme is proposed to guarantee high rejection probability of deceptive jamming. At the analysis stage, the performance of the detector is assessed in comparison with the conventional MIMO scenario and other detectors. The performance assessment shows that the proposed solutions are effective in the presence of electronic countermeasure (ECM) systems, especially when facing mainlobe deceptive jamming.","Title: New Matrix Bounds And Iterative Algorithms For The Discrete Coupled Algebraic Riccati Equation\nYear: 2017\n\nAbstract: The discrete coupled algebraic Riccati equation (DCARE) has wide applications in control theory and linear system. In general, for the DCARE, one discusses every term of the coupled term, respectively. In this paper, we consider the coupled term as a whole, which is different from the recent results. When applying eigenvalue inequalities to discuss the coupled term, our method has less error. In terms of the properties of special matrices and eigenvalue inequalities, we propose several upper and lower matrix bounds for the solution of DCARE. Further, we discuss the iterative algorithms for the solution of the DCARE. In the fixed point iterative algorithms, the scope of Lipschitz factor is wider than the recent results. Finally, we offer corresponding numerical examples to illustrate the effectiveness of the derived results.","Title: The Nekrasov diagonally dominant degree on the Schur complement of Nekrasov matrices and its applications.\nYear: 2018\n\nAbstract: In this paper, we estimate the Nekrasov diagonally dominant degree on the Schur complement of Nekrasov matrices. As an application, we offer new bounds of the determinant for several special matrices, which improve the related results in certain case. Further, we give an estimation on the infinity norm bounds for the inverse of Schur complement of Nekrasov matrices. Finally, we introduce new methods called Schur-based super relaxation iteration (SSSOR) method and Schur-based conjugate gradient (SCG) method to solve the linear equation by reducing order. The numerical examples illustrate the effectiveness of the derived result.","Title: The Solution Bounds And Fixed Point Iterative Algorithm For The Discrete Coupled Algebraic Riccati Equation Applied To Automatic Control\nYear: 2017\n\nAbstract: In this article, applying the special properties of nonnegative matrices and symmetric matrices, we consider the coupled term as a whole and derive the solution bounds of the discrete coupled algebraic Riccati equation. This reduces the error of taking apart the coupled term separately when applying inequality techniques to get the bounds in general. Then, using Cauchy-Schwarz inequality, matrix norm inequalities and a fixed point theorem, we discuss the existence uniqueness condition and fixed point iteration for the solution of this equation. Finally, we offer corresponding numerical examples to illustrate the effectiveness of our results.","Title: SCPR: Secure Crowdsourcing-Based Parking Reservation System\nYear: 2017\n\nAbstract: AbstractThe crowdsourcing-based parking reservation system is a new computing paradigm, where private owners can rent their parking spots out. Security is the main concern for parking reservation systems. However, current schemes cannot provide user privacy protection for drivers and have no key agreement functions, resulting in a lot of security problems. Moreover, current schemes are typically based on the time-consuming bilinear pairing and not suitable for real-time applications. To solve these security and efficiency problems, we present a novel security protocol with user privacy called SCPR. Similar to protocols of this field, SCPR can authenticate drivers involved in the parking reservation system. However, different from other well-known approaches, SCPR uses pseudonyms instead of real identities for providing user privacy protection for drivers and designs a novel pseudonym-based key agreement protocol. Finally, to reduce the time cost, SCPR designs several novel cryptographic algorithms based on the algebraic signature technique. By doing so, SCPR can satisfy a number of security requirements and enjoy high efficiency. Experimental results show SCPR is feasible for real world applications.","Title: Acmhs: Efficient Access Control For Mobile Health Care System\nYear: 2018\n\nAbstract: To provide privacy protection in mobile health care system, medical users typically encrypt their personal health information before publishing it to the health care center. And other medical users download, decrypt, and process it using an access control protocol. However, current access control protocols are mainly focusing the privacy of personal health information and lacking the protection for medical users' real identities, leading to a variety of security issues. At the same time, current access control protocols for mobile health care systems are usually based on bilinear map, resulting in high computation costs. Observing the above issues, we introduce a novel access control protocol with privacy-preserving called Access Control for Mobile Healthcare System (ACMHS). Similar to protocols of this field, ACMHS can provide privacy protection for mobile health care systems. However, different from other well-known approaches, ACMHS uses pseudonyms instead of real identities, which can provide privacy protection for medical users' real identities. Moreover, to reduce the computation cost of bilinear map, we introduce the algebraic signature to ACMHS and design a novel access control protocol, which is much more efficient than current bilinear-map-based protocols. By doing so, ACMHS can achieve high efficiency while still enjoying required security requirements. Experimental results show that ACMHS is feasible for real-world applications.","Title: Low Rank Variation Dictionary and Inverse Projection Group Sparse Representation Model for Breast Tumor Classification.\nYear: 2018\n\nAbstract: Sparse representation classification achieves good results by addressing recognition problem with sufficient training samples per subject. However, SRC performs not very well for small sample data. In this paper, an inverse-projection group sparse representation model is presented for breast tumor classification, which is based on constructing low-rank variation dictionary. The proposed low-rank variation dictionary tackles tumor recognition problem from the viewpoint of detecting and using variations in gene expression profiles of normal and patients, rather than directly using these samples. The inverse projection group sparsity representation model is constructed based on taking full using of exist samples and group effect of microarray gene data. Extensive experiments on public breast tumor microarray gene expression datasets demonstrate the proposed technique is competitive with state-of-the-art methods. The results of Breast-1, Breast-2 and Breast-3 databases are 80.81%, 89.10% and 100% respectively, which are better than the latest literature.","Title: Breast Tumor Classification Based on Decision Information Genes and Inverse Projection Sparse Representation.\nYear: 2018\n\nAbstract: Microarray gene expression data-based breast tumor classification is an active and challenging issue. In this paper, a robust breast tumor recognition framework is presented based on considering reducing clinical misdiagnosis rate and exploiting available information in existing samples. A wrapper gene selection method is established from a new perspective of reducing clinical misdiagnosis rate. The further feature selection of information genes is fulfilled by a modified NMF model, which is motivated by hierarchical learning and layer-wise pre-training strategy in deep learning. For completing the classification, an inverse projection sparse representation (IPSR) model is constructed to exploit information embedded in existing samples, especially in test ones. Moreover, the IPSR model is optimized via generalized ADMM and the corresponding convergence is analyzed. Extensive experiments on three public breast tumor datasets show that the proposed method is stable and effective for breast tumor classification. Compared to the latest literature, there is 14% higher in classification accuracy. Specificity and sensitivity achieve 94.17% and 97.5%.","Title: Relay Translation Policies Based On State Pruning For Ad Hoc Networks\nYear: 2018\n\nAbstract: In this paper, we consider the multi-hop cooperative ad hoc networks with dynamic network status, limited node energy, and fading channel environment. It is difficult to solve the multi-objective cost function with large state space. We aim to find the optimal policies to reduce these states for arbitrary topologies with links affected by path loss. Therefore, this paper presents the algorithm of state pruning program based on inverse probability of metropolis (SPP_IPM) which defines the inverse probability of metropolis as the refuse acceptance criteria and realizes the pruning for state space. It achieves the pruning upper bound by scaling method and reduces the number of states according to a certain probability bound. Thus, we reduce the computation complexity through this pruning technique. The simulation results demonstrate SPP IPM is better than other typical algorithms such as RTDP (real-time dynamic programming) and FRTDP (focused real-time dynamic programming) with respect to the state space, the success probability, and the transmission delay. Furthermore, SPP_IPM has a good control effect on the growth of the network state space when the network scene changes.","Title: Multiperspective Target Tracking Approach for Doppler Through-Wall Radar.\nYear: 2018\n\nAbstract: In this letter, a multiperspective target tracking approach is proposed for Doppler through-wall radar (TWR). To properly identify stationary and tangentially moving targets, an expansion receiver is added to the traditional cost-effective Doppler TWR, and a data fusion process is applied to track targets. Using the expansion receiver, the information on the target is acquired from another perspec...","Title: An approximation scheme for the time fractional convection-diffusion equation.\nYear: 2018\n\nAbstract: In this paper, a discrete form is proposed for solving time fractional convection–diffusion equation. Firstly, we obtain a time discrete scheme based on finite difference method. Secondly, we prove that the time discrete scheme is unconditionally stable, and the numerical solution converges to the exact one with order O(τ2−α), where τ is the time step size. Finally, two numerical examples are proposed respectively, to verify the order of convergence.","Title: Stable and realistic crack pattern generation using a cracking node method.\nYear: 2018\n\nAbstract: This paper presents a method for simulating surface crack patterns appearing in ceramic glaze, glass, wood and mud. It uses a physically and heuristically combined method to model this type of crack pattern. A stress field is defined heuristically over the triangle mesh of an object. Then, a first-order quasi-static cracking node method (CNM) is used to model deformation. A novel combined stress and energy combined crack criterion is employed to address crack initiation and propagation separately according to physics. Meanwhile, a highest-stress-first rule is applied in crack initiation, and a breadth-first rule is applied in crack propagation. Finally, a local stress relaxation step is employed to evolve the stress field and avoid shattering artifacts. Other related issues are also discussed, such as the elimination of quadrature sub-cells, the prevention of parallel cracks and spurious crack procession. Using this method, a variety of crack patterns observed in the real world can be reproduced by changing a set of parameters. Consequently, our method is robust because the computational mesh is independent of dynamic cracks and has no sliver elements. We evaluate the realism of our results by comparing them with photographs of real-world examples. Further, we demonstrate the controllability of our method by varying different parameters.","Title: Supply chain decisions with reference quality effect under the O2O environment.\nYear: 2018\n\nAbstract: The reference quality effect is an important factor that influences consumer purchasing behavior. To investigate how firms should incorporate the reference quality effect under different business models, we focus on a supply chain consisting of a supplier and a retailer where the retailer can be an offline store, a pure online store or a combination of offline and online stores within an offline to online model. We formulate the reference quality effect with a modified Nerlove-Arrow model and use two different sales functions to reflect the fact that the reference quality effect will affect consumers in various ways when the business model varies. Utilizing differential game theory, the equilibrium decisions of the channel members are derived, and the analysis illustrates how the firms should adjust their decisions when the retailers use different retail patterns. The basic model does not allow product returns, but then this assumption is relaxed. Comparison between the two cases shows under what conditions consumers will benefit from the retailer’s allowing product returns.","Title: Damage Detection of L-Shaped Concrete Filled Steel Tube (L-CFST) Columns under Cyclic Loading Using Embedded Piezoceramic Transducers.\nYear: 2018\n\nAbstract: L-shaped concrete filled steel tube (L-CFST) columns are used frequently in civil engineering, and the concrete damage inside the L-CFST column is difficult to monitor. This research aims to develop a new method to monitor the internal concrete damage in the L-CFST column by using embedded piezoceramic smart aggregates (SAs) under low frequency cyclic loading. The SA enabled active method is used to monitor the concrete damages near the bottom of the L-CFST columns, and the wavelet packet analysis is used to establish a damage index, which is used to analyze the acquired data. During the experiment, three L-CFST columns with different wall thickness of the steel tube were tested. The experimental results find that the structural damage indices under the low-frequency cyclic loading are basically consistent with the results of the hysteretic curves and the skeleton curve of the specimens, and are in good agreement with the experimental phenomena. We conclude that the use of smart aggregate can directly and clearly reflect the damage process of the concrete core, demonstrating the feasibility of using piezoceramic smart aggregates to monitor the internal concrete damage of the L-CFST column.","Title: Individualized analysis of differentially expressed miRNAs with application to the identification of miRNAs deregulated commonly in lung cancer tissues.\nYear: 2018\n\nAbstract: Identifying differentially expressed microRNAs (DE miRNAs) between cancer samples and normal controls is a common way to investigate carcinogenesis mechanisms. However, for a DE miRNA detected at the population-level, we do not know whether it is DE in a particular cancer sample. Here, based on the finding that the within-sample relative expression orderings of miRNA pairs are highly stable in a particular type of normal tissues but widely disrupted in the corresponding cancer tissues, we proposed a method, called RankMiRNA, to identify DE miRNAs in each cancer tissue compared with its own normal state. Evaluated with pair-matchedmiRNA expression profiles of cancer tissues and adjacent normal tissues for lung and liver cancers, RankMiRNA exhibited excellent performance. Finally, we exemplified an application of the individual-level differential expression analysis by finding miRNAs DE in at least 90% lung cancer tissues, defined as common DE miRNAs of lung cancer. After identifying DE miRNAs for each of 991 lung cancer samples from The Cancer Genome Atlas with RankMiRNA, we found that hsa-mir-210 was upregulated, while hsa-mir-490 and hsa-mir-486 were downregulated in > 90% of the 991 lung cancer samples. These common DE miRNAs were validated in independent pair-matched samples of cancer tissues and adjacent normal tissues measured with different platforms. In conclusion, RankMiRNA provides us a novel tool to find common and subtype-specific miRNAs for a type of cancer, allowing us to study cancer mechanisms in a novel way.","Title: SIM-RPM: Secure Incentive Mechanism for Crowdsourcing-Based Road Pavement Monitoring System.\nYear: 2018\n\nAbstract: Crowdsourcing-based road pavement monitoring is a new computing paradigm, where some drivers detect road surface information and publish it to the data center, while other drivers download it for use. However, current road pavement monitoring schemes lack an incentive mechanism, which discourages drivers from publishing road surface information. More importantly, current security protocols for crowdsourcing-based systems are built on real identities of drivers, resulting in the user privacy problem. At the same time, current security protocols for crowdsourcing-based systems use time-consuming bilinear map for designing cryptographic algorithms, resulting in the efficiency problem. To solve the above problems, we present a secure incentive mechanism for road pavement monitoring systems called SIM-RPM. Different from protocols of this field, SIM-RPM is built on pseudonyms, which can provide user privacy protection for drivers. Moreover, by designing an access control protocol, SIM-RPM enables drivers to sell road surface information, which encourages drivers to attend the road pavement monitoring system. Finally, to reduce the time cost, we introduce the Bloom filter technique to SIM-RPM, and design novel light-weight cryptographic algorithms. By doing so, SIM-RPM provides a secure and efficient incentive mechanism for crowdsourcing-based road pavement monitoring systems. Experimential results show SIM-RPM is feasible for real world applications.","Title: Retrieval of the Fine-Mode Aerosol Optical Depth over East China Using a Grouped Residual Error Sorting (GRES) Method from Multi-Angle and Polarized Satellite Data.\nYear: 2018\n\nAbstract: The fine-mode aerosol optical depth (AOD(f)) is an important parameter for the environment and climate change study, which mainly represents the anthropogenic aerosols component. The Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar (PARASOL) instrument can detect polarized signal from multi-angle observation and the polarized signal mainly comes from the radiation contribution of the fine-mode aerosols, which provides an opportunity to obtain AOD(f) directly. However, the currently operational algorithm of Laboratoire d'Optique Atmospherique (LOA) has a poor AOD(f) retrieval accuracy over East China on high aerosol loading days. This study focused on solving this issue and proposed a grouped residual error sorting (GRES) method to determine the optimal aerosol model in AOD(f) retrieval using the traditional look-up table (LUT) approach and then the AOD(f) retrieval accuracy over East China was improved. The comparisons between the GRES retrieved and the Aerosol Robotic Network (AERONET) ground-based AOD(f) at Beijing, Xianghe, Taihu and Hong_Kong_PolyU sites produced high correlation coefficients (r) of 0.900, 0.933, 0.957 and 0.968, respectively. The comparisons of the GRES retrieved AOD(f) and PARASOL AOD(f) product with those of the AERONET observations produced a mean absolute error (MAE) of 0.054 versus 0.104 on high aerosol loading days (AERONET mean AOD(f) at 865 nm = 0.283). An application using the GRES method for total AOD (AOD(t)) retrieval also showed a good expandability for multi-angle aerosol retrieval of this method.","Title: Retrieving articulated 3-d models using medial surfaces and their graph spectra\nYear: 2005\n\nAbstract: We consider the use of medial surfaces to represent symmetries of 3-D objects. This allows for a qualitative abstraction based on a directed acyclic graph of components and also a degree of invariance to a variety of transformations including the articulation and deformation of parts. We demonstrate the use of this representation for both indexing and matching 3-D object models. Our formulation uses the geometric information associated with each node along with an eigenvalue labeling of the adjacency matrix of the subgraph rooted at that node. We present comparative results against the techniques of shape distributions [17] and harmonic spheres [12] on a database of 320 models representing 13 object classes. The results demonstrate that medial surface based graph matching significantly outperforms these techniques for objects with articulating parts.","Title: Data-driven modeling for chinese ancient architectures\nYear: 2017\n\nAbstract: Chinese ancient architecture is well known for its unique shape styles and complex constructions, which results in a complicated 3D modeling process. In contrast to the existing procedural modeling method which generates architectures relying on fixed rules, our data-driven approach aggregate information from 3D model collections to realize analysis, inference and synthesis of components spontaneously. For proprietary properties of ancient building models, this paper describes the framework of the complete procedure from shape representation to component assembly. The plausible combinations for structural components are also learned from 3D model dataset by the Bayesian network. As a periodic research, some remaining works at each stage are proposed in this paper. We conclude these works with ideas that can inspire future research in the modeling issue for Chinese ancient architecture.","Title: Assessing reappearance factors of H7N9 avian influenza in China.\nYear: 2017\n\nAbstract: The model considers the seasonal migration of bird and periodic decay rate of virus in environment.Both temperature cycle and closing and reopening markets can lead to the observed recurrence.Migratory birds have little or no effect on local epidemic spread.Closing markets is the most effective measure to control the epidemic to be at a low level. H7N9, a kind of subgroup of influenza viruses, has lasted for over three years in China. Although it has not yet aroused large-scale outbreak in human, it reappeared and peaked every winter. According to the obtained information and literatures, possible reasons to induce recurrence of human cases every winter can be speculated as migration of birds, temperature cycling, or reopening of live poultry trading markets. However, which one is the major driving factor is unclear. As a result, a dynamical model about migrant birds, resident birds and domestic poultry, considering temperature-dependent decay rate of virus and periodical closure of trading markets, is established to determine internal critical driving factors, and to propose the most effective prevention measure by sensitivity analysis. By numerical analysis, it is concluded that temperature cycling may be the main driving mechanism of the periodicity of infected human cases. Closing trading markets is not the main factor, but it is the most effective measure to control the epidemic to be at a low level. Consequently, periodically closing trading markets is proposed to prevent and control the spread of epidemic.","Title: Experimental Study on Stress Monitoring of Sand-Filled Steel Tube during Impact Using Piezoceramic Smart Aggregates.\nYear: 2017\n\nAbstract: The filling of thin-walled steel tubes with quartz sand can help to prevent the premature buckling of the steel tube at a low cost. During an impact, the internal stress of the quartz sand-filled steel tube column is subjected to not only axial force but also lateral confining force, resulting in complicated internal stress. A suitable sensor for monitoring the internal stress of such a structure under an impact is important for structural health monitoring. In this paper, piezoceramic Smart Aggregates (SAs) are embedded into a quartz Sand-Filled Steel Tube Column (SFSTC) to monitor the internal structural stress during impacts. The piezoceramic smart aggregates are first calibrated by an impact hammer. Tests are conducted to study the feasibility of monitoring the internal stress of a structure. The results reflect that the calibration value of the piezoceramic smart aggregate sensitivity test is in good agreement with the theoretical value, and the output voltage value of the piezoceramic smart aggregate has a good linear relationship with external forces. Impact tests are conducted on the sand-filled steel tube with embedded piezoceramic smart aggregates. By analyzing the output signal of the piezoceramic smart aggregates, the internal stress state of the structure can be obtained. Experimental results demonstrated that, under the action of impact loads, the piezoceramic smart aggregates monitor the compressive stress at different locations in the steel tube, which verifies the feasibility of using piezoceramic smart aggregate to monitor the internal stress of a structure.","Title: Efficient identity-based data transmission for VANET.\nYear: 2018\n\nAbstract: To simplify the key management processes, data transmission protocols for vehicular ad-hoc networks typically use identity-based cryptography for protecting the transmitted data. However, current identity-based data transmission protocols are mainly based on the bilinear map technique, which requires high computation costs on vehicles and road-side units, resulting in high latency. At the same time, to provide location privacy protection, data transmission protocols typically use pseudonyms instead of vehicles’ real identities, resulting in complicated pseudonym management processes. To address these issues, we present a novel identity-based data transmission protocol called EIBDT. Similar to the protocols of this field, EIBDT can provide integrity and confidentiality protections for the transmitted data. However, compared to other well-known approaches, EIBDT uses Lagrange interpolation instead of bilinear map for integrity protection, which employs only a few modular exponentiation operations. Since modular exponentiation is much more efficient than bilinear pairing, EIBDT can reduce the computation costs of vehicles and RSUs significantly. Moreover, for location privacy protection, EIBDT encrypts real identities of vehicles using an algebraic-signature-and-identity-based algorithm which is highly efficient. Therefore, the complicated pseudonym management process is avoided and the computation costs are reduced. Experimental results show that the proposed protocol is feasible for real applications.","Title: The Matrix Bounds And Fixed-Point Iteration For The Solution Of The Discrete Algebraic Riccati Equation\nYear: 2019\n\nAbstract: In this paper, by constructing the equivalent form of the discrete algebraic Riccati equation (DARE), applying the eigenvalue inequalities of matrix's sum and product, we provide new lower and upper matrix bounds for the solution of the DARE, which improve some of the recent results. Further, using the derived matrix bounds and a fixed point theorem, we discuss the existence and uniqueness for the solution of the DARE. Finally, we give corresponding numerical examples to demonstrate the effectiveness of our results.","Title: Data Fusion-Based Multi-Object Tracking for Unconstrained Visual Sensor Networks.\nYear: 2018\n\nAbstract: Camera node perception capability is one of the crucial issues for visual sensor networks, which belongs to the field of Internet of Things. Multi-object tracking is an important feature in analyzing object trajectories across multiple cameras, thus allowing synthesis of data and security analysis of images in various scenarios. Despite intensive research in the last decades, it remains challenging for tracking systems to perform in real-world situations. We therefore focus on key issues of multi-object state estimation for unconstrained multi-camera systems, e.g., data fusion of multiple sensors and data association. Unlike previous work that rely on camera network topology inference, we construct a graph from 2-D observations of all camera pairs without any assumption of network configuration. We apply the shortest path algorithm to the graph to find fused 3-D observation groups. Our approach is thus able to reject false positive reconstructions automatically, and also minimize computational complexity to guarantee feasible data fusion. Particle filters are applied as the 3-D tracker to form tracklets that integrate local features. These tracklets are modeled by a graph and linked into full tracks incorporating global spatial-temporal features. Experiments on the real-world PETS2009 S2/L1 sequence show the accuracy of our approach. Analyses of the different components of our approach provide meaningful insights for object tracking using multiple cameras. Evidence is provided for selecting the best view for a visual sensor network.","Title: The Optimization Of Parallel Convolutional Rbm Based On Spark\nYear: 2019\n\nAbstract: With the extensive attention and research of the scholars in deep learning, the convolutional restricted Boltzmann machine (CRBM) model based on restricted Boltzmann machine (RBM) is widely used in image recognition, speech recognition, etc. However, time consuming training still seems to be an unneglectable issue. To solve this problem, this paper mainly uses optimized parallel CRBM based on Spark, and proposes a parallel comparison divergence algorithm based on Spark and uses it to train the CRBM model to improve the training speed. The experiments show that the method is faster than traditional sequential algorithm. We train the CRBM with the method and apply it to breast X-ray image classification. The experiments show that it can improve the precision and the speed of training compared with traditional algorithm.","Title: Upper And Lower Eigenvalue Summation Bounds Of The Lyapunov Matrix Differential Equation And The Application In A Class Time-Varying Nonlinear System\nYear: 2020\n\nAbstract: In this paper, we first show a class relation between the eigenvalue of functional matrix derivative and the derivative of function matrix eigenvalue. Applying the relation, we transform the time-varying linear matrix differential equation into eigenvalue differential equation. Furthermore, by using singular value decomposition and majorisation inequalities, we derive upper and lower bounds on eigenvalue summation of the solution for the Lyapunov matrix differential equation, which improve the recent results. As an application in control and optimisation, we show that our bounds could be used to discuss the stability of a class time-varying nonlinear system. Finally, we give a corresponding numerical example to show the superiority and effectiveness of the derived bounds.","Title: Lower bounds of error estimates for singular perturbation problems with Jacobi-Spectral approximations.\nYear: 2019\n\nAbstract: With weighted orthogonal Jacobi polynomials, we study spectral approximations for singular perturbation problems on an interval. The singular parameters of the model are included in the basis functions, and then its stiff matrix is diagonal. Considering the estimations for weighted orthogonal coefficients, a special technique is proposed to investigate the a posteriori error estimates. In view of the difficulty of a posteriori error estimates for spectral approximations, we employ a truncation projection to study lower bounds for the models. Specially, we present the lower bounds of a posteriori error estimates with two different weighted norms in details.","Title: Damage Detection of Concrete-Filled Square Steel Tube (CFSST) Column Joints under Cyclic Loading Using Piezoceramic Transducers.\nYear: 2018\n\nAbstract: Concrete-filled square steel tube column (CFSSTC) joints are the most important parts of concrete-filled steel tube frame structures. It is of great significance to study the damage of CFSSTC joints under the seismic loads. In this paper, embedded piezoceramic transducers are used to monitor the damage of core concrete of CFSSTC joints under cyclic loading and surface-bonded piezoceramic disks are used to monitor the debonding damage of the steel tube and core concrete of two specimens. The damages of the joints under different loading levels and different loading cycles are evaluated by the received signal of the piezoceramic transducers. The experimental results show that the amplitude of the signal attenuates obviously with the appearance of damage in the joints, and the degree of attenuation increases with the development of the damage. The monitoring results from piezoceramic transducers are basically consistent with the hysteresis loops and skeleton curves of the CFSSTC joints during the cyclic loading. The effectiveness of the piezoceramic transducers are verified by the experimental results in structural health monitoring of the CFSSTC joint under cyclic loading.","Title: SCRPM: securing crowdsourcing-based road pavement monitoring system with location privacy\nYear: 2020\n\nAbstract: To provide privacy protection, security protocols for crowdsourcing-based systems typically encrypt data before uploading it to the server. However, current security protocols cannot provide protection for real identities, resulting in the location privacy issue. Moreover, current security protocols for crowdsourcing-based systems are mainly based on bilinear map, which is quite time-consuming. To address the above security and efficiency issues, we propose a novel security protocol with location privacy called SCRPM. Similar to protocols of this field, SCRPM can provide privacy protection for uploaded data. However, different from other well-known approaches, SCRPM uses pseudonyms instead of real identities, which can provide location privacy protection for cars. At the same time, to avoid using bilinear pairing, we introduce the algebraic signature technique to SCRPM and design novel signcryption algorithms, which is quite light weight. By doing so, SCRPM can provide location privacy protection for cars while still enjoying high efficiency. Experimental results show SCRPM is feasible for real world applications.","Title: Single Image Snow Removal via Composition Generative Adversarial Networks.\nYear: 2019\n\nAbstract: Snowflakes attached to the camera lens can severely affect the visibility of the background scene and compromise the image quality. In this paper, we solve this problem by visually removing snowflakes to convert the snowy image into a clean one. The problem is troublesome; the information about the background of the occluded regions is completely lost for the most part. For removing snowflakes from a single image, we proposed a composition generative adversarial network. Different from the previous generative adversarial networks, our generator network comprises clean background module and a snow mask estimate module. The clean background module aims to generate a clear image from an input snowy image, and snow mask estimate module is used to produce the snow mask in an input image. During the training step, we put forward a composition loss between the input snowy image and composition of the generated clean image and estimated snow mask. We use a dataset named Snow 100K(2) including indoor and outdoor scenes to train and test the proposed method. The extensive experiments on both synthetic and real-world images show that our network has a good effect and it is superior to the other state-of-the-art methods.","Title: An Integrated Inverse Space Sparse Representation Framework for Tumor Classification.\nYear: 2019\n\nAbstract: •An integrated inverse space sparse representation model is proposed for gene-based tumor classification.•A gene selection method is proposed to improve the model's representation ability to small sample problem.•A feature representation learning method is proposed to enhance the model's representation ability and stability.•The model is optimized and the convergence is analyzed.•Extensive experiments are conducted on six microarray gene expression datasets, which contain early diagnosis, tumor type recognition and postoperative metastasis.","Title: Polarimetric multiple-radar architectures with distributed antennas for discriminating between radar targets and deception jamming\nYear: 2019\n\nAbstract: The potential abilities of polarimetric multiple-radar architectures with distributed antennas in electronic counter-countermeasures (ECCMs) are analyzed based on differences between target echoes and deception jamming in scattering coefficients properties. According to the differences in generation mechanism between the target scatter and jamming, a signal model of real targets and deception jamming for two-dimensional vector sensors is established. By applying the Neyman-Pearson (NP) criterion and a hybrid NP-Maximum Posteriori (MAP) criterion, a two-stage detection/discrimination algorithm is proposed. In our proposed algorithm, the targets and jamming are detected in the first stage from a Gaussian noise background. Then, a discriminator based on the generalized likelihood ratio test is designed by exploiting the property of polarization discrimination. A theoretical analysis is also given to evaluate the performance of the second stage of the algorithm. Moreover, the discrimination performance can be improved by optimizing the transmitter polarization parameters. Simulation results indicate that the proposed ECCM strategy is effective against DRFM jamming, especially when JNR is high and transmit polarization is optimally designed.","Title: Lie Symmetry Analysis and Exact Solutions of Generalized Fractional Zakharov-Kuznetsov Equations\nYear: 2019\n\nAbstract: This paper considers the Lie symmetry analysis of a class of fractional Zakharov-Kuznetsov equations. We systematically show the procedure to obtain the Lie point symmetries for the equation. Accordingly, we study the vector fields of this equation. Meantime, the symmetry reductions of this equation are performed. Finally, by employing the obtained symmetry properties, we can get some new exact solutions to this fractional Zakharov-Kuznetsov equation.","Title: Dynamical Analysis of the Model for Brucellosis Transmission to the Dairy Cows with Immunological Threshold.\nYear: 2019\n\nAbstract: As we all know, bacteria is different from virus which with certain types can be killed by the immune cells in the body. The brucellosis, a bacterial disease, can invade the body by indirect transmission from environment, which has not been researched by combining with immune cells. Considering the effects of immune cells, we put a minimum infection dose of brucellosis invading into the dairy cows as an immunological threshold and get a switch model. In this paper, we accomplish a thorough dynamics analysis of a SEIB switch model. On the one hand, we can get a disease-free and bacteria-free steady state and up to three endemic steady states which may be thoroughly analyzed in different cases of a minimum infection dose in a switch model. On the other hand, we calculate the basic reproduction number R0 and know that the disease-free and bacteria-free steady state is a global stability when R0<1, and the one of the endemic steady state is a conditionally global stability when R0>1. We find that different amounts of R0 may lead to different steady states of brucellosis, and considering the effects of immunology is more serious in mathematics and biology.","Title: DD-CycleGAN: Unpaired image dehazing via Double-Discriminator Cycle-Consistent Generative Adversarial Network\nYear: 2019\n\nAbstract: Despite the recent progress in image dehazing, the task remains tremendous challenging. To improve the performance of haze removal, we propose a scheme for haze removal based on Double-Discriminator Cycle-Consistent Generative Adversarial Network (DD-CycleGAN), which leverages CycleGAN to translate a hazy image to the corresponding haze-free image. Unlike other methods, it does not need pairs of haze and their corresponding haze-free images for training. Extensive experiments demonstrate that the proposed method achieves significant improvements over the existing methods, both quantitatively as well as qualitatively. And our method can also achieve good effects qualitatively when applied to the real scenes too.","Title: Depth Camera Based Fluid Reconstruction and its Solid-fluid Interaction\nYear: 2019\n\nAbstract: Fluid animation has great value in study and application in many fields, such as video special effects, virtual reality and so on. However, due to the complexity and irregularity of fluid's motion, the existing simulation methods cannot make a good tradeoff between computational efficiency and realism. In this paper, a method of fluid animation synthesis based on depth camera is proposed. The RGB-D (red, green, blue-depth) data, captured by a RealSense camera, are used to reconstruct the fluid surface. By analysis the relation between color intensity and height field gradient of water surface, we use the RGB image as guidance to denoise the depth image, then possion reconstruction is employed to repair the incomplete data of depth image and fuse detail feature from the RGB to the reconstruction fluid surface. In addition, we propose a real-time method to simulate the interaction between solid and fluid, which is implemented on the wave particle system that fitted to the reconstruction fluid surface. Experiments show that the presented method can reconstruct fluid effectively and generate fluid animation of solid-fluid interaction plausibly.","Title: Discrimination of Active False Targets Based on Hermitian Distance for Distributed Multiple-Radar Architectures.\nYear: 2019\n\nAbstract: In a distributed multiple-radar architecture, the spatial scattering properties of targets can be utilized to counter active deception jamming effectively. When the cooperative detection is performed, the available method to discriminate active false targets needs to know the precise jammer location, and its performance will suffer significant deterioration with little location error. Aiming at the problem, we propose a novel method to discriminate false targets based on Hermitian distance. The difference in spatial scattering property leads to the difference in the Hermitian distances of different target combinations. The Hermitian distance between two false targets is much greater than that between two physical targets or that between a physical target and a false target, especially in high jamming-to-noise ratio. Based on the difference, hypothesis testing is performed to discriminate false targets. The proposed method does not require any prior information about the jamming environment and can discriminate the targets effectively in one pulse repetition interval. Moreover, the proposed method can restrict the upper bound of the misjudgment probability for physical targets. The theoretical analysis and simulation verify the feasibility and validity of the proposed discrimination method.","Title: A healthy node selection method based on particle swarm optimization under network intrusion environment\nYear: 2019\n\nAbstract: In the network intrusion environment, how to realize the accurate selection of healthy nodes and use them for secure communication is a research hotspot of network security technology. In this paper, a healthy node selection method based on particle swarm optimization under the network intrusion environment is proposed. First, a fuzzy mathematical model is established for node features to constrain the cost of healthy node selection. Then, we introduce the particle swarm optimization algorithm and combine the uncertain factors to optimize the parameters to achieve the selection of healthy nodes. The experimental results show that, compared with the traditional BP neural network method, our healthy node selection method improves the accuracy of healthy node selection, shortens the running time, and can control the error after invasion to a reasonable range.","Title: System Revenue Maximization for Offloading Decisions in Mobile Edge Computing\nYear: 2021\n\nAbstract: Offloading decisions in mobile edge computing have been extended with multiple objectives, such as revenue maximization, energy conservation and latency reduction. Revenues of network/service operators, as the realistic and ultimate goal at intensive competitive markets, have not been thoroughly studied under a pricing scheme in combination with offloading decisions, especially with the aims of reducing and restricting energy consumption and latency. To bridge this important gap, this paper studies the revenue maximization of network operators through a pricing scheme in mobile edge computing, by explicitly formulating energy consumption and latency into the offloading strategy. A two-stage game-theory framework based on the Stackelberg game is established, through which the optimal price for both the network operator and the customer can be reached. The offloading data size can be dynamically adjusted according to the agreed price. The existence of equilibrium in the Stackelberg game is proved, and experiments are conducted to verify the effectiveness of our proposed model.","Title: Observer-Based Output Feedback Event-Triggered Adaptive Control for Linear Multiagent Systems Under Switching Topologies\nYear: 2022\n\nAbstract: The consensus problem of general linear multiagent systems (MASs) is studied under switching topologies by using observer-based event-triggered control method in this article. On the basis of the output information of agents, two kinds of novel event-triggered adaptive control schemes are designed to achieve the leaderless and leader-follower consensus problems, which do not need to utilize the global information of the communication networks. Finally, two simulation examples are introduced to show that the consensus error converges to zero and Zeno behavior is eliminated in MASs. Compared with the existing output feedback control research, one of the significant advantages of our methods is that the controller protocols and triggering mechanisms do not rely on any global information, are independent of the network scale, and are fully distributed ways.","Title: Dfbdehazenet: An End-To-End Dense Feedback Network For Single Image Dehazing\nYear: 2021\n\nAbstract: The feedback mechanism method of simulating the biological vision system has not been widely used in deep learning dehazing algorithms. To alleviate the difficulty of feature interaction, we combine the feedback mechanism with dense skip connections to fuse features of different levels in a dehazing network. Inspired by the feedback network in which previous network layers can have access to rich information processed by the following network layers, we propose an end-to-end dense feedback network (DFBDehazeNet) for single image dehazing that implements the feedback mechanism using hidden states of constrained RNN. The low-level hazy feature information can be continuously corrected by the high-level feature information obtained from the dense feedback block via the recurrent feedback connection. The top-down feedback mechanism is adopted in DFBDehazeNet to refine the low-level hazy feature information, thereby achieving a powerful image restoration effect. The ablation experiment proves that the iterative structure of DFBDehazeNet and the projection unit play an important role in removing haze from images. The experimental results show that the results of image haze removal are superior to the great majority of existing methods both qualitatively and quantitatively. (c) 2021 SPIE and IS&T [DOI: 10.1117/1.JEI.30.3.033004]Under the influence of severe weather, the quality of images collected by the camera system drops sharply. These degraded images not only affect people's subjective judgment but also lead to poor results on advanced computer vision tasks such as object detection,1 semantic segmentation,2 action recognition,3 and so on. The restoration of images in severe weather4 is of great significance to computer vision. Image dehazing algorithms are designed to recover high-quality clear images from low-quality hazy images for advanced computer vision tasks. The image dehazing task assists in the driving technology of unmanned vehicles and the unmanned toll system of highways in severe weather. In the past two decades, many image haze removal methods have been proposed. Image dehazing has become one focus of computer vision. Traditional methods mostly use auxiliary tools or prior knowledge to obtain the depth information of images and then estimate the medium","Title: Integral Reinforcement Learning-Based Guaranteed Cost Control For Unknown Nonlinear Systems Subject To Input Constraints And Uncertainties\nYear: 2021\n\nAbstract: This paper investigates guaranteed cost control (GCC) problem for nonlinear systems subject to input constraints and disturbances by utilizing the reinforcement-learning (RL) algorithm. Firstly, by establishing a modified Hamilton-Jacobi-Bellman (HJI) equation, which is difficult to be solved, a model-based policy iteration (PI) GCC algorithm is designed for input-constrained nonlinear systems with disturbances. Moreover, without requiring any knowledge of system dynamics, by designing an auxiliary system with a control law and an auxiliary disturbance policy, an online model-free GCC approach is developed by utilizing integral reinforcement learning (IRL) algorithm. To implement the proposed control algorithm, the actor and disturbance NNs are constructed to approximate the optimal control input and worst-case disturbance policy, while the critic NN is utilized to approximate optimal value function. Further, a synchronization weight update law is developed to minimize the NN approximation residual errors. The asymptotic stability of controlled systems is analyzed by applying the Lyapunov's method. Finally, the effectiveness and feasibility of the proposed control method are verified by two nonlinear simulation examples. (C) 2021 Elsevier Inc. All rights reserved.","Title: Consensus Control For Nonlinear Multi-Agent Systems With Event-Triggered Communications\nYear: 2021\n\nAbstract: This paper investigates the consensus problems for a class of nonlinear multi-agent systems (MASs) by distributed event-based control protocols. Firstly, we design the static distributed event-triggered control protocols to achieve leader-follower and leaderless consensus problems, respectively, and the systems exclude Zeno behavior under the proposed control schemes. In order to avoid utilizing the global information of the system networks, that is, the minimum eigenvalues of the correlation matrices. Then, the fully distributed event-based control schemes are designed by introducing the time-varying coupling weights to achieve the leader-follower and leaderless consensus problems, respectively. It has been shown that the Zeno behavior is ruled out in the MASs under the fully distributed event-based control protocols. All static and dynamic event-based control schemes are able to be constructed based on the solvability of linear matrix inequalities (LMIs). Finally, the rationality of the theoretical results is illustrated by the example.(c) 2021 Elsevier Inc. All rights reserved.","Title: Analysis Of Grid Operation State Based On Improved Maximum Eigenvalue Sample Covariance Matrix Algorithm\nYear: 2021\n\nAbstract: Traditional static threshold-based state analysis methods can be applied to specific signal-to-noise ratio situations but may present poor performance in the presence of large sizes and complexity of power system. In this article, an improved maximum eigenvalue sample covariance matrix algorithm is proposed, where a Marchenko-Pastur law-based dynamic threshold is introduced by taking all the eigenvalues exceeding the supremum into account for different signal-to-noise ratio situations, to improve the calculation efficiency and widen the application fields of existing methods. The comparison analysis based on IEEE 39-Bus system shows that the proposed algorithm outperforms the existing solutions in terms of calculation speed, anti-interference ability, and universality to different signal-to-noise ratio situations.","Title: A GENERAL ALTERNATING-DIRECTION IMPLICIT FRAMEWORK WITH GAUSSIAN PROCESS REGRESSION PARAMETER PREDICTION FOR LARGE SPARSE LINEAR SYSTEMS\nYear: 2022\n\nAbstract: This paper proposes an efficient general alternating-direction implicit (GADI) framework for solving large sparse linear systems. The convergence property of the GADI framework is discussed. Most of existing ADI methods can be unified in the developed framework. Meanwhile the GADI framework can derive new ADI methods. Moreover, as the algorithm efficiency is sensitive to the splitting parameters, we offer a data-driven approach, the Gaussian process regression (GPR) method based on the Bayesian inference, to predict the GADI framework's relatively optimal parameters. The GPR method only requires a small training data set to learn the regression prediction mapping, can predict accurate splitting parameters, and has high generalization capability. It allows us to efficiently solve linear systems with a one-shot computation, and does not require any repeated computations. Finally, we use the three-dimensional convection-diffusion equation, two-dimensional parabolic equation, and continuous Sylvester matrix equation to examine the performance of our proposed methods. Numerical results demonstrate that the proposed framework is faster tens to thousands of times than the existing ADI methods, such as (inexact) Hermitian and skew-Hermitian splitting type methods in which the consumption of obtaining relatively optimal splitting parameters is ignored. As a result, our proposed methods can solve much larger linear systems which these existing ADI methods have not reached.","Title: Fault-tolerant control for uncertain switched random systems with multiple interval time-varying delays and intermittent faults\nYear: 2021\n\nAbstract: This paper focuses on the passive fault-tolerant control for an uncertain switched nonlinear random system with multiple interval time-varying delays and intermittent faults in sensors and actuators. There are also nonlinear functions, exogenous disturbances and measurement noise in the system. There are few tries to realize noise-to-state exponentially mean-square stability for switched random nonlinear systems subject to multiple interval time-varying delays allowing various time delay cases. Random differential equations are more common than stochastic differential equations in practice. Thus, the suggested method is more practicable. First, a nonlinear dynamic output feedback fault-tolerant controller is constructed. Next, an augmented closed-loop system can be obtained in the framework of multiple interval time-varying delays. Then piecewise Lyapunov function is utilized to realize stability analysis for the closed-loop system. At last, this feasibility of this approach presented in this study is verified through two examples.","Title: Quantitative Evaluation of the Soil Signal Effect on the Correlation between Sentinel-1 Cross Ratio and Snow Depth\nYear: 2021\n\nAbstract: High-resolution Synthetic Aperture Radar (SAR), as an efficient Earth observation technology, can be used as a complementary means of observation for snow depth (SD) and can address the spatial heterogeneity of mountain snow. However, there is still uncertainty in the SD retrieval algorithm based on SAR data, due to soil surface scattering. The aim of this study is to quantify the impact of soil signals on the SD retrieval method based on the cross-ratio (CR) of high-spatial resolution SAR images. Utilizing ascending Sentinel-1 observation data during the period from November 2016 to March 2020 and a CR method based on VH- and VV-polarization, we quantitatively analyzed the CR variability characteristics of rock and soil areas within typical thick snow study areas in the Northern Hemisphere from temporal and spatial perspectives. The correlation analysis demonstrated that the CR signal in rock areas at a daily timescale shows a strong correlation (mean value > 0.60) with snow depth. Furthermore, the soil areas are more influenced by freeze-thaw cycles, such that the monthly CR changes showed no or negative trend during the snow accumulation period. This study highlights the complexity of the physical mechanisms of snow scattering during winter processes and the influencing factors that cause uncertainty in the SD retrieval, which help to promote the development of high-spatial resolution C-band data for snow characterization applications.","Title: Analysis and Suppression of Phase Unbalance Induced Force Ripples in Permanent Magnet Linear Motors\nYear: 2022\n\nAbstract: Low force ripples from permanent magnet linear motors (PMLMs) are typically essential requirements in a wide range of demanding control applications with high motion quality. Apart from the sources present in rotary PM machines, phase unbalance due to the end effect is another major contributor of the thrust force ripples in the PMLMs. In this paper, the phase unbalance and induced force ripples are investigated in the case of a planar PMLM typically equipped with single-layer concentrated windings and quasi-Halbach magnets. It demonstrates that although the PMLM is quite carefully deigned to minimize the end effect, the phase unbalance still exists, including both the amplitude and phase position unbalances. And the phase unbalances give rise to even order force ripple harmonics, in which the 2(nd)-order force ripple is particularly significant. In order to compensate the end effect and the unbalance between the three phases, a novel PMLM topology is presented, in which the width of the two end teeth (instead of the edge teeth) is adjusted to reduce the force ripple. And the impact of the end teeth width on the ripple force magnitude is further revealed. It is shown that by proper design of the end teeth width, the 2(nd)-order force ripple of the PMLM can be minimized to quite a low extent. In comparison with the original design, the 2(nd)-order force ripple factor of the novel topology has been suppressed from 0.4% to less than 0.03% in the case study. Such a technique has provided a new way of force ripple minimization, and is applicable to other types of linear motors or actuators with appropriate changes.","Title: A coherent signal beamforming technique based on sub-array cross correlation\nYear: 2022\n\nAbstract: In this paper, a new coherent signal beamforming technique based on sub-array cross correlation (SCC) is proposed for the signal cancellation caused by traditional adaptive beamforming in multipath environment. Firstly, the array is uniformly divided into two mutual non-overlapped sub-arrays, and the preprocess of blocking expected signal is performed inside every sub-array to remove the expected signal component from the received signal on the premise that the direction of expected signal is known in advance. Then, minimum variance distortionless response (MVDR) allows us to obtain the desired signal with noise. Afterwards, based on the fact that the noise between different elements is not correlated, the MVDR output of one sub-array can be taken as the expected signal of the other sub-array to perform the beamforming with minimum mean square error (MMSE) criterion. Finally, the weight for the full array is obtained by combining the MMSE beamforming weights of the two sub-arrays. The proposed method can effectively solve the above coherent beamforming problem. What's more, the SCC beamformer performs well in the case with small samples. Both theoretical derivation and simulation experiment verify the feasibility and validity of the proposed beamformer. (C) 2021 Elsevier Inc. All rights reserved.","Title: Double-Branch Dehazing Network based on Self-Calibrated Attentional Convolution\nYear: 2022\n\nAbstract: Single image dehazing is an essential preprocessing task to improve the image’s visual effect. In this paper, we propose a Double-Branch Dehazing Network based on Self-Calibrated Attentional Convolution (DBDN-SCAC), which effectively captures the multi-scale features of important information for solving dehazing problems. More specifically, our proposed DBDN-SCAC uses an encoder–decoder architecture based on a Laplacian Pyramid to learn information at different scales. At the same time, the self-calibration attention convolution module is incorporated after each scaling to expand the receptive field and enrich the output feature. Furthermore, to avoid information loss caused by downsampling, a new self-calibration branch is established to help the image recover more detailed information. Finally, a Content-aware Upsampling module is added in two branches to allow the network in reconstructing better images. The effectiveness of our approach, which is compared with the state-of-the-art methods in the public datasets, is justified by our achieving the best results.","Title: Time-varying formation control with general linear multi-agent systems by distributed event-triggered mechanisms under fixed and switching topologies\nYear: 2022\n\nAbstract: This paper investigates the time-varying formation control (TVFC) problem of multi-agent systems (MASs) with general linear dynamics under fixed topology by utilizing event-triggered mechanisms. In order to achieve the TVFC problem, two kinds of distributed event-triggered formation control schemes are proposed. Moreover, the Zeno behavior is excluded under these event-triggered mechanisms. Since these control protocols are constructed on the basis of the sampling information of the event time instants instead of the real-time information of each agent, each agent does not require to communicate continuously with its neighbors, thus effectively reducing the bandwidth requirement of communication and greatly reducing the use of energy in MASs. It deserves to be mentioned that the second control protocol introduces time-varying coupling weights, does not require to utilize any global information of the system networks, and is more suitable for systems with large-scale network. Moreover, the proposed event-triggered formation control protocols are suitable for arbitrarily switching topologies, and its related results are extended to the case of switching topologies. Finally, several examples are introduced to achieve the TVFC problem by utilizing the adaptive event-triggered control protocol, and Zeno behavior is not observed during the process of simulation.","Title: Semi-discretized numerical solution for time fractional convection-diffusion equation by RBF-FD\nYear: 2022\n\nAbstract: In this paper, a numerical method based on radial basis functions finite difference (RBF-FD) has been developed for solving the time fractional convection-diffusion equation. We first approximate the time fractional derivative by FD and obtain the semi-discretized scheme. The unconditional stability and convergence of the semi-discretized scheme are given. After that, we use the multiquadric RBF to approximate the spatial derivatives in the numerical experiments. The aim of this paper is to show that the RBF-FD method is better than the classical FD method for solving our mentioned equation. Finally, two numerical examples are proposed respectively to verify the correctness of our theoretical analysis and to demonstrate the superiority of the RBF-FD method. (C) 2021 Elsevier Ltd. All rights reserved.","Title: Efficient block preconditioners for integral constrained elliptic optimal control problems with finite element approximations\nYear: 2022\n\nAbstract: In this paper, focusing on a distributed optimal control problem for the elliptic equations with integral control constraint, we propose efficient block diagonal preconditioners to solve the corresponding linearized algebraic system with finite element methods. We derive the first order necessary and sufficient optimality conditions for an integral constraint on the control, and divide the discretized optimal conditions into three matrix forms. Then block-diagonal preconditioners for the corresponding linear algebraic systems are constructed. With respect to both the mesh size and the regularization parameter, the robustness of our preconditioners is proved in detail. In fact, the condition numbers of the preconditioned matrices are a constant for different parameters. Meanwhile, based on the equivalent matrix forms, an algorithm is proposed for this kind of constrained optimal control problems. Numerical experiments are given to depict the efficiency of our proposed preconditioners.","Title: A texture-aware U-Net for identifying incomplete blinking from eye videography\nYear: 2022\n\nAbstract: •Three convolutional blocks were introduced based on element-wise subtraction operations.•A novel segmentation network was developed by integrating the introduced blocks with the classical U-Net.•Extensive experiments showed a unique advantage of the developed network in extracting palpebral fissures from frame images of eye videography.•The developed network showed the feasibility of identifying incomplete blinking based on eye videography.","Title: Generalized finite difference method for three-dimensional eigenproblems of Helmholtz equation\nYear: 2022\n\nAbstract: In this paper, a meshless numerical procedure, based on the generalized finite difference method (GFDM) is proposed to efficiently and accurately solve the three-dimensional eigenproblems of the Helmholtz equation. The eigenvalues and eigenvectors are very important to various engineering applications in three-dimensional acoustics, optics and electromagnetics, so it is essential to develop an efficient numerical model to analyze the three-dimensional eigenproblems in irregular domains. In the GFDM, the Taylor series and the moving-least squares method are used to derive the expressions at every node. By enforcing the satisfactions of governing equation at interior nodes and boundary conditions at boundary nodes, the resultant system of linear algebraic equations can be expressed as the eigenproblems of matrix and then the eigenvalues and eigenvectors can be efficiently acquired. In this paper, four numerical examples are provided to validate the accuracy and simplicity of the proposed numerical scheme. Furthermore, the numerical results are compared with analytical solutions and other numerical results to verify the merits of the proposed method.","Title: Improved CLEAN Algorithm for RFI Mitigation of Aperture Synthesis Radiometer Images\nYear: 2022\n\nAbstract: For synthetic aperture radiometer, radio frequency interference (RFI) seriously contaminates the observed data and affects the quality of the brightness temperature (BT) image. CLEAN algorithm is usually used to mitigate the Gibbs effect generated by RFI. However, the closely distributed RFI sources cannot be well mitigated by the original CLEAN algorithm. This letter proposes an improved adaptive CLEAN algorithm to enhance the performance of RFI mitigation, especially the closely distributed RFI sources. The loop factor is forced to synchronize with residual BT of RFI for every iteration to avoid over mitigation. Over mitigation can result in a BT vacancy area or an extra BT point generated at the position of RFI. Soil Moisture and Ocean Salinity (SMOS) L1B real data are applied to verify the effectiveness of the improved CLEAN algorithm. The simulation shows that the improved CLEAN algorithm can be competent in eliminating isolated and closely distributed RFIs better than the classic CLEAN algorithm, and the over mitigation problem is solved by the proposed method.","Title: Resource Allocation Scheme Based on Complete Planning Process for Immediate and Advance Reservation in SDM-EONs\nYear: 2021\n\nAbstract: Immediate reservation (IR) and advanced reservation (AR) become two common traffic models for future space division multiplexing-elastic optical networks (SDM-EONs). However, it is crucial to reduce conflicts between different types of requests and to accommodate more requests with limited resources. In this paper, we propose a resource allocation scheme based on complete planning process (RA-CPP) for IR and AR in SDM-EONs, including request provisioning, resource planning, passive adjustment, and active adjustment. In the request provisioning, the starting time of requests is considered and the earlier transmitted requests have higher resource selection priority. Next, the spectrum blocks with minimal resource spacing between adjacent requests (RS-AR) are planned for requests to maximize the free resources of the network in the resource planning. In addition, for the new request that would be blocked, passive adjustment begins. After planning reserved resources for the new request, conflicting AR requests are re-provisioned. Finally, the active adjustment re-optimizes the resources by increasing the tightness of spectrum resources. Simulation results show that the proposed RA-CPP scheme has greater advantages in blocking probability and spectrum utilization compared with the benchmark schemes.","Title: Semi-supervised learning for raindrop removal on a single image\nYear: 2022\n\nAbstract: This article propose s a network that is mainly used to deal with a single image polluted by raindrops in rainy weather to get a clean image without raindrops. In the existing solutions, most of the methods rely on paired images, that is, the rain image and the real image without rain in the same scene. However, in many cases, the paired images are difficult to obtain, which makes it impossible to apply the raindrop removal network in many scenarios. Therefore this article proposes a semi-supervised rain-removing network apply to unpaired images. The model contains two parts: a supervised network and an unsupervised network. After the model is trained, the unsupervised network does not require paired images and it can get a clean image without raindrops. In particular, our network can perform training on paired and unpaired samples. The experimental results show that the best results are achieved not only on the supervised rain-removing network, but also on the unsupervised rain-removing network.","Title: A recurrent skip deep learning network for accurate image segmentation\nYear: 2022\n\nAbstract: Accurate image segmentation plays a vital role in quantitatively assessing various diseases and their prognosis. In this study, we described a novel deep learning network termed Recurrent Skip Network (RS-Net) by integrating a backward skip connection and an attention-aware convolutional block with the available BiO-Net. To validate its performance and merits, we applied it (1) for the segmentation of three corneal layers (i.e., epithelium layer, Bowman's layer, and stroma layer) depicted on optical coherence tomography (OCT) images and (2) for the segmentation of the optic disc (OD) and cup (OC) depicted on color fundus photography (CFP). Our experiments showed that RS-Net achieved an average Dice score of 0.9327 and 0.8868, respectively for the two different segmentation tasks, demonstrating a unique performance as compared with BiO-Net and other networks.","Title: A cumulative prospect theory-based method for group medical emergency decision-making with interval uncertainty\nYear: 2022\n\nAbstract: An emergency response to a medical situation is generally considered to be a risk decision-making problem. When an emergency event occurs, it makes sense to take into account more than one decision maker’s opinions and psychological behaviors. The existing research tends to ignore these multidimensional aspects. To fill this literature gap, we propose a multi-attribute model.\n The model is based on cumulative prospect theory (CPT), considering multiple experts’ psychological factors. By not assuming full rationality, we extend existing models to allow multiple experts’ risk preferences to be incorporated into the decision-making process in the case of an emergency. Then, traditional CPT is extended by allowing for multiple attributes. In addition, rather than using crisp data, interval values are adopted to tackle the usual uncertainties in reality. The multi-attribute CPT based model proposed can deal with the selection of potential emergency alternatives. The model incorporates interval values to allow more uncertainty and the comparative studies show that the optimal solution changes under different scenarios. Our illustrative example and comparative study show that considering multiple experts and multiple attributes is more reasonable, especially in complicated situations under an emergency. In addition, decision-makers’ risk preferences highly affect the selection outcomes, highlighting their importance in the medical decision-making process. Our proposed model can be applied to similar fields with appropriate modifications.","Title: Adaptive Event-Triggered Leader-Follower Consensus of Linear Multiagent Systems Under Directed Graph With Nonzero Leader Input\nYear: 2022\n\nAbstract: This brief investigates the leader-follower consensus problem of linear multiagent systems (MASs) with nonzero leader input under the directed graph. By proposing the innovative adaptive event-triggered mechanism, the proposed control protocol does not need continuous communication among neighbors, and the control protocol can be updated intermittently for each follower. Moreover, Zeno behavior can be excluded for each agent. Note that the existence of the leader's nonzero bounded input enormously increases the difficulty of solving the leader-follower consensus problem. At last, two numerical examples are introduced to show the effectiveness and advantages of the designed protocol.","Title: Positive Solutions for Third-Order Boundary Value Problems with the Integral Boundary Conditions and Dependence on the First-Order Derivatives\nYear: 2022\n\nAbstract: In this paper, by the use of a new fixed point theorem and the Green function of BVPs, the existence of at least one positive solution for the third-order boundary value problem with the integral boundary conditions is considered,where there is a nonnegative continuous function. Finally, an example which to illustrate the main conclusions of this paper is given.","Title: Automated Full Scene Parsing for Marine ASVs Using Monocular Vision\nYear: 2022\n\nAbstract: Perceiving and parsing a maritime scene automatically and in real time is a key task for autonomous surface vehicle navigation. We propose a panoptic segmentation framework that allows end-to-end training and multiple task cascading to meet the navigational challenges of scene parsing in a complex maritime environment. In our framework, the feature extraction backbone is based on Res2Net combined with improved FPN. The fusion network neck adds a mask branch to the latest YOLO detector and embeds a bottleneck attention module. We address possible inference conflict between semantic segmentation and instance segmentation with a panoptic fusion head that resolves conflict using Dezert-Smarandache theory. We also constructed the first maritime scene parsing dataset MarPS-1395, which is completely and fully annotated. MarPS-1395 is the first panoptic segmentation dataset in this field. We validated our model on MarPS-1395 as well as the publicly available dataset to investigate the real-time performance and the accuracy of multitask implementation in panoptic segmentation, which included object detection and classification, instance segmentation, and semantic segmentation. The experimental results also show that our method can robustly accomplish full scene parsing in a complex maritime environment, and achieved a good balance between accuracy of segmentation and speed of computing.","Title: Reinforcement Learning-Based Parallel Approach Control of Micro-Assembly Manipulators\nYear: 2022\n\nAbstract: Micro-devices are usually assembled by micro-assembly robot operating multi-manipulators in a narrow assembly space. To ensure assembly accuracy, manipulators are required to assemble multiple parts in parallel. However, in the traditional assembly, in order to prevent the parts from interfering, the movement trajectory of each manipulator must be manually input, which leads to low planning effici...","Title: Brain-activation-based individual identification reveals individually unique activation patterns elicited by pain and touch\nYear: 2022\n\nAbstract: Pain is subjective and perceived differently in different people. However, individual differences in pain-elicited brain activations are largely overlooked and often discarded as noises. Here, we used a brain-activation-based individual identification procedure to investigate the uniqueness of the activation patterns within the whole brain or brain regions elicited by nociceptive (laser) and tactile (electrical) stimuli in each of 62 healthy participants. Specifically, brain activation patterns were used as “fingerprints” to identify each individual participant within and across sensory modalities, and individual identification accuracy was calculated to measure each individual's identifiability. We found that individual participants could be successfully identified using their brain activation patterns elicited by nociceptive stimuli, tactile stimuli, or even across modalities. However, different participants had different identifiability; importantly, the within-pain, but not within-touch or cross-modality, individual identifiability obtained from three brain regions (i.e., the left superior frontal gyrus, the middle temporal gyrus and the insular gyrus) were inversely correlated with the scores of Pain Vigilance and Awareness Questionnaire (i.e., how a person is alerted to pain) across participants. These results suggest that each individual has a unique pattern of brain responses to nociceptive stimuli which contains both modality-nonspecific and pain-specific information and may be associated with pain-related behaviors shaped by his/her own personal experiences and highlight the importance of a transition from group-level to individual-level characterization of brain activity in neuroimaging studies.","Title: Post-earthquake resilience optimization of a rural “road-bridge” transportation network system\nYear: 2022\n\nAbstract: •It proposes an optimization model of post-earthquake recovery scheduling for bridges as key links in rural “road-bridge” transportation network system.•The remaining function of the bridge after the earthquake is quantified based on the seismic fragility function.•The non-equilibrium probability random assignment model is used to simulate the travel behavior of users in recovery after the earthquake.•The resilience metric for the restoration scheduling of damaged bridges is proposed based on the total recovery time and the cumulative benefit rate.","Title: Adaptive Event-Triggered Consensus of Linear Multiagent Systems With Resilience to Communication Link Faults for Digraphs\nYear: 2022\n\nAbstract: This brief investigates the leader-follower consensus problem for multiagent systems (MASs) with resilience to unknown communication link faults. To deal with the unknown faults and avoid using global information, the adaptive event-triggered observers are designed to estimate the leader state, namely, resilience for communication link faults is transformed into the design observers by adaptive control technology. Then, based on the designed observers, controllers are designed to stabilize the given MASs. Finally, an example is introduced to demonstrate the effectiveness of the obtained results.","Title: Influence of Open-Pit Coal Mining on Ground Surface Deformation of Permafrost in the Muli Region in the Qinghai-Tibet Plateau, China\nYear: 2022\n\nAbstract: The Qinghai-Tibet Plateau (QTP) is the largest mid-to low latitude and high-altitude permafrost. Open-pit coal mining and other activities have caused serious damage to the alpine ecological environment and have accelerated the degradation of permafrost on the QTP. In this study, the influence of open-pit coal mining on the time series ground surface deformation of the permafrost in the Muli region of the QTP was analyzed from 19 January 2018 to 22 December 2020 based on Landsat, Gaofen, and Sentinel remote sensing data. The primary methods include human-computer interactive visual interpretation and the small baseline subsets interferometric synthetic aperture radar (SBAS-InSAR) method. The results showed that the spatial distribution of displacement velocity exhibits a considerably different pattern in the Muli region. Alpine meadow is the main land use/land cover (LULC) in the Muli region, and the surface displacement was mainly subsidence. The surface subsidence trend in alpine marsh meadows was obvious, with a subsidence displacement velocity of 10-30 mm/a. Under the influence of changes in temperature, the permafrost surface displacement was characteristics of regular thaw subsidence and freeze uplift. Surface deformation of the mining area is relatively severe, with maximum uplift displacement velocity of 74.31 mm/a and maximum subsidence displacement velocity of 167.51 mm/a. Open-pit coal mining had resulted in the destruction of 48.73 km(2) of natural landscape in the Muli region. Mining development in the Muli region had increased the soil moisture of the alpine marsh meadow around the mining area, resulting in considerable cumulative displacement near the mining area and the acceleration of permafrost degradation.","Title: Multiple delay-dependent finite-time fault-tolerant control for switched fuzzy systems\nYear: 2022\n\nAbstract: The work is concerned with this issue of multiple delay-dependent finite-time fault-tolerant control (FTC) for uncertain switched Takagi-Sugeno (T-S) fuzzy systems against state and input constraints. There exist intermittent faults (IFs), exogenous disturbances along with measurement noise in the models. There are few attempts for this theme. A nonlinear dynamic output feedback controller is explored for these uncertain switched T-S fuzzy systems first. And an augmented system with respect to realizing FTC is acquired. Moreover, sufficient conditions of robust finite-time H∞ control are constructed by piecewise fuzzy Lyapunov function. At the same time, finite-time boundedness (FTB) as well as input-output finite-time stability (IO-FTS) is achieved. The effectiveness as well as feasibility of this approach is verified through an example.","Title: Tracking Control of Discrete-Time System With Dynamic Event-Based Adaptive Dynamic Programming\nYear: 2022\n\nAbstract: In this brief, a novel formulation of the value function with dynamic event-triggered strategy is introduced for the optimal tracking problem (TP) of nonlinear discrete-time systems to eliminate the tracking error by using adaptive dynamic programming (ADP). Firstly, different from the existing ADP methods, a creative formulation of the cost function introduces the control input into the tracking error, and ignores the quadratic form of the control input directly. Second, a novel static triggering mechanism is designed. The proposed approach samples the states and updates the control signal only when the triggered condition is satisfied, and critic-actor Neural network (NN) is designed to approach the performance index and control input. The stability analysis of the closed-loop system is provided based on the Lyapunov’s theorem. Finally, in order to obtain a larger event triggering time interval, a dynamic triggering mechanism is given. Theoretical results show that the system state and the network weight errors are uniformly ultimately bounded (UUB). A simulation result also verify the theoretical claims.","Title: Cooperative Output Regulation Problem for Multiagent System Under Directed Topology Using Dynamic Triggering Strategy\nYear: 2022\n\nAbstract: This brief focuses on cooperative output regulation (COR) problem for linear multiagent systems (MASs) under the directed topology. The fully distributed event-triggered control protocol is designed to solve COR problem. In order to reduce the communication frequency, the dynamic event-triggered mechanism is proposed by introducing positive internal variable. One main advantage is that the method inherits the advantages of event-triggered control and adaptive control to deal with the COR problem, and does not depend on global information. At last, a numerical example is given in the supplementary material to verify the validity of the obtained results.","Title: Three-Dimensional Coordinate Extraction Based on Radargrammetry for Single-Channel Curvilinear SAR System\nYear: 2022\n\nAbstract: With the rapid development of high-resolution synthetic aperture radar (SAR) systems, the technique that utilizes multiple two-dimensional (2-D) SAR images with different view angles to extract three-dimensional (3-D) coordinates of targets has gained wide concern in recent years. Unlike the traditional multi-channel SAR utilized for 3-D coordinate extraction, the single-channel curvilinear SAR (CLSAR) has the advantages of large variation of view angle, requiring fewer acquisition data, and lower device cost. However, due to the complex aerodynamic configuration and flight characteristics, important issues should be considered, including the mathematical model establishment, imaging geometry analysis, and high-precision extraction model design. In this paper, to address these challenges, a 3-D vector model of CLSAR was presented and the imaging geometries under different view angles were analyzed. Then, a novel 3-D coordinate extraction approach based on radargrammetry was proposed, in which the unique property of the SAR system, called cylindrical symmetry, was utilized to establish a novel extraction model. Compared with the conventional approach, the proposed one has fewer constraints on the trajectory of radar platform, requires fewer model parameters, and can obtain higher extraction accuracy without the assistance of extra ground control points (GCPs). Numerical results using simulated data demonstrated the effectiveness of the proposed approach.","Title: A Novel Iterative Inner-Pulse Integration Target Detection Method for Bistatic Radar\nYear: 2022\n\nAbstract: A large time-bandwidth product bistatic radar offers several advantages in high-resolution target detection and motion parameter estimation, but the scale factor and inner-pulse Doppler will also be introduced in the radar echoes when detecting high-speed targets. Under this condition, the conventional matched filter will cause a certain energy loss and a nonnegligible shift of the range center, which is called mismatch effect. Considering the special geometries of the bistatic radar, we first establish the precise echo signal (PES) model to describe the radar echoes for high-speed targets in a space-air-based bistatic radar system. By analyzing the mismatch effect within the pulse and the migration between pulses, we have obtained the mathematic relationship between the scale factor, inner-pulse Doppler shift, range, equivalent velocity, and acceleration. Following that, we perceive that the parameters of the matched filter are related to the target motion parameters, i.e., a priori unknown, which inspires us to propose an iterative coherent integration method to achieve the intrapulse and interpulse integration. A PES model matched filter with initial parameters is defined and a roughly motion parameter estimation is acquired by the PES model-based Keystone transform (PES-KT) and inner-pulse chirp Fourier transform (CFT). The estimated parameters are used for matched filter construction and coherent integration. This process is performed over multiple iterations to provide an accurate motion parameter result. In the end, a target detection experiment is given to show the effectiveness of the proposed method using space-air-based bistatic radar.","Title: Designing an Interactive Communication Assistance System for Hearing-Impaired College Students Based on Gesture Recognition and Representation\nYear: 2022\n\nAbstract: Developing a smart classroom can make the modern classroom more efficient and intelligent. Much research has been conducted pertaining to smart classrooms for hearing-impaired college students. However, there have been few significant breakthroughs in mobilizing students' learning efficiency as measured by information transmission, communication, and interaction in class. This research collects data through nonparticipatory observation and in-depth interviews and analyzes available data on classroom interaction needs of these students. We found that diversified explanations, recordable interactive contents, and interaction between teachers and students could improve the learning effects in the classroom. We also propose a tracking-processing method based on gesture recognition and representation and present a design for a processing system based on AT89C52 microcontroller and Kinect. In this way, sign language can be translated into text and all students can receive the information and participate in the interaction, which greatly improves students' autonomy and enthusiasm of learning. This design enables deaf students to fully use classroom learning resources, reduces learning time costs, and improves learning efficiency. It can assist teachers in teaching and tutoring students to enhance their experience.","Title: An Oblique Projection-Based Beamforming Method for Coherent Signals Receiving\nYear: 2022\n\nAbstract: Within a complex sea or ground surface background, multipath signals are strongly correlated or even completely coherent, which leads to signal cancellation when conventional optimal beamforming is performed. Aiming at the above problem, a coherent signal-receiving algorithm is proposed based on oblique projection technology in this paper. The direction of arrival (DOA) of incident signals is estimated firstly by the space smoothing-based MUSIC method. The composite steering vector of multipath coherent signals is then obtained utilizing the oblique projection matrix constructed with the estimated angles. The weight vector is thereby derived with the minimum variance distortionless response criteria. The proposed oblique projection-based beamformer can receive the multipath coherent signals effectively. Moreover, the proposed beamformer is more robust and converges to optimal beamformer rapidly without aperture loss. The theoretical analysis and simulation verify the validity and superiority of the proposed coherent signal beamformer.","Title: Dissipativity-Based Intermittent Fault Detection and Fault-Tolerant Control for Uncertain Switched Random Nonlinear Systems With Multiple Delays\nYear: 2022\n\nAbstract: The issue of fault detection (FD) and fault-tolerant control is developed for a switched random nonlinear system against multiple delays in this work. There also exist parameter uncertainties, exogenous disturbances, nonlinear functions as well as measurement noise in this model. This is one of the few tries to investigate the dissipativity issue and an design FD-based observer for the switched random system. Based on the dimensionally adjustable observer, the controller is designed to make this system noise-to-state exponentially mean-square stable. Then, these sufficient stability conditions can be earned by the piecewise Lyapunov function. Concurrently, this strict \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$\\left({\\mathsf {Q}, \\mathsf {S}, \\mathsf {R}}\\right)-{\\upsilon }-$ </tex-math></inline-formula>\n dissipativity performance, this elaborate \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$H_{\\infty }$ </tex-math></inline-formula>\n performance as well as this elaborate \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$H\\_{}$ </tex-math></inline-formula>\n performance can be gained, separately. In addition, the merits and feasibility are presented via a numerical simulation and a practical example."],"string":"[\n \"Title: High positive correlation between soil temperature and NDVI from 1982 to 2006 in alpine meadow of the Three-River Source Region on the Qinghai-Tibetan Plateau\\nYear: 2011\\n\\nAbstract: Using satellite-observed Normalized Difference Vegetation Index (NDVI) data and Rotated Empirical Orthogonal Function (REOF) method, we analyzed the spatio-temporal variation of vegetation during growing seasons from May to September in the Three-River Source Region, alpine meadow in the Qinghai-Tibetan Plateau from 1982 to 2006. We found that NDVI in the centre and east of the region, where the vegetation cover is low, showed a consistent but slight increase before 2003 and remarkable increase in 2004 and 2005. Impact factors analysis indicted that among air temperature, precipitation, humid index, soil surface temperature, and soil temperature at 10 cm and 20 cm depth, annual variation of NDVI was highly positive correlated with the soil surface temperature of the period from March to July. Further analysis revealed that the correlation between the vegetation and temperature was insignificant before 1995, but statistically significant from 1995. The study indicates that temperature is the major controlling factor of vegetation change in the Three-River Source Region, and the currently increase of temperature may increase vegetation coverage and/or density in the area. In addition, ecological restoration project started from 2005 in Three-River Source Region has a certain role in promoting the recovery of vegetation. Keywords NDVI (Normalized Differential Vegetation Index) Qinghai-Tibetan Plateau Temperature Vegetation\",\n \"Title: Influence of removable devices on computer worms: Dynamic analysis and control strategies\\nYear: 2011\\n\\nAbstract: Worms spreading via both Web-based scanning and removable devices account for a major part of threats on internet. However, their dynamical behavior and controlling methods remain unclear. As a result, we present a computer worm model incorporating specific features unique to those worms, in this paper. The threshold value R\\\"0 determining whether the worms die out is obtained. Theoretical analysis shows that if R\\\"0\",\n \"Title: The numerical solution of fifth-order boundary value problems by the variational iteration method\\nYear: 2009\\n\\nAbstract: This paper applies the variational iteration method to solve fifth-order boundary value problems; just one iteration results in highly accurate solutions. A comparison of the results with exact ones is made to confirm the validity and efficiency.\",\n \"Title: Comparison Of Power Transfer Characteristics Between Cpt And Ipt System And Mutual Inductance Optimization For Ipt System\\nYear: 2012\\n\\nAbstract: The capacitive power transfer (CPT) system and inductively power transfer (IPT) system are the two typical wireless power transfer systems. Based on the power transfer characteristics, the power transfer capacity of the two wireless power transfer systems were analyzed. Firstly, the maximum power transfer capacity and its existing condition of the two wireless power transfer systems were analyzed, and the choose gist of wireless power transfer system was presented according to the analysis result; then, the mutual inductance of 4 typical IPT systems was optimized for achieving maximum power transfer; finally, the theoretical research was justified via a simulation and experiment.\",\n \"Title: Bounds for the eigenvalues of the continuous algebraic Riccati equation\\nYear: 2011\\n\\nAbstract: By using singular value decomposition and majorisation inequalities, we propose new upper and lower bounds for summations of eigenvalues (including the trace) of the solution of the continuous algebraic Riccati equation. These bounds improve and extend some of the previous results. Finally, we give corresponding numerical examples to illustrate the effectiveness of our results.\",\n \"Title: The dominant degree and disc theorem for the Schur complement of matrix\\nYear: 2010\\n\\nAbstract: The theory of Schur complement plays an important role in many fields such as control theory and computational mathematics. In this paper, we obtain some estimates for the diagonally, @c-diagonally and product @c-diagonally dominant degree on the Schur complement of matrices, which improve some relative results. As application we present some bounds for the eigenvalues of Schur complement by the entries of the original matrix instead of those of the Schur complement. Particularly, we obtain that the eigenvalues of the Schur complements are located in the Gerschgorin Circles of the original matrices under certain conditions.\",\n \"Title: Computer-aided diagnosis of breast DCE-MRI images using bilateral asymmetry of contrast enhancement between two breasts.\\nYear: 2014\\n\\nAbstract: Dynamic contrast material-enhanced magnetic resonance imaging (DCE-MRI) of breasts is an important imaging modality in breast cancer diagnosis with higher sensitivity but relatively lower specificity. The objective of this study is to investigate a new approach to help improve diagnostic performance of DCE-MRI examinations based on the automated detection and analysis of bilateral asymmetry of characteristic kinetic features between the left and right breast. An image dataset involving 130 DCE-MRI examinations was assembled and used in which 80 were biopsy-proved malignant and 50 were benign. A computer-aided diagnosis (CAD) scheme was developed to segment breast areas depicted on each MR image, register images acquired from the sequential MR image scan series, compute average contrast enhancement of all pixels in one breast, and a set of kinetic features related to the difference of contrast enhancement between the left and right breast, and then use a multi-feature based Bayesian belief network to classify between malignant and benign cases. A leave-one-case-out validation method was applied to test CAD performance. The computed area under a receiver operating characteristic (ROC) curve is 0.78 ± 0.04. The positive and negative predictive values are 0.77 and 0.64, respectively. The study indicates that bilateral asymmetry of kinetic features between the left and right breasts is a potentially useful image biomarker to enhance the detection of angiogenesis associated with malignancy. It also demonstrates the feasibility of applying a simple CAD approach to classify between malignant and benign DCE-MRI examinations based on this new image biomarker.\",\n \"Title: Using Genetic Algorithm to Improve Fuzzy k-NN\\nYear: 2008\\n\\nAbstract: This paper presents a method of improving fuzzy k-nearest neighbor (k-NN) using genetic algorithm (GA). k-NN is an important classification algorithm, However, a major drawback of the method is that each of the patterns of known classification is considered equally important in the assignment of the pattern to be classified. This can cause difficulties in regions where pattern data overlap. To overcome this drawback, A fuzzy k-NN was proposed for increasing classification correct rate of overlapping pattern data . In this paper, Experiments are carried out and illustrate that both k-NN and fuzzy k-NN are still sensitive to the initial k and parameter m. GA is applied to calculate initial k and parameter m. Experimental results show that the our proposed approach has the higher classification correct rate than the fuzzy k-NN.\",\n \"Title: Classification tree modeling to identify severe and moderate vehicular injuries in young and middle-aged adults\\nYear: 2009\\n\\nAbstract: Objectives: Motor vehicle crashes are a leading cause of mortality and morbidity worldwide. Even though trauma centers provide the gold standard of care for motor vehicle crash patients with life- or limb-threatening injuries, many whose lives might be saved by trauma center care are treated instead at non-trauma center hospitals. Triage algorithms, designed to identify patients with life- or limb-threatening injuries who should be transported to a trauma center, lack appropriate sensitivity to many of these injuries. The challenge to the trauma community is differentiating patients with life- or limb-threatening injuries from those with less severe injuries at the crash scene so that the patients can be transported to the most appropriate level of care. The purpose of this study was to use crash scene data available to emergency responders to classify adults with moderate and severe injuries. These classifiers might be useful to guide triage decision making. Methods and material: Records of 74,626 adults, age 18-64 years, from the National Automotive Sampling System Crashworthiness Data Systems database were analyzed using classification and regression trees (CART) analysis. Both CART models (moderate injury and severe injury) included 13 predictor variables. The response variables were the targeted injury severity score cut points for moderate and severe injury. Two final classification trees were developed: one that classified occupants based on moderate injury and the other on severe injury. Misclassification costs were manipulated to achieve the best model fit for each tree. Results: The moderate injury classification tree had three splitters: police-estimated injury severity, restraint use, and number of persons injured. The severe injury classification tree had four splitters: police-estimated injury severity, manner of collision, number of persons injured in the crash, and age. Sensitivity and specificity of the classification trees were 93.70%, 77.53% (moderate) and 99.18%, 73.96% (severe), respectively. Conclusions: CART analysis can be used to classify injury severity using crash scene information that is available to emergency responders. This procedure offers an opportunity to examine alternative methods of identifying injury severity that might assist emergency responders to differentiate more accurately persons who should receive trauma center care from those who can be treated safely at a non-trauma center hospital.\",\n \"Title: A CPN-based Software Testing Approach.\\nYear: 2011\\n\\nAbstract: As a graphical and mathematical modeling tool, CPN (Colored Petri Net) is often used to describe the transition of states for an information system. The advantage of CPN model is that the model can be simulated dynamically. This paper presents an approach to generating test cases based on a transition graph of CPN model. The transition graph provides a solid basis for test cases generation in a form that can be easily manipulated. The case for vending machine illustrates the effectiveness of this method. © 2011 ACADEMY PUBLISHER.\",\n \"Title: Improved mammographic mass retrieval performance using multi-view information\\nYear: 2010\\n\\nAbstract: Breast cancer is the most common malignant disease in women. Mammographic mass retrieval system can help radiologists to improve the diagnostic accuracy by retrieving biopsy-proven masses which are similar with the diagnostic ones. However, although screening mammograms usually consists of two-view(MLO and CC) mammography of the same breast, most breast CAD systems incorporate with image retrieval techniques are based on a single-view principle where query ROI within a view is analyzed independently. In this paper, a mammographic mass retrieval approach based on multi-view information is proposed. In this work, the query example is a multi-view(MLO and CC) mass pair instead of the single view mass in the traditional image retrieval framework. In the experiments, several visual features are used for retrieval evaluation. Both distance similarity measures, such as Euclidean distance, and k-NN regression model based non-distance similarity measures are used for comparison. Experimental study was carried out on a database with 126 biopsy-proven masses(63 mass pairs). Preliminary results showed that multi-view based retrieval approach achieves better retrieval accuracy than single-view based one, especially for the k-NN regression model based similairy metric.\",\n \"Title: Application of Fuzzy Mathematics Assessment to Groundwater Quality in Jining City\\nYear: 2009\\n\\nAbstract: The water quality of groundwater in Jining has been assessed comprehensively based on the environmental investigation and monitoring in order to provide the scientific basis for the pollution treatment in the Jining city. This paper discussed the choice of the evaluated factors, the operations of the weight value, and compares with the status of the water quality in the course of the evaluation. The conclusions indicated that the result of the fuzzy mathematics evaluation represented the water quality of groundwater in Jining city. The evaluation result was compared with that of the traditional method and the reported results in Jining. It is indicated that the performance of the proposed model is practically feasible in the application of groundwater quality assessment and its application is simple.\",\n \"Title: Modeling Software Testing Process Using HTCPN\\nYear: 2009\\n\\nAbstract: Software testing process has being separated gradually from software development. As a relatively independent process, software testing process simulation and optimization has been also started to catch people's attention. It is particularly important to improve the reuse of software testing. This paper proposes a systematic way to model the software testing processes STP-NET based on HTCPN, in order to reuse them. The performance of the process, such as average time and average waiting time, is simulated and analyzed using CPN Tools. According to the results, the process configuration parameters, the license allocation model of automatic testing tool, can be optimized. The efficiency of the testing process can be improved by simulation and optimization using CPN Tools.\",\n \"Title: Parallel algorithm design for remote sensing image processing in the PC cluster environment\\nYear: 2010\\n\\nAbstract: In the PC cluster environment, parallel algorithms can significantly improve the efficiency of remote sensing image processing. The remote sensing dataset is the raster data stored by order of band, therefore, it is feasible to assign executable tasks to some computing nodes by band and complete the processing tasks together through communicating each other amount the computing nodes by MPI interface. In addition, the multi-thread processing algorithm is scheduled based on OpenMP library toward the single computing node with multi-core CPU. The experiment evaluates image radiation performance about the four-band HJ-1A CCD remote sensing image in the PC cluster environment which consists of 4 sets of dual-core computers. Its performance is increased by 6 times to 6.5 times comparing with a single PC. Through validating, the dual level design pattern is available based on integrating MPI and OpenMP to improve the efficiency of remote sensing image processing.\",\n \"Title: Position/force hybrid control system for high precision aligning of small gripper to ring object\\nYear: 2013\\n\\nAbstract: A position/force hybrid control system based on impedance control scheme is designed to align a small gripper to a special ring object. The vision information provided by microscope vision system is used as the feedback to indicate the position relationship between the gripper and the ring object. Multiple image features of the gripper and the ring object are extracted to estimate the relative positions between them. The end-effector of the gripper is tracked using the extracted features to keep the gripper moving in the field of view. The force information from the force sensor serves as the feedback to ensure that the contact force between the gripper and the ring object is limited in a small safe range. Experimental results verify the effectiveness of the proposed control strategy.\",\n \"Title: New Matrix Bounds, An Existence Uniqueness And A Fixed-Point Iterative Algorithm For The Solution Of The Unified Coupled Algebraic Riccati Equation\\nYear: 2012\\n\\nAbstract: In this paper, combining the equivalent form of the unified coupled algebraic Riccati equation (UCARE) with the eigenvalue inequalities of a matrix's sum and product, using the properties of an M-matrix and its inverse matrix, we offer new lower and upper matrix bounds for the solution of the UCARE. Furthermore, applying the derived lower and upper matrix bounds and a fixed-point theorem, an existence uniqueness condition of the solution of the UCARE is proposed. Then, we propose a new fixed-point iterative algorithm for the solution of the UCARE. Finally, we present a corresponding numerical example to demonstrate the effectiveness of our results.\",\n \"Title: The application of nonaffine network structural model in sine pulsating flow field\\nYear: 2004\\n\\nAbstract: In order to describe the rheological behavior of polymer melts in a practical vibration force field, the nonaffine network structural model is generalized from a simple oscillatory shear force field to a sine pulsating flow field. Through experiments and theoretic analyses, we prove that this model can be described preferably the viscous and elastic behavior of polymer melts in sine pulsating flow field, and the change amplitude of the shear stress increases with the vibration frequency or amplitude increasing.\",\n \"Title: QP based framework for development and formal verification of flight control software of UAV\\nYear: 2011\\n\\nAbstract: UAV is widely invested both in military and academic fields. The development and verification of UAV flight control software is an importance issues and hot topic. In this paper, a QP based method is present to develop and formally verify the UAV flight control software. The method combines the UML and OCL constraint language and output the UML file that constrained by OCL to XMI file. Then we input the XMI file to rule inference engine as facts. Aided by safety rules, the flight control software could be automatically verified. The method has many advantages and is hopeful to enhance the quality of UAV flight control software. It can also be used in similar scenarios.\",\n \"Title: Pattern formation of a spatial predator-prey system.\\nYear: 2012\\n\\nAbstract: There are random and directed movements of predator and prey populations in many natural systems which are strongly influenced and modified by spatial factors. To investigate how these migration (directed movement) and diffusion (random movement) affect predator–prey systems, we have studied the spatiotemporal complexity in a predator–prey system with Holling–Tanner form. A theoretical analysis of emerging spatial pattern is presented and wavelength and pattern speed are calculated. At the same time, we present the properties of pattern solutions. The results of numerical simulations show that migration has prominent effect on the pattern formation of the population, i.e., changing Turing pattern into traveling pattern. This study suggests that modelling by migration and diffusion in predator–prey systems can account for the dynamical complexity of ecosystems.\",\n \"Title: Web Document Classification Based on Fuzzy k-NN Algorithm\\nYear: 2009\\n\\nAbstract: Web document classification is an important technique of Web mining. Web pages classification has been studied extensively since the Internet has become a huge database of information. The k-NN is a simple classification algorithm that is used to assign patterns of unknown classification to the class of the majority of its k nearest neighbors of known classification according to the distance measure, but a main drawback of the method is that each of the patterns of known classification is considered equally important in the assignment of the pattern to be classified. Fuzzy k-nearest neighbor (fuzzy k-NN) is improving algorithm of k-NN, which is applied successfully in structural data classification. This paper presents the Web document classification based on fuzzy k-NN network, in the process of classification, TF/IDF (term frequency/inverse document frequency) is adopted for selecting features of document, to increase the accuracy and suit for real world, membership grade is used. Experimental results show that classification performance is better than both k-NN and support vector machine (SVM).\",\n \"Title: Using Ontology to Build Information Recommendation System in e-Commerce.\\nYear: 2011\\n\\nAbstract: The aim of ontologies is to formalize domain knowledge in a generic way and provide a common understanding of a domain, which may be used and shared by applications and groups. This study attempts to propose a framework to improve and upgrade the effectiveness of E-C recommendation by ontology building algorithms. As a result, a e-commerce system is built using the existing standards cited above by overcoming their limits and sharing their different advantages. This paper puts forward using ontology to build information recommendation system in e-commerce. The experimental results have shown our suggested information system will increase precision and performance.\",\n \"Title: Scale-Free Networks with Different Types of Nodes\\nYear: 2009\\n\\nAbstract: In many natural and social networks, nodes may play different roles or have different functions. In this paper, we propose a simple model with different types of nodes and deterministic selective linking rule. We investigate the structural properties by theoretical predictions. It is found that the given model exhibits a power-law distribution. In addition, we make the model become the weighted network by giving the links the weight and analyze the probability distribution of the node strength.\",\n \"Title: A new multi-view identification scheme for breast masses in mammography using multi-agent algorithm\\nYear: 2010\\n\\nAbstract: The classification of breast masses into benign and malignant categories plays an important role in the area of computer-aided diagnosis (CAD) of breast cancer. In this paper, in order to improve the accuracy and the robustness of the classification and reduce the false positive rates, we proposed one novel scheme that was based on information fusion in multi views. A series of contour and shape features of the masses were chosen, and new contour features were used. Then, a multi-classifier fusion based on multi-agent algorithm was introduced. And finally, a multi-view information fusion method was applied. Experimental results demonstrated that the proposed classification scheme achieved a higher accuracy than those schemes using the individual classifiers and the multi-classifier fusion technique in single-view.\",\n \"Title: An efficient ensemble method for classifying skewed data streams\\nYear: 2011\\n\\nAbstract: Class distributions of data streams in real application are usually unbalanced, they are hence called Skewed Data Streams (abbreviated as SDS). However, in the classification of SDS, it is a challenge for traditional methods because of the difficulty in the recognition of minority classes. Therefore, many approaches have been proposed to improve the recognition rate of minority classes, while they are time-consuming. Motivated by this, we propose an efficient Ensemble method for Classifying SDS called ECSDS. Our algorithm creates multiple classifiers based on C4.5, and adopts the threshold of F1-value to limit the updating frequency of classifiers. Meanwhile, it adds misclassified positive instances into the training data to guarantee the effectiveness of classifiers when updating. Experimental studies demonstrate that our proposed method enables reducing the time overhead and maintains a good performance on the classification accuracy.\",\n \"Title: Dynamical analysis and perturbation solution of an SEIR epidemic model.\\nYear: 2014\\n\\nAbstract: In this paper we present a kind of technique to obtain the asymptotical solution of an SEIR (where S is the susceptible population, E is the exposed population, I is the infectious population and R is the recovered population) epidemic model by employing the method of perturbation. At first, we investigate the epidemic model by analysing its dynamical behavior. Then, we use the method of perturbation to obtain the analytical solution of the model. We assign values for parameters and draw figures to observe the magnitude of error of the perturbation method in a macro view. Finally, we analyse macroscopically the two comparison chart on analytical solution and the exact solution, and know that it is feasible to analyse the solution of the epidemic model by using the perturbation method.\",\n \"Title: Variational approach to solitary wave solution of the generalized Zakharov equation\\nYear: 2007\\n\\nAbstract: He's semi-inverse method is applied to search for the solitary wave solution of the generalized Zakharov equation (GZE). The solution process reveals that the method is easy and straightforward.\",\n \"Title: Clustering coefficients in a growing network model of polymer melts\\nYear: 2011\\n\\nAbstract: In this paper, we apply the mean-field approach to analyze the clustering coefficient of the growing network model of polymer melts and conduct numerical simulations by computer. It reveals the rules of the changes of the clustering coefficients with various parameters, which provides an important guide for polymer melts research.\",\n \"Title: A zone-diffusion based routing protocol for LEO satellite networks\\nYear: 2011\\n\\nAbstract: A Zone-Diffusion based Routing Protocol (ZDRP) for LEO satellite networks is proposed in this paper. The proposed routing protocol aims at reducing the broadcast overhead, which is a big problem in LEO satellite networks because of the topological dynamics of LEO constellation. The protocol divides a satellite network into zones, and restricts the broadcasting to the domain of zone. ZDRP operates in an on-demand mode to set up paths between each source-destination pair, and consists of two routing mechanisms, i.e., Route Discovery and Route Maintenance. The two mechanisms work together to allow nodes to discover and maintain source routes to arbitrary destinations in the LEO network. Consequently, the broadcast overhead incurred by routing protocol is decreased greatly. Simulation results show that ZDRP has a good performance in terms of end-to-end delay. Moreover, the packet loss rate is decreased significantly when ZDRP is applied to LEO network.\",\n \"Title: Design And Implementation Of Crop Recommendation Fertilization Decision System Based On Webgis At Village Scale\\nYear: 2010\\n\\nAbstract: To study crop recommendation fertilization of the rural farmers as the main in our country, the paper took towns and villages of Hua County as the study area, took recommendation fertilization of wheat, maize and peanut as the study object, designed model components of crop balance fertilization by using Object-Oriented technique, and developed the decision-making system about crop recommendation fertilization based on ArcGIS Server at village scale. The decision-making system realized farmland nutrient management and fertilization recommendations decision-making according to soil output capacity, agricultural production level and crop target yield. It was successfully applied in crop production in Hua County. The research results show that the system has the characteristic of better expansibility than before, and it is significantly simple and practical to reduce crop production cost and increase agricultural production efficiency, which provides technical support for crop fertilization decision-making and is significant to improve agricultural ecological environment and increase the comprehensive production capacity of farmland.\",\n \"Title: Hybridizing Differential Evolution and Particle Swarm Optimization to Design Powerful Optimizers: A Review and Taxonomy\\nYear: 2012\\n\\nAbstract: Differential evolution (DE) and particle swarm optimization (PSO) are two formidable population-based optimizers (POs) that follow different philosophies and paradigms, which are successfully and widely applied in scientific and engineering research. The hybridization between DE and PSO represents a promising way to create more powerful optimizers, especially for specific problem solving. In the past decade, numerous hybrids of DE and PSO have emerged with diverse design ideas from many researchers. This paper attempts to comprehensively review the existing hybrids based on DE and PSO with the goal of collection of different ideas to build a systematic taxonomy of hybridization strategies. Taking into account five hybridization factors, i.e., the relationship between parent optimizers, hybridization level, operating order (OO), type of information transfer (TIT), and type of transferred information (TTI), we propose several classification mechanisms and a versatile taxonomy to differentiate and analyze various hybridization strategies. A large number of hybrids, which include the hybrids of DE and PSO and several other representative hybrids, are categorized according to the taxonomy. The taxonomy can be utilized not only as a tool to identify different hybridization strategies, but also as a reference to design hybrid optimizers. The tradeoff between exploration and exploitation regarding hybridization design is discussed and highlighted. Based on the taxonomy proposed, this paper also indicates several promising lines of research that are worthy of devotion in future.\",\n \"Title: A complex network model of polymer melts without growth\\nYear: 2011\\n\\nAbstract: In order to describe the entangled network structure in polymer melts visually, a complex network model without growth is proposed. The analysis shows that the exponential distribution can be exhibited from the statistic of the network after a long time accumulation, the average number of entanglements can be decreased if the probability of deleted edges is not less than that of created edges, and the simulation result and the theoretical one are the same, approximately.\",\n \"Title: New solution bounds for the continuous algebraic Riccati equation\\nYear: 2011\\n\\nAbstract: In this paper, by constructing the equivalent form of the continuous algebraic Riccati equation and utilizing the eigenvalue and singular value inequalities of matrix's sum and product, we propose new lower and upper matrix bounds for the solution of the continuous algebraic Riccati equation. Finally, we give corresponding numerical examples to illustrate the effectiveness of our results.\",\n \"Title: Retrieving articulated 3-D models using medial surfaces\\nYear: 2008\\n\\nAbstract: We consider the use of medial surfaces to represent symmetries of 3-D objects. This allows for a qualitative abstraction based on a directed acyclic graph of components and also a degree of invariance to a variety of transformations including the articulation of parts. We demonstrate the use of this representation for 3-D object model retrieval. Our formulation uses the geometric information associated with each node along with an eigenvalue labeling of the adjacency matrix of the subgraph rooted at that node. We present comparative retrieval results against the techniques of shape distributions (Osada et al.) and harmonic spheres (Kazhdan et al.) on 425 models from the McGill Shape Benchmark, representing 19 object classes. For objects with articulating parts, the precision vs recall curves using our method are consistently above and to the right of those of the other two techniques, demonstrating superior retrieval performance. For objects that are rigid, our method gives results that compare favorably with these methods.\",\n \"Title: Passive data storage based housewares store management system\\nYear: 2011\\n\\nAbstract: After referring several cases of management information system and RFID system, we design a novel and practical system for housewares store. We use the RFID tags in a different way - Passive Data Storage. In the paper, the structure and implementation of the system is described in detail. A case study is also given for illustration.\",\n \"Title: The combining prediction of the RMB exchange rate series based on diverse architectural artificial neural network ensemble methodology.\\nYear: 2010\\n\\nAbstract: Motivated by the neural network ensemble approach, this paper puts forward a diverse architectural artificial neural network (ANN) ensemble method to optimize the combining prediction of the RMB exchange rates. On the one hand, four types of architectures are adopted here including multilayer perceptron (MLP), recurrent neural networks (RNNs) to diversify the learning mechanism. On the other hand, the non parametric kernel smoothing technique is applied to make combining forecasts, which can overcome the drawbacks of traditional methods. The empirical results show that the proposed method has significantly improved the forecasting performance of the optimal single ANNs and random walk model, especially in RMB exchange rate series forecasting. © 2010 IEEE.\",\n \"Title: Ant Colony Optimization Algorithm Based on Immunity Vaccine and Dynamic Pheromone Updating\\nYear: 2010\\n\\nAbstract: To solve the problem of complexity and uncertainty in logistics and distribution system, a kind of Ant Colony Optimization algorithm based on immunity vaccine and dynamic pheromone updating is put forward in this paper. In this new Algorithm, initial antibody is vaccinated first to produce better solutions, and initial parameters are then set depending on these better solutions by the Ant Colony. Mechanism of dynamic adjustment and pheromone updating is also introduced into this algorithm to slow down the increment of the pheromone concentration difference between the paths, avoiding sectional solution of the algorithm. Experiment results show that DACOIV can effectively find optimal path for logistics and distribution system.\",\n \"Title: Improving the prediction of chemotherapeutic sensitivity of tumors in breast cancer via optimizing the selection of candidate genes.\\nYear: 2014\\n\\nAbstract: •The prediction of chemotherapeutic sensitivity is important in cancer treatment.•False positive genes seriously impact the performance of predictive models.•We proposed a combined strategy for optimizing the selection of candidate genes.•The performance of predictive models is improved when using the refined genes.•This research will benefit the guidance of the personalized chemotherapy.\",\n \"Title: Professional tennis player ranking strategy based Monte Carlo feature selection\\nYear: 2011\\n\\nAbstract: Extracting significant features from high-dimensional and small sample-size microarray data is a challenging problem. Other than wrapper or filter methods, we propose a novel feature selection algorithm which integrates the ideas of professional tennis players ranking, such as seed players and dynamic ranking with Monte Carlo simulation. Seed players make the 'game' more competitive and selective, hence improve the selection efficiency. Besides, the ranks of features are dynamically updated and this ensures that it is always the current best players to take part in each competitions. The proposed algorithm is tested on widely used public datasets. Results demonstrate that the proposed method comparatively converges faster, more stable and has good performance in classification and therefore is an efficient algorithm for feature selection.\",\n \"Title: Identifying Ovarian Cancer Chemotherapy Response Relevant Gene Cliques\\nYear: 2011\\n\\nAbstract: Operation with adjuvant chemotherapy is still the principal means to treat Ovarian cancer. Identifying Ovarian Cancer Chemotherapy Response (OCCR) relevant genes and describe their interactions is thus an important issue. However the problems of high dimensional micro array data and the scarcity of biological priors make building a complete OCCR biological network intractable. To this end, we combine liquid association (LA) algorithm with biological knowledgebase searching to identify OCCR relevant gene clique and describe their interactions. Rather than trying to build a gene network, our approach focus on identifying OCCR relevant gene cliques and then patching them up. Statistical analysis and biological validation show that the identified gene cliques play important roles in tumor genesis, immunity, cells proliferation and migration etc and significantly OCCR relevant. More importantly, the connection of independent gene cliques is established and the associations of genes are described. Methodologically, the proposed method avoids the problem of complex computation, relies only on available biological priors and provides a novel way to build gene network.\",\n \"Title: Investigating the concordance of Gene Ontology terms reveals the intra- and inter-platform reproducibility of enrichment analysis.\\nYear: 2013\\n\\nAbstract: Reliability and Reproducibility of differentially expressed genes (DEGs) are essential for the biological interpretation of microarray data. The microarray quality control (MAQC) project launched by US Food and Drug Administration (FDA) elucidated that the lists of DEGs generated by intra- and inter-platform comparisons can reach a high level of concordance, which mainly depended on the statistical criteria used for ranking and selecting DEGs. Generally, it will produce reproducible lists of DEGs when combining fold change ranking with a non-stringent p-value cutoff. For further interpretation of the gene expression data, statistical methods of gene enrichment analysis provide powerful tools for associating the DEGs with prior biological knowledge, e.g. Gene Ontology (GO) terms and pathways, and are widely used in genome-wide research. Although the DEG lists generated from the same compared conditions proved to be reliable, the reproducible enrichment results are still crucial to the discovery of the underlying molecular mechanism differentiating the two conditions. Therefore, it is important to know whether the enrichment results are still reproducible, when using the lists of DEGs generated by different statistic criteria from inter-laboratory and cross-platform comparisons. In our study, we used the MAQC data sets for systematically accessing the intra- and inter-platform concordance of GO terms enriched by Gene Set Enrichment Analysis (GSEA) and LRpath.In intra-platform comparisons, the overlapped percentage of enriched GO terms was as high as ~80% when the inputted lists of DEGs were generated by fold change ranking and Significance Analysis of Microarrays (SAM), whereas the percentages decreased about 20% when generating the lists of DEGs by using fold change ranking and t-test, or by using SAM and t-test. Similar results were found in inter-platform comparisons.Our results demonstrated that the lists of DEGs in a high level of concordance can ensure the high concordance of enrichment results. Importantly, based on the lists of DEGs generated by a straightforward method of combining fold change ranking with a non-stringent p-value cutoff, enrichment analysis will produce reproducible enriched GO terms for the biological interpretation.\",\n \"Title: Evaluating Software Maintainability Using Fuzzy Entropy Theory\\nYear: 2010\\n\\nAbstract: With the development of information technology, a large number of software products enter the maintenance phase. The maintainability of software products continues to attract people's attention. This paper proposes a new synthetic maintainability evaluation model basing on fuzzy entropy theory. In the model, the weight of different indexes is calculated by combining the entropy weight and expert weight. The entropy weight can be obtained through measuring the difference of membership degree of the index which reflects the consistency of the expert opinion. The evaluation result is more objective and reasonable. It provides a guideline to design a software product with high maintainability.\",\n \"Title: Analysis of a periodic bacteria-immunity model with delayed quorum sensing\\nYear: 2008\\n\\nAbstract: Based on [J. Zhang, M. Cerasuolo, P. Fergola, Z.E. Ma, On the influence of quorum sensing in the competition between bacteria and immune system (in press)], a bacteria-immunity model with periodically bacterial quorum sensing is formulated which describes the competition between bacteria and immune cells. A discrete delay is introduced to characterize the time in which bacteria receive signal molecules and combat with immune cells. In this paper, we focus on a subsystem of the bacteria-immunity model, investigate the existence and stability of the positively periodic solution, and discuss the existence of subharmonic bifurcation by the perturbation method.\",\n \"Title: Optimal Contraction Theorem for Exploration–Exploitation Tradeoff in Search and Optimization\\nYear: 2009\\n\\nAbstract: Global optimization process can often be divided into two subprocesses: exploration and exploitation. The tradeoff between exploration and exploitation (T:Er&Ei) is crucial in search and optimization, having a great effect on global optimization performance, e.g., accuracy and convergence speed of optimization algorithms. In this paper, definitions of exploration and exploitation are first given based on information correlation among samplings. Then, some general indicators of optimization hardness are presented to characterize problem difficulties. By analyzing a typical contraction-based three-stage optimization process, optimal contraction theorem is presented to show that T:Er&Ei depends on the optimization hardness of problems to be optimized. T:Er&Ei will gradually lean toward exploration as optimization hardness increases. In the case of great optimization hardness, exploration-dominated optimizers outperform exploitation-dominated optimizers. In particular, random sampling will become an outstanding optimizer when optimization hardness reaches a certain degree. Besides, the optimal number of contraction stages increases with optimization hardness. In an optimal contraction way, the whole sampling cost is evenly distributed in all contraction stages, and each contraction takes the same contracting ratio. Furthermore, the characterization of optimization hardness is discussed in detail. The experiments with several typical global optimization algorithms used to optimize three groups of test problems validate the correctness of the conclusions made by T:Er&Ei analysis.\",\n \"Title: A compartmental model for the analysis of SARS transmission patterns and outbreak control measures in China\\nYear: 2005\\n\\nAbstract: We propose a compartmental model BloComp(2,7) that mimics the SARS control strategies implemented by the Chinese government after the middle of April 2003: the division of the whole population into two parallel blocks corresponding to the so-called free environment and the isolated environment and the partition of these blocks further into the compartments of susceptible, exposed, infective, possible, diagnosed, removed and the health care workers. We introduce a novel approach to calculate the transfer rate from the free environment to the isolated environment, and we incorporate into the model the fact that many individuals were misdiagnosed as SARS suspected and hence were mistakenly put in the isolated environment due to lack of fast and effective SARS diagnostic tests. We develop some methods for the parameter identification using the daily reported data from the Ministry of Health of China. Simulations based on these parameters agree with the accural data well, thus provide additional validation of the model. We then vary some parameters to assess the effectiveness of different control measures: these new parameters correspond to the situation when the quarantine measures in the free-environment were prematurely relaxed (we thus observe the second outbreak with the maximal number of daily SARS patients much higher than the first outbreak) or when the quarantine time of SARS patients is postponed (we observe delayed peak time but with much higher number of SARS patients at the peak). We also calculate the basic reproductive number and the basic adequate contact rate.\",\n \"Title: Test Case Reuse Based on Ontology\\nYear: 2009\\n\\nAbstract: Test cases are one of the most important assets in the testing process. This paper presents the testing ontology based SWEBOK and software quality model. The management and retrieval of test cases will play a vital role in test cases reuse. The keyword-based, as well as facet-based retrieval cannot meet user's flexible query requirement because of lack of semantic information. SWEBOK provides a broad agreement on the content of the software engineering discipline. At last this paper discusses the management and retrieval of test cases based on the semantic similarity of two test concepts in two ontologies according to difference sets of superconcept, subconcept, extension, and intension.\",\n \"Title: The Research of Construction Ontology-Based Material Management System in e-Commerce.\\nYear: 2011\\n\\nAbstract: Ontologies are formal, explicit specifications of shared conceptuallizations of a given domain of discourse. The goal of materials management is to provide an unbroken chain of components for production to manufacture goods on time for the customer base. This paper attempts to propose a framework to improve and upgrade the effectiveness of E-C materials management by ontology construction algorithms. Our approach consists of a methodology that aids actors in understanding information generated from multiple construction participants. This paper presents using ontology to build materials management system in e-commerce. The experimental results have shown our suggested materials management system will increase precision and performance.\",\n \"Title: Self-calibration method without joint iteration for distributed small satellite SAR systems.\\nYear: 2013\\n\\nAbstract: The performance of distributed small satellite synthetic aperture radar systems degrades significantly due to the unavoidable array errors, including gain, phase, and position errors, in real operating scenarios. In the conventional method proposed in (IEEE T Aero. Elec. Sys. 42:436–451, 2006), the spectrum components within one Doppler bin are considered as calibration sources. However, it is found in this article that the gain error estimation and the position error estimation in the conventional method can interact with each other. The conventional method may converge to suboptimal solutions in large position errors since it requires the joint iteration between gain-phase error estimation and position error estimation. In addition, it is also found that phase errors can be estimated well regardless of position errors when the zero Doppler bin is chosen. In this article, we propose a method obtained by modifying the conventional one, based on these two observations. In this modified method, gain errors are firstly estimated and compensated, which eliminates the interaction between gain error estimation and position error estimation. Then, by using the zero Doppler bin data, the phase error estimation can be performed well independent of position errors. Finally, position errors are estimated based on the Taylor-series expansion. Meanwhile, the joint iteration between gain-phase error estimation and position error estimation is not required. Therefore, the problem of suboptimal convergence, which occurs in the conventional method, can be avoided with low computational method. The modified method has merits of faster convergence and lower estimation error compared to the conventional one. Theoretical analysis and computer simulation results verified the effectiveness of the modified method.\",\n \"Title: Evolutionary decision-makings for the dynamic weapon-target assignment problem.\\nYear: 2009\\n\\nAbstract: The dynamic weapon-target assignment (DWTA) problem is an important issue in the field of military command and control. An\\n asset-based DWTA optimization model was proposed with four kinds of constraints considered, including capability constraints,\\n strategy constraints, resource constraints and engagement feasibility constraints. A general “virtual” representation of decisions\\n was presented to facilitate the generation of feasible decisions. The representation is in essence the permutation of all\\n assignment pairs. A construction procedure converts the permutations into real feasible decisions. In order to solve this\\n problem, three evolutionary decision-making algorithms, including a genetic algorithm and two memetic algorithms, were developed.\\n Experimental results show that the memetic algorithm based on greedy local search can generate obviously better DWTA decisions,\\n especially for large-scale problems, than the genetic algorithm and the memetic algorithm based on steepest local search.\",\n \"Title: An Efficient Rule-Based Constructive Heuristic to Solve Dynamic Weapon-Target Assignment Problem\\nYear: 2011\\n\\nAbstract: In this paper, we propose an efficient rule-based heuristic to solve asset-based dynamic weapon-target assignment (DWTA) problems. The main idea of the proposed heuristic is to utilize the domain knowledge of DWTA problems to directly achieve weapon assignment, without large number of function evaluations. We update the saturation states of constraints in the assignment process to guarantee the feasibility of generated solutions. For the purpose of testing the performance of the proposed heuristic, we build a general Monte Carlo simulation-based DWTA framework. For comparison, we also employ a Monte Carlo method (MCM) to make DWTA decisions in different defense scenarios. From simulations with DWTA instances under different scales, the heuristic has obvious advantages over the MCM with regard to solution quality and computation time. The proposed method can solve large-scale DWTA problems (e.g., those including 100 weapons, 100 targets, and four defense stages) within only a few seconds.\",\n \"Title: Efficient Decision Makings for Dynamic Weapon-Target Assignment by Virtual Permutation and Tabu Search Heuristics\\nYear: 2010\\n\\nAbstract: The dynamic weapon-target assignment (DWTA) problem is a typical constrained combinatorial optimization problem with the objective of maximizing the total value of surviving assets threatened by hostile targets through all defense stages. A generic asset-based DWTA model is established, especially for the warfare scenario of force coordination, to formulate this problem. Four categories of constraints, involving capability constraints, strategy constraints, resource constraints (i.e., ammunition constraints), and engagement feasibility constraints, are taken into account in the DWTA model. The concept of virtual permutation (VP) is proposed to facilitate the generation of feasible decisions. A construction procedure (CP) converts VPs into feasible DWTA decisions. With constraint satisfaction guaranteed by the synergy of VPs and the CP, an elaborate local search (LS) operator, namely move-to-head operator, is constructed to avoid repeatedly generating the same decisions. The operator is integrated into two tabu search (TS) algorithms to solve DWTA problems. Comparative experiments involving a random sampling method, an LS method, a hybrid genetic algorithm, a hybrid ant-colony optimization algorithm, and our TS algorithms show that the proposed TS heuristics for DWTA outperform their competitors in most test cases and they are competent for high-quality real-time DWTA decision makings.\",\n \"Title: Intensity based image registration by minimizing exponential function weighted residual complexity.\\nYear: 2013\\n\\nAbstract: In this paper, we propose a novel intensity-based similarity measure for medical image registration. Traditional intensity-based methods are sensitive to intensity distortions, contrast agent and noise. Although residual complexity can solve this problem in certain situations, relative modification of the parameter can generate dramatically different results. By introducing a specifically designed exponential weighting function to the residual term in residual complexity, the proposed similarity measure performed well due to automatically weighting the residual image between the reference image and the warped floating image. We utilized local variance of the reference image to model the exponential weighting function. The proposed technique was applied to brain magnetic resonance images, dynamic contrast enhanced magnetic resonance images (DCE-MRI) of breasts and contrast enhanced 3D CT liver images. The experimental results clearly indicated that the proposed approach has achieved more accurate and robust performance than mutual information, residual complexity and Jensen-Tsallis.\",\n \"Title: Multi-instance learning for mass retrieval in digitized mammograms\\nYear: 2012\\n\\nAbstract: Breast cancer is one of the most common malignant tumors in women. In mammogram retrieval system, the query mass is ambiguity and difficult to be described because in which the lesion and the normal tissue are physically adjacent. If the query mass can be processed as an image bag, then the ambiguity can be tackled by multi-instance learning (MIL) techniques. In this paper, we presented a preliminary study of MIL for mass retrieval in digitized mammograms, and proposed three image bag generators named J-Bag, A-Bag and K-Bag, respectively. Diverse Density (DD), EM-DD and BP-MIP were applied as MIL algorithms for mass retrieval. Experimental study was carried out on DDSM database and another database in which images were collected from the Zhejiang Cancer Hospital in China. Preliminary experiments showed that the MIL techniques can be applied to the problem of mass retrieval in digitized mammograms and the proposed bag generators A-Bag and K-Bag can achieve more efficient results than the existing bag generator SBN.\",\n \"Title: Perceptual mass segmentation using eye-tracking and seed-growing\\nYear: 2012\\n\\nAbstract: In the paper, we propose a novel scheme for breast mass segmentation in mammography, which is based on visual perception and consists of two steps. Firstly, radiologists' eye-gazing data is recorded by the eye-tracker during reading and then clustered with a density-based spatial clustering of applications with noise (DBSCAN) algorithm to achieve seeds locating radiologists' regions of interest (ROIs). The seeds-based region growing (SBRG) algorithm is applied to buckle ROIs containing suspicious lesions. Secondly, in order to achieve fine lesion contour as final result, the ROIs are segmented with a multi-scale mass segmentation approach using active contour models. The result of applying the proposed method to the mammograms from both DDSM and Zhejiang Cancer Hospital shows that the achieved average of overlap rate is 0.5915 and the achieved average of misclassification rate is 0.6342. The innovative point of the proposed approach is to introduce visual perception into breast mass segmentation, which makes the result of mass segmentation meet radiologists' subjective demand.\",\n \"Title: Association between bilateral asymmetry of kinetic features computed from the DCE-MRI images and breast cancer\\nYear: 2013\\n\\nAbstract: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of breast yields high sensitivity but relatively lower specificity. To improve diagnostic accuracy of DCE-MRI, we investigated the association between bilateral asymmetry of kinetic features computed from the left and right breasts and breast cancer detection with the hypothesis that due to the growth of angiogenesis associated with malignant lesions, the average dynamic contrast enhancement computed from the breasts depicting malignant lesions should be higher than negative or benign breasts. To test this hypothesis, we assembled a database involving 130 DCE-MRI examinations including 81 malignant and 49 benign cases. We developed a computerized scheme that automatically segments breast areas depicted on MR images and computes kinetic features related to the bilateral asymmetry of contrast enhancement ratio between two breasts. An artificial neural network (ANN) was then used to classify between malignant and benign cases. To identify the optimal approach to compute the bilateral kinetic feature asymmetry, we tested 4 different thresholds to select the enhanced pixels (voxels) from DCE-MRI images and compute the kinetic features. Using the optimal threshold, the ANN had a classification performance measured by the area under the ROC curve of AUC=0.79 +/- 0.04. The positive and negative predictive values were 0.75 and 0.67, respectively. The study suggested that the bilateral asymmetry of kinetic features or contrast enhancement of breast background tissue could provide valuable supplementary information to distinguish between the malignant and benign cases, which can be fused into existing computer-aided detection schemes to improve classification performance.\",\n \"Title: Energy-Efficient Policy Based On Cross-Layer Cooperation In Wireless Communication\\nYear: 2014\\n\\nAbstract: Cooperative communication has emerged as a new wireless network communication concept, in which parameter optimization such as cross-layer cooperation plays an important role. Heuristic evaluation postdecision state learning algorithm (HE-PDS) is proposed in cross-layer cooperation. The proposed algorithm exploits the determinate state information and jointly considers the transmitting power and channel state condition at the physical layer and the buffer congestion control at the media access control layer. The experimental results show that the cumulative average total costs of HE-PDS algorithm decrease about ten times and 8% under the maximum delay and throughput constraints and the power costs decrease about 50% and 28% under various delay limits and about 100% and 56% under the different throughput constraints than the traditional Q algorithm and PDS algorithm, demonstrating that the proposed algorithm has much better energy-efficient performance and faster convergence speed and outperforms the traditional Q learning algorithm and PDS learning algorithm.\",\n \"Title: Transmission dynamics of a multi-group brucellosis model with mixed cross infection in public farm.\\nYear: 2014\\n\\nAbstract: Brucellosis is one of the major public health problems in China, since there is a very large number of cattle and sheep. While the mixed feeding between cattle and sheep in public farm is one of the most typical characteristics in brucellosis outbreak regions, which has a large influence on the spreading of brucellosis. In this paper, we propose a multi-group SEIRV dynamical model with bidirectional mixed cross infection between cattle and sheep, and aim to investigate the influence of cross infection of mixed feeding on the brucellosis transmission. Firstly, we obtain the basic reproduction number R0, confirm the uniform existence and unique positive equilibrium of dynamic model when R0>1 and prove the global stability of endemic equilibrium when R0>1. In addition, sensitivity analysis of the infectious herds with different system in term of some parameter values are given. It is found that if there exists the bidirectional of mixed cross infection between cattle and sheep, the disease cannot disappear even though R01<1 and R02<1. These findings have important implication for brucellosis control: if we want to eliminate the brucellosis, firstly we should take measures to remove mixed cross infection, i.e., to prohibit mixed feeding.\",\n \"Title: A two-phase approximation of cylindrical branching models\\nYear: 2014\\n\\nAbstract: This paper presents a scheme of approximating cylindrical models using convolution surfaces and subdivision surfaces. Firstly, an inverse Catmul-Clark subdivision is utilized to approximate the cylindrical models. Then the subdivided mesh is evolved onto the target convolution surface-based shape to produce a natural blending at branches. The initial inverse subdivision reduce the evolution iterations efficiently. Results show that our method approximate the skeletal structures well.\",\n \"Title: A subjective quality evaluation for 3D point cloud models\\nYear: 2014\\n\\nAbstract: Research over the last decade has developed a wide application of 3D models. Much of this work, however, is not related to quality evaluation of 3D color models. This paper presents a novel subjective quality evaluation model that can be used to compute the difference between an original 3D color model and the other processed one. Our model jointly takes into account human visual perception of color and shape in 3D color models. As observed from the results of color and shape stimulations, our model can efficiently test the evaluation outcomes and realize a subjective metric of 3D color models. Experimental results show that our proposed evaluation model gives a reasonable correspondence with the human perception, and supports our relationship formulation between the changes in shape and color for the similarity measurements of 3D color models.\",\n \"Title: Research on 3D laser scanning technology based on point cloud data acquisition\\nYear: 2014\\n\\nAbstract: Three-dimensional terrestrial laser scanning technology is a high-tech and sophisticated development in recent years, so understanding how to use the laser scanner and knowing the acquisition process of point cloud data is very important. This paper focuses specifically on point cloud data acquisition process by using the FARO Focus3D 120 laser scanner. The paper explores whether light affects the accuracy of point cloud data scanned. A total of six tests were conducted in adequate light, low light and moderate light scan scenes in order to assess data accuracy associated with current scanning light environment.\",\n \"Title: Bibliometric analysis for the literature of traditional Chinese medicine in PubMed\\nYear: 2015\\n\\nAbstract: The aim of this study is to conduct a retrospective bibliometric analysis of articles about traditional Chinese medicine (TCM) research in PubMed and to learn about the development and perspective of TCM. A systematic bibliometric search was performed based on the PubMed database covering related publications between January 1, 1995, and December 31, 2014. Numbers and type of articles, countries and language of publications, as well as major journals were analyzed in accordance with bibliometrics methodologies. The retrieve results were analyzed and described in the form of texts, tables, and graphs. A total of 42,192 articles were identified from the PubMed database, among which 43.56 % were published as original articles. The articles were originated from 102 countries and territories. China was ranked first with 20,121 articles, followed by United States with 2207 articles. 57.74 % of the articles are published in English. 4364 articles were published by Zhongguo Zhong Yao Za Zhi. And complementary medicine was the most focused research are involving 30,544 articles. The publication activity of TCM literature increased rapidly in the past 20 years, indicating enhanced attention attracted to TCM and more research input. In view of its great advances achieved in scientific studies, TCM will continue to play an important role in medical research.\",\n \"Title: Global dynamics of a predator-prey system modeling by metaphysiological approach.\\nYear: 2016\\n\\nAbstract: We showed the detailed analysis for a predator-prey system.Transcritical and Hopf bifurcations are investigated.We get four types of bubbles.Our results link with biological systems in the real world. In this paper, a systematical analysis of a predator-prey system based on metaphysiological approach is proposed, which considers changes in the aggregate biomass density rather than changes in the demographics. Bifurcation behaviors including transcritical and Hopf bifurcation are observed in this type of system, and bubble phenomena can be seized by setting appropriate values for parameters. The obtained results provide some new insights in population dynamics, which play important roles in maintaining population diversity.\",\n \"Title: The Existence Uniqueness And The Fixed Iterative Algorithm Of The Solution For The Discrete Coupled Algebraic Riccati Equation\\nYear: 2011\\n\\nAbstract: In this article, applying the properties of M-matrix and non-negative matrix, utilising eigenvalue inequalities of matrix's sum and product, we firstly develop new upper and lower matrix bounds of the solution for discrete coupled algebraic Riccati equation (DCARE). Secondly, we discuss the solution existence uniqueness condition of the DCARE using the developed upper and lower matrix bounds and a fixed point theorem. Thirdly, a new fixed iterative algorithm of the solution for the DCARE is shown. Finally, the corresponding numerical examples are given to illustrate the effectiveness of the developed results.\",\n \"Title: Circuit simulation on control and synchronization of fractional order switching chaotic system\\nYear: 2015\\n\\nAbstract: In view of obtaining a larger key space and thus improve the level of security attainable in chaotic communication, this paper examines the dynamics of a switched, fractional-order chaotic system. Taking the fractional-order Chen system as a starting point, the dynamics of a modified version of that system is examined, and the composed system is hereafter realized by allowing switching between the two different subsystems. Our work first demonstrates how fractional-order chaotic systems can be realized physically. For the fractional-order switching chaotic system, a linear feedback controller and a number of synchronization schemes are designed. Our simulations show that control and synchronization of the individual subsystems can be achieved with the same feedback and synchronization controller.\",\n \"Title: Upper Solution Bounds Of The Continuous Coupled Algebraic Riccati Matrix Equation\\nYear: 2011\\n\\nAbstract: In this article, by using some matrix identities, we construct the equivalent form of the continuous coupled algebraic Riccati equation (CCARE). Further, with the aid of the eigenvalue inequalities of matrix's product, by solving the linear inequalities utilising the properties of M-matrix and its inverse matrix, new upper matrix bounds for the solutions of the CCARE are established, which improve and extend some of the recent results. Finally, a corresponding numerical example is proposed to illustrate the effectiveness of the derived results.\",\n \"Title: Efficient privacy-preserving third-party auditing for ambient intelligence systems.\\nYear: 2016\\n\\nAbstract: Due to limited resources of actors, third-party auditing has vital significance for ambient intelligence systems, which ensures the integrity of data stored in the information manager. However, Current public key based third-party auditing schemes are costly, while simple MAC based third-party auditing schemes can’t fulfill two security requirements, namely the privacy-preserving and the integrity-against-TPA requirements. Taking both security and efficiency into account, this paper proposes a novel simple MAC based third-party auditing scheme, which is much more efficient than current public key based schemes, while still fulfills the above two security requirements. Therefore, it is feasible to be deployed in ambient intelligence systems.\",\n \"Title: PKDIP: Efficient Public-Key-Based Data Integrity Protection for Wireless Image Sensors\\nYear: 2015\\n\\nAbstract: Due to limited energy of \\\"wireless image sensors (WISs),\\\" existing data integrity protection mechanisms typically employ a hash-function-based signing algorithm to generate \\\"message authentication codes (MACs)\\\" for long image frames. However, hash-function-based signing algorithm requires the WIS and the \\\"end user (EU)\\\" sharing a secret, which leads to a new security issue: Once the EU becomes malicious due to some reasons, it will be able to forge the WIS's data since it holds the shared secret. Therefore, public-key cryptography is desirable. Unfortunately, public-key cryptographic operations are quite time-consuming for energy-restrained WISs. Facing this dilemma, we present a novel data integrity protection protocol named PKDIP in this paper. Similar to the mechanisms of this field, PKDIP generates MACs for data integrity protection. However, different from other well-known approaches, PKDIP introduces the \\\"Montgomery Modular Multiplication (MontMM)\\\" technique to current public-key-based signing algorithms. Since MontMM is much more efficient than hash functions, PKDIP can reduce the signing cost significantly. Experimental results show PKDIP can even be more efficient than hash-function-based schemes.\",\n \"Title: Formal Modelling And Analysis Of Hla Architectural Style\\nYear: 2010\\n\\nAbstract: The high level architecture (HLA) was accepted as an IEEE standard in the year 2000 as reference architecture and common infrastructure for large scale distributed interactive simulation systems. But the problems caused by the informal description of the standard are widely recognised. The special requirements of HLA formalisation are analysed and considered as a new architectural style. Combined with the comparison of many architecture description languages (ADLs), the formal models of HLA style are introduced with a newly proposed formal language, p-ADL. These models are grouped as specific architectural concepts of composite, component and connector types. With respect to the different HLA concepts, the models are further described by internal behaviour and external interfaces. They are then tested and analysed with a simple HLA example using the p-ADL compiler p-ADLVM. An HLA internal deadlock reason is found automatically. With the formal specification, a new HLA system design environment is established, which improves the efficiency of the system development.\",\n \"Title: Research on the Application of Face Symbol in Thematic Map Making\\nYear: 2015\\n\\nAbstract: Currently, map symbols that represent multivariable data sets in thematic map are simple and not good at combined using, they can't satisfy the needs of displaying multiple-index data in mapping process. In this paper, face symbols are used to visualize multiple-variable data sets. Firstly, it analyzes the development of face symbol, then summarizes the steps and rules to design face symbol. At last, it compares the application effect of face symbol and conventional statistics symbol. The results show that face symbol represents multiple-variable data better in thematic map.\",\n \"Title: Practical and Secure Outsourcing of Linear Algebra in the Cloud\\nYear: 2013\\n\\nAbstract: Cloud computing provide an economically promising paradigm of outsourcing computation. Input/output privacy and verification are becoming the major security concern, and the efficiency of client and cloud side is becoming the main practice concern. Focusing on the existed linear algebra outsourcing schemes used fully homomorphic encryption scheme or somewhat homomorphic encryption scheme are far from practical goal. We use matrix fully homomorphic encryption scheme to construct an efficient linear algebra outsourcing scheme. The first basic property of the scheme is the protection of input/output privacy. The second basic property is the high efficiency of the client's encryption, decryption and verification algorithms and the cloud side computation.\",\n \"Title: The a posteriori error estimates of Chebyshev-Petrov-Galerkin methods for second-order equations.\\nYear: 2016\\n\\nAbstract: In this paper, the a posteriori error estimates of Chebyshev–Petrov–Galerkin approximations are investigated. For simplicity, we choose the Poisson equation with Dirichlet boundary conditions to discuss the a posteriori error estimators, and deduce their efficient and reliable properties. Some numerical experiments are performed to verify the theoretical analysis for the a posteriori error estimators.\",\n \"Title: Galerkin spectral approximations for optimal control problems governed by the fourth order equation with an integral constraint on state.\\nYear: 2016\\n\\nAbstract: An integral constraint on state optimal control problem governed by the first bi-harmonic equation is analyzed and its Legendre Galerkin spectral approximations are constructed. The optimality conditions of the continuous and the discretized optimal control problems are presented by the Kuhn–Tucker condition, respectively. The a priori error estimates of high accuracy are obtained. Furthermore, an efficient projection algorithm is proposed, and the convergent rate is discussed with precise details. Ample numerical examples are carried out to validate the theoretical results.\",\n \"Title: Identity-based key management for wireless sensor networks using lagrange interpolation.\\nYear: 2016\\n\\nAbstract: To simplify the certificate management in public key infrastructure, key management protocols for wireless sensor networks typically use identity-based cryptography for negotiating the shared key. However, current identity-based key management protocols are mainly based on the bilinear map technique, which requires high computation costs on sensor nodes, resulting in short lifetimes of sensor nodes. To address this issue, we present a novel identity-based key management protocol called Identity-Based Key Management using Lagrange Interpolation IBKML. Similar to the protocols of this field, IBKML can provide confidentiality and integrity protections for the negotiated key. However, compared to other well-known approaches, IBKML is based on Lagrange interpolation instead of bilinear map, which employs only a few modular multiplication operations. Because modular multiplication is much more efficient than bilinear pairing, IBKML can reduce the computation costs on sensor nodes significantly. Experimental results show that the proposed protocol is feasible for real applications. Copyright © 2016 John Wiley & Sons, Ltd.\",\n \"Title: Echo Correlation Analysis Of Targets With Dominant Scatterers In Diversity Mimo Radar\\nYear: 2016\\n\\nAbstract: A huge limitation of the correlation analysis in existing scatterer model is that the former model, only consisting of uniformly distributed scatterers, ignores the variability of complex scattering intensity in real situation. In fact, each part of the target has a different scattering architecture. Moreover, the scattering intensity of pinnacle or boundary scattering structure is an order of magnitude larger than the normal scatterers. To solve this problem, this paper studies the correlation coefficient for scattering model which has several dominant scatterers in spatial-frequency diversity MIMO radar. And the positions of the dominant scatterers assumed to be variables to make an equivalent of different real targets. The proposed analysis enhances the authenticity of scattering model and can simulate the real model accurately. Simulation results show the effectiveness of the proposed approach.\",\n \"Title: An automatic positioning algorithm for archaeological fragments.\\nYear: 2016\\n\\nAbstract: One of the main challenges on digital preservation of cultural heritage is to reassemble broken fragments. However, large amounts of fragments are mixed randomly when discovered, which brings terrific difficulties for their reassembly. This paper introduces an automatic approach for positioning large mixed archaeological fragments which come from a particular kind of artifact. The main idea is to position fragments based on partial matching between fragments and a complete artifact model using the multi-scale, informative and robust Heat Kernel Signature. The positioning pipeline contains four steps: feature points extraction for fragments and the complete artifact model based on Heat Kernel Signature; initial matching between feature points by comparing their Heat Kernel Signature curves; wrong matches removing using a basis points driven refinement procedure and rigid transformation generating by selecting three pairs of points among the correct matching results using farthest points sampling. After these steps, archaeological fragments can be positioned to different positions compared with the template model, which provides not only the classification information, but also the accurate relative position. The main contributions of this paper are using a novel feature extraction algorithm based on Heat Kernel Signature to assist partial matching, and a basis points driven refinement procedure to remove wrong matches. The proposed algorithm has been tested on the Terracotta Warriors fragments, and the results prove the effectiveness of the proposed positioning algorithm.\",\n \"Title: A mesh-insensitive elastic model for simulation of solid bodies.\\nYear: 2016\\n\\nAbstract: FEM-based elasticity models are popular in solid body simulation. To avoid its problems of mesh sensitivity and overly stiff, a novel smoothed pseudo-linear elasticity model is presented. First, the smoothed finite element method is employed to alleviate mesh distortion and overly stiff problems instead of the traditional spatial adaptive smoothing method. Then, we propose a smoothing domain-based stiffness warping technique to compensate the nonlinear errors introduced by linear elasticity models. With this approach, transient displacements are slightly affected by mesh distortion and total volumes are preserved under large rotations. It also shows apparently softening effects in the experiments. Simulation results are generated without adding significant complexity or computational cost to the standard corotational FEM.\",\n \"Title: Comparative Analysis Of The Supply Situation Of Chinese Medicine Service System In Hubei Province\\nYear: 2016\\n\\nAbstract: The policy of the traditional Chinese medicine was researched in this paper. Firstly, the stages of Chinese medicine policy since 1949 were introduced. Secondly, the characteristics and advantages of the traditional Chinese medicine in Hubei province were studied. Thirdly, the policy measures and economic indicators of Hubei province were compared with other provinces. Fourthly, the problems which Hubei confronted with to develop Chinese medicine were analyzed. Fifthly, the conclusions were drawn. According to this paper, in order to fully play the role of Chinese medicine for the public's health and promote the development of the traditional Chinese medicine in Hubei, national government should increase heavy investment for developing the Chinese medicine industry. And provincial health department should implement the government's preferential policies to the prosperity of the state hospital for Chinese medicine. Then the medical institutions should be encouraged to provide the service and technology of the traditional Chinese medicine. In addition, the service capabilities of Chinese medicine should be strengthened and improved.\",\n \"Title: Error estimates of spectral element methods with generalized Jacobi polynomials on an interval.\\nYear: 2017\\n\\nAbstract: Based on the generalized Jacobi polynomials with indices (−1,−1), a new upper bound for a posteriori error estimates is proposed, investigated and implemented for generalized Jacobi–Galerkin spectral element approximations. To simplify discussion for the error estimates, the second-order partial differential equation with homogeneous Dirichlet boundary conditions is considered on a unit interval.\",\n \"Title: A 6gs/S 8-Bit Current Steering Dac With Optimized Current Switch Drive\\nYear: 2017\\n\\nAbstract: A 6GS/s 8-bit current-steering DAC in 0.13 mu m CMOS technology is presented. The 5-31 binary to thermometer decoder and the switch drive signal are optimized, leading to obvious improvements in dynamic performance. A minimum spurious free dynamic range (SFDR) of 34 dB has been achieved over the full Nyquist bandwidth at 6GS/s. Total system power consumption is 95.44mW at 6GS/s with 2.98GHz input signal. Core area of DAC occupies 0.13mm2 without pads.\",\n \"Title: The Improved Eigenvalue Bounds For The Solution Of The Discrete Algebraic Riccati Equation\\nYear: 2017\\n\\nAbstract: In this paper, by using matrix eigenvalue inequalities and the properties of the positive definite solution for the discrete algebraic Riccati equation (DARE), we present upper and lower eigenvalue bounds for the solution of this equation. Moreover, applying majorization inequalities and eigenvalue summation (product) inequalities of special matrices, based on the derived results, we propose upper and lower bounds on eigenvalue summation and product for the solution of the DARE, which improve some of the recent results. The numerical examples show the effectiveness of the derived results.\",\n \"Title: Lower Bounds On Eigenvalue Summation For The Solution Of The Lyapunov Matrix Differential Equation\\nYear: 2017\\n\\nAbstract: The Lyapunov matrix differential equation has widely applications in control theory and linear system. In this paper, applying the properties of exponential matrices and integrable functions, we obtain lower bounds on eigenvalue summation for the solution of this equation using eigenvalue inequalities and majorization inequalities. Finally, we give a corresponding numerical example to show the effectiveness of the derived bounds.\",\n \"Title: Adaptive detection with conic rejection to suppress deceptive jamming for frequency diverse MIMO radar.\\nYear: 2017\\n\\nAbstract: With traditional radar systems, it is not easy to classify and suppress the intended false targets, especially in case of the mainlobe deceptive jamming. In this paper, the deceptive jamming suppression issue is formulated as a problem of detecting the presence of target signal while rejecting potential deceptive jamming under the multiple-input multiple-output (MIMO) radar with frequency diverse array (FDA) as the transmit array. The problem at hand is settled by resorting to the generalized likelihood ratio test (GLRT); at the design stage we define the sets where the false target and true target lie in. Since FDA-MIMO radar provides controllable degrees of freedom in both range and angle domains, the sets are range–angle related. This implies that both range and angle properties can be used to suppress the false targets. Based on the detector, a two-pulse scheme is proposed to guarantee high rejection probability of deceptive jamming. At the analysis stage, the performance of the detector is assessed in comparison with the conventional MIMO scenario and other detectors. The performance assessment shows that the proposed solutions are effective in the presence of electronic countermeasure (ECM) systems, especially when facing mainlobe deceptive jamming.\",\n \"Title: New Matrix Bounds And Iterative Algorithms For The Discrete Coupled Algebraic Riccati Equation\\nYear: 2017\\n\\nAbstract: The discrete coupled algebraic Riccati equation (DCARE) has wide applications in control theory and linear system. In general, for the DCARE, one discusses every term of the coupled term, respectively. In this paper, we consider the coupled term as a whole, which is different from the recent results. When applying eigenvalue inequalities to discuss the coupled term, our method has less error. In terms of the properties of special matrices and eigenvalue inequalities, we propose several upper and lower matrix bounds for the solution of DCARE. Further, we discuss the iterative algorithms for the solution of the DCARE. In the fixed point iterative algorithms, the scope of Lipschitz factor is wider than the recent results. Finally, we offer corresponding numerical examples to illustrate the effectiveness of the derived results.\",\n \"Title: The Nekrasov diagonally dominant degree on the Schur complement of Nekrasov matrices and its applications.\\nYear: 2018\\n\\nAbstract: In this paper, we estimate the Nekrasov diagonally dominant degree on the Schur complement of Nekrasov matrices. As an application, we offer new bounds of the determinant for several special matrices, which improve the related results in certain case. Further, we give an estimation on the infinity norm bounds for the inverse of Schur complement of Nekrasov matrices. Finally, we introduce new methods called Schur-based super relaxation iteration (SSSOR) method and Schur-based conjugate gradient (SCG) method to solve the linear equation by reducing order. The numerical examples illustrate the effectiveness of the derived result.\",\n \"Title: The Solution Bounds And Fixed Point Iterative Algorithm For The Discrete Coupled Algebraic Riccati Equation Applied To Automatic Control\\nYear: 2017\\n\\nAbstract: In this article, applying the special properties of nonnegative matrices and symmetric matrices, we consider the coupled term as a whole and derive the solution bounds of the discrete coupled algebraic Riccati equation. This reduces the error of taking apart the coupled term separately when applying inequality techniques to get the bounds in general. Then, using Cauchy-Schwarz inequality, matrix norm inequalities and a fixed point theorem, we discuss the existence uniqueness condition and fixed point iteration for the solution of this equation. Finally, we offer corresponding numerical examples to illustrate the effectiveness of our results.\",\n \"Title: SCPR: Secure Crowdsourcing-Based Parking Reservation System\\nYear: 2017\\n\\nAbstract: AbstractThe crowdsourcing-based parking reservation system is a new computing paradigm, where private owners can rent their parking spots out. Security is the main concern for parking reservation systems. However, current schemes cannot provide user privacy protection for drivers and have no key agreement functions, resulting in a lot of security problems. Moreover, current schemes are typically based on the time-consuming bilinear pairing and not suitable for real-time applications. To solve these security and efficiency problems, we present a novel security protocol with user privacy called SCPR. Similar to protocols of this field, SCPR can authenticate drivers involved in the parking reservation system. However, different from other well-known approaches, SCPR uses pseudonyms instead of real identities for providing user privacy protection for drivers and designs a novel pseudonym-based key agreement protocol. Finally, to reduce the time cost, SCPR designs several novel cryptographic algorithms based on the algebraic signature technique. By doing so, SCPR can satisfy a number of security requirements and enjoy high efficiency. Experimental results show SCPR is feasible for real world applications.\",\n \"Title: Acmhs: Efficient Access Control For Mobile Health Care System\\nYear: 2018\\n\\nAbstract: To provide privacy protection in mobile health care system, medical users typically encrypt their personal health information before publishing it to the health care center. And other medical users download, decrypt, and process it using an access control protocol. However, current access control protocols are mainly focusing the privacy of personal health information and lacking the protection for medical users' real identities, leading to a variety of security issues. At the same time, current access control protocols for mobile health care systems are usually based on bilinear map, resulting in high computation costs. Observing the above issues, we introduce a novel access control protocol with privacy-preserving called Access Control for Mobile Healthcare System (ACMHS). Similar to protocols of this field, ACMHS can provide privacy protection for mobile health care systems. However, different from other well-known approaches, ACMHS uses pseudonyms instead of real identities, which can provide privacy protection for medical users' real identities. Moreover, to reduce the computation cost of bilinear map, we introduce the algebraic signature to ACMHS and design a novel access control protocol, which is much more efficient than current bilinear-map-based protocols. By doing so, ACMHS can achieve high efficiency while still enjoying required security requirements. Experimental results show that ACMHS is feasible for real-world applications.\",\n \"Title: Low Rank Variation Dictionary and Inverse Projection Group Sparse Representation Model for Breast Tumor Classification.\\nYear: 2018\\n\\nAbstract: Sparse representation classification achieves good results by addressing recognition problem with sufficient training samples per subject. However, SRC performs not very well for small sample data. In this paper, an inverse-projection group sparse representation model is presented for breast tumor classification, which is based on constructing low-rank variation dictionary. The proposed low-rank variation dictionary tackles tumor recognition problem from the viewpoint of detecting and using variations in gene expression profiles of normal and patients, rather than directly using these samples. The inverse projection group sparsity representation model is constructed based on taking full using of exist samples and group effect of microarray gene data. Extensive experiments on public breast tumor microarray gene expression datasets demonstrate the proposed technique is competitive with state-of-the-art methods. The results of Breast-1, Breast-2 and Breast-3 databases are 80.81%, 89.10% and 100% respectively, which are better than the latest literature.\",\n \"Title: Breast Tumor Classification Based on Decision Information Genes and Inverse Projection Sparse Representation.\\nYear: 2018\\n\\nAbstract: Microarray gene expression data-based breast tumor classification is an active and challenging issue. In this paper, a robust breast tumor recognition framework is presented based on considering reducing clinical misdiagnosis rate and exploiting available information in existing samples. A wrapper gene selection method is established from a new perspective of reducing clinical misdiagnosis rate. The further feature selection of information genes is fulfilled by a modified NMF model, which is motivated by hierarchical learning and layer-wise pre-training strategy in deep learning. For completing the classification, an inverse projection sparse representation (IPSR) model is constructed to exploit information embedded in existing samples, especially in test ones. Moreover, the IPSR model is optimized via generalized ADMM and the corresponding convergence is analyzed. Extensive experiments on three public breast tumor datasets show that the proposed method is stable and effective for breast tumor classification. Compared to the latest literature, there is 14% higher in classification accuracy. Specificity and sensitivity achieve 94.17% and 97.5%.\",\n \"Title: Relay Translation Policies Based On State Pruning For Ad Hoc Networks\\nYear: 2018\\n\\nAbstract: In this paper, we consider the multi-hop cooperative ad hoc networks with dynamic network status, limited node energy, and fading channel environment. It is difficult to solve the multi-objective cost function with large state space. We aim to find the optimal policies to reduce these states for arbitrary topologies with links affected by path loss. Therefore, this paper presents the algorithm of state pruning program based on inverse probability of metropolis (SPP_IPM) which defines the inverse probability of metropolis as the refuse acceptance criteria and realizes the pruning for state space. It achieves the pruning upper bound by scaling method and reduces the number of states according to a certain probability bound. Thus, we reduce the computation complexity through this pruning technique. The simulation results demonstrate SPP IPM is better than other typical algorithms such as RTDP (real-time dynamic programming) and FRTDP (focused real-time dynamic programming) with respect to the state space, the success probability, and the transmission delay. Furthermore, SPP_IPM has a good control effect on the growth of the network state space when the network scene changes.\",\n \"Title: Multiperspective Target Tracking Approach for Doppler Through-Wall Radar.\\nYear: 2018\\n\\nAbstract: In this letter, a multiperspective target tracking approach is proposed for Doppler through-wall radar (TWR). To properly identify stationary and tangentially moving targets, an expansion receiver is added to the traditional cost-effective Doppler TWR, and a data fusion process is applied to track targets. Using the expansion receiver, the information on the target is acquired from another perspec...\",\n \"Title: An approximation scheme for the time fractional convection-diffusion equation.\\nYear: 2018\\n\\nAbstract: In this paper, a discrete form is proposed for solving time fractional convection–diffusion equation. Firstly, we obtain a time discrete scheme based on finite difference method. Secondly, we prove that the time discrete scheme is unconditionally stable, and the numerical solution converges to the exact one with order O(τ2−α), where τ is the time step size. Finally, two numerical examples are proposed respectively, to verify the order of convergence.\",\n \"Title: Stable and realistic crack pattern generation using a cracking node method.\\nYear: 2018\\n\\nAbstract: This paper presents a method for simulating surface crack patterns appearing in ceramic glaze, glass, wood and mud. It uses a physically and heuristically combined method to model this type of crack pattern. A stress field is defined heuristically over the triangle mesh of an object. Then, a first-order quasi-static cracking node method (CNM) is used to model deformation. A novel combined stress and energy combined crack criterion is employed to address crack initiation and propagation separately according to physics. Meanwhile, a highest-stress-first rule is applied in crack initiation, and a breadth-first rule is applied in crack propagation. Finally, a local stress relaxation step is employed to evolve the stress field and avoid shattering artifacts. Other related issues are also discussed, such as the elimination of quadrature sub-cells, the prevention of parallel cracks and spurious crack procession. Using this method, a variety of crack patterns observed in the real world can be reproduced by changing a set of parameters. Consequently, our method is robust because the computational mesh is independent of dynamic cracks and has no sliver elements. We evaluate the realism of our results by comparing them with photographs of real-world examples. Further, we demonstrate the controllability of our method by varying different parameters.\",\n \"Title: Supply chain decisions with reference quality effect under the O2O environment.\\nYear: 2018\\n\\nAbstract: The reference quality effect is an important factor that influences consumer purchasing behavior. To investigate how firms should incorporate the reference quality effect under different business models, we focus on a supply chain consisting of a supplier and a retailer where the retailer can be an offline store, a pure online store or a combination of offline and online stores within an offline to online model. We formulate the reference quality effect with a modified Nerlove-Arrow model and use two different sales functions to reflect the fact that the reference quality effect will affect consumers in various ways when the business model varies. Utilizing differential game theory, the equilibrium decisions of the channel members are derived, and the analysis illustrates how the firms should adjust their decisions when the retailers use different retail patterns. The basic model does not allow product returns, but then this assumption is relaxed. Comparison between the two cases shows under what conditions consumers will benefit from the retailer’s allowing product returns.\",\n \"Title: Damage Detection of L-Shaped Concrete Filled Steel Tube (L-CFST) Columns under Cyclic Loading Using Embedded Piezoceramic Transducers.\\nYear: 2018\\n\\nAbstract: L-shaped concrete filled steel tube (L-CFST) columns are used frequently in civil engineering, and the concrete damage inside the L-CFST column is difficult to monitor. This research aims to develop a new method to monitor the internal concrete damage in the L-CFST column by using embedded piezoceramic smart aggregates (SAs) under low frequency cyclic loading. The SA enabled active method is used to monitor the concrete damages near the bottom of the L-CFST columns, and the wavelet packet analysis is used to establish a damage index, which is used to analyze the acquired data. During the experiment, three L-CFST columns with different wall thickness of the steel tube were tested. The experimental results find that the structural damage indices under the low-frequency cyclic loading are basically consistent with the results of the hysteretic curves and the skeleton curve of the specimens, and are in good agreement with the experimental phenomena. We conclude that the use of smart aggregate can directly and clearly reflect the damage process of the concrete core, demonstrating the feasibility of using piezoceramic smart aggregates to monitor the internal concrete damage of the L-CFST column.\",\n \"Title: Individualized analysis of differentially expressed miRNAs with application to the identification of miRNAs deregulated commonly in lung cancer tissues.\\nYear: 2018\\n\\nAbstract: Identifying differentially expressed microRNAs (DE miRNAs) between cancer samples and normal controls is a common way to investigate carcinogenesis mechanisms. However, for a DE miRNA detected at the population-level, we do not know whether it is DE in a particular cancer sample. Here, based on the finding that the within-sample relative expression orderings of miRNA pairs are highly stable in a particular type of normal tissues but widely disrupted in the corresponding cancer tissues, we proposed a method, called RankMiRNA, to identify DE miRNAs in each cancer tissue compared with its own normal state. Evaluated with pair-matchedmiRNA expression profiles of cancer tissues and adjacent normal tissues for lung and liver cancers, RankMiRNA exhibited excellent performance. Finally, we exemplified an application of the individual-level differential expression analysis by finding miRNAs DE in at least 90% lung cancer tissues, defined as common DE miRNAs of lung cancer. After identifying DE miRNAs for each of 991 lung cancer samples from The Cancer Genome Atlas with RankMiRNA, we found that hsa-mir-210 was upregulated, while hsa-mir-490 and hsa-mir-486 were downregulated in > 90% of the 991 lung cancer samples. These common DE miRNAs were validated in independent pair-matched samples of cancer tissues and adjacent normal tissues measured with different platforms. In conclusion, RankMiRNA provides us a novel tool to find common and subtype-specific miRNAs for a type of cancer, allowing us to study cancer mechanisms in a novel way.\",\n \"Title: SIM-RPM: Secure Incentive Mechanism for Crowdsourcing-Based Road Pavement Monitoring System.\\nYear: 2018\\n\\nAbstract: Crowdsourcing-based road pavement monitoring is a new computing paradigm, where some drivers detect road surface information and publish it to the data center, while other drivers download it for use. However, current road pavement monitoring schemes lack an incentive mechanism, which discourages drivers from publishing road surface information. More importantly, current security protocols for crowdsourcing-based systems are built on real identities of drivers, resulting in the user privacy problem. At the same time, current security protocols for crowdsourcing-based systems use time-consuming bilinear map for designing cryptographic algorithms, resulting in the efficiency problem. To solve the above problems, we present a secure incentive mechanism for road pavement monitoring systems called SIM-RPM. Different from protocols of this field, SIM-RPM is built on pseudonyms, which can provide user privacy protection for drivers. Moreover, by designing an access control protocol, SIM-RPM enables drivers to sell road surface information, which encourages drivers to attend the road pavement monitoring system. Finally, to reduce the time cost, we introduce the Bloom filter technique to SIM-RPM, and design novel light-weight cryptographic algorithms. By doing so, SIM-RPM provides a secure and efficient incentive mechanism for crowdsourcing-based road pavement monitoring systems. Experimential results show SIM-RPM is feasible for real world applications.\",\n \"Title: Retrieval of the Fine-Mode Aerosol Optical Depth over East China Using a Grouped Residual Error Sorting (GRES) Method from Multi-Angle and Polarized Satellite Data.\\nYear: 2018\\n\\nAbstract: The fine-mode aerosol optical depth (AOD(f)) is an important parameter for the environment and climate change study, which mainly represents the anthropogenic aerosols component. The Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar (PARASOL) instrument can detect polarized signal from multi-angle observation and the polarized signal mainly comes from the radiation contribution of the fine-mode aerosols, which provides an opportunity to obtain AOD(f) directly. However, the currently operational algorithm of Laboratoire d'Optique Atmospherique (LOA) has a poor AOD(f) retrieval accuracy over East China on high aerosol loading days. This study focused on solving this issue and proposed a grouped residual error sorting (GRES) method to determine the optimal aerosol model in AOD(f) retrieval using the traditional look-up table (LUT) approach and then the AOD(f) retrieval accuracy over East China was improved. The comparisons between the GRES retrieved and the Aerosol Robotic Network (AERONET) ground-based AOD(f) at Beijing, Xianghe, Taihu and Hong_Kong_PolyU sites produced high correlation coefficients (r) of 0.900, 0.933, 0.957 and 0.968, respectively. The comparisons of the GRES retrieved AOD(f) and PARASOL AOD(f) product with those of the AERONET observations produced a mean absolute error (MAE) of 0.054 versus 0.104 on high aerosol loading days (AERONET mean AOD(f) at 865 nm = 0.283). An application using the GRES method for total AOD (AOD(t)) retrieval also showed a good expandability for multi-angle aerosol retrieval of this method.\",\n \"Title: Retrieving articulated 3-d models using medial surfaces and their graph spectra\\nYear: 2005\\n\\nAbstract: We consider the use of medial surfaces to represent symmetries of 3-D objects. This allows for a qualitative abstraction based on a directed acyclic graph of components and also a degree of invariance to a variety of transformations including the articulation and deformation of parts. We demonstrate the use of this representation for both indexing and matching 3-D object models. Our formulation uses the geometric information associated with each node along with an eigenvalue labeling of the adjacency matrix of the subgraph rooted at that node. We present comparative results against the techniques of shape distributions [17] and harmonic spheres [12] on a database of 320 models representing 13 object classes. The results demonstrate that medial surface based graph matching significantly outperforms these techniques for objects with articulating parts.\",\n \"Title: Data-driven modeling for chinese ancient architectures\\nYear: 2017\\n\\nAbstract: Chinese ancient architecture is well known for its unique shape styles and complex constructions, which results in a complicated 3D modeling process. In contrast to the existing procedural modeling method which generates architectures relying on fixed rules, our data-driven approach aggregate information from 3D model collections to realize analysis, inference and synthesis of components spontaneously. For proprietary properties of ancient building models, this paper describes the framework of the complete procedure from shape representation to component assembly. The plausible combinations for structural components are also learned from 3D model dataset by the Bayesian network. As a periodic research, some remaining works at each stage are proposed in this paper. We conclude these works with ideas that can inspire future research in the modeling issue for Chinese ancient architecture.\",\n \"Title: Assessing reappearance factors of H7N9 avian influenza in China.\\nYear: 2017\\n\\nAbstract: The model considers the seasonal migration of bird and periodic decay rate of virus in environment.Both temperature cycle and closing and reopening markets can lead to the observed recurrence.Migratory birds have little or no effect on local epidemic spread.Closing markets is the most effective measure to control the epidemic to be at a low level. H7N9, a kind of subgroup of influenza viruses, has lasted for over three years in China. Although it has not yet aroused large-scale outbreak in human, it reappeared and peaked every winter. According to the obtained information and literatures, possible reasons to induce recurrence of human cases every winter can be speculated as migration of birds, temperature cycling, or reopening of live poultry trading markets. However, which one is the major driving factor is unclear. As a result, a dynamical model about migrant birds, resident birds and domestic poultry, considering temperature-dependent decay rate of virus and periodical closure of trading markets, is established to determine internal critical driving factors, and to propose the most effective prevention measure by sensitivity analysis. By numerical analysis, it is concluded that temperature cycling may be the main driving mechanism of the periodicity of infected human cases. Closing trading markets is not the main factor, but it is the most effective measure to control the epidemic to be at a low level. Consequently, periodically closing trading markets is proposed to prevent and control the spread of epidemic.\",\n \"Title: Experimental Study on Stress Monitoring of Sand-Filled Steel Tube during Impact Using Piezoceramic Smart Aggregates.\\nYear: 2017\\n\\nAbstract: The filling of thin-walled steel tubes with quartz sand can help to prevent the premature buckling of the steel tube at a low cost. During an impact, the internal stress of the quartz sand-filled steel tube column is subjected to not only axial force but also lateral confining force, resulting in complicated internal stress. A suitable sensor for monitoring the internal stress of such a structure under an impact is important for structural health monitoring. In this paper, piezoceramic Smart Aggregates (SAs) are embedded into a quartz Sand-Filled Steel Tube Column (SFSTC) to monitor the internal structural stress during impacts. The piezoceramic smart aggregates are first calibrated by an impact hammer. Tests are conducted to study the feasibility of monitoring the internal stress of a structure. The results reflect that the calibration value of the piezoceramic smart aggregate sensitivity test is in good agreement with the theoretical value, and the output voltage value of the piezoceramic smart aggregate has a good linear relationship with external forces. Impact tests are conducted on the sand-filled steel tube with embedded piezoceramic smart aggregates. By analyzing the output signal of the piezoceramic smart aggregates, the internal stress state of the structure can be obtained. Experimental results demonstrated that, under the action of impact loads, the piezoceramic smart aggregates monitor the compressive stress at different locations in the steel tube, which verifies the feasibility of using piezoceramic smart aggregate to monitor the internal stress of a structure.\",\n \"Title: Efficient identity-based data transmission for VANET.\\nYear: 2018\\n\\nAbstract: To simplify the key management processes, data transmission protocols for vehicular ad-hoc networks typically use identity-based cryptography for protecting the transmitted data. However, current identity-based data transmission protocols are mainly based on the bilinear map technique, which requires high computation costs on vehicles and road-side units, resulting in high latency. At the same time, to provide location privacy protection, data transmission protocols typically use pseudonyms instead of vehicles’ real identities, resulting in complicated pseudonym management processes. To address these issues, we present a novel identity-based data transmission protocol called EIBDT. Similar to the protocols of this field, EIBDT can provide integrity and confidentiality protections for the transmitted data. However, compared to other well-known approaches, EIBDT uses Lagrange interpolation instead of bilinear map for integrity protection, which employs only a few modular exponentiation operations. Since modular exponentiation is much more efficient than bilinear pairing, EIBDT can reduce the computation costs of vehicles and RSUs significantly. Moreover, for location privacy protection, EIBDT encrypts real identities of vehicles using an algebraic-signature-and-identity-based algorithm which is highly efficient. Therefore, the complicated pseudonym management process is avoided and the computation costs are reduced. Experimental results show that the proposed protocol is feasible for real applications.\",\n \"Title: The Matrix Bounds And Fixed-Point Iteration For The Solution Of The Discrete Algebraic Riccati Equation\\nYear: 2019\\n\\nAbstract: In this paper, by constructing the equivalent form of the discrete algebraic Riccati equation (DARE), applying the eigenvalue inequalities of matrix's sum and product, we provide new lower and upper matrix bounds for the solution of the DARE, which improve some of the recent results. Further, using the derived matrix bounds and a fixed point theorem, we discuss the existence and uniqueness for the solution of the DARE. Finally, we give corresponding numerical examples to demonstrate the effectiveness of our results.\",\n \"Title: Data Fusion-Based Multi-Object Tracking for Unconstrained Visual Sensor Networks.\\nYear: 2018\\n\\nAbstract: Camera node perception capability is one of the crucial issues for visual sensor networks, which belongs to the field of Internet of Things. Multi-object tracking is an important feature in analyzing object trajectories across multiple cameras, thus allowing synthesis of data and security analysis of images in various scenarios. Despite intensive research in the last decades, it remains challenging for tracking systems to perform in real-world situations. We therefore focus on key issues of multi-object state estimation for unconstrained multi-camera systems, e.g., data fusion of multiple sensors and data association. Unlike previous work that rely on camera network topology inference, we construct a graph from 2-D observations of all camera pairs without any assumption of network configuration. We apply the shortest path algorithm to the graph to find fused 3-D observation groups. Our approach is thus able to reject false positive reconstructions automatically, and also minimize computational complexity to guarantee feasible data fusion. Particle filters are applied as the 3-D tracker to form tracklets that integrate local features. These tracklets are modeled by a graph and linked into full tracks incorporating global spatial-temporal features. Experiments on the real-world PETS2009 S2/L1 sequence show the accuracy of our approach. Analyses of the different components of our approach provide meaningful insights for object tracking using multiple cameras. Evidence is provided for selecting the best view for a visual sensor network.\",\n \"Title: The Optimization Of Parallel Convolutional Rbm Based On Spark\\nYear: 2019\\n\\nAbstract: With the extensive attention and research of the scholars in deep learning, the convolutional restricted Boltzmann machine (CRBM) model based on restricted Boltzmann machine (RBM) is widely used in image recognition, speech recognition, etc. However, time consuming training still seems to be an unneglectable issue. To solve this problem, this paper mainly uses optimized parallel CRBM based on Spark, and proposes a parallel comparison divergence algorithm based on Spark and uses it to train the CRBM model to improve the training speed. The experiments show that the method is faster than traditional sequential algorithm. We train the CRBM with the method and apply it to breast X-ray image classification. The experiments show that it can improve the precision and the speed of training compared with traditional algorithm.\",\n \"Title: Upper And Lower Eigenvalue Summation Bounds Of The Lyapunov Matrix Differential Equation And The Application In A Class Time-Varying Nonlinear System\\nYear: 2020\\n\\nAbstract: In this paper, we first show a class relation between the eigenvalue of functional matrix derivative and the derivative of function matrix eigenvalue. Applying the relation, we transform the time-varying linear matrix differential equation into eigenvalue differential equation. Furthermore, by using singular value decomposition and majorisation inequalities, we derive upper and lower bounds on eigenvalue summation of the solution for the Lyapunov matrix differential equation, which improve the recent results. As an application in control and optimisation, we show that our bounds could be used to discuss the stability of a class time-varying nonlinear system. Finally, we give a corresponding numerical example to show the superiority and effectiveness of the derived bounds.\",\n \"Title: Lower bounds of error estimates for singular perturbation problems with Jacobi-Spectral approximations.\\nYear: 2019\\n\\nAbstract: With weighted orthogonal Jacobi polynomials, we study spectral approximations for singular perturbation problems on an interval. The singular parameters of the model are included in the basis functions, and then its stiff matrix is diagonal. Considering the estimations for weighted orthogonal coefficients, a special technique is proposed to investigate the a posteriori error estimates. In view of the difficulty of a posteriori error estimates for spectral approximations, we employ a truncation projection to study lower bounds for the models. Specially, we present the lower bounds of a posteriori error estimates with two different weighted norms in details.\",\n \"Title: Damage Detection of Concrete-Filled Square Steel Tube (CFSST) Column Joints under Cyclic Loading Using Piezoceramic Transducers.\\nYear: 2018\\n\\nAbstract: Concrete-filled square steel tube column (CFSSTC) joints are the most important parts of concrete-filled steel tube frame structures. It is of great significance to study the damage of CFSSTC joints under the seismic loads. In this paper, embedded piezoceramic transducers are used to monitor the damage of core concrete of CFSSTC joints under cyclic loading and surface-bonded piezoceramic disks are used to monitor the debonding damage of the steel tube and core concrete of two specimens. The damages of the joints under different loading levels and different loading cycles are evaluated by the received signal of the piezoceramic transducers. The experimental results show that the amplitude of the signal attenuates obviously with the appearance of damage in the joints, and the degree of attenuation increases with the development of the damage. The monitoring results from piezoceramic transducers are basically consistent with the hysteresis loops and skeleton curves of the CFSSTC joints during the cyclic loading. The effectiveness of the piezoceramic transducers are verified by the experimental results in structural health monitoring of the CFSSTC joint under cyclic loading.\",\n \"Title: SCRPM: securing crowdsourcing-based road pavement monitoring system with location privacy\\nYear: 2020\\n\\nAbstract: To provide privacy protection, security protocols for crowdsourcing-based systems typically encrypt data before uploading it to the server. However, current security protocols cannot provide protection for real identities, resulting in the location privacy issue. Moreover, current security protocols for crowdsourcing-based systems are mainly based on bilinear map, which is quite time-consuming. To address the above security and efficiency issues, we propose a novel security protocol with location privacy called SCRPM. Similar to protocols of this field, SCRPM can provide privacy protection for uploaded data. However, different from other well-known approaches, SCRPM uses pseudonyms instead of real identities, which can provide location privacy protection for cars. At the same time, to avoid using bilinear pairing, we introduce the algebraic signature technique to SCRPM and design novel signcryption algorithms, which is quite light weight. By doing so, SCRPM can provide location privacy protection for cars while still enjoying high efficiency. Experimental results show SCRPM is feasible for real world applications.\",\n \"Title: Single Image Snow Removal via Composition Generative Adversarial Networks.\\nYear: 2019\\n\\nAbstract: Snowflakes attached to the camera lens can severely affect the visibility of the background scene and compromise the image quality. In this paper, we solve this problem by visually removing snowflakes to convert the snowy image into a clean one. The problem is troublesome; the information about the background of the occluded regions is completely lost for the most part. For removing snowflakes from a single image, we proposed a composition generative adversarial network. Different from the previous generative adversarial networks, our generator network comprises clean background module and a snow mask estimate module. The clean background module aims to generate a clear image from an input snowy image, and snow mask estimate module is used to produce the snow mask in an input image. During the training step, we put forward a composition loss between the input snowy image and composition of the generated clean image and estimated snow mask. We use a dataset named Snow 100K(2) including indoor and outdoor scenes to train and test the proposed method. The extensive experiments on both synthetic and real-world images show that our network has a good effect and it is superior to the other state-of-the-art methods.\",\n \"Title: An Integrated Inverse Space Sparse Representation Framework for Tumor Classification.\\nYear: 2019\\n\\nAbstract: •An integrated inverse space sparse representation model is proposed for gene-based tumor classification.•A gene selection method is proposed to improve the model's representation ability to small sample problem.•A feature representation learning method is proposed to enhance the model's representation ability and stability.•The model is optimized and the convergence is analyzed.•Extensive experiments are conducted on six microarray gene expression datasets, which contain early diagnosis, tumor type recognition and postoperative metastasis.\",\n \"Title: Polarimetric multiple-radar architectures with distributed antennas for discriminating between radar targets and deception jamming\\nYear: 2019\\n\\nAbstract: The potential abilities of polarimetric multiple-radar architectures with distributed antennas in electronic counter-countermeasures (ECCMs) are analyzed based on differences between target echoes and deception jamming in scattering coefficients properties. According to the differences in generation mechanism between the target scatter and jamming, a signal model of real targets and deception jamming for two-dimensional vector sensors is established. By applying the Neyman-Pearson (NP) criterion and a hybrid NP-Maximum Posteriori (MAP) criterion, a two-stage detection/discrimination algorithm is proposed. In our proposed algorithm, the targets and jamming are detected in the first stage from a Gaussian noise background. Then, a discriminator based on the generalized likelihood ratio test is designed by exploiting the property of polarization discrimination. A theoretical analysis is also given to evaluate the performance of the second stage of the algorithm. Moreover, the discrimination performance can be improved by optimizing the transmitter polarization parameters. Simulation results indicate that the proposed ECCM strategy is effective against DRFM jamming, especially when JNR is high and transmit polarization is optimally designed.\",\n \"Title: Lie Symmetry Analysis and Exact Solutions of Generalized Fractional Zakharov-Kuznetsov Equations\\nYear: 2019\\n\\nAbstract: This paper considers the Lie symmetry analysis of a class of fractional Zakharov-Kuznetsov equations. We systematically show the procedure to obtain the Lie point symmetries for the equation. Accordingly, we study the vector fields of this equation. Meantime, the symmetry reductions of this equation are performed. Finally, by employing the obtained symmetry properties, we can get some new exact solutions to this fractional Zakharov-Kuznetsov equation.\",\n \"Title: Dynamical Analysis of the Model for Brucellosis Transmission to the Dairy Cows with Immunological Threshold.\\nYear: 2019\\n\\nAbstract: As we all know, bacteria is different from virus which with certain types can be killed by the immune cells in the body. The brucellosis, a bacterial disease, can invade the body by indirect transmission from environment, which has not been researched by combining with immune cells. Considering the effects of immune cells, we put a minimum infection dose of brucellosis invading into the dairy cows as an immunological threshold and get a switch model. In this paper, we accomplish a thorough dynamics analysis of a SEIB switch model. On the one hand, we can get a disease-free and bacteria-free steady state and up to three endemic steady states which may be thoroughly analyzed in different cases of a minimum infection dose in a switch model. On the other hand, we calculate the basic reproduction number R0 and know that the disease-free and bacteria-free steady state is a global stability when R0<1, and the one of the endemic steady state is a conditionally global stability when R0>1. We find that different amounts of R0 may lead to different steady states of brucellosis, and considering the effects of immunology is more serious in mathematics and biology.\",\n \"Title: DD-CycleGAN: Unpaired image dehazing via Double-Discriminator Cycle-Consistent Generative Adversarial Network\\nYear: 2019\\n\\nAbstract: Despite the recent progress in image dehazing, the task remains tremendous challenging. To improve the performance of haze removal, we propose a scheme for haze removal based on Double-Discriminator Cycle-Consistent Generative Adversarial Network (DD-CycleGAN), which leverages CycleGAN to translate a hazy image to the corresponding haze-free image. Unlike other methods, it does not need pairs of haze and their corresponding haze-free images for training. Extensive experiments demonstrate that the proposed method achieves significant improvements over the existing methods, both quantitatively as well as qualitatively. And our method can also achieve good effects qualitatively when applied to the real scenes too.\",\n \"Title: Depth Camera Based Fluid Reconstruction and its Solid-fluid Interaction\\nYear: 2019\\n\\nAbstract: Fluid animation has great value in study and application in many fields, such as video special effects, virtual reality and so on. However, due to the complexity and irregularity of fluid's motion, the existing simulation methods cannot make a good tradeoff between computational efficiency and realism. In this paper, a method of fluid animation synthesis based on depth camera is proposed. The RGB-D (red, green, blue-depth) data, captured by a RealSense camera, are used to reconstruct the fluid surface. By analysis the relation between color intensity and height field gradient of water surface, we use the RGB image as guidance to denoise the depth image, then possion reconstruction is employed to repair the incomplete data of depth image and fuse detail feature from the RGB to the reconstruction fluid surface. In addition, we propose a real-time method to simulate the interaction between solid and fluid, which is implemented on the wave particle system that fitted to the reconstruction fluid surface. Experiments show that the presented method can reconstruct fluid effectively and generate fluid animation of solid-fluid interaction plausibly.\",\n \"Title: Discrimination of Active False Targets Based on Hermitian Distance for Distributed Multiple-Radar Architectures.\\nYear: 2019\\n\\nAbstract: In a distributed multiple-radar architecture, the spatial scattering properties of targets can be utilized to counter active deception jamming effectively. When the cooperative detection is performed, the available method to discriminate active false targets needs to know the precise jammer location, and its performance will suffer significant deterioration with little location error. Aiming at the problem, we propose a novel method to discriminate false targets based on Hermitian distance. The difference in spatial scattering property leads to the difference in the Hermitian distances of different target combinations. The Hermitian distance between two false targets is much greater than that between two physical targets or that between a physical target and a false target, especially in high jamming-to-noise ratio. Based on the difference, hypothesis testing is performed to discriminate false targets. The proposed method does not require any prior information about the jamming environment and can discriminate the targets effectively in one pulse repetition interval. Moreover, the proposed method can restrict the upper bound of the misjudgment probability for physical targets. The theoretical analysis and simulation verify the feasibility and validity of the proposed discrimination method.\",\n \"Title: A healthy node selection method based on particle swarm optimization under network intrusion environment\\nYear: 2019\\n\\nAbstract: In the network intrusion environment, how to realize the accurate selection of healthy nodes and use them for secure communication is a research hotspot of network security technology. In this paper, a healthy node selection method based on particle swarm optimization under the network intrusion environment is proposed. First, a fuzzy mathematical model is established for node features to constrain the cost of healthy node selection. Then, we introduce the particle swarm optimization algorithm and combine the uncertain factors to optimize the parameters to achieve the selection of healthy nodes. The experimental results show that, compared with the traditional BP neural network method, our healthy node selection method improves the accuracy of healthy node selection, shortens the running time, and can control the error after invasion to a reasonable range.\",\n \"Title: System Revenue Maximization for Offloading Decisions in Mobile Edge Computing\\nYear: 2021\\n\\nAbstract: Offloading decisions in mobile edge computing have been extended with multiple objectives, such as revenue maximization, energy conservation and latency reduction. Revenues of network/service operators, as the realistic and ultimate goal at intensive competitive markets, have not been thoroughly studied under a pricing scheme in combination with offloading decisions, especially with the aims of reducing and restricting energy consumption and latency. To bridge this important gap, this paper studies the revenue maximization of network operators through a pricing scheme in mobile edge computing, by explicitly formulating energy consumption and latency into the offloading strategy. A two-stage game-theory framework based on the Stackelberg game is established, through which the optimal price for both the network operator and the customer can be reached. The offloading data size can be dynamically adjusted according to the agreed price. The existence of equilibrium in the Stackelberg game is proved, and experiments are conducted to verify the effectiveness of our proposed model.\",\n \"Title: Observer-Based Output Feedback Event-Triggered Adaptive Control for Linear Multiagent Systems Under Switching Topologies\\nYear: 2022\\n\\nAbstract: The consensus problem of general linear multiagent systems (MASs) is studied under switching topologies by using observer-based event-triggered control method in this article. On the basis of the output information of agents, two kinds of novel event-triggered adaptive control schemes are designed to achieve the leaderless and leader-follower consensus problems, which do not need to utilize the global information of the communication networks. Finally, two simulation examples are introduced to show that the consensus error converges to zero and Zeno behavior is eliminated in MASs. Compared with the existing output feedback control research, one of the significant advantages of our methods is that the controller protocols and triggering mechanisms do not rely on any global information, are independent of the network scale, and are fully distributed ways.\",\n \"Title: Dfbdehazenet: An End-To-End Dense Feedback Network For Single Image Dehazing\\nYear: 2021\\n\\nAbstract: The feedback mechanism method of simulating the biological vision system has not been widely used in deep learning dehazing algorithms. To alleviate the difficulty of feature interaction, we combine the feedback mechanism with dense skip connections to fuse features of different levels in a dehazing network. Inspired by the feedback network in which previous network layers can have access to rich information processed by the following network layers, we propose an end-to-end dense feedback network (DFBDehazeNet) for single image dehazing that implements the feedback mechanism using hidden states of constrained RNN. The low-level hazy feature information can be continuously corrected by the high-level feature information obtained from the dense feedback block via the recurrent feedback connection. The top-down feedback mechanism is adopted in DFBDehazeNet to refine the low-level hazy feature information, thereby achieving a powerful image restoration effect. The ablation experiment proves that the iterative structure of DFBDehazeNet and the projection unit play an important role in removing haze from images. The experimental results show that the results of image haze removal are superior to the great majority of existing methods both qualitatively and quantitatively. (c) 2021 SPIE and IS&T [DOI: 10.1117/1.JEI.30.3.033004]Under the influence of severe weather, the quality of images collected by the camera system drops sharply. These degraded images not only affect people's subjective judgment but also lead to poor results on advanced computer vision tasks such as object detection,1 semantic segmentation,2 action recognition,3 and so on. The restoration of images in severe weather4 is of great significance to computer vision. Image dehazing algorithms are designed to recover high-quality clear images from low-quality hazy images for advanced computer vision tasks. The image dehazing task assists in the driving technology of unmanned vehicles and the unmanned toll system of highways in severe weather. In the past two decades, many image haze removal methods have been proposed. Image dehazing has become one focus of computer vision. Traditional methods mostly use auxiliary tools or prior knowledge to obtain the depth information of images and then estimate the medium\",\n \"Title: Integral Reinforcement Learning-Based Guaranteed Cost Control For Unknown Nonlinear Systems Subject To Input Constraints And Uncertainties\\nYear: 2021\\n\\nAbstract: This paper investigates guaranteed cost control (GCC) problem for nonlinear systems subject to input constraints and disturbances by utilizing the reinforcement-learning (RL) algorithm. Firstly, by establishing a modified Hamilton-Jacobi-Bellman (HJI) equation, which is difficult to be solved, a model-based policy iteration (PI) GCC algorithm is designed for input-constrained nonlinear systems with disturbances. Moreover, without requiring any knowledge of system dynamics, by designing an auxiliary system with a control law and an auxiliary disturbance policy, an online model-free GCC approach is developed by utilizing integral reinforcement learning (IRL) algorithm. To implement the proposed control algorithm, the actor and disturbance NNs are constructed to approximate the optimal control input and worst-case disturbance policy, while the critic NN is utilized to approximate optimal value function. Further, a synchronization weight update law is developed to minimize the NN approximation residual errors. The asymptotic stability of controlled systems is analyzed by applying the Lyapunov's method. Finally, the effectiveness and feasibility of the proposed control method are verified by two nonlinear simulation examples. (C) 2021 Elsevier Inc. All rights reserved.\",\n \"Title: Consensus Control For Nonlinear Multi-Agent Systems With Event-Triggered Communications\\nYear: 2021\\n\\nAbstract: This paper investigates the consensus problems for a class of nonlinear multi-agent systems (MASs) by distributed event-based control protocols. Firstly, we design the static distributed event-triggered control protocols to achieve leader-follower and leaderless consensus problems, respectively, and the systems exclude Zeno behavior under the proposed control schemes. In order to avoid utilizing the global information of the system networks, that is, the minimum eigenvalues of the correlation matrices. Then, the fully distributed event-based control schemes are designed by introducing the time-varying coupling weights to achieve the leader-follower and leaderless consensus problems, respectively. It has been shown that the Zeno behavior is ruled out in the MASs under the fully distributed event-based control protocols. All static and dynamic event-based control schemes are able to be constructed based on the solvability of linear matrix inequalities (LMIs). Finally, the rationality of the theoretical results is illustrated by the example.(c) 2021 Elsevier Inc. All rights reserved.\",\n \"Title: Analysis Of Grid Operation State Based On Improved Maximum Eigenvalue Sample Covariance Matrix Algorithm\\nYear: 2021\\n\\nAbstract: Traditional static threshold-based state analysis methods can be applied to specific signal-to-noise ratio situations but may present poor performance in the presence of large sizes and complexity of power system. In this article, an improved maximum eigenvalue sample covariance matrix algorithm is proposed, where a Marchenko-Pastur law-based dynamic threshold is introduced by taking all the eigenvalues exceeding the supremum into account for different signal-to-noise ratio situations, to improve the calculation efficiency and widen the application fields of existing methods. The comparison analysis based on IEEE 39-Bus system shows that the proposed algorithm outperforms the existing solutions in terms of calculation speed, anti-interference ability, and universality to different signal-to-noise ratio situations.\",\n \"Title: A GENERAL ALTERNATING-DIRECTION IMPLICIT FRAMEWORK WITH GAUSSIAN PROCESS REGRESSION PARAMETER PREDICTION FOR LARGE SPARSE LINEAR SYSTEMS\\nYear: 2022\\n\\nAbstract: This paper proposes an efficient general alternating-direction implicit (GADI) framework for solving large sparse linear systems. The convergence property of the GADI framework is discussed. Most of existing ADI methods can be unified in the developed framework. Meanwhile the GADI framework can derive new ADI methods. Moreover, as the algorithm efficiency is sensitive to the splitting parameters, we offer a data-driven approach, the Gaussian process regression (GPR) method based on the Bayesian inference, to predict the GADI framework's relatively optimal parameters. The GPR method only requires a small training data set to learn the regression prediction mapping, can predict accurate splitting parameters, and has high generalization capability. It allows us to efficiently solve linear systems with a one-shot computation, and does not require any repeated computations. Finally, we use the three-dimensional convection-diffusion equation, two-dimensional parabolic equation, and continuous Sylvester matrix equation to examine the performance of our proposed methods. Numerical results demonstrate that the proposed framework is faster tens to thousands of times than the existing ADI methods, such as (inexact) Hermitian and skew-Hermitian splitting type methods in which the consumption of obtaining relatively optimal splitting parameters is ignored. As a result, our proposed methods can solve much larger linear systems which these existing ADI methods have not reached.\",\n \"Title: Fault-tolerant control for uncertain switched random systems with multiple interval time-varying delays and intermittent faults\\nYear: 2021\\n\\nAbstract: This paper focuses on the passive fault-tolerant control for an uncertain switched nonlinear random system with multiple interval time-varying delays and intermittent faults in sensors and actuators. There are also nonlinear functions, exogenous disturbances and measurement noise in the system. There are few tries to realize noise-to-state exponentially mean-square stability for switched random nonlinear systems subject to multiple interval time-varying delays allowing various time delay cases. Random differential equations are more common than stochastic differential equations in practice. Thus, the suggested method is more practicable. First, a nonlinear dynamic output feedback fault-tolerant controller is constructed. Next, an augmented closed-loop system can be obtained in the framework of multiple interval time-varying delays. Then piecewise Lyapunov function is utilized to realize stability analysis for the closed-loop system. At last, this feasibility of this approach presented in this study is verified through two examples.\",\n \"Title: Quantitative Evaluation of the Soil Signal Effect on the Correlation between Sentinel-1 Cross Ratio and Snow Depth\\nYear: 2021\\n\\nAbstract: High-resolution Synthetic Aperture Radar (SAR), as an efficient Earth observation technology, can be used as a complementary means of observation for snow depth (SD) and can address the spatial heterogeneity of mountain snow. However, there is still uncertainty in the SD retrieval algorithm based on SAR data, due to soil surface scattering. The aim of this study is to quantify the impact of soil signals on the SD retrieval method based on the cross-ratio (CR) of high-spatial resolution SAR images. Utilizing ascending Sentinel-1 observation data during the period from November 2016 to March 2020 and a CR method based on VH- and VV-polarization, we quantitatively analyzed the CR variability characteristics of rock and soil areas within typical thick snow study areas in the Northern Hemisphere from temporal and spatial perspectives. The correlation analysis demonstrated that the CR signal in rock areas at a daily timescale shows a strong correlation (mean value > 0.60) with snow depth. Furthermore, the soil areas are more influenced by freeze-thaw cycles, such that the monthly CR changes showed no or negative trend during the snow accumulation period. This study highlights the complexity of the physical mechanisms of snow scattering during winter processes and the influencing factors that cause uncertainty in the SD retrieval, which help to promote the development of high-spatial resolution C-band data for snow characterization applications.\",\n \"Title: Analysis and Suppression of Phase Unbalance Induced Force Ripples in Permanent Magnet Linear Motors\\nYear: 2022\\n\\nAbstract: Low force ripples from permanent magnet linear motors (PMLMs) are typically essential requirements in a wide range of demanding control applications with high motion quality. Apart from the sources present in rotary PM machines, phase unbalance due to the end effect is another major contributor of the thrust force ripples in the PMLMs. In this paper, the phase unbalance and induced force ripples are investigated in the case of a planar PMLM typically equipped with single-layer concentrated windings and quasi-Halbach magnets. It demonstrates that although the PMLM is quite carefully deigned to minimize the end effect, the phase unbalance still exists, including both the amplitude and phase position unbalances. And the phase unbalances give rise to even order force ripple harmonics, in which the 2(nd)-order force ripple is particularly significant. In order to compensate the end effect and the unbalance between the three phases, a novel PMLM topology is presented, in which the width of the two end teeth (instead of the edge teeth) is adjusted to reduce the force ripple. And the impact of the end teeth width on the ripple force magnitude is further revealed. It is shown that by proper design of the end teeth width, the 2(nd)-order force ripple of the PMLM can be minimized to quite a low extent. In comparison with the original design, the 2(nd)-order force ripple factor of the novel topology has been suppressed from 0.4% to less than 0.03% in the case study. Such a technique has provided a new way of force ripple minimization, and is applicable to other types of linear motors or actuators with appropriate changes.\",\n \"Title: A coherent signal beamforming technique based on sub-array cross correlation\\nYear: 2022\\n\\nAbstract: In this paper, a new coherent signal beamforming technique based on sub-array cross correlation (SCC) is proposed for the signal cancellation caused by traditional adaptive beamforming in multipath environment. Firstly, the array is uniformly divided into two mutual non-overlapped sub-arrays, and the preprocess of blocking expected signal is performed inside every sub-array to remove the expected signal component from the received signal on the premise that the direction of expected signal is known in advance. Then, minimum variance distortionless response (MVDR) allows us to obtain the desired signal with noise. Afterwards, based on the fact that the noise between different elements is not correlated, the MVDR output of one sub-array can be taken as the expected signal of the other sub-array to perform the beamforming with minimum mean square error (MMSE) criterion. Finally, the weight for the full array is obtained by combining the MMSE beamforming weights of the two sub-arrays. The proposed method can effectively solve the above coherent beamforming problem. What's more, the SCC beamformer performs well in the case with small samples. Both theoretical derivation and simulation experiment verify the feasibility and validity of the proposed beamformer. (C) 2021 Elsevier Inc. All rights reserved.\",\n \"Title: Double-Branch Dehazing Network based on Self-Calibrated Attentional Convolution\\nYear: 2022\\n\\nAbstract: Single image dehazing is an essential preprocessing task to improve the image’s visual effect. In this paper, we propose a Double-Branch Dehazing Network based on Self-Calibrated Attentional Convolution (DBDN-SCAC), which effectively captures the multi-scale features of important information for solving dehazing problems. More specifically, our proposed DBDN-SCAC uses an encoder–decoder architecture based on a Laplacian Pyramid to learn information at different scales. At the same time, the self-calibration attention convolution module is incorporated after each scaling to expand the receptive field and enrich the output feature. Furthermore, to avoid information loss caused by downsampling, a new self-calibration branch is established to help the image recover more detailed information. Finally, a Content-aware Upsampling module is added in two branches to allow the network in reconstructing better images. The effectiveness of our approach, which is compared with the state-of-the-art methods in the public datasets, is justified by our achieving the best results.\",\n \"Title: Time-varying formation control with general linear multi-agent systems by distributed event-triggered mechanisms under fixed and switching topologies\\nYear: 2022\\n\\nAbstract: This paper investigates the time-varying formation control (TVFC) problem of multi-agent systems (MASs) with general linear dynamics under fixed topology by utilizing event-triggered mechanisms. In order to achieve the TVFC problem, two kinds of distributed event-triggered formation control schemes are proposed. Moreover, the Zeno behavior is excluded under these event-triggered mechanisms. Since these control protocols are constructed on the basis of the sampling information of the event time instants instead of the real-time information of each agent, each agent does not require to communicate continuously with its neighbors, thus effectively reducing the bandwidth requirement of communication and greatly reducing the use of energy in MASs. It deserves to be mentioned that the second control protocol introduces time-varying coupling weights, does not require to utilize any global information of the system networks, and is more suitable for systems with large-scale network. Moreover, the proposed event-triggered formation control protocols are suitable for arbitrarily switching topologies, and its related results are extended to the case of switching topologies. Finally, several examples are introduced to achieve the TVFC problem by utilizing the adaptive event-triggered control protocol, and Zeno behavior is not observed during the process of simulation.\",\n \"Title: Semi-discretized numerical solution for time fractional convection-diffusion equation by RBF-FD\\nYear: 2022\\n\\nAbstract: In this paper, a numerical method based on radial basis functions finite difference (RBF-FD) has been developed for solving the time fractional convection-diffusion equation. We first approximate the time fractional derivative by FD and obtain the semi-discretized scheme. The unconditional stability and convergence of the semi-discretized scheme are given. After that, we use the multiquadric RBF to approximate the spatial derivatives in the numerical experiments. The aim of this paper is to show that the RBF-FD method is better than the classical FD method for solving our mentioned equation. Finally, two numerical examples are proposed respectively to verify the correctness of our theoretical analysis and to demonstrate the superiority of the RBF-FD method. (C) 2021 Elsevier Ltd. All rights reserved.\",\n \"Title: Efficient block preconditioners for integral constrained elliptic optimal control problems with finite element approximations\\nYear: 2022\\n\\nAbstract: In this paper, focusing on a distributed optimal control problem for the elliptic equations with integral control constraint, we propose efficient block diagonal preconditioners to solve the corresponding linearized algebraic system with finite element methods. We derive the first order necessary and sufficient optimality conditions for an integral constraint on the control, and divide the discretized optimal conditions into three matrix forms. Then block-diagonal preconditioners for the corresponding linear algebraic systems are constructed. With respect to both the mesh size and the regularization parameter, the robustness of our preconditioners is proved in detail. In fact, the condition numbers of the preconditioned matrices are a constant for different parameters. Meanwhile, based on the equivalent matrix forms, an algorithm is proposed for this kind of constrained optimal control problems. Numerical experiments are given to depict the efficiency of our proposed preconditioners.\",\n \"Title: A texture-aware U-Net for identifying incomplete blinking from eye videography\\nYear: 2022\\n\\nAbstract: •Three convolutional blocks were introduced based on element-wise subtraction operations.•A novel segmentation network was developed by integrating the introduced blocks with the classical U-Net.•Extensive experiments showed a unique advantage of the developed network in extracting palpebral fissures from frame images of eye videography.•The developed network showed the feasibility of identifying incomplete blinking based on eye videography.\",\n \"Title: Generalized finite difference method for three-dimensional eigenproblems of Helmholtz equation\\nYear: 2022\\n\\nAbstract: In this paper, a meshless numerical procedure, based on the generalized finite difference method (GFDM) is proposed to efficiently and accurately solve the three-dimensional eigenproblems of the Helmholtz equation. The eigenvalues and eigenvectors are very important to various engineering applications in three-dimensional acoustics, optics and electromagnetics, so it is essential to develop an efficient numerical model to analyze the three-dimensional eigenproblems in irregular domains. In the GFDM, the Taylor series and the moving-least squares method are used to derive the expressions at every node. By enforcing the satisfactions of governing equation at interior nodes and boundary conditions at boundary nodes, the resultant system of linear algebraic equations can be expressed as the eigenproblems of matrix and then the eigenvalues and eigenvectors can be efficiently acquired. In this paper, four numerical examples are provided to validate the accuracy and simplicity of the proposed numerical scheme. Furthermore, the numerical results are compared with analytical solutions and other numerical results to verify the merits of the proposed method.\",\n \"Title: Improved CLEAN Algorithm for RFI Mitigation of Aperture Synthesis Radiometer Images\\nYear: 2022\\n\\nAbstract: For synthetic aperture radiometer, radio frequency interference (RFI) seriously contaminates the observed data and affects the quality of the brightness temperature (BT) image. CLEAN algorithm is usually used to mitigate the Gibbs effect generated by RFI. However, the closely distributed RFI sources cannot be well mitigated by the original CLEAN algorithm. This letter proposes an improved adaptive CLEAN algorithm to enhance the performance of RFI mitigation, especially the closely distributed RFI sources. The loop factor is forced to synchronize with residual BT of RFI for every iteration to avoid over mitigation. Over mitigation can result in a BT vacancy area or an extra BT point generated at the position of RFI. Soil Moisture and Ocean Salinity (SMOS) L1B real data are applied to verify the effectiveness of the improved CLEAN algorithm. The simulation shows that the improved CLEAN algorithm can be competent in eliminating isolated and closely distributed RFIs better than the classic CLEAN algorithm, and the over mitigation problem is solved by the proposed method.\",\n \"Title: Resource Allocation Scheme Based on Complete Planning Process for Immediate and Advance Reservation in SDM-EONs\\nYear: 2021\\n\\nAbstract: Immediate reservation (IR) and advanced reservation (AR) become two common traffic models for future space division multiplexing-elastic optical networks (SDM-EONs). However, it is crucial to reduce conflicts between different types of requests and to accommodate more requests with limited resources. In this paper, we propose a resource allocation scheme based on complete planning process (RA-CPP) for IR and AR in SDM-EONs, including request provisioning, resource planning, passive adjustment, and active adjustment. In the request provisioning, the starting time of requests is considered and the earlier transmitted requests have higher resource selection priority. Next, the spectrum blocks with minimal resource spacing between adjacent requests (RS-AR) are planned for requests to maximize the free resources of the network in the resource planning. In addition, for the new request that would be blocked, passive adjustment begins. After planning reserved resources for the new request, conflicting AR requests are re-provisioned. Finally, the active adjustment re-optimizes the resources by increasing the tightness of spectrum resources. Simulation results show that the proposed RA-CPP scheme has greater advantages in blocking probability and spectrum utilization compared with the benchmark schemes.\",\n \"Title: Semi-supervised learning for raindrop removal on a single image\\nYear: 2022\\n\\nAbstract: This article propose s a network that is mainly used to deal with a single image polluted by raindrops in rainy weather to get a clean image without raindrops. In the existing solutions, most of the methods rely on paired images, that is, the rain image and the real image without rain in the same scene. However, in many cases, the paired images are difficult to obtain, which makes it impossible to apply the raindrop removal network in many scenarios. Therefore this article proposes a semi-supervised rain-removing network apply to unpaired images. The model contains two parts: a supervised network and an unsupervised network. After the model is trained, the unsupervised network does not require paired images and it can get a clean image without raindrops. In particular, our network can perform training on paired and unpaired samples. The experimental results show that the best results are achieved not only on the supervised rain-removing network, but also on the unsupervised rain-removing network.\",\n \"Title: A recurrent skip deep learning network for accurate image segmentation\\nYear: 2022\\n\\nAbstract: Accurate image segmentation plays a vital role in quantitatively assessing various diseases and their prognosis. In this study, we described a novel deep learning network termed Recurrent Skip Network (RS-Net) by integrating a backward skip connection and an attention-aware convolutional block with the available BiO-Net. To validate its performance and merits, we applied it (1) for the segmentation of three corneal layers (i.e., epithelium layer, Bowman's layer, and stroma layer) depicted on optical coherence tomography (OCT) images and (2) for the segmentation of the optic disc (OD) and cup (OC) depicted on color fundus photography (CFP). Our experiments showed that RS-Net achieved an average Dice score of 0.9327 and 0.8868, respectively for the two different segmentation tasks, demonstrating a unique performance as compared with BiO-Net and other networks.\",\n \"Title: A cumulative prospect theory-based method for group medical emergency decision-making with interval uncertainty\\nYear: 2022\\n\\nAbstract: An emergency response to a medical situation is generally considered to be a risk decision-making problem. When an emergency event occurs, it makes sense to take into account more than one decision maker’s opinions and psychological behaviors. The existing research tends to ignore these multidimensional aspects. To fill this literature gap, we propose a multi-attribute model.\\n The model is based on cumulative prospect theory (CPT), considering multiple experts’ psychological factors. By not assuming full rationality, we extend existing models to allow multiple experts’ risk preferences to be incorporated into the decision-making process in the case of an emergency. Then, traditional CPT is extended by allowing for multiple attributes. In addition, rather than using crisp data, interval values are adopted to tackle the usual uncertainties in reality. The multi-attribute CPT based model proposed can deal with the selection of potential emergency alternatives. The model incorporates interval values to allow more uncertainty and the comparative studies show that the optimal solution changes under different scenarios. Our illustrative example and comparative study show that considering multiple experts and multiple attributes is more reasonable, especially in complicated situations under an emergency. In addition, decision-makers’ risk preferences highly affect the selection outcomes, highlighting their importance in the medical decision-making process. Our proposed model can be applied to similar fields with appropriate modifications.\",\n \"Title: Adaptive Event-Triggered Leader-Follower Consensus of Linear Multiagent Systems Under Directed Graph With Nonzero Leader Input\\nYear: 2022\\n\\nAbstract: This brief investigates the leader-follower consensus problem of linear multiagent systems (MASs) with nonzero leader input under the directed graph. By proposing the innovative adaptive event-triggered mechanism, the proposed control protocol does not need continuous communication among neighbors, and the control protocol can be updated intermittently for each follower. Moreover, Zeno behavior can be excluded for each agent. Note that the existence of the leader's nonzero bounded input enormously increases the difficulty of solving the leader-follower consensus problem. At last, two numerical examples are introduced to show the effectiveness and advantages of the designed protocol.\",\n \"Title: Positive Solutions for Third-Order Boundary Value Problems with the Integral Boundary Conditions and Dependence on the First-Order Derivatives\\nYear: 2022\\n\\nAbstract: In this paper, by the use of a new fixed point theorem and the Green function of BVPs, the existence of at least one positive solution for the third-order boundary value problem with the integral boundary conditions is considered,where there is a nonnegative continuous function. Finally, an example which to illustrate the main conclusions of this paper is given.\",\n \"Title: Automated Full Scene Parsing for Marine ASVs Using Monocular Vision\\nYear: 2022\\n\\nAbstract: Perceiving and parsing a maritime scene automatically and in real time is a key task for autonomous surface vehicle navigation. We propose a panoptic segmentation framework that allows end-to-end training and multiple task cascading to meet the navigational challenges of scene parsing in a complex maritime environment. In our framework, the feature extraction backbone is based on Res2Net combined with improved FPN. The fusion network neck adds a mask branch to the latest YOLO detector and embeds a bottleneck attention module. We address possible inference conflict between semantic segmentation and instance segmentation with a panoptic fusion head that resolves conflict using Dezert-Smarandache theory. We also constructed the first maritime scene parsing dataset MarPS-1395, which is completely and fully annotated. MarPS-1395 is the first panoptic segmentation dataset in this field. We validated our model on MarPS-1395 as well as the publicly available dataset to investigate the real-time performance and the accuracy of multitask implementation in panoptic segmentation, which included object detection and classification, instance segmentation, and semantic segmentation. The experimental results also show that our method can robustly accomplish full scene parsing in a complex maritime environment, and achieved a good balance between accuracy of segmentation and speed of computing.\",\n \"Title: Reinforcement Learning-Based Parallel Approach Control of Micro-Assembly Manipulators\\nYear: 2022\\n\\nAbstract: Micro-devices are usually assembled by micro-assembly robot operating multi-manipulators in a narrow assembly space. To ensure assembly accuracy, manipulators are required to assemble multiple parts in parallel. However, in the traditional assembly, in order to prevent the parts from interfering, the movement trajectory of each manipulator must be manually input, which leads to low planning effici...\",\n \"Title: Brain-activation-based individual identification reveals individually unique activation patterns elicited by pain and touch\\nYear: 2022\\n\\nAbstract: Pain is subjective and perceived differently in different people. However, individual differences in pain-elicited brain activations are largely overlooked and often discarded as noises. Here, we used a brain-activation-based individual identification procedure to investigate the uniqueness of the activation patterns within the whole brain or brain regions elicited by nociceptive (laser) and tactile (electrical) stimuli in each of 62 healthy participants. Specifically, brain activation patterns were used as “fingerprints” to identify each individual participant within and across sensory modalities, and individual identification accuracy was calculated to measure each individual's identifiability. We found that individual participants could be successfully identified using their brain activation patterns elicited by nociceptive stimuli, tactile stimuli, or even across modalities. However, different participants had different identifiability; importantly, the within-pain, but not within-touch or cross-modality, individual identifiability obtained from three brain regions (i.e., the left superior frontal gyrus, the middle temporal gyrus and the insular gyrus) were inversely correlated with the scores of Pain Vigilance and Awareness Questionnaire (i.e., how a person is alerted to pain) across participants. These results suggest that each individual has a unique pattern of brain responses to nociceptive stimuli which contains both modality-nonspecific and pain-specific information and may be associated with pain-related behaviors shaped by his/her own personal experiences and highlight the importance of a transition from group-level to individual-level characterization of brain activity in neuroimaging studies.\",\n \"Title: Post-earthquake resilience optimization of a rural “road-bridge” transportation network system\\nYear: 2022\\n\\nAbstract: •It proposes an optimization model of post-earthquake recovery scheduling for bridges as key links in rural “road-bridge” transportation network system.•The remaining function of the bridge after the earthquake is quantified based on the seismic fragility function.•The non-equilibrium probability random assignment model is used to simulate the travel behavior of users in recovery after the earthquake.•The resilience metric for the restoration scheduling of damaged bridges is proposed based on the total recovery time and the cumulative benefit rate.\",\n \"Title: Adaptive Event-Triggered Consensus of Linear Multiagent Systems With Resilience to Communication Link Faults for Digraphs\\nYear: 2022\\n\\nAbstract: This brief investigates the leader-follower consensus problem for multiagent systems (MASs) with resilience to unknown communication link faults. To deal with the unknown faults and avoid using global information, the adaptive event-triggered observers are designed to estimate the leader state, namely, resilience for communication link faults is transformed into the design observers by adaptive control technology. Then, based on the designed observers, controllers are designed to stabilize the given MASs. Finally, an example is introduced to demonstrate the effectiveness of the obtained results.\",\n \"Title: Influence of Open-Pit Coal Mining on Ground Surface Deformation of Permafrost in the Muli Region in the Qinghai-Tibet Plateau, China\\nYear: 2022\\n\\nAbstract: The Qinghai-Tibet Plateau (QTP) is the largest mid-to low latitude and high-altitude permafrost. Open-pit coal mining and other activities have caused serious damage to the alpine ecological environment and have accelerated the degradation of permafrost on the QTP. In this study, the influence of open-pit coal mining on the time series ground surface deformation of the permafrost in the Muli region of the QTP was analyzed from 19 January 2018 to 22 December 2020 based on Landsat, Gaofen, and Sentinel remote sensing data. The primary methods include human-computer interactive visual interpretation and the small baseline subsets interferometric synthetic aperture radar (SBAS-InSAR) method. The results showed that the spatial distribution of displacement velocity exhibits a considerably different pattern in the Muli region. Alpine meadow is the main land use/land cover (LULC) in the Muli region, and the surface displacement was mainly subsidence. The surface subsidence trend in alpine marsh meadows was obvious, with a subsidence displacement velocity of 10-30 mm/a. Under the influence of changes in temperature, the permafrost surface displacement was characteristics of regular thaw subsidence and freeze uplift. Surface deformation of the mining area is relatively severe, with maximum uplift displacement velocity of 74.31 mm/a and maximum subsidence displacement velocity of 167.51 mm/a. Open-pit coal mining had resulted in the destruction of 48.73 km(2) of natural landscape in the Muli region. Mining development in the Muli region had increased the soil moisture of the alpine marsh meadow around the mining area, resulting in considerable cumulative displacement near the mining area and the acceleration of permafrost degradation.\",\n \"Title: Multiple delay-dependent finite-time fault-tolerant control for switched fuzzy systems\\nYear: 2022\\n\\nAbstract: The work is concerned with this issue of multiple delay-dependent finite-time fault-tolerant control (FTC) for uncertain switched Takagi-Sugeno (T-S) fuzzy systems against state and input constraints. There exist intermittent faults (IFs), exogenous disturbances along with measurement noise in the models. There are few attempts for this theme. A nonlinear dynamic output feedback controller is explored for these uncertain switched T-S fuzzy systems first. And an augmented system with respect to realizing FTC is acquired. Moreover, sufficient conditions of robust finite-time H∞ control are constructed by piecewise fuzzy Lyapunov function. At the same time, finite-time boundedness (FTB) as well as input-output finite-time stability (IO-FTS) is achieved. The effectiveness as well as feasibility of this approach is verified through an example.\",\n \"Title: Tracking Control of Discrete-Time System With Dynamic Event-Based Adaptive Dynamic Programming\\nYear: 2022\\n\\nAbstract: In this brief, a novel formulation of the value function with dynamic event-triggered strategy is introduced for the optimal tracking problem (TP) of nonlinear discrete-time systems to eliminate the tracking error by using adaptive dynamic programming (ADP). Firstly, different from the existing ADP methods, a creative formulation of the cost function introduces the control input into the tracking error, and ignores the quadratic form of the control input directly. Second, a novel static triggering mechanism is designed. The proposed approach samples the states and updates the control signal only when the triggered condition is satisfied, and critic-actor Neural network (NN) is designed to approach the performance index and control input. The stability analysis of the closed-loop system is provided based on the Lyapunov’s theorem. Finally, in order to obtain a larger event triggering time interval, a dynamic triggering mechanism is given. Theoretical results show that the system state and the network weight errors are uniformly ultimately bounded (UUB). A simulation result also verify the theoretical claims.\",\n \"Title: Cooperative Output Regulation Problem for Multiagent System Under Directed Topology Using Dynamic Triggering Strategy\\nYear: 2022\\n\\nAbstract: This brief focuses on cooperative output regulation (COR) problem for linear multiagent systems (MASs) under the directed topology. The fully distributed event-triggered control protocol is designed to solve COR problem. In order to reduce the communication frequency, the dynamic event-triggered mechanism is proposed by introducing positive internal variable. One main advantage is that the method inherits the advantages of event-triggered control and adaptive control to deal with the COR problem, and does not depend on global information. At last, a numerical example is given in the supplementary material to verify the validity of the obtained results.\",\n \"Title: Three-Dimensional Coordinate Extraction Based on Radargrammetry for Single-Channel Curvilinear SAR System\\nYear: 2022\\n\\nAbstract: With the rapid development of high-resolution synthetic aperture radar (SAR) systems, the technique that utilizes multiple two-dimensional (2-D) SAR images with different view angles to extract three-dimensional (3-D) coordinates of targets has gained wide concern in recent years. Unlike the traditional multi-channel SAR utilized for 3-D coordinate extraction, the single-channel curvilinear SAR (CLSAR) has the advantages of large variation of view angle, requiring fewer acquisition data, and lower device cost. However, due to the complex aerodynamic configuration and flight characteristics, important issues should be considered, including the mathematical model establishment, imaging geometry analysis, and high-precision extraction model design. In this paper, to address these challenges, a 3-D vector model of CLSAR was presented and the imaging geometries under different view angles were analyzed. Then, a novel 3-D coordinate extraction approach based on radargrammetry was proposed, in which the unique property of the SAR system, called cylindrical symmetry, was utilized to establish a novel extraction model. Compared with the conventional approach, the proposed one has fewer constraints on the trajectory of radar platform, requires fewer model parameters, and can obtain higher extraction accuracy without the assistance of extra ground control points (GCPs). Numerical results using simulated data demonstrated the effectiveness of the proposed approach.\",\n \"Title: A Novel Iterative Inner-Pulse Integration Target Detection Method for Bistatic Radar\\nYear: 2022\\n\\nAbstract: A large time-bandwidth product bistatic radar offers several advantages in high-resolution target detection and motion parameter estimation, but the scale factor and inner-pulse Doppler will also be introduced in the radar echoes when detecting high-speed targets. Under this condition, the conventional matched filter will cause a certain energy loss and a nonnegligible shift of the range center, which is called mismatch effect. Considering the special geometries of the bistatic radar, we first establish the precise echo signal (PES) model to describe the radar echoes for high-speed targets in a space-air-based bistatic radar system. By analyzing the mismatch effect within the pulse and the migration between pulses, we have obtained the mathematic relationship between the scale factor, inner-pulse Doppler shift, range, equivalent velocity, and acceleration. Following that, we perceive that the parameters of the matched filter are related to the target motion parameters, i.e., a priori unknown, which inspires us to propose an iterative coherent integration method to achieve the intrapulse and interpulse integration. A PES model matched filter with initial parameters is defined and a roughly motion parameter estimation is acquired by the PES model-based Keystone transform (PES-KT) and inner-pulse chirp Fourier transform (CFT). The estimated parameters are used for matched filter construction and coherent integration. This process is performed over multiple iterations to provide an accurate motion parameter result. In the end, a target detection experiment is given to show the effectiveness of the proposed method using space-air-based bistatic radar.\",\n \"Title: Designing an Interactive Communication Assistance System for Hearing-Impaired College Students Based on Gesture Recognition and Representation\\nYear: 2022\\n\\nAbstract: Developing a smart classroom can make the modern classroom more efficient and intelligent. Much research has been conducted pertaining to smart classrooms for hearing-impaired college students. However, there have been few significant breakthroughs in mobilizing students' learning efficiency as measured by information transmission, communication, and interaction in class. This research collects data through nonparticipatory observation and in-depth interviews and analyzes available data on classroom interaction needs of these students. We found that diversified explanations, recordable interactive contents, and interaction between teachers and students could improve the learning effects in the classroom. We also propose a tracking-processing method based on gesture recognition and representation and present a design for a processing system based on AT89C52 microcontroller and Kinect. In this way, sign language can be translated into text and all students can receive the information and participate in the interaction, which greatly improves students' autonomy and enthusiasm of learning. This design enables deaf students to fully use classroom learning resources, reduces learning time costs, and improves learning efficiency. It can assist teachers in teaching and tutoring students to enhance their experience.\",\n \"Title: An Oblique Projection-Based Beamforming Method for Coherent Signals Receiving\\nYear: 2022\\n\\nAbstract: Within a complex sea or ground surface background, multipath signals are strongly correlated or even completely coherent, which leads to signal cancellation when conventional optimal beamforming is performed. Aiming at the above problem, a coherent signal-receiving algorithm is proposed based on oblique projection technology in this paper. The direction of arrival (DOA) of incident signals is estimated firstly by the space smoothing-based MUSIC method. The composite steering vector of multipath coherent signals is then obtained utilizing the oblique projection matrix constructed with the estimated angles. The weight vector is thereby derived with the minimum variance distortionless response criteria. The proposed oblique projection-based beamformer can receive the multipath coherent signals effectively. Moreover, the proposed beamformer is more robust and converges to optimal beamformer rapidly without aperture loss. The theoretical analysis and simulation verify the validity and superiority of the proposed coherent signal beamformer.\",\n \"Title: Dissipativity-Based Intermittent Fault Detection and Fault-Tolerant Control for Uncertain Switched Random Nonlinear Systems With Multiple Delays\\nYear: 2022\\n\\nAbstract: The issue of fault detection (FD) and fault-tolerant control is developed for a switched random nonlinear system against multiple delays in this work. There also exist parameter uncertainties, exogenous disturbances, nonlinear functions as well as measurement noise in this model. This is one of the few tries to investigate the dissipativity issue and an design FD-based observer for the switched random system. Based on the dimensionally adjustable observer, the controller is designed to make this system noise-to-state exponentially mean-square stable. Then, these sufficient stability conditions can be earned by the piecewise Lyapunov function. Concurrently, this strict \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"> <tex-math notation=\\\"LaTeX\\\">$\\\\left({\\\\mathsf {Q}, \\\\mathsf {S}, \\\\mathsf {R}}\\\\right)-{\\\\upsilon }-$ </tex-math></inline-formula>\\n dissipativity performance, this elaborate \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"> <tex-math notation=\\\"LaTeX\\\">$H_{\\\\infty }$ </tex-math></inline-formula>\\n performance as well as this elaborate \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"> <tex-math notation=\\\"LaTeX\\\">$H\\\\_{}$ </tex-math></inline-formula>\\n performance can be gained, separately. In addition, the merits and feasibility are presented via a numerical simulation and a practical example.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":156,"string":"156"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13676,"string":"13,676"}}},{"rowIdx":13677,"cells":{"question_id":{"kind":"string","value":"Alexandre Seuret"},"question":{"kind":"string","value":"Generate an abstract for the title \"Stabilization of time-delay systems through linear differential equations using a descriptor representation\" using the following items: 1. Stability\n2. State feedback controller\n3. Time-delay systems\n4. Lyapunov-Krasovskii functionals\n5. Linear matrix inequalities"},"answer":{"kind":"string","value":"In this article, a new method to assess stability and to design static state feedback controller for linear time- delay systems is introduced. The method based on linear differential equations allows considering explicit Lyapunov- Krasovskii functionals with non constant matrix parameters. The stability conditions considering constant delays are delay- dependent and expressed using easy computable linear matrix inequalities. An example is introduced to show the efficiency of the stabilization criteria."},"correct_answer":{"kind":"string","value":"In this article, a new method to assess stability and to design static state feedback controller for linear time- delay systems is introduced. The method based on linear differential equations allows considering explicit Lyapunov- Krasovskii functionals with non constant matrix parameters. The stability conditions considering constant delays are delay- dependent and expressed using easy computable linear matrix inequalities. An example is introduced to show the efficiency of the stabilization criteria."},"documents":{"kind":"list like","value":["Title: Design of a Pressure Control System With Dead Band and Time Delay\nYear: 2007\n\nAbstract: This paper investigates the control of pressure in a hydraulic circuit containing a dead band and a time varying delay. The dead band is considered as a linear term and a perturbation. A sliding mode controller is designed. Stability conditions are established by making use of Lyapunov Krasovskii functionals, non-perfect time delay estimation is studied and a condition for the effect of uncertainties on the dead zone on stability is derived. Also the effect of different LMI formulations on conservativeness is studied. The control law is tested in practice.","Title: A looped-functional approach for robust stability analysis of linear impulsive systems\nYear: 2012\n\nAbstract: A new functional-based approach is developed for the stability analysis of linear impulsive systems. The new method, which introduces looped functionals, considers non-monotonic Lyapunov functions and leads to LMI conditions devoid of exponential terms. This allows one to easily formulate dwell-time results, for both certain and uncertain systems. It is also shown that this approach may be applied to a wider class of impulsive systems than existing methods. Some examples, notably on sampled-data systems, illustrate the efficiency of the approach.","Title: Stability Analysis of Sampled-Data Systems Using Sum of Squares.\nYear: 2013\n\nAbstract: This technical brief proposes a new approach to stability analysis of linear systems with sampled-data inputs or channels. The method, based on a variation of the discrete-time Lyapunov approach, provides stability conditions using functional variables subject to convex constraints. These stability conditions can be solved using the sum of squares methodology with little or no conservatism in both the case of synchronous and asynchronous sampling. Numerical examples are included to show convergence.","Title: A novel stability analysis of linear systems under asynchronous samplings.\nYear: 2012\n\nAbstract: This article proposes a novel approach to assess the stability of continuous linear systems with sampled-data inputs. The method, which is based on the discrete-time Lyapunov theorem, provides easy tractable stability conditions for the continuous-time model. Sufficient conditions for asymptotic and exponential stability are provided dealing with synchronous and asynchronous samplings and uncertain systems. An additional stability analysis is provided for the cases of multiple sampling periods and packet losses. Several examples show the efficiency of the method.","Title: Collaborative estimation of gradient direction by a formation of AUVs.\nYear: 2011\n\nAbstract: This work deals with the source-seeking problem in which the task is to locate the source of some signal using a fleet of AUVs (autonomous underwater vehicles). The present paper proposes a distributed solution in which a group of vehicles uniformly distributed in a fixed circular formation, estimates the gradient direction of the signal propagation. The distributed algorithm takes into account the communication constraints and depends on direct signal measurements. Our approach is based on the previous results in formation control to stabilize the fleet in a circular formation with time-varying center and in a collaborative source-seeking algorithm. The results are supported through computer simulations.","Title: Static Output Feedback Sliding Mode Control Design via an Artificial Stabilizing Delay\nYear: 2009\n\nAbstract: It is well known that for linear, uncertain systems, a static output feedback sliding mode controller can only be deter- mined if a particular triple associated with the reduced order dy- namics in the sliding mode is stabilizable. This paper shows that the staticoutput feedbackslidingmodecontrol designproblemcan be solved for a broader class of systems if a known delay term is deliberately introduced into the switching function. Effectively the reduced order sliding mode dynamics are stabilized by the intro- duction of this artificial delay.","Title: Collaborative Estimation Of Gradient Direction By A Formation Of Auvs Under Communication Constraints\nYear: 2011\n\nAbstract: This work deals with the source-seeking problem in which the task is to locate the source of some signal using a fleet of Autonomous Underwater Vehicles (AUVs). The present paper proposes a distributed solution in which a group of vehicles uniformly distributed in a fixed circular formation, estimates the gradient direction of the signal propagation. The distributed algorithm takes into account the communication constraints and depends on direct signal measurements. Our approach is based on the previous results in formation control to stabilize the fleet in a circular formation with time-varying center and in a collaborative source-seeking algorithm. The results are supported through computer simulations.","Title: Taking into account period variations and actuator saturation in sampled-data systems.\nYear: 2012\n\nAbstract: This paper deals with the problem of stability and stabilization of sampled-data systems under asynchronous samplings and actuator saturation. The method is based, on the one hand, on the use of a novel class of Lyapunov functionals whose derivative is negative along the trajectories of the continuous-time model of the sampled data system. It is shown that this fact guarantees that a quadratic Lyapunov function is strictly decreasing for the discrete-time asynchronous system. On the other hand, the control saturation is taken into account from the use of a generalized sector condition. These ingredients lead to the formulation of improved LMI conditions that can be cast in optimization problems aiming at enlarging estimates of the region of attraction of the closed-loop system or maximizing the bounds on the sampling period jitter for which stability and stabilization are ensured.","Title: Robust Control Under Weakened Real-Time Constraints\nYear: 2011\n\nAbstract: A weakened implementation scheme for real-time feedback controllers is proposed to reduce the conservatism due to traditional worst-cases considerations, while preserving the stability and control performance. Based on recent results to assess stability of linear systems with delayed and sampled-data inputs, this paper takes into account both the effects of deadline misses of control tasks and uncertainties in the plant. The methodology is applied to the pitch control of an aircraft, showing that weakening the real-time constraints allows for saving computing power while preserving the system's stability.","Title: Event-Triggered Sampling Algorithms Based On A Lyapunov Function\nYear: 2011\n\nAbstract: Usually feedback laws are implemented in a periodic fashion on digital hardware. The main reason for using this periodicity in the hardware comes from the difficulties to analyze the stability of aperiodic or asynchronous systems. However it also seems natural to hold the same control input longer if the system behaves in a suitable way or shorter if the system requires an updated input. In this paper, an algorithm is suggested to sample the control input based on the behavior of a Lyapunov-like function. This algorithm is called event-triggered since the Lyapunov-like function directly depends on the state of the systems.","Title: Implementation of an Internet-controlled system under variable delays\nYear: 2006\n\nAbstract: This work deals with the control and the observation of a remote system using Internet as a communication line. The process consists in a Slave part S, with poor computing capacity, connected to a Master M through the Internet. The internal times of both M and S are synchronized thanks to a common GPS clock. The global system must ensure some speed performance whatever the delay variations. The main technical restriction is that the delays are less than some known bound, even if the framework we consider takes into account the perturbations generated by the Internet communication delays, by the packet losses and by the sampling phenomenon as well. In what concerns the design of the observer and controller, most of the theorems to be used here refer to proofs given in (Seuret et al., 2006), and this will allow for focussing on the implementation aspects.","Title: Robust sampled-data stabilization of linear systems: an input delay approach\nYear: 2004\n\nAbstract: A new approach to robust sampled-data control is introduced. The system is modelled as a continuous-time one, where the control input has a piecewise-continuous delay. Sufficient linear matrix inequalities (LMIs) conditions for sampled-data state-feedback stabilization of such systems are derived via descriptor approach to time-delay systems. The only restriction on the sampling is that the distance between the sequel sampling times is not greater than some prechosen h>0 for which the LMIs are feasible. For h→0 the conditions coincide with the necessary and sufficient conditions for continuous-time state-feedback stabilization. Our approach is applied to two problems: to sampled-data stabilization of systems with polytopic type uncertainities and to regional stabilization by sampled-data saturated state-feedback.","Title: Improved Consensus Algorithms Using Memory Effects\nYear: 2011\n\nAbstract: This paper deals with simple integrator consensus problems. The objective is the design of an improved consensus algorithm for continuous-time multi-agent systems using memory effects. The novel algorithm proposes to sample, in an appropriate manner, part of the multi-agent systems information such that the algorithm converges, assuming that at each instant, agent's control laws will also consider the sampled past information of its neighbors. Stability conditions expressed in terms of LMI's and based on algebraic communication matrix structure are provided. The efficiency of the method is tested for different network communication schemes.","Title: Translation Control Of A Fleet Circular Formation Of Auvs Under Finite Communication Range\nYear: 2009\n\nAbstract: This work proposes a control algorithm to stabilize a circular formation of AUVs tracking a time-varying center. We also consider the problem of uniform distribution of all the agents along the circle from two approaches: all-to-all and limited communication. We tackle with this communication constraint using a cooperative control strategy which includes the Laplacian matrix of the communication graph (fixed or distance-dependent). The system was implemented in computer simulation, accessible though Web(1).","Title: Cooperative Control Design for Time-Varying Formations of Multi-Agent Systems.\nYear: 2014\n\nAbstract: This paper deals with cooperative control design for nonlinear multi-agent systems. The control objective is to ensure that a group of agents reaches a formation characterized by external time-varying parameters. First, a translation control design is presented to stabilize the multi-agent system to a circular motion tracking a time-varying center. Then, we propose a new framework based on affine transformations to extend previous results to more complex time-varying formations. Moreover, both control laws are improved adding a cooperative term to distribute the agents uniformly along the formation.","Title: Stability of non-linear systems by means of event-triggered sampling algorithms.\nYear: 2014\n\nAbstract: In this paper, the problem of feedback control implementation for non-linear systems is considered. Some conditions for holding the same control input until an event occurs are derived. With respect to classical approaches, where feedback laws are implemented in a periodical fashion, it is suggested new algorithms to use the same control input. By means of these algorithms, some jumps of the contr...","Title: Jensen's and Wirtinger's Inequalities for Time-Delay Systems.\nYear: 2013\n\nAbstract: In the huge literature dedicated to stability of time-delay systems, the most popular approach remains the use of Lyapunov-Krasovskii functionals. This framework allows to study a large class of time-delay systems including constant or time-varying delays. Since several years, the main challenge is to propose new functionals and techniques for deriving less and less conservative stability conditions. Nevertheless, all these approaches usually adopt the same procedure which is based on the well-known Jensen's inequality which generally induces some conservatism difficult to overcome. This paper analyses firstly the conservatism induced by the Jensen's inequality and secondly proposes a wide class of new parametrized inequalities. All these are based on an extensive use of Wirtinger inequality which has been recently introduced in Liu and Fridman [2012] and Seuret and Gouaisbaut [2012b] for stability analysis.","Title: On the Use of the Wirtinger Inequalities for Time-Delay Systems.\nYear: 2012\n\nAbstract: The paper addresses the stability problem of linear time delay system. In the literature, the most popular approach to tackle this problem relies on the use of Lyapunov-Krasovskii functionals. Many results have proposed new functionals and techniques for deriving less and less conservative stability conditions. Nevertheless, all these approaches use the same trick, the well-known Jensen's inequality which generally induces some conservatism difficult to overcome. In light of those observations, we propose to reduce the conservatism of Lyapunov-Krasovskii functionals by introducing new classes of integral inequalities called Wirtinger inequalities. This integral type inequality is firstly shown to encompass Jensen's inequality and is then employed to derive new stability conditions. To this end, a slightly modified Lyapunov functional is proposed. Several examples illustrate the effectiveness of our methodology.","Title: Robust stability for delayed port-Hamiltonian systems using improved Wirtinger-based inequality\nYear: 2014\n\nAbstract: This paper addresses robust stability issues of interconnected port-Hamiltonian systems with polytopic uncertainty and time-varying delay. On the basis of a Lyapunov-Krasovskii functional and the Wirtinger's inequality (known to be less conservative than the popular Jensen's inequality) we show the improvements of the newly proposed criterion with respect to other existing ones. The stability analysis is derived based on a delay independent criterion. A classical nonlinear example taken from the literature illustrates the relevance of the results.","Title: Stability analysis of asynchronous sampled-data systems with discrete-time constant input delay\nYear: 2014\n\nAbstract: Aperiodic sampled-data systems controlled by a delayed state-feedback controller arise naturally in networked control systems. We propose a novel way to analyze the stability of sampled-data systems with discrete-time constant input delay by, first, reformulating the system into an aperiodic discrete-time system with constant delay and by, then, considering a mixture of a discrete-time Lyapunov-Krasovskii functional and a looped-functional in order to account for the presence of the delay and the sampling aperiodicity, respectively. Stability criteria, expressed as linear matrix inequalities, are derived for both certain and uncertain (time-varying) systems. An example is given for illustration.","Title: Hybrid control of a three-agent network cluster\nYear: 2014\n\nAbstract: We consider the management problem for a cluster of three identical servers treating an external request. We assume that each server has a constant computational speed and we build a deterministic hybrid dynamical model of the overall system. Then, inspired by consensus theory, we propose a centralized control law distributing the computational load among the servers and deciding when to turn on or off each server, for energy efficiency, while ensuring that the queues of each server asymptotically converge to a desirable level. We prove the properties of the proposed law and highlight some interesting peculiarities in terms of non-uniform convergence.","Title: Networked control using GPS synchronization\nYear: 2006\n\nAbstract: This work concerns the control, the observation and then, the implementation principles of a remote system (Master and Slave parts) through the Internet network. This communication link introduces variable delays that have to be taken into account in the control-observation loop. The data-sampling effects will also be considered, even in the aperiodic case. The Slave part is considered to be a linear system. But, since its computation power is supposed to be limited, the control complexity (which, here, is an observer- based state feedback) has to stay in the Master part. The global system must ensure speed performance whatever the delay variation. Such a performance is obtained by showing the robust, exponential stability property, which is proven by using adequate Lyapunov-Krasovskii functionals. This makes possible to compute the controller and observer gains by using LMI optimization. The technical solution we propose is based on a GPS system, which guarantees the Master and Slave clocks to be synchronized. Then, the control and measurement packets are sent together with time-stamps that allow for reconstructing a non-symmetric delay information. It means that Master-to-Slave and Slave-to-Master delays are separately reconstructed by the system (and not only the global RTT, round-trip-time). The last part of the paper provides an example where the Slave is a second-order system.","Title: Consensus under communication delays\nYear: 2008\n\nAbstract: This paper deals with the consensus problem under communication network inducing delays. It is well-known that introducing a delay leads in general to a reduction of the performance or to instability due to the fact that time- delay systems are infinite dimensional. For instance, the set of initial conditions of a time-delay system is not a vector but a function taken in an interval. Therefore, investigating the effect of time-delays in the consensus problem is an important issue. In the present paper, we assume that each agent receives instantaneously its own state information but receives the state information from its neighbors after a constant delay. Two stability criteria are provided based on the frequency approach and on Lyapunov-Krasovskii techniques given in terms of LMI. An analytic expression of the consensus equilibrium which depends on the delay and on the initial conditions taken in an interval is derived. The efficiency of the method is tested for different network communication schemes.","Title: Convex Dwell-Time Characterizations for Uncertain Linear Impulsive Systems\nYear: 2012\n\nAbstract: New sufficient conditions for the characterization of dwell-times for linear impulsive systems are proposed and shown to coincide with continuous decrease conditions of a certain class of looped-functionals, a recently introduced type of functionals suitable for the analysis of hybrid systems. This approach allows to consider Lyapunov functions that evolve nonmonotonically along the flow of the system in a new way, broadening then the admissible class of systems which may be analyzed. As a byproduct, the particular structure of the obtained conditions makes the method is easily extendable to uncertain systems by exploiting some convexity properties. Several examples illustrate the approach.","Title: Affine Characterizations of Minimal and Mode-Dependent Dwell-Times for Uncertain Linear Switched Systems\nYear: 2012\n\nAbstract: An alternative approach for minimum and mode-dependent dwell-time characterization for switched systems is derived. While minimum-dwell time results require the subsystems to be asymptotically stable, mode-dependent dwell-time results can consider unstable subsystems and dwell-times within a, possibly unbounded, range of values. The proposed approach is related to Lyapunov looped-functionals, a new type of functionals leading to stability conditions affine in the system matrices, unlike standard results for minimum dwell-time. These conditions are expressed as infinite-dimensional LMIs which can be solved using recent polynomial optimization techniques such as sum-of-squares. The specific structure of the conditions is finally utilized in order to derive dwell-time stability results for uncertain switched systems. Several examples illustrate the efficiency of the approach.","Title: Bessel inequality for robust stability analysis of time-delay system\nYear: 2013\n\nAbstract: This paper addresses the problem of the stability analysis for a linear time-delay systems via a robust analysis approach and especially the quadratic separation framework. To this end, we use the Bessel inequality for building operators that depend on the delay. They not only allow us to model the system as an uncertain feedback system but also to control the accuracy of the approximations made. Then, a set of LMIs conditions are proposed which tends on examples to the analytical bounds for both delay dependent stability and delay range stability.","Title: Elastic formation control based on affine transformations\nYear: 2011\n\nAbstract: This paper deals with the control of a fleet of non-linear systems representing AUVs (autonomous underwater vehicles). The purpose is here to design a control law to stabilize the fleet to time-varying formations which are not only circular. A novel framework is proposed to express a general control law for a large class of formations. This is produced by applying a sequence of affine transformations such as translations, rotations and scalings. The paper also includes a cooperative control to distribute the agents along the formation which takes into account the communication constraints. The system was implemented in computer simulation, accessible through Web.","Title: Stability analysis for sampled-data systems with a time-varying period\nYear: 2009\n\nAbstract: This paper proposes a novel stability analysis of linear systems with sampled-data inputs. Inspired by the input- delay approach and the stability of impulsive systems, this method provides novel sufficient stability conditions. The stabil- ity analysis concerns both constant and time-varying sampling periods. The delay-dependent conditions are expressed using computable linear matrix inequalities. Several examples show the efficiency and the limitation of such stability criteria. Index Terms— Sampled data controller, Time-delay, delay- dependent stability, Lyapunov-Krasovskii functionals (LKF), linear matrix inequality (LMI)","Title: Contraction Control of a Fleet Circular Formation of AUVs under Limited Communication Range\nYear: 2010\n\nAbstract: This work proposes a novel control algorithm ded- icated to multi-agent systems with nonlinear dynamics. The aim of this paper is for the agents to form a circular formation whose center is fixed and whose radius is given by a time-varying reference. The problem of uniform distribution of all the agents along the circle is also addressed under the assumption of limited communication range. This communication constraint is tackled by using a cooperative control scheme which includes the Laplacian matrix of the communication graph (distance- dependent). The multi-agent system is simulated with Matlab. Videos showing the simulations are accessible though Web1.","Title: Stability of Discrete-Time Systems with Time-Varying Delays via a Novel Summation Inequality\nYear: 2015\n\nAbstract: This paper is concerned with the stability analysis of discrete linear systems with time-varying delays. The novelty of the paper comes from the consideration of a new inequality which is less conservative than the celebrated Jensen inequality employed in the context of discretetime delay systems. This inequality is a discrete-time counterpart of the Wirtinger-based integral inequality that was recently employed for the improved analysis of continuous-tine systems with delays. However, differently from the continuous-time case, the proof of the new inequality is not based on the Wirtinger inequality. The method is also combined with an efficient representation of the improved reciprocally convex combination inequality in order to reduce the conservatism induced by the LMIs optimization setup. The effectiveness of the proposed result is illustrated by some classical examples from the literature.","Title: Exponential stability of a PI plus reset integrator controller by a sampled-data system approach.\nYear: 2016\n\nAbstract: The paper deals with the stability analysis of time delay reset control systems, for which the resetting law is assumed to satisfy a time-dependent condition. A stability analysis of the closed-loop system is performed based on an appropriate sampled-data system. New linear matrix inequality (LMI) conditions are proposed to ensure the exponential stability of the closed-loop system resulting from the connection of a plant with a proportional and integral controller together with a reset integrator (PI+RI).","Title: Hierarchy of LMI conditions for the stability analysis of time-delay systems\nYear: 2015\n\nAbstract: Assessing stability of time-delay systems based on the Lyapunov–Krasovskii functionals has been the subject of many contributions. Most of the results are based, first, on an a priori design of functionals and, finally, on the use of the famous Jensen’s inequality. In contrast with this design process, the present paper aims at providing a generic set of integral inequalities which are asymptotically non conservative and then to design functionals driven by these inequalities. The resulting stability conditions form a hierarchy of LMI which is competitive with the most efficient existing methods (delay-partitioning, discretization and sum of squares), in terms of conservatism and of complexity. Finally, some examples show the efficiency of the method.","Title: On The Necessity Of Looped-Functionals Arising In The Analysis Of Pseudo-Periodic, Sampled-Data And Hybrid Systems\nYear: 2015\n\nAbstract: Looped-functionals have been shown to be relevant for the analysis of a wide variety of systems. However, the conditions obtained in previous papers on the analysis of sampled-data, impulsive and switched systems have only been shown to be sufficient for the characterisation of their associated discrete-time stability conditions. We prove here that these conditions are also necessary. This result is derived for a wider class of linear systems, referred to as impulsive pseudo-periodic systems, that encompass periodic, impulsive, sampled-data and switched systems as special cases.","Title: Complete quadratic Lyapunov functionals for distributed delay systems\nYear: 2015\n\nAbstract: This paper is concerned with the stability analysis of distributed delay systems using complete-Lyapunov functionals. Numerous articles aim at approximating their parameters thanks to a discretization method or polynomial modeling. The interest of such approximations is the design of tractable sufficient stability conditions. In the present article, we provide an alternative method based on polynomial approximation which takes advantages of the Legendre polynomials and their properties. The resulting stability conditions are scalable with respect to the maximum degree of the polynomials and are expressed in terms of tractable linear matrix inequalities. Several examples of delayed systems are tested to show the effectiveness of the method.","Title: Stability analysis of uncertain sampled-data systems with incremental delay using looped-functionals\nYear: 2015\n\nAbstract: The robust stability analysis of asynchronous and uncertain sampled-data systems with constant incremental input delay is addressed in the looped-functional framework. These functionals have been shown to be suitable for the analysis of impulsive systems as they allow one to express discrete-time stability conditions in an affine way, enabling then the consideration of uncertain and time-varying systems. The stability conditions are obtained by first reformulating the sampled-data system as an impulsive system, and by then considering a tailored looped-functional along with Wirtinger's inequality, a recently introduced inequality that has been shown to be less conservative than Jensen's inequality. Several examples are given for illustration.","Title: Regional Stability Analysis of Discrete-Time Dynamic Output Feedback Under Aperiodic Sampling and Input Saturation.\nYear: 2016\n\nAbstract: This technical note addresses the problem of regional stability analysis of a continuous-time plant controlled by a discrete-time dynamic output feedback control law with saturation constraints under aperiodic sampling. To cope with the aperiodic sampling problem, the proposed approach is based on an impulsive system modeling and the use of looped functionals. A generalized sector-based relation is applied to tackle the control saturation effects. From these ingredients, conditions to ensure regional asymptotic stability of the closed-loop system under aperiodic sampling are derived. Based on these conditions, LMI-based optimization problems are proposed to compute estimates of the region of attraction of the closed-loop system or to maximize the bound on the maximal admissible interval between two successive sampling instants, for which the regional stability can be ensured with respect to a given set of admissible states. A numerical example illustrates the application of the results.","Title: Recent developments on the stability of systems with aperiodic sampling: An overview.\nYear: 2017\n\nAbstract: This article presents basic concepts and recent research directions about the stability of sampled-data systems with aperiodic sampling. We focus mainly on the stability problem for systems with arbitrary time-varying sampling intervals which has been addressed in several areas of research in Control Theory. Systems with aperiodic sampling can be seen as time-delay systems, hybrid systems, Input/Output interconnections, discrete-time systems with time-varying parameters, etc. The goal of the article is to provide a structural overview of the progress made on the stability analysis problem. Without being exhaustive, which would be neither possible nor useful, we try to bring together results from diverse communities and present them in a unified manner. For each of the existing approaches, the basic concepts, fundamental results, converse stability theorems (when available), and relations with the other approaches are discussed in detail. Results concerning extensions of Lyapunov and frequency domain methods for systems with aperiodic sampling are recalled, as they allow to derive constructive stability conditions. Furthermore, numerical criteria are presented while indicating the sources of conservatism, the problems that remain open and the possible directions of improvement. At last, some emerging research directions, such as the design of stabilizing sampling sequences, are briefly discussed.","Title: Stability analysis of systems with time-varying delays via the second-order Bessel-Legendre inequality.\nYear: 2017\n\nAbstract: This paper is concerned with the delay-dependent stability of systems with time-varying delays. The novelty relies on the use of the second-order Bessel–Legendre integral inequality which is less conservative than the Jensen and Wirtinger-based inequalities. Unlike similar contributions, the features of this inequality are fully integrated into the construction of augmented Lyapunov–Krasovskii functionals leading to novel stability criteria expressed in terms of linear matrix inequalities. The stability condition is tested on some classical numerical examples illustrating the efficiency of the proposed method.","Title: LQ-based event-triggered controller co-design for saturated linear systems.\nYear: 2016\n\nAbstract: This paper deals with the simultaneous design of a state feedback law and an event-triggering condition ensuring local exponential stability and LQ performance in the presence of plant input saturation and of a communication channel between the controller output and the saturated plant input. To this aim, we adopt Lyapunov-based techniques in a hybrid framework. The design of the event-triggered control is based on two conditions: one to solve the event-triggered control co-design for LQ stabilization; the second one to adjust the co-design among all possible solutions of the first condition thanks to a tunable parameter. The proposed Lyapunov formulation yields an event-triggered algorithm to update the saturated plant input based on conditions involving the closed-loop state, while an estimate of the domain of attraction is provided. Furthermore, a trade-off is highlighted relating the optimality level, the size of the estimate of the basin of attraction and the reduction of the amount of transmissions.","Title: Weighted Consensus Protocols Design Based on Network Centrality for Multi-Agent Systems With Sampled-Data.\nYear: 2017\n\nAbstract: This technical note aims at constructing and analyzing an efficient framework for the leader-following consensus protocol in multi-agent systems (MASs). We propose two novel consensus protocols weighted by calculating the betweenness and eigenvector centralities for agent and link which are determined by the interconnection structure of MASs. The concepts of centrality were introduced in the field of social science. Ultimately, the use of the proposed protocols can be described with regard to not only the number of each agent's neighbors, which was utilized in the existing works, but also more information about agents through considering two such centralities. By utilizing the Lyapunov method and some mathematical techniques, the leader-following guaranteed cost consensus conditions for MASs with the proposed protocols and sampled-data will be established in terms of linear matrix inequalities (LMIs). Based on the result of consensus criteria, two new protocol design methods which utilize the betweenness and eigenvector centralities will be proposed. Finally, some simulation results are given to illustrate the advantages of the proposed protocols in point of the robustness on sampling interval and the transient consensus performance.","Title: Stability analysis of linear impulsive delay dynamical systems via looped-functionals.\nYear: 2017\n\nAbstract: This article investigates the asymptotic stability of impulsive delay dynamical systems (IDDS) by using the LyapunovKrasovskii method and looped-functionals. The proposed conditions reduce the conservatism of the results found in the literature by allowing the functionals to grow during both the continuous dynamics and the discrete dynamics. Sufficient conditions for asymptotic stability in the form of linear matrix inequalities (LMI) are provided for the case of impulsive delay dynamical systems with linear and time-invariant (LTI) base systems (non-impulsive actions). Several numerical examples illustrate the effectiveness of the method.","Title: Real-time control systems: feedback, scheduling and robustness.\nYear: 2017\n\nAbstract: The efficient control of real-time distributed systems, where continuous components are governed through digital devices and communication networks, needs a careful examination of the constraints arising from the different involved domains inside co-design approaches. Thanks to the robustness of feedback control, both new control methodologies and slackened real-time scheduling schemes are proposed beyond the frontiers between these traditionally separated fields. A methodology to design robust aperiodic controllers is provided, where the sampling interval is considered as a control variable of the system. Promising experimental results are provided to show the feasibility and robustness of the approach.","Title: An improved reciprocally convex inequality and an augmented Lyapunov-Krasovskii functional for stability of linear systems with time-varying delay.\nYear: 2017\n\nAbstract: This paper is concerned with stability of a linear system with a time-varying delay. First, an improved reciprocally convex inequality including some existing ones as its special cases is derived. Compared with an extended reciprocally convex inequality recently reported, the improved reciprocally convex inequality can provide a maximum lower bound with less slack matrix variables for some reciprocally convex combinations. Second, an augmented Lyapunov–Krasovskii functional is tailored for the use of a second-order Bessel–Legendre inequality. Third, a stability criterion is derived by employing the proposed reciprocally convex inequality and the augmented Lyapunov–Krasovskii functional. Finally, two well-studied numerical examples are given to show that the obtained stability criterion can produce a larger upper bound of the time-varying delay than some existing criteria.","Title: Dissipative analysis of linear coupled differential-difference systems with distributed delays.\nYear: 2017\n\nAbstract: In this paper, we present a new method for the dissipativity and stability analysis of a linear coupled differential-difference system (CDDS) with general distributed delays at both state and output. More precisely, the distributed delay terms under consideration can contain any $fL^{2}$ functions which are approximated via a class of elementary functions which includes the option of Legendre polynomials. By using this broader class of functions compared to the existing Legendre polynomials approximation approach, one can construct a Liapunov-Krasovskii functional which is parameterized by non-polynomial functions . Furthermore, a novel generalized integral inequality is also proposed to incorporate approximation error in our stability (dissipativity) conditions. Based on the proposed approximation scenario with the proposed integral inequality, sufficient conditions determining the dissipativity and stability of a CDDS are derived in terms of linear matrix inequalities. In addition, several hierarchies in terms of the feasibility of the proposed conditions are derived under certain constraints. Finally, several numerical examples are presented in this paper to show the effectiveness of our proposed methodologies.","Title: Tractable sufficient stability conditions for a system coupling linear transport and differential equations.\nYear: 2017\n\nAbstract: This paper deals with the stability analysis of a system of finite dimension coupled to a vectorial transport equation. We develop here a new method to study the stability of such a system, coupling ordinary and partial differential equations, using linear matrix inequalities led by the choice of an appropriate Lyapunov functional. To this end, we exploit Legendre polynomials and their properties, and use a Bessel inequality to measure the contribution of our approximation. The exponential stability of a wide class of delay systems is a direct consequence of this study, but above all, we are detailing here a new approach in the consideration of systems coupling infinite and finite dimensional dynamics. The coupling with a vectorial transport equation is a first step that already prove the interest of the method, bringing hierarchized conditions for stability. We will give exponential stability results and their proofs. Our approach will finally be tested on several academic examples.","Title: Stability of Linear Systems With Time-Varying Delays Using Bessel–Legendre Inequalities\nYear: 2018\n\nAbstract: This paper addresses the stability problem of linear systems with a time-varying delay. Hierarchical stability conditions based on linear matrix inequalities are obtained from an extensive use of the Bessel inequality applied to Legendre polynomials of arbitrary orders. While this inequality has been only used for constant discrete and distributed delays, this paper generalizes the same methodology to time-varying delays. We take advantages of the dependence of the stability criteria on both the delay and its derivative to propose a new definition of allowable delay sets. A light and smart modification in their definition leads to relevant conclusions on the numerical results.","Title: Comparison of bounding methods for stability analysis of systems with time-varying delays.\nYear: 2017\n\nAbstract: Integral inequalities for quadratic functions play an important role in the derivation of delay-dependent stability criteria for linear time-delay systems. Based on the Jensen inequality, a reciprocally convex combination approach was introduced by Park et al. (2011) for deriving delay-dependent stability criterion, which achieves the same upper bound of the time-varying delay as the one on the use of the Moon’s et al. inequality. Recently, a new inequality called Wirtinger-based inequality that encompasses the Jensen inequality was proposed by Seuret and Gouaisbaut (2013) for the stability analysis of time-delay systems. In this paper, it is revealed that the reciprocally convex combination approach is effective only with the use of Jensen inequality. When the Jensen inequality is replaced by Wirtinger-based inequality, the Moon’s et al.inequality together with convex analysis can lead to less conservative stability conditions than the reciprocally convex combination inequality.\nMoreover, we prove that the feasibility of an LMI condition derived by the Moon’s et al.inequality as well as convex analysis implies the feasibility of an LMI condition induced by the reciprocally convex combination inequality.\nFinally, the efficiency of the methods is demonstrated by some numerical examples, even though the corresponding system with zero-delay as well as the system without the delayed term are not stable.","Title: Closeness-Centrality-Based Synchronization Criteria for Complex Dynamical Networks With Interval Time-Varying Coupling Delays.\nYear: 2018\n\nAbstract: This paper investigates synchronization in complex dynamical networks (CDNs) with interval time-varying delays. The CDNs are representative of systems composed of a large number of interconnected dynamical units, and for the purpose of the mathematical analysis, the leading work is to model them as graphs whose nodes represent the dynamical units. At this time, we take note of the importance of ea...","Title: Generalized reciprocally convex combination lemmas and its application to time-delay systems.\nYear: 2018\n\nAbstract: Various efficient matrix inequalities have recently been proposed to deal with the stability analysis of linear systems with time-varying delays. This paper provides more insights on the relationship between some of them. We present an equivalent formulation of Moon et al.’s inequality, allowing us to discover strong links not only with the most recent and efficient matrix inequalities such as the reciprocally convex combination lemma and also its relaxed version but also with some previous inequalities such as the approximation inequality introduced in Shao (2009) or free-matrix-based inequality. More especially, it is proved that these existing inequalities can be captured as particular cases of Moon et al.’s inequality. Examples show the best tradeoff between the reduction of conservatism and the numerical complexity.","Title: Distributed Control Of Compact Formations For Multi-Robot Swarms\nYear: 2018\n\nAbstract: This article proposes a distributed algorithm for the compact deployment of robots, using both distance-and angular-based arguments in the controllers' design. Our objective is to achieve a configuration maximizing the coverage of the environment while increasing the graph's connectivity. First, we provide: (i) a dispersion protocol guaranteeing connectivity maintenance; and (ii) a compactness controller with static and variable control gains that minimizes the inter-agent angles. Second, we present a sequential, multi-stage strategy and analyse its stability. Finally, we validate our theoretical results with simulations, where a group of robots are deployed to carry out sensing or communication tasks.","Title: Wirtinger-Like Lyapunov-Krasovskii Functionals For Discrete-Time Delay Systems\nYear: 2018\n\nAbstract: Time-dependent Lyapunov functionals appeared to be very efficient for sampled-data systems. Recently, new Lyapunov functionals were constructed for sampled-data control in the presence of a constant input delay. The construction of these functionals was based on Wirtinger's inequality leading to simplified and efficient stability conditions in terms of linear matrix inequalities. In this article, we extend the latter results to the discrete-time sampled-data systems. We show that the proposed approach is less conservative on some examples with a lower number of decision variables.","Title: Lyapunov Stability Analysis of a String Equation Coupled With an Ordinary Differential System.\nYear: 2018\n\nAbstract: This paper considers the stability problem of a linear time invariant system in feedback with a string equation. A new Lyapunov functional is proposed using augmented states that enriches and encompasses the classical functionals of the literature. It results in tractable stability conditions expressed in terms of linear matrix inequalities. This methodology follows from the application of the Bes...","Title: Sufficient Stability Condition For Delayed Port-Hamiltonian Systems Subject To Input Saturation\nYear: 2018\n\nAbstract: This work deals with a class of delayed port-Hamiltonian systems with control law given as a bounded output feedback. A sufficient stability condition of the resulting closed-loop dynamics is obtained combining Wirtinger-based integral inequality and Lyapunov-Krasovkii theorem. This condition is formulated in terms of (parametrized) linear matrix inequality. A numerical example shows reduction of the conservatism regarding literature result.","Title: Static State And Output Feedback Synthesis For Time-Delay Systems\nYear: 2018\n\nAbstract: In this paper, the design of a static feedback gain for a linear system subject to an input delay is studied. This synthesis is based on a stability analysis conducted using Lyapunov-Krasovskii theorem and Bessel-Legendre inequalities. It is expressed in terms of linear matrix inequalities. Some bilinear non-convex matrix inequalities are obtained to go from analysis to synthesis. They are then difficult to solve and an iterative procedure is given which takes advantage of the elimination lemma. Naturally, slack variables are introduced and values coming from an optimization process are proposed to reduce the conservatism. The two main corollaries discuss the static state and output feedback synthesis. Finally, a comparison is proposed and shows that this formulation introduces small conservatism.","Title: Stability analysis of a system coupled to a heat equation.\nYear: 2019\n\nAbstract: As a first approach to the study of systems coupling finite and infinite dimensional natures, this article addresses the stability of a system of ordinary differential equations coupled with a classic heat equation using a Lyapunov functional technique. Inspired from recent developments in the area of time delay systems, a new methodology to study the stability of such a class of distributed parameter systems is presented here. The idea is to use a polynomial approximation of the infinite dimensional state of the heat equation in order to build an enriched energy functional. A well known efficient integral inequality (Bessel inequality) will allow to obtain stability conditions expressed in terms of linear matrix inequalities. We will eventually test our approach on academic examples in order to illustrate the efficiency of our theoretical results.","Title: Distributed estimation based on multi-hop subspace decomposition.\nYear: 2019\n\nAbstract: This paper deals with the problem of distributedly estimating the state of an LTI plant through an interconnected network of agents. The proposed approach results in an observer structure that incorporates consensus among the agents and that can be distributedly designed, achieving a robust solution with a good estimation performance. The developed solution is based on an iterative decomposition of the plant in the local observable staircase forms. The proposed observer has several positive features compared to recent results in the literature, which include milder assumptions on the network connectivity and the ability to set the convergence rate.","Title: Stability Of Distributed Delay System With Infinite Support\nYear: 2018\n\nAbstract: This paper is devoted to the stability analysis of infinite distributed delay systems. The basic idea is to model the original time-delay system into an interconnected feedback system in order to use robust analysis and especially quadratic separation. This approach has been widely used to study classical pointwise time-delay system. The stability analysis is performed by introducing new quadratic inequalities based on Laguerre polynomials particularly well fitted to deal with infinite distributed delay. It allows to develop new stability results which accuracy depends on the chosen polynomial degree.","Title: Stabilization Of An Unstable Wave Equation Using An Infinite Dimensional Dynamic Controller\nYear: 2018\n\nAbstract: This paper deals with the stabilization of an anti-stable string equation with Dirichlet actuation where the instability appears because of the uncontrolled boundary condition. Then, infinitely many unstable poles are generated and an infinite dimensional control law is therefore proposed to exponentially stabilize the system. The idea behind the choice of the controller is to extend the domain of the PDE so that the anti-damping term is compensated by a damping at the other boundary condition. Additionally, notice that the system can then be exponentially stabilized with a chosen decay-rate and is robust to uncertainties on the wave speed and the antidamped coefficient of the wave equation, with the only use of a point-wise boundary measurement. The efficiency of this new control strategy is then compared to the backstepping approach.","Title: Exponential Lyapunov Stability Analysis Of A Drilling Mechanism\nYear: 2018\n\nAbstract: This article deals with the stability analysis of a drilling system which is modelled as a coupled ordinary differential equation / string equation. The string is damped at the two boundaries but leading to a stable open-loop system. The aim is to derive a linear matrix inequality ensuring the exponential stability with a guaranteed decay-rate of this interconnected system. A strictly proper dynamic controller based on boundary measurements is proposed to accelerate the system dynamics and its effects are investigated through the stability theorem and simulations. It results in an efficient finite dimension controller which subsequently improves the system performances.","Title: Circular formation control for cooperative target tracking with limited information.\nYear: 2019\n\nAbstract: This paper addresses the problem of encircling and tracking a moving target with a fleet of unicycle-like vehicles. A new control law is developed to steer the vehicles to an evenly spaced formation along a circumference, the center of which tracks the motion of the target. The strategy proposed relies only on the relative positions of the agents with respect to the target, expressed in the local frame of each vehicle. The absolute position, velocity and acceleration of the target are unknown. Additionally, the robustness of the proposed control law in the presence of external disturbances is analyzed. Communication among agents is used to maintain the vehicles equally spaced in the circular formation. Simulation results illustrate the effectiveness of the proposed strategies.","Title: A Nonsmooth Hybrid Invariance Principle Applied to Robust Event-Triggered Design\nYear: 2019\n\nAbstract: We first propose a nonsmooth hybrid invariance principle with relaxed conditions stemming from the fact that flowing solutions evolve only in the tangent cone, and complete jumping solutions cannot jump outside the jump and flow sets. We then show an application consisting in the design of event-triggered rules to stabilize a class of uncertain linear control systems. The event-triggering rule depends only on local information, that is it uses only the output signals available to the controller. The approach proposed combines hybrid and sampled-data tools. The proposed design conditions are formulated in terms of linear matrix inequalities (LMIs) ensuring global robust asymptotic stability of the closed-loop system. A tunable parameter is also available to guarantee an adjustable dwell-time property of the solutions. The effectiveness of the approach is evaluated through an example borrowed from the literature.","Title: Synchronization on a limit cycle of multi-agent systems governed by discrete-time switched affine dynamics\nYear: 2021\n\nAbstract: This paper addresses the problem of synchronizing a group of interacting discrete-time switched affine systems with centralized control laws. Two different state-dependent control laws are proposed, based on recent literature on switched affine systems and are evaluated on an academical example with a multi-agent system.","Title: Delayed Stabilization Of Parabolic Pdes Via Augmented Lyapunov Functionals And Legendre Polynomials\nYear: 2021\n\nAbstract: We first study stabilization of heat equation with globally Lipschitz nonlinearity. We consider the point measurements with constant delay and use spatial decomposition. Inspired by recent developments in the area of ordinary differential equations (ODEs) with time-delays, for the stability analysis, we suggest an augmented Lyapunov functional depending on the state derivative that is based on Legendre polynomials. Global exponential stability conditions are derived in terms of linear matrix inequalities (LMIs) that depend on the degree N of Legendre polynomials. The stability conditions form a hierarchy of LMIs: if the LMIs hold for N, they hold for N + 1. The dual observer design problem with constant delay is also formulated. We further consider stabilization of Korteweg-de Vries-Burgers (KdVB) equation using the point measurements with constant delay. Due to the third-order partial derivative in KdVB equation, the Lyapunov functionals that depend on the state derivative are not applicable here, which is different from the case of heat equation. We suggest a novel augmented Lyapunov functional depending on the state only that leads to improved regional stability conditions in terms of LMIs. Finally, numerical examples illustrate the efficiency of the method. (C) 2021 Elsevier B.V. All rights reserved.","Title: Distributed Hybrid Observer With Prescribed Convergence Rate for a Linear Plant Using Multi-Hop Decomposition.\nYear: 2023\n\nAbstract: With a continuous-time formulation of the multihop decomposition, we propose a distributed hybrid observer for a sensor network where the plant and local observers run in continuous time and the information exchange among the sensing nodes is sampled-data. Process disturbances, measurement noise and communication noise are considered, and we prove that under some necessary detectability assumptions the observer gains can be tuned to guarantee exponential ISS with a prescribeda convergence rate. Simulations illustrate the performance of the proposed observer.","Title: Distributed Hybrid Observer With Prescribed Convergence Rate for a Linear Plant Using Multi-Hop Decomposition\nYear: 2023\n\nAbstract: We propose a distributed hybrid observer for a sensor network where the plant and local observers run in continuous time and the information exchange among the sensing nodes is sampled-data. Process disturbances, measurement noise and communication noise are considered, and we prove that under some necessary detectability assumptions the observer gains can be tuned to guarantee exponential Input-to-State Stability with a prescribed convergence rate. Simulations illustrate the performance of the proposed observer.","Title: Stability analysis of an ordinary differential equation interconnected with the reaction–diffusion equation\nYear: 2022\n\nAbstract: This paper deals with the stability analysis of the reaction–diffusion equation interconnected with a finite-dimensional system. In this situation, stability is no longer straightforward to assess, and one needs to look for dedicated tools to provide accurate numerical stability tests. Here, the objective is to provide a Lyapunov analysis leading to efficient and precise stability criteria. Thanks to the Legendre orthogonal basis, the study is possible using dedicated Lyapunov functionals. Such functionals contain information based on the state of an augmented finite-dimensional system and a remaining infinite-dimensional system, associated with the first Legendre coefficients and the remainder of the Legendre expansion, respectively. Furthermore, the Legendre’s development provides efficient formulations of Bessel and Wirtinger inequalities and leads to sufficient stability conditions expressed as scalable linear matrix inequalities. Numerical examples finally illustrate the accuracy and the potential of such stability results."],"string":"[\n \"Title: Design of a Pressure Control System With Dead Band and Time Delay\\nYear: 2007\\n\\nAbstract: This paper investigates the control of pressure in a hydraulic circuit containing a dead band and a time varying delay. The dead band is considered as a linear term and a perturbation. A sliding mode controller is designed. Stability conditions are established by making use of Lyapunov Krasovskii functionals, non-perfect time delay estimation is studied and a condition for the effect of uncertainties on the dead zone on stability is derived. Also the effect of different LMI formulations on conservativeness is studied. The control law is tested in practice.\",\n \"Title: A looped-functional approach for robust stability analysis of linear impulsive systems\\nYear: 2012\\n\\nAbstract: A new functional-based approach is developed for the stability analysis of linear impulsive systems. The new method, which introduces looped functionals, considers non-monotonic Lyapunov functions and leads to LMI conditions devoid of exponential terms. This allows one to easily formulate dwell-time results, for both certain and uncertain systems. It is also shown that this approach may be applied to a wider class of impulsive systems than existing methods. Some examples, notably on sampled-data systems, illustrate the efficiency of the approach.\",\n \"Title: Stability Analysis of Sampled-Data Systems Using Sum of Squares.\\nYear: 2013\\n\\nAbstract: This technical brief proposes a new approach to stability analysis of linear systems with sampled-data inputs or channels. The method, based on a variation of the discrete-time Lyapunov approach, provides stability conditions using functional variables subject to convex constraints. These stability conditions can be solved using the sum of squares methodology with little or no conservatism in both the case of synchronous and asynchronous sampling. Numerical examples are included to show convergence.\",\n \"Title: A novel stability analysis of linear systems under asynchronous samplings.\\nYear: 2012\\n\\nAbstract: This article proposes a novel approach to assess the stability of continuous linear systems with sampled-data inputs. The method, which is based on the discrete-time Lyapunov theorem, provides easy tractable stability conditions for the continuous-time model. Sufficient conditions for asymptotic and exponential stability are provided dealing with synchronous and asynchronous samplings and uncertain systems. An additional stability analysis is provided for the cases of multiple sampling periods and packet losses. Several examples show the efficiency of the method.\",\n \"Title: Collaborative estimation of gradient direction by a formation of AUVs.\\nYear: 2011\\n\\nAbstract: This work deals with the source-seeking problem in which the task is to locate the source of some signal using a fleet of AUVs (autonomous underwater vehicles). The present paper proposes a distributed solution in which a group of vehicles uniformly distributed in a fixed circular formation, estimates the gradient direction of the signal propagation. The distributed algorithm takes into account the communication constraints and depends on direct signal measurements. Our approach is based on the previous results in formation control to stabilize the fleet in a circular formation with time-varying center and in a collaborative source-seeking algorithm. The results are supported through computer simulations.\",\n \"Title: Static Output Feedback Sliding Mode Control Design via an Artificial Stabilizing Delay\\nYear: 2009\\n\\nAbstract: It is well known that for linear, uncertain systems, a static output feedback sliding mode controller can only be deter- mined if a particular triple associated with the reduced order dy- namics in the sliding mode is stabilizable. This paper shows that the staticoutput feedbackslidingmodecontrol designproblemcan be solved for a broader class of systems if a known delay term is deliberately introduced into the switching function. Effectively the reduced order sliding mode dynamics are stabilized by the intro- duction of this artificial delay.\",\n \"Title: Collaborative Estimation Of Gradient Direction By A Formation Of Auvs Under Communication Constraints\\nYear: 2011\\n\\nAbstract: This work deals with the source-seeking problem in which the task is to locate the source of some signal using a fleet of Autonomous Underwater Vehicles (AUVs). The present paper proposes a distributed solution in which a group of vehicles uniformly distributed in a fixed circular formation, estimates the gradient direction of the signal propagation. The distributed algorithm takes into account the communication constraints and depends on direct signal measurements. Our approach is based on the previous results in formation control to stabilize the fleet in a circular formation with time-varying center and in a collaborative source-seeking algorithm. The results are supported through computer simulations.\",\n \"Title: Taking into account period variations and actuator saturation in sampled-data systems.\\nYear: 2012\\n\\nAbstract: This paper deals with the problem of stability and stabilization of sampled-data systems under asynchronous samplings and actuator saturation. The method is based, on the one hand, on the use of a novel class of Lyapunov functionals whose derivative is negative along the trajectories of the continuous-time model of the sampled data system. It is shown that this fact guarantees that a quadratic Lyapunov function is strictly decreasing for the discrete-time asynchronous system. On the other hand, the control saturation is taken into account from the use of a generalized sector condition. These ingredients lead to the formulation of improved LMI conditions that can be cast in optimization problems aiming at enlarging estimates of the region of attraction of the closed-loop system or maximizing the bounds on the sampling period jitter for which stability and stabilization are ensured.\",\n \"Title: Robust Control Under Weakened Real-Time Constraints\\nYear: 2011\\n\\nAbstract: A weakened implementation scheme for real-time feedback controllers is proposed to reduce the conservatism due to traditional worst-cases considerations, while preserving the stability and control performance. Based on recent results to assess stability of linear systems with delayed and sampled-data inputs, this paper takes into account both the effects of deadline misses of control tasks and uncertainties in the plant. The methodology is applied to the pitch control of an aircraft, showing that weakening the real-time constraints allows for saving computing power while preserving the system's stability.\",\n \"Title: Event-Triggered Sampling Algorithms Based On A Lyapunov Function\\nYear: 2011\\n\\nAbstract: Usually feedback laws are implemented in a periodic fashion on digital hardware. The main reason for using this periodicity in the hardware comes from the difficulties to analyze the stability of aperiodic or asynchronous systems. However it also seems natural to hold the same control input longer if the system behaves in a suitable way or shorter if the system requires an updated input. In this paper, an algorithm is suggested to sample the control input based on the behavior of a Lyapunov-like function. This algorithm is called event-triggered since the Lyapunov-like function directly depends on the state of the systems.\",\n \"Title: Implementation of an Internet-controlled system under variable delays\\nYear: 2006\\n\\nAbstract: This work deals with the control and the observation of a remote system using Internet as a communication line. The process consists in a Slave part S, with poor computing capacity, connected to a Master M through the Internet. The internal times of both M and S are synchronized thanks to a common GPS clock. The global system must ensure some speed performance whatever the delay variations. The main technical restriction is that the delays are less than some known bound, even if the framework we consider takes into account the perturbations generated by the Internet communication delays, by the packet losses and by the sampling phenomenon as well. In what concerns the design of the observer and controller, most of the theorems to be used here refer to proofs given in (Seuret et al., 2006), and this will allow for focussing on the implementation aspects.\",\n \"Title: Robust sampled-data stabilization of linear systems: an input delay approach\\nYear: 2004\\n\\nAbstract: A new approach to robust sampled-data control is introduced. The system is modelled as a continuous-time one, where the control input has a piecewise-continuous delay. Sufficient linear matrix inequalities (LMIs) conditions for sampled-data state-feedback stabilization of such systems are derived via descriptor approach to time-delay systems. The only restriction on the sampling is that the distance between the sequel sampling times is not greater than some prechosen h>0 for which the LMIs are feasible. For h→0 the conditions coincide with the necessary and sufficient conditions for continuous-time state-feedback stabilization. Our approach is applied to two problems: to sampled-data stabilization of systems with polytopic type uncertainities and to regional stabilization by sampled-data saturated state-feedback.\",\n \"Title: Improved Consensus Algorithms Using Memory Effects\\nYear: 2011\\n\\nAbstract: This paper deals with simple integrator consensus problems. The objective is the design of an improved consensus algorithm for continuous-time multi-agent systems using memory effects. The novel algorithm proposes to sample, in an appropriate manner, part of the multi-agent systems information such that the algorithm converges, assuming that at each instant, agent's control laws will also consider the sampled past information of its neighbors. Stability conditions expressed in terms of LMI's and based on algebraic communication matrix structure are provided. The efficiency of the method is tested for different network communication schemes.\",\n \"Title: Translation Control Of A Fleet Circular Formation Of Auvs Under Finite Communication Range\\nYear: 2009\\n\\nAbstract: This work proposes a control algorithm to stabilize a circular formation of AUVs tracking a time-varying center. We also consider the problem of uniform distribution of all the agents along the circle from two approaches: all-to-all and limited communication. We tackle with this communication constraint using a cooperative control strategy which includes the Laplacian matrix of the communication graph (fixed or distance-dependent). The system was implemented in computer simulation, accessible though Web(1).\",\n \"Title: Cooperative Control Design for Time-Varying Formations of Multi-Agent Systems.\\nYear: 2014\\n\\nAbstract: This paper deals with cooperative control design for nonlinear multi-agent systems. The control objective is to ensure that a group of agents reaches a formation characterized by external time-varying parameters. First, a translation control design is presented to stabilize the multi-agent system to a circular motion tracking a time-varying center. Then, we propose a new framework based on affine transformations to extend previous results to more complex time-varying formations. Moreover, both control laws are improved adding a cooperative term to distribute the agents uniformly along the formation.\",\n \"Title: Stability of non-linear systems by means of event-triggered sampling algorithms.\\nYear: 2014\\n\\nAbstract: In this paper, the problem of feedback control implementation for non-linear systems is considered. Some conditions for holding the same control input until an event occurs are derived. With respect to classical approaches, where feedback laws are implemented in a periodical fashion, it is suggested new algorithms to use the same control input. By means of these algorithms, some jumps of the contr...\",\n \"Title: Jensen's and Wirtinger's Inequalities for Time-Delay Systems.\\nYear: 2013\\n\\nAbstract: In the huge literature dedicated to stability of time-delay systems, the most popular approach remains the use of Lyapunov-Krasovskii functionals. This framework allows to study a large class of time-delay systems including constant or time-varying delays. Since several years, the main challenge is to propose new functionals and techniques for deriving less and less conservative stability conditions. Nevertheless, all these approaches usually adopt the same procedure which is based on the well-known Jensen's inequality which generally induces some conservatism difficult to overcome. This paper analyses firstly the conservatism induced by the Jensen's inequality and secondly proposes a wide class of new parametrized inequalities. All these are based on an extensive use of Wirtinger inequality which has been recently introduced in Liu and Fridman [2012] and Seuret and Gouaisbaut [2012b] for stability analysis.\",\n \"Title: On the Use of the Wirtinger Inequalities for Time-Delay Systems.\\nYear: 2012\\n\\nAbstract: The paper addresses the stability problem of linear time delay system. In the literature, the most popular approach to tackle this problem relies on the use of Lyapunov-Krasovskii functionals. Many results have proposed new functionals and techniques for deriving less and less conservative stability conditions. Nevertheless, all these approaches use the same trick, the well-known Jensen's inequality which generally induces some conservatism difficult to overcome. In light of those observations, we propose to reduce the conservatism of Lyapunov-Krasovskii functionals by introducing new classes of integral inequalities called Wirtinger inequalities. This integral type inequality is firstly shown to encompass Jensen's inequality and is then employed to derive new stability conditions. To this end, a slightly modified Lyapunov functional is proposed. Several examples illustrate the effectiveness of our methodology.\",\n \"Title: Robust stability for delayed port-Hamiltonian systems using improved Wirtinger-based inequality\\nYear: 2014\\n\\nAbstract: This paper addresses robust stability issues of interconnected port-Hamiltonian systems with polytopic uncertainty and time-varying delay. On the basis of a Lyapunov-Krasovskii functional and the Wirtinger's inequality (known to be less conservative than the popular Jensen's inequality) we show the improvements of the newly proposed criterion with respect to other existing ones. The stability analysis is derived based on a delay independent criterion. A classical nonlinear example taken from the literature illustrates the relevance of the results.\",\n \"Title: Stability analysis of asynchronous sampled-data systems with discrete-time constant input delay\\nYear: 2014\\n\\nAbstract: Aperiodic sampled-data systems controlled by a delayed state-feedback controller arise naturally in networked control systems. We propose a novel way to analyze the stability of sampled-data systems with discrete-time constant input delay by, first, reformulating the system into an aperiodic discrete-time system with constant delay and by, then, considering a mixture of a discrete-time Lyapunov-Krasovskii functional and a looped-functional in order to account for the presence of the delay and the sampling aperiodicity, respectively. Stability criteria, expressed as linear matrix inequalities, are derived for both certain and uncertain (time-varying) systems. An example is given for illustration.\",\n \"Title: Hybrid control of a three-agent network cluster\\nYear: 2014\\n\\nAbstract: We consider the management problem for a cluster of three identical servers treating an external request. We assume that each server has a constant computational speed and we build a deterministic hybrid dynamical model of the overall system. Then, inspired by consensus theory, we propose a centralized control law distributing the computational load among the servers and deciding when to turn on or off each server, for energy efficiency, while ensuring that the queues of each server asymptotically converge to a desirable level. We prove the properties of the proposed law and highlight some interesting peculiarities in terms of non-uniform convergence.\",\n \"Title: Networked control using GPS synchronization\\nYear: 2006\\n\\nAbstract: This work concerns the control, the observation and then, the implementation principles of a remote system (Master and Slave parts) through the Internet network. This communication link introduces variable delays that have to be taken into account in the control-observation loop. The data-sampling effects will also be considered, even in the aperiodic case. The Slave part is considered to be a linear system. But, since its computation power is supposed to be limited, the control complexity (which, here, is an observer- based state feedback) has to stay in the Master part. The global system must ensure speed performance whatever the delay variation. Such a performance is obtained by showing the robust, exponential stability property, which is proven by using adequate Lyapunov-Krasovskii functionals. This makes possible to compute the controller and observer gains by using LMI optimization. The technical solution we propose is based on a GPS system, which guarantees the Master and Slave clocks to be synchronized. Then, the control and measurement packets are sent together with time-stamps that allow for reconstructing a non-symmetric delay information. It means that Master-to-Slave and Slave-to-Master delays are separately reconstructed by the system (and not only the global RTT, round-trip-time). The last part of the paper provides an example where the Slave is a second-order system.\",\n \"Title: Consensus under communication delays\\nYear: 2008\\n\\nAbstract: This paper deals with the consensus problem under communication network inducing delays. It is well-known that introducing a delay leads in general to a reduction of the performance or to instability due to the fact that time- delay systems are infinite dimensional. For instance, the set of initial conditions of a time-delay system is not a vector but a function taken in an interval. Therefore, investigating the effect of time-delays in the consensus problem is an important issue. In the present paper, we assume that each agent receives instantaneously its own state information but receives the state information from its neighbors after a constant delay. Two stability criteria are provided based on the frequency approach and on Lyapunov-Krasovskii techniques given in terms of LMI. An analytic expression of the consensus equilibrium which depends on the delay and on the initial conditions taken in an interval is derived. The efficiency of the method is tested for different network communication schemes.\",\n \"Title: Convex Dwell-Time Characterizations for Uncertain Linear Impulsive Systems\\nYear: 2012\\n\\nAbstract: New sufficient conditions for the characterization of dwell-times for linear impulsive systems are proposed and shown to coincide with continuous decrease conditions of a certain class of looped-functionals, a recently introduced type of functionals suitable for the analysis of hybrid systems. This approach allows to consider Lyapunov functions that evolve nonmonotonically along the flow of the system in a new way, broadening then the admissible class of systems which may be analyzed. As a byproduct, the particular structure of the obtained conditions makes the method is easily extendable to uncertain systems by exploiting some convexity properties. Several examples illustrate the approach.\",\n \"Title: Affine Characterizations of Minimal and Mode-Dependent Dwell-Times for Uncertain Linear Switched Systems\\nYear: 2012\\n\\nAbstract: An alternative approach for minimum and mode-dependent dwell-time characterization for switched systems is derived. While minimum-dwell time results require the subsystems to be asymptotically stable, mode-dependent dwell-time results can consider unstable subsystems and dwell-times within a, possibly unbounded, range of values. The proposed approach is related to Lyapunov looped-functionals, a new type of functionals leading to stability conditions affine in the system matrices, unlike standard results for minimum dwell-time. These conditions are expressed as infinite-dimensional LMIs which can be solved using recent polynomial optimization techniques such as sum-of-squares. The specific structure of the conditions is finally utilized in order to derive dwell-time stability results for uncertain switched systems. Several examples illustrate the efficiency of the approach.\",\n \"Title: Bessel inequality for robust stability analysis of time-delay system\\nYear: 2013\\n\\nAbstract: This paper addresses the problem of the stability analysis for a linear time-delay systems via a robust analysis approach and especially the quadratic separation framework. To this end, we use the Bessel inequality for building operators that depend on the delay. They not only allow us to model the system as an uncertain feedback system but also to control the accuracy of the approximations made. Then, a set of LMIs conditions are proposed which tends on examples to the analytical bounds for both delay dependent stability and delay range stability.\",\n \"Title: Elastic formation control based on affine transformations\\nYear: 2011\\n\\nAbstract: This paper deals with the control of a fleet of non-linear systems representing AUVs (autonomous underwater vehicles). The purpose is here to design a control law to stabilize the fleet to time-varying formations which are not only circular. A novel framework is proposed to express a general control law for a large class of formations. This is produced by applying a sequence of affine transformations such as translations, rotations and scalings. The paper also includes a cooperative control to distribute the agents along the formation which takes into account the communication constraints. The system was implemented in computer simulation, accessible through Web.\",\n \"Title: Stability analysis for sampled-data systems with a time-varying period\\nYear: 2009\\n\\nAbstract: This paper proposes a novel stability analysis of linear systems with sampled-data inputs. Inspired by the input- delay approach and the stability of impulsive systems, this method provides novel sufficient stability conditions. The stabil- ity analysis concerns both constant and time-varying sampling periods. The delay-dependent conditions are expressed using computable linear matrix inequalities. Several examples show the efficiency and the limitation of such stability criteria. Index Terms— Sampled data controller, Time-delay, delay- dependent stability, Lyapunov-Krasovskii functionals (LKF), linear matrix inequality (LMI)\",\n \"Title: Contraction Control of a Fleet Circular Formation of AUVs under Limited Communication Range\\nYear: 2010\\n\\nAbstract: This work proposes a novel control algorithm ded- icated to multi-agent systems with nonlinear dynamics. The aim of this paper is for the agents to form a circular formation whose center is fixed and whose radius is given by a time-varying reference. The problem of uniform distribution of all the agents along the circle is also addressed under the assumption of limited communication range. This communication constraint is tackled by using a cooperative control scheme which includes the Laplacian matrix of the communication graph (distance- dependent). The multi-agent system is simulated with Matlab. Videos showing the simulations are accessible though Web1.\",\n \"Title: Stability of Discrete-Time Systems with Time-Varying Delays via a Novel Summation Inequality\\nYear: 2015\\n\\nAbstract: This paper is concerned with the stability analysis of discrete linear systems with time-varying delays. The novelty of the paper comes from the consideration of a new inequality which is less conservative than the celebrated Jensen inequality employed in the context of discretetime delay systems. This inequality is a discrete-time counterpart of the Wirtinger-based integral inequality that was recently employed for the improved analysis of continuous-tine systems with delays. However, differently from the continuous-time case, the proof of the new inequality is not based on the Wirtinger inequality. The method is also combined with an efficient representation of the improved reciprocally convex combination inequality in order to reduce the conservatism induced by the LMIs optimization setup. The effectiveness of the proposed result is illustrated by some classical examples from the literature.\",\n \"Title: Exponential stability of a PI plus reset integrator controller by a sampled-data system approach.\\nYear: 2016\\n\\nAbstract: The paper deals with the stability analysis of time delay reset control systems, for which the resetting law is assumed to satisfy a time-dependent condition. A stability analysis of the closed-loop system is performed based on an appropriate sampled-data system. New linear matrix inequality (LMI) conditions are proposed to ensure the exponential stability of the closed-loop system resulting from the connection of a plant with a proportional and integral controller together with a reset integrator (PI+RI).\",\n \"Title: Hierarchy of LMI conditions for the stability analysis of time-delay systems\\nYear: 2015\\n\\nAbstract: Assessing stability of time-delay systems based on the Lyapunov–Krasovskii functionals has been the subject of many contributions. Most of the results are based, first, on an a priori design of functionals and, finally, on the use of the famous Jensen’s inequality. In contrast with this design process, the present paper aims at providing a generic set of integral inequalities which are asymptotically non conservative and then to design functionals driven by these inequalities. The resulting stability conditions form a hierarchy of LMI which is competitive with the most efficient existing methods (delay-partitioning, discretization and sum of squares), in terms of conservatism and of complexity. Finally, some examples show the efficiency of the method.\",\n \"Title: On The Necessity Of Looped-Functionals Arising In The Analysis Of Pseudo-Periodic, Sampled-Data And Hybrid Systems\\nYear: 2015\\n\\nAbstract: Looped-functionals have been shown to be relevant for the analysis of a wide variety of systems. However, the conditions obtained in previous papers on the analysis of sampled-data, impulsive and switched systems have only been shown to be sufficient for the characterisation of their associated discrete-time stability conditions. We prove here that these conditions are also necessary. This result is derived for a wider class of linear systems, referred to as impulsive pseudo-periodic systems, that encompass periodic, impulsive, sampled-data and switched systems as special cases.\",\n \"Title: Complete quadratic Lyapunov functionals for distributed delay systems\\nYear: 2015\\n\\nAbstract: This paper is concerned with the stability analysis of distributed delay systems using complete-Lyapunov functionals. Numerous articles aim at approximating their parameters thanks to a discretization method or polynomial modeling. The interest of such approximations is the design of tractable sufficient stability conditions. In the present article, we provide an alternative method based on polynomial approximation which takes advantages of the Legendre polynomials and their properties. The resulting stability conditions are scalable with respect to the maximum degree of the polynomials and are expressed in terms of tractable linear matrix inequalities. Several examples of delayed systems are tested to show the effectiveness of the method.\",\n \"Title: Stability analysis of uncertain sampled-data systems with incremental delay using looped-functionals\\nYear: 2015\\n\\nAbstract: The robust stability analysis of asynchronous and uncertain sampled-data systems with constant incremental input delay is addressed in the looped-functional framework. These functionals have been shown to be suitable for the analysis of impulsive systems as they allow one to express discrete-time stability conditions in an affine way, enabling then the consideration of uncertain and time-varying systems. The stability conditions are obtained by first reformulating the sampled-data system as an impulsive system, and by then considering a tailored looped-functional along with Wirtinger's inequality, a recently introduced inequality that has been shown to be less conservative than Jensen's inequality. Several examples are given for illustration.\",\n \"Title: Regional Stability Analysis of Discrete-Time Dynamic Output Feedback Under Aperiodic Sampling and Input Saturation.\\nYear: 2016\\n\\nAbstract: This technical note addresses the problem of regional stability analysis of a continuous-time plant controlled by a discrete-time dynamic output feedback control law with saturation constraints under aperiodic sampling. To cope with the aperiodic sampling problem, the proposed approach is based on an impulsive system modeling and the use of looped functionals. A generalized sector-based relation is applied to tackle the control saturation effects. From these ingredients, conditions to ensure regional asymptotic stability of the closed-loop system under aperiodic sampling are derived. Based on these conditions, LMI-based optimization problems are proposed to compute estimates of the region of attraction of the closed-loop system or to maximize the bound on the maximal admissible interval between two successive sampling instants, for which the regional stability can be ensured with respect to a given set of admissible states. A numerical example illustrates the application of the results.\",\n \"Title: Recent developments on the stability of systems with aperiodic sampling: An overview.\\nYear: 2017\\n\\nAbstract: This article presents basic concepts and recent research directions about the stability of sampled-data systems with aperiodic sampling. We focus mainly on the stability problem for systems with arbitrary time-varying sampling intervals which has been addressed in several areas of research in Control Theory. Systems with aperiodic sampling can be seen as time-delay systems, hybrid systems, Input/Output interconnections, discrete-time systems with time-varying parameters, etc. The goal of the article is to provide a structural overview of the progress made on the stability analysis problem. Without being exhaustive, which would be neither possible nor useful, we try to bring together results from diverse communities and present them in a unified manner. For each of the existing approaches, the basic concepts, fundamental results, converse stability theorems (when available), and relations with the other approaches are discussed in detail. Results concerning extensions of Lyapunov and frequency domain methods for systems with aperiodic sampling are recalled, as they allow to derive constructive stability conditions. Furthermore, numerical criteria are presented while indicating the sources of conservatism, the problems that remain open and the possible directions of improvement. At last, some emerging research directions, such as the design of stabilizing sampling sequences, are briefly discussed.\",\n \"Title: Stability analysis of systems with time-varying delays via the second-order Bessel-Legendre inequality.\\nYear: 2017\\n\\nAbstract: This paper is concerned with the delay-dependent stability of systems with time-varying delays. The novelty relies on the use of the second-order Bessel–Legendre integral inequality which is less conservative than the Jensen and Wirtinger-based inequalities. Unlike similar contributions, the features of this inequality are fully integrated into the construction of augmented Lyapunov–Krasovskii functionals leading to novel stability criteria expressed in terms of linear matrix inequalities. The stability condition is tested on some classical numerical examples illustrating the efficiency of the proposed method.\",\n \"Title: LQ-based event-triggered controller co-design for saturated linear systems.\\nYear: 2016\\n\\nAbstract: This paper deals with the simultaneous design of a state feedback law and an event-triggering condition ensuring local exponential stability and LQ performance in the presence of plant input saturation and of a communication channel between the controller output and the saturated plant input. To this aim, we adopt Lyapunov-based techniques in a hybrid framework. The design of the event-triggered control is based on two conditions: one to solve the event-triggered control co-design for LQ stabilization; the second one to adjust the co-design among all possible solutions of the first condition thanks to a tunable parameter. The proposed Lyapunov formulation yields an event-triggered algorithm to update the saturated plant input based on conditions involving the closed-loop state, while an estimate of the domain of attraction is provided. Furthermore, a trade-off is highlighted relating the optimality level, the size of the estimate of the basin of attraction and the reduction of the amount of transmissions.\",\n \"Title: Weighted Consensus Protocols Design Based on Network Centrality for Multi-Agent Systems With Sampled-Data.\\nYear: 2017\\n\\nAbstract: This technical note aims at constructing and analyzing an efficient framework for the leader-following consensus protocol in multi-agent systems (MASs). We propose two novel consensus protocols weighted by calculating the betweenness and eigenvector centralities for agent and link which are determined by the interconnection structure of MASs. The concepts of centrality were introduced in the field of social science. Ultimately, the use of the proposed protocols can be described with regard to not only the number of each agent's neighbors, which was utilized in the existing works, but also more information about agents through considering two such centralities. By utilizing the Lyapunov method and some mathematical techniques, the leader-following guaranteed cost consensus conditions for MASs with the proposed protocols and sampled-data will be established in terms of linear matrix inequalities (LMIs). Based on the result of consensus criteria, two new protocol design methods which utilize the betweenness and eigenvector centralities will be proposed. Finally, some simulation results are given to illustrate the advantages of the proposed protocols in point of the robustness on sampling interval and the transient consensus performance.\",\n \"Title: Stability analysis of linear impulsive delay dynamical systems via looped-functionals.\\nYear: 2017\\n\\nAbstract: This article investigates the asymptotic stability of impulsive delay dynamical systems (IDDS) by using the LyapunovKrasovskii method and looped-functionals. The proposed conditions reduce the conservatism of the results found in the literature by allowing the functionals to grow during both the continuous dynamics and the discrete dynamics. Sufficient conditions for asymptotic stability in the form of linear matrix inequalities (LMI) are provided for the case of impulsive delay dynamical systems with linear and time-invariant (LTI) base systems (non-impulsive actions). Several numerical examples illustrate the effectiveness of the method.\",\n \"Title: Real-time control systems: feedback, scheduling and robustness.\\nYear: 2017\\n\\nAbstract: The efficient control of real-time distributed systems, where continuous components are governed through digital devices and communication networks, needs a careful examination of the constraints arising from the different involved domains inside co-design approaches. Thanks to the robustness of feedback control, both new control methodologies and slackened real-time scheduling schemes are proposed beyond the frontiers between these traditionally separated fields. A methodology to design robust aperiodic controllers is provided, where the sampling interval is considered as a control variable of the system. Promising experimental results are provided to show the feasibility and robustness of the approach.\",\n \"Title: An improved reciprocally convex inequality and an augmented Lyapunov-Krasovskii functional for stability of linear systems with time-varying delay.\\nYear: 2017\\n\\nAbstract: This paper is concerned with stability of a linear system with a time-varying delay. First, an improved reciprocally convex inequality including some existing ones as its special cases is derived. Compared with an extended reciprocally convex inequality recently reported, the improved reciprocally convex inequality can provide a maximum lower bound with less slack matrix variables for some reciprocally convex combinations. Second, an augmented Lyapunov–Krasovskii functional is tailored for the use of a second-order Bessel–Legendre inequality. Third, a stability criterion is derived by employing the proposed reciprocally convex inequality and the augmented Lyapunov–Krasovskii functional. Finally, two well-studied numerical examples are given to show that the obtained stability criterion can produce a larger upper bound of the time-varying delay than some existing criteria.\",\n \"Title: Dissipative analysis of linear coupled differential-difference systems with distributed delays.\\nYear: 2017\\n\\nAbstract: In this paper, we present a new method for the dissipativity and stability analysis of a linear coupled differential-difference system (CDDS) with general distributed delays at both state and output. More precisely, the distributed delay terms under consideration can contain any $fL^{2}$ functions which are approximated via a class of elementary functions which includes the option of Legendre polynomials. By using this broader class of functions compared to the existing Legendre polynomials approximation approach, one can construct a Liapunov-Krasovskii functional which is parameterized by non-polynomial functions . Furthermore, a novel generalized integral inequality is also proposed to incorporate approximation error in our stability (dissipativity) conditions. Based on the proposed approximation scenario with the proposed integral inequality, sufficient conditions determining the dissipativity and stability of a CDDS are derived in terms of linear matrix inequalities. In addition, several hierarchies in terms of the feasibility of the proposed conditions are derived under certain constraints. Finally, several numerical examples are presented in this paper to show the effectiveness of our proposed methodologies.\",\n \"Title: Tractable sufficient stability conditions for a system coupling linear transport and differential equations.\\nYear: 2017\\n\\nAbstract: This paper deals with the stability analysis of a system of finite dimension coupled to a vectorial transport equation. We develop here a new method to study the stability of such a system, coupling ordinary and partial differential equations, using linear matrix inequalities led by the choice of an appropriate Lyapunov functional. To this end, we exploit Legendre polynomials and their properties, and use a Bessel inequality to measure the contribution of our approximation. The exponential stability of a wide class of delay systems is a direct consequence of this study, but above all, we are detailing here a new approach in the consideration of systems coupling infinite and finite dimensional dynamics. The coupling with a vectorial transport equation is a first step that already prove the interest of the method, bringing hierarchized conditions for stability. We will give exponential stability results and their proofs. Our approach will finally be tested on several academic examples.\",\n \"Title: Stability of Linear Systems With Time-Varying Delays Using Bessel–Legendre Inequalities\\nYear: 2018\\n\\nAbstract: This paper addresses the stability problem of linear systems with a time-varying delay. Hierarchical stability conditions based on linear matrix inequalities are obtained from an extensive use of the Bessel inequality applied to Legendre polynomials of arbitrary orders. While this inequality has been only used for constant discrete and distributed delays, this paper generalizes the same methodology to time-varying delays. We take advantages of the dependence of the stability criteria on both the delay and its derivative to propose a new definition of allowable delay sets. A light and smart modification in their definition leads to relevant conclusions on the numerical results.\",\n \"Title: Comparison of bounding methods for stability analysis of systems with time-varying delays.\\nYear: 2017\\n\\nAbstract: Integral inequalities for quadratic functions play an important role in the derivation of delay-dependent stability criteria for linear time-delay systems. Based on the Jensen inequality, a reciprocally convex combination approach was introduced by Park et al. (2011) for deriving delay-dependent stability criterion, which achieves the same upper bound of the time-varying delay as the one on the use of the Moon’s et al. inequality. Recently, a new inequality called Wirtinger-based inequality that encompasses the Jensen inequality was proposed by Seuret and Gouaisbaut (2013) for the stability analysis of time-delay systems. In this paper, it is revealed that the reciprocally convex combination approach is effective only with the use of Jensen inequality. When the Jensen inequality is replaced by Wirtinger-based inequality, the Moon’s et al.inequality together with convex analysis can lead to less conservative stability conditions than the reciprocally convex combination inequality.\\nMoreover, we prove that the feasibility of an LMI condition derived by the Moon’s et al.inequality as well as convex analysis implies the feasibility of an LMI condition induced by the reciprocally convex combination inequality.\\nFinally, the efficiency of the methods is demonstrated by some numerical examples, even though the corresponding system with zero-delay as well as the system without the delayed term are not stable.\",\n \"Title: Closeness-Centrality-Based Synchronization Criteria for Complex Dynamical Networks With Interval Time-Varying Coupling Delays.\\nYear: 2018\\n\\nAbstract: This paper investigates synchronization in complex dynamical networks (CDNs) with interval time-varying delays. The CDNs are representative of systems composed of a large number of interconnected dynamical units, and for the purpose of the mathematical analysis, the leading work is to model them as graphs whose nodes represent the dynamical units. At this time, we take note of the importance of ea...\",\n \"Title: Generalized reciprocally convex combination lemmas and its application to time-delay systems.\\nYear: 2018\\n\\nAbstract: Various efficient matrix inequalities have recently been proposed to deal with the stability analysis of linear systems with time-varying delays. This paper provides more insights on the relationship between some of them. We present an equivalent formulation of Moon et al.’s inequality, allowing us to discover strong links not only with the most recent and efficient matrix inequalities such as the reciprocally convex combination lemma and also its relaxed version but also with some previous inequalities such as the approximation inequality introduced in Shao (2009) or free-matrix-based inequality. More especially, it is proved that these existing inequalities can be captured as particular cases of Moon et al.’s inequality. Examples show the best tradeoff between the reduction of conservatism and the numerical complexity.\",\n \"Title: Distributed Control Of Compact Formations For Multi-Robot Swarms\\nYear: 2018\\n\\nAbstract: This article proposes a distributed algorithm for the compact deployment of robots, using both distance-and angular-based arguments in the controllers' design. Our objective is to achieve a configuration maximizing the coverage of the environment while increasing the graph's connectivity. First, we provide: (i) a dispersion protocol guaranteeing connectivity maintenance; and (ii) a compactness controller with static and variable control gains that minimizes the inter-agent angles. Second, we present a sequential, multi-stage strategy and analyse its stability. Finally, we validate our theoretical results with simulations, where a group of robots are deployed to carry out sensing or communication tasks.\",\n \"Title: Wirtinger-Like Lyapunov-Krasovskii Functionals For Discrete-Time Delay Systems\\nYear: 2018\\n\\nAbstract: Time-dependent Lyapunov functionals appeared to be very efficient for sampled-data systems. Recently, new Lyapunov functionals were constructed for sampled-data control in the presence of a constant input delay. The construction of these functionals was based on Wirtinger's inequality leading to simplified and efficient stability conditions in terms of linear matrix inequalities. In this article, we extend the latter results to the discrete-time sampled-data systems. We show that the proposed approach is less conservative on some examples with a lower number of decision variables.\",\n \"Title: Lyapunov Stability Analysis of a String Equation Coupled With an Ordinary Differential System.\\nYear: 2018\\n\\nAbstract: This paper considers the stability problem of a linear time invariant system in feedback with a string equation. A new Lyapunov functional is proposed using augmented states that enriches and encompasses the classical functionals of the literature. It results in tractable stability conditions expressed in terms of linear matrix inequalities. This methodology follows from the application of the Bes...\",\n \"Title: Sufficient Stability Condition For Delayed Port-Hamiltonian Systems Subject To Input Saturation\\nYear: 2018\\n\\nAbstract: This work deals with a class of delayed port-Hamiltonian systems with control law given as a bounded output feedback. A sufficient stability condition of the resulting closed-loop dynamics is obtained combining Wirtinger-based integral inequality and Lyapunov-Krasovkii theorem. This condition is formulated in terms of (parametrized) linear matrix inequality. A numerical example shows reduction of the conservatism regarding literature result.\",\n \"Title: Static State And Output Feedback Synthesis For Time-Delay Systems\\nYear: 2018\\n\\nAbstract: In this paper, the design of a static feedback gain for a linear system subject to an input delay is studied. This synthesis is based on a stability analysis conducted using Lyapunov-Krasovskii theorem and Bessel-Legendre inequalities. It is expressed in terms of linear matrix inequalities. Some bilinear non-convex matrix inequalities are obtained to go from analysis to synthesis. They are then difficult to solve and an iterative procedure is given which takes advantage of the elimination lemma. Naturally, slack variables are introduced and values coming from an optimization process are proposed to reduce the conservatism. The two main corollaries discuss the static state and output feedback synthesis. Finally, a comparison is proposed and shows that this formulation introduces small conservatism.\",\n \"Title: Stability analysis of a system coupled to a heat equation.\\nYear: 2019\\n\\nAbstract: As a first approach to the study of systems coupling finite and infinite dimensional natures, this article addresses the stability of a system of ordinary differential equations coupled with a classic heat equation using a Lyapunov functional technique. Inspired from recent developments in the area of time delay systems, a new methodology to study the stability of such a class of distributed parameter systems is presented here. The idea is to use a polynomial approximation of the infinite dimensional state of the heat equation in order to build an enriched energy functional. A well known efficient integral inequality (Bessel inequality) will allow to obtain stability conditions expressed in terms of linear matrix inequalities. We will eventually test our approach on academic examples in order to illustrate the efficiency of our theoretical results.\",\n \"Title: Distributed estimation based on multi-hop subspace decomposition.\\nYear: 2019\\n\\nAbstract: This paper deals with the problem of distributedly estimating the state of an LTI plant through an interconnected network of agents. The proposed approach results in an observer structure that incorporates consensus among the agents and that can be distributedly designed, achieving a robust solution with a good estimation performance. The developed solution is based on an iterative decomposition of the plant in the local observable staircase forms. The proposed observer has several positive features compared to recent results in the literature, which include milder assumptions on the network connectivity and the ability to set the convergence rate.\",\n \"Title: Stability Of Distributed Delay System With Infinite Support\\nYear: 2018\\n\\nAbstract: This paper is devoted to the stability analysis of infinite distributed delay systems. The basic idea is to model the original time-delay system into an interconnected feedback system in order to use robust analysis and especially quadratic separation. This approach has been widely used to study classical pointwise time-delay system. The stability analysis is performed by introducing new quadratic inequalities based on Laguerre polynomials particularly well fitted to deal with infinite distributed delay. It allows to develop new stability results which accuracy depends on the chosen polynomial degree.\",\n \"Title: Stabilization Of An Unstable Wave Equation Using An Infinite Dimensional Dynamic Controller\\nYear: 2018\\n\\nAbstract: This paper deals with the stabilization of an anti-stable string equation with Dirichlet actuation where the instability appears because of the uncontrolled boundary condition. Then, infinitely many unstable poles are generated and an infinite dimensional control law is therefore proposed to exponentially stabilize the system. The idea behind the choice of the controller is to extend the domain of the PDE so that the anti-damping term is compensated by a damping at the other boundary condition. Additionally, notice that the system can then be exponentially stabilized with a chosen decay-rate and is robust to uncertainties on the wave speed and the antidamped coefficient of the wave equation, with the only use of a point-wise boundary measurement. The efficiency of this new control strategy is then compared to the backstepping approach.\",\n \"Title: Exponential Lyapunov Stability Analysis Of A Drilling Mechanism\\nYear: 2018\\n\\nAbstract: This article deals with the stability analysis of a drilling system which is modelled as a coupled ordinary differential equation / string equation. The string is damped at the two boundaries but leading to a stable open-loop system. The aim is to derive a linear matrix inequality ensuring the exponential stability with a guaranteed decay-rate of this interconnected system. A strictly proper dynamic controller based on boundary measurements is proposed to accelerate the system dynamics and its effects are investigated through the stability theorem and simulations. It results in an efficient finite dimension controller which subsequently improves the system performances.\",\n \"Title: Circular formation control for cooperative target tracking with limited information.\\nYear: 2019\\n\\nAbstract: This paper addresses the problem of encircling and tracking a moving target with a fleet of unicycle-like vehicles. A new control law is developed to steer the vehicles to an evenly spaced formation along a circumference, the center of which tracks the motion of the target. The strategy proposed relies only on the relative positions of the agents with respect to the target, expressed in the local frame of each vehicle. The absolute position, velocity and acceleration of the target are unknown. Additionally, the robustness of the proposed control law in the presence of external disturbances is analyzed. Communication among agents is used to maintain the vehicles equally spaced in the circular formation. Simulation results illustrate the effectiveness of the proposed strategies.\",\n \"Title: A Nonsmooth Hybrid Invariance Principle Applied to Robust Event-Triggered Design\\nYear: 2019\\n\\nAbstract: We first propose a nonsmooth hybrid invariance principle with relaxed conditions stemming from the fact that flowing solutions evolve only in the tangent cone, and complete jumping solutions cannot jump outside the jump and flow sets. We then show an application consisting in the design of event-triggered rules to stabilize a class of uncertain linear control systems. The event-triggering rule depends only on local information, that is it uses only the output signals available to the controller. The approach proposed combines hybrid and sampled-data tools. The proposed design conditions are formulated in terms of linear matrix inequalities (LMIs) ensuring global robust asymptotic stability of the closed-loop system. A tunable parameter is also available to guarantee an adjustable dwell-time property of the solutions. The effectiveness of the approach is evaluated through an example borrowed from the literature.\",\n \"Title: Synchronization on a limit cycle of multi-agent systems governed by discrete-time switched affine dynamics\\nYear: 2021\\n\\nAbstract: This paper addresses the problem of synchronizing a group of interacting discrete-time switched affine systems with centralized control laws. Two different state-dependent control laws are proposed, based on recent literature on switched affine systems and are evaluated on an academical example with a multi-agent system.\",\n \"Title: Delayed Stabilization Of Parabolic Pdes Via Augmented Lyapunov Functionals And Legendre Polynomials\\nYear: 2021\\n\\nAbstract: We first study stabilization of heat equation with globally Lipschitz nonlinearity. We consider the point measurements with constant delay and use spatial decomposition. Inspired by recent developments in the area of ordinary differential equations (ODEs) with time-delays, for the stability analysis, we suggest an augmented Lyapunov functional depending on the state derivative that is based on Legendre polynomials. Global exponential stability conditions are derived in terms of linear matrix inequalities (LMIs) that depend on the degree N of Legendre polynomials. The stability conditions form a hierarchy of LMIs: if the LMIs hold for N, they hold for N + 1. The dual observer design problem with constant delay is also formulated. We further consider stabilization of Korteweg-de Vries-Burgers (KdVB) equation using the point measurements with constant delay. Due to the third-order partial derivative in KdVB equation, the Lyapunov functionals that depend on the state derivative are not applicable here, which is different from the case of heat equation. We suggest a novel augmented Lyapunov functional depending on the state only that leads to improved regional stability conditions in terms of LMIs. Finally, numerical examples illustrate the efficiency of the method. (C) 2021 Elsevier B.V. All rights reserved.\",\n \"Title: Distributed Hybrid Observer With Prescribed Convergence Rate for a Linear Plant Using Multi-Hop Decomposition.\\nYear: 2023\\n\\nAbstract: With a continuous-time formulation of the multihop decomposition, we propose a distributed hybrid observer for a sensor network where the plant and local observers run in continuous time and the information exchange among the sensing nodes is sampled-data. Process disturbances, measurement noise and communication noise are considered, and we prove that under some necessary detectability assumptions the observer gains can be tuned to guarantee exponential ISS with a prescribeda convergence rate. Simulations illustrate the performance of the proposed observer.\",\n \"Title: Distributed Hybrid Observer With Prescribed Convergence Rate for a Linear Plant Using Multi-Hop Decomposition\\nYear: 2023\\n\\nAbstract: We propose a distributed hybrid observer for a sensor network where the plant and local observers run in continuous time and the information exchange among the sensing nodes is sampled-data. Process disturbances, measurement noise and communication noise are considered, and we prove that under some necessary detectability assumptions the observer gains can be tuned to guarantee exponential Input-to-State Stability with a prescribed convergence rate. Simulations illustrate the performance of the proposed observer.\",\n \"Title: Stability analysis of an ordinary differential equation interconnected with the reaction–diffusion equation\\nYear: 2022\\n\\nAbstract: This paper deals with the stability analysis of the reaction–diffusion equation interconnected with a finite-dimensional system. In this situation, stability is no longer straightforward to assess, and one needs to look for dedicated tools to provide accurate numerical stability tests. Here, the objective is to provide a Lyapunov analysis leading to efficient and precise stability criteria. Thanks to the Legendre orthogonal basis, the study is possible using dedicated Lyapunov functionals. Such functionals contain information based on the state of an augmented finite-dimensional system and a remaining infinite-dimensional system, associated with the first Legendre coefficients and the remainder of the Legendre expansion, respectively. Furthermore, the Legendre’s development provides efficient formulations of Bessel and Wirtinger inequalities and leads to sufficient stability conditions expressed as scalable linear matrix inequalities. Numerical examples finally illustrate the accuracy and the potential of such stability results.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":66,"string":"66"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13677,"string":"13,677"}}},{"rowIdx":13678,"cells":{"question_id":{"kind":"string","value":"Yang Zhou"},"question":{"kind":"string","value":"Generate an abstract for the title \"Modified BM3D algorithm for image denoising using nonlocal centralization prior.\" using the following items: 1. BM3D, 2. Image denoising, 3. Nonlocal centralization prior, 4. Nonlocal shrinkage functions, 5. State-of-the-art den"},"answer":{"kind":"string","value":"Block matching and 3D collaborative filtering (BM3D) has shown great power for image denoising. For grouped image blocks, this letter proposes to remove the 1D transform inter-blocks and introduce the nonlocal centralization prior to better utilize both local sparsity of wavelet coefficients and nonlocal similarity of grouped blocks. Three nonlocal shrinkage functions are developed under different norm restrictions of wavelet coefficients intra- and inter-blocks. Such shrinkage functions are verified to be competitive or better than the BM3D algorithm and other state-of-the-art denoising methods."},"correct_answer":{"kind":"string","value":"Block matching and 3D collaborative filtering (BM3D) has shown great power for image denoising. For grouped image blocks, this letter proposes to remove the 1D transform inter-blocks and introduce the nonlocal centralization prior to better utilize both local sparsity of wavelet coefficients and nonlocal similarity of grouped blocks. Three nonlocal shrinkage functions are developed under different norm restrictions of wavelet coefficients intra- and inter-blocks. Such shrinkage functions are verified to be competitive or better than the BM3D algorithm and other state-of-the-art denoising methods."},"documents":{"kind":"list like","value":["Title: Fast Iterative Graph Computation with Resource Aware Graph Parallel Abstractions\nYear: 2015\n\nAbstract: Iterative computation on large graphs has challenged system research from two aspects: (1) how to conduct high performance parallel processing for both in-memory and out-of-core graphs; and (2) how to handle large graphs that exceed the resource boundary of traditional systems by resource aware graph partitioning such that it is feasible to run large-scale graph analysis on a single PC. This paper presents GraphLego, a resource adaptive graph processing system with multi-level programmable graph parallel abstractions. GraphLego is novel in three aspects: (1) we argue that vertex-centric or edge-centric graph partitioning are ineffective for parallel processing of large graphs and we introduce three alternative graph parallel abstractions to enable a large graph to be partitioned at the granularity of subgraphs by slice, strip and dice based partitioning; (2) we use dice-based data placement algorithm to store a large graph on disk by minimizing non-sequential disk access and enabling more structured in-memory access; and (3) we dynamically determine the right level of graph parallel abstraction to maximize sequential access and minimize random access. GraphLego can run efficiently on different computers with diverse resource capacities and respond to different memory requirements by real-world graphs of different complexity. Extensive experiments show the competitiveness of GraphLego against existing representative graph processing systems, such as GraphChi, GraphLab and X-Stream.","Title: Skeleton-guided 3D shape distance field metamorphosis.\nYear: 2016\n\nAbstract: We introduce an automatic 3D shape morphing method without the need of manually placing anchor correspondence points. Given a source and a target shape, we extract their skeletons and automatically estimate the meaningful anchor points based on their skeleton node correspondences. Based on the anchors, dense correspondences between the interior of source and target shape can be established using earth mover's distance (EMD) optimization. Skeleton node correspondence, estimated with a voting-based method, leads to part correspondence which can be used to confine the dense correspondence within matched part pairs. This produces smooth and plausible morphing sequence based on distance field interpolation (DFI). We demonstrate the effectiveness of our algorithm over shapes with large geometric variation and structural difference. (C) 2016 Elsevier Inc. All rights reserved.","Title: Space-based passive tracking of non-cooperative space target using robust filtering algorithm.\nYear: 2016\n\nAbstract: Commonly, a non-cooperative space target is tracked using angle-only data. An approach utilised in many tracking problems is to perform an initial orbit determination (IOD) step and then transition to sequential estimation. In this paper, a space-based passive tracking strategy is proposed that combines IOD and sequential estimation. For the space-based IOD problem, a modified Laplace's method is derived, in which the non-spherical effect of Earth is considered and more than three pairs of angle measurements are fused. For the sequential estimation, a robust filtering algorithm, termed the robust square-root cubature Kalman filter, is developed. An adaptive factor is introduced in the developed algorithm, to ensure robustness to measurement malfunctions. Simulation results demonstrate that the proposed strategy can be used in actual engineering applications.","Title: Biologically inspired model of path integration based on head direction cells and grid cells.\nYear: 2016\n\nAbstract: Some neurons in the brain of freely moving rodents show special firing pattern. The firing of head direction cells (HDCs) and grid cells (GCs) is related to the moving direction and distance, respectively. Thus, it is considered that these cells play an important role in the rodents’ path integration. To provide a bionic approach for the vehicle to achieve path integration, we present a biologically inspired model of path integration based on the firing characteristics of HDCs and GCs. The detailed implementation process of this model is discussed. Besides, the proposed model is realized by simulation, and the path integration performance is analyzed under different conditions. Simulations validate that the proposed model is effective and stable.","Title: Sentiment processing of social media information from both wireless and wired network.\nYear: 2016\n\nAbstract: Recent years, information spreading under the environment of wireless communication has attracted increasing interest. Microblog platform on mobile terminals, as one product of wireless communication, facilitate information spreading and evolution by conveying message from peer to peer. Furthermore, sentiments from microblog reflect the attitude of peers on goods or events. Analysis of the sentiment can help in decision-making. Research work focuses on analyzing sentiment orientation for specific aspects of product with explicit names. However, it is not suitable for sentiment analysis of events using microblog data since users prefer to express their feelings in individual ways, namely the same object may be expressed in several ways. In this paper, a framework is proposed to calculate sentiment for aspects of events. First, we introduce some effective technologies in processing natural language, such as wordvec, HMM, and TextRank. Then, based on the state-of-art technologies, we build up a flowchart to get sentiment for aspects of events. At last, experiments are designed to prove these technologies on computing sentiment. During the process, name entities with the same meaning are clustered and sentiment carrier is filtered, with which sentiment can be got even users express their feeling for the same object with different words.","Title: Sentiment Analysis of Name Entity for Text.\nYear: 2016\n\nAbstract: Recent years, big data has attracted increasing interest. Sentiment analysis from microblog as one kind of big data also receive great attention. Some recent research works are not suitable for sentiment analysis as the result that users prefer to express their feelings in individual ways. In this paper, a framework is proposed to calculate sentiment for aspects of event. Based on some state of art technologies, we build up one flowchart to get sentiment for aspects of event. During the process, name entities with the same meaning are clustered and sentiment carrier are filtered. In this way sentiment can be got even user express feeling for the same object with different words.","Title: Probabilistic Rank-One Matrix Analysis with Concurrent Regularization.\nYear: 2016\n\nAbstract: As a classical subspace learning method, Probabilistic PCA (PPCA) has been extended to several bilinear variants for dealing with matrix observations. However, they are all based on the Tucker model, leading to a restricted subspace representation and the problem of rotational ambiguity. To address these problems, this paper proposes a bilinear PPCA method named as Probabilistic Rank-One Matrix Analysis (PROMA). PROMA is based on the CP model, which leads to a more flexible subspace representation and does not suffer from rotational ambiguity. For better generalization, concurrent regularization is introduced to regularize the whole matrix subspace, rather than column and row factors separately. Experiments on both synthetic and real-world data demonstrate the superiority of PROMA in subspace estimation and classification as well as the effectiveness of concurrent regularization in regularizing bilinear PPCAs.","Title: Environmental Incidents Detection from Chinese Microblog Based on Sentiment Analysis.\nYear: 2016\n\nAbstract: Environmental incidents affect the stable development of economy and society. If environmental incidents can be detected timely through microblog, it will be possible to reduce risk factors and improve social stability. Microblog has now become an important platform for generating and propagating incidents on the Web, it is an ideal field to detect incidents. However, since the texts of microblog messages are very short and unstructured, it is a challenging task to detect incidents from microblogs. Due to the diversity of Chinese microblogs, the results identified by Dynamic Query Expansion DQE always contain a considerable number of unrelated events. This paper proposes to filter microblogs by calculating emotion values via Sentiment Analysis SA, thus to improve the accuracy of detection. Experimental results demonstrated that DQEi¾ź+i¾źSA can be more accurate and effective to detect environmental incidents.","Title: Predicting Mass Incidents from Weibo.\nYear: 2016\n\nAbstract: The outbreak of mass incidents severely affects the stability of society. If we can predict mass incidents in advance, we may find the solution to avoid the confliction in time. Some of the existing approaches rely on emotional modeling. Much research has been conducted on microblog incident detection using statistical models, like LASSO regression method, Dynamic Query Expansion DQE and so on. In this paper, we propose to combine sentiment analysis and statistical methods, and uses LASSO regression method for mass incidents prediction. Experiments on Qingdao demonstrated that our proposed approach achieves a good performance.","Title: The Particle Swarm Differential Evolution Algorithm for Ecological Sensor Network Coverage Optimization.\nYear: 2016\n\nAbstract: The problem of coverage optimization is the challengingly important and key part in the research and application of ecology sensor network related with the ecological monitoring of Poyang Lake. A modified differential evolution algorithm (PSI-DE) combined with particle swarm intelligence is proposed to solve the coverage optimization problem. First, an improved version of the mutation rule combined with self-cognitive and social-cognitive items is introduced. Then, the influence on the coverage optimization performance of the PSI-DE algorithm brought by the five factors - namely, population size, number of iterations, sensing radius size, raster size, and number of nodes - is discussed and analyzed. The statistical results about the best coverage rate, average coverage rate, worst coverage rate, and variance are respectively obtained through a lot of simulation experiments. A series of the coverage rate curves, the line chart, and the node layout are drawn in this paper, and finally, the figures and the statistical results are proven to confirm each other.","Title: Remote Sensing Image Transfer Classification Based on Weighted Extreme Learning Machine.\nYear: 2016\n\nAbstract: It is expensive in time or resources to obtain adequate labeled data for a new remote sensing image to be categorized. The cost of manual interpretation can be reduced if labeled samples collected from previous temporal images can be reused to classify a new image over the same investigated area. However, it is reasonable to consider that the distributions of the target data and the historical dat...","Title: Neural network-based adaptive second order sliding mode control of Lorentz-augmented spacecraft formation.\nYear: 2017\n\nAbstract: The Lorentz force acting on an electrostatically charged spacecraft provides a new means of propulsion for orbital maneuvers such as Lorentz-augmented spacecraft formation flying. Modeling the Earth's magnetic field as a tilted dipole corotating with Earth, a nonlinear dynamical model that describes the orbital motion of a Lorentz spacecraft about an arbitrary elliptic orbit is developed. Then, the optimal open-loop control trajectories of formation establishment are solved by the pseudospectral method. To guarantee trajectory tracking in the presence of external perturbations and system uncertainties, a closed-loop neural network-based adaptive second order fast terminal sliding mode controller is designed using state feedback, which simultaneously solves the singularity and chattering problems that generally exist in conventional terminal sliding mode. Neural networks are employed to approximate the unknown nonlinearities in the system dynamics. Meanwhile, to ensure closed-loop tracking control without velocity measurements, an output feedback controller is also proposed with an observer introduced to capture the velocity signals. The overall stabilities for both control schemes are proved by a Lyapunov-based method. Numerical simulations are presented to verify the performance of the proposed controllers. Furthermore, the controllers could be applied to other Lorentz-augmented relative orbital control problems. An almost propellantless formation establishment is achieved via Lorentz force.Optimal open-loop hybrid inputs are derived by pseudospectral method.A closed-loop adaptive sliding mode controller using neural networks is proposed.An output feedback controller is designed for cases without velocity measurements.","Title: A Model of Generating Visual Place Cells Based on Environment Perception and Similar Measure.\nYear: 2016\n\nAbstract: It is an important content to generate visual place cells (VPCs) in the field of bioinspired navigation. By analyzing the firing characteristic of biological place cells and the existing methods for generating VPCs, a model of generating visual place cells based on environment perception and similar measure is abstracted in this paper. VPCs’ generation process is divided into three phases, including environment perception, similar measure, and recruiting of a new place cell. According to this process, a specific method for generating VPCs is presented. External reference landmarks are obtained based on local invariant characteristics of image and a similar measure function is designed based on Euclidean distance and Gaussian function. Simulation validates the proposed method is available. The firing characteristic of the generated VPCs is similar to that of biological place cells, and VPCs’ firing fields can be adjusted flexibly by changing the adjustment factor of firing field (AFFF) and firing rate’s threshold (FRT).","Title: CSM: A Cloud Service Marketplace for Complex Service Acquisition.\nYear: 2016\n\nAbstract: The cloud service marketplace (CSM) is an exploratory project aiming to provide “an AppStore for Services.” It is an intelligent online marketplace that facilitates service discovery and acquisition for enterprise customers. Traditional service discovery and acquisition are time-consuming. In the era of OneClick Checkout and pay-as-you-go service plans, users expect services to be purchased online efficiently and conveniently. However, as services are complex and different from software apps, the currently prevailing App Store based on keyword search is inadequate for services. In CSM, exploring and configuring services are an iterative process. Customers provide their requirements in natural language and interact with the system through questioning and answering. Learning from the input, the system can incrementally clarify users’ intention, narrow down the candidate services, and profile the configuration information for the candidates at the same time. CSM’s back end is built around the Services Knowledge Graph (SKG) and leverages data mining technologies to enable the semantic understanding of customers’ requirements. To quantitatively assess the value of CSM, empirical evaluation on real and synthetic datasets and case studies are given to demonstrate the efficacy and effectiveness of the proposed system.","Title: A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories.\nYear: 2017\n\nAbstract: Trajectory analysis is essential in many applications. In this paper, we address the problem of representing motion trajectories in a highly informative way, and consequently utilize it for analyzing trajectories. Our approach first leverages the complete information from given trajectories to construct a thermal transfer field which provides a context-rich way to describe the global motion patter...","Title: Optimal Estimation in Wireless Sensor Networks With Energy Harvesting.\nYear: 2016\n\nAbstract: In this paper, the source estimation problem is investigated in a wireless sensor network (WSN) with energy-harvesting (EH) nodes. We first consider a deterministic EH model where the amount of harvested energy at each node is known prior to transmissions. For such a model, we investigate the average mean-square-error (MSE) minimization problem over a finite horizon of multiple estimation periods ...","Title: Manifold Partition Discriminant Analysis\nYear: 2017\n\nAbstract: We propose a novel algorithm for supervised dimensionality reduction named manifold partition discriminant analysis (MPDA). It aims to find a linear embedding space where the within-class similarity is achieved along the direction that is consistent with the local variation of the data manifold, while nearby data belonging to different classes are well separated. By partitioning the data manifold ...","Title: Blind 3D image quality assessment based on self-similarity of binocular features.\nYear: 2017\n\nAbstract: The perceived quality assessment of three-dimensional (3D) images has emerged as a challenging research topic in the field of 3D imaging in recent years. Especially, blind quality assessment of 3D images encounters challenges due to prior information about the original 3D images is not available. In this paper, we propose a blind 3D image quality assessment (IQA) metric that utilizes the self-similarity of binocular features. The primary contribution of this study is that the proposed metric considers the binocular visual property and the local visual structural property for blind 3D-IQA. We calculate the self-similarity of binocular rivalry response as well as binocular orientation selectivity in the distorted 3D image. We then extract the inter- and intra-pixel binocular quality-predictive features from these self-similarity measures. Following feature extraction, we use machine learning based on the support vector regression (SVR) procedure to drive the overall quality score. Our results on publicly accessible 3D databases confirmed that the proposed metric is highly efficient and robust.","Title: A parallelized screen-based method for rendering polylines and polygons on terrain surfaces.\nYear: 2017\n\nAbstract: Two dimensional (2D) vector features, when rendered on terrain as reference or highlight features, can improve the intelligibility of three dimensional (3D) scenes. This research proposes a new screen-space based method to render solid color 2D polylines and polygons on terrain. Using the z-buffer value and window coordinates in screen space, a pixel's spatial extent in real world coordinates can be calculated. The pixel's color is changed, if its spatial extent intersects with a polyline or polygon feature, to reflect the fact that there is a polyline going through or a polygon covering the pixel. Compared to other existing methods, our method eliminates undesirable rendering artifacts. A parallel computing schema was proposed and implemented as well to improve rendering performance. New screen-space based method to render 2D polylines and polygons on terrain.Undesirable rendering artifacts are eliminated.Parallelized for rendering performance.","Title: A Large-scale Distributed Video Parsing and Evaluation Platform.\nYear: 2016\n\nAbstract: Visual surveillance systems have become one of the largest data sources of Big Visual Data in real world. However, existing systems for video analysis still lack the ability to handle the problems of scalability, expansibility and error-prone, though great advances have been achieved in a number of visual recognition tasks and surveillance applications, e.g., pedestrian/vehicle detection, people/vehicle counting. Moreover, few algorithms explore the specific values/characteristics in large-scale surveillance videos. To address these problems in large-scale video analysis, we develop a scalable video parsing and evaluation platform through combining some advanced techniques for Big Data processing, including Spark Streaming, Kafka and Hadoop Distributed Filesystem ( HDFS). Also, a Web User Interface is designed in the system, to collect users' degrees of satisfaction on the recognition tasks so as to evaluate the performance of the whole system. Furthermore, the highly extensible platform running on the long-term surveillance videos makes it possible to develop more intelligent incremental algorithms to enhance the performance of various visual recognition tasks.","Title: ESSE: an early software size estimation method based on auto-extracted requirements features.\nYear: 2016\n\nAbstract: Software size estimation is a crucial step in project management. According to the Standish Chaos Report, 65% of software projects are over budget or deadline; therefore, a good size estimation method is very important. However, existing estimation methods are complicated and human-effort consuming. In many industrial projects, project technical leads (PTLs) do not use these methods but just give a rough estimation based on their experience. To decrease human effort, we propose an early software size estimation (ESSE) method, which can extract semantic features from natural language requirements automatically, and build size estimation models for project. Firstly, ESSE makes a two-level semantic analysis of requirements specification documents by information extraction and activation spreading. Then, complexity-related features are extracted from the results of semantic analysis. Finally, a size estimation model is trained to predict size of new projects by regression algorithms. Experiments in real industrial datasets show that our method is effective and can be applied to real industrial projects.","Title: Cascaded Interactional Targeting Network For Egocentric Video Analysis\nYear: 2016\n\nAbstract: Knowing how hands move and what object is being manipulated are two key sub-tasks for analyzing first-person (egocentric) action. However, lack of fully annotated hand data as well as imprecise foreground segmentation make either sub-task challenging. This work aims to explicitly address these two issues via introducing a cascaded interactional targeting (i.e., infer both hand and active object regions) deep neural network. Firstly, a novel EM-like learning framework is proposed to train the pixel-level deep convolutional neural network (DCNN) by seamlessly integrating weakly supervised data (i.e., massive bounding box annotations) with a small set of strongly supervised data (i.e., fully annotated hand segmentation maps) to achieve state-of-the-art hand segmentation performance. Secondly, the resulting high-quality hand segmentation maps are further paired with the corresponding motion maps and object feature maps, in order to explore the contextual information among object, motion and hand to generate interactional foreground regions (operated objects). The resulting interactional target maps (hand + active object) from our cascaded DCNN are further utilized to form discriminative action representation. Experiments show that our framework has achieved the state-of-the-art egocentric action recognition performance on the benchmark dataset Activities of Daily Living (ADL).","Title: Thermal Infrared Contrast Between Different Types of Oil Slicks on Top of Water Bodies.\nYear: 2017\n\nAbstract: Thermal remote sensing is an effective technique for marine oil slick detection. However, many factors, such as the oil type, slick thickness, sensor capability, and the background environment, can together have an impact on the remotely sensed thermal imagery. These cross-coupling effects can usually be clarified by ground-based experiments. In this letter, four different types of oil slicks on w...","Title: Toward Understanding the Cold, Hot, and Neutral Nature of Chinese Medicines Using in Silico Mode-of-Action Analysis.\nYear: 2017\n\nAbstract: One important, however, poorly understood, concept of Traditional Chinese Medicine (TCM) is that of hot, cold, and neutral nature of its bioactive principles. To advance the field, in this study, we analyzed compound nature pairs from TCM on a large scale (>23 000 structures) via chemical space visualizations to understand its physicochemical domain and in silico target prediction to understand differences related to their modes-of-action (MoA) against proteins. We found that overall TCM natures spread into different subclusters with specific molecular patterns, as opposed to forming coherent global groups. Compounds associated with cold nature had a lower clog? and contain more aliphatic rings than the other groups and were found to control detoxification, heat-clearing, heart development processes, and have sedative function, associated with \"Mental and behavioural disorders\" diseases. While compounds associated with hot nature were on average of lower molecular weight, have more aromatic ring systems than other groups, frequently seemed to control body temperature, have cardio-protection function, improve fertility and sexual function, and represent excitatory or activating effects, associated with \"endocrine, nutritional and metabolic diseases\" and \"diseases of the circulatory system\". Compounds associated with neutral nature had a higher polar surface area and contain more cyclohexene moieties than other groups and seem to be related to memory function, suggesting that their nature may be a useful guide for their utility in neural degenerative diseases. We were hence able to elucidate the difference between different nature classes in TCM on the molecular level, and on a large data set, for the first time, thereby helping a better understanding of TCM nature theory and bridging the gap between traditional medicine and our current understanding of the human body.","Title: Local gradient patterns (LGP): An effective local-statistical-feature extraction scheme for no-reference image quality assessment.\nYear: 2017\n\nAbstract: Gradient features are known to be effective for full-reference (FR) image quality assessment (IQA). However, only a few metrics utilize gradient features for no-reference (NR) IQA. To investigate the potential benefits of gradient magnitude and phase for NR-IQA, we propose a novel and effective local-statistical-feature extraction metric, namely Local Gradient Patterns (LGP), for general-purpose NR-IQA. Using a Gaussian partial derivative filter, the image is first decomposed into two complementary components: gradient magnitude and phase. The local statistical features (e.g., the conditional probability distributions) are then extracted from the complementary components, using the derived local gradient magnitude and phase pattern operators. Finally, to facilitate NR-IQA, local statistical features that convey important structural information are mapped to the subjective mean opinion score of the image, using a support vector regression (SVR) procedure. We evaluated our proposed LGP metric using images from two publicly available test databases; the results confirm that the proposed LGP metric provides predictive performance that is superior to most state-of-the-art NR-IQA metrics and has an acceptable level of computational complexity.","Title: A QoS Enhancement Scheme through Joint Control of Clear Channel Assessment Threshold and Contending Window for IEEE 802.11e Broadcasting.\nYear: 2017\n\nAbstract: In a WLAN, when a great many nodes coexist, the network may readily be congested, thus causing packets dropping and network performance degradation. To solve this issue, a lot of schemes have been proposed. However, most of the previous works attempt to avoid the possible channel congestion by controlling the packets generation rate and/or transmitting power of nodes, while the effects of Clear Channel Assessment (CCA) threshold are not well examined. In our paper, a Joint CCA threshold and contending window control algorithm (JCCA) is proposed to avoid channel congestion or reduce the congestion probability of broadcasting in an IEEE 802.11e network. Both the network conditions and the priorities of messages are taken into account to improve the broadcasting performance in our paper. According to the simulation results, it can be concluded that our scheme can significantly increase the network throughput as well as packets delivery ratio and reduce the packet transmission delay compared to the IEEE 802.11e and Adaptive Carrier Sensing-Based MAC Designs (ACSBM) protocol.","Title: A congestion avoidance game for information exchange on intersections in heterogeneous vehicular networks.\nYear: 2017\n\nAbstract: Recently, the concept of HetVNET (Heterogeneous Vehicular NETwork) is greatly motivated by the fact that present wireless networks not only offers their attractive benefits, but also bring their shortcomings together. In this paper, by integrating the GIS (Geographic Information System), GPS (Global Position System), Sensor network, Vehicular Ad Hoc Networks (VANETs) and other localization technologies into our work, we propose a RoS (Requirement of Safety) aware services scheduling in the HetVNET to improve the packets delivery ratio, increase the spectral efficiency and alleviate the packet congestion as well. In addition, through utilizing the SINR-based (Signal to Interference plus Noise Ratio) utility and social optima searching strategy, our proposed algorithm outperforms the IEEE 802.11p in terms of the throughput as well as service miss ratio with the EDF (Earliest Deadline First) queuing mechanism. In addition, to reflect the increased transmission opportunities, the channel busy ratio under different configurations is also compared with a previous work DynB (Dynamic Beaconing).","Title: Robust adaptive multi-task tracking control of redundant manipulators with dynamic and kinematic uncertainties and unknown disturbances.\nYear: 2017\n\nAbstract: In this paper, a novel adaptive multi-priority controller for redundant manipulators is proposed to accomplish the multi-task tracking when kinematic/dynamic uncertainties and unknown disturbances exist. Prioritized redundancy resolution in kinematic level is incorporated into this passivity-based control framework. The kinematic and dynamic parameter adaptations are driven by both tracking error and prediction error. Moreover, the tracking information from both primary and subtasks are all utilized to accelerate the parameter estimation when the tasks are independent, whereas the inevitable tracking error of the subtasks due to algorithmic singularities is properly eliminated in the adaptation laws when the tasks are dependent. Potential ill-conditioned solution of the pseudoinverse is avoided using an improved singularity-robust inverse of the projected Jacobian. Along with the improvement of the multi-task tracking performance, smoothness of the commanded torques is still guaranteed for easy application. Measurements of the noisy joint acceleration and task velocity are avoided. The controller is mathematically derived based on Lyapunov stability analysis. Simulation results of the two cases are presented to verify the effectiveness and superiority of the proposed controller.","Title: Topology design and cross-layer optimization for wireless body sensor networks.\nYear: 2017\n\nAbstract: Wireless Body Sensor Networks play a crucial role in digital health care nowadays. Due to the size limitation on the sensor nodes and the life critical characteristics of the signals, there are stringent requirements on networks reliability and energy efficiency. In this article, we propose a mathematical optimization problem that jointly considers network topology design and cross-layer optimization in WBSNs. We introduce multilevel primal and dual decomposition methods and manage to solve the proposed non-convex mixed-integer optimization problem. A solution with fast convergence rate based on binary search is provided. Simulation results have been supplemented to show that our proposed method yields much better performance than existing solutions.","Title: Analysis and Controlled Synthesis of Inhomogeneous Textures.\nYear: 2017\n\nAbstract: Many interesting real-world textures are inhomogeneous and/or anisotropic. An inhomogeneous texture is one where various visual properties exhibit significant changes across the texture's spatial domain. Examples include perceptible changes in surface color, lighting, local texture pattern and/or its apparent scale, and weathering effects, which may vary abruptly, or in a continuous fashion. An anisotropic texture is one where the local patterns exhibit a preferred orientation, which also may vary across the spatial domain. While many example-based texture synthesis methods can be highly effective when synthesizing uniform stationary isotropic textures, synthesizing highly non-uniform textures, or ones with spatially varying orientation, is a considerably more challenging task, which so far has remained underexplored. In this paper, we propose a new method for automatic analysis and controlled synthesis of such textures. Given an input texture exemplar, our method generates a source guidance map comprising: i a scalar progression channel that attempts to capture the low frequency spatial changes in color, lighting, and local pattern combined, and ii a direction field that captures the local dominant orientation of the texture. Having augmented the texture exemplar with this guidance map, users can exercise better control over the synthesized result by providing easily specified target guidance maps, which are used to constrain the synthesis process.","Title: Blind quality estimator for 3D images based on binocular combination and extreme learning machine.\nYear: 2017\n\nAbstract: •Two binocular combinations of stimuli are generated using different combination strategies.•LoG and GPD are utilized to characterize the visual semantic structures.•ELMs can achieve higher learning accuracy with much faster learning speeds.","Title: Design and Implementation of Delay-Dependent Wide-Area Damping Control for Stability Enhancement of Power Systems.\nYear: 2017\n\nAbstract: In this paper, the hardware and software design and implementation are proposed for the wide-area damping control (WADC) strategies. First, the hardware-in-the-loop (HIL) real-time (RT) simulation system based on the RT-Lab is established as the closed-loop test platform for the WADC applied in multi-machine interconnected power system. The hardware, which contains the microcontroller, the periphe...","Title: Online sponsored search auction matching problem with advertiser credibility\nYear: 2017\n\nAbstract: How does a search engine company decide which advertisements to display for each query to maximize its revenue? This turns out to be a generalization of the online bipartite matching problem. In this paper, search engines decide the strategy to allocate resources with an advertiser credibility factor under budget constraints. Based on the optimal algorithm, which is the notion of a trade-off revealing LP, this paper remains the competitive ratio as 1 - 1/e with an advertiser credibility factor. During the ranking in the keywords auctions, CTR(Click Through Rate) and credibility factor are added to the trade-off function. In the long term, users tend to use the search engine with high credibility, which will bring greater revenues. The search engine will attract advertisers to bid on keywords and improve their credibility actively.","Title: An Integrated Approach For Sustainable Container Supply Chain Design And Evaluation -A Simulation Approach With Case Study\nYear: 2016\n\nAbstract: Traditionally, the container is usually regarded as a 'black box' in container supply chain design and analysis, which ignores the interactions between container, inner packaging and logistics operations and their impact on sustainability performance of the logistics solution. But according to UK and international insurance organisations, more than half of incidents in container shipping are cargo loss or damage, and more than one third of those value loss incidents are contributed by poorly or incorrect packed containers. Since the packaging related factors are tightly related to container cargo value loss in logistics operations and further influence the sustainability of the container supply chain, this paper introduces packaging logistics concept and risk management tools into container shipping to integrate the packaging, cargo and logistics operation as a holistic system in container supply chain evaluation and design. An integrated framework and approach is proposed in this paper for sustainable container supply chain evaluation and design taking packaging related risks into account. And simulation model is developed to apply this integrated method in container shipping scenario.","Title: The Evaluation On Resource Configuration Of Telecom Assets\nYear: 2016\n\nAbstract: Telecom regulatory departments issued many policies to keep the healthy development of telecom assets market, while the governments pay little attention to the resource configuration of telecom assets, and some of the existing researches have not got verified in academia. In this paper, firstly it puts forward the evaluation model for resource configuration of telecom assets, and indicators of cause and outcome are considered in this model; secondly, the data of provinces, municipalities and autonomous regions in China is collected related to the evaluation indicators of resource configuration in telecom assets; and then PCA method is developed to take empirical researches on configuration evaluation of telecom assets. Thus, the overall resource configuration of telecom assets can be got quantitatively. At last, some important conclusions are obtained.","Title: Bilinear Probabilistic Canonical Correlation Analysis via Hybrid Concatenations.\nYear: 2017\n\nAbstract: Canonical Correlation Analysis (CCA) is a classical technique for two-view correlation analysis, while Probabilistic CCA (PCCA) provides a generative and more general viewpoint for this task. Recently, PCCA has been extended to bilinear cases for dealing with two-view matrices in order to preserve and exploit the matrix structures in PCCA. However, existing bilinear PCCAs impose restrictive model assumptions for matrix structure preservation, sacrificing generative correctness or model flexibility. To overcome these drawbacks, we propose BPCCA, a new bilinear extension of PCCA, by introducing a hybrid joint model. Our new model preserves matrix structures indirectly via hybrid vector-based and matrix-based concatenations. This enables BPCCA to gain more model flexibility in capturing two-view correlations and obtain close-form solutions in parameter estimation. Experimental results on two real-world applications demonstrate the superior performance of BPCCA over competing methods.","Title: A Density-Based Seed-Centric Community Detection Algorithm\nYear: 2016\n\nAbstract: Although many different community detection algorithms have been proposed to detect community structures in complex networks, how to effectively detect community structures is still a great challenge. Seed-centric methods is one of the most effective solutions for community detection. To more, in this paper, we propose a novel density-based seed expansion algorithm, namely, DenSeC, which can easily find cores of communities. By taking nodes with higher density locally as cores of communities, DenSeC first detects cores by searching from sparsely connected area to densely connected area. Then, starting from cores, along the path from densely connected area to sparsely connected area, it constructs a community by iteratively adding nodes which are similar to the nodes in the community. Numerical experiments have verified the effectiveness of DenSeC.","Title: Construction of the diagnosis and treatment process of dermatosis based on data-driven approach\nYear: 2017\n\nAbstract: In this study, we selected the dermatology data, constructed a dictionary of dermatology semantic classification, and established a corpus of dermatology literature. Based on the context calculation model we took the case of erythema skin disease as the example, identified the semantic meaning of the dermatology data, and formed the concept attribute of dermatology knowledge, constructed the dermatology ontology. We established a priori database and verified each other with the running data to correct the data error and improve the data definition. In this study, we used the natural language to describe the characteristic information of skin diseases, and carried out the semantic searching. Finally, we constructed the mathematical model of the diagnosis and treatment of dermatology based on the corpus, the ontology and the priori database. The purpose is to supply the gap of unbalance of doctors' personal level of the dermatosis diagnosis and treatment, improve the level and accuracy of clinical diagnosis and treatment of dermatosis, and to achieve effective drive of medical literature data for clinical diagnosis and treatment.","Title: Pyramid sketch: a sketch framework for frequency estimation of data streams\nYear: 2017\n\nAbstract: AbstractSketch is a probabilistic data structure, and is used to store and query the frequency of any item in a given multiset. Due to its high memory efficiency, it has been applied to various fields in computer science, such as stream database, network traffic measurement, etc. The key metrics of sketches for data streams are accuracy, speed, and memory usage. Various sketches have been proposed, but they cannot achieve both high accuracy and high speed using limited memory, especially for skewed datasets. To address this issue, we propose a sketch framework, the Pyramid sketch, which can significantly improve accuracy as well as update and query speed. To verify the effectiveness and efficiency of our framework, we applied our framework to four typical sketches. Extensive experimental results show that the accuracy is improved up to 3.50 times, while the speed is improved up to 2.10 times. We have released our source codes at Github [1].","Title: Who Will You Share a Ride With: Factors that Influence Trust of Potential Rideshare Partners.\nYear: 2017\n\nAbstract: Ridesharing can save energy, reduce pollution, and alleviate congestion. Ridesharing systems allow peer-to-peer ride matching through mobile-phone applications. In many systems people have little information about the driver and co-riders. Trust heavily influences the usage of a ridesharing system, however, work in this area is limited. In this paper, we investigate the role of social media in shaping trust and delineate factors that influence the decisions of trust placed on a driver or co-rider. We design a ridesharing system where a rider can see the Instagram profile and 10 recent pictures of a co-rider and ask them to make judgments on six trust related questions. We discovered three important factors that shape trust forming: textit{social proof}, textit{social approval}, and textit{self-disclosure}. These three factors account for 40%, 33%, and 13% of the variance, and subsequent factors extracted accounted for substantially less variance. Using these three factors, we successfully build a trust prediction model with F-measure of 87.4%, an improvement from 78.6% on using all factors collected.","Title: Predicting the Trends of Social Events on Chinese Social Media.\nYear: 2017\n\nAbstract: Growing interest in social events on social media came along with the rapid development of the Internet. Social events that occur in the \"real\" world can spread on social media (e.g., Sina Weibo) rapidly, which may trigger severe consequences and thus require the government's timely attention and responses. This article proposes to predict the trends of social events on Sina Weibo, which is currently the most popular social media in China. Based on the theories of social psychology and communication sciences, we extract an unprecedented amount of comprehensive and effective features that relate to the trends of social events on Chinese social media, and we construct the trends of prediction models by using three classical regression algorithms. We found that lasso regression performed better with the precision 0.78 and the recall 0.88. The results of our experiments demonstrated the effectiveness of our proposed approach.","Title: Visual comfort prediction for stereoscopic image using stereoscopic visual saliency.\nYear: 2017\n\nAbstract: Perceptually salient regions of stereoscopic images significantly affect visual comfort (VC). In this paper, we propose a new objective approach for predicting VC of stereoscopic images according to visual saliency. The proposed approach includes two stages. The first stage involves the extraction of foreground saliency and depth contrast from a disparity map to generate a depth saliency map, which in turn is combined with 2D saliency to obtain a stereoscopic visual saliency map. The second stage involves the extraction of saliency-weighted VC features, and feeding them into a prediction metric to produce VC scores of the stereoscopic images. We demonstrate the effectiveness of the proposed approach compared with the conventional prediction methods on the IVY Lab database, with performance gain ranging from 0.016 to 0.198 in terms of correlation coefficients.","Title: Simultaneous Coherent and Random Noise Attenuation by Morphological Filtering With Dual-Directional Structuring Element.\nYear: 2017\n\nAbstract: Seismic data are highly corrupted by noise or unwanted energies arising from different kinds of sources. In general, seismic noise can be divided into two categories, namely, coherent noise and random noise, and is treated with essentially different methods. Traditional methods often utilize the differences in frequency, wavenumber, or amplitude to separate signal and noise. However, the applicati...","Title: Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55.\nYear: 2017\n\nAbstract: We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database. The benchmark consists of two tasks: part-level segmentation of 3D shapes and 3D reconstruction from single view images. Ten teams have participated in the challenge and the best performing teams have outperformed state-of-the-art approaches on both tasks. A few novel deep learning architectures have been proposed on various 3D representations on both tasks. We report the techniques used by each team and the corresponding performances. In addition, we summarize the major discoveries from the reported results and possible trends for the future work in the field.","Title: A Matching Approach to Factor Scores Based on Online Sponsored Search Auction\nYear: 2018\n\nAbstract: AbstractHow does a search engine company decide which advertisements to display for each query to maximize its revenue? This turns out to be a generalization of the online bipartite matching problem. In this paper, search engines decide the strategy to allocate resources with an advertiser credibility factor under budget constraints. Based on the optimal algorithm, which is the notion of a trade-off revealing LP, this paper remains the competitive ratio as 1-1/e with an advertiser credibility factor. During the ranking in the keywords auctions, the authors calculate factor scores. The new arrival keywords should match to the advertisement which has the maximum factor scores. The amount of spent money, budget constraints, CTR Click Through Rate and a credibility factor are added to the trade-off function using in the authors' algorithms. In the long term, users tend to use the search engine with high credibility, which will bring greater revenues. The search engine will attract advertisers to bid on keywords and improve their credibility actively.","Title: A New Visibility Model for Surface Reconstruction.\nYear: 2017\n\nAbstract: In this paper, we propose a new visibility model for scene reconstruction. To yield out surface meshes with enough scene details, we introduce a new visibility model, which keeps the relaxed visibility constraints and takes the distribution of points into consideration, thus it is efficient for preserving details. To strengthen the robustness to noise, a new likelihood energy term is introduced to the binary labeling problem of Delaunay tetrahedra, and its implementation is presented. The experimental results show that our method performs well in terms of detail preservation.","Title: An efficient all-zero block detection algorithm for high efficiency video coding with RDOQ.\nYear: 2018\n\nAbstract: Rate distortion optimized HEVC video coding is challenged by the extremely high complexity caused by hierarchical prediction structure and advanced coding tools. All-zero-block (AZB) detection is an efficient tool to decrease the complexity of mode decision in HEVC coders in the sense of rate distortion optimization (RDO). Traditional AZB detection schemes were usually designed by employing hard-decision quantization (HDQ), and the AZB detection thresholds were generally derived according to the individual block’s characteristic parameter such as SAD or SATD. However, soft-decision quantization (SDQ) is preferably employed in HEVC encoders for coding performance improvement, and the AZB detection thresholds are supposed to be determined according to the ensemble’s statistical characteristics in the sense of statistical inference. This paper proposes an adaptive AZB detection algorithm well-suited for SDQ, more specifically RD optimized quantization (RDOQ). Inspired by Bayesian inference, a more accurate coefficient-level zero-quantized threshold model in the RDO sense is proposed by fully simulating RDOQ to aid in the following AZB detection. In addition, a local parameter depicting the individual block’s characteristics regarding the inter-coefficient distribution is jointly combined with SATD to derive an adaptive threshold model for AZB detection. The experimental results demonstrate that the proposed algorithm detects 91.9% 4×4 AZB with smaller than 1.9% FAR and 8.1% FRR false alarm rate on average, at the cost of negligible rate-distortion performance loss. This work is well-suited for fast RD optimized HEVC coding.","Title: One Memory Access Sketch: A More Accurate and Faster Sketch for Per-Flow Measurement.\nYear: 2017\n\nAbstract: Sketch is a probabilistic data structure widely used for per-flow measurement in the real network. The key metrics of sketches for per-flow measurement are their memory usage, accuracy, and speed. There are a variety of sketches, but they cannot achieve both high accuracy and high speed at the same time given a fixed memory size. To address this issue, we propose a new sketch, namely the OM (One Memory) sketch. It achieves much higher accuracy than the state-of-the-art, and achieves close to one memory access and one hash computation for each insertion or query. The key methodology of our OM sketch is to leverage word constraint and fingerprint techniques based on a hierarchical structure. Extensive experiments based on real IP traces show that the accuracy is improved up to 10.64 times while the speed is improved up to 2.50 times, compared with the well-known CM sketch [1]. All the related source code has been released at GitHub [2].","Title: Node-centric route mutation for large-scale SDN based on 3D earth mover's distance model\nYear: 2017\n\nAbstract: Exploiting software-defined networking techniques, randomly and instantly mutating routes can disguise strategically important infrastructure, and protect the integrity of data networks. Route mutation has been to date formulated as NP-complete constraint satisfaction problems where feasible sets of routes need to be generated with exponential computational complexities, limiting algorithmic scalability to large-scale networks. In this paper, we propose a novel node-centric route mutation method which interprets route mutation as a signature matching problem. We formulate the route mutation problem as a three-dimensional earth mover's distance (EMD) model and solve it by using a binary branch and bound method. Considering the scalability, we further propose a heuristic method yields significantly lower computational complexities with marginal loss of robustness against eavesdropping. Simulation results show that our proposed methods can effectively disguise key infrastructure by reducing the difference of historically accumulative traffic among different switches. With significantly reduced complexities, our algorithms are of particular interest to safeguard large-scale networks.","Title: Detecting Postpartum Depression In Depressed People By Speech Features\nYear: 2017\n\nAbstract: Postpartum depression (PPD) is a depressive disorder with peripartum onset, which brings heavy burden to individuals and their families. In this paper, we propose to detect PPD in depressed people via voices. We used openSMILE for feature extraction, selected Sequential Floating Forward Selection (SFFS) algorithm for feature selection, tried different settings of features, set 5-fold cross validation and applied Support Vector Machine (SVM) on Weka for training and testing different models. The best predictive performance among our models is 69%, which suggests that the speech features could be used as a potential behavioral indicator for identifying PPD in depression. We also found that a combined impact of features and content of questions contribute to the prediction. After dimension reduction, the average value of F-measure was increased 5.2%, and the precision of PPD was rose to 75%. Comparing with demographic questions, the features of emotional induction questions have better predictive effects.","Title: Inferring Social Functions Available in the Metro Station Area from Passengers' Staying Activities in Smart Card Data.\nYear: 2017\n\nAbstract: The function of a metro station area is vital for city planners to consider when establishing a context-aware Transit-Oriented Development policy around the station area. However, the functions of metro station areas are hard to infer using the static land use distribution and other traditional survey datasets. In this paper, we propose a method to infer the functions occurring around the metro station catchment areas according to the patterns of staying activities derived from smart card data. We first define the staying activities by the spatial and temporal constraints of the two consecutive alighting and boarding records from the individual travel profile. Then we cluster and label the whole staying activities by considering the features of duration, frequency, and start time. By analyzing the percentage of different types of aggregated activities happening around each metro station, we cluster and explore the functions of the metro station area. Taking Wuhan as a case study, we analyze the results of Wuhan metro systems and discuss the similarities and differences between the functions and the land use distribution around the station area. The results show that although there exist some agreements, there is also a gap between the human activities and the land uses around the station area. These findings could give us deeper insight into how people act around the stations by metro systems, which will ultimately benefit the urban planning and policy development.","Title: Scalable Node-Centric Route Mutation for Defense of Large-Scale Software-Defined Networks.\nYear: 2017\n\nAbstract: Exploiting software-defined networking techniques, randomly and instantly mutating routes can disguise strategically important infrastructure and protect the integrity of data networks. Route mutation has been to date formulated as NP-complete constraint satisfaction problem where feasible sets of routes need to be generated with exponential computational complexities, limiting algorithmic scalability to large-scale networks. In this paper, we propose a novel node-centric route mutation method which interprets route mutation as a signature matching problem. We formulate the route mutation problem as a three-dimensional earth mover's distance (EMD) model and solve it by using a binary branch and bound method. Considering the scalability, we further propose that a heuristic method yields significantly lower computational complexities with marginal loss of robustness against eavesdropping. Simulation results show that our proposed methods can effectively disguise key infrastructure by reducing the difference of historically accumulative traffic among different switches. With significantly reduced complexities, our algorithms are of particular interest to safeguard large-scale networks.","Title: Accurate Mesh-Based Alignment For Ground And Aerial Multi-View Stereo Models\nYear: 2017\n\nAbstract: We propose a method for accurate alignment of ground and aerial multi-view stereo (MVS) models. We achieve this goal by reconstructing the surface meshes from MVS point clouds generated by aerial and ground images respectively, and then iteratively removing the gap between them. The key issue is how to establish reliable correspondences between two meshes. To address this issue, we introduce a new set called the skeleton facet set (SFS) to represent the locally smooth part on the mesh, and then compute the transformation matrix by comparing the depths of the facets in SFS between aerial and ground models. Experimental results show that the proposed method is able to yield accurate alignment results and is robust to noise as well.","Title: Elastic Switch Migration for Control Plane Load Balancing in SDN.\nYear: 2018\n\nAbstract: Software-defined network (SDN) provides a solution for the scalable network framework with decoupled control and data plane. Migrating switches can balance the resource utilization of controllers and improve network performance. Switch migration problem has to date been formulated as a resource utilization maximization problem to address the scalability of the control plane. However, this problem is NP-hard with high-computational complexities and without addressing the security challenges of the control plane. In this paper, we propose a switch migration method, which interprets switch migration as a signature matching problem and is formulated as a 3-D earth movers distance model to protect strategically important controllers in the network. Considering the scalability, we further propose a heuristic method which is time-efficient and suitable to large-scale networks. Simulation results show that our proposed methods can disguise strategically important controllers by diminishing the difference of traffic load between controllers. Moreover, our proposed methods can significantly relieve the traffic pressure of controllers and prevent saturation attacks.","Title: Adaptive Navigation Algorithm Under Abnormal Measurements in Libration-Point Mission.\nYear: 2018\n\nAbstract: This paper investigates X-ray pulsar navigation for earth-moon libration-point mission. A comprehensive analysis shows that abnormal measurements are inevitable and bring quite adverse effects for navigation results. To approach this problem, a new algorithm called robust square-root cubature Kalman filter is proposed. An innovation-based adaptive scale factor is introduced to adjust measurement noise covariance so that the adverse effects of abnormal measurements can be suppressed. Effectiveness of the proposed method is demonstrated in simulation results.","Title: Local and Global Feature Learning for Blind Quality Evaluation of Screen Content and Natural Scene Images.\nYear: 2018\n\nAbstract: The blind quality evaluation of screen content images (SCIs) and natural scene images (NSIs) has become an important, yet very challenging issue. In this paper, we present an effective blind quality evaluation technique for SCIs and NSIs based on a dictionary of learned local and global quality features. First, a local dictionary is constructed using local normalized image patches and conventional...","Title: The heterogeneity of thermal sensitivity of residential electricity load between China and USA\nYear: 2017\n\nAbstract: Thermal-sensitive electricity usage accounts for the majority of residential electricity use worldwide. Moreover, it has substantial impacts on the power system and the society during the extreme weathers. We propose a novel data-driven method to identify the thermal-sensitive electricity usage and quantify the associated thermal sensitivity directly from widely deployed smart meter data, where the Multivariate Adaptive Regression Splines model provides both the flexibility and interpretability. We apply the proposed method to analyze the thermal sensitivity of individual residential electricity use for two representative urban areas in the United States and China, San Francisco Bay Area and Shanghai, respectively. More than 120,000 households' smart meter data sampled from Bay Area and Shanghai is used for the analysis. To best of our knowledge, we for the first time reveal the heterogeneous patterns of thermal sensitivity of households from USA and China at the individual level. The difference of thermal sensitivity serves as an important factor for personalized techniques and policies towards mitigating the impact of extreme weathers to power systems and improving the buildings' energy efficiency.","Title: Privacy-Preserving Publishing of Multilevel Utility-Controlled Graph Datasets.\nYear: 2018\n\nAbstract: Conventional private data publication schemes are targeted at publication of sensitive datasets either after the k-anonymization process or through differential privacy constraints. Typically these schemes are designed with the objective of retaining as much utility as possible for the aggregate queries while ensuring the privacy of the individual records. Such an approach, though suitable for publishing aggregate information as public datasets, is inapplicable when users have different levels of access to the same data. We argue that existing schemes either result in increased disclosure of private information or lead to reduced utility when some users have more access privileges than the others. In this article, we present an anonymization framework for publishing large datasets with the goals of providing different levels of utility to the users based on their access privilege levels. We design and implement our proposed multilevel utility-controlled anonymization schemes in the context of large association graphs considering three levels of user utility, namely, (1) users having access to only the graph structure, (2) users having access to the graph structure and aggregate query results, and (3) users having access to the graph structure, aggregate query results, and individual associations. Our experiments on real large association graphs show that the proposed techniques are effective and scalable and yield the required level of privacy and utility for each user privacy and access privilege level.","Title: Cold Filter: A Meta-Framework for Faster and More Accurate Stream Processing.\nYear: 2018\n\nAbstract: Approximate stream processing algorithms, such as Count-Min sketch, Space-Saving, etc., support numerous applications in databases, storage systems, networking, and other domains. However, the unbalanced distribution in real data streams poses great challenges to existing algorithms. To enhance these algorithms, we propose a meta-framework, called Cold Filter (CF), that enables faster and more accurate stream processing.\n\nDifferent from existing filters that mainly focus on hot items, our filter captures cold items in the first stage, and hot items in the second stage. Also, existing filters require two-direction communication - with frequent exchanges between the two stages; our filter on the other hand is one-direction - each item enters one stage at most once. Our filter can accurately estimate both cold and hot items, giving it a genericity that makes it applicable to many stream processing tasks. To illustrate the benefits of our filter, we deploy it on three typical stream processing tasks and experimental results show speed improvements of up to 4.7 times, and accuracy improvements of up to 51 times. All source code is made publicly available at Github.","Title: Development of distributed cache strategy for analytic cluster in an Internet of Things system\nYear: 2018\n\nAbstract: This paper discusses the development of a distributed cache strategy for an analytic cluster in an Internet of Things (IoT) system. In this paper, Least Recently Used (LRU), Proactive Cache and distributed system related concepts are discussed. The study about the approaches for performance optimization, nodes and data distributing in the IoT system is also performed. The novelty about this research is the concept of a placeholder and its use together with Proactive Cache to optimize the traditional LRU algorithm in the use case of a data analytic cluster in an IoT system, which can reduce the response delay of the query up to 90% in the test case.","Title: Single Hash: Use One Hash Function to Build Faster Hash Based Data Structures\nYear: 2018\n\nAbstract: With the scale of data to store or monitor in nowadays network constantly increasing, hash based data structures are more and more widely used because of their high memory efficiency and high speed. Most of them, like Bloom filters, sketches and d-Ieft hash tables use more than one hash function. Furthermore, in order to achieve good randomicity, the hash functions used, like MD5 and SHA1, are very complicated and consume a lot of CPU cycles to carry out. As a consequence, the implementation of these hash functions will be time-consuming, In order to address this issue, we propose Single Hash technique in this paper. It is based on the observation that the hash functions we use produce 32-bit or M-bit values which have much bigger value ranges than that we need in practice. We usually have to carry out modular operation to map the hash results into a smaller range in the data structures listed above. In this procedure, information carried by the high bits may be discarded. For example, if in a Bloom filter the length of the bit array is 220 while the hash functions we use are 32-bit hash functions, there are 12 bits in the results of the hash functions discarded in the procedure of modular. We can use these bits to produce more hash values. Therefore, we propose to use a few bit operations to make full use of the information produced by one hash function and generate multiple hash values which can be used in these data structures. Single Hash technique can be applied to most of the hash based data structures. It can significantly improve their speed, because instead of carrying out multiple hash functions, we only need to compute one hash function and a few simple operations (e.g., bit shift and XOR). Other aspects of performance, like memory efficiency and accuracy of these data structures will not be influenced by Single Hash technique. In this paper, we apply it to three kinds of classic hash based data structures, i.e., Bloom filters, CM sketches and d-Ieft hash tables as case studies, and evaluate their performance with both mathematical analysis and extensive experiments. We make all our codes open source on Github.","Title: Risk index assessment for urban natural gas pipeline leakage based on artificial neural network\nYear: 2017\n\nAbstract: With large-scale construction of urban natural gas pipelines, the occurrence of accidents such as fire, explosion owing to natural gas pipeline leakage has increased. How to make appropriate response strategy for urban natural gas pipeline leakage is an important topic for urban safety planner. This paper proposed an assessment program to evaluate risk of urban natural gas pipeline leakage. This system uses artificial neural network mode, which includes 10 inputs such as methane concentration, weather, corrosion, to simulate risk index of pipeline leakage. The 97-day field operation results showed that the risk index match well with field situation, which indicates the reliability and practicability of the assessment program.","Title: A rear-end collision prediction scheme based on deep learning in the Internet of Vehicles.\nYear: 2018\n\nAbstract: Recently, the deep learning schemes have been well investigated for improving the driving safety and efficiency in the transportation systems. In this paper, a probabilistic model named as CPGN (Collision Prediction model based on GA-optimized Neural Network) for decision-making in the rear-end collision avoidance system is proposed, targeting modeling the impact of important influential factors of collisions on the occurring probability of possible accidents in the Internet of Vehicles (IoV). The decision on how to serve the chauffeur is determined by a typical deep learning model, i.e., the BP neural network through evaluating the possible collision risk with V2I (Vehicle-to-Infrastructure) communication, V2V (Vehicle-to-Vehicle) communication and GPS infrastructure supporting. The proper structure of our BP neural network model is deeply learned with training data generated from VISSIM with multiple influential factors considered. In addition, since the selection of the connection coefficient array and thresholds of the neural network has great randomness, a local optimization issue is readily occurring during the modeling procedure. To overcome this problem and consider the ability to efficiently find out a global optimization, this paper chooses the genetic algorithm to optimize the coefficient array and thresholds of proposed neural network. For the purpose of enhancing the convergence speed of the proposed model, we further adjust the studying rate according to the relationship between the actual and predicated values of two adjacent iterations. Simulation results demonstrate that the proposed collision risk evaluation framework could offer rationale estimations to the possible collision risk in car-following scenarios for the next discrete monitoring interval.","Title: VisemeNet: Audio-Driven Animator-Centric Speech Animation.\nYear: 2018\n\nAbstract: We present a novel deep-learning based approach to producing animator-centric speech motion curves that drive a JALI or standard FACS-based production face-rig, directly from input audio. Our three-stage Long Short-Term Memory (LSTM) network architecture is motivated by psycho-linguistic insights: segmenting speech audio into a stream of phonetic-groups is sufficient for viseme construction; speech styles like mumbling or shouting are strongly co-related to the motion of facial landmarks; and animator style is encoded in viseme motion curve profiles. Our contribution is an automatic real-time lip-synchronization from audio solution that integrates seamlessly into existing animation pipelines. We evaluate our results by: cross-validation to ground-truth data; animator critique and edits; visual comparison to recent deep-learning lip-synchronization solutions; and showing our approach to be resilient to diversity in speaker and language.","Title: Non-Stationary Texture Synthesis by Adversarial Expansion.\nYear: 2018\n\nAbstract: The real world exhibits an abundance of non-stationary textures. Examples include textures with large scale structures, as well as spatially variant and inhomogeneous textures. While existing example-based texture synthesis methods can cope well with stationary textures, non-stationary textures still pose a considerable challenge, which remains unresolved. In this paper, we propose a new approach for example-based non-stationary texture synthesis. Our approach uses a generative adversarial network (GAN), trained to double the spatial extent of texture blocks extracted from a specific texture exemplar. Once trained, the fully convolutional generator is able to expand the size of the entire exemplar, as well as of any of its sub-blocks. We demonstrate that this conceptually simple approach is highly effective for capturing large scale structures, as well as other non-stationary attributes of the input exemplar. As a result, it can cope with challenging textures, which, to our knowledge, no other existing method can handle.","Title: Full 3D Reconstruction of Transparent Objects.\nYear: 2018\n\nAbstract: Numerous techniques have been proposed for reconstructing 3D models for opaque objects in past decades. However, none of them can be directly applied to transparent objects. This paper presents a fully automatic approach for reconstructing complete 3D shapes of transparent objects. Through positioning an object on a turntable, its silhouettes and light refraction paths under different viewing directions are captured. Then, starting from an initial rough model generated from space carving, our algorithm progressively optimizes the model under three constraints: surface and refraction normal consistency, surface projection and silhouette consistency, and surface smoothness. Experimental results on both synthetic and real objects demonstrate that our method can successfully recover the complex shapes of transparent objects and faithfully reproduce their light refraction properties.","Title: 2D Scene Sketch-Based 3D Scene Retrieval.\nYear: 2018\n\nAbstract: Sketch-based 3D model retrieval has the intuitiveness advantage over other types of retrieval schemes. Currently, there is a lot of research in sketch-based 3D model retrieval, which usually targets the problem of retrieving a list of candidate 3D models using a single sketch as input. 2D scene sketch-based 3D scene retrieval is a brand new research topic in the field of 3D object retrieval. Unlike traditional sketch-based 3D model retrieval which ideally assumes that a query sketch contains only a single object, this is a new 3D model retrieval topic within the context of a 2D scene sketch which contains several objects that may overlap with each other and thus be occluded and also have relative location configurations. It is challenging due to the semantic gap existing between the iconic 2D representation of sketches and more accurate 3D representation of 3D models. But it also has vast applications such as 3D scene reconstruction, autonomous driving cars, 3D geometry video retrieval, and 3D AR/VR Entertainment. Therefore, this research topic deserves our further exploration.To promote this interesting research, we organize this SHREC track and build the first 2D scene sketch-based 3D scene retrieval benchmark by collecting 3D scenes from Google 3D Warehouse and utilizing our previously proposed 2D scene sketch dataset Scene 250. The objective of this track is to evaluate the performance of different 2D scene sketch-based 3D scene retrieval algorithms using a 2D sketch query dataset and a 3D Warehouse model dataset. The benchmark contains 250 scene sketches and 1000 3D scene models, and both are equally classified into 10 classes. In this track, six groups from five countries (China, Chile, USA, UK, and Vietnam) have registered for the track, while due to many challenges involved, only 3 groups have successfully submitted 8 runs. The retrieval performance of submitted results has been evaluated using 7 commonly used retrieval performance metrics. We also conduct a thorough analysis and discussion on those methods, and suggest several future research directions to tackle this research problem. We wish this publicly available [YLL18] benchmark, comparative evaluation results and corresponding evaluation code, will further enrich and advance the research of 2D scene sketch-based 3D scene retrieval and its applications.","Title: Accurate Per-Flow Measurement With Bloom Sketch\nYear: 2018\n\nAbstract: Sketch is a probabilistic data structure, and is widely used for per-flow measurement in network. The most common sketches are the CM sketch and its several variants. However, given a limited memory size, these sketches always significantly overestimate some flows, exhibiting poor accuracy. To address this issue, we proposed a novel sketch named the Bloom sketch, combining the sketch with the Bloom filter, another well-known probabilistic data structure widely used for membership queries. Extensive experiments based on real IP traces show that our Bloom sketch achieves up to 14:47x higher accurac y compared with the CM sketch, while exhibiting comparable insertion and query speed. Our source code is available at Github [1].","Title: Elastic sketch: adaptive and fast network-wide measurements.\nYear: 2018\n\nAbstract: When network is undergoing problems such as congestion, scan attack, DDoS attack, etc., measurements are much more important than usual. In this case, traffic characteristics including available bandwidth, packet rate, and flow size distribution vary drastically, significantly degrading the performance of measurements. To address this issue, we propose the Elastic sketch. It is adaptive to currently traffic characteristics. Besides, it is generic to measurement tasks and platforms. We implement the Elastic sketch on six platforms: P4, FPGA, GPU, CPU, multi-core CPU, and OVS, to process six typical measurement tasks. Experimental results and theoretical analysis show that the Elastic sketch can adapt well to traffic characteristics. Compared to the state-of-the-art, the Elastic sketch achieves 44.6 ∼ 45.2 times faster speed and 2.0 ∼ 273.7 smaller error rate.","Title: Resource scheduling optimisation algorithm for containerised microservice architecture in cloud computing.\nYear: 2018\n\nAbstract: Currently, the containerised microservice architecture has aroused great concern. The single application is developed as a suite of small services to facilitate the application deployment, expansion and management. The traditional scheduling of microservice tends to focus on the load balancing of cluster, ignoring the quality of service (QoS). Therefore, this paper proposes a prediction model of component relevance, by adopting the optimised artificial bee colony algorithm (OABC) on the containerised microservice scheduling. Different assessment strategies are adopted according to the differences in the correlation among components. Two-point crossover operator is introduced to improve the exploration ability of the algorithm. The mutation operator is added to enhance the local search ability, and the mutation probability is set to the dynamic value which varies with the number of iterations to speed up the convergence of the algorithm. The experimental results show that the OABC is preferable to the arti...","Title: Local and global temporal correlations for longitudinal data.\nYear: 2018\n\nAbstract: Quantifying the association between a pair of random functions recorded over a time period is of general interest. Several methods have been proposed in the literature but they either suffer from an ill-posed problem intrinsic to functional data or are suitable only for intensely recorded longitudinal data. In this paper we provide new methods that overcome these challenges by investigating the temporal Pearson correlation between paired random functions. We investigate both a local temporal correlation measure and a global summary measure of the dynamic temporal correlations and propose a nonparametric estimation method that covers both intensely observed and sparsely observed longitudinal data. Asymptotic results of the estimators are derived under mild conditions and the method is illustrated via simulations and a benchmark data set.","Title: Frame Level Quantization Control with Temporal Distortion Propagation Model for Video Coding.\nYear: 2018\n\nAbstract: In video coder, inter-frame prediction causes distortion propagation among temporally adjacent frames, which complicates frame level bit allocation and quantization control. Quantization parameter cascading (QPC) is generally employed to determine a sequence of quantization parameter for dependent rate distortion optimization (RDO). This paper proposes a general framework for temporal dependency analysis by lever-aging a distortion propagation model. The amount of distortion propagated from the temporally adjacent frames is measured by tree-style dependent analysis. Then, a trellis comprised of frame level quantization parameters of one GOP is constructed to achieve global optimization via branch-prune based dynamic programming. The simulation results verify that the frame level QPC algorithm with the proposed distortion model achieves up to 1.2dB—1.5dB PSNR improvement on average, with smaller temporal distortion fluctuation contributed by efficient bit allocation.","Title: Fine-Level Semantic Labeling of Large-Scale 3D Model by Active Learning\nYear: 2018\n\nAbstract: Semantic labeling of 3D models has been a challenging task in recent years. Due to the various categories and shapes of 3D objects in different scenes, it is hard to develop a versatile method suitable for most scenes. In this paper, we propose an Active Learning based method to tackle the problem. The proposed method takes a 3D mesh model generated from images using SfM and MVS, as well as the calibrated images, as the input, and outputs a semantic mesh model in which each facet takes a fine-level semantic label. Starting with a small annotated image set, we progressively fine-tune a Convolutional Neural Network (CNN) with the ever-enlarging annotated image set for image semantic segmentation. In each iteration, by back-projecting the pixel labels to the 3D model and fusing them in 3D space, a semantic 3D model is generated. The semantic 3D model functions as a supervisor to select a batch of worthy images for annotation to boost the performance of the CNN in next iteration. This process iterates until the label assignment of the 3D model becomes steady. By making full use of the 3D geometric information, the proposed method could significantly reduce the annotation cost without losing the labeling quality of 3D models. Experimental results of fine-level labeling on two large-scale ancient Chinese architectures demonstrate the effectiveness of the proposed method.","Title: Accelerating network measurement in software.\nYear: 2018\n\nAbstract: Network measurement plays an important role for many network functions such as detecting network anomalies and identifying big flows. However, most existing measurement solutions fail to achieve high performance in software as they often incorporate heavy computations and a large number of random memory accesses. We present Agg-Evict, a generic framework for accelerating network measurement in software. Agg-Evict aggregates the incoming packets on the same flows and sends them as a batch, reducing the number of computations and random memory accesses in the subsequent measurement solutions. We perform extensive experiments on top of DPDK with 10G NIC and observe that almost all the tested measurement solutions under Agg-Evict can achieve 14.88 Mpps throughput and see up to 5.7X lower average processing latency per packet.","Title: Sino-InSpace: A Digital Simulation Platform for Virtual Space Environments.\nYear: 2018\n\nAbstract: The implementation of increased space exploration missions reduces the distance between human beings and outer space. Although it is impossible for everyone to enter the remote outer space, virtual environments could provide computer-based digital spaces that we can observe, participate in, and experience. In this study, Sino-InSpace, a digital simulation platform, was developed to support the construction of virtual space environments. The input data are divided into two types, the environment element and the entity object, that are then supported by the unified time-space datum. The platform adopted the pyramid model and octree index to preprocess the geographic and space environment data, which ensured the efficiency of data loading and browsing. To describe objects perfectly, they were abstracted and modeled based on four aspects including attributes, ephemeris, geometry, and behavior. Then, the platform performed the organization of a visual scenario based on logical modeling and data modeling; in addition, it ensured smooth and flexible visual scenario displays using efficient data and rendering engines. Multilevel modes (application directly, visualization development, and scientific analysis) were designed to support multilevel applications for users from different grades and fields. Each mode provided representative case studies, which also demonstrated the capabilities of the platform for data integration, visualization, process deduction, and auxiliary analysis. Finally, a user study with human participants was conducted from multiple views (usability, user acceptance, presence, and software design). The results indicate that Sino-InSpace performs well in simulation for virtual space environments, while a virtual reality setup is beneficial for promoting the experience.","Title: Pair-wise Exchangeable Feature Extraction for Arbitrary Style Transfer.\nYear: 2018\n\nAbstract: Style transfer has been an important topic in both computer vision and graphics. Gatys et al. first prove that deep features extracted by the pre-trained VGG network represent both content and style features of an image and hence, style transfer can be achieved through optimization in feature space. Huang et al. then show that real-time arbitrary style transfer can be done by simply aligning the mean and variance of each feature channel. In this paper, however, we argue that only aligning the global statistics of deep features cannot always guarantee a good style transfer. Instead, we propose to jointly analyze the input image pair and extract common/exchangeable style features between the two. Besides, a new fusion mode is developed for combining content and style information in feature space. Qualitative and quantitative experiments demonstrate the advantages of our approach.","Title: Probabilistic Rank-One Discriminant Analysis via Collective and Individual Variation Modeling.\nYear: 2020\n\nAbstract: Linear discriminant analysis (LDA) is a classical supervised subspace learning technique that has wide applications. However, it is designed for vector only, which cannot exploit the tensor structures and may lead to suboptimal results when dealing with tensorial data. To address this problem, several multilinear LDA (MLDA) methods have been proposed to learn the subspaces from tensors. By exploiting the tensor structures, they achieve compact subspace representations, reduced parameter sizes, and improved robustness against the small sample size problem. However, existing MLDA methods do not take data uncertainty into account, fail to converge properly, or have to introduce additional tuning parameters for good convergence properties. In this paper, we therefore solve these limitations by proposing a probabilistic MLDA method for matrix inputs. Specifically, we propose a new generative model to incorporate structural information into the probabilistic framework, where each observed matrix is represented as a linear combination of collective and individual rank-one matrices. This provides our method with both the expressiveness of capturing discriminative features and nondiscriminative noise, and the capability of exploiting the 2-D tensor structures. To overcome the convergence problem of existing MLDAs, we develop an EM-type algorithm for parameter estimation, which has closed-form solutions with convergence guarantees. Experimental results on real-world datasets show the superiority of the proposed method to other probabilistic and MLDA variants.","Title: A class of optimal estimators for the covariance operator in reproducing kernel Hilbert spaces.\nYear: 2019\n\nAbstract: The covariance operator plays an important role in modern statistical methods and is critical for inference. It is most often estimated by the empirical covariance operator. In spite of its simple and appealing properties, however, this estimator can be improved by a class of shrinkage operators. In this paper, we study shrinkage estimation of the covariance operator in reproducing kernel Hilbert spaces. A data-driven shrinkage estimator enjoying desirable theoretical and computational properties is proposed. The procedure is easily implemented and its numerical performance is investigated through simulations. In finite samples, the estimator outperforms the empirical covariance operator, especially when the data dimension is much larger than the sample size. We also show that the rate of convergence in Hilbert–Schmidt norm is of the order n−1∕2. Furthermore, we establish the minimax optimal rate of convergence over suitable classes of probability measures and demonstrate that these shrinkage operators are all minimax rate-optimal.","Title: A Context-Awareness Positioning Scheme In Hospital Wlan Environment\nYear: 2018\n\nAbstract: Positioning technologies can be generally divided into indoor and outdoor application scenarios. This paper focuses on an indoor positioning system which is able to be used in the Wireless Local Area Network environment in the hospitals. Compared with traditional techniques, the newly-proposed positioning scheme takes user's context into consideration, using Radio Frequency Fingerprint Vector (RFFV) as the signal feature to identify and detect the location of the user in the hospital. Besides, space and time factors are considered as the constraints to reduce the positioning errors. Experiment result in hospital environment proves the performance of the scheme is better than those traditional schemes without error correction thus can successfully judge patient's activity and prevent some accidents.","Title: An Extension of Yuan’s Lemma to Fourth-Order Tensor System\nYear: 2019\n\nAbstract: Yuan’s lemma is a basic proposition on homogeneous quadratic function systems. In this note, we extend Yuan’s lemma to the fourth-order tensor system. We first give generalized definition of positive semidefinite of fourth-order tensor, and based on it, an extension of Yuan’s lemma is proposed. As an application, we establish the strong duality result of a class of quadratic semidefinite programming problems using the extended Yuan’s lemma.","Title: A Security Reinforcement Technology of Telematics Box Based on Secure Element.\nYear: 2018\n\nAbstract: The telematics box is a vehicle communication terminal in the Internet of Vehicles, used to collect data from electronic control units and transfer data between users and application server. With the rapid development of Internet of Vehicles, T-Box provides users personalized service and abundant entertainment. However, it also becomes easy to cause security problems, such as disclosure of private information of owners, illegal control of vehicles and so on. Therefore, it is very important to reinforce the security of T-Box. To reinforce the security of T-Box, this paper will present a security reinforcement technology, which uses a secure element and traditional security measures to implements secure boot based on TrustZone and security service. This paper designs a software hierarchical model of security service based on the research of AUTOSAR and make innovative improvements to Transport Layer Security. Besides, to validate the feasibility of the solution, this paper also implements a security reinforcement technology and verifies the validity of it.","Title: Adaptive LSB quantum watermarking method using tri-way pixel value differencing\nYear: 2019\n\nAbstract: As an important way to protect copyright by embedding watermark in digital images, quantum watermarking catches more and more attentions. In this study, a novel quantum watermarking method on the basis of tri-way pixel value differencing and modified least significant bit (LSB) substitution is proposed. A quantum cover image using the novel-enhanced quantum image representation is partitioned into non-overlapping 2 × 2 blocks with four pixels firstly. To classify the block as a smooth area or an edge area, the tri-way pixel value differences are calculated and compared with a predefined threshold. The quantum watermark image, which is expanded and scrambled, is then embedded into a quantum cover image by the k-bit LSB substitution method, where k is decided by the level of each block. The embedded quantum watermark can be extracted from the quantum stego-image without the assistance of original quantum cover image. Theoretical analysis and simulation-based experiments demonstrate both the feasibility and capabilities of the proposed quantum watermarking method, which has good visual quality, better robustness, and higher security.","Title: Classical Formula Ontology Construction and Application in the Diagnosis and Treatment of Dermatosis\nYear: 2018\n\nAbstract: The knowledge discovery and effective using are insufficient in the study of classical formula which needs to be further mined. In order to solve the problem, this paper constructed classical formula Ontology based on knowledge processing, and realized the sharing and reuse of classical formula knowledge. Finally, this paper constructed the mathematical model in the diagnosis and treatment of dermatosis based on the classical formula corpus and Ontology. Our purpose was to perfect dermatologist's different levels in diagnosing and treating dermatosis, improve the level and accuracy of clinical diagnosis and treatment, and achieve effective drive of classical formula literatures for clinical diagnosis and treatment.","Title: Evaluation of a Wi-Fi Signal Based System for Freeway Traffic States Monitoring: An Exploratory Field Test.\nYear: 2019\n\nAbstract: Monitoring traffic states from the road is arousing increasing concern from traffic management authorities. To complete the picture of real-time traffic states, novel data sources have been introduced and studied in the transportation community for decades. This paper explores a supplementary and novel data source, Wi-Fi signal data, to extract traffic information through a well-designed system. An IoT (Internet of Things)-based Wi-Fi signal detector consisting of a solar power module, high capacity module, and IoT functioning module was constructed to collect Wi-Fi signal data. On this basis, a filtration and mining algorithm was developed to extract traffic state information (i.e., travel time, traffic volume, and speed). In addition, to evaluate the performance of the proposed system, a practical field test was conducted through the use of the system to monitor traffic states of a major corridor in China. The comparison results with loop data indicated that traffic speed obtained from the system was consistent with that collected from loop detectors. The mean absolute percentage error reached 3.55% in the best case. Furthermore, the preliminary analysis proved the existence of the highly correlated relationship between volumes obtained from the system and from loop detectors. The evaluation confirmed the feasibility of applying Wi-Fi signal data to acquisition of traffic information, indicating that Wi-Fi signal data could be used as a supplementary data source for monitoring real-time traffic states.","Title: Tank Container Operators' profit maximization through dynamic operations planning integrated with the quotation-booking process under multiple uncertainties.\nYear: 2019\n\nAbstract: •Dynamic operations planning integrated with quotation-booking process.•Multiple uncertainties arising from tank cleaning and transport service reliability.•Planned leasing and emergent leasing for additional tank container capacity.•Simulation-based two-stage optimization model proposed.•Solution procedure combining multiple techniques to tackle dynamic planning horizon.","Title: Optimizing New User Experience in Online Services.\nYear: 2018\n\nAbstract: \"Well begun is half done\". This proverb is especially true for a web product when it comes to creating a delightful and proactive user experience. This article describes our work in the last few years optimizing new user experience at LinkedIn, driven by application of data science and advanced analytical methods. Through mining the logs generated by new users in the past, we uncovered signals from their initial session that can predict their retention. We established the difference between creating models for predictions and creating models to inform product strategy. We found that persistent features, such as a user's number of connections, and having a confirmed channel of communication (email, phone or app), more strongly predict new user retention than most transient features such as how long they spend on the registration form or how many page views they have visited. We further constructed a true north metric (Quality Signup) to drive our Growth team towards the right focus as they iterated through multiple versions of new user onboarding flows. The strong positive correlation between the Quality Signup metric and long-term retention, as well as the positive impact we have seen on the product over the last two years, validate our strategy to drive product roadmap through data-informed metrics.","Title: A robot state estimator based on multi-sensor information fusion\nYear: 2018\n\nAbstract: Mobile robot technology plays an important role in the service and manufacturing industries. The mobile robot technology includes a series of key technologies, such as robot localization, navigation and path planning. Furthermore, the state estimation problem of the robot is more critical. The state estimation of the robot mainly includes the calculation of the parameters such as the robot's position, attitude and the speed. The accuracy of the state estimation of the robot will greatly affect the working effect of the robot, including the construction of the environment map, and navigation, path planning, and so on. In recent years, a large number of scholars and experts have carried out a lot of research on the state estimation of robots. They have used various methods to estimate the state of robots. The sensors commonly used for acquiring robot state information mainly include odometers, inertial measurement units, and lasers, cameras. The characteristics of various sensors are different. Considering the complementary characteristics of sensors, this paper mainly uses multi-sensor information for estimating robot status. The extended Kalman filter is used to fuse the information coming from the camera, odometer, inertial measurement unit, and laser to estimate the robot state.","Title: Experimental study of the effect of light source spot size on measure error of PSD.\nYear: 2019\n\nAbstract: The energy distribution of the incident light spot on position-sensitive detector (PSD) screen is uneven and the output coordinate value is the energy centre of the light spot. Measurement error caused by the deviation between the geometric and gravity centre of the light spot will occur. The deviation will increase with the increase of the diameter of the light spot. There is no quantitative research on the position error caused by the changes of the light spot diameter and the distance from the light spot to the PSD. To evaluate the magnitude of the error, the infrared spotlight is selected as the target light. The changing pattern that the spot diameter varies with the distance between the light source and lens is studied. Additionally, the effect of the spot size on the PSD measure error is studied. The research provides reference for error evaluation of the distance and position detection of PSD. [Submitted 5 July 2017; Accepted 24 December 2017]","Title: Video Super-Resolution via Dynamic Local Filter Network\nYear: 2018\n\nAbstract: Conventional Convolutional Neural Network (CNN) based video super-resolution (VSR) methods heavily depend on explicit motion compensation. Input frames are warped according to flow-like information to eliminate inter-frame differences. These methods have to make a trade-off between the distraction caused by spatio-temporal inconsistency and the pixel-wise detail damage caused by compensation. In this paper, we propose a novel video super-resolution method based on dynamic local filter network. Unlike traditional VSR techniques, our method implicitly performs motion estimation, compensation and fusion simultaneously via local convolutions with dynamically generated filter kernels. An optional autoencoder based refinement module is also proposed to sharpen edges and remove artifacts. The experimental results demonstrate that our method outperforms the best existing VSR algorithm by 0.53 dB in terms of PSNR, and provides superior visual quality.","Title: A New Method With Hilbert Transform and Slip-SVD-Based Noise-Suppression Algorithm for Noisy Power Quality Monitoring\nYear: 2019\n\nAbstract: This paper presents a new method for automatic monitoring of noisy power quality, which is based on the Hilbert transform (HT) and the proposed slip-singular value decomposition (SVD)-based noise-suppression algorithm. The proposed method first employs the fast Fourier transform (FFT)-based low-pass filter and HT to obtain the instantaneous fundamental amplitude and the FFT sequence of the signal. Second, the slip-SVD-based noise-suppression algorithm and threshold filtering are used to extract cleaned singular value characteristic waveform of the high-frequency signal. Through judging the instantaneous fundamental amplitude, cleaned singular value characteristic waveform, and the FFT sequence, the presence of disturbances including single and combined disturbances can be easily detected by the proposed method. To demonstrate the effectiveness of the proposed method, extensive tests are conducted on the diverse simulation disturbances and the actual data obtained from the practical power systems of China. The test results show that the proposed method has the advantages such as low false detection rate, good noise tolerance capability, short computational time, fewer parameters, practicability, and compatibility in comparison with the traditional disturbance detection methods. Besides, the proposed method can provide some important features such as amplitude, duration, and frequency for classification. Such advantages make the proposed method to be a good choice for real-time applications.","Title: Lightweight Indexing and Querying Services for Big Spatial Data\nYear: 2019\n\nAbstract: With the widespread use of GPS-equipped smartphones and Internet of Things devices, a huge amount of data with location information is being generated at an unprecedented rate. To gain a deeper insight into such a plethora of spatial data, scientists and engineers are widely using spatial queries for their big data applications. However, because of not only the massive spatial data size but also t...","Title: EMC: Energy-Aware Morphable Cache Design for Non-Volatile Processors\nYear: 2019\n\nAbstract: Wearable, implantable and Internet of Things devices are attracting increasing attention from both research and industry fields. Energy harvesting is a promising alternative of battery to power these embedded systems. However, the intrinsic instability of energy harvesting systems leads to potential frequent power interruptions. In traditional volatile processor, all the status will be lost at power failures and the program needs to re-start after power resumes. In order to survive the power failures and enable accumulative execution, non-volatile processor (NVP) is proposed to back up volatile information before power depletion and recover the system status after power resumes. Non-volatile memory (NVM) is typically attached for cache and main memory backup. There are researches working on optimization of the backup. However, little of them involve multiple level cell (MLC) NVM. In this work, we first discuss the benefit of applying MLC NVM for cache backup and the architecture of morphable hybrid cache, and then propose a three-stage energy-aware cache management strategy to improve the system performance and energy utilization while guaranteeing successful backups. Backup-aware cache replacement policies are also developed for backup optimization. Evaluation shows that the proposed EMC scheme can achieve 10.6 percent performance improvement and simultaneous 25.2 percent energy reduction when compared with the single level cell (SLC) based hybrid cache.","Title: Monitoring Landslides Using Multi-frequency SAR Data in Danba County, Sichuan Province, China.\nYear: 2016\n\nAbstract: Danba County, located at the northwestern Sichuan, is one of the areas prone to severe landslides in China. The landslides in this area present great threaten upon the local public safety and the famous heritage architectures. Therefore, monitoring landslide is of great importance for the sustainable developments in Danba. In this paper, InSAR techniques is employed to investigate typical landslide activities, based on the multi-frequency SAR images acquired from C-band Envisat, X-band TerraSAR-X, and L-band ALOS-1/2 satellites. Firstly, differential InSAR (D-InSAR) is used to recognize known landslides and find potential unstable slopes in a region scale. Then, for a specific landslide, advanced multi-temporal InSAR method is exploited to characterize its surface deformation by obtaining time-series displacement on coherent targets. Furthermore, the triggering factors are discussed based on the deformation results and on-site surveys.","Title: A biologically inspired model based on a multi-scale spatial representation for goal-directed navigation.\nYear: 2017\n\nAbstract: Inspired by the multi-scale nature of hippocampal place cells, a biologically inspired model based on a multi-scale spatial representation for goal-directed navigation is proposed in order to achieve robotic spatial cognition and autonomous navigation. First, a map of the place cells is constructed in different scales, which is used for encoding the spatial environment. Then, the firing rate of the place cells in each layer is calculated by the Gaussian function as the input of the Q-learning process. The robot decides on its next direction for movement through several candidate actions according to the rules of action selection. After several training trials, the robot can accumulate experiential knowledge and thus learn an appropriate navigation policy to find its goal. The results in simulation show that, in contrast to the other two methods(G-Q, S-Q), the multi-scale model presented in this paper is not only in line with the multi-scale nature of place cells, but also has a faster learning potential to find the optimized path to the goal. Additionally, this method also has a good ability to complete the goal-directed navigation task in large space and in the environments with obstacles.","Title: Development and Usage of Chinese Medicine Supporting System Based on Post-Controlled Machinery\nYear: 2009\n\nAbstract: This article makes deep study on Chinese medicine literature supporting system’s development and its function’s realization ,which is established on ancient Chinese medicine literature ’s manner of writing and structure and focused on design theory, technology index and function. In this sense this article makes good try to research on method and model of Chinese ancient technology literature’s digitalizing, which could supply suggestion for the other relative scholarships’ deep study.","Title: Communication Distance Correlates Positively with Electrode Distance in Underwater Electrocommunication\nYear: 2019\n\nAbstract: Underwater wireless communication technology has been restricting the development of undersea resources for decades. We have proposed an effective underwater communication method (electrocommunication) for small underwater robots in our previous studies. In this article, we further study how electrode distance/spacing affects the communication range of electrocommunication between the robotic fish models. The working principle of electrocommunication is simplified by an electric dipole model. Based on the electric dipole model, the maximum theoretical distance of electrocommunication is derived as r=K√[3]d2 where d2 is the distance between the receiving electrodes, and K is a constant related to the system and environmental parameters. Extensive experiments were conducted by varying the distance between the transmitting electrodes. The results show that the maximum experimental distance is basically consistent with the theoretical predictions, and that the communication distance is positively correlated with the electrode distance. © 2018 IEEE.","Title: Automatically difficulty grading method of \"instruction system\" question bank based on knowledge tree.\nYear: 2017\n\nAbstract: The aim of this study is to propose a model, which can automatically grade difficulty for a question from \\\"Instruction System\\\" question bank. The system mainly uses attributes which are employed to be input. A knowledge tree model which was established based on the proper nouns from Chinese \\\"Instruction System\\\" teaching material and a machine learning algorithm are utilized as important parts for classification. The experimental dataset comes from our built \\\"Principles of Computer Organization\\\" online education system, the accuracy result of difficulty classification could be 79.41% which is much higher than the accuracy of random guess 50%.","Title: Detail Preserved Surface Reconstruction from Point Cloud.\nYear: 2019\n\nAbstract: In this paper, we put forward a new method for surface reconstruction from image-based point clouds. In particular, we introduce a new visibility model for each line of sight to preserve scene details without decreasing the noise filtering ability. To make the proposed method suitable for point clouds with heavy noise, we introduce a new likelihood energy term to the total energy of the binary labeling problem of Delaunay tetrahedra, and we give its s-t graph implementation. Besides, we further improve the performance of the proposed method with the dense visibility technique, which helps to keep the object edge sharp. The experimental result shows that the proposed method rivalled the state-of-the-art methods in terms of accuracy and completeness, and performed better with reference to detail preservation.","Title: 600-Ghz-Wave Beam Steering By Terahertz-Wave Combiner\nYear: 2018\n\nAbstract: We achieved 600-GHz-wave power combination with directional gain by optically phase tuning of arrayed photomixers. Using this phase control technique for phase shift, we also demonstrated the beam steering within an angle of 35 degrees.","Title: Learning and evaluation of latent dependency forest models\nYear: 2019\n\nAbstract: Latent dependency forest models (LDFMs) are a new type of probabilistic models with dynamic dependency structures over random variables. They distinguish themselves from other probabilistic models by the fact that there is no need for structure search when learning the models. However, parameter learning of LDFMs is still quite challenging since the partition function cannot be tractably calculated. In this paper, we investigate and empirically compare several algorithms of learning parameters of LDFMs which either approximate or ignore the partition function in the learning objective. Furthermore, we propose an approximate algorithm to estimate the partition function of LDFM. Experimental results show that (1) our learning algorithms can achieve better results than the previous learning algorithm of LDFMs, and (2) our partition function estimation algorithm is accurate.","Title: Learning and evaluation of latent dependency forest models\nYear: 2019\n\nAbstract: Latent dependency forest models (LDFMs) are a new type of probabilistic models with dynamic dependency structures over random variables. They distinguish themselves from other probabilistic models by the fact that there is no need for structure search when learning the models. However, parameter learning of LDFMs is still quite challenging since the partition function cannot be tractably calculated. In this paper, we investigate and empirically compare several algorithms of learning parameters of LDFMs which either approximate or ignore the partition function in the learning objective. Furthermore, we propose an approximate algorithm to estimate the partition function of LDFM. Experimental results show that (1) our learning algorithms can achieve better results than the previous learning algorithm of LDFMs, and (2) our partition function estimation algorithm is accurate.","Title: A Three-Dimensional Elasto-Plastic Dynamic Response Procedure For Concrete-Faced Rockfill Dams\nYear: 2018\n\nAbstract: Equivalent linear dynamic analysis is the primary method of analysis for concrete-faced rockfill dams during earthquake. However, this method cannot be directly used to estimate permanent deformation during earthquake, which is important to evaluate the dynamic safety of concrete-faced rockfill dams. To bridge this gap, an elasto-plastic dynamic response procedure based on finite element method is presented to estimate the construction, impoundment, and dynamic characteristics of concrete-faced rockfill dams. This procedure involves a generalized plasticity model for rockfill materials and an ideal elasto-plastic model for interface between concrete face slab and rockfill material. The construction, impounding process, and seismic behavior of a 150-m-tall concrete-faced rockfill dam were simulated using seismic motion based on Chinese code for seismic design of hydraulic structures of hydropower project with the peak ground acceleration of 0.3g as an excitation to validate the procedure. On the basis of the numerical simulation results of the step-by-step construction, reservoir impoundment and seismic response of a concrete-faced rockfill dam, dam settlement, joint deformation, stress, and deformation of slab were analyzed. The results indicate that the dam settlement and deformation of joint can be directly obtained by the procedure during earthquake. The construction, impoundment, and seismic analysis could be considered in one procedure.","Title: Multi-Island Genetic Algorithm and Kriging Model-Based Design of Vehicle Product Comprising Multi-Material.\nYear: 2018\n\nAbstract: In recent years, improvement in multi-material additive manufacturing technology has resulted in technical improvements in multi-material design employed by the automotive industry. Therefore, in this work, an internal trim part (corresponding to an original product composed of polypropylene) of a vehicle was divided into four components using a multi-material design method considering PLA composites. The PLA was reinforced with basalt fibers for realization of the required mechanical properties. The mechanical properties associated with different fiber content (from 0 to 60%) were determined via tensile tests. To reduce the mass of the product, an optimization process combining a Kriging surrogate model with a Multi-Island Genetic Algorithm was used to search for the Pareto solution. The coefficient of determination (R-2) and response surface methodology (RSM) surrogate model were used to confirm the validity and accuracy of the Kriging model. The values of R-2 were all >0.92 and the low error value of both results demonstrated the effectiveness of the optimization process. Owing to the optimization process, the mass of the PLA composite product was reduced by nearly 9%. Correlation analysis indicated that x(2) has the strongest impact on the total mass. Therefore, the optimization process proposed for the multi-material optimal design is feasible and contributes significantly to the attainment of light-weight vehicle parts.","Title: Dynamic Responses Of Concrete-Faced Rockfill Dam Due To Different Seismic Motion Input Methods\nYear: 2018\n\nAbstract: The concrete-faced rockfill dam valley foundation was considered as an open energy system and a reasonable non-uniform seismic motion input method was applied to the dynamic analysis of a concrete-faced rockfill dam based on the generalized plastic model. First, the corresponding program was validated by means of the scattering question of an idealized semicircle valley. Subsequently, the seismic elasto-plastic finite element analyses were performed to compare and investigate the performances of a concrete-faced rockfill dam under different seismic motion input methods. The results show that the dynamic responses of the concrete-faced rockfill dam are decreased by 10%-30% approximately with the use of non-uniform seismic motion input method. As a result, the traditional uniform seismic motion input method would overestimate the responses of the dam. From the perspective of seismic safety evaluation, the overestimations would disturb the reasonable assessment of the aseismic capacity of the dam. Moreover, the slope stability analysis results might be conservative and unreasonable due to overestimating the accelerations during the earthquake.","Title: Least-Element Time-Stepping Methods for Simulation of Linear Networks with Ideal Switches\nYear: 2019\n\nAbstract: Linear networks with ideal switches have various applications in power converters, signal processing and control problems, which can be modeled by linear complementarity systems (LCSs). This paper presents new results on the least-element time-stepping method for simulation of linear networks with ideal switches for a class of LCSs. The method is efficient and stable and can be easily implemented. The convergence results and preliminary numerical results show that the least-element time-stepping method is efficient for verifying accuracy of approximate solutions.","Title: Blind screen content image quality measurement based on sparse feature learning.\nYear: 2019\n\nAbstract: Recently, the perceived quality measurement of screen content images (SCIs) has become an active research topic. In this paper, a blind image quality measurement (IQM) metric for SCIs based on the learning of sparse features via dictionary learning is proposed. First, to extract the sparse features, histogram representations from multi-scale local gradient patterns are integrated to form a dictionary. Subsequently, using a pursuit algorithm, the sparse features of the distorted SCIs are efficiently coded by this dictionary. Finally, to obtain the final quality of the distorted SCIs, a machine learning algorithm is utilised to combine the sparse features into a final quality score. The results of extensive simulations conducted show that the proposed blind IQM metric consistently obtains competitive performance and is in line with human beings perceive.","Title: Research on Container Load Strategy Based on Microservices\nYear: 2018\n\nAbstract: For the problems of unbalanced load and long service response time in microservice-based container environments, this paper designs a load model that is easy to expand and maintain, which combines the service discovery of microservices, performance monitoring of servers and containers, and container load balancing, and as for the shortcomings of load balancing algorithm-the premature convergence of Ant Colony algorithm as well as the shortcomings of the load balancing algorithm in springcloud-the long service response time of Minimum Concurrency Algorithm, it proposes an Optimized Ant Colony algorithm (OACO), OACO optimizes the Ant Colony algorithm by introducing a fuse mechanism and an open circuit recovery mechanism. The simulation results show that the Optimized Ant Colony algorithm (OACO) has advantages in load balance and service response time over Ant Colony algorithm and the service response time is improved with respect to Minimum Concurrency Algorithm.","Title: Probabilistic Rank-One Tensor Analysis With Concurrent Regularizations.\nYear: 2021\n\nAbstract: Subspace learning for tensors attracts increasing interest in recent years, leading to the development of multilinear extensions of principal component analysis (PCA) and probabilistic PCA (PPCA). Existing multilinear PPCAs are based on the Tucker or CANDECOMP/PARAFAC (CP) models. Although both kinds of multilinear PPCAs have shown their effectiveness in dealing with tensors, they also have their ...","Title: Projective Latent Dependency Forest Models\nYear: 2019\n\nAbstract: Latent dependence forest models (LDFM) are a new type of probabilistic models with the advantage of not requiring the difficult procedure of structure learning in model learning. However, normalized joint probability computation and marginal inference are intractable for LDFM. In this paper, we proposed projective LDFMs (PLDFMs), a variant of LDFM, for which joint and marginal probabilities become tractable (cubic time with respect to the number of random variables) to compute while learning remains easy. We show that PLDFMs can be seen as a special case of sum-product networks (SPNs). We then propose sum-product projective dependence networks, a combination of PLDFMs and SPNs that scales up to a large number of random variables. Our extensive experiments on 29 datasets show that our models achieve competitive results with other probabilistic models.","Title: An Efficient and Reliable QoF Routing for Urban VANETs With Backbone Nodes.\nYear: 2019\n\nAbstract: Because of the dynamic topology, high mobility of nodes, and complicated channel environments in urban areas, existing routings methods are susceptible to frequent link interruptions and channel congestions. To address these issues, a Quality of Forwarding (QoF)-based reliable geographic routing (QFRG) in urban vehicular ad hoc networks (VANETs) is proposed, where the best route is determined by guaranteeing the QoF and satisfying the link reliability requirement. Two theoretical models for QoF and link reliability analysis are first presented. Taking into consideration the transmission cost and the packet delivery ratio, the QoF is, then, employed to provide the quantitative evaluation to the road segments through the presented road weight evaluation (RWE) scheme, which takes into account the impact of the relative position of links on the network performance. Next, to accommodate the network scale of an urban city, the city map is divided into smaller grid zones. Based on the position of the destination, different transmission strategies are presented for packet forwarding. The extensive simulations demonstrate our protocol's superiority in both transmission delay and packet delivery ratio compared with the existing schemes.","Title: Some remarks on Jacobi and Gauss–Seidel-type iteration methods for the matrix equation AXB=C\nYear: 2019\n\nAbstract: Tian, et al. proposed in [5] several Jacobi and Gauss–Seidel-type iterative methods for solving matrix equation AXB=C. Those methods were demonstrated to be effective by the given numerical experiments. However, we find that there is a technical error in the proof of the main theorem (Theorem 3.3). In this note we first show this erratum by an example. Then we establish a new convergence theorem which contains the Theorem 3.3 in [5] as a special case.","Title: Deep blind quality evaluator for multiply distorted images based on monogenic binary coding\nYear: 2019\n\nAbstract: Perceptual quality evaluation of multiply distorted images has become a very challenging research topic. In this paper, we present a novel and efficient deep blind quality evaluator for multiply distorted images based on monogenic binary coding (MBC). Local complementary structural information and a deep learning method are employed to blindly evaluate the quality of multiply distorted images. First, a monogenic signal representation is utilized to decompose a multiply distorted image into three complementary components: orientation, phase, and magnitude. The quality-predictive features are then determined from the complementary components. Finally, the features are mapped to the human quality score of the multiply distorted image based on the deep neural network. The results on two newly established multiply distorted image subjective databases confirm that our metric has a better prediction performance than existing state-of-the-art full-reference and classical blind metrics.","Title: Bidirectional recurrent autoencoder for 3D skeleton motion data refinement\nYear: 2019\n\nAbstract: •The proposed BRA can generate motion data that have much less reproduction error compared to the CNN architecture.•The proposed BRA does not require postprocessing procedure.•The proposed BRA does not require the noise amplitude as a priori knowledge.","Title: Disruptive Online Service Innovation In The Presence Of Competition Among Disruptors: The Case Of 3rd-Party Online Payment\nYear: 2019\n\nAbstract: While the competition between incumbent and disruptor has been well documented, the competition among disruptors is less well understood. This study aims to identify factors influenced the competitiveness of a disruptive online service innovation when there is a competition among disruptors. Focusing on the 3rd-party online payment, authors conducted a case study by interviewing 20 key figures in this field and reviewing a variety of published information resources. Findings indicate that the variations in competitiveness arise from three sources: 1) competition basis; 2) competition environment; 3) competition strategy. Important factors within these categories include the service's performance on security and availability, the regulations limited the business scope, the interrelationship with the members in the value network, and the strategies to build and protect the market share.","Title: Bayesian Low-Tubal-Rank Robust Tensor Factorization with Multi-Rank Determination\nYear: 2021\n\nAbstract: Robust tensor factorization is a fundamental problem in machine learning and computer vision, which aims at decomposing tensors into low-rank and sparse components. However, existing methods either suffer from limited modeling power in preserving low-rank structures, or have difficulties in determining the target tensor rank and the trade-off between the low-rank and sparse components. To address these problems, we propose a fully Bayesian treatment of robust tensor factorization along with a generalized sparsity-inducing prior. By adapting the recently proposed low-tubal-rank model in a generative manner, our method is effective in preserving low-rank structures. Moreover, benefiting from the proposed prior and the Bayesian framework, the proposed method can automatically determine the tensor rank while inferring the trade-off between the low-rank and sparse components. For model estimation, we develop a variational inference algorithm, and further improve its efficiency by reformulating the variational updates in the frequency domain. Experimental results on both synthetic and real-world datasets demonstrate the effectiveness of the proposed method in multi-rank determination as well as its superiority in image denoising and background modeling over state-of-the-art approaches.","Title: Place Representation Based Bike Demand Prediction\nYear: 2019\n\nAbstract: Bike-sharing service as an important public transportation system has grown over 1800% from 2014 to 2018 in worldwide. Accurate bike demand prediction of bike-sharing system is critical for increasing user satisfaction, improving the system efficiency, and expand bike stations to new regions. There are two major challenges for existing bike demand prediction methods: First, several current works heavily rely on historical bike transition records and can not predict bike demands for new stations. Second, people movement patterns as an important factor for bike demand prediction are not considered. To address these challenges, in this paper, we develop a place representation based bike demand prediction framework, which uses large scale people movement data to analyze bike demand. The framework is based on an origin-destination algorithm, which is a novel approach for learning latent representation of places, and a feature selection process to reduce place's embedding size to a proper number. Hourly bike check-ins/check-outs of each station is predicted by integrating three factors: place embedding, station geography, and POI distribution. Experiments on NYC CitiBike system show that compared with the state-of-the-art function zone based method, our model reduces error rate by 6% and root mean squared logarithmic error by 4% in prediction of existing bike station demand and by 6% and 5% for new stations in error rate and root mean squared logarithmic error respectively.","Title: Trust Inference For Rideshare Through Co-Training On Social Media Data\nYear: 2019\n\nAbstract: Ridesharing can save energy, reduce pollution, and alleviate congestion. These societal benefits can only occur, however, if sufficient trust is established between rideshare partners. Trust judgments heavily influence the usage of ridesharing platforms. Towards trust building, ridesharing systems are starting to allow drivers to provide personal information about themselves. Some previous work already built a trust prediction model using information from Instagram profiles. Their results indicated both social account information and picture information factors influenced judgments of trust of potential rideshare partners. In this paper, we advance these findings by describing a co-training approach that uses social account information and picture information as two independent views to predict trust judgments. Specifically, this analysis addresses the problem of accurate labeling of complex pictures from social media by using the hashtags of pictures for trust inference. We use a topic model to summarize the hashtags of pictures into a small number of groups called topics. The final results show that using hashtags as picture information yields a better result than the use of picture category. The co-training approach with social account information and hashtags outperforms supervised and semi-supervised classifiers. Often, in order to make an accurate trust prediction model, large amounts of expensive labeled training data are needed. The co-training approach described allows for faster labeling of large datasets, saving resources by avoiding intensive human labeling effort.","Title: Semi-Supervised Classification-Based Local Vertex Ranking Via Dual Generative Adversarial Nets\nYear: 2019\n\nAbstract: Real-world graphs are usually very sparse in terms of inadequate edges and labels as well as have poor quality due to a large amount of noisy data. In this paper, we propose a classification-based local vertex ranking architecture through dual generative adversarial networks in the semi-supervised setting, DQGAN, for analyzing sparse noisy graphs with rarely labeled data. First, we develop a quadruple generative adversarial ClassNet model to address the noisy data and data sparsity issues as well as to classify each vertex into K classes by automatically creating imaginary/real-looking supplementary labeled vertices with the quite different/similar distributions as real vertices, without the high cost of multi-step graph propagation, heterogeneous graph mining, and iterative weight learning. In addition, the vertex label vicinity is incorporated into the classification model to capture the pairwise vertex closeness based on the labeling and align the vertex label vicinity with the well-known vertex homophily for preserving the original structural semantics in the classification space. Second, we present a quintuple generative adversarial RankNet framework to locally rank each vertex on each of K classes by designing the game of multiple competitors utilizing the mix of real and noisy data to fight against each other, for improving the robustness of local vertex ranking to noisy data with few help from human efforts. The cycle ranking consistency strategy is designed to make the ranking quality verifiable through the bidirectional information-lossless translations between the original features and the ranking features. We propose to utilize the relaxed local PageRank property to produce high-quality local vertex ranking results in the context of information networks. Third but last, extensive evaluation on real graph datasets demonstrates that DQGAN outperforms existing representative methods in terms of both classification and ranking in the semi-supervised setting.","Title: Integrating Local Vertex/Edge Embedding Via Deep Matrix Fusion And Siamese Multi-Label Classification\nYear: 2019\n\nAbstract: Network embedding techniques aim to encode each vertex/edge as a low-dimensional vector, enabling easy integration with existing graph mining algorithms. This paper presents a novel network embedding framework, VEEMBEDCLASS, that combines local vertex/edge embedding with deep matrix fusion and Siamese multi-label classification for facilitating classification-based local network embedding. First, we propose to perform the embeddings of each vertex/edge on K local vertex/edge embedding models respectively, with the joint optimization by considering both intra-class and inter-class correlations, to learn their latent local features on each class. The deep matrix fusion technique is developed to preserve the first-order and second-order proximity of vertices and edges on each of K classes simultaneously. Second, a Student t-distribution based Siamese multi-label classification method is designed to train associated vertices and edges with similar local characteristics together and learn their class membership probabilities, in response to the power-law vertex degree distribution widespread in real graphs. A principle of vertex-edge homophily is introduced to guarantee that the common edge/vertex shared by two associated vertices/edges and themselves are similar in terms of both structural correlations and class memberships. Finally, we integrate local vertex/edge embedding and Siamese multi-label classification into a unified model by mutually enhancing each other.","Title: Abc: A Practicable Sketch Framework For Non-Uniform Multisets\nYear: 2017\n\nAbstract: Sketch is a data structure used to record frequencies of items in a multiset, which is widely used in data streams, data graph, distributed datasets processing, etc. It works with small memory usage and a high speed at the cost of a slight inaccuracy. In practice, frequencies of items in many datasets are non-uniformly distributed. Unfortunately, existing sketches can hardly work well on non-uniform datasets. To address this issue, we propose a new sketch framework, namely ABC framework, which can be applied to most existing sketches and can significantly improve the accuracy on non-uniform datasets. The key idea behind our framework is that when a counter overflows, it makes use of the space from the adjacent counters by operations of bits-borrowing and combination. Extensive experimental results show that our ABC framework improves the accuracy by 4.10 times and 4.49 times in average, respectively. A demo and all the related source codes are available on our homepage [1].","Title: A comparison of methods for 3D scene shape retrieval\nYear: 2020\n\nAbstract: 3D scene shape retrieval is a brand new but important research direction in content-based 3D shape retrieval. To promote this research area, two Shape Retrieval Contest (SHREC) tracks on 2D scene sketch-based and image-based 3D scene model retrieval have been organized by us in 2018 and 2019, respectively. In 2018, we built the first benchmark for each track which contains 2D and 3D scene data for ten (10) categories, while they share the same 3D scene target dataset. Four and five distinct 3D scene shape retrieval methods have competed with each other in these two contests, respectively. In 2019, to measure and compare the scalability performance of the participating and other promising Query-by-Sketch or Query-by-Image 3D scene shape retrieval methods, we built a much larger extended benchmark for each type of retrieval which has thirty (30) classes and organized two extended tracks. Again, two and three different 3D scene shape retrieval methods have contended in these two tracks, separately. To solicit state-of-the-art approaches, we perform a comprehensive comparison of all the above methods and an additional new retrieval methods by evaluating them on the two benchmarks. The benchmarks, evaluation results and tools are publicly available at our track websites (Yuan et al., 2019 [1]; Abdul-Rashid et al., 2019 [2]; Yuan et al., 2019 [3]; Abdul-Rashid et al., 2019 [4]), while code for the evaluated methods are also available: http://github.com/3DSceneRetrieval.","Title: What The Role Is Vs. What Plays The Role: Semi-Supervised Event Argument Extraction Via Dual Question Answering\nYear: 2021\n\nAbstract: Event argument extraction is an essential task in event extraction, and become particularly challenging in the case of low-resource scenarios. We solve the issues in existing studies under low-resource situations from two sides. From the perspective of the model, the existing methods always suffer from the concern of insufficient parameter sharing and do not consider the semantics of roles, which is not conducive to dealing with sparse data. And from the perspective of the data, most existing methods focus on data generation and data augmentation. However, these methods rely heavily on external resources, which is more laborious to create than obtain unlabeled data. In this paper, we propose DualQA, a novel framework, which models the event argument extraction task as question answering to alleviate the problem of data sparseness and leverage the duality of event argument recognition which is to ask \"What plays the role\", as well as event role recognition which is to ask \"What the role is\", to mutually improve each other. Experimental results on two datasets prove the effectiveness of our approach, especially in extremely low-resource situations.","Title: Robust Network Alignment via Attack Signal Scaling and Adversarial Perturbation Elimination\nYear: 2021\n\nAbstract: ABSTRACT Recent studies have shown that graph learning models are highly vulnerable to adversarial attacks, and network alignment methods are no exception. How to enhance the robustness of network alignment against adversarial attacks remains an open research problem. In this paper, we propose a robust network alignment solution, RNA, for offering preemptive protection of existing network alignment algorithms, enhanced with the guidance of effective adversarial attacks. First, we analyze how popular iterative gradient-based adversarial attack techniques suffer from gradient vanishing issues and show a fake sense of attack effectiveness. Based on dynamical isometry theory, an attack signal scaling (ASS) method with established upper bound of feasible signal scaling is introduced to alleviate the gradient vanishing issues for effective adversarial attacks while maintaining the decision boundary of network alignment. Second, we develop an adversarial perturbation elimination (APE) model to neutralize adversarial nodes in vulnerable space to adversarial-free nodes in safe area, by integrating Dirac delta approximation (DDA) techniques and the LSTM models. Our proposed APE method is able to provide proactive protection to existing network alignment algorithms against adversarial attacks. The theoretical analysis demonstrates the existence of an optimal distribution for the APE model to reach a lower bound. Last but not least, extensive evaluation on real datasets presents that RNA is able to offer the preemptive protection to trained network alignment methods against three popular adversarial attack models.","Title: TC4JPF: Using Trace Compass to Visualize JPF Traces\nYear: 2021\n\nAbstract: AbstractJava Path nder (JPF) is a very versatile program analysis tool, but understanding the error traces it generates is challenging. Visualizing traces can facilitate their understanding. Earlier attempts to visualize traces have resulted in specialized tools that do not interoperate with other frameworks. We present TC4JPF, which builds on Eclipse Trace Compass and enables Trace Compass to visualize JPF traces. With TC4JPF, we leverage the scalability and capabilities of Trace Compass and provide the rst solution that visualizes JPF traces in a general-purpose trace visualization tool.","Title: The Effect Of The Decorative Surface Layer On The Dynamic Properties Of A Symmetric Concrete Slab\nYear: 2021\n\nAbstract: In the past few years, the immense advances in building materials and construction techniques have inspired the development of large span, light, and flexible structures with low damping. The low frequency and low damping properties of the mentioned structures result in the problem of serviceability caused by human-induced vibrations. An evaluation of the serviceability of a structure requires obtaining the modes and natural frequencies of the structure via the finite element method (FEM). In the design stage, the structural model considers the contribution of involved elements made to the stiffness of the whole structure, such as beams, slabs, and columns, while the decorative surface layer above the floor is often regarded as an additional mass, regardless of its contribution to the stiffness of the floor slab. In this study, the dynamic properties of a symmetric concrete slab were tested with an ambient excitation method to obtain the dynamic properties of the original empty structure and the structure decorated with a tiled surface, a marble surface, and a terrazzo surface, respectively. The results show that the first-order natural frequencies of floor slabs decorated with tile, marble, and terrazzo finishes are decreased compared to the original empty structure, while the second- and third-order ones are increased, which indicates that it is improper to treat decorative finishes purely as an additional mass. By equating the decorative layer to a certain thickness of additional concrete layer in the finite element model, it is found that, if the decorative surface layer is equated to a 29-31 mm thick additional layer and the weight of the equivalent additional layer is the same as that of the actual decorative surface, the simulation results will be in good agreement with the measured results. Moreover, the test results indicate that the first-order shape function of the structure is symmetric and its second- and third-order shape functions are antisymmetric, which is consistent with the results of simulations under FEM method. This provides a basis for designers to evaluate the contribution of the additional layers in structural serviceability analysis.","Title: Sdimpute: A Statistical Block Imputation Method Based On Cell-Level And Gene-Level Information For Dropouts In Single-Cell Rna-Seq Data\nYear: 2021\n\nAbstract: The single-cell RNA sequencing (scRNA-seq) technologies obtain gene expression at single-cell resolution and provide a tool for exploring cell heterogeneity and cell types. As the low amount of extracted mRNA copies per cell, scRNA-seq data exhibit a large number of dropouts, which hinders the downstream analysis of the scRNA-seq data. We propose a statistical method, SDImpute (Single-cell RNA-seq Dropout Imputation), to implement block imputation for dropout events in scRNA-seq data. SDImpute automatically identifies the dropout events based on the gene expression levels and the variations of gene expression across similar cells and similar genes, and it implements block imputation for dropouts by utilizing gene expression unaffected by dropouts from similar cells. In the experiments, the results of the simulated datasets and real datasets suggest that SDImpute is an effective tool to recover the data and preserve the heterogeneity of gene expression across cells. Compared with the state-of-the-art imputation methods, SDImpute improves the accuracy of the downstream analysis including clustering, visualization, and differential expression analysis.","Title: Research on Term Extraction in The Analysis of Classical Prescriptions\nYear: 2020\n\nAbstract: In this paper, classical prescriptions refer to ancient Chinese medicine prescriptions created by Zhang Zhongjing in Han Dynasty. For the analysis of the components of classical prescriptions, it has been a great difficulty because of the constituent medicines and substance components. In order to extract the constituent medicines and substance components, we constructed the extraction model of cl...","Title: Gradient-Based Multi-Focus Image Fusion Method Using Convolution Neural Network\nYear: 2021\n\nAbstract: Due to limitation of optical lenses, obtaining all-in-focus images is difficult. However, lots of multi-focus image fusion methods cause undesirable artifacts around the focused and defocused boundaries in fusion images. Usually, these boundaries are at the edges of objects in images while the gradient information can reflect edge information intuitively. Based on the above ideas, a Gradient-based method using convolution neural network (CNN) is proposed to produce all-in-focus image. Specifically, we transmit the original images and corresponding four kinds of gradient images into five CNN models to generate the five initial focus score maps, respectively. Then, the final segmented focus map is obtained via merging the initial focus score maps. Finally, we combine the final segmented focus map and source images to obtain the fused image. The experimental results demonstrate that the proposed method has a better performance on both quality and quantitative evaluations than other state-of-the-art methods.Due to limitation of optical lenses, obtaining all-in-focus images is difficult. However, lots of multi-focus image fusion methods cause undesirable artifacts around the focused and defocused boundaries in fusion images. Usually, these boundaries are at the edges of objects in images while the gradient information can reflect edge information intuitively. Based on the above ideas, a Gradient-based method using convolution neural network (CNN) is proposed to produce all-in-focus image. Specifically, we transmit the original images and corresponding four kinds of gradient images into five CNN models to generate the five initial focus score maps, respectively. Then, the final segmented focus map is obtained via merging the initial focus score maps. Finally, we combine the final segmented focus map and source images to obtain the fused image. The experimental results demonstrate that the proposed method has a better performance on both quality and quantitative evaluations than other state-of-the-art methods.","Title: A directional margin paradigm for noise suppression in face recognition\nYear: 2021\n\nAbstract: Convolutional neural networks (CNN) have achieved outstanding face recognition (FR) performance with increasing large-scale face datasets. With face dataset size grown, noisy data will inevitably increase, undoubtedly bringing difficulties to data cleaning. In this paper, the probability that the sample belongs to noise can be determined based on the cosine distance (cosθ) of normalized angle center and face feature vector in the margin-based loss functions. According to this finding, we propose a two-step learning method integrated into the loss function. The new proposed directional margin loss function combines the noise probability with the label as the supervision information. Experiments show that our method can tolerate noisy data and get high FR accuracy when the training datasets mix with more than 30% noise. Our approach can also achieve a great result of 79.33% in MegaFace challenge one using a noisy training dataset.","Title: A Survey Of 3d Object Detection\nYear: 2021\n\nAbstract: Due to the rapid development of science and technology, object detection has become a promising research direction in computer vision. In recent years, most object detection frameworks proposed in the existing research are 2D. However, 2D object detection cannot take three-dimensional space into account, resulting in its inability to be used to solve problems in real world. Hence, we conduct this 3D object detection survey in the hope that 3D object detection methods can be better applied to the contexts of intelligent video surveillance, robot navigation and autonomous driving technology. There exist various 3D object detection methods while in this paper we only focus on the popular deep learning based methods. We divide these approaches into four categories according to the input data category. Besides, we discuss the innovations of these frames and compare their experimental results in terms of accuracy. Finally, we indicate the technical difficulties associated with current 3D object detection and discuss future research directions.","Title: Analysis Of Propagation And Distribution Characteristics Of Leakage Acoustic Waves In Water Supply Pipelines\nYear: 2021\n\nAbstract: Leakage detection methods based on the analysis of leakage acoustic signals provide an effective technical approach for detecting small leaks in water supply pipelines. From a technical perspective, the study of the propagation characteristics of acoustic waves generated by the leakage in the water supply pipeline is necessary for detecting the leak location on the basis of acoustic signals. In this study, a 3D transient leakage acoustic wave propagation equation was derived by combining the principles of fluid dynamics and Lighthill acoustic analogy theory. The propagation of the leakage-induced noise in water supply pipeline was modelled theoretically. We simulated the propagation of a leakage acoustic wave under different conditions for different target scenarios encountered in actual pipeline inspections. Specifically, we analysed the effect of different factors, such as the pipe size and acoustic source characteristics, on acoustic propagation. Finally, the simulated experiments were practically performed using a self-designed simulated water supply pipeline and self-developed spherical water supply pipeline detector to validate the simulation analysis. The results of this study provide a theoretical guidance and basis for the analysis of characteristics of leakage acoustic wave signals and the recognition of leakage conditions in water supply pipelines.","Title: Approximation Algorithms For Fuzzy C-Means Problem Based On Seeding Method\nYear: 2021\n\nAbstract: As a kind of important soft clustering model, the fuzzy C-means method is widely applied in many fields. In this method, instead of the strict distributive ability in the classical k-means method, all the sample points are endowed with degrees of membership to each center to depict the fuzzy clustering. In this paper, we show that the fuzzy C-means++ algorithm, which introduces the k-means++ algorithm as a seeding strategy, gives a solution for which the approximation guarantee is O(k(2) ln k). A novel seeding algorithm is then designed based on the contribution of the fuzzy potential function, which improves the approximation ratio to O(k lnk). Preliminary numerical experiments are proposed to support the theoretical results of this paper. (C) 2021 Elsevier B.V. All rights reserved.","Title: Vectorization for Fast, Analytic, and Differentiable Visibility\nYear: 2021\n\nAbstract: AbstractIn Computer Graphics, the two main approaches to rendering and visibility involve ray tracing and rasterization. However, a limitation of both approaches is that they essentially use point sampling. This is the source of noise and aliasing, and also leads to significant difficulties for differentiable rendering. In this work, we present a new rendering method, which we call vectorization, that computes 2D point-to-region integrals analytically, thus eliminating point sampling in the 2D integration domain such as for pixel footprints and area lights. Our vectorization revisits the concept of beam tracing, and handles the hidden surface removal problem robustly and accurately. That is, for each intersecting triangle inserted into the viewport of a beam in an arbitrary order, we are able to maintain all the visible regions formed by intersections and occlusions, thanks to our Visibility Bounding Volume Hierarchy structure. As a result, our vectorization produces perfectly anti-aliased visibility, accurate and analytic shading and shadows, and most important, fast and noise-free gradients with Automatic Differentiation or Finite Differences that directly enables differentiable rendering without any changes to our rendering pipeline. Our results are inherently high-quality and noise-free, and our gradients are one to two orders of magnitude faster than those computed with existing differentiable rendering methods.","Title: Impact of YouTube Advertising on Sales with Regression Analysis and Statistical Modeling: Usefulness of Online Media in Business\nYear: 2021\n\nAbstract: Computer technology plays a prominent role in almost every aspect of daily life including education, health care, online shopping, advertising, and even in homes. Computers help to make daily tasks much easier and convenient. Among social media, YouTube is a well-known social sharing networking service. As more and more people join social media and become everyday users, brands have also increased their online engagement. However, it is still unclear how to effectively measure value and return on advertising using social media. As of 2021, more than 31 million YouTube channels around the globe have been opened. In this paper, we consider YouTube advertising to check its effectiveness and benefits gained. Certain statistical tools are adopted to measure the extent of advertising benefits and their correlation in creating effective advertising campaigns on YouTube. Simple linear regression analysis is performed on the data representing the YouTube advertising budget of a company and the sales data of that company. Furthermore, we develop a new statistical distribution to provide the best description of the YouTube advertising data. The result of this research shows that YouTube is an effective medium for advertising and has a strong relationship with sales.","Title: Forecasting Stock Prices Using Stock Correlation Graph: A Graph Convolutional Network Approach\nYear: 2021\n\nAbstract: Accurate forecasting of stock prices plays an important role in stock investment. With the advancement of AI in Fintech applications, various deep learning models have recently been developed for stock price forecasting. However, these models focus on designing sequence models to capture the temporal dependence from a stock's historical prices (and other information such as technical indicators an...","Title: Object Relocation Visual Tracking based on Siamese network\nYear: 2021\n\nAbstract: Siamese network based trackers treat the visual tracking task as a similarity matching process, which significantly increases the performance of trackers. However, there are still many remaining problems as following. 1) The lightweight backbone of Siamese network limits its feature representation ability, and the tracker is prone to failure under the interference distractors (e.g., similar object...","Title: Monocular Pseudo-LiDAR 3D Object Detection Method Based on Confidence and Feature optimization\nYear: 2021\n\nAbstract: 3D object detection based on the monocular Pseudo-LiDAR is cost-effective compared with multi-sensor solutions in autonomous driving, and made great progress in recent. However, these methods still suffer from the following disadvantages, i.e., (1) the depth estimation error of the background points in the Pseudo-LiDAR is larger than that of the object points, which leads to the degradation of the...","Title: A Novel SAR Images Change Detection Method Based on Dynamic TUNET-CRF Model\nYear: 2021\n\nAbstract: There are two problems in SAR images change detection at present: 1) it is difficult to extract the semantic change information; 2) the change boundary is blur. In this paper, the transformers-UNET (TUNET) model is built by embedding the transformers (TR) layer with the internal global self-concern mechanism in the UNET structure to overcome the limitation of modeling remote dependence of UNET net...","Title: Multi-Attribute Group Decision-Making Based On Interval-Valued Q-Rung Orthopair Fuzzy Power Generalized Maclaurin Symmetric Mean Operator And Its Application In Online Education Platform Performance Evaluation\nYear: 2021\n\nAbstract: This paper aims to propose a novel multi-attribute group decision-making (MAGDM) method based on interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs). The IVq-ROFSs have been proved to be effective in handling MAGDM problems, and several novel decision-making methods have been proposed. Nevertheless, it is worth pointing out that these approaches still have some limitations, and there still exist some realistic situations that cannot be solved by existing MAGDM methods. Hence, the objective of this paper is to introduce a novel MAGDM method, which can overcome some of the drawbacks of existing approaches. To effectively and appropriately aggregate interval-valued q-rung orthopair fuzzy numbers (IVq-ROFNs), we combine the power average with generalized Maclaurin symmetric mean (GMSM), propose the power GMSM operator and extend it into IVq-ROFSs. Afterwards, a collection of new aggregation operators for IVq-ROFNs are developed. In this paper, we study definitions of these operators and investigate their characteristics as well as special cases. Then, based on the new aggregation operators, we present a new MAGDM method. Finally, we apply the proposed MAGDM method in online education platform performance evaluation to illustrate its effectiveness and validity. In addition, we also provide comparative analysis to explain why decision-makers should use our method instead of the others.","Title: Energy Management Strategy of Distributed Electric Propulsion Aircraft Hybrid Power System based on State Machine\nYear: 2021\n\nAbstract: Fuel cell has received more and more attention in the field of green aviation in recent years due to their zero-emission and pollution-free advantages. In this paper, a flight mission model of a distributed electric propulsion aircraft is established, and an energy management strategy based on an improved multi-dimensional coupled state machine is designed by considering factors such as multi-mission profile power requirements and failure emergency measures for the aviation electric propulsion hybrid power system. The strategy is based on a state machine and divides the operation of the distributed electric propulsion aircraft into four states: climb, cruise, descent, and malfunction. The overall operation test of the aircraft model is carried out based on the four states, and the research results indicate that compared with the load-following mode control strategy, the proposed strategy increases the average fuel cell (FC) efficiency by 3.83% In addition, the proposed strategy saves 7.95% of hydrogen and can effectively ensure flight safety in the event of a malfunction.","Title: Low-dose dobutamine cardiovascular magnetic resonance segmental strain study of early phase of intramyocardial hemorrhage rats\nYear: 2021\n\nAbstract: Background This study investigates the segmental myocardial strain of the early phase of intramyocardial hemorrhage (IMH) caused by reperfused myocardial infarction (MI) in rats by low-dose dobutamine (LDD) cardiovascular magnetic resonance (CMR) feature-tracking. Methods Nine sham rats and nine rats with 60-min myocardial ischemia followed by 48-h reperfusion were investigated using CMR, including T2*-mapping sequence and fast imaging with steady-state precession (FISP)-cine sequence. Another FISP-cine sequence was acquired after 2 min of dobutamine injection; the MI, IMH, and Non-MI (NMI) areas were identified. The values of peak radial strains (PRS) and peak circumferential strains (PCS) of the MI, IMH and NMI segments were acquired. The efficiency of PRS and PCS (EPRS and EPCS, respectively) were calculated on the basis of the time of every single heartbeat. Results The PRS, PCS, EPRS, and EPCS of the sham group increased after LDD injection. However, the PRS, PCS, EPRS, and EPCS of the IMH segment did not increase. Moreover, the PRS and PCS of the MI and NMI segments did not increase, but the EPRS and EPCS of these segments increased. The PRS, PCS, EPRS, and EPCS of the IMH segment were lower than those of the MI and NMI segments before and after LDD injection, but without a significant difference between MI segment and NMI segment before and after LDD injection. Conclusions LDD could help assess dysfunctions in segments with IMH, especially using the efficiency of strain. IMH was a crucial factor that decreased segmental movement and reserved function.","Title: Histogram analysis of diffusion-weighted magnetic resonance imaging as a biomarker to predict LNM in T3 stage rectal carcinoma\nYear: 2021\n\nAbstract: Background Preoperative identification of rectal cancer lymph node status is crucial for patient prognosis and treatment decisions. Rectal magnetic resonance imaging (MRI) plays an essential role in the preoperative staging of rectal cancer, but its ability to predict lymph node metastasis (LNM) is insufficient. This study explored the value of histogram features of primary lesions on multi-parametric MRI for predicting LNM of stage T3 rectal carcinoma. Methods We retrospectively analyzed 175 patients with stage T3 rectal cancer who underwent preoperative MRI, including diffusion-weighted imaging (DWI) before surgery. 62 patients were included in the LNM group, and 113 patients were included in the non-LNM group. Texture features were calculated from histograms derived from T2 weighted imaging (T2WI), DWI, ADC, and T2 maps. Stepwise logistic regression analysis was used to screen independent predictors of LNM from clinical features, imaging features, and histogram features. Predictive performance was evaluated by receiver operating characteristic (ROC) curve analysis. Finally, a nomogram was established for predicting the risk of LNM. Results The clinical, imaging and histogram features were analyzed by stepwise logistic regression. Preoperative carbohydrate antigen 199 level (p = 0.009), MRN stage (p < 0.001), (T2WI)Kurtosis (p = 0.010), (DWI)Mode (p = 0.038), DWICV (p = 0.038), and (T2-map)P5 (p = 0.007) were independent predictors of LNM. These factors were combined to form the best predictive model. The model reached an area under the ROC curve (AUC) of 0.860, with a sensitivity of 72.8% and a specificity of 85.5%. Conclusion The histogram features on multi-parametric MRI of the primary tumor in rectal cancer were related to LN status, which is helpful for improving the ability to predict LNM of stage T3 rectal cancer.","Title: The Construction And Application Of Expectations Index On Monetary Policy\nYear: 2017\n\nAbstract: Given the complexity of macro-economic situation, in the decision-making of monetary policy, it is vital to first grasp and then effectively manage and guide the public expectations. However, expectation is an index hard to measure. With the emergence of big data application, measuring expectations through constructing Internet public opinion index has become an important research direction. According to the characteristics of monetary policy expectations, this paper determines the relevant key words, conducts research on online public opinions by mining and crawling the netnews and search data, and construct the expectations index on monetary policy. The practicability of the expectations index is then verified by measuring its correlation with the macroeconomic variables. The result indicates that the index can indeed represent the public expectations, which means it can be applied to analyze the pattern of public expectations on the monetary policy. Apart from that, the fact that it changes ahead of the price variable can serve as the basis for farsighted decisions.","Title: Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory.\nYear: 2021\n\nAbstract: Despite achieving remarkable efficiency, traditional network pruning techniques often follow manually-crafted heuristics to generate pruned sparse networks. Such heuristic pruning strategies are hard to guarantee that the pruned networks achieve test accuracy comparable to the original dense ones. Recent works have empirically identified and verified the Lottery Ticket Hypothesis (LTH): a randomly-initialized dense neural network contains an extremely sparse subnetwork, which can be trained to achieve similar accuracy to the former. Due to the lack of theoretical evidence, they often need to run multiple rounds of expensive training and pruning over the original large networks to discover the sparse subnetworks with low accuracy loss. By leveraging dynamical systems theory and inertial manifold theory, this work theoretically verifies the validity of the LTH. We explore the possibility of theoretically lossless pruning as well as one-time pruning, compared with existing neural network pruning and LTH techniques. We reformulate the neural network optimization problem as a gradient dynamical system and reduce this high-dimensional system onto inertial manifolds to obtain a low-dimensional system regarding pruned subnetworks. We demonstrate the precondition and existence of pruned subnetworks and prune the original networks in terms of the gap in their spectrum that make the subnetworks have the smallest dimensions.","Title: Efficient Device Scheduling with Multi-Job Federated Learning.\nYear: 2022\n\nAbstract: Recent years have witnessed a large amount of decentralized data in multiple (edge) devices of end-users, while the aggregation of the decentralized data remains difficult for machine learning jobs due to laws or regulations. Federated Learning (FL) emerges as an effective approach to handling decentralized data without sharing the sensitive raw data, while collaboratively training global machine learning models. The servers in FL need to select (and schedule) devices during the training process. However, the scheduling of devices for multiple jobs with FL remains a critical and open problem. In this paper, we propose a novel multi-job FL framework to enable the parallel training process of multiple jobs. The framework consists of a system model and two scheduling methods. In the system model, we propose a parallel training process of multiple jobs, and construct a cost model based on the training time and the data fairness of various devices during the training process of diverse jobs. We propose a reinforcement learning-based method and a Bayesian optimization-based method to schedule devices for multiple jobs while minimizing the cost. We conduct extensive experimentation with multiple jobs and datasets. The experimental results show that our proposed approaches significantly outperform baseline approaches in terms of training time (up to 8.67 times faster) and accuracy (up to 44.6% higher).","Title: Multi-Robot Collaborative Perception With Graph Neural Networks\nYear: 2022\n\nAbstract: Multi-robot systems such as swarms of aerial robots are naturally suited to offer additional flexibility, resilience, and robustness in several tasks compared to a single robot by enabling cooperation among the agents. To enhance the autonomous robot decision-making process and situational awareness, multi-robot systems have to coordinate their perception capabilities to collect, share, and fuse environment information among the agents efficiently to obtain context-appropriate information or gain resilience to sensor noise or failures. In this letter, we propose a general-purpose Graph Neural Network (GNN) with the main goal to increase, in multi-robot perception tasks, single robots' inference perception accuracy as well as resilience to sensor failures and disturbances. We show that the proposed framework can address multi-view visual perception problems such as monocular depth estimation and semantic segmentation. Several experiments both using photo-realistic and real data gathered from multiple aerial robots' viewpoints show the effectiveness of the proposed approach in challenging inference conditions including images corrupted by heavy noise and camera occlusions or failures.","Title: Unsupervised Adversarial Network Alignment with Reinforcement Learning\nYear: 2022\n\nAbstract: AbstractNetwork alignment, which aims at learning a matching between the same entities across multiple information networks, often suffers challenges from feature inconsistency, high-dimensional features, to unstable alignment results. This article presents a novel network alignment framework, Unsupervised Adversarial learning based Network Alignment(UANA), that combines generative adversarial network (GAN) and reinforcement learning (RL) techniques to tackle the above critical challenges. First, we propose a bidirectional adversarial network distribution matching model to perform the bidirectional cross-network alignment translations between two networks, such that the distributions of real and translated networks completely overlap together. In addition, two cross-network alignment translation cycles are constructed for training the unsupervised alignment without the need of prior alignment knowledge. Second, in order to address the feature inconsistency issue, we integrate a dual adversarial autoencoder module with an adversarial binary classification model together to project two copies of the same vertices with high-dimensional inconsistent features into the same low-dimensional embedding space. This facilitates the translations of the distributions of two networks in the adversarial network distribution matching model. Finally, we develop an RL based optimization approach to solve the vertex matching problem in the discrete space of the GAN model, i.e., directly select the vertices in target networks most relevant to the vertices in source networks, without unstable similarity computation that is sensitive to discriminative features and similarity metrics. Extensive evaluation on real-world graph datasets demonstrates the outstanding capability of UANA to address the unsupervised network alignment problem, in terms of both effectiveness and scalability.","Title: Correction: SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data\nYear: 2022\n\nAbstract: [This corrects the article DOI: 10.1371/journal.pcbi.1009118.].","Title: Real-time Multiple-Channel Shoulder EMG Processing for a Rehabilitative Upper-limb Exoskeleton Motion Control Using ANN Machine Learning\nYear: 2021\n\nAbstract: Machine learning (ML) has been proposed for surface electromyography (sEMG) signal processing to control assistive robots. However, most of the studies using ML focused on recording sEMG for off-line data analysis and pattern recognition. Only a few studies reported online ML EMG processing for one DoF hand motion pattern recognition and instant robot control. Using ML for multiple channels of sho...","Title: Pro-PULSE: Learning Progressive Encoders of Latent Semantics in GANs for Photo Upsampling\nYear: 2022\n\nAbstract: The state-of-the-art photo upsampling method, PULSE, demonstrates that a sharp, high-resolution (HR) version of a given low-resolution (LR) input can be obtained by exploring the latent space of generative models. However, mapping an extreme LR input (16\n<sup xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>\n) directly to an HR image (1024\n<sup xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>\n) is too ambiguous to preserve faithful local facial semantics. In this paper, we propose an enhanced upsampling approach, Pro-PULSE, that addresses the issues of semantic inconsistency and optimization complexity. Our idea is to learn an encoder that progressively constructs the HR latent codes in the extended \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$\\mathcal {W}+$ </tex-math></inline-formula>\n latent space of StyleGAN. This design divides the complex \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$64\\times $ </tex-math></inline-formula>\n upsampling problem into several steps, and therefore small-scale facial semantics can be inherited from one end to the other. In particular, we train two encoders, the base encoder maps latent vectors in \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$\\mathcal {W}$ </tex-math></inline-formula>\n space and serves as a foundation of the HR latent vector, while the second scale-specific encoder performed in \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$\\mathcal {W}+$ </tex-math></inline-formula>\n space gradually replaces the previous vector produced by the base encoder at each scale. This process produces intermediate side-outputs, which injects deep supervision into the training of encoder. Extensive experiments demonstrate superiorities over the latest latent space exploration methods, in terms of efficiency, quantitative quality metrics, and qualitative visual results.","Title: Flood Discharge Prediction Based on Remote-Sensed Spatiotemporal Features Fusion and Graph Attention\nYear: 2021\n\nAbstract: Floods have brought a great threat to the life and property of human beings. Under the premise of strengthening flood control engineering measures and following the strategic thinking of sustainable development, many achievements have been made in flood forecasting recently. However, due to the complexity of the traditional lumped model and distributed model, the hydrologic parameter calibration process is full of difficulties, leading to a long development cycle of a reasonable hydrologic prediction model. Even for modern data-driven models, the spatial distribution characteristics of the rainfall data are also not fully mined. Based on this situation, this paper abstracts the rainfall data into the graph structure data, uses remote sensing images to extract the elevation information, introduces the graph attention mechanism to extract the spatial characteristics of rainfall, and employs long-term and short-term memory (LSTM) network to fuse the spatial and temporal characteristics for flood prediction. Through well-designed experiments, the forecasting effect of flood peak value and flood arrival time is verified. Furthermore, compared with the LSTM model and BIGRU model without spatial feature extraction, the advantages of spatiotemporal feature fusion are highlighted. The specific performance is that the RMSE (the root means square error) and R2 (coefficient of determination) of the GA-RNN model have been significantly improved. Finally, we conduct experiments on the observed ten rainfall events in the history of the target watershed. According to the hydrological prediction specifications, the model can be evaluated as a Class B flood forecasting model.","Title: Logging Data Completion Based on an MC-GAN-BiLSTM Model\nYear: 2022\n\nAbstract: Due to environmental interference and operational errors, problems such as incomplete and random missing logging data have occurred during the geophysical logging data collection process. Since it is difficult to establish a geophysical model based on logging data and geological information, the data complementation effect of conventional methods is not very satisfactory. In this paper, we propose an MC-GAN-BiLSTM model based on spatiotemporal sequence prediction. In the model, we adopt a generative adversarial network (GAN) as a network framework, and a long short-term memory (LSTM) neural network and a bi-directional long short-term memory (Bi-LSTM) as the basic modules. We use the LSTM instead of a fully-connected layer in the GAN to extract the potential information in the logging data depth domain. We complete the logging data missing values through an encoding-decoding structure that includes the Bi-LSTM. In addition, the generator module also uses multiscale convolution to fully extract the logging data features. We use logging data random missing values and consecutive missing values to simulate a field data acquisition environment and threshold control to simulate a laboratory processing environment for experiments. The experimental results show that the coefficient of determination (R-2) of the GAN-LSTM model reaches 0.906 when 30% of random logging data are missing and 0.851 when 30% of consecutive logging data are missing. The effect of the model proposed in this paper is significantly higher than the commonly used random forest (RF), sequence to sequence (seq2seq) and generative adversarial interpolation network (GAIN) models.","Title: 3D Segmentation Guided Style-Based Generative Adversarial Networks for PET Synthesis\nYear: 2022\n\nAbstract: Potential radioactive hazards in full-dose positron emission tomography (PET) imaging remain a concern, whereas the quality of low-dose images is never desirable for clinical use. So it is of great interest to translate low-dose PET images into full-dose. Previous studies based on deep learning methods usually directly extract hierarchical features for reconstruction. We notice that the importance of each feature is different and they should be weighted dissimilarly so that tiny information can be captured by the neural network. Furthermore, the synthesis on some regions of interest is important in some applications. Here we propose a novel segmentation guided style-based generative adversarial network (SGSGAN) for PET synthesis. (1) We put forward a style-based generator employing style modulation, which specifically controls the hierarchical features in the translation process, to generate images with more realistic textures. (2) We adopt a task-driven strategy that couples a segmentation task with a generative adversarial network (GAN) framework to improve the translation performance. Extensive experiments show the superiority of our overall framework in PET synthesis, especially on those regions of interest.","Title: Automatic repetition instruction generation for air traffic control training using multi-task learning with an improved copy network\nYear: 2022\n\nAbstract: To eliminate the need for human pseudo-pilots in air traffic controller training, a multi-task framework with a copy mechanism is proposed to automatically generate a repetition instruction, which can greatly reduce human and device resources. The proposed framework is implemented using a sequence-to-sequence architecture and is optimized through multi-task learning, including text instruction understanding (TIU) and repetition instruction generation (RIG). In the proposed framework, a shared encoder is designed to learn the representations from the input sequence. Two task-specific attention modules are proposed to extract task-specific features, and different decoders are applied to generate the TIU and RIG outputs. The TIU module provides both word- and sentence-level implicit semantic representations through slot filling and intent detection tasks. In the decoding procedure of the RIG task, the slot context vector is regarded as an additional input used to solve the problem of ambivalent words. Simultaneously, an improved copy network based on multi-task learning is proposed to consider the correlations between the input instruction and the output repetition in the RIG module. To avoid a loss imbalance for the TIU and RIG tasks, the gradient normalization algorithm is applied to learn the loss weights automatically by adjusting the loss gradient. Finally, the proposed framework was trained and evaluated using a real-world air traffic control corpus. The experiment results demonstrate that the proposed framework significantly outperforms other state-of-the-art methods for the RIG task, achieving a novel integrated metric of 97.19%.","Title: Lesion Localization in OCT by Semi-Supervised Object Detection\nYear: 2022\n\nAbstract: Over 300 million people worldwide are affected by various retinal diseases. By noninvasive Optical Coherence Tomography (OCT) scans, a number of abnormal structural changes in the retina, namely retinal lesions, can be identified. Automated lesion localization in OCT is thus important for detecting retinal diseases at their early stage. To conquer the lack of manual annotation for deep supervised learning, this paper presents a first study on utilizing semi-supervised object detection (SSOD) for lesion localization in OCT images. To that end, we develop a taxonomy to provide a unified and structured viewpoint of the current SSOD methods, and consequently identify key modules in these methods. To evaluate the influence of these modules in the new task, we build OCT-SS, a new dataset consisting of over 1k expert-labeled OCT B-scan images and over 13k unlabeled B-scans. Extensive experiments on OCT-SS identify Unbiased Teacher (UnT) as the best current SSOD method for lesion localization. Moreover, we improve over this strong baseline, with mAP increased from 49.34 to 50.86.","Title: FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server.\nYear: 2022\n\nAbstract: Despite achieving remarkable performance, Federated Learning (FL) suffers from two critical challenges, i.e., limited computational resources and low training efficiency. In this paper, we propose a novel FL framework, i.e., FedDUAP, with two original contributions, to exploit the insensitive data on the server and the decentralized data in edge devices to further improve the training efficiency. First, a dynamic server update algorithm is designed to exploit the insensitive data on the server, in order to dynamically determine the optimal steps of the server update for improving the convergence and accuracy of the global model. Second, a layer-adaptive model pruning method is developed to perform unique pruning operations adapted to the different dimensions and importance of multiple layers, to achieve a good balance between efficiency and effectiveness. By integrating the two original techniques together, our proposed FL model, FedDUAP, significantly outperforms baseline approaches in terms of accuracy (up to 4.8% higher), efficiency (up to 2.8 times faster), and computational cost (up to 61.9% smaller).","Title: Human-Centered Prior-Guided and Task-Dependent Multi-Task Representation Learning for Action Recognition Pre-Training\nYear: 2022\n\nAbstract: Recently, much progress has been made for self-supervised action recognition. Most existing approaches emphasize the contrastive relations among videos, including appearance and motion consistency. However, two main issues remain for existing pre-training methods: 1) the learned representation is neutral and not informative for a specific task; 2) multi-task learning-based pre-training sometimes leads to sub-optimal solutions due to inconsistent domains of different tasks. To address the above issues, we propose a novel action recognition pre-training framework, which exploits human-centered prior knowledge that generates more informative representation, and avoids the conflict between multiple tasks by using task-dependent representations. Specifically, we distill knowledge from a human parsing model to enrich the semantic capability of representation. In addition, we combine knowledge distillation with contrastive learning to constitute a task-dependent multi-task framework. We achieve state-of-the-art performance on two popular benchmarks for action recognition task, i.e., UCF101 and HMDB51, verifying the effectiveness of our method.","Title: None Class Ranking Loss for Document-Level Relation Extraction.\nYear: 2022\n\nAbstract: Document-level relation extraction (RE) aims at extracting relations among entities expressed across multiple sentences, which can be viewed as a multi-label classification problem. In a typical document, most entity pairs do not express any pre-defined relation and are labeled as \"none\" or \"no relation\". For good document-level RE performance, it is crucial to distinguish such \\textit{none} class instances (entity pairs) from those of pre-defined classes (relations). However, most existing methods only estimate the probability of pre-defined relations independently without considering the probability of \"no relation\". This ignores the context of entity pairs and the label correlations between the none class and pre-defined classes, leading to sub-optimal predictions. To address this problem, we propose a new multi-label loss that encourages large \\textit{margins} of label confidence scores between each pre-defined class and the none class, which enables captured label correlations and context-dependent thresholding for label prediction. To gain further robustness against positive-negative imbalance and mislabeled data that could appear in real-world RE datasets, we propose a margin regularization and a margin shifting technique. Experimental results demonstrate that our method significantly outperforms existing multi-label losses for document-level RE and works well in other multi-label tasks such as emotion classification when none class instances are available for training.","Title: REU-Net: Region-enhanced nuclei segmentation network\nYear: 2022\n\nAbstract: Nuclei segmentation is a key technique for automatic pathological screening. Although many methods have been proposed, it remains a challenge because of numerous nuclei clusters, high variability of object appearances and complex backgrounds. To address these issues, we propose a novel multi-task region-enhanced nuclei segmentation network (REU-Net). It stacks three U-shaped structures by combining serial and parallel approaches to construct a multi-task architecture. The model employs two auxiliary tasks, i.e., contour extraction and rough segmentation to help the main task of fine segmentation. The saliency regions are enhanced by the prediction results of the auxiliary tasks, and the enhanced images are further segmented through the main task. In addition, the spatial and texture features in auxiliary tasks are aggregated by attention gates, helping the main task to refine the details of nuclei and contours. Extensive experiments are conducted to evaluate the proposed method qualitatively and quantitatively. Experimental results show that REU-Net outperforms the state-of-the-art methods on HUSTS, MoNuSeg, CoNSep and CPM-17 datasets.","Title: The AISP-SJTU Simultaneous Translation System for IWSLT 2022.\nYear: 2022\n\nAbstract: This paper describes AISP-SJTU’s submissions for the IWSLT 2022 Simultaneous Translation task. We participate in the text-to-text and speech-to-text simultaneous translation from English to Mandarin Chinese. The training of the CAAT is improved by training across multiple values of right context window size, which achieves good online performance without setting a prior right context window size for training. For speech-to-text task, the best model we submitted achieves 25.87, 26.21, 26.45 BLEU in low, medium and high regimes on tst-COMMON, corresponding to 27.94, 28.31, 28.43 BLEU in text-to-text task.","Title: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection\nYear: 2022\n\nAbstract: As an inherently ill-posed problem, depth estimation from single images is the most challenging part of monocular 3D object detection (M3OD). Many existing methods rely on preconceived assumptions to bridge the missing spatial information in monocular images, and predict a sole depth value for every object of interest. However, these assumptions do not always hold in practical applications. To tackle this problem, we propose a depth solving system that fully explores the visual clues from the subtasks in M3OD and generates multiple estimations for the depth of each target. Since the depth estimations rely on different assumptions in essence, they present diverse distributions. Even if some assumptions collapse, the estimations established on the remaining assumptions are still reliable. In addition, we develop a depth selection and combination strategy. This strategy is able to remove abnormal estimations caused by collapsed assumptions, and adaptively combine the remaining estimations into a single one. In this way, our depth solving system becomes more precise and robust. Exploiting the clues from multiple subtasks of M3OD and without introducing any extra information, our method surpasses the current best method by more than 20% relatively on the Moderate level of test split in the KITTI 3D object detection benchmark, while still maintaining real-time efficiency.","Title: An eXtreme Gradient Boosting Algorithm Combining Artificial Bee Colony Parameters Optimized Technique for Single Sand Body Identification\nYear: 2021\n\nAbstract: Due to the problems of traditional artificial single sand body identification methods such as strong subjectivity, heavy workload and low efficiency, we propose a fast and objective ABC-XGBoost. The algorithm consists of two parts: eXtreme gradient boosting (XGBoost) and artificial bee colony algorithm (ABC). XGBoost introduces a regular term, which can effectively prevent overfitting, and uses the second derivative to make the identification result more accurate. However, a large number of parameters in XGBoost need to be adjusted manually, which affects the efficiency of the algorithm. In this regard, ABC is used to optimize the parameters based on XGBoost, and then the single sand body can be identified quickly and effectively. We take the C6 21 oil-bearing layer in the second area of Dalugou, Jing'an Oilfield as the research object, and use the ABC-XGBoost to identify the single sand body in the research area. Based on the reasonable selection of physical parameter data and logging data, the partition and interlayer data should be eliminated first to avoid data redundancy. The results indicate that ABC-XGBoost is more efficient and accurate than the existing mainstream machine algorithms, such as support vector machines (SVM), random forests (RF), and XGBoost using trial and error tuning under the same logging data and computer hardware conditions. The accuracy can reach 90.6%, which has certain practical application value in the middle and late development of oil and gas fields.","Title: Pyramid Family: Generic Frameworks for Accurate and Fast Flow Size Measurement\nYear: 2022\n\nAbstract: AbstractSketches, as a kind of probabilistic data structures, have been considered as the most promising solution for network measurement in recent years. Most sketches do not work well for skewed network traffic. To address this problem, we propose a family of sketch frameworks, namely the Pyramid family. The first member of our Pyramid family is the S-Pyramid framework, which includes two techniques: counter-pair sharing for high accuracy, and word acceleration for fast speed. The second member of our Pyramid family is the Mini-Pyramid framework, which projects the S-Pyramid framework into one counter, bringing more flexibility in application while keeping the accuracy. To demonstrate the generality of our Pyramid family, we apply both frameworks to sketches of CM, CU, Count, and Augmented. To demonstrate the flexibility of the Mini-Pyramid framework, we further apply Mini-Pyramid to SBF and the On-Off sketch. The experimental results show that, the S-Pyramid framework can reduce the ARE by up to 7.12 times compared with the original sketches, while improving the throughput by up to 2.37 times; the Mini-Pyramid framework can reduce the ARE by up to 29.2 times, at the cost of 21.3% lower throughput on average.","Title: Spatiotemporal just noticeable difference modeling with heterogeneous temporal visual features\nYear: 2021\n\nAbstract: Developing accurate Just-noticeable difference (JND) models are challenged by complicated HVS characteristics and nonstationary features of video sequence. Great efforts have been devoted to JND modeling, and inspiring performance improvements are witnessed in the literature, especially spatial JND models. However, there are not only urgent requirement but also technical potentiality for improving temporal JND models fully accounting for the temporal perception characteristics. In terms of temporal JND modeling, there are two challenges, one is how to extract perceptual feature parameters of source video, and the other is how to quantitatively characterize the interaction relationship between feature parameters and HVS characteristics? Firstly, this work extracts contentaware temporal feature parameters having predominate impacts on vision perception, including motion (foreground/background), pixel-correspondence duration and inter-frame residue fluctuation intensity along temporal trajectory, and investigates the HVS responses to these four heterogeneous feature parameters. Secondly, this work proposes respective probability density functions (PDF) in the perception sense to quantitatively depict the attention and suppression perception responses of feature parameters, accounting for the temporal perception characteristics. Using these PDF models, we fuse the heterogeneous feature parameters from the viewpoint of uniform dimension,i.e. self-information measured visual attention and information entropy measured masking uncertainty, achieving heterogeneous parameter homogenization. Thirdly, with self-information and entropy results, this work then proposes a temporal weight model, by striking the balance between visual attention and masking suppression, to adjust the spatial JND threshold, and then develops the improved spatiotemporal JND model. Intensive simulation results verity the effectiveness of the proposed spatiotemporal JND profile, with competitive model accuracy compared with the-state-of-the-art candidate models.","Title: Earth Imagery Segmentation on Terrain Surface with Limited Training Labels: A Semi-supervised Approach based on Physics-Guided Graph Co-Training\nYear: 2022\n\nAbstract: AbstractGiven earth imagery with spectral features on a terrain surface, this paper studies surface segmentation based on both explanatory features and surface topology. The problem is important in many spatial and spatiotemporal applications such as flood extent mapping in hydrology. The problem is uniquely challenging for several reasons: first, the size of earth imagery on a terrain surface is often much larger than the input of popular deep convolutional neural networks; second, there exists topological structure dependency between pixel classes on the surface, and such dependency can follow an unknown and non-linear distribution; third, there are often limited training labels. Existing methods for earth imagery segmentation often divide the imagery into patches and consider the elevation as an additional feature channel. These methods do not fully incorporate the spatial topological structural constraint within and across surface patches and thus often show poor results, especially when training labels are limited. Existing methods on semi-supervised and unsupervised learning for earth imagery often focus on learning representation without explicitly incorporating surface topology. In contrast, we propose a novel framework that explicitly models the topological skeleton of a terrain surface with a contour tree from computational topology, which is guided by the physical constraint (e.g., water flow direction on terrains). Our framework consists of two neural networks: a convolutional neural network (CNN) to learn spatial contextual features on a 2D image grid, and a graph neural network (GNN) to learn the statistical distribution of physics-guided spatial topological dependency on the contour tree. The two models are co-trained via variational EM. Evaluations on the real-world flood mapping datasets show that the proposed models outperform baseline methods in classification accuracy, especially when training labels are limited.","Title: Improved Residual Network for Automatic Classification Grading of Lettuce Freshness\nYear: 2022\n\nAbstract: To solve the problem of the low efficiency of traditional lettuce freshness classification methods and sample damage, we proposed an automatic lettuce freshness classification method based on improved deep residuals convolutional neural network (Im-ResNet). We built an image acquisition system to obtain the freshness classification dataset of lettuce leaves. For improving the classification accuracy, we developed an image acquisition system for curating the freshness of lettuce leaves. Then, we proposed a novel method that was derived from the existing ResNet-50 (which uses ReLU activation function) known as Improved Residual Networks (Im-ResNet): the new method factored extra convolutional layer, pooling layer, fully-connected layers, and a random ReLU (RReLU) activation function. We also performed the corresponding experiments using the Im-ResNet network compared with four network architectures (AlexNet, GoogleNet, VGG16 and ResNet50). The experimental results showed that the proposed network had more significant advantages in the recognition accuracy and loss value of lettuce freshness compared with the traditional deep networks. The recognition accuracy of the validation set of the proposed model can reach to 95.60%. Different from the physical and chemical methods, our scheme can automatically and non-destructively classify the freshness of lettuce.","Title: Wireless Performance Evaluation of Building Materials Integrated With Antenna Arrays\nYear: 2022\n\nAbstract: To avoid negative visual and weight impact of antenna arrays in indoor multiple-input multiple-output (MIMO) systems, the antenna arrays can be integrated in building materials. In this letter, we model building materials integrated with antenna arrays and develop an approach that can characterize and evaluate their wireless performance using a ray-tracing model that incorporates multipath propaga...","Title: Multi-Timescale Load Forecast of Large Power Customers Based on Online Data Recovery and Time Series Neural Networks\nYear: 2022\n\nAbstract: This paper presents a multi-timescale receding horizon framework for the load forecast of large power customers. The future load pattern of individual users could be very difficult to predict because of its chronological and high volatile properties. Also, the sampling of nonaggregated load data may suffer from severe information missing issues. To address these challenges, we first develop an online singular value thresholding (SVT) algorithm, which utilizes the approximate low-rank property of load data matrices to efficiently recover the missing information. Then, a combinatorial deep learning method is developed, which applies the multi-layer perception (MLP) neural network and the long short-term memory (LSTM) neural network with gated recurrent unit (GRU) to deal with the short-term and ultra-short-term load forecast, respectively. Specifically, an early stopping strategy is designed and implemented to avoid the over-fitting of model training. Moreover, the receding time window is imposed to dynamically update the data recovery and load forecast outcomes, which supports the online computing on a Spark platform. Numerical experiments on real-world load data from North China confirms the effectiveness of the proposed methodology, which can support more complex applications in embedded systems and cyber physical systems.","Title: A Distributed and Privacy-Preserving Random Forest Evaluation Scheme with Fine Grained Access Control\nYear: 2022\n\nAbstract: Random forest is a simple and effective model for ensemble learning with wide potential applications. Implementation of random forest evaluations while preserving privacy for the source data is demanding but also challenging. In this paper, we propose a practical and fault-tolerant privacy-preserving random forest evaluation scheme based on asymmetric encryption. The user can use asymmetric encryption to encrypt the data outsourced to the cloud platform and specify who can access the final evaluation results. After receiving the encrypted inputs from the user, the cloud platform evaluates via a random forest model and outputs the aggregated results where only the designated recipient can decrypt them. Threat analyses prove that the proposed scheme achieves the desirable security properties, such as correctness, confidentiality and robustness. Moreover, efficiency analyses demonstrate that the scheme is practical for real-world applications.","Title: A Lightweight NMS-Free Framework for Real-Time Visual Fault Detection System of Freight Trains\nYear: 2022\n\nAbstract: Real-time vision-based system of fault detection (RVBS-FD) for freight trains is an essential part of ensuring railway transportation safety. Most existing vision-based methods still have high computational costs based on convolutional neural networks. The computational cost is mainly reflected in the backbone, neck, and postprocessing, i.e., nonmaximum suppression (NMS). In this article, we propose a lightweight NMS-free framework to achieve real-time detection and high accuracy simultaneously. First, we use a lightweight backbone for feature extraction and design a fault detection pyramid to process features. This fault detection pyramid includes three novel individual modules using attention mechanism, bottleneck, and dilated convolution for feature enhancement and computation reduction. Instead of using NMS, we calculate different loss functions, including classification and location costs in the detection head, to further reduce computation. Experimental results show that our framework achieves over 83-frames/s speed with a smaller model size and higher accuracy than the state-of-the-art detectors. Meanwhile, the hardware resource requirements of our method are low during the training and testing process.","Title: Seismic Random Noise Removal Based on a Multiscale Convolution and Densely Connected Network for Noise Level Evaluation\nYear: 2022\n\nAbstract: Traditional denoising methods for seismic exploration data design a corresponding mathematical denoising model batch according to the different properties of different random noises, which is a tedious and time-consuming process. To solve this problem, this paper proposes a deep convolutional neural network denoising model based on noise estimation (MCD-DCNN). This model is primarily composed of two modules, the noise estimation module and the denoising module. The noise estimation module uses a multiscale convolutional neural network to better extract the characteristics of random noise in the seismic data. To make full use of the extracted features, a dense connection method is adopted between the multiscale convolutions in the noise estimation module. In the denoising module, we use multiscale convolutions and dense connections to replace the original convolutional neural network and use the residual structure (ResNet) and batch normalization (BN) to improve the denoising effect and running speed of the model. In this experiment, single trace and simple and complex profile data are used as input to simulate the real data processing environment. Finally, we compare the denoising effects of the MCD-DCNN model proposed in this paper with the current mainstream feed-forward denoising convolutional neural network (DnCNN) and a fast and flexible denoising convolutional neural network (FFDNet) models. The comprehensive results show that under the condition of a given prior noise level, the denoising performance of the FFDNet and MCD-DCNN models are comparable. In the absence of a priori noise level, the denoising performance of the FFDNet model drops sharply, while the denoising performance of MCD-DCNN is not affected; therefore, MCD-DCNN is more in line with actual seismic denoising.","Title: APES: Articulated Part Extraction from Sprite Sheets\nYear: 2022\n\nAbstract: Rigged puppets are one of the most prevalent representations to create 2D character animations. Creating these puppets requires partitioning characters into independently moving parts. In this work, we present a method to automatically identify such articulated parts from a small set of character poses shown in a sprite sheet, which is an illustration of the character that artists often draw before puppet creation. Our method is trained to infer articulated parts, e.g. head, torso and limbs, that can be reassembled to best reconstruct the given poses. Our results demonstrate significantly better performance than alternatives qualitatively and quantitatively. Our project page https://zhan-xu.github.io/parts/ includes our code and data.","Title: Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing.\nYear: 2022\n\nAbstract: Only recently, researchers attempt to provide classification algorithms with provable group fairness guarantees. Most of these algorithms suffer from harassment caused by the requirement that the training and deployment data follow the same distribution. This paper proposes an input-agnostic certified group fairness algorithm, FairSmooth, for improving the fairness of classification models while maintaining the remarkable prediction accuracy. A Gaussian parameter smoothing method is developed to transform base classifiers into their smooth versions. An optimal individual smooth classifier is learnt for each group with only the data regarding the group and an overall smooth classifier for all groups is generated by averaging the parameters of all the individual smooth ones. By leveraging the theory of nonlinear functional analysis, the smooth classifiers are reformulated as output functions of a Nemytskii operator. Theoretical analysis is conducted to derive that the Nemytskii operator is smooth and induces a Frechet differentiable smooth manifold. We theoretically demonstrate that the smooth manifold has a global Lipschitz constant that is independent of the domain of the input data, which derives the input-agnostic certified group fairness.","Title: Accelerated Federated Learning with Decoupled Adaptive Optimization.\nYear: 2022\n\nAbstract: The federated learning (FL) framework enables edge clients to collaboratively learn a shared inference model while keeping privacy of training data on clients. Recently, many heuristics efforts have been made to generalize centralized adaptive optimization methods, such as SGDM, Adam, AdaGrad, etc., to federated settings for improving convergence and accuracy. However, there is still a paucity of theoretical principles on where to and how to design and utilize adaptive optimization methods in federated settings. This work aims to develop novel adaptive optimization methods for FL from the perspective of dynamics of ordinary differential equations (ODEs). First, an analytic framework is established to build a connection between federated optimization methods and decompositions of ODEs of corresponding centralized optimizers. Second, based on this analytic framework, a momentum decoupling adaptive optimization method, FedDA, is developed to fully utilize the global momentum on each local iteration and accelerate the training convergence. Last but not least, full batch gradients are utilized to mimic centralized optimization in the end of the training process to ensure the convergence and overcome the possible inconsistency caused by adaptive optimization methods.","Title: Audio-driven Neural Gesture Reenactment with Video Motion Graphs\nYear: 2022\n\nAbstract: Human speech is often accompanied by body gestures including arm and hand gestures. We present a method that reenacts a high-quality video with gestures matching a target speech audio. The key idea of our method is to split and re-assemble clips from a reference video through a novel video motion graph encoding valid transitions between clips. To seamlessly connect different clips in the reenactment, we propose a pose-aware video blending network which synthesizes video frames around the stitched frames between two clips. Moreover, we developed an audio-based gesture searching algorithm to find the optimal order of the reenacted frames. Our system generates reen-actments that are consistent with both the audio rhythms and the speech content. We evaluate our synthesized video quality quantitatively, qualitatively, and with user studies, demonstrating that our method produces videos of much higher quality and consistency with the target audio compared to previous work and baselines. Our project page https://github.com/yzhou359/vid-reenact includes code and data.","Title: Faithful Extreme Rescaling via Generative Prior Reciprocated Invertible Representations\nYear: 2022\n\nAbstract: This paper presents a Generative prior ReciprocAted Invertible rescaling Network (GRAIN) for generating faithful high-resolution (HR) images from low-resolution (LR) invertible images with an extreme upscaling factor (64×). Previous researches have leveraged the prior knowledge of a pretrained GAN model to generate high-quality upscaling results. However, they fail to produce pixel-accurate results due to the highly ambiguous extreme mapping process. We remedy this problem by introducing a reciprocated invertible image rescaling process, in which high-resolution information can be delicately embedded into an invertible low-resolution image and generative prior for a faithful HR reconstruction. In particular, the invertible LR features not only carry significant HR semantics, but also are trained to predict scale-specific latent codes, yielding a preferable utilization of generative features. On the other hand, the enhanced generative prior is re-injected to the rescaling process, compensating the lost details of the invertible rescaling. Our reciprocal mechanism perfectly integrates the advantages of invertible encoding and generative prior, leading to the first feasible extreme rescaling solution. Extensive experiments demonstrate superior performance against state-of-the-art upscaling methods. Code is available at https://github.com/cszzx/GRAIN.","Title: A Miniaturized Ultrawideband Active Electric Probe With High Sensitivity for Near-Field Measurement\nYear: 2022\n\nAbstract: A miniaturized ultrawideband active electric probe with high sensitivity for near-field measurement is proposed in this article. The probe is designed with a four-layer printed circuit board (PCB) with mainly two chips, an amplification chip (HMC797APM5E) which can work from de to 22 GHz and a power management chip (HMC980LP4E). The probe can work from 9 kHz to 18 GHz with 15-dB average gain compared to the passive probe, and the flatness is about +/- 4 dB in terms of vertical bar S-21 vertical bar (transfer factor) from 2.5 to 18 GHz. The maximum size of the probe is 64 mm x 41.5 mm with 1-mm spatial resolution, and the width of the tip is only 4.1 mm, so the probe can handle most test environments. On the whole, the probe is very efficient for near-field measurement to detect weak signals in a wide bandwidth.","Title: An edge intelligence empowered flooding process prediction using Internet of things in smart city\nYear: 2022\n\nAbstract: Floods result in substantial damage throughout the world every year. Accurate predictions of floods can significantly alleviate casualties and property losses. However, due to the complexity of hydrology process especially in a city with complicated pipe network, the accuracy of traditional flood forecasting models suffer from the performance degradation with the increasing of required prediction period. In the work, based on the collected historical data of Xixian City, Henan Province, China, using the Internet of Things system (IoT) in 2011-2018, a Bidirectional Gated Recurrent Unit (BiGRU) multi-step flood prediction model with attention mechanism is proposed. In our model, the attention mechanism is used to automatically adjust the matching degree between the input features and output. Besides, we use a bidirectional GRU model, which can process the input sequence from two directions of time series (chronologically and antichronologically), then merge their representations together. Compared with the prediction model using Long Short Term Memory (LSTM), our method can generate better prediction result, as can be seen from the arrival time error and peak error of floods during multi-step predictions.","Title: LEDet: localization estimation detector with data augmentation for ship detection based on unmanned surface vehicle\nYear: 2022\n\nAbstract: Ship detection is significant for monitoring ports, especially contributing to the safe driving of Unmanned Surface Vehicle (USV). However, recent ship detection based on deep learning lacks complete ship datasets and uses the classification score as the ranking basis, which harms their performance. To address the problems, we present a one-stage localization estimation detector (LEDet) with ship-customized data augmentation. Specifically, we integrate the localization quality estimation into the classification branch as a soft label localization score. We further apply ship-customized data augmentation named “cutting-transform-paste” to expand ship datasets without manual annotation. Hence, a large number of diverse ship datasets can be created. Extensive experiments show that our LEDet consistently exceeds the strong baseline by 8.0% COCO-style Average Precision (AP) with ResNet-50. It significantly improves the ship detection performance.","Title: An infrared pedestrian detection method based on segmentation and domain adaptation learning\nYear: 2022\n\nAbstract: Benefiting from the capability of night viewing, infrared images has been widely applied to surveillance systems as an effective complement to visible-light images. However, the development of infrared pedestrian detection is still impeded by weak features and limited diversity of infrared images. Aiming at these two problems, we designed a multi-task learning framework for pedestrian detection by incorporating a semantic segmentation branch and a domain adaptation branch. Composed of UNet network with Swin Transformer, the semantic segmentation could apply spatial constraints to pedestrian detection. The domain adaptation branch aligns the features between infrared and visible-light images to improve the scene diversity. In addition, three tasks shared a basic feature extraction network to reduce computation cost. The experiment results show that the average precision (AP) of our method is superior to the EfficientDet network by 2.0% on the XDU-NIR2020 dataset and 2.2% on the CVC-09 dataset respectively.","Title: PrefixFPM: a parallel framework for general-purpose mining of frequent and closed patterns\nYear: 2022\n\nAbstract: A frequent pattern is a substructure that appears in a database with frequency (aka. support) no less than a user-specified threshold, while a closed pattern is one that has no super-pattern that has the same support. Here, a substructure can refer to different structural forms, such as itemsets, subsequences, subtrees, and subgraphs, and mining such substructures is important in many real applications such as product recommendation and feature extraction. Currently, there lacks a general programming framework that can be easily customized to mine different types of patterns, and existing parallel and distributed solutions are IO-bound rendering CPU cores underutilized. Since mining frequent and/or closed patterns are NP-hard, it is important to fully utilize the available CPU cores. This paper presents such a general-purpose framework called PrefixFPM. The framework is based on the idea of prefix projection which allows a divide-and-conquer mining paradigm. PrefixFPM exposes a unified programming interface to users who can readily customize it to mine their desired patterns. We have adapted the state-of-the-art serial algorithms for mining patterns including subsequences, subtrees, and subgraphs on top of PrefixFPM, and extensive experiments demonstrate an excellent speedup ratio of PrefixFPM with the number of CPU cores.","Title: LNMF: lightweight network for multi-focus image fusion\nYear: 2022\n\nAbstract: Due to limitations of the optical lenses, using the digital single-lens camera to obtain an all-in-focus image is difficult. In order to overcome this difficulty, a lot of multi-focus image fusion methods have been proposed to produce all-in-focus images. With the development of Deep Learning (DL), the Convolutional Neural Networks (CNNs) have been successfully applied to image fusion. Currently proposed DL-based multi-focus fusion methods use deeper and wider networks to obtain better fusion results. However, due to the large computational complexity and memory consumption of standard convolution operations and deeper networks, these methods are unable to be applied to small devices. To address this issue, we propose a novel multi-focus image fusion method using a lightweight network. In this paper, we convert the fusion problem into a classification problem firstly. Then, using a lightweight network to classify focus and defocus areas of source images. Finally, focus areas are fused to obtain an all-in-focus image. The proposed lightweight network reduces operation complexity and memory consumption while achieving good fusion effects. The experimental results demonstrate that the proposed method with fewer parameters and computational costs has a better performance on quality and quantitative evaluations than other state-of-the-art methods.","Title: Vision-based Relative Detection and Tracking for Teams of Micro Aerial Vehicles\nYear: 2022\n\nAbstract: In this paper, we address the vision-based detection and tracking problems of multiple aerial vehicles using a single camera and Inertial Measurement Unit (IMU) as well as the corresponding perception consensus problem (i.e., uniqueness and identical IDs across all observing agents). We design several vision-based decentralized Bayesian multi-tracking filtering strategies to resolve the association between the incoming unsorted measurements obtained by a visual detector algorithm and the tracked agents. We compare their accuracy in different operating conditions as well as their scalability according to the number of agents in the team. This analysis provides useful insights about the most appropriate design choice for the given task. We further show that the proposed perception and inference pipeline which includes a Deep Neural Network (DNN) as visual target detector is lightweight and capable of concurrently running control and planning with Size, Weight, and Power (SWaP) constrained robots on-board. Experimental results show the effective tracking of multiple drones in various challenging scenarios such as heavy occlusions.","Title: Discovering unknowns: Context-enhanced anomaly detection for curiosity-driven autonomous underwater exploration\nYear: 2022\n\nAbstract: •Anomaly detection for unknowns towards to autonomous underwater exploration.•Autoencoder and autoregressive network to identify anomalies in unstructured dynamic underwater moving views.•Novel context-enhanced autoregressive network to learn feature dependence.•Patch learning paradigm to build an accurate latent feature space.•Validation on two benchmarks, simulation, and real data, outperforms state-of-arts.","Title: Skeleton-Free Pose Transfer for Stylized 3D Characters.\nYear: 2022\n\nAbstract: We present the first method that automatically transfers poses between stylized 3D characters without skeletal rigging. In contrast to previous attempts to learn pose transformations on fixed or topology-equivalent skeleton templates, our method focuses on a novel scenario to handle skeleton-free characters with diverse shapes, topologies, and mesh connectivities. The key idea of our method is to represent the characters in a unified articulation model so that the pose can be transferred through the correspondent parts. To achieve this, we propose a novel pose transfer network that predicts the character skinning weights and deformation transformations jointly to articulate the target character to match the desired pose. Our method is trained in a semi-supervised manner absorbing all existing character data with paired/unpaired poses and stylized shapes. It generalizes well to unseen stylized characters and inanimate objects. We conduct extensive experiments and demonstrate the effectiveness of our method on this novel task.","Title: Mechanistic Studies on the Stereoselectivity of FFAR1 Modulators\nYear: 2022\n\nAbstract: Free fatty acid receptor 1 (FFAR1) is a potential therapeutic target for the treatment of type 2 diabetes (T2D). It has been validated that agonists targeting FFAR1 can achieve the initial therapeutic endpoints of T2D, and the epimer agonists (R,S) AM-8596 can activate FFAR1 differently, with one acting as a partial agonist and the other as a full agonist. Up to now, the origin of the stereoselectivity of FFAR1 agonists remains elusive. In this work, we used molecular simulation methods to elucidate the mechanism of the stereoselectivity of the FFAR1 agonists (R)-AM-8596 and (S)-AM-8596. We found that the full agonist (R)-AM-8596 disrupts the residue interaction network around the receptor binding pocket and promotes the opening of the binding site for the G-protein, thereby resulting in the full activation of FFAR1. In contrast, the partial agonist (S)-AM-8596 forms stable electrostatic interactions with FFAR1, which stabilizes the residue network and hinders the conformational transition of the receptor. Our work thus clarifies the selectivity and underlying molecular activation mechanism of FFAR1 agonists.","Title: Quantifying and Reducing Registration Uncertainty of Spatial Vector Labels on Earth Imagery\nYear: 2022\n\nAbstract: Given raster imagery features and imperfect vector training labels with registration uncertainty, this paper studies a deep learning framework that can quantify and reduce the registration uncertainty of training labels as well as train neural network parameters simultaneously. The problem is important in broad applications such as streamline classification on Earth imagery or tissue segmentation on medical imagery, whereby annotating precise vector labels is expensive and time-consuming. However, the problem is challenging due to the gap between the vector representation of class labels and the raster representation of image features and the need for training neural networks with uncertain label locations. Existing research on uncertain training labels often focuses on uncertainty in label class semantics or characterizes label registration uncertainty at the pixel level (not contiguous vectors). To fill the gap, this paper proposes a novel learning framework that explicitly quantifies vector labels' registration uncertainty. We propose a registration-uncertainty-aware loss function and design an iterative uncertainty reduction algorithm by re-estimating the posterior of true vector label locations distribution based on a Gaussian process. Evaluations on real-world datasets in National Hydrography Dataset refinement show that the proposed approach significantly outperforms several baselines in the registration uncertainty estimations performance and classification performance.","Title: Learning Visibility for Robust Dense Human Body Estimation.\nYear: 2022\n\nAbstract: Estimating 3D human pose and shape from 2D images is a crucial yet challenging task. While prior methods with model-based representations can perform reasonably well on whole-body images, they often fail when parts of the body are occluded or outside the frame. Moreover, these results usually do not faithfully capture the human silhouettes due to their limited representation power of deformable models (e.g., representing only the naked body). An alternative approach is to estimate dense vertices of a predefined template body in the image space. Such representations are effective in localizing vertices within an image but cannot handle out-of-frame body parts. In this work, we learn dense human body estimation that is robust to partial observations. We explicitly model the visibility of human joints and vertices in the x, y, and z axes separately. The visibility in x and y axes help distinguishing out-of-frame cases, and the visibility in depth axis corresponds to occlusions (either self-occlusions or occlusions by other objects). We obtain pseudo ground-truths of visibility labels from dense UV correspondences and train a neural network to predict visibility along with 3D coordinates. We show that visibility can serve as 1) an additional signal to resolve depth ordering ambiguities of self-occluded vertices and 2) a regularization term when fitting a human body model to the predictions. Extensive experiments on multiple 3D human datasets demonstrate that visibility modeling significantly improves the accuracy of human body estimation, especially for partial-body cases. Our project page with code is at: https://github.com/chhankyao/visdb.","Title: An independent steering driving system to realize headland turning of unmanned tractors\nYear: 2022\n\nAbstract: •A 360°steering auxiliary wheel is installed on the tractor to realize efficient turning at the headland.•The auxiliary wheel system has the functions of automatic steering, lifting and driving.•The minimum turning radius of the modified tractor is 0 m.•The auxiliary wheel steering method decreases the ground space occupancy at the headland by 50 %.","Title: On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities\nYear: 2022\n\nAbstract: As the smartphone market leader, Android has been a prominent target for malware attacks. The number of malicious applications (apps) identified for it has increased continually over the past decade, creating an immense challenge for all parties involved. For market holders and researchers, in particular, the large number of samples has made manual malware detection unfeasible, leading to an influx of research that investigate Machine Learning (ML) approaches to automate this process. However, while some of the proposed approaches achieve high performance, rapidly evolving Android malware has made them unable to maintain their accuracy over time. This has created a need in the community to conduct further research, and build more flexible ML pipelines. Doing so, however, is currently hindered by a lack of systematic overview of the existing literature, to learn from and improve upon the existing solutions. Existing survey papers often focus only on parts of the ML process (e.g., data collection or model deployment), while omitting other important stages, such as model evaluation and explanation. In this paper, we address this problem with a review of 42 highly-cited papers, spanning a decade of research (from 2011 to 2021). We introduce a novel procedural taxonomy of the published literature, covering how they have used ML algorithms, what features they have engineered, which dimensionality reduction techniques they have employed, what datasets they have employed for training, and what their evaluation and explanation strategies are. Drawing from this taxonomy, we also identify gaps in knowledge and provide ideas for improvement and future work.","Title: Research on Size System of Disposable PE Gloves Based on Hand Contour Measurement\nYear: 2022\n\nAbstract: Disposable PE gloves are in great demand in the catering industry, and although their use accuracy is not high, disposable PE gloves of inconsistent size will still cause great discomfort to consumers. Therefore, how to adjust the current disposable PE glove production size system is the guarantee for the better development of the industry. In this study, the photo measurement method was used to measure the size and contour of the adult hand. Through cluster analysis and variance analysis, three types of disposable PE gloves size systems were determined. This size system provides one type of gloves for women, and two types of gloves for men with a height of 180cm as the dividing line.","Title: Cooperative Critical Turning Point-Based Decision-Making and Planning for CAVH Intersection Management System\nYear: 2022\n\nAbstract: The intersection is a critical traffic problem from the perspective of safety and traffic efficiency. As wireless communication technology advances, vehicle infrastructure cooperative approaches have received increased attention. In this paper, we propose a cooperative critical turning point method to help the cooperation between vehicles and infrastructures to improve the traffic efficiencies. The idea of cooperative critical turning point improves the cooperation between connected automated vehicles, the surrounding traffic and roadside infrastructures in order to provide high efficiencies of the intersection. An intersection management system using such a method is implemented based on the framework of the connected automated vehicle highway system. Such system can efficiently allow a roadside infrastructure receives state information from vehicles, reserve the associated intersection time-space occupancy, and then provide decision-making and planning feedback to the vehicles. The vehicles covered by the system then adjust their trajectories to meet their assigned time slot. The study validates the proposed system that considers the uncertainties of the driving environment by formulating the problem into a POMDP problem and solves it using an online solver. Based on preliminary simulation experiments, the proposed strategy can significantly reduce travel delays, decrease stops and improve the sustainability of the traffic system.","Title: A Novel Distributed Predefined-Time Sliding Mode Controller for Performance Enhancement of Power System Under Input Saturation\nYear: 2022\n\nAbstract: This article proposes a novel distributed predefined-time sliding mode controller (DPTSMC) to gain exceptional performances of power system under input saturation. Firstly, considering the saturation of excitation input to avoid the overvoltage of the coil, an auxiliary system is taken to compensate the effect of the excitation saturation. Then, the sliding mode control (SMC) and the predefined-time stability theory are taken to devise the DPTSMC to make the power system stable in a predefined time, which has an upper limit and is directly equivalent to an adjustable parameter, thus satisfying the high requirement of the power system for expected convergence time. Meanwhile, the estimated upper bound convergence time of the power system under the proposed DPTSMC is less conservative than some existing fixed-time control strategies. Moreover, the distributed control is taken to devise the DPTSMC and the controller can receive the real-time information from the phasor measurement units (PMU). Hence, the central controller is not required and the communication network is simple, thus releasing the pressure in complicated calculation and communication, and gaining rapid and coordinated response capacity. Next, the stabilization of the power system under the DPTSMC within a predefined upper limited time is proved through the Lyapunov function. Finally, the exceptional performance of the proposed DPTSMC than the existing control methods is verified through the simulation in the IEEE 39-bus 10-machine New England power system under different operation scenarios.","Title: Water Gauge Image Augmentation based on Generative Adversarial Network\nYear: 2022\n\nAbstract: Water level monitoring based on water gauge is a very widely used way because of its cheapness and portability. However, the insufficiency and low quality of water gauge images restrict the performance of water level measuring task based on deep learning methods such as object detection and semantic segmentation. In this article, we proposed a generative adversarial network (GAN) named Contextual adjustment GAN (CA-GAN) for data augmentation of water gauge images obtained from Wuyuan, Jiangxi Province in China, i.e. CA-GAN can generate high-quality images containing various scales and types water gauge, which provide image data for application such as deep-learning based water level measuring method. First, a improved downsampling module is designed with the help of segmentation map for the semantic activation modulation. Then, the Unet++ structure with the improved downsampling module is applied in the generator. Finally, to modulate the semantic relationship, a contextual adjustment scheme is de-signed between adjacent layers. This article conducts detailed experiments, proving that the improved downsampling module contributes to the maintenance of semantic information of water gauge images. It is illustrated that the water gauge images generated by CA-GAN have higher quality comparing with other three GAN models. And our method is expected to promote the water level measurement and hydrological monitoring application development.","Title: Maximal Directed Quasi-Clique Mining\nYear: 2022\n\nAbstract: Quasi-cliques are a type of dense subgraphs that generalize the notion of cliques, important for applications such as community/module detection in various social and biological networks. However, the existing quasi-clique definition and algorithms are only applicable to undirected graphs. In this paper, we generalize the concept of quasi-cliques to directed graphs by proposing (.1,.2)-quasi-cliques which have density requirements in both inbound and outbound directions of each vertex in a quasi-clique subgraph. An efficient recursive algorithm is proposed to find maximal (.1,.2)-quasi-cliques which integrates many effective pruning rules that are validated by ablation studies. We also study the finding of top-k large quasi-cliques directly by bootstrapping the search from more compact quasicliques, to scale the mining to larger networks. The algorithms are parallelized with effective load balancing, and we demonstrate that they can scale up effectively with the number of CPU cores.","Title: Automatic Field Extraction of Extended TLV for Binary Protocol Reverse Engineering\nYear: 2022\n\nAbstract: Type Length Value (TLV) is one of the main structures commonly used in network protocols. A large number of proprietary protocols, whose specification is unknown to the public, run in the current Internet as well as domain-specific Internet of Things (IoT) applications. It is critical to infer the TLV fields within a packet because this information can help network administrators quickly identify abnormal traffic and potential attacks. Inferring TLV fields belongs to the general task of protocol reverse engineering and is particularly challenging for binary protocols, where the boundaries of TLV fields have many possible positions. Existing methods for reverse engineering binary protocols involve many parameters and only work for protocols strictly following the conventional TLV format. We extend the concept of TLV to accommodate a broader category of structural patterns in various binary protocols, such as TCP, IP, ModBus, and MQTT. We then design algorithms to automatically extract the extended - TLV fields from packets. Via a series of experiments over several protocols, we demonstrate that our algorithms can accurately and quickly identify the extended-TLV fields in all the tested protocols. Our approach can thus be deployed as a general method for automatically reverse engineering binary protocol format.","Title: Bidirectional Relation Attention for Entity Alignment Based on Graph Convolutional Network\nYear: 2022\n\nAbstract: Entity alignment is an effective solution to integrate resources from different knowledge graphs (KGs). It aims to establish link between entities in different KGs that refer to the same object in the real world. Most of the existing embedding-based methods embed entities and relations into the same vector space or utilize a graph neural network (GNN) for nodes aggregation to learn the topology of the KG. However, these approaches do not sufficiently distinguish the effects of different relations on node features and ignore the dependencies between relations and entities. Some latent semantic information hidden in the relations is underutilized. This paper proposes a bidirectional relation attention mechanism to jointly learn entity and relation embeddings. We employ a graph convolutional network (GCN) to obtain the structure embedding for the KGs, then the attention mechanism used in relation embeddings facilitates the flow of information from entities to relations and strengthen the dependency between them. Finally the relation attention exploited in the aggregation stage integrates both direction information and connected relation semantics of neighborhoods that capture different impacts of relation on the neighborhood nodes. Experiments conducted on the DBP15K dataset validate the effectiveness of our proposed model and show that our model outperforms the baseline methods.","Title: Debris-Flow Susceptibility Assessment in China: A Comparison between Traditional Statistical and Machine Learning Methods\nYear: 2022\n\nAbstract: Debris flows, triggered by dual interferences extrinsically and intrinsically, have been widespread in China. The debris-flow susceptibility (DFS) assessment is acknowledged as the benchmark for the mitigation and prevention of debris flow risks, but DFS assessments at the national level are lacking. The role of human activities in the DFS assessment has always been overlooked. On the basis of a detailed inventory of debris-flow sites and a large set of environmental and human-related characteristics, this research presents the comparative performance of the well-known information value (IV), logistic regression (LR) and random forest (RF) models for DFS assessments in China. Twelve causative factors, namely, elevation, slope, aspect, rainfall, the normalized difference vegetation index (NDVI), land use, landform, geology, distance to faults, density of villages, distance to rivers and distance to roads, were considered. Debris-flow susceptibility maps were then generated after the nonlinear relationship between the debris-flow occurrence and the causative factors was captured. Finally, the predictive performance of the three maps was evaluated through receiver operating characteristic (ROC) curves, and the validation results showed that areas under the ROC curves were 81.98%, 79.96% and 97.38% for the IV, LR and RF models, respectively, indicating that the RF model outperformed the other two traditional statistical methods. The importance ranking of the RF model also revealed that distance to roads, slope and rainfall dominated the spatial distribution of debris flows. This is the first experiment to compare between the traditional statistical and machine learning methods in DFS studies for the whole of China. Our results could provide some empirical support for China's policymakers and local practitioners in their efforts to enable residents to be less vulnerable to disasters.","Title: Traffic Volatility Forecasting Using an Omnibus Family GARCH Modeling Framework\nYear: 2022\n\nAbstract: Traffic volatility modeling has been highly valued in recent years because of its advantages in describing the uncertainty of traffic flow during the short-term forecasting process. A few generalized autoregressive conditional heteroscedastic (GARCH) models have been developed to capture and hence forecast the volatility of traffic flow. Although these models have been confirmed to be capable of producing more reliable forecasts than traditional point forecasting models, the more or less imposed restrictions on parameter estimations may make the asymmetric property of traffic volatility be not or insufficiently considered. Furthermore, the performance of the models has not been fully evaluated and compared in the traffic forecasting context, rendering the choice of the models dilemmatic for traffic volatility modeling. In this study, an omnibus traffic volatility forecasting framework is proposed, where various traffic volatility models with symmetric and asymmetric properties can be developed in a unifying way by fixing or flexibly estimating three key parameters, namely the Box-Cox transformation coefficient A, the shift factor b, and the rotation factor c. Extensive traffic speed datasets collected from urban roads of Kunshan city, China, and from freeway segments of the San Diego Region, USA, were used to evaluate the proposed framework and develop traffic volatility forecasting models in a number of case studies. The models include the standard GARCH, the threshold GARCH (TGARCH), the nonlinear ARCH (NGARCH), the nonlinear-asymmetric GARCH (NAGARCH), the Glosten-Jagannathan-Runkle GARCH (GJR-GARCH), and the family GARCH (FGARCH). The mean forecasting performance of the models was measured with mean absolute error (MAE) and mean absolute percentage error (MAPE), while the volatility forecasting performance of the models was measured with volatility mean absolute error (VMAE), directional accuracy (DA), kickoff percentage (KP), and average confidence length (ACL). Experimental results demonstrate the effectiveness and flexibility of the proposed framework and provide insights into how to develop and select proper traffic volatility forecasting models in different situations.","Title: Fast algorithms for supermodular and non-supermodular minimization via bi-criteria strategy\nYear: 2022\n\nAbstract: In this paper, we concentrate on exploring fast algorithms for the minimization of a non-increasing supermodular or non-supermodular function f subject to a cardinality constraint. As for the non-supermodular minimization problem with the weak supermodularity ratio r, we can obtain a (1 + epsilon)-approximation algorithm with adaptivity O(n/epsilon log rn.f (empty set)/epsilon.OPT) under the bi-criteria strategy, where OPT denotes the optimal objective value of the problem. That is, instead of selecting at most k elements on behalf of the constraint, the cardinality of the output may reach to k/r log f (empty set)/epsilon.OPT. Moreover, for the supermodular minimization problem, we propose two (1 + epsilon)-approximation algorithms forwhich the output solution X is of size vertical bar X-0 vertical bar+ O (k log f (X-0)/epsilon.OPT. The adaptivities of this two algorithms are O (log(2) n . log f (X-0)/epsilon.OPT and O (log n . log f (X-0)/epsilon.OPT), where X-0 is an input set and OPT is the optimal value. Applications to group sparse linear regression problems and fuzzy C-means problems are studied at the end."],"string":"[\n \"Title: Fast Iterative Graph Computation with Resource Aware Graph Parallel Abstractions\\nYear: 2015\\n\\nAbstract: Iterative computation on large graphs has challenged system research from two aspects: (1) how to conduct high performance parallel processing for both in-memory and out-of-core graphs; and (2) how to handle large graphs that exceed the resource boundary of traditional systems by resource aware graph partitioning such that it is feasible to run large-scale graph analysis on a single PC. This paper presents GraphLego, a resource adaptive graph processing system with multi-level programmable graph parallel abstractions. GraphLego is novel in three aspects: (1) we argue that vertex-centric or edge-centric graph partitioning are ineffective for parallel processing of large graphs and we introduce three alternative graph parallel abstractions to enable a large graph to be partitioned at the granularity of subgraphs by slice, strip and dice based partitioning; (2) we use dice-based data placement algorithm to store a large graph on disk by minimizing non-sequential disk access and enabling more structured in-memory access; and (3) we dynamically determine the right level of graph parallel abstraction to maximize sequential access and minimize random access. GraphLego can run efficiently on different computers with diverse resource capacities and respond to different memory requirements by real-world graphs of different complexity. Extensive experiments show the competitiveness of GraphLego against existing representative graph processing systems, such as GraphChi, GraphLab and X-Stream.\",\n \"Title: Skeleton-guided 3D shape distance field metamorphosis.\\nYear: 2016\\n\\nAbstract: We introduce an automatic 3D shape morphing method without the need of manually placing anchor correspondence points. Given a source and a target shape, we extract their skeletons and automatically estimate the meaningful anchor points based on their skeleton node correspondences. Based on the anchors, dense correspondences between the interior of source and target shape can be established using earth mover's distance (EMD) optimization. Skeleton node correspondence, estimated with a voting-based method, leads to part correspondence which can be used to confine the dense correspondence within matched part pairs. This produces smooth and plausible morphing sequence based on distance field interpolation (DFI). We demonstrate the effectiveness of our algorithm over shapes with large geometric variation and structural difference. (C) 2016 Elsevier Inc. All rights reserved.\",\n \"Title: Space-based passive tracking of non-cooperative space target using robust filtering algorithm.\\nYear: 2016\\n\\nAbstract: Commonly, a non-cooperative space target is tracked using angle-only data. An approach utilised in many tracking problems is to perform an initial orbit determination (IOD) step and then transition to sequential estimation. In this paper, a space-based passive tracking strategy is proposed that combines IOD and sequential estimation. For the space-based IOD problem, a modified Laplace's method is derived, in which the non-spherical effect of Earth is considered and more than three pairs of angle measurements are fused. For the sequential estimation, a robust filtering algorithm, termed the robust square-root cubature Kalman filter, is developed. An adaptive factor is introduced in the developed algorithm, to ensure robustness to measurement malfunctions. Simulation results demonstrate that the proposed strategy can be used in actual engineering applications.\",\n \"Title: Biologically inspired model of path integration based on head direction cells and grid cells.\\nYear: 2016\\n\\nAbstract: Some neurons in the brain of freely moving rodents show special firing pattern. The firing of head direction cells (HDCs) and grid cells (GCs) is related to the moving direction and distance, respectively. Thus, it is considered that these cells play an important role in the rodents’ path integration. To provide a bionic approach for the vehicle to achieve path integration, we present a biologically inspired model of path integration based on the firing characteristics of HDCs and GCs. The detailed implementation process of this model is discussed. Besides, the proposed model is realized by simulation, and the path integration performance is analyzed under different conditions. Simulations validate that the proposed model is effective and stable.\",\n \"Title: Sentiment processing of social media information from both wireless and wired network.\\nYear: 2016\\n\\nAbstract: Recent years, information spreading under the environment of wireless communication has attracted increasing interest. Microblog platform on mobile terminals, as one product of wireless communication, facilitate information spreading and evolution by conveying message from peer to peer. Furthermore, sentiments from microblog reflect the attitude of peers on goods or events. Analysis of the sentiment can help in decision-making. Research work focuses on analyzing sentiment orientation for specific aspects of product with explicit names. However, it is not suitable for sentiment analysis of events using microblog data since users prefer to express their feelings in individual ways, namely the same object may be expressed in several ways. In this paper, a framework is proposed to calculate sentiment for aspects of events. First, we introduce some effective technologies in processing natural language, such as wordvec, HMM, and TextRank. Then, based on the state-of-art technologies, we build up a flowchart to get sentiment for aspects of events. At last, experiments are designed to prove these technologies on computing sentiment. During the process, name entities with the same meaning are clustered and sentiment carrier is filtered, with which sentiment can be got even users express their feeling for the same object with different words.\",\n \"Title: Sentiment Analysis of Name Entity for Text.\\nYear: 2016\\n\\nAbstract: Recent years, big data has attracted increasing interest. Sentiment analysis from microblog as one kind of big data also receive great attention. Some recent research works are not suitable for sentiment analysis as the result that users prefer to express their feelings in individual ways. In this paper, a framework is proposed to calculate sentiment for aspects of event. Based on some state of art technologies, we build up one flowchart to get sentiment for aspects of event. During the process, name entities with the same meaning are clustered and sentiment carrier are filtered. In this way sentiment can be got even user express feeling for the same object with different words.\",\n \"Title: Probabilistic Rank-One Matrix Analysis with Concurrent Regularization.\\nYear: 2016\\n\\nAbstract: As a classical subspace learning method, Probabilistic PCA (PPCA) has been extended to several bilinear variants for dealing with matrix observations. However, they are all based on the Tucker model, leading to a restricted subspace representation and the problem of rotational ambiguity. To address these problems, this paper proposes a bilinear PPCA method named as Probabilistic Rank-One Matrix Analysis (PROMA). PROMA is based on the CP model, which leads to a more flexible subspace representation and does not suffer from rotational ambiguity. For better generalization, concurrent regularization is introduced to regularize the whole matrix subspace, rather than column and row factors separately. Experiments on both synthetic and real-world data demonstrate the superiority of PROMA in subspace estimation and classification as well as the effectiveness of concurrent regularization in regularizing bilinear PPCAs.\",\n \"Title: Environmental Incidents Detection from Chinese Microblog Based on Sentiment Analysis.\\nYear: 2016\\n\\nAbstract: Environmental incidents affect the stable development of economy and society. If environmental incidents can be detected timely through microblog, it will be possible to reduce risk factors and improve social stability. Microblog has now become an important platform for generating and propagating incidents on the Web, it is an ideal field to detect incidents. However, since the texts of microblog messages are very short and unstructured, it is a challenging task to detect incidents from microblogs. Due to the diversity of Chinese microblogs, the results identified by Dynamic Query Expansion DQE always contain a considerable number of unrelated events. This paper proposes to filter microblogs by calculating emotion values via Sentiment Analysis SA, thus to improve the accuracy of detection. Experimental results demonstrated that DQEi¾ź+i¾źSA can be more accurate and effective to detect environmental incidents.\",\n \"Title: Predicting Mass Incidents from Weibo.\\nYear: 2016\\n\\nAbstract: The outbreak of mass incidents severely affects the stability of society. If we can predict mass incidents in advance, we may find the solution to avoid the confliction in time. Some of the existing approaches rely on emotional modeling. Much research has been conducted on microblog incident detection using statistical models, like LASSO regression method, Dynamic Query Expansion DQE and so on. In this paper, we propose to combine sentiment analysis and statistical methods, and uses LASSO regression method for mass incidents prediction. Experiments on Qingdao demonstrated that our proposed approach achieves a good performance.\",\n \"Title: The Particle Swarm Differential Evolution Algorithm for Ecological Sensor Network Coverage Optimization.\\nYear: 2016\\n\\nAbstract: The problem of coverage optimization is the challengingly important and key part in the research and application of ecology sensor network related with the ecological monitoring of Poyang Lake. A modified differential evolution algorithm (PSI-DE) combined with particle swarm intelligence is proposed to solve the coverage optimization problem. First, an improved version of the mutation rule combined with self-cognitive and social-cognitive items is introduced. Then, the influence on the coverage optimization performance of the PSI-DE algorithm brought by the five factors - namely, population size, number of iterations, sensing radius size, raster size, and number of nodes - is discussed and analyzed. The statistical results about the best coverage rate, average coverage rate, worst coverage rate, and variance are respectively obtained through a lot of simulation experiments. A series of the coverage rate curves, the line chart, and the node layout are drawn in this paper, and finally, the figures and the statistical results are proven to confirm each other.\",\n \"Title: Remote Sensing Image Transfer Classification Based on Weighted Extreme Learning Machine.\\nYear: 2016\\n\\nAbstract: It is expensive in time or resources to obtain adequate labeled data for a new remote sensing image to be categorized. The cost of manual interpretation can be reduced if labeled samples collected from previous temporal images can be reused to classify a new image over the same investigated area. However, it is reasonable to consider that the distributions of the target data and the historical dat...\",\n \"Title: Neural network-based adaptive second order sliding mode control of Lorentz-augmented spacecraft formation.\\nYear: 2017\\n\\nAbstract: The Lorentz force acting on an electrostatically charged spacecraft provides a new means of propulsion for orbital maneuvers such as Lorentz-augmented spacecraft formation flying. Modeling the Earth's magnetic field as a tilted dipole corotating with Earth, a nonlinear dynamical model that describes the orbital motion of a Lorentz spacecraft about an arbitrary elliptic orbit is developed. Then, the optimal open-loop control trajectories of formation establishment are solved by the pseudospectral method. To guarantee trajectory tracking in the presence of external perturbations and system uncertainties, a closed-loop neural network-based adaptive second order fast terminal sliding mode controller is designed using state feedback, which simultaneously solves the singularity and chattering problems that generally exist in conventional terminal sliding mode. Neural networks are employed to approximate the unknown nonlinearities in the system dynamics. Meanwhile, to ensure closed-loop tracking control without velocity measurements, an output feedback controller is also proposed with an observer introduced to capture the velocity signals. The overall stabilities for both control schemes are proved by a Lyapunov-based method. Numerical simulations are presented to verify the performance of the proposed controllers. Furthermore, the controllers could be applied to other Lorentz-augmented relative orbital control problems. An almost propellantless formation establishment is achieved via Lorentz force.Optimal open-loop hybrid inputs are derived by pseudospectral method.A closed-loop adaptive sliding mode controller using neural networks is proposed.An output feedback controller is designed for cases without velocity measurements.\",\n \"Title: A Model of Generating Visual Place Cells Based on Environment Perception and Similar Measure.\\nYear: 2016\\n\\nAbstract: It is an important content to generate visual place cells (VPCs) in the field of bioinspired navigation. By analyzing the firing characteristic of biological place cells and the existing methods for generating VPCs, a model of generating visual place cells based on environment perception and similar measure is abstracted in this paper. VPCs’ generation process is divided into three phases, including environment perception, similar measure, and recruiting of a new place cell. According to this process, a specific method for generating VPCs is presented. External reference landmarks are obtained based on local invariant characteristics of image and a similar measure function is designed based on Euclidean distance and Gaussian function. Simulation validates the proposed method is available. The firing characteristic of the generated VPCs is similar to that of biological place cells, and VPCs’ firing fields can be adjusted flexibly by changing the adjustment factor of firing field (AFFF) and firing rate’s threshold (FRT).\",\n \"Title: CSM: A Cloud Service Marketplace for Complex Service Acquisition.\\nYear: 2016\\n\\nAbstract: The cloud service marketplace (CSM) is an exploratory project aiming to provide “an AppStore for Services.” It is an intelligent online marketplace that facilitates service discovery and acquisition for enterprise customers. Traditional service discovery and acquisition are time-consuming. In the era of OneClick Checkout and pay-as-you-go service plans, users expect services to be purchased online efficiently and conveniently. However, as services are complex and different from software apps, the currently prevailing App Store based on keyword search is inadequate for services. In CSM, exploring and configuring services are an iterative process. Customers provide their requirements in natural language and interact with the system through questioning and answering. Learning from the input, the system can incrementally clarify users’ intention, narrow down the candidate services, and profile the configuration information for the candidates at the same time. CSM’s back end is built around the Services Knowledge Graph (SKG) and leverages data mining technologies to enable the semantic understanding of customers’ requirements. To quantitatively assess the value of CSM, empirical evaluation on real and synthetic datasets and case studies are given to demonstrate the efficacy and effectiveness of the proposed system.\",\n \"Title: A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories.\\nYear: 2017\\n\\nAbstract: Trajectory analysis is essential in many applications. In this paper, we address the problem of representing motion trajectories in a highly informative way, and consequently utilize it for analyzing trajectories. Our approach first leverages the complete information from given trajectories to construct a thermal transfer field which provides a context-rich way to describe the global motion patter...\",\n \"Title: Optimal Estimation in Wireless Sensor Networks With Energy Harvesting.\\nYear: 2016\\n\\nAbstract: In this paper, the source estimation problem is investigated in a wireless sensor network (WSN) with energy-harvesting (EH) nodes. We first consider a deterministic EH model where the amount of harvested energy at each node is known prior to transmissions. For such a model, we investigate the average mean-square-error (MSE) minimization problem over a finite horizon of multiple estimation periods ...\",\n \"Title: Manifold Partition Discriminant Analysis\\nYear: 2017\\n\\nAbstract: We propose a novel algorithm for supervised dimensionality reduction named manifold partition discriminant analysis (MPDA). It aims to find a linear embedding space where the within-class similarity is achieved along the direction that is consistent with the local variation of the data manifold, while nearby data belonging to different classes are well separated. By partitioning the data manifold ...\",\n \"Title: Blind 3D image quality assessment based on self-similarity of binocular features.\\nYear: 2017\\n\\nAbstract: The perceived quality assessment of three-dimensional (3D) images has emerged as a challenging research topic in the field of 3D imaging in recent years. Especially, blind quality assessment of 3D images encounters challenges due to prior information about the original 3D images is not available. In this paper, we propose a blind 3D image quality assessment (IQA) metric that utilizes the self-similarity of binocular features. The primary contribution of this study is that the proposed metric considers the binocular visual property and the local visual structural property for blind 3D-IQA. We calculate the self-similarity of binocular rivalry response as well as binocular orientation selectivity in the distorted 3D image. We then extract the inter- and intra-pixel binocular quality-predictive features from these self-similarity measures. Following feature extraction, we use machine learning based on the support vector regression (SVR) procedure to drive the overall quality score. Our results on publicly accessible 3D databases confirmed that the proposed metric is highly efficient and robust.\",\n \"Title: A parallelized screen-based method for rendering polylines and polygons on terrain surfaces.\\nYear: 2017\\n\\nAbstract: Two dimensional (2D) vector features, when rendered on terrain as reference or highlight features, can improve the intelligibility of three dimensional (3D) scenes. This research proposes a new screen-space based method to render solid color 2D polylines and polygons on terrain. Using the z-buffer value and window coordinates in screen space, a pixel's spatial extent in real world coordinates can be calculated. The pixel's color is changed, if its spatial extent intersects with a polyline or polygon feature, to reflect the fact that there is a polyline going through or a polygon covering the pixel. Compared to other existing methods, our method eliminates undesirable rendering artifacts. A parallel computing schema was proposed and implemented as well to improve rendering performance. New screen-space based method to render 2D polylines and polygons on terrain.Undesirable rendering artifacts are eliminated.Parallelized for rendering performance.\",\n \"Title: A Large-scale Distributed Video Parsing and Evaluation Platform.\\nYear: 2016\\n\\nAbstract: Visual surveillance systems have become one of the largest data sources of Big Visual Data in real world. However, existing systems for video analysis still lack the ability to handle the problems of scalability, expansibility and error-prone, though great advances have been achieved in a number of visual recognition tasks and surveillance applications, e.g., pedestrian/vehicle detection, people/vehicle counting. Moreover, few algorithms explore the specific values/characteristics in large-scale surveillance videos. To address these problems in large-scale video analysis, we develop a scalable video parsing and evaluation platform through combining some advanced techniques for Big Data processing, including Spark Streaming, Kafka and Hadoop Distributed Filesystem ( HDFS). Also, a Web User Interface is designed in the system, to collect users' degrees of satisfaction on the recognition tasks so as to evaluate the performance of the whole system. Furthermore, the highly extensible platform running on the long-term surveillance videos makes it possible to develop more intelligent incremental algorithms to enhance the performance of various visual recognition tasks.\",\n \"Title: ESSE: an early software size estimation method based on auto-extracted requirements features.\\nYear: 2016\\n\\nAbstract: Software size estimation is a crucial step in project management. According to the Standish Chaos Report, 65% of software projects are over budget or deadline; therefore, a good size estimation method is very important. However, existing estimation methods are complicated and human-effort consuming. In many industrial projects, project technical leads (PTLs) do not use these methods but just give a rough estimation based on their experience. To decrease human effort, we propose an early software size estimation (ESSE) method, which can extract semantic features from natural language requirements automatically, and build size estimation models for project. Firstly, ESSE makes a two-level semantic analysis of requirements specification documents by information extraction and activation spreading. Then, complexity-related features are extracted from the results of semantic analysis. Finally, a size estimation model is trained to predict size of new projects by regression algorithms. Experiments in real industrial datasets show that our method is effective and can be applied to real industrial projects.\",\n \"Title: Cascaded Interactional Targeting Network For Egocentric Video Analysis\\nYear: 2016\\n\\nAbstract: Knowing how hands move and what object is being manipulated are two key sub-tasks for analyzing first-person (egocentric) action. However, lack of fully annotated hand data as well as imprecise foreground segmentation make either sub-task challenging. This work aims to explicitly address these two issues via introducing a cascaded interactional targeting (i.e., infer both hand and active object regions) deep neural network. Firstly, a novel EM-like learning framework is proposed to train the pixel-level deep convolutional neural network (DCNN) by seamlessly integrating weakly supervised data (i.e., massive bounding box annotations) with a small set of strongly supervised data (i.e., fully annotated hand segmentation maps) to achieve state-of-the-art hand segmentation performance. Secondly, the resulting high-quality hand segmentation maps are further paired with the corresponding motion maps and object feature maps, in order to explore the contextual information among object, motion and hand to generate interactional foreground regions (operated objects). The resulting interactional target maps (hand + active object) from our cascaded DCNN are further utilized to form discriminative action representation. Experiments show that our framework has achieved the state-of-the-art egocentric action recognition performance on the benchmark dataset Activities of Daily Living (ADL).\",\n \"Title: Thermal Infrared Contrast Between Different Types of Oil Slicks on Top of Water Bodies.\\nYear: 2017\\n\\nAbstract: Thermal remote sensing is an effective technique for marine oil slick detection. However, many factors, such as the oil type, slick thickness, sensor capability, and the background environment, can together have an impact on the remotely sensed thermal imagery. These cross-coupling effects can usually be clarified by ground-based experiments. In this letter, four different types of oil slicks on w...\",\n \"Title: Toward Understanding the Cold, Hot, and Neutral Nature of Chinese Medicines Using in Silico Mode-of-Action Analysis.\\nYear: 2017\\n\\nAbstract: One important, however, poorly understood, concept of Traditional Chinese Medicine (TCM) is that of hot, cold, and neutral nature of its bioactive principles. To advance the field, in this study, we analyzed compound nature pairs from TCM on a large scale (>23 000 structures) via chemical space visualizations to understand its physicochemical domain and in silico target prediction to understand differences related to their modes-of-action (MoA) against proteins. We found that overall TCM natures spread into different subclusters with specific molecular patterns, as opposed to forming coherent global groups. Compounds associated with cold nature had a lower clog? and contain more aliphatic rings than the other groups and were found to control detoxification, heat-clearing, heart development processes, and have sedative function, associated with \\\"Mental and behavioural disorders\\\" diseases. While compounds associated with hot nature were on average of lower molecular weight, have more aromatic ring systems than other groups, frequently seemed to control body temperature, have cardio-protection function, improve fertility and sexual function, and represent excitatory or activating effects, associated with \\\"endocrine, nutritional and metabolic diseases\\\" and \\\"diseases of the circulatory system\\\". Compounds associated with neutral nature had a higher polar surface area and contain more cyclohexene moieties than other groups and seem to be related to memory function, suggesting that their nature may be a useful guide for their utility in neural degenerative diseases. We were hence able to elucidate the difference between different nature classes in TCM on the molecular level, and on a large data set, for the first time, thereby helping a better understanding of TCM nature theory and bridging the gap between traditional medicine and our current understanding of the human body.\",\n \"Title: Local gradient patterns (LGP): An effective local-statistical-feature extraction scheme for no-reference image quality assessment.\\nYear: 2017\\n\\nAbstract: Gradient features are known to be effective for full-reference (FR) image quality assessment (IQA). However, only a few metrics utilize gradient features for no-reference (NR) IQA. To investigate the potential benefits of gradient magnitude and phase for NR-IQA, we propose a novel and effective local-statistical-feature extraction metric, namely Local Gradient Patterns (LGP), for general-purpose NR-IQA. Using a Gaussian partial derivative filter, the image is first decomposed into two complementary components: gradient magnitude and phase. The local statistical features (e.g., the conditional probability distributions) are then extracted from the complementary components, using the derived local gradient magnitude and phase pattern operators. Finally, to facilitate NR-IQA, local statistical features that convey important structural information are mapped to the subjective mean opinion score of the image, using a support vector regression (SVR) procedure. We evaluated our proposed LGP metric using images from two publicly available test databases; the results confirm that the proposed LGP metric provides predictive performance that is superior to most state-of-the-art NR-IQA metrics and has an acceptable level of computational complexity.\",\n \"Title: A QoS Enhancement Scheme through Joint Control of Clear Channel Assessment Threshold and Contending Window for IEEE 802.11e Broadcasting.\\nYear: 2017\\n\\nAbstract: In a WLAN, when a great many nodes coexist, the network may readily be congested, thus causing packets dropping and network performance degradation. To solve this issue, a lot of schemes have been proposed. However, most of the previous works attempt to avoid the possible channel congestion by controlling the packets generation rate and/or transmitting power of nodes, while the effects of Clear Channel Assessment (CCA) threshold are not well examined. In our paper, a Joint CCA threshold and contending window control algorithm (JCCA) is proposed to avoid channel congestion or reduce the congestion probability of broadcasting in an IEEE 802.11e network. Both the network conditions and the priorities of messages are taken into account to improve the broadcasting performance in our paper. According to the simulation results, it can be concluded that our scheme can significantly increase the network throughput as well as packets delivery ratio and reduce the packet transmission delay compared to the IEEE 802.11e and Adaptive Carrier Sensing-Based MAC Designs (ACSBM) protocol.\",\n \"Title: A congestion avoidance game for information exchange on intersections in heterogeneous vehicular networks.\\nYear: 2017\\n\\nAbstract: Recently, the concept of HetVNET (Heterogeneous Vehicular NETwork) is greatly motivated by the fact that present wireless networks not only offers their attractive benefits, but also bring their shortcomings together. In this paper, by integrating the GIS (Geographic Information System), GPS (Global Position System), Sensor network, Vehicular Ad Hoc Networks (VANETs) and other localization technologies into our work, we propose a RoS (Requirement of Safety) aware services scheduling in the HetVNET to improve the packets delivery ratio, increase the spectral efficiency and alleviate the packet congestion as well. In addition, through utilizing the SINR-based (Signal to Interference plus Noise Ratio) utility and social optima searching strategy, our proposed algorithm outperforms the IEEE 802.11p in terms of the throughput as well as service miss ratio with the EDF (Earliest Deadline First) queuing mechanism. In addition, to reflect the increased transmission opportunities, the channel busy ratio under different configurations is also compared with a previous work DynB (Dynamic Beaconing).\",\n \"Title: Robust adaptive multi-task tracking control of redundant manipulators with dynamic and kinematic uncertainties and unknown disturbances.\\nYear: 2017\\n\\nAbstract: In this paper, a novel adaptive multi-priority controller for redundant manipulators is proposed to accomplish the multi-task tracking when kinematic/dynamic uncertainties and unknown disturbances exist. Prioritized redundancy resolution in kinematic level is incorporated into this passivity-based control framework. The kinematic and dynamic parameter adaptations are driven by both tracking error and prediction error. Moreover, the tracking information from both primary and subtasks are all utilized to accelerate the parameter estimation when the tasks are independent, whereas the inevitable tracking error of the subtasks due to algorithmic singularities is properly eliminated in the adaptation laws when the tasks are dependent. Potential ill-conditioned solution of the pseudoinverse is avoided using an improved singularity-robust inverse of the projected Jacobian. Along with the improvement of the multi-task tracking performance, smoothness of the commanded torques is still guaranteed for easy application. Measurements of the noisy joint acceleration and task velocity are avoided. The controller is mathematically derived based on Lyapunov stability analysis. Simulation results of the two cases are presented to verify the effectiveness and superiority of the proposed controller.\",\n \"Title: Topology design and cross-layer optimization for wireless body sensor networks.\\nYear: 2017\\n\\nAbstract: Wireless Body Sensor Networks play a crucial role in digital health care nowadays. Due to the size limitation on the sensor nodes and the life critical characteristics of the signals, there are stringent requirements on networks reliability and energy efficiency. In this article, we propose a mathematical optimization problem that jointly considers network topology design and cross-layer optimization in WBSNs. We introduce multilevel primal and dual decomposition methods and manage to solve the proposed non-convex mixed-integer optimization problem. A solution with fast convergence rate based on binary search is provided. Simulation results have been supplemented to show that our proposed method yields much better performance than existing solutions.\",\n \"Title: Analysis and Controlled Synthesis of Inhomogeneous Textures.\\nYear: 2017\\n\\nAbstract: Many interesting real-world textures are inhomogeneous and/or anisotropic. An inhomogeneous texture is one where various visual properties exhibit significant changes across the texture's spatial domain. Examples include perceptible changes in surface color, lighting, local texture pattern and/or its apparent scale, and weathering effects, which may vary abruptly, or in a continuous fashion. An anisotropic texture is one where the local patterns exhibit a preferred orientation, which also may vary across the spatial domain. While many example-based texture synthesis methods can be highly effective when synthesizing uniform stationary isotropic textures, synthesizing highly non-uniform textures, or ones with spatially varying orientation, is a considerably more challenging task, which so far has remained underexplored. In this paper, we propose a new method for automatic analysis and controlled synthesis of such textures. Given an input texture exemplar, our method generates a source guidance map comprising: i a scalar progression channel that attempts to capture the low frequency spatial changes in color, lighting, and local pattern combined, and ii a direction field that captures the local dominant orientation of the texture. Having augmented the texture exemplar with this guidance map, users can exercise better control over the synthesized result by providing easily specified target guidance maps, which are used to constrain the synthesis process.\",\n \"Title: Blind quality estimator for 3D images based on binocular combination and extreme learning machine.\\nYear: 2017\\n\\nAbstract: •Two binocular combinations of stimuli are generated using different combination strategies.•LoG and GPD are utilized to characterize the visual semantic structures.•ELMs can achieve higher learning accuracy with much faster learning speeds.\",\n \"Title: Design and Implementation of Delay-Dependent Wide-Area Damping Control for Stability Enhancement of Power Systems.\\nYear: 2017\\n\\nAbstract: In this paper, the hardware and software design and implementation are proposed for the wide-area damping control (WADC) strategies. First, the hardware-in-the-loop (HIL) real-time (RT) simulation system based on the RT-Lab is established as the closed-loop test platform for the WADC applied in multi-machine interconnected power system. The hardware, which contains the microcontroller, the periphe...\",\n \"Title: Online sponsored search auction matching problem with advertiser credibility\\nYear: 2017\\n\\nAbstract: How does a search engine company decide which advertisements to display for each query to maximize its revenue? This turns out to be a generalization of the online bipartite matching problem. In this paper, search engines decide the strategy to allocate resources with an advertiser credibility factor under budget constraints. Based on the optimal algorithm, which is the notion of a trade-off revealing LP, this paper remains the competitive ratio as 1 - 1/e with an advertiser credibility factor. During the ranking in the keywords auctions, CTR(Click Through Rate) and credibility factor are added to the trade-off function. In the long term, users tend to use the search engine with high credibility, which will bring greater revenues. The search engine will attract advertisers to bid on keywords and improve their credibility actively.\",\n \"Title: An Integrated Approach For Sustainable Container Supply Chain Design And Evaluation -A Simulation Approach With Case Study\\nYear: 2016\\n\\nAbstract: Traditionally, the container is usually regarded as a 'black box' in container supply chain design and analysis, which ignores the interactions between container, inner packaging and logistics operations and their impact on sustainability performance of the logistics solution. But according to UK and international insurance organisations, more than half of incidents in container shipping are cargo loss or damage, and more than one third of those value loss incidents are contributed by poorly or incorrect packed containers. Since the packaging related factors are tightly related to container cargo value loss in logistics operations and further influence the sustainability of the container supply chain, this paper introduces packaging logistics concept and risk management tools into container shipping to integrate the packaging, cargo and logistics operation as a holistic system in container supply chain evaluation and design. An integrated framework and approach is proposed in this paper for sustainable container supply chain evaluation and design taking packaging related risks into account. And simulation model is developed to apply this integrated method in container shipping scenario.\",\n \"Title: The Evaluation On Resource Configuration Of Telecom Assets\\nYear: 2016\\n\\nAbstract: Telecom regulatory departments issued many policies to keep the healthy development of telecom assets market, while the governments pay little attention to the resource configuration of telecom assets, and some of the existing researches have not got verified in academia. In this paper, firstly it puts forward the evaluation model for resource configuration of telecom assets, and indicators of cause and outcome are considered in this model; secondly, the data of provinces, municipalities and autonomous regions in China is collected related to the evaluation indicators of resource configuration in telecom assets; and then PCA method is developed to take empirical researches on configuration evaluation of telecom assets. Thus, the overall resource configuration of telecom assets can be got quantitatively. At last, some important conclusions are obtained.\",\n \"Title: Bilinear Probabilistic Canonical Correlation Analysis via Hybrid Concatenations.\\nYear: 2017\\n\\nAbstract: Canonical Correlation Analysis (CCA) is a classical technique for two-view correlation analysis, while Probabilistic CCA (PCCA) provides a generative and more general viewpoint for this task. Recently, PCCA has been extended to bilinear cases for dealing with two-view matrices in order to preserve and exploit the matrix structures in PCCA. However, existing bilinear PCCAs impose restrictive model assumptions for matrix structure preservation, sacrificing generative correctness or model flexibility. To overcome these drawbacks, we propose BPCCA, a new bilinear extension of PCCA, by introducing a hybrid joint model. Our new model preserves matrix structures indirectly via hybrid vector-based and matrix-based concatenations. This enables BPCCA to gain more model flexibility in capturing two-view correlations and obtain close-form solutions in parameter estimation. Experimental results on two real-world applications demonstrate the superior performance of BPCCA over competing methods.\",\n \"Title: A Density-Based Seed-Centric Community Detection Algorithm\\nYear: 2016\\n\\nAbstract: Although many different community detection algorithms have been proposed to detect community structures in complex networks, how to effectively detect community structures is still a great challenge. Seed-centric methods is one of the most effective solutions for community detection. To more, in this paper, we propose a novel density-based seed expansion algorithm, namely, DenSeC, which can easily find cores of communities. By taking nodes with higher density locally as cores of communities, DenSeC first detects cores by searching from sparsely connected area to densely connected area. Then, starting from cores, along the path from densely connected area to sparsely connected area, it constructs a community by iteratively adding nodes which are similar to the nodes in the community. Numerical experiments have verified the effectiveness of DenSeC.\",\n \"Title: Construction of the diagnosis and treatment process of dermatosis based on data-driven approach\\nYear: 2017\\n\\nAbstract: In this study, we selected the dermatology data, constructed a dictionary of dermatology semantic classification, and established a corpus of dermatology literature. Based on the context calculation model we took the case of erythema skin disease as the example, identified the semantic meaning of the dermatology data, and formed the concept attribute of dermatology knowledge, constructed the dermatology ontology. We established a priori database and verified each other with the running data to correct the data error and improve the data definition. In this study, we used the natural language to describe the characteristic information of skin diseases, and carried out the semantic searching. Finally, we constructed the mathematical model of the diagnosis and treatment of dermatology based on the corpus, the ontology and the priori database. The purpose is to supply the gap of unbalance of doctors' personal level of the dermatosis diagnosis and treatment, improve the level and accuracy of clinical diagnosis and treatment of dermatosis, and to achieve effective drive of medical literature data for clinical diagnosis and treatment.\",\n \"Title: Pyramid sketch: a sketch framework for frequency estimation of data streams\\nYear: 2017\\n\\nAbstract: AbstractSketch is a probabilistic data structure, and is used to store and query the frequency of any item in a given multiset. Due to its high memory efficiency, it has been applied to various fields in computer science, such as stream database, network traffic measurement, etc. The key metrics of sketches for data streams are accuracy, speed, and memory usage. Various sketches have been proposed, but they cannot achieve both high accuracy and high speed using limited memory, especially for skewed datasets. To address this issue, we propose a sketch framework, the Pyramid sketch, which can significantly improve accuracy as well as update and query speed. To verify the effectiveness and efficiency of our framework, we applied our framework to four typical sketches. Extensive experimental results show that the accuracy is improved up to 3.50 times, while the speed is improved up to 2.10 times. We have released our source codes at Github [1].\",\n \"Title: Who Will You Share a Ride With: Factors that Influence Trust of Potential Rideshare Partners.\\nYear: 2017\\n\\nAbstract: Ridesharing can save energy, reduce pollution, and alleviate congestion. Ridesharing systems allow peer-to-peer ride matching through mobile-phone applications. In many systems people have little information about the driver and co-riders. Trust heavily influences the usage of a ridesharing system, however, work in this area is limited. In this paper, we investigate the role of social media in shaping trust and delineate factors that influence the decisions of trust placed on a driver or co-rider. We design a ridesharing system where a rider can see the Instagram profile and 10 recent pictures of a co-rider and ask them to make judgments on six trust related questions. We discovered three important factors that shape trust forming: textit{social proof}, textit{social approval}, and textit{self-disclosure}. These three factors account for 40%, 33%, and 13% of the variance, and subsequent factors extracted accounted for substantially less variance. Using these three factors, we successfully build a trust prediction model with F-measure of 87.4%, an improvement from 78.6% on using all factors collected.\",\n \"Title: Predicting the Trends of Social Events on Chinese Social Media.\\nYear: 2017\\n\\nAbstract: Growing interest in social events on social media came along with the rapid development of the Internet. Social events that occur in the \\\"real\\\" world can spread on social media (e.g., Sina Weibo) rapidly, which may trigger severe consequences and thus require the government's timely attention and responses. This article proposes to predict the trends of social events on Sina Weibo, which is currently the most popular social media in China. Based on the theories of social psychology and communication sciences, we extract an unprecedented amount of comprehensive and effective features that relate to the trends of social events on Chinese social media, and we construct the trends of prediction models by using three classical regression algorithms. We found that lasso regression performed better with the precision 0.78 and the recall 0.88. The results of our experiments demonstrated the effectiveness of our proposed approach.\",\n \"Title: Visual comfort prediction for stereoscopic image using stereoscopic visual saliency.\\nYear: 2017\\n\\nAbstract: Perceptually salient regions of stereoscopic images significantly affect visual comfort (VC). In this paper, we propose a new objective approach for predicting VC of stereoscopic images according to visual saliency. The proposed approach includes two stages. The first stage involves the extraction of foreground saliency and depth contrast from a disparity map to generate a depth saliency map, which in turn is combined with 2D saliency to obtain a stereoscopic visual saliency map. The second stage involves the extraction of saliency-weighted VC features, and feeding them into a prediction metric to produce VC scores of the stereoscopic images. We demonstrate the effectiveness of the proposed approach compared with the conventional prediction methods on the IVY Lab database, with performance gain ranging from 0.016 to 0.198 in terms of correlation coefficients.\",\n \"Title: Simultaneous Coherent and Random Noise Attenuation by Morphological Filtering With Dual-Directional Structuring Element.\\nYear: 2017\\n\\nAbstract: Seismic data are highly corrupted by noise or unwanted energies arising from different kinds of sources. In general, seismic noise can be divided into two categories, namely, coherent noise and random noise, and is treated with essentially different methods. Traditional methods often utilize the differences in frequency, wavenumber, or amplitude to separate signal and noise. However, the applicati...\",\n \"Title: Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55.\\nYear: 2017\\n\\nAbstract: We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database. The benchmark consists of two tasks: part-level segmentation of 3D shapes and 3D reconstruction from single view images. Ten teams have participated in the challenge and the best performing teams have outperformed state-of-the-art approaches on both tasks. A few novel deep learning architectures have been proposed on various 3D representations on both tasks. We report the techniques used by each team and the corresponding performances. In addition, we summarize the major discoveries from the reported results and possible trends for the future work in the field.\",\n \"Title: A Matching Approach to Factor Scores Based on Online Sponsored Search Auction\\nYear: 2018\\n\\nAbstract: AbstractHow does a search engine company decide which advertisements to display for each query to maximize its revenue? This turns out to be a generalization of the online bipartite matching problem. In this paper, search engines decide the strategy to allocate resources with an advertiser credibility factor under budget constraints. Based on the optimal algorithm, which is the notion of a trade-off revealing LP, this paper remains the competitive ratio as 1-1/e with an advertiser credibility factor. During the ranking in the keywords auctions, the authors calculate factor scores. The new arrival keywords should match to the advertisement which has the maximum factor scores. The amount of spent money, budget constraints, CTR Click Through Rate and a credibility factor are added to the trade-off function using in the authors' algorithms. In the long term, users tend to use the search engine with high credibility, which will bring greater revenues. The search engine will attract advertisers to bid on keywords and improve their credibility actively.\",\n \"Title: A New Visibility Model for Surface Reconstruction.\\nYear: 2017\\n\\nAbstract: In this paper, we propose a new visibility model for scene reconstruction. To yield out surface meshes with enough scene details, we introduce a new visibility model, which keeps the relaxed visibility constraints and takes the distribution of points into consideration, thus it is efficient for preserving details. To strengthen the robustness to noise, a new likelihood energy term is introduced to the binary labeling problem of Delaunay tetrahedra, and its implementation is presented. The experimental results show that our method performs well in terms of detail preservation.\",\n \"Title: An efficient all-zero block detection algorithm for high efficiency video coding with RDOQ.\\nYear: 2018\\n\\nAbstract: Rate distortion optimized HEVC video coding is challenged by the extremely high complexity caused by hierarchical prediction structure and advanced coding tools. All-zero-block (AZB) detection is an efficient tool to decrease the complexity of mode decision in HEVC coders in the sense of rate distortion optimization (RDO). Traditional AZB detection schemes were usually designed by employing hard-decision quantization (HDQ), and the AZB detection thresholds were generally derived according to the individual block’s characteristic parameter such as SAD or SATD. However, soft-decision quantization (SDQ) is preferably employed in HEVC encoders for coding performance improvement, and the AZB detection thresholds are supposed to be determined according to the ensemble’s statistical characteristics in the sense of statistical inference. This paper proposes an adaptive AZB detection algorithm well-suited for SDQ, more specifically RD optimized quantization (RDOQ). Inspired by Bayesian inference, a more accurate coefficient-level zero-quantized threshold model in the RDO sense is proposed by fully simulating RDOQ to aid in the following AZB detection. In addition, a local parameter depicting the individual block’s characteristics regarding the inter-coefficient distribution is jointly combined with SATD to derive an adaptive threshold model for AZB detection. The experimental results demonstrate that the proposed algorithm detects 91.9% 4×4 AZB with smaller than 1.9% FAR and 8.1% FRR false alarm rate on average, at the cost of negligible rate-distortion performance loss. This work is well-suited for fast RD optimized HEVC coding.\",\n \"Title: One Memory Access Sketch: A More Accurate and Faster Sketch for Per-Flow Measurement.\\nYear: 2017\\n\\nAbstract: Sketch is a probabilistic data structure widely used for per-flow measurement in the real network. The key metrics of sketches for per-flow measurement are their memory usage, accuracy, and speed. There are a variety of sketches, but they cannot achieve both high accuracy and high speed at the same time given a fixed memory size. To address this issue, we propose a new sketch, namely the OM (One Memory) sketch. It achieves much higher accuracy than the state-of-the-art, and achieves close to one memory access and one hash computation for each insertion or query. The key methodology of our OM sketch is to leverage word constraint and fingerprint techniques based on a hierarchical structure. Extensive experiments based on real IP traces show that the accuracy is improved up to 10.64 times while the speed is improved up to 2.50 times, compared with the well-known CM sketch [1]. All the related source code has been released at GitHub [2].\",\n \"Title: Node-centric route mutation for large-scale SDN based on 3D earth mover's distance model\\nYear: 2017\\n\\nAbstract: Exploiting software-defined networking techniques, randomly and instantly mutating routes can disguise strategically important infrastructure, and protect the integrity of data networks. Route mutation has been to date formulated as NP-complete constraint satisfaction problems where feasible sets of routes need to be generated with exponential computational complexities, limiting algorithmic scalability to large-scale networks. In this paper, we propose a novel node-centric route mutation method which interprets route mutation as a signature matching problem. We formulate the route mutation problem as a three-dimensional earth mover's distance (EMD) model and solve it by using a binary branch and bound method. Considering the scalability, we further propose a heuristic method yields significantly lower computational complexities with marginal loss of robustness against eavesdropping. Simulation results show that our proposed methods can effectively disguise key infrastructure by reducing the difference of historically accumulative traffic among different switches. With significantly reduced complexities, our algorithms are of particular interest to safeguard large-scale networks.\",\n \"Title: Detecting Postpartum Depression In Depressed People By Speech Features\\nYear: 2017\\n\\nAbstract: Postpartum depression (PPD) is a depressive disorder with peripartum onset, which brings heavy burden to individuals and their families. In this paper, we propose to detect PPD in depressed people via voices. We used openSMILE for feature extraction, selected Sequential Floating Forward Selection (SFFS) algorithm for feature selection, tried different settings of features, set 5-fold cross validation and applied Support Vector Machine (SVM) on Weka for training and testing different models. The best predictive performance among our models is 69%, which suggests that the speech features could be used as a potential behavioral indicator for identifying PPD in depression. We also found that a combined impact of features and content of questions contribute to the prediction. After dimension reduction, the average value of F-measure was increased 5.2%, and the precision of PPD was rose to 75%. Comparing with demographic questions, the features of emotional induction questions have better predictive effects.\",\n \"Title: Inferring Social Functions Available in the Metro Station Area from Passengers' Staying Activities in Smart Card Data.\\nYear: 2017\\n\\nAbstract: The function of a metro station area is vital for city planners to consider when establishing a context-aware Transit-Oriented Development policy around the station area. However, the functions of metro station areas are hard to infer using the static land use distribution and other traditional survey datasets. In this paper, we propose a method to infer the functions occurring around the metro station catchment areas according to the patterns of staying activities derived from smart card data. We first define the staying activities by the spatial and temporal constraints of the two consecutive alighting and boarding records from the individual travel profile. Then we cluster and label the whole staying activities by considering the features of duration, frequency, and start time. By analyzing the percentage of different types of aggregated activities happening around each metro station, we cluster and explore the functions of the metro station area. Taking Wuhan as a case study, we analyze the results of Wuhan metro systems and discuss the similarities and differences between the functions and the land use distribution around the station area. The results show that although there exist some agreements, there is also a gap between the human activities and the land uses around the station area. These findings could give us deeper insight into how people act around the stations by metro systems, which will ultimately benefit the urban planning and policy development.\",\n \"Title: Scalable Node-Centric Route Mutation for Defense of Large-Scale Software-Defined Networks.\\nYear: 2017\\n\\nAbstract: Exploiting software-defined networking techniques, randomly and instantly mutating routes can disguise strategically important infrastructure and protect the integrity of data networks. Route mutation has been to date formulated as NP-complete constraint satisfaction problem where feasible sets of routes need to be generated with exponential computational complexities, limiting algorithmic scalability to large-scale networks. In this paper, we propose a novel node-centric route mutation method which interprets route mutation as a signature matching problem. We formulate the route mutation problem as a three-dimensional earth mover's distance (EMD) model and solve it by using a binary branch and bound method. Considering the scalability, we further propose that a heuristic method yields significantly lower computational complexities with marginal loss of robustness against eavesdropping. Simulation results show that our proposed methods can effectively disguise key infrastructure by reducing the difference of historically accumulative traffic among different switches. With significantly reduced complexities, our algorithms are of particular interest to safeguard large-scale networks.\",\n \"Title: Accurate Mesh-Based Alignment For Ground And Aerial Multi-View Stereo Models\\nYear: 2017\\n\\nAbstract: We propose a method for accurate alignment of ground and aerial multi-view stereo (MVS) models. We achieve this goal by reconstructing the surface meshes from MVS point clouds generated by aerial and ground images respectively, and then iteratively removing the gap between them. The key issue is how to establish reliable correspondences between two meshes. To address this issue, we introduce a new set called the skeleton facet set (SFS) to represent the locally smooth part on the mesh, and then compute the transformation matrix by comparing the depths of the facets in SFS between aerial and ground models. Experimental results show that the proposed method is able to yield accurate alignment results and is robust to noise as well.\",\n \"Title: Elastic Switch Migration for Control Plane Load Balancing in SDN.\\nYear: 2018\\n\\nAbstract: Software-defined network (SDN) provides a solution for the scalable network framework with decoupled control and data plane. Migrating switches can balance the resource utilization of controllers and improve network performance. Switch migration problem has to date been formulated as a resource utilization maximization problem to address the scalability of the control plane. However, this problem is NP-hard with high-computational complexities and without addressing the security challenges of the control plane. In this paper, we propose a switch migration method, which interprets switch migration as a signature matching problem and is formulated as a 3-D earth movers distance model to protect strategically important controllers in the network. Considering the scalability, we further propose a heuristic method which is time-efficient and suitable to large-scale networks. Simulation results show that our proposed methods can disguise strategically important controllers by diminishing the difference of traffic load between controllers. Moreover, our proposed methods can significantly relieve the traffic pressure of controllers and prevent saturation attacks.\",\n \"Title: Adaptive Navigation Algorithm Under Abnormal Measurements in Libration-Point Mission.\\nYear: 2018\\n\\nAbstract: This paper investigates X-ray pulsar navigation for earth-moon libration-point mission. A comprehensive analysis shows that abnormal measurements are inevitable and bring quite adverse effects for navigation results. To approach this problem, a new algorithm called robust square-root cubature Kalman filter is proposed. An innovation-based adaptive scale factor is introduced to adjust measurement noise covariance so that the adverse effects of abnormal measurements can be suppressed. Effectiveness of the proposed method is demonstrated in simulation results.\",\n \"Title: Local and Global Feature Learning for Blind Quality Evaluation of Screen Content and Natural Scene Images.\\nYear: 2018\\n\\nAbstract: The blind quality evaluation of screen content images (SCIs) and natural scene images (NSIs) has become an important, yet very challenging issue. In this paper, we present an effective blind quality evaluation technique for SCIs and NSIs based on a dictionary of learned local and global quality features. First, a local dictionary is constructed using local normalized image patches and conventional...\",\n \"Title: The heterogeneity of thermal sensitivity of residential electricity load between China and USA\\nYear: 2017\\n\\nAbstract: Thermal-sensitive electricity usage accounts for the majority of residential electricity use worldwide. Moreover, it has substantial impacts on the power system and the society during the extreme weathers. We propose a novel data-driven method to identify the thermal-sensitive electricity usage and quantify the associated thermal sensitivity directly from widely deployed smart meter data, where the Multivariate Adaptive Regression Splines model provides both the flexibility and interpretability. We apply the proposed method to analyze the thermal sensitivity of individual residential electricity use for two representative urban areas in the United States and China, San Francisco Bay Area and Shanghai, respectively. More than 120,000 households' smart meter data sampled from Bay Area and Shanghai is used for the analysis. To best of our knowledge, we for the first time reveal the heterogeneous patterns of thermal sensitivity of households from USA and China at the individual level. The difference of thermal sensitivity serves as an important factor for personalized techniques and policies towards mitigating the impact of extreme weathers to power systems and improving the buildings' energy efficiency.\",\n \"Title: Privacy-Preserving Publishing of Multilevel Utility-Controlled Graph Datasets.\\nYear: 2018\\n\\nAbstract: Conventional private data publication schemes are targeted at publication of sensitive datasets either after the k-anonymization process or through differential privacy constraints. Typically these schemes are designed with the objective of retaining as much utility as possible for the aggregate queries while ensuring the privacy of the individual records. Such an approach, though suitable for publishing aggregate information as public datasets, is inapplicable when users have different levels of access to the same data. We argue that existing schemes either result in increased disclosure of private information or lead to reduced utility when some users have more access privileges than the others. In this article, we present an anonymization framework for publishing large datasets with the goals of providing different levels of utility to the users based on their access privilege levels. We design and implement our proposed multilevel utility-controlled anonymization schemes in the context of large association graphs considering three levels of user utility, namely, (1) users having access to only the graph structure, (2) users having access to the graph structure and aggregate query results, and (3) users having access to the graph structure, aggregate query results, and individual associations. Our experiments on real large association graphs show that the proposed techniques are effective and scalable and yield the required level of privacy and utility for each user privacy and access privilege level.\",\n \"Title: Cold Filter: A Meta-Framework for Faster and More Accurate Stream Processing.\\nYear: 2018\\n\\nAbstract: Approximate stream processing algorithms, such as Count-Min sketch, Space-Saving, etc., support numerous applications in databases, storage systems, networking, and other domains. However, the unbalanced distribution in real data streams poses great challenges to existing algorithms. To enhance these algorithms, we propose a meta-framework, called Cold Filter (CF), that enables faster and more accurate stream processing.\\n\\nDifferent from existing filters that mainly focus on hot items, our filter captures cold items in the first stage, and hot items in the second stage. Also, existing filters require two-direction communication - with frequent exchanges between the two stages; our filter on the other hand is one-direction - each item enters one stage at most once. Our filter can accurately estimate both cold and hot items, giving it a genericity that makes it applicable to many stream processing tasks. To illustrate the benefits of our filter, we deploy it on three typical stream processing tasks and experimental results show speed improvements of up to 4.7 times, and accuracy improvements of up to 51 times. All source code is made publicly available at Github.\",\n \"Title: Development of distributed cache strategy for analytic cluster in an Internet of Things system\\nYear: 2018\\n\\nAbstract: This paper discusses the development of a distributed cache strategy for an analytic cluster in an Internet of Things (IoT) system. In this paper, Least Recently Used (LRU), Proactive Cache and distributed system related concepts are discussed. The study about the approaches for performance optimization, nodes and data distributing in the IoT system is also performed. The novelty about this research is the concept of a placeholder and its use together with Proactive Cache to optimize the traditional LRU algorithm in the use case of a data analytic cluster in an IoT system, which can reduce the response delay of the query up to 90% in the test case.\",\n \"Title: Single Hash: Use One Hash Function to Build Faster Hash Based Data Structures\\nYear: 2018\\n\\nAbstract: With the scale of data to store or monitor in nowadays network constantly increasing, hash based data structures are more and more widely used because of their high memory efficiency and high speed. Most of them, like Bloom filters, sketches and d-Ieft hash tables use more than one hash function. Furthermore, in order to achieve good randomicity, the hash functions used, like MD5 and SHA1, are very complicated and consume a lot of CPU cycles to carry out. As a consequence, the implementation of these hash functions will be time-consuming, In order to address this issue, we propose Single Hash technique in this paper. It is based on the observation that the hash functions we use produce 32-bit or M-bit values which have much bigger value ranges than that we need in practice. We usually have to carry out modular operation to map the hash results into a smaller range in the data structures listed above. In this procedure, information carried by the high bits may be discarded. For example, if in a Bloom filter the length of the bit array is 220 while the hash functions we use are 32-bit hash functions, there are 12 bits in the results of the hash functions discarded in the procedure of modular. We can use these bits to produce more hash values. Therefore, we propose to use a few bit operations to make full use of the information produced by one hash function and generate multiple hash values which can be used in these data structures. Single Hash technique can be applied to most of the hash based data structures. It can significantly improve their speed, because instead of carrying out multiple hash functions, we only need to compute one hash function and a few simple operations (e.g., bit shift and XOR). Other aspects of performance, like memory efficiency and accuracy of these data structures will not be influenced by Single Hash technique. In this paper, we apply it to three kinds of classic hash based data structures, i.e., Bloom filters, CM sketches and d-Ieft hash tables as case studies, and evaluate their performance with both mathematical analysis and extensive experiments. We make all our codes open source on Github.\",\n \"Title: Risk index assessment for urban natural gas pipeline leakage based on artificial neural network\\nYear: 2017\\n\\nAbstract: With large-scale construction of urban natural gas pipelines, the occurrence of accidents such as fire, explosion owing to natural gas pipeline leakage has increased. How to make appropriate response strategy for urban natural gas pipeline leakage is an important topic for urban safety planner. This paper proposed an assessment program to evaluate risk of urban natural gas pipeline leakage. This system uses artificial neural network mode, which includes 10 inputs such as methane concentration, weather, corrosion, to simulate risk index of pipeline leakage. The 97-day field operation results showed that the risk index match well with field situation, which indicates the reliability and practicability of the assessment program.\",\n \"Title: A rear-end collision prediction scheme based on deep learning in the Internet of Vehicles.\\nYear: 2018\\n\\nAbstract: Recently, the deep learning schemes have been well investigated for improving the driving safety and efficiency in the transportation systems. In this paper, a probabilistic model named as CPGN (Collision Prediction model based on GA-optimized Neural Network) for decision-making in the rear-end collision avoidance system is proposed, targeting modeling the impact of important influential factors of collisions on the occurring probability of possible accidents in the Internet of Vehicles (IoV). The decision on how to serve the chauffeur is determined by a typical deep learning model, i.e., the BP neural network through evaluating the possible collision risk with V2I (Vehicle-to-Infrastructure) communication, V2V (Vehicle-to-Vehicle) communication and GPS infrastructure supporting. The proper structure of our BP neural network model is deeply learned with training data generated from VISSIM with multiple influential factors considered. In addition, since the selection of the connection coefficient array and thresholds of the neural network has great randomness, a local optimization issue is readily occurring during the modeling procedure. To overcome this problem and consider the ability to efficiently find out a global optimization, this paper chooses the genetic algorithm to optimize the coefficient array and thresholds of proposed neural network. For the purpose of enhancing the convergence speed of the proposed model, we further adjust the studying rate according to the relationship between the actual and predicated values of two adjacent iterations. Simulation results demonstrate that the proposed collision risk evaluation framework could offer rationale estimations to the possible collision risk in car-following scenarios for the next discrete monitoring interval.\",\n \"Title: VisemeNet: Audio-Driven Animator-Centric Speech Animation.\\nYear: 2018\\n\\nAbstract: We present a novel deep-learning based approach to producing animator-centric speech motion curves that drive a JALI or standard FACS-based production face-rig, directly from input audio. Our three-stage Long Short-Term Memory (LSTM) network architecture is motivated by psycho-linguistic insights: segmenting speech audio into a stream of phonetic-groups is sufficient for viseme construction; speech styles like mumbling or shouting are strongly co-related to the motion of facial landmarks; and animator style is encoded in viseme motion curve profiles. Our contribution is an automatic real-time lip-synchronization from audio solution that integrates seamlessly into existing animation pipelines. We evaluate our results by: cross-validation to ground-truth data; animator critique and edits; visual comparison to recent deep-learning lip-synchronization solutions; and showing our approach to be resilient to diversity in speaker and language.\",\n \"Title: Non-Stationary Texture Synthesis by Adversarial Expansion.\\nYear: 2018\\n\\nAbstract: The real world exhibits an abundance of non-stationary textures. Examples include textures with large scale structures, as well as spatially variant and inhomogeneous textures. While existing example-based texture synthesis methods can cope well with stationary textures, non-stationary textures still pose a considerable challenge, which remains unresolved. In this paper, we propose a new approach for example-based non-stationary texture synthesis. Our approach uses a generative adversarial network (GAN), trained to double the spatial extent of texture blocks extracted from a specific texture exemplar. Once trained, the fully convolutional generator is able to expand the size of the entire exemplar, as well as of any of its sub-blocks. We demonstrate that this conceptually simple approach is highly effective for capturing large scale structures, as well as other non-stationary attributes of the input exemplar. As a result, it can cope with challenging textures, which, to our knowledge, no other existing method can handle.\",\n \"Title: Full 3D Reconstruction of Transparent Objects.\\nYear: 2018\\n\\nAbstract: Numerous techniques have been proposed for reconstructing 3D models for opaque objects in past decades. However, none of them can be directly applied to transparent objects. This paper presents a fully automatic approach for reconstructing complete 3D shapes of transparent objects. Through positioning an object on a turntable, its silhouettes and light refraction paths under different viewing directions are captured. Then, starting from an initial rough model generated from space carving, our algorithm progressively optimizes the model under three constraints: surface and refraction normal consistency, surface projection and silhouette consistency, and surface smoothness. Experimental results on both synthetic and real objects demonstrate that our method can successfully recover the complex shapes of transparent objects and faithfully reproduce their light refraction properties.\",\n \"Title: 2D Scene Sketch-Based 3D Scene Retrieval.\\nYear: 2018\\n\\nAbstract: Sketch-based 3D model retrieval has the intuitiveness advantage over other types of retrieval schemes. Currently, there is a lot of research in sketch-based 3D model retrieval, which usually targets the problem of retrieving a list of candidate 3D models using a single sketch as input. 2D scene sketch-based 3D scene retrieval is a brand new research topic in the field of 3D object retrieval. Unlike traditional sketch-based 3D model retrieval which ideally assumes that a query sketch contains only a single object, this is a new 3D model retrieval topic within the context of a 2D scene sketch which contains several objects that may overlap with each other and thus be occluded and also have relative location configurations. It is challenging due to the semantic gap existing between the iconic 2D representation of sketches and more accurate 3D representation of 3D models. But it also has vast applications such as 3D scene reconstruction, autonomous driving cars, 3D geometry video retrieval, and 3D AR/VR Entertainment. Therefore, this research topic deserves our further exploration.To promote this interesting research, we organize this SHREC track and build the first 2D scene sketch-based 3D scene retrieval benchmark by collecting 3D scenes from Google 3D Warehouse and utilizing our previously proposed 2D scene sketch dataset Scene 250. The objective of this track is to evaluate the performance of different 2D scene sketch-based 3D scene retrieval algorithms using a 2D sketch query dataset and a 3D Warehouse model dataset. The benchmark contains 250 scene sketches and 1000 3D scene models, and both are equally classified into 10 classes. In this track, six groups from five countries (China, Chile, USA, UK, and Vietnam) have registered for the track, while due to many challenges involved, only 3 groups have successfully submitted 8 runs. The retrieval performance of submitted results has been evaluated using 7 commonly used retrieval performance metrics. We also conduct a thorough analysis and discussion on those methods, and suggest several future research directions to tackle this research problem. We wish this publicly available [YLL18] benchmark, comparative evaluation results and corresponding evaluation code, will further enrich and advance the research of 2D scene sketch-based 3D scene retrieval and its applications.\",\n \"Title: Accurate Per-Flow Measurement With Bloom Sketch\\nYear: 2018\\n\\nAbstract: Sketch is a probabilistic data structure, and is widely used for per-flow measurement in network. The most common sketches are the CM sketch and its several variants. However, given a limited memory size, these sketches always significantly overestimate some flows, exhibiting poor accuracy. To address this issue, we proposed a novel sketch named the Bloom sketch, combining the sketch with the Bloom filter, another well-known probabilistic data structure widely used for membership queries. Extensive experiments based on real IP traces show that our Bloom sketch achieves up to 14:47x higher accurac y compared with the CM sketch, while exhibiting comparable insertion and query speed. Our source code is available at Github [1].\",\n \"Title: Elastic sketch: adaptive and fast network-wide measurements.\\nYear: 2018\\n\\nAbstract: When network is undergoing problems such as congestion, scan attack, DDoS attack, etc., measurements are much more important than usual. In this case, traffic characteristics including available bandwidth, packet rate, and flow size distribution vary drastically, significantly degrading the performance of measurements. To address this issue, we propose the Elastic sketch. It is adaptive to currently traffic characteristics. Besides, it is generic to measurement tasks and platforms. We implement the Elastic sketch on six platforms: P4, FPGA, GPU, CPU, multi-core CPU, and OVS, to process six typical measurement tasks. Experimental results and theoretical analysis show that the Elastic sketch can adapt well to traffic characteristics. Compared to the state-of-the-art, the Elastic sketch achieves 44.6 ∼ 45.2 times faster speed and 2.0 ∼ 273.7 smaller error rate.\",\n \"Title: Resource scheduling optimisation algorithm for containerised microservice architecture in cloud computing.\\nYear: 2018\\n\\nAbstract: Currently, the containerised microservice architecture has aroused great concern. The single application is developed as a suite of small services to facilitate the application deployment, expansion and management. The traditional scheduling of microservice tends to focus on the load balancing of cluster, ignoring the quality of service (QoS). Therefore, this paper proposes a prediction model of component relevance, by adopting the optimised artificial bee colony algorithm (OABC) on the containerised microservice scheduling. Different assessment strategies are adopted according to the differences in the correlation among components. Two-point crossover operator is introduced to improve the exploration ability of the algorithm. The mutation operator is added to enhance the local search ability, and the mutation probability is set to the dynamic value which varies with the number of iterations to speed up the convergence of the algorithm. The experimental results show that the OABC is preferable to the arti...\",\n \"Title: Local and global temporal correlations for longitudinal data.\\nYear: 2018\\n\\nAbstract: Quantifying the association between a pair of random functions recorded over a time period is of general interest. Several methods have been proposed in the literature but they either suffer from an ill-posed problem intrinsic to functional data or are suitable only for intensely recorded longitudinal data. In this paper we provide new methods that overcome these challenges by investigating the temporal Pearson correlation between paired random functions. We investigate both a local temporal correlation measure and a global summary measure of the dynamic temporal correlations and propose a nonparametric estimation method that covers both intensely observed and sparsely observed longitudinal data. Asymptotic results of the estimators are derived under mild conditions and the method is illustrated via simulations and a benchmark data set.\",\n \"Title: Frame Level Quantization Control with Temporal Distortion Propagation Model for Video Coding.\\nYear: 2018\\n\\nAbstract: In video coder, inter-frame prediction causes distortion propagation among temporally adjacent frames, which complicates frame level bit allocation and quantization control. Quantization parameter cascading (QPC) is generally employed to determine a sequence of quantization parameter for dependent rate distortion optimization (RDO). This paper proposes a general framework for temporal dependency analysis by lever-aging a distortion propagation model. The amount of distortion propagated from the temporally adjacent frames is measured by tree-style dependent analysis. Then, a trellis comprised of frame level quantization parameters of one GOP is constructed to achieve global optimization via branch-prune based dynamic programming. The simulation results verify that the frame level QPC algorithm with the proposed distortion model achieves up to 1.2dB—1.5dB PSNR improvement on average, with smaller temporal distortion fluctuation contributed by efficient bit allocation.\",\n \"Title: Fine-Level Semantic Labeling of Large-Scale 3D Model by Active Learning\\nYear: 2018\\n\\nAbstract: Semantic labeling of 3D models has been a challenging task in recent years. Due to the various categories and shapes of 3D objects in different scenes, it is hard to develop a versatile method suitable for most scenes. In this paper, we propose an Active Learning based method to tackle the problem. The proposed method takes a 3D mesh model generated from images using SfM and MVS, as well as the calibrated images, as the input, and outputs a semantic mesh model in which each facet takes a fine-level semantic label. Starting with a small annotated image set, we progressively fine-tune a Convolutional Neural Network (CNN) with the ever-enlarging annotated image set for image semantic segmentation. In each iteration, by back-projecting the pixel labels to the 3D model and fusing them in 3D space, a semantic 3D model is generated. The semantic 3D model functions as a supervisor to select a batch of worthy images for annotation to boost the performance of the CNN in next iteration. This process iterates until the label assignment of the 3D model becomes steady. By making full use of the 3D geometric information, the proposed method could significantly reduce the annotation cost without losing the labeling quality of 3D models. Experimental results of fine-level labeling on two large-scale ancient Chinese architectures demonstrate the effectiveness of the proposed method.\",\n \"Title: Accelerating network measurement in software.\\nYear: 2018\\n\\nAbstract: Network measurement plays an important role for many network functions such as detecting network anomalies and identifying big flows. However, most existing measurement solutions fail to achieve high performance in software as they often incorporate heavy computations and a large number of random memory accesses. We present Agg-Evict, a generic framework for accelerating network measurement in software. Agg-Evict aggregates the incoming packets on the same flows and sends them as a batch, reducing the number of computations and random memory accesses in the subsequent measurement solutions. We perform extensive experiments on top of DPDK with 10G NIC and observe that almost all the tested measurement solutions under Agg-Evict can achieve 14.88 Mpps throughput and see up to 5.7X lower average processing latency per packet.\",\n \"Title: Sino-InSpace: A Digital Simulation Platform for Virtual Space Environments.\\nYear: 2018\\n\\nAbstract: The implementation of increased space exploration missions reduces the distance between human beings and outer space. Although it is impossible for everyone to enter the remote outer space, virtual environments could provide computer-based digital spaces that we can observe, participate in, and experience. In this study, Sino-InSpace, a digital simulation platform, was developed to support the construction of virtual space environments. The input data are divided into two types, the environment element and the entity object, that are then supported by the unified time-space datum. The platform adopted the pyramid model and octree index to preprocess the geographic and space environment data, which ensured the efficiency of data loading and browsing. To describe objects perfectly, they were abstracted and modeled based on four aspects including attributes, ephemeris, geometry, and behavior. Then, the platform performed the organization of a visual scenario based on logical modeling and data modeling; in addition, it ensured smooth and flexible visual scenario displays using efficient data and rendering engines. Multilevel modes (application directly, visualization development, and scientific analysis) were designed to support multilevel applications for users from different grades and fields. Each mode provided representative case studies, which also demonstrated the capabilities of the platform for data integration, visualization, process deduction, and auxiliary analysis. Finally, a user study with human participants was conducted from multiple views (usability, user acceptance, presence, and software design). The results indicate that Sino-InSpace performs well in simulation for virtual space environments, while a virtual reality setup is beneficial for promoting the experience.\",\n \"Title: Pair-wise Exchangeable Feature Extraction for Arbitrary Style Transfer.\\nYear: 2018\\n\\nAbstract: Style transfer has been an important topic in both computer vision and graphics. Gatys et al. first prove that deep features extracted by the pre-trained VGG network represent both content and style features of an image and hence, style transfer can be achieved through optimization in feature space. Huang et al. then show that real-time arbitrary style transfer can be done by simply aligning the mean and variance of each feature channel. In this paper, however, we argue that only aligning the global statistics of deep features cannot always guarantee a good style transfer. Instead, we propose to jointly analyze the input image pair and extract common/exchangeable style features between the two. Besides, a new fusion mode is developed for combining content and style information in feature space. Qualitative and quantitative experiments demonstrate the advantages of our approach.\",\n \"Title: Probabilistic Rank-One Discriminant Analysis via Collective and Individual Variation Modeling.\\nYear: 2020\\n\\nAbstract: Linear discriminant analysis (LDA) is a classical supervised subspace learning technique that has wide applications. However, it is designed for vector only, which cannot exploit the tensor structures and may lead to suboptimal results when dealing with tensorial data. To address this problem, several multilinear LDA (MLDA) methods have been proposed to learn the subspaces from tensors. By exploiting the tensor structures, they achieve compact subspace representations, reduced parameter sizes, and improved robustness against the small sample size problem. However, existing MLDA methods do not take data uncertainty into account, fail to converge properly, or have to introduce additional tuning parameters for good convergence properties. In this paper, we therefore solve these limitations by proposing a probabilistic MLDA method for matrix inputs. Specifically, we propose a new generative model to incorporate structural information into the probabilistic framework, where each observed matrix is represented as a linear combination of collective and individual rank-one matrices. This provides our method with both the expressiveness of capturing discriminative features and nondiscriminative noise, and the capability of exploiting the 2-D tensor structures. To overcome the convergence problem of existing MLDAs, we develop an EM-type algorithm for parameter estimation, which has closed-form solutions with convergence guarantees. Experimental results on real-world datasets show the superiority of the proposed method to other probabilistic and MLDA variants.\",\n \"Title: A class of optimal estimators for the covariance operator in reproducing kernel Hilbert spaces.\\nYear: 2019\\n\\nAbstract: The covariance operator plays an important role in modern statistical methods and is critical for inference. It is most often estimated by the empirical covariance operator. In spite of its simple and appealing properties, however, this estimator can be improved by a class of shrinkage operators. In this paper, we study shrinkage estimation of the covariance operator in reproducing kernel Hilbert spaces. A data-driven shrinkage estimator enjoying desirable theoretical and computational properties is proposed. The procedure is easily implemented and its numerical performance is investigated through simulations. In finite samples, the estimator outperforms the empirical covariance operator, especially when the data dimension is much larger than the sample size. We also show that the rate of convergence in Hilbert–Schmidt norm is of the order n−1∕2. Furthermore, we establish the minimax optimal rate of convergence over suitable classes of probability measures and demonstrate that these shrinkage operators are all minimax rate-optimal.\",\n \"Title: A Context-Awareness Positioning Scheme In Hospital Wlan Environment\\nYear: 2018\\n\\nAbstract: Positioning technologies can be generally divided into indoor and outdoor application scenarios. This paper focuses on an indoor positioning system which is able to be used in the Wireless Local Area Network environment in the hospitals. Compared with traditional techniques, the newly-proposed positioning scheme takes user's context into consideration, using Radio Frequency Fingerprint Vector (RFFV) as the signal feature to identify and detect the location of the user in the hospital. Besides, space and time factors are considered as the constraints to reduce the positioning errors. Experiment result in hospital environment proves the performance of the scheme is better than those traditional schemes without error correction thus can successfully judge patient's activity and prevent some accidents.\",\n \"Title: An Extension of Yuan’s Lemma to Fourth-Order Tensor System\\nYear: 2019\\n\\nAbstract: Yuan’s lemma is a basic proposition on homogeneous quadratic function systems. In this note, we extend Yuan’s lemma to the fourth-order tensor system. We first give generalized definition of positive semidefinite of fourth-order tensor, and based on it, an extension of Yuan’s lemma is proposed. As an application, we establish the strong duality result of a class of quadratic semidefinite programming problems using the extended Yuan’s lemma.\",\n \"Title: A Security Reinforcement Technology of Telematics Box Based on Secure Element.\\nYear: 2018\\n\\nAbstract: The telematics box is a vehicle communication terminal in the Internet of Vehicles, used to collect data from electronic control units and transfer data between users and application server. With the rapid development of Internet of Vehicles, T-Box provides users personalized service and abundant entertainment. However, it also becomes easy to cause security problems, such as disclosure of private information of owners, illegal control of vehicles and so on. Therefore, it is very important to reinforce the security of T-Box. To reinforce the security of T-Box, this paper will present a security reinforcement technology, which uses a secure element and traditional security measures to implements secure boot based on TrustZone and security service. This paper designs a software hierarchical model of security service based on the research of AUTOSAR and make innovative improvements to Transport Layer Security. Besides, to validate the feasibility of the solution, this paper also implements a security reinforcement technology and verifies the validity of it.\",\n \"Title: Adaptive LSB quantum watermarking method using tri-way pixel value differencing\\nYear: 2019\\n\\nAbstract: As an important way to protect copyright by embedding watermark in digital images, quantum watermarking catches more and more attentions. In this study, a novel quantum watermarking method on the basis of tri-way pixel value differencing and modified least significant bit (LSB) substitution is proposed. A quantum cover image using the novel-enhanced quantum image representation is partitioned into non-overlapping 2 × 2 blocks with four pixels firstly. To classify the block as a smooth area or an edge area, the tri-way pixel value differences are calculated and compared with a predefined threshold. The quantum watermark image, which is expanded and scrambled, is then embedded into a quantum cover image by the k-bit LSB substitution method, where k is decided by the level of each block. The embedded quantum watermark can be extracted from the quantum stego-image without the assistance of original quantum cover image. Theoretical analysis and simulation-based experiments demonstrate both the feasibility and capabilities of the proposed quantum watermarking method, which has good visual quality, better robustness, and higher security.\",\n \"Title: Classical Formula Ontology Construction and Application in the Diagnosis and Treatment of Dermatosis\\nYear: 2018\\n\\nAbstract: The knowledge discovery and effective using are insufficient in the study of classical formula which needs to be further mined. In order to solve the problem, this paper constructed classical formula Ontology based on knowledge processing, and realized the sharing and reuse of classical formula knowledge. Finally, this paper constructed the mathematical model in the diagnosis and treatment of dermatosis based on the classical formula corpus and Ontology. Our purpose was to perfect dermatologist's different levels in diagnosing and treating dermatosis, improve the level and accuracy of clinical diagnosis and treatment, and achieve effective drive of classical formula literatures for clinical diagnosis and treatment.\",\n \"Title: Evaluation of a Wi-Fi Signal Based System for Freeway Traffic States Monitoring: An Exploratory Field Test.\\nYear: 2019\\n\\nAbstract: Monitoring traffic states from the road is arousing increasing concern from traffic management authorities. To complete the picture of real-time traffic states, novel data sources have been introduced and studied in the transportation community for decades. This paper explores a supplementary and novel data source, Wi-Fi signal data, to extract traffic information through a well-designed system. An IoT (Internet of Things)-based Wi-Fi signal detector consisting of a solar power module, high capacity module, and IoT functioning module was constructed to collect Wi-Fi signal data. On this basis, a filtration and mining algorithm was developed to extract traffic state information (i.e., travel time, traffic volume, and speed). In addition, to evaluate the performance of the proposed system, a practical field test was conducted through the use of the system to monitor traffic states of a major corridor in China. The comparison results with loop data indicated that traffic speed obtained from the system was consistent with that collected from loop detectors. The mean absolute percentage error reached 3.55% in the best case. Furthermore, the preliminary analysis proved the existence of the highly correlated relationship between volumes obtained from the system and from loop detectors. The evaluation confirmed the feasibility of applying Wi-Fi signal data to acquisition of traffic information, indicating that Wi-Fi signal data could be used as a supplementary data source for monitoring real-time traffic states.\",\n \"Title: Tank Container Operators' profit maximization through dynamic operations planning integrated with the quotation-booking process under multiple uncertainties.\\nYear: 2019\\n\\nAbstract: •Dynamic operations planning integrated with quotation-booking process.•Multiple uncertainties arising from tank cleaning and transport service reliability.•Planned leasing and emergent leasing for additional tank container capacity.•Simulation-based two-stage optimization model proposed.•Solution procedure combining multiple techniques to tackle dynamic planning horizon.\",\n \"Title: Optimizing New User Experience in Online Services.\\nYear: 2018\\n\\nAbstract: \\\"Well begun is half done\\\". This proverb is especially true for a web product when it comes to creating a delightful and proactive user experience. This article describes our work in the last few years optimizing new user experience at LinkedIn, driven by application of data science and advanced analytical methods. Through mining the logs generated by new users in the past, we uncovered signals from their initial session that can predict their retention. We established the difference between creating models for predictions and creating models to inform product strategy. We found that persistent features, such as a user's number of connections, and having a confirmed channel of communication (email, phone or app), more strongly predict new user retention than most transient features such as how long they spend on the registration form or how many page views they have visited. We further constructed a true north metric (Quality Signup) to drive our Growth team towards the right focus as they iterated through multiple versions of new user onboarding flows. The strong positive correlation between the Quality Signup metric and long-term retention, as well as the positive impact we have seen on the product over the last two years, validate our strategy to drive product roadmap through data-informed metrics.\",\n \"Title: A robot state estimator based on multi-sensor information fusion\\nYear: 2018\\n\\nAbstract: Mobile robot technology plays an important role in the service and manufacturing industries. The mobile robot technology includes a series of key technologies, such as robot localization, navigation and path planning. Furthermore, the state estimation problem of the robot is more critical. The state estimation of the robot mainly includes the calculation of the parameters such as the robot's position, attitude and the speed. The accuracy of the state estimation of the robot will greatly affect the working effect of the robot, including the construction of the environment map, and navigation, path planning, and so on. In recent years, a large number of scholars and experts have carried out a lot of research on the state estimation of robots. They have used various methods to estimate the state of robots. The sensors commonly used for acquiring robot state information mainly include odometers, inertial measurement units, and lasers, cameras. The characteristics of various sensors are different. Considering the complementary characteristics of sensors, this paper mainly uses multi-sensor information for estimating robot status. The extended Kalman filter is used to fuse the information coming from the camera, odometer, inertial measurement unit, and laser to estimate the robot state.\",\n \"Title: Experimental study of the effect of light source spot size on measure error of PSD.\\nYear: 2019\\n\\nAbstract: The energy distribution of the incident light spot on position-sensitive detector (PSD) screen is uneven and the output coordinate value is the energy centre of the light spot. Measurement error caused by the deviation between the geometric and gravity centre of the light spot will occur. The deviation will increase with the increase of the diameter of the light spot. There is no quantitative research on the position error caused by the changes of the light spot diameter and the distance from the light spot to the PSD. To evaluate the magnitude of the error, the infrared spotlight is selected as the target light. The changing pattern that the spot diameter varies with the distance between the light source and lens is studied. Additionally, the effect of the spot size on the PSD measure error is studied. The research provides reference for error evaluation of the distance and position detection of PSD. [Submitted 5 July 2017; Accepted 24 December 2017]\",\n \"Title: Video Super-Resolution via Dynamic Local Filter Network\\nYear: 2018\\n\\nAbstract: Conventional Convolutional Neural Network (CNN) based video super-resolution (VSR) methods heavily depend on explicit motion compensation. Input frames are warped according to flow-like information to eliminate inter-frame differences. These methods have to make a trade-off between the distraction caused by spatio-temporal inconsistency and the pixel-wise detail damage caused by compensation. In this paper, we propose a novel video super-resolution method based on dynamic local filter network. Unlike traditional VSR techniques, our method implicitly performs motion estimation, compensation and fusion simultaneously via local convolutions with dynamically generated filter kernels. An optional autoencoder based refinement module is also proposed to sharpen edges and remove artifacts. The experimental results demonstrate that our method outperforms the best existing VSR algorithm by 0.53 dB in terms of PSNR, and provides superior visual quality.\",\n \"Title: A New Method With Hilbert Transform and Slip-SVD-Based Noise-Suppression Algorithm for Noisy Power Quality Monitoring\\nYear: 2019\\n\\nAbstract: This paper presents a new method for automatic monitoring of noisy power quality, which is based on the Hilbert transform (HT) and the proposed slip-singular value decomposition (SVD)-based noise-suppression algorithm. The proposed method first employs the fast Fourier transform (FFT)-based low-pass filter and HT to obtain the instantaneous fundamental amplitude and the FFT sequence of the signal. Second, the slip-SVD-based noise-suppression algorithm and threshold filtering are used to extract cleaned singular value characteristic waveform of the high-frequency signal. Through judging the instantaneous fundamental amplitude, cleaned singular value characteristic waveform, and the FFT sequence, the presence of disturbances including single and combined disturbances can be easily detected by the proposed method. To demonstrate the effectiveness of the proposed method, extensive tests are conducted on the diverse simulation disturbances and the actual data obtained from the practical power systems of China. The test results show that the proposed method has the advantages such as low false detection rate, good noise tolerance capability, short computational time, fewer parameters, practicability, and compatibility in comparison with the traditional disturbance detection methods. Besides, the proposed method can provide some important features such as amplitude, duration, and frequency for classification. Such advantages make the proposed method to be a good choice for real-time applications.\",\n \"Title: Lightweight Indexing and Querying Services for Big Spatial Data\\nYear: 2019\\n\\nAbstract: With the widespread use of GPS-equipped smartphones and Internet of Things devices, a huge amount of data with location information is being generated at an unprecedented rate. To gain a deeper insight into such a plethora of spatial data, scientists and engineers are widely using spatial queries for their big data applications. However, because of not only the massive spatial data size but also t...\",\n \"Title: EMC: Energy-Aware Morphable Cache Design for Non-Volatile Processors\\nYear: 2019\\n\\nAbstract: Wearable, implantable and Internet of Things devices are attracting increasing attention from both research and industry fields. Energy harvesting is a promising alternative of battery to power these embedded systems. However, the intrinsic instability of energy harvesting systems leads to potential frequent power interruptions. In traditional volatile processor, all the status will be lost at power failures and the program needs to re-start after power resumes. In order to survive the power failures and enable accumulative execution, non-volatile processor (NVP) is proposed to back up volatile information before power depletion and recover the system status after power resumes. Non-volatile memory (NVM) is typically attached for cache and main memory backup. There are researches working on optimization of the backup. However, little of them involve multiple level cell (MLC) NVM. In this work, we first discuss the benefit of applying MLC NVM for cache backup and the architecture of morphable hybrid cache, and then propose a three-stage energy-aware cache management strategy to improve the system performance and energy utilization while guaranteeing successful backups. Backup-aware cache replacement policies are also developed for backup optimization. Evaluation shows that the proposed EMC scheme can achieve 10.6 percent performance improvement and simultaneous 25.2 percent energy reduction when compared with the single level cell (SLC) based hybrid cache.\",\n \"Title: Monitoring Landslides Using Multi-frequency SAR Data in Danba County, Sichuan Province, China.\\nYear: 2016\\n\\nAbstract: Danba County, located at the northwestern Sichuan, is one of the areas prone to severe landslides in China. The landslides in this area present great threaten upon the local public safety and the famous heritage architectures. Therefore, monitoring landslide is of great importance for the sustainable developments in Danba. In this paper, InSAR techniques is employed to investigate typical landslide activities, based on the multi-frequency SAR images acquired from C-band Envisat, X-band TerraSAR-X, and L-band ALOS-1/2 satellites. Firstly, differential InSAR (D-InSAR) is used to recognize known landslides and find potential unstable slopes in a region scale. Then, for a specific landslide, advanced multi-temporal InSAR method is exploited to characterize its surface deformation by obtaining time-series displacement on coherent targets. Furthermore, the triggering factors are discussed based on the deformation results and on-site surveys.\",\n \"Title: A biologically inspired model based on a multi-scale spatial representation for goal-directed navigation.\\nYear: 2017\\n\\nAbstract: Inspired by the multi-scale nature of hippocampal place cells, a biologically inspired model based on a multi-scale spatial representation for goal-directed navigation is proposed in order to achieve robotic spatial cognition and autonomous navigation. First, a map of the place cells is constructed in different scales, which is used for encoding the spatial environment. Then, the firing rate of the place cells in each layer is calculated by the Gaussian function as the input of the Q-learning process. The robot decides on its next direction for movement through several candidate actions according to the rules of action selection. After several training trials, the robot can accumulate experiential knowledge and thus learn an appropriate navigation policy to find its goal. The results in simulation show that, in contrast to the other two methods(G-Q, S-Q), the multi-scale model presented in this paper is not only in line with the multi-scale nature of place cells, but also has a faster learning potential to find the optimized path to the goal. Additionally, this method also has a good ability to complete the goal-directed navigation task in large space and in the environments with obstacles.\",\n \"Title: Development and Usage of Chinese Medicine Supporting System Based on Post-Controlled Machinery\\nYear: 2009\\n\\nAbstract: This article makes deep study on Chinese medicine literature supporting system’s development and its function’s realization ,which is established on ancient Chinese medicine literature ’s manner of writing and structure and focused on design theory, technology index and function. In this sense this article makes good try to research on method and model of Chinese ancient technology literature’s digitalizing, which could supply suggestion for the other relative scholarships’ deep study.\",\n \"Title: Communication Distance Correlates Positively with Electrode Distance in Underwater Electrocommunication\\nYear: 2019\\n\\nAbstract: Underwater wireless communication technology has been restricting the development of undersea resources for decades. We have proposed an effective underwater communication method (electrocommunication) for small underwater robots in our previous studies. In this article, we further study how electrode distance/spacing affects the communication range of electrocommunication between the robotic fish models. The working principle of electrocommunication is simplified by an electric dipole model. Based on the electric dipole model, the maximum theoretical distance of electrocommunication is derived as r=K√[3]d2 where d2 is the distance between the receiving electrodes, and K is a constant related to the system and environmental parameters. Extensive experiments were conducted by varying the distance between the transmitting electrodes. The results show that the maximum experimental distance is basically consistent with the theoretical predictions, and that the communication distance is positively correlated with the electrode distance. © 2018 IEEE.\",\n \"Title: Automatically difficulty grading method of \\\"instruction system\\\" question bank based on knowledge tree.\\nYear: 2017\\n\\nAbstract: The aim of this study is to propose a model, which can automatically grade difficulty for a question from \\\\\\\"Instruction System\\\\\\\" question bank. The system mainly uses attributes which are employed to be input. A knowledge tree model which was established based on the proper nouns from Chinese \\\\\\\"Instruction System\\\\\\\" teaching material and a machine learning algorithm are utilized as important parts for classification. The experimental dataset comes from our built \\\\\\\"Principles of Computer Organization\\\\\\\" online education system, the accuracy result of difficulty classification could be 79.41% which is much higher than the accuracy of random guess 50%.\",\n \"Title: Detail Preserved Surface Reconstruction from Point Cloud.\\nYear: 2019\\n\\nAbstract: In this paper, we put forward a new method for surface reconstruction from image-based point clouds. In particular, we introduce a new visibility model for each line of sight to preserve scene details without decreasing the noise filtering ability. To make the proposed method suitable for point clouds with heavy noise, we introduce a new likelihood energy term to the total energy of the binary labeling problem of Delaunay tetrahedra, and we give its s-t graph implementation. Besides, we further improve the performance of the proposed method with the dense visibility technique, which helps to keep the object edge sharp. The experimental result shows that the proposed method rivalled the state-of-the-art methods in terms of accuracy and completeness, and performed better with reference to detail preservation.\",\n \"Title: 600-Ghz-Wave Beam Steering By Terahertz-Wave Combiner\\nYear: 2018\\n\\nAbstract: We achieved 600-GHz-wave power combination with directional gain by optically phase tuning of arrayed photomixers. Using this phase control technique for phase shift, we also demonstrated the beam steering within an angle of 35 degrees.\",\n \"Title: Learning and evaluation of latent dependency forest models\\nYear: 2019\\n\\nAbstract: Latent dependency forest models (LDFMs) are a new type of probabilistic models with dynamic dependency structures over random variables. They distinguish themselves from other probabilistic models by the fact that there is no need for structure search when learning the models. However, parameter learning of LDFMs is still quite challenging since the partition function cannot be tractably calculated. In this paper, we investigate and empirically compare several algorithms of learning parameters of LDFMs which either approximate or ignore the partition function in the learning objective. Furthermore, we propose an approximate algorithm to estimate the partition function of LDFM. Experimental results show that (1) our learning algorithms can achieve better results than the previous learning algorithm of LDFMs, and (2) our partition function estimation algorithm is accurate.\",\n \"Title: Learning and evaluation of latent dependency forest models\\nYear: 2019\\n\\nAbstract: Latent dependency forest models (LDFMs) are a new type of probabilistic models with dynamic dependency structures over random variables. They distinguish themselves from other probabilistic models by the fact that there is no need for structure search when learning the models. However, parameter learning of LDFMs is still quite challenging since the partition function cannot be tractably calculated. In this paper, we investigate and empirically compare several algorithms of learning parameters of LDFMs which either approximate or ignore the partition function in the learning objective. Furthermore, we propose an approximate algorithm to estimate the partition function of LDFM. Experimental results show that (1) our learning algorithms can achieve better results than the previous learning algorithm of LDFMs, and (2) our partition function estimation algorithm is accurate.\",\n \"Title: A Three-Dimensional Elasto-Plastic Dynamic Response Procedure For Concrete-Faced Rockfill Dams\\nYear: 2018\\n\\nAbstract: Equivalent linear dynamic analysis is the primary method of analysis for concrete-faced rockfill dams during earthquake. However, this method cannot be directly used to estimate permanent deformation during earthquake, which is important to evaluate the dynamic safety of concrete-faced rockfill dams. To bridge this gap, an elasto-plastic dynamic response procedure based on finite element method is presented to estimate the construction, impoundment, and dynamic characteristics of concrete-faced rockfill dams. This procedure involves a generalized plasticity model for rockfill materials and an ideal elasto-plastic model for interface between concrete face slab and rockfill material. The construction, impounding process, and seismic behavior of a 150-m-tall concrete-faced rockfill dam were simulated using seismic motion based on Chinese code for seismic design of hydraulic structures of hydropower project with the peak ground acceleration of 0.3g as an excitation to validate the procedure. On the basis of the numerical simulation results of the step-by-step construction, reservoir impoundment and seismic response of a concrete-faced rockfill dam, dam settlement, joint deformation, stress, and deformation of slab were analyzed. The results indicate that the dam settlement and deformation of joint can be directly obtained by the procedure during earthquake. The construction, impoundment, and seismic analysis could be considered in one procedure.\",\n \"Title: Multi-Island Genetic Algorithm and Kriging Model-Based Design of Vehicle Product Comprising Multi-Material.\\nYear: 2018\\n\\nAbstract: In recent years, improvement in multi-material additive manufacturing technology has resulted in technical improvements in multi-material design employed by the automotive industry. Therefore, in this work, an internal trim part (corresponding to an original product composed of polypropylene) of a vehicle was divided into four components using a multi-material design method considering PLA composites. The PLA was reinforced with basalt fibers for realization of the required mechanical properties. The mechanical properties associated with different fiber content (from 0 to 60%) were determined via tensile tests. To reduce the mass of the product, an optimization process combining a Kriging surrogate model with a Multi-Island Genetic Algorithm was used to search for the Pareto solution. The coefficient of determination (R-2) and response surface methodology (RSM) surrogate model were used to confirm the validity and accuracy of the Kriging model. The values of R-2 were all >0.92 and the low error value of both results demonstrated the effectiveness of the optimization process. Owing to the optimization process, the mass of the PLA composite product was reduced by nearly 9%. Correlation analysis indicated that x(2) has the strongest impact on the total mass. Therefore, the optimization process proposed for the multi-material optimal design is feasible and contributes significantly to the attainment of light-weight vehicle parts.\",\n \"Title: Dynamic Responses Of Concrete-Faced Rockfill Dam Due To Different Seismic Motion Input Methods\\nYear: 2018\\n\\nAbstract: The concrete-faced rockfill dam valley foundation was considered as an open energy system and a reasonable non-uniform seismic motion input method was applied to the dynamic analysis of a concrete-faced rockfill dam based on the generalized plastic model. First, the corresponding program was validated by means of the scattering question of an idealized semicircle valley. Subsequently, the seismic elasto-plastic finite element analyses were performed to compare and investigate the performances of a concrete-faced rockfill dam under different seismic motion input methods. The results show that the dynamic responses of the concrete-faced rockfill dam are decreased by 10%-30% approximately with the use of non-uniform seismic motion input method. As a result, the traditional uniform seismic motion input method would overestimate the responses of the dam. From the perspective of seismic safety evaluation, the overestimations would disturb the reasonable assessment of the aseismic capacity of the dam. Moreover, the slope stability analysis results might be conservative and unreasonable due to overestimating the accelerations during the earthquake.\",\n \"Title: Least-Element Time-Stepping Methods for Simulation of Linear Networks with Ideal Switches\\nYear: 2019\\n\\nAbstract: Linear networks with ideal switches have various applications in power converters, signal processing and control problems, which can be modeled by linear complementarity systems (LCSs). This paper presents new results on the least-element time-stepping method for simulation of linear networks with ideal switches for a class of LCSs. The method is efficient and stable and can be easily implemented. The convergence results and preliminary numerical results show that the least-element time-stepping method is efficient for verifying accuracy of approximate solutions.\",\n \"Title: Blind screen content image quality measurement based on sparse feature learning.\\nYear: 2019\\n\\nAbstract: Recently, the perceived quality measurement of screen content images (SCIs) has become an active research topic. In this paper, a blind image quality measurement (IQM) metric for SCIs based on the learning of sparse features via dictionary learning is proposed. First, to extract the sparse features, histogram representations from multi-scale local gradient patterns are integrated to form a dictionary. Subsequently, using a pursuit algorithm, the sparse features of the distorted SCIs are efficiently coded by this dictionary. Finally, to obtain the final quality of the distorted SCIs, a machine learning algorithm is utilised to combine the sparse features into a final quality score. The results of extensive simulations conducted show that the proposed blind IQM metric consistently obtains competitive performance and is in line with human beings perceive.\",\n \"Title: Research on Container Load Strategy Based on Microservices\\nYear: 2018\\n\\nAbstract: For the problems of unbalanced load and long service response time in microservice-based container environments, this paper designs a load model that is easy to expand and maintain, which combines the service discovery of microservices, performance monitoring of servers and containers, and container load balancing, and as for the shortcomings of load balancing algorithm-the premature convergence of Ant Colony algorithm as well as the shortcomings of the load balancing algorithm in springcloud-the long service response time of Minimum Concurrency Algorithm, it proposes an Optimized Ant Colony algorithm (OACO), OACO optimizes the Ant Colony algorithm by introducing a fuse mechanism and an open circuit recovery mechanism. The simulation results show that the Optimized Ant Colony algorithm (OACO) has advantages in load balance and service response time over Ant Colony algorithm and the service response time is improved with respect to Minimum Concurrency Algorithm.\",\n \"Title: Probabilistic Rank-One Tensor Analysis With Concurrent Regularizations.\\nYear: 2021\\n\\nAbstract: Subspace learning for tensors attracts increasing interest in recent years, leading to the development of multilinear extensions of principal component analysis (PCA) and probabilistic PCA (PPCA). Existing multilinear PPCAs are based on the Tucker or CANDECOMP/PARAFAC (CP) models. Although both kinds of multilinear PPCAs have shown their effectiveness in dealing with tensors, they also have their ...\",\n \"Title: Projective Latent Dependency Forest Models\\nYear: 2019\\n\\nAbstract: Latent dependence forest models (LDFM) are a new type of probabilistic models with the advantage of not requiring the difficult procedure of structure learning in model learning. However, normalized joint probability computation and marginal inference are intractable for LDFM. In this paper, we proposed projective LDFMs (PLDFMs), a variant of LDFM, for which joint and marginal probabilities become tractable (cubic time with respect to the number of random variables) to compute while learning remains easy. We show that PLDFMs can be seen as a special case of sum-product networks (SPNs). We then propose sum-product projective dependence networks, a combination of PLDFMs and SPNs that scales up to a large number of random variables. Our extensive experiments on 29 datasets show that our models achieve competitive results with other probabilistic models.\",\n \"Title: An Efficient and Reliable QoF Routing for Urban VANETs With Backbone Nodes.\\nYear: 2019\\n\\nAbstract: Because of the dynamic topology, high mobility of nodes, and complicated channel environments in urban areas, existing routings methods are susceptible to frequent link interruptions and channel congestions. To address these issues, a Quality of Forwarding (QoF)-based reliable geographic routing (QFRG) in urban vehicular ad hoc networks (VANETs) is proposed, where the best route is determined by guaranteeing the QoF and satisfying the link reliability requirement. Two theoretical models for QoF and link reliability analysis are first presented. Taking into consideration the transmission cost and the packet delivery ratio, the QoF is, then, employed to provide the quantitative evaluation to the road segments through the presented road weight evaluation (RWE) scheme, which takes into account the impact of the relative position of links on the network performance. Next, to accommodate the network scale of an urban city, the city map is divided into smaller grid zones. Based on the position of the destination, different transmission strategies are presented for packet forwarding. The extensive simulations demonstrate our protocol's superiority in both transmission delay and packet delivery ratio compared with the existing schemes.\",\n \"Title: Some remarks on Jacobi and Gauss–Seidel-type iteration methods for the matrix equation AXB=C\\nYear: 2019\\n\\nAbstract: Tian, et al. proposed in [5] several Jacobi and Gauss–Seidel-type iterative methods for solving matrix equation AXB=C. Those methods were demonstrated to be effective by the given numerical experiments. However, we find that there is a technical error in the proof of the main theorem (Theorem 3.3). In this note we first show this erratum by an example. Then we establish a new convergence theorem which contains the Theorem 3.3 in [5] as a special case.\",\n \"Title: Deep blind quality evaluator for multiply distorted images based on monogenic binary coding\\nYear: 2019\\n\\nAbstract: Perceptual quality evaluation of multiply distorted images has become a very challenging research topic. In this paper, we present a novel and efficient deep blind quality evaluator for multiply distorted images based on monogenic binary coding (MBC). Local complementary structural information and a deep learning method are employed to blindly evaluate the quality of multiply distorted images. First, a monogenic signal representation is utilized to decompose a multiply distorted image into three complementary components: orientation, phase, and magnitude. The quality-predictive features are then determined from the complementary components. Finally, the features are mapped to the human quality score of the multiply distorted image based on the deep neural network. The results on two newly established multiply distorted image subjective databases confirm that our metric has a better prediction performance than existing state-of-the-art full-reference and classical blind metrics.\",\n \"Title: Bidirectional recurrent autoencoder for 3D skeleton motion data refinement\\nYear: 2019\\n\\nAbstract: •The proposed BRA can generate motion data that have much less reproduction error compared to the CNN architecture.•The proposed BRA does not require postprocessing procedure.•The proposed BRA does not require the noise amplitude as a priori knowledge.\",\n \"Title: Disruptive Online Service Innovation In The Presence Of Competition Among Disruptors: The Case Of 3rd-Party Online Payment\\nYear: 2019\\n\\nAbstract: While the competition between incumbent and disruptor has been well documented, the competition among disruptors is less well understood. This study aims to identify factors influenced the competitiveness of a disruptive online service innovation when there is a competition among disruptors. Focusing on the 3rd-party online payment, authors conducted a case study by interviewing 20 key figures in this field and reviewing a variety of published information resources. Findings indicate that the variations in competitiveness arise from three sources: 1) competition basis; 2) competition environment; 3) competition strategy. Important factors within these categories include the service's performance on security and availability, the regulations limited the business scope, the interrelationship with the members in the value network, and the strategies to build and protect the market share.\",\n \"Title: Bayesian Low-Tubal-Rank Robust Tensor Factorization with Multi-Rank Determination\\nYear: 2021\\n\\nAbstract: Robust tensor factorization is a fundamental problem in machine learning and computer vision, which aims at decomposing tensors into low-rank and sparse components. However, existing methods either suffer from limited modeling power in preserving low-rank structures, or have difficulties in determining the target tensor rank and the trade-off between the low-rank and sparse components. To address these problems, we propose a fully Bayesian treatment of robust tensor factorization along with a generalized sparsity-inducing prior. By adapting the recently proposed low-tubal-rank model in a generative manner, our method is effective in preserving low-rank structures. Moreover, benefiting from the proposed prior and the Bayesian framework, the proposed method can automatically determine the tensor rank while inferring the trade-off between the low-rank and sparse components. For model estimation, we develop a variational inference algorithm, and further improve its efficiency by reformulating the variational updates in the frequency domain. Experimental results on both synthetic and real-world datasets demonstrate the effectiveness of the proposed method in multi-rank determination as well as its superiority in image denoising and background modeling over state-of-the-art approaches.\",\n \"Title: Place Representation Based Bike Demand Prediction\\nYear: 2019\\n\\nAbstract: Bike-sharing service as an important public transportation system has grown over 1800% from 2014 to 2018 in worldwide. Accurate bike demand prediction of bike-sharing system is critical for increasing user satisfaction, improving the system efficiency, and expand bike stations to new regions. There are two major challenges for existing bike demand prediction methods: First, several current works heavily rely on historical bike transition records and can not predict bike demands for new stations. Second, people movement patterns as an important factor for bike demand prediction are not considered. To address these challenges, in this paper, we develop a place representation based bike demand prediction framework, which uses large scale people movement data to analyze bike demand. The framework is based on an origin-destination algorithm, which is a novel approach for learning latent representation of places, and a feature selection process to reduce place's embedding size to a proper number. Hourly bike check-ins/check-outs of each station is predicted by integrating three factors: place embedding, station geography, and POI distribution. Experiments on NYC CitiBike system show that compared with the state-of-the-art function zone based method, our model reduces error rate by 6% and root mean squared logarithmic error by 4% in prediction of existing bike station demand and by 6% and 5% for new stations in error rate and root mean squared logarithmic error respectively.\",\n \"Title: Trust Inference For Rideshare Through Co-Training On Social Media Data\\nYear: 2019\\n\\nAbstract: Ridesharing can save energy, reduce pollution, and alleviate congestion. These societal benefits can only occur, however, if sufficient trust is established between rideshare partners. Trust judgments heavily influence the usage of ridesharing platforms. Towards trust building, ridesharing systems are starting to allow drivers to provide personal information about themselves. Some previous work already built a trust prediction model using information from Instagram profiles. Their results indicated both social account information and picture information factors influenced judgments of trust of potential rideshare partners. In this paper, we advance these findings by describing a co-training approach that uses social account information and picture information as two independent views to predict trust judgments. Specifically, this analysis addresses the problem of accurate labeling of complex pictures from social media by using the hashtags of pictures for trust inference. We use a topic model to summarize the hashtags of pictures into a small number of groups called topics. The final results show that using hashtags as picture information yields a better result than the use of picture category. The co-training approach with social account information and hashtags outperforms supervised and semi-supervised classifiers. Often, in order to make an accurate trust prediction model, large amounts of expensive labeled training data are needed. The co-training approach described allows for faster labeling of large datasets, saving resources by avoiding intensive human labeling effort.\",\n \"Title: Semi-Supervised Classification-Based Local Vertex Ranking Via Dual Generative Adversarial Nets\\nYear: 2019\\n\\nAbstract: Real-world graphs are usually very sparse in terms of inadequate edges and labels as well as have poor quality due to a large amount of noisy data. In this paper, we propose a classification-based local vertex ranking architecture through dual generative adversarial networks in the semi-supervised setting, DQGAN, for analyzing sparse noisy graphs with rarely labeled data. First, we develop a quadruple generative adversarial ClassNet model to address the noisy data and data sparsity issues as well as to classify each vertex into K classes by automatically creating imaginary/real-looking supplementary labeled vertices with the quite different/similar distributions as real vertices, without the high cost of multi-step graph propagation, heterogeneous graph mining, and iterative weight learning. In addition, the vertex label vicinity is incorporated into the classification model to capture the pairwise vertex closeness based on the labeling and align the vertex label vicinity with the well-known vertex homophily for preserving the original structural semantics in the classification space. Second, we present a quintuple generative adversarial RankNet framework to locally rank each vertex on each of K classes by designing the game of multiple competitors utilizing the mix of real and noisy data to fight against each other, for improving the robustness of local vertex ranking to noisy data with few help from human efforts. The cycle ranking consistency strategy is designed to make the ranking quality verifiable through the bidirectional information-lossless translations between the original features and the ranking features. We propose to utilize the relaxed local PageRank property to produce high-quality local vertex ranking results in the context of information networks. Third but last, extensive evaluation on real graph datasets demonstrates that DQGAN outperforms existing representative methods in terms of both classification and ranking in the semi-supervised setting.\",\n \"Title: Integrating Local Vertex/Edge Embedding Via Deep Matrix Fusion And Siamese Multi-Label Classification\\nYear: 2019\\n\\nAbstract: Network embedding techniques aim to encode each vertex/edge as a low-dimensional vector, enabling easy integration with existing graph mining algorithms. This paper presents a novel network embedding framework, VEEMBEDCLASS, that combines local vertex/edge embedding with deep matrix fusion and Siamese multi-label classification for facilitating classification-based local network embedding. First, we propose to perform the embeddings of each vertex/edge on K local vertex/edge embedding models respectively, with the joint optimization by considering both intra-class and inter-class correlations, to learn their latent local features on each class. The deep matrix fusion technique is developed to preserve the first-order and second-order proximity of vertices and edges on each of K classes simultaneously. Second, a Student t-distribution based Siamese multi-label classification method is designed to train associated vertices and edges with similar local characteristics together and learn their class membership probabilities, in response to the power-law vertex degree distribution widespread in real graphs. A principle of vertex-edge homophily is introduced to guarantee that the common edge/vertex shared by two associated vertices/edges and themselves are similar in terms of both structural correlations and class memberships. Finally, we integrate local vertex/edge embedding and Siamese multi-label classification into a unified model by mutually enhancing each other.\",\n \"Title: Abc: A Practicable Sketch Framework For Non-Uniform Multisets\\nYear: 2017\\n\\nAbstract: Sketch is a data structure used to record frequencies of items in a multiset, which is widely used in data streams, data graph, distributed datasets processing, etc. It works with small memory usage and a high speed at the cost of a slight inaccuracy. In practice, frequencies of items in many datasets are non-uniformly distributed. Unfortunately, existing sketches can hardly work well on non-uniform datasets. To address this issue, we propose a new sketch framework, namely ABC framework, which can be applied to most existing sketches and can significantly improve the accuracy on non-uniform datasets. The key idea behind our framework is that when a counter overflows, it makes use of the space from the adjacent counters by operations of bits-borrowing and combination. Extensive experimental results show that our ABC framework improves the accuracy by 4.10 times and 4.49 times in average, respectively. A demo and all the related source codes are available on our homepage [1].\",\n \"Title: A comparison of methods for 3D scene shape retrieval\\nYear: 2020\\n\\nAbstract: 3D scene shape retrieval is a brand new but important research direction in content-based 3D shape retrieval. To promote this research area, two Shape Retrieval Contest (SHREC) tracks on 2D scene sketch-based and image-based 3D scene model retrieval have been organized by us in 2018 and 2019, respectively. In 2018, we built the first benchmark for each track which contains 2D and 3D scene data for ten (10) categories, while they share the same 3D scene target dataset. Four and five distinct 3D scene shape retrieval methods have competed with each other in these two contests, respectively. In 2019, to measure and compare the scalability performance of the participating and other promising Query-by-Sketch or Query-by-Image 3D scene shape retrieval methods, we built a much larger extended benchmark for each type of retrieval which has thirty (30) classes and organized two extended tracks. Again, two and three different 3D scene shape retrieval methods have contended in these two tracks, separately. To solicit state-of-the-art approaches, we perform a comprehensive comparison of all the above methods and an additional new retrieval methods by evaluating them on the two benchmarks. The benchmarks, evaluation results and tools are publicly available at our track websites (Yuan et al., 2019 [1]; Abdul-Rashid et al., 2019 [2]; Yuan et al., 2019 [3]; Abdul-Rashid et al., 2019 [4]), while code for the evaluated methods are also available: http://github.com/3DSceneRetrieval.\",\n \"Title: What The Role Is Vs. What Plays The Role: Semi-Supervised Event Argument Extraction Via Dual Question Answering\\nYear: 2021\\n\\nAbstract: Event argument extraction is an essential task in event extraction, and become particularly challenging in the case of low-resource scenarios. We solve the issues in existing studies under low-resource situations from two sides. From the perspective of the model, the existing methods always suffer from the concern of insufficient parameter sharing and do not consider the semantics of roles, which is not conducive to dealing with sparse data. And from the perspective of the data, most existing methods focus on data generation and data augmentation. However, these methods rely heavily on external resources, which is more laborious to create than obtain unlabeled data. In this paper, we propose DualQA, a novel framework, which models the event argument extraction task as question answering to alleviate the problem of data sparseness and leverage the duality of event argument recognition which is to ask \\\"What plays the role\\\", as well as event role recognition which is to ask \\\"What the role is\\\", to mutually improve each other. Experimental results on two datasets prove the effectiveness of our approach, especially in extremely low-resource situations.\",\n \"Title: Robust Network Alignment via Attack Signal Scaling and Adversarial Perturbation Elimination\\nYear: 2021\\n\\nAbstract: ABSTRACT Recent studies have shown that graph learning models are highly vulnerable to adversarial attacks, and network alignment methods are no exception. How to enhance the robustness of network alignment against adversarial attacks remains an open research problem. In this paper, we propose a robust network alignment solution, RNA, for offering preemptive protection of existing network alignment algorithms, enhanced with the guidance of effective adversarial attacks. First, we analyze how popular iterative gradient-based adversarial attack techniques suffer from gradient vanishing issues and show a fake sense of attack effectiveness. Based on dynamical isometry theory, an attack signal scaling (ASS) method with established upper bound of feasible signal scaling is introduced to alleviate the gradient vanishing issues for effective adversarial attacks while maintaining the decision boundary of network alignment. Second, we develop an adversarial perturbation elimination (APE) model to neutralize adversarial nodes in vulnerable space to adversarial-free nodes in safe area, by integrating Dirac delta approximation (DDA) techniques and the LSTM models. Our proposed APE method is able to provide proactive protection to existing network alignment algorithms against adversarial attacks. The theoretical analysis demonstrates the existence of an optimal distribution for the APE model to reach a lower bound. Last but not least, extensive evaluation on real datasets presents that RNA is able to offer the preemptive protection to trained network alignment methods against three popular adversarial attack models.\",\n \"Title: TC4JPF: Using Trace Compass to Visualize JPF Traces\\nYear: 2021\\n\\nAbstract: AbstractJava Path nder (JPF) is a very versatile program analysis tool, but understanding the error traces it generates is challenging. Visualizing traces can facilitate their understanding. Earlier attempts to visualize traces have resulted in specialized tools that do not interoperate with other frameworks. We present TC4JPF, which builds on Eclipse Trace Compass and enables Trace Compass to visualize JPF traces. With TC4JPF, we leverage the scalability and capabilities of Trace Compass and provide the rst solution that visualizes JPF traces in a general-purpose trace visualization tool.\",\n \"Title: The Effect Of The Decorative Surface Layer On The Dynamic Properties Of A Symmetric Concrete Slab\\nYear: 2021\\n\\nAbstract: In the past few years, the immense advances in building materials and construction techniques have inspired the development of large span, light, and flexible structures with low damping. The low frequency and low damping properties of the mentioned structures result in the problem of serviceability caused by human-induced vibrations. An evaluation of the serviceability of a structure requires obtaining the modes and natural frequencies of the structure via the finite element method (FEM). In the design stage, the structural model considers the contribution of involved elements made to the stiffness of the whole structure, such as beams, slabs, and columns, while the decorative surface layer above the floor is often regarded as an additional mass, regardless of its contribution to the stiffness of the floor slab. In this study, the dynamic properties of a symmetric concrete slab were tested with an ambient excitation method to obtain the dynamic properties of the original empty structure and the structure decorated with a tiled surface, a marble surface, and a terrazzo surface, respectively. The results show that the first-order natural frequencies of floor slabs decorated with tile, marble, and terrazzo finishes are decreased compared to the original empty structure, while the second- and third-order ones are increased, which indicates that it is improper to treat decorative finishes purely as an additional mass. By equating the decorative layer to a certain thickness of additional concrete layer in the finite element model, it is found that, if the decorative surface layer is equated to a 29-31 mm thick additional layer and the weight of the equivalent additional layer is the same as that of the actual decorative surface, the simulation results will be in good agreement with the measured results. Moreover, the test results indicate that the first-order shape function of the structure is symmetric and its second- and third-order shape functions are antisymmetric, which is consistent with the results of simulations under FEM method. This provides a basis for designers to evaluate the contribution of the additional layers in structural serviceability analysis.\",\n \"Title: Sdimpute: A Statistical Block Imputation Method Based On Cell-Level And Gene-Level Information For Dropouts In Single-Cell Rna-Seq Data\\nYear: 2021\\n\\nAbstract: The single-cell RNA sequencing (scRNA-seq) technologies obtain gene expression at single-cell resolution and provide a tool for exploring cell heterogeneity and cell types. As the low amount of extracted mRNA copies per cell, scRNA-seq data exhibit a large number of dropouts, which hinders the downstream analysis of the scRNA-seq data. We propose a statistical method, SDImpute (Single-cell RNA-seq Dropout Imputation), to implement block imputation for dropout events in scRNA-seq data. SDImpute automatically identifies the dropout events based on the gene expression levels and the variations of gene expression across similar cells and similar genes, and it implements block imputation for dropouts by utilizing gene expression unaffected by dropouts from similar cells. In the experiments, the results of the simulated datasets and real datasets suggest that SDImpute is an effective tool to recover the data and preserve the heterogeneity of gene expression across cells. Compared with the state-of-the-art imputation methods, SDImpute improves the accuracy of the downstream analysis including clustering, visualization, and differential expression analysis.\",\n \"Title: Research on Term Extraction in The Analysis of Classical Prescriptions\\nYear: 2020\\n\\nAbstract: In this paper, classical prescriptions refer to ancient Chinese medicine prescriptions created by Zhang Zhongjing in Han Dynasty. For the analysis of the components of classical prescriptions, it has been a great difficulty because of the constituent medicines and substance components. In order to extract the constituent medicines and substance components, we constructed the extraction model of cl...\",\n \"Title: Gradient-Based Multi-Focus Image Fusion Method Using Convolution Neural Network\\nYear: 2021\\n\\nAbstract: Due to limitation of optical lenses, obtaining all-in-focus images is difficult. However, lots of multi-focus image fusion methods cause undesirable artifacts around the focused and defocused boundaries in fusion images. Usually, these boundaries are at the edges of objects in images while the gradient information can reflect edge information intuitively. Based on the above ideas, a Gradient-based method using convolution neural network (CNN) is proposed to produce all-in-focus image. Specifically, we transmit the original images and corresponding four kinds of gradient images into five CNN models to generate the five initial focus score maps, respectively. Then, the final segmented focus map is obtained via merging the initial focus score maps. Finally, we combine the final segmented focus map and source images to obtain the fused image. The experimental results demonstrate that the proposed method has a better performance on both quality and quantitative evaluations than other state-of-the-art methods.Due to limitation of optical lenses, obtaining all-in-focus images is difficult. However, lots of multi-focus image fusion methods cause undesirable artifacts around the focused and defocused boundaries in fusion images. Usually, these boundaries are at the edges of objects in images while the gradient information can reflect edge information intuitively. Based on the above ideas, a Gradient-based method using convolution neural network (CNN) is proposed to produce all-in-focus image. Specifically, we transmit the original images and corresponding four kinds of gradient images into five CNN models to generate the five initial focus score maps, respectively. Then, the final segmented focus map is obtained via merging the initial focus score maps. Finally, we combine the final segmented focus map and source images to obtain the fused image. The experimental results demonstrate that the proposed method has a better performance on both quality and quantitative evaluations than other state-of-the-art methods.\",\n \"Title: A directional margin paradigm for noise suppression in face recognition\\nYear: 2021\\n\\nAbstract: Convolutional neural networks (CNN) have achieved outstanding face recognition (FR) performance with increasing large-scale face datasets. With face dataset size grown, noisy data will inevitably increase, undoubtedly bringing difficulties to data cleaning. In this paper, the probability that the sample belongs to noise can be determined based on the cosine distance (cosθ) of normalized angle center and face feature vector in the margin-based loss functions. According to this finding, we propose a two-step learning method integrated into the loss function. The new proposed directional margin loss function combines the noise probability with the label as the supervision information. Experiments show that our method can tolerate noisy data and get high FR accuracy when the training datasets mix with more than 30% noise. Our approach can also achieve a great result of 79.33% in MegaFace challenge one using a noisy training dataset.\",\n \"Title: A Survey Of 3d Object Detection\\nYear: 2021\\n\\nAbstract: Due to the rapid development of science and technology, object detection has become a promising research direction in computer vision. In recent years, most object detection frameworks proposed in the existing research are 2D. However, 2D object detection cannot take three-dimensional space into account, resulting in its inability to be used to solve problems in real world. Hence, we conduct this 3D object detection survey in the hope that 3D object detection methods can be better applied to the contexts of intelligent video surveillance, robot navigation and autonomous driving technology. There exist various 3D object detection methods while in this paper we only focus on the popular deep learning based methods. We divide these approaches into four categories according to the input data category. Besides, we discuss the innovations of these frames and compare their experimental results in terms of accuracy. Finally, we indicate the technical difficulties associated with current 3D object detection and discuss future research directions.\",\n \"Title: Analysis Of Propagation And Distribution Characteristics Of Leakage Acoustic Waves In Water Supply Pipelines\\nYear: 2021\\n\\nAbstract: Leakage detection methods based on the analysis of leakage acoustic signals provide an effective technical approach for detecting small leaks in water supply pipelines. From a technical perspective, the study of the propagation characteristics of acoustic waves generated by the leakage in the water supply pipeline is necessary for detecting the leak location on the basis of acoustic signals. In this study, a 3D transient leakage acoustic wave propagation equation was derived by combining the principles of fluid dynamics and Lighthill acoustic analogy theory. The propagation of the leakage-induced noise in water supply pipeline was modelled theoretically. We simulated the propagation of a leakage acoustic wave under different conditions for different target scenarios encountered in actual pipeline inspections. Specifically, we analysed the effect of different factors, such as the pipe size and acoustic source characteristics, on acoustic propagation. Finally, the simulated experiments were practically performed using a self-designed simulated water supply pipeline and self-developed spherical water supply pipeline detector to validate the simulation analysis. The results of this study provide a theoretical guidance and basis for the analysis of characteristics of leakage acoustic wave signals and the recognition of leakage conditions in water supply pipelines.\",\n \"Title: Approximation Algorithms For Fuzzy C-Means Problem Based On Seeding Method\\nYear: 2021\\n\\nAbstract: As a kind of important soft clustering model, the fuzzy C-means method is widely applied in many fields. In this method, instead of the strict distributive ability in the classical k-means method, all the sample points are endowed with degrees of membership to each center to depict the fuzzy clustering. In this paper, we show that the fuzzy C-means++ algorithm, which introduces the k-means++ algorithm as a seeding strategy, gives a solution for which the approximation guarantee is O(k(2) ln k). A novel seeding algorithm is then designed based on the contribution of the fuzzy potential function, which improves the approximation ratio to O(k lnk). Preliminary numerical experiments are proposed to support the theoretical results of this paper. (C) 2021 Elsevier B.V. All rights reserved.\",\n \"Title: Vectorization for Fast, Analytic, and Differentiable Visibility\\nYear: 2021\\n\\nAbstract: AbstractIn Computer Graphics, the two main approaches to rendering and visibility involve ray tracing and rasterization. However, a limitation of both approaches is that they essentially use point sampling. This is the source of noise and aliasing, and also leads to significant difficulties for differentiable rendering. In this work, we present a new rendering method, which we call vectorization, that computes 2D point-to-region integrals analytically, thus eliminating point sampling in the 2D integration domain such as for pixel footprints and area lights. Our vectorization revisits the concept of beam tracing, and handles the hidden surface removal problem robustly and accurately. That is, for each intersecting triangle inserted into the viewport of a beam in an arbitrary order, we are able to maintain all the visible regions formed by intersections and occlusions, thanks to our Visibility Bounding Volume Hierarchy structure. As a result, our vectorization produces perfectly anti-aliased visibility, accurate and analytic shading and shadows, and most important, fast and noise-free gradients with Automatic Differentiation or Finite Differences that directly enables differentiable rendering without any changes to our rendering pipeline. Our results are inherently high-quality and noise-free, and our gradients are one to two orders of magnitude faster than those computed with existing differentiable rendering methods.\",\n \"Title: Impact of YouTube Advertising on Sales with Regression Analysis and Statistical Modeling: Usefulness of Online Media in Business\\nYear: 2021\\n\\nAbstract: Computer technology plays a prominent role in almost every aspect of daily life including education, health care, online shopping, advertising, and even in homes. Computers help to make daily tasks much easier and convenient. Among social media, YouTube is a well-known social sharing networking service. As more and more people join social media and become everyday users, brands have also increased their online engagement. However, it is still unclear how to effectively measure value and return on advertising using social media. As of 2021, more than 31 million YouTube channels around the globe have been opened. In this paper, we consider YouTube advertising to check its effectiveness and benefits gained. Certain statistical tools are adopted to measure the extent of advertising benefits and their correlation in creating effective advertising campaigns on YouTube. Simple linear regression analysis is performed on the data representing the YouTube advertising budget of a company and the sales data of that company. Furthermore, we develop a new statistical distribution to provide the best description of the YouTube advertising data. The result of this research shows that YouTube is an effective medium for advertising and has a strong relationship with sales.\",\n \"Title: Forecasting Stock Prices Using Stock Correlation Graph: A Graph Convolutional Network Approach\\nYear: 2021\\n\\nAbstract: Accurate forecasting of stock prices plays an important role in stock investment. With the advancement of AI in Fintech applications, various deep learning models have recently been developed for stock price forecasting. However, these models focus on designing sequence models to capture the temporal dependence from a stock's historical prices (and other information such as technical indicators an...\",\n \"Title: Object Relocation Visual Tracking based on Siamese network\\nYear: 2021\\n\\nAbstract: Siamese network based trackers treat the visual tracking task as a similarity matching process, which significantly increases the performance of trackers. However, there are still many remaining problems as following. 1) The lightweight backbone of Siamese network limits its feature representation ability, and the tracker is prone to failure under the interference distractors (e.g., similar object...\",\n \"Title: Monocular Pseudo-LiDAR 3D Object Detection Method Based on Confidence and Feature optimization\\nYear: 2021\\n\\nAbstract: 3D object detection based on the monocular Pseudo-LiDAR is cost-effective compared with multi-sensor solutions in autonomous driving, and made great progress in recent. However, these methods still suffer from the following disadvantages, i.e., (1) the depth estimation error of the background points in the Pseudo-LiDAR is larger than that of the object points, which leads to the degradation of the...\",\n \"Title: A Novel SAR Images Change Detection Method Based on Dynamic TUNET-CRF Model\\nYear: 2021\\n\\nAbstract: There are two problems in SAR images change detection at present: 1) it is difficult to extract the semantic change information; 2) the change boundary is blur. In this paper, the transformers-UNET (TUNET) model is built by embedding the transformers (TR) layer with the internal global self-concern mechanism in the UNET structure to overcome the limitation of modeling remote dependence of UNET net...\",\n \"Title: Multi-Attribute Group Decision-Making Based On Interval-Valued Q-Rung Orthopair Fuzzy Power Generalized Maclaurin Symmetric Mean Operator And Its Application In Online Education Platform Performance Evaluation\\nYear: 2021\\n\\nAbstract: This paper aims to propose a novel multi-attribute group decision-making (MAGDM) method based on interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs). The IVq-ROFSs have been proved to be effective in handling MAGDM problems, and several novel decision-making methods have been proposed. Nevertheless, it is worth pointing out that these approaches still have some limitations, and there still exist some realistic situations that cannot be solved by existing MAGDM methods. Hence, the objective of this paper is to introduce a novel MAGDM method, which can overcome some of the drawbacks of existing approaches. To effectively and appropriately aggregate interval-valued q-rung orthopair fuzzy numbers (IVq-ROFNs), we combine the power average with generalized Maclaurin symmetric mean (GMSM), propose the power GMSM operator and extend it into IVq-ROFSs. Afterwards, a collection of new aggregation operators for IVq-ROFNs are developed. In this paper, we study definitions of these operators and investigate their characteristics as well as special cases. Then, based on the new aggregation operators, we present a new MAGDM method. Finally, we apply the proposed MAGDM method in online education platform performance evaluation to illustrate its effectiveness and validity. In addition, we also provide comparative analysis to explain why decision-makers should use our method instead of the others.\",\n \"Title: Energy Management Strategy of Distributed Electric Propulsion Aircraft Hybrid Power System based on State Machine\\nYear: 2021\\n\\nAbstract: Fuel cell has received more and more attention in the field of green aviation in recent years due to their zero-emission and pollution-free advantages. In this paper, a flight mission model of a distributed electric propulsion aircraft is established, and an energy management strategy based on an improved multi-dimensional coupled state machine is designed by considering factors such as multi-mission profile power requirements and failure emergency measures for the aviation electric propulsion hybrid power system. The strategy is based on a state machine and divides the operation of the distributed electric propulsion aircraft into four states: climb, cruise, descent, and malfunction. The overall operation test of the aircraft model is carried out based on the four states, and the research results indicate that compared with the load-following mode control strategy, the proposed strategy increases the average fuel cell (FC) efficiency by 3.83% In addition, the proposed strategy saves 7.95% of hydrogen and can effectively ensure flight safety in the event of a malfunction.\",\n \"Title: Low-dose dobutamine cardiovascular magnetic resonance segmental strain study of early phase of intramyocardial hemorrhage rats\\nYear: 2021\\n\\nAbstract: Background This study investigates the segmental myocardial strain of the early phase of intramyocardial hemorrhage (IMH) caused by reperfused myocardial infarction (MI) in rats by low-dose dobutamine (LDD) cardiovascular magnetic resonance (CMR) feature-tracking. Methods Nine sham rats and nine rats with 60-min myocardial ischemia followed by 48-h reperfusion were investigated using CMR, including T2*-mapping sequence and fast imaging with steady-state precession (FISP)-cine sequence. Another FISP-cine sequence was acquired after 2 min of dobutamine injection; the MI, IMH, and Non-MI (NMI) areas were identified. The values of peak radial strains (PRS) and peak circumferential strains (PCS) of the MI, IMH and NMI segments were acquired. The efficiency of PRS and PCS (EPRS and EPCS, respectively) were calculated on the basis of the time of every single heartbeat. Results The PRS, PCS, EPRS, and EPCS of the sham group increased after LDD injection. However, the PRS, PCS, EPRS, and EPCS of the IMH segment did not increase. Moreover, the PRS and PCS of the MI and NMI segments did not increase, but the EPRS and EPCS of these segments increased. The PRS, PCS, EPRS, and EPCS of the IMH segment were lower than those of the MI and NMI segments before and after LDD injection, but without a significant difference between MI segment and NMI segment before and after LDD injection. Conclusions LDD could help assess dysfunctions in segments with IMH, especially using the efficiency of strain. IMH was a crucial factor that decreased segmental movement and reserved function.\",\n \"Title: Histogram analysis of diffusion-weighted magnetic resonance imaging as a biomarker to predict LNM in T3 stage rectal carcinoma\\nYear: 2021\\n\\nAbstract: Background Preoperative identification of rectal cancer lymph node status is crucial for patient prognosis and treatment decisions. Rectal magnetic resonance imaging (MRI) plays an essential role in the preoperative staging of rectal cancer, but its ability to predict lymph node metastasis (LNM) is insufficient. This study explored the value of histogram features of primary lesions on multi-parametric MRI for predicting LNM of stage T3 rectal carcinoma. Methods We retrospectively analyzed 175 patients with stage T3 rectal cancer who underwent preoperative MRI, including diffusion-weighted imaging (DWI) before surgery. 62 patients were included in the LNM group, and 113 patients were included in the non-LNM group. Texture features were calculated from histograms derived from T2 weighted imaging (T2WI), DWI, ADC, and T2 maps. Stepwise logistic regression analysis was used to screen independent predictors of LNM from clinical features, imaging features, and histogram features. Predictive performance was evaluated by receiver operating characteristic (ROC) curve analysis. Finally, a nomogram was established for predicting the risk of LNM. Results The clinical, imaging and histogram features were analyzed by stepwise logistic regression. Preoperative carbohydrate antigen 199 level (p = 0.009), MRN stage (p < 0.001), (T2WI)Kurtosis (p = 0.010), (DWI)Mode (p = 0.038), DWICV (p = 0.038), and (T2-map)P5 (p = 0.007) were independent predictors of LNM. These factors were combined to form the best predictive model. The model reached an area under the ROC curve (AUC) of 0.860, with a sensitivity of 72.8% and a specificity of 85.5%. Conclusion The histogram features on multi-parametric MRI of the primary tumor in rectal cancer were related to LN status, which is helpful for improving the ability to predict LNM of stage T3 rectal cancer.\",\n \"Title: The Construction And Application Of Expectations Index On Monetary Policy\\nYear: 2017\\n\\nAbstract: Given the complexity of macro-economic situation, in the decision-making of monetary policy, it is vital to first grasp and then effectively manage and guide the public expectations. However, expectation is an index hard to measure. With the emergence of big data application, measuring expectations through constructing Internet public opinion index has become an important research direction. According to the characteristics of monetary policy expectations, this paper determines the relevant key words, conducts research on online public opinions by mining and crawling the netnews and search data, and construct the expectations index on monetary policy. The practicability of the expectations index is then verified by measuring its correlation with the macroeconomic variables. The result indicates that the index can indeed represent the public expectations, which means it can be applied to analyze the pattern of public expectations on the monetary policy. Apart from that, the fact that it changes ahead of the price variable can serve as the basis for farsighted decisions.\",\n \"Title: Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory.\\nYear: 2021\\n\\nAbstract: Despite achieving remarkable efficiency, traditional network pruning techniques often follow manually-crafted heuristics to generate pruned sparse networks. Such heuristic pruning strategies are hard to guarantee that the pruned networks achieve test accuracy comparable to the original dense ones. Recent works have empirically identified and verified the Lottery Ticket Hypothesis (LTH): a randomly-initialized dense neural network contains an extremely sparse subnetwork, which can be trained to achieve similar accuracy to the former. Due to the lack of theoretical evidence, they often need to run multiple rounds of expensive training and pruning over the original large networks to discover the sparse subnetworks with low accuracy loss. By leveraging dynamical systems theory and inertial manifold theory, this work theoretically verifies the validity of the LTH. We explore the possibility of theoretically lossless pruning as well as one-time pruning, compared with existing neural network pruning and LTH techniques. We reformulate the neural network optimization problem as a gradient dynamical system and reduce this high-dimensional system onto inertial manifolds to obtain a low-dimensional system regarding pruned subnetworks. We demonstrate the precondition and existence of pruned subnetworks and prune the original networks in terms of the gap in their spectrum that make the subnetworks have the smallest dimensions.\",\n \"Title: Efficient Device Scheduling with Multi-Job Federated Learning.\\nYear: 2022\\n\\nAbstract: Recent years have witnessed a large amount of decentralized data in multiple (edge) devices of end-users, while the aggregation of the decentralized data remains difficult for machine learning jobs due to laws or regulations. Federated Learning (FL) emerges as an effective approach to handling decentralized data without sharing the sensitive raw data, while collaboratively training global machine learning models. The servers in FL need to select (and schedule) devices during the training process. However, the scheduling of devices for multiple jobs with FL remains a critical and open problem. In this paper, we propose a novel multi-job FL framework to enable the parallel training process of multiple jobs. The framework consists of a system model and two scheduling methods. In the system model, we propose a parallel training process of multiple jobs, and construct a cost model based on the training time and the data fairness of various devices during the training process of diverse jobs. We propose a reinforcement learning-based method and a Bayesian optimization-based method to schedule devices for multiple jobs while minimizing the cost. We conduct extensive experimentation with multiple jobs and datasets. The experimental results show that our proposed approaches significantly outperform baseline approaches in terms of training time (up to 8.67 times faster) and accuracy (up to 44.6% higher).\",\n \"Title: Multi-Robot Collaborative Perception With Graph Neural Networks\\nYear: 2022\\n\\nAbstract: Multi-robot systems such as swarms of aerial robots are naturally suited to offer additional flexibility, resilience, and robustness in several tasks compared to a single robot by enabling cooperation among the agents. To enhance the autonomous robot decision-making process and situational awareness, multi-robot systems have to coordinate their perception capabilities to collect, share, and fuse environment information among the agents efficiently to obtain context-appropriate information or gain resilience to sensor noise or failures. In this letter, we propose a general-purpose Graph Neural Network (GNN) with the main goal to increase, in multi-robot perception tasks, single robots' inference perception accuracy as well as resilience to sensor failures and disturbances. We show that the proposed framework can address multi-view visual perception problems such as monocular depth estimation and semantic segmentation. Several experiments both using photo-realistic and real data gathered from multiple aerial robots' viewpoints show the effectiveness of the proposed approach in challenging inference conditions including images corrupted by heavy noise and camera occlusions or failures.\",\n \"Title: Unsupervised Adversarial Network Alignment with Reinforcement Learning\\nYear: 2022\\n\\nAbstract: AbstractNetwork alignment, which aims at learning a matching between the same entities across multiple information networks, often suffers challenges from feature inconsistency, high-dimensional features, to unstable alignment results. This article presents a novel network alignment framework, Unsupervised Adversarial learning based Network Alignment(UANA), that combines generative adversarial network (GAN) and reinforcement learning (RL) techniques to tackle the above critical challenges. First, we propose a bidirectional adversarial network distribution matching model to perform the bidirectional cross-network alignment translations between two networks, such that the distributions of real and translated networks completely overlap together. In addition, two cross-network alignment translation cycles are constructed for training the unsupervised alignment without the need of prior alignment knowledge. Second, in order to address the feature inconsistency issue, we integrate a dual adversarial autoencoder module with an adversarial binary classification model together to project two copies of the same vertices with high-dimensional inconsistent features into the same low-dimensional embedding space. This facilitates the translations of the distributions of two networks in the adversarial network distribution matching model. Finally, we develop an RL based optimization approach to solve the vertex matching problem in the discrete space of the GAN model, i.e., directly select the vertices in target networks most relevant to the vertices in source networks, without unstable similarity computation that is sensitive to discriminative features and similarity metrics. Extensive evaluation on real-world graph datasets demonstrates the outstanding capability of UANA to address the unsupervised network alignment problem, in terms of both effectiveness and scalability.\",\n \"Title: Correction: SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data\\nYear: 2022\\n\\nAbstract: [This corrects the article DOI: 10.1371/journal.pcbi.1009118.].\",\n \"Title: Real-time Multiple-Channel Shoulder EMG Processing for a Rehabilitative Upper-limb Exoskeleton Motion Control Using ANN Machine Learning\\nYear: 2021\\n\\nAbstract: Machine learning (ML) has been proposed for surface electromyography (sEMG) signal processing to control assistive robots. However, most of the studies using ML focused on recording sEMG for off-line data analysis and pattern recognition. Only a few studies reported online ML EMG processing for one DoF hand motion pattern recognition and instant robot control. Using ML for multiple channels of sho...\",\n \"Title: Pro-PULSE: Learning Progressive Encoders of Latent Semantics in GANs for Photo Upsampling\\nYear: 2022\\n\\nAbstract: The state-of-the-art photo upsampling method, PULSE, demonstrates that a sharp, high-resolution (HR) version of a given low-resolution (LR) input can be obtained by exploring the latent space of generative models. However, mapping an extreme LR input (16\\n<sup xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">2</sup>\\n) directly to an HR image (1024\\n<sup xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">2</sup>\\n) is too ambiguous to preserve faithful local facial semantics. In this paper, we propose an enhanced upsampling approach, Pro-PULSE, that addresses the issues of semantic inconsistency and optimization complexity. Our idea is to learn an encoder that progressively constructs the HR latent codes in the extended \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"> <tex-math notation=\\\"LaTeX\\\">$\\\\mathcal {W}+$ </tex-math></inline-formula>\\n latent space of StyleGAN. This design divides the complex \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"> <tex-math notation=\\\"LaTeX\\\">$64\\\\times $ </tex-math></inline-formula>\\n upsampling problem into several steps, and therefore small-scale facial semantics can be inherited from one end to the other. In particular, we train two encoders, the base encoder maps latent vectors in \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"> <tex-math notation=\\\"LaTeX\\\">$\\\\mathcal {W}$ </tex-math></inline-formula>\\n space and serves as a foundation of the HR latent vector, while the second scale-specific encoder performed in \\n<inline-formula xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"> <tex-math notation=\\\"LaTeX\\\">$\\\\mathcal {W}+$ </tex-math></inline-formula>\\n space gradually replaces the previous vector produced by the base encoder at each scale. This process produces intermediate side-outputs, which injects deep supervision into the training of encoder. Extensive experiments demonstrate superiorities over the latest latent space exploration methods, in terms of efficiency, quantitative quality metrics, and qualitative visual results.\",\n \"Title: Flood Discharge Prediction Based on Remote-Sensed Spatiotemporal Features Fusion and Graph Attention\\nYear: 2021\\n\\nAbstract: Floods have brought a great threat to the life and property of human beings. Under the premise of strengthening flood control engineering measures and following the strategic thinking of sustainable development, many achievements have been made in flood forecasting recently. However, due to the complexity of the traditional lumped model and distributed model, the hydrologic parameter calibration process is full of difficulties, leading to a long development cycle of a reasonable hydrologic prediction model. Even for modern data-driven models, the spatial distribution characteristics of the rainfall data are also not fully mined. Based on this situation, this paper abstracts the rainfall data into the graph structure data, uses remote sensing images to extract the elevation information, introduces the graph attention mechanism to extract the spatial characteristics of rainfall, and employs long-term and short-term memory (LSTM) network to fuse the spatial and temporal characteristics for flood prediction. Through well-designed experiments, the forecasting effect of flood peak value and flood arrival time is verified. Furthermore, compared with the LSTM model and BIGRU model without spatial feature extraction, the advantages of spatiotemporal feature fusion are highlighted. The specific performance is that the RMSE (the root means square error) and R2 (coefficient of determination) of the GA-RNN model have been significantly improved. Finally, we conduct experiments on the observed ten rainfall events in the history of the target watershed. According to the hydrological prediction specifications, the model can be evaluated as a Class B flood forecasting model.\",\n \"Title: Logging Data Completion Based on an MC-GAN-BiLSTM Model\\nYear: 2022\\n\\nAbstract: Due to environmental interference and operational errors, problems such as incomplete and random missing logging data have occurred during the geophysical logging data collection process. Since it is difficult to establish a geophysical model based on logging data and geological information, the data complementation effect of conventional methods is not very satisfactory. In this paper, we propose an MC-GAN-BiLSTM model based on spatiotemporal sequence prediction. In the model, we adopt a generative adversarial network (GAN) as a network framework, and a long short-term memory (LSTM) neural network and a bi-directional long short-term memory (Bi-LSTM) as the basic modules. We use the LSTM instead of a fully-connected layer in the GAN to extract the potential information in the logging data depth domain. We complete the logging data missing values through an encoding-decoding structure that includes the Bi-LSTM. In addition, the generator module also uses multiscale convolution to fully extract the logging data features. We use logging data random missing values and consecutive missing values to simulate a field data acquisition environment and threshold control to simulate a laboratory processing environment for experiments. The experimental results show that the coefficient of determination (R-2) of the GAN-LSTM model reaches 0.906 when 30% of random logging data are missing and 0.851 when 30% of consecutive logging data are missing. The effect of the model proposed in this paper is significantly higher than the commonly used random forest (RF), sequence to sequence (seq2seq) and generative adversarial interpolation network (GAIN) models.\",\n \"Title: 3D Segmentation Guided Style-Based Generative Adversarial Networks for PET Synthesis\\nYear: 2022\\n\\nAbstract: Potential radioactive hazards in full-dose positron emission tomography (PET) imaging remain a concern, whereas the quality of low-dose images is never desirable for clinical use. So it is of great interest to translate low-dose PET images into full-dose. Previous studies based on deep learning methods usually directly extract hierarchical features for reconstruction. We notice that the importance of each feature is different and they should be weighted dissimilarly so that tiny information can be captured by the neural network. Furthermore, the synthesis on some regions of interest is important in some applications. Here we propose a novel segmentation guided style-based generative adversarial network (SGSGAN) for PET synthesis. (1) We put forward a style-based generator employing style modulation, which specifically controls the hierarchical features in the translation process, to generate images with more realistic textures. (2) We adopt a task-driven strategy that couples a segmentation task with a generative adversarial network (GAN) framework to improve the translation performance. Extensive experiments show the superiority of our overall framework in PET synthesis, especially on those regions of interest.\",\n \"Title: Automatic repetition instruction generation for air traffic control training using multi-task learning with an improved copy network\\nYear: 2022\\n\\nAbstract: To eliminate the need for human pseudo-pilots in air traffic controller training, a multi-task framework with a copy mechanism is proposed to automatically generate a repetition instruction, which can greatly reduce human and device resources. The proposed framework is implemented using a sequence-to-sequence architecture and is optimized through multi-task learning, including text instruction understanding (TIU) and repetition instruction generation (RIG). In the proposed framework, a shared encoder is designed to learn the representations from the input sequence. Two task-specific attention modules are proposed to extract task-specific features, and different decoders are applied to generate the TIU and RIG outputs. The TIU module provides both word- and sentence-level implicit semantic representations through slot filling and intent detection tasks. In the decoding procedure of the RIG task, the slot context vector is regarded as an additional input used to solve the problem of ambivalent words. Simultaneously, an improved copy network based on multi-task learning is proposed to consider the correlations between the input instruction and the output repetition in the RIG module. To avoid a loss imbalance for the TIU and RIG tasks, the gradient normalization algorithm is applied to learn the loss weights automatically by adjusting the loss gradient. Finally, the proposed framework was trained and evaluated using a real-world air traffic control corpus. The experiment results demonstrate that the proposed framework significantly outperforms other state-of-the-art methods for the RIG task, achieving a novel integrated metric of 97.19%.\",\n \"Title: Lesion Localization in OCT by Semi-Supervised Object Detection\\nYear: 2022\\n\\nAbstract: Over 300 million people worldwide are affected by various retinal diseases. By noninvasive Optical Coherence Tomography (OCT) scans, a number of abnormal structural changes in the retina, namely retinal lesions, can be identified. Automated lesion localization in OCT is thus important for detecting retinal diseases at their early stage. To conquer the lack of manual annotation for deep supervised learning, this paper presents a first study on utilizing semi-supervised object detection (SSOD) for lesion localization in OCT images. To that end, we develop a taxonomy to provide a unified and structured viewpoint of the current SSOD methods, and consequently identify key modules in these methods. To evaluate the influence of these modules in the new task, we build OCT-SS, a new dataset consisting of over 1k expert-labeled OCT B-scan images and over 13k unlabeled B-scans. Extensive experiments on OCT-SS identify Unbiased Teacher (UnT) as the best current SSOD method for lesion localization. Moreover, we improve over this strong baseline, with mAP increased from 49.34 to 50.86.\",\n \"Title: FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server.\\nYear: 2022\\n\\nAbstract: Despite achieving remarkable performance, Federated Learning (FL) suffers from two critical challenges, i.e., limited computational resources and low training efficiency. In this paper, we propose a novel FL framework, i.e., FedDUAP, with two original contributions, to exploit the insensitive data on the server and the decentralized data in edge devices to further improve the training efficiency. First, a dynamic server update algorithm is designed to exploit the insensitive data on the server, in order to dynamically determine the optimal steps of the server update for improving the convergence and accuracy of the global model. Second, a layer-adaptive model pruning method is developed to perform unique pruning operations adapted to the different dimensions and importance of multiple layers, to achieve a good balance between efficiency and effectiveness. By integrating the two original techniques together, our proposed FL model, FedDUAP, significantly outperforms baseline approaches in terms of accuracy (up to 4.8% higher), efficiency (up to 2.8 times faster), and computational cost (up to 61.9% smaller).\",\n \"Title: Human-Centered Prior-Guided and Task-Dependent Multi-Task Representation Learning for Action Recognition Pre-Training\\nYear: 2022\\n\\nAbstract: Recently, much progress has been made for self-supervised action recognition. Most existing approaches emphasize the contrastive relations among videos, including appearance and motion consistency. However, two main issues remain for existing pre-training methods: 1) the learned representation is neutral and not informative for a specific task; 2) multi-task learning-based pre-training sometimes leads to sub-optimal solutions due to inconsistent domains of different tasks. To address the above issues, we propose a novel action recognition pre-training framework, which exploits human-centered prior knowledge that generates more informative representation, and avoids the conflict between multiple tasks by using task-dependent representations. Specifically, we distill knowledge from a human parsing model to enrich the semantic capability of representation. In addition, we combine knowledge distillation with contrastive learning to constitute a task-dependent multi-task framework. We achieve state-of-the-art performance on two popular benchmarks for action recognition task, i.e., UCF101 and HMDB51, verifying the effectiveness of our method.\",\n \"Title: None Class Ranking Loss for Document-Level Relation Extraction.\\nYear: 2022\\n\\nAbstract: Document-level relation extraction (RE) aims at extracting relations among entities expressed across multiple sentences, which can be viewed as a multi-label classification problem. In a typical document, most entity pairs do not express any pre-defined relation and are labeled as \\\"none\\\" or \\\"no relation\\\". For good document-level RE performance, it is crucial to distinguish such \\\\textit{none} class instances (entity pairs) from those of pre-defined classes (relations). However, most existing methods only estimate the probability of pre-defined relations independently without considering the probability of \\\"no relation\\\". This ignores the context of entity pairs and the label correlations between the none class and pre-defined classes, leading to sub-optimal predictions. To address this problem, we propose a new multi-label loss that encourages large \\\\textit{margins} of label confidence scores between each pre-defined class and the none class, which enables captured label correlations and context-dependent thresholding for label prediction. To gain further robustness against positive-negative imbalance and mislabeled data that could appear in real-world RE datasets, we propose a margin regularization and a margin shifting technique. Experimental results demonstrate that our method significantly outperforms existing multi-label losses for document-level RE and works well in other multi-label tasks such as emotion classification when none class instances are available for training.\",\n \"Title: REU-Net: Region-enhanced nuclei segmentation network\\nYear: 2022\\n\\nAbstract: Nuclei segmentation is a key technique for automatic pathological screening. Although many methods have been proposed, it remains a challenge because of numerous nuclei clusters, high variability of object appearances and complex backgrounds. To address these issues, we propose a novel multi-task region-enhanced nuclei segmentation network (REU-Net). It stacks three U-shaped structures by combining serial and parallel approaches to construct a multi-task architecture. The model employs two auxiliary tasks, i.e., contour extraction and rough segmentation to help the main task of fine segmentation. The saliency regions are enhanced by the prediction results of the auxiliary tasks, and the enhanced images are further segmented through the main task. In addition, the spatial and texture features in auxiliary tasks are aggregated by attention gates, helping the main task to refine the details of nuclei and contours. Extensive experiments are conducted to evaluate the proposed method qualitatively and quantitatively. Experimental results show that REU-Net outperforms the state-of-the-art methods on HUSTS, MoNuSeg, CoNSep and CPM-17 datasets.\",\n \"Title: The AISP-SJTU Simultaneous Translation System for IWSLT 2022.\\nYear: 2022\\n\\nAbstract: This paper describes AISP-SJTU’s submissions for the IWSLT 2022 Simultaneous Translation task. We participate in the text-to-text and speech-to-text simultaneous translation from English to Mandarin Chinese. The training of the CAAT is improved by training across multiple values of right context window size, which achieves good online performance without setting a prior right context window size for training. For speech-to-text task, the best model we submitted achieves 25.87, 26.21, 26.45 BLEU in low, medium and high regimes on tst-COMMON, corresponding to 27.94, 28.31, 28.43 BLEU in text-to-text task.\",\n \"Title: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection\\nYear: 2022\\n\\nAbstract: As an inherently ill-posed problem, depth estimation from single images is the most challenging part of monocular 3D object detection (M3OD). Many existing methods rely on preconceived assumptions to bridge the missing spatial information in monocular images, and predict a sole depth value for every object of interest. However, these assumptions do not always hold in practical applications. To tackle this problem, we propose a depth solving system that fully explores the visual clues from the subtasks in M3OD and generates multiple estimations for the depth of each target. Since the depth estimations rely on different assumptions in essence, they present diverse distributions. Even if some assumptions collapse, the estimations established on the remaining assumptions are still reliable. In addition, we develop a depth selection and combination strategy. This strategy is able to remove abnormal estimations caused by collapsed assumptions, and adaptively combine the remaining estimations into a single one. In this way, our depth solving system becomes more precise and robust. Exploiting the clues from multiple subtasks of M3OD and without introducing any extra information, our method surpasses the current best method by more than 20% relatively on the Moderate level of test split in the KITTI 3D object detection benchmark, while still maintaining real-time efficiency.\",\n \"Title: An eXtreme Gradient Boosting Algorithm Combining Artificial Bee Colony Parameters Optimized Technique for Single Sand Body Identification\\nYear: 2021\\n\\nAbstract: Due to the problems of traditional artificial single sand body identification methods such as strong subjectivity, heavy workload and low efficiency, we propose a fast and objective ABC-XGBoost. The algorithm consists of two parts: eXtreme gradient boosting (XGBoost) and artificial bee colony algorithm (ABC). XGBoost introduces a regular term, which can effectively prevent overfitting, and uses the second derivative to make the identification result more accurate. However, a large number of parameters in XGBoost need to be adjusted manually, which affects the efficiency of the algorithm. In this regard, ABC is used to optimize the parameters based on XGBoost, and then the single sand body can be identified quickly and effectively. We take the C6 21 oil-bearing layer in the second area of Dalugou, Jing'an Oilfield as the research object, and use the ABC-XGBoost to identify the single sand body in the research area. Based on the reasonable selection of physical parameter data and logging data, the partition and interlayer data should be eliminated first to avoid data redundancy. The results indicate that ABC-XGBoost is more efficient and accurate than the existing mainstream machine algorithms, such as support vector machines (SVM), random forests (RF), and XGBoost using trial and error tuning under the same logging data and computer hardware conditions. The accuracy can reach 90.6%, which has certain practical application value in the middle and late development of oil and gas fields.\",\n \"Title: Pyramid Family: Generic Frameworks for Accurate and Fast Flow Size Measurement\\nYear: 2022\\n\\nAbstract: AbstractSketches, as a kind of probabilistic data structures, have been considered as the most promising solution for network measurement in recent years. Most sketches do not work well for skewed network traffic. To address this problem, we propose a family of sketch frameworks, namely the Pyramid family. The first member of our Pyramid family is the S-Pyramid framework, which includes two techniques: counter-pair sharing for high accuracy, and word acceleration for fast speed. The second member of our Pyramid family is the Mini-Pyramid framework, which projects the S-Pyramid framework into one counter, bringing more flexibility in application while keeping the accuracy. To demonstrate the generality of our Pyramid family, we apply both frameworks to sketches of CM, CU, Count, and Augmented. To demonstrate the flexibility of the Mini-Pyramid framework, we further apply Mini-Pyramid to SBF and the On-Off sketch. The experimental results show that, the S-Pyramid framework can reduce the ARE by up to 7.12 times compared with the original sketches, while improving the throughput by up to 2.37 times; the Mini-Pyramid framework can reduce the ARE by up to 29.2 times, at the cost of 21.3% lower throughput on average.\",\n \"Title: Spatiotemporal just noticeable difference modeling with heterogeneous temporal visual features\\nYear: 2021\\n\\nAbstract: Developing accurate Just-noticeable difference (JND) models are challenged by complicated HVS characteristics and nonstationary features of video sequence. Great efforts have been devoted to JND modeling, and inspiring performance improvements are witnessed in the literature, especially spatial JND models. However, there are not only urgent requirement but also technical potentiality for improving temporal JND models fully accounting for the temporal perception characteristics. In terms of temporal JND modeling, there are two challenges, one is how to extract perceptual feature parameters of source video, and the other is how to quantitatively characterize the interaction relationship between feature parameters and HVS characteristics? Firstly, this work extracts contentaware temporal feature parameters having predominate impacts on vision perception, including motion (foreground/background), pixel-correspondence duration and inter-frame residue fluctuation intensity along temporal trajectory, and investigates the HVS responses to these four heterogeneous feature parameters. Secondly, this work proposes respective probability density functions (PDF) in the perception sense to quantitatively depict the attention and suppression perception responses of feature parameters, accounting for the temporal perception characteristics. Using these PDF models, we fuse the heterogeneous feature parameters from the viewpoint of uniform dimension,i.e. self-information measured visual attention and information entropy measured masking uncertainty, achieving heterogeneous parameter homogenization. Thirdly, with self-information and entropy results, this work then proposes a temporal weight model, by striking the balance between visual attention and masking suppression, to adjust the spatial JND threshold, and then develops the improved spatiotemporal JND model. Intensive simulation results verity the effectiveness of the proposed spatiotemporal JND profile, with competitive model accuracy compared with the-state-of-the-art candidate models.\",\n \"Title: Earth Imagery Segmentation on Terrain Surface with Limited Training Labels: A Semi-supervised Approach based on Physics-Guided Graph Co-Training\\nYear: 2022\\n\\nAbstract: AbstractGiven earth imagery with spectral features on a terrain surface, this paper studies surface segmentation based on both explanatory features and surface topology. The problem is important in many spatial and spatiotemporal applications such as flood extent mapping in hydrology. The problem is uniquely challenging for several reasons: first, the size of earth imagery on a terrain surface is often much larger than the input of popular deep convolutional neural networks; second, there exists topological structure dependency between pixel classes on the surface, and such dependency can follow an unknown and non-linear distribution; third, there are often limited training labels. Existing methods for earth imagery segmentation often divide the imagery into patches and consider the elevation as an additional feature channel. These methods do not fully incorporate the spatial topological structural constraint within and across surface patches and thus often show poor results, especially when training labels are limited. Existing methods on semi-supervised and unsupervised learning for earth imagery often focus on learning representation without explicitly incorporating surface topology. In contrast, we propose a novel framework that explicitly models the topological skeleton of a terrain surface with a contour tree from computational topology, which is guided by the physical constraint (e.g., water flow direction on terrains). Our framework consists of two neural networks: a convolutional neural network (CNN) to learn spatial contextual features on a 2D image grid, and a graph neural network (GNN) to learn the statistical distribution of physics-guided spatial topological dependency on the contour tree. The two models are co-trained via variational EM. Evaluations on the real-world flood mapping datasets show that the proposed models outperform baseline methods in classification accuracy, especially when training labels are limited.\",\n \"Title: Improved Residual Network for Automatic Classification Grading of Lettuce Freshness\\nYear: 2022\\n\\nAbstract: To solve the problem of the low efficiency of traditional lettuce freshness classification methods and sample damage, we proposed an automatic lettuce freshness classification method based on improved deep residuals convolutional neural network (Im-ResNet). We built an image acquisition system to obtain the freshness classification dataset of lettuce leaves. For improving the classification accuracy, we developed an image acquisition system for curating the freshness of lettuce leaves. Then, we proposed a novel method that was derived from the existing ResNet-50 (which uses ReLU activation function) known as Improved Residual Networks (Im-ResNet): the new method factored extra convolutional layer, pooling layer, fully-connected layers, and a random ReLU (RReLU) activation function. We also performed the corresponding experiments using the Im-ResNet network compared with four network architectures (AlexNet, GoogleNet, VGG16 and ResNet50). The experimental results showed that the proposed network had more significant advantages in the recognition accuracy and loss value of lettuce freshness compared with the traditional deep networks. The recognition accuracy of the validation set of the proposed model can reach to 95.60%. Different from the physical and chemical methods, our scheme can automatically and non-destructively classify the freshness of lettuce.\",\n \"Title: Wireless Performance Evaluation of Building Materials Integrated With Antenna Arrays\\nYear: 2022\\n\\nAbstract: To avoid negative visual and weight impact of antenna arrays in indoor multiple-input multiple-output (MIMO) systems, the antenna arrays can be integrated in building materials. In this letter, we model building materials integrated with antenna arrays and develop an approach that can characterize and evaluate their wireless performance using a ray-tracing model that incorporates multipath propaga...\",\n \"Title: Multi-Timescale Load Forecast of Large Power Customers Based on Online Data Recovery and Time Series Neural Networks\\nYear: 2022\\n\\nAbstract: This paper presents a multi-timescale receding horizon framework for the load forecast of large power customers. The future load pattern of individual users could be very difficult to predict because of its chronological and high volatile properties. Also, the sampling of nonaggregated load data may suffer from severe information missing issues. To address these challenges, we first develop an online singular value thresholding (SVT) algorithm, which utilizes the approximate low-rank property of load data matrices to efficiently recover the missing information. Then, a combinatorial deep learning method is developed, which applies the multi-layer perception (MLP) neural network and the long short-term memory (LSTM) neural network with gated recurrent unit (GRU) to deal with the short-term and ultra-short-term load forecast, respectively. Specifically, an early stopping strategy is designed and implemented to avoid the over-fitting of model training. Moreover, the receding time window is imposed to dynamically update the data recovery and load forecast outcomes, which supports the online computing on a Spark platform. Numerical experiments on real-world load data from North China confirms the effectiveness of the proposed methodology, which can support more complex applications in embedded systems and cyber physical systems.\",\n \"Title: A Distributed and Privacy-Preserving Random Forest Evaluation Scheme with Fine Grained Access Control\\nYear: 2022\\n\\nAbstract: Random forest is a simple and effective model for ensemble learning with wide potential applications. Implementation of random forest evaluations while preserving privacy for the source data is demanding but also challenging. In this paper, we propose a practical and fault-tolerant privacy-preserving random forest evaluation scheme based on asymmetric encryption. The user can use asymmetric encryption to encrypt the data outsourced to the cloud platform and specify who can access the final evaluation results. After receiving the encrypted inputs from the user, the cloud platform evaluates via a random forest model and outputs the aggregated results where only the designated recipient can decrypt them. Threat analyses prove that the proposed scheme achieves the desirable security properties, such as correctness, confidentiality and robustness. Moreover, efficiency analyses demonstrate that the scheme is practical for real-world applications.\",\n \"Title: A Lightweight NMS-Free Framework for Real-Time Visual Fault Detection System of Freight Trains\\nYear: 2022\\n\\nAbstract: Real-time vision-based system of fault detection (RVBS-FD) for freight trains is an essential part of ensuring railway transportation safety. Most existing vision-based methods still have high computational costs based on convolutional neural networks. The computational cost is mainly reflected in the backbone, neck, and postprocessing, i.e., nonmaximum suppression (NMS). In this article, we propose a lightweight NMS-free framework to achieve real-time detection and high accuracy simultaneously. First, we use a lightweight backbone for feature extraction and design a fault detection pyramid to process features. This fault detection pyramid includes three novel individual modules using attention mechanism, bottleneck, and dilated convolution for feature enhancement and computation reduction. Instead of using NMS, we calculate different loss functions, including classification and location costs in the detection head, to further reduce computation. Experimental results show that our framework achieves over 83-frames/s speed with a smaller model size and higher accuracy than the state-of-the-art detectors. Meanwhile, the hardware resource requirements of our method are low during the training and testing process.\",\n \"Title: Seismic Random Noise Removal Based on a Multiscale Convolution and Densely Connected Network for Noise Level Evaluation\\nYear: 2022\\n\\nAbstract: Traditional denoising methods for seismic exploration data design a corresponding mathematical denoising model batch according to the different properties of different random noises, which is a tedious and time-consuming process. To solve this problem, this paper proposes a deep convolutional neural network denoising model based on noise estimation (MCD-DCNN). This model is primarily composed of two modules, the noise estimation module and the denoising module. The noise estimation module uses a multiscale convolutional neural network to better extract the characteristics of random noise in the seismic data. To make full use of the extracted features, a dense connection method is adopted between the multiscale convolutions in the noise estimation module. In the denoising module, we use multiscale convolutions and dense connections to replace the original convolutional neural network and use the residual structure (ResNet) and batch normalization (BN) to improve the denoising effect and running speed of the model. In this experiment, single trace and simple and complex profile data are used as input to simulate the real data processing environment. Finally, we compare the denoising effects of the MCD-DCNN model proposed in this paper with the current mainstream feed-forward denoising convolutional neural network (DnCNN) and a fast and flexible denoising convolutional neural network (FFDNet) models. The comprehensive results show that under the condition of a given prior noise level, the denoising performance of the FFDNet and MCD-DCNN models are comparable. In the absence of a priori noise level, the denoising performance of the FFDNet model drops sharply, while the denoising performance of MCD-DCNN is not affected; therefore, MCD-DCNN is more in line with actual seismic denoising.\",\n \"Title: APES: Articulated Part Extraction from Sprite Sheets\\nYear: 2022\\n\\nAbstract: Rigged puppets are one of the most prevalent representations to create 2D character animations. Creating these puppets requires partitioning characters into independently moving parts. In this work, we present a method to automatically identify such articulated parts from a small set of character poses shown in a sprite sheet, which is an illustration of the character that artists often draw before puppet creation. Our method is trained to infer articulated parts, e.g. head, torso and limbs, that can be reassembled to best reconstruct the given poses. Our results demonstrate significantly better performance than alternatives qualitatively and quantitatively. Our project page https://zhan-xu.github.io/parts/ includes our code and data.\",\n \"Title: Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing.\\nYear: 2022\\n\\nAbstract: Only recently, researchers attempt to provide classification algorithms with provable group fairness guarantees. Most of these algorithms suffer from harassment caused by the requirement that the training and deployment data follow the same distribution. This paper proposes an input-agnostic certified group fairness algorithm, FairSmooth, for improving the fairness of classification models while maintaining the remarkable prediction accuracy. A Gaussian parameter smoothing method is developed to transform base classifiers into their smooth versions. An optimal individual smooth classifier is learnt for each group with only the data regarding the group and an overall smooth classifier for all groups is generated by averaging the parameters of all the individual smooth ones. By leveraging the theory of nonlinear functional analysis, the smooth classifiers are reformulated as output functions of a Nemytskii operator. Theoretical analysis is conducted to derive that the Nemytskii operator is smooth and induces a Frechet differentiable smooth manifold. We theoretically demonstrate that the smooth manifold has a global Lipschitz constant that is independent of the domain of the input data, which derives the input-agnostic certified group fairness.\",\n \"Title: Accelerated Federated Learning with Decoupled Adaptive Optimization.\\nYear: 2022\\n\\nAbstract: The federated learning (FL) framework enables edge clients to collaboratively learn a shared inference model while keeping privacy of training data on clients. Recently, many heuristics efforts have been made to generalize centralized adaptive optimization methods, such as SGDM, Adam, AdaGrad, etc., to federated settings for improving convergence and accuracy. However, there is still a paucity of theoretical principles on where to and how to design and utilize adaptive optimization methods in federated settings. This work aims to develop novel adaptive optimization methods for FL from the perspective of dynamics of ordinary differential equations (ODEs). First, an analytic framework is established to build a connection between federated optimization methods and decompositions of ODEs of corresponding centralized optimizers. Second, based on this analytic framework, a momentum decoupling adaptive optimization method, FedDA, is developed to fully utilize the global momentum on each local iteration and accelerate the training convergence. Last but not least, full batch gradients are utilized to mimic centralized optimization in the end of the training process to ensure the convergence and overcome the possible inconsistency caused by adaptive optimization methods.\",\n \"Title: Audio-driven Neural Gesture Reenactment with Video Motion Graphs\\nYear: 2022\\n\\nAbstract: Human speech is often accompanied by body gestures including arm and hand gestures. We present a method that reenacts a high-quality video with gestures matching a target speech audio. The key idea of our method is to split and re-assemble clips from a reference video through a novel video motion graph encoding valid transitions between clips. To seamlessly connect different clips in the reenactment, we propose a pose-aware video blending network which synthesizes video frames around the stitched frames between two clips. Moreover, we developed an audio-based gesture searching algorithm to find the optimal order of the reenacted frames. Our system generates reen-actments that are consistent with both the audio rhythms and the speech content. We evaluate our synthesized video quality quantitatively, qualitatively, and with user studies, demonstrating that our method produces videos of much higher quality and consistency with the target audio compared to previous work and baselines. Our project page https://github.com/yzhou359/vid-reenact includes code and data.\",\n \"Title: Faithful Extreme Rescaling via Generative Prior Reciprocated Invertible Representations\\nYear: 2022\\n\\nAbstract: This paper presents a Generative prior ReciprocAted Invertible rescaling Network (GRAIN) for generating faithful high-resolution (HR) images from low-resolution (LR) invertible images with an extreme upscaling factor (64×). Previous researches have leveraged the prior knowledge of a pretrained GAN model to generate high-quality upscaling results. However, they fail to produce pixel-accurate results due to the highly ambiguous extreme mapping process. We remedy this problem by introducing a reciprocated invertible image rescaling process, in which high-resolution information can be delicately embedded into an invertible low-resolution image and generative prior for a faithful HR reconstruction. In particular, the invertible LR features not only carry significant HR semantics, but also are trained to predict scale-specific latent codes, yielding a preferable utilization of generative features. On the other hand, the enhanced generative prior is re-injected to the rescaling process, compensating the lost details of the invertible rescaling. Our reciprocal mechanism perfectly integrates the advantages of invertible encoding and generative prior, leading to the first feasible extreme rescaling solution. Extensive experiments demonstrate superior performance against state-of-the-art upscaling methods. Code is available at https://github.com/cszzx/GRAIN.\",\n \"Title: A Miniaturized Ultrawideband Active Electric Probe With High Sensitivity for Near-Field Measurement\\nYear: 2022\\n\\nAbstract: A miniaturized ultrawideband active electric probe with high sensitivity for near-field measurement is proposed in this article. The probe is designed with a four-layer printed circuit board (PCB) with mainly two chips, an amplification chip (HMC797APM5E) which can work from de to 22 GHz and a power management chip (HMC980LP4E). The probe can work from 9 kHz to 18 GHz with 15-dB average gain compared to the passive probe, and the flatness is about +/- 4 dB in terms of vertical bar S-21 vertical bar (transfer factor) from 2.5 to 18 GHz. The maximum size of the probe is 64 mm x 41.5 mm with 1-mm spatial resolution, and the width of the tip is only 4.1 mm, so the probe can handle most test environments. On the whole, the probe is very efficient for near-field measurement to detect weak signals in a wide bandwidth.\",\n \"Title: An edge intelligence empowered flooding process prediction using Internet of things in smart city\\nYear: 2022\\n\\nAbstract: Floods result in substantial damage throughout the world every year. Accurate predictions of floods can significantly alleviate casualties and property losses. However, due to the complexity of hydrology process especially in a city with complicated pipe network, the accuracy of traditional flood forecasting models suffer from the performance degradation with the increasing of required prediction period. In the work, based on the collected historical data of Xixian City, Henan Province, China, using the Internet of Things system (IoT) in 2011-2018, a Bidirectional Gated Recurrent Unit (BiGRU) multi-step flood prediction model with attention mechanism is proposed. In our model, the attention mechanism is used to automatically adjust the matching degree between the input features and output. Besides, we use a bidirectional GRU model, which can process the input sequence from two directions of time series (chronologically and antichronologically), then merge their representations together. Compared with the prediction model using Long Short Term Memory (LSTM), our method can generate better prediction result, as can be seen from the arrival time error and peak error of floods during multi-step predictions.\",\n \"Title: LEDet: localization estimation detector with data augmentation for ship detection based on unmanned surface vehicle\\nYear: 2022\\n\\nAbstract: Ship detection is significant for monitoring ports, especially contributing to the safe driving of Unmanned Surface Vehicle (USV). However, recent ship detection based on deep learning lacks complete ship datasets and uses the classification score as the ranking basis, which harms their performance. To address the problems, we present a one-stage localization estimation detector (LEDet) with ship-customized data augmentation. Specifically, we integrate the localization quality estimation into the classification branch as a soft label localization score. We further apply ship-customized data augmentation named “cutting-transform-paste” to expand ship datasets without manual annotation. Hence, a large number of diverse ship datasets can be created. Extensive experiments show that our LEDet consistently exceeds the strong baseline by 8.0% COCO-style Average Precision (AP) with ResNet-50. It significantly improves the ship detection performance.\",\n \"Title: An infrared pedestrian detection method based on segmentation and domain adaptation learning\\nYear: 2022\\n\\nAbstract: Benefiting from the capability of night viewing, infrared images has been widely applied to surveillance systems as an effective complement to visible-light images. However, the development of infrared pedestrian detection is still impeded by weak features and limited diversity of infrared images. Aiming at these two problems, we designed a multi-task learning framework for pedestrian detection by incorporating a semantic segmentation branch and a domain adaptation branch. Composed of UNet network with Swin Transformer, the semantic segmentation could apply spatial constraints to pedestrian detection. The domain adaptation branch aligns the features between infrared and visible-light images to improve the scene diversity. In addition, three tasks shared a basic feature extraction network to reduce computation cost. The experiment results show that the average precision (AP) of our method is superior to the EfficientDet network by 2.0% on the XDU-NIR2020 dataset and 2.2% on the CVC-09 dataset respectively.\",\n \"Title: PrefixFPM: a parallel framework for general-purpose mining of frequent and closed patterns\\nYear: 2022\\n\\nAbstract: A frequent pattern is a substructure that appears in a database with frequency (aka. support) no less than a user-specified threshold, while a closed pattern is one that has no super-pattern that has the same support. Here, a substructure can refer to different structural forms, such as itemsets, subsequences, subtrees, and subgraphs, and mining such substructures is important in many real applications such as product recommendation and feature extraction. Currently, there lacks a general programming framework that can be easily customized to mine different types of patterns, and existing parallel and distributed solutions are IO-bound rendering CPU cores underutilized. Since mining frequent and/or closed patterns are NP-hard, it is important to fully utilize the available CPU cores. This paper presents such a general-purpose framework called PrefixFPM. The framework is based on the idea of prefix projection which allows a divide-and-conquer mining paradigm. PrefixFPM exposes a unified programming interface to users who can readily customize it to mine their desired patterns. We have adapted the state-of-the-art serial algorithms for mining patterns including subsequences, subtrees, and subgraphs on top of PrefixFPM, and extensive experiments demonstrate an excellent speedup ratio of PrefixFPM with the number of CPU cores.\",\n \"Title: LNMF: lightweight network for multi-focus image fusion\\nYear: 2022\\n\\nAbstract: Due to limitations of the optical lenses, using the digital single-lens camera to obtain an all-in-focus image is difficult. In order to overcome this difficulty, a lot of multi-focus image fusion methods have been proposed to produce all-in-focus images. With the development of Deep Learning (DL), the Convolutional Neural Networks (CNNs) have been successfully applied to image fusion. Currently proposed DL-based multi-focus fusion methods use deeper and wider networks to obtain better fusion results. However, due to the large computational complexity and memory consumption of standard convolution operations and deeper networks, these methods are unable to be applied to small devices. To address this issue, we propose a novel multi-focus image fusion method using a lightweight network. In this paper, we convert the fusion problem into a classification problem firstly. Then, using a lightweight network to classify focus and defocus areas of source images. Finally, focus areas are fused to obtain an all-in-focus image. The proposed lightweight network reduces operation complexity and memory consumption while achieving good fusion effects. The experimental results demonstrate that the proposed method with fewer parameters and computational costs has a better performance on quality and quantitative evaluations than other state-of-the-art methods.\",\n \"Title: Vision-based Relative Detection and Tracking for Teams of Micro Aerial Vehicles\\nYear: 2022\\n\\nAbstract: In this paper, we address the vision-based detection and tracking problems of multiple aerial vehicles using a single camera and Inertial Measurement Unit (IMU) as well as the corresponding perception consensus problem (i.e., uniqueness and identical IDs across all observing agents). We design several vision-based decentralized Bayesian multi-tracking filtering strategies to resolve the association between the incoming unsorted measurements obtained by a visual detector algorithm and the tracked agents. We compare their accuracy in different operating conditions as well as their scalability according to the number of agents in the team. This analysis provides useful insights about the most appropriate design choice for the given task. We further show that the proposed perception and inference pipeline which includes a Deep Neural Network (DNN) as visual target detector is lightweight and capable of concurrently running control and planning with Size, Weight, and Power (SWaP) constrained robots on-board. Experimental results show the effective tracking of multiple drones in various challenging scenarios such as heavy occlusions.\",\n \"Title: Discovering unknowns: Context-enhanced anomaly detection for curiosity-driven autonomous underwater exploration\\nYear: 2022\\n\\nAbstract: •Anomaly detection for unknowns towards to autonomous underwater exploration.•Autoencoder and autoregressive network to identify anomalies in unstructured dynamic underwater moving views.•Novel context-enhanced autoregressive network to learn feature dependence.•Patch learning paradigm to build an accurate latent feature space.•Validation on two benchmarks, simulation, and real data, outperforms state-of-arts.\",\n \"Title: Skeleton-Free Pose Transfer for Stylized 3D Characters.\\nYear: 2022\\n\\nAbstract: We present the first method that automatically transfers poses between stylized 3D characters without skeletal rigging. In contrast to previous attempts to learn pose transformations on fixed or topology-equivalent skeleton templates, our method focuses on a novel scenario to handle skeleton-free characters with diverse shapes, topologies, and mesh connectivities. The key idea of our method is to represent the characters in a unified articulation model so that the pose can be transferred through the correspondent parts. To achieve this, we propose a novel pose transfer network that predicts the character skinning weights and deformation transformations jointly to articulate the target character to match the desired pose. Our method is trained in a semi-supervised manner absorbing all existing character data with paired/unpaired poses and stylized shapes. It generalizes well to unseen stylized characters and inanimate objects. We conduct extensive experiments and demonstrate the effectiveness of our method on this novel task.\",\n \"Title: Mechanistic Studies on the Stereoselectivity of FFAR1 Modulators\\nYear: 2022\\n\\nAbstract: Free fatty acid receptor 1 (FFAR1) is a potential therapeutic target for the treatment of type 2 diabetes (T2D). It has been validated that agonists targeting FFAR1 can achieve the initial therapeutic endpoints of T2D, and the epimer agonists (R,S) AM-8596 can activate FFAR1 differently, with one acting as a partial agonist and the other as a full agonist. Up to now, the origin of the stereoselectivity of FFAR1 agonists remains elusive. In this work, we used molecular simulation methods to elucidate the mechanism of the stereoselectivity of the FFAR1 agonists (R)-AM-8596 and (S)-AM-8596. We found that the full agonist (R)-AM-8596 disrupts the residue interaction network around the receptor binding pocket and promotes the opening of the binding site for the G-protein, thereby resulting in the full activation of FFAR1. In contrast, the partial agonist (S)-AM-8596 forms stable electrostatic interactions with FFAR1, which stabilizes the residue network and hinders the conformational transition of the receptor. Our work thus clarifies the selectivity and underlying molecular activation mechanism of FFAR1 agonists.\",\n \"Title: Quantifying and Reducing Registration Uncertainty of Spatial Vector Labels on Earth Imagery\\nYear: 2022\\n\\nAbstract: Given raster imagery features and imperfect vector training labels with registration uncertainty, this paper studies a deep learning framework that can quantify and reduce the registration uncertainty of training labels as well as train neural network parameters simultaneously. The problem is important in broad applications such as streamline classification on Earth imagery or tissue segmentation on medical imagery, whereby annotating precise vector labels is expensive and time-consuming. However, the problem is challenging due to the gap between the vector representation of class labels and the raster representation of image features and the need for training neural networks with uncertain label locations. Existing research on uncertain training labels often focuses on uncertainty in label class semantics or characterizes label registration uncertainty at the pixel level (not contiguous vectors). To fill the gap, this paper proposes a novel learning framework that explicitly quantifies vector labels' registration uncertainty. We propose a registration-uncertainty-aware loss function and design an iterative uncertainty reduction algorithm by re-estimating the posterior of true vector label locations distribution based on a Gaussian process. Evaluations on real-world datasets in National Hydrography Dataset refinement show that the proposed approach significantly outperforms several baselines in the registration uncertainty estimations performance and classification performance.\",\n \"Title: Learning Visibility for Robust Dense Human Body Estimation.\\nYear: 2022\\n\\nAbstract: Estimating 3D human pose and shape from 2D images is a crucial yet challenging task. While prior methods with model-based representations can perform reasonably well on whole-body images, they often fail when parts of the body are occluded or outside the frame. Moreover, these results usually do not faithfully capture the human silhouettes due to their limited representation power of deformable models (e.g., representing only the naked body). An alternative approach is to estimate dense vertices of a predefined template body in the image space. Such representations are effective in localizing vertices within an image but cannot handle out-of-frame body parts. In this work, we learn dense human body estimation that is robust to partial observations. We explicitly model the visibility of human joints and vertices in the x, y, and z axes separately. The visibility in x and y axes help distinguishing out-of-frame cases, and the visibility in depth axis corresponds to occlusions (either self-occlusions or occlusions by other objects). We obtain pseudo ground-truths of visibility labels from dense UV correspondences and train a neural network to predict visibility along with 3D coordinates. We show that visibility can serve as 1) an additional signal to resolve depth ordering ambiguities of self-occluded vertices and 2) a regularization term when fitting a human body model to the predictions. Extensive experiments on multiple 3D human datasets demonstrate that visibility modeling significantly improves the accuracy of human body estimation, especially for partial-body cases. Our project page with code is at: https://github.com/chhankyao/visdb.\",\n \"Title: An independent steering driving system to realize headland turning of unmanned tractors\\nYear: 2022\\n\\nAbstract: •A 360°steering auxiliary wheel is installed on the tractor to realize efficient turning at the headland.•The auxiliary wheel system has the functions of automatic steering, lifting and driving.•The minimum turning radius of the modified tractor is 0 m.•The auxiliary wheel steering method decreases the ground space occupancy at the headland by 50 %.\",\n \"Title: On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities\\nYear: 2022\\n\\nAbstract: As the smartphone market leader, Android has been a prominent target for malware attacks. The number of malicious applications (apps) identified for it has increased continually over the past decade, creating an immense challenge for all parties involved. For market holders and researchers, in particular, the large number of samples has made manual malware detection unfeasible, leading to an influx of research that investigate Machine Learning (ML) approaches to automate this process. However, while some of the proposed approaches achieve high performance, rapidly evolving Android malware has made them unable to maintain their accuracy over time. This has created a need in the community to conduct further research, and build more flexible ML pipelines. Doing so, however, is currently hindered by a lack of systematic overview of the existing literature, to learn from and improve upon the existing solutions. Existing survey papers often focus only on parts of the ML process (e.g., data collection or model deployment), while omitting other important stages, such as model evaluation and explanation. In this paper, we address this problem with a review of 42 highly-cited papers, spanning a decade of research (from 2011 to 2021). We introduce a novel procedural taxonomy of the published literature, covering how they have used ML algorithms, what features they have engineered, which dimensionality reduction techniques they have employed, what datasets they have employed for training, and what their evaluation and explanation strategies are. Drawing from this taxonomy, we also identify gaps in knowledge and provide ideas for improvement and future work.\",\n \"Title: Research on Size System of Disposable PE Gloves Based on Hand Contour Measurement\\nYear: 2022\\n\\nAbstract: Disposable PE gloves are in great demand in the catering industry, and although their use accuracy is not high, disposable PE gloves of inconsistent size will still cause great discomfort to consumers. Therefore, how to adjust the current disposable PE glove production size system is the guarantee for the better development of the industry. In this study, the photo measurement method was used to measure the size and contour of the adult hand. Through cluster analysis and variance analysis, three types of disposable PE gloves size systems were determined. This size system provides one type of gloves for women, and two types of gloves for men with a height of 180cm as the dividing line.\",\n \"Title: Cooperative Critical Turning Point-Based Decision-Making and Planning for CAVH Intersection Management System\\nYear: 2022\\n\\nAbstract: The intersection is a critical traffic problem from the perspective of safety and traffic efficiency. As wireless communication technology advances, vehicle infrastructure cooperative approaches have received increased attention. In this paper, we propose a cooperative critical turning point method to help the cooperation between vehicles and infrastructures to improve the traffic efficiencies. The idea of cooperative critical turning point improves the cooperation between connected automated vehicles, the surrounding traffic and roadside infrastructures in order to provide high efficiencies of the intersection. An intersection management system using such a method is implemented based on the framework of the connected automated vehicle highway system. Such system can efficiently allow a roadside infrastructure receives state information from vehicles, reserve the associated intersection time-space occupancy, and then provide decision-making and planning feedback to the vehicles. The vehicles covered by the system then adjust their trajectories to meet their assigned time slot. The study validates the proposed system that considers the uncertainties of the driving environment by formulating the problem into a POMDP problem and solves it using an online solver. Based on preliminary simulation experiments, the proposed strategy can significantly reduce travel delays, decrease stops and improve the sustainability of the traffic system.\",\n \"Title: A Novel Distributed Predefined-Time Sliding Mode Controller for Performance Enhancement of Power System Under Input Saturation\\nYear: 2022\\n\\nAbstract: This article proposes a novel distributed predefined-time sliding mode controller (DPTSMC) to gain exceptional performances of power system under input saturation. Firstly, considering the saturation of excitation input to avoid the overvoltage of the coil, an auxiliary system is taken to compensate the effect of the excitation saturation. Then, the sliding mode control (SMC) and the predefined-time stability theory are taken to devise the DPTSMC to make the power system stable in a predefined time, which has an upper limit and is directly equivalent to an adjustable parameter, thus satisfying the high requirement of the power system for expected convergence time. Meanwhile, the estimated upper bound convergence time of the power system under the proposed DPTSMC is less conservative than some existing fixed-time control strategies. Moreover, the distributed control is taken to devise the DPTSMC and the controller can receive the real-time information from the phasor measurement units (PMU). Hence, the central controller is not required and the communication network is simple, thus releasing the pressure in complicated calculation and communication, and gaining rapid and coordinated response capacity. Next, the stabilization of the power system under the DPTSMC within a predefined upper limited time is proved through the Lyapunov function. Finally, the exceptional performance of the proposed DPTSMC than the existing control methods is verified through the simulation in the IEEE 39-bus 10-machine New England power system under different operation scenarios.\",\n \"Title: Water Gauge Image Augmentation based on Generative Adversarial Network\\nYear: 2022\\n\\nAbstract: Water level monitoring based on water gauge is a very widely used way because of its cheapness and portability. However, the insufficiency and low quality of water gauge images restrict the performance of water level measuring task based on deep learning methods such as object detection and semantic segmentation. In this article, we proposed a generative adversarial network (GAN) named Contextual adjustment GAN (CA-GAN) for data augmentation of water gauge images obtained from Wuyuan, Jiangxi Province in China, i.e. CA-GAN can generate high-quality images containing various scales and types water gauge, which provide image data for application such as deep-learning based water level measuring method. First, a improved downsampling module is designed with the help of segmentation map for the semantic activation modulation. Then, the Unet++ structure with the improved downsampling module is applied in the generator. Finally, to modulate the semantic relationship, a contextual adjustment scheme is de-signed between adjacent layers. This article conducts detailed experiments, proving that the improved downsampling module contributes to the maintenance of semantic information of water gauge images. It is illustrated that the water gauge images generated by CA-GAN have higher quality comparing with other three GAN models. And our method is expected to promote the water level measurement and hydrological monitoring application development.\",\n \"Title: Maximal Directed Quasi-Clique Mining\\nYear: 2022\\n\\nAbstract: Quasi-cliques are a type of dense subgraphs that generalize the notion of cliques, important for applications such as community/module detection in various social and biological networks. However, the existing quasi-clique definition and algorithms are only applicable to undirected graphs. In this paper, we generalize the concept of quasi-cliques to directed graphs by proposing (.1,.2)-quasi-cliques which have density requirements in both inbound and outbound directions of each vertex in a quasi-clique subgraph. An efficient recursive algorithm is proposed to find maximal (.1,.2)-quasi-cliques which integrates many effective pruning rules that are validated by ablation studies. We also study the finding of top-k large quasi-cliques directly by bootstrapping the search from more compact quasicliques, to scale the mining to larger networks. The algorithms are parallelized with effective load balancing, and we demonstrate that they can scale up effectively with the number of CPU cores.\",\n \"Title: Automatic Field Extraction of Extended TLV for Binary Protocol Reverse Engineering\\nYear: 2022\\n\\nAbstract: Type Length Value (TLV) is one of the main structures commonly used in network protocols. A large number of proprietary protocols, whose specification is unknown to the public, run in the current Internet as well as domain-specific Internet of Things (IoT) applications. It is critical to infer the TLV fields within a packet because this information can help network administrators quickly identify abnormal traffic and potential attacks. Inferring TLV fields belongs to the general task of protocol reverse engineering and is particularly challenging for binary protocols, where the boundaries of TLV fields have many possible positions. Existing methods for reverse engineering binary protocols involve many parameters and only work for protocols strictly following the conventional TLV format. We extend the concept of TLV to accommodate a broader category of structural patterns in various binary protocols, such as TCP, IP, ModBus, and MQTT. We then design algorithms to automatically extract the extended - TLV fields from packets. Via a series of experiments over several protocols, we demonstrate that our algorithms can accurately and quickly identify the extended-TLV fields in all the tested protocols. Our approach can thus be deployed as a general method for automatically reverse engineering binary protocol format.\",\n \"Title: Bidirectional Relation Attention for Entity Alignment Based on Graph Convolutional Network\\nYear: 2022\\n\\nAbstract: Entity alignment is an effective solution to integrate resources from different knowledge graphs (KGs). It aims to establish link between entities in different KGs that refer to the same object in the real world. Most of the existing embedding-based methods embed entities and relations into the same vector space or utilize a graph neural network (GNN) for nodes aggregation to learn the topology of the KG. However, these approaches do not sufficiently distinguish the effects of different relations on node features and ignore the dependencies between relations and entities. Some latent semantic information hidden in the relations is underutilized. This paper proposes a bidirectional relation attention mechanism to jointly learn entity and relation embeddings. We employ a graph convolutional network (GCN) to obtain the structure embedding for the KGs, then the attention mechanism used in relation embeddings facilitates the flow of information from entities to relations and strengthen the dependency between them. Finally the relation attention exploited in the aggregation stage integrates both direction information and connected relation semantics of neighborhoods that capture different impacts of relation on the neighborhood nodes. Experiments conducted on the DBP15K dataset validate the effectiveness of our proposed model and show that our model outperforms the baseline methods.\",\n \"Title: Debris-Flow Susceptibility Assessment in China: A Comparison between Traditional Statistical and Machine Learning Methods\\nYear: 2022\\n\\nAbstract: Debris flows, triggered by dual interferences extrinsically and intrinsically, have been widespread in China. The debris-flow susceptibility (DFS) assessment is acknowledged as the benchmark for the mitigation and prevention of debris flow risks, but DFS assessments at the national level are lacking. The role of human activities in the DFS assessment has always been overlooked. On the basis of a detailed inventory of debris-flow sites and a large set of environmental and human-related characteristics, this research presents the comparative performance of the well-known information value (IV), logistic regression (LR) and random forest (RF) models for DFS assessments in China. Twelve causative factors, namely, elevation, slope, aspect, rainfall, the normalized difference vegetation index (NDVI), land use, landform, geology, distance to faults, density of villages, distance to rivers and distance to roads, were considered. Debris-flow susceptibility maps were then generated after the nonlinear relationship between the debris-flow occurrence and the causative factors was captured. Finally, the predictive performance of the three maps was evaluated through receiver operating characteristic (ROC) curves, and the validation results showed that areas under the ROC curves were 81.98%, 79.96% and 97.38% for the IV, LR and RF models, respectively, indicating that the RF model outperformed the other two traditional statistical methods. The importance ranking of the RF model also revealed that distance to roads, slope and rainfall dominated the spatial distribution of debris flows. This is the first experiment to compare between the traditional statistical and machine learning methods in DFS studies for the whole of China. Our results could provide some empirical support for China's policymakers and local practitioners in their efforts to enable residents to be less vulnerable to disasters.\",\n \"Title: Traffic Volatility Forecasting Using an Omnibus Family GARCH Modeling Framework\\nYear: 2022\\n\\nAbstract: Traffic volatility modeling has been highly valued in recent years because of its advantages in describing the uncertainty of traffic flow during the short-term forecasting process. A few generalized autoregressive conditional heteroscedastic (GARCH) models have been developed to capture and hence forecast the volatility of traffic flow. Although these models have been confirmed to be capable of producing more reliable forecasts than traditional point forecasting models, the more or less imposed restrictions on parameter estimations may make the asymmetric property of traffic volatility be not or insufficiently considered. Furthermore, the performance of the models has not been fully evaluated and compared in the traffic forecasting context, rendering the choice of the models dilemmatic for traffic volatility modeling. In this study, an omnibus traffic volatility forecasting framework is proposed, where various traffic volatility models with symmetric and asymmetric properties can be developed in a unifying way by fixing or flexibly estimating three key parameters, namely the Box-Cox transformation coefficient A, the shift factor b, and the rotation factor c. Extensive traffic speed datasets collected from urban roads of Kunshan city, China, and from freeway segments of the San Diego Region, USA, were used to evaluate the proposed framework and develop traffic volatility forecasting models in a number of case studies. The models include the standard GARCH, the threshold GARCH (TGARCH), the nonlinear ARCH (NGARCH), the nonlinear-asymmetric GARCH (NAGARCH), the Glosten-Jagannathan-Runkle GARCH (GJR-GARCH), and the family GARCH (FGARCH). The mean forecasting performance of the models was measured with mean absolute error (MAE) and mean absolute percentage error (MAPE), while the volatility forecasting performance of the models was measured with volatility mean absolute error (VMAE), directional accuracy (DA), kickoff percentage (KP), and average confidence length (ACL). Experimental results demonstrate the effectiveness and flexibility of the proposed framework and provide insights into how to develop and select proper traffic volatility forecasting models in different situations.\",\n \"Title: Fast algorithms for supermodular and non-supermodular minimization via bi-criteria strategy\\nYear: 2022\\n\\nAbstract: In this paper, we concentrate on exploring fast algorithms for the minimization of a non-increasing supermodular or non-supermodular function f subject to a cardinality constraint. As for the non-supermodular minimization problem with the weak supermodularity ratio r, we can obtain a (1 + epsilon)-approximation algorithm with adaptivity O(n/epsilon log rn.f (empty set)/epsilon.OPT) under the bi-criteria strategy, where OPT denotes the optimal objective value of the problem. That is, instead of selecting at most k elements on behalf of the constraint, the cardinality of the output may reach to k/r log f (empty set)/epsilon.OPT. Moreover, for the supermodular minimization problem, we propose two (1 + epsilon)-approximation algorithms forwhich the output solution X is of size vertical bar X-0 vertical bar+ O (k log f (X-0)/epsilon.OPT. The adaptivities of this two algorithms are O (log(2) n . log f (X-0)/epsilon.OPT and O (log n . log f (X-0)/epsilon.OPT), where X-0 is an input set and OPT is the optimal value. Applications to group sparse linear regression problems and fuzzy C-means problems are studied at the end.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":200,"string":"200"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13678,"string":"13,678"}}},{"rowIdx":13679,"cells":{"question_id":{"kind":"string","value":"James C Hoe"},"question":{"kind":"string","value":"Generate an abstract for the title \"2009 MEMOCODE co-design contest\" using the following items: MEMOCODE, Co-Design, Contest, Design methodology, Platform."},"answer":{"kind":"string","value":"The 2009 MEMOCODE Co-Design Contest is the third in the series of annual design contests organized by the MEMOCODE Conference. Contestants have one month to create the best performing design solution to a posted design challenge. The contest is open to all interested participants, and the contest rules are designed to not exclude or favor any one design methodology or platform. The goal of the contest is to invite developers of tools and platforms to showcase their technology in a leveled competition and to encourage hands-on design activities in the fields of interest of the MEMOCODE Conference. Please see http://www.memocode-conference.com for current information about this contest."},"correct_answer":{"kind":"string","value":"The 2009 MEMOCODE Co-Design Contest is the third in the series of annual design contests organized by the MEMOCODE Conference. Contestants have one month to create the best performing design solution to a posted design challenge. The contest is open to all interested participants, and the contest rules are designed to not exclude or favor any one design methodology or platform. The goal of the contest is to invite developers of tools and platforms to showcase their technology in a leveled competition and to encourage hands-on design activities in the fields of interest of the MEMOCODE Conference. Please see http://www.memocode-conference.com for current information about this contest."},"documents":{"kind":"list like","value":["Title: Automatic pipelining from transactional datapath specifications\nYear: 2010\n\nAbstract: We present a transactional datapath specification (T-spec) and the tool (T-piper) to synthesize automatically an in-order pipelined implementation from it. T-spec abstractly views a datapath as executing one transaction at a time, computing next system states based on current ones. From a T-spec, T-piper can synthesize a pipelined implementation that preserves original transaction semantics, while allowing simultaneous execution of multiple overlapped transactions across pipeline stages. T-piper not only ensures the correctness of pipelined executions, but can also employ forwarding and speculation to minimize performance loss due to data dependencies. Design case studies on RISC and CISC processor pipeline development are reported.","Title: A personal supercomputer for climate research\nYear: 1999\n\nAbstract: We describe and analyze the performance of a cluster of personal computers dedicated to coupledclimate simulations. This climate modeling system performs comparably to state-of-the-art supercomputersand yet is affordable by individual research groups, thus enabling more spontaneous applicationof high-end numerical models to climate science. The cluster's novelty centers around the Arctic SwitchFabric and the StarT-X network interface, a system-area interconnect substrate developed at MIT....","Title: Algorithm and architecture optimization for large size two dimensional discrete fourier transform (abstract only)\nYear: 2012\n\nAbstract: We present a poster showcasing our FPGA implementations of two-dimensional discrete Fourier transform (2D-DFT) on large datasets that must reside off-chip in DRAM. These memory-bound large 2D-DFT computations are at the heart of important scientific computing and image processing applications. The central challenge in creating high-performance implementations is in the carefully orchestrated use of the available off-chip memory bandwidth and on-chip temporary storage. Our implementations derive their efficiency from a combined attention to both the algorithm design to enable efficient DRAM access patterns and datapath design to extract the maximum compute throughput at a given level of memory bandwidth. The poster reports results including a 1024x1024 double-precision 2D-DFT implementation on an Altera DE4 platform (based on a Stratix IV EP4SGX530 with 12 GB/s DRAM bandwidth) that reached over 16 Gflop/s, achieving a much higher ratio of performance-to-memory-bandwidth than both state-of-the-art CPU and GPU implementations.","Title: MPI-StarT: delivering network performance to numerical applications\nYear: 1998\n\nAbstract: We describe an MPI implementation for a cluster of SMPs interconnected by a high-performance interconnect. This work is a collaboration between a numerical applications programmer and a cluster interconnect architect. The collaboration started with the modest goal of satisfying the communication needs of a specific numerical application, MITMatlab. However, by supporting the MPI standard MPI-StarT readily extends support to a host of applications. MPI-StarT is derived from MPICH by developing a custom implementation of the Channel Interface. Some changes in MPICH's ADI and Protocol Layers are also necessary for correct and optimal operation.MPI-StarT relies on the host SMPs' shared memory mechanism for intra-SMP communication. Inter-SMP communication is supported through StarT-X. The StarT-X NIU allows a cluster of PCI-equipped host platforms to communicate over the Arctic Switch Fabric. Currently, StarT-X is utilized by a cluster of SUN E5000 SMPs as well as a cluster of Intel Pentium-II workstations. On a SUN E5000 with StarT-X, a processor can send and receive a 64-byte message in less than 0.4 and 3.5 usec respectively and incur less than 5.6 usec user-to-user one-way latency. StarT-X's remote memory-to-memory DMA mechanism can transfer large data blocks at 60 MByte/sec between SUN E5000s.This paper outlines our effort to preserve and deliver this level of communication performance through MPI-StarT to user applications. We have studied the requirements of MITMatlab and the capabilities of StarT-X and have formulated an implementation strategy for the Channel Interface. In this paper, we discuss some performance and correctness issues and their resolutions in MPI-StarT. The correctness issues range from the handling of arbitrarily large message sizes to deadlock-free support of nonblocking MPI operations. Performance optimizations include a shared-memory-based transport mechanism for intra-SMP communication and a broadcast mechanism that is aware of the performance difference between intra-SMP and the slower inter-SMP communication.We characterize the performance of MPI-StarT on a cluster of SUN E5000s. On SUN E5000s, MPI processes within the same SMP can communicate at over 150 MByte/sec using shared memory. When communicating between SMPs over StarT-X, MPI-StarT has a peak bandwidth of 56 MByte/sec. While fine-tuning of MPI-StarT is ongoing, we demonstrate that MPI-StarT is effective in enabling the speedup of MITMatlab on a cluster of SMPs by reporting on the performance of some representative numerical operations.","Title: Synchronous extensions to operation centric hardware description languages\nYear: 2004\n\nAbstract: The Abstract Transition System (ATS) is a high-level hardware description framework. ATS's operation-centric abstraction permits perspicuous descriptions of complex concurrent hardware behavior as a sequence of atomic state transitions. However non-determinism in the ATS semantics prevents it from capturing the behavior of systems whose correctness depends upon both function and exact synchronous timing. To address this shortcoming, we present two extensions to ATS-committing transitions and synchronously delayed expressions-to support the specification of synchronous behaviors and interfaces. The new synchronous extensions compose naturally with the original ATS. We describe a compilation strategy for the synchronous extensions that leverages existing ATS synthesis capabilities. We also evaluate the new extensions' ease of description and synthesis quality in several design examples.","Title: Automatic multithreaded pipeline synthesis from transactional datapath specifications\nYear: 2010\n\nAbstract: We present a technique to automatically synthesize a multithreaded in-order pipeline from a high-level unpipelined datapath specification. This work extends the previously proposed transactional specification (T-spec) and synthesis technology (T-piper). The technique not only works with instruction processors but also flexible enough to accept any sequential datapath. It maintains previously proposed non-threaded pipeline features and is enhanced with multithreading features. We report a design space exploration study of 32 multithreaded x86 processor pipelines, all synthesized from a single T-spec.","Title: Superscalar out-of-order demystified in four instructions\nYear: 2003\n\nAbstract: This paper describes a processor design project intended to illustrate the detail inner workings of modern superscalar out-of-order processors. In the project, the students implement a cycle-accurate RTL-level model of an out-of-order core--including rename, issue, execute, completion and retirement stages--based on the MIPS R10000. The processor core only supports four instruction types. First, the basic integer subtract instruction is included to exercise the mechanisms related to register-renamed out-of-order execution. Second, two types of branch instructions, resolving correctly and incorrectly respectively, exercise speculative execution and branch rewind capabilities. Lastly, an exception-triggering instruction tests the support for precise exceptions. The project is designed to be completed in six weeks by a team of two to three students with solid background and strong interest in computer architecture and digital design. This project has been used twice in an advanced graduate computer architecture course (CMU 18-744 Hardware Systems Engineering) and has received favorable feedback from students and industry recruiters. The project handout and required Verilog source files can be downloaded from http://www.ece.cmu.edu/~jhoe/superscalar.","Title: Implementing a high-performance multithreaded microprocessor: a case study in high-level design and validation\nYear: 2009\n\nAbstract: We have developed a 16-way multithreaded microprocessor called BlueSPARC. This in-order, high-throughput processor incorporates complex features such as privileged operations, memory management, and a non-blocking cache subsystem. When supported by a hybrid simulation technique that handles rare, unimplemented behaviors in a software host, the BlueSPARC microprocessor runs unmodified UltraSPARC III-based commercial applications on Solaris 8 while hosted on a single Xilinx XCV2P70 FPGA clocked at 90MHz. This significant effort was achieved in under one man-year using a high-level language and a high-level validation approach. In the first part of the paper, we describe our experience in applying the Bluespec SystemVerilog (BSV) language to develop a large hardware design that must meet specific area and performance requirements. In the second part of the paper, we present the FPGA-accelerated validation approach we employed to check the correct execution of real multithreaded programs running on the BlueSPARC processor. We discuss the challenges and our solutions to validation in the presence of full-system interactions and microarchitectural nondeterminism.","Title: High-level modeling and FPGA prototyping of microprocessors\nYear: 2003\n\nAbstract: Emerging high-level hardware description and synthesis technologies in conjunction with field-programmable gate arrays (FPGAs) have significantly lowered the threshold for hardware development. Opportunities exist to integrate these technologies into a tool for exploring and evaluating microarchitectural designs. This paper presents a case study in developing the synthesizable high-level model of a superscalar processor and producing a working prototype in FPGA. Using an experimental operation-centric hardware description language, we have created the synthesizable model of a superscalar speculative out-of-order core for the integer subset of SimpleScalar PISA. A prototype implementation is produced by synthesizing the high-level model for the Spyder FPGA prototyping board. In addition, we have modified the baseline processor model to create derivative processor designs that add newly proposed experimental mechanisms. The derivative models are useful both in testing the completeness and correctness of new mechanisms and in assessing the mechanisms' impact on implementation area and cycle time.","Title: Automatic generation of streaming datapaths for arbitrary fixed permutations\nYear: 2009\n\nAbstract: This paper presents a technique to perform arbitrary fixed permutations on streaming data. We describe a parameterized architecture that takes as input n data points streamed at a rate of w per cycle, performs a permutation over all n points, and outputs the result in the same streaming format. We describe the system and its requirements mathematically and use this mathematical description to show that the datapaths resulting from our technique can sustain a full throughput of w words per cycle without stalling. Additionally, we provide an algorithm to configure the datapath for a given permutation and streaming width. Using this technique, we have constructed a full synthesis system that takes as input a permutation and a streaming width and outputs a register-transfer level Verilog description of the datapath. We present an evaluation of our generated designs over varying problem sizes and streaming widths, synthesized for a Xilinx Virtex-5 FPGA.","Title: PROToFLEX: FPGA-accelerated Hybrid Functional Simulator\nYear: 2007\n\nAbstract: PROTOFLEX is an FPGA-accelerated hybrid simulation/emulation platform designed to support large-scale multiprocessor hardware and software research. Unlike prior attempts at FPGA multiprocessor system emulators, PROTOFLEX emulates full-system fidelity-i.e., runs stock commercial operating systems with I/O support. This is accomplished without undue effort by leveraging a hybrid emulation technique called transplanting. Our transplant technology uses FPGAs to accelerate only common-case behaviors while relegating infrequent, complex behaviors (e.g., I/O devices) to software simulation. By working in concert with existing full-system simulators, transplanting avoids the costly and unnecessary construction of the entire target system in FPGA. We report preliminary findings from a working hybrid PROTOFLEX emulator of an UltraSPARC workstation running Solaris 8. We have also started developing a novel multiprocessor emulation approach that interleaves the execution of many (10s to 100s) processor contexts onto a shared emulation engine. This approach decouples the scale and complexity of the FPGA host from the simulated system size but nevertheless enables us to scale the desired emulation performance by the number of emulation engines used. Together, the transplant and interleaving techniques enable us to develop full-system FPGA emulators of up to thousands of processors without an overwhelming development effort.","Title: Computer Generation of Hardware for Linear Digital Signal Processing Transforms\nYear: 2012\n\nAbstract: Linear signal transforms such as the discrete Fourier transform (DFT) are very widely used in digital signal processing and other domains. Due to high performance or efficiency requirements, these transforms are often implemented in hardware. This implementation is challenging due to the large number of algorithmic options (e.g., fast Fourier transform algorithms or FFTs), the variety of ways that a fixed algorithm can be mapped to a sequential datapath, and the design of the components of this datapath. The best choices depend heavily on the resource budget and the performance goals of the target application. Thus, it is difficult for a designer to determine which set of options will best meet a given set of requirements. In this article we introduce the Spiral hardware generation framework and system for linear transforms. The system takes a problem specification as input as well as directives that define characteristics of the desired datapath. Using a mathematical language to represent and explore transform algorithms and datapath characteristics, the system automatically generates an algorithm, maps it to a datapath, and outputs a synthesizable register transfer level Verilog description suitable for FPGA or ASIC implementation. The quality of the generated designs rivals the best available handwritten IP cores.","Title: Fast bilateral filtering by adapting block size\nYear: 2010\n\nAbstract: Direct implementations of bilateral filtering show O(r2) computational complexity per pixel, where r is the filter window radius. Several lower complexity methods have been developed. State-of-the-art low complexity algorithm is an O(1) bilateral filtering, in which computational cost per pixel is nearly constant for large image size. Although the overall computational complexity does not go up with the window radius, it is linearly proportional to the number of quantization levels of bilateral filtering computed per pixel in the algorithm. In this paper, we show that overall runtime depends on two factors, computing time per pixel per level and average number of levels per pixel. We explain a fundamental trade-off between these two factors, which can be controlled by adjusting block size. We establish a model to estimate run time and search for the optimal block size. Using this model, we demonstrate an average speedup of 1.2-26.0x over the previous method for typical bilateral filtering parameters.","Title: Automatic generation of customized discrete fourier transform IPs\nYear: 2005\n\nAbstract: This paper presents a parameterized soft core generator for the discrete Fourier transform (DFT). Reusable IPs of digital signal processing (DSP) kernels are important time-saving resources in DSP hardware development. Unfortunately, reusable IPs, however optimized, could introduce inefficiencies because they cannot fit the exact requirements of every application context. Given the well-understood and regular computation in DSP kernels, an automatic tool could generate high-quality ready-to-use IPs customized to user-specified cost/performance tradeoffs (beyond basic parameters such as input size and data format). The paper shows that the generated DFT cores could match closely the performance and cost of DFT cores from the Xilinx LogiCore library. Furthermore, the generator could yield DFT cores over a range of different performance/cost tradeoff points that are not available from the library.","Title: ProtoFlex: Towards Scalable, Full-System Multiprocessor Simulations Using FPGAs\nYear: 2009\n\nAbstract: Functional full-system simulators are powerful and versatile research tools for accelerating architectural exploration and advanced software development. Their main shortcoming is limited throughput when simulating large multiprocessor systems with hundreds or thousands of processors or when instrumentation is introduced. We propose the ProtoFlex simulation architecture, which uses FPGAs to accelerate full-system multiprocessor simulation and to facilitate high-performance instrumentation. Prior FPGA approaches that prototype a complete system in hardware are either too complex when scaling to large-scale configurations or require significant effort to provide full-system support. In contrast, ProtoFlex virtualizes the execution of many logical processors onto a consolidated number of multiple-context execution engines on the FPGA. Through virtualization, the number of engines can be judiciously scaled, as needed, to deliver on necessary simulation performance at a large savings in complexity. Further, to achieve low-complexity full-system support, a hybrid simulation technique called transplanting allows implementing in the FPGA only the frequently encountered behaviors, while a software simulator preserves the abstraction of a complete system. We have created a first instance of the ProtoFlex simulation architecture, which is an FPGA-based, full-system functional simulator for a 16-way UltraSPARC III symmetric multiprocessor server, hosted on a single Xilinx Virtex-II XCV2P70 FPGA. On average, the simulator achieves a 38x speedup (and as high as 49×) over comparable software simulation across a suite of applications, including OLTP on a commercial database server. We also demonstrate the advantages of minimal-overhead FPGA-accelerated instrumentation through a CMP cache simulation technique that runs orders-of-magnitude faster than software.","Title: Chip-Level Redundancy in Distributed Shared-Memory Multiprocessors\nYear: 2009\n\nAbstract: Distributed shared-memory (DSM) multiprocessors provide a scalable hardware platform, but lack the necessary redundancy for mainframe-level reliability and availability. Chip-level redundancy in a DSM server faces a key challenge: the increased latency to check results among redundant components. To address performance overheads, we propose a checking filter that reduces the number of checking operations impeding the critical path of execution. Furthermore, we propose to decouple checking operations from the coherence protocol, which simplifies the implementation and permits reuse of existing coherence controller hardware. Our simulation results of commercial workloads indicate average performance overhead is within 4% (9% maximum) of tightly coupled DMR solutions.","Title: FFT Compiler: from math to efficient hardware HLDVT invited short paper\nYear: 2007\n\nAbstract: This paper presents a high-level compiler that generates hardware implementations of the discrete Fourier transform (DFT) from mathematical specifications. The matrix formula input language captures not only the DFT calculation but also the implementation options at the algorithmic and architectural levels. By selecting the appropriate formula, the resulting hardware implementations (described in a synthesizable Verilog description) can achieve a wide range of tradeoffs between implementation cost and performance. The compiler is also parameterized for a set of technology-specific optimizations, to allow it to target specific implementation platforms. This paper gives a brief overview of the system and presents synthesis results.","Title: 3D Point Cloud Reduction Using Mixed-Integer Quadratic Programming\nYear: 2013\n\nAbstract: Large scale 3D image localization requires computationally expensive matching between 2D feature points in the query image and a 3D point cloud. In this paper, we present a method to accelerate the matching process and to reduce the memory footprint by analyzing the view-statistics of points in a training corpus. Given a training image set that is representative of common views of a scene, our approach identifies a compact subset of the 3D point cloud for efficient localization, while achieving comparable localization performance to using the full 3D point cloud. We demonstrate that the problem can be precisely formulated as a mixed-integer quadratic program and present a pointwise descriptor calibration process to improve matching. We show that our algorithm outperforms the state-of-theart greedy algorithm on standard datasets, on measures of both point-cloud compression and localization accuracy.","Title: Generating FPGA-Accelerated DFT Libraries\nYear: 2007\n\nAbstract: We present a domain-specific approach to generate high-performance hardware-software partitioned implementations of the discrete Fourier transform (DFT) in fixed point precision. The partitioning strategy is a heuristic based on the DFT's divide-and-conquer algorithmic structure and fine tuned by the feedback-driven exploration of candidate designs. We have integrated this approach in the Spiral linear-transform code-generation framework to support push-button automatic implementation. We present evaluations of hardware-software DFT implementations running on the embedded PowerPC processor and the reconfigurable fabric of the Xilinx Virtex-II Pro FPGA. In our experiments, the 1D and 2D DFT's FPGA-accelerated libraries exhibit between 2 and 7.5 times higher performance (operations per second) and up to 2.5 times better energy efficiency (operations per Joule) than the software-only version.","Title: CoRAM: an in-fabric memory architecture for FPGA-based computing\nYear: 2011\n\nAbstract: FPGAs have been used in many applications to achieve orders-of-magnitude improvement in absolute performance and energy efficiency relative to conventional microprocessors. Despite their promise in both processing performance and efficiency, FPGAs have not yet gained widespread acceptance as mainstream computing devices. A fundamental obstacle to FPGA-based computing today is the FPGA's lack of a common, scalable memory architecture. When developing applications for FPGAs, designers are often directly responsible for crafting the application-specific infrastructure logic that manages and transports data to and from the processing kernels. This infrastructure not only increases design time and effort but will frequently lock a design to a particular FPGA product line, hindering scalability and portability. We propose a new FPGA memory architecture called Connected RAM (CoRAM) to serve as a portable bridge between the distributed computation kernels and the external memory interfaces. In addition to improving performance and efficiency, the CoRAM architecture provides a virtualized memory environment as seen by the hardware kernels to simplify development and to improve an application's portability and scalability.","Title: Integrating formal verification and high-level processor pipeline synthesis\nYear: 2011\n\nAbstract: When a processor implementation is synthesized from a specification using an automatic framework, this implementation still should be verified against its specification to ensure the automatic framework introduced no error. This paper presents our effort in integrating fully automated formal verification with a high-level processor pipeline synthesis framework. As an integral part of the pipeline synthesis, our framework also emits SMV models for checking the functional equivalence between the output pipelined processor implementation and its input non-pipelined specification. Well known compositional model checking techniques are automatically applied to curtail state explosion during model checking. The paper reports case studies of applying this integrated framework to synthesize and formally verify pipelined RISC and CISC processors.","Title: Memory Bandwidth Efficient Two-Dimensional Fast Fourier Transform Algorithm and Implementation for Large Problem Sizes\nYear: 2012\n\nAbstract: Prevailing VLSI trends point to a growing gap between the scaling of on-chip processing throughput and off-chip memory bandwidth. An efficient use of memory bandwidth must become a first-class design consideration in order to fully utilize the processing capability of highly concurrent processing platforms like FPGAs. In this paper, we present key aspects of this challenge in developing FPGA-based implementations of two-dimensional fast Fourier transform (2D-FFT) where the large datasets must reside off-chip in DRAM. Our scalable implementations address the memory bandwidth bottleneck through both (1) algorithm design to enable efficient DRAM access patterns and (2) data path design to extract the maximum compute throughput for a given level of memory bandwidth. We present results for double-precision 2D-FFT up to size 2,048-by-2,048. On an Alter a DE4 platform our implementation of the 2,048-by-2,048 2D-FFT can achieve over 19.2 Gflop/s from the 12 GByte/s maximum DRAM bandwidth available. The results also show that our FPGA-based implementations of 2D-FFT are more efficient than 2D-FFT running on state-of-the-art CPUs and GPUs in terms of the bandwidth and power efficiency.","Title: Hardware Synthesis from Term Rewriting Systems\nYear: 1999\n\nAbstract: Term Rewriting System (TRS) is a good formalism for describing concurrent systemsthat embody asynchronous and nondeterministic behavior in their specifications.Elsewhere, we have used TRS's to describe speculative micro-architecturesand complex cache-coherence protocols, and proven the correctness of these systems.In this paper, we describe the compilation of TRS's into a subset of Verilogthat can be simulated and synthesized using commercial tools. TRAC, TermRewriting Architecture...","Title: RAMP: Research Accelerator for Multiple Processors\nYear: 2007\n\nAbstract: The RAMP project's goal is to enable the intensive, multidisciplinary innovation that the computing industry will need to tackle the problems of parallel processing. RAMP itself is an open-source, community-developed, FPGA-based emulator of parallel architectures. Its design framework lets a large, collaborative community develop and contribute reusable, composable design modules. Three complete designs--for transactional memory, distributed systems, and distributed-shared memory--demonstrate the platform's potential.","Title: Permuting streaming data using RAMs\nYear: 2009\n\nAbstract: This article presents a method for constructing hardware structures that perform a fixed permutation on streaming data. The method applies to permutations that can be represented as linear mappings on the bit-level representation of the data locations. This subclass includes many important permutations such as stride permutations (corner turn, perfect shuffle, etc.), the bit reversal, the Hadamard reordering, and the Gray code reordering. The datapath for performing the streaming permutation consists of several independent banks of memory and two interconnection networks. These structures are built for a given streaming width (i.e., number of inputs and outputs per cycle) and operate at full throughput for this streaming width. We provide an algorithm that completely specifies the datapath and control logic given the desired permutation and streaming width. Further, we provide lower bounds on the achievable cost of a solution and show that for an important subclass of permutations our solution is optimal. We apply our algorithm to derive datapaths for several important permutations, including a detailed example that carefully illustrates each aspect of the design process. Lastly, we compare our permutation structures to those of Järvinen et al. [2004], which are specialized for stride permutations.","Title: SimFlex: a fast, accurate, flexible full-system simulation framework for performance evaluation of server architecture\nYear: 2004\n\nAbstract: The new focus on commercial workloads in simulation studies of server systems has caused a drastic increase in the complexity and decrease in the speed of simulation tools. The complexity of a large-scale full-system model makes development of a monolithic simulation tool a prohibitively difficult task. Furthermore, detailed full-system models simulate so slowly that experimental results must be based on simulations of only fractions of a second of execution of the modelled system.This paper presents SIMFLEX, a simulation framework which uses component-based design and rigorous statistical sampling to enable development of complex models and ensure representative measurement results with fast simulation turnaround. The novelty of SIMFLEX lies in its combination of a unique, compile-time approach to component interconnection and a methodology for obtaining accurate results from sampled simulations on a platform capable of evaluating unmodified commercial workloads.","Title: In-system FPGA prototyping of an itanium microarchitecture\nYear: 2004\n\nAbstract: This work is part of our on-going effort to prototype an Itanium microarchitecture on an FPGA. To conserve time and effort in model development, we described our microarchitecture in Bluespec, a synthesizable high-level hardware description language. The microarchitecture model currently supports a subset of the Itanium instruction set architecture (ISA). The model includes details such as multi-bundle instruction fetch, decode and issue, parallel pipelined execution units with scoreboarding and bypassing, and multiple levels of cache hierarchies. The microarchitecture model is synthesized and prototyped on an FPGA that interfaces directly to the memory bus of a host PC. The prototyped microprocessor core executes the supported ISA subset at 100MHz and directly references the host-PC's DRAM and I/O resources through the memory bus at up to 800MB/sec of bandwidth. This effort is a first step toward developing a convenient in-system microprocessor prototyping platform capable of executing realistic full-scale applications and operating systems.","Title: Efficient Resource Sharing in Concurrent Error Detecting Superscalar Microarchitectures\nYear: 2004\n\nAbstract: Previous proposals for soft-error tolerance have called for redundantly executing a program as two concurrent threads on a superscalar microarchitecture. In a balanced superscalar design, the extra workload from redundant execution induces a severe performance penalty due to increased contention for resources throughout the datapath. This paper identifies and analyzes four key factors that affect the performance of redundant execution, namely 1) issue bandwidth and functional unit contention, 2) issue queue and reorder buffer capacity contention, 3) decode and retirement bandwidth contention, and 4) coupling between redundant threads' dynamic resource requirements. Based on this analysis, we propose the SHREC microarchitecture for asymmetric and staggered redundant execution. This microarchitecture addresses the four factors in an integrated design without requiring prohibitive additional hardware resources. In comparison to conventional single-threaded execution on a state-of-the-art superscalar microarchitecture with comparable cost, SHREC reduces the average performance penalty to within 4% on integer and 15% on floating-point SPEC2K benchmarks by sharing resources more efficiently between the redundant threads.","Title: A complexity-effective architecture for accelerating full-system multiprocessor simulations using FPGAs\nYear: 2008\n\nAbstract: Functional full-system simulators are powerful and versatile research tools for accelerating architectural exploration and advanced software development. Their main shortcoming is limited throughput when simulating systems with hundreds of processors or more. To overcome this bottleneck, we propose the PROTOFLEX simulation architecture, which uses FPGAs to accelerate simulation. Prior FPGA approaches that prototype a complete system in hardware are either too complex when scaling to large-scale configurations or require significant effort to provide full-system support. In contrast, PROTOFLEX reduces complexity by virtualizing the execution of many logical processors onto a consolidated set of multiple-context execution engines on the FPGA. Through virtualization, the number of engines can be judiciously scaled, as needed, to deliver on necessary simulation performance. To achieve low-complexity full-system support, a hybrid simulation technique called transplanting allows implementing in the FPGA only the frequently encountered behaviors, while a software simulator preserves the abstraction of a complete system We have created a first instance of the PROTOFLEX simulation architecture, which is an FPGA-based, full-system functional simulator for a 16-way UltraSPARC III symmetric multiprocessor server hosted on a single Xilinx Virtex-II XCV2P70 FPGA. On average, the simulator achieves a 39x speedup (and as high as 49x) over comparable software simulation across a suite of applications, including OLTP on a commercial database server.","Title: AUTOMATIC COST MINIMIZATION FOR MULTIPLIERLESS IMPLEMENTATIONS OF DISCRETE SIGNAL TRANSFORMS\nYear: 2004\n\nAbstract: The computation of linear DSP transforms consists entirely of ad- ditions and multiplications by constants, which, in a hardware re- alization, can be implemented as a network of wired shifts and additions. Thus, a light weight fixed point implementation that ap- proximates an exact transform can be built from only adders. This paper presents an automatic approach for minimizing the number of additions required for a given transform under the constraint of a particular quality measure. We present an evaluation of our approach. For example, one experiment shows that the IMDCT transform within an MP3 decoder can be reduced from 572 ad- ditions to 260 additions while maintaining Limited Accuracy as defined by the MP3 ISO standard.","Title: Truss: A Reliable, Scalable Server Architecture\nYear: 2005\n\nAbstract: Traditional reliable servers require costly design changes to the processor, use custom system or application software, or cannot scale beyond a few processing elements. We present TRUSS, a family of server architectures providing reliable, scalable computation from distributed shared-memory hardware while requiring no changes to software. The TRUSS paradigm centers around a logical division of computation and memory that isolates errors in processing from memory storage and vice versa. In this paper, we present the key mechanisms that enable this separation and use full-system simulation to evaluate the impact on a range of commercial and scientific workloads.","Title: PAI: A Lightweight Mechanism for Single-Node Memory Recovery in DSM Servers\nYear: 2007\n\nAbstract: Several recent studies identify the memory system as the most frequent source of hardware failures in commercial servers. Techniques to protect the memory system from failures must continue to service memory requests, despite hardware failures. Furthermore, to support existing OS's, the physical address space must be retained following reconfiguration. Existing techniques either suffer from a high performance overhead or require pervasive hardware changes to support transparent recovery. In this paper, we propose Physical Address Indirection (PAI), a lightweight, hardware-based mechanism for memory system failure recovery. PAI provides a simple hardware mapping to transparently reconstruct affected data in alternate locations, while maintaining high performance and avoiding physical address changes. With full-system simulation of commercial and scientific workloads on a 16-node distributed shared memory server, we show that prior techniques have an average degraded mode performance loss of 14% and 51% for commercial and scientific workloads, respectively. Using PAI's dataswap reconstruction, the same workloads have 1% and 32% average performance losses.","Title: SMARTS: accelerating microarchitecture simulation via rigorous statistical sampling\nYear: 2003\n\nAbstract: Current software-based microarchitecture simulators are many orders of magnitude slower than the hardware they simulate. Hence, most microarchitecture design studies draw their conclusions from drastically truncated benchmark simulations that are often inaccurate and misleading. This paper presents the Sampling Microarchitecture Simulation (SMARTS) framework as an approach to enable fast and accurate performance measurements of full-length benchmarks. SMARTS accelerates simulation by selectively measuring in detail only an appropriate benchmark subset. SMARTS prescribes a statistically sound procedure for configuring a systematic sampling simulation run to achieve a desired quantifiable confidence in estimates.Analysis of 41 of the 45 possible SPEC2K benchmark/input combinations show CPI and energy per instruction (EPI) can be estimated to within ±3% with 99.7% confidence by measuring fewer than 50 million instructions per benchmark. In practice, inaccuracy in microarchitectural state initialization introduces an additional uncertainty which we empirically bound to ∼2% for the tested benchmarks. Our implementation of SMARTS achieves an actual average error of only 0.64% on CPI and 0.59% on EPI for the tested benchmarks, running with average speedups of 35 and 60 over detailed simulation of 8-way and 16-way out-of-order processors, respectively.","Title: MEMOCODE 2007 Co-Design Contest\nYear: 2008\n\nAbstract: New to the 2007 MEMOCODE conference is the HW/SW Co-Design Contest. Members of the technical and steering committees from MEMOCODE 2006 thought that the co-design practice is distinct from conventional hardware or software design practice. A co-design contest was conceived to help elucidate these issues and to foster greater interest in the design aspects of the MEMOCODE conference. The contest would also serve to showcase advances in co-design tools and methodologies. Below, we describe briefly the development of the contest format and the thinking behind the choices. The actual call-for-participation that appeared on the contest website (http://memocode07.ece.cmu.edu/contest.html) is reproduced in the appendix section.","Title: Cross-platform FPGA accelerator development using CoRAM and CONNECT\nYear: 2013\n\nAbstract: The CoRAM memory architecture is an easy-to-use and portable abstraction for FPGA accelerator development [1, 2]. Using the CoRAM framework, FPGA developers can write their applications once and re-target them automatically to different FPGA platforms and devices (e.g., Xilinx ML605, Altera DE4, ZYNQ-702, etc). In this tutorial, participants will learn the key concepts of the CoRAM Virtual Architecture and the underlying CONNECT Network-on-Chip generation framework [3]. The tutorial is organized into three parts. The first part will provide an overview of the CoRAM Virtual Architecture and include a hands-on section where participants will work on a small example to get first-hand experience with the CoRAM development flow. The second part of the tutorial will provide a beneath-the-hood look at CoRAM and cover more advanced topics. These topics include memory loading, user I/O, debugging, as well as a segment on the CONNECT NoC generation framework which serves as the on-chip interconnect for CoRAM. The final part of the tutorial will be devoted to more advanced exercises and demos, as well as a Q&A session for CoRAM and CONNECT. The tutorial assumes a basic understanding of RTL design and C programming. To join in on the hands-on exercise, the attendees need laptops with 15GB of free space and VirtualBox installed. Please visit http://www.ece.cmu.edu/~coram for information about CoRAM and updates on this tutorial.","Title: Dependable VLSI: Device, design and architecture - How should they cooperate?\nYear: 2009\n\nAbstract: VLSI dependability is one of the most significant issues in the modern world. Here the panelists will discuss the key technologies for it as well as the cost optimization among device, design and architecture.","Title: Formal datapath representation and manipulation for implementing DSP transforms\nYear: 2008\n\nAbstract: We present a domain-specific approach to representing datapaths for hardware implementations of linear signal transform algorithms. We extend the tensor structure for describing linear transform algorithms, adding the ability to explicitly characterize two important dimensions of datapath architecture. This representation allows both algorithm and datapath to be specified within a single formula and gives the designer the ability to easily consider a wide space of possible datapaths at a high level of abstraction. We have constructed a formula manipulation system based on this representation and have written a compiler that can translate a formula into a hardware implementation. This enables an automatic \"push button\" compilation flow that produces a register transfer level hardware description from high-level datapath directives and an algorithm (written as a formula). In our experimental results, we demonstrate that this approach yields efficient designs over a large tradeoff space.","Title: Real time stereo vision using exponential step cost aggregation on GPU\nYear: 2009\n\nAbstract: In this paper, we propose a local cost aggregation approach for real time stereo vision on a graphics processing unit (GPU). Recent research shows that local approaches based on carefully designed cost aggregation strategies can outperform many global approaches. Among those local aggregation approaches, adaptive-weight window produces the best quality disparity map under real-time constraint, but it is slower than other local approaches. We propose a very fast adaptive-weight aggregation method based on exponential step information propagation. The basic idea is to propagate information from long distance pixels within a few iterations. We also discuss important techniques of efficient implementation on GPU platform, which result in 10.5à speed up than a straightforward implementation. Compared to existing real time adaptive-weight approach, our technique reduces the computation time by more than half at improved accuracy. Detailed experimental results show that our technique is Pareto-optimal among existing real time or near real time stereo algorithms in the accuracy-speed trade-off space.","Title: FIST: A Fast, Lightweight, FPGA-Friendly Packet Latency Estimator for NoC Modeling in Full-System Simulations\nYear: 2011\n\nAbstract: FIST (Fast Interconnect Simulation Techniques) is a fast and simple packet latency estimator to replace time-consuming detailed Network-on-Chip (NoC) models in full-system performance simulators. FIST combines ideas from analytical network modeling and execution-driven simulation models. The main idea is to abstractly model each router as a load-delay curve and sum load-dependent delay at each visited router to obtain a packet's latency by tracking each router's load at runtime. The resulting latency estimator can accurately capture subtle load-dependent behaviors of a NoC but is much simpler than a full-blown execution-driven model. We study two variations of FIST in the context of a software-based, cycle-level simulation of a tiled chip-multiprocessor (CMP). We evaluate FIST's accuracy and performance relative to the CMP simulator's original execution-driven 2D-mesh NoC model. A static FIST approach (trained offine using uniform random synthetic traffic) achieves less than 6% average error in packet latency and up to 43× average speedup for a 16×16 mesh. A dynamic FIST approach that adds periodic online training reduces the average packet latency error to less than 2% and still maintains an average speedup of up to 18× for a 16×16 mesh. Moreover, an FPGA-based realization of FIST can simulate 2D-mesh networks up to 24×24 nodes, at 3 to 4 orders of magnitude speedup over software-based simulators.","Title: Statistical sampling of microarchitecture simulation\nYear: 2006\n\nAbstract: Current software-based microarchitecture simulators are many orders of magnitude slower than the hardware they simulate. Hence, most microarchitecture design studies draw their conclusions from drastically truncated benchmark simulations that are often inaccurate and misleading. This article presents the Sampling Microarchitecture Simulation (SMARTS) framework as an approach to enable fast and accurate performance measurements of full-length benchmarks. SMARTS accelerates simulation by selectively measuring in detail only an appropriate benchmark subset. SMARTS prescribes a statistically sound procedure for configuring a systematic sampling simulation run to achieve a desired quantifiable confidence in estimates.Analysis of the SPEC CPU2000 benchmark suite shows that CPI and energy per instruction (EPI) can be estimated to within ±3% with 99.7% confidence by measuring fewer than 50 million instructions per benchmark. In practice, inaccuracy in microarchitectural state initialization introduces an additional uncertainty which we empirically bound to ∼2% for the tested benchmarks. Our implementation of SMARTS achieves an actual average error of only 0.64% on CPI and 0.59% on EPI for the tested benchmarks, running with average speedups of 35 and 60 over detailed simulation of 8-way and 16-way out-of-order processors, respectively.","Title: TurboSMARTS: accurate microarchitecture simulation sampling in minutes\nYear: 2005\n\nAbstract: Recent research proposes accelerating processor microarchitecture simulation through statistical sampling. Prior simulation sampling approaches construct accurate model state for each measurement by continuously warming large microarchitectural structures (e.g., caches and the branch predictor) while emulating the billions of instructions between measurements. This approach, called functional warming, occupies hours of runtime while the detailed simulation that is measured requires mere minutes.To eliminate the functional warming bottleneck, we propose TurboSMARTS, a simulation framework that stores functionally-warmed state in a library of small, reusable checkpoints. TurboSMARTS enables the creation of the thousands of checkpoints necessary for accurate sampling by storing only the subset of warmed state accessed during simulation of each brief execution window. TurboSMARTS matches the accuracy of prior simulation sampling techniques (i.e., ±3% error with 99.7% confidence), while estimating the performance of an 8-way out-of-order super-scalar processor running SPEC CPU2000 in 91 seconds per benchmark, on average, using a 12 GB checkpoint library.","Title: Fast and accurate resource estimation of automatically generated custom DFT IP cores\nYear: 2006\n\nAbstract: This paper presents an equation-based resource utilization model for automatically generated discrete Fourier transform (DFT) soft core IPs. The parameterized DFT IP generator allows a user to make customized tradeoffs between cost and performance and between utilization of different resource classes. The equation-based resource model permits immediate and accurate estimation of resource requirements as the user considers the different generator options. Furthermore, the fast turnaround of the model allows it to be combined with a search algorithm such that the user could query automatically for an optimal design within the stated performance and resource constraints.Following a brief review of the DFT IP generator, this paper presents the development of the equation-based models for estimating slice and hard macro utilizations in the Xilinx Virtex-II Pro FPGA family. The evaluation section shows that an average error of 6.1% is achievable by a model of linear equations that can be evaluated in sub-microseconds. The paper further offers a demonstration of the automatic design exploration capability.","Title: Highly Efficient Performance Portable Tracking of Evolving Surfaces\nYear: 2012\n\nAbstract: In this paper we present a framework to obtain highly efficient implementations for the narrow band level set method on commercial off-the-shelf (COTS) multicore CPU systems with a cache-based memory hierarchy such as Intel Xeon and Atom processors. The narrow-band level set algorithm tracks wave-fronts in discretized volumes (for instance, explosion shock waves), and is computationally very demanding. At the core of our optimization framework is a novel projection-based approach to enhance data locality and enable reuse for sparse surfaces in dense discretized volumes. The method reduces stencil operations on sparse and changing sets of pixels belonging to an evolving surface into dense stencil operations on meta-pixels in a lower-dimensional projection of the pixel space. These meta-pixels are then amenable to standard techniques like time tiling. However, the complexity introduced by ever-changing meta-pixels requires us to revisit and adapt all other necessary optimizations. We apply adapted versions of SIMDization, multi-threading, DAG scheduling for basic tiles, and specialization through code generation to extract maximum performance. The system is implemented as highly parameterized code skeleton that is auto-tuned and uses program generation. We evaluated our framework on a dual-socket 2.8 GHz Xeon 5560 and a 1.6 GHz Atom N270. Our single-core performance reaches 26%-35% of the machine peak on the Xeon, and 12%- 20% on the Atom across a range of image sizes. We see up to 6.5x speedup on 8 cores of the dual-socket Xeon. For cacheresident sizes our code outperforms the best available thirdparty code (C pre-compiled into a DLL) by about 10x and for the largest out-of-cache sizes the speedup approaches around 200x. Experiments fully explain the high speedup numbers.","Title: CONNECT: re-examining conventional wisdom for designing nocs in the context of FPGAs\nYear: 2012\n\nAbstract: An FPGA is a peculiar hardware realization substrate in terms of the relative speed and cost of logic vs. wires vs. memory. In this paper, we present a Network-on-Chip (NoC) design study from the mindset of NoC as a synthesizable infrastructural element to support emerging System-on-Chip (SoC) applications on FPGAs. To support our study, we developed CONNECT, an NoC generator that can produce synthesizable RTL designs of FPGA-tuned multi-node NoCs of arbitrary topology. The CONNECT NoC architecture embodies a set of FPGA-motivated design principles that uniquely influence key NoC design decisions, such as topology, link width, router pipeline depth, network buffer sizing, and flow control. We evaluate CONNECT against a high-quality publicly available synthesizable RTL-level NoC design intended for ASICs. Our evaluation shows a significant gain in specializing NoC design decisions to FPGAs' unique mapping and operating characteristics. For example, in the case of a 4x4 mesh configuration evaluated using a set of synthetic traffic patterns, we obtain comparable or better performance than the state-of-the-art NoC while reducing logic resource cost by 58%, or alternatively, achieve 3-4x better performance for approximately the same logic resource usage. Finally, to demonstrate CONNECT's flexibility and extensive design space coverage, we also report synthesis and network performance results for several router configurations and for entire CONNECT networks.","Title: START-NG: Delivering Seamless Parallel Computing\nYear: 1995\n\nAbstract: StarT-ng is a joint MIT-Motorola project to build a high-performance message passing machine from commercial systems. Each site of the machine consists of a PowerPC 620-based Motorola symmetric multiprocessor (SMP) running the AIX 4.1 operating system. Every processor is connected to a low-latency, high-bandwidth network that is directly accessible from user-level code. In addition to fast message passing capabilities, the machine has experimental support for cachecoherent shared memory across sites. When the machine requires memory to be kept globally coherent, one processor on each site is devoted to supporting shared memory. When globally coherent shared memory is not required, that processor can be used for normal computation tasks. StarT-ng will be delivered at about the time the base SMP is introduced into the marketplace. The ability to be both a collection of standard SMP and an aggressive message passing machine with coherent shared memory makes StarT-ng a good building block for incrementally expandable parallel machines.","Title: Custom-optimized multiplierless implementations of DSP algorithms\nYear: 2004\n\nAbstract: Linear DSP kernels such as transforms and filters are comprised exclusively of additions and multiplications by constants. These multiplications may be realized as networks of additions and wired shifts in hardware. The cost of such a \"multiplierless\" implementation is determined by the number of additions, which in turn depends on the value and precision of these constants. For a given transform or filter, the set of constants and their required precision is affected by algorithmic and implementation choices and hence provides a degree of freedom for optimization. In This work we present an automated method to generate, for a given linear transform, a minimum addition multiplierless implementation that satisfies a given quality constraint. The method combines automatic algorithm selection to improve numerical robustness and automatic search methods to minimize constant precisions in a chosen algorithm. We present experiments that show the trade-offs between cost and quality, including custom optimizations of the transforms used in JPEG image and MP3 audio decoders.","Title: Prototype and evaluation of the CoRAM memory architecture for FPGA-based computing\nYear: 2012\n\nAbstract: The CoRAM memory architecture for FPGA-based computing augments traditional reconfigurable fabric with a natural and effective way for applications to interact with off-chip memory and I/O. The two central tenets of the CoRAM memory architecture are (1) the deliberate separation of concerns between computation versus data marshalling and (2) the use of a multithreaded software abstraction to replace FSM-based memory control logic. To evaluate the viability of the CoRAM memory architecture, we developed a full RTL implementation of a CoRAM microarchitecture instance that can be synthesized for standard cells or emulated on FPGAs. The results of our evaluation show that a soft emulation of the CoRAM memory architecture on current FPGAs can be impractical for memory-intensive, large-scale applications due to the high performance and area penalties incurred by the soft mechanisms. The results further show that in an envisioned FPGA built with CoRAM in mind, the introduction of hard macro blocks for data distribution can mitigate these inefficiencies---allowing applications to take advantage of the CoRAM memory architecture for ease of programmability and portability while still enjoying performance and efficiency comparable to RTL-level application development on conventional FPGAs.","Title: Multiple constant multiplication by time-multiplexed mapping of addition chains\nYear: 2004\n\nAbstract: An important primitive in the hardware implementations of linear DSP transforms is a circuit that can multiply an input value by one of several different preset constants. We propose a novel implementation of this circuit based on combining the addition chains of the constituent constants. We present an algorithm to automatically generate such a circuit for a given set of constants. The quality of the resulting circuits is evaluated after synthesis for a commercial 0.18um standard cell library. We compare the area and latency efficiency of this addition chain based approach against a straightforward approach based on a constant table and a full multiplier.","Title: SimFlex: Statistical Sampling of Computer System Simulation\nYear: 2006\n\nAbstract: Timing-accurate full-system multiprocessor simulations can take years because of architecture and application complexity. Statistical sampling makes simulation-based studies feasibly by providing ten-thousand-fold reductions in simulation runtime and enabling thousand-way simulation parallelism.","Title: C-to-CoRAM: compiling perfect loop nests to the portable CoRAM abstraction\nYear: 2013\n\nAbstract: This paper presents initial work on developing a C compiler for the CoRAM FPGA computing abstraction. The presented effort focuses on compiling fixed-bound perfect loop nests that operate on large data sets in external DRAM. As required by the CoRAM abstraction, the compiler partitions source code into two separate implementation components: (1) hardware kernel pipelines to be mapped onto the reconfigurable logic fabric; and (2) control threads that express, in a C-like language, the sequencing and coordination of data transfers between the hardware kernels and external DRAM. The compiler performs optimizations to increase parallelism and use DRAM bandwidth efficiently. It can target different FPGA platforms that support the CoRAM abstraction, either natively in a future FPGA or in soft-logic on today's devices. The CoRAM abstraction provides a convenient high-level compilation target to simplify the task of design optimization and system generation. The compiler is evaluated using three test programs (matrix-matrix multiplication, k-nearest neighbor, and 2D convolution) on the Xilinx ML605 and the Altera DE4. Results show that our compiler is able to target the different platforms and effectively exploit their dissimilar capacities and features. Depending on the application, the compiler-generated implementations achieve performance ranging from a factor of 4 slower to a factor of 2 faster relative to hand-designed implementations, as measured on actual hardware.","Title: DELPHI: a framework for RTL-based architecture design evaluation using DSENT models\nYear: 2015\n\nAbstract: Computer architects are increasingly interested in evaluating their ideas at the register-transfer level (RTL) to gain more precise insights on the key characteristics (frequency, area, power) of a micro/architectural design proposal. However, the RTL synthesis process is notoriously tedious, slow, and errorprone and is often outside the area of expertise of a typical computer architect, as it requires familiarity with complex CAD flows, hard-to-get tools and standard cell libraries. The effort is further multiplied when targeting multiple technology nodes and standard cell variants to study technology dependence. This paper presents DELPHI, a flexible, open framework that leverages the DSENT modeling engine for faster, easier, and more efficient characterization of RTL hardware designs. DELPHI first synthesizes a Verilog or VHDL RTL design (either using the industry-standard Synopsys Design Compiler tool or a combination of open-source tools) to an intermediate structural netlist. It then processes the resulting synthesized netlist to generate a technology-independent DSENT design model. This model can then be used within a modified version of the DSENT flow to perform very fast???one to two orders of magnitude faster than full RTL synthesis???estimation of hardware performance characteristics, such as frequency, area, and power across a variety of DSENT technology models (e.g., 65nm Bulk, 32nm SOI, 11nm Tri-Gate, etc.). In our evaluation using 26 RTL design examples, DELPHI and DSENT were consistently able to closely track and capture design trends of conventional RTL synthesis results without the associated delay and complexity. We are releasing the full DELPHI framework (including a fully open-source flow) at http://www.ece.cmu.edu/CALCM/delphi/.","Title: GraphGen: An FPGA Framework for Vertex-Centric Graph Computation.\nYear: 2014\n\nAbstract: Vertex-centric graph computations are widely used in many machine learning and data mining applications that operate on graph data structures. This paper presents GraphGen, a vertex-centric framework that targets FPGA for hardware acceleration of graph computations. GraphGen accepts a vertex-centric graph specification and automatically compiles it onto an application-specific synthesized graph processor and memory system for the target FPGA platform. We report design case studies using GraphGen to implement stereo matching and handwriting recognition graph applications on Terasic DE4 and Xilinx ML605 FPGA boards. Results show up to 14.6x and 2.9x speedups over software on Intel Core i7 CPU for the two applications, respectively.","Title: Algorithm/hardware co-optimized SAR image reconstruction with 3D-stacked logic in memory\nYear: 2014\n\nAbstract: Real-time system level implementations of complex Synthetic Aperture Radar (SAR) image reconstruction algorithms have always been challenging due to their data intensive characteristics. In this paper, we propose a basis vector transform based novel algorithm to alleviate the data intensity and a 3D-stacked logic in memory based hardware accelerator as the implementation platform. Experimental results indicate that this proposed algorithm/hardware co-optimized system can achieve an accuracy of 91 dB PSNR compared to a reference algorithm implemented in Matlab and energy efficiency of 72 GFLOPS/W for a 8k×8k SAR image reconstruction.","Title: HAMLeT: Hardware accelerated memory layout transform within 3D-stacked DRAM\nYear: 2014\n\nAbstract: Memory layout transformations via data reorganization are very common operations, which occur as a part of the computation or as a performance optimization in data-intensive applications. These operations require inefficient memory access patterns and roundtrip data movement through the memory hierarchy, failing to utilize the performance and energy-efficiency potentials of the memory subsystem. This paper proposes a high-bandwidth and energy-efficient hardware accelerated memory layout transform (HAMLeT) system integrated within a 3D-stacked DRAM. HAMLeT uses a low-overhead hardware that exploits the existing infrastructure in the logic layer of 3D-stacked DRAMs, and does not require any changes to the DRAM layers, yet it can fully exploit the locality and parallelism within the stack by implementing efficient layout transform algorithms. We analyze matrix layout transform operations (such as matrix transpose, matrix blocking and 3D matrix rotation) and demonstrate that HAMLeT can achieve close to peak system utilization, offering up to an order of magnitude performance improvement compared to the CPU and GPU memory subsystems which does not employ HAMLeT.","Title: FFTS with near-optimal memory access through block data layouts\nYear: 2014\n\nAbstract: Fast Fourier transform algorithms on large data sets achieve poor performance on various platforms because of the inefficient strided memory access patterns. These inefficient access patterns need to be reshaped to achieve high performance implementations. In this paper we formally restructure 1D, 2D and 3D FFTs targeting a generic machine model with a two-level memory hierarchy requiring block data transfers, and derive memory access pattern efficient algorithms using custom block data layouts. Using the Kronecker product formalism, we integrate our optimizations into Spiral framework. In our evaluations we demonstrate that Spiral generated hardware designs achieve close to theoretical peak performance of the targeted platform and offer significant speed-up (up to 6.5x) compared to naive baseline algorithms.","Title: Understanding the design space of DRAM-optimized hardware FFT accelerators\nYear: 2014\n\nAbstract: As technology scaling is reaching its limits, pointing to the well-known memory and power wall problems, achieving high-performance and energy-efficient systems is becoming a significant challenge. Especially for data-intensive computing, efficient utilization of the memory subsystem is the key to achieve high performance and energy efficiency.We address this challenge in DRAM-optimized hardware accelerators for 1D, 2D and 3D fast Fourier transforms (FFT) on large datasets. When the dataset has to be stored in external DRAM, the main challenge for FFT algorithm design lies in reshaping DRAM-unfriendly memory access patterns to eliminate excessive DRAM row buffer misses. More importantly, these algorithms need to be carefully mapped to the targeted platform's architecture, particularly the memory subsystem, to fully utilize performance and energy efficiency potentials. We use automatic design generation techniques to consider a family of DRAM-optimized FFT algorithms and their hardware implementation design space. In our evaluations, we demonstrate DRAM-optimized accelerator designs over a large tradeoff space given various problem (single/double precision 1D, 2D and 3D FFTs) and hardware platform (off-chip DRAM, 3D-stacked DRAM, ASIC, FPGA, etc.) parameters. We show that generated pareto-optimal designs can yield up to 5.5× energy consumption and order of magnitude memory bandwidth utilization improvements in DRAM, which lead to overall system performance and power efficiency improvements of up to 6× and 6.5× respectively over conventional row-column FFT algorithms.","Title: Highly-parallel special-purpose multicore architecture for SystemC/TLM simulations\nYear: 2014\n\nAbstract: The complexity of SystemC virtual prototyping is continuously increasing. Accelerating RTL/TLM SystemC simulations is essential to control future SoC development cost and time-to-market. In this paper, we present RAVES, a highly-parallel special-purpose multicore architecture that achieves simulation performance more efficiently by parallel execution of light-weight user-level threads on many small cores. We present a design study based on the virtual prototype of RAVES processors running a co-designed custom SystemC kernel. Our evaluation suggests that a 64-core RAVES processor can deliver up to 4.47× more simulation performance than a high-end x86 processor.","Title: Spiral: Joint Runtime and Energy Optimization of Linear Transforms\nYear: 2006\n\nAbstract: There is much interest into joint runtime and energy opti- mization of implementations of signal processing algorithms. Applications in domains such as embedded computing, sen- sor networks, and mobile communications often require pro- cessing of signals under simultaneous runtime, energy and/or power constraints. Hence, in addition to runtime, power and energy are first-order design considerations for both hardware and software developers in those domains. This paper studies the automatic generation of software implementations of digi- tal signal processing (DSP) transforms that are optimized with respect to both runtime and energy. We explore the impact of algorithm selection (a software technique) and voltage- frequency scaling (a hardware technique) on the runtime and energy of computing fast linear transforms. We use SPIRAL, a code generation system, to enumerate automatically many alternative algorithms for the discrete Fourier transform. We measure the runtime and energy of these algorithms at dif- ferent voltage-frequency settings of an Intel Pentium M mi- croprocessor. We report experimental results supporting that algorithm selection and voltage-frequency scaling do achieve the following: (1) have large impact on the runtime and en- ergy of computing the discrete Fourier transform on a micro- processor; and (2) enable the optimization of important joint runtime-energy objectives.","Title: Dual use of superscalar datapath for transient-fault detection and recovery\nYear: 2001\n\nAbstract: Diminutive devices and high clock frequency of future microprocessor generations are causing increased concerns for transient soft failures in hardware, necessitating fault detection and recovery mechanisms even in commodity processors. In this paper, we propose a fault-tolerant extension for modern superscalar out-of-order datapath that can be supported by only modest additional hardware. In the proposed extensions, error-detection is achieved by verifying the redundant results of dynamically replicated threads of executions, while the error-recovery scheme employs the instruction-rewind mechanism to restart at a failed instruction. We study the performance impact of augmenting superscalar microarchitectures with this fault tolerance mechanism. An analytical performance model is used in conjunction with a performance simulator. The simulation results of 11 SPEC95 and SPEC2000 benchmarks show that in the absence of faults, error detection causes a 2% to 45% reduction in throughput, which is in line with other proposed detection schemes. In the presence of transient faults, the fast error recovery scheme contributes very little additional slowdown.","Title: Reunion: Complexity-Effective Multicore Redundancy\nYear: 2006\n\nAbstract: To protect processor logic from soft errors, multicore redundant architectures execute two copies of a program on separate cores of a chip multiprocessor (CMP). Maintaining identical instruction streams is challenging because redundant cores operate independently, yet must still receive the same inputs (e.g., load values and shared-memory invalidations). Past proposals strictly replicate load values across two cores, requiring significant changes to the highly-optimized core. We make the key observation that, in the common case, both cores load identical values without special hardware. When the cores do receive different load values (e.g., due to a data race), the same mechanisms employed for soft error detection and recovery can correct the difference. This observation permits designs that relax input replication, while still providing correct redundant execution. In this paper, we present Reunion, an execution model that provides relaxed input replication and preserves the existing memory interface, coherence protocols, and consistency models. We evaluate a CMP-based implementation of the Reunion execution model with full-system, cycle-accurate simulation. We show that the performance overhead of relaxed input replication is only 5% and 6% for commercial and scientific workloads, respectively.","Title: MEMOCODE 2008 Co-Design Contest\nYear: 2007\n\nAbstract: The second MEMOCODE hardware/software codesign contest invites participants to solve a practical hardware/software codesign problem within the time span of one month. The larger objective for this contest is to be a showcase of advances in co-design tools and methodologies, in combination with design ingenuity and creativity. In the second installment of the contest, we received 9 submissions. In this short writeup, we review this year's design problem, and we consider relevant contest statistics.","Title: High-Level Design and Validation of the BlueSPARC Multithreaded Processor\nYear: 2010\n\nAbstract: This paper presents our experiences in using high-level methods to design and validate a 16-way multithreaded microprocessor called BlueSPARC. BlueSPARC is an in-order, high-throughput processor supporting complex features such as privileged-mode operations, memory management, and a nonblocking cache subsystem. Using a high-level design language called Bluespec System Verilog (BSV), our final implementation achieves comparable synthesis quality to a similar commercial microprocessor developed using conventional register transfer level flows, and is capable of running unmodified commercial applications while hosted on a Xilinx XCV2P70 field-programmable gate array (FPGA) at 90 MHz. To validate our implementation, an FPGA-accelerated approach was developed to efficiently check the correct execution of real, nondeterministic multithreaded programs running on the BlueSPARC processor. Together, the high-level language features of BSV along with our validation approach enabled us to achieve a working FPGA-based implementation in less than one man-year.","Title: Single-Chip Heterogeneous Computing: Does the Future Include Custom Logic, FPGAs, and GPGPUs?\nYear: 2010\n\nAbstract: To extend the exponential performance scaling of future chip multiprocessors, improving energy efficiency has become a first-class priority. Single-chip heterogeneous computing has the potential to achieve greater energy efficiency by combining traditional processors with unconventional cores (U-cores) such as custom logic, FPGAs, or GPGPUs. Although U-cores are effective at increasing performance, their benefits can also diminish given the scarcity of projected bandwidth in the future. To understand the relative merits between different approaches in the face of technology constraints, this work builds on prior modeling of heterogeneous multicores to support U-cores. Unlike prior models that trade performance, power, and area using well-known relationships between simple and complex processors, our model must consider the less-obvious relationships between conventional processors and a diverse set of U-cores. Further, our model supports speculation of future designs from scaling trends predicted by the ITRS road map. The predictive power of our model depends upon U-core-specific parameters derived by measuring performance and power of tuned applications on today's state-of-the-art multicores, GPUs, FPGAs, and ASICs. Our results reinforce some current-day understandings of the potential and limitations of U-cores and also provides new insights on their relative merits.","Title: Hardware implementation of the discrete fourier transform with non-power-of-two problem size\nYear: 2010\n\nAbstract: In this paper, we examine several algorithms suitable for the hardware implementation of the discrete Fourier transform (DFT) with non-power-of two problem size. We incorporate these algorithms into Spiral, a tool capable of automatically generating corresponding hardware implementations. We dis- cuss how each algorithm can be used to generate different types of hardware structures, and we demonstrate that our tool is able to produce hardware implementations of non-power- of-two sized DFTs over a wide range of cost/performance tradeoff points.","Title: HAMLeT Architecture for Parallel Data Reorganization in Memory\nYear: 2016\n\nAbstract: 3D-stacked integration of DRAM and logic layers using through-silicon via (TSV) technology has given rise to a new interpretation of near-data processing (NDP) concepts that were proposed decades ago. However, processing capability within the stack is limited by stringent power and thermal constraints. Simple processing mechanisms with intensive memory accesses, such as data reorganization, are an effective means of exploiting 3D stacking-based NDP. Data reorganization handled completely in memory improves the host processoru0027s memory access performance. However, in-memory data reorganization performed in parallel with host memory accesses raises issues, including interference, bandwidth allocation, and coherence. Previous work has mainly focused on performing data reorganization while blocking host accesses. This article details data reorganization performed in parallel with host memory accesses, providing mechanisms to address host/NDP interference, flexible bandwidth allocation, and in-memory coherence.","Title: The CONNECT Network-on-Chip Generator\nYear: 2015\n\nAbstract: Efficiently supporting the communication needs of systems on chip with tens to hundreds of interacting modules requires a systematic and flexible network-on-chip (NoC) infrastructure. The freely available CONNECT generator lets users quickly navigate a range of design parameters to produce tailored NoC design instances in Verilog. To date, it has generated nearly 4,000 designs.","Title: Nautilus: fast automated IP design space search using guided genetic algorithms\nYear: 2015\n\nAbstract: Today's offerings of parameterized hardware IP generators permit very high degrees of performance and implementation customization. Nevertheless, it is ultimately still left to the IP users to set IP parameters to achieve the desired tuning effects. For the average IP user, the knowledge and effort required to navigate a complex IP's design space can significantly offset the productivity gain from using the IP. This paper presents an approach that builds into an IP generator an extended genetic algorithm (GA) to perform automatic IP parameter tuning. In particular, we propose extensions that allow IP authors to embed pertinent designer knowledge to improve GA performance. In the context of several IP generators, our evaluations show that (1) GA is an effective solution to this problem and (2) our modified IP author guided GA can reach the same quality of results up to an order of magnitude faster compared to the basic GA.","Title: A Study of Pointer-Chasing Performance on Shared-Memory Processor-FPGA Systems.\nYear: 2016\n\nAbstract: The advent of FPGA acceleration platforms with direct coherent access to processor memory creates an opportunity for accelerating applications with irregular parallelism governed by large in-memory pointer-based data structures. This paper uses the simple reference behavior of a linked-list traversal as a proxy to study the performance potentials of accelerating these applications on shared-memory processor-FPGA systems. The linked-list traversal is parameterized by node layout in memory, per-node data payload size, payload dependence, and traversal concurrency to capture the main performance effects of different pointer-based data structures and algorithms. The paper explores the trade-offs over a wide range of implementation options available on shared-memory processor-FPGA architectures, including using tightly-coupled processor assistance. We make observations of the key effects on currently available systems including the Xilinx Zynq, the Intel QuickAssist QPI FPGA Platform, and the Convey HC-2. The key results show: (1) the FPGA fabric is least efficient when traversing a single list with non-sequential node layout and a small payload size; (2) processor assistance can help alleviate this shortcoming; and (3) when appropriate, a fabric only approach that interleaves multiple linked list traversals is an effective way to maximize traversal performance.","Title: Data reorganization in memory using 3D-stacked DRAM\nYear: 2015\n\nAbstract: In this paper we focus on common data reorganization operations such as shuffle, pack/unpack, swap, transpose, and layout transformations. Although these operations simply relocate the data in the memory, they are costly on conventional systems mainly due to inefficient access patterns, limited data reuse and roundtrip data traversal throughout the memory hierarchy. This paper presents a two pronged approach for efficient data reorganization, which combines (i) a proposed DRAM-aware reshape accelerator integrated within 3D-stacked DRAM, and (ii) a mathematical framework that is used to represent and optimize the reorganization operations. We evaluate our proposed system through two major use cases. First, we demonstrate the reshape accelerator in performing a physical address remapping via data layout transform to utilize the internal parallelism/locality of the 3D-stacked DRAM structure more efficiently for general purpose workloads. Then, we focus on offloading and accelerating commonly used data reorganization routines selected from the Intel Math Kernel Library package. We evaluate the energy and performance benefits of our approach by comparing it against existing optimized implementations on state-of-the-art GPUs and CPUs. For the various test cases, in-memory data reorganization provides orders of magnitude performance and energy efficiency improvements via low overhead hardware.","Title: Coram Plus Plus : Supporting Data-Structure-Specific Memory Interfaces For Fpga Computing\nYear: 2015\n\nAbstract: Facilitating DRAM access is an essential part of an application programming environment for FPGA computing. Existing FPGA application programming environments primarily focus on support for simple, regular memory access patterns, such as block copy and streaming. This paper presents CoRAM++, a programming environment for FPGA computing that is based on an extensible set of data-structure-specific memory interfaces. CoRAM++ supports complex data structures, such as multidimensional arrays and linked lists, in addition to simple data access patterns. CoRAM++ defines an appropriate application-level interface for each supported data structure, and provides a specialized soft-logic implementation of the supporting data-path to memory We evaluated the effectiveness of the CoRAM++ data-structure-specific approach in three application scenarios based on streams, multi-dimensional arrays and linked lists. Our results show that the CoRAM++ programming environment can offer convenient application-level interfaces without penalizing DRAM access performance.","Title: Enabling portable energy efficiency with memory accelerated library\nYear: 2015\n\nAbstract: Over the last decade, the looming power wall has spurred a flurry of interest in developing heterogeneous systems with hardware accelerators. The questions, then, are what and how accelerators should be designed, and what software support is required. Our accelerator design approach stems from the observation that many efficient and portable software implementations rely on high performance software libraries with well-established application programming interfaces (APIs). We propose the integration of hardware accelerators on 3D-stacked memory that explicitly targets the memory-bounded operations within high performance libraries. The fixed APIs with limited configurability simplify the design of the accelerators, while ensuring that the accelerators have wide applicability. With our software support that automatically converts library APIs to accelerator invocations, an additional advantage of our approach is that library-based legacy code automatically gains the benefit of memory-side accelerators without requiring a reimplementation. On average, the legacy code using our proposed MEmory Accelerated Library (MEALib) improves performance and energy efficiency for individual operations in Intel's Math Kernel Library (MKL) by 38x and 75x, respectively. For a real-world signal processing application that employs Intel MKL, MEALib attains more than 10x better energy efficiency.","Title: Using Vivado-HLS for Structural Design: a NoC Case Study (Abstract Only).\nYear: 2017\n\nAbstract: There have been ample successful examples of applying Xilinx Vivado's \\\"function-to-module\\\" high-level synthesis (HLS) where the subject is algorithmic in nature. In this work, we carried out a design study to assess the effectiveness of applying Vivado-HLS in structural design. We employed Vivado-HLS to synthesize C functions corresponding to standalone network-on-chip (NoC) routers as well as complete multi-endpoint NoCs. Interestingly, we find that describing a complete NoC comprising router submodules faces fundamental difficulties not present in describing the routers as standalone modules. Ultimately, we succeeded in using Vivado-HLS to produce router and NoC modules that are exact cycle- and bit-accurate replacements of our reference RTL-based router and NoC modules. Furthermore, the routers and NoCs resulting from HLS and RTL are comparable in resource utilization and critical path delay. Our experience subjectively suggests that HLS is able to simplify the design effort even though much of the structural details had to be provided in the HLS description through a combination of coding discipline and explicit pragmas. The C++ source code and a more extensive description of this work can be found at http://www.ece.cmu.edu/calcm/connect_hls.","Title: Amorphous Dynamic Partial Reconfiguration with Flexible Boundaries to Remove Fragmentation.\nYear: 2017\n\nAbstract: Dynamic partial reconfiguration (DPR) allows one region of an field-programmable gate array (FPGA) fabric to be reconfigured without affecting the operations on the rest of the fabric. To use an FPGA as a dynamically shared compute resource, one could partition and manage an FPGA fabric as multiple DPR partitions that can be independently reconfigured at runtime with different application function units (AFUs). Unfortunately, dividing a fabric into DPR partitions with fixed boundaries causes the available fabric resources to become fragmented. An AFU of a given size cannot be loaded unless a sufficiently large DPR partition was floorplanned at build time. To overcome this inefficiency, we devised an DPR technique that is compatible with current device and tool support but does not require the DPR partition boundaries to be a priori fixed. A collection of AFU bitstreams can be simultaneously loaded on the fabric if their footprints (the actual area used by an AFU) in the fabric do not overlap. We verified the feasibility of amorphous DPR on Xilinx Zynq System-on-Chip (SoC) FPGAs using Vivado. We evaluated the benefits of amorphous DPR in the context of a dynamically reconfigurable vision processing pipeline framework.","Title: Using Vivado-HLS for Structural Design: a NoC Case Study.\nYear: 2017\n\nAbstract: There have been ample successful examples of applying Xilinx Vivadou0027s function-to-module high-level synthesis (HLS) where the subject is algorithmic in nature. In this work, we carried out a design study to assess the effectiveness of applying Vivado-HLS in structural design. We employed Vivado-HLS to synthesize C functions corresponding to standalone network-on-chip (NoC) routers as well as complete multi-endpoint NoCs. Interestingly, we find that describing a complete NoC comprising router submodules faces fundamental difficulties not present in describing the routers as standalone modules. Ultimately, we succeeded in using Vivado-HLS to produce router and NoC modules that are exact cycle- and bit-accurate replacements of our reference RTL-based router and NoC modules. Furthermore, the routers and NoCs resulting from HLS and RTL are comparable in resource utilization and critical path delay. Our experience subjectively suggests that HLS is able to simplify the design effort even though much of the structural details had to be provided in the HLS description through a combination of coding discipline and explicit pragmas. The C++ source code can be found at this http URL","Title: SPIRAL: Extreme Performance Portability.\nYear: 2018\n\nAbstract: In this paper, we address the question of how to automatically map computational kernels to highly efficient code for a wide range of computing platforms and establish the correctness of the synthesized code. More specifically, we focus on two fundamental problems that software developers are faced with: performance portability across the ever-changing landscape of parallel platforms and correctne...","Title: Time-Shared Execution of Realtime Computer Vision Pipelines by Dynamic Partial Reconfiguration\nYear: 2018\n\nAbstract: This paper presents an FPGA runtime framework that demonstrates the feasibility of using dynamic partial reconfiguration (DPR) for time-sharing an FPGA by multiple realtime computer vision pipelines. The presented time-sharing runtime framework manages an FPGA fabric that can be round-robin time-shared by different pipelines at the time scale of individual frames. In this new use-case, the challenge is to achieve useful performance despite high reconfiguration time. The paper describes the basic runtime support as well as four optimizations necessary to achieve realtime performance given the limitations of DPR on today's FPGAs. The paper provides a characterization of a working runtime framework prototype on a Xilinx ZC706 development board. The paper also reports the performance of streaming vision pipelines when time-shared.","Title: PageRank Acceleration for Large Graphs with Scalable Hardware and Two-Step SpMV\nYear: 2018\n\nAbstract: PageRank is an important vertex ranking algorithm that suffers from poor performance and efficiency due to notorious memory access behavior. Furthermore, when graphs become bigger and sparser, PageRank applications are inhibited as most current solutions profoundly rely on large random access fast memory, which is not easily scalable. In this paper we present a 16nm ASIC based shared memory platform for PageRank implementation that fundamentally accelerates Sparse Matrix dense Vector multiplication (SpMV), the core kernel of PageRank. This accelerator is scalable, guarantees full DRAM streaming and reduces off-chip communication. More importantly, it is capable of handling very large graphs (~2 billion vertices) despite using significantly less fast random access memory than current solutions. Experimental results show that our proposed accelerator is able to yield order of magnitude improvement in both energy efficiency and performance over state of the art shared memory commercial off-the-shelf (COTS) solutions.","Title: Processor Assisted Worklist Scheduling for FPGA Accelerated Graph Processing on a Shared-Memory Platform\nYear: 2019\n\nAbstract: FPGA-based processing has gained much attention for accelerating graph analytics because of the demand in performance and energy efficiency. However, while priority scheduling has been shown to be an effective optimization for improving performance for worklist-based graph computations, it is rarely used in accelerator designs due to its implementation complexity and memory-access overhead. In this paper, we present a heterogeneous processing approach for priority scheduling on a shared-memory CPU-FPGA platform. By exploiting the closely-coupled integration of the host processor and the FPGA accelerator, our system dynamically offloads the task of scheduling to a software scheduler on the processor for its programmability, high-capacity cache and low memory latency, while the FPGA graph processing accelerator enjoys the scheduling benefit and delivers higher performance at excellent energy efficiency. To understand the effectiveness of our solution, we compared it with FPGA-only solutions for two scheduling schemes: the well-known Dijkstra scheduling for Single Source Shortest Path and a new scheduling optimization we developed for improving the data locality of Breadth First Search. Whereas the FPGA-only solution requires an impractical amount of on-chip storage to implement a priority queue, the proposed processor-assisted scheduling that moves the task of scheduling to the processor consumes a negligible load on the processor and retains most of the performance benefit from priority scheduling.","Title: Achieving 100gbps Intrusion Prevention On A Single Server\nYear: 2020\n\nAbstract: Intrusion Detection and Prevention Systems (IDS/IPS) are among the most demanding stateful network functions. Today's network operators are faced with securing 100Gbps networks with 100K+ concurrent connections by deploying IDS/IPSes to search for 10K+ rules concurrently. In this paper we set an ambitious goal: Can we do all of the above in a single server? Through the Pigasus IDS/IPS, we show that this goal is achievable, perhaps for the first time, by building on recent advances in FPGA-capable SmartNICs. Pigasus' design takes an FPGA first approach, where the majority of processing, and all state and control flow are managed on the FPGA. However, doing so requires careful design of algorithms and data structures to ensure fast common-case performance while densely utilizing system memory resources. Our experiments with a variety of traces show that Pigasus can support 100Gbps using an average of 5 cores and 1 FPGA, using 38x less power than a CPU-only approach.","Title: Flexible Hardware Accelerator Design Generation with Spiral\nYear: 2022\n\nAbstract: Hardware specialization has become a widely employed technique for approaching higher performance and en-ergy efficiency in computer systems. Yet obtaining efficient cus-tom hardware designs remains a challenging and tedious task, calling for the automated approaches. In the past, Spiral has been used for generating high-throughput streaming hardware designs for linear transform kernels. This paper is motivated by an observation that a memory-based iterative computing model may allow us to trade off throughput for algorithmic flexibility. In this paper, we present a hardware generation approach that generates and optimizes algorithms using Spiral's multi-level domain-specific languages (DSLs), targeting a scalar load-store architecture. We have incorporated this approach as a hardware backend into the Spiral system. Our evaluation of this approach on several fundamental kernels shows flexibility with reasonable performance and resource utilization."],"string":"[\n \"Title: Automatic pipelining from transactional datapath specifications\\nYear: 2010\\n\\nAbstract: We present a transactional datapath specification (T-spec) and the tool (T-piper) to synthesize automatically an in-order pipelined implementation from it. T-spec abstractly views a datapath as executing one transaction at a time, computing next system states based on current ones. From a T-spec, T-piper can synthesize a pipelined implementation that preserves original transaction semantics, while allowing simultaneous execution of multiple overlapped transactions across pipeline stages. T-piper not only ensures the correctness of pipelined executions, but can also employ forwarding and speculation to minimize performance loss due to data dependencies. Design case studies on RISC and CISC processor pipeline development are reported.\",\n \"Title: A personal supercomputer for climate research\\nYear: 1999\\n\\nAbstract: We describe and analyze the performance of a cluster of personal computers dedicated to coupledclimate simulations. This climate modeling system performs comparably to state-of-the-art supercomputersand yet is affordable by individual research groups, thus enabling more spontaneous applicationof high-end numerical models to climate science. The cluster's novelty centers around the Arctic SwitchFabric and the StarT-X network interface, a system-area interconnect substrate developed at MIT....\",\n \"Title: Algorithm and architecture optimization for large size two dimensional discrete fourier transform (abstract only)\\nYear: 2012\\n\\nAbstract: We present a poster showcasing our FPGA implementations of two-dimensional discrete Fourier transform (2D-DFT) on large datasets that must reside off-chip in DRAM. These memory-bound large 2D-DFT computations are at the heart of important scientific computing and image processing applications. The central challenge in creating high-performance implementations is in the carefully orchestrated use of the available off-chip memory bandwidth and on-chip temporary storage. Our implementations derive their efficiency from a combined attention to both the algorithm design to enable efficient DRAM access patterns and datapath design to extract the maximum compute throughput at a given level of memory bandwidth. The poster reports results including a 1024x1024 double-precision 2D-DFT implementation on an Altera DE4 platform (based on a Stratix IV EP4SGX530 with 12 GB/s DRAM bandwidth) that reached over 16 Gflop/s, achieving a much higher ratio of performance-to-memory-bandwidth than both state-of-the-art CPU and GPU implementations.\",\n \"Title: MPI-StarT: delivering network performance to numerical applications\\nYear: 1998\\n\\nAbstract: We describe an MPI implementation for a cluster of SMPs interconnected by a high-performance interconnect. This work is a collaboration between a numerical applications programmer and a cluster interconnect architect. The collaboration started with the modest goal of satisfying the communication needs of a specific numerical application, MITMatlab. However, by supporting the MPI standard MPI-StarT readily extends support to a host of applications. MPI-StarT is derived from MPICH by developing a custom implementation of the Channel Interface. Some changes in MPICH's ADI and Protocol Layers are also necessary for correct and optimal operation.MPI-StarT relies on the host SMPs' shared memory mechanism for intra-SMP communication. Inter-SMP communication is supported through StarT-X. The StarT-X NIU allows a cluster of PCI-equipped host platforms to communicate over the Arctic Switch Fabric. Currently, StarT-X is utilized by a cluster of SUN E5000 SMPs as well as a cluster of Intel Pentium-II workstations. On a SUN E5000 with StarT-X, a processor can send and receive a 64-byte message in less than 0.4 and 3.5 usec respectively and incur less than 5.6 usec user-to-user one-way latency. StarT-X's remote memory-to-memory DMA mechanism can transfer large data blocks at 60 MByte/sec between SUN E5000s.This paper outlines our effort to preserve and deliver this level of communication performance through MPI-StarT to user applications. We have studied the requirements of MITMatlab and the capabilities of StarT-X and have formulated an implementation strategy for the Channel Interface. In this paper, we discuss some performance and correctness issues and their resolutions in MPI-StarT. The correctness issues range from the handling of arbitrarily large message sizes to deadlock-free support of nonblocking MPI operations. Performance optimizations include a shared-memory-based transport mechanism for intra-SMP communication and a broadcast mechanism that is aware of the performance difference between intra-SMP and the slower inter-SMP communication.We characterize the performance of MPI-StarT on a cluster of SUN E5000s. On SUN E5000s, MPI processes within the same SMP can communicate at over 150 MByte/sec using shared memory. When communicating between SMPs over StarT-X, MPI-StarT has a peak bandwidth of 56 MByte/sec. While fine-tuning of MPI-StarT is ongoing, we demonstrate that MPI-StarT is effective in enabling the speedup of MITMatlab on a cluster of SMPs by reporting on the performance of some representative numerical operations.\",\n \"Title: Synchronous extensions to operation centric hardware description languages\\nYear: 2004\\n\\nAbstract: The Abstract Transition System (ATS) is a high-level hardware description framework. ATS's operation-centric abstraction permits perspicuous descriptions of complex concurrent hardware behavior as a sequence of atomic state transitions. However non-determinism in the ATS semantics prevents it from capturing the behavior of systems whose correctness depends upon both function and exact synchronous timing. To address this shortcoming, we present two extensions to ATS-committing transitions and synchronously delayed expressions-to support the specification of synchronous behaviors and interfaces. The new synchronous extensions compose naturally with the original ATS. We describe a compilation strategy for the synchronous extensions that leverages existing ATS synthesis capabilities. We also evaluate the new extensions' ease of description and synthesis quality in several design examples.\",\n \"Title: Automatic multithreaded pipeline synthesis from transactional datapath specifications\\nYear: 2010\\n\\nAbstract: We present a technique to automatically synthesize a multithreaded in-order pipeline from a high-level unpipelined datapath specification. This work extends the previously proposed transactional specification (T-spec) and synthesis technology (T-piper). The technique not only works with instruction processors but also flexible enough to accept any sequential datapath. It maintains previously proposed non-threaded pipeline features and is enhanced with multithreading features. We report a design space exploration study of 32 multithreaded x86 processor pipelines, all synthesized from a single T-spec.\",\n \"Title: Superscalar out-of-order demystified in four instructions\\nYear: 2003\\n\\nAbstract: This paper describes a processor design project intended to illustrate the detail inner workings of modern superscalar out-of-order processors. In the project, the students implement a cycle-accurate RTL-level model of an out-of-order core--including rename, issue, execute, completion and retirement stages--based on the MIPS R10000. The processor core only supports four instruction types. First, the basic integer subtract instruction is included to exercise the mechanisms related to register-renamed out-of-order execution. Second, two types of branch instructions, resolving correctly and incorrectly respectively, exercise speculative execution and branch rewind capabilities. Lastly, an exception-triggering instruction tests the support for precise exceptions. The project is designed to be completed in six weeks by a team of two to three students with solid background and strong interest in computer architecture and digital design. This project has been used twice in an advanced graduate computer architecture course (CMU 18-744 Hardware Systems Engineering) and has received favorable feedback from students and industry recruiters. The project handout and required Verilog source files can be downloaded from http://www.ece.cmu.edu/~jhoe/superscalar.\",\n \"Title: Implementing a high-performance multithreaded microprocessor: a case study in high-level design and validation\\nYear: 2009\\n\\nAbstract: We have developed a 16-way multithreaded microprocessor called BlueSPARC. This in-order, high-throughput processor incorporates complex features such as privileged operations, memory management, and a non-blocking cache subsystem. When supported by a hybrid simulation technique that handles rare, unimplemented behaviors in a software host, the BlueSPARC microprocessor runs unmodified UltraSPARC III-based commercial applications on Solaris 8 while hosted on a single Xilinx XCV2P70 FPGA clocked at 90MHz. This significant effort was achieved in under one man-year using a high-level language and a high-level validation approach. In the first part of the paper, we describe our experience in applying the Bluespec SystemVerilog (BSV) language to develop a large hardware design that must meet specific area and performance requirements. In the second part of the paper, we present the FPGA-accelerated validation approach we employed to check the correct execution of real multithreaded programs running on the BlueSPARC processor. We discuss the challenges and our solutions to validation in the presence of full-system interactions and microarchitectural nondeterminism.\",\n \"Title: High-level modeling and FPGA prototyping of microprocessors\\nYear: 2003\\n\\nAbstract: Emerging high-level hardware description and synthesis technologies in conjunction with field-programmable gate arrays (FPGAs) have significantly lowered the threshold for hardware development. Opportunities exist to integrate these technologies into a tool for exploring and evaluating microarchitectural designs. This paper presents a case study in developing the synthesizable high-level model of a superscalar processor and producing a working prototype in FPGA. Using an experimental operation-centric hardware description language, we have created the synthesizable model of a superscalar speculative out-of-order core for the integer subset of SimpleScalar PISA. A prototype implementation is produced by synthesizing the high-level model for the Spyder FPGA prototyping board. In addition, we have modified the baseline processor model to create derivative processor designs that add newly proposed experimental mechanisms. The derivative models are useful both in testing the completeness and correctness of new mechanisms and in assessing the mechanisms' impact on implementation area and cycle time.\",\n \"Title: Automatic generation of streaming datapaths for arbitrary fixed permutations\\nYear: 2009\\n\\nAbstract: This paper presents a technique to perform arbitrary fixed permutations on streaming data. We describe a parameterized architecture that takes as input n data points streamed at a rate of w per cycle, performs a permutation over all n points, and outputs the result in the same streaming format. We describe the system and its requirements mathematically and use this mathematical description to show that the datapaths resulting from our technique can sustain a full throughput of w words per cycle without stalling. Additionally, we provide an algorithm to configure the datapath for a given permutation and streaming width. Using this technique, we have constructed a full synthesis system that takes as input a permutation and a streaming width and outputs a register-transfer level Verilog description of the datapath. We present an evaluation of our generated designs over varying problem sizes and streaming widths, synthesized for a Xilinx Virtex-5 FPGA.\",\n \"Title: PROToFLEX: FPGA-accelerated Hybrid Functional Simulator\\nYear: 2007\\n\\nAbstract: PROTOFLEX is an FPGA-accelerated hybrid simulation/emulation platform designed to support large-scale multiprocessor hardware and software research. Unlike prior attempts at FPGA multiprocessor system emulators, PROTOFLEX emulates full-system fidelity-i.e., runs stock commercial operating systems with I/O support. This is accomplished without undue effort by leveraging a hybrid emulation technique called transplanting. Our transplant technology uses FPGAs to accelerate only common-case behaviors while relegating infrequent, complex behaviors (e.g., I/O devices) to software simulation. By working in concert with existing full-system simulators, transplanting avoids the costly and unnecessary construction of the entire target system in FPGA. We report preliminary findings from a working hybrid PROTOFLEX emulator of an UltraSPARC workstation running Solaris 8. We have also started developing a novel multiprocessor emulation approach that interleaves the execution of many (10s to 100s) processor contexts onto a shared emulation engine. This approach decouples the scale and complexity of the FPGA host from the simulated system size but nevertheless enables us to scale the desired emulation performance by the number of emulation engines used. Together, the transplant and interleaving techniques enable us to develop full-system FPGA emulators of up to thousands of processors without an overwhelming development effort.\",\n \"Title: Computer Generation of Hardware for Linear Digital Signal Processing Transforms\\nYear: 2012\\n\\nAbstract: Linear signal transforms such as the discrete Fourier transform (DFT) are very widely used in digital signal processing and other domains. Due to high performance or efficiency requirements, these transforms are often implemented in hardware. This implementation is challenging due to the large number of algorithmic options (e.g., fast Fourier transform algorithms or FFTs), the variety of ways that a fixed algorithm can be mapped to a sequential datapath, and the design of the components of this datapath. The best choices depend heavily on the resource budget and the performance goals of the target application. Thus, it is difficult for a designer to determine which set of options will best meet a given set of requirements. In this article we introduce the Spiral hardware generation framework and system for linear transforms. The system takes a problem specification as input as well as directives that define characteristics of the desired datapath. Using a mathematical language to represent and explore transform algorithms and datapath characteristics, the system automatically generates an algorithm, maps it to a datapath, and outputs a synthesizable register transfer level Verilog description suitable for FPGA or ASIC implementation. The quality of the generated designs rivals the best available handwritten IP cores.\",\n \"Title: Fast bilateral filtering by adapting block size\\nYear: 2010\\n\\nAbstract: Direct implementations of bilateral filtering show O(r2) computational complexity per pixel, where r is the filter window radius. Several lower complexity methods have been developed. State-of-the-art low complexity algorithm is an O(1) bilateral filtering, in which computational cost per pixel is nearly constant for large image size. Although the overall computational complexity does not go up with the window radius, it is linearly proportional to the number of quantization levels of bilateral filtering computed per pixel in the algorithm. In this paper, we show that overall runtime depends on two factors, computing time per pixel per level and average number of levels per pixel. We explain a fundamental trade-off between these two factors, which can be controlled by adjusting block size. We establish a model to estimate run time and search for the optimal block size. Using this model, we demonstrate an average speedup of 1.2-26.0x over the previous method for typical bilateral filtering parameters.\",\n \"Title: Automatic generation of customized discrete fourier transform IPs\\nYear: 2005\\n\\nAbstract: This paper presents a parameterized soft core generator for the discrete Fourier transform (DFT). Reusable IPs of digital signal processing (DSP) kernels are important time-saving resources in DSP hardware development. Unfortunately, reusable IPs, however optimized, could introduce inefficiencies because they cannot fit the exact requirements of every application context. Given the well-understood and regular computation in DSP kernels, an automatic tool could generate high-quality ready-to-use IPs customized to user-specified cost/performance tradeoffs (beyond basic parameters such as input size and data format). The paper shows that the generated DFT cores could match closely the performance and cost of DFT cores from the Xilinx LogiCore library. Furthermore, the generator could yield DFT cores over a range of different performance/cost tradeoff points that are not available from the library.\",\n \"Title: ProtoFlex: Towards Scalable, Full-System Multiprocessor Simulations Using FPGAs\\nYear: 2009\\n\\nAbstract: Functional full-system simulators are powerful and versatile research tools for accelerating architectural exploration and advanced software development. Their main shortcoming is limited throughput when simulating large multiprocessor systems with hundreds or thousands of processors or when instrumentation is introduced. We propose the ProtoFlex simulation architecture, which uses FPGAs to accelerate full-system multiprocessor simulation and to facilitate high-performance instrumentation. Prior FPGA approaches that prototype a complete system in hardware are either too complex when scaling to large-scale configurations or require significant effort to provide full-system support. In contrast, ProtoFlex virtualizes the execution of many logical processors onto a consolidated number of multiple-context execution engines on the FPGA. Through virtualization, the number of engines can be judiciously scaled, as needed, to deliver on necessary simulation performance at a large savings in complexity. Further, to achieve low-complexity full-system support, a hybrid simulation technique called transplanting allows implementing in the FPGA only the frequently encountered behaviors, while a software simulator preserves the abstraction of a complete system. We have created a first instance of the ProtoFlex simulation architecture, which is an FPGA-based, full-system functional simulator for a 16-way UltraSPARC III symmetric multiprocessor server, hosted on a single Xilinx Virtex-II XCV2P70 FPGA. On average, the simulator achieves a 38x speedup (and as high as 49×) over comparable software simulation across a suite of applications, including OLTP on a commercial database server. We also demonstrate the advantages of minimal-overhead FPGA-accelerated instrumentation through a CMP cache simulation technique that runs orders-of-magnitude faster than software.\",\n \"Title: Chip-Level Redundancy in Distributed Shared-Memory Multiprocessors\\nYear: 2009\\n\\nAbstract: Distributed shared-memory (DSM) multiprocessors provide a scalable hardware platform, but lack the necessary redundancy for mainframe-level reliability and availability. Chip-level redundancy in a DSM server faces a key challenge: the increased latency to check results among redundant components. To address performance overheads, we propose a checking filter that reduces the number of checking operations impeding the critical path of execution. Furthermore, we propose to decouple checking operations from the coherence protocol, which simplifies the implementation and permits reuse of existing coherence controller hardware. Our simulation results of commercial workloads indicate average performance overhead is within 4% (9% maximum) of tightly coupled DMR solutions.\",\n \"Title: FFT Compiler: from math to efficient hardware HLDVT invited short paper\\nYear: 2007\\n\\nAbstract: This paper presents a high-level compiler that generates hardware implementations of the discrete Fourier transform (DFT) from mathematical specifications. The matrix formula input language captures not only the DFT calculation but also the implementation options at the algorithmic and architectural levels. By selecting the appropriate formula, the resulting hardware implementations (described in a synthesizable Verilog description) can achieve a wide range of tradeoffs between implementation cost and performance. The compiler is also parameterized for a set of technology-specific optimizations, to allow it to target specific implementation platforms. This paper gives a brief overview of the system and presents synthesis results.\",\n \"Title: 3D Point Cloud Reduction Using Mixed-Integer Quadratic Programming\\nYear: 2013\\n\\nAbstract: Large scale 3D image localization requires computationally expensive matching between 2D feature points in the query image and a 3D point cloud. In this paper, we present a method to accelerate the matching process and to reduce the memory footprint by analyzing the view-statistics of points in a training corpus. Given a training image set that is representative of common views of a scene, our approach identifies a compact subset of the 3D point cloud for efficient localization, while achieving comparable localization performance to using the full 3D point cloud. We demonstrate that the problem can be precisely formulated as a mixed-integer quadratic program and present a pointwise descriptor calibration process to improve matching. We show that our algorithm outperforms the state-of-theart greedy algorithm on standard datasets, on measures of both point-cloud compression and localization accuracy.\",\n \"Title: Generating FPGA-Accelerated DFT Libraries\\nYear: 2007\\n\\nAbstract: We present a domain-specific approach to generate high-performance hardware-software partitioned implementations of the discrete Fourier transform (DFT) in fixed point precision. The partitioning strategy is a heuristic based on the DFT's divide-and-conquer algorithmic structure and fine tuned by the feedback-driven exploration of candidate designs. We have integrated this approach in the Spiral linear-transform code-generation framework to support push-button automatic implementation. We present evaluations of hardware-software DFT implementations running on the embedded PowerPC processor and the reconfigurable fabric of the Xilinx Virtex-II Pro FPGA. In our experiments, the 1D and 2D DFT's FPGA-accelerated libraries exhibit between 2 and 7.5 times higher performance (operations per second) and up to 2.5 times better energy efficiency (operations per Joule) than the software-only version.\",\n \"Title: CoRAM: an in-fabric memory architecture for FPGA-based computing\\nYear: 2011\\n\\nAbstract: FPGAs have been used in many applications to achieve orders-of-magnitude improvement in absolute performance and energy efficiency relative to conventional microprocessors. Despite their promise in both processing performance and efficiency, FPGAs have not yet gained widespread acceptance as mainstream computing devices. A fundamental obstacle to FPGA-based computing today is the FPGA's lack of a common, scalable memory architecture. When developing applications for FPGAs, designers are often directly responsible for crafting the application-specific infrastructure logic that manages and transports data to and from the processing kernels. This infrastructure not only increases design time and effort but will frequently lock a design to a particular FPGA product line, hindering scalability and portability. We propose a new FPGA memory architecture called Connected RAM (CoRAM) to serve as a portable bridge between the distributed computation kernels and the external memory interfaces. In addition to improving performance and efficiency, the CoRAM architecture provides a virtualized memory environment as seen by the hardware kernels to simplify development and to improve an application's portability and scalability.\",\n \"Title: Integrating formal verification and high-level processor pipeline synthesis\\nYear: 2011\\n\\nAbstract: When a processor implementation is synthesized from a specification using an automatic framework, this implementation still should be verified against its specification to ensure the automatic framework introduced no error. This paper presents our effort in integrating fully automated formal verification with a high-level processor pipeline synthesis framework. As an integral part of the pipeline synthesis, our framework also emits SMV models for checking the functional equivalence between the output pipelined processor implementation and its input non-pipelined specification. Well known compositional model checking techniques are automatically applied to curtail state explosion during model checking. The paper reports case studies of applying this integrated framework to synthesize and formally verify pipelined RISC and CISC processors.\",\n \"Title: Memory Bandwidth Efficient Two-Dimensional Fast Fourier Transform Algorithm and Implementation for Large Problem Sizes\\nYear: 2012\\n\\nAbstract: Prevailing VLSI trends point to a growing gap between the scaling of on-chip processing throughput and off-chip memory bandwidth. An efficient use of memory bandwidth must become a first-class design consideration in order to fully utilize the processing capability of highly concurrent processing platforms like FPGAs. In this paper, we present key aspects of this challenge in developing FPGA-based implementations of two-dimensional fast Fourier transform (2D-FFT) where the large datasets must reside off-chip in DRAM. Our scalable implementations address the memory bandwidth bottleneck through both (1) algorithm design to enable efficient DRAM access patterns and (2) data path design to extract the maximum compute throughput for a given level of memory bandwidth. We present results for double-precision 2D-FFT up to size 2,048-by-2,048. On an Alter a DE4 platform our implementation of the 2,048-by-2,048 2D-FFT can achieve over 19.2 Gflop/s from the 12 GByte/s maximum DRAM bandwidth available. The results also show that our FPGA-based implementations of 2D-FFT are more efficient than 2D-FFT running on state-of-the-art CPUs and GPUs in terms of the bandwidth and power efficiency.\",\n \"Title: Hardware Synthesis from Term Rewriting Systems\\nYear: 1999\\n\\nAbstract: Term Rewriting System (TRS) is a good formalism for describing concurrent systemsthat embody asynchronous and nondeterministic behavior in their specifications.Elsewhere, we have used TRS's to describe speculative micro-architecturesand complex cache-coherence protocols, and proven the correctness of these systems.In this paper, we describe the compilation of TRS's into a subset of Verilogthat can be simulated and synthesized using commercial tools. TRAC, TermRewriting Architecture...\",\n \"Title: RAMP: Research Accelerator for Multiple Processors\\nYear: 2007\\n\\nAbstract: The RAMP project's goal is to enable the intensive, multidisciplinary innovation that the computing industry will need to tackle the problems of parallel processing. RAMP itself is an open-source, community-developed, FPGA-based emulator of parallel architectures. Its design framework lets a large, collaborative community develop and contribute reusable, composable design modules. Three complete designs--for transactional memory, distributed systems, and distributed-shared memory--demonstrate the platform's potential.\",\n \"Title: Permuting streaming data using RAMs\\nYear: 2009\\n\\nAbstract: This article presents a method for constructing hardware structures that perform a fixed permutation on streaming data. The method applies to permutations that can be represented as linear mappings on the bit-level representation of the data locations. This subclass includes many important permutations such as stride permutations (corner turn, perfect shuffle, etc.), the bit reversal, the Hadamard reordering, and the Gray code reordering. The datapath for performing the streaming permutation consists of several independent banks of memory and two interconnection networks. These structures are built for a given streaming width (i.e., number of inputs and outputs per cycle) and operate at full throughput for this streaming width. We provide an algorithm that completely specifies the datapath and control logic given the desired permutation and streaming width. Further, we provide lower bounds on the achievable cost of a solution and show that for an important subclass of permutations our solution is optimal. We apply our algorithm to derive datapaths for several important permutations, including a detailed example that carefully illustrates each aspect of the design process. Lastly, we compare our permutation structures to those of Järvinen et al. [2004], which are specialized for stride permutations.\",\n \"Title: SimFlex: a fast, accurate, flexible full-system simulation framework for performance evaluation of server architecture\\nYear: 2004\\n\\nAbstract: The new focus on commercial workloads in simulation studies of server systems has caused a drastic increase in the complexity and decrease in the speed of simulation tools. The complexity of a large-scale full-system model makes development of a monolithic simulation tool a prohibitively difficult task. Furthermore, detailed full-system models simulate so slowly that experimental results must be based on simulations of only fractions of a second of execution of the modelled system.This paper presents SIMFLEX, a simulation framework which uses component-based design and rigorous statistical sampling to enable development of complex models and ensure representative measurement results with fast simulation turnaround. The novelty of SIMFLEX lies in its combination of a unique, compile-time approach to component interconnection and a methodology for obtaining accurate results from sampled simulations on a platform capable of evaluating unmodified commercial workloads.\",\n \"Title: In-system FPGA prototyping of an itanium microarchitecture\\nYear: 2004\\n\\nAbstract: This work is part of our on-going effort to prototype an Itanium microarchitecture on an FPGA. To conserve time and effort in model development, we described our microarchitecture in Bluespec, a synthesizable high-level hardware description language. The microarchitecture model currently supports a subset of the Itanium instruction set architecture (ISA). The model includes details such as multi-bundle instruction fetch, decode and issue, parallel pipelined execution units with scoreboarding and bypassing, and multiple levels of cache hierarchies. The microarchitecture model is synthesized and prototyped on an FPGA that interfaces directly to the memory bus of a host PC. The prototyped microprocessor core executes the supported ISA subset at 100MHz and directly references the host-PC's DRAM and I/O resources through the memory bus at up to 800MB/sec of bandwidth. This effort is a first step toward developing a convenient in-system microprocessor prototyping platform capable of executing realistic full-scale applications and operating systems.\",\n \"Title: Efficient Resource Sharing in Concurrent Error Detecting Superscalar Microarchitectures\\nYear: 2004\\n\\nAbstract: Previous proposals for soft-error tolerance have called for redundantly executing a program as two concurrent threads on a superscalar microarchitecture. In a balanced superscalar design, the extra workload from redundant execution induces a severe performance penalty due to increased contention for resources throughout the datapath. This paper identifies and analyzes four key factors that affect the performance of redundant execution, namely 1) issue bandwidth and functional unit contention, 2) issue queue and reorder buffer capacity contention, 3) decode and retirement bandwidth contention, and 4) coupling between redundant threads' dynamic resource requirements. Based on this analysis, we propose the SHREC microarchitecture for asymmetric and staggered redundant execution. This microarchitecture addresses the four factors in an integrated design without requiring prohibitive additional hardware resources. In comparison to conventional single-threaded execution on a state-of-the-art superscalar microarchitecture with comparable cost, SHREC reduces the average performance penalty to within 4% on integer and 15% on floating-point SPEC2K benchmarks by sharing resources more efficiently between the redundant threads.\",\n \"Title: A complexity-effective architecture for accelerating full-system multiprocessor simulations using FPGAs\\nYear: 2008\\n\\nAbstract: Functional full-system simulators are powerful and versatile research tools for accelerating architectural exploration and advanced software development. Their main shortcoming is limited throughput when simulating systems with hundreds of processors or more. To overcome this bottleneck, we propose the PROTOFLEX simulation architecture, which uses FPGAs to accelerate simulation. Prior FPGA approaches that prototype a complete system in hardware are either too complex when scaling to large-scale configurations or require significant effort to provide full-system support. In contrast, PROTOFLEX reduces complexity by virtualizing the execution of many logical processors onto a consolidated set of multiple-context execution engines on the FPGA. Through virtualization, the number of engines can be judiciously scaled, as needed, to deliver on necessary simulation performance. To achieve low-complexity full-system support, a hybrid simulation technique called transplanting allows implementing in the FPGA only the frequently encountered behaviors, while a software simulator preserves the abstraction of a complete system We have created a first instance of the PROTOFLEX simulation architecture, which is an FPGA-based, full-system functional simulator for a 16-way UltraSPARC III symmetric multiprocessor server hosted on a single Xilinx Virtex-II XCV2P70 FPGA. On average, the simulator achieves a 39x speedup (and as high as 49x) over comparable software simulation across a suite of applications, including OLTP on a commercial database server.\",\n \"Title: AUTOMATIC COST MINIMIZATION FOR MULTIPLIERLESS IMPLEMENTATIONS OF DISCRETE SIGNAL TRANSFORMS\\nYear: 2004\\n\\nAbstract: The computation of linear DSP transforms consists entirely of ad- ditions and multiplications by constants, which, in a hardware re- alization, can be implemented as a network of wired shifts and additions. Thus, a light weight fixed point implementation that ap- proximates an exact transform can be built from only adders. This paper presents an automatic approach for minimizing the number of additions required for a given transform under the constraint of a particular quality measure. We present an evaluation of our approach. For example, one experiment shows that the IMDCT transform within an MP3 decoder can be reduced from 572 ad- ditions to 260 additions while maintaining Limited Accuracy as defined by the MP3 ISO standard.\",\n \"Title: Truss: A Reliable, Scalable Server Architecture\\nYear: 2005\\n\\nAbstract: Traditional reliable servers require costly design changes to the processor, use custom system or application software, or cannot scale beyond a few processing elements. We present TRUSS, a family of server architectures providing reliable, scalable computation from distributed shared-memory hardware while requiring no changes to software. The TRUSS paradigm centers around a logical division of computation and memory that isolates errors in processing from memory storage and vice versa. In this paper, we present the key mechanisms that enable this separation and use full-system simulation to evaluate the impact on a range of commercial and scientific workloads.\",\n \"Title: PAI: A Lightweight Mechanism for Single-Node Memory Recovery in DSM Servers\\nYear: 2007\\n\\nAbstract: Several recent studies identify the memory system as the most frequent source of hardware failures in commercial servers. Techniques to protect the memory system from failures must continue to service memory requests, despite hardware failures. Furthermore, to support existing OS's, the physical address space must be retained following reconfiguration. Existing techniques either suffer from a high performance overhead or require pervasive hardware changes to support transparent recovery. In this paper, we propose Physical Address Indirection (PAI), a lightweight, hardware-based mechanism for memory system failure recovery. PAI provides a simple hardware mapping to transparently reconstruct affected data in alternate locations, while maintaining high performance and avoiding physical address changes. With full-system simulation of commercial and scientific workloads on a 16-node distributed shared memory server, we show that prior techniques have an average degraded mode performance loss of 14% and 51% for commercial and scientific workloads, respectively. Using PAI's dataswap reconstruction, the same workloads have 1% and 32% average performance losses.\",\n \"Title: SMARTS: accelerating microarchitecture simulation via rigorous statistical sampling\\nYear: 2003\\n\\nAbstract: Current software-based microarchitecture simulators are many orders of magnitude slower than the hardware they simulate. Hence, most microarchitecture design studies draw their conclusions from drastically truncated benchmark simulations that are often inaccurate and misleading. This paper presents the Sampling Microarchitecture Simulation (SMARTS) framework as an approach to enable fast and accurate performance measurements of full-length benchmarks. SMARTS accelerates simulation by selectively measuring in detail only an appropriate benchmark subset. SMARTS prescribes a statistically sound procedure for configuring a systematic sampling simulation run to achieve a desired quantifiable confidence in estimates.Analysis of 41 of the 45 possible SPEC2K benchmark/input combinations show CPI and energy per instruction (EPI) can be estimated to within ±3% with 99.7% confidence by measuring fewer than 50 million instructions per benchmark. In practice, inaccuracy in microarchitectural state initialization introduces an additional uncertainty which we empirically bound to ∼2% for the tested benchmarks. Our implementation of SMARTS achieves an actual average error of only 0.64% on CPI and 0.59% on EPI for the tested benchmarks, running with average speedups of 35 and 60 over detailed simulation of 8-way and 16-way out-of-order processors, respectively.\",\n \"Title: MEMOCODE 2007 Co-Design Contest\\nYear: 2008\\n\\nAbstract: New to the 2007 MEMOCODE conference is the HW/SW Co-Design Contest. Members of the technical and steering committees from MEMOCODE 2006 thought that the co-design practice is distinct from conventional hardware or software design practice. A co-design contest was conceived to help elucidate these issues and to foster greater interest in the design aspects of the MEMOCODE conference. The contest would also serve to showcase advances in co-design tools and methodologies. Below, we describe briefly the development of the contest format and the thinking behind the choices. The actual call-for-participation that appeared on the contest website (http://memocode07.ece.cmu.edu/contest.html) is reproduced in the appendix section.\",\n \"Title: Cross-platform FPGA accelerator development using CoRAM and CONNECT\\nYear: 2013\\n\\nAbstract: The CoRAM memory architecture is an easy-to-use and portable abstraction for FPGA accelerator development [1, 2]. Using the CoRAM framework, FPGA developers can write their applications once and re-target them automatically to different FPGA platforms and devices (e.g., Xilinx ML605, Altera DE4, ZYNQ-702, etc). In this tutorial, participants will learn the key concepts of the CoRAM Virtual Architecture and the underlying CONNECT Network-on-Chip generation framework [3]. The tutorial is organized into three parts. The first part will provide an overview of the CoRAM Virtual Architecture and include a hands-on section where participants will work on a small example to get first-hand experience with the CoRAM development flow. The second part of the tutorial will provide a beneath-the-hood look at CoRAM and cover more advanced topics. These topics include memory loading, user I/O, debugging, as well as a segment on the CONNECT NoC generation framework which serves as the on-chip interconnect for CoRAM. The final part of the tutorial will be devoted to more advanced exercises and demos, as well as a Q&A session for CoRAM and CONNECT. The tutorial assumes a basic understanding of RTL design and C programming. To join in on the hands-on exercise, the attendees need laptops with 15GB of free space and VirtualBox installed. Please visit http://www.ece.cmu.edu/~coram for information about CoRAM and updates on this tutorial.\",\n \"Title: Dependable VLSI: Device, design and architecture - How should they cooperate?\\nYear: 2009\\n\\nAbstract: VLSI dependability is one of the most significant issues in the modern world. Here the panelists will discuss the key technologies for it as well as the cost optimization among device, design and architecture.\",\n \"Title: Formal datapath representation and manipulation for implementing DSP transforms\\nYear: 2008\\n\\nAbstract: We present a domain-specific approach to representing datapaths for hardware implementations of linear signal transform algorithms. We extend the tensor structure for describing linear transform algorithms, adding the ability to explicitly characterize two important dimensions of datapath architecture. This representation allows both algorithm and datapath to be specified within a single formula and gives the designer the ability to easily consider a wide space of possible datapaths at a high level of abstraction. We have constructed a formula manipulation system based on this representation and have written a compiler that can translate a formula into a hardware implementation. This enables an automatic \\\"push button\\\" compilation flow that produces a register transfer level hardware description from high-level datapath directives and an algorithm (written as a formula). In our experimental results, we demonstrate that this approach yields efficient designs over a large tradeoff space.\",\n \"Title: Real time stereo vision using exponential step cost aggregation on GPU\\nYear: 2009\\n\\nAbstract: In this paper, we propose a local cost aggregation approach for real time stereo vision on a graphics processing unit (GPU). Recent research shows that local approaches based on carefully designed cost aggregation strategies can outperform many global approaches. Among those local aggregation approaches, adaptive-weight window produces the best quality disparity map under real-time constraint, but it is slower than other local approaches. We propose a very fast adaptive-weight aggregation method based on exponential step information propagation. The basic idea is to propagate information from long distance pixels within a few iterations. We also discuss important techniques of efficient implementation on GPU platform, which result in 10.5à speed up than a straightforward implementation. Compared to existing real time adaptive-weight approach, our technique reduces the computation time by more than half at improved accuracy. Detailed experimental results show that our technique is Pareto-optimal among existing real time or near real time stereo algorithms in the accuracy-speed trade-off space.\",\n \"Title: FIST: A Fast, Lightweight, FPGA-Friendly Packet Latency Estimator for NoC Modeling in Full-System Simulations\\nYear: 2011\\n\\nAbstract: FIST (Fast Interconnect Simulation Techniques) is a fast and simple packet latency estimator to replace time-consuming detailed Network-on-Chip (NoC) models in full-system performance simulators. FIST combines ideas from analytical network modeling and execution-driven simulation models. The main idea is to abstractly model each router as a load-delay curve and sum load-dependent delay at each visited router to obtain a packet's latency by tracking each router's load at runtime. The resulting latency estimator can accurately capture subtle load-dependent behaviors of a NoC but is much simpler than a full-blown execution-driven model. We study two variations of FIST in the context of a software-based, cycle-level simulation of a tiled chip-multiprocessor (CMP). We evaluate FIST's accuracy and performance relative to the CMP simulator's original execution-driven 2D-mesh NoC model. A static FIST approach (trained offine using uniform random synthetic traffic) achieves less than 6% average error in packet latency and up to 43× average speedup for a 16×16 mesh. A dynamic FIST approach that adds periodic online training reduces the average packet latency error to less than 2% and still maintains an average speedup of up to 18× for a 16×16 mesh. Moreover, an FPGA-based realization of FIST can simulate 2D-mesh networks up to 24×24 nodes, at 3 to 4 orders of magnitude speedup over software-based simulators.\",\n \"Title: Statistical sampling of microarchitecture simulation\\nYear: 2006\\n\\nAbstract: Current software-based microarchitecture simulators are many orders of magnitude slower than the hardware they simulate. Hence, most microarchitecture design studies draw their conclusions from drastically truncated benchmark simulations that are often inaccurate and misleading. This article presents the Sampling Microarchitecture Simulation (SMARTS) framework as an approach to enable fast and accurate performance measurements of full-length benchmarks. SMARTS accelerates simulation by selectively measuring in detail only an appropriate benchmark subset. SMARTS prescribes a statistically sound procedure for configuring a systematic sampling simulation run to achieve a desired quantifiable confidence in estimates.Analysis of the SPEC CPU2000 benchmark suite shows that CPI and energy per instruction (EPI) can be estimated to within ±3% with 99.7% confidence by measuring fewer than 50 million instructions per benchmark. In practice, inaccuracy in microarchitectural state initialization introduces an additional uncertainty which we empirically bound to ∼2% for the tested benchmarks. Our implementation of SMARTS achieves an actual average error of only 0.64% on CPI and 0.59% on EPI for the tested benchmarks, running with average speedups of 35 and 60 over detailed simulation of 8-way and 16-way out-of-order processors, respectively.\",\n \"Title: TurboSMARTS: accurate microarchitecture simulation sampling in minutes\\nYear: 2005\\n\\nAbstract: Recent research proposes accelerating processor microarchitecture simulation through statistical sampling. Prior simulation sampling approaches construct accurate model state for each measurement by continuously warming large microarchitectural structures (e.g., caches and the branch predictor) while emulating the billions of instructions between measurements. This approach, called functional warming, occupies hours of runtime while the detailed simulation that is measured requires mere minutes.To eliminate the functional warming bottleneck, we propose TurboSMARTS, a simulation framework that stores functionally-warmed state in a library of small, reusable checkpoints. TurboSMARTS enables the creation of the thousands of checkpoints necessary for accurate sampling by storing only the subset of warmed state accessed during simulation of each brief execution window. TurboSMARTS matches the accuracy of prior simulation sampling techniques (i.e., ±3% error with 99.7% confidence), while estimating the performance of an 8-way out-of-order super-scalar processor running SPEC CPU2000 in 91 seconds per benchmark, on average, using a 12 GB checkpoint library.\",\n \"Title: Fast and accurate resource estimation of automatically generated custom DFT IP cores\\nYear: 2006\\n\\nAbstract: This paper presents an equation-based resource utilization model for automatically generated discrete Fourier transform (DFT) soft core IPs. The parameterized DFT IP generator allows a user to make customized tradeoffs between cost and performance and between utilization of different resource classes. The equation-based resource model permits immediate and accurate estimation of resource requirements as the user considers the different generator options. Furthermore, the fast turnaround of the model allows it to be combined with a search algorithm such that the user could query automatically for an optimal design within the stated performance and resource constraints.Following a brief review of the DFT IP generator, this paper presents the development of the equation-based models for estimating slice and hard macro utilizations in the Xilinx Virtex-II Pro FPGA family. The evaluation section shows that an average error of 6.1% is achievable by a model of linear equations that can be evaluated in sub-microseconds. The paper further offers a demonstration of the automatic design exploration capability.\",\n \"Title: Highly Efficient Performance Portable Tracking of Evolving Surfaces\\nYear: 2012\\n\\nAbstract: In this paper we present a framework to obtain highly efficient implementations for the narrow band level set method on commercial off-the-shelf (COTS) multicore CPU systems with a cache-based memory hierarchy such as Intel Xeon and Atom processors. The narrow-band level set algorithm tracks wave-fronts in discretized volumes (for instance, explosion shock waves), and is computationally very demanding. At the core of our optimization framework is a novel projection-based approach to enhance data locality and enable reuse for sparse surfaces in dense discretized volumes. The method reduces stencil operations on sparse and changing sets of pixels belonging to an evolving surface into dense stencil operations on meta-pixels in a lower-dimensional projection of the pixel space. These meta-pixels are then amenable to standard techniques like time tiling. However, the complexity introduced by ever-changing meta-pixels requires us to revisit and adapt all other necessary optimizations. We apply adapted versions of SIMDization, multi-threading, DAG scheduling for basic tiles, and specialization through code generation to extract maximum performance. The system is implemented as highly parameterized code skeleton that is auto-tuned and uses program generation. We evaluated our framework on a dual-socket 2.8 GHz Xeon 5560 and a 1.6 GHz Atom N270. Our single-core performance reaches 26%-35% of the machine peak on the Xeon, and 12%- 20% on the Atom across a range of image sizes. We see up to 6.5x speedup on 8 cores of the dual-socket Xeon. For cacheresident sizes our code outperforms the best available thirdparty code (C pre-compiled into a DLL) by about 10x and for the largest out-of-cache sizes the speedup approaches around 200x. Experiments fully explain the high speedup numbers.\",\n \"Title: CONNECT: re-examining conventional wisdom for designing nocs in the context of FPGAs\\nYear: 2012\\n\\nAbstract: An FPGA is a peculiar hardware realization substrate in terms of the relative speed and cost of logic vs. wires vs. memory. In this paper, we present a Network-on-Chip (NoC) design study from the mindset of NoC as a synthesizable infrastructural element to support emerging System-on-Chip (SoC) applications on FPGAs. To support our study, we developed CONNECT, an NoC generator that can produce synthesizable RTL designs of FPGA-tuned multi-node NoCs of arbitrary topology. The CONNECT NoC architecture embodies a set of FPGA-motivated design principles that uniquely influence key NoC design decisions, such as topology, link width, router pipeline depth, network buffer sizing, and flow control. We evaluate CONNECT against a high-quality publicly available synthesizable RTL-level NoC design intended for ASICs. Our evaluation shows a significant gain in specializing NoC design decisions to FPGAs' unique mapping and operating characteristics. For example, in the case of a 4x4 mesh configuration evaluated using a set of synthetic traffic patterns, we obtain comparable or better performance than the state-of-the-art NoC while reducing logic resource cost by 58%, or alternatively, achieve 3-4x better performance for approximately the same logic resource usage. Finally, to demonstrate CONNECT's flexibility and extensive design space coverage, we also report synthesis and network performance results for several router configurations and for entire CONNECT networks.\",\n \"Title: START-NG: Delivering Seamless Parallel Computing\\nYear: 1995\\n\\nAbstract: StarT-ng is a joint MIT-Motorola project to build a high-performance message passing machine from commercial systems. Each site of the machine consists of a PowerPC 620-based Motorola symmetric multiprocessor (SMP) running the AIX 4.1 operating system. Every processor is connected to a low-latency, high-bandwidth network that is directly accessible from user-level code. In addition to fast message passing capabilities, the machine has experimental support for cachecoherent shared memory across sites. When the machine requires memory to be kept globally coherent, one processor on each site is devoted to supporting shared memory. When globally coherent shared memory is not required, that processor can be used for normal computation tasks. StarT-ng will be delivered at about the time the base SMP is introduced into the marketplace. The ability to be both a collection of standard SMP and an aggressive message passing machine with coherent shared memory makes StarT-ng a good building block for incrementally expandable parallel machines.\",\n \"Title: Custom-optimized multiplierless implementations of DSP algorithms\\nYear: 2004\\n\\nAbstract: Linear DSP kernels such as transforms and filters are comprised exclusively of additions and multiplications by constants. These multiplications may be realized as networks of additions and wired shifts in hardware. The cost of such a \\\"multiplierless\\\" implementation is determined by the number of additions, which in turn depends on the value and precision of these constants. For a given transform or filter, the set of constants and their required precision is affected by algorithmic and implementation choices and hence provides a degree of freedom for optimization. In This work we present an automated method to generate, for a given linear transform, a minimum addition multiplierless implementation that satisfies a given quality constraint. The method combines automatic algorithm selection to improve numerical robustness and automatic search methods to minimize constant precisions in a chosen algorithm. We present experiments that show the trade-offs between cost and quality, including custom optimizations of the transforms used in JPEG image and MP3 audio decoders.\",\n \"Title: Prototype and evaluation of the CoRAM memory architecture for FPGA-based computing\\nYear: 2012\\n\\nAbstract: The CoRAM memory architecture for FPGA-based computing augments traditional reconfigurable fabric with a natural and effective way for applications to interact with off-chip memory and I/O. The two central tenets of the CoRAM memory architecture are (1) the deliberate separation of concerns between computation versus data marshalling and (2) the use of a multithreaded software abstraction to replace FSM-based memory control logic. To evaluate the viability of the CoRAM memory architecture, we developed a full RTL implementation of a CoRAM microarchitecture instance that can be synthesized for standard cells or emulated on FPGAs. The results of our evaluation show that a soft emulation of the CoRAM memory architecture on current FPGAs can be impractical for memory-intensive, large-scale applications due to the high performance and area penalties incurred by the soft mechanisms. The results further show that in an envisioned FPGA built with CoRAM in mind, the introduction of hard macro blocks for data distribution can mitigate these inefficiencies---allowing applications to take advantage of the CoRAM memory architecture for ease of programmability and portability while still enjoying performance and efficiency comparable to RTL-level application development on conventional FPGAs.\",\n \"Title: Multiple constant multiplication by time-multiplexed mapping of addition chains\\nYear: 2004\\n\\nAbstract: An important primitive in the hardware implementations of linear DSP transforms is a circuit that can multiply an input value by one of several different preset constants. We propose a novel implementation of this circuit based on combining the addition chains of the constituent constants. We present an algorithm to automatically generate such a circuit for a given set of constants. The quality of the resulting circuits is evaluated after synthesis for a commercial 0.18um standard cell library. We compare the area and latency efficiency of this addition chain based approach against a straightforward approach based on a constant table and a full multiplier.\",\n \"Title: SimFlex: Statistical Sampling of Computer System Simulation\\nYear: 2006\\n\\nAbstract: Timing-accurate full-system multiprocessor simulations can take years because of architecture and application complexity. Statistical sampling makes simulation-based studies feasibly by providing ten-thousand-fold reductions in simulation runtime and enabling thousand-way simulation parallelism.\",\n \"Title: C-to-CoRAM: compiling perfect loop nests to the portable CoRAM abstraction\\nYear: 2013\\n\\nAbstract: This paper presents initial work on developing a C compiler for the CoRAM FPGA computing abstraction. The presented effort focuses on compiling fixed-bound perfect loop nests that operate on large data sets in external DRAM. As required by the CoRAM abstraction, the compiler partitions source code into two separate implementation components: (1) hardware kernel pipelines to be mapped onto the reconfigurable logic fabric; and (2) control threads that express, in a C-like language, the sequencing and coordination of data transfers between the hardware kernels and external DRAM. The compiler performs optimizations to increase parallelism and use DRAM bandwidth efficiently. It can target different FPGA platforms that support the CoRAM abstraction, either natively in a future FPGA or in soft-logic on today's devices. The CoRAM abstraction provides a convenient high-level compilation target to simplify the task of design optimization and system generation. The compiler is evaluated using three test programs (matrix-matrix multiplication, k-nearest neighbor, and 2D convolution) on the Xilinx ML605 and the Altera DE4. Results show that our compiler is able to target the different platforms and effectively exploit their dissimilar capacities and features. Depending on the application, the compiler-generated implementations achieve performance ranging from a factor of 4 slower to a factor of 2 faster relative to hand-designed implementations, as measured on actual hardware.\",\n \"Title: DELPHI: a framework for RTL-based architecture design evaluation using DSENT models\\nYear: 2015\\n\\nAbstract: Computer architects are increasingly interested in evaluating their ideas at the register-transfer level (RTL) to gain more precise insights on the key characteristics (frequency, area, power) of a micro/architectural design proposal. However, the RTL synthesis process is notoriously tedious, slow, and errorprone and is often outside the area of expertise of a typical computer architect, as it requires familiarity with complex CAD flows, hard-to-get tools and standard cell libraries. The effort is further multiplied when targeting multiple technology nodes and standard cell variants to study technology dependence. This paper presents DELPHI, a flexible, open framework that leverages the DSENT modeling engine for faster, easier, and more efficient characterization of RTL hardware designs. DELPHI first synthesizes a Verilog or VHDL RTL design (either using the industry-standard Synopsys Design Compiler tool or a combination of open-source tools) to an intermediate structural netlist. It then processes the resulting synthesized netlist to generate a technology-independent DSENT design model. This model can then be used within a modified version of the DSENT flow to perform very fast???one to two orders of magnitude faster than full RTL synthesis???estimation of hardware performance characteristics, such as frequency, area, and power across a variety of DSENT technology models (e.g., 65nm Bulk, 32nm SOI, 11nm Tri-Gate, etc.). In our evaluation using 26 RTL design examples, DELPHI and DSENT were consistently able to closely track and capture design trends of conventional RTL synthesis results without the associated delay and complexity. We are releasing the full DELPHI framework (including a fully open-source flow) at http://www.ece.cmu.edu/CALCM/delphi/.\",\n \"Title: GraphGen: An FPGA Framework for Vertex-Centric Graph Computation.\\nYear: 2014\\n\\nAbstract: Vertex-centric graph computations are widely used in many machine learning and data mining applications that operate on graph data structures. This paper presents GraphGen, a vertex-centric framework that targets FPGA for hardware acceleration of graph computations. GraphGen accepts a vertex-centric graph specification and automatically compiles it onto an application-specific synthesized graph processor and memory system for the target FPGA platform. We report design case studies using GraphGen to implement stereo matching and handwriting recognition graph applications on Terasic DE4 and Xilinx ML605 FPGA boards. Results show up to 14.6x and 2.9x speedups over software on Intel Core i7 CPU for the two applications, respectively.\",\n \"Title: Algorithm/hardware co-optimized SAR image reconstruction with 3D-stacked logic in memory\\nYear: 2014\\n\\nAbstract: Real-time system level implementations of complex Synthetic Aperture Radar (SAR) image reconstruction algorithms have always been challenging due to their data intensive characteristics. In this paper, we propose a basis vector transform based novel algorithm to alleviate the data intensity and a 3D-stacked logic in memory based hardware accelerator as the implementation platform. Experimental results indicate that this proposed algorithm/hardware co-optimized system can achieve an accuracy of 91 dB PSNR compared to a reference algorithm implemented in Matlab and energy efficiency of 72 GFLOPS/W for a 8k×8k SAR image reconstruction.\",\n \"Title: HAMLeT: Hardware accelerated memory layout transform within 3D-stacked DRAM\\nYear: 2014\\n\\nAbstract: Memory layout transformations via data reorganization are very common operations, which occur as a part of the computation or as a performance optimization in data-intensive applications. These operations require inefficient memory access patterns and roundtrip data movement through the memory hierarchy, failing to utilize the performance and energy-efficiency potentials of the memory subsystem. This paper proposes a high-bandwidth and energy-efficient hardware accelerated memory layout transform (HAMLeT) system integrated within a 3D-stacked DRAM. HAMLeT uses a low-overhead hardware that exploits the existing infrastructure in the logic layer of 3D-stacked DRAMs, and does not require any changes to the DRAM layers, yet it can fully exploit the locality and parallelism within the stack by implementing efficient layout transform algorithms. We analyze matrix layout transform operations (such as matrix transpose, matrix blocking and 3D matrix rotation) and demonstrate that HAMLeT can achieve close to peak system utilization, offering up to an order of magnitude performance improvement compared to the CPU and GPU memory subsystems which does not employ HAMLeT.\",\n \"Title: FFTS with near-optimal memory access through block data layouts\\nYear: 2014\\n\\nAbstract: Fast Fourier transform algorithms on large data sets achieve poor performance on various platforms because of the inefficient strided memory access patterns. These inefficient access patterns need to be reshaped to achieve high performance implementations. In this paper we formally restructure 1D, 2D and 3D FFTs targeting a generic machine model with a two-level memory hierarchy requiring block data transfers, and derive memory access pattern efficient algorithms using custom block data layouts. Using the Kronecker product formalism, we integrate our optimizations into Spiral framework. In our evaluations we demonstrate that Spiral generated hardware designs achieve close to theoretical peak performance of the targeted platform and offer significant speed-up (up to 6.5x) compared to naive baseline algorithms.\",\n \"Title: Understanding the design space of DRAM-optimized hardware FFT accelerators\\nYear: 2014\\n\\nAbstract: As technology scaling is reaching its limits, pointing to the well-known memory and power wall problems, achieving high-performance and energy-efficient systems is becoming a significant challenge. Especially for data-intensive computing, efficient utilization of the memory subsystem is the key to achieve high performance and energy efficiency.We address this challenge in DRAM-optimized hardware accelerators for 1D, 2D and 3D fast Fourier transforms (FFT) on large datasets. When the dataset has to be stored in external DRAM, the main challenge for FFT algorithm design lies in reshaping DRAM-unfriendly memory access patterns to eliminate excessive DRAM row buffer misses. More importantly, these algorithms need to be carefully mapped to the targeted platform's architecture, particularly the memory subsystem, to fully utilize performance and energy efficiency potentials. We use automatic design generation techniques to consider a family of DRAM-optimized FFT algorithms and their hardware implementation design space. In our evaluations, we demonstrate DRAM-optimized accelerator designs over a large tradeoff space given various problem (single/double precision 1D, 2D and 3D FFTs) and hardware platform (off-chip DRAM, 3D-stacked DRAM, ASIC, FPGA, etc.) parameters. We show that generated pareto-optimal designs can yield up to 5.5× energy consumption and order of magnitude memory bandwidth utilization improvements in DRAM, which lead to overall system performance and power efficiency improvements of up to 6× and 6.5× respectively over conventional row-column FFT algorithms.\",\n \"Title: Highly-parallel special-purpose multicore architecture for SystemC/TLM simulations\\nYear: 2014\\n\\nAbstract: The complexity of SystemC virtual prototyping is continuously increasing. Accelerating RTL/TLM SystemC simulations is essential to control future SoC development cost and time-to-market. In this paper, we present RAVES, a highly-parallel special-purpose multicore architecture that achieves simulation performance more efficiently by parallel execution of light-weight user-level threads on many small cores. We present a design study based on the virtual prototype of RAVES processors running a co-designed custom SystemC kernel. Our evaluation suggests that a 64-core RAVES processor can deliver up to 4.47× more simulation performance than a high-end x86 processor.\",\n \"Title: Spiral: Joint Runtime and Energy Optimization of Linear Transforms\\nYear: 2006\\n\\nAbstract: There is much interest into joint runtime and energy opti- mization of implementations of signal processing algorithms. Applications in domains such as embedded computing, sen- sor networks, and mobile communications often require pro- cessing of signals under simultaneous runtime, energy and/or power constraints. Hence, in addition to runtime, power and energy are first-order design considerations for both hardware and software developers in those domains. This paper studies the automatic generation of software implementations of digi- tal signal processing (DSP) transforms that are optimized with respect to both runtime and energy. We explore the impact of algorithm selection (a software technique) and voltage- frequency scaling (a hardware technique) on the runtime and energy of computing fast linear transforms. We use SPIRAL, a code generation system, to enumerate automatically many alternative algorithms for the discrete Fourier transform. We measure the runtime and energy of these algorithms at dif- ferent voltage-frequency settings of an Intel Pentium M mi- croprocessor. We report experimental results supporting that algorithm selection and voltage-frequency scaling do achieve the following: (1) have large impact on the runtime and en- ergy of computing the discrete Fourier transform on a micro- processor; and (2) enable the optimization of important joint runtime-energy objectives.\",\n \"Title: Dual use of superscalar datapath for transient-fault detection and recovery\\nYear: 2001\\n\\nAbstract: Diminutive devices and high clock frequency of future microprocessor generations are causing increased concerns for transient soft failures in hardware, necessitating fault detection and recovery mechanisms even in commodity processors. In this paper, we propose a fault-tolerant extension for modern superscalar out-of-order datapath that can be supported by only modest additional hardware. In the proposed extensions, error-detection is achieved by verifying the redundant results of dynamically replicated threads of executions, while the error-recovery scheme employs the instruction-rewind mechanism to restart at a failed instruction. We study the performance impact of augmenting superscalar microarchitectures with this fault tolerance mechanism. An analytical performance model is used in conjunction with a performance simulator. The simulation results of 11 SPEC95 and SPEC2000 benchmarks show that in the absence of faults, error detection causes a 2% to 45% reduction in throughput, which is in line with other proposed detection schemes. In the presence of transient faults, the fast error recovery scheme contributes very little additional slowdown.\",\n \"Title: Reunion: Complexity-Effective Multicore Redundancy\\nYear: 2006\\n\\nAbstract: To protect processor logic from soft errors, multicore redundant architectures execute two copies of a program on separate cores of a chip multiprocessor (CMP). Maintaining identical instruction streams is challenging because redundant cores operate independently, yet must still receive the same inputs (e.g., load values and shared-memory invalidations). Past proposals strictly replicate load values across two cores, requiring significant changes to the highly-optimized core. We make the key observation that, in the common case, both cores load identical values without special hardware. When the cores do receive different load values (e.g., due to a data race), the same mechanisms employed for soft error detection and recovery can correct the difference. This observation permits designs that relax input replication, while still providing correct redundant execution. In this paper, we present Reunion, an execution model that provides relaxed input replication and preserves the existing memory interface, coherence protocols, and consistency models. We evaluate a CMP-based implementation of the Reunion execution model with full-system, cycle-accurate simulation. We show that the performance overhead of relaxed input replication is only 5% and 6% for commercial and scientific workloads, respectively.\",\n \"Title: MEMOCODE 2008 Co-Design Contest\\nYear: 2007\\n\\nAbstract: The second MEMOCODE hardware/software codesign contest invites participants to solve a practical hardware/software codesign problem within the time span of one month. The larger objective for this contest is to be a showcase of advances in co-design tools and methodologies, in combination with design ingenuity and creativity. In the second installment of the contest, we received 9 submissions. In this short writeup, we review this year's design problem, and we consider relevant contest statistics.\",\n \"Title: High-Level Design and Validation of the BlueSPARC Multithreaded Processor\\nYear: 2010\\n\\nAbstract: This paper presents our experiences in using high-level methods to design and validate a 16-way multithreaded microprocessor called BlueSPARC. BlueSPARC is an in-order, high-throughput processor supporting complex features such as privileged-mode operations, memory management, and a nonblocking cache subsystem. Using a high-level design language called Bluespec System Verilog (BSV), our final implementation achieves comparable synthesis quality to a similar commercial microprocessor developed using conventional register transfer level flows, and is capable of running unmodified commercial applications while hosted on a Xilinx XCV2P70 field-programmable gate array (FPGA) at 90 MHz. To validate our implementation, an FPGA-accelerated approach was developed to efficiently check the correct execution of real, nondeterministic multithreaded programs running on the BlueSPARC processor. Together, the high-level language features of BSV along with our validation approach enabled us to achieve a working FPGA-based implementation in less than one man-year.\",\n \"Title: Single-Chip Heterogeneous Computing: Does the Future Include Custom Logic, FPGAs, and GPGPUs?\\nYear: 2010\\n\\nAbstract: To extend the exponential performance scaling of future chip multiprocessors, improving energy efficiency has become a first-class priority. Single-chip heterogeneous computing has the potential to achieve greater energy efficiency by combining traditional processors with unconventional cores (U-cores) such as custom logic, FPGAs, or GPGPUs. Although U-cores are effective at increasing performance, their benefits can also diminish given the scarcity of projected bandwidth in the future. To understand the relative merits between different approaches in the face of technology constraints, this work builds on prior modeling of heterogeneous multicores to support U-cores. Unlike prior models that trade performance, power, and area using well-known relationships between simple and complex processors, our model must consider the less-obvious relationships between conventional processors and a diverse set of U-cores. Further, our model supports speculation of future designs from scaling trends predicted by the ITRS road map. The predictive power of our model depends upon U-core-specific parameters derived by measuring performance and power of tuned applications on today's state-of-the-art multicores, GPUs, FPGAs, and ASICs. Our results reinforce some current-day understandings of the potential and limitations of U-cores and also provides new insights on their relative merits.\",\n \"Title: Hardware implementation of the discrete fourier transform with non-power-of-two problem size\\nYear: 2010\\n\\nAbstract: In this paper, we examine several algorithms suitable for the hardware implementation of the discrete Fourier transform (DFT) with non-power-of two problem size. We incorporate these algorithms into Spiral, a tool capable of automatically generating corresponding hardware implementations. We dis- cuss how each algorithm can be used to generate different types of hardware structures, and we demonstrate that our tool is able to produce hardware implementations of non-power- of-two sized DFTs over a wide range of cost/performance tradeoff points.\",\n \"Title: HAMLeT Architecture for Parallel Data Reorganization in Memory\\nYear: 2016\\n\\nAbstract: 3D-stacked integration of DRAM and logic layers using through-silicon via (TSV) technology has given rise to a new interpretation of near-data processing (NDP) concepts that were proposed decades ago. However, processing capability within the stack is limited by stringent power and thermal constraints. Simple processing mechanisms with intensive memory accesses, such as data reorganization, are an effective means of exploiting 3D stacking-based NDP. Data reorganization handled completely in memory improves the host processoru0027s memory access performance. However, in-memory data reorganization performed in parallel with host memory accesses raises issues, including interference, bandwidth allocation, and coherence. Previous work has mainly focused on performing data reorganization while blocking host accesses. This article details data reorganization performed in parallel with host memory accesses, providing mechanisms to address host/NDP interference, flexible bandwidth allocation, and in-memory coherence.\",\n \"Title: The CONNECT Network-on-Chip Generator\\nYear: 2015\\n\\nAbstract: Efficiently supporting the communication needs of systems on chip with tens to hundreds of interacting modules requires a systematic and flexible network-on-chip (NoC) infrastructure. The freely available CONNECT generator lets users quickly navigate a range of design parameters to produce tailored NoC design instances in Verilog. To date, it has generated nearly 4,000 designs.\",\n \"Title: Nautilus: fast automated IP design space search using guided genetic algorithms\\nYear: 2015\\n\\nAbstract: Today's offerings of parameterized hardware IP generators permit very high degrees of performance and implementation customization. Nevertheless, it is ultimately still left to the IP users to set IP parameters to achieve the desired tuning effects. For the average IP user, the knowledge and effort required to navigate a complex IP's design space can significantly offset the productivity gain from using the IP. This paper presents an approach that builds into an IP generator an extended genetic algorithm (GA) to perform automatic IP parameter tuning. In particular, we propose extensions that allow IP authors to embed pertinent designer knowledge to improve GA performance. In the context of several IP generators, our evaluations show that (1) GA is an effective solution to this problem and (2) our modified IP author guided GA can reach the same quality of results up to an order of magnitude faster compared to the basic GA.\",\n \"Title: A Study of Pointer-Chasing Performance on Shared-Memory Processor-FPGA Systems.\\nYear: 2016\\n\\nAbstract: The advent of FPGA acceleration platforms with direct coherent access to processor memory creates an opportunity for accelerating applications with irregular parallelism governed by large in-memory pointer-based data structures. This paper uses the simple reference behavior of a linked-list traversal as a proxy to study the performance potentials of accelerating these applications on shared-memory processor-FPGA systems. The linked-list traversal is parameterized by node layout in memory, per-node data payload size, payload dependence, and traversal concurrency to capture the main performance effects of different pointer-based data structures and algorithms. The paper explores the trade-offs over a wide range of implementation options available on shared-memory processor-FPGA architectures, including using tightly-coupled processor assistance. We make observations of the key effects on currently available systems including the Xilinx Zynq, the Intel QuickAssist QPI FPGA Platform, and the Convey HC-2. The key results show: (1) the FPGA fabric is least efficient when traversing a single list with non-sequential node layout and a small payload size; (2) processor assistance can help alleviate this shortcoming; and (3) when appropriate, a fabric only approach that interleaves multiple linked list traversals is an effective way to maximize traversal performance.\",\n \"Title: Data reorganization in memory using 3D-stacked DRAM\\nYear: 2015\\n\\nAbstract: In this paper we focus on common data reorganization operations such as shuffle, pack/unpack, swap, transpose, and layout transformations. Although these operations simply relocate the data in the memory, they are costly on conventional systems mainly due to inefficient access patterns, limited data reuse and roundtrip data traversal throughout the memory hierarchy. This paper presents a two pronged approach for efficient data reorganization, which combines (i) a proposed DRAM-aware reshape accelerator integrated within 3D-stacked DRAM, and (ii) a mathematical framework that is used to represent and optimize the reorganization operations. We evaluate our proposed system through two major use cases. First, we demonstrate the reshape accelerator in performing a physical address remapping via data layout transform to utilize the internal parallelism/locality of the 3D-stacked DRAM structure more efficiently for general purpose workloads. Then, we focus on offloading and accelerating commonly used data reorganization routines selected from the Intel Math Kernel Library package. We evaluate the energy and performance benefits of our approach by comparing it against existing optimized implementations on state-of-the-art GPUs and CPUs. For the various test cases, in-memory data reorganization provides orders of magnitude performance and energy efficiency improvements via low overhead hardware.\",\n \"Title: Coram Plus Plus : Supporting Data-Structure-Specific Memory Interfaces For Fpga Computing\\nYear: 2015\\n\\nAbstract: Facilitating DRAM access is an essential part of an application programming environment for FPGA computing. Existing FPGA application programming environments primarily focus on support for simple, regular memory access patterns, such as block copy and streaming. This paper presents CoRAM++, a programming environment for FPGA computing that is based on an extensible set of data-structure-specific memory interfaces. CoRAM++ supports complex data structures, such as multidimensional arrays and linked lists, in addition to simple data access patterns. CoRAM++ defines an appropriate application-level interface for each supported data structure, and provides a specialized soft-logic implementation of the supporting data-path to memory We evaluated the effectiveness of the CoRAM++ data-structure-specific approach in three application scenarios based on streams, multi-dimensional arrays and linked lists. Our results show that the CoRAM++ programming environment can offer convenient application-level interfaces without penalizing DRAM access performance.\",\n \"Title: Enabling portable energy efficiency with memory accelerated library\\nYear: 2015\\n\\nAbstract: Over the last decade, the looming power wall has spurred a flurry of interest in developing heterogeneous systems with hardware accelerators. The questions, then, are what and how accelerators should be designed, and what software support is required. Our accelerator design approach stems from the observation that many efficient and portable software implementations rely on high performance software libraries with well-established application programming interfaces (APIs). We propose the integration of hardware accelerators on 3D-stacked memory that explicitly targets the memory-bounded operations within high performance libraries. The fixed APIs with limited configurability simplify the design of the accelerators, while ensuring that the accelerators have wide applicability. With our software support that automatically converts library APIs to accelerator invocations, an additional advantage of our approach is that library-based legacy code automatically gains the benefit of memory-side accelerators without requiring a reimplementation. On average, the legacy code using our proposed MEmory Accelerated Library (MEALib) improves performance and energy efficiency for individual operations in Intel's Math Kernel Library (MKL) by 38x and 75x, respectively. For a real-world signal processing application that employs Intel MKL, MEALib attains more than 10x better energy efficiency.\",\n \"Title: Using Vivado-HLS for Structural Design: a NoC Case Study (Abstract Only).\\nYear: 2017\\n\\nAbstract: There have been ample successful examples of applying Xilinx Vivado's \\\\\\\"function-to-module\\\\\\\" high-level synthesis (HLS) where the subject is algorithmic in nature. In this work, we carried out a design study to assess the effectiveness of applying Vivado-HLS in structural design. We employed Vivado-HLS to synthesize C functions corresponding to standalone network-on-chip (NoC) routers as well as complete multi-endpoint NoCs. Interestingly, we find that describing a complete NoC comprising router submodules faces fundamental difficulties not present in describing the routers as standalone modules. Ultimately, we succeeded in using Vivado-HLS to produce router and NoC modules that are exact cycle- and bit-accurate replacements of our reference RTL-based router and NoC modules. Furthermore, the routers and NoCs resulting from HLS and RTL are comparable in resource utilization and critical path delay. Our experience subjectively suggests that HLS is able to simplify the design effort even though much of the structural details had to be provided in the HLS description through a combination of coding discipline and explicit pragmas. The C++ source code and a more extensive description of this work can be found at http://www.ece.cmu.edu/calcm/connect_hls.\",\n \"Title: Amorphous Dynamic Partial Reconfiguration with Flexible Boundaries to Remove Fragmentation.\\nYear: 2017\\n\\nAbstract: Dynamic partial reconfiguration (DPR) allows one region of an field-programmable gate array (FPGA) fabric to be reconfigured without affecting the operations on the rest of the fabric. To use an FPGA as a dynamically shared compute resource, one could partition and manage an FPGA fabric as multiple DPR partitions that can be independently reconfigured at runtime with different application function units (AFUs). Unfortunately, dividing a fabric into DPR partitions with fixed boundaries causes the available fabric resources to become fragmented. An AFU of a given size cannot be loaded unless a sufficiently large DPR partition was floorplanned at build time. To overcome this inefficiency, we devised an DPR technique that is compatible with current device and tool support but does not require the DPR partition boundaries to be a priori fixed. A collection of AFU bitstreams can be simultaneously loaded on the fabric if their footprints (the actual area used by an AFU) in the fabric do not overlap. We verified the feasibility of amorphous DPR on Xilinx Zynq System-on-Chip (SoC) FPGAs using Vivado. We evaluated the benefits of amorphous DPR in the context of a dynamically reconfigurable vision processing pipeline framework.\",\n \"Title: Using Vivado-HLS for Structural Design: a NoC Case Study.\\nYear: 2017\\n\\nAbstract: There have been ample successful examples of applying Xilinx Vivadou0027s function-to-module high-level synthesis (HLS) where the subject is algorithmic in nature. In this work, we carried out a design study to assess the effectiveness of applying Vivado-HLS in structural design. We employed Vivado-HLS to synthesize C functions corresponding to standalone network-on-chip (NoC) routers as well as complete multi-endpoint NoCs. Interestingly, we find that describing a complete NoC comprising router submodules faces fundamental difficulties not present in describing the routers as standalone modules. Ultimately, we succeeded in using Vivado-HLS to produce router and NoC modules that are exact cycle- and bit-accurate replacements of our reference RTL-based router and NoC modules. Furthermore, the routers and NoCs resulting from HLS and RTL are comparable in resource utilization and critical path delay. Our experience subjectively suggests that HLS is able to simplify the design effort even though much of the structural details had to be provided in the HLS description through a combination of coding discipline and explicit pragmas. The C++ source code can be found at this http URL\",\n \"Title: SPIRAL: Extreme Performance Portability.\\nYear: 2018\\n\\nAbstract: In this paper, we address the question of how to automatically map computational kernels to highly efficient code for a wide range of computing platforms and establish the correctness of the synthesized code. More specifically, we focus on two fundamental problems that software developers are faced with: performance portability across the ever-changing landscape of parallel platforms and correctne...\",\n \"Title: Time-Shared Execution of Realtime Computer Vision Pipelines by Dynamic Partial Reconfiguration\\nYear: 2018\\n\\nAbstract: This paper presents an FPGA runtime framework that demonstrates the feasibility of using dynamic partial reconfiguration (DPR) for time-sharing an FPGA by multiple realtime computer vision pipelines. The presented time-sharing runtime framework manages an FPGA fabric that can be round-robin time-shared by different pipelines at the time scale of individual frames. In this new use-case, the challenge is to achieve useful performance despite high reconfiguration time. The paper describes the basic runtime support as well as four optimizations necessary to achieve realtime performance given the limitations of DPR on today's FPGAs. The paper provides a characterization of a working runtime framework prototype on a Xilinx ZC706 development board. The paper also reports the performance of streaming vision pipelines when time-shared.\",\n \"Title: PageRank Acceleration for Large Graphs with Scalable Hardware and Two-Step SpMV\\nYear: 2018\\n\\nAbstract: PageRank is an important vertex ranking algorithm that suffers from poor performance and efficiency due to notorious memory access behavior. Furthermore, when graphs become bigger and sparser, PageRank applications are inhibited as most current solutions profoundly rely on large random access fast memory, which is not easily scalable. In this paper we present a 16nm ASIC based shared memory platform for PageRank implementation that fundamentally accelerates Sparse Matrix dense Vector multiplication (SpMV), the core kernel of PageRank. This accelerator is scalable, guarantees full DRAM streaming and reduces off-chip communication. More importantly, it is capable of handling very large graphs (~2 billion vertices) despite using significantly less fast random access memory than current solutions. Experimental results show that our proposed accelerator is able to yield order of magnitude improvement in both energy efficiency and performance over state of the art shared memory commercial off-the-shelf (COTS) solutions.\",\n \"Title: Processor Assisted Worklist Scheduling for FPGA Accelerated Graph Processing on a Shared-Memory Platform\\nYear: 2019\\n\\nAbstract: FPGA-based processing has gained much attention for accelerating graph analytics because of the demand in performance and energy efficiency. However, while priority scheduling has been shown to be an effective optimization for improving performance for worklist-based graph computations, it is rarely used in accelerator designs due to its implementation complexity and memory-access overhead. In this paper, we present a heterogeneous processing approach for priority scheduling on a shared-memory CPU-FPGA platform. By exploiting the closely-coupled integration of the host processor and the FPGA accelerator, our system dynamically offloads the task of scheduling to a software scheduler on the processor for its programmability, high-capacity cache and low memory latency, while the FPGA graph processing accelerator enjoys the scheduling benefit and delivers higher performance at excellent energy efficiency. To understand the effectiveness of our solution, we compared it with FPGA-only solutions for two scheduling schemes: the well-known Dijkstra scheduling for Single Source Shortest Path and a new scheduling optimization we developed for improving the data locality of Breadth First Search. Whereas the FPGA-only solution requires an impractical amount of on-chip storage to implement a priority queue, the proposed processor-assisted scheduling that moves the task of scheduling to the processor consumes a negligible load on the processor and retains most of the performance benefit from priority scheduling.\",\n \"Title: Achieving 100gbps Intrusion Prevention On A Single Server\\nYear: 2020\\n\\nAbstract: Intrusion Detection and Prevention Systems (IDS/IPS) are among the most demanding stateful network functions. Today's network operators are faced with securing 100Gbps networks with 100K+ concurrent connections by deploying IDS/IPSes to search for 10K+ rules concurrently. In this paper we set an ambitious goal: Can we do all of the above in a single server? Through the Pigasus IDS/IPS, we show that this goal is achievable, perhaps for the first time, by building on recent advances in FPGA-capable SmartNICs. Pigasus' design takes an FPGA first approach, where the majority of processing, and all state and control flow are managed on the FPGA. However, doing so requires careful design of algorithms and data structures to ensure fast common-case performance while densely utilizing system memory resources. Our experiments with a variety of traces show that Pigasus can support 100Gbps using an average of 5 cores and 1 FPGA, using 38x less power than a CPU-only approach.\",\n \"Title: Flexible Hardware Accelerator Design Generation with Spiral\\nYear: 2022\\n\\nAbstract: Hardware specialization has become a widely employed technique for approaching higher performance and en-ergy efficiency in computer systems. Yet obtaining efficient cus-tom hardware designs remains a challenging and tedious task, calling for the automated approaches. In the past, Spiral has been used for generating high-throughput streaming hardware designs for linear transform kernels. This paper is motivated by an observation that a memory-based iterative computing model may allow us to trade off throughput for algorithmic flexibility. In this paper, we present a hardware generation approach that generates and optimizes algorithms using Spiral's multi-level domain-specific languages (DSLs), targeting a scalar load-store architecture. We have incorporated this approach as a hardware backend into the Spiral system. Our evaluation of this approach on several fundamental kernels shows flexibility with reasonable performance and resource utilization.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":80,"string":"80"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13679,"string":"13,679"}}},{"rowIdx":13680,"cells":{"question_id":{"kind":"string","value":"Omprakash Gnawali"},"question":{"kind":"string","value":"Generate an abstract for the title \"Dynamic Reconfiguration of Wireless Sensor Networks to Support Heterogeneous Applications\" using the following items: Wireless Sensor Network, Heterogeneous Applications, Network Protocol, Reconfiguration, Dynamic Scheduling."},"answer":{"kind":"string","value":"As larger numbers of Wireless Sensor Network (WSN) applications get deployed in our homes and offices, it is desirable to use the same network to run different applications. We present and analyze the problem of scheduling and supporting the execution of multiple heterogeneous applications on top of the same WSN. First, we establish that using the same MAC or network protocol is not sufficient to obtain acceptable performance across a set of applications that require different types of communication services from the protocol stack (e.g., low-rate reliable many-toone collection vs point-to-point low-latency bulk-data streaming). Hence, we propose a framework to dynamically reconfigure the WSN and adapt its power consumption, transmission reliability, and data throughput to the different requirements of the applications. The framework makes it possible to specify, at design time, distinct network, MAC and radio protocols for each application as well as the events and policies triggering the WSN reconfigurations. At run-time, the WSN automatically reconfigures itself in response to these events and according to these policies. Through experiments on a 119-node testbed, we show that the proposed approach can reconfigure the whole network in few hundreds of milliseconds while incurring little memory and control overhead."},"correct_answer":{"kind":"string","value":"As larger numbers of Wireless Sensor Network (WSN) applications get deployed in our homes and offices, it is desirable to use the same network to run different applications. We present and analyze the problem of scheduling and supporting the execution of multiple heterogeneous applications on top of the same WSN. First, we establish that using the same MAC or network protocol is not sufficient to obtain acceptable performance across a set of applications that require different types of communication services from the protocol stack (e.g., low-rate reliable many-toone collection vs point-to-point low-latency bulk-data streaming). Hence, we propose a framework to dynamically reconfigure the WSN and adapt its power consumption, transmission reliability, and data throughput to the different requirements of the applications. The framework makes it possible to specify, at design time, distinct network, MAC and radio protocols for each application as well as the events and policies triggering the WSN reconfigurations. At run-time, the WSN automatically reconfigures itself in response to these events and according to these policies. Through experiments on a 119-node testbed, we show that the proposed approach can reconfigure the whole network in few hundreds of milliseconds while incurring little memory and control overhead."},"documents":{"kind":"list like","value":["Title: Collection tree protocol\nYear: 2009\n\nAbstract: This paper presents and evaluates two principles for wire- less routing protocols. The first is datapath validation: data traffic quickly discovers and fixes routing inconsistencies. The second is adaptive beaconing: extending the Trickle al- gorithm to routing control traffic reduces route repair latency and sends fewer beacons. We evaluate datapath validation and adaptive beaconing in CTP Noe, a sensor network tree collection protocol. We use 12 different testbeds ranging in size from 20-310 nodes, comprising seven platforms, and six different link layers, on both interference-free and interference-prone channels. In all cases, CTP Noe delivers > 90% of packets. Many experi- ments achieve 99.9%. Compared to standard beaconing, CTP Noe sends 73% fewer beacons while reducing topology re- pair latency by 99.8%. Finally, when using low-power link layers, CTP Noe has duty cycles of 3% while supporting ag- gregate loads of 30 packets/minute.","Title: A case for evaluating sensor network protocols concurrently\nYear: 2010\n\nAbstract: Researchers typically evaluate and compare protocols on the testbeds by running them one at a time. This methodology ignores the variation in link qualities and wireless environment across these experiments. These variations can introduce significant noise in the results. Evaluating two protocols concurrently, however, suffers from inter-protocol interactions. These interactions can perturb performance even under very light load, especially timing and timing sensitive protocols. We argue that the benefits of running protocols concurrently greatly outweigh the disadvantages. Protocols rarely run in isolation in real networks, and so considering such interactions is valuable. Although the wireless environment is still uncontrolled, concurrent evaluations make comparisons fair and more statistically sound. Through experiments on two testbeds, we make the case for evaluating and comparing low data-rate sensor network protocols by running them concurrently.","Title: Performance of RPL under wireless interference\nYear: 2013\n\nAbstract: Smart homes and environments will consist of a large number of low power wireless devices such as sensors and actuators. Recently IETF standardized a network protocol called RPL that is designed to run on these nodes. In this article, we study the performance of RPL on a variety of scenarios that these nodes will encounter when they are deployed in practice. We deploy a network of 23 sensor nodes in a computer lab to monitor energy used by each computer across the applications and users. We subject the network to four different levels of interference that are representative of the types and levels of interference that these networks might encounter in deployment. Our study finds that RPL reliability degrades even with an access point in overlapping channel under normal network traffic. With high interference, the packet delivery reliability goes down to 10 percent. The scenario that resulted in this performance is not unimaginable in smart home or environment. This performance degradation is partly due to lack of coordination across the layers of the protocol stack: RPL is unaware of the changes in the wireless environment underneath and proceeds as usual. As other research has shown, coordination across the network stack is essential for network protocols to work reliably in the presence of interference. This will require a more coordinated approach to standards across the bodies.","Title: Application-informed radio duty-cycling in a re-taskable multi-user sensing system\nYear: 2009\n\nAbstract: As sensor networks mature, there will be an increasing need for re-usable, dynamically taskable software systems that support multiple concurrent applications. In this paper, we consider the problem of energy management in such systems, taking Tenet as a case study. Our work considers energy management under three new constraints: dynamic multi-hop routing and tasking, multiple concurrent applications, and reliable end-toend data delivery. We present AEM, an energy management system that satisfies these constraints. AEM statically analyzes and infers the traffic profile for the application and accordingly tunes the duty-cycling protocol to provide the best trade-off in latency and data delivery performance. Furthermore, unlike other duty-cycling protocols with pre-computed or fixed transmission and reception time slots, AEM uses elastic schedules that allows it to adapt to dynamics while enabling bounded latency of event detection. Our experiments show that AEM achieves 1–3% duty-cycles, while allowing concurrent applications to transmit 100% of the sensor data in a multi-hop 40-node network testbed.","Title: A real-time selective speaker cancellation system for relieving social anxiety in autistics\nYear: 2013\n\nAbstract: Due to hypersensitivity to sound, patients with autism spectrum disorders (ASD) can feel frustrated and even profoundly fearful when talking with multiple speakers. This exacerbates their impairments in social interaction and communication. We propose a fully interactive system that allows ASD patient to focus on a single auditory stream (a person's voice) according to their preference during conversations. The system has the capacity to filter out other speakers' voices based on distinguishing their locations. The experimental results have demonstrated our prototyping system works reliably in regular conversations.","Title: CodeDrip: Data Dissemination Protocol with Network Coding for Wireless Sensor Networks.\nYear: 2014\n\nAbstract: In this paper, we present CodeDrip, a data dissemination protocol for Wireless Sensor Networks. Dissemination is typically used to query nodes, send commands, and reconfigure the network. CodeDrip utilizes Network Coding to improve energy efficiency, reliability, and speed of dissemination. Network coding allows recovery of lost packets by combining the received packets thereby making dissemination robust to packet losses. While previous work in combining network coding and dissemination focused on bulk data dissemination, we optimize the design of CodeDrip for dissemination of small values. We perform extensive evaluation of CodeDrip on simulations and a large-scale testbed and compare against the implementations of Drip, DIP and DHV protocols. Results show that CodeDrip is faster, smaller and sends fewer messages than Drip, DHV and DIP protocols.","Title: Green enterprise computing data: Assumptions and realities\nYear: 2012\n\nAbstract: Until now, green computing research has largely relied on few, short-term power measurements to characterize the energy use of enterprise computing. This paper brings new and comprehensive power datasets through Powernet, a hybrid sensor network that monitors the power and utilization of the IT systems in a large academic building. Over more than two years, we have collected power data from 250+ individual computing devices and have monitored a subset of CPU and network loads. This dense, long-term monitoring allows us to extrapolate the data to a detailed breakdown of electricity use across the building's computing systems. Our datasets provide an opportunity to examine assumptions commonly made in green computing. We show that power variability both between similar devices and over time for a single device can lead to cost or savings estimates that are off by 15–20%. Extending the coverage of measured devices and the duration (to at least one month) significantly reduces errors. Lastly, our experiences with collecting data and the subsequent analysis lead to a better understanding of how one should go about power characterization studies. We provide several methodology guidelines for future green computing research.","Title: Speaker localization and cancellation for improving social attention\nYear: 2013\n\nAbstract: Autistic individuals suffer from sensory impairment and can become frustrated in conversational settings involving multiple people [3]. For this reason, many choose to avoid certain social settings filled with different sounds. Such behavior only adds to their lack of necessary social skills and inhibits them from joining others in regular conversation. In this paper, we describe a fully-interactive system that allows autistic persons to choose which person's speech they would like to focus on, thereby giving them a sense of control.","Title: Understanding Desktop Energy Footprint in an Academic Computer Lab\nYear: 2012\n\nAbstract: In this paper, we present results from our measurement and analysis of energy use and user behavior in an academic computer lab. We use wireless power sensors to collect power readings from 22 computers in the lab. We use software monitor to log various user activities in the computer. We collected a total of 59.6 million power readings and 220.3 million user activity logs over one month. We analyze the data collected from this instrumentation to not only understand how much energy is used but to also drill down and reveal a detailed understanding of which machines, processes, and users contribute the most to computing energy footprint of the lab. Our results show the power draw on different machines in the lab are different despite the identical hardware and software settings. Our study attributes this difference to different users presenting different types and lengths of load to the computers, preferring specific physical computers, which leads to some computer being used more than others. The results show that the majority of energy was wasted while the computer was left in idle mode, and individual user behavior affects the energy consumption.","Title: Data stashing: energy-efficient information delivery to mobile sinks through trajectory prediction\nYear: 2010\n\nAbstract: In this paper, we present a routing scheme that exploits knowledge about the behavior of mobile sinks within a network of data sources to minimize energy consumption and network congestion. For delay-tolerant network applications, we propose to route data not to the sink directly, but to send it instead to a relay node along an announced or predicted path of the mobile node that is close to the data source. The relay node will stash the information until the mobile node passes by and picks up the data. We use linear programming to find optimal relay nodes that minimize the number of necessary transmissions while guaranteeing robustness against link and node failures, as well as trajectory uncertainty. We show that this technique can drastically reduce the number of transmissions necessary to deliver data to mobile sinks. We derive mobility and association models from real-world data traces and evaluate our data stashing technique in simulations. We examine the influence of uncertainty in the trajectory prediction on the performance and robustness of the routing scheme.","Title: Computerized-eyewear based face recognition system for improving social lives of prosopagnosics\nYear: 2013\n\nAbstract: Prosopagnosia (PA), or the inability to recognize faces, is widespread around the world, with no effective cure for the disease. We propose a wearable system to improve prosopagnosic's daily life. The system utilizes a real-time face recognition application that runs on a smartphone, and a portable eyewear that displays descriptive information when a person appears in range of camera mounted on the eyewear. The system helps these patients identify faces, reminds them to make social reactions and ultimately prevents them from developing social anxiety disorders, characterized by fear and avoidance of social situations that may cause embarrassment. Lastly, we implement the system in a real life demonstration to test its performance.","Title: The impact of network topology on collection performance\nYear: 2011\n\nAbstract: The network topology has a significant impact on the performance of collection protocols in wireless sensor networks. In this paper, we introduce an unobtrusive methodology to quantify the impact of the topology on the performance of collection protocols. Specifically, we propose a protocol-independent metric, the Expected Network Delivery, that quantifies the delivery performance that a collection protocol can be expected to achieve given the network topology. Experimental evidence obtained with two collection protocols on numerous topologies on testbeds shows that our approach enables a systematic evaluation of protocol performance.","Title: END: a topology-aware collection metric for sensor networks\nYear: 2010\n\nAbstract: The performance of sensor network protocols is greatly affected by the network topology: the network layout, the link dynamics, and the sink placement. We propose the Expected Network Delivery (END), a protocol-independent collection metric that captures the impact of the topology on protocol performance. Our preliminary results with two collection protocols on various topologies on the MoteLab testbed show that the END metric enables a more systematic and topology-aware evaluation of protocol performance.","Title: Inferring mobile trajectories using a network of binary proximity sensors\nYear: 2011\n\nAbstract: Understanding human mobility in an environment can be approached in many forms, one of which is to recover the underlying structure of user movement. In our work, we show that we can use a network of binary proximity sensors to detect paths between nodes and also extract highly popular trajectories users take. We show that with sufficient amount of these binary data, even with no prior knowledge of the location of these sensors, we can capture a correlation between the detection timestamps in the case where a physical path exists between any two nodes. Our algorithm also generates characteristics of the path, such as the distribution of transition times and volume. We further show that with sampling techniques we can estimate the underlying trajectories that generated the time stamps. We have tested our algorithm on a simulator and two sensor network deployments. We found that, despite the lack of position information about the sensor nodes, with timestamps alone our algorithm can accurately detect the trajectories and is robust enough to use in a real-world office building.","Title: Twonet: large-scale wireless sensor network testbed with dual-radio nodes\nYear: 2013\n\nAbstract: We present Twonet, a large-scale sensor network testbed with dual-radio nodes. Twonet has 100 Opal nodes with low-power 32-bit ARM CPU and 2.4 GHz and 900 MHz radios. These nodes are managed by a network of 20 Raspberry Pi nodes at tier 2 and a PC server at tier 1. These nodes together provide a robust testbed for public access. Twonet represents a major addition to the collection of wireless sensor network testbeds that are publicly available. We hope Twonet's availability will foster sensor network research based on a modern 32-bit sensor node architecture and multi-channel wireless networking.","Title: Implementing a Sensor Database System using a Generic Data Dissemination Mechanism\nYear: 2005\n\nAbstract: Abstract The vision of a sensor network as a database has been reasonably well explored in the recent literature. Several sensor database systems have been prototyped [1, 11], and some have even been deployed [2]. However, these systems have largely integrated the query processing and the routing mechanisms. In this paper, we explore the design and implementation of a sensor database system (specifically, TinyDB [11]) on top of a generic sensor network data dissemination mechanism (Directed Diffusion [8]). Such a decoupled design is desirable, since it allows us to significantly re-use functionality and promotes overall system robustness. In conducting this exercise, we found that TinyDB influenced the re-design of Diffusion in several important ways.","Title: CTP: An efficient, robust, and reliable collection tree protocol for wireless sensor networks\nYear: 2013\n\nAbstract: We describe CTP, a collection routing protocol for wireless sensor networks. CTP uses three techniques to provide efficient, robust, and reliable routing in highly dynamic network conditions. CTP's link estimator accurately estimates link qualities by using feedback from both the data and control planes, using information from multiple layers through narrow, platform-independent interfaces. Second, CTP uses the Trickle algorithm to time the control traffic, sending few beacons in stable topologies yet quickly adapting to changes. Finally, CTP actively probes the topology with data traffic, quickly discovering and fixing routing failures. Through experiments on 13 different testbeds, encompassing seven platforms, six link layers, and multiple densities and frequencies, and detailed observations of a long-running sensor network application that uses CTP, we study how these three techniques contribute to CTP's overall performance.","Title: The Tenet architecture for tiered sensor networks\nYear: 2010\n\nAbstract: Most sensor network research and software design has been guided by an architectural principle that permits multinode data fusion on small-form-factor, resource-poor nodes, or motes. While we were among the earliest promoters of this approach, through experience we found that this principle leads to fragile and unmanageable systems and explore an alternative. The Tenet architecture is motivated by the observation that future large-scale sensor network deployments will be tiered, consisting of motes in the lower tier and masters, relatively unconstrained 32-bit platform nodes, in the upper tier. Tenet constrains multinode fusion to the master tier while allowing motes to process locally-generated sensor data. This simplifies application development and allows mote-tier software to be reused. Applications running on masters task motes by composing task descriptions from a novel tasklet library. Our Tenet implementation also contains a robust and scalable networking subsystem for disseminating tasks and reliably delivering responses. We show that a Tenet pursuit-evasion application exhibits performance comparable to a mote-native implementation while being considerably more compact. We also present two real-world deployments of Tenet system: a structural vibration monitoring application at Vincent Thomas Bridge and an imaging-based habitat monitoring application at James Reserve, and show that tiered architecture scales network capacity and allows reliable delivery of high rate data.1","Title: Visualizing sensor network data with Powertron\nYear: 2010\n\nAbstract: Powertron is a web-based application that visualizes wireless sensor network deployment data. In this particular demo, we use Powertron to show application-level power data collected from more than 250 sensor nodes. In addition, we expose the routing layer of the deployment by providing real-time interactive visual representation of links, routes, and CTP-related statistics. We hope that the community will have feedback on how such a tool can be extended and generalized to fit a variety of wireless sensor network applications.","Title: TinyWiFi: making network protocol evaluation portable across multiple phy-link layers\nYear: 2011\n\nAbstract: Multihop wireless networks, such as sensor-, ad hoc- and mesh-networks, although different share some common characteristics. All these networks exhibit link dynamics. Protocols designed for these wireless networks must overcome the challenge of link dynamics and the resulting churn in network topology. Due to structural and topological similarities, protocols developed for one class of wireless network should also be applicable in the other classes. However, network-layer protocols are usually developed for and tested in only one class of wireless network due to the lack of a platform that allows testing of protocols across different classes of networks. As a result, we unnecessarily constrain the range of settings and scenarios in which we test network protocols. In this paper, we present TinyWifi, a platform for executing native sensornet protocols on Linux-driven wireless devices. TinyWifi builds on nesC code base that abstracts from TinyOS and enables the execution of nesC-based protocols in Linux. Using this abstraction, we expand the applicability and means of protocol execution from one class of wireless network to another without re-implementation. We demonstrate the generality of TinyWifi by evaluating four well-established protocols on IEEE 802.11 and 802.15.4 based testbeds using a single implementation.","Title: Forwarder Selection in Multi-transmitter Networks\nYear: 2013\n\nAbstract: Recent work has shown that network protocols which rely on precisely-timed concurrent transmissions can achieve reliable, energy-efficient, and conceptually simple network flooding. While these multi-transmitter schemes work well, they require all nodes in the network to forward every data packet, which has inherent inefficiencies for non-flooding traffic patterns (where not all nodes need to receive the data). In this work, we formalize the concept of the \"useful\" forwarder set for point-to-point transmissions in low power multi-transmitter networks, those nodes which help forward data to the destination. We present a mechanism for approximating membership in this set based on simple heuristics. Incorporating forwarder selection on our 66-node testbed reduced radio duty cycle by 30% and increased throughput by 49% relative to concurrent flooding while preserving a 99.4% end-to-end packet reception ratio under the collection traffic pattern. We envision forwarder selection as a fundamental task in an efficient multi-transmitter networking stack. This work demonstrates that adding forwarder selection can improve energy efficiency and throughput while providing similar end-to-end packet delivery rates to flooding.","Title: Routing without routes: the backpressure collection protocol\nYear: 2010\n\nAbstract: Current data collection protocols for wireless sensor networks are mostly based on quasi-static minimum-cost routing trees. We consider an alternative, highly-agile approach called backpressure routing, in which routing and forwarding decisions are made on a per-packet basis. Although there is a considerable theoretical literature on backpressure routing, it has not been implemented on practical systems to date due to concerns about packet looping, the effect of link losses, large packet delays, and scalability. Addressing these concerns, we present the Backpressure Collection Protocol (BCP) for sensor networks, the first ever implementation of dynamic backpressure routing in wireless networks. In particular, we demonstrate for the first time that replacing the traditional FIFO queue service in backpressure routing with LIFO queues reduces the average end-to-end packet delays for delivered packets drastically (75% under high load, 98% under low load). Further, we improve backpressure scalability by introducing a new concept of floating queues into the backpressure framework. Under static network settings, BCP shows a more than 60% improvement in max-min rate over the state of the art Collection Tree Protocol (CTP). We also empirically demonstrate the superior delivery performance of BCP in highly dynamic network settings, including conditions of extreme external interference and highly mobile sinks.","Title: Surviving sensor network software faults\nYear: 2009\n\nAbstract: We describe Neutron, a version of the TinyOS operating system that efficiently recovers from memory safety bugs. Where existing schemes reboot an entire node on an error, Neutron's compiler and runtime extensions divide programs into recovery units and reboot only the faulting unit. The TinyOS kernel itself is a recovery unit: a kernel safety violation appears to applications as the processor being unavailable for 10-20 milliseconds. Neutron further minimizes safety violation cost by supporting \"precious\" state that persists across reboots. Application data, time synchronization state, and routing tables can all be declared as precious. Neutron's reboot sequence conservatively checks that precious state is not the source of a fault before preserving it. Together, recovery units and precious state allow Neutron to reduce a safety violation's cost to time synchronization by 94% and to a routing protocol by 99.5%. Neutron also protects applications from losing data. Neutron provides this recovery on the very limited resources of a tiny, low-power microcontroller.","Title: Network Warehouses: Efficient Information Distribution To Mobile Users\nYear: 2011\n\nAbstract: We consider the problem of distributing time-sensitive information from a collection of sources to mobile users traversing a wireless mesh network. Our strategy is to distributively select a set of well-placed nodes (warehouses) to act as intermediaries between the information sources and clusters of users. Warehouses are selected via the distributed construction of Hierarchical Well-Separated Trees (HSTs), which are sparse structures that induce a natural spatial clustering of the network.Unlike many traditional multicast protocols, our approach is not data driven. Rather, it is agnostic to the number and position of sources as well as to the mobility patterns of users. Whereas source-rooted tree multicast algorithms construct a separate routing infrastructure to support each source, our sparse and flexible infrastructure is precomputed and efficiently reused by sources and users, its cost amortized over time. Moreover, the route acquisition delay inherent in on-demand wireless ad hoc network protocols is avoided by exploiting the HST addressing scheme. Our algorithm ensures with high probability a guaranteed stretch bound for the information delivery path, and is robust to lossy links and node failure by providing alternative HST-induced routes. Nearby users are clustered and their requests aggregated, further reducing communication overhead.","Title: On the Effectiveness of Energy Metering on Every Node\nYear: 2013\n\nAbstract: Making wireless sensor node platforms energy-efficient is one of the major research thrusts in the sensor network community. Energy metering lies at the foundation of this research, either by providing direct measurements for profiling, or by serving as the base for the formulation and fitting of energy-usage models. Most of the literature and tools, however, make their measurements on a very small subset of the node population, and usually at a single point in time, before deployment. In this paper we set out to evaluate the cost, in loss of precision, of not having constant and ubiquitous measurement. Through experiments on a 240-node sensor-network testbed, we find that the variations in energy consumption due to temperature change are small, and we establish a model between environmental temperature changes and power consumption of Quanto testbed motes. We also find that different nodes of the same kind can have up to 15% variation in power draw, suggesting a need to deploy instrumentation on a subset of nodes. We quantify the energy estimation error of different metering techniques and characterize the conditions in which the errors disappear. Overall, we find that a small number of measurements in time and across nodes is adequate for accurate estimation of network-wide energy use.","Title: Macro-programming wireless sensor networks using Kairos\nYear: 2005\n\nAbstract: The literature on programming sensor networks has focused so far on providing higher-level abstractions for expressing local node behavior. Kairos is a natural next step in sensor network programming in that it allows the programmer to express, in a centralized fashion, the desired global behavior of a distributed computation on the entire sensor network. Kairos’ compile-time and runtime subsystems expose a small set of programming primitives, while hiding from the programmer the details of distributed-code generation and instantiation, remote data access and management, and inter-node program flow coordination. In this paper, we describe Kairos’ programming model, and demonstrate its suitability, through actual implementation, for a variety of distributed programs—both infrastructure services and signal processing tasks—typically encountered in sensor network literature: routing tree construction, localization, and object tracking. Our experimental results suggest that Kairos does not adversely affect the performance or accuracy of distributed programs, while our implementation experiences suggest that it greatly raises the level of abstraction presented to the programmer.","Title: DualMOP-RPL: Supporting Multiple Modes of Downward Routing in a Single RPL Network\nYear: 2015\n\nAbstract: RPL is an IPv6 routing protocol for low-power and lossy networks (LLNs) designed to meet the requirements of a wide range of LLN applications including smart grid AMIs, home and building automation, industrial and environmental monitoring, health care, wireless sensor networks, and the Internet of Things (IoT) in general with thousands and millions of nodes interconnected through multihop mesh networks. RPL constructs tree-like routing topology rooted at an LLN border router (LBR) and supports bidirectional IPv6 communication to and from the mesh devices by providing both upward and downward routing over the routing tree. In this article, we focus on the interoperability of downward routing and supporting its two modes of operations (MOPs) defined in the RPL standard (RFC 6550). Specifically, we show that there exists a serious connectivity problem in RPL protocol when two MOPs are mixed within a single network, even for standard-compliant implementations, which may result in network partitions. To address this problem, this article proposes DualMOP-RPL, an enhanced version of RPL, which supports nodes with different MOPs for downward routing to communicate gracefully in a single RPL network while preserving the high bidirectional data delivery performance. DualMOP-RPL allows multiple overlapping RPL networks in the same geographical regions to cooperate as a single densely connected network even if those networks are using different MOPs. This will not only improve the link qualities and routing performances of the networks but also allow for network migrations and alternate routing in the case of LBR failures. We evaluate DualMOP-RPL through extensive simulations and testbed experiments and show that our proposal eliminates all the problems we have identified.","Title: Recycling Corrupt Packets Over Multiple Hops\nYear: 2015\n\nAbstract: We propose a Corrupt Packet Recycling (CPR) approach for WSN that processes and forwards partially-corrupt packets over multiple hops without necessitating their complete recovery. We motivate this approach with two insights: address-agnostic routing in WSN can forgive header errors since intermediate nodes know the next hop and the destination; and that payload errors can be either interpolated, due to error-tolerant nature of information in WSN applications, or rectified using spatio-temporal redundancies. CPR, without introducing any transmission overhead, improves information delivery rate by up to 4x.","Title: Revealing Protocol Information And Activity From Energy Instrumentation In Wireless Sensor Network\nYear: 2015\n\nAbstract: In this paper, we present a novel approach to study and reveal network and protocol information from energy instrumentation in wireless sensor network. Unlike prior approaches which focused on analyzing the aggregate statistics of energy efficiency of a network or a protocol, our approach aims at revealing network protocols, application workloads, and topology information by fine-grained energy instrumentation on the nodes. We design a set of features based on various aspects of energy data and use those features to classify and reveal network activity. Results from experiments on three testbeds indicate that our approach can achieve 97% accuracy to identify the routing protocols, and infer the network topology with 98% accuracy.","Title: Eye contact reminder system for people with autism\nYear: 2014\n\nAbstract: Avoiding eye contact behavior has been characteristic of individuals with autism. Such behavior prevents intrinsic development of social and communication skills. In this paper we present a directional eye contact reminder system which reminds people with autism to generally focus their eyes in the direction of a human speaker. This device detects a speaker's voice, calculates the sound direction, and directs their eyes by displaying a prompt on the computerized-eyewear which is a Head-Mounted Display, in the direction of the speaker. The experiments demonstrate the feasibility of our prototyping system.","Title: Enhancing independence for people with low vision to use daily panel-interface machines\nYear: 2014\n\nAbstract: Those that suffer from macular or vision degenerative diseases report lower levels of ease and ability to participate in daily activities, even when using the most advanced corrective lens technologies. They desire to gain independence in daily tasks, including using everyday machines. Plenty of these machines, such as microwaves, vending machines, and elevators, have a control panel as a user interface. Some have Braille (i.e. bumpy dots) on the panel for people with low vision to recognize. However, many of them have been designed without considering low-vision people's convenience. Though many techniques have been proposed to improve the usability of these panels for people with low vision, they are difficult to deploy due to the high cost and low financial benefit. In this paper, we propose a panel reading assistive system which is wearable instead of being deployed on the machine's side. The conceptual system instructs the user to operate on the panel of an everyday machine. It creates a plan according to the user's need, and guides the user to implement it step by step. The user moves the finger under the system's instruction via audio channel. Finally, the experiments in real life scenarios have demonstrated the feasibility and effectiveness of the system.","Title: Concurrent Wireless Channel Survey on Dual Band Sensor Network Testbed\nYear: 2014\n\nAbstract: Researchers have proposed many multi-channel and dual-band communication systems to address the limitations of single-channel hardware and software. The most common dual-band communication for sensor network applications use 900 MHz and 2.4 GHz radios. There are now some testbeds such as Flocklab and Twonet that allow experimentation with dual-radio systems. However, there is no widely accepted efficient and comprehensive mechanism to survey all the channels of the dual band systems during networking experiments. In this work, we evaluate two mechanisms that can survey the RF environment in multiple channels in both the bands concurrently. We implement these two mechanisms on Twonet and evaluate them. We find that the channel scanning methodology is generally sound but sometimes static channel assignment may be attractive due to its simplicity.","Title: Multi Channel Performance of Dual Band Low Power Wireless Network\nYear: 2014\n\nAbstract: Wireless sensor network platforms share the wireless communication channels with Wi-Fi and Bluetooth based networks, resulting in heavy use of these bands. As a consequence, platforms in wireless sensor networks need to carefully consider external interference to achieve reliable communication. In this paper, we present an experimental analysis of wireless channels for wireless sensor network operating on dual frequency bands. Specifically, we designed a set of detailed experiments aiming to find out correlation patterns in 900 MHz and 2.4 GHz ISM bands. We conducted our experiments on two testbeds and investigated the band correlation between two distinctive radio transceivers in two different office-space environments. From our data samples, we quantified frequency channel and band correlations in parallel experiments that eliminate artifacts stemming from different external activity on the test site. We found that network formed in 900 MHz band has 15% more connectivity than network formed in 2.4 GHz band, even on radio channels that minimize overlap with Wi-Fi networks.","Title: Robust time synchronization in wireless sensor networks using real time clock\nYear: 2014\n\nAbstract: Time synchronization is an essential service in many sensor network applications. Harsh environment which causes nodes to fail, go offline, or reboot can challenge many time synchronization protocols. In this work, we first characterize this challenge and use a real time clock in one of the nodes in the network to improve robustness of time synchronization. Our experiments show that our approach improves the robustness of state-of-the-art offline time synchronization protocols.","Title: Understanding radio activity signature of wireless sensor network protocols\nYear: 2014\n\nAbstract: In this poster, we present a novel approach to study and reveal network protocol information from radio activities instrumentation in wireless sensor network. Recent studies have analyzed radio activities; however, most of these studies focus on estimating energy consumption, since radio chip usually dominates the energy consumption of nodes. In our work, we analyze radio activities with a different purpose, which aims to reveal network protocols and application workloads by an analysis of fine-grained low level radio activities on the nodes. We design a feature called Radio Awake Length Counter and use it to classify and reveal network activity. Results from experiments on a real world testbed indicate that our approach can achieve up to 97% accuracy to identify the routing protocols, average 85% accuracy to distinguish application workloads.","Title: Large-scale network protocol emulation on commodity cloud\nYear: 2014\n\nAbstract: Network emulation allows us to evaluate network protocol implementations, typically in higher fidelity than simulations. This advantage comes at a cost. Emulation often requires much larger IO or computational resources than simulations. As a result, it is common to see some research projects doing simulations with up to hundred thousand nodes while emulations typically scale up to a few hundred nodes. In this paper, we present CloudNet, a network protocol emulation platform that leverages the commodity cloud computing service to scale emulations to thousands of nodes. CloudNet uses a light-weight virtualization technique called LXC containers to emulate a single node. The network protocol code and the protocol state for each node is maintained in its respective container. CloudNet then uses properties of the network topology to determine where to place these containers among many physical machines researchers might rent on the cloud service. CloudNet's careful mapping of nodes to the containers makes network performance more predictable and suitable for emulation even on a shared commodity cloud, which were previously thought to be unsuitable for serious network emulation. Through extensive experiments, we establish that CloudNet is scalable to thousand-node networks while providing accurate emulation results.","Title: Language independent analysis and classification of discussion threads in Coursera MOOC forums\nYear: 2014\n\nAbstract: In this work, we analyze the discussion threads from the forums of 60 Massive Open Online Courses (MOOCs) offered by Coursera and taught in 4 different languages. The types of interactions in such threads vary: there are discussions on close ended problems (e.g. solutions to assignments), open ended topics, course logistics, or just small talk among fellow students. We first study the evolution of the forum activities with respect to the normalized course duration. Then we investigate several language independent features to classify the discussion threads based on the types of the interactions among the users. We use default Coursera subforum categories (Study Groups, Assignments, Lectures, ...) to define the classes of interest and so the labels. We extract features related to structure, popularity, temporal dynamics of threads and diversity of the ids of the users. Text related features, word count aside, are avoided to apply the methods across discussion threads written in different languages and with various technical terminologies. Experiments show a classification performance with ROCAUC between 0.58 and 0.89, depending on the subforum class considered and with possibly noisy labels.","Title: Interactive analysis and simulation of VANETs using MOWINE\nYear: 2010\n\nAbstract: We present a mobile wireless network evaluation tool (MOWINE) that can analyze mobility and wireless connectivity traces to quantify performance of different network protocols in a given application scenario. We model the highly dynamic connectivity of mobile networks using time-expanded graphs which allow us to study the best-case performance of network protocols. MOWINE also integrates with a network simulator that can evaluate the performance of recent VANET routing protocols with a user specified network load. This combination of network modeling and simulation enables users to gain deeper insights into the performance of routing protocols, for example, by distinguishing the limitations of a particular routing protocol from the fundamental limitations of the underlying network. Network engineers can use MOWINE to study and fine-tune performance of real-world network protocols in different scenarios before deploying the network, and hence engineer the mobile ad hoc network. We demonstrate the effectiveness of MOWINE by analyzing and understanding the performance of six different VANET routing protocols using real-world taxi cab traces collected in San Francisco.","Title: Predictive Data Delivery to Mobile Users through Mobility Learning in Wireless Sensor Networks\nYear: 2015\n\nAbstract: We consider applications, such as indoor navigation, evacuation, or targeted advertising, where mobile users equipped with a smart-phone class device require access to sensor network data measured in their proximity. Specifically, we focus on efficient communication protocols between static sensors and users with changing location. Our main contribution is to predict a set of possible future paths for each user and store data at sensor nodes that the user is likely to associate with. We use historical data of radio connectivity between users and static sensor nodes to predict the future user-node associations and propose a network optimization process, called data stashing, which uses the predictions to minimize network and energy overheads of packet transmissions. We show that data stashing significantly decreases routing cost for delivering data from stationary sensor nodes to multiple mobile users compared to routing protocols where sensor nodes immediately deliver data to the last known association nodes of mobile users. We also show that the scheme provides better load balancing, avoiding collisions and consuming energy resources evenly throughout the network, leading to longer overall network lifetime. Finally, we demonstrate that even limited knowledge of the future users location can lead to significant improvements in routing performance.","Title: Embedded Visible Light Communication: Link Measurements and Interpretation.\nYear: 2016\n\nAbstract: Embedded visible light communication (VLC) systems built with low-cost commodity hardware are starting to attract the interest of the embedded systems community, but are yet unexplored in many aspects. The performance of VLC channels is not comprehensively studied under different communication settings and scenarios. In this paper we present an experimental characterization of the performance of low-end VLC channels and investigate the impact of various transmitter/receiver settings, protocol-level parameters and deployment aspects using both low-power and highpower LEDs. Our goal is to discover the strengths, weaknesses and limitations of embedded VLC systems.","Title: Interconnecting WiFi Devices with IEEE 802.15.4 Devices without Using a Gateway\nYear: 2015\n\nAbstract: In many wireless sensing and control application deployments, there is often a gateway device to bridge between the low power IEEE 802.15.4 network and the Internet. The bridge has at least two interfaces. One interface communicates with the 802.15.4 wireless. The other interface either communicates with WiFi or wired network. When a user wants to send a command to the wireless controller, lets say at a smart home, the user may use a smartphone and send command over WiFi to the gateway, often through a cloud service provider. Then gateway shuttles the message from the wired or WiFi chip to the 802.15.4 chip. Then the gateway transmits the messages over the 802.15.4 chip into the 802.15.4 network. In this work, we design a novel modulation technique that runs on the WiFi devices (e.g., Smartphone) and demodulation technique that runs on 802.15.4 devices (e.g., A wireless controller in a smart home) to enable WiFi devices to directly communicate with 802.15.4 devices without any gateway. The key idea is to utilize crosstalk between 802.11 and 802.15.4 channels as the medium for communication. We implemented the proposed technique on multiple platforms and are able to successfully achieve a data rate of 2 bytes per second with less than 10% bit error rate in uncontrolled environments.","Title: Eye Contact Conditioning in Autistic Children Using Virtual Reality Technology\nYear: 2014\n\nAbstract: Children afflicted with developmental disabilities, namely autism, suffer from a natural aversion to dyadic (i.e., eye-to-eye) contact. Research has shown this aversion to be an early indicator of slower development of linguistic skills, a narrow vocabulary, as well as social issues later in life. In addition, this aversion may also result in the loss of already acquired abilities such as language and life skills. Consequently, manual prompt techniques have been adopted to address this issue. However, they are plagued with some inherent flaws: (i) the teacher must make unnatural movements when using a manual prompt such as gesturing towards the face; (ii) The child's attention will follow this prompt as it is removed from the face defeating the purpose as it detracts the child's attention from the teacher's eyes. To tackle these issues we have developed a system that can utilize effective prompt methodologies aimed at conditioning these children to establish and maintain dyadic contact. Our system not only reduces, but eliminates shortcomings present in the current manual method. This is accomplished through the use of a stereo camera and virtual reality headset to augment the child's vision when eye contact is not being established. The prompt is displayed in the child's vision over the eyes of the teacher to attract their attention. Once dyadic contact has been ascertained, the prompt is gradually fading leaving the child only to focus on the eyes of the teacher as is needed.","Title: Low-Cost, Flexible and Open Platform for Visible Light Communication Networks\nYear: 2015\n\nAbstract: Built around a cost-effective embedded Linux system, OpenVLC1.0 is an open source, flexible, software-defined, and low-cost platform for research in Visible Light Communication (VLC) Networks. OpenVLC1.0 consists of simple electronic hardware for optical transmission and reception, and of software implementation that runs the MAC layer, part of the PHY layer, and offers an interface to Internet protocols. We have designed and developed a printed circuit board (OpenVLC1.0 cape) that implements a flexible optical front-end. Researchers can plug the cape into the main Beaglebone board and swiftly build and prototype innovative PHY and MAC protocols using the software implementation (OpenVLC1.0 driver). In this work, we provide preliminary measurement results that demonstrate the flexibility of the platform in a few but yet representative scenarios.","Title: Demo: OpenVLC1.0 Platform for Research in Visible Light Communication Networks\nYear: 2015\n\nAbstract: Built around a cost-effective embedded Linux platform, OpenVLC is an open source project (www.openvlc.org) for research in Visible Light Communication (VLC) Networks. In this work, we introduce and demonstrate the OpenVLC1.0 platform, a flexible, software-defined, and low-cost research platform. OpenVLC1.0 consists of a simple electronic design, and a new driver of the Linux operating system that implements the MAC, part of the PHY layers and it offers an interface to Internet protocols. The electronics of OpenVLC implement a flexible optical front-end consisting of commodity low- and high-power Light Emitting Diodes (LEDs), photodiodes (PDs), and ancillary electronic circuitry. In order to quickly start playing with VLC Networks, we have designed and developed a printed circuit board (OpenVLC1.0 cape). The cape can be plugged into the main embedded Beaglebone board. Researchers can then swiftly build PHY and MAC protocols using the software implementation (OpenVLC1.0 driver), and prototype innovative solutions in realistic network setups. In this demo, we show that OpenVLC1.0 can switch between different MAC protocols, it can choose different optical channel for data transmission and reception, and it can be employed jointly with standard TCP/IP diagnostic tools.","Title: Modeling and implementation of energy neutral sensing systems\nYear: 2013\n\nAbstract: We present the modeling, implementation, and evaluation of a single wireless sensor network that executes energy-harvesting algorithms and sensing applications. The network energy-management is modeled as a feedback control system. An asynchronous execution of the energy-management and the application processes is modeled as a finite state machine. To evaluate our approach, we designed energy neutral sensing systems for two applications and implemented them with Fennec Fox.","Title: Poster Abstract: Scaling IoT Device APIs and Analytics\nYear: 2016\n\nAbstract: Many IoT applications consist of two types of actions: interaction with the device, which can be sensors or actuators, and interaction with the data, for example, to reveal insights. In this poster, we introduce a software stack that provides these functionalities in a scalable manner. The API for device interaction is designed with generality in mind so that widest possible array of devices are supported and in large numbers. The analytics framework, called Composer, is designed to allow user code to be easily integrated into data analytics. We present the design, describe the implementation and deployment, and present some evaluation results. We share the performance data from a live deployment with tens of thousands of active users to demonstrate the scalability of the design.","Title: Poster Abstract: The Impact of User Engagement in the Effectiveness of Energy Saving Programs\nYear: 2016\n\nAbstract: Significant energy wasted in private homes each year. Most of the times, the residents do not know the cause of energy waste in their homes. We designed several activities to encourage the homeowners to learn about how energy is used at their homes and start thinking about eliminating those waste. The program consists of twelve weekly activities in which the homeowners participate. We analyzed monthly electricity bills for all the program's participants and found that energy savings achieved by the participants has close relationship to level of their engagement in the program. People who were highly involved in program saved much more energy (5%) compared to participants who were involved in less than 25% of activities.","Title: Proactive patrol dispatch surveillance system by inferring mobile trajectories of multiple intruders using binary proximity sensors\nYear: 2016\n\nAbstract: In this paper, we consider the problem of distributing patrol officers inside a building to maximize the probability of catching multiple intruders while minimizing the distance the patrol officers travel to reach the locations of the intruders. In our problem setting, the patrol officers are assisted by the information collected by a network of binary proximity sensors installed in the building. We claim that learning even common movement sub-patterns that originate due to the constrained physical environment helps to find likely locations of intruders where each major location is instrumented using a sensor node. We use a series of binary detection events to infer likely future trajectories in a real-world building. For a given set of detectable nodes on the inferred future trajectories, we aim to find the optimal patrol dispatch node location with high exposure to intruders' future appearance using patrol officers in limited numbers, ideally fewer than the intruders. In order to prevent possible crime and perform responsive defense against potential intruders, our algorithm also tries to reduce the travel distance from patrols current positions to their dispatched positions at the same time. We validate our proposed scheme in terms of detection accuracy by varying the number of intruders, robustness against missing events, and responsiveness compared to a practical baseline counterpart through real-world system experiments.","Title: CodeDrip: Improving data dissemination for wireless sensor networks with network coding.\nYear: 2017\n\nAbstract: Network Coding is a technique that combines packets in the network with the potential to mitigate packet lost. In wireless communication systems, traditionally, dropped packets are recovered using retransmissions. By combining packets using network coding, it is possible to recover the transmitted information without needing to retransmit all the lost packets to all the nodes. In this paper, we present a show-case that applying Network Coding to data dissemination for Wireless Sensor Networks provides benefits even for small values. CodeDrip is a data dissemination protocol with Network Coding capability. Dissemination is typically used to query nodes, send commands, and reconfigure the network. CodeDrip utilizes Network Coding to improve energy efficiency, reliability, and speed of dissemination. While previous work in combining network coding and dissemination focused on bulk data dissemination, we optimize the design of CodeDrip for dissemination of small values. We perform extensive evaluation of CodeDrip on simulations and a large-scale testbed and compare against the implementations of Drip, DIP and DHV protocols. We evaluate with more than 350 physical sensor nodes. Results show that CodeDrip is faster, smaller and sends fewer messages than Drip, DHV and DIP protocols.","Title: A Benchmark for Low-power Wireless Networking: Poster Abstract.\nYear: 2016\n\nAbstract: Experimental research in low-power wireless networking lacks a reference benchmark. While other communities such as databases or machine learning have standardized benchmarks, our community still uses ad-hoc setups for its experiments and struggles to provide a fair comparison between communication protocols. Reasons for this include the diversity of network scenarios and the stochastic nature of wireless experiments. Leveraging on the excellent testbeds and tools that have been built to support experimental validation, we make the case for a reference benchmark to promote a fair comparison and reproducibility of results. This abstract describes early design elements and a benchmarking methodology with the goal to gather feedback from the community rather than propose a definite solution.","Title: Nonintrusive Occupant Identification by Sensing Body Shape and Movement.\nYear: 2016\n\nAbstract: The ability to identify people has numerous applications including in smart buildings where the building can be customized to the needs of its occupants or for other applications such as in assisted living and customer behavior analysis in commercial settings. There are different methods used for occupant identification. Some are intrusive such as using cameras or microphone and others require the users to carry mobile gadgets to be identified. In this paper, we present a nonintrusive method to identify people by sensing their body shape and movement. Such information is derived from using ultrasonic sensors to measure the height and width as the occupant walks through the instrumental doorway. In fact, height and width are not unique to every occupant, but extracting a set of features from the variations in height and width makes identification possible. In this study, our system senses a stream of height and width data, recognizes the walking event when a person walks through the door, extracts features that capture a person's movement as well as physical shape. These features are fed to our clustering algorithm that associates each occupant with a distinct cluster. We deployed our system for 1 month. We found out that our approach achieves 95% accuracy with 20 occupants suggesting the suitability of our approach in commercial building settings. In addition, we found out that using girth to distinguish between occupants is more successful than using height.","Title: Does QUIC Make the Web Faster?\nYear: 2016\n\nAbstract: Increase in size and complexity of web pages has challenged the efficiency of HTTP. Recent developments to speed up the web have resulted in two promising protocols, HTTP/2 (RFC 7540) at the application layer and QUIC (multiplexed stream transport over UDP). Google servers are using HTTP/2 and QUIC whereas other major sites like Facebook and Twitter have begun using HTTP/2. In this paper, we compare the performance of HTTP/2 vs QUIC+SPDY 3.1 by studying the Web page load times. In the first set of experiments, we serve synthetic pages (only static objects) over both protocols in emulated controlled network conditions and then extend it to real network, both wired and cellular (2G/3G in India and 3G/4GLTE in US). Further, we conduct experiments on a set of web pages on the most popular sites from the Internet (Alexa Rankings) in controlled conditions. We find QUIC to perform better overall under poor network conditions (low bandwidth, high latency and high loss), for e.g. more than 90% of synthetic pages loaded faster with QUIC in 2G compared to 60% in 4GLTE. This is due to the lower connection establishment latency and improved congestion control mechanism in QUIC. However, QUIC does not offer significant advantage when a webpage consists of many smallsized objects.","Title: WiFi Access Point as a Sensing Platform.\nYear: 2016\n\nAbstract: The growth of the Internet of Things and the trends to deploy more sensors everywhere has led to search for cost effective ways to connect the devices to the Internet. In the most common network architecture for home IoT, the devices use low-power wireless or WiFi to connect to an AP and access the Internet backend. We present a study on the feasibility and efficiency of an alternate network architecture for home IoT. In the proposed architecture, sensors and devices are directly attached to WiFi access points and utilize the computing resources of WiFi APs. We design and implement several sensing applications based on the proposed architecture and report on the performance and limitations of the approach. We find that the proposed architecture allows WiFi APs to become the computational, networking, and storage host for sensing applications without degrading the AP's primary function of providing Internet access to the home users.","Title: Towards Embedded Visible Light Communication Robust to Dynamic Ambient Light.\nYear: 2016\n\nAbstract: The presence of ambient light is a key challenge for reliable and robust low cost embedded visible light communication system. The photodetector used by these systems can perform poorly when subjected to bright ambient light or fluctuating ambient light. To solve this problem, we present an ambient light cancellation mechanism for low cost embedded LED to photodiode communication systems that utilizes a digital potentiometer to adaptively nullify the ambient light to provide an always ZERO output no matter what the ambient light intensity is. The proposed technique allows the receiver to correctly receive the light transmitted by the transmitter without any interference from the ambient light. We provide a detailed description of the modulation and demodulation schemes as well as ambient light cancellation mechanism, and their evaluations. The results show our proposed system can provide a reliable and robust visible light communication with extremely low symbol error rate (almost 0) and an acceptable data rate up to 3kbps given an operating distance of 50 centimeters.","Title: Measuring People-Flow through Doorways Using Easy-to-Install IR Array Sensors\nYear: 2017\n\nAbstract: People counting has many applications in smart buildings. For example, adjusting HVAC systems based on the number of occupants in each room can save a significant amount of energy. In addition, security and safety of the building can be managed by determining the number and location of occupants. Different technologies and sensing platforms have proposed for accurate and efficient people counting. However, these solutions are expensive, hard to deploy, or privacy invasive. We investigate the possibility of placing an 8×8 IR array sensor at the doorways and counting the number of people inside rooms. Our solution is real-time, inexpensive, privacy preserving with much less deployment constraints compared to its competitors. The proposed solution deals with realistic and dynamic changes in the sensing environment by leveraging a combination of Otsus thresholding and modeling thermal noise distribution. We evaluated our solution via several controlled and uncontrolled real-world environments. The results show an average of 93% accuracy in estimating the number of occupants in rooms.","Title: Sonicdoor: scaling person identification with ultrasonic sensors by novel modeling of shape, behavior and walking patterns\nYear: 2017\n\nAbstract: Non-intrusive occupant identification enables numerous applications in Smart Buildings such as personalization of climate and lighting. Current non-intrusive identification techniques do not scale beyond 20 people whereas commercial buildings can have 100 or more people. This paper proposes a new method to identify occupants by sensing their body shape, movement and walking patterns as they walk through a SonicDoor, a door instrumented with three ultrasonic sensors. The proposed method infers contextual information such as path detection and historical walks through different doors of the building in order to enhance the identification accuracy. Each SonicDoor is instrumented with ultrasonic ping sensors, one on the top to sense height and two on the sides of the door to sense width of the person walking through the door. SonicDoor detects a walking event and analyzes it to infer whether the Walker is using a phone, holding a handbag, or wearing a backpack. It extracts a set of features from the walking event and corrects them using a set of transformation functions to mitigate the bias. We deployed five SonicDoors in a real building for two months and collected data consisting of over 9000 walking events spanning over 170 people. The proposed method identifies up to 100 occupants with an accuracy of 90.2%, which makes it suitable for large-scale realistic buildings. SonicDoor method surpasses the state of the art by a factor of five, which is limited to 20 people.","Title: Effectiveness of a task-based residential energy efficiency program in Oahu.\nYear: 2017\n\nAbstract: Energy wasted in buildings is a major fraction of total energy wasted in today's cities. Most people are not aware of inefficiencies and energy wasted in their homes. Making people aware about energy wasted in their homes and inefficiencies in their energy consumption behaviors could contribute to large energy savings at the city scale. Many energy-saving programs, run by the governments, non-profits, or utilities, are designed to help people improve their energy efficiency at homes by providing feedback. The critical factor in the effectiveness of energy saving programs is user engagement. Best energy saving tips will not save noticeable energy if users do not use those tips. In this project, we design several intentionally simple energy saving activities with a goal to help citizens understand how they use energy and find ways to save energy. To evaluate the effectiveness of proposed energy-saving guidelines, 740 volunteers at Oahu, Hawaii are provided smart meters and are asked to follow proposed activities in their daily life. Results from the program indicate that our program helps users reduce their bill by 2.83%, which is comparable with similar programs but the simplicity of our proposed energy saving activities boosted up participation rate to 35%.","Title: Toward Standard Non-Line-of-Sight Benchmarking of Ultra-Wideband Radio-Based Localization\nYear: 2018\n\nAbstract: Performance of Ultra-wideband (UWB) radios in non-line-of-sight (NLoS) environments has been a topic of interest among researchers, especially when it comes to indoor localization applications. It is known that NLoS propagation of electromagnetic waves can severely affect the localization accuracy. Despite the interest in indoor localization performance, it is still difficult to compare results from different studies without proper evaluation standards. Understanding the types of materials used in a testing environment could be a proper technique for benchmarking different localization solutions in different scenarios. We provide a systematic study to investigate effects of signal refraction and attenuation on UWB signals in different construction materials by examining the Channel Impulse Response (CIR) and ranging accuracy. Further, we present failure scenarios for common NLoS identification and mitigation techniques.","Title: IoTBench: Towards a Benchmark for Low-Power Wireless Networking\nYear: 2018\n\nAbstract: Unlike other fields of computing and communications, low-power wireless networking is plagued by one major issue: the absence of a well-defined, agreed-upon yardstick to compare the performance of systems, namely, a benchmark. We argue that this situation may eventually represent a hampering factor for a technology expected to be key in the Internet of Things (IoT) and Cyber-physical Systems (CPS). This paper describes a recent initiative to remedy this situation, seeking to enlarge the participation from the community.","Title: SonicDoor: A Person Identification System Based on Modeling of Shape, Behavior, and Walking Patterns.\nYear: 2018\n\nAbstract: Non-intrusive occupant identification enables numerous applications in Smart Buildings such as personalization of climate and lighting. Current techniques do not scale beyond 20 people, whereas commercial buildings have 100 or more people. This article proposes a new method to identify occupants by sensing their body shape, movement, and walking patterns as they walk through a SonicDoor, a door instrumented with three ultrasonic sensors. The proposed method infers contextual information, such as paths and historical walks through different doors of the building. Each SonicDoor is instrumented with ultrasonic ping sensors, one on top sensing height and two on the sides of the door sensing width of the person walking through the door. SonicDoor detects a walking event and analyzes it to infer whether the Walker is using a phone, holding a handbag, or wearing a backpack. It extracts a set of features from the walking event and corrects them using a set of transformation functions to mitigate the bias. We deployed five SonicDoors in a real building for two months and collected data consisting of over 9,000 walking events spanning over 170 people. The proposed method identifies 100 occupants with an accuracy of 90.2%, which makes it suitable for commercial buildings.","Title: Scaling and Effectiveness of Email Masquerade Attacks: Exploiting Natural Language Generation.\nYear: 2017\n\nAbstract: We focus on email-based attacks, a rich field with well-publicized consequences. We show how current Natural Language Generation (NLG) technology allows an attacker to generate masquerade attacks on scale, and study their effectiveness with a within-subjects study. We also gather insights on what parts of an email do users focus on and how users identify attacks in this realm, by planting signals and also by asking them for their reasoning. We find that: (i) 17% of participants could not identify any of the signals that were inserted in emails, and (ii) Participants were unable to perform better than random guessing on these attacks. The insights gathered and the tools and techniques employed could help defenders in: (i) implementing new, customized anti-phishing solutions for Internet users including training next-generation email filters that go beyond vanilla spam filters and capable of addressing masquerade, (ii) more effectively training and upgrading the skills of email users, and (iii) understanding the dynamics of this novel attack and its ability of tricking humans.","Title: On the ability of mobile sensor networks to diffuse information.\nYear: 2018\n\nAbstract: We examine the ability of networks formed by mobile sensor nodes to diffuse information in the case when communication is only possible during opportunistic encounters. Our setting assumes that mobile nodes are continuously sensing the world and acquiring new information. We form an abstract model of this situation and show by theoretical analysis, simulation, and real mobility data that the diffusion of information in this setting cannot be as efficient as when we allow arbitrary contact patterns between the nodes with the same overall contact statistics. This establishes a fundamental asymptotic limitation on the information diffusion capacity of such opportunistic mobile sensor networks --- the encounter patterns arising out of physical motions in a geometric space are not ideal for information diffusion.","Title: Uwb-Based Single-Anchor Indoor Localization Using Reflected Multipath Components\nYear: 2019\n\nAbstract: Indoor localization has many applications in the context of smart buildings that interact with objects and humans. Due to their resilience to multipath fading, high penetration rate and low duty cycles, Ultra-wideband (UWB) radio have been utilized in indoor localization. In the presence of Line-of-Sight (LoS) signals, UWB-based indoor localization system can locate a target with small errors (< 5cm) but in situations with Non-Line-of-Sight (NLoS) signals, UWB systems work at a much reduced accuracy. In this work, NLoS signals are utilized to improve the robustness of the positioning system. The proposed system uses the statistical characteristics of Reflected Multipath Components (RMCs) of UWB signals in different locations as fingerprints and locates the target node based on previously seen locations. We implement and evaluate our proposed system on Decawave platform. Our evaluation shows that the proposed solution can locate objects within squares of 20cm x 20cm, with an accuracy of 96% using only one anchor which outperforms existing solutions in robustness and accuracy.","Title: Device-Free Activity Recognition Using Ultra-Wideband Radios\nYear: 2019\n\nAbstract: Human Activity Recognition (HAR) is a fundamental building block for the current trend of smart devices in Internet of Things (IoT). Ultra-Wideband RF technology has been used in localization research while Wi-Fi Channel State Information (CSI) has been widely investigated for non-obtrusive activity recognition in the literature. This paper investigates the feasibility of using UWB technology for Human Activity Recognition (HAR). The key idea is to use machine learning classification algorithms most suited to train models to classify different activities using the Channel Impulse Response (CIR) data of the UWB signals. Our experiments show that by using CIR data as features we can classify simple activities such as standing, sitting, lying with an accuracy of 95%. To compare this performance, we have also trained statistical models using Wi-Fi CSI. We found that, for all models UWB CIR significantly outperformed Wi-Fi CSI. Thus, we believe UWB to be a very effective technology in the context of device-free activity recognition.","Title: Users Issues in using the Internet of Things Systems.\nYear: 2019\n\nAbstract: Internet of Things (IoT) systems are bundles of networked sensors and actuators that are deployed in an environment and act upon the sensory data that they receive. These systems, especially consumer electronics, have two main cooperating components: a device and a mobile app. The unique combination of hardware and software in IoT systems presents challenges that are lesser known to mainstream software developers and might require innovative solutions to support the development and integration of such systems. In this paper, we analyze the more than 90,000 reviews of ten IoT devices and their corresponding apps and extract the issues that users encountered in using these systems. Our results indicate that issues with connectivity, timing, and update are particularly prevalent in the reviews. Our results call for a new software-hardware development framework to assist the development of reliable IoT systems.","Title: Topics of Concern: Identifying User Issues in Reviews of IoT Apps and Devices.\nYear: 2019\n\nAbstract: Internet of Things (IoT) systems are bundles of networked sensors and actuators that are deployed in an environment and act upon the sensory data that they receive. These systems, especially consumer electronics, have two main cooperating components: a device and a mobile app. The unique combination of hardware and software in IoT systems presents challenges that are lesser known to mainstream software developers. They might require innovative solutions to support the development and integration of such systems. In this paper, we analyze more than 90,000 reviews of ten IoT devices and their corresponding apps and extract the issues that users encountered while using these systems. Our results indicate that issues with connectivity, timing, and updates are particularly prevalent in the reviews. Our results call for a new software-hardware development framework to assist the development of reliable IoT systems.","Title: Visible Light Communication: Concepts, Applications and Challenges\nYear: 2019\n\nAbstract: During the last decade, the exponential growth of mobile devices and wireless services created a huge demand for radio frequency-based technologies. Meanwhile, the lighting industry has been revolutionized due to the popularization of LED light bulbs, which are more economical and efficient. In that context, visible light communication (VLC) is a disruptive technology based on LEDs that offers a free spectrum and high data rate, which can potentially serve as a complementary technology to the current radio frequency standards. In this paper, we present a comprehensive state-of-the-art survey of VLC, as well as the main concepts and challenges related to this emergent area. We overview VLC technology, from its physical aspects and communication architecture to its main applications and research challenges. Finally, we present the main research platforms available today, along with a deep analysis of the system design and future directions in the field.","Title: Newswire versus Social Media for Disaster Response and Recovery\nYear: 2019\n\nAbstract: In a disaster situation, first responders need to quickly acquire situational awareness and prioritize response based on the need, resources available and impact. Can they do this based on digital media such as Twitter alone, or newswire alone, or some combination of the two? We examine this question in the context of the 2015 Nepal earthquakes. We propose a method to link tweets and newswire, so that we can compare their key characteristics: timeliness, whether tweets appear earlier than their corresponding newswires, and content. Whenever possible, we present both quantitative and qualitative evaluations. One of our main findings is that tweets and newswire articles provide complementary perspectives that form a holistic view of the disaster situation. Another finding is that during the Nepal earthquake, significant information of earthquake-related news appeared on Twitter before newswire. Our results also show that 9.08% of the tweets published before their matched newswire contain actionable information regarding the Nepal earthquake.","Title: Anchor-oriented Time and Phase-based Concurrent Self-localization using UWB Radios\nYear: 2021\n\nAbstract: Positioning plays an important role in many IoT applications. Ultra-wideband (UWB)-based positioning is an alternative to GPS in indoor environments due to its multipath resilience and its accuracy and precision. In the presence of a large number of targets to localize, conventional UWB localization fails to provide a practical location update rate. UWB concurrency in conjunction with self-localiz...","Title: A Domain-Independent Holistic Approach to Deception Detection.\nYear: 2021\n\nAbstract: The deception in the text can be of different forms in different domains, including fake news, rumor tweets, and spam emails. Irrespective of the domain, the main intent of the deceptive text is to deceit the reader. Although domain-specific deception detection exists, domain-independent deception detection can provide a holistic picture, which can be crucial to understand how deception occurs in the text. In this paper, we detect deception in a domain-independent setting using deep learning architectures. Our method outperforms the State-of-the-Art (SOTA) performance of most benchmark datasets with an overall accuracy of 93.42% and F1-Score of 93.22%. The domain-independent training allows us to capture subtler nuances of deceptive writing style. Furthermore, we analyze how much in-domain data may be helpful to accurately detect deception, especially for the cases where data may not be readily available to train. Our results and analysis indicate that there may be a universal pattern of deception lying in-between the text independent of the domain, which can create a novel area of research and open up new avenues in the field of deception detection."],"string":"[\n \"Title: Collection tree protocol\\nYear: 2009\\n\\nAbstract: This paper presents and evaluates two principles for wire- less routing protocols. The first is datapath validation: data traffic quickly discovers and fixes routing inconsistencies. The second is adaptive beaconing: extending the Trickle al- gorithm to routing control traffic reduces route repair latency and sends fewer beacons. We evaluate datapath validation and adaptive beaconing in CTP Noe, a sensor network tree collection protocol. We use 12 different testbeds ranging in size from 20-310 nodes, comprising seven platforms, and six different link layers, on both interference-free and interference-prone channels. In all cases, CTP Noe delivers > 90% of packets. Many experi- ments achieve 99.9%. Compared to standard beaconing, CTP Noe sends 73% fewer beacons while reducing topology re- pair latency by 99.8%. Finally, when using low-power link layers, CTP Noe has duty cycles of 3% while supporting ag- gregate loads of 30 packets/minute.\",\n \"Title: A case for evaluating sensor network protocols concurrently\\nYear: 2010\\n\\nAbstract: Researchers typically evaluate and compare protocols on the testbeds by running them one at a time. This methodology ignores the variation in link qualities and wireless environment across these experiments. These variations can introduce significant noise in the results. Evaluating two protocols concurrently, however, suffers from inter-protocol interactions. These interactions can perturb performance even under very light load, especially timing and timing sensitive protocols. We argue that the benefits of running protocols concurrently greatly outweigh the disadvantages. Protocols rarely run in isolation in real networks, and so considering such interactions is valuable. Although the wireless environment is still uncontrolled, concurrent evaluations make comparisons fair and more statistically sound. Through experiments on two testbeds, we make the case for evaluating and comparing low data-rate sensor network protocols by running them concurrently.\",\n \"Title: Performance of RPL under wireless interference\\nYear: 2013\\n\\nAbstract: Smart homes and environments will consist of a large number of low power wireless devices such as sensors and actuators. Recently IETF standardized a network protocol called RPL that is designed to run on these nodes. In this article, we study the performance of RPL on a variety of scenarios that these nodes will encounter when they are deployed in practice. We deploy a network of 23 sensor nodes in a computer lab to monitor energy used by each computer across the applications and users. We subject the network to four different levels of interference that are representative of the types and levels of interference that these networks might encounter in deployment. Our study finds that RPL reliability degrades even with an access point in overlapping channel under normal network traffic. With high interference, the packet delivery reliability goes down to 10 percent. The scenario that resulted in this performance is not unimaginable in smart home or environment. This performance degradation is partly due to lack of coordination across the layers of the protocol stack: RPL is unaware of the changes in the wireless environment underneath and proceeds as usual. As other research has shown, coordination across the network stack is essential for network protocols to work reliably in the presence of interference. This will require a more coordinated approach to standards across the bodies.\",\n \"Title: Application-informed radio duty-cycling in a re-taskable multi-user sensing system\\nYear: 2009\\n\\nAbstract: As sensor networks mature, there will be an increasing need for re-usable, dynamically taskable software systems that support multiple concurrent applications. In this paper, we consider the problem of energy management in such systems, taking Tenet as a case study. Our work considers energy management under three new constraints: dynamic multi-hop routing and tasking, multiple concurrent applications, and reliable end-toend data delivery. We present AEM, an energy management system that satisfies these constraints. AEM statically analyzes and infers the traffic profile for the application and accordingly tunes the duty-cycling protocol to provide the best trade-off in latency and data delivery performance. Furthermore, unlike other duty-cycling protocols with pre-computed or fixed transmission and reception time slots, AEM uses elastic schedules that allows it to adapt to dynamics while enabling bounded latency of event detection. Our experiments show that AEM achieves 1–3% duty-cycles, while allowing concurrent applications to transmit 100% of the sensor data in a multi-hop 40-node network testbed.\",\n \"Title: A real-time selective speaker cancellation system for relieving social anxiety in autistics\\nYear: 2013\\n\\nAbstract: Due to hypersensitivity to sound, patients with autism spectrum disorders (ASD) can feel frustrated and even profoundly fearful when talking with multiple speakers. This exacerbates their impairments in social interaction and communication. We propose a fully interactive system that allows ASD patient to focus on a single auditory stream (a person's voice) according to their preference during conversations. The system has the capacity to filter out other speakers' voices based on distinguishing their locations. The experimental results have demonstrated our prototyping system works reliably in regular conversations.\",\n \"Title: CodeDrip: Data Dissemination Protocol with Network Coding for Wireless Sensor Networks.\\nYear: 2014\\n\\nAbstract: In this paper, we present CodeDrip, a data dissemination protocol for Wireless Sensor Networks. Dissemination is typically used to query nodes, send commands, and reconfigure the network. CodeDrip utilizes Network Coding to improve energy efficiency, reliability, and speed of dissemination. Network coding allows recovery of lost packets by combining the received packets thereby making dissemination robust to packet losses. While previous work in combining network coding and dissemination focused on bulk data dissemination, we optimize the design of CodeDrip for dissemination of small values. We perform extensive evaluation of CodeDrip on simulations and a large-scale testbed and compare against the implementations of Drip, DIP and DHV protocols. Results show that CodeDrip is faster, smaller and sends fewer messages than Drip, DHV and DIP protocols.\",\n \"Title: Green enterprise computing data: Assumptions and realities\\nYear: 2012\\n\\nAbstract: Until now, green computing research has largely relied on few, short-term power measurements to characterize the energy use of enterprise computing. This paper brings new and comprehensive power datasets through Powernet, a hybrid sensor network that monitors the power and utilization of the IT systems in a large academic building. Over more than two years, we have collected power data from 250+ individual computing devices and have monitored a subset of CPU and network loads. This dense, long-term monitoring allows us to extrapolate the data to a detailed breakdown of electricity use across the building's computing systems. Our datasets provide an opportunity to examine assumptions commonly made in green computing. We show that power variability both between similar devices and over time for a single device can lead to cost or savings estimates that are off by 15–20%. Extending the coverage of measured devices and the duration (to at least one month) significantly reduces errors. Lastly, our experiences with collecting data and the subsequent analysis lead to a better understanding of how one should go about power characterization studies. We provide several methodology guidelines for future green computing research.\",\n \"Title: Speaker localization and cancellation for improving social attention\\nYear: 2013\\n\\nAbstract: Autistic individuals suffer from sensory impairment and can become frustrated in conversational settings involving multiple people [3]. For this reason, many choose to avoid certain social settings filled with different sounds. Such behavior only adds to their lack of necessary social skills and inhibits them from joining others in regular conversation. In this paper, we describe a fully-interactive system that allows autistic persons to choose which person's speech they would like to focus on, thereby giving them a sense of control.\",\n \"Title: Understanding Desktop Energy Footprint in an Academic Computer Lab\\nYear: 2012\\n\\nAbstract: In this paper, we present results from our measurement and analysis of energy use and user behavior in an academic computer lab. We use wireless power sensors to collect power readings from 22 computers in the lab. We use software monitor to log various user activities in the computer. We collected a total of 59.6 million power readings and 220.3 million user activity logs over one month. We analyze the data collected from this instrumentation to not only understand how much energy is used but to also drill down and reveal a detailed understanding of which machines, processes, and users contribute the most to computing energy footprint of the lab. Our results show the power draw on different machines in the lab are different despite the identical hardware and software settings. Our study attributes this difference to different users presenting different types and lengths of load to the computers, preferring specific physical computers, which leads to some computer being used more than others. The results show that the majority of energy was wasted while the computer was left in idle mode, and individual user behavior affects the energy consumption.\",\n \"Title: Data stashing: energy-efficient information delivery to mobile sinks through trajectory prediction\\nYear: 2010\\n\\nAbstract: In this paper, we present a routing scheme that exploits knowledge about the behavior of mobile sinks within a network of data sources to minimize energy consumption and network congestion. For delay-tolerant network applications, we propose to route data not to the sink directly, but to send it instead to a relay node along an announced or predicted path of the mobile node that is close to the data source. The relay node will stash the information until the mobile node passes by and picks up the data. We use linear programming to find optimal relay nodes that minimize the number of necessary transmissions while guaranteeing robustness against link and node failures, as well as trajectory uncertainty. We show that this technique can drastically reduce the number of transmissions necessary to deliver data to mobile sinks. We derive mobility and association models from real-world data traces and evaluate our data stashing technique in simulations. We examine the influence of uncertainty in the trajectory prediction on the performance and robustness of the routing scheme.\",\n \"Title: Computerized-eyewear based face recognition system for improving social lives of prosopagnosics\\nYear: 2013\\n\\nAbstract: Prosopagnosia (PA), or the inability to recognize faces, is widespread around the world, with no effective cure for the disease. We propose a wearable system to improve prosopagnosic's daily life. The system utilizes a real-time face recognition application that runs on a smartphone, and a portable eyewear that displays descriptive information when a person appears in range of camera mounted on the eyewear. The system helps these patients identify faces, reminds them to make social reactions and ultimately prevents them from developing social anxiety disorders, characterized by fear and avoidance of social situations that may cause embarrassment. Lastly, we implement the system in a real life demonstration to test its performance.\",\n \"Title: The impact of network topology on collection performance\\nYear: 2011\\n\\nAbstract: The network topology has a significant impact on the performance of collection protocols in wireless sensor networks. In this paper, we introduce an unobtrusive methodology to quantify the impact of the topology on the performance of collection protocols. Specifically, we propose a protocol-independent metric, the Expected Network Delivery, that quantifies the delivery performance that a collection protocol can be expected to achieve given the network topology. Experimental evidence obtained with two collection protocols on numerous topologies on testbeds shows that our approach enables a systematic evaluation of protocol performance.\",\n \"Title: END: a topology-aware collection metric for sensor networks\\nYear: 2010\\n\\nAbstract: The performance of sensor network protocols is greatly affected by the network topology: the network layout, the link dynamics, and the sink placement. We propose the Expected Network Delivery (END), a protocol-independent collection metric that captures the impact of the topology on protocol performance. Our preliminary results with two collection protocols on various topologies on the MoteLab testbed show that the END metric enables a more systematic and topology-aware evaluation of protocol performance.\",\n \"Title: Inferring mobile trajectories using a network of binary proximity sensors\\nYear: 2011\\n\\nAbstract: Understanding human mobility in an environment can be approached in many forms, one of which is to recover the underlying structure of user movement. In our work, we show that we can use a network of binary proximity sensors to detect paths between nodes and also extract highly popular trajectories users take. We show that with sufficient amount of these binary data, even with no prior knowledge of the location of these sensors, we can capture a correlation between the detection timestamps in the case where a physical path exists between any two nodes. Our algorithm also generates characteristics of the path, such as the distribution of transition times and volume. We further show that with sampling techniques we can estimate the underlying trajectories that generated the time stamps. We have tested our algorithm on a simulator and two sensor network deployments. We found that, despite the lack of position information about the sensor nodes, with timestamps alone our algorithm can accurately detect the trajectories and is robust enough to use in a real-world office building.\",\n \"Title: Twonet: large-scale wireless sensor network testbed with dual-radio nodes\\nYear: 2013\\n\\nAbstract: We present Twonet, a large-scale sensor network testbed with dual-radio nodes. Twonet has 100 Opal nodes with low-power 32-bit ARM CPU and 2.4 GHz and 900 MHz radios. These nodes are managed by a network of 20 Raspberry Pi nodes at tier 2 and a PC server at tier 1. These nodes together provide a robust testbed for public access. Twonet represents a major addition to the collection of wireless sensor network testbeds that are publicly available. We hope Twonet's availability will foster sensor network research based on a modern 32-bit sensor node architecture and multi-channel wireless networking.\",\n \"Title: Implementing a Sensor Database System using a Generic Data Dissemination Mechanism\\nYear: 2005\\n\\nAbstract: Abstract The vision of a sensor network as a database has been reasonably well explored in the recent literature. Several sensor database systems have been prototyped [1, 11], and some have even been deployed [2]. However, these systems have largely integrated the query processing and the routing mechanisms. In this paper, we explore the design and implementation of a sensor database system (specifically, TinyDB [11]) on top of a generic sensor network data dissemination mechanism (Directed Diffusion [8]). Such a decoupled design is desirable, since it allows us to significantly re-use functionality and promotes overall system robustness. In conducting this exercise, we found that TinyDB influenced the re-design of Diffusion in several important ways.\",\n \"Title: CTP: An efficient, robust, and reliable collection tree protocol for wireless sensor networks\\nYear: 2013\\n\\nAbstract: We describe CTP, a collection routing protocol for wireless sensor networks. CTP uses three techniques to provide efficient, robust, and reliable routing in highly dynamic network conditions. CTP's link estimator accurately estimates link qualities by using feedback from both the data and control planes, using information from multiple layers through narrow, platform-independent interfaces. Second, CTP uses the Trickle algorithm to time the control traffic, sending few beacons in stable topologies yet quickly adapting to changes. Finally, CTP actively probes the topology with data traffic, quickly discovering and fixing routing failures. Through experiments on 13 different testbeds, encompassing seven platforms, six link layers, and multiple densities and frequencies, and detailed observations of a long-running sensor network application that uses CTP, we study how these three techniques contribute to CTP's overall performance.\",\n \"Title: The Tenet architecture for tiered sensor networks\\nYear: 2010\\n\\nAbstract: Most sensor network research and software design has been guided by an architectural principle that permits multinode data fusion on small-form-factor, resource-poor nodes, or motes. While we were among the earliest promoters of this approach, through experience we found that this principle leads to fragile and unmanageable systems and explore an alternative. The Tenet architecture is motivated by the observation that future large-scale sensor network deployments will be tiered, consisting of motes in the lower tier and masters, relatively unconstrained 32-bit platform nodes, in the upper tier. Tenet constrains multinode fusion to the master tier while allowing motes to process locally-generated sensor data. This simplifies application development and allows mote-tier software to be reused. Applications running on masters task motes by composing task descriptions from a novel tasklet library. Our Tenet implementation also contains a robust and scalable networking subsystem for disseminating tasks and reliably delivering responses. We show that a Tenet pursuit-evasion application exhibits performance comparable to a mote-native implementation while being considerably more compact. We also present two real-world deployments of Tenet system: a structural vibration monitoring application at Vincent Thomas Bridge and an imaging-based habitat monitoring application at James Reserve, and show that tiered architecture scales network capacity and allows reliable delivery of high rate data.1\",\n \"Title: Visualizing sensor network data with Powertron\\nYear: 2010\\n\\nAbstract: Powertron is a web-based application that visualizes wireless sensor network deployment data. In this particular demo, we use Powertron to show application-level power data collected from more than 250 sensor nodes. In addition, we expose the routing layer of the deployment by providing real-time interactive visual representation of links, routes, and CTP-related statistics. We hope that the community will have feedback on how such a tool can be extended and generalized to fit a variety of wireless sensor network applications.\",\n \"Title: TinyWiFi: making network protocol evaluation portable across multiple phy-link layers\\nYear: 2011\\n\\nAbstract: Multihop wireless networks, such as sensor-, ad hoc- and mesh-networks, although different share some common characteristics. All these networks exhibit link dynamics. Protocols designed for these wireless networks must overcome the challenge of link dynamics and the resulting churn in network topology. Due to structural and topological similarities, protocols developed for one class of wireless network should also be applicable in the other classes. However, network-layer protocols are usually developed for and tested in only one class of wireless network due to the lack of a platform that allows testing of protocols across different classes of networks. As a result, we unnecessarily constrain the range of settings and scenarios in which we test network protocols. In this paper, we present TinyWifi, a platform for executing native sensornet protocols on Linux-driven wireless devices. TinyWifi builds on nesC code base that abstracts from TinyOS and enables the execution of nesC-based protocols in Linux. Using this abstraction, we expand the applicability and means of protocol execution from one class of wireless network to another without re-implementation. We demonstrate the generality of TinyWifi by evaluating four well-established protocols on IEEE 802.11 and 802.15.4 based testbeds using a single implementation.\",\n \"Title: Forwarder Selection in Multi-transmitter Networks\\nYear: 2013\\n\\nAbstract: Recent work has shown that network protocols which rely on precisely-timed concurrent transmissions can achieve reliable, energy-efficient, and conceptually simple network flooding. While these multi-transmitter schemes work well, they require all nodes in the network to forward every data packet, which has inherent inefficiencies for non-flooding traffic patterns (where not all nodes need to receive the data). In this work, we formalize the concept of the \\\"useful\\\" forwarder set for point-to-point transmissions in low power multi-transmitter networks, those nodes which help forward data to the destination. We present a mechanism for approximating membership in this set based on simple heuristics. Incorporating forwarder selection on our 66-node testbed reduced radio duty cycle by 30% and increased throughput by 49% relative to concurrent flooding while preserving a 99.4% end-to-end packet reception ratio under the collection traffic pattern. We envision forwarder selection as a fundamental task in an efficient multi-transmitter networking stack. This work demonstrates that adding forwarder selection can improve energy efficiency and throughput while providing similar end-to-end packet delivery rates to flooding.\",\n \"Title: Routing without routes: the backpressure collection protocol\\nYear: 2010\\n\\nAbstract: Current data collection protocols for wireless sensor networks are mostly based on quasi-static minimum-cost routing trees. We consider an alternative, highly-agile approach called backpressure routing, in which routing and forwarding decisions are made on a per-packet basis. Although there is a considerable theoretical literature on backpressure routing, it has not been implemented on practical systems to date due to concerns about packet looping, the effect of link losses, large packet delays, and scalability. Addressing these concerns, we present the Backpressure Collection Protocol (BCP) for sensor networks, the first ever implementation of dynamic backpressure routing in wireless networks. In particular, we demonstrate for the first time that replacing the traditional FIFO queue service in backpressure routing with LIFO queues reduces the average end-to-end packet delays for delivered packets drastically (75% under high load, 98% under low load). Further, we improve backpressure scalability by introducing a new concept of floating queues into the backpressure framework. Under static network settings, BCP shows a more than 60% improvement in max-min rate over the state of the art Collection Tree Protocol (CTP). We also empirically demonstrate the superior delivery performance of BCP in highly dynamic network settings, including conditions of extreme external interference and highly mobile sinks.\",\n \"Title: Surviving sensor network software faults\\nYear: 2009\\n\\nAbstract: We describe Neutron, a version of the TinyOS operating system that efficiently recovers from memory safety bugs. Where existing schemes reboot an entire node on an error, Neutron's compiler and runtime extensions divide programs into recovery units and reboot only the faulting unit. The TinyOS kernel itself is a recovery unit: a kernel safety violation appears to applications as the processor being unavailable for 10-20 milliseconds. Neutron further minimizes safety violation cost by supporting \\\"precious\\\" state that persists across reboots. Application data, time synchronization state, and routing tables can all be declared as precious. Neutron's reboot sequence conservatively checks that precious state is not the source of a fault before preserving it. Together, recovery units and precious state allow Neutron to reduce a safety violation's cost to time synchronization by 94% and to a routing protocol by 99.5%. Neutron also protects applications from losing data. Neutron provides this recovery on the very limited resources of a tiny, low-power microcontroller.\",\n \"Title: Network Warehouses: Efficient Information Distribution To Mobile Users\\nYear: 2011\\n\\nAbstract: We consider the problem of distributing time-sensitive information from a collection of sources to mobile users traversing a wireless mesh network. Our strategy is to distributively select a set of well-placed nodes (warehouses) to act as intermediaries between the information sources and clusters of users. Warehouses are selected via the distributed construction of Hierarchical Well-Separated Trees (HSTs), which are sparse structures that induce a natural spatial clustering of the network.Unlike many traditional multicast protocols, our approach is not data driven. Rather, it is agnostic to the number and position of sources as well as to the mobility patterns of users. Whereas source-rooted tree multicast algorithms construct a separate routing infrastructure to support each source, our sparse and flexible infrastructure is precomputed and efficiently reused by sources and users, its cost amortized over time. Moreover, the route acquisition delay inherent in on-demand wireless ad hoc network protocols is avoided by exploiting the HST addressing scheme. Our algorithm ensures with high probability a guaranteed stretch bound for the information delivery path, and is robust to lossy links and node failure by providing alternative HST-induced routes. Nearby users are clustered and their requests aggregated, further reducing communication overhead.\",\n \"Title: On the Effectiveness of Energy Metering on Every Node\\nYear: 2013\\n\\nAbstract: Making wireless sensor node platforms energy-efficient is one of the major research thrusts in the sensor network community. Energy metering lies at the foundation of this research, either by providing direct measurements for profiling, or by serving as the base for the formulation and fitting of energy-usage models. Most of the literature and tools, however, make their measurements on a very small subset of the node population, and usually at a single point in time, before deployment. In this paper we set out to evaluate the cost, in loss of precision, of not having constant and ubiquitous measurement. Through experiments on a 240-node sensor-network testbed, we find that the variations in energy consumption due to temperature change are small, and we establish a model between environmental temperature changes and power consumption of Quanto testbed motes. We also find that different nodes of the same kind can have up to 15% variation in power draw, suggesting a need to deploy instrumentation on a subset of nodes. We quantify the energy estimation error of different metering techniques and characterize the conditions in which the errors disappear. Overall, we find that a small number of measurements in time and across nodes is adequate for accurate estimation of network-wide energy use.\",\n \"Title: Macro-programming wireless sensor networks using Kairos\\nYear: 2005\\n\\nAbstract: The literature on programming sensor networks has focused so far on providing higher-level abstractions for expressing local node behavior. Kairos is a natural next step in sensor network programming in that it allows the programmer to express, in a centralized fashion, the desired global behavior of a distributed computation on the entire sensor network. Kairos’ compile-time and runtime subsystems expose a small set of programming primitives, while hiding from the programmer the details of distributed-code generation and instantiation, remote data access and management, and inter-node program flow coordination. In this paper, we describe Kairos’ programming model, and demonstrate its suitability, through actual implementation, for a variety of distributed programs—both infrastructure services and signal processing tasks—typically encountered in sensor network literature: routing tree construction, localization, and object tracking. Our experimental results suggest that Kairos does not adversely affect the performance or accuracy of distributed programs, while our implementation experiences suggest that it greatly raises the level of abstraction presented to the programmer.\",\n \"Title: DualMOP-RPL: Supporting Multiple Modes of Downward Routing in a Single RPL Network\\nYear: 2015\\n\\nAbstract: RPL is an IPv6 routing protocol for low-power and lossy networks (LLNs) designed to meet the requirements of a wide range of LLN applications including smart grid AMIs, home and building automation, industrial and environmental monitoring, health care, wireless sensor networks, and the Internet of Things (IoT) in general with thousands and millions of nodes interconnected through multihop mesh networks. RPL constructs tree-like routing topology rooted at an LLN border router (LBR) and supports bidirectional IPv6 communication to and from the mesh devices by providing both upward and downward routing over the routing tree. In this article, we focus on the interoperability of downward routing and supporting its two modes of operations (MOPs) defined in the RPL standard (RFC 6550). Specifically, we show that there exists a serious connectivity problem in RPL protocol when two MOPs are mixed within a single network, even for standard-compliant implementations, which may result in network partitions. To address this problem, this article proposes DualMOP-RPL, an enhanced version of RPL, which supports nodes with different MOPs for downward routing to communicate gracefully in a single RPL network while preserving the high bidirectional data delivery performance. DualMOP-RPL allows multiple overlapping RPL networks in the same geographical regions to cooperate as a single densely connected network even if those networks are using different MOPs. This will not only improve the link qualities and routing performances of the networks but also allow for network migrations and alternate routing in the case of LBR failures. We evaluate DualMOP-RPL through extensive simulations and testbed experiments and show that our proposal eliminates all the problems we have identified.\",\n \"Title: Recycling Corrupt Packets Over Multiple Hops\\nYear: 2015\\n\\nAbstract: We propose a Corrupt Packet Recycling (CPR) approach for WSN that processes and forwards partially-corrupt packets over multiple hops without necessitating their complete recovery. We motivate this approach with two insights: address-agnostic routing in WSN can forgive header errors since intermediate nodes know the next hop and the destination; and that payload errors can be either interpolated, due to error-tolerant nature of information in WSN applications, or rectified using spatio-temporal redundancies. CPR, without introducing any transmission overhead, improves information delivery rate by up to 4x.\",\n \"Title: Revealing Protocol Information And Activity From Energy Instrumentation In Wireless Sensor Network\\nYear: 2015\\n\\nAbstract: In this paper, we present a novel approach to study and reveal network and protocol information from energy instrumentation in wireless sensor network. Unlike prior approaches which focused on analyzing the aggregate statistics of energy efficiency of a network or a protocol, our approach aims at revealing network protocols, application workloads, and topology information by fine-grained energy instrumentation on the nodes. We design a set of features based on various aspects of energy data and use those features to classify and reveal network activity. Results from experiments on three testbeds indicate that our approach can achieve 97% accuracy to identify the routing protocols, and infer the network topology with 98% accuracy.\",\n \"Title: Eye contact reminder system for people with autism\\nYear: 2014\\n\\nAbstract: Avoiding eye contact behavior has been characteristic of individuals with autism. Such behavior prevents intrinsic development of social and communication skills. In this paper we present a directional eye contact reminder system which reminds people with autism to generally focus their eyes in the direction of a human speaker. This device detects a speaker's voice, calculates the sound direction, and directs their eyes by displaying a prompt on the computerized-eyewear which is a Head-Mounted Display, in the direction of the speaker. The experiments demonstrate the feasibility of our prototyping system.\",\n \"Title: Enhancing independence for people with low vision to use daily panel-interface machines\\nYear: 2014\\n\\nAbstract: Those that suffer from macular or vision degenerative diseases report lower levels of ease and ability to participate in daily activities, even when using the most advanced corrective lens technologies. They desire to gain independence in daily tasks, including using everyday machines. Plenty of these machines, such as microwaves, vending machines, and elevators, have a control panel as a user interface. Some have Braille (i.e. bumpy dots) on the panel for people with low vision to recognize. However, many of them have been designed without considering low-vision people's convenience. Though many techniques have been proposed to improve the usability of these panels for people with low vision, they are difficult to deploy due to the high cost and low financial benefit. In this paper, we propose a panel reading assistive system which is wearable instead of being deployed on the machine's side. The conceptual system instructs the user to operate on the panel of an everyday machine. It creates a plan according to the user's need, and guides the user to implement it step by step. The user moves the finger under the system's instruction via audio channel. Finally, the experiments in real life scenarios have demonstrated the feasibility and effectiveness of the system.\",\n \"Title: Concurrent Wireless Channel Survey on Dual Band Sensor Network Testbed\\nYear: 2014\\n\\nAbstract: Researchers have proposed many multi-channel and dual-band communication systems to address the limitations of single-channel hardware and software. The most common dual-band communication for sensor network applications use 900 MHz and 2.4 GHz radios. There are now some testbeds such as Flocklab and Twonet that allow experimentation with dual-radio systems. However, there is no widely accepted efficient and comprehensive mechanism to survey all the channels of the dual band systems during networking experiments. In this work, we evaluate two mechanisms that can survey the RF environment in multiple channels in both the bands concurrently. We implement these two mechanisms on Twonet and evaluate them. We find that the channel scanning methodology is generally sound but sometimes static channel assignment may be attractive due to its simplicity.\",\n \"Title: Multi Channel Performance of Dual Band Low Power Wireless Network\\nYear: 2014\\n\\nAbstract: Wireless sensor network platforms share the wireless communication channels with Wi-Fi and Bluetooth based networks, resulting in heavy use of these bands. As a consequence, platforms in wireless sensor networks need to carefully consider external interference to achieve reliable communication. In this paper, we present an experimental analysis of wireless channels for wireless sensor network operating on dual frequency bands. Specifically, we designed a set of detailed experiments aiming to find out correlation patterns in 900 MHz and 2.4 GHz ISM bands. We conducted our experiments on two testbeds and investigated the band correlation between two distinctive radio transceivers in two different office-space environments. From our data samples, we quantified frequency channel and band correlations in parallel experiments that eliminate artifacts stemming from different external activity on the test site. We found that network formed in 900 MHz band has 15% more connectivity than network formed in 2.4 GHz band, even on radio channels that minimize overlap with Wi-Fi networks.\",\n \"Title: Robust time synchronization in wireless sensor networks using real time clock\\nYear: 2014\\n\\nAbstract: Time synchronization is an essential service in many sensor network applications. Harsh environment which causes nodes to fail, go offline, or reboot can challenge many time synchronization protocols. In this work, we first characterize this challenge and use a real time clock in one of the nodes in the network to improve robustness of time synchronization. Our experiments show that our approach improves the robustness of state-of-the-art offline time synchronization protocols.\",\n \"Title: Understanding radio activity signature of wireless sensor network protocols\\nYear: 2014\\n\\nAbstract: In this poster, we present a novel approach to study and reveal network protocol information from radio activities instrumentation in wireless sensor network. Recent studies have analyzed radio activities; however, most of these studies focus on estimating energy consumption, since radio chip usually dominates the energy consumption of nodes. In our work, we analyze radio activities with a different purpose, which aims to reveal network protocols and application workloads by an analysis of fine-grained low level radio activities on the nodes. We design a feature called Radio Awake Length Counter and use it to classify and reveal network activity. Results from experiments on a real world testbed indicate that our approach can achieve up to 97% accuracy to identify the routing protocols, average 85% accuracy to distinguish application workloads.\",\n \"Title: Large-scale network protocol emulation on commodity cloud\\nYear: 2014\\n\\nAbstract: Network emulation allows us to evaluate network protocol implementations, typically in higher fidelity than simulations. This advantage comes at a cost. Emulation often requires much larger IO or computational resources than simulations. As a result, it is common to see some research projects doing simulations with up to hundred thousand nodes while emulations typically scale up to a few hundred nodes. In this paper, we present CloudNet, a network protocol emulation platform that leverages the commodity cloud computing service to scale emulations to thousands of nodes. CloudNet uses a light-weight virtualization technique called LXC containers to emulate a single node. The network protocol code and the protocol state for each node is maintained in its respective container. CloudNet then uses properties of the network topology to determine where to place these containers among many physical machines researchers might rent on the cloud service. CloudNet's careful mapping of nodes to the containers makes network performance more predictable and suitable for emulation even on a shared commodity cloud, which were previously thought to be unsuitable for serious network emulation. Through extensive experiments, we establish that CloudNet is scalable to thousand-node networks while providing accurate emulation results.\",\n \"Title: Language independent analysis and classification of discussion threads in Coursera MOOC forums\\nYear: 2014\\n\\nAbstract: In this work, we analyze the discussion threads from the forums of 60 Massive Open Online Courses (MOOCs) offered by Coursera and taught in 4 different languages. The types of interactions in such threads vary: there are discussions on close ended problems (e.g. solutions to assignments), open ended topics, course logistics, or just small talk among fellow students. We first study the evolution of the forum activities with respect to the normalized course duration. Then we investigate several language independent features to classify the discussion threads based on the types of the interactions among the users. We use default Coursera subforum categories (Study Groups, Assignments, Lectures, ...) to define the classes of interest and so the labels. We extract features related to structure, popularity, temporal dynamics of threads and diversity of the ids of the users. Text related features, word count aside, are avoided to apply the methods across discussion threads written in different languages and with various technical terminologies. Experiments show a classification performance with ROCAUC between 0.58 and 0.89, depending on the subforum class considered and with possibly noisy labels.\",\n \"Title: Interactive analysis and simulation of VANETs using MOWINE\\nYear: 2010\\n\\nAbstract: We present a mobile wireless network evaluation tool (MOWINE) that can analyze mobility and wireless connectivity traces to quantify performance of different network protocols in a given application scenario. We model the highly dynamic connectivity of mobile networks using time-expanded graphs which allow us to study the best-case performance of network protocols. MOWINE also integrates with a network simulator that can evaluate the performance of recent VANET routing protocols with a user specified network load. This combination of network modeling and simulation enables users to gain deeper insights into the performance of routing protocols, for example, by distinguishing the limitations of a particular routing protocol from the fundamental limitations of the underlying network. Network engineers can use MOWINE to study and fine-tune performance of real-world network protocols in different scenarios before deploying the network, and hence engineer the mobile ad hoc network. We demonstrate the effectiveness of MOWINE by analyzing and understanding the performance of six different VANET routing protocols using real-world taxi cab traces collected in San Francisco.\",\n \"Title: Predictive Data Delivery to Mobile Users through Mobility Learning in Wireless Sensor Networks\\nYear: 2015\\n\\nAbstract: We consider applications, such as indoor navigation, evacuation, or targeted advertising, where mobile users equipped with a smart-phone class device require access to sensor network data measured in their proximity. Specifically, we focus on efficient communication protocols between static sensors and users with changing location. Our main contribution is to predict a set of possible future paths for each user and store data at sensor nodes that the user is likely to associate with. We use historical data of radio connectivity between users and static sensor nodes to predict the future user-node associations and propose a network optimization process, called data stashing, which uses the predictions to minimize network and energy overheads of packet transmissions. We show that data stashing significantly decreases routing cost for delivering data from stationary sensor nodes to multiple mobile users compared to routing protocols where sensor nodes immediately deliver data to the last known association nodes of mobile users. We also show that the scheme provides better load balancing, avoiding collisions and consuming energy resources evenly throughout the network, leading to longer overall network lifetime. Finally, we demonstrate that even limited knowledge of the future users location can lead to significant improvements in routing performance.\",\n \"Title: Embedded Visible Light Communication: Link Measurements and Interpretation.\\nYear: 2016\\n\\nAbstract: Embedded visible light communication (VLC) systems built with low-cost commodity hardware are starting to attract the interest of the embedded systems community, but are yet unexplored in many aspects. The performance of VLC channels is not comprehensively studied under different communication settings and scenarios. In this paper we present an experimental characterization of the performance of low-end VLC channels and investigate the impact of various transmitter/receiver settings, protocol-level parameters and deployment aspects using both low-power and highpower LEDs. Our goal is to discover the strengths, weaknesses and limitations of embedded VLC systems.\",\n \"Title: Interconnecting WiFi Devices with IEEE 802.15.4 Devices without Using a Gateway\\nYear: 2015\\n\\nAbstract: In many wireless sensing and control application deployments, there is often a gateway device to bridge between the low power IEEE 802.15.4 network and the Internet. The bridge has at least two interfaces. One interface communicates with the 802.15.4 wireless. The other interface either communicates with WiFi or wired network. When a user wants to send a command to the wireless controller, lets say at a smart home, the user may use a smartphone and send command over WiFi to the gateway, often through a cloud service provider. Then gateway shuttles the message from the wired or WiFi chip to the 802.15.4 chip. Then the gateway transmits the messages over the 802.15.4 chip into the 802.15.4 network. In this work, we design a novel modulation technique that runs on the WiFi devices (e.g., Smartphone) and demodulation technique that runs on 802.15.4 devices (e.g., A wireless controller in a smart home) to enable WiFi devices to directly communicate with 802.15.4 devices without any gateway. The key idea is to utilize crosstalk between 802.11 and 802.15.4 channels as the medium for communication. We implemented the proposed technique on multiple platforms and are able to successfully achieve a data rate of 2 bytes per second with less than 10% bit error rate in uncontrolled environments.\",\n \"Title: Eye Contact Conditioning in Autistic Children Using Virtual Reality Technology\\nYear: 2014\\n\\nAbstract: Children afflicted with developmental disabilities, namely autism, suffer from a natural aversion to dyadic (i.e., eye-to-eye) contact. Research has shown this aversion to be an early indicator of slower development of linguistic skills, a narrow vocabulary, as well as social issues later in life. In addition, this aversion may also result in the loss of already acquired abilities such as language and life skills. Consequently, manual prompt techniques have been adopted to address this issue. However, they are plagued with some inherent flaws: (i) the teacher must make unnatural movements when using a manual prompt such as gesturing towards the face; (ii) The child's attention will follow this prompt as it is removed from the face defeating the purpose as it detracts the child's attention from the teacher's eyes. To tackle these issues we have developed a system that can utilize effective prompt methodologies aimed at conditioning these children to establish and maintain dyadic contact. Our system not only reduces, but eliminates shortcomings present in the current manual method. This is accomplished through the use of a stereo camera and virtual reality headset to augment the child's vision when eye contact is not being established. The prompt is displayed in the child's vision over the eyes of the teacher to attract their attention. Once dyadic contact has been ascertained, the prompt is gradually fading leaving the child only to focus on the eyes of the teacher as is needed.\",\n \"Title: Low-Cost, Flexible and Open Platform for Visible Light Communication Networks\\nYear: 2015\\n\\nAbstract: Built around a cost-effective embedded Linux system, OpenVLC1.0 is an open source, flexible, software-defined, and low-cost platform for research in Visible Light Communication (VLC) Networks. OpenVLC1.0 consists of simple electronic hardware for optical transmission and reception, and of software implementation that runs the MAC layer, part of the PHY layer, and offers an interface to Internet protocols. We have designed and developed a printed circuit board (OpenVLC1.0 cape) that implements a flexible optical front-end. Researchers can plug the cape into the main Beaglebone board and swiftly build and prototype innovative PHY and MAC protocols using the software implementation (OpenVLC1.0 driver). In this work, we provide preliminary measurement results that demonstrate the flexibility of the platform in a few but yet representative scenarios.\",\n \"Title: Demo: OpenVLC1.0 Platform for Research in Visible Light Communication Networks\\nYear: 2015\\n\\nAbstract: Built around a cost-effective embedded Linux platform, OpenVLC is an open source project (www.openvlc.org) for research in Visible Light Communication (VLC) Networks. In this work, we introduce and demonstrate the OpenVLC1.0 platform, a flexible, software-defined, and low-cost research platform. OpenVLC1.0 consists of a simple electronic design, and a new driver of the Linux operating system that implements the MAC, part of the PHY layers and it offers an interface to Internet protocols. The electronics of OpenVLC implement a flexible optical front-end consisting of commodity low- and high-power Light Emitting Diodes (LEDs), photodiodes (PDs), and ancillary electronic circuitry. In order to quickly start playing with VLC Networks, we have designed and developed a printed circuit board (OpenVLC1.0 cape). The cape can be plugged into the main embedded Beaglebone board. Researchers can then swiftly build PHY and MAC protocols using the software implementation (OpenVLC1.0 driver), and prototype innovative solutions in realistic network setups. In this demo, we show that OpenVLC1.0 can switch between different MAC protocols, it can choose different optical channel for data transmission and reception, and it can be employed jointly with standard TCP/IP diagnostic tools.\",\n \"Title: Modeling and implementation of energy neutral sensing systems\\nYear: 2013\\n\\nAbstract: We present the modeling, implementation, and evaluation of a single wireless sensor network that executes energy-harvesting algorithms and sensing applications. The network energy-management is modeled as a feedback control system. An asynchronous execution of the energy-management and the application processes is modeled as a finite state machine. To evaluate our approach, we designed energy neutral sensing systems for two applications and implemented them with Fennec Fox.\",\n \"Title: Poster Abstract: Scaling IoT Device APIs and Analytics\\nYear: 2016\\n\\nAbstract: Many IoT applications consist of two types of actions: interaction with the device, which can be sensors or actuators, and interaction with the data, for example, to reveal insights. In this poster, we introduce a software stack that provides these functionalities in a scalable manner. The API for device interaction is designed with generality in mind so that widest possible array of devices are supported and in large numbers. The analytics framework, called Composer, is designed to allow user code to be easily integrated into data analytics. We present the design, describe the implementation and deployment, and present some evaluation results. We share the performance data from a live deployment with tens of thousands of active users to demonstrate the scalability of the design.\",\n \"Title: Poster Abstract: The Impact of User Engagement in the Effectiveness of Energy Saving Programs\\nYear: 2016\\n\\nAbstract: Significant energy wasted in private homes each year. Most of the times, the residents do not know the cause of energy waste in their homes. We designed several activities to encourage the homeowners to learn about how energy is used at their homes and start thinking about eliminating those waste. The program consists of twelve weekly activities in which the homeowners participate. We analyzed monthly electricity bills for all the program's participants and found that energy savings achieved by the participants has close relationship to level of their engagement in the program. People who were highly involved in program saved much more energy (5%) compared to participants who were involved in less than 25% of activities.\",\n \"Title: Proactive patrol dispatch surveillance system by inferring mobile trajectories of multiple intruders using binary proximity sensors\\nYear: 2016\\n\\nAbstract: In this paper, we consider the problem of distributing patrol officers inside a building to maximize the probability of catching multiple intruders while minimizing the distance the patrol officers travel to reach the locations of the intruders. In our problem setting, the patrol officers are assisted by the information collected by a network of binary proximity sensors installed in the building. We claim that learning even common movement sub-patterns that originate due to the constrained physical environment helps to find likely locations of intruders where each major location is instrumented using a sensor node. We use a series of binary detection events to infer likely future trajectories in a real-world building. For a given set of detectable nodes on the inferred future trajectories, we aim to find the optimal patrol dispatch node location with high exposure to intruders' future appearance using patrol officers in limited numbers, ideally fewer than the intruders. In order to prevent possible crime and perform responsive defense against potential intruders, our algorithm also tries to reduce the travel distance from patrols current positions to their dispatched positions at the same time. We validate our proposed scheme in terms of detection accuracy by varying the number of intruders, robustness against missing events, and responsiveness compared to a practical baseline counterpart through real-world system experiments.\",\n \"Title: CodeDrip: Improving data dissemination for wireless sensor networks with network coding.\\nYear: 2017\\n\\nAbstract: Network Coding is a technique that combines packets in the network with the potential to mitigate packet lost. In wireless communication systems, traditionally, dropped packets are recovered using retransmissions. By combining packets using network coding, it is possible to recover the transmitted information without needing to retransmit all the lost packets to all the nodes. In this paper, we present a show-case that applying Network Coding to data dissemination for Wireless Sensor Networks provides benefits even for small values. CodeDrip is a data dissemination protocol with Network Coding capability. Dissemination is typically used to query nodes, send commands, and reconfigure the network. CodeDrip utilizes Network Coding to improve energy efficiency, reliability, and speed of dissemination. While previous work in combining network coding and dissemination focused on bulk data dissemination, we optimize the design of CodeDrip for dissemination of small values. We perform extensive evaluation of CodeDrip on simulations and a large-scale testbed and compare against the implementations of Drip, DIP and DHV protocols. We evaluate with more than 350 physical sensor nodes. Results show that CodeDrip is faster, smaller and sends fewer messages than Drip, DHV and DIP protocols.\",\n \"Title: A Benchmark for Low-power Wireless Networking: Poster Abstract.\\nYear: 2016\\n\\nAbstract: Experimental research in low-power wireless networking lacks a reference benchmark. While other communities such as databases or machine learning have standardized benchmarks, our community still uses ad-hoc setups for its experiments and struggles to provide a fair comparison between communication protocols. Reasons for this include the diversity of network scenarios and the stochastic nature of wireless experiments. Leveraging on the excellent testbeds and tools that have been built to support experimental validation, we make the case for a reference benchmark to promote a fair comparison and reproducibility of results. This abstract describes early design elements and a benchmarking methodology with the goal to gather feedback from the community rather than propose a definite solution.\",\n \"Title: Nonintrusive Occupant Identification by Sensing Body Shape and Movement.\\nYear: 2016\\n\\nAbstract: The ability to identify people has numerous applications including in smart buildings where the building can be customized to the needs of its occupants or for other applications such as in assisted living and customer behavior analysis in commercial settings. There are different methods used for occupant identification. Some are intrusive such as using cameras or microphone and others require the users to carry mobile gadgets to be identified. In this paper, we present a nonintrusive method to identify people by sensing their body shape and movement. Such information is derived from using ultrasonic sensors to measure the height and width as the occupant walks through the instrumental doorway. In fact, height and width are not unique to every occupant, but extracting a set of features from the variations in height and width makes identification possible. In this study, our system senses a stream of height and width data, recognizes the walking event when a person walks through the door, extracts features that capture a person's movement as well as physical shape. These features are fed to our clustering algorithm that associates each occupant with a distinct cluster. We deployed our system for 1 month. We found out that our approach achieves 95% accuracy with 20 occupants suggesting the suitability of our approach in commercial building settings. In addition, we found out that using girth to distinguish between occupants is more successful than using height.\",\n \"Title: Does QUIC Make the Web Faster?\\nYear: 2016\\n\\nAbstract: Increase in size and complexity of web pages has challenged the efficiency of HTTP. Recent developments to speed up the web have resulted in two promising protocols, HTTP/2 (RFC 7540) at the application layer and QUIC (multiplexed stream transport over UDP). Google servers are using HTTP/2 and QUIC whereas other major sites like Facebook and Twitter have begun using HTTP/2. In this paper, we compare the performance of HTTP/2 vs QUIC+SPDY 3.1 by studying the Web page load times. In the first set of experiments, we serve synthetic pages (only static objects) over both protocols in emulated controlled network conditions and then extend it to real network, both wired and cellular (2G/3G in India and 3G/4GLTE in US). Further, we conduct experiments on a set of web pages on the most popular sites from the Internet (Alexa Rankings) in controlled conditions. We find QUIC to perform better overall under poor network conditions (low bandwidth, high latency and high loss), for e.g. more than 90% of synthetic pages loaded faster with QUIC in 2G compared to 60% in 4GLTE. This is due to the lower connection establishment latency and improved congestion control mechanism in QUIC. However, QUIC does not offer significant advantage when a webpage consists of many smallsized objects.\",\n \"Title: WiFi Access Point as a Sensing Platform.\\nYear: 2016\\n\\nAbstract: The growth of the Internet of Things and the trends to deploy more sensors everywhere has led to search for cost effective ways to connect the devices to the Internet. In the most common network architecture for home IoT, the devices use low-power wireless or WiFi to connect to an AP and access the Internet backend. We present a study on the feasibility and efficiency of an alternate network architecture for home IoT. In the proposed architecture, sensors and devices are directly attached to WiFi access points and utilize the computing resources of WiFi APs. We design and implement several sensing applications based on the proposed architecture and report on the performance and limitations of the approach. We find that the proposed architecture allows WiFi APs to become the computational, networking, and storage host for sensing applications without degrading the AP's primary function of providing Internet access to the home users.\",\n \"Title: Towards Embedded Visible Light Communication Robust to Dynamic Ambient Light.\\nYear: 2016\\n\\nAbstract: The presence of ambient light is a key challenge for reliable and robust low cost embedded visible light communication system. The photodetector used by these systems can perform poorly when subjected to bright ambient light or fluctuating ambient light. To solve this problem, we present an ambient light cancellation mechanism for low cost embedded LED to photodiode communication systems that utilizes a digital potentiometer to adaptively nullify the ambient light to provide an always ZERO output no matter what the ambient light intensity is. The proposed technique allows the receiver to correctly receive the light transmitted by the transmitter without any interference from the ambient light. We provide a detailed description of the modulation and demodulation schemes as well as ambient light cancellation mechanism, and their evaluations. The results show our proposed system can provide a reliable and robust visible light communication with extremely low symbol error rate (almost 0) and an acceptable data rate up to 3kbps given an operating distance of 50 centimeters.\",\n \"Title: Measuring People-Flow through Doorways Using Easy-to-Install IR Array Sensors\\nYear: 2017\\n\\nAbstract: People counting has many applications in smart buildings. For example, adjusting HVAC systems based on the number of occupants in each room can save a significant amount of energy. In addition, security and safety of the building can be managed by determining the number and location of occupants. Different technologies and sensing platforms have proposed for accurate and efficient people counting. However, these solutions are expensive, hard to deploy, or privacy invasive. We investigate the possibility of placing an 8×8 IR array sensor at the doorways and counting the number of people inside rooms. Our solution is real-time, inexpensive, privacy preserving with much less deployment constraints compared to its competitors. The proposed solution deals with realistic and dynamic changes in the sensing environment by leveraging a combination of Otsus thresholding and modeling thermal noise distribution. We evaluated our solution via several controlled and uncontrolled real-world environments. The results show an average of 93% accuracy in estimating the number of occupants in rooms.\",\n \"Title: Sonicdoor: scaling person identification with ultrasonic sensors by novel modeling of shape, behavior and walking patterns\\nYear: 2017\\n\\nAbstract: Non-intrusive occupant identification enables numerous applications in Smart Buildings such as personalization of climate and lighting. Current non-intrusive identification techniques do not scale beyond 20 people whereas commercial buildings can have 100 or more people. This paper proposes a new method to identify occupants by sensing their body shape, movement and walking patterns as they walk through a SonicDoor, a door instrumented with three ultrasonic sensors. The proposed method infers contextual information such as path detection and historical walks through different doors of the building in order to enhance the identification accuracy. Each SonicDoor is instrumented with ultrasonic ping sensors, one on the top to sense height and two on the sides of the door to sense width of the person walking through the door. SonicDoor detects a walking event and analyzes it to infer whether the Walker is using a phone, holding a handbag, or wearing a backpack. It extracts a set of features from the walking event and corrects them using a set of transformation functions to mitigate the bias. We deployed five SonicDoors in a real building for two months and collected data consisting of over 9000 walking events spanning over 170 people. The proposed method identifies up to 100 occupants with an accuracy of 90.2%, which makes it suitable for large-scale realistic buildings. SonicDoor method surpasses the state of the art by a factor of five, which is limited to 20 people.\",\n \"Title: Effectiveness of a task-based residential energy efficiency program in Oahu.\\nYear: 2017\\n\\nAbstract: Energy wasted in buildings is a major fraction of total energy wasted in today's cities. Most people are not aware of inefficiencies and energy wasted in their homes. Making people aware about energy wasted in their homes and inefficiencies in their energy consumption behaviors could contribute to large energy savings at the city scale. Many energy-saving programs, run by the governments, non-profits, or utilities, are designed to help people improve their energy efficiency at homes by providing feedback. The critical factor in the effectiveness of energy saving programs is user engagement. Best energy saving tips will not save noticeable energy if users do not use those tips. In this project, we design several intentionally simple energy saving activities with a goal to help citizens understand how they use energy and find ways to save energy. To evaluate the effectiveness of proposed energy-saving guidelines, 740 volunteers at Oahu, Hawaii are provided smart meters and are asked to follow proposed activities in their daily life. Results from the program indicate that our program helps users reduce their bill by 2.83%, which is comparable with similar programs but the simplicity of our proposed energy saving activities boosted up participation rate to 35%.\",\n \"Title: Toward Standard Non-Line-of-Sight Benchmarking of Ultra-Wideband Radio-Based Localization\\nYear: 2018\\n\\nAbstract: Performance of Ultra-wideband (UWB) radios in non-line-of-sight (NLoS) environments has been a topic of interest among researchers, especially when it comes to indoor localization applications. It is known that NLoS propagation of electromagnetic waves can severely affect the localization accuracy. Despite the interest in indoor localization performance, it is still difficult to compare results from different studies without proper evaluation standards. Understanding the types of materials used in a testing environment could be a proper technique for benchmarking different localization solutions in different scenarios. We provide a systematic study to investigate effects of signal refraction and attenuation on UWB signals in different construction materials by examining the Channel Impulse Response (CIR) and ranging accuracy. Further, we present failure scenarios for common NLoS identification and mitigation techniques.\",\n \"Title: IoTBench: Towards a Benchmark for Low-Power Wireless Networking\\nYear: 2018\\n\\nAbstract: Unlike other fields of computing and communications, low-power wireless networking is plagued by one major issue: the absence of a well-defined, agreed-upon yardstick to compare the performance of systems, namely, a benchmark. We argue that this situation may eventually represent a hampering factor for a technology expected to be key in the Internet of Things (IoT) and Cyber-physical Systems (CPS). This paper describes a recent initiative to remedy this situation, seeking to enlarge the participation from the community.\",\n \"Title: SonicDoor: A Person Identification System Based on Modeling of Shape, Behavior, and Walking Patterns.\\nYear: 2018\\n\\nAbstract: Non-intrusive occupant identification enables numerous applications in Smart Buildings such as personalization of climate and lighting. Current techniques do not scale beyond 20 people, whereas commercial buildings have 100 or more people. This article proposes a new method to identify occupants by sensing their body shape, movement, and walking patterns as they walk through a SonicDoor, a door instrumented with three ultrasonic sensors. The proposed method infers contextual information, such as paths and historical walks through different doors of the building. Each SonicDoor is instrumented with ultrasonic ping sensors, one on top sensing height and two on the sides of the door sensing width of the person walking through the door. SonicDoor detects a walking event and analyzes it to infer whether the Walker is using a phone, holding a handbag, or wearing a backpack. It extracts a set of features from the walking event and corrects them using a set of transformation functions to mitigate the bias. We deployed five SonicDoors in a real building for two months and collected data consisting of over 9,000 walking events spanning over 170 people. The proposed method identifies 100 occupants with an accuracy of 90.2%, which makes it suitable for commercial buildings.\",\n \"Title: Scaling and Effectiveness of Email Masquerade Attacks: Exploiting Natural Language Generation.\\nYear: 2017\\n\\nAbstract: We focus on email-based attacks, a rich field with well-publicized consequences. We show how current Natural Language Generation (NLG) technology allows an attacker to generate masquerade attacks on scale, and study their effectiveness with a within-subjects study. We also gather insights on what parts of an email do users focus on and how users identify attacks in this realm, by planting signals and also by asking them for their reasoning. We find that: (i) 17% of participants could not identify any of the signals that were inserted in emails, and (ii) Participants were unable to perform better than random guessing on these attacks. The insights gathered and the tools and techniques employed could help defenders in: (i) implementing new, customized anti-phishing solutions for Internet users including training next-generation email filters that go beyond vanilla spam filters and capable of addressing masquerade, (ii) more effectively training and upgrading the skills of email users, and (iii) understanding the dynamics of this novel attack and its ability of tricking humans.\",\n \"Title: On the ability of mobile sensor networks to diffuse information.\\nYear: 2018\\n\\nAbstract: We examine the ability of networks formed by mobile sensor nodes to diffuse information in the case when communication is only possible during opportunistic encounters. Our setting assumes that mobile nodes are continuously sensing the world and acquiring new information. We form an abstract model of this situation and show by theoretical analysis, simulation, and real mobility data that the diffusion of information in this setting cannot be as efficient as when we allow arbitrary contact patterns between the nodes with the same overall contact statistics. This establishes a fundamental asymptotic limitation on the information diffusion capacity of such opportunistic mobile sensor networks --- the encounter patterns arising out of physical motions in a geometric space are not ideal for information diffusion.\",\n \"Title: Uwb-Based Single-Anchor Indoor Localization Using Reflected Multipath Components\\nYear: 2019\\n\\nAbstract: Indoor localization has many applications in the context of smart buildings that interact with objects and humans. Due to their resilience to multipath fading, high penetration rate and low duty cycles, Ultra-wideband (UWB) radio have been utilized in indoor localization. In the presence of Line-of-Sight (LoS) signals, UWB-based indoor localization system can locate a target with small errors (< 5cm) but in situations with Non-Line-of-Sight (NLoS) signals, UWB systems work at a much reduced accuracy. In this work, NLoS signals are utilized to improve the robustness of the positioning system. The proposed system uses the statistical characteristics of Reflected Multipath Components (RMCs) of UWB signals in different locations as fingerprints and locates the target node based on previously seen locations. We implement and evaluate our proposed system on Decawave platform. Our evaluation shows that the proposed solution can locate objects within squares of 20cm x 20cm, with an accuracy of 96% using only one anchor which outperforms existing solutions in robustness and accuracy.\",\n \"Title: Device-Free Activity Recognition Using Ultra-Wideband Radios\\nYear: 2019\\n\\nAbstract: Human Activity Recognition (HAR) is a fundamental building block for the current trend of smart devices in Internet of Things (IoT). Ultra-Wideband RF technology has been used in localization research while Wi-Fi Channel State Information (CSI) has been widely investigated for non-obtrusive activity recognition in the literature. This paper investigates the feasibility of using UWB technology for Human Activity Recognition (HAR). The key idea is to use machine learning classification algorithms most suited to train models to classify different activities using the Channel Impulse Response (CIR) data of the UWB signals. Our experiments show that by using CIR data as features we can classify simple activities such as standing, sitting, lying with an accuracy of 95%. To compare this performance, we have also trained statistical models using Wi-Fi CSI. We found that, for all models UWB CIR significantly outperformed Wi-Fi CSI. Thus, we believe UWB to be a very effective technology in the context of device-free activity recognition.\",\n \"Title: Users Issues in using the Internet of Things Systems.\\nYear: 2019\\n\\nAbstract: Internet of Things (IoT) systems are bundles of networked sensors and actuators that are deployed in an environment and act upon the sensory data that they receive. These systems, especially consumer electronics, have two main cooperating components: a device and a mobile app. The unique combination of hardware and software in IoT systems presents challenges that are lesser known to mainstream software developers and might require innovative solutions to support the development and integration of such systems. In this paper, we analyze the more than 90,000 reviews of ten IoT devices and their corresponding apps and extract the issues that users encountered in using these systems. Our results indicate that issues with connectivity, timing, and update are particularly prevalent in the reviews. Our results call for a new software-hardware development framework to assist the development of reliable IoT systems.\",\n \"Title: Topics of Concern: Identifying User Issues in Reviews of IoT Apps and Devices.\\nYear: 2019\\n\\nAbstract: Internet of Things (IoT) systems are bundles of networked sensors and actuators that are deployed in an environment and act upon the sensory data that they receive. These systems, especially consumer electronics, have two main cooperating components: a device and a mobile app. The unique combination of hardware and software in IoT systems presents challenges that are lesser known to mainstream software developers. They might require innovative solutions to support the development and integration of such systems. In this paper, we analyze more than 90,000 reviews of ten IoT devices and their corresponding apps and extract the issues that users encountered while using these systems. Our results indicate that issues with connectivity, timing, and updates are particularly prevalent in the reviews. Our results call for a new software-hardware development framework to assist the development of reliable IoT systems.\",\n \"Title: Visible Light Communication: Concepts, Applications and Challenges\\nYear: 2019\\n\\nAbstract: During the last decade, the exponential growth of mobile devices and wireless services created a huge demand for radio frequency-based technologies. Meanwhile, the lighting industry has been revolutionized due to the popularization of LED light bulbs, which are more economical and efficient. In that context, visible light communication (VLC) is a disruptive technology based on LEDs that offers a free spectrum and high data rate, which can potentially serve as a complementary technology to the current radio frequency standards. In this paper, we present a comprehensive state-of-the-art survey of VLC, as well as the main concepts and challenges related to this emergent area. We overview VLC technology, from its physical aspects and communication architecture to its main applications and research challenges. Finally, we present the main research platforms available today, along with a deep analysis of the system design and future directions in the field.\",\n \"Title: Newswire versus Social Media for Disaster Response and Recovery\\nYear: 2019\\n\\nAbstract: In a disaster situation, first responders need to quickly acquire situational awareness and prioritize response based on the need, resources available and impact. Can they do this based on digital media such as Twitter alone, or newswire alone, or some combination of the two? We examine this question in the context of the 2015 Nepal earthquakes. We propose a method to link tweets and newswire, so that we can compare their key characteristics: timeliness, whether tweets appear earlier than their corresponding newswires, and content. Whenever possible, we present both quantitative and qualitative evaluations. One of our main findings is that tweets and newswire articles provide complementary perspectives that form a holistic view of the disaster situation. Another finding is that during the Nepal earthquake, significant information of earthquake-related news appeared on Twitter before newswire. Our results also show that 9.08% of the tweets published before their matched newswire contain actionable information regarding the Nepal earthquake.\",\n \"Title: Anchor-oriented Time and Phase-based Concurrent Self-localization using UWB Radios\\nYear: 2021\\n\\nAbstract: Positioning plays an important role in many IoT applications. Ultra-wideband (UWB)-based positioning is an alternative to GPS in indoor environments due to its multipath resilience and its accuracy and precision. In the presence of a large number of targets to localize, conventional UWB localization fails to provide a practical location update rate. UWB concurrency in conjunction with self-localiz...\",\n \"Title: A Domain-Independent Holistic Approach to Deception Detection.\\nYear: 2021\\n\\nAbstract: The deception in the text can be of different forms in different domains, including fake news, rumor tweets, and spam emails. Irrespective of the domain, the main intent of the deceptive text is to deceit the reader. Although domain-specific deception detection exists, domain-independent deception detection can provide a holistic picture, which can be crucial to understand how deception occurs in the text. In this paper, we detect deception in a domain-independent setting using deep learning architectures. Our method outperforms the State-of-the-Art (SOTA) performance of most benchmark datasets with an overall accuracy of 93.42% and F1-Score of 93.22%. The domain-independent training allows us to capture subtler nuances of deceptive writing style. Furthermore, we analyze how much in-domain data may be helpful to accurately detect deception, especially for the cases where data may not be readily available to train. Our results and analysis indicate that there may be a universal pattern of deception lying in-between the text independent of the domain, which can create a novel area of research and open up new avenues in the field of deception detection.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":70,"string":"70"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13680,"string":"13,680"}}},{"rowIdx":13681,"cells":{"question_id":{"kind":"string","value":"Fabian Kuhn"},"question":{"kind":"string","value":"Generate an abstract for the title \"Geometric ad-hoc routing: of theory and practice\" using the following items: 1. Ad-hoc routing\n2. Geometric routing algorithm\n3. Worst-case optimal\n4. Cost metrics\n5. Practical networks."},"answer":{"kind":"string","value":"All too often a seemingly insurmountable divide between theory and practice can be witnessed. In this paper we try to contribute to narrowing this gap in the field of ad-hoc routing. In particular we consider two aspects: We propose a new geometric routing algorithm which is outstandingly efficient on practical average-case networks, however is also in theory asymptotically worst-case optimal. On the other hand we are able to drop the formerly necessary assumption that the distance between network nodes may not fall below a constant value, an assumption that cannot be maintained for practical networks. Abandoning this assumption we identify from a theoretical point of view two fundamentamentally different classes of cost metrics for routing in ad-hoc networks."},"correct_answer":{"kind":"string","value":"All too often a seemingly insurmountable divide between theory and practice can be witnessed. In this paper we try to contribute to narrowing this gap in the field of ad-hoc routing. In particular we consider two aspects: We propose a new geometric routing algorithm which is outstandingly efficient on practical average-case networks, however is also in theory asymptotically worst-case optimal. On the other hand we are able to drop the formerly necessary assumption that the distance between network nodes may not fall below a constant value, an assumption that cannot be maintained for practical networks. Abandoning this assumption we identify from a theoretical point of view two fundamentamentally different classes of cost metrics for routing in ad-hoc networks."},"documents":{"kind":"list like","value":["Title: Distributed connectivity decomposition\nYear: 2013\n\nAbstract: A fundamental problem in distributed network algorithms is to manage congestion and obtain information flow matching the graph's connectivity. In this paper, we present time-efficient distributed algorithms for decomposing graphs with large edge or vertex connectivity into multiple spanning or dominating trees, respectively. These decompositions allow us to achieve a flow with size close to the connectivity by parallelizing it along the trees. More specifically, our distributed decomposition algorithms are as follows: - A decomposition of each undirected graph with vertex-connectivity k into (fractionally) vertex-disjoint weighted dominating trees with total weight Ω(k/log n), in ~O(D+√n) rounds. - A decomposition of each undirected graph with edge-connectivity λ into (fractionally) edge-disjoint weighted spanning trees with total weight ⌈λ-1/2⌉(1-ε), in ~{O}(D+√nλ) rounds. We also show round complexity lower bounds of ~](D+√n/k) and ~Ω(D+√n/λ) for the above two decompositions, using techniques of [Das Sarma et al., STOC'11]. Moreover, our vertex-connectivity decomposition extends to centralized algorithms and improves the time complexity of [Censor-Hillel et al., SODA'14] from O(n3) to near-optimal ~O(m). Additional implications of our results are: a near-linear time centralized approximation of vertex connectivity which can be seen as a step towards a conjecture of Aho, Hopcroft and Ullman), the first distributed approximating of vertex connectivity, and distributed algorithms with near-optimal competitiveness for oblivious broadcast routing.","Title: Improved approximation algorithms for connected sensor cover\nYear: 2007\n\nAbstract: Wireless sensor networks have recently posed many new system building challenges. One of the main problems is energy conservation since most of the sensors are devices with limited battery life and it is infeasible to replenish energy via replacing batteries. An effective approach for energy conservation is scheduling sleep intervals for some sensors, while the remaining sensors stay active providing continuous service. In this paper we consider the problem of selecting a set of active sensors of minimum cardinality so that sensing coverage and network connectivity are maintained. We show that the greedy algorithm that provides complete coverage has an approximation factor no better than Ω(log n), where n is the number of sensor nodes. Then we present algorithms that provide approximate coverage while the number of nodes selected is a constant factor far from the optimal solution. Finally, we show how to connect a set of sensors that already provides coverage.","Title: A new perspective on vertex connectivity\nYear: 2014\n\nAbstract: Edge connectivity and vertex connectivity are two fundamental concepts in graph theory. Although by now there is a good understanding of the structure of graphs based on their edge connectivity our knowledge in the case of vertex connectivity is much more limited. An essential tool in capturing edge connectivity are the classical results of Tutte and Nash-Williams from 1961 which show that a λ-edge-connected graph contains ⌈(λ -- 1)/2⌉ edge-disjoint spanning trees. We argue that connected dominating set partitions and packings are the natural analogues of edge-disjoint spanning trees in the context of vertex connectivity and we use them to obtain structural results about vertex connectivity in the spirit of those for edge connectivity. More specifically connected dominating set (CDS) partitions and packings are counterparts of edge-disjoint spanning trees, focusing on vertex-disjointness rather than edge-disjointness, and their sizes are always upper bounded by the vertex connectivity k. We constructively show that every k-vertex-connected graph with n nodes has CDS packings and partitions with sizes, respectively, Ω(k/log n) and Ω(k/log5 n), and we prove that the former bound is existentially optimal. Beautiful results by Karger show that when edges of a λ-edge-connected graph are independently sampled with probability p, the sampled graph has edge connectivity Ω(λp). Obtaining such a result for vertex sampling remained open. We illustrate the strength of our approach by proving that when vertices of a k-vertex-connected graph are independently sampled with probability p, the graph induced by the sampled vertices has vertex connectivity Ω(kp2). This bound is optimal up to poly-log factors and is proven by building an Ω(kp2) size CDS packing on the sampled vertices while sampling happens. As an additional important application, we show CDS packings to be tightly related to the throughput of routing-based algorithms and use our new toolbox to yield a routing-based broadcast algorithm with optimal throughput Ω(k/log n + 1), improving the (previously best-known) trivial throughput of Θ(1).","Title: Constant-time distributed dominating set approximation\nYear: 2005\n\nAbstract: Finding a small dominating set is one of the most fundamental problems of traditional graph theory. In this paper, we present a new fully distributed approximation algorithm based on LP relaxation techniques. For an arbitrary parameter k and maximum degree Δ, our algorithm computes a dominating set of expected size O(kΔ2/k log Δ|DSOPT|) in O(k2) rounds where each node has to send O(k2Δ) messages of size O(logΔ). This is the first algorithm which achieves a non-trivial approximation ratio in a constant number of rounds.","Title: Distributed Minimum Cut Approximation\nYear: 2013\n\nAbstract: We study the problem of computing approximate minimum edge cuts by distributed algorithms. We use a standard synchronous message passing model where in each round, O(log n) bits can be transmitted over each edge (a.k.a. the CONGEST model). The first algorithm is based on a simple and new approach for analyzing random edge sampling, which we call the random layering technique. For any weighted graph and any epsilon is an element of (0, 1), the algorithm with high probability finds a cut of size at most O(epsilon(-1)lambda) in O(D) + (O) over tilde (n(1/2+epsilon)) rounds, where lambda is the size of the minimum cut and the (O) over tilde -notation hides poly-logarithmic factors in n. In addition, based on a centralized algorithm due to Matula [SODA '93], we present a randomized distributed algorithm that with high probability computes a cut of size at most (2 + epsilon)lambda in (O) over tilde((D + root n)/epsilon(5)) rounds for any epsilon > 0.The time complexities of our algorithms almost match the (Omega) over tilde (D + root n) lower bound of Das Sarma et al. [STOC '11], thus leading to an answer to an open question raised by Elkin [SIGACT-News '04] and Das Sarma et al. [STOC '11].To complement our upper bound results, we also strengthen the (Omega) over tilde (D + v n) lower bound of Das Sarma et al. by extending it to unweighted graphs. We show that the same lower bound also holds for unweighted multigraphs (or equivalently for weighted graphs in which O(w log n) bits can be transmitted in each round over an edge of weight w). For unweighted simple graphs, we show that computing an alpha-approximate minimum cut requires time at least (Omega) over tilde (D + root n/alpha(1/4)).","Title: Weak graph colorings: distributed algorithms and applications\nYear: 2009\n\nAbstract: We study deterministic, distributed algorithms for two weak variants of the standard graph coloring problem. We consider defective colorings, i.e., colorings where nodes of a color class may induce a graph of maximum degree d for some parameter d0. We also look at colorings where a minimum number of multi-chromatic edges is required. For an integer k0, we call a coloring k-partially proper if every node v has at least min{k,deg(v)} neighbors with a different color. We show that for all d∈{1,...,Δ}, it is possible to compute a O(Δ2/d2)-coloring with defect d in time O(log*n) where Δ is the largest degree of the network graph. Similarly, for all k∈{1,...,Δ}, a k-partially proper O(k2)-coloring can be computed in O(log*n) rounds. As an application of our weak defective coloring algorithm, we obtain a faster deterministic algorithm for the standard vertex coloring problem on graphs with moderate degrees. We show that in time O(Δ+log*n), a (Δ+1)-coloring can be computed, a task for which the best previous algorithm required time O(Δ*log(Δ) + log*n). The same result holds for the problem of computing a maximal independent set.","Title: Structuring unreliable radio networks\nYear: 2014\n\nAbstract: In this paper we study the problem of building a connected dominating set with constant degree (CCDS) in the dual graph radio network model [4,9,10]. This model includes two types of links: reliable, which always deliver messages, and unreliable, which sometimes fail to deliver messages. Real networks compensate for this differing quality by deploying low-layer detection protocols to filter unreliable from reliable links. With this in mind, we begin by presenting an algorithm that solves the CCDS problem in the dual graph model under the assumption that every process u is provided a local link detector set consisting of every neighbor connected to u by a reliable link. The algorithm solves the CCDS problem in O(Δ\\log2 n/b + log3 n) rounds, with high probability, where Δ is the maximum degree in the reliable link graph, n is the network size, and b is an upper bound in bits on the message size. The algorithm works by first building a Maximal Independent Set (MIS) in log3 n time, and then leveraging the local topology knowledge to efficiently connect nearby MIS processes. A natural follow up question is whether the link detector must be perfectly reliable to solve the CCDS problem. With this in mind, we first describe an algorithm that builds a CCDS in O(Δpolylog(n)) time under the assumption of O(1) unreliable links included in each link detector set. We then prove this algorithm to be (almost) tight by showing that the possible inclusion of only a single unreliable link in each process's local link detector set is sufficient to require Ω(Δ) rounds to solve the CCDS problem, regardless of message size. We conclude by discussing how to apply our algorithm in the setting where the topology of reliable and unreliable links can change over time.","Title: The Abstract MAC Layer\nYear: 2011\n\nAbstract: A diversity of possible communication assumptions complicates the study of algorithms and lower bounds for radio networks.\n We address this problem by defining an abstract MAC layer. This service provides reliable local broadcast communication, with\n timing guarantees stated in terms of a collection of abstract delay functions applied to the relevant contention. Algorithm designers can analyze their algorithms in terms of these functions, independently\n of specific channel behavior. Concrete implementations of the abstract MAC layer over basic radio network models generate\n concrete definitions for these delay functions, automatically adapting bounds proven for the abstract service to bounds for\n the specific radio network under consideration. To illustrate this approach, we use the abstract MAC layer to study the new\n problem of Multi-Message Broadcast, a generalization of standard single-message broadcast in which multiple messages can originate at different times and locations\n in the network. We present and analyze two algorithms for Multi-Message Broadcast in static networks: a simple greedy algorithm\n and one that uses regional leaders. We then indicate how these results can be extended to mobile networks.","Title: Dynamic Analysis of the Arrow Distributed Protocol\nYear: 2006\n\nAbstract: Distributed queuing is a fundamental coordination problem that arises in a variety of applications, including distributed directories, totally ordered multicast, and distributed mutual exclusion. The arrow protocol is a solution to distributed queuing that is based on path reversal on a pre-selected spanning tree of the network. We present a novel and comprehensive competitive analysis of the arrow protocol. We consider the total cost of handling a finite number of queuing requests, which may or may not be issued concurrently, and show that the arrow protocol is $O(s\\cdot \\log D)$-competitive to the optimal queuing protocol, where s and D are the stretch and the diameter, respectively, of the spanning tree. In addition, we show that our analysis is almost tight by proving that for every spanning tree chosen for execution, the arrow protocol is $\\Omega(s \\cdot \\log(D/s)/{\\log}\\log(D/s))$-competitive to the optimal queuing protocol. Our analysis reveals an intriguing connection between the arrow protocol and the nearest neighbor traveling salesperson tour on an appropriately defined graph.","Title: The wireless synchronization problem\nYear: 2009\n\nAbstract: In this paper, we study the wireless synchronization prob- lem which requires devices activated at dierent times on a congested single-hop radio network to synchronize their round numbering. We assume a collection of n synchronous devices with access to a shared band of the radio spectrum, divided into F narrowband frequencies. We assume that the communication medium suers from unpredictable, perhaps even malicious interference, which we model by an adversary that can disrupt up to t frequencies per round. Devices be- gin executing in dierent rounds and the exact number of participants is not known in advance. We first prove a lower bound, demonstrating that at least \" log2 n (F t) log log n + Ft F t logn","Title: Unit disk graph approximation\nYear: 2004\n\nAbstract: Finding a good embedding of a unit disk graph given by its connectivity information is a problem of practical importance in a variety of fields. In wireless ad hoc and sensor networks, such an embedding can be used to obtain virtual coordinates. In this paper, we prove a non-approximability result for the problem of embedding a given unit disk graph. Particularly, we show that if non-neighboring nodes are not allowed to be closer to each other than distance 1, then two neighbors can be as far apart as √3/2 - ε, where ε goes to 0 as n goes to infinity, unless P=NP. We further show that finding a realization of a d-quasi unit disk graph with d ≥ 1/√2 is NP-hard.","Title: Reconstructing approximate tree metrics\nYear: 2007\n\nAbstract: We introduce a novel measure called \"-four-points condition (\"- 4PC), which assigns a value \" 2 (0;1) to every metric space quan- tifying how close the metric is to a tree metric. Data-sets taken from real Internet measurements indicate remarkable closeness of Internet latencies to tree metrics based on this condition. We study embeddings of \"-4PC metric spaces into trees and prove tight upper and lower bounds. Specifically, we show that there are constants c1 and c2 such that, (1) every metric (X;d) which satisfies the \"-4PC can be embedded into a tree with distortion (1 + \")c 1 log jXj , and (2) for every \" 2 (0;1) and any number of nodes, there is a metric space (X;d) satisfying the \"-4PC that does not embed into a tree with distortion less than (1 + \")c 2 log jXj . In addition, we prove a lower bound on approximate distance labelings of \"-4PC metrics, and give tight bounds for tree embeddings with additive error guar- antees.","Title: Maximal independent sets in multichannel radio networks\nYear: 2013\n\nAbstract: We present new upper bounds for fundamental problems in multichannel wireless networks. These bounds address the benefits of dynamic spectrum access, i.e., to what extent multiple communication channels can be used to improve performance. In more detail, we study a multichannel generalization of the standard graph-based wireless model without collision detection, and assume the network topology satisfies polynomially bounded independence. Our core technical result is an algorithm that constructs a maximal independent set (MIS) in O(log2 n/F)+ Õ(logn) rounds, in networks of size n with F channels, where the Õ-notation hides polynomial factors in log log n. Moreover, we use this MIS algorithm as a subroutine to build a constant-degree connected dominating set in the same asymptotic time. Leveraging this structure, we are able to solve global broadcast and leader election within O(D + log2 n/F + Õ(logn) rounds, where D is the diameter of the graph, and k-message multi-message broadcast in O(D + k + log2 n/F}\\big)+Õ(logn) rounds for unrestricted message size (with a slow down of only a log factor on the k term under the assumption of restricted message size). In all five cases above, we prove: (a) our results hold with high probability (i.e., at least 1--1/n); (b) our results are within polyloglog factors of the relevant lower bounds for multichannel networks; and (c) our results beat the relevant lower bounds for single channel networks. These new (near) optimal algorithms significantly expand the number of problems now known to be solvable faster in multichannel versus single channel wireless networks.","Title: An algorithmic approach to geographic routing in ad hoc and sensor networks\nYear: 2008\n\nAbstract: The one type of routing in ad hoc and sensor networks that currently appears to be most amenable to algorithmic analysis is geographic routing. This paper contains an introduction to the problem field of geographic routing, presents a specific routing algorithm based on a synthesis of the greedy forwarding and face routing approaches, and provides an algorithmic analysis of the presented algorithm from both a worst-case and an average-case perspective.","Title: Random Walks Revisited: Extensions of Pollard's Rho Algorithm for Computing Multiple Discrete Logarithms\nYear: 2001\n\nAbstract: This paper extends the analysis of Pollard's rho algorithm for solving a single instance of the discrete logarithm problem in a finite cyclic group G to the case of solving more than one instance of the discrete logarithm problem in the same group G. We analyze Pollard's rho algorithm when used to iteratively solve all the instances. We also analyze the situation when the goal is to solve any one of the multiple instances using any DLP algorithm.","Title: Synchrony and asynchrony in neural networks\nYear: 2010\n\nAbstract: The dynamics of large networks is an important and fascinating problem. Key examples are the Internet, social networks, and the human brain. In this paper we consider a model introduced by DeVille and Peskin [6] for a stochastic pulse-coupled neural network. The key feature and novelty in their approach is that they describe the interactions of a neuronal system as a discrete-state stochastic dynamical network. This idealization has two benefits: it captures essential features of neuronal behavior, and it allows the study of spontaneous synchronization, an important phenomenon in neuronal networks that is well-studied but unfortunately far from being well-understood. In synchronous behavior the firing of one neuron leads to the firing of other neurons, which in turn may set off a chain reaction that often involves a substantial proportion of the neurons. In this paper we rigorously analyze their model. In particular, by applying methods and tools that are frequently used in theoretical computer science, we provide a very precise picture of the dynamics and the evolution of the given system. In particular, we obtain insights into the coexistence of synchronous and asynchronous behavior and the conditions that trigger a \"spontaneous\" transition from one state to another.","Title: MAC design for analog network coding\nYear: 2011\n\nAbstract: Most medium access control (MAC) mechanisms discard collided packets and consider interference harmful. Recent work on Analog Network Coding (ANC) suggests a different approach, in which multiple interfering transmissions are strategically scheduled. Receiving nodes collect the results of collisions and then use a decoding process, such as ZigZag decoding, to extract the packets involved in the collisions. In this paper, we present an algebraic representation of collisions and describe a general approach to recovering collisions using ANC. To study the effects of using ANC on the performance of MAC layers, we develop an ANC-based MAC algorithm, CMAC, and analyze its performance in terms of probabilistic latency guarantees for local packet delivery. Specifically, we prove that CMAC implements an abstract MAC layer service, as defined in [14, 13]. This study shows that ANC can significantly improve the performance of the abstract MAC layer service compared to conventional probabilistic transmission approaches. We illustrate how this improvement in the MAC layer can translate into faster higher-level algorithms, by analyzing the time complexity of a multi-message network-wide broadcast algorithm that uses CMAC.","Title: Efficient distributed approximation algorithms via probabilistic tree embeddings\nYear: 2012\n\nAbstract: We present a uniform approach to design e cient distributed ap- proximation algorithms for various network optimization problems. Our approach is randomized and based on a probabilistic tree em- bedding due to Fakcharoenphol, Rao, and Talwar (12) (FRT embed- ding). We show how to e ciently compute an (implicit) FRT em- bedding in a decentralized manner and how to use the embedding to obtain expected O(log n)-approximate distributed algorithms for the generalized Steiner forest problem, the minimum routing cost spanning tree problem, and the k-source shortest paths problem in arbitrary networks. The time complexities of our algorithms are within a polylogarithmic factor of the optimum. The distributed construction of the FRT embedding is based on the computation of least elements (LE) lists, a distributed data struc- ture that can be of independent interest. Assuming a global order on the nodes of a network, the LE list of a node stores the smallest node (with respect to the given order) within every distance d (cf. Cohen (6), Cohen and Kaplan (7)). For weighted graphs, we give an almost-optimal distributed algorithm for computing LE lists, as- suming a random order on the nodes. For unweighted graphs, our LE lists computation has asymptotically optimal time complexity O(D), where D is the diameter of the network. As a byproduct, we get an improved leader election algorithm in general networks that is both time-optimal and almost message-optimal with high proba- bility.","Title: The communication complexity of distributed task allocation\nYear: 2012\n\nAbstract: We consider a distributed task allocation problem in which m players must divide a set of n tasks between them. Each player i receives as input a set Xi of tasks such that the union of all input sets covers the task set. The goal is for each player to output a subset Yi ⊆ Xi, such that the outputs (Y1,...,Ym) form a partition of the set of tasks. The problem can be viewed as a distributed one-shot variant of the well-known k-server problem, and we also show that it is closely related to the problem of finding a rooted spanning tree in directed broadcast networks. We study the communication complexity and round complexity of the task allocation problem. We begin with the classical two-player communication model, and show that the randomized communication complexity of task allocation is Ω(n), even when the set of tasks is known to the players in advance. For the multi-player setting with m = O(n) we give two upper bounds in the shared-blackboard model of communication. We show that the problem can be solved in O(log n) rounds and O(n log n) total bits for arbitrary inputs; moreover, if for any set X of tasks, there are at least α|X| players that have at least one task from X in their inputs, then O((1/α + log m)log n) rounds suffice even if each player can only write O(log n) bits on the blackboard in each round. Finally, we extend our results to the case where the players communicate over an arbitrary directed communication graph instead of a shared blackboard. As an application of these results, we also consider the related problem of constructing a directed spanning tree in strongly-connected directed networks and we show lower and upper bounds for that problem.","Title: Beeping a maximal independent set\nYear: 2011\n\nAbstract: We consider the problem of computing a maximal independent set (MIS) in an extremely harsh broadcast model that relies only on carrier sensing. The model consists of an anonymous broadcast network in which nodes have no knowledge about the topology of the network or even an upper bound on its size. Furthermore, it is assumed that nodes wake up asynchronously. At each time slot a node can either beep (i.e., emit a signal) or be silent. At a particular time slot, beeping nodes receive no feedback, while silent nodes can only differentiate between none of its neighbors beeping, or at least one neighbor beeping. We start by proving a lower bound that shows that in this model, it is not possible to locally converge to an MIS in sub-polynomial time. We then study four different relaxations of the model which allow us to circumvent the lower bound and compute an MIS in polylogarithmic time. First, we show that if a polynomial upper bound on the network size is known, it is possible to find an MIS in O(log3 n) time. Second, if sleeping nodes are awoken by neighboring beeps, then we can also find an MIS in O(log3 n) time. Third, if in addition to this wakeup assumption we allow beeping nodes to receive feedback to identify if at least one neighboring node is beeping concurrently (i.e., sender-side collision detection) we can find an MIS in O(log2 n) time. Finally, if instead we endow nodes with synchronous clocks, it is also possible to compute an MIS in O(log2 n) time.","Title: Towards worst-case churn resistant peer-to-peer systems\nYear: 2010\n\nAbstract: Until now, the analysis of fault tolerance of peer- to-peer systems usually only covers random faults of some kind. Contrary to traditional algorithmic research, faults as well as joins and leaves occurring in a worst-case manner are hardly considered. In this article, we devise techniques to build dynamic peer-to-peer systems which remain fully functional in spite of an adversary who continuously adds and removes peers. We exemplify our algorithms on hyper- cube and pancake topologies and present a system which maintains small peer degree and network diameter.","Title: Broadcasting in unreliable radio networks\nYear: 2010\n\nAbstract: Practitioners agree that unreliable links, which sometimes deliver messages and sometime do not, are an important characteristic of wireless networks. In contrast, most theoretical models of radio networks fix a static set of links and assume that these links are reliable. This gap between theory and practice motivates us to investigate how unreliable links affect theoretical bounds on broadcast in radio networks. To that end we consider a model that includes two types of links: reliable links, which always deliver messages, and unreliable links, which sometimes fail to deliver messages. We assume that the reliable links induce a connected graph, and that unreliable links are controlled by a worst-case adversary. In the new model we show an Ω(n log n) lower bound on deterministic broadcast in undirected graphs, even when all processes are initially awake and have collision detection, and an Ω(n) lower bound on randomized broadcast in undirected networks of constant diameter. This separates the new model from the classical, reliable model. On the positive side, we give two algorithms that tolerate unreliability: an O(n3/2 √log n)-time deterministic algorithm and a randomized algorithm which terminates in O(n log2 n) rounds with high probability.","Title: Deploying wireless networks with beeps\nYear: 2010\n\nAbstract: We present the discrete beeping communication model, which assumes nodes have minimal knowledge about their environment and severely limited communication capabilities. Specifically, nodes have no information regarding the local or global structure of the network, do not have access to synchronized clocks and are woken up by an adversary. Moreover, instead on communicating through messages they rely solely on carrier sensing to exchange information. This model is interesting from a practical point of view, because it is possible to implement it (or emulate it) even in extremely restricted radio network environments. From a theory point of view, it shows that complex problems (such as vertex coloring) can be solved efficiently even without strong assumptions on properties of the communication model. We study the problem of interval coloring, a variant of vertex coloring specially suited for the studied beeping model. Given a set of resources, the goal of interval coloring is to assign every node a large contiguous fraction of the resources, such that neighboring nodes have disjoint resources. A k-interval coloring is one where every node gets at least a 1/k fraction of the resources. To highlight the importance of the discreteness of the model, we contrast it against a continuous variant described in [17]. We present an O(1) time algorithm that with probability 1 produces a O(Δ)-interval coloring. This improves an O(log n) time algorithm with the same guarantees presented in [17], and accentuates the unrealistic assumptions of the continuous model. Under the more realistic discrete model, we present a Las Vegas algorithm that solves O(Δ)- interval coloring in O(log n) time with high probability and describe how to adapt the algorithm for dynamic networks where nodes may join or leave. For constant degree graphs we prove a lower bound of Ω(log n) on the time required to solve interval coloring for this model against randomized algorithms. This lower bound implies that our algorithm is asymptotically optimal for constant degree graphs.","Title: Efficient adaptive collect using randomization\nYear: 2006\n\nAbstract: An adaptive algorithm, whose step complexity adjusts to the number of active processes, is attractive for distributed systems with a highly-variable number of processes. The cornerstone of many adaptive algorithms is an adaptive mechanism to collect up-to-date information from all participating processes. To date, all known collect algorithms either have non-linear step complexity or they are impractical because of unrealistic memory overhead.This paper presents new randomized collect algorithms with asymptotically optimal O(k) step complexity and linear memory overhead only. In addition we present a new deterministic collect algorithm that beats the best step complexity for previous polynomial-memory algorithms.","Title: Coordinated consensus in dynamic networks\nYear: 2011\n\nAbstract: We study several variants of coordinated consensus in dynamic networks. We assume a synchronous model, where the communication graph for each round is chosen by a worst-case adversary. The network topology is always connected, but can change completely from one round to the next. The model captures mobile and wireless networks, where communication can be unpredictable. In this setting we study the fundamental problems of eventual, simultaneous, and Δ-coordinated consensus, as well as their relationship to other distributed problems, such as determining the size of the network. We show that in the absence of a good initial upper bound on the size of the network, eventual consensus is as hard as computing deterministic functions of the input, e.g., the minimum or maximum of inputs to the nodes. We also give an algorithm for computing such functions that is optimal in every execution. Next, we show that simultaneous consensus can never be achieved in less than n - 1 rounds in any execution, where n is the size of the network; consequently, simultaneous consensus is as hard as computing an upper bound on the number of nodes in the network. For Δ-coordinated consensus, we show that if the ratio between nodes with input 0 and input 1 is bounded away from 1, it is possible to decide in time n-Θ(√ nΔ), where Δ bounds the time from the first decision until all nodes decide. If the dynamic graph has diameter D, the time to decide is min{O(nD/Δ),n-Ω(nΔ/D)}, even if D is not known in advance. Finally, we show that (a) there is a dynamic graph such that for every input, no node can decide before time n-O(Δ0.28n0.72); and (b) for any diameter D = O(Δ), there is an execution with diameter D where no node can decide before time Ω(nD / Δ). To our knowledge, our work constitutes the first study of Δ-coordinated consensus in general graphs.","Title: Distributed computation of the mode\nYear: 2008\n\nAbstract: This paper studies the problem of computing the most fre- quent element (the mode) by means of a distributed algo- rithm where the elements are located at the nodes of a net- work. Let k denote the number of distinct elements and fur- ther let mi be the number of occurrences of the element ei in the ordered list of occurrences m1 > m2 ‚ ::: ‚ mk. We give a deterministic distributed algorithm with time com- plexity O(D+k) where D denotes the diameter of the graph, which is essentially tight. As our main contribution, a Monte Carlo algorithm is presented which computes the mode in O(D + F2=m2 1 ¢ logk) time with high probability, where the frequency moment F' is deflned as F' = Pk i=1 m ' i. This algo- rithm is substantially faster than the deterministic algorithm for various relevant frequency distributions. Moreover, we provide a lower bound of ›(D+F5=(m5 1B)), where B is the maximum message size, that captures the efiect of the fre- quency distribution on the time complexity to compute the mode.","Title: Distributed computation in dynamic networks\nYear: 2010\n\nAbstract: In this paper we investigate distributed computation in dynamic networks in which the network topology changes from round to round. We consider a worst-case model in which the communication links for each round are chosen by an adversary, and nodes do not know who their neighbors for the current round are before they broadcast their messages. The model captures mobile networks and wireless networks, in which mobility and interference render communication unpredictable. In contrast to much of the existing work on dynamic networks, we do not assume that the network eventually stops changing; we require correctness and termination even in networks that change continually. We introduce a stability property called T -interval connectivity (for T = 1), which stipulates that for every T consecutive rounds there exists a stable connected spanning subgraph. For T = 1 this means that the graph is connected in every round, but changes arbitrarily between rounds. We show that in 1-interval connected graphs it is possible for nodes to determine the size of the network and compute any com- putable function of their initial inputs in O(n2) rounds using messages of size O(log n + d), where d is the size of the input to a single node. Further, if the graph is T-interval connected for T 1, the computation can be sped up by a factor of T, and any function can be computed in O(n + n2/T) rounds using messages of size O(log n + d). We also give two lower bounds on the token dissemination problem, which requires the nodes to disseminate k pieces of information to all the nodes in the network. The T-interval connected dynamic graph model is a novel model, which we believe opens new avenues for research in the theory of distributed computing in wireless, mobile and dynamic networks.","Title: Radio Network Clustering from Scratch\nYear: 2004\n\nAbstract: We propose a novel randomized algorithm for computing a dominating set based clustering in wireless ad-hoc and sensor networks. The algorithm works under a model which captures the characteristics of the set-up phase of such multi-hop radio networks: asynchronous wake-up, the hidden terminal problem, and scarce knowledge about the topology of the network graph. When modelling the network as a unit disk graph, the algorithm computes a dominating set in polylogarithmic time and achieves a constant approximation ratio.","Title: Initializing newly deployed ad hoc and sensor networks\nYear: 2004\n\nAbstract: A newly deployed multi-hop radio network is unstructured and lacks a reliable and efficient communication scheme. In this paper, we take a step towards analyzing the problems existing during the initialization phase of ad hoc and sensor networks. Particularly, we model the network as a multi-hop quasi unit disk graph and allow nodes to wake up asynchronously at any time. Further, nodes do not feature a reliable collision detection mechanism, and they have only limited knowledge about the network topology. We show that even for this restricted model, a good clustering can be computed efficiently. Our algorithm efficiently computes an asymptotically optimal clustering. Based on this algorithm, we describe a protocol for quickly establishing synchronized sleep and listen schedule between nodes within a cluster. Additionally, we provide simulation results in a variety of settings.","Title: Optimal gradient clock synchronization in dynamic networks\nYear: 2010\n\nAbstract: We study the problem of clock synchronization in highly dynamic networks, where communication links can appear or disappear at any time. The nodes in the network are equipped with hardware clocks, but the rate of the hardware clocks can vary arbitrarily within specific bounds, and the estimates that nodes can obtain about the clock values of other nodes are inherently inaccurate. Our goal in this setting is to output a logical clock at each node, such that the logical clocks of any two nodes are not too far apart, and nodes that remain close to each other in the network for a long time are better synchronized than distant nodes. This property is called gradient clock synchronization. Gradient clock synchronization has been widely studied in the static setting. We show that the bounds for the static case also apply to our highly dynamic setting: if two nodes remain at distance d from each other for sufficiently long, it is possible to synchronize their clocks to within O(d log(D/d)), where D is the diameter of the network. This is known to be optimal for static networks, and since a static network is a special case of a dynamic network, it is optimal for dynamic networks as well. Furthermore, we show that our algorithm has optimal stabilization time: when a path of length d appears between two nodes, the time required until the skew between the two nodes is reduced to O(d log(D/d)) is O(D), which we prove is optimal.","Title: On the locality of bounded growth\nYear: 2005\n\nAbstract: Many large-scale networks such as ad hoc and sensor networks, peer-to-peer networks, or the Internet have the property that the number of independent nodes does not grow arbitrarily when looking at neighborhoods of increasing size. Due to this bounded \"volume growth,\" one could expect that distributed algorithms are able to solve many problems more efficiently than on general graphs. The goal of this paper is to help understanding the distributed complexity of problems on \"bounded growth\" graphs. We show that on the widely used unit disk graph, covering and packing linear programs can be approximated by constant factors in constant time. For a more general network model which is based on the assumption that nodes are in a metric space of constant doubling dimension, we show that in O(log*!n) rounds it is possible to construct a (O(1), O(1))-network decomposition. This results in asymptotically optimal O(log*!n) time algorithms for many important problems.","Title: Oblivious low-congestion multicast routing in wireless networks\nYear: 2012\n\nAbstract: We propose a routing scheme to implement multicast communication in wireless networks. The scheme is oblivious, compact, and completely decentralized. It is intended to support dynamic and diverse multicast requests typical of, for example, publish/subscribe and content-based communication. The scheme is built on top of a geographical routing layer. Each message is transmitted along the geometric minimum spanning tree that connects the source and all the destinations. Then, for each edge in this tree, the scheme routes a message through a random intermediate node, chosen independently of the set of multicast requests. The intermediate node is chosen in the vicinity of the corresponding edge such that congestion is reduced without stretching the routes by more than a constant factor. We first evaluate the scheme analytically, showing that it achieves a theoretically optimal level of congestion. We then evaluate the scheme in simulation, showing that its performance is also good in practice.","Title: On the treeness of internet latency and bandwidth\nYear: 2009\n\nAbstract: Existing empirical studies of Internet structure and path properties indicate that the Internet is tree-like. This work quantifies the degree to which at least two important Internet measures--latency and bandwidth--approximate tree metrics. We evaluate our ability to model end-to-end measures using tree embeddings by actually building tree representations. In addition to being simple and intuitive models, these trees provide a range of commonly-required functionality beyond serving as an analytical tool. The contributions of our study are twofold. First, we investigate the ability to portray the inherent hierarchical structure of the Internet using the most pure and compact topology, trees. Second, we evaluate the ability of our compact representation to facilitate many natural tasks, such as the selection of servers with short latency or high bandwidth from a client. Experiments show that these tasks can be done with high degree of success and modest overhead.","Title: Efficient symmetry breaking in multi-channel radio networks\nYear: 2012\n\nAbstract: We investigate the complexity of basic symmetry breaking problems in multihop radio networks with multiple communication channels. We assume a network of synchronous nodes, where each node can be awakened individually in an arbitrary time slot by an adversary. In each time slot, each awake node can transmit or listen (without collision detection) on one of multiple available shared channels. The network topology is assumed to satisfy a natural generalization of the well-known unit disk graph model. We study the classic wake-up problem and a new variant we call active wake-up. For the former we prove a lower bound that shows the advantage of multiple channels disappears for any network of more than one hop. For the active version however, we describe an algorithm that outperforms any single channel solution. We then extend this algorithm to compute a constant approximation for the minimum dominating set (MDS) problem in the same time bound. Combined, these results for the increasingly relevant multi-channel model show that it is often possible to leverage channel diversity to beat classic lower bounds, but not always.","Title: Worst-Case optimal and average-case efficient geometric ad-hoc routing\nYear: 2003\n\nAbstract: In this paper we present GOAFR, a new geometric ad-hoc routing algorithm combining greedy and face routing. We evaluate this algorithm by both rigorous analysis and comprehensive simulation. GOAFR is the first ad-hoc algorithm to be both asymptotically optimal and average-case efficient. For our simulations we identify a network density range critical for any routing algorithm. We study a dozen of routing algorithms and show that GOAFR outperforms other prominent algorithms, such as GPSR or AFR.","Title: Keeping mobile robot swarms connected\nYear: 2009\n\nAbstract: Designing robust algorithms for mobile agents with reliable communication is difficult due to the distributed nature of computation, in mobile ad hoc networks (MANETs) the matter is exacerbated by the need to ensure connectivity. Existing distributed algorithms provide coordination but typically assume connectivity is ensured by other means. We present a connectivity service that encapsulates an arbitrary motion planner and can refine any plan to preserve connectivity (the graph of agents remains connected) and ensure progress (the agents advance towards their goal). The service is realized by a distributed algorithm that is modular in that it makes no assumptions of the motion-planning mechanism except the ability for an agent to query its position and intended goal position, local in that it uses 1-hop broadcast to communicate with nearby agents but doesn't need any network routing infrastructure, and oblivious in that it does not depend on previous computations. We prove the progress of the algorithm in one round is at least Ω(min(d, r)), where d is the minimum distance between an agent and its target and r is the communication radius. We characterize the worst case configuration and show that when d ≥ r this bound is tight and the algorithm is optimal, since no algorithm can guarantee greater progress. Finally we show all agents get Ɛ-close to their targets within O(D0/r+n2/Ɛ) rounds where n is the number of agents and D0 is the sum of the initial distances to the targets.","Title: Brief announcement: hardness of broadcasting in wireless networks with unreliable communication\nYear: 2009\n\nAbstract: We prove two broadcast lower bounds for a wireless network model that includes unreliable links. For deterministic algorithms, we show n − 1 rounds are required, where n is the number of processes. For randomized algorithms, ε(n − 1) rounds are required for success probability ε. In both cases, the bounds are proved for a network in which constant-time broadcast is possible.","Title: Computing a Maximal Independent Set Using Beeps\nYear: 2011\n\nAbstract: We consider the problem of finding a maximal independent set (MIS) in the discrete beeping model. At each time, a node in the network can either beep (i.e., emit a signal) or be silent. Silent nodes can only differentiate between no neighbor beeping, or at least one neighbor beeping. This basic communication model relies only on carrier-sensing. Furthermore, we assume nothing about the underlying communication graph and allow nodes to wake up (and crash) arbitrarily. We show that if a polynomial upper bound on the size of the network n is known, then with high probability every node becomes stable in O(\\log^3 n) time after it is woken up. To contrast this, we establish a polynomial lower bound when no a priori upper bound on the network size is known. This holds even in the much stronger model of local message broadcast with collision detection. Finally, if we assume nodes have access to synchronized clocks or we consider a somewhat restricted wake up, we can solve the MIS problem in O(\\log^2 n) time without requiring an upper bound on the size of the network, thereby achieving the same bit complexity as Luby's MIS algorithm.","Title: A self-repairing peer-to-peer system resilient to dynamic adversarial churn\nYear: 2005\n\nAbstract: We present a dynamic distributed hash table where peers may join and leave at any time. Our system tolerates a powerful adversary which has complete visibility of the entire state of the system and can continuously add and remove peers. Our system provides worst-case fault-tolerance, maintaining desirable properties such as a low peer degree and a low network diameter.","Title: Fault-Tolerant Clustering in Ad Hoc and Sensor Networks\nYear: 2006\n\nAbstract: In this paper, we study distributed approximation algorithms for fault-tolerant clustering in wireless ad hoc and sensor networks. A k-fold dominating set of a graph G = (V,E) is a subset S of V such that every node v \\in V \\ S has at least k neighbors in S. We study the problem in two network models. In general graphs, for arbitrary parameter t, we propose a distributed algorithm that runs in time O(t^2) and achieves an approximation ratio of O(t\\delta^2/t log\\delta), where n and \\delta denote the number of nodes in the network and the maximal degree, respectively. When the network is modeled as a unit disk graph, we give a probabilistic algorithm that runs in time O(log log n) and achieves an O(1) approximation in expectation. Both algorithms require only small messages of size O(log n) bits.","Title: Dependable peer-to-peer systems withstanding dynamic adversarial churn\nYear: 2006\n\nAbstract: The most essential difference between classical distributed data structures and peer-to-peer systems is the dynamic behavior of the latter. Unlike traditional systems which consist of a fixed set of machines of which a few might occasionally fail in some way, peer-to-peer systems are characterized by continuous joins and leaves at a high rate (called churn). We address this dynamism in two ways. We present a general information aggregation method which can be used to implement deterministic join and leave protocols which keep the network in a well-balanced state. We also use the information aggregation algorithm together with a primitive called token distribution to obtain a general way of constructing efficient peer-to-peer systems which are resilient to dynamic, adversarial joins and leaves. In each time step, an adversary is allowed to insert and delete a bounded number of arbitrary peers. The system adapts to this churn by rearranging peers or adjusting the topology whenever necessary.","Title: Tight bounds for distributed selection\nYear: 2007\n\nAbstract: We revisit the problem of distributed k-selection where, given a general connected graph of diameter D consisting of n nodes in which each node holds a numeric element, the goal is to determine the kth smallest of these elements. In our model, there is no imposed relation between the magnitude of the stored elements and the number of nodes in the graph. We propose a randomized algorithm whose time complexity is O(DlogD n) with high probability. Additionally, a deterministic algorithm with a worst-case time complexity of O(Dlog2D n) is presented which considerably improves the best known bound for deterministic algorithms. Moreover, we prove a lower bound of Ω(D logDn) for any randomized or deterministic algorithm, implying that the randomized algorithm is asymptotically optimal.","Title: Taming Dynamic and Selfish Peers.\nYear: 2006\n\nAbstract: Abstract This paper addresses two important challenges for current P2P systems, namely churn and selfishness. First, we report on a system [19] whose desirable properties (small peer degree, small network diam- eter, etc.) are maintained in spite of ongoing and concurrent membership changes. Thereby, dynamic peers are “tamed” by redundancy. Due to the worst- case churn, this system may never be fully repaired, but always fully functional. However, it relies on the fact that peers act according to the protocol. In the second part of the paper (based on [22]), we study the impact of selfish peers which follow those pro- tocols which maximize their utility. In particular, the efficiency of topologies formed by selfish peers is considered. We show that even in the absence of joins and leaves, the resulting system may never sta- bilize. How to “tame” selfish peers towards a more cooperative behavior remains an open issue. 1 CHURN Many existing file sharing systems are faced with fre-","Title: Local approximation schemes for ad hoc and sensor networks\nYear: 2005\n\nAbstract: We present two local approaches that yield polynomial-time approximation schemes (PTAS) for the Maximum Independent Set and Minimum Dominating Set problem in unit disk graphs. The algorithms run locally in each node and compute a (1+ε)-approximation to the problems at hand for any given ε 0. The time complexity of both algorithms is O(TMIS + log*! n/εO(1)), where TMIS is the time required to compute a maximal independent set in the graph, and n denotes the number of nodes. We then extend these results to a more general class of graphs in which the maximum number of pair-wise independent nodes in every r-neighborhood is at most polynomial in r. Such graphs of polynomially bounded growth are introduced as a more realistic model for wireless networks and they generalize existing models, such as unit disk graphs or coverage area graphs.","Title: Vertex cover in graphs with locally few colors\nYear: 2011\n\nAbstract: In [13], Erdős et al. defined the local chromatic number of a graph as the minimum number of colors that must appear within\n distance 1 of a vertex. For any Δ ≥ 2, there are graphs with arbitrarily large chromatic number that can be colored so that\n (i) no vertex neighborhood contains more than Δ different colors (bounded local colorability), and (ii) adjacent vertices from two color classes induce a complete bipartite graph (biclique coloring).\n \n \n We investigate the weighted vertex cover problem in graphs when a locally bounded coloring is given. This generalizes the\n vertex cover problem in bounded degree graphs to a class of graphs with arbitrarily large chromatic number. Assuming the Unique\n Game Conjecture, we provide a tight characterization. We prove that it is UGC-hard to improve the approximation ratio of 2 − 2/(Δ + 1)\n if the given local coloring is not a biclique coloring. A matching upper bound is also provided. Vice versa, when properties\n (i) and (ii) hold, we present a randomized algorithm with approximation ratio of \n\n\n\n\n\n2-</font\n> W</font\n>(1)\\fraclnlnD</font\n>lnD</font\n>2- \\Omega(1)\\frac{\\ln \\ln \\Delta}{\\ln \\Delta}. This matches known inapproximability results for the special case of bounded degree graphs.\n \n \n \n Moreover, we show that the obtained result finds a natural application in a classical scheduling problem, namely the precedence\n constrained single machine scheduling problem to minimize the total weighted completion time. In a series of recent papers\n it was established that this scheduling problem is a special case of the minimum weighted vertex cover in graphs G\n \n P\n of incomparable pairs defined in the dimension theory of partial orders. We show that G\n \n P\n satisfies properties (i) and (ii) where Δ − 1 is the maximum number of predecessors (or successors) of each job.","Title: Distributed selection: a missing piece of data aggregation\nYear: 2008\n\nAbstract: In this article, we study the problem of distributed selection from a theoretical point of view. Given a general connected graph of diameter D consisting of n nodes in which each node holds a numeric element, the goal of a k-selection algorithm is to determine the kth smallest of these elements. We prove that distributed selection indeed requires more work than other aggregation functions such as, e.g., the computation of the average or the maximum of all elements. On the other hand, we show that the kth smallest element can be computed efficiently by providing both a randomized and a deterministic k-selection algorithm, dispelling the misconception that solving distributed selection through in-network aggregation is infeasible.","Title: Leader election in shared spectrum radio networks\nYear: 2012\n\nAbstract: We study the leader election problem in the context of a congested single-hop radio network. We assume a collection of N synchronous devices with access to a shared band of the radio spectrum, divided into F frequencies. To model unpredictable congestion, we assume an abstract interference adversary that can choose up to t F frequencies in each round to disrupt, preventing communication. The devices are individually activated in arbitrary rounds by an adversary. On activation, a device does not know how many other devices (if any) are also active. The goal of the leader election problem is for each active device to output the id of a leader as soon as possible after activation, while preserving the safety constraint that all devices output the same leader, with high probability. We begin by establishing a lower bound of Ω(log2N ⁄ F-t)log logN + ⁄ Ft F-t ⋅ logN) rounds, through reduction to an existing result in this model [5]. We then set out to prove this bound tight (within log log N factors). For the case where t=0, we present a novel randomized algorithm, based on a strategy of recruiting herald no-des, that works in O(⁄log2NF+log N) time. For 1 ≤ t ≤ F/6, we present a variant of our herald algorithm in which multiple real (potentially disrupted) frequencies are used to simulate each non-disrupted frequency from the t=0 case. This algorithm works in O(⁄log2NF+tlog N) time. Finally, for t F 6 we show how to improve the trapdoor protocol of [5], used to solve a similar problem in a non-optimal manner, to solve leader election in optimal O(⁄ logN + F t⁄F-t ⋅ logN ) time, for (only) these large values of t. We also observe that if F=ω(1) and t ≤(1-ε)F for a constant ε0, our protocols beat the classic Ω(log2N) bound on wake-up in a single frequency radio network, underscoring the observation that more frequencies in a radio network allows for more algorithmic efficiency - even if devices can each only participate on a single frequency at a time, and a significant fraction of these frequencies are disrupted adversarially.","Title: Local Multicoloring Algorithms: Computing a Nearly-Optimal TDMA Schedule in Constant Time\nYear: 2009\n\nAbstract: The described multicoloring problem has direct applications in the context of wireless ad hoc and sensor networks. In order to coordinate the access to the shared wireless medium, the nodes of such a network need to employ some medium access control (MAC) protocol. Typical MAC protocols control the access to the shared channel by time (TDMA), frequency (FDMA), or code division multiple access (CDMA) schemes. Many channel access schemes assign a fixed set of time slots, frequencies, or (orthogonal) codes to the nodes of a network such that nodes that interfere with each other receive disjoint sets of time slots, frequencies, or code sets. Finding a valid assignment of time slots, frequencies, or codes hence directly corresponds to computing a multicoloring of a graph $G$. The scarcity of bandwidth, energy, and computing resources in ad hoc and sensor networks, as well as the often highly dynamic nature of these networks require that the multicoloring can be computed based on as little and as local information as possible.","Title: Ad hoc networks beyond unit disk graphs\nYear: 2008\n\nAbstract: In this paper, we study an algorithmic model for wireless ad hoc and sensor networks that aims to be sufficiently close to reality as to represent practical real-world networks while at the same time being concise enough to promote strong theoretical results. The quasi unit disk graph model contains all edges shorter than a parameter d between 0 and 1 and no edges longer than 1. We show that--in comparison to the cost known for unit disk graphs--the complexity results of geographic routing in this model contain the additional factor 1/d2. We prove that in quasi unit disk graphs flooding is an asymptotically message-optimal routing technique, we provide a geographic routing algorithm being most efficient in dense networks, and we show that classic geographic routing is possible with the same asymptotic performance guarantees as for unit disk graphs if d ≥ 1/√2.","Title: Gradient Clock Synchronization Using Reference Broadcasts\nYear: 2009\n\nAbstract: Reference-Broadcast Synchronization (RBS) is a technique that allows a set of receivers in a broadcast network to accurately estimate each others' clock values. RBS provides a relative time-frame for conversion between the local clocks of different nodes, and can be used to synchronize nodes to an external time-source such as GPS. However, RBS by itself does not output a logical clock at every node, and so it does not solve internal clock synchronization. In this work we study the theoretical properties of RBS in the worst-case model, in which the performance of a clock synchronization algorithm is measured by the worst-case skew it can incur. We suggest a method by which RBS can be incorporated in standard internal clock synchronization algorithms. This is achieved by separating the task of estimating the clock values of other nodes in the network from the task of using these estimates to output a logical clock value. The separation is modelled using a virtual estimate graph , overlaid on top of the real network graph, which represents the information various nodes can obtain about each other. RBS estimates are represented in the estimate graph as edges between nodes at distance 2 from each other in the original network graph. A clock synchronization algorithm then operates on the estimate graph as though it were the original network. To illustrate the merits of this approach, we modify a recent optimal gradient clock synchronization algorithm to work in this setting. The modified algorithm transparently takes advantage of RBS estimates. Its quality of synchronization depends on the diameter of the estimate graph, which is typically much smaller than the diameter of the original network graph.","Title: Gradient Clock Synchronization in Dynamic Networks\nYear: 2011\n\nAbstract: Over the last years, large-scale decentralized computer networks such as peer-to-peer and mobile ad hoc networks have become increasingly prevalent. The topologies of many of these networks are often highly dynamic. This is especially true for ad hoc networks formed by mobile wireless devices. In this paper, we study the fundamental problem of clock synchronization in dynamic networks. We show that there is an inherent trade-off between the skew $\\mathcal{S}$ guaranteed along sufficiently old links and the time needed to guarantee a small skew along new links: for any sufficiently large initial skew on a new link, there are executions in which the time required to reduce the skew on the link to $O(\\mathcal{S})$ is at least $\\Omega(n/\\mathcal{S})$. We show that this bound is tight for moderately small values of $\\mathcal{S}$. Assuming a fixed set of n nodes, an arbitrary pattern of edge insertions and removals, and a weak dynamic connectivity requirement, we present an algorithm that always maintains a skew of O(n) between any two nodes in the network. For a parameter $\\mathcal{S}=\\Omega(\\sqrt{\\rho n})$, where ρ is the maximum hardware clock drift, it is further guaranteed that if a communication link between two nodes u,v persists in the network for $\\Theta(n/\\mathcal{S})$ time, the clock skew between u and v is reduced to no more than $O(\\mathcal{S})$.","Title: Local Computation: Lower and Upper Bounds\nYear: 2010\n\nAbstract: The question of what can be computed, and how efficiently, are at the core of\ncomputer science. Not surprisingly, in distributed systems and networking\nresearch, an equally fundamental question is what can be computed in a\ndistributed fashion. More precisely, if nodes of a network must base their\ndecision on information in their local neighborhood only, how well can they\ncompute or approximate a global (optimization) problem? In this paper we give\nthe first substantial lower bound on such local computation for (optimization)\nproblems including minimum vertex cover, minimum (connected) dominating set,\nmaximum matching, maximal independent set, and maximal matching. In addition we\npresent a new distributed algorithm for solving general covering and packing\nlinear programs. For some problems this algorithm is tight with the lower\nbounds, for others it is a distributed approximation scheme. Together, our\nlower and upper bounds establish the local computability and approximability of\na large class of problems, characterizing how much local information is\nrequired to solve these tasks.","Title: Fast deterministic distributed maximal independent set computation on growth-bounded graphs\nYear: 2005\n\nAbstract: The distributed complexity of computing a maximal independent set in a graph is of both practical and theoretical importance. While there exists an elegant O(log n) time randomized algorithm for general graphs [20], no deterministic polylogarithmic algorithm is known. In this paper, we study the problem in graphs with bounded growth, an important family of graphs which includes the well-known unit disk graph and many variants thereof. Particularly, we propose a deterministic algorithm that computes a maximal independent set in time O(log Δ· log*n) in graphs with bounded growth, where n and Δ denote the number of nodes and the maximal degree in G, respectively.","Title: Decomposing broadcast algorithms using abstract MAC layers\nYear: 2010\n\nAbstract: In much of the theoretical literature on wireless algorithms, issues of message dissemination are considered together with issues of contention management. This combination leads to complicated algorithms and analysis, and makes it difficult to extend the work to harder communication problems. In this paper, we present results of a current project aimed at simplifying such algorithms and analysis by decomposing the treatment into two levels, using abstract \"MAC layer\" specifications to encapsulate the contention management. We use two different abstract MAC layers: the basic one of [14, 15] and a new probabilistic layer. We first present a typical randomized contention-manageent algorithm for a standard graph-based radio network model We show that it implements both abstract MAC layers. We combine this algorithm with greedy algorithms for single-message and multi-message global broadcast and analyze the combination, using both abstract MAC layers as intermediate layers. Using the basic MAC layer, we prove a bound of O(D log(n/∈) log Δ) for the time to deliver a single message everywhere with probability 1 -- ∈, where D is the network diameter, n is the number of nodes, and Δ is the maximum node degree. Using the probabilistic layer, we prove a bound of O((D + log(n/∈)) log Δ), which matches the best previously-known bound for single-message broadcast over the physical network model. For multi-message broadcast, we obtain bounds of O((D + kΔ) log(n/∈) log Δ) using the basic layer and O((D + kΔ log(n/∈)) log Δ) using the probabilistic layer, for the time to deliver a message everywhere in the presence of at most k concurrent messages.","Title: Decomposing broadcast algorithms using abstract MAC layers\nYear: 2014\n\nAbstract: In much of the theoretical literature on global broadcast algorithms for wireless networks, issues of message dissemination are considered together with issues of contention management. This combination leads to complicated algorithms and analysis, and makes it difficult to extend the work to more difficult communication problems. In this paper, we present results aimed at simplifying such algorithms and analysis by decomposing the treatment into two levels, using abstract ''MAC layer'' specifications to encapsulate contention management. We use two different abstract MAC layers: the basic layer of [1,2] and a new probabilistic layer. We first present a typical randomized contention-management algorithm for a standard graph-based radio network model and show that it implements both abstract MAC layers. Then we combine this algorithm with greedy algorithms for single-message and multi-message global broadcast and analyze the combinations, using both abstract MAC layers as intermediate layers. Using the basic MAC layer, we prove a bound of ODlogn@?log(@D) for the time to deliver a single message everywhere with probability 1-@?, where D is the network diameter, n is the number of nodes, and @D is the maximum node degree. Using the probabilistic layer, we prove a bound of OD+logn@?log(@D), which matches the best previously-known bound for single-message broadcast over the physical network model. For multi-message broadcast, we obtain bounds of O(D+k@D)logn@?log(@D) using the basic layer and OD+k@Dlogn@?log(@D) using the probabilistic layer, for the time to deliver a message everywhere in the presence of at most k concurrent messages.","Title: Serving Online Demands with Movable Centers.\nYear: 2014\n\nAbstract: We study an online problem in which a set of mobile resources or centers have to be moved in order to optimally serve a set of demands. There is a set of $n$ nodes and a set of $k$ resources that are placed at some of the nodes. Each node can potentially host several resources and the resources can be moved between the nodes. At the nodes, demands arrive in an online fashion, and the cost for serving the demands is a function of the number of demands and resources at the different nodes. An online algorithm has to move resources in order to keep the service cost low, however as moving resources is expensive, the objective of an online algorithm is to minimize the total number of movements. Specifically, we give a deterministic online algorithm, which for parameters $\\alpha\\geq 1$ and $\\beta\\geq 0$ guarantees that at all times, the service cost is within a multiplicative factor $\\alpha$ and an additive term $\\beta$ of the optimal service cost and where also the movement cost is within a small multiplicative factor and an additive term of the optimal overall cost. Roughly, for $\\alpha=1$ and $\\beta=\\Omega(k)$, we show that the movement cost by time $t$ is upper bounded by the optimal service cost at time $t$ plus an additive term of order $O(k\\log k)$. For $\\alpha>1$ and arbitrary $\\beta$, we show that the movement cost by time $t$ is only linear in $k\\log k$ and logarithmic in the optimal service cost at time $t$. We also show that both bounds are almost asymptotically tight.","Title: Broadcast In The Ad Hoc Sinr Model\nYear: 2013\n\nAbstract: An increasing amount of attention is being turned toward the study of distributed algorithms in wireless network models based on calculations of the signal to noise and interference ratio (SINR). In this paper we introduce the ad hoc SINR model, which, we argue, reduces the gap between theory results and real world deployment. We then use it to study upper and lower bounds for the canonical problem of broadcast on the graph induced by both strong and weak links. For strong connectivity broadcast, we present a new randomized algorithm that solves the problem in O(Dlog (n) polylog(R)) rounds in networks of size n, with link graph diameter D, and a ratio between longest and shortest links bounded by R. We then show that for back-off style algorithms (a common type of algorithm where nodes do not explicitly coordinate with each other) and compact networks (a practice-motivated model variant that treats the distance from very close nodes as equivalent), there exist networks in which centralized algorithms can solve broadcast in O(1) rounds, but distributed solutions require Omega(n) rounds. We then turn our attention to weak connectivity broadcast, where we show a similar Omega(n) lower bound for all types of algorithms, which we (nearly) match with a back-off style O(n log(2) n)-round upper bound. Our broadcast algorithms are the first known for SINR-style models that do not assume synchronous starts, as well as the first known not to depend on power control, tunable carrier sensing, geographic information and/or exact knowledge of network parameters.","Title: Asymptotically optimal geometric mobile ad-hoc routing\nYear: 2002\n\nAbstract: In this paper we present AFR, a new geometric mobile ad-hoc routing algorithm. The algorithm is completely distributed; nodes need to communicate only with direct neighbors in their transmission range. We show that if a best route has cost c, AFR finds a route and terminates with cost &Ogr;(c2) in the worst case. AFR is the first algorithm with cost bounded by a function of the optimal route. We also give a tight lower bound by showing that any geometric routing algorithm has worst-case cost $Ogr;(c2). Thus AFR is asymptotically optimal. We give a non-geometric algorithm that also matches the lower bound, but needs some memory at each node. This establishes an intriguing trade-off between geometry and memory.","Title: Internal DLA: Efficient Simulation of a Physical Growth Model - (Extended Abstract).\nYear: 2014\n\nAbstract: The internal diffusion limited aggregation (IDLA) process places n particles on the two dimensional integer grid. The first particle is placed on the origin; every subsequent particle starts at the origin and performs an unbiased random walk until it reaches an unoccupied position. In this work we study the computational complexity of determining the subset that is generated after n particles have been placed. We develop the first algorithm that provably outperforms the naive step-bystep simulation of all particles. Particularly, our algorithm has a running time of O(n log(2) n) and a sublinear space requirement of O(n(1/2) log n), both in expectation and with high probability. In contrast to some speedups proposed for similar models in the physics community, our algorithm samples from the exact distribution. To simulate a single particle fast we have to develop techniques for combining multiple steps of a random walk to large jumps without hitting a forbidden set of grid points. These techniques might be of independent interest for speeding up other problems based on random walks.","Title: On the power of the congested clique model\nYear: 2014\n\nAbstract: We study the computation power of the congested clique, a model of distributed computation where n players communicate with each other over a complete network in order to compute some function of their inputs. The number of bits that can be sent on any edge in a round is bounded by a parameter b We consider two versions of the model: in the first, the players communicate by unicast, allowing them to send a different message on each of their links in one round; in the second, the players communicate by broadcast, sending one message to all their neighbors. It is known that the unicast version of the model is quite powerful; to date, no lower bounds for this model are known. In this paper we provide a partial explanation by showing that the unicast congested clique can simulate powerful classes of bounded-depth circuits, implying that even slightly super-constant lower bounds for the congested clique would give new lower bounds in circuit complexity. Moreover, under a widely-believed conjecture on matrix multiplication, the triangle detection problem, studied in [8], can be solved in O(nε) time for any ε > 0. The broadcast version of the congested clique is the well-known multi-party shared-blackboard model of communication complexity (with number-in-hand input). This version is more amenable to lower bounds, and in this paper we show that the subgraph detection problem studied in [8] requires polynomially many rounds for several classes of subgraphs. We also give upper bounds for the subgraph detection problem, and relate the hardness of triangle detection in the broadcast congested clique to the communication complexity of set disjointness in the 3-party number-on-forehead model.","Title: A distributed perspective on graph connectivity and cuts\nYear: 2014\n\nAbstract: Edge and vertex connectivity, as well as edge and vertex cuts are among the most basic and fundamental concepts in graph theory. In particular, they are naturally significant in a networking context as they are a measure for the rate at which information can be transferred across a network. While in a traditional, sequential setting, there is a rich literature (in particular on problems related to edge connectivity and edge cuts), until recently, much less was known from a distributed algorithms point of view. In my talk, I will discuss and give partial answers to some of the following basic questions. Using distributed algorithms, how fast can we compute or approximate the edge or vertex connectivity and is it possible to efficiently find small cuts in a network? Assuming, we have a network with good connectivity properties, to what extent is it possible to exploit this in order to speed up distributed computations? Where such properties can be exploited, are there network structures that allow to make use of good connectivity in a structured (and somewhat canonical) way and can we construct such structures in a distributed manner?","Title: Tight bounds on vertex connectivity under vertex sampling\nYear: 2015\n\nAbstract: A fundamental result by Karger [10] states that for any λ-edge-connected graph with n nodes, independently sampling each edge with probability p = Ω(log n/λ) results in a graph that has edge connectivity Ω(λp), with high probability. This paper proves the analogous result for vertex connectivity, when sampling vertices. We show that for any k-vertex-connected graph G with n nodes, if each node is independently sampled with probability p = Ω([EQUATION]log n/k), then the subgraph induced by the sampled nodes has vertex connectivity Ω(kp2), with high probability. This bound improves upon the recent results of Censor-Hillel et al. [6], and is existentially optimal.","Title: Understanding Radio Irregularity in Wireless Networks\nYear: 2008\n\nAbstract: In an effort to better understand connectivity and capacity in wireless networks, the log-normal shadowing radio propagation model is used to capture radio irregularities and ob- stacles in the transmission path. Existing results indicat e that log- normal shadowing results in higher connectivity and interference levels as shadowing (i.e., the radio irregularity) increas es. In this paper we demonstrate that such a behavior is mainly caused by an unnatural bias of the log-normal shadowing radio propagation model that results in a larger transmission range as shadowing increases. To avoid this effect, we analyze connectivity and interference under log-normal shadowing using a normalization that compensates for the enlarged radio transmission range. Our analysis shows that log-normal shadowing still improves the connectivity of a wireless network and even reduces interference. We explain this behavior by studying in detail what network parameters are affected by shadowing. Our results indicate that, when it comes to connectivity and interference, an analysis based on a circular transmission range leads to worst case results. I. I NTRODUCTION Understanding connectivity and radio interference in wire- less networks is an important step to determine their overall throughput capacity. In an effort to overcome the limitatio ns of the deterministic path loss model (where the transmission range is a perfect circle), connectivity ((4), (1), (7), (6) , (12)) and capacity ((10), (11)) have recently been studied in the context of the log-normal shadowing radio propagation model (9), (14). In this model, the radio irregularity can be contr olled through a single parameter: the shadowing deviation. As the shadowing deviation grows, the transmission range turns into a more irregular shape, mirroring what happens in reality with antennas that are not ideal (not perfectly isotropic) a nd obstacles that cut the transmission range short in a given direction. In this paper, we use the log-normal shadowing radio prop- agation model to explore the impact of radio irregularity on connectivity and interference in a wireless network. We show that the log-normal shadowing model introduces an unnatural bias into the analysis: as the shadowing deviation grows, the radio transmission range not only becomes more irregular, but also enlarges. This naturally leads to an improved connectivity. At the same time, the enlarged transmission range leads to an increase in interference. These are results already mentio ned The work presented in this paper was supported (in part) by the National Competence Center in Research on Mobile Information and Communication Systems (NCCR-MICS), a center supported by the Swiss National Science Foundation under grant number 5005-67322.","Title: Near-Optimal Distributed Maximum Flow: Extended Abstract\nYear: 2015\n\nAbstract: We present a near-optimal distributed algorithm for (1+o(1))-approximation of single-commodity maximum flow in undirected weighted networks that runs in (D+ √n)⋅ no(1) communication rounds in the Congest model. Here, n and D denote the number of nodes and the network diameter, respectively. This is the first improvement over the trivial O(m) time bound, and it nearly matches the Ω(D+√n) round complexity lower bound. The algorithm contains two sub-algorithms of independent interest, both with running time (D+√n)⋅ no(1): Distributed construction of a spanning tree of average stretch no(1). Distributed construction of an no(1)-congestion approximator consisting of the cuts induced by O(log n) virtual trees. The distributed representation of the cut approximator allows for evaluation in (D+√n)⋅ no(1) rounds. All our algorithms make use of randomization and succeed with high probability.","Title: Efficient Communication in Cognitive Radio Networks\nYear: 2015\n\nAbstract: Devices in a cognitive radio network use advanced radios to identify pockets of usable spectrum in a crowded band and make them available to higher layers of the network stack. A core challenge in designing algorithms for this model is that different devices might have different views of the network. In this paper, we study two problems for this setting that are well-motivated but not yet well-understood: local broadcast and data aggregation. We consider a single hop cognitive radio network with n nodes that each has access to c channels. We assume each pair of nodes overlaps on at least 1 We first describe and analyze CogCast, a randomized algorithm that solves local broadcast in O( (c/k)*max{1,c/n}*lg(n) ) time, with high probability, by spreading information in an epidemic manner through the network. We then propose CogComp, a randomized algorithm that solves data aggregation in O( (c/k)*max{1,c/n}*lg(n) + n ) time, with high probability. The CogComp algorithm uses CogCast as a key primitive to establish a spanning tree among the nodes, so that data can be aggregated from leaves to root. We conclude with a collection of lower bounds that show CogCast is near optimal (in particular, within a lg(n) factor) in many cases. These bounds introduce new techniques of potential standalone interest for those concerned with proving fundamental limits in the cognitive radio network setting.","Title: Serving Online Requests with Mobile Servers.\nYear: 2015\n\nAbstract: We study an online problem in which mobile servers have to be moved in order to efficiently serve at set of online requests. More formally, there is a set of n nodes and a set of k mobile servers that are placed at some of the nodes. Each node can potentially host several servers and the servers can be moved between the nodes. There are requests 1, 2,... that are adversarially issued at nodes one at a time, where a request issued at time t needs to be served at all times t' >= t. The cost for serving the requests is a function of the number of servers and requests at the different nodes. The requirements on how to serve the requests are governed by two parameters alpha >= 1 and beta >= 0. An algorithm needs to guarantee that at all times, the total service cost remains within a multiplicative factor a and an additive term beta of the current optimal service cost. We consider online algorithms for two different minimization objectives. We first consider the natural problem of minimizing the total number of server movements. We show that in this case for every k, the competitive ratio of every deterministic online algorithm needs to be at least Omega(n). Given this negative result, we then extend the minimization objective to also include the current service cost. We give almost tight bounds on the competitive ratio of the online problem where one needs to minimize the sum of the total number of movements and the current service cost. In particular, we show that at the cost of an additional additive term which is roughly linear in k, it is possible to achieve a multiplicative competitive ratio of 1 + epsilon for every constant epsilon > 0.","Title: Tight Bounds for MIS in Multichannel Radio Networks\nYear: 2015\n\nAbstract: In [8] an algorithm has been presented that computes a maximal independent set MIS within $$O\\\\log ^2 n/\\\\mathcal {F}+\\\\log n {{\\\\mathrm{\\\\mathrm {polyloglog~}}}} n$$ rounds in an n-node multichannel radio network with $$\\\\mathcal {F}$$ communication channels. The paper uses a multichannel variant of the standard graph-based radio network model without collision detection and it assumes that the network graph is a polynomially bounded independence graph BIG, a natural combinatorial generalization of well-known geographic families. The upper bound of [8] is known to be optimal up to the $${{\\\\mathrm{\\\\mathrm {polyloglog~}}}} n$$ factor. In this paper, we adapt this algorithm and its analysis to improve the result of [8] in two ways. Mainly, we get rid of the $${{\\\\mathrm{\\\\mathrm {polyloglog~}}}} n$$ factor in the running time and we thus obtain an asymptotically optimal multichannel radio network MIS algorithm. In addition, our new analysis allows to generalize the class of graphs from those with polynomially bounded local independence to graphs where the local independence is bounded by an arbitrary function of the neighborhood radius.","Title: Brief Announcement: Local Independent Set Approximation.\nYear: 2016\n\nAbstract: We show that the first phase of the Linial-Saks network decomposition algorithm gives a randomized distributed O(nε)-approximation algorithm for the maximum independent set problem that operates in O(1/ε) rounds, and we give a matching lower bound that holds even for bipartite graphs.","Title: Multi-Message Broadcast in Dynamic Radio Networks.\nYear: 2016\n\nAbstract: We continue the recent line of research studying information dissemination problems in adversarial dynamic radio networks. We give two generic algorithms which allow to transform generalized version of single-message broadcast algorithms into multi-message broadcast algorithms. Based on these generic algorithms, we obtain multi-message broadcast algorithms for dynamic radio networks for a number of different dynamic network settings. For one of the modeling assumptions, our algorithms are complemented by a lower bound which shows that the upper bound is close to optimal.","Title: Distributed Sparse Cut Approximation.\nYear: 2015\n\nAbstract: We study the problem of computing a sparse cut in an undirected network graph G=(V,E). We measure the sparsity of a cut (S,VS) by its conductance phi(S), i.e., by the ratio of the number of edges crossing the cut and the sum of the degrees on the smaller of the two sides. We present an efficient distributed algorithm to compute a cut of low conductance. Specifically, given two parameters b and phi, if there exists a cut of balance at least b and conductance at most phi, our algorithm outputs a cut of balance at least b/2 and conductance at most ~O(sqrt{phi}), where ~O(.) hides polylogarithmic factors in the number of nodes n. Our distributed algorithm works in the congest model, i.e., it only requires to send messages of size at most O(log(n)) bits. The time complexity of the algorithm is ~O(D + 1/b*phi), where D is the diameter of G. This is a significant improvement over a result by Das Sarma et al. [ICDCN 2015], where it is shown that a cut of the same quality can be computed in time ~O(n + 1/b*phi). The improved running time is in particular achieved by devising and applying an efficient distributed algorithm for the all-prefix-sums problem in a distributed search tree. This algorithm, which is based on the classic parallel all-prefix-sums algorithm, might be of independent interest.","Title: Polynomial Lower Bound for Distributed Graph Coloring in a Weak LOCAL Model.\nYear: 2016\n\nAbstract: We show an (varOmega big (varDelta ^{frac{1}{3}-frac{eta }{3}}big )) lower bound on the runtime of any deterministic distributed (mathcal {O}big (varDelta ^{1+eta }big ))-graph coloring algorithm in a weak variant of the (mathsf {LOCAL}) model.","Title: Deterministic Distributed Edge-Coloring via Hypergraph Maximal Matching\nYear: 2017\n\nAbstract: We present a deterministic distributed algorithm that computes a (2Δ-1)-edge-coloring, or even list-edge-coloring, in any n-node graph with maximum degree Δ, in O(log\n<sup xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">8</sup>\n Δ·log n) rounds. This answers one of the long-standing open questions of distributed graph algorithms} from the late 1980s, which asked for a polylogarithmic-time algorithm. See, e.g., Open Problem 4 in the Distributed Graph Coloring book of Barenboim and Elkin. The previous best round complexities were 2\n<sup xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">O(√(log n)</sup>\n by Panconesi and Srinivasan [STOC'92] and Õ(√(Δ)) + O(log* n) by Fraigniaud, Heinrich, and Kosowski [FOCS'16]. A corollary of our deterministic list-edge-coloring also improves the randomized complexity of (2Δ-1)-edge-coloring to poly(log log n) rounds. The key technical ingredient is a deterministic distributed algorithm for hypergraph maximal matching, which we believe will be of interest beyond this result. In any hypergraph of rank r - where each hyperedge has at most r vertices - with n nodes and maximum degree Δ, this algorithm computes a maximal matching in O(r\n<sup xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">5</sup>\n log\n<sup xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">6+log r</sup>\n Δ·log n) rounds. This hypergraph matching algorithm and its extensions also lead to a number of other results. In particular, we obtain a polylogarithmic-time deterministic distributed maximal independent set (MIS) algorithm for graphs with bounded neighborhood independence, hence answering Open Problem 5 of Barenboim and Elkins book, a ((log Δ/ε)\n<sup xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">O(log 1/ε)</sup>\n)-round deterministic algorithm for (1+ε)-approximation of maximum matching, and a quasi-polylogarithmic-time deterministic distributed algorithm for orienting λ-arboricity graphs with out-degree at most ⌈(1+ε)λ⌉, for any constant ε>0, hence partially answering Open Problem 10 of Barenboim and Elkin's book.","Title: Dynamic Analysis of the Arrow Distributed Directory Protocol in General Networks.\nYear: 2017\n\nAbstract: The Arrow protocol is a simple and elegant protocol to coordinate exclusive access to a shared object in a network. The protocol solves the underlying distributed queueing problem by using path reversal on a pre-computed spanning tree (or any other tree topology simulated on top of the given network).It is known that the Arrow protocol solves the problem with a competitive ratio of O(log D) on trees of diameter D. This implies a distributed queueing algorithm with competitive ratio O(s log D) for general networks with a spanning tree of diameter D and stretch s. In this work we show that when running the Arrow protocol on top of the well-known probabilistic tree embedding of Fakcharoenphol, Rao, and Talwar [STOCu002703], we obtain a randomized distributed online queueing algorithm with expected competitive ratio O(log n) against an oblivious adversary even on general n-node network topologies. The result holds even if the queueing requests occur in an arbitrarily dynamic and concurrent fashion and even if communication is asynchronous. The main technical result of the paper shows that the competitive ratio of the Arrow protocol is constant on a special family of tree topologies, known as hierarchically well separated trees.","Title: Tight Bounds on Vertex Connectivity Under Sampling.\nYear: 2017\n\nAbstract: A fundamental result by Karger [10] states that for any λ-edge-connected graph with n nodes, independently sampling each edge with probability p = Ω(log (n)/λ) results in a graph that has edge connectivity Ω(λp), with high probability. This article proves the analogous result for vertex connectivity, when either vertices or edges are sampled. We show that for any k-vertex-connected graph G with n nodes, if each node is independently sampled with probability p=Ω(&sqrt;log(n)/k), then the subgraph induced by the sampled nodes has vertex connectivity Ω(kp2), with high probability. If edges are sampled with probability p = Ω(log (n)/k), then the sampled subgraph has vertex connectivity Ω(kp), with high probability. Both bounds are existentially optimal.","Title: ICALP 2017 - Call for Papers.\nYear: 2016\n\nAbstract: The 44th International Colloquium on Automata, Languages, and Programming (ICALP) will take place in Warsaw, Poland, on 10-14 July 2017. ICALP is the main conference and annual meeting of the European Association for Theoretical Computer Science (EATCS). As usual, ICALP will be preceded by a series of workshops, which will take place on July 10.","Title: Distributed MST and Routing in Almost Mixing Time.\nYear: 2017\n\nAbstract: We present a randomized distributed algorithm that computes a minimum spanning tree in τ(G) · 2O(√(log n log log n))) rounds, in any n-node graph G with mixing time τ(G). This result provides a sub-polynomial complexity for a wide range of graphs of practical interest, and goes below the celebrated Ω(D+ √n) lower bound of Das Sarma et al. [STOC'11] which holds for some worst-case general graphs. The core novelty in this result is a distributed method for permutation routing. In this problem, one is given a number of source-destination pairs, and we should deliver one packet from each source to its destination, all in parallel, in the shortest span of time possible. Our algorithm allows us to route and deliver all these packets in τ(G) · 2O(√(log n log log n)) rounds, assuming that each node v is the source or destination for at most dG(v) packets. The main technical ingredient in this routing result is a certain hierarchical embedding of good-expansion random graphs on the base graph, which we believe can be of interest well beyond this work.","Title: Nearest Common Ancestors: Universal Trees and Improved Labeling Schemes.\nYear: 2017\n\nAbstract: We investigate the nearest common ancestor (NCA) function in rooted trees. the main conceptual contribution, the paper introduces universal trees for the NCA function: For a given family of rooted trees, an NCA-universal tree $S$ is a rooted tree such that any tree $T$ of the family can be embedded into $S$ such that the embedding of the NCA in $T$ of two nodes of $T$ is equal to the NCA in $S$ of the embeddings of the two nodes. As the main technical result we give explicit constructions of NCA-universal trees of size $n^{2.318}$ for the family of rooted $n$-vertex trees and of size $n^{1.894}$ for the family of rooted binary $n$-vertex trees. A direct consequence is the explicit construction of NCA-labeling schemes with labels of size $2.318log_2 n$ and $1.894log_2 n$ for the two families of rooted trees. This improves on the best known such labeling schemes established by Alstrup, Halvorsen and Larsen [SODA 2014].","Title: On Derandomizing Local Distributed Algorithms\nYear: 2018\n\nAbstract: The gap between the known randomized and deterministic local distributed algorithms underlies arguably the most fundamental and central open question in distributed graph algorithms. In this paper, we combine the method of conditional expectation with network decompositions to obtain a generic and clean recipe for derandomizing LOCAL algorithms. This leads to significant improvements on a number of problems, in cases resolving known open problems. Two main results are: - An improved deterministic distributed algorithm for hypergraph maximal matching, improving on Fischer, Ghaffari, and Kuhn [FOCS '17]. This yields improved algorithms for edge-coloring, maximum matching approximation, and low out-degree edge orientation. The last result gives the first positive resolution in the Open Problem 11.10 in the book of Barenboim and Elkin. - Improved randomized and deterministic distributed algorithms for the Lovász Local Lemma, which get closer to a conjecture of Chang and Pettie [FOCS '17].","Title: Labeling Schemes for Nearest Common Ancestors through Minor-Universal Trees.\nYear: 2018\n\nAbstract: Preprocessing a tree for finding the nearest common ancestor of two nodes is a basic tool with multiple applications. Quite a few linear-space constant-time solutions are known and the problem seems to be well-understood. This is however not so clear if we want to design a labeling scheme. In this model, the structure should be distributed: every node receives a distinct binary string, called its label, so that given the labels of two nodes (and no further information about the topology of the tree) we can compute the label of their nearest common ancestor. The goal is to make the labels as short as possible. Alstrup, Gavoille, Kaplan, and Rauhe [Theor. Comput. Syst. 37(3):441--456 2004] showed that O(log n)-bit labels are enough, with a somewhat large constant. More recently, Alstrup, Halvorsen, and Larsen [SODA 2014] refined this to only 2.772 log n, and provided a lower bound of 1.008 log n.\n\nWe connect the question of designing a labeling scheme for nearest common ancestors to the existence of a tree, called a minor-universal tree, that contains every tree on n nodes as a topological minor. Even though it is not clear if a labeling scheme must be based on such a notion, we argue that all already existing schemes can be reformulated as such. Further, we show that this notion allows us to easily obtain clean and good bounds on the length of the labels. As the main upper bound, we show that 2.318 log n-bit labels are enough. Surprisingly, the notion of a minor-universal tree for binary trees on n nodes has been already used in a different context by Hrubes et al. [CCC 2010], and Young, Chu, and Wong [J. ACM 46(3):416--435, 1999] introduced a very closely related (but not equivalent) notion of a universal tree. On the lower bound side, we show that any minor-universal tree for trees on n nodes must contain at least Ω(n2.174) nodes. This highlights a natural limitation for all approaches based on defining a minor-universal tree. We complement the existential results with a generic transformation that allows us, for any labeling scheme for nearest common ancestors based on a minor-universal tree, to decrease the query time to constant, while increasing the length of the labels only by lower order terms.","Title: Local Distributed Algorithms in Highly Dynamic Networks\nYear: 2018\n\nAbstract: We define a generalization of local distributed graph problems to (synchronous round-based) dynamic networks and present a framework for developing algorithms for these problems. The algorithms should satisfy non-trivial guarantees in every round. The guarantees should be stronger the more stable the graph has been during the last few rounds and coincide with the definition of the static graph problem if no topological change appeared recently. Moreover, if only a constant neighborhood around some part of the graph is stable during an interval, the algorithms should quickly converge to a solution for this part of the graph that remains unchanged throughout the interval. We demonstrate our generic framework with two classic distributed graph problems, namely (degree+1)-vertex coloring and maximal independent set (MIS). To illustrate the given guarantees consider the vertex coloring problem: Any conflict between two nodes caused by a newly inserted edge is resolved within T = O(logn) rounds. During this conflict resolving both nodes always output colors that are not in conflict with their respective `old` neighbors. The largest color that a node is allowed to output is determined by the number of distinct neighbors that it has seen in the last T rounds.","Title: Improved Distributed Delta-Coloring\nYear: 2021\n\nAbstract: We present a randomized distributed algorithm that computes a Delta-coloring in any non-complete graph with maximum degree Delta >= 4 in O(log Delta)+ 2(O)(root(log log n)) rounds, as well as a randomized algorithm that computes a Delta-coloring in O((log log n)(2)) rounds when Delta is an element of [3, O(1)]. Both these algorithms improve on an O(log(3) n/log Delta)-round algorithm of Panconesi and Srinivasan (STOC'93), which has remained the state of the art for the past 25 years. Moreover, the latter algorithm gets (exponentially) closer to an Omega (log log n) round lower bound of Brandt et al. (STOC'16).","Title: Distributed Computation in the Node-Congested Clique.\nYear: 2018\n\nAbstract: The Congested Clique model of distributed computing, which was introduced by Lotker, Patt-Shamir, Pavlov, and Peleg [SPAAu002703, SICOMPu002705] and was motivated as simple model for overlay networks, has received extensive attention over the past few years. In this model, nodes of the system are connected as a clique and can communicate in synchronous rounds, where per round each node can send $O(log n)$ bits to each other node, all the same time. The fact that this model allows each node to send and receive a linear number of messages at the same time seems to limit the relevance of the model for overlay networks. Towards addressing this issue, in this paper, we introduce the Node-Congested Clique as a general communication network model. Similarly to the Congested Clique model, the nodes are connected as a clique and messages are sent in synchronous communication rounds. However, here, per round, every node can send and receive only $O(log n)$ many messages of size $O(log n)$. To initiate research on our network model, we present distributed algorithms for the Minimum Spanning Tree, BFS Tree, Maximal Independent Set, Maximal Matching, and Coloring problem for an input graph $G=(V,E)$, where each clique node initially only knows a single node of $G$ and its incident edges. For the Minimum Spanning Tree problem, our runtime is polylogarithmic. In all other cases the runtime of our algorithms mainly depends on the arboricity $a$ of $G$, which is a constant for many important graph families such as planar graphs. At the core of these algorithms is a distributed algorithm that assigns directions to the edges of $G$ so that at the end, every node is incident to at most $O(a)$ outgoing edges.","Title: Efficient Distributed Computation of MIS and Generalized MIS in Linear Hypergraphs.\nYear: 2018\n\nAbstract: Given a graph, a maximal independent set (MIS) is a maximal subset of pairwise non-adjacent vertices. Finding an MIS is a fundamental problem distributed computing. Although the problem is extensively studied and well understood simple graphs, our knowledge is still quite limited when solving it hypergraphs, especially the distributed CONGEST model. In this paper, we focus on linear hypergraphs---a class of hypergraphs which any two hyperedges overlap on at most one node. first present a randomized algorithm for computing an MIS linear hypergraphs. It has poly-logarithmic runtime and it works the CONGEST model. The algorithm uses a network decomposition to achieve fast parallel processing. Within each cluster of the decomposition, we run a distributed variant of a parallel hypergraph MIS algorithm by Luczak and Szymanska. then propose the concept of a generalized maximal independent set (GMIS) as an extension to the classical MIS hypergraphs. More specifically, a GMIS, for each hyperedge $e$ a hypergraph $mathcal{H}$, we associate an integer threshold $t_e$ the range $[1, |e|-1]$, and the goal is to find a maximal subset $mathcal{I}$ of vertices that do not violate any threshold constraints: $forall e in E(mathcal{H}), |e cap mathcal{I}| leq t_e$. We hope that GMIS might capture a broader class of real-world problems than MIS; we also believe that GMIS is an interesting and challenging symmetry breaking problem on its own. Our second upper bound result is a distributed algorithm for computing a GMIS linear hypergraphs, subject to the constraint that the maximum hyperedge size is bounded by some constant. Again, the algorithm has poly-logarithmic runtime and it works the CONGEST model. It is obtained by generalizing our previous (linear) hypergraph MIS algorithm.","Title: Deterministic Distributed Ruling Sets of Line Graphs.\nYear: 2018\n\nAbstract: An ((alpha ,beta ))-ruling set of a graph (G=(V,E)) is a set (Rsubseteq V) such that for any node (vin V) there is a node (uin R) in distance at most (beta ) from v and such that any two nodes in R are at distance at least (alpha ) from each other. The concept of ruling sets can naturally be extended to edges, i.e., a subset (Fsubseteq E) is an ((alpha ,beta ))-ruling edge set of a graph (G=(V,E)) if the corresponding nodes form an ((alpha ,beta ))-ruling set in the line graph of G. This paper presents a simple deterministic, distributed algorithm, in the (mathsf {CONGEST}) model, for computing (2, 2)-ruling edge sets in (O(log ^{*}n)) rounds. Furthermore, we extend the algorithm to compute ruling sets of graphs with bounded diversity. Roughly speaking, the diversity of a graph is the maximum number of maximal cliques a vertex belongs to. We devise ((2,O(mathcal {D})))-ruling sets on graphs with diversity (mathcal {D}) in (O(mathcal {D}+log ^{*}n)) rounds. This also implies a fast, deterministic ((2,O( ell )))-ruling edge set algorithm for hypergraphs with rank at most ( ell ).","Title: Possibilities and Impossibilities for Distributed Subgraph Detection.\nYear: 2018\n\nAbstract: In the distributed subgraph detection problem, we are given a fixed subgraph H , and the network must decide whether the network graph contains a copy of H or not. Subgraph detection can be solved in a constant number of rounds if message size is unbounded, but in the CONGEST model, where each message has bounded size, it can have high round complexity. Distributed subgraph detection has received significant attention recently, with new upper and lower bounds, but several fundamental questions remain open. In this paper we prove new possibility and impossibility results for subgraph detection in the CONGEST model. We show for the first time that some subgraphs require superlinear --- in fact, nearly quadratic --- running time, even in small-diameter networks. We also study cycle-detection, and show that any even cycle can be detected in sublinear time (in contrast to odd cycles, which require linear time). For the special case of triangle-detection, we show that deterministic algorithms require $Ømega(łog n)$ total communication even in graphs of degree 2, and that one-round randomized algorithms must send $Ømega(Δ)$ bits in graphs of degree Δ, improving on the recent results of [Abboud et. al.]. Finally, we extend a recent lower bound of [Izumi, Le Gall] on listing all triangles to cliques of any size.","Title: The Communication Cost of Information Spreading in Dynamic Networks\nYear: 2018\n\nAbstract: This paper investigates the message complexity of distributed information spreading in adversarial dynamic networks. While distributed computations in dynamic networks have been studied intensively over the last years, almost all of the existing work solely focuses on the time complexity of distributed algorithms. In information spreading, the goal is to spread k tokens of information to every node on an n-node network. We consider the amortized (average) message complexity of spreading a token, assuming that the number of tokens is large. In a static network, this basic problem can be solved using (asymptotically optimal) O(n) amortized messages per token. Our focus is on token-forwarding algorithms, which do not manipulate tokens in any way other than storing, copying, and forwarding them. We present two sets of results depending on how nodes send messages to their neighbors: 1. Local broadcast: We show a tight lower bound of Ω̃(n\n<sup xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>\n) on the number of amortized local broadcasts, which is matched by the naive flooding algorithm. The lower bound holds for randomized algorithms against a strongly adaptive adversary. 2. Unicast: We study the message complexity as a function of the number of dynamic changes in the network. To facilitate this, we introduce adversary-competitive message complexity as a natural complexity measure for analyzing dynamic networks: The adversary pays a unit cost for every topological change and the message cost of an algorithm is determined as the actual number of messages sent minus the total cost of the adversary. Under this model, we give a deterministic algorithm that obtains an optimal amortized message complexity of O(n) if the number of tokens k is sufficiently large. We also present a randomized algorithm that achieves subquadratic amortized message complexity for much smaller k under an oblivious adversary.","Title: Shape Recognition by a Finite Automaton Robot.\nYear: 2018\n\nAbstract: Motivated by the problem of shape recognition by nanoscale computing agents, we investigate the problem of detecting the geometric shape of a structure composed of hexagonal tiles by a finite-state automaton robot. In particular, in this paper we consider the question of recognizing whether the tiles are assembled into a parallelogram whose longer side has length l = f(h), for a given function f(*), where h is the length of the shorter side. To determine the computational power of the finite-state automaton robot, we identify functions that can or cannot be decided when the robot is given a certain number of pebbles. We show that the robot can decide whether l = ah+b for constant integers a and b without any pebbles, but cannot detect whether l = f(h) for any function f(x) = omega(x). For a robot with a single pebble, we present an algorithm to decide whether l = p(h) for a given polynomial p(*) of constant degree. We contrast this result by showing that, for any constant k, any function f(x) = omega(x^(6k + 2)) cannot be decided by a robot with k states and a single pebble. We further present exponential functions that can be decided using two pebbles. Finally, we present a family of functions f_n(*) such that the robot needs more than n pebbles to decide whether l = f_n(h).","Title: Improved Distributed Delta-Coloring.\nYear: 2018\n\nAbstract: We present a randomized distributed algorithm that computes a Δ- coloring in any non-complete graph with maximum degree Δ ≥ 4 in O(log Δ) +2O( √ log log n) rounds, as well as a randomized algorithm that computes a Δ-coloring in O((log logn)2) rounds when Δ ε [3,O(1)]. Both these algorithms improve on an O(log3 n/ log Δ)- round algorithm of Panconesi and Srinivasan [STOC'1993], which has remained the state of the art for the past 25 years. Moreover, the latter algorithm gets (exponentially) closer to an Ω(log logn) round lower bound of Brandt et al. [STOC'16].","Title: Distributed Approximate Maximum Matching in the CONGEST Model.\nYear: 2018\n\nAbstract: We study distributed algorithms for the maximum matching problem in the CONGEST model, where each message must be bounded in size. We give new deterministic upper bounds, and a new lower bound on the problem.We begin by giving a distributed algorithm that computes an exact maximum (unweighted) matching in bipartite graphs, in O(n log n) rounds. Next, we give a distributed algorithm that approximates the fractional weighted maximum matching problem in general graphs. In a graph with maximum degree at most Delta, the algorithm computes a (1-epsilon)-approximation for the problem in time O(log(Delta W)/epsilon^2), where W is a bound on the ratio between the largest and the smallest edge weight. Next, we show a slightly improved and generalized version of the deterministic rounding algorithm of Fischer [DISC u002717]. Given a fractional weighted maximum matching solution of value f for a given graph G, we show that in time O((log^2(Delta)+log^*n)/epsilon), the fractional solution can be turned into an integer solution of value at least (1-epsilon)f for bipartite graphs and (1-epsilon) * (g-1)/g * f for general graphs, where g is the length of the shortest odd cycle of G. Together with the above fractional maximum matching algorithm, this implies a deterministic algorithm that computes a (1-epsilon)* (g-1)/g-approximation for the weighted maximum matching problem in time O(log(Delta W)/epsilon^2 + (log^2(Delta)+log^* n)/epsilon).On the lower-bound front, we show that even for unweighted fractional maximum matching in bipartite graphs, computing an (1 - O(1/sqrt{n}))-approximate solution requires at least Omega~(D+sqrt{n}) rounds in CONGEST. This lower bound requires the introduction of a new 2-party communication problem, for which we prove a tight lower bound.","Title: Optimal strategies for patrolling fences.\nYear: 2018\n\nAbstract: A classical multi-agent fence patrolling problem asks: What is the maximum length $L$ of a line that $k$ agents with maximum speeds $v_1,ldots,v_k$ can patrol if each point on the line needs to be visited at least once every unit of time. It is easy to see that $L = alpha sum_{i=1}^k v_i$ for some efficiency $alpha [frac{1}{2},1)$. After a series of works giving better and better efficiencies, it was conjectured that the best possible efficiency approaches $frac{2}{3}$. No upper bounds on the efficiency below $1$ were known. We prove the first such upper bounds and tightly bound the optimal efficiency in terms of the minimum ratio of speeds $s = frac{v_{max}}{v_{min}}$ and the number of agents $k$. Our bounds of $alpha leq frac{1}{1 + frac{1}{s}}$ and $alpha leq 1 - frac{1}{2sqrt{k}}$ imply that in order to achieve efficiency $1 - epsilon$, at least $k geq Omega(epsilon^{-2})$ agents with a speed ratio of $s geq Omega(epsilon^{-1})$ are necessary. Guided by our upper bounds, we construct a scheme whose efficiency approaches $1$, disproving the conjecture of Kawamura and Soejima. Our scheme asymptotically matches our upper bounds in terms of the maximal speed difference and the number of agents used, proving them to be asymptotically tight.","Title: Interference in cellular networks: the minimum membership set cover problem\nYear: 2005\n\nAbstract: The infrastructure for mobile distributed tasks is often formed by cellular networks. One of the major issues in such networks is interference. In this paper we tackle interference reduction by suitable assignment of transmission power levels to base stations. This task is formalized introducing the Minimum Membership Set Cover combinatorial optimization problem. On the one hand we prove that in polynomial time the optimal solution of the problem cannot be approximated more closely than with a factor ln n. On the other hand we present an algorithm exploiting linear programming relaxation techniques which asymptotically matches this lower bound.","Title: Lower bounds on information dissemination in dynamic networks\nYear: 2012\n\nAbstract: We study lower bounds on information dissemination in adversarial dynamic networks. Initially, k pieces of information (henceforth called tokens) are distributed among n nodes. The tokens need to be broadcast to all nodes through a synchronous network in which the topology can change arbitrarily from round to round provided that some connectivity requirements are satisfied. If the network is guaranteed to be connected in every round and each node can broadcast a single token per round to its neighbors, there is a simple token dissemination algorithm that manages to deliver all k tokens to all the nodes in O(nk) rounds. Interestingly, in a recent paper, Dutta et al. proved an almost matching Ω(n+nk/logn) lower bound for deterministic token-forwarding algorithms that are not allowed to combine, split, or change tokens in any way. In the present paper, we extend this bound in different ways. If nodes are allowed to forward b≤k tokens instead of only one token in every round, a straight-forward extension of the O(nk) algorithm disseminates all k tokens in time O(nk/b). We show that for any randomized token-forwarding algorithm, Ω(n+nk/(b2lognloglogn)) rounds are necessary. If nodes can only send a single token per round, but we are guaranteed that the network graph is c-vertex connected in every round, we show a lower bound of Ω(nk/(clog3/2n)), which almost matches the currently best O(nk/c) upper bound. Further, if the network is T-interval connected, a notion that captures connection stability over time, we prove that Ω(n+nk/(T2logn)) rounds are needed. The best known upper bound in this case manages to solve the problem in O(n+nk/T) rounds. Finally, we show that even if each node only needs to obtain a δ-fraction of all the tokens for some δ∈[0,1], Ω(nkδ3/logn) are still required.","Title: The complexity of data aggregation in directed networks\nYear: 2011\n\nAbstract: We study problems of data aggregation, such as approximate counting and computing the minimum input value, in synchronous directed networks with bounded message bandwidth B = Ω(log n). In undirected networks of diameter D, many such problems can easily be solved in O(D) rounds, using O(log n)- size messages. We show that for directed networks this is not the case: when the bandwidth B is small, several classical data aggregation problems have a time complexity that depends polynomially on the size of the network, even when the diameter of the network is constant. We show that computing an ε-approximation to the size n of the network requires Ω(min{n, 1/ε2}/B) rounds, even in networks of diameter 2. We also show that computing a sensitive function (e.g., minimum and maximum) requires Ω(√n/B) rounds in networks of diameter 2, provided that the diameter is not known in advance to be o(√n/B). Our lower bounds are established by reduction from several well-known problems in communication complexity. On the positive side, we give a nearly optimal Õ(D+√n/B)-round algorithm for computing simple sensitive functions using messages of size B = Ω(logN), where N is a loose upper bound on the size of the network and D is the diameter.","Title: On the Complexity of Local Distributed Graph Problems.\nYear: 2017\n\nAbstract: This paper is centered on the complexity of graph problems in the well-studied LOCAL model of distributed computing, introduced by Linial [FOCS â87]. It is widely known that for many of the classic distributed graph problems (including maximal independent set (MIS) and (Î+1)-vertex coloring), the randomized complexity is at most polylogarithmic in the size n of the network, while the best deterministic complexity is typically 2O(âlogn). Understanding and potentially narrowing down this exponential gap is considered to be one of the central long-standing open questions in the area of distributed graph algorithms. We investigate the problem by introducing a complexity-theoretic framework that allows us to shed some light on the role of randomness in the LOCAL model. We define the SLOCAL model as a sequential version of the LOCAL model. Our framework allows us to prove completeness results with respect to the class of problems which can be solved efficiently in the SLOCAL model, implying that if any of the complete problems can be solved deterministically in logn rounds in the LOCAL model, we can deterministically solve all efficient SLOCAL-problems (including MIS and (Î+1)-coloring) in logn rounds in the LOCAL model. Perhaps most surprisingly, we show that a rather rudimentary looking graph coloring problem is complete in the above sense: Color the nodes of a graph with colors red and blue such that each node of sufficiently large polylogarithmic degree has at least one neighbor of each color. The problem admits a trivial zero-round randomized solution. The result can be viewed as showing that the only obstacle to getting efficient determinstic algorithms in the LOCAL model is an efficient algorithm to approximately round fractional values into integer values. In addition, our formal framework also allows us to develop polylogarithmic-time randomized distributed algorithms in a simpler way. As a result, we provide a polylog-time distributed approximation scheme for arbitrary distributed covering and packing integer linear programs.","Title: Communication Primitives in Cognitive Radio Networks.\nYear: 2017\n\nAbstract: Cognitive radio networks are a new type of multi-channel wireless network in which different nodes can have access to different sets of channels. By providing multiple channels, they improve the efficiency and reliability of wireless communication. However, the heterogeneous nature of cognitive radio networks also brings new challenges to the design and analysis of distributed algorithms. In this paper, we focus on two fundamental problems in cognitive radio networks: neighbor discovery, and global broadcast. We consider a network containing n nodes, each of which has access to c channels. We assume the network has diameter D, and each pair of neighbors have at least k≥1, and at most kmax≤c, shared channels. We also assume each node has at most Δ neighbors. For the neighbor discovery problem, we design a randomized algorithm CSeek which has time complexity Õ( (c2/k) + (kmax/k)*Δ ). CSeek is flexible and robust, which allows us to use it as a generic \\\"filter\\\" to find \\\"well-connected\\\" neighbors with an even shorter running time. We then move on to the global broadcast problem, and propose CGCast, a randomized algorithm which takes Õ( (c2/k) + (kmax/k)*Δ + D*Δ) time. CGCast uses CSeek to achieve communication among neighbors, and uses edge coloring to establish an efficient schedule for fast message dissemination. Towards the end of the paper, we give lower bounds for solving the two problems. These lower bounds demonstrate that in many situations, CSeek and CGCast are near optimal.","Title: Distributed approximation of capacitated dominating sets\nYear: 2007\n\nAbstract: We study local, distributed algorithms for the capacitated minimum dominating set (CapMDS) problem, which arises in various distributed network applications. Given a network graph G = (V,E), and a capacity cap(v) ∈ N for each node v ∈ V , the CapMDS problem asks for a subset S ⊆ V of minimal cardinality, such that every network node not in S is covered by at least one neighbor in S, and every node v ∈ S covers at most cap(v) of its neighbors. We prove that in general graphs and even with uniform capacities, the problem is inherently non-local, i.e., every distributed algorithm achieving a non-trivial approximation ratio must have a time complexity that essentially grows linearly with the network diameter. On the other hand, if for some parameter ε 0, capacities can be violated by a factor of 1 + ε, CapMDS becomes much more local. Particularly, based on a novel distributed randomized rounding technique, we present a distributed bi-criteria algorithm that achieves an O(log Δ)-approximation in time O(log3n + log(n)/ε), where n and Δ denote the number of nodes and the maximal degree in G, respectively. Finally, we prove that in geometric network graphs typically arising in wireless settings, the uniform problem can be approximated within a constant factor in logarithmic time, whereas the non-uniform problem remains entirely non-local.","Title: Dynamic analysis of the arrow distributed protocol\nYear: 2004\n\nAbstract: Arrow is a prominent distributed protocol which globally orders requests initiated by the nodes in a distributed system. In this paper we present a dynamic analysis of the Arrow protocol. We prove that Arrow is O(log D)-competitive, where D is the diameter of the spanning tree on which Arrow operates. In addition, we show that our analysis is almost tight by proving that for all trees the competitive ratio of Arrow is Ω(log D/log log D).","Title: An Efficient Communication Abstraction for Dense Wireless Networks.\nYear: 2017\n\nAbstract: In this paper we study the problem of developing efficient distributed algorithms for dense wireless networks. For many problems in this setting, fast solutions must leverage the reality that radio signals fade with distance, which can be exploited to enable concurrent communication among multiple sender/receiver pairs. To simplify the development of these algorithms we describe a new communication abstraction called FadingMAC which exposes the benefits of this concurrent communication, but also hides the details of the underlying low-level radio signal behavior. This approach splits efforts between those who develop useful algorithms that run on the abstraction, and those who implement the abstraction in concrete low-level wireless models, or on real hardware.After defining FadingMAC, we describe and analyze an efficient implementation of the abstraction in a standard low-level SINR-style network model. We then describe solutions to the following problems that run on the abstraction: max, min, sum, and mean computed over input values; process renaming; consensus and leader election; and optimal packet scheduling. Combining our abstraction implementation with these applications that run on the abstraction, we obtain near-optimal solutions to these problems in our low-level SINR model - significantly advancing the known results for distributed algorithms in this setting. Of equal importance to these concrete bounds, however, is the general idea advanced by this paper: as wireless networks become more dense, both theoreticians and practitioners must explore new communication abstractions that can help tame this density.","Title: Contention Resolution on a Fading Channel.\nYear: 2016\n\nAbstract: In this paper, we study upper and lower bounds for contention resolution on a single hop fading channel; i.e., a channel where receive behavior is determined by a signal to interference and noise ratio (SINR) equation. The best known previous solution solves the problem in this setting in O(log2 n log log n) rounds, with high probability in the system size n. We describe and analyze an algorithm that solves the problem in O(log n + log R) rounds, where R is the ratio between the longest and shortest link, and is a value upper bounded by a polynomial in n for most feasible deployments. We complement this result with an Ω(log n) lower bound that proves the bound tight for reasonable R. We note that in the classical radio network model (which does not include signal fading), high probability contention resolution requires Ω(log 2 n) rounds. Our algorithm, therefore, affirms the conjecture that the spectrum reuse enabled by fading should allow distributed algorithms to achieve a significant improvement on this log2 n speed limit. In addition, we argue that the new techniques required to prove our upper and lower bounds are of general use for analyzing other distributed algorithms in this increasingly well-studied fading channel setting.","Title: Competitive Concurrent Distributed Scheduling.\nYear: 2019\n\nAbstract: We introduce a new scheduling problem in distributed computing that we call the DSMS problem. We are given a set of $k geq 1$ mobile identical servers that are initially hosted by some processors of the given network. Further, we are given a set of requests that are issued by the processors possibly at any time in an online fashion, and the servers must serve them. A request must be scheduled before the request is served. The delay for scheduling a request is the time taken since the request is issued until it is scheduled. The goal is to minimize the average delay. Navigating mobile servers in a large-scale distributed system needs a scalable location service. We devise the distributed GNN protocol, a novel linked-reversal-based protocol for the DSMS problem that works on overlay trees. We prove that GNN is a starvation-free protocol that correctly integrates locating the servers and synchronizing the concurrent access to the servers despite asynchrony. Further, we analyze the GNN protocol for one-shot executions where all requests are simultaneously issued. We show that when running the GNN protocol on top of a special family of tree topologies---known as hierarchically well-separated trees (HSTs)---we obtain a randomized distributed protocol with an expected competitive ratio of $O(log n)$ on general network topologies for the DSMS problem where $n$ is the number of processors in the given network. From a technical point of view, the main result of our paper shows that the GNN protocol optimally solves the DSMS problem on HSTs for one-shot executions. Our results hold even if communication is asynchronous.","Title: Latency, Capacity, and Distributed MST.\nYear: 2019\n\nAbstract: Consider the problem of building a minimum-weight spanning tree for a given graph $G$. In this paper, we study the cost of distributed MST construction where each edge has a latency and a capacity, along with the weight. Edge latencies capture the delay on the links of the communication network, while capacity captures their throughput (in this case the rate at which messages can be sent). Depending on how the edge latencies relate to the edge weights, we provide several tight bounds on the time required to construct an MST. When there is no correlation between the latencies and the weights, we show that (unlike the sub-linear time algorithms in the standard textsf{CONGEST} model, on small diameter graphs), the best time complexity that can be achieved is $tilde{Theta}(D+n/c)$, where edges have capacity $c$ and $D$ refers to the latency diameter of the graph. However, if we restrict all edges to have equal latency $ell$ and capacity $c$, we give an algorithm that constructs an MST in $tilde{O}(D + sqrt{nell/c})$ time. Next, we consider the case where latencies are exactly equal to the weights. Here we show that, perhaps surprisingly, the bottleneck parameter in determining the running time of an algorithm is the total weight $W$ of the constructed MST by showing a tight bound of $tilde{Theta}(D + sqrt{W/c})$. In each case, we provide matching lower bounds.","Title: On the Use of Randomness in Local Distributed Graph Algorithms.\nYear: 2019\n\nAbstract: We attempt to better understand randomization in local distributed graph algorithms by exploring how randomness is used and what we can gain from it: We first ask the question of how much randomness is needed to obtain efficient randomized algorithms. We show that for all locally checkable problems with poly log n-time randomized algorithms, there are such algorithms even if either (I) there is a only a single (private) independent random bit in each poly log n-neighborhood of the graph, (II) the (private) bits of randomness of different nodes are only poly log n-wise independent, or (III) there are only poly log n bits of global shared randomness (and no private randomness). Second, we study how much we can improve the error probability of randomized algorithms. For all locally checkable problems with poly log n-time randomized algorithms, we show that there are such algorithms that succeed with probability 1-n-2 ε(log log n) 2 and more generally T-round algorithms, for T ≥ poly log n, with success probability 1-n-2 εlog 2T. We also show that poly log n-time randomized algorithms with success probability 1-2-2 log ε n for some ε > 0 can be derandomized to poly log n-time deterministic algorithms. Both of the directions mentioned above, reducing the amount of randomness and improving the success probability, can be seen as partial derandomization of existing randomized algorithms. In all the above cases, we also show that any significant improvement of our results would lead to a major breakthrough, as it would imply significantly more efficient deterministic distributed algorithms for a wide class of problems.","Title: Deterministic Distributed Dominating Set Approximation in the CONGEST Model.\nYear: 2019\n\nAbstract: We develop deterministic approximation algorithms for the minimum dominating set problem in the CONGEST model with an almost optimal approximation guarantee. For ε 1/ poly log Δ we obtain two algorithms with approximation factor (1 + ε)(1 + ł n (Δ + 1)) and with runtimes 2O(√ log n log log n) and O(Δ poly log Δ + poly log Δ log* n), respectively. Further we show how dominating set approximations can be deterministically transformed into a connected dominating set in the CONGEST model while only increasing the approximation guarantee by a constant factor. This results in a deterministic O(log Δ)-approximation algorithm for the minimum connected dominating set with time complexity 2O(√ log n log log n).","Title: On the Complexity of Distributed Splitting Problems.\nYear: 2019\n\nAbstract: One of the fundamental open problems in the area of distributed graph algorithms is whether randomization is needed for efficient symmetry breaking. While there are poly log n-time randomized algorithms for all the classic symmetry breaking problems, for many of them, the best deterministic algorithms are almost exponentially slower. The following basic local splitting problem, which is known as weak splitting, takes a central role in this context: Each node of a graph G=(V,E) has to be colored red or blue such that each node of sufficiently large degree has at least one neighbor of each color. Ghaffari, Kuhn, and Maus [STOC '17] showed that this seemingly simple problem is complete w.r.t. the above fundamental open question in the following sense: If there is an efficient poly log n-time determinstic distributed algorithm for weak splitting, then there is such an algorithm for all locally checkable graph problems for which an efficient randomized algorithm exists. We investigate the distributed complexity of weak splitting and some closely related problems and we in particular obtain the following results: We obtain efficient algorithms for special cases of weak splitting in nearly regular graphs. We show that if δ=Ø(log n) and Δ are the minimum and maximum degrees of G, weak splitting can be solved deterministically in time O #916;(√ over δ • poly(log n)). Further, if δ = Ø(log log n) and Δ ≤ 2ε δ, the time complexity is O(Δ over δ⋅poly(log log n)). We prove that the following two related problems are also complete in the same sense: (I) Color the nodes of a graph with C ≤ poly log n colors such that each node with a sufficiently large polylogarithmic degree has at least 2 log n different colors among its neighbors, and (II) Color the nodes with a large constant number of colors so that for each node of a sufficiently large at least logarithmic degree d(v), the number of neighbors of each color is at most (1-εd(v) for some constant ε > 0.","Title: Near-Optimal Distributed Degree+1 Coloring\nYear: 2022\n\nAbstract: We present a new approach to randomized distributed graph coloring that is simpler and more efficient than previous ones. In particular, it allows us to tackle the (deg +1)-list-coloring (D1LC) problem, where each node upsilon of degree dv is assigned a palette of d(upsilon)+ 1 colors, and the objective is to find a proper coloring using these palettes. While for (Delta+ 1)-coloring (where Delta is the maximum degree), there is a fast randomized distributed O(log(3) logn)-round algorithm due to Chang, Li, and Pettie, no o(logn)-round algorithms are known for the D1LC problem. We give a randomized distributed algorithm for D1LC that is optimal under plausible assumptions about the deterministic complexity of the problem. Using the recent deterministic algorithm of Ghaffari and Kuhn, our algorithm runs inO(log3 logn) time, matching the best bound known for (Lambda + 1)-coloring. A key contribution is a subroutine to generate slack for D1LC, which almost immediately leads to a palette sparsification theorem for D1LC when placed into the framework of works by Assadi, Chen, and Khanna and Alon and Assadi. That gives fast algorithms for D1LC in three different models: an O(1)-round algorithm in the MPC model with O(n) memory per machine; a single-pass semi-streaming algorithm in dynamic streams; and an O(n root n)-time query algorithm.","Title: Routing Schemes and Distance Oracles in the Hybrid Model.\nYear: 2022\n\nAbstract: The $\\mathsf{HYBRID}$ model was introduced as a means for theoretical study of distributed networks that use various communication modes. Conceptually, it is a synchronous message passing model with a local communication mode, where in each round each node can send large messages to all its neighbors in a local network (a graph), and a global communication mode, where each node is allotted limited (polylogarithmic) bandwidth per round which it can use to communicate with any node in the network. Prior work has often focused on shortest paths problems in the local network, as their global nature makes these an interesting case study how combining communication modes in the $\\mathsf{HYBRID}$ model can overcome the individual lower bounds of either mode. In this work we consider a similar problem, namely computation of distance oracles and routing schemes. In the former, all nodes have to compute local tables, which allows them to look up the distance (estimates) to any target node in the local network when provided with the label of the target. In the latter, it suffices that nodes give the next node on an (approximately) shortest path to the target. Our goal is to compute these local tables as fast as possible with labels as small as possible. We show that this can be done exactly in $\\widetilde O(n^{1/3})$ communication rounds and labels of size $\\Theta(n^{2/3})$ bits. For constant stretch approximations we achieve labels of size $O(\\log n)$ in the same time. Further, as our main technical contribution, we provide computational lower bounds for a variety of problem parameters. For instance, we show that computing solutions with stretch below a certain constant takes $\\widetilde \\Omega(n^{1/3})$ rounds even for labels of size $O(n^{2/3})$.","Title: Sublinear-time distributed algorithms for detecting small cliques and even cycles\nYear: 2022\n\nAbstract: In this paper we give sublinear-time distributed algorithms in the \n \n \n \n $$\\mathsf {CONGEST}$$\n \n model for finding or listing cliques and even-length cycles. We show for the first time that all copies of 4-cliques and 5-cliques in the network graph can be detected and listed in sublinear time, \n \n \n \n $$O(n^{5/6+o(1)})$$\n \n rounds and \n \n \n \n $$O(n^{73/75+o(1)})$$\n \n rounds, respectively. For even-length cycles, \n \n \n \n $$C_{2k}$$\n \n , we give an improved sublinear-time algorithm, which exploits a new connection to extremal combinatorics. For example, for 6-cycles we improve the running time from \n \n \n \n $${\\tilde{O}}(n^{5/6})$$\n \n to \n \n \n \n $${\\tilde{O}}(n^{3/4})$$\n \n rounds. We also show two obstacles on proving lower bounds for \n \n \n \n $$C_{2k}$$\n \n -freeness: first, we use the new connection to extremal combinatorics to show that the current lower bound of \n \n \n \n $${\\tilde{\\varOmega }}(\\sqrt{n})$$\n \n rounds for 6-cycle freeness cannot be improved using partition-based reductions from 2-party communication complexity, the technique by which all known lower bounds on subgraph detection have been proven to date. Second, we show that there is some fixed constant \n \n \n \n $$\\delta \\in (0,1/2)$$\n \n such that for any k, a lower bound of \n \n \n \n $$\\varOmega (n^{1/2+\\delta })$$\n \n on \n \n \n \n $$C_{2k}$$\n \n -freeness would imply new lower bounds in circuit complexity. We use the same technique to show a barrier for proving any polynomial lower bound on triangle-freeness. For general subgraphs, it was shown by Fischer et al. that for any fixed k, there exists a subgraph H of size k such that H-freeness requires \n \n \n \n $${\\tilde{\\varOmega }}(n^{2-\\varTheta (1/k)})$$\n \n rounds. It was left as an open problem whether this is tight, or whether some constant-sized subgraph requires truly quadratic time to detect. We show that in fact, for any subgraph H of constant size k, the H-freeness problem can be solved in \n \n \n \n $$O(n^{2 - \\varTheta (1/k)})$$\n \n rounds, nearly matching the lower bound.","Title: Latency, capacity, and distributed minimum spanning trees\nYear: 2022\n\nAbstract: We study the cost of distributed MST construction in the setting where each edge has a latency and a capacity, along with the weight. Edge latencies capture the delay on the links of the communication network, while capacity captures their throughput (in this case, the rate at which messages can be sent). Depending on how the edge latencies relate to the edge weights, we provide several tight bounds on the time and messages required to construct an MST. When edge weights exactly correspond with the latencies, we show that, perhaps interestingly, the bottleneck parameter in determining the running time of an algorithm is the total weight W of the MST (rather than the total number of nodes n, as in the standard CONGEST model). That is, we show a tight bound of Θ˜(D+W/c) rounds, where D refers to the latency diameter of the graph, W refers to the total weight of the constructed MST and edges have capacity c. The proposed algorithm sends O˜(m+W) messages, where m, the total number of edges in the network graph under consideration, is a known lower bound on message complexity for MST construction. We also show that Ω(W) is a lower bound for fast MST constructions. When the edge latencies and the corresponding edge weights are unrelated, and either can take arbitrary values, we show that (unlike the sub-linear time algorithms in the standard CONGEST model, on small diameter graphs), the best time complexity that can be achieved is Θ˜(D+n/c). However, if we restrict all edges to have equal latency ℓ and capacity c while having possibly different weights (weights could deviate arbitrarily from ℓ), we give an algorithm that constructs an MST in O˜(D+nℓ/c) time. In each case, we provide nearly matching upper and lower bounds.","Title: Contention Resolution for Coded Radio Networks\nYear: 2022\n\nAbstract: BSTRACTRandomized backoff protocols, such as exponential backoff, are a powerful tool for managing access to a shared resource, often a wireless communication channel (e.g., [1]). For a wireless device to transmit successfully, it uses a backoff protocol to ensure exclusive access to the channel. Modern radios, however, do not need exclusive access to the channel to communicate; in particular, they have the ability to receive useful information even when more than one device transmits at the same time. These capabilities have now been exploited for many years by systems that rely on interference cancellation, physical layer network coding and analog network coding to improve efficiency. For example, Zigzag decoding [56] demonstrated how a base station can decode messages sent by multiple devices simultaneously. In this paper, we address the following question: Can we design a backoff protocol that is better than exponential backoff when exclusive channel access is not required. We define the Coded Radio Network Model, which generalizes traditional radio network models (e.g., [30]). We then introduce the Decodable Backoff Algorithm, a randomized backoff protocol that achieves an optimal throughput of 1 - o (1). (Throughput 1 is optimal, as simultaneous reception does not increase the channel capacity.) The algorithm breaks the constant throughput lower bound for traditional radio networks [47-49], showing the power of these new hardware capabilities.","Title: Node and Edge Averaged Complexities of Local Graph Problems\nYear: 2022\n\nAbstract: BSTRACTWe continue the recently started line of work on the distributed node-averaged complexity of distributed graph algorithms. The node-averaged complexity of a distributed algorithm running on a graph G=(V,E) is the average over the times at which the nodes V of G finish their computation and commit to their outputs. We study the node-averaged complexity for some of the central distributed symmetry breaking problems. As our main result, we show that the randomized node-averaged complexity of computing a maximal independent set (MIS) is at least Ε(min(log Δ / log log Δ, √(log n/log log n))) in n-node graphs of maximum degree Δ. This bound is obtained by a novel adaptation of the well-known lower bound of Kuhn, Moscibroda, and Wattenhofer [JACM'16]. As a side result, we obtain that the worst-case randomized round complexity for computing an MIS in trees is also Ω(min(log Δ / log log Δ,√(log n/log log n)))---this essentially answers open problem 11.15 in the book of Barenboim and Elkin and resolves the complexity of MIS on trees up to an O(√(log log n)) factor. We also show that, perhaps surprisingly, a minimal relaxation of MIS, which is the same as (2,1)-ruling set, to the (2,2)-ruling set problem drops the randomized node-averaged complexity to O(1). For the problem of computing a maximal matching, we show that while the randomized node-averaged complexity is at least Ω(min(log Δ / log log Δ,√(log n/log log n))), the randomized edge-averaged complexity is O(1). Further, we show that the deterministic edge-averaged complexity of maximal matching is O(log2Δ + log* n) and the deterministic node-averaged complexity of maximal matching is O(log3Δ + log* n). Finally, we consider the problem of computing a sinkless orientation of a graph. The deterministic worst-case complexity of the problem is known to be Θ(log n), even on bounded-degree graphs. We show that the problem can be solved deterministically with node-averaged complexity O(log* n), while keeping the worst-case complexity in O(log n).","Title: Distributed Delta-Coloring Plays Hide-and-Seek*\nYear: 2022\n\nAbstract: We prove several new tight or near-tight distributed lower bounds for classic symmetry breaking problems in graphs. As a basic tool, we first provide a new insightful proof that any deterministic distributed algorithm that computes a Delta-coloring on.-regular trees requires Omega( log(Delta) n) rounds and any randomized such algorithm requires Omega( log(Delta) logn) rounds. We prove this by showing that a natural relaxation of the Delta-coloring problem is a fixed point in the round elimination framework. As a first application, we show that our.-coloring lower bound proof directly extends to arbdefective colorings. An arbdefective Delta-coloring of a graph G = (V, E) is given by a c-coloring of V and an orientation of E, where the arbdefect of a color.. is the maximum number of monochromatic outgoing edges of any node of color... We exactly characterize which variants of the arbdefective coloring problem can be solved in O (f (Delta)+log*n) rounds, for some function.., and which of them instead require Omega(log(Delta)n) rounds for deterministic algorithms and Omega(log(Delta) logn) rounds for randomized ones. As a second application, which we see as our main contribution, we use the structure of the fixed point as a building block to prove lower bounds as a function of. for problems that, in some sense, are much easier than Delta-coloring, as they can be solved in O(log*n) deterministic rounds in bounded-degree graphs. More specifically, we prove lower bounds as a function of. for a large class of distributed symmetry breaking problems, which can all be solved by a simple sequential greedy algorithm. For example, we obtain novel results for the fundamental problem of computing a (2,beta)-ruling set, i.e., for computing an independent set S subset of V such that every node upsilon is an element of V is within distance <= beta of some node in S. We in particular show that Omega(beta Delta(1)/(beta)) rounds are needed even if initially an O(Delta)-coloring of the graph is given. With an initial O(Delta)-coloring, this lower bound is tight and without, it still nearly matches the existing O (beta Delta(2/(beta+1)) + log*n) upper bound. The new ( 2,beta)-ruling set lower bound is an exponential improvement over the best existing lower bound for the problem, which was proven in [FOCS '20]. As a special case of the lower bound, we also obtain a tight linear-in-. lower bound for computing a maximal independent set (MIS) in trees. While such an MIS lower bound was known for general graphs, the best previous MIS lower bounds for trees was O( log.). Our lower bound even applies to a much more general family of problems that allows for almost arbitrary combinations of natural constraints from coloring problems, orientation problems, and independent set problems, and provides a single unified proof for known and new lower bound results for these types of problems. All of our lower bounds as a function of Delta also imply substantial lower bounds as a function of n. For instance, we obtain that the maximal independent set problem, on trees, requires Omega( logn/log logn) rounds for deterministic algorithms, which is tight.","Title: Distributed Edge Coloring in Time Polylogarithmic in Δ\nYear: 2022\n\nAbstract: BSTRACTWe provide new deterministic algorithms for the edge coloring problem, which is one of the classic and highly studied distributed local symmetry breaking problems. As our main result, we show that a (2Δ - 1)-edge coloring can be computed in time poly log Δ + O(log* n) in the LOCAL model. This improves a result of Balliu, Kuhn, and Olivetti [PODC '20], who gave an algorithm with a quasi-polylogarithmic dependency on Δ. We further show that in the CONGEST model, an (8 + ε)Δ-edge coloring can be computed in poly log Δ + O(log* n) rounds. The best previous O(Δ)-edge coloring algorithm that can be implemented in the CONGEST model is by Barenboim and Elkin [PODC '11] and it computes a 2O(1/ε)Δ- edge coloring in time O(Δε + log* n) for any ε ∈ (0, 1]."],"string":"[\n \"Title: Distributed connectivity decomposition\\nYear: 2013\\n\\nAbstract: A fundamental problem in distributed network algorithms is to manage congestion and obtain information flow matching the graph's connectivity. In this paper, we present time-efficient distributed algorithms for decomposing graphs with large edge or vertex connectivity into multiple spanning or dominating trees, respectively. These decompositions allow us to achieve a flow with size close to the connectivity by parallelizing it along the trees. More specifically, our distributed decomposition algorithms are as follows: - A decomposition of each undirected graph with vertex-connectivity k into (fractionally) vertex-disjoint weighted dominating trees with total weight Ω(k/log n), in ~O(D+√n) rounds. - A decomposition of each undirected graph with edge-connectivity λ into (fractionally) edge-disjoint weighted spanning trees with total weight ⌈λ-1/2⌉(1-ε), in ~{O}(D+√nλ) rounds. We also show round complexity lower bounds of ~](D+√n/k) and ~Ω(D+√n/λ) for the above two decompositions, using techniques of [Das Sarma et al., STOC'11]. Moreover, our vertex-connectivity decomposition extends to centralized algorithms and improves the time complexity of [Censor-Hillel et al., SODA'14] from O(n3) to near-optimal ~O(m). Additional implications of our results are: a near-linear time centralized approximation of vertex connectivity which can be seen as a step towards a conjecture of Aho, Hopcroft and Ullman), the first distributed approximating of vertex connectivity, and distributed algorithms with near-optimal competitiveness for oblivious broadcast routing.\",\n \"Title: Improved approximation algorithms for connected sensor cover\\nYear: 2007\\n\\nAbstract: Wireless sensor networks have recently posed many new system building challenges. One of the main problems is energy conservation since most of the sensors are devices with limited battery life and it is infeasible to replenish energy via replacing batteries. An effective approach for energy conservation is scheduling sleep intervals for some sensors, while the remaining sensors stay active providing continuous service. In this paper we consider the problem of selecting a set of active sensors of minimum cardinality so that sensing coverage and network connectivity are maintained. We show that the greedy algorithm that provides complete coverage has an approximation factor no better than Ω(log n), where n is the number of sensor nodes. Then we present algorithms that provide approximate coverage while the number of nodes selected is a constant factor far from the optimal solution. Finally, we show how to connect a set of sensors that already provides coverage.\",\n \"Title: A new perspective on vertex connectivity\\nYear: 2014\\n\\nAbstract: Edge connectivity and vertex connectivity are two fundamental concepts in graph theory. Although by now there is a good understanding of the structure of graphs based on their edge connectivity our knowledge in the case of vertex connectivity is much more limited. An essential tool in capturing edge connectivity are the classical results of Tutte and Nash-Williams from 1961 which show that a λ-edge-connected graph contains ⌈(λ -- 1)/2⌉ edge-disjoint spanning trees. We argue that connected dominating set partitions and packings are the natural analogues of edge-disjoint spanning trees in the context of vertex connectivity and we use them to obtain structural results about vertex connectivity in the spirit of those for edge connectivity. More specifically connected dominating set (CDS) partitions and packings are counterparts of edge-disjoint spanning trees, focusing on vertex-disjointness rather than edge-disjointness, and their sizes are always upper bounded by the vertex connectivity k. We constructively show that every k-vertex-connected graph with n nodes has CDS packings and partitions with sizes, respectively, Ω(k/log n) and Ω(k/log5 n), and we prove that the former bound is existentially optimal. Beautiful results by Karger show that when edges of a λ-edge-connected graph are independently sampled with probability p, the sampled graph has edge connectivity Ω(λp). Obtaining such a result for vertex sampling remained open. We illustrate the strength of our approach by proving that when vertices of a k-vertex-connected graph are independently sampled with probability p, the graph induced by the sampled vertices has vertex connectivity Ω(kp2). This bound is optimal up to poly-log factors and is proven by building an Ω(kp2) size CDS packing on the sampled vertices while sampling happens. As an additional important application, we show CDS packings to be tightly related to the throughput of routing-based algorithms and use our new toolbox to yield a routing-based broadcast algorithm with optimal throughput Ω(k/log n + 1), improving the (previously best-known) trivial throughput of Θ(1).\",\n \"Title: Constant-time distributed dominating set approximation\\nYear: 2005\\n\\nAbstract: Finding a small dominating set is one of the most fundamental problems of traditional graph theory. In this paper, we present a new fully distributed approximation algorithm based on LP relaxation techniques. For an arbitrary parameter k and maximum degree Δ, our algorithm computes a dominating set of expected size O(kΔ2/k log Δ|DSOPT|) in O(k2) rounds where each node has to send O(k2Δ) messages of size O(logΔ). This is the first algorithm which achieves a non-trivial approximation ratio in a constant number of rounds.\",\n \"Title: Distributed Minimum Cut Approximation\\nYear: 2013\\n\\nAbstract: We study the problem of computing approximate minimum edge cuts by distributed algorithms. We use a standard synchronous message passing model where in each round, O(log n) bits can be transmitted over each edge (a.k.a. the CONGEST model). The first algorithm is based on a simple and new approach for analyzing random edge sampling, which we call the random layering technique. For any weighted graph and any epsilon is an element of (0, 1), the algorithm with high probability finds a cut of size at most O(epsilon(-1)lambda) in O(D) + (O) over tilde (n(1/2+epsilon)) rounds, where lambda is the size of the minimum cut and the (O) over tilde -notation hides poly-logarithmic factors in n. In addition, based on a centralized algorithm due to Matula [SODA '93], we present a randomized distributed algorithm that with high probability computes a cut of size at most (2 + epsilon)lambda in (O) over tilde((D + root n)/epsilon(5)) rounds for any epsilon > 0.The time complexities of our algorithms almost match the (Omega) over tilde (D + root n) lower bound of Das Sarma et al. [STOC '11], thus leading to an answer to an open question raised by Elkin [SIGACT-News '04] and Das Sarma et al. [STOC '11].To complement our upper bound results, we also strengthen the (Omega) over tilde (D + v n) lower bound of Das Sarma et al. by extending it to unweighted graphs. We show that the same lower bound also holds for unweighted multigraphs (or equivalently for weighted graphs in which O(w log n) bits can be transmitted in each round over an edge of weight w). For unweighted simple graphs, we show that computing an alpha-approximate minimum cut requires time at least (Omega) over tilde (D + root n/alpha(1/4)).\",\n \"Title: Weak graph colorings: distributed algorithms and applications\\nYear: 2009\\n\\nAbstract: We study deterministic, distributed algorithms for two weak variants of the standard graph coloring problem. We consider defective colorings, i.e., colorings where nodes of a color class may induce a graph of maximum degree d for some parameter d0. We also look at colorings where a minimum number of multi-chromatic edges is required. For an integer k0, we call a coloring k-partially proper if every node v has at least min{k,deg(v)} neighbors with a different color. We show that for all d∈{1,...,Δ}, it is possible to compute a O(Δ2/d2)-coloring with defect d in time O(log*n) where Δ is the largest degree of the network graph. Similarly, for all k∈{1,...,Δ}, a k-partially proper O(k2)-coloring can be computed in O(log*n) rounds. As an application of our weak defective coloring algorithm, we obtain a faster deterministic algorithm for the standard vertex coloring problem on graphs with moderate degrees. We show that in time O(Δ+log*n), a (Δ+1)-coloring can be computed, a task for which the best previous algorithm required time O(Δ*log(Δ) + log*n). The same result holds for the problem of computing a maximal independent set.\",\n \"Title: Structuring unreliable radio networks\\nYear: 2014\\n\\nAbstract: In this paper we study the problem of building a connected dominating set with constant degree (CCDS) in the dual graph radio network model [4,9,10]. This model includes two types of links: reliable, which always deliver messages, and unreliable, which sometimes fail to deliver messages. Real networks compensate for this differing quality by deploying low-layer detection protocols to filter unreliable from reliable links. With this in mind, we begin by presenting an algorithm that solves the CCDS problem in the dual graph model under the assumption that every process u is provided a local link detector set consisting of every neighbor connected to u by a reliable link. The algorithm solves the CCDS problem in O(Δ\\\\log2 n/b + log3 n) rounds, with high probability, where Δ is the maximum degree in the reliable link graph, n is the network size, and b is an upper bound in bits on the message size. The algorithm works by first building a Maximal Independent Set (MIS) in log3 n time, and then leveraging the local topology knowledge to efficiently connect nearby MIS processes. A natural follow up question is whether the link detector must be perfectly reliable to solve the CCDS problem. With this in mind, we first describe an algorithm that builds a CCDS in O(Δpolylog(n)) time under the assumption of O(1) unreliable links included in each link detector set. We then prove this algorithm to be (almost) tight by showing that the possible inclusion of only a single unreliable link in each process's local link detector set is sufficient to require Ω(Δ) rounds to solve the CCDS problem, regardless of message size. We conclude by discussing how to apply our algorithm in the setting where the topology of reliable and unreliable links can change over time.\",\n \"Title: The Abstract MAC Layer\\nYear: 2011\\n\\nAbstract: A diversity of possible communication assumptions complicates the study of algorithms and lower bounds for radio networks.\\n We address this problem by defining an abstract MAC layer. This service provides reliable local broadcast communication, with\\n timing guarantees stated in terms of a collection of abstract delay functions applied to the relevant contention. Algorithm designers can analyze their algorithms in terms of these functions, independently\\n of specific channel behavior. Concrete implementations of the abstract MAC layer over basic radio network models generate\\n concrete definitions for these delay functions, automatically adapting bounds proven for the abstract service to bounds for\\n the specific radio network under consideration. To illustrate this approach, we use the abstract MAC layer to study the new\\n problem of Multi-Message Broadcast, a generalization of standard single-message broadcast in which multiple messages can originate at different times and locations\\n in the network. We present and analyze two algorithms for Multi-Message Broadcast in static networks: a simple greedy algorithm\\n and one that uses regional leaders. We then indicate how these results can be extended to mobile networks.\",\n \"Title: Dynamic Analysis of the Arrow Distributed Protocol\\nYear: 2006\\n\\nAbstract: Distributed queuing is a fundamental coordination problem that arises in a variety of applications, including distributed directories, totally ordered multicast, and distributed mutual exclusion. The arrow protocol is a solution to distributed queuing that is based on path reversal on a pre-selected spanning tree of the network. We present a novel and comprehensive competitive analysis of the arrow protocol. We consider the total cost of handling a finite number of queuing requests, which may or may not be issued concurrently, and show that the arrow protocol is $O(s\\\\cdot \\\\log D)$-competitive to the optimal queuing protocol, where s and D are the stretch and the diameter, respectively, of the spanning tree. In addition, we show that our analysis is almost tight by proving that for every spanning tree chosen for execution, the arrow protocol is $\\\\Omega(s \\\\cdot \\\\log(D/s)/{\\\\log}\\\\log(D/s))$-competitive to the optimal queuing protocol. Our analysis reveals an intriguing connection between the arrow protocol and the nearest neighbor traveling salesperson tour on an appropriately defined graph.\",\n \"Title: The wireless synchronization problem\\nYear: 2009\\n\\nAbstract: In this paper, we study the wireless synchronization prob- lem which requires devices activated at dierent times on a congested single-hop radio network to synchronize their round numbering. We assume a collection of n synchronous devices with access to a shared band of the radio spectrum, divided into F narrowband frequencies. We assume that the communication medium suers from unpredictable, perhaps even malicious interference, which we model by an adversary that can disrupt up to t frequencies per round. Devices be- gin executing in dierent rounds and the exact number of participants is not known in advance. We first prove a lower bound, demonstrating that at least \\\" log2 n (F t) log log n + Ft F t logn\",\n \"Title: Unit disk graph approximation\\nYear: 2004\\n\\nAbstract: Finding a good embedding of a unit disk graph given by its connectivity information is a problem of practical importance in a variety of fields. In wireless ad hoc and sensor networks, such an embedding can be used to obtain virtual coordinates. In this paper, we prove a non-approximability result for the problem of embedding a given unit disk graph. Particularly, we show that if non-neighboring nodes are not allowed to be closer to each other than distance 1, then two neighbors can be as far apart as √3/2 - ε, where ε goes to 0 as n goes to infinity, unless P=NP. We further show that finding a realization of a d-quasi unit disk graph with d ≥ 1/√2 is NP-hard.\",\n \"Title: Reconstructing approximate tree metrics\\nYear: 2007\\n\\nAbstract: We introduce a novel measure called \\\"-four-points condition (\\\"- 4PC), which assigns a value \\\" 2 (0;1) to every metric space quan- tifying how close the metric is to a tree metric. Data-sets taken from real Internet measurements indicate remarkable closeness of Internet latencies to tree metrics based on this condition. We study embeddings of \\\"-4PC metric spaces into trees and prove tight upper and lower bounds. Specifically, we show that there are constants c1 and c2 such that, (1) every metric (X;d) which satisfies the \\\"-4PC can be embedded into a tree with distortion (1 + \\\")c 1 log jXj , and (2) for every \\\" 2 (0;1) and any number of nodes, there is a metric space (X;d) satisfying the \\\"-4PC that does not embed into a tree with distortion less than (1 + \\\")c 2 log jXj . In addition, we prove a lower bound on approximate distance labelings of \\\"-4PC metrics, and give tight bounds for tree embeddings with additive error guar- antees.\",\n \"Title: Maximal independent sets in multichannel radio networks\\nYear: 2013\\n\\nAbstract: We present new upper bounds for fundamental problems in multichannel wireless networks. These bounds address the benefits of dynamic spectrum access, i.e., to what extent multiple communication channels can be used to improve performance. In more detail, we study a multichannel generalization of the standard graph-based wireless model without collision detection, and assume the network topology satisfies polynomially bounded independence. Our core technical result is an algorithm that constructs a maximal independent set (MIS) in O(log2 n/F)+ Õ(logn) rounds, in networks of size n with F channels, where the Õ-notation hides polynomial factors in log log n. Moreover, we use this MIS algorithm as a subroutine to build a constant-degree connected dominating set in the same asymptotic time. Leveraging this structure, we are able to solve global broadcast and leader election within O(D + log2 n/F + Õ(logn) rounds, where D is the diameter of the graph, and k-message multi-message broadcast in O(D + k + log2 n/F}\\\\big)+Õ(logn) rounds for unrestricted message size (with a slow down of only a log factor on the k term under the assumption of restricted message size). In all five cases above, we prove: (a) our results hold with high probability (i.e., at least 1--1/n); (b) our results are within polyloglog factors of the relevant lower bounds for multichannel networks; and (c) our results beat the relevant lower bounds for single channel networks. These new (near) optimal algorithms significantly expand the number of problems now known to be solvable faster in multichannel versus single channel wireless networks.\",\n \"Title: An algorithmic approach to geographic routing in ad hoc and sensor networks\\nYear: 2008\\n\\nAbstract: The one type of routing in ad hoc and sensor networks that currently appears to be most amenable to algorithmic analysis is geographic routing. This paper contains an introduction to the problem field of geographic routing, presents a specific routing algorithm based on a synthesis of the greedy forwarding and face routing approaches, and provides an algorithmic analysis of the presented algorithm from both a worst-case and an average-case perspective.\",\n \"Title: Random Walks Revisited: Extensions of Pollard's Rho Algorithm for Computing Multiple Discrete Logarithms\\nYear: 2001\\n\\nAbstract: This paper extends the analysis of Pollard's rho algorithm for solving a single instance of the discrete logarithm problem in a finite cyclic group G to the case of solving more than one instance of the discrete logarithm problem in the same group G. We analyze Pollard's rho algorithm when used to iteratively solve all the instances. We also analyze the situation when the goal is to solve any one of the multiple instances using any DLP algorithm.\",\n \"Title: Synchrony and asynchrony in neural networks\\nYear: 2010\\n\\nAbstract: The dynamics of large networks is an important and fascinating problem. Key examples are the Internet, social networks, and the human brain. In this paper we consider a model introduced by DeVille and Peskin [6] for a stochastic pulse-coupled neural network. The key feature and novelty in their approach is that they describe the interactions of a neuronal system as a discrete-state stochastic dynamical network. This idealization has two benefits: it captures essential features of neuronal behavior, and it allows the study of spontaneous synchronization, an important phenomenon in neuronal networks that is well-studied but unfortunately far from being well-understood. In synchronous behavior the firing of one neuron leads to the firing of other neurons, which in turn may set off a chain reaction that often involves a substantial proportion of the neurons. In this paper we rigorously analyze their model. In particular, by applying methods and tools that are frequently used in theoretical computer science, we provide a very precise picture of the dynamics and the evolution of the given system. In particular, we obtain insights into the coexistence of synchronous and asynchronous behavior and the conditions that trigger a \\\"spontaneous\\\" transition from one state to another.\",\n \"Title: MAC design for analog network coding\\nYear: 2011\\n\\nAbstract: Most medium access control (MAC) mechanisms discard collided packets and consider interference harmful. Recent work on Analog Network Coding (ANC) suggests a different approach, in which multiple interfering transmissions are strategically scheduled. Receiving nodes collect the results of collisions and then use a decoding process, such as ZigZag decoding, to extract the packets involved in the collisions. In this paper, we present an algebraic representation of collisions and describe a general approach to recovering collisions using ANC. To study the effects of using ANC on the performance of MAC layers, we develop an ANC-based MAC algorithm, CMAC, and analyze its performance in terms of probabilistic latency guarantees for local packet delivery. Specifically, we prove that CMAC implements an abstract MAC layer service, as defined in [14, 13]. This study shows that ANC can significantly improve the performance of the abstract MAC layer service compared to conventional probabilistic transmission approaches. We illustrate how this improvement in the MAC layer can translate into faster higher-level algorithms, by analyzing the time complexity of a multi-message network-wide broadcast algorithm that uses CMAC.\",\n \"Title: Efficient distributed approximation algorithms via probabilistic tree embeddings\\nYear: 2012\\n\\nAbstract: We present a uniform approach to design e cient distributed ap- proximation algorithms for various network optimization problems. Our approach is randomized and based on a probabilistic tree em- bedding due to Fakcharoenphol, Rao, and Talwar (12) (FRT embed- ding). We show how to e ciently compute an (implicit) FRT em- bedding in a decentralized manner and how to use the embedding to obtain expected O(log n)-approximate distributed algorithms for the generalized Steiner forest problem, the minimum routing cost spanning tree problem, and the k-source shortest paths problem in arbitrary networks. The time complexities of our algorithms are within a polylogarithmic factor of the optimum. The distributed construction of the FRT embedding is based on the computation of least elements (LE) lists, a distributed data struc- ture that can be of independent interest. Assuming a global order on the nodes of a network, the LE list of a node stores the smallest node (with respect to the given order) within every distance d (cf. Cohen (6), Cohen and Kaplan (7)). For weighted graphs, we give an almost-optimal distributed algorithm for computing LE lists, as- suming a random order on the nodes. For unweighted graphs, our LE lists computation has asymptotically optimal time complexity O(D), where D is the diameter of the network. As a byproduct, we get an improved leader election algorithm in general networks that is both time-optimal and almost message-optimal with high proba- bility.\",\n \"Title: The communication complexity of distributed task allocation\\nYear: 2012\\n\\nAbstract: We consider a distributed task allocation problem in which m players must divide a set of n tasks between them. Each player i receives as input a set Xi of tasks such that the union of all input sets covers the task set. The goal is for each player to output a subset Yi ⊆ Xi, such that the outputs (Y1,...,Ym) form a partition of the set of tasks. The problem can be viewed as a distributed one-shot variant of the well-known k-server problem, and we also show that it is closely related to the problem of finding a rooted spanning tree in directed broadcast networks. We study the communication complexity and round complexity of the task allocation problem. We begin with the classical two-player communication model, and show that the randomized communication complexity of task allocation is Ω(n), even when the set of tasks is known to the players in advance. For the multi-player setting with m = O(n) we give two upper bounds in the shared-blackboard model of communication. We show that the problem can be solved in O(log n) rounds and O(n log n) total bits for arbitrary inputs; moreover, if for any set X of tasks, there are at least α|X| players that have at least one task from X in their inputs, then O((1/α + log m)log n) rounds suffice even if each player can only write O(log n) bits on the blackboard in each round. Finally, we extend our results to the case where the players communicate over an arbitrary directed communication graph instead of a shared blackboard. As an application of these results, we also consider the related problem of constructing a directed spanning tree in strongly-connected directed networks and we show lower and upper bounds for that problem.\",\n \"Title: Beeping a maximal independent set\\nYear: 2011\\n\\nAbstract: We consider the problem of computing a maximal independent set (MIS) in an extremely harsh broadcast model that relies only on carrier sensing. The model consists of an anonymous broadcast network in which nodes have no knowledge about the topology of the network or even an upper bound on its size. Furthermore, it is assumed that nodes wake up asynchronously. At each time slot a node can either beep (i.e., emit a signal) or be silent. At a particular time slot, beeping nodes receive no feedback, while silent nodes can only differentiate between none of its neighbors beeping, or at least one neighbor beeping. We start by proving a lower bound that shows that in this model, it is not possible to locally converge to an MIS in sub-polynomial time. We then study four different relaxations of the model which allow us to circumvent the lower bound and compute an MIS in polylogarithmic time. First, we show that if a polynomial upper bound on the network size is known, it is possible to find an MIS in O(log3 n) time. Second, if sleeping nodes are awoken by neighboring beeps, then we can also find an MIS in O(log3 n) time. Third, if in addition to this wakeup assumption we allow beeping nodes to receive feedback to identify if at least one neighboring node is beeping concurrently (i.e., sender-side collision detection) we can find an MIS in O(log2 n) time. Finally, if instead we endow nodes with synchronous clocks, it is also possible to compute an MIS in O(log2 n) time.\",\n \"Title: Towards worst-case churn resistant peer-to-peer systems\\nYear: 2010\\n\\nAbstract: Until now, the analysis of fault tolerance of peer- to-peer systems usually only covers random faults of some kind. Contrary to traditional algorithmic research, faults as well as joins and leaves occurring in a worst-case manner are hardly considered. In this article, we devise techniques to build dynamic peer-to-peer systems which remain fully functional in spite of an adversary who continuously adds and removes peers. We exemplify our algorithms on hyper- cube and pancake topologies and present a system which maintains small peer degree and network diameter.\",\n \"Title: Broadcasting in unreliable radio networks\\nYear: 2010\\n\\nAbstract: Practitioners agree that unreliable links, which sometimes deliver messages and sometime do not, are an important characteristic of wireless networks. In contrast, most theoretical models of radio networks fix a static set of links and assume that these links are reliable. This gap between theory and practice motivates us to investigate how unreliable links affect theoretical bounds on broadcast in radio networks. To that end we consider a model that includes two types of links: reliable links, which always deliver messages, and unreliable links, which sometimes fail to deliver messages. We assume that the reliable links induce a connected graph, and that unreliable links are controlled by a worst-case adversary. In the new model we show an Ω(n log n) lower bound on deterministic broadcast in undirected graphs, even when all processes are initially awake and have collision detection, and an Ω(n) lower bound on randomized broadcast in undirected networks of constant diameter. This separates the new model from the classical, reliable model. On the positive side, we give two algorithms that tolerate unreliability: an O(n3/2 √log n)-time deterministic algorithm and a randomized algorithm which terminates in O(n log2 n) rounds with high probability.\",\n \"Title: Deploying wireless networks with beeps\\nYear: 2010\\n\\nAbstract: We present the discrete beeping communication model, which assumes nodes have minimal knowledge about their environment and severely limited communication capabilities. Specifically, nodes have no information regarding the local or global structure of the network, do not have access to synchronized clocks and are woken up by an adversary. Moreover, instead on communicating through messages they rely solely on carrier sensing to exchange information. This model is interesting from a practical point of view, because it is possible to implement it (or emulate it) even in extremely restricted radio network environments. From a theory point of view, it shows that complex problems (such as vertex coloring) can be solved efficiently even without strong assumptions on properties of the communication model. We study the problem of interval coloring, a variant of vertex coloring specially suited for the studied beeping model. Given a set of resources, the goal of interval coloring is to assign every node a large contiguous fraction of the resources, such that neighboring nodes have disjoint resources. A k-interval coloring is one where every node gets at least a 1/k fraction of the resources. To highlight the importance of the discreteness of the model, we contrast it against a continuous variant described in [17]. We present an O(1) time algorithm that with probability 1 produces a O(Δ)-interval coloring. This improves an O(log n) time algorithm with the same guarantees presented in [17], and accentuates the unrealistic assumptions of the continuous model. Under the more realistic discrete model, we present a Las Vegas algorithm that solves O(Δ)- interval coloring in O(log n) time with high probability and describe how to adapt the algorithm for dynamic networks where nodes may join or leave. For constant degree graphs we prove a lower bound of Ω(log n) on the time required to solve interval coloring for this model against randomized algorithms. This lower bound implies that our algorithm is asymptotically optimal for constant degree graphs.\",\n \"Title: Efficient adaptive collect using randomization\\nYear: 2006\\n\\nAbstract: An adaptive algorithm, whose step complexity adjusts to the number of active processes, is attractive for distributed systems with a highly-variable number of processes. The cornerstone of many adaptive algorithms is an adaptive mechanism to collect up-to-date information from all participating processes. To date, all known collect algorithms either have non-linear step complexity or they are impractical because of unrealistic memory overhead.This paper presents new randomized collect algorithms with asymptotically optimal O(k) step complexity and linear memory overhead only. In addition we present a new deterministic collect algorithm that beats the best step complexity for previous polynomial-memory algorithms.\",\n \"Title: Coordinated consensus in dynamic networks\\nYear: 2011\\n\\nAbstract: We study several variants of coordinated consensus in dynamic networks. We assume a synchronous model, where the communication graph for each round is chosen by a worst-case adversary. The network topology is always connected, but can change completely from one round to the next. The model captures mobile and wireless networks, where communication can be unpredictable. In this setting we study the fundamental problems of eventual, simultaneous, and Δ-coordinated consensus, as well as their relationship to other distributed problems, such as determining the size of the network. We show that in the absence of a good initial upper bound on the size of the network, eventual consensus is as hard as computing deterministic functions of the input, e.g., the minimum or maximum of inputs to the nodes. We also give an algorithm for computing such functions that is optimal in every execution. Next, we show that simultaneous consensus can never be achieved in less than n - 1 rounds in any execution, where n is the size of the network; consequently, simultaneous consensus is as hard as computing an upper bound on the number of nodes in the network. For Δ-coordinated consensus, we show that if the ratio between nodes with input 0 and input 1 is bounded away from 1, it is possible to decide in time n-Θ(√ nΔ), where Δ bounds the time from the first decision until all nodes decide. If the dynamic graph has diameter D, the time to decide is min{O(nD/Δ),n-Ω(nΔ/D)}, even if D is not known in advance. Finally, we show that (a) there is a dynamic graph such that for every input, no node can decide before time n-O(Δ0.28n0.72); and (b) for any diameter D = O(Δ), there is an execution with diameter D where no node can decide before time Ω(nD / Δ). To our knowledge, our work constitutes the first study of Δ-coordinated consensus in general graphs.\",\n \"Title: Distributed computation of the mode\\nYear: 2008\\n\\nAbstract: This paper studies the problem of computing the most fre- quent element (the mode) by means of a distributed algo- rithm where the elements are located at the nodes of a net- work. Let k denote the number of distinct elements and fur- ther let mi be the number of occurrences of the element ei in the ordered list of occurrences m1 > m2 ‚ ::: ‚ mk. We give a deterministic distributed algorithm with time com- plexity O(D+k) where D denotes the diameter of the graph, which is essentially tight. As our main contribution, a Monte Carlo algorithm is presented which computes the mode in O(D + F2=m2 1 ¢ logk) time with high probability, where the frequency moment F' is deflned as F' = Pk i=1 m ' i. This algo- rithm is substantially faster than the deterministic algorithm for various relevant frequency distributions. Moreover, we provide a lower bound of ›(D+F5=(m5 1B)), where B is the maximum message size, that captures the efiect of the fre- quency distribution on the time complexity to compute the mode.\",\n \"Title: Distributed computation in dynamic networks\\nYear: 2010\\n\\nAbstract: In this paper we investigate distributed computation in dynamic networks in which the network topology changes from round to round. We consider a worst-case model in which the communication links for each round are chosen by an adversary, and nodes do not know who their neighbors for the current round are before they broadcast their messages. The model captures mobile networks and wireless networks, in which mobility and interference render communication unpredictable. In contrast to much of the existing work on dynamic networks, we do not assume that the network eventually stops changing; we require correctness and termination even in networks that change continually. We introduce a stability property called T -interval connectivity (for T = 1), which stipulates that for every T consecutive rounds there exists a stable connected spanning subgraph. For T = 1 this means that the graph is connected in every round, but changes arbitrarily between rounds. We show that in 1-interval connected graphs it is possible for nodes to determine the size of the network and compute any com- putable function of their initial inputs in O(n2) rounds using messages of size O(log n + d), where d is the size of the input to a single node. Further, if the graph is T-interval connected for T 1, the computation can be sped up by a factor of T, and any function can be computed in O(n + n2/T) rounds using messages of size O(log n + d). We also give two lower bounds on the token dissemination problem, which requires the nodes to disseminate k pieces of information to all the nodes in the network. The T-interval connected dynamic graph model is a novel model, which we believe opens new avenues for research in the theory of distributed computing in wireless, mobile and dynamic networks.\",\n \"Title: Radio Network Clustering from Scratch\\nYear: 2004\\n\\nAbstract: We propose a novel randomized algorithm for computing a dominating set based clustering in wireless ad-hoc and sensor networks. The algorithm works under a model which captures the characteristics of the set-up phase of such multi-hop radio networks: asynchronous wake-up, the hidden terminal problem, and scarce knowledge about the topology of the network graph. When modelling the network as a unit disk graph, the algorithm computes a dominating set in polylogarithmic time and achieves a constant approximation ratio.\",\n \"Title: Initializing newly deployed ad hoc and sensor networks\\nYear: 2004\\n\\nAbstract: A newly deployed multi-hop radio network is unstructured and lacks a reliable and efficient communication scheme. In this paper, we take a step towards analyzing the problems existing during the initialization phase of ad hoc and sensor networks. Particularly, we model the network as a multi-hop quasi unit disk graph and allow nodes to wake up asynchronously at any time. Further, nodes do not feature a reliable collision detection mechanism, and they have only limited knowledge about the network topology. We show that even for this restricted model, a good clustering can be computed efficiently. Our algorithm efficiently computes an asymptotically optimal clustering. Based on this algorithm, we describe a protocol for quickly establishing synchronized sleep and listen schedule between nodes within a cluster. Additionally, we provide simulation results in a variety of settings.\",\n \"Title: Optimal gradient clock synchronization in dynamic networks\\nYear: 2010\\n\\nAbstract: We study the problem of clock synchronization in highly dynamic networks, where communication links can appear or disappear at any time. The nodes in the network are equipped with hardware clocks, but the rate of the hardware clocks can vary arbitrarily within specific bounds, and the estimates that nodes can obtain about the clock values of other nodes are inherently inaccurate. Our goal in this setting is to output a logical clock at each node, such that the logical clocks of any two nodes are not too far apart, and nodes that remain close to each other in the network for a long time are better synchronized than distant nodes. This property is called gradient clock synchronization. Gradient clock synchronization has been widely studied in the static setting. We show that the bounds for the static case also apply to our highly dynamic setting: if two nodes remain at distance d from each other for sufficiently long, it is possible to synchronize their clocks to within O(d log(D/d)), where D is the diameter of the network. This is known to be optimal for static networks, and since a static network is a special case of a dynamic network, it is optimal for dynamic networks as well. Furthermore, we show that our algorithm has optimal stabilization time: when a path of length d appears between two nodes, the time required until the skew between the two nodes is reduced to O(d log(D/d)) is O(D), which we prove is optimal.\",\n \"Title: On the locality of bounded growth\\nYear: 2005\\n\\nAbstract: Many large-scale networks such as ad hoc and sensor networks, peer-to-peer networks, or the Internet have the property that the number of independent nodes does not grow arbitrarily when looking at neighborhoods of increasing size. Due to this bounded \\\"volume growth,\\\" one could expect that distributed algorithms are able to solve many problems more efficiently than on general graphs. The goal of this paper is to help understanding the distributed complexity of problems on \\\"bounded growth\\\" graphs. We show that on the widely used unit disk graph, covering and packing linear programs can be approximated by constant factors in constant time. For a more general network model which is based on the assumption that nodes are in a metric space of constant doubling dimension, we show that in O(log*!n) rounds it is possible to construct a (O(1), O(1))-network decomposition. This results in asymptotically optimal O(log*!n) time algorithms for many important problems.\",\n \"Title: Oblivious low-congestion multicast routing in wireless networks\\nYear: 2012\\n\\nAbstract: We propose a routing scheme to implement multicast communication in wireless networks. The scheme is oblivious, compact, and completely decentralized. It is intended to support dynamic and diverse multicast requests typical of, for example, publish/subscribe and content-based communication. The scheme is built on top of a geographical routing layer. Each message is transmitted along the geometric minimum spanning tree that connects the source and all the destinations. Then, for each edge in this tree, the scheme routes a message through a random intermediate node, chosen independently of the set of multicast requests. The intermediate node is chosen in the vicinity of the corresponding edge such that congestion is reduced without stretching the routes by more than a constant factor. We first evaluate the scheme analytically, showing that it achieves a theoretically optimal level of congestion. We then evaluate the scheme in simulation, showing that its performance is also good in practice.\",\n \"Title: On the treeness of internet latency and bandwidth\\nYear: 2009\\n\\nAbstract: Existing empirical studies of Internet structure and path properties indicate that the Internet is tree-like. This work quantifies the degree to which at least two important Internet measures--latency and bandwidth--approximate tree metrics. We evaluate our ability to model end-to-end measures using tree embeddings by actually building tree representations. In addition to being simple and intuitive models, these trees provide a range of commonly-required functionality beyond serving as an analytical tool. The contributions of our study are twofold. First, we investigate the ability to portray the inherent hierarchical structure of the Internet using the most pure and compact topology, trees. Second, we evaluate the ability of our compact representation to facilitate many natural tasks, such as the selection of servers with short latency or high bandwidth from a client. Experiments show that these tasks can be done with high degree of success and modest overhead.\",\n \"Title: Efficient symmetry breaking in multi-channel radio networks\\nYear: 2012\\n\\nAbstract: We investigate the complexity of basic symmetry breaking problems in multihop radio networks with multiple communication channels. We assume a network of synchronous nodes, where each node can be awakened individually in an arbitrary time slot by an adversary. In each time slot, each awake node can transmit or listen (without collision detection) on one of multiple available shared channels. The network topology is assumed to satisfy a natural generalization of the well-known unit disk graph model. We study the classic wake-up problem and a new variant we call active wake-up. For the former we prove a lower bound that shows the advantage of multiple channels disappears for any network of more than one hop. For the active version however, we describe an algorithm that outperforms any single channel solution. We then extend this algorithm to compute a constant approximation for the minimum dominating set (MDS) problem in the same time bound. Combined, these results for the increasingly relevant multi-channel model show that it is often possible to leverage channel diversity to beat classic lower bounds, but not always.\",\n \"Title: Worst-Case optimal and average-case efficient geometric ad-hoc routing\\nYear: 2003\\n\\nAbstract: In this paper we present GOAFR, a new geometric ad-hoc routing algorithm combining greedy and face routing. We evaluate this algorithm by both rigorous analysis and comprehensive simulation. GOAFR is the first ad-hoc algorithm to be both asymptotically optimal and average-case efficient. For our simulations we identify a network density range critical for any routing algorithm. We study a dozen of routing algorithms and show that GOAFR outperforms other prominent algorithms, such as GPSR or AFR.\",\n \"Title: Keeping mobile robot swarms connected\\nYear: 2009\\n\\nAbstract: Designing robust algorithms for mobile agents with reliable communication is difficult due to the distributed nature of computation, in mobile ad hoc networks (MANETs) the matter is exacerbated by the need to ensure connectivity. Existing distributed algorithms provide coordination but typically assume connectivity is ensured by other means. We present a connectivity service that encapsulates an arbitrary motion planner and can refine any plan to preserve connectivity (the graph of agents remains connected) and ensure progress (the agents advance towards their goal). The service is realized by a distributed algorithm that is modular in that it makes no assumptions of the motion-planning mechanism except the ability for an agent to query its position and intended goal position, local in that it uses 1-hop broadcast to communicate with nearby agents but doesn't need any network routing infrastructure, and oblivious in that it does not depend on previous computations. We prove the progress of the algorithm in one round is at least Ω(min(d, r)), where d is the minimum distance between an agent and its target and r is the communication radius. We characterize the worst case configuration and show that when d ≥ r this bound is tight and the algorithm is optimal, since no algorithm can guarantee greater progress. Finally we show all agents get Ɛ-close to their targets within O(D0/r+n2/Ɛ) rounds where n is the number of agents and D0 is the sum of the initial distances to the targets.\",\n \"Title: Brief announcement: hardness of broadcasting in wireless networks with unreliable communication\\nYear: 2009\\n\\nAbstract: We prove two broadcast lower bounds for a wireless network model that includes unreliable links. For deterministic algorithms, we show n − 1 rounds are required, where n is the number of processes. For randomized algorithms, ε(n − 1) rounds are required for success probability ε. In both cases, the bounds are proved for a network in which constant-time broadcast is possible.\",\n \"Title: Computing a Maximal Independent Set Using Beeps\\nYear: 2011\\n\\nAbstract: We consider the problem of finding a maximal independent set (MIS) in the discrete beeping model. At each time, a node in the network can either beep (i.e., emit a signal) or be silent. Silent nodes can only differentiate between no neighbor beeping, or at least one neighbor beeping. This basic communication model relies only on carrier-sensing. Furthermore, we assume nothing about the underlying communication graph and allow nodes to wake up (and crash) arbitrarily. We show that if a polynomial upper bound on the size of the network n is known, then with high probability every node becomes stable in O(\\\\log^3 n) time after it is woken up. To contrast this, we establish a polynomial lower bound when no a priori upper bound on the network size is known. This holds even in the much stronger model of local message broadcast with collision detection. Finally, if we assume nodes have access to synchronized clocks or we consider a somewhat restricted wake up, we can solve the MIS problem in O(\\\\log^2 n) time without requiring an upper bound on the size of the network, thereby achieving the same bit complexity as Luby's MIS algorithm.\",\n \"Title: A self-repairing peer-to-peer system resilient to dynamic adversarial churn\\nYear: 2005\\n\\nAbstract: We present a dynamic distributed hash table where peers may join and leave at any time. Our system tolerates a powerful adversary which has complete visibility of the entire state of the system and can continuously add and remove peers. Our system provides worst-case fault-tolerance, maintaining desirable properties such as a low peer degree and a low network diameter.\",\n \"Title: Fault-Tolerant Clustering in Ad Hoc and Sensor Networks\\nYear: 2006\\n\\nAbstract: In this paper, we study distributed approximation algorithms for fault-tolerant clustering in wireless ad hoc and sensor networks. A k-fold dominating set of a graph G = (V,E) is a subset S of V such that every node v \\\\in V \\\\ S has at least k neighbors in S. We study the problem in two network models. In general graphs, for arbitrary parameter t, we propose a distributed algorithm that runs in time O(t^2) and achieves an approximation ratio of O(t\\\\delta^2/t log\\\\delta), where n and \\\\delta denote the number of nodes in the network and the maximal degree, respectively. When the network is modeled as a unit disk graph, we give a probabilistic algorithm that runs in time O(log log n) and achieves an O(1) approximation in expectation. Both algorithms require only small messages of size O(log n) bits.\",\n \"Title: Dependable peer-to-peer systems withstanding dynamic adversarial churn\\nYear: 2006\\n\\nAbstract: The most essential difference between classical distributed data structures and peer-to-peer systems is the dynamic behavior of the latter. Unlike traditional systems which consist of a fixed set of machines of which a few might occasionally fail in some way, peer-to-peer systems are characterized by continuous joins and leaves at a high rate (called churn). We address this dynamism in two ways. We present a general information aggregation method which can be used to implement deterministic join and leave protocols which keep the network in a well-balanced state. We also use the information aggregation algorithm together with a primitive called token distribution to obtain a general way of constructing efficient peer-to-peer systems which are resilient to dynamic, adversarial joins and leaves. In each time step, an adversary is allowed to insert and delete a bounded number of arbitrary peers. The system adapts to this churn by rearranging peers or adjusting the topology whenever necessary.\",\n \"Title: Tight bounds for distributed selection\\nYear: 2007\\n\\nAbstract: We revisit the problem of distributed k-selection where, given a general connected graph of diameter D consisting of n nodes in which each node holds a numeric element, the goal is to determine the kth smallest of these elements. In our model, there is no imposed relation between the magnitude of the stored elements and the number of nodes in the graph. We propose a randomized algorithm whose time complexity is O(DlogD n) with high probability. Additionally, a deterministic algorithm with a worst-case time complexity of O(Dlog2D n) is presented which considerably improves the best known bound for deterministic algorithms. Moreover, we prove a lower bound of Ω(D logDn) for any randomized or deterministic algorithm, implying that the randomized algorithm is asymptotically optimal.\",\n \"Title: Taming Dynamic and Selfish Peers.\\nYear: 2006\\n\\nAbstract: Abstract This paper addresses two important challenges for current P2P systems, namely churn and selfishness. First, we report on a system [19] whose desirable properties (small peer degree, small network diam- eter, etc.) are maintained in spite of ongoing and concurrent membership changes. Thereby, dynamic peers are “tamed” by redundancy. Due to the worst- case churn, this system may never be fully repaired, but always fully functional. However, it relies on the fact that peers act according to the protocol. In the second part of the paper (based on [22]), we study the impact of selfish peers which follow those pro- tocols which maximize their utility. In particular, the efficiency of topologies formed by selfish peers is considered. We show that even in the absence of joins and leaves, the resulting system may never sta- bilize. How to “tame” selfish peers towards a more cooperative behavior remains an open issue. 1 CHURN Many existing file sharing systems are faced with fre-\",\n \"Title: Local approximation schemes for ad hoc and sensor networks\\nYear: 2005\\n\\nAbstract: We present two local approaches that yield polynomial-time approximation schemes (PTAS) for the Maximum Independent Set and Minimum Dominating Set problem in unit disk graphs. The algorithms run locally in each node and compute a (1+ε)-approximation to the problems at hand for any given ε 0. The time complexity of both algorithms is O(TMIS + log*! n/εO(1)), where TMIS is the time required to compute a maximal independent set in the graph, and n denotes the number of nodes. We then extend these results to a more general class of graphs in which the maximum number of pair-wise independent nodes in every r-neighborhood is at most polynomial in r. Such graphs of polynomially bounded growth are introduced as a more realistic model for wireless networks and they generalize existing models, such as unit disk graphs or coverage area graphs.\",\n \"Title: Vertex cover in graphs with locally few colors\\nYear: 2011\\n\\nAbstract: In [13], Erdős et al. defined the local chromatic number of a graph as the minimum number of colors that must appear within\\n distance 1 of a vertex. For any Δ ≥ 2, there are graphs with arbitrarily large chromatic number that can be colored so that\\n (i) no vertex neighborhood contains more than Δ different colors (bounded local colorability), and (ii) adjacent vertices from two color classes induce a complete bipartite graph (biclique coloring).\\n \\n \\n We investigate the weighted vertex cover problem in graphs when a locally bounded coloring is given. This generalizes the\\n vertex cover problem in bounded degree graphs to a class of graphs with arbitrarily large chromatic number. Assuming the Unique\\n Game Conjecture, we provide a tight characterization. We prove that it is UGC-hard to improve the approximation ratio of 2 − 2/(Δ + 1)\\n if the given local coloring is not a biclique coloring. A matching upper bound is also provided. Vice versa, when properties\\n (i) and (ii) hold, we present a randomized algorithm with approximation ratio of \\n\\n\\n\\n\\n\\n2-</font\\n> W</font\\n>(1)\\\\fraclnlnD</font\\n>lnD</font\\n>2- \\\\Omega(1)\\\\frac{\\\\ln \\\\ln \\\\Delta}{\\\\ln \\\\Delta}. This matches known inapproximability results for the special case of bounded degree graphs.\\n \\n \\n \\n Moreover, we show that the obtained result finds a natural application in a classical scheduling problem, namely the precedence\\n constrained single machine scheduling problem to minimize the total weighted completion time. In a series of recent papers\\n it was established that this scheduling problem is a special case of the minimum weighted vertex cover in graphs G\\n \\n P\\n of incomparable pairs defined in the dimension theory of partial orders. We show that G\\n \\n P\\n satisfies properties (i) and (ii) where Δ − 1 is the maximum number of predecessors (or successors) of each job.\",\n \"Title: Distributed selection: a missing piece of data aggregation\\nYear: 2008\\n\\nAbstract: In this article, we study the problem of distributed selection from a theoretical point of view. Given a general connected graph of diameter D consisting of n nodes in which each node holds a numeric element, the goal of a k-selection algorithm is to determine the kth smallest of these elements. We prove that distributed selection indeed requires more work than other aggregation functions such as, e.g., the computation of the average or the maximum of all elements. On the other hand, we show that the kth smallest element can be computed efficiently by providing both a randomized and a deterministic k-selection algorithm, dispelling the misconception that solving distributed selection through in-network aggregation is infeasible.\",\n \"Title: Leader election in shared spectrum radio networks\\nYear: 2012\\n\\nAbstract: We study the leader election problem in the context of a congested single-hop radio network. We assume a collection of N synchronous devices with access to a shared band of the radio spectrum, divided into F frequencies. To model unpredictable congestion, we assume an abstract interference adversary that can choose up to t F frequencies in each round to disrupt, preventing communication. The devices are individually activated in arbitrary rounds by an adversary. On activation, a device does not know how many other devices (if any) are also active. The goal of the leader election problem is for each active device to output the id of a leader as soon as possible after activation, while preserving the safety constraint that all devices output the same leader, with high probability. We begin by establishing a lower bound of Ω(log2N ⁄ F-t)log logN + ⁄ Ft F-t ⋅ logN) rounds, through reduction to an existing result in this model [5]. We then set out to prove this bound tight (within log log N factors). For the case where t=0, we present a novel randomized algorithm, based on a strategy of recruiting herald no-des, that works in O(⁄log2NF+log N) time. For 1 ≤ t ≤ F/6, we present a variant of our herald algorithm in which multiple real (potentially disrupted) frequencies are used to simulate each non-disrupted frequency from the t=0 case. This algorithm works in O(⁄log2NF+tlog N) time. Finally, for t F 6 we show how to improve the trapdoor protocol of [5], used to solve a similar problem in a non-optimal manner, to solve leader election in optimal O(⁄ logN + F t⁄F-t ⋅ logN ) time, for (only) these large values of t. We also observe that if F=ω(1) and t ≤(1-ε)F for a constant ε0, our protocols beat the classic Ω(log2N) bound on wake-up in a single frequency radio network, underscoring the observation that more frequencies in a radio network allows for more algorithmic efficiency - even if devices can each only participate on a single frequency at a time, and a significant fraction of these frequencies are disrupted adversarially.\",\n \"Title: Local Multicoloring Algorithms: Computing a Nearly-Optimal TDMA Schedule in Constant Time\\nYear: 2009\\n\\nAbstract: The described multicoloring problem has direct applications in the context of wireless ad hoc and sensor networks. In order to coordinate the access to the shared wireless medium, the nodes of such a network need to employ some medium access control (MAC) protocol. Typical MAC protocols control the access to the shared channel by time (TDMA), frequency (FDMA), or code division multiple access (CDMA) schemes. Many channel access schemes assign a fixed set of time slots, frequencies, or (orthogonal) codes to the nodes of a network such that nodes that interfere with each other receive disjoint sets of time slots, frequencies, or code sets. Finding a valid assignment of time slots, frequencies, or codes hence directly corresponds to computing a multicoloring of a graph $G$. The scarcity of bandwidth, energy, and computing resources in ad hoc and sensor networks, as well as the often highly dynamic nature of these networks require that the multicoloring can be computed based on as little and as local information as possible.\",\n \"Title: Ad hoc networks beyond unit disk graphs\\nYear: 2008\\n\\nAbstract: In this paper, we study an algorithmic model for wireless ad hoc and sensor networks that aims to be sufficiently close to reality as to represent practical real-world networks while at the same time being concise enough to promote strong theoretical results. The quasi unit disk graph model contains all edges shorter than a parameter d between 0 and 1 and no edges longer than 1. We show that--in comparison to the cost known for unit disk graphs--the complexity results of geographic routing in this model contain the additional factor 1/d2. We prove that in quasi unit disk graphs flooding is an asymptotically message-optimal routing technique, we provide a geographic routing algorithm being most efficient in dense networks, and we show that classic geographic routing is possible with the same asymptotic performance guarantees as for unit disk graphs if d ≥ 1/√2.\",\n \"Title: Gradient Clock Synchronization Using Reference Broadcasts\\nYear: 2009\\n\\nAbstract: Reference-Broadcast Synchronization (RBS) is a technique that allows a set of receivers in a broadcast network to accurately estimate each others' clock values. RBS provides a relative time-frame for conversion between the local clocks of different nodes, and can be used to synchronize nodes to an external time-source such as GPS. However, RBS by itself does not output a logical clock at every node, and so it does not solve internal clock synchronization. In this work we study the theoretical properties of RBS in the worst-case model, in which the performance of a clock synchronization algorithm is measured by the worst-case skew it can incur. We suggest a method by which RBS can be incorporated in standard internal clock synchronization algorithms. This is achieved by separating the task of estimating the clock values of other nodes in the network from the task of using these estimates to output a logical clock value. The separation is modelled using a virtual estimate graph , overlaid on top of the real network graph, which represents the information various nodes can obtain about each other. RBS estimates are represented in the estimate graph as edges between nodes at distance 2 from each other in the original network graph. A clock synchronization algorithm then operates on the estimate graph as though it were the original network. To illustrate the merits of this approach, we modify a recent optimal gradient clock synchronization algorithm to work in this setting. The modified algorithm transparently takes advantage of RBS estimates. Its quality of synchronization depends on the diameter of the estimate graph, which is typically much smaller than the diameter of the original network graph.\",\n \"Title: Gradient Clock Synchronization in Dynamic Networks\\nYear: 2011\\n\\nAbstract: Over the last years, large-scale decentralized computer networks such as peer-to-peer and mobile ad hoc networks have become increasingly prevalent. The topologies of many of these networks are often highly dynamic. This is especially true for ad hoc networks formed by mobile wireless devices. In this paper, we study the fundamental problem of clock synchronization in dynamic networks. We show that there is an inherent trade-off between the skew $\\\\mathcal{S}$ guaranteed along sufficiently old links and the time needed to guarantee a small skew along new links: for any sufficiently large initial skew on a new link, there are executions in which the time required to reduce the skew on the link to $O(\\\\mathcal{S})$ is at least $\\\\Omega(n/\\\\mathcal{S})$. We show that this bound is tight for moderately small values of $\\\\mathcal{S}$. Assuming a fixed set of n nodes, an arbitrary pattern of edge insertions and removals, and a weak dynamic connectivity requirement, we present an algorithm that always maintains a skew of O(n) between any two nodes in the network. For a parameter $\\\\mathcal{S}=\\\\Omega(\\\\sqrt{\\\\rho n})$, where ρ is the maximum hardware clock drift, it is further guaranteed that if a communication link between two nodes u,v persists in the network for $\\\\Theta(n/\\\\mathcal{S})$ time, the clock skew between u and v is reduced to no more than $O(\\\\mathcal{S})$.\",\n \"Title: Local Computation: Lower and Upper Bounds\\nYear: 2010\\n\\nAbstract: The question of what can be computed, and how efficiently, are at the core of\\ncomputer science. Not surprisingly, in distributed systems and networking\\nresearch, an equally fundamental question is what can be computed in a\\ndistributed fashion. More precisely, if nodes of a network must base their\\ndecision on information in their local neighborhood only, how well can they\\ncompute or approximate a global (optimization) problem? In this paper we give\\nthe first substantial lower bound on such local computation for (optimization)\\nproblems including minimum vertex cover, minimum (connected) dominating set,\\nmaximum matching, maximal independent set, and maximal matching. In addition we\\npresent a new distributed algorithm for solving general covering and packing\\nlinear programs. For some problems this algorithm is tight with the lower\\nbounds, for others it is a distributed approximation scheme. Together, our\\nlower and upper bounds establish the local computability and approximability of\\na large class of problems, characterizing how much local information is\\nrequired to solve these tasks.\",\n \"Title: Fast deterministic distributed maximal independent set computation on growth-bounded graphs\\nYear: 2005\\n\\nAbstract: The distributed complexity of computing a maximal independent set in a graph is of both practical and theoretical importance. While there exists an elegant O(log n) time randomized algorithm for general graphs [20], no deterministic polylogarithmic algorithm is known. In this paper, we study the problem in graphs with bounded growth, an important family of graphs which includes the well-known unit disk graph and many variants thereof. Particularly, we propose a deterministic algorithm that computes a maximal independent set in time O(log Δ· log*n) in graphs with bounded growth, where n and Δ denote the number of nodes and the maximal degree in G, respectively.\",\n \"Title: Decomposing broadcast algorithms using abstract MAC layers\\nYear: 2010\\n\\nAbstract: In much of the theoretical literature on wireless algorithms, issues of message dissemination are considered together with issues of contention management. This combination leads to complicated algorithms and analysis, and makes it difficult to extend the work to harder communication problems. In this paper, we present results of a current project aimed at simplifying such algorithms and analysis by decomposing the treatment into two levels, using abstract \\\"MAC layer\\\" specifications to encapsulate the contention management. We use two different abstract MAC layers: the basic one of [14, 15] and a new probabilistic layer. We first present a typical randomized contention-manageent algorithm for a standard graph-based radio network model We show that it implements both abstract MAC layers. We combine this algorithm with greedy algorithms for single-message and multi-message global broadcast and analyze the combination, using both abstract MAC layers as intermediate layers. Using the basic MAC layer, we prove a bound of O(D log(n/∈) log Δ) for the time to deliver a single message everywhere with probability 1 -- ∈, where D is the network diameter, n is the number of nodes, and Δ is the maximum node degree. Using the probabilistic layer, we prove a bound of O((D + log(n/∈)) log Δ), which matches the best previously-known bound for single-message broadcast over the physical network model. For multi-message broadcast, we obtain bounds of O((D + kΔ) log(n/∈) log Δ) using the basic layer and O((D + kΔ log(n/∈)) log Δ) using the probabilistic layer, for the time to deliver a message everywhere in the presence of at most k concurrent messages.\",\n \"Title: Decomposing broadcast algorithms using abstract MAC layers\\nYear: 2014\\n\\nAbstract: In much of the theoretical literature on global broadcast algorithms for wireless networks, issues of message dissemination are considered together with issues of contention management. This combination leads to complicated algorithms and analysis, and makes it difficult to extend the work to more difficult communication problems. In this paper, we present results aimed at simplifying such algorithms and analysis by decomposing the treatment into two levels, using abstract ''MAC layer'' specifications to encapsulate contention management. We use two different abstract MAC layers: the basic layer of [1,2] and a new probabilistic layer. We first present a typical randomized contention-management algorithm for a standard graph-based radio network model and show that it implements both abstract MAC layers. Then we combine this algorithm with greedy algorithms for single-message and multi-message global broadcast and analyze the combinations, using both abstract MAC layers as intermediate layers. Using the basic MAC layer, we prove a bound of ODlogn@?log(@D) for the time to deliver a single message everywhere with probability 1-@?, where D is the network diameter, n is the number of nodes, and @D is the maximum node degree. Using the probabilistic layer, we prove a bound of OD+logn@?log(@D), which matches the best previously-known bound for single-message broadcast over the physical network model. For multi-message broadcast, we obtain bounds of O(D+k@D)logn@?log(@D) using the basic layer and OD+k@Dlogn@?log(@D) using the probabilistic layer, for the time to deliver a message everywhere in the presence of at most k concurrent messages.\",\n \"Title: Serving Online Demands with Movable Centers.\\nYear: 2014\\n\\nAbstract: We study an online problem in which a set of mobile resources or centers have to be moved in order to optimally serve a set of demands. There is a set of $n$ nodes and a set of $k$ resources that are placed at some of the nodes. Each node can potentially host several resources and the resources can be moved between the nodes. At the nodes, demands arrive in an online fashion, and the cost for serving the demands is a function of the number of demands and resources at the different nodes. An online algorithm has to move resources in order to keep the service cost low, however as moving resources is expensive, the objective of an online algorithm is to minimize the total number of movements. Specifically, we give a deterministic online algorithm, which for parameters $\\\\alpha\\\\geq 1$ and $\\\\beta\\\\geq 0$ guarantees that at all times, the service cost is within a multiplicative factor $\\\\alpha$ and an additive term $\\\\beta$ of the optimal service cost and where also the movement cost is within a small multiplicative factor and an additive term of the optimal overall cost. Roughly, for $\\\\alpha=1$ and $\\\\beta=\\\\Omega(k)$, we show that the movement cost by time $t$ is upper bounded by the optimal service cost at time $t$ plus an additive term of order $O(k\\\\log k)$. For $\\\\alpha>1$ and arbitrary $\\\\beta$, we show that the movement cost by time $t$ is only linear in $k\\\\log k$ and logarithmic in the optimal service cost at time $t$. We also show that both bounds are almost asymptotically tight.\",\n \"Title: Broadcast In The Ad Hoc Sinr Model\\nYear: 2013\\n\\nAbstract: An increasing amount of attention is being turned toward the study of distributed algorithms in wireless network models based on calculations of the signal to noise and interference ratio (SINR). In this paper we introduce the ad hoc SINR model, which, we argue, reduces the gap between theory results and real world deployment. We then use it to study upper and lower bounds for the canonical problem of broadcast on the graph induced by both strong and weak links. For strong connectivity broadcast, we present a new randomized algorithm that solves the problem in O(Dlog (n) polylog(R)) rounds in networks of size n, with link graph diameter D, and a ratio between longest and shortest links bounded by R. We then show that for back-off style algorithms (a common type of algorithm where nodes do not explicitly coordinate with each other) and compact networks (a practice-motivated model variant that treats the distance from very close nodes as equivalent), there exist networks in which centralized algorithms can solve broadcast in O(1) rounds, but distributed solutions require Omega(n) rounds. We then turn our attention to weak connectivity broadcast, where we show a similar Omega(n) lower bound for all types of algorithms, which we (nearly) match with a back-off style O(n log(2) n)-round upper bound. Our broadcast algorithms are the first known for SINR-style models that do not assume synchronous starts, as well as the first known not to depend on power control, tunable carrier sensing, geographic information and/or exact knowledge of network parameters.\",\n \"Title: Asymptotically optimal geometric mobile ad-hoc routing\\nYear: 2002\\n\\nAbstract: In this paper we present AFR, a new geometric mobile ad-hoc routing algorithm. The algorithm is completely distributed; nodes need to communicate only with direct neighbors in their transmission range. We show that if a best route has cost c, AFR finds a route and terminates with cost &Ogr;(c2) in the worst case. AFR is the first algorithm with cost bounded by a function of the optimal route. We also give a tight lower bound by showing that any geometric routing algorithm has worst-case cost $Ogr;(c2). Thus AFR is asymptotically optimal. We give a non-geometric algorithm that also matches the lower bound, but needs some memory at each node. This establishes an intriguing trade-off between geometry and memory.\",\n \"Title: Internal DLA: Efficient Simulation of a Physical Growth Model - (Extended Abstract).\\nYear: 2014\\n\\nAbstract: The internal diffusion limited aggregation (IDLA) process places n particles on the two dimensional integer grid. The first particle is placed on the origin; every subsequent particle starts at the origin and performs an unbiased random walk until it reaches an unoccupied position. In this work we study the computational complexity of determining the subset that is generated after n particles have been placed. We develop the first algorithm that provably outperforms the naive step-bystep simulation of all particles. Particularly, our algorithm has a running time of O(n log(2) n) and a sublinear space requirement of O(n(1/2) log n), both in expectation and with high probability. In contrast to some speedups proposed for similar models in the physics community, our algorithm samples from the exact distribution. To simulate a single particle fast we have to develop techniques for combining multiple steps of a random walk to large jumps without hitting a forbidden set of grid points. These techniques might be of independent interest for speeding up other problems based on random walks.\",\n \"Title: On the power of the congested clique model\\nYear: 2014\\n\\nAbstract: We study the computation power of the congested clique, a model of distributed computation where n players communicate with each other over a complete network in order to compute some function of their inputs. The number of bits that can be sent on any edge in a round is bounded by a parameter b We consider two versions of the model: in the first, the players communicate by unicast, allowing them to send a different message on each of their links in one round; in the second, the players communicate by broadcast, sending one message to all their neighbors. It is known that the unicast version of the model is quite powerful; to date, no lower bounds for this model are known. In this paper we provide a partial explanation by showing that the unicast congested clique can simulate powerful classes of bounded-depth circuits, implying that even slightly super-constant lower bounds for the congested clique would give new lower bounds in circuit complexity. Moreover, under a widely-believed conjecture on matrix multiplication, the triangle detection problem, studied in [8], can be solved in O(nε) time for any ε > 0. The broadcast version of the congested clique is the well-known multi-party shared-blackboard model of communication complexity (with number-in-hand input). This version is more amenable to lower bounds, and in this paper we show that the subgraph detection problem studied in [8] requires polynomially many rounds for several classes of subgraphs. We also give upper bounds for the subgraph detection problem, and relate the hardness of triangle detection in the broadcast congested clique to the communication complexity of set disjointness in the 3-party number-on-forehead model.\",\n \"Title: A distributed perspective on graph connectivity and cuts\\nYear: 2014\\n\\nAbstract: Edge and vertex connectivity, as well as edge and vertex cuts are among the most basic and fundamental concepts in graph theory. In particular, they are naturally significant in a networking context as they are a measure for the rate at which information can be transferred across a network. While in a traditional, sequential setting, there is a rich literature (in particular on problems related to edge connectivity and edge cuts), until recently, much less was known from a distributed algorithms point of view. In my talk, I will discuss and give partial answers to some of the following basic questions. Using distributed algorithms, how fast can we compute or approximate the edge or vertex connectivity and is it possible to efficiently find small cuts in a network? Assuming, we have a network with good connectivity properties, to what extent is it possible to exploit this in order to speed up distributed computations? Where such properties can be exploited, are there network structures that allow to make use of good connectivity in a structured (and somewhat canonical) way and can we construct such structures in a distributed manner?\",\n \"Title: Tight bounds on vertex connectivity under vertex sampling\\nYear: 2015\\n\\nAbstract: A fundamental result by Karger [10] states that for any λ-edge-connected graph with n nodes, independently sampling each edge with probability p = Ω(log n/λ) results in a graph that has edge connectivity Ω(λp), with high probability. This paper proves the analogous result for vertex connectivity, when sampling vertices. We show that for any k-vertex-connected graph G with n nodes, if each node is independently sampled with probability p = Ω([EQUATION]log n/k), then the subgraph induced by the sampled nodes has vertex connectivity Ω(kp2), with high probability. This bound improves upon the recent results of Censor-Hillel et al. [6], and is existentially optimal.\",\n \"Title: Understanding Radio Irregularity in Wireless Networks\\nYear: 2008\\n\\nAbstract: In an effort to better understand connectivity and capacity in wireless networks, the log-normal shadowing radio propagation model is used to capture radio irregularities and ob- stacles in the transmission path. Existing results indicat e that log- normal shadowing results in higher connectivity and interference levels as shadowing (i.e., the radio irregularity) increas es. In this paper we demonstrate that such a behavior is mainly caused by an unnatural bias of the log-normal shadowing radio propagation model that results in a larger transmission range as shadowing increases. To avoid this effect, we analyze connectivity and interference under log-normal shadowing using a normalization that compensates for the enlarged radio transmission range. Our analysis shows that log-normal shadowing still improves the connectivity of a wireless network and even reduces interference. We explain this behavior by studying in detail what network parameters are affected by shadowing. Our results indicate that, when it comes to connectivity and interference, an analysis based on a circular transmission range leads to worst case results. I. I NTRODUCTION Understanding connectivity and radio interference in wire- less networks is an important step to determine their overall throughput capacity. In an effort to overcome the limitatio ns of the deterministic path loss model (where the transmission range is a perfect circle), connectivity ((4), (1), (7), (6) , (12)) and capacity ((10), (11)) have recently been studied in the context of the log-normal shadowing radio propagation model (9), (14). In this model, the radio irregularity can be contr olled through a single parameter: the shadowing deviation. As the shadowing deviation grows, the transmission range turns into a more irregular shape, mirroring what happens in reality with antennas that are not ideal (not perfectly isotropic) a nd obstacles that cut the transmission range short in a given direction. In this paper, we use the log-normal shadowing radio prop- agation model to explore the impact of radio irregularity on connectivity and interference in a wireless network. We show that the log-normal shadowing model introduces an unnatural bias into the analysis: as the shadowing deviation grows, the radio transmission range not only becomes more irregular, but also enlarges. This naturally leads to an improved connectivity. At the same time, the enlarged transmission range leads to an increase in interference. These are results already mentio ned The work presented in this paper was supported (in part) by the National Competence Center in Research on Mobile Information and Communication Systems (NCCR-MICS), a center supported by the Swiss National Science Foundation under grant number 5005-67322.\",\n \"Title: Near-Optimal Distributed Maximum Flow: Extended Abstract\\nYear: 2015\\n\\nAbstract: We present a near-optimal distributed algorithm for (1+o(1))-approximation of single-commodity maximum flow in undirected weighted networks that runs in (D+ √n)⋅ no(1) communication rounds in the Congest model. Here, n and D denote the number of nodes and the network diameter, respectively. This is the first improvement over the trivial O(m) time bound, and it nearly matches the Ω(D+√n) round complexity lower bound. The algorithm contains two sub-algorithms of independent interest, both with running time (D+√n)⋅ no(1): Distributed construction of a spanning tree of average stretch no(1). Distributed construction of an no(1)-congestion approximator consisting of the cuts induced by O(log n) virtual trees. The distributed representation of the cut approximator allows for evaluation in (D+√n)⋅ no(1) rounds. All our algorithms make use of randomization and succeed with high probability.\",\n \"Title: Efficient Communication in Cognitive Radio Networks\\nYear: 2015\\n\\nAbstract: Devices in a cognitive radio network use advanced radios to identify pockets of usable spectrum in a crowded band and make them available to higher layers of the network stack. A core challenge in designing algorithms for this model is that different devices might have different views of the network. In this paper, we study two problems for this setting that are well-motivated but not yet well-understood: local broadcast and data aggregation. We consider a single hop cognitive radio network with n nodes that each has access to c channels. We assume each pair of nodes overlaps on at least 1 We first describe and analyze CogCast, a randomized algorithm that solves local broadcast in O( (c/k)*max{1,c/n}*lg(n) ) time, with high probability, by spreading information in an epidemic manner through the network. We then propose CogComp, a randomized algorithm that solves data aggregation in O( (c/k)*max{1,c/n}*lg(n) + n ) time, with high probability. The CogComp algorithm uses CogCast as a key primitive to establish a spanning tree among the nodes, so that data can be aggregated from leaves to root. We conclude with a collection of lower bounds that show CogCast is near optimal (in particular, within a lg(n) factor) in many cases. These bounds introduce new techniques of potential standalone interest for those concerned with proving fundamental limits in the cognitive radio network setting.\",\n \"Title: Serving Online Requests with Mobile Servers.\\nYear: 2015\\n\\nAbstract: We study an online problem in which mobile servers have to be moved in order to efficiently serve at set of online requests. More formally, there is a set of n nodes and a set of k mobile servers that are placed at some of the nodes. Each node can potentially host several servers and the servers can be moved between the nodes. There are requests 1, 2,... that are adversarially issued at nodes one at a time, where a request issued at time t needs to be served at all times t' >= t. The cost for serving the requests is a function of the number of servers and requests at the different nodes. The requirements on how to serve the requests are governed by two parameters alpha >= 1 and beta >= 0. An algorithm needs to guarantee that at all times, the total service cost remains within a multiplicative factor a and an additive term beta of the current optimal service cost. We consider online algorithms for two different minimization objectives. We first consider the natural problem of minimizing the total number of server movements. We show that in this case for every k, the competitive ratio of every deterministic online algorithm needs to be at least Omega(n). Given this negative result, we then extend the minimization objective to also include the current service cost. We give almost tight bounds on the competitive ratio of the online problem where one needs to minimize the sum of the total number of movements and the current service cost. In particular, we show that at the cost of an additional additive term which is roughly linear in k, it is possible to achieve a multiplicative competitive ratio of 1 + epsilon for every constant epsilon > 0.\",\n \"Title: Tight Bounds for MIS in Multichannel Radio Networks\\nYear: 2015\\n\\nAbstract: In [8] an algorithm has been presented that computes a maximal independent set MIS within $$O\\\\\\\\log ^2 n/\\\\\\\\mathcal {F}+\\\\\\\\log n {{\\\\\\\\mathrm{\\\\\\\\mathrm {polyloglog~}}}} n$$ rounds in an n-node multichannel radio network with $$\\\\\\\\mathcal {F}$$ communication channels. The paper uses a multichannel variant of the standard graph-based radio network model without collision detection and it assumes that the network graph is a polynomially bounded independence graph BIG, a natural combinatorial generalization of well-known geographic families. The upper bound of [8] is known to be optimal up to the $${{\\\\\\\\mathrm{\\\\\\\\mathrm {polyloglog~}}}} n$$ factor. In this paper, we adapt this algorithm and its analysis to improve the result of [8] in two ways. Mainly, we get rid of the $${{\\\\\\\\mathrm{\\\\\\\\mathrm {polyloglog~}}}} n$$ factor in the running time and we thus obtain an asymptotically optimal multichannel radio network MIS algorithm. In addition, our new analysis allows to generalize the class of graphs from those with polynomially bounded local independence to graphs where the local independence is bounded by an arbitrary function of the neighborhood radius.\",\n \"Title: Brief Announcement: Local Independent Set Approximation.\\nYear: 2016\\n\\nAbstract: We show that the first phase of the Linial-Saks network decomposition algorithm gives a randomized distributed O(nε)-approximation algorithm for the maximum independent set problem that operates in O(1/ε) rounds, and we give a matching lower bound that holds even for bipartite graphs.\",\n \"Title: Multi-Message Broadcast in Dynamic Radio Networks.\\nYear: 2016\\n\\nAbstract: We continue the recent line of research studying information dissemination problems in adversarial dynamic radio networks. We give two generic algorithms which allow to transform generalized version of single-message broadcast algorithms into multi-message broadcast algorithms. Based on these generic algorithms, we obtain multi-message broadcast algorithms for dynamic radio networks for a number of different dynamic network settings. For one of the modeling assumptions, our algorithms are complemented by a lower bound which shows that the upper bound is close to optimal.\",\n \"Title: Distributed Sparse Cut Approximation.\\nYear: 2015\\n\\nAbstract: We study the problem of computing a sparse cut in an undirected network graph G=(V,E). We measure the sparsity of a cut (S,VS) by its conductance phi(S), i.e., by the ratio of the number of edges crossing the cut and the sum of the degrees on the smaller of the two sides. We present an efficient distributed algorithm to compute a cut of low conductance. Specifically, given two parameters b and phi, if there exists a cut of balance at least b and conductance at most phi, our algorithm outputs a cut of balance at least b/2 and conductance at most ~O(sqrt{phi}), where ~O(.) hides polylogarithmic factors in the number of nodes n. Our distributed algorithm works in the congest model, i.e., it only requires to send messages of size at most O(log(n)) bits. The time complexity of the algorithm is ~O(D + 1/b*phi), where D is the diameter of G. This is a significant improvement over a result by Das Sarma et al. [ICDCN 2015], where it is shown that a cut of the same quality can be computed in time ~O(n + 1/b*phi). The improved running time is in particular achieved by devising and applying an efficient distributed algorithm for the all-prefix-sums problem in a distributed search tree. This algorithm, which is based on the classic parallel all-prefix-sums algorithm, might be of independent interest.\",\n \"Title: Polynomial Lower Bound for Distributed Graph Coloring in a Weak LOCAL Model.\\nYear: 2016\\n\\nAbstract: We show an (varOmega big (varDelta ^{frac{1}{3}-frac{eta }{3}}big )) lower bound on the runtime of any deterministic distributed (mathcal {O}big (varDelta ^{1+eta }big ))-graph coloring algorithm in a weak variant of the (mathsf {LOCAL}) model.\",\n \"Title: Deterministic Distributed Edge-Coloring via Hypergraph Maximal Matching\\nYear: 2017\\n\\nAbstract: We present a deterministic distributed algorithm that computes a (2Δ-1)-edge-coloring, or even list-edge-coloring, in any n-node graph with maximum degree Δ, in O(log\\n<sup xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">8</sup>\\n Δ·log n) rounds. This answers one of the long-standing open questions of distributed graph algorithms} from the late 1980s, which asked for a polylogarithmic-time algorithm. See, e.g., Open Problem 4 in the Distributed Graph Coloring book of Barenboim and Elkin. The previous best round complexities were 2\\n<sup xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">O(√(log n)</sup>\\n by Panconesi and Srinivasan [STOC'92] and Õ(√(Δ)) + O(log* n) by Fraigniaud, Heinrich, and Kosowski [FOCS'16]. A corollary of our deterministic list-edge-coloring also improves the randomized complexity of (2Δ-1)-edge-coloring to poly(log log n) rounds. The key technical ingredient is a deterministic distributed algorithm for hypergraph maximal matching, which we believe will be of interest beyond this result. In any hypergraph of rank r - where each hyperedge has at most r vertices - with n nodes and maximum degree Δ, this algorithm computes a maximal matching in O(r\\n<sup xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">5</sup>\\n log\\n<sup xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">6+log r</sup>\\n Δ·log n) rounds. This hypergraph matching algorithm and its extensions also lead to a number of other results. In particular, we obtain a polylogarithmic-time deterministic distributed maximal independent set (MIS) algorithm for graphs with bounded neighborhood independence, hence answering Open Problem 5 of Barenboim and Elkins book, a ((log Δ/ε)\\n<sup xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">O(log 1/ε)</sup>\\n)-round deterministic algorithm for (1+ε)-approximation of maximum matching, and a quasi-polylogarithmic-time deterministic distributed algorithm for orienting λ-arboricity graphs with out-degree at most ⌈(1+ε)λ⌉, for any constant ε>0, hence partially answering Open Problem 10 of Barenboim and Elkin's book.\",\n \"Title: Dynamic Analysis of the Arrow Distributed Directory Protocol in General Networks.\\nYear: 2017\\n\\nAbstract: The Arrow protocol is a simple and elegant protocol to coordinate exclusive access to a shared object in a network. The protocol solves the underlying distributed queueing problem by using path reversal on a pre-computed spanning tree (or any other tree topology simulated on top of the given network).It is known that the Arrow protocol solves the problem with a competitive ratio of O(log D) on trees of diameter D. This implies a distributed queueing algorithm with competitive ratio O(s log D) for general networks with a spanning tree of diameter D and stretch s. In this work we show that when running the Arrow protocol on top of the well-known probabilistic tree embedding of Fakcharoenphol, Rao, and Talwar [STOCu002703], we obtain a randomized distributed online queueing algorithm with expected competitive ratio O(log n) against an oblivious adversary even on general n-node network topologies. The result holds even if the queueing requests occur in an arbitrarily dynamic and concurrent fashion and even if communication is asynchronous. The main technical result of the paper shows that the competitive ratio of the Arrow protocol is constant on a special family of tree topologies, known as hierarchically well separated trees.\",\n \"Title: Tight Bounds on Vertex Connectivity Under Sampling.\\nYear: 2017\\n\\nAbstract: A fundamental result by Karger [10] states that for any λ-edge-connected graph with n nodes, independently sampling each edge with probability p = Ω(log (n)/λ) results in a graph that has edge connectivity Ω(λp), with high probability. This article proves the analogous result for vertex connectivity, when either vertices or edges are sampled. We show that for any k-vertex-connected graph G with n nodes, if each node is independently sampled with probability p=Ω(&sqrt;log(n)/k), then the subgraph induced by the sampled nodes has vertex connectivity Ω(kp2), with high probability. If edges are sampled with probability p = Ω(log (n)/k), then the sampled subgraph has vertex connectivity Ω(kp), with high probability. Both bounds are existentially optimal.\",\n \"Title: ICALP 2017 - Call for Papers.\\nYear: 2016\\n\\nAbstract: The 44th International Colloquium on Automata, Languages, and Programming (ICALP) will take place in Warsaw, Poland, on 10-14 July 2017. ICALP is the main conference and annual meeting of the European Association for Theoretical Computer Science (EATCS). As usual, ICALP will be preceded by a series of workshops, which will take place on July 10.\",\n \"Title: Distributed MST and Routing in Almost Mixing Time.\\nYear: 2017\\n\\nAbstract: We present a randomized distributed algorithm that computes a minimum spanning tree in τ(G) · 2O(√(log n log log n))) rounds, in any n-node graph G with mixing time τ(G). This result provides a sub-polynomial complexity for a wide range of graphs of practical interest, and goes below the celebrated Ω(D+ √n) lower bound of Das Sarma et al. [STOC'11] which holds for some worst-case general graphs. The core novelty in this result is a distributed method for permutation routing. In this problem, one is given a number of source-destination pairs, and we should deliver one packet from each source to its destination, all in parallel, in the shortest span of time possible. Our algorithm allows us to route and deliver all these packets in τ(G) · 2O(√(log n log log n)) rounds, assuming that each node v is the source or destination for at most dG(v) packets. The main technical ingredient in this routing result is a certain hierarchical embedding of good-expansion random graphs on the base graph, which we believe can be of interest well beyond this work.\",\n \"Title: Nearest Common Ancestors: Universal Trees and Improved Labeling Schemes.\\nYear: 2017\\n\\nAbstract: We investigate the nearest common ancestor (NCA) function in rooted trees. the main conceptual contribution, the paper introduces universal trees for the NCA function: For a given family of rooted trees, an NCA-universal tree $S$ is a rooted tree such that any tree $T$ of the family can be embedded into $S$ such that the embedding of the NCA in $T$ of two nodes of $T$ is equal to the NCA in $S$ of the embeddings of the two nodes. As the main technical result we give explicit constructions of NCA-universal trees of size $n^{2.318}$ for the family of rooted $n$-vertex trees and of size $n^{1.894}$ for the family of rooted binary $n$-vertex trees. A direct consequence is the explicit construction of NCA-labeling schemes with labels of size $2.318log_2 n$ and $1.894log_2 n$ for the two families of rooted trees. This improves on the best known such labeling schemes established by Alstrup, Halvorsen and Larsen [SODA 2014].\",\n \"Title: On Derandomizing Local Distributed Algorithms\\nYear: 2018\\n\\nAbstract: The gap between the known randomized and deterministic local distributed algorithms underlies arguably the most fundamental and central open question in distributed graph algorithms. In this paper, we combine the method of conditional expectation with network decompositions to obtain a generic and clean recipe for derandomizing LOCAL algorithms. This leads to significant improvements on a number of problems, in cases resolving known open problems. Two main results are: - An improved deterministic distributed algorithm for hypergraph maximal matching, improving on Fischer, Ghaffari, and Kuhn [FOCS '17]. This yields improved algorithms for edge-coloring, maximum matching approximation, and low out-degree edge orientation. The last result gives the first positive resolution in the Open Problem 11.10 in the book of Barenboim and Elkin. - Improved randomized and deterministic distributed algorithms for the Lovász Local Lemma, which get closer to a conjecture of Chang and Pettie [FOCS '17].\",\n \"Title: Labeling Schemes for Nearest Common Ancestors through Minor-Universal Trees.\\nYear: 2018\\n\\nAbstract: Preprocessing a tree for finding the nearest common ancestor of two nodes is a basic tool with multiple applications. Quite a few linear-space constant-time solutions are known and the problem seems to be well-understood. This is however not so clear if we want to design a labeling scheme. In this model, the structure should be distributed: every node receives a distinct binary string, called its label, so that given the labels of two nodes (and no further information about the topology of the tree) we can compute the label of their nearest common ancestor. The goal is to make the labels as short as possible. Alstrup, Gavoille, Kaplan, and Rauhe [Theor. Comput. Syst. 37(3):441--456 2004] showed that O(log n)-bit labels are enough, with a somewhat large constant. More recently, Alstrup, Halvorsen, and Larsen [SODA 2014] refined this to only 2.772 log n, and provided a lower bound of 1.008 log n.\\n\\nWe connect the question of designing a labeling scheme for nearest common ancestors to the existence of a tree, called a minor-universal tree, that contains every tree on n nodes as a topological minor. Even though it is not clear if a labeling scheme must be based on such a notion, we argue that all already existing schemes can be reformulated as such. Further, we show that this notion allows us to easily obtain clean and good bounds on the length of the labels. As the main upper bound, we show that 2.318 log n-bit labels are enough. Surprisingly, the notion of a minor-universal tree for binary trees on n nodes has been already used in a different context by Hrubes et al. [CCC 2010], and Young, Chu, and Wong [J. ACM 46(3):416--435, 1999] introduced a very closely related (but not equivalent) notion of a universal tree. On the lower bound side, we show that any minor-universal tree for trees on n nodes must contain at least Ω(n2.174) nodes. This highlights a natural limitation for all approaches based on defining a minor-universal tree. We complement the existential results with a generic transformation that allows us, for any labeling scheme for nearest common ancestors based on a minor-universal tree, to decrease the query time to constant, while increasing the length of the labels only by lower order terms.\",\n \"Title: Local Distributed Algorithms in Highly Dynamic Networks\\nYear: 2018\\n\\nAbstract: We define a generalization of local distributed graph problems to (synchronous round-based) dynamic networks and present a framework for developing algorithms for these problems. The algorithms should satisfy non-trivial guarantees in every round. The guarantees should be stronger the more stable the graph has been during the last few rounds and coincide with the definition of the static graph problem if no topological change appeared recently. Moreover, if only a constant neighborhood around some part of the graph is stable during an interval, the algorithms should quickly converge to a solution for this part of the graph that remains unchanged throughout the interval. We demonstrate our generic framework with two classic distributed graph problems, namely (degree+1)-vertex coloring and maximal independent set (MIS). To illustrate the given guarantees consider the vertex coloring problem: Any conflict between two nodes caused by a newly inserted edge is resolved within T = O(logn) rounds. During this conflict resolving both nodes always output colors that are not in conflict with their respective `old` neighbors. The largest color that a node is allowed to output is determined by the number of distinct neighbors that it has seen in the last T rounds.\",\n \"Title: Improved Distributed Delta-Coloring\\nYear: 2021\\n\\nAbstract: We present a randomized distributed algorithm that computes a Delta-coloring in any non-complete graph with maximum degree Delta >= 4 in O(log Delta)+ 2(O)(root(log log n)) rounds, as well as a randomized algorithm that computes a Delta-coloring in O((log log n)(2)) rounds when Delta is an element of [3, O(1)]. Both these algorithms improve on an O(log(3) n/log Delta)-round algorithm of Panconesi and Srinivasan (STOC'93), which has remained the state of the art for the past 25 years. Moreover, the latter algorithm gets (exponentially) closer to an Omega (log log n) round lower bound of Brandt et al. (STOC'16).\",\n \"Title: Distributed Computation in the Node-Congested Clique.\\nYear: 2018\\n\\nAbstract: The Congested Clique model of distributed computing, which was introduced by Lotker, Patt-Shamir, Pavlov, and Peleg [SPAAu002703, SICOMPu002705] and was motivated as simple model for overlay networks, has received extensive attention over the past few years. In this model, nodes of the system are connected as a clique and can communicate in synchronous rounds, where per round each node can send $O(log n)$ bits to each other node, all the same time. The fact that this model allows each node to send and receive a linear number of messages at the same time seems to limit the relevance of the model for overlay networks. Towards addressing this issue, in this paper, we introduce the Node-Congested Clique as a general communication network model. Similarly to the Congested Clique model, the nodes are connected as a clique and messages are sent in synchronous communication rounds. However, here, per round, every node can send and receive only $O(log n)$ many messages of size $O(log n)$. To initiate research on our network model, we present distributed algorithms for the Minimum Spanning Tree, BFS Tree, Maximal Independent Set, Maximal Matching, and Coloring problem for an input graph $G=(V,E)$, where each clique node initially only knows a single node of $G$ and its incident edges. For the Minimum Spanning Tree problem, our runtime is polylogarithmic. In all other cases the runtime of our algorithms mainly depends on the arboricity $a$ of $G$, which is a constant for many important graph families such as planar graphs. At the core of these algorithms is a distributed algorithm that assigns directions to the edges of $G$ so that at the end, every node is incident to at most $O(a)$ outgoing edges.\",\n \"Title: Efficient Distributed Computation of MIS and Generalized MIS in Linear Hypergraphs.\\nYear: 2018\\n\\nAbstract: Given a graph, a maximal independent set (MIS) is a maximal subset of pairwise non-adjacent vertices. Finding an MIS is a fundamental problem distributed computing. Although the problem is extensively studied and well understood simple graphs, our knowledge is still quite limited when solving it hypergraphs, especially the distributed CONGEST model. In this paper, we focus on linear hypergraphs---a class of hypergraphs which any two hyperedges overlap on at most one node. first present a randomized algorithm for computing an MIS linear hypergraphs. It has poly-logarithmic runtime and it works the CONGEST model. The algorithm uses a network decomposition to achieve fast parallel processing. Within each cluster of the decomposition, we run a distributed variant of a parallel hypergraph MIS algorithm by Luczak and Szymanska. then propose the concept of a generalized maximal independent set (GMIS) as an extension to the classical MIS hypergraphs. More specifically, a GMIS, for each hyperedge $e$ a hypergraph $mathcal{H}$, we associate an integer threshold $t_e$ the range $[1, |e|-1]$, and the goal is to find a maximal subset $mathcal{I}$ of vertices that do not violate any threshold constraints: $forall e in E(mathcal{H}), |e cap mathcal{I}| leq t_e$. We hope that GMIS might capture a broader class of real-world problems than MIS; we also believe that GMIS is an interesting and challenging symmetry breaking problem on its own. Our second upper bound result is a distributed algorithm for computing a GMIS linear hypergraphs, subject to the constraint that the maximum hyperedge size is bounded by some constant. Again, the algorithm has poly-logarithmic runtime and it works the CONGEST model. It is obtained by generalizing our previous (linear) hypergraph MIS algorithm.\",\n \"Title: Deterministic Distributed Ruling Sets of Line Graphs.\\nYear: 2018\\n\\nAbstract: An ((alpha ,beta ))-ruling set of a graph (G=(V,E)) is a set (Rsubseteq V) such that for any node (vin V) there is a node (uin R) in distance at most (beta ) from v and such that any two nodes in R are at distance at least (alpha ) from each other. The concept of ruling sets can naturally be extended to edges, i.e., a subset (Fsubseteq E) is an ((alpha ,beta ))-ruling edge set of a graph (G=(V,E)) if the corresponding nodes form an ((alpha ,beta ))-ruling set in the line graph of G. This paper presents a simple deterministic, distributed algorithm, in the (mathsf {CONGEST}) model, for computing (2, 2)-ruling edge sets in (O(log ^{*}n)) rounds. Furthermore, we extend the algorithm to compute ruling sets of graphs with bounded diversity. Roughly speaking, the diversity of a graph is the maximum number of maximal cliques a vertex belongs to. We devise ((2,O(mathcal {D})))-ruling sets on graphs with diversity (mathcal {D}) in (O(mathcal {D}+log ^{*}n)) rounds. This also implies a fast, deterministic ((2,O( ell )))-ruling edge set algorithm for hypergraphs with rank at most ( ell ).\",\n \"Title: Possibilities and Impossibilities for Distributed Subgraph Detection.\\nYear: 2018\\n\\nAbstract: In the distributed subgraph detection problem, we are given a fixed subgraph H , and the network must decide whether the network graph contains a copy of H or not. Subgraph detection can be solved in a constant number of rounds if message size is unbounded, but in the CONGEST model, where each message has bounded size, it can have high round complexity. Distributed subgraph detection has received significant attention recently, with new upper and lower bounds, but several fundamental questions remain open. In this paper we prove new possibility and impossibility results for subgraph detection in the CONGEST model. We show for the first time that some subgraphs require superlinear --- in fact, nearly quadratic --- running time, even in small-diameter networks. We also study cycle-detection, and show that any even cycle can be detected in sublinear time (in contrast to odd cycles, which require linear time). For the special case of triangle-detection, we show that deterministic algorithms require $Ømega(łog n)$ total communication even in graphs of degree 2, and that one-round randomized algorithms must send $Ømega(Δ)$ bits in graphs of degree Δ, improving on the recent results of [Abboud et. al.]. Finally, we extend a recent lower bound of [Izumi, Le Gall] on listing all triangles to cliques of any size.\",\n \"Title: The Communication Cost of Information Spreading in Dynamic Networks\\nYear: 2018\\n\\nAbstract: This paper investigates the message complexity of distributed information spreading in adversarial dynamic networks. While distributed computations in dynamic networks have been studied intensively over the last years, almost all of the existing work solely focuses on the time complexity of distributed algorithms. In information spreading, the goal is to spread k tokens of information to every node on an n-node network. We consider the amortized (average) message complexity of spreading a token, assuming that the number of tokens is large. In a static network, this basic problem can be solved using (asymptotically optimal) O(n) amortized messages per token. Our focus is on token-forwarding algorithms, which do not manipulate tokens in any way other than storing, copying, and forwarding them. We present two sets of results depending on how nodes send messages to their neighbors: 1. Local broadcast: We show a tight lower bound of Ω̃(n\\n<sup xmlns:mml=\\\"http://www.w3.org/1998/Math/MathML\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">2</sup>\\n) on the number of amortized local broadcasts, which is matched by the naive flooding algorithm. The lower bound holds for randomized algorithms against a strongly adaptive adversary. 2. Unicast: We study the message complexity as a function of the number of dynamic changes in the network. To facilitate this, we introduce adversary-competitive message complexity as a natural complexity measure for analyzing dynamic networks: The adversary pays a unit cost for every topological change and the message cost of an algorithm is determined as the actual number of messages sent minus the total cost of the adversary. Under this model, we give a deterministic algorithm that obtains an optimal amortized message complexity of O(n) if the number of tokens k is sufficiently large. We also present a randomized algorithm that achieves subquadratic amortized message complexity for much smaller k under an oblivious adversary.\",\n \"Title: Shape Recognition by a Finite Automaton Robot.\\nYear: 2018\\n\\nAbstract: Motivated by the problem of shape recognition by nanoscale computing agents, we investigate the problem of detecting the geometric shape of a structure composed of hexagonal tiles by a finite-state automaton robot. In particular, in this paper we consider the question of recognizing whether the tiles are assembled into a parallelogram whose longer side has length l = f(h), for a given function f(*), where h is the length of the shorter side. To determine the computational power of the finite-state automaton robot, we identify functions that can or cannot be decided when the robot is given a certain number of pebbles. We show that the robot can decide whether l = ah+b for constant integers a and b without any pebbles, but cannot detect whether l = f(h) for any function f(x) = omega(x). For a robot with a single pebble, we present an algorithm to decide whether l = p(h) for a given polynomial p(*) of constant degree. We contrast this result by showing that, for any constant k, any function f(x) = omega(x^(6k + 2)) cannot be decided by a robot with k states and a single pebble. We further present exponential functions that can be decided using two pebbles. Finally, we present a family of functions f_n(*) such that the robot needs more than n pebbles to decide whether l = f_n(h).\",\n \"Title: Improved Distributed Delta-Coloring.\\nYear: 2018\\n\\nAbstract: We present a randomized distributed algorithm that computes a Δ- coloring in any non-complete graph with maximum degree Δ ≥ 4 in O(log Δ) +2O( √ log log n) rounds, as well as a randomized algorithm that computes a Δ-coloring in O((log logn)2) rounds when Δ ε [3,O(1)]. Both these algorithms improve on an O(log3 n/ log Δ)- round algorithm of Panconesi and Srinivasan [STOC'1993], which has remained the state of the art for the past 25 years. Moreover, the latter algorithm gets (exponentially) closer to an Ω(log logn) round lower bound of Brandt et al. [STOC'16].\",\n \"Title: Distributed Approximate Maximum Matching in the CONGEST Model.\\nYear: 2018\\n\\nAbstract: We study distributed algorithms for the maximum matching problem in the CONGEST model, where each message must be bounded in size. We give new deterministic upper bounds, and a new lower bound on the problem.We begin by giving a distributed algorithm that computes an exact maximum (unweighted) matching in bipartite graphs, in O(n log n) rounds. Next, we give a distributed algorithm that approximates the fractional weighted maximum matching problem in general graphs. In a graph with maximum degree at most Delta, the algorithm computes a (1-epsilon)-approximation for the problem in time O(log(Delta W)/epsilon^2), where W is a bound on the ratio between the largest and the smallest edge weight. Next, we show a slightly improved and generalized version of the deterministic rounding algorithm of Fischer [DISC u002717]. Given a fractional weighted maximum matching solution of value f for a given graph G, we show that in time O((log^2(Delta)+log^*n)/epsilon), the fractional solution can be turned into an integer solution of value at least (1-epsilon)f for bipartite graphs and (1-epsilon) * (g-1)/g * f for general graphs, where g is the length of the shortest odd cycle of G. Together with the above fractional maximum matching algorithm, this implies a deterministic algorithm that computes a (1-epsilon)* (g-1)/g-approximation for the weighted maximum matching problem in time O(log(Delta W)/epsilon^2 + (log^2(Delta)+log^* n)/epsilon).On the lower-bound front, we show that even for unweighted fractional maximum matching in bipartite graphs, computing an (1 - O(1/sqrt{n}))-approximate solution requires at least Omega~(D+sqrt{n}) rounds in CONGEST. This lower bound requires the introduction of a new 2-party communication problem, for which we prove a tight lower bound.\",\n \"Title: Optimal strategies for patrolling fences.\\nYear: 2018\\n\\nAbstract: A classical multi-agent fence patrolling problem asks: What is the maximum length $L$ of a line that $k$ agents with maximum speeds $v_1,ldots,v_k$ can patrol if each point on the line needs to be visited at least once every unit of time. It is easy to see that $L = alpha sum_{i=1}^k v_i$ for some efficiency $alpha [frac{1}{2},1)$. After a series of works giving better and better efficiencies, it was conjectured that the best possible efficiency approaches $frac{2}{3}$. No upper bounds on the efficiency below $1$ were known. We prove the first such upper bounds and tightly bound the optimal efficiency in terms of the minimum ratio of speeds $s = frac{v_{max}}{v_{min}}$ and the number of agents $k$. Our bounds of $alpha leq frac{1}{1 + frac{1}{s}}$ and $alpha leq 1 - frac{1}{2sqrt{k}}$ imply that in order to achieve efficiency $1 - epsilon$, at least $k geq Omega(epsilon^{-2})$ agents with a speed ratio of $s geq Omega(epsilon^{-1})$ are necessary. Guided by our upper bounds, we construct a scheme whose efficiency approaches $1$, disproving the conjecture of Kawamura and Soejima. Our scheme asymptotically matches our upper bounds in terms of the maximal speed difference and the number of agents used, proving them to be asymptotically tight.\",\n \"Title: Interference in cellular networks: the minimum membership set cover problem\\nYear: 2005\\n\\nAbstract: The infrastructure for mobile distributed tasks is often formed by cellular networks. One of the major issues in such networks is interference. In this paper we tackle interference reduction by suitable assignment of transmission power levels to base stations. This task is formalized introducing the Minimum Membership Set Cover combinatorial optimization problem. On the one hand we prove that in polynomial time the optimal solution of the problem cannot be approximated more closely than with a factor ln n. On the other hand we present an algorithm exploiting linear programming relaxation techniques which asymptotically matches this lower bound.\",\n \"Title: Lower bounds on information dissemination in dynamic networks\\nYear: 2012\\n\\nAbstract: We study lower bounds on information dissemination in adversarial dynamic networks. Initially, k pieces of information (henceforth called tokens) are distributed among n nodes. The tokens need to be broadcast to all nodes through a synchronous network in which the topology can change arbitrarily from round to round provided that some connectivity requirements are satisfied. If the network is guaranteed to be connected in every round and each node can broadcast a single token per round to its neighbors, there is a simple token dissemination algorithm that manages to deliver all k tokens to all the nodes in O(nk) rounds. Interestingly, in a recent paper, Dutta et al. proved an almost matching Ω(n+nk/logn) lower bound for deterministic token-forwarding algorithms that are not allowed to combine, split, or change tokens in any way. In the present paper, we extend this bound in different ways. If nodes are allowed to forward b≤k tokens instead of only one token in every round, a straight-forward extension of the O(nk) algorithm disseminates all k tokens in time O(nk/b). We show that for any randomized token-forwarding algorithm, Ω(n+nk/(b2lognloglogn)) rounds are necessary. If nodes can only send a single token per round, but we are guaranteed that the network graph is c-vertex connected in every round, we show a lower bound of Ω(nk/(clog3/2n)), which almost matches the currently best O(nk/c) upper bound. Further, if the network is T-interval connected, a notion that captures connection stability over time, we prove that Ω(n+nk/(T2logn)) rounds are needed. The best known upper bound in this case manages to solve the problem in O(n+nk/T) rounds. Finally, we show that even if each node only needs to obtain a δ-fraction of all the tokens for some δ∈[0,1], Ω(nkδ3/logn) are still required.\",\n \"Title: The complexity of data aggregation in directed networks\\nYear: 2011\\n\\nAbstract: We study problems of data aggregation, such as approximate counting and computing the minimum input value, in synchronous directed networks with bounded message bandwidth B = Ω(log n). In undirected networks of diameter D, many such problems can easily be solved in O(D) rounds, using O(log n)- size messages. We show that for directed networks this is not the case: when the bandwidth B is small, several classical data aggregation problems have a time complexity that depends polynomially on the size of the network, even when the diameter of the network is constant. We show that computing an ε-approximation to the size n of the network requires Ω(min{n, 1/ε2}/B) rounds, even in networks of diameter 2. We also show that computing a sensitive function (e.g., minimum and maximum) requires Ω(√n/B) rounds in networks of diameter 2, provided that the diameter is not known in advance to be o(√n/B). Our lower bounds are established by reduction from several well-known problems in communication complexity. On the positive side, we give a nearly optimal Õ(D+√n/B)-round algorithm for computing simple sensitive functions using messages of size B = Ω(logN), where N is a loose upper bound on the size of the network and D is the diameter.\",\n \"Title: On the Complexity of Local Distributed Graph Problems.\\nYear: 2017\\n\\nAbstract: This paper is centered on the complexity of graph problems in the well-studied LOCAL model of distributed computing, introduced by Linial [FOCS â87]. It is widely known that for many of the classic distributed graph problems (including maximal independent set (MIS) and (Î+1)-vertex coloring), the randomized complexity is at most polylogarithmic in the size n of the network, while the best deterministic complexity is typically 2O(âlogn). Understanding and potentially narrowing down this exponential gap is considered to be one of the central long-standing open questions in the area of distributed graph algorithms. We investigate the problem by introducing a complexity-theoretic framework that allows us to shed some light on the role of randomness in the LOCAL model. We define the SLOCAL model as a sequential version of the LOCAL model. Our framework allows us to prove completeness results with respect to the class of problems which can be solved efficiently in the SLOCAL model, implying that if any of the complete problems can be solved deterministically in logn rounds in the LOCAL model, we can deterministically solve all efficient SLOCAL-problems (including MIS and (Î+1)-coloring) in logn rounds in the LOCAL model. Perhaps most surprisingly, we show that a rather rudimentary looking graph coloring problem is complete in the above sense: Color the nodes of a graph with colors red and blue such that each node of sufficiently large polylogarithmic degree has at least one neighbor of each color. The problem admits a trivial zero-round randomized solution. The result can be viewed as showing that the only obstacle to getting efficient determinstic algorithms in the LOCAL model is an efficient algorithm to approximately round fractional values into integer values. In addition, our formal framework also allows us to develop polylogarithmic-time randomized distributed algorithms in a simpler way. As a result, we provide a polylog-time distributed approximation scheme for arbitrary distributed covering and packing integer linear programs.\",\n \"Title: Communication Primitives in Cognitive Radio Networks.\\nYear: 2017\\n\\nAbstract: Cognitive radio networks are a new type of multi-channel wireless network in which different nodes can have access to different sets of channels. By providing multiple channels, they improve the efficiency and reliability of wireless communication. However, the heterogeneous nature of cognitive radio networks also brings new challenges to the design and analysis of distributed algorithms. In this paper, we focus on two fundamental problems in cognitive radio networks: neighbor discovery, and global broadcast. We consider a network containing n nodes, each of which has access to c channels. We assume the network has diameter D, and each pair of neighbors have at least k≥1, and at most kmax≤c, shared channels. We also assume each node has at most Δ neighbors. For the neighbor discovery problem, we design a randomized algorithm CSeek which has time complexity Õ( (c2/k) + (kmax/k)*Δ ). CSeek is flexible and robust, which allows us to use it as a generic \\\\\\\"filter\\\\\\\" to find \\\\\\\"well-connected\\\\\\\" neighbors with an even shorter running time. We then move on to the global broadcast problem, and propose CGCast, a randomized algorithm which takes Õ( (c2/k) + (kmax/k)*Δ + D*Δ) time. CGCast uses CSeek to achieve communication among neighbors, and uses edge coloring to establish an efficient schedule for fast message dissemination. Towards the end of the paper, we give lower bounds for solving the two problems. These lower bounds demonstrate that in many situations, CSeek and CGCast are near optimal.\",\n \"Title: Distributed approximation of capacitated dominating sets\\nYear: 2007\\n\\nAbstract: We study local, distributed algorithms for the capacitated minimum dominating set (CapMDS) problem, which arises in various distributed network applications. Given a network graph G = (V,E), and a capacity cap(v) ∈ N for each node v ∈ V , the CapMDS problem asks for a subset S ⊆ V of minimal cardinality, such that every network node not in S is covered by at least one neighbor in S, and every node v ∈ S covers at most cap(v) of its neighbors. We prove that in general graphs and even with uniform capacities, the problem is inherently non-local, i.e., every distributed algorithm achieving a non-trivial approximation ratio must have a time complexity that essentially grows linearly with the network diameter. On the other hand, if for some parameter ε 0, capacities can be violated by a factor of 1 + ε, CapMDS becomes much more local. Particularly, based on a novel distributed randomized rounding technique, we present a distributed bi-criteria algorithm that achieves an O(log Δ)-approximation in time O(log3n + log(n)/ε), where n and Δ denote the number of nodes and the maximal degree in G, respectively. Finally, we prove that in geometric network graphs typically arising in wireless settings, the uniform problem can be approximated within a constant factor in logarithmic time, whereas the non-uniform problem remains entirely non-local.\",\n \"Title: Dynamic analysis of the arrow distributed protocol\\nYear: 2004\\n\\nAbstract: Arrow is a prominent distributed protocol which globally orders requests initiated by the nodes in a distributed system. In this paper we present a dynamic analysis of the Arrow protocol. We prove that Arrow is O(log D)-competitive, where D is the diameter of the spanning tree on which Arrow operates. In addition, we show that our analysis is almost tight by proving that for all trees the competitive ratio of Arrow is Ω(log D/log log D).\",\n \"Title: An Efficient Communication Abstraction for Dense Wireless Networks.\\nYear: 2017\\n\\nAbstract: In this paper we study the problem of developing efficient distributed algorithms for dense wireless networks. For many problems in this setting, fast solutions must leverage the reality that radio signals fade with distance, which can be exploited to enable concurrent communication among multiple sender/receiver pairs. To simplify the development of these algorithms we describe a new communication abstraction called FadingMAC which exposes the benefits of this concurrent communication, but also hides the details of the underlying low-level radio signal behavior. This approach splits efforts between those who develop useful algorithms that run on the abstraction, and those who implement the abstraction in concrete low-level wireless models, or on real hardware.After defining FadingMAC, we describe and analyze an efficient implementation of the abstraction in a standard low-level SINR-style network model. We then describe solutions to the following problems that run on the abstraction: max, min, sum, and mean computed over input values; process renaming; consensus and leader election; and optimal packet scheduling. Combining our abstraction implementation with these applications that run on the abstraction, we obtain near-optimal solutions to these problems in our low-level SINR model - significantly advancing the known results for distributed algorithms in this setting. Of equal importance to these concrete bounds, however, is the general idea advanced by this paper: as wireless networks become more dense, both theoreticians and practitioners must explore new communication abstractions that can help tame this density.\",\n \"Title: Contention Resolution on a Fading Channel.\\nYear: 2016\\n\\nAbstract: In this paper, we study upper and lower bounds for contention resolution on a single hop fading channel; i.e., a channel where receive behavior is determined by a signal to interference and noise ratio (SINR) equation. The best known previous solution solves the problem in this setting in O(log2 n log log n) rounds, with high probability in the system size n. We describe and analyze an algorithm that solves the problem in O(log n + log R) rounds, where R is the ratio between the longest and shortest link, and is a value upper bounded by a polynomial in n for most feasible deployments. We complement this result with an Ω(log n) lower bound that proves the bound tight for reasonable R. We note that in the classical radio network model (which does not include signal fading), high probability contention resolution requires Ω(log 2 n) rounds. Our algorithm, therefore, affirms the conjecture that the spectrum reuse enabled by fading should allow distributed algorithms to achieve a significant improvement on this log2 n speed limit. In addition, we argue that the new techniques required to prove our upper and lower bounds are of general use for analyzing other distributed algorithms in this increasingly well-studied fading channel setting.\",\n \"Title: Competitive Concurrent Distributed Scheduling.\\nYear: 2019\\n\\nAbstract: We introduce a new scheduling problem in distributed computing that we call the DSMS problem. We are given a set of $k geq 1$ mobile identical servers that are initially hosted by some processors of the given network. Further, we are given a set of requests that are issued by the processors possibly at any time in an online fashion, and the servers must serve them. A request must be scheduled before the request is served. The delay for scheduling a request is the time taken since the request is issued until it is scheduled. The goal is to minimize the average delay. Navigating mobile servers in a large-scale distributed system needs a scalable location service. We devise the distributed GNN protocol, a novel linked-reversal-based protocol for the DSMS problem that works on overlay trees. We prove that GNN is a starvation-free protocol that correctly integrates locating the servers and synchronizing the concurrent access to the servers despite asynchrony. Further, we analyze the GNN protocol for one-shot executions where all requests are simultaneously issued. We show that when running the GNN protocol on top of a special family of tree topologies---known as hierarchically well-separated trees (HSTs)---we obtain a randomized distributed protocol with an expected competitive ratio of $O(log n)$ on general network topologies for the DSMS problem where $n$ is the number of processors in the given network. From a technical point of view, the main result of our paper shows that the GNN protocol optimally solves the DSMS problem on HSTs for one-shot executions. Our results hold even if communication is asynchronous.\",\n \"Title: Latency, Capacity, and Distributed MST.\\nYear: 2019\\n\\nAbstract: Consider the problem of building a minimum-weight spanning tree for a given graph $G$. In this paper, we study the cost of distributed MST construction where each edge has a latency and a capacity, along with the weight. Edge latencies capture the delay on the links of the communication network, while capacity captures their throughput (in this case the rate at which messages can be sent). Depending on how the edge latencies relate to the edge weights, we provide several tight bounds on the time required to construct an MST. When there is no correlation between the latencies and the weights, we show that (unlike the sub-linear time algorithms in the standard textsf{CONGEST} model, on small diameter graphs), the best time complexity that can be achieved is $tilde{Theta}(D+n/c)$, where edges have capacity $c$ and $D$ refers to the latency diameter of the graph. However, if we restrict all edges to have equal latency $ell$ and capacity $c$, we give an algorithm that constructs an MST in $tilde{O}(D + sqrt{nell/c})$ time. Next, we consider the case where latencies are exactly equal to the weights. Here we show that, perhaps surprisingly, the bottleneck parameter in determining the running time of an algorithm is the total weight $W$ of the constructed MST by showing a tight bound of $tilde{Theta}(D + sqrt{W/c})$. In each case, we provide matching lower bounds.\",\n \"Title: On the Use of Randomness in Local Distributed Graph Algorithms.\\nYear: 2019\\n\\nAbstract: We attempt to better understand randomization in local distributed graph algorithms by exploring how randomness is used and what we can gain from it: We first ask the question of how much randomness is needed to obtain efficient randomized algorithms. We show that for all locally checkable problems with poly log n-time randomized algorithms, there are such algorithms even if either (I) there is a only a single (private) independent random bit in each poly log n-neighborhood of the graph, (II) the (private) bits of randomness of different nodes are only poly log n-wise independent, or (III) there are only poly log n bits of global shared randomness (and no private randomness). Second, we study how much we can improve the error probability of randomized algorithms. For all locally checkable problems with poly log n-time randomized algorithms, we show that there are such algorithms that succeed with probability 1-n-2 ε(log log n) 2 and more generally T-round algorithms, for T ≥ poly log n, with success probability 1-n-2 εlog 2T. We also show that poly log n-time randomized algorithms with success probability 1-2-2 log ε n for some ε > 0 can be derandomized to poly log n-time deterministic algorithms. Both of the directions mentioned above, reducing the amount of randomness and improving the success probability, can be seen as partial derandomization of existing randomized algorithms. In all the above cases, we also show that any significant improvement of our results would lead to a major breakthrough, as it would imply significantly more efficient deterministic distributed algorithms for a wide class of problems.\",\n \"Title: Deterministic Distributed Dominating Set Approximation in the CONGEST Model.\\nYear: 2019\\n\\nAbstract: We develop deterministic approximation algorithms for the minimum dominating set problem in the CONGEST model with an almost optimal approximation guarantee. For ε 1/ poly log Δ we obtain two algorithms with approximation factor (1 + ε)(1 + ł n (Δ + 1)) and with runtimes 2O(√ log n log log n) and O(Δ poly log Δ + poly log Δ log* n), respectively. Further we show how dominating set approximations can be deterministically transformed into a connected dominating set in the CONGEST model while only increasing the approximation guarantee by a constant factor. This results in a deterministic O(log Δ)-approximation algorithm for the minimum connected dominating set with time complexity 2O(√ log n log log n).\",\n \"Title: On the Complexity of Distributed Splitting Problems.\\nYear: 2019\\n\\nAbstract: One of the fundamental open problems in the area of distributed graph algorithms is whether randomization is needed for efficient symmetry breaking. While there are poly log n-time randomized algorithms for all the classic symmetry breaking problems, for many of them, the best deterministic algorithms are almost exponentially slower. The following basic local splitting problem, which is known as weak splitting, takes a central role in this context: Each node of a graph G=(V,E) has to be colored red or blue such that each node of sufficiently large degree has at least one neighbor of each color. Ghaffari, Kuhn, and Maus [STOC '17] showed that this seemingly simple problem is complete w.r.t. the above fundamental open question in the following sense: If there is an efficient poly log n-time determinstic distributed algorithm for weak splitting, then there is such an algorithm for all locally checkable graph problems for which an efficient randomized algorithm exists. We investigate the distributed complexity of weak splitting and some closely related problems and we in particular obtain the following results: We obtain efficient algorithms for special cases of weak splitting in nearly regular graphs. We show that if δ=Ø(log n) and Δ are the minimum and maximum degrees of G, weak splitting can be solved deterministically in time O #916;(√ over δ • poly(log n)). Further, if δ = Ø(log log n) and Δ ≤ 2ε δ, the time complexity is O(Δ over δ⋅poly(log log n)). We prove that the following two related problems are also complete in the same sense: (I) Color the nodes of a graph with C ≤ poly log n colors such that each node with a sufficiently large polylogarithmic degree has at least 2 log n different colors among its neighbors, and (II) Color the nodes with a large constant number of colors so that for each node of a sufficiently large at least logarithmic degree d(v), the number of neighbors of each color is at most (1-εd(v) for some constant ε > 0.\",\n \"Title: Near-Optimal Distributed Degree+1 Coloring\\nYear: 2022\\n\\nAbstract: We present a new approach to randomized distributed graph coloring that is simpler and more efficient than previous ones. In particular, it allows us to tackle the (deg +1)-list-coloring (D1LC) problem, where each node upsilon of degree dv is assigned a palette of d(upsilon)+ 1 colors, and the objective is to find a proper coloring using these palettes. While for (Delta+ 1)-coloring (where Delta is the maximum degree), there is a fast randomized distributed O(log(3) logn)-round algorithm due to Chang, Li, and Pettie, no o(logn)-round algorithms are known for the D1LC problem. We give a randomized distributed algorithm for D1LC that is optimal under plausible assumptions about the deterministic complexity of the problem. Using the recent deterministic algorithm of Ghaffari and Kuhn, our algorithm runs inO(log3 logn) time, matching the best bound known for (Lambda + 1)-coloring. A key contribution is a subroutine to generate slack for D1LC, which almost immediately leads to a palette sparsification theorem for D1LC when placed into the framework of works by Assadi, Chen, and Khanna and Alon and Assadi. That gives fast algorithms for D1LC in three different models: an O(1)-round algorithm in the MPC model with O(n) memory per machine; a single-pass semi-streaming algorithm in dynamic streams; and an O(n root n)-time query algorithm.\",\n \"Title: Routing Schemes and Distance Oracles in the Hybrid Model.\\nYear: 2022\\n\\nAbstract: The $\\\\mathsf{HYBRID}$ model was introduced as a means for theoretical study of distributed networks that use various communication modes. Conceptually, it is a synchronous message passing model with a local communication mode, where in each round each node can send large messages to all its neighbors in a local network (a graph), and a global communication mode, where each node is allotted limited (polylogarithmic) bandwidth per round which it can use to communicate with any node in the network. Prior work has often focused on shortest paths problems in the local network, as their global nature makes these an interesting case study how combining communication modes in the $\\\\mathsf{HYBRID}$ model can overcome the individual lower bounds of either mode. In this work we consider a similar problem, namely computation of distance oracles and routing schemes. In the former, all nodes have to compute local tables, which allows them to look up the distance (estimates) to any target node in the local network when provided with the label of the target. In the latter, it suffices that nodes give the next node on an (approximately) shortest path to the target. Our goal is to compute these local tables as fast as possible with labels as small as possible. We show that this can be done exactly in $\\\\widetilde O(n^{1/3})$ communication rounds and labels of size $\\\\Theta(n^{2/3})$ bits. For constant stretch approximations we achieve labels of size $O(\\\\log n)$ in the same time. Further, as our main technical contribution, we provide computational lower bounds for a variety of problem parameters. For instance, we show that computing solutions with stretch below a certain constant takes $\\\\widetilde \\\\Omega(n^{1/3})$ rounds even for labels of size $O(n^{2/3})$.\",\n \"Title: Sublinear-time distributed algorithms for detecting small cliques and even cycles\\nYear: 2022\\n\\nAbstract: In this paper we give sublinear-time distributed algorithms in the \\n \\n \\n \\n $$\\\\mathsf {CONGEST}$$\\n \\n model for finding or listing cliques and even-length cycles. We show for the first time that all copies of 4-cliques and 5-cliques in the network graph can be detected and listed in sublinear time, \\n \\n \\n \\n $$O(n^{5/6+o(1)})$$\\n \\n rounds and \\n \\n \\n \\n $$O(n^{73/75+o(1)})$$\\n \\n rounds, respectively. For even-length cycles, \\n \\n \\n \\n $$C_{2k}$$\\n \\n , we give an improved sublinear-time algorithm, which exploits a new connection to extremal combinatorics. For example, for 6-cycles we improve the running time from \\n \\n \\n \\n $${\\\\tilde{O}}(n^{5/6})$$\\n \\n to \\n \\n \\n \\n $${\\\\tilde{O}}(n^{3/4})$$\\n \\n rounds. We also show two obstacles on proving lower bounds for \\n \\n \\n \\n $$C_{2k}$$\\n \\n -freeness: first, we use the new connection to extremal combinatorics to show that the current lower bound of \\n \\n \\n \\n $${\\\\tilde{\\\\varOmega }}(\\\\sqrt{n})$$\\n \\n rounds for 6-cycle freeness cannot be improved using partition-based reductions from 2-party communication complexity, the technique by which all known lower bounds on subgraph detection have been proven to date. Second, we show that there is some fixed constant \\n \\n \\n \\n $$\\\\delta \\\\in (0,1/2)$$\\n \\n such that for any k, a lower bound of \\n \\n \\n \\n $$\\\\varOmega (n^{1/2+\\\\delta })$$\\n \\n on \\n \\n \\n \\n $$C_{2k}$$\\n \\n -freeness would imply new lower bounds in circuit complexity. We use the same technique to show a barrier for proving any polynomial lower bound on triangle-freeness. For general subgraphs, it was shown by Fischer et al. that for any fixed k, there exists a subgraph H of size k such that H-freeness requires \\n \\n \\n \\n $${\\\\tilde{\\\\varOmega }}(n^{2-\\\\varTheta (1/k)})$$\\n \\n rounds. It was left as an open problem whether this is tight, or whether some constant-sized subgraph requires truly quadratic time to detect. We show that in fact, for any subgraph H of constant size k, the H-freeness problem can be solved in \\n \\n \\n \\n $$O(n^{2 - \\\\varTheta (1/k)})$$\\n \\n rounds, nearly matching the lower bound.\",\n \"Title: Latency, capacity, and distributed minimum spanning trees\\nYear: 2022\\n\\nAbstract: We study the cost of distributed MST construction in the setting where each edge has a latency and a capacity, along with the weight. Edge latencies capture the delay on the links of the communication network, while capacity captures their throughput (in this case, the rate at which messages can be sent). Depending on how the edge latencies relate to the edge weights, we provide several tight bounds on the time and messages required to construct an MST. When edge weights exactly correspond with the latencies, we show that, perhaps interestingly, the bottleneck parameter in determining the running time of an algorithm is the total weight W of the MST (rather than the total number of nodes n, as in the standard CONGEST model). That is, we show a tight bound of Θ˜(D+W/c) rounds, where D refers to the latency diameter of the graph, W refers to the total weight of the constructed MST and edges have capacity c. The proposed algorithm sends O˜(m+W) messages, where m, the total number of edges in the network graph under consideration, is a known lower bound on message complexity for MST construction. We also show that Ω(W) is a lower bound for fast MST constructions. When the edge latencies and the corresponding edge weights are unrelated, and either can take arbitrary values, we show that (unlike the sub-linear time algorithms in the standard CONGEST model, on small diameter graphs), the best time complexity that can be achieved is Θ˜(D+n/c). However, if we restrict all edges to have equal latency ℓ and capacity c while having possibly different weights (weights could deviate arbitrarily from ℓ), we give an algorithm that constructs an MST in O˜(D+nℓ/c) time. In each case, we provide nearly matching upper and lower bounds.\",\n \"Title: Contention Resolution for Coded Radio Networks\\nYear: 2022\\n\\nAbstract: BSTRACTRandomized backoff protocols, such as exponential backoff, are a powerful tool for managing access to a shared resource, often a wireless communication channel (e.g., [1]). For a wireless device to transmit successfully, it uses a backoff protocol to ensure exclusive access to the channel. Modern radios, however, do not need exclusive access to the channel to communicate; in particular, they have the ability to receive useful information even when more than one device transmits at the same time. These capabilities have now been exploited for many years by systems that rely on interference cancellation, physical layer network coding and analog network coding to improve efficiency. For example, Zigzag decoding [56] demonstrated how a base station can decode messages sent by multiple devices simultaneously. In this paper, we address the following question: Can we design a backoff protocol that is better than exponential backoff when exclusive channel access is not required. We define the Coded Radio Network Model, which generalizes traditional radio network models (e.g., [30]). We then introduce the Decodable Backoff Algorithm, a randomized backoff protocol that achieves an optimal throughput of 1 - o (1). (Throughput 1 is optimal, as simultaneous reception does not increase the channel capacity.) The algorithm breaks the constant throughput lower bound for traditional radio networks [47-49], showing the power of these new hardware capabilities.\",\n \"Title: Node and Edge Averaged Complexities of Local Graph Problems\\nYear: 2022\\n\\nAbstract: BSTRACTWe continue the recently started line of work on the distributed node-averaged complexity of distributed graph algorithms. The node-averaged complexity of a distributed algorithm running on a graph G=(V,E) is the average over the times at which the nodes V of G finish their computation and commit to their outputs. We study the node-averaged complexity for some of the central distributed symmetry breaking problems. As our main result, we show that the randomized node-averaged complexity of computing a maximal independent set (MIS) is at least Ε(min(log Δ / log log Δ, √(log n/log log n))) in n-node graphs of maximum degree Δ. This bound is obtained by a novel adaptation of the well-known lower bound of Kuhn, Moscibroda, and Wattenhofer [JACM'16]. As a side result, we obtain that the worst-case randomized round complexity for computing an MIS in trees is also Ω(min(log Δ / log log Δ,√(log n/log log n)))---this essentially answers open problem 11.15 in the book of Barenboim and Elkin and resolves the complexity of MIS on trees up to an O(√(log log n)) factor. We also show that, perhaps surprisingly, a minimal relaxation of MIS, which is the same as (2,1)-ruling set, to the (2,2)-ruling set problem drops the randomized node-averaged complexity to O(1). For the problem of computing a maximal matching, we show that while the randomized node-averaged complexity is at least Ω(min(log Δ / log log Δ,√(log n/log log n))), the randomized edge-averaged complexity is O(1). Further, we show that the deterministic edge-averaged complexity of maximal matching is O(log2Δ + log* n) and the deterministic node-averaged complexity of maximal matching is O(log3Δ + log* n). Finally, we consider the problem of computing a sinkless orientation of a graph. The deterministic worst-case complexity of the problem is known to be Θ(log n), even on bounded-degree graphs. We show that the problem can be solved deterministically with node-averaged complexity O(log* n), while keeping the worst-case complexity in O(log n).\",\n \"Title: Distributed Delta-Coloring Plays Hide-and-Seek*\\nYear: 2022\\n\\nAbstract: We prove several new tight or near-tight distributed lower bounds for classic symmetry breaking problems in graphs. As a basic tool, we first provide a new insightful proof that any deterministic distributed algorithm that computes a Delta-coloring on.-regular trees requires Omega( log(Delta) n) rounds and any randomized such algorithm requires Omega( log(Delta) logn) rounds. We prove this by showing that a natural relaxation of the Delta-coloring problem is a fixed point in the round elimination framework. As a first application, we show that our.-coloring lower bound proof directly extends to arbdefective colorings. An arbdefective Delta-coloring of a graph G = (V, E) is given by a c-coloring of V and an orientation of E, where the arbdefect of a color.. is the maximum number of monochromatic outgoing edges of any node of color... We exactly characterize which variants of the arbdefective coloring problem can be solved in O (f (Delta)+log*n) rounds, for some function.., and which of them instead require Omega(log(Delta)n) rounds for deterministic algorithms and Omega(log(Delta) logn) rounds for randomized ones. As a second application, which we see as our main contribution, we use the structure of the fixed point as a building block to prove lower bounds as a function of. for problems that, in some sense, are much easier than Delta-coloring, as they can be solved in O(log*n) deterministic rounds in bounded-degree graphs. More specifically, we prove lower bounds as a function of. for a large class of distributed symmetry breaking problems, which can all be solved by a simple sequential greedy algorithm. For example, we obtain novel results for the fundamental problem of computing a (2,beta)-ruling set, i.e., for computing an independent set S subset of V such that every node upsilon is an element of V is within distance <= beta of some node in S. We in particular show that Omega(beta Delta(1)/(beta)) rounds are needed even if initially an O(Delta)-coloring of the graph is given. With an initial O(Delta)-coloring, this lower bound is tight and without, it still nearly matches the existing O (beta Delta(2/(beta+1)) + log*n) upper bound. The new ( 2,beta)-ruling set lower bound is an exponential improvement over the best existing lower bound for the problem, which was proven in [FOCS '20]. As a special case of the lower bound, we also obtain a tight linear-in-. lower bound for computing a maximal independent set (MIS) in trees. While such an MIS lower bound was known for general graphs, the best previous MIS lower bounds for trees was O( log.). Our lower bound even applies to a much more general family of problems that allows for almost arbitrary combinations of natural constraints from coloring problems, orientation problems, and independent set problems, and provides a single unified proof for known and new lower bound results for these types of problems. All of our lower bounds as a function of Delta also imply substantial lower bounds as a function of n. For instance, we obtain that the maximal independent set problem, on trees, requires Omega( logn/log logn) rounds for deterministic algorithms, which is tight.\",\n \"Title: Distributed Edge Coloring in Time Polylogarithmic in Δ\\nYear: 2022\\n\\nAbstract: BSTRACTWe provide new deterministic algorithms for the edge coloring problem, which is one of the classic and highly studied distributed local symmetry breaking problems. As our main result, we show that a (2Δ - 1)-edge coloring can be computed in time poly log Δ + O(log* n) in the LOCAL model. This improves a result of Balliu, Kuhn, and Olivetti [PODC '20], who gave an algorithm with a quasi-polylogarithmic dependency on Δ. We further show that in the CONGEST model, an (8 + ε)Δ-edge coloring can be computed in poly log Δ + O(log* n) rounds. The best previous O(Δ)-edge coloring algorithm that can be implemented in the CONGEST model is by Barenboim and Elkin [PODC '11] and it computes a 2O(1/ε)Δ- edge coloring in time O(Δε + log* n) for any ε ∈ (0, 1].\"\n]","isConversational":false},"profile_size":{"kind":"number","value":112,"string":"112"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13681,"string":"13,681"}}},{"rowIdx":13682,"cells":{"question_id":{"kind":"string","value":"Kamala Krithivasan"},"question":{"kind":"string","value":"Generate an abstract for the title \"P Systems Generating Hexagonal Picture Languages\" using the following items: 1. P systems\n2. Rectangular arrays\n3. Hexagonal arrays\n4. Triangular grids\n5. Capabilities demonstration"},"answer":{"kind":"string","value":"P systems generating rectangular arrays have been recently introduced in [1,2,3], thus bringing together the two areas of theoretical computer science, namely membrane computing and picture grammars. In this paper, hexagonal arrays on triangular grids are considered and the capabilities of the three approaches [1,2,3] that construct P systems for generation of arrays on rectangular grids, to handle arrays on triangular grids, are demonstrated."},"correct_answer":{"kind":"string","value":"P systems generating rectangular arrays have been recently introduced in [1,2,3], thus bringing together the two areas of theoretical computer science, namely membrane computing and picture grammars. In this paper, hexagonal arrays on triangular grids are considered and the capabilities of the three approaches [1,2,3] that construct P systems for generation of arrays on rectangular grids, to handle arrays on triangular grids, are demonstrated."},"documents":{"kind":"list like","value":["Title: Binding-blocking automata\nYear: 2013\n\nAbstract: We propose an automaton model called as binding-blocking automaton BBA. It is a finite state automaton together with the ability to block some symbols and postpone them for reading by the head at a later time. The idea of blocking some symbols from being read by the head and unblocking when the system requires to read is motivated by peptide computing where some parts of peptide sequences are blocked by attaching an antibody with higher affinity and unblocked at a later point by the removal of the appropriate antibody. We study the variants of such systems, analyse the power of each variants and show various hierarchy results involving them. We also define normal-forms of binding-blocking automata for one of its variants and also show that the acceptance power of this variant of BBA is strictly less than that of multi-head finite automata.","Title: Array Splicing Systems\nYear: 1997\n\nAbstract: In this paper the concept of splicing is extended to arrays and array or 2D splicing systems are defined. Various subclasses of 2D splicing systems are defined and a restricted class viz. finite simple splicing systems is studied. The hierarchy among the various subclasses of finite simple splicing systems and their relationship to the strictly locally testable languages are established.","Title: Graph splicing systems\nYear: 2007\n\nAbstract: In this paper, extended graph splicing systems are defined. It is shown that when strings are represented as linear graphs, any recursively enumerable set can be generated by an extended graph splicing system. It is also shown that the computational completeness of extended graph splicing systems can be proved under some constraints too.","Title: Hexagonal Pattern Languages\nYear: 2004\n\nAbstract: Hexagonal tiling systems, hexagonal local picture languages and hexagonal recognizable picture languages are defined in this\n paper. Hexagonal Wang tiles and systems are also introduced. It is noticed that the family of hexagonal picture languages\n defined by hexagonal Wang systems coincides with the family of hexagonal picture languages recognized by hexagonal tiling\n system. Similar to hv-domino systems describing rectangular arrays, we define xyz-domino systems and characterize hexagonal\n picture languages using this. Unary hexagonal picture languages are also considered and we analyze some of their properties.","Title: Contextual P Systems\nYear: 2002\n\nAbstract: Generally, in P systems with string-objects one uses the Chomsky way of rewriting for processing the objects. In this paper we consider the contextual way of handling string-objects in P systems. We introduce some variants of contextual grammars and prove that contextual P systems with rules corresponding to these variants are more powerful than ordinary contextual grammars and their variants. We also show that one-sided contextual P systems with right-sided erased contexts and insertion contextual P systems with right-sided erased contexts are computationally complete.","Title: Control Words of Transition P Systems.\nYear: 2012\n\nAbstract: A new way of associating a language with the computation of a P system is considered. A label is assigned to every rule in a P system, where the labels are chosen from a finite alphabet or A. We associate a string, called control word, that is obtained by concatenating the labels of the rules in the transition sequence corresponding to a computation. We study the generative capacity of such control languages comparing them with family of languages such as regular, context-free, context-sensitive and recursively enumerable languages of Chomskian hierarchy.","Title: Contextual Array P Systems\nYear: 2004\n\nAbstract: We define external and internal array contextual P systems, which generate rectangular arrays. We also introduce external array contextual P systems with erased contexts. Some properties of these systems are discussed. We obtain the relation between external array contextual P systems and external array contextual grammars with regular control.","Title: Distributed Processing in Automata\nYear: 1999\n\nAbstract: With distributed computing beginning to play a major role in modern Computer Science, the theory of grammar systems and distributed automata has been developed in order to model distributed computing. In this paper, we introduce the notion of distributed automata in the sequential sense. Distributed Automata are a group of automata working in unison to accept one language. We build the theory of distributed automata for FSA and PDA in dierent modes of acceptance like the t-mode, *-mode, = k-mode, k-mode and k-mode. We then analyze the acceptance power of each automata in all the above modes. We present proofs that distributed FSA do not have any additional power over \\centralized\" FSAs in any of the modes, while distributed PDA with only two components is as powerful as Turing Machines in all of the modes. We give proofs for the equivalence of all modes in the case of PDAs. We also study a restricted version of distributed PDA called k-turn distributed PDA.","Title: On Controlled P Systems\nYear: 2014\n\nAbstract: We introduce and briefly investigate P systems with controlled computations. First, P systems with label restricted transitions are considered in each step, all rules used have either the same label, or, possibly, the empty label, λ, then P systems with the computations controlled by languages as in context-free controlled grammars. The relationships between the families of sets of numbers computed by the various classes of controlled P systems are investigated, also comparing them with length sets of languages in Chomsky and Lindenmayer hierarchies characterizations of the length sets of ET0L and of recursively enumerable languages are obtained in this framework. A series of open problems and research topics are formulated.","Title: Efficient two-dimensional pattern matching in the presence of errors\nYear: 1987\n\nAbstract: We give an algorithm for two-dimensional pattern matching in the presence of errors. We find that the complexity of our algorithm is O(kn1n2 log n2 + n12n2 + kn1m1m2), where the pattern is an n1 × n2 array, the text is an m1 × m2 array and k is the number of mismatches allowed.","Title: Network of evolutionary processors with splicing rules and forbidding context\nYear: 2005\n\nAbstract: In this paper we consider networks of evolutionary processors with splicing rules and forbidding context (NEPFS) as language generating and computational devices. Such a network consists of several processors placed on the nodes of a virtual graph and are able to perform splicing (which is a biologically motivated operation) on the words present in that node, according to the splicing rules present there. Before applying the splicing operation on words, we check for the absence of certain symbols (forbidding context) in the strings on which the rule is applied. Each node is associated with an input and output filter. When the filters are based on random context conditions, one gets the computational power of Turing machines with networks of size two. We also show how these networks can be used to solve NP–complete problems in linear time.","Title: Network of evolutionary processors with splicing rules and permitting context.\nYear: 2007\n\nAbstract: In this paper we consider networks of evolutionary processors with splicing rules and permitting context (NEPPS) as language generating and computational devices. Such a network consists of several processors placed on the nodes of a virtual graph and are able to perform splicing (which is a biologically motivated operation) on the words present in that node, according to the splicing rules present there. Before applying the splicing operation on words, we check for the presence of certain symbols (permitting context) in the strings on which the rule is applied. Each node is associated with an input and output filter. When the filters are based on random context conditions, one gets the computational power of Turing machines with networks of size two. We also show how these networks can be used to solve NP-complete problems in linear time.","Title: Rewriting Tissue P Systems\nYear: 2004\n\nAbstract: By considering string-objects and rewriting rules, we propose a variant of tissue P systems, namely, rewriting tissue P systems. We show the computational efficiency of rewriting tissue P systems by solving the Satisfiability and the Hamiltionian path problems in linear time. We study the computational capacity of rewriting tissue P systems and show that rewriting tissue P systems with at most two cells and four states are computationally universal. We also show the universality result of rewriting tissue P systems with at most one cell and five states. Finally we propose some new directions for future work.","Title: A Survey of Some Variants of P Systems\nYear: 2002\n\nAbstract: In this paper we give a brief survey of some variants of P systems and their computational capacity. An improvement of a known result about rewriting P systems with leftmost derivation is given. We also give the generalized definition for normal forms of rewriting P systems.","Title: A Variant of Distributed P Systems for Real Time Cross Layer Optimization.\nYear: 2012\n\nAbstract: Membrane computing models (also known as P Systems) that solve optimisation problems using genetic algorithms, ant colony optimisation, quantum-inspired evolutionary algorithm and particle swarm optimisation have been defined and are efficiently used in several applications. This paper describes the design of a variant of the existing Distributed P system (dP system) that is augmented with new features that enable centralised monitoring and communication with all the other components of the distributed system. This proposed model is titled Monitored Distributed P System (MDP System) and its innovative application in performing Cross Layer Optimisation in wireless adhoc networks is also presented. In the proposed MDP System each node in the network is represented by a P system that can independently perform Cross Layer Optimisation using particle swarm optimisation. Discussions on the communication complexity of the proposed model and the experimental results presented are also suggestive of the fact that the proposed Monitored Distributed P System is suitable for real time optimisation in a dynamic and distributed environment.","Title: An efficient reconstruction of 2D-tiling with t1,2, t2,1, t1,1 tiles\nYear: 2006\n\nAbstract: We define the projection of a tiling as a matrix P = (pij) where pi1 is number of t1,2 tiles in row i and pi2 is the number of t2,1 tiles in row i. We give an efficient algorithm to tile a 2D-square grid with only t1,2, t2,1, t1,1 tiles such that the projection of this tiling is the same as the given projection.","Title: Image Representation using Distributed Weighted Finite Automata\nYear: 2001\n\nAbstract: Weighted finite automata (WFA) define real functions, in particular, grayness functions of graytone images. Inference algorithm that converts an arbitrary function (graytone image) into a WFA that can regenerate it is given in [7]. In this paper we define the theoretical construct of Cooperating Distributed Weighted Finite Automata with n-components (n-WFA) and study the power of this construct in various modes of acceptance. We give an inference algorithm and the de-inference algorithm for the n-WFA.","Title: On the study of ambiguity and the trade-off between measures and ambiguity in insertion–deletion languages\nYear: 2011\n\nAbstract: Gene insertion and deletion are the operations that occur commonly in DNA processing and RNA editing. Based on these operations, a computing model has been formulated in formal language theory known as insertion–deletion systems. In this paper we study about ambiguity issues of these systems. First, we define six levels of ambiguity for insertion–deletion systems that are based on the components used in the derivation such as axiom, contexts and strings. Next, we show that there are inherently i-ambiguous insertion–deletion languages which are j-unambiguous for the combinations (i,j)∈{(5,4),(4,3),(4,2),(3,1),(2,1),(1,0),(0,1)}. As an application, we discuss with an example that how some of these ambiguity levels can be interpreted in gene sequences. Further, we prove an important result that the ambiguity problem of insertion–deletion systems is undecidable. Then, we define six new measures for insertion–deletion systems based on used contexts and strings. Finally, we analyze the trade-off between ambiguity levels and measures. We note that there are languages which are inherently i-ambiguous (for i=5,4,2,0) when a measure M is minimal for the languages but they are i-unambiguous otherwise.","Title: Network of evolutionary processors with splicing rules\nYear: 2005\n\nAbstract: In this paper we consider networks of evolutionary processors with splicing rules (NEPS) as language generating and computational devices. Such a network consists of several processors placed on the nodes of a virtual graph and are able to perform splicing (which is a biologically motivated operation) on the words present in that node, according to the splicing rules present there. Each node is associated with an input and output filter. When the filters are regular languages one gets the computational power of Turing machines with networks of size two. We also show how these networks can be used to solve NP–complete problems in linear time.","Title: Context dependent Table matrix L systems\nYear: 1985\n\nAbstract: Table matrix L systems with context dependent rules generating rectangular arrays are introduced. It is seen that this generates some interesting classes of pictures. The typical hierarchy result is proved in this case. Closure operations on this model and its one dimensional restriction are considered.","Title: An efficient randomized algorithm for the closest pair problem on colored point sets\nYear: 1995\n\nAbstract: We present a simple randomized algorithm for finding the Closest Foreign Pair, in a set of n points in D-dimensional space, where D ≥ 2 is a fixed constant and the distances are measured in the L1 and the L∞ metric. The algorithm runs in O(nlogD-1 n/log log n) time with high probability for the L∞ metric and in O(n log2D-1-1 n/log log n) time with high probability for the L1 metric.","Title: Relativised Cellular Automata and Complexity Classes\nYear: 1991\n\nAbstract: Some of the fundamental problems concerning cellular automata (CA) are as follows:","Title: On the Relative Expressive Power of Contextual Grammars with Maximal and Depth-First Derivations\nYear: 2009\n\nAbstract: In the recent years, several new classes of contextual grammars have been introduced to give an appropriate model description to natural languages. With this aim, some new families of contextual languages have been introduced based on maximal and depth-first conditions and analyzed in the framework of so-called mildly context sensitive languages. However, the relationship among these families of languages have not yet been analyzed in detail. In this paper, we investigate the relationship between the families of languages whose grammars are based on maximal and depth-first conditions. We prove an interesting result that all these families of languages are incomparable to each other, but they are not disjoint.","Title: Binary 3D-Matrices Under the Microscope: A Tomographical Problem\nYear: 2005\n\nAbstract: A binary 3D-matrix can be scanned by moving a fixed 3D-window across it. We define scan matrix based on this and define smooth matrix. We present algorithm to reconstruct the original matrix from its 3D-scan matrix in the case of smooth matrix.","Title: Efficient Algorithms for Reconstruction of 2D-Arrays from Extended Parikh Images\nYear: 2008\n\nAbstract: The concept of a Parikh matrix or an extended Parikh mapping of words introduced by Mateescu et al (2001) is formulated here for two-dimensional (2D) arrays. A polynomial time algorithm is proposed to reconstruct an unknown 2D-array over { 0,1 } from its image under the extended Parikh mapping along a single direction. On the other hand the problem of reconstructing a 2D-array over { 0,1 } from its image under the extended Parikh mapping along three or more directions is shown to be NP-hard. Also a polynomial time algorithm to reconstruct a 2D-array over {0,1} with a maximum number of ones close to the main diagonal of the array is presented by reducing the problem to Min-cost Max-flow problem.","Title: Finite Automata And Digital Images\nYear: 2000\n\nAbstract: In this paper, we initially consider representation of 2D black-white images and 3D objects using finite state automata. We describe transformation of scaling on the 2D image by an operation on the FSA. We also give constructions for getting the projections of a 3D object on to coordinate planes and for reconstructing the 3D object from its projections. We define minimization of nondeterministic FSAs and give an O(e(2)) (e is the number of edges in the FSA) algorithm for minimization of NFAs. Later, we define a WFA and describe various properties of WFA. We define four normal forms of WFA and show how a WFA can be normalized into any of these forms. We show the equivalence of WFAs with epsilon edges and epsilon-free WFAs. Then, we define minimization of WFAs and present an algorithm to minimize a WFA.","Title: Intersection Removal For Simple Polygons\nYear: 1997\n\nAbstract: Given two intersecting simple polygons A and B and a direction, theta, we have to find the minimum distance by which B should be translated along direction theta so that A and B no longer intersect. We present two algorithms for this problem for the case that A and B are in 2 dimensions and one algorithm for the case that they are in 3 dimensions. The 2 dimensional algorithms run in O(N-4) and O(N-3 log N) time respectively where N is the total number of vertices in both the polygons while the 3 dimensional algorithm runs in O(N-4) time.","Title: Control Languages Associated With Tissue P Systems\nYear: 2013\n\nAbstract: We consider a way to associate a language with the computations of a tissue P system. We assign a label to every rule, where the labels are chosen from an alphabet or the label can be.. The rules used in a transition should have either the empty label or the same label from the chosen alphabet. In this way, a string is associated with each halting computation, called the control word of the computation. The set of all control words associated with computations in a tP system form the control language of the system. We study the family of control languages of tP systems in comparison with the families of finite, regular, context-free, context-sensitive, and recursively enumerable languages.","Title: Terminal weighted grammars and picture description\nYear: 1985\n\nAbstract: Motivated by the idea of describing parquet deformations using grammars and also of describing an infinite number of terminals starting with only a finite set, this paper defines a terminal weighted grammar, where the terminal generated at any step of a derivation is defined as a function of time. It is seen that terminal weighted regular grammars generate exactly the class of recursively enumerable sets. Terminal weighted matrix grammars are used to describe parquet deformations. The extension of terminal weights to array grammars is also discussed.","Title: Nondeterministic, probabilistic and alternating computations on cellular array models\nYear: 1995\n\nAbstract: A new mechanism for introducing nondeterminism on the cellular automaton model is introduced. It is shown that this form of nondeterminism corresponds to the traditional notion in the unbounded-time case, but there appear to be differences when real-time or linear-time cellular automata are considered. The notion is then generalised to include probabilistic and alternating computations. Restricted nondeterminism classes are also defined and studied, in an attempt to refine the power of nondeterminism.","Title: An optimal algorithm for one-separation of a set of isothetic polygons\nYear: 2004\n\nAbstract: We consider the problem of separating a collection of isothetic polygons in the plane by translating one polygon at a time to infinity. The directions of translation are the four isothetic (parallel to the axes) directions, but a particular polygon can be translated only in one of these four directions. Our algorithm detects whether a scene is separable in this sense and computes a translational ordering of the polygons. The time and space complexities of our algorithm are O(n log n) and O(n) respectively, where n is the total number of vertices of the polygons in the scene. The best previous algorithm in the plane for this problem has complexities of O(n log2 n) time and O(n log n) space.","Title: Learning Algorithms for Grammars of Variable Arity Trees\nYear: 2007\n\nAbstract: Grammatical Inference is the technique by which a grammar that best describes a given set of input samples is inferred. This paper considers the inference of tree grammars from a set of sample input trees. Inference of grammars for fixed arity trees is well studied, in this paper we extend the method to give algorithms for inference of grammars for variable arity trees. We give algorithms for inference of local, single type and regular grammar and also consider the use of negative samples. The variable arity trees we consider can be used for representation of XML documents and the algorithms we have given can be used for validation as well as for schema inference.","Title: Peptide Computing - Universality and Complexity\nYear: 2001\n\nAbstract: This paper considers a computational model using the peptide-antibody interactions. These interactions which are carried out in parallel can be used to solve NP-complete problems. In this paper we show how to use peptide experiments to solve the Hamiltonian Path Problem (HPP) and a particular version of Set Cover problem called Exact Cover by 3-Sets problem. We also prove that this of model of computation is computationally complete.","Title: Probabilistic Parallel Communicating Grammar Systems\nYear: 2002\n\nAbstract: Grammar systems are theoretical models of distributed computing which play a major role in modern Computer Science. In this paper, we define and study a variant of Parallel Communicating(PC) grammar systems namely, Probabilistic PC grammar systems which serves as a grammatical model for random distributed processing. We study the construct with two different models of communication protocols namely, communication by request and communication by command. We explore the generative capacity of the variant with regular, context-free and context-sensitive rules in both models of communication.","Title: Path Planning with Local Information\nYear: 1988\n\nAbstract: We consider the problem of path-planning for a point mobile automaton in the presence of unknown obstacles in a two dimensional space. We present an algorithm which has better worst case complexity than that of Lumelsky and Stepanov [4], which is the best known at present.","Title: Length synchronization context-free grammars\nYear: 2004\n\nAbstract: We propose a new type of regulation on the derivation of a context-free grammar: the productions used for passing from a level of a derivation tree to the next level should have the right-hand members of the same length. We prove that such length synchronized context-free grammars characterize the family of ETOL languages, and therefore are equivalent with the synchronization context-free grammars of H. Jüirgensen and K. Salomaa. In this way, a conjecture of H. Jürgensen and K. Salomaa is disproved, about a language which was conjectured not to be generated by a synchronization context-free grammar.","Title: On The Ambiguity Of Insertion Systems\nYear: 2011\n\nAbstract: Gene insertion and deletion are the operations that occur commonly in DNA processing and RNA editing. Based on these evolutionary transformations, a computing model has been formulated in formal language theory known as insertion-deletion systems. In this paper, we study about the ambiguity issues of insertion systems. First, we define six levels of ambiguity for insertion systems based on the components used in the derivation such as axiom, contexts and strings. Next, we show that there are inherently i-ambiguous insertion languages which are j-unambiguous for the combinations (i, j) is an element of {(5, 0), (5, 4), (4, 3), (4, 2), (3, 1), (2, 1), (1, 0), (0, 1)}. Finally, we prove an important result that the ambiguity problem of insertion systems is undecidable.","Title: Some variants in communication of parallel communicating pushdown automata\nYear: 2003\n\nAbstract: We consider automata systems consisting of several pushdown automata working in parallel and communicating the contents of their stacks by request. We show that centralized non-returning parallel communicating pushdown automata systems with three components recognize all recursively enumerable languages. We also show that centralized returning pushdown automata systems accept non-ET0L languages. We study two variants of communication: one uses filters in communication and in the other only specified number of symbols are communicated. We prove that all these systems accept the family of recursively enumerable languages both in the centralized and in the non-centralized strategies and in both returning and non-returning communication modes with only two components.","Title: On rule-number complexity of components of probabilistic cooperating distributed grammar systems\nYear: 2002\n\nAbstract: In this paper we discuss a size property of Probabilistic Cooperating Distributed Grammar Systems, grammatical constructs designed for modelling distributed random processing. We show that, with respect to the number of productions of a component grammar as a complexity measure, these systems exhibit a language specification of low complexity.","Title: Modelling and analysis of spiking neural P systems with anti-spikes using Pnet lab\nYear: 2011\n\nAbstract: Petri Nets are promising methods for modelling and simulating biological systems. Spiking Neural P system with anti-spikes (SN PA systems) is a biologically inspired computing model that incorporates two types of objects called spikes and anti-spikes thus representing binary information in a natural way. In this paper, we propose a methodology to simulate SN PA systems using a Petri net tool called Pnet Lab. It provides a promising way for SN PA systems because of its parallel execution semantics and appropriateness to represent typical working processes of these systems. This enables us to verify system properties, system soundness and to simulate the dynamic behaviour.","Title: Tree topology inference from leaf-to-leaf path lengths using Prüfer sequence\nYear: 2014\n\nAbstract: In this paper, we propose an algorithm to reconstruct the set of nodes and their topology relationships, particularly in case of a topological tree where all edge weights are 1, given the set of terminal vertices or leaves and their pairwise distances or path lengths. Prüfer encoding and decoding techniques showed a one-to-one correspondence between n-labelled trees and n − 2-tuples of vertex labels. We consider Prüfer encoding technique in discovering the intermediate vertices from the end-to-end path length between all pairs of terminal vertices. The results are useful in inferring graph characteristics, topology in particular, in the field of network tomography where the internal structure and link-level performance are inferred from end-to-end measurements.","Title: An Adaptive e-Learning Environment Using Distributed Spiking Neural P Systems\nYear: 2011\n\nAbstract: The motivation behind the proposed research work is the need for an innovative e-learning system that can adapt to the learning capability of every individual. Adaptive e-learning systems create new opportunities and at the same time have several research challenges that need to be addressed. The primary requirement of such adaptive systems is the need to create and represent adaptable content effectively. This paper presents a membrane computing model to demonstrate how adaptable content can be represented and used efficiently. The Spiking Neural P System (SNP) is a membrane computing model inspired by the way neurons communicate by means of spikes. This paper proposes the Distributed Spiking Neural P System (DSNP), a variant of the existing Distributed P System, that can be used to represent dynamic and distibuted systems. Temporal relations captured on a time line during authoring of the ecourse, can be automatically converted into an SNP system using the algorithm presented in the paper. An algorithm for the automatic generation of the DSNP from the e-course compositions represented using a linked list of SNPs is also presented in the paper along with experimental results to prove the efficiency and scalability of the proposed model.","Title: Some Results On Contextual Grammars\nYear: 2000\n\nAbstract: Contextual Grammars are widely investigated as models of natural languages and currently find applications related to DNA Computing. In this paper, we extend the concept of control languages to contextual grammars. We prove that control languages indeed add to the power of contextual grammars and investigate certain hierarchy results.","Title: On String Languages Generated By Spiking Neural P Systems With Anti-Spikes\nYear: 2011\n\nAbstract: An Spiking Neural P system with anti-spikes uses two types of objects called spikes and anti-spikes which can encode binary digits in a natural way. The step when system emits a spike or an anti-spike is associated with symbol 1 and 0, respectively. Here we consider these computing devices as language generators. They allow non-determinism between the rules a(c) -> a and a(c) -> (a) over bar, c is an element of N, thus help to generate languages which cannot be generated using simple SN P systems.","Title: On Characterizing Recursively Enumerable Languages by Insertion Grammars\nYear: 2005\n\nAbstract: Insertion grammars have been introduced in [1] and their computational power has been studied in several places. In [7] it is proved that insertion grammars with weight at least 7 can characterize recursively enumerable languages (modulo a weak coding and an inverse morphism), and the question was formulated whether or not this result can be improved. In this paper, we come up with a positive answer to this question, by decreasing the weight of the insertion grammar used to 5. We also give a characterization of recursively enumerable languages in terms of right quotients of insertion languages.","Title: Clique-Detection Algorithm Using Clique-Self-Assembly\nYear: 2011\n\nAbstract: Self-assembly is a process in which simple objects autonomously combine themselves into larger objects. It is considered as a promising technique in nano-technology. Two simple graphs G1 and G2 with a clique of same size overlap and a new self-assembled graph is formed. Besides studying the properties of self assembled graphs on cliques, we answer the question: Can a given set of graphs be generated through the self-assembly of cliques? If so, how to find the generator that could generate the given set of graphs by the process of clique-self-assembly. The question of the existence of minimal generator is also discussed. The necessary and sufficient condition for a graph H to be obtained by the iterated clique-self-assembly of the graph G is also answered. We also conclude that the problem of finding the generator is decidable. We note the importance of our work with respect to several closely related clique finding problem.","Title: On A Class Of P Automata\nYear: 2003\n\nAbstract: In this paper, we propose a class of P automata in which each membrane has a state, like in tissue P systems [5], and the computation starts at some initial state and ends in a final state. Unlike the automaton considered in [2], where rules are used in sequential manner, here we consider a variant such that the rules can be applied in maximal mode (as defined in tissue P systems), We show that P automata characterize the recursively enumerable sets of vectors of natural numbers.","Title: P Systems with Membrane Creation: Universality and Efficiency\nYear: 2001\n\nAbstract: P systems, introduced by Gh. Paun form a new class of distributed computing model. Several variants of P systems were already shown to be computationally universal. In this paper, we propose a new variant of P systems, P systems with membrane creation, in which some objects are productive and create membranes. This new variant of P systems is capable of solving the Hamiltonian Path Problem in linear time. We show that P systems with membrane creation are computationally complete.","Title: Minimum Area Polygons With Two Reflex Angles Enclosing K Points\nYear: 2001\n\nAbstract: We present in this paper, algorithms for finding the minimum area isothetic polygons having two reflex angles containing k points; (k < n). Our algorithms are based on line sweep paradigm. The idea is to enumerate all the polygons in each kind and find the minimum one, We also analyse the complexities of each of the algorithms. We find that out of the four orthogonal polygons with two reflex angles, three are orthoconvex and the algorithms resented for these polygons work in 0((n - k)(2)n(2) + n, n log n) time, where n, could be 0(n - k)(5) in the worst case. The algorithm for the non-orthoconvex polygon has a complexity of 0((n - k)(2)n(2) + n, n log n) where n, could be 0((n - k)(5)n) in the worst case.","Title: Local And Recognizable Hexagonal Picture Languages\nYear: 2005\n\nAbstract: In this paper we consider hexagonal arrays on triangular grids and introduce hexagonal local picture languages and hexagonal tiling systems defining hexagonal recognizable picture languages, motivated by an analogous study of rectangular arrays by Giammarresi and Restivo. We also introduce hexagonal Wang tiles to define hexagonal Wang systems (HWS) as a formalism to describe hexagonal picture languages. It is noticed that the family of hexagonal picture languages defined by hexagonal Wang systems and the family recognized by hexagonal tiling systems coincide. Analogous to hv-domino systems describing rectangular arrays, we define xyz-domino systems and prove that recognizable hexagonal picture languages are characterized as projections of xyz-local picture languages.","Title: Breaking the data encryption standard using networks of evolutionary processors with parallel string rewriting rules\nYear: 2009\n\nAbstract: In this paper we introduce a biologically inspired distributed computing model called networks of evolutionary processors with parallel string rewriting rules (NEPPS), which is a variation of the hybrid networks of evolutionary processors introduced by Martin-Vide et al. Such a network contains simple processors that are located in the nodes of a virtual graph. Each processor has strings (each string having multiple copies) and string rewriting rules. The rules are applied parallely on the strings. After the strings have been rewritten, they are communicated among the processors through filters. We show that we can theoretically break the DES (data encryption standard), which is the most widely used cryptosystem, using NEPPS. We prove that, given an arbitrary pair, one can recover the DES key in a constant number of steps.","Title: Real time cross layer design using particle swarm optimization\nYear: 2011\n\nAbstract: Cross Layer Optimization (CLO) strategies are currently being incorporated in network operating system for efficient utilization of resources to enable effective information management. In wireless adhoc networks real time optimizations need to be performed and hence CLO strategies that have faster response time are required. In this paper we propose a Cross Layer Optimization strategy that uses a variant of the Particle Swarm Optimization (PSO) for real time cross layer design of the network. The variant of the PSO used in this research work uses digital pheromones for improved performance. The proposed PSO-CLO strategy can be used for delay sensitive, bandwidth intensive and loss-tolerant wireless multimedia transmissions that have an ever demanding need for better Quality of Service. Our experimental results show that the proposed PSO-CLO strategy has significantly faster response time in comparison with the classical CLO solutions.","Title: Universality Results for Some Variants of P Systems\nYear: 2000\n\nAbstract: P systems, introduced by Gh. P驴un, form a new class of distributed computing models. Many variants of P systems were shown to be computationally universal. In this paper, we consider several classes of P systems with symbol-objects where we allow the catalysts to move in and out of a membrane. We prove universality results for these variants of P systems with a very small number of membranes.","Title: Returning and non-returning parallel communicating finite automata are equivalent\nYear: 2007\n\nAbstract: A parallel communicating automata system consists of several automata working independently in parallel and communicating with each other by request with the aim of recognizing a word. Rather surprisingly, returning parallel communicating finite automata systems are equivalent to the non- returning variants. We show this result by proving the equivalence of both with multihead finite automata. Some open problems are finally formulated.","Title: On the Trade-off Between Ambiguity and Complexity in Contextual Languages\nYear: 2013\n\nAbstract: Contextual grammars are introduced by Solomon Marcus in 1969 based on the fundamental concept of descriptive linguistics of insertion of strings in given contexts. Internal contextual grammars are introduced by Păun and Nguyen in 1980. For contextual grammars several descriptional complexity measures and levels of ambiguity have been defined. In this paper, we analyze the trade-off between ambiguity and complexity of languages generated by internal contextual grammars. The notion of a pseudo inherently ambiguous language with respect to two complexity measures is introduced and investigated. These languages can be generated by unambiguous grammars which are minimal with respect to one measure and ambiguous if they are minimal with respect to the other measure. An open problem from [15] is solved in this framework.","Title: Communicating Distributed H systems with Simple Splicing Rules\nYear: 2006\n\nAbstract: Abstractó In this paper we dene communicating distributed H systems with simple splicing rules of types (1,3), (1,4) and (2,3) and study the generative capacity. keyword: Splicing systems, contextfree languages, simple H systems, test tube systems, communicating distributed H systems","Title: Efficient Randomized Incremental Algorithm For The Closest Pair Problem Using Leafary Trees\nYear: 1995\n\nAbstract: We present a new data structure, the Leafary tree, for designing an efficient randomized algorithm for the Closest Pair Problem. Using this data structure, we show that the Closest Pair of n points in D-dimensional space, where, D2, is a fixed constant, can be found in O(n log n/log log n) expected time. The algorithm does not employ hashing.","Title: On the generative power of simple H systems\nYear: 2000\n\nAbstract: In this paper, we prove that the power of Simple H-systems of the(2,3) type with permitting contexts and target alphabet is equal toExtended H-systems with permitting contexts and radius of the rulesequal to one. We also prove interesting results on Simple ExtendedH-systems and Extended H-systems with forbidden contexts.Keywords: Splicing systems, simple H systems, permitting and forbiddencontexts and cardinality of context.1","Title: Small Universal Spiking Neural P Systems with Cooperating Rules as Function Computing Devices.\nYear: 2014\n\nAbstract: The paper considers spiking neural P systems (SN P systems) with cooperating rules where each neuron has the same number of sets of rules, labelled identically. Each set is called a component (maybe empty). At each step only one of the components can be active for the whole system, and only the rules from the active component are enabled. Each neuron with enabled rules from this active component can fire. By using 59 neurons, a small universal SN P system with two components, working in the terminating mode, is constructed for computing functions.","Title: An entropy maximization problem in shortest path routing networks\nYear: 2014\n\nAbstract: In the context of an IP network, we investigate an interesting case of the inverse shortest path problem using the concept of network centrality. For a given network, the centrality distribution associated with the links of a network can be determined based on the number of shortest paths passing through each link. An entropy measure for this distribution is defined, and we then forumulate the inverse shortest problem in terms of maximizing this entropy. We then obtain a centrality distribution that is as broadly distributed as possible subject to the topology constraints. An appropriate change in the weight of a link alters the number of shortest paths that pass through it, thereby modifying the centrality distribution. The idea is to obtain a centrality distribution that maximizes the entropy. This problem is shown to be NP-hard, and a heuristic approach is proposed. An application to handling link failure scenarios in Open Shortest Path First routing is discussed.","Title: Distance realization problem in Network Tomography: A heuristic approach\nYear: 2013\n\nAbstract: This paper proposes a heuristic approach for the distance realization problem, which arises in Network Tomography. Network Tomography is the study of estimating internal network structure and link-level performance from end-to-end measurements. A distance realization problem is to reconstruct a graph or topology from its distance matrix, i.e., the matrix containing the pairwise distances between the terminal nodes. The graph, thus realized from the pairwise distances of terminal nodes, can either be a tree or a general graph. There are efficient polynomial algorithms developed for the case of tree realization. However, the problem of finding optimal realization (i.e., the total length of the graph realized is minimum) of distance matrix for a general graph is shown to be NP-hard. Our proposed heuristic approach for distance realization consists of three stages: (i) find a closer tree realizable distance matrix based on the shortest paths, (ii) construct a tree and (iii) fix the differences between the tree realizable distance matrix and the original distance matrix. It also attempts to maximize the `entropy of betweenness-centrality' measure in the network while satisfying the distance constraints.","Title: Modeling spiking neural P systems using timed Petri nets\nYear: 2009\n\nAbstract: This paper shows that deterministic P-timed Petri nets with inhibitory and test arcs can simulate an SN P system. A method is proposed to translate an SN P system into Petri net model and is illustrated with an example.","Title: Array Grammars and Kolam\nYear: 1974\n\nAbstract: Kolam designs of South Indian folk art are treated as examples of two-dimensional picture languages, Many of the complicated kolain patterns are seen to be generable by context-free array grammars. Two methods of kolam generation are discussed. One method is to generate the kolam patterns by array grammars with a finite number of primitives as terminals. The other is to generate labeled dots by array grammars and then to give a finite number of simple instructions to draw the kolam pattern on the framework of labeled dots.","Title: Abstract families of matrices and picture languages\nYear: 1972\n\nAbstract: A linguistic model to generate matrices (arrays of terminals) is presented. The sixclosure axioms which define abstract families of languages (AFL) are extended to matrices to obtain abstract families of matrices (AFM). Corresponding automata to recognize matrices are defined and equivalence established. Immediate application is shown in picture processing by generating a wide class of interesting pictures. Illustrations include generation of kolam patterns, wall paper designs and geometric patterns replicated several times. Simple transformations of a picture such as reflection, translation, halftum, and turning upside down are very naturally obtained.","Title: A Centrality Entropy Maximization Problem in Shortest Path Routing Networks.\nYear: 2016\n\nAbstract: In the context of an IP network, this paper investigates an interesting case of the inverse shortest path problem using the concept of network centrality. For a given network, a special probability distribution, namely the centrality distribution associated with the links of a network can be determined based on the number of the shortest paths passing through each link. An entropy measure for this distribution is defined, and the inverse shortest path problem is formulated in terms of maximizing this entropy. We then obtain a centrality distribution that is as broadly distributed as possible subject to the topology constraints. A maximum entropy distribution signifies the decentralization of the network. An appropriate change in the weight of a link alters the number of the shortest paths that pass through it, thereby modifying the centrality distribution. The idea is to obtain a centrality distribution that maximizes the entropy. This problem is shown to be NP-hard, and a heuristic approach is proposed. An application to handling link failure scenarios in Open Shortest Path First routing is discussed.","Title: On Succinct Description Of Certain Context-Free Languages By Ins-Del And Matrix Ins-Del Systems\nYear: 2016\n\nAbstract: In this paper, we introduce some basic measures for insertion-deletion system and matrix insertion-deletion system. These measures are based on the number of variables, the number of productions and the number of symbols in a grammar. We show that with respect to these measures, both the systems are more succinct over context free grammars in representing certain families of context-free languages.","Title: A Note on Hybrid P Systems\nYear: 2002\n\nAbstract: Generally, in rewriting P systems (Martin-Vide and Paun, 2000) one uses Chomsky rules (Hopcroft and Ullman, 1979), whereas in contextual P systems (Madhu and Krithivasan, 2002) we considered contextual rules (Marcus, 1969), (Paun, 1997) for processing string-objects. By combining Chomsky rules and contextual rules, a new class of P systems were introduced in Krishna et al. (2001), the hybrid P systems. In this paper we continue the study of hybrid P systems, and show that systems with two membranes are universal in the case of contextual rules with a regular choice, and systems with four membranes are universal in the case of contextual rules with finite selection.","Title: Realizing Switching Functions Using Peptide-Antibody Interactions\nYear: 2004\n\nAbstract: We propose a theoretical model for performing gate operations - OR, AND, and NOT - using peptide-antibody interactions. The idea is extended to further gates, such as XOR, NAND, and NOR.","Title: A min-cost-max-flow based algorithm for reconstructing binary image from two projections using similar images\nYear: 2008\n\nAbstract: The aim of this paper is to study the reconstruction of binary images from two projections using a priori images that are similar to the unknown image. Reconstruction of images from a few projections is preferred to reduce radiation hazards. It is well known that the problem of reconstructing images from a few projections is ill-posed. To handle the ill-posedness of the problem, a priori information such as convexity, connectivity and periodicity are used to limit the number of possible solutions. We use a priori images that are similar to the unknown image, to reduce the class of images having the same two projections. The a priori similar images may be obtained in many ways such as by considering images of neighboring slices or images of the same slice, taken in previous time instances. In this paper, we give a polynomial time algorithm to reconstruct binary image from two projections such that the reconstructed image is optimally close to the a priori similar images. We obtain a solution to our problem by reducing our problem to min cost integral max flow problem.","Title: Spiking Neural P Systems with Cooperating Rules.\nYear: 2014\n\nAbstract: The concept of cooperation and distribution as known from grammar systems is introduced to spiking neural P systems (in short, SN P systems) in which each neuron has a finite number of sets (called components) of rules. During computations, at each step only one of the components can be active for the whole system and one of the enabled rules from this active component of each neuron fires. The switching between the components occurs under different cooperation strategies. This paper considers the terminating mode, in which the switching occurs when no rule is enabled in the active component of any neuron in the system. By introducing this new mechanism, the computational power of asynchronous and sequential SN P systems with standard rules is investigated. The results are that asynchronous standard SN P systems with two components and strongly sequential unbounded SN P systems with two components are Turing complete.","Title: Generalized normal form for rewriting P systems\nYear: 2002\n\nAbstract: . P systems, introduced by Gh. Păun [9] as a new theoretical model for molecular computations, are based on the notion of membrane\n structure. Several variants of P systems have been proposed and shown to be computationally universal. One of such variant\n is the rewriting P systems, where we consider string-objects and process them using rewriting rules. Particular cases of normal\n forms for rewriting P systems were proposed in [11–13]. In this work we introduce the generalized normal form for rewriting\n P systems which take into consideration the depth of the membrane structure and the number of rewriting rules present in each\n membrane. Such generalized normal forms are given for rewriting P systems with priorities, and for partially parallel rewriting\n P systems. In this way, several results from the literature are generalized and improved.","Title: Visibility Of Rectangular Objects In L-1 Metric\nYear: 1998\n\nAbstract: Given a set of n rectangles (2-dimensional) parallel to xy-plane, and a point P(x, y, z) in 3-dimensional space, we find the visibility of all rectangles i.e., the set of all visible points of rectangles from P in L-1 metric, in O(n(4)) time and O(n(3)) space. We also discuss the cases where the point P moves along a straight line and the rectangles are inserted and deleted dynamically."],"string":"[\n \"Title: Binding-blocking automata\\nYear: 2013\\n\\nAbstract: We propose an automaton model called as binding-blocking automaton BBA. It is a finite state automaton together with the ability to block some symbols and postpone them for reading by the head at a later time. The idea of blocking some symbols from being read by the head and unblocking when the system requires to read is motivated by peptide computing where some parts of peptide sequences are blocked by attaching an antibody with higher affinity and unblocked at a later point by the removal of the appropriate antibody. We study the variants of such systems, analyse the power of each variants and show various hierarchy results involving them. We also define normal-forms of binding-blocking automata for one of its variants and also show that the acceptance power of this variant of BBA is strictly less than that of multi-head finite automata.\",\n \"Title: Array Splicing Systems\\nYear: 1997\\n\\nAbstract: In this paper the concept of splicing is extended to arrays and array or 2D splicing systems are defined. Various subclasses of 2D splicing systems are defined and a restricted class viz. finite simple splicing systems is studied. The hierarchy among the various subclasses of finite simple splicing systems and their relationship to the strictly locally testable languages are established.\",\n \"Title: Graph splicing systems\\nYear: 2007\\n\\nAbstract: In this paper, extended graph splicing systems are defined. It is shown that when strings are represented as linear graphs, any recursively enumerable set can be generated by an extended graph splicing system. It is also shown that the computational completeness of extended graph splicing systems can be proved under some constraints too.\",\n \"Title: Hexagonal Pattern Languages\\nYear: 2004\\n\\nAbstract: Hexagonal tiling systems, hexagonal local picture languages and hexagonal recognizable picture languages are defined in this\\n paper. Hexagonal Wang tiles and systems are also introduced. It is noticed that the family of hexagonal picture languages\\n defined by hexagonal Wang systems coincides with the family of hexagonal picture languages recognized by hexagonal tiling\\n system. Similar to hv-domino systems describing rectangular arrays, we define xyz-domino systems and characterize hexagonal\\n picture languages using this. Unary hexagonal picture languages are also considered and we analyze some of their properties.\",\n \"Title: Contextual P Systems\\nYear: 2002\\n\\nAbstract: Generally, in P systems with string-objects one uses the Chomsky way of rewriting for processing the objects. In this paper we consider the contextual way of handling string-objects in P systems. We introduce some variants of contextual grammars and prove that contextual P systems with rules corresponding to these variants are more powerful than ordinary contextual grammars and their variants. We also show that one-sided contextual P systems with right-sided erased contexts and insertion contextual P systems with right-sided erased contexts are computationally complete.\",\n \"Title: Control Words of Transition P Systems.\\nYear: 2012\\n\\nAbstract: A new way of associating a language with the computation of a P system is considered. A label is assigned to every rule in a P system, where the labels are chosen from a finite alphabet or A. We associate a string, called control word, that is obtained by concatenating the labels of the rules in the transition sequence corresponding to a computation. We study the generative capacity of such control languages comparing them with family of languages such as regular, context-free, context-sensitive and recursively enumerable languages of Chomskian hierarchy.\",\n \"Title: Contextual Array P Systems\\nYear: 2004\\n\\nAbstract: We define external and internal array contextual P systems, which generate rectangular arrays. We also introduce external array contextual P systems with erased contexts. Some properties of these systems are discussed. We obtain the relation between external array contextual P systems and external array contextual grammars with regular control.\",\n \"Title: Distributed Processing in Automata\\nYear: 1999\\n\\nAbstract: With distributed computing beginning to play a major role in modern Computer Science, the theory of grammar systems and distributed automata has been developed in order to model distributed computing. In this paper, we introduce the notion of distributed automata in the sequential sense. Distributed Automata are a group of automata working in unison to accept one language. We build the theory of distributed automata for FSA and PDA in dierent modes of acceptance like the t-mode, *-mode, = k-mode, k-mode and k-mode. We then analyze the acceptance power of each automata in all the above modes. We present proofs that distributed FSA do not have any additional power over \\\\centralized\\\" FSAs in any of the modes, while distributed PDA with only two components is as powerful as Turing Machines in all of the modes. We give proofs for the equivalence of all modes in the case of PDAs. We also study a restricted version of distributed PDA called k-turn distributed PDA.\",\n \"Title: On Controlled P Systems\\nYear: 2014\\n\\nAbstract: We introduce and briefly investigate P systems with controlled computations. First, P systems with label restricted transitions are considered in each step, all rules used have either the same label, or, possibly, the empty label, λ, then P systems with the computations controlled by languages as in context-free controlled grammars. The relationships between the families of sets of numbers computed by the various classes of controlled P systems are investigated, also comparing them with length sets of languages in Chomsky and Lindenmayer hierarchies characterizations of the length sets of ET0L and of recursively enumerable languages are obtained in this framework. A series of open problems and research topics are formulated.\",\n \"Title: Efficient two-dimensional pattern matching in the presence of errors\\nYear: 1987\\n\\nAbstract: We give an algorithm for two-dimensional pattern matching in the presence of errors. We find that the complexity of our algorithm is O(kn1n2 log n2 + n12n2 + kn1m1m2), where the pattern is an n1 × n2 array, the text is an m1 × m2 array and k is the number of mismatches allowed.\",\n \"Title: Network of evolutionary processors with splicing rules and forbidding context\\nYear: 2005\\n\\nAbstract: In this paper we consider networks of evolutionary processors with splicing rules and forbidding context (NEPFS) as language generating and computational devices. Such a network consists of several processors placed on the nodes of a virtual graph and are able to perform splicing (which is a biologically motivated operation) on the words present in that node, according to the splicing rules present there. Before applying the splicing operation on words, we check for the absence of certain symbols (forbidding context) in the strings on which the rule is applied. Each node is associated with an input and output filter. When the filters are based on random context conditions, one gets the computational power of Turing machines with networks of size two. We also show how these networks can be used to solve NP–complete problems in linear time.\",\n \"Title: Network of evolutionary processors with splicing rules and permitting context.\\nYear: 2007\\n\\nAbstract: In this paper we consider networks of evolutionary processors with splicing rules and permitting context (NEPPS) as language generating and computational devices. Such a network consists of several processors placed on the nodes of a virtual graph and are able to perform splicing (which is a biologically motivated operation) on the words present in that node, according to the splicing rules present there. Before applying the splicing operation on words, we check for the presence of certain symbols (permitting context) in the strings on which the rule is applied. Each node is associated with an input and output filter. When the filters are based on random context conditions, one gets the computational power of Turing machines with networks of size two. We also show how these networks can be used to solve NP-complete problems in linear time.\",\n \"Title: Rewriting Tissue P Systems\\nYear: 2004\\n\\nAbstract: By considering string-objects and rewriting rules, we propose a variant of tissue P systems, namely, rewriting tissue P systems. We show the computational efficiency of rewriting tissue P systems by solving the Satisfiability and the Hamiltionian path problems in linear time. We study the computational capacity of rewriting tissue P systems and show that rewriting tissue P systems with at most two cells and four states are computationally universal. We also show the universality result of rewriting tissue P systems with at most one cell and five states. Finally we propose some new directions for future work.\",\n \"Title: A Survey of Some Variants of P Systems\\nYear: 2002\\n\\nAbstract: In this paper we give a brief survey of some variants of P systems and their computational capacity. An improvement of a known result about rewriting P systems with leftmost derivation is given. We also give the generalized definition for normal forms of rewriting P systems.\",\n \"Title: A Variant of Distributed P Systems for Real Time Cross Layer Optimization.\\nYear: 2012\\n\\nAbstract: Membrane computing models (also known as P Systems) that solve optimisation problems using genetic algorithms, ant colony optimisation, quantum-inspired evolutionary algorithm and particle swarm optimisation have been defined and are efficiently used in several applications. This paper describes the design of a variant of the existing Distributed P system (dP system) that is augmented with new features that enable centralised monitoring and communication with all the other components of the distributed system. This proposed model is titled Monitored Distributed P System (MDP System) and its innovative application in performing Cross Layer Optimisation in wireless adhoc networks is also presented. In the proposed MDP System each node in the network is represented by a P system that can independently perform Cross Layer Optimisation using particle swarm optimisation. Discussions on the communication complexity of the proposed model and the experimental results presented are also suggestive of the fact that the proposed Monitored Distributed P System is suitable for real time optimisation in a dynamic and distributed environment.\",\n \"Title: An efficient reconstruction of 2D-tiling with t1,2, t2,1, t1,1 tiles\\nYear: 2006\\n\\nAbstract: We define the projection of a tiling as a matrix P = (pij) where pi1 is number of t1,2 tiles in row i and pi2 is the number of t2,1 tiles in row i. We give an efficient algorithm to tile a 2D-square grid with only t1,2, t2,1, t1,1 tiles such that the projection of this tiling is the same as the given projection.\",\n \"Title: Image Representation using Distributed Weighted Finite Automata\\nYear: 2001\\n\\nAbstract: Weighted finite automata (WFA) define real functions, in particular, grayness functions of graytone images. Inference algorithm that converts an arbitrary function (graytone image) into a WFA that can regenerate it is given in [7]. In this paper we define the theoretical construct of Cooperating Distributed Weighted Finite Automata with n-components (n-WFA) and study the power of this construct in various modes of acceptance. We give an inference algorithm and the de-inference algorithm for the n-WFA.\",\n \"Title: On the study of ambiguity and the trade-off between measures and ambiguity in insertion–deletion languages\\nYear: 2011\\n\\nAbstract: Gene insertion and deletion are the operations that occur commonly in DNA processing and RNA editing. Based on these operations, a computing model has been formulated in formal language theory known as insertion–deletion systems. In this paper we study about ambiguity issues of these systems. First, we define six levels of ambiguity for insertion–deletion systems that are based on the components used in the derivation such as axiom, contexts and strings. Next, we show that there are inherently i-ambiguous insertion–deletion languages which are j-unambiguous for the combinations (i,j)∈{(5,4),(4,3),(4,2),(3,1),(2,1),(1,0),(0,1)}. As an application, we discuss with an example that how some of these ambiguity levels can be interpreted in gene sequences. Further, we prove an important result that the ambiguity problem of insertion–deletion systems is undecidable. Then, we define six new measures for insertion–deletion systems based on used contexts and strings. Finally, we analyze the trade-off between ambiguity levels and measures. We note that there are languages which are inherently i-ambiguous (for i=5,4,2,0) when a measure M is minimal for the languages but they are i-unambiguous otherwise.\",\n \"Title: Network of evolutionary processors with splicing rules\\nYear: 2005\\n\\nAbstract: In this paper we consider networks of evolutionary processors with splicing rules (NEPS) as language generating and computational devices. Such a network consists of several processors placed on the nodes of a virtual graph and are able to perform splicing (which is a biologically motivated operation) on the words present in that node, according to the splicing rules present there. Each node is associated with an input and output filter. When the filters are regular languages one gets the computational power of Turing machines with networks of size two. We also show how these networks can be used to solve NP–complete problems in linear time.\",\n \"Title: Context dependent Table matrix L systems\\nYear: 1985\\n\\nAbstract: Table matrix L systems with context dependent rules generating rectangular arrays are introduced. It is seen that this generates some interesting classes of pictures. The typical hierarchy result is proved in this case. Closure operations on this model and its one dimensional restriction are considered.\",\n \"Title: An efficient randomized algorithm for the closest pair problem on colored point sets\\nYear: 1995\\n\\nAbstract: We present a simple randomized algorithm for finding the Closest Foreign Pair, in a set of n points in D-dimensional space, where D ≥ 2 is a fixed constant and the distances are measured in the L1 and the L∞ metric. The algorithm runs in O(nlogD-1 n/log log n) time with high probability for the L∞ metric and in O(n log2D-1-1 n/log log n) time with high probability for the L1 metric.\",\n \"Title: Relativised Cellular Automata and Complexity Classes\\nYear: 1991\\n\\nAbstract: Some of the fundamental problems concerning cellular automata (CA) are as follows:\",\n \"Title: On the Relative Expressive Power of Contextual Grammars with Maximal and Depth-First Derivations\\nYear: 2009\\n\\nAbstract: In the recent years, several new classes of contextual grammars have been introduced to give an appropriate model description to natural languages. With this aim, some new families of contextual languages have been introduced based on maximal and depth-first conditions and analyzed in the framework of so-called mildly context sensitive languages. However, the relationship among these families of languages have not yet been analyzed in detail. In this paper, we investigate the relationship between the families of languages whose grammars are based on maximal and depth-first conditions. We prove an interesting result that all these families of languages are incomparable to each other, but they are not disjoint.\",\n \"Title: Binary 3D-Matrices Under the Microscope: A Tomographical Problem\\nYear: 2005\\n\\nAbstract: A binary 3D-matrix can be scanned by moving a fixed 3D-window across it. We define scan matrix based on this and define smooth matrix. We present algorithm to reconstruct the original matrix from its 3D-scan matrix in the case of smooth matrix.\",\n \"Title: Efficient Algorithms for Reconstruction of 2D-Arrays from Extended Parikh Images\\nYear: 2008\\n\\nAbstract: The concept of a Parikh matrix or an extended Parikh mapping of words introduced by Mateescu et al (2001) is formulated here for two-dimensional (2D) arrays. A polynomial time algorithm is proposed to reconstruct an unknown 2D-array over { 0,1 } from its image under the extended Parikh mapping along a single direction. On the other hand the problem of reconstructing a 2D-array over { 0,1 } from its image under the extended Parikh mapping along three or more directions is shown to be NP-hard. Also a polynomial time algorithm to reconstruct a 2D-array over {0,1} with a maximum number of ones close to the main diagonal of the array is presented by reducing the problem to Min-cost Max-flow problem.\",\n \"Title: Finite Automata And Digital Images\\nYear: 2000\\n\\nAbstract: In this paper, we initially consider representation of 2D black-white images and 3D objects using finite state automata. We describe transformation of scaling on the 2D image by an operation on the FSA. We also give constructions for getting the projections of a 3D object on to coordinate planes and for reconstructing the 3D object from its projections. We define minimization of nondeterministic FSAs and give an O(e(2)) (e is the number of edges in the FSA) algorithm for minimization of NFAs. Later, we define a WFA and describe various properties of WFA. We define four normal forms of WFA and show how a WFA can be normalized into any of these forms. We show the equivalence of WFAs with epsilon edges and epsilon-free WFAs. Then, we define minimization of WFAs and present an algorithm to minimize a WFA.\",\n \"Title: Intersection Removal For Simple Polygons\\nYear: 1997\\n\\nAbstract: Given two intersecting simple polygons A and B and a direction, theta, we have to find the minimum distance by which B should be translated along direction theta so that A and B no longer intersect. We present two algorithms for this problem for the case that A and B are in 2 dimensions and one algorithm for the case that they are in 3 dimensions. The 2 dimensional algorithms run in O(N-4) and O(N-3 log N) time respectively where N is the total number of vertices in both the polygons while the 3 dimensional algorithm runs in O(N-4) time.\",\n \"Title: Control Languages Associated With Tissue P Systems\\nYear: 2013\\n\\nAbstract: We consider a way to associate a language with the computations of a tissue P system. We assign a label to every rule, where the labels are chosen from an alphabet or the label can be.. The rules used in a transition should have either the empty label or the same label from the chosen alphabet. In this way, a string is associated with each halting computation, called the control word of the computation. The set of all control words associated with computations in a tP system form the control language of the system. We study the family of control languages of tP systems in comparison with the families of finite, regular, context-free, context-sensitive, and recursively enumerable languages.\",\n \"Title: Terminal weighted grammars and picture description\\nYear: 1985\\n\\nAbstract: Motivated by the idea of describing parquet deformations using grammars and also of describing an infinite number of terminals starting with only a finite set, this paper defines a terminal weighted grammar, where the terminal generated at any step of a derivation is defined as a function of time. It is seen that terminal weighted regular grammars generate exactly the class of recursively enumerable sets. Terminal weighted matrix grammars are used to describe parquet deformations. The extension of terminal weights to array grammars is also discussed.\",\n \"Title: Nondeterministic, probabilistic and alternating computations on cellular array models\\nYear: 1995\\n\\nAbstract: A new mechanism for introducing nondeterminism on the cellular automaton model is introduced. It is shown that this form of nondeterminism corresponds to the traditional notion in the unbounded-time case, but there appear to be differences when real-time or linear-time cellular automata are considered. The notion is then generalised to include probabilistic and alternating computations. Restricted nondeterminism classes are also defined and studied, in an attempt to refine the power of nondeterminism.\",\n \"Title: An optimal algorithm for one-separation of a set of isothetic polygons\\nYear: 2004\\n\\nAbstract: We consider the problem of separating a collection of isothetic polygons in the plane by translating one polygon at a time to infinity. The directions of translation are the four isothetic (parallel to the axes) directions, but a particular polygon can be translated only in one of these four directions. Our algorithm detects whether a scene is separable in this sense and computes a translational ordering of the polygons. The time and space complexities of our algorithm are O(n log n) and O(n) respectively, where n is the total number of vertices of the polygons in the scene. The best previous algorithm in the plane for this problem has complexities of O(n log2 n) time and O(n log n) space.\",\n \"Title: Learning Algorithms for Grammars of Variable Arity Trees\\nYear: 2007\\n\\nAbstract: Grammatical Inference is the technique by which a grammar that best describes a given set of input samples is inferred. This paper considers the inference of tree grammars from a set of sample input trees. Inference of grammars for fixed arity trees is well studied, in this paper we extend the method to give algorithms for inference of grammars for variable arity trees. We give algorithms for inference of local, single type and regular grammar and also consider the use of negative samples. The variable arity trees we consider can be used for representation of XML documents and the algorithms we have given can be used for validation as well as for schema inference.\",\n \"Title: Peptide Computing - Universality and Complexity\\nYear: 2001\\n\\nAbstract: This paper considers a computational model using the peptide-antibody interactions. These interactions which are carried out in parallel can be used to solve NP-complete problems. In this paper we show how to use peptide experiments to solve the Hamiltonian Path Problem (HPP) and a particular version of Set Cover problem called Exact Cover by 3-Sets problem. We also prove that this of model of computation is computationally complete.\",\n \"Title: Probabilistic Parallel Communicating Grammar Systems\\nYear: 2002\\n\\nAbstract: Grammar systems are theoretical models of distributed computing which play a major role in modern Computer Science. In this paper, we define and study a variant of Parallel Communicating(PC) grammar systems namely, Probabilistic PC grammar systems which serves as a grammatical model for random distributed processing. We study the construct with two different models of communication protocols namely, communication by request and communication by command. We explore the generative capacity of the variant with regular, context-free and context-sensitive rules in both models of communication.\",\n \"Title: Path Planning with Local Information\\nYear: 1988\\n\\nAbstract: We consider the problem of path-planning for a point mobile automaton in the presence of unknown obstacles in a two dimensional space. We present an algorithm which has better worst case complexity than that of Lumelsky and Stepanov [4], which is the best known at present.\",\n \"Title: Length synchronization context-free grammars\\nYear: 2004\\n\\nAbstract: We propose a new type of regulation on the derivation of a context-free grammar: the productions used for passing from a level of a derivation tree to the next level should have the right-hand members of the same length. We prove that such length synchronized context-free grammars characterize the family of ETOL languages, and therefore are equivalent with the synchronization context-free grammars of H. Jüirgensen and K. Salomaa. In this way, a conjecture of H. Jürgensen and K. Salomaa is disproved, about a language which was conjectured not to be generated by a synchronization context-free grammar.\",\n \"Title: On The Ambiguity Of Insertion Systems\\nYear: 2011\\n\\nAbstract: Gene insertion and deletion are the operations that occur commonly in DNA processing and RNA editing. Based on these evolutionary transformations, a computing model has been formulated in formal language theory known as insertion-deletion systems. In this paper, we study about the ambiguity issues of insertion systems. First, we define six levels of ambiguity for insertion systems based on the components used in the derivation such as axiom, contexts and strings. Next, we show that there are inherently i-ambiguous insertion languages which are j-unambiguous for the combinations (i, j) is an element of {(5, 0), (5, 4), (4, 3), (4, 2), (3, 1), (2, 1), (1, 0), (0, 1)}. Finally, we prove an important result that the ambiguity problem of insertion systems is undecidable.\",\n \"Title: Some variants in communication of parallel communicating pushdown automata\\nYear: 2003\\n\\nAbstract: We consider automata systems consisting of several pushdown automata working in parallel and communicating the contents of their stacks by request. We show that centralized non-returning parallel communicating pushdown automata systems with three components recognize all recursively enumerable languages. We also show that centralized returning pushdown automata systems accept non-ET0L languages. We study two variants of communication: one uses filters in communication and in the other only specified number of symbols are communicated. We prove that all these systems accept the family of recursively enumerable languages both in the centralized and in the non-centralized strategies and in both returning and non-returning communication modes with only two components.\",\n \"Title: On rule-number complexity of components of probabilistic cooperating distributed grammar systems\\nYear: 2002\\n\\nAbstract: In this paper we discuss a size property of Probabilistic Cooperating Distributed Grammar Systems, grammatical constructs designed for modelling distributed random processing. We show that, with respect to the number of productions of a component grammar as a complexity measure, these systems exhibit a language specification of low complexity.\",\n \"Title: Modelling and analysis of spiking neural P systems with anti-spikes using Pnet lab\\nYear: 2011\\n\\nAbstract: Petri Nets are promising methods for modelling and simulating biological systems. Spiking Neural P system with anti-spikes (SN PA systems) is a biologically inspired computing model that incorporates two types of objects called spikes and anti-spikes thus representing binary information in a natural way. In this paper, we propose a methodology to simulate SN PA systems using a Petri net tool called Pnet Lab. It provides a promising way for SN PA systems because of its parallel execution semantics and appropriateness to represent typical working processes of these systems. This enables us to verify system properties, system soundness and to simulate the dynamic behaviour.\",\n \"Title: Tree topology inference from leaf-to-leaf path lengths using Prüfer sequence\\nYear: 2014\\n\\nAbstract: In this paper, we propose an algorithm to reconstruct the set of nodes and their topology relationships, particularly in case of a topological tree where all edge weights are 1, given the set of terminal vertices or leaves and their pairwise distances or path lengths. Prüfer encoding and decoding techniques showed a one-to-one correspondence between n-labelled trees and n − 2-tuples of vertex labels. We consider Prüfer encoding technique in discovering the intermediate vertices from the end-to-end path length between all pairs of terminal vertices. The results are useful in inferring graph characteristics, topology in particular, in the field of network tomography where the internal structure and link-level performance are inferred from end-to-end measurements.\",\n \"Title: An Adaptive e-Learning Environment Using Distributed Spiking Neural P Systems\\nYear: 2011\\n\\nAbstract: The motivation behind the proposed research work is the need for an innovative e-learning system that can adapt to the learning capability of every individual. Adaptive e-learning systems create new opportunities and at the same time have several research challenges that need to be addressed. The primary requirement of such adaptive systems is the need to create and represent adaptable content effectively. This paper presents a membrane computing model to demonstrate how adaptable content can be represented and used efficiently. The Spiking Neural P System (SNP) is a membrane computing model inspired by the way neurons communicate by means of spikes. This paper proposes the Distributed Spiking Neural P System (DSNP), a variant of the existing Distributed P System, that can be used to represent dynamic and distibuted systems. Temporal relations captured on a time line during authoring of the ecourse, can be automatically converted into an SNP system using the algorithm presented in the paper. An algorithm for the automatic generation of the DSNP from the e-course compositions represented using a linked list of SNPs is also presented in the paper along with experimental results to prove the efficiency and scalability of the proposed model.\",\n \"Title: Some Results On Contextual Grammars\\nYear: 2000\\n\\nAbstract: Contextual Grammars are widely investigated as models of natural languages and currently find applications related to DNA Computing. In this paper, we extend the concept of control languages to contextual grammars. We prove that control languages indeed add to the power of contextual grammars and investigate certain hierarchy results.\",\n \"Title: On String Languages Generated By Spiking Neural P Systems With Anti-Spikes\\nYear: 2011\\n\\nAbstract: An Spiking Neural P system with anti-spikes uses two types of objects called spikes and anti-spikes which can encode binary digits in a natural way. The step when system emits a spike or an anti-spike is associated with symbol 1 and 0, respectively. Here we consider these computing devices as language generators. They allow non-determinism between the rules a(c) -> a and a(c) -> (a) over bar, c is an element of N, thus help to generate languages which cannot be generated using simple SN P systems.\",\n \"Title: On Characterizing Recursively Enumerable Languages by Insertion Grammars\\nYear: 2005\\n\\nAbstract: Insertion grammars have been introduced in [1] and their computational power has been studied in several places. In [7] it is proved that insertion grammars with weight at least 7 can characterize recursively enumerable languages (modulo a weak coding and an inverse morphism), and the question was formulated whether or not this result can be improved. In this paper, we come up with a positive answer to this question, by decreasing the weight of the insertion grammar used to 5. We also give a characterization of recursively enumerable languages in terms of right quotients of insertion languages.\",\n \"Title: Clique-Detection Algorithm Using Clique-Self-Assembly\\nYear: 2011\\n\\nAbstract: Self-assembly is a process in which simple objects autonomously combine themselves into larger objects. It is considered as a promising technique in nano-technology. Two simple graphs G1 and G2 with a clique of same size overlap and a new self-assembled graph is formed. Besides studying the properties of self assembled graphs on cliques, we answer the question: Can a given set of graphs be generated through the self-assembly of cliques? If so, how to find the generator that could generate the given set of graphs by the process of clique-self-assembly. The question of the existence of minimal generator is also discussed. The necessary and sufficient condition for a graph H to be obtained by the iterated clique-self-assembly of the graph G is also answered. We also conclude that the problem of finding the generator is decidable. We note the importance of our work with respect to several closely related clique finding problem.\",\n \"Title: On A Class Of P Automata\\nYear: 2003\\n\\nAbstract: In this paper, we propose a class of P automata in which each membrane has a state, like in tissue P systems [5], and the computation starts at some initial state and ends in a final state. Unlike the automaton considered in [2], where rules are used in sequential manner, here we consider a variant such that the rules can be applied in maximal mode (as defined in tissue P systems), We show that P automata characterize the recursively enumerable sets of vectors of natural numbers.\",\n \"Title: P Systems with Membrane Creation: Universality and Efficiency\\nYear: 2001\\n\\nAbstract: P systems, introduced by Gh. Paun form a new class of distributed computing model. Several variants of P systems were already shown to be computationally universal. In this paper, we propose a new variant of P systems, P systems with membrane creation, in which some objects are productive and create membranes. This new variant of P systems is capable of solving the Hamiltonian Path Problem in linear time. We show that P systems with membrane creation are computationally complete.\",\n \"Title: Minimum Area Polygons With Two Reflex Angles Enclosing K Points\\nYear: 2001\\n\\nAbstract: We present in this paper, algorithms for finding the minimum area isothetic polygons having two reflex angles containing k points; (k < n). Our algorithms are based on line sweep paradigm. The idea is to enumerate all the polygons in each kind and find the minimum one, We also analyse the complexities of each of the algorithms. We find that out of the four orthogonal polygons with two reflex angles, three are orthoconvex and the algorithms resented for these polygons work in 0((n - k)(2)n(2) + n, n log n) time, where n, could be 0(n - k)(5) in the worst case. The algorithm for the non-orthoconvex polygon has a complexity of 0((n - k)(2)n(2) + n, n log n) where n, could be 0((n - k)(5)n) in the worst case.\",\n \"Title: Local And Recognizable Hexagonal Picture Languages\\nYear: 2005\\n\\nAbstract: In this paper we consider hexagonal arrays on triangular grids and introduce hexagonal local picture languages and hexagonal tiling systems defining hexagonal recognizable picture languages, motivated by an analogous study of rectangular arrays by Giammarresi and Restivo. We also introduce hexagonal Wang tiles to define hexagonal Wang systems (HWS) as a formalism to describe hexagonal picture languages. It is noticed that the family of hexagonal picture languages defined by hexagonal Wang systems and the family recognized by hexagonal tiling systems coincide. Analogous to hv-domino systems describing rectangular arrays, we define xyz-domino systems and prove that recognizable hexagonal picture languages are characterized as projections of xyz-local picture languages.\",\n \"Title: Breaking the data encryption standard using networks of evolutionary processors with parallel string rewriting rules\\nYear: 2009\\n\\nAbstract: In this paper we introduce a biologically inspired distributed computing model called networks of evolutionary processors with parallel string rewriting rules (NEPPS), which is a variation of the hybrid networks of evolutionary processors introduced by Martin-Vide et al. Such a network contains simple processors that are located in the nodes of a virtual graph. Each processor has strings (each string having multiple copies) and string rewriting rules. The rules are applied parallely on the strings. After the strings have been rewritten, they are communicated among the processors through filters. We show that we can theoretically break the DES (data encryption standard), which is the most widely used cryptosystem, using NEPPS. We prove that, given an arbitrary pair, one can recover the DES key in a constant number of steps.\",\n \"Title: Real time cross layer design using particle swarm optimization\\nYear: 2011\\n\\nAbstract: Cross Layer Optimization (CLO) strategies are currently being incorporated in network operating system for efficient utilization of resources to enable effective information management. In wireless adhoc networks real time optimizations need to be performed and hence CLO strategies that have faster response time are required. In this paper we propose a Cross Layer Optimization strategy that uses a variant of the Particle Swarm Optimization (PSO) for real time cross layer design of the network. The variant of the PSO used in this research work uses digital pheromones for improved performance. The proposed PSO-CLO strategy can be used for delay sensitive, bandwidth intensive and loss-tolerant wireless multimedia transmissions that have an ever demanding need for better Quality of Service. Our experimental results show that the proposed PSO-CLO strategy has significantly faster response time in comparison with the classical CLO solutions.\",\n \"Title: Universality Results for Some Variants of P Systems\\nYear: 2000\\n\\nAbstract: P systems, introduced by Gh. P驴un, form a new class of distributed computing models. Many variants of P systems were shown to be computationally universal. In this paper, we consider several classes of P systems with symbol-objects where we allow the catalysts to move in and out of a membrane. We prove universality results for these variants of P systems with a very small number of membranes.\",\n \"Title: Returning and non-returning parallel communicating finite automata are equivalent\\nYear: 2007\\n\\nAbstract: A parallel communicating automata system consists of several automata working independently in parallel and communicating with each other by request with the aim of recognizing a word. Rather surprisingly, returning parallel communicating finite automata systems are equivalent to the non- returning variants. We show this result by proving the equivalence of both with multihead finite automata. Some open problems are finally formulated.\",\n \"Title: On the Trade-off Between Ambiguity and Complexity in Contextual Languages\\nYear: 2013\\n\\nAbstract: Contextual grammars are introduced by Solomon Marcus in 1969 based on the fundamental concept of descriptive linguistics of insertion of strings in given contexts. Internal contextual grammars are introduced by Păun and Nguyen in 1980. For contextual grammars several descriptional complexity measures and levels of ambiguity have been defined. In this paper, we analyze the trade-off between ambiguity and complexity of languages generated by internal contextual grammars. The notion of a pseudo inherently ambiguous language with respect to two complexity measures is introduced and investigated. These languages can be generated by unambiguous grammars which are minimal with respect to one measure and ambiguous if they are minimal with respect to the other measure. An open problem from [15] is solved in this framework.\",\n \"Title: Communicating Distributed H systems with Simple Splicing Rules\\nYear: 2006\\n\\nAbstract: Abstractó In this paper we dene communicating distributed H systems with simple splicing rules of types (1,3), (1,4) and (2,3) and study the generative capacity. keyword: Splicing systems, contextfree languages, simple H systems, test tube systems, communicating distributed H systems\",\n \"Title: Efficient Randomized Incremental Algorithm For The Closest Pair Problem Using Leafary Trees\\nYear: 1995\\n\\nAbstract: We present a new data structure, the Leafary tree, for designing an efficient randomized algorithm for the Closest Pair Problem. Using this data structure, we show that the Closest Pair of n points in D-dimensional space, where, D2, is a fixed constant, can be found in O(n log n/log log n) expected time. The algorithm does not employ hashing.\",\n \"Title: On the generative power of simple H systems\\nYear: 2000\\n\\nAbstract: In this paper, we prove that the power of Simple H-systems of the(2,3) type with permitting contexts and target alphabet is equal toExtended H-systems with permitting contexts and radius of the rulesequal to one. We also prove interesting results on Simple ExtendedH-systems and Extended H-systems with forbidden contexts.Keywords: Splicing systems, simple H systems, permitting and forbiddencontexts and cardinality of context.1\",\n \"Title: Small Universal Spiking Neural P Systems with Cooperating Rules as Function Computing Devices.\\nYear: 2014\\n\\nAbstract: The paper considers spiking neural P systems (SN P systems) with cooperating rules where each neuron has the same number of sets of rules, labelled identically. Each set is called a component (maybe empty). At each step only one of the components can be active for the whole system, and only the rules from the active component are enabled. Each neuron with enabled rules from this active component can fire. By using 59 neurons, a small universal SN P system with two components, working in the terminating mode, is constructed for computing functions.\",\n \"Title: An entropy maximization problem in shortest path routing networks\\nYear: 2014\\n\\nAbstract: In the context of an IP network, we investigate an interesting case of the inverse shortest path problem using the concept of network centrality. For a given network, the centrality distribution associated with the links of a network can be determined based on the number of shortest paths passing through each link. An entropy measure for this distribution is defined, and we then forumulate the inverse shortest problem in terms of maximizing this entropy. We then obtain a centrality distribution that is as broadly distributed as possible subject to the topology constraints. An appropriate change in the weight of a link alters the number of shortest paths that pass through it, thereby modifying the centrality distribution. The idea is to obtain a centrality distribution that maximizes the entropy. This problem is shown to be NP-hard, and a heuristic approach is proposed. An application to handling link failure scenarios in Open Shortest Path First routing is discussed.\",\n \"Title: Distance realization problem in Network Tomography: A heuristic approach\\nYear: 2013\\n\\nAbstract: This paper proposes a heuristic approach for the distance realization problem, which arises in Network Tomography. Network Tomography is the study of estimating internal network structure and link-level performance from end-to-end measurements. A distance realization problem is to reconstruct a graph or topology from its distance matrix, i.e., the matrix containing the pairwise distances between the terminal nodes. The graph, thus realized from the pairwise distances of terminal nodes, can either be a tree or a general graph. There are efficient polynomial algorithms developed for the case of tree realization. However, the problem of finding optimal realization (i.e., the total length of the graph realized is minimum) of distance matrix for a general graph is shown to be NP-hard. Our proposed heuristic approach for distance realization consists of three stages: (i) find a closer tree realizable distance matrix based on the shortest paths, (ii) construct a tree and (iii) fix the differences between the tree realizable distance matrix and the original distance matrix. It also attempts to maximize the `entropy of betweenness-centrality' measure in the network while satisfying the distance constraints.\",\n \"Title: Modeling spiking neural P systems using timed Petri nets\\nYear: 2009\\n\\nAbstract: This paper shows that deterministic P-timed Petri nets with inhibitory and test arcs can simulate an SN P system. A method is proposed to translate an SN P system into Petri net model and is illustrated with an example.\",\n \"Title: Array Grammars and Kolam\\nYear: 1974\\n\\nAbstract: Kolam designs of South Indian folk art are treated as examples of two-dimensional picture languages, Many of the complicated kolain patterns are seen to be generable by context-free array grammars. Two methods of kolam generation are discussed. One method is to generate the kolam patterns by array grammars with a finite number of primitives as terminals. The other is to generate labeled dots by array grammars and then to give a finite number of simple instructions to draw the kolam pattern on the framework of labeled dots.\",\n \"Title: Abstract families of matrices and picture languages\\nYear: 1972\\n\\nAbstract: A linguistic model to generate matrices (arrays of terminals) is presented. The sixclosure axioms which define abstract families of languages (AFL) are extended to matrices to obtain abstract families of matrices (AFM). Corresponding automata to recognize matrices are defined and equivalence established. Immediate application is shown in picture processing by generating a wide class of interesting pictures. Illustrations include generation of kolam patterns, wall paper designs and geometric patterns replicated several times. Simple transformations of a picture such as reflection, translation, halftum, and turning upside down are very naturally obtained.\",\n \"Title: A Centrality Entropy Maximization Problem in Shortest Path Routing Networks.\\nYear: 2016\\n\\nAbstract: In the context of an IP network, this paper investigates an interesting case of the inverse shortest path problem using the concept of network centrality. For a given network, a special probability distribution, namely the centrality distribution associated with the links of a network can be determined based on the number of the shortest paths passing through each link. An entropy measure for this distribution is defined, and the inverse shortest path problem is formulated in terms of maximizing this entropy. We then obtain a centrality distribution that is as broadly distributed as possible subject to the topology constraints. A maximum entropy distribution signifies the decentralization of the network. An appropriate change in the weight of a link alters the number of the shortest paths that pass through it, thereby modifying the centrality distribution. The idea is to obtain a centrality distribution that maximizes the entropy. This problem is shown to be NP-hard, and a heuristic approach is proposed. An application to handling link failure scenarios in Open Shortest Path First routing is discussed.\",\n \"Title: On Succinct Description Of Certain Context-Free Languages By Ins-Del And Matrix Ins-Del Systems\\nYear: 2016\\n\\nAbstract: In this paper, we introduce some basic measures for insertion-deletion system and matrix insertion-deletion system. These measures are based on the number of variables, the number of productions and the number of symbols in a grammar. We show that with respect to these measures, both the systems are more succinct over context free grammars in representing certain families of context-free languages.\",\n \"Title: A Note on Hybrid P Systems\\nYear: 2002\\n\\nAbstract: Generally, in rewriting P systems (Martin-Vide and Paun, 2000) one uses Chomsky rules (Hopcroft and Ullman, 1979), whereas in contextual P systems (Madhu and Krithivasan, 2002) we considered contextual rules (Marcus, 1969), (Paun, 1997) for processing string-objects. By combining Chomsky rules and contextual rules, a new class of P systems were introduced in Krishna et al. (2001), the hybrid P systems. In this paper we continue the study of hybrid P systems, and show that systems with two membranes are universal in the case of contextual rules with a regular choice, and systems with four membranes are universal in the case of contextual rules with finite selection.\",\n \"Title: Realizing Switching Functions Using Peptide-Antibody Interactions\\nYear: 2004\\n\\nAbstract: We propose a theoretical model for performing gate operations - OR, AND, and NOT - using peptide-antibody interactions. The idea is extended to further gates, such as XOR, NAND, and NOR.\",\n \"Title: A min-cost-max-flow based algorithm for reconstructing binary image from two projections using similar images\\nYear: 2008\\n\\nAbstract: The aim of this paper is to study the reconstruction of binary images from two projections using a priori images that are similar to the unknown image. Reconstruction of images from a few projections is preferred to reduce radiation hazards. It is well known that the problem of reconstructing images from a few projections is ill-posed. To handle the ill-posedness of the problem, a priori information such as convexity, connectivity and periodicity are used to limit the number of possible solutions. We use a priori images that are similar to the unknown image, to reduce the class of images having the same two projections. The a priori similar images may be obtained in many ways such as by considering images of neighboring slices or images of the same slice, taken in previous time instances. In this paper, we give a polynomial time algorithm to reconstruct binary image from two projections such that the reconstructed image is optimally close to the a priori similar images. We obtain a solution to our problem by reducing our problem to min cost integral max flow problem.\",\n \"Title: Spiking Neural P Systems with Cooperating Rules.\\nYear: 2014\\n\\nAbstract: The concept of cooperation and distribution as known from grammar systems is introduced to spiking neural P systems (in short, SN P systems) in which each neuron has a finite number of sets (called components) of rules. During computations, at each step only one of the components can be active for the whole system and one of the enabled rules from this active component of each neuron fires. The switching between the components occurs under different cooperation strategies. This paper considers the terminating mode, in which the switching occurs when no rule is enabled in the active component of any neuron in the system. By introducing this new mechanism, the computational power of asynchronous and sequential SN P systems with standard rules is investigated. The results are that asynchronous standard SN P systems with two components and strongly sequential unbounded SN P systems with two components are Turing complete.\",\n \"Title: Generalized normal form for rewriting P systems\\nYear: 2002\\n\\nAbstract: . P systems, introduced by Gh. Păun [9] as a new theoretical model for molecular computations, are based on the notion of membrane\\n structure. Several variants of P systems have been proposed and shown to be computationally universal. One of such variant\\n is the rewriting P systems, where we consider string-objects and process them using rewriting rules. Particular cases of normal\\n forms for rewriting P systems were proposed in [11–13]. In this work we introduce the generalized normal form for rewriting\\n P systems which take into consideration the depth of the membrane structure and the number of rewriting rules present in each\\n membrane. Such generalized normal forms are given for rewriting P systems with priorities, and for partially parallel rewriting\\n P systems. In this way, several results from the literature are generalized and improved.\",\n \"Title: Visibility Of Rectangular Objects In L-1 Metric\\nYear: 1998\\n\\nAbstract: Given a set of n rectangles (2-dimensional) parallel to xy-plane, and a point P(x, y, z) in 3-dimensional space, we find the visibility of all rectangles i.e., the set of all visible points of rectangles from P in L-1 metric, in O(n(4)) time and O(n(3)) space. We also discuss the cases where the point P moves along a straight line and the rectangles are inserted and deleted dynamically.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":72,"string":"72"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13682,"string":"13,682"}}},{"rowIdx":13683,"cells":{"question_id":{"kind":"string","value":"Viorel Negru"},"question":{"kind":"string","value":"Generate an abstract for the title \"Sensors Data-Stream Processing Middleware Based On Multi-Agent Model\" using the following items: intelligent sensor data processing, multi-agent model, workflows, healthcare system, ambient assisted living."},"answer":{"kind":"string","value":"The goal of this study is to propose an architecture for an intelligent sensor data processing middleware. In order to fulfill the ambient assisted living data processing requirements we design a flexible and scalable architecture based on multi-agent model. This architecture allows acquisition, interpretation and aggregation of sensor data-streams. Our system is able to process different sensor data-streams, to adapt to different levels of abstraction, to define different data-processing workflows. An extended operators over data stream language is used to define workflows. Different types of agents (simple sensor agent, logic sensor agent, virtual sensor agent etc) are defined. The designed system is a domain independent multi-agent system which can be instantiated for particular AmI problems. The middleware architecture will use a healthcare system for validation."},"correct_answer":{"kind":"string","value":"The goal of this study is to propose an architecture for an intelligent sensor data processing middleware. In order to fulfill the ambient assisted living data processing requirements we design a flexible and scalable architecture based on multi-agent model. This architecture allows acquisition, interpretation and aggregation of sensor data-streams. Our system is able to process different sensor data-streams, to adapt to different levels of abstraction, to define different data-processing workflows. An extended operators over data stream language is used to define workflows. Different types of agents (simple sensor agent, logic sensor agent, virtual sensor agent etc) are defined. The designed system is a domain independent multi-agent system which can be instantiated for particular AmI problems. The middleware architecture will use a healthcare system for validation."},"documents":{"kind":"list like","value":["Title: Neuroevolution Based Multi-Agent System With Ontology Based Template Creation For Micromanagement In Real-Time Strategy Games\nYear: 2014\n\nAbstract: This paper presents a multi-agent system that handles unit micromanagement using online machine learning in real time strategy games. We used rtNEAT algorithm in order to obtain customized neural network topologies, thus avoiding to complex network architecture. We use an ontology based template to create suitable input and outputs for unit agents enabling them to cooperate and form teams for their mutual benefit and eliminating communication overhead. The AI system was implemented using the JADE framework and the BWAPI handled communication between our system and the game. We have chosen Starcraft as a testbed. As a baseline we compared the in game AI as well as several other AI solutions that use adaptive mechanisms.","Title: Human behavior analysis in ubiquitous environments for energy efficiency improvement\nYear: 2011\n\nAbstract: The goal of this paper is to discover human habits using the received sensors data-streams, in order to improve the energy efficiency. We propose a multi-agent architecture and a formalism to describe scenarios in ubiquitous environments based on a wireless sensors network. We have used sensor data and simulations about a four person family, to validate our prototype.","Title: A highly-efficient memory-compression approach for GPU-Accelerated virus signature matching\nYear: 2012\n\nAbstract: We are proposing an approach for implementing highly compressed Aho-Corasick and Commentz-Walter automatons for performing GPU-accelerated virus scanning, suitable for implementation in real-world software and hardware systems. We are performing experiments using the set of virus signatures from ClamAV and a CUDA-based graphics card, showing how memory consumption can be improved dramatically (along with run-time performance), both in the pre-processing stage and at run-time. Our approach also ensures maximum bandwidth for the data transfer required in the pre-processing stage, between the host and the device memory, making it ideal for implementation in real-time virus scanners. Finally, we show how using this model and an efficient combination of the two automata can result in much lower memory requirements in real-world implementations.","Title: Multi-Agent Stock Trading Algorithm - A Neural Network Approach.\nYear: 2012\n\nAbstract: The goal of this paper is to create a hybrid model that helps the investor to make a good decision on when to buy/sell on a stock market in order to maximize his gain. Artificial Neural Networks are often used in stock market prediction because of their ability to find patterns and correlations in past data. We propose a Multi-Agent Architecture that by combining techniques from fundamental and technical analysis with Neural Networks (namely Multi-Layer Perceptron) tries to compare the result given by all three of them in order to give a much valuable result on which can be a good moment to buy/sell on a stock market. In order to validate our model a prototype was developed.","Title: A Combinative Method for Decision Tree Construction\nYear: 2005\n\nAbstract: The selection of the splitting attribute in decision tree construction process is the key point for the size and quality of the tree. Although several criteria have been proposed and there are good papers that compare their results, no consensus have been adopted regarding the best method. In this paper we'll present a new approach in which each candidate attribute is evaluated using a set of available criteria and the attribute voted the best by most of the criteria will be selected as the winning splitting attribute. Each criterion will be evaluated based on the contingency table created at each splitting node. An approach based on OLAP is used for faster contingency table aggregation.","Title: The Effect Of Temporary Links In Randomly Generated Networks Of Constraints\nYear: 2012\n\nAbstract: Additional communication links between unconnected agents are used in asynchronous searching, in order to detect obsolete information. A first way to remove obsolete information is to add new communication links, which allow a nogood owner to determine whether this nogood is obsolete or not. The second solution consists in temporarily keeping the links. A new link is maintained until a fixed number of messages have been exchanged through it. This article investigates different values for the number of messages, values that are either statically or dynamically, during the run time, determined. In the case of processing all the messages, we adapt a dynamical solution for determining the number of necessary messages for maintaining a connection. The experiments show a better efficiency in comparison with the standard Asynchronous Backtracking. In this paper we examine the effect of temporary links for the random binary constraints problem. Experiments with asynchronous search techniques are conducted on randomly generated networks of constraints. Experimental results show that the dynamical solution for the temporary links allows obtaining better results for the majority of classes of problems investigated.","Title: AgentDiscover: A Multi-Agent System for Knowledge Discovery from Databases\nYear: 2007\n\nAbstract: AgentDiscover is a multi-agent based intelligent recommendation system, supporting real-time control and feedback, for building and execution workflows for knowledge discovery from databases (KDD). The aim of the proposed system is to deal with the complexity of KDD processes and to offer a tool that supports both researchers exploring KDD methods and non-expert users looking for quick results in this field. A prototype was developed in JADE and the approach was tested for a medical dataset.","Title: Negotiation Policies for Provisioning of Cloud Resources.\nYear: 2012\n\nAbstract: Cloud represents today a computing market where users can buy resources according to a pay-per-use business model. Cloud providers offer different kind of services which can be characterized by different service levels. Negotiation of the best resource can be very difficult because there is a semantic gap between the different provider SLAs and the requirements of an user's application. We address the negotiation issue within the research activity of the mOSAIC project by designing and developing an agents based service at platform level for provisioning of cloud resources. In order to allow the execution of the application and fulfill the developer's requirements, the service uses a policy based approach that is able to choose, at infrastructure level, the best solution, in terms of a collection of cloud resources from different providers.","Title: Multi-agent Stock Trading Algorithm Model\nYear: 2011\n\nAbstract: Stock Trading Algorithm Models are an important problem researchers dealt with through time that implied knowledge in technical and fundamental analysis, time series combined with knowledge expertise in computer science or programming in order to find solutions of how to have a stock gain. This paper proposes a multi-agent architecture that assists a user in making a successful investment on the stock market. This implies the use of technical and fundamental analysis in order to make a useful prediction of the security's trend.","Title: Multi-Agent Architecture for Knowledge Discovery\nYear: 2006\n\nAbstract: Knowledge Discovery from Databases (KDD) is a complex process composed of several phases: business understanding, data understanding, data preparation, modeling, evaluation and deployment. For each of the phases, there are many algorithms and methods available, the end-user having to select one of them. The AgentDiscover is a multi-agent based intelligent recommendation system for selection of the most appropriate solving method for each phase. This brings added value for both novice and experienced users.","Title: Managing Risk Behavior on an Evolutionary Market -- A Risk Limits and Value-at-Risk Measures Approach\nYear: 2013\n\nAbstract: The goal of this paper is to develop a system able to coordinate a trader in optimizing a stock market portfolio in order to improve the profitability of a short or medium time period investment. The system is able to classify the risk and quantifies its effect on an investment based on sentiment analysis, certain characteristics, the traders confidence level, and by measuring the potential loss over a certain period of time. The system id also able to create certain types of portfolios based on different methods of computing the investment risk and on the associated level of confidence. We proposed a multi-agent system that uses sentiment analysis, volatility, Monte Carlo simulation, trust models and risk models/limits in order to choose the appropriate mix of investments in order to minimize the risk and maximize the gain on a stock portfolio taking in consideration also the traders possibilities to enter in an investment. A prototype was developed on which we validated our research.","Title: Experimental Study on Parallel Methods for Solving Systems of Equations\nYear: 2012\n\nAbstract: The paper presents experimental results on parallel variants of some classical methods for solving systems of equations. The following four methods are studied: Newton method, Chebyshev method, Gradient method with Fridman control sequence, a method of conjugate gradient type. The main conclusion is that the precondition of the sparse systems (both linear and nonlinear) improves in a great extent the performance of the parallel algorithms.","Title: Particularities of evolutionary parameter estimation in multi-stage compartmental models of thymocyte dynamics\nYear: 2013\n\nAbstract: The aim of this paper is twofold. Firstly, it presents an extension of a multi-stage compartmental model in order to make it more appropriate in modelling various perturbations of thymocyte dynamics.Secondly, it proposes an evolutionary approach, based on the JADE algorithm, for simultaneously estimating the number of division stages, the rates associated to cellular processes (e.g. proliferation, death, migration) and the parameters corresponding to the proposed perturbation functions. Several quality of fit measures are investigated and their relationship with the variability of experimental data is exploited in order to select the optimization criterion.","Title: From Cloud Governance to IoT Governance\nYear: 2013\n\nAbstract: Recent migration towards the cloud has lead to boom of initiatives that address key issues related to it. One of these issues, integrating various available cloud services while providing automated lifecycle management, was addressed by cloud governance. However, while mostly until now computers and phones were linked to the internet, new devices like tablets, sensors, smart devices (embedded devices) are now making their way into this environment. This, in turn, raises questions related to managing, governing these devices. Similarly to how cloud governance extends SOA governance, this article will detail the way in which IoT governance extends cloud governance.","Title: Ontology-Based Modeling and Execution of Workflows for Virtual ISP\nYear: 2008\n\nAbstract: Most of the small ISPs are constrained to resell wholesale services of incumbent operators owning the access infrastructure. They are struggling to gain competitive advantages by lowering their pricing on typical services (e.g. e-mail, hosting) and by their capability to provide specific added-value services which are more difficult to offer by large entities. The production of tailored solutions targeted to local business needs is strongly facilitated by an appropriate conceptual and executable model of the ISP domain. Innovative technologies, like ontologies, web technologies and workflow technologies, network modeling and Directory Enabled Networks are combined to build a software platform that enables a cluster of SMEs to operate as a single business entity. The goal of this paper is to highlight the role of ontologies in the implementation and execution of the software platform.","Title: Neural Network Predictions of Stock Price Fluctuations\nYear: 2012\n\nAbstract: The goal of this paper is to create a hybrid system based on a Multi-Agent Architecture that will investigate the evolution of some neural network methods along with technical and fundamental analysis methods on stock market indexes and how this information influences the stock market behavior in order to improve the profitability of a short or medium time period investment. The proposed system compares the results of Standard Feed Forward Neural Network, Elman and Jordan Recurrent Neural Networks and a Neural Network evolved with Neuro Evolution of Augmenting Topologies (NEAT) in order to investigate which network gives the most accurate result and time performance by taking in consideration the close price of a stock. The system also finds correlations between the pattern recognition methods and technical and fundamental methods results in order to find the direction of the market trend, to predict the next day price of a stock and to trigger a useful buy/sell signal. We are also interested in finding a correlation between the evolution of price, volume, number of transactions in order to have a better view on which is the effect of stock liquidity on a stock price. In order to validate our model a prototype was developed and applied to the Bucharest Stock Exchange Market indexes.","Title: Towards an Ontology for Cloud Services\nYear: 2012\n\nAbstract: The emergence of cloud computing has created a shift in the service lifecycle, a shift meant to meet the requirements of cloud environments. Anticipating this, cloud governance, a step forward from SOA governance, tries to consolidate an environment where collaboration between various enterprises can be easily achieved. Cloud governance lays the groundwork for enabling easy application development and deployment by providing critical services that range from service management to security, monitoring and audit. As means of support for cloud computing, several ontologies have tried to bridge gaps and provide interoperability. None, however, have taken the time to analyze and define aspects that relate to service lifecycle and cloud governance. In this regard, our article proposes an ontology that is meant to complement existing ones.","Title: Towards a Service Friendly Cloud Ecosystem\nYear: 2012\n\nAbstract: After the large penetration of Cloud Computing, more and more developers are taking into account migrating their applications to the Cloud, in order to take advantage of the characteristics of this new environment. In close relation with application migration, an increasing number of development and execution platforms, delivered as PaaS solutions (such as mOSAIC, 4CaaSt, Cloud Foundry, Open Shift, Stackato, and others) are offering their services for development, deployment, and execution of applications that are using in an optimum manner the five characteristics of the Cloud. Following this massive migration of applications, especially from SOA, to Cloud environments, new requirements for application development could be identified in order to enable the construction of complex solutions, and to exploit a business level on the top of various *-as-a-Service layers. The introduction of a centralized component, the Cloud Governance, is necessary in order to enable the development of complex cloud ecosystems. This centralized component is extending, complementing, completing and integrating core features from the PaaS layer, like monitoring, provisioning, negotiation, and others, and integrate features of various Cloud management solutions.","Title: Multi-Agent Architecture In Semantic Services Environment\nYear: 2012\n\nAbstract: A semantic enabled multi-agent architecture for solving non-linear equations systems by using a service oriented approach is proposed. The service oriented approach allows us to access already implemented methods for solving complex mathematical problems. The semantic descriptions of these services provide support for intelligent agents. The proposed architecture is a framework with two extension areas, agent society and domain ontology, with possible application in other domains too.","Title: Framework for Enabling Scalable Learning Game AI.\nYear: 2013\n\nAbstract: The video game industry is a multibillion-dollar industry in which, due to general short deadlines, game visuals as well as gameplay elements are worked in parallel up until the very last minute. This means that even if the AI system has been designed in parallel with the other game elements, once a change has been made in the late stages of the game development, the AI may prove to be inadequate to the given job. Our article covers some of the existing frameworks for game AI and proposes a multi-agent system which serves as a framework for scalable learning game AI through integration of existing machine learning techniques.","Title: Knowledge Management in Expert System Creator\nYear: 2002\n\nAbstract: Recent explosive growth in data and the availability of large data-sets in multiple disciplines, generates the need for new techniques and tools that can intelligently transform the data into useful information and knowledge. The machine learning algorithms synthesize various knowledge representation forms, most of them equivalent and inter-changeable. A new approach is needed to support and combine the knowledge representation form with various data sources (represented by database management systems). In this paper, a powerful, visual CASE tool for knowledge management is introduced. It supports the integration of high-level knowledge \"beans\" into host projects. A scalable approach to the problem of data integration from conventional database systems in expert systems is also over-viewed.","Title: Neuroevolution based multi-agent system for micromanagement in real-time strategy games\nYear: 2012\n\nAbstract: The main goal of this paper is the design of a multi-agent system (MAS) that handles unit micromanagement in real time strategy games and is able to adapt/learn during game play. To achieve this we adopted the rtNEAT approach in order to obtain customized neural network topologies, thus avoiding the generation of too complex architectures. Also by defining internal and external inputs for each agent we managed to create independent agents that are able to cooperate and form teams for their mutual benefit and at the same time eliminate unnecessary communication overhead. The MAS was implemented for the real time strategy game StarCraft using JADE multi-agent platform and BWAPI to ensure the interface with the game. We used as a baseline the in game AI and also tested it against other adapting AI systems in order to compare their performance against our system.","Title: An Evolutionary Approach for SLA-based Cloud Resource Provisioning\nYear: 2013\n\nAbstract: As the number of existing cloud vendors rises, resource count and types are ever increasing leading to a need of cloud management solutions which facilitate easy cloud adoption. While providing several services, cloud management's primary role is resource provisioning. In order to meet application needs in terms of resources, cloud developers must carefully choose among the existing offers in order to deploy their applications. The research presented in this paper enables developers to automate the process of resource provisioning by specifying their preference towards resources and resource attributes based on which the system can propose solutions for their requirements.","Title: Service Datastores in Cloud Governance\nYear: 2012\n\nAbstract: A direct consequence of adopting Cloud Computing is the creation of new marketplaces for small and medium sized enterprises (SMEs). Having a strong support offered by Platform as a Service (PaaS) paradigm in terms of automation and management, combined with cloud governance, SMEs have a real chance to get back in competition with the large enterprises. The implementation of Cloud Governance solutions offers the necessary support for storing and manipulating data which is relevant for various aspects of applications, ranging from business to technical support for deployed services. Our paper focuses on analysing and detailing governance specific data stores and the way they are able to provide system-wide services.","Title: DL-AgentRecom - A Multi-Agent Based Recommendation System for Scientific Documents\nYear: 2008\n\nAbstract: The goal is to propose a recommendation system for scientific documents. The paper presents the architecture and theprinciples of functioning of the system. The way a user organizesits documents is called the user's perspective over the set ofdocuments. Operations with user perspectives are defined, also thesystem can have and build it's own perspective. The usersperspective can be mixed with system perspective.","Title: A Survey of Adaptive Game AI: Considerations for Cloud Deployment.\nYear: 2013\n\nAbstract: Modern video games have become an important part of AI research in the past years, largely thanks to the characteristics of their environment and the challenges they pose to AI researchers. This paper is a a survey of the current game AI state of the art and highlights important achievements in this field. An adaptive multi-agent system that can be deployed on a cloud infrastructure to solve computational constraints of advanced machine learning methods is also presented.","Title: Applying ontologies for workflow modelling and execution for a virtual ISP\nYear: 2008\n\nAbstract: Small Internet Service Providers (ISPs) are constrained to resell the wholesale services of incumbent operators that own the access infrastructure. They struggle to gain competitive advantages by improving the typical services provided (e.g., e-mail, hosting) and through their capability to provide specific added-value services, which are more difficult for larger entities to offer. The production of tailored solutions targeted at the needs of local businesses is strongly facilitated by an appropriate conceptual and executable model of the ISP domain. Innovative technologies, like ontologies, web technologies, workflow technologies, network modelling and Directory Enabled Networks, are combined to build a platform that enables a cluster of Small- and Medium-sized Enterprises (SMEs) to operate as a single business entity. The goal of this paper is to highlight the role of ontologies in the implementation and execution of the aforementioned platform.","Title: An Event Driven Multi-agent Architecture for Enabling Cloud Governance\nYear: 2012\n\nAbstract: Cloud adoption is consistent within IT-based industries at different maturity levels. While cloud migration is an ongoing process, its base characteristics are not yet fully exploited. As Cloud Governance is built on top of requirements like security, reliability, trust, portability, interoperability, or fail over, a highly distributed and concurrent, and fault tolerant solution is required in order to achieve above specified requirements. This paper describes the core of an event-driven multi-agent architecture for supporting Cloud Governance activities, built around Akka/Clojure actor characteristics and fully exploiting the Enterprise Integration Patterns. The modeling process follows the Gaia methodology for agent-oriented analysis and design.","Title: Service Lifecycle in the Cloud Environment\nYear: 2012\n\nAbstract: Cloud governance's approach to development in the cloud environment allows for a way in which services are well defined and unambiguous, where they can be easily designed, implemented, deployed and executed, for a way in which they can be consumed. This approach is beneficial to SMEs which can now empower themselves by adopting cloud-based technologies and platforms. They now have the opportunity to develop niche solutions, or to group themselves in specialized clusters, in order to build complex, targeted solutions, tailored on customers' needs. The focus of this paper is detailing the service lifecycle from the point of view of our proposed cloud governance architecture that is based on a cloud management solution, the Cloud Agency, a multi-agent system that is part of the mOSAIC project.","Title: The Impact of the \"Nogood Processor\" Technique in Scale-Free Networks.\nYear: 2013\n\nAbstract: DisCSPs are composed of agents that manage variables which are connected by constraints, various algorithms for solving DisCSPs are searching through this network of constraints. The scale-free graphs have been proposed as a generic and universal model of network topologies that exhibit power-law distributions in the connectivity of network nodes. Little research was done concerning the network structure for DisCSP and in particular for scale-free networks. The asynchronous searching techniques are characterized by the occurrence of the nogood values during the search for the solution. In this article we analyzed the distribution of nogood values to agents and the way to use the information stored in the nogood, what we will call the nogood processor technique. We examine the effect of nogood processor for networks that have a scale-free structure. We develop a novel way for the distribution of nogood values to agents, the experiments show that it is more effective for several families of asynchronous techniques.","Title: An Extensible Environment for Expert System Development\nYear: 2003\n\nAbstract: The use of expert systems in the speed-up of human professional work has been in two orders of magnitude with resulting increases in human productivity and financial returns. Last decade shows that a growing number of organizations shift their informational systems towards a knowledge-based approach. This fact generates the need for new tools and environments that intelligently port the legacy systems in modern, extensible and scalable knowledge-integrated systems. This paper presents an extensible, user-friendly environment for expert system development, which supports the integration of high-level knowledge \"beans\" into host projects, data integration from conventional database systems and system's verification, debugging and profiling.","Title: Experimental Analysis of the Impact of the Message Management in the Case of the ABT Family\nYear: 2007\n\nAbstract: In this article are analyzed the message queues aiming to improve the message management, more precisely to reduce or eliminate the obsolete messages and, thus, reduce the costs for obtaining the solution. This thing allows the improvement of the performances of the ABT family techniques. Asynchronous techniques use some message processing routines. Those procedures process sequentially or in batches the messages that are in the message queues. Typically, each agent extracts one or more messages from its communication channel and calls the appropriate message processing routine, this operation is achieved based on a strategy of message processing, strategy that specifies the order and the way of treating the messages based on a formal protocol. In this article we analyzed this protocol for message management for the purpose of identifying the conditions where this could be applyed to allow us to decrease or eliminate the redundant messages. A new strategy for message management is proposed, strategy that allows obtaining variants of more efficient asynchronous techniques, in the case of the ABT family. An improved protocol for complete treating of the messages is proposed. The performances of the ABT family techniques are investigated, the experiments showing a decrease of the necessary costs for obtaining a solution in the case of applying the message management. A comparative study of the ABT versions is presented, the sequential versions are compared with the versions with redundant messages filtering, those based on various strategies of message processing.","Title: Optimizing Cloud Resources Allocation For An Internet Of Things Architecture\nYear: 2014\n\nAbstract: The optimization of the cloud resources used to power a multi-agent Internet of Things architecture is an important issue which has an important impact on the overall operation cost of the architecture. The resources tenancy is a costly operation, thus their allocation and management should be optimized based on the usage patterns. The infrastructure for the multi-agent system should not be affected by the deployment or maintenance life cycle, operations require parts of the system, or even the entire system to be offline during the execution of scheduled procedures. This paper outlines of the importance of the infrastructure audit, which offers a good insight of how the resources are used, the geographical areas which are heavily used and where the allocation or release of used resources is mandatory. Also, the security audit, in a distributed multi-agent architecture that handles a large number of heterogeneous devices, represents a good mechanism for performance improvement.","Title: Efficient parallel automata construction for hybrid resource-impelled data-matching.\nYear: 2014\n\nAbstract: We are presenting an innovative, massively-parallel heterogeneous architecture for the very fast construction and implementation of very large Aho–Corasick and Commentz-Walter pattern-matching automata, commonly used in data-matching applications, and validate its use with large sets of data actively used in intrusion detection systems. Our approach represents the first known hybrid-parallel model for the construction of such automata and the first to allow self-adjusting pattern-matching automata in real-time by allowing full-duplex transfers at maximum throughput between the host (CPU) and the device (GPU). The architecture we propose is easily scalable to multi-GPU and multi-CPU systems and benefits greatly from GPU acceleration, also relying on a highly-efficient storage model for the automata and includes on-demand support for regular-expression matching, as well as support for custom heuristics to be built on top of the architecture, at different processing stages.","Title: Evolutionary Estimation of Parameters in Computational Models of Thymocyte Dynamics.\nYear: 2013\n\nAbstract: This paper presents an evolutionary-based parameter estimation procedure able to deal with the particularities of the constraints arising in mathematical models of biological systems. A measure of the constraint satisfaction degree and several feasibility-based ranking rules are proposed and comparatively analyzed for the problem of estimating the parameters involved in a model describing the dynamics of thymocytes. The numerical results illustrate the effectiveness of the procedure in inferring models which fit well the experimental data and also satisfy the biological constraints.","Title: Enhancing DisCSP-Netlogo from Simulation to Real-execution of Agents in Distributed Constraints.\nYear: 2014\n\nAbstract: The use of agent-based simulation models in NetLogo for research is growing rapidly in a number of fields of science and en- gineering, including in computer science, for example for analyzing the performances of asynchronous search techniques based on distributed constraints. Distributed Constraint programming (DisCSP/DCOP) is a programming approach used to describe and solve large classes of problems such as searching, combinatorial and planning problems. The purpose of this paper is to present an open-source and complete solution in NetLogo that allows modeling, simulation and evaluation of the distributed constraints using single computer or clusters, simulation or real execution. Our tool allows the use of various search techniques and also the evaluation and analysis of the performance of the asynchronous search techniques in two manners: simulation mode and real exe- cution. We also explain our methodology for running the NetLogo models in a cluster computing environment (simulation mode) or on a network of computers (simulation real execution). This tool is aimed to allow the evaluation of distributed algorithms in conditions as similar as possible to the real situations.","Title: Intelligent Stock Market Analysis System - A Fundamental and Macro-economical Analysis Approach\nYear: 2014\n\nAbstract: Stock Market Forecasting implies the use of a series of techniques that helps in determining the stock price evolution. The paper describes a multi-agent system that uses numerical, financial and economical data in order to evaluate the company's position on the market, profitability, performance, future expectations in the company's evolution. Determining the effect of political, governmental and social decisions along with detecting the way in which the price is constructed based on technical and fundamental analysis methods and the bid/ask situation helps in determining a more precise buy/sell signals, reducing the false signals and determining some risk/gain positions on different periods of time. In order to validate the results a prototype was developed.","Title: Multi-agent architecture for solving nonlinear equations systems in semantic services environment\nYear: 2011\n\nAbstract: A semantic enabled multi-agent architecture for solving nonlinear equations systems by using a service oriented approach is proposed. The service oriented approach allows us to access already implemented methods for solving complex mathematical problems. The semantic descriptions of these services provide support for intelligent agents.","Title: Steps towards cloud governance. a survey\nYear: 2012\n\nAbstract: Special characteristics of cloud computing have enhanced its rate of adoption for small medium-sized enterprises. Its associated economic model allow for small actors to become more visible, and enable them to collaborate and integrate their solutions for competing with big players on the IT market. As means of exploiting these opportunities, there is a necessity for adopting of solutions which enhance the efficacy of in the cloud activities - cloud management -and allow the migration of business and operational aspects altogether - cloud governance -while integrating services in order to build complex, targeted, solutions for their customers' needs.","Title: Stock market analysis - Strongest performing stocks influence on an evolutionary market\nYear: 2013\n\nAbstract: On the stock market, it is said that the strongest stock gives the direction of those stocks that are in the same domain (IT, services, oil, and others). Determining the strongest stock plays an important part in finding a better moment to enter on a position. This paper presents a Multi-Agent architecture that combines Technical Analysis, Neural Networks and Statistical Methods in order to find the strongest stock and to make a better forecast on the market future trend and trigger an entry/exit signal based on the market basket classification. A prototype was developed and applied on the Bucharest Stock Exchange Market (BSE).","Title: Classifying and quantifying certain phenomena effect\nYear: 2013\n\nAbstract: The goal of this paper is to create a hybrid system that will investigate possible stock market changes immediately after financial news article appear and how this information influences the stock market behavior in order to improve the profitability of a short or medium time period investment. We proposed a multi-agent system that uses text mining, information extraction, pattern recognition, sentiment analysis and a trust model. The system classifies and quantifies certain phenomena (financial news influence) in order to compute the effect of some properties and its size on the stock market and also checks if we can use turbulence to detect disasters. It also searches a correlation between the effect of news articles and the trader's behavior on the market. In order to validate our model a prototype was developed.","Title: Data mining considerations for knowledge acquisition in real time strategy games\nYear: 2013\n\nAbstract: Adaptive Game AI has been one of the key topics being researched in the field of academic game AI research. In this paper we present a comparison of several domain independent machine learning methods with the aid of which we extract expert knowledge from game logs. Each game log is represented as a feature vector that encodes cardinality and timing for player actions. We compare a wide variety of classification methods and highlight which ones are best for deployment for an adaptive game AI systems.","Title: Investment portfolio optimization based on risk and trust management\nYear: 2013\n\nAbstract: The goal of this paper is to develop a system able to coordinate a trader in optimizing a stock market portfolio in order to improve the profitability of a short or medium time period investment. The system is able to classify the risk and quantifies its effect on an investment based on sentiment analysis, certain characteristics and the traders level of confidence. It also uses an ontology to describe the markets status and expectations based on future events (text features) and to determine the relationship and correlation between news articles. The system also verifies the effect of a similar percentage of positive and negative news articles, and which of this information will influence more the stock price movement and trend. We proposed a multi-agent system that uses text mining, ontology, sentiment analysis, trust and risk models in order to choose the appropriate mix of investments to minimize the risk and maximize the gain on a stock portfolio. A prototype was developed on which we validated our research.","Title: Evolutionary Approach to Negotiation in Game AI\nYear: 2013\n\nAbstract: As modern games become more and more sophisticated graphically, so does the level of artificial intelligence that animates them thus, the larger the game budget, the more work is put into improving the AI. Unfortunately many of these games feature AIs that are standalone and do not communicate with each other, they do not try to negotiate in order to improve their individual standing. The current work focuses on analysing existing game types in order to establish types of negotiation that can be achieved between AI entities. Moreover, an evolutionary approach which focuses on achieving negotiation between these entities and tackles the problem of having multiple negotiation items with discrete values is presented.","Title: Evolutionary pruning of non-nested generalized exemplars\nYear: 2011\n\nAbstract: This paper investigates the ability of an evolutionary pruning mechanism to improve the predictive accuracy of a classifier based on non-nested generalized exemplars. Two pruning algorithms are proposed: one which selects the most representative generalized exemplars and the other one which simultaneously selects both relevant exemplars and relevant attributes. Experimental studies conducted for a set of twenty-one datasets illustrated that both algorithms induce a significant improvement on the classification ability of the selected set of non-nested generalized exemplars.","Title: Improving the Performances of Asynchronous Search Algorithms in Scale-Free Networks Using the Nogood Processor Technique.\nYear: 2015\n\nAbstract: The scale-free graphs were proposed as a generic and universal model of network topologies that exhibit power-law distributions in the connectivity of network nodes. In recent years various complex networks were identified as having a scale-free structure. Little research was done concerning the network structure for DisCSP, and in particular, for scale-free networks. The asynchronous searching techniques are characterized by the occurrence of nogood values during the search for a solution. In this article we analyze the distribution of nogood values to agents and the way how to use the information from the nogood; that is called the nogood processor technique. We examine the effect of nogood processor for networks that have a scale-free structure aiming to develop search algorithms specialized for scale-free networks of constraints, algorithms that require minimum costs for obtaining the solution. We develop a novel way for distributing nogood values to agents, thus obtaining a new hybrid search technique that uses the information from the stored nogoods. The experiments show that it is more effective for several families of asynchronous techniques; we perform tests with the model running on a cluster of computers. Also, we examine the effect of synchronization of agents' execution and of processing messages by packets in scale-free networks.","Title: A taxonomic view of cloud computing services.\nYear: 2015\n\nAbstract: With the massive adoption of cloud computing, a growing number of small and medium enterprises SMEs now have the opportunity, together with the immediate goal of reducing cost, to effectively expose their services or group themselves into clusters or virtual enterprises in order to build and expose customised offerings. Different requirements, ranging from interoperability and portability, provisioning and reconfiguration, security and privacy, or monitoring and auditing, can all be controlled through the lifecycle support of cloud services and grouped under the umbrella of different virtual enterprises. A semantic support is essential to enable such an entity, and to foster collaboration among different virtual enterprises by providing specific mechanisms by which the construction of complex services from elementary building blocks can be made possible. At the same time it actively supports tasks like automation of cloud resource usage, efficient monitoring and scaling of cloud services, either individually or based on specific dependencies, or replacement and termination of services when the initial offers are no longer available.","Title: Natural Language Agreement in the Generation Mechanism based on Stratified Graphs\nYear: 2015\n\nAbstract: A Stratified Graph can generate natural language constructions by acting as a transition network with arc labels referring to word categories and word forms. In this representation, each grammar rule can be transposed in a sequence of labeled paths. The generation mechanism defined by means of stratified graph uses a bottom-up approach. The advantage of this generation mechanism relies on its bottom-up approach characteristic: this mechanism never suggests constructions that are not correct based on the involved representation elements. As it will be presented, implementing syntactic agreement rules in this natural language generation mechanism can determine different results even if identical words are considered in the generation mechanism.","Title: Stock Market Trading Strategies Applying Risk and Decision Analysis Models for Detecting Financial Turbulence.\nYear: 2015\n\nAbstract: Risk handling and evaluation plays an importantrole in optimizing an investment portfolio. This paper's goal isto describe a system that determines, classifies and handles riskassociated to any type of investment based on sentiment analysis, price movement, information related to companies, certain characteristics, the traders confidence level, and by measuring thepotential loss over a certain period of time. This research impliesanalyzing trader's risk, market risk, risk associated to eachevaluated company or financial group, political and governmentalrisk. The system is able to create different types of portfoliooptions based on the investor/trader profile, which is build basedon the user's tolerance to risk (determined by the results froman interactive quiz that the user must complete when entering thesystem). We propose a multi-agent system that uses different typeof data(numerical, textual) in order to choose the appropriate mixof investments in order to minimize the risk and maximize thegain on a stock portfolio. In order to validate the result a systemwas constructed.","Title: Text Mining News System - Quantifying Certain Phenomena Effect on the Stock Market Behavior.\nYear: 2015\n\nAbstract: Stock market prediction is influenced by manyinternal and external factors. One of these factors are the newsarticles and financial reports related to each listed company. This paper describes a system that is able to extract relevantinformation from this type of textual documents, correlate themwith the stock price movement and determine whether ornot a new released news can and in which proportion willinfluence the market behavior. Predefined ontologies are used forclassifying the news articles and automated ontology extractionfor classifying concepts and super - concepts, on an attempt tomake a semantic mining of the text news. The system is basedon a Multi-Agent Architecture that will investigate, extract andcorrelate the textual data message with the price evolution inorder to better determine buy/sell moments, the trend directionand optimize an investment portfolio. In order to validate ourmodel a prototype was developed and applied to the BucharestStock Exchange Market listed companies.","Title: A Multi-agent Architecture for Ontology-Based Diagnosis of Mental Disorders.\nYear: 2015\n\nAbstract: This paper presents a Multi-agent system that facilitates the remote monitoring of the elderly patients which are susceptible to mental disorder diseases. In order to find early signs of health condition depreciation we have assessed four of the most common mental disorder diseases to find which kind of sensors can detect specific symptoms with the main purpose of creating an early warning system. The diagnosis component is based on an ontology that defines the relations between sensors, symptoms and diseases. Based on these relationships a specialized agent can inform the medical personnel about the detected symptoms.","Title: Real-Time Hybrid Compression of Pattern Matching Automata for Heterogeneous Signature-Based Intrusion Detection\nYear: 2015\n\nAbstract: We are proposing a new hybrid approach to achieving real-time compression of pattern matching automata in signature-based intrusion detection systems, with particular emphasis on heterogeneous CPU/GPU architectures. We also provide details of the implementation and show how a hybrid approach can lead to improved compression ratios while performing real-time changes to the automata. By testing our methodology in a real-world scenario using sets taken from the ClamAV signature database the Snort rules database, we show that the approach we propose performs better than the current solutions, significantly reducing the storage required and paving the way for high-throughput CPU/GPU heterogeneous processing for such type of automata.","Title: A Heterogeneous Fault-Resilient Architecture for Mining Anomalous Activity Patterns in Smart Homes\nYear: 2015\n\nAbstract: We are presenting a massively parallel heterogeneous cloud-based architecture oriented towards anomalous activity detection in smart homes. The architecture has very high resilience to both hardware and software faults, it is capable of collecting activity from various data sources and performing anomaly detection in real-time. We corroborate the approach with an efficient checkpointing mechanism for data processing which allows the implementation of hybrid (CPU/GPU) fault-resilience and anomaly detection through pattern mining techniques, at the same time offering high throughput.","Title: Infrastructure Management Support in a Multi-agent Architecture for Internet of Things\nYear: 2014\n\nAbstract: This paper examines the cloud resources management for a multi-agent IoT architecture. The resources tenancy is a costly operation, thus their allocation and management should be approached in an effective manner. On the other hand, the infrastructure should not be affected by the deployment or maintenance life cycle, operations that could put parts of the system offline, or even the entire system. We emphasize the need for infrastructure audit, which offers a good insight of how the resources are used, the geographical areas with an increased number of failures and where the allocation of supplementary resources is mandatory. Also, the security audit and its impact over a distributed multi-agent architecture that handles a large number of heterogeneous devices is discussed.","Title: An Efficient Heterogeneous Approach to Building Compressed Automata for Malware Signature Matching\nYear: 2014\n\nAbstract: We are presenting an innovative, deterministic approach to constructing highly compressed automata commonly used in malware signature scanning. Our implementation allows building a very efficient (storage-wise) approach for automata, with particular focus on the Aho-Corasick and Commentz-Walter algorithms, using a heterogeneous architecture that not only performs faster, but also supports much larger automata. Experimental results have shown that the memory required for the construction process of our approach is two times lower than in the classic CPU-only approach, while the overall construction time for the automata is improved by at least 50% on average in our experiments.","Title: Behavioral Trading System - Detecting Crisis, Risk and Stability in Financial Markets\nYear: 2016\n\nAbstract: The goal of this paper is to create a hybrid system based on a Multi-Agent Architecture that will investigate the evolution of some prediction strategies (Elliott Wave, Lucas, GANN) along with technical, fundamental and macro-economical analysis methods on stock market indexes and how this information influences the stock market behavior in order to improve the profitability on a short or medium time period investment. The proposed system correlates the results from Elliott Wave, GANN and Lucas methods in order to determine a better prediction of the stock price position on the trend and based on this to determine which will be its future direction. The system also finds correlations between the pattern recognition methods and technical and fundamental methods results in order to find the direction of the market trend, to predict the next day price of a stock and to trigger a useful buy/sell signal. In order to validate our model a prototype was developed and applied to the Bucharest Stock Exchange Market indexes.","Title: A methodology for improving complex sales success in CRM Systems\nYear: 2017\n\nAbstract: In this paper we propose a methodology for extracting complex sales expert rules by analyzing the data from the past lost/won deals stored in Customer Relationship Management Systems. We first used Multi-Adaptive Regression Splines model to identify the features importance, then we created a classification tree of lost/won sales using Random Forest and lastly we used the tree for extraction of the expert rules that gives insights into the rules of successful complex sales. The proposed methodology was successfully validated using complex sales data from a CRM application and the results are presented and discussed in this paper.","Title: FAST: A High-Performance Architecture for Heterogeneous Big Data Forensics.\nYear: 2017\n\nAbstract: We are presenting a highly-efficient, novel architecture (which we call FAST, or Forensic Analysis of Sensitive Traces) for high-performance big data forensics for heterogeneous systems (CPU and GPU-based). Our model uses a highly-compact storage format of the widely known Aho-Corasick algorithm [1], as well as a partial pruning mechanism to ensure the lowest possible memory footprint, while maximizing throughput performance. We are comparing our performance with classic methods used in data forensics and observe significant memory footprint improvements, as well as massive throughput improvements throughout all stages of big data processing.","Title: Accelerating DNA Biometrics in Criminal Investigations Through GPU-Based Pattern Matching.\nYear: 2018\n\nAbstract: With the ever-increasing capabilities of modern hardware and breakthroughs in the DNA biometrics field, we are presenting a new, scalable and innovative method to accelerate the DNA analysis process used in criminal investigations, by building an improved methodology for using large-scale GPU-based automata for performing high-throughput pattern-matching. Our approach focuses on all important stages of preparing for the pattern-matching process, tackling with all major steps, from creation, to preprocessing, to the runtime performance. Finally, we experiment using real-world DNA sequences and apply the process to the human DNA genome, for an evaluation of our implementation.","Title: Optimizing Pattern-Matching for Memory-Efficient Heterogeneous DNA Processing in Bioinformatics\nYear: 2018\n\nAbstract: We are proposing a new, memory-efficient approach to optimizing DNA pattern-matching in bioinformatics through a heterogeneous implementation and new architectural layout, that poses several advantages over usual approaches, which we discuss in detail. We are applying our approach on a subset of DNA sequences part of the FASTA open database, under different hardware settings, and observe a significant performance increase in our heterogeneous implementation. With a practical reduction of 23 times less memory usage than a classic implementation of the same algorithm, and massive scaling capabilities for high-throughput DNA-matching, our approach proves its feasibility for scalable heterogeneous architectures.","Title: Improving Lost/Won Classification in CRM Systems Using Sentiment Analysis\nYear: 2017\n\nAbstract: In this work, we are proposing several approaches to enhance lost/won classification of complex deals using sentiment analysis. The analysis of sentiments is done by text mining the activity notes recorded in CRM Systems used to manage complex sales. Using a baseline SVM model, we extended the baseline features with opinion predictors gathered using various techniques that included different preprocessing approaches of the CRM notes, scoring and counting of opinion sentences and inference of sentiment level features. We analyzed and compared the accuracy and f1-measure gained in comparison to the baseline and we discovered that, among the approaches analyzed, counting the polarity sentences gives the highest gain.","Title: Towards a Complex Evolutionary Agent-Based System for Medical Sensor Data\nYear: 2017\n\nAbstract: Various gadgets and medical devices collect data from patients continuously. Processing this information and learning from it is a challenge for data scientists as this kind of data comes in complex and large quantities, making the decision of what kind of algorithms to apply for the best results difficult and time consuming. In this paper we propose a Multi-agent system that enables autonomous selection and application of machine learning methods for data sets belonging to medical systems, by applying genetic algorithms to agents.","Title: Towards a Multi-Agent System for Medical Records Processing and Knowledge Discovery\nYear: 2016\n\nAbstract: Medical files and observation papers have always been an important source of knowledge. Unfortunately, most of the times they are still stored as physical documents either printed or handwritten, thus making it difficult to transfer this precious information from one place to another, or centralizing and extracting new knowledge from it. But with nowadays advancements in computer science this problem may be handled. In this paper we propose a patient centered Multi-agent system project that is able to extract relevant information from the patients' health records and store that knowledge on a centralized data-store, based on a predefined ontology scheme. The system's purpose is to standardize and enrich the knowledge by performing various mining tasks on the given text. The final purpose is to provide different hospital departments with a tool which they can query for useful information about the patients' medication, treatments and/or scheduled tests and exams and get suggestions regarding their treatment intentions.","Title: A Self Developing System for Medical Data Analysis\nYear: 2018\n\nAbstract: In this paper we present a concept project for a self developing system based on agents built for a hospital. The system monitors patients during and after being released from hospitalization, with the aim of understanding patterns and predicting future problems. Due to its complexity and dynamism the agents must be automatically generated. They need to cooperate and \"compete\" with each other in order to get good results. By combining meta-heuristic algorithms with reinforcement and clustering techniques we target a large degree of autonomy in decision making.","Title: Survey on Feasibility of Pattern Matching Techniques In Heterogeneous Architectures for Bioinformatics\nYear: 2018\n\nAbstract: Pattern-matching techniques are very common in major areas of bioinformatics, in multiple forms: from exact (accurate) to partial matching, the process itself is vital to multiple niches of research. In this paper, we prepare a survey of recent breakthroughs in the field of pattern-matching applied to bioinformatics, from a heterogeneous implementation standpoint, focusing especially on the ones based on SIMD (Single Instruction Multiple Data) or GPGPU (General-Purpose computing on Graphics Processing Units) architectures. We focus on the most important aspects of such data processing and their effectiveness, with particular focus on the technological challenges that such heterogeneous implementations bring, while also analyzing their feasibility of application to particular research niches, such as DNA analysis and protein sequence alignment.","Title: Activity-Aware Vital Signmonitoring Based On A Multi-Agent Architecture\nYear: 2021\n\nAbstract: Vital sign monitoring outside the clinical environment based on wearable sensors ensures better support in assessing a patient's health condition, and in case of health deterioration, automatic alerts can be sent to the care providers. In everyday life, the users can perform different physical activities, and considering that vital sign measurements depend on the intensity of the activity, we proposed an architecture based on the multi-agent paradigm to handle this issue dynamically. Different types of agents were proposed that processed different sensor signals and recognized simple activities of daily living. The system was validated using a real-life dataset where subjects wore accelerometer sensors on the chest, wrist, and ankle. The system relied on ontology-based models to address the data heterogeneity and combined different wearable sensor sources in order to achieve better performance. The results showed an accuracy of 95.25% on intersubject activity classification. Moreover, the proposed method, which automatically extracted vital sign threshold ranges for each physical activity recognized by the system, showed promising results for remote health status evaluation.","Title: A Fast Heterogeneous Approach to Enhanced Blockchain Attack Resilience and Mitigation\nYear: 2021\n\nAbstract: We are introducing a new computational model for heterogeneous architectures for performing enhanced security validations and processing of transactions on-chain, a model applicable to both permissioned and permissionless blockchains. By proposing an efficient storage model for the Merkle trees involved in the transaction and security validation processing, and combining it with an efficient heterogeneous approach for processing it, we are outlining the improved computational advantages to ensuring attack resilience on-chain, which surpasses the performance of traditional CPU-based approaches by a significant factor."],"string":"[\n \"Title: Neuroevolution Based Multi-Agent System With Ontology Based Template Creation For Micromanagement In Real-Time Strategy Games\\nYear: 2014\\n\\nAbstract: This paper presents a multi-agent system that handles unit micromanagement using online machine learning in real time strategy games. We used rtNEAT algorithm in order to obtain customized neural network topologies, thus avoiding to complex network architecture. We use an ontology based template to create suitable input and outputs for unit agents enabling them to cooperate and form teams for their mutual benefit and eliminating communication overhead. The AI system was implemented using the JADE framework and the BWAPI handled communication between our system and the game. We have chosen Starcraft as a testbed. As a baseline we compared the in game AI as well as several other AI solutions that use adaptive mechanisms.\",\n \"Title: Human behavior analysis in ubiquitous environments for energy efficiency improvement\\nYear: 2011\\n\\nAbstract: The goal of this paper is to discover human habits using the received sensors data-streams, in order to improve the energy efficiency. We propose a multi-agent architecture and a formalism to describe scenarios in ubiquitous environments based on a wireless sensors network. We have used sensor data and simulations about a four person family, to validate our prototype.\",\n \"Title: A highly-efficient memory-compression approach for GPU-Accelerated virus signature matching\\nYear: 2012\\n\\nAbstract: We are proposing an approach for implementing highly compressed Aho-Corasick and Commentz-Walter automatons for performing GPU-accelerated virus scanning, suitable for implementation in real-world software and hardware systems. We are performing experiments using the set of virus signatures from ClamAV and a CUDA-based graphics card, showing how memory consumption can be improved dramatically (along with run-time performance), both in the pre-processing stage and at run-time. Our approach also ensures maximum bandwidth for the data transfer required in the pre-processing stage, between the host and the device memory, making it ideal for implementation in real-time virus scanners. Finally, we show how using this model and an efficient combination of the two automata can result in much lower memory requirements in real-world implementations.\",\n \"Title: Multi-Agent Stock Trading Algorithm - A Neural Network Approach.\\nYear: 2012\\n\\nAbstract: The goal of this paper is to create a hybrid model that helps the investor to make a good decision on when to buy/sell on a stock market in order to maximize his gain. Artificial Neural Networks are often used in stock market prediction because of their ability to find patterns and correlations in past data. We propose a Multi-Agent Architecture that by combining techniques from fundamental and technical analysis with Neural Networks (namely Multi-Layer Perceptron) tries to compare the result given by all three of them in order to give a much valuable result on which can be a good moment to buy/sell on a stock market. In order to validate our model a prototype was developed.\",\n \"Title: A Combinative Method for Decision Tree Construction\\nYear: 2005\\n\\nAbstract: The selection of the splitting attribute in decision tree construction process is the key point for the size and quality of the tree. Although several criteria have been proposed and there are good papers that compare their results, no consensus have been adopted regarding the best method. In this paper we'll present a new approach in which each candidate attribute is evaluated using a set of available criteria and the attribute voted the best by most of the criteria will be selected as the winning splitting attribute. Each criterion will be evaluated based on the contingency table created at each splitting node. An approach based on OLAP is used for faster contingency table aggregation.\",\n \"Title: The Effect Of Temporary Links In Randomly Generated Networks Of Constraints\\nYear: 2012\\n\\nAbstract: Additional communication links between unconnected agents are used in asynchronous searching, in order to detect obsolete information. A first way to remove obsolete information is to add new communication links, which allow a nogood owner to determine whether this nogood is obsolete or not. The second solution consists in temporarily keeping the links. A new link is maintained until a fixed number of messages have been exchanged through it. This article investigates different values for the number of messages, values that are either statically or dynamically, during the run time, determined. In the case of processing all the messages, we adapt a dynamical solution for determining the number of necessary messages for maintaining a connection. The experiments show a better efficiency in comparison with the standard Asynchronous Backtracking. In this paper we examine the effect of temporary links for the random binary constraints problem. Experiments with asynchronous search techniques are conducted on randomly generated networks of constraints. Experimental results show that the dynamical solution for the temporary links allows obtaining better results for the majority of classes of problems investigated.\",\n \"Title: AgentDiscover: A Multi-Agent System for Knowledge Discovery from Databases\\nYear: 2007\\n\\nAbstract: AgentDiscover is a multi-agent based intelligent recommendation system, supporting real-time control and feedback, for building and execution workflows for knowledge discovery from databases (KDD). The aim of the proposed system is to deal with the complexity of KDD processes and to offer a tool that supports both researchers exploring KDD methods and non-expert users looking for quick results in this field. A prototype was developed in JADE and the approach was tested for a medical dataset.\",\n \"Title: Negotiation Policies for Provisioning of Cloud Resources.\\nYear: 2012\\n\\nAbstract: Cloud represents today a computing market where users can buy resources according to a pay-per-use business model. Cloud providers offer different kind of services which can be characterized by different service levels. Negotiation of the best resource can be very difficult because there is a semantic gap between the different provider SLAs and the requirements of an user's application. We address the negotiation issue within the research activity of the mOSAIC project by designing and developing an agents based service at platform level for provisioning of cloud resources. In order to allow the execution of the application and fulfill the developer's requirements, the service uses a policy based approach that is able to choose, at infrastructure level, the best solution, in terms of a collection of cloud resources from different providers.\",\n \"Title: Multi-agent Stock Trading Algorithm Model\\nYear: 2011\\n\\nAbstract: Stock Trading Algorithm Models are an important problem researchers dealt with through time that implied knowledge in technical and fundamental analysis, time series combined with knowledge expertise in computer science or programming in order to find solutions of how to have a stock gain. This paper proposes a multi-agent architecture that assists a user in making a successful investment on the stock market. This implies the use of technical and fundamental analysis in order to make a useful prediction of the security's trend.\",\n \"Title: Multi-Agent Architecture for Knowledge Discovery\\nYear: 2006\\n\\nAbstract: Knowledge Discovery from Databases (KDD) is a complex process composed of several phases: business understanding, data understanding, data preparation, modeling, evaluation and deployment. For each of the phases, there are many algorithms and methods available, the end-user having to select one of them. The AgentDiscover is a multi-agent based intelligent recommendation system for selection of the most appropriate solving method for each phase. This brings added value for both novice and experienced users.\",\n \"Title: Managing Risk Behavior on an Evolutionary Market -- A Risk Limits and Value-at-Risk Measures Approach\\nYear: 2013\\n\\nAbstract: The goal of this paper is to develop a system able to coordinate a trader in optimizing a stock market portfolio in order to improve the profitability of a short or medium time period investment. The system is able to classify the risk and quantifies its effect on an investment based on sentiment analysis, certain characteristics, the traders confidence level, and by measuring the potential loss over a certain period of time. The system id also able to create certain types of portfolios based on different methods of computing the investment risk and on the associated level of confidence. We proposed a multi-agent system that uses sentiment analysis, volatility, Monte Carlo simulation, trust models and risk models/limits in order to choose the appropriate mix of investments in order to minimize the risk and maximize the gain on a stock portfolio taking in consideration also the traders possibilities to enter in an investment. A prototype was developed on which we validated our research.\",\n \"Title: Experimental Study on Parallel Methods for Solving Systems of Equations\\nYear: 2012\\n\\nAbstract: The paper presents experimental results on parallel variants of some classical methods for solving systems of equations. The following four methods are studied: Newton method, Chebyshev method, Gradient method with Fridman control sequence, a method of conjugate gradient type. The main conclusion is that the precondition of the sparse systems (both linear and nonlinear) improves in a great extent the performance of the parallel algorithms.\",\n \"Title: Particularities of evolutionary parameter estimation in multi-stage compartmental models of thymocyte dynamics\\nYear: 2013\\n\\nAbstract: The aim of this paper is twofold. Firstly, it presents an extension of a multi-stage compartmental model in order to make it more appropriate in modelling various perturbations of thymocyte dynamics.Secondly, it proposes an evolutionary approach, based on the JADE algorithm, for simultaneously estimating the number of division stages, the rates associated to cellular processes (e.g. proliferation, death, migration) and the parameters corresponding to the proposed perturbation functions. Several quality of fit measures are investigated and their relationship with the variability of experimental data is exploited in order to select the optimization criterion.\",\n \"Title: From Cloud Governance to IoT Governance\\nYear: 2013\\n\\nAbstract: Recent migration towards the cloud has lead to boom of initiatives that address key issues related to it. One of these issues, integrating various available cloud services while providing automated lifecycle management, was addressed by cloud governance. However, while mostly until now computers and phones were linked to the internet, new devices like tablets, sensors, smart devices (embedded devices) are now making their way into this environment. This, in turn, raises questions related to managing, governing these devices. Similarly to how cloud governance extends SOA governance, this article will detail the way in which IoT governance extends cloud governance.\",\n \"Title: Ontology-Based Modeling and Execution of Workflows for Virtual ISP\\nYear: 2008\\n\\nAbstract: Most of the small ISPs are constrained to resell wholesale services of incumbent operators owning the access infrastructure. They are struggling to gain competitive advantages by lowering their pricing on typical services (e.g. e-mail, hosting) and by their capability to provide specific added-value services which are more difficult to offer by large entities. The production of tailored solutions targeted to local business needs is strongly facilitated by an appropriate conceptual and executable model of the ISP domain. Innovative technologies, like ontologies, web technologies and workflow technologies, network modeling and Directory Enabled Networks are combined to build a software platform that enables a cluster of SMEs to operate as a single business entity. The goal of this paper is to highlight the role of ontologies in the implementation and execution of the software platform.\",\n \"Title: Neural Network Predictions of Stock Price Fluctuations\\nYear: 2012\\n\\nAbstract: The goal of this paper is to create a hybrid system based on a Multi-Agent Architecture that will investigate the evolution of some neural network methods along with technical and fundamental analysis methods on stock market indexes and how this information influences the stock market behavior in order to improve the profitability of a short or medium time period investment. The proposed system compares the results of Standard Feed Forward Neural Network, Elman and Jordan Recurrent Neural Networks and a Neural Network evolved with Neuro Evolution of Augmenting Topologies (NEAT) in order to investigate which network gives the most accurate result and time performance by taking in consideration the close price of a stock. The system also finds correlations between the pattern recognition methods and technical and fundamental methods results in order to find the direction of the market trend, to predict the next day price of a stock and to trigger a useful buy/sell signal. We are also interested in finding a correlation between the evolution of price, volume, number of transactions in order to have a better view on which is the effect of stock liquidity on a stock price. In order to validate our model a prototype was developed and applied to the Bucharest Stock Exchange Market indexes.\",\n \"Title: Towards an Ontology for Cloud Services\\nYear: 2012\\n\\nAbstract: The emergence of cloud computing has created a shift in the service lifecycle, a shift meant to meet the requirements of cloud environments. Anticipating this, cloud governance, a step forward from SOA governance, tries to consolidate an environment where collaboration between various enterprises can be easily achieved. Cloud governance lays the groundwork for enabling easy application development and deployment by providing critical services that range from service management to security, monitoring and audit. As means of support for cloud computing, several ontologies have tried to bridge gaps and provide interoperability. None, however, have taken the time to analyze and define aspects that relate to service lifecycle and cloud governance. In this regard, our article proposes an ontology that is meant to complement existing ones.\",\n \"Title: Towards a Service Friendly Cloud Ecosystem\\nYear: 2012\\n\\nAbstract: After the large penetration of Cloud Computing, more and more developers are taking into account migrating their applications to the Cloud, in order to take advantage of the characteristics of this new environment. In close relation with application migration, an increasing number of development and execution platforms, delivered as PaaS solutions (such as mOSAIC, 4CaaSt, Cloud Foundry, Open Shift, Stackato, and others) are offering their services for development, deployment, and execution of applications that are using in an optimum manner the five characteristics of the Cloud. Following this massive migration of applications, especially from SOA, to Cloud environments, new requirements for application development could be identified in order to enable the construction of complex solutions, and to exploit a business level on the top of various *-as-a-Service layers. The introduction of a centralized component, the Cloud Governance, is necessary in order to enable the development of complex cloud ecosystems. This centralized component is extending, complementing, completing and integrating core features from the PaaS layer, like monitoring, provisioning, negotiation, and others, and integrate features of various Cloud management solutions.\",\n \"Title: Multi-Agent Architecture In Semantic Services Environment\\nYear: 2012\\n\\nAbstract: A semantic enabled multi-agent architecture for solving non-linear equations systems by using a service oriented approach is proposed. The service oriented approach allows us to access already implemented methods for solving complex mathematical problems. The semantic descriptions of these services provide support for intelligent agents. The proposed architecture is a framework with two extension areas, agent society and domain ontology, with possible application in other domains too.\",\n \"Title: Framework for Enabling Scalable Learning Game AI.\\nYear: 2013\\n\\nAbstract: The video game industry is a multibillion-dollar industry in which, due to general short deadlines, game visuals as well as gameplay elements are worked in parallel up until the very last minute. This means that even if the AI system has been designed in parallel with the other game elements, once a change has been made in the late stages of the game development, the AI may prove to be inadequate to the given job. Our article covers some of the existing frameworks for game AI and proposes a multi-agent system which serves as a framework for scalable learning game AI through integration of existing machine learning techniques.\",\n \"Title: Knowledge Management in Expert System Creator\\nYear: 2002\\n\\nAbstract: Recent explosive growth in data and the availability of large data-sets in multiple disciplines, generates the need for new techniques and tools that can intelligently transform the data into useful information and knowledge. The machine learning algorithms synthesize various knowledge representation forms, most of them equivalent and inter-changeable. A new approach is needed to support and combine the knowledge representation form with various data sources (represented by database management systems). In this paper, a powerful, visual CASE tool for knowledge management is introduced. It supports the integration of high-level knowledge \\\"beans\\\" into host projects. A scalable approach to the problem of data integration from conventional database systems in expert systems is also over-viewed.\",\n \"Title: Neuroevolution based multi-agent system for micromanagement in real-time strategy games\\nYear: 2012\\n\\nAbstract: The main goal of this paper is the design of a multi-agent system (MAS) that handles unit micromanagement in real time strategy games and is able to adapt/learn during game play. To achieve this we adopted the rtNEAT approach in order to obtain customized neural network topologies, thus avoiding the generation of too complex architectures. Also by defining internal and external inputs for each agent we managed to create independent agents that are able to cooperate and form teams for their mutual benefit and at the same time eliminate unnecessary communication overhead. The MAS was implemented for the real time strategy game StarCraft using JADE multi-agent platform and BWAPI to ensure the interface with the game. We used as a baseline the in game AI and also tested it against other adapting AI systems in order to compare their performance against our system.\",\n \"Title: An Evolutionary Approach for SLA-based Cloud Resource Provisioning\\nYear: 2013\\n\\nAbstract: As the number of existing cloud vendors rises, resource count and types are ever increasing leading to a need of cloud management solutions which facilitate easy cloud adoption. While providing several services, cloud management's primary role is resource provisioning. In order to meet application needs in terms of resources, cloud developers must carefully choose among the existing offers in order to deploy their applications. The research presented in this paper enables developers to automate the process of resource provisioning by specifying their preference towards resources and resource attributes based on which the system can propose solutions for their requirements.\",\n \"Title: Service Datastores in Cloud Governance\\nYear: 2012\\n\\nAbstract: A direct consequence of adopting Cloud Computing is the creation of new marketplaces for small and medium sized enterprises (SMEs). Having a strong support offered by Platform as a Service (PaaS) paradigm in terms of automation and management, combined with cloud governance, SMEs have a real chance to get back in competition with the large enterprises. The implementation of Cloud Governance solutions offers the necessary support for storing and manipulating data which is relevant for various aspects of applications, ranging from business to technical support for deployed services. Our paper focuses on analysing and detailing governance specific data stores and the way they are able to provide system-wide services.\",\n \"Title: DL-AgentRecom - A Multi-Agent Based Recommendation System for Scientific Documents\\nYear: 2008\\n\\nAbstract: The goal is to propose a recommendation system for scientific documents. The paper presents the architecture and theprinciples of functioning of the system. The way a user organizesits documents is called the user's perspective over the set ofdocuments. Operations with user perspectives are defined, also thesystem can have and build it's own perspective. The usersperspective can be mixed with system perspective.\",\n \"Title: A Survey of Adaptive Game AI: Considerations for Cloud Deployment.\\nYear: 2013\\n\\nAbstract: Modern video games have become an important part of AI research in the past years, largely thanks to the characteristics of their environment and the challenges they pose to AI researchers. This paper is a a survey of the current game AI state of the art and highlights important achievements in this field. An adaptive multi-agent system that can be deployed on a cloud infrastructure to solve computational constraints of advanced machine learning methods is also presented.\",\n \"Title: Applying ontologies for workflow modelling and execution for a virtual ISP\\nYear: 2008\\n\\nAbstract: Small Internet Service Providers (ISPs) are constrained to resell the wholesale services of incumbent operators that own the access infrastructure. They struggle to gain competitive advantages by improving the typical services provided (e.g., e-mail, hosting) and through their capability to provide specific added-value services, which are more difficult for larger entities to offer. The production of tailored solutions targeted at the needs of local businesses is strongly facilitated by an appropriate conceptual and executable model of the ISP domain. Innovative technologies, like ontologies, web technologies, workflow technologies, network modelling and Directory Enabled Networks, are combined to build a platform that enables a cluster of Small- and Medium-sized Enterprises (SMEs) to operate as a single business entity. The goal of this paper is to highlight the role of ontologies in the implementation and execution of the aforementioned platform.\",\n \"Title: An Event Driven Multi-agent Architecture for Enabling Cloud Governance\\nYear: 2012\\n\\nAbstract: Cloud adoption is consistent within IT-based industries at different maturity levels. While cloud migration is an ongoing process, its base characteristics are not yet fully exploited. As Cloud Governance is built on top of requirements like security, reliability, trust, portability, interoperability, or fail over, a highly distributed and concurrent, and fault tolerant solution is required in order to achieve above specified requirements. This paper describes the core of an event-driven multi-agent architecture for supporting Cloud Governance activities, built around Akka/Clojure actor characteristics and fully exploiting the Enterprise Integration Patterns. The modeling process follows the Gaia methodology for agent-oriented analysis and design.\",\n \"Title: Service Lifecycle in the Cloud Environment\\nYear: 2012\\n\\nAbstract: Cloud governance's approach to development in the cloud environment allows for a way in which services are well defined and unambiguous, where they can be easily designed, implemented, deployed and executed, for a way in which they can be consumed. This approach is beneficial to SMEs which can now empower themselves by adopting cloud-based technologies and platforms. They now have the opportunity to develop niche solutions, or to group themselves in specialized clusters, in order to build complex, targeted solutions, tailored on customers' needs. The focus of this paper is detailing the service lifecycle from the point of view of our proposed cloud governance architecture that is based on a cloud management solution, the Cloud Agency, a multi-agent system that is part of the mOSAIC project.\",\n \"Title: The Impact of the \\\"Nogood Processor\\\" Technique in Scale-Free Networks.\\nYear: 2013\\n\\nAbstract: DisCSPs are composed of agents that manage variables which are connected by constraints, various algorithms for solving DisCSPs are searching through this network of constraints. The scale-free graphs have been proposed as a generic and universal model of network topologies that exhibit power-law distributions in the connectivity of network nodes. Little research was done concerning the network structure for DisCSP and in particular for scale-free networks. The asynchronous searching techniques are characterized by the occurrence of the nogood values during the search for the solution. In this article we analyzed the distribution of nogood values to agents and the way to use the information stored in the nogood, what we will call the nogood processor technique. We examine the effect of nogood processor for networks that have a scale-free structure. We develop a novel way for the distribution of nogood values to agents, the experiments show that it is more effective for several families of asynchronous techniques.\",\n \"Title: An Extensible Environment for Expert System Development\\nYear: 2003\\n\\nAbstract: The use of expert systems in the speed-up of human professional work has been in two orders of magnitude with resulting increases in human productivity and financial returns. Last decade shows that a growing number of organizations shift their informational systems towards a knowledge-based approach. This fact generates the need for new tools and environments that intelligently port the legacy systems in modern, extensible and scalable knowledge-integrated systems. This paper presents an extensible, user-friendly environment for expert system development, which supports the integration of high-level knowledge \\\"beans\\\" into host projects, data integration from conventional database systems and system's verification, debugging and profiling.\",\n \"Title: Experimental Analysis of the Impact of the Message Management in the Case of the ABT Family\\nYear: 2007\\n\\nAbstract: In this article are analyzed the message queues aiming to improve the message management, more precisely to reduce or eliminate the obsolete messages and, thus, reduce the costs for obtaining the solution. This thing allows the improvement of the performances of the ABT family techniques. Asynchronous techniques use some message processing routines. Those procedures process sequentially or in batches the messages that are in the message queues. Typically, each agent extracts one or more messages from its communication channel and calls the appropriate message processing routine, this operation is achieved based on a strategy of message processing, strategy that specifies the order and the way of treating the messages based on a formal protocol. In this article we analyzed this protocol for message management for the purpose of identifying the conditions where this could be applyed to allow us to decrease or eliminate the redundant messages. A new strategy for message management is proposed, strategy that allows obtaining variants of more efficient asynchronous techniques, in the case of the ABT family. An improved protocol for complete treating of the messages is proposed. The performances of the ABT family techniques are investigated, the experiments showing a decrease of the necessary costs for obtaining a solution in the case of applying the message management. A comparative study of the ABT versions is presented, the sequential versions are compared with the versions with redundant messages filtering, those based on various strategies of message processing.\",\n \"Title: Optimizing Cloud Resources Allocation For An Internet Of Things Architecture\\nYear: 2014\\n\\nAbstract: The optimization of the cloud resources used to power a multi-agent Internet of Things architecture is an important issue which has an important impact on the overall operation cost of the architecture. The resources tenancy is a costly operation, thus their allocation and management should be optimized based on the usage patterns. The infrastructure for the multi-agent system should not be affected by the deployment or maintenance life cycle, operations require parts of the system, or even the entire system to be offline during the execution of scheduled procedures. This paper outlines of the importance of the infrastructure audit, which offers a good insight of how the resources are used, the geographical areas which are heavily used and where the allocation or release of used resources is mandatory. Also, the security audit, in a distributed multi-agent architecture that handles a large number of heterogeneous devices, represents a good mechanism for performance improvement.\",\n \"Title: Efficient parallel automata construction for hybrid resource-impelled data-matching.\\nYear: 2014\\n\\nAbstract: We are presenting an innovative, massively-parallel heterogeneous architecture for the very fast construction and implementation of very large Aho–Corasick and Commentz-Walter pattern-matching automata, commonly used in data-matching applications, and validate its use with large sets of data actively used in intrusion detection systems. Our approach represents the first known hybrid-parallel model for the construction of such automata and the first to allow self-adjusting pattern-matching automata in real-time by allowing full-duplex transfers at maximum throughput between the host (CPU) and the device (GPU). The architecture we propose is easily scalable to multi-GPU and multi-CPU systems and benefits greatly from GPU acceleration, also relying on a highly-efficient storage model for the automata and includes on-demand support for regular-expression matching, as well as support for custom heuristics to be built on top of the architecture, at different processing stages.\",\n \"Title: Evolutionary Estimation of Parameters in Computational Models of Thymocyte Dynamics.\\nYear: 2013\\n\\nAbstract: This paper presents an evolutionary-based parameter estimation procedure able to deal with the particularities of the constraints arising in mathematical models of biological systems. A measure of the constraint satisfaction degree and several feasibility-based ranking rules are proposed and comparatively analyzed for the problem of estimating the parameters involved in a model describing the dynamics of thymocytes. The numerical results illustrate the effectiveness of the procedure in inferring models which fit well the experimental data and also satisfy the biological constraints.\",\n \"Title: Enhancing DisCSP-Netlogo from Simulation to Real-execution of Agents in Distributed Constraints.\\nYear: 2014\\n\\nAbstract: The use of agent-based simulation models in NetLogo for research is growing rapidly in a number of fields of science and en- gineering, including in computer science, for example for analyzing the performances of asynchronous search techniques based on distributed constraints. Distributed Constraint programming (DisCSP/DCOP) is a programming approach used to describe and solve large classes of problems such as searching, combinatorial and planning problems. The purpose of this paper is to present an open-source and complete solution in NetLogo that allows modeling, simulation and evaluation of the distributed constraints using single computer or clusters, simulation or real execution. Our tool allows the use of various search techniques and also the evaluation and analysis of the performance of the asynchronous search techniques in two manners: simulation mode and real exe- cution. We also explain our methodology for running the NetLogo models in a cluster computing environment (simulation mode) or on a network of computers (simulation real execution). This tool is aimed to allow the evaluation of distributed algorithms in conditions as similar as possible to the real situations.\",\n \"Title: Intelligent Stock Market Analysis System - A Fundamental and Macro-economical Analysis Approach\\nYear: 2014\\n\\nAbstract: Stock Market Forecasting implies the use of a series of techniques that helps in determining the stock price evolution. The paper describes a multi-agent system that uses numerical, financial and economical data in order to evaluate the company's position on the market, profitability, performance, future expectations in the company's evolution. Determining the effect of political, governmental and social decisions along with detecting the way in which the price is constructed based on technical and fundamental analysis methods and the bid/ask situation helps in determining a more precise buy/sell signals, reducing the false signals and determining some risk/gain positions on different periods of time. In order to validate the results a prototype was developed.\",\n \"Title: Multi-agent architecture for solving nonlinear equations systems in semantic services environment\\nYear: 2011\\n\\nAbstract: A semantic enabled multi-agent architecture for solving nonlinear equations systems by using a service oriented approach is proposed. The service oriented approach allows us to access already implemented methods for solving complex mathematical problems. The semantic descriptions of these services provide support for intelligent agents.\",\n \"Title: Steps towards cloud governance. a survey\\nYear: 2012\\n\\nAbstract: Special characteristics of cloud computing have enhanced its rate of adoption for small medium-sized enterprises. Its associated economic model allow for small actors to become more visible, and enable them to collaborate and integrate their solutions for competing with big players on the IT market. As means of exploiting these opportunities, there is a necessity for adopting of solutions which enhance the efficacy of in the cloud activities - cloud management -and allow the migration of business and operational aspects altogether - cloud governance -while integrating services in order to build complex, targeted, solutions for their customers' needs.\",\n \"Title: Stock market analysis - Strongest performing stocks influence on an evolutionary market\\nYear: 2013\\n\\nAbstract: On the stock market, it is said that the strongest stock gives the direction of those stocks that are in the same domain (IT, services, oil, and others). Determining the strongest stock plays an important part in finding a better moment to enter on a position. This paper presents a Multi-Agent architecture that combines Technical Analysis, Neural Networks and Statistical Methods in order to find the strongest stock and to make a better forecast on the market future trend and trigger an entry/exit signal based on the market basket classification. A prototype was developed and applied on the Bucharest Stock Exchange Market (BSE).\",\n \"Title: Classifying and quantifying certain phenomena effect\\nYear: 2013\\n\\nAbstract: The goal of this paper is to create a hybrid system that will investigate possible stock market changes immediately after financial news article appear and how this information influences the stock market behavior in order to improve the profitability of a short or medium time period investment. We proposed a multi-agent system that uses text mining, information extraction, pattern recognition, sentiment analysis and a trust model. The system classifies and quantifies certain phenomena (financial news influence) in order to compute the effect of some properties and its size on the stock market and also checks if we can use turbulence to detect disasters. It also searches a correlation between the effect of news articles and the trader's behavior on the market. In order to validate our model a prototype was developed.\",\n \"Title: Data mining considerations for knowledge acquisition in real time strategy games\\nYear: 2013\\n\\nAbstract: Adaptive Game AI has been one of the key topics being researched in the field of academic game AI research. In this paper we present a comparison of several domain independent machine learning methods with the aid of which we extract expert knowledge from game logs. Each game log is represented as a feature vector that encodes cardinality and timing for player actions. We compare a wide variety of classification methods and highlight which ones are best for deployment for an adaptive game AI systems.\",\n \"Title: Investment portfolio optimization based on risk and trust management\\nYear: 2013\\n\\nAbstract: The goal of this paper is to develop a system able to coordinate a trader in optimizing a stock market portfolio in order to improve the profitability of a short or medium time period investment. The system is able to classify the risk and quantifies its effect on an investment based on sentiment analysis, certain characteristics and the traders level of confidence. It also uses an ontology to describe the markets status and expectations based on future events (text features) and to determine the relationship and correlation between news articles. The system also verifies the effect of a similar percentage of positive and negative news articles, and which of this information will influence more the stock price movement and trend. We proposed a multi-agent system that uses text mining, ontology, sentiment analysis, trust and risk models in order to choose the appropriate mix of investments to minimize the risk and maximize the gain on a stock portfolio. A prototype was developed on which we validated our research.\",\n \"Title: Evolutionary Approach to Negotiation in Game AI\\nYear: 2013\\n\\nAbstract: As modern games become more and more sophisticated graphically, so does the level of artificial intelligence that animates them thus, the larger the game budget, the more work is put into improving the AI. Unfortunately many of these games feature AIs that are standalone and do not communicate with each other, they do not try to negotiate in order to improve their individual standing. The current work focuses on analysing existing game types in order to establish types of negotiation that can be achieved between AI entities. Moreover, an evolutionary approach which focuses on achieving negotiation between these entities and tackles the problem of having multiple negotiation items with discrete values is presented.\",\n \"Title: Evolutionary pruning of non-nested generalized exemplars\\nYear: 2011\\n\\nAbstract: This paper investigates the ability of an evolutionary pruning mechanism to improve the predictive accuracy of a classifier based on non-nested generalized exemplars. Two pruning algorithms are proposed: one which selects the most representative generalized exemplars and the other one which simultaneously selects both relevant exemplars and relevant attributes. Experimental studies conducted for a set of twenty-one datasets illustrated that both algorithms induce a significant improvement on the classification ability of the selected set of non-nested generalized exemplars.\",\n \"Title: Improving the Performances of Asynchronous Search Algorithms in Scale-Free Networks Using the Nogood Processor Technique.\\nYear: 2015\\n\\nAbstract: The scale-free graphs were proposed as a generic and universal model of network topologies that exhibit power-law distributions in the connectivity of network nodes. In recent years various complex networks were identified as having a scale-free structure. Little research was done concerning the network structure for DisCSP, and in particular, for scale-free networks. The asynchronous searching techniques are characterized by the occurrence of nogood values during the search for a solution. In this article we analyze the distribution of nogood values to agents and the way how to use the information from the nogood; that is called the nogood processor technique. We examine the effect of nogood processor for networks that have a scale-free structure aiming to develop search algorithms specialized for scale-free networks of constraints, algorithms that require minimum costs for obtaining the solution. We develop a novel way for distributing nogood values to agents, thus obtaining a new hybrid search technique that uses the information from the stored nogoods. The experiments show that it is more effective for several families of asynchronous techniques; we perform tests with the model running on a cluster of computers. Also, we examine the effect of synchronization of agents' execution and of processing messages by packets in scale-free networks.\",\n \"Title: A taxonomic view of cloud computing services.\\nYear: 2015\\n\\nAbstract: With the massive adoption of cloud computing, a growing number of small and medium enterprises SMEs now have the opportunity, together with the immediate goal of reducing cost, to effectively expose their services or group themselves into clusters or virtual enterprises in order to build and expose customised offerings. Different requirements, ranging from interoperability and portability, provisioning and reconfiguration, security and privacy, or monitoring and auditing, can all be controlled through the lifecycle support of cloud services and grouped under the umbrella of different virtual enterprises. A semantic support is essential to enable such an entity, and to foster collaboration among different virtual enterprises by providing specific mechanisms by which the construction of complex services from elementary building blocks can be made possible. At the same time it actively supports tasks like automation of cloud resource usage, efficient monitoring and scaling of cloud services, either individually or based on specific dependencies, or replacement and termination of services when the initial offers are no longer available.\",\n \"Title: Natural Language Agreement in the Generation Mechanism based on Stratified Graphs\\nYear: 2015\\n\\nAbstract: A Stratified Graph can generate natural language constructions by acting as a transition network with arc labels referring to word categories and word forms. In this representation, each grammar rule can be transposed in a sequence of labeled paths. The generation mechanism defined by means of stratified graph uses a bottom-up approach. The advantage of this generation mechanism relies on its bottom-up approach characteristic: this mechanism never suggests constructions that are not correct based on the involved representation elements. As it will be presented, implementing syntactic agreement rules in this natural language generation mechanism can determine different results even if identical words are considered in the generation mechanism.\",\n \"Title: Stock Market Trading Strategies Applying Risk and Decision Analysis Models for Detecting Financial Turbulence.\\nYear: 2015\\n\\nAbstract: Risk handling and evaluation plays an importantrole in optimizing an investment portfolio. This paper's goal isto describe a system that determines, classifies and handles riskassociated to any type of investment based on sentiment analysis, price movement, information related to companies, certain characteristics, the traders confidence level, and by measuring thepotential loss over a certain period of time. This research impliesanalyzing trader's risk, market risk, risk associated to eachevaluated company or financial group, political and governmentalrisk. The system is able to create different types of portfoliooptions based on the investor/trader profile, which is build basedon the user's tolerance to risk (determined by the results froman interactive quiz that the user must complete when entering thesystem). We propose a multi-agent system that uses different typeof data(numerical, textual) in order to choose the appropriate mixof investments in order to minimize the risk and maximize thegain on a stock portfolio. In order to validate the result a systemwas constructed.\",\n \"Title: Text Mining News System - Quantifying Certain Phenomena Effect on the Stock Market Behavior.\\nYear: 2015\\n\\nAbstract: Stock market prediction is influenced by manyinternal and external factors. One of these factors are the newsarticles and financial reports related to each listed company. This paper describes a system that is able to extract relevantinformation from this type of textual documents, correlate themwith the stock price movement and determine whether ornot a new released news can and in which proportion willinfluence the market behavior. Predefined ontologies are used forclassifying the news articles and automated ontology extractionfor classifying concepts and super - concepts, on an attempt tomake a semantic mining of the text news. The system is basedon a Multi-Agent Architecture that will investigate, extract andcorrelate the textual data message with the price evolution inorder to better determine buy/sell moments, the trend directionand optimize an investment portfolio. In order to validate ourmodel a prototype was developed and applied to the BucharestStock Exchange Market listed companies.\",\n \"Title: A Multi-agent Architecture for Ontology-Based Diagnosis of Mental Disorders.\\nYear: 2015\\n\\nAbstract: This paper presents a Multi-agent system that facilitates the remote monitoring of the elderly patients which are susceptible to mental disorder diseases. In order to find early signs of health condition depreciation we have assessed four of the most common mental disorder diseases to find which kind of sensors can detect specific symptoms with the main purpose of creating an early warning system. The diagnosis component is based on an ontology that defines the relations between sensors, symptoms and diseases. Based on these relationships a specialized agent can inform the medical personnel about the detected symptoms.\",\n \"Title: Real-Time Hybrid Compression of Pattern Matching Automata for Heterogeneous Signature-Based Intrusion Detection\\nYear: 2015\\n\\nAbstract: We are proposing a new hybrid approach to achieving real-time compression of pattern matching automata in signature-based intrusion detection systems, with particular emphasis on heterogeneous CPU/GPU architectures. We also provide details of the implementation and show how a hybrid approach can lead to improved compression ratios while performing real-time changes to the automata. By testing our methodology in a real-world scenario using sets taken from the ClamAV signature database the Snort rules database, we show that the approach we propose performs better than the current solutions, significantly reducing the storage required and paving the way for high-throughput CPU/GPU heterogeneous processing for such type of automata.\",\n \"Title: A Heterogeneous Fault-Resilient Architecture for Mining Anomalous Activity Patterns in Smart Homes\\nYear: 2015\\n\\nAbstract: We are presenting a massively parallel heterogeneous cloud-based architecture oriented towards anomalous activity detection in smart homes. The architecture has very high resilience to both hardware and software faults, it is capable of collecting activity from various data sources and performing anomaly detection in real-time. We corroborate the approach with an efficient checkpointing mechanism for data processing which allows the implementation of hybrid (CPU/GPU) fault-resilience and anomaly detection through pattern mining techniques, at the same time offering high throughput.\",\n \"Title: Infrastructure Management Support in a Multi-agent Architecture for Internet of Things\\nYear: 2014\\n\\nAbstract: This paper examines the cloud resources management for a multi-agent IoT architecture. The resources tenancy is a costly operation, thus their allocation and management should be approached in an effective manner. On the other hand, the infrastructure should not be affected by the deployment or maintenance life cycle, operations that could put parts of the system offline, or even the entire system. We emphasize the need for infrastructure audit, which offers a good insight of how the resources are used, the geographical areas with an increased number of failures and where the allocation of supplementary resources is mandatory. Also, the security audit and its impact over a distributed multi-agent architecture that handles a large number of heterogeneous devices is discussed.\",\n \"Title: An Efficient Heterogeneous Approach to Building Compressed Automata for Malware Signature Matching\\nYear: 2014\\n\\nAbstract: We are presenting an innovative, deterministic approach to constructing highly compressed automata commonly used in malware signature scanning. Our implementation allows building a very efficient (storage-wise) approach for automata, with particular focus on the Aho-Corasick and Commentz-Walter algorithms, using a heterogeneous architecture that not only performs faster, but also supports much larger automata. Experimental results have shown that the memory required for the construction process of our approach is two times lower than in the classic CPU-only approach, while the overall construction time for the automata is improved by at least 50% on average in our experiments.\",\n \"Title: Behavioral Trading System - Detecting Crisis, Risk and Stability in Financial Markets\\nYear: 2016\\n\\nAbstract: The goal of this paper is to create a hybrid system based on a Multi-Agent Architecture that will investigate the evolution of some prediction strategies (Elliott Wave, Lucas, GANN) along with technical, fundamental and macro-economical analysis methods on stock market indexes and how this information influences the stock market behavior in order to improve the profitability on a short or medium time period investment. The proposed system correlates the results from Elliott Wave, GANN and Lucas methods in order to determine a better prediction of the stock price position on the trend and based on this to determine which will be its future direction. The system also finds correlations between the pattern recognition methods and technical and fundamental methods results in order to find the direction of the market trend, to predict the next day price of a stock and to trigger a useful buy/sell signal. In order to validate our model a prototype was developed and applied to the Bucharest Stock Exchange Market indexes.\",\n \"Title: A methodology for improving complex sales success in CRM Systems\\nYear: 2017\\n\\nAbstract: In this paper we propose a methodology for extracting complex sales expert rules by analyzing the data from the past lost/won deals stored in Customer Relationship Management Systems. We first used Multi-Adaptive Regression Splines model to identify the features importance, then we created a classification tree of lost/won sales using Random Forest and lastly we used the tree for extraction of the expert rules that gives insights into the rules of successful complex sales. The proposed methodology was successfully validated using complex sales data from a CRM application and the results are presented and discussed in this paper.\",\n \"Title: FAST: A High-Performance Architecture for Heterogeneous Big Data Forensics.\\nYear: 2017\\n\\nAbstract: We are presenting a highly-efficient, novel architecture (which we call FAST, or Forensic Analysis of Sensitive Traces) for high-performance big data forensics for heterogeneous systems (CPU and GPU-based). Our model uses a highly-compact storage format of the widely known Aho-Corasick algorithm [1], as well as a partial pruning mechanism to ensure the lowest possible memory footprint, while maximizing throughput performance. We are comparing our performance with classic methods used in data forensics and observe significant memory footprint improvements, as well as massive throughput improvements throughout all stages of big data processing.\",\n \"Title: Accelerating DNA Biometrics in Criminal Investigations Through GPU-Based Pattern Matching.\\nYear: 2018\\n\\nAbstract: With the ever-increasing capabilities of modern hardware and breakthroughs in the DNA biometrics field, we are presenting a new, scalable and innovative method to accelerate the DNA analysis process used in criminal investigations, by building an improved methodology for using large-scale GPU-based automata for performing high-throughput pattern-matching. Our approach focuses on all important stages of preparing for the pattern-matching process, tackling with all major steps, from creation, to preprocessing, to the runtime performance. Finally, we experiment using real-world DNA sequences and apply the process to the human DNA genome, for an evaluation of our implementation.\",\n \"Title: Optimizing Pattern-Matching for Memory-Efficient Heterogeneous DNA Processing in Bioinformatics\\nYear: 2018\\n\\nAbstract: We are proposing a new, memory-efficient approach to optimizing DNA pattern-matching in bioinformatics through a heterogeneous implementation and new architectural layout, that poses several advantages over usual approaches, which we discuss in detail. We are applying our approach on a subset of DNA sequences part of the FASTA open database, under different hardware settings, and observe a significant performance increase in our heterogeneous implementation. With a practical reduction of 23 times less memory usage than a classic implementation of the same algorithm, and massive scaling capabilities for high-throughput DNA-matching, our approach proves its feasibility for scalable heterogeneous architectures.\",\n \"Title: Improving Lost/Won Classification in CRM Systems Using Sentiment Analysis\\nYear: 2017\\n\\nAbstract: In this work, we are proposing several approaches to enhance lost/won classification of complex deals using sentiment analysis. The analysis of sentiments is done by text mining the activity notes recorded in CRM Systems used to manage complex sales. Using a baseline SVM model, we extended the baseline features with opinion predictors gathered using various techniques that included different preprocessing approaches of the CRM notes, scoring and counting of opinion sentences and inference of sentiment level features. We analyzed and compared the accuracy and f1-measure gained in comparison to the baseline and we discovered that, among the approaches analyzed, counting the polarity sentences gives the highest gain.\",\n \"Title: Towards a Complex Evolutionary Agent-Based System for Medical Sensor Data\\nYear: 2017\\n\\nAbstract: Various gadgets and medical devices collect data from patients continuously. Processing this information and learning from it is a challenge for data scientists as this kind of data comes in complex and large quantities, making the decision of what kind of algorithms to apply for the best results difficult and time consuming. In this paper we propose a Multi-agent system that enables autonomous selection and application of machine learning methods for data sets belonging to medical systems, by applying genetic algorithms to agents.\",\n \"Title: Towards a Multi-Agent System for Medical Records Processing and Knowledge Discovery\\nYear: 2016\\n\\nAbstract: Medical files and observation papers have always been an important source of knowledge. Unfortunately, most of the times they are still stored as physical documents either printed or handwritten, thus making it difficult to transfer this precious information from one place to another, or centralizing and extracting new knowledge from it. But with nowadays advancements in computer science this problem may be handled. In this paper we propose a patient centered Multi-agent system project that is able to extract relevant information from the patients' health records and store that knowledge on a centralized data-store, based on a predefined ontology scheme. The system's purpose is to standardize and enrich the knowledge by performing various mining tasks on the given text. The final purpose is to provide different hospital departments with a tool which they can query for useful information about the patients' medication, treatments and/or scheduled tests and exams and get suggestions regarding their treatment intentions.\",\n \"Title: A Self Developing System for Medical Data Analysis\\nYear: 2018\\n\\nAbstract: In this paper we present a concept project for a self developing system based on agents built for a hospital. The system monitors patients during and after being released from hospitalization, with the aim of understanding patterns and predicting future problems. Due to its complexity and dynamism the agents must be automatically generated. They need to cooperate and \\\"compete\\\" with each other in order to get good results. By combining meta-heuristic algorithms with reinforcement and clustering techniques we target a large degree of autonomy in decision making.\",\n \"Title: Survey on Feasibility of Pattern Matching Techniques In Heterogeneous Architectures for Bioinformatics\\nYear: 2018\\n\\nAbstract: Pattern-matching techniques are very common in major areas of bioinformatics, in multiple forms: from exact (accurate) to partial matching, the process itself is vital to multiple niches of research. In this paper, we prepare a survey of recent breakthroughs in the field of pattern-matching applied to bioinformatics, from a heterogeneous implementation standpoint, focusing especially on the ones based on SIMD (Single Instruction Multiple Data) or GPGPU (General-Purpose computing on Graphics Processing Units) architectures. We focus on the most important aspects of such data processing and their effectiveness, with particular focus on the technological challenges that such heterogeneous implementations bring, while also analyzing their feasibility of application to particular research niches, such as DNA analysis and protein sequence alignment.\",\n \"Title: Activity-Aware Vital Signmonitoring Based On A Multi-Agent Architecture\\nYear: 2021\\n\\nAbstract: Vital sign monitoring outside the clinical environment based on wearable sensors ensures better support in assessing a patient's health condition, and in case of health deterioration, automatic alerts can be sent to the care providers. In everyday life, the users can perform different physical activities, and considering that vital sign measurements depend on the intensity of the activity, we proposed an architecture based on the multi-agent paradigm to handle this issue dynamically. Different types of agents were proposed that processed different sensor signals and recognized simple activities of daily living. The system was validated using a real-life dataset where subjects wore accelerometer sensors on the chest, wrist, and ankle. The system relied on ontology-based models to address the data heterogeneity and combined different wearable sensor sources in order to achieve better performance. The results showed an accuracy of 95.25% on intersubject activity classification. Moreover, the proposed method, which automatically extracted vital sign threshold ranges for each physical activity recognized by the system, showed promising results for remote health status evaluation.\",\n \"Title: A Fast Heterogeneous Approach to Enhanced Blockchain Attack Resilience and Mitigation\\nYear: 2021\\n\\nAbstract: We are introducing a new computational model for heterogeneous architectures for performing enhanced security validations and processing of transactions on-chain, a model applicable to both permissioned and permissionless blockchains. By proposing an efficient storage model for the Merkle trees involved in the transaction and security validation processing, and combining it with an efficient heterogeneous approach for processing it, we are outlining the improved computational advantages to ensuring attack resilience on-chain, which surpasses the performance of traditional CPU-based approaches by a significant factor.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":67,"string":"67"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13683,"string":"13,683"}}},{"rowIdx":13684,"cells":{"question_id":{"kind":"string","value":"Ali Hamzeh"},"question":{"kind":"string","value":"Generate an abstract for the title \"Community detection in dynamic social networks\" using the following items: Social network communities, data mining, graph mining, artificial intelligence, community detection."},"answer":{"kind":"string","value":"AbstractCommunities in social networks are groups of individuals who are connected with specific goals. Discovering information on the structure, members and types of changes of communities have always been of great interest. Despite the extensive global researches conducted on these, discovery has not been confirmed yet and researchers try to find methods and improve estimated techniques by using Data Mining tools, Graph Mining tools and artificial intelligence techniques. This paper proposes a novel two-phase approach based on global and local information to detect communities in social network. It explores the global information in the first phase and then exploits the local information in the second phase to discover communities more accurately. It also proposes a novel algorithm which exploits the local information and mines deeply for the second phase. Experimental results show that the proposed method has better performance and achieves more accurate results compared with the previous ones."},"correct_answer":{"kind":"string","value":"AbstractCommunities in social networks are groups of individuals who are connected with specific goals. Discovering information on the structure, members and types of changes of communities have always been of great interest. Despite the extensive global researches conducted on these, discovery has not been confirmed yet and researchers try to find methods and improve estimated techniques by using Data Mining tools, Graph Mining tools and artificial intelligence techniques. This paper proposes a novel two-phase approach based on global and local information to detect communities in social network. It explores the global information in the first phase and then exploits the local information in the second phase to discover communities more accurately. It also proposes a novel algorithm which exploits the local information and mines deeply for the second phase. Experimental results show that the proposed method has better performance and achieves more accurate results compared with the previous ones."},"documents":{"kind":"list like","value":["Title: Using reinforcement learning to find an optimal set of features\nYear: 2013\n\nAbstract: Identifying the most characterizing features of observed data is critical for minimizing the classification error. Feature selection is the process of identifying a small subset of highly predictive features out of a large set of candidate features. In the literature, many feature selection methods approach the task as a search problem, where each state in the search space is a possible feature subset. In this study, we consider feature selection problem as a reinforcement learning problem in general and use a well-known method, temporal difference, to traverse the state space and select the best subset of features. Specifically, first, we consider the state space as a Markov decision process, and then we introduce an optimal graph search to overcome the complexity of the problem of concern. Since this approach needs a state evaluation paradigm as an aid to traverse the promising regions in the state space, the presence of a low-cost evaluation function is necessary. This method initially explores the lattice of feature sets, and then exploits the obtained experiments. Finally, two methods, based on filters and wrappers, are proposed for the ultimate selection of features. Our empirical evaluation shows that this strategy performs well in comparison with other commonly used feature selection strategies, while maintaining compatibility with all datasets in hand.","Title: An Effective Expert Team Formation in Social Networks Based on Skill Grading\nYear: 2011\n\nAbstract: In this paper, a novel approach for forming an effective expert team in social network is proposed by using of skill grading and minimum communication cost. In this work, first a framework is recommended to determine skill level of experts based on their skill abilities and their collaboration among expert neighbors. Second, the existing diameter algorithm is extended to its more generalized version. Third, the communication cost is customized by presenting the cost metric containing both distance and skill grade of individuals. The experimental results show the advantages of this method which using DBLP co-authorship graph through an effective and efficient expert team in practice.","Title: Modeling information diffusion and community membership using stochastic optimization\nYear: 2013\n\nAbstract: Communities are vehicles for efficiently disseminating news, rumors, and opinions in human social networks. Modeling information diffusion through a network can enable us to reach a superior functional understanding of the effect of network structures such as communities on information propagation. The intrinsic assumption is that form follows function---rational actors exercise social choice mechanisms to join communities that best serve their information needs. Particle Swarm Optimization (PSO) was originally designed to simulate aggregate social behavior; our proposed diffusion model, PSODM (Particle Swarm Optimization Diffusion Model) models information flow in a network by creating particle swarms for local network neighborhoods that optimize a continuous version of Holland's hyperplane-defined objective functions. In this paper, we show how our approach differs from prior modeling work in the area and demonstrate that it outperforms existing model-based community detection methods on several social network datasets.","Title: Detecting overlapping communities in social networks by game theory and structural equivalence concept\nYear: 2011\n\nAbstract: Most complex networks demonstrate a significant property 'community structure', meaning that the network nodes are often joined together in tightly knit groups or communities, while there are only looser connections between them. Detecting these groups is of great importance and has immediate applications, especially in the popular online social networks like Facebook and Twitter. Many of these networks are divided into overlapping communities, i.e. communities with nodes belonging to more than one community simultaneously. Unfortunately most of the works cannot detect such communities. In this paper, we consider the formation of communities in social networks as an iterative game in a multiagent environment, in which, each node is regarded as an agent trying to be in the communities with members structurally equivalent to her. Remarkable results on the real world and benchmark graphs show efficiency of our approach in detecting overlapping communities compared to the other similar methods.","Title: DBFS: An effective Density Based Feature Selection scheme for small sample size and high dimensional imbalanced data sets\nYear: 2012\n\nAbstract: Nowadays, imbalanced data sets are pervasive in real world human practices, and hence, become a very interesting research area within machine learning communities. Imbalanced data sets introduce a significant reduction in performance of standard classifiers when they are invoked to learn data underlying concepts. The problem becomes even more sever when imbalanced data sets are involved with high dimensions. This paper presents a novel feature ranking approach based on the probability density estimation to cope with these issues. The idea behind our approach, named Density Based Feature Selection (DBFS), is that features' distributions over classes can bring significant benefits to feature selection algorithms. In other words, to explore the contribution of each attribute and assign it an appropriate rank, DBFS takes into account features' corresponding distributions over all classes along with their correlations. To show the effectiveness of the presented approach, well-known feature ranking methods are implemented and compared with our approach across varieties of small sample size and high dimensional data sets from microarray, mass spectrometry and text mining domains. Our theoretical analysis and experimental observations reveal that our approach is the method of choice by offering a simple yet effective feature ranking method based on well-known statistical evaluation measures.","Title: Improving security of pixel value differencing steganographic method\nYear: 2009\n\nAbstract: Pixel value differencing (PVD) is a steganographic method which embeds secret data in images based on spatial information. PVD has relatively high capacity but its effectiveness has been questioned by variety of steganalysis attacks. These attacks use the difference histogram quantization to reveal existence of secret data in a stego image. In this paper, we propose some modifications to PVD method to make it more robust to histogram quantization. Hence, at the expense of slight reduction in the capacity of the original algorithm, PVD's stability against histogram quantization attacks is significantly improved.","Title: A levelwise spectral co-clustering algorithm for collaborative filtering\nYear: 2012\n\nAbstract: Collaborative Filtering (CF) is a widely used approach in recommendation systems that predicts user preferences by learning from user-item ratings. To extract either user relationship or item dependencies, there exists several well known approaches; among them clustering is of great importance. Inspired by this understanding, this paper presents a novel levelwise paradigm whose first level clusters users and items separately using their pair wise similarity. Once the primary clusters of first level formed, the second level, does the simultaneously clustering user and item clusters by the means of Co-clustering. The main advantages of our approach include: First, it is able to alleviate the well known notion of sparsity problem intrinsically exists in CF. Second, the proposed method offers an elegant dimensionality reduction what in turn leads to better performance. Third experimental results on Movielens dataset have demonstrated that the proposed method can effectively reveal the subset aggregates of users and items which are closely related.","Title: TeamFinder: A Co-clustering based Framework for Finding an Effective Team of Experts in Social Networks\nYear: 2012\n\nAbstract: A lot of research in social networks has been devoted in the recommendation of individuals. Most of the work has focused on finding appropriate people one in question, regarding the input social graph and their attributes. However, for many applications one is interested in finding a team of experts for a given query. For a given social graph and a task including a set of required skills, we study the problem of finding a team of experts that their expertises match the given task and the relationships among them represent how well team members work together. Our proposed framework, named Team Finder, first provide a grouping method to aggregate the set of experts that are strongly correlated based on their skills as well as the best connection among them. By considering the groups, search space is significantly reduced and moreover it causes to prevent from the growth of redundant communication costs and team cardinality while assigning the team members. Second, the identified groups are reconstructed as a bipartite graph an a Co-clustering method is applied to find the qualified clusters containing related experts and skills and finally, an adapted version of team formation method is used to discover the effective experts for accomplishing the given task. Team Finder method is developed in a scalable and effective manner and making it ideal for large scale applications. We conduct extensive experiments on DBLP co-authorship graph and the experimental results demonstrate the effectiveness and scalability of proposed method in practice and give useful and intuitive results.","Title: Anomaly detection in categorical datasets using bayesian networks\nYear: 2011\n\nAbstract: In this paper we present a method for finding anomalous records in categorical or mixed datasets in an unsupervised fashion. Since the data in many problems consist of normal records with a small minority of anomalies, many approaches build a model from the training data and compare the test records against it. But instead of building a model, we keep track of the number of occurrences of different attribute value combinations. We also consider a more meaningful definition of anomalies and incorporate the Bayesian network structure in it. A scoring technique is defined for each test record. In this procedure we combine supports of different rules according to the Bayesian network structure in order to determine the label of the test instances. As it is shown in the results, our proposed method has a higher or similar f-measure and precision compared to a Bayesian network based approach in all cases.","Title: A novel steganalysis for TPVD steganographic method based on differences of pixel difference histogram\nYear: 2012\n\nAbstract: Tri-way Pixel Value Differencing (TPVD) steganographic method is a new modified version of another well-known method called PVD, which intents to increase embedding capacity and security of its successor by hiding secret bits in both vertical and diagonal edges of a cover image, in addition to the horizontal edges used in PVD. In this paper, it is shown that the histogram of difference values of a stego image under the TPVD algorithms is vulnerable to a particular statistical analysis. So, a new steganalytic measure named Growing Anomalies is introduced that its value has a linear relationship with secret message rate. It is shown empirically and theoretically that proposed steganalysis method based on this measure can estimate the amount of secret bits with a negligible error rate. The proposed steganalyser can classify test images as stego or cover with 97% accuracy when they contain more that 10% secret data. Implementation results indicate that proposed method can estimate secret message rate with an average accuracy of 95%.","Title: Intelligent exploration method for XCS\nYear: 2005\n\nAbstract: Exploration/Exploitation equilibrium is one of the most challenging issues in reinforcement learning area as well as learning classifier systems such as XCS. In this paper, an intelligent method is proposed to control exploration rate in XCS to improve its long-term performance. This method is called Intelligent Exploration Method (IEM) and is applied to some benchmark problems to show advantages of adaptive exploration rate for XCS. It","Title: To handle real valued input in XCS: using fuzzy hyper-trapezoidal membership in classifier condition\nYear: 2010\n\nAbstract: Learning classifier systems (LCSs) are evolutionary learning mechanisms that combine genetic algorithms (GAs) with the power of the reinforcement learning paradigm. XCS, eXtended Classifier System, is currently considered as state of the art learning classifier systems due to its effectiveness in data analysis and its success in applying to varieties of learning problems. Generalization and the size of evolved population in XCS is one of the most challenging issues in XCS. This paper suggests that rule representation in XCS is not matured to the point where the condition parts of classifiers are covering the problem space in an effective manner with minimum redundancy. The key idea in the proposed system, named XCS-HT, is to use a novel representation scheme based on presenting a subspace of problem space with two kinds of regions named certain and vague regions. Using such mechanism significantly boils the number of evolved XCS-HT's classifiers down, while changing the performance only marginally. The experimental results show that XCS-HT has a better ability to solve problems having indistinguishable class boundaries comparing to XCSR, a common extension of XCS standing for handling real valued data.","Title: A Precise Statistical approach for concept change detection in unlabeled data streams\nYear: 2011\n\nAbstract: Recently data stream has been extensively explored due to its emergence in a great deal of applications such as sensor networks, web click streams and network flows. One of the most important challenges in data streams is concept change where data underlying distributions change from time to time. A vast majority of researches in the context of data stream mining are devoted to labeled data, whereas, in real word human practice label of data are rarely available to the learning algorithms. Moreover, most of the methods that detect changes in unlabeled data stream merely deal with numerical data sets, and also, they are facing considerable difficulty when dimension of data tends to increase. In this paper, we present a Precise Statistical approach for Concept Change Detection in unlabeled data streams, which, abbreviated as PSCCD, detects changes using an exchangeable test. This hypothesis test is driven from a martingale which is based on Doob’s Maximal Inequality. The advantages of our approach are three fold. First, it does not require a sliding window on the data stream whose size is a well-known challenging issue; second, it works well in multi-dimensional data stream, and last but not the least, it is applicable to different types of data including categorical, numerical and mixed-attribute data streams. To explore the advantages of our approach, quite a lot of experiments with different settings and specifications are conducted. The obtained results are very promising.","Title: An enriched game-theoretic framework for multi-objective clustering\nYear: 2013\n\nAbstract: The framework of multi-objective clustering can serve as a competent technique in nowadays human issues ranging from decision making process to machine learning and pattern recognition problems. Multi-objective clustering basically aims at placing similar objects into the same groups based on some conflicting objectives, which substantially supports the use of game theory to come to a resolution. Based on these understandings, this paper suggests Enriched Game Theory K-means, called EGTKMeans, as a novel multi-objective clustering technique based on the notion of game theory. EGTKMeans is specially designed to optimize two intrinsically conflicting objectives, named, compaction and equi-partitioning. The key contributions of the proposed approach are three folds. First, it formulates an elegant and novel payoff definition which considers both objectives with equal priority. The presented payoff function incorporates a desirable fairness into the final clustering results. Second, EGTKMeans performs better off by utilizing the advantages of mixed strategies as well as those of pure ones, considering the existence of mixed Nash Equilibrium in every game. The last but not the least is that EGTKMeans approaches the optimal solution in a very promising manner by optimizing both objectives simultaneously. The experimental results suggest that the proposed approach significantly outperforms other rival methods across real world and synthetic data sets with reasonable time complexity.","Title: Agent Based Decision Tree Learning: A Novel Approach\nYear: 2009\n\nAbstract: Decision trees are one of the most effective and widely used induction methods that have received a great deal of attention over the past twenty years. When decision tree induction algorithms were used with uncertain rather than deterministic data, the result is a complete tree, which can classify most of the unseen samples correctly. This tree would be pruned in order to reduce its classification error and over-fitting. Recently, multi agent researchers concentrated on learning from large databases. In this paper we present a novel multi agent learning method that is able to induce a decision tree from distributed training sets. Our method is based on combination of separate decision trees each provided by one agent. Hence an agent is provided to aggregate results of the other agents and induces the final tree. Our empirical results suggest that the proposed method can provide significant benefits to distributed data classification.","Title: Discovering overlapping communities in social networks: A novel game-theoretic approach\nYear: 2013\n\nAbstract: Identifying communities in social networks has been receiving the increasing attentions recently. However, the overlapping concept has received little attentions in the literature, although it is observed in almost all social networks. In this study, we propose a framework based on the game theory and the structural equivalence concept to address the detection of overlapping communities in social networks. Specifically, we consider the underlying graph as a hypothetical social networking website and regard each vertex of this graph as an agent performing in this multiagent environment. Since each agent may belong to several communities simultaneously, we are able to find overlapping community structure of social networks. The rigorous proof of the existence of Nash equilibrium in this game is provided which shows that the method always reaches to the final solution. Experimental results on the benchmark and real world graphs show superiority of our approach over the other state-of-the-art methods.","Title: Approximating Arbitrary Reinforcement Signal by Learning Classifier Systems using Micro Genetic Algorithm\nYear: 2008\n\nAbstract: Learning Classifier Systems are Evolutionary Learning mechanisms which combine Genetic Algorithm and the Reinforcement Learning paradigm. Learning Classifier Systems try to evolve state-action-reward mappings to propose the best action for each environmental state to maximize the achieved reward. In the first versions of learning classifier systems, state-action pairs can only be mapped to a constant real-valued reward. So to model a fairly complex environment, LCSs had to develop redundant state-action pairs which had to be mapped to different reward values. But an extension to a well-known LCS, called Accuracy Based Learning Classifier System or XCS, was recently developed which was able to map state-action pairs to a linear reward function. This new extension, called XCSF, can develop a more compact population than the original XCS. But some further researches have shown that this new extension is not able to develop proper mappings when the input parameters are from certain intervals. As a solution to this issue, in our previous works, we proposed a novel solution inspired by the idea of using evolutionary approach to approximate the reward landscape. The first results seem promising, but our approach, called XCSFG, converged to the goal very slowly. In this paper, we propose a new extension to XCSFG which employs micro-GA which its needed population is extremely smaller than simple GA. So we expect micro-GA to help XCSFG to converge faster. Reported results show that this new extension can be assumed as an alternative approach in XCSF family with respect to its convergence speed, approximation accuracy and population compactness.","Title: A Novel Strategy Selection Method for Multi-Objective Clustering Algorithms Using Game Theory\nYear: 2012\n\nAbstract: The most important factors which contribute to the efficiency of game-theoretical algorithms are time and game complexity. In this study, we have offered an elegant method to deal with high complexity of game theoretic multi-objective clustering methods in large-sized data sets. Here, we have developed a method which selects a subset of strategies from strategies profile for each player. In this case, the size of payoff matrices reduces significantly which has a remarkable impact on time complexity. Therefore, practical problems with more data are tractable with less computational complexity. Although strategies set may grow with increasing the number of data points, the presented model of strategy selection reduces the strategy space, considerably, where clusters are subdivided into several sub-clusters in each local game. The remarkable results demonstrate the efficiency of the presented approach in reducing computational complexity of the problem of concern.","Title: Reducing the Computational Cost in Multi-objective Evolutionary Algorithms by Filtering Worthless Individuals.\nYear: 2014\n\nAbstract: The large number of exact fitness function evaluations makes evolutionary algorithms to have computational cost. In some real-world problems, reducing number of these evaluations is much more valuable even by increasing computational complexity and spending more time. To fulfill this target, we introduce an effective factor, in spite of applied factor in Adaptive Fuzzy Fitness Granulation with Non-dominated Sorting Genetic Algorithm-II, to filter out worthless individuals more precisely. Our proposed approach is compared with respect to Adaptive Fuzzy Fitness Granulation with Non-dominated Sorting Genetic Algorithm-II, using the Hyper volume and the Inverted Generational Distance performance measures. The proposed method is applied to 1 traditional and 1 state-of-the-art benchmarks with considering 3 different dimensions. From an average performance view, the results indicate that although decreasing the number of fitness evaluations leads to have performance reduction but it is not tangible compared to what we gain.","Title: Community Detection in Social Networks Using Information Diffusion\nYear: 2012\n\nAbstract: Discovering communities in popular social networks like Facebook has been receiving significant attentions recently. In this paper, inspired from real life, we have addressed the community detection problem by a framework based on Information Diffusion Model and Game Theory. In this approach, we consider each node of the social network as a selfish agent which has interactions with its neighbors and tries to maximize its total utility (i.e. received information). Finally community structure of the graph reveals after reaching to the local Nash equilibrium of the game. Experimental results on the benchmark social media datasets, synthetic and real world graphs demonstrate that our method is superior compared with the other state-of-the-art methods.","Title: A pattern based evolutionary approach to prediction computation in XCSF\nYear: 2006\n\nAbstract: XCSF is a new version of XCS with the ability of computing the environmental rewards. In the early versions of XCSF, this computation was done by approximating the coefficients of the associated polynomial reward functions. However, recent researches show that this approximation method suffers from some significant drawbacks such as input range dependency. In this paper, we propose a new method to approximate the reward functions using Genetic Algorithms. Our proposed method uses a new representation scheme for chromosomes and some newly introduced pattern based operators which are adapted for function approximation.","Title: Using Noun Similarity to Adapt an Acceptability Measure for Persian Light Verb Constructions.\nYear: 2012\n\nAbstract: Light verb constructions (LVCs), such as take a walk and make a decision, are a common subclass of multiword expressions (MWEs), whose distinct syntactic and semantic properties call for a special treatment within a computational system. In particular, LVCs are formed semi-productively: often a semantically-general verb (such as take) combines with a number of semantically-similar nouns to form semantically-related LVCs, as in make a decision/choice/commitment. Nonetheless, there are restrictions as to which verbs combine with which class of nouns. A proper computational account of LVCs is even more important for languages such as Persian, in which most verbs are of the form of LVCs. Recently, there has been some work on the automatic identification of MWEs (including LVCs) in resource-rich languages, such as English and Dutch. We adapt such existing techniques for the automatic identification of LVCs in Persian, an under-resourced language. Specifically, we extend an existing statistical measure of the acceptability of English LVCs (Fazly et al., 2007) to make explicit use of semantic classes of noun, and show that such classes are in particular useful for determining the LVC acceptability of new combinations.","Title: CBDL: Context-based distance learning for categorical attributes\nYear: 2011\n\nAbstract: Distance learning is an important notion and has played a critical role in success of various machine learning algorithms. Any learning algorithm that requires dissimilarity/similarity measures has to assume some forms of distance functions, either explicitly or implicitly. Hence, in recent years a considerable amount of research has been devoted to distance learning. Despite great achievements in this field, a number of important issues need to be further explored for real world datasets mainly containing categorical attributes. Based on these considerations, the current research presents a Context-Based Distance Learning approach (CBDL) to advance the state of the art existing researches on distance metric learning for categorical datasets. CBDL is designed and developed based on the idea that distance between two values of a given categorical attribute can be estimated by using information inherently exists within subset of attributes called context. CBDL composes of two main components: context extraction component and distance learning component. Context extraction component is responsible for extracting the relevant subset of feature set for a given attribute, while distance learning component tries to learn distance between each pair of values based on the extracted context. To have a comprehensive analysis, we conduct wide range of experiments in both supervised and unsupervised environments in the presence of noise. Our experimental results reveal that CBDL is the method of choice distance learning approach by offering a comparable or better performance compared to the state of the art existing distance learning schemes according to studied evaluation measures. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.","Title: A Recursive Classifier System for Partially Observable Environments\nYear: 2009\n\nAbstract: Petitcolas has proposed a steganographic technique called MP3Stego which can hide secret messages in a MP3 audio. This technique is well-known because of its high capacity. However, in rare cases, the normal audio encoding process will be terminated ...","Title: A New Architecture for Learning Classifier Systems to Solve POMDP Problems\nYear: 2008\n\nAbstract: Reinforcement Learning is a learning paradigm that helps the agent to learn to act optimally in an unknown environment through trial and error. An RL-based agent senses its environmental state, proposes an action, and applies it to the environment. Then a reinforcement signal, called the reward, is sent back from the environment to the agent. The agent is expected to learn how to maximize overall environmental reward through its internal mechanisms. One of the most challenging issues in the RL area arises as a result of the sensory ability of the agent, when it is not able to sense its current environmental state completely. These environments are called partially observable environments. In these environments, the agent may fail to distinguish the actual environmental state and so may fail to propose the optimal action in particular environmental states. So an extended mechanism must be added to the architecture of the agent to enable it to perform optimally in these environments. On the other hand, one of the most-used approaches to reinforcement learning is the evolutionary learning approach and one of the most-used techniques in this family is learning classifier systems. Learning classifier systems try to evolve state-action-reward mappings to model their current environment through trial and error. In this paper we propose a new architecture for learning classifier systems that is able to perform optimally in partially observable environments. This new architecture uses a novel method to detect aliased states in the environment and disambiguates them through multiple instances of classifier systems that interact with the environment in parallel. This model is applied to some well-known benchmark problems and is compared with some of the best classifier systems proposed for these environments. Our results and detailed discussion show that our approach is one of the best techniques among other learning classifier systems in partially observable environments.","Title: A fuzzy system to control exploration rate in XCS\nYear: 2005\n\nAbstract: Exploration/Exploitation dilemma is one of the most challenging issues in reinforcement learning area as well as learning classifier systems such as XCS. In this paper, an intelligent method is proposed to control the exploration rate in XCS to improve its long-term performance. This method is called Intelligent Exploration Method (IEM) and is applied to some benchmark problems to show advantages of adaptive exploration rate for XCS.","Title: Link Prediction in Social Network Using Co-clustering Based Approach\nYear: 2012\n\nAbstract: This paper introduces an approach to derive whether an individual is related to an item or not. In our approach, the well-known DBLP dataset is used and we try to find some skills that are related to an author that we were not aware of before. To realize our objective, we cluster authors and skills using Spectral Graph Clustering algorithm, then simultaneously obtain user and movie clusters via Bipartite Graph (Bigraph) Spectral Co-clustering approach, and then generate predictions based on the outputs of clustering and co-clustering steps. Accordingly, we utilize clustering and co-clustering advantages to predict the probability of link existing between an author and a skill. Experimental results on DBLP dataset show that our approach works well in the specified task.","Title: SPCF: a stepwise partitioning for collaborative filtering to alleviate sparsity problems\nYear: 2012\n\nAbstract: Collaborative filtering is a widely used approach in recommendation systems which predict user preferences by learning from user-item ratings. To extract either user relationship or item dependencies, there exist several well known approaches; among them clustering is of great importance. Traditional clustering methods in collaborative filtering usually suffer from two fundamental problems: sparsity and scalability. Sparsity refers to a situation where most users rate only a small number of items, while scalability denotes a huge number of both users and items. Inspired by these problems, this paper presents a novel stepwise paradigm, SPCF, which in the first step clusters users and items separately using their latent similarity. Once the primary clusters of the first level are formed, the second level simultaneously clusters the user and item clusters by means of co-clustering. The advantages of SPCF are threefold; first, it is able to alleviate the well known sparsity problem which intrinsically exists in collaborative filtering; second, the proposed method offers an elegant solution to the scalability problem based on dimensionality reduction which in turn leads to better performance of the model; third, experimental results on two versions of a Movielens dataset for prediction have demonstrated that the proposed method can reveal major interests of users or items in promising manner.","Title: An evolutionary function approximation approach to compute prediction in XCSF\nYear: 2005\n\nAbstract: XCSF is a new extension to XCS that is developed to extend XCS's reward calculation capability via computing. This new feature is called computable prediction. The first version of XCSF tries to find the most appropriate equation to compute each classifier's reward using a weight update mechanism. In this paper, we try to propose a new evolutionary mechanism to compute these equations using genetic algorithms.","Title: Harmony-Based Feature Weighting to Improve the Nearest Neighbor Classification.\nYear: 2012\n\nAbstract: This paper introduces the use of Harmony Search with novel fitness function in order to assign higher weights to informative features while noisy irrelevant features are given low weights. The fitness function is based on the Area Under the receiver operating characteristics Curve (AUC). The aim of this feature weighting is to improve the performance of the k-NN algorithm. Experimental results show that the proposed method can improve the classification performance of the k-NN algorithm in comparison with the other important method in realm of feature weighting such as Mutual Information, Genetic Algorithm, Tabu Search and chi-squared (chi 2). Furthermore, on synthetic data sets, this method is able to allocate very low weight to the noisy irrelevant features which may be considered as the eliminated features from the data set.","Title: A New Architecture of XCS to Approximate Real-Valued Functions Based on High Order Polynomials Using Variable-Length GA\nYear: 2007\n\nAbstract: XCSFG is a new version of XCSF with the ability of computing the environmental payoff using Genetic Algorithm. In the first version of XCSFG, this computation was done by evolving coefficients of the associated linear payoff functions. In this paper, we extend XCSFG to approximate the payoff functions in the form of higher order polynomials. Our newly proposed method uses GA with variable-length chromosome with real-valued representation. This new version of XCSFG is called XCSFG, the Continuous version or XCSFGC. It is applied to some benchmark problems and is compared with the original XCSF and its newly introduced extensions.","Title: A Grid Based Cooperative Co-evolutionary Multi-Objective Algorithm\nYear: 2009\n\nAbstract: In this paper, a well performing approach in the context of Multi-Objective Evolutionary Algorithm (MOEA) is investigated due to its complexity. This approach called NSCCGA is based on previously introduced approach called NSGA-II. NSCCGA performs better than NSGA-II but with a heavy load of computational complexity. Here, a novel approach called GBCCGA is introduced based on MOCCGA with some modifications. The main difference between GBCCGA and MOCCGA is in their niching technique which instead of the traditional sharing mechanism in MOCCGA, a novel grid-based technique is used in GBCCGA. The reported results show that GBCCGA performs roughly the same as NSCCGA but with very low computational complexity with respect to the original MOCCGA.","Title: Towards efficient and effective negative selection algorithm: a convex hull representation scheme\nYear: 2010\n\nAbstract: Negative Selection Algorithm (NSA) is one of several algorithms inspired by the principles of natural immune system. The algorithm received the researchers attention due to its applicability in various research areas and a number of valuable efforts are made to increase the effectiveness and efficiency of it. The heart of NSA is to somehow find rules called detectors to discriminate self and anomaly areas. Each detector in NSA defines a subspace of problem space where no self data is located. One of the major issues in NSA is detector's shape or representation of detectors which can affect the detection performance significantly. This paper for the first time proposes a new representation for detectors based on convex hull. Since convex hull is a general form of other geometric shapes, it retains the benefits of other shapes meanwhile it provides some new features like the asymmetric shape. Experimental results show a significant enhancement in the accuracy of negative selection algorithm compared to other common representation shapes.","Title: Towards final rule set reduction in XCS: a fuzzy representation approach\nYear: 2011\n\nAbstract: Generalization is the most challenging issue in XCS research area. One of the main components of XCS managing to remedy this issue is knowledge representation. In this paper, a knowledge representation based on fuzzy membership function offering certain and vague regions is described. We extend the Michigan learning classifier system using this approach to be improved in terms of both performance and interpretability. The contribution of this paper is three-folds: 1) updating main parameters of classifiers based on their certainty factor in matching of incoming data, 2) enhancing essential components of XCS to be compatible with such fuzzy representation schema and 3) proposing a novel rule set reduction method named Reduction based on Least Reward Prediction (RLRP) to improve the interpretability of the evolved model. Furthermore, an inference methodology which is compatible with RLRP is suggested to maintain the similar performance. The obtained results are promising due to the effectiveness of proposed method in dealing with real world problems. Furthermore, the proposed reduction method can upgrade the interpretability of final rule set by boiling its size down by 94% on average while slightly degrading the prediction accuracy.","Title: Hc-Moea: A Hierarchical Clustering Approach For Increasing The Solution'S Diversity In Multiobjective Evolutionary Algorithms\nYear: 2015\n\nAbstract: Recently, Indicator-based Evolutionary Algorithms are considered the main issue for researchers in the evolutionary multi-objective frameworks. Due to the capability of the Indicator-based approaches in obtaining a finest non-dominated solutions and the potential of these approaches on achieving the well-distributed solutions, these approaches become popular among modern Multi-Objective Evolutionary Algorithms (MOEAs). Most modern MOEAs are intended to converge to the Pareto optimal front through preserving the population diversity in the objective space. In this regard, the intention of this work is presenting a novel MOEA to enhance the population diversity among the non-dominated vectors in the solution space. The idea of this method is inspired by the Hierarchical clustering. In this attitude, an adept approach is planned to present a new indicator as a selection method during the optimization cycle. The gain of this technique is a desirable set with more diverse solutions in the solution space during the environmental selection operator. In the last part, this work also improved the rate of the convergence by introducing a parent selection mechanism. The selection method is simple and effective, which is worked base on the selection of proper members of parents' population instead of a random mechanism. This bright parent selection is adopted to accelerate the convergence of the proposed method. This work is applied to a wide range of well-established test problems. The obtained results validate the motivation on the basis of diversity and performance measures in comparison with the state of the art algorithms.","Title: A Martingale Approach to Detect Peak of News in Social Network.\nYear: 2014\n\nAbstract: Nowadays, social medias such as Twitter, Memetracker and Blogs have become powerful tools to propagate information. They facilitate quick dissemination sequence of information such as news article, blog posts, user's interests and thoughts through large scale. Providing strong means to analyzing social networks structure and how information diffuse through them is essential. Many recent studies emphasize on modeling information diffusion and their patterns to gain some useful knowledge. In this paper, we propose a statistical approach to online detect peak points of news when spread over social networks, to the best of our knowledge has never investigated before. The proposed model use martingale approach to predict peak points when news reached the peak of its popularity. Experimental results on real datasets show good performance of our approach to online detect these peak points.","Title: Trust based latency aware influence maximization in social networks\nYear: 2015\n\nAbstract: Influence maximization is the problem of finding a small set of nodes that maximizes the aggregated influence in social networks. The problem of influence maximization in social networks has been explored in many previous researches. They have mainly relied on similar temporal chances for every node to influence another; whereas in reality, time plays a major role in pairwise propagation rates in social networks. However, there is little research done on influence maximization considering temporal dynamics of the networks and existing approaches merely offers a mediocre performance due to ignoring trust aspects of the diffusion process. In this paper, we propose a Trust based Latency aware Influence Maximization model, abbreviated as TLIM, which selects the most influential nodes in social networks with considering time and trust simultaneously. To the best of our knowledge, we are the first to study trust in classic IC model and also the first to consider both important time and trust factors jointly in influence maximization problem. The main contributions of this paper are listed as follows: first, we extend the classic IC model to include time and trust simultaneously, which is more applicable in existing social networks. Second, we find the most influential nodes in social networks with considering time and trust together; and the last but not the least, it is applicable to well-known real social networks such as Epinions, Slashdot and Wikipedia. To explore the advantages of our approach, quite a lot of experiments with different specifications are conducted. The obtained results are very promising.","Title: An enriched social behavioural information diffusion model in social networks\nYear: 2015\n\nAbstract: Online social networks have recently become an innovative and effective method for spreading information among people around the world. Information diffusion, rumour spreading and diseases infection are all instances of stochastic processes that occur over the edges of social networks. Many prior works have carried out empirical studies and diffusion models to understand how information propagates in online social networks; however they suffer from problems. In this paper, we propose an information diffusion model inspired by information propagation among people. Our proposed Social Behavioural Information Diffusion Model, abbreviated as SBIDM, considers the effect of mainstream media like TV and radio, as well as interaction with the neighbours. The advantages of our approach are four-fold. First, it models information diffusion in social networks inspired by social life, which considers the effect of aggregate social behaviour to diffuse information; second, it allows partial knowledge to be held in each individual; third, it considers the effects of social media in propagating information as well as the effects of interacting with neighbours; and last but not least, it is applicable to different types of data including synthetic and well-known real social networks like Facebook, Amazon, Epinions and DBLP. To explore the advantages of our approach, many experiments with different settings and specifications were conducted. The obtained results are very promising.","Title: An ensemble game theoretic approach for multi-objective optimization.\nYear: 2015\n\nAbstract: Recently multi-objective clustering has been extensively explored due to its appearance of new applications in many domains. However, in many applications, there is more than a single objective which is needed to be optimized in the context of the application, such as facility location, ad hoc networks and sensor networks. These domains must optimize two objectives of compactness and equi-partitioning which may be conflicted in some situations. Existing algorithms have high complexity. In this paper, we propose an Ensemble Game Theoretic approach for multi-objective clustering method which optimizes two objectives of compactness and equi-partitioning, simultaneously. We compare our algorithm on variety of data sets including synthetic and real ones. The remarkable results are very promising and demonstrate the efficiency of presented approach both in performance and complexity.","Title: A fuzzy based approach for fitness approximation in multi-objective evolutionary algorithms.\nYear: 2015\n\nAbstract: Evolutionary Algorithm provides a framework that is largely applicable to particular problems including multiobjective optimization problems, basically for the ease of their implementation and their capability to perform efficient parallel search. Indeed, in some cases, expensive multiobjective optimization evaluations might be a challenge to restrict the number of explicit fitness evaluations in multiobjective evolutionary algorithms. Accordingly, this article presents a novel approach that tackles this problem so as to not only decrease the number of fitness evaluations but also to improve the performance. During evolution, our proposed approach selects fit individuals based on the knowledge acquired throughout the search, and performs explicit fitness evaluations on these individuals. A comprehensive comparative analysis of a wide range of well-established test problems, selected from both traditional and state-of-the-art benchmarks, has been presented. Afterward, the effectiveness of the obtained results is compared with some of the state-of-the-art methods using two well-known metrics-i.e. Hypervolume and Inverted Generational Distance (IGD). The experiments of our implemented approach is performed to illustrate that our proposal seems to be promising and would prove more efficient than other approaches in terms of both the performance and the computational cost.","Title: A Coevolutionary Approach To Many Objective Optimization Based On A Novel Ranking Method\nYear: 2016\n\nAbstract: Ranking solutions of the population in an evolutionary algorithm that solves a many objective optimization problem is a challenging task which has been vastly studied in recent years. Loss in the hypervolume of the population when a solution is omitted could be a good measure for ranking solutions but calculating this value for high dimensional problems is not tractable. In the selection operator of evolutionary algorithms, the actual hypervolume values are not important, with only a relative knowledge of these values we can select best solutions. In this paper we have proposed a novel method for approximating the ranking induced by the hypervolume indicator. This method is compared to the similar methods and its efficiency and performance is proved via proper experiments. The method is tested using benchmark test problems from the Walking Fish Group problem family which are scalable both in number of variables and objectives.","Title: Scaling Up The Hybrid Particle Swarm Optimization Algorithm For Nominal Data-Sets\nYear: 2015\n\nAbstract: Recently, the hybrid Particle Swarm Optimisation/Ant Colony Optimisation (PSO/ACO) has been proposed for discovery of classification rules. An improved version of this hybrid scheme, PSO/ACO(2) algorithm, can directly cope with nominal attributes without converting them into numerical ones. Although PSO/ACO(2) can handle nominal values, it suffers from high computational complexity for large datasets. Beside variety of classification methods which exist to provide more compact set of rules, this study propose an approach which reduces the computational complexity of PSO/ACO(2) in order to make it suitable for classification of large datasets. This work is developed the K-mode as a method of sampling from the datasets. In this regard a modification is employed to this algorithm in order to decrease the computational time as well as maintaining the accuracy of the algorithm. Further contribution of this paper is utilizing a new fitness measure for the algorithm. This measure has a robust theoretical background. The combination of the proposed modified K-mode method and the introduced fitness measure led to speed the obtained results up. The experimental result shows the efficiency of the proposed algorithm in comparison with its competitors.","Title: Team Formation In Social Networks Based On Local Distance Metric\nYear: 2015\n\nAbstract: The tremendous growth of social networking in last decades has resulted in an extensive research into this area. Social network users are enabled to easily communicate and collaborate with each other. Due to the significant role of network users, a new research topic in social networking analysis is team formation. The social network is modeled as a graph where nodes represent experts and edges show the initial collaborations between experts. The present research aims to tackle the problem to find an expert team in social network in order to complete a project that requires a set of skills. An algorithm is thus proposed to find the best suited team for the assigned project: a new method is devised as well to determine the distance between pairs of experts. Experimental results on DBLP dataset present the efficiency of our proposed method.","Title: A diversity control mechanism in many objective optimizations.\nYear: 2016\n\nAbstract: Multi-objective evolutionary optimization algorithms are among the best optimizers for solving problems in control systems, engineering and industrial planning. The performance of these algorithms degrades severely due to the loss of selection pressure exerted by the Pareto dominance relation which will cause the algorithm to act randomly. Various recent methods tried to provide more selection pressure but this would cause the population to converge to a specific region which is not desirable. Diversity reduction in high dimensional problems which decreases the capabilities of these approaches is a decisive factor in the overall performance of these algorithms. The novelty of this paper is to propose a new diversity measure and a diversity control mechanism which can be used in combination to remedy the mentioned problem. This measure is based on shortest Hamiltonian path for capturing an order of the population in any dimension. In order to control the diversity of population, we designed an adaptive framework which adjusts the selection operator according to diversity variation in the population using different diversity measures as well as our proposed one. This study incorporates the proposed framework in MOEA/D, an efficient widely used evolutionary algorithm. The obtained results validate the motivation on the basis of diversity and performance measures in comparison with the state-of-the-art algorithms and demonstrate the applicability of our algorithm/method in handling many-objective problems. Moreover, an extensive comparison with several diversity measure algorithms reveals the competitiveness of our proposed measure.","Title: A parameter-free method for detecting local communities based on attainable information.\nYear: 2016\n\nAbstract: Community detection approaches are useful tools for revealing the structure and properties of networks. There are many approaches for identifying communities, some require inaccessible information, others need initializing parameters to perform well. Many existing algorithms require centralized decision maker to reveal communities. This paper proposes a non-centralized parameter-free method that only uses attainable information. The proposed method is examined using real as well as synthetic social networks. Experimental results suggest that this approach can perform as well as centralized approaches that require global information for detecting communities.","Title: A heuristic local community detection method (HLCD).\nYear: 2017\n\nAbstract: The advances in social networks has led to the concentration of research on analyzing people's behaviors in these networks. Accordingly, detecting communities and the interactions between their members is one of the most important issues addressed by these studies. After the proposition of new community detection methods in recent years, due to the extensive volume of the information generated in social networks and the increasing growth in the size of these networks, researchers became more interested in local, rather than global, detection methods. This paper proposes a heuristic approach to detecting communities by investigating local information. Comparing this method with state-of-the-art approaches, it is observed that the proposed approach outperforms the compared methods in detecting communities and their members and provides more accurate results.","Title: Continuous states latency aware influence maximization in social networks.\nYear: 2017\n\nAbstract: Influence maximization is the problem of finding a small set of nodes that maximizes the aggregated influence in social networks. The problem of influence maximization in social networks has been explored in many previous researches. All the existing methods have mainly relied on the constraint of binary state for each node which is either inactive or active. However in reality, changing the state of a node from inactive to active happens gradually and under the continuous effects of all its inspiring neighbors. Accordingly, a node can be inactive, active, or in any state between these two, indicating the gradual process of inspiration. In this paper, we propose a novel influence propagation model which considers continuous states for each node instead of discrete ones. Furthermore, regarding the major role of time in pairwise propagation rates in social networks, we build on a time constrained framework to solve the influence maximization problem. In our work, we first extend the classic IC model to include continuous states and also time delays for transitions between states. Second, we find the most influential nodes in social networks considering continuity and time of the influence process simultaneously. And the last but not the least, it is applicable to well-known real social networks such as Epinions and Wikipedia.","Title: Discovering the Hidden Structure of a Social Network: A Semi Supervised Approach.\nYear: 2017\n\nAbstract: Contagions such as information, rumors, infectious diseases, actions, and influence diffuse as cascades in large networks. Each contagion appears in some node and spreads through the nodes over the underlying network. In most cases, the network structure is hidden and we can only observe the times at which nodes are infected by contagions. So, inferring network structures and analyzing information...","Title: Graph embedding as a new approach for unknown malware detection.\nYear: 2017\n\nAbstract: Malware is any type of computer program which is developed to harm computers, networks, and information. Noticeable growth of malware development has made computer and network security a significant and challenging area in recent years. There is an intensive competition between malwares and antiviruses. Malware authors make every effort to develop new harmful codes using various programming tricks and exploits which are unseen for detection techniques. On the other hand, antivirus developers upgrade their methods and algorithms to recognize unknown malware. Therefore, an accurate and rapid detection method is an irrefutable demand in computer security area. This paper proposes a new malware detection method based on the OpCodes within an executable file. Proposed method generates a graph of operational codes (OpCode) within an executable file and then embeds this graph into eigenspace using “Power Iteration” method. This will help us represent an executable file as a linear combination of eigenvectors proportionate to their eigenvalues, which is beneficial to train machine learning classifiers such as k-nearest neighbor (KNN) and support vector machine (SVM). The main advantages of our proposed method are high detection rate despite utilizing simple classifiers like KNN, acceptable computational complexity even in large scale datasets against rival methods, and low false positive rate.","Title: Evaluation of random forest classifier in security domain.\nYear: 2017\n\nAbstract: There is an intrinsic adversarial nature in the security domain such as spam filtering and malware detection systems that attempt to mislead the detection system. This adversarial nature makes security applications different from the classical machine learning problems; for instance, an adversary (attacker) might change the distribution of test data and violate the data stationarity, a common assumption in machine learning techniques. Since machine learning methods are not inherently adversary-aware, a classifier designer should investigate the robustness of a learning system under attack. In this respect, recent studies have modeled the identified attacks against machine learning-based detection systems. Based on this, a classifier designer can evaluate the performance of a learning system leveraging the modeled attacks. Prior research explored a gradient-based approach in order to devise an attack against a classifier with differentiable discriminant function like SVM. However, there are several powerful classifiers with non-differentiable decision boundary such as Random Forest, which are commonly used in different security domain and applications. In this paper, we present a novel approach to model an attack against classifiers with non-differentiable decision boundary. In the experimentation, we first present an example that visually shows the effect of a successful attack on the handwritten digits classification task. Then we conduct experiments for two well-known applications in the security domain: spam filtering and malware detection in PDF files. The experimental results demonstrate that the proposed attack successfully evades Random Forest classifier and effectively degrades the classifier’s performance.","Title: Modelling information diffusion based on non-dominated friends in social networks\nYear: 2017\n\nAbstract: AbstractIn recent years, social networks have played a strong role in diffusing information among people all around the globe. Therefore, the ability to analyse the diffusion pattern is essential. A diffusion model can identify the information dissemination pattern in a social network. One of the most important components of a diffusion model is information perception which determines the source each node receives its information from. Previous studies have assumed information perception to be just based on a single factor, that is, each individual receives information from their friend with the highest amount of information, whereas in reality, there exist other factors, such as trust, that affect the decision of people for selecting the friend who would supply information. These factors might be in conflict with each other, and modelling diffusion process with respect to a single factor can give rise to unacceptable results with respect to the other factors. In this article, we propose a novel information diffusion model based on non-dominated friends IDNDF. Non-dominated friends are a set of friends of a node for whom there is no friend better than them in the set based on all considered factors, considering different factors simultaneously significantly enhance the proposed information diffusion model. Moreover, our model gives a chance to all non-dominated friends to be selected. Also, IDNDF allows having partial knowledge by each node of the social network. Finally, IDNDF is applicable to different types of data, including well-known real social networks like Epinions, WikiPedia, Advogato and so on. Extensive experiments are performed to assess the performance of the proposed model. The results show the efficiency of the IDNDF in diffusion of information in varieties of social networks.","Title: Multi-Objective Perception Model For Diffusion Of Information In Social Networks\nYear: 2017\n\nAbstract: Social networks have recently become important tools for sharing and exchanging data. They facilitate the rapid propagation and dissemination of information. Therefore, it is essential to provide means for analysing the phenomenon of diffusion in such networks. One of the most important components of a diffusion model is information perception which determines who each node receives its information from. Previous researches assumed the information perception to be based on a single objective, whereas in reality, there exist other objectives that affects the people's decision for selecting the neighbour who would supply information, which might conflict with each other, and modelling diffusion process with respect to a single objective can give rise to unacceptable results with respect to the other objectives. This paper presents a novel multi-objective perception model for diffusion of information, which is abbreviated as MOPDI. Considering different objectives simultaneously significantly enhances the proposed information diffusion model.","Title: Change-aware community detection approach for dynamic social networks.\nYear: 2018\n\nAbstract: Community mining is one of the most popular issues in social network analysis. Although various changes may occur in a dynamic social network, they can be classified into two categories, gradual changes and abrupt changes. Many researchers have attempted to propose a method to discover communities in dynamic social networks with various changes more accurately. Most of them have assumed that changes in dynamic social networks occur gradually. This presumption for the dynamic social network in which abrupt changes may occur misleads the problem. Few methods have tried to detect abrupt changes, but they used the statistical approach which has such disadvantages as the need for a lot of snapshots. In this paper, we propose a novel method to detect the type of changes using the least information of social networks and then, apply it to a new community detection framework named change-aware model. The experimental results on different benchmark and real-life datasets confirmed that the new method and framework have improved the performance of community detection algorithms.","Title: Human Strategy (HS) Optimization Algorithm.\nYear: 2018\n\nAbstract: In this paper, a new group of optimization algorithms named Human Strategy Algorithm (HS) is proposed which is inspired by human strategies to problem solving. The main idea of HS is based on human actions to find the problem’s optima by means of accessible instruments. As the environment of an unknown problem assumed to be a black box, it is supposed that the environment of our problem is a dark room occupied by several men named blind men. The main mission of these men is to look for the optimum solution. Each man has at least one instrument as his assistance. Like real life, the instrument might be any tool such as stick, billy, rope, stone, yoyo, sweep. Any instrument by its unique features is suitable in some situations. In fact, this algorithm maps problem space and searches agents to dark room and people, respectively. In this paper, one sample algorithm of the group of human strategy, YOYO Blind Man Algorithm (YOYO-BMA), is introduced which uses yoyos as men’s accessible instruments. The performance of the YOYO-BMA is evaluated on a set of benchmark problems provided for CEC’2010 Special Session and Competition on Large-Scale Global Optimization (Tang et al. 2010). The results show superior performance of proposed algorithm in comparison with others. Moreover, the problem of designing urban traffic network is solve to evaluate the algorithm using a real complex problem.","Title: Fsim: A Fast And Scalable Influence Maximization Algorithm Based On Community Detection\nYear: 2018\n\nAbstract: Influence maximization algorithms try to select a set of individuals in social networks that are more influential. The Influence maximization problem is important in marketing and many researchers has researched on it and proposed new algorithms. All proposed algorithms are not scalable and are very time consuming for very large social networks generally. In this paper, a fast and scalable influence maximization algorithm called FSIM is proposed based on community detection. FSIM algorithm decreases number of nodes that must be examined without loss of the operations quality therefore it can find seeds quickly. FSIM can maximize influence in large social networks. Experimental results show FSIM is faster and more scalable than existing algorithms.","Title: Domain invariant feature extraction against evasion attack.\nYear: 2018\n\nAbstract: In the security application, an attacker might violate the data stationary assumption that is a common assumption in the most machine learning techniques. This problem named as the domain shift problem arises when training (source) and test (target) data follow different distributions. The inherent adversarial nature of the security applications considerably effects on the robustness of a learning system. For that, a classifier designer needs to evaluate the robustness of a learning system under potential attacks during the design phase. The previous studies investigate the effect of reduced feature vector on the security evaluation of a learning classifier. They demonstrate that traditional feature selection techniques lead to even worsen performance. Therefore, an adversary-aware feature selection algorithm is proposed to improve the robustness of the learning systems. However, prior studies in domain adaptation techniques which are fundamental in addressing domain shift problem demonstrate that original space may not be directly suitable for refining this distribution mismatch, because some features may have been distorted by the domain shift. In this paper, we propose domain invariant feature extraction model based on domain adaptation technique in order to address domain shift problem caused by an adversary. We conduct an experiment that graphically shows the effect of a successful attack on the MNIST handwritten digits classification task. After that, we design synthetic datasets to investigate the effect of reduced feature vector on the performance of a learning system under attack. Moreover, our proposed feature extraction model significantly outperforms the adversarial-aware feature selection and traditional feature selection models on the application of spam filtering","Title: Incremental community miner for dynamic networks\nYear: 2018\n\nAbstract: Human relationships have led to complex communication networks among different individuals in a society. As the nature of relationship is change, these networks will change over the time too which makes them dynamic networks including several consecutive snapshots. Nowadays, the pervasiveness of electronic communication networks, so called Social Networks, has facilitated obtaining this valuable communication information and highlighted as one of the most interesting researchers in the field of data mining, called social network mining. One of the most challenging issues in the field of social network mining is community detection. It means to detect hidden communities in a social network based on the available information. This study proposes an appropriate solution to find and track communities in a dynamic social network based on the local information. Our approach tries to detect communities by finding initial kernels and maintaining them in the next snapshots. Using well-known datasets, the investigation and comparison of the proposed method with some state-of-the-art approaches indicates that the performance and computation complexity of our method is promising and can outperform its competitors.","Title: Using Domain Adaptation In Adversarial Environment\nYear: 2017\n\nAbstract: There is an inherent adversarial nature in security applications like malware detection systems that intends to mislead the detection system. This adversarial nature makes security applications different from the classical machine-learning problem. For instance, an adversary (attacker) might violate the data stationary assumption that is a common assumption in machine learning techniques. This problem, named as the domain shift problem, arises when training and test data follow different distributions. Previous studies proposed an adversary-aware feature selection algorithm to improve robustness of the learning systems. However, prior studies in domain adaptation techniques which are fundamental in addressing domain shift problem demonstrate that original space may not be directly suitable for refining this distribution mismatch, because some features may have been distorted by the domain shift. In this paper, we propose an adversarial-aware feature extraction (AFE) model based on domain adaptation technique in order to address domain shift problem. The experimental results acknowledge that our model effectively improves the robustness of the learning system under attack.","Title: Visual malware detection using local malicious pattern\nYear: 2019\n\nAbstract: In recent years, malware authors have had significant developments in offering new generations of malware and have tried to use different methods to make malware detection hard, so detecting malware has become one of the most important challenges for the security of computer systems. These developments have made detection of malware using conventional methods rather difficult and in many cases impossible. Thus, inventing new methods for detecting malware is critical. In this paper, a new method is proposed to detect unknown malware based on micro-patterns within the executable files. In the proposed method, for extracting required micro-patterns, one of the well-known methods in machine vision field is used. The proposed method works as follows: first executable files are converted into digital images; second, these images are used to extract visual features of the executable files; finally, machine learning methods are used to detect malware. The main idea of the proposed method is based on differences in the behavior and functionality of malware and benign files, where different behavior results in different micro-patterns which can be used to distinguish between malware and benign files. Accordingly, in this paper a textural image classification method is used which aims to extract micro-patterns of digital textural images, to detect and extract micro-patterns of executable files and use them to detect malware.","Title: Music classification as a new approach for malware detection\nYear: 2019\n\nAbstract: Each year, a huge number of malicious programs are released which causes malware detection to become a critical task in computer security. Antiviruses use various methods for detecting malware, such as signature-based and heuristic-based techniques. Polymorphic and metamorphic malwares employ obfuscation techniques to bypass traditional detection methods used by antiviruses. Recently, the number of these malware has increased dramatically. Most of the previously proposed methods to detect malware are based on high-level features such as opcodes, function calls or program’s control flow graph (CFG). Due to new obfuscation techniques, extracting high-level features is tough, fallible and time-consuming; hence approaches using program’s bytes are quicker and more accurate. In this paper, a novel byte-level method for detecting malware by audio signal processing techniques is presented. In our proposed method, program’s bytes are converted to a meaningful audio signal, then Music Information Retrieval (MIR) techniques are employed to construct a machine learning music classification model from audio signals to detect new and unseen instances. Experiments evaluate the influence of different strategies converting bytes to audio signals and the effectiveness of the method.","Title: A new approach for malware detection based on evolutionary algorithm.\nYear: 2019\n\nAbstract: Malware is a malicious code which intends to harm computers and networks. Each year, a huge number of malicious programs are released. Therefore, detecting malware has become one of the most important challenges for the security of computer systems. Various methods have been defined for detecting and classifying malware, such as signature-based and heuristic-based techniques. This paper proposes a new malware detection method based on the operational codes (OpCodes) within an executable file by using the evolutionary algorithm. There are several steps in the proposed method, which includes disassembling the executable files, generating a graph of OpCodes and using the evolutionary algorithm to find the most similar graph to each suspicious instance. Finally, the label of each suspicious instance is detected based on the most similar graph obtained from the evolutionary algorithm with each class (family of malware and benign). The results show that, the proposed method can be used as a method for malware detection and malware category.","Title: A novel method for malware detection based on hardware events using deep neural networks\nYear: 2021\n\nAbstract: With the increasing availability of internet access, the number of malware is growing dramatically. So, defence against malware is an important issue in the security of computers and networks. Hence, malware detection systems are important. The most common method for malware detection is a signature-based technique. Nowadays, malware attempt to deceive all kinds of anti-malware, using obfuscation mechanisms and polymorphic behaviours, thus this method is not able to detect such malware and is not very effective. One way to deal with these types of malware is through dynamic malware analysis that requires executing the malware and monitoring its behaviour. In this paper, a novel dynamic malware detection method is presented which utilizes hardware events during file execution such as hardware port status, CPU internal status, main memory status, etc., as features of the classification model. Then, two Deep Neural Network (DNN) based algorithms, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), are used to construct the machine learning model. Finally, the voting network is used between the outputs of CNNs and the LSTM network, and the label of the suspicious sample is determined. The results show that the combination of hardware events as feature inputs, with LSTM and voting network can be effective in detecting new malware.","Title: A PE header-based method for malware detection using clustering and deep embedding techniques\nYear: 2021\n\nAbstract: Recent years have witnessed the dramatic growth of malware programs in a wide range of malicious intentions following the expansion of computer systems. Hence, highly effective systems to detect malware are extremely demanded. Most of the recent approaches use machine learning techniques along with the features extracted from files such as byte sequence, API-Calls, Op-Code sequence, and hardware events to detect malware. Utilizing the executable file header to extract features is a wide-spread way in this field since it contains efficient and prominent content to distinguish malware and benign programs. In this paper, a novel deep learning method is proposed to learn different embedding representations for malware and benign programs. To this end, the deep neural network uses a clustering algorithm in the training process. During the training process, samples are embedded through the neural network, and then the output of the neural network is fed into the k-means clustering algorithm, which is segmenting samples into two clusters of malware and benign. The network parameters are then updated based on the clustering result. By repeating this training process, the network representations and clustering assignments refine iteratively to the point that the network learns different representations for malware and benign programs. The proposed method utilizes raw bytes of the PE files header. Due to the lightweight network and utilizing the raw byte, which is fast to extract, the proposed method has a considerably low-computational overhead, and a set of experiments showed that this method is highly fast to use as a real-time malware detection method with high performance.","Title: FAIMCS: A fast and accurate influence maximization algorithm in social networks based on community structures\nYear: 2021\n\nAbstract: Finding a number of nodes that are able to maximize the spread of influence through the social network and are called influence maximization has numerous applications in marketing. One such application is to find influential members for promoting a product across a large network. Even though numerous algorithms have been proposed, challenges such as scalability, time constraints, and low accuracy have motivated the researchers for better solutions. Some of the newly proposed algorithms are scalable, but fail to provide adequate accuracy. On the other hand, some greedy algorithms provide a good level of accuracy but are very time consuming for large networks. In this paper, an algorithm is proposed called FAIMCS that can quickly find influential nodes across large networks with high accuracy. FAIMCS, reduces computational overhead considerably by eliminating major portions of the social network graph which have little influence. FAIMCS uses community detection algorithm to determine each community's quota of influential nodes based on the structure of that community. Finally, it obtains influential nodes from the candidate nodes. Experiment results show FAIMCS is faster than current algorithms and provides a high level of accuracy for large social networks.","Title: Using Negative Ideas as a Tool to Maximize Positive Influence in Trust Social Networks\nYear: 2022\n\nAbstract: Influence Maximization (IM) is the problem of finding k influential nodes in a network to increase the spread or influence of a specific matter. It has been an amazing topic through recent years. Later studies proposed algorithms and heuristics to either accelerate seed selection process or introduce new diffusion models. Two of important aspects in diffusion are trust and time. There have been several studies in either aspect, but to our best knowledge there is a great lack in considering both time and trust together in articles. Still one other important problem is the propagation of negative ideas. It is obvious that we cannot neglect this type of effects and must find a way to reduce it or \"USE\" it in a wise way. We chose to use this effect against their selves and maximize the positive effect. In addition, we considered time constraint to better sculpt the real world in our diffusion model. We used a greedy based framework to select seed nodes. By utilizing an approximation for marginal influence, the seed selection process is accelerated. Comparing with other related methods, results show the superiority of our proposed method while dealing with two important and complex matters.","Title: How to Boost the Performance of Recommender Systems by Social Trust? Studying the Challenges and Proposing a Solution\nYear: 2022\n\nAbstract: With the increasing number of items in electronic retailers, news websites, etc., finding interesting items concerning the taste of users is becoming more challenging. Recommender Systems (RS) are a well-known solution to this issue. Collaborative filtering (CF) is a widely accepted and popular technique to implement an RS. However, cold-start and data sparsity problems reduce the performance of CF methods. One promising solution for these issues is to use the social trust information. However, how to properly use social trust information is a hot and still open question. In this paper, we propose a similarity measure and a simple link prediction method to address this question and employ them in trust-aware matrix factorization. Especially, our proposed similarity measure is asymmetric to consider the nature of social relationships. Also, to have a more accurate similarity estimation, we have considered both the user's historical ratings and trust relations, and we have determined the weight of each source. Finally, we have used the item-based model and the level of interest a user's trustee has for an item to improve the performance of the proposed method for sparse datasets. We conduct extensive performance evaluations in terms of rate prediction and interesting items found. Experimental results on three real-world datasets demonstrate the effectiveness of the proposed method, especially in terms of Mean Absolute Error.","Title: Ransomware Detection Based on PE Header Using Convolutional Neural Networks\nYear: 2022\n\nAbstract: With the spread of information technology in human life, data protection is a critical task. On the other hand, malicious programs are developed, which can manipulate sensitive and critical data and restrict access to this data. Ransomware is an example of such a malicious program that encrypts data, restricts users' access to the system or their data, and then request a ransom payment. Many types of research have been proposed for ransomware detection. Most of these methods attempt to identify ransomware by relying on program behavior during execution. The main weakness of these methods is that it is not explicit how long the program should be monitored to show its real behavior. Therefore, sometimes, these researches cannot detect ransomware early. In this paper, a new method for ransomware detection is proposed that does not need executing the program and uses the PE header of the executable file. To extract effective features from the PE header file, an image is constructed based on PE header. Then, according to the advantages of convolutional neural networks (CNN) in extracting features from images and classifying them, CNN is used. The proposed method achieves high detection rates. Our results indicate the usefulness and practicality of our method for ransomware detection. (C) 2020 ISC. All rights reserved."],"string":"[\n \"Title: Using reinforcement learning to find an optimal set of features\\nYear: 2013\\n\\nAbstract: Identifying the most characterizing features of observed data is critical for minimizing the classification error. Feature selection is the process of identifying a small subset of highly predictive features out of a large set of candidate features. In the literature, many feature selection methods approach the task as a search problem, where each state in the search space is a possible feature subset. In this study, we consider feature selection problem as a reinforcement learning problem in general and use a well-known method, temporal difference, to traverse the state space and select the best subset of features. Specifically, first, we consider the state space as a Markov decision process, and then we introduce an optimal graph search to overcome the complexity of the problem of concern. Since this approach needs a state evaluation paradigm as an aid to traverse the promising regions in the state space, the presence of a low-cost evaluation function is necessary. This method initially explores the lattice of feature sets, and then exploits the obtained experiments. Finally, two methods, based on filters and wrappers, are proposed for the ultimate selection of features. Our empirical evaluation shows that this strategy performs well in comparison with other commonly used feature selection strategies, while maintaining compatibility with all datasets in hand.\",\n \"Title: An Effective Expert Team Formation in Social Networks Based on Skill Grading\\nYear: 2011\\n\\nAbstract: In this paper, a novel approach for forming an effective expert team in social network is proposed by using of skill grading and minimum communication cost. In this work, first a framework is recommended to determine skill level of experts based on their skill abilities and their collaboration among expert neighbors. Second, the existing diameter algorithm is extended to its more generalized version. Third, the communication cost is customized by presenting the cost metric containing both distance and skill grade of individuals. The experimental results show the advantages of this method which using DBLP co-authorship graph through an effective and efficient expert team in practice.\",\n \"Title: Modeling information diffusion and community membership using stochastic optimization\\nYear: 2013\\n\\nAbstract: Communities are vehicles for efficiently disseminating news, rumors, and opinions in human social networks. Modeling information diffusion through a network can enable us to reach a superior functional understanding of the effect of network structures such as communities on information propagation. The intrinsic assumption is that form follows function---rational actors exercise social choice mechanisms to join communities that best serve their information needs. Particle Swarm Optimization (PSO) was originally designed to simulate aggregate social behavior; our proposed diffusion model, PSODM (Particle Swarm Optimization Diffusion Model) models information flow in a network by creating particle swarms for local network neighborhoods that optimize a continuous version of Holland's hyperplane-defined objective functions. In this paper, we show how our approach differs from prior modeling work in the area and demonstrate that it outperforms existing model-based community detection methods on several social network datasets.\",\n \"Title: Detecting overlapping communities in social networks by game theory and structural equivalence concept\\nYear: 2011\\n\\nAbstract: Most complex networks demonstrate a significant property 'community structure', meaning that the network nodes are often joined together in tightly knit groups or communities, while there are only looser connections between them. Detecting these groups is of great importance and has immediate applications, especially in the popular online social networks like Facebook and Twitter. Many of these networks are divided into overlapping communities, i.e. communities with nodes belonging to more than one community simultaneously. Unfortunately most of the works cannot detect such communities. In this paper, we consider the formation of communities in social networks as an iterative game in a multiagent environment, in which, each node is regarded as an agent trying to be in the communities with members structurally equivalent to her. Remarkable results on the real world and benchmark graphs show efficiency of our approach in detecting overlapping communities compared to the other similar methods.\",\n \"Title: DBFS: An effective Density Based Feature Selection scheme for small sample size and high dimensional imbalanced data sets\\nYear: 2012\\n\\nAbstract: Nowadays, imbalanced data sets are pervasive in real world human practices, and hence, become a very interesting research area within machine learning communities. Imbalanced data sets introduce a significant reduction in performance of standard classifiers when they are invoked to learn data underlying concepts. The problem becomes even more sever when imbalanced data sets are involved with high dimensions. This paper presents a novel feature ranking approach based on the probability density estimation to cope with these issues. The idea behind our approach, named Density Based Feature Selection (DBFS), is that features' distributions over classes can bring significant benefits to feature selection algorithms. In other words, to explore the contribution of each attribute and assign it an appropriate rank, DBFS takes into account features' corresponding distributions over all classes along with their correlations. To show the effectiveness of the presented approach, well-known feature ranking methods are implemented and compared with our approach across varieties of small sample size and high dimensional data sets from microarray, mass spectrometry and text mining domains. Our theoretical analysis and experimental observations reveal that our approach is the method of choice by offering a simple yet effective feature ranking method based on well-known statistical evaluation measures.\",\n \"Title: Improving security of pixel value differencing steganographic method\\nYear: 2009\\n\\nAbstract: Pixel value differencing (PVD) is a steganographic method which embeds secret data in images based on spatial information. PVD has relatively high capacity but its effectiveness has been questioned by variety of steganalysis attacks. These attacks use the difference histogram quantization to reveal existence of secret data in a stego image. In this paper, we propose some modifications to PVD method to make it more robust to histogram quantization. Hence, at the expense of slight reduction in the capacity of the original algorithm, PVD's stability against histogram quantization attacks is significantly improved.\",\n \"Title: A levelwise spectral co-clustering algorithm for collaborative filtering\\nYear: 2012\\n\\nAbstract: Collaborative Filtering (CF) is a widely used approach in recommendation systems that predicts user preferences by learning from user-item ratings. To extract either user relationship or item dependencies, there exists several well known approaches; among them clustering is of great importance. Inspired by this understanding, this paper presents a novel levelwise paradigm whose first level clusters users and items separately using their pair wise similarity. Once the primary clusters of first level formed, the second level, does the simultaneously clustering user and item clusters by the means of Co-clustering. The main advantages of our approach include: First, it is able to alleviate the well known notion of sparsity problem intrinsically exists in CF. Second, the proposed method offers an elegant dimensionality reduction what in turn leads to better performance. Third experimental results on Movielens dataset have demonstrated that the proposed method can effectively reveal the subset aggregates of users and items which are closely related.\",\n \"Title: TeamFinder: A Co-clustering based Framework for Finding an Effective Team of Experts in Social Networks\\nYear: 2012\\n\\nAbstract: A lot of research in social networks has been devoted in the recommendation of individuals. Most of the work has focused on finding appropriate people one in question, regarding the input social graph and their attributes. However, for many applications one is interested in finding a team of experts for a given query. For a given social graph and a task including a set of required skills, we study the problem of finding a team of experts that their expertises match the given task and the relationships among them represent how well team members work together. Our proposed framework, named Team Finder, first provide a grouping method to aggregate the set of experts that are strongly correlated based on their skills as well as the best connection among them. By considering the groups, search space is significantly reduced and moreover it causes to prevent from the growth of redundant communication costs and team cardinality while assigning the team members. Second, the identified groups are reconstructed as a bipartite graph an a Co-clustering method is applied to find the qualified clusters containing related experts and skills and finally, an adapted version of team formation method is used to discover the effective experts for accomplishing the given task. Team Finder method is developed in a scalable and effective manner and making it ideal for large scale applications. We conduct extensive experiments on DBLP co-authorship graph and the experimental results demonstrate the effectiveness and scalability of proposed method in practice and give useful and intuitive results.\",\n \"Title: Anomaly detection in categorical datasets using bayesian networks\\nYear: 2011\\n\\nAbstract: In this paper we present a method for finding anomalous records in categorical or mixed datasets in an unsupervised fashion. Since the data in many problems consist of normal records with a small minority of anomalies, many approaches build a model from the training data and compare the test records against it. But instead of building a model, we keep track of the number of occurrences of different attribute value combinations. We also consider a more meaningful definition of anomalies and incorporate the Bayesian network structure in it. A scoring technique is defined for each test record. In this procedure we combine supports of different rules according to the Bayesian network structure in order to determine the label of the test instances. As it is shown in the results, our proposed method has a higher or similar f-measure and precision compared to a Bayesian network based approach in all cases.\",\n \"Title: A novel steganalysis for TPVD steganographic method based on differences of pixel difference histogram\\nYear: 2012\\n\\nAbstract: Tri-way Pixel Value Differencing (TPVD) steganographic method is a new modified version of another well-known method called PVD, which intents to increase embedding capacity and security of its successor by hiding secret bits in both vertical and diagonal edges of a cover image, in addition to the horizontal edges used in PVD. In this paper, it is shown that the histogram of difference values of a stego image under the TPVD algorithms is vulnerable to a particular statistical analysis. So, a new steganalytic measure named Growing Anomalies is introduced that its value has a linear relationship with secret message rate. It is shown empirically and theoretically that proposed steganalysis method based on this measure can estimate the amount of secret bits with a negligible error rate. The proposed steganalyser can classify test images as stego or cover with 97% accuracy when they contain more that 10% secret data. Implementation results indicate that proposed method can estimate secret message rate with an average accuracy of 95%.\",\n \"Title: Intelligent exploration method for XCS\\nYear: 2005\\n\\nAbstract: Exploration/Exploitation equilibrium is one of the most challenging issues in reinforcement learning area as well as learning classifier systems such as XCS. In this paper, an intelligent method is proposed to control exploration rate in XCS to improve its long-term performance. This method is called Intelligent Exploration Method (IEM) and is applied to some benchmark problems to show advantages of adaptive exploration rate for XCS. It\",\n \"Title: To handle real valued input in XCS: using fuzzy hyper-trapezoidal membership in classifier condition\\nYear: 2010\\n\\nAbstract: Learning classifier systems (LCSs) are evolutionary learning mechanisms that combine genetic algorithms (GAs) with the power of the reinforcement learning paradigm. XCS, eXtended Classifier System, is currently considered as state of the art learning classifier systems due to its effectiveness in data analysis and its success in applying to varieties of learning problems. Generalization and the size of evolved population in XCS is one of the most challenging issues in XCS. This paper suggests that rule representation in XCS is not matured to the point where the condition parts of classifiers are covering the problem space in an effective manner with minimum redundancy. The key idea in the proposed system, named XCS-HT, is to use a novel representation scheme based on presenting a subspace of problem space with two kinds of regions named certain and vague regions. Using such mechanism significantly boils the number of evolved XCS-HT's classifiers down, while changing the performance only marginally. The experimental results show that XCS-HT has a better ability to solve problems having indistinguishable class boundaries comparing to XCSR, a common extension of XCS standing for handling real valued data.\",\n \"Title: A Precise Statistical approach for concept change detection in unlabeled data streams\\nYear: 2011\\n\\nAbstract: Recently data stream has been extensively explored due to its emergence in a great deal of applications such as sensor networks, web click streams and network flows. One of the most important challenges in data streams is concept change where data underlying distributions change from time to time. A vast majority of researches in the context of data stream mining are devoted to labeled data, whereas, in real word human practice label of data are rarely available to the learning algorithms. Moreover, most of the methods that detect changes in unlabeled data stream merely deal with numerical data sets, and also, they are facing considerable difficulty when dimension of data tends to increase. In this paper, we present a Precise Statistical approach for Concept Change Detection in unlabeled data streams, which, abbreviated as PSCCD, detects changes using an exchangeable test. This hypothesis test is driven from a martingale which is based on Doob’s Maximal Inequality. The advantages of our approach are three fold. First, it does not require a sliding window on the data stream whose size is a well-known challenging issue; second, it works well in multi-dimensional data stream, and last but not the least, it is applicable to different types of data including categorical, numerical and mixed-attribute data streams. To explore the advantages of our approach, quite a lot of experiments with different settings and specifications are conducted. The obtained results are very promising.\",\n \"Title: An enriched game-theoretic framework for multi-objective clustering\\nYear: 2013\\n\\nAbstract: The framework of multi-objective clustering can serve as a competent technique in nowadays human issues ranging from decision making process to machine learning and pattern recognition problems. Multi-objective clustering basically aims at placing similar objects into the same groups based on some conflicting objectives, which substantially supports the use of game theory to come to a resolution. Based on these understandings, this paper suggests Enriched Game Theory K-means, called EGTKMeans, as a novel multi-objective clustering technique based on the notion of game theory. EGTKMeans is specially designed to optimize two intrinsically conflicting objectives, named, compaction and equi-partitioning. The key contributions of the proposed approach are three folds. First, it formulates an elegant and novel payoff definition which considers both objectives with equal priority. The presented payoff function incorporates a desirable fairness into the final clustering results. Second, EGTKMeans performs better off by utilizing the advantages of mixed strategies as well as those of pure ones, considering the existence of mixed Nash Equilibrium in every game. The last but not the least is that EGTKMeans approaches the optimal solution in a very promising manner by optimizing both objectives simultaneously. The experimental results suggest that the proposed approach significantly outperforms other rival methods across real world and synthetic data sets with reasonable time complexity.\",\n \"Title: Agent Based Decision Tree Learning: A Novel Approach\\nYear: 2009\\n\\nAbstract: Decision trees are one of the most effective and widely used induction methods that have received a great deal of attention over the past twenty years. When decision tree induction algorithms were used with uncertain rather than deterministic data, the result is a complete tree, which can classify most of the unseen samples correctly. This tree would be pruned in order to reduce its classification error and over-fitting. Recently, multi agent researchers concentrated on learning from large databases. In this paper we present a novel multi agent learning method that is able to induce a decision tree from distributed training sets. Our method is based on combination of separate decision trees each provided by one agent. Hence an agent is provided to aggregate results of the other agents and induces the final tree. Our empirical results suggest that the proposed method can provide significant benefits to distributed data classification.\",\n \"Title: Discovering overlapping communities in social networks: A novel game-theoretic approach\\nYear: 2013\\n\\nAbstract: Identifying communities in social networks has been receiving the increasing attentions recently. However, the overlapping concept has received little attentions in the literature, although it is observed in almost all social networks. In this study, we propose a framework based on the game theory and the structural equivalence concept to address the detection of overlapping communities in social networks. Specifically, we consider the underlying graph as a hypothetical social networking website and regard each vertex of this graph as an agent performing in this multiagent environment. Since each agent may belong to several communities simultaneously, we are able to find overlapping community structure of social networks. The rigorous proof of the existence of Nash equilibrium in this game is provided which shows that the method always reaches to the final solution. Experimental results on the benchmark and real world graphs show superiority of our approach over the other state-of-the-art methods.\",\n \"Title: Approximating Arbitrary Reinforcement Signal by Learning Classifier Systems using Micro Genetic Algorithm\\nYear: 2008\\n\\nAbstract: Learning Classifier Systems are Evolutionary Learning mechanisms which combine Genetic Algorithm and the Reinforcement Learning paradigm. Learning Classifier Systems try to evolve state-action-reward mappings to propose the best action for each environmental state to maximize the achieved reward. In the first versions of learning classifier systems, state-action pairs can only be mapped to a constant real-valued reward. So to model a fairly complex environment, LCSs had to develop redundant state-action pairs which had to be mapped to different reward values. But an extension to a well-known LCS, called Accuracy Based Learning Classifier System or XCS, was recently developed which was able to map state-action pairs to a linear reward function. This new extension, called XCSF, can develop a more compact population than the original XCS. But some further researches have shown that this new extension is not able to develop proper mappings when the input parameters are from certain intervals. As a solution to this issue, in our previous works, we proposed a novel solution inspired by the idea of using evolutionary approach to approximate the reward landscape. The first results seem promising, but our approach, called XCSFG, converged to the goal very slowly. In this paper, we propose a new extension to XCSFG which employs micro-GA which its needed population is extremely smaller than simple GA. So we expect micro-GA to help XCSFG to converge faster. Reported results show that this new extension can be assumed as an alternative approach in XCSF family with respect to its convergence speed, approximation accuracy and population compactness.\",\n \"Title: A Novel Strategy Selection Method for Multi-Objective Clustering Algorithms Using Game Theory\\nYear: 2012\\n\\nAbstract: The most important factors which contribute to the efficiency of game-theoretical algorithms are time and game complexity. In this study, we have offered an elegant method to deal with high complexity of game theoretic multi-objective clustering methods in large-sized data sets. Here, we have developed a method which selects a subset of strategies from strategies profile for each player. In this case, the size of payoff matrices reduces significantly which has a remarkable impact on time complexity. Therefore, practical problems with more data are tractable with less computational complexity. Although strategies set may grow with increasing the number of data points, the presented model of strategy selection reduces the strategy space, considerably, where clusters are subdivided into several sub-clusters in each local game. The remarkable results demonstrate the efficiency of the presented approach in reducing computational complexity of the problem of concern.\",\n \"Title: Reducing the Computational Cost in Multi-objective Evolutionary Algorithms by Filtering Worthless Individuals.\\nYear: 2014\\n\\nAbstract: The large number of exact fitness function evaluations makes evolutionary algorithms to have computational cost. In some real-world problems, reducing number of these evaluations is much more valuable even by increasing computational complexity and spending more time. To fulfill this target, we introduce an effective factor, in spite of applied factor in Adaptive Fuzzy Fitness Granulation with Non-dominated Sorting Genetic Algorithm-II, to filter out worthless individuals more precisely. Our proposed approach is compared with respect to Adaptive Fuzzy Fitness Granulation with Non-dominated Sorting Genetic Algorithm-II, using the Hyper volume and the Inverted Generational Distance performance measures. The proposed method is applied to 1 traditional and 1 state-of-the-art benchmarks with considering 3 different dimensions. From an average performance view, the results indicate that although decreasing the number of fitness evaluations leads to have performance reduction but it is not tangible compared to what we gain.\",\n \"Title: Community Detection in Social Networks Using Information Diffusion\\nYear: 2012\\n\\nAbstract: Discovering communities in popular social networks like Facebook has been receiving significant attentions recently. In this paper, inspired from real life, we have addressed the community detection problem by a framework based on Information Diffusion Model and Game Theory. In this approach, we consider each node of the social network as a selfish agent which has interactions with its neighbors and tries to maximize its total utility (i.e. received information). Finally community structure of the graph reveals after reaching to the local Nash equilibrium of the game. Experimental results on the benchmark social media datasets, synthetic and real world graphs demonstrate that our method is superior compared with the other state-of-the-art methods.\",\n \"Title: A pattern based evolutionary approach to prediction computation in XCSF\\nYear: 2006\\n\\nAbstract: XCSF is a new version of XCS with the ability of computing the environmental rewards. In the early versions of XCSF, this computation was done by approximating the coefficients of the associated polynomial reward functions. However, recent researches show that this approximation method suffers from some significant drawbacks such as input range dependency. In this paper, we propose a new method to approximate the reward functions using Genetic Algorithms. Our proposed method uses a new representation scheme for chromosomes and some newly introduced pattern based operators which are adapted for function approximation.\",\n \"Title: Using Noun Similarity to Adapt an Acceptability Measure for Persian Light Verb Constructions.\\nYear: 2012\\n\\nAbstract: Light verb constructions (LVCs), such as take a walk and make a decision, are a common subclass of multiword expressions (MWEs), whose distinct syntactic and semantic properties call for a special treatment within a computational system. In particular, LVCs are formed semi-productively: often a semantically-general verb (such as take) combines with a number of semantically-similar nouns to form semantically-related LVCs, as in make a decision/choice/commitment. Nonetheless, there are restrictions as to which verbs combine with which class of nouns. A proper computational account of LVCs is even more important for languages such as Persian, in which most verbs are of the form of LVCs. Recently, there has been some work on the automatic identification of MWEs (including LVCs) in resource-rich languages, such as English and Dutch. We adapt such existing techniques for the automatic identification of LVCs in Persian, an under-resourced language. Specifically, we extend an existing statistical measure of the acceptability of English LVCs (Fazly et al., 2007) to make explicit use of semantic classes of noun, and show that such classes are in particular useful for determining the LVC acceptability of new combinations.\",\n \"Title: CBDL: Context-based distance learning for categorical attributes\\nYear: 2011\\n\\nAbstract: Distance learning is an important notion and has played a critical role in success of various machine learning algorithms. Any learning algorithm that requires dissimilarity/similarity measures has to assume some forms of distance functions, either explicitly or implicitly. Hence, in recent years a considerable amount of research has been devoted to distance learning. Despite great achievements in this field, a number of important issues need to be further explored for real world datasets mainly containing categorical attributes. Based on these considerations, the current research presents a Context-Based Distance Learning approach (CBDL) to advance the state of the art existing researches on distance metric learning for categorical datasets. CBDL is designed and developed based on the idea that distance between two values of a given categorical attribute can be estimated by using information inherently exists within subset of attributes called context. CBDL composes of two main components: context extraction component and distance learning component. Context extraction component is responsible for extracting the relevant subset of feature set for a given attribute, while distance learning component tries to learn distance between each pair of values based on the extracted context. To have a comprehensive analysis, we conduct wide range of experiments in both supervised and unsupervised environments in the presence of noise. Our experimental results reveal that CBDL is the method of choice distance learning approach by offering a comparable or better performance compared to the state of the art existing distance learning schemes according to studied evaluation measures. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.\",\n \"Title: A Recursive Classifier System for Partially Observable Environments\\nYear: 2009\\n\\nAbstract: Petitcolas has proposed a steganographic technique called MP3Stego which can hide secret messages in a MP3 audio. This technique is well-known because of its high capacity. However, in rare cases, the normal audio encoding process will be terminated ...\",\n \"Title: A New Architecture for Learning Classifier Systems to Solve POMDP Problems\\nYear: 2008\\n\\nAbstract: Reinforcement Learning is a learning paradigm that helps the agent to learn to act optimally in an unknown environment through trial and error. An RL-based agent senses its environmental state, proposes an action, and applies it to the environment. Then a reinforcement signal, called the reward, is sent back from the environment to the agent. The agent is expected to learn how to maximize overall environmental reward through its internal mechanisms. One of the most challenging issues in the RL area arises as a result of the sensory ability of the agent, when it is not able to sense its current environmental state completely. These environments are called partially observable environments. In these environments, the agent may fail to distinguish the actual environmental state and so may fail to propose the optimal action in particular environmental states. So an extended mechanism must be added to the architecture of the agent to enable it to perform optimally in these environments. On the other hand, one of the most-used approaches to reinforcement learning is the evolutionary learning approach and one of the most-used techniques in this family is learning classifier systems. Learning classifier systems try to evolve state-action-reward mappings to model their current environment through trial and error. In this paper we propose a new architecture for learning classifier systems that is able to perform optimally in partially observable environments. This new architecture uses a novel method to detect aliased states in the environment and disambiguates them through multiple instances of classifier systems that interact with the environment in parallel. This model is applied to some well-known benchmark problems and is compared with some of the best classifier systems proposed for these environments. Our results and detailed discussion show that our approach is one of the best techniques among other learning classifier systems in partially observable environments.\",\n \"Title: A fuzzy system to control exploration rate in XCS\\nYear: 2005\\n\\nAbstract: Exploration/Exploitation dilemma is one of the most challenging issues in reinforcement learning area as well as learning classifier systems such as XCS. In this paper, an intelligent method is proposed to control the exploration rate in XCS to improve its long-term performance. This method is called Intelligent Exploration Method (IEM) and is applied to some benchmark problems to show advantages of adaptive exploration rate for XCS.\",\n \"Title: Link Prediction in Social Network Using Co-clustering Based Approach\\nYear: 2012\\n\\nAbstract: This paper introduces an approach to derive whether an individual is related to an item or not. In our approach, the well-known DBLP dataset is used and we try to find some skills that are related to an author that we were not aware of before. To realize our objective, we cluster authors and skills using Spectral Graph Clustering algorithm, then simultaneously obtain user and movie clusters via Bipartite Graph (Bigraph) Spectral Co-clustering approach, and then generate predictions based on the outputs of clustering and co-clustering steps. Accordingly, we utilize clustering and co-clustering advantages to predict the probability of link existing between an author and a skill. Experimental results on DBLP dataset show that our approach works well in the specified task.\",\n \"Title: SPCF: a stepwise partitioning for collaborative filtering to alleviate sparsity problems\\nYear: 2012\\n\\nAbstract: Collaborative filtering is a widely used approach in recommendation systems which predict user preferences by learning from user-item ratings. To extract either user relationship or item dependencies, there exist several well known approaches; among them clustering is of great importance. Traditional clustering methods in collaborative filtering usually suffer from two fundamental problems: sparsity and scalability. Sparsity refers to a situation where most users rate only a small number of items, while scalability denotes a huge number of both users and items. Inspired by these problems, this paper presents a novel stepwise paradigm, SPCF, which in the first step clusters users and items separately using their latent similarity. Once the primary clusters of the first level are formed, the second level simultaneously clusters the user and item clusters by means of co-clustering. The advantages of SPCF are threefold; first, it is able to alleviate the well known sparsity problem which intrinsically exists in collaborative filtering; second, the proposed method offers an elegant solution to the scalability problem based on dimensionality reduction which in turn leads to better performance of the model; third, experimental results on two versions of a Movielens dataset for prediction have demonstrated that the proposed method can reveal major interests of users or items in promising manner.\",\n \"Title: An evolutionary function approximation approach to compute prediction in XCSF\\nYear: 2005\\n\\nAbstract: XCSF is a new extension to XCS that is developed to extend XCS's reward calculation capability via computing. This new feature is called computable prediction. The first version of XCSF tries to find the most appropriate equation to compute each classifier's reward using a weight update mechanism. In this paper, we try to propose a new evolutionary mechanism to compute these equations using genetic algorithms.\",\n \"Title: Harmony-Based Feature Weighting to Improve the Nearest Neighbor Classification.\\nYear: 2012\\n\\nAbstract: This paper introduces the use of Harmony Search with novel fitness function in order to assign higher weights to informative features while noisy irrelevant features are given low weights. The fitness function is based on the Area Under the receiver operating characteristics Curve (AUC). The aim of this feature weighting is to improve the performance of the k-NN algorithm. Experimental results show that the proposed method can improve the classification performance of the k-NN algorithm in comparison with the other important method in realm of feature weighting such as Mutual Information, Genetic Algorithm, Tabu Search and chi-squared (chi 2). Furthermore, on synthetic data sets, this method is able to allocate very low weight to the noisy irrelevant features which may be considered as the eliminated features from the data set.\",\n \"Title: A New Architecture of XCS to Approximate Real-Valued Functions Based on High Order Polynomials Using Variable-Length GA\\nYear: 2007\\n\\nAbstract: XCSFG is a new version of XCSF with the ability of computing the environmental payoff using Genetic Algorithm. In the first version of XCSFG, this computation was done by evolving coefficients of the associated linear payoff functions. In this paper, we extend XCSFG to approximate the payoff functions in the form of higher order polynomials. Our newly proposed method uses GA with variable-length chromosome with real-valued representation. This new version of XCSFG is called XCSFG, the Continuous version or XCSFGC. It is applied to some benchmark problems and is compared with the original XCSF and its newly introduced extensions.\",\n \"Title: A Grid Based Cooperative Co-evolutionary Multi-Objective Algorithm\\nYear: 2009\\n\\nAbstract: In this paper, a well performing approach in the context of Multi-Objective Evolutionary Algorithm (MOEA) is investigated due to its complexity. This approach called NSCCGA is based on previously introduced approach called NSGA-II. NSCCGA performs better than NSGA-II but with a heavy load of computational complexity. Here, a novel approach called GBCCGA is introduced based on MOCCGA with some modifications. The main difference between GBCCGA and MOCCGA is in their niching technique which instead of the traditional sharing mechanism in MOCCGA, a novel grid-based technique is used in GBCCGA. The reported results show that GBCCGA performs roughly the same as NSCCGA but with very low computational complexity with respect to the original MOCCGA.\",\n \"Title: Towards efficient and effective negative selection algorithm: a convex hull representation scheme\\nYear: 2010\\n\\nAbstract: Negative Selection Algorithm (NSA) is one of several algorithms inspired by the principles of natural immune system. The algorithm received the researchers attention due to its applicability in various research areas and a number of valuable efforts are made to increase the effectiveness and efficiency of it. The heart of NSA is to somehow find rules called detectors to discriminate self and anomaly areas. Each detector in NSA defines a subspace of problem space where no self data is located. One of the major issues in NSA is detector's shape or representation of detectors which can affect the detection performance significantly. This paper for the first time proposes a new representation for detectors based on convex hull. Since convex hull is a general form of other geometric shapes, it retains the benefits of other shapes meanwhile it provides some new features like the asymmetric shape. Experimental results show a significant enhancement in the accuracy of negative selection algorithm compared to other common representation shapes.\",\n \"Title: Towards final rule set reduction in XCS: a fuzzy representation approach\\nYear: 2011\\n\\nAbstract: Generalization is the most challenging issue in XCS research area. One of the main components of XCS managing to remedy this issue is knowledge representation. In this paper, a knowledge representation based on fuzzy membership function offering certain and vague regions is described. We extend the Michigan learning classifier system using this approach to be improved in terms of both performance and interpretability. The contribution of this paper is three-folds: 1) updating main parameters of classifiers based on their certainty factor in matching of incoming data, 2) enhancing essential components of XCS to be compatible with such fuzzy representation schema and 3) proposing a novel rule set reduction method named Reduction based on Least Reward Prediction (RLRP) to improve the interpretability of the evolved model. Furthermore, an inference methodology which is compatible with RLRP is suggested to maintain the similar performance. The obtained results are promising due to the effectiveness of proposed method in dealing with real world problems. Furthermore, the proposed reduction method can upgrade the interpretability of final rule set by boiling its size down by 94% on average while slightly degrading the prediction accuracy.\",\n \"Title: Hc-Moea: A Hierarchical Clustering Approach For Increasing The Solution'S Diversity In Multiobjective Evolutionary Algorithms\\nYear: 2015\\n\\nAbstract: Recently, Indicator-based Evolutionary Algorithms are considered the main issue for researchers in the evolutionary multi-objective frameworks. Due to the capability of the Indicator-based approaches in obtaining a finest non-dominated solutions and the potential of these approaches on achieving the well-distributed solutions, these approaches become popular among modern Multi-Objective Evolutionary Algorithms (MOEAs). Most modern MOEAs are intended to converge to the Pareto optimal front through preserving the population diversity in the objective space. In this regard, the intention of this work is presenting a novel MOEA to enhance the population diversity among the non-dominated vectors in the solution space. The idea of this method is inspired by the Hierarchical clustering. In this attitude, an adept approach is planned to present a new indicator as a selection method during the optimization cycle. The gain of this technique is a desirable set with more diverse solutions in the solution space during the environmental selection operator. In the last part, this work also improved the rate of the convergence by introducing a parent selection mechanism. The selection method is simple and effective, which is worked base on the selection of proper members of parents' population instead of a random mechanism. This bright parent selection is adopted to accelerate the convergence of the proposed method. This work is applied to a wide range of well-established test problems. The obtained results validate the motivation on the basis of diversity and performance measures in comparison with the state of the art algorithms.\",\n \"Title: A Martingale Approach to Detect Peak of News in Social Network.\\nYear: 2014\\n\\nAbstract: Nowadays, social medias such as Twitter, Memetracker and Blogs have become powerful tools to propagate information. They facilitate quick dissemination sequence of information such as news article, blog posts, user's interests and thoughts through large scale. Providing strong means to analyzing social networks structure and how information diffuse through them is essential. Many recent studies emphasize on modeling information diffusion and their patterns to gain some useful knowledge. In this paper, we propose a statistical approach to online detect peak points of news when spread over social networks, to the best of our knowledge has never investigated before. The proposed model use martingale approach to predict peak points when news reached the peak of its popularity. Experimental results on real datasets show good performance of our approach to online detect these peak points.\",\n \"Title: Trust based latency aware influence maximization in social networks\\nYear: 2015\\n\\nAbstract: Influence maximization is the problem of finding a small set of nodes that maximizes the aggregated influence in social networks. The problem of influence maximization in social networks has been explored in many previous researches. They have mainly relied on similar temporal chances for every node to influence another; whereas in reality, time plays a major role in pairwise propagation rates in social networks. However, there is little research done on influence maximization considering temporal dynamics of the networks and existing approaches merely offers a mediocre performance due to ignoring trust aspects of the diffusion process. In this paper, we propose a Trust based Latency aware Influence Maximization model, abbreviated as TLIM, which selects the most influential nodes in social networks with considering time and trust simultaneously. To the best of our knowledge, we are the first to study trust in classic IC model and also the first to consider both important time and trust factors jointly in influence maximization problem. The main contributions of this paper are listed as follows: first, we extend the classic IC model to include time and trust simultaneously, which is more applicable in existing social networks. Second, we find the most influential nodes in social networks with considering time and trust together; and the last but not the least, it is applicable to well-known real social networks such as Epinions, Slashdot and Wikipedia. To explore the advantages of our approach, quite a lot of experiments with different specifications are conducted. The obtained results are very promising.\",\n \"Title: An enriched social behavioural information diffusion model in social networks\\nYear: 2015\\n\\nAbstract: Online social networks have recently become an innovative and effective method for spreading information among people around the world. Information diffusion, rumour spreading and diseases infection are all instances of stochastic processes that occur over the edges of social networks. Many prior works have carried out empirical studies and diffusion models to understand how information propagates in online social networks; however they suffer from problems. In this paper, we propose an information diffusion model inspired by information propagation among people. Our proposed Social Behavioural Information Diffusion Model, abbreviated as SBIDM, considers the effect of mainstream media like TV and radio, as well as interaction with the neighbours. The advantages of our approach are four-fold. First, it models information diffusion in social networks inspired by social life, which considers the effect of aggregate social behaviour to diffuse information; second, it allows partial knowledge to be held in each individual; third, it considers the effects of social media in propagating information as well as the effects of interacting with neighbours; and last but not least, it is applicable to different types of data including synthetic and well-known real social networks like Facebook, Amazon, Epinions and DBLP. To explore the advantages of our approach, many experiments with different settings and specifications were conducted. The obtained results are very promising.\",\n \"Title: An ensemble game theoretic approach for multi-objective optimization.\\nYear: 2015\\n\\nAbstract: Recently multi-objective clustering has been extensively explored due to its appearance of new applications in many domains. However, in many applications, there is more than a single objective which is needed to be optimized in the context of the application, such as facility location, ad hoc networks and sensor networks. These domains must optimize two objectives of compactness and equi-partitioning which may be conflicted in some situations. Existing algorithms have high complexity. In this paper, we propose an Ensemble Game Theoretic approach for multi-objective clustering method which optimizes two objectives of compactness and equi-partitioning, simultaneously. We compare our algorithm on variety of data sets including synthetic and real ones. The remarkable results are very promising and demonstrate the efficiency of presented approach both in performance and complexity.\",\n \"Title: A fuzzy based approach for fitness approximation in multi-objective evolutionary algorithms.\\nYear: 2015\\n\\nAbstract: Evolutionary Algorithm provides a framework that is largely applicable to particular problems including multiobjective optimization problems, basically for the ease of their implementation and their capability to perform efficient parallel search. Indeed, in some cases, expensive multiobjective optimization evaluations might be a challenge to restrict the number of explicit fitness evaluations in multiobjective evolutionary algorithms. Accordingly, this article presents a novel approach that tackles this problem so as to not only decrease the number of fitness evaluations but also to improve the performance. During evolution, our proposed approach selects fit individuals based on the knowledge acquired throughout the search, and performs explicit fitness evaluations on these individuals. A comprehensive comparative analysis of a wide range of well-established test problems, selected from both traditional and state-of-the-art benchmarks, has been presented. Afterward, the effectiveness of the obtained results is compared with some of the state-of-the-art methods using two well-known metrics-i.e. Hypervolume and Inverted Generational Distance (IGD). The experiments of our implemented approach is performed to illustrate that our proposal seems to be promising and would prove more efficient than other approaches in terms of both the performance and the computational cost.\",\n \"Title: A Coevolutionary Approach To Many Objective Optimization Based On A Novel Ranking Method\\nYear: 2016\\n\\nAbstract: Ranking solutions of the population in an evolutionary algorithm that solves a many objective optimization problem is a challenging task which has been vastly studied in recent years. Loss in the hypervolume of the population when a solution is omitted could be a good measure for ranking solutions but calculating this value for high dimensional problems is not tractable. In the selection operator of evolutionary algorithms, the actual hypervolume values are not important, with only a relative knowledge of these values we can select best solutions. In this paper we have proposed a novel method for approximating the ranking induced by the hypervolume indicator. This method is compared to the similar methods and its efficiency and performance is proved via proper experiments. The method is tested using benchmark test problems from the Walking Fish Group problem family which are scalable both in number of variables and objectives.\",\n \"Title: Scaling Up The Hybrid Particle Swarm Optimization Algorithm For Nominal Data-Sets\\nYear: 2015\\n\\nAbstract: Recently, the hybrid Particle Swarm Optimisation/Ant Colony Optimisation (PSO/ACO) has been proposed for discovery of classification rules. An improved version of this hybrid scheme, PSO/ACO(2) algorithm, can directly cope with nominal attributes without converting them into numerical ones. Although PSO/ACO(2) can handle nominal values, it suffers from high computational complexity for large datasets. Beside variety of classification methods which exist to provide more compact set of rules, this study propose an approach which reduces the computational complexity of PSO/ACO(2) in order to make it suitable for classification of large datasets. This work is developed the K-mode as a method of sampling from the datasets. In this regard a modification is employed to this algorithm in order to decrease the computational time as well as maintaining the accuracy of the algorithm. Further contribution of this paper is utilizing a new fitness measure for the algorithm. This measure has a robust theoretical background. The combination of the proposed modified K-mode method and the introduced fitness measure led to speed the obtained results up. The experimental result shows the efficiency of the proposed algorithm in comparison with its competitors.\",\n \"Title: Team Formation In Social Networks Based On Local Distance Metric\\nYear: 2015\\n\\nAbstract: The tremendous growth of social networking in last decades has resulted in an extensive research into this area. Social network users are enabled to easily communicate and collaborate with each other. Due to the significant role of network users, a new research topic in social networking analysis is team formation. The social network is modeled as a graph where nodes represent experts and edges show the initial collaborations between experts. The present research aims to tackle the problem to find an expert team in social network in order to complete a project that requires a set of skills. An algorithm is thus proposed to find the best suited team for the assigned project: a new method is devised as well to determine the distance between pairs of experts. Experimental results on DBLP dataset present the efficiency of our proposed method.\",\n \"Title: A diversity control mechanism in many objective optimizations.\\nYear: 2016\\n\\nAbstract: Multi-objective evolutionary optimization algorithms are among the best optimizers for solving problems in control systems, engineering and industrial planning. The performance of these algorithms degrades severely due to the loss of selection pressure exerted by the Pareto dominance relation which will cause the algorithm to act randomly. Various recent methods tried to provide more selection pressure but this would cause the population to converge to a specific region which is not desirable. Diversity reduction in high dimensional problems which decreases the capabilities of these approaches is a decisive factor in the overall performance of these algorithms. The novelty of this paper is to propose a new diversity measure and a diversity control mechanism which can be used in combination to remedy the mentioned problem. This measure is based on shortest Hamiltonian path for capturing an order of the population in any dimension. In order to control the diversity of population, we designed an adaptive framework which adjusts the selection operator according to diversity variation in the population using different diversity measures as well as our proposed one. This study incorporates the proposed framework in MOEA/D, an efficient widely used evolutionary algorithm. The obtained results validate the motivation on the basis of diversity and performance measures in comparison with the state-of-the-art algorithms and demonstrate the applicability of our algorithm/method in handling many-objective problems. Moreover, an extensive comparison with several diversity measure algorithms reveals the competitiveness of our proposed measure.\",\n \"Title: A parameter-free method for detecting local communities based on attainable information.\\nYear: 2016\\n\\nAbstract: Community detection approaches are useful tools for revealing the structure and properties of networks. There are many approaches for identifying communities, some require inaccessible information, others need initializing parameters to perform well. Many existing algorithms require centralized decision maker to reveal communities. This paper proposes a non-centralized parameter-free method that only uses attainable information. The proposed method is examined using real as well as synthetic social networks. Experimental results suggest that this approach can perform as well as centralized approaches that require global information for detecting communities.\",\n \"Title: A heuristic local community detection method (HLCD).\\nYear: 2017\\n\\nAbstract: The advances in social networks has led to the concentration of research on analyzing people's behaviors in these networks. Accordingly, detecting communities and the interactions between their members is one of the most important issues addressed by these studies. After the proposition of new community detection methods in recent years, due to the extensive volume of the information generated in social networks and the increasing growth in the size of these networks, researchers became more interested in local, rather than global, detection methods. This paper proposes a heuristic approach to detecting communities by investigating local information. Comparing this method with state-of-the-art approaches, it is observed that the proposed approach outperforms the compared methods in detecting communities and their members and provides more accurate results.\",\n \"Title: Continuous states latency aware influence maximization in social networks.\\nYear: 2017\\n\\nAbstract: Influence maximization is the problem of finding a small set of nodes that maximizes the aggregated influence in social networks. The problem of influence maximization in social networks has been explored in many previous researches. All the existing methods have mainly relied on the constraint of binary state for each node which is either inactive or active. However in reality, changing the state of a node from inactive to active happens gradually and under the continuous effects of all its inspiring neighbors. Accordingly, a node can be inactive, active, or in any state between these two, indicating the gradual process of inspiration. In this paper, we propose a novel influence propagation model which considers continuous states for each node instead of discrete ones. Furthermore, regarding the major role of time in pairwise propagation rates in social networks, we build on a time constrained framework to solve the influence maximization problem. In our work, we first extend the classic IC model to include continuous states and also time delays for transitions between states. Second, we find the most influential nodes in social networks considering continuity and time of the influence process simultaneously. And the last but not the least, it is applicable to well-known real social networks such as Epinions and Wikipedia.\",\n \"Title: Discovering the Hidden Structure of a Social Network: A Semi Supervised Approach.\\nYear: 2017\\n\\nAbstract: Contagions such as information, rumors, infectious diseases, actions, and influence diffuse as cascades in large networks. Each contagion appears in some node and spreads through the nodes over the underlying network. In most cases, the network structure is hidden and we can only observe the times at which nodes are infected by contagions. So, inferring network structures and analyzing information...\",\n \"Title: Graph embedding as a new approach for unknown malware detection.\\nYear: 2017\\n\\nAbstract: Malware is any type of computer program which is developed to harm computers, networks, and information. Noticeable growth of malware development has made computer and network security a significant and challenging area in recent years. There is an intensive competition between malwares and antiviruses. Malware authors make every effort to develop new harmful codes using various programming tricks and exploits which are unseen for detection techniques. On the other hand, antivirus developers upgrade their methods and algorithms to recognize unknown malware. Therefore, an accurate and rapid detection method is an irrefutable demand in computer security area. This paper proposes a new malware detection method based on the OpCodes within an executable file. Proposed method generates a graph of operational codes (OpCode) within an executable file and then embeds this graph into eigenspace using “Power Iteration” method. This will help us represent an executable file as a linear combination of eigenvectors proportionate to their eigenvalues, which is beneficial to train machine learning classifiers such as k-nearest neighbor (KNN) and support vector machine (SVM). The main advantages of our proposed method are high detection rate despite utilizing simple classifiers like KNN, acceptable computational complexity even in large scale datasets against rival methods, and low false positive rate.\",\n \"Title: Evaluation of random forest classifier in security domain.\\nYear: 2017\\n\\nAbstract: There is an intrinsic adversarial nature in the security domain such as spam filtering and malware detection systems that attempt to mislead the detection system. This adversarial nature makes security applications different from the classical machine learning problems; for instance, an adversary (attacker) might change the distribution of test data and violate the data stationarity, a common assumption in machine learning techniques. Since machine learning methods are not inherently adversary-aware, a classifier designer should investigate the robustness of a learning system under attack. In this respect, recent studies have modeled the identified attacks against machine learning-based detection systems. Based on this, a classifier designer can evaluate the performance of a learning system leveraging the modeled attacks. Prior research explored a gradient-based approach in order to devise an attack against a classifier with differentiable discriminant function like SVM. However, there are several powerful classifiers with non-differentiable decision boundary such as Random Forest, which are commonly used in different security domain and applications. In this paper, we present a novel approach to model an attack against classifiers with non-differentiable decision boundary. In the experimentation, we first present an example that visually shows the effect of a successful attack on the handwritten digits classification task. Then we conduct experiments for two well-known applications in the security domain: spam filtering and malware detection in PDF files. The experimental results demonstrate that the proposed attack successfully evades Random Forest classifier and effectively degrades the classifier’s performance.\",\n \"Title: Modelling information diffusion based on non-dominated friends in social networks\\nYear: 2017\\n\\nAbstract: AbstractIn recent years, social networks have played a strong role in diffusing information among people all around the globe. Therefore, the ability to analyse the diffusion pattern is essential. A diffusion model can identify the information dissemination pattern in a social network. One of the most important components of a diffusion model is information perception which determines the source each node receives its information from. Previous studies have assumed information perception to be just based on a single factor, that is, each individual receives information from their friend with the highest amount of information, whereas in reality, there exist other factors, such as trust, that affect the decision of people for selecting the friend who would supply information. These factors might be in conflict with each other, and modelling diffusion process with respect to a single factor can give rise to unacceptable results with respect to the other factors. In this article, we propose a novel information diffusion model based on non-dominated friends IDNDF. Non-dominated friends are a set of friends of a node for whom there is no friend better than them in the set based on all considered factors, considering different factors simultaneously significantly enhance the proposed information diffusion model. Moreover, our model gives a chance to all non-dominated friends to be selected. Also, IDNDF allows having partial knowledge by each node of the social network. Finally, IDNDF is applicable to different types of data, including well-known real social networks like Epinions, WikiPedia, Advogato and so on. Extensive experiments are performed to assess the performance of the proposed model. The results show the efficiency of the IDNDF in diffusion of information in varieties of social networks.\",\n \"Title: Multi-Objective Perception Model For Diffusion Of Information In Social Networks\\nYear: 2017\\n\\nAbstract: Social networks have recently become important tools for sharing and exchanging data. They facilitate the rapid propagation and dissemination of information. Therefore, it is essential to provide means for analysing the phenomenon of diffusion in such networks. One of the most important components of a diffusion model is information perception which determines who each node receives its information from. Previous researches assumed the information perception to be based on a single objective, whereas in reality, there exist other objectives that affects the people's decision for selecting the neighbour who would supply information, which might conflict with each other, and modelling diffusion process with respect to a single objective can give rise to unacceptable results with respect to the other objectives. This paper presents a novel multi-objective perception model for diffusion of information, which is abbreviated as MOPDI. Considering different objectives simultaneously significantly enhances the proposed information diffusion model.\",\n \"Title: Change-aware community detection approach for dynamic social networks.\\nYear: 2018\\n\\nAbstract: Community mining is one of the most popular issues in social network analysis. Although various changes may occur in a dynamic social network, they can be classified into two categories, gradual changes and abrupt changes. Many researchers have attempted to propose a method to discover communities in dynamic social networks with various changes more accurately. Most of them have assumed that changes in dynamic social networks occur gradually. This presumption for the dynamic social network in which abrupt changes may occur misleads the problem. Few methods have tried to detect abrupt changes, but they used the statistical approach which has such disadvantages as the need for a lot of snapshots. In this paper, we propose a novel method to detect the type of changes using the least information of social networks and then, apply it to a new community detection framework named change-aware model. The experimental results on different benchmark and real-life datasets confirmed that the new method and framework have improved the performance of community detection algorithms.\",\n \"Title: Human Strategy (HS) Optimization Algorithm.\\nYear: 2018\\n\\nAbstract: In this paper, a new group of optimization algorithms named Human Strategy Algorithm (HS) is proposed which is inspired by human strategies to problem solving. The main idea of HS is based on human actions to find the problem’s optima by means of accessible instruments. As the environment of an unknown problem assumed to be a black box, it is supposed that the environment of our problem is a dark room occupied by several men named blind men. The main mission of these men is to look for the optimum solution. Each man has at least one instrument as his assistance. Like real life, the instrument might be any tool such as stick, billy, rope, stone, yoyo, sweep. Any instrument by its unique features is suitable in some situations. In fact, this algorithm maps problem space and searches agents to dark room and people, respectively. In this paper, one sample algorithm of the group of human strategy, YOYO Blind Man Algorithm (YOYO-BMA), is introduced which uses yoyos as men’s accessible instruments. The performance of the YOYO-BMA is evaluated on a set of benchmark problems provided for CEC’2010 Special Session and Competition on Large-Scale Global Optimization (Tang et al. 2010). The results show superior performance of proposed algorithm in comparison with others. Moreover, the problem of designing urban traffic network is solve to evaluate the algorithm using a real complex problem.\",\n \"Title: Fsim: A Fast And Scalable Influence Maximization Algorithm Based On Community Detection\\nYear: 2018\\n\\nAbstract: Influence maximization algorithms try to select a set of individuals in social networks that are more influential. The Influence maximization problem is important in marketing and many researchers has researched on it and proposed new algorithms. All proposed algorithms are not scalable and are very time consuming for very large social networks generally. In this paper, a fast and scalable influence maximization algorithm called FSIM is proposed based on community detection. FSIM algorithm decreases number of nodes that must be examined without loss of the operations quality therefore it can find seeds quickly. FSIM can maximize influence in large social networks. Experimental results show FSIM is faster and more scalable than existing algorithms.\",\n \"Title: Domain invariant feature extraction against evasion attack.\\nYear: 2018\\n\\nAbstract: In the security application, an attacker might violate the data stationary assumption that is a common assumption in the most machine learning techniques. This problem named as the domain shift problem arises when training (source) and test (target) data follow different distributions. The inherent adversarial nature of the security applications considerably effects on the robustness of a learning system. For that, a classifier designer needs to evaluate the robustness of a learning system under potential attacks during the design phase. The previous studies investigate the effect of reduced feature vector on the security evaluation of a learning classifier. They demonstrate that traditional feature selection techniques lead to even worsen performance. Therefore, an adversary-aware feature selection algorithm is proposed to improve the robustness of the learning systems. However, prior studies in domain adaptation techniques which are fundamental in addressing domain shift problem demonstrate that original space may not be directly suitable for refining this distribution mismatch, because some features may have been distorted by the domain shift. In this paper, we propose domain invariant feature extraction model based on domain adaptation technique in order to address domain shift problem caused by an adversary. We conduct an experiment that graphically shows the effect of a successful attack on the MNIST handwritten digits classification task. After that, we design synthetic datasets to investigate the effect of reduced feature vector on the performance of a learning system under attack. Moreover, our proposed feature extraction model significantly outperforms the adversarial-aware feature selection and traditional feature selection models on the application of spam filtering\",\n \"Title: Incremental community miner for dynamic networks\\nYear: 2018\\n\\nAbstract: Human relationships have led to complex communication networks among different individuals in a society. As the nature of relationship is change, these networks will change over the time too which makes them dynamic networks including several consecutive snapshots. Nowadays, the pervasiveness of electronic communication networks, so called Social Networks, has facilitated obtaining this valuable communication information and highlighted as one of the most interesting researchers in the field of data mining, called social network mining. One of the most challenging issues in the field of social network mining is community detection. It means to detect hidden communities in a social network based on the available information. This study proposes an appropriate solution to find and track communities in a dynamic social network based on the local information. Our approach tries to detect communities by finding initial kernels and maintaining them in the next snapshots. Using well-known datasets, the investigation and comparison of the proposed method with some state-of-the-art approaches indicates that the performance and computation complexity of our method is promising and can outperform its competitors.\",\n \"Title: Using Domain Adaptation In Adversarial Environment\\nYear: 2017\\n\\nAbstract: There is an inherent adversarial nature in security applications like malware detection systems that intends to mislead the detection system. This adversarial nature makes security applications different from the classical machine-learning problem. For instance, an adversary (attacker) might violate the data stationary assumption that is a common assumption in machine learning techniques. This problem, named as the domain shift problem, arises when training and test data follow different distributions. Previous studies proposed an adversary-aware feature selection algorithm to improve robustness of the learning systems. However, prior studies in domain adaptation techniques which are fundamental in addressing domain shift problem demonstrate that original space may not be directly suitable for refining this distribution mismatch, because some features may have been distorted by the domain shift. In this paper, we propose an adversarial-aware feature extraction (AFE) model based on domain adaptation technique in order to address domain shift problem. The experimental results acknowledge that our model effectively improves the robustness of the learning system under attack.\",\n \"Title: Visual malware detection using local malicious pattern\\nYear: 2019\\n\\nAbstract: In recent years, malware authors have had significant developments in offering new generations of malware and have tried to use different methods to make malware detection hard, so detecting malware has become one of the most important challenges for the security of computer systems. These developments have made detection of malware using conventional methods rather difficult and in many cases impossible. Thus, inventing new methods for detecting malware is critical. In this paper, a new method is proposed to detect unknown malware based on micro-patterns within the executable files. In the proposed method, for extracting required micro-patterns, one of the well-known methods in machine vision field is used. The proposed method works as follows: first executable files are converted into digital images; second, these images are used to extract visual features of the executable files; finally, machine learning methods are used to detect malware. The main idea of the proposed method is based on differences in the behavior and functionality of malware and benign files, where different behavior results in different micro-patterns which can be used to distinguish between malware and benign files. Accordingly, in this paper a textural image classification method is used which aims to extract micro-patterns of digital textural images, to detect and extract micro-patterns of executable files and use them to detect malware.\",\n \"Title: Music classification as a new approach for malware detection\\nYear: 2019\\n\\nAbstract: Each year, a huge number of malicious programs are released which causes malware detection to become a critical task in computer security. Antiviruses use various methods for detecting malware, such as signature-based and heuristic-based techniques. Polymorphic and metamorphic malwares employ obfuscation techniques to bypass traditional detection methods used by antiviruses. Recently, the number of these malware has increased dramatically. Most of the previously proposed methods to detect malware are based on high-level features such as opcodes, function calls or program’s control flow graph (CFG). Due to new obfuscation techniques, extracting high-level features is tough, fallible and time-consuming; hence approaches using program’s bytes are quicker and more accurate. In this paper, a novel byte-level method for detecting malware by audio signal processing techniques is presented. In our proposed method, program’s bytes are converted to a meaningful audio signal, then Music Information Retrieval (MIR) techniques are employed to construct a machine learning music classification model from audio signals to detect new and unseen instances. Experiments evaluate the influence of different strategies converting bytes to audio signals and the effectiveness of the method.\",\n \"Title: A new approach for malware detection based on evolutionary algorithm.\\nYear: 2019\\n\\nAbstract: Malware is a malicious code which intends to harm computers and networks. Each year, a huge number of malicious programs are released. Therefore, detecting malware has become one of the most important challenges for the security of computer systems. Various methods have been defined for detecting and classifying malware, such as signature-based and heuristic-based techniques. This paper proposes a new malware detection method based on the operational codes (OpCodes) within an executable file by using the evolutionary algorithm. There are several steps in the proposed method, which includes disassembling the executable files, generating a graph of OpCodes and using the evolutionary algorithm to find the most similar graph to each suspicious instance. Finally, the label of each suspicious instance is detected based on the most similar graph obtained from the evolutionary algorithm with each class (family of malware and benign). The results show that, the proposed method can be used as a method for malware detection and malware category.\",\n \"Title: A novel method for malware detection based on hardware events using deep neural networks\\nYear: 2021\\n\\nAbstract: With the increasing availability of internet access, the number of malware is growing dramatically. So, defence against malware is an important issue in the security of computers and networks. Hence, malware detection systems are important. The most common method for malware detection is a signature-based technique. Nowadays, malware attempt to deceive all kinds of anti-malware, using obfuscation mechanisms and polymorphic behaviours, thus this method is not able to detect such malware and is not very effective. One way to deal with these types of malware is through dynamic malware analysis that requires executing the malware and monitoring its behaviour. In this paper, a novel dynamic malware detection method is presented which utilizes hardware events during file execution such as hardware port status, CPU internal status, main memory status, etc., as features of the classification model. Then, two Deep Neural Network (DNN) based algorithms, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), are used to construct the machine learning model. Finally, the voting network is used between the outputs of CNNs and the LSTM network, and the label of the suspicious sample is determined. The results show that the combination of hardware events as feature inputs, with LSTM and voting network can be effective in detecting new malware.\",\n \"Title: A PE header-based method for malware detection using clustering and deep embedding techniques\\nYear: 2021\\n\\nAbstract: Recent years have witnessed the dramatic growth of malware programs in a wide range of malicious intentions following the expansion of computer systems. Hence, highly effective systems to detect malware are extremely demanded. Most of the recent approaches use machine learning techniques along with the features extracted from files such as byte sequence, API-Calls, Op-Code sequence, and hardware events to detect malware. Utilizing the executable file header to extract features is a wide-spread way in this field since it contains efficient and prominent content to distinguish malware and benign programs. In this paper, a novel deep learning method is proposed to learn different embedding representations for malware and benign programs. To this end, the deep neural network uses a clustering algorithm in the training process. During the training process, samples are embedded through the neural network, and then the output of the neural network is fed into the k-means clustering algorithm, which is segmenting samples into two clusters of malware and benign. The network parameters are then updated based on the clustering result. By repeating this training process, the network representations and clustering assignments refine iteratively to the point that the network learns different representations for malware and benign programs. The proposed method utilizes raw bytes of the PE files header. Due to the lightweight network and utilizing the raw byte, which is fast to extract, the proposed method has a considerably low-computational overhead, and a set of experiments showed that this method is highly fast to use as a real-time malware detection method with high performance.\",\n \"Title: FAIMCS: A fast and accurate influence maximization algorithm in social networks based on community structures\\nYear: 2021\\n\\nAbstract: Finding a number of nodes that are able to maximize the spread of influence through the social network and are called influence maximization has numerous applications in marketing. One such application is to find influential members for promoting a product across a large network. Even though numerous algorithms have been proposed, challenges such as scalability, time constraints, and low accuracy have motivated the researchers for better solutions. Some of the newly proposed algorithms are scalable, but fail to provide adequate accuracy. On the other hand, some greedy algorithms provide a good level of accuracy but are very time consuming for large networks. In this paper, an algorithm is proposed called FAIMCS that can quickly find influential nodes across large networks with high accuracy. FAIMCS, reduces computational overhead considerably by eliminating major portions of the social network graph which have little influence. FAIMCS uses community detection algorithm to determine each community's quota of influential nodes based on the structure of that community. Finally, it obtains influential nodes from the candidate nodes. Experiment results show FAIMCS is faster than current algorithms and provides a high level of accuracy for large social networks.\",\n \"Title: Using Negative Ideas as a Tool to Maximize Positive Influence in Trust Social Networks\\nYear: 2022\\n\\nAbstract: Influence Maximization (IM) is the problem of finding k influential nodes in a network to increase the spread or influence of a specific matter. It has been an amazing topic through recent years. Later studies proposed algorithms and heuristics to either accelerate seed selection process or introduce new diffusion models. Two of important aspects in diffusion are trust and time. There have been several studies in either aspect, but to our best knowledge there is a great lack in considering both time and trust together in articles. Still one other important problem is the propagation of negative ideas. It is obvious that we cannot neglect this type of effects and must find a way to reduce it or \\\"USE\\\" it in a wise way. We chose to use this effect against their selves and maximize the positive effect. In addition, we considered time constraint to better sculpt the real world in our diffusion model. We used a greedy based framework to select seed nodes. By utilizing an approximation for marginal influence, the seed selection process is accelerated. Comparing with other related methods, results show the superiority of our proposed method while dealing with two important and complex matters.\",\n \"Title: How to Boost the Performance of Recommender Systems by Social Trust? Studying the Challenges and Proposing a Solution\\nYear: 2022\\n\\nAbstract: With the increasing number of items in electronic retailers, news websites, etc., finding interesting items concerning the taste of users is becoming more challenging. Recommender Systems (RS) are a well-known solution to this issue. Collaborative filtering (CF) is a widely accepted and popular technique to implement an RS. However, cold-start and data sparsity problems reduce the performance of CF methods. One promising solution for these issues is to use the social trust information. However, how to properly use social trust information is a hot and still open question. In this paper, we propose a similarity measure and a simple link prediction method to address this question and employ them in trust-aware matrix factorization. Especially, our proposed similarity measure is asymmetric to consider the nature of social relationships. Also, to have a more accurate similarity estimation, we have considered both the user's historical ratings and trust relations, and we have determined the weight of each source. Finally, we have used the item-based model and the level of interest a user's trustee has for an item to improve the performance of the proposed method for sparse datasets. We conduct extensive performance evaluations in terms of rate prediction and interesting items found. Experimental results on three real-world datasets demonstrate the effectiveness of the proposed method, especially in terms of Mean Absolute Error.\",\n \"Title: Ransomware Detection Based on PE Header Using Convolutional Neural Networks\\nYear: 2022\\n\\nAbstract: With the spread of information technology in human life, data protection is a critical task. On the other hand, malicious programs are developed, which can manipulate sensitive and critical data and restrict access to this data. Ransomware is an example of such a malicious program that encrypts data, restricts users' access to the system or their data, and then request a ransom payment. Many types of research have been proposed for ransomware detection. Most of these methods attempt to identify ransomware by relying on program behavior during execution. The main weakness of these methods is that it is not explicit how long the program should be monitored to show its real behavior. Therefore, sometimes, these researches cannot detect ransomware early. In this paper, a new method for ransomware detection is proposed that does not need executing the program and uses the PE header of the executable file. To extract effective features from the PE header file, an image is constructed based on PE header. Then, according to the advantages of convolutional neural networks (CNN) in extracting features from images and classifying them, CNN is used. The proposed method achieves high detection rates. Our results indicate the usefulness and practicality of our method for ransomware detection. (C) 2020 ISC. All rights reserved.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":67,"string":"67"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13684,"string":"13,684"}}},{"rowIdx":13685,"cells":{"question_id":{"kind":"string","value":"J rgen Giesl"},"question":{"kind":"string","value":"Generate an abstract for the title \"Termination of Constructor Systems\" using the following items: 1. Termination \n2. Constructor systems \n3. Dependency pairs \n4. Automation \n5. Well-founded orderings"},"answer":{"kind":"string","value":"We present a method to prove termination of constructor systems automatically.Our approach takes advantage of the special form of these rewritesystems because for constructor systems instead of left- and right-hand sidesof rules it is sufficient to compare so-called dependency pairs [Art96]. Unfortunately,standard techniques for the generation of well-founded orderingscannot be directly used for the automation of the dependency pair approach.To solve this problem we have developed a..."},"correct_answer":{"kind":"string","value":"We present a method to prove termination of constructor systems automatically.Our approach takes advantage of the special form of these rewritesystems because for constructor systems instead of left- and right-hand sidesof rules it is sufficient to compare so-called dependency pairs [Art96]. Unfortunately,standard techniques for the generation of well-founded orderingscannot be directly used for the automation of the dependency pair approach.To solve this problem we have developed a..."},"documents":{"kind":"list like","value":["Title: Improving Dependency Pairs\nYear: 2003\n\nAbstract: The dependency pair approach is one of the most powerful techniques for termination and innermost termination proofs of term rewrite systems (TRSs). For any TRS, it generates inequality constraints that have to be satisfied by weakly monotonic well-founded orders. We improve the dependency pair approach by considerably reducing the number of constraints produced for (innermost) termination proofs. Moreover, we extend transformation techniques to manipulate dependency pairs which simplify (innermost) termination proofs significantly. In order to fully automate the dependency pair approach, we show how transformation techniques and the search for suitable orders can be mechanized efficiently. We implemented our results in the automated termination prover AProVE and evaluated them on large collections of examples.","Title: Eliminating Dummy Elimination\nYear: 2000\n\nAbstract: This paper is concerned with methods that automatically prove termination of term rewrite systems. The aim of dummy elimina- tion, a method to prove termination introduced by Ferreira and Zantema, is to transform a given rewrite system into a rewrite system whose termi- nation is easier to prove. We show that dummy elimination is subsumed by the more recent dependency pair method of Arts and Giesl. More pre- cisely, if dummy elimination succeeds in transforming a rewrite system into a so-called simply terminating rewrite system then termination of the given rewrite system can be directly proved by the dependency pair technique. Even stronger, using dummy elimination as a preprocessing step to the dependency pair technique does not have any advantages either. We show that to a large extent these results also hold for the argument ltering transformation of Kusakari et al.","Title: Deaccumulation - Improving Provability\nYear: 2003\n\nAbstract: Several induction theorem provers were developed to verify functional programs mechanically. Unfortunately, automated verification usually fails for functions with accumulating arguments. In particular, this holds for tail-recursive functions that correspond to imperative programs, but also for programs with nested recursion. Based on results from the theory of tree transducers, we develop an automatic transformation technique. It transforms accumulative functional programs into non-accumulative ones, which are much better suited for automated verification by induction theorem provers. Hence, in contrast to classical program transformations aiming at improving the efficiency, the goal of our deaccumulation technique is to improve the provability.","Title: Deciding Innermost Loops\nYear: 2008\n\nAbstract: We present the rst method to disprove innermost termina- tion of term rewrite systems automatically. To this end, we rst develop a suitable notion of an innermost loop. Second, we show how to detect in- nermost loops: One can start with any technique amenable to nd loops. Then our novel procedure can be applied to decide whether a given loop is an innermost loop. We implemented and successfully evaluated our method in the termination prover AProVE.","Title: Liveness in Rewriting\nYear: 2003\n\nAbstract: In this paper, we show how the problem of verifying liveness properties is related to termination of term rewrite systems (TRSs). We formalize liveness in the framework of rewriting and present a sound and complete transformation to transform particular liveness problems into TRSs. Then the transformed TRS terminates if and only if the original liveness property holds. This shows that liveness and termination are essentially equivalent. To apply our approach in practice, we introduce a simpler sound transformation which only satisfies the 'only if'-part. By refining existing techniques for proving termination of TRSs we show how liveness properties can be verified automatically. As examples, we prove a liveness property of a waiting line protocol for a network of processes and a liveness property of a protocol on a ring of processes.","Title: Termination of Isabelle functions via termination of rewriting\nYear: 2011\n\nAbstract: We show how to automate termination proofs for recursive functions in (a first-order subset of) Isabelle/HOL by encoding them as term rewrite systems and invoking an external termination prover. Our link to the external prover includes full proof reconstruction, where all necessary properties are derived inside Isabelle/HOL without oracles. Apart from the certification of the imported proof, the main challenge is the formal reduction of the proof obligation produced by Isabelle/HOL to the termination of the corresponding term rewrite system. We automate this reduction via suitable tactics which we added to the IsaFoR library.","Title: Generating Polynomial Orderings for Termination Proofs\nYear: 1995\n\nAbstract: Most systems for the automation of termination proofs using polynomial orderings are only semi-automatic, i.e. the "right" polynomial ordering has to be given by the user. We show that a variation of Lankford's partial derivative technique leads to an easier and slightly more powerful method than most other semi-automatic approaches. Based on this technique we develop a method for the automated synthesis of a suited polynomial ordering.","Title: Modular Termination Proofs of Recursive Java Bytecode Programs by Term Rewriting\nYear: 2011\n\nAbstract: In [5, 15] we presented an approach to prove termination of non-recursive Java Bytecode (JBC) programs automatically. Here, JBC programs are first transformed to finite termination graphs which represent all possible runs of the program. Afterwards, the termination graphs are translated to term rewrite systems (TRSs) such that termination of the resulting TRSs implies termination of the original JBC programs. So in this way, existing techniques and tools from term rewriting can be used to prove termination of JBC automatically. In this paper, we improve this approach substantially in two ways: (1) We extend it in order to also analyze recursive JBC programs. To this end, one has to represent call stacks of arbitrary size. (2) To handle JBC programs with several methods, we modularize our approach in order to reuse termination graphs and TRSs for the separate methods and to prove termination of the resulting TRS in a modular way. We implemented our approach in the tool AProVE. Our experiments show that the new contributions increase the power of termination analysis for JBC significantly.","Title: Loops under Strategies ... Continued\nYear: 2010\n\nAbstract: While there are many approaches for automatically proving termination of term\nrewrite systems, up to now there exist only few techniques to disprove their\ntermination automatically. Almost all of these techniques try to find loops,\nwhere the existence of a loop implies non-termination of the rewrite system.\nHowever, most programming languages use specific evaluation strategies, whereas\nloop detection techniques usually do not take strategies into account. So even\nif a rewrite system has a loop, it may still be terminating under certain\nstrategies.\n Therefore, our goal is to develop decision procedures which can determine\nwhether a given loop is also a loop under the respective evaluation strategy.\nIn earlier work, such procedures were presented for the strategies of\ninnermost, outermost, and context-sensitive evaluation. In the current paper,\nwe build upon this work and develop such decision procedures for important\nstrategies like leftmost-innermost, leftmost-outermost,\n(max-)parallel-innermost, (max-)parallel-outermost, and forbidden patterns\n(which generalize innermost, outermost, and context-sensitive strategies). In\nthis way, we obtain the first approach to disprove termination under these\nstrategies automatically.","Title: Strategies for Semantical Contractions\nYear: 1994\n\nAbstract: Without Abstract","Title: Automated termination proofs for haskell by term rewriting\nYear: 2011\n\nAbstract: There are many powerful techniques for automated termination analysis of term rewriting. However, up to now they have hardly been used for real programming languages. We present a new approach which permits the application of existing techniques from term rewriting to prove termination of most functions defined in Haskell programs. In particular, we show how termination techniques for ordinary rewriting can be used to handle those features of Haskell which are missing in term rewriting (e.g., lazy evaluation, polymorphic types, and higher-order functions). We implemented our results in the termination prover AProVE and successfully evaluated them on existing Haskell libraries.","Title: SAT-based termination analysis using monotonicity constraints over the integers.\nYear: 2011\n\nAbstract: We describe an algorithm for proving termination of programs abstracted to systems of monotonicity constraints in the integer domain. Monotonicity constraints are a nontrivial extension of the well-known size-change termination method. While deciding termination for systems of monotonicity constraints is PSPACE complete, we focus on a well-defined and significant subset, which we call MCNP (for \"monotonicity constraints in NP\"), designed to be amenable to a SAT-based solution. Our technique is based on the search for a special type of ranking function defined in terms of bounded differences between multisets of integer values. We describe the application of our approach as the back end for the termination analysis of Java Bytecode. At the front end, systems of monotonicity constraints are obtained by abstracting information, using two different termination analyzers: AProVE and COSTA. Preliminary results reveal that our approach provides a good trade-off between precision and cost of analysis.","Title: Proving and disproving termination of higher-order functions\nYear: 2005\n\nAbstract: The dependency pair technique is a powerful modular method for automated termination proofs of term rewrite systems (TRSs). We present two important extensions of this technique: First, we show how to prove termination of higher-order functions using dependency pairs. To this end, the dependency pair technique is extended to handle (untyped) applicative TRSs. Second, we introduce a method to prove non-termination with dependency pairs, while up to now dependency pairs were only used to verify termination. Our results lead to a framework for combining termination and non-termination techniques for first- and higher-order functions in a very flexible way. We implemented and evaluated our results in the automated termination prover AProVE.","Title: Automatically Proving Termination Where Simplification Orderings Fail\nYear: 1997\n\nAbstract: To prove termination of term rewriting systems (TRSs), several methodshave been developed to synthesize suitable well-founded orderings automatically.However, virtually all orderings that are amenable to automation areso-called simplification orderings. Unfortunately, there exist numerous interestingand relevant TRSs that cannot be oriented by orderings of this restrictedclass and therefore their termination cannot be proved automatically with theexisting techniques.In this paper we...","Title: SAT solving for termination analysis with polynomial interpretations\nYear: 2007\n\nAbstract: Polynomial interpretations are one of the most popular techniques for automated termination analysis and the search for such interpretations is a main bottleneck in most termination provers. We show that one can obtain speedups in orders of magnitude by encoding this task as a SAT problem and by applying modern SAT solvers.","Title: Proving Termination of Integer Term Rewriting\nYear: 2009\n\nAbstract: When using rewrite techniques for termination analysis of programs, a main problem are pre-defined data types like integers. We extend term rewriting by built-in integers and adapt the dependency pair framework to prove termination of integer term rewriting automatically.","Title: Automatic Termination Proofs in the Dependency Pair Framework\nYear: 2006\n\nAbstract: AProVE 1.2 is one of the most powerful systems for automated termination proofs of term rewrite systems (TRSs). It is the first tool\n which automates the new dependency pair framework [8] and therefore permits a completely flexible combination of different termination proof techniques. Due to this framework,\n AProVE 1.2 is also the first termination prover which can be fully configured by the user.","Title: Symbolic evaluation graphs and term rewriting: a general methodology for analyzing logic programs\nYear: 2012\n\nAbstract: There exist many powerful techniques to analyze termination and complexity of term rewrite systems (TRSs). Our goal is to use these techniques for the analysis of other programming languages as well. For instance, approaches to prove termination of definite logic programs by a transformation to TRSs have been studied for decades. However, a challenge is to handle languages with more complex evaluation strategies (such as Prolog, where predicates like the cut influence the control flow). In this paper, we present a general methodology for the analysis of such programs. Here, the logic program is first transformed into a symbolic evaluation graph which represents all possible evaluations in a finite way. Afterwards, different analyses can be performed on these graphs. In particular, one can generate TRSs from such graphs and apply existing tools for termination or complexity analysis of TRSs to infer information on the termination or complexity of the original logic program.","Title: Context-Moving Transformations for Function Verification\nYear: 1999\n\nAbstract: Several induction theorem provers have been developed which support mechanized verification of functional programs. Unfortunately, a major problem is that they often fail in verifying tail recursive functions (which correspond to imperative programs). However, in practice imperative programs are used almost exclusively. We present an automatic transformation to tackle this problem. It transforms functions which are hard to verify into functions whose correctness can be shown by the existing...","Title: Proving non-looping non-termination automatically\nYear: 2012\n\nAbstract: We introduce a technique to prove non-termination of term rewrite systems automatically. Our technique improves over previous approaches substantially, as it can also detect non-looping non-termination.","Title: A linear operational semantics for termination and complexity analysis of ISO prolog\nYear: 2011\n\nAbstract: We present a new operational semantics for Prolog which covers all constructs in the corresponding ISO standard (including \"non-logical\" concepts like cuts, meta-programming, \"all solution\" predicates, dynamic predicates, and exception handling). In contrast to the classical operational semantics for logic programming, our semantics is linear and not based on search trees. This has the advantage that it is particularly suitable for automated program analyses such as termination and complexity analysis. We prove that our new semantics is equivalent to the ISOProlog semantics, i.e., it computes the same answer substitutions and the derivations in both semantics have essentially the same length.","Title: Closure Induction in a Z-Like Language\nYear: 2000\n\nAbstract: Simply-typed set-theoretic languages such as Z and B are widely used for program and system specifications. The main technique for reasoning about such specifications is induction. However, while partiality is an important concept in these languages, many standard approaches to automating induction proofs rely on the totality of all occurring functions. Reinterpreting the second author's recently proposed induction technique for partial functional programs, we introduce in this paper the new principle of \"closure induction\" for reasoning about the inductive properties of partial functions in simply-typed set-theoretic languages. In particular, closure induction allows us to prove partial correctness, that is, to prove those instances of conjectures for which designated partial functions are explicitly defined.","Title: Improved Modular Termination Proofs Using Dependency Pairs\nYear: 2004\n\nAbstract: The dependency pair approach is one of the most powerful techniques for automated (innermost) termination proofs of term rewrite systems (TRSs). For any TRS, it generates inequality constraints that have to be satisfied by well-founded orders. However, proving innermost termination is considerably easier than termination, since the constraints for innermost termination are a subset of those for termination. We show that surprisingly, the dependency pair approach for termination can be improved by only generating the same constraints as for innermost termination. In other words, proving full termination becomes virtually as easy as proving innermost termination. Our results are based on splitting the termination proof into several modular independent subproofs. We implemented our contributions in the automated termination prover AProVE and evaluated them on large collections of examples. These experiments show that our improvements increase the power and efficiency of automated termination proving substantially.","Title: Size-change termination for term rewriting\nYear: 2003\n\nAbstract: In [13], a new size-change principle was proposed to verify termination of functional programs automatically. We extend this principle in order to prove termination and innermost termination of arbitrary term rewrite systems (TRSs). Moreover, we compare this approach with existing techniques for termination analysis of TRSs (such as recursive path orderings or dependency pairs). It turns out that the size-change principle on its own fails for many examples that can be handled by standard techniques for rewriting, but there are also TRSs where it succeeds whereas existing rewriting techniques fail. In order to benefit from their respective advantages, we show how to combine the size-change principle with classical orderings and with dependency pairs. In this way, we obtain a new approach for automated termination proofs of TRSs which is more powerful than previous approaches.","Title: The dependency triple framework for termination of logic programs\nYear: 2009\n\nAbstract: We show how to combine the two most powerful approaches for automated termination analysis of logic programs (LPs): the direct approach which operates directly on LPs and the transformational approach which transforms LPs to term rewrite systems (TRSs) and tries to prove termination of the resulting TRSs. To this end, we adapt the well-known dependency pair framework from TRSs to LPs. With the resulting method, one can combine arbitrary termination techniques for LPs in a completely modular way and one can use both direct and transformational techniques for different parts of the same LP.","Title: Termination of Nested and Mutually Recursive Algorithms\nYear: 1997\n\nAbstract: This paper deals with automated termination analysis for functional programs. Previously developed methods for automated termination proofs of functional programs often fail for algorithms with nested recursion and they cannot handle algorithms with mutual recursion.We show that termination proofs for nested and mutually recursive algorithms can be performed without having to prove the correctness of the algorithms simultaneously. Using this result, nested and mutually recursive algorithms do no longer constitute a special problem and the existing methods for automated termination analysis can be extended to nested and mutual recursion in a straightforward way. We give some examples of algorithms whose termination can now be proved automatically (including well-known challenge problems such as McCarthy’s f_91 function).","Title: Automated termination proofs for logic programs by term rewriting\nYear: 2008\n\nAbstract: There are two kinds of approaches for termination analysis of logic programs: “transformational” and “direct” ones. Direct approaches prove termination directly on the basis of the logic program. Transformational approaches transform a logic program into a Term Rewrite System (TRS) and then analyze termination of the resulting TRS instead. Thus, transformational approaches make all methods previously developed for TRSs available for logic programs as well. However, the applicability of most existing transformations is quite restricted, as they can only be used for certain subclasses of logic programs. (Most of them are restricted to well-moded programs.) In this article we improve these transformations such that they become applicable for any definite logic program. To simulate the behavior of logic programs by TRSs, we slightly modify the notion of rewriting by permitting infinite terms. We show that our transformation results in TRSs which are indeed suitable for automated termination analysis. In contrast to most other methods for termination of logic programs, our technique is also sound for logic programming without occur check, which is typically used in practice. We implemented our approach in the termination prover AProVE and successfully evaluated it on a large collection of examples.","Title: Automated termination analysis for logic programs by term rewriting\nYear: 2006\n\nAbstract: There are two kinds of approaches for termination analysis of logic programs: \"transformational\" and \"direct\" ones. Direct approaches prove termination directly on the basis of the logic program. Transformational approaches transform a logic program into a term rewrite system (TRS) and then analyze termination of the resulting TRS instead. Thus, transformational approaches make all methods previously developed for TRSs available for logic programs as well. However, the applicability of most existing transformations is quite restricted, as they can only be used for certain subclasses of logic programs. (Most of them are restricted to well-moded programs.) In this paper we improve these transformations such that they become applicable for any definite logic program. To simulate the behavior of logic programs by TRSs, we slightly modify the notion of rewriting by permitting infinite terms. We show that our transformation results in TRSs which are indeed suitable for automated termination analysis. In contrast to most other methods for termination of logic programs, our technique is also sound for logic programming without occur check, which is typically used in practice. We implemented our approach in the termination prover AProVE and successfully evaluated it on a large collection of examples.","Title: Automated termination proofs for Java programs with cyclic data\nYear: 2012\n\nAbstract: In earlier work, we developed a technique to prove termination of Java programs automatically: first, Java programs are automatically transformed to term rewrite systems (TRSs) and then, existing methods and tools are used to prove termination of the resulting TRSs. In this paper, we extend our technique in order to prove termination of algorithms on cyclic data such as cyclic lists or graphs automatically. We implemented our technique in the tool AProVE and performed extensive experiments to evaluate its practical applicability.","Title: The Dependency Pair Framework: Combining Techniques for Automated Termination Proofs.\nYear: 2004\n\nAbstract: The dependency pair approach is one of the most powerful techniques for automated termination proofs of term rewrite systems. Up to now, it was regarded as one of several possible methods to prove termination. In this paper, we show that dependency pairs can instead be used as a general concept to integrate arbitrary techniques for termination analysis. In this way, the benefits of different techniques can be combined and their modularity and power are increased significantly. We refer to this new concept as the \"dependency pair framework\" to distinguish it from the old \"dependency pair approach\". Moreover, this framework facilitates the development of new methods for termination analysis. To demonstrate this, we present several new techniques within the dependency pair framework which simplify termination problems considerably. We implemented the dependency pair framework in our termination prover AProVE and evaluated it on large collections of examples.","Title: SAT Solving for Termination Proofs with Recursive Path Orders and Dependency Pairs\nYear: 2012\n\nAbstract: This paper introduces a propositional encoding for recursive path orders (RPO), in connection with dependency pairs. Hence, we capture in a uniform setting all common instances of RPO, i.e., lexicographic path orders (LPO), multiset path orders (MPO), and lexicographic path orders with status (LPOS). This facilitates the application of SAT solvers for termination analysis of term rewrite systems (TRSs). We address four main inter-related issues and show how to encode them as satisfiability problems of propositional formulas that can be efficiently handled by SAT solving: (A) the lexicographic comparison w.r.t. a permutation of the arguments; (B) the multiset extension of a base order; (C) the combined search for a path order together with an argument filter to orient a set of inequalities; and (D) how the choice of the argument filter influences the set of inequalities that have to be oriented (so-called usable rules). We have implemented our contributions in the termination prover AProVE. Extensive experiments show that by our encoding and the application of SAT solvers one obtains speedups in orders of magnitude as well as increased termination proving power.","Title: Equational Termination by Semantic Labelling\nYear: 2000\n\nAbstract: Semantic labelling is a powerful tool for proving termination of term rewrite systems. The usefulness of the extension to equational term rewriting described in Zantema [24] is however rather limited. In this paper we introduce a stronger version of equational semantical labelling, parameterized by three choices: (1) the order on the underlying algebra (partial order vs. quasi-order), (2) the relation between the algebra and the rewrite system (model vs. quasi-model), and (3) the labelling of the function symbols appearing in the equations (forbidden vs. allowed). We present soundness and completeness results for the various instantiations and analyze the relationships between them. Applications of our equational semantic labelling technique include a short proof of the main result of Ferreira et al. [7]--the correctness of a version of dummy elimination for AC-rewriting which completely removes the AC-axioms-- and an extension of Zantema's distribution elimination technique [23] to the equational setting.","Title: Termination Analysis for Functional Programs using Term Orderings\nYear: 1995\n\nAbstract: . To prove the termination of a functional program there hasto be a well-founded ordering such that the arguments in each recursivecall are smaller than the corresponding inputs. In this paper we presenta procedure for automated termination proofs of functional programs. Incontrast to previously presented methods a suited well-founded orderingdoes not have to be fixed in advance by the user, but can be synthesizedautomatically.For that purpose we use approaches developed in the area of...","Title: Improving Context-Sensitive Dependency Pairs\nYear: 2008\n\nAbstract: Context-sensitive dependency pairs (CS-DPs) are currently the most powerful method for automated termination analysis of context-sensitive rewriting. However, compared to DPs for ordinary rewriting, CS-DPs suffer from two main drawbacks: (a) CS-DPs can be collapsing . This complicates the handling of CS-DPs and makes them less powerful in practice. (b) There does not exist a \"DP framework \" for CS-DPs which would allow one to apply them in a flexible and modular way. This paper solves drawback (a) by introducing a new definition of CS-DPs. With our definition, CS-DPs are always non-collapsing and thus, they can be handled like ordinary DPs. This allows us to solve drawback (b) as well, i.e., we extend the existing DP framework for ordinary DPs to context-sensitive rewriting. We implemented our results in the tool AProVE and successfully evaluated them on a large collection of examples.","Title: Induction Proofs with Partial Functions\nYear: 2001\n\nAbstract: In this paper we present a method for automated induction proofs about partial functions. We show that most well-known techniques developed for (explicit) induction theorem proving are unsound when dealing with partial functions. But surprisingly, by slightly restricting the application of these techniques, it is possible to develop a calculus for automated induction proofs with partial functions. In particular, under certain conditions one may even generate induction schemes from the recursions of nonterminating algorithms. The need for such induction schemes and the power of our calculus have been demonstrated on a large collection of nontrivial theorems (including Knuth and Bendix's critical pair lemma). In this way, existing induction theorem provers can be directly extended to partial functions without major changes of their logical framework.","Title: Polytool: Polynomial interpretations as a basis for termination analysis of logic programs\nYear: 2009\n\nAbstract: Our goal is to study the feasibility of porting termination analysis techniques developed for one programming paradigm to another paradigm. In this paper, we show how to adapt termination analysis techniques based on polynomial interpretations—very well known in the context of term rewrite systems—to obtain new (nontransformational) termination analysis techniques for definite logic programs (LPs). This leads to an approach that can be seen as a direct generalization of the traditional techniques in termination analysis of LPs, where linear norms and level mappings are used. Our extension generalizes these to arbitrary polynomials. We extend a number of standard concepts and results on termination analysis to the context of polynomial interpretations. We also propose a constraint-based approach for automatically generating polynomial interpretations that satisfy the termination conditions. Based on this approach, we implemented a new tool, called Polytool, for automatic termination analysis of LPs.","Title: Proving Termination Using Recursive Path Orders and SAT Solving\nYear: 2007\n\nAbstract: We introduce a propositional encoding of the recursive path order with status (RPO). RPO is a combination of a multiset path order and a lexicographic path order which considers permutations of the arguments in the lexicographic comparison. Our encoding allows us to apply SAT solvers in order to determine whether a given term rewrite system is RPO-terminating. Furthermore, to apply RPO within the dependency pair framework, we combined our novel encoding for RPO with an existing encoding for argument filters. We implemented our contributions in the termination prover AProVE. Our experiments show that due to our encoding, combining termination provers with SAT solvers improves the performance of RPO-implementations by orders of magnitude.","Title: The size-change principle and dependency pairs for termination of term rewriting\nYear: 2005\n\nAbstract: In [24], a new size-change principle was proposed to verify termination of functional programs automatically. We extend this principle in order to prove termination and innermost termination of arbitrary term rewrite systems (TRSs). Moreover, we compare this approach with existing techniques for termination analysis of TRSs (such as recursive path orders or dependency pairs). It turns out that the size-change principle on its own fails for many examples that can be handled by standard techniques for rewriting, but there are also TRSs where it succeeds whereas existing rewriting techniques fail. Moreover, we also compare the complexity of the respective methods. To this end, we develop the first complexity analysis for the dependency pair approach. While the size-change principle is PSPACE-complete, we prove that the dependency pair approach (in combination with classical path orders) is only -complete. To benefit from their respective advantages, we show how to combine the size-change principle with classical orders and with dependency pairs. In this way, we obtain a new approach for automated termination proofs of TRSs which is more powerful than previous approaches. We also show that the combination with dependency pairs does not increase the complexity of the size-change principle, i.e., the combined approach is still PSPACE-complete.","Title: Approximating the domains of functional and imperative programs\nYear: 1999\n\nAbstract: This paper deals with automated termination analysis of partial functional programs, that is, of functional programs which do not terminate for some input. We present a method to determine their domains (respectively non-trivial subsets of their domains) automatically. More precisely, for each functional program a termination predicate algorithm is synthesized that only returns true for inputs where the program is terminating. To ease subsequent reasoning about the generated termination predicates we also present a procedure for their simplification. Finally, we show that our method can also be used for automated termination analysis of imperative programs.","Title: Automated Termination Analysis: From Term Rewriting to Programming Languages.\nYear: 2013\n\nAbstract: Termination is a crucial property of programs. Therefore, techniques to analyze termination automatically are highly important for program verification. Traditionally, techniques for automated termination analysis were mainly studied for declarative programming paradigms such as logic programming and term rewriting. However, in the last years, several powerful techniques and tools have been developed which analyze the termination of programs in many programming languages including Java, C, Haskell, and Prolog.","Title: Transformation techniques for context-sensitive rewrite systems\nYear: 2004\n\nAbstract: Context-sensitive rewriting is a computational restriction of term rewriting used to model non-strict (lazy) evaluation in functional programming. The goal of this paper is the study and development of techniques to analyze the termination behavior of context-sensitive rewrite systems. For that purpose, several methods have been proposed in the literature which transform context-sensitive rewrite systems into ordinary rewrite systems such that termination of the transformed ordinary system implies termination of the original context-sensitive system. In this way, the huge variety of existing techniques for termination analysis of ordinary rewriting can be used for context-sensitive rewriting, too. We analyze the existing transformation techniques for proving termination of context-sensitive rewriting and we suggest two new transformations. Our first method is simple, sound, and more powerful than the previously proposed transformations. However, it is not complete, i.e., there are terminating context-sensitive rewrite systems that are transformed into non-terminating term rewrite systems. The second method that we present in this paper is both sound and complete. All these observations also hold for rewriting modulo associativity and commutativity.","Title: Adding constants to string rewriting\nYear: 2008\n\nAbstract: We consider unary term rewriting, i.e., term rewriting with unary signatures where all function symbols are either unary or constants. Terms over such signatures can be transformed into strings by just reading all symbols in the term from left to right, ignoring the optional variable. By lifting this transformation to rewrite rules, any unary term rewrite system (TRS) is transformed into a corresponding string rewrite system (SRS). We investigate which properties are preserved by this transformation. It turns out that any TRS over a unary signature is terminating if and only if the corresponding SRS is terminating. In this way tools for proving termination of string rewriting can be applied for proving termination of unary TRSs. For other rewriting properties including confluence, unique normal form property, weak normalization and relative termination, we show that a similar corresponding preservation property does not hold.","Title: Modularity of Termination Using Dependency pairs\nYear: 1998\n\nAbstract: The framework of dependency pairs allows automated termination and innermost termination proofs for many TRSs where such proofs were not possible before. In this paper we present\n a refinement of this framework in order to prove termination in a modular way. Our modularity results significantly increase the class of term rewriting systems where termination resp. innermost\n termination can be proved automatically. Moreover, the modular approach to dependency pairs yields new modularity criteria\n which extend previous results in this area. In particular, existing results for modularity of innermost termination can easily\n be obtained as direct consequences of our new criteria.","Title: Automated Termination Proofs with AProVE\nYear: 2004\n\nAbstract: We describe the system ProVE, an automated prover to verify (innermost) termination of term rewrite systems (TRSs). For this system, we have developed and implemented efficient algorithms based on classical simplification orders, dependency pairs, and the size-change principle. In particular, it contains many new improvements of the dependency pair approach that make automated termination proving more powerful and efficient. In ProVE, termination proofs can be performed with a user-friendly graphical interface and the system is currently among the most powerful termination provers available.","Title: Deciding Inductive Validity of Equations\nYear: 2003\n\nAbstract: Kapur and Subramaniam [12] defined syntactical classes of equations where inductive validity can be decided automatically. However, these classes are quite restrictive, since defined function symbols with recursive definitions may only appear on one side of the equations. In this paper, we expand the decidable class of equations significantly by allowing both sides of equations to be expressed using defined function symbols. The definitions of these function symbols must satisfy certain restrictions which can be checked mechanically. These results are crucial to increase the applicability of decision procedures for induction.","Title: Decision Procedures for Loop Detection\nYear: 2007\n\nAbstract: The dependency pair technique is a powerful modular meth- od for automated termination proofs of term rewrite systems. We first show that dependency pairs are also suitable for disproving termination: loops can be detected more easily. In a second step we analyze how to disprove innermost termination. Here, we present a novel procedure to decide whether a given loop is an innermost loop. All results have been implemented in the termination prover AProVE.","Title: Dependency Pairs for Equational Rewriting\nYear: 2001\n\nAbstract: The dependency pair technique of Arts and Giesl [1, 2, 3] for termination proofs of term rewrite systems (TRSs) is extended to rewriting modulo equations. Up to now, such an extension was only known in the special case of AC-rewriting [15, 17]. In contrast to that, the proposed technique works for arbitrary non-collapsing equations (satisfying a certain linearity condition). With the proposed approach, it is now possible to perform automated termination proofs for many systems where this was not possible before. In other words, the power of dependency pairs can now also be used for rewriting modulo equations.","Title: Deaccumulation techniques for improving provability\nYear: 2007\n\nAbstract: Several induction theorem provers were developed to verify functional programs mechanically. Unfortunately, automatic verification often fails for functions with accumulating arguments. Using concepts from the theory of tree transducers and extending on earlier work, the paper develops automatic transformations from accumulative functional programs into non-accumulative ones, which are much better suited for mechanized verification. The overall goal is to reduce the need for generalizing induction hypotheses in (semi-)automatic provers. Via the correspondence between imperative programs and tail-recursive functions, the presented approach can also help to reduce the need for inventing loop invariants in the verification of imperative programs.","Title: Automated termination analysis for logic programs with cut*\nYear: 2010\n\nAbstract: Termination is an important and well-studied property for logic programs. However, almost all approaches for automated termination analysis focus on definite logic programs, whereas real-world Prolog programs typically use the cut operator. We introduce a novel pre-processing method which automatically transforms Prolog programs into logic programs without cuts, where termination of the cut-free program implies termination of the original program. Hence after this pre-processing, any technique for proving termination of definite logic programs can be applied. We implemented this pre-processing in our termination prover AProVE and evaluated it successfully with extensive experiments.","Title: Current Trends in Automated Deduction\nYear: 2010\n\nAbstract: Automated deduction is one of the key areas in artificial intelligence. In this short article we give an overview on some\n of the main current research topics in automated deduction.","Title: Decidable Classes of Inductive Theorems\nYear: 2001\n\nAbstract: Kapur and Subramaniam [8] defined syntactical classes of equations where inductive validity is decidable. Thus, their validity can be checked without any user interaction and hence, this allows an integration of (a restricted form of) induction in fully automated reasoning tools such as model checkers. However, the results of [8] were only restricted to equations. This paper extends the classes of conjectures considered in [8] to a larger class of arbitrary quantifier-free formulas (e.g., conjectures also containing negation, conjunction, disjunction, etc.).","Title: Proving Termination by Bounded Increase\nYear: 2007\n\nAbstract: Most methods for termination analysis of term rewrite systems (TRSs) essentially try to find arguments of functions that decreasein recursive calls. However, they fail if the reason for termination is that an argument is increasedin recursive calls repeatedly until it reaches a bound. In this paper, we solve that problem and show how to prove innermost termination of TRSs with bounded increase automatically.","Title: Termination Analysis of Logic Programs Based on Dependency Graphs\nYear: 2007\n\nAbstract: This paper introduces a modular framework for termination analysis of logic programming. To this end, we adapt the notions of dependency pairs and dependency graphs (which were developed for term rewriting) to the logic programming domain. The main idea of the approach is that termination conditions for a program are established based on the decomposition of its dependency graph into its strongly connected components. These conditions can then be analysed separately by possibly different well-founded orders. We propose a constraint-based approach for automating the framework. Then, for example, termination techniques based on polynomial interpretations can be plugged in as a component to generate well-founded orders.","Title: Applying Rewriting Techniques to the Verification of Erlang Processes\nYear: 1999\n\nAbstract: Erlang is a functional programming language developed by Ericsson Telecom which is particularly well suited for implementing concurrent processes. In this paper we show how methods from the area of term rewriting are presently used at Ericsson. To verify properties of processes, such a property is transformed into a termination problem of a conditional term rewriting system (CTRS). Subsequently, this termination proof can be performed automatically using dependency pairs. The paper illustrates how the dependency pair technique can be applied for termination proofs of conditional TRSs. Secondly, we present two refinements of this technique, viz. narrowing and rewriting dependency pairs. These refinements are not only of use in the industrial application sketched in this paper, but they are generally applicable to arbitrary (C)TRSs. Thus, in this way dependency pairs can be used to prove termination of even more (C)TRSs automatically.","Title: Mechanizing and Improving Dependency Pairs\nYear: 2006\n\nAbstract: To simplify the task of proving termination and AC-termination of term rewriting systems, elimination transformations have been vigorously studied since the 1990s. Dummy elimination, distribution elimination, general dummy elimination, and improved general ...","Title: Proving and Disproving Termination in the Dependency Pair Framework\nYear: 2005\n\nAbstract: The dependency pair approach is one of the most powerful techniques for auto- mated termination proofs of term rewrite systems. Up to now, it was regarded as one of several possible methods to prove termination. We show that dependency pairs can instead be used as a general concept to integrate arbitrary techniques for termination analysis. In this way, the benets of dieren t techniques can be combined and their modularity and power are increased signican tly. We refer to this new concept as the \\dependency pair framework\" to distinguish it from the old \\dependency pair approach\". Moreover, this framework facilitates the development of new methods for termination analysis. Traditionally, the research on termination focused on methods which prove termination and there were hardly any approaches for disproving termination. We show that with the dependency pair framework, one can combine the search for a proof and for a disproof of termination. In this way, we obtain the rst powerful method which can also verify non-termination of term rewrite systems. Moreover, the combination of termination and non-termination techniques is particularly useful: On the one hand, termination techniques also help for dis- proving termination, because they identify those parts of a term rewrite system which may cause non-termination. On the other hand, non-termination tech- niques are helpful for proving termination, because they can detect \\dead ends\" during a termination proof attempt. We implemented and evaluated our contributions in the automated termina- tion prover AProVE. Due to these results, AProVE was the winning tool in the International Competition of Termination Provers 2005, both for proving and for disproving termination of term rewriting.","Title: Automated Termination Analysis of Java Bytecode by Term Rewriting\nYear: 2010\n\nAbstract: We present an automated approach to prove termination of Java Bytecode (JBC) programs by automatically transforming them to term rewrite systems (TRSs). In this way, the numerous techniques and tools developed for TRS termination can now be used for imperative object-oriented languages like Java, which can be compiled into JBC.","Title: Modular termination proofs for rewriting using dependency pairs\nYear: 2002\n\nAbstract: Recently, Arts and Giesl developed the dependency pair approach which allows automated termination and innermost termination proofs for many term rewriting systems (TRSs) for which such proofs were not possible before. The motivation for this approach was that virtually all previous techniques for automated termination proofs of TRSs were based on simplification orderings. In practice, however, many rewrite systems are not simply terminating, i.e. their termination cannot be verified by any simplification ordering.In this paper we introduce a refinement of the dependency pair framework which further extends the class of TRSs for which termination or innermost termination can be shown automatically. By means of this refinement, one can now prove termination in a modular way. Thus, this refinement is inevitable in order to verify the termination of large rewrite systems occurring in practice. To be more precise, one may use several different orderings in one termination proof.Subsequently, we present several new modularity results based on dependency pairs. First, we show that the well-known modularity of simple termination for disjoint unions can be extended to DP quasi-simple termination, i.e. to the class of rewrite systems where termination can be shown automatically by the dependency pair technique in combination with quasi-simplification orderings. Under certain additional conditions, this new result also holds for constructor-sharing and composable systems. Second, the above-mentioned refinement of the dependency pair method yields new modularity criteria for innermost termination which extend previous results in this area considerably. In particular, existing results for modularity of innermost termination can easily be shown to be direct consequences of our new criteria.","Title: A dependency pair framework for innermost complexity analysis of term rewrite systems\nYear: 2011\n\nAbstract: We present a modular framework to analyze the innermost runtime complexity of term rewrite systems automatically. Our method is based on the dependency pair framework for termination analysis. In contrast to previous work, we developed a direct adaptation of successful termination techniques from the dependency pair framework in order to use them for complexity analysis. By extensive experimental results, we demonstrate the power of our method compared to existing techniques.","Title: Proving Termination by Dependency Pairs and Inductive Theorem Proving\nYear: 2011\n\nAbstract: Current techniques and tools for automated termination analysis of term rewrite systems (TRSs) are already very powerful. However, they fail for algorithms whose termination is essentially due to an inductive argument. Therefore, we show how to couple the dependency pair method for termination of TRSs with inductive theorem proving. As confirmed by the implementation of our new approach in the tool AProVE, now TRS termination techniques are also successful on this important class of algorithms.","Title: Simulating Liveness by Reduction Strategies\nYear: 2003\n\nAbstract: We define a general framework to handle liveness and related properties by reduction strategies in abstract reduction and term rewriting. Classically, reduction strategies in rewriting are used to simulate the evaluation process in programming languages. The aim of our work is to use reduction strategies to also study liveness questions which are of high importance in practice (e.g., in protocol verification for distributed processes). In particular, we show how the problem of verifying liveness is related to termination of term rewrite systems (TRSs). Using our results, techniques for proving termination of TRSs can be used to verify liveness properties.","Title: Termination of term rewriting using dependency pairs\nYear: 2000\n\nAbstract: We present techniques to prove termination and innermost termination of term rewriting systems automatically. In contrast to previous approaches, we do not compare left- and right-hand sides of rewrite rules, but introduce the notion of dependency pairs to compare left-hand sides with special subterms of the right-hand sides. This results in a technique which allows to apply existing methods for automated termination proofs to term rewriting systems where they failed up to now. In particular, there are numerous term rewriting systems where a direct termination proof with simplification orderings is not possible, but in combination with our technique, well-known simplification orderings (such as the recursive path ordering, polynomial orderings, or the Knuth–Bendix ordering) can now be used to prove termination automatically. Unlike previous methods, our technique for proving innermost termination automatically can also be applied to prove innermost termination of term rewriting systems that are not terminating. Moreover, as innermost termination implies termination for certain classes of term rewriting systems, this technique can also be used for termination proofs of such systems.","Title: SAT solving for argument filterings\nYear: 2006\n\nAbstract: This paper introduces a propositional encoding for lexicographic path orders in connection with dependency pairs. This facilitates the application of SAT solvers for termination analysis of term rewrite systems based on the dependency pair method. We address two main inter-related issues and encode them as satisfiability problems of propositional formulas that can be efficiently handled by SAT solving: (1) the combined search for a lexicographic path order together with an argument filtering to orient a set of inequalities; and (2) how the choice of the argument filtering influences the set of inequalities that have to be oriented. We have implemented our contributions in the termination prover AProVE. Extensive experiments show that by our encoding and the application of SAT solvers one obtains speedups in orders of magnitude as well as increased termination proving power.","Title: Termination Analysis for Partial Functions\nYear: 1996\n\nAbstract: . This paper deals with automated termination analysis forpartial functional programs, i.e. for functional programs which do notterminate for each input. We present a method to determine their domains (resp. non-trivial subsets of their domains) automatically. Moreprecisely, for each functional program a termination predicate algorithmis synthesized, which only returns true for inputs where the program isterminating. To ease subsequent reasoning about the generated terminationpredicates ...","Title: Termination Analysis by Inductive Evaluation\nYear: 1998\n\nAbstract: . We present a new approach for automatic termination analysis of functional programs.Several methods have been presented which try to find a well-founded ordering suchthat the arguments in the recursive calls are smaller than the corresponding inputs. However,previously developed approaches for automated termination analysis often disregardthe conditions under which the recursive calls are evaluated. Hence, the existing methods failfor an important class of algorithms where the...","Title: Transforming Context-Sensitive Rewrite Systems\nYear: 1999\n\nAbstract: We present two new transformation techniques for proving termination of context-sensitive rewriting. Our first method is simple, sound, and more powerful than previously suggested transformations. However, it is not complete, i.e., there are terminating context-sensitive rewrite systems that are transformed into non-terminating term rewrite systems. The second method that we present in this paper is both sound and complete. This latter result can be interpreted as stating that from a termination perspective there is no reason to study context-sensitive rewriting.","Title: Termination graphs for Java bytecode\nYear: 2010\n\nAbstract: To prove termination of Java Bytecode (JBC) automatically, we transform JBC to finite termination graphs which represent all possible runs of the program. Afterwards, the graph can be translated into \"simple\" formalisms like term rewriting and existing tools can be used to prove termination of the resulting term rewrite system (TRS). In this paper we show that termination graphs indeed capture the semantics of JBC correctly. Hence, termination of the TRS resulting from the termination graph implies termination of the original JBC program.","Title: The Semantics of Rational Contractions\nYear: 1993\n\nAbstract: The Logic of Theory Change developed by Alchourrón, Gärdenfors and Makinson is concerned with the revision of beliefs in the face of new and possibly contradicting information. This nonmonotonic process consists of a contraction and an expansion transforming one belief into another. Beliefs are represented by consistent deductively closed sets of formulas. To achieve minimal change Alchourrón, Gärdenfors and Makinson suggested widely accepted postulates that rational contractions have to fulfill.","Title: Innermost Termination of Context-Sensitive Rewriting\nYear: 2002\n\nAbstract: Context-sensitive rewriting is a restriction of term rewriting used to model evaluation strategies in functional programming and in programming languages like OBJ. For example, under certain conditions termination of an OBJ program is equivalent to innermost termination of the corresponding context-sensitive rewrite system (18). To prove ter- mination of context-sensitive rewriting, several methods have been pro- posed in the literature which transform context-sensitive rewrite systems into ordinary rewrite systems such that termination of the transformed ordinary system implies termination of the original context-sensitive sys- tem. Most of these transformations are not very satisfactory when it comes to proving innermost termination. We investigate the relation- ship between termination and innermost termination of context-sensitive rewriting and we examine the applicability of the different transforma- tions for innermost termination proofs. Finally, we present a simple trans- formation which is both sound and complete for innermost termination.","Title: Pushing the Frontiers of Combining Rewrite Systems Farther Outwards.\nYear: 1998\n\nAbstract: It is well known that simple termination is modular for certain kindsof combinations of term rewriting systems (TRSs). This result is ofpractical relevance because most techniques for (automated) terminationproofs use simplification orderings, so they show in fact simple termination.On the other hand, in practice many systems are non-simplyterminating. In order to cope with such systems, Arts and Giesl developedthe dependency pair approach. By using (quasi-)simplificationorderings in...","Title: Automated detection of non-termination and nullpointerexceptions for Java Bytecode\nYear: 2011\n\nAbstract: Recently, we developed an approach for automated termination proofs of Java Bytecode (JBC), which is based on constructing and analyzing termination graphs. These graphs represent all possible program executions in a finite way. In this paper, we show that this approach can also be used to detect non-termination or NullPointerExceptions. Our approach automatically generates witnesses, i.e., calling the program with these witness arguments indeed leads to non-termination resp. to a NullPointerException. Thus, we never obtain \"false positives\". We implemented our results in the termination prover AProVE and provide experimental evidence for the power of our approach.","Title: Analyzing Innermost Runtime Complexity of Term Rewriting by Dependency Pairs.\nYear: 2013\n\nAbstract: We present a modular framework to analyze the innermost runtime complexity of term rewrite systems automatically. Our method is based on the dependency pair framework for termination analysis. In contrast to previous work, we developed a adaptation of successful termination techniques from the dependency pair framework in order to use them for complexity analysis. By extensive experimental results, we demonstrate the power of our method compared to existing techniques.","Title: Implementing RPO and POLO using SAT\nYear: 2007\n\nAbstract: Well-founded orders are the most basic, but also most impor- tant ingredient to virtually all termination analyses. Numerous fully au- tomated search algorithms for these classes have therefore been devised and implemented in termination tools. Unfortunately, for termination problems occurring in practice, the performance of existing algorithms is often insufficient. Performance can be improved significantly by reducing these search prob- lems to decision problems for which more efficient algorithmsalready exist. Here, we introduce an encoding of RPO and POLO to the satis- fiability of propositional logic (SAT). We implemented these encodings in our termination tool AProVE. Extensive experiments have shown that one can obtain speedups in orders of magnitude by this encoding and the application of modern SAT solvers.","Title: Termination Analysis by Dependency Pairs and Inductive Theorem Proving\nYear: 2009\n\nAbstract: Current techniques and tools for automated termination analysis of term rewrite systems (TRSs) are already very powerful. However, they fail for algorithms whose termination is essentially due to an inductive argument. Therefore, we show how to couple the dependency pair method for TRS termination with inductive theorem proving. As confirmed by the implementation of our new approach in the tool AProVE, now TRS termination techniques are also successful on this important class of algorithms.","Title: Verification of Erlang Processes by Dependency Pairs\nYear: 2001\n\nAbstract: Erlang is a functional programming language developed by Ericsson Telecom, which is particularly well suited for implementing concurrent processes. In this paper we show how methods from the area of term rewriting are presently used at Ericsson. To verify properties of processes, such a property is transformed into a termination problem of a conditional term rewriting system (CTRS). Subsequently, this termination proof can be performed automatically using dependency pairs. The paper illustrates ...","Title: Lazy abstraction for size-change termination\nYear: 2010\n\nAbstract: Size-change termination is a widely used means of proving termination where source programs are first abstracted to size-change graphs which are then analyzed to determine if they satisfy the size-change termination property. Here, the choice of the abstraction is crucial to the success of the method, and it is an open problem how to choose an abstraction such that no critical loss of precision occurs. This paper shows how to couple the search for a suitable abstraction and the test for size-change termination via an encoding to a single SAT instance. In this way, the problem of choosing the right abstraction is solved en passant by a SAT solver. We show that for the setting of term rewriting, the integration of this approach into the dependency pair framework works smoothly and gives rise to a new class of size-change reduction pairs. We implemented size-change reduction pairs in the termination prover AProVE and evaluated their usefulness in extensive experiments.","Title: Automated termination analysis for Haskell: from term rewriting to programming languages\nYear: 2006\n\nAbstract: There are many powerful techniques for automated termination analysis of term rewriting. However, up to now they have hardly been used for real programming languages. We present a new approach which permits the application of existing techniques from term rewriting in order to prove termination of programs in the functional language Haskell. In particular, we show how termination techniques for ordinary rewriting can be used to handle those features of Haskell which are missing in term rewriting (e.g., lazy evaluation, polymorphic types, and higher-order functions). We implemented our results in the termination prover AProVE and successfully evaluated them on existing Haskell-libraries.","Title: Automated Termination Proofs with Measure Functions\nYear: 1995\n\nAbstract: . This paper deals with the automation of termination proofsfor recursively defined algorithms (i.e. algorithms in a pure functionallanguage). Previously developed methods for their termination proofseither had a low degree of automation or they were restricted to onesingle fixed measure function to compare data objects. To overcome thesedrawbacks we introduce a calculus for automated termination proofswhich is able to handle arbitrary measure functions based on polynomialnorms.1...","Title: Search Techniques for Rational Polynomial Orders\nYear: 2008\n\nAbstract: Polynomial interpretations are a standard technique used in almost all tools for proving termination of term rewrite systems (TRSs) automatically. Traditionally, one applies interpretations with polynomials over the naturals. But recently, it was shown that interpretations with polynomials over the rationals can be significantly more powerful. However, searching for such interpretations is considerably more difficult than for natural polynomials. Moreover, while there exist highly efficient SAT-based techniques for finding natural polynomials, no such techniques had been developed for rational polynomials yet. In this paper, we tackle the two main problems when applying rational polynomial interpretations in practice: (1) We develop new criteria to decide when to use rational instead of natural polynomial interpretations. (2) Afterwards, we present SAT-based methods for finding rational polynomial interpretations and evaluate them empirically.","Title: Dependency triples for improving termination analysis of logic programs with cut\nYear: 2010\n\nAbstract: In very recent work, we introduced a non-termination preserving transformation from logic programs with cut to definite logic programs. While that approach allows us to prove termination of a large class of logic programs with cut automatically, in several cases the transformation results in a non-terminating definite logic program. In this paper we extend the transformation such that logic programs with cut are no longer transformed into definite logic programs, but into dependency triple problems. By the implementation of our new method and extensive experiments, we empirically evaluate the practical benefit of our contributions.","Title: Proving Termination of Programs Automatically with AProVE.\nYear: 2014\n\nAbstract: AProVE is a system for automatic termination and complexity proofs of Java, C, Haskell, Prolog, and term rewrite systems (TRSs). To analyze programs in high-level languages, AProVE automatically converts them to TRSs. Then, a wide range of techniques is employed to prove termination and to infer complexity bounds for the resulting TRSs. The generated proofs can be exported to check their correctness using automatic certifiers. For use in software construction, we present an AProVE plug-in for the popular Eclipse software development environment.","Title: Proving Termination and Memory Safety for Programs with Pointer Arithmetic.\nYear: 2014\n\nAbstract: Proving termination automatically for programs with explicit pointer arithmetic is still an open problem. To close this gap, we introduce a novel abstract domain that can track allocated memory in detail. We use it to automatically construct a symbolic execution graph that represents all possible runs of the program and that can be used to prove memory safety. This graph is then transformed into an integer transition system, whose termination can be proved by standard techniques. We implemented this approach in the automated termination prover AProVE and demonstrate its capability of analyzing C programs with pointer arithmetic that existing tools cannot handle.","Title: Termination Competition (termCOMP 2015)\nYear: 2015\n\nAbstract: The termination competition focuses on automated termination analysis for all kinds of programming paradigms, including categories for term rewriting, imperative programming, logic programming, and functional programming. Moreover, the competition also features categories for automated complexity analysis. In all categories, the competition also welcomes the participation of tools providing certified proofs. The goal of the termination competition is to demonstrate the power of the leading tools in each of these areas.","Title: Inferring Lower Bounds for Runtime Complexity.\nYear: 2015\n\nAbstract: We present the first approach to deduce lower bounds for innermost runtime complexity of term rewrite systems (TRSs) automatically. Inferring lower runtime bounds is useful to detect bugs and to complement existing techniques that compute upper complexitybounds. The key idea of our approach is to generate suitable families of rewrite sequences of a TRS and to find a relation between the length of such a rewrite sequence and the size of the first term in the sequence. We implemented our approach inthe tool AProVE and evaluated it by extensive experiments.","Title: Lower Runtime Bounds for Integer Programs.\nYear: 2016\n\nAbstract: We present a technique to infer lower bounds on the worst-case runtime complexity of integer programs. To this end, we construct symbolic representations of program executions using a framework for iterative, under-approximating program simplification. The core of this simplification is a method for under-approximating program acceleration based on recurrence solving and a variation of ranking functions. Afterwards, we deduce asymptotic lower bounds from the resulting simplified programs. We implemented our technique in our tool LoAT and show that it infers non-trivial lower bounds for a large number of examples.","Title: Proving Termination of Programs with Bitvector Arithmetic by Symbolic Execution.\nYear: 2016\n\nAbstract: In earlier work, we developed an approach for automated termination analysis of C programs with explicit pointer arithmetic, which is based on symbolic execution. However, similar to many other termination techniques, this approach assumed the program variables to range over mathematical integers instead of bitvectors. This eases mathematical reasoning but is unsound in general. In this paper, we extend our approach in order to handle fixed-width bitvector integers. Thus, we present the first technique for termination analysis of C programs that covers both byte-accurate pointer arithmetic and bit-precise modeling of integers. We implemented our approach in the automated termination prover AProVE and evaluate its power by extensive experiments.","Title: Analyzing Runtime and Size Complexity of Integer Programs.\nYear: 2016\n\nAbstract: We present a modular approach to automatic complexity analysis of integer programs. Based on a novel alternation between finding symbolic time bounds for program parts and using these to infer bounds on the absolute values of program variables, we can restrict each analysis step to a small part of the program while maintaining a high level of precision. The bounds computed by our method are polynomial or exponential expressions that depend on the absolute values of input parameters. We show how to extend our approach to arbitrary cost measures, allowing the use of our technique to find upper bounds for other expended resources, such as network requests or memory consumption. Our contributions are implemented in the open-source tool KoAT, and extensive experiments show the performance and power of our implementation in comparison with other tools.","Title: Automatically Proving Termination and Memory Safety for Programs with Pointer Arithmetic.\nYear: 2017\n\nAbstract: While automated verification of imperative programs has been studied intensively, proving termination of programs with explicit pointer arithmetic fully automatically was still an open problem. To close this gap, we introduce a novel abstract domain that can track allocated memory in detail. We use it to automatically construct a symbolic execution graph that over-approximates all possible runs of a program and that can be used to prove memory safety. This graph is then transformed into an integer transition system, whose termination can be proved by standard techniques. We implemented this approach in the automated termination prover AProVE and demonstrate its capability of analyzing C programs with pointer arithmetic that existing tools cannot handle.","Title: Lower Bounds for Runtime Complexity of Term Rewriting.\nYear: 2017\n\nAbstract: We present the first approach to deduce lower bounds for (worst-case) runtime complexity of term rewrite systems (TRSs) automatically. Inferring lower runtime bounds is useful to detect bugs and to complement existing methods that compute upper complexity bounds. Our approach is based on two techniques: the induction technique generates suitable families of rewrite sequences and uses induction proofs to find a relation between the length of a rewrite sequence and the size of the first term in the sequence. The loop detection technique searches for \\\"decreasing loops\\\". Decreasing loops generalize the notion of loops for TRSs, and allow us to detect families of rewrite sequences with linear, exponential, or infinite length. We implemented our approach in the tool AProVE and evaluated it by extensive experiments.","Title: Complexity Analysis for Term Rewriting by Integer Transition Systems.\nYear: 2017\n\nAbstract: We present a new method to infer upper bounds on the innermost runtime complexity of term rewrite systems (TRSs), which benefits from recent advances on complexity analysis of integer transition systems (ITSs). To this end, we develop a transformation from TRSs to a generalized notion of ITSs with (possibly non-tail) recursion. To analyze their complexity, we introduce a modular technique which allows us to use existing tools for standard ITSs in order to infer complexity bounds for our generalized ITSs. The key idea of our technique is a summarization method that allows us to analyze components of the transition system independently. We implemented our contributions in the tool AProVE, and our experiments show that one can now infer bounds for significantly more TRSs than with previous state-of-the-art tools for term rewriting.","Title: Constant runtime complexity of term rewriting is semi-decidable.\nYear: 2018\n\nAbstract: •It is semi-decidable if the runtime complexity of a term rewrite system is constant.•Constant complexity corresponds to termination of a restricted form of narrowing.•This termination property can be checked by only examining finitely many terms.•The new semi-decision procedure was implemented in the tool AProVE.•The implementation solves many examples where automatic tools failed before.","Title: Proving Innermost Normalisation Automatically\nYear: 1997\n\nAbstract: We present a technique to prove innermost normalisation of term rewritingsystems (TRSs) automatically. In contrast to previous methods, our techniqueis able to prove innermost normalisation of TRSs that are not terminating.","Title: Analyzing Program Termination and Complexity Automatically with AProVE.\nYear: 2017\n\nAbstract: In this system description, we present the tool AProVE for automatic termination and complexity proofs of Java, C, Haskell, Prolog, and rewrite systems. In addition to classical term rewrite systems (TRSs), AProVE also supports rewrite systems containing built-in integers (int-TRSs). To analyze programs in high-level languages, AProVE automatically converts them to (int-)TRSs. Then, a wide range of techniques is employed to prove termination and to infer complexity bounds for the resulting rewrite systems. The generated proofs can be exported to check their correctness using automatic certifiers. To use AProVE in software construction, we present a corresponding plug-in for the popular Eclipse software development environment.","Title: Complexity Analysis for Java with AProVE.\nYear: 2017\n\nAbstract: While AProVE is one of the most powerful tools for termination analysis of Java since many years, we now extend our approach in order to analyze the complexity of Java programs as well. Based on a symbolic execution of the program, we develop a novel transformation of (possibly heap-manipulating) Java programs to integer transition systems (ITSs). This allows us to use existing complexity analyzers for ITSs to infer runtime bounds for Java programs. We demonstrate the power of our implementation on an established standard benchmark set.","Title: Termination and complexity analysis for programs with bitvector arithmetic by symbolic execution.\nYear: 2018\n\nAbstract: In earlier work, we developed an approach for automated termination analysis of C programs with explicit pointer arithmetic, which is based on symbolic execution. However, similar to many other termination techniques, this approach assumed the program variables to range over mathematical integers instead of bitvectors. This eases mathematical reasoning but is unsound in general. In this paper, we extend our approach in order to handle fixed-width bitvector integers. Thus, we present the first technique for termination analysis of C programs that covers both byte-accurate pointer arithmetic and bit-precise modeling of integers. Moreover, we show that our approach can also be used to analyze the runtime complexity of bitvector programs. We implemented our contributions in the automated termination prover AProVE and evaluate its power by extensive experiments.","Title: Proving Non-Termination and Lower Runtime Bounds with LoAT (System Description).\nYear: 2022\n\nAbstract: We present the new version of the Loop Acceleration Tool (LoAT), a powerful tool for proving non-termination and worst-case lower bounds for programs operating on integers. It is based on novel calculi for loop acceleration, i.e., transforming loops into non-deterministic straight-line code, and for finding non-terminating configurations. To implement them efficiently, LoAT uses a new approach based on SMT solving and unsat cores. An extensive evaluation shows that LoAT is highly competitive with other state-of-the-art tools for proving non-termination. While no other tool is able to deduce worst-case lower bounds for full integer programs, we also demonstrate that LoAT significantly outperforms its predecessors.","Title: Automatic Complexity Analysis of Integer Programs via Triangular Weakly Non-Linear Loops.\nYear: 2022\n\nAbstract: There exist several results on deciding termination and computing runtime bounds for triangular weakly non-linear loops (twn-loops). We show how to use results on such subclasses of programs where complexity bounds are computable within incomplete approaches for complexity analysis of full integer programs. To this end, we present a novel modular approach which computes local runtime bounds for subprograms which can be transformed into twn-loops. These local runtime bounds are then lifted to global runtime bounds for the whole program. The power of our approach is shown by our implementation in the tool KoAT which analyzes complexity of programs where all other state-of-the-art tools fail."],"string":"[\n \"Title: Improving Dependency Pairs\\nYear: 2003\\n\\nAbstract: The dependency pair approach is one of the most powerful techniques for termination and innermost termination proofs of term rewrite systems (TRSs). For any TRS, it generates inequality constraints that have to be satisfied by weakly monotonic well-founded orders. We improve the dependency pair approach by considerably reducing the number of constraints produced for (innermost) termination proofs. Moreover, we extend transformation techniques to manipulate dependency pairs which simplify (innermost) termination proofs significantly. In order to fully automate the dependency pair approach, we show how transformation techniques and the search for suitable orders can be mechanized efficiently. We implemented our results in the automated termination prover AProVE and evaluated them on large collections of examples.\",\n \"Title: Eliminating Dummy Elimination\\nYear: 2000\\n\\nAbstract: This paper is concerned with methods that automatically prove termination of term rewrite systems. The aim of dummy elimina- tion, a method to prove termination introduced by Ferreira and Zantema, is to transform a given rewrite system into a rewrite system whose termi- nation is easier to prove. We show that dummy elimination is subsumed by the more recent dependency pair method of Arts and Giesl. More pre- cisely, if dummy elimination succeeds in transforming a rewrite system into a so-called simply terminating rewrite system then termination of the given rewrite system can be directly proved by the dependency pair technique. Even stronger, using dummy elimination as a preprocessing step to the dependency pair technique does not have any advantages either. We show that to a large extent these results also hold for the argument ltering transformation of Kusakari et al.\",\n \"Title: Deaccumulation - Improving Provability\\nYear: 2003\\n\\nAbstract: Several induction theorem provers were developed to verify functional programs mechanically. Unfortunately, automated verification usually fails for functions with accumulating arguments. In particular, this holds for tail-recursive functions that correspond to imperative programs, but also for programs with nested recursion. Based on results from the theory of tree transducers, we develop an automatic transformation technique. It transforms accumulative functional programs into non-accumulative ones, which are much better suited for automated verification by induction theorem provers. Hence, in contrast to classical program transformations aiming at improving the efficiency, the goal of our deaccumulation technique is to improve the provability.\",\n \"Title: Deciding Innermost Loops\\nYear: 2008\\n\\nAbstract: We present the rst method to disprove innermost termina- tion of term rewrite systems automatically. To this end, we rst develop a suitable notion of an innermost loop. Second, we show how to detect in- nermost loops: One can start with any technique amenable to nd loops. Then our novel procedure can be applied to decide whether a given loop is an innermost loop. We implemented and successfully evaluated our method in the termination prover AProVE.\",\n \"Title: Liveness in Rewriting\\nYear: 2003\\n\\nAbstract: In this paper, we show how the problem of verifying liveness properties is related to termination of term rewrite systems (TRSs). We formalize liveness in the framework of rewriting and present a sound and complete transformation to transform particular liveness problems into TRSs. Then the transformed TRS terminates if and only if the original liveness property holds. This shows that liveness and termination are essentially equivalent. To apply our approach in practice, we introduce a simpler sound transformation which only satisfies the 'only if'-part. By refining existing techniques for proving termination of TRSs we show how liveness properties can be verified automatically. As examples, we prove a liveness property of a waiting line protocol for a network of processes and a liveness property of a protocol on a ring of processes.\",\n \"Title: Termination of Isabelle functions via termination of rewriting\\nYear: 2011\\n\\nAbstract: We show how to automate termination proofs for recursive functions in (a first-order subset of) Isabelle/HOL by encoding them as term rewrite systems and invoking an external termination prover. Our link to the external prover includes full proof reconstruction, where all necessary properties are derived inside Isabelle/HOL without oracles. Apart from the certification of the imported proof, the main challenge is the formal reduction of the proof obligation produced by Isabelle/HOL to the termination of the corresponding term rewrite system. We automate this reduction via suitable tactics which we added to the IsaFoR library.\",\n \"Title: Generating Polynomial Orderings for Termination Proofs\\nYear: 1995\\n\\nAbstract: Most systems for the automation of termination proofs using polynomial orderings are only semi-automatic, i.e. the "right" polynomial ordering has to be given by the user. We show that a variation of Lankford's partial derivative technique leads to an easier and slightly more powerful method than most other semi-automatic approaches. Based on this technique we develop a method for the automated synthesis of a suited polynomial ordering.\",\n \"Title: Modular Termination Proofs of Recursive Java Bytecode Programs by Term Rewriting\\nYear: 2011\\n\\nAbstract: In [5, 15] we presented an approach to prove termination of non-recursive Java Bytecode (JBC) programs automatically. Here, JBC programs are first transformed to finite termination graphs which represent all possible runs of the program. Afterwards, the termination graphs are translated to term rewrite systems (TRSs) such that termination of the resulting TRSs implies termination of the original JBC programs. So in this way, existing techniques and tools from term rewriting can be used to prove termination of JBC automatically. In this paper, we improve this approach substantially in two ways: (1) We extend it in order to also analyze recursive JBC programs. To this end, one has to represent call stacks of arbitrary size. (2) To handle JBC programs with several methods, we modularize our approach in order to reuse termination graphs and TRSs for the separate methods and to prove termination of the resulting TRS in a modular way. We implemented our approach in the tool AProVE. Our experiments show that the new contributions increase the power of termination analysis for JBC significantly.\",\n \"Title: Loops under Strategies ... Continued\\nYear: 2010\\n\\nAbstract: While there are many approaches for automatically proving termination of term\\nrewrite systems, up to now there exist only few techniques to disprove their\\ntermination automatically. Almost all of these techniques try to find loops,\\nwhere the existence of a loop implies non-termination of the rewrite system.\\nHowever, most programming languages use specific evaluation strategies, whereas\\nloop detection techniques usually do not take strategies into account. So even\\nif a rewrite system has a loop, it may still be terminating under certain\\nstrategies.\\n Therefore, our goal is to develop decision procedures which can determine\\nwhether a given loop is also a loop under the respective evaluation strategy.\\nIn earlier work, such procedures were presented for the strategies of\\ninnermost, outermost, and context-sensitive evaluation. In the current paper,\\nwe build upon this work and develop such decision procedures for important\\nstrategies like leftmost-innermost, leftmost-outermost,\\n(max-)parallel-innermost, (max-)parallel-outermost, and forbidden patterns\\n(which generalize innermost, outermost, and context-sensitive strategies). In\\nthis way, we obtain the first approach to disprove termination under these\\nstrategies automatically.\",\n \"Title: Strategies for Semantical Contractions\\nYear: 1994\\n\\nAbstract: Without Abstract\",\n \"Title: Automated termination proofs for haskell by term rewriting\\nYear: 2011\\n\\nAbstract: There are many powerful techniques for automated termination analysis of term rewriting. However, up to now they have hardly been used for real programming languages. We present a new approach which permits the application of existing techniques from term rewriting to prove termination of most functions defined in Haskell programs. In particular, we show how termination techniques for ordinary rewriting can be used to handle those features of Haskell which are missing in term rewriting (e.g., lazy evaluation, polymorphic types, and higher-order functions). We implemented our results in the termination prover AProVE and successfully evaluated them on existing Haskell libraries.\",\n \"Title: SAT-based termination analysis using monotonicity constraints over the integers.\\nYear: 2011\\n\\nAbstract: We describe an algorithm for proving termination of programs abstracted to systems of monotonicity constraints in the integer domain. Monotonicity constraints are a nontrivial extension of the well-known size-change termination method. While deciding termination for systems of monotonicity constraints is PSPACE complete, we focus on a well-defined and significant subset, which we call MCNP (for \\\"monotonicity constraints in NP\\\"), designed to be amenable to a SAT-based solution. Our technique is based on the search for a special type of ranking function defined in terms of bounded differences between multisets of integer values. We describe the application of our approach as the back end for the termination analysis of Java Bytecode. At the front end, systems of monotonicity constraints are obtained by abstracting information, using two different termination analyzers: AProVE and COSTA. Preliminary results reveal that our approach provides a good trade-off between precision and cost of analysis.\",\n \"Title: Proving and disproving termination of higher-order functions\\nYear: 2005\\n\\nAbstract: The dependency pair technique is a powerful modular method for automated termination proofs of term rewrite systems (TRSs). We present two important extensions of this technique: First, we show how to prove termination of higher-order functions using dependency pairs. To this end, the dependency pair technique is extended to handle (untyped) applicative TRSs. Second, we introduce a method to prove non-termination with dependency pairs, while up to now dependency pairs were only used to verify termination. Our results lead to a framework for combining termination and non-termination techniques for first- and higher-order functions in a very flexible way. We implemented and evaluated our results in the automated termination prover AProVE.\",\n \"Title: Automatically Proving Termination Where Simplification Orderings Fail\\nYear: 1997\\n\\nAbstract: To prove termination of term rewriting systems (TRSs), several methodshave been developed to synthesize suitable well-founded orderings automatically.However, virtually all orderings that are amenable to automation areso-called simplification orderings. Unfortunately, there exist numerous interestingand relevant TRSs that cannot be oriented by orderings of this restrictedclass and therefore their termination cannot be proved automatically with theexisting techniques.In this paper we...\",\n \"Title: SAT solving for termination analysis with polynomial interpretations\\nYear: 2007\\n\\nAbstract: Polynomial interpretations are one of the most popular techniques for automated termination analysis and the search for such interpretations is a main bottleneck in most termination provers. We show that one can obtain speedups in orders of magnitude by encoding this task as a SAT problem and by applying modern SAT solvers.\",\n \"Title: Proving Termination of Integer Term Rewriting\\nYear: 2009\\n\\nAbstract: When using rewrite techniques for termination analysis of programs, a main problem are pre-defined data types like integers. We extend term rewriting by built-in integers and adapt the dependency pair framework to prove termination of integer term rewriting automatically.\",\n \"Title: Automatic Termination Proofs in the Dependency Pair Framework\\nYear: 2006\\n\\nAbstract: AProVE 1.2 is one of the most powerful systems for automated termination proofs of term rewrite systems (TRSs). It is the first tool\\n which automates the new dependency pair framework [8] and therefore permits a completely flexible combination of different termination proof techniques. Due to this framework,\\n AProVE 1.2 is also the first termination prover which can be fully configured by the user.\",\n \"Title: Symbolic evaluation graphs and term rewriting: a general methodology for analyzing logic programs\\nYear: 2012\\n\\nAbstract: There exist many powerful techniques to analyze termination and complexity of term rewrite systems (TRSs). Our goal is to use these techniques for the analysis of other programming languages as well. For instance, approaches to prove termination of definite logic programs by a transformation to TRSs have been studied for decades. However, a challenge is to handle languages with more complex evaluation strategies (such as Prolog, where predicates like the cut influence the control flow). In this paper, we present a general methodology for the analysis of such programs. Here, the logic program is first transformed into a symbolic evaluation graph which represents all possible evaluations in a finite way. Afterwards, different analyses can be performed on these graphs. In particular, one can generate TRSs from such graphs and apply existing tools for termination or complexity analysis of TRSs to infer information on the termination or complexity of the original logic program.\",\n \"Title: Context-Moving Transformations for Function Verification\\nYear: 1999\\n\\nAbstract: Several induction theorem provers have been developed which support mechanized verification of functional programs. Unfortunately, a major problem is that they often fail in verifying tail recursive functions (which correspond to imperative programs). However, in practice imperative programs are used almost exclusively. We present an automatic transformation to tackle this problem. It transforms functions which are hard to verify into functions whose correctness can be shown by the existing...\",\n \"Title: Proving non-looping non-termination automatically\\nYear: 2012\\n\\nAbstract: We introduce a technique to prove non-termination of term rewrite systems automatically. Our technique improves over previous approaches substantially, as it can also detect non-looping non-termination.\",\n \"Title: A linear operational semantics for termination and complexity analysis of ISO prolog\\nYear: 2011\\n\\nAbstract: We present a new operational semantics for Prolog which covers all constructs in the corresponding ISO standard (including \\\"non-logical\\\" concepts like cuts, meta-programming, \\\"all solution\\\" predicates, dynamic predicates, and exception handling). In contrast to the classical operational semantics for logic programming, our semantics is linear and not based on search trees. This has the advantage that it is particularly suitable for automated program analyses such as termination and complexity analysis. We prove that our new semantics is equivalent to the ISOProlog semantics, i.e., it computes the same answer substitutions and the derivations in both semantics have essentially the same length.\",\n \"Title: Closure Induction in a Z-Like Language\\nYear: 2000\\n\\nAbstract: Simply-typed set-theoretic languages such as Z and B are widely used for program and system specifications. The main technique for reasoning about such specifications is induction. However, while partiality is an important concept in these languages, many standard approaches to automating induction proofs rely on the totality of all occurring functions. Reinterpreting the second author's recently proposed induction technique for partial functional programs, we introduce in this paper the new principle of \\\"closure induction\\\" for reasoning about the inductive properties of partial functions in simply-typed set-theoretic languages. In particular, closure induction allows us to prove partial correctness, that is, to prove those instances of conjectures for which designated partial functions are explicitly defined.\",\n \"Title: Improved Modular Termination Proofs Using Dependency Pairs\\nYear: 2004\\n\\nAbstract: The dependency pair approach is one of the most powerful techniques for automated (innermost) termination proofs of term rewrite systems (TRSs). For any TRS, it generates inequality constraints that have to be satisfied by well-founded orders. However, proving innermost termination is considerably easier than termination, since the constraints for innermost termination are a subset of those for termination. We show that surprisingly, the dependency pair approach for termination can be improved by only generating the same constraints as for innermost termination. In other words, proving full termination becomes virtually as easy as proving innermost termination. Our results are based on splitting the termination proof into several modular independent subproofs. We implemented our contributions in the automated termination prover AProVE and evaluated them on large collections of examples. These experiments show that our improvements increase the power and efficiency of automated termination proving substantially.\",\n \"Title: Size-change termination for term rewriting\\nYear: 2003\\n\\nAbstract: In [13], a new size-change principle was proposed to verify termination of functional programs automatically. We extend this principle in order to prove termination and innermost termination of arbitrary term rewrite systems (TRSs). Moreover, we compare this approach with existing techniques for termination analysis of TRSs (such as recursive path orderings or dependency pairs). It turns out that the size-change principle on its own fails for many examples that can be handled by standard techniques for rewriting, but there are also TRSs where it succeeds whereas existing rewriting techniques fail. In order to benefit from their respective advantages, we show how to combine the size-change principle with classical orderings and with dependency pairs. In this way, we obtain a new approach for automated termination proofs of TRSs which is more powerful than previous approaches.\",\n \"Title: The dependency triple framework for termination of logic programs\\nYear: 2009\\n\\nAbstract: We show how to combine the two most powerful approaches for automated termination analysis of logic programs (LPs): the direct approach which operates directly on LPs and the transformational approach which transforms LPs to term rewrite systems (TRSs) and tries to prove termination of the resulting TRSs. To this end, we adapt the well-known dependency pair framework from TRSs to LPs. With the resulting method, one can combine arbitrary termination techniques for LPs in a completely modular way and one can use both direct and transformational techniques for different parts of the same LP.\",\n \"Title: Termination of Nested and Mutually Recursive Algorithms\\nYear: 1997\\n\\nAbstract: This paper deals with automated termination analysis for functional programs. Previously developed methods for automated termination proofs of functional programs often fail for algorithms with nested recursion and they cannot handle algorithms with mutual recursion.We show that termination proofs for nested and mutually recursive algorithms can be performed without having to prove the correctness of the algorithms simultaneously. Using this result, nested and mutually recursive algorithms do no longer constitute a special problem and the existing methods for automated termination analysis can be extended to nested and mutual recursion in a straightforward way. We give some examples of algorithms whose termination can now be proved automatically (including well-known challenge problems such as McCarthy’s f_91 function).\",\n \"Title: Automated termination proofs for logic programs by term rewriting\\nYear: 2008\\n\\nAbstract: There are two kinds of approaches for termination analysis of logic programs: “transformational” and “direct” ones. Direct approaches prove termination directly on the basis of the logic program. Transformational approaches transform a logic program into a Term Rewrite System (TRS) and then analyze termination of the resulting TRS instead. Thus, transformational approaches make all methods previously developed for TRSs available for logic programs as well. However, the applicability of most existing transformations is quite restricted, as they can only be used for certain subclasses of logic programs. (Most of them are restricted to well-moded programs.) In this article we improve these transformations such that they become applicable for any definite logic program. To simulate the behavior of logic programs by TRSs, we slightly modify the notion of rewriting by permitting infinite terms. We show that our transformation results in TRSs which are indeed suitable for automated termination analysis. In contrast to most other methods for termination of logic programs, our technique is also sound for logic programming without occur check, which is typically used in practice. We implemented our approach in the termination prover AProVE and successfully evaluated it on a large collection of examples.\",\n \"Title: Automated termination analysis for logic programs by term rewriting\\nYear: 2006\\n\\nAbstract: There are two kinds of approaches for termination analysis of logic programs: \\\"transformational\\\" and \\\"direct\\\" ones. Direct approaches prove termination directly on the basis of the logic program. Transformational approaches transform a logic program into a term rewrite system (TRS) and then analyze termination of the resulting TRS instead. Thus, transformational approaches make all methods previously developed for TRSs available for logic programs as well. However, the applicability of most existing transformations is quite restricted, as they can only be used for certain subclasses of logic programs. (Most of them are restricted to well-moded programs.) In this paper we improve these transformations such that they become applicable for any definite logic program. To simulate the behavior of logic programs by TRSs, we slightly modify the notion of rewriting by permitting infinite terms. We show that our transformation results in TRSs which are indeed suitable for automated termination analysis. In contrast to most other methods for termination of logic programs, our technique is also sound for logic programming without occur check, which is typically used in practice. We implemented our approach in the termination prover AProVE and successfully evaluated it on a large collection of examples.\",\n \"Title: Automated termination proofs for Java programs with cyclic data\\nYear: 2012\\n\\nAbstract: In earlier work, we developed a technique to prove termination of Java programs automatically: first, Java programs are automatically transformed to term rewrite systems (TRSs) and then, existing methods and tools are used to prove termination of the resulting TRSs. In this paper, we extend our technique in order to prove termination of algorithms on cyclic data such as cyclic lists or graphs automatically. We implemented our technique in the tool AProVE and performed extensive experiments to evaluate its practical applicability.\",\n \"Title: The Dependency Pair Framework: Combining Techniques for Automated Termination Proofs.\\nYear: 2004\\n\\nAbstract: The dependency pair approach is one of the most powerful techniques for automated termination proofs of term rewrite systems. Up to now, it was regarded as one of several possible methods to prove termination. In this paper, we show that dependency pairs can instead be used as a general concept to integrate arbitrary techniques for termination analysis. In this way, the benefits of different techniques can be combined and their modularity and power are increased significantly. We refer to this new concept as the \\\"dependency pair framework\\\" to distinguish it from the old \\\"dependency pair approach\\\". Moreover, this framework facilitates the development of new methods for termination analysis. To demonstrate this, we present several new techniques within the dependency pair framework which simplify termination problems considerably. We implemented the dependency pair framework in our termination prover AProVE and evaluated it on large collections of examples.\",\n \"Title: SAT Solving for Termination Proofs with Recursive Path Orders and Dependency Pairs\\nYear: 2012\\n\\nAbstract: This paper introduces a propositional encoding for recursive path orders (RPO), in connection with dependency pairs. Hence, we capture in a uniform setting all common instances of RPO, i.e., lexicographic path orders (LPO), multiset path orders (MPO), and lexicographic path orders with status (LPOS). This facilitates the application of SAT solvers for termination analysis of term rewrite systems (TRSs). We address four main inter-related issues and show how to encode them as satisfiability problems of propositional formulas that can be efficiently handled by SAT solving: (A) the lexicographic comparison w.r.t. a permutation of the arguments; (B) the multiset extension of a base order; (C) the combined search for a path order together with an argument filter to orient a set of inequalities; and (D) how the choice of the argument filter influences the set of inequalities that have to be oriented (so-called usable rules). We have implemented our contributions in the termination prover AProVE. Extensive experiments show that by our encoding and the application of SAT solvers one obtains speedups in orders of magnitude as well as increased termination proving power.\",\n \"Title: Equational Termination by Semantic Labelling\\nYear: 2000\\n\\nAbstract: Semantic labelling is a powerful tool for proving termination of term rewrite systems. The usefulness of the extension to equational term rewriting described in Zantema [24] is however rather limited. In this paper we introduce a stronger version of equational semantical labelling, parameterized by three choices: (1) the order on the underlying algebra (partial order vs. quasi-order), (2) the relation between the algebra and the rewrite system (model vs. quasi-model), and (3) the labelling of the function symbols appearing in the equations (forbidden vs. allowed). We present soundness and completeness results for the various instantiations and analyze the relationships between them. Applications of our equational semantic labelling technique include a short proof of the main result of Ferreira et al. [7]--the correctness of a version of dummy elimination for AC-rewriting which completely removes the AC-axioms-- and an extension of Zantema's distribution elimination technique [23] to the equational setting.\",\n \"Title: Termination Analysis for Functional Programs using Term Orderings\\nYear: 1995\\n\\nAbstract: . To prove the termination of a functional program there hasto be a well-founded ordering such that the arguments in each recursivecall are smaller than the corresponding inputs. In this paper we presenta procedure for automated termination proofs of functional programs. Incontrast to previously presented methods a suited well-founded orderingdoes not have to be fixed in advance by the user, but can be synthesizedautomatically.For that purpose we use approaches developed in the area of...\",\n \"Title: Improving Context-Sensitive Dependency Pairs\\nYear: 2008\\n\\nAbstract: Context-sensitive dependency pairs (CS-DPs) are currently the most powerful method for automated termination analysis of context-sensitive rewriting. However, compared to DPs for ordinary rewriting, CS-DPs suffer from two main drawbacks: (a) CS-DPs can be collapsing . This complicates the handling of CS-DPs and makes them less powerful in practice. (b) There does not exist a \\\"DP framework \\\" for CS-DPs which would allow one to apply them in a flexible and modular way. This paper solves drawback (a) by introducing a new definition of CS-DPs. With our definition, CS-DPs are always non-collapsing and thus, they can be handled like ordinary DPs. This allows us to solve drawback (b) as well, i.e., we extend the existing DP framework for ordinary DPs to context-sensitive rewriting. We implemented our results in the tool AProVE and successfully evaluated them on a large collection of examples.\",\n \"Title: Induction Proofs with Partial Functions\\nYear: 2001\\n\\nAbstract: In this paper we present a method for automated induction proofs about partial functions. We show that most well-known techniques developed for (explicit) induction theorem proving are unsound when dealing with partial functions. But surprisingly, by slightly restricting the application of these techniques, it is possible to develop a calculus for automated induction proofs with partial functions. In particular, under certain conditions one may even generate induction schemes from the recursions of nonterminating algorithms. The need for such induction schemes and the power of our calculus have been demonstrated on a large collection of nontrivial theorems (including Knuth and Bendix's critical pair lemma). In this way, existing induction theorem provers can be directly extended to partial functions without major changes of their logical framework.\",\n \"Title: Polytool: Polynomial interpretations as a basis for termination analysis of logic programs\\nYear: 2009\\n\\nAbstract: Our goal is to study the feasibility of porting termination analysis techniques developed for one programming paradigm to another paradigm. In this paper, we show how to adapt termination analysis techniques based on polynomial interpretations—very well known in the context of term rewrite systems—to obtain new (nontransformational) termination analysis techniques for definite logic programs (LPs). This leads to an approach that can be seen as a direct generalization of the traditional techniques in termination analysis of LPs, where linear norms and level mappings are used. Our extension generalizes these to arbitrary polynomials. We extend a number of standard concepts and results on termination analysis to the context of polynomial interpretations. We also propose a constraint-based approach for automatically generating polynomial interpretations that satisfy the termination conditions. Based on this approach, we implemented a new tool, called Polytool, for automatic termination analysis of LPs.\",\n \"Title: Proving Termination Using Recursive Path Orders and SAT Solving\\nYear: 2007\\n\\nAbstract: We introduce a propositional encoding of the recursive path order with status (RPO). RPO is a combination of a multiset path order and a lexicographic path order which considers permutations of the arguments in the lexicographic comparison. Our encoding allows us to apply SAT solvers in order to determine whether a given term rewrite system is RPO-terminating. Furthermore, to apply RPO within the dependency pair framework, we combined our novel encoding for RPO with an existing encoding for argument filters. We implemented our contributions in the termination prover AProVE. Our experiments show that due to our encoding, combining termination provers with SAT solvers improves the performance of RPO-implementations by orders of magnitude.\",\n \"Title: The size-change principle and dependency pairs for termination of term rewriting\\nYear: 2005\\n\\nAbstract: In [24], a new size-change principle was proposed to verify termination of functional programs automatically. We extend this principle in order to prove termination and innermost termination of arbitrary term rewrite systems (TRSs). Moreover, we compare this approach with existing techniques for termination analysis of TRSs (such as recursive path orders or dependency pairs). It turns out that the size-change principle on its own fails for many examples that can be handled by standard techniques for rewriting, but there are also TRSs where it succeeds whereas existing rewriting techniques fail. Moreover, we also compare the complexity of the respective methods. To this end, we develop the first complexity analysis for the dependency pair approach. While the size-change principle is PSPACE-complete, we prove that the dependency pair approach (in combination with classical path orders) is only -complete. To benefit from their respective advantages, we show how to combine the size-change principle with classical orders and with dependency pairs. In this way, we obtain a new approach for automated termination proofs of TRSs which is more powerful than previous approaches. We also show that the combination with dependency pairs does not increase the complexity of the size-change principle, i.e., the combined approach is still PSPACE-complete.\",\n \"Title: Approximating the domains of functional and imperative programs\\nYear: 1999\\n\\nAbstract: This paper deals with automated termination analysis of partial functional programs, that is, of functional programs which do not terminate for some input. We present a method to determine their domains (respectively non-trivial subsets of their domains) automatically. More precisely, for each functional program a termination predicate algorithm is synthesized that only returns true for inputs where the program is terminating. To ease subsequent reasoning about the generated termination predicates we also present a procedure for their simplification. Finally, we show that our method can also be used for automated termination analysis of imperative programs.\",\n \"Title: Automated Termination Analysis: From Term Rewriting to Programming Languages.\\nYear: 2013\\n\\nAbstract: Termination is a crucial property of programs. Therefore, techniques to analyze termination automatically are highly important for program verification. Traditionally, techniques for automated termination analysis were mainly studied for declarative programming paradigms such as logic programming and term rewriting. However, in the last years, several powerful techniques and tools have been developed which analyze the termination of programs in many programming languages including Java, C, Haskell, and Prolog.\",\n \"Title: Transformation techniques for context-sensitive rewrite systems\\nYear: 2004\\n\\nAbstract: Context-sensitive rewriting is a computational restriction of term rewriting used to model non-strict (lazy) evaluation in functional programming. The goal of this paper is the study and development of techniques to analyze the termination behavior of context-sensitive rewrite systems. For that purpose, several methods have been proposed in the literature which transform context-sensitive rewrite systems into ordinary rewrite systems such that termination of the transformed ordinary system implies termination of the original context-sensitive system. In this way, the huge variety of existing techniques for termination analysis of ordinary rewriting can be used for context-sensitive rewriting, too. We analyze the existing transformation techniques for proving termination of context-sensitive rewriting and we suggest two new transformations. Our first method is simple, sound, and more powerful than the previously proposed transformations. However, it is not complete, i.e., there are terminating context-sensitive rewrite systems that are transformed into non-terminating term rewrite systems. The second method that we present in this paper is both sound and complete. All these observations also hold for rewriting modulo associativity and commutativity.\",\n \"Title: Adding constants to string rewriting\\nYear: 2008\\n\\nAbstract: We consider unary term rewriting, i.e., term rewriting with unary signatures where all function symbols are either unary or constants. Terms over such signatures can be transformed into strings by just reading all symbols in the term from left to right, ignoring the optional variable. By lifting this transformation to rewrite rules, any unary term rewrite system (TRS) is transformed into a corresponding string rewrite system (SRS). We investigate which properties are preserved by this transformation. It turns out that any TRS over a unary signature is terminating if and only if the corresponding SRS is terminating. In this way tools for proving termination of string rewriting can be applied for proving termination of unary TRSs. For other rewriting properties including confluence, unique normal form property, weak normalization and relative termination, we show that a similar corresponding preservation property does not hold.\",\n \"Title: Modularity of Termination Using Dependency pairs\\nYear: 1998\\n\\nAbstract: The framework of dependency pairs allows automated termination and innermost termination proofs for many TRSs where such proofs were not possible before. In this paper we present\\n a refinement of this framework in order to prove termination in a modular way. Our modularity results significantly increase the class of term rewriting systems where termination resp. innermost\\n termination can be proved automatically. Moreover, the modular approach to dependency pairs yields new modularity criteria\\n which extend previous results in this area. In particular, existing results for modularity of innermost termination can easily\\n be obtained as direct consequences of our new criteria.\",\n \"Title: Automated Termination Proofs with AProVE\\nYear: 2004\\n\\nAbstract: We describe the system ProVE, an automated prover to verify (innermost) termination of term rewrite systems (TRSs). For this system, we have developed and implemented efficient algorithms based on classical simplification orders, dependency pairs, and the size-change principle. In particular, it contains many new improvements of the dependency pair approach that make automated termination proving more powerful and efficient. In ProVE, termination proofs can be performed with a user-friendly graphical interface and the system is currently among the most powerful termination provers available.\",\n \"Title: Deciding Inductive Validity of Equations\\nYear: 2003\\n\\nAbstract: Kapur and Subramaniam [12] defined syntactical classes of equations where inductive validity can be decided automatically. However, these classes are quite restrictive, since defined function symbols with recursive definitions may only appear on one side of the equations. In this paper, we expand the decidable class of equations significantly by allowing both sides of equations to be expressed using defined function symbols. The definitions of these function symbols must satisfy certain restrictions which can be checked mechanically. These results are crucial to increase the applicability of decision procedures for induction.\",\n \"Title: Decision Procedures for Loop Detection\\nYear: 2007\\n\\nAbstract: The dependency pair technique is a powerful modular meth- od for automated termination proofs of term rewrite systems. We first show that dependency pairs are also suitable for disproving termination: loops can be detected more easily. In a second step we analyze how to disprove innermost termination. Here, we present a novel procedure to decide whether a given loop is an innermost loop. All results have been implemented in the termination prover AProVE.\",\n \"Title: Dependency Pairs for Equational Rewriting\\nYear: 2001\\n\\nAbstract: The dependency pair technique of Arts and Giesl [1, 2, 3] for termination proofs of term rewrite systems (TRSs) is extended to rewriting modulo equations. Up to now, such an extension was only known in the special case of AC-rewriting [15, 17]. In contrast to that, the proposed technique works for arbitrary non-collapsing equations (satisfying a certain linearity condition). With the proposed approach, it is now possible to perform automated termination proofs for many systems where this was not possible before. In other words, the power of dependency pairs can now also be used for rewriting modulo equations.\",\n \"Title: Deaccumulation techniques for improving provability\\nYear: 2007\\n\\nAbstract: Several induction theorem provers were developed to verify functional programs mechanically. Unfortunately, automatic verification often fails for functions with accumulating arguments. Using concepts from the theory of tree transducers and extending on earlier work, the paper develops automatic transformations from accumulative functional programs into non-accumulative ones, which are much better suited for mechanized verification. The overall goal is to reduce the need for generalizing induction hypotheses in (semi-)automatic provers. Via the correspondence between imperative programs and tail-recursive functions, the presented approach can also help to reduce the need for inventing loop invariants in the verification of imperative programs.\",\n \"Title: Automated termination analysis for logic programs with cut*\\nYear: 2010\\n\\nAbstract: Termination is an important and well-studied property for logic programs. However, almost all approaches for automated termination analysis focus on definite logic programs, whereas real-world Prolog programs typically use the cut operator. We introduce a novel pre-processing method which automatically transforms Prolog programs into logic programs without cuts, where termination of the cut-free program implies termination of the original program. Hence after this pre-processing, any technique for proving termination of definite logic programs can be applied. We implemented this pre-processing in our termination prover AProVE and evaluated it successfully with extensive experiments.\",\n \"Title: Current Trends in Automated Deduction\\nYear: 2010\\n\\nAbstract: Automated deduction is one of the key areas in artificial intelligence. In this short article we give an overview on some\\n of the main current research topics in automated deduction.\",\n \"Title: Decidable Classes of Inductive Theorems\\nYear: 2001\\n\\nAbstract: Kapur and Subramaniam [8] defined syntactical classes of equations where inductive validity is decidable. Thus, their validity can be checked without any user interaction and hence, this allows an integration of (a restricted form of) induction in fully automated reasoning tools such as model checkers. However, the results of [8] were only restricted to equations. This paper extends the classes of conjectures considered in [8] to a larger class of arbitrary quantifier-free formulas (e.g., conjectures also containing negation, conjunction, disjunction, etc.).\",\n \"Title: Proving Termination by Bounded Increase\\nYear: 2007\\n\\nAbstract: Most methods for termination analysis of term rewrite systems (TRSs) essentially try to find arguments of functions that decreasein recursive calls. However, they fail if the reason for termination is that an argument is increasedin recursive calls repeatedly until it reaches a bound. In this paper, we solve that problem and show how to prove innermost termination of TRSs with bounded increase automatically.\",\n \"Title: Termination Analysis of Logic Programs Based on Dependency Graphs\\nYear: 2007\\n\\nAbstract: This paper introduces a modular framework for termination analysis of logic programming. To this end, we adapt the notions of dependency pairs and dependency graphs (which were developed for term rewriting) to the logic programming domain. The main idea of the approach is that termination conditions for a program are established based on the decomposition of its dependency graph into its strongly connected components. These conditions can then be analysed separately by possibly different well-founded orders. We propose a constraint-based approach for automating the framework. Then, for example, termination techniques based on polynomial interpretations can be plugged in as a component to generate well-founded orders.\",\n \"Title: Applying Rewriting Techniques to the Verification of Erlang Processes\\nYear: 1999\\n\\nAbstract: Erlang is a functional programming language developed by Ericsson Telecom which is particularly well suited for implementing concurrent processes. In this paper we show how methods from the area of term rewriting are presently used at Ericsson. To verify properties of processes, such a property is transformed into a termination problem of a conditional term rewriting system (CTRS). Subsequently, this termination proof can be performed automatically using dependency pairs. The paper illustrates how the dependency pair technique can be applied for termination proofs of conditional TRSs. Secondly, we present two refinements of this technique, viz. narrowing and rewriting dependency pairs. These refinements are not only of use in the industrial application sketched in this paper, but they are generally applicable to arbitrary (C)TRSs. Thus, in this way dependency pairs can be used to prove termination of even more (C)TRSs automatically.\",\n \"Title: Mechanizing and Improving Dependency Pairs\\nYear: 2006\\n\\nAbstract: To simplify the task of proving termination and AC-termination of term rewriting systems, elimination transformations have been vigorously studied since the 1990s. Dummy elimination, distribution elimination, general dummy elimination, and improved general ...\",\n \"Title: Proving and Disproving Termination in the Dependency Pair Framework\\nYear: 2005\\n\\nAbstract: The dependency pair approach is one of the most powerful techniques for auto- mated termination proofs of term rewrite systems. Up to now, it was regarded as one of several possible methods to prove termination. We show that dependency pairs can instead be used as a general concept to integrate arbitrary techniques for termination analysis. In this way, the benets of dieren t techniques can be combined and their modularity and power are increased signican tly. We refer to this new concept as the \\\\dependency pair framework\\\" to distinguish it from the old \\\\dependency pair approach\\\". Moreover, this framework facilitates the development of new methods for termination analysis. Traditionally, the research on termination focused on methods which prove termination and there were hardly any approaches for disproving termination. We show that with the dependency pair framework, one can combine the search for a proof and for a disproof of termination. In this way, we obtain the rst powerful method which can also verify non-termination of term rewrite systems. Moreover, the combination of termination and non-termination techniques is particularly useful: On the one hand, termination techniques also help for dis- proving termination, because they identify those parts of a term rewrite system which may cause non-termination. On the other hand, non-termination tech- niques are helpful for proving termination, because they can detect \\\\dead ends\\\" during a termination proof attempt. We implemented and evaluated our contributions in the automated termina- tion prover AProVE. Due to these results, AProVE was the winning tool in the International Competition of Termination Provers 2005, both for proving and for disproving termination of term rewriting.\",\n \"Title: Automated Termination Analysis of Java Bytecode by Term Rewriting\\nYear: 2010\\n\\nAbstract: We present an automated approach to prove termination of Java Bytecode (JBC) programs by automatically transforming them to term rewrite systems (TRSs). In this way, the numerous techniques and tools developed for TRS termination can now be used for imperative object-oriented languages like Java, which can be compiled into JBC.\",\n \"Title: Modular termination proofs for rewriting using dependency pairs\\nYear: 2002\\n\\nAbstract: Recently, Arts and Giesl developed the dependency pair approach which allows automated termination and innermost termination proofs for many term rewriting systems (TRSs) for which such proofs were not possible before. The motivation for this approach was that virtually all previous techniques for automated termination proofs of TRSs were based on simplification orderings. In practice, however, many rewrite systems are not simply terminating, i.e. their termination cannot be verified by any simplification ordering.In this paper we introduce a refinement of the dependency pair framework which further extends the class of TRSs for which termination or innermost termination can be shown automatically. By means of this refinement, one can now prove termination in a modular way. Thus, this refinement is inevitable in order to verify the termination of large rewrite systems occurring in practice. To be more precise, one may use several different orderings in one termination proof.Subsequently, we present several new modularity results based on dependency pairs. First, we show that the well-known modularity of simple termination for disjoint unions can be extended to DP quasi-simple termination, i.e. to the class of rewrite systems where termination can be shown automatically by the dependency pair technique in combination with quasi-simplification orderings. Under certain additional conditions, this new result also holds for constructor-sharing and composable systems. Second, the above-mentioned refinement of the dependency pair method yields new modularity criteria for innermost termination which extend previous results in this area considerably. In particular, existing results for modularity of innermost termination can easily be shown to be direct consequences of our new criteria.\",\n \"Title: A dependency pair framework for innermost complexity analysis of term rewrite systems\\nYear: 2011\\n\\nAbstract: We present a modular framework to analyze the innermost runtime complexity of term rewrite systems automatically. Our method is based on the dependency pair framework for termination analysis. In contrast to previous work, we developed a direct adaptation of successful termination techniques from the dependency pair framework in order to use them for complexity analysis. By extensive experimental results, we demonstrate the power of our method compared to existing techniques.\",\n \"Title: Proving Termination by Dependency Pairs and Inductive Theorem Proving\\nYear: 2011\\n\\nAbstract: Current techniques and tools for automated termination analysis of term rewrite systems (TRSs) are already very powerful. However, they fail for algorithms whose termination is essentially due to an inductive argument. Therefore, we show how to couple the dependency pair method for termination of TRSs with inductive theorem proving. As confirmed by the implementation of our new approach in the tool AProVE, now TRS termination techniques are also successful on this important class of algorithms.\",\n \"Title: Simulating Liveness by Reduction Strategies\\nYear: 2003\\n\\nAbstract: We define a general framework to handle liveness and related properties by reduction strategies in abstract reduction and term rewriting. Classically, reduction strategies in rewriting are used to simulate the evaluation process in programming languages. The aim of our work is to use reduction strategies to also study liveness questions which are of high importance in practice (e.g., in protocol verification for distributed processes). In particular, we show how the problem of verifying liveness is related to termination of term rewrite systems (TRSs). Using our results, techniques for proving termination of TRSs can be used to verify liveness properties.\",\n \"Title: Termination of term rewriting using dependency pairs\\nYear: 2000\\n\\nAbstract: We present techniques to prove termination and innermost termination of term rewriting systems automatically. In contrast to previous approaches, we do not compare left- and right-hand sides of rewrite rules, but introduce the notion of dependency pairs to compare left-hand sides with special subterms of the right-hand sides. This results in a technique which allows to apply existing methods for automated termination proofs to term rewriting systems where they failed up to now. In particular, there are numerous term rewriting systems where a direct termination proof with simplification orderings is not possible, but in combination with our technique, well-known simplification orderings (such as the recursive path ordering, polynomial orderings, or the Knuth–Bendix ordering) can now be used to prove termination automatically. Unlike previous methods, our technique for proving innermost termination automatically can also be applied to prove innermost termination of term rewriting systems that are not terminating. Moreover, as innermost termination implies termination for certain classes of term rewriting systems, this technique can also be used for termination proofs of such systems.\",\n \"Title: SAT solving for argument filterings\\nYear: 2006\\n\\nAbstract: This paper introduces a propositional encoding for lexicographic path orders in connection with dependency pairs. This facilitates the application of SAT solvers for termination analysis of term rewrite systems based on the dependency pair method. We address two main inter-related issues and encode them as satisfiability problems of propositional formulas that can be efficiently handled by SAT solving: (1) the combined search for a lexicographic path order together with an argument filtering to orient a set of inequalities; and (2) how the choice of the argument filtering influences the set of inequalities that have to be oriented. We have implemented our contributions in the termination prover AProVE. Extensive experiments show that by our encoding and the application of SAT solvers one obtains speedups in orders of magnitude as well as increased termination proving power.\",\n \"Title: Termination Analysis for Partial Functions\\nYear: 1996\\n\\nAbstract: . This paper deals with automated termination analysis forpartial functional programs, i.e. for functional programs which do notterminate for each input. We present a method to determine their domains (resp. non-trivial subsets of their domains) automatically. Moreprecisely, for each functional program a termination predicate algorithmis synthesized, which only returns true for inputs where the program isterminating. To ease subsequent reasoning about the generated terminationpredicates ...\",\n \"Title: Termination Analysis by Inductive Evaluation\\nYear: 1998\\n\\nAbstract: . We present a new approach for automatic termination analysis of functional programs.Several methods have been presented which try to find a well-founded ordering suchthat the arguments in the recursive calls are smaller than the corresponding inputs. However,previously developed approaches for automated termination analysis often disregardthe conditions under which the recursive calls are evaluated. Hence, the existing methods failfor an important class of algorithms where the...\",\n \"Title: Transforming Context-Sensitive Rewrite Systems\\nYear: 1999\\n\\nAbstract: We present two new transformation techniques for proving termination of context-sensitive rewriting. Our first method is simple, sound, and more powerful than previously suggested transformations. However, it is not complete, i.e., there are terminating context-sensitive rewrite systems that are transformed into non-terminating term rewrite systems. The second method that we present in this paper is both sound and complete. This latter result can be interpreted as stating that from a termination perspective there is no reason to study context-sensitive rewriting.\",\n \"Title: Termination graphs for Java bytecode\\nYear: 2010\\n\\nAbstract: To prove termination of Java Bytecode (JBC) automatically, we transform JBC to finite termination graphs which represent all possible runs of the program. Afterwards, the graph can be translated into \\\"simple\\\" formalisms like term rewriting and existing tools can be used to prove termination of the resulting term rewrite system (TRS). In this paper we show that termination graphs indeed capture the semantics of JBC correctly. Hence, termination of the TRS resulting from the termination graph implies termination of the original JBC program.\",\n \"Title: The Semantics of Rational Contractions\\nYear: 1993\\n\\nAbstract: The Logic of Theory Change developed by Alchourrón, Gärdenfors and Makinson is concerned with the revision of beliefs in the face of new and possibly contradicting information. This nonmonotonic process consists of a contraction and an expansion transforming one belief into another. Beliefs are represented by consistent deductively closed sets of formulas. To achieve minimal change Alchourrón, Gärdenfors and Makinson suggested widely accepted postulates that rational contractions have to fulfill.\",\n \"Title: Innermost Termination of Context-Sensitive Rewriting\\nYear: 2002\\n\\nAbstract: Context-sensitive rewriting is a restriction of term rewriting used to model evaluation strategies in functional programming and in programming languages like OBJ. For example, under certain conditions termination of an OBJ program is equivalent to innermost termination of the corresponding context-sensitive rewrite system (18). To prove ter- mination of context-sensitive rewriting, several methods have been pro- posed in the literature which transform context-sensitive rewrite systems into ordinary rewrite systems such that termination of the transformed ordinary system implies termination of the original context-sensitive sys- tem. Most of these transformations are not very satisfactory when it comes to proving innermost termination. We investigate the relation- ship between termination and innermost termination of context-sensitive rewriting and we examine the applicability of the different transforma- tions for innermost termination proofs. Finally, we present a simple trans- formation which is both sound and complete for innermost termination.\",\n \"Title: Pushing the Frontiers of Combining Rewrite Systems Farther Outwards.\\nYear: 1998\\n\\nAbstract: It is well known that simple termination is modular for certain kindsof combinations of term rewriting systems (TRSs). This result is ofpractical relevance because most techniques for (automated) terminationproofs use simplification orderings, so they show in fact simple termination.On the other hand, in practice many systems are non-simplyterminating. In order to cope with such systems, Arts and Giesl developedthe dependency pair approach. By using (quasi-)simplificationorderings in...\",\n \"Title: Automated detection of non-termination and nullpointerexceptions for Java Bytecode\\nYear: 2011\\n\\nAbstract: Recently, we developed an approach for automated termination proofs of Java Bytecode (JBC), which is based on constructing and analyzing termination graphs. These graphs represent all possible program executions in a finite way. In this paper, we show that this approach can also be used to detect non-termination or NullPointerExceptions. Our approach automatically generates witnesses, i.e., calling the program with these witness arguments indeed leads to non-termination resp. to a NullPointerException. Thus, we never obtain \\\"false positives\\\". We implemented our results in the termination prover AProVE and provide experimental evidence for the power of our approach.\",\n \"Title: Analyzing Innermost Runtime Complexity of Term Rewriting by Dependency Pairs.\\nYear: 2013\\n\\nAbstract: We present a modular framework to analyze the innermost runtime complexity of term rewrite systems automatically. Our method is based on the dependency pair framework for termination analysis. In contrast to previous work, we developed a adaptation of successful termination techniques from the dependency pair framework in order to use them for complexity analysis. By extensive experimental results, we demonstrate the power of our method compared to existing techniques.\",\n \"Title: Implementing RPO and POLO using SAT\\nYear: 2007\\n\\nAbstract: Well-founded orders are the most basic, but also most impor- tant ingredient to virtually all termination analyses. Numerous fully au- tomated search algorithms for these classes have therefore been devised and implemented in termination tools. Unfortunately, for termination problems occurring in practice, the performance of existing algorithms is often insufficient. Performance can be improved significantly by reducing these search prob- lems to decision problems for which more efficient algorithmsalready exist. Here, we introduce an encoding of RPO and POLO to the satis- fiability of propositional logic (SAT). We implemented these encodings in our termination tool AProVE. Extensive experiments have shown that one can obtain speedups in orders of magnitude by this encoding and the application of modern SAT solvers.\",\n \"Title: Termination Analysis by Dependency Pairs and Inductive Theorem Proving\\nYear: 2009\\n\\nAbstract: Current techniques and tools for automated termination analysis of term rewrite systems (TRSs) are already very powerful. However, they fail for algorithms whose termination is essentially due to an inductive argument. Therefore, we show how to couple the dependency pair method for TRS termination with inductive theorem proving. As confirmed by the implementation of our new approach in the tool AProVE, now TRS termination techniques are also successful on this important class of algorithms.\",\n \"Title: Verification of Erlang Processes by Dependency Pairs\\nYear: 2001\\n\\nAbstract: Erlang is a functional programming language developed by Ericsson Telecom, which is particularly well suited for implementing concurrent processes. In this paper we show how methods from the area of term rewriting are presently used at Ericsson. To verify properties of processes, such a property is transformed into a termination problem of a conditional term rewriting system (CTRS). Subsequently, this termination proof can be performed automatically using dependency pairs. The paper illustrates ...\",\n \"Title: Lazy abstraction for size-change termination\\nYear: 2010\\n\\nAbstract: Size-change termination is a widely used means of proving termination where source programs are first abstracted to size-change graphs which are then analyzed to determine if they satisfy the size-change termination property. Here, the choice of the abstraction is crucial to the success of the method, and it is an open problem how to choose an abstraction such that no critical loss of precision occurs. This paper shows how to couple the search for a suitable abstraction and the test for size-change termination via an encoding to a single SAT instance. In this way, the problem of choosing the right abstraction is solved en passant by a SAT solver. We show that for the setting of term rewriting, the integration of this approach into the dependency pair framework works smoothly and gives rise to a new class of size-change reduction pairs. We implemented size-change reduction pairs in the termination prover AProVE and evaluated their usefulness in extensive experiments.\",\n \"Title: Automated termination analysis for Haskell: from term rewriting to programming languages\\nYear: 2006\\n\\nAbstract: There are many powerful techniques for automated termination analysis of term rewriting. However, up to now they have hardly been used for real programming languages. We present a new approach which permits the application of existing techniques from term rewriting in order to prove termination of programs in the functional language Haskell. In particular, we show how termination techniques for ordinary rewriting can be used to handle those features of Haskell which are missing in term rewriting (e.g., lazy evaluation, polymorphic types, and higher-order functions). We implemented our results in the termination prover AProVE and successfully evaluated them on existing Haskell-libraries.\",\n \"Title: Automated Termination Proofs with Measure Functions\\nYear: 1995\\n\\nAbstract: . This paper deals with the automation of termination proofsfor recursively defined algorithms (i.e. algorithms in a pure functionallanguage). Previously developed methods for their termination proofseither had a low degree of automation or they were restricted to onesingle fixed measure function to compare data objects. To overcome thesedrawbacks we introduce a calculus for automated termination proofswhich is able to handle arbitrary measure functions based on polynomialnorms.1...\",\n \"Title: Search Techniques for Rational Polynomial Orders\\nYear: 2008\\n\\nAbstract: Polynomial interpretations are a standard technique used in almost all tools for proving termination of term rewrite systems (TRSs) automatically. Traditionally, one applies interpretations with polynomials over the naturals. But recently, it was shown that interpretations with polynomials over the rationals can be significantly more powerful. However, searching for such interpretations is considerably more difficult than for natural polynomials. Moreover, while there exist highly efficient SAT-based techniques for finding natural polynomials, no such techniques had been developed for rational polynomials yet. In this paper, we tackle the two main problems when applying rational polynomial interpretations in practice: (1) We develop new criteria to decide when to use rational instead of natural polynomial interpretations. (2) Afterwards, we present SAT-based methods for finding rational polynomial interpretations and evaluate them empirically.\",\n \"Title: Dependency triples for improving termination analysis of logic programs with cut\\nYear: 2010\\n\\nAbstract: In very recent work, we introduced a non-termination preserving transformation from logic programs with cut to definite logic programs. While that approach allows us to prove termination of a large class of logic programs with cut automatically, in several cases the transformation results in a non-terminating definite logic program. In this paper we extend the transformation such that logic programs with cut are no longer transformed into definite logic programs, but into dependency triple problems. By the implementation of our new method and extensive experiments, we empirically evaluate the practical benefit of our contributions.\",\n \"Title: Proving Termination of Programs Automatically with AProVE.\\nYear: 2014\\n\\nAbstract: AProVE is a system for automatic termination and complexity proofs of Java, C, Haskell, Prolog, and term rewrite systems (TRSs). To analyze programs in high-level languages, AProVE automatically converts them to TRSs. Then, a wide range of techniques is employed to prove termination and to infer complexity bounds for the resulting TRSs. The generated proofs can be exported to check their correctness using automatic certifiers. For use in software construction, we present an AProVE plug-in for the popular Eclipse software development environment.\",\n \"Title: Proving Termination and Memory Safety for Programs with Pointer Arithmetic.\\nYear: 2014\\n\\nAbstract: Proving termination automatically for programs with explicit pointer arithmetic is still an open problem. To close this gap, we introduce a novel abstract domain that can track allocated memory in detail. We use it to automatically construct a symbolic execution graph that represents all possible runs of the program and that can be used to prove memory safety. This graph is then transformed into an integer transition system, whose termination can be proved by standard techniques. We implemented this approach in the automated termination prover AProVE and demonstrate its capability of analyzing C programs with pointer arithmetic that existing tools cannot handle.\",\n \"Title: Termination Competition (termCOMP 2015)\\nYear: 2015\\n\\nAbstract: The termination competition focuses on automated termination analysis for all kinds of programming paradigms, including categories for term rewriting, imperative programming, logic programming, and functional programming. Moreover, the competition also features categories for automated complexity analysis. In all categories, the competition also welcomes the participation of tools providing certified proofs. The goal of the termination competition is to demonstrate the power of the leading tools in each of these areas.\",\n \"Title: Inferring Lower Bounds for Runtime Complexity.\\nYear: 2015\\n\\nAbstract: We present the first approach to deduce lower bounds for innermost runtime complexity of term rewrite systems (TRSs) automatically. Inferring lower runtime bounds is useful to detect bugs and to complement existing techniques that compute upper complexitybounds. The key idea of our approach is to generate suitable families of rewrite sequences of a TRS and to find a relation between the length of such a rewrite sequence and the size of the first term in the sequence. We implemented our approach inthe tool AProVE and evaluated it by extensive experiments.\",\n \"Title: Lower Runtime Bounds for Integer Programs.\\nYear: 2016\\n\\nAbstract: We present a technique to infer lower bounds on the worst-case runtime complexity of integer programs. To this end, we construct symbolic representations of program executions using a framework for iterative, under-approximating program simplification. The core of this simplification is a method for under-approximating program acceleration based on recurrence solving and a variation of ranking functions. Afterwards, we deduce asymptotic lower bounds from the resulting simplified programs. We implemented our technique in our tool LoAT and show that it infers non-trivial lower bounds for a large number of examples.\",\n \"Title: Proving Termination of Programs with Bitvector Arithmetic by Symbolic Execution.\\nYear: 2016\\n\\nAbstract: In earlier work, we developed an approach for automated termination analysis of C programs with explicit pointer arithmetic, which is based on symbolic execution. However, similar to many other termination techniques, this approach assumed the program variables to range over mathematical integers instead of bitvectors. This eases mathematical reasoning but is unsound in general. In this paper, we extend our approach in order to handle fixed-width bitvector integers. Thus, we present the first technique for termination analysis of C programs that covers both byte-accurate pointer arithmetic and bit-precise modeling of integers. We implemented our approach in the automated termination prover AProVE and evaluate its power by extensive experiments.\",\n \"Title: Analyzing Runtime and Size Complexity of Integer Programs.\\nYear: 2016\\n\\nAbstract: We present a modular approach to automatic complexity analysis of integer programs. Based on a novel alternation between finding symbolic time bounds for program parts and using these to infer bounds on the absolute values of program variables, we can restrict each analysis step to a small part of the program while maintaining a high level of precision. The bounds computed by our method are polynomial or exponential expressions that depend on the absolute values of input parameters. We show how to extend our approach to arbitrary cost measures, allowing the use of our technique to find upper bounds for other expended resources, such as network requests or memory consumption. Our contributions are implemented in the open-source tool KoAT, and extensive experiments show the performance and power of our implementation in comparison with other tools.\",\n \"Title: Automatically Proving Termination and Memory Safety for Programs with Pointer Arithmetic.\\nYear: 2017\\n\\nAbstract: While automated verification of imperative programs has been studied intensively, proving termination of programs with explicit pointer arithmetic fully automatically was still an open problem. To close this gap, we introduce a novel abstract domain that can track allocated memory in detail. We use it to automatically construct a symbolic execution graph that over-approximates all possible runs of a program and that can be used to prove memory safety. This graph is then transformed into an integer transition system, whose termination can be proved by standard techniques. We implemented this approach in the automated termination prover AProVE and demonstrate its capability of analyzing C programs with pointer arithmetic that existing tools cannot handle.\",\n \"Title: Lower Bounds for Runtime Complexity of Term Rewriting.\\nYear: 2017\\n\\nAbstract: We present the first approach to deduce lower bounds for (worst-case) runtime complexity of term rewrite systems (TRSs) automatically. Inferring lower runtime bounds is useful to detect bugs and to complement existing methods that compute upper complexity bounds. Our approach is based on two techniques: the induction technique generates suitable families of rewrite sequences and uses induction proofs to find a relation between the length of a rewrite sequence and the size of the first term in the sequence. The loop detection technique searches for \\\\\\\"decreasing loops\\\\\\\". Decreasing loops generalize the notion of loops for TRSs, and allow us to detect families of rewrite sequences with linear, exponential, or infinite length. We implemented our approach in the tool AProVE and evaluated it by extensive experiments.\",\n \"Title: Complexity Analysis for Term Rewriting by Integer Transition Systems.\\nYear: 2017\\n\\nAbstract: We present a new method to infer upper bounds on the innermost runtime complexity of term rewrite systems (TRSs), which benefits from recent advances on complexity analysis of integer transition systems (ITSs). To this end, we develop a transformation from TRSs to a generalized notion of ITSs with (possibly non-tail) recursion. To analyze their complexity, we introduce a modular technique which allows us to use existing tools for standard ITSs in order to infer complexity bounds for our generalized ITSs. The key idea of our technique is a summarization method that allows us to analyze components of the transition system independently. We implemented our contributions in the tool AProVE, and our experiments show that one can now infer bounds for significantly more TRSs than with previous state-of-the-art tools for term rewriting.\",\n \"Title: Constant runtime complexity of term rewriting is semi-decidable.\\nYear: 2018\\n\\nAbstract: •It is semi-decidable if the runtime complexity of a term rewrite system is constant.•Constant complexity corresponds to termination of a restricted form of narrowing.•This termination property can be checked by only examining finitely many terms.•The new semi-decision procedure was implemented in the tool AProVE.•The implementation solves many examples where automatic tools failed before.\",\n \"Title: Proving Innermost Normalisation Automatically\\nYear: 1997\\n\\nAbstract: We present a technique to prove innermost normalisation of term rewritingsystems (TRSs) automatically. In contrast to previous methods, our techniqueis able to prove innermost normalisation of TRSs that are not terminating.\",\n \"Title: Analyzing Program Termination and Complexity Automatically with AProVE.\\nYear: 2017\\n\\nAbstract: In this system description, we present the tool AProVE for automatic termination and complexity proofs of Java, C, Haskell, Prolog, and rewrite systems. In addition to classical term rewrite systems (TRSs), AProVE also supports rewrite systems containing built-in integers (int-TRSs). To analyze programs in high-level languages, AProVE automatically converts them to (int-)TRSs. Then, a wide range of techniques is employed to prove termination and to infer complexity bounds for the resulting rewrite systems. The generated proofs can be exported to check their correctness using automatic certifiers. To use AProVE in software construction, we present a corresponding plug-in for the popular Eclipse software development environment.\",\n \"Title: Complexity Analysis for Java with AProVE.\\nYear: 2017\\n\\nAbstract: While AProVE is one of the most powerful tools for termination analysis of Java since many years, we now extend our approach in order to analyze the complexity of Java programs as well. Based on a symbolic execution of the program, we develop a novel transformation of (possibly heap-manipulating) Java programs to integer transition systems (ITSs). This allows us to use existing complexity analyzers for ITSs to infer runtime bounds for Java programs. We demonstrate the power of our implementation on an established standard benchmark set.\",\n \"Title: Termination and complexity analysis for programs with bitvector arithmetic by symbolic execution.\\nYear: 2018\\n\\nAbstract: In earlier work, we developed an approach for automated termination analysis of C programs with explicit pointer arithmetic, which is based on symbolic execution. However, similar to many other termination techniques, this approach assumed the program variables to range over mathematical integers instead of bitvectors. This eases mathematical reasoning but is unsound in general. In this paper, we extend our approach in order to handle fixed-width bitvector integers. Thus, we present the first technique for termination analysis of C programs that covers both byte-accurate pointer arithmetic and bit-precise modeling of integers. Moreover, we show that our approach can also be used to analyze the runtime complexity of bitvector programs. We implemented our contributions in the automated termination prover AProVE and evaluate its power by extensive experiments.\",\n \"Title: Proving Non-Termination and Lower Runtime Bounds with LoAT (System Description).\\nYear: 2022\\n\\nAbstract: We present the new version of the Loop Acceleration Tool (LoAT), a powerful tool for proving non-termination and worst-case lower bounds for programs operating on integers. It is based on novel calculi for loop acceleration, i.e., transforming loops into non-deterministic straight-line code, and for finding non-terminating configurations. To implement them efficiently, LoAT uses a new approach based on SMT solving and unsat cores. An extensive evaluation shows that LoAT is highly competitive with other state-of-the-art tools for proving non-termination. While no other tool is able to deduce worst-case lower bounds for full integer programs, we also demonstrate that LoAT significantly outperforms its predecessors.\",\n \"Title: Automatic Complexity Analysis of Integer Programs via Triangular Weakly Non-Linear Loops.\\nYear: 2022\\n\\nAbstract: There exist several results on deciding termination and computing runtime bounds for triangular weakly non-linear loops (twn-loops). We show how to use results on such subclasses of programs where complexity bounds are computable within incomplete approaches for complexity analysis of full integer programs. To this end, we present a novel modular approach which computes local runtime bounds for subprograms which can be transformed into twn-loops. These local runtime bounds are then lifted to global runtime bounds for the whole program. The power of our approach is shown by our implementation in the tool KoAT which analyzes complexity of programs where all other state-of-the-art tools fail.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":97,"string":"97"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13685,"string":"13,685"}}},{"rowIdx":13686,"cells":{"question_id":{"kind":"string","value":"Nikos Paragios"},"question":{"kind":"string","value":"Generate an abstract for the title \"Fast illumination-invariant background subtraction using two views: error analysis, sensor placement and applications\" using the following items: background modeling, multiple cameras, subtraction, false detections, sensor configuration"},"answer":{"kind":"string","value":"Background modeling and subtraction to detect new or moving objects in a scene is an important component of many intelligent video applications. Compared to a single camera, the use of multiple cameras leads to better handling of shadows, specularities and illumination changes due to the utilization of geometric information. Although the result of stereo matching can be used as the feature for detection, it has been shown that the detection process can be made much faster by a simple subtraction of the intensities observed at stereo-generated conjugate pairs in the two views. The methodology however, suffers from false and missed detections due to some geometric considerations. In this paper, we perform a detailed analysis of such errors. Then, we propose a sensor configuration that eliminates false detections. Algorithms are also proposed that effectively eliminate most detection errors due to missed detections, specular reflections and objects being geometrically close to the background. Experiments on several scenes illustrate the utility and enhanced performance of the proposed approach compared to existing techniques."},"correct_answer":{"kind":"string","value":"Background modeling and subtraction to detect new or moving objects in a scene is an important component of many intelligent video applications. Compared to a single camera, the use of multiple cameras leads to better handling of shadows, specularities and illumination changes due to the utilization of geometric information. Although the result of stereo matching can be used as the feature for detection, it has been shown that the detection process can be made much faster by a simple subtraction of the intensities observed at stereo-generated conjugate pairs in the two views. The methodology however, suffers from false and missed detections due to some geometric considerations. In this paper, we perform a detailed analysis of such errors. Then, we propose a sensor configuration that eliminates false detections. Algorithms are also proposed that effectively eliminate most detection errors due to missed detections, specular reflections and objects being geometrically close to the background. Experiments on several scenes illustrate the utility and enhanced performance of the proposed approach compared to existing techniques."},"documents":{"kind":"list like","value":["Title: Postarthroplasty Examination Using X-Ray Images\nYear: 2009\n\nAbstract: Arthroplasty, the implantation of prostheses into joints, is a surgical procedure that is affecting a larger and larger number of patients over time. As a result, it is increasingly important to develop imaging techniques to noninvasively examine joints with prostheses after surgery, both statically and dynamically in 3-D. The static problem is considered here, with the aim to create a 3-D shape model of the bone as well as the prosthesis using a set of 2-D X-rays from various viewpoints. The most important challenge to be addressed is the lack of texture, the most common feature to recover shape from multiple views. In order to overcome this limitation, we reformulate the problem using a novel multiview segmentation approach where an active contours 3-D surface evolution with level-set implementation is used to recover the shape of bones and prostheses in postoperative joints. The recovered shape may then be used to track 3-D motions in dynamic X-ray sequences to obtain kinematic information.","Title: Shape registration in implicit spaces using information theory and free form deformations.\nYear: 2006\n\nAbstract: We present a novel, variational and statistical approach for shape registration. Shapes of interest are implicitly embedded in a higher-dimensional space of distance transforms. In this implicit embedding space, registration is formulated in a hierarchical manner: the Mutual Information criterion supports various transformation models and is optimized to perform global registration; then, a B-spline-based Incremental Free Form Deformations (IFFD) model is used to minimize a Sum-of-Squared-Differences (SSD) measure and further recover a dense local nonrigid registration field. The key advantage of such framework is twofold: (1) it naturally deals with shapes of arbitrary dimension (2D, 3D, or higher) and arbitrary topology (multiple parts, closed/open) and (2) it preserves shape topology during local deformation and produces local registration fields that are smooth, continuous, and establish one-to-one correspondences. Its invariance to initial conditions is evaluated through empirical validation, and various hard 2D/3D geometric shape registration examples are used to show its robustness to noise, severe occlusion, and missing parts. We demonstrate the power of the proposed framework using two applications: one for statistical modeling of anatomical structures, another for 3D face scan registration and expression tracking. We also compare the performance of our algorithm with that of several other well-known shape registration algorithms.","Title: 4D ventricular segmentation and wall motion estimation using efficient discrete optimization\nYear: 2007\n\nAbstract: In this paper we propose a novel approach to ventricular motion estimation and segmentation. Our method is based on a MRF formulation where an optimal intensity-based separation between the endocardium and the rest of the cardiac volume is to be determined. Such a term is defined in the spatiotemporal domain, where the ventricular wall motion is introduced to account for correspondences between the consecutive segmentation maps. The estimation of the deformations is done through a continuous deformation field (FFD) where the displacements of the control points are determined using discrete labeling approach. Principles from linear programming and in particular the Primal/Dual Schema is used to recover the optimal solution in both spaces. Promising experimental results obtained on 13 MR spatiotemporal data sets demonstrate the potentials of our method.","Title: Curvilinear Structures Extraction In Cluttered Bioimaging Data With Discrete Optimization Methods\nYear: 2011\n\nAbstract: Filamentary structures extraction in medical and biological images is a challenging problem. Muscular/Neural fibers, neurites and blood arteries are some examples. Their delineation is particularly problematic due to the lack of solid visual support that is also compromised by the presence of clutter and low signal to noise ratios.In this article, we propose a modular approach to curvilinear structures extraction based on recent advances in discrete optimization on the basis of aggregate clustering. Given an initial clustering of the detected points, first a pair-wise Markov Random Field (MRF) is considered to determine consistent elongated structures while penalizing their number and rejecting outliers. The outcome of this process is then locally refined through a non-submodular MRF aiming at center-line extraction process and guided by local geometric consistency between segments expressing the intra-cluster variability. Promising results for microtubules delineation in TIRFM images as well as for guidewires segmentation in fluoroscopic images demonstrate the potentials of the method.","Title: Variational model-based 3d building extraction from remote sensing data\nYear: 2009\n\nAbstract: In this paper, we introduce a variational framework towards automatic 3D building reconstruction from optical and Lidar data. Multiple 3D competing building priors are considered under a recognition-driven way. These models, under a certain hierarchical representation, describe the space of solutions and under a fruitful synergy with an inferential procedure recover the observed scene's geometry. Our formulation allows the cue with the higher spatial resolution to constrain properly the boundaries detection procedure ensuring, in this way, optimal results in terms of accuracy. Such an integrated approach is defined in a variational context, solves segmentation in both spaces, addresses fusion in a natural manner and allows multiple competing priors to determine the pose and 3D geometry from the observed data. Very promising experimental results demonstrate the potentials of our approach.","Title: Surface/volume-based articulated 3D spine inference through Markov random fields.\nYear: 2009\n\nAbstract: This paper presents a method towards inferring personalized 3D spine models to intraoperative CT data acquired for corrective spinal surgery. An accurate 3D reconstruction from standard X-rays is obtained before surgery to provide the geometry of vertebrae. The outcome of this procedure is used as basis to derive an articulated spine model that is represented by consecutive sets of intervertebral articulations relative to rotation and translation parameters (6 degrees of freedom). Inference with respect to the model parameters is then performed using an integrated and interconnected Markov Random Field graph that involves singleton and pairwise costs. Singleton potentials measure the support from the data (surface or image-based) with respect to the model parameters, while pairwise constraints encode geometrical dependencies between vertebrae. Optimization of model parameters in a multi-modal context is achieved using efficient linear programming and duality. We show successful image registration results from simulated and real data experiments aimed for image-guidance fusion.","Title: Approximated Curvature Penalty in Non-rigid Registration Using Pairwise MRFs\nYear: 2009\n\nAbstract: Labeling of discrete Markov Random Fields (MRFs) has become an attractive approach for solving the problem of non-rigid image registration. Here, regularization plays an important role in order to obtain smooth deformations for the inherent ill-posed problem. Smoothness is achieved by penalizing the derivatives of the displacement field. However, efficient optimization strategies (based on iterative graph-cuts) are only available for second-order MRFs which contain cliques of size up to two. Higher-order cliques require graph modifications and insertion of auxiliary nodes, while pairwise interactions actually allow only regularization based on the first-order derivatives. In this paper, we propose an approximated curvature penalty using second-order derivatives defined on the MRF pairwise potentials. In our experiments, we demonstrate that our approximated term has similar properties as higher-order approaches (invariance to linear transformations), while the computational efficiency of pairwise models is preserved.","Title: Image reconstruction using particle filters and multiple hypotheses testing.\nYear: 2010\n\nAbstract: In this paper, we introduce a reconstruction framework that explicitly accounts for image geometry when defining the spatial interaction between pixels in the filtering process. To this end, image structure is captured using local co-occurrence statistics and is incorporated to the enhancement algorithm in a sequential fashion using the particle filtering technique. In this context, the reconstruction process is modeled using a dynamical system with multiple states and its evolution is guided by the prior density describing the image structure. Towards optimal exploration of the image geometry, an evaluation process of the state of the system is performed at each iteration. The resulting framework explores optimally spatial dependencies between image content towards variable bandwidth image reconstruction. Promising results using additive noise models demonstrate the potentials of such an explicit modeling of the geometry.","Title: Simultaneous geometric--iconic registration.\nYear: 2010\n\nAbstract: In this paper, we introduce a novel approach to bridge the gap between the landmark-based and the iconic-based voxel-wise registration methods. The registration problem is formulated with the use of Markov Random Field theory resulting in a discrete objective function consisting of thee parts. The first part of the energy accounts for the iconic-based volumetric registration problem while the second one for establishing geometrically meaningful correspondences by optimizing over a set of automatically generated mutually salient candidate pairs of points. The last part of the energy penalizes locally the difference between the dense deformation field due to the iconic-based registration and the implied displacements due to the obtained correspondences. Promising results in real MR brain data demonstrate the potentials of our approach.","Title: Learning deformation and structure simultaneously: in situ endograft deformation analysis.\nYear: 2011\n\nAbstract: The learning of the shape and appearance behavior of complex anatomical structures is of growing importance in the successful use of medical imaging data. We propose a method to simultaneously learn a model of shape variation and the behavioral structure of objects in volumetric data sets. The algorithm performs a group-wise registration of a set of examples, and accounts for the heterogeneous deformation or variability properties of the data. We use the method for the in situ analysis of endograft deformation in the thoracic aorta during the cardiac cycle. The method is based on an emerging model of the shape variation, which is learned autonomously from a gated computed tomography sequence. It automatically adapts to the highly non-uniform elasticity properties of the structure during learning. The resulting deformation model is used for the measurement of global and local characteristics of the endograft movement. The method allows for the in situ localization of the stent during the cardiac cycle, and the measurement of its deformation. Furthermore, it makes the comparison of different endograft designs possible, and can serve as a basis for fitting a physical model of the endograft- and vessel surface to individual patients. The latter is essential for long-term risk assessment of the impact of endografts in highly mobile areas. We evaluate the approach on 10 data sets from patients that underwent endograft placement after traumatic ruptures of the thoracic aorta.","Title: Markov Random Field-based fitting of a subdivision-based geometric atlas\nYear: 2011\n\nAbstract: An accurate labeling of a multi-part, complex anatomical structure (e.g., brain) is required in order to compare data across images for spatial analysis. It can be achieved by fitting an object-specific geometric atlas that is constructed using a partitioned, high-resolution deformable mesh and tagging each of its polygons with a region label. Subdivision meshes have been used to construct such an atlas because they can provide a compact representation of a partitioned, multi-resolution, object-specific mesh structure using only a few control points. However, automated fitting of a subdivision mesh-based geometric atlas to an anatomical structure in an image is a difficult problem and has not been sufficiently addressed. In this paper, we propose a novel Markov Random Field-based method for fitting a planar, multi-part subdivision mesh to anatomical data. The optimal fitting of the atlas is obtained by determining the optimal locations of the control points. We also tackle the problem of landmark matching in tandem with atlas fitting by constructing a single graphical model to impose pose-invariant, landmark-based geometric constraints on atlas deformation. The atlas deformation is also governed by additional constraints imposed by the mesh's geometric properties and the object boundary. We demonstrate the potential of the proposed method on the difficult problem of segmenting a mouse brain and its interior regions in gene expression images which exhibit large intensity and shape variability. We obtain promising results when compared with manual annotations and prior methods.","Title: Random walks, constrained multiple hypothesis testing and image enhancement\nYear: 2006\n\nAbstract: Image restoration is a keen problem of low level vision. In this paper, we propose a novel – assumption-free on the noise model – technique based on random walks for image enhancement. Our method explores multiple neighbors sets (or hypotheses) that can be used for pixel denoising, through a particle filtering approach. This technique associates weights for each hypotheses according to its relevance and its contribution in the denoising process. Towards accounting for the image structure, we introduce perturbations based on local statistical properties of the image. In other words, particle evolution are controlled by the image structure leading to a filtering window adapted to the image content. Promising experimental results demonstrate the potential of such an approach.","Title: Detecting Multiple Moving Targets Using Deformable Contours\nYear: 1997\n\nAbstract: This papers presents a framework for detecting multiple moving moving objects in a sequence of images. Using a statistical approach, where the inter-frame difference is modeled by a mixture of two Laplacian distributions and a deformable contour-based energy minimization approach, we reformulate the motion detection problem as a front propagation problem. Following the work of geodesic active contours [2], we transform the detection of moving objects problem into an equivalent problem of geodesic computation, which is solved using a level set formulation scheme. To reduce the computational cost required by a direct implementation of the formulation scheme [5] the Narrow Band technique is used. In order to further reduce the CPU time, a multi-scale approach has also been considered. Very promising experimental results are provided using real video sequences.","Title: Shape Priors And Discrete Mrfs For Knowledge-Based Segmentation\nYear: 2009\n\nAbstract: In this paper we introduce a new approach to knowledge-based segmentation. Our method consists of a novel representation to model shape variations as well as an efficient inference procedure to fit the model to new data. The considered shape model is similarity-invariant and refers to an incomplete graph that consists of intra and intercluster connections representing the inter-dependencies of control points. The clusters are determined according to the co-dependencies of the deformations of the control points within the training set. The connections between the components of a cluster represent the local structure while the connections between the clusters account for the global structure. The distributions of the normalized distances between the connected control points encode the prior model. During search, this model is used together with a discrete markov random field (MRF) based segmentation, where the unknown variables are the positions of the control points in the image domain. To encode the image support, a Voronoi decomposition of the domain is considered and regional based statistics are used. The resulting model is computationally efficient, can encode complex statistical models of shape variations and benefits from the image support of the entire spatial domain.","Title: Modelling Shapes with Uncertainties: Higher Order Polynomials, Variable Bandwidth Kernels and Non Parametric Density Estimation\nYear: 2005\n\nAbstract: We propose a novel approach for shape-based segmentation based on a specially designed level set function format. This format permits us to better control the process of object registration which is an important part in the shapebased segmentation framework. ...","Title: Linear intensity-based image registration by Markov random fields and discrete optimization.\nYear: 2010\n\nAbstract: We propose a framework for intensity-based registration of images by linear transformations, based on a discrete Markov random field (MRF) formulation. Here, the challenge arises from the fact that optimizing the energy associated with this problem requires a high-order MRF model. Currently, methods for optimizing such high-order models are less general, easy to use, and efficient, than methods for the popular second-order models.","Title: A Variational Approach for the Segmentation of the Left Ventricle in Cardiac Image Analysis\nYear: 2002\n\nAbstract: In this paper we propose a level set method to segment MR cardiac images. Our approach is based on a coupled propagation of two cardiac contours and integrates visual information with anatomical constraints. The visual information is expressed through a gradient vector flow-based boundary component and a region term that aims at best separating the cardiac contours/regions according to their global intensity properties. In order to deal with misleading visual support, an anatomical constraint is considered that couples the propagation of the cardiac contours according to their relative distance. The resulting motion equations are implemented using a level set approach and a fast and stable numerical approximation scheme, the Additive Operator Splitting. Encouraging experimental results are provided using real data.","Title: Variable Bandwidth Image Denoising Using Image-Based Noise Models\nYear: 2007\n\nAbstract: This paper introduces a variational formulation for image denoising based on a quadratic function over kernels of variable bandwidth. These kernels are scale adaptive and reflect spatial and photometric similarities between pixels. The bandwidth of the kernels is observation-dependent towards improving the accuracy of the reconstruction process and is constrained to be locally smooth. We analyze the evolution of the noise model form the RAW space to the RGB one, by propagating it over the image formation process. The experimental results demonstrate that the use of a variable bandwidth approach and an image intensity dependent noise variance ensures better restoration quality.","Title: Beyond Loose LP-Relaxations: Optimizing MRFs by Repairing Cycles\nYear: 2008\n\nAbstract: This paper presents a new MRF optimization algorithm, which is derived from Linear Programming and manages to go beyond current state-of-the-art techniques (such as those based on graph-cuts or belief propagation). It does so by relying on a much tighter class of LP-relaxations, called cycle-relaxations. With the help of this class of relaxations, our algorithm tries to deal with a difficulty lying at the heart of MRF optimization: the existence of inconsistent cycles. To this end, it uses an operation called cycle-repairing. The goal of that operation is to fix any inconsistent cycles that may appear during optimization, instead of simply ignoring them as usually done up to now. The more the repaired cycles, the tighter the underlying LP relaxation becomes. As a result of this procedure, our algorithm is capable of providing almost optimal solutions even for very general MRFs with arbitrary potentials. Experimental results verify its effectiveness on difficult MRF problems, as well as its better performance compared to the state of the art.","Title: Task-specific functional brain geometry from model maps.\nYear: 2008\n\nAbstract: In this paper we propose model maps to derive and represent the intrinsic functional geometry of a brain from functional magnetic resonance imaging (fMRI) data for a specific task. Model maps represent the coherence of behavior of individual fMRI-measurements for a set of observations, or a time sequence. The maps establish a relation between individual positions in the brain by encoding the blood oxygen level dependent (BOLD) signal over a time period in a Markov chain. They represent this relation by mapping spatial positions to a new metric space, the model map. In this map the Euclidean distance between two points relates to the joint modeling behavior of their signals and thus the co-dependencies of the corresponding signals. The map reflects the functional as opposed to the anatomical geometry of the brain. It provides a quantitative tool to explore and study global and local patterns of resource allocation in the brain. To demonstrate the merit of this representation, we report quantitative experimental results on 29 fMRI time sequences, each with sub-sequences corresponding to 4 different conditions for two groups of individuals. We demonstrate that drug abusers exhibit lower differentiation in brain interactivity between baseline and reward related tasks, which could not be quantified until now.","Title: Image transport regression using mixture of experts and discrete Markov random fields\nYear: 2010\n\nAbstract: The registration of multi-modal images is the process of finding a transformation which maps one image to the other according to a given similarity metric. In this paper, we introduce a novel approach for metric learning, aiming to address highly non functional correspondences through the integration of statistical regression and multi-label classification. We developed a position-invariant method that models the variations of intensities through the use of linear combinations of kernels that are able to handle intensity shifts. Such transport functions are considered as the singleton potentials of a Markov Random Field (MRF) where pair-wise connections encode smoothness as well as prior knowledge through a local neighborhood system. We use recent advances in the field of discrete optimization towards recovering the lowest potential of the designed cost function. Promising results on real data demonstrate the potentials of our approach.","Title: Primal/dual linear programming and statistical atlases for cartilage segmentation.\nYear: 2007\n\nAbstract: In this paper we propose a novel approach for automatic segmentation of cartilage using a statistical atlas and efficient primal/dual linear programming. To this end, a novel statistical atlas construction is considered from registered training examples. Segmentation is then solved through registration which aims at deforming the atlas such that the conditional posterior of the learned (atlas) density is maximized with respect to the image. Such a task is reformulated using a discrete set of deformations and segmentation becomes equivalent to finding the set of local deformations which optimally match the model to the image. We evaluate our method on 56 MRI data sets (28 used for the model and 28 used for evaluation) and obtain a fully automatic segmentation of patella cartilage volume with an overlap ratio of 0.84 with a sensitivity and specificity of 94.06% and 99.92%, respectively.","Title: Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation\nYear: 2002\n\nAbstract: This paper presents a novel variational framework to deal with frame partition problems in Computer Vision. This framework exploits boundary and region-based segmentation modules under a curve-based optimization objective function. The task of supervised texture segmentation is considered to demonstrate the potentials of the proposed framework. The textured feature space is generated by filtering the given textured images using isotropic and anisotropic filters, and analyzing their responses as multi-component conditional probability density functions. The texture segmentation is obtained by unifying region and boundary-based information as an improved Geodesic Active Contour Model. The defined objective function is minimized using a gradient-descent method where a level set approach is used to implement the obtained PDE. According to this PDE, the curve propagation towards the final solution is guided by boundary and region-based segmentation forces, and is constrained by a regularity force. The level set implementation is performed using a fast front propagation algorithm where topological changes are naturally handled. The performance of our method is demonstrated on a variety of synthetic and real textured frames.","Title: Sparse Classification with MRI Based Markers for Neuromuscular Disease Categorization\nYear: 2013\n\nAbstract: In this paper, we present a novel method for disease classification between two patient populations based on features extracted from Magnetic Resonance Imaging (MRI) data. Anatomically meaningful features are extracted from structural data (T1- and T2-weighted MR images) and Diffusion Tensor Imaging (DTI) data, and used to train a new machine learning algorithm, the k-support SVM (ksup-SVM). The k-support regularized SVM has an inherent feature selection property, and thus it eliminates the requirement for a separate feature selection step. Our dataset consists of patients that suffer from facioscapulohumeral muscular dystrophy (FSH) and Myotonic muscular dystrophy type 1 (DM1) and our proposed method achieves a high performance. More specifically, it achieves a mean Area Under the Curve (AUC) of 0.7141 and mean accuracy 77%ä±0.013. Moreover, we provide a sparsity visualization of the features in order to indentify their discriminative value. The results suggest the potential of the combined use of MR markers to diagnose myopathies, and the general utility of the ksup-SVM. Source code is also available at <ExternalRef> <RefSource> <Emphasis FontCategory=\\\"NonProportional\\\"https://gitorious.org/ksup-svm</Emphasis> </RefSource> <RefTarget Address=\\\"https://gitorious.org/ksup-svm\\\" TargetType=\\\"URL\\\"/ </ExternalRef>.","Title: DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting\nYear: 2011\n\nAbstract: A general-purpose deformable registration algorithm referred to as “DRAMMS” is presented in this paper. DRAMMS bridges the gap between the traditional voxel-wise methods and landmark/feature-based methods with primarily two contributions. First, DRAMMS renders each voxel relatively distinctively identifiable by a rich set of attributes, therefore largely reducing matching ambiguities. In particular, a set of multi-scale and multi-orientation Gabor attributes are extracted and the optimal components are selected, so that they form a highly distinctive morphological signature reflecting the anatomical and geometric context around each voxel. Moreover, the way in which the optimal Gabor attributes are constructed is independent of the underlying image modalities or contents, which renders DRAMMS generally applicable to diverse registration tasks. A second contribution of DRAMMS is that it modulates the registration by assigning higher weights to those voxels having higher ability to establish unique (hence reliable) correspondences across images, therefore reducing the negative impact of those regions that are less capable of finding correspondences (such as outlier regions). A continuously-valued weighting function named “mutual-saliency” is developed to reflect the matching uniqueness between a pair of voxels implied by the tentative transformation. As a result, voxels do not contribute equally as in most voxel-wise methods, nor in isolation as in landmark/feature-based methods. Instead, they contribute according to the continuously-valued mutual-saliency map, which dynamically evolves during the registration process. Experiments in simulated images, inter-subject images, single-/multi-modality images, from brain, heart, and prostate have demonstrated the general applicability and the accuracy of DRAMMS.","Title: Landmark/Image-based Deformable Registration of Gene Expression Data.\nYear: 2011\n\nAbstract: Analysis of gene expression patterns in brain images obtained from high-throughput in situ hybridization requires accurate and consistent annotations of anatomical regions/subregions. Such annotations are obtained by mapping an anatomical atlas onto the gene expression images through intensity- and/or landmark-based registration methods or deformable model-based segmentation methods. Due to the complex appearance of the gene expression images, these approaches require a pre-processing step to determine landmark correspondences in order to incorporate landmark-based geometric constraints. In this paper, we propose a novel method for landmark-constrained, intensity-based registration without determining landmark correspondences a priori. The proposed method performs dense image registration and identifies the landmark correspondences, simultaneously, using a single higher-order Markov Random Field model. In addition, a machine learning technique is used to improve the discriminating properties of local descriptors for landmark matching by projecting them in a Hamming space of lower dimension. We qualitatively show that our method achieves promising results and also compares well, quantitatively, with the expert's annotations, outperforming previous methods.","Title: Linear image registration through MRF optimization\nYear: 2009\n\nAbstract: We propose a Markov Random Field formulation for the linear image registration problem. Transformation parameters are represented by nodes in a fully connected graph where the edges model pairwise dependencies. Parameter estimation is then solved through iterative discrete labeling and discrete optimization while a label space refinement strategy is employed to achieve sub-millimeter accuracy. Our framework can encode any similarity measure, allows for automatic reduction of the degrees of freedom by simple changes on the MRF topology, and is robust to initialization. Promising results on real data and random studies demonstrate the potential of our approach.","Title: Higher order polynomials, free form deformations and optical flow estimation\nYear: 2005\n\nAbstract: In this paper, we propose a novel technique to represent and recover optical flow through free form deformations. Such a technique is based on representing the motion field using regular connected grids according to higher order polynomials, a compromise between dense motion estimation and parametric motion models. Optical flow is determined through the deformation of the grid - derived from the optimization of a cost function - and consequently of the underlying image structures towards satisfying the constant brightness constraint. Smoothness conditions are implicitly accounted for through the free form deformation approach. Promising results demonstrate the potentials of our approach.","Title: Guide-wire extraction through perceptual organization of local segments in fluoroscopic images.\nYear: 2010\n\nAbstract: Segmentation of surgical devices in fluoroscopic images and in particular of guide-wires is a valuable element during surgery. In cardiac angioplasty, the problem is particularly challenging due to the following reasons: (i) low signal to noise ratio, (ii) the use of 2D images that accumulate information from the whole volume, and (iii) the similarity between the structure of interest and adjacent anatomical structures. In this paper we propose a novel approach to address these challenges, that combines efficiently low-level detection using machine learning techniques, local unsupervised clustering detections and finally high-level perceptual organization of these segments towards its complete reconstruction. The latter handles miss-detections and is based on a local search algorithm. Very promising results were obtained.","Title: A variational approach to monocular hand-pose estimation\nYear: 2010\n\nAbstract: In this paper, we propose a model-based approach to recover 3D hand pose from 2D images. To this end, we describe the hand structure using a compact 3D articulated model and reformulate pose estimation as a binary image segmentation problem aiming to separate the hand from the background. We propose generative models for hand and background pixels leading to a log-likelihood objective function which aims to enclose hand-like pixels within the projected silhouette of the 3D model while excluding background-like pixels. Segmentation and hand-pose estimation are jointly addressed through the minimization of a single likelihood function. Pose is determined through gradient descent in the hand parameter space of such an area-based objective function. Furthermore, we propose a new constrained variable metric gradient descent to speed up convergence and finally the so called smart particle filter to deal with occlusions and local minima through multiple hypotheses. Promising experimental results demonstrate the potentials of our approach.","Title: Atlas-based deformable mutual population segmentation\nYear: 2009\n\nAbstract: Segmentation is one of the most critical problems in medical imaging. State-of-the art methods often are based on prior knowledge that can either encode geometry, appearance or both. Despite enormous work in the field, the mainstream is based on the concept of case-to-case segmentation. In this paper, we introduce the notion of mutual population segmentation using discrete optimization where results from a given example influence results for the rest of the examples towards improving the overall segmentation performance. The aim is to combine prior knowledge along with consistency through the simultaneous segmentation of the whole population. This is achieved through their mutual deformation towards the atlas, while being constrained through a simultaneous all-to-all deformable diffeomorphic registration. Promising results demonstrate the potentials of the method.","Title: Discriminative parameter estimation for random walks segmentation.\nYear: 2013\n\nAbstract: The Random Walks (RW) algorithm is one of the most efficient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate segmentations of medical images in a fully automated manner. However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned. we propose a novel discriminative learning framework that estimates the parameters using a training dataset. The main challenge we face is that the training samples are not fully supervised. Specifically, they provide a hard segmentation of the images, instead of a probabilistic segmentation. We overcome this challenge by treating the optimal probabilistic segmentation that is compatible with the given hard segmentation as a latent variable. This allows us to employ the latent support vector machine formulation for parameter estimation. We show that our approach significantly outperforms the baseline methods on a challenging dataset consisting of real clinical 3D MRI volumes of skeletal muscles.","Title: A Generic Deformation Model for Dense Non-rigid Surface Registration: A Higher-Order MRF-Based Approach\nYear: 2013\n\nAbstract: We propose a novel approach for dense non-rigid 3D surface registration, which brings together Riemannian geometry and graphical models. To this end, we first introduce a generic deformation model, called Canonical Distortion Coefficients (CDCs), by characterizing the deformation of every point on a surface using the distortions along its two principle directions. This model subsumes the deformation groups commonly used in surface registration such as isometry and conformality, and is able to handle more complex deformations. We also derive its discrete counterpart which can be computed very efficiently in a closed form. Based on these, we introduce a higher-order Markov Random Field (MRF) model which seamlessly integrates our deformation model and a geometry/texture similarity metric. Then we jointly establish the optimal correspondences for all the points via maximum a posteriori (MAP) inference. Moreover, we develop a parallel optimization algorithm to efficiently perform the inference for the proposed higher-order MRF model. The resulting registration algorithm outperforms state-of-the-art methods in both dense non-rigid 3D surface registration and tracking.","Title: Particle filters, a quasi-Monte-Carlo-solution for segmentation of coronaries.\nYear: 2005\n\nAbstract: In this paper we propose a Particle Filter-based approach for the segmentation of coronary arteries. To this end, successive planes of the vessel are modeled as unknown states of a sequential process. Such states consist of the orientation, position, shape model and appearance (in statistical terms) of the vessel that are recovered in an incremental fashion, using a sequential Bayesian filter (Particle Filter). In order to account for bifurcations and branchings, we consider a Monte Carlo sampling rule that propagates in parallel multiple hypotheses. Promising results on the segmentation of coronary arteries demonstrate the potential of the proposed approach.","Title: Graphical models and deformable diffeomorphic population registration using global and local metrics.\nYear: 2009\n\nAbstract: In this paper we propose a novel framework to unite a population to an optimal (unknown) pose through their mutual deformation. The registration criterion comprises three terms, the first imposes compactness on appearance of the registered population at the pixel level, the second tries to minimize the individual distances between all possible pairs of images, while the last is a regularization one imposing smoothness on the deformation fields. The problem is reformulated as a graphical model that consists of hidden (deformation fields) and observed variables (intensities). A novel deformation grid-based scheme is proposed that guarantees the diffeomorphism of the deformation and is computationally favorably compared to standard deformation methods. Towards addressing important deformations we propose a compositional approach where the deformations are recovered through the sub-optimal solutions of successive discrete MRFs by using efficient linear programming. Promising experimental results using real 2D data demonstrate the potentials of our approach.","Title: Motion-based enhancement of optical imaging\nYear: 2009\n\nAbstract: Optical imaging offers the possibility to study and image biology at molecular scale in small animal with applications such as oncology or gene expression studies. New device enable cinematic acquisitions but in some cases signals could suffer from weak spatial information. The aim of this paper is to improve localization of the luminescent data for a freely moving animal using motion information obtained with an additional camera. To this end, we propose an approach that performs a non-rigid registration in the scene video and use the corresponding motion vectors to filter the optical one. Due to the lack of contrast and texture, our method introduces silhouette constraints and landmarks on the mouse skin within a variation framework. Motion is represented using thin-platesplines and the objective function is optimized using a gradient conjugate descent approach. Experimental results demonstrate the potentials of the proposed framework.","Title: Joint Object Segmentation And Behavior Classification In Image Sequences\nYear: 2007\n\nAbstract: In this paper, we propose a general framework for fusing bottom-up segmentation with top-down object behavior classification over an image sequence. This approach is beneficial for both tasks, since it enables them to cooperate so that knowledge relevant to each can aid in the resolution of the other, thus enhancing the final result. In particular classification offers dynamic probabilistic priors to guide segmentation, while segmentation supplies its results to classification, ensuring that they are consistent both with prior knowledge and with new image information. We demonstrate the effectiveness of our framework via a particular implementation for a hand gesture recognition application. The prior models are learned from training data using principal components analysis and they adapt dynamically to the content of new images. Our experimental results illustrate the robustness of our joint approach to segmentation and behavior classification in challenging conditions involving occlusions of the target object before a complex background.","Title: Random Exploration of the Procedural Space for Single-View 3D Modeling of Buildings\nYear: 2011\n\nAbstract: In this paper we tackle the problem of 3D modeling for urban environment using a modular, flexible and powerful approach driven from procedural generation. To this end, typologies of architectures are modeled through shape grammars that consist of a set of derivation rules and a set of shape/dictionary elements. Appearance (from statistical point of view with respect to the individual pixel's properties) of the dictionary elements is then learned using a set of training images. Image classifiers are trained towards recovering image support with respect to the semantics. Then, given a new image and the corresponding footprint, the modeling problem is formulated as a search of the space of shapes, that can be generated on-the-fly by deriving the grammar on the input axiom. Defining an image-based score function for the produced instances using the trained classifiers, the best rules are selected, making sure that we keep exploring the space by allowing some rules to be randomly selected. New rules are then generated by resampling around the selected rules. At the finest level, these rules define the 3D model of the building. Promising results on complex and varying architectural styles demonstrate the potential of the presented method.","Title: An Application of Graph Commute Times to Image Indexing\nYear: 2008\n\nAbstract: In this paper we provide an overview of an image represen- tation approach based on the description of layout and ap- pearance properties of groups of features. In each image a graph of quantized features of interest is constructed. The features that are assigned to the same codebook bin are then grouped to produce a collapsed graph; the image contentn is represented by the matrix of commute times of this collapsed graph. This novel image descriptor can be used to label satellite image databases; we demonstrate the relevance and the effi- ciency of our approach by addressing classification problem s on a dataset of 0.6m resolution Quickbird images.","Title: A convex semi-definite positive framework for DTI estimation and regularization\nYear: 2007\n\nAbstract: In this paper we introduce a novel variational method for joint estimation and regularization of diffusion tensor fields from noisy raw data. To this end, we use the classic quadratic data fidelity term derived from the Stejskal-Tanner equation with a new smoothness term leading to a convex objective function. The regularization term is based on the assumption that the signal can be reconstructed using a weighted average of observations on a local neighborhood. The weights measure the similarity between tensors and are computed directly from the diffusion images. We preserve the positive semi-definiteness constraint using a projected gradient descent. Experimental validation and comparisons with a similar method using synthetic data with known noise model, as well as classification of tensors towards understanding the anatomy of human skeletal muscle demonstrate the potential of our method.","Title: Uniform and textured regions separation in natural images towards MPM adaptive denoising\nYear: 2007\n\nAbstract: Natural images consist of texture, structure and smooth regions and this makes the task of filtering challenging mainly when it aims at edge and texture preservation. In this paper, we present a novel adaptive filtering technique based on a partition of the image to \"noisy smooth zones\" and \"texture or edge + noise\" zones. To this end, an analysis of local features is used to recover a statistical model that associates to each pixel a probability measure corresponding to a membership degree for each class. This probability function is then encoded in a new denoising process based on a MPM (Marginal Posterior Mode) estimation technique. The posterior density is computed through a non parametric density estimation method with variable kernel bandwidth that aims to adapt the denoising process to image structure. In our algorithm the selection of the bandwidth relies on a non linear function of the membership probabilities. Encouraging, experimental results demonstrate the potential of our approach.","Title: MRI Composing for Whole Body Imaging\nYear: 2009\n\nAbstract: Whole-body magnetic resonance imaging is an emerging ap- plication gaining vast clinical interest during the last years. Although recent technological advances shortened the longish acquisition time, this is still the limiting factor avoiding its wide-spread clinical usage. The ac- quisition of images with large fleld-of-view helps to relieve this drawback, but leads to signiflcantly distorted images. In this paper, a novel scheme for MRI composing is presented. The approach is based on simultaneous registration of two MRI volumes to their linear weighted average. The method successfully compensates for the distortions and allows to gener- ate high-resolution whole body images. Results on several in-vivo data sets are presented.","Title: Model-Based 3D Hand Pose Estimation from Monocular Video\nYear: 2011\n\nAbstract: A novel model-based approach to 3D hand tracking from monocular video is presented. The 3D hand pose, the hand texture, and the illuminant are dynamically estimated through minimization of an objective function. Derived from an inverse problem formulation, the objective function enables explicit use of temporal texture continuity and shading information while handling important self-occlusions and time-varying illumination. The minimization is done efficiently using a quasi-Newton method, for which we provide a rigorous derivation of the objective function gradient. Particular attention is given to terms related to the change of visibility near self-occlusion boundaries that are neglected in existing formulations. To this end, we introduce new occlusion forces and show that using all gradient terms greatly improves the performance of the method. Qualitative and quantitative experimental results demonstrate the potential of the approach.","Title: Manifold-Enhanced Segmentation Through Random Walks On Linear Subspace Priors\nYear: 2012\n\nAbstract: In this paper we propose a novel method for knowledge-based segmentation. Our contribution lies on the introduction of linear sub-spaces constraints within the random-walk segmentation framework. Prior knowledge is obtained through principal component analysis that is then combined with conventional boundary constraints for image segmentation. The approach is validated on a challenging clinical setting that is multicomponent segmentation of the human upper leg skeletal muscle in Magnetic Resonance Imaging, where there is limited visual differentiation support between muscle classes.","Title: Uncertainty-Driven non-parametric knowledge-based segmentation: the corpus callosum case\nYear: 2005\n\nAbstract: In this paper we propose a novel variational technique for the knowledge based segmentation of two dimensional objects. One of the elements of our approach is the use of higher order implicit polynomials to represent shapes. The most important contribution is the estimation of uncertainties on the registered shapes, which can be used with a variable bandwidth kernel-based non-parametric density estimation process to model prior knowledge about the object of interest. Such a non-linear model with uncertainty measures is integrated with an adaptive visual-driven data term that aims to separate the object of interest from the background. Promising results obtained for the segmentation of the corpus callosum in MR mid-sagittal brain slices demonstrate the potential of such a framework.","Title: Boosted Metric Learning For 3d Multi-Modal Deformable Registration\nYear: 2011\n\nAbstract: Defining a suitable metric is one of the biggest challenges in deformable image fusion from different modalities. In this paper, we propose a novel approach for multi-modal metric learning in the deformable registration framework that consists of embedding data from both modalities into a common metric space whose metric is used to parametrize the similarity. Specifically, we use image representation in the Fourier/Gabor space which introduces invariance to the local pose parameters, and the Hamming metric as the target embedding space, which allows constructing the embedding using boosted learning algorithms. The resulting metric is incorporated into a discrete optimization framework. Very promising results demonstrate the potential of the proposed method.","Title: Corrections to 'Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects'\nYear: 2000\n\nAbstract: First Page of the Article","Title: Knowledge-based Registration & Segmentation of the Left Ventricle: A Level Set Approach\nYear: 2002\n\nAbstract: In this paper, we propose a level set formulation to dealwith the segmentation and registration of the left ventriclein Magnetic Resonance (MR) images. Our approach isbased on the integration of visual information, anatomicalconstraints and a flexible shape-driven cardiac model. Thevisual information is expressed through an intensity-basedgrouping module. The anatomical constraint accounts forthe relative positions of the structures of interest. Globalshape consistency is introduced by seeking for the lowestpotential of the distance between the solution and the priormodel. Registration is obtained using the same criterionwhere the transformation that aligns the latest segmentationmap to either the shape model or to the previous segmentationresult (temporal domain) is to be recovered.","Title: Compressed sensing dynamic reconstruction in rotational angiography.\nYear: 2012\n\nAbstract: This work tackles three-dimensional reconstruction of tomographic acquisitions in C-arm-based rotational angiography. The relatively slow rotation speed of C-arm systems involves motion artifacts that limit the use of three-dimensional imaging in interventional procedures. The main contribution of this paper is a reconstruction algorithm that deals with the temporal variations due to intra-arterial injections. Based on a compressed-sensing approach, we propose a multiple phase reconstruction with spatio-temporal constraints. The algorithm was evaluated by qualitative and quantitative assessment of image quality on both numerical phantom experiments and clinical data from vascular C-arm systems. In this latter case, motion artifacts reduction was obtained in spite of the cone-beam geometry, the short-scan acquisition, and the truncated and subsampled data.","Title: Registration with uncertainties and statistical modeling of shapes with variable metric kernels.\nYear: 2009\n\nAbstract: Registration and modeling of shapes are two important problems in computer vision and pattern recognition. Despite enormous progress made over the past decade, these problems are still open. In this paper, we advance the state of the art in both directions. First we consider an efficient registration method that aims to recover a one-to-one correspondence between shapes and introduce measures of uncertainties driven from the data which explain the local support of the recovered transformations. To this end, a free form deformation is used to describe the deformation model. The transformation is combined with an objective function defined in the space of implicit functions used to represent shapes. Once the registration parameters have been recovered, we introduce a novel technique for model building and statistical interpretation of the training examples based on a variable bandwidth kernel approach. The support on the kernels varies spatially and is determined according to the uncertainties of the registration process. Such a technique introduces the ability to account for potential registration errors in the model. Hand-written character recognition and knowledge-based object extraction in medical images are examples of applications that demonstrate the potentials of the proposed framework.","Title: Graph-based multi-resolution temporal-based face reconstruction\nYear: 2006\n\nAbstract: Reproducing high quality facial expressions is an important challenge in human-computer interaction. Laser-scanners offer an expensive solution to such a problem with image based alternatives being a low-resolution alternative. In this paper, we propose a new method for stereo reconstruction from multiple video pairs that is capable of producing high resolution facial models. To this end, a combinatorial optimization approach is considered and is coupled in time to produce high resolution depth maps. Such optimization is addressed with the use of graph-cuts leading to precise reconstruction of facial expressions that can then be used for animation.","Title: Image contextual representation and matching through hierarchies and higher order graphs\nYear: 2012\n\nAbstract: We present a region matching algorithm which establishes correspondences between regions from two segmented images. An abstract graph-based representation conceals the image in a hierarchical graph, exploiting the scene properties at two levels. First, the similarity and spatial consistency of the image semantic objects is encoded in a graph of commute times. Second, the cluttered regions of the semantic objects are represented with a shape descriptor. Many-to-many matching of regions is specially challenging due to the instability of the segmentation under slight image changes, and we explicitly handle it through high order potentials. We demonstrate the matching approach applied to images of world famous buildings, captured under different conditions, showing the robustness of our method to large variations in illumination and viewpoint.","Title: Uncalibrated Stereo Rectification For Automatic 3d Surveillance\nYear: 2004\n\nAbstract: We describe a stereo rectification method suitable for automatic 3D surveillance. We take advantage of the fact that in a typical urban scene. there is ordinarily a small number of dominant planes. Given two views of the scene, we align a dominant plane in one view with the other. Conjugate epipolar lines between the reference view and plane-aligned image become geometrically identical and can be added to the rectified image pair line by line. Selecting conjugate epipolar lines to cover the whole image is simplified Since they are geometrically identical. In addition, the polarities of conjugate epipolar lines are automatically preserved by plane alignment, which simplifies stereo matching.","Title: Fast Dichotomic Multiple Search Algorithm for Shortest Cirular Path\nYear: 2006\n\nAbstract: Circular shortest path algorithms for polar objects segmentation have been proposed in [1, 11, 10] to address discrete case and extended in [2] to the continuous domain for closed global optimal geodesic calculation. The best method up to date relies on a branch and bound approach and runs in O(u1.6v) on average while O(u2v) in worst case for a u 脳 v discrete trellis warped in the direction of v. We propose an new algorithm called dichotomic multiple search (DMS) which finds the global minimum with a O(ulog2(u)v) worst case scenario complexity. Our algorithm relies on the fact that two minimal paths never cross more than once. This allows to sequentially partition the trellis in a dichotomic manner. Each computed circular minimal path with chosen starting point allows cutting the trellis into two sub trellis. The algorithm is then recursively applied on each sub trellis. Application to object segmentation is presented.","Title: Background Modeling and Subtraction of Dynamic Scenes\nYear: 2003\n\nAbstract: Background modeling and subtraction is a core componentin motion analysis. The central idea behind such moduleis to create a probabilistic representation of the staticscene that is compared with the current input to performsubtraction. Such approach is efficient when the scene to bemodeled refers to a static structure with limited perturbation.In this paper, we address the problem of modeling dynamicscenes where the assumption of a static backgroundis not valid. Waving trees, beaches, escalators, naturalscenes with rain or snow are examples. Inspired by the workproposed in [4], we propose an on-line auto-regressivemodel to capture and predict the behavior of such scenes.Towards detection of events we introduce a new metric thatis based on a state-driven comparison between the predictionand the actual frame. Promising results demonstratethe potentials of the proposed framework.","Title: Image Renaissance Using Discrete Optimization\nYear: 2006\n\nAbstract: In this paper we propose a novel technique to image completion that addresses image renaissance through a graph-based matching process. To this end, a number of candidate seeds with content similar to the one of the area to be inpainted are considered. They are selected through a particle filter method and then positioned over the missing area. Markov Random Fields are used to formalize inpainting as a labeling estimation problem while a combinatorial approach is used to recover the optimal partition of patches that completes the missing area with the á-expansion process. Promising results in image and texture completion demonstrate the potentials of the proposed method.","Title: Editorial: Special Issue on Vision and Medical Imaging Activities at Siemens Corporate Research\nYear: 2006\n\nAbstract: Without Abstract","Title: Globally optimal active contours, sequential monte carlo and on-line learning for vessel segmentation\nYear: 2006\n\nAbstract: In this paper we propose a Particle Filter-based propagation approach for the segmentation of vascular structures in 3D volumes. Because of pathologies and inhomogeneities, many deterministic methods fail to segment certain types of vessel. Statistical methods represent the solution using a probability density function (pdf). This pdf does not only indicate the best possible solution, but also valuable information about the solution's variance. Particle Filters are used to learn the variations of direction and appearance of the vessel as the segmentation goes. These variations are used in turn in the particle filters framework to control the perturbations introduced in the Sampling Importance Resampling step (SIR). For the segmentation itself, successive planes of the vessel are modeled as states of a Particle Filter. Such states consist of the orientation, position and appearance (in statistical terms) of the vessel. The shape of the vessel and subsequently the particles pdf are recovered using globally active contours, implemented using circular shortest paths by branch and bound [1] that guarantees the global optimal solution. Promising results on the segmentation of coronary arteries demonstrate the potential of the proposed approach.","Title: Scene modeling and change detection in dynamic scenes: A subspace approach\nYear: 2009\n\nAbstract: Background modeling and subtraction are core components in video processing. To this end, one aims to recover and continuously update a representation of the scene that is compared with the current input to perform subtraction. Most of the existing methods treat each pixel independently and attempt to model the background perturbation through statistical modeling such as a mixture of Gaussians. While such methods have satisfactory performance in many scenarios, they do not model the relationships and correlation amongst nearby pixels. Such correlation between pixels exists both in space and across time especially when the scene consists of image structures moving across space. Waving trees, beach, escalators and natural scenes with rain or snow are examples of such scenes. In this paper, we propose a method for differentiating between image structures and motion that are persistent and repeated from those that are ''new''. Towards capturing the appearance characteristics of such scenes, we propose the use of an appropriate subspace created from image structures. Furthermore, the dynamical characteristics are captured by the use of a prediction mechanism in such subspace. Since the model must adapt to long-term changes in the background, an incremental method for fast online adaptation of the model parameters is proposed. Given such adaptive models, robust and meaningful measures for detection that consider both structural and motion changes are considered. Promising experimental results that include qualitative and quantitative comparisons with existing background modeling/subtraction techniques demonstrate the very promising performance of the proposed framework when dealing with complex backgrounds.","Title: Clustering of the human skeletal muscle fibers using linear programming and angular Hilbertian metrics.\nYear: 2009\n\nAbstract: In this paper, we present a manifold clustering method fo the classification of fibers obtained from diffusion tensor images (DTI) of the human skeletal muscle. Using a linear programming formulation of prototype-based clustering, we propose a novel fiber classification algorithm over manifolds that circumvents the necessity to embed the data in low dimensional spaces and determines automatically the number of clusters. Furthermore, we propose the use of angular Hilbertian metrics between multivariate normal distributions to define a family of distances between tensors that we generalize to fibers. These metrics are used to approximate the geodesic distances over the fiber manifold. We also discuss the case where only geodesic distances to a reduced set of landmark fibers are available. The experimental validation of the method is done using a manually annotated significant dataset of DTI of the calf muscle for healthy and diseased subjects.","Title: Unifying Boundary and Region-Based Information for Geodesic Active Tracking\nYear: 1999\n\nAbstract: This paper addresses the problem of tracking several non-rigid objects over a sequence of frames acquired from a static observer using boundary and region-based informa- tion under a coupled geodesic active contour framework. Given the current frame, a statistical analysis is performe d on the observed difference frame which provides a mea- surement that distinguishes between the static and mobile regions in terms of conditional probabilities. An objec- tive function is defined that integrates boundary-based and region-based module by seeking curves that attract the ob- ject boundaries and maximize the a posteriori segmentation probability on the interior curve regions with respect to in - tensity and motion properties. This function is minimized using a gradient descent method. The associated Euler- Lagrange PDE is implemented using a Level-Set approach, where a very fast front propagation algorithm evolves the initial curve towards the final tracking result. Very promis - ing experimental results are provided using real video se- quences.","Title: Automatic inference of articulated spine models in CT images using high-order Markov Random Fields.\nYear: 2011\n\nAbstract: In this paper, we introduce a novel and efficient approach for inferring articulated 3D spine models from operative images. The problem is formulated as a Markov Random Field which has the ability to encode global structural dependencies to align CT volume images. A personalized geometrical model is first reconstructed from preoperative images before surgery, and subsequently decomposed as a series of intervertebral transformations based on rotation and translation parameters. The shape transformation between the standing and lying poses is achieved by optimizing the deformations applied to the intervertebral transformations. Singleton and pairwise potentials measure the support from the data and geometrical dependencies between neighboring vertebrae respectively, while higher-order cliques (groups of vertebrae) are introduced to integrate consistency in regional curves. Local vertebra modifications are achieved through a constrained mesh relaxation technique. Optimization of model parameters in a multimodal context is achieved using efficient linear programming and duality. Experimental and clinical evaluation of the vertebra model alignment obtained from the proposed method gave promising results. Quantitative comparison to expert identification yields an accuracy of 1.8±0.7mm based on the localization of surgical landmarks.","Title: Implicit Active Shape Models for 3D Segmentation in MR Imaging\nYear: 2004\n\nAbstract: Extraction of structures of interest in medical images is often an arduous task because of noisy or incomplete data. However, hand-segmented data are often available and most of the structures to be extracted have a similar shape from one subject to an other. Then, the possibility of modeling a family of shapes and restricting the new structure to be extracted within this class is of particular interest. This approach is commonly implemented using active shape models [2] and the definition of the image term is the most challenging component of such an approach. In parallel, level set methods [8] define a powerful optimization framework, that can be used to recover objects of interest by the propagation of curves or surfaces. They can support complex topologies, considered in higher dimensions, are implicit, intrinsic and parameter free. In this paper we re-visit active shape models and introduce a level set variant of them. Such an approach can account for prior shape knowledge quite efficiently as well as use data/image terms of various form and complexity. Promising results on the extraction of brain ventricles in MR images demonstrate the potential of our approach.","Title: Matching Distance Functions: A Shape-to-Area Variational Approach for Global-to-Local Registration\nYear: 2002\n\nAbstract: This paper deals with the matching of geometric shapes. Our primary contribution is the use of a simple, robust, rich and efficient way to represent shapes, the level set representations according to singed distance transforms. Based on these representations we propose a variational framework for global as well as local shape registration that can be extended to deal with structures of higher dimension. The optimization criterion is invariant to rotation, translation and scale and combines efficiently a global motion model with local pixel-wise deformations. Promising results are obtained on examples showing small and large global deformations as well as arbitrary topological changes.","Title: Dense registration with deformation priors.\nYear: 2009\n\nAbstract: In this paper we propose a novel approach to define task-driven regularization constraints in deformable image registration using learned deformation priors. Our method consists of representing deformation through a set of control points and an interpolation strategy. Then, using a training set of images and the corresponding deformations we seek for a weakly connected graph on the control points where edges define the prior knowledge on the deformation. This graph is obtained using a clustering technique which models the co-dependencies between the displacements of the control points. The resulting classification is used to encode regularization constraints through connections between cluster centers and cluster elements. Additionally, the global structure of the deformation is encoded through a fully connected graph on the cluster centers. Then, registration of a new pair of images consists of displacing the set of control points where on top of conventional image correspondence costs, we introduce costs that are based on the relative deformation of two control points with respect to the learned deformation. The resulting paradigm is implemented using a discrete Markov Random Field which is optimized using efficient linear programming. Promising experimental results on synthetic and real data demonstrate the potential of our approach.","Title: Joint tumor segmentation and dense deformable registration of brain MR images.\nYear: 2012\n\nAbstract: In this paper we propose a novel graph-based concurrent registration and segmentation framework. Registration is modeled with a pairwise graphical model formulation that is modular with respect to the data and regularization term. Segmentation is addressed by adopting a similar graphical model, using image-based classification techniques while producing a smooth solution. The two problems are coupled via a relaxation of the registration criterion in the presence of tumors as well as a segmentation through a registration term aiming the separation between healthy and diseased tissues. Efficient linear programming is used to solve both problems simultaneously. State of the art results demonstrate the potential of our method on a large and challenging low-grade glioma data set.","Title: Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge.\nYear: 2011\n\nAbstract: EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intra patient thoracic CT image pairs. Evaluation of nonrigid registration techniques is a nontrivial task. This is compounded by the fact that researchers typically test only on their own data, which varies wid...","Title: Left ventricle segmentation using diffusion wavelets and boosting.\nYear: 2009\n\nAbstract: We propose a method for the segmentation of medical images based on a novel parameterization of prior shape knowledge and a search scheme based on classifying local appearance. The method uses diffusion wavelets to capture arbitrary and continuous interdependencies in the training data and uses them for an efficient shape model. The lack of classic visual consistency in complex medical imaging data, is tackled by a manifold learning approach handling optimal high-dimensional local features by Gentle Boosting. Appearance saliency is encoded in the model and segmentation is performed through the extraction and classification of the corresponding features in a new data set, as well as a diffusion wavelet based shape model constraint. Our framework supports hierarchies both in the model and the search space, can encode complex geometric and photometric dependencies of the structure of interest, and can deal with arbitrary topologies. Promising results are reported for heart CT data sets, proving the impact of the soft parameterization, and the efficiency of our approach.","Title: Clustering via LP-based Stabilities\nYear: 2008\n\nAbstract: A novel center-based clustering algorithm is proposed in this paper. We first formulate clustering as an NP-hard linear integer program and we then use linear programming and the duality theory to derive the solution of this optimization problem. This leads to an efficient and very general algorithm, which works in the dual domain, and can cluster data based on an arbitrary set of distances. Despite its generality, it is independent of initialization (unlike EM-like methods such as K-means), has guaranteed convergence, and can also provide online optimality bounds about the quality of the estimated clustering solutions. To deal with the most critical issue in a center-based clustering algorithm (selection of cluster centers), we also introduce the notion of stability of a cluster center, which is a well defined LP-based quantity that plays a key role to our algorithm's success. Furthermore, we also introduce, what we call, the margins (another key ingredient in our algorithm), which can be roughly thought of as dual counterparts to stabilities and allow us to obtain computationally efficient approximations to the latter. Promising experimental results demonstrate the potentials of our method.","Title: Similarity-Based appearance-prior for fitting a subdivision mesh in gene expression images\nYear: 2012\n\nAbstract: Automated segmentation of multi-part anatomical objects in images is a challenging task. In this paper, we propose a similarity-based appearance-prior to fit a compartmental geometric atlas of the mouse brain in gene expression images. A subdivision mesh which is used to model the geometry is deformed using a Markov Random Field (MRF) framework. The proposed appearance-prior is computed as a function of the similarity between local patches at corresponding atlas locations from two images. In addition, we introduce a similarity-saliency score to select the mesh points that are relevant for the computation of the proposed prior. Our method significantly improves the accuracy of the atlas fitting, especially in the regions that are influenced by the selected similarity-salient points, and outperforms the previous subdivision mesh fitting methods for gene expression images.","Title: Estimating shadows with the bright channel cue\nYear: 2010\n\nAbstract: In this paper, we introduce a simple but efficient cue for the extraction of shadows from a single color image, the bright channel cue. We discuss its limitations and offer two methods to refine the bright channel: by computing confidence values for the cast shadows, based on a shadow-dependent feature, such as hue; and by combining the bright channel with illumination invariant representations of the original image in a flexible way using an MRF model. We present qualitative and quantitative results for shadow detection, as well as results in illumination estimation from shadows. Our results show that our method achieves satisfying results despite the simplicity of the approach.","Title: Coupled Geodesic Active Regions for Image Segmentation: A Level Set Approach\nYear: 2000\n\nAbstract: This paper presents a novel variational method for image segmentation that unifies boundary and region-based information sources under the Geodesic Active Region framework. A statistical analysis based on the Minimum Description Length criterion and the Maximum Likelihood Principle for the observed density function (image histogram) using a mixture of Gaussian elements, indicates the number of the different regions and their intensity properties. Then, the boundary information is determined using a probabilistic edge detector, while the region information is estimated using the Gaussian components of the mixture model. The defined objective function is minimized using a gradient-descent method where a level set approach is used to implement the resulting PDE system. According to the motion equations, the set of initial curves is propagated toward the segmentation result under the influence of boundary and region-based segmentation forces, and being constrained by a regularity force. The changes of topology are naturally handled thanks to the level set implementation, while a coupled multi-phase propagation is adopted that increases the robustness and the convergence rate by imposing the idea of mutually exclusive propagating curves. Finally, to reduce the required computational cost and the risk of convergence to local minima, a multi-scale approach is also considered. The performance of our method is demonstrated on a variety of real images.","Title: Active shape models and segmentation of the left ventricle in echocardiography\nYear: 2005\n\nAbstract: Segmentation of the left ventricle in echocardiographic images is a task with important diagnostic power. We propose a model-based approach that aims at extracting the left ventricle for each frame of the cardiac cycle. Our approach exhibits several novel elements. Modelling consists of two separate components, one for the systolic and one for the diastolic moment. Segmentation is considered in two steps. During the first step a linear combination of the systolic and the diastolic model is to be recovered – that dictates the new model – along with a similarity transformation that projects this model to the desired image features. During the second step, a linear combination of the modes of variation for the systolic and diastolic models is recovered for precise extraction of the endocardium boundaries. The process is considered in the temporal domain where constraints are introduced to couple information across frames and to lead to a smooth solution. Promising results demonstrate the potentials of the presented framework.","Title: Dense non-rigid surface registration using high-order graph matching\nYear: 2010\n\nAbstract: In this paper, we propose a high-order graph matching formulation to address non-rigid surface matching. The singleton terms capture the geometric and appearance similarities (e.g., curvature and texture) while the high-order terms model the intrinsic embedding energy. The novelty of this paper includes: 1. casting 3D surface registration into a graph matching problem that combines both geometric and appearance similarities and intrinsic embedding information, 2. the first implementation of high-order graph matching algorithm that solves a non-convex optimization problem, and 3. an efficient two-stage optimization approach to constrain the search space for dense surface registration. Our method is validated through a series of experiments demonstrating its accuracy and efficiency, notably in challenging cases of large and/or non-isometric deformations, or meshes that are partially occluded.","Title: Data Fusion Through Cross-Modality Metric Learning Using Similarity-Sensitive Hashing\nYear: 2010\n\nAbstract: Visual understanding is often based on measuring similarity between observations. Learning similarities specific to a certain perception task from a set of examples has been shown advantageous in various computer vision and pattern recognition problems. In many important applications, the data that one needs to compare come from different representations or modalities, and the similarity between such data operates on objects that may have different and often incommensurable structure and dimensionality. In this paper, we propose a framework for supervised similarity learning based on embedding the input data from two arbitrary spaces into the Hamming space. The mapping is expressed as a binary classification problem with positive and negative examples, and can be efficiently learned using boosting algorithms. The utility and efficiency of such a generic approach is demonstrated on several challenging applications including cross-representation shape retrieval and alignment of multi-modal medical images.","Title: Non-rigid 2D-3D medical image registration using markov random fields.\nYear: 2013\n\nAbstract: The aim of this paper is to propose a novel mapping algorithm between 2D images and a 3D volume seeking simultaneously a linear plane transformation and an in-plane dense deformation. We adopt a metric free locally over-parametrized graphical model that combines linear and deformable parameters within a coupled formulation on a 5-dimensional space. Image similarity is encoded in singleton terms, while geometric linear consistency of the solution (common/single plane) and in-plane deformations smoothness are modeled in a pair-wise term. The robustness of the method and its promising results with respect to the state of the art demonstrate the extreme potential of this approach.","Title: Liver segmentation using sparse 3D prior models with optimal data support.\nYear: 2007\n\nAbstract: Volume segmentation is a relatively slow process and, in certain circumstances, the enormous amount of prior knowledge available is underused. Model-based liver segmentation suffers from the large shape variability of this organ, and from structures of similar appearance that juxtapose the liver. The technique presented in this paper is devoted to combine a statistical analysis of the data with a reconstruction model from sparse information: only the most reliable information in the image is used, and the rest of the liver's shape is inferred from the model and the sparse observation. The resulting process is more efficient than standard segmentation since most of the workload is concentrated on the critical points, but also more robust, since the interpolated volume is consistent with the prior knowledge statistics. The experimental results on liver datasets prove the sparse information model has the same potential as PCA, if not better, to represent the shape of the liver. Furthermore, the performance assessment from measurement statistics on the liver's volume, distance between reconstructed surfaces and ground truth, and inter-observer variability demonstrates the liver is efficiently segmented using sparse information.","Title: Uncertainty-Driven Efficiently-Sampled Sparse Graphical Models for Concurrent Tumor Segmentation and Atlas Registration\nYear: 2013\n\nAbstract: Graph-based methods have become popular in recent years and have successfully addressed tasks like segmentation and deformable registration. Their main strength is optimality of the obtained solution while their main limitation is the lack of precision due to the grid-like representations and the discrete nature of the quantized search space. In this paper we introduce a novel approach for combined segmentation/registration of brain tumors that adapts graph and sampling resolution according to the image content. To this end we estimate the segmentation and registration marginals towards adaptive graph resolution and intelligent definition of the search space. This information is considered in a hierarchical framework where uncertainties are propagated in a natural manner. State of the art results in the joint segmentation/registration of brain images with low-grade gliomas demonstrate the potential of our approach.","Title: A variational framework for the simultaneous segmentation and object behavior classification of image sequences\nYear: 2007\n\nAbstract: In this paper, we advance the state of the art in variational image segmentation through the fusion of bottom-up segmentation and top-down classification of object behavior over an image sequence. Such an approach is beneficial for both tasks and is carried out through a joint optimization, which enables the two tasks to cooperate, such that knowledge relevant to each can aid in the resolution of the other, thereby enhancing the final result. In particular, classification offers dynamic probabilistic priors to guide segmentation, while segmentation supplies its results to classification, ensuring that they are consistent with prior knowledge. The prior models are learned from training data and updated dynamically, based on segmentations of earlier images in the sequence. We demonstrate the potential of our approach in a hand gesture recognition application, where the combined use of segmentation and classification improves robustness in the presence of occlusion and background complexity.","Title: Discriminative Parameter Estimation for Random Walks Segmentation: Technical Report.\nYear: 2013\n\nAbstract: The Random Walks (RW) algorithm is one of the most e - cient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate segmentations of medical images in a fully automated manner. However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned. we propose a novel discriminative learning framework that estimates the parameters using a training dataset. The main challenge we face is that the training samples are not fully supervised. Speci cally, they provide a hard segmentation of the images, instead of a proba-bilistic segmentation. We overcome this challenge by treating the optimal probabilistic segmentation that is compatible with the given hard segmentation as a latent variable. This allows us to employ the latent support vector machine formulation for parameter estimation. We show that our approach signi cantly outperforms the baseline methods on a challenging dataset consisting of real clinical 3D MRI volumes of skeletal muscles.","Title: Conjoint Use Of Coded-Aperture Collimators And Mlem Algoritem: Towards Large Blood Vessels Reconstruction At 511 Kev\nYear: 2008\n\nAbstract: In this paper, we propose a novel approach to improve the trade-off between the spatial resolution and the sensitivity of existing nuclear imaging systems in emission tomography for 511 keV-photons. Although emission tomography suffers from the low amount of emitted photons in the object, partial transparency of collimators and low sensitivity of detectors, we are able to reconstruct radioactive spatial distribution from projections obtained through a specifically designed coded-aperture collimator, with a MLEM-algorithm. We address both the selection of the collimator and the reconstruction approach. Very promising experiments towards 3D-vessels reconstruction demonstrate the potentials of our approach.","Title: Simultaneous cast shadows, illumination and geometry inference using hypergraphs.\nYear: 2013\n\nAbstract: The cast shadows in an image provide important information about illumination and geometry. In this paper, we utilize this information in a novel framework in order to jointly recover the illumination environment, a set of geometry parameters, and an estimate of the cast shadows in the scene given a single image and coarse initial 3D geometry. We model the interaction of illumination and geometry in the scene and associate it with image evidence for cast shadows using a higher order Markov Random Field (MRF) illumination model, while we also introduce a method to obtain approximate image evidence for cast shadows. Capturing the interaction between light sources and geometry in the proposed graphical model necessitates higher order cliques and continuous-valued variables, which make inference challenging. Taking advantage of domain knowledge, we provide a two-stage minimization technique for the MRF energy of our model. We evaluate our method in different datasets, both synthetic and real. Our model is robust to rough knowledge of geometry and inaccurate initial shadow estimates, allowing a generic coarse 3D model to represent a whole class of objects for the task of illumination estimation, or the estimation of geometry parameters to refine our initial knowledge of scene geometry, simultaneously with illumination estimation.","Title: Pose-invariant 3D proximal femur estimation through bi-planar image segmentation with hierarchical higher-order graph-based priors.\nYear: 2011\n\nAbstract: Low-dose CT-like imaging systems offer numerous perspectives in terms of clinical application, in particular for osteoarticular diseases. In this paper, we address the challenging problem of 3D femur modeling and estimation from bi-planar views. Our contributions are threefold. First, we propose a non-uniform hierarchical decomposition of the shape prior of increasing clinical-relevant precision which is achieved through curvature driven unsupervised clustering acting on the geodesic distances between vertices. Second, we introduce a graphical-model representation of the femur which can be learned from a small number of training examples and involves third-order and fourth-order priors, while being similarity and mirror-symmetry invariant and providing means of measuring regional and boundary supports in the bi-planar views. Last but not least, we adopt an efficient dual-decomposition optimization approach for efficient inference of the 3D femur configuration from bi-planar views. Promising results demonstrate the potential of our method.","Title: Extraction of layers of similar motion through combinatorial techniques\nYear: 2005\n\nAbstract: In this paper we present a new technique to extract layers in a video sequence. To this end, we assume that the observed scene is composed of several transparent layers, that their motion in the 2D plane can be approximated with an affine model. The objective of our approach is the estimation of these motion models as well as the estimation of their support in the image domain. Our technique is based on an iterative process that integrates robust motion estimation, MRF-based formulation, combinatorial optimization and the use of visual as well as motion features to recover the parameters of the motion models as well as their support layers. Special handling of occlusions as well as adaptive techniques to detect new objects in the scene are also considered. Promising results demonstrate the potentials of our approach.","Title: Group-wise MDL based registration of small animals in video sequences\nYear: 2008\n\nAbstract: We propose a method for the non-rigid tracking of small animals in video sequences. Based on an image sequence showing the animal, first a sub-set of images with coherent pose is chosen automatically. Then a robust rigid registration determines the coarse animal body pose in the set of frames and a subsequent non-rigid group-wise registration using the minimum description length principle learns a deformation model of the animal. It obtains a non-rigid mapping between the individual positions and shapes of the animal during the sequence, based on discrete sets of landmark candidates and according texture features. This is of high relevance in small animal research to integrate signals from multiple frames, and cannot be achieved by standard continuous registration methods. We report first experimental results on video sequences of a rat.","Title: Model-based multi-view fusion of cinematic flow and optical imaging.\nYear: 2010\n\nAbstract: Bioluminescence imaging (BLI) offers the possibility to study and image biology at molecular scale in small animals with applications in oncology or gene expression studies. Here we present a novel model-based approach to 3D animal tracking from monocular video which allows the quantification of bioluminescence signal on freely moving animals. The 3D animal pose and the illumination are dynamically estimated through minimization of an objective function with constraints on the bioluminescence signal position. Derived from an inverse problem formulation, the objective function enables explicit use of temporal continuity and shading information, while handling important self-occlusions and time-varying illumination. In this model-based framework, we include a constraint on the 3D position of bioluminescence signal to enforce tracking of the biologically produced signal. The minimization is done efficiently using a quasi-Newton method, with a rigorous derivation of the objective function gradient. Promising experimental results demonstrate the potentials of our approach for 3D accurate measurement with freely moving animal.","Title: Compressed sensing based 3D tomographic reconstruction for rotational angiography.\nYear: 2011\n\nAbstract: In this paper, we address three-dimensional tomographic reconstruction of rotational angiography acquisitions. In clinical routine, angular subsampling commonly occurs, due to the technical limitations of C-arm systems or possible improper injection. Standard methods such as filtered backprojection yield a reconstruction that is deteriorated by sampling artifacts, which potentially hampers medical interpretation. Recent developments of compressed sensing have demonstrated that it is possible to significantly improve reconstruction of subsampled datasets by generating sparse approximations through l1-penalized minimization. Based on these results, we present an extension of the iterative filtered backprojection that includes a sparsity constraint called soft background subtraction. This approach is shown to provide sampling artifact reduction when reconstructing sparse objects, and more interestingly, when reconstructing sparse objects over a non-sparse background. The relevance of our approach is evaluated in cone-beam geometry on real clinical data.","Title: Geodesic active regions and level set methods for motion estimation and tracking\nYear: 2005\n\nAbstract: Motion analysis in computer vision is a well studied problem with numerous ap- plications. In particular, the tasks of optical flow estimation and tracking are of increasing interest. In this paper, we propose a level set approach to address both aspects of motion analysis. Our approach relies on the propagation of smooth in- terfaces to perform tracking while using an incremental estimation of the motion models. Implicit representations are used to represent moving objects, and capture their motion parameters. Information from different sources like a boundary attrac- tion term, a background subtraction component and a visual consistency constraint are considered. The Euler-Lagrange equations within a gradient descent method lead to a flow that deforms a set of initial curve towards the object boundaries as well an incremental robust estimator of their apparent motion. Partial extension of the proposed framework to address desne motion estimation and the case of moving observer is also presented. Promising results demonstrate the performance of the method.","Title: Registration of 3d angiographic and x-ray images using sequential monte carlo sampling\nYear: 2005\n\nAbstract: Digital subtraction angiography (DSA) reconstructions and 3D Magnetic Resonance Angiography (MRA) are the modalities of choice for diagnosis of vascular diseases. However, when it comes to treatment through an endovascular intervention, only two dimensional lower resolution information such as angiograms or fluoroscopic images are usually available. Overlaying the pre-operative information from high resoluion acquisition onto the images acquired during intervention greatly helps physician in performing the operation. We propose to register pre-operative DSA or MRS with intra-operative images to bring the two data sets into a single coordinate frame. The method uses the vascular structure, which is present and visible from most of DSA, MRA and x-ray angiogram and fluoroscopic images, to determine the registration parameters. A robust multiple hypothesis framework is built to minimize a fitness measure between the 3D volume and the 2D projection. The measure is based on the distance map computed from the vascular segmentation. Particle Filters are used to resample the hypothesis, and direct them toward the feature space’s zones of maximum likelihood. Promising experimental results demonstrate the potentials of the method.","Title: Landmark-Based Segmentation Of Lungs While Handling Partial Correspondences Using Sparse Graph-Based Priors\nYear: 2011\n\nAbstract: In this paper, we propose a new segmentation algorithm that combines a graph-based shape model with image cues based on boosted features. The landmark-based shape model encodes prior constraints through the normalized Euclidean distances between pairs of control points, alleviating the need of a large database for the training. Moreover, the graph topology is deduced from the dataset using manifold learning and unsupervised clustering. In a graph-matching-like manner, we formulate the segmentation task as a labeling problem where we seek to match the model landmarks to image points that are extracted using the boosted classifiers. We also propose to overcome the limitation of missing correspondences by incorporating an additional label to account for outliers. Then, we repair the outlier positions to complete the segmentation. State-of-the-art discrete optimization techniques are used to provide our experimental results for the segmentation of the right lung in 2D chest radiographs, demonstrating the potentials of our method.","Title: Gradient vector flow fast geometric active contours.\nYear: 2001\n\nAbstract: In this paper, we propose an edge-driven bidirectional geometric flow for boundary extraction. To this end, we combine the geodesic active contour flow and the gradient vector flow external force for snakes. The resulting motion equation is considered within a level set formulation, can deal with topological changes and important shape deformations. An efficient numerical schema is used for the flow implementation that exhibits robust behavior and has fast convergence rate. Promising results on real and synthetic images demonstrate the potentials of the flow.","Title: Joint model-pixel segmentation with pose-invariant deformable graph-priors.\nYear: 2013\n\nAbstract: This paper proposes a novel framework for image segmentation through a unified model-based and pixel-driven integrated graphical model. Prior knowledge is expressed through the deformation of a discrete model that consists of decomposing the shape of interest into a set of higher order cliques (triplets). Such decomposition allows the introduction of region-driven image statistics as well as pose-invariant (i. e. translation, rotation and scale) constraints whose accumulation introduces global deformation constraints on the model. Regional triangles are associated with pixels labeling which aims to create consistency between the model and the image space. The proposed formulation is pose-invariant, can integrate regional statistics in a natural and efficient manner while being able to produce solutions unobserved during training. The challenging problem of tagged cardiac MR image segmentation is used to demonstrate the performance potentials of the method.","Title: Prior Knowledge, Level Set Representations & Visual Grouping\nYear: 2008\n\nAbstract: In this paper, we propose a level set method for shape-driven object extraction. We introduce a voxel-wise probabilistic level set formulation to account for prior knowledge. To this end, objects are represented in an implicit form. Constraints on the segmentation process are imposed by seeking a projection to the image plane of the prior model modulo a similarity transformation. The optimization of a statistical metric between the evolving contour and the model leads to motion equations that evolve the contour toward the desired image properties while recovering the pose of the object in the new image. Upon convergence, a solution that is similarity invariant with respect to the model and the corresponding transformation are recovered. Promising experimental results demonstrate the potential of such an approach.","Title: Establishing Local Correspondences towards Compact Representations of Anatomical Structures\nYear: 2003\n\nAbstract: Computer-aided diagnosis is often based on comparing a structure of interest with prior models. Such a comparison requires automatic techniques in determining prior models from a set of examples and establishing local correspondences between the structure and the model. In this paper we propose a variational technique for solving the correspondence problem. The proposed method integrates a powerful representation for shapes (implicit functions), a state-of-the-art criterion for global registration (mutual information) and an efficient technique to recover local correspondences (free form deformations) that guarantees one-to-one mapping. Local correspondences can then be used to build compact representations for a structure of interest according to a set of training examples. The registration and statistical modeling of Systolic Left Ventricle shapes in Ultrasonic images demonstrate the potential of the proposed technique.","Title: 3D knowledge-based segmentation using pose-invariant higher-order graphs.\nYear: 2010\n\nAbstract: Segmentation is a fundamental problem in medical image analysis. The use of prior knowledge is often considered to address the ill-posedness of the process. Such a process consists in bringing all training examples in the same reference pose, and then building statistics. During inference, pose parameters are usually estimated first, and then one seeks a compromise between data-attraction and model-fitness with the prior model. In this paper, we propose a novel higher-order Markov Random Field (MRF) model to encode pose-invariant priors and perform 3D segmentation of challenging data. The approach encodes data support in the singleton terms that are obtained using machine learning, and prior constraints in the higher-order terms. A dual-decomposition-based inference method is used to recover the optimal solution. Promising results on challenging data involving segmentation of tissue classes of the human skeletal muscle demonstrate the potentials of the method.","Title: Trajectory-based representation of human actions\nYear: 2007\n\nAbstract: This work addresses the problem of human action recognition by introducing a representation of a human action as a collection of short trajectories that are extracted in areas of the scene with significant amount of visual activity. The trajectories are extracted by an auxiliary particle filtering tracking scheme that is initialized at points that are considered salient both in space and time. The spatiotemporal salient points are detected by measuring the variations in the information content of pixel neighborhoods in space and time. We implement an online background estimation algorithm in order to deal with inadequate localization of the salient points on the moving parts in the scene, and to improve the overall performance of the particle filter tracking scheme.We use a variant of the Longest Common Subsequence algorithm (LCSS) in order to compare different sets of trajectories corresponding to different actions. We use Relevance Vector Machines (RVM) in order to address the classification problem. We propose new kernels for use by the RVM, which are specifically tailored to the proposed representation of short trajectories. The basis of these kernels is the modified LCSS distance of the previous step. We present results on real image sequences from a small database depicting people performing 12 aerobic exercises.","Title: Deformable mosaicing for whole-body MRI.\nYear: 2008\n\nAbstract: Whole-body magnetic resonance imaging is an emerging application gaining vast clinical interest during the last years. Although recent technological advances shortened the longish acquisition time, this is still the limiting factor avoiding its wide-spread clinical usage. The acquisition of images with large field-of-view helps to relieve this drawback, but leads to significantly distorted images. Therefore, we propose a deformable mosaicing approach, based on the simultaneous registration to linear weighted averages, to correct for distortions in the overlapping area. This method produces good results on in-vivo data and has the advantage that a seamless integration into the clinical workflow is possible.","Title: 3D cardiac segmentation with pose-invariant higher-order MRFs\nYear: 2012\n\nAbstract: This paper proposes a novel pose-invariant segmentation approach for left ventricle in 3D CT images. The proposed formulation is modular with respect to the image support (i.e. landmarks, edges and regional statistics). The prior is represented as a third-order Markov Random Field (MRF) where triplets of points result to a low-rank statistical prior while inheriting invariance to global transformations. The ventricle surface is determined through triangulation where image discontinuities can be easily evaluated and the Divergence theorem provides an exact calculation of regional statistics acting on the image or a derived feature space. Promising results using boosting along with the learned prior demonstrate the potential of our method.","Title: Monocular Hand Pose Estimation Using Variable Metric Gradient-Descent\nYear: 2006\n\nAbstract: In this paper, we propose a novel model-based approach to recover 3D hand pose from 2D images through a compact articulated 3D hand model whose parameters are inferred in a Bayesian manner. To this end, we pro- pose generative models for hand and background pixels leading to a log- likelihood objective function which aims at enclosing hand-like pixels within the silhouette of the projected 3D model while excluding background-like pixels.Segmentation and hand pose estimation are unified through the mini- mization of a single likelihood function, which is novel and improve overall robustness. We derive the gradient in the hand parameter space of such an area-based objective function, which is new and allows faster convergence rate than gradient free methods. Furthermore , we propose a new constrained variable metric gradient descent to speed up convergence and finally the so called smart particle filter is used to improve robustness through multiple hy- potheses and to exploit temporal coherence. Very promising experimental results demonstrate the potentials of our approach.","Title: Recognition-Driven Two-Dimensional Competing Priors Toward Automatic and Accurate Building Detection\nYear: 2009\n\nAbstract: In this paper, a novel recognition-driven variational framework, toward multiple building extraction from aerial and satellite images, is introduced. To this end, competing shape priors are considered, and building extraction is addressed through an image segmentation approach that involves the use of a data-driven term constrained from the prior models. The proposed framework extends previous app...","Title: Performance vs computational efficiency for optimizing single and dynamic MRFs: Setting the state of the art with primal-dual strategies\nYear: 2008\n\nAbstract: In this paper we introduce a novel method to address minimization of static and dynamic MRFs. Our approach is based on principles from linear programming and, in particular, on primal-dual strategies. It generalizes prior state-of-the-art methods such as @a-expansion, while it can also be used for efficiently minimizing NP-hard problems with complex pair-wise potential functions. Furthermore, it offers a substantial speedup - of a magnitude 10 - over existing techniques, due to the fact that it exploits information coming not only from the original MRF problem, but also from a dual one. The proposed technique consists of recovering pair of solutions for the primal and the dual such that the gap between them is minimized. Therefore, it can also boost performance of dynamic MRFs, where one should expect that the new pair of primal-dual solutions is closed to the previous one. Promising results in a number of applications, and theoretical, as well as numerical comparisons with the state of the art demonstrate the extreme potentials of this approach.","Title: Discrete minimum distortion correspondence problems for non-rigid shape matching\nYear: 2011\n\nAbstract: Similarity and correspondence are two fundamental archetype problems in shape analysis, encountered in numerous application in computer vision and pattern recognition. Many methods for shape similarity and correspondence boil down to the minimum-distortion correspondence problem, in which two shapes are endowed with certain structure, and one attempts to find the matching with smallest structure distortion between them. Defining structures invariant to some class of shape transformations results in an invariant minimum-distortion correspondence or similarity. In this paper, we model shapes using local and global structures, formulate the invariant correspondence problem as binary graph labeling, and show how different choice of structure results in invariance under various classes of deformations.","Title: User-Aided Boundary Delineation through the Propagation of Implicit Representations\nYear: 2003\n\nAbstract: In this paper we introduce user-defined segmentation constraints within the level set methods. Snake-driven methods are powerful and widely explored techniques for object extraction. Level set representations is a mathematical framework technique to implement such methods. This formulation is implicit, intrinsic and parameter/topology free. Introducing shape-driven knowledge within the level set method for segmentation is a recently explored topic. User interactive constraints are more simplistic forms of prior shape knowledge. To this end, we propose a simple formulation that converts user interaction to objective function terms that aim to guide the segmentation solution through the user edits.","Title: Graph based spatial position mapping of low-grade gliomas.\nYear: 2011\n\nAbstract: Low-grade gliomas (WHO grade II) are diffusively infiltrative brain tumors arising from glial cells. Spatial classification that is usually based on cerebral lobes lacks accuracy and is far from being able to provide some pattern or statistical interpretation of their appearance. In this paper, we propose a novel approach to understand and infer position of low-grade gliomas using a graphical model. The problem is formulated as a graph topology optimization problem. Graph nodes correspond to extracted tumors and graph connections to the spatial and content dependencies among them. The task of spatial position mapping is then expressed as an unsupervised clustering problem, where cluster centers correspond to centers with position appearance prior, and cluster samples to nodes with strong statistical dependencies on their position with respect to the cluster center. Promising results using leave-one-out cross-validation outperform conventional dimensionality reduction methods and seem to coincide with conclusions drawn in physiological studies regarding the expected tumor spatial distributions and interactions.","Title: Concurrent tumor segmentation and registration with uncertainty-based sparse non-uniform graphs.\nYear: 2014\n\nAbstract: In this paper, we present a graph-based concurrent brain tumor segmentation and atlas to diseased patient registration framework. Both segmentation and registration problems are modeled using a unified pairwise discrete Markov Random Field model on a sparse grid superimposed to the image domain. Segmentation is addressed based on pattern classification techniques, while registration is performed by maximizing the similarity between volumes and is modular with respect to the matching criterion. The two problems are coupled by relaxing the registration term in the tumor area, corresponding to areas of high classification score and high dissimilarity between volumes. In order to overcome the main shortcomings of discrete approaches regarding appropriate sampling of the solution space as well as important memory requirements, content driven samplings of the discrete displacement set and the sparse grid are considered, based on the local segmentation and registration uncertainties recovered by the min marginal energies. State of the art results on a substantial low-grade glioma database demonstrate the potential of our method, while our proposed approach shows maintained performance and strongly reduced complexity of the model.","Title: Graph-based geometric-iconic guide-wire tracking.\nYear: 2011\n\nAbstract: In this paper we introduce a novel hybrid graph-based approach for Guide-wire tracking. The image support is captured by steerable filters and improved through tensor voting. Then, a graphical model is considered that represents guide-wire extraction/tracking through a B-spline control-point model. Points with strong geometric interest (landmarks) are automatically determined and anchored to such a representation. Tracking is then performed through discrete MRFs that optimize the spatio-temporal positions of the control points while establishing landmark temporal correspondences. Promising results demonstrate the potentials of our method.","Title: Segmentation, ordering and multi-object tracking using graphical models\nYear: 2009\n\nAbstract: In this paper, we propose a unified graphical-model framework to interpret a scene composed of multiple objects in monocular video sequences. Using a single pairwise Markov random field (MRF), all the observed and hidden variables of interest such as image intensities, pixels' states (associated object's index and relative depth), objects' states (model motion parameters and relative depth) are jointly considered. Particular attention is given to occlusion handling by introducing a rigorous visibility modeling within the MRF formulation. Through minimizing the MRF's energy, we simultaneously segment, track and sort by depth the objects. Promising experimental results demonstrate the potential of this framework and its robustness to image noise, cluttered background, moving camera and background, and even complete occlusions.","Title: Efficient parallel message computation for MAP inference\nYear: 2011\n\nAbstract: First order Markov Random Fields (MRFs) have become a predominant tool in Computer Vision over the past decade. Such a success was mostly due to the development of efficient optimization algorithms both in terms of speed as well as in terms of optimality properties. Message passing algorithms are among the most popular methods due to their good performance for a wide range of pairwise potential functions (PPFs). Their main bottleneck is computational complexity. In this paper, we revisit message computation as a distance transformation using a more formal setting than [8] to generalize it to arbitrary PPFs. The method is based on [20] yielding accurate results for a specific class of PPFs and in most other cases a close approximation. The proposed algorithm is parallel and thus enables us to fully take advantage of the computational power of parallel processing architectures. The proposed scheme coupled with an efficient belief propagation algorithm [8] and implemented on a massively parallel coprocessor provides results as accurate as state of the art inference methods, though is in general one order of magnitude faster in terms of speed.","Title: Support Vector Driven Markov Random Fields Towards Dti Segmentation Of The Human Skeletal Muscle\nYear: 2008\n\nAbstract: In this paper we propose a classification-based method towards the segmentation of diffusion tensor images. We use Support Vector Machines to classify diffusion tensors and we extend linear classification to the non linear case. To this end, we discuss and evaluate three different classes of kernels on the space of symmetric definite positive matrices that are well suited for the classification of tensor data. We impose spatial constraints by means of a Markov random field model that takes into account the result of SVM classification. Experimental results are provided for diffusion tensor images of human skeletal muscles. They demonstrate the potential of our method in discriminating the different muscle groups.","Title: Implicit free-form-deformations for multi-frame segmentation and tracking\nYear: 2005\n\nAbstract: In this paper, we propose a novel technique to address motion estimation and tracking. Such technique represents the motion field using a regular grid of thin-plate splines, and the moving objects using an implicit function on the image plane that is a cubic interpolation of a ”level set function” defined on this grid. Optical flow is determined through the deformation of the grid and consequently of the underlying image structures towards satisfying the constant brightness constraint. Tracking is performed in similar fashion through the consistent recovery in the temporal domain of the zero iso-surfaces of a level set that is the projection of the Free Form Deformation (FFD) implicit function according to the cubic spline formulation. Such an approach is a compromise between dense motion estimation and parametric motion models, introduces smoothness in an implicit fashion, is intrinsic, and can cope with important object deformations. Promising results demonstrate the potentials of our approach.","Title: High-Level Bottom-Up Cues for Top-Down Parsing of Facade Images\nYear: 2012\n\nAbstract: We address the problem of parsing images of building facades. The goal is to segment images, assigning to the resulting regions semantic labels that correspond to the basic architectural elements. We assume a top-down parsing framework based on a 2D shape grammar that encodes a prior knowledge on the possible composition of facades. The algorithm explores the space of feasible solutions by generating the possible configurations of the facade and comparing it to the input data by means of a local, pixel- or patch-based classifier. We propose new bottom-up cues for the algorithm, both for evaluation of a candidate parse and for guiding the exploration of the space of feasible solutions. The method that we propose benefits from detection-based information and leverages on the similar appearance of elements that repeat in a given facade. Experiments performed on standard datasets show that this use of more discriminative bottom-up cues improves the convergence in comparison to state-of-the-art algorithms, and gives better results in terms of precision and recall, as well as computation time and performance deviation.","Title: 3d Model-Based Reconstruction Of The Proximal Femur From Low-Dose Biplanar X-Ray Images\nYear: 2011\n\nAbstract: The 3D modeling of the proximal femur is a valuable diagnostic tool for orthopedic surgery planning. The use of computed tomography is the most prominent modality to visualize bones both in terms of resolution as well as in term of bone/tissue separation. Towards reducing the impact of radiation to the patient, low-dose X-ray imaging systems have been introduced while still providing partial views with rather low signal-to-noise ratio. In this paper, we focus on automating the 3D proximal femur reconstruction from simultaneously acquired 2D views. A deformable model represented by triangulated mesh surfaces extends to a linear sub-space describing the variations across individuals. Segmentation consists of inferring a global deformation of 3D model followed by a local adaption based on the most prominent combination of the sub-space parameters. The basis of which relies on the minimization of a cost function based on the biplanar projection of this model. To this end, we employ an active region model that aims at optimizing the 3D model parameters such that projection of surface is attracted from edge potentials, while creating an optimal partition between the bone class and the remaining structures. The global parameters of the model and the local ones are optimized through a gradient-free approach. Promising results demonstrate the potentials of our method compared to a supervised reconstruction technique.","Title: Dense image registration through MRFs and efficient linear programming.\nYear: 2008\n\nAbstract: In this paper, we introduce a novel and efficient approach to dense image registration, which does not require a derivative of the employed cost function. In such a context, the registration problem is formulated using a discrete Markov random field objective function. First, towards dimensionality reduction on the variables we assume that the dense deformation field can be expressed using a small number of control points (registration grid) and an interpolation strategy. Then, the registration cost is expressed using a discrete sum over image costs (using an arbitrary similarity measure) projected on the control points, and a smoothness term that penalizes local deviations on the deformation field according to a neighborhood system on the grid. Towards a discrete approach, the search space is quantized resulting in a fully discrete model. In order to account for large deformations and produce results on a high resolution level, a multi-scale incremental approach is considered where the optimal solution is iteratively updated. This is done through successive morphings of the source towards the target image. Efficient linear programming using the primal dual principles is considered to recover the lowest potential of the cost function. Very promising results using synthetic data with known deformations and real data demonstrate the potentials of our approach.","Title: A PDE-Based Level-Set Approach for Detection and Tracking of Moving Objects\nYear: 1998\n\nAbstract: This papers pr esents a framework for detecting and tracking moving objects in a sequence of images. Using a statistical approach, where the inter-frame difference is modeled by a mixture of two Laplacian or Gaussian distributions, and an energy minimization based approach, we reformulate the motion detection and tracking problem as a front propagation problem. The Euler-Lagrange equation of the designed energy functional is first derived and the flow minimizing the energy is then obtained. Following the work by Caselles et al [CKS95],and Malladi et al [MSV95, MSV93], the contours to be detected and tracked are modeled as geodesic active contours evolving toward the minimum of the designed energy, under the influence of internal and external image dependent forces. Using the level set formulation scheme of Osher and Sethian [OS88] complex curves can be detected and tracked and topological changes for the evolving curves are naturally managed. To reduce the computational cost required by a direct implementation of the formulation scheme of Osher and Sethian [OS88], a new approach exploiting aspects from the classical Narrow Band [AS95] and Fast Marching [Set96] methods is proposed and favorably compared to them. In order to further reduce the CPU time, a multi-scale approach has also been considered. Very promising experimental results are provided using real video sequences.","Title: Large-Scale Building Reconstruction Through Information Fusion and 3-D Priors\nYear: 2010\n\nAbstract: In this paper, a novel variational framework is introduced toward automatic 3-D building reconstruction from remote-sensing data. We consider a subset of building models that involve the footprint, their elevation, and the roof type. These models, under a certain hierarchical representation, describe the space of solutions and, under a fruitful synergy with an inferential procedure, recover the observed scene's geometry. Such an integrated approach is defined in a variational context, solves segmentation both in optical images and digital elevation maps, and allows multiple competing priors to determine their pose and 3-D geometry from the observed data. The very promising experimental results and the performed quantitative evaluation demonstrate the potentials of our approach.","Title: Nonlinear embedding towards articulated spine shape inference using higher-order MRFs.\nYear: 2010\n\nAbstract: In this paper we introduce a novel approach for inferring articulated spine models from images. A low-dimensional manifold embedding is created from a training set of prior mesh models to establish the patterns of global shape variations. Local appearance is captured from neighborhoods in the manifold once the overall representation converges. Inference with respect to the manifold and shape parameters is performed using a Markov Random Field (MRF). Singleton and pairwise potentials measure the support from the data and shape coherence from neighboring models respectively, while higher-order cliques encode geometrical modes of variation for local vertebra shape warping. Optimization of model parameters is achieved using efficient linear programming and duality. The resulting model is geometrically intuitive, captures the statistical distribution of the underlying manifold and respects image support in the spatial domain. Experimental results on spinal column geometry estimation from CT demonstrate the approach's potential.","Title: Non-rigid registration using distance functions\nYear: 2003\n\nAbstract: Feature-based methods for non-rigid registration frequently encounter the correspondence problem. Regardless of whether points, lines, curves or surface parameterizations are used, feature-based non-rigid matching requires us to automatically solve for ...","Title: Border Detection on Short Axis Echocardiographic Views Using a Region Based Ellipse-Driven Framework\nYear: 2004\n\nAbstract: In this paper, we propose a robust technique that integrates spatial and temporal information for consistent recovery of the endcocardium. To account for the low image quality we introduce a local variant of the Mumford-Shah that is coupled with a model of limited parameters to describe the ventricle, namely an ellipse. The objective function is defined on the implicit space of ellipses, separates locally the blood pool from the heart wall and explores geometric constraints on the deformations of the endocardium to impose temporal consistency. Promising experimental results demonstrate the potentials of our method.","Title: Shape Priors for Level Set Representations\nYear: 2002\n\nAbstract: Level Set Representations, the pioneering framework introduced by Osher and Sethian [14] is the most common choice for the implementation of variational frameworks in Computer Vision since it is implicit, intrinsic, parameter and topology free. However, many Computer vision applications refer to entities with physical meanings that follow a shape form with a certain degree of variability. In this paper, we propose a novel energetic form to introduce shape constraints to level set representations. This formulation exploits all advantages of these representations resulting on a very elegant approach that can deal with a large number of parametric as well as continuous transformations. Furthermore, it can be combined with existing well known level set-based segmentation approaches leading to paradigms that can deal with noisy, occluded and missing or physically corrupted data. Encouraging experimental results are obtained using synthetic and real images.","Title: Markov Random Field modeling, inference & learning in computer vision & image understanding: A survey\nYear: 2013\n\nAbstract: In this paper, we present a comprehensive survey of Markov Random Fields (MRFs) in computer vision and image understanding, with respect to the modeling, the inference and the learning. While MRFs were introduced into the computer vision field about two decades ago, they started to become a ubiquitous tool for solving visual perception problems around the turn of the millennium following the emergence of efficient inference methods. During the past decade, a variety of MRF models as well as inference and learning methods have been developed for addressing numerous low, mid and high-level vision problems. While most of the literature concerns pairwise MRFs, in recent years we have also witnessed significant progress in higher-order MRFs, which substantially enhances the expressiveness of graph-based models and expands the domain of solvable problems. This survey provides a compact and informative summary of the major literature in this research topic.","Title: Robust guidewire segmentation through boosting, clustering and linear programming\nYear: 2010\n\nAbstract: Fluroscopic imaging provides means to assess the motion of the internal structures and therefore is of great use during surgery. In this paper we propose a novel approach for the segmentation of curvilinear structures in these images. The main challenge to be addressed is the lack of visual support due to the low SNR where traditional edge-based methods fail. Our approach combines machine learning techniques, unsupervised clustering and linear programming. In particular, numerous invariant to position/rotation classifiers are combined to detect candidate pixels of curvilinear structure. These candidates are grouped into consistent geometric segments through the use of a state-of-the art unsupervised clustering algorithm. The complete curvilinear structure is obtained through an ordering of these segments using the elastica model in a linear programming framework. Very promising results were obtained on guide wire segmentation in fluoroscopic images.","Title: Beyond Pairwise Energies: Efficient Optimization For Higher-Order Mrfs\nYear: 2009\n\nAbstract: In this paper, we introduce a higher-order MRF optimization framework. On the one hand, it is very general; we thus use it to derive a generic optimizer that can be applied to almost any higher-order MRF and that provably optimizes a dual relaxation related to the input MRF problem. On the other hand, it is also extremely flexible and thus can be easily adapted to yield far more powerful algorithms when dealing with subclasses of high-order MRFs. We thus introduce a new powerful class of high-order potentials, which are shown to offer enough expressive power and to be useful for many vision tasks. To address them, we derive, based on the same framework, a novel and extremely efficient message-passing algorithm, which goes beyond the aforementioned generic optimizer and is able to deliver almost optimal solutions of very high quality. Experimental results on vision problems demonstrate the extreme effectiveness of our approach. For instance, we show that in some cases we are even able to compute the global optimum for NP-hard higher-order MRFs in a very efficient manner.","Title: Expression mimicking: from 2D monocular sequences to 3D animations\nYear: 2007\n\nAbstract: In this paper we present a novel approach for mimicking expressions in 3D from a monocular video sequence. To this end, first we construct a high resolution semantic mesh model through automatic global and local registration of a low resolution range data. Such a model is represented using predefined set of control points in a compact fashion, and animated using radial basis functions. In order to recover the 2D positions of the 3D control points in the observed sequence, we use cascade Adaboost-driven search. The search space is reduced through the use of predictive expression modeling. The optimal configuration of the Adaboost responses is determined using combinatorial linear programming which enforces the anthropometric nature of the model. Then the displacement can be reproduced on any version of the model, registered on another face. Our method doesn't require dense stereo estimation and can then produce realistic animations, using any 3D model. Promising experimental results demonstrate the potential of our approach.","Title: Tagged cardiac MR image segmentation using boundary & regional-support and graph-based deformable priors\nYear: 2011\n\nAbstract: Segmentation and tracking of tagged MR images is a critical component of in vivo understanding for the heart dynamics. In this paper, we propose a novel approach which uses multi-dimensional features and casts the left ventricle (LV) extraction problem as a maximum posteriori estimation process in both the feature and the shape spaces. Exact integration of multi-dimensional boundary and regional statistics is achieved through a global formulation. Prior is enforced through a point-distribution model, where distances between landmark positions are learned and enforced during the segmentation process. The use of divergence theorem leads to an elegant pairwise formulation where image support and prior knowledge are jointly encoded within a pairwise MRF and the segmentation is achieved efficiently by employing MRF inference algorithms. Promising results on numerous examples demonstrate the potentials of our method.","Title: Inter and intra-modal deformable registration: continuous deformations meet efficient optimal linear programming.\nYear: 2007\n\nAbstract: In this paper we propose a novel non-rigid volume registration based on discrete labeling and linear programming. The proposed framework reformulates registration as a minimal path extraction in a weighted graph. The space of solutions is represented using a set of a labels which are assigned to predefined displacements. The graph topology corresponds to a superimposed regular grid onto the volume. Links between neighborhood control points introduce smoothness, while links between the graph nodes and the labels (end-nodes) measure the cost induced to the objective function through the selection of a particular deformation for a given control point once projected to the entire volume domain, Higher order polynomials are used to express the volume deformation from the ones of the control points. Efficient linear programming that can guarantee the optimal solution up to (a user-defined) bound is considered to recover the optimal registration parameters. Therefore, the method is gradient free, can encode various similarity metrics (simple changes on the graph construction), can guarantee a globally sub-optimal solution and is computational tractable. Experimental validation using simulated data with known deformation, as well as manually segmented data demonstrate the extreme potentials of our approach.","Title: Viewpoint invariant 3D landmark model inference from monocular 2D images using higher-order priors\nYear: 2011\n\nAbstract: In this paper, we propose a novel one-shot optimization approach to simultaneously determine both the optimal 3D landmark model and the corresponding 2D projections without explicit estimation of the camera viewpoint, which is also able to deal with misdetections as well as partial occlusions. To this end, a 3D shape manifold is built upon fourth-order interactions of landmarks from a training set where pose-invariant statistics are obtained in this space. The 3D-2D consistency is also encoded in such high-order interactions, which eliminate the necessity of viewpoint estimation. Furthermore, the modeling of visibility improves further the performance of the method by handling missing correspondences and occlusions. The inference is addressed through a MAP formulation which is naturally transformed into a higher-order MRF optimization problem and is solved using a dual-decomposition-based method. Promising results on standard face benchmarks demonstrate the potential of our approach.","Title: Prior knowledge, random walks and human skeletal muscle segmentation\nYear: 2012\n\nAbstract: In this paper, we propose a novel approach for segmenting the skeletal muscles in MRI automatically. In order to deal with the absence of contrast between the different muscle classes, we proposed a principled mathematical formulation that integrates prior knowledge with a random walks graph-based formulation. Prior knowledge is represented using a statistical shape atlas that once coupled with the random walks segmentation leads to an efficient iterative linear optimization system. We reveal the potential of our approach on a challenging set of real clinical data.","Title: Efficient and Automated Multimodal Satellite Data Registration through MRFs and Linear Programming\nYear: 2014\n\nAbstract: The accurate and automated registration of multimodal remote sensing data is of fundamental importance for numerous emerging geospatial environmental and engineering applications. However, the registration of very large multimodal, multitemporal, with different spatial resolutions data is, still, an open matter. To this end, we propose a generic and automated registration framework based on Markov Random Fields (MRFs) and efficient linear programming. The discrete optimization setting along with the introduced data-specific energy terms form a modular approach with respect to the similarity criterion allowing to fully exploit the spectral properties of multimodal remote sensing datasets. The proposed approach was validated both qualitatively and quantitatively demonstrating its potentials on very large (more than 100M pixels) multitemporal remote sensing datasets. In particular, in terms of spatial accuracy the geometry of the optical and radar data has been recovered with displacement errors of less than 2 and 3 pixels, respectively. In terms of computational efficiency the optical data term can converge after 7-8 minutes, while the radar data term after less than 15 minutes.","Title: Discriminative learning of deformable contour models.\nYear: 2014\n\nAbstract: In this work we propose a machine learning approach to improve shape detection accuracy inmedical images with deformable contour models (DCMs). Our DCMs can efficiently recover globally optimal solutions that take into account constraints on shape and appearance in the model fitting criterion; our model can also deal with global scale variations by operating in a multi-scale pyramid.Our main contribution consists in formulating the task of learning the DCM score function as a large-margin structured prediction problem. Our algorithm trains DCMs in an joint manner - all the parameters are learned simultaneously, while we use rich local features for landmark localization.We evaluate our method on lung field, heart, and clavicle segmentation tasks using 247 standard posterior-anterior (PA) chest radiographs from the Segmentation in Chest Radiographs (SCR) benchmark. Our learned DCMs systematically outperform the state of the art methods according to a host of validation measures including the overlap coefficient, mean contour distance and pixel error rate.","Title: Discrete Visual Perception\nYear: 2014\n\nAbstract: Computational vision and biomedical image have made tremendous progress of the past decade. This is mostly due the development of efficient learning and inference algorithms which allow better, faster and richer modeling of visual perception tasks. Graph-based representations are among the most prominent tools to address such perception through the casting of perception as a graph optimization problem. In this paper, we briefly introduce the interest of such representations, discuss their strength and limitations and present their application to address a variety of problems in computer vision and biomedical image analysis.","Title: Motion-based background subtraction using adaptive kernel density estimation\nYear: 2004\n\nAbstract: Background modeling is an important component of many vision systems. Existing work in the area has mostly addressed scenes that consist of static or quasi-static structures. When the scene exhibits a persistent dynamic behavior in time, such an assumption is violated and detection performance deteriorates. In this paper, we propose a new method for the modeling and subtraction of such scenes. Towards the modeling of the dynamic characteristics, optical flow is computed and utilized as a feature in a higher dimensional space. Inherent ambiguities in the computation of features are addressed by using a data-dependent bandwidth for density estimation using kernels. Extensive experiments demonstrate the utility and performance of the proposed approach.","Title: Variational methods and partial differential equations in cardiac image analysis\nYear: 2004\n\nAbstract: Cardio-vascular diseases is a major cause of deaths world- wide. Early diagnosis is quite often associated with more efficient treatment leading to a significant decrease of the mortality rate. In this paper, we present tools developed to procces dif- ferent cardiac modalities over the past years with aim to faciliate/automate computer aided diagnosis. In particular we present algorithms and advanced techniques for the seg- mentation of the left ventrile for magentic resonance and echocardiographic imaging. Furthermore, to account for user interaction, we extent our method towards user-aided boundary delineation where the user has the ability to cor- rect local discrepancies of automated results.","Title: Geodesic active contours for supervised texture segmentation\nYear: 1999\n\nAbstract: This paper presents a variational method for supervised texture segmentation, which is based on ideas coming from the curve propagation theory. We assume that a preferable texture pattern is known (e.g. the pattern that we want to distinguish from the rest of the image). The textured feature space is generated by filtering the input and the preferable pattern image using Gabor filters, and analyzing their re- sponses as multi-component conditional probability density functions. The texture segmentation is obtained by mini- mizing a Geodesic Active Contour Model objective func- tion where the boundary-based information is expressed via discontinuities on the statistical space associated with the multi-modal textured feature space. This function is minimized using a gradient descent method where the ob- tained PDE is implemented using a level set approach, that handles naturally the topological changes. Finally, a fast method is used for the level set implementation. The perfor- mance of our method is demonstrated on a variety of syn- thetic and real textured images.","Title: Geodesic active regions for supervised texture segmentation\nYear: 1999\n\nAbstract: The paper presents a novel variational method for supervised texture segmentation. The textured feature space is generated by filtering the given textured images using isotropic and anisotropic filters, and analyzing their responses as multi-component conditional probability density functions. The texture segmentation is obtained by unifying region and boundary based information as an improved Geodesic Active Contour Model. The defined objective function is minimized using a gradient-descent method where a level set approach is used to implement the obtained PDE. According to this PDE, the curve propagation towards the final solution is guided by boundary and region based segmentation forces, and is constrained by a regularity force. The level set implementation is performed using a fast front propagation algorithm where topological changes are naturally handled. The performance of our method is demonstrated on a variety of synthetic and real textured frames","Title: Geodesic active regions for motion estimation and tracking\nYear: 1999\n\nAbstract: This paper proposes a new front propagation method to deal accurately with the challenging problem of tracking non-rigid moving objects. This is obtained by employing a Geodesic Active Region model where the designed ob- jective function is composed of boundary and region-based terms and optimizes the curve position with respect to mo- tion and intensity properties. The main novelty of our ap- proach is that we deal with the motion estimation (linear models are assumed) and the tracking problem simultane- ously. In other words, the optimization problem contains a coupled set of unknown variables; the curve position and the corresponding motion model. The designed objec- tive function is minimized using a gradient descent method; the curve is propagated towards the object boundaries un- der the influence of boundary, intensity and motion-based forces using a PDE, while given the curve position an in- cremental analytical solution is obtained for the motion model. Besides, this PDE is implemented using a level set approachwhere topological changes are naturally handled. Very promising experimental results are provided using real video sequences.","Title: Geodesic active contours and level sets for the detection and tracking of moving objects\nYear: 2000\n\nAbstract: This paper presents a new variational framework for detecting and tracking multiple moving objects in image sequences. Motion detection is performed using a statistical framework for which the observed interframe difference density function is approximated using a mixture model. This model is composed of two components, namely, the static (background) and the mobile (moving objects) one. Both components are zero-mean and obey Laplacian or Gaussian law. This statistical framework is used to provide the motion detection boundaries. Additionally, the original frame is used to provide the moving object boundaries. Then, the detection and the tracking problem are addressed in a common framework that employs a geodesic active contour objective function. This function is minimized using a gradient descent method, where a flow deforms the initial curve towards the minimum of the objective function, under the influence of internal and external image dependent forces. Using the level set formulation scheme, complex curves can be detected and tracked while topological changes for the evolving curves are naturally managed. To reduce the computational cost required by a direct implementation of the level set formulation scheme, a new approach named Hermes is proposed. Hermes exploits aspects from the well-known front propagation algorithms (Narrow Band, Fast Marching) and compares favorably to them. Very promising experimental results are provided using real video sequences.","Title: A MRF-based approach for real-time subway monitoring\nYear: 2001\n\nAbstract: There has been an increase in the use of video surveil- lance and monitoring in public areas to improve safety and security. Change detection and crowding/congestion den- sity estimation are two sub-tasks in a subway monitoring system. We propose a method that decomposes this problem into two steps. The first step consists of a change detec- tion algorithm that distinguishes the background from the foreground. This is done using a discontinuity preserving MRF-based approach where the information from differ- ent sources (background subtraction, intensity modeling) is combined with spatial constraints to provide a smooth mo- tion detection map. Then, the obtained change detection map is combined with a geometry module that performs a soft auto-calibration to estimate a measure of congestion of the observed area (platform). Extensive experimental re- sults in a metro station of a metropolitan city demonstrates the performance and the potential of our method.","Title: LOCALLY ADAPTIVE AUTOREGRESSIVE ACTIVE MODELS FOR SEGMENTATION OF 3D ANATOMICAL STRUCTURES\nYear: 2007\n\nAbstract: Many techniques of knowledge-based segmentation consist of building statistical models that describe the deformations of the structure of interest, and then fit these models to the image data. In this paper, we introduce a novel family of shape prior models that aim to capture such varying support. To this end, 3D segmentation is considered by modeling the relationship between contours on consecutive slices using autoregression. Then, the segmentation is performed progressively on the 2D slices in a qualitative fashion, starting from the ones with strong data support toward the ones of limited support. Successive segmentation maps are linked through a locally adaptive autoregressive prediction mechanism - that is learned through training - where confidence of the data from prior slices constrains the results. Such prediction is integrated with a contour minimization technique, leading to a Bayesian sequential procedure that iteratively predicts and corrects 2D contours leading to complete reconstruction of 3D anatomical structures. A quantitative comparative study with 3D active shape models demonstrate the potential of the method.","Title: Fast, Approximately Optimal Solutions for Single and Dynamic MRFs\nYear: 2007\n\nAbstract: A newefficientMRF optimizationalgorithm, calledFast- PD, is proposed, which generalizes �-expansion. One of its main advantages is that it offers a substantial speedup over that method, e.g. it can be at least 3-9 times faster than �-expansion. Its efficiency is a result of the fact that Fast-PDexploitsinformationcomingnotonlyfrom theorig- inal MRF problem, but also from a dual problem. Further- more, besides static MRFs, it can also be used for boost- ing the performance of dynamic MRFs, i.e. MRFs varying over time. On top of that, Fast-PD makes no compromise about the optimality of its solutions: it can compute exactly the same answer as �-expansion, but, unlike that method, it can also guarantee an almost optimal solution for a much wider class of NP-hard MRF problems. Results on static and dynamic MRFs demonstrate the algorithm's efficiency and power. E.g., Fast-PD has been able to compute dispar- ity forstereoscopicsequencesin real time, with theresulting disparity coinciding with that of �-expansion. can that method also offer a computational advantage for the case of dynamic MRFs? With respect to the questions raised above, this work makes the following contributions. Efficiency for single MRFs: �-expansion works by solving a series of max-flow problems. Its efficiency is thus largely determined from the efficiency of these max-flow problems, which, in turn, depends on the number of augmenting paths per max-flow. Here, we build upon recent work of (6), and propose a new primal-dual MRF optimization method, called Fast-PD. This method, like (6) or �-expansion, also ends up solving a max-flow problem for a series of graphs. However, unlike these techniques, the graphs constructed by Fast-PD ensure that the number of augmentations per max-flow decreases dramatically over time, thus boosting the efficiency of MRF inference. To show this, we prove a generalized relationship between the number of augmentations and the so-called primal-dual gap associated with the original MRF problem and its dual. Furthermore, to fully exploit the above property, 2 new ex- tensions are also proposed: an adapted max-flow algorithm, as well as an incremental graph construction method. Optimality properties: Despite its efficiency, our method also makes no compromise regarding the optimality of its solutions. So, if d(� , � ) is a metric, Fast-PD is as pow- erful as �-expansion,i.e. it computes exactly the same solu- tion, but with a substantial speedup. Moreover, it applies to a much wider class of MRFs1, e.g. even with a non-metric d(� , � ), while still guaranteeing an almost optimal solution. Efficiency for dynamic MRFs: Furthermore, our","Title: Automatic detection of liver tumors\nYear: 2008\n\nAbstract: Tumor detection in CT liver images is a challenging task. The nature of tumor has a direct effect on the number of voxels being contaminated, as well as on the changes in the observed CT scan. In order to deal with this challenge, in this paper we propose the use of advanced non-linear machine learning techniques to determine the optimal features, as well as the hyperplane that use these features to separate tumoral voxels from voxels corresponding to healthy tissues. Very promising classification results using an important volume of clinically annotated data (86% sensitivity, 82% specificity) demonstrate the potentials of our approach.","Title: Model-based hand tracking with texture, shading and self-occlusions\nYear: 2008\n\nAbstract: A novel model-based approach to 3D hand tracking from monocular video is presented. The 3D hand pose, the hand texture and the illuminant are dynamically estimated through minimization of an objective function. Derived from an inverse problem formulation, the objective function enables explicit use of texture temporal continuity and shad- ing information, while handling important self-occlusions and time-varying illumination. The minimization is done efficiently using a quasi-Newton method, for which we pro- pose a rigorous derivation of the objective function gra- dient. Particular attention is given to terms related to the change of visibility near self-occlusion boundaries that are neglected in existing formulations. In doing so we introduce new occlusion forces and show that using all gradient terms greatly improves the performance of the method. Experi- mental results demonstrate the potential of the formulation.","Title: Graph-based knowledge-driven discrete segmentation of the left ventricle\nYear: 2009\n\nAbstract: In this paper, we propose a novel similarity-invariant approach to model-based segmentation of the left ventricle. The method assumes a control point representation of the model and an arbitrary interpolation strategy. First, we construct the prior manifold using the distributions of the relative normalized distances between pairs of control points within the training set. Then, we introduce a geometric partition of the space using a Voronoi decomposition that aims to determine relationships between the control points and the image domain. Knowledge-based segmentation can then be expressed using a Markov Random Field, where the pairwise potentials encode the variation of the shape, while the singleton potentials refer to the data term through the Voronoi decomposition of the space. State-of-the art techniques from linear programming are considered to optimize the designed function.","Title: Robust shadow and illumination estimation using a mixture model\nYear: 2009\n\nAbstract: Illuminant estimation from shadows typically relies on accurate segmentation of the shadows and knowledge of ex- act 3D geometry, while shadow estimation is difficult in the presence of texture. These can be onerous requirements; in this paper we propose a graphical model to estimate the illumination environment and detect the shadows of a scene with textured surfaces from a single image and only coarse 3D information. We represent the illumination en- vironment as a mixture of von Mises-Fisher distributions. Then, each shadow pixel becomes the combination of sam- ples generated from this illumination environment. We inte- grate a number of low-level, illumination-invariant 2D cues in a graphical model to detect and estimate cast shadows on textured surfaces. Both 2D cues and approximate 3D rea- soning are combined to infer a set of labels that identify the shadows in the image and estimate the positions, shapes and intensities of the light sources. Our results demonstrate that the probabilistic combination of multiple cues, unlike prior approaches, manages to differentiate both hard and soft shadows from the underlying surface texture even when we can only coarsely anticipate the effect of 3D geometry. We also experimentally demonstrate how correct estimation of the sharpness and shape of the light sources improves the Augmented Reality results.","Title: MRF Energy Minimization and Beyond via Dual Decomposition\nYear: 2011\n\nAbstract: This paper introduces a new rigorous theoretical framework to address discrete MRF-based optimization in computer vision. Such a framework exploits the powerful technique of Dual Decomposition. It is based on a projected subgradient scheme that attempts to solve an MRF optimization problem by first decomposing it into a set of appropriately chosen subproblems, and then combining their solutions in a principled way. In order to determine the limits of this method, we analyze the conditions that these subproblems have to satisfy and demonstrate the extreme generality and flexibility of such an approach. We thus show that by appropriately choosing what subproblems to use, one can design novel and very powerful MRF optimization algorithms. For instance, in this manner we are able to derive algorithms that: 1) generalize and extend state-of-the-art message-passing methods, 2) optimize very tight LP-relaxations to MRF optimization, and 3) take full advantage of the special structure that may exist in particular MRFs, allowing the use of efficient inference techniques such as, e.g., graph-cut-based methods. Theoretical analysis on the bounds related with the different algorithms derived from our framework and experimental results/comparisons using synthetic and real data for a variety of tasks in computer vision demonstrate the extreme potentials of our approach.","Title: Bioluminescence enhancement through fusion of optical imaging and cinematic video flow\nYear: 2010\n\nAbstract: Optical imaging is an efficient mean to measure biological signal. However, it can suffer from low spatial and temporal resolution while animal deformable displacements could also degrade significantly the localization of the measurements. In this paper, we propose a novel approach to perform fusion of cinematic flow and optical imaging towards enhancement of the biological signal. To this end, fusion is reformulated as a population (all vs. all) registration problem where the two (being spatially aligned) signals are registered in time using the same deformation field. Implicit silhouette and landmark matching are considered for the cinematic images and are combined with global statistical congealing-type measurements of the optical one. The problem is reformulated using a discrete MRF, where optical imaging costs are expressed in singleton (global) potentials, while smoothness constraints as well as cinematic measurements through pair-wise potentials. Promising experimental results demonstrate the potentials of our approach.","Title: Segmentation of building facades using procedural shape priors\nYear: 2010\n\nAbstract: In this paper we propose a novel approach to the perceptual interpretation of building facades that combines shape grammars, supervised classification and random walks. Procedural modeling is used to model the geometric and the photometric variation of buildings. This is fused with visual classification techniques (randomized forests) that provide a crude probabilistic interpretation of the observation space in order to measure the appropriateness of a procedural generation with respect to the image. A random exploration of the grammar space is used to optimize the sequence of derivation rules towards a semantico-geometric interpretation of the observations. Experiments conducted on complex architecture facades with ground truth validate the approach.","Title: Finger-spelling recognition within a collaborative segmentation/behavior inference framework\nYear: 2008\n\nAbstract: We introduce a new approach for finger-spelling recognition from video sequences, relying on the collaboration between the feature extraction and behavior inference processes. The inference process dynamically guides the segmentation-based feature extraction pro- cess towards the most likely location of the signer's hand (based on its attributes). Reciprocally, segmentation offers to the inference process hand object attributes extracted from each image, combin- ing the received guidance and new image information. This collab- oration is beneficial for both processes, yielding not only accurate segmentations of the spelling hand, but also a robust recognition scheme, which can cope with complex backgrounds, typical of real life situations.","Title: Hierarchical 3D diffusion wavelet shape priors\nYear: 2009\n\nAbstract: In this paper, we propose a novel representation of prior knowledge for image segmentation, using diffusion wavelets that can reflect arbitrary continuous interdependencies in shape data. The application of diffusion wavelets has, so far, largely been confined to signal processing. In our approach, and in contrast to state-of-the-art methods, we optimize the coefficients, the number and the position of landmarks, and the object topology - the domain on which the wavelets are defined - during the model learning phase, in a coarse-to-fine manner. The resulting paradigm supports hierarchies both in the model and the search space, can encode complex geometric and photometric dependencies of the structure of interest, and can deal with arbitrary topologies. We report results on two challenging medical data sets, that illustrate the impact of the soft parameterization and the potential of the diffusion operator.","Title: Multimodal inference of articulated spine models from higher order energy functions of discrete MRFS\nYear: 2010\n\nAbstract: In this paper, we introduce a novel approach based on higher order energy functions which have the ability to encode global structural dependencies to infer articulated 3D spine models to CT volume data. A personalized geometrical model is reconstructed from biplanar X-rays before spinal surgery in order to create a spinal column representation which is modeled by a series of intervertebral transformations based on rotation and translation parameters. The shape transformation between the standing and lying poses is then achieved through a Markov Random Field optimization graph, where the unknown variables are the deformations applied to the intervertebral transformations. Singleton and pairwise potentials measure the support from the data and geometrical dependencies between neighboring vertebrae respectively, while higher order cliques are introduced to integrate consistency in regional curves. Optimization of model parameters in a multi-modal context is achieved using efficient linear programming and duality. A qualitative evaluation of the vertebra model alignment obtained from the proposed method gave promising results while the quantitative comparison to expert identification yields an accuracy of 1.8 ± 0.7 mm based on the localization of surgical landmarks.","Title: Needle tracking through higher-order MRF optimization\nYear: 2010\n\nAbstract: We propose a Markov Random Field formulation for the tracking of needles in fluoroscopic images. A novel motion model makes it possible to capture the primarily rigid motion as well as deformations of the needle in a single second-order MRF graph. Needles are represented by B-splines and each control point is associated with a random variable in a MAP-MRF formulation. In addition to the control points we introduce a single additional random variable representing the rigid transformation needles undergo during interventions. The incorporation of rigid transformations allows to recover transformations even in the presence of large displacements which is not possible with existing MRF models for medical tool tracking.","Title: An explicit shape-constrained MRF-based contour evolution method for 2-D medical image segmentation.\nYear: 2014\n\nAbstract: Image segmentation is, in general, an ill-posed problem and additional constraints need to be imposed in order to achieve the desired segmentation result. While segmenting organs in medical images, which is the topic of this paper, a significant amount of prior knowledge about the shape, appearance, and location of the organs is available that can be used to constrain the solution space of the segmentation problem. Among the various types of prior information, the incorporation of prior information about shape, in particular, is very challenging. In this paper, we present an explicit shape-constrained MAP-MRF-based contour evolution method for the segmentation of organs in 2-D medical images. Specifically, we represent the segmentation contour explicitly as a chain of control points. We then cast the segmentation problem as a contour evolution problem, wherein the evolution of the contour is performed by iteratively solving a MAP-MRF labeling problem. The evolution of the contour is governed by three types of prior information, namely: (i) appearance prior, (ii) boundary-edgeness prior, and (iii) shape prior, each of which is incorporated as clique potentials into the MAP-MRF problem. We use the master-slave dual decomposition framework to solve the MAP-MRF labeling problem in each iteration. In our experiments, we demonstrate the application of the proposed method to the challenging problem of heart segmentation in non-contrast computed tomography data.","Title: Deformable Medical Image Registration: A Survey\nYear: 2013\n\nAbstract: Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.","Title: Slice-to-volume deformable registration: efficient one-shot consensus between plane selection and in-plane deformation.\nYear: 2015\n\nAbstract: This paper introduces a novel decomposed graphical model to deal with slice-to-volume registration in the context of medical images and image-guided surgeries.We present a new non-rigid slice-to-volume registration method whose main contribution is the ability to decouple the plane selection and the in-plane deformation parts of the transformation-through two distinct graphs-toward reducing the complexity of the model while being able to obtain simultaneously the solution for both of them. To this end, the plane selection process is expressed as a local graph-labeling problem endowed with planarity satisfaction constraints, which is then directly linked with the deformable part through the data registration likelihoods. The resulting model is modular with respect to the image metric, can cope with arbitrary in-plane regularization terms and inherits excellent properties in terms of computational efficiency.The proof of concept for the proposed formulation is done using cardiac MR sequences of a beating heart (an artificially generated 2D temporal sequence is extracted using real data with known ground truth) as well as multimodal brain images involving ultrasound and computed tomography images. We achieve state-of-the-art results while decreasing the computational time when we compare with another method based on similar techniques.We confirm that graphical models and discrete optimization techniques are suitable to solve non-rigid slice-to-volume registration problems. Moreover, we show that decoupling the graphical model and labeling it using two lower-dimensional label spaces, we can achieve state-of-the-art results while substantially reducing the complexity of our method and moving the approach close to real clinical applications once considered in the context of modern parallel architectures.","Title: Sub-cortical brain structure segmentation using F-CNN'S\nYear: 2016\n\nAbstract: In this paper we propose a deep learning approach for segmenting sub-cortical structures of the human brain in Magnetic Resonance (MR) image data. We draw inspiration from a state-of-the-art Fully-Convolutional Neural Network (F-CNN) architecture for semantic segmentation of objects in natural images, and adapt it to our task. Unlike previous CNN-based methods that operate on image patches, our model is applied on a full blown 2D image, without any alignment or registration steps at testing time. We further improve segmentation results by interpreting the CNN output as potentials of a Markov Random Field (MRF), whose topology corresponds to a volumetric grid. Alpha-expansion is used to perform approximate inference imposing spatial volumetric homogeneity to the CNN priors. We compare the performance of the proposed pipeline with a similar system using Random Forest-based priors, as well as state-of-art segmentation algorithms, and show promising results on two different brain MRI datasets.","Title: A Framework for Efficient Structured Max-Margin Learning of High-Order MRF Models\nYear: 2015\n\nAbstract: We present a very general algorithm for structured prediction learning that is able to efficiently handle discrete MRFs/CRFs (including both pairwise and higher-order models) so long as they can admit a decomposition into tractable subproblems. At its core, it relies on a dual decomposition principle that has been recently employed in the task of MRF optimization. By properly combining such an app...","Title: Guest Editors’ Introduction: Special Section on Higher Order Graphical Models in Computer Vision\nYear: 2015\n\nAbstract: The papers in this special section address the programs and services supported by graphical models in computer vision. This section explores the main challenges in this framework???modeling novel priors, learning, inference???and presents innovative solutions. The papers cover the aspects of modeling novel priors, inference algorithms and parameter learning methods in the context of higher order graphical models.","Title: Building Detection In Very High Resolution Multispectral Data With Deep Learning Features\nYear: 2015\n\nAbstract: The automated man-made object detection and building extraction from single satellite images is, still, one of the most challenging tasks for various urban planning and monitoring engineering applications. To this end, in this paper we propose an automated building detection framework from very high resolution remote sensing data based on deep convolutional neural networks. The core of the developed method is based on a supervised classification procedure employing a very large training dataset. An MRF model is then responsible for obtaining the optimal labels regarding the detection of scene buildings. The experimental results and the performed quantitative validation indicate the quite promising potentials of the developed approach.","Title: A Machine Learning Methodology for Enzyme Functional Classification Combining Structural and Protein Sequence Descriptors.\nYear: 2016\n\nAbstract: The massive expansion of the worldwide Protein Data Bank (PDB) provides new opportunities for computational approaches which can learn from available data and extrapolate the knowledge into new coming instances. The aim of this work is to apply machine learning in order to train prediction models using data acquired by costly experimental procedures and perform enzyme functional classification. Enzymes constitute key pharmacological targets and the knowledge on the chemical reactions they catalyze is very important for the development of potent molecular agents that will either suppress or enhance the function of the given enzyme, thus modulating a pathogenicity, an illness or even the phenotype. Classification is performed on two levels: (i) using structural information into a Support Vector Machines (SVM) classifier and (ii) based on amino acid sequence alignment and Nearest Neighbor (NN) classification. The classification accuracy is increased by fusing the two classifiers and reaches 93.4% on a large dataset of 39,251 proteins from the PDB database. The method is very competitive with respect to accuracy of classification into the 6 enzymatic classes, while at the same time its computational cost during prediction is very small.","Title: Simultaneous registration and change detection in multitemporal, very high resolution remote sensing data\nYear: 2015\n\nAbstract: In order to exploit the currently continuous streams of massive, multi-temporal, high-resolution remote sensing datasets there is an emerging need to address efficiently the image registration and change detection challenges. To this end, in this paper we propose a modular, scalable, metric free single shot change detection/registration method. The approach exploits a decomposed interconnected graphical model formulation where registration similarity constraints are relaxed in the presence of change detection. The deformation space is discretized, while efficient linear programming and duality principles are used to optimize a joint solution space where local consistency is imposed on the deformation and the detection space. Promising results on large scale experiments demonstrate the extreme potentials of our method.","Title: Graph-Based Motion-Driven Segmentation of the Carotid Atherosclerotique Plaque in 2D Ultrasound Sequences.\nYear: 2015\n\nAbstract: Carotid plaque segmentation in ultrasound images is a crucial step for carotid atherosclerosis. However, image quality, important deformations and lack of texture are prohibiting factors towards manual or accurate carotid segmentation. We propose a novel fully automated methodology to identify the plaque region by exploiting kinematic dependencies between the atherosclerotic and the normal arterial wall. The proposed methodology exploits group-wise image registration towards recovering the deformation field, on which information theory criteria are used to determine dominant motion classes and a map reflecting kinematic dependencies, which is then segmented using Markov random fields. The algorithm was evaluated on 120 cases, for which manually-traced plaque contours by an experienced physician were available. Promising evaluation results showed the enhanced performance of the algorithm in automatically segmenting the plaque region, while future experiments on additional datasets are expected to further elucidate its potential.","Title: Modular Linear Iconic Matching Using Higher Order Graphs\nYear: 2015\n\nAbstract: We introduce a novel approach to tackle iconic linear mapping between two images. We adopt a grid-based parametrization of the deformation field that is encoded by a higher order graphical model. In the proposed formulation, latent variables correspond to local grid displacement vectors and unary potentials locally quantify the level of alignment between the two images. Higher order constraints that involve third and forth order potentials, enforce the linearity of the resulting transformation. The resulting formulation is modular with respect to the image metric used to evaluate the correctness of mapping as well as with respect to the nature of the linear transformation ( rigid, similarity, or affine). Inference on this graph is performed through dual decomposition. Comparison with classic algorithms demonstrates the potential of our approach.","Title: Implicit Planar And In-Plane Deformable Mapping In Medical Images Through High Order Graphs\nYear: 2015\n\nAbstract: In this paper we propose a novel method based on discrete optimization of high order graphs, to perform deformable slice-to-volume registration of 2D images and 3D volumes. To this end, a 2D grid superimposed to the image is considered with control points deforming in 3D and their deformations corresponding to the label space. Geometrical consistency (unique plane selection) and deformation smoothness (in-plane deformations) as well as image similarity (visual matching) are encoded in different third order cliques. The proposed formulation is optimized through its mapping to a factor graph using conventional graph optimization methods. A dataset composed of 2D slices and 3D MRI volumes of the heart was used to evaluate its accuracy leading to very promising results.","Title: Local atlas selection for discrete multi-atlas segmentation\nYear: 2015\n\nAbstract: Multi-atlas segmentation is commonly performed in two separate steps: i) multiple pairwise registrations, and ii) fusion of the deformed segmentation masks towards labeling objects of interest. In this paper we propose an approach for integrated volume segmentation through multi-atlas registration. To tackle this problem, we opt for a graphical model where registration and segmentation nodes are coupled. The aim is to recover simultaneously all atlas deformations along with selection masks quantifying the participation of each atlas per segmentation voxel. The above is modeled using a pairwise graphical model where deformation and segmentation variables are modeled explicitly. A sequential optimization relaxation is proposed for efficient inference. Promising performance is reported on the IBSR dataset when comparing to majority voting and local appearance-based weighted voting.","Title: (Hyper)-Graphs Inference through Convex Relaxations and Move Making Algorithms: Contributions and Applications in Artificial Vision.\nYear: 2016\n\nAbstract: Computational visual perception seeks to reproduce human vision through the combination of visual sensors, artificial intelligence, and computing. To this end, computer vision tasks are often reformulated as mathematical inference problems where the objective is to determine the set of parameters corresponding to the lowest potential of a task-specific objective function. Graphical models have been the most popular formulation in the field over the past two decades where the problem is viewed as a discrete assignment labeling one. Modularity, scalability, and portability are the main strengths of these methods which once combined with efficient inference algorithms they could lead to state of the art results. This monograph focuses on the inference component of the problem and in particular discusses in a systematic manner the most commonly used optimization principles in the context of graphical models. It looks at inference over low rank models (interactions between variables are constrained to pairs) as well as higher order ones (arbitrary set of variables determine hyper-cliques on which constraints are introduced) and seeks a concise, self-contained presentation of prior art as well as the presentation of the current state of the art methods in the field.","Title: Prior-based Coregistration and Cosegmentation.\nYear: 2016\n\nAbstract: We propose a modular and scalable framework for dense coregistration and cosegmentation with two key characteristics: first, we substitute ground truth data with the semantic map output of a classifier; second, we combine this output with population deformable registration to improve both alignment and segmentation. Our approach deforms all volumes towards consensus, taking into account image similarities and label consistency. Our pipeline can incorporate any classifier and similarity metric. Results on two datasets, containing annotations of challenging brain structures, demonstrate the potential of our method.","Title: Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields.\nYear: 2016\n\nAbstract: Rigid slice-to-volume registration is a challenging task, which finds application in medical imaging problems like image fusion for image guided surgeries and motion correction for volume reconstruction. It is usually formulated as an optimization problem and solved using standard continuous methods. In this paper, we discuss how this task be formulated as a discrete labeling problem on a graph. Inspired by previous works on discrete estimation of linear transformations using Markov Random Fields (MRFs), we model it using a pairwise MRF, where the nodes are associated to the rigid parameters, and the edges encode the relation between the variables. We compare the performance of the proposed method to a continuous formulation optimized using simplex, and we discuss how it can be used to further improve the accuracy of our approach. Promising results are obtained using a monomodal dataset composed of magnetic resonance images (MRI) of a beating heart.","Title: (Hyper)-graphical models in biomedical image analysis.\nYear: 2016\n\nAbstract: •We briefly present the interest of graphical models in biomedical imaging.•We present/briefly review the state of the art optimization algorithms for their inference.•We provide a snapshot of methods of increasing complexity/richness to address core biomedical imaging problems.•We provide some insights as it concerns future developments and impact of these methods for the decade to come.","Title: Simultaneous Registration, Segmentation And Change Detection From Multisensor, Multitemporal Satellite Image Pairs\nYear: 2016\n\nAbstract: In this paper, a novel generic framework has been designed, developed and validated for addressing simultaneously the tasks of image registration, segmentation and change detection from multisensor, multiresolution, multitemporal satellite image pairs. Our approach models the inter- dependencies of variables through a higher order graph. The proposed formulation is modular with respect to the nature of images (various similarity metrics can be considered), the nature of deformations (arbitrary interpolation strategies), and the nature of segmentation likelihoods (various classification approaches can be employed). Inference of the proposed formulation is achieved through its mapping to an overparametrized pairwise graph which is then optimized using linear programming. Experimental results and the performed quantitative evaluation indicate the high potentials of the developed method.","Title: Comparison of Multi-resolution Analysis Patterns for Texture Classification of Breast Tumors Based on DCE-MRI.\nYear: 2016\n\nAbstract: Although Fourier and Wavelet Transform have been widely used for texture classification methods in medical images, the discrimination performance of FDCT has not been investigated so far in respect to breast cancer detection..n this paper, three multi-resolution transforms, namely the Discrete Wavelet Transform (DWT), the Stationary Wavelet Transform (SWT) and the Fast Discrete Curvelet Transform (FDCT) were comparatively assessed with respect to their ability to discriminate between malignant and benign breast tumors in Dynamic Contrast-Enhanced Magnetic Resonance Images (DCE-MRI). The mean and entropy of the detail sub-images for each decomposition scheme were used as texture features, which were subsequently fed as input into several classifiers. FDCT features fed to a Linear Discriminant Analysis (LDA) classifier produced the highest overall classification performance (93.18 % Accuracy).","Title: Slice-to-volume medical image registration: a survey.\nYear: 2017\n\nAbstract: •The first comprehensive survey of the literature about slice-to-volume registration.•A categorical study of the algorithms according to an ad-hoc defined taxonomy.•We discuss advantages and disadvantages of alternative slice-to-volume methods.•We elaborate conclusions from this analysis and discuss future perspectives in the field.","Title: Optimal Estimation of Diffusion in DW-MRI by High-Order MRF-Based Joint Deformable Registration and Diffusion Modeling\nYear: 2016\n\nAbstract: Over the last years, the apparent diffusion coefficient (ADC), computed from diffusion-weighted magnetic resonance (DW-MR) images, has become an important imaging biomarker for evaluating and managing patients with neoplastic or cerebrovascular disease. Standard methods for the calculation of ADC ignore the presence of noise and motion between successive (in time) DW-MR images acquired by changing the b-value. In order to accurately quantify the diffusion process during image acquisition, we introduce a method based on a high-order Markov Random Field (MRF) formulation that jointly registers the DWMR images and models the spatiotemporal diffusion. Spatial smoothness on the ADC map, as well as spatiotemporal deformation smoothness, is imposed towards producing anatomically meaningful representations. The high-order dependencies in our MRF model are handled through Dual Decomposition. Performance of registration is compared to a state-of-the art registration approach in terms of obtained fitting error of the diffusion model in the core of the tumor. Preliminary results reveal a marginally better performance of our method when compared against the standard ADC map used in clinical practice, which indicates its potential as a means for extracting imaging biomarkers.","Title: EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation.\nYear: 2017\n\nAbstract: During the past decade, with the significant progress of computational power as well as ever-rising data availability, deep learning techniques became increasingly popular due to their excellent performance on computer vision problems. The size of the Protein Data Bank (PDB) has increased more than 15-fold since 1999, which enabled the expansion of models that aim at predicting enzymatic function via their amino acid composition. Amino acid sequence, however, is less conserved in nature than protein structure and therefore considered a less reliable predictor of protein function. This paper presents EnzyNet, a novel 3D convolutional neural networks classifier that predicts the Enzyme Commission number of enzymes based only on their voxel-based spatial structure. The spatial distribution of biochemical properties was also examined as complementary information. The two-layer architecture was investigated on a large dataset of 63,558 enzymes from the PDB and achieved an accuracy of 78.4% by exploiting only the binary representation of the protein shape. Code and datasets are available at https://github.com/shervinea/enzynet.","Title: Deformable Registration through Learning of Context-Specific Metric Aggregation.\nYear: 2017\n\nAbstract: We propose a novel weakly supervised discriminative algorithm for learning context specific registration metrics as a linear combination of conventional similarity measures. Conventional metrics have been extensively used over the past two decades and therefore both their strengths and limitations are known. The challenge is to find the optimal relative weighting (or parameters) of different metrics forming the similarity measure of the registration algorithm. Hand-tuning these parameters would result in sub optimal solutions and quickly become infeasible as the number of metrics increases. Furthermore, such hand-crafted combination can only happen at global scale (entire volume) and therefore will not be able to account for the different tissue properties. We propose a learning algorithm for estimating these parameters locally, conditioned to the data semantic classes. The objective function of our formulation is a special case of non-convex function, difference of convex function, which we optimize using the concave convex procedure. As a proof of concept, we show the impact of our approach on three challenging datasets for different anatomical structures and modalities.","Title: Graph-Based Slice-to-Volume Deformable Registration.\nYear: 2018\n\nAbstract: Deformable image registration is a fundamental problem in computer vision and medical image computing. In this paper we investigate the use of graphical models in the context of a particular type of image registration problem, known as slice-to-volume registration. We introduce a scalable, modular and flexible formulation that can accommodate low-rank and high order terms, that simultaneously selects the plane and estimates the in-plane deformation through a single shot optimization approach. The proposed framework is instantiated into different variants seeking either a compromise between computational efficiency (soft plane selection constraints and approximate definition of the data similarity terms through pair-wise components) or exact definition of the data terms and the constraints on the plane selection. Simulated and real-data in the context of ultrasound and magnetic resonance registration (where both framework instantiations as well as different optimization strategies are considered) demonstrate the potentials of our method.","Title: Deep Patch-Based Priors Under A Fully Convolutional Encoder-Decoder Architecture For Interstitial Lung Disease Segmentation\nYear: 2018\n\nAbstract: Interstitial lung diseases (ILD) encompass a large spectrum of diseases sharing similarities in their physiopathology and computed tomography (CT) appearance. In this paper, we propose the adaption of a deep convolutional encoder-decoder (CED) that has shown high accuracy for image segmentation. Such architectures require annotation of the total region with pathological findings. This is difficult to acquire, due to uncertainty in the definition and extent of disease patterns and the need of significant human effort, especially for large datasets. Therefore, often current methods use patch-based implementations of convolutional neural networks, which however tend to produce spatially inhomogeneous segmentations due to their local contextual view. We exploit the advantages of both architectures by using the output of a patch-based classifier as a prior to a CED. Our method could advance the state-of-the-art in lung tissue segmentation using only a small number of newly annotated images.","Title: Cooperative Object Segmentation and Behavior Inference in Image Sequences\nYear: 2009\n\nAbstract: In this paper, we propose a general framework for fusing bottom-up segmentation with top-down object behavior inference over an image sequence. This approach is beneficial for both tasks, since it enables them to cooperate so that knowledge relevant to each can aid in the resolution of the other, thus enhancing the final result. In particular, the behavior inference process offers dynamic probabilistic priors to guide segmentation. At the same time, segmentation supplies its results to the inference process, ensuring that they are consistent both with prior knowledge and with new image information. The prior models are learned from training data and they adapt dynamically, based on newly analyzed images. We demonstrate the effectiveness of our framework via particular implementations that we have employed in the resolution of two hand gesture recognition applications. Our experimental results illustrate the robustness of our joint approach to segmentation and behavior inference in challenging conditions involving complex backgrounds and occlusions of the target object.","Title: AtlasNet: Multi-atlas Non-linear Deep Networks for Medical Image Segmentation.\nYear: 2018\n\nAbstract: Deep learning methods have gained increasing attention in addressing segmentation problems for medical images analysis despite the challenges inherited from the medical domain, such as limited data availability, lack of consistent textural or salient patterns, and high dimensionality of the data. In this paper, we introduce a novel multi-network architecture that exploits domain knowledge to address those challenges. The proposed architecture consists of multiple deep neural networks that are trained after co-aligning multiple anatomies through multi-metric deformable registration. This multi-network architecture can be trained with fewer examples and leads to better performance, robustness and generalization through consensus. Comparable to human accuracy, highly promising results on the challenging task of interstitial lung disease segmentation demonstrate the potential of our approach.","Title: Weakly-Supervised Learning of Metric Aggregations for Deformable Image Registration.\nYear: 2018\n\nAbstract: Deformable registration has been one of the pillars of biomedical image computing. Conventional approaches refer to the definition of a similarity criterion that, once endowed with a deformation model and a smoothness constraint, determines the optimal transformation to align two given images. The definition of this metric function is among the most critical aspects of the registration process. We argue that incorporating semantic information (in the form of anatomical segmentation maps) into the registration process will further improve the accuracy of the results. In this paper, we propose a novel weakly supervised approach to learn domain specific aggregations of conventional metrics using anatomical segmentations. This combination is learned using latent structured support vector machines (LSSVM). The learned matching criterion is integrated within a metric free optimization framework based on graphical models, resulting in a multi-metric algorithm endowed with a spatially varying similarity metric function conditioned on the anatomical structures. We provide extensive evaluation on three different datasets of CT and MRI images, showing that learned multi-metric registration outperforms single-metric approaches based on conventional similarity measures.","Title: Multitemporal Very High Resolution From Space: Outcome of the 2016 IEEE GRSS Data Fusion Contest.\nYear: 2017\n\nAbstract: In this paper, the scientific outcomes of the 2016 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society are discussed. The 2016 Contest was an open topic competition based on a multitemporal and multimodal dataset, which included a temporal pair of very high resolution panchromatic and multispectral Deimos-2 image...","Title: Graph-Based Registration, Change Detection, and Classification in Very High Resolution Multitemporal Remote Sensing Data.\nYear: 2016\n\nAbstract: In this paper, we propose a modular, scalable, metric-free, single-shot change detection/registration method. The developed framework exploits the relation between the registration and change detection problems, while under a fruitful synergy, the coupling energy term constrains adequately both tasks. In particular, through a decomposed interconnected graphical model, the registration similarity c...","Title: Quasi Real-Time Sub-Space 3d Deformable Fusion\nYear: 2015\n\nAbstract: In this paper we propose a novel and robust approach for deformable fusion using a metric defined in an appropriate subspace which is adaptive to the image-content/image-modality. We adopt a graph-based formulation that assumes that intensities of corresponding pixels in the two image domains are related through an unknown piece-wise constant linear function. This relation is propagated to an appropriate sub-space (wavelets coefficients) where a criterion that couples the estimation of the deformation field with optimal transport function on the subspace and the smoothness of the deformation is considered. Message passing methods efficiently implemented using jump flooding are considered to get the optimal parameters of the deformation field and the transport function. Promising results are obtained in near real-time with a unique parameter setting.","Title: Spatiotemporal decomposition in object-space along reconstruction in emission tomography.\nYear: 2008\n\nAbstract: Emission tomography has provided a new insight in brain mechanisms past years. Although reconstructions are nowadays mostly static, trend is going toward dynamic acquisitions and reconstructions. This opens a new range of investigations, for instance for drugs discovery. Indeed new drugs are studied through the dynamic ability of tissues to catch them. However, it is required to know radiotracer concentration of blood that irrigates tissues in order to draw conclusions on potentials of these drugs. This concentration is called 'input function' and this paper presents a new method for measuring it in a non-invasive way. Our new method relies on simultaneous estimations of vessels kinetics and vessels spatial distribution. These estimations are performed during the reconstruction process and take into account the statistical nature of measured signals. Indeed, this method is based on the maximisation of the likelihood of counts in detectors. It takes advantages of a non-negative matrix factorisation which separate spatial and temporal components. Results are very promising, since it estimates arterial input function accurately although object emits just a limited amount of photons, especially within the first minutes.","Title: A Discrete MRF Framework for Integrated Multi-Atlas Registration and Segmentation.\nYear: 2017\n\nAbstract: Multi-atlas segmentation has emerged in recent years as a simple yet powerful approach in medical image segmentation. It commonly comprises two steps: (1) a series of pairwise registrations that establish correspondences between a query image and a number of atlases, and (2) the fusion of the available segmentation hypotheses towards labeling objects of interest. In this paper, we introduce a novel approach that solves simultaneously for the underlying segmentation labels and the multi-atlas registration. The proposed approach is formulated as a pairwise Markov Random Field, where registration and segmentation nodes are coupled towards simultaneously recovering all atlas deformations and labeling the query image. The coupling is achieved by promoting the consistency between selected deformed atlas segmentations and the estimated query segmentation. Additional membership fields are estimated, determining the participation of each atlas in labeling each voxel. Inference is performed by using a sequential relaxation scheme. The proposed approach is validated on the IBSR dataset and is compared against standard post-registration label fusion strategies. Promising results demonstrate the potential of our method.","Title: Understanding 3d emotions through compact anthropometric autoregressive models\nYear: 2006\n\nAbstract: Reproducing realistic facial expressions is an important challenge in human computer interaction. In this paper we propose a novel method of modeling and recovering the transitions between different expressions through the use of an autoregressive process. In order to account for computational complexity, we adopt a compact face representation inspired from MPEG-4 standards while in terms of expressions a well known Facial Action Unit System (FACS) comprising the six dominant ones is considered. Then, transitions between expressions are modeled through a time series according to a linear model. Explicit constraints driven from face anthropometry and points interaction are inherited in this model and minimize the risk of producing non-realistic configurations. Towards optimal animation performance, a particular hardware architecture is used to provide the 3D depth information of the corresponding facial elements during the learning stage and the Random Sampling Consensus algorithm for the robust estimation of the model parameters. Promising experimental results demonstrate the potential of such an approach.","Title: MRF-Based blind image deconvolution\nYear: 2012\n\nAbstract: This paper proposes an optimization-based blind image deconvolution method. The proposed method relies on imposing a discrete MRF prior on the deconvolved image. The use of such a prior leads to a very efficient and powerful deconvolution algorithm that carefully combines advanced optimization techniques. We demonstrate the extreme effectiveness of our method1 by applying it on a wide variety of very challenging cases that involve the inference of large and complicated blur kernels.","Title: Gene Expression High-Dimensional Clustering Towards A Novel, Robust, Clinically Relevant And Highly Compact Cancer Signature\nYear: 2019\n\nAbstract: Precision medicine, a highly disruptive paradigm shift in healthcare targeting the personalizing treatment, heavily relies on genomic data. However, the complexity of the biological interactions, the important number of genes as well as the lack of substantial patient's clinical data consist a tremendous bottleneck on the clinical implementation of precision medicine. In this work, we introduce a generic, low dimensional gene signature that represents adequately the tumor type. Our gene signature is produced using LP-stability algorithm, a high dimensional center-based unsupervised clustering algorithm working in the dual domain, and is very versatile as it can consider any arbitrary distance metric between genes. The gene signature produced by LP-stability reports at least 10 times better statistical significance and 35% better biological significance than the ones produced by two referential unsupervised clustering methods. Moreover, our experiments demonstrate that our low dimensional biomarker (27 genes) surpass significantly existing state of the art methods both in terms of qualitative and quantitative assessment while providing better associations to tumor types than methods widely used in the literature that rely on several omics data.","Title: AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia\nYear: 2021\n\nAbstract: •An algorithm for automatic COVID-19 quantification based on 2D & 3D deep convolutional neural networks is presented.•A COVID-19 holistic, highly compact signature integrating imaging and clinical/ biological attributes is presented•An automatic Short and Long-term prognosis is presented, offering complementary means to facilitate triage for COVID-19.•A clinically-relevant quantification and staging tool validated by comparison with clinical experts is reported.","Title: Exploring Deep Registration Latent Spaces\nYear: 2021\n\nAbstract: Explainability of deep neural networks is one of the most challenging and interesting problems in the field. In this study, we investigate the topic focusing on the interpretability of deep learning-based registration methods. In particular, with the appropriate model architecture and using a simple linear projection, we decompose the encoding space, generating a new basis, and we empirically show that this basis captures various decomposed anatomically aware geometrical transformations. We perform experiments using two different datasets focusing on lungs and hippocampus MRI. We show that such an approach can decompose the highly convoluted latent spaces of registration pipelines in an orthogonal space with several interesting properties. We hope that this work could shed some light on a better understanding of deep learning-based registration methods.","Title: Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment.\nYear: 2021\n\nAbstract: Sarcopenia is a medical condition characterized by a reduction in muscle mass and function. A quantitative diagnosis technique consists of localizing the CT slice passing through the middle of the third lumbar area (L3) and segmenting muscles at this level. In this paper, we propose a deep reinforcement learning method for accurate localization of the L3 CT slice. Our method trains a reinforcement learning agent by incentivizing it to discover the right position. Specifically, a Deep Q-Network is trained to find the best policy to follow for this problem. Visualizing the training process shows that the agent mimics the scrolling of an experienced radiologist. Extensive experiments against other state-of-the-art deep learning based methods for L3 localization prove the superiority of our technique which performs well even with limited amount of data and annotations.","Title: Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology\nYear: 2021\n\nAbstract: The examination of histopathology images is considered to be the gold standard for the diagnosis and stratification of cancer patients. A key challenge in the analysis of such images is their size, which can run into the gigapixels and can require tedious screening by clinicians. With the recent advances in computational medicine, automatic tools have been proposed to assist clinicians in their ev...","Title: High-Particle Simulation of Monte-Carlo Dose Distribution with 3D ConvLSTMs\nYear: 2021\n\nAbstract: Monte-Carlo simulation of radiotherapy dose remains an extremely time-consuming task, despite being still the most precise tool for radiation transport calculation. To circumvent this issue, deep learning offers promising avenues. In this paper, we extend ConvLSTM to handle 3D data and introduce a 3D recurrent and fully convolutional neural network architecture. Our model's purpose is to infer a computationally expensive Monte Carlo dose calculation result for VMAT plans with a high number of particles from a sequence of simulations with a low number of particles. We benchmark our framework against other learning methods commonly used for denoising and other medical tasks. Our model outperforms the other methods with regards to several evaluation metrics used to assess the clinical viability of the predictions.","Title: Joint Deformable Image Registration and ADC Map Regularization: Application to DWI-Based Lymphoma Classification\nYear: 2022\n\nAbstract: The Apparent Diffusion Coefficient (ADC) is considered an importantimaging biomarker contributing to the assessment of tissue microstructure and pathophy- siology. It is calculated from Diffusion-Weighted Magnetic Resonance Imaging (DWI) by means of a diffusion model, usually without considering any motion during image acquisition. We propose a method to improve the computation of the ADC by coping jointly with both motion artifacts in whole-body DWI (through group-wise registration) and possible instrumental noise in the diffusion model. The proposed deformable registration method yielded on average the lowest ADC reconstruction error on data with simulated motion and diffusion. Moreover, our approach was applied on whole-body diffusion weighted images obtained with five different b-values from a cohort of 38 patients with histologically confirmed lymphomas of three different types (Hodgkin, diffuse large B-cell lymphoma and follicular lymphoma). Evaluation on the real data showed that ADC-based features, extracted using our joint optimization approach classified lymphomas with an accuracy of approximately 78.6% (yielding a 11% increase in respect to the standard features extracted from unregistered diffusion-weighted images). Furthermore, the correlation between diffusion characteristics and histopathological findings was higher than any other previous approach of ADC computation.","Title: Region-Guided CycleGANs for Stain Transfer in Whole Slide Images\nYear: 2022\n\nAbstract: In whole slide imaging, commonly used staining techniques based on hematoxylin and eosin (H&E) and immunohistochemistry (IHC) stains accentuate different aspects of the tissue landscape. In the case of detecting metastases, IHC provides a distinct readout that is readily interpretable by pathologists. IHC, however, is a more expensive approach and not available at all medical centers. Virtually generating IHC images from H&E using deep neural networks thus becomes an attractive alternative. Deep generative models such as CycleGANs learn a semantically-consistent mapping between two image domains, while emulating the textural properties of each domain. They are therefore a suitable choice for stain transfer applications. However, they remain fully unsupervised, and possess no mechanism for enforcing biological consistency in stain transfer. In this paper, we propose an extension to CycleGANs in the form of a region of interest discriminator. This allows the CycleGAN to learn from unpaired datasets where, in addition, there is a partial annotation of objects for which one wishes to enforce consistency. We present a use case on whole slide images, where an IHC stain provides an experimentally generated signal for metastatic cells. We demonstrate the superiority of our approach over prior art in stain transfer on histopathology tiles over two datasets. Our code and model are available at https://github.com/jcboyd/miccai2022-roigan."],"string":"[\n \"Title: Postarthroplasty Examination Using X-Ray Images\\nYear: 2009\\n\\nAbstract: Arthroplasty, the implantation of prostheses into joints, is a surgical procedure that is affecting a larger and larger number of patients over time. As a result, it is increasingly important to develop imaging techniques to noninvasively examine joints with prostheses after surgery, both statically and dynamically in 3-D. The static problem is considered here, with the aim to create a 3-D shape model of the bone as well as the prosthesis using a set of 2-D X-rays from various viewpoints. The most important challenge to be addressed is the lack of texture, the most common feature to recover shape from multiple views. In order to overcome this limitation, we reformulate the problem using a novel multiview segmentation approach where an active contours 3-D surface evolution with level-set implementation is used to recover the shape of bones and prostheses in postoperative joints. The recovered shape may then be used to track 3-D motions in dynamic X-ray sequences to obtain kinematic information.\",\n \"Title: Shape registration in implicit spaces using information theory and free form deformations.\\nYear: 2006\\n\\nAbstract: We present a novel, variational and statistical approach for shape registration. Shapes of interest are implicitly embedded in a higher-dimensional space of distance transforms. In this implicit embedding space, registration is formulated in a hierarchical manner: the Mutual Information criterion supports various transformation models and is optimized to perform global registration; then, a B-spline-based Incremental Free Form Deformations (IFFD) model is used to minimize a Sum-of-Squared-Differences (SSD) measure and further recover a dense local nonrigid registration field. The key advantage of such framework is twofold: (1) it naturally deals with shapes of arbitrary dimension (2D, 3D, or higher) and arbitrary topology (multiple parts, closed/open) and (2) it preserves shape topology during local deformation and produces local registration fields that are smooth, continuous, and establish one-to-one correspondences. Its invariance to initial conditions is evaluated through empirical validation, and various hard 2D/3D geometric shape registration examples are used to show its robustness to noise, severe occlusion, and missing parts. We demonstrate the power of the proposed framework using two applications: one for statistical modeling of anatomical structures, another for 3D face scan registration and expression tracking. We also compare the performance of our algorithm with that of several other well-known shape registration algorithms.\",\n \"Title: 4D ventricular segmentation and wall motion estimation using efficient discrete optimization\\nYear: 2007\\n\\nAbstract: In this paper we propose a novel approach to ventricular motion estimation and segmentation. Our method is based on a MRF formulation where an optimal intensity-based separation between the endocardium and the rest of the cardiac volume is to be determined. Such a term is defined in the spatiotemporal domain, where the ventricular wall motion is introduced to account for correspondences between the consecutive segmentation maps. The estimation of the deformations is done through a continuous deformation field (FFD) where the displacements of the control points are determined using discrete labeling approach. Principles from linear programming and in particular the Primal/Dual Schema is used to recover the optimal solution in both spaces. Promising experimental results obtained on 13 MR spatiotemporal data sets demonstrate the potentials of our method.\",\n \"Title: Curvilinear Structures Extraction In Cluttered Bioimaging Data With Discrete Optimization Methods\\nYear: 2011\\n\\nAbstract: Filamentary structures extraction in medical and biological images is a challenging problem. Muscular/Neural fibers, neurites and blood arteries are some examples. Their delineation is particularly problematic due to the lack of solid visual support that is also compromised by the presence of clutter and low signal to noise ratios.In this article, we propose a modular approach to curvilinear structures extraction based on recent advances in discrete optimization on the basis of aggregate clustering. Given an initial clustering of the detected points, first a pair-wise Markov Random Field (MRF) is considered to determine consistent elongated structures while penalizing their number and rejecting outliers. The outcome of this process is then locally refined through a non-submodular MRF aiming at center-line extraction process and guided by local geometric consistency between segments expressing the intra-cluster variability. Promising results for microtubules delineation in TIRFM images as well as for guidewires segmentation in fluoroscopic images demonstrate the potentials of the method.\",\n \"Title: Variational model-based 3d building extraction from remote sensing data\\nYear: 2009\\n\\nAbstract: In this paper, we introduce a variational framework towards automatic 3D building reconstruction from optical and Lidar data. Multiple 3D competing building priors are considered under a recognition-driven way. These models, under a certain hierarchical representation, describe the space of solutions and under a fruitful synergy with an inferential procedure recover the observed scene's geometry. Our formulation allows the cue with the higher spatial resolution to constrain properly the boundaries detection procedure ensuring, in this way, optimal results in terms of accuracy. Such an integrated approach is defined in a variational context, solves segmentation in both spaces, addresses fusion in a natural manner and allows multiple competing priors to determine the pose and 3D geometry from the observed data. Very promising experimental results demonstrate the potentials of our approach.\",\n \"Title: Surface/volume-based articulated 3D spine inference through Markov random fields.\\nYear: 2009\\n\\nAbstract: This paper presents a method towards inferring personalized 3D spine models to intraoperative CT data acquired for corrective spinal surgery. An accurate 3D reconstruction from standard X-rays is obtained before surgery to provide the geometry of vertebrae. The outcome of this procedure is used as basis to derive an articulated spine model that is represented by consecutive sets of intervertebral articulations relative to rotation and translation parameters (6 degrees of freedom). Inference with respect to the model parameters is then performed using an integrated and interconnected Markov Random Field graph that involves singleton and pairwise costs. Singleton potentials measure the support from the data (surface or image-based) with respect to the model parameters, while pairwise constraints encode geometrical dependencies between vertebrae. Optimization of model parameters in a multi-modal context is achieved using efficient linear programming and duality. We show successful image registration results from simulated and real data experiments aimed for image-guidance fusion.\",\n \"Title: Approximated Curvature Penalty in Non-rigid Registration Using Pairwise MRFs\\nYear: 2009\\n\\nAbstract: Labeling of discrete Markov Random Fields (MRFs) has become an attractive approach for solving the problem of non-rigid image registration. Here, regularization plays an important role in order to obtain smooth deformations for the inherent ill-posed problem. Smoothness is achieved by penalizing the derivatives of the displacement field. However, efficient optimization strategies (based on iterative graph-cuts) are only available for second-order MRFs which contain cliques of size up to two. Higher-order cliques require graph modifications and insertion of auxiliary nodes, while pairwise interactions actually allow only regularization based on the first-order derivatives. In this paper, we propose an approximated curvature penalty using second-order derivatives defined on the MRF pairwise potentials. In our experiments, we demonstrate that our approximated term has similar properties as higher-order approaches (invariance to linear transformations), while the computational efficiency of pairwise models is preserved.\",\n \"Title: Image reconstruction using particle filters and multiple hypotheses testing.\\nYear: 2010\\n\\nAbstract: In this paper, we introduce a reconstruction framework that explicitly accounts for image geometry when defining the spatial interaction between pixels in the filtering process. To this end, image structure is captured using local co-occurrence statistics and is incorporated to the enhancement algorithm in a sequential fashion using the particle filtering technique. In this context, the reconstruction process is modeled using a dynamical system with multiple states and its evolution is guided by the prior density describing the image structure. Towards optimal exploration of the image geometry, an evaluation process of the state of the system is performed at each iteration. The resulting framework explores optimally spatial dependencies between image content towards variable bandwidth image reconstruction. Promising results using additive noise models demonstrate the potentials of such an explicit modeling of the geometry.\",\n \"Title: Simultaneous geometric--iconic registration.\\nYear: 2010\\n\\nAbstract: In this paper, we introduce a novel approach to bridge the gap between the landmark-based and the iconic-based voxel-wise registration methods. The registration problem is formulated with the use of Markov Random Field theory resulting in a discrete objective function consisting of thee parts. The first part of the energy accounts for the iconic-based volumetric registration problem while the second one for establishing geometrically meaningful correspondences by optimizing over a set of automatically generated mutually salient candidate pairs of points. The last part of the energy penalizes locally the difference between the dense deformation field due to the iconic-based registration and the implied displacements due to the obtained correspondences. Promising results in real MR brain data demonstrate the potentials of our approach.\",\n \"Title: Learning deformation and structure simultaneously: in situ endograft deformation analysis.\\nYear: 2011\\n\\nAbstract: The learning of the shape and appearance behavior of complex anatomical structures is of growing importance in the successful use of medical imaging data. We propose a method to simultaneously learn a model of shape variation and the behavioral structure of objects in volumetric data sets. The algorithm performs a group-wise registration of a set of examples, and accounts for the heterogeneous deformation or variability properties of the data. We use the method for the in situ analysis of endograft deformation in the thoracic aorta during the cardiac cycle. The method is based on an emerging model of the shape variation, which is learned autonomously from a gated computed tomography sequence. It automatically adapts to the highly non-uniform elasticity properties of the structure during learning. The resulting deformation model is used for the measurement of global and local characteristics of the endograft movement. The method allows for the in situ localization of the stent during the cardiac cycle, and the measurement of its deformation. Furthermore, it makes the comparison of different endograft designs possible, and can serve as a basis for fitting a physical model of the endograft- and vessel surface to individual patients. The latter is essential for long-term risk assessment of the impact of endografts in highly mobile areas. We evaluate the approach on 10 data sets from patients that underwent endograft placement after traumatic ruptures of the thoracic aorta.\",\n \"Title: Markov Random Field-based fitting of a subdivision-based geometric atlas\\nYear: 2011\\n\\nAbstract: An accurate labeling of a multi-part, complex anatomical structure (e.g., brain) is required in order to compare data across images for spatial analysis. It can be achieved by fitting an object-specific geometric atlas that is constructed using a partitioned, high-resolution deformable mesh and tagging each of its polygons with a region label. Subdivision meshes have been used to construct such an atlas because they can provide a compact representation of a partitioned, multi-resolution, object-specific mesh structure using only a few control points. However, automated fitting of a subdivision mesh-based geometric atlas to an anatomical structure in an image is a difficult problem and has not been sufficiently addressed. In this paper, we propose a novel Markov Random Field-based method for fitting a planar, multi-part subdivision mesh to anatomical data. The optimal fitting of the atlas is obtained by determining the optimal locations of the control points. We also tackle the problem of landmark matching in tandem with atlas fitting by constructing a single graphical model to impose pose-invariant, landmark-based geometric constraints on atlas deformation. The atlas deformation is also governed by additional constraints imposed by the mesh's geometric properties and the object boundary. We demonstrate the potential of the proposed method on the difficult problem of segmenting a mouse brain and its interior regions in gene expression images which exhibit large intensity and shape variability. We obtain promising results when compared with manual annotations and prior methods.\",\n \"Title: Random walks, constrained multiple hypothesis testing and image enhancement\\nYear: 2006\\n\\nAbstract: Image restoration is a keen problem of low level vision. In this paper, we propose a novel – assumption-free on the noise model – technique based on random walks for image enhancement. Our method explores multiple neighbors sets (or hypotheses) that can be used for pixel denoising, through a particle filtering approach. This technique associates weights for each hypotheses according to its relevance and its contribution in the denoising process. Towards accounting for the image structure, we introduce perturbations based on local statistical properties of the image. In other words, particle evolution are controlled by the image structure leading to a filtering window adapted to the image content. Promising experimental results demonstrate the potential of such an approach.\",\n \"Title: Detecting Multiple Moving Targets Using Deformable Contours\\nYear: 1997\\n\\nAbstract: This papers presents a framework for detecting multiple moving moving objects in a sequence of images. Using a statistical approach, where the inter-frame difference is modeled by a mixture of two Laplacian distributions and a deformable contour-based energy minimization approach, we reformulate the motion detection problem as a front propagation problem. Following the work of geodesic active contours [2], we transform the detection of moving objects problem into an equivalent problem of geodesic computation, which is solved using a level set formulation scheme. To reduce the computational cost required by a direct implementation of the formulation scheme [5] the Narrow Band technique is used. In order to further reduce the CPU time, a multi-scale approach has also been considered. Very promising experimental results are provided using real video sequences.\",\n \"Title: Shape Priors And Discrete Mrfs For Knowledge-Based Segmentation\\nYear: 2009\\n\\nAbstract: In this paper we introduce a new approach to knowledge-based segmentation. Our method consists of a novel representation to model shape variations as well as an efficient inference procedure to fit the model to new data. The considered shape model is similarity-invariant and refers to an incomplete graph that consists of intra and intercluster connections representing the inter-dependencies of control points. The clusters are determined according to the co-dependencies of the deformations of the control points within the training set. The connections between the components of a cluster represent the local structure while the connections between the clusters account for the global structure. The distributions of the normalized distances between the connected control points encode the prior model. During search, this model is used together with a discrete markov random field (MRF) based segmentation, where the unknown variables are the positions of the control points in the image domain. To encode the image support, a Voronoi decomposition of the domain is considered and regional based statistics are used. The resulting model is computationally efficient, can encode complex statistical models of shape variations and benefits from the image support of the entire spatial domain.\",\n \"Title: Modelling Shapes with Uncertainties: Higher Order Polynomials, Variable Bandwidth Kernels and Non Parametric Density Estimation\\nYear: 2005\\n\\nAbstract: We propose a novel approach for shape-based segmentation based on a specially designed level set function format. This format permits us to better control the process of object registration which is an important part in the shapebased segmentation framework. ...\",\n \"Title: Linear intensity-based image registration by Markov random fields and discrete optimization.\\nYear: 2010\\n\\nAbstract: We propose a framework for intensity-based registration of images by linear transformations, based on a discrete Markov random field (MRF) formulation. Here, the challenge arises from the fact that optimizing the energy associated with this problem requires a high-order MRF model. Currently, methods for optimizing such high-order models are less general, easy to use, and efficient, than methods for the popular second-order models.\",\n \"Title: A Variational Approach for the Segmentation of the Left Ventricle in Cardiac Image Analysis\\nYear: 2002\\n\\nAbstract: In this paper we propose a level set method to segment MR cardiac images. Our approach is based on a coupled propagation of two cardiac contours and integrates visual information with anatomical constraints. The visual information is expressed through a gradient vector flow-based boundary component and a region term that aims at best separating the cardiac contours/regions according to their global intensity properties. In order to deal with misleading visual support, an anatomical constraint is considered that couples the propagation of the cardiac contours according to their relative distance. The resulting motion equations are implemented using a level set approach and a fast and stable numerical approximation scheme, the Additive Operator Splitting. Encouraging experimental results are provided using real data.\",\n \"Title: Variable Bandwidth Image Denoising Using Image-Based Noise Models\\nYear: 2007\\n\\nAbstract: This paper introduces a variational formulation for image denoising based on a quadratic function over kernels of variable bandwidth. These kernels are scale adaptive and reflect spatial and photometric similarities between pixels. The bandwidth of the kernels is observation-dependent towards improving the accuracy of the reconstruction process and is constrained to be locally smooth. We analyze the evolution of the noise model form the RAW space to the RGB one, by propagating it over the image formation process. The experimental results demonstrate that the use of a variable bandwidth approach and an image intensity dependent noise variance ensures better restoration quality.\",\n \"Title: Beyond Loose LP-Relaxations: Optimizing MRFs by Repairing Cycles\\nYear: 2008\\n\\nAbstract: This paper presents a new MRF optimization algorithm, which is derived from Linear Programming and manages to go beyond current state-of-the-art techniques (such as those based on graph-cuts or belief propagation). It does so by relying on a much tighter class of LP-relaxations, called cycle-relaxations. With the help of this class of relaxations, our algorithm tries to deal with a difficulty lying at the heart of MRF optimization: the existence of inconsistent cycles. To this end, it uses an operation called cycle-repairing. The goal of that operation is to fix any inconsistent cycles that may appear during optimization, instead of simply ignoring them as usually done up to now. The more the repaired cycles, the tighter the underlying LP relaxation becomes. As a result of this procedure, our algorithm is capable of providing almost optimal solutions even for very general MRFs with arbitrary potentials. Experimental results verify its effectiveness on difficult MRF problems, as well as its better performance compared to the state of the art.\",\n \"Title: Task-specific functional brain geometry from model maps.\\nYear: 2008\\n\\nAbstract: In this paper we propose model maps to derive and represent the intrinsic functional geometry of a brain from functional magnetic resonance imaging (fMRI) data for a specific task. Model maps represent the coherence of behavior of individual fMRI-measurements for a set of observations, or a time sequence. The maps establish a relation between individual positions in the brain by encoding the blood oxygen level dependent (BOLD) signal over a time period in a Markov chain. They represent this relation by mapping spatial positions to a new metric space, the model map. In this map the Euclidean distance between two points relates to the joint modeling behavior of their signals and thus the co-dependencies of the corresponding signals. The map reflects the functional as opposed to the anatomical geometry of the brain. It provides a quantitative tool to explore and study global and local patterns of resource allocation in the brain. To demonstrate the merit of this representation, we report quantitative experimental results on 29 fMRI time sequences, each with sub-sequences corresponding to 4 different conditions for two groups of individuals. We demonstrate that drug abusers exhibit lower differentiation in brain interactivity between baseline and reward related tasks, which could not be quantified until now.\",\n \"Title: Image transport regression using mixture of experts and discrete Markov random fields\\nYear: 2010\\n\\nAbstract: The registration of multi-modal images is the process of finding a transformation which maps one image to the other according to a given similarity metric. In this paper, we introduce a novel approach for metric learning, aiming to address highly non functional correspondences through the integration of statistical regression and multi-label classification. We developed a position-invariant method that models the variations of intensities through the use of linear combinations of kernels that are able to handle intensity shifts. Such transport functions are considered as the singleton potentials of a Markov Random Field (MRF) where pair-wise connections encode smoothness as well as prior knowledge through a local neighborhood system. We use recent advances in the field of discrete optimization towards recovering the lowest potential of the designed cost function. Promising results on real data demonstrate the potentials of our approach.\",\n \"Title: Primal/dual linear programming and statistical atlases for cartilage segmentation.\\nYear: 2007\\n\\nAbstract: In this paper we propose a novel approach for automatic segmentation of cartilage using a statistical atlas and efficient primal/dual linear programming. To this end, a novel statistical atlas construction is considered from registered training examples. Segmentation is then solved through registration which aims at deforming the atlas such that the conditional posterior of the learned (atlas) density is maximized with respect to the image. Such a task is reformulated using a discrete set of deformations and segmentation becomes equivalent to finding the set of local deformations which optimally match the model to the image. We evaluate our method on 56 MRI data sets (28 used for the model and 28 used for evaluation) and obtain a fully automatic segmentation of patella cartilage volume with an overlap ratio of 0.84 with a sensitivity and specificity of 94.06% and 99.92%, respectively.\",\n \"Title: Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation\\nYear: 2002\\n\\nAbstract: This paper presents a novel variational framework to deal with frame partition problems in Computer Vision. This framework exploits boundary and region-based segmentation modules under a curve-based optimization objective function. The task of supervised texture segmentation is considered to demonstrate the potentials of the proposed framework. The textured feature space is generated by filtering the given textured images using isotropic and anisotropic filters, and analyzing their responses as multi-component conditional probability density functions. The texture segmentation is obtained by unifying region and boundary-based information as an improved Geodesic Active Contour Model. The defined objective function is minimized using a gradient-descent method where a level set approach is used to implement the obtained PDE. According to this PDE, the curve propagation towards the final solution is guided by boundary and region-based segmentation forces, and is constrained by a regularity force. The level set implementation is performed using a fast front propagation algorithm where topological changes are naturally handled. The performance of our method is demonstrated on a variety of synthetic and real textured frames.\",\n \"Title: Sparse Classification with MRI Based Markers for Neuromuscular Disease Categorization\\nYear: 2013\\n\\nAbstract: In this paper, we present a novel method for disease classification between two patient populations based on features extracted from Magnetic Resonance Imaging (MRI) data. Anatomically meaningful features are extracted from structural data (T1- and T2-weighted MR images) and Diffusion Tensor Imaging (DTI) data, and used to train a new machine learning algorithm, the k-support SVM (ksup-SVM). The k-support regularized SVM has an inherent feature selection property, and thus it eliminates the requirement for a separate feature selection step. Our dataset consists of patients that suffer from facioscapulohumeral muscular dystrophy (FSH) and Myotonic muscular dystrophy type 1 (DM1) and our proposed method achieves a high performance. More specifically, it achieves a mean Area Under the Curve (AUC) of 0.7141 and mean accuracy 77%ä±0.013. Moreover, we provide a sparsity visualization of the features in order to indentify their discriminative value. The results suggest the potential of the combined use of MR markers to diagnose myopathies, and the general utility of the ksup-SVM. Source code is also available at <ExternalRef> <RefSource> <Emphasis FontCategory=\\\\\\\"NonProportional\\\\\\\"https://gitorious.org/ksup-svm</Emphasis> </RefSource> <RefTarget Address=\\\\\\\"https://gitorious.org/ksup-svm\\\\\\\" TargetType=\\\\\\\"URL\\\\\\\"/ </ExternalRef>.\",\n \"Title: DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting\\nYear: 2011\\n\\nAbstract: A general-purpose deformable registration algorithm referred to as “DRAMMS” is presented in this paper. DRAMMS bridges the gap between the traditional voxel-wise methods and landmark/feature-based methods with primarily two contributions. First, DRAMMS renders each voxel relatively distinctively identifiable by a rich set of attributes, therefore largely reducing matching ambiguities. In particular, a set of multi-scale and multi-orientation Gabor attributes are extracted and the optimal components are selected, so that they form a highly distinctive morphological signature reflecting the anatomical and geometric context around each voxel. Moreover, the way in which the optimal Gabor attributes are constructed is independent of the underlying image modalities or contents, which renders DRAMMS generally applicable to diverse registration tasks. A second contribution of DRAMMS is that it modulates the registration by assigning higher weights to those voxels having higher ability to establish unique (hence reliable) correspondences across images, therefore reducing the negative impact of those regions that are less capable of finding correspondences (such as outlier regions). A continuously-valued weighting function named “mutual-saliency” is developed to reflect the matching uniqueness between a pair of voxels implied by the tentative transformation. As a result, voxels do not contribute equally as in most voxel-wise methods, nor in isolation as in landmark/feature-based methods. Instead, they contribute according to the continuously-valued mutual-saliency map, which dynamically evolves during the registration process. Experiments in simulated images, inter-subject images, single-/multi-modality images, from brain, heart, and prostate have demonstrated the general applicability and the accuracy of DRAMMS.\",\n \"Title: Landmark/Image-based Deformable Registration of Gene Expression Data.\\nYear: 2011\\n\\nAbstract: Analysis of gene expression patterns in brain images obtained from high-throughput in situ hybridization requires accurate and consistent annotations of anatomical regions/subregions. Such annotations are obtained by mapping an anatomical atlas onto the gene expression images through intensity- and/or landmark-based registration methods or deformable model-based segmentation methods. Due to the complex appearance of the gene expression images, these approaches require a pre-processing step to determine landmark correspondences in order to incorporate landmark-based geometric constraints. In this paper, we propose a novel method for landmark-constrained, intensity-based registration without determining landmark correspondences a priori. The proposed method performs dense image registration and identifies the landmark correspondences, simultaneously, using a single higher-order Markov Random Field model. In addition, a machine learning technique is used to improve the discriminating properties of local descriptors for landmark matching by projecting them in a Hamming space of lower dimension. We qualitatively show that our method achieves promising results and also compares well, quantitatively, with the expert's annotations, outperforming previous methods.\",\n \"Title: Linear image registration through MRF optimization\\nYear: 2009\\n\\nAbstract: We propose a Markov Random Field formulation for the linear image registration problem. Transformation parameters are represented by nodes in a fully connected graph where the edges model pairwise dependencies. Parameter estimation is then solved through iterative discrete labeling and discrete optimization while a label space refinement strategy is employed to achieve sub-millimeter accuracy. Our framework can encode any similarity measure, allows for automatic reduction of the degrees of freedom by simple changes on the MRF topology, and is robust to initialization. Promising results on real data and random studies demonstrate the potential of our approach.\",\n \"Title: Higher order polynomials, free form deformations and optical flow estimation\\nYear: 2005\\n\\nAbstract: In this paper, we propose a novel technique to represent and recover optical flow through free form deformations. Such a technique is based on representing the motion field using regular connected grids according to higher order polynomials, a compromise between dense motion estimation and parametric motion models. Optical flow is determined through the deformation of the grid - derived from the optimization of a cost function - and consequently of the underlying image structures towards satisfying the constant brightness constraint. Smoothness conditions are implicitly accounted for through the free form deformation approach. Promising results demonstrate the potentials of our approach.\",\n \"Title: Guide-wire extraction through perceptual organization of local segments in fluoroscopic images.\\nYear: 2010\\n\\nAbstract: Segmentation of surgical devices in fluoroscopic images and in particular of guide-wires is a valuable element during surgery. In cardiac angioplasty, the problem is particularly challenging due to the following reasons: (i) low signal to noise ratio, (ii) the use of 2D images that accumulate information from the whole volume, and (iii) the similarity between the structure of interest and adjacent anatomical structures. In this paper we propose a novel approach to address these challenges, that combines efficiently low-level detection using machine learning techniques, local unsupervised clustering detections and finally high-level perceptual organization of these segments towards its complete reconstruction. The latter handles miss-detections and is based on a local search algorithm. Very promising results were obtained.\",\n \"Title: A variational approach to monocular hand-pose estimation\\nYear: 2010\\n\\nAbstract: In this paper, we propose a model-based approach to recover 3D hand pose from 2D images. To this end, we describe the hand structure using a compact 3D articulated model and reformulate pose estimation as a binary image segmentation problem aiming to separate the hand from the background. We propose generative models for hand and background pixels leading to a log-likelihood objective function which aims to enclose hand-like pixels within the projected silhouette of the 3D model while excluding background-like pixels. Segmentation and hand-pose estimation are jointly addressed through the minimization of a single likelihood function. Pose is determined through gradient descent in the hand parameter space of such an area-based objective function. Furthermore, we propose a new constrained variable metric gradient descent to speed up convergence and finally the so called smart particle filter to deal with occlusions and local minima through multiple hypotheses. Promising experimental results demonstrate the potentials of our approach.\",\n \"Title: Atlas-based deformable mutual population segmentation\\nYear: 2009\\n\\nAbstract: Segmentation is one of the most critical problems in medical imaging. State-of-the art methods often are based on prior knowledge that can either encode geometry, appearance or both. Despite enormous work in the field, the mainstream is based on the concept of case-to-case segmentation. In this paper, we introduce the notion of mutual population segmentation using discrete optimization where results from a given example influence results for the rest of the examples towards improving the overall segmentation performance. The aim is to combine prior knowledge along with consistency through the simultaneous segmentation of the whole population. This is achieved through their mutual deformation towards the atlas, while being constrained through a simultaneous all-to-all deformable diffeomorphic registration. Promising results demonstrate the potentials of the method.\",\n \"Title: Discriminative parameter estimation for random walks segmentation.\\nYear: 2013\\n\\nAbstract: The Random Walks (RW) algorithm is one of the most efficient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate segmentations of medical images in a fully automated manner. However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned. we propose a novel discriminative learning framework that estimates the parameters using a training dataset. The main challenge we face is that the training samples are not fully supervised. Specifically, they provide a hard segmentation of the images, instead of a probabilistic segmentation. We overcome this challenge by treating the optimal probabilistic segmentation that is compatible with the given hard segmentation as a latent variable. This allows us to employ the latent support vector machine formulation for parameter estimation. We show that our approach significantly outperforms the baseline methods on a challenging dataset consisting of real clinical 3D MRI volumes of skeletal muscles.\",\n \"Title: A Generic Deformation Model for Dense Non-rigid Surface Registration: A Higher-Order MRF-Based Approach\\nYear: 2013\\n\\nAbstract: We propose a novel approach for dense non-rigid 3D surface registration, which brings together Riemannian geometry and graphical models. To this end, we first introduce a generic deformation model, called Canonical Distortion Coefficients (CDCs), by characterizing the deformation of every point on a surface using the distortions along its two principle directions. This model subsumes the deformation groups commonly used in surface registration such as isometry and conformality, and is able to handle more complex deformations. We also derive its discrete counterpart which can be computed very efficiently in a closed form. Based on these, we introduce a higher-order Markov Random Field (MRF) model which seamlessly integrates our deformation model and a geometry/texture similarity metric. Then we jointly establish the optimal correspondences for all the points via maximum a posteriori (MAP) inference. Moreover, we develop a parallel optimization algorithm to efficiently perform the inference for the proposed higher-order MRF model. The resulting registration algorithm outperforms state-of-the-art methods in both dense non-rigid 3D surface registration and tracking.\",\n \"Title: Particle filters, a quasi-Monte-Carlo-solution for segmentation of coronaries.\\nYear: 2005\\n\\nAbstract: In this paper we propose a Particle Filter-based approach for the segmentation of coronary arteries. To this end, successive planes of the vessel are modeled as unknown states of a sequential process. Such states consist of the orientation, position, shape model and appearance (in statistical terms) of the vessel that are recovered in an incremental fashion, using a sequential Bayesian filter (Particle Filter). In order to account for bifurcations and branchings, we consider a Monte Carlo sampling rule that propagates in parallel multiple hypotheses. Promising results on the segmentation of coronary arteries demonstrate the potential of the proposed approach.\",\n \"Title: Graphical models and deformable diffeomorphic population registration using global and local metrics.\\nYear: 2009\\n\\nAbstract: In this paper we propose a novel framework to unite a population to an optimal (unknown) pose through their mutual deformation. The registration criterion comprises three terms, the first imposes compactness on appearance of the registered population at the pixel level, the second tries to minimize the individual distances between all possible pairs of images, while the last is a regularization one imposing smoothness on the deformation fields. The problem is reformulated as a graphical model that consists of hidden (deformation fields) and observed variables (intensities). A novel deformation grid-based scheme is proposed that guarantees the diffeomorphism of the deformation and is computationally favorably compared to standard deformation methods. Towards addressing important deformations we propose a compositional approach where the deformations are recovered through the sub-optimal solutions of successive discrete MRFs by using efficient linear programming. Promising experimental results using real 2D data demonstrate the potentials of our approach.\",\n \"Title: Motion-based enhancement of optical imaging\\nYear: 2009\\n\\nAbstract: Optical imaging offers the possibility to study and image biology at molecular scale in small animal with applications such as oncology or gene expression studies. New device enable cinematic acquisitions but in some cases signals could suffer from weak spatial information. The aim of this paper is to improve localization of the luminescent data for a freely moving animal using motion information obtained with an additional camera. To this end, we propose an approach that performs a non-rigid registration in the scene video and use the corresponding motion vectors to filter the optical one. Due to the lack of contrast and texture, our method introduces silhouette constraints and landmarks on the mouse skin within a variation framework. Motion is represented using thin-platesplines and the objective function is optimized using a gradient conjugate descent approach. Experimental results demonstrate the potentials of the proposed framework.\",\n \"Title: Joint Object Segmentation And Behavior Classification In Image Sequences\\nYear: 2007\\n\\nAbstract: In this paper, we propose a general framework for fusing bottom-up segmentation with top-down object behavior classification over an image sequence. This approach is beneficial for both tasks, since it enables them to cooperate so that knowledge relevant to each can aid in the resolution of the other, thus enhancing the final result. In particular classification offers dynamic probabilistic priors to guide segmentation, while segmentation supplies its results to classification, ensuring that they are consistent both with prior knowledge and with new image information. We demonstrate the effectiveness of our framework via a particular implementation for a hand gesture recognition application. The prior models are learned from training data using principal components analysis and they adapt dynamically to the content of new images. Our experimental results illustrate the robustness of our joint approach to segmentation and behavior classification in challenging conditions involving occlusions of the target object before a complex background.\",\n \"Title: Random Exploration of the Procedural Space for Single-View 3D Modeling of Buildings\\nYear: 2011\\n\\nAbstract: In this paper we tackle the problem of 3D modeling for urban environment using a modular, flexible and powerful approach driven from procedural generation. To this end, typologies of architectures are modeled through shape grammars that consist of a set of derivation rules and a set of shape/dictionary elements. Appearance (from statistical point of view with respect to the individual pixel's properties) of the dictionary elements is then learned using a set of training images. Image classifiers are trained towards recovering image support with respect to the semantics. Then, given a new image and the corresponding footprint, the modeling problem is formulated as a search of the space of shapes, that can be generated on-the-fly by deriving the grammar on the input axiom. Defining an image-based score function for the produced instances using the trained classifiers, the best rules are selected, making sure that we keep exploring the space by allowing some rules to be randomly selected. New rules are then generated by resampling around the selected rules. At the finest level, these rules define the 3D model of the building. Promising results on complex and varying architectural styles demonstrate the potential of the presented method.\",\n \"Title: An Application of Graph Commute Times to Image Indexing\\nYear: 2008\\n\\nAbstract: In this paper we provide an overview of an image represen- tation approach based on the description of layout and ap- pearance properties of groups of features. In each image a graph of quantized features of interest is constructed. The features that are assigned to the same codebook bin are then grouped to produce a collapsed graph; the image contentn is represented by the matrix of commute times of this collapsed graph. This novel image descriptor can be used to label satellite image databases; we demonstrate the relevance and the effi- ciency of our approach by addressing classification problem s on a dataset of 0.6m resolution Quickbird images.\",\n \"Title: A convex semi-definite positive framework for DTI estimation and regularization\\nYear: 2007\\n\\nAbstract: In this paper we introduce a novel variational method for joint estimation and regularization of diffusion tensor fields from noisy raw data. To this end, we use the classic quadratic data fidelity term derived from the Stejskal-Tanner equation with a new smoothness term leading to a convex objective function. The regularization term is based on the assumption that the signal can be reconstructed using a weighted average of observations on a local neighborhood. The weights measure the similarity between tensors and are computed directly from the diffusion images. We preserve the positive semi-definiteness constraint using a projected gradient descent. Experimental validation and comparisons with a similar method using synthetic data with known noise model, as well as classification of tensors towards understanding the anatomy of human skeletal muscle demonstrate the potential of our method.\",\n \"Title: Uniform and textured regions separation in natural images towards MPM adaptive denoising\\nYear: 2007\\n\\nAbstract: Natural images consist of texture, structure and smooth regions and this makes the task of filtering challenging mainly when it aims at edge and texture preservation. In this paper, we present a novel adaptive filtering technique based on a partition of the image to \\\"noisy smooth zones\\\" and \\\"texture or edge + noise\\\" zones. To this end, an analysis of local features is used to recover a statistical model that associates to each pixel a probability measure corresponding to a membership degree for each class. This probability function is then encoded in a new denoising process based on a MPM (Marginal Posterior Mode) estimation technique. The posterior density is computed through a non parametric density estimation method with variable kernel bandwidth that aims to adapt the denoising process to image structure. In our algorithm the selection of the bandwidth relies on a non linear function of the membership probabilities. Encouraging, experimental results demonstrate the potential of our approach.\",\n \"Title: MRI Composing for Whole Body Imaging\\nYear: 2009\\n\\nAbstract: Whole-body magnetic resonance imaging is an emerging ap- plication gaining vast clinical interest during the last years. Although recent technological advances shortened the longish acquisition time, this is still the limiting factor avoiding its wide-spread clinical usage. The ac- quisition of images with large fleld-of-view helps to relieve this drawback, but leads to signiflcantly distorted images. In this paper, a novel scheme for MRI composing is presented. The approach is based on simultaneous registration of two MRI volumes to their linear weighted average. The method successfully compensates for the distortions and allows to gener- ate high-resolution whole body images. Results on several in-vivo data sets are presented.\",\n \"Title: Model-Based 3D Hand Pose Estimation from Monocular Video\\nYear: 2011\\n\\nAbstract: A novel model-based approach to 3D hand tracking from monocular video is presented. The 3D hand pose, the hand texture, and the illuminant are dynamically estimated through minimization of an objective function. Derived from an inverse problem formulation, the objective function enables explicit use of temporal texture continuity and shading information while handling important self-occlusions and time-varying illumination. The minimization is done efficiently using a quasi-Newton method, for which we provide a rigorous derivation of the objective function gradient. Particular attention is given to terms related to the change of visibility near self-occlusion boundaries that are neglected in existing formulations. To this end, we introduce new occlusion forces and show that using all gradient terms greatly improves the performance of the method. Qualitative and quantitative experimental results demonstrate the potential of the approach.\",\n \"Title: Manifold-Enhanced Segmentation Through Random Walks On Linear Subspace Priors\\nYear: 2012\\n\\nAbstract: In this paper we propose a novel method for knowledge-based segmentation. Our contribution lies on the introduction of linear sub-spaces constraints within the random-walk segmentation framework. Prior knowledge is obtained through principal component analysis that is then combined with conventional boundary constraints for image segmentation. The approach is validated on a challenging clinical setting that is multicomponent segmentation of the human upper leg skeletal muscle in Magnetic Resonance Imaging, where there is limited visual differentiation support between muscle classes.\",\n \"Title: Uncertainty-Driven non-parametric knowledge-based segmentation: the corpus callosum case\\nYear: 2005\\n\\nAbstract: In this paper we propose a novel variational technique for the knowledge based segmentation of two dimensional objects. One of the elements of our approach is the use of higher order implicit polynomials to represent shapes. The most important contribution is the estimation of uncertainties on the registered shapes, which can be used with a variable bandwidth kernel-based non-parametric density estimation process to model prior knowledge about the object of interest. Such a non-linear model with uncertainty measures is integrated with an adaptive visual-driven data term that aims to separate the object of interest from the background. Promising results obtained for the segmentation of the corpus callosum in MR mid-sagittal brain slices demonstrate the potential of such a framework.\",\n \"Title: Boosted Metric Learning For 3d Multi-Modal Deformable Registration\\nYear: 2011\\n\\nAbstract: Defining a suitable metric is one of the biggest challenges in deformable image fusion from different modalities. In this paper, we propose a novel approach for multi-modal metric learning in the deformable registration framework that consists of embedding data from both modalities into a common metric space whose metric is used to parametrize the similarity. Specifically, we use image representation in the Fourier/Gabor space which introduces invariance to the local pose parameters, and the Hamming metric as the target embedding space, which allows constructing the embedding using boosted learning algorithms. The resulting metric is incorporated into a discrete optimization framework. Very promising results demonstrate the potential of the proposed method.\",\n \"Title: Corrections to 'Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects'\\nYear: 2000\\n\\nAbstract: First Page of the Article\",\n \"Title: Knowledge-based Registration & Segmentation of the Left Ventricle: A Level Set Approach\\nYear: 2002\\n\\nAbstract: In this paper, we propose a level set formulation to dealwith the segmentation and registration of the left ventriclein Magnetic Resonance (MR) images. Our approach isbased on the integration of visual information, anatomicalconstraints and a flexible shape-driven cardiac model. Thevisual information is expressed through an intensity-basedgrouping module. The anatomical constraint accounts forthe relative positions of the structures of interest. Globalshape consistency is introduced by seeking for the lowestpotential of the distance between the solution and the priormodel. Registration is obtained using the same criterionwhere the transformation that aligns the latest segmentationmap to either the shape model or to the previous segmentationresult (temporal domain) is to be recovered.\",\n \"Title: Compressed sensing dynamic reconstruction in rotational angiography.\\nYear: 2012\\n\\nAbstract: This work tackles three-dimensional reconstruction of tomographic acquisitions in C-arm-based rotational angiography. The relatively slow rotation speed of C-arm systems involves motion artifacts that limit the use of three-dimensional imaging in interventional procedures. The main contribution of this paper is a reconstruction algorithm that deals with the temporal variations due to intra-arterial injections. Based on a compressed-sensing approach, we propose a multiple phase reconstruction with spatio-temporal constraints. The algorithm was evaluated by qualitative and quantitative assessment of image quality on both numerical phantom experiments and clinical data from vascular C-arm systems. In this latter case, motion artifacts reduction was obtained in spite of the cone-beam geometry, the short-scan acquisition, and the truncated and subsampled data.\",\n \"Title: Registration with uncertainties and statistical modeling of shapes with variable metric kernels.\\nYear: 2009\\n\\nAbstract: Registration and modeling of shapes are two important problems in computer vision and pattern recognition. Despite enormous progress made over the past decade, these problems are still open. In this paper, we advance the state of the art in both directions. First we consider an efficient registration method that aims to recover a one-to-one correspondence between shapes and introduce measures of uncertainties driven from the data which explain the local support of the recovered transformations. To this end, a free form deformation is used to describe the deformation model. The transformation is combined with an objective function defined in the space of implicit functions used to represent shapes. Once the registration parameters have been recovered, we introduce a novel technique for model building and statistical interpretation of the training examples based on a variable bandwidth kernel approach. The support on the kernels varies spatially and is determined according to the uncertainties of the registration process. Such a technique introduces the ability to account for potential registration errors in the model. Hand-written character recognition and knowledge-based object extraction in medical images are examples of applications that demonstrate the potentials of the proposed framework.\",\n \"Title: Graph-based multi-resolution temporal-based face reconstruction\\nYear: 2006\\n\\nAbstract: Reproducing high quality facial expressions is an important challenge in human-computer interaction. Laser-scanners offer an expensive solution to such a problem with image based alternatives being a low-resolution alternative. In this paper, we propose a new method for stereo reconstruction from multiple video pairs that is capable of producing high resolution facial models. To this end, a combinatorial optimization approach is considered and is coupled in time to produce high resolution depth maps. Such optimization is addressed with the use of graph-cuts leading to precise reconstruction of facial expressions that can then be used for animation.\",\n \"Title: Image contextual representation and matching through hierarchies and higher order graphs\\nYear: 2012\\n\\nAbstract: We present a region matching algorithm which establishes correspondences between regions from two segmented images. An abstract graph-based representation conceals the image in a hierarchical graph, exploiting the scene properties at two levels. First, the similarity and spatial consistency of the image semantic objects is encoded in a graph of commute times. Second, the cluttered regions of the semantic objects are represented with a shape descriptor. Many-to-many matching of regions is specially challenging due to the instability of the segmentation under slight image changes, and we explicitly handle it through high order potentials. We demonstrate the matching approach applied to images of world famous buildings, captured under different conditions, showing the robustness of our method to large variations in illumination and viewpoint.\",\n \"Title: Uncalibrated Stereo Rectification For Automatic 3d Surveillance\\nYear: 2004\\n\\nAbstract: We describe a stereo rectification method suitable for automatic 3D surveillance. We take advantage of the fact that in a typical urban scene. there is ordinarily a small number of dominant planes. Given two views of the scene, we align a dominant plane in one view with the other. Conjugate epipolar lines between the reference view and plane-aligned image become geometrically identical and can be added to the rectified image pair line by line. Selecting conjugate epipolar lines to cover the whole image is simplified Since they are geometrically identical. In addition, the polarities of conjugate epipolar lines are automatically preserved by plane alignment, which simplifies stereo matching.\",\n \"Title: Fast Dichotomic Multiple Search Algorithm for Shortest Cirular Path\\nYear: 2006\\n\\nAbstract: Circular shortest path algorithms for polar objects segmentation have been proposed in [1, 11, 10] to address discrete case and extended in [2] to the continuous domain for closed global optimal geodesic calculation. The best method up to date relies on a branch and bound approach and runs in O(u1.6v) on average while O(u2v) in worst case for a u 脳 v discrete trellis warped in the direction of v. We propose an new algorithm called dichotomic multiple search (DMS) which finds the global minimum with a O(ulog2(u)v) worst case scenario complexity. Our algorithm relies on the fact that two minimal paths never cross more than once. This allows to sequentially partition the trellis in a dichotomic manner. Each computed circular minimal path with chosen starting point allows cutting the trellis into two sub trellis. The algorithm is then recursively applied on each sub trellis. Application to object segmentation is presented.\",\n \"Title: Background Modeling and Subtraction of Dynamic Scenes\\nYear: 2003\\n\\nAbstract: Background modeling and subtraction is a core componentin motion analysis. The central idea behind such moduleis to create a probabilistic representation of the staticscene that is compared with the current input to performsubtraction. Such approach is efficient when the scene to bemodeled refers to a static structure with limited perturbation.In this paper, we address the problem of modeling dynamicscenes where the assumption of a static backgroundis not valid. Waving trees, beaches, escalators, naturalscenes with rain or snow are examples. Inspired by the workproposed in [4], we propose an on-line auto-regressivemodel to capture and predict the behavior of such scenes.Towards detection of events we introduce a new metric thatis based on a state-driven comparison between the predictionand the actual frame. Promising results demonstratethe potentials of the proposed framework.\",\n \"Title: Image Renaissance Using Discrete Optimization\\nYear: 2006\\n\\nAbstract: In this paper we propose a novel technique to image completion that addresses image renaissance through a graph-based matching process. To this end, a number of candidate seeds with content similar to the one of the area to be inpainted are considered. They are selected through a particle filter method and then positioned over the missing area. Markov Random Fields are used to formalize inpainting as a labeling estimation problem while a combinatorial approach is used to recover the optimal partition of patches that completes the missing area with the á-expansion process. Promising results in image and texture completion demonstrate the potentials of the proposed method.\",\n \"Title: Editorial: Special Issue on Vision and Medical Imaging Activities at Siemens Corporate Research\\nYear: 2006\\n\\nAbstract: Without Abstract\",\n \"Title: Globally optimal active contours, sequential monte carlo and on-line learning for vessel segmentation\\nYear: 2006\\n\\nAbstract: In this paper we propose a Particle Filter-based propagation approach for the segmentation of vascular structures in 3D volumes. Because of pathologies and inhomogeneities, many deterministic methods fail to segment certain types of vessel. Statistical methods represent the solution using a probability density function (pdf). This pdf does not only indicate the best possible solution, but also valuable information about the solution's variance. Particle Filters are used to learn the variations of direction and appearance of the vessel as the segmentation goes. These variations are used in turn in the particle filters framework to control the perturbations introduced in the Sampling Importance Resampling step (SIR). For the segmentation itself, successive planes of the vessel are modeled as states of a Particle Filter. Such states consist of the orientation, position and appearance (in statistical terms) of the vessel. The shape of the vessel and subsequently the particles pdf are recovered using globally active contours, implemented using circular shortest paths by branch and bound [1] that guarantees the global optimal solution. Promising results on the segmentation of coronary arteries demonstrate the potential of the proposed approach.\",\n \"Title: Scene modeling and change detection in dynamic scenes: A subspace approach\\nYear: 2009\\n\\nAbstract: Background modeling and subtraction are core components in video processing. To this end, one aims to recover and continuously update a representation of the scene that is compared with the current input to perform subtraction. Most of the existing methods treat each pixel independently and attempt to model the background perturbation through statistical modeling such as a mixture of Gaussians. While such methods have satisfactory performance in many scenarios, they do not model the relationships and correlation amongst nearby pixels. Such correlation between pixels exists both in space and across time especially when the scene consists of image structures moving across space. Waving trees, beach, escalators and natural scenes with rain or snow are examples of such scenes. In this paper, we propose a method for differentiating between image structures and motion that are persistent and repeated from those that are ''new''. Towards capturing the appearance characteristics of such scenes, we propose the use of an appropriate subspace created from image structures. Furthermore, the dynamical characteristics are captured by the use of a prediction mechanism in such subspace. Since the model must adapt to long-term changes in the background, an incremental method for fast online adaptation of the model parameters is proposed. Given such adaptive models, robust and meaningful measures for detection that consider both structural and motion changes are considered. Promising experimental results that include qualitative and quantitative comparisons with existing background modeling/subtraction techniques demonstrate the very promising performance of the proposed framework when dealing with complex backgrounds.\",\n \"Title: Clustering of the human skeletal muscle fibers using linear programming and angular Hilbertian metrics.\\nYear: 2009\\n\\nAbstract: In this paper, we present a manifold clustering method fo the classification of fibers obtained from diffusion tensor images (DTI) of the human skeletal muscle. Using a linear programming formulation of prototype-based clustering, we propose a novel fiber classification algorithm over manifolds that circumvents the necessity to embed the data in low dimensional spaces and determines automatically the number of clusters. Furthermore, we propose the use of angular Hilbertian metrics between multivariate normal distributions to define a family of distances between tensors that we generalize to fibers. These metrics are used to approximate the geodesic distances over the fiber manifold. We also discuss the case where only geodesic distances to a reduced set of landmark fibers are available. The experimental validation of the method is done using a manually annotated significant dataset of DTI of the calf muscle for healthy and diseased subjects.\",\n \"Title: Unifying Boundary and Region-Based Information for Geodesic Active Tracking\\nYear: 1999\\n\\nAbstract: This paper addresses the problem of tracking several non-rigid objects over a sequence of frames acquired from a static observer using boundary and region-based informa- tion under a coupled geodesic active contour framework. Given the current frame, a statistical analysis is performe d on the observed difference frame which provides a mea- surement that distinguishes between the static and mobile regions in terms of conditional probabilities. An objec- tive function is defined that integrates boundary-based and region-based module by seeking curves that attract the ob- ject boundaries and maximize the a posteriori segmentation probability on the interior curve regions with respect to in - tensity and motion properties. This function is minimized using a gradient descent method. The associated Euler- Lagrange PDE is implemented using a Level-Set approach, where a very fast front propagation algorithm evolves the initial curve towards the final tracking result. Very promis - ing experimental results are provided using real video se- quences.\",\n \"Title: Automatic inference of articulated spine models in CT images using high-order Markov Random Fields.\\nYear: 2011\\n\\nAbstract: In this paper, we introduce a novel and efficient approach for inferring articulated 3D spine models from operative images. The problem is formulated as a Markov Random Field which has the ability to encode global structural dependencies to align CT volume images. A personalized geometrical model is first reconstructed from preoperative images before surgery, and subsequently decomposed as a series of intervertebral transformations based on rotation and translation parameters. The shape transformation between the standing and lying poses is achieved by optimizing the deformations applied to the intervertebral transformations. Singleton and pairwise potentials measure the support from the data and geometrical dependencies between neighboring vertebrae respectively, while higher-order cliques (groups of vertebrae) are introduced to integrate consistency in regional curves. Local vertebra modifications are achieved through a constrained mesh relaxation technique. Optimization of model parameters in a multimodal context is achieved using efficient linear programming and duality. Experimental and clinical evaluation of the vertebra model alignment obtained from the proposed method gave promising results. Quantitative comparison to expert identification yields an accuracy of 1.8±0.7mm based on the localization of surgical landmarks.\",\n \"Title: Implicit Active Shape Models for 3D Segmentation in MR Imaging\\nYear: 2004\\n\\nAbstract: Extraction of structures of interest in medical images is often an arduous task because of noisy or incomplete data. However, hand-segmented data are often available and most of the structures to be extracted have a similar shape from one subject to an other. Then, the possibility of modeling a family of shapes and restricting the new structure to be extracted within this class is of particular interest. This approach is commonly implemented using active shape models [2] and the definition of the image term is the most challenging component of such an approach. In parallel, level set methods [8] define a powerful optimization framework, that can be used to recover objects of interest by the propagation of curves or surfaces. They can support complex topologies, considered in higher dimensions, are implicit, intrinsic and parameter free. In this paper we re-visit active shape models and introduce a level set variant of them. Such an approach can account for prior shape knowledge quite efficiently as well as use data/image terms of various form and complexity. Promising results on the extraction of brain ventricles in MR images demonstrate the potential of our approach.\",\n \"Title: Matching Distance Functions: A Shape-to-Area Variational Approach for Global-to-Local Registration\\nYear: 2002\\n\\nAbstract: This paper deals with the matching of geometric shapes. Our primary contribution is the use of a simple, robust, rich and efficient way to represent shapes, the level set representations according to singed distance transforms. Based on these representations we propose a variational framework for global as well as local shape registration that can be extended to deal with structures of higher dimension. The optimization criterion is invariant to rotation, translation and scale and combines efficiently a global motion model with local pixel-wise deformations. Promising results are obtained on examples showing small and large global deformations as well as arbitrary topological changes.\",\n \"Title: Dense registration with deformation priors.\\nYear: 2009\\n\\nAbstract: In this paper we propose a novel approach to define task-driven regularization constraints in deformable image registration using learned deformation priors. Our method consists of representing deformation through a set of control points and an interpolation strategy. Then, using a training set of images and the corresponding deformations we seek for a weakly connected graph on the control points where edges define the prior knowledge on the deformation. This graph is obtained using a clustering technique which models the co-dependencies between the displacements of the control points. The resulting classification is used to encode regularization constraints through connections between cluster centers and cluster elements. Additionally, the global structure of the deformation is encoded through a fully connected graph on the cluster centers. Then, registration of a new pair of images consists of displacing the set of control points where on top of conventional image correspondence costs, we introduce costs that are based on the relative deformation of two control points with respect to the learned deformation. The resulting paradigm is implemented using a discrete Markov Random Field which is optimized using efficient linear programming. Promising experimental results on synthetic and real data demonstrate the potential of our approach.\",\n \"Title: Joint tumor segmentation and dense deformable registration of brain MR images.\\nYear: 2012\\n\\nAbstract: In this paper we propose a novel graph-based concurrent registration and segmentation framework. Registration is modeled with a pairwise graphical model formulation that is modular with respect to the data and regularization term. Segmentation is addressed by adopting a similar graphical model, using image-based classification techniques while producing a smooth solution. The two problems are coupled via a relaxation of the registration criterion in the presence of tumors as well as a segmentation through a registration term aiming the separation between healthy and diseased tissues. Efficient linear programming is used to solve both problems simultaneously. State of the art results demonstrate the potential of our method on a large and challenging low-grade glioma data set.\",\n \"Title: Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge.\\nYear: 2011\\n\\nAbstract: EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intra patient thoracic CT image pairs. Evaluation of nonrigid registration techniques is a nontrivial task. This is compounded by the fact that researchers typically test only on their own data, which varies wid...\",\n \"Title: Left ventricle segmentation using diffusion wavelets and boosting.\\nYear: 2009\\n\\nAbstract: We propose a method for the segmentation of medical images based on a novel parameterization of prior shape knowledge and a search scheme based on classifying local appearance. The method uses diffusion wavelets to capture arbitrary and continuous interdependencies in the training data and uses them for an efficient shape model. The lack of classic visual consistency in complex medical imaging data, is tackled by a manifold learning approach handling optimal high-dimensional local features by Gentle Boosting. Appearance saliency is encoded in the model and segmentation is performed through the extraction and classification of the corresponding features in a new data set, as well as a diffusion wavelet based shape model constraint. Our framework supports hierarchies both in the model and the search space, can encode complex geometric and photometric dependencies of the structure of interest, and can deal with arbitrary topologies. Promising results are reported for heart CT data sets, proving the impact of the soft parameterization, and the efficiency of our approach.\",\n \"Title: Clustering via LP-based Stabilities\\nYear: 2008\\n\\nAbstract: A novel center-based clustering algorithm is proposed in this paper. We first formulate clustering as an NP-hard linear integer program and we then use linear programming and the duality theory to derive the solution of this optimization problem. This leads to an efficient and very general algorithm, which works in the dual domain, and can cluster data based on an arbitrary set of distances. Despite its generality, it is independent of initialization (unlike EM-like methods such as K-means), has guaranteed convergence, and can also provide online optimality bounds about the quality of the estimated clustering solutions. To deal with the most critical issue in a center-based clustering algorithm (selection of cluster centers), we also introduce the notion of stability of a cluster center, which is a well defined LP-based quantity that plays a key role to our algorithm's success. Furthermore, we also introduce, what we call, the margins (another key ingredient in our algorithm), which can be roughly thought of as dual counterparts to stabilities and allow us to obtain computationally efficient approximations to the latter. Promising experimental results demonstrate the potentials of our method.\",\n \"Title: Similarity-Based appearance-prior for fitting a subdivision mesh in gene expression images\\nYear: 2012\\n\\nAbstract: Automated segmentation of multi-part anatomical objects in images is a challenging task. In this paper, we propose a similarity-based appearance-prior to fit a compartmental geometric atlas of the mouse brain in gene expression images. A subdivision mesh which is used to model the geometry is deformed using a Markov Random Field (MRF) framework. The proposed appearance-prior is computed as a function of the similarity between local patches at corresponding atlas locations from two images. In addition, we introduce a similarity-saliency score to select the mesh points that are relevant for the computation of the proposed prior. Our method significantly improves the accuracy of the atlas fitting, especially in the regions that are influenced by the selected similarity-salient points, and outperforms the previous subdivision mesh fitting methods for gene expression images.\",\n \"Title: Estimating shadows with the bright channel cue\\nYear: 2010\\n\\nAbstract: In this paper, we introduce a simple but efficient cue for the extraction of shadows from a single color image, the bright channel cue. We discuss its limitations and offer two methods to refine the bright channel: by computing confidence values for the cast shadows, based on a shadow-dependent feature, such as hue; and by combining the bright channel with illumination invariant representations of the original image in a flexible way using an MRF model. We present qualitative and quantitative results for shadow detection, as well as results in illumination estimation from shadows. Our results show that our method achieves satisfying results despite the simplicity of the approach.\",\n \"Title: Coupled Geodesic Active Regions for Image Segmentation: A Level Set Approach\\nYear: 2000\\n\\nAbstract: This paper presents a novel variational method for image segmentation that unifies boundary and region-based information sources under the Geodesic Active Region framework. A statistical analysis based on the Minimum Description Length criterion and the Maximum Likelihood Principle for the observed density function (image histogram) using a mixture of Gaussian elements, indicates the number of the different regions and their intensity properties. Then, the boundary information is determined using a probabilistic edge detector, while the region information is estimated using the Gaussian components of the mixture model. The defined objective function is minimized using a gradient-descent method where a level set approach is used to implement the resulting PDE system. According to the motion equations, the set of initial curves is propagated toward the segmentation result under the influence of boundary and region-based segmentation forces, and being constrained by a regularity force. The changes of topology are naturally handled thanks to the level set implementation, while a coupled multi-phase propagation is adopted that increases the robustness and the convergence rate by imposing the idea of mutually exclusive propagating curves. Finally, to reduce the required computational cost and the risk of convergence to local minima, a multi-scale approach is also considered. The performance of our method is demonstrated on a variety of real images.\",\n \"Title: Active shape models and segmentation of the left ventricle in echocardiography\\nYear: 2005\\n\\nAbstract: Segmentation of the left ventricle in echocardiographic images is a task with important diagnostic power. We propose a model-based approach that aims at extracting the left ventricle for each frame of the cardiac cycle. Our approach exhibits several novel elements. Modelling consists of two separate components, one for the systolic and one for the diastolic moment. Segmentation is considered in two steps. During the first step a linear combination of the systolic and the diastolic model is to be recovered – that dictates the new model – along with a similarity transformation that projects this model to the desired image features. During the second step, a linear combination of the modes of variation for the systolic and diastolic models is recovered for precise extraction of the endocardium boundaries. The process is considered in the temporal domain where constraints are introduced to couple information across frames and to lead to a smooth solution. Promising results demonstrate the potentials of the presented framework.\",\n \"Title: Dense non-rigid surface registration using high-order graph matching\\nYear: 2010\\n\\nAbstract: In this paper, we propose a high-order graph matching formulation to address non-rigid surface matching. The singleton terms capture the geometric and appearance similarities (e.g., curvature and texture) while the high-order terms model the intrinsic embedding energy. The novelty of this paper includes: 1. casting 3D surface registration into a graph matching problem that combines both geometric and appearance similarities and intrinsic embedding information, 2. the first implementation of high-order graph matching algorithm that solves a non-convex optimization problem, and 3. an efficient two-stage optimization approach to constrain the search space for dense surface registration. Our method is validated through a series of experiments demonstrating its accuracy and efficiency, notably in challenging cases of large and/or non-isometric deformations, or meshes that are partially occluded.\",\n \"Title: Data Fusion Through Cross-Modality Metric Learning Using Similarity-Sensitive Hashing\\nYear: 2010\\n\\nAbstract: Visual understanding is often based on measuring similarity between observations. Learning similarities specific to a certain perception task from a set of examples has been shown advantageous in various computer vision and pattern recognition problems. In many important applications, the data that one needs to compare come from different representations or modalities, and the similarity between such data operates on objects that may have different and often incommensurable structure and dimensionality. In this paper, we propose a framework for supervised similarity learning based on embedding the input data from two arbitrary spaces into the Hamming space. The mapping is expressed as a binary classification problem with positive and negative examples, and can be efficiently learned using boosting algorithms. The utility and efficiency of such a generic approach is demonstrated on several challenging applications including cross-representation shape retrieval and alignment of multi-modal medical images.\",\n \"Title: Non-rigid 2D-3D medical image registration using markov random fields.\\nYear: 2013\\n\\nAbstract: The aim of this paper is to propose a novel mapping algorithm between 2D images and a 3D volume seeking simultaneously a linear plane transformation and an in-plane dense deformation. We adopt a metric free locally over-parametrized graphical model that combines linear and deformable parameters within a coupled formulation on a 5-dimensional space. Image similarity is encoded in singleton terms, while geometric linear consistency of the solution (common/single plane) and in-plane deformations smoothness are modeled in a pair-wise term. The robustness of the method and its promising results with respect to the state of the art demonstrate the extreme potential of this approach.\",\n \"Title: Liver segmentation using sparse 3D prior models with optimal data support.\\nYear: 2007\\n\\nAbstract: Volume segmentation is a relatively slow process and, in certain circumstances, the enormous amount of prior knowledge available is underused. Model-based liver segmentation suffers from the large shape variability of this organ, and from structures of similar appearance that juxtapose the liver. The technique presented in this paper is devoted to combine a statistical analysis of the data with a reconstruction model from sparse information: only the most reliable information in the image is used, and the rest of the liver's shape is inferred from the model and the sparse observation. The resulting process is more efficient than standard segmentation since most of the workload is concentrated on the critical points, but also more robust, since the interpolated volume is consistent with the prior knowledge statistics. The experimental results on liver datasets prove the sparse information model has the same potential as PCA, if not better, to represent the shape of the liver. Furthermore, the performance assessment from measurement statistics on the liver's volume, distance between reconstructed surfaces and ground truth, and inter-observer variability demonstrates the liver is efficiently segmented using sparse information.\",\n \"Title: Uncertainty-Driven Efficiently-Sampled Sparse Graphical Models for Concurrent Tumor Segmentation and Atlas Registration\\nYear: 2013\\n\\nAbstract: Graph-based methods have become popular in recent years and have successfully addressed tasks like segmentation and deformable registration. Their main strength is optimality of the obtained solution while their main limitation is the lack of precision due to the grid-like representations and the discrete nature of the quantized search space. In this paper we introduce a novel approach for combined segmentation/registration of brain tumors that adapts graph and sampling resolution according to the image content. To this end we estimate the segmentation and registration marginals towards adaptive graph resolution and intelligent definition of the search space. This information is considered in a hierarchical framework where uncertainties are propagated in a natural manner. State of the art results in the joint segmentation/registration of brain images with low-grade gliomas demonstrate the potential of our approach.\",\n \"Title: A variational framework for the simultaneous segmentation and object behavior classification of image sequences\\nYear: 2007\\n\\nAbstract: In this paper, we advance the state of the art in variational image segmentation through the fusion of bottom-up segmentation and top-down classification of object behavior over an image sequence. Such an approach is beneficial for both tasks and is carried out through a joint optimization, which enables the two tasks to cooperate, such that knowledge relevant to each can aid in the resolution of the other, thereby enhancing the final result. In particular, classification offers dynamic probabilistic priors to guide segmentation, while segmentation supplies its results to classification, ensuring that they are consistent with prior knowledge. The prior models are learned from training data and updated dynamically, based on segmentations of earlier images in the sequence. We demonstrate the potential of our approach in a hand gesture recognition application, where the combined use of segmentation and classification improves robustness in the presence of occlusion and background complexity.\",\n \"Title: Discriminative Parameter Estimation for Random Walks Segmentation: Technical Report.\\nYear: 2013\\n\\nAbstract: The Random Walks (RW) algorithm is one of the most e - cient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate segmentations of medical images in a fully automated manner. However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned. we propose a novel discriminative learning framework that estimates the parameters using a training dataset. The main challenge we face is that the training samples are not fully supervised. Speci cally, they provide a hard segmentation of the images, instead of a proba-bilistic segmentation. We overcome this challenge by treating the optimal probabilistic segmentation that is compatible with the given hard segmentation as a latent variable. This allows us to employ the latent support vector machine formulation for parameter estimation. We show that our approach signi cantly outperforms the baseline methods on a challenging dataset consisting of real clinical 3D MRI volumes of skeletal muscles.\",\n \"Title: Conjoint Use Of Coded-Aperture Collimators And Mlem Algoritem: Towards Large Blood Vessels Reconstruction At 511 Kev\\nYear: 2008\\n\\nAbstract: In this paper, we propose a novel approach to improve the trade-off between the spatial resolution and the sensitivity of existing nuclear imaging systems in emission tomography for 511 keV-photons. Although emission tomography suffers from the low amount of emitted photons in the object, partial transparency of collimators and low sensitivity of detectors, we are able to reconstruct radioactive spatial distribution from projections obtained through a specifically designed coded-aperture collimator, with a MLEM-algorithm. We address both the selection of the collimator and the reconstruction approach. Very promising experiments towards 3D-vessels reconstruction demonstrate the potentials of our approach.\",\n \"Title: Simultaneous cast shadows, illumination and geometry inference using hypergraphs.\\nYear: 2013\\n\\nAbstract: The cast shadows in an image provide important information about illumination and geometry. In this paper, we utilize this information in a novel framework in order to jointly recover the illumination environment, a set of geometry parameters, and an estimate of the cast shadows in the scene given a single image and coarse initial 3D geometry. We model the interaction of illumination and geometry in the scene and associate it with image evidence for cast shadows using a higher order Markov Random Field (MRF) illumination model, while we also introduce a method to obtain approximate image evidence for cast shadows. Capturing the interaction between light sources and geometry in the proposed graphical model necessitates higher order cliques and continuous-valued variables, which make inference challenging. Taking advantage of domain knowledge, we provide a two-stage minimization technique for the MRF energy of our model. We evaluate our method in different datasets, both synthetic and real. Our model is robust to rough knowledge of geometry and inaccurate initial shadow estimates, allowing a generic coarse 3D model to represent a whole class of objects for the task of illumination estimation, or the estimation of geometry parameters to refine our initial knowledge of scene geometry, simultaneously with illumination estimation.\",\n \"Title: Pose-invariant 3D proximal femur estimation through bi-planar image segmentation with hierarchical higher-order graph-based priors.\\nYear: 2011\\n\\nAbstract: Low-dose CT-like imaging systems offer numerous perspectives in terms of clinical application, in particular for osteoarticular diseases. In this paper, we address the challenging problem of 3D femur modeling and estimation from bi-planar views. Our contributions are threefold. First, we propose a non-uniform hierarchical decomposition of the shape prior of increasing clinical-relevant precision which is achieved through curvature driven unsupervised clustering acting on the geodesic distances between vertices. Second, we introduce a graphical-model representation of the femur which can be learned from a small number of training examples and involves third-order and fourth-order priors, while being similarity and mirror-symmetry invariant and providing means of measuring regional and boundary supports in the bi-planar views. Last but not least, we adopt an efficient dual-decomposition optimization approach for efficient inference of the 3D femur configuration from bi-planar views. Promising results demonstrate the potential of our method.\",\n \"Title: Extraction of layers of similar motion through combinatorial techniques\\nYear: 2005\\n\\nAbstract: In this paper we present a new technique to extract layers in a video sequence. To this end, we assume that the observed scene is composed of several transparent layers, that their motion in the 2D plane can be approximated with an affine model. The objective of our approach is the estimation of these motion models as well as the estimation of their support in the image domain. Our technique is based on an iterative process that integrates robust motion estimation, MRF-based formulation, combinatorial optimization and the use of visual as well as motion features to recover the parameters of the motion models as well as their support layers. Special handling of occlusions as well as adaptive techniques to detect new objects in the scene are also considered. Promising results demonstrate the potentials of our approach.\",\n \"Title: Group-wise MDL based registration of small animals in video sequences\\nYear: 2008\\n\\nAbstract: We propose a method for the non-rigid tracking of small animals in video sequences. Based on an image sequence showing the animal, first a sub-set of images with coherent pose is chosen automatically. Then a robust rigid registration determines the coarse animal body pose in the set of frames and a subsequent non-rigid group-wise registration using the minimum description length principle learns a deformation model of the animal. It obtains a non-rigid mapping between the individual positions and shapes of the animal during the sequence, based on discrete sets of landmark candidates and according texture features. This is of high relevance in small animal research to integrate signals from multiple frames, and cannot be achieved by standard continuous registration methods. We report first experimental results on video sequences of a rat.\",\n \"Title: Model-based multi-view fusion of cinematic flow and optical imaging.\\nYear: 2010\\n\\nAbstract: Bioluminescence imaging (BLI) offers the possibility to study and image biology at molecular scale in small animals with applications in oncology or gene expression studies. Here we present a novel model-based approach to 3D animal tracking from monocular video which allows the quantification of bioluminescence signal on freely moving animals. The 3D animal pose and the illumination are dynamically estimated through minimization of an objective function with constraints on the bioluminescence signal position. Derived from an inverse problem formulation, the objective function enables explicit use of temporal continuity and shading information, while handling important self-occlusions and time-varying illumination. In this model-based framework, we include a constraint on the 3D position of bioluminescence signal to enforce tracking of the biologically produced signal. The minimization is done efficiently using a quasi-Newton method, with a rigorous derivation of the objective function gradient. Promising experimental results demonstrate the potentials of our approach for 3D accurate measurement with freely moving animal.\",\n \"Title: Compressed sensing based 3D tomographic reconstruction for rotational angiography.\\nYear: 2011\\n\\nAbstract: In this paper, we address three-dimensional tomographic reconstruction of rotational angiography acquisitions. In clinical routine, angular subsampling commonly occurs, due to the technical limitations of C-arm systems or possible improper injection. Standard methods such as filtered backprojection yield a reconstruction that is deteriorated by sampling artifacts, which potentially hampers medical interpretation. Recent developments of compressed sensing have demonstrated that it is possible to significantly improve reconstruction of subsampled datasets by generating sparse approximations through l1-penalized minimization. Based on these results, we present an extension of the iterative filtered backprojection that includes a sparsity constraint called soft background subtraction. This approach is shown to provide sampling artifact reduction when reconstructing sparse objects, and more interestingly, when reconstructing sparse objects over a non-sparse background. The relevance of our approach is evaluated in cone-beam geometry on real clinical data.\",\n \"Title: Geodesic active regions and level set methods for motion estimation and tracking\\nYear: 2005\\n\\nAbstract: Motion analysis in computer vision is a well studied problem with numerous ap- plications. In particular, the tasks of optical flow estimation and tracking are of increasing interest. In this paper, we propose a level set approach to address both aspects of motion analysis. Our approach relies on the propagation of smooth in- terfaces to perform tracking while using an incremental estimation of the motion models. Implicit representations are used to represent moving objects, and capture their motion parameters. Information from different sources like a boundary attrac- tion term, a background subtraction component and a visual consistency constraint are considered. The Euler-Lagrange equations within a gradient descent method lead to a flow that deforms a set of initial curve towards the object boundaries as well an incremental robust estimator of their apparent motion. Partial extension of the proposed framework to address desne motion estimation and the case of moving observer is also presented. Promising results demonstrate the performance of the method.\",\n \"Title: Registration of 3d angiographic and x-ray images using sequential monte carlo sampling\\nYear: 2005\\n\\nAbstract: Digital subtraction angiography (DSA) reconstructions and 3D Magnetic Resonance Angiography (MRA) are the modalities of choice for diagnosis of vascular diseases. However, when it comes to treatment through an endovascular intervention, only two dimensional lower resolution information such as angiograms or fluoroscopic images are usually available. Overlaying the pre-operative information from high resoluion acquisition onto the images acquired during intervention greatly helps physician in performing the operation. We propose to register pre-operative DSA or MRS with intra-operative images to bring the two data sets into a single coordinate frame. The method uses the vascular structure, which is present and visible from most of DSA, MRA and x-ray angiogram and fluoroscopic images, to determine the registration parameters. A robust multiple hypothesis framework is built to minimize a fitness measure between the 3D volume and the 2D projection. The measure is based on the distance map computed from the vascular segmentation. Particle Filters are used to resample the hypothesis, and direct them toward the feature space’s zones of maximum likelihood. Promising experimental results demonstrate the potentials of the method.\",\n \"Title: Landmark-Based Segmentation Of Lungs While Handling Partial Correspondences Using Sparse Graph-Based Priors\\nYear: 2011\\n\\nAbstract: In this paper, we propose a new segmentation algorithm that combines a graph-based shape model with image cues based on boosted features. The landmark-based shape model encodes prior constraints through the normalized Euclidean distances between pairs of control points, alleviating the need of a large database for the training. Moreover, the graph topology is deduced from the dataset using manifold learning and unsupervised clustering. In a graph-matching-like manner, we formulate the segmentation task as a labeling problem where we seek to match the model landmarks to image points that are extracted using the boosted classifiers. We also propose to overcome the limitation of missing correspondences by incorporating an additional label to account for outliers. Then, we repair the outlier positions to complete the segmentation. State-of-the-art discrete optimization techniques are used to provide our experimental results for the segmentation of the right lung in 2D chest radiographs, demonstrating the potentials of our method.\",\n \"Title: Gradient vector flow fast geometric active contours.\\nYear: 2001\\n\\nAbstract: In this paper, we propose an edge-driven bidirectional geometric flow for boundary extraction. To this end, we combine the geodesic active contour flow and the gradient vector flow external force for snakes. The resulting motion equation is considered within a level set formulation, can deal with topological changes and important shape deformations. An efficient numerical schema is used for the flow implementation that exhibits robust behavior and has fast convergence rate. Promising results on real and synthetic images demonstrate the potentials of the flow.\",\n \"Title: Joint model-pixel segmentation with pose-invariant deformable graph-priors.\\nYear: 2013\\n\\nAbstract: This paper proposes a novel framework for image segmentation through a unified model-based and pixel-driven integrated graphical model. Prior knowledge is expressed through the deformation of a discrete model that consists of decomposing the shape of interest into a set of higher order cliques (triplets). Such decomposition allows the introduction of region-driven image statistics as well as pose-invariant (i. e. translation, rotation and scale) constraints whose accumulation introduces global deformation constraints on the model. Regional triangles are associated with pixels labeling which aims to create consistency between the model and the image space. The proposed formulation is pose-invariant, can integrate regional statistics in a natural and efficient manner while being able to produce solutions unobserved during training. The challenging problem of tagged cardiac MR image segmentation is used to demonstrate the performance potentials of the method.\",\n \"Title: Prior Knowledge, Level Set Representations & Visual Grouping\\nYear: 2008\\n\\nAbstract: In this paper, we propose a level set method for shape-driven object extraction. We introduce a voxel-wise probabilistic level set formulation to account for prior knowledge. To this end, objects are represented in an implicit form. Constraints on the segmentation process are imposed by seeking a projection to the image plane of the prior model modulo a similarity transformation. The optimization of a statistical metric between the evolving contour and the model leads to motion equations that evolve the contour toward the desired image properties while recovering the pose of the object in the new image. Upon convergence, a solution that is similarity invariant with respect to the model and the corresponding transformation are recovered. Promising experimental results demonstrate the potential of such an approach.\",\n \"Title: Establishing Local Correspondences towards Compact Representations of Anatomical Structures\\nYear: 2003\\n\\nAbstract: Computer-aided diagnosis is often based on comparing a structure of interest with prior models. Such a comparison requires automatic techniques in determining prior models from a set of examples and establishing local correspondences between the structure and the model. In this paper we propose a variational technique for solving the correspondence problem. The proposed method integrates a powerful representation for shapes (implicit functions), a state-of-the-art criterion for global registration (mutual information) and an efficient technique to recover local correspondences (free form deformations) that guarantees one-to-one mapping. Local correspondences can then be used to build compact representations for a structure of interest according to a set of training examples. The registration and statistical modeling of Systolic Left Ventricle shapes in Ultrasonic images demonstrate the potential of the proposed technique.\",\n \"Title: 3D knowledge-based segmentation using pose-invariant higher-order graphs.\\nYear: 2010\\n\\nAbstract: Segmentation is a fundamental problem in medical image analysis. The use of prior knowledge is often considered to address the ill-posedness of the process. Such a process consists in bringing all training examples in the same reference pose, and then building statistics. During inference, pose parameters are usually estimated first, and then one seeks a compromise between data-attraction and model-fitness with the prior model. In this paper, we propose a novel higher-order Markov Random Field (MRF) model to encode pose-invariant priors and perform 3D segmentation of challenging data. The approach encodes data support in the singleton terms that are obtained using machine learning, and prior constraints in the higher-order terms. A dual-decomposition-based inference method is used to recover the optimal solution. Promising results on challenging data involving segmentation of tissue classes of the human skeletal muscle demonstrate the potentials of the method.\",\n \"Title: Trajectory-based representation of human actions\\nYear: 2007\\n\\nAbstract: This work addresses the problem of human action recognition by introducing a representation of a human action as a collection of short trajectories that are extracted in areas of the scene with significant amount of visual activity. The trajectories are extracted by an auxiliary particle filtering tracking scheme that is initialized at points that are considered salient both in space and time. The spatiotemporal salient points are detected by measuring the variations in the information content of pixel neighborhoods in space and time. We implement an online background estimation algorithm in order to deal with inadequate localization of the salient points on the moving parts in the scene, and to improve the overall performance of the particle filter tracking scheme.We use a variant of the Longest Common Subsequence algorithm (LCSS) in order to compare different sets of trajectories corresponding to different actions. We use Relevance Vector Machines (RVM) in order to address the classification problem. We propose new kernels for use by the RVM, which are specifically tailored to the proposed representation of short trajectories. The basis of these kernels is the modified LCSS distance of the previous step. We present results on real image sequences from a small database depicting people performing 12 aerobic exercises.\",\n \"Title: Deformable mosaicing for whole-body MRI.\\nYear: 2008\\n\\nAbstract: Whole-body magnetic resonance imaging is an emerging application gaining vast clinical interest during the last years. Although recent technological advances shortened the longish acquisition time, this is still the limiting factor avoiding its wide-spread clinical usage. The acquisition of images with large field-of-view helps to relieve this drawback, but leads to significantly distorted images. Therefore, we propose a deformable mosaicing approach, based on the simultaneous registration to linear weighted averages, to correct for distortions in the overlapping area. This method produces good results on in-vivo data and has the advantage that a seamless integration into the clinical workflow is possible.\",\n \"Title: 3D cardiac segmentation with pose-invariant higher-order MRFs\\nYear: 2012\\n\\nAbstract: This paper proposes a novel pose-invariant segmentation approach for left ventricle in 3D CT images. The proposed formulation is modular with respect to the image support (i.e. landmarks, edges and regional statistics). The prior is represented as a third-order Markov Random Field (MRF) where triplets of points result to a low-rank statistical prior while inheriting invariance to global transformations. The ventricle surface is determined through triangulation where image discontinuities can be easily evaluated and the Divergence theorem provides an exact calculation of regional statistics acting on the image or a derived feature space. Promising results using boosting along with the learned prior demonstrate the potential of our method.\",\n \"Title: Monocular Hand Pose Estimation Using Variable Metric Gradient-Descent\\nYear: 2006\\n\\nAbstract: In this paper, we propose a novel model-based approach to recover 3D hand pose from 2D images through a compact articulated 3D hand model whose parameters are inferred in a Bayesian manner. To this end, we pro- pose generative models for hand and background pixels leading to a log- likelihood objective function which aims at enclosing hand-like pixels within the silhouette of the projected 3D model while excluding background-like pixels.Segmentation and hand pose estimation are unified through the mini- mization of a single likelihood function, which is novel and improve overall robustness. We derive the gradient in the hand parameter space of such an area-based objective function, which is new and allows faster convergence rate than gradient free methods. Furthermore , we propose a new constrained variable metric gradient descent to speed up convergence and finally the so called smart particle filter is used to improve robustness through multiple hy- potheses and to exploit temporal coherence. Very promising experimental results demonstrate the potentials of our approach.\",\n \"Title: Recognition-Driven Two-Dimensional Competing Priors Toward Automatic and Accurate Building Detection\\nYear: 2009\\n\\nAbstract: In this paper, a novel recognition-driven variational framework, toward multiple building extraction from aerial and satellite images, is introduced. To this end, competing shape priors are considered, and building extraction is addressed through an image segmentation approach that involves the use of a data-driven term constrained from the prior models. The proposed framework extends previous app...\",\n \"Title: Performance vs computational efficiency for optimizing single and dynamic MRFs: Setting the state of the art with primal-dual strategies\\nYear: 2008\\n\\nAbstract: In this paper we introduce a novel method to address minimization of static and dynamic MRFs. Our approach is based on principles from linear programming and, in particular, on primal-dual strategies. It generalizes prior state-of-the-art methods such as @a-expansion, while it can also be used for efficiently minimizing NP-hard problems with complex pair-wise potential functions. Furthermore, it offers a substantial speedup - of a magnitude 10 - over existing techniques, due to the fact that it exploits information coming not only from the original MRF problem, but also from a dual one. The proposed technique consists of recovering pair of solutions for the primal and the dual such that the gap between them is minimized. Therefore, it can also boost performance of dynamic MRFs, where one should expect that the new pair of primal-dual solutions is closed to the previous one. Promising results in a number of applications, and theoretical, as well as numerical comparisons with the state of the art demonstrate the extreme potentials of this approach.\",\n \"Title: Discrete minimum distortion correspondence problems for non-rigid shape matching\\nYear: 2011\\n\\nAbstract: Similarity and correspondence are two fundamental archetype problems in shape analysis, encountered in numerous application in computer vision and pattern recognition. Many methods for shape similarity and correspondence boil down to the minimum-distortion correspondence problem, in which two shapes are endowed with certain structure, and one attempts to find the matching with smallest structure distortion between them. Defining structures invariant to some class of shape transformations results in an invariant minimum-distortion correspondence or similarity. In this paper, we model shapes using local and global structures, formulate the invariant correspondence problem as binary graph labeling, and show how different choice of structure results in invariance under various classes of deformations.\",\n \"Title: User-Aided Boundary Delineation through the Propagation of Implicit Representations\\nYear: 2003\\n\\nAbstract: In this paper we introduce user-defined segmentation constraints within the level set methods. Snake-driven methods are powerful and widely explored techniques for object extraction. Level set representations is a mathematical framework technique to implement such methods. This formulation is implicit, intrinsic and parameter/topology free. Introducing shape-driven knowledge within the level set method for segmentation is a recently explored topic. User interactive constraints are more simplistic forms of prior shape knowledge. To this end, we propose a simple formulation that converts user interaction to objective function terms that aim to guide the segmentation solution through the user edits.\",\n \"Title: Graph based spatial position mapping of low-grade gliomas.\\nYear: 2011\\n\\nAbstract: Low-grade gliomas (WHO grade II) are diffusively infiltrative brain tumors arising from glial cells. Spatial classification that is usually based on cerebral lobes lacks accuracy and is far from being able to provide some pattern or statistical interpretation of their appearance. In this paper, we propose a novel approach to understand and infer position of low-grade gliomas using a graphical model. The problem is formulated as a graph topology optimization problem. Graph nodes correspond to extracted tumors and graph connections to the spatial and content dependencies among them. The task of spatial position mapping is then expressed as an unsupervised clustering problem, where cluster centers correspond to centers with position appearance prior, and cluster samples to nodes with strong statistical dependencies on their position with respect to the cluster center. Promising results using leave-one-out cross-validation outperform conventional dimensionality reduction methods and seem to coincide with conclusions drawn in physiological studies regarding the expected tumor spatial distributions and interactions.\",\n \"Title: Concurrent tumor segmentation and registration with uncertainty-based sparse non-uniform graphs.\\nYear: 2014\\n\\nAbstract: In this paper, we present a graph-based concurrent brain tumor segmentation and atlas to diseased patient registration framework. Both segmentation and registration problems are modeled using a unified pairwise discrete Markov Random Field model on a sparse grid superimposed to the image domain. Segmentation is addressed based on pattern classification techniques, while registration is performed by maximizing the similarity between volumes and is modular with respect to the matching criterion. The two problems are coupled by relaxing the registration term in the tumor area, corresponding to areas of high classification score and high dissimilarity between volumes. In order to overcome the main shortcomings of discrete approaches regarding appropriate sampling of the solution space as well as important memory requirements, content driven samplings of the discrete displacement set and the sparse grid are considered, based on the local segmentation and registration uncertainties recovered by the min marginal energies. State of the art results on a substantial low-grade glioma database demonstrate the potential of our method, while our proposed approach shows maintained performance and strongly reduced complexity of the model.\",\n \"Title: Graph-based geometric-iconic guide-wire tracking.\\nYear: 2011\\n\\nAbstract: In this paper we introduce a novel hybrid graph-based approach for Guide-wire tracking. The image support is captured by steerable filters and improved through tensor voting. Then, a graphical model is considered that represents guide-wire extraction/tracking through a B-spline control-point model. Points with strong geometric interest (landmarks) are automatically determined and anchored to such a representation. Tracking is then performed through discrete MRFs that optimize the spatio-temporal positions of the control points while establishing landmark temporal correspondences. Promising results demonstrate the potentials of our method.\",\n \"Title: Segmentation, ordering and multi-object tracking using graphical models\\nYear: 2009\\n\\nAbstract: In this paper, we propose a unified graphical-model framework to interpret a scene composed of multiple objects in monocular video sequences. Using a single pairwise Markov random field (MRF), all the observed and hidden variables of interest such as image intensities, pixels' states (associated object's index and relative depth), objects' states (model motion parameters and relative depth) are jointly considered. Particular attention is given to occlusion handling by introducing a rigorous visibility modeling within the MRF formulation. Through minimizing the MRF's energy, we simultaneously segment, track and sort by depth the objects. Promising experimental results demonstrate the potential of this framework and its robustness to image noise, cluttered background, moving camera and background, and even complete occlusions.\",\n \"Title: Efficient parallel message computation for MAP inference\\nYear: 2011\\n\\nAbstract: First order Markov Random Fields (MRFs) have become a predominant tool in Computer Vision over the past decade. Such a success was mostly due to the development of efficient optimization algorithms both in terms of speed as well as in terms of optimality properties. Message passing algorithms are among the most popular methods due to their good performance for a wide range of pairwise potential functions (PPFs). Their main bottleneck is computational complexity. In this paper, we revisit message computation as a distance transformation using a more formal setting than [8] to generalize it to arbitrary PPFs. The method is based on [20] yielding accurate results for a specific class of PPFs and in most other cases a close approximation. The proposed algorithm is parallel and thus enables us to fully take advantage of the computational power of parallel processing architectures. The proposed scheme coupled with an efficient belief propagation algorithm [8] and implemented on a massively parallel coprocessor provides results as accurate as state of the art inference methods, though is in general one order of magnitude faster in terms of speed.\",\n \"Title: Support Vector Driven Markov Random Fields Towards Dti Segmentation Of The Human Skeletal Muscle\\nYear: 2008\\n\\nAbstract: In this paper we propose a classification-based method towards the segmentation of diffusion tensor images. We use Support Vector Machines to classify diffusion tensors and we extend linear classification to the non linear case. To this end, we discuss and evaluate three different classes of kernels on the space of symmetric definite positive matrices that are well suited for the classification of tensor data. We impose spatial constraints by means of a Markov random field model that takes into account the result of SVM classification. Experimental results are provided for diffusion tensor images of human skeletal muscles. They demonstrate the potential of our method in discriminating the different muscle groups.\",\n \"Title: Implicit free-form-deformations for multi-frame segmentation and tracking\\nYear: 2005\\n\\nAbstract: In this paper, we propose a novel technique to address motion estimation and tracking. Such technique represents the motion field using a regular grid of thin-plate splines, and the moving objects using an implicit function on the image plane that is a cubic interpolation of a ”level set function” defined on this grid. Optical flow is determined through the deformation of the grid and consequently of the underlying image structures towards satisfying the constant brightness constraint. Tracking is performed in similar fashion through the consistent recovery in the temporal domain of the zero iso-surfaces of a level set that is the projection of the Free Form Deformation (FFD) implicit function according to the cubic spline formulation. Such an approach is a compromise between dense motion estimation and parametric motion models, introduces smoothness in an implicit fashion, is intrinsic, and can cope with important object deformations. Promising results demonstrate the potentials of our approach.\",\n \"Title: High-Level Bottom-Up Cues for Top-Down Parsing of Facade Images\\nYear: 2012\\n\\nAbstract: We address the problem of parsing images of building facades. The goal is to segment images, assigning to the resulting regions semantic labels that correspond to the basic architectural elements. We assume a top-down parsing framework based on a 2D shape grammar that encodes a prior knowledge on the possible composition of facades. The algorithm explores the space of feasible solutions by generating the possible configurations of the facade and comparing it to the input data by means of a local, pixel- or patch-based classifier. We propose new bottom-up cues for the algorithm, both for evaluation of a candidate parse and for guiding the exploration of the space of feasible solutions. The method that we propose benefits from detection-based information and leverages on the similar appearance of elements that repeat in a given facade. Experiments performed on standard datasets show that this use of more discriminative bottom-up cues improves the convergence in comparison to state-of-the-art algorithms, and gives better results in terms of precision and recall, as well as computation time and performance deviation.\",\n \"Title: 3d Model-Based Reconstruction Of The Proximal Femur From Low-Dose Biplanar X-Ray Images\\nYear: 2011\\n\\nAbstract: The 3D modeling of the proximal femur is a valuable diagnostic tool for orthopedic surgery planning. The use of computed tomography is the most prominent modality to visualize bones both in terms of resolution as well as in term of bone/tissue separation. Towards reducing the impact of radiation to the patient, low-dose X-ray imaging systems have been introduced while still providing partial views with rather low signal-to-noise ratio. In this paper, we focus on automating the 3D proximal femur reconstruction from simultaneously acquired 2D views. A deformable model represented by triangulated mesh surfaces extends to a linear sub-space describing the variations across individuals. Segmentation consists of inferring a global deformation of 3D model followed by a local adaption based on the most prominent combination of the sub-space parameters. The basis of which relies on the minimization of a cost function based on the biplanar projection of this model. To this end, we employ an active region model that aims at optimizing the 3D model parameters such that projection of surface is attracted from edge potentials, while creating an optimal partition between the bone class and the remaining structures. The global parameters of the model and the local ones are optimized through a gradient-free approach. Promising results demonstrate the potentials of our method compared to a supervised reconstruction technique.\",\n \"Title: Dense image registration through MRFs and efficient linear programming.\\nYear: 2008\\n\\nAbstract: In this paper, we introduce a novel and efficient approach to dense image registration, which does not require a derivative of the employed cost function. In such a context, the registration problem is formulated using a discrete Markov random field objective function. First, towards dimensionality reduction on the variables we assume that the dense deformation field can be expressed using a small number of control points (registration grid) and an interpolation strategy. Then, the registration cost is expressed using a discrete sum over image costs (using an arbitrary similarity measure) projected on the control points, and a smoothness term that penalizes local deviations on the deformation field according to a neighborhood system on the grid. Towards a discrete approach, the search space is quantized resulting in a fully discrete model. In order to account for large deformations and produce results on a high resolution level, a multi-scale incremental approach is considered where the optimal solution is iteratively updated. This is done through successive morphings of the source towards the target image. Efficient linear programming using the primal dual principles is considered to recover the lowest potential of the cost function. Very promising results using synthetic data with known deformations and real data demonstrate the potentials of our approach.\",\n \"Title: A PDE-Based Level-Set Approach for Detection and Tracking of Moving Objects\\nYear: 1998\\n\\nAbstract: This papers pr esents a framework for detecting and tracking moving objects in a sequence of images. Using a statistical approach, where the inter-frame difference is modeled by a mixture of two Laplacian or Gaussian distributions, and an energy minimization based approach, we reformulate the motion detection and tracking problem as a front propagation problem. The Euler-Lagrange equation of the designed energy functional is first derived and the flow minimizing the energy is then obtained. Following the work by Caselles et al [CKS95],and Malladi et al [MSV95, MSV93], the contours to be detected and tracked are modeled as geodesic active contours evolving toward the minimum of the designed energy, under the influence of internal and external image dependent forces. Using the level set formulation scheme of Osher and Sethian [OS88] complex curves can be detected and tracked and topological changes for the evolving curves are naturally managed. To reduce the computational cost required by a direct implementation of the formulation scheme of Osher and Sethian [OS88], a new approach exploiting aspects from the classical Narrow Band [AS95] and Fast Marching [Set96] methods is proposed and favorably compared to them. In order to further reduce the CPU time, a multi-scale approach has also been considered. Very promising experimental results are provided using real video sequences.\",\n \"Title: Large-Scale Building Reconstruction Through Information Fusion and 3-D Priors\\nYear: 2010\\n\\nAbstract: In this paper, a novel variational framework is introduced toward automatic 3-D building reconstruction from remote-sensing data. We consider a subset of building models that involve the footprint, their elevation, and the roof type. These models, under a certain hierarchical representation, describe the space of solutions and, under a fruitful synergy with an inferential procedure, recover the observed scene's geometry. Such an integrated approach is defined in a variational context, solves segmentation both in optical images and digital elevation maps, and allows multiple competing priors to determine their pose and 3-D geometry from the observed data. The very promising experimental results and the performed quantitative evaluation demonstrate the potentials of our approach.\",\n \"Title: Nonlinear embedding towards articulated spine shape inference using higher-order MRFs.\\nYear: 2010\\n\\nAbstract: In this paper we introduce a novel approach for inferring articulated spine models from images. A low-dimensional manifold embedding is created from a training set of prior mesh models to establish the patterns of global shape variations. Local appearance is captured from neighborhoods in the manifold once the overall representation converges. Inference with respect to the manifold and shape parameters is performed using a Markov Random Field (MRF). Singleton and pairwise potentials measure the support from the data and shape coherence from neighboring models respectively, while higher-order cliques encode geometrical modes of variation for local vertebra shape warping. Optimization of model parameters is achieved using efficient linear programming and duality. The resulting model is geometrically intuitive, captures the statistical distribution of the underlying manifold and respects image support in the spatial domain. Experimental results on spinal column geometry estimation from CT demonstrate the approach's potential.\",\n \"Title: Non-rigid registration using distance functions\\nYear: 2003\\n\\nAbstract: Feature-based methods for non-rigid registration frequently encounter the correspondence problem. Regardless of whether points, lines, curves or surface parameterizations are used, feature-based non-rigid matching requires us to automatically solve for ...\",\n \"Title: Border Detection on Short Axis Echocardiographic Views Using a Region Based Ellipse-Driven Framework\\nYear: 2004\\n\\nAbstract: In this paper, we propose a robust technique that integrates spatial and temporal information for consistent recovery of the endcocardium. To account for the low image quality we introduce a local variant of the Mumford-Shah that is coupled with a model of limited parameters to describe the ventricle, namely an ellipse. The objective function is defined on the implicit space of ellipses, separates locally the blood pool from the heart wall and explores geometric constraints on the deformations of the endocardium to impose temporal consistency. Promising experimental results demonstrate the potentials of our method.\",\n \"Title: Shape Priors for Level Set Representations\\nYear: 2002\\n\\nAbstract: Level Set Representations, the pioneering framework introduced by Osher and Sethian [14] is the most common choice for the implementation of variational frameworks in Computer Vision since it is implicit, intrinsic, parameter and topology free. However, many Computer vision applications refer to entities with physical meanings that follow a shape form with a certain degree of variability. In this paper, we propose a novel energetic form to introduce shape constraints to level set representations. This formulation exploits all advantages of these representations resulting on a very elegant approach that can deal with a large number of parametric as well as continuous transformations. Furthermore, it can be combined with existing well known level set-based segmentation approaches leading to paradigms that can deal with noisy, occluded and missing or physically corrupted data. Encouraging experimental results are obtained using synthetic and real images.\",\n \"Title: Markov Random Field modeling, inference & learning in computer vision & image understanding: A survey\\nYear: 2013\\n\\nAbstract: In this paper, we present a comprehensive survey of Markov Random Fields (MRFs) in computer vision and image understanding, with respect to the modeling, the inference and the learning. While MRFs were introduced into the computer vision field about two decades ago, they started to become a ubiquitous tool for solving visual perception problems around the turn of the millennium following the emergence of efficient inference methods. During the past decade, a variety of MRF models as well as inference and learning methods have been developed for addressing numerous low, mid and high-level vision problems. While most of the literature concerns pairwise MRFs, in recent years we have also witnessed significant progress in higher-order MRFs, which substantially enhances the expressiveness of graph-based models and expands the domain of solvable problems. This survey provides a compact and informative summary of the major literature in this research topic.\",\n \"Title: Robust guidewire segmentation through boosting, clustering and linear programming\\nYear: 2010\\n\\nAbstract: Fluroscopic imaging provides means to assess the motion of the internal structures and therefore is of great use during surgery. In this paper we propose a novel approach for the segmentation of curvilinear structures in these images. The main challenge to be addressed is the lack of visual support due to the low SNR where traditional edge-based methods fail. Our approach combines machine learning techniques, unsupervised clustering and linear programming. In particular, numerous invariant to position/rotation classifiers are combined to detect candidate pixels of curvilinear structure. These candidates are grouped into consistent geometric segments through the use of a state-of-the art unsupervised clustering algorithm. The complete curvilinear structure is obtained through an ordering of these segments using the elastica model in a linear programming framework. Very promising results were obtained on guide wire segmentation in fluoroscopic images.\",\n \"Title: Beyond Pairwise Energies: Efficient Optimization For Higher-Order Mrfs\\nYear: 2009\\n\\nAbstract: In this paper, we introduce a higher-order MRF optimization framework. On the one hand, it is very general; we thus use it to derive a generic optimizer that can be applied to almost any higher-order MRF and that provably optimizes a dual relaxation related to the input MRF problem. On the other hand, it is also extremely flexible and thus can be easily adapted to yield far more powerful algorithms when dealing with subclasses of high-order MRFs. We thus introduce a new powerful class of high-order potentials, which are shown to offer enough expressive power and to be useful for many vision tasks. To address them, we derive, based on the same framework, a novel and extremely efficient message-passing algorithm, which goes beyond the aforementioned generic optimizer and is able to deliver almost optimal solutions of very high quality. Experimental results on vision problems demonstrate the extreme effectiveness of our approach. For instance, we show that in some cases we are even able to compute the global optimum for NP-hard higher-order MRFs in a very efficient manner.\",\n \"Title: Expression mimicking: from 2D monocular sequences to 3D animations\\nYear: 2007\\n\\nAbstract: In this paper we present a novel approach for mimicking expressions in 3D from a monocular video sequence. To this end, first we construct a high resolution semantic mesh model through automatic global and local registration of a low resolution range data. Such a model is represented using predefined set of control points in a compact fashion, and animated using radial basis functions. In order to recover the 2D positions of the 3D control points in the observed sequence, we use cascade Adaboost-driven search. The search space is reduced through the use of predictive expression modeling. The optimal configuration of the Adaboost responses is determined using combinatorial linear programming which enforces the anthropometric nature of the model. Then the displacement can be reproduced on any version of the model, registered on another face. Our method doesn't require dense stereo estimation and can then produce realistic animations, using any 3D model. Promising experimental results demonstrate the potential of our approach.\",\n \"Title: Tagged cardiac MR image segmentation using boundary & regional-support and graph-based deformable priors\\nYear: 2011\\n\\nAbstract: Segmentation and tracking of tagged MR images is a critical component of in vivo understanding for the heart dynamics. In this paper, we propose a novel approach which uses multi-dimensional features and casts the left ventricle (LV) extraction problem as a maximum posteriori estimation process in both the feature and the shape spaces. Exact integration of multi-dimensional boundary and regional statistics is achieved through a global formulation. Prior is enforced through a point-distribution model, where distances between landmark positions are learned and enforced during the segmentation process. The use of divergence theorem leads to an elegant pairwise formulation where image support and prior knowledge are jointly encoded within a pairwise MRF and the segmentation is achieved efficiently by employing MRF inference algorithms. Promising results on numerous examples demonstrate the potentials of our method.\",\n \"Title: Inter and intra-modal deformable registration: continuous deformations meet efficient optimal linear programming.\\nYear: 2007\\n\\nAbstract: In this paper we propose a novel non-rigid volume registration based on discrete labeling and linear programming. The proposed framework reformulates registration as a minimal path extraction in a weighted graph. The space of solutions is represented using a set of a labels which are assigned to predefined displacements. The graph topology corresponds to a superimposed regular grid onto the volume. Links between neighborhood control points introduce smoothness, while links between the graph nodes and the labels (end-nodes) measure the cost induced to the objective function through the selection of a particular deformation for a given control point once projected to the entire volume domain, Higher order polynomials are used to express the volume deformation from the ones of the control points. Efficient linear programming that can guarantee the optimal solution up to (a user-defined) bound is considered to recover the optimal registration parameters. Therefore, the method is gradient free, can encode various similarity metrics (simple changes on the graph construction), can guarantee a globally sub-optimal solution and is computational tractable. Experimental validation using simulated data with known deformation, as well as manually segmented data demonstrate the extreme potentials of our approach.\",\n \"Title: Viewpoint invariant 3D landmark model inference from monocular 2D images using higher-order priors\\nYear: 2011\\n\\nAbstract: In this paper, we propose a novel one-shot optimization approach to simultaneously determine both the optimal 3D landmark model and the corresponding 2D projections without explicit estimation of the camera viewpoint, which is also able to deal with misdetections as well as partial occlusions. To this end, a 3D shape manifold is built upon fourth-order interactions of landmarks from a training set where pose-invariant statistics are obtained in this space. The 3D-2D consistency is also encoded in such high-order interactions, which eliminate the necessity of viewpoint estimation. Furthermore, the modeling of visibility improves further the performance of the method by handling missing correspondences and occlusions. The inference is addressed through a MAP formulation which is naturally transformed into a higher-order MRF optimization problem and is solved using a dual-decomposition-based method. Promising results on standard face benchmarks demonstrate the potential of our approach.\",\n \"Title: Prior knowledge, random walks and human skeletal muscle segmentation\\nYear: 2012\\n\\nAbstract: In this paper, we propose a novel approach for segmenting the skeletal muscles in MRI automatically. In order to deal with the absence of contrast between the different muscle classes, we proposed a principled mathematical formulation that integrates prior knowledge with a random walks graph-based formulation. Prior knowledge is represented using a statistical shape atlas that once coupled with the random walks segmentation leads to an efficient iterative linear optimization system. We reveal the potential of our approach on a challenging set of real clinical data.\",\n \"Title: Efficient and Automated Multimodal Satellite Data Registration through MRFs and Linear Programming\\nYear: 2014\\n\\nAbstract: The accurate and automated registration of multimodal remote sensing data is of fundamental importance for numerous emerging geospatial environmental and engineering applications. However, the registration of very large multimodal, multitemporal, with different spatial resolutions data is, still, an open matter. To this end, we propose a generic and automated registration framework based on Markov Random Fields (MRFs) and efficient linear programming. The discrete optimization setting along with the introduced data-specific energy terms form a modular approach with respect to the similarity criterion allowing to fully exploit the spectral properties of multimodal remote sensing datasets. The proposed approach was validated both qualitatively and quantitatively demonstrating its potentials on very large (more than 100M pixels) multitemporal remote sensing datasets. In particular, in terms of spatial accuracy the geometry of the optical and radar data has been recovered with displacement errors of less than 2 and 3 pixels, respectively. In terms of computational efficiency the optical data term can converge after 7-8 minutes, while the radar data term after less than 15 minutes.\",\n \"Title: Discriminative learning of deformable contour models.\\nYear: 2014\\n\\nAbstract: In this work we propose a machine learning approach to improve shape detection accuracy inmedical images with deformable contour models (DCMs). Our DCMs can efficiently recover globally optimal solutions that take into account constraints on shape and appearance in the model fitting criterion; our model can also deal with global scale variations by operating in a multi-scale pyramid.Our main contribution consists in formulating the task of learning the DCM score function as a large-margin structured prediction problem. Our algorithm trains DCMs in an joint manner - all the parameters are learned simultaneously, while we use rich local features for landmark localization.We evaluate our method on lung field, heart, and clavicle segmentation tasks using 247 standard posterior-anterior (PA) chest radiographs from the Segmentation in Chest Radiographs (SCR) benchmark. Our learned DCMs systematically outperform the state of the art methods according to a host of validation measures including the overlap coefficient, mean contour distance and pixel error rate.\",\n \"Title: Discrete Visual Perception\\nYear: 2014\\n\\nAbstract: Computational vision and biomedical image have made tremendous progress of the past decade. This is mostly due the development of efficient learning and inference algorithms which allow better, faster and richer modeling of visual perception tasks. Graph-based representations are among the most prominent tools to address such perception through the casting of perception as a graph optimization problem. In this paper, we briefly introduce the interest of such representations, discuss their strength and limitations and present their application to address a variety of problems in computer vision and biomedical image analysis.\",\n \"Title: Motion-based background subtraction using adaptive kernel density estimation\\nYear: 2004\\n\\nAbstract: Background modeling is an important component of many vision systems. Existing work in the area has mostly addressed scenes that consist of static or quasi-static structures. When the scene exhibits a persistent dynamic behavior in time, such an assumption is violated and detection performance deteriorates. In this paper, we propose a new method for the modeling and subtraction of such scenes. Towards the modeling of the dynamic characteristics, optical flow is computed and utilized as a feature in a higher dimensional space. Inherent ambiguities in the computation of features are addressed by using a data-dependent bandwidth for density estimation using kernels. Extensive experiments demonstrate the utility and performance of the proposed approach.\",\n \"Title: Variational methods and partial differential equations in cardiac image analysis\\nYear: 2004\\n\\nAbstract: Cardio-vascular diseases is a major cause of deaths world- wide. Early diagnosis is quite often associated with more efficient treatment leading to a significant decrease of the mortality rate. In this paper, we present tools developed to procces dif- ferent cardiac modalities over the past years with aim to faciliate/automate computer aided diagnosis. In particular we present algorithms and advanced techniques for the seg- mentation of the left ventrile for magentic resonance and echocardiographic imaging. Furthermore, to account for user interaction, we extent our method towards user-aided boundary delineation where the user has the ability to cor- rect local discrepancies of automated results.\",\n \"Title: Geodesic active contours for supervised texture segmentation\\nYear: 1999\\n\\nAbstract: This paper presents a variational method for supervised texture segmentation, which is based on ideas coming from the curve propagation theory. We assume that a preferable texture pattern is known (e.g. the pattern that we want to distinguish from the rest of the image). The textured feature space is generated by filtering the input and the preferable pattern image using Gabor filters, and analyzing their re- sponses as multi-component conditional probability density functions. The texture segmentation is obtained by mini- mizing a Geodesic Active Contour Model objective func- tion where the boundary-based information is expressed via discontinuities on the statistical space associated with the multi-modal textured feature space. This function is minimized using a gradient descent method where the ob- tained PDE is implemented using a level set approach, that handles naturally the topological changes. Finally, a fast method is used for the level set implementation. The perfor- mance of our method is demonstrated on a variety of syn- thetic and real textured images.\",\n \"Title: Geodesic active regions for supervised texture segmentation\\nYear: 1999\\n\\nAbstract: The paper presents a novel variational method for supervised texture segmentation. The textured feature space is generated by filtering the given textured images using isotropic and anisotropic filters, and analyzing their responses as multi-component conditional probability density functions. The texture segmentation is obtained by unifying region and boundary based information as an improved Geodesic Active Contour Model. The defined objective function is minimized using a gradient-descent method where a level set approach is used to implement the obtained PDE. According to this PDE, the curve propagation towards the final solution is guided by boundary and region based segmentation forces, and is constrained by a regularity force. The level set implementation is performed using a fast front propagation algorithm where topological changes are naturally handled. The performance of our method is demonstrated on a variety of synthetic and real textured frames\",\n \"Title: Geodesic active regions for motion estimation and tracking\\nYear: 1999\\n\\nAbstract: This paper proposes a new front propagation method to deal accurately with the challenging problem of tracking non-rigid moving objects. This is obtained by employing a Geodesic Active Region model where the designed ob- jective function is composed of boundary and region-based terms and optimizes the curve position with respect to mo- tion and intensity properties. The main novelty of our ap- proach is that we deal with the motion estimation (linear models are assumed) and the tracking problem simultane- ously. In other words, the optimization problem contains a coupled set of unknown variables; the curve position and the corresponding motion model. The designed objec- tive function is minimized using a gradient descent method; the curve is propagated towards the object boundaries un- der the influence of boundary, intensity and motion-based forces using a PDE, while given the curve position an in- cremental analytical solution is obtained for the motion model. Besides, this PDE is implemented using a level set approachwhere topological changes are naturally handled. Very promising experimental results are provided using real video sequences.\",\n \"Title: Geodesic active contours and level sets for the detection and tracking of moving objects\\nYear: 2000\\n\\nAbstract: This paper presents a new variational framework for detecting and tracking multiple moving objects in image sequences. Motion detection is performed using a statistical framework for which the observed interframe difference density function is approximated using a mixture model. This model is composed of two components, namely, the static (background) and the mobile (moving objects) one. Both components are zero-mean and obey Laplacian or Gaussian law. This statistical framework is used to provide the motion detection boundaries. Additionally, the original frame is used to provide the moving object boundaries. Then, the detection and the tracking problem are addressed in a common framework that employs a geodesic active contour objective function. This function is minimized using a gradient descent method, where a flow deforms the initial curve towards the minimum of the objective function, under the influence of internal and external image dependent forces. Using the level set formulation scheme, complex curves can be detected and tracked while topological changes for the evolving curves are naturally managed. To reduce the computational cost required by a direct implementation of the level set formulation scheme, a new approach named Hermes is proposed. Hermes exploits aspects from the well-known front propagation algorithms (Narrow Band, Fast Marching) and compares favorably to them. Very promising experimental results are provided using real video sequences.\",\n \"Title: A MRF-based approach for real-time subway monitoring\\nYear: 2001\\n\\nAbstract: There has been an increase in the use of video surveil- lance and monitoring in public areas to improve safety and security. Change detection and crowding/congestion den- sity estimation are two sub-tasks in a subway monitoring system. We propose a method that decomposes this problem into two steps. The first step consists of a change detec- tion algorithm that distinguishes the background from the foreground. This is done using a discontinuity preserving MRF-based approach where the information from differ- ent sources (background subtraction, intensity modeling) is combined with spatial constraints to provide a smooth mo- tion detection map. Then, the obtained change detection map is combined with a geometry module that performs a soft auto-calibration to estimate a measure of congestion of the observed area (platform). Extensive experimental re- sults in a metro station of a metropolitan city demonstrates the performance and the potential of our method.\",\n \"Title: LOCALLY ADAPTIVE AUTOREGRESSIVE ACTIVE MODELS FOR SEGMENTATION OF 3D ANATOMICAL STRUCTURES\\nYear: 2007\\n\\nAbstract: Many techniques of knowledge-based segmentation consist of building statistical models that describe the deformations of the structure of interest, and then fit these models to the image data. In this paper, we introduce a novel family of shape prior models that aim to capture such varying support. To this end, 3D segmentation is considered by modeling the relationship between contours on consecutive slices using autoregression. Then, the segmentation is performed progressively on the 2D slices in a qualitative fashion, starting from the ones with strong data support toward the ones of limited support. Successive segmentation maps are linked through a locally adaptive autoregressive prediction mechanism - that is learned through training - where confidence of the data from prior slices constrains the results. Such prediction is integrated with a contour minimization technique, leading to a Bayesian sequential procedure that iteratively predicts and corrects 2D contours leading to complete reconstruction of 3D anatomical structures. A quantitative comparative study with 3D active shape models demonstrate the potential of the method.\",\n \"Title: Fast, Approximately Optimal Solutions for Single and Dynamic MRFs\\nYear: 2007\\n\\nAbstract: A newefficientMRF optimizationalgorithm, calledFast- PD, is proposed, which generalizes �-expansion. One of its main advantages is that it offers a substantial speedup over that method, e.g. it can be at least 3-9 times faster than �-expansion. Its efficiency is a result of the fact that Fast-PDexploitsinformationcomingnotonlyfrom theorig- inal MRF problem, but also from a dual problem. Further- more, besides static MRFs, it can also be used for boost- ing the performance of dynamic MRFs, i.e. MRFs varying over time. On top of that, Fast-PD makes no compromise about the optimality of its solutions: it can compute exactly the same answer as �-expansion, but, unlike that method, it can also guarantee an almost optimal solution for a much wider class of NP-hard MRF problems. Results on static and dynamic MRFs demonstrate the algorithm's efficiency and power. E.g., Fast-PD has been able to compute dispar- ity forstereoscopicsequencesin real time, with theresulting disparity coinciding with that of �-expansion. can that method also offer a computational advantage for the case of dynamic MRFs? With respect to the questions raised above, this work makes the following contributions. Efficiency for single MRFs: �-expansion works by solving a series of max-flow problems. Its efficiency is thus largely determined from the efficiency of these max-flow problems, which, in turn, depends on the number of augmenting paths per max-flow. Here, we build upon recent work of (6), and propose a new primal-dual MRF optimization method, called Fast-PD. This method, like (6) or �-expansion, also ends up solving a max-flow problem for a series of graphs. However, unlike these techniques, the graphs constructed by Fast-PD ensure that the number of augmentations per max-flow decreases dramatically over time, thus boosting the efficiency of MRF inference. To show this, we prove a generalized relationship between the number of augmentations and the so-called primal-dual gap associated with the original MRF problem and its dual. Furthermore, to fully exploit the above property, 2 new ex- tensions are also proposed: an adapted max-flow algorithm, as well as an incremental graph construction method. Optimality properties: Despite its efficiency, our method also makes no compromise regarding the optimality of its solutions. So, if d(� , � ) is a metric, Fast-PD is as pow- erful as �-expansion,i.e. it computes exactly the same solu- tion, but with a substantial speedup. Moreover, it applies to a much wider class of MRFs1, e.g. even with a non-metric d(� , � ), while still guaranteeing an almost optimal solution. Efficiency for dynamic MRFs: Furthermore, our\",\n \"Title: Automatic detection of liver tumors\\nYear: 2008\\n\\nAbstract: Tumor detection in CT liver images is a challenging task. The nature of tumor has a direct effect on the number of voxels being contaminated, as well as on the changes in the observed CT scan. In order to deal with this challenge, in this paper we propose the use of advanced non-linear machine learning techniques to determine the optimal features, as well as the hyperplane that use these features to separate tumoral voxels from voxels corresponding to healthy tissues. Very promising classification results using an important volume of clinically annotated data (86% sensitivity, 82% specificity) demonstrate the potentials of our approach.\",\n \"Title: Model-based hand tracking with texture, shading and self-occlusions\\nYear: 2008\\n\\nAbstract: A novel model-based approach to 3D hand tracking from monocular video is presented. The 3D hand pose, the hand texture and the illuminant are dynamically estimated through minimization of an objective function. Derived from an inverse problem formulation, the objective function enables explicit use of texture temporal continuity and shad- ing information, while handling important self-occlusions and time-varying illumination. The minimization is done efficiently using a quasi-Newton method, for which we pro- pose a rigorous derivation of the objective function gra- dient. Particular attention is given to terms related to the change of visibility near self-occlusion boundaries that are neglected in existing formulations. In doing so we introduce new occlusion forces and show that using all gradient terms greatly improves the performance of the method. Experi- mental results demonstrate the potential of the formulation.\",\n \"Title: Graph-based knowledge-driven discrete segmentation of the left ventricle\\nYear: 2009\\n\\nAbstract: In this paper, we propose a novel similarity-invariant approach to model-based segmentation of the left ventricle. The method assumes a control point representation of the model and an arbitrary interpolation strategy. First, we construct the prior manifold using the distributions of the relative normalized distances between pairs of control points within the training set. Then, we introduce a geometric partition of the space using a Voronoi decomposition that aims to determine relationships between the control points and the image domain. Knowledge-based segmentation can then be expressed using a Markov Random Field, where the pairwise potentials encode the variation of the shape, while the singleton potentials refer to the data term through the Voronoi decomposition of the space. State-of-the art techniques from linear programming are considered to optimize the designed function.\",\n \"Title: Robust shadow and illumination estimation using a mixture model\\nYear: 2009\\n\\nAbstract: Illuminant estimation from shadows typically relies on accurate segmentation of the shadows and knowledge of ex- act 3D geometry, while shadow estimation is difficult in the presence of texture. These can be onerous requirements; in this paper we propose a graphical model to estimate the illumination environment and detect the shadows of a scene with textured surfaces from a single image and only coarse 3D information. We represent the illumination en- vironment as a mixture of von Mises-Fisher distributions. Then, each shadow pixel becomes the combination of sam- ples generated from this illumination environment. We inte- grate a number of low-level, illumination-invariant 2D cues in a graphical model to detect and estimate cast shadows on textured surfaces. Both 2D cues and approximate 3D rea- soning are combined to infer a set of labels that identify the shadows in the image and estimate the positions, shapes and intensities of the light sources. Our results demonstrate that the probabilistic combination of multiple cues, unlike prior approaches, manages to differentiate both hard and soft shadows from the underlying surface texture even when we can only coarsely anticipate the effect of 3D geometry. We also experimentally demonstrate how correct estimation of the sharpness and shape of the light sources improves the Augmented Reality results.\",\n \"Title: MRF Energy Minimization and Beyond via Dual Decomposition\\nYear: 2011\\n\\nAbstract: This paper introduces a new rigorous theoretical framework to address discrete MRF-based optimization in computer vision. Such a framework exploits the powerful technique of Dual Decomposition. It is based on a projected subgradient scheme that attempts to solve an MRF optimization problem by first decomposing it into a set of appropriately chosen subproblems, and then combining their solutions in a principled way. In order to determine the limits of this method, we analyze the conditions that these subproblems have to satisfy and demonstrate the extreme generality and flexibility of such an approach. We thus show that by appropriately choosing what subproblems to use, one can design novel and very powerful MRF optimization algorithms. For instance, in this manner we are able to derive algorithms that: 1) generalize and extend state-of-the-art message-passing methods, 2) optimize very tight LP-relaxations to MRF optimization, and 3) take full advantage of the special structure that may exist in particular MRFs, allowing the use of efficient inference techniques such as, e.g., graph-cut-based methods. Theoretical analysis on the bounds related with the different algorithms derived from our framework and experimental results/comparisons using synthetic and real data for a variety of tasks in computer vision demonstrate the extreme potentials of our approach.\",\n \"Title: Bioluminescence enhancement through fusion of optical imaging and cinematic video flow\\nYear: 2010\\n\\nAbstract: Optical imaging is an efficient mean to measure biological signal. However, it can suffer from low spatial and temporal resolution while animal deformable displacements could also degrade significantly the localization of the measurements. In this paper, we propose a novel approach to perform fusion of cinematic flow and optical imaging towards enhancement of the biological signal. To this end, fusion is reformulated as a population (all vs. all) registration problem where the two (being spatially aligned) signals are registered in time using the same deformation field. Implicit silhouette and landmark matching are considered for the cinematic images and are combined with global statistical congealing-type measurements of the optical one. The problem is reformulated using a discrete MRF, where optical imaging costs are expressed in singleton (global) potentials, while smoothness constraints as well as cinematic measurements through pair-wise potentials. Promising experimental results demonstrate the potentials of our approach.\",\n \"Title: Segmentation of building facades using procedural shape priors\\nYear: 2010\\n\\nAbstract: In this paper we propose a novel approach to the perceptual interpretation of building facades that combines shape grammars, supervised classification and random walks. Procedural modeling is used to model the geometric and the photometric variation of buildings. This is fused with visual classification techniques (randomized forests) that provide a crude probabilistic interpretation of the observation space in order to measure the appropriateness of a procedural generation with respect to the image. A random exploration of the grammar space is used to optimize the sequence of derivation rules towards a semantico-geometric interpretation of the observations. Experiments conducted on complex architecture facades with ground truth validate the approach.\",\n \"Title: Finger-spelling recognition within a collaborative segmentation/behavior inference framework\\nYear: 2008\\n\\nAbstract: We introduce a new approach for finger-spelling recognition from video sequences, relying on the collaboration between the feature extraction and behavior inference processes. The inference process dynamically guides the segmentation-based feature extraction pro- cess towards the most likely location of the signer's hand (based on its attributes). Reciprocally, segmentation offers to the inference process hand object attributes extracted from each image, combin- ing the received guidance and new image information. This collab- oration is beneficial for both processes, yielding not only accurate segmentations of the spelling hand, but also a robust recognition scheme, which can cope with complex backgrounds, typical of real life situations.\",\n \"Title: Hierarchical 3D diffusion wavelet shape priors\\nYear: 2009\\n\\nAbstract: In this paper, we propose a novel representation of prior knowledge for image segmentation, using diffusion wavelets that can reflect arbitrary continuous interdependencies in shape data. The application of diffusion wavelets has, so far, largely been confined to signal processing. In our approach, and in contrast to state-of-the-art methods, we optimize the coefficients, the number and the position of landmarks, and the object topology - the domain on which the wavelets are defined - during the model learning phase, in a coarse-to-fine manner. The resulting paradigm supports hierarchies both in the model and the search space, can encode complex geometric and photometric dependencies of the structure of interest, and can deal with arbitrary topologies. We report results on two challenging medical data sets, that illustrate the impact of the soft parameterization and the potential of the diffusion operator.\",\n \"Title: Multimodal inference of articulated spine models from higher order energy functions of discrete MRFS\\nYear: 2010\\n\\nAbstract: In this paper, we introduce a novel approach based on higher order energy functions which have the ability to encode global structural dependencies to infer articulated 3D spine models to CT volume data. A personalized geometrical model is reconstructed from biplanar X-rays before spinal surgery in order to create a spinal column representation which is modeled by a series of intervertebral transformations based on rotation and translation parameters. The shape transformation between the standing and lying poses is then achieved through a Markov Random Field optimization graph, where the unknown variables are the deformations applied to the intervertebral transformations. Singleton and pairwise potentials measure the support from the data and geometrical dependencies between neighboring vertebrae respectively, while higher order cliques are introduced to integrate consistency in regional curves. Optimization of model parameters in a multi-modal context is achieved using efficient linear programming and duality. A qualitative evaluation of the vertebra model alignment obtained from the proposed method gave promising results while the quantitative comparison to expert identification yields an accuracy of 1.8 ± 0.7 mm based on the localization of surgical landmarks.\",\n \"Title: Needle tracking through higher-order MRF optimization\\nYear: 2010\\n\\nAbstract: We propose a Markov Random Field formulation for the tracking of needles in fluoroscopic images. A novel motion model makes it possible to capture the primarily rigid motion as well as deformations of the needle in a single second-order MRF graph. Needles are represented by B-splines and each control point is associated with a random variable in a MAP-MRF formulation. In addition to the control points we introduce a single additional random variable representing the rigid transformation needles undergo during interventions. The incorporation of rigid transformations allows to recover transformations even in the presence of large displacements which is not possible with existing MRF models for medical tool tracking.\",\n \"Title: An explicit shape-constrained MRF-based contour evolution method for 2-D medical image segmentation.\\nYear: 2014\\n\\nAbstract: Image segmentation is, in general, an ill-posed problem and additional constraints need to be imposed in order to achieve the desired segmentation result. While segmenting organs in medical images, which is the topic of this paper, a significant amount of prior knowledge about the shape, appearance, and location of the organs is available that can be used to constrain the solution space of the segmentation problem. Among the various types of prior information, the incorporation of prior information about shape, in particular, is very challenging. In this paper, we present an explicit shape-constrained MAP-MRF-based contour evolution method for the segmentation of organs in 2-D medical images. Specifically, we represent the segmentation contour explicitly as a chain of control points. We then cast the segmentation problem as a contour evolution problem, wherein the evolution of the contour is performed by iteratively solving a MAP-MRF labeling problem. The evolution of the contour is governed by three types of prior information, namely: (i) appearance prior, (ii) boundary-edgeness prior, and (iii) shape prior, each of which is incorporated as clique potentials into the MAP-MRF problem. We use the master-slave dual decomposition framework to solve the MAP-MRF labeling problem in each iteration. In our experiments, we demonstrate the application of the proposed method to the challenging problem of heart segmentation in non-contrast computed tomography data.\",\n \"Title: Deformable Medical Image Registration: A Survey\\nYear: 2013\\n\\nAbstract: Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.\",\n \"Title: Slice-to-volume deformable registration: efficient one-shot consensus between plane selection and in-plane deformation.\\nYear: 2015\\n\\nAbstract: This paper introduces a novel decomposed graphical model to deal with slice-to-volume registration in the context of medical images and image-guided surgeries.We present a new non-rigid slice-to-volume registration method whose main contribution is the ability to decouple the plane selection and the in-plane deformation parts of the transformation-through two distinct graphs-toward reducing the complexity of the model while being able to obtain simultaneously the solution for both of them. To this end, the plane selection process is expressed as a local graph-labeling problem endowed with planarity satisfaction constraints, which is then directly linked with the deformable part through the data registration likelihoods. The resulting model is modular with respect to the image metric, can cope with arbitrary in-plane regularization terms and inherits excellent properties in terms of computational efficiency.The proof of concept for the proposed formulation is done using cardiac MR sequences of a beating heart (an artificially generated 2D temporal sequence is extracted using real data with known ground truth) as well as multimodal brain images involving ultrasound and computed tomography images. We achieve state-of-the-art results while decreasing the computational time when we compare with another method based on similar techniques.We confirm that graphical models and discrete optimization techniques are suitable to solve non-rigid slice-to-volume registration problems. Moreover, we show that decoupling the graphical model and labeling it using two lower-dimensional label spaces, we can achieve state-of-the-art results while substantially reducing the complexity of our method and moving the approach close to real clinical applications once considered in the context of modern parallel architectures.\",\n \"Title: Sub-cortical brain structure segmentation using F-CNN'S\\nYear: 2016\\n\\nAbstract: In this paper we propose a deep learning approach for segmenting sub-cortical structures of the human brain in Magnetic Resonance (MR) image data. We draw inspiration from a state-of-the-art Fully-Convolutional Neural Network (F-CNN) architecture for semantic segmentation of objects in natural images, and adapt it to our task. Unlike previous CNN-based methods that operate on image patches, our model is applied on a full blown 2D image, without any alignment or registration steps at testing time. We further improve segmentation results by interpreting the CNN output as potentials of a Markov Random Field (MRF), whose topology corresponds to a volumetric grid. Alpha-expansion is used to perform approximate inference imposing spatial volumetric homogeneity to the CNN priors. We compare the performance of the proposed pipeline with a similar system using Random Forest-based priors, as well as state-of-art segmentation algorithms, and show promising results on two different brain MRI datasets.\",\n \"Title: A Framework for Efficient Structured Max-Margin Learning of High-Order MRF Models\\nYear: 2015\\n\\nAbstract: We present a very general algorithm for structured prediction learning that is able to efficiently handle discrete MRFs/CRFs (including both pairwise and higher-order models) so long as they can admit a decomposition into tractable subproblems. At its core, it relies on a dual decomposition principle that has been recently employed in the task of MRF optimization. By properly combining such an app...\",\n \"Title: Guest Editors’ Introduction: Special Section on Higher Order Graphical Models in Computer Vision\\nYear: 2015\\n\\nAbstract: The papers in this special section address the programs and services supported by graphical models in computer vision. This section explores the main challenges in this framework???modeling novel priors, learning, inference???and presents innovative solutions. The papers cover the aspects of modeling novel priors, inference algorithms and parameter learning methods in the context of higher order graphical models.\",\n \"Title: Building Detection In Very High Resolution Multispectral Data With Deep Learning Features\\nYear: 2015\\n\\nAbstract: The automated man-made object detection and building extraction from single satellite images is, still, one of the most challenging tasks for various urban planning and monitoring engineering applications. To this end, in this paper we propose an automated building detection framework from very high resolution remote sensing data based on deep convolutional neural networks. The core of the developed method is based on a supervised classification procedure employing a very large training dataset. An MRF model is then responsible for obtaining the optimal labels regarding the detection of scene buildings. The experimental results and the performed quantitative validation indicate the quite promising potentials of the developed approach.\",\n \"Title: A Machine Learning Methodology for Enzyme Functional Classification Combining Structural and Protein Sequence Descriptors.\\nYear: 2016\\n\\nAbstract: The massive expansion of the worldwide Protein Data Bank (PDB) provides new opportunities for computational approaches which can learn from available data and extrapolate the knowledge into new coming instances. The aim of this work is to apply machine learning in order to train prediction models using data acquired by costly experimental procedures and perform enzyme functional classification. Enzymes constitute key pharmacological targets and the knowledge on the chemical reactions they catalyze is very important for the development of potent molecular agents that will either suppress or enhance the function of the given enzyme, thus modulating a pathogenicity, an illness or even the phenotype. Classification is performed on two levels: (i) using structural information into a Support Vector Machines (SVM) classifier and (ii) based on amino acid sequence alignment and Nearest Neighbor (NN) classification. The classification accuracy is increased by fusing the two classifiers and reaches 93.4% on a large dataset of 39,251 proteins from the PDB database. The method is very competitive with respect to accuracy of classification into the 6 enzymatic classes, while at the same time its computational cost during prediction is very small.\",\n \"Title: Simultaneous registration and change detection in multitemporal, very high resolution remote sensing data\\nYear: 2015\\n\\nAbstract: In order to exploit the currently continuous streams of massive, multi-temporal, high-resolution remote sensing datasets there is an emerging need to address efficiently the image registration and change detection challenges. To this end, in this paper we propose a modular, scalable, metric free single shot change detection/registration method. The approach exploits a decomposed interconnected graphical model formulation where registration similarity constraints are relaxed in the presence of change detection. The deformation space is discretized, while efficient linear programming and duality principles are used to optimize a joint solution space where local consistency is imposed on the deformation and the detection space. Promising results on large scale experiments demonstrate the extreme potentials of our method.\",\n \"Title: Graph-Based Motion-Driven Segmentation of the Carotid Atherosclerotique Plaque in 2D Ultrasound Sequences.\\nYear: 2015\\n\\nAbstract: Carotid plaque segmentation in ultrasound images is a crucial step for carotid atherosclerosis. However, image quality, important deformations and lack of texture are prohibiting factors towards manual or accurate carotid segmentation. We propose a novel fully automated methodology to identify the plaque region by exploiting kinematic dependencies between the atherosclerotic and the normal arterial wall. The proposed methodology exploits group-wise image registration towards recovering the deformation field, on which information theory criteria are used to determine dominant motion classes and a map reflecting kinematic dependencies, which is then segmented using Markov random fields. The algorithm was evaluated on 120 cases, for which manually-traced plaque contours by an experienced physician were available. Promising evaluation results showed the enhanced performance of the algorithm in automatically segmenting the plaque region, while future experiments on additional datasets are expected to further elucidate its potential.\",\n \"Title: Modular Linear Iconic Matching Using Higher Order Graphs\\nYear: 2015\\n\\nAbstract: We introduce a novel approach to tackle iconic linear mapping between two images. We adopt a grid-based parametrization of the deformation field that is encoded by a higher order graphical model. In the proposed formulation, latent variables correspond to local grid displacement vectors and unary potentials locally quantify the level of alignment between the two images. Higher order constraints that involve third and forth order potentials, enforce the linearity of the resulting transformation. The resulting formulation is modular with respect to the image metric used to evaluate the correctness of mapping as well as with respect to the nature of the linear transformation ( rigid, similarity, or affine). Inference on this graph is performed through dual decomposition. Comparison with classic algorithms demonstrates the potential of our approach.\",\n \"Title: Implicit Planar And In-Plane Deformable Mapping In Medical Images Through High Order Graphs\\nYear: 2015\\n\\nAbstract: In this paper we propose a novel method based on discrete optimization of high order graphs, to perform deformable slice-to-volume registration of 2D images and 3D volumes. To this end, a 2D grid superimposed to the image is considered with control points deforming in 3D and their deformations corresponding to the label space. Geometrical consistency (unique plane selection) and deformation smoothness (in-plane deformations) as well as image similarity (visual matching) are encoded in different third order cliques. The proposed formulation is optimized through its mapping to a factor graph using conventional graph optimization methods. A dataset composed of 2D slices and 3D MRI volumes of the heart was used to evaluate its accuracy leading to very promising results.\",\n \"Title: Local atlas selection for discrete multi-atlas segmentation\\nYear: 2015\\n\\nAbstract: Multi-atlas segmentation is commonly performed in two separate steps: i) multiple pairwise registrations, and ii) fusion of the deformed segmentation masks towards labeling objects of interest. In this paper we propose an approach for integrated volume segmentation through multi-atlas registration. To tackle this problem, we opt for a graphical model where registration and segmentation nodes are coupled. The aim is to recover simultaneously all atlas deformations along with selection masks quantifying the participation of each atlas per segmentation voxel. The above is modeled using a pairwise graphical model where deformation and segmentation variables are modeled explicitly. A sequential optimization relaxation is proposed for efficient inference. Promising performance is reported on the IBSR dataset when comparing to majority voting and local appearance-based weighted voting.\",\n \"Title: (Hyper)-Graphs Inference through Convex Relaxations and Move Making Algorithms: Contributions and Applications in Artificial Vision.\\nYear: 2016\\n\\nAbstract: Computational visual perception seeks to reproduce human vision through the combination of visual sensors, artificial intelligence, and computing. To this end, computer vision tasks are often reformulated as mathematical inference problems where the objective is to determine the set of parameters corresponding to the lowest potential of a task-specific objective function. Graphical models have been the most popular formulation in the field over the past two decades where the problem is viewed as a discrete assignment labeling one. Modularity, scalability, and portability are the main strengths of these methods which once combined with efficient inference algorithms they could lead to state of the art results. This monograph focuses on the inference component of the problem and in particular discusses in a systematic manner the most commonly used optimization principles in the context of graphical models. It looks at inference over low rank models (interactions between variables are constrained to pairs) as well as higher order ones (arbitrary set of variables determine hyper-cliques on which constraints are introduced) and seeks a concise, self-contained presentation of prior art as well as the presentation of the current state of the art methods in the field.\",\n \"Title: Prior-based Coregistration and Cosegmentation.\\nYear: 2016\\n\\nAbstract: We propose a modular and scalable framework for dense coregistration and cosegmentation with two key characteristics: first, we substitute ground truth data with the semantic map output of a classifier; second, we combine this output with population deformable registration to improve both alignment and segmentation. Our approach deforms all volumes towards consensus, taking into account image similarities and label consistency. Our pipeline can incorporate any classifier and similarity metric. Results on two datasets, containing annotations of challenging brain structures, demonstrate the potential of our method.\",\n \"Title: Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields.\\nYear: 2016\\n\\nAbstract: Rigid slice-to-volume registration is a challenging task, which finds application in medical imaging problems like image fusion for image guided surgeries and motion correction for volume reconstruction. It is usually formulated as an optimization problem and solved using standard continuous methods. In this paper, we discuss how this task be formulated as a discrete labeling problem on a graph. Inspired by previous works on discrete estimation of linear transformations using Markov Random Fields (MRFs), we model it using a pairwise MRF, where the nodes are associated to the rigid parameters, and the edges encode the relation between the variables. We compare the performance of the proposed method to a continuous formulation optimized using simplex, and we discuss how it can be used to further improve the accuracy of our approach. Promising results are obtained using a monomodal dataset composed of magnetic resonance images (MRI) of a beating heart.\",\n \"Title: (Hyper)-graphical models in biomedical image analysis.\\nYear: 2016\\n\\nAbstract: •We briefly present the interest of graphical models in biomedical imaging.•We present/briefly review the state of the art optimization algorithms for their inference.•We provide a snapshot of methods of increasing complexity/richness to address core biomedical imaging problems.•We provide some insights as it concerns future developments and impact of these methods for the decade to come.\",\n \"Title: Simultaneous Registration, Segmentation And Change Detection From Multisensor, Multitemporal Satellite Image Pairs\\nYear: 2016\\n\\nAbstract: In this paper, a novel generic framework has been designed, developed and validated for addressing simultaneously the tasks of image registration, segmentation and change detection from multisensor, multiresolution, multitemporal satellite image pairs. Our approach models the inter- dependencies of variables through a higher order graph. The proposed formulation is modular with respect to the nature of images (various similarity metrics can be considered), the nature of deformations (arbitrary interpolation strategies), and the nature of segmentation likelihoods (various classification approaches can be employed). Inference of the proposed formulation is achieved through its mapping to an overparametrized pairwise graph which is then optimized using linear programming. Experimental results and the performed quantitative evaluation indicate the high potentials of the developed method.\",\n \"Title: Comparison of Multi-resolution Analysis Patterns for Texture Classification of Breast Tumors Based on DCE-MRI.\\nYear: 2016\\n\\nAbstract: Although Fourier and Wavelet Transform have been widely used for texture classification methods in medical images, the discrimination performance of FDCT has not been investigated so far in respect to breast cancer detection..n this paper, three multi-resolution transforms, namely the Discrete Wavelet Transform (DWT), the Stationary Wavelet Transform (SWT) and the Fast Discrete Curvelet Transform (FDCT) were comparatively assessed with respect to their ability to discriminate between malignant and benign breast tumors in Dynamic Contrast-Enhanced Magnetic Resonance Images (DCE-MRI). The mean and entropy of the detail sub-images for each decomposition scheme were used as texture features, which were subsequently fed as input into several classifiers. FDCT features fed to a Linear Discriminant Analysis (LDA) classifier produced the highest overall classification performance (93.18 % Accuracy).\",\n \"Title: Slice-to-volume medical image registration: a survey.\\nYear: 2017\\n\\nAbstract: •The first comprehensive survey of the literature about slice-to-volume registration.•A categorical study of the algorithms according to an ad-hoc defined taxonomy.•We discuss advantages and disadvantages of alternative slice-to-volume methods.•We elaborate conclusions from this analysis and discuss future perspectives in the field.\",\n \"Title: Optimal Estimation of Diffusion in DW-MRI by High-Order MRF-Based Joint Deformable Registration and Diffusion Modeling\\nYear: 2016\\n\\nAbstract: Over the last years, the apparent diffusion coefficient (ADC), computed from diffusion-weighted magnetic resonance (DW-MR) images, has become an important imaging biomarker for evaluating and managing patients with neoplastic or cerebrovascular disease. Standard methods for the calculation of ADC ignore the presence of noise and motion between successive (in time) DW-MR images acquired by changing the b-value. In order to accurately quantify the diffusion process during image acquisition, we introduce a method based on a high-order Markov Random Field (MRF) formulation that jointly registers the DWMR images and models the spatiotemporal diffusion. Spatial smoothness on the ADC map, as well as spatiotemporal deformation smoothness, is imposed towards producing anatomically meaningful representations. The high-order dependencies in our MRF model are handled through Dual Decomposition. Performance of registration is compared to a state-of-the art registration approach in terms of obtained fitting error of the diffusion model in the core of the tumor. Preliminary results reveal a marginally better performance of our method when compared against the standard ADC map used in clinical practice, which indicates its potential as a means for extracting imaging biomarkers.\",\n \"Title: EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation.\\nYear: 2017\\n\\nAbstract: During the past decade, with the significant progress of computational power as well as ever-rising data availability, deep learning techniques became increasingly popular due to their excellent performance on computer vision problems. The size of the Protein Data Bank (PDB) has increased more than 15-fold since 1999, which enabled the expansion of models that aim at predicting enzymatic function via their amino acid composition. Amino acid sequence, however, is less conserved in nature than protein structure and therefore considered a less reliable predictor of protein function. This paper presents EnzyNet, a novel 3D convolutional neural networks classifier that predicts the Enzyme Commission number of enzymes based only on their voxel-based spatial structure. The spatial distribution of biochemical properties was also examined as complementary information. The two-layer architecture was investigated on a large dataset of 63,558 enzymes from the PDB and achieved an accuracy of 78.4% by exploiting only the binary representation of the protein shape. Code and datasets are available at https://github.com/shervinea/enzynet.\",\n \"Title: Deformable Registration through Learning of Context-Specific Metric Aggregation.\\nYear: 2017\\n\\nAbstract: We propose a novel weakly supervised discriminative algorithm for learning context specific registration metrics as a linear combination of conventional similarity measures. Conventional metrics have been extensively used over the past two decades and therefore both their strengths and limitations are known. The challenge is to find the optimal relative weighting (or parameters) of different metrics forming the similarity measure of the registration algorithm. Hand-tuning these parameters would result in sub optimal solutions and quickly become infeasible as the number of metrics increases. Furthermore, such hand-crafted combination can only happen at global scale (entire volume) and therefore will not be able to account for the different tissue properties. We propose a learning algorithm for estimating these parameters locally, conditioned to the data semantic classes. The objective function of our formulation is a special case of non-convex function, difference of convex function, which we optimize using the concave convex procedure. As a proof of concept, we show the impact of our approach on three challenging datasets for different anatomical structures and modalities.\",\n \"Title: Graph-Based Slice-to-Volume Deformable Registration.\\nYear: 2018\\n\\nAbstract: Deformable image registration is a fundamental problem in computer vision and medical image computing. In this paper we investigate the use of graphical models in the context of a particular type of image registration problem, known as slice-to-volume registration. We introduce a scalable, modular and flexible formulation that can accommodate low-rank and high order terms, that simultaneously selects the plane and estimates the in-plane deformation through a single shot optimization approach. The proposed framework is instantiated into different variants seeking either a compromise between computational efficiency (soft plane selection constraints and approximate definition of the data similarity terms through pair-wise components) or exact definition of the data terms and the constraints on the plane selection. Simulated and real-data in the context of ultrasound and magnetic resonance registration (where both framework instantiations as well as different optimization strategies are considered) demonstrate the potentials of our method.\",\n \"Title: Deep Patch-Based Priors Under A Fully Convolutional Encoder-Decoder Architecture For Interstitial Lung Disease Segmentation\\nYear: 2018\\n\\nAbstract: Interstitial lung diseases (ILD) encompass a large spectrum of diseases sharing similarities in their physiopathology and computed tomography (CT) appearance. In this paper, we propose the adaption of a deep convolutional encoder-decoder (CED) that has shown high accuracy for image segmentation. Such architectures require annotation of the total region with pathological findings. This is difficult to acquire, due to uncertainty in the definition and extent of disease patterns and the need of significant human effort, especially for large datasets. Therefore, often current methods use patch-based implementations of convolutional neural networks, which however tend to produce spatially inhomogeneous segmentations due to their local contextual view. We exploit the advantages of both architectures by using the output of a patch-based classifier as a prior to a CED. Our method could advance the state-of-the-art in lung tissue segmentation using only a small number of newly annotated images.\",\n \"Title: Cooperative Object Segmentation and Behavior Inference in Image Sequences\\nYear: 2009\\n\\nAbstract: In this paper, we propose a general framework for fusing bottom-up segmentation with top-down object behavior inference over an image sequence. This approach is beneficial for both tasks, since it enables them to cooperate so that knowledge relevant to each can aid in the resolution of the other, thus enhancing the final result. In particular, the behavior inference process offers dynamic probabilistic priors to guide segmentation. At the same time, segmentation supplies its results to the inference process, ensuring that they are consistent both with prior knowledge and with new image information. The prior models are learned from training data and they adapt dynamically, based on newly analyzed images. We demonstrate the effectiveness of our framework via particular implementations that we have employed in the resolution of two hand gesture recognition applications. Our experimental results illustrate the robustness of our joint approach to segmentation and behavior inference in challenging conditions involving complex backgrounds and occlusions of the target object.\",\n \"Title: AtlasNet: Multi-atlas Non-linear Deep Networks for Medical Image Segmentation.\\nYear: 2018\\n\\nAbstract: Deep learning methods have gained increasing attention in addressing segmentation problems for medical images analysis despite the challenges inherited from the medical domain, such as limited data availability, lack of consistent textural or salient patterns, and high dimensionality of the data. In this paper, we introduce a novel multi-network architecture that exploits domain knowledge to address those challenges. The proposed architecture consists of multiple deep neural networks that are trained after co-aligning multiple anatomies through multi-metric deformable registration. This multi-network architecture can be trained with fewer examples and leads to better performance, robustness and generalization through consensus. Comparable to human accuracy, highly promising results on the challenging task of interstitial lung disease segmentation demonstrate the potential of our approach.\",\n \"Title: Weakly-Supervised Learning of Metric Aggregations for Deformable Image Registration.\\nYear: 2018\\n\\nAbstract: Deformable registration has been one of the pillars of biomedical image computing. Conventional approaches refer to the definition of a similarity criterion that, once endowed with a deformation model and a smoothness constraint, determines the optimal transformation to align two given images. The definition of this metric function is among the most critical aspects of the registration process. We argue that incorporating semantic information (in the form of anatomical segmentation maps) into the registration process will further improve the accuracy of the results. In this paper, we propose a novel weakly supervised approach to learn domain specific aggregations of conventional metrics using anatomical segmentations. This combination is learned using latent structured support vector machines (LSSVM). The learned matching criterion is integrated within a metric free optimization framework based on graphical models, resulting in a multi-metric algorithm endowed with a spatially varying similarity metric function conditioned on the anatomical structures. We provide extensive evaluation on three different datasets of CT and MRI images, showing that learned multi-metric registration outperforms single-metric approaches based on conventional similarity measures.\",\n \"Title: Multitemporal Very High Resolution From Space: Outcome of the 2016 IEEE GRSS Data Fusion Contest.\\nYear: 2017\\n\\nAbstract: In this paper, the scientific outcomes of the 2016 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society are discussed. The 2016 Contest was an open topic competition based on a multitemporal and multimodal dataset, which included a temporal pair of very high resolution panchromatic and multispectral Deimos-2 image...\",\n \"Title: Graph-Based Registration, Change Detection, and Classification in Very High Resolution Multitemporal Remote Sensing Data.\\nYear: 2016\\n\\nAbstract: In this paper, we propose a modular, scalable, metric-free, single-shot change detection/registration method. The developed framework exploits the relation between the registration and change detection problems, while under a fruitful synergy, the coupling energy term constrains adequately both tasks. In particular, through a decomposed interconnected graphical model, the registration similarity c...\",\n \"Title: Quasi Real-Time Sub-Space 3d Deformable Fusion\\nYear: 2015\\n\\nAbstract: In this paper we propose a novel and robust approach for deformable fusion using a metric defined in an appropriate subspace which is adaptive to the image-content/image-modality. We adopt a graph-based formulation that assumes that intensities of corresponding pixels in the two image domains are related through an unknown piece-wise constant linear function. This relation is propagated to an appropriate sub-space (wavelets coefficients) where a criterion that couples the estimation of the deformation field with optimal transport function on the subspace and the smoothness of the deformation is considered. Message passing methods efficiently implemented using jump flooding are considered to get the optimal parameters of the deformation field and the transport function. Promising results are obtained in near real-time with a unique parameter setting.\",\n \"Title: Spatiotemporal decomposition in object-space along reconstruction in emission tomography.\\nYear: 2008\\n\\nAbstract: Emission tomography has provided a new insight in brain mechanisms past years. Although reconstructions are nowadays mostly static, trend is going toward dynamic acquisitions and reconstructions. This opens a new range of investigations, for instance for drugs discovery. Indeed new drugs are studied through the dynamic ability of tissues to catch them. However, it is required to know radiotracer concentration of blood that irrigates tissues in order to draw conclusions on potentials of these drugs. This concentration is called 'input function' and this paper presents a new method for measuring it in a non-invasive way. Our new method relies on simultaneous estimations of vessels kinetics and vessels spatial distribution. These estimations are performed during the reconstruction process and take into account the statistical nature of measured signals. Indeed, this method is based on the maximisation of the likelihood of counts in detectors. It takes advantages of a non-negative matrix factorisation which separate spatial and temporal components. Results are very promising, since it estimates arterial input function accurately although object emits just a limited amount of photons, especially within the first minutes.\",\n \"Title: A Discrete MRF Framework for Integrated Multi-Atlas Registration and Segmentation.\\nYear: 2017\\n\\nAbstract: Multi-atlas segmentation has emerged in recent years as a simple yet powerful approach in medical image segmentation. It commonly comprises two steps: (1) a series of pairwise registrations that establish correspondences between a query image and a number of atlases, and (2) the fusion of the available segmentation hypotheses towards labeling objects of interest. In this paper, we introduce a novel approach that solves simultaneously for the underlying segmentation labels and the multi-atlas registration. The proposed approach is formulated as a pairwise Markov Random Field, where registration and segmentation nodes are coupled towards simultaneously recovering all atlas deformations and labeling the query image. The coupling is achieved by promoting the consistency between selected deformed atlas segmentations and the estimated query segmentation. Additional membership fields are estimated, determining the participation of each atlas in labeling each voxel. Inference is performed by using a sequential relaxation scheme. The proposed approach is validated on the IBSR dataset and is compared against standard post-registration label fusion strategies. Promising results demonstrate the potential of our method.\",\n \"Title: Understanding 3d emotions through compact anthropometric autoregressive models\\nYear: 2006\\n\\nAbstract: Reproducing realistic facial expressions is an important challenge in human computer interaction. In this paper we propose a novel method of modeling and recovering the transitions between different expressions through the use of an autoregressive process. In order to account for computational complexity, we adopt a compact face representation inspired from MPEG-4 standards while in terms of expressions a well known Facial Action Unit System (FACS) comprising the six dominant ones is considered. Then, transitions between expressions are modeled through a time series according to a linear model. Explicit constraints driven from face anthropometry and points interaction are inherited in this model and minimize the risk of producing non-realistic configurations. Towards optimal animation performance, a particular hardware architecture is used to provide the 3D depth information of the corresponding facial elements during the learning stage and the Random Sampling Consensus algorithm for the robust estimation of the model parameters. Promising experimental results demonstrate the potential of such an approach.\",\n \"Title: MRF-Based blind image deconvolution\\nYear: 2012\\n\\nAbstract: This paper proposes an optimization-based blind image deconvolution method. The proposed method relies on imposing a discrete MRF prior on the deconvolved image. The use of such a prior leads to a very efficient and powerful deconvolution algorithm that carefully combines advanced optimization techniques. We demonstrate the extreme effectiveness of our method1 by applying it on a wide variety of very challenging cases that involve the inference of large and complicated blur kernels.\",\n \"Title: Gene Expression High-Dimensional Clustering Towards A Novel, Robust, Clinically Relevant And Highly Compact Cancer Signature\\nYear: 2019\\n\\nAbstract: Precision medicine, a highly disruptive paradigm shift in healthcare targeting the personalizing treatment, heavily relies on genomic data. However, the complexity of the biological interactions, the important number of genes as well as the lack of substantial patient's clinical data consist a tremendous bottleneck on the clinical implementation of precision medicine. In this work, we introduce a generic, low dimensional gene signature that represents adequately the tumor type. Our gene signature is produced using LP-stability algorithm, a high dimensional center-based unsupervised clustering algorithm working in the dual domain, and is very versatile as it can consider any arbitrary distance metric between genes. The gene signature produced by LP-stability reports at least 10 times better statistical significance and 35% better biological significance than the ones produced by two referential unsupervised clustering methods. Moreover, our experiments demonstrate that our low dimensional biomarker (27 genes) surpass significantly existing state of the art methods both in terms of qualitative and quantitative assessment while providing better associations to tumor types than methods widely used in the literature that rely on several omics data.\",\n \"Title: AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia\\nYear: 2021\\n\\nAbstract: •An algorithm for automatic COVID-19 quantification based on 2D & 3D deep convolutional neural networks is presented.•A COVID-19 holistic, highly compact signature integrating imaging and clinical/ biological attributes is presented•An automatic Short and Long-term prognosis is presented, offering complementary means to facilitate triage for COVID-19.•A clinically-relevant quantification and staging tool validated by comparison with clinical experts is reported.\",\n \"Title: Exploring Deep Registration Latent Spaces\\nYear: 2021\\n\\nAbstract: Explainability of deep neural networks is one of the most challenging and interesting problems in the field. In this study, we investigate the topic focusing on the interpretability of deep learning-based registration methods. In particular, with the appropriate model architecture and using a simple linear projection, we decompose the encoding space, generating a new basis, and we empirically show that this basis captures various decomposed anatomically aware geometrical transformations. We perform experiments using two different datasets focusing on lungs and hippocampus MRI. We show that such an approach can decompose the highly convoluted latent spaces of registration pipelines in an orthogonal space with several interesting properties. We hope that this work could shed some light on a better understanding of deep learning-based registration methods.\",\n \"Title: Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment.\\nYear: 2021\\n\\nAbstract: Sarcopenia is a medical condition characterized by a reduction in muscle mass and function. A quantitative diagnosis technique consists of localizing the CT slice passing through the middle of the third lumbar area (L3) and segmenting muscles at this level. In this paper, we propose a deep reinforcement learning method for accurate localization of the L3 CT slice. Our method trains a reinforcement learning agent by incentivizing it to discover the right position. Specifically, a Deep Q-Network is trained to find the best policy to follow for this problem. Visualizing the training process shows that the agent mimics the scrolling of an experienced radiologist. Extensive experiments against other state-of-the-art deep learning based methods for L3 localization prove the superiority of our technique which performs well even with limited amount of data and annotations.\",\n \"Title: Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology\\nYear: 2021\\n\\nAbstract: The examination of histopathology images is considered to be the gold standard for the diagnosis and stratification of cancer patients. A key challenge in the analysis of such images is their size, which can run into the gigapixels and can require tedious screening by clinicians. With the recent advances in computational medicine, automatic tools have been proposed to assist clinicians in their ev...\",\n \"Title: High-Particle Simulation of Monte-Carlo Dose Distribution with 3D ConvLSTMs\\nYear: 2021\\n\\nAbstract: Monte-Carlo simulation of radiotherapy dose remains an extremely time-consuming task, despite being still the most precise tool for radiation transport calculation. To circumvent this issue, deep learning offers promising avenues. In this paper, we extend ConvLSTM to handle 3D data and introduce a 3D recurrent and fully convolutional neural network architecture. Our model's purpose is to infer a computationally expensive Monte Carlo dose calculation result for VMAT plans with a high number of particles from a sequence of simulations with a low number of particles. We benchmark our framework against other learning methods commonly used for denoising and other medical tasks. Our model outperforms the other methods with regards to several evaluation metrics used to assess the clinical viability of the predictions.\",\n \"Title: Joint Deformable Image Registration and ADC Map Regularization: Application to DWI-Based Lymphoma Classification\\nYear: 2022\\n\\nAbstract: The Apparent Diffusion Coefficient (ADC) is considered an importantimaging biomarker contributing to the assessment of tissue microstructure and pathophy- siology. It is calculated from Diffusion-Weighted Magnetic Resonance Imaging (DWI) by means of a diffusion model, usually without considering any motion during image acquisition. We propose a method to improve the computation of the ADC by coping jointly with both motion artifacts in whole-body DWI (through group-wise registration) and possible instrumental noise in the diffusion model. The proposed deformable registration method yielded on average the lowest ADC reconstruction error on data with simulated motion and diffusion. Moreover, our approach was applied on whole-body diffusion weighted images obtained with five different b-values from a cohort of 38 patients with histologically confirmed lymphomas of three different types (Hodgkin, diffuse large B-cell lymphoma and follicular lymphoma). Evaluation on the real data showed that ADC-based features, extracted using our joint optimization approach classified lymphomas with an accuracy of approximately 78.6% (yielding a 11% increase in respect to the standard features extracted from unregistered diffusion-weighted images). Furthermore, the correlation between diffusion characteristics and histopathological findings was higher than any other previous approach of ADC computation.\",\n \"Title: Region-Guided CycleGANs for Stain Transfer in Whole Slide Images\\nYear: 2022\\n\\nAbstract: In whole slide imaging, commonly used staining techniques based on hematoxylin and eosin (H&E) and immunohistochemistry (IHC) stains accentuate different aspects of the tissue landscape. In the case of detecting metastases, IHC provides a distinct readout that is readily interpretable by pathologists. IHC, however, is a more expensive approach and not available at all medical centers. Virtually generating IHC images from H&E using deep neural networks thus becomes an attractive alternative. Deep generative models such as CycleGANs learn a semantically-consistent mapping between two image domains, while emulating the textural properties of each domain. They are therefore a suitable choice for stain transfer applications. However, they remain fully unsupervised, and possess no mechanism for enforcing biological consistency in stain transfer. In this paper, we propose an extension to CycleGANs in the form of a region of interest discriminator. This allows the CycleGAN to learn from unpaired datasets where, in addition, there is a partial annotation of objects for which one wishes to enforce consistency. We present a use case on whole slide images, where an IHC stain provides an experimentally generated signal for metastatic cells. We demonstrate the superiority of our approach over prior art in stain transfer on histopathology tiles over two datasets. Our code and model are available at https://github.com/jcboyd/miccai2022-roigan.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":193,"string":"193"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13686,"string":"13,686"}}},{"rowIdx":13687,"cells":{"question_id":{"kind":"string","value":"Yann Hang Lee"},"question":{"kind":"string","value":"Generate an abstract for the title \"A nonblocking algorithm for shared queues using compare-and-swap\" using the following items: Nonblocking algorithms, Concurrent objects, Shared FIFO queues, Compare & swap operation, System latency."},"answer":{"kind":"string","value":"Nonblocking algorithms for concurrent objects guarantee that an object is always accessible, in contrast to blocking algorithms in which a slow or halted process can render part or all of the data structure inaccessible to other processes. A number of algorithms have been proposed for shared FIFO queues, but nonblocking implementations are few and either limit the concurrency or provide inefficient solutions. The authors present a simple and efficient nonblocking shared FIFO queue algorithm with O(n) system latency, no additional memory requirements, and enqueuing and dequeuing times independent of the size of the queue. They use the compare & swap operation as the basic synchronization primitive. They model their algorithm analytically and with a simulation, and compare its performance with that of a blocking FIFO queue. They find that the nonblocking queue has better performance if processors are occasionally slow, but worse performance if some processors are always slower than others."},"correct_answer":{"kind":"string","value":"Nonblocking algorithms for concurrent objects guarantee that an object is always accessible, in contrast to blocking algorithms in which a slow or halted process can render part or all of the data structure inaccessible to other processes. A number of algorithms have been proposed for shared FIFO queues, but nonblocking implementations are few and either limit the concurrency or provide inefficient solutions. The authors present a simple and efficient nonblocking shared FIFO queue algorithm with O(n) system latency, no additional memory requirements, and enqueuing and dequeuing times independent of the size of the queue. They use the compare & swap operation as the basic synchronization primitive. They model their algorithm analytically and with a simulation, and compare its performance with that of a blocking FIFO queue. They find that the nonblocking queue has better performance if processors are occasionally slow, but worse performance if some processors are always slower than others."},"documents":{"kind":"list like","value":["Title: RTSOA: Real-Time Service-Oriented Architecture\nYear: 2006\n\nAbstract: This paper extends the traditional Service-Oriented Architecture (SOA) to a new Real-Time SOA (RTSOA) and proposes a framework for the new architecture, by providing real-time service modeling, design, code generation, simulation, deployment, execution, orchestration, and management. The concepts, architecture, and enabling technique are studied for Real-time SOA. In addition, an efficient algorithm is derived in the paper to find the optimal composition of real-time services subject to the real-time constraint.","Title: STUBcast - efficient support for concurrency control in broadcast-based asymmetric communication environment\nYear: 2001\n\nAbstract: Observing that it is impractical to use traditional methods to control concurrency in a broadcast-based asymmetric communication environment, we introduce a concurrency control protocol designed for broadcast-based transaction processing called STUBcast (Server Timestamp and Update Broadcast Supported Concurrency). STUBcast supports two new correctness criteria proposed - single serializability and local serializability. These criteria are weaker than global serializability but are practical and easier to achieve in a broadcast environment. This article also shows some simulation results. These results suggest that STUBcast could be very efficient in a realistic application environment","Title: ITB: intrusion-tolerant broadcast protocol in wireless sensor networks\nYear: 2006\n\nAbstract: A large-scale wireless sensor network relies on broadcast protocol for data communication. This paper presents an idea for a base station to reliably and securely broadcast announcing messages to the network in an intrusion-tolerant way. Our Intrusion-Tolerant Broadcast Protocol based on Mutual Verification scheme guarantees that no forged information can be propagated to the network if an intruder is assumed to not compromise the base station. We show that our proposed protocol successfully runs and also demonstrate the effectiveness of the protocol by introducing a theorem and the proof of the theorem.","Title: Look-ahead routing switches for multistage interconnection networks\nYear: 1993\n\nAbstract: Multistage interconnection networks have been implemented in several prototype parallel systems as well as marketed products. One major performance drawback of this type network is due to link contention. This contention may occur when messages share part of their routes with each other. Especially, under hot-spot traffic, the network can be easily saturated and provides a drastically degraded performance. To remedy this problem, we propose a switch design which uses two independent routes between each input port and each output port within a switch. With proper routing control mechanisms, the adverse effect on link conflicts can be greatly reduced. The design needs neither additional connection between switches nor message resequencing for maintaining serialization. Various simulations are used to illustrate the performance improvement of this new design under both uniform and hot-spot traffic patterns.","Title: Efficient Java Native Interface for Android Based Mobile Devices\nYear: 2011\n\nAbstract: Java has been making its way into the embedded systems and mobile devices like Android. The Java platform specifies the Java Native Interface (JNI) which allows Java code that runs within a JVM to interoperate with applications or libraries that are written in other languages and compiled to the host CPU. JNI plays an important role in embedded system as it provides a mechanism to interact with libraries specific to the platform and to take the advantage of fast execution of native programs. To address the overhead incurred in the JNI due to reflection and serialization, this paper proposes to cache class, field, and method information obtained from reflection for subsequent usage. It also provides a function to pin objects to their memory locations such that they can be accesses through the known reference. The Android emulator is used to evaluate the performance of these techniques and we observed that there was 10-30 % performance gain in the Java Native Interface for two Android applications.","Title: Java Real-Time Publish-Subscribe Middleware for Distributed Embedded Systems\nYear: 2000\n\nAbstract: As the complexity of distributed embedded systems is increasing, and frequent upgrades and maintenance are expected, conventional communication models, such as point-to-point and the client-server model are not adequate to keep up with these trends. So we propose a generic Java real-time communication model based on the publish-subscribe model which has proper characteristics for distributed applications. The proposed Java publish-subscribe model has been implemented as middleware, named Java Embedded Bus (JEB). To easily support heterogeneous communication technologies, we propose a middleware-independent plug-and-play JEB device driver model. We have also prototyped the middleware on the demonstrative distributed embedded systems which comprised embedded machines and workstations connected by VME bus and Ethernet.","Title: Addendum to Voltage-Clock-Scaling Adaptive Scheduling Techniques for Low Power in Hard Real-Time Systems\nYear: 2004\n\nAbstract: First Page of the Article","Title: An Efficient Scheduling Discipline for Packet Switching Networks Using Earliest Deadline First Round Robin\nYear: 2003\n\nAbstract: This paper addresses a frame-oriented scheduling discipline, EDF-RR (earliest deadline first round robin), for OQ (output-queued) switch architecture and data traffic consisting of fixed-length cells. Bandwidth reservation for an active session is performed by holding a number of cell slots for the session in a repeatedly-transferred frame. Each cell that is going to be transferred in the frame is assigned a virtual release time and a virtual deadline according to the bandwidth reservation scheme. The transmitting order of the cells in the frame is determined by non-preemptive non-idling EDF algorithm so that cells of a backlogged session in the frame are distributed as uniformly as possible. Through the analysis applying real-time scheduling theory and network calculus as well as network simulation, EDF-RR takes the advantage of O(1) computational complexity, and possesses tight delay bounds and lenient buffer requirements. The proposed scheduling discipline is appropriate for distributed real-time systems as we show that sessions can be configured based on message traffic models and deadline requirements. Also, a modified version of EDF-RR, called EDF-DRR, can be applied as traffic regulator when jitter requirements exist among active sessions.","Title: Voltage-Clock Scaling for Low Energy Consumption in Real-Time Embedded Systems\nYear: 1999\n\nAbstract: Low power and energy consumption will always be an essential requirement in many real-time embedded applications. Voltage scaling is a relative novel approach to reducing energy consumption. The idea is that a processor can be run either at high or at low voltage: at high voltage, the clock rate is high but so is the power consumption; at low voltage the clock rate is lower, but the power consumption drops by a greater factor. This immediately suggests a powerful approach to lowering energy consumption in real-time systems. In this paper, we introduce static and dynamic algorithms to control processor voltage to reduce energy consumption. We demonstrate by simulation that our algorithms can significantly reduce total energy consumption.","Title: Voltage-Clock Scaling for Low Energy Consumption in Fixed-Priority Real-Time Systems\nYear: 2003\n\nAbstract: Power and energy constraints are becoming increasingly prevalent in real-time embedded systems. Voltage-scaling is a promising technique to reduce energy and power consumption: clock speed tends to decrease linearly with supply voltage while power consumption goes down quadratically. We therefore have a tradeoff between the energy consumption of a task and the speed with which it can be completed. The timing constraints associated with real-time tasks can be used to resolve this tradeoff. In this paper, we present two algorithms for voltage-scaling. Assuming that a processor can operate in one of two modes: high voltage and low voltage, we show how to schedule the voltage settings so that deadlines are met while reducing the total energy consumed. We show that significant reductions can be made in energy consumption.","Title: Ontology-Based Service Composition Framework for Syndicating Building Intelligence\nYear: 2009\n\nAbstract: Building Information Model (BIM) encompasses building geometry, spatial relationships, geographic information, and quantities and properties of building components; however, it does not include any information related to the functions that a building might require. On the other side, Service–Oriented computing emerged as a promising technology to enable fast, efficient software development. Even though features such as standard protocols, loosely coupled components, ontology-enabled dynamic discovery and composition of SOA seems a perfect fit for the building construction domain, the potential gain of applying SOA techniques has not really been discovered. In the paper, an ontology-based service composition framework for construction domain is proposed, and BIM is extended to include function requirements and service specifications. An example is shown to demonstrate the advantages over traditional techniques also.","Title: The performance of a watchdog protocol for wireless network security\nYear: 2007\n\nAbstract: In wireless networks, a watchdog mechanism can be a part of the security architecture used to identify nodes that fail to forward the packets they agree to forward. In this paper, we propose two protocols to address the ambiguous collision limitation of watchdogs. The Watchdog Alert (WA) and Watchdog Confirmation (WC) protocols are variants of the Carrier Sense Multiple Access with Collision Avoidance Protocol (CSMA/CA). We analyse the trade-offs between throughput and watchdog success probability of each protocol, comparing it to CSMA/CA.","Title: Measurement and Application of Fault Latency\nYear: 1986\n\nAbstract: The time interval between the occurrence of a fault and the detection of the error caused by the fault is divided by the generation of that error into two parts: fault latency and error latency. Since the moment of error generation is not directly observable, all related works in the literature have dealt with only the sum of fault and error latencies, thereby making the analysis of their separate effects impossible. To remedy this deficiency, we 1) present a new methodology for indirectly measuring fault latency, 2) derive the distribution of fault latency from the methodology, and 3) apply the knowledge of fault latency to the analysis of two important examples.","Title: Integrated scheduling with garbage collection for real-time embedded applications in CLI\nYear: 2006\n\nAbstract: We present a schedulable garbage collection for realtime applications in virtual machine environments. The design objective is to make the pause time caused by garbage collection operations controllable, and the invocation of garbage collection predictable. Thus, real-time applications can be schedulable along with garbage collection. We develop a prototype for a schedulable garbage collection in MONO CLI execution environment. A cost model of garbage collection is established based on measured WCET to predict the execution time and overhead of garbage collection operations. A scheduling algorithm of garbage collection and application tasks is presented to illustrate how the time and memory constraints of real-time systems can be met. The experiment result of the scheduling algorithm for a periodic task set on the prototype is included in the paper","Title: Software architecture supporting integrated real-time systems\nYear: 2003\n\nAbstract: To achieve reliability, reusability, and cost reduction, a significant trend in building large complex real-time systems is to integrate separate application modules of different criticalities in a common hardware platform. An essential requirement of integrated real-time systems is to guarantee spatial and temporal partitioning among applications in order te ensure an exclusive access of physical and temporal resources to the applications. In this paper we propose software architecture, implemented as SPIRIT-µKernel, for strongly partitioned integrated real-time systems. The SPIRIT-µKernel has been designed and implemented based on a two-level hierarchical scheduling methodology such that the real-time constraints of each application can be guaranteed. To demonstrate the feasibility of the SPIRIT-µKernel, we have ported two real-time operating systems (RTOS), WindRiver's VxWorks and Cygnus's eCos, on the top of the microkernel. Thus, different RTOS can be applied in various partitions to provide required features for each application. Based on the measured performance results, the SPIRIT-µKernel architecture is practical and appealing due to its low overheads of kernel services and the support for dependable integration of real-time applications via scheduling algorithm.","Title: Replay Debugging for Multi-threaded Embedded Software\nYear: 2010\n\nAbstract: The non-deterministic behavior of multi-threaded embedded software makes cyclic debugging difficult. Even with the same input data, consecutive runs may result in different executions and reproducing the same bug is itself a challenge. Despite the fact that several approaches have been proposed for deterministic replay, none of them attends to the capabilities and functionalities that replay can comprise for better debugging. This paper introduces a practical replay mechanism for multi-threaded embedded software. The Replay Debugger, based on Lamport clock, offers a user controlled debugging environment in which the program execution follows the identical partially ordered happened-before dependency among threads and IO events as that of the recorded run. With the order of thread synchronizations assured, users can focus their debugging effort in the program behavior of any threads while having a comprehension of thread-level concurrency. Using a set of benchmark programs, experiment results of a prototyped implementation show that, in average, the software based approach incurs a small probe effect of 3.3% in its record stage.","Title: Resource Scheduling in Dependable Integrated Modular Avionics\nYear: 2000\n\nAbstract: In the recent development of avionics systems, Integrated Modular Avionics (IMA) is advocated for next generation architecture that needs integration of mixed-criticality real-time applications. These integrated applications meet their own timing constraints while sharing avionics computer resources. To guarantee timing constraints and dependability of each application, an IMA-based system is equipped with the schemes for spatial and temporal partitioning. We refer the model as SP-RTS (Strongly Partitioned Real-Time System), which deals with processor partitions and communication channels as its basic scheduling entities.This paper presents a partition and channel-scheduling algorithm for the SP-RTS. The basic idea of the algorithm is to use a two-level hierarchical schedule that activates partitions (or channels) following a distance-constraints guaranteed cyclic schedule and then dispatches tasks (or messages) according to a fixed priority schedule. To enhance schedulability, we devised heuristic algorithms for deadline decomposition and channel combining. The simulation results show the schedulability analysis of the two-level scheduling algorithm and the beneficial characteristics of the proposed deadline decomposition and channel combining algorithms.","Title: Ontology-Based Smart Home Solution and Service Composition\nYear: 2009\n\nAbstract: Given the diversity of home environment, appliances, and residents, the applications for smart homes must be configurable and adaptive. Instead of programming each household, we propose an ontology-based framework to facilitate the automatic composition of appropriate applications. The system composes appropriate services depending upon the available equipments in each individual household automatically. Meanwhile, it dynamically adjusts the environment parameters to match the customer needs and to encompass the available resource. With its supporting on customized function template editing, customers are able to specify their usual behavior templates as different mode.","Title: SPIRIT-/spl mu/Kernel for strongly partitioned real-time systems\nYear: 2000\n\nAbstract: To achieve reliability, reusability and cost reduction, a significant trend of building large, complex real-time systems is to integrate separated application modules. An essential requirement of integrated real-time systems is to guarantee strong partitioning among applications. In this paper, we propose a microkernel, called the SPIRIT (Strongly Partitioned Integrated Real-tIme sysTem) /spl mu/Kernel, for strongly partitioned real-time systems. The SPIRIT-/spl mu/Kernel has been designed and implemented based on a two-level hierarchical scheduling methodology such that the real-time constraints of each application can be guaranteed. It provides a minimal set of kernel functions, such as address management, interrupt/exception dispatching, inter-application communication and application scheduling. To demonstrate the feasibility of the SPIRIT-/spl mu/Kernel, we have ported two different application-level real-time operating systems (RTOSs)-WindRiver's VxWorks and Cygnus's eCos-on the top of the microkernel. The SPIRIT-/spl mu/Kernel architecture is practical and appealing due to its low overheads of kernel services and the support for dependable integration of real-time applications.","Title: Schedulable online testing framework for real-time embedded applications in VM\nYear: 2007\n\nAbstract: The paper suggests a VM-based online testing approach in which software testing is piggybacked at runtime on a system that operates to serve actual mission. Online testing in VM is facilitated with a framework that uses persistence service to initialize the testing operation with a consistent system state. The testing operation then runs in an isolated domain which can be scheduled independently of the operating version. Thus, testing operation cannot cause unbounded pause time nor spoil the normal operation. We evaluate the feasibility of schedulable online testing with a prototype developed in MONO CLI (Common Language Infrastructure) and the experiment on the prototype.","Title: Optimal Scheduling of Signature Analysis for VLSI Testing\nYear: 1991\n\nAbstract: A simple algorithm that shows how to optimally schedule the test-application and the signature-analysis phases of VLSI testing is presented. The testing process is broken into subintervals, the signature is analyzed at the end of each subinterval, and future tests are aborted if the circuit is found to be faulty, thus saving test time. The mathematical proofs associated with the algorithm are given.","Title: Secure localization and location verification in sensor networks\nYear: 2005\n\nAbstract: Evolving networks of wireless sensing nodes have many attractive applications. Localization, that is to determine the locations of sensor nodes, is a key enabler for many applications of sensor networks. As sensor networks move closer to extensive deployments, security becomes a major concern, resulting in a new demand for secure localization and location proving systems. In this paper, we propose a secure localization system based on a secure hop algorithm. A location verification system that allows the base station to verify the locations of sensor nodes is also presented. It prevents a node from falsifying reported location information. The evaluation of the secure localization system shows that the proposed scheme performs well under a range of scenarios tested and is resilient against an attacker creating incorrect location information in other nodes.","Title: On robust transaction routing and load sharing\nYear: 1991\n\nAbstract: In this paper we examine the issue of robust transaction routing in a locally distributed database environment where transaction characteristics such as reference locality imply that certain processing systems can be identified as being more suitable than others for a given transaction class. A response time based routing strategy can strike a balance between indiscriminate sharing of the load and routing based only on transaction affinity. Since response time estimates depend on workload and system parameters that may not be readily available, it is important to examine the robustness of routing decisions to information accuracy. We find that a strategy which strictly tries to minimize the response time of incoming transactions is sensitive to the accuracy of certain parameter values. On the other hand, naive strategies, that simply ignore the parameters in making routing decisions, have even worse performance. Three alternative strategies are therefore examined: threshold, discriminatory, and adaptive. Instead of just optimizing an incoming transaction's response time, the first two strategies pursue a strategy that is somewhat more oriented towards global optimization. This is achieved by being more restrictive on either the condition or the candidate for balancing the load. The third strategy, while trying to minimize the response time of individual incoming transactions, employs a feedback process to adaptively adjust future response time estimates. It monitors the discrepancy between the actual and estimated response times and introduces a correction factor based on regression analysis. All three strategies are shown to be robust with respect to the accuracy of workload and system parameters used in the response time estimation.","Title: A Systematic Approach for Integrating Fault Trees into System Statecharts\nYear: 2008\n\nAbstract: As software systems are encompassing a wide range of fields and applications, software reliability becomes a crucial step. The need for safety analysis and test cases that have high probability to uncover plausible faults are necessities in proving software quality. System models that represent only the operational behavioral of a system are incomplete sources for deriving test cases and performing safety analysis before the implementation process. Therefore, a system model that encompasses faults is required. This paper presents a technique that formalizes a safety model through the incorporation of faults with system specifications. The technique focuses on introducing semantic faults through the integration of fault trees with system specifications or statechart. The method uses a set of systematic transformation rules that tries to maintain the semantics of both fault trees and statechart representations during the transformation of fault trees into statechart notations.","Title: A schedulable garbage collection for embedded applications in CLI\nYear: 2005\n\nAbstract: Common language infrastructure (CLI) has been introduced as a core technology of Microsoft .NET. It enables \"writing in multiple languages, running in multiple platforms\" by providing virtual execution system (VES), common intermediate language, and common type system etc. The advantages of using CLI, including portability, compactness, and interoperability, could benefit the productivity of application software development and deployment. However, for embedded real-time systems, the applications' time-constraints cannot be satisfied easily due to several features of CLI runtime environment, such as thread priority, thread scheduling, garbage collection etc. In this paper, we aim to have a garbage collection mechanism applicable on real-time applications in CLI and other virtual machine environments. We achieve the goal by making the pause time of garbage collection operations predictable, and the invocation of garbage collection and applications schedulable. A cost model based on measured WCET is established to predict the execution time and overhead of garbage collection operations.","Title: Optimization criteria for checkpoint placement\nYear: 1984\n\nAbstract: Mean response time and availability as optimization criteria for checkpoint placement are better replaced by workable formulas that calculate the ratio between the marginal gain accrued to users who experience system failure and the (presumably slight) loss suffered on average by all users.","Title: A Genetic Algorithm Based Approach for Event Synchronization Analysis in Real-Time Embedded Systems\nYear: 2009\n\nAbstract: In real-time embedded systems, due to race conditions, synchronization order between events may be different from one execution to another. This behavior is permissible as in concurrent systems, but should be fully analyzed to ensure the correctness of the system. In this paper, a new intelligent method is presented to analyze event synchronization sequence in embedded systems. Our goal is to identify the feasible sequence, and to determine timing parameters that lead to these sequences. Our approach adopts timed event automata (TEA) to model the targeted embedded system and use a race condition graph (RCG) to specify event synchronization sequence (SYN-Spec). A genetic algorithm working with simulation is used to analyze the timing parameters in the target model and to verify whether a defined SYN-Spec is satisfied or not. A case study shows that the method proposed is able to find potential execution sequences according to the event synchronization orders.","Title: Improving multistage network performance under uniform and hot-spot traffics\nYear: 1990\n\nAbstract: The major performance drawbacks of multistage interconnection networks are due to link contention and message blockage. To remedy this problem, the authors propose a switch design which uses two independent routes between each input port and each output port within a switch. With proper routing control mechanisms, the adverse effect on link conflicts can be greatly reduced. Various simulations are used to illustrate the performance improvement of this new design under both uniform and hot-spot traffic patterns","Title: Location verification using bidirectional one-way hash function in wireless sensor networks\nYear: 2007\n\nAbstract: Evolving networks of wireless ad hoc sensing nodes have many attractive applications. Localisation, that is to determine where a given node is physically located, is a key enabler for many applications of sensor networks. As sensor networks move closer to extensive deployments, security becomes a major concern, resulting in a new demand for secure localisation and location proving systems. We propose a secure localisation system based on a secure hop algorithm. A location verification system that allows the base station to verify the location claims of sensor nodes is also presented. It prevents a node from falsifying reported location information and can be used for location-based access control. In order to support the resource-constrained sensor nodes, the proposed approach makes use of efficient one-way hash functions. Finally, we report the evaluation of the secure localisation system. The proposed scheme performs well under a range of scenarios tested and is resilient against an attacker creating incorrect location information in other nodes.","Title: Key management in wireless sensor networks\nYear: 2004\n\nAbstract: Wireless sensor networks hold the promise of facilitating large-scale, real-time data processing in complex environments. As sensor networks edge closer towards widespread deployment, security issues become a central concern. Key management in wireless sensor networks is a challenging problem. Computationally complex asymmetric crypto techniques are unsuitable for use in resource-constrained sensor nodes and use of symmetric cryptography makes the entire network vulnerable in the event of node compromise. This paper presents a key management scheme that satisfies both operational and security requirements of distributed wireless sensor networks. The proposed scheme accommodates other techniques like data aggregation for energy efficiency and watchdog mechanism for intrusion monitoring. Also, the proposed scheme is generic enough so that it can be applied to variety of sensor network protocols.","Title: Optimal reconfiguration strategy for a degradable multimodule computing system\nYear: 1987\n\nAbstract: A new quantitative approach to the problem of reconfiguring a degradable multimodule system is presented. The approach is concerned with both assigning some modules for computation and arranging others for reliability. Conventionally, a fault-tolerant system performs reconfiguration only upon a subsystem failure. Since there exists an inherent trade-off between the computation capacity and fault tolerance of a multimodule computing system, the conventional approach is a passive action and does not yield a configuration that provides an optimal compromise for the trade-off. By using the expected total reward as the optimal criterion, the need and existence of an active reconfiguration strategy, in which the system reconfigures itself on the basis of not only the occurrence of a failure but also the progression of the mission, are shown.Following the problem formulation, some important properties of an optimal reconfiguration strategy, which specify (i) the times at which the system should undergo reconfiguration and (ii) the configurations to which the system should change, are investigated. Then, the optimal reconfiguration problem is converted to integer nonlinear knapsack and fractional programming problems. The algorithms for solving these problems and a demonstrative example are given. Extensions of the optimal reconfiguration problem are also discussed.","Title: Partition Scheduling in APEX Runtime Environment for Embedded Avionics Software\nYear: 1998\n\nAbstract: Advances in the computer technology encouraged the avionics industry to replace the federated design of control units with an integrated suite of control modules that share the computing resources. The new approach, which is called integrated modular avionics (IMA), cart achieve substantial cost reduction in the development, operation and maintenance of airplanes. A set of guidelines has been developed by the avionics industry to facilitate the development and certification of integrated systems. Among them, a software architecture is recommended to address real-time and fault-tolerance requirements. According to the architecture, applications are classified into partitions supervised by an operating system executive. A general-purpose application/executive (APEX) interface is defined which identifies the minimum functionality provided to the application software of an NMA system. To support the temporal partitioning between applications, APEX interface requires a deterministic cyclic scheduling of partitions at the O/S level and a fixed priority scheduling among processes within each partition. In this paper we propose a scheduling scheme for partitions in APEX. The scheme determines the frequency that each partition must be invoked and the assignment of processor capacity on every invocation. Then a cyclic schedule at the O/S level can be constructed and all processes within each partition can meet their deadline requirements.","Title: Schedulable garbage collection in CLI virtual execution system\nYear: 2007\n\nAbstract: Virtual software execution environment, known as Virtual Machine (VM), has been gaining popularity through Java Virtual Machine (JVM) and Common Language Infrastructure (CLI). Given their advantages in portability, productivity, and safety, etc., applying VM to real-time embedded systems can leverage production cost, fast time-to-market, and software integrity. However, this approach can only become practical once the VM operations and application tasks are made schedulable jointly. In this paper, we present a schedulable garbage collection algorithm applicable on real-time applications in CLI virtual machine environment. To facilitate the scheduling of real-time applications and garbage collection operations, we make the pause time due to garbage collection controllable, and the invocation of garbage collection predictable. To demonstrate the approach, a prototype for a schedulable garbage collection has been implemented in CLI execution environment. The garbage collection is carried out by a concurrent thread while meeting a targeted pause time and satisfying the memory requests of applications. A cost model of garbage collection is established based on measured WCET such that the execution time and overhead of garbage collection operations can be predicted. Finally, we illustrate a joint scheduling algorithm to meet the time and memory constraints of real-time systems.","Title: Design of HM2p A Hierarchical Multimicroprocessor for General-Purpose Applications\nYear: 1982\n\nAbstract: This paper presents a tree-structured multiprocessor called the hierarchical multimicroprocessor (HM2p), each node of which is composed of a cluster of processor modules (PM's), common memory, DMA interface, switches, communication lines, and a data processor associated with it. The HM2p consists of two different hierarchies, one for data processing and the other for data distribution, which provide clean, structured separation between processing components and user interface components.","Title: Schedulable persistence system for teal-time applications in virtual machine\nYear: 2006\n\nAbstract: Persistence in applications saves a computation state that can be used to facilitate system recovery upon failures. As we begin to adopt virtual execution environments (VMs) for mission-critical real-time embedded applications, persistence service will become an essential part of VM to ensure high availability of the systems.In this paper, we focus in a schedulable persistence system in VMs and show a prototype persistence system constructed on CLI 's open source platform, MONO. By employing object serialization, the system enables concurrent and preemptible persistence operation, i.e., the task in charge of persistence service runs concurrently with application tasks and is a target of real-time scheduling. Thus, the execution of application tasks can be interleaved with the operations of persistence service, and the task timeliness can be guaranteed as the pause time caused by persistence service is bounded. The experiment output on the prototyped system illustrates that persistence service is appropriate for realtime applications because of its controllable pause time and its optimized overhead.","Title: DC/sup 2/ scheduling for aperiodic tasks in strongly partitioned real-time systems\nYear: 2000\n\nAbstract: The strongly partitioned real time system (SP-RTS) provides temporal and spatial partitioning for its integrated real time applications. The SP-RTS architecture adopts a two-level hierarchical scheduling mechanism. In the lower level, multiple partitions (applications) are dispatched under a cyclic scheduling, whereas, in the higher level, multiple periodic tasks of each partition are scheduled according to a fixed priority algorithm. The authors focus on designing algorithms for online scheduling of aperiodic tasks in the SP-RTS environment. The Distance Constraint guaranteed Dynamic Cyclic (DC/sup 2/) scheduler that is proposed uses these basic operations: Left Sliding (LS), Right Putting (RP), and Compacting. These operations dynamically schedule aperiodic tasks within a distance-constrained cyclic schedule. We show that the dynamic applications of these operations do not violate the distance constraint characteristics of a partition cyclic schedule. In addition, the slack time calculation of these dynamic operations can be applied for scheduling hard aperiodic tasks. With the simulation studies, we observed that the DC/sup 2/ can result in a significant performance enhancement in terms of the average response time of soft aperiodic tasks and the acceptance rate for hard aperiodic tasks.","Title: Service-oriented smart home applications: composition, code generation, deployment, and execution\nYear: 2012\n\nAbstract: A smart home usually has a variety of devices or home appliance, instead of designing software for a specific home, this paper proposes a service-oriented framework with a set of ontology systems to support service and device publishing, discovery of devices and their services, composition of control software using existing control services that wrap devices, deployment, and execution of the composed service in an computing environment, monitoring the execution, and recovery from device failure. The ontology systems specify semantic information about devices, services, and workflows used in various smart home, and users can compose and recompose services for their specific needs. New devices, workflows, and services can be added into ontology. Most of the steps in this process can be automated including code generation. For example, service composition will be carried out in three steps: abstract workflow design, function construction, and device discovery, and different codes can be generated for different computing platforms such as Java and Open Services Gateway initiative environments. In this way, a variety of smart home can be constructed rapidly using the framework by discovery and composition using existing services and workflows. This paper illustrates this framework using a media control example to illustrate the ontology, discovery, composition, deployment, execution, monitoring, and recovery.","Title: Design and Evaluation of a Fault-Tolerant Multiprocessor Using Hardware Recovery Blocks\nYear: 1984\n\nAbstract: In this paper we consider the design and evaluation of a fault-tolerant multiprocessor with a rollback recovery mechanism.","Title: Bounded energy allocation and scheduling for real-time Embedded Systems\nYear: 2005\n\nAbstract: For energy-constrained real-time embedded systems, the power-delay tradeoff property of Volta ge-Clock Scaling (VCS) needs to be carefully considered in scheduling real-time tasks for meeting strict timing requirements. In addition, non-real-time tasks require minimized response times. Thus, a bounded energy allocation model should be adopted to solve a combination of optimization objectives in systems having mixed hard and soft real-time tasks. In this paper, we propose an energy sharing model that allocates the energy budget among hard and soft real-time tasks, exploiting the interplay between utilization and energy consumption in VCS-based earliest-deadline-first scheduling (VCS-EDF). Also a dynamic scheduling is presented which is designed to reduce energy consumption by switching between two scheduling policies and utilizing an explicit pattern of event occurrences at run-time. Through simulations, we show that this Dual-Policy Dynamic Scheduling can outperform in the reduction of energy consumption while introducing small delay in average response time of non-real-time tasks.","Title: Optimal Partitioning of Heterogeneous Traffic Sources in Mobile Communications Networks\nYear: 1997\n\nAbstract: Given n heterogeneous traffic sources which generate multiple types of traffic among themselves, we consider the problem of finding a set of disjoint clusters to cover n traffic sources. The objective is to minimize the total communication cost for the entire system in the context that the intracluster communication is less expensive than the intercluster communication. Different from the general graph partitioning problem, our work takes into account the physical topology constraints of the linear arrangement of physical cells in highway cellular systems and the hexagonal mesh arrangement of physical cells in cellular systems. In our partitioning schemes, the optimal partitioning problem is transformed into an equivalent problem with a relative cost function, which generates the communication cost differences between the intracluster communications and the intercluster communications.For highway cellular systems, we have designed an efficient optimal partitioning algorithm of O(mn2) by dynamic programming, where m is the number of clusters of n base stations. The algorithm also finds all the valid partitions in the same polynomial time, given the size constraint on a cluster and the total allowable communication cost for the entire system. For hexagonal cellular systems, we have developed four heuristics for the optimal partitioning based on the techniques of moving or interchanging boundary nodes between adjacent clusters. The heuristics have been evaluated and compared through experimental testing and analysis.","Title: EDF scheduling using two-mode voltage-clock-scaling for hard real-time systems\nYear: 2001\n\nAbstract: Scaling down power supply voltage yields a quadratic reduction in dynamic power dissipation and also requires a reduction in clock frequency. In order to meet task deadlines in hard real-time systems, the delay penalty in voltage scaling needs to be carefully considered to achieve low power consumption. In this paper, we focus on dynamic reclaiming of early released resources in Earliest Deadline First (EDF) scheduling using voltage scaling. In addition to a static voltage assignment, we propose a new dynamic-mode assignment, which has a flexible voltage mode setting at run-time enabling much larger energy savings. Using simulation results and exploiting the interplay between power supply voltage, frequency, and circuit delay in CMOS technology, we find the optimal two-level voltage settings that minimize energy consumption.","Title: Optimal Dynamic Control of Resources in a Distributed System\nYear: 1989\n\nAbstract: First Page of the Article","Title: Sharing i/o in strongly partitioned real-time systems\nYear: 2004\n\nAbstract: Strongly partitioned real-time systems have been adopted to provide an integrated run time environment for applications with varied criticalities. This is achieved by guaranteeing spatial as well as temporal partitioning for the applications. To enable accesses to shared I/O devices in such an environment, this paper provides an effective model that the co-existence of any application does not hinder the execution of IO operations or spatial and temporal requirements of other applications. The model utilizes a microkernel-based approach to regulate the work of drive drivers and thus integrate the device-sharing model with the microkernel.","Title: Evaluation of Error Recovery Blocks Used for Cooperating Processes\nYear: 1984\n\nAbstract: Three alternatives for implementing recovery blocks (RB's) are conceivable for backward error recovery in concurrent processing. These are the asynchronous, synchronous, and the pseudorecovery point implementations. Asynchronous RB's are based on the concept of maximum autonomy in each of concurrent processes. Consequently, establishment of RB's in a process is made independently of others and unbounded rollback propagations become a serious problem. In order to completely avoid unbounded rollback propagations, it is necessary to synchronize the establishment of recovery blocks in all cooperating processes. Process autonomy is sacrificed and processes are forced to wait for commitments from others to establish a recovery line, leading to inefficiency in time utilization. As a compromise between asynchronous and synchronous RB's we propose to insert pseudorecovery points (PRP's) so that unbounded rollback propagations may be avoided while maintaining process autonomy. We developed probabilistic models for analyzing these three methods under standard assumptions in computer performance analysis, i.e., exponential distributions for related random variables. With these models we have estimated 1) the interval between two successive recovery lines for asynchronous RB's, 2) mean loss in computation power for the synchronized method, and 3) additional overhead and rollback distance in case PRP's are used.","Title: Dynamic Analysis of Embedded Software Using Execution Replay\nYear: 2014\n\nAbstract: For program optimization and debugging, dynamic analysis tools, e.g., profiler, data race detector, are widely used. To gather execution information, software instrumentation is often employed for its portability and convenience. Unfortunately, instrumentation overhead may change the execution of a program and lead to distorted analysis results, i.e., probe effect. In embedded software which usually consists of multiple threads and external inputs, program executions are determined by the timing of external inputs and the order of thread executions. Hence, probe effect incurred in an analysis of embedded software will be more prominent than in desktop software. This paper presents a reliable dynamic analysis method for embedded software using deterministic replay. The idea is to record thread executions and I/O with minimal record overhead and to apply dynamic analysis tools in replayed execution. For this end, we have developed a record/replay framework called P-Replayer, based on Lamport's happens-before relation. Our experimental results show that dynamic analyses can be managed in the replay execution enabled by P-Replayer as if there is no instrumentation on the program.","Title: Efficient Data Race Detection for C/C++ Programs Using Dynamic Granularity\nYear: 2014\n\nAbstract: To detect races precisely without false alarms, vector clock based race detectors can be applied if the overhead in time and space can be contained. This is indeed the case for the applications developed in object-oriented programming language where objects can be used as detection units. On the other hand, embedded applications, often written in C/C++, necessitate the use of fine-grained detection approaches that lead to significant execution overhead. In this paper, we present a dynamic granularity algorithm for vector clock based data race detectors. The algorithm exploits the fact that neigh boring memory locations tend to be accessed together and can share the same vector clock archiving dynamic granularity of detection. The algorithm is implemented on top of Fast Track and uses Intel PIN tool for dynamic binary instrumentation. Experimental results on benchmarks show that, on average, the race detection tool using the dynamic granularity algorithm is 43% faster than the Fast Track with byte granularity and is with 60% less memory usage. Comparison with existing industrial tools, Val grind DRD and Intel Inspector XE, also suggests that the proposed dynamic granularity approach is very viable.","Title: On the existence of probe effect in multi-threaded embedded programs\nYear: 2014\n\nAbstract: Software instrumentation has been a convenient and portable approach for dynamic analysis, debugging, or profiling of program execution. Unfortunately, instrumentation may change the temporal behavior of multi-threaded program execution and result in different ordering of thread operations, which is called probe effect. While the approaches to reduce instrumentation overhead, to enable reproducible execution, and to enforce deterministic threading have been studied, no research has yet answered if an instrumented execution has the same behavior as the program execution without any instrumentation and how the execution gets changed if there were any. In this paper, we propose a simulation-based analysis to detect the changes of execution event ordering that are induced by instrumentation operations. The execution model of a program is constructed from the trace of instrumented program execution and is used in a simulation analysis where instrumentation overhead is removed. As a consequence, we can infer the ordering of events in the original program execution and verify the existence of probe effect resulted from instrumentation.","Title: Scanning the issue - Special issue on real-time systems\nYear: 2003\n\nAbstract: Provides an overview of the technical articles and features presented in this issue.","Title: Voltage-clock-scaling adaptive scheduling techniques for low power in hard real-time systems\nYear: 2003\n\nAbstract: Abstract--Many embedded systems operate under severe power and energy constraints. Voltage clock scaling is one mechanism by which energy consumption may be reduced: It is based on the fact that power consumption is a quadratic function of the voltage, while the speed is a linear function. In this paper, we show how voltage scaling can be scheduled to reduce energy usage while still meeting real-time deadlines.","Title: Error Detection Process—Model, Design, and Its Impact on Computer Performance\nYear: 1984\n\nAbstract: Conventionally, reliability analyses either assume that a fault/error is detected immediately as it occurs, or ignore damage caused by imperfect detection mechanisms and error latency, namely, the time interval between the occurrence of an error and the detection of that error.","Title: Routing algorithm of minimizing maximum link congestion on grid networks.\nYear: 2015\n\nAbstract: As regular topology networks, grid networks are widely adopted in network deployment. Link congestion and routing path length are two critical factors that affect the delay and throughput of a network. In this paper, we study the routing problem in grid networks concerning these two factors. The main objective of our routing algorithms is to minimize the maximum link congestion. The shortest path minimum maximum (SPMM) link congestion and non-shortest path minimum maximum (NSPMM) link congestion routing problems are studied. The two problems are first formulated as integer optimization problems. Then, corresponding routing algorithms (SPMM and NSPMM routing algorithms) are proposed. For SPMM routing algorithm, the path length is optimal, while for NSPMM routing algorithm, the path is limited in a submesh. Thus, the path length can be bounded. At last, we compare the proposed routing algorithms under different scenarios with other popular routing algorithms (RowColumn, ZigZag, Greedy, Random routing algorithms). The performances are evaluated through different metrics including link congestion, path length, path congestion, path congestion to path length ratio, delay and throughput.","Title: Automatic Parallelization of Simulink Models for Multi-core Architectures\nYear: 2015\n\nAbstract: This paper addresses the problem of parallelizing existing single-rate Simulink models for embedded control applications on multi-core architectures considering communication cost between blocks on different CPU cores. Utilizing the block diagram of the Simulink model, we derive the dependency graph between the different blocks. In order to solve the scheduling problem, we describe a Mixed Integer Linear Programming (MILP) formulation for optimally mapping the Simulink blocks to different CPU cores. Since the number of variables and constraints for MILP solver grows exponentially when model size increases, solving this problem in a reasonable time becomes harder. For addressing this issue, we introduce a set of techniques for reducing the number of constraints in the MILP formulation. By using the proposed techniques, the MILP solver finds solutions that are closer to the optimal solution within a given time bound. We study the scalability and efficiency of our consisting approach with synthetic benchmarks of randomly generated directed acyclic graphs. We also use the \\\"Fault-Tolerant Fuel Control System\\\" demo from Simulink and a Diesel engine controller from Toyota as case studies for demonstrating applicability of our approach to real world problems.","Title: Teaching microprocessor systems design using a SoC and Embedded Linux platform\nYear: 2005\n\nAbstract: In traditional microprocessor systems design courses, students learn to develop assembly language programs to control peripherals, handle interrupts, and perform I/O operations. We adopt a 32-bit StrongARM architecture on the Motorola MX1ADS board with Embedded Linux to present a modern microprocessor system design course. With this new platform, we use a high-level language to develop projects that accelerate the students' learning curve. Embedded Linux also provides the necessary flexibility and tool set required for students to debug their own projects. Our students' responded very positively to this change. They were excited about the renewed course structure, the updated learning environment, and the challenging projects.","Title: Real-Time Support of Flash Memory File System for Embedded Applications\nYear: 2006\n\nAbstract: NAND flash memory has emerged as an attractive proposition for embedded storage applications due to its properties of robustness, low price, a high number of write-erase cycles, and large capacities. To make it suitable for real-time embedded applications, this paper presents the design of a log structured file system that utilizes data redundancy and parallel operations to guarantee bounded read-write access latencies to real-time tasks, in the presence of requests from non real-time tasks. The proposed scheme requires minimal support from the underlying operating system. In addition, the paper proposes a storage scheme to reduce RAM footprint for the log structured file system while preserving reliability and minimal read/write overhead.","Title: Automatic Parallelization of Multirate Block Diagrams of Control Systems on Multicore Platforms.\nYear: 2016\n\nAbstract: This article addresses the problem of parallelizing model block diagrams for real-time embedded applications on multicore architectures. We describe a Mixed Integer Linear Programming formulation for finding a feasible mapping of the blocks to different CPU cores. For single-rate models, we use an objective function that minimizes the overall worst-case execution time. We introduce a set of heuristics to solve the problem for large models in a reasonable time. For multirate models, we solve the feasibility problem for finding a valid mapping. We study the scalability and efficiency of our approach with synthetic benchmarks and an engine controller from Toyota.","Title: A Parallel FastTrack Data Race Detector on Multi-core Systems\nYear: 2017\n\nAbstract: Detecting data races in multithreaded programs is critical to ensure the correctness of the programs. To discover data races precisely without false alarms, dynamic detection approaches are often applied. However, the overhead of the existing dynamic detection approaches, even with recent innovations, is still substantially high. In this paper, we present a simple but efficient approach to parallelize data race detection in multicore SMP (Symmetric Multiprocessing) machines. In our approach, data access information needed for dynamic detection is collected at application threads and passed to de-tection threads. The access information is distributed in a way that the operation performed by each detection thread is inde-pendent of that of other detection threads. As a consequence, the overhead caused by locking operations in data race detection can be alleviated and multiple cores can be fully utilized to speed up and scale up the detection. Furthermore, each detection thread deals with only its own assigned memory access region rather than the whole address space. The executions of detection threads can exploit the spatial locality of accesses leading to an improved cache performance. We have applied our parallel approach on the FastTrack algorithm and demon-strated the validity of our approach on an Intel Xeon machine. Our experimental results show that the parallel FastTrack detector, on average, runs 2.2 times faster than the original FastTrack detector on the 8 core machine.","Title: A Smart Gateway Framework for IOT Services\nYear: 2016\n\nAbstract: With various connectivity and communication protocols, it is conceivable that all raw data from sensor devices can be made available in the Internet. However, this imposes a significant intricacy when dealing with the amount of information and the management of the devices. To alleviate the problem, this paper presents a design and implementation of a prototype IoT gateway framework that exposes high-level abstraction for accessing and manipulating the devices of an ambient environment and the data that they produce. Along with integrating and consolidating edge devices, the gateway framework enables the construction of high-level knowledge to represent the property of the environment and to support privacy protection. Thus, intelligent services, created with reasoning rules or event triggers, can be adaptable to application environment. To illustrate the capability of the IoT gateway framework, an application scenario and its performance evaluation are included in the paper.","Title: Dynamic Analysis of Multi-threaded Embedded Software to Expose Atomicity Violations\nYear: 2016\n\nAbstract: Concurrency bugs are one of the most notorious software bugs and may not be observed easily. Significant work has been done on detection of atomicity violations bugs for high performance systems but there is not much work related to detect these bugs for embedded systems. Although criteria to claim existence of bugs remains same, approach changes a bit for embedded systems. The main focus of this research is to develop a systemic methodology to address the issue from embedded systems perspective. A framework is developed which predicts the access interleaving patterns that may violate atomicity using memory references of shared variables and provides support to force and analyze these patterns for any output change, system fault, or change in execution path.","Title: SPDA: a security protocol for data aggregation in large-scale wireless sensor networks\nYear: 2006\n\nAbstract: In this paper we propose a new key establishment protocol enabling any data aggregation protocol to be operated securely. This is accomplished by a bidirectional key distribution scheme based on Forward Key Setup and Backward Key Setup developed by using synchronized broadcast and multi-level key concept. Our protocol, called SPDA(Security Protocol for Data Aggregation) is well suited for any data aggregation algorithms and applications. Our analysis results prove SPDA's efficiency meaning that its communication cost is manageable","Title: Emerging COTS architecture support for real-time TSN ethernet.\nYear: 2019\n\nAbstract: The IEEE family of standards referred to as Time Sensitive Networking (TSN) is gaining popularity in many real-time embedded applications, including industrial automation and automotive. Based on a synchronized clock at the network layer, the TSN standards aim at deterministic behavior of message communication over an Ethernet. However, the data access time between a CPU and its network interface may be affected by the contention for shared resources and buses at each node. Any jitter on this final segment of the communication path can have a negative impact on clock synchronization and network message transmission. In this paper, we investigate the potential performance impacts to real-time TSN Ethernet caused by the level of precision to which the network clock and the CPU clock is synchronized as well as the magnitude of transmission jitter under various load conditions. Specifically, we look into the emerging architecture features in x86 systems that are considered to be commercial-off-the-shelf (COTS) platforms for embedded applications. We show that the latest generation of COTS hardware significantly reduces the jitters of clock synchronization between the software visible clock and the network, as well as reducing the jitter from when software attempts to transmit an Ethernet frame to when it actually appears on the wire. As a consequence, a significant performance improvement for software applications utilizing real-time TSN Ethernet can be attained in the emerging COTS systems.","Title: Multi-Core Intra-Process Clock Synchronization\nYear: 2021\n\nAbstract: Real-time applications require not only a precise understanding of now (what time it is), but also the ability to initiate an externally visible event at the correct time. We define this as Intra-process clock synchronization. Our findings show that in the latest commercial, off-the-shelf (COTS) multi-core systems running with preempt-RT Linux, the uncertainty in the software visible clock is near..."],"string":"[\n \"Title: RTSOA: Real-Time Service-Oriented Architecture\\nYear: 2006\\n\\nAbstract: This paper extends the traditional Service-Oriented Architecture (SOA) to a new Real-Time SOA (RTSOA) and proposes a framework for the new architecture, by providing real-time service modeling, design, code generation, simulation, deployment, execution, orchestration, and management. The concepts, architecture, and enabling technique are studied for Real-time SOA. In addition, an efficient algorithm is derived in the paper to find the optimal composition of real-time services subject to the real-time constraint.\",\n \"Title: STUBcast - efficient support for concurrency control in broadcast-based asymmetric communication environment\\nYear: 2001\\n\\nAbstract: Observing that it is impractical to use traditional methods to control concurrency in a broadcast-based asymmetric communication environment, we introduce a concurrency control protocol designed for broadcast-based transaction processing called STUBcast (Server Timestamp and Update Broadcast Supported Concurrency). STUBcast supports two new correctness criteria proposed - single serializability and local serializability. These criteria are weaker than global serializability but are practical and easier to achieve in a broadcast environment. This article also shows some simulation results. These results suggest that STUBcast could be very efficient in a realistic application environment\",\n \"Title: ITB: intrusion-tolerant broadcast protocol in wireless sensor networks\\nYear: 2006\\n\\nAbstract: A large-scale wireless sensor network relies on broadcast protocol for data communication. This paper presents an idea for a base station to reliably and securely broadcast announcing messages to the network in an intrusion-tolerant way. Our Intrusion-Tolerant Broadcast Protocol based on Mutual Verification scheme guarantees that no forged information can be propagated to the network if an intruder is assumed to not compromise the base station. We show that our proposed protocol successfully runs and also demonstrate the effectiveness of the protocol by introducing a theorem and the proof of the theorem.\",\n \"Title: Look-ahead routing switches for multistage interconnection networks\\nYear: 1993\\n\\nAbstract: Multistage interconnection networks have been implemented in several prototype parallel systems as well as marketed products. One major performance drawback of this type network is due to link contention. This contention may occur when messages share part of their routes with each other. Especially, under hot-spot traffic, the network can be easily saturated and provides a drastically degraded performance. To remedy this problem, we propose a switch design which uses two independent routes between each input port and each output port within a switch. With proper routing control mechanisms, the adverse effect on link conflicts can be greatly reduced. The design needs neither additional connection between switches nor message resequencing for maintaining serialization. Various simulations are used to illustrate the performance improvement of this new design under both uniform and hot-spot traffic patterns.\",\n \"Title: Efficient Java Native Interface for Android Based Mobile Devices\\nYear: 2011\\n\\nAbstract: Java has been making its way into the embedded systems and mobile devices like Android. The Java platform specifies the Java Native Interface (JNI) which allows Java code that runs within a JVM to interoperate with applications or libraries that are written in other languages and compiled to the host CPU. JNI plays an important role in embedded system as it provides a mechanism to interact with libraries specific to the platform and to take the advantage of fast execution of native programs. To address the overhead incurred in the JNI due to reflection and serialization, this paper proposes to cache class, field, and method information obtained from reflection for subsequent usage. It also provides a function to pin objects to their memory locations such that they can be accesses through the known reference. The Android emulator is used to evaluate the performance of these techniques and we observed that there was 10-30 % performance gain in the Java Native Interface for two Android applications.\",\n \"Title: Java Real-Time Publish-Subscribe Middleware for Distributed Embedded Systems\\nYear: 2000\\n\\nAbstract: As the complexity of distributed embedded systems is increasing, and frequent upgrades and maintenance are expected, conventional communication models, such as point-to-point and the client-server model are not adequate to keep up with these trends. So we propose a generic Java real-time communication model based on the publish-subscribe model which has proper characteristics for distributed applications. The proposed Java publish-subscribe model has been implemented as middleware, named Java Embedded Bus (JEB). To easily support heterogeneous communication technologies, we propose a middleware-independent plug-and-play JEB device driver model. We have also prototyped the middleware on the demonstrative distributed embedded systems which comprised embedded machines and workstations connected by VME bus and Ethernet.\",\n \"Title: Addendum to Voltage-Clock-Scaling Adaptive Scheduling Techniques for Low Power in Hard Real-Time Systems\\nYear: 2004\\n\\nAbstract: First Page of the Article\",\n \"Title: An Efficient Scheduling Discipline for Packet Switching Networks Using Earliest Deadline First Round Robin\\nYear: 2003\\n\\nAbstract: This paper addresses a frame-oriented scheduling discipline, EDF-RR (earliest deadline first round robin), for OQ (output-queued) switch architecture and data traffic consisting of fixed-length cells. Bandwidth reservation for an active session is performed by holding a number of cell slots for the session in a repeatedly-transferred frame. Each cell that is going to be transferred in the frame is assigned a virtual release time and a virtual deadline according to the bandwidth reservation scheme. The transmitting order of the cells in the frame is determined by non-preemptive non-idling EDF algorithm so that cells of a backlogged session in the frame are distributed as uniformly as possible. Through the analysis applying real-time scheduling theory and network calculus as well as network simulation, EDF-RR takes the advantage of O(1) computational complexity, and possesses tight delay bounds and lenient buffer requirements. The proposed scheduling discipline is appropriate for distributed real-time systems as we show that sessions can be configured based on message traffic models and deadline requirements. Also, a modified version of EDF-RR, called EDF-DRR, can be applied as traffic regulator when jitter requirements exist among active sessions.\",\n \"Title: Voltage-Clock Scaling for Low Energy Consumption in Real-Time Embedded Systems\\nYear: 1999\\n\\nAbstract: Low power and energy consumption will always be an essential requirement in many real-time embedded applications. Voltage scaling is a relative novel approach to reducing energy consumption. The idea is that a processor can be run either at high or at low voltage: at high voltage, the clock rate is high but so is the power consumption; at low voltage the clock rate is lower, but the power consumption drops by a greater factor. This immediately suggests a powerful approach to lowering energy consumption in real-time systems. In this paper, we introduce static and dynamic algorithms to control processor voltage to reduce energy consumption. We demonstrate by simulation that our algorithms can significantly reduce total energy consumption.\",\n \"Title: Voltage-Clock Scaling for Low Energy Consumption in Fixed-Priority Real-Time Systems\\nYear: 2003\\n\\nAbstract: Power and energy constraints are becoming increasingly prevalent in real-time embedded systems. Voltage-scaling is a promising technique to reduce energy and power consumption: clock speed tends to decrease linearly with supply voltage while power consumption goes down quadratically. We therefore have a tradeoff between the energy consumption of a task and the speed with which it can be completed. The timing constraints associated with real-time tasks can be used to resolve this tradeoff. In this paper, we present two algorithms for voltage-scaling. Assuming that a processor can operate in one of two modes: high voltage and low voltage, we show how to schedule the voltage settings so that deadlines are met while reducing the total energy consumed. We show that significant reductions can be made in energy consumption.\",\n \"Title: Ontology-Based Service Composition Framework for Syndicating Building Intelligence\\nYear: 2009\\n\\nAbstract: Building Information Model (BIM) encompasses building geometry, spatial relationships, geographic information, and quantities and properties of building components; however, it does not include any information related to the functions that a building might require. On the other side, Service–Oriented computing emerged as a promising technology to enable fast, efficient software development. Even though features such as standard protocols, loosely coupled components, ontology-enabled dynamic discovery and composition of SOA seems a perfect fit for the building construction domain, the potential gain of applying SOA techniques has not really been discovered. In the paper, an ontology-based service composition framework for construction domain is proposed, and BIM is extended to include function requirements and service specifications. An example is shown to demonstrate the advantages over traditional techniques also.\",\n \"Title: The performance of a watchdog protocol for wireless network security\\nYear: 2007\\n\\nAbstract: In wireless networks, a watchdog mechanism can be a part of the security architecture used to identify nodes that fail to forward the packets they agree to forward. In this paper, we propose two protocols to address the ambiguous collision limitation of watchdogs. The Watchdog Alert (WA) and Watchdog Confirmation (WC) protocols are variants of the Carrier Sense Multiple Access with Collision Avoidance Protocol (CSMA/CA). We analyse the trade-offs between throughput and watchdog success probability of each protocol, comparing it to CSMA/CA.\",\n \"Title: Measurement and Application of Fault Latency\\nYear: 1986\\n\\nAbstract: The time interval between the occurrence of a fault and the detection of the error caused by the fault is divided by the generation of that error into two parts: fault latency and error latency. Since the moment of error generation is not directly observable, all related works in the literature have dealt with only the sum of fault and error latencies, thereby making the analysis of their separate effects impossible. To remedy this deficiency, we 1) present a new methodology for indirectly measuring fault latency, 2) derive the distribution of fault latency from the methodology, and 3) apply the knowledge of fault latency to the analysis of two important examples.\",\n \"Title: Integrated scheduling with garbage collection for real-time embedded applications in CLI\\nYear: 2006\\n\\nAbstract: We present a schedulable garbage collection for realtime applications in virtual machine environments. The design objective is to make the pause time caused by garbage collection operations controllable, and the invocation of garbage collection predictable. Thus, real-time applications can be schedulable along with garbage collection. We develop a prototype for a schedulable garbage collection in MONO CLI execution environment. A cost model of garbage collection is established based on measured WCET to predict the execution time and overhead of garbage collection operations. A scheduling algorithm of garbage collection and application tasks is presented to illustrate how the time and memory constraints of real-time systems can be met. The experiment result of the scheduling algorithm for a periodic task set on the prototype is included in the paper\",\n \"Title: Software architecture supporting integrated real-time systems\\nYear: 2003\\n\\nAbstract: To achieve reliability, reusability, and cost reduction, a significant trend in building large complex real-time systems is to integrate separate application modules of different criticalities in a common hardware platform. An essential requirement of integrated real-time systems is to guarantee spatial and temporal partitioning among applications in order te ensure an exclusive access of physical and temporal resources to the applications. In this paper we propose software architecture, implemented as SPIRIT-µKernel, for strongly partitioned integrated real-time systems. The SPIRIT-µKernel has been designed and implemented based on a two-level hierarchical scheduling methodology such that the real-time constraints of each application can be guaranteed. To demonstrate the feasibility of the SPIRIT-µKernel, we have ported two real-time operating systems (RTOS), WindRiver's VxWorks and Cygnus's eCos, on the top of the microkernel. Thus, different RTOS can be applied in various partitions to provide required features for each application. Based on the measured performance results, the SPIRIT-µKernel architecture is practical and appealing due to its low overheads of kernel services and the support for dependable integration of real-time applications via scheduling algorithm.\",\n \"Title: Replay Debugging for Multi-threaded Embedded Software\\nYear: 2010\\n\\nAbstract: The non-deterministic behavior of multi-threaded embedded software makes cyclic debugging difficult. Even with the same input data, consecutive runs may result in different executions and reproducing the same bug is itself a challenge. Despite the fact that several approaches have been proposed for deterministic replay, none of them attends to the capabilities and functionalities that replay can comprise for better debugging. This paper introduces a practical replay mechanism for multi-threaded embedded software. The Replay Debugger, based on Lamport clock, offers a user controlled debugging environment in which the program execution follows the identical partially ordered happened-before dependency among threads and IO events as that of the recorded run. With the order of thread synchronizations assured, users can focus their debugging effort in the program behavior of any threads while having a comprehension of thread-level concurrency. Using a set of benchmark programs, experiment results of a prototyped implementation show that, in average, the software based approach incurs a small probe effect of 3.3% in its record stage.\",\n \"Title: Resource Scheduling in Dependable Integrated Modular Avionics\\nYear: 2000\\n\\nAbstract: In the recent development of avionics systems, Integrated Modular Avionics (IMA) is advocated for next generation architecture that needs integration of mixed-criticality real-time applications. These integrated applications meet their own timing constraints while sharing avionics computer resources. To guarantee timing constraints and dependability of each application, an IMA-based system is equipped with the schemes for spatial and temporal partitioning. We refer the model as SP-RTS (Strongly Partitioned Real-Time System), which deals with processor partitions and communication channels as its basic scheduling entities.This paper presents a partition and channel-scheduling algorithm for the SP-RTS. The basic idea of the algorithm is to use a two-level hierarchical schedule that activates partitions (or channels) following a distance-constraints guaranteed cyclic schedule and then dispatches tasks (or messages) according to a fixed priority schedule. To enhance schedulability, we devised heuristic algorithms for deadline decomposition and channel combining. The simulation results show the schedulability analysis of the two-level scheduling algorithm and the beneficial characteristics of the proposed deadline decomposition and channel combining algorithms.\",\n \"Title: Ontology-Based Smart Home Solution and Service Composition\\nYear: 2009\\n\\nAbstract: Given the diversity of home environment, appliances, and residents, the applications for smart homes must be configurable and adaptive. Instead of programming each household, we propose an ontology-based framework to facilitate the automatic composition of appropriate applications. The system composes appropriate services depending upon the available equipments in each individual household automatically. Meanwhile, it dynamically adjusts the environment parameters to match the customer needs and to encompass the available resource. With its supporting on customized function template editing, customers are able to specify their usual behavior templates as different mode.\",\n \"Title: SPIRIT-/spl mu/Kernel for strongly partitioned real-time systems\\nYear: 2000\\n\\nAbstract: To achieve reliability, reusability and cost reduction, a significant trend of building large, complex real-time systems is to integrate separated application modules. An essential requirement of integrated real-time systems is to guarantee strong partitioning among applications. In this paper, we propose a microkernel, called the SPIRIT (Strongly Partitioned Integrated Real-tIme sysTem) /spl mu/Kernel, for strongly partitioned real-time systems. The SPIRIT-/spl mu/Kernel has been designed and implemented based on a two-level hierarchical scheduling methodology such that the real-time constraints of each application can be guaranteed. It provides a minimal set of kernel functions, such as address management, interrupt/exception dispatching, inter-application communication and application scheduling. To demonstrate the feasibility of the SPIRIT-/spl mu/Kernel, we have ported two different application-level real-time operating systems (RTOSs)-WindRiver's VxWorks and Cygnus's eCos-on the top of the microkernel. The SPIRIT-/spl mu/Kernel architecture is practical and appealing due to its low overheads of kernel services and the support for dependable integration of real-time applications.\",\n \"Title: Schedulable online testing framework for real-time embedded applications in VM\\nYear: 2007\\n\\nAbstract: The paper suggests a VM-based online testing approach in which software testing is piggybacked at runtime on a system that operates to serve actual mission. Online testing in VM is facilitated with a framework that uses persistence service to initialize the testing operation with a consistent system state. The testing operation then runs in an isolated domain which can be scheduled independently of the operating version. Thus, testing operation cannot cause unbounded pause time nor spoil the normal operation. We evaluate the feasibility of schedulable online testing with a prototype developed in MONO CLI (Common Language Infrastructure) and the experiment on the prototype.\",\n \"Title: Optimal Scheduling of Signature Analysis for VLSI Testing\\nYear: 1991\\n\\nAbstract: A simple algorithm that shows how to optimally schedule the test-application and the signature-analysis phases of VLSI testing is presented. The testing process is broken into subintervals, the signature is analyzed at the end of each subinterval, and future tests are aborted if the circuit is found to be faulty, thus saving test time. The mathematical proofs associated with the algorithm are given.\",\n \"Title: Secure localization and location verification in sensor networks\\nYear: 2005\\n\\nAbstract: Evolving networks of wireless sensing nodes have many attractive applications. Localization, that is to determine the locations of sensor nodes, is a key enabler for many applications of sensor networks. As sensor networks move closer to extensive deployments, security becomes a major concern, resulting in a new demand for secure localization and location proving systems. In this paper, we propose a secure localization system based on a secure hop algorithm. A location verification system that allows the base station to verify the locations of sensor nodes is also presented. It prevents a node from falsifying reported location information. The evaluation of the secure localization system shows that the proposed scheme performs well under a range of scenarios tested and is resilient against an attacker creating incorrect location information in other nodes.\",\n \"Title: On robust transaction routing and load sharing\\nYear: 1991\\n\\nAbstract: In this paper we examine the issue of robust transaction routing in a locally distributed database environment where transaction characteristics such as reference locality imply that certain processing systems can be identified as being more suitable than others for a given transaction class. A response time based routing strategy can strike a balance between indiscriminate sharing of the load and routing based only on transaction affinity. Since response time estimates depend on workload and system parameters that may not be readily available, it is important to examine the robustness of routing decisions to information accuracy. We find that a strategy which strictly tries to minimize the response time of incoming transactions is sensitive to the accuracy of certain parameter values. On the other hand, naive strategies, that simply ignore the parameters in making routing decisions, have even worse performance. Three alternative strategies are therefore examined: threshold, discriminatory, and adaptive. Instead of just optimizing an incoming transaction's response time, the first two strategies pursue a strategy that is somewhat more oriented towards global optimization. This is achieved by being more restrictive on either the condition or the candidate for balancing the load. The third strategy, while trying to minimize the response time of individual incoming transactions, employs a feedback process to adaptively adjust future response time estimates. It monitors the discrepancy between the actual and estimated response times and introduces a correction factor based on regression analysis. All three strategies are shown to be robust with respect to the accuracy of workload and system parameters used in the response time estimation.\",\n \"Title: A Systematic Approach for Integrating Fault Trees into System Statecharts\\nYear: 2008\\n\\nAbstract: As software systems are encompassing a wide range of fields and applications, software reliability becomes a crucial step. The need for safety analysis and test cases that have high probability to uncover plausible faults are necessities in proving software quality. System models that represent only the operational behavioral of a system are incomplete sources for deriving test cases and performing safety analysis before the implementation process. Therefore, a system model that encompasses faults is required. This paper presents a technique that formalizes a safety model through the incorporation of faults with system specifications. The technique focuses on introducing semantic faults through the integration of fault trees with system specifications or statechart. The method uses a set of systematic transformation rules that tries to maintain the semantics of both fault trees and statechart representations during the transformation of fault trees into statechart notations.\",\n \"Title: A schedulable garbage collection for embedded applications in CLI\\nYear: 2005\\n\\nAbstract: Common language infrastructure (CLI) has been introduced as a core technology of Microsoft .NET. It enables \\\"writing in multiple languages, running in multiple platforms\\\" by providing virtual execution system (VES), common intermediate language, and common type system etc. The advantages of using CLI, including portability, compactness, and interoperability, could benefit the productivity of application software development and deployment. However, for embedded real-time systems, the applications' time-constraints cannot be satisfied easily due to several features of CLI runtime environment, such as thread priority, thread scheduling, garbage collection etc. In this paper, we aim to have a garbage collection mechanism applicable on real-time applications in CLI and other virtual machine environments. We achieve the goal by making the pause time of garbage collection operations predictable, and the invocation of garbage collection and applications schedulable. A cost model based on measured WCET is established to predict the execution time and overhead of garbage collection operations.\",\n \"Title: Optimization criteria for checkpoint placement\\nYear: 1984\\n\\nAbstract: Mean response time and availability as optimization criteria for checkpoint placement are better replaced by workable formulas that calculate the ratio between the marginal gain accrued to users who experience system failure and the (presumably slight) loss suffered on average by all users.\",\n \"Title: A Genetic Algorithm Based Approach for Event Synchronization Analysis in Real-Time Embedded Systems\\nYear: 2009\\n\\nAbstract: In real-time embedded systems, due to race conditions, synchronization order between events may be different from one execution to another. This behavior is permissible as in concurrent systems, but should be fully analyzed to ensure the correctness of the system. In this paper, a new intelligent method is presented to analyze event synchronization sequence in embedded systems. Our goal is to identify the feasible sequence, and to determine timing parameters that lead to these sequences. Our approach adopts timed event automata (TEA) to model the targeted embedded system and use a race condition graph (RCG) to specify event synchronization sequence (SYN-Spec). A genetic algorithm working with simulation is used to analyze the timing parameters in the target model and to verify whether a defined SYN-Spec is satisfied or not. A case study shows that the method proposed is able to find potential execution sequences according to the event synchronization orders.\",\n \"Title: Improving multistage network performance under uniform and hot-spot traffics\\nYear: 1990\\n\\nAbstract: The major performance drawbacks of multistage interconnection networks are due to link contention and message blockage. To remedy this problem, the authors propose a switch design which uses two independent routes between each input port and each output port within a switch. With proper routing control mechanisms, the adverse effect on link conflicts can be greatly reduced. Various simulations are used to illustrate the performance improvement of this new design under both uniform and hot-spot traffic patterns\",\n \"Title: Location verification using bidirectional one-way hash function in wireless sensor networks\\nYear: 2007\\n\\nAbstract: Evolving networks of wireless ad hoc sensing nodes have many attractive applications. Localisation, that is to determine where a given node is physically located, is a key enabler for many applications of sensor networks. As sensor networks move closer to extensive deployments, security becomes a major concern, resulting in a new demand for secure localisation and location proving systems. We propose a secure localisation system based on a secure hop algorithm. A location verification system that allows the base station to verify the location claims of sensor nodes is also presented. It prevents a node from falsifying reported location information and can be used for location-based access control. In order to support the resource-constrained sensor nodes, the proposed approach makes use of efficient one-way hash functions. Finally, we report the evaluation of the secure localisation system. The proposed scheme performs well under a range of scenarios tested and is resilient against an attacker creating incorrect location information in other nodes.\",\n \"Title: Key management in wireless sensor networks\\nYear: 2004\\n\\nAbstract: Wireless sensor networks hold the promise of facilitating large-scale, real-time data processing in complex environments. As sensor networks edge closer towards widespread deployment, security issues become a central concern. Key management in wireless sensor networks is a challenging problem. Computationally complex asymmetric crypto techniques are unsuitable for use in resource-constrained sensor nodes and use of symmetric cryptography makes the entire network vulnerable in the event of node compromise. This paper presents a key management scheme that satisfies both operational and security requirements of distributed wireless sensor networks. The proposed scheme accommodates other techniques like data aggregation for energy efficiency and watchdog mechanism for intrusion monitoring. Also, the proposed scheme is generic enough so that it can be applied to variety of sensor network protocols.\",\n \"Title: Optimal reconfiguration strategy for a degradable multimodule computing system\\nYear: 1987\\n\\nAbstract: A new quantitative approach to the problem of reconfiguring a degradable multimodule system is presented. The approach is concerned with both assigning some modules for computation and arranging others for reliability. Conventionally, a fault-tolerant system performs reconfiguration only upon a subsystem failure. Since there exists an inherent trade-off between the computation capacity and fault tolerance of a multimodule computing system, the conventional approach is a passive action and does not yield a configuration that provides an optimal compromise for the trade-off. By using the expected total reward as the optimal criterion, the need and existence of an active reconfiguration strategy, in which the system reconfigures itself on the basis of not only the occurrence of a failure but also the progression of the mission, are shown.Following the problem formulation, some important properties of an optimal reconfiguration strategy, which specify (i) the times at which the system should undergo reconfiguration and (ii) the configurations to which the system should change, are investigated. Then, the optimal reconfiguration problem is converted to integer nonlinear knapsack and fractional programming problems. The algorithms for solving these problems and a demonstrative example are given. Extensions of the optimal reconfiguration problem are also discussed.\",\n \"Title: Partition Scheduling in APEX Runtime Environment for Embedded Avionics Software\\nYear: 1998\\n\\nAbstract: Advances in the computer technology encouraged the avionics industry to replace the federated design of control units with an integrated suite of control modules that share the computing resources. The new approach, which is called integrated modular avionics (IMA), cart achieve substantial cost reduction in the development, operation and maintenance of airplanes. A set of guidelines has been developed by the avionics industry to facilitate the development and certification of integrated systems. Among them, a software architecture is recommended to address real-time and fault-tolerance requirements. According to the architecture, applications are classified into partitions supervised by an operating system executive. A general-purpose application/executive (APEX) interface is defined which identifies the minimum functionality provided to the application software of an NMA system. To support the temporal partitioning between applications, APEX interface requires a deterministic cyclic scheduling of partitions at the O/S level and a fixed priority scheduling among processes within each partition. In this paper we propose a scheduling scheme for partitions in APEX. The scheme determines the frequency that each partition must be invoked and the assignment of processor capacity on every invocation. Then a cyclic schedule at the O/S level can be constructed and all processes within each partition can meet their deadline requirements.\",\n \"Title: Schedulable garbage collection in CLI virtual execution system\\nYear: 2007\\n\\nAbstract: Virtual software execution environment, known as Virtual Machine (VM), has been gaining popularity through Java Virtual Machine (JVM) and Common Language Infrastructure (CLI). Given their advantages in portability, productivity, and safety, etc., applying VM to real-time embedded systems can leverage production cost, fast time-to-market, and software integrity. However, this approach can only become practical once the VM operations and application tasks are made schedulable jointly. In this paper, we present a schedulable garbage collection algorithm applicable on real-time applications in CLI virtual machine environment. To facilitate the scheduling of real-time applications and garbage collection operations, we make the pause time due to garbage collection controllable, and the invocation of garbage collection predictable. To demonstrate the approach, a prototype for a schedulable garbage collection has been implemented in CLI execution environment. The garbage collection is carried out by a concurrent thread while meeting a targeted pause time and satisfying the memory requests of applications. A cost model of garbage collection is established based on measured WCET such that the execution time and overhead of garbage collection operations can be predicted. Finally, we illustrate a joint scheduling algorithm to meet the time and memory constraints of real-time systems.\",\n \"Title: Design of HM2p A Hierarchical Multimicroprocessor for General-Purpose Applications\\nYear: 1982\\n\\nAbstract: This paper presents a tree-structured multiprocessor called the hierarchical multimicroprocessor (HM2p), each node of which is composed of a cluster of processor modules (PM's), common memory, DMA interface, switches, communication lines, and a data processor associated with it. The HM2p consists of two different hierarchies, one for data processing and the other for data distribution, which provide clean, structured separation between processing components and user interface components.\",\n \"Title: Schedulable persistence system for teal-time applications in virtual machine\\nYear: 2006\\n\\nAbstract: Persistence in applications saves a computation state that can be used to facilitate system recovery upon failures. As we begin to adopt virtual execution environments (VMs) for mission-critical real-time embedded applications, persistence service will become an essential part of VM to ensure high availability of the systems.In this paper, we focus in a schedulable persistence system in VMs and show a prototype persistence system constructed on CLI 's open source platform, MONO. By employing object serialization, the system enables concurrent and preemptible persistence operation, i.e., the task in charge of persistence service runs concurrently with application tasks and is a target of real-time scheduling. Thus, the execution of application tasks can be interleaved with the operations of persistence service, and the task timeliness can be guaranteed as the pause time caused by persistence service is bounded. The experiment output on the prototyped system illustrates that persistence service is appropriate for realtime applications because of its controllable pause time and its optimized overhead.\",\n \"Title: DC/sup 2/ scheduling for aperiodic tasks in strongly partitioned real-time systems\\nYear: 2000\\n\\nAbstract: The strongly partitioned real time system (SP-RTS) provides temporal and spatial partitioning for its integrated real time applications. The SP-RTS architecture adopts a two-level hierarchical scheduling mechanism. In the lower level, multiple partitions (applications) are dispatched under a cyclic scheduling, whereas, in the higher level, multiple periodic tasks of each partition are scheduled according to a fixed priority algorithm. The authors focus on designing algorithms for online scheduling of aperiodic tasks in the SP-RTS environment. The Distance Constraint guaranteed Dynamic Cyclic (DC/sup 2/) scheduler that is proposed uses these basic operations: Left Sliding (LS), Right Putting (RP), and Compacting. These operations dynamically schedule aperiodic tasks within a distance-constrained cyclic schedule. We show that the dynamic applications of these operations do not violate the distance constraint characteristics of a partition cyclic schedule. In addition, the slack time calculation of these dynamic operations can be applied for scheduling hard aperiodic tasks. With the simulation studies, we observed that the DC/sup 2/ can result in a significant performance enhancement in terms of the average response time of soft aperiodic tasks and the acceptance rate for hard aperiodic tasks.\",\n \"Title: Service-oriented smart home applications: composition, code generation, deployment, and execution\\nYear: 2012\\n\\nAbstract: A smart home usually has a variety of devices or home appliance, instead of designing software for a specific home, this paper proposes a service-oriented framework with a set of ontology systems to support service and device publishing, discovery of devices and their services, composition of control software using existing control services that wrap devices, deployment, and execution of the composed service in an computing environment, monitoring the execution, and recovery from device failure. The ontology systems specify semantic information about devices, services, and workflows used in various smart home, and users can compose and recompose services for their specific needs. New devices, workflows, and services can be added into ontology. Most of the steps in this process can be automated including code generation. For example, service composition will be carried out in three steps: abstract workflow design, function construction, and device discovery, and different codes can be generated for different computing platforms such as Java and Open Services Gateway initiative environments. In this way, a variety of smart home can be constructed rapidly using the framework by discovery and composition using existing services and workflows. This paper illustrates this framework using a media control example to illustrate the ontology, discovery, composition, deployment, execution, monitoring, and recovery.\",\n \"Title: Design and Evaluation of a Fault-Tolerant Multiprocessor Using Hardware Recovery Blocks\\nYear: 1984\\n\\nAbstract: In this paper we consider the design and evaluation of a fault-tolerant multiprocessor with a rollback recovery mechanism.\",\n \"Title: Bounded energy allocation and scheduling for real-time Embedded Systems\\nYear: 2005\\n\\nAbstract: For energy-constrained real-time embedded systems, the power-delay tradeoff property of Volta ge-Clock Scaling (VCS) needs to be carefully considered in scheduling real-time tasks for meeting strict timing requirements. In addition, non-real-time tasks require minimized response times. Thus, a bounded energy allocation model should be adopted to solve a combination of optimization objectives in systems having mixed hard and soft real-time tasks. In this paper, we propose an energy sharing model that allocates the energy budget among hard and soft real-time tasks, exploiting the interplay between utilization and energy consumption in VCS-based earliest-deadline-first scheduling (VCS-EDF). Also a dynamic scheduling is presented which is designed to reduce energy consumption by switching between two scheduling policies and utilizing an explicit pattern of event occurrences at run-time. Through simulations, we show that this Dual-Policy Dynamic Scheduling can outperform in the reduction of energy consumption while introducing small delay in average response time of non-real-time tasks.\",\n \"Title: Optimal Partitioning of Heterogeneous Traffic Sources in Mobile Communications Networks\\nYear: 1997\\n\\nAbstract: Given n heterogeneous traffic sources which generate multiple types of traffic among themselves, we consider the problem of finding a set of disjoint clusters to cover n traffic sources. The objective is to minimize the total communication cost for the entire system in the context that the intracluster communication is less expensive than the intercluster communication. Different from the general graph partitioning problem, our work takes into account the physical topology constraints of the linear arrangement of physical cells in highway cellular systems and the hexagonal mesh arrangement of physical cells in cellular systems. In our partitioning schemes, the optimal partitioning problem is transformed into an equivalent problem with a relative cost function, which generates the communication cost differences between the intracluster communications and the intercluster communications.For highway cellular systems, we have designed an efficient optimal partitioning algorithm of O(mn2) by dynamic programming, where m is the number of clusters of n base stations. The algorithm also finds all the valid partitions in the same polynomial time, given the size constraint on a cluster and the total allowable communication cost for the entire system. For hexagonal cellular systems, we have developed four heuristics for the optimal partitioning based on the techniques of moving or interchanging boundary nodes between adjacent clusters. The heuristics have been evaluated and compared through experimental testing and analysis.\",\n \"Title: EDF scheduling using two-mode voltage-clock-scaling for hard real-time systems\\nYear: 2001\\n\\nAbstract: Scaling down power supply voltage yields a quadratic reduction in dynamic power dissipation and also requires a reduction in clock frequency. In order to meet task deadlines in hard real-time systems, the delay penalty in voltage scaling needs to be carefully considered to achieve low power consumption. In this paper, we focus on dynamic reclaiming of early released resources in Earliest Deadline First (EDF) scheduling using voltage scaling. In addition to a static voltage assignment, we propose a new dynamic-mode assignment, which has a flexible voltage mode setting at run-time enabling much larger energy savings. Using simulation results and exploiting the interplay between power supply voltage, frequency, and circuit delay in CMOS technology, we find the optimal two-level voltage settings that minimize energy consumption.\",\n \"Title: Optimal Dynamic Control of Resources in a Distributed System\\nYear: 1989\\n\\nAbstract: First Page of the Article\",\n \"Title: Sharing i/o in strongly partitioned real-time systems\\nYear: 2004\\n\\nAbstract: Strongly partitioned real-time systems have been adopted to provide an integrated run time environment for applications with varied criticalities. This is achieved by guaranteeing spatial as well as temporal partitioning for the applications. To enable accesses to shared I/O devices in such an environment, this paper provides an effective model that the co-existence of any application does not hinder the execution of IO operations or spatial and temporal requirements of other applications. The model utilizes a microkernel-based approach to regulate the work of drive drivers and thus integrate the device-sharing model with the microkernel.\",\n \"Title: Evaluation of Error Recovery Blocks Used for Cooperating Processes\\nYear: 1984\\n\\nAbstract: Three alternatives for implementing recovery blocks (RB's) are conceivable for backward error recovery in concurrent processing. These are the asynchronous, synchronous, and the pseudorecovery point implementations. Asynchronous RB's are based on the concept of maximum autonomy in each of concurrent processes. Consequently, establishment of RB's in a process is made independently of others and unbounded rollback propagations become a serious problem. In order to completely avoid unbounded rollback propagations, it is necessary to synchronize the establishment of recovery blocks in all cooperating processes. Process autonomy is sacrificed and processes are forced to wait for commitments from others to establish a recovery line, leading to inefficiency in time utilization. As a compromise between asynchronous and synchronous RB's we propose to insert pseudorecovery points (PRP's) so that unbounded rollback propagations may be avoided while maintaining process autonomy. We developed probabilistic models for analyzing these three methods under standard assumptions in computer performance analysis, i.e., exponential distributions for related random variables. With these models we have estimated 1) the interval between two successive recovery lines for asynchronous RB's, 2) mean loss in computation power for the synchronized method, and 3) additional overhead and rollback distance in case PRP's are used.\",\n \"Title: Dynamic Analysis of Embedded Software Using Execution Replay\\nYear: 2014\\n\\nAbstract: For program optimization and debugging, dynamic analysis tools, e.g., profiler, data race detector, are widely used. To gather execution information, software instrumentation is often employed for its portability and convenience. Unfortunately, instrumentation overhead may change the execution of a program and lead to distorted analysis results, i.e., probe effect. In embedded software which usually consists of multiple threads and external inputs, program executions are determined by the timing of external inputs and the order of thread executions. Hence, probe effect incurred in an analysis of embedded software will be more prominent than in desktop software. This paper presents a reliable dynamic analysis method for embedded software using deterministic replay. The idea is to record thread executions and I/O with minimal record overhead and to apply dynamic analysis tools in replayed execution. For this end, we have developed a record/replay framework called P-Replayer, based on Lamport's happens-before relation. Our experimental results show that dynamic analyses can be managed in the replay execution enabled by P-Replayer as if there is no instrumentation on the program.\",\n \"Title: Efficient Data Race Detection for C/C++ Programs Using Dynamic Granularity\\nYear: 2014\\n\\nAbstract: To detect races precisely without false alarms, vector clock based race detectors can be applied if the overhead in time and space can be contained. This is indeed the case for the applications developed in object-oriented programming language where objects can be used as detection units. On the other hand, embedded applications, often written in C/C++, necessitate the use of fine-grained detection approaches that lead to significant execution overhead. In this paper, we present a dynamic granularity algorithm for vector clock based data race detectors. The algorithm exploits the fact that neigh boring memory locations tend to be accessed together and can share the same vector clock archiving dynamic granularity of detection. The algorithm is implemented on top of Fast Track and uses Intel PIN tool for dynamic binary instrumentation. Experimental results on benchmarks show that, on average, the race detection tool using the dynamic granularity algorithm is 43% faster than the Fast Track with byte granularity and is with 60% less memory usage. Comparison with existing industrial tools, Val grind DRD and Intel Inspector XE, also suggests that the proposed dynamic granularity approach is very viable.\",\n \"Title: On the existence of probe effect in multi-threaded embedded programs\\nYear: 2014\\n\\nAbstract: Software instrumentation has been a convenient and portable approach for dynamic analysis, debugging, or profiling of program execution. Unfortunately, instrumentation may change the temporal behavior of multi-threaded program execution and result in different ordering of thread operations, which is called probe effect. While the approaches to reduce instrumentation overhead, to enable reproducible execution, and to enforce deterministic threading have been studied, no research has yet answered if an instrumented execution has the same behavior as the program execution without any instrumentation and how the execution gets changed if there were any. In this paper, we propose a simulation-based analysis to detect the changes of execution event ordering that are induced by instrumentation operations. The execution model of a program is constructed from the trace of instrumented program execution and is used in a simulation analysis where instrumentation overhead is removed. As a consequence, we can infer the ordering of events in the original program execution and verify the existence of probe effect resulted from instrumentation.\",\n \"Title: Scanning the issue - Special issue on real-time systems\\nYear: 2003\\n\\nAbstract: Provides an overview of the technical articles and features presented in this issue.\",\n \"Title: Voltage-clock-scaling adaptive scheduling techniques for low power in hard real-time systems\\nYear: 2003\\n\\nAbstract: Abstract--Many embedded systems operate under severe power and energy constraints. Voltage clock scaling is one mechanism by which energy consumption may be reduced: It is based on the fact that power consumption is a quadratic function of the voltage, while the speed is a linear function. In this paper, we show how voltage scaling can be scheduled to reduce energy usage while still meeting real-time deadlines.\",\n \"Title: Error Detection Process—Model, Design, and Its Impact on Computer Performance\\nYear: 1984\\n\\nAbstract: Conventionally, reliability analyses either assume that a fault/error is detected immediately as it occurs, or ignore damage caused by imperfect detection mechanisms and error latency, namely, the time interval between the occurrence of an error and the detection of that error.\",\n \"Title: Routing algorithm of minimizing maximum link congestion on grid networks.\\nYear: 2015\\n\\nAbstract: As regular topology networks, grid networks are widely adopted in network deployment. Link congestion and routing path length are two critical factors that affect the delay and throughput of a network. In this paper, we study the routing problem in grid networks concerning these two factors. The main objective of our routing algorithms is to minimize the maximum link congestion. The shortest path minimum maximum (SPMM) link congestion and non-shortest path minimum maximum (NSPMM) link congestion routing problems are studied. The two problems are first formulated as integer optimization problems. Then, corresponding routing algorithms (SPMM and NSPMM routing algorithms) are proposed. For SPMM routing algorithm, the path length is optimal, while for NSPMM routing algorithm, the path is limited in a submesh. Thus, the path length can be bounded. At last, we compare the proposed routing algorithms under different scenarios with other popular routing algorithms (RowColumn, ZigZag, Greedy, Random routing algorithms). The performances are evaluated through different metrics including link congestion, path length, path congestion, path congestion to path length ratio, delay and throughput.\",\n \"Title: Automatic Parallelization of Simulink Models for Multi-core Architectures\\nYear: 2015\\n\\nAbstract: This paper addresses the problem of parallelizing existing single-rate Simulink models for embedded control applications on multi-core architectures considering communication cost between blocks on different CPU cores. Utilizing the block diagram of the Simulink model, we derive the dependency graph between the different blocks. In order to solve the scheduling problem, we describe a Mixed Integer Linear Programming (MILP) formulation for optimally mapping the Simulink blocks to different CPU cores. Since the number of variables and constraints for MILP solver grows exponentially when model size increases, solving this problem in a reasonable time becomes harder. For addressing this issue, we introduce a set of techniques for reducing the number of constraints in the MILP formulation. By using the proposed techniques, the MILP solver finds solutions that are closer to the optimal solution within a given time bound. We study the scalability and efficiency of our consisting approach with synthetic benchmarks of randomly generated directed acyclic graphs. We also use the \\\\\\\"Fault-Tolerant Fuel Control System\\\\\\\" demo from Simulink and a Diesel engine controller from Toyota as case studies for demonstrating applicability of our approach to real world problems.\",\n \"Title: Teaching microprocessor systems design using a SoC and Embedded Linux platform\\nYear: 2005\\n\\nAbstract: In traditional microprocessor systems design courses, students learn to develop assembly language programs to control peripherals, handle interrupts, and perform I/O operations. We adopt a 32-bit StrongARM architecture on the Motorola MX1ADS board with Embedded Linux to present a modern microprocessor system design course. With this new platform, we use a high-level language to develop projects that accelerate the students' learning curve. Embedded Linux also provides the necessary flexibility and tool set required for students to debug their own projects. Our students' responded very positively to this change. They were excited about the renewed course structure, the updated learning environment, and the challenging projects.\",\n \"Title: Real-Time Support of Flash Memory File System for Embedded Applications\\nYear: 2006\\n\\nAbstract: NAND flash memory has emerged as an attractive proposition for embedded storage applications due to its properties of robustness, low price, a high number of write-erase cycles, and large capacities. To make it suitable for real-time embedded applications, this paper presents the design of a log structured file system that utilizes data redundancy and parallel operations to guarantee bounded read-write access latencies to real-time tasks, in the presence of requests from non real-time tasks. The proposed scheme requires minimal support from the underlying operating system. In addition, the paper proposes a storage scheme to reduce RAM footprint for the log structured file system while preserving reliability and minimal read/write overhead.\",\n \"Title: Automatic Parallelization of Multirate Block Diagrams of Control Systems on Multicore Platforms.\\nYear: 2016\\n\\nAbstract: This article addresses the problem of parallelizing model block diagrams for real-time embedded applications on multicore architectures. We describe a Mixed Integer Linear Programming formulation for finding a feasible mapping of the blocks to different CPU cores. For single-rate models, we use an objective function that minimizes the overall worst-case execution time. We introduce a set of heuristics to solve the problem for large models in a reasonable time. For multirate models, we solve the feasibility problem for finding a valid mapping. We study the scalability and efficiency of our approach with synthetic benchmarks and an engine controller from Toyota.\",\n \"Title: A Parallel FastTrack Data Race Detector on Multi-core Systems\\nYear: 2017\\n\\nAbstract: Detecting data races in multithreaded programs is critical to ensure the correctness of the programs. To discover data races precisely without false alarms, dynamic detection approaches are often applied. However, the overhead of the existing dynamic detection approaches, even with recent innovations, is still substantially high. In this paper, we present a simple but efficient approach to parallelize data race detection in multicore SMP (Symmetric Multiprocessing) machines. In our approach, data access information needed for dynamic detection is collected at application threads and passed to de-tection threads. The access information is distributed in a way that the operation performed by each detection thread is inde-pendent of that of other detection threads. As a consequence, the overhead caused by locking operations in data race detection can be alleviated and multiple cores can be fully utilized to speed up and scale up the detection. Furthermore, each detection thread deals with only its own assigned memory access region rather than the whole address space. The executions of detection threads can exploit the spatial locality of accesses leading to an improved cache performance. We have applied our parallel approach on the FastTrack algorithm and demon-strated the validity of our approach on an Intel Xeon machine. Our experimental results show that the parallel FastTrack detector, on average, runs 2.2 times faster than the original FastTrack detector on the 8 core machine.\",\n \"Title: A Smart Gateway Framework for IOT Services\\nYear: 2016\\n\\nAbstract: With various connectivity and communication protocols, it is conceivable that all raw data from sensor devices can be made available in the Internet. However, this imposes a significant intricacy when dealing with the amount of information and the management of the devices. To alleviate the problem, this paper presents a design and implementation of a prototype IoT gateway framework that exposes high-level abstraction for accessing and manipulating the devices of an ambient environment and the data that they produce. Along with integrating and consolidating edge devices, the gateway framework enables the construction of high-level knowledge to represent the property of the environment and to support privacy protection. Thus, intelligent services, created with reasoning rules or event triggers, can be adaptable to application environment. To illustrate the capability of the IoT gateway framework, an application scenario and its performance evaluation are included in the paper.\",\n \"Title: Dynamic Analysis of Multi-threaded Embedded Software to Expose Atomicity Violations\\nYear: 2016\\n\\nAbstract: Concurrency bugs are one of the most notorious software bugs and may not be observed easily. Significant work has been done on detection of atomicity violations bugs for high performance systems but there is not much work related to detect these bugs for embedded systems. Although criteria to claim existence of bugs remains same, approach changes a bit for embedded systems. The main focus of this research is to develop a systemic methodology to address the issue from embedded systems perspective. A framework is developed which predicts the access interleaving patterns that may violate atomicity using memory references of shared variables and provides support to force and analyze these patterns for any output change, system fault, or change in execution path.\",\n \"Title: SPDA: a security protocol for data aggregation in large-scale wireless sensor networks\\nYear: 2006\\n\\nAbstract: In this paper we propose a new key establishment protocol enabling any data aggregation protocol to be operated securely. This is accomplished by a bidirectional key distribution scheme based on Forward Key Setup and Backward Key Setup developed by using synchronized broadcast and multi-level key concept. Our protocol, called SPDA(Security Protocol for Data Aggregation) is well suited for any data aggregation algorithms and applications. Our analysis results prove SPDA's efficiency meaning that its communication cost is manageable\",\n \"Title: Emerging COTS architecture support for real-time TSN ethernet.\\nYear: 2019\\n\\nAbstract: The IEEE family of standards referred to as Time Sensitive Networking (TSN) is gaining popularity in many real-time embedded applications, including industrial automation and automotive. Based on a synchronized clock at the network layer, the TSN standards aim at deterministic behavior of message communication over an Ethernet. However, the data access time between a CPU and its network interface may be affected by the contention for shared resources and buses at each node. Any jitter on this final segment of the communication path can have a negative impact on clock synchronization and network message transmission. In this paper, we investigate the potential performance impacts to real-time TSN Ethernet caused by the level of precision to which the network clock and the CPU clock is synchronized as well as the magnitude of transmission jitter under various load conditions. Specifically, we look into the emerging architecture features in x86 systems that are considered to be commercial-off-the-shelf (COTS) platforms for embedded applications. We show that the latest generation of COTS hardware significantly reduces the jitters of clock synchronization between the software visible clock and the network, as well as reducing the jitter from when software attempts to transmit an Ethernet frame to when it actually appears on the wire. As a consequence, a significant performance improvement for software applications utilizing real-time TSN Ethernet can be attained in the emerging COTS systems.\",\n \"Title: Multi-Core Intra-Process Clock Synchronization\\nYear: 2021\\n\\nAbstract: Real-time applications require not only a precise understanding of now (what time it is), but also the ability to initiate an externally visible event at the correct time. We define this as Intra-process clock synchronization. Our findings show that in the latest commercial, off-the-shelf (COTS) multi-core systems running with preempt-RT Linux, the uncertainty in the software visible clock is near...\"\n]","isConversational":false},"profile_size":{"kind":"number","value":61,"string":"61"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13687,"string":"13,687"}}},{"rowIdx":13688,"cells":{"question_id":{"kind":"string","value":"Yongsheng Gao"},"question":{"kind":"string","value":"Generate an abstract for the title \"3D local derivative pattern for hyperspectral face recognition\" using the following items: 1. Hyperspectral imaging\n2. 3D texture pattern descriptor\n3. Spatial-spectral features\n4. Local derivative pattern\n5. State-of-the-art performance"},"answer":{"kind":"string","value":"Traditional 2D face recognition has been studied for many years and has achieved great success. Nonetheless, there is high demand to explore unrevealed information other than structure and texture in spatial domain in the faces. Hyperspectral imaging meets such requirements by providing additional spectrum information on objects, in completion to the traditional spatial features extracted in 2D images. In this paper, we propose a novel 3D high-order texture pattern descriptor for hyperspectral face recognition, which effectively exploit both spatial and spectral features in hyperspectral images. Based on the local derivative pattern, our method encodes the hyperspectral faces with multi-directional derivatives and binarization function in spatial-spectral space. Then a spatial-spectral feature descriptor is generated by applying a 3D histogram on the derivative pattern, which can be used to convert hyperspectral face images into vectorized representations. Compared to traditional face recognition methods, our method is able to describe the distinctive micro-patterns which integrate the spatial and spectral information in faces. Experiments on the real hyperspectral face datasets prove that our method has outperformed several state-of-the-art hyperspectral face recognition approaches."},"correct_answer":{"kind":"string","value":"Traditional 2D face recognition has been studied for many years and has achieved great success. Nonetheless, there is high demand to explore unrevealed information other than structure and texture in spatial domain in the faces. Hyperspectral imaging meets such requirements by providing additional spectrum information on objects, in completion to the traditional spatial features extracted in 2D images. In this paper, we propose a novel 3D high-order texture pattern descriptor for hyperspectral face recognition, which effectively exploit both spatial and spectral features in hyperspectral images. Based on the local derivative pattern, our method encodes the hyperspectral faces with multi-directional derivatives and binarization function in spatial-spectral space. Then a spatial-spectral feature descriptor is generated by applying a 3D histogram on the derivative pattern, which can be used to convert hyperspectral face images into vectorized representations. Compared to traditional face recognition methods, our method is able to describe the distinctive micro-patterns which integrate the spatial and spectral information in faces. Experiments on the real hyperspectral face datasets prove that our method has outperformed several state-of-the-art hyperspectral face recognition approaches."},"documents":{"kind":"list like","value":["Title: A multiview facial analysis technique for identity authentication\nYear: 2003\n\nAbstract: Biometric systems, which use people's physiological characteristics for identification or authentication, have become increasingly popular for countering fraud In this article, we describe a face recognition system that uses a new distance measure for authentication. It performs matching on a fusion of multiple views of each person.","Title: Sobel-LBP\nYear: 2008\n\nAbstract: This paper presents a new Sobel-LBP, an extension of existing local binary pattern (LBP), for facial image representation. The face image is filtered by Sobel operator to enhance the edge information. Sobel-LBP feature distributions are then extracted and concatenated into a spatial histogram to be used as a face descriptor. The proposed method is compared with the original LBP on both gray-level images and Gabor real and imaginary features for face recognition. The experimental results indicate that Sobel-LBP provides a significantly better performance than LBP under various conditions.","Title: High-Order Circular Derivative Pattern for Image Representation and Recognition\nYear: 2010\n\nAbstract: Micropattern based image representation and recognition, e.g. Local Binary Pattern (LBP), has been proved successful over the past few years due to its advantages of illumination tolerance and computational efficiency. However, LBP only encodes the first-order radial-directional derivatives of spatial images and is inadequate to completely describe the discriminative features for classification. This paper proposes a new Circular Derivative Pattern (CDP) which extracts high-order derivative information of images along circular directions. We argue that the high-order circular derivatives contain more detailed and more discriminative information than the first-order LBP in terms of recognition accuracy. Experimental evaluation through face recognition on the FERET database and insect classification on the NICTA Biosecurity Dataset demonstrated the effectiveness of the proposed method.","Title: Towards a BioAPI compliant face verification system\nYear: 2004\n\nAbstract: Standards are important to the growth and acceptance of emerging industries such as the biometric industry. To promote biometric standard, the BioAPI Consortium has developed a widely accepted BioAPI specification that defines Application Programming Interface (API) for application developers and Service Provider Interface (SPI) for biometric technology developers. In this paper, we propose an implementation of a BioAPI compliant face verification BSP (Biometric Service Provider). In the face verification BSP, Line Edge Map (LEM) is used as the face recognition engine. Primitive biometric functions such as Capture, CreateTemplate, Process and VerifyMatch have been developed to provide support for face verification. In addition, to demonstrate the proposed face verification BSP, we have developed a BioAPI compliant face verification application for door access control. The performance of the face verification application is also evaluated.","Title: A comparative evaluation of Average Face on holistic and local face recognition approaches\nYear: 2008\n\nAbstract: This study focuses on a recent paper ldquo100% Accuracy in Automatic Face Recognitionrdquo published on Science, in which an ldquoAverage Facerdquo is proposed and claimed to be capable of dramatically improving performance of a face recognition system. To reveal its working mechanism, we perform the averaging process using pose-varied synthetic images generated from 3D face database and conduct a comparative study to observe its effectiveness on holistic and local face recognition approaches. Two representative methods, i.e. eigenface and local binary pattern (LBP) are employed to perform the experiments. It is interesting to find from our experiments that the performance of the ldquoAverage Facerdquo is not independent of the face recognition approaches. Although face averaging increases the recognition accuracy of eigenface method, it impairs the performance of LBP method.","Title: Fast Kernel Sparse Representation\nYear: 2011\n\nAbstract: Two efficient algorithms are proposed to seek the sparse representation on high-dimensional Hilbert space. By proving that all the calculations in Orthogonal Match Pursuit (OMP) are essentially inner-product combinations, we modify the OMP algorithm to apply the kernel-trick. The proposed Kernel OMP (KOMP) is much faster than the existing methods, and illustrates higher accuracy in some scenarios. Furthermore, inspired by the success of group-sparsity, we enforce a rigid group-sparsity constraint on KOMP which leads to a noniterative variation. The constrained cousin of KOMP, dubbed as Single-Step KOMP (S-KOMP), merely takes one step to achieve the sparse coefficients. A remarkable improvement (up to 2,750 times) in efficiency is reported for S-KOMP, with only a negligible loss of accuracy.","Title: Learning kernel subspace classifier\nYear: 2007\n\nAbstract: Subspace classifiers are well-known in pattern recognition, which represent pattern classes by linear subspaces spanned by the class specific basis vectors through simple mathematical operations like SVD. Recently, kernel based subspace methods have been proposed to extend the functionalities by directly applying the Kernel Principal Component Analysis (KPCA). The projection variance in kernel space as applied in these earlier proposed kernel subspace methods, however, is not a trustworthy criteria for class discrimination and they simply fail in many recognition problems as we encountered in biometrics research. We address this issue by proposing a learning kernel subspace classifier which attempts to reconstruct data in input space through the kernel subspace projection. While the pre-image methods aiming at finding an approximate pre-image for each input by minimization of the reconstruction error in kernel space, we emphasize the problem of how to estimate a kernel subspace as a model for a specific class. Using the occluded face recognition as examples, our experimental results demonstrated the efficiency of the proposed method.","Title: Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor.\nYear: 2010\n\nAbstract: This paper proposes a novel high-order local pattern descriptor, local derivative pattern (LDP), for face recognition. LDP is a general framework to encode directional pattern features based on local derivative variations. The n(th)-order LDP is proposed to encode the (n-1)(th) -order local derivative direction variations, which can capture more detailed information than the first-order local pattern used in local binary pattern (LBP). Different from LBP encoding the relationship between the central point and its neighbors, the LDP templates extract high-order local information by encoding various distinctive spatial relationships contained in a given local region. Both gray-level images and Gabor feature images are used to evaluate the comparative performances of LDP and LBP. Extensive experimental results on FERET, CAS-PEAL, CMU-PIE, Extended Yale B, and FRGC databases show that the high-order LDP consistently performs much better than LBP for both face identification and face verification under various conditions.","Title: A Minutiae-Based Fingerprint Matching Algorithm Using Phase Correlation\nYear: 2007\n\nAbstract: Minutiae-based method is the most popular approach in fingerprint matching. However, most existing methods need to search for the best correspondence of minutiae pairs or use reference points (core and delta points) to estimate the alignment parameters. The problem of lost minutiae or spurious minutiae always occurs during the minutiae detection process. Hence, the corresponding pairs or reference points may not be found under this condition. This paper proposes a new minutiae-based fingerprint matching algorithm using phase correlation. We define a new representation called Minutiae Direction Map (MDM). First, we convert minutiae sets into 2D image spaces. Then the transformation parameters are calculated using phase correlation between two MDMs to align two fingerprints to be matched. The similarity of two fingerprints is determined by the distance between two minutiae sets. Our approach does not need to search for the corresponding minutiae pairs. Experimental results show that the proposed approach performed well in matching fingerprint minutiae sets, which greatly improved the economy of storage space.","Title: Face Localization Using an Effective Co-evolutionary Genetic Algorithm\nYear: 2010\n\nAbstract: In this paper, a new method for face localization in color images, which is based on co-evolutionary systems, is introduced. The proposed method uses a co-evolutionary system to locate the eyes in a face image. The used coevolutionary system involves two genetic algorithm models. The first GA model searches for a solution in the given environment, and the second GA model searches for useful genetic information in the first GA model. In the next step, by using the location of eyes in image the parameters of face's bounding ellipse (center, orientation, major and minor axis) are computed. To evaluate and compare the proposed method with other methods, high order Pseudo Zernike Moments (PZM) are utilized to produce feature vectors and a Radial Basis Function (RBF) neural network is used as the classifier. Simulation results indicate that the speed and accuracy of the new system using the proposed face localization method which uses a co-evolutionary approach is higher than the system proposed in [10].","Title: Lighting Analysis and Texture Modification of 3D Human Face Scans\nYear: 2007\n\nAbstract: The 3D scanning of human faces is an important tool to acquire accurate shape and texture information of heads and faces for both animation and recognition purposes. One of the most popular scanning tools is Cyberware head and face 3D colour scanner. Currently, 3D face databases scanned using the Cyberware head and face 3D colour scanner databases are publicly available and have been used in many face recognition system as the training data, one of which is USF human ID 3-D database. The mechanism of facial texture capturing, however, remains unclear due to the special settings of the light sources during scanning. The direct application of the acquired texture intensity as the albedo of the face surfaces leads to suboptimal modelling results. This paper analyses the lighting configuration of the scanner and proposes an appropriate reflectance model to describe the reflecting mechanism when scanning. The texture modifications can be carried on in the existing scanning results without any further scans. Experiment results show that the proposed reflectance model can better approximate the reflecting situation of scanning so that the acquired textures can be directly used as diffuse reflectance coefficients and achieve better rendering results. Key words: 3D face model, 3D scanner, facial textures, lighting condition, reflection model.","Title: Recognizing face profiles in the presence of hairs/glasses interferences\nYear: 2010\n\nAbstract: Facial profile provides a complementary structure of the face that is not present in frontal faces, which has been used in personal identification, face perception research and 3D face construction. In this paper, we present a novel local attributed string matching (LAStrM) approach to recognize face profiles in the presence of interferences. The conventional profile recognition algorithms heavily depend on the accuracy of the facial area cropping. However, in realistic scenarios the facial area may be difficult to localize due to interferences (e.g., glasses, hairstyles). The proposed approach is able to efficiently find the most discriminative local parts between face profiles addressing the recognition problem with interferences. Experimental results have shown that the proposed matching scheme is robust to interferences compared against several primary approaches using two profile image databases (Bern and FERET). It has potential capability for partially occluded shape classification.","Title: Locality-Regularized Linear Regression for face recognition.\nYear: 2012\n\nAbstract: Linear Regression Classification (LRC) based face recognition achieves high accuracy while being highly efficient. As with most other linear-subspace-based methods, the faces of a subject are assumed to reside on a linear manifold; however, where occlusion or disturbances are involved, this assumption may be inaccurate. In this paper, a manifold-learning procedure is used to expand on conventional LRC by excluding faces not fitting the original assumption (of linearity), thereby localizing the manifold subspace, increasing the accuracy over conventional LRC and reducing the number of faces for which the regression must be performed. The algorithm is evaluated using two standard databases and shown to outperform the conventional LRC.","Title: Compressed Sensing Ensemble Classifier for Human Detection.\nYear: 2013\n\nAbstract: This paper proposes a novel Compressed Sensing Ensemble Classifier (CSEC) for human detection. The proposed CSEC employs the compressed sensing technique to get a more sparse model with a more reasonable selection of base classifiers. The major contributions of this paper are: 1) a novel principled framework for ensemble classifier design based on compressed sensing; 2) a new concept of considering both the simplicity of ensemble classifier and irrelevance of base classifiers towards optimal classifier design; and 3) a quadratic function for CSEC optimization which includes a new optimizable positive semi-definite relevance matrix to simultaneously select appropriate base classifiers with minimized relevance. Experimental results on INRIA and SDL databases show that the performance of CSEC is better than two most popular classifiers SVM and AdaBoost, as well as a most recent method CLML. © 2013 Springer-Verlag Berlin Heidelberg.","Title: Recognizing partially occluded faces from a single sample per class using string-based matching\nYear: 2010\n\nAbstract: Automatically recognizing human faces with partial occlusions is one of the most challenging problems in face analysis community. This paper presents a novel string-based face recognition approach to address the partial occlusion problem in face recognition. In this approach, a new face representation, Stringface, is constructed to integrate the relational organization of intermediate-level features (line segments) into a high-level global structure (string). The matching of two faces is done by matching two Stringfaces through a string-to-string matching scheme, which is able to efficiently find the most discriminative local parts (substrings) for recognition without making any assumption on the distributions of the deformed facial regions. The proposed approach is compared against the state-of-the-art algorithms using both the AR database and FRGC (Face Recognition Grand Challenge) ver2.0 database. Very encouraging experimental results demonstrate, for the first time, the feasibility and effectiveness of a high-level syntactic method in face recognition, showing a new strategy for face representation and recognition.","Title: Face Recognition by Combining Kernel Associative Memory and Gabor Transforms\nYear: 2006\n\nAbstract: Kernel associative memory (KAM) has previously been proposed as an efficient scheme for face recognition. In this paper, a hybrid method of combining KAM and Gabor wavelet transform is proposed. In this method, face images of each person are first decomposed into their spatial/ frequency domains by Gabor transforms, which are then modelled by a KAM. While Gabor properties of orientation selectivity and spatial frequency selectivity provide discriminating features, KAM offers the means to capture the important intra-class variations. Experimental results obtained on two standard face databases demonstrated that the proposed method consistently improved the system performance.","Title: 3D face modeling using image warping in pose-invariant face recognition\nYear: 2002\n\nAbstract: One of the key remaining problems in face recognition is that of handling the variability in appearance due to changes in pose. One strategy is to synthesize virtual face views from real views. In this paper, a new 3D face shape modeling algorithm - orthogonal reverse projection model (ORPM) is presented. Our method makes only use of two orthogonal real view of a face, i.e., the frontal and profile views. To obtain the 3D shape information of the face, a modified feature-based 2D image warping technique is employed to establish point correspondence between the frontal and profile views. Then the 3D face shape model is generated by orthogonal reverse projection using the corresponding point pairs. The virtual views can be synthesized by simply rotating the 3D face model. The modeling results suggest the validity of the ORPM algorithm for 3D face modeling and 2D face image synthesizing under different poses.","Title: Colour Adjustment and Specular Removal for Non-uniform Shape from Shading\nYear: 2010\n\nAbstract: Surface colour changes and specular reflections are two major problems in 3D modelling using shape-from-shading (SFS). This paper proposes to pre-process the input image for a typical SFS algorithm, so that the resultant image has no colour changes and specular reflection. First, a chromaticity-based specular reflection removal algorithm is applied to achieve a pure diffuse (Lambertian) reflected image. Then, a novel chromaticity-based colour adjustment approach is proposed to generate an image without surface colour changes. The standard SFS algorithms can then be applied successfully onto the processed images to produce plausible 3D models. In experiments, the proposed approach was tested on standard SFS datasets with complex surface colours. The experimental results show it's promising to facilitate SFS algorithms to handle SFS problems with more complex surface properties and illumination conditions.","Title: Characteristics of an effective design plan system to support reuse in case-based mechanical design\nYear: 1998\n\nAbstract: Design plan systems play an important role in applying case-based reasoning (CBR) to mechanical design. They are responsible for the representation and evaluation of design plans. Design plans are stored as the actual cases in the case base of a CBR system. In this paper, we discuss design reuse and related open issues: plan modification and plan adaptation. It is shown that these issues need to utilize design knowledge associated with both design procedure and design rationale. An overview of current design systems is given. These design systems have not been able to address the issues associated with design reuse. This has, in effect, made them unsuitable for use in a robust CBR system for mechanical design. Based on this discussion, this paper presents the characteristics of an effective design plan system to support reuse in case-based mechanical design. It is concluded that a design plan system should be a mixture of both the design procedure model and the design rationale model. An implementation of such a design plan system is presented with an example.","Title: Gabor feature constrained statistical model for efficient landmark localization and face recognition\nYear: 2009\n\nAbstract: Feature extraction and classification using Gabor wavelets have proven to be successful in computer vision and pattern recognition. Gabor feature-based Elastic Bunch Graph Matching (EBGM), which demonstrated excellent performance in the FERET evaluation test, has been considered as one of the best algorithms for face recognition due to its robustness against expression, illumination and pose variations. However, EBGM involves considerable computational complexity in its rigid and deformable matching process, preventing its use in many real-time applications. This paper presents a new Constrained Profile Model (CPM), in cooperation with Flexible Shape Model (FSM) to form an efficient localization framework. Through Gabor feature constrained local alignment, the proposed method not only avoids local minima in landmark localization, but also circumvents the exhaustive global optimization. Experiments on CAS-PEAL and FERET databases demonstrated the effectiveness and efficiency of the proposed method.","Title: Soil Biochar Quantification via Hyperspectral Unmixing\nYear: 2013\n\nAbstract: Biochar has unique function to improve soil chemo-physical and biological properties for crop growth. Because changes of biochar in soil may affect its long-term effectiveness as an amendment, it is important to quantify and monitor biochar after application. In this paper, we propose a solution for this problem via hyperspectral image analysis. We treat the soil image as a mixture of soil and biochar signals, and then apply hyperspectral unmixing methods to predict the biochar abundance at each pixel. The final percentage of biochar can be calculated by taking the mean of the abundance of hyperspectral pixels. We have compared several hyperspectral unmixing methods based on least squares estimation and nonnegative matrix factorization with various sparsity constraints. Experimental results are evaluated by polynomial regression and root mean square errors against the ground truth data collected in the environmental labs. The results show that hyperspectral unmixing is a promising method to measure the percentage of biochar in the soil.","Title: 2.5D face recognition using Patch Geodesic Moments\nYear: 2012\n\nAbstract: In this paper, we propose a novel Patch Geodesic Distance (PGD) to transform the texture map of an object through its shape data for robust 2.5D object recognition. Local geodesic paths within patches and global geodesic paths for patches are combined in a coarse to fine hierarchical computation of PGD for each surface point to tackle the missing data problem in 2.5D images. Shape adjusted texture patches are encoded into local patterns for similarity measurement between two 2.5D images with different viewing angles and/or shape deformations. An extensive experimental investigation is conducted on 2.5 face images using the publicly available BU-3DFE and Bosphorus databases covering face recognition under expression and pose changes. The performance of the proposed method is compared with that of three benchmark approaches. The experimental results demonstrate that the proposed method provides a very encouraging new solution for 2.5D object recognition.","Title: Safety in multi-robot teleoperation system through Internet.\nYear: 2004\n\nAbstract: Internet based multi-operator multi-robot (MOMR) teleoperation system allow the accomplishment of tasks that a single robot could not approach due to restrictions associated to the product and /or the robot. The safety issue in the system is an important problem according to its complexity. This paper describes the development of multi-layer safety structure of the safety subsystem which consist of software modules as well as hardware components after the threat analysis of the system using fault tree analysis (FTA) technology. The subsystem is divided into three layers with its logic architecture: interactive monitor layer, collaborative control layer and real-time control layer. The safety problems and the related strategy are clarified by detailed analysis of each layer and relationship among the layers. So we can get a high performance MOMR teleoperation system with multi-layer safety architecture. © 2004 IEEE.","Title: Recognizing Rotated Faces From Frontal and Side Views: An Approach Toward Effective Use of Mugshot Databases\nYear: 2008\n\nAbstract: Mug shot photography has been used to identify criminals by the police for more than a century. However, the common scenario of face recognition using frontal and side-view mug shots as gallery remains largely uninvestigated in computerized face recognition across pose. This paper presents a novel appearance-based approach using frontal and sideface images to handle pose variations in face recognition, which has great potential in forensic and security applications involving police mugshot databases. Virtual views in different poses are generated in two steps: 1) shape modelling and 2) texture synthesis. In the shape modelling step, a multilevel variation minimization approach is applied to generate personalized 3-D face shapes. In the texture synthesis step, face surface properties are analyzed and virtual views in arbitrary viewing conditions are rendered, taking diffuse and specular reflections into account. Appearance-based face recognition is performed with the augmentation of synthesized virtual views covering possible viewing angles to recognize probe views in arbitrary conditions. The encouraging experimental results demonstrated that the proposed approach by using frontal and side-view images is a feasible and effective solution to recognizing rotated faces, which can lead to a better and practical use of existing forensic databases in computerized human face-recognition applications.","Title: Significant jet point for facial image representation and recognition\nYear: 2008\n\nAbstract: Gabor wavelet related feature extraction and classification is an important topic in image analysis and pattern recognition. Gabor features can be used either holistically or analytically. While holistic approaches involve significant computational complexity, existing analytic approaches require explicit correspondence of predefined feature points for classification. Different from these approaches, this paper presents a new analytic Gabor method for face recognition. The proposed method attaches Gabor features on a set of shape-driven sparse points to describe both geometric and textural information. Neither the number nor the correspondence of these points is needed. A variant of Hausdorff distance is employed to recognize faces. The experiments performed on AR database demonstrated that the proposed algorithm is effective to identify individuals in various circumstances, such as under expression and illumination changes.","Title: Complex Background Modeling And Motion Detection Based On Texture Pattern Flow\nYear: 2008\n\nAbstract: This paper proposes a novel Texture Pattern Flow (TPF) for complex background modeling and motion detection. The Pattern Flow is proposed to encode the binary pattern changes among the neighborhoods in the space-time domain. To model the distribution of the TPF, the TPF integral histograms are used to extract the discriminative features to represent the input video. Experimental results on the public videos testify the effectiveness of the proposed method in comparison to LBP and GMM based background modeling methods.","Title: Multilevel Quadratic Variation Minimization for 3D Face Modeling and Virtual View Synthesis\nYear: 2005\n\nAbstract: One of the key remaining problems in face recognition is that of handling the variability in appearance due to changes in pose. One strategy is to synthesize virtual face views from real views. In this paper, a novel 3D face shape-modeling algorithm, Multilevel Quadratic Variation Minimization (MQVM), is proposed. Our method makes sole use of two orthogonal real views of a face, i.e., the frontal and profile views. By applying quadratic variation minimization iteratively in a coarse-to-fine hierarchy of control lattices, the MQVM algorithm can generate C虏-smooth 3D face surfaces. Then realistic virtual face views can be synthesized by rotating the 3D models. The algorithm works properly on sparse constraint points and large images. It is much more efficient than single-level quadratic variation minimization. The modeling results suggest the validity of the MQVM algorithm for 3D face modeling and 2D face view synthesis under different poses.","Title: A Novel Pose Invariant Face Recognition Approach Using a 2D-3D Searching Strategy\nYear: 2010\n\nAbstract: Many Face Recognition techniques focus on 2D-2D comparison or 3D-3D comparison, however few techniques explore the idea of cross-dimensional comparison. This paper presents a novel face recognition approach that implements cross-dimensional comparison to solve the issue of pose invariance. Our approach implements a Gabor representation during comparison to allow for variations in texture, illumination, expression and pose. Kernel scaling is used to reduce comparison time during the branching search, which determines the facial pose of input images. The conducted experiments prove the viability of this approach, with our larger kernel experiments returning 91.6% - 100% accuracy on a database comprised of both local data, and data from the USF Human ID 3D database.","Title: Automatic Texture Synthesis for Face Recognition from Single Views\nYear: 2006\n\nAbstract: One possible solution for pose- and illumination-invariant face recognition is to employ appearance-based approaches, which rely greatly on correct facial textures. However, existing facial texture analysis algorithms are suboptimal, because they usually neglect specular reflections and require numerous training images for virtual view synthesis. This paper presents a novel texture synthesis approach from a single frontal view for face recognition. Using a generic 3D face shape, facial textures are analyzed with consideration of all of the ambient, diffuse, and specular reflections. Virtual views are synthesized under different poses and illuminations. The proposed approach was evaluated using the CMU-PIE face database. Encouraging results show that the proposed approach improves face recognition performances across pose and illumination variations","Title: Expression-Invariant 3D Face Recognition Using Patched Geodesic Texture Transform\nYear: 2010\n\nAbstract: Numerous methods have been proposed for the expression-invariant 3D face recognition, but a little attention is given to the local-based representation for the texture of the 3D images. In this paper, we propose an expression-invariant 3D face recognition approach based on the locally extracted moments of the texture when only one exemplar per person is available. We use a geodesic texture transform accompanied by Pseudo Zernike Moments to extract local feature vectors from the texture of a face. An extensive experimental investigation is conducted using publicly available BU-3DFE face databases covering face recognition under expression variations. The performance of the proposed method is compared with the performance of two benchmark approaches. The encouraging experimental results demonstrate that the proposed method can be used for 3D face recognition in single model databases.","Title: On averaging face images for recognition under pose variations\nYear: 2008\n\nAbstract: Recently, psychological studies showed that averaging human face images greatly improves the performance of face recognition under various pose, illumination, expression, and/or aging conditions. This paper investigates quantitatively the mechanism of the face averaging process in face recognition specifically against pose variations. Facilitated with 3D face dataset, the process of face averaging is tested on face images free from human errors and misalignments. Single images are chosen as gallery and the averaged views as probe in all the experiments. Three different scenarios are experimented, i.e., identification using single gallery images, identification using different gallery images, and averaging using unbalanced range of input image. The experimental results show that the averaging process under pose variations is equivalent to generating a face view in an average pose and the improvement in face recognition is subject to the conditions that the gallery pose is close to the average probe pose.","Title: An Automatic On-Site Fire Ant Screening System\nYear: 2012\n\nAbstract: This paper proposes the first attempt for semi-automatic screening and identification of red imported fire ants (Solenopsis invicta) in Australia. As an exotic ant species to Australia, fire ants were imported from South America in 2001 and have since been regarded as dangerous pests that could severely damage the environment and many industries. We followed two of the three major identification keys defined by entomologists and proposed: 1) A fusion of two different image features (i.e., the perpendicular median intensity and the perpendicular width) for antenna segment detection; and 2) A weighted histogramming of micropattern features for petiole classification. Our experimental results show that automatic on-site fire ant screening is feasible and the proposed weighted histogramming of micropattern features performs better than the original micropattern representation.","Title: Local Kernel Feature Analysis (LKFA) for object recognition\nYear: 2011\n\nAbstract: This paper proposes a new Local Kernel Feature Analysis (LKFA) method for object recognition. LKFA captures the nonlinear local relationship in an image via kernel functions. Different from traditional kernel methods for object recognition, the proposed method does not need to reserve the training samples. LKFA is designed to extract the eigenvalue features from the Hermite matrix of a local feature representation, which we have theoretically proven its robustness to noise and perturbations. Experiment results on palmprint and face recognitions demonstrated the effectiveness of the proposed LKFA that significantly improved the performance of the local feature based object recognition method.","Title: A 3D Polygonal Line Chains Matching Method for Face Recognition\nYear: 2013\n\nAbstract: In this paper, a novel 3D polygonal line chains matching method is proposed. Different from traditional method that use points and meshes to represent and match 3D shapes, our method represents 3D surfaces using 3D polygonal line chains generated from ridge and valley curves. Then a 3D polygonal line segment Hausdorff distance measure is developed to compute the similarity between two 3D surfaces. This representation, along with the distance metric, can effectively harness structural and spatial information on a 3D surface. The added information can provide more and better discrimination power for object recognition. It strengthens and improves the matching process of similar 3D objects such as 3D faces. Experiments on FRGC v2 database leads to a rank one recognition rate of 96.1%.","Title: Establishing point correspondence using multidirectional binary pattern for face recognition\nYear: 2008\n\nAbstract: This paper presents a new Multidirectional Binary Pattern (MBP) for face recognition. Different from most Local Binary Pattern (LBP) related approaches which cluster LBP occurrences from whole image or partitioned subimage patches and use single or concatenated histogram measurement for recognition, MBP is applied on a sparse set of shape-driven points. The new representation is designed for describing both global structure and local texture, and also significantly reduces the high dimensionality of LBP histogram description. Composed of binary patterns from multiple directions, MBP is capable of extracting more discriminative features than LBP. The experiments on face recognition demonstrated the effectiveness of the proposed algorithm against expression and lighting variations.","Title: Feature-Level Fusion in Personal Identification\nYear: 2005\n\nAbstract: The existing studies of multi-modal and multi-view personal identification focused on combining the outputs of multiple classifiers at the decision level. In this study, we investigated the fusion at the feature level to combine multiple views and modals in personal identification. A new similarity measure is proposed, which integrates multiple 2-D view features representing a visual identity of a 3-D object seen from different viewpoints and from different sensors. The robustness to non-rigid distortions is achieved by the proximity correspondence manner in the similarity computation. The feasibility and capability of the proposed technique for personal identification were evaluated on multiple view human faces and palmprints. This research demonstrates that the feature-level fusion provides a new way to combine multiple modals and views for personal identification.","Title: Study on the BeiHang Keystroke Dynamics Database\nYear: 2011\n\nAbstract: This paper introduces a new BeiHang (BH) Keystroke Dynamics Database for testing and evaluation of biometric approaches. Different from the existing keystroke dynamics researches which solely rely on laboratory experiments, the developed database is collected from a real commercialized system and thus is more comprehensive and more faithful to human behavior. Moreover, our database comes with ready-to-use benchmark results of three keystroke dynamics methods, Nearest Neighbor classifier, Gaussian Model and One-Class Support Vector Machine. Both the database and benchmark results are open to the public and provide a significant experimental platform for international researchers in the keystroke dynamics area.","Title: Combined Classification Of Multiple Views Using Facial Corners\nYear: 2002\n\nAbstract: The profile view of a face provides a complementary structure that is not seen in the frontal view. The classification system combining both frontal and profile views of faces can improve the classification accuracy. And it would be more foolproof because it is difficult to fool the profile face identification by a mask. This paper proposes a new face recognition approach, which can be applied on both frontal and profile faces, to build a robust combined multiple view face identification system. The recognition employs a novel facial corner coding and matching method, and integrates the outline and interior facial parts in the profile matching. The proposed multiview modified Hausdorff distance fuses multiple views of faces to achieve an improved system performance.","Title: Optimizing Sharpness Measure for Bright Lesion Detection in Retinal Image Analysis\nYear: 2009\n\nAbstract: Due to the spherical shape nature of retina and the illumination effect, detecting bright lesions in a retinal image is a challenging problem. Existing methods depend heavily on a prior knowledge about lesions, which either a user-defined parameter is employed or a supervised learning technique is adopted to estimate the parameter. In this paper, a novel sharpness measure is proposed, which indicates the degree of sharpness of bright lesions in the whole retinal image. It has a sudden jump at the optimal parameter. A polynomial fitting technique is used to capture this jump. We have tested our method on a public available dataset. Experimental results show that the proposed unsupervised approach is able to detect bright lesions accurately in an unhealthy retinal image and it outperforms existing supervised learning method. Also, the proposed method reports no abnormality for a healthy retinal image.","Title: Performance Evaluation of Micropattern Representation on Gabor Features for Face Recognition\nYear: 2010\n\nAbstract: Face recognition using micropattern representation has recently received much attention in the computer vision and pattern recognition community. Previous researches demonstrated that micropattern representation based on Gabor features achieves better performance than its direct usage on gray-level images. This paper conducts a comparative performance evaluation of micropattern representations on four forms of Gabor features for face recognition. Three evaluation rules are proposed and observed for a fair comparison. To reduce the high feature dimensionality problem, uniform quantization is used to partition the spatial histograms. The experimental results reveal that: 1) micropattern representation based on Gabor magnitude features outperforms the other three representations, and the performances of the other three are comparable; and 2) micropattern representation based on the combination of Gabor magnitude and phase features performs the best.","Title: Parametric manifold of an object under different viewing directions\nYear: 2012\n\nAbstract: The appearance of a 3D object depends on both the viewing directions and illumination conditions. It is proven that all n-pixel images of a convex object with Lambertian surface under variable lighting from infinity form a convex polyhedral cone (called illumination cone) in n-dimensional space. This paper tries to answer the other half of the question: What is the set of images of an object under all viewing directions? A novel image representation is proposed, which transforms any n-pixel image of a 3D object to a vector in a 2n-dimensional pose space. In such a pose space, we prove that the transformed images of a 3D object under all viewing directions form a parametric manifold in a 6-dimensional linear subspace. With in-depth rotations along a single axis in particular, this manifold is an ellipse. Furthermore, we show that this parametric pose manifold of a convex object can be estimated from a few images in different poses and used to predict object's appearances under unseen viewing directions. These results immediately suggest a number of approaches to object recognition, scene detection, and 3D modelling. Experiments on both synthetic data and real images were reported, which demonstrates the validity of the proposed representation.","Title: Architecture of Multi-Agent-Based Multi-Operator Multi-Mobile-Manipulator Teleoperation System\nYear: 2008\n\nAbstract: Being characteristics of motion, coordination, distribution, non-determinism etc, multi-operator multi-mobile-manipulator teleoperation system has difficulties in controlling and implementation. Multi-Agent-Based architecture of the system can distinctly describe the information interactive relationship between components of the system, and optimize intelligence distribution between human and robot, so it helps to reduce difficulties in controlling and implementation of the system. After the physical structure of the system was analyzed, the system was divided into several sub-systems with given function, and then a Multi-Agent-Based layered architecture with shared control mode of the system was proposed and the function of each Agent was planned. Multi-Agent's mixed communication structure was designed by both of blackboard model and Peer-to-Peer model. According to characteristics of the system, a kind of general mixed architecture for Agent was proposed, and an example of implementation method of Agent was given, then the validity of the mixed architecture was verified by designing experiment.","Title: Human face profile recognition using attributed string\nYear: 2002\n\nAbstract: A new attributed string matching method for human face profile recognition is proposed in this work. It is a novel idea to apply structural and syntactic technique on face profile matching. The approach works on a chain of profile line segments and highlights the favor of curve matching by suppressing the operations of insert and delete. The technique relies mainly on the merge and change operations of string to tackle the inconsistency problem of feature point detection. A quadratic penalty function is proposed to prohibit large angle changes and overmerging. The method produces very encouraging results and is found to be suitable for similar shape classification.","Title: Learning Discriminative Local Patterns With Unrestricted Structure For Face Recognition\nYear: 2013\n\nAbstract: Local binary patterns are a popular local texture feature for describing textures and objects. The standard method and many derivatives use a hand-crafted structure of point comparisons to encode the local texture to build the descriptors. In this paper we propose automatically learning a discriminative pattern structure from an extended pool of candidate pattern elements, without restricting the possible configurations. The learnt pattern structure may contain elements describing many different scales and gradient orientations that are not available in LBP (and related patterns), thus allowing the flexibility to construct structures capable of better representing the objects under test. We show through experimentation on two face recognition databases that this approach consistently outperforms other methods, in terms of training speed and recognition accuracy in every tested case.","Title: Face Recognition Using Ensemble String Matching\nYear: 2013\n\nAbstract: In this paper, we present a syntactic string matching approach to solve the frontal face recognition problem. String matching is a powerful partial matching technique, but is not suitable for frontal face recognition due to its requirement of globally sequential representation and the complex nature of human faces, containing discontinuous and non-sequential features. Here, we build a compact syntactic Stringface representation, which is an ensemble of strings. A novel ensemble string matching approach that can perform non-sequential string matching between two Stringfaces is proposed. It is invariant to the sequential order of strings and the direction of each string. The embedded partial matching mechanism enables our method to automatically use every piece of non-occluded region, regardless of shape, in the recognition process. The encouraging results demonstrate the feasibility and effectiveness of using syntactic methods for face recognition from a single exemplar image per person, breaking the barrier that prevents string matching techniques from being used for addressing complex image recognition problems. The proposed method not only achieved significantly better performance in recognizing partially occluded faces, but also showed its ability to perform direct matching between sketch faces and photo faces.","Title: Face recognition using line edge map\nYear: 2002\n\nAbstract: The automatic recognition of human faces presents a significant challenge to the pattern recognition research community. Typically, human faces are very similar in structure with minor differences from person to person. They are actually within one class of \"human face\". Furthermore, lighting conditions change, while facial expressions and pose variations further complicate the face recognition task as one of the difficult problems in pattern analysis. This paper proposes a novel concept: namely, that faces can be recognized using a line edge map (LEM). The LEM, a compact face feature, is generated for face coding and recognition. A thorough investigation of the proposed concept is conducted which covers all aspects of human face recognition, i.e. face recognition under (1) controlled/ideal conditions and size variations, (2) varying lighting conditions, (3) varying facial expressions, and (4) varying pose. The system performance is also compared with the eigenface method, one of the best face recognition techniques, and with reported experimental results of other methods. A face pre-filtering technique is proposed to speed up the search process. It is a very encouraging to find that the proposed face recognition technique has performed better than the eigenface method in most of the comparison experiments. This research demonstrates that the LEM, together with the proposed generic line-segment Hausdorff distance measure, provides a new method for face coding and recognition","Title: Face Recognition using Optimal Representation Ensemble\nYear: 2011\n\nAbstract: Recently, the face recognizers based on linear representations have been shown to deliver state-of-the-art performance. In real-world applications, however, face images usually suffer from expressions, disguises and random occlusions. The problematic facial parts undermine the validity of the linear-subspace assumption and thus the recognition performance deteriorates significantly. In this work, we address the problem in a learning-inference-mixed fashion. By observing that the linear-subspace assumption is more reliable on certain face patches rather than on the holistic face, some Bayesian Patch Representations (BPRs) are randomly generated and interpreted according to the Bayes' theory. We then train an ensemble model over the patch-representations by minimizing the empirical risk w.r.t the \"leave-one-out margins\". The obtained model is termed Optimal Representation Ensemble (ORE), since it guarantees the optimality from the perspective of Empirical Risk Minimization. To handle the unknown patterns in test faces, a robust version of BPR is proposed by taking the non-face category into consideration. Equipped with the Robust-BPRs, the inference ability of ORE is increased dramatically and several record-breaking accuracies (99.9% on Yale-B and 99.5% on AR) and desirable efficiencies (below 20 ms per face in Matlab) are achieved. It also overwhelms other modular heuristics on the faces with random occlusions, extreme expressions and disguises. Furthermore, to accommodate immense BPRs sets, a boosting-like algorithm is also derived. The boosted model, a.k.a Boosted-ORE, obtains similar performance to its prototype. Besides the empirical superiorities, two desirable features of the proposed methods, namely, the training-determined model-selection and the data-weight-free boosting procedure, are also theoretically verified.","Title: Face recognition across pose: A review\nYear: 2009\n\nAbstract: One of the major challenges encountered by current face recognition techniques lies in the difficulties of handling varying poses, i.e., recognition of faces in arbitrary in-depth rotations. The face image differences caused by rotations are often larger than the inter-person differences used in distinguishing identities. Face recognition across pose, on the other hand, has great potentials in many applications dealing with uncooperative subjects, in which the full power of face recognition being a passive biometric technique can be implemented and utilised. Extensive efforts have been put into the research toward pose-invariant face recognition in recent years and many prominent approaches have been proposed. However, several issues in face recognition across pose still remain open, such as lack of understanding about subspaces of pose variant images, problem intractability in 3D face modelling, complex face surface reflection mechanism, etc. This paper provides a critical survey of researches on image-based face recognition across pose. The existing techniques are comprehensively reviewed and discussed. They are classified into different categories according to their methodologies in handling pose variations. Their strategies, advantages/disadvantages and performances are elaborated. By generalising different tactics in handling pose variations and evaluating their performances, several promising directions for future research have been suggested.","Title: On transforming statistical models for non-frontal face verification\nYear: 2006\n\nAbstract: We address the pose mismatch problem which can occur in face verification systems that have only a single (frontal) face image available for training. In the framework of a Bayesian classifier based on mixtures of gaussians, the problem is tackled through extending each frontal face model with artificially synthesized models for non-frontal views. The synthesis methods are based on several implementations of maximum likelihood linear regression (MLLR), as well as standard multi-variate linear regression (LinReg). All synthesis techniques rely on prior information and learn how face models for the frontal view are related to face models for non-frontal views. The synthesis and extension approach is evaluated by applying it to two face verification systems: a holistic system (based on PCA-derived features) and a local feature system (based on DCT-derived features). Experiments on the FERET database suggest that for the holistic system, the LinReg-based technique is more suited than the MLLR-based techniques; for the local feature system, the results show that synthesis via a new MLLR implementation obtains better performance than synthesis based on traditional MLLR. The results further suggest that extending frontal models considerably reduces errors. It is also shown that the local feature system is less affected by view changes than the holistic system; this can be attributed to the parts based representation of the face, and, due to the classifier based on mixtures of gaussians, the lack of constraints on spatial relations between the face parts, allowing for deformations and movements of face areas.","Title: Recognition of expression variant faces from one sample image per enrolled subject\nYear: 2009\n\nAbstract: Despite remarkable progress on face recognition, little attention has been given to robustly recognize expression variant faces from a single sample image per person. One way to deal with the recognition of faces under above conditions is by using local statistical approaches which appear to be more robust against variations in facial expression. In this paper, we propose a new weighted matching method based on our recent work of AWPPZMA to recognize expression variant faces when only one exemplar image per enrolled subject is available. The proposed weighting method gives more significance to those parts of the face with facial expression variations that change less compared to neutral face image and less significance to those parts that change more. In this contribution, we use the difference between local area in the input face and its corresponding local area in the neutral face image as a measure of observable structure changes. The encouraging experimental results demonstrate that the proposed method provides a new solution to the problem of robustly recognizing expression variant faces in single model databases.","Title: Width Distributions for Shape Description\nYear: 2011\n\nAbstract: Measuring the similarity between articulated shapes is a fundamental yet challenging problem. This paper proposes a novel shape descriptor based on Width Distributions (WD), which is robust to articulations. We show that the width distributions are articulation insensitive yet descriptive to distinguish different shapes with varied part structures. With measurements on distributions only, the proposed method does not require any alignment between two objects and thus is more robust than correspondence-based measurements. First, the medial axes of the objects are extracted and fitted to B-Splines to remove outliners. The width of the object shape perpendicular to the medial axis can be calculated for each position on the medial axis. The histograms of those widths are compared using chi-square method for similarity. Experiments on standard 2D shape database show that the proposed method performed better than standard shape distribution algorithms and similarly to other articulation-robust shape descriptors, such as inner-distance. The speed of the proposed method is much faster than the more sophisticated inner-distance, as the proposed method is much simpler in nature and requires limited image processing and pattern classification. These results suggested it could be an efficient and effective method to describe and to match articulated shapes.","Title: Single model face database retrieval by directional corner points\nYear: 2004\n\nAbstract: This paper presents a new face image retrieval approach using directional corner points (DCPs). Though much work is being done on face similarity matching techniques, little attention is given to the design of face matching scheme suitable for visual retrieval in single model databases where accuracy, robustness to scale and environmental changes, and computational efficiency are three important issues to be concerned. This research demonstrates that the proposed DCP approach provides a new solution, which is both robust to scale and environmental changes, and efficient in computation, for retrieving human faces in single model databases.","Title: Heterogeneous Specular and Diffuse 3-D Surface Approximation for Face Recognition Across Pose\nYear: 2012\n\nAbstract: This paper proposes a novel heterogeneous specular and diffuse (HSD) 3-D surface approximation which considers spatial variability of specular and diffuse reflections in face modelling and recognition. Traditional 3-D face modelling and recognition methods constrain human faces with either the Lambertian assumption or the homogeneity assumption, resulting in suboptimal shape and texture models. The proposed HSD approach allows both specular and diffuse reflectance coefficients to vary spatially to better accommodate surface properties of real human faces. From a small number of face images of a person under different lighting conditions, 3-D shape and surface reflectivity property are estimated using a localized stochastic optimization method. The resultant personalized 3-D face model is used to render novel gallery views under different poses for recognition across pose. The proposed approach is evaluated on both synthetic and real face datasets and benchmarked against the state-of-the-art approaches. Experimental results demonstrated that it can achieve a higher level of performances in modelling accuracy, algorithm reliability, and recognition accuracy, which suggests that face modelling and recognition beyond the Lambertian and homogeneity assumptions is a feasible and better solution towards pose-invariant face recognition.","Title: Multi-View Combined Visual Similarity Retrieval For Face Database\nYear: 2002\n\nAbstract: This work proposed a new multi-view combined visual similarity retrieval approach for face database. The search and retrieval employ a novel facial corner coding and matching approach, which delivers fast searching speed for database retrieval. The experimental results demonstrate that the facial corners can be a useful identity feature for human face database retrieval. The profile view of a face provides a complementary structure of the face that is not seen in the frontal view. The proposed multi-view modified Hausdorff distance combines the facial corners of multiple views and achieved an improved accuracy. This technique is not limited to two view combined retrieval but a generic approach for multiple view combined similarity retrieval.","Title: Matching Non-Aligned Objects Using A Relational String-Graph\nYear: 2013\n\nAbstract: Localising and aligning objects is a challenging task in computer vision that still remains largely unsolved. Utilising the syntactic power of graph representation, we define a relational string-graph matching algorithm that seeks to perform these tasks simultaneously. By matching the relations between vertices, where vertices represent high-level primitives, the relational string-graph is able to overcome the noisy and inconsistent nature of the vertices themselves. For each possible relation correspondence between two graphs, we calculate the rotation, translation, and scale parameters required to transform a relation into its counterpart. We plot these parameters in 4D space and use Gaussian mixture models and the expectation-maximisation algorithm to estimate the underlying parameters. Our method is tested on face alignment and recognition, but is equally (if not more) applicable for generic object alignment.","Title: Robust Face Recognition by Hierarchical Kernel Associative Memory Models Based on Spatial Domain Gabor Transforms\nYear: 2006\n\nAbstract: Face recognition can be studied as an associative memory (AM) problem and kernel-based AM models have been proven efficient. In this paper, a hierarchical Kernel Associative Memory (KAM) face recognition scheme with a multiscale Gabor transform, is proposed. The pyrami- dal multiscale Gabor decomposition proposed by Nestares, Navarro, Portilla and Tabernero not only provides a very efficient implementation of the Gabor transform in the spatial domain, but also permits a fast reconstruction of images. In our method, face images of each person are first decomposed into their multiscale representations by a quasicomplete Gabor transform, which are then modelled by Kernel Associative Memories. In the recognition stage, a query face image is also represented by a Gabor mul- tiresolution pyramid and the reconstructions from different KAM models corresponding to even Gabor channels are then simply summed to give the recall. The recognition scheme was thoroughly tested using several benchmarking face datasets, including the AR faces, UMIST faces, JAFFE faces and Yale A faces, which include different kind of face variations from occlusions, pose, expression and illu- mination. The experiment results show that the proposed method demonstrated strong robustness in recognizing faces under different conditions, particularly under occlusions, pose alterations and expression changes.","Title: 3D face recognition using topographic high-order derivatives\nYear: 2013\n\nAbstract: This paper presents a novel feature, Topographic High-order Derivatives (THD) for 3D face recognition. THD is based on the high-order micro-pattern information extracted from face topography maps. Face topography maps are partitioned into polar sectors, and THDs are computed using directional highorder derivatives within the sectors. Local features are extracted by encoding directional high-order derivatives within polar neighborhoods. To evaluate the proposed method, we use Bosphorus and FRGC 3D face databases which include pose and expression changes. The performance of the proposed method is higher compared to the state-of-the-art benchmark approaches in 3D face recognition.","Title: Fast Screening in Large Face Databases Using Merit-Based Dominant Points\nYear: 2005\n\nAbstract: Current face identification approaches require computer systems to search through large quantity of face feature sets in the database and pick the ones that best match the features of an unknown input face. In this paper, a fast screening method for large face database searching is proposed. The method utilizes dominant points instead of edge maps as features for similarity measurement. A new formulation of Hausdorff distance is designed for merit-based dominant point matching. The screening experiments demonstrated that the proposed face screening method significantly improves the computational speed and the storage economy. It provides a very efficient way for large face databases searching and screening.","Title: Gender Classification Using Interlaced Derivative Patterns\nYear: 2010\n\nAbstract: Automated gender recognition has become an interesting and challenging research problem in recent years with its potential applications in security industry and human-computer interaction systems. In this paper we present a novel feature representation, namely Interlaced Derivative Patterns (IDP), which is a derivative-based technique to extract discriminative facial features for gender classification. The proposed technique operates on a neighborhood around a pixel and concatenates the extracted regional feature distributions to form a feature vector. The experimental results demonstrate the effectiveness of the IDP method for gender classification, showing that the proposed approach achieves 29.6% relative error reduction compared to Local Binary Patterns (LBP), while it performs over four times faster than Local Derivative Patterns (LDP).","Title: Facial expression recognition from line-based caricatures\nYear: 2003\n\nAbstract: The automatic recognition of facial expression presents a significant challenge to the pattern analysis and man-machine interaction research community. Recognition from a single static image is particularly a difficult task. In this paper, we present a methodology for facial expression recognition from a single static image using line-based caricatures. The recognition process is completely automatic. It also addresses the computational expensive problem and is thus suitable for real-time applications. The proposed approach uses structural and geometrical features of a user sketched expression model to match the line edge map (LEM) descriptor of an input face image. A disparity measure that is robust to expression variations is defined. The effectiveness of the proposed technique has been evaluated and promising results are obtained. This work has proven the proposed idea that facial expressions can be characterized and recognized by caricatures.","Title: Mobile Plant Leaf Identification Using Smart-Phones\nYear: 2013\n\nAbstract: A novel shape description method is proposed for mobile retrieval of leaf images to aid in plant recognition. In this method, traveling the shape contour, the convexity and concavity properties of the arches of various levels are measured, respectively, to generate a multiscale shape descriptor. Its performance has been tested on two leaf datasets and the experimental results indicated higher recognition accuracies than the state-of-the-art approaches with a speed improvement of more than 170 times. The proposed method has been successfully applied to develop a prototype system of online plant leaf identification working on a consumer mobile platform.","Title: Primitive-based 3D structure inference from a single 2D image for insect modeling: Towards an electronic field guide for insect identification\nYear: 2010\n\nAbstract: 3D insect models are useful to overcome viewing angle variations and self-occlusions in computer-assisted insect taxonomy for electronic field guides. The acquisition of 3D information is, however, unreliable due to the flexibility and small size of the insect bodies. This paper explores how to infer 3D insect models from a single 2D insect image, which will assist both insect description and identification. The 3D structure of the insect body is modeled from two geometric primitives, generalized cylinders and deformable ellipsoids. The primitives are fitted and warped based on both edge and medial axis constraints of the 2D image. Individualized 3D models are then built to approximate the insect structure. The proposed approach results in seemingly useful 3D insect models capable of representing the major morphological characteristics for a variety of insects with different body types. This method could be a helpful assistance for computer-assisted insect taxonomy and insect identification by entomologists and the public.","Title: Kernel Similarity Modeling of Texture Pattern Flow for Motion Detection in Complex Background\nYear: 2011\n\nAbstract: This paper proposes a novel kernel similarity modeling of texture pattern flow (KSM-TPF) for background modeling and motion detection in complex and dynamic environments. The texture pattern flow encodes the binary pattern changes in both spatial and temporal neighborhoods. The integral histogram of texture pattern flow is employed to extract the discriminative features from the input videos. Different from existing uniform threshold based motion detection approaches which are only effective for simple background, the kernel similarity modeling is proposed to produce an adaptive threshold for complex background. The adaptive threshold is computed from the mean and variance of an extended Gaussian mixture model. The proposed KSM-TPF approach incorporates machine learning method with feature extraction method in a homogenous way. Experimental results on the publicly available video sequences demonstrate that the proposed approach provides an effective and efficient way for background modeling and motion detection.","Title: Face recognition using adaptively weighted patch PZM array from a single exemplar image per person\nYear: 2008\n\nAbstract: Though numerous approaches have been proposed for face recognition, little attention is given to the moment-based face recognition techniques. In this paper we propose a novel face recognition approach based on adaptively weighted patch pseudo Zernike moment array (AWPPZMA) when only one exemplar image per person is available. In this approach, a face image is represented as an array of patch pseudo Zernike moments (PPZM) extracted from a partitioned face image containing moment information of local areas instead of global information of a face. An adaptively weighting scheme is used to assign proper weights to each PPZM to adjust the contribution of each local area of a face in terms of the quantity of identity information that a patch contains and the likelihood of a patch is occluded. An extensive experimental investigation is conducted using AR and Yale face databases covering face recognition under controlled/ideal conditions, different illumination conditions, different facial expressions and partial occlusion. The system performance is compared with the performance of four benchmark approaches. The encouraging experimental results demonstrate that moments can be used for face recognition and patch-based moment array provides a novel way for face representation and recognition in single model databases.","Title: General Retinal Vessel Segmentation Using Regularization-Based Multiconcavity Modeling.\nYear: 2010\n\nAbstract: Detecting blood vessels in retinal images with the presence of bright and dark lesions is a challenging unsolved problem. In this paper, a novel multiconcavity modeling approach is proposed to handle both healthy and unhealthy retinas simultaneously. The differentiable concavity measure is proposed to handle bright lesions in a perceptive space. The line-shape concavity measure is proposed to remo...","Title: Fast and effective retrieval of plant leaf shapes\nYear: 2012\n\nAbstract: In this paper, a novel shape description and matching method based on multi-level curve segment measures (MLCSM) is proposed for plant leaf image retrieval. MLCSM extracts the statistical features of shape contour via measuring the curve bending, convexity and concavity of the curve segments with different length of shape contour to describe the shape. This method not only finely captures the global and local features, but also is very compact and has very low computational complexity. The performance of the proposed method is evaluated on the widely used Swedish leaf database and the leaf databases collected by ourselves which contains 1200 images and 100 plant leaf species. All the experiments show the superiority of our method over the state-of-the-art shape retrieval methods.","Title: Textural Hausdorff Distance for wider-range tolerance to pose variation and misalignment in 2D face recognition\nYear: 2009\n\nAbstract: This paper addresses two critical but rarely concerned issues in 2D face recognition: wider-range tolerance to pose variation and misalignment. We propose a new Textural Hausdorff Distance (THD), which is a compound measurement integrating both spatial and textural features. The THD is applied to a Significant Jet Point (SJP) representation of face images, where a varied number of shape-driven SJPs are detected automatically from low-level edge map with rich information content. The comparative experiments conducted on publicly available FERET and AR face databases demonstrated that the proposed approach has a considerably wider range of tolerance against both in-depth head rotation and face misalignment.","Title: Robust visual similarity retrieval in single model face databases\nYear: 2005\n\nAbstract: In this paper, we introduce a novel visual similarity measuring technique to retrieve face images in photo album databases for law enforcement. Though much work is being done on face similarity matching techniques, little attention is given to the design of face matching schemes suitable for visual retrieval in single model databases where accuracy, robustness to scale and environmental changes, and computational efficiency are three important issues to be considered. This paper presents a robust face retrieval approach using structural and spatial point correspondence in which the directional corner points (DCPs) are generated for efficient face coding and retrieval. A complete investigation on the proposed method is conducted, which covers face retrieval under controlled/ideal condition, scale variations, environmental changes and subject actions. The system performance is compared with the performance of the eigenface method. It is an attractive finding that the proposed DCP retrieval technique has performed superior to the eigenface method in most of the comparison experiments. This research demonstrates that the proposed DCP approach provides a new way, which is both robust to scale and environmental changes, and efficient in computation, for retrieving human faces in single model databases.","Title: Significance-Based Multi-View Hausdorff Distance for Non-Rigid Object Registration\nYear: 2003\n\nAbstract: In this paper, we propose a new similarity measure to combine multiple views of 3-D objects for non-rigid registration. It is a metric that integrates multiple 2-D view features representing a visual identity of a 3-D object seen from different viewpoints. The robustness to non-rigid distortions is achieved by the proximity correspondence manner. The human face, a typical non-rigid object, was chosen to evaluate the capability of the proposed object matching technique. Very encouraging results were obtained which showed that the proposed Significant-Based Multi-View Hausdorff Distance (SMVHD) provides a new fusion method for non-rigid 3-D object registration.","Title: Noisy logo recognition using line segment Hausdorff distance\nYear: 2003\n\nAbstract: Logo recognition is of great interest in the document and shape analysis domain. In order to develop a recognition method that is robust to employ under adverse conditions such as different scale/orientation, broken curves, added noise and occlusion, a modified line segment Hausdorff distance is proposed in this paper. The new approach has the advantage to incorporate structural and spatial information to compute dissimilarity between two sets of line segments rather than two sets of points. The proposed technique has been applied on line segments generated from logos with encouraging results. Clear cut distinction between the correct and incorrect matches has been observed. This suggests a strong potential for logo and shape recognition system.","Title: Generalised ambient reflection models for Lambertian and Phong surfaces\nYear: 2009\n\nAbstract: Ambient reflection is widely present in many applications of computer graphics and image processing, which is traditionally modelled as a constant free from environmental factors. This paper reconsiders ambient reflection modelling of Lambertian and Phong surfaces and calculates it as reflection integrations of infinitesimal incident beams from the environment. It reveals that ambient reflection exists in variable forms for Lambertian surfaces of non-convex objects and Phong surfaces of all objects. For convex objects with Lambertian surfaces, ambient reflectance coefficient is actually the diffuse reflectance coefficient. Generalised ambient reflection models are proposed to calculate ambient reflection using the same reflection model as used in calculation of other reflections. Based on this analysis, new ambient reflection formulations of Lambertian and Phong surfaces are derived to enable efficient computations in computer graphics and image processing.","Title: Automated Face Pose Estimation Using Elastic Energy Models\nYear: 2006\n\nAbstract: Face pose estimation forms an important part in a face recognition system. However, fully automated and accurate pose determination still remains an unsolved problem in the research community. In this paper, we propose a novel elastic energy model to automatically estimate face poses. Our method employs statistical energy contributions of a set of feature points, which can avoid over-trusting selected anchor points. It provides a robust solution to the feature localisation inaccuracy problem, which is inevitable in practical applications with cluttered backgrounds. As a general configuration, our model can be easily implemented and extended to other non-rigid objects. Its effectiveness and robustness are revealed in our experiments","Title: Recognizing Rotated Faces from Two Orthogonal Views in Mugshot Databases\nYear: 2006\n\nAbstract: Tolerance to pose variations is one of the key remaining problems in face recognition. It is of great interest in airport surveillance systems using mugshot databases to screen travellers' faces. This paper presents a novel poseinvariant face recognition approach using two orthogonal face images from mugshot databases. Virtual views under different poses are generated in two steps: shape modeling and texture synthesis. In the shape modeling step, a feature-based multilevel quadratic variation minimization approach is applied to generate smooth 3D face shapes. In the texture synthesis step, a non-Lambertian reflectance model is explored to synthesize facial textures taking into account both diffuse and specular reflections. A view-based face recognizer is used to examine the feasibility and effectiveness of the proposed pose-invariant face recognition. The experimental results show that the proposed method provides a new solution to the problem of recognizing rotated faces.","Title: 3d Face Recognition Using Geodesic Pzm Array From A Single Model Per Person\nYear: 2011\n\nAbstract: For the 3D face recognition numerous methods have been proposed, but little attention has been given to the local-based representation, for the texture map of the 3D models. In this paper, we propose a novel 3D face recognition approach based on locally extracted Geodesic Pseudo Zernike Moment Array (GPZMA) of the texture map when only one exemplar per person is available. In the proposed method, the function of the PZM is controlled by the geodesic deformations to tackle the problem of face recognition under the expression and pose variations. The feasibility and effectiveness investigation for the proposed method is conducted through a wide range of experiments using publicly available BU-3DFE and Bosphorus databases including samples with different expression and pose variations. The performance of the proposed method is compared with the performance of three state-of-the-art benchmark approaches. The encouraging experimental results demonstrate that the proposed method achieves much higher accuracy than the benchmarks in single-model databases.","Title: Recognition of driving postures by multiwavelet transform and multilayer perceptron classifier\nYear: 2012\n\nAbstract: To develop Human-centric Driver Assistance Systems (HDAS) for automatic understanding and charactering of driver behaviors, an efficient feature extraction of driving postures based on Geronimo-Hardin-Massopust (GHM) multiwavelet transform is proposed, and Multilayer Perceptron (MLP) classifiers with three layers are then exploited in order to recognize four pre-defined classes of driving postures. With features extracted from a driving posture dataset created at Southeast University (SEU), the holdout and cross-validation experiments on driving posture classification are conducted by MLP classifiers, compared with the Intersection Kernel Support Vector Machines (IKSVMs), the k-Nearest Neighbor (kNN) classifier and the Parzen classifier. The experimental results show that feature extraction based on GHM multwavelet transform and MLP classifier, using softmax activation function in the output layer and hyperbolic tangent activation function in the hidden layer, offer the best classification performance compared to IKSVMs, kNN and Parzen classifiers. The experimental results also show that talking on a cellular phone is the most difficult one to classify among four predefined classes, which are 83.01% and 84.04% in the holdout and cross-validation experiments respectively. These results show the effectiveness of the feature extraction approach using GHM multiwavelet transform and MLP classifier in automatically understanding and characterizing driver behaviors towards Human-centric Driver Assistance Systems (HDAS).","Title: 3D Reconstruction from Hyperspectral Images\nYear: 2015\n\nAbstract: 3D reconstruction from hyper spectral images has seldom been addressed in the literature. This is a challenging problem because 3D models reconstructed from different spectral bands demonstrate different properties. If we use a single band or covert the hyper spectral image to gray scale image for the reconstruction, fine structural information may be lost. In this paper, we present a novel method to reconstruct a 3D model from hyper spectral images. Our proposed method first generates 3D point sets from images at each wavelength using the typical structure from motion approach. A structural descriptor is developed to characterize the spatial relationship between the points, which allows robust point matching between two 3D models at different wavelength. Then a 3D registration method is introduced to combine all band-level models into a single and complete hyper spectral 3D model. As far as we know, this is the first attempt in reconstructing a complete 3D model from hyper spectral images. This work allows fine structural-spectral information of an object be captured and integrated into the 3D model, which can be used to support further research and applications.","Title: Polygonal approximation using integer particle swarm optimization.\nYear: 2014\n\nAbstract: Polygonal approximation is an effective yet challenging digital curve representation for image analysis, pattern recognition and computer vision. This paper proposes a novel approach, integer particle swarm optimization (iPSO), for polygonal approximation. When compared to the traditional binary version of particle swarm optimization (bPSO), the new iPSO directly uses an integer vector to represent the candidate solution and provides a more efficient and convenient means for solution processing. The velocity and position updating mechanisms in iPSO not only have clear physical meaning, but also guarantee the optimality of the solutions. The method is suitable for polygonal approximation which could otherwise be an intractable optimization problem. The proposed method has been tested on commonly used synthesized shapes and lake contours extracted from the maps of four famous lakes in the world. The experimental results show that the proposed iPSO has better solution quality and computational efficiency than the bPSO-based methods and better solution quality than the other state-of-the-art methods.","Title: MARCH: Multiscale-arch-height description for mobile retrieval of leaf images.\nYear: 2015\n\nAbstract: In this paper, we propose a novel shape description method for mobile retrieval of leaf images. In this method, termed multiscale arch height (MARCH), hierarchical arch height features at different chord spans are extracted from each contour point to provide a compact, multiscale shape descriptor. Both the global and detailed features of the leaf shape can be effectively captured by the proposed algorithm. MARCH descriptors are compared using a simple L1-norm based dissimilarity measurement providing very fast shape matching. The algorithm has been tested on four publicly available leaf image datasets including the Swedish leaf dataset, the Flavia leaf dataset, the ICL leaf dataset and the scanned subset of the ImageCLEF leaf dataset. The experiments indicate that the proposed method can achieve a higher classification rate and retrieval accuracy than the state-of-the-art benchmarks with a more than 500 times faster retrieval speed. A mobile retrieval system embedding the proposed algorithms has been developed for the real application of leaf image retrieval.","Title: Adaptive sliding bandlimited multiple fourier linear combiner for estimation of pathological tremor.\nYear: 2014\n\nAbstract: •The analysis of frequency characteristic of physiological and pathological tremor is proposed.•The algorithm incorporates Butterworth filter, WFLC, frequency modulation block and BMFLC.•The method could estimate desired signals with sliding frequency band and zero-phase lag.•One mechanical platform for simulating tremor and intention movements is developed.•The method successfully extracts the voluntary motion from the raw measured signal.","Title: Hierarchical String Cuts: A Translation, Rotation, Scale and Mirror Invariant Descriptor for Fast Shape Retrieval.\nYear: 2014\n\nAbstract: This paper presents a novel approach for both fast and accurately retrieving similar shapes. A hierarchical string cuts (HSC) method is proposed to partition a shape into multiple level curve segments of different lengths from a point moving around the contour to describe the shape gradually and completely from the global information to the finest details. At each hierarchical level, the curve segments are cut by strings to extract features that characterize the geometric and distribution properties in that particular level of details. The translation, rotation, scale and mirror invariant HSC descriptor enables a fast metric based matching to achieve the desired high accuracy. Encouraging experimental results on four databases demonstrated that the proposed method can consistently achieve higher (or similar) retrieval accuracies than the state-of-the-art benchmarks with a more than 120 times faster speed. This may suggest a new way of developing shape retrieval techniques in which a high accuracy can be achieved by a fast metric matching algorithm without using the time-consuming correspondence optimisation strategy.","Title: Efficient subgraph matching using topological node feature constraints.\nYear: 2015\n\nAbstract: This paper presents techniques designed to minimise the number of states which are explored during subgraph isomorphism detection. A set of advanced topological node features, calculated from n-neighbourhood graphs, is presented and shown to outperform existing features. Further, the pruning effectiveness of both the new and existing topological node features is significantly improved through the introduction of strengthening techniques. In addition to topological node features, these strengthening techniques can also be used to enhance application-specific node labels using a proposed novel extension to existing pruning algorithms. Through the combination of these techniques, the number of explored search states can be reduced to near-optimal levels.","Title: Face Recognition Using 3D Directional Corner Points\nYear: 2014\n\nAbstract: In this paper, we present a novel face recognition approach using 3D directional corner points (3D DCPs). Traditionally, points and meshes are applied to represent and match 3D shapes. Here we represent 3D surfaces by 3D DCPs derived from ridge and valley curves. Then we develop a 3D DCP matching method to compute the similarity of two different 3D surfaces. This representation, along with the similarity metric can effectively integrate structural and spatial information on 3D surfaces. The added information can provide more and better discriminative power for object recognition. It strengthens and improves the matching process of similar 3D objects such as faces. To evaluate the performance of our method for 3D face recognition, we have performed experiments on Face Recognition Grand Challenge v2.0 database (FRGC v2.0) and resulted in a rank-one recognition rate of 97.1%. This study demonstrates that 3D DCPs provides a new solution for 3D face recognition, which may also find its application in general 3D object representation and recognition.","Title: Dual Graph Regularized NMF for Hyperspectral Unmixing\nYear: 2014\n\nAbstract: Hyperspectral unmixing is an important technique for estimating fraction of different land cover types from remote sensing imagery. In recent years, nonnegative matrix factorization (NMF) with various constraints have been introduced into hyperspectral unmixing. Among these methods, graph based constraint have been proved to be useful in capturing the latent manifold structure of the hyperspectral data in the feature space. In this paper, we propose to integrate graph-based constraints based on manifold assumption in feature spaces and consistency of spatial space to regularize the NMF method. Results on both synthetic and real data have validated the effectiveness of the proposed method.","Title: Motion analysis for face capturing\nYear: 2002\n\nAbstract: Face recognition is one of the most suitable biometric methods for supporting systems that require high security. The success of a face recognition system involves more than just the comparison algorithm. Face data acquisition is the first step towards developing face recognition systems. The accuracy of a face recognition system greatly depends on the face data acquired. This paper proposes the head-nodding method for face capturing using motion analysis. By analyzing the head-nodding motion of the user, the head-nodding method captures face images with consistent orientation to support face verification systems. In this paper, the design and implementation of the head-nodding method is presented. Preliminary experimental results are also presented.","Title: On local features for GMM based face verification\nYear: 2005\n\nAbstract: It has been recently shown that local feature approaches to face verification are considerably more robust than holistic approaches, in terms of translations (caused by automatic face localization) and pose variations. In this paper we first investigate whether features based on local Principal Component Analysis (LPCA) are more discriminative than features based on the 2D Discrete Cosine Transform (2D DCT). We also investigate several methods for modifying the two feature extraction techniques in order to counteract the effects of linear and non-linear illumination changes, without losing discriminative information. Results on the XM2VTS database show that when using a Bayesian classifier based on Gaussian Mixture Models (GMMs), the performances of 2D DCT and LPCA techniques are quite similar, suggesting that the 2D DCT technique is preferable due to its lower computational complexity. When using 8脳8 blocks, modifying the 2D DCT and LPCA techniques by removing the first coefficient, which is the most affected by illumination changes, enhances robustness with little change in discrimination ability; removing further coefficients causes a noticeable reduction in performance on clean images and provides little gain in robustness. When using the 2D DCT with 16脳16 blocks, the first three coefficients need to be removed in order to achieve good robustness. It is further shown that contrary to previously published results, the use of deltas of low-order coefficients (to alleviate performance losses caused by removing coefficients) can adversely affect robustness.","Title: Line-Based Affine Invariant Object Location Using Transformation Space Decomposition\nYear: 2006\n\nAbstract: This paper presents a novel line-based affine invariant object location methodology. Our algorithm employs a new line-based transformation space decomposition technique to exploit intrinsic structural information provided by line features. Furthermore, we propose a new line-based distance transform to integrate with our algorithm to provide efficient transformation cell evaluation and subdivision in a coarse to fine manner. The algorithm is able to rapidly accelerate the searching process while maintaining high discriminative power and minimal storage requirement. The efficiency and discriminative power of this methodology are demonstrated using real-world examples with promising results.","Title: Pose-invariant 2.5D face recognition using Geodesic Texture Warping\nYear: 2010\n\nAbstract: In recent years, 3D face recognition has become a popular solution to deal with the problem of pose-invariant face recognition. The majority of 3D face data are, however, actually 2.5D which are sensitive to pose variations. This paper presents a novel Geodesic Texture Warping (GTW) solution for 2.5D pose-invariant face recognition. In this method, we use the geodesic distance computed on a 2.5D face scan to warp the texture of a rotated face to that of a frontal one to perform matching. A feasibility and effectiveness investigation for the proposed method is conducted using a wide range of experiments including samples with different face rotations. The encouraging experimental results demonstrate that the proposed method achieves much higher accuracy than the state-of-the-art method with a low computational cost.","Title: Topological features and iterative node elimination for speeding up subgraph isomorphism detection\nYear: 2012\n\nAbstract: In this paper we tackle the problem of subgraph isomorphism detection on large graphs, which may commonly be intractable, even with state of the art algorithms. Rather than competing with other matching algorithms, we define enhancements that can be used by (almost) any subgraph isomorphism algorithm, both current and future. These enhancements consist of a number of topological features to be added to the nodes, and a technique which we term “iterative node elimination”. The fusion of these enhancements is shown to be able to reduce subgraph isomorphism matching times by a factor of over 4,500.","Title: Prediction of pathological tremor using adaptive multiple oscillators linear combiner.\nYear: 2016\n\nAbstract: •Pathological tremor signal prediction algorithm AMOLC based on Hopf oscillator is proposed.•The optimum parameters of AMOLC are obtained by building experimental platform which can generates simulated tremor motion.•The corresponding optimal parameters are verified by actual pathological tremor signals.•The AMOLC is zero phase lag and it can be used in real-time application.","Title: Spectral Regression dimension reduction for multiple features facial image retrieval\nYear: 2012\n\nAbstract: Face retrieval has received much attention in recent years. This paper comparatively studied five feature description methods for face representation, including Local Binary Pattern (LBP), Gabor feature, Gray Level Co-occurrence Matrices (GLCM), Pyramid Histogram of Oriented Gradient (PHOG) and Curvelet Transform (CT). The problem of large dimensionalities of the extracted features was addressed by employing a manifold learning method called Spectral Regression (SR). A fusion scheme was proposed by aggregating the distance metrics. Experiments illustrated that dimension reduced features are more efficient and the fusion scheme can offer much enhanced performance. A 98% rank 1 accuracy was obtained for the AR faces and 92% for the FERET faces.","Title: Model Identification Of Emp Electromagnetic Coupling Using Volterra Kernel\nYear: 2011\n\nAbstract: Electromagnetic pulses (EMP) are capable of causing effects like malfunctions, performance degradation, interferences, and destructions in electronic and electrical systems. The EMP effect simulation is one of key points from home and abroad. Furthermore, EMP coupling is the promise and foundation of effect evolution and protection. In this paper, a new method, based on the combination of system identification theory and Volterra series algorithm, was proposed for the analysis of the EMP electromagnetic coupling phenomena from an external field to an inner electric system. In particular, we focused on the ultra wide band (UWB) pulses, which were able to cause malfunctions or even destructions of complex electronic systems and the coupling effect has not yet been sufficiently analysed. Based on experience data, we designed a new method using Volterra series to identify EMP electromagnetic coupling model. We firstly analysed the theory foundation of non-parameters model evolution. And then, to verify the validity of this method, we designed UWB pulses electromagnetic coupling table and gained radiation coupling data. Finally, model testing results showed that the increased model accuracy and smaller computation will improve EMP effect evolution, also which can apply this method to EMP electromagnetic coupling model identification.","Title: Non-Linear System Identification Of Gear Crack Failure Based On Second Order Volterra Kernel\nYear: 2010\n\nAbstract: Non-linear system identification has attracted great interest in mechanical fields. Against the weak vibration signals of gear crack failure and the considerable but ignored influence by input variation in gear tooth system non-linear working condition, which put forward a huge challenge to gear crack failure detection and identification's accurate, we have proposed a new non-linear non-parameter method that used the second order Volterra kernel function to analyse the inner contact between crack failure and the corresponding non-linear changes in whole system term. After identifying gear tooth system second order kernel function with input and output time series, we analysed the non-linear information indicated by frequency spectrum in detail. Anglicisation and comparison results show that gear tooth crack failure's second order Volterra kernel function considered the fault identification's correction influenced by input elements and was sensitive to system non-linear changes which were caused by different fault conditions. And, this non-linear system identification method can solve the diagnosis' fuzziness and indeterminacy problems caused by traditional frequency sideband theory. This new method is also helpful for mechanical system modelling, security running monitoring, dynamic design, the study of dynamic characteristics, and the fault diagnosis of rotary systems.","Title: The Design Of Bio-Inspired Esd Protection Model For Digital Circuits Based On Cell Structure\nYear: 2012\n\nAbstract: As the semiconductor feature size decreases and the number of transistors on a single chip increases, digital circuits are frequently used in harsh and complex electromagnetic interference (EMI) environments. Typical protection problems are gradually prominent, such as malfunctions, performance degradation and destructions in component and/or system. This paper proposed a bio-inspired model to fulfil the requirements of digital circuits' protection performance. Firstly, extensive experiments have been carried out and are presented here to simulate the process of field program gate array (FPGA) chips damaged by human body model (HBM) electrostatic discharge (ESD), applying the contact discharge method. And then, this paper builds the functional fault model of the above process and verifies the similarity in the aftermath of damaging electrical and biological systems. Thus for electronic system design, we proposed the virtual cell model with redundancy structure, self-organising and self-healing operational methodology which is built upon artificial evolution. Finally, we implemented Markov model to analyse the steady-state stability of the virtual cell model in order to prove its durability when it comes to heavy and frequent ESD damage.","Title: Prediction Of Joint Angle By Combining Multiple Linear Regression With Autoregressive (Ar) Model And Kalman Filter\nYear: 2015\n\nAbstract: In this paper, a new prediction algorithm combining multiple linear regression with autoregressive model and Kalman filter (MLRAR-KF) is proposed to predict the elbow joint angle. The MLRAR model updating weights with Kalman filter is shown to be able to predict joint motion with high accuracy and well robustness. In comparison to existing prediction algorithms, MLRAR-KF can predict joint angle with higher accuracy and better robustness. A data acquisition system was used to collect sEMG and elbow joint angle signals of human upper limb. The experimental results demonstrate the benefits of MLRAR-KF prediction algorithm. Comparison of computational complexity about some existing prediction methods and MLRAR-KF is conducted to analyze the real-time performance.","Title: Analysis And Simulation Of The Neural Oscillator For Tremor Suppression By Fes\nYear: 2015\n\nAbstract: Tremor with a roughly sinusoidal profile impacts the individuals' living activities. Recently, Functional Electric Stimulation (FES) was intensively applied to activate the antagonist muscles with the anti-phase stimulation patterns for compensating the tremor. Considering the similarity between the rhythmic movements and the central neural oscillator, the Matsuoka model is introduced to reciprocally modulate the stimulations intensity of the antagonistic muscles. However, the nonlinear threshold function of the Matsuoka model complicates the system and limits the analysis. In this study, the linearly approximated method is employed to develop the explicit relationship between the model parameters and the frequency/amplitude of the sustained oscillation. The simulation results demonstrate that the output of roughly approximated oscillator is in accordance with that of the original oscillator. Besides, this study brings insight on the effect of the sensory feedback on the neural oscillator. Further, the treatment of tremor with the Matsuoka model as the feed-forward controller and the kinematic signals as the feedback is feasible.","Title: Relative Depth Estimation from Hyperspectral Data\nYear: 2015\n\nAbstract: This paper addresses the problem of relative depth estimation using spatial defocus and spectral chromatic aberration presented in hyperspectral data. Our approach produces merged relative sparse depth map using two different methods. The first method constructs a histogram descriptor for edge pixels in each spectral band image. Due to the spectral chromatic aberration, different edge statistical information can be extracted from each band even at the same location. Variance among histogram bins provides input data for band-wise spatial defocus calculation. These band-wise statistical data are later combined to give the first sparse depth map. The second approach uses difference of neighboring spectral vectors to estimate relative depth. The two sparse maps with distinguishing features are finally combined and optimized to generate final sparse depth map. During the last step, normalization and smoothing are used to guarantee better consistency among edge pixels. Experimental results show that our method can generate better sparse depth map than alternative methods which operate on RGB images.","Title: On the Sampling Strategy for Evaluation of Spectral-spatial Methods in Hyperspectral Image Classification.\nYear: 2017\n\nAbstract: Spectral-spatial processing has been increasingly explored in remote sensing hyperspectral image classification. While extensive studies have focused on developing methods to improve the classification accuracy, experimental setting and design for method evaluation have drawn little attention. In the scope of supervised classification, we find that traditional experimental designs for spectral pro...","Title: Nonnegative-Matrix-Factorization-Based Hyperspectral Unmixing With Partially Known Endmembers.\nYear: 2016\n\nAbstract: Hyperspectral unmixing is an important technique for estimating fractions of various materials from remote sensing imagery. Most unmixing methods make the assumption that no prior knowledge of endmembers is available before the estimation. This is, however, not true for some unmixing tasks for which part of the endmember signatures may be known in advance. In this paper, we address the hyperspectr...","Title: Surface geodesic pattern for 3D deformable texture matching.\nYear: 2017\n\nAbstract: This paper presents a Surface Geodesic Pattern (SGP) representation for matching textured 3D deformable surfaces. SGP encodes the local variations of the surface texture derivatives to extract local information from distinctive textural relationships contained in a geodesic neighborhood. Thus, SGP derives its strength from the fusion of surface texture and shape information at the data level in a way that is invariant to non-rigid deformations. We also propose Gabor Topography Wavelet (GTW) for direct feature extraction from the range data. Both features are combined using a multi-view sparse representation to achieve higher discrimination capability while matching non-rigid 3D surfaces. The performance of the proposed method is evaluated extensively on the Bosphorus face database, the FRGC v2 face database, and the PolyU contact-free hand database and the results are compared to state-of-the-art methods. Experimental results show the effectiveness and superiority of the proposed method in recognizing objects under non-rigid surface deformations.","Title: Structure Integral Transform Versus Radon Transform: A 2D Mathematical Tool for Invariant Shape Recognition.\nYear: 2016\n\nAbstract: In this paper, we present a novel mathematical tool, Structure Integral Transform (SIT), for invariant shape description and recognition. Different from the Radon Transform (RT), which integrates the shape image function over a 1D line in the image plane, the proposed SIT builds upon two orthogonal integrals over a 2D $K$ -cross dissecting structure spanning across all rotation angles by which the shape regions are bisected in each integral. The proposed SIT brings the following advantages over the RT: 1) it has the extra function of describing the interior structural relationship within the shape which provides a more powerful discriminative ability for shape recognition; 2) the shape regions are dissected by the $K$ -cross in a coarse to fine hierarchical order that can characterize the shape in a better spatial organization scanning from the center to the periphery; and 3) it is easier to build a completely invariant shape descriptor. The experimental results of applying SIT to shape recognition demonstrate its superior performance over the well-known Radon transform, and the well-known shape contexts and the polar harmonic transforms.","Title: Leaf image retrieval using combined feature of vein and contour\nYear: 2015\n\nAbstract: This paper proposes a novel method of recognizing plant leaf image using a combination of venation and contour features. In this method, the shape is cut into pieces under different scales to describe the leaf image in a multiscale way. Both leaf venation and contour features extracted from the cut pieces are utilized to provide comprehensive description under each scale. The performance of the proposed method has been evaluated on two leaf datasets. And the experimental results indicates that the proposed method can achieve higher (or similar) recognition accuracies than the state-of-the-art approaches with acceptable speed.","Title: Segmentation Of The Left Ventricle In Echocardiography Using Contextual Shape Model\nYear: 2016\n\nAbstract: Accurate localization of the left ventricle (LV) boundary from echocardiogram images is of vital importance for the diagnosis and treatment of heart disease. Statistical shape models such as active shape models (ASM) have been commonly used to perform automatic detection of this boundary. Such models perform well when there is low variability in the underlying shape subspace and an accurate initialization can be provided, however in the absence of these conditions results are often much poorer. In the case of LV echocardiogram images, such variability is often encountered in patients with abnormal LV function. In this paper we propose a fully automatic segmentation technique using deep learning in a Bayesian nonparametric framework. Our model uses a dynamic statistical shape model comprised of training shapes from select weighted subsets of the feature subspace. Subsets are chosen during the iterative segmentation process according to a latent temporal component allocation variable, determined from joint deep features and LV landmark information using a Dirichlet process mixture model with Chinese restaurant process prior. Testing is performed on a data set comprising images of the LV acquired from patients exhibiting both normal and abnormal LV function, and the results using our technique compared to both ASM and a state of the art technique. Results from this testing show an improvement in the LV localization accuracy, particularly when LV function is abnormal.","Title: Automated Image Analysis on Insect Soups\nYear: 2016\n\nAbstract: Insect soup is a term which refers to the bulk ethanol samples stored in many insect collections which contain unsorted material and augment the dried and pinned collections. These samples often contain rare or unique specimens, and it would be beneficial to researchers or even the general public to have an easy way to access them to view and annotate their contents. In this paper we propose an image analysis approach for analysing insect soup images and making their content readily available through the web. The analysis includes insect segmentation, scale bar segmentation, measurements, and insect sorting. The image analysis results will be used for online access of insect images for insect classification, research, species discovery, or educational purposes.","Title: Affine Invariant Point-Set Matching Using Convex Hull Bisection\nYear: 2016\n\nAbstract: Affine invariant point-set matching is an important issue in computer vision and pattern recognition. Using reference points derived from the convex hull of the point-set is an existing idea to solve this problem. However, how to choose proper and enough reference points for extracting affine invariant and powerful discriminative descriptors is an open problem. In this paper, a novel method termed convex hull bisection (CHB) is proposed for affine invariant point-set matching. In CHB, for each point in the point-set, two reference points are derived by bisecting the convex hull using the line connecting the point and the centroid of the convex hull. The resulting reference points and other reference points, such as the centroid point of the convex hull and the mean point of the point-set, are utilised to yield an 11-dimensional affine invariant feature vector associated with any point within the point-set. The obtained point based descriptors are used for point-set matching. The proposed method works well even in the case that reference points are overlapping or collinear, which overcomes the limitations of the existing convex hull based methods. The effectiveness of the proposed method is validated by an extensive experimental investigation.","Title: Multiscale Crossing Representation Using Combined Feature of Contour and Venation for Leaf Image Identification\nYear: 2016\n\nAbstract: In this paper, we propose a novel method for plant identification using a multiscale crossing representation of leaf contour and venation. By extracting the combined features in multiple scale, the proposed method is capable of representing features from global to local regions with mirror, scale, translation and rotation invariance. Three leaf datasets including the Swedish Leaf dataset, the Flavia Leaf dataset and the Soybean Cultivar Leaf dataset are adopted in the experiments to evaluate the performance of the proposed method. Comparative experimental results show that the proposed method can achieve consistently higher or similar recognition accuracy than the state-of-the-art methods among these leaf datasets, which may indicate a new solution to the leaf identification problem.","Title: A Local Scale Selection Scheme for Multiscale Area Integral Invariants\nYear: 2016\n\nAbstract: Area integral invariant (AII) is a functional obtained by performing integral operations on the closed planar contour of a shape via the convolution with disc kernels. This shape descriptor is insensitive to noise and robust with respect to occlusions. AII intrinsically introduces the notion of scale using the size of kernel radius. However how to select an optimal scale remains unresolved. In this paper, we propose a local scale selection scheme for generating multiscale area integral invariants. For the same scale level, the disc kernel size is not fixed and varies with the contour point where the disc is centered. This scheme also provides a scale assignment for emphasising the features extraction at finer scales. The strong discriminative power of the multiscale area integral invariant derived from the proposed scale selection scheme has been validated through experiments on very challenging leaf image retrievals.","Title: Robust Facial Landmark Localization Using LBP Histogram Correlation Based Initialization\nYear: 2017\n\nAbstract: Facial landmark localization on images with occlusions is an important and challenging task in many visual applications. Recently, the cascaded pose regression has attracted increasing attention, since it achieved superior performance in terms of facial landmark localization under occlusions. However, such approach is sensitive to initialization, where an improper initialization will decrease the performance sharply. In this paper, we propose a novel initialization method to get a robust initial shape by analysing correlation of Local Binary Patterns (LBP) histograms between the estimated face and training faces. The shape of the training face that is most correlated with the estimated face, will be selected as the initialization for the regression. The selected shape is closer to the real shape of the estimated face, which makes the landmark localization more accurate. Besides, in order to make the initial shape more robust to occlusions, we propose a boosted smart restarts technique by checking location and occlusion jointly instead of checking location only. We show that the proposed method significantly improves performance over existing landmark localization methods on the challenging dataset of COFW. The experimental results demonstrate that the proposed method reduces error by 11.9% and failure cases by 20.8% on COFW dataset. Moreover, it detects face occlusions with 85/40% precision/recall.","Title: Low-rank double dictionary learning from corrupted data for robust image classification.\nYear: 2017\n\nAbstract: •A novel low-rank double dictionary learning (LRD2L) approach is proposed for robust image classification.•It integrates the low-rank matrix recovery technique with the class-specific and class-shared dictionary learning.•It can effectively handle the image corruptions in both training and testing samples, which are inevitable in real-world applications.•The experimental results on three datasets demonstrate the effectiveness and superiority of the proposed approach.","Title: Multiple Graph Regularized Nmf For Hyperspectral Unmixing\nYear: 2015\n\nAbstract: Hyperspectral unmixing is an important technique for estimating fraction of different land covers from remote sensing imagery. In recent years, nonnegative matrix factorization (NMF) methods with various constraints have been introduced into hyperspectral unmixing. Among these methods, graph based constraint has been proved to be useful in capturing the latent manifold structure of the hyper spectral data in the feature domain. However, due to the complexity of the data, only using single graph can not adequately reflect the intrinsic property of the data. In this paper, we propose a multiple graph regularized NMF method for hyperspectral unmixing, which approximates the manifold and consistency of data by a linear combination of several graphs constructed in different scales. Results on both synthetic and real data have validated the effectiveness of the proposed method, and shown that it has outperformed several state of -the-arts hyperspectral unmixing methods.","Title: On The Sampling Strategies For Evaluation Of Joint Spectral-Spatial Information Based Classifiers\nYear: 2015\n\nAbstract: Joint spectral-spatial information based classification is an active topic in hyperspectral remote sensing. Current classification approaches adopt a random sampling strategy to evaluate the performance of various classification systems. Due to the limitation of benchmark data, sampling of training and testing data is performed on the same image. In this paper, we point out that while training with random sampling is practical for hyperspectral image classification, it has intrinsic problems in evaluating spectral-spatial information based classifiers. This statement is supported by several experiments, and has lead to the proposal of a new sampling strategy for comparing spectral spatial information based classifiers.","Title: Continuous estimation of joint angle from electromyography using multiple time-delayed features and random forests.\nYear: 2018\n\nAbstract: •Multiple time-delayed feature signals of sEMG are applied to represnt the role of sEMG in the course of motion estimation.•The execution time of the proposed method (RF using MTDF) is the least.•RF using MTDF method can be used to estimate the isometric trials and dynamic trials of human joint motion.•RF using MTDF method can select the important features of sEMG automatically.","Title: Dynamic Texture Comparison Using Derivative Sparse Representation: Application to Video-Based Face Recognition.\nYear: 2017\n\nAbstract: Video-based face, expression, and scene recognition are fundamental problems in human-machine interaction, especially when there is a short-length video. In this paper, we present a new derivative sparse representation approach for face and texture recognition using short-length videos. First, it builds local linear subspaces of dynamic texture segments by computing spatiotemporal directional deri...","Title: Conditional Random Field and Deep Feature Learning for Hyperspectral Image Segmentation.\nYear: 2017\n\nAbstract: Image segmentation is considered to be one of the critical tasks in hyperspectral remote sensing image processing. Recently, convolutional neural network (CNN) has established itself as a powerful model in segmentation and classification by demonstrating excellent performances. The use of a graphical model such as a conditional random field (CRF) contributes further in capturing contextual information and thus improving the segmentation performance. In this paper, we propose a method to segment hyperspectral images by considering both spectral and spatial information via a combined framework consisting of CNN and CRF. We use multiple spectral cubes to learn deep features using CNN, and then formulate deep CRF with CNN-based unary and pairwise potential functions to effectively extract the semantic correlations between patches consisting of three-dimensional data cubes. Effective piecewise training is applied in order to avoid the computationally expensive iterative CRF inference. Furthermore, we introduce a deep deconvolution network that improves the segmentation masks. We also introduce a new dataset and experimented our proposed method on it along with several widely adopted benchmark datasets to evaluate the effectiveness of our method. By comparing our results with those from several state-of-the-art models, we show the promising potential of our method.","Title: Robust Facial Landmark Localization Based on Texture and Pose Correlated Initialization.\nYear: 2018\n\nAbstract: Robust facial landmark localization remains a challenging task when faces are partially occluded. Recently, the cascaded pose regression has attracted increasing attentions, due to itu0027s superior performance in facial landmark localization and occlusion detection. However, such an approach is sensitive to initialization, where an improper initialization can severly degrade the performance. In this paper, we propose a Robust Initialization for Cascaded Pose Regression (RICPR) by providing texture and pose correlated initial shapes for the testing face. By examining the correlation of local binary patterns histograms between the testing face and the training faces, the shapes of the training faces that are most correlated with the testing face are selected as the texture correlated initialization. To make the initialization more robust to various poses, we estimate the rough pose of the testing face according to five fiducial landmarks located by multitask cascaded convolutional networks. Then the pose correlated initial shapes are constructed by the mean faceu0027s shape and the rough testing face pose. Finally, the texture correlated and the pose correlated initial shapes are joined together as the robust initialization. We evaluate RICPR on the challenging dataset of COFW. The experimental results demonstrate that the proposed scheme achieves better performances than the state-of-the-art methods in facial landmark localization and occlusion detection.","Title: Few-Example Affine Invariant Ear Detection in the Wild.\nYear: 2018\n\nAbstract: Ear detection in the wild with the varying pose, lighting, and complex background is a challenging unsolved problem. In this paper, we study affine invariant ear detection in the wild using only a small number of ear example images and formulate the problem of affine invariant ear detection as a task of locating an affine transformation of an ear model in an image. Ear shapes are represented by line segments, which incorporate structural information of line orientation and line-point association. Then a novel fast line based Hausdorff distance (FLHD) is developed to match two sets of line segments. Compared to existing line segment Hausdorff distance, FLHD is one order of magnitude faster with similar discriminative power. As there are a large number of transformations to consider, an efficient global search using branch-and-bound scheme is presented to locate the ear. This makes our algorithm be able to handle arbitrary 2D affine transformations. Experimental results on real-world images that were acquired in the wild and Point Head Pose database show the effectiveness and robustness of the proposed method.","Title: Matching Pursuit Based On Kernel Non-Second Order Minimization\nYear: 2018\n\nAbstract: The orthogonal matching pursuit (OMP) is an important sparse approximation algorithm to recover sparse signals from compressed measurements. However, most MP algorithms are based on the mean square error(MSE) to minimize the recovery error, which is suboptimal when there are outliers. In this paper, we present a new robust OMP algorithm based on kernel non-second order statistics (KNS-OMP), which not only takes advantages of the outlier resistance ability of correntropy but also further extends the second order statistics based correntropy to a non-second order similarity measurement to improve its robustness. The resulted framework is more accurate than the second order ones in reducing the effect of outliers. Experimental results on synthetic and real data show that the proposed method achieves better performances compared with existing methods.","Title: Multi-Scale Piecewise Line Integral Strategy For Structure Integral Transform\nYear: 2018\n\nAbstract: Structure Integral transform (SIT) is a mathematical tool for invariant shape recognition. SIT has a superior ability to capture the interior structure information by integrating the shape image function over 2D dissecting structure, but the details on the dissecting structure are discarded. In this paper, for the first time, we propose a novel multi-scale piecewise line integral strategy for SIT which introduces the grayscale information. At a higher scale, the integral line on dissecting structure will be equally divided more times, which brings to a coarse to fine description for each integral line. The proposed multi-scale piecewise integral strategy has been validated to extend SIT from binary shape to grayscale shape successfully through experiments on Leeds' Butterflies dataset and Ponce Group's Butterflies dataset.","Title: Data Augmentation Using Synthetic Lesions Improves Machine Learning Detection of Microbleeds from MRI.\nYear: 2018\n\nAbstract: Machine learning applied to medical imaging for lesions detection, such as cerebral microbleeds (CMB) from Magnetic Resonance Imaging (MRI), is challenged by the relatively small datasets available for which only subjective and tedious visual reading is available, and by the low prevalence of lesions (a few in similar to 10% of a typical elderly cohort) resulting in unbalanced classes. Moreover, the lack of actual ground truth might limit the performance of any machine learning method to that of human performance. Yet, the automatic identification of those lesions is relevant to quantify cerebrovascular burden associated with dementia, such as identifying co-morbidity for Alzheimer's disease. In this paper, we investigated a novel approach consisting of simulating synthetic CMB on SWI MRI scans from healthy individuals to create a large and well characterized training dataset, as a data augmentation strategy. Firstly, we characterized actual CMBs from MRI SWI scans and designed a method to create realistic synthetic CMBs whose location, shape, appearance, and size are similar to actual CMBs. We then tested a supervised neural network classifier using various combinations of actual CMB and synthetic CMBs for training. Augmenting data with synthetic CMBs resulted in a large improvement over training on only actual CMBs only when tested on unseen lesions, and provided better results than other standard data augmentation approaches. Our results suggest that data augmentation using synthetic lesions can address the lack of ground truth and low prevalence limitations for medical imaging analysis allowing the deployment of data hungry supervised learning techniques such as deep learning.","Title: Generative Adversarial Product Quantisation.\nYear: 2018\n\nAbstract: Product Quantisation (PQ) has been recognised as an effective encoding technique for scalable multimedia content analysis. In this paper, we propose a novel learning framework that enables an end-to-end encoding strategy from raw images to compact PQ codes. The system aims to learn both PQ encoding functions and codewords for content-based image retrieval. In detail, we first design a trainable encoding layer that is pluggable into neural networks, so the codewords can be trained in back-forward propagation. Then we integrate it into a Deep Convolutional Generative Adversarial Network (DC-GAN). In our proposed encoding framework, the raw images are directly encoded by passing through the convolutional and encoding layers, and the generator aims to use the codewords as constrained inputs to generate full image representations that are visually similar to the original images. By taking the advantages of the generative adversarial model, our proposed system can produce high-quality PQ codewords and encoding functions for scalable multimedia retrieval tasks. Experiments show that the proposed architecture GA-PQ outperforms the state-of-the-art encoding techniques on three public image datasets.","Title: Exploring Structural Consistency in Graph Regularized Joint Spectral-Spatial Sparse Coding for Hyperspectral Image Classification.\nYear: 2017\n\nAbstract: In hyperspectral image classification, both spectral and spatial data distributions are important in describing and identifying different materials and objects in the image. Furthermore, consistent spatial structures across bands can be useful in capturing inherent structural information of objects. These imply that three properties should be considered when reconstructing an image using sparse co...","Title: Classifier-Free Extraction Of Power Line Wires From Point Cloud Data\nYear: 2018\n\nAbstract: This paper proposes a classifier-free method for extraction of power line wires from aerial point cloud data. It combines the advantages of both grid-and point-based processing of the input data. In addition to the non-ground point cloud data, the input to the proposed method includes the pylon locations, which are automatically extracted by a previous method. The proposed method first counts the number of wires in a span between the two successive pylons using two masks: vertical and horizontal. Then, the initial wire segments are obtained and refined iteratively. Finally, the initial segments are extended on both ends and each individual wire points are modelled as a 3D polynomial curve. Experimental results show both the object-based completeness and correctness are 97%, while the point-based completeness and correctness are 99% and 88%, respectively.","Title: Face Recognition with Multi-channel Local Mesh High-order Pattern Descriptor and Convolutional Neural Network\nYear: 2018\n\nAbstract: In this paper, we propose a novel Local Mesh High-order Pattern Descriptor (LMHPD) for face recognition. This description is constructed in a high-order derivative space and is integrated with a Convolutional Neural Network (CNN) architecture. Based on the information collected at a local neighborhood of reference pixel with diverse radiuses and mesh angles, a vectorized feature representation of the reference pixel is generated to provide micro-patterns. They are then converted to multi-channels to use in conjunction with the CNN. The CNN adopted in the proposed architecture is generic and very compact with a small number of convolutional layers. However, LMHPD is derived in such a way that it can work with most of the available CNN architectures. For keeping the computational cost and time complexity at the minimum, we propose a lighter approach of high-order texture descriptor with CNN architecture that can effectively extract discriminative face features. Extensive experiments on Extended Yale B and CMU-PIE datasets show that our method consistently outperforms several alternative descriptors for face recognition under various circumstances.","Title: Colour Analysis of Strawberries on a Real Time Production Line\nYear: 2018\n\nAbstract: A novel system has been designed where colour analysis algorithms facilitate grading ripeness of packed strawberries on a fast-paced production line. The Strawberry quality system acquires images at the rate of 2punnets/s, and feeds the images to the two algorithms. Using CIELAB and HSV colourspaces, both underripe and overripe colour features are analysed resulting in F1 scores of 94.7% and 90.6% respectively, when measured on multiple defect samples. The single defect class results scored 80.1% and 77.1%. The algorithms total time for the current hardware configuration is 121ms maximum and 80ms average, which is well below the required time window of 500ms. 105, 542 punnets have been assessed by the algorithm and has rejected 4, 952 in total (4.9%), helping to ensure the quality of the product being shipped to customers and avoiding costly returns.","Title: Bay Lobsters Moulting Stage Analysis Based on High-Order Texture Descriptor\nYear: 2018\n\nAbstract: In this paper, we introduce the world's first method to automatically classify the moulting stage of Bay lobsters, formally known as Thenus orientális, in a controlled environment. Our classification approach only requires top view images of exoskeleton of bay lobsters. We analyzed the texture of exoskeleton to categorize into normal, moulting stage, and freshly moulted classes. To meet the efficiency and robustness requirements of production platform, we leverage traditional approach such as Local Binary Pattern and Local Derivative Pattern with enhanced encoding scheme for underwater imagery. We also build a dataset of 315 bay lobster images captured at the controlled under water environment. Experimental results on this dataset demonstrated that the proposed method can effectively classify bay lobsters with a high accuracy.","Title: Online Relational Manifold Learning for Multiview Segmentation in Echocardiography\nYear: 2018\n\nAbstract: Accurate delineation of the left ventricle (LV) endocardial border in echocardiography is of vital importance for the diagnosis and treatment of heart disease. Effective segmentation of the LV is challenging due to low contrast, signal dropout and acoustic noise. In the situation where low level and region-based image cues are unable to define the LV boundary, shape prior models are critical to infer shape. These models perform well when there is low variability in the underlying shape subspace and the shape instance produced by appearance cues does not contain gross errors, however in the absence of these conditions results are often much poorer. In this paper, we first propose a shape model to overcome the problem of modelling complex shape subspaces. Our method connects the implicit relationship between image features and shape by extending graph regularized sparse nonnegative matrix factorization (NMF) to jointly learn the structure and connection between two low dimensional manifolds comprising image features and shapes, respectively. We extend conventional NMF learning to an online learning-based approach where the input image is used to leverage the learning and connection of each manifold to the most relevant subspace regions. This ensures robust shape inference and a shape model constructed from contextually relevant shapes. A fully automatic segmentation approach using a probabilistic framework is then proposed to detect the LV endocardial border. Our method is applied to a diverse dataset that contains multiple views of the LV. Results show the effectiveness of our approach compared to state-of-the-art methods.","Title: Real- Time Computing for Droplet Detection and Recognition.\nYear: 2018\n\nAbstract: In this paper, we propose a novel framework to achieve real-time droplet detection and recognition. In a fluidics system, water drops can be generated to carry objects. It is important to use such droplets to analyze the properties of the carried tiny objects. In real applications, the movement of droplets are captured by a high-speed camera, and processed by an image-based computing system. This imposes high requirements on the computational efficiency of the image processing and pattern recognition methods in the system. To tackle this challenge, we propose to use efficient object tracking and classification methods to build the framework. We first use the Hough transform as the basic droplet detection method, and then explore the relationship of the same droplet in different frames to track their movement. For the classification step, we use the area of particle content as the basic feature, and conduct classification using a linear support vector machine. The experiments on real-time droplet analysis system verifies that our proposed framework can process images at 1000 frames per second, while achieving 99.3% in classification accuracy.","Title: Fusion of Hyperspectral and LiDAR Data Using Discriminant Correlation Analysis for Land Cover Classification.\nYear: 2018\n\nAbstract: It is evident that using complementary features from different sensors is effective for land cover classification. Therefore, combining complementary information from hyperspectral (HS) and light detection and ranging (LiDAR) data can greatly assist in such applications. In this paper, we propose a model for land cover classification, which extracts effective features representing different charac...","Title: Conditional Random Field and Deep Feature Learning for Hyperspectral Image Classification\nYear: 2019\n\nAbstract: Image classification is considered to be one of the critical tasks in hyperspectral remote sensing image processing. Recently, a convolutional neural network (CNN) has established itself as a powerful model in classification by demonstrating excellent performances. The use of a graphical model such as a conditional random field (CRF) contributes further in capturing contextual information and thus improving the classification performance. In this paper, we propose a method to classify hyperspectral images by considering both spectral and spatial information via a combined framework consisting of CNN and CRF. We use multiple spectral band groups to learn deep features using CNN, and then formulate deep CRF with CNN-based unary and pairwise potential functions to effectively extract the semantic correlations between patches consisting of 3-D data cubes. Furthermore, we introduce a deep deconvolution network that improves the final classification performance. We also introduced a new data set and experimented our proposed method on it along with several widely adopted benchmark data sets to evaluate the effectiveness of our method. By comparing our results with those from several state-of-the-art models, we show the promising potential of our method.","Title: Region-Based Structure Preserving Nonnegative Matrix Factorization for Hyperspectral Unmixing\nYear: 2017\n\nAbstract: Hyperspectral unmixing is one of the most important techniques in the remote sensing image analysis. In recent years, the nonnegative matrix factorization (NMF) method is widely used in hyperspectral unmixing. In order to solve the nonconvex problem of the NMF method, a number of constraints have been introduced into NMF models, including sparsity, manifold, smoothness, etc. However, these constra...","Title: Robust Image Classification via Low-Rank Double Dictionary Learning.\nYear: 2017\n\nAbstract: In recent years, dictionary learning has been widely used in various image classification applications. However, how to construct an effective dictionary for robust image classification task, in which both the training and the testing image samples are corrupted, is still an open problem. To address this, we propose a novel low-rank double dictionary learning (LRD2L) method. Unlike traditional dictionary learning methods, LRD2L simultaneously learns three components from training data: (1) a low-rank class-specific sub-dictionary for each class to capture the most discriminative features owned by each class, (2) a low-rank class-shared dictionary which models the common patterns shared by different classes and (3) a sparse error container to fit the noises in data. As a result, the class-specific information, the class-shared information and the noises contained in data are separated from each other. Therefore, the dictionaries learned by LRD2L are noiseless, and the class-specific sub-dictionary of each class can be more discriminative. Also since the common features across different classes, which are essential to the reconstruction of image samples, are preserved in class-shared dictionary, LRD2L has a powerful reconstructive capability for newly coming testing samples. Experimental results on three public available datasets reveal the effectiveness and the superiority of our approach compared to the state-of-the-art dictionary learning methods.","Title: Boosting Radial Strings for 3D Face Recognition with Expressions and Occlusions\nYear: 2016\n\nAbstract: In this paper, we present a new radial string representation and matching approach for 3D face recognition under expression variations and partial occlusions. The radial strings are an indexed collection of strings emanating from the nose tip of a face scan. The matching between two radial strings is conducted through a dynamic programming process, in which a partial matching mechanism is established to effectively find those un-occluded substrings. Moreover, the most discriminative and stable radial strings are selected optimally by the well-known AdaBoost algorithm to achieve a composite classifier for 3D face recognition under facial expression changes. Experimental results on the GavabDB and the Bosphorus databases show that the proposed approach achieves promising results for human face recognition with expressions and occlusions.","Title: Human Intention Understanding From Multiple Demonstrations And Behavior Generalization In Dynamic Movement Primitives Framework\nYear: 2019\n\nAbstract: Human's interference in the process of skill learning can improve the performance of the robot greatly. However, learning from demonstration to generate a new action with human behavioral characteristics in the varying situation is challenging. Generally, dynamic movement primitives (DMPs) method can generalize the trajectory imitating the demonstration, but cannot integrate the feature of multiple trajectories of different targets. In this paper, the proposed method contains two aspects of learning and generating. The statistical method Gaussian mixture model and Gaussian mixture regression (GMM-GMR) is used to extract the common characteristic and eliminate the uncertainty of the multiple demonstrations. To exert the ability of DMPs to generate a human-like motion to a new goal, and we model the shape parameter with locally weighted regression (LWR) method. To enhance the ability of DMPs in multiple trajectories learning, we propose the multivariate Gaussian process regression (MV-GPR) method to construct the regression model of shape parameters to reflect the human intentions, with respect to the target position. To verify the feasibility of the proposed method, we design a peg-in-hole experiment with proving generalization and obstacle avoidance performance. The results have shown that the strategy integrated the generalization of DMPs and feature regeneration ability of MV-GPR method, and the generated valid trajectory could achieve the peg-in-hole task with 6-DOF whole-arm avoidance.","Title: ST-CNN: Spatial-Temporal Convolutional Neural Network for crowd counting in videos\nYear: 2019\n\nAbstract: •We propose a crowd counting network (ST-CNN) to accurately calculate the number of people in the scene.•We utilized both spatial and temporal features to solve the problem of crowd counting.•Experiments on two challenging datasets, Mall and WorldExpo010, prove the effectiveness of our method.","Title: Chord Bunch Walks for Recognizing Naturally Self-overlapped and Compound Leaves.\nYear: 2019\n\nAbstract: Effectively describing and recognizing leaf shapes under arbitrary variations, particularly from a large database, remains an unsolved problem. In this research, we attempted a new strategy of describing leaf shapes by walking and measuring along a bunch of chords that pass through the shape. A novel chord bunch walks (CBW) descriptor is developed through the chord walking behaviour that effectively integrates the shape image function over the walked chord to reflect both the contour features and the inner properties of the shape. For each contour point, the chord bunch groups multiple pairs of chords to build a hierarchical framework for a coarse-to-fine description that can effectively characterize not only the subtle differences among leaf margin patterns but also the interior part of the shape contour formed inside a self-overlapped or compound leaf. Instead of using optimal correspondence based matching, a Log-Min distance that encourages one-to-one correspondences is proposed for efficient and effective CBW matching. The proposed CBW shape analysis method is invariant to rotation, scaling, translation, and mirror transforms. Five experiments, including image retrieval of compound leaves, image retrieval of naturally self-overlapped leaves, and retrieval of mixed leaves on three large scale datasets, are conducted. The proposed method achieved large accuracy increases with low computational costs over the state-of-the-art benchmarks, which indicates the research potential along this direction.","Title: Multiscale Contour Steered Region Integral and Its Application for Cultivar Classification.\nYear: 2019\n\nAbstract: In this paper, a multiscale contour steered region integral (MCSRI) method is proposed to classify highly similar shapes with flexible interior connection architectures. A component distance map (CDM) is developed to robustly characterize the flexible interior connection structure, shape of the exterior contour, and their inter-relationship in a shape image. A novel multiscale region transform (MReT) is proposed to perform region integral over different contour-steered strips at all possible scales to effectively integrate patch features, and thus enables a better description of the shape image in a coarse-to-fine manner. It is applied to solve a challenging problem of classifying cultivars from leaf images, which is a new attempt in both biology and computer vision research communities. A soybean cultivar leaf vein database (SoyCultivarVein), which is the first cultivar leaf vein database, is created and presented for performance evaluation. The experimental results demonstrate the superiority of the proposed method over the state-of-the-art methods in similar shape classification and the possibility of cultivar recognition via leaf pattern analysis, which may lead to a new research interest towards fine-level shape analysis on cultivar classification.","Title: Triplet Constrained Deep Feature Extraction for Hyperspectral Image Classification\nYear: 2018\n\nAbstract: Convolutional neural networks (CNNs) have demonstrated significant performance in various visual recognition problems in recent years. Recent research has shown that training multilayer neural networks can extensively improve the performance of hyperspectral image (HSI) classification. In this paper, we apply a triplet constraint property on a 3D CNN. This method directly learns a mapping from images to a Euclidean space in which distances directly correspond to a measure of spectral-spatial similarity. Once this embedding has been established, classification can be implemented with such embeddings as feature vectors. Moreover, we also augment the size of the training samples in different band groups. This produces different yet useful estimation of spectral-spatial characteristics of HSI data and contributes considerably in accurate classification. This method is evaluated on a new dataset and compared with several state-of-the-art models, which shows the promising potential of our method.","Title: Feature Fusion Vision Transformer for Fine-Grained Visual Categorization.\nYear: 2021\n\nAbstract: The core for tackling the fine-grained visual categorization (FGVC) is to learn subtleyet discriminative features. Most previous works achieve this by explicitly selecting thediscriminative parts or integrating the attention mechanism via CNN-based approaches.However, these methods enhance the computational complexity and make the modeldominated by the regions containing the most of the objects. Recently, vision trans-former (ViT) has achieved SOTA performance on general image recognition tasks. Theself-attention mechanism aggregates and weights the information from all patches to theclassification token, making it perfectly suitable for FGVC. Nonetheless, the classifi-cation token in the deep layer pays more attention to the global information, lackingthe local and low-level features that are essential for FGVC. In this work, we proposea novel pure transformer-based framework Feature Fusion Vision Transformer (FFVT)where we aggregate the important tokens from each transformer layer to compensate thelocal, low-level and middle-level information. We design a novel token selection mod-ule called mutual attention weight selection (MAWS) to guide the network effectivelyand efficiently towards selecting discriminative tokens without introducing extra param-eters. We verify the effectiveness of FFVT on three benchmarks where FFVT achievesthe state-of-the-art performance.","Title: Self-Supervised Video Object Segmentation by Motion-Aware Mask Propagation\nYear: 2022\n\nAbstract: We propose a self-supervised spatio-temporal matching method coined Motion-Aware Mask Propagation (MAMP) for semi-supervised video object segmentation. During training, MAMP leverages the frame reconstruction task to train the model without the need for annotations. During inference, MAMP extracts high-resolution features from each frame to build a memory bank from the features as well as the predicted masks of selected past frames. MAMP then propagates the masks from the memory bank to subsequent frames according to our motion-aware spatio-temporal matching module, also proposed in this paper. Evaluation on DAVIS-2017 and YouTube-VOS datasets show that MAMP achieves state-of-the-art performance with stronger generalization ability compared to existing self-supervised methods, i.e. 4.9\\% higher mean $\\mathcal{J}\\&\\mathcal{F}$ on DAVIS-2017 and 4.85\\% higher mean $\\mathcal{J}\\&\\mathcal{F}$ on the unseen categories of YouTube-VOS than the nearest competitor. Moreover, MAMP performs on par with many supervised video object segmentation methods. Our code is available at: \\url{https://github.com/bo-miao/MAMP}.","Title: MaskCOV: A random mask covariance network for ultra-fine-grained visual categorization\nYear: 2021\n\nAbstract: •This paper proposes a novel random mask covariance network (MaskCOV) to study the challenging problem of ultra-fine-grained visual categorization (ultra-FGVC).•A new self-supervised learning with a powerful in-image data augmentation scheme is proposed for enabling the network to localise those discriminative yet compact regions.•The proposed MaskCOV focuses training within the local discriminative regions, enforcing the regional and correlation learning for subtle detail classification in the ultra-FGVC.•Encouraging experimental results demonstrate the capability of MaskCOV in advancing research from the fine-grained to the ultra-fine-grained visual categorization.","Title: A unified weight learning and low-rank regression model for robust complex error modeling\nYear: 2021\n\nAbstract: •This is the first attempt to use a unified function to address both error distribution fitting and structure estimation.•A generalized-correntropy-based weight learning theory is proposed to better fit complex error distribution in face images.•A new low-rank approximation estimator based on the generalized correntropy is proposed for contiguous error structure estimation.•We propose an optimization scheme based on the majorization minimization (MM) and alternating direction method of multipliers (ADMM).","Title: Mask-Guided Feature Extraction and Augmentation for Ultra-Fine-Grained Visual Categorization\nYear: 2021\n\nAbstract: While the fine-grained visual categorization (FGVC) problems have been greatly developed in the past years, the Ultra-fine-grained visual categorization (Ultra-FGVC) problems have been understudied. FGVC aims at classifying objects from the same species (very similar categories), while the Ultra-FGVC targets at more challenging problems of classifying images at an ultra-fine granularity where even...","Title: A Compositional Feature Embedding and Similarity Metric for Ultra-Fine-Grained Visual Categorization\nYear: 2021\n\nAbstract: Fine-grained visual categorization (FGVC), which aims at classifying objects with small inter-class variances, has been significantly advanced in recent years. However, ultra-fine-grained visual categorization (ultra-FGVC), which targets at identifying subclasses with extremely similar patterns, has not received much attention. In ultra-FGVC datasets, the samples per category are always scarce as ...","Title: Upwardly Abstracted Definition-Based Subontologies\nYear: 2021\n\nAbstract: ABSTRACTIn this paper, we present a method for extracting subontologies from $\\mathcalELH $ ontologies for a set of symbols. The approach is focused on the generation of upwardly abstracted definitions of concepts, which is a technique for computing definitions expressed using closest primitive ancestors. The subontologies returned by the method are evaluated for quality and compared to extracts computed with locality-based modularisation and uniform interpolation. Our subontology generation method produces promising results in terms of size and relevance to the needs of domain experts.","Title: Learning discriminative region representation for person retrieval\nYear: 2022\n\nAbstract: •We propose HRS, a weakly-supervised region segmentation method to predict informative region for improved person retrieval.•HRS coherently incorporates global appearance information and local granularity cue in an end-to-end learning.•Extensive experimental results on three benchmark datasets show HRS achieves new state-of-the-arts for person retrieval.","Title: Obstacle Avoidance Methods Based on Geometric Information Under the DMPs Framework\nYear: 2021\n\nAbstract: Dynamical Movement Primitives (DMPs) can equip the robot with humanoid characteristics and make it efficiently complete the task. However, research on obstacle avoidance strategies combined with DMPs is still being explored nowadays. In this paper, we proposed new obstacle avoidance methods based on geometric information under the DMPs framework, in order that the robot can still complete the task in an unstructured environment. We first generalized new trajectories by DMPs after demonstration. Then according to the interference area of a static or a moving obstacle after extracting the geometric information of the obstacle, we quantitatively adjusted the generalized trajectories by adding proper offset at a certain direction. Finally we used proportional-derivative (PD) control to ensure that the modified trajectories converge to the original one and the goal. The methods make the robot successfully avoid the interference of a static or a moving obstacle, and besides, they maintain all the advantages of the DMPs framework, such as fast goal convergence, simple mathematical principles and high imitation similarity. We verified the effectiveness of the methods in two dimensional and three dimensional simulation, and set up the real experiments using a static or a moving obstacle respectively, and finally proved the feasibility and validity of the avoidance methods.","Title: Composite Description Based On Color Vector Quantization And Visual Primary Features For Cbir Tasks\nYear: 2021\n\nAbstract: This paper presents a novel method for content-based color image retrieval that combines color vector quantization and visual primary features into a compact feature representation. Color vector quantization is proposed to describe the image in a compressed stream by preserving the contrast of an image and produce two color quantizers which are processed by vector quantization to preserve the content of a color image. Loosely inspired by human visual system and its mechanism in effectively recognizing objects in an image by its edges and color distribution, we propose extraction of visual primary features based on edge texture orientation and color moments features. The representation of the proposed method utilizes histogram-based features which are populated by color quantization and visual primary features that are used to measure the similarity between the two color images by a specific distance metric computation. The proposed method proves to be efficient and adaptive to the particulars of image retrieval, while it does not require any training information, making it suitable for real time color CBIR applications.The smaller feature size is an additional benefit of the proposed methodology which makes it ideal to use for embedded computing, mobile computing, energy efficient computing and high-throughput systems. An extensive experimental evaluation conducted on four publicly available datasets namely Wang, Vistex-640, Corel-5k and Corel-10k datasets against well-known fusion based, non-fusion based, and deep learning methods highlights the effectiveness and efficiency of the proposed method.","Title: Benchmark Platform for Ultra-Fine-Grained Visual Categorization Beyond Human Performance.\nYear: 2021\n\nAbstract: Deep learning methods have achieved remarkable success in fine-grained visual categorization. Such successful categorization at sub-ordinate level, e.g., different animal or plant species, however relies heavily on the visual differences that human can observe and the ground-truths are labelled on the basis of such human visual observation. In contrast, few research has been done for visual categorization at the ultra-fine-grained level, i.e., a granularity where even human experts can hardly identify the visual differences or are not yet able to give affirmative labels by inferring observed pattern differences. This paper reports our efforts towards mitigating this research gap. We introduce the ultra-fine-grained (UFG) image dataset, a large collection of 47,114 images from 3,526 categories. All the images in the proposed UFG image dataset are grouped into categories with different confirmed cultivar names. In addition, we perform an extensive evaluation of state-of-the-art fine-grained classification methods on the proposed UFG image dataset as comparative baselines. The proposed UFG image dataset and evaluation protocols is intended to serve as a benchmark platform that can advance research of visual classification from approaching human performance to beyond human ability, via facilitating benchmark data of artificial intelligence (AI) not to be limited by the labels of human intelligence (HI). The dataset is available online at https://github.com/XiaohanYu-GU/Ultra-FGVC.","Title: A Dual-Band Radio-Over-Fiber Link for Future 5G Communication System\nYear: 2021\n\nAbstract: In the existing fifth-generation (5G) communication system, the remote radio unit (RRU) has a large weight, size and power consumption, and there are also compatibility problems between the low-frequency and the high-frequency millimeter wave frequency band system. Based on the microwave photonics technology, a dual-band radio over fiber (RoF) link is proposed, which can realize the simultaneous transmission and channel switching of the low-frequency (3.5 GHz) and high-frequency (28 GHz). In the low-frequency band, the nonlinear distortion is suppressed, and in the high-frequency millimeter wave band, the power fading caused by dispersion is suppressed.","Title: Large Spurious-free Dynamic Range RoF Link with Tunable CSR\nYear: 2021\n\nAbstract: In this paper, a linearized radio-over-fiber (RoF) link is proposed. The suppression for the third-order intermodulation distortion (IMID3) and periodic power fading can be simultaneously realized in the proposed link. Adjustable carrier-to-sideband ratio (CSR) can also be achieved. Compared with the traditional RoF link, the spurious free dynamic range (SFDR) of the proposed link is improved by 19 dB.","Title: Distribution-Aware Margin Calibration for Semantic Segmentation in Images\nYear: 2022\n\nAbstract: The Jaccard index, also known as Intersection-over-Union (IoU), is one of the most critical evaluation metrics in image semantic segmentation. However, direct optimization of IoU score is very difficult because the learning objective is neither differentiable nor decomposable. Although some algorithms have been proposed to optimize its surrogates, there is no guarantee provided for the generalization ability. In this paper, we propose a margin calibration method, which can be directly used as a learning objective, for an improved generalization of IoU over the data-distribution, underpinned by a rigid lower bound. This scheme theoretically ensures a better segmentation performance in terms of IoU score. We evaluated the effectiveness of the proposed margin calibration method on seven image datasets, showing substantial improvements in IoU score over other learning objectives using deep segmentation models.","Title: EAR-NET: Error Attention Refining Network For Retinal Vessel Segmentation\nYear: 2021\n\nAbstract: The precise detection of blood vessels in retinal images is crucial to the early diagnosis of the retinal vascular diseases, e.g., diabetic, hypertensive and solar retinopathies. Existing works often fail in predicting the abnormal areas, e.g, sudden brighter and darker areas and are inclined to predict a pixel to background due to the significant class imbalance, leading to high accuracy and spec...","Title: Early Detection of Sugarcane Smut Disease in Hyperspectral Images\nYear: 2021\n\nAbstract: Sugarcane smut, caused by the fungus Sporisorium scitamineum, is a serious sugarcane disease in Queensland, which can cause 30-100% production loss. Early detection of smut disease is a key step towards disease management. However, early-stage smut symptoms are not visible to the human eye. To address this challenge, we leverage the capability of hyperspectral imaging in data acquisition beyond th...","Title: Imitation of Manipulation Skills Using Multiple Geometries.\nYear: 2022\n\nAbstract: Daily manipulation tasks are characterized by geometric primitives related to actions and object shapes. Such geometric descriptors are poorly represented by only using Cartesian coordinate systems. In this paper, we propose a learning approach to extract the optimal representation from a dictionary of coordinate systems to encode an observed movement/behavior. This is achieved by using an extension of Gaussian distributions on Riemannian manifolds, which is used to analyse a set of user demonstrations statistically, by considering multiple geometries as candidate representations of the task. We formulate the reproduction problem as a general optimal control problem based on an iterative linear quadratic regulator (iLQR), where the Gaussian distribution in the extracted coordinate systems are used to define the cost function. We apply our approach to object grasping and box opening tasks in simulation and on a 7-axis Franka Emika robot. The results show that the robot can exploit several geometries to execute the manipulation task and generalize it to new situations, by maintaining the invariant characteristics of the task in the coordinate system(s) of interest.","Title: An Attention-Based Lattice Network for Hyperspectral Image Classification\nYear: 2022\n\nAbstract: Convolutional neural networks (CNNs) with 3-D convolutional kernels are widely used for hyperspectral image (HSI) classification, which bring notable benefits in capturing joint spectral and spatial features. However, they suffer from poor computational efficiency, causing the low training/inference speed of the model. On the contrary, CNN-based methods with 1-D and 2-D kernels are efficient but mostly restricted to extracting either spectral or spatial features. Moreover, most CNN-based HSI classification frameworks are incapable of simultaneously taking advantage of residual and dense aggregations without over-allocating parameters or information loss for feature reusage. This article presents a novel attention-based lattice network (ALN) to overcome these shortcomings. The proposed 2-D lattice framework can effectively harness the advantages of residual and dense aggregations to achieve outstanding accuracy performance and computational efficiency simultaneously. Furthermore, the ALN employs a unique joint spectral-spatial attention mechanism to capture both spectral and spatial information effectively. In particular, a new pointwise spectral attention mechanism is adopted to fully capture spectral dependences for every pixel. Our extensive experimental investigation verifies the effectiveness and efficiency of the ALN architecture for HSI classification.","Title: Symmetric Binary Tree Based Co-occurrence Texture Pattern Mining for Fine-grained Plant Leaf Image Retrieval\nYear: 2022\n\nAbstract: •This work focuses on leaf image retrieval in cultivar level known as a very fine-grained image recognition problem.•Symmetric binary tree (SBT) is designed for mining co-occurrence texture patterns.•A novel feature fusion scheme, named K-NN Based Handcrafted and Deep Features Fusion, is proposed.•The effectiveness of the proposed method has been validated on the soybean cultivar leaf dataset and peanut cultivar leaf dataset.","Title: Generalization of orientation trajectories and force–torque profiles for learning human assembly skill\nYear: 2022\n\nAbstract: Constructing system models in industrial tasks is difficult, especially when multiple sensing information is involved. The peg-in-hole task is typically complex in industrial manufacturing. It is hard to model different assembly states precisely. Learning from demonstration can simplify the modelling process based on human demonstration. In this paper, we transfer a new kind of assembly skill to the robot. To intuitively represent the orientation of the assembly, we propose a novel expression named assembly-angle based on direction cosine and rotation matrix, which offers a good differential performance and avoids the singularity problem. Meanwhile, this approach can be encoded by the dynamic constraint dynamic movement primitives (DC-DMPs) method, which is proposed to consider the constraints among the assembly-angle during movement generalization. The RBF network is employed to generalize force–torque information to improve the accuracy of force–torque profiles prediction. We utilize the vision system and apply admittance control to locate the hole precisely. This proposed assembly skill learning framework can complete the task from random deviation of the pose, which makes it suitable for realistic assembly scenarios. To validate our method’s generalization ability, pegs of two kinds of materials were designed, and the deviations of the initial pose were set from small to large. The success rate was 93.75% for high stiffness assembly and 86.25% for low stiffness assembly. The result validated the effectiveness of our method in the peg-in-hole task.","Title: A Lie algebra representation for efficient 2D shape classification\nYear: 2023\n\nAbstract: •This paper presents a novel solution to address high computational cost of current Riemannian framework based methods.•We propose a novel representation of Block Diagonal Symmetric Positive Definite Matrix Lie Algebra (BDSPDMLA) for 2D shapes.•A new classification algorithm is introduced to achieve ten thousand times increase in speed over manifold methods.•Experimental results on five datasets demonstrate BDSPDMLA’s effectiveness in computation times and classification accuracy."],"string":"[\n \"Title: A multiview facial analysis technique for identity authentication\\nYear: 2003\\n\\nAbstract: Biometric systems, which use people's physiological characteristics for identification or authentication, have become increasingly popular for countering fraud In this article, we describe a face recognition system that uses a new distance measure for authentication. It performs matching on a fusion of multiple views of each person.\",\n \"Title: Sobel-LBP\\nYear: 2008\\n\\nAbstract: This paper presents a new Sobel-LBP, an extension of existing local binary pattern (LBP), for facial image representation. The face image is filtered by Sobel operator to enhance the edge information. Sobel-LBP feature distributions are then extracted and concatenated into a spatial histogram to be used as a face descriptor. The proposed method is compared with the original LBP on both gray-level images and Gabor real and imaginary features for face recognition. The experimental results indicate that Sobel-LBP provides a significantly better performance than LBP under various conditions.\",\n \"Title: High-Order Circular Derivative Pattern for Image Representation and Recognition\\nYear: 2010\\n\\nAbstract: Micropattern based image representation and recognition, e.g. Local Binary Pattern (LBP), has been proved successful over the past few years due to its advantages of illumination tolerance and computational efficiency. However, LBP only encodes the first-order radial-directional derivatives of spatial images and is inadequate to completely describe the discriminative features for classification. This paper proposes a new Circular Derivative Pattern (CDP) which extracts high-order derivative information of images along circular directions. We argue that the high-order circular derivatives contain more detailed and more discriminative information than the first-order LBP in terms of recognition accuracy. Experimental evaluation through face recognition on the FERET database and insect classification on the NICTA Biosecurity Dataset demonstrated the effectiveness of the proposed method.\",\n \"Title: Towards a BioAPI compliant face verification system\\nYear: 2004\\n\\nAbstract: Standards are important to the growth and acceptance of emerging industries such as the biometric industry. To promote biometric standard, the BioAPI Consortium has developed a widely accepted BioAPI specification that defines Application Programming Interface (API) for application developers and Service Provider Interface (SPI) for biometric technology developers. In this paper, we propose an implementation of a BioAPI compliant face verification BSP (Biometric Service Provider). In the face verification BSP, Line Edge Map (LEM) is used as the face recognition engine. Primitive biometric functions such as Capture, CreateTemplate, Process and VerifyMatch have been developed to provide support for face verification. In addition, to demonstrate the proposed face verification BSP, we have developed a BioAPI compliant face verification application for door access control. The performance of the face verification application is also evaluated.\",\n \"Title: A comparative evaluation of Average Face on holistic and local face recognition approaches\\nYear: 2008\\n\\nAbstract: This study focuses on a recent paper ldquo100% Accuracy in Automatic Face Recognitionrdquo published on Science, in which an ldquoAverage Facerdquo is proposed and claimed to be capable of dramatically improving performance of a face recognition system. To reveal its working mechanism, we perform the averaging process using pose-varied synthetic images generated from 3D face database and conduct a comparative study to observe its effectiveness on holistic and local face recognition approaches. Two representative methods, i.e. eigenface and local binary pattern (LBP) are employed to perform the experiments. It is interesting to find from our experiments that the performance of the ldquoAverage Facerdquo is not independent of the face recognition approaches. Although face averaging increases the recognition accuracy of eigenface method, it impairs the performance of LBP method.\",\n \"Title: Fast Kernel Sparse Representation\\nYear: 2011\\n\\nAbstract: Two efficient algorithms are proposed to seek the sparse representation on high-dimensional Hilbert space. By proving that all the calculations in Orthogonal Match Pursuit (OMP) are essentially inner-product combinations, we modify the OMP algorithm to apply the kernel-trick. The proposed Kernel OMP (KOMP) is much faster than the existing methods, and illustrates higher accuracy in some scenarios. Furthermore, inspired by the success of group-sparsity, we enforce a rigid group-sparsity constraint on KOMP which leads to a noniterative variation. The constrained cousin of KOMP, dubbed as Single-Step KOMP (S-KOMP), merely takes one step to achieve the sparse coefficients. A remarkable improvement (up to 2,750 times) in efficiency is reported for S-KOMP, with only a negligible loss of accuracy.\",\n \"Title: Learning kernel subspace classifier\\nYear: 2007\\n\\nAbstract: Subspace classifiers are well-known in pattern recognition, which represent pattern classes by linear subspaces spanned by the class specific basis vectors through simple mathematical operations like SVD. Recently, kernel based subspace methods have been proposed to extend the functionalities by directly applying the Kernel Principal Component Analysis (KPCA). The projection variance in kernel space as applied in these earlier proposed kernel subspace methods, however, is not a trustworthy criteria for class discrimination and they simply fail in many recognition problems as we encountered in biometrics research. We address this issue by proposing a learning kernel subspace classifier which attempts to reconstruct data in input space through the kernel subspace projection. While the pre-image methods aiming at finding an approximate pre-image for each input by minimization of the reconstruction error in kernel space, we emphasize the problem of how to estimate a kernel subspace as a model for a specific class. Using the occluded face recognition as examples, our experimental results demonstrated the efficiency of the proposed method.\",\n \"Title: Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor.\\nYear: 2010\\n\\nAbstract: This paper proposes a novel high-order local pattern descriptor, local derivative pattern (LDP), for face recognition. LDP is a general framework to encode directional pattern features based on local derivative variations. The n(th)-order LDP is proposed to encode the (n-1)(th) -order local derivative direction variations, which can capture more detailed information than the first-order local pattern used in local binary pattern (LBP). Different from LBP encoding the relationship between the central point and its neighbors, the LDP templates extract high-order local information by encoding various distinctive spatial relationships contained in a given local region. Both gray-level images and Gabor feature images are used to evaluate the comparative performances of LDP and LBP. Extensive experimental results on FERET, CAS-PEAL, CMU-PIE, Extended Yale B, and FRGC databases show that the high-order LDP consistently performs much better than LBP for both face identification and face verification under various conditions.\",\n \"Title: A Minutiae-Based Fingerprint Matching Algorithm Using Phase Correlation\\nYear: 2007\\n\\nAbstract: Minutiae-based method is the most popular approach in fingerprint matching. However, most existing methods need to search for the best correspondence of minutiae pairs or use reference points (core and delta points) to estimate the alignment parameters. The problem of lost minutiae or spurious minutiae always occurs during the minutiae detection process. Hence, the corresponding pairs or reference points may not be found under this condition. This paper proposes a new minutiae-based fingerprint matching algorithm using phase correlation. We define a new representation called Minutiae Direction Map (MDM). First, we convert minutiae sets into 2D image spaces. Then the transformation parameters are calculated using phase correlation between two MDMs to align two fingerprints to be matched. The similarity of two fingerprints is determined by the distance between two minutiae sets. Our approach does not need to search for the corresponding minutiae pairs. Experimental results show that the proposed approach performed well in matching fingerprint minutiae sets, which greatly improved the economy of storage space.\",\n \"Title: Face Localization Using an Effective Co-evolutionary Genetic Algorithm\\nYear: 2010\\n\\nAbstract: In this paper, a new method for face localization in color images, which is based on co-evolutionary systems, is introduced. The proposed method uses a co-evolutionary system to locate the eyes in a face image. The used coevolutionary system involves two genetic algorithm models. The first GA model searches for a solution in the given environment, and the second GA model searches for useful genetic information in the first GA model. In the next step, by using the location of eyes in image the parameters of face's bounding ellipse (center, orientation, major and minor axis) are computed. To evaluate and compare the proposed method with other methods, high order Pseudo Zernike Moments (PZM) are utilized to produce feature vectors and a Radial Basis Function (RBF) neural network is used as the classifier. Simulation results indicate that the speed and accuracy of the new system using the proposed face localization method which uses a co-evolutionary approach is higher than the system proposed in [10].\",\n \"Title: Lighting Analysis and Texture Modification of 3D Human Face Scans\\nYear: 2007\\n\\nAbstract: The 3D scanning of human faces is an important tool to acquire accurate shape and texture information of heads and faces for both animation and recognition purposes. One of the most popular scanning tools is Cyberware head and face 3D colour scanner. Currently, 3D face databases scanned using the Cyberware head and face 3D colour scanner databases are publicly available and have been used in many face recognition system as the training data, one of which is USF human ID 3-D database. The mechanism of facial texture capturing, however, remains unclear due to the special settings of the light sources during scanning. The direct application of the acquired texture intensity as the albedo of the face surfaces leads to suboptimal modelling results. This paper analyses the lighting configuration of the scanner and proposes an appropriate reflectance model to describe the reflecting mechanism when scanning. The texture modifications can be carried on in the existing scanning results without any further scans. Experiment results show that the proposed reflectance model can better approximate the reflecting situation of scanning so that the acquired textures can be directly used as diffuse reflectance coefficients and achieve better rendering results. Key words: 3D face model, 3D scanner, facial textures, lighting condition, reflection model.\",\n \"Title: Recognizing face profiles in the presence of hairs/glasses interferences\\nYear: 2010\\n\\nAbstract: Facial profile provides a complementary structure of the face that is not present in frontal faces, which has been used in personal identification, face perception research and 3D face construction. In this paper, we present a novel local attributed string matching (LAStrM) approach to recognize face profiles in the presence of interferences. The conventional profile recognition algorithms heavily depend on the accuracy of the facial area cropping. However, in realistic scenarios the facial area may be difficult to localize due to interferences (e.g., glasses, hairstyles). The proposed approach is able to efficiently find the most discriminative local parts between face profiles addressing the recognition problem with interferences. Experimental results have shown that the proposed matching scheme is robust to interferences compared against several primary approaches using two profile image databases (Bern and FERET). It has potential capability for partially occluded shape classification.\",\n \"Title: Locality-Regularized Linear Regression for face recognition.\\nYear: 2012\\n\\nAbstract: Linear Regression Classification (LRC) based face recognition achieves high accuracy while being highly efficient. As with most other linear-subspace-based methods, the faces of a subject are assumed to reside on a linear manifold; however, where occlusion or disturbances are involved, this assumption may be inaccurate. In this paper, a manifold-learning procedure is used to expand on conventional LRC by excluding faces not fitting the original assumption (of linearity), thereby localizing the manifold subspace, increasing the accuracy over conventional LRC and reducing the number of faces for which the regression must be performed. The algorithm is evaluated using two standard databases and shown to outperform the conventional LRC.\",\n \"Title: Compressed Sensing Ensemble Classifier for Human Detection.\\nYear: 2013\\n\\nAbstract: This paper proposes a novel Compressed Sensing Ensemble Classifier (CSEC) for human detection. The proposed CSEC employs the compressed sensing technique to get a more sparse model with a more reasonable selection of base classifiers. The major contributions of this paper are: 1) a novel principled framework for ensemble classifier design based on compressed sensing; 2) a new concept of considering both the simplicity of ensemble classifier and irrelevance of base classifiers towards optimal classifier design; and 3) a quadratic function for CSEC optimization which includes a new optimizable positive semi-definite relevance matrix to simultaneously select appropriate base classifiers with minimized relevance. Experimental results on INRIA and SDL databases show that the performance of CSEC is better than two most popular classifiers SVM and AdaBoost, as well as a most recent method CLML. © 2013 Springer-Verlag Berlin Heidelberg.\",\n \"Title: Recognizing partially occluded faces from a single sample per class using string-based matching\\nYear: 2010\\n\\nAbstract: Automatically recognizing human faces with partial occlusions is one of the most challenging problems in face analysis community. This paper presents a novel string-based face recognition approach to address the partial occlusion problem in face recognition. In this approach, a new face representation, Stringface, is constructed to integrate the relational organization of intermediate-level features (line segments) into a high-level global structure (string). The matching of two faces is done by matching two Stringfaces through a string-to-string matching scheme, which is able to efficiently find the most discriminative local parts (substrings) for recognition without making any assumption on the distributions of the deformed facial regions. The proposed approach is compared against the state-of-the-art algorithms using both the AR database and FRGC (Face Recognition Grand Challenge) ver2.0 database. Very encouraging experimental results demonstrate, for the first time, the feasibility and effectiveness of a high-level syntactic method in face recognition, showing a new strategy for face representation and recognition.\",\n \"Title: Face Recognition by Combining Kernel Associative Memory and Gabor Transforms\\nYear: 2006\\n\\nAbstract: Kernel associative memory (KAM) has previously been proposed as an efficient scheme for face recognition. In this paper, a hybrid method of combining KAM and Gabor wavelet transform is proposed. In this method, face images of each person are first decomposed into their spatial/ frequency domains by Gabor transforms, which are then modelled by a KAM. While Gabor properties of orientation selectivity and spatial frequency selectivity provide discriminating features, KAM offers the means to capture the important intra-class variations. Experimental results obtained on two standard face databases demonstrated that the proposed method consistently improved the system performance.\",\n \"Title: 3D face modeling using image warping in pose-invariant face recognition\\nYear: 2002\\n\\nAbstract: One of the key remaining problems in face recognition is that of handling the variability in appearance due to changes in pose. One strategy is to synthesize virtual face views from real views. In this paper, a new 3D face shape modeling algorithm - orthogonal reverse projection model (ORPM) is presented. Our method makes only use of two orthogonal real view of a face, i.e., the frontal and profile views. To obtain the 3D shape information of the face, a modified feature-based 2D image warping technique is employed to establish point correspondence between the frontal and profile views. Then the 3D face shape model is generated by orthogonal reverse projection using the corresponding point pairs. The virtual views can be synthesized by simply rotating the 3D face model. The modeling results suggest the validity of the ORPM algorithm for 3D face modeling and 2D face image synthesizing under different poses.\",\n \"Title: Colour Adjustment and Specular Removal for Non-uniform Shape from Shading\\nYear: 2010\\n\\nAbstract: Surface colour changes and specular reflections are two major problems in 3D modelling using shape-from-shading (SFS). This paper proposes to pre-process the input image for a typical SFS algorithm, so that the resultant image has no colour changes and specular reflection. First, a chromaticity-based specular reflection removal algorithm is applied to achieve a pure diffuse (Lambertian) reflected image. Then, a novel chromaticity-based colour adjustment approach is proposed to generate an image without surface colour changes. The standard SFS algorithms can then be applied successfully onto the processed images to produce plausible 3D models. In experiments, the proposed approach was tested on standard SFS datasets with complex surface colours. The experimental results show it's promising to facilitate SFS algorithms to handle SFS problems with more complex surface properties and illumination conditions.\",\n \"Title: Characteristics of an effective design plan system to support reuse in case-based mechanical design\\nYear: 1998\\n\\nAbstract: Design plan systems play an important role in applying case-based reasoning (CBR) to mechanical design. They are responsible for the representation and evaluation of design plans. Design plans are stored as the actual cases in the case base of a CBR system. In this paper, we discuss design reuse and related open issues: plan modification and plan adaptation. It is shown that these issues need to utilize design knowledge associated with both design procedure and design rationale. An overview of current design systems is given. These design systems have not been able to address the issues associated with design reuse. This has, in effect, made them unsuitable for use in a robust CBR system for mechanical design. Based on this discussion, this paper presents the characteristics of an effective design plan system to support reuse in case-based mechanical design. It is concluded that a design plan system should be a mixture of both the design procedure model and the design rationale model. An implementation of such a design plan system is presented with an example.\",\n \"Title: Gabor feature constrained statistical model for efficient landmark localization and face recognition\\nYear: 2009\\n\\nAbstract: Feature extraction and classification using Gabor wavelets have proven to be successful in computer vision and pattern recognition. Gabor feature-based Elastic Bunch Graph Matching (EBGM), which demonstrated excellent performance in the FERET evaluation test, has been considered as one of the best algorithms for face recognition due to its robustness against expression, illumination and pose variations. However, EBGM involves considerable computational complexity in its rigid and deformable matching process, preventing its use in many real-time applications. This paper presents a new Constrained Profile Model (CPM), in cooperation with Flexible Shape Model (FSM) to form an efficient localization framework. Through Gabor feature constrained local alignment, the proposed method not only avoids local minima in landmark localization, but also circumvents the exhaustive global optimization. Experiments on CAS-PEAL and FERET databases demonstrated the effectiveness and efficiency of the proposed method.\",\n \"Title: Soil Biochar Quantification via Hyperspectral Unmixing\\nYear: 2013\\n\\nAbstract: Biochar has unique function to improve soil chemo-physical and biological properties for crop growth. Because changes of biochar in soil may affect its long-term effectiveness as an amendment, it is important to quantify and monitor biochar after application. In this paper, we propose a solution for this problem via hyperspectral image analysis. We treat the soil image as a mixture of soil and biochar signals, and then apply hyperspectral unmixing methods to predict the biochar abundance at each pixel. The final percentage of biochar can be calculated by taking the mean of the abundance of hyperspectral pixels. We have compared several hyperspectral unmixing methods based on least squares estimation and nonnegative matrix factorization with various sparsity constraints. Experimental results are evaluated by polynomial regression and root mean square errors against the ground truth data collected in the environmental labs. The results show that hyperspectral unmixing is a promising method to measure the percentage of biochar in the soil.\",\n \"Title: 2.5D face recognition using Patch Geodesic Moments\\nYear: 2012\\n\\nAbstract: In this paper, we propose a novel Patch Geodesic Distance (PGD) to transform the texture map of an object through its shape data for robust 2.5D object recognition. Local geodesic paths within patches and global geodesic paths for patches are combined in a coarse to fine hierarchical computation of PGD for each surface point to tackle the missing data problem in 2.5D images. Shape adjusted texture patches are encoded into local patterns for similarity measurement between two 2.5D images with different viewing angles and/or shape deformations. An extensive experimental investigation is conducted on 2.5 face images using the publicly available BU-3DFE and Bosphorus databases covering face recognition under expression and pose changes. The performance of the proposed method is compared with that of three benchmark approaches. The experimental results demonstrate that the proposed method provides a very encouraging new solution for 2.5D object recognition.\",\n \"Title: Safety in multi-robot teleoperation system through Internet.\\nYear: 2004\\n\\nAbstract: Internet based multi-operator multi-robot (MOMR) teleoperation system allow the accomplishment of tasks that a single robot could not approach due to restrictions associated to the product and /or the robot. The safety issue in the system is an important problem according to its complexity. This paper describes the development of multi-layer safety structure of the safety subsystem which consist of software modules as well as hardware components after the threat analysis of the system using fault tree analysis (FTA) technology. The subsystem is divided into three layers with its logic architecture: interactive monitor layer, collaborative control layer and real-time control layer. The safety problems and the related strategy are clarified by detailed analysis of each layer and relationship among the layers. So we can get a high performance MOMR teleoperation system with multi-layer safety architecture. © 2004 IEEE.\",\n \"Title: Recognizing Rotated Faces From Frontal and Side Views: An Approach Toward Effective Use of Mugshot Databases\\nYear: 2008\\n\\nAbstract: Mug shot photography has been used to identify criminals by the police for more than a century. However, the common scenario of face recognition using frontal and side-view mug shots as gallery remains largely uninvestigated in computerized face recognition across pose. This paper presents a novel appearance-based approach using frontal and sideface images to handle pose variations in face recognition, which has great potential in forensic and security applications involving police mugshot databases. Virtual views in different poses are generated in two steps: 1) shape modelling and 2) texture synthesis. In the shape modelling step, a multilevel variation minimization approach is applied to generate personalized 3-D face shapes. In the texture synthesis step, face surface properties are analyzed and virtual views in arbitrary viewing conditions are rendered, taking diffuse and specular reflections into account. Appearance-based face recognition is performed with the augmentation of synthesized virtual views covering possible viewing angles to recognize probe views in arbitrary conditions. The encouraging experimental results demonstrated that the proposed approach by using frontal and side-view images is a feasible and effective solution to recognizing rotated faces, which can lead to a better and practical use of existing forensic databases in computerized human face-recognition applications.\",\n \"Title: Significant jet point for facial image representation and recognition\\nYear: 2008\\n\\nAbstract: Gabor wavelet related feature extraction and classification is an important topic in image analysis and pattern recognition. Gabor features can be used either holistically or analytically. While holistic approaches involve significant computational complexity, existing analytic approaches require explicit correspondence of predefined feature points for classification. Different from these approaches, this paper presents a new analytic Gabor method for face recognition. The proposed method attaches Gabor features on a set of shape-driven sparse points to describe both geometric and textural information. Neither the number nor the correspondence of these points is needed. A variant of Hausdorff distance is employed to recognize faces. The experiments performed on AR database demonstrated that the proposed algorithm is effective to identify individuals in various circumstances, such as under expression and illumination changes.\",\n \"Title: Complex Background Modeling And Motion Detection Based On Texture Pattern Flow\\nYear: 2008\\n\\nAbstract: This paper proposes a novel Texture Pattern Flow (TPF) for complex background modeling and motion detection. The Pattern Flow is proposed to encode the binary pattern changes among the neighborhoods in the space-time domain. To model the distribution of the TPF, the TPF integral histograms are used to extract the discriminative features to represent the input video. Experimental results on the public videos testify the effectiveness of the proposed method in comparison to LBP and GMM based background modeling methods.\",\n \"Title: Multilevel Quadratic Variation Minimization for 3D Face Modeling and Virtual View Synthesis\\nYear: 2005\\n\\nAbstract: One of the key remaining problems in face recognition is that of handling the variability in appearance due to changes in pose. One strategy is to synthesize virtual face views from real views. In this paper, a novel 3D face shape-modeling algorithm, Multilevel Quadratic Variation Minimization (MQVM), is proposed. Our method makes sole use of two orthogonal real views of a face, i.e., the frontal and profile views. By applying quadratic variation minimization iteratively in a coarse-to-fine hierarchy of control lattices, the MQVM algorithm can generate C虏-smooth 3D face surfaces. Then realistic virtual face views can be synthesized by rotating the 3D models. The algorithm works properly on sparse constraint points and large images. It is much more efficient than single-level quadratic variation minimization. The modeling results suggest the validity of the MQVM algorithm for 3D face modeling and 2D face view synthesis under different poses.\",\n \"Title: A Novel Pose Invariant Face Recognition Approach Using a 2D-3D Searching Strategy\\nYear: 2010\\n\\nAbstract: Many Face Recognition techniques focus on 2D-2D comparison or 3D-3D comparison, however few techniques explore the idea of cross-dimensional comparison. This paper presents a novel face recognition approach that implements cross-dimensional comparison to solve the issue of pose invariance. Our approach implements a Gabor representation during comparison to allow for variations in texture, illumination, expression and pose. Kernel scaling is used to reduce comparison time during the branching search, which determines the facial pose of input images. The conducted experiments prove the viability of this approach, with our larger kernel experiments returning 91.6% - 100% accuracy on a database comprised of both local data, and data from the USF Human ID 3D database.\",\n \"Title: Automatic Texture Synthesis for Face Recognition from Single Views\\nYear: 2006\\n\\nAbstract: One possible solution for pose- and illumination-invariant face recognition is to employ appearance-based approaches, which rely greatly on correct facial textures. However, existing facial texture analysis algorithms are suboptimal, because they usually neglect specular reflections and require numerous training images for virtual view synthesis. This paper presents a novel texture synthesis approach from a single frontal view for face recognition. Using a generic 3D face shape, facial textures are analyzed with consideration of all of the ambient, diffuse, and specular reflections. Virtual views are synthesized under different poses and illuminations. The proposed approach was evaluated using the CMU-PIE face database. Encouraging results show that the proposed approach improves face recognition performances across pose and illumination variations\",\n \"Title: Expression-Invariant 3D Face Recognition Using Patched Geodesic Texture Transform\\nYear: 2010\\n\\nAbstract: Numerous methods have been proposed for the expression-invariant 3D face recognition, but a little attention is given to the local-based representation for the texture of the 3D images. In this paper, we propose an expression-invariant 3D face recognition approach based on the locally extracted moments of the texture when only one exemplar per person is available. We use a geodesic texture transform accompanied by Pseudo Zernike Moments to extract local feature vectors from the texture of a face. An extensive experimental investigation is conducted using publicly available BU-3DFE face databases covering face recognition under expression variations. The performance of the proposed method is compared with the performance of two benchmark approaches. The encouraging experimental results demonstrate that the proposed method can be used for 3D face recognition in single model databases.\",\n \"Title: On averaging face images for recognition under pose variations\\nYear: 2008\\n\\nAbstract: Recently, psychological studies showed that averaging human face images greatly improves the performance of face recognition under various pose, illumination, expression, and/or aging conditions. This paper investigates quantitatively the mechanism of the face averaging process in face recognition specifically against pose variations. Facilitated with 3D face dataset, the process of face averaging is tested on face images free from human errors and misalignments. Single images are chosen as gallery and the averaged views as probe in all the experiments. Three different scenarios are experimented, i.e., identification using single gallery images, identification using different gallery images, and averaging using unbalanced range of input image. The experimental results show that the averaging process under pose variations is equivalent to generating a face view in an average pose and the improvement in face recognition is subject to the conditions that the gallery pose is close to the average probe pose.\",\n \"Title: An Automatic On-Site Fire Ant Screening System\\nYear: 2012\\n\\nAbstract: This paper proposes the first attempt for semi-automatic screening and identification of red imported fire ants (Solenopsis invicta) in Australia. As an exotic ant species to Australia, fire ants were imported from South America in 2001 and have since been regarded as dangerous pests that could severely damage the environment and many industries. We followed two of the three major identification keys defined by entomologists and proposed: 1) A fusion of two different image features (i.e., the perpendicular median intensity and the perpendicular width) for antenna segment detection; and 2) A weighted histogramming of micropattern features for petiole classification. Our experimental results show that automatic on-site fire ant screening is feasible and the proposed weighted histogramming of micropattern features performs better than the original micropattern representation.\",\n \"Title: Local Kernel Feature Analysis (LKFA) for object recognition\\nYear: 2011\\n\\nAbstract: This paper proposes a new Local Kernel Feature Analysis (LKFA) method for object recognition. LKFA captures the nonlinear local relationship in an image via kernel functions. Different from traditional kernel methods for object recognition, the proposed method does not need to reserve the training samples. LKFA is designed to extract the eigenvalue features from the Hermite matrix of a local feature representation, which we have theoretically proven its robustness to noise and perturbations. Experiment results on palmprint and face recognitions demonstrated the effectiveness of the proposed LKFA that significantly improved the performance of the local feature based object recognition method.\",\n \"Title: A 3D Polygonal Line Chains Matching Method for Face Recognition\\nYear: 2013\\n\\nAbstract: In this paper, a novel 3D polygonal line chains matching method is proposed. Different from traditional method that use points and meshes to represent and match 3D shapes, our method represents 3D surfaces using 3D polygonal line chains generated from ridge and valley curves. Then a 3D polygonal line segment Hausdorff distance measure is developed to compute the similarity between two 3D surfaces. This representation, along with the distance metric, can effectively harness structural and spatial information on a 3D surface. The added information can provide more and better discrimination power for object recognition. It strengthens and improves the matching process of similar 3D objects such as 3D faces. Experiments on FRGC v2 database leads to a rank one recognition rate of 96.1%.\",\n \"Title: Establishing point correspondence using multidirectional binary pattern for face recognition\\nYear: 2008\\n\\nAbstract: This paper presents a new Multidirectional Binary Pattern (MBP) for face recognition. Different from most Local Binary Pattern (LBP) related approaches which cluster LBP occurrences from whole image or partitioned subimage patches and use single or concatenated histogram measurement for recognition, MBP is applied on a sparse set of shape-driven points. The new representation is designed for describing both global structure and local texture, and also significantly reduces the high dimensionality of LBP histogram description. Composed of binary patterns from multiple directions, MBP is capable of extracting more discriminative features than LBP. The experiments on face recognition demonstrated the effectiveness of the proposed algorithm against expression and lighting variations.\",\n \"Title: Feature-Level Fusion in Personal Identification\\nYear: 2005\\n\\nAbstract: The existing studies of multi-modal and multi-view personal identification focused on combining the outputs of multiple classifiers at the decision level. In this study, we investigated the fusion at the feature level to combine multiple views and modals in personal identification. A new similarity measure is proposed, which integrates multiple 2-D view features representing a visual identity of a 3-D object seen from different viewpoints and from different sensors. The robustness to non-rigid distortions is achieved by the proximity correspondence manner in the similarity computation. The feasibility and capability of the proposed technique for personal identification were evaluated on multiple view human faces and palmprints. This research demonstrates that the feature-level fusion provides a new way to combine multiple modals and views for personal identification.\",\n \"Title: Study on the BeiHang Keystroke Dynamics Database\\nYear: 2011\\n\\nAbstract: This paper introduces a new BeiHang (BH) Keystroke Dynamics Database for testing and evaluation of biometric approaches. Different from the existing keystroke dynamics researches which solely rely on laboratory experiments, the developed database is collected from a real commercialized system and thus is more comprehensive and more faithful to human behavior. Moreover, our database comes with ready-to-use benchmark results of three keystroke dynamics methods, Nearest Neighbor classifier, Gaussian Model and One-Class Support Vector Machine. Both the database and benchmark results are open to the public and provide a significant experimental platform for international researchers in the keystroke dynamics area.\",\n \"Title: Combined Classification Of Multiple Views Using Facial Corners\\nYear: 2002\\n\\nAbstract: The profile view of a face provides a complementary structure that is not seen in the frontal view. The classification system combining both frontal and profile views of faces can improve the classification accuracy. And it would be more foolproof because it is difficult to fool the profile face identification by a mask. This paper proposes a new face recognition approach, which can be applied on both frontal and profile faces, to build a robust combined multiple view face identification system. The recognition employs a novel facial corner coding and matching method, and integrates the outline and interior facial parts in the profile matching. The proposed multiview modified Hausdorff distance fuses multiple views of faces to achieve an improved system performance.\",\n \"Title: Optimizing Sharpness Measure for Bright Lesion Detection in Retinal Image Analysis\\nYear: 2009\\n\\nAbstract: Due to the spherical shape nature of retina and the illumination effect, detecting bright lesions in a retinal image is a challenging problem. Existing methods depend heavily on a prior knowledge about lesions, which either a user-defined parameter is employed or a supervised learning technique is adopted to estimate the parameter. In this paper, a novel sharpness measure is proposed, which indicates the degree of sharpness of bright lesions in the whole retinal image. It has a sudden jump at the optimal parameter. A polynomial fitting technique is used to capture this jump. We have tested our method on a public available dataset. Experimental results show that the proposed unsupervised approach is able to detect bright lesions accurately in an unhealthy retinal image and it outperforms existing supervised learning method. Also, the proposed method reports no abnormality for a healthy retinal image.\",\n \"Title: Performance Evaluation of Micropattern Representation on Gabor Features for Face Recognition\\nYear: 2010\\n\\nAbstract: Face recognition using micropattern representation has recently received much attention in the computer vision and pattern recognition community. Previous researches demonstrated that micropattern representation based on Gabor features achieves better performance than its direct usage on gray-level images. This paper conducts a comparative performance evaluation of micropattern representations on four forms of Gabor features for face recognition. Three evaluation rules are proposed and observed for a fair comparison. To reduce the high feature dimensionality problem, uniform quantization is used to partition the spatial histograms. The experimental results reveal that: 1) micropattern representation based on Gabor magnitude features outperforms the other three representations, and the performances of the other three are comparable; and 2) micropattern representation based on the combination of Gabor magnitude and phase features performs the best.\",\n \"Title: Parametric manifold of an object under different viewing directions\\nYear: 2012\\n\\nAbstract: The appearance of a 3D object depends on both the viewing directions and illumination conditions. It is proven that all n-pixel images of a convex object with Lambertian surface under variable lighting from infinity form a convex polyhedral cone (called illumination cone) in n-dimensional space. This paper tries to answer the other half of the question: What is the set of images of an object under all viewing directions? A novel image representation is proposed, which transforms any n-pixel image of a 3D object to a vector in a 2n-dimensional pose space. In such a pose space, we prove that the transformed images of a 3D object under all viewing directions form a parametric manifold in a 6-dimensional linear subspace. With in-depth rotations along a single axis in particular, this manifold is an ellipse. Furthermore, we show that this parametric pose manifold of a convex object can be estimated from a few images in different poses and used to predict object's appearances under unseen viewing directions. These results immediately suggest a number of approaches to object recognition, scene detection, and 3D modelling. Experiments on both synthetic data and real images were reported, which demonstrates the validity of the proposed representation.\",\n \"Title: Architecture of Multi-Agent-Based Multi-Operator Multi-Mobile-Manipulator Teleoperation System\\nYear: 2008\\n\\nAbstract: Being characteristics of motion, coordination, distribution, non-determinism etc, multi-operator multi-mobile-manipulator teleoperation system has difficulties in controlling and implementation. Multi-Agent-Based architecture of the system can distinctly describe the information interactive relationship between components of the system, and optimize intelligence distribution between human and robot, so it helps to reduce difficulties in controlling and implementation of the system. After the physical structure of the system was analyzed, the system was divided into several sub-systems with given function, and then a Multi-Agent-Based layered architecture with shared control mode of the system was proposed and the function of each Agent was planned. Multi-Agent's mixed communication structure was designed by both of blackboard model and Peer-to-Peer model. According to characteristics of the system, a kind of general mixed architecture for Agent was proposed, and an example of implementation method of Agent was given, then the validity of the mixed architecture was verified by designing experiment.\",\n \"Title: Human face profile recognition using attributed string\\nYear: 2002\\n\\nAbstract: A new attributed string matching method for human face profile recognition is proposed in this work. It is a novel idea to apply structural and syntactic technique on face profile matching. The approach works on a chain of profile line segments and highlights the favor of curve matching by suppressing the operations of insert and delete. The technique relies mainly on the merge and change operations of string to tackle the inconsistency problem of feature point detection. A quadratic penalty function is proposed to prohibit large angle changes and overmerging. The method produces very encouraging results and is found to be suitable for similar shape classification.\",\n \"Title: Learning Discriminative Local Patterns With Unrestricted Structure For Face Recognition\\nYear: 2013\\n\\nAbstract: Local binary patterns are a popular local texture feature for describing textures and objects. The standard method and many derivatives use a hand-crafted structure of point comparisons to encode the local texture to build the descriptors. In this paper we propose automatically learning a discriminative pattern structure from an extended pool of candidate pattern elements, without restricting the possible configurations. The learnt pattern structure may contain elements describing many different scales and gradient orientations that are not available in LBP (and related patterns), thus allowing the flexibility to construct structures capable of better representing the objects under test. We show through experimentation on two face recognition databases that this approach consistently outperforms other methods, in terms of training speed and recognition accuracy in every tested case.\",\n \"Title: Face Recognition Using Ensemble String Matching\\nYear: 2013\\n\\nAbstract: In this paper, we present a syntactic string matching approach to solve the frontal face recognition problem. String matching is a powerful partial matching technique, but is not suitable for frontal face recognition due to its requirement of globally sequential representation and the complex nature of human faces, containing discontinuous and non-sequential features. Here, we build a compact syntactic Stringface representation, which is an ensemble of strings. A novel ensemble string matching approach that can perform non-sequential string matching between two Stringfaces is proposed. It is invariant to the sequential order of strings and the direction of each string. The embedded partial matching mechanism enables our method to automatically use every piece of non-occluded region, regardless of shape, in the recognition process. The encouraging results demonstrate the feasibility and effectiveness of using syntactic methods for face recognition from a single exemplar image per person, breaking the barrier that prevents string matching techniques from being used for addressing complex image recognition problems. The proposed method not only achieved significantly better performance in recognizing partially occluded faces, but also showed its ability to perform direct matching between sketch faces and photo faces.\",\n \"Title: Face recognition using line edge map\\nYear: 2002\\n\\nAbstract: The automatic recognition of human faces presents a significant challenge to the pattern recognition research community. Typically, human faces are very similar in structure with minor differences from person to person. They are actually within one class of \\\"human face\\\". Furthermore, lighting conditions change, while facial expressions and pose variations further complicate the face recognition task as one of the difficult problems in pattern analysis. This paper proposes a novel concept: namely, that faces can be recognized using a line edge map (LEM). The LEM, a compact face feature, is generated for face coding and recognition. A thorough investigation of the proposed concept is conducted which covers all aspects of human face recognition, i.e. face recognition under (1) controlled/ideal conditions and size variations, (2) varying lighting conditions, (3) varying facial expressions, and (4) varying pose. The system performance is also compared with the eigenface method, one of the best face recognition techniques, and with reported experimental results of other methods. A face pre-filtering technique is proposed to speed up the search process. It is a very encouraging to find that the proposed face recognition technique has performed better than the eigenface method in most of the comparison experiments. This research demonstrates that the LEM, together with the proposed generic line-segment Hausdorff distance measure, provides a new method for face coding and recognition\",\n \"Title: Face Recognition using Optimal Representation Ensemble\\nYear: 2011\\n\\nAbstract: Recently, the face recognizers based on linear representations have been shown to deliver state-of-the-art performance. In real-world applications, however, face images usually suffer from expressions, disguises and random occlusions. The problematic facial parts undermine the validity of the linear-subspace assumption and thus the recognition performance deteriorates significantly. In this work, we address the problem in a learning-inference-mixed fashion. By observing that the linear-subspace assumption is more reliable on certain face patches rather than on the holistic face, some Bayesian Patch Representations (BPRs) are randomly generated and interpreted according to the Bayes' theory. We then train an ensemble model over the patch-representations by minimizing the empirical risk w.r.t the \\\"leave-one-out margins\\\". The obtained model is termed Optimal Representation Ensemble (ORE), since it guarantees the optimality from the perspective of Empirical Risk Minimization. To handle the unknown patterns in test faces, a robust version of BPR is proposed by taking the non-face category into consideration. Equipped with the Robust-BPRs, the inference ability of ORE is increased dramatically and several record-breaking accuracies (99.9% on Yale-B and 99.5% on AR) and desirable efficiencies (below 20 ms per face in Matlab) are achieved. It also overwhelms other modular heuristics on the faces with random occlusions, extreme expressions and disguises. Furthermore, to accommodate immense BPRs sets, a boosting-like algorithm is also derived. The boosted model, a.k.a Boosted-ORE, obtains similar performance to its prototype. Besides the empirical superiorities, two desirable features of the proposed methods, namely, the training-determined model-selection and the data-weight-free boosting procedure, are also theoretically verified.\",\n \"Title: Face recognition across pose: A review\\nYear: 2009\\n\\nAbstract: One of the major challenges encountered by current face recognition techniques lies in the difficulties of handling varying poses, i.e., recognition of faces in arbitrary in-depth rotations. The face image differences caused by rotations are often larger than the inter-person differences used in distinguishing identities. Face recognition across pose, on the other hand, has great potentials in many applications dealing with uncooperative subjects, in which the full power of face recognition being a passive biometric technique can be implemented and utilised. Extensive efforts have been put into the research toward pose-invariant face recognition in recent years and many prominent approaches have been proposed. However, several issues in face recognition across pose still remain open, such as lack of understanding about subspaces of pose variant images, problem intractability in 3D face modelling, complex face surface reflection mechanism, etc. This paper provides a critical survey of researches on image-based face recognition across pose. The existing techniques are comprehensively reviewed and discussed. They are classified into different categories according to their methodologies in handling pose variations. Their strategies, advantages/disadvantages and performances are elaborated. By generalising different tactics in handling pose variations and evaluating their performances, several promising directions for future research have been suggested.\",\n \"Title: On transforming statistical models for non-frontal face verification\\nYear: 2006\\n\\nAbstract: We address the pose mismatch problem which can occur in face verification systems that have only a single (frontal) face image available for training. In the framework of a Bayesian classifier based on mixtures of gaussians, the problem is tackled through extending each frontal face model with artificially synthesized models for non-frontal views. The synthesis methods are based on several implementations of maximum likelihood linear regression (MLLR), as well as standard multi-variate linear regression (LinReg). All synthesis techniques rely on prior information and learn how face models for the frontal view are related to face models for non-frontal views. The synthesis and extension approach is evaluated by applying it to two face verification systems: a holistic system (based on PCA-derived features) and a local feature system (based on DCT-derived features). Experiments on the FERET database suggest that for the holistic system, the LinReg-based technique is more suited than the MLLR-based techniques; for the local feature system, the results show that synthesis via a new MLLR implementation obtains better performance than synthesis based on traditional MLLR. The results further suggest that extending frontal models considerably reduces errors. It is also shown that the local feature system is less affected by view changes than the holistic system; this can be attributed to the parts based representation of the face, and, due to the classifier based on mixtures of gaussians, the lack of constraints on spatial relations between the face parts, allowing for deformations and movements of face areas.\",\n \"Title: Recognition of expression variant faces from one sample image per enrolled subject\\nYear: 2009\\n\\nAbstract: Despite remarkable progress on face recognition, little attention has been given to robustly recognize expression variant faces from a single sample image per person. One way to deal with the recognition of faces under above conditions is by using local statistical approaches which appear to be more robust against variations in facial expression. In this paper, we propose a new weighted matching method based on our recent work of AWPPZMA to recognize expression variant faces when only one exemplar image per enrolled subject is available. The proposed weighting method gives more significance to those parts of the face with facial expression variations that change less compared to neutral face image and less significance to those parts that change more. In this contribution, we use the difference between local area in the input face and its corresponding local area in the neutral face image as a measure of observable structure changes. The encouraging experimental results demonstrate that the proposed method provides a new solution to the problem of robustly recognizing expression variant faces in single model databases.\",\n \"Title: Width Distributions for Shape Description\\nYear: 2011\\n\\nAbstract: Measuring the similarity between articulated shapes is a fundamental yet challenging problem. This paper proposes a novel shape descriptor based on Width Distributions (WD), which is robust to articulations. We show that the width distributions are articulation insensitive yet descriptive to distinguish different shapes with varied part structures. With measurements on distributions only, the proposed method does not require any alignment between two objects and thus is more robust than correspondence-based measurements. First, the medial axes of the objects are extracted and fitted to B-Splines to remove outliners. The width of the object shape perpendicular to the medial axis can be calculated for each position on the medial axis. The histograms of those widths are compared using chi-square method for similarity. Experiments on standard 2D shape database show that the proposed method performed better than standard shape distribution algorithms and similarly to other articulation-robust shape descriptors, such as inner-distance. The speed of the proposed method is much faster than the more sophisticated inner-distance, as the proposed method is much simpler in nature and requires limited image processing and pattern classification. These results suggested it could be an efficient and effective method to describe and to match articulated shapes.\",\n \"Title: Single model face database retrieval by directional corner points\\nYear: 2004\\n\\nAbstract: This paper presents a new face image retrieval approach using directional corner points (DCPs). Though much work is being done on face similarity matching techniques, little attention is given to the design of face matching scheme suitable for visual retrieval in single model databases where accuracy, robustness to scale and environmental changes, and computational efficiency are three important issues to be concerned. This research demonstrates that the proposed DCP approach provides a new solution, which is both robust to scale and environmental changes, and efficient in computation, for retrieving human faces in single model databases.\",\n \"Title: Heterogeneous Specular and Diffuse 3-D Surface Approximation for Face Recognition Across Pose\\nYear: 2012\\n\\nAbstract: This paper proposes a novel heterogeneous specular and diffuse (HSD) 3-D surface approximation which considers spatial variability of specular and diffuse reflections in face modelling and recognition. Traditional 3-D face modelling and recognition methods constrain human faces with either the Lambertian assumption or the homogeneity assumption, resulting in suboptimal shape and texture models. The proposed HSD approach allows both specular and diffuse reflectance coefficients to vary spatially to better accommodate surface properties of real human faces. From a small number of face images of a person under different lighting conditions, 3-D shape and surface reflectivity property are estimated using a localized stochastic optimization method. The resultant personalized 3-D face model is used to render novel gallery views under different poses for recognition across pose. The proposed approach is evaluated on both synthetic and real face datasets and benchmarked against the state-of-the-art approaches. Experimental results demonstrated that it can achieve a higher level of performances in modelling accuracy, algorithm reliability, and recognition accuracy, which suggests that face modelling and recognition beyond the Lambertian and homogeneity assumptions is a feasible and better solution towards pose-invariant face recognition.\",\n \"Title: Multi-View Combined Visual Similarity Retrieval For Face Database\\nYear: 2002\\n\\nAbstract: This work proposed a new multi-view combined visual similarity retrieval approach for face database. The search and retrieval employ a novel facial corner coding and matching approach, which delivers fast searching speed for database retrieval. The experimental results demonstrate that the facial corners can be a useful identity feature for human face database retrieval. The profile view of a face provides a complementary structure of the face that is not seen in the frontal view. The proposed multi-view modified Hausdorff distance combines the facial corners of multiple views and achieved an improved accuracy. This technique is not limited to two view combined retrieval but a generic approach for multiple view combined similarity retrieval.\",\n \"Title: Matching Non-Aligned Objects Using A Relational String-Graph\\nYear: 2013\\n\\nAbstract: Localising and aligning objects is a challenging task in computer vision that still remains largely unsolved. Utilising the syntactic power of graph representation, we define a relational string-graph matching algorithm that seeks to perform these tasks simultaneously. By matching the relations between vertices, where vertices represent high-level primitives, the relational string-graph is able to overcome the noisy and inconsistent nature of the vertices themselves. For each possible relation correspondence between two graphs, we calculate the rotation, translation, and scale parameters required to transform a relation into its counterpart. We plot these parameters in 4D space and use Gaussian mixture models and the expectation-maximisation algorithm to estimate the underlying parameters. Our method is tested on face alignment and recognition, but is equally (if not more) applicable for generic object alignment.\",\n \"Title: Robust Face Recognition by Hierarchical Kernel Associative Memory Models Based on Spatial Domain Gabor Transforms\\nYear: 2006\\n\\nAbstract: Face recognition can be studied as an associative memory (AM) problem and kernel-based AM models have been proven efficient. In this paper, a hierarchical Kernel Associative Memory (KAM) face recognition scheme with a multiscale Gabor transform, is proposed. The pyrami- dal multiscale Gabor decomposition proposed by Nestares, Navarro, Portilla and Tabernero not only provides a very efficient implementation of the Gabor transform in the spatial domain, but also permits a fast reconstruction of images. In our method, face images of each person are first decomposed into their multiscale representations by a quasicomplete Gabor transform, which are then modelled by Kernel Associative Memories. In the recognition stage, a query face image is also represented by a Gabor mul- tiresolution pyramid and the reconstructions from different KAM models corresponding to even Gabor channels are then simply summed to give the recall. The recognition scheme was thoroughly tested using several benchmarking face datasets, including the AR faces, UMIST faces, JAFFE faces and Yale A faces, which include different kind of face variations from occlusions, pose, expression and illu- mination. The experiment results show that the proposed method demonstrated strong robustness in recognizing faces under different conditions, particularly under occlusions, pose alterations and expression changes.\",\n \"Title: 3D face recognition using topographic high-order derivatives\\nYear: 2013\\n\\nAbstract: This paper presents a novel feature, Topographic High-order Derivatives (THD) for 3D face recognition. THD is based on the high-order micro-pattern information extracted from face topography maps. Face topography maps are partitioned into polar sectors, and THDs are computed using directional highorder derivatives within the sectors. Local features are extracted by encoding directional high-order derivatives within polar neighborhoods. To evaluate the proposed method, we use Bosphorus and FRGC 3D face databases which include pose and expression changes. The performance of the proposed method is higher compared to the state-of-the-art benchmark approaches in 3D face recognition.\",\n \"Title: Fast Screening in Large Face Databases Using Merit-Based Dominant Points\\nYear: 2005\\n\\nAbstract: Current face identification approaches require computer systems to search through large quantity of face feature sets in the database and pick the ones that best match the features of an unknown input face. In this paper, a fast screening method for large face database searching is proposed. The method utilizes dominant points instead of edge maps as features for similarity measurement. A new formulation of Hausdorff distance is designed for merit-based dominant point matching. The screening experiments demonstrated that the proposed face screening method significantly improves the computational speed and the storage economy. It provides a very efficient way for large face databases searching and screening.\",\n \"Title: Gender Classification Using Interlaced Derivative Patterns\\nYear: 2010\\n\\nAbstract: Automated gender recognition has become an interesting and challenging research problem in recent years with its potential applications in security industry and human-computer interaction systems. In this paper we present a novel feature representation, namely Interlaced Derivative Patterns (IDP), which is a derivative-based technique to extract discriminative facial features for gender classification. The proposed technique operates on a neighborhood around a pixel and concatenates the extracted regional feature distributions to form a feature vector. The experimental results demonstrate the effectiveness of the IDP method for gender classification, showing that the proposed approach achieves 29.6% relative error reduction compared to Local Binary Patterns (LBP), while it performs over four times faster than Local Derivative Patterns (LDP).\",\n \"Title: Facial expression recognition from line-based caricatures\\nYear: 2003\\n\\nAbstract: The automatic recognition of facial expression presents a significant challenge to the pattern analysis and man-machine interaction research community. Recognition from a single static image is particularly a difficult task. In this paper, we present a methodology for facial expression recognition from a single static image using line-based caricatures. The recognition process is completely automatic. It also addresses the computational expensive problem and is thus suitable for real-time applications. The proposed approach uses structural and geometrical features of a user sketched expression model to match the line edge map (LEM) descriptor of an input face image. A disparity measure that is robust to expression variations is defined. The effectiveness of the proposed technique has been evaluated and promising results are obtained. This work has proven the proposed idea that facial expressions can be characterized and recognized by caricatures.\",\n \"Title: Mobile Plant Leaf Identification Using Smart-Phones\\nYear: 2013\\n\\nAbstract: A novel shape description method is proposed for mobile retrieval of leaf images to aid in plant recognition. In this method, traveling the shape contour, the convexity and concavity properties of the arches of various levels are measured, respectively, to generate a multiscale shape descriptor. Its performance has been tested on two leaf datasets and the experimental results indicated higher recognition accuracies than the state-of-the-art approaches with a speed improvement of more than 170 times. The proposed method has been successfully applied to develop a prototype system of online plant leaf identification working on a consumer mobile platform.\",\n \"Title: Primitive-based 3D structure inference from a single 2D image for insect modeling: Towards an electronic field guide for insect identification\\nYear: 2010\\n\\nAbstract: 3D insect models are useful to overcome viewing angle variations and self-occlusions in computer-assisted insect taxonomy for electronic field guides. The acquisition of 3D information is, however, unreliable due to the flexibility and small size of the insect bodies. This paper explores how to infer 3D insect models from a single 2D insect image, which will assist both insect description and identification. The 3D structure of the insect body is modeled from two geometric primitives, generalized cylinders and deformable ellipsoids. The primitives are fitted and warped based on both edge and medial axis constraints of the 2D image. Individualized 3D models are then built to approximate the insect structure. The proposed approach results in seemingly useful 3D insect models capable of representing the major morphological characteristics for a variety of insects with different body types. This method could be a helpful assistance for computer-assisted insect taxonomy and insect identification by entomologists and the public.\",\n \"Title: Kernel Similarity Modeling of Texture Pattern Flow for Motion Detection in Complex Background\\nYear: 2011\\n\\nAbstract: This paper proposes a novel kernel similarity modeling of texture pattern flow (KSM-TPF) for background modeling and motion detection in complex and dynamic environments. The texture pattern flow encodes the binary pattern changes in both spatial and temporal neighborhoods. The integral histogram of texture pattern flow is employed to extract the discriminative features from the input videos. Different from existing uniform threshold based motion detection approaches which are only effective for simple background, the kernel similarity modeling is proposed to produce an adaptive threshold for complex background. The adaptive threshold is computed from the mean and variance of an extended Gaussian mixture model. The proposed KSM-TPF approach incorporates machine learning method with feature extraction method in a homogenous way. Experimental results on the publicly available video sequences demonstrate that the proposed approach provides an effective and efficient way for background modeling and motion detection.\",\n \"Title: Face recognition using adaptively weighted patch PZM array from a single exemplar image per person\\nYear: 2008\\n\\nAbstract: Though numerous approaches have been proposed for face recognition, little attention is given to the moment-based face recognition techniques. In this paper we propose a novel face recognition approach based on adaptively weighted patch pseudo Zernike moment array (AWPPZMA) when only one exemplar image per person is available. In this approach, a face image is represented as an array of patch pseudo Zernike moments (PPZM) extracted from a partitioned face image containing moment information of local areas instead of global information of a face. An adaptively weighting scheme is used to assign proper weights to each PPZM to adjust the contribution of each local area of a face in terms of the quantity of identity information that a patch contains and the likelihood of a patch is occluded. An extensive experimental investigation is conducted using AR and Yale face databases covering face recognition under controlled/ideal conditions, different illumination conditions, different facial expressions and partial occlusion. The system performance is compared with the performance of four benchmark approaches. The encouraging experimental results demonstrate that moments can be used for face recognition and patch-based moment array provides a novel way for face representation and recognition in single model databases.\",\n \"Title: General Retinal Vessel Segmentation Using Regularization-Based Multiconcavity Modeling.\\nYear: 2010\\n\\nAbstract: Detecting blood vessels in retinal images with the presence of bright and dark lesions is a challenging unsolved problem. In this paper, a novel multiconcavity modeling approach is proposed to handle both healthy and unhealthy retinas simultaneously. The differentiable concavity measure is proposed to handle bright lesions in a perceptive space. The line-shape concavity measure is proposed to remo...\",\n \"Title: Fast and effective retrieval of plant leaf shapes\\nYear: 2012\\n\\nAbstract: In this paper, a novel shape description and matching method based on multi-level curve segment measures (MLCSM) is proposed for plant leaf image retrieval. MLCSM extracts the statistical features of shape contour via measuring the curve bending, convexity and concavity of the curve segments with different length of shape contour to describe the shape. This method not only finely captures the global and local features, but also is very compact and has very low computational complexity. The performance of the proposed method is evaluated on the widely used Swedish leaf database and the leaf databases collected by ourselves which contains 1200 images and 100 plant leaf species. All the experiments show the superiority of our method over the state-of-the-art shape retrieval methods.\",\n \"Title: Textural Hausdorff Distance for wider-range tolerance to pose variation and misalignment in 2D face recognition\\nYear: 2009\\n\\nAbstract: This paper addresses two critical but rarely concerned issues in 2D face recognition: wider-range tolerance to pose variation and misalignment. We propose a new Textural Hausdorff Distance (THD), which is a compound measurement integrating both spatial and textural features. The THD is applied to a Significant Jet Point (SJP) representation of face images, where a varied number of shape-driven SJPs are detected automatically from low-level edge map with rich information content. The comparative experiments conducted on publicly available FERET and AR face databases demonstrated that the proposed approach has a considerably wider range of tolerance against both in-depth head rotation and face misalignment.\",\n \"Title: Robust visual similarity retrieval in single model face databases\\nYear: 2005\\n\\nAbstract: In this paper, we introduce a novel visual similarity measuring technique to retrieve face images in photo album databases for law enforcement. Though much work is being done on face similarity matching techniques, little attention is given to the design of face matching schemes suitable for visual retrieval in single model databases where accuracy, robustness to scale and environmental changes, and computational efficiency are three important issues to be considered. This paper presents a robust face retrieval approach using structural and spatial point correspondence in which the directional corner points (DCPs) are generated for efficient face coding and retrieval. A complete investigation on the proposed method is conducted, which covers face retrieval under controlled/ideal condition, scale variations, environmental changes and subject actions. The system performance is compared with the performance of the eigenface method. It is an attractive finding that the proposed DCP retrieval technique has performed superior to the eigenface method in most of the comparison experiments. This research demonstrates that the proposed DCP approach provides a new way, which is both robust to scale and environmental changes, and efficient in computation, for retrieving human faces in single model databases.\",\n \"Title: Significance-Based Multi-View Hausdorff Distance for Non-Rigid Object Registration\\nYear: 2003\\n\\nAbstract: In this paper, we propose a new similarity measure to combine multiple views of 3-D objects for non-rigid registration. It is a metric that integrates multiple 2-D view features representing a visual identity of a 3-D object seen from different viewpoints. The robustness to non-rigid distortions is achieved by the proximity correspondence manner. The human face, a typical non-rigid object, was chosen to evaluate the capability of the proposed object matching technique. Very encouraging results were obtained which showed that the proposed Significant-Based Multi-View Hausdorff Distance (SMVHD) provides a new fusion method for non-rigid 3-D object registration.\",\n \"Title: Noisy logo recognition using line segment Hausdorff distance\\nYear: 2003\\n\\nAbstract: Logo recognition is of great interest in the document and shape analysis domain. In order to develop a recognition method that is robust to employ under adverse conditions such as different scale/orientation, broken curves, added noise and occlusion, a modified line segment Hausdorff distance is proposed in this paper. The new approach has the advantage to incorporate structural and spatial information to compute dissimilarity between two sets of line segments rather than two sets of points. The proposed technique has been applied on line segments generated from logos with encouraging results. Clear cut distinction between the correct and incorrect matches has been observed. This suggests a strong potential for logo and shape recognition system.\",\n \"Title: Generalised ambient reflection models for Lambertian and Phong surfaces\\nYear: 2009\\n\\nAbstract: Ambient reflection is widely present in many applications of computer graphics and image processing, which is traditionally modelled as a constant free from environmental factors. This paper reconsiders ambient reflection modelling of Lambertian and Phong surfaces and calculates it as reflection integrations of infinitesimal incident beams from the environment. It reveals that ambient reflection exists in variable forms for Lambertian surfaces of non-convex objects and Phong surfaces of all objects. For convex objects with Lambertian surfaces, ambient reflectance coefficient is actually the diffuse reflectance coefficient. Generalised ambient reflection models are proposed to calculate ambient reflection using the same reflection model as used in calculation of other reflections. Based on this analysis, new ambient reflection formulations of Lambertian and Phong surfaces are derived to enable efficient computations in computer graphics and image processing.\",\n \"Title: Automated Face Pose Estimation Using Elastic Energy Models\\nYear: 2006\\n\\nAbstract: Face pose estimation forms an important part in a face recognition system. However, fully automated and accurate pose determination still remains an unsolved problem in the research community. In this paper, we propose a novel elastic energy model to automatically estimate face poses. Our method employs statistical energy contributions of a set of feature points, which can avoid over-trusting selected anchor points. It provides a robust solution to the feature localisation inaccuracy problem, which is inevitable in practical applications with cluttered backgrounds. As a general configuration, our model can be easily implemented and extended to other non-rigid objects. Its effectiveness and robustness are revealed in our experiments\",\n \"Title: Recognizing Rotated Faces from Two Orthogonal Views in Mugshot Databases\\nYear: 2006\\n\\nAbstract: Tolerance to pose variations is one of the key remaining problems in face recognition. It is of great interest in airport surveillance systems using mugshot databases to screen travellers' faces. This paper presents a novel poseinvariant face recognition approach using two orthogonal face images from mugshot databases. Virtual views under different poses are generated in two steps: shape modeling and texture synthesis. In the shape modeling step, a feature-based multilevel quadratic variation minimization approach is applied to generate smooth 3D face shapes. In the texture synthesis step, a non-Lambertian reflectance model is explored to synthesize facial textures taking into account both diffuse and specular reflections. A view-based face recognizer is used to examine the feasibility and effectiveness of the proposed pose-invariant face recognition. The experimental results show that the proposed method provides a new solution to the problem of recognizing rotated faces.\",\n \"Title: 3d Face Recognition Using Geodesic Pzm Array From A Single Model Per Person\\nYear: 2011\\n\\nAbstract: For the 3D face recognition numerous methods have been proposed, but little attention has been given to the local-based representation, for the texture map of the 3D models. In this paper, we propose a novel 3D face recognition approach based on locally extracted Geodesic Pseudo Zernike Moment Array (GPZMA) of the texture map when only one exemplar per person is available. In the proposed method, the function of the PZM is controlled by the geodesic deformations to tackle the problem of face recognition under the expression and pose variations. The feasibility and effectiveness investigation for the proposed method is conducted through a wide range of experiments using publicly available BU-3DFE and Bosphorus databases including samples with different expression and pose variations. The performance of the proposed method is compared with the performance of three state-of-the-art benchmark approaches. The encouraging experimental results demonstrate that the proposed method achieves much higher accuracy than the benchmarks in single-model databases.\",\n \"Title: Recognition of driving postures by multiwavelet transform and multilayer perceptron classifier\\nYear: 2012\\n\\nAbstract: To develop Human-centric Driver Assistance Systems (HDAS) for automatic understanding and charactering of driver behaviors, an efficient feature extraction of driving postures based on Geronimo-Hardin-Massopust (GHM) multiwavelet transform is proposed, and Multilayer Perceptron (MLP) classifiers with three layers are then exploited in order to recognize four pre-defined classes of driving postures. With features extracted from a driving posture dataset created at Southeast University (SEU), the holdout and cross-validation experiments on driving posture classification are conducted by MLP classifiers, compared with the Intersection Kernel Support Vector Machines (IKSVMs), the k-Nearest Neighbor (kNN) classifier and the Parzen classifier. The experimental results show that feature extraction based on GHM multwavelet transform and MLP classifier, using softmax activation function in the output layer and hyperbolic tangent activation function in the hidden layer, offer the best classification performance compared to IKSVMs, kNN and Parzen classifiers. The experimental results also show that talking on a cellular phone is the most difficult one to classify among four predefined classes, which are 83.01% and 84.04% in the holdout and cross-validation experiments respectively. These results show the effectiveness of the feature extraction approach using GHM multiwavelet transform and MLP classifier in automatically understanding and characterizing driver behaviors towards Human-centric Driver Assistance Systems (HDAS).\",\n \"Title: 3D Reconstruction from Hyperspectral Images\\nYear: 2015\\n\\nAbstract: 3D reconstruction from hyper spectral images has seldom been addressed in the literature. This is a challenging problem because 3D models reconstructed from different spectral bands demonstrate different properties. If we use a single band or covert the hyper spectral image to gray scale image for the reconstruction, fine structural information may be lost. In this paper, we present a novel method to reconstruct a 3D model from hyper spectral images. Our proposed method first generates 3D point sets from images at each wavelength using the typical structure from motion approach. A structural descriptor is developed to characterize the spatial relationship between the points, which allows robust point matching between two 3D models at different wavelength. Then a 3D registration method is introduced to combine all band-level models into a single and complete hyper spectral 3D model. As far as we know, this is the first attempt in reconstructing a complete 3D model from hyper spectral images. This work allows fine structural-spectral information of an object be captured and integrated into the 3D model, which can be used to support further research and applications.\",\n \"Title: Polygonal approximation using integer particle swarm optimization.\\nYear: 2014\\n\\nAbstract: Polygonal approximation is an effective yet challenging digital curve representation for image analysis, pattern recognition and computer vision. This paper proposes a novel approach, integer particle swarm optimization (iPSO), for polygonal approximation. When compared to the traditional binary version of particle swarm optimization (bPSO), the new iPSO directly uses an integer vector to represent the candidate solution and provides a more efficient and convenient means for solution processing. The velocity and position updating mechanisms in iPSO not only have clear physical meaning, but also guarantee the optimality of the solutions. The method is suitable for polygonal approximation which could otherwise be an intractable optimization problem. The proposed method has been tested on commonly used synthesized shapes and lake contours extracted from the maps of four famous lakes in the world. The experimental results show that the proposed iPSO has better solution quality and computational efficiency than the bPSO-based methods and better solution quality than the other state-of-the-art methods.\",\n \"Title: MARCH: Multiscale-arch-height description for mobile retrieval of leaf images.\\nYear: 2015\\n\\nAbstract: In this paper, we propose a novel shape description method for mobile retrieval of leaf images. In this method, termed multiscale arch height (MARCH), hierarchical arch height features at different chord spans are extracted from each contour point to provide a compact, multiscale shape descriptor. Both the global and detailed features of the leaf shape can be effectively captured by the proposed algorithm. MARCH descriptors are compared using a simple L1-norm based dissimilarity measurement providing very fast shape matching. The algorithm has been tested on four publicly available leaf image datasets including the Swedish leaf dataset, the Flavia leaf dataset, the ICL leaf dataset and the scanned subset of the ImageCLEF leaf dataset. The experiments indicate that the proposed method can achieve a higher classification rate and retrieval accuracy than the state-of-the-art benchmarks with a more than 500 times faster retrieval speed. A mobile retrieval system embedding the proposed algorithms has been developed for the real application of leaf image retrieval.\",\n \"Title: Adaptive sliding bandlimited multiple fourier linear combiner for estimation of pathological tremor.\\nYear: 2014\\n\\nAbstract: •The analysis of frequency characteristic of physiological and pathological tremor is proposed.•The algorithm incorporates Butterworth filter, WFLC, frequency modulation block and BMFLC.•The method could estimate desired signals with sliding frequency band and zero-phase lag.•One mechanical platform for simulating tremor and intention movements is developed.•The method successfully extracts the voluntary motion from the raw measured signal.\",\n \"Title: Hierarchical String Cuts: A Translation, Rotation, Scale and Mirror Invariant Descriptor for Fast Shape Retrieval.\\nYear: 2014\\n\\nAbstract: This paper presents a novel approach for both fast and accurately retrieving similar shapes. A hierarchical string cuts (HSC) method is proposed to partition a shape into multiple level curve segments of different lengths from a point moving around the contour to describe the shape gradually and completely from the global information to the finest details. At each hierarchical level, the curve segments are cut by strings to extract features that characterize the geometric and distribution properties in that particular level of details. The translation, rotation, scale and mirror invariant HSC descriptor enables a fast metric based matching to achieve the desired high accuracy. Encouraging experimental results on four databases demonstrated that the proposed method can consistently achieve higher (or similar) retrieval accuracies than the state-of-the-art benchmarks with a more than 120 times faster speed. This may suggest a new way of developing shape retrieval techniques in which a high accuracy can be achieved by a fast metric matching algorithm without using the time-consuming correspondence optimisation strategy.\",\n \"Title: Efficient subgraph matching using topological node feature constraints.\\nYear: 2015\\n\\nAbstract: This paper presents techniques designed to minimise the number of states which are explored during subgraph isomorphism detection. A set of advanced topological node features, calculated from n-neighbourhood graphs, is presented and shown to outperform existing features. Further, the pruning effectiveness of both the new and existing topological node features is significantly improved through the introduction of strengthening techniques. In addition to topological node features, these strengthening techniques can also be used to enhance application-specific node labels using a proposed novel extension to existing pruning algorithms. Through the combination of these techniques, the number of explored search states can be reduced to near-optimal levels.\",\n \"Title: Face Recognition Using 3D Directional Corner Points\\nYear: 2014\\n\\nAbstract: In this paper, we present a novel face recognition approach using 3D directional corner points (3D DCPs). Traditionally, points and meshes are applied to represent and match 3D shapes. Here we represent 3D surfaces by 3D DCPs derived from ridge and valley curves. Then we develop a 3D DCP matching method to compute the similarity of two different 3D surfaces. This representation, along with the similarity metric can effectively integrate structural and spatial information on 3D surfaces. The added information can provide more and better discriminative power for object recognition. It strengthens and improves the matching process of similar 3D objects such as faces. To evaluate the performance of our method for 3D face recognition, we have performed experiments on Face Recognition Grand Challenge v2.0 database (FRGC v2.0) and resulted in a rank-one recognition rate of 97.1%. This study demonstrates that 3D DCPs provides a new solution for 3D face recognition, which may also find its application in general 3D object representation and recognition.\",\n \"Title: Dual Graph Regularized NMF for Hyperspectral Unmixing\\nYear: 2014\\n\\nAbstract: Hyperspectral unmixing is an important technique for estimating fraction of different land cover types from remote sensing imagery. In recent years, nonnegative matrix factorization (NMF) with various constraints have been introduced into hyperspectral unmixing. Among these methods, graph based constraint have been proved to be useful in capturing the latent manifold structure of the hyperspectral data in the feature space. In this paper, we propose to integrate graph-based constraints based on manifold assumption in feature spaces and consistency of spatial space to regularize the NMF method. Results on both synthetic and real data have validated the effectiveness of the proposed method.\",\n \"Title: Motion analysis for face capturing\\nYear: 2002\\n\\nAbstract: Face recognition is one of the most suitable biometric methods for supporting systems that require high security. The success of a face recognition system involves more than just the comparison algorithm. Face data acquisition is the first step towards developing face recognition systems. The accuracy of a face recognition system greatly depends on the face data acquired. This paper proposes the head-nodding method for face capturing using motion analysis. By analyzing the head-nodding motion of the user, the head-nodding method captures face images with consistent orientation to support face verification systems. In this paper, the design and implementation of the head-nodding method is presented. Preliminary experimental results are also presented.\",\n \"Title: On local features for GMM based face verification\\nYear: 2005\\n\\nAbstract: It has been recently shown that local feature approaches to face verification are considerably more robust than holistic approaches, in terms of translations (caused by automatic face localization) and pose variations. In this paper we first investigate whether features based on local Principal Component Analysis (LPCA) are more discriminative than features based on the 2D Discrete Cosine Transform (2D DCT). We also investigate several methods for modifying the two feature extraction techniques in order to counteract the effects of linear and non-linear illumination changes, without losing discriminative information. Results on the XM2VTS database show that when using a Bayesian classifier based on Gaussian Mixture Models (GMMs), the performances of 2D DCT and LPCA techniques are quite similar, suggesting that the 2D DCT technique is preferable due to its lower computational complexity. When using 8脳8 blocks, modifying the 2D DCT and LPCA techniques by removing the first coefficient, which is the most affected by illumination changes, enhances robustness with little change in discrimination ability; removing further coefficients causes a noticeable reduction in performance on clean images and provides little gain in robustness. When using the 2D DCT with 16脳16 blocks, the first three coefficients need to be removed in order to achieve good robustness. It is further shown that contrary to previously published results, the use of deltas of low-order coefficients (to alleviate performance losses caused by removing coefficients) can adversely affect robustness.\",\n \"Title: Line-Based Affine Invariant Object Location Using Transformation Space Decomposition\\nYear: 2006\\n\\nAbstract: This paper presents a novel line-based affine invariant object location methodology. Our algorithm employs a new line-based transformation space decomposition technique to exploit intrinsic structural information provided by line features. Furthermore, we propose a new line-based distance transform to integrate with our algorithm to provide efficient transformation cell evaluation and subdivision in a coarse to fine manner. The algorithm is able to rapidly accelerate the searching process while maintaining high discriminative power and minimal storage requirement. The efficiency and discriminative power of this methodology are demonstrated using real-world examples with promising results.\",\n \"Title: Pose-invariant 2.5D face recognition using Geodesic Texture Warping\\nYear: 2010\\n\\nAbstract: In recent years, 3D face recognition has become a popular solution to deal with the problem of pose-invariant face recognition. The majority of 3D face data are, however, actually 2.5D which are sensitive to pose variations. This paper presents a novel Geodesic Texture Warping (GTW) solution for 2.5D pose-invariant face recognition. In this method, we use the geodesic distance computed on a 2.5D face scan to warp the texture of a rotated face to that of a frontal one to perform matching. A feasibility and effectiveness investigation for the proposed method is conducted using a wide range of experiments including samples with different face rotations. The encouraging experimental results demonstrate that the proposed method achieves much higher accuracy than the state-of-the-art method with a low computational cost.\",\n \"Title: Topological features and iterative node elimination for speeding up subgraph isomorphism detection\\nYear: 2012\\n\\nAbstract: In this paper we tackle the problem of subgraph isomorphism detection on large graphs, which may commonly be intractable, even with state of the art algorithms. Rather than competing with other matching algorithms, we define enhancements that can be used by (almost) any subgraph isomorphism algorithm, both current and future. These enhancements consist of a number of topological features to be added to the nodes, and a technique which we term “iterative node elimination”. The fusion of these enhancements is shown to be able to reduce subgraph isomorphism matching times by a factor of over 4,500.\",\n \"Title: Prediction of pathological tremor using adaptive multiple oscillators linear combiner.\\nYear: 2016\\n\\nAbstract: •Pathological tremor signal prediction algorithm AMOLC based on Hopf oscillator is proposed.•The optimum parameters of AMOLC are obtained by building experimental platform which can generates simulated tremor motion.•The corresponding optimal parameters are verified by actual pathological tremor signals.•The AMOLC is zero phase lag and it can be used in real-time application.\",\n \"Title: Spectral Regression dimension reduction for multiple features facial image retrieval\\nYear: 2012\\n\\nAbstract: Face retrieval has received much attention in recent years. This paper comparatively studied five feature description methods for face representation, including Local Binary Pattern (LBP), Gabor feature, Gray Level Co-occurrence Matrices (GLCM), Pyramid Histogram of Oriented Gradient (PHOG) and Curvelet Transform (CT). The problem of large dimensionalities of the extracted features was addressed by employing a manifold learning method called Spectral Regression (SR). A fusion scheme was proposed by aggregating the distance metrics. Experiments illustrated that dimension reduced features are more efficient and the fusion scheme can offer much enhanced performance. A 98% rank 1 accuracy was obtained for the AR faces and 92% for the FERET faces.\",\n \"Title: Model Identification Of Emp Electromagnetic Coupling Using Volterra Kernel\\nYear: 2011\\n\\nAbstract: Electromagnetic pulses (EMP) are capable of causing effects like malfunctions, performance degradation, interferences, and destructions in electronic and electrical systems. The EMP effect simulation is one of key points from home and abroad. Furthermore, EMP coupling is the promise and foundation of effect evolution and protection. In this paper, a new method, based on the combination of system identification theory and Volterra series algorithm, was proposed for the analysis of the EMP electromagnetic coupling phenomena from an external field to an inner electric system. In particular, we focused on the ultra wide band (UWB) pulses, which were able to cause malfunctions or even destructions of complex electronic systems and the coupling effect has not yet been sufficiently analysed. Based on experience data, we designed a new method using Volterra series to identify EMP electromagnetic coupling model. We firstly analysed the theory foundation of non-parameters model evolution. And then, to verify the validity of this method, we designed UWB pulses electromagnetic coupling table and gained radiation coupling data. Finally, model testing results showed that the increased model accuracy and smaller computation will improve EMP effect evolution, also which can apply this method to EMP electromagnetic coupling model identification.\",\n \"Title: Non-Linear System Identification Of Gear Crack Failure Based On Second Order Volterra Kernel\\nYear: 2010\\n\\nAbstract: Non-linear system identification has attracted great interest in mechanical fields. Against the weak vibration signals of gear crack failure and the considerable but ignored influence by input variation in gear tooth system non-linear working condition, which put forward a huge challenge to gear crack failure detection and identification's accurate, we have proposed a new non-linear non-parameter method that used the second order Volterra kernel function to analyse the inner contact between crack failure and the corresponding non-linear changes in whole system term. After identifying gear tooth system second order kernel function with input and output time series, we analysed the non-linear information indicated by frequency spectrum in detail. Anglicisation and comparison results show that gear tooth crack failure's second order Volterra kernel function considered the fault identification's correction influenced by input elements and was sensitive to system non-linear changes which were caused by different fault conditions. And, this non-linear system identification method can solve the diagnosis' fuzziness and indeterminacy problems caused by traditional frequency sideband theory. This new method is also helpful for mechanical system modelling, security running monitoring, dynamic design, the study of dynamic characteristics, and the fault diagnosis of rotary systems.\",\n \"Title: The Design Of Bio-Inspired Esd Protection Model For Digital Circuits Based On Cell Structure\\nYear: 2012\\n\\nAbstract: As the semiconductor feature size decreases and the number of transistors on a single chip increases, digital circuits are frequently used in harsh and complex electromagnetic interference (EMI) environments. Typical protection problems are gradually prominent, such as malfunctions, performance degradation and destructions in component and/or system. This paper proposed a bio-inspired model to fulfil the requirements of digital circuits' protection performance. Firstly, extensive experiments have been carried out and are presented here to simulate the process of field program gate array (FPGA) chips damaged by human body model (HBM) electrostatic discharge (ESD), applying the contact discharge method. And then, this paper builds the functional fault model of the above process and verifies the similarity in the aftermath of damaging electrical and biological systems. Thus for electronic system design, we proposed the virtual cell model with redundancy structure, self-organising and self-healing operational methodology which is built upon artificial evolution. Finally, we implemented Markov model to analyse the steady-state stability of the virtual cell model in order to prove its durability when it comes to heavy and frequent ESD damage.\",\n \"Title: Prediction Of Joint Angle By Combining Multiple Linear Regression With Autoregressive (Ar) Model And Kalman Filter\\nYear: 2015\\n\\nAbstract: In this paper, a new prediction algorithm combining multiple linear regression with autoregressive model and Kalman filter (MLRAR-KF) is proposed to predict the elbow joint angle. The MLRAR model updating weights with Kalman filter is shown to be able to predict joint motion with high accuracy and well robustness. In comparison to existing prediction algorithms, MLRAR-KF can predict joint angle with higher accuracy and better robustness. A data acquisition system was used to collect sEMG and elbow joint angle signals of human upper limb. The experimental results demonstrate the benefits of MLRAR-KF prediction algorithm. Comparison of computational complexity about some existing prediction methods and MLRAR-KF is conducted to analyze the real-time performance.\",\n \"Title: Analysis And Simulation Of The Neural Oscillator For Tremor Suppression By Fes\\nYear: 2015\\n\\nAbstract: Tremor with a roughly sinusoidal profile impacts the individuals' living activities. Recently, Functional Electric Stimulation (FES) was intensively applied to activate the antagonist muscles with the anti-phase stimulation patterns for compensating the tremor. Considering the similarity between the rhythmic movements and the central neural oscillator, the Matsuoka model is introduced to reciprocally modulate the stimulations intensity of the antagonistic muscles. However, the nonlinear threshold function of the Matsuoka model complicates the system and limits the analysis. In this study, the linearly approximated method is employed to develop the explicit relationship between the model parameters and the frequency/amplitude of the sustained oscillation. The simulation results demonstrate that the output of roughly approximated oscillator is in accordance with that of the original oscillator. Besides, this study brings insight on the effect of the sensory feedback on the neural oscillator. Further, the treatment of tremor with the Matsuoka model as the feed-forward controller and the kinematic signals as the feedback is feasible.\",\n \"Title: Relative Depth Estimation from Hyperspectral Data\\nYear: 2015\\n\\nAbstract: This paper addresses the problem of relative depth estimation using spatial defocus and spectral chromatic aberration presented in hyperspectral data. Our approach produces merged relative sparse depth map using two different methods. The first method constructs a histogram descriptor for edge pixels in each spectral band image. Due to the spectral chromatic aberration, different edge statistical information can be extracted from each band even at the same location. Variance among histogram bins provides input data for band-wise spatial defocus calculation. These band-wise statistical data are later combined to give the first sparse depth map. The second approach uses difference of neighboring spectral vectors to estimate relative depth. The two sparse maps with distinguishing features are finally combined and optimized to generate final sparse depth map. During the last step, normalization and smoothing are used to guarantee better consistency among edge pixels. Experimental results show that our method can generate better sparse depth map than alternative methods which operate on RGB images.\",\n \"Title: On the Sampling Strategy for Evaluation of Spectral-spatial Methods in Hyperspectral Image Classification.\\nYear: 2017\\n\\nAbstract: Spectral-spatial processing has been increasingly explored in remote sensing hyperspectral image classification. While extensive studies have focused on developing methods to improve the classification accuracy, experimental setting and design for method evaluation have drawn little attention. In the scope of supervised classification, we find that traditional experimental designs for spectral pro...\",\n \"Title: Nonnegative-Matrix-Factorization-Based Hyperspectral Unmixing With Partially Known Endmembers.\\nYear: 2016\\n\\nAbstract: Hyperspectral unmixing is an important technique for estimating fractions of various materials from remote sensing imagery. Most unmixing methods make the assumption that no prior knowledge of endmembers is available before the estimation. This is, however, not true for some unmixing tasks for which part of the endmember signatures may be known in advance. In this paper, we address the hyperspectr...\",\n \"Title: Surface geodesic pattern for 3D deformable texture matching.\\nYear: 2017\\n\\nAbstract: This paper presents a Surface Geodesic Pattern (SGP) representation for matching textured 3D deformable surfaces. SGP encodes the local variations of the surface texture derivatives to extract local information from distinctive textural relationships contained in a geodesic neighborhood. Thus, SGP derives its strength from the fusion of surface texture and shape information at the data level in a way that is invariant to non-rigid deformations. We also propose Gabor Topography Wavelet (GTW) for direct feature extraction from the range data. Both features are combined using a multi-view sparse representation to achieve higher discrimination capability while matching non-rigid 3D surfaces. The performance of the proposed method is evaluated extensively on the Bosphorus face database, the FRGC v2 face database, and the PolyU contact-free hand database and the results are compared to state-of-the-art methods. Experimental results show the effectiveness and superiority of the proposed method in recognizing objects under non-rigid surface deformations.\",\n \"Title: Structure Integral Transform Versus Radon Transform: A 2D Mathematical Tool for Invariant Shape Recognition.\\nYear: 2016\\n\\nAbstract: In this paper, we present a novel mathematical tool, Structure Integral Transform (SIT), for invariant shape description and recognition. Different from the Radon Transform (RT), which integrates the shape image function over a 1D line in the image plane, the proposed SIT builds upon two orthogonal integrals over a 2D $K$ -cross dissecting structure spanning across all rotation angles by which the shape regions are bisected in each integral. The proposed SIT brings the following advantages over the RT: 1) it has the extra function of describing the interior structural relationship within the shape which provides a more powerful discriminative ability for shape recognition; 2) the shape regions are dissected by the $K$ -cross in a coarse to fine hierarchical order that can characterize the shape in a better spatial organization scanning from the center to the periphery; and 3) it is easier to build a completely invariant shape descriptor. The experimental results of applying SIT to shape recognition demonstrate its superior performance over the well-known Radon transform, and the well-known shape contexts and the polar harmonic transforms.\",\n \"Title: Leaf image retrieval using combined feature of vein and contour\\nYear: 2015\\n\\nAbstract: This paper proposes a novel method of recognizing plant leaf image using a combination of venation and contour features. In this method, the shape is cut into pieces under different scales to describe the leaf image in a multiscale way. Both leaf venation and contour features extracted from the cut pieces are utilized to provide comprehensive description under each scale. The performance of the proposed method has been evaluated on two leaf datasets. And the experimental results indicates that the proposed method can achieve higher (or similar) recognition accuracies than the state-of-the-art approaches with acceptable speed.\",\n \"Title: Segmentation Of The Left Ventricle In Echocardiography Using Contextual Shape Model\\nYear: 2016\\n\\nAbstract: Accurate localization of the left ventricle (LV) boundary from echocardiogram images is of vital importance for the diagnosis and treatment of heart disease. Statistical shape models such as active shape models (ASM) have been commonly used to perform automatic detection of this boundary. Such models perform well when there is low variability in the underlying shape subspace and an accurate initialization can be provided, however in the absence of these conditions results are often much poorer. In the case of LV echocardiogram images, such variability is often encountered in patients with abnormal LV function. In this paper we propose a fully automatic segmentation technique using deep learning in a Bayesian nonparametric framework. Our model uses a dynamic statistical shape model comprised of training shapes from select weighted subsets of the feature subspace. Subsets are chosen during the iterative segmentation process according to a latent temporal component allocation variable, determined from joint deep features and LV landmark information using a Dirichlet process mixture model with Chinese restaurant process prior. Testing is performed on a data set comprising images of the LV acquired from patients exhibiting both normal and abnormal LV function, and the results using our technique compared to both ASM and a state of the art technique. Results from this testing show an improvement in the LV localization accuracy, particularly when LV function is abnormal.\",\n \"Title: Automated Image Analysis on Insect Soups\\nYear: 2016\\n\\nAbstract: Insect soup is a term which refers to the bulk ethanol samples stored in many insect collections which contain unsorted material and augment the dried and pinned collections. These samples often contain rare or unique specimens, and it would be beneficial to researchers or even the general public to have an easy way to access them to view and annotate their contents. In this paper we propose an image analysis approach for analysing insect soup images and making their content readily available through the web. The analysis includes insect segmentation, scale bar segmentation, measurements, and insect sorting. The image analysis results will be used for online access of insect images for insect classification, research, species discovery, or educational purposes.\",\n \"Title: Affine Invariant Point-Set Matching Using Convex Hull Bisection\\nYear: 2016\\n\\nAbstract: Affine invariant point-set matching is an important issue in computer vision and pattern recognition. Using reference points derived from the convex hull of the point-set is an existing idea to solve this problem. However, how to choose proper and enough reference points for extracting affine invariant and powerful discriminative descriptors is an open problem. In this paper, a novel method termed convex hull bisection (CHB) is proposed for affine invariant point-set matching. In CHB, for each point in the point-set, two reference points are derived by bisecting the convex hull using the line connecting the point and the centroid of the convex hull. The resulting reference points and other reference points, such as the centroid point of the convex hull and the mean point of the point-set, are utilised to yield an 11-dimensional affine invariant feature vector associated with any point within the point-set. The obtained point based descriptors are used for point-set matching. The proposed method works well even in the case that reference points are overlapping or collinear, which overcomes the limitations of the existing convex hull based methods. The effectiveness of the proposed method is validated by an extensive experimental investigation.\",\n \"Title: Multiscale Crossing Representation Using Combined Feature of Contour and Venation for Leaf Image Identification\\nYear: 2016\\n\\nAbstract: In this paper, we propose a novel method for plant identification using a multiscale crossing representation of leaf contour and venation. By extracting the combined features in multiple scale, the proposed method is capable of representing features from global to local regions with mirror, scale, translation and rotation invariance. Three leaf datasets including the Swedish Leaf dataset, the Flavia Leaf dataset and the Soybean Cultivar Leaf dataset are adopted in the experiments to evaluate the performance of the proposed method. Comparative experimental results show that the proposed method can achieve consistently higher or similar recognition accuracy than the state-of-the-art methods among these leaf datasets, which may indicate a new solution to the leaf identification problem.\",\n \"Title: A Local Scale Selection Scheme for Multiscale Area Integral Invariants\\nYear: 2016\\n\\nAbstract: Area integral invariant (AII) is a functional obtained by performing integral operations on the closed planar contour of a shape via the convolution with disc kernels. This shape descriptor is insensitive to noise and robust with respect to occlusions. AII intrinsically introduces the notion of scale using the size of kernel radius. However how to select an optimal scale remains unresolved. In this paper, we propose a local scale selection scheme for generating multiscale area integral invariants. For the same scale level, the disc kernel size is not fixed and varies with the contour point where the disc is centered. This scheme also provides a scale assignment for emphasising the features extraction at finer scales. The strong discriminative power of the multiscale area integral invariant derived from the proposed scale selection scheme has been validated through experiments on very challenging leaf image retrievals.\",\n \"Title: Robust Facial Landmark Localization Using LBP Histogram Correlation Based Initialization\\nYear: 2017\\n\\nAbstract: Facial landmark localization on images with occlusions is an important and challenging task in many visual applications. Recently, the cascaded pose regression has attracted increasing attention, since it achieved superior performance in terms of facial landmark localization under occlusions. However, such approach is sensitive to initialization, where an improper initialization will decrease the performance sharply. In this paper, we propose a novel initialization method to get a robust initial shape by analysing correlation of Local Binary Patterns (LBP) histograms between the estimated face and training faces. The shape of the training face that is most correlated with the estimated face, will be selected as the initialization for the regression. The selected shape is closer to the real shape of the estimated face, which makes the landmark localization more accurate. Besides, in order to make the initial shape more robust to occlusions, we propose a boosted smart restarts technique by checking location and occlusion jointly instead of checking location only. We show that the proposed method significantly improves performance over existing landmark localization methods on the challenging dataset of COFW. The experimental results demonstrate that the proposed method reduces error by 11.9% and failure cases by 20.8% on COFW dataset. Moreover, it detects face occlusions with 85/40% precision/recall.\",\n \"Title: Low-rank double dictionary learning from corrupted data for robust image classification.\\nYear: 2017\\n\\nAbstract: •A novel low-rank double dictionary learning (LRD2L) approach is proposed for robust image classification.•It integrates the low-rank matrix recovery technique with the class-specific and class-shared dictionary learning.•It can effectively handle the image corruptions in both training and testing samples, which are inevitable in real-world applications.•The experimental results on three datasets demonstrate the effectiveness and superiority of the proposed approach.\",\n \"Title: Multiple Graph Regularized Nmf For Hyperspectral Unmixing\\nYear: 2015\\n\\nAbstract: Hyperspectral unmixing is an important technique for estimating fraction of different land covers from remote sensing imagery. In recent years, nonnegative matrix factorization (NMF) methods with various constraints have been introduced into hyperspectral unmixing. Among these methods, graph based constraint has been proved to be useful in capturing the latent manifold structure of the hyper spectral data in the feature domain. However, due to the complexity of the data, only using single graph can not adequately reflect the intrinsic property of the data. In this paper, we propose a multiple graph regularized NMF method for hyperspectral unmixing, which approximates the manifold and consistency of data by a linear combination of several graphs constructed in different scales. Results on both synthetic and real data have validated the effectiveness of the proposed method, and shown that it has outperformed several state of -the-arts hyperspectral unmixing methods.\",\n \"Title: On The Sampling Strategies For Evaluation Of Joint Spectral-Spatial Information Based Classifiers\\nYear: 2015\\n\\nAbstract: Joint spectral-spatial information based classification is an active topic in hyperspectral remote sensing. Current classification approaches adopt a random sampling strategy to evaluate the performance of various classification systems. Due to the limitation of benchmark data, sampling of training and testing data is performed on the same image. In this paper, we point out that while training with random sampling is practical for hyperspectral image classification, it has intrinsic problems in evaluating spectral-spatial information based classifiers. This statement is supported by several experiments, and has lead to the proposal of a new sampling strategy for comparing spectral spatial information based classifiers.\",\n \"Title: Continuous estimation of joint angle from electromyography using multiple time-delayed features and random forests.\\nYear: 2018\\n\\nAbstract: •Multiple time-delayed feature signals of sEMG are applied to represnt the role of sEMG in the course of motion estimation.•The execution time of the proposed method (RF using MTDF) is the least.•RF using MTDF method can be used to estimate the isometric trials and dynamic trials of human joint motion.•RF using MTDF method can select the important features of sEMG automatically.\",\n \"Title: Dynamic Texture Comparison Using Derivative Sparse Representation: Application to Video-Based Face Recognition.\\nYear: 2017\\n\\nAbstract: Video-based face, expression, and scene recognition are fundamental problems in human-machine interaction, especially when there is a short-length video. In this paper, we present a new derivative sparse representation approach for face and texture recognition using short-length videos. First, it builds local linear subspaces of dynamic texture segments by computing spatiotemporal directional deri...\",\n \"Title: Conditional Random Field and Deep Feature Learning for Hyperspectral Image Segmentation.\\nYear: 2017\\n\\nAbstract: Image segmentation is considered to be one of the critical tasks in hyperspectral remote sensing image processing. Recently, convolutional neural network (CNN) has established itself as a powerful model in segmentation and classification by demonstrating excellent performances. The use of a graphical model such as a conditional random field (CRF) contributes further in capturing contextual information and thus improving the segmentation performance. In this paper, we propose a method to segment hyperspectral images by considering both spectral and spatial information via a combined framework consisting of CNN and CRF. We use multiple spectral cubes to learn deep features using CNN, and then formulate deep CRF with CNN-based unary and pairwise potential functions to effectively extract the semantic correlations between patches consisting of three-dimensional data cubes. Effective piecewise training is applied in order to avoid the computationally expensive iterative CRF inference. Furthermore, we introduce a deep deconvolution network that improves the segmentation masks. We also introduce a new dataset and experimented our proposed method on it along with several widely adopted benchmark datasets to evaluate the effectiveness of our method. By comparing our results with those from several state-of-the-art models, we show the promising potential of our method.\",\n \"Title: Robust Facial Landmark Localization Based on Texture and Pose Correlated Initialization.\\nYear: 2018\\n\\nAbstract: Robust facial landmark localization remains a challenging task when faces are partially occluded. Recently, the cascaded pose regression has attracted increasing attentions, due to itu0027s superior performance in facial landmark localization and occlusion detection. However, such an approach is sensitive to initialization, where an improper initialization can severly degrade the performance. In this paper, we propose a Robust Initialization for Cascaded Pose Regression (RICPR) by providing texture and pose correlated initial shapes for the testing face. By examining the correlation of local binary patterns histograms between the testing face and the training faces, the shapes of the training faces that are most correlated with the testing face are selected as the texture correlated initialization. To make the initialization more robust to various poses, we estimate the rough pose of the testing face according to five fiducial landmarks located by multitask cascaded convolutional networks. Then the pose correlated initial shapes are constructed by the mean faceu0027s shape and the rough testing face pose. Finally, the texture correlated and the pose correlated initial shapes are joined together as the robust initialization. We evaluate RICPR on the challenging dataset of COFW. The experimental results demonstrate that the proposed scheme achieves better performances than the state-of-the-art methods in facial landmark localization and occlusion detection.\",\n \"Title: Few-Example Affine Invariant Ear Detection in the Wild.\\nYear: 2018\\n\\nAbstract: Ear detection in the wild with the varying pose, lighting, and complex background is a challenging unsolved problem. In this paper, we study affine invariant ear detection in the wild using only a small number of ear example images and formulate the problem of affine invariant ear detection as a task of locating an affine transformation of an ear model in an image. Ear shapes are represented by line segments, which incorporate structural information of line orientation and line-point association. Then a novel fast line based Hausdorff distance (FLHD) is developed to match two sets of line segments. Compared to existing line segment Hausdorff distance, FLHD is one order of magnitude faster with similar discriminative power. As there are a large number of transformations to consider, an efficient global search using branch-and-bound scheme is presented to locate the ear. This makes our algorithm be able to handle arbitrary 2D affine transformations. Experimental results on real-world images that were acquired in the wild and Point Head Pose database show the effectiveness and robustness of the proposed method.\",\n \"Title: Matching Pursuit Based On Kernel Non-Second Order Minimization\\nYear: 2018\\n\\nAbstract: The orthogonal matching pursuit (OMP) is an important sparse approximation algorithm to recover sparse signals from compressed measurements. However, most MP algorithms are based on the mean square error(MSE) to minimize the recovery error, which is suboptimal when there are outliers. In this paper, we present a new robust OMP algorithm based on kernel non-second order statistics (KNS-OMP), which not only takes advantages of the outlier resistance ability of correntropy but also further extends the second order statistics based correntropy to a non-second order similarity measurement to improve its robustness. The resulted framework is more accurate than the second order ones in reducing the effect of outliers. Experimental results on synthetic and real data show that the proposed method achieves better performances compared with existing methods.\",\n \"Title: Multi-Scale Piecewise Line Integral Strategy For Structure Integral Transform\\nYear: 2018\\n\\nAbstract: Structure Integral transform (SIT) is a mathematical tool for invariant shape recognition. SIT has a superior ability to capture the interior structure information by integrating the shape image function over 2D dissecting structure, but the details on the dissecting structure are discarded. In this paper, for the first time, we propose a novel multi-scale piecewise line integral strategy for SIT which introduces the grayscale information. At a higher scale, the integral line on dissecting structure will be equally divided more times, which brings to a coarse to fine description for each integral line. The proposed multi-scale piecewise integral strategy has been validated to extend SIT from binary shape to grayscale shape successfully through experiments on Leeds' Butterflies dataset and Ponce Group's Butterflies dataset.\",\n \"Title: Data Augmentation Using Synthetic Lesions Improves Machine Learning Detection of Microbleeds from MRI.\\nYear: 2018\\n\\nAbstract: Machine learning applied to medical imaging for lesions detection, such as cerebral microbleeds (CMB) from Magnetic Resonance Imaging (MRI), is challenged by the relatively small datasets available for which only subjective and tedious visual reading is available, and by the low prevalence of lesions (a few in similar to 10% of a typical elderly cohort) resulting in unbalanced classes. Moreover, the lack of actual ground truth might limit the performance of any machine learning method to that of human performance. Yet, the automatic identification of those lesions is relevant to quantify cerebrovascular burden associated with dementia, such as identifying co-morbidity for Alzheimer's disease. In this paper, we investigated a novel approach consisting of simulating synthetic CMB on SWI MRI scans from healthy individuals to create a large and well characterized training dataset, as a data augmentation strategy. Firstly, we characterized actual CMBs from MRI SWI scans and designed a method to create realistic synthetic CMBs whose location, shape, appearance, and size are similar to actual CMBs. We then tested a supervised neural network classifier using various combinations of actual CMB and synthetic CMBs for training. Augmenting data with synthetic CMBs resulted in a large improvement over training on only actual CMBs only when tested on unseen lesions, and provided better results than other standard data augmentation approaches. Our results suggest that data augmentation using synthetic lesions can address the lack of ground truth and low prevalence limitations for medical imaging analysis allowing the deployment of data hungry supervised learning techniques such as deep learning.\",\n \"Title: Generative Adversarial Product Quantisation.\\nYear: 2018\\n\\nAbstract: Product Quantisation (PQ) has been recognised as an effective encoding technique for scalable multimedia content analysis. In this paper, we propose a novel learning framework that enables an end-to-end encoding strategy from raw images to compact PQ codes. The system aims to learn both PQ encoding functions and codewords for content-based image retrieval. In detail, we first design a trainable encoding layer that is pluggable into neural networks, so the codewords can be trained in back-forward propagation. Then we integrate it into a Deep Convolutional Generative Adversarial Network (DC-GAN). In our proposed encoding framework, the raw images are directly encoded by passing through the convolutional and encoding layers, and the generator aims to use the codewords as constrained inputs to generate full image representations that are visually similar to the original images. By taking the advantages of the generative adversarial model, our proposed system can produce high-quality PQ codewords and encoding functions for scalable multimedia retrieval tasks. Experiments show that the proposed architecture GA-PQ outperforms the state-of-the-art encoding techniques on three public image datasets.\",\n \"Title: Exploring Structural Consistency in Graph Regularized Joint Spectral-Spatial Sparse Coding for Hyperspectral Image Classification.\\nYear: 2017\\n\\nAbstract: In hyperspectral image classification, both spectral and spatial data distributions are important in describing and identifying different materials and objects in the image. Furthermore, consistent spatial structures across bands can be useful in capturing inherent structural information of objects. These imply that three properties should be considered when reconstructing an image using sparse co...\",\n \"Title: Classifier-Free Extraction Of Power Line Wires From Point Cloud Data\\nYear: 2018\\n\\nAbstract: This paper proposes a classifier-free method for extraction of power line wires from aerial point cloud data. It combines the advantages of both grid-and point-based processing of the input data. In addition to the non-ground point cloud data, the input to the proposed method includes the pylon locations, which are automatically extracted by a previous method. The proposed method first counts the number of wires in a span between the two successive pylons using two masks: vertical and horizontal. Then, the initial wire segments are obtained and refined iteratively. Finally, the initial segments are extended on both ends and each individual wire points are modelled as a 3D polynomial curve. Experimental results show both the object-based completeness and correctness are 97%, while the point-based completeness and correctness are 99% and 88%, respectively.\",\n \"Title: Face Recognition with Multi-channel Local Mesh High-order Pattern Descriptor and Convolutional Neural Network\\nYear: 2018\\n\\nAbstract: In this paper, we propose a novel Local Mesh High-order Pattern Descriptor (LMHPD) for face recognition. This description is constructed in a high-order derivative space and is integrated with a Convolutional Neural Network (CNN) architecture. Based on the information collected at a local neighborhood of reference pixel with diverse radiuses and mesh angles, a vectorized feature representation of the reference pixel is generated to provide micro-patterns. They are then converted to multi-channels to use in conjunction with the CNN. The CNN adopted in the proposed architecture is generic and very compact with a small number of convolutional layers. However, LMHPD is derived in such a way that it can work with most of the available CNN architectures. For keeping the computational cost and time complexity at the minimum, we propose a lighter approach of high-order texture descriptor with CNN architecture that can effectively extract discriminative face features. Extensive experiments on Extended Yale B and CMU-PIE datasets show that our method consistently outperforms several alternative descriptors for face recognition under various circumstances.\",\n \"Title: Colour Analysis of Strawberries on a Real Time Production Line\\nYear: 2018\\n\\nAbstract: A novel system has been designed where colour analysis algorithms facilitate grading ripeness of packed strawberries on a fast-paced production line. The Strawberry quality system acquires images at the rate of 2punnets/s, and feeds the images to the two algorithms. Using CIELAB and HSV colourspaces, both underripe and overripe colour features are analysed resulting in F1 scores of 94.7% and 90.6% respectively, when measured on multiple defect samples. The single defect class results scored 80.1% and 77.1%. The algorithms total time for the current hardware configuration is 121ms maximum and 80ms average, which is well below the required time window of 500ms. 105, 542 punnets have been assessed by the algorithm and has rejected 4, 952 in total (4.9%), helping to ensure the quality of the product being shipped to customers and avoiding costly returns.\",\n \"Title: Bay Lobsters Moulting Stage Analysis Based on High-Order Texture Descriptor\\nYear: 2018\\n\\nAbstract: In this paper, we introduce the world's first method to automatically classify the moulting stage of Bay lobsters, formally known as Thenus orientális, in a controlled environment. Our classification approach only requires top view images of exoskeleton of bay lobsters. We analyzed the texture of exoskeleton to categorize into normal, moulting stage, and freshly moulted classes. To meet the efficiency and robustness requirements of production platform, we leverage traditional approach such as Local Binary Pattern and Local Derivative Pattern with enhanced encoding scheme for underwater imagery. We also build a dataset of 315 bay lobster images captured at the controlled under water environment. Experimental results on this dataset demonstrated that the proposed method can effectively classify bay lobsters with a high accuracy.\",\n \"Title: Online Relational Manifold Learning for Multiview Segmentation in Echocardiography\\nYear: 2018\\n\\nAbstract: Accurate delineation of the left ventricle (LV) endocardial border in echocardiography is of vital importance for the diagnosis and treatment of heart disease. Effective segmentation of the LV is challenging due to low contrast, signal dropout and acoustic noise. In the situation where low level and region-based image cues are unable to define the LV boundary, shape prior models are critical to infer shape. These models perform well when there is low variability in the underlying shape subspace and the shape instance produced by appearance cues does not contain gross errors, however in the absence of these conditions results are often much poorer. In this paper, we first propose a shape model to overcome the problem of modelling complex shape subspaces. Our method connects the implicit relationship between image features and shape by extending graph regularized sparse nonnegative matrix factorization (NMF) to jointly learn the structure and connection between two low dimensional manifolds comprising image features and shapes, respectively. We extend conventional NMF learning to an online learning-based approach where the input image is used to leverage the learning and connection of each manifold to the most relevant subspace regions. This ensures robust shape inference and a shape model constructed from contextually relevant shapes. A fully automatic segmentation approach using a probabilistic framework is then proposed to detect the LV endocardial border. Our method is applied to a diverse dataset that contains multiple views of the LV. Results show the effectiveness of our approach compared to state-of-the-art methods.\",\n \"Title: Real- Time Computing for Droplet Detection and Recognition.\\nYear: 2018\\n\\nAbstract: In this paper, we propose a novel framework to achieve real-time droplet detection and recognition. In a fluidics system, water drops can be generated to carry objects. It is important to use such droplets to analyze the properties of the carried tiny objects. In real applications, the movement of droplets are captured by a high-speed camera, and processed by an image-based computing system. This imposes high requirements on the computational efficiency of the image processing and pattern recognition methods in the system. To tackle this challenge, we propose to use efficient object tracking and classification methods to build the framework. We first use the Hough transform as the basic droplet detection method, and then explore the relationship of the same droplet in different frames to track their movement. For the classification step, we use the area of particle content as the basic feature, and conduct classification using a linear support vector machine. The experiments on real-time droplet analysis system verifies that our proposed framework can process images at 1000 frames per second, while achieving 99.3% in classification accuracy.\",\n \"Title: Fusion of Hyperspectral and LiDAR Data Using Discriminant Correlation Analysis for Land Cover Classification.\\nYear: 2018\\n\\nAbstract: It is evident that using complementary features from different sensors is effective for land cover classification. Therefore, combining complementary information from hyperspectral (HS) and light detection and ranging (LiDAR) data can greatly assist in such applications. In this paper, we propose a model for land cover classification, which extracts effective features representing different charac...\",\n \"Title: Conditional Random Field and Deep Feature Learning for Hyperspectral Image Classification\\nYear: 2019\\n\\nAbstract: Image classification is considered to be one of the critical tasks in hyperspectral remote sensing image processing. Recently, a convolutional neural network (CNN) has established itself as a powerful model in classification by demonstrating excellent performances. The use of a graphical model such as a conditional random field (CRF) contributes further in capturing contextual information and thus improving the classification performance. In this paper, we propose a method to classify hyperspectral images by considering both spectral and spatial information via a combined framework consisting of CNN and CRF. We use multiple spectral band groups to learn deep features using CNN, and then formulate deep CRF with CNN-based unary and pairwise potential functions to effectively extract the semantic correlations between patches consisting of 3-D data cubes. Furthermore, we introduce a deep deconvolution network that improves the final classification performance. We also introduced a new data set and experimented our proposed method on it along with several widely adopted benchmark data sets to evaluate the effectiveness of our method. By comparing our results with those from several state-of-the-art models, we show the promising potential of our method.\",\n \"Title: Region-Based Structure Preserving Nonnegative Matrix Factorization for Hyperspectral Unmixing\\nYear: 2017\\n\\nAbstract: Hyperspectral unmixing is one of the most important techniques in the remote sensing image analysis. In recent years, the nonnegative matrix factorization (NMF) method is widely used in hyperspectral unmixing. In order to solve the nonconvex problem of the NMF method, a number of constraints have been introduced into NMF models, including sparsity, manifold, smoothness, etc. However, these constra...\",\n \"Title: Robust Image Classification via Low-Rank Double Dictionary Learning.\\nYear: 2017\\n\\nAbstract: In recent years, dictionary learning has been widely used in various image classification applications. However, how to construct an effective dictionary for robust image classification task, in which both the training and the testing image samples are corrupted, is still an open problem. To address this, we propose a novel low-rank double dictionary learning (LRD2L) method. Unlike traditional dictionary learning methods, LRD2L simultaneously learns three components from training data: (1) a low-rank class-specific sub-dictionary for each class to capture the most discriminative features owned by each class, (2) a low-rank class-shared dictionary which models the common patterns shared by different classes and (3) a sparse error container to fit the noises in data. As a result, the class-specific information, the class-shared information and the noises contained in data are separated from each other. Therefore, the dictionaries learned by LRD2L are noiseless, and the class-specific sub-dictionary of each class can be more discriminative. Also since the common features across different classes, which are essential to the reconstruction of image samples, are preserved in class-shared dictionary, LRD2L has a powerful reconstructive capability for newly coming testing samples. Experimental results on three public available datasets reveal the effectiveness and the superiority of our approach compared to the state-of-the-art dictionary learning methods.\",\n \"Title: Boosting Radial Strings for 3D Face Recognition with Expressions and Occlusions\\nYear: 2016\\n\\nAbstract: In this paper, we present a new radial string representation and matching approach for 3D face recognition under expression variations and partial occlusions. The radial strings are an indexed collection of strings emanating from the nose tip of a face scan. The matching between two radial strings is conducted through a dynamic programming process, in which a partial matching mechanism is established to effectively find those un-occluded substrings. Moreover, the most discriminative and stable radial strings are selected optimally by the well-known AdaBoost algorithm to achieve a composite classifier for 3D face recognition under facial expression changes. Experimental results on the GavabDB and the Bosphorus databases show that the proposed approach achieves promising results for human face recognition with expressions and occlusions.\",\n \"Title: Human Intention Understanding From Multiple Demonstrations And Behavior Generalization In Dynamic Movement Primitives Framework\\nYear: 2019\\n\\nAbstract: Human's interference in the process of skill learning can improve the performance of the robot greatly. However, learning from demonstration to generate a new action with human behavioral characteristics in the varying situation is challenging. Generally, dynamic movement primitives (DMPs) method can generalize the trajectory imitating the demonstration, but cannot integrate the feature of multiple trajectories of different targets. In this paper, the proposed method contains two aspects of learning and generating. The statistical method Gaussian mixture model and Gaussian mixture regression (GMM-GMR) is used to extract the common characteristic and eliminate the uncertainty of the multiple demonstrations. To exert the ability of DMPs to generate a human-like motion to a new goal, and we model the shape parameter with locally weighted regression (LWR) method. To enhance the ability of DMPs in multiple trajectories learning, we propose the multivariate Gaussian process regression (MV-GPR) method to construct the regression model of shape parameters to reflect the human intentions, with respect to the target position. To verify the feasibility of the proposed method, we design a peg-in-hole experiment with proving generalization and obstacle avoidance performance. The results have shown that the strategy integrated the generalization of DMPs and feature regeneration ability of MV-GPR method, and the generated valid trajectory could achieve the peg-in-hole task with 6-DOF whole-arm avoidance.\",\n \"Title: ST-CNN: Spatial-Temporal Convolutional Neural Network for crowd counting in videos\\nYear: 2019\\n\\nAbstract: •We propose a crowd counting network (ST-CNN) to accurately calculate the number of people in the scene.•We utilized both spatial and temporal features to solve the problem of crowd counting.•Experiments on two challenging datasets, Mall and WorldExpo010, prove the effectiveness of our method.\",\n \"Title: Chord Bunch Walks for Recognizing Naturally Self-overlapped and Compound Leaves.\\nYear: 2019\\n\\nAbstract: Effectively describing and recognizing leaf shapes under arbitrary variations, particularly from a large database, remains an unsolved problem. In this research, we attempted a new strategy of describing leaf shapes by walking and measuring along a bunch of chords that pass through the shape. A novel chord bunch walks (CBW) descriptor is developed through the chord walking behaviour that effectively integrates the shape image function over the walked chord to reflect both the contour features and the inner properties of the shape. For each contour point, the chord bunch groups multiple pairs of chords to build a hierarchical framework for a coarse-to-fine description that can effectively characterize not only the subtle differences among leaf margin patterns but also the interior part of the shape contour formed inside a self-overlapped or compound leaf. Instead of using optimal correspondence based matching, a Log-Min distance that encourages one-to-one correspondences is proposed for efficient and effective CBW matching. The proposed CBW shape analysis method is invariant to rotation, scaling, translation, and mirror transforms. Five experiments, including image retrieval of compound leaves, image retrieval of naturally self-overlapped leaves, and retrieval of mixed leaves on three large scale datasets, are conducted. The proposed method achieved large accuracy increases with low computational costs over the state-of-the-art benchmarks, which indicates the research potential along this direction.\",\n \"Title: Multiscale Contour Steered Region Integral and Its Application for Cultivar Classification.\\nYear: 2019\\n\\nAbstract: In this paper, a multiscale contour steered region integral (MCSRI) method is proposed to classify highly similar shapes with flexible interior connection architectures. A component distance map (CDM) is developed to robustly characterize the flexible interior connection structure, shape of the exterior contour, and their inter-relationship in a shape image. A novel multiscale region transform (MReT) is proposed to perform region integral over different contour-steered strips at all possible scales to effectively integrate patch features, and thus enables a better description of the shape image in a coarse-to-fine manner. It is applied to solve a challenging problem of classifying cultivars from leaf images, which is a new attempt in both biology and computer vision research communities. A soybean cultivar leaf vein database (SoyCultivarVein), which is the first cultivar leaf vein database, is created and presented for performance evaluation. The experimental results demonstrate the superiority of the proposed method over the state-of-the-art methods in similar shape classification and the possibility of cultivar recognition via leaf pattern analysis, which may lead to a new research interest towards fine-level shape analysis on cultivar classification.\",\n \"Title: Triplet Constrained Deep Feature Extraction for Hyperspectral Image Classification\\nYear: 2018\\n\\nAbstract: Convolutional neural networks (CNNs) have demonstrated significant performance in various visual recognition problems in recent years. Recent research has shown that training multilayer neural networks can extensively improve the performance of hyperspectral image (HSI) classification. In this paper, we apply a triplet constraint property on a 3D CNN. This method directly learns a mapping from images to a Euclidean space in which distances directly correspond to a measure of spectral-spatial similarity. Once this embedding has been established, classification can be implemented with such embeddings as feature vectors. Moreover, we also augment the size of the training samples in different band groups. This produces different yet useful estimation of spectral-spatial characteristics of HSI data and contributes considerably in accurate classification. This method is evaluated on a new dataset and compared with several state-of-the-art models, which shows the promising potential of our method.\",\n \"Title: Feature Fusion Vision Transformer for Fine-Grained Visual Categorization.\\nYear: 2021\\n\\nAbstract: The core for tackling the fine-grained visual categorization (FGVC) is to learn subtleyet discriminative features. Most previous works achieve this by explicitly selecting thediscriminative parts or integrating the attention mechanism via CNN-based approaches.However, these methods enhance the computational complexity and make the modeldominated by the regions containing the most of the objects. Recently, vision trans-former (ViT) has achieved SOTA performance on general image recognition tasks. Theself-attention mechanism aggregates and weights the information from all patches to theclassification token, making it perfectly suitable for FGVC. Nonetheless, the classifi-cation token in the deep layer pays more attention to the global information, lackingthe local and low-level features that are essential for FGVC. In this work, we proposea novel pure transformer-based framework Feature Fusion Vision Transformer (FFVT)where we aggregate the important tokens from each transformer layer to compensate thelocal, low-level and middle-level information. We design a novel token selection mod-ule called mutual attention weight selection (MAWS) to guide the network effectivelyand efficiently towards selecting discriminative tokens without introducing extra param-eters. We verify the effectiveness of FFVT on three benchmarks where FFVT achievesthe state-of-the-art performance.\",\n \"Title: Self-Supervised Video Object Segmentation by Motion-Aware Mask Propagation\\nYear: 2022\\n\\nAbstract: We propose a self-supervised spatio-temporal matching method coined Motion-Aware Mask Propagation (MAMP) for semi-supervised video object segmentation. During training, MAMP leverages the frame reconstruction task to train the model without the need for annotations. During inference, MAMP extracts high-resolution features from each frame to build a memory bank from the features as well as the predicted masks of selected past frames. MAMP then propagates the masks from the memory bank to subsequent frames according to our motion-aware spatio-temporal matching module, also proposed in this paper. Evaluation on DAVIS-2017 and YouTube-VOS datasets show that MAMP achieves state-of-the-art performance with stronger generalization ability compared to existing self-supervised methods, i.e. 4.9\\\\% higher mean $\\\\mathcal{J}\\\\&\\\\mathcal{F}$ on DAVIS-2017 and 4.85\\\\% higher mean $\\\\mathcal{J}\\\\&\\\\mathcal{F}$ on the unseen categories of YouTube-VOS than the nearest competitor. Moreover, MAMP performs on par with many supervised video object segmentation methods. Our code is available at: \\\\url{https://github.com/bo-miao/MAMP}.\",\n \"Title: MaskCOV: A random mask covariance network for ultra-fine-grained visual categorization\\nYear: 2021\\n\\nAbstract: •This paper proposes a novel random mask covariance network (MaskCOV) to study the challenging problem of ultra-fine-grained visual categorization (ultra-FGVC).•A new self-supervised learning with a powerful in-image data augmentation scheme is proposed for enabling the network to localise those discriminative yet compact regions.•The proposed MaskCOV focuses training within the local discriminative regions, enforcing the regional and correlation learning for subtle detail classification in the ultra-FGVC.•Encouraging experimental results demonstrate the capability of MaskCOV in advancing research from the fine-grained to the ultra-fine-grained visual categorization.\",\n \"Title: A unified weight learning and low-rank regression model for robust complex error modeling\\nYear: 2021\\n\\nAbstract: •This is the first attempt to use a unified function to address both error distribution fitting and structure estimation.•A generalized-correntropy-based weight learning theory is proposed to better fit complex error distribution in face images.•A new low-rank approximation estimator based on the generalized correntropy is proposed for contiguous error structure estimation.•We propose an optimization scheme based on the majorization minimization (MM) and alternating direction method of multipliers (ADMM).\",\n \"Title: Mask-Guided Feature Extraction and Augmentation for Ultra-Fine-Grained Visual Categorization\\nYear: 2021\\n\\nAbstract: While the fine-grained visual categorization (FGVC) problems have been greatly developed in the past years, the Ultra-fine-grained visual categorization (Ultra-FGVC) problems have been understudied. FGVC aims at classifying objects from the same species (very similar categories), while the Ultra-FGVC targets at more challenging problems of classifying images at an ultra-fine granularity where even...\",\n \"Title: A Compositional Feature Embedding and Similarity Metric for Ultra-Fine-Grained Visual Categorization\\nYear: 2021\\n\\nAbstract: Fine-grained visual categorization (FGVC), which aims at classifying objects with small inter-class variances, has been significantly advanced in recent years. However, ultra-fine-grained visual categorization (ultra-FGVC), which targets at identifying subclasses with extremely similar patterns, has not received much attention. In ultra-FGVC datasets, the samples per category are always scarce as ...\",\n \"Title: Upwardly Abstracted Definition-Based Subontologies\\nYear: 2021\\n\\nAbstract: ABSTRACTIn this paper, we present a method for extracting subontologies from $\\\\mathcalELH $ ontologies for a set of symbols. The approach is focused on the generation of upwardly abstracted definitions of concepts, which is a technique for computing definitions expressed using closest primitive ancestors. The subontologies returned by the method are evaluated for quality and compared to extracts computed with locality-based modularisation and uniform interpolation. Our subontology generation method produces promising results in terms of size and relevance to the needs of domain experts.\",\n \"Title: Learning discriminative region representation for person retrieval\\nYear: 2022\\n\\nAbstract: •We propose HRS, a weakly-supervised region segmentation method to predict informative region for improved person retrieval.•HRS coherently incorporates global appearance information and local granularity cue in an end-to-end learning.•Extensive experimental results on three benchmark datasets show HRS achieves new state-of-the-arts for person retrieval.\",\n \"Title: Obstacle Avoidance Methods Based on Geometric Information Under the DMPs Framework\\nYear: 2021\\n\\nAbstract: Dynamical Movement Primitives (DMPs) can equip the robot with humanoid characteristics and make it efficiently complete the task. However, research on obstacle avoidance strategies combined with DMPs is still being explored nowadays. In this paper, we proposed new obstacle avoidance methods based on geometric information under the DMPs framework, in order that the robot can still complete the task in an unstructured environment. We first generalized new trajectories by DMPs after demonstration. Then according to the interference area of a static or a moving obstacle after extracting the geometric information of the obstacle, we quantitatively adjusted the generalized trajectories by adding proper offset at a certain direction. Finally we used proportional-derivative (PD) control to ensure that the modified trajectories converge to the original one and the goal. The methods make the robot successfully avoid the interference of a static or a moving obstacle, and besides, they maintain all the advantages of the DMPs framework, such as fast goal convergence, simple mathematical principles and high imitation similarity. We verified the effectiveness of the methods in two dimensional and three dimensional simulation, and set up the real experiments using a static or a moving obstacle respectively, and finally proved the feasibility and validity of the avoidance methods.\",\n \"Title: Composite Description Based On Color Vector Quantization And Visual Primary Features For Cbir Tasks\\nYear: 2021\\n\\nAbstract: This paper presents a novel method for content-based color image retrieval that combines color vector quantization and visual primary features into a compact feature representation. Color vector quantization is proposed to describe the image in a compressed stream by preserving the contrast of an image and produce two color quantizers which are processed by vector quantization to preserve the content of a color image. Loosely inspired by human visual system and its mechanism in effectively recognizing objects in an image by its edges and color distribution, we propose extraction of visual primary features based on edge texture orientation and color moments features. The representation of the proposed method utilizes histogram-based features which are populated by color quantization and visual primary features that are used to measure the similarity between the two color images by a specific distance metric computation. The proposed method proves to be efficient and adaptive to the particulars of image retrieval, while it does not require any training information, making it suitable for real time color CBIR applications.The smaller feature size is an additional benefit of the proposed methodology which makes it ideal to use for embedded computing, mobile computing, energy efficient computing and high-throughput systems. An extensive experimental evaluation conducted on four publicly available datasets namely Wang, Vistex-640, Corel-5k and Corel-10k datasets against well-known fusion based, non-fusion based, and deep learning methods highlights the effectiveness and efficiency of the proposed method.\",\n \"Title: Benchmark Platform for Ultra-Fine-Grained Visual Categorization Beyond Human Performance.\\nYear: 2021\\n\\nAbstract: Deep learning methods have achieved remarkable success in fine-grained visual categorization. Such successful categorization at sub-ordinate level, e.g., different animal or plant species, however relies heavily on the visual differences that human can observe and the ground-truths are labelled on the basis of such human visual observation. In contrast, few research has been done for visual categorization at the ultra-fine-grained level, i.e., a granularity where even human experts can hardly identify the visual differences or are not yet able to give affirmative labels by inferring observed pattern differences. This paper reports our efforts towards mitigating this research gap. We introduce the ultra-fine-grained (UFG) image dataset, a large collection of 47,114 images from 3,526 categories. All the images in the proposed UFG image dataset are grouped into categories with different confirmed cultivar names. In addition, we perform an extensive evaluation of state-of-the-art fine-grained classification methods on the proposed UFG image dataset as comparative baselines. The proposed UFG image dataset and evaluation protocols is intended to serve as a benchmark platform that can advance research of visual classification from approaching human performance to beyond human ability, via facilitating benchmark data of artificial intelligence (AI) not to be limited by the labels of human intelligence (HI). The dataset is available online at https://github.com/XiaohanYu-GU/Ultra-FGVC.\",\n \"Title: A Dual-Band Radio-Over-Fiber Link for Future 5G Communication System\\nYear: 2021\\n\\nAbstract: In the existing fifth-generation (5G) communication system, the remote radio unit (RRU) has a large weight, size and power consumption, and there are also compatibility problems between the low-frequency and the high-frequency millimeter wave frequency band system. Based on the microwave photonics technology, a dual-band radio over fiber (RoF) link is proposed, which can realize the simultaneous transmission and channel switching of the low-frequency (3.5 GHz) and high-frequency (28 GHz). In the low-frequency band, the nonlinear distortion is suppressed, and in the high-frequency millimeter wave band, the power fading caused by dispersion is suppressed.\",\n \"Title: Large Spurious-free Dynamic Range RoF Link with Tunable CSR\\nYear: 2021\\n\\nAbstract: In this paper, a linearized radio-over-fiber (RoF) link is proposed. The suppression for the third-order intermodulation distortion (IMID3) and periodic power fading can be simultaneously realized in the proposed link. Adjustable carrier-to-sideband ratio (CSR) can also be achieved. Compared with the traditional RoF link, the spurious free dynamic range (SFDR) of the proposed link is improved by 19 dB.\",\n \"Title: Distribution-Aware Margin Calibration for Semantic Segmentation in Images\\nYear: 2022\\n\\nAbstract: The Jaccard index, also known as Intersection-over-Union (IoU), is one of the most critical evaluation metrics in image semantic segmentation. However, direct optimization of IoU score is very difficult because the learning objective is neither differentiable nor decomposable. Although some algorithms have been proposed to optimize its surrogates, there is no guarantee provided for the generalization ability. In this paper, we propose a margin calibration method, which can be directly used as a learning objective, for an improved generalization of IoU over the data-distribution, underpinned by a rigid lower bound. This scheme theoretically ensures a better segmentation performance in terms of IoU score. We evaluated the effectiveness of the proposed margin calibration method on seven image datasets, showing substantial improvements in IoU score over other learning objectives using deep segmentation models.\",\n \"Title: EAR-NET: Error Attention Refining Network For Retinal Vessel Segmentation\\nYear: 2021\\n\\nAbstract: The precise detection of blood vessels in retinal images is crucial to the early diagnosis of the retinal vascular diseases, e.g., diabetic, hypertensive and solar retinopathies. Existing works often fail in predicting the abnormal areas, e.g, sudden brighter and darker areas and are inclined to predict a pixel to background due to the significant class imbalance, leading to high accuracy and spec...\",\n \"Title: Early Detection of Sugarcane Smut Disease in Hyperspectral Images\\nYear: 2021\\n\\nAbstract: Sugarcane smut, caused by the fungus Sporisorium scitamineum, is a serious sugarcane disease in Queensland, which can cause 30-100% production loss. Early detection of smut disease is a key step towards disease management. However, early-stage smut symptoms are not visible to the human eye. To address this challenge, we leverage the capability of hyperspectral imaging in data acquisition beyond th...\",\n \"Title: Imitation of Manipulation Skills Using Multiple Geometries.\\nYear: 2022\\n\\nAbstract: Daily manipulation tasks are characterized by geometric primitives related to actions and object shapes. Such geometric descriptors are poorly represented by only using Cartesian coordinate systems. In this paper, we propose a learning approach to extract the optimal representation from a dictionary of coordinate systems to encode an observed movement/behavior. This is achieved by using an extension of Gaussian distributions on Riemannian manifolds, which is used to analyse a set of user demonstrations statistically, by considering multiple geometries as candidate representations of the task. We formulate the reproduction problem as a general optimal control problem based on an iterative linear quadratic regulator (iLQR), where the Gaussian distribution in the extracted coordinate systems are used to define the cost function. We apply our approach to object grasping and box opening tasks in simulation and on a 7-axis Franka Emika robot. The results show that the robot can exploit several geometries to execute the manipulation task and generalize it to new situations, by maintaining the invariant characteristics of the task in the coordinate system(s) of interest.\",\n \"Title: An Attention-Based Lattice Network for Hyperspectral Image Classification\\nYear: 2022\\n\\nAbstract: Convolutional neural networks (CNNs) with 3-D convolutional kernels are widely used for hyperspectral image (HSI) classification, which bring notable benefits in capturing joint spectral and spatial features. However, they suffer from poor computational efficiency, causing the low training/inference speed of the model. On the contrary, CNN-based methods with 1-D and 2-D kernels are efficient but mostly restricted to extracting either spectral or spatial features. Moreover, most CNN-based HSI classification frameworks are incapable of simultaneously taking advantage of residual and dense aggregations without over-allocating parameters or information loss for feature reusage. This article presents a novel attention-based lattice network (ALN) to overcome these shortcomings. The proposed 2-D lattice framework can effectively harness the advantages of residual and dense aggregations to achieve outstanding accuracy performance and computational efficiency simultaneously. Furthermore, the ALN employs a unique joint spectral-spatial attention mechanism to capture both spectral and spatial information effectively. In particular, a new pointwise spectral attention mechanism is adopted to fully capture spectral dependences for every pixel. Our extensive experimental investigation verifies the effectiveness and efficiency of the ALN architecture for HSI classification.\",\n \"Title: Symmetric Binary Tree Based Co-occurrence Texture Pattern Mining for Fine-grained Plant Leaf Image Retrieval\\nYear: 2022\\n\\nAbstract: •This work focuses on leaf image retrieval in cultivar level known as a very fine-grained image recognition problem.•Symmetric binary tree (SBT) is designed for mining co-occurrence texture patterns.•A novel feature fusion scheme, named K-NN Based Handcrafted and Deep Features Fusion, is proposed.•The effectiveness of the proposed method has been validated on the soybean cultivar leaf dataset and peanut cultivar leaf dataset.\",\n \"Title: Generalization of orientation trajectories and force–torque profiles for learning human assembly skill\\nYear: 2022\\n\\nAbstract: Constructing system models in industrial tasks is difficult, especially when multiple sensing information is involved. The peg-in-hole task is typically complex in industrial manufacturing. It is hard to model different assembly states precisely. Learning from demonstration can simplify the modelling process based on human demonstration. In this paper, we transfer a new kind of assembly skill to the robot. To intuitively represent the orientation of the assembly, we propose a novel expression named assembly-angle based on direction cosine and rotation matrix, which offers a good differential performance and avoids the singularity problem. Meanwhile, this approach can be encoded by the dynamic constraint dynamic movement primitives (DC-DMPs) method, which is proposed to consider the constraints among the assembly-angle during movement generalization. The RBF network is employed to generalize force–torque information to improve the accuracy of force–torque profiles prediction. We utilize the vision system and apply admittance control to locate the hole precisely. This proposed assembly skill learning framework can complete the task from random deviation of the pose, which makes it suitable for realistic assembly scenarios. To validate our method’s generalization ability, pegs of two kinds of materials were designed, and the deviations of the initial pose were set from small to large. The success rate was 93.75% for high stiffness assembly and 86.25% for low stiffness assembly. The result validated the effectiveness of our method in the peg-in-hole task.\",\n \"Title: A Lie algebra representation for efficient 2D shape classification\\nYear: 2023\\n\\nAbstract: •This paper presents a novel solution to address high computational cost of current Riemannian framework based methods.•We propose a novel representation of Block Diagonal Symmetric Positive Definite Matrix Lie Algebra (BDSPDMLA) for 2D shapes.•A new classification algorithm is introduced to achieve ten thousand times increase in speed over manifold methods.•Experimental results on five datasets demonstrate BDSPDMLA’s effectiveness in computation times and classification accuracy.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":157,"string":"157"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13688,"string":"13,688"}}},{"rowIdx":13689,"cells":{"question_id":{"kind":"string","value":"Dominique Gruyer"},"question":{"kind":"string","value":"Generate an abstract for the title \"Kalman filters predictive steps comparison for vehicle localization\" using the following items: Kalman filters, nonlinear systems, limitations, new filters, vehicle localization."},"answer":{"kind":"string","value":"The aim of this paper is to perform a comparison among several different Kalman filters algorithms designed for nonlinear systems. After presenting the most popular of them and showing its limitations, we introduce some new Kalman filters in order to compare them in the vehicle localization context. This comparison is based on the sole use of their predictive steps that corresponds to the worst case that it can occur in vehicle localization (corrective data are unavailable)."},"correct_answer":{"kind":"string","value":"The aim of this paper is to perform a comparison among several different Kalman filters algorithms designed for nonlinear systems. After presenting the most popular of them and showing its limitations, we introduce some new Kalman filters in order to compare them in the vehicle localization context. This comparison is based on the sole use of their predictive steps that corresponds to the worst case that it can occur in vehicle localization (corrective data are unavailable)."},"documents":{"kind":"list like","value":["Title: Consistent outdoor vehicle localization by bounded-error state estimation\nYear: 2009\n\nAbstract: Localization is a part of many automotive applications where safety is of crucial importance. We think that the best way to guarantee the safety in these applications is to guarantee the results of their embedded localization algorithms. Unfortunately localization of vehicles is mostly solved by Bayesian methods which (due to their structure) can only guarantee their results in a probabilistic way. Interval analysis allows an alternative approach with bounded-error state estimation. Such an approach provides a bounded set of configurations that is guaranteed to surround the actual vehicle configuration. We have validated the bounded-error state estimation with an outdoor vehicle equipped with odometers, a GPS receiver and a gyro. With the experimental results we compare the bounded-error state estimation with the particle filter localization in terms of consistency and computation time.","Title: Maneuver-based trajectory planning for highly autonomous vehicles on real road with traffic and driver interaction\nYear: 2010\n\nAbstract: This paper presents the design and first test on a simulator of a vehicle trajectory-planning algorithm that adapts to traffic on a lane-structured infrastructure such as highways. The proposed algorithm is designed to run on a fail-safe embedded environment with low computational power, such as an engine control unit, to be implementable in commercial vehicles of the near future. The target platform has a clock frequency of less than 150 MHz, 150 kB RAM of memory, and a 3-MB program memory. The trajectory planning is performed by a two-step algorithm. The first step defines the feasible maneuvers with respect to the environment, aiming at minimizing the risk of a collision. The output of this step is a target group of maneuvers in the longitudinal direction (accelerating or decelerating), in the lateral direction (changing lanes), and in the combination of both directions. The second step is a more detailed evaluation of several possible trajectories within these maneuvers. The trajectories are optimized to additional performance indicators such as travel time, traffic rules, consumption, and comfort. The output of this module is a trajectory in the vehicle frame that represents the recommended vehicle state (position, heading, speed, and acceleration) for the following seconds.","Title: Fast prototyping of a Highly Autonomous Cooperative Driving System for public roads.\nYear: 2010\n\nAbstract: This paper presents a framework for a fast prototyping of Advanced Driving Assistance Systems (ADAS). The simulation tool SiVIC is proposed for drastically reducing development time and costs of a vehicle system design. (RT)Maps (R) is used as a platform for easily encapsulating the system component algorithms and for effortlessly transferring them from a simulation environment to a physical vehicle. With these tools a Highly Autonomous Cooperative Driving System (HACS) has been designed. A perception component uses a combination of sensors to map the environment. In this paper a cooperative, extended perception with infrastructure-to-vehicle communication (I2V) will be proposed. A co-pilot integrates a fast Total Trajectory Exploration (TTE) method that finds a trajectory that is optimal with respect to the sensed environment. A simple controller on the vehicle actuators is used for guiding the vehicle on this trajectory. The cooperation between human and automation is managed by a Driving Mode Selection Unit (MSU) and a Human Machine Interface (HMI). In this paper a vehicle system which allows highly autonomous driving with human cooperation is called a co-system.","Title: A packing model to estimate VANET capacity.\nYear: 2012\n\nAbstract: In IEEE 802.11p VANET networks the transmission scheduling is distributed and asynchronous. The number of simultaneous transmitters is thus closely related to the CSMA/CA mechanism which limits the spatial reuse of the channel. The capacity is bounded by a determined constant whatever the number of nodes and the type of routing schemes. This paper aims to evaluate the spatial reuse of a VANET (using CSMA/CA) and to deduce its maximum capacity. The proposed model is an extension of a classical packing problem. We prove formally that the intensity of the maximum number of simultaneous transmitters (the maximal spatial reuse) converges to a constant and propose a simple estimate of this constant. Realistic simulations show that the theoretical capacity offers a very tight bound on the real capacity of the network.","Title: A new robust cooperative-reactive Filter for vehicle localization: The Extended Kalman Particle Swarm 'EKPS'.\nYear: 2013\n\nAbstract: This paper introduces a proposal for a collaborative intelligent localization algorithm inspired from the Particle Swarm Optimization (PSO) technique and applied to highly dynamic road vehicle localization. This approach performs a reactive cooperative vehicle localization by considering a PSO of the vehicle position in a dynamic environment with an adaptive dynamic 'fitness' function. In order to manage the uncertainties, the PSO algorithm is coupled with an Extended Kalman Filter (EKF). This new localization approach is tested and validated using real world data obtained from embedded sensors (GPS, INS, Odometer, Gyrometer, Steering wheel angle sensor and a Centimetrik RTK GPS) in comparison with the classical EKF performances. The first results obtained are better in terms of accuracy and robustness.","Title: Topology control in VANET and capacity estimation\nYear: 2013\n\nAbstract: Some safety applications using VANET exchange a large amount of data, and consequently require an important network capacity. In this paper, we focus on extended perception map applications, that use information from local and distant sensors to offer driving assistance (autonomous driving, collision warning, etc). Extended perception requires a high bandwidth that might not be available in practice in classical IEEE 802.11p ad hoc networks. Therefore, we propose an adaptive power control algorithm optimized for this particular application. We show through an analytical model and a large set of simulations that the network capacity is then significantly increased.","Title: Development of Full Speed Range ACC with SiVIC, a virtual platform for ADAS Prototyping, test and evaluation.\nYear: 2013\n\nAbstract: LIVIC-IFSTTAR develops driving assistance services in order to improve the driving safety. These systems are tested on several real prototypes equipped with sensors and perception, decision and control modules. But tests on real prototypes are not always available, effectively some hardware architectures could be too expensive to implement, scenario may lead to hazardous situations. Moreover, lots of reasons could lead to the inability to obtain both sensors and ground truth data for ADAS evaluation. However, safety applications must be tested in order to guaranty their reliability. For this task, simulation appears as a good alternative to the real prototyping and testing stages. In this context, the simulation must provide the same opportunities as reality, by providing all the necessary data to develop and to prototype different types of ADAS based on local or extended environment perception. The sensor data provided by simulation must be as noised and imperfect as those obtained with real sensors. To address this issue, the SiVIC platform has been developed; it provides a virtual road environment including realistic dynamic models of mobile entities (vehicles), realistic sensors, and sensors for ground truth. To test real embedded applications, an interconnection has been developed between SiVIC and third party applications (ie. RTMaps). In this way, the prototyped application can be directly embedded in real prototypes in order to test it in real conditions. A Full Speed Range ACC application is presented in this paper to illustrate the capabilities and the functionalities of this virtual platform.","Title: Comparing cooperative and non-cooperative crash risk-assessment.\nYear: 2013\n\nAbstract: Cooperative Systems provide, through the multiplication of information sources over the road, a lot of potential to improve the assessment of the road risk describing a particular driving situation. In this paper, we compare the performance of a cooperative risk assessment approach against a non-cooperative approach; we used an advanced simulation framework, allowing for accurate and detailed, close-to-reality simulations. Risk is estimated, in both cases, with combinations of indicators based on the TTC. For the noncooperative approach, vehicles are equipped only with an AAC-like forward-facing ranging sensor. On the other hand, for the cooperative approach, vehicles share information through 802.11p IVC and create an augmented map representing their environment; risk indicators are then extracted from this map. Our system shows that the cooperative risk assessment provides a systematic increase of forward warning to most of the vehicles involved in a freeway emergency braking scenario, compared to a non-cooperative system.","Title: Safe path planning in an uncertain-configuration space\nYear: 2003\n\nAbstract: The objective of this paper is to bring an effective response to the safe path planning problem which should be solved in an uncertain-configuration space. First, a path planning method dealing with localization uncertainties is proposed, where the uncertainties in both position and orientation of a non-holonomic mobile robot are considered. The safety of this method is due to the mixing of the planning phase and the navigation phase using the same process of localization (the Kalman filter). Next, while previous works planned safe paths in the configuration space, we show that it is necessary to plan safe paths in an uncertain-configuration space. Then, we introduce the novel concept of \"towers of uncertainties\" and show the effectiveness of this concept with some examples.","Title: Analysis of Intervehicle Communication to Reduce Road Crashes.\nYear: 2011\n\nAbstract: The aim of this paper is to study the contribution of intervehicle communication (IVC) to reduce the number of secondary collisions caused by an accident. We assess the impact of broadcast protocols used to disseminate warning messages and the impact of IVC penetration ratio on the number of collisions. There are two main contributions in this paper. First, it addresses the analytical evaluation o...","Title: Improved Visibility Of Road Scene Images Under Heterogeneous Fog\nYear: 2010\n\nAbstract: One source of accidents when driving a vehicle is the presence of homogeneous and heterogeneous fog. Fog fades the colors and reduces the contrast of the observed objects with respect to their distances. Various camera-based Advanced Driver Assistance Systems (ADAS) can be improved if efficient algorithms are designed for visibility enhancement of road images. The visibility enhancement algorithm proposed in [1] is not dedicated to road images and thus it leads to limited quality results on images of this kind. In this paper, we interpret the algorithm in [1] as the inference of the local atmospheric veil subject to two constraints. From this interpretation, we propose an extended algorithm which better handles road images by taking into account that a large part of the image can be assumed to be a planar road. The advantages of the proposed local algorithm are its speed, the possibility to handle both color images or gray-level images, and its small number of parameters. A comparative study and quantitative evaluation with other state-of-the-art algorithms is proposed on synthetic images with several types of generated fog. This evaluation demonstrates that the new algorithm produces similar quality results with homogeneous fog and that it is able to better deal with the presence of heterogeneous fog.","Title: Vision Enhancement in Homogeneous and Heterogeneous Fog\nYear: 2012\n\nAbstract: One source of accidents when driving a vehicle is the presence of fog. Fog fades the colors and reduces the contrasts in the scene with respect to their distances from the driver. Various camera-based Advanced Driver Assistance Systems (ADAS) can be improved if efficient algorithms are designed for visibility enhancement in road images. The visibility enhancement algorithm proposed in [1] is not optimized for road images. In this paper, we reformulate the problem as the inference of the local atmospheric veil from constraints. The algorithm in [1] thus becomes a particular case. From this new derivation, we propose to better handle road images by introducing an extra constraint taking into account that a large part of the image can be assumed to be a planar road. The advantages of the proposed local algorithm are the speed, the possibility to handle both color and gray-level images, and the small number of parameters. A new scheme is proposed for rating visibility enhancement algorithms based on the addition of several types of generated fog on synthetic and camera images. A comparative study and quantitative evaluation with other state-of-the-art algorithms is thus proposed. This evaluation demonstrates that the new algorithm produces better results with homogeneous fog and that it is able to deal better with the presence of heterogeneous fog. Finally, we also propose a model allowing to evaluate the potential safety benefit of an ADAS based on the display of defogged images.","Title: How To Manage Conflict And Ambiguities In Localization And Map Matching\nYear: 2014\n\nAbstract: Since the use of systems of satellite positioning such as the global positioning system (GPS), applications have tried to locate vehicles on maps representing the environment with their attributes. For one decade, this has led to both localization and navigation services for users. Recently, new researches have begun in order to extend the functionalities of the existing systems and thus to develop new applications using these technologies in the design of driver assistance systems. These new systems will indeed allow us to anticipate road departures or prevent overspeed turn approaches. Nevertheless, to deploy such new functionalities, it is imperative to ensure the association of vehicle position with one of the roadmap segments. In this article, we propose a new approach based on the belief theory taking into account the imperfections of available data in order to ensure the positioning and tracking of a vehicle on a roadmap and to manage conflicts and ambiguities using a multi-hypotheses decision.","Title: A computational model of the car driver interfaced with a simulation platform for future Virtual Human Centred Design applications: COSMO-SIVIC\nYear: 2012\n\nAbstract: This paper presents the first step of a research programme implemented by IFSTTAR in order to develop an integrative simulation platform able to support a Human Centred Design (HCD) method for virtual design of driving assistances. This virtual platform, named COSMO-SiVIC, implements a COgnitive Simulation MOdel of the DRIVEr (i.e. COSMODRIVE) into a Vehicle-Environment-Sensors platform (named SiVIC, for Simulateur Vehicule-Infrastructure-Capteur). From this simulation tool based on a computational driver model, the design costs of driving assistances is expected to reduce in the future, and the end-users needs during the design process are also better taken into account. This article is mainly focussed on the description of the driver model developed and implemented on the SiVIC virtual platform, which is only the first step towards a future Virtual HCD integrated tool. The first section will discuss the research context and objective, and the second one will present the theoretical background in cognitive sciences supporting our driver modelling approach. Then, the SiVIC tool is used in this research as a methodological and technical support for both empirical data collection among human drivers and as a virtual road environment to be interfaced with the COSMODRIVE model. In the result section, the functional architecture of COSMO-SIVIC (based on three complementary modules of Perception, Decision and Action) will be described, and an example of virtual simulation of human driver's errors due to visual distraction while driving will be presented. The perspectives concerning future use of COSMO-SIVIC for virtual HCD will be then discussed in the conclusion section.","Title: Cooperative Fusion for Multi-Obstacles Detection With Use of Stereovision and Laser Scanner\nYear: 2005\n\nAbstract: We propose a new cooperative fusion approach between stereovision and laser scanner in order to take advantage of the best features and cope with the drawbacks of these two sensors to perform robust, accurate and real time-detection of multi-obstacles in the automotive context. The proposed system is able to estimate the position and the height, width and depth of generic obstacles at video frame rate (25 frames per second). The vehicle pitch, estimated by stereovision, is used to filter laser scanner raw data. Objects out of the road are removed using road lane information computed by stereovision. Various fusion schemes are proposed and one is experimented. Results of experiments in real driving situations (multi-pedestrians and multi-vehicles detection) are presented and stress the benefits of our approach.","Title: Vehicle Detection And Tracking By Collaborative Fusion Between Laser Scanner And Camera\nYear: 2013\n\nAbstract: This paper presents a new approach to fuse 3D and 2D information in a driver assistance setup, in particular to perform obstacle detection and tracking. We propose a new cooperative fusion method between two exteroceptive sensors: it is able to address highly non linear dynamic configuration without any assumption on the driving maneuver. Information are provided by a mono-layer laser scanner and a monocular camera which are unsynchronized. The initial detection stage is performed using the 1D laser data, which computes clusters of points which might correspond to vehicles present on the road. These clusters are projected to the image to define targets, which will be tracked using image registration techniques. This multi-object association and tracking scheme is implemented using belief theory integrating temporal and spatial information, which allows the estimation of the dynamic state of the tracks and to monitor appearance and disappearance of obstacles. Accuracy of the method is evaluated on a database made publicly available, focus is cast on the relative localization of the vehicle ahead: estimations of its longitudinal and lateral distances are analysed.","Title: Multi-objects association in perception of dynamical situation\nYear: 2013\n\nAbstract: In current perception systems applied to the rebuilding of the environment for intelligent vehicles, the part reserved to object association for the tracking is increasingly significant. This allows firstly to follow the objects temporal evolution and secondly to increase the reliability of environment perception. We propose in this communication the development of a multiobjects association algorithm with ambiguity removal entering into the design of such a dynamic perception system for intelligent vehicles. This algorithm uses the belief theory and data modelling with fuzzy mathematics in order to be able to handle inaccurate as well as uncertain information due to imperfect sensors. These theories also allow the fusion of numerical as well as symbolic data. We develop in this article the problem of matching between known and perceived objects. This makes it possible to update a dynamic environment map for a vehicle. The belief theory will enable us to quantify the belief in the association of each perceived object with each known object. Conflicts can appear in the case of object appearance or disappearance, or in the case of a confused situation or bad perception. These conflicts are removed or solved using an assignment algorithm, giving a solution called the «best» and so ensuring the tracking of some objects present in our environment.","Title: New likelihood updating for the IMM approach application to outdoor vehicles localization\nYear: 2009\n\nAbstract: This paper presents the problematic of outdoor vehicle localization under the IMM (Interacting Multiple Model) approach. The IMM is now a well known modular approach, which is based on the discretization of the vehicle evolution space into simple maneuvers, represented each by a simple dynamic model such as constant velocity or constant turning etc. This allows the method to be optimized for highly dynamic vehicles. Unfortunately classical IMM shows some drawbacks concerning some real time multi sensors applications. In this work, we focus on outdoor vehicle localization with asynchronous sensors in order to report these drawbacks and then propose a new solution. Many tests carried out with simulated and real data confirm the interest for using such a solution in our applications.","Title: Development Of An Particle Swarm Algorithm For Vehicle Localization\nYear: 2012\n\nAbstract: This paper describes the development of a filter algorithm based on the behaviour of biological swarms. The main goal of the algorithm is to perform vehicle localization by combining the data from different sensors - GPS, IMU, speedometers, etc. - and digital maps. In this sense, the algorithm considers several solutions at the same time like Particles Filters. The algorithm has been developed off-line using real data captured from an instrumented vehicle at LIVIC. Performance of the algorithm has been validated and compared with and EKF with encouraging results.","Title: A New Decentralized Bayesian Approach for Cooperative Vehicle Localization Based on Fusion of GPS and VANET Based Inter-Vehicle Distance Measurement\nYear: 2015\n\nAbstract: Accurate and reliable vehicle localization is a key component of numerous automotive and Intelligent Transportation System (ITS) applications, including active vehicle safety systems, real time estimation of traffic conditions, and high occupancy tolling [1]. Various safety critical vehicle applications in particular, such as collision avoidance or mitigation, lane change management or emergency braking assistance systems, rely principally on the accurate and reliable knowledge of vehicles? positioning within given vicinity.","Title: Proposition Of Generic Validation Criteria Using Stereo-Vision For On-Road Obstacle Detection\nYear: 2014\n\nAbstract: Real-time obstacle detection is an essential function for the future of Advanced Driver Assistance Systems (ADAS), but its applications to the driving safety require a very high reliability: the detection rate must be high, while the false detection rate must remain extremely low. Such features seem antinomic for obstacle detection systems, especially when using a single sensor. Multi-sensor fusion is often considered as a mean to reduce this limitation. In this paper, we propose to use stereo-vision as a post-process to improve the reliability of any obstacle detection system, by reducing the number of false positives. Our algorithm, which is both generic and real-time confirms detections by locally using the stereoscopic data. We evaluated and validated our approach with an initial detection based on a vision system and a laser scanner. The evaluation dataset is real on-road data and contains more than 20,000 images.","Title: A New Model-Free Design For Vehicle Control And Its Validation Through An Advanced Simulation Platform\nYear: 2015\n\nAbstract: A new model-free setting and the corresponding \"intelligent\" P and PD controllers are employed for the longitudinal and lateral motions of a vehicle. This new approach has been developed and used in order to ensure simultaneously a best profile tracking for the longitudinal and lateral behaviors. The longitudinal speed and the derivative of the lateral deviation, on one hand, the driving/braking torque and the steering angle, on the other hand, are respectively the output and the input variables. Let us emphasize that a \"good\" mathematical modeling, which is quite difficult, if not impossible to obtain, is not needed for such a design. An important part of this publication is focused on the presentation of simulation results with actual and virtual data. The actual data, used in Matlab as reference trajectories, have been obtained from a properly instrumented car (Peugeot 406). Other virtual sets of data have been generated through the interconnected platform SiVIC/RTMaps. It is a dedicated virtual simulation platform for prototyping and validation of advanced driving assistance systems.","Title: COSMO-SIVIC: A first step towards a virtual platform for human centred design of driving assistances.\nYear: 2010\n\nAbstract: This paper presents the first step of research work implemented by INRETS in the frame of the ISi-PADAS European project, in order to develop a simulation platform able to support a Human Centred Design (HCD) method for virtual design of driving assistances. This HCD tool (called COSMO-SiVIC) integrates a cognitive simulation model of the Driver (called COSMODRIVE) on a virtual Vehicle-Environment platform (SiVIC). From this future tool, it is expected to compare since the earlier stages of the technological design, virtual simulation of driving performances with and without driving assistance, and thus to appreciate the potential benefits, interests and risks of vehicle automation on road safety.","Title: A performance test for a new reactive-cooperative filter in an ego-vehicle localization application.\nYear: 2014\n\nAbstract: This paper presents the Optimized Kalman Particle Swarm (OKPS) filter. This filter is a new robust data fusion approach adapted for ego-vehicle localization in degraded signal conditions. The OKPS is the improved version of the hybridization of the Particle Filter (PF) by Particle Swarm Optimization notions (PSO). Taking also some features from the Extended Kalman filter (EKF), the OKPS is designed for being more robust to noises such as GPS multipaths and also more reactive. The OKPS has the challenge of merging reactivity and resistance to noises. For high dynamic on-road vehicles localization, the balance between reactivity and robustness is critical. This paper introduces an intelligent collaborative localization algorithm inspired by PSO techniques that adresses this challenge. The OKPS filter outline integrates Particle Filter ( PF) tracking, PSO evolutionary optimization and EKF self-diagnose. Using real world data, the OKPS is tested in comparison to the EKF and PF approaches performances. The comparison is done following new specific criteria, designed for ego-localization filter performances analysis. Competitive results are reached for a high dynamic on-road vehicle localization application.","Title: Map-aided localization with lateral perception\nYear: 2014\n\nAbstract: Accurate localization of a vehicle is a challenging task as GPS available on the market are not designed for lane-level accuracy application. Although dead reckoning helps, cumulative errors from inertial sensors result in a integration drift. This paper presents a new method of localization based on sensors data fusion. An accurate digital map of the lane marking is used as a powerful additional sensor. Road markings are detected by processing two lateral cameras to estimate their distance to the vehicle. Coupled with the map data in a EKF filter it improves the ego-localization obtained with inertial and GPS measurements. The result is a vehicle localization at an ego-lane level of accuracy, with a lateral error of less than 10 centimeters.","Title: From virtual to reality, how to prototype, test and evaluate new ADAS: Application to automatic car parking\nYear: 2014\n\nAbstract: Over the past decade, a lot of researches have been done on the development of advanced driver assistance systems (ADAS). Most of these ADAS are now active and need to be tested and evaluated before large deployment. In these ADAS, the prototyping and the implementation of the control stages are risky stages and not so easy to carry out. Indeed, the prototyping and the test of such reactive algorithms need heavy hardware and software supports (dedicated vehicle, actuators, hardware architecture, software architecture, sensors). To achieve such active devices, additional developments and implementation of numerous expensive embedded devices are required. Therefore, in order to reduce both time and risk, in early design stage, it becomes necessary to have a very realistic simulation environment dedicated to the development and to the evaluation of these ADAS. For such virtual platform, it is mandatory to provide physics-driven road environments, virtual embedded sensors, and physics-based vehicle models. In this publication, we present a dedicated couple of platforms with their efficient interconnection for the prototyping of such ADAS. Initially, the SiVIC simulation platform has been developed to generate the virtual world (environments, sensors, actuators, vehicles). In order to improve the real time prototyping capabilities of SiVIC, an efficient interconnection of this first platform has been done with RTMaps platform. This second one is mainly dedicated to the multi-sensors data processing (data management, fusion, flow recording and replaying). In this paper we will show the interest of such bi-directionnal interconnected platforms to prototype complex and real time embedded ADAS. This interconnection can be done not only on one computer but also on a distributed and distant computers architecture. The relevance of this approach will be illustrated with an automatic parking application.","Title: Impact of reduced visibility from fog on traffic sign detection\nYear: 2014\n\nAbstract: In camera-based Advance Driver Assistance System (ADAS) such as traffic sign recognition, some failure may be inferred by adverse meteorological conditions, in particular under foggy weather. This paper investigates the effects of reduced visibility from fog in an ADAS operating range, more specifically a traffic sign detection algorithm. For this purpose, we produced a database of synthetic images containing road signs with and without fog, that is intended to be shared with the scientific community. The database enables a study of the effects of reduced visibility from fog on a gradient-based geometrical model of traffic signs. After analysing the tolerance of the algorithm to additive noise and blurring, its performance is measured under increasing level of fog. Its operating range is measured with regard to the fog density: we discuss the way the distance required to detect a sign increases with the meteorological visibility distance and its impact on safety.","Title: Dual multi-targets tracking for ambiguities' identification and solving\nYear: 2014\n\nAbstract: In this paper a new algorithm for multi-targets tracking for roadway environment is proposed. This new approach is based on two parallel tracking stages. Its objective is to improve associations between targets and tracks by avoiding wrong associations which can cause errors on track's path determination. Another interesting point of the proposed approach lies in the fact that the two trackings stages are operated together only when association ambiguities are detected. otherwise, only one tracking is used. This mechanism leads to save computational resources. This contribution comes after previous works achieved at the LIVIC (Laboratory on interactions between vehicles, road network and drivers) regarding to Multi-Hypothesis Tracking (MHT) using the Dempster-Shafer Theory. These previous works discussed the potential interest of considering at the same time multi-hypothesis solutions instead of mono-hypothesis ones. This new approach is more focused on the identification of ambiguities, and runs simultaneously two tracking stages in order to solve these ambiguities thanks to the Dempster-Shafer multi-criteria association rules. The paper will therefore explain quickly the basis of the MHT and then describe the Dual Tracking Ambiguities' Solving (DTAS) algorithm. Finally, a relevant case of study showing the interest of the DTAS will be discussed.","Title: A fast Monte Carlo algorithm for collision probability estimation\nYear: 2008\n\nAbstract: In order to navigate safely, it is important to detect and to react to a potentially dangerous situation. Such a situation can be underlined by a judicious use of the locations and the uncertainties of both the navigating vehicle and the obstacles. We propose to build an estimation of the collision probability from the environment perception with its probabilistic modelling. The probability of collision is computed from integrals of Gaussians and takes into account the uncertain configurations and the volume of both the vehicle and the obstacles.","Title: Modeling and validation of a new generic virtual optical sensor for ADAS prototyping\nYear: 2012\n\nAbstract: In the early design stages of embedded applications, it becomes necessary to have a very realistic simulation environment dedicated to the prototyping and to the evaluation of these Advanced Driving Assistance Systems (ADAS). This Numerical simulation stage is gradually becoming a strong advantage in active safety. The use of realistic numerical models enabling to substitute real data by simulated data is primordial. For such virtual platform it is mandatory to provide physics-driven road environments, virtual embedded sensors, and physics-based vehicle models. In this publication, a generic solution for cameras modelling is presented. The use of this optical sensor simulation can easily and efficiently replace real camera test campaigns. This optical sensor is very important due to the great number of applications and algorithms based on it. The presented model involves a filter mechanism in order to reproduce, in the most realistic way, the behaviour of optical sensors. The main filters used in ADAS developments will be presented. Moreover, an optical analysis of these virtual sensors has been achieved allowing the confrontation between real and simulated results. An optical platform has been developed to characterize and validate any camera, permitting to measure their performances. By comparing real and simulated sensors with this platform, this paper demonstrates this virtual platform (Pro-SiVIC™) accurately reproduces real optical sensors' behaviour.","Title: Robust positioning in safety applications for the CVIS project\nYear: 2010\n\nAbstract: This paper describes hybrid fusion module used in a strong localization context (POMA) for embedded vehicle applications. This work has been developed in order to give an answer to the POMA (Positioning, Maps and local referencing) sub project objectives. These objectives are to provide, for a set of high level applications, a positioning service included a service quality, a metric accuracy (lane) and a robust result. This work is involved in CVIS European project. The use of IMM approach in the Hybrid Fusion module will be justified in comparison to the different current probabilistic methods. The IMM, contrary to the non modular methods, is based on the discretization of the vehicle evolution space into simple maneuvers, represented each by a simple dynamic model such as constant velocity or constant turning etc. This allows the method to be optimized for highly dynamic vehicles. The application of this positioning service will be presented in a real time embedded architecture. The presented results are based on real measurements collected from representatives scenarios (test track, peri-urban road, highway). These results show a real interest in using the new IMM method in order to reach the POMA's objectives.","Title: Design of a modular demonstrator for safety application systems: The CVIS project\nYear: 2010\n\nAbstract: This article describes the design of a modular demonstrator (Enhanced Driver Awareness - EDA) aimed at integrating various safety embedded vehicular applications. Two goals are achieved with this demonstrator: the first one is to provide a complete set of useful function in order to develop safety applications; the other one is to allow multiple services to run simultaneously on the same vehicle sharing information to improve their potentials. This demonstrator has been used in the European CVIS project, which aim is to provide a set of technologies defined for embedded safety vehicle application.","Title: Validation and benchmarking for pedestrian video detection based on a sensors simulation platform\nYear: 2010\n\nAbstract: The evaluation stage is an important part in the validation of ADAS robustness. Moreover, the control and the repetitiveness of the experimentations were very difficult to conduct on real road due to safety reasons. Moreover, the lack of data/sensors or the complexity of the experiment are often very penalizing for a correct and exhaustive evaluation. It is for these reasons that LIVIC launched the development of a software simulation architecture (SIVTC), to support its research activities on ADAS. The use of such a simulation platform can provide both simulated sensors data and a ground truth reference for the validation stages.","Title: Integrated automated vehicle string longitudinal control\nYear: 2013\n\nAbstract: This paper presents the design and simulation test of an automated vehicle string longitudinal control. The vehicle string is constituted of three highly cooperative vehicles following a leader. The string follows a leading vehicle which is supposed manually driven or at least not cooperative. The first vehicle of the string uses only its on-board sensor to obtain relative speed and inter-distance to the leader. Based on Lyapunov theory and invariant sets, the control law of the string is able to guarantee that the state trajectories remain bounded under bounded disturbance inputs, such as leading vehicle acceleration. The control law is designed in such a way that variable time headway strategy is naturally handled. The control law is computed using Linear and Bilinear Matrix Inequalities (LMI-BMI) methods. Some design parameters can be adjusted to handle the trade-off between safety constraints and comfort specifications.","Title: Weighted V-disparity approach for obstacles localization in highway environments\nYear: 2013\n\nAbstract: The employment of embedded passive sensors in order to perceive environment for reducing the accident risk level is a tendency of intelligent vehicles research. From such sensors, one can extract useful informations which can assist the driver to identify hazardous situations. While safety improvement is a substantial requirement for driving assistance, localizing and tracking obstacles in complex road environment became an important task. One promising approach is to use the V-disparity based on the stereovision technique. It is a cumulative space estimated from the disparity image. We propose a sound framework and a complete system based on a real-time stereovision for detection, 3D localization and tracking of dynamic obstacles in highway environment. The main contribution we propose is the improvement of the V-disparity approach by extending the basic approach by merging it with a confidence term. This consists on weighting each pixel in the V-disparity space according to a confidence value which measures the probability of associating a pair of pixels. Furthermore, we propose a tracking system which is based on the belief theory. The tracking task is done on the image space which takes into account uncertainties, handles conflicts, and automatically dealt with targets appearance and disappearce as well as their spatial and temporal propogation. Extensive experiments on simulated and real dataset demonstrate the effectiveness and the robustness of the weighted V-disparity approach.","Title: Credibilistic IMM likelihood updating applied to outdoor vehicle robust ego-localization\nYear: 2011\n\nAbstract: This paper deals with the ego-localization problem for an outdoor vehicle equipped with proprioceptive sensors. In a current way, the ego-localisation consists in two stages (prediction and updating of the vehicle state) in order to provide a new vehicle state with its associated uncertainty. Previous works have proved the efficiency of several type of algorithms. Among them, we can quote the IMM (Interacting Multiple Model) approach. IMM is based on the discretization of the vehicle motion space modeled by simple manoeuvres, such as Constant Velocity (CT) or Constant Turn (CT) models. This yields a very adapted approach for heavily manoeuvring vehicles. Unfortunately, without GPS data, this approach becomes difficult to use. The idea is to combine different kind of proprioceptive information by using the IMM (Interacting Multiple Model) approach in a credibilistic way for the likelihood model updating in the prediction stage. In our case, data are coming from dead reckoning sensors. This kind of sensors complement the GPS for updating the vehicle state as well as the model likelihood.","Title: An improved approach for robust road marking detection and tracking applied to multi-lane estimation\nYear: 2013\n\nAbstract: In this paper, an original and innovative algorithm for multi-lane detection and estimation is proposed. Based on a three-step process, (1) road primitives extraction, (2) road markings detection and tracking, (3) lanes shape estimation. This algorithm combines several advantages at each processing level and is quite robust to the extraction method and more specifically to the choice of the extraction threshold. The detection step is so efficient, by using robust poly-fitting based on the point intensity of extracted points, that correction step is almost not necessary anymore. This approach has been used in several project in real condition and its performances have been evaluated with the sensor data generated from SiVIC platform. This validation stage has been done with a sequence of 2500 simulated images. Results are very encouraging : more than 95% of marking lines are detected for less than 2% of false alarm, with 3 cm accuracy at a range of 60 m.","Title: A Novel Approach for Improved Vehicular Positioning Using Cooperative Map Matching and Dynamic Base Station DGPS Concept\nYear: 2016\n\nAbstract: In this paper, a novel approach for improving vehicular positioning is presented. This method is based on the cooperation of the vehicles by communicating their measured information about their position. This method consists of two steps. In the first step, we introduce our cooperative map matching method. This map matching method uses the V2V communication in a vehicular ad hoc network (VANET) to exchange global positioning system (GPS) information between vehicles. Having a precise road map, vehicles can apply the road constraints of other vehicles in their own map matching process and acquire a significant improvement in their positioning. After that, we have proposed the concept of a dynamic base station DGPS (DDGPS), which is used by vehicles in the second step to generate and broadcast the GPS pseudorange corrections that can be used by newly arrived vehicles to improve their positioning. The DDGPS is a decentralized cooperative method that aims to improve the GPS positioning by estimating and compensating the common error in GPS pseudorange measurements. It can be seen as an extension of DGPS where the base stations are not necessarily static with an exact known position. In the DDGPS method, the pseudorange corrections are estimated based on the receiver's belief on its positioning and its uncertainty and then broadcasted to other GPS receivers. The performance of the proposed algorithm has been verified with simulations in several realistic scenarios.","Title: Accurate lateral positioning from map data and road marking detection\nYear: 2016\n\nAbstract: •Accurate ego-localization using visual features from two lateral cameras.•Digital map integrated as a powerful additional sensor.•Road marking detection coupled with the map data in a fusion filter.•Vehicle localization at an ego-lane level of accuracy.","Title: Road sign-aided estimation of visibility conditions\nYear: 2015\n\nAbstract: Reduced visibility on roadways caused by localized fog can impact the traffic flow in many ways: traffic speed, travel time delay, reduced capacity and accident risks. This paper presents a novel approach to estimate visibility conditions using an onboard camera and a digital map. Based on a traffic sign detector's characteristics in the fog, and registering detection by vision and information encoded in the map, we are able to accurately determine the current visual range in hazy conditions. Quantitative results are provided on a large experimental data set of driving environment with various level of fogginess.","Title: PerSEE: A central sensors fusion electronic control unit for the development of perception-based ADAS\nYear: 2015\n\nAbstract: Automated vehicles and Advanced Driver Assistance Systems (ADAS) face a variety of complex situations that are dealt with numerous sensors for the perception of the local driving area. Going forward, we see an increasing use of multiple, different sensors inputs with radar, camera and inertial measurement the most common sensor types. Each system has its own purpose and either displays information or performs an activity without consideration for any other ADAS systems, which does not make the best use of the systems. This paper presents an embedded real-time system to combine the attributes of obstacles, roadway and ego-vehicle features in order to build a collaborative local map. This embedded architecture is called PerSEE: a library of vision-based state-of-the-art algorithms was implemented and distributed in processors of a main fusion electronic board and on smart-cameras board. The embedded hardware architecture of the full PerSEE platform is detailed, with block diagrams to illustrate the partition of the algorithm on the different processors and electronic boards. The communications interfaces as well as the development environment are described.","Title: Ecodriving Performances Of Human Drivers In A Virtual And Realistic World\nYear: 2015\n\nAbstract: In this study, results of an ecodriving challenge that took place during the Paris Motor Show in 2014 are presented. The principle of this challenge was to drive a simulated passenger car as far as possible with a limited quantity of energy (15 cL). The experimental setup, constituted of the SiVIC software, an Oculus Rift Helmet and a fuel consumption model, is also detailed. 1211 trips of visitors were validated during the 17 days of the event. Results showed that high acceleration without kickdown is desirable and that constant speed can lead to significant reduction in energy consumption. Next work will concentrate on improving the simulation and the scenario to increase the immersion realism and the ecodriving behavior sensitivity.","Title: Implementation of a multi-criteria tracking based on the dempster-Shafer theory\nYear: 2015\n\nAbstract: This paper aims to present how the Belief Theory (also known as the Dempster-Shafer theory) can be relevant to implement powerful tracking systems. As the Belief theory belongs to the group of information-theories, it is very suitable for solving the track-to-target association problem which is one of the main issue of tracking systems. The data association problem is about associating the measurement at a given time-stamp with the objects that are being tracked along the time. In this paper, we present two methods based on the belief theory that can be used to improve the data association reliability. After that we propose as an example of tracking implementation and discuss the effect of using a multi-criteria track-to-target association algorithm.","Title: Distributed simulation platform for cooperative ADAS testing and validation\nYear: 2016\n\nAbstract: Cooperative Advanced Driving Assistance Systems (C-ADAS) require both efficient perception and communication technologies. Experimentation of such systems in real world conditions is needed before their deployment. However, this is a difficult task since it deals with complex road scenarios and substantial experimental cost. To limit ADAS real experimentation constraints, we propose the design and development of a new distributed and interoperable simulation platform dedicated to cooperative vehicular application prototyping and testing. This simulation platform is based on the interconnection of three main simulators: the network simulator, NS3 (Network Simulator 3), the sensor and environment simulator, Pro-Sivic and the ADAS data management framework, RTMaps. In this paper, we present the design of our platform and some primary tests that prove the concept of the already cited simulators interconnection.","Title: A Cooperative Fusion Architecture For Robust Localization: Application To Autonomous Driving\nYear: 2016\n\nAbstract: The localization of a vehicle is a central task of autonomous driving. Most of the time, it is solved by considering a single algorithm with a few sensors. In this paper, we propose a cooperative fusion architecture based on two main algorithms: a laser-based Simultaneous Localization And Mapping (SLAM) process and a lane detection and tracking approach using a single camera. Both algorithms are designed individually as cooperative fusion processes where other sensors (GPS and proprioceptive information) and dedicated maps are integrated to strengthen the advantages of each system. The whole architecture is formalized around key components (ego-vehicle, roadway, obstacle and environment). A final decision layer, that takes into account the state of each algorithm, allows the system to choose the most appropriate ego-vehicle localization mean based on the current road situation and the environmental context.","Title: A lane marker estimation method for improving lane detection\nYear: 2016\n\nAbstract: In this paper we present a new lane markers detection and estimation algorithm aiming to improve lane detection methods. We first estimate the area of lane marking using the profile of the lane estimation in a confidence map. After that a fitting method is applied to improve the lane marker detection accuracy. To track our lane markers over time and make the association between two iteration, we use transferable belief model. The final lane markers are then used to filter out noise and to improve the lane estimation. The presented method is validated on both real and synthetic datasets. Without the integration of lane markers, we obtain a 98% ego detection rate on synthetic data for the lane detection algorithm presented in [8]. With the use of our lane marker detection algorithm, we are able to improve ours results by 1.5% thus reaching a detection rate of 99.5%. For the real validation, we use a highway sequence of about 4000 pictures and get 95.02% of good detection with 0.45% of false alarm for the first thousand images and 99% for ego-lane detection with no false alarm for all the sequence.","Title: Real Time Cooperative Localization For Autonomous Vehicles\nYear: 2016\n\nAbstract: This paper describes a new strategy for real-time cooperative localization of autonomous vehicles. The strategy aims to improve the vehicles localization accuracy and reduce the impact of computing time of multi-sensor data fusion algorithms and vehicle-to-vehicle communication on parallel architectures. The method aims to solve localization issues in a cluster of autonomous vehicles, equipped with low-cost navigation systems in an unknown environment. It stands on multiple forms of the Kalman filter derivatives to estimate the vehicles' nonlinear model vector state, named local fusion node. The vehicles exchange their local state estimate and Covariance Intersection algorithm for merging the local vehicles' state estimate in the second node (named global data fusion node). This strategy simultaneously exploits the proprioceptive and sensors -a Global Positioning System, and a vehicle-to-vehicle transmitter and receiver-and an exteroceptive sensor, range finder, to sense their surroundings for more accurate and reliable collaborative localization.","Title: An efficient model-free setting for longitudinal and lateral vehicle control. Validation through the interconnected pro-SiVIC/RTMaps prototyping platform.\nYear: 2018\n\nAbstract: In this paper, the problem of tracking desired longitudinal and lateral motions for a vehicle is addressed here. Let us point out that a “good” modeling is often quite difficult or even impossible to obtain. It is due for example to parametric uncertainties, for the vehicle mass, inertia or for the interaction forces between the wheels and the road pavement. To overcome this type of difficulties, ...","Title: Performance evaluation of AODV, DSR, GRP and OLSR for VANET with real-world trajectories\nYear: 2017\n\nAbstract: Vehicular communications can be achieved through the infrastructure (Vehicle-to-infrastructure network, V2I), as well as directly through vehicle-to-vehicle communication (V2V) via ad hoc networks. In V2V communications, the routing protocols are designed in order to optimize the dissemination of messages. This paper presents an evaluation of routing protocols such as the Optimized Link State Routing (OLSR), Ad hoc On-demand Distance Vector (AODV), Dynamic Source Routing (DSR), and Geographic Routing Protocol (GRP), while considering both vehicular safety application requirements and mobility models based on real-world traces of vehicular traffic. The results show that, though proactive routing protocols perform better in this context, the four routing protocols fail to fulfill the safety application requirements on the delay metric even for a reasonable number of vehicles.","Title: Intelligent Vehicle Embedded Sensors Fault Detection and Isolation Using Analytical Redundancy and Nonlinear Transformations\nYear: 2017\n\nAbstract: This work proposes a fault detection architecture for vehicle embedded sensors, allowing to deal with both system nonlinearity and environmental disturbances and degradations. The proposed method uses analytical redundancy and a nonlinear transformation to generate the residual value allowing the fault detection. A strategy dedicated to the optimization of the detection parameters choice is also developed.","Title: Multi-Hypotheses Tracking using the Dempster-Shafer Theory, application to ambiguous road context\nYear: 2016\n\nAbstract: We develop a new method to solve ambiguities in tracks-target association in vehicular environments.Previous work with the Dempster-Shafer theory is improved by incorporating conflictual data in a beneficial way.Two Multi-Hypotheses Tracking methods are examined with their pro- and cons.Ambiguity removal is demonstrated with two examples: pedestrians crossing in front of a vehicle-mounted laserscanner, and an overtaking manoeuvre. This paper presents a Multi-Hypotheses Tracking (MHT) approach that allows solving ambiguities that arise with previous methods of associating targets and tracks within a highly volatile vehicular environment. The previous approach based on the Dempster-Shafer Theory assumes that associations between tracks and targets are unique; this was shown to allow the formation of ghost tracks when there was too much ambiguity or conflict for the system to take a meaningful decision. The MHT algorithm described in this paper removes this uniqueness condition, allowing the system to include ambiguity and even to prevent making any decision if available data are poor. We provide a general introduction to the Dempster-Shafer Theory and present the previously used approach. Then, we explain our MHT mechanism and provide evidence of its increased performance in reducing the amount of ghost tracks and false positive processed by the tracking system.","Title: Perception, information processing and modeling: Critical stages for autonomous driving applications.\nYear: 2017\n\nAbstract: •\tWe proposed an overview on reliability and high robustness of driving automation solutions.•\tWe described a global architecture for automated driving with the human in the loop.•\tWe proposed a literature review of driving automation solutions (perception and sensors).•\tWe proposed a discussion about sensors and methods limits.","Title: Chain-Branch-Leaf: A clustering scheme for vehicular networks using only V2V communications.\nYear: 2018\n\nAbstract: The development of vehicular networks leads to a plethora of new applications that are already becoming essential to the drivers and to cooperative vehicles. One important condition to the continuity of these new services is the existence of an ubiquitous network access, which cannot be completely guaranteed. In this context, developing reliable ad hoc vehicle-to-vehicle communications is a good alternative that also becomes mandatory. This work proposes a clustering scheme that combines the information on road configuration, vehicle mobility and link quality in order to build a structure similar to a vehicular network infrastructure, while relying only on the vehicles. This clustering scheme can be integrated into any reactive, proactive, or geographic ad hoc routing protocol in order to optimize the flooding of messages and simplify routing operations. The evaluations realized through simulation show that the proposed clustering scheme allows creating and maintaining a stable backbone during a significant time. Moreover, the simulation results show that the proposed clustering scheme reduces significantly the impact of broadcast traffic flooding in comparison with well-established techniques such as multipoint relaying, to mention few.","Title: Fast Semi-Dense 3d Semantic Mapping With Monocular Visual Slam\nYear: 2017\n\nAbstract: Fast 3D reconstruction with semantic information on road scenarios involves issues of geometry and appearance in the field of computer vision. An important idea is that fusion of geometry and appearance would boost the performance of each other. Stereo cameras and RGB-D sensors are widely used to realise 3D reconstruction and trajectory tracking, but they need heavy computation cost and storage in a dense way. Moreover, they lack flexibility of seamless switch between various scaled environments, i.e., indoor and outdoor scenes. Besides, autonomous vehicle would benefit from semantic information on the road, but it is hard to be sufficiently accurate and consistent in the 3D mapping. We address this challenge by fusion of direct Simultaneous Localisation and Mapping (SLAM) from a monocular camera in a semi-dense way and the state-of-the-art approaches of deep neural network. In our approach, 2D semantic information is transferred to 3D mapping via correspondence between consecutive Keyframes with spatial consistency. Since there are lots of semantic redundancies in consecutive frames, it is not necessary to process semantic segmentation for each frame in the entire sequence. Consequently, the segmentation could run in a reasonable speed (about 20Hz). We thoroughly evaluate our methods on road scene datasets. The experiments show a promising 3D semantic mapping in various road scenes.","Title: Fault Detection Architecture for Proprioceptive Sensors based on a Multi Model Approach and Fuzzy Logic Decisions.\nYear: 2015\n\nAbstract: In this paper a new fault detection architecture will be presented. Inspired by multi-model data fusion algorithms and fuzzy logic decisions, it consists in the comparison between the estimation of a dynamic mode using each sensor independently. This method is used to deal with important non-linearity and strong interaction with the environment usually encountered in the domain of the intelligent vehicles localization. The concept of analytic redundancy is also used to ignore model uncertainties","Title: Fault Detection and Isolation via the Interacting Multiple Model Approach Applied to Drive-By-Wire Vehicles.\nYear: 2018\n\nAbstract: The place of driving assistance systems is currently increasing drastically for road vehicles. Paving the road to the fully autonomous vehicle, the drive-by-wire technology could improve the potential of the vehicle control. The implementation of these new embedded systems is still limited, mainly for reliability reasons, thus requiring the development of diagnostic mechanisms. In this paper, we investigate the detection and the identification of sensor and actuator faults for a drive-by-wire road vehicle. An Interacting Multiple Model approach is proposed, based on a non-linear vehicle dynamics observer. The adequacy of different probabilistic observers is discussed. The results, based on experimental vehicle signals, show a fast and robust identification of sensor faults while the actuator faults are more challenging.","Title: Multi-Criteria Decision Making for Autonomous Vehicles using Fuzzy Dempster-Shafer Reasoning.\nYear: 2018\n\nAbstract: This article considers the problem of high-level decision process for autonomous vehicles on highways. The goal is to select a predictive reference trajectory among a set of candidate ones, issued from a trajectory generator. This selection aims at optimizing multi-criteria functions, such as safety, legal rules, preferences and comfort of passengers, or energy consumption. This work introduces a new framework for Multi-Criteria Decision Making (MCDM). The proposed approach adopts fuzzy logic theory to deal with heterogeneous criteria and arbitrary functions. Moreover, the consideration of uncertain vehicleu0027s sensors data is done using the Dempster-Shafer Theory with fuzzy sets in order to provide a risk assessment. Simulation results using datasets collected under the NGSIM program are presented on car following cases, and extended to lane changing situations.","Title: A Cooperative Vehicle Ego-localization Application Using V2V Communications with CBL Clustering.\nYear: 2018\n\nAbstract: The cooperative vehicle paradigm offers new opportunities for enhancing various vehicle functions through distributed applications. The localization function is one of the most important since its services are needed by numerous applications ranging from driver navigation to autonomous vehicle guiding. Though the GPS (Global Positioning System) provides honorable service for driver navigation, its precision is not accurate for allowing an autonomous vehicle to localize itself correctly on the road lanes. Recently an ego-localization technique where a group of vehicles exchange their position and the related correction through V2V communications has been proposed in order to enhance the precision of the 10- cation of each node in the group. In this paper, we evaluate the performance that this application can expect from V2V communication services supplied by the CBL (Chain-Branch-Leaf) clustering scheme. The simulation results show that CBL achieves delays and packet delivery ratio adapted to various rates of the ego-localization application traffic.","Title: Optimized self-adaptive PID speed control for autonomous vehicles\nYear: 2021\n\nAbstract: The main control tasks in autonomous vehicles are steering (lateral) and speed (longitudinal) control. PID controllers are widely used in the industry because of their simplicity and good performance, but they are difficult to tune and need additional adaptation to control nonlinear systems with varying parameters. In this paper, the longitudinal control task is addressed by implementing adaptive ..."],"string":"[\n \"Title: Consistent outdoor vehicle localization by bounded-error state estimation\\nYear: 2009\\n\\nAbstract: Localization is a part of many automotive applications where safety is of crucial importance. We think that the best way to guarantee the safety in these applications is to guarantee the results of their embedded localization algorithms. Unfortunately localization of vehicles is mostly solved by Bayesian methods which (due to their structure) can only guarantee their results in a probabilistic way. Interval analysis allows an alternative approach with bounded-error state estimation. Such an approach provides a bounded set of configurations that is guaranteed to surround the actual vehicle configuration. We have validated the bounded-error state estimation with an outdoor vehicle equipped with odometers, a GPS receiver and a gyro. With the experimental results we compare the bounded-error state estimation with the particle filter localization in terms of consistency and computation time.\",\n \"Title: Maneuver-based trajectory planning for highly autonomous vehicles on real road with traffic and driver interaction\\nYear: 2010\\n\\nAbstract: This paper presents the design and first test on a simulator of a vehicle trajectory-planning algorithm that adapts to traffic on a lane-structured infrastructure such as highways. The proposed algorithm is designed to run on a fail-safe embedded environment with low computational power, such as an engine control unit, to be implementable in commercial vehicles of the near future. The target platform has a clock frequency of less than 150 MHz, 150 kB RAM of memory, and a 3-MB program memory. The trajectory planning is performed by a two-step algorithm. The first step defines the feasible maneuvers with respect to the environment, aiming at minimizing the risk of a collision. The output of this step is a target group of maneuvers in the longitudinal direction (accelerating or decelerating), in the lateral direction (changing lanes), and in the combination of both directions. The second step is a more detailed evaluation of several possible trajectories within these maneuvers. The trajectories are optimized to additional performance indicators such as travel time, traffic rules, consumption, and comfort. The output of this module is a trajectory in the vehicle frame that represents the recommended vehicle state (position, heading, speed, and acceleration) for the following seconds.\",\n \"Title: Fast prototyping of a Highly Autonomous Cooperative Driving System for public roads.\\nYear: 2010\\n\\nAbstract: This paper presents a framework for a fast prototyping of Advanced Driving Assistance Systems (ADAS). The simulation tool SiVIC is proposed for drastically reducing development time and costs of a vehicle system design. (RT)Maps (R) is used as a platform for easily encapsulating the system component algorithms and for effortlessly transferring them from a simulation environment to a physical vehicle. With these tools a Highly Autonomous Cooperative Driving System (HACS) has been designed. A perception component uses a combination of sensors to map the environment. In this paper a cooperative, extended perception with infrastructure-to-vehicle communication (I2V) will be proposed. A co-pilot integrates a fast Total Trajectory Exploration (TTE) method that finds a trajectory that is optimal with respect to the sensed environment. A simple controller on the vehicle actuators is used for guiding the vehicle on this trajectory. The cooperation between human and automation is managed by a Driving Mode Selection Unit (MSU) and a Human Machine Interface (HMI). In this paper a vehicle system which allows highly autonomous driving with human cooperation is called a co-system.\",\n \"Title: A packing model to estimate VANET capacity.\\nYear: 2012\\n\\nAbstract: In IEEE 802.11p VANET networks the transmission scheduling is distributed and asynchronous. The number of simultaneous transmitters is thus closely related to the CSMA/CA mechanism which limits the spatial reuse of the channel. The capacity is bounded by a determined constant whatever the number of nodes and the type of routing schemes. This paper aims to evaluate the spatial reuse of a VANET (using CSMA/CA) and to deduce its maximum capacity. The proposed model is an extension of a classical packing problem. We prove formally that the intensity of the maximum number of simultaneous transmitters (the maximal spatial reuse) converges to a constant and propose a simple estimate of this constant. Realistic simulations show that the theoretical capacity offers a very tight bound on the real capacity of the network.\",\n \"Title: A new robust cooperative-reactive Filter for vehicle localization: The Extended Kalman Particle Swarm 'EKPS'.\\nYear: 2013\\n\\nAbstract: This paper introduces a proposal for a collaborative intelligent localization algorithm inspired from the Particle Swarm Optimization (PSO) technique and applied to highly dynamic road vehicle localization. This approach performs a reactive cooperative vehicle localization by considering a PSO of the vehicle position in a dynamic environment with an adaptive dynamic 'fitness' function. In order to manage the uncertainties, the PSO algorithm is coupled with an Extended Kalman Filter (EKF). This new localization approach is tested and validated using real world data obtained from embedded sensors (GPS, INS, Odometer, Gyrometer, Steering wheel angle sensor and a Centimetrik RTK GPS) in comparison with the classical EKF performances. The first results obtained are better in terms of accuracy and robustness.\",\n \"Title: Topology control in VANET and capacity estimation\\nYear: 2013\\n\\nAbstract: Some safety applications using VANET exchange a large amount of data, and consequently require an important network capacity. In this paper, we focus on extended perception map applications, that use information from local and distant sensors to offer driving assistance (autonomous driving, collision warning, etc). Extended perception requires a high bandwidth that might not be available in practice in classical IEEE 802.11p ad hoc networks. Therefore, we propose an adaptive power control algorithm optimized for this particular application. We show through an analytical model and a large set of simulations that the network capacity is then significantly increased.\",\n \"Title: Development of Full Speed Range ACC with SiVIC, a virtual platform for ADAS Prototyping, test and evaluation.\\nYear: 2013\\n\\nAbstract: LIVIC-IFSTTAR develops driving assistance services in order to improve the driving safety. These systems are tested on several real prototypes equipped with sensors and perception, decision and control modules. But tests on real prototypes are not always available, effectively some hardware architectures could be too expensive to implement, scenario may lead to hazardous situations. Moreover, lots of reasons could lead to the inability to obtain both sensors and ground truth data for ADAS evaluation. However, safety applications must be tested in order to guaranty their reliability. For this task, simulation appears as a good alternative to the real prototyping and testing stages. In this context, the simulation must provide the same opportunities as reality, by providing all the necessary data to develop and to prototype different types of ADAS based on local or extended environment perception. The sensor data provided by simulation must be as noised and imperfect as those obtained with real sensors. To address this issue, the SiVIC platform has been developed; it provides a virtual road environment including realistic dynamic models of mobile entities (vehicles), realistic sensors, and sensors for ground truth. To test real embedded applications, an interconnection has been developed between SiVIC and third party applications (ie. RTMaps). In this way, the prototyped application can be directly embedded in real prototypes in order to test it in real conditions. A Full Speed Range ACC application is presented in this paper to illustrate the capabilities and the functionalities of this virtual platform.\",\n \"Title: Comparing cooperative and non-cooperative crash risk-assessment.\\nYear: 2013\\n\\nAbstract: Cooperative Systems provide, through the multiplication of information sources over the road, a lot of potential to improve the assessment of the road risk describing a particular driving situation. In this paper, we compare the performance of a cooperative risk assessment approach against a non-cooperative approach; we used an advanced simulation framework, allowing for accurate and detailed, close-to-reality simulations. Risk is estimated, in both cases, with combinations of indicators based on the TTC. For the noncooperative approach, vehicles are equipped only with an AAC-like forward-facing ranging sensor. On the other hand, for the cooperative approach, vehicles share information through 802.11p IVC and create an augmented map representing their environment; risk indicators are then extracted from this map. Our system shows that the cooperative risk assessment provides a systematic increase of forward warning to most of the vehicles involved in a freeway emergency braking scenario, compared to a non-cooperative system.\",\n \"Title: Safe path planning in an uncertain-configuration space\\nYear: 2003\\n\\nAbstract: The objective of this paper is to bring an effective response to the safe path planning problem which should be solved in an uncertain-configuration space. First, a path planning method dealing with localization uncertainties is proposed, where the uncertainties in both position and orientation of a non-holonomic mobile robot are considered. The safety of this method is due to the mixing of the planning phase and the navigation phase using the same process of localization (the Kalman filter). Next, while previous works planned safe paths in the configuration space, we show that it is necessary to plan safe paths in an uncertain-configuration space. Then, we introduce the novel concept of \\\"towers of uncertainties\\\" and show the effectiveness of this concept with some examples.\",\n \"Title: Analysis of Intervehicle Communication to Reduce Road Crashes.\\nYear: 2011\\n\\nAbstract: The aim of this paper is to study the contribution of intervehicle communication (IVC) to reduce the number of secondary collisions caused by an accident. We assess the impact of broadcast protocols used to disseminate warning messages and the impact of IVC penetration ratio on the number of collisions. There are two main contributions in this paper. First, it addresses the analytical evaluation o...\",\n \"Title: Improved Visibility Of Road Scene Images Under Heterogeneous Fog\\nYear: 2010\\n\\nAbstract: One source of accidents when driving a vehicle is the presence of homogeneous and heterogeneous fog. Fog fades the colors and reduces the contrast of the observed objects with respect to their distances. Various camera-based Advanced Driver Assistance Systems (ADAS) can be improved if efficient algorithms are designed for visibility enhancement of road images. The visibility enhancement algorithm proposed in [1] is not dedicated to road images and thus it leads to limited quality results on images of this kind. In this paper, we interpret the algorithm in [1] as the inference of the local atmospheric veil subject to two constraints. From this interpretation, we propose an extended algorithm which better handles road images by taking into account that a large part of the image can be assumed to be a planar road. The advantages of the proposed local algorithm are its speed, the possibility to handle both color images or gray-level images, and its small number of parameters. A comparative study and quantitative evaluation with other state-of-the-art algorithms is proposed on synthetic images with several types of generated fog. This evaluation demonstrates that the new algorithm produces similar quality results with homogeneous fog and that it is able to better deal with the presence of heterogeneous fog.\",\n \"Title: Vision Enhancement in Homogeneous and Heterogeneous Fog\\nYear: 2012\\n\\nAbstract: One source of accidents when driving a vehicle is the presence of fog. Fog fades the colors and reduces the contrasts in the scene with respect to their distances from the driver. Various camera-based Advanced Driver Assistance Systems (ADAS) can be improved if efficient algorithms are designed for visibility enhancement in road images. The visibility enhancement algorithm proposed in [1] is not optimized for road images. In this paper, we reformulate the problem as the inference of the local atmospheric veil from constraints. The algorithm in [1] thus becomes a particular case. From this new derivation, we propose to better handle road images by introducing an extra constraint taking into account that a large part of the image can be assumed to be a planar road. The advantages of the proposed local algorithm are the speed, the possibility to handle both color and gray-level images, and the small number of parameters. A new scheme is proposed for rating visibility enhancement algorithms based on the addition of several types of generated fog on synthetic and camera images. A comparative study and quantitative evaluation with other state-of-the-art algorithms is thus proposed. This evaluation demonstrates that the new algorithm produces better results with homogeneous fog and that it is able to deal better with the presence of heterogeneous fog. Finally, we also propose a model allowing to evaluate the potential safety benefit of an ADAS based on the display of defogged images.\",\n \"Title: How To Manage Conflict And Ambiguities In Localization And Map Matching\\nYear: 2014\\n\\nAbstract: Since the use of systems of satellite positioning such as the global positioning system (GPS), applications have tried to locate vehicles on maps representing the environment with their attributes. For one decade, this has led to both localization and navigation services for users. Recently, new researches have begun in order to extend the functionalities of the existing systems and thus to develop new applications using these technologies in the design of driver assistance systems. These new systems will indeed allow us to anticipate road departures or prevent overspeed turn approaches. Nevertheless, to deploy such new functionalities, it is imperative to ensure the association of vehicle position with one of the roadmap segments. In this article, we propose a new approach based on the belief theory taking into account the imperfections of available data in order to ensure the positioning and tracking of a vehicle on a roadmap and to manage conflicts and ambiguities using a multi-hypotheses decision.\",\n \"Title: A computational model of the car driver interfaced with a simulation platform for future Virtual Human Centred Design applications: COSMO-SIVIC\\nYear: 2012\\n\\nAbstract: This paper presents the first step of a research programme implemented by IFSTTAR in order to develop an integrative simulation platform able to support a Human Centred Design (HCD) method for virtual design of driving assistances. This virtual platform, named COSMO-SiVIC, implements a COgnitive Simulation MOdel of the DRIVEr (i.e. COSMODRIVE) into a Vehicle-Environment-Sensors platform (named SiVIC, for Simulateur Vehicule-Infrastructure-Capteur). From this simulation tool based on a computational driver model, the design costs of driving assistances is expected to reduce in the future, and the end-users needs during the design process are also better taken into account. This article is mainly focussed on the description of the driver model developed and implemented on the SiVIC virtual platform, which is only the first step towards a future Virtual HCD integrated tool. The first section will discuss the research context and objective, and the second one will present the theoretical background in cognitive sciences supporting our driver modelling approach. Then, the SiVIC tool is used in this research as a methodological and technical support for both empirical data collection among human drivers and as a virtual road environment to be interfaced with the COSMODRIVE model. In the result section, the functional architecture of COSMO-SIVIC (based on three complementary modules of Perception, Decision and Action) will be described, and an example of virtual simulation of human driver's errors due to visual distraction while driving will be presented. The perspectives concerning future use of COSMO-SIVIC for virtual HCD will be then discussed in the conclusion section.\",\n \"Title: Cooperative Fusion for Multi-Obstacles Detection With Use of Stereovision and Laser Scanner\\nYear: 2005\\n\\nAbstract: We propose a new cooperative fusion approach between stereovision and laser scanner in order to take advantage of the best features and cope with the drawbacks of these two sensors to perform robust, accurate and real time-detection of multi-obstacles in the automotive context. The proposed system is able to estimate the position and the height, width and depth of generic obstacles at video frame rate (25 frames per second). The vehicle pitch, estimated by stereovision, is used to filter laser scanner raw data. Objects out of the road are removed using road lane information computed by stereovision. Various fusion schemes are proposed and one is experimented. Results of experiments in real driving situations (multi-pedestrians and multi-vehicles detection) are presented and stress the benefits of our approach.\",\n \"Title: Vehicle Detection And Tracking By Collaborative Fusion Between Laser Scanner And Camera\\nYear: 2013\\n\\nAbstract: This paper presents a new approach to fuse 3D and 2D information in a driver assistance setup, in particular to perform obstacle detection and tracking. We propose a new cooperative fusion method between two exteroceptive sensors: it is able to address highly non linear dynamic configuration without any assumption on the driving maneuver. Information are provided by a mono-layer laser scanner and a monocular camera which are unsynchronized. The initial detection stage is performed using the 1D laser data, which computes clusters of points which might correspond to vehicles present on the road. These clusters are projected to the image to define targets, which will be tracked using image registration techniques. This multi-object association and tracking scheme is implemented using belief theory integrating temporal and spatial information, which allows the estimation of the dynamic state of the tracks and to monitor appearance and disappearance of obstacles. Accuracy of the method is evaluated on a database made publicly available, focus is cast on the relative localization of the vehicle ahead: estimations of its longitudinal and lateral distances are analysed.\",\n \"Title: Multi-objects association in perception of dynamical situation\\nYear: 2013\\n\\nAbstract: In current perception systems applied to the rebuilding of the environment for intelligent vehicles, the part reserved to object association for the tracking is increasingly significant. This allows firstly to follow the objects temporal evolution and secondly to increase the reliability of environment perception. We propose in this communication the development of a multiobjects association algorithm with ambiguity removal entering into the design of such a dynamic perception system for intelligent vehicles. This algorithm uses the belief theory and data modelling with fuzzy mathematics in order to be able to handle inaccurate as well as uncertain information due to imperfect sensors. These theories also allow the fusion of numerical as well as symbolic data. We develop in this article the problem of matching between known and perceived objects. This makes it possible to update a dynamic environment map for a vehicle. The belief theory will enable us to quantify the belief in the association of each perceived object with each known object. Conflicts can appear in the case of object appearance or disappearance, or in the case of a confused situation or bad perception. These conflicts are removed or solved using an assignment algorithm, giving a solution called the «best» and so ensuring the tracking of some objects present in our environment.\",\n \"Title: New likelihood updating for the IMM approach application to outdoor vehicles localization\\nYear: 2009\\n\\nAbstract: This paper presents the problematic of outdoor vehicle localization under the IMM (Interacting Multiple Model) approach. The IMM is now a well known modular approach, which is based on the discretization of the vehicle evolution space into simple maneuvers, represented each by a simple dynamic model such as constant velocity or constant turning etc. This allows the method to be optimized for highly dynamic vehicles. Unfortunately classical IMM shows some drawbacks concerning some real time multi sensors applications. In this work, we focus on outdoor vehicle localization with asynchronous sensors in order to report these drawbacks and then propose a new solution. Many tests carried out with simulated and real data confirm the interest for using such a solution in our applications.\",\n \"Title: Development Of An Particle Swarm Algorithm For Vehicle Localization\\nYear: 2012\\n\\nAbstract: This paper describes the development of a filter algorithm based on the behaviour of biological swarms. The main goal of the algorithm is to perform vehicle localization by combining the data from different sensors - GPS, IMU, speedometers, etc. - and digital maps. In this sense, the algorithm considers several solutions at the same time like Particles Filters. The algorithm has been developed off-line using real data captured from an instrumented vehicle at LIVIC. Performance of the algorithm has been validated and compared with and EKF with encouraging results.\",\n \"Title: A New Decentralized Bayesian Approach for Cooperative Vehicle Localization Based on Fusion of GPS and VANET Based Inter-Vehicle Distance Measurement\\nYear: 2015\\n\\nAbstract: Accurate and reliable vehicle localization is a key component of numerous automotive and Intelligent Transportation System (ITS) applications, including active vehicle safety systems, real time estimation of traffic conditions, and high occupancy tolling [1]. Various safety critical vehicle applications in particular, such as collision avoidance or mitigation, lane change management or emergency braking assistance systems, rely principally on the accurate and reliable knowledge of vehicles? positioning within given vicinity.\",\n \"Title: Proposition Of Generic Validation Criteria Using Stereo-Vision For On-Road Obstacle Detection\\nYear: 2014\\n\\nAbstract: Real-time obstacle detection is an essential function for the future of Advanced Driver Assistance Systems (ADAS), but its applications to the driving safety require a very high reliability: the detection rate must be high, while the false detection rate must remain extremely low. Such features seem antinomic for obstacle detection systems, especially when using a single sensor. Multi-sensor fusion is often considered as a mean to reduce this limitation. In this paper, we propose to use stereo-vision as a post-process to improve the reliability of any obstacle detection system, by reducing the number of false positives. Our algorithm, which is both generic and real-time confirms detections by locally using the stereoscopic data. We evaluated and validated our approach with an initial detection based on a vision system and a laser scanner. The evaluation dataset is real on-road data and contains more than 20,000 images.\",\n \"Title: A New Model-Free Design For Vehicle Control And Its Validation Through An Advanced Simulation Platform\\nYear: 2015\\n\\nAbstract: A new model-free setting and the corresponding \\\"intelligent\\\" P and PD controllers are employed for the longitudinal and lateral motions of a vehicle. This new approach has been developed and used in order to ensure simultaneously a best profile tracking for the longitudinal and lateral behaviors. The longitudinal speed and the derivative of the lateral deviation, on one hand, the driving/braking torque and the steering angle, on the other hand, are respectively the output and the input variables. Let us emphasize that a \\\"good\\\" mathematical modeling, which is quite difficult, if not impossible to obtain, is not needed for such a design. An important part of this publication is focused on the presentation of simulation results with actual and virtual data. The actual data, used in Matlab as reference trajectories, have been obtained from a properly instrumented car (Peugeot 406). Other virtual sets of data have been generated through the interconnected platform SiVIC/RTMaps. It is a dedicated virtual simulation platform for prototyping and validation of advanced driving assistance systems.\",\n \"Title: COSMO-SIVIC: A first step towards a virtual platform for human centred design of driving assistances.\\nYear: 2010\\n\\nAbstract: This paper presents the first step of research work implemented by INRETS in the frame of the ISi-PADAS European project, in order to develop a simulation platform able to support a Human Centred Design (HCD) method for virtual design of driving assistances. This HCD tool (called COSMO-SiVIC) integrates a cognitive simulation model of the Driver (called COSMODRIVE) on a virtual Vehicle-Environment platform (SiVIC). From this future tool, it is expected to compare since the earlier stages of the technological design, virtual simulation of driving performances with and without driving assistance, and thus to appreciate the potential benefits, interests and risks of vehicle automation on road safety.\",\n \"Title: A performance test for a new reactive-cooperative filter in an ego-vehicle localization application.\\nYear: 2014\\n\\nAbstract: This paper presents the Optimized Kalman Particle Swarm (OKPS) filter. This filter is a new robust data fusion approach adapted for ego-vehicle localization in degraded signal conditions. The OKPS is the improved version of the hybridization of the Particle Filter (PF) by Particle Swarm Optimization notions (PSO). Taking also some features from the Extended Kalman filter (EKF), the OKPS is designed for being more robust to noises such as GPS multipaths and also more reactive. The OKPS has the challenge of merging reactivity and resistance to noises. For high dynamic on-road vehicles localization, the balance between reactivity and robustness is critical. This paper introduces an intelligent collaborative localization algorithm inspired by PSO techniques that adresses this challenge. The OKPS filter outline integrates Particle Filter ( PF) tracking, PSO evolutionary optimization and EKF self-diagnose. Using real world data, the OKPS is tested in comparison to the EKF and PF approaches performances. The comparison is done following new specific criteria, designed for ego-localization filter performances analysis. Competitive results are reached for a high dynamic on-road vehicle localization application.\",\n \"Title: Map-aided localization with lateral perception\\nYear: 2014\\n\\nAbstract: Accurate localization of a vehicle is a challenging task as GPS available on the market are not designed for lane-level accuracy application. Although dead reckoning helps, cumulative errors from inertial sensors result in a integration drift. This paper presents a new method of localization based on sensors data fusion. An accurate digital map of the lane marking is used as a powerful additional sensor. Road markings are detected by processing two lateral cameras to estimate their distance to the vehicle. Coupled with the map data in a EKF filter it improves the ego-localization obtained with inertial and GPS measurements. The result is a vehicle localization at an ego-lane level of accuracy, with a lateral error of less than 10 centimeters.\",\n \"Title: From virtual to reality, how to prototype, test and evaluate new ADAS: Application to automatic car parking\\nYear: 2014\\n\\nAbstract: Over the past decade, a lot of researches have been done on the development of advanced driver assistance systems (ADAS). Most of these ADAS are now active and need to be tested and evaluated before large deployment. In these ADAS, the prototyping and the implementation of the control stages are risky stages and not so easy to carry out. Indeed, the prototyping and the test of such reactive algorithms need heavy hardware and software supports (dedicated vehicle, actuators, hardware architecture, software architecture, sensors). To achieve such active devices, additional developments and implementation of numerous expensive embedded devices are required. Therefore, in order to reduce both time and risk, in early design stage, it becomes necessary to have a very realistic simulation environment dedicated to the development and to the evaluation of these ADAS. For such virtual platform, it is mandatory to provide physics-driven road environments, virtual embedded sensors, and physics-based vehicle models. In this publication, we present a dedicated couple of platforms with their efficient interconnection for the prototyping of such ADAS. Initially, the SiVIC simulation platform has been developed to generate the virtual world (environments, sensors, actuators, vehicles). In order to improve the real time prototyping capabilities of SiVIC, an efficient interconnection of this first platform has been done with RTMaps platform. This second one is mainly dedicated to the multi-sensors data processing (data management, fusion, flow recording and replaying). In this paper we will show the interest of such bi-directionnal interconnected platforms to prototype complex and real time embedded ADAS. This interconnection can be done not only on one computer but also on a distributed and distant computers architecture. The relevance of this approach will be illustrated with an automatic parking application.\",\n \"Title: Impact of reduced visibility from fog on traffic sign detection\\nYear: 2014\\n\\nAbstract: In camera-based Advance Driver Assistance System (ADAS) such as traffic sign recognition, some failure may be inferred by adverse meteorological conditions, in particular under foggy weather. This paper investigates the effects of reduced visibility from fog in an ADAS operating range, more specifically a traffic sign detection algorithm. For this purpose, we produced a database of synthetic images containing road signs with and without fog, that is intended to be shared with the scientific community. The database enables a study of the effects of reduced visibility from fog on a gradient-based geometrical model of traffic signs. After analysing the tolerance of the algorithm to additive noise and blurring, its performance is measured under increasing level of fog. Its operating range is measured with regard to the fog density: we discuss the way the distance required to detect a sign increases with the meteorological visibility distance and its impact on safety.\",\n \"Title: Dual multi-targets tracking for ambiguities' identification and solving\\nYear: 2014\\n\\nAbstract: In this paper a new algorithm for multi-targets tracking for roadway environment is proposed. This new approach is based on two parallel tracking stages. Its objective is to improve associations between targets and tracks by avoiding wrong associations which can cause errors on track's path determination. Another interesting point of the proposed approach lies in the fact that the two trackings stages are operated together only when association ambiguities are detected. otherwise, only one tracking is used. This mechanism leads to save computational resources. This contribution comes after previous works achieved at the LIVIC (Laboratory on interactions between vehicles, road network and drivers) regarding to Multi-Hypothesis Tracking (MHT) using the Dempster-Shafer Theory. These previous works discussed the potential interest of considering at the same time multi-hypothesis solutions instead of mono-hypothesis ones. This new approach is more focused on the identification of ambiguities, and runs simultaneously two tracking stages in order to solve these ambiguities thanks to the Dempster-Shafer multi-criteria association rules. The paper will therefore explain quickly the basis of the MHT and then describe the Dual Tracking Ambiguities' Solving (DTAS) algorithm. Finally, a relevant case of study showing the interest of the DTAS will be discussed.\",\n \"Title: A fast Monte Carlo algorithm for collision probability estimation\\nYear: 2008\\n\\nAbstract: In order to navigate safely, it is important to detect and to react to a potentially dangerous situation. Such a situation can be underlined by a judicious use of the locations and the uncertainties of both the navigating vehicle and the obstacles. We propose to build an estimation of the collision probability from the environment perception with its probabilistic modelling. The probability of collision is computed from integrals of Gaussians and takes into account the uncertain configurations and the volume of both the vehicle and the obstacles.\",\n \"Title: Modeling and validation of a new generic virtual optical sensor for ADAS prototyping\\nYear: 2012\\n\\nAbstract: In the early design stages of embedded applications, it becomes necessary to have a very realistic simulation environment dedicated to the prototyping and to the evaluation of these Advanced Driving Assistance Systems (ADAS). This Numerical simulation stage is gradually becoming a strong advantage in active safety. The use of realistic numerical models enabling to substitute real data by simulated data is primordial. For such virtual platform it is mandatory to provide physics-driven road environments, virtual embedded sensors, and physics-based vehicle models. In this publication, a generic solution for cameras modelling is presented. The use of this optical sensor simulation can easily and efficiently replace real camera test campaigns. This optical sensor is very important due to the great number of applications and algorithms based on it. The presented model involves a filter mechanism in order to reproduce, in the most realistic way, the behaviour of optical sensors. The main filters used in ADAS developments will be presented. Moreover, an optical analysis of these virtual sensors has been achieved allowing the confrontation between real and simulated results. An optical platform has been developed to characterize and validate any camera, permitting to measure their performances. By comparing real and simulated sensors with this platform, this paper demonstrates this virtual platform (Pro-SiVIC™) accurately reproduces real optical sensors' behaviour.\",\n \"Title: Robust positioning in safety applications for the CVIS project\\nYear: 2010\\n\\nAbstract: This paper describes hybrid fusion module used in a strong localization context (POMA) for embedded vehicle applications. This work has been developed in order to give an answer to the POMA (Positioning, Maps and local referencing) sub project objectives. These objectives are to provide, for a set of high level applications, a positioning service included a service quality, a metric accuracy (lane) and a robust result. This work is involved in CVIS European project. The use of IMM approach in the Hybrid Fusion module will be justified in comparison to the different current probabilistic methods. The IMM, contrary to the non modular methods, is based on the discretization of the vehicle evolution space into simple maneuvers, represented each by a simple dynamic model such as constant velocity or constant turning etc. This allows the method to be optimized for highly dynamic vehicles. The application of this positioning service will be presented in a real time embedded architecture. The presented results are based on real measurements collected from representatives scenarios (test track, peri-urban road, highway). These results show a real interest in using the new IMM method in order to reach the POMA's objectives.\",\n \"Title: Design of a modular demonstrator for safety application systems: The CVIS project\\nYear: 2010\\n\\nAbstract: This article describes the design of a modular demonstrator (Enhanced Driver Awareness - EDA) aimed at integrating various safety embedded vehicular applications. Two goals are achieved with this demonstrator: the first one is to provide a complete set of useful function in order to develop safety applications; the other one is to allow multiple services to run simultaneously on the same vehicle sharing information to improve their potentials. This demonstrator has been used in the European CVIS project, which aim is to provide a set of technologies defined for embedded safety vehicle application.\",\n \"Title: Validation and benchmarking for pedestrian video detection based on a sensors simulation platform\\nYear: 2010\\n\\nAbstract: The evaluation stage is an important part in the validation of ADAS robustness. Moreover, the control and the repetitiveness of the experimentations were very difficult to conduct on real road due to safety reasons. Moreover, the lack of data/sensors or the complexity of the experiment are often very penalizing for a correct and exhaustive evaluation. It is for these reasons that LIVIC launched the development of a software simulation architecture (SIVTC), to support its research activities on ADAS. The use of such a simulation platform can provide both simulated sensors data and a ground truth reference for the validation stages.\",\n \"Title: Integrated automated vehicle string longitudinal control\\nYear: 2013\\n\\nAbstract: This paper presents the design and simulation test of an automated vehicle string longitudinal control. The vehicle string is constituted of three highly cooperative vehicles following a leader. The string follows a leading vehicle which is supposed manually driven or at least not cooperative. The first vehicle of the string uses only its on-board sensor to obtain relative speed and inter-distance to the leader. Based on Lyapunov theory and invariant sets, the control law of the string is able to guarantee that the state trajectories remain bounded under bounded disturbance inputs, such as leading vehicle acceleration. The control law is designed in such a way that variable time headway strategy is naturally handled. The control law is computed using Linear and Bilinear Matrix Inequalities (LMI-BMI) methods. Some design parameters can be adjusted to handle the trade-off between safety constraints and comfort specifications.\",\n \"Title: Weighted V-disparity approach for obstacles localization in highway environments\\nYear: 2013\\n\\nAbstract: The employment of embedded passive sensors in order to perceive environment for reducing the accident risk level is a tendency of intelligent vehicles research. From such sensors, one can extract useful informations which can assist the driver to identify hazardous situations. While safety improvement is a substantial requirement for driving assistance, localizing and tracking obstacles in complex road environment became an important task. One promising approach is to use the V-disparity based on the stereovision technique. It is a cumulative space estimated from the disparity image. We propose a sound framework and a complete system based on a real-time stereovision for detection, 3D localization and tracking of dynamic obstacles in highway environment. The main contribution we propose is the improvement of the V-disparity approach by extending the basic approach by merging it with a confidence term. This consists on weighting each pixel in the V-disparity space according to a confidence value which measures the probability of associating a pair of pixels. Furthermore, we propose a tracking system which is based on the belief theory. The tracking task is done on the image space which takes into account uncertainties, handles conflicts, and automatically dealt with targets appearance and disappearce as well as their spatial and temporal propogation. Extensive experiments on simulated and real dataset demonstrate the effectiveness and the robustness of the weighted V-disparity approach.\",\n \"Title: Credibilistic IMM likelihood updating applied to outdoor vehicle robust ego-localization\\nYear: 2011\\n\\nAbstract: This paper deals with the ego-localization problem for an outdoor vehicle equipped with proprioceptive sensors. In a current way, the ego-localisation consists in two stages (prediction and updating of the vehicle state) in order to provide a new vehicle state with its associated uncertainty. Previous works have proved the efficiency of several type of algorithms. Among them, we can quote the IMM (Interacting Multiple Model) approach. IMM is based on the discretization of the vehicle motion space modeled by simple manoeuvres, such as Constant Velocity (CT) or Constant Turn (CT) models. This yields a very adapted approach for heavily manoeuvring vehicles. Unfortunately, without GPS data, this approach becomes difficult to use. The idea is to combine different kind of proprioceptive information by using the IMM (Interacting Multiple Model) approach in a credibilistic way for the likelihood model updating in the prediction stage. In our case, data are coming from dead reckoning sensors. This kind of sensors complement the GPS for updating the vehicle state as well as the model likelihood.\",\n \"Title: An improved approach for robust road marking detection and tracking applied to multi-lane estimation\\nYear: 2013\\n\\nAbstract: In this paper, an original and innovative algorithm for multi-lane detection and estimation is proposed. Based on a three-step process, (1) road primitives extraction, (2) road markings detection and tracking, (3) lanes shape estimation. This algorithm combines several advantages at each processing level and is quite robust to the extraction method and more specifically to the choice of the extraction threshold. The detection step is so efficient, by using robust poly-fitting based on the point intensity of extracted points, that correction step is almost not necessary anymore. This approach has been used in several project in real condition and its performances have been evaluated with the sensor data generated from SiVIC platform. This validation stage has been done with a sequence of 2500 simulated images. Results are very encouraging : more than 95% of marking lines are detected for less than 2% of false alarm, with 3 cm accuracy at a range of 60 m.\",\n \"Title: A Novel Approach for Improved Vehicular Positioning Using Cooperative Map Matching and Dynamic Base Station DGPS Concept\\nYear: 2016\\n\\nAbstract: In this paper, a novel approach for improving vehicular positioning is presented. This method is based on the cooperation of the vehicles by communicating their measured information about their position. This method consists of two steps. In the first step, we introduce our cooperative map matching method. This map matching method uses the V2V communication in a vehicular ad hoc network (VANET) to exchange global positioning system (GPS) information between vehicles. Having a precise road map, vehicles can apply the road constraints of other vehicles in their own map matching process and acquire a significant improvement in their positioning. After that, we have proposed the concept of a dynamic base station DGPS (DDGPS), which is used by vehicles in the second step to generate and broadcast the GPS pseudorange corrections that can be used by newly arrived vehicles to improve their positioning. The DDGPS is a decentralized cooperative method that aims to improve the GPS positioning by estimating and compensating the common error in GPS pseudorange measurements. It can be seen as an extension of DGPS where the base stations are not necessarily static with an exact known position. In the DDGPS method, the pseudorange corrections are estimated based on the receiver's belief on its positioning and its uncertainty and then broadcasted to other GPS receivers. The performance of the proposed algorithm has been verified with simulations in several realistic scenarios.\",\n \"Title: Accurate lateral positioning from map data and road marking detection\\nYear: 2016\\n\\nAbstract: •Accurate ego-localization using visual features from two lateral cameras.•Digital map integrated as a powerful additional sensor.•Road marking detection coupled with the map data in a fusion filter.•Vehicle localization at an ego-lane level of accuracy.\",\n \"Title: Road sign-aided estimation of visibility conditions\\nYear: 2015\\n\\nAbstract: Reduced visibility on roadways caused by localized fog can impact the traffic flow in many ways: traffic speed, travel time delay, reduced capacity and accident risks. This paper presents a novel approach to estimate visibility conditions using an onboard camera and a digital map. Based on a traffic sign detector's characteristics in the fog, and registering detection by vision and information encoded in the map, we are able to accurately determine the current visual range in hazy conditions. Quantitative results are provided on a large experimental data set of driving environment with various level of fogginess.\",\n \"Title: PerSEE: A central sensors fusion electronic control unit for the development of perception-based ADAS\\nYear: 2015\\n\\nAbstract: Automated vehicles and Advanced Driver Assistance Systems (ADAS) face a variety of complex situations that are dealt with numerous sensors for the perception of the local driving area. Going forward, we see an increasing use of multiple, different sensors inputs with radar, camera and inertial measurement the most common sensor types. Each system has its own purpose and either displays information or performs an activity without consideration for any other ADAS systems, which does not make the best use of the systems. This paper presents an embedded real-time system to combine the attributes of obstacles, roadway and ego-vehicle features in order to build a collaborative local map. This embedded architecture is called PerSEE: a library of vision-based state-of-the-art algorithms was implemented and distributed in processors of a main fusion electronic board and on smart-cameras board. The embedded hardware architecture of the full PerSEE platform is detailed, with block diagrams to illustrate the partition of the algorithm on the different processors and electronic boards. The communications interfaces as well as the development environment are described.\",\n \"Title: Ecodriving Performances Of Human Drivers In A Virtual And Realistic World\\nYear: 2015\\n\\nAbstract: In this study, results of an ecodriving challenge that took place during the Paris Motor Show in 2014 are presented. The principle of this challenge was to drive a simulated passenger car as far as possible with a limited quantity of energy (15 cL). The experimental setup, constituted of the SiVIC software, an Oculus Rift Helmet and a fuel consumption model, is also detailed. 1211 trips of visitors were validated during the 17 days of the event. Results showed that high acceleration without kickdown is desirable and that constant speed can lead to significant reduction in energy consumption. Next work will concentrate on improving the simulation and the scenario to increase the immersion realism and the ecodriving behavior sensitivity.\",\n \"Title: Implementation of a multi-criteria tracking based on the dempster-Shafer theory\\nYear: 2015\\n\\nAbstract: This paper aims to present how the Belief Theory (also known as the Dempster-Shafer theory) can be relevant to implement powerful tracking systems. As the Belief theory belongs to the group of information-theories, it is very suitable for solving the track-to-target association problem which is one of the main issue of tracking systems. The data association problem is about associating the measurement at a given time-stamp with the objects that are being tracked along the time. In this paper, we present two methods based on the belief theory that can be used to improve the data association reliability. After that we propose as an example of tracking implementation and discuss the effect of using a multi-criteria track-to-target association algorithm.\",\n \"Title: Distributed simulation platform for cooperative ADAS testing and validation\\nYear: 2016\\n\\nAbstract: Cooperative Advanced Driving Assistance Systems (C-ADAS) require both efficient perception and communication technologies. Experimentation of such systems in real world conditions is needed before their deployment. However, this is a difficult task since it deals with complex road scenarios and substantial experimental cost. To limit ADAS real experimentation constraints, we propose the design and development of a new distributed and interoperable simulation platform dedicated to cooperative vehicular application prototyping and testing. This simulation platform is based on the interconnection of three main simulators: the network simulator, NS3 (Network Simulator 3), the sensor and environment simulator, Pro-Sivic and the ADAS data management framework, RTMaps. In this paper, we present the design of our platform and some primary tests that prove the concept of the already cited simulators interconnection.\",\n \"Title: A Cooperative Fusion Architecture For Robust Localization: Application To Autonomous Driving\\nYear: 2016\\n\\nAbstract: The localization of a vehicle is a central task of autonomous driving. Most of the time, it is solved by considering a single algorithm with a few sensors. In this paper, we propose a cooperative fusion architecture based on two main algorithms: a laser-based Simultaneous Localization And Mapping (SLAM) process and a lane detection and tracking approach using a single camera. Both algorithms are designed individually as cooperative fusion processes where other sensors (GPS and proprioceptive information) and dedicated maps are integrated to strengthen the advantages of each system. The whole architecture is formalized around key components (ego-vehicle, roadway, obstacle and environment). A final decision layer, that takes into account the state of each algorithm, allows the system to choose the most appropriate ego-vehicle localization mean based on the current road situation and the environmental context.\",\n \"Title: A lane marker estimation method for improving lane detection\\nYear: 2016\\n\\nAbstract: In this paper we present a new lane markers detection and estimation algorithm aiming to improve lane detection methods. We first estimate the area of lane marking using the profile of the lane estimation in a confidence map. After that a fitting method is applied to improve the lane marker detection accuracy. To track our lane markers over time and make the association between two iteration, we use transferable belief model. The final lane markers are then used to filter out noise and to improve the lane estimation. The presented method is validated on both real and synthetic datasets. Without the integration of lane markers, we obtain a 98% ego detection rate on synthetic data for the lane detection algorithm presented in [8]. With the use of our lane marker detection algorithm, we are able to improve ours results by 1.5% thus reaching a detection rate of 99.5%. For the real validation, we use a highway sequence of about 4000 pictures and get 95.02% of good detection with 0.45% of false alarm for the first thousand images and 99% for ego-lane detection with no false alarm for all the sequence.\",\n \"Title: Real Time Cooperative Localization For Autonomous Vehicles\\nYear: 2016\\n\\nAbstract: This paper describes a new strategy for real-time cooperative localization of autonomous vehicles. The strategy aims to improve the vehicles localization accuracy and reduce the impact of computing time of multi-sensor data fusion algorithms and vehicle-to-vehicle communication on parallel architectures. The method aims to solve localization issues in a cluster of autonomous vehicles, equipped with low-cost navigation systems in an unknown environment. It stands on multiple forms of the Kalman filter derivatives to estimate the vehicles' nonlinear model vector state, named local fusion node. The vehicles exchange their local state estimate and Covariance Intersection algorithm for merging the local vehicles' state estimate in the second node (named global data fusion node). This strategy simultaneously exploits the proprioceptive and sensors -a Global Positioning System, and a vehicle-to-vehicle transmitter and receiver-and an exteroceptive sensor, range finder, to sense their surroundings for more accurate and reliable collaborative localization.\",\n \"Title: An efficient model-free setting for longitudinal and lateral vehicle control. Validation through the interconnected pro-SiVIC/RTMaps prototyping platform.\\nYear: 2018\\n\\nAbstract: In this paper, the problem of tracking desired longitudinal and lateral motions for a vehicle is addressed here. Let us point out that a “good” modeling is often quite difficult or even impossible to obtain. It is due for example to parametric uncertainties, for the vehicle mass, inertia or for the interaction forces between the wheels and the road pavement. To overcome this type of difficulties, ...\",\n \"Title: Performance evaluation of AODV, DSR, GRP and OLSR for VANET with real-world trajectories\\nYear: 2017\\n\\nAbstract: Vehicular communications can be achieved through the infrastructure (Vehicle-to-infrastructure network, V2I), as well as directly through vehicle-to-vehicle communication (V2V) via ad hoc networks. In V2V communications, the routing protocols are designed in order to optimize the dissemination of messages. This paper presents an evaluation of routing protocols such as the Optimized Link State Routing (OLSR), Ad hoc On-demand Distance Vector (AODV), Dynamic Source Routing (DSR), and Geographic Routing Protocol (GRP), while considering both vehicular safety application requirements and mobility models based on real-world traces of vehicular traffic. The results show that, though proactive routing protocols perform better in this context, the four routing protocols fail to fulfill the safety application requirements on the delay metric even for a reasonable number of vehicles.\",\n \"Title: Intelligent Vehicle Embedded Sensors Fault Detection and Isolation Using Analytical Redundancy and Nonlinear Transformations\\nYear: 2017\\n\\nAbstract: This work proposes a fault detection architecture for vehicle embedded sensors, allowing to deal with both system nonlinearity and environmental disturbances and degradations. The proposed method uses analytical redundancy and a nonlinear transformation to generate the residual value allowing the fault detection. A strategy dedicated to the optimization of the detection parameters choice is also developed.\",\n \"Title: Multi-Hypotheses Tracking using the Dempster-Shafer Theory, application to ambiguous road context\\nYear: 2016\\n\\nAbstract: We develop a new method to solve ambiguities in tracks-target association in vehicular environments.Previous work with the Dempster-Shafer theory is improved by incorporating conflictual data in a beneficial way.Two Multi-Hypotheses Tracking methods are examined with their pro- and cons.Ambiguity removal is demonstrated with two examples: pedestrians crossing in front of a vehicle-mounted laserscanner, and an overtaking manoeuvre. This paper presents a Multi-Hypotheses Tracking (MHT) approach that allows solving ambiguities that arise with previous methods of associating targets and tracks within a highly volatile vehicular environment. The previous approach based on the Dempster-Shafer Theory assumes that associations between tracks and targets are unique; this was shown to allow the formation of ghost tracks when there was too much ambiguity or conflict for the system to take a meaningful decision. The MHT algorithm described in this paper removes this uniqueness condition, allowing the system to include ambiguity and even to prevent making any decision if available data are poor. We provide a general introduction to the Dempster-Shafer Theory and present the previously used approach. Then, we explain our MHT mechanism and provide evidence of its increased performance in reducing the amount of ghost tracks and false positive processed by the tracking system.\",\n \"Title: Perception, information processing and modeling: Critical stages for autonomous driving applications.\\nYear: 2017\\n\\nAbstract: •\\tWe proposed an overview on reliability and high robustness of driving automation solutions.•\\tWe described a global architecture for automated driving with the human in the loop.•\\tWe proposed a literature review of driving automation solutions (perception and sensors).•\\tWe proposed a discussion about sensors and methods limits.\",\n \"Title: Chain-Branch-Leaf: A clustering scheme for vehicular networks using only V2V communications.\\nYear: 2018\\n\\nAbstract: The development of vehicular networks leads to a plethora of new applications that are already becoming essential to the drivers and to cooperative vehicles. One important condition to the continuity of these new services is the existence of an ubiquitous network access, which cannot be completely guaranteed. In this context, developing reliable ad hoc vehicle-to-vehicle communications is a good alternative that also becomes mandatory. This work proposes a clustering scheme that combines the information on road configuration, vehicle mobility and link quality in order to build a structure similar to a vehicular network infrastructure, while relying only on the vehicles. This clustering scheme can be integrated into any reactive, proactive, or geographic ad hoc routing protocol in order to optimize the flooding of messages and simplify routing operations. The evaluations realized through simulation show that the proposed clustering scheme allows creating and maintaining a stable backbone during a significant time. Moreover, the simulation results show that the proposed clustering scheme reduces significantly the impact of broadcast traffic flooding in comparison with well-established techniques such as multipoint relaying, to mention few.\",\n \"Title: Fast Semi-Dense 3d Semantic Mapping With Monocular Visual Slam\\nYear: 2017\\n\\nAbstract: Fast 3D reconstruction with semantic information on road scenarios involves issues of geometry and appearance in the field of computer vision. An important idea is that fusion of geometry and appearance would boost the performance of each other. Stereo cameras and RGB-D sensors are widely used to realise 3D reconstruction and trajectory tracking, but they need heavy computation cost and storage in a dense way. Moreover, they lack flexibility of seamless switch between various scaled environments, i.e., indoor and outdoor scenes. Besides, autonomous vehicle would benefit from semantic information on the road, but it is hard to be sufficiently accurate and consistent in the 3D mapping. We address this challenge by fusion of direct Simultaneous Localisation and Mapping (SLAM) from a monocular camera in a semi-dense way and the state-of-the-art approaches of deep neural network. In our approach, 2D semantic information is transferred to 3D mapping via correspondence between consecutive Keyframes with spatial consistency. Since there are lots of semantic redundancies in consecutive frames, it is not necessary to process semantic segmentation for each frame in the entire sequence. Consequently, the segmentation could run in a reasonable speed (about 20Hz). We thoroughly evaluate our methods on road scene datasets. The experiments show a promising 3D semantic mapping in various road scenes.\",\n \"Title: Fault Detection Architecture for Proprioceptive Sensors based on a Multi Model Approach and Fuzzy Logic Decisions.\\nYear: 2015\\n\\nAbstract: In this paper a new fault detection architecture will be presented. Inspired by multi-model data fusion algorithms and fuzzy logic decisions, it consists in the comparison between the estimation of a dynamic mode using each sensor independently. This method is used to deal with important non-linearity and strong interaction with the environment usually encountered in the domain of the intelligent vehicles localization. The concept of analytic redundancy is also used to ignore model uncertainties\",\n \"Title: Fault Detection and Isolation via the Interacting Multiple Model Approach Applied to Drive-By-Wire Vehicles.\\nYear: 2018\\n\\nAbstract: The place of driving assistance systems is currently increasing drastically for road vehicles. Paving the road to the fully autonomous vehicle, the drive-by-wire technology could improve the potential of the vehicle control. The implementation of these new embedded systems is still limited, mainly for reliability reasons, thus requiring the development of diagnostic mechanisms. In this paper, we investigate the detection and the identification of sensor and actuator faults for a drive-by-wire road vehicle. An Interacting Multiple Model approach is proposed, based on a non-linear vehicle dynamics observer. The adequacy of different probabilistic observers is discussed. The results, based on experimental vehicle signals, show a fast and robust identification of sensor faults while the actuator faults are more challenging.\",\n \"Title: Multi-Criteria Decision Making for Autonomous Vehicles using Fuzzy Dempster-Shafer Reasoning.\\nYear: 2018\\n\\nAbstract: This article considers the problem of high-level decision process for autonomous vehicles on highways. The goal is to select a predictive reference trajectory among a set of candidate ones, issued from a trajectory generator. This selection aims at optimizing multi-criteria functions, such as safety, legal rules, preferences and comfort of passengers, or energy consumption. This work introduces a new framework for Multi-Criteria Decision Making (MCDM). The proposed approach adopts fuzzy logic theory to deal with heterogeneous criteria and arbitrary functions. Moreover, the consideration of uncertain vehicleu0027s sensors data is done using the Dempster-Shafer Theory with fuzzy sets in order to provide a risk assessment. Simulation results using datasets collected under the NGSIM program are presented on car following cases, and extended to lane changing situations.\",\n \"Title: A Cooperative Vehicle Ego-localization Application Using V2V Communications with CBL Clustering.\\nYear: 2018\\n\\nAbstract: The cooperative vehicle paradigm offers new opportunities for enhancing various vehicle functions through distributed applications. The localization function is one of the most important since its services are needed by numerous applications ranging from driver navigation to autonomous vehicle guiding. Though the GPS (Global Positioning System) provides honorable service for driver navigation, its precision is not accurate for allowing an autonomous vehicle to localize itself correctly on the road lanes. Recently an ego-localization technique where a group of vehicles exchange their position and the related correction through V2V communications has been proposed in order to enhance the precision of the 10- cation of each node in the group. In this paper, we evaluate the performance that this application can expect from V2V communication services supplied by the CBL (Chain-Branch-Leaf) clustering scheme. The simulation results show that CBL achieves delays and packet delivery ratio adapted to various rates of the ego-localization application traffic.\",\n \"Title: Optimized self-adaptive PID speed control for autonomous vehicles\\nYear: 2021\\n\\nAbstract: The main control tasks in autonomous vehicles are steering (lateral) and speed (longitudinal) control. PID controllers are widely used in the industry because of their simplicity and good performance, but they are difficult to tune and need additional adaptation to control nonlinear systems with varying parameters. In this paper, the longitudinal control task is addressed by implementing adaptive ...\"\n]","isConversational":false},"profile_size":{"kind":"number","value":59,"string":"59"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13689,"string":"13,689"}}},{"rowIdx":13690,"cells":{"question_id":{"kind":"string","value":"Di Ma"},"question":{"kind":"string","value":"Generate an abstract for the title \"Enabling secure location-based services in mobile cloud computing\" using the following items: LBS, Security, Access control, Authentication, Privacy."},"answer":{"kind":"string","value":"The increasing spread of location-based services (LBSs) has led to a renewed research interest in the security of services. To ensure the credibility and availability of LBSs, there is a pressing requirement for addressing access control, authentication and privacy issues of LBSs in a synergistic way. In this paper, we propose an innovative location-based fine-grained access control mechanism for LBSs, enabling effective fine-grained access control, location-based authentication and privacy protection. Our proposed approach is based on the construction of a spatio-temporal predicate-based encryption by means of efficient secure integer comparison. Our experimental results not only validate the effectiveness of our scheme, but also demonstrate that the proposed integer comparison scheme performs better than previous bitwise comparison scheme."},"correct_answer":{"kind":"string","value":"The increasing spread of location-based services (LBSs) has led to a renewed research interest in the security of services. To ensure the credibility and availability of LBSs, there is a pressing requirement for addressing access control, authentication and privacy issues of LBSs in a synergistic way. In this paper, we propose an innovative location-based fine-grained access control mechanism for LBSs, enabling effective fine-grained access control, location-based authentication and privacy protection. Our proposed approach is based on the construction of a spatio-temporal predicate-based encryption by means of efficient secure integer comparison. Our experimental results not only validate the effectiveness of our scheme, but also demonstrate that the proposed integer comparison scheme performs better than previous bitwise comparison scheme."},"documents":{"kind":"list like","value":["Title: Secure Data Retrieval of Outsourced Data with Complex Query Support\nYear: 2012\n\nAbstract: The ability to efficiently retrieve data in their encrypted form is important for the cloud to become a truly secure and practical data sharing platform. Although this field has attracted tremendous research attention in the past a few years, most existing solutions do not support efficient searches with complex query conditions. Also, there has been little work to explore various comparison methods to specify complex query conditions. In this paper, we propose a framework of multi-keyword secure data retrieval to meet the needs of secure data retrieval with complex query support by leveraging recent development in attribute-based encryptions. Moreover, we propose a novel cryptographic comparison method based on forward and backward deviation functions (FDF/BDF). This method supports dual range comparisons and tree-oriented keyword search, as well as almost constant complexity for large size of integer range. We also provide several authentication mechanisms for preventing unpermitted access and verifying validity of protocol output. We implement a prototype system and our experimental results demonstrate its efficiency for practical usage.","Title: Authenticating query results in data publishing\nYear: 2005\n\nAbstract: We propose a communication-efficient authentication scheme to authenticate query results disseminated by untrusted data publishing servers. In our scheme, signatures of multiple tuples in the result set are aggregated into one and thus the communication overhead incurred by the signature keeps constant. Next attr-MHTs (tuple based Merkle Hash Tree) are built to further reduce the communication overhead incurred by auxiliary authentication information (AAI). Besides the property of communication-efficiency, our scheme also supports dynamic SET operations (UNION, INTERSECTION) and dynamic JOIN with immunity to reordering attack.","Title: An application of host identity tags to DKIM for identifying signers\nYear: 2009\n\nAbstract: The DomainKeys Identified Mail (DKIM) protocol gives a solution to guarantee the authentication and the integrity of e-mail messages. And in practice, to make DKIM come from theory to engineering, a concept of ADSP (Author Domain Signing Practice) has been put forth in an IETF draft. However, on the aspects of identifying a signer and delegating the signing ability, ADSP fails to give a specification. Yet the HITs (Host Identity Tags), put forth in RFC 5201, are born to identify signers featuring asymmetric key pairs and HITs will offer extra functions for security of the keys involved and management of the identifiers. This paper proposes to apply HITs to DKIM for identifying signers.","Title: A New Approach to Secure Logging\nYear: 2009\n\nAbstract: The need for secure logging is well-understood by the security researchers and practitioners. The ability to efficiently verify\n all (or some) log entries is important to any application employing secure logging techniques. In this paper, we begin by\n examining the state-of-the-art in secure logging and identify some problems inherent to systems based on trusted third-party\n servers. We then propose a different approach based upon recently developed Forward-Secure Sequential Aggregate (FssAgg) authentication\n techniques. Our approach offers both space-efficiency and provable security. We illustrate two concrete schemes – one private-verifiable\n and one public-verifiable – that offer practical secure logging without any reliance on on-line trusted third parties or secure\n hardware. We evaluate proposed schemes and report on our experience with implementing them within a secure logging system.","Title: DISH: Distributed Self-Healing\nYear: 2008\n\nAbstract: Unattended wireless sensor networks (UWSNs) operating in hostile environments face the risk of compromise. Unable to off-load\n collected data to a sink or some other trusted external entity, sensors must protect themselves by attempting to mitigate\n potential compromise and safeguarding their data. In this paper, we focus on techniques that allow unattended sensors to recover\n from intrusions by soliciting help from peer sensors. We define a realistic adversarial model and show how certain simple\n defense methods can result in sensors re-gaining secrecy and authenticity of collected data, despite adversary’s efforts to\n the contrary. We present an extensive analysis and a set of simulation results that support our observations and demonstrate\n the effectiveness of proposed techniques.","Title: Successful IP Business Models\nYear: 2006\n\nAbstract: The onward march of Moore's Law obviously brings with it a host of challenges, not the least of which is how to design to the space that is now available on an average leading edge semiconductor device. With millions of available transistors and a variety of technology options to choose from, it's small wonder that IP providers have managed to continue to build a viable industry. But that industry is changing, with numerous business models and sources of IP emerging. How will the interaction between IP providers and semiconductor manufacturers change? How will the industry benefit, in terms of quality and availability of IP? Dr. Di Ma will present a foundry perspective, including interesting statistics on IP usage and how the future interplay of foundry and IP provider might develop.","Title: A Middleware Services Simulation Platform for Wireless Sensor Networks\nYear: 2008\n\nAbstract: In the recent years, wireless sensor networks have attracted a lot of attention due to their broad application areas. In many application scenarios, hundreds or thousands of sensor nodes may be deployed in the physical environment. To design, implement, manage and maintain such large-scale networks efficiently is a major challenge which requires an integrated middleware development approach. In this paper, we focus on the development of a simulation platform specifically designed to support the development of sensor network middleware services. This novel platform is designed in a modular manner to facilitate the rapid integration and testing of different middleware algorithms and services. We present the simulator's software architecture and its operation. A case study to illustrate the usage of our simulation platform is also presented. The simulation platform shows potentials to support the research and development of middleware servicesfor wireless sensor networks.","Title: Broadcast based on layered diffusion in wireless ad hoc and sensor networks.\nYear: 2012\n\nAbstract: Broadcast is a fundamental operation in wireless ad hoc and sensor networks. This paper proposes a simple and efficient broadcast protocol, called Broadcast based on Layered Diffusion (BLD), which is a stateless broadcast protocol with very low message overheads and very low computation requirements. In BLD, nodes do not need to exchange neighborhood information for building a broadcast backbone, but make their rebroadcast decisions locally. The design idea of BLD is to emulate the triangular tessellation for complete area coverage of the network field. It does not require nodes' location information for such tessellation emulation but only exploits the hop count information for each node to make local rebroadcast decision and timing. Simulation results show the effectiveness and efficiency of the BLD protocol and its robustness to localization errors and transmission errors. Copyright (C) 2009 John Wiley & Sons, Ltd.","Title: MR-DBSCAN: An Efficient Parallel Density-Based Clustering Algorithm Using MapReduce\nYear: 2011\n\nAbstract: Data clustering is an important data mining technology that plays a crucial role in numerous scientific applications. However, it is challenging due to the size of datasets has been growing rapidly to extra-large scale in the real world. Meanwhile, MapReduce is a desirable parallel programming platform that is widely applied in kinds of data process fields. In this paper, we propose an efficient parallel density-based clustering algorithm and implement it by a 4-stages MapReduce paradigm. Furthermore, we adopt a quick partitioning strategy for large scale non-indexed data. We study the metric of merge among bordering partitions and make optimizations on it. At last, we evaluate our work on real large scale datasets using Hadoop platform. Results reveal that the speedup and scale up of our work are very efficient.","Title: DISH: Distributed Self-Healing in Unattended Sensor Networks.\nYear: 2008\n\nAbstract: Unattended wireless sensor networks (UWSNs) operating in hostile environments face the risk of compromise. Unable to off-load collected data to a sink or some other trusted external entity, sensors must protect themselves by attempting to mitigate potential compromise and safeguarding their data. In this paper, we focus on techniques that allow unattended sensors to recover from intrusions by soliciting help from peer sensors. We define a realistic adversarial model and show how certain simple defense methods call result in sensors re-gaining secrecy and authenticity of collected data, despite adversary's efforts to the contrary. We present an extensive analysis and a, set of simulation results that support, our observations and demonstrate the effectiveness of proposed techniques.","Title: Addressing flaws in RFID authentication protocols\nYear: 2011\n\nAbstract: The development of RFID systems in sensitive applications like e-passport, e-health, credit cards, and personal devices, makes it necessary to consider the related security and privacy issues in great detail. Among other security characteristic of an RFID authentication protocol, untraceability and synchronization are the most important attributes. The former is strongly related to the privacy of tags and their holders, while the latter has a significant role in the security and availability parameters. In this paper, we investigate three RFID authentication protocols proposed by Duc and Kim, Song and Mitchell, and Cho, Yeo and Kim in terms of privacy and security. We analyze the protocol proposed by Duc and Kim and present desynchronization and traceability attacks. By initiating traceability, backward traceability and desynchronization attacks, we show that the protocol proposed by Song and Mitchell lacks location privacy and availability. In addition, we study the weaknesses in Cho et al.'s protocol and address its defects by applying desynchronization, traceability and backward traceability attacks. We also propose revisions to secure the Cho et al.'s protocol against the cited attacks.","Title: Efficient identity-based encryption without pairings and key escrow for mobile devices\nYear: 2013\n\nAbstract: We propose a new construction of identity-based encryption without key escrow over the tradition cryptosystems. The security of our scheme follows from the decisional Diffie-Hellman assumption and the difficulty of a new problem --- modular inversion hidden number problem with error (MIHNPwE). The latter can be seen as a generalization of the modular inversion hidden number problem. We give an analysis on the hardness of MIHNPwE by lattice techniques. In our construction, we generate each user's partial private key in the form of an MIHNPwE instance. The hardness of MIHNPwE provides our scheme with resistance against key-collusion attacks from any number of traitors.","Title: Sensing-Enabled Defenses To Rfid Unauthorized Reading And Relay Attacks Without Changing The Usage Model\nYear: 2012\n\nAbstract: Many RFID tags store valuable information privy to their users that can easily be subject to unauthorized reading, leading to owner tracking or impersonation. RFID tags are also susceptible to different forms of relay attacks. This paper presents novel sensing-enabled defenses to unauthorized reading and relay attacks against RFID systems without necessitating any changes to the traditional RFID usage model.More specifically, the paper proposes the use of on-board tag sensors to (automatically) acquire useful contextual information about the tag's environment (or its owner, or the tag itself). It suggests how this information can be used to achieve two security functionalities. First, such context recognition can be leveraged for the purpose of selective tag unlocking - the tag will respond selectively to reader interrogations, i.e., only when it is deemed safe to do so. Second, context recognition can be used as a basis for transaction verification in order to provide protection against a severe form of relay attacks involving malicious RFID readers. To demonstrate the feasibility of the overall idea, a novel selective unlocking mechanism based on owner's posture recognition is presented. The evaluation of the proposed mechanism shows its effectiveness in significantly raising the bar against many different RFID attacks.","Title: Wireless medical implant: A case study on artificial pancreas\nYear: 2012\n\nAbstract: Medical implants are devices manufactured to replace damaged biological organs or structures. Once implanted, these devices communicate with other devices outside the body wirelessly. In this paper, we present our work on a particular type of WMI (wireless medical implants): artificial pancreas. We investigate challenges and issues in the context of artificial pancreas designs. In particular, we focus on how to achieve a low power design for WMI and propose a Virtual Patient system to evaluate control algorithms and communication protocols without an actual implantation of the device. Simulation and experiment results presented in this paper serves as design guidelines for wireless medical implant development.","Title: Hop-Count Based Node-to-Anchor Distance Estimation in Wireless Sensor Networks\nYear: 2010\n\nAbstract: Localization is one of the most important research issues in wireless sensor networks (WSNs). Hop-count based localization has been proposed as a cost-effective alternative to the expensive hardware-based localization schemes. In this paper, we propose a new method to estimate distances between anchors and nodes based on hop-count information. Localization is then achieved using the estimated distances. Simulation results show that the performance of our proposed algorithm is much better than that of the DV-hop algorithm in terms of the node-to- anchor distance estimation and localization accuracy, and the improvement is proportional to the node density.","Title: Cryptographic Spatio-temporal Predicates for Location-Based Services\nYear: 2013\n\nAbstract: The increasing spread of location-based services (LBSs) has led to a renewed research interest in location-based security, especially location-based access control. It also raises a lot of concern on potential privacy violation due to the possibility of identifying the user who requests a given service based on her/his location information at the time of the request. To ensure the credibility and availability of LBSs, there is a pressing requirement for addressing security and privacy issues of LBSs in a synergistic way. In this paper, we propose an innovative access control mechanism for LBSs, enabling both fine-grained access control and effective privacy protection. Our proposed approach is based on the construction of cryptographic spatio-temporal predicates by means of efficient secure integer comparison. Our experimental results not only validate the effectiveness of our scheme, but also demonstrate that the proposed integer comparison scheme performs better than previous bit wise comparison scheme.","Title: Range-free wireless sensor networks localization based on hop-count quantization\nYear: 2012\n\nAbstract: Localization is one of the most important research issues in Wireless Sensor Networks (WSNs). Recently, hop-count-based localization has been proposed as a cost-effective alternative to many expensive hardware-based\n localization algorithms. The basic idea of many hop-count-based localization algorithms is to seek a transformation from hop-count\n information to distance (e.g. DV-hop algorithm of Niculescu and Nath in Global Telecommunications Conference, vol. 5, pp. 2926–2931,\n 2001) or location (e.g. MDS algorithm of Shang et al. in International Symposium on Mobile Ad Hoc Networking and Computing, pp. 201–212,\n 2003) information. Traditionally, hop-counts between any pair of nodes can only take on integer value regardless of relative positions\n of nodes in the hop. We argue that by partitioning a node’s one-hop neighbor set into three disjoint subsets according to\n their hop-count values, the integer hop-count can be transformed into a real number accordingly. The transformed real number\n hop-count is then a more accurate representation of a node’s relative position than an integer-valued hop-count. In this paper,\n we present a novel algorithm termed HCQ (hop-count quantization) to perform such transformation. We then use the transformed\n real number hop-count to solve WSNs localization problems based on the MDS (multidimensional scaling) method. Simulation results\n show that the performance of the MDS algorithm using the real number hop-count outperforms those which use integer hop-count\n values.","Title: Secure Collaborative Integrity Verification For Hybrid Cloud Environments\nYear: 2012\n\nAbstract: A hybrid cloud is a cloud computing environment in which an organization provides and manages some internal resources and has others provided externally. However, this new environment could bring irretrievable losses to the clients due to a lack of integrity verification mechanism for distributed data outsourcing. To support scalable service and data migration, in this paper we address the construction of a collaborative integrity verification mechanism in hybrid clouds where we consider the existence of multiple cloud service providers to collaboratively store and maintain the clients' data. We propose a collaborative provable data possession scheme adopting the techniques of homomorphic verifiable responses and hash index hierarchy. In addition, we articulate the performance optimization mechanisms for our scheme and prove the security of our scheme based on multi-prover zero-knowledge proof system, which can satisfy the properties of completeness, knowledge soundness, and zero-knowledge. Our experiments also show that our proposed solution only incurs a small constant amount of communications overhead.","Title: A survey of movement strategies for improving network coverage in wireless sensor networks\nYear: 2009\n\nAbstract: The emerging technology of wireless sensor network (WSN) is expected to provide a broad range of applications, such as battlefield surveillance, environmental monitoring, smart spaces and so on. The coverage problem is a fundamental issue in WSN, which mainly concerns with a fundamental question: How well a sensor field is observed by the deployed sensors? To optimize network coverage, the traditional approach is to deploy a large amount of stationary sensor nodes and then to schedule their sensing activities in an efficient way. Recently, mobile sensor nodes have received many attentions since network performance can be greatly improved by using just a few of mobile nodes. We categorize three typical application scenarios of applying mobile nodes, and we then highlight the issues and identify the objectives of optimizing network coverage by leveraging mobile nodes. We further survey recent contributions on the design of nodes’ movement strategy to optimize network coverage in each application scenario.","Title: Practical forward secure sequential aggregate signatures\nYear: 2008\n\nAbstract: A forward secure sequential aggregate (FssAgg) signature scheme allows a signer to iteratively combine signatures generated in different time intervals - and with different keys - into a single constant-size signature. Such a signature offers forward security, storage/communication efficiency, as well as overall integrity of the signed messages. FssAgg schemes are therefore suitable for data-intensive applications on untrusted and/or unattended devices, e.g., logging systems. The first FssAgg signature scheme [21] is based on bilinear maps and is thus rather costly. In this paper, we propose two more practical FssAgg1 signature schemes. A FssAgg1 signature scheme is a special FssAgg signature scheme where exactly ONE message can be signed at each time interval and key update is invoked immediately after each signature generation. Both new schemes are derived from existing forward secure signature schemes. Unlike the scheme in [21], each new scheme has constant-size public and private keys, constant-size signatures as well as constant-time key update and signature generation complexity. We show how to apply proposed schemes in secure logging applications.","Title: Security and privacy in emerging wireless networks\nYear: 2010\n\nAbstract: Wireless communication is continuing to make inroads into many facets of society and is gradually becoming more and more ubiquitous. While in the past wireless communication (as well as mobility) was largely limited to the first and last transmission hops, today's wireless networks are starting to offer purely wireless, often mobile, and even opportunistically connected operation. The purpose of this article is to examine security and privacy issues in some new and emerging types of wireless networks, and attempt to identify directions for future research.","Title: Tap-Wave-Rub: lightweight malware prevention for smartphones using intuitive human gestures\nYear: 2013\n\nAbstract: We introduce a lightweight permission enforcement approach - Tap-Wave-Rub (TWR) - for smartphone malware prevention. TWR is based on simple human gestures (implicit or explicit) that are very quick and intuitive but less likely to be exhibited in users' daily activities. Presence or absence of such gestures, prior to accessing an application, can effectively inform the OS whether the access request is benign or malicious. In this paper, we focus on the design of an accelerometer-based phone tapping detection mechanism. This implicit tapping detection mechanism is geared to prevent malicious access to NFC services, where a user is usually required to tap her phone with another device. We present a variety of novel experiments to evaluate the proposed mechanism. Our results suggest that our approach could be very effective for malware prevention, with quite low false positives and false negatives, while imposing no additional burden on the users. As part of the TWR framework, we also briefly explore explicit gestures (finger tapping, rubbing or hand waving based on proximity sensor), which could be used to protect services which do not have a unique implicit gesture associated with them.","Title: Efficient Object-Based Stream Authentication\nYear: 2002\n\nAbstract: This paper presents an object-based stream authentication scheme for practical end-to-end applications on present network infrastructure and protocols. In this proposal, a stream is divided into objects that are delivered sequentially. Each object consists of a unique identifier, content and operations. At the sender side, a locked object identifier is produced by encrypting the object identifier with a key generated from MAC of the object content. The object content and locked identifier are transmitted to the receiver. The receiver can recover the object identifier and check it to authenticate the object. The scheme tolerates packet loss, and incurs no additional overhead if an identifier is ingeniously chosen. In the experiment, we overload some particular fields in the headers of standard protocols as object identifier to implement a real time video stream authentication scheme.","Title: Self-healing in unattended wireless sensor networks\nYear: 2012\n\nAbstract: Wireless sensor networks (WSNs) appeal to a wide range of applications that involve the monitoring of various physical phenomena. However, WSNs are subject to many threats. In particular, lack of pervasive tamper-resistant hardware results in sensors being easy targets for compromise. Having compromised a sensor, the adversary learns all the sensor secrets, allowing it to later encrypt/decrypt or authenticate messages on behalf of that sensor. This threat is particularly relevant in the novel unattended wireless sensor networks (UWSNs) scenario. UWSNs operate without constant supervision by a trusted sink. UWSN's unattended nature and increased exposure to attacks prompts the need for special techniques geared towards regaining security after being compromised. In this article, we investigate cooperative self-healing in UWSNs and propose various techniques to allow unattended sensors to recover security after compromise. Our techniques provide seamless healing rates even against a very agile and powerful adversary. The effectiveness and viability of our proposed techniques are assessed by thorough analysis and supported by simulation results. Finally, we introduce some real-world issues affecting UWSN deployment and provide some solutions for them as well as a few open problems calling for further investigation.","Title: Seamline Network Refinement Based on Area Voronoi Diagrams With Overlap\nYear: 2014\n\nAbstract: The area Voronoi diagrams with overlap (AVDO) method was recently presented and has been used to generate a seamline network for the mosaicking of orthoimages. The method shows considerable potential advantages for seamless mosaics covering a large geographic region. In this paper, the method is further improved, and a seamline network refinement approach based on AVDO is presented. The improvements of the presented approach include the detection of valid regions for orthoimages, a more general algorithm for the generation of bisectors, and the refinement of the seamline network combining the bottleneck model and the Dijkstra's algorithm. Finally, a number of digital aerial orthoimages are employed to test the proposed approach, and the experimental results demonstrate its efficacy.","Title: A coverage-aware clustering protocol for wireless sensor networks\nYear: 2010\n\nAbstract: In energy-limited wireless sensor networks, network clustering and sensor scheduling are two efficient techniques for minimizing node energy consumption and maximizing network coverage lifetime. When integrating the two techniques, the challenges include how to decide the most energy-efficient cluster size and how to select cluster heads and active nodes. In this paper, we provide a computation method for the optimal cluster size to minimize the average energy consumption rate per unit area. In the proposed coverage-aware clustering protocol, we define a cost metric that favors those nodes being more energy-redundantly covered as better candidates for cluster heads and select active nodes in a way that tries to emulate the most efficient tessellation for area coverage. Our simulation results validate our computation and show the significant improvement of the network coverage lifetime.","Title: A novel approach toward source-to-sink distance estimation in wireless sensor networks\nYear: 2010\n\nAbstract: Estimation of the Euclidean distance between a source node and a sink node by using the hop-counts between them is an interesting research problem in WSNs (wireless sensor networks). In this paper, we propose a novel source-to-sink distance estimation method based on the observation that the neighbors of a sink can have different hop-counts relative to the same source. The proposed method not only uses the hop-count information of a sink, but also exploits the hop-count information of the sink's neighbors. Simulation results show that the proposed method outperforms other well-known hop-count-based source-to-sink distance estimation methods.","Title: Analysis of Hop-Count-Based Source-to-Destination Distance Estimation in Wireless Sensor Networks With Applications in Localization\nYear: 2010\n\nAbstract: In wireless sensor networks (WSNs), estimation of the true distance between a source and its destination based on the hop counts between them is an important research problem. The basic idea of several hop-count-based distance-estimation algorithms is to seek an analytical/heuristic transformation from the observed destination's hop-count information to an unknown source-to-destination distance (s...","Title: Flexible Access Control to JPEG 2000 Image Code-Streams\nYear: 2007\n\nAbstract: JPEG 2000 is an international standard for still image compression in the 21st century. Part 8 of the standard, named JPSEC, is concerned with all the security aspects, in particular to access control and authentication. This paper presents a novel access control scheme for JPEG 2000 image code-streams. The proposed scheme is secure against collusion attacks and highly efficient. The scheme is also very flexible, allowing access control to JPEG 2000 image code-streams according to any combination of resolution, quality layer and region of interest. The \"encrypt once, decrypt many ways\" property of our scheme is designed to work seamlessly with the \"compress once, decompress many ways\" feature of the JPEG 2000 image code-streams. Our prototype implementation shows that the scheme is practical and is completely compatible with the core part of the JPEG 2000 standard.","Title: Host-identifier-based scheme for source accountability of the internet\nYear: 2011\n\nAbstract: This paper explores the current Internet's potential to provide accountability. We present a new IPv6 address management and certification hierarchy to achieve host-oriented accountability of the Internet, with the current addressing system remaining unchanged. With the design of HIA (Host-Identifier-Embedded IPv6 Address), we present the framework for host identity authentication, packet source validation and the host reputation system construction. This paper also discusses the feasibility and deployment issues about our design.","Title: Progressive protection of JPEG2000 codestreams\nYear: 2004\n\nAbstract: This paper presents an authentication scheme and an access control scheme for JPEG2000 image codestreams based on hash chains. Both schemes are fully compatible with the core part of JPEG2000 standard. The authentication scheme allows users to verify the authenticity of sub-images progressively extracted from a single codestream protected with a single signature, and the access control scheme allows users to access sub-images partially decrypted from a single encrypted JPEG2000 codestream. In addition, the two schemes can be integrated to provide both authenticity and access control simultaneously. Our experiments demonstrate the two scheme are very efficient and highly scalable.","Title: Scalable trusted online dissemination of JPEG2000 images\nYear: 2005\n\nAbstract: n important aspect of JPEG2000 is its \"compress once, decompress many ways\" property [1], i.e., it allows users with different preferences, privileges or capabilities to extract various sub-images all from a single compressed image code-stream. In this paper, we present a flexible and scalable scheme to authenticate JPEG2000 images disseminated by a untrusted third-party server over open networks. The proposed scheme is fully compatible with JPEG2000 and possesses a \"sign once, verify many ways\" property, i.e., it allows users to verify the authenticity and integrity of different sub-images extracted from a single compressed code-stream protected with a single digital signature. Furthermore, the use of aggregated digital signatures reduces both computation and communication overhead on the user side for batch image authentication.","Title: A Physical and SPICE-Compatible Model for the MOS Depletion Device\nYear: 1985\n\nAbstract: An MOS depletion device model that is compatible with SPICE circuit simulation program and based on device physics is described. The depletion device is modeled by an equivalent circuit consisting of various well-characterized semiconductor devices, for example, the enhancement MOS device and the JFET. IT has the advantages of both the existing physical and empirical models. The model is applicable to immediate circuit simulation and device fabrication. Derivation of the model along with its parameters for the Gaussian impurity distribution is given. Implementation of circuit simulation with SPICE is demonstrated. More accurate results compared to the empirical model are obtained.","Title: Secure and efficient constructions of hash, MAC and PRF for mobile devices\nYear: 2012\n\nAbstract: Numerous cryptographic techniques have been developed to be used on mobile devices for various security and privacy protections. However, these cryptographic primitives, working under different mathematical assumptions, tend to become more and more complex and intricate, which makes it increasingly more difficult for proper implementation and management. Thus, it is desired to simplify management and improve efficiency by means of designing a general function family to meet a variety of security requirements. In this paper, we present such a family of square functions, including SqHash, SqMAC and SqPRF, based on a specially truncated function (MSB or LSB). We further improve the efficiency of these algorithms by using “circular convolution with carry bits” which makes parallel processing possible. We prove the security of these functions based on the privacy in hidden number problem and hard-core predicate of one-way function. We also show that the proposed schemes achieve better performance with a complexity reduction from O(n2) to O(kn/w) for n-bit message, k-bit output and w-bit word size.","Title: Location-Aware and Safer Cards: Enhancing RFID Security and Privacy via Location Sensing\nYear: 2012\n\nAbstract: In this paper, we report on a new approach for enhancing security and privacy in certain RFID applications whereby location or location-related information (such as speed) can serve as a legitimate access context. Examples of these applications include access cards, toll cards, credit cards, and other payment tokens. We show that location awareness can be used by both tags and back-end servers for defending against unauthorized reading and relay attacks on RFID systems. On the tag side, we design a location-aware selective unlocking mechanism using which tags can selectively respond to reader interrogations rather than doing so promiscuously. On the server side, we design a location-aware secure transaction verification scheme that allows a bank server to decide whether to approve or deny a payment transaction and detect a specific type of relay attack involving malicious readers. The premise of our work is a current technological advancement that can enable RFID tags with low-cost location (GPS) sensing capabilities. Unlike prior research on this subject, our defenses do not rely on auxiliary devices or require any explicit user involvement.","Title: A Server Friendly File Synchronization Mechanism for Cloud Storage\nYear: 2013\n\nAbstract: The rapid development of cloud computing has made traditional data centers energy hungry. Thus it is very important to reduce the amount of calculations on the cloud. In this paper we focus on the energy saving in cloud storage service. First we analyze rsync algorithm widely used in the cloud synchronization service and reveal how this algorithm will lead to high CPU load on cloud. Then we design and implement an improved algorithm that moves most calculations out of the cloud. The experiments show that the synchronization related CPU load in server dropped significantly when using our improved method. The improved mechanism can be applied not only in cloud storage service, but also in any centralized synchronization scenario to reduce CPU load on the central nodes.","Title: Incorporating social actions into recommender systems\nYear: 2013\n\nAbstract: With the rapidly growing amount of information available on the internet, recommender systems become popular tools to promote relevant online information to a given user. Although collaborative filtering is the most popular approach to build recommender systems and has been widely deployed in many applications, it still pay little attention to social actions, which are widely common in social networks and we believe could make a significant improvement in recommender systems. In this paper, we incorporate users' social actions into a model-based approach for recommendation using probabilistic matrix factorization. Compared with previous work, users' social actions are taken as a new relation to optimize previous trust-based recommender systems. To achieve this, we propose a social recommendation graphical model employing users' relations based on their social actions. We make use of users' commenting action in our approach and conduct experiments on a real life dataset, extracted from the Douban movie ratings and comments system. Our experiments demonstrate that incorporating users' social action information leads to a significant improvement in recommender systems.","Title: Building Trust for lambda-Congenial Secret Groups\nYear: 2011\n\nAbstract: Establishing trust while preserving privacy is a challenging research problem. In this paper we introduce lambda -congenial secret groups which allow users to recognize trusted partners based on common attributes while preserving their anonymity and privacy. Such protocols are different from authentication protocols, since the latter are based on identities, while the former are based on attributes. Introducing attributes in trust establishment allows a greater flexibility but also brings up several issues. In this paper, we investigate the problem of building trust with attributes by presenting motivating examples, analyzing the security requirements and giving an informal definition. We also survey one of the most related techniques, namely private matching, and finally present solutions based on it.","Title: End-to-end aggregate authentication of time-series data\nYear: 2013\n\nAbstract: We consider the problem of providing integrity of aggregate result in the presence of an untrusted data aggregator who may introduce errors into data fusion, causing the final aggregate result to far deviate from the true result determined by participating values. We proposes a construction that allows a group of participants to periodically upload authenticated data values to a data aggregator, such that the aggregator is able to derive an aggregate signature over the true aggregate result, but is unable to do so if any error is introduced into the aggregate result. To our knowledge, it is the first secure agggregate authentication scheme that achieves end-to-end security without resorting to additional communication or third parties. Our construction supports iterative aggregation thus it can be used in a networked environment where there exists multi-level of aggregators. Our construction is efficient in term of both computation and communication. Especially, the online computation cost of aggregate tag verification is constant and independent of the number of underlying individual signatures. We prove the security of our construction under the assumption of CDH problem.","Title: Secure feedback service in wireless sensor networks\nYear: 2007\n\nAbstract: In many sensor network applications, it is critical for the base station to know the delivery (or execution) status of its broadcast messages (or commands). One straightforward way to do so is to let every sensor node send an authenticated acknowledgement (ACK) to the BS directly. However this naive solution is highly communication inefficient and may result in severe ACK implosion near the BS. In this paper, we propose a communication efficient scheme to provide secure feedback service in sensor networks. In our basic scheme, we use ACK aggregation to reduce the ACK traffic. Meanwhile we store audit information for each aggregation operation so that the BS can use the audit information to locate errors in the network. We further improve the basic scheme by constructing a balanced aggregation tree to reduce localization delay and using Bloom filters to reduce storage requirement in each sensor for storing audit information. We analyze the performance of the proposed scheme and show it achieves good bandwidth gain over the naive approach.","Title: A Context-Aware Approach for Enhanced Security and Privacy in RFID Electronic Toll Collection Systems\nYear: 2011\n\nAbstract: RFID Electronic Toll Collection (ETC) systems have been deployed worldwide to improve toll collection efficiency, reduce road congestion, increase road safety and traveler satisfiability. However, the use of such systems raises a number of security and privacy issues due to unauthorized reading and relay attacks. Unfortunately, currently deployed or proposed solutions targeting these attacks often fail to satisfy the constraints and requirements of the underlying RFID toll road application in terms of (one or more of) efficiency, security, and usability. In this paper, we report our initial work toward a new approach, one that utilizes sensing technologies, to tackle the problems of unauthorized reading and relay attacks in RFID ETC systems by considering efficiency, security, and usability simultaneously. In our approach, on-board tag sensors are used to collect contextual information (location, speed) about the tag. Such contextual information is then used to design context-aware selective unlocking mechanisms for toll cards such that they can selectively respond to reader interrogations and thus minimize the likelihood of unauthorized reading and relay attacks. The premise of our work is a current technological advancement that enables many RFID tags with sensing capabilities.","Title: A Soldier Health Monitoring System for Military Applications\nYear: 2010\n\nAbstract: With recent advances in technology, various wearable sensors have been developed for the monitoring of human physiological parameters. A Body Sensor Network (BSN) consisting of such physiological and biomedical sensor nodes placed on, near or within a human body can be used for real-time health monitoring. In this paper, we describe an on-going effort to develop a system consisting of interconnected BSNs for real-time health monitoring of soldiers. We discuss the background and an application scenario for this project. We describe the preliminary prototype of the system and present a blast source localization application.","Title: IRRES: Intrusion-Resilient Remote Email Storage\nYear: 2010\n\nAbstract: Both individuals and corporations increasingly rely on email to exchange important and, often sensitive, information. With the advent of ubiquitous computing and miniaturization of end-devices, many users leave email on remote servers, thus facilitating anywhere/anytime access from any networked device. Since private and sensitive information is often contained in email, it is very important to prevent its unauthorized disclosure. In this paper, we consider the problem of repeated intrusions of third-party email servers. Specifically, we design IRRES: Intrusion-Resilient Remote Email Storage system that prevents email leakage despite the presence of a powerful mobile adversary. IRRES eliminates sender compliance requirements by shifting encryption from email sender to email server. Incoming email messages are encrypted by the server with one-time keys obtained from an intrusion-resilient cooperative randomness generation process. Thus, even if the server is compromised twice, all email encrypted in the interim remains secure.","Title: POSH: Proactive co-Operative Self-Healing in Unattended Wireless Sensor Networks\nYear: 2008\n\nAbstract: We identify here various kinds of failure conditions and robustness issues that arise in context-aware pervasive computing applications. Such conditions are related to failures in an application's interactions with ambient services, failures in resource ...","Title: A Framework for Cloud-Based Large-Scale Data Analytics and Visualization: Case Study on Multiscale Climate Data\nYear: 2011\n\nAbstract: In this paper, we present a cloud framework to provide cloud clustering, workflow scheduling and management, fault tolerance and distributed data storage, data analytics and visualisation services. Using a practical case study, we show that in the process of analyzing multiscale climate data, typical problems plaguing data analysts are faced. These include large datasets and limited computational resources, data complexity and limited knowledge, and varying data structures/formats and the need to integrate different tools. The implementation of our framework to climate studies was a success. This can be seen in its ability to perform spatio-temporal data analysis and visualization of a large multi-dimensional climate dataset with reduced processing time. The framework demonstrates great flexibility and simplicity for end users intending to perform data analysis by aiding the integration of data and tools and enabling interactive visualization on-the-fly. This is coupled with effective utilization of computational resources and data storage systems.","Title: A parameter selection strategy for particle swarm optimization based on particle positions.\nYear: 2014\n\nAbstract: •A relationship between the dynamic process of PSO and the transition process is proposed.•A novel parameter strategy for PSO is proven using the overshoot and the peak time.•The parameters obtained from our strategy are coincided with the published results.•The new strategy is applied to equally spaced linear array synthesis examples.","Title: Lattice-based key exchange on small integer solution problem.\nYear: 2014\n\nAbstract: In this paper, we propose a new hard problem, called bilateral inhomogeneous small integer solution (Bi-ISIS), which can be seen as an extension of the small integer solution problem on lattices. The main idea is that, instead of choosing a rectangle matrix, we choose a square matrix with small rank to generate Bi-ISIS problem without affecting the hardness of the underlying SIS problem. Based on this new problem, we present two new hardness problems: computational Bi-ISIS and decisional problems. As a direct application of these problems, we construct a new lattice-based key exchange (KE) protocol, which is analogous to the classic Diffie-Hellman KE protocol. We prove the security of this protocol and show that it provides better security in case of worst-case hardness of lattice problems, relatively efficient implementations, and great simplicity.","Title: Identity-Set-based Broadcast Encryption supporting \\\"Cut-or-Select\\\" with Short Ciphertext\nYear: 2015\n\nAbstract: In this paper we present an identity-set-based broadcast encryption scheme with three working modes: positive membership (Select-mode), all member (All-mode), and negative membership (Cut-mode) over the user identity set, simultaneously. The core of our scheme is the implementation of cryptographic representation of subset by using two aggregation functions: Zeros-based aggregation and Poles-based aggregation. These two aggregation functions are capable of compressing any subset into one element in a bilinear map group for determining the membership between an element and a subset. Our scheme achieves the optimal bound of O(1)-size for either ciphertext (consisting of just two elements) or decryption key (one element) for an identity set of large size. We prove that our scheme is secure under the General Diffie-Hellman Exponent (GDHE) assumption.","Title: A context-aware approach to defend against unauthorized reading and relay attacks in RFID systems.\nYear: 2014\n\nAbstract: Radio frequency identification (RFID) systems are becoming increasingly ubiquitous in both public and private domains. However, because of the inherent weaknesses of underlying wireless radio communications, RFID systems are plagued with a wide variety of security and privacy threats. A large number of these threats arise because of the tag's promiscuous response to any reader requests. This renders sensitive tag information easily subject to unauthorized reading. Promiscuous tag response also incites different forms of relay attacks whereby a malicious colluding pair, relaying messages between a tag and a reader, can successfully impersonate the tag without actually possessing it. Because of the increasing ubiquity of RFID devices, there is a pressing need for the development of security primitives and protocols to defeat unauthorized reading and relay attacks. However, currently deployed or proposed solutions often fail to satisfy the constraints and requirements of the underlying RFID applications in terms of (one or more of) efficiency, security, and usability. This paper proposes a novel research direction, one that utilizes sensing technologies, to tackle the problems of unauthorized reading and relay attacks with a goal of reconciling the requirements of efficiency, security, and usability. The premise of the proposed work is based on a current technological advancement that enables many RFID tags with low-cost sensing capabilities. The on-board tag sensors will be used to acquire useful contextual information about the tag's environment (or its owner, or the tag itself). For defense against unauthorized reading and relay attacks, such context information can be leveraged in two ways. First, contextual information can be used to design context-aware selective unlocking mechanisms so that tags can selectively respond to reader interrogations and thus minimize the likelihood of unauthorized reading and ghost-and-leech relay attacks. Second, contextual information can be used as a basis for context-aware secure transaction verification to defend against special types of relay attacks involving malicious readers. Copyright (c) 2011 John Wiley & Sons, Ltd. This paper proposes a novel research direction, one that utilizes sensing technologies to tackle the challenging problems of unauthorized reading and relay attacks in radio frequency identification systems. First, contextual information is used to design context-aware selective unlocking mechanisms, so that tags can selectively respond to reader interrogations and, thus, minimize the likelihood of unauthorized reading and ghost-and-leech relay attacks. Second, contextual information is used as a basis for context-aware secure transaction verification to defend against special types of relay attacks involving malicious readers.","Title: Secure and efficient random functions with variable-length output.\nYear: 2014\n\nAbstract: Many random functions, like Hash, MAC, PRG, have been used in various network applications for different security choices. However, they are either fast but insecure or cryptographic secure but slow. To integrate them together, in this paper we present a new family of square random functions, including SqHash, SqMAC and SqPRG, based on a specially truncated function (MSB or LSB), as well as circular convolution with carry bits. Provable security is provided by the privacy property in hidden number problem and Hard-core unpredication of one-way function. The experiment results show that these schemes have better performance under different input and output lengths. We also perform four types of statistical tests for randomness. The experiments indicate that our construction has good average-case randomness than SHA-2 and original Square algorithm.","Title: Context-Aware Defenses to RFID Unauthorized Reading and Relay Attacks\nYear: 2013\n\nAbstract: Many RFID tags store valuable information privy to their users that can easily be subject to unauthorized reading, leading to owner tracking, or impersonation. RFID tags are also susceptible to different forms of relay attacks. This paper presents novel sensing-enabled defenses to unauthorized reading and relay attacks against RFID systems without necessitating any changes to the traditional RFID usage model. Specifically, this paper proposes the use of cyber-physical interfaces, on-board tag sensors, to (automatically) acquire useful contextual information about the tag's environment (or its owner, or the tag itself). First, such context recognition is leveraged for the purpose of selective tag unlocking-the tag will respond selectively to reader interrogations. In particular, novel mechanisms based on an owner's posture recognition are presented. Second, context recognition is used as a basis for transaction verification in order to provide protection against a severe form of relay attacks involving malicious RFID readers. A new mechanism is developed that can determine the proximity between a valid tag and a valid reader by correlating certain (specifically audio) sensor data extracted from the two devices. Our evaluation of the proposed mechanisms demonstrate their feasibility in significantly raising the bar against RFID attacks.","Title: How to use attribute-based encryption to implement role-based access control in the cloud\nYear: 2013\n\nAbstract: This paper addresses how to construct a RBAC-compatible attribute-based encryption (ABE) for secure cloud storage, which provides a user-friendly and easy-to-manage security mechanism without user intervention. Similar to role hierarchy in RBAC, attribute lattice introduced into ABE is used to define a seniority relation among all values of an attribute, whereby a user holding the senior attribute values acquires permissions of their juniors. Based on these notations, we present a new ABE scheme called Attribute-Based Encryption with Attribute Lattice (ABE-AL) that provides an efficient approach to implement comparison operations between attribute values on a poset derived from attribute lattice. By using bilinear groups of composite order, we propose a practical construction of ABE-AL based on forward and backward derivation functions. Compared with prior solutions, our scheme offers a compact policy representation solution, which can significantly reduce the size of privatekeys and ciphertexts. Furthermore, our solution provides a richer expressive power of access policies to facilitate flexible access control for ABE scheme.","Title: Control strategy PSO.\nYear: 2016\n\nAbstract: •An evaluation index called “Control Strategy PSO” is developed.•It can be applied to other intelligent algorithms.•We present a detailed theoretical and empirical analysis.","Title: Tap-Wave-Rub: Lightweight Human Interaction Approach to Curb Emerging Smartphone Malware\nYear: 2015\n\nAbstract: Malware is a burgeoning threat for smartphones and continuing advancing. Traditional defenses to malware, however, are not suitable for smartphones due to their resource intensive nature. This necessitates the design of novel mechanisms that can take into account the specifics of the smartphone malware and smartphones themselves. In this paper, we introduce a lightweight permission enforcement approach – Tap-Wave-Rub (TWR) – for smartphone malware prevention. TWR is based on simple cyber-physical human interactions, i.e., human gestures, that are very quick and intuitive but less likely to be exhibited in users’ daily activities. Presence or absence of such gestures, prior to accessing an application, can effectively inform the OS whether the access request is benign or malicious. Specifically, we present the design of two mechanisms: (1) acceleration-based phone tapping detection; and (2) proximity-based finger tapping, rubbing or hand waving detection. The first mechanism is geared for NFC applications, which usually require the user to tap her phone with another device. The second mechanism involves very simple gestures, i.e., tapping or rubbing a finger near the top of phone’s screen or waving a hand close to the phone, and broadly appeals to many applications (e.g., SMS). In addition, we present the TWR-enhanced Android permission model, the prototypes implementing the underlying gesture recognition mechanisms, and a variety of novel experiments to evaluate these mechanisms. Our results suggest the proposed approach could be very effective for malware detection/prevention, with quite low false positives and false negatives, while imposing little to no additional burden on the users.","Title: Secure Snaps: A New Forward Secrecy Cryptosystem for Self-Destructing Messages in Mobile Services\nYear: 2015\n\nAbstract: Recently, mobile services of ephemeral communications, such as Snap chat, are becoming increasingly popular because of their \\\"burn after reading\\\" functionality. However, recent events show that these services do not achieve the claimed functionality supporting truly self-destructing messages. In this paper, we address the problem of privacy protection in ephemeral communications involving untrusted service providers. We present Secure Snaps, a forward secrecy cryptosystem with self-destructing messages. In this system, we introduce a trusted module supervised by a trusted third party in order to ensure the security of user's private keys, so that untrusted service providers cannot access the communications among users. We take a new approach, called cryptographic counting control, to implement message's self-destruction functionality which meets the requirement for forward secrecy. By adopting this approach, no one, including the original sender, service provider, and key management module, can recover past messages once these messages have entered the \\\"destroyed state\\\". We prove that the proposed scheme is provably secure against chosen plaintext attacks. Our experiment results demonstrate its efficiency and practicality for large-size group of users.","Title: CGMOS: Certainty Guided Minority OverSampling.\nYear: 2016\n\nAbstract: Handling imbalanced datasets is a challenging problem that if not treated correctly results in reduced classification performance. Imbalanced datasets are commonly handled using minority oversampling, whereas the SMOTE algorithm is a successful oversampling algorithm with numerous extensions. SMOTE extensions do not have a theoretical guarantee during training to work better than SMOTE and in many instances their performance is data dependent. In this paper we propose a novel extension to the SMOTE algorithm with a theoretical guarantee for improved classification performance. The proposed approach considers the classification performance of both the majority and minority classes. In the proposed approach CGMOS (Certainty Guided Minority OverSampling) new data points are added by considering certainty changes in the dataset. The paper provides a proof that the proposed algorithm is guaranteed to work better than SMOTE for training data. Further experimental results on 30 real-world datasets show that CGMOS works better than existing algorithms when using 6 different classifiers.","Title: Demographics Inference Through Wi-Fi Network Traffic Analysis\nYear: 2016\n\nAbstract: Although privacy leaking through content analysis of Wi-Fi traffic has received an increased attention, privacy inference through meta-data (e.g. IP, Host) analysis of Wi-Fi traffic represents a potentially more serious threat to user privacy. Firstly, it represents a more efficient and scalable approach to infer users' sensitive information without checking the content of Wi-Fi traffic. Secondly, meta-data based demographics inference can work on both unencrypted and encrypted traffic (e.g., HTTPS traffic). In this study, we present a novel approach to infer user demographic information by exploiting the meta-data of Wi-Fi traffic. We develop a proof-of-concept prototype, Demographic Information Predictor (DIP) system, and evaluate its performance on a real-world dataset, which includes the Wi-Fi access of 28,158 users in 5 months. DIP extracts four kinds of features from real-world Wi-Fi traffic and proposes a novel machine learning based inference technique to predict user demographics. Our analytical results show that, for unencrypted traffic, DIP can predict gender and education level of users with an accuracy of 78% and 74% respectively. It is surprising to show that, even for HTTPS traffic, user demographics can still be predicted at a precision of 67% and 72% respectively, which well demonstrates the practicality of the proposed privacy inference scheme.","Title: Social Community-Oriented Social Attribute Analysis: An Empirical Study On Qq Group Data\nYear: 2015\n\nAbstract: Social networks have got increasing attention in recent years, and Social Networking Services (SNS), such as QQ, Facebook and Twitter, are among the most popular network applications on the internet. The popularity of these SNS attract more and more researchers to focus on the study of attributes of social network, especially the groups which are ubiquitous in social networks, both to improve current systems and to design new applications of social networks. In this paper, we study statistically 1.4 billion personal social information from QQ and 80 million group social information from QQ, and systemically analysis the distribution of attributes of all these social context data. And then we discover that both the frequency of age and the frequency of age for each gender conform to normal distributions. In addition, we analysis the QQ user registration time and the relationship between digital number of QQ and their age. Moreover, the distribution of the number of users in QQ group conforms to a power- law distribution. Furthermore, as the KL distance between the sample data and the whole data is within 0.1, i.e. the similarity of the sample data and the whole data is high, we can use the results based on the sample data to estimate the properties of the whole data in the integrated QQ network.","Title: A low complexity algorithm for across range unit effect correction of the moving target via range frequency polynomial-phase transform.\nYear: 2017\n\nAbstract: Based on the moving target detection, this paper proposes a low complexity algorithm for moving target across range unit effect correction on the basis of the range frequency polynomial-phase transform, utilizing which to remove the coupling of the range frequency and the slow time, then the across range unit effect can be corrected effectively. The algorithm does not need to search the target's parameters and estimate the Doppler ambiguity integer, and its computational complexity is low. The simulation results have proved the effectiveness of the proposed algorithm.","Title: Risk Assessment for Cooperative Automated Driving.\nYear: 2016\n\nAbstract: Global investment and recent advancements in vehicle automation are making autonomous and cooperative automated driving (AD) a reality. Not only will automated vehicles incorporate more electronics and connectivity than ever before, but also, notably, they will transfer control and responsibility of monitoring the environment from a human driver to a robotic system. While prior work has assessed and provided security solutions for non-automated vehicles, there is much to understand regarding the security implications of AD. In this work, we begin to address this gap in understanding. This paper reports on a risk assessment framework for autonomous and cooperative AD. We aggregate the state of the art in AD research to define a reference architecture for automated vehicles, describing the new attack surfaces and data flow. Employing existing automotive threat models, we propose a novel application-based threat enumeration and analysis approach that is able to address different AD applications across all levels of automation. We demonstrate this framework with an example application assessment and summarize the results and security insights from analyses of other applications. The results of our risk assessment and future assessments with this framework will inform on the design of security solutions and secure architectures for production AD systems.","Title: Ecosystem particle swarm optimization.\nYear: 2017\n\nAbstract: Particle swarm optimization (PSO) is a well-known swarm intelligence algorithm inspired by the foraging behavior of bird flocking. PSO has been widely used in many optimization and engineering problems due to its simplicity and efficiency, even though there still exist some disadvantages. The standard PSO often suffers with premature convergence or slow convergence when the optimization problem is multimodal or high-dimensional. To overcome these drawbacks, an ecosystem PSO (ESPSO) inspired by the characteristic that a natural ecosystem can excellently keep the biological diversity and make the whole ecosystem be in a dynamic balance is presented in this paper. ESPSO not only prevents the algorithm trapping into local optima but also balances the exploration and exploitation in both unimodal and multimodal problems as compared to other PSO variants. Twenty benchmark functions including unimodal functions and multimodal nonlinear functions are used to test the searching ability of ESPSO. Experimental results show that ESPSO considerably improves the searching accuracy, the algorithm reliability and the searching efficiency in comparison with other six well-known PSO variants and four evolutionary algorithms. Moreover, ESPSO was successfully applied to the antenna array pattern synthesis design and gained satisfactory results.","Title: A Functional Co-Design towards Safe and Secure Vehicle Platooning.\nYear: 2017\n\nAbstract: Cooperative adaptive cruise control (CACC) or platooning recently becomes promising as vehicles can learn of nearby vehicles? intentions and dynamics through wireless vehicle to vehicle (V2V) communication and advanced on-board sensing technologies. Violation of cybersecurity often results in serious safety issues as been demonstrated in recent studies. However, safety and security in a vehicle platoon so far have been considered separately by different sets of experts. Consequently no existing solution solves both safety and security in a coherent way. In this paper, we show cyber attacks on an automated platoon system could have the most severe level of safety impact with large scale car crash and argue the importance of safety-security co-design for safety critical cyber physical systems (CPS). We propose a safety-security co-design engineering process to derive functional security requirements for a safe automated vehicle platoon system based on a deep comprehension on the interrelation of safety and security. To our best knowledge, we are the first to apply the safety-security co-design concept to a concrete application. Through this engineering process, we propose a general approach for designing a safe and secure platooning. Following the general approach, we come up with a new platoon control algorithm that takes into account both safety and security. Our defense mechanism implicitly defends against safety-related cyber-attacks and greatly shortens the safe distance required when the platoon is not protected.","Title: Hybrid de-anonymization across real-world heterogeneous social networks.\nYear: 2017\n\nAbstract: To enjoy various utility and services, people are active in multiple social networks nowadays. With tons of data generated on platforms, multiple accounts of the same user in different social networks can be used to de-anonymize the user in a large scale. The aggregation of user profiles poses a threat to user privacy. With a concern of privacy leakage, de-anonymization techniques, including graph based approaches and profile based approaches, are widely studied in recent years. However, few works throw light on the deanonymization between real-world heterogeneous social networks. In this paper, we propose a Hybrid De-anonymization Scheme (HDS) aiming at de-anonymizing heterogeneous social networks. HDS firstly leverages the network graph structure to significantly reduce the size of candidate set, then exploits user profile information to identify the correct mapping users with a high confidence. Performance evaluation on real-world social network datasets shows that HDS has considerable accuracy on de-anonymization and significantly outperforms the prior schemes.","Title: Exploiting AUTOSAR Safety Mechanisms to Launch Security Attacks.\nYear: 2017\n\nAbstract: Automotive Electronic Control Units (ECUs) rely on both hardware and software mechanisms to ensure safety is maintained in the face of hazards that result from both random and systematic failures. In the presence of a malicious attacker, these safety mechanisms can serve as attack vectors to launch Denial of Service (DoS) attacks. This can be achieved by disabling critical system functions through the malicious creation of safety relevant fault conditions. In this paper, we explore some of the exploitable safety mechanisms within the Automotive Open System Architecture (AUTOSAR), and we demonstrate two successful attacks on an authenticated CAN FD bus system by introducing safety critical failures.","Title: Lecture Vdeo Indexing Using Boosted Margin Maximizing Neural Networks\nYear: 2017\n\nAbstract: This paper presents a novel approach for lecture video indexing using a boosted deep convolutional neural network system. The indexing is performed by matching high quality slide images, for which text is either known or extracted, to lower resolution video frames with possible noise, perspective distortion, and occlusions. We propose a deep neural network integrated with a boosting framework composed of two sub-networks targeting feature extraction and similarity determination to perform the matching. The trained network is given as input a pair of slide image and a candidate video frame image and produces the similarity between them. A boosting framework is integrated into our proposed network during the training process. Experimental results show that the proposed approach is much more capable of handling occlusion, spatial transformations, and other types of noises when compared with known approaches.","Title: Demographic Information Inference through Meta-Data Analysis of Wi-Fi Traffic.\nYear: 2018\n\nAbstract: Privacy inference through meta-data (e.g., IP, Host) analysis of Wi-Fi traffic poses a potentially more serious threat to user privacy. First, it provides a more efficient and scalable approach to infer users' sensitive information without checking the content of Wi-Fi traffic. Second, meta-data based demographics inference can work on both unencrypted and encrypted traffic (e.g., HTTPS traffic). In this study, we present a novel approach to infer user demographic information by exploiting the meta-data of Wi-Fi traffic. We develop an inference framework based on machine learning and evaluate its performance on a real-world dataset, which includes the Wi-Fi access of 28,158 users in five months. The framework extracts four kinds of features from real-world Wi-Fi traffic and applies a novel machine learning technique (XGBoost) to predict user demographics. Our analytical results show that, the overall accuracy of inferring gender and education level of users can be 82 and 78 percent, respectively. It is surprising to show that, even for HTTPS traffic, user demographics can still be predicted at accuracy of 69 and 76 percent, respectively, which well demonstrates the practicality of the proposed privacy inference scheme. Finally, we discuss and evaluate potential mitigation methods for such inference attacks.","Title: The hop count shift problem and its impacts on protocol design in wireless ad hoc networks\nYear: 2010\n\nAbstract: The hop count information has been exploited in the design of networking protocols in wireless ad hoc multi-hop networks. The hop count setup process normally assumes a perfect disk communication model and uses a simple controlled flooding approach. However, the practical communication model may not be such a disk communication model but time-varying and lossy. Sometimes transmissions can be successful beyond the nominal transmission range, i.e., the radius of such a disk model. The defacto hop count which are setup via time-varying and lossy radio channels may be different from the one based on the disk communication model. This paper introduces the hop count shift problem in realistic radio channels and investigates its impacts, via extensive simulations, on some representative hop count based protocols. Our simulation results suggest that these protocols’ performance generally suffers from the hop count shift problem, and the degradation is dependent on how the practical communication model deviates from the disk communication model. We also propose a strategy to combat the hop count shift problem and conduct simulations to show its effectiveness. The study of this paper necessitates reexaminations for the design of new hop count setup mechanism and the hop count based networking protocols.","Title: Generating Image Sequence From Description With Lstm Conditional Gan\nYear: 2018\n\nAbstract: Generating images from word descriptions is a challenging task. Generative adversarial networks(GANs) are shown to be able to generate realistic images of real-life objects. In this paper, we propose a new neural network architecture of LSTM Conditional Generative Adversarial Networks to generate images of real-life objects. Our proposed model is trained on the Oxford-102 Flowers and Caltech-UCSD Birds-200-2011 datasets. We demonstrate that our proposed model produces the better results surpassing other state-of-art approaches.","Title: Provably Secure Cryptographic ABAC System to Enhance Reliability and Privacy Using Real-Time Token and Dynamic Policy\nYear: 2018\n\nAbstract: In this paper we address the problem of reliability and security in an open-access data sharing system. We propose a new framework, called cryptographic attribute-based access control (CABAC), in consistent with the standard ABAC model. Moreover, two new mechanisms, real-time Tokens and secure policy decision-making, are introduced for ensuring secure attribute authorization and verifiable policy decision-making. More important, we present a practical CABAC system to support adaptability and flexibility using dynamically chosen policy and real-time attribute acquisition. We prove that our CABAC system is provably secure in four aspects: the attribute Tokens are existentially unforgeable against chosen-time and chosen-attribute attacks, respectively; the secure policy is existentially unforgeable against chosen-object attack under eBDH assumption; and our entire system is semantically secure against chosen-plaintext attack with Token and policy queries under eGDHE assumption.","Title: Heterogeneous multi-output classification by structured conditional risk minimization.\nYear: 2018\n\nAbstract: •For the first time, the mixture of different output types (heterogeneous output space) is studied.•The heterogeneous output space is transformed to a new homogeneous space.•A new structured prediction method is proposed to solve the transformed problem.•Experiments on real-world datasets show the effectiveness of the proposed method.","Title: Multi-target Core Network-Based Networked Multi-label Classification\nYear: 2018\n\nAbstract: As the increasing popularity of label classification, networked multi-label classification is becoming a hot topic in the field of data mining, where the networked multi-label means that each entity has more than one label during classification in network environments. In the existing works on networked multi-label classification, although only the labels of certain nodes are required to be determined, the labels of all nodes in the network have to be inferred. This works well for small networks, but not for large networks, especially not for large-scale networks with big data, as a plenty of time has been spent to compute a lot of unrequired labels. In this paper, we introduce a core network which is composed of the shortest paths that link some sources (i.e., some nodes with known labels) and some targets (i.e., some nodes with unknown labels required to be determined), as these paths have the most significant directly influence on label classification. Then we propose a novel heuristic MultI-TargeT corE Network discovery algorithm MITTEN to discover a core network, which aims to achieve the relatively accuracy of predicted labels with a relatively short time. Compared with existing networked multi-label classification approaches, the experimental results executed on real networks show that our proposed MITTEN can predict labels in network environments more precisely and more efficiently.","Title: Traffic Aware Virtual Machine Packing in Cloud Data Centers\nYear: 2016\n\nAbstract: This paper investigates the virtual machine packing problem in cloud data centers. A cloud tenant expresses computation requirement for each virtual machine (VM) and bandwidth requirement for each pair of VMs. A cloud provider places the VMs and routes the traffic among the VMs in a way that minimizes the total number of servers used while providing both computation and bandwidth guarantees. We formulate a special Multi-Capacity Bin Packing problem that consolidates VMs into the smallest number of servers. A server is represented by a bin with multiple capacities corresponding to the computation resource (CPU, memory, and storage) and communication bandwidth resource available. Unlike bin packing, in the traffic-aware VM packing, the cumulative bandwidth requirement of all VMs hosted in a server can be smaller than the sum of bandwidth demand of all VMs due to the co-location. Our experimental results show the proposed solutions significantly reduce the number of servers needed as well as the total inter-server traffic, and will have great potential for reducing energy consumption in data centers.","Title: Who Moved My Cheese: Towards Automatic and Fine-Grained Classification and Modeling Ad Network.\nYear: 2016\n\nAbstract: The mobile advertisement (ad) network is gaining an increasing interest due to the high popularity of smart phones. Previous researches on the security issues of ad network primarily focus on the privacy, permission and malware detection while less attention has been paid to the traffic consumption issue incurred by ad network. Though it is well known that ad network plays an important role in network consumption, it represents a great challenge of giving a fine-grained classification of ad networks. Inspired by this, different from any previous researches, in this study, we take the initial step towards modeling the network consumption of Ad network in Android. We develop an automatic ad analysis platform to quantify the ad network traffic consumed by android applications (app). To achieve a fine-grained quantification, we combine two sources of network traffic. On one hand, we modify the android webview and log system in system level to capture network traffic accurately. On the other hand, we capture network traffic in router level to collect detailed information of traffic packets, such as packet size and URI. We have evaluated the developed system in terms of normal apps, repacked apps and malicious apps based on the real-world dataset, which is comprised of 93 Android apps. We find out that ad traffic takes major percentage of the whole network traffic caused by Android app. We have also studied the ad library mechanism for 10 popular ad libraries. We found ads from some ad libraries use much more network traffic because they have to be fetched from remote ad libraries each time they are shown to users while other ad libraries allow apps to store ads locally.","Title: Privacy Leakage via De-anonymization and Aggregation in Heterogeneous Social Networks\nYear: 2020\n\nAbstract: Though representing a promising approach for personalization, targeting, and recommendation, aggregation of user profiles from multiple social networks will inevitably incur a serious privacy leakage issue. In this paper, we propose a Novel Heterogeneous De-anonymization Scheme (NHDS) aiming at de-anonymizing heterogeneous social networks. NHDS first leverages the network graph structure to significantly reduce the size of candidate set, then exploits user profile information to identify the correct mapping users with a high confidence. Performance evaluation on real-world social network datasets shows that NHDS significantly outperforms the prior schemes. Finally, we perform an empirical study on privacy leakage arising from cross-network aggregation based on four real-world social network datasets. Our findings show that 39.9 percent more information is disclosed through de-anonymization and the de-anonymized ratio is 84 percent. The detailed privacy leakage of user demographics and interests is also examined, which demonstrates the practicality of the identified privacy leakage issue.","Title: Layered Optical Flow Estimation Using a Deep Neural Network with a Soft Mask.\nYear: 2018\n\nAbstract: Using a layered representation for motion estimation has the advantage of being able to cope with discontinuities and occlusions. In this paper, we learn to estimate optical flow by combining a layered motion representation with deep learning. Instead of pre-segmenting the image to layers, the proposed approach automatically generates a layered representation of optical flow using the proposed soft-mask module. The essential components of the soft-mask module are maxout and fuse operations, which enable a disjoint layered representation of optical flow and more accurate flow estimation. We show that by using masks the motion estimate results in a quadratic function of input features in the output layer. The proposed soft-mask module can be added to any existing optical flow estimation networks by replacing their flow output layer. In this work, we use FlowNet as the base network to which we add the soft-mask module. The resulting network is tested on three well-known benchmarks with both supervised and unsupervised flow estimation tasks. Evaluation results show that the proposed network achieve better results compared with the original FlowNet.","Title: Defending AUTOSAR Safety Critical Systems Against Code Reuse Attacks.\nYear: 2019\n\nAbstract: With the emergence of AUTOSAR as the dominant software platform for deeply embedded automotive systems, securing AUTOSAR against software based attacks becomes essential to securing most vehicles. However security countermeasures have to fit within the constraints of automotive systems, such as available system resources, legacy code reuse and sensitivity to cost. Since the primary goal of such systems is to control safety critical vehicle functions, any security countermeasures need to harmonize the safety and security response. In this paper, we investigate the susceptibility of safety critical automotive systems to code reuse attacks that aim to violate the vehicle safety. We perform a case study using the vehicle diagnostics protocol with the aim to bypass the security access checks and execute a safety critical Routine Control service. Then we propose an HSM based monitoring system to detect or prevent such attacks.","Title: An intelligent intrusion detection system\nYear: 2019\n\nAbstract: With the introduction of emerging technologies cybersecurity has become an inherited and amplified problem. New technologies bring significant developments but also come with new challenges in the cybersecurity area. The fight against malicious attacks is an everyday battle for every company. Challenges brought by security breaches can be devastating for a company and sometimes bring un-survivable circumstances. In this paper, we propose a novel two-stage intelligent intrusion detection system (IDS) to detect and protect from such malicious attacks. Intrusion Detection Systems are feasible solutions for cybersecurity problems, but they come with implementation challenges. Anomaly based IDS usually have a high rate of false positives (FP) and they require considerable computational requirements. The approach proposed in this paper consists of a two-stage architecture based on machine learning algorithms. In the first stage, the IDS uses K-Means to detect attacks and the second stage uses supervised learning to classify such attacks and eliminate the number of false positives. The implementation of this approach results in a computationally efficient IDS able to detect and classify attacks at a 99.97% accuracy while lowering the number of false positives to 0. The paper also evaluates the performance results and compares them with other relevant research papers. The performance of this proposed IDS is superior to the current state of the art.","Title: 2D compressed learning: support matrix machine with bilinear random projections\nYear: 2019\n\nAbstract: Support matrix machine (SMM) is an efficient matrix classification method that can leverage the structure information within the matrix to improve the classification performance. However, its computational and storage costs are still expensive for high-dimensional data. To address these problems, in this paper, we consider a 2D compressed learning paradigm to learn the SMM classifier in some compressed data domain. Specifically, we use the Kronecker compressed sensing (KCS) to obtain the compressive measurements and learn the SMM classifier. We show that the Kronecker product measurement matrices used by KCS satisfies the restricted isometry property (RIP), which is a property to ensure the learnability of the compressed data. We further give a lower bound on the number of measurements required for KCS. Though this lower bound shows that KCS requires more measurements than the regular CS to satisfy the same RIP condition, KCS itself still enjoys lower computational and storage complexities. Then, using the RIP condition, we verify that the learned SMM classifier in the compressed domain can perform almost as well as the best linear classifier in the original uncompressed domain. Finally, our experimental results also demonstrate the feasibility of 2D compressed learning.","Title: BVI-DVC: A Training Database for Deep Video Compression\nYear: 2022\n\nAbstract: Deep learning methods are increasingly being applied in the optimisation of video compression algorithms and can achieve significantly enhanced coding gains, compared to conventional approaches. Such approaches often employ Convolutional Neural Networks (CNNs) which are trained on databases with relatively limited content coverage. In this paper, a new extensive and representative video database, BVI-DVC,is presented for training CNN-based video compression systems, with specific emphasis on machine learning tools that enhance conventional coding architectures, including spatial resolution and bit depth up-sampling, post-processing and in-loop filtering. BVI-DVC contains 800 sequences at various spatial resolutions from 270p to 2160p and has been evaluated on ten existing network architectures for four different coding tools. Experimental results show that this database produces significant improvements in terms of coding gains over five existing (commonly used) image/video training databases under the same training and evaluation configurations. The overall additional coding improvements by using the proposed database for all tested coding modules and CNN architectures are up to 10.3% based on the assessment of PSNR and 8.1% based on VMAF.","Title: Distribution agnostic Bayesian matching pursuit based on the exponential embedded family\nYear: 2020\n\nAbstract: Compressed sensing (CS) is an emerging field that allows to recover high-dimensional sparse signal from a few compressed measurements. Classical CS algorithms using the Bayesian framework generally impose a sparseness-promoting prior on the signal, which may not characterize the real signals with diversity. Moreover, estimating the parameters involved in the prior is challenging especially for non-i.i.d. signals. In this paper, we propose an efficient prior-agnostic Bayesian matching pursuit for sparse signal recovery, which avoids the risk of imposing mismatched prior on the signals and enjoys lower complexity than Bayesian approaches due to the elimination of prior parameter estimation. Specifically, we utilize the noise model and the reduced exponential embedded family (EEF) to obtain an approximate likelihood of interest, and then find the most dominant supports with maximum approximate likelihoods in a greedy manner to give an approximate minimum mean squared error (MMSE) estimate for the sparse signal. Experiment results demonstrate that our method achieves lower normalized mean squared error (NMSE) while higher efficiency compared to the state-of-the-art methods.","Title: The Horizontal Inr Conflict-Detection Algorithm: Revealing Inr Reallocation And Reauthorization In Rpki\nYear: 2021\n\nAbstract: Resource Public Key Infrastructure (RPKI) is a promising security enhancement to the Border Gateway Protocol, but it only requires the relying party (RP) to validate Internet Number Resource (INR) allocation or authorization relationships expressed in parent-child certificate pairs vertically. Therefore, conflicts in INR allocation and authorization may exist because of the limitations of the validation procedure of the RP software, in other words, certification authority malfunctions in issuing RPKI objects within a publication point cannot be detected by the RP. We develop a model of such conflicts and propose a horizontal INR conflict-detection algorithm with acceptable build time and query time. The proposed algorithm was tested on real-world RPKI data to identify actual and potential INR conflicts and its accurateness has been tried to be evaluated. This paper also discusses the deployment issues and the accuracy dependence about our algorithm design.","Title: Fuzzy Based Threat Assessment Model (FTAM)\nYear: 2019\n\nAbstract: Threat assessment is the process of identifying and characterizing a cybersecurity threat by evaluating and assessing its properties. This is a tool used by many organizations around the globe in order to evaluate their systems and perform a risk analysis against common threats. SAE J3061 guidelines provide a set of principles for automotive cybersecurity which includes threat identification, assessment, and analysis. This standard defines threat assessment as “an analysis technique that is applied in the concept phase to help identify potential threats to a feature and to assess the risk associated with the identified threats”. This paper presents an innovative, Fuzzy Based Threat Assessment Model (FTAM). FTAM leverages the threat characterization from established threat assessment models, while focusing on improving its assessment capabilities by using Fuzzy Logic. Through this methodology, FTAM is able to improve the efficiency and accuracy of the threat assessment process to determine the “degree” of the threat. This is different from using subjective assessment processes based on table look-ups or scoring like some of the current threat assessment models do. This paper provides an analysis on the design and performance of FTAM as well as benchmarking with other existing models.","Title: Continuous improvement of script-driven verifiable random functions for reducing computing power in blockchain consensus protocols\nYear: 2022\n\nAbstract: In order to solve the problem of low efficiency and high energy consumption of the Proof-of-Work (PoW) consensus protocol in blockchain within a peer-to-peer network, some new protocols based on Verifiable Random Function (VRF) have emerged recently. However, these VRF-based consensus protocols do not actually give a concrete and efficient VRF construction. In view of this, we present three simple and practical VRF constructions from the RSA hardness assumption, the Decisional Diffie-Hellman (DDH) assumption and the Leftover Hash Lemma (LHL) respectively, the output size of which is continuously reduced for the design of efficient consensus protocol in blockchain. We also give a complete security analysis of our VRF constructions. Furthermore, we show a specific application of our VRF constructions in the famous Algorand consensus protocol. We illustrate a general approach to integrate our VRF constructions with block structure in blockchain. Comparing with PoW-based mining, we demonstrate the detailed process of VRF-based consensus protocol. Meanwhile, three new opcodes are designed for the scripting system in blockchain to develop a script pair, scriptProof and scriptHash, which provides secure and efficient block verification. Finally, we evaluate the performance of our VRF constructions in terms of storage and computational overheads, and the experimental evaluation results show our VRF constructions can significantly reduce the computing power of consensus protocol in blockchain.","Title: A Hybrid Approach Toward Efficient and Accurate Intrusion Detection for In-Vehicle Networks\nYear: 2022\n\nAbstract: With recent advancements in the automotive world and the introduction of autonomous vehicles, automotive security has become a real and important issue. Modern vehicles have tens of Electronic Control Units (ECUs) connected to in-vehicle networks. As a de facto standard for in-vehicle network communication, the Controller Area Network (CAN) has become a target of cyber attacks. Anomaly-based Intrusion Detection System (IDS) is considered as an effective approach to secure CAN and detect malicious attacks. Currently, there are two primary approaches used for intrusion detection: rule-based and machine learning-based. Rule-based approach is efficient but limited in the detection accuracy while machine learning-based detection has comparably higher detection accuracy but higher computation cost at the same time. In this paper, we propose a novel hybrid IDS that combines the benefits of both rule-based and machine learning-based approaches. More specifically, we use machine learning methods to achieve a high detection rate while keeping the low computational requirement by offsetting the detection with a rule-based component. Our experiments with CAN traces collected from four different vehicle models demonstrate the effectiveness and efficiency of the proposed hybrid IDS."],"string":"[\n \"Title: Secure Data Retrieval of Outsourced Data with Complex Query Support\\nYear: 2012\\n\\nAbstract: The ability to efficiently retrieve data in their encrypted form is important for the cloud to become a truly secure and practical data sharing platform. Although this field has attracted tremendous research attention in the past a few years, most existing solutions do not support efficient searches with complex query conditions. Also, there has been little work to explore various comparison methods to specify complex query conditions. In this paper, we propose a framework of multi-keyword secure data retrieval to meet the needs of secure data retrieval with complex query support by leveraging recent development in attribute-based encryptions. Moreover, we propose a novel cryptographic comparison method based on forward and backward deviation functions (FDF/BDF). This method supports dual range comparisons and tree-oriented keyword search, as well as almost constant complexity for large size of integer range. We also provide several authentication mechanisms for preventing unpermitted access and verifying validity of protocol output. We implement a prototype system and our experimental results demonstrate its efficiency for practical usage.\",\n \"Title: Authenticating query results in data publishing\\nYear: 2005\\n\\nAbstract: We propose a communication-efficient authentication scheme to authenticate query results disseminated by untrusted data publishing servers. In our scheme, signatures of multiple tuples in the result set are aggregated into one and thus the communication overhead incurred by the signature keeps constant. Next attr-MHTs (tuple based Merkle Hash Tree) are built to further reduce the communication overhead incurred by auxiliary authentication information (AAI). Besides the property of communication-efficiency, our scheme also supports dynamic SET operations (UNION, INTERSECTION) and dynamic JOIN with immunity to reordering attack.\",\n \"Title: An application of host identity tags to DKIM for identifying signers\\nYear: 2009\\n\\nAbstract: The DomainKeys Identified Mail (DKIM) protocol gives a solution to guarantee the authentication and the integrity of e-mail messages. And in practice, to make DKIM come from theory to engineering, a concept of ADSP (Author Domain Signing Practice) has been put forth in an IETF draft. However, on the aspects of identifying a signer and delegating the signing ability, ADSP fails to give a specification. Yet the HITs (Host Identity Tags), put forth in RFC 5201, are born to identify signers featuring asymmetric key pairs and HITs will offer extra functions for security of the keys involved and management of the identifiers. This paper proposes to apply HITs to DKIM for identifying signers.\",\n \"Title: A New Approach to Secure Logging\\nYear: 2009\\n\\nAbstract: The need for secure logging is well-understood by the security researchers and practitioners. The ability to efficiently verify\\n all (or some) log entries is important to any application employing secure logging techniques. In this paper, we begin by\\n examining the state-of-the-art in secure logging and identify some problems inherent to systems based on trusted third-party\\n servers. We then propose a different approach based upon recently developed Forward-Secure Sequential Aggregate (FssAgg) authentication\\n techniques. Our approach offers both space-efficiency and provable security. We illustrate two concrete schemes – one private-verifiable\\n and one public-verifiable – that offer practical secure logging without any reliance on on-line trusted third parties or secure\\n hardware. We evaluate proposed schemes and report on our experience with implementing them within a secure logging system.\",\n \"Title: DISH: Distributed Self-Healing\\nYear: 2008\\n\\nAbstract: Unattended wireless sensor networks (UWSNs) operating in hostile environments face the risk of compromise. Unable to off-load\\n collected data to a sink or some other trusted external entity, sensors must protect themselves by attempting to mitigate\\n potential compromise and safeguarding their data. In this paper, we focus on techniques that allow unattended sensors to recover\\n from intrusions by soliciting help from peer sensors. We define a realistic adversarial model and show how certain simple\\n defense methods can result in sensors re-gaining secrecy and authenticity of collected data, despite adversary’s efforts to\\n the contrary. We present an extensive analysis and a set of simulation results that support our observations and demonstrate\\n the effectiveness of proposed techniques.\",\n \"Title: Successful IP Business Models\\nYear: 2006\\n\\nAbstract: The onward march of Moore's Law obviously brings with it a host of challenges, not the least of which is how to design to the space that is now available on an average leading edge semiconductor device. With millions of available transistors and a variety of technology options to choose from, it's small wonder that IP providers have managed to continue to build a viable industry. But that industry is changing, with numerous business models and sources of IP emerging. How will the interaction between IP providers and semiconductor manufacturers change? How will the industry benefit, in terms of quality and availability of IP? Dr. Di Ma will present a foundry perspective, including interesting statistics on IP usage and how the future interplay of foundry and IP provider might develop.\",\n \"Title: A Middleware Services Simulation Platform for Wireless Sensor Networks\\nYear: 2008\\n\\nAbstract: In the recent years, wireless sensor networks have attracted a lot of attention due to their broad application areas. In many application scenarios, hundreds or thousands of sensor nodes may be deployed in the physical environment. To design, implement, manage and maintain such large-scale networks efficiently is a major challenge which requires an integrated middleware development approach. In this paper, we focus on the development of a simulation platform specifically designed to support the development of sensor network middleware services. This novel platform is designed in a modular manner to facilitate the rapid integration and testing of different middleware algorithms and services. We present the simulator's software architecture and its operation. A case study to illustrate the usage of our simulation platform is also presented. The simulation platform shows potentials to support the research and development of middleware servicesfor wireless sensor networks.\",\n \"Title: Broadcast based on layered diffusion in wireless ad hoc and sensor networks.\\nYear: 2012\\n\\nAbstract: Broadcast is a fundamental operation in wireless ad hoc and sensor networks. This paper proposes a simple and efficient broadcast protocol, called Broadcast based on Layered Diffusion (BLD), which is a stateless broadcast protocol with very low message overheads and very low computation requirements. In BLD, nodes do not need to exchange neighborhood information for building a broadcast backbone, but make their rebroadcast decisions locally. The design idea of BLD is to emulate the triangular tessellation for complete area coverage of the network field. It does not require nodes' location information for such tessellation emulation but only exploits the hop count information for each node to make local rebroadcast decision and timing. Simulation results show the effectiveness and efficiency of the BLD protocol and its robustness to localization errors and transmission errors. Copyright (C) 2009 John Wiley & Sons, Ltd.\",\n \"Title: MR-DBSCAN: An Efficient Parallel Density-Based Clustering Algorithm Using MapReduce\\nYear: 2011\\n\\nAbstract: Data clustering is an important data mining technology that plays a crucial role in numerous scientific applications. However, it is challenging due to the size of datasets has been growing rapidly to extra-large scale in the real world. Meanwhile, MapReduce is a desirable parallel programming platform that is widely applied in kinds of data process fields. In this paper, we propose an efficient parallel density-based clustering algorithm and implement it by a 4-stages MapReduce paradigm. Furthermore, we adopt a quick partitioning strategy for large scale non-indexed data. We study the metric of merge among bordering partitions and make optimizations on it. At last, we evaluate our work on real large scale datasets using Hadoop platform. Results reveal that the speedup and scale up of our work are very efficient.\",\n \"Title: DISH: Distributed Self-Healing in Unattended Sensor Networks.\\nYear: 2008\\n\\nAbstract: Unattended wireless sensor networks (UWSNs) operating in hostile environments face the risk of compromise. Unable to off-load collected data to a sink or some other trusted external entity, sensors must protect themselves by attempting to mitigate potential compromise and safeguarding their data. In this paper, we focus on techniques that allow unattended sensors to recover from intrusions by soliciting help from peer sensors. We define a realistic adversarial model and show how certain simple defense methods call result in sensors re-gaining secrecy and authenticity of collected data, despite adversary's efforts to the contrary. We present an extensive analysis and a, set of simulation results that support, our observations and demonstrate the effectiveness of proposed techniques.\",\n \"Title: Addressing flaws in RFID authentication protocols\\nYear: 2011\\n\\nAbstract: The development of RFID systems in sensitive applications like e-passport, e-health, credit cards, and personal devices, makes it necessary to consider the related security and privacy issues in great detail. Among other security characteristic of an RFID authentication protocol, untraceability and synchronization are the most important attributes. The former is strongly related to the privacy of tags and their holders, while the latter has a significant role in the security and availability parameters. In this paper, we investigate three RFID authentication protocols proposed by Duc and Kim, Song and Mitchell, and Cho, Yeo and Kim in terms of privacy and security. We analyze the protocol proposed by Duc and Kim and present desynchronization and traceability attacks. By initiating traceability, backward traceability and desynchronization attacks, we show that the protocol proposed by Song and Mitchell lacks location privacy and availability. In addition, we study the weaknesses in Cho et al.'s protocol and address its defects by applying desynchronization, traceability and backward traceability attacks. We also propose revisions to secure the Cho et al.'s protocol against the cited attacks.\",\n \"Title: Efficient identity-based encryption without pairings and key escrow for mobile devices\\nYear: 2013\\n\\nAbstract: We propose a new construction of identity-based encryption without key escrow over the tradition cryptosystems. The security of our scheme follows from the decisional Diffie-Hellman assumption and the difficulty of a new problem --- modular inversion hidden number problem with error (MIHNPwE). The latter can be seen as a generalization of the modular inversion hidden number problem. We give an analysis on the hardness of MIHNPwE by lattice techniques. In our construction, we generate each user's partial private key in the form of an MIHNPwE instance. The hardness of MIHNPwE provides our scheme with resistance against key-collusion attacks from any number of traitors.\",\n \"Title: Sensing-Enabled Defenses To Rfid Unauthorized Reading And Relay Attacks Without Changing The Usage Model\\nYear: 2012\\n\\nAbstract: Many RFID tags store valuable information privy to their users that can easily be subject to unauthorized reading, leading to owner tracking or impersonation. RFID tags are also susceptible to different forms of relay attacks. This paper presents novel sensing-enabled defenses to unauthorized reading and relay attacks against RFID systems without necessitating any changes to the traditional RFID usage model.More specifically, the paper proposes the use of on-board tag sensors to (automatically) acquire useful contextual information about the tag's environment (or its owner, or the tag itself). It suggests how this information can be used to achieve two security functionalities. First, such context recognition can be leveraged for the purpose of selective tag unlocking - the tag will respond selectively to reader interrogations, i.e., only when it is deemed safe to do so. Second, context recognition can be used as a basis for transaction verification in order to provide protection against a severe form of relay attacks involving malicious RFID readers. To demonstrate the feasibility of the overall idea, a novel selective unlocking mechanism based on owner's posture recognition is presented. The evaluation of the proposed mechanism shows its effectiveness in significantly raising the bar against many different RFID attacks.\",\n \"Title: Wireless medical implant: A case study on artificial pancreas\\nYear: 2012\\n\\nAbstract: Medical implants are devices manufactured to replace damaged biological organs or structures. Once implanted, these devices communicate with other devices outside the body wirelessly. In this paper, we present our work on a particular type of WMI (wireless medical implants): artificial pancreas. We investigate challenges and issues in the context of artificial pancreas designs. In particular, we focus on how to achieve a low power design for WMI and propose a Virtual Patient system to evaluate control algorithms and communication protocols without an actual implantation of the device. Simulation and experiment results presented in this paper serves as design guidelines for wireless medical implant development.\",\n \"Title: Hop-Count Based Node-to-Anchor Distance Estimation in Wireless Sensor Networks\\nYear: 2010\\n\\nAbstract: Localization is one of the most important research issues in wireless sensor networks (WSNs). Hop-count based localization has been proposed as a cost-effective alternative to the expensive hardware-based localization schemes. In this paper, we propose a new method to estimate distances between anchors and nodes based on hop-count information. Localization is then achieved using the estimated distances. Simulation results show that the performance of our proposed algorithm is much better than that of the DV-hop algorithm in terms of the node-to- anchor distance estimation and localization accuracy, and the improvement is proportional to the node density.\",\n \"Title: Cryptographic Spatio-temporal Predicates for Location-Based Services\\nYear: 2013\\n\\nAbstract: The increasing spread of location-based services (LBSs) has led to a renewed research interest in location-based security, especially location-based access control. It also raises a lot of concern on potential privacy violation due to the possibility of identifying the user who requests a given service based on her/his location information at the time of the request. To ensure the credibility and availability of LBSs, there is a pressing requirement for addressing security and privacy issues of LBSs in a synergistic way. In this paper, we propose an innovative access control mechanism for LBSs, enabling both fine-grained access control and effective privacy protection. Our proposed approach is based on the construction of cryptographic spatio-temporal predicates by means of efficient secure integer comparison. Our experimental results not only validate the effectiveness of our scheme, but also demonstrate that the proposed integer comparison scheme performs better than previous bit wise comparison scheme.\",\n \"Title: Range-free wireless sensor networks localization based on hop-count quantization\\nYear: 2012\\n\\nAbstract: Localization is one of the most important research issues in Wireless Sensor Networks (WSNs). Recently, hop-count-based localization has been proposed as a cost-effective alternative to many expensive hardware-based\\n localization algorithms. The basic idea of many hop-count-based localization algorithms is to seek a transformation from hop-count\\n information to distance (e.g. DV-hop algorithm of Niculescu and Nath in Global Telecommunications Conference, vol. 5, pp. 2926–2931,\\n 2001) or location (e.g. MDS algorithm of Shang et al. in International Symposium on Mobile Ad Hoc Networking and Computing, pp. 201–212,\\n 2003) information. Traditionally, hop-counts between any pair of nodes can only take on integer value regardless of relative positions\\n of nodes in the hop. We argue that by partitioning a node’s one-hop neighbor set into three disjoint subsets according to\\n their hop-count values, the integer hop-count can be transformed into a real number accordingly. The transformed real number\\n hop-count is then a more accurate representation of a node’s relative position than an integer-valued hop-count. In this paper,\\n we present a novel algorithm termed HCQ (hop-count quantization) to perform such transformation. We then use the transformed\\n real number hop-count to solve WSNs localization problems based on the MDS (multidimensional scaling) method. Simulation results\\n show that the performance of the MDS algorithm using the real number hop-count outperforms those which use integer hop-count\\n values.\",\n \"Title: Secure Collaborative Integrity Verification For Hybrid Cloud Environments\\nYear: 2012\\n\\nAbstract: A hybrid cloud is a cloud computing environment in which an organization provides and manages some internal resources and has others provided externally. However, this new environment could bring irretrievable losses to the clients due to a lack of integrity verification mechanism for distributed data outsourcing. To support scalable service and data migration, in this paper we address the construction of a collaborative integrity verification mechanism in hybrid clouds where we consider the existence of multiple cloud service providers to collaboratively store and maintain the clients' data. We propose a collaborative provable data possession scheme adopting the techniques of homomorphic verifiable responses and hash index hierarchy. In addition, we articulate the performance optimization mechanisms for our scheme and prove the security of our scheme based on multi-prover zero-knowledge proof system, which can satisfy the properties of completeness, knowledge soundness, and zero-knowledge. Our experiments also show that our proposed solution only incurs a small constant amount of communications overhead.\",\n \"Title: A survey of movement strategies for improving network coverage in wireless sensor networks\\nYear: 2009\\n\\nAbstract: The emerging technology of wireless sensor network (WSN) is expected to provide a broad range of applications, such as battlefield surveillance, environmental monitoring, smart spaces and so on. The coverage problem is a fundamental issue in WSN, which mainly concerns with a fundamental question: How well a sensor field is observed by the deployed sensors? To optimize network coverage, the traditional approach is to deploy a large amount of stationary sensor nodes and then to schedule their sensing activities in an efficient way. Recently, mobile sensor nodes have received many attentions since network performance can be greatly improved by using just a few of mobile nodes. We categorize three typical application scenarios of applying mobile nodes, and we then highlight the issues and identify the objectives of optimizing network coverage by leveraging mobile nodes. We further survey recent contributions on the design of nodes’ movement strategy to optimize network coverage in each application scenario.\",\n \"Title: Practical forward secure sequential aggregate signatures\\nYear: 2008\\n\\nAbstract: A forward secure sequential aggregate (FssAgg) signature scheme allows a signer to iteratively combine signatures generated in different time intervals - and with different keys - into a single constant-size signature. Such a signature offers forward security, storage/communication efficiency, as well as overall integrity of the signed messages. FssAgg schemes are therefore suitable for data-intensive applications on untrusted and/or unattended devices, e.g., logging systems. The first FssAgg signature scheme [21] is based on bilinear maps and is thus rather costly. In this paper, we propose two more practical FssAgg1 signature schemes. A FssAgg1 signature scheme is a special FssAgg signature scheme where exactly ONE message can be signed at each time interval and key update is invoked immediately after each signature generation. Both new schemes are derived from existing forward secure signature schemes. Unlike the scheme in [21], each new scheme has constant-size public and private keys, constant-size signatures as well as constant-time key update and signature generation complexity. We show how to apply proposed schemes in secure logging applications.\",\n \"Title: Security and privacy in emerging wireless networks\\nYear: 2010\\n\\nAbstract: Wireless communication is continuing to make inroads into many facets of society and is gradually becoming more and more ubiquitous. While in the past wireless communication (as well as mobility) was largely limited to the first and last transmission hops, today's wireless networks are starting to offer purely wireless, often mobile, and even opportunistically connected operation. The purpose of this article is to examine security and privacy issues in some new and emerging types of wireless networks, and attempt to identify directions for future research.\",\n \"Title: Tap-Wave-Rub: lightweight malware prevention for smartphones using intuitive human gestures\\nYear: 2013\\n\\nAbstract: We introduce a lightweight permission enforcement approach - Tap-Wave-Rub (TWR) - for smartphone malware prevention. TWR is based on simple human gestures (implicit or explicit) that are very quick and intuitive but less likely to be exhibited in users' daily activities. Presence or absence of such gestures, prior to accessing an application, can effectively inform the OS whether the access request is benign or malicious. In this paper, we focus on the design of an accelerometer-based phone tapping detection mechanism. This implicit tapping detection mechanism is geared to prevent malicious access to NFC services, where a user is usually required to tap her phone with another device. We present a variety of novel experiments to evaluate the proposed mechanism. Our results suggest that our approach could be very effective for malware prevention, with quite low false positives and false negatives, while imposing no additional burden on the users. As part of the TWR framework, we also briefly explore explicit gestures (finger tapping, rubbing or hand waving based on proximity sensor), which could be used to protect services which do not have a unique implicit gesture associated with them.\",\n \"Title: Efficient Object-Based Stream Authentication\\nYear: 2002\\n\\nAbstract: This paper presents an object-based stream authentication scheme for practical end-to-end applications on present network infrastructure and protocols. In this proposal, a stream is divided into objects that are delivered sequentially. Each object consists of a unique identifier, content and operations. At the sender side, a locked object identifier is produced by encrypting the object identifier with a key generated from MAC of the object content. The object content and locked identifier are transmitted to the receiver. The receiver can recover the object identifier and check it to authenticate the object. The scheme tolerates packet loss, and incurs no additional overhead if an identifier is ingeniously chosen. In the experiment, we overload some particular fields in the headers of standard protocols as object identifier to implement a real time video stream authentication scheme.\",\n \"Title: Self-healing in unattended wireless sensor networks\\nYear: 2012\\n\\nAbstract: Wireless sensor networks (WSNs) appeal to a wide range of applications that involve the monitoring of various physical phenomena. However, WSNs are subject to many threats. In particular, lack of pervasive tamper-resistant hardware results in sensors being easy targets for compromise. Having compromised a sensor, the adversary learns all the sensor secrets, allowing it to later encrypt/decrypt or authenticate messages on behalf of that sensor. This threat is particularly relevant in the novel unattended wireless sensor networks (UWSNs) scenario. UWSNs operate without constant supervision by a trusted sink. UWSN's unattended nature and increased exposure to attacks prompts the need for special techniques geared towards regaining security after being compromised. In this article, we investigate cooperative self-healing in UWSNs and propose various techniques to allow unattended sensors to recover security after compromise. Our techniques provide seamless healing rates even against a very agile and powerful adversary. The effectiveness and viability of our proposed techniques are assessed by thorough analysis and supported by simulation results. Finally, we introduce some real-world issues affecting UWSN deployment and provide some solutions for them as well as a few open problems calling for further investigation.\",\n \"Title: Seamline Network Refinement Based on Area Voronoi Diagrams With Overlap\\nYear: 2014\\n\\nAbstract: The area Voronoi diagrams with overlap (AVDO) method was recently presented and has been used to generate a seamline network for the mosaicking of orthoimages. The method shows considerable potential advantages for seamless mosaics covering a large geographic region. In this paper, the method is further improved, and a seamline network refinement approach based on AVDO is presented. The improvements of the presented approach include the detection of valid regions for orthoimages, a more general algorithm for the generation of bisectors, and the refinement of the seamline network combining the bottleneck model and the Dijkstra's algorithm. Finally, a number of digital aerial orthoimages are employed to test the proposed approach, and the experimental results demonstrate its efficacy.\",\n \"Title: A coverage-aware clustering protocol for wireless sensor networks\\nYear: 2010\\n\\nAbstract: In energy-limited wireless sensor networks, network clustering and sensor scheduling are two efficient techniques for minimizing node energy consumption and maximizing network coverage lifetime. When integrating the two techniques, the challenges include how to decide the most energy-efficient cluster size and how to select cluster heads and active nodes. In this paper, we provide a computation method for the optimal cluster size to minimize the average energy consumption rate per unit area. In the proposed coverage-aware clustering protocol, we define a cost metric that favors those nodes being more energy-redundantly covered as better candidates for cluster heads and select active nodes in a way that tries to emulate the most efficient tessellation for area coverage. Our simulation results validate our computation and show the significant improvement of the network coverage lifetime.\",\n \"Title: A novel approach toward source-to-sink distance estimation in wireless sensor networks\\nYear: 2010\\n\\nAbstract: Estimation of the Euclidean distance between a source node and a sink node by using the hop-counts between them is an interesting research problem in WSNs (wireless sensor networks). In this paper, we propose a novel source-to-sink distance estimation method based on the observation that the neighbors of a sink can have different hop-counts relative to the same source. The proposed method not only uses the hop-count information of a sink, but also exploits the hop-count information of the sink's neighbors. Simulation results show that the proposed method outperforms other well-known hop-count-based source-to-sink distance estimation methods.\",\n \"Title: Analysis of Hop-Count-Based Source-to-Destination Distance Estimation in Wireless Sensor Networks With Applications in Localization\\nYear: 2010\\n\\nAbstract: In wireless sensor networks (WSNs), estimation of the true distance between a source and its destination based on the hop counts between them is an important research problem. The basic idea of several hop-count-based distance-estimation algorithms is to seek an analytical/heuristic transformation from the observed destination's hop-count information to an unknown source-to-destination distance (s...\",\n \"Title: Flexible Access Control to JPEG 2000 Image Code-Streams\\nYear: 2007\\n\\nAbstract: JPEG 2000 is an international standard for still image compression in the 21st century. Part 8 of the standard, named JPSEC, is concerned with all the security aspects, in particular to access control and authentication. This paper presents a novel access control scheme for JPEG 2000 image code-streams. The proposed scheme is secure against collusion attacks and highly efficient. The scheme is also very flexible, allowing access control to JPEG 2000 image code-streams according to any combination of resolution, quality layer and region of interest. The \\\"encrypt once, decrypt many ways\\\" property of our scheme is designed to work seamlessly with the \\\"compress once, decompress many ways\\\" feature of the JPEG 2000 image code-streams. Our prototype implementation shows that the scheme is practical and is completely compatible with the core part of the JPEG 2000 standard.\",\n \"Title: Host-identifier-based scheme for source accountability of the internet\\nYear: 2011\\n\\nAbstract: This paper explores the current Internet's potential to provide accountability. We present a new IPv6 address management and certification hierarchy to achieve host-oriented accountability of the Internet, with the current addressing system remaining unchanged. With the design of HIA (Host-Identifier-Embedded IPv6 Address), we present the framework for host identity authentication, packet source validation and the host reputation system construction. This paper also discusses the feasibility and deployment issues about our design.\",\n \"Title: Progressive protection of JPEG2000 codestreams\\nYear: 2004\\n\\nAbstract: This paper presents an authentication scheme and an access control scheme for JPEG2000 image codestreams based on hash chains. Both schemes are fully compatible with the core part of JPEG2000 standard. The authentication scheme allows users to verify the authenticity of sub-images progressively extracted from a single codestream protected with a single signature, and the access control scheme allows users to access sub-images partially decrypted from a single encrypted JPEG2000 codestream. In addition, the two schemes can be integrated to provide both authenticity and access control simultaneously. Our experiments demonstrate the two scheme are very efficient and highly scalable.\",\n \"Title: Scalable trusted online dissemination of JPEG2000 images\\nYear: 2005\\n\\nAbstract: n important aspect of JPEG2000 is its \\\"compress once, decompress many ways\\\" property [1], i.e., it allows users with different preferences, privileges or capabilities to extract various sub-images all from a single compressed image code-stream. In this paper, we present a flexible and scalable scheme to authenticate JPEG2000 images disseminated by a untrusted third-party server over open networks. The proposed scheme is fully compatible with JPEG2000 and possesses a \\\"sign once, verify many ways\\\" property, i.e., it allows users to verify the authenticity and integrity of different sub-images extracted from a single compressed code-stream protected with a single digital signature. Furthermore, the use of aggregated digital signatures reduces both computation and communication overhead on the user side for batch image authentication.\",\n \"Title: A Physical and SPICE-Compatible Model for the MOS Depletion Device\\nYear: 1985\\n\\nAbstract: An MOS depletion device model that is compatible with SPICE circuit simulation program and based on device physics is described. The depletion device is modeled by an equivalent circuit consisting of various well-characterized semiconductor devices, for example, the enhancement MOS device and the JFET. IT has the advantages of both the existing physical and empirical models. The model is applicable to immediate circuit simulation and device fabrication. Derivation of the model along with its parameters for the Gaussian impurity distribution is given. Implementation of circuit simulation with SPICE is demonstrated. More accurate results compared to the empirical model are obtained.\",\n \"Title: Secure and efficient constructions of hash, MAC and PRF for mobile devices\\nYear: 2012\\n\\nAbstract: Numerous cryptographic techniques have been developed to be used on mobile devices for various security and privacy protections. However, these cryptographic primitives, working under different mathematical assumptions, tend to become more and more complex and intricate, which makes it increasingly more difficult for proper implementation and management. Thus, it is desired to simplify management and improve efficiency by means of designing a general function family to meet a variety of security requirements. In this paper, we present such a family of square functions, including SqHash, SqMAC and SqPRF, based on a specially truncated function (MSB or LSB). We further improve the efficiency of these algorithms by using “circular convolution with carry bits” which makes parallel processing possible. We prove the security of these functions based on the privacy in hidden number problem and hard-core predicate of one-way function. We also show that the proposed schemes achieve better performance with a complexity reduction from O(n2) to O(kn/w) for n-bit message, k-bit output and w-bit word size.\",\n \"Title: Location-Aware and Safer Cards: Enhancing RFID Security and Privacy via Location Sensing\\nYear: 2012\\n\\nAbstract: In this paper, we report on a new approach for enhancing security and privacy in certain RFID applications whereby location or location-related information (such as speed) can serve as a legitimate access context. Examples of these applications include access cards, toll cards, credit cards, and other payment tokens. We show that location awareness can be used by both tags and back-end servers for defending against unauthorized reading and relay attacks on RFID systems. On the tag side, we design a location-aware selective unlocking mechanism using which tags can selectively respond to reader interrogations rather than doing so promiscuously. On the server side, we design a location-aware secure transaction verification scheme that allows a bank server to decide whether to approve or deny a payment transaction and detect a specific type of relay attack involving malicious readers. The premise of our work is a current technological advancement that can enable RFID tags with low-cost location (GPS) sensing capabilities. Unlike prior research on this subject, our defenses do not rely on auxiliary devices or require any explicit user involvement.\",\n \"Title: A Server Friendly File Synchronization Mechanism for Cloud Storage\\nYear: 2013\\n\\nAbstract: The rapid development of cloud computing has made traditional data centers energy hungry. Thus it is very important to reduce the amount of calculations on the cloud. In this paper we focus on the energy saving in cloud storage service. First we analyze rsync algorithm widely used in the cloud synchronization service and reveal how this algorithm will lead to high CPU load on cloud. Then we design and implement an improved algorithm that moves most calculations out of the cloud. The experiments show that the synchronization related CPU load in server dropped significantly when using our improved method. The improved mechanism can be applied not only in cloud storage service, but also in any centralized synchronization scenario to reduce CPU load on the central nodes.\",\n \"Title: Incorporating social actions into recommender systems\\nYear: 2013\\n\\nAbstract: With the rapidly growing amount of information available on the internet, recommender systems become popular tools to promote relevant online information to a given user. Although collaborative filtering is the most popular approach to build recommender systems and has been widely deployed in many applications, it still pay little attention to social actions, which are widely common in social networks and we believe could make a significant improvement in recommender systems. In this paper, we incorporate users' social actions into a model-based approach for recommendation using probabilistic matrix factorization. Compared with previous work, users' social actions are taken as a new relation to optimize previous trust-based recommender systems. To achieve this, we propose a social recommendation graphical model employing users' relations based on their social actions. We make use of users' commenting action in our approach and conduct experiments on a real life dataset, extracted from the Douban movie ratings and comments system. Our experiments demonstrate that incorporating users' social action information leads to a significant improvement in recommender systems.\",\n \"Title: Building Trust for lambda-Congenial Secret Groups\\nYear: 2011\\n\\nAbstract: Establishing trust while preserving privacy is a challenging research problem. In this paper we introduce lambda -congenial secret groups which allow users to recognize trusted partners based on common attributes while preserving their anonymity and privacy. Such protocols are different from authentication protocols, since the latter are based on identities, while the former are based on attributes. Introducing attributes in trust establishment allows a greater flexibility but also brings up several issues. In this paper, we investigate the problem of building trust with attributes by presenting motivating examples, analyzing the security requirements and giving an informal definition. We also survey one of the most related techniques, namely private matching, and finally present solutions based on it.\",\n \"Title: End-to-end aggregate authentication of time-series data\\nYear: 2013\\n\\nAbstract: We consider the problem of providing integrity of aggregate result in the presence of an untrusted data aggregator who may introduce errors into data fusion, causing the final aggregate result to far deviate from the true result determined by participating values. We proposes a construction that allows a group of participants to periodically upload authenticated data values to a data aggregator, such that the aggregator is able to derive an aggregate signature over the true aggregate result, but is unable to do so if any error is introduced into the aggregate result. To our knowledge, it is the first secure agggregate authentication scheme that achieves end-to-end security without resorting to additional communication or third parties. Our construction supports iterative aggregation thus it can be used in a networked environment where there exists multi-level of aggregators. Our construction is efficient in term of both computation and communication. Especially, the online computation cost of aggregate tag verification is constant and independent of the number of underlying individual signatures. We prove the security of our construction under the assumption of CDH problem.\",\n \"Title: Secure feedback service in wireless sensor networks\\nYear: 2007\\n\\nAbstract: In many sensor network applications, it is critical for the base station to know the delivery (or execution) status of its broadcast messages (or commands). One straightforward way to do so is to let every sensor node send an authenticated acknowledgement (ACK) to the BS directly. However this naive solution is highly communication inefficient and may result in severe ACK implosion near the BS. In this paper, we propose a communication efficient scheme to provide secure feedback service in sensor networks. In our basic scheme, we use ACK aggregation to reduce the ACK traffic. Meanwhile we store audit information for each aggregation operation so that the BS can use the audit information to locate errors in the network. We further improve the basic scheme by constructing a balanced aggregation tree to reduce localization delay and using Bloom filters to reduce storage requirement in each sensor for storing audit information. We analyze the performance of the proposed scheme and show it achieves good bandwidth gain over the naive approach.\",\n \"Title: A Context-Aware Approach for Enhanced Security and Privacy in RFID Electronic Toll Collection Systems\\nYear: 2011\\n\\nAbstract: RFID Electronic Toll Collection (ETC) systems have been deployed worldwide to improve toll collection efficiency, reduce road congestion, increase road safety and traveler satisfiability. However, the use of such systems raises a number of security and privacy issues due to unauthorized reading and relay attacks. Unfortunately, currently deployed or proposed solutions targeting these attacks often fail to satisfy the constraints and requirements of the underlying RFID toll road application in terms of (one or more of) efficiency, security, and usability. In this paper, we report our initial work toward a new approach, one that utilizes sensing technologies, to tackle the problems of unauthorized reading and relay attacks in RFID ETC systems by considering efficiency, security, and usability simultaneously. In our approach, on-board tag sensors are used to collect contextual information (location, speed) about the tag. Such contextual information is then used to design context-aware selective unlocking mechanisms for toll cards such that they can selectively respond to reader interrogations and thus minimize the likelihood of unauthorized reading and relay attacks. The premise of our work is a current technological advancement that enables many RFID tags with sensing capabilities.\",\n \"Title: A Soldier Health Monitoring System for Military Applications\\nYear: 2010\\n\\nAbstract: With recent advances in technology, various wearable sensors have been developed for the monitoring of human physiological parameters. A Body Sensor Network (BSN) consisting of such physiological and biomedical sensor nodes placed on, near or within a human body can be used for real-time health monitoring. In this paper, we describe an on-going effort to develop a system consisting of interconnected BSNs for real-time health monitoring of soldiers. We discuss the background and an application scenario for this project. We describe the preliminary prototype of the system and present a blast source localization application.\",\n \"Title: IRRES: Intrusion-Resilient Remote Email Storage\\nYear: 2010\\n\\nAbstract: Both individuals and corporations increasingly rely on email to exchange important and, often sensitive, information. With the advent of ubiquitous computing and miniaturization of end-devices, many users leave email on remote servers, thus facilitating anywhere/anytime access from any networked device. Since private and sensitive information is often contained in email, it is very important to prevent its unauthorized disclosure. In this paper, we consider the problem of repeated intrusions of third-party email servers. Specifically, we design IRRES: Intrusion-Resilient Remote Email Storage system that prevents email leakage despite the presence of a powerful mobile adversary. IRRES eliminates sender compliance requirements by shifting encryption from email sender to email server. Incoming email messages are encrypted by the server with one-time keys obtained from an intrusion-resilient cooperative randomness generation process. Thus, even if the server is compromised twice, all email encrypted in the interim remains secure.\",\n \"Title: POSH: Proactive co-Operative Self-Healing in Unattended Wireless Sensor Networks\\nYear: 2008\\n\\nAbstract: We identify here various kinds of failure conditions and robustness issues that arise in context-aware pervasive computing applications. Such conditions are related to failures in an application's interactions with ambient services, failures in resource ...\",\n \"Title: A Framework for Cloud-Based Large-Scale Data Analytics and Visualization: Case Study on Multiscale Climate Data\\nYear: 2011\\n\\nAbstract: In this paper, we present a cloud framework to provide cloud clustering, workflow scheduling and management, fault tolerance and distributed data storage, data analytics and visualisation services. Using a practical case study, we show that in the process of analyzing multiscale climate data, typical problems plaguing data analysts are faced. These include large datasets and limited computational resources, data complexity and limited knowledge, and varying data structures/formats and the need to integrate different tools. The implementation of our framework to climate studies was a success. This can be seen in its ability to perform spatio-temporal data analysis and visualization of a large multi-dimensional climate dataset with reduced processing time. The framework demonstrates great flexibility and simplicity for end users intending to perform data analysis by aiding the integration of data and tools and enabling interactive visualization on-the-fly. This is coupled with effective utilization of computational resources and data storage systems.\",\n \"Title: A parameter selection strategy for particle swarm optimization based on particle positions.\\nYear: 2014\\n\\nAbstract: •A relationship between the dynamic process of PSO and the transition process is proposed.•A novel parameter strategy for PSO is proven using the overshoot and the peak time.•The parameters obtained from our strategy are coincided with the published results.•The new strategy is applied to equally spaced linear array synthesis examples.\",\n \"Title: Lattice-based key exchange on small integer solution problem.\\nYear: 2014\\n\\nAbstract: In this paper, we propose a new hard problem, called bilateral inhomogeneous small integer solution (Bi-ISIS), which can be seen as an extension of the small integer solution problem on lattices. The main idea is that, instead of choosing a rectangle matrix, we choose a square matrix with small rank to generate Bi-ISIS problem without affecting the hardness of the underlying SIS problem. Based on this new problem, we present two new hardness problems: computational Bi-ISIS and decisional problems. As a direct application of these problems, we construct a new lattice-based key exchange (KE) protocol, which is analogous to the classic Diffie-Hellman KE protocol. We prove the security of this protocol and show that it provides better security in case of worst-case hardness of lattice problems, relatively efficient implementations, and great simplicity.\",\n \"Title: Identity-Set-based Broadcast Encryption supporting \\\\\\\"Cut-or-Select\\\\\\\" with Short Ciphertext\\nYear: 2015\\n\\nAbstract: In this paper we present an identity-set-based broadcast encryption scheme with three working modes: positive membership (Select-mode), all member (All-mode), and negative membership (Cut-mode) over the user identity set, simultaneously. The core of our scheme is the implementation of cryptographic representation of subset by using two aggregation functions: Zeros-based aggregation and Poles-based aggregation. These two aggregation functions are capable of compressing any subset into one element in a bilinear map group for determining the membership between an element and a subset. Our scheme achieves the optimal bound of O(1)-size for either ciphertext (consisting of just two elements) or decryption key (one element) for an identity set of large size. We prove that our scheme is secure under the General Diffie-Hellman Exponent (GDHE) assumption.\",\n \"Title: A context-aware approach to defend against unauthorized reading and relay attacks in RFID systems.\\nYear: 2014\\n\\nAbstract: Radio frequency identification (RFID) systems are becoming increasingly ubiquitous in both public and private domains. However, because of the inherent weaknesses of underlying wireless radio communications, RFID systems are plagued with a wide variety of security and privacy threats. A large number of these threats arise because of the tag's promiscuous response to any reader requests. This renders sensitive tag information easily subject to unauthorized reading. Promiscuous tag response also incites different forms of relay attacks whereby a malicious colluding pair, relaying messages between a tag and a reader, can successfully impersonate the tag without actually possessing it. Because of the increasing ubiquity of RFID devices, there is a pressing need for the development of security primitives and protocols to defeat unauthorized reading and relay attacks. However, currently deployed or proposed solutions often fail to satisfy the constraints and requirements of the underlying RFID applications in terms of (one or more of) efficiency, security, and usability. This paper proposes a novel research direction, one that utilizes sensing technologies, to tackle the problems of unauthorized reading and relay attacks with a goal of reconciling the requirements of efficiency, security, and usability. The premise of the proposed work is based on a current technological advancement that enables many RFID tags with low-cost sensing capabilities. The on-board tag sensors will be used to acquire useful contextual information about the tag's environment (or its owner, or the tag itself). For defense against unauthorized reading and relay attacks, such context information can be leveraged in two ways. First, contextual information can be used to design context-aware selective unlocking mechanisms so that tags can selectively respond to reader interrogations and thus minimize the likelihood of unauthorized reading and ghost-and-leech relay attacks. Second, contextual information can be used as a basis for context-aware secure transaction verification to defend against special types of relay attacks involving malicious readers. Copyright (c) 2011 John Wiley & Sons, Ltd. This paper proposes a novel research direction, one that utilizes sensing technologies to tackle the challenging problems of unauthorized reading and relay attacks in radio frequency identification systems. First, contextual information is used to design context-aware selective unlocking mechanisms, so that tags can selectively respond to reader interrogations and, thus, minimize the likelihood of unauthorized reading and ghost-and-leech relay attacks. Second, contextual information is used as a basis for context-aware secure transaction verification to defend against special types of relay attacks involving malicious readers.\",\n \"Title: Secure and efficient random functions with variable-length output.\\nYear: 2014\\n\\nAbstract: Many random functions, like Hash, MAC, PRG, have been used in various network applications for different security choices. However, they are either fast but insecure or cryptographic secure but slow. To integrate them together, in this paper we present a new family of square random functions, including SqHash, SqMAC and SqPRG, based on a specially truncated function (MSB or LSB), as well as circular convolution with carry bits. Provable security is provided by the privacy property in hidden number problem and Hard-core unpredication of one-way function. The experiment results show that these schemes have better performance under different input and output lengths. We also perform four types of statistical tests for randomness. The experiments indicate that our construction has good average-case randomness than SHA-2 and original Square algorithm.\",\n \"Title: Context-Aware Defenses to RFID Unauthorized Reading and Relay Attacks\\nYear: 2013\\n\\nAbstract: Many RFID tags store valuable information privy to their users that can easily be subject to unauthorized reading, leading to owner tracking, or impersonation. RFID tags are also susceptible to different forms of relay attacks. This paper presents novel sensing-enabled defenses to unauthorized reading and relay attacks against RFID systems without necessitating any changes to the traditional RFID usage model. Specifically, this paper proposes the use of cyber-physical interfaces, on-board tag sensors, to (automatically) acquire useful contextual information about the tag's environment (or its owner, or the tag itself). First, such context recognition is leveraged for the purpose of selective tag unlocking-the tag will respond selectively to reader interrogations. In particular, novel mechanisms based on an owner's posture recognition are presented. Second, context recognition is used as a basis for transaction verification in order to provide protection against a severe form of relay attacks involving malicious RFID readers. A new mechanism is developed that can determine the proximity between a valid tag and a valid reader by correlating certain (specifically audio) sensor data extracted from the two devices. Our evaluation of the proposed mechanisms demonstrate their feasibility in significantly raising the bar against RFID attacks.\",\n \"Title: How to use attribute-based encryption to implement role-based access control in the cloud\\nYear: 2013\\n\\nAbstract: This paper addresses how to construct a RBAC-compatible attribute-based encryption (ABE) for secure cloud storage, which provides a user-friendly and easy-to-manage security mechanism without user intervention. Similar to role hierarchy in RBAC, attribute lattice introduced into ABE is used to define a seniority relation among all values of an attribute, whereby a user holding the senior attribute values acquires permissions of their juniors. Based on these notations, we present a new ABE scheme called Attribute-Based Encryption with Attribute Lattice (ABE-AL) that provides an efficient approach to implement comparison operations between attribute values on a poset derived from attribute lattice. By using bilinear groups of composite order, we propose a practical construction of ABE-AL based on forward and backward derivation functions. Compared with prior solutions, our scheme offers a compact policy representation solution, which can significantly reduce the size of privatekeys and ciphertexts. Furthermore, our solution provides a richer expressive power of access policies to facilitate flexible access control for ABE scheme.\",\n \"Title: Control strategy PSO.\\nYear: 2016\\n\\nAbstract: •An evaluation index called “Control Strategy PSO” is developed.•It can be applied to other intelligent algorithms.•We present a detailed theoretical and empirical analysis.\",\n \"Title: Tap-Wave-Rub: Lightweight Human Interaction Approach to Curb Emerging Smartphone Malware\\nYear: 2015\\n\\nAbstract: Malware is a burgeoning threat for smartphones and continuing advancing. Traditional defenses to malware, however, are not suitable for smartphones due to their resource intensive nature. This necessitates the design of novel mechanisms that can take into account the specifics of the smartphone malware and smartphones themselves. In this paper, we introduce a lightweight permission enforcement approach – Tap-Wave-Rub (TWR) – for smartphone malware prevention. TWR is based on simple cyber-physical human interactions, i.e., human gestures, that are very quick and intuitive but less likely to be exhibited in users’ daily activities. Presence or absence of such gestures, prior to accessing an application, can effectively inform the OS whether the access request is benign or malicious. Specifically, we present the design of two mechanisms: (1) acceleration-based phone tapping detection; and (2) proximity-based finger tapping, rubbing or hand waving detection. The first mechanism is geared for NFC applications, which usually require the user to tap her phone with another device. The second mechanism involves very simple gestures, i.e., tapping or rubbing a finger near the top of phone’s screen or waving a hand close to the phone, and broadly appeals to many applications (e.g., SMS). In addition, we present the TWR-enhanced Android permission model, the prototypes implementing the underlying gesture recognition mechanisms, and a variety of novel experiments to evaluate these mechanisms. Our results suggest the proposed approach could be very effective for malware detection/prevention, with quite low false positives and false negatives, while imposing little to no additional burden on the users.\",\n \"Title: Secure Snaps: A New Forward Secrecy Cryptosystem for Self-Destructing Messages in Mobile Services\\nYear: 2015\\n\\nAbstract: Recently, mobile services of ephemeral communications, such as Snap chat, are becoming increasingly popular because of their \\\\\\\"burn after reading\\\\\\\" functionality. However, recent events show that these services do not achieve the claimed functionality supporting truly self-destructing messages. In this paper, we address the problem of privacy protection in ephemeral communications involving untrusted service providers. We present Secure Snaps, a forward secrecy cryptosystem with self-destructing messages. In this system, we introduce a trusted module supervised by a trusted third party in order to ensure the security of user's private keys, so that untrusted service providers cannot access the communications among users. We take a new approach, called cryptographic counting control, to implement message's self-destruction functionality which meets the requirement for forward secrecy. By adopting this approach, no one, including the original sender, service provider, and key management module, can recover past messages once these messages have entered the \\\\\\\"destroyed state\\\\\\\". We prove that the proposed scheme is provably secure against chosen plaintext attacks. Our experiment results demonstrate its efficiency and practicality for large-size group of users.\",\n \"Title: CGMOS: Certainty Guided Minority OverSampling.\\nYear: 2016\\n\\nAbstract: Handling imbalanced datasets is a challenging problem that if not treated correctly results in reduced classification performance. Imbalanced datasets are commonly handled using minority oversampling, whereas the SMOTE algorithm is a successful oversampling algorithm with numerous extensions. SMOTE extensions do not have a theoretical guarantee during training to work better than SMOTE and in many instances their performance is data dependent. In this paper we propose a novel extension to the SMOTE algorithm with a theoretical guarantee for improved classification performance. The proposed approach considers the classification performance of both the majority and minority classes. In the proposed approach CGMOS (Certainty Guided Minority OverSampling) new data points are added by considering certainty changes in the dataset. The paper provides a proof that the proposed algorithm is guaranteed to work better than SMOTE for training data. Further experimental results on 30 real-world datasets show that CGMOS works better than existing algorithms when using 6 different classifiers.\",\n \"Title: Demographics Inference Through Wi-Fi Network Traffic Analysis\\nYear: 2016\\n\\nAbstract: Although privacy leaking through content analysis of Wi-Fi traffic has received an increased attention, privacy inference through meta-data (e.g. IP, Host) analysis of Wi-Fi traffic represents a potentially more serious threat to user privacy. Firstly, it represents a more efficient and scalable approach to infer users' sensitive information without checking the content of Wi-Fi traffic. Secondly, meta-data based demographics inference can work on both unencrypted and encrypted traffic (e.g., HTTPS traffic). In this study, we present a novel approach to infer user demographic information by exploiting the meta-data of Wi-Fi traffic. We develop a proof-of-concept prototype, Demographic Information Predictor (DIP) system, and evaluate its performance on a real-world dataset, which includes the Wi-Fi access of 28,158 users in 5 months. DIP extracts four kinds of features from real-world Wi-Fi traffic and proposes a novel machine learning based inference technique to predict user demographics. Our analytical results show that, for unencrypted traffic, DIP can predict gender and education level of users with an accuracy of 78% and 74% respectively. It is surprising to show that, even for HTTPS traffic, user demographics can still be predicted at a precision of 67% and 72% respectively, which well demonstrates the practicality of the proposed privacy inference scheme.\",\n \"Title: Social Community-Oriented Social Attribute Analysis: An Empirical Study On Qq Group Data\\nYear: 2015\\n\\nAbstract: Social networks have got increasing attention in recent years, and Social Networking Services (SNS), such as QQ, Facebook and Twitter, are among the most popular network applications on the internet. The popularity of these SNS attract more and more researchers to focus on the study of attributes of social network, especially the groups which are ubiquitous in social networks, both to improve current systems and to design new applications of social networks. In this paper, we study statistically 1.4 billion personal social information from QQ and 80 million group social information from QQ, and systemically analysis the distribution of attributes of all these social context data. And then we discover that both the frequency of age and the frequency of age for each gender conform to normal distributions. In addition, we analysis the QQ user registration time and the relationship between digital number of QQ and their age. Moreover, the distribution of the number of users in QQ group conforms to a power- law distribution. Furthermore, as the KL distance between the sample data and the whole data is within 0.1, i.e. the similarity of the sample data and the whole data is high, we can use the results based on the sample data to estimate the properties of the whole data in the integrated QQ network.\",\n \"Title: A low complexity algorithm for across range unit effect correction of the moving target via range frequency polynomial-phase transform.\\nYear: 2017\\n\\nAbstract: Based on the moving target detection, this paper proposes a low complexity algorithm for moving target across range unit effect correction on the basis of the range frequency polynomial-phase transform, utilizing which to remove the coupling of the range frequency and the slow time, then the across range unit effect can be corrected effectively. The algorithm does not need to search the target's parameters and estimate the Doppler ambiguity integer, and its computational complexity is low. The simulation results have proved the effectiveness of the proposed algorithm.\",\n \"Title: Risk Assessment for Cooperative Automated Driving.\\nYear: 2016\\n\\nAbstract: Global investment and recent advancements in vehicle automation are making autonomous and cooperative automated driving (AD) a reality. Not only will automated vehicles incorporate more electronics and connectivity than ever before, but also, notably, they will transfer control and responsibility of monitoring the environment from a human driver to a robotic system. While prior work has assessed and provided security solutions for non-automated vehicles, there is much to understand regarding the security implications of AD. In this work, we begin to address this gap in understanding. This paper reports on a risk assessment framework for autonomous and cooperative AD. We aggregate the state of the art in AD research to define a reference architecture for automated vehicles, describing the new attack surfaces and data flow. Employing existing automotive threat models, we propose a novel application-based threat enumeration and analysis approach that is able to address different AD applications across all levels of automation. We demonstrate this framework with an example application assessment and summarize the results and security insights from analyses of other applications. The results of our risk assessment and future assessments with this framework will inform on the design of security solutions and secure architectures for production AD systems.\",\n \"Title: Ecosystem particle swarm optimization.\\nYear: 2017\\n\\nAbstract: Particle swarm optimization (PSO) is a well-known swarm intelligence algorithm inspired by the foraging behavior of bird flocking. PSO has been widely used in many optimization and engineering problems due to its simplicity and efficiency, even though there still exist some disadvantages. The standard PSO often suffers with premature convergence or slow convergence when the optimization problem is multimodal or high-dimensional. To overcome these drawbacks, an ecosystem PSO (ESPSO) inspired by the characteristic that a natural ecosystem can excellently keep the biological diversity and make the whole ecosystem be in a dynamic balance is presented in this paper. ESPSO not only prevents the algorithm trapping into local optima but also balances the exploration and exploitation in both unimodal and multimodal problems as compared to other PSO variants. Twenty benchmark functions including unimodal functions and multimodal nonlinear functions are used to test the searching ability of ESPSO. Experimental results show that ESPSO considerably improves the searching accuracy, the algorithm reliability and the searching efficiency in comparison with other six well-known PSO variants and four evolutionary algorithms. Moreover, ESPSO was successfully applied to the antenna array pattern synthesis design and gained satisfactory results.\",\n \"Title: A Functional Co-Design towards Safe and Secure Vehicle Platooning.\\nYear: 2017\\n\\nAbstract: Cooperative adaptive cruise control (CACC) or platooning recently becomes promising as vehicles can learn of nearby vehicles? intentions and dynamics through wireless vehicle to vehicle (V2V) communication and advanced on-board sensing technologies. Violation of cybersecurity often results in serious safety issues as been demonstrated in recent studies. However, safety and security in a vehicle platoon so far have been considered separately by different sets of experts. Consequently no existing solution solves both safety and security in a coherent way. In this paper, we show cyber attacks on an automated platoon system could have the most severe level of safety impact with large scale car crash and argue the importance of safety-security co-design for safety critical cyber physical systems (CPS). We propose a safety-security co-design engineering process to derive functional security requirements for a safe automated vehicle platoon system based on a deep comprehension on the interrelation of safety and security. To our best knowledge, we are the first to apply the safety-security co-design concept to a concrete application. Through this engineering process, we propose a general approach for designing a safe and secure platooning. Following the general approach, we come up with a new platoon control algorithm that takes into account both safety and security. Our defense mechanism implicitly defends against safety-related cyber-attacks and greatly shortens the safe distance required when the platoon is not protected.\",\n \"Title: Hybrid de-anonymization across real-world heterogeneous social networks.\\nYear: 2017\\n\\nAbstract: To enjoy various utility and services, people are active in multiple social networks nowadays. With tons of data generated on platforms, multiple accounts of the same user in different social networks can be used to de-anonymize the user in a large scale. The aggregation of user profiles poses a threat to user privacy. With a concern of privacy leakage, de-anonymization techniques, including graph based approaches and profile based approaches, are widely studied in recent years. However, few works throw light on the deanonymization between real-world heterogeneous social networks. In this paper, we propose a Hybrid De-anonymization Scheme (HDS) aiming at de-anonymizing heterogeneous social networks. HDS firstly leverages the network graph structure to significantly reduce the size of candidate set, then exploits user profile information to identify the correct mapping users with a high confidence. Performance evaluation on real-world social network datasets shows that HDS has considerable accuracy on de-anonymization and significantly outperforms the prior schemes.\",\n \"Title: Exploiting AUTOSAR Safety Mechanisms to Launch Security Attacks.\\nYear: 2017\\n\\nAbstract: Automotive Electronic Control Units (ECUs) rely on both hardware and software mechanisms to ensure safety is maintained in the face of hazards that result from both random and systematic failures. In the presence of a malicious attacker, these safety mechanisms can serve as attack vectors to launch Denial of Service (DoS) attacks. This can be achieved by disabling critical system functions through the malicious creation of safety relevant fault conditions. In this paper, we explore some of the exploitable safety mechanisms within the Automotive Open System Architecture (AUTOSAR), and we demonstrate two successful attacks on an authenticated CAN FD bus system by introducing safety critical failures.\",\n \"Title: Lecture Vdeo Indexing Using Boosted Margin Maximizing Neural Networks\\nYear: 2017\\n\\nAbstract: This paper presents a novel approach for lecture video indexing using a boosted deep convolutional neural network system. The indexing is performed by matching high quality slide images, for which text is either known or extracted, to lower resolution video frames with possible noise, perspective distortion, and occlusions. We propose a deep neural network integrated with a boosting framework composed of two sub-networks targeting feature extraction and similarity determination to perform the matching. The trained network is given as input a pair of slide image and a candidate video frame image and produces the similarity between them. A boosting framework is integrated into our proposed network during the training process. Experimental results show that the proposed approach is much more capable of handling occlusion, spatial transformations, and other types of noises when compared with known approaches.\",\n \"Title: Demographic Information Inference through Meta-Data Analysis of Wi-Fi Traffic.\\nYear: 2018\\n\\nAbstract: Privacy inference through meta-data (e.g., IP, Host) analysis of Wi-Fi traffic poses a potentially more serious threat to user privacy. First, it provides a more efficient and scalable approach to infer users' sensitive information without checking the content of Wi-Fi traffic. Second, meta-data based demographics inference can work on both unencrypted and encrypted traffic (e.g., HTTPS traffic). In this study, we present a novel approach to infer user demographic information by exploiting the meta-data of Wi-Fi traffic. We develop an inference framework based on machine learning and evaluate its performance on a real-world dataset, which includes the Wi-Fi access of 28,158 users in five months. The framework extracts four kinds of features from real-world Wi-Fi traffic and applies a novel machine learning technique (XGBoost) to predict user demographics. Our analytical results show that, the overall accuracy of inferring gender and education level of users can be 82 and 78 percent, respectively. It is surprising to show that, even for HTTPS traffic, user demographics can still be predicted at accuracy of 69 and 76 percent, respectively, which well demonstrates the practicality of the proposed privacy inference scheme. Finally, we discuss and evaluate potential mitigation methods for such inference attacks.\",\n \"Title: The hop count shift problem and its impacts on protocol design in wireless ad hoc networks\\nYear: 2010\\n\\nAbstract: The hop count information has been exploited in the design of networking protocols in wireless ad hoc multi-hop networks. The hop count setup process normally assumes a perfect disk communication model and uses a simple controlled flooding approach. However, the practical communication model may not be such a disk communication model but time-varying and lossy. Sometimes transmissions can be successful beyond the nominal transmission range, i.e., the radius of such a disk model. The defacto hop count which are setup via time-varying and lossy radio channels may be different from the one based on the disk communication model. This paper introduces the hop count shift problem in realistic radio channels and investigates its impacts, via extensive simulations, on some representative hop count based protocols. Our simulation results suggest that these protocols’ performance generally suffers from the hop count shift problem, and the degradation is dependent on how the practical communication model deviates from the disk communication model. We also propose a strategy to combat the hop count shift problem and conduct simulations to show its effectiveness. The study of this paper necessitates reexaminations for the design of new hop count setup mechanism and the hop count based networking protocols.\",\n \"Title: Generating Image Sequence From Description With Lstm Conditional Gan\\nYear: 2018\\n\\nAbstract: Generating images from word descriptions is a challenging task. Generative adversarial networks(GANs) are shown to be able to generate realistic images of real-life objects. In this paper, we propose a new neural network architecture of LSTM Conditional Generative Adversarial Networks to generate images of real-life objects. Our proposed model is trained on the Oxford-102 Flowers and Caltech-UCSD Birds-200-2011 datasets. We demonstrate that our proposed model produces the better results surpassing other state-of-art approaches.\",\n \"Title: Provably Secure Cryptographic ABAC System to Enhance Reliability and Privacy Using Real-Time Token and Dynamic Policy\\nYear: 2018\\n\\nAbstract: In this paper we address the problem of reliability and security in an open-access data sharing system. We propose a new framework, called cryptographic attribute-based access control (CABAC), in consistent with the standard ABAC model. Moreover, two new mechanisms, real-time Tokens and secure policy decision-making, are introduced for ensuring secure attribute authorization and verifiable policy decision-making. More important, we present a practical CABAC system to support adaptability and flexibility using dynamically chosen policy and real-time attribute acquisition. We prove that our CABAC system is provably secure in four aspects: the attribute Tokens are existentially unforgeable against chosen-time and chosen-attribute attacks, respectively; the secure policy is existentially unforgeable against chosen-object attack under eBDH assumption; and our entire system is semantically secure against chosen-plaintext attack with Token and policy queries under eGDHE assumption.\",\n \"Title: Heterogeneous multi-output classification by structured conditional risk minimization.\\nYear: 2018\\n\\nAbstract: •For the first time, the mixture of different output types (heterogeneous output space) is studied.•The heterogeneous output space is transformed to a new homogeneous space.•A new structured prediction method is proposed to solve the transformed problem.•Experiments on real-world datasets show the effectiveness of the proposed method.\",\n \"Title: Multi-target Core Network-Based Networked Multi-label Classification\\nYear: 2018\\n\\nAbstract: As the increasing popularity of label classification, networked multi-label classification is becoming a hot topic in the field of data mining, where the networked multi-label means that each entity has more than one label during classification in network environments. In the existing works on networked multi-label classification, although only the labels of certain nodes are required to be determined, the labels of all nodes in the network have to be inferred. This works well for small networks, but not for large networks, especially not for large-scale networks with big data, as a plenty of time has been spent to compute a lot of unrequired labels. In this paper, we introduce a core network which is composed of the shortest paths that link some sources (i.e., some nodes with known labels) and some targets (i.e., some nodes with unknown labels required to be determined), as these paths have the most significant directly influence on label classification. Then we propose a novel heuristic MultI-TargeT corE Network discovery algorithm MITTEN to discover a core network, which aims to achieve the relatively accuracy of predicted labels with a relatively short time. Compared with existing networked multi-label classification approaches, the experimental results executed on real networks show that our proposed MITTEN can predict labels in network environments more precisely and more efficiently.\",\n \"Title: Traffic Aware Virtual Machine Packing in Cloud Data Centers\\nYear: 2016\\n\\nAbstract: This paper investigates the virtual machine packing problem in cloud data centers. A cloud tenant expresses computation requirement for each virtual machine (VM) and bandwidth requirement for each pair of VMs. A cloud provider places the VMs and routes the traffic among the VMs in a way that minimizes the total number of servers used while providing both computation and bandwidth guarantees. We formulate a special Multi-Capacity Bin Packing problem that consolidates VMs into the smallest number of servers. A server is represented by a bin with multiple capacities corresponding to the computation resource (CPU, memory, and storage) and communication bandwidth resource available. Unlike bin packing, in the traffic-aware VM packing, the cumulative bandwidth requirement of all VMs hosted in a server can be smaller than the sum of bandwidth demand of all VMs due to the co-location. Our experimental results show the proposed solutions significantly reduce the number of servers needed as well as the total inter-server traffic, and will have great potential for reducing energy consumption in data centers.\",\n \"Title: Who Moved My Cheese: Towards Automatic and Fine-Grained Classification and Modeling Ad Network.\\nYear: 2016\\n\\nAbstract: The mobile advertisement (ad) network is gaining an increasing interest due to the high popularity of smart phones. Previous researches on the security issues of ad network primarily focus on the privacy, permission and malware detection while less attention has been paid to the traffic consumption issue incurred by ad network. Though it is well known that ad network plays an important role in network consumption, it represents a great challenge of giving a fine-grained classification of ad networks. Inspired by this, different from any previous researches, in this study, we take the initial step towards modeling the network consumption of Ad network in Android. We develop an automatic ad analysis platform to quantify the ad network traffic consumed by android applications (app). To achieve a fine-grained quantification, we combine two sources of network traffic. On one hand, we modify the android webview and log system in system level to capture network traffic accurately. On the other hand, we capture network traffic in router level to collect detailed information of traffic packets, such as packet size and URI. We have evaluated the developed system in terms of normal apps, repacked apps and malicious apps based on the real-world dataset, which is comprised of 93 Android apps. We find out that ad traffic takes major percentage of the whole network traffic caused by Android app. We have also studied the ad library mechanism for 10 popular ad libraries. We found ads from some ad libraries use much more network traffic because they have to be fetched from remote ad libraries each time they are shown to users while other ad libraries allow apps to store ads locally.\",\n \"Title: Privacy Leakage via De-anonymization and Aggregation in Heterogeneous Social Networks\\nYear: 2020\\n\\nAbstract: Though representing a promising approach for personalization, targeting, and recommendation, aggregation of user profiles from multiple social networks will inevitably incur a serious privacy leakage issue. In this paper, we propose a Novel Heterogeneous De-anonymization Scheme (NHDS) aiming at de-anonymizing heterogeneous social networks. NHDS first leverages the network graph structure to significantly reduce the size of candidate set, then exploits user profile information to identify the correct mapping users with a high confidence. Performance evaluation on real-world social network datasets shows that NHDS significantly outperforms the prior schemes. Finally, we perform an empirical study on privacy leakage arising from cross-network aggregation based on four real-world social network datasets. Our findings show that 39.9 percent more information is disclosed through de-anonymization and the de-anonymized ratio is 84 percent. The detailed privacy leakage of user demographics and interests is also examined, which demonstrates the practicality of the identified privacy leakage issue.\",\n \"Title: Layered Optical Flow Estimation Using a Deep Neural Network with a Soft Mask.\\nYear: 2018\\n\\nAbstract: Using a layered representation for motion estimation has the advantage of being able to cope with discontinuities and occlusions. In this paper, we learn to estimate optical flow by combining a layered motion representation with deep learning. Instead of pre-segmenting the image to layers, the proposed approach automatically generates a layered representation of optical flow using the proposed soft-mask module. The essential components of the soft-mask module are maxout and fuse operations, which enable a disjoint layered representation of optical flow and more accurate flow estimation. We show that by using masks the motion estimate results in a quadratic function of input features in the output layer. The proposed soft-mask module can be added to any existing optical flow estimation networks by replacing their flow output layer. In this work, we use FlowNet as the base network to which we add the soft-mask module. The resulting network is tested on three well-known benchmarks with both supervised and unsupervised flow estimation tasks. Evaluation results show that the proposed network achieve better results compared with the original FlowNet.\",\n \"Title: Defending AUTOSAR Safety Critical Systems Against Code Reuse Attacks.\\nYear: 2019\\n\\nAbstract: With the emergence of AUTOSAR as the dominant software platform for deeply embedded automotive systems, securing AUTOSAR against software based attacks becomes essential to securing most vehicles. However security countermeasures have to fit within the constraints of automotive systems, such as available system resources, legacy code reuse and sensitivity to cost. Since the primary goal of such systems is to control safety critical vehicle functions, any security countermeasures need to harmonize the safety and security response. In this paper, we investigate the susceptibility of safety critical automotive systems to code reuse attacks that aim to violate the vehicle safety. We perform a case study using the vehicle diagnostics protocol with the aim to bypass the security access checks and execute a safety critical Routine Control service. Then we propose an HSM based monitoring system to detect or prevent such attacks.\",\n \"Title: An intelligent intrusion detection system\\nYear: 2019\\n\\nAbstract: With the introduction of emerging technologies cybersecurity has become an inherited and amplified problem. New technologies bring significant developments but also come with new challenges in the cybersecurity area. The fight against malicious attacks is an everyday battle for every company. Challenges brought by security breaches can be devastating for a company and sometimes bring un-survivable circumstances. In this paper, we propose a novel two-stage intelligent intrusion detection system (IDS) to detect and protect from such malicious attacks. Intrusion Detection Systems are feasible solutions for cybersecurity problems, but they come with implementation challenges. Anomaly based IDS usually have a high rate of false positives (FP) and they require considerable computational requirements. The approach proposed in this paper consists of a two-stage architecture based on machine learning algorithms. In the first stage, the IDS uses K-Means to detect attacks and the second stage uses supervised learning to classify such attacks and eliminate the number of false positives. The implementation of this approach results in a computationally efficient IDS able to detect and classify attacks at a 99.97% accuracy while lowering the number of false positives to 0. The paper also evaluates the performance results and compares them with other relevant research papers. The performance of this proposed IDS is superior to the current state of the art.\",\n \"Title: 2D compressed learning: support matrix machine with bilinear random projections\\nYear: 2019\\n\\nAbstract: Support matrix machine (SMM) is an efficient matrix classification method that can leverage the structure information within the matrix to improve the classification performance. However, its computational and storage costs are still expensive for high-dimensional data. To address these problems, in this paper, we consider a 2D compressed learning paradigm to learn the SMM classifier in some compressed data domain. Specifically, we use the Kronecker compressed sensing (KCS) to obtain the compressive measurements and learn the SMM classifier. We show that the Kronecker product measurement matrices used by KCS satisfies the restricted isometry property (RIP), which is a property to ensure the learnability of the compressed data. We further give a lower bound on the number of measurements required for KCS. Though this lower bound shows that KCS requires more measurements than the regular CS to satisfy the same RIP condition, KCS itself still enjoys lower computational and storage complexities. Then, using the RIP condition, we verify that the learned SMM classifier in the compressed domain can perform almost as well as the best linear classifier in the original uncompressed domain. Finally, our experimental results also demonstrate the feasibility of 2D compressed learning.\",\n \"Title: BVI-DVC: A Training Database for Deep Video Compression\\nYear: 2022\\n\\nAbstract: Deep learning methods are increasingly being applied in the optimisation of video compression algorithms and can achieve significantly enhanced coding gains, compared to conventional approaches. Such approaches often employ Convolutional Neural Networks (CNNs) which are trained on databases with relatively limited content coverage. In this paper, a new extensive and representative video database, BVI-DVC,is presented for training CNN-based video compression systems, with specific emphasis on machine learning tools that enhance conventional coding architectures, including spatial resolution and bit depth up-sampling, post-processing and in-loop filtering. BVI-DVC contains 800 sequences at various spatial resolutions from 270p to 2160p and has been evaluated on ten existing network architectures for four different coding tools. Experimental results show that this database produces significant improvements in terms of coding gains over five existing (commonly used) image/video training databases under the same training and evaluation configurations. The overall additional coding improvements by using the proposed database for all tested coding modules and CNN architectures are up to 10.3% based on the assessment of PSNR and 8.1% based on VMAF.\",\n \"Title: Distribution agnostic Bayesian matching pursuit based on the exponential embedded family\\nYear: 2020\\n\\nAbstract: Compressed sensing (CS) is an emerging field that allows to recover high-dimensional sparse signal from a few compressed measurements. Classical CS algorithms using the Bayesian framework generally impose a sparseness-promoting prior on the signal, which may not characterize the real signals with diversity. Moreover, estimating the parameters involved in the prior is challenging especially for non-i.i.d. signals. In this paper, we propose an efficient prior-agnostic Bayesian matching pursuit for sparse signal recovery, which avoids the risk of imposing mismatched prior on the signals and enjoys lower complexity than Bayesian approaches due to the elimination of prior parameter estimation. Specifically, we utilize the noise model and the reduced exponential embedded family (EEF) to obtain an approximate likelihood of interest, and then find the most dominant supports with maximum approximate likelihoods in a greedy manner to give an approximate minimum mean squared error (MMSE) estimate for the sparse signal. Experiment results demonstrate that our method achieves lower normalized mean squared error (NMSE) while higher efficiency compared to the state-of-the-art methods.\",\n \"Title: The Horizontal Inr Conflict-Detection Algorithm: Revealing Inr Reallocation And Reauthorization In Rpki\\nYear: 2021\\n\\nAbstract: Resource Public Key Infrastructure (RPKI) is a promising security enhancement to the Border Gateway Protocol, but it only requires the relying party (RP) to validate Internet Number Resource (INR) allocation or authorization relationships expressed in parent-child certificate pairs vertically. Therefore, conflicts in INR allocation and authorization may exist because of the limitations of the validation procedure of the RP software, in other words, certification authority malfunctions in issuing RPKI objects within a publication point cannot be detected by the RP. We develop a model of such conflicts and propose a horizontal INR conflict-detection algorithm with acceptable build time and query time. The proposed algorithm was tested on real-world RPKI data to identify actual and potential INR conflicts and its accurateness has been tried to be evaluated. This paper also discusses the deployment issues and the accuracy dependence about our algorithm design.\",\n \"Title: Fuzzy Based Threat Assessment Model (FTAM)\\nYear: 2019\\n\\nAbstract: Threat assessment is the process of identifying and characterizing a cybersecurity threat by evaluating and assessing its properties. This is a tool used by many organizations around the globe in order to evaluate their systems and perform a risk analysis against common threats. SAE J3061 guidelines provide a set of principles for automotive cybersecurity which includes threat identification, assessment, and analysis. This standard defines threat assessment as “an analysis technique that is applied in the concept phase to help identify potential threats to a feature and to assess the risk associated with the identified threats”. This paper presents an innovative, Fuzzy Based Threat Assessment Model (FTAM). FTAM leverages the threat characterization from established threat assessment models, while focusing on improving its assessment capabilities by using Fuzzy Logic. Through this methodology, FTAM is able to improve the efficiency and accuracy of the threat assessment process to determine the “degree” of the threat. This is different from using subjective assessment processes based on table look-ups or scoring like some of the current threat assessment models do. This paper provides an analysis on the design and performance of FTAM as well as benchmarking with other existing models.\",\n \"Title: Continuous improvement of script-driven verifiable random functions for reducing computing power in blockchain consensus protocols\\nYear: 2022\\n\\nAbstract: In order to solve the problem of low efficiency and high energy consumption of the Proof-of-Work (PoW) consensus protocol in blockchain within a peer-to-peer network, some new protocols based on Verifiable Random Function (VRF) have emerged recently. However, these VRF-based consensus protocols do not actually give a concrete and efficient VRF construction. In view of this, we present three simple and practical VRF constructions from the RSA hardness assumption, the Decisional Diffie-Hellman (DDH) assumption and the Leftover Hash Lemma (LHL) respectively, the output size of which is continuously reduced for the design of efficient consensus protocol in blockchain. We also give a complete security analysis of our VRF constructions. Furthermore, we show a specific application of our VRF constructions in the famous Algorand consensus protocol. We illustrate a general approach to integrate our VRF constructions with block structure in blockchain. Comparing with PoW-based mining, we demonstrate the detailed process of VRF-based consensus protocol. Meanwhile, three new opcodes are designed for the scripting system in blockchain to develop a script pair, scriptProof and scriptHash, which provides secure and efficient block verification. Finally, we evaluate the performance of our VRF constructions in terms of storage and computational overheads, and the experimental evaluation results show our VRF constructions can significantly reduce the computing power of consensus protocol in blockchain.\",\n \"Title: A Hybrid Approach Toward Efficient and Accurate Intrusion Detection for In-Vehicle Networks\\nYear: 2022\\n\\nAbstract: With recent advancements in the automotive world and the introduction of autonomous vehicles, automotive security has become a real and important issue. Modern vehicles have tens of Electronic Control Units (ECUs) connected to in-vehicle networks. As a de facto standard for in-vehicle network communication, the Controller Area Network (CAN) has become a target of cyber attacks. Anomaly-based Intrusion Detection System (IDS) is considered as an effective approach to secure CAN and detect malicious attacks. Currently, there are two primary approaches used for intrusion detection: rule-based and machine learning-based. Rule-based approach is efficient but limited in the detection accuracy while machine learning-based detection has comparably higher detection accuracy but higher computation cost at the same time. In this paper, we propose a novel hybrid IDS that combines the benefits of both rule-based and machine learning-based approaches. More specifically, we use machine learning methods to achieve a high detection rate while keeping the low computational requirement by offsetting the detection with a rule-based component. Our experiments with CAN traces collected from four different vehicle models demonstrate the effectiveness and efficiency of the proposed hybrid IDS.\"\n]","isConversational":false},"profile_size":{"kind":"number","value":84,"string":"84"},"task":{"kind":"string","value":"longlamp_2"},"split_kind":{"kind":"string","value":"user_based"},"id":{"kind":"number","value":13690,"string":"13,690"}}},{"rowIdx":13691,"cells":{"question_id":{"kind":"string","value":"Martin Haenggi"},"question":{"kind":"string","value":"Generate an abstract for the title \"Joint Design of Channel and Network Coding for Star Networks\" using the following items: 1. Channel coding \n2. Rateless erasure correcting schemes \n3. Network coding \n4. Throughput advantage \n5. TDMA vs. RLNC"},"answer":{"kind":"string","value":"Channel coding alone is not sufficient to reliably transmit a message of finite length $K$ from a source to one or more destinations as in, e.g., file transfer. To ensure that no data is lost, it must be combined with rateless erasure correcting schemes on a higher layer, such as a time-division multiple access (TDMA) system paired with automatic repeat request (ARQ) or random linear network coding (RLNC). We consider binary channel coding on a binary symmetric channel (BSC) and q-ary RLNC for erasure correction in a star network, where Y sources send messages to each other with the help of a central relay. In this scenario RLNC has been shown to have a throughput advantage over TDMA schemes as K and q tend to infinity. In this paper we focus on finite block lengths and compare the expected throughputs of RLNC and TDMA. For a total message length of K bits, which can be subdivided into blocks of smaller size prior to channel coding, we obtain the channel coding rate and the number of blocks that maximize the expected throughput of both RLNC and TDMA, and we find that TDMA is more throughput-efficient for small message lengths K and small q."},"correct_answer":{"kind":"string","value":"Channel coding alone is not sufficient to reliably transmit a message of finite length $K$ from a source to one or more destinations as in, e.g., file transfer. To ensure that no data is lost, it must be combined with rateless erasure correcting schemes on a higher layer, such as a time-division multiple access (TDMA) system paired with automatic repeat request (ARQ) or random linear network coding (RLNC). We consider binary channel coding on a binary symmetric channel (BSC) and q-ary RLNC for erasure correction in a star network, where Y sources send messages to each other with the help of a central relay. In this scenario RLNC has been shown to have a throughput advantage over TDMA schemes as K and q tend to infinity. In this paper we focus on finite block lengths and compare the expected throughputs of RLNC and TDMA. For a total message length of K bits, which can be subdivided into blocks of smaller size prior to channel coding, we obtain the channel coding rate and the number of blocks that maximize the expected throughput of both RLNC and TDMA, and we find that TDMA is more throughput-efficient for small message lengths K and small q."},"documents":{"kind":"list like","value":["Title: ALOHA performs optimal power control in Poisson networks\nYear: 2012\n\nAbstract: This paper studies power control strategies in interference-limited wireless networks with Poisson distributed nodes. We focus on the case where each transmitter knows its distance to its desired receiver but does not know the topology of the rest of the network. We study three sets of strategies: the single-node optimal power control (SNOPC) strategy, the Nash equilibrium power control (NEPC) strategy, and the globally optimal power control (GOPC) strategy. SNOPC strategies maximize the expected throughput of the power controllable link given that all the other transmitters do not use power control. Under NEPC strategies, no individual node of the network can achieve a higher expected throughput by unilaterally deviating from these strategies. The GOPC strategy maximizes the throughput of a typical node in the network. This paper shows that under mean and peak power constraints at each transmitter, all of the three strategies are ALOHA-type random on-off power control policies in bipolar networks. For links of iid random distances, we show both SNOPC and NEPC strategies are ALOHA-type random on-off policies. These results suggest that ALOHA can be viewed not only as a MAC scheme but also as an efficient and stable power control scheme.","Title: The TASEP: A Statistical Mechanics Tool to Study the Performance of Wireless Line Networks\nYear: 2010\n\nAbstract: We consider a multihop wireless line network with a single unidirectional data flow and show that by limiting the buffer sizes at the relay nodes to unity, the flow of traffic in the system can be efficiently regulated in a completely distributed fashion. Upon exerting this simple transmission policy, we find that the transport of packets in the wireless network is analogous to the flow of particles in the totally asymmetric simple exclusion process (TASEP). Using existing results from statistical mechanics, we characterize the end-to-end delay and throughput performance of multihop wireless line networks for two different channel access schemes. Additionally, we apply our findings towards the design of long networks. This paper also aims at promoting the TASEP as a powerful tool for analyzing the performance of ad hoc networks.","Title: Spatial and temporal correlation of the interference in ALOHA ad hoc networks\nYear: 2009\n\nAbstract: Interference is a main limiting factor of the performance of a wireless ad hoc network. The temporal and the spatial correlation of the interference makes the outages correlated temporally (important for retransmissions) and spatially correlated (important for routing). In this letter we quantify the temporal and spatial correlation of the interference in a wireless ad hoc network whose nodes are distributed as a Poisson point process on the plane when ALOHA is used as the multiple-access scheme.","Title: Interference in Lattice Networks\nYear: 2010\n\nAbstract: Lattices are important as models for the node locations in wireless networks\nfor two main reasons: (1) When network designers have control over the\nplacement of the nodes, they often prefer a regular arrangement in a lattice\nfor coverage and interference reasons. (2) If nodes are randomly distributed or\nmobile, good channel access schemes ensure that concurrent transmitters are\nregularly spaced, hence the locations of the transmitting nodes are well\napproximated by a lattice. In this paper, we introduce general interference\nbounding techniques that permit the derivation of tight closed-form upper and\nlower bounds for all lattice networks, and we present and analyze optimum or\nnear-optimum channel access schemes for one-dimensional, square, and triangular\nlattices.","Title: ALOHA Performs Delay-Optimum Power Control\nYear: 2011\n\nAbstract: As a fundamental source of delay in wireless networks, the local delay is defined as the mean time, in number of time slots, until a packet is successfully received (decoded) over a link. This paper shows that with mean power and peak power constraints at each node, power control can significantly reduce the local delay. We show that, for links with Rayleigh fading and random length, there exists a simple power control strategy, which turns out to be optimal in reducing the local delay. This strategy acts as an ALOHA-type random on-off power control policy whose parameters depend on the link distance. The optimal power control policy as well as its variations are compared with constant power transmission and other simple random power control policies.","Title: Distance Distributions in Finite Uniformly Random Networks: Theory and Applications\nYear: 2010\n\nAbstract: In wireless networks, knowledge of internode distances is essential for performance analysis and protocol design. When determining distance distributions in random networks, the underlying nodal arrangement is almost universally taken to be a stationary Poisson point process. While this may be a good approximation in some cases, there are also certain shortcomings to this model, such as the fact that, in practical networks, the number of nodes in disjoint areas is not independent. This paper considers a more-realistic network model where a known and fixed number of nodes are independently distributed in a given region and characterizes the distribution of the Euclidean internode distances. The key finding is that, when the nodes are uniformly randomly placed inside a ball of arbitrary dimensions, the probability density function (pdf) of the internode distances follows a generalized beta distribution. This result is applied to study wireless network characteristics such as energy consumption, interference, outage, and connectivity.","Title: The Local Delay in Mobile Poisson Networks\nYear: 2013\n\nAbstract: For communication between two neighboring nodes in wireless networks, the local delay is defined as the time it takes a node to successfully transmit a packet. Previous research focuses on the local delay in static or infinitely mobile Poisson networks with ALOHA. In this paper, we extend the local delay results to Poisson networks with finite mobility. The results obtained show that mobility helps reduce the local delay. Bounds of the local delay in mobile Poisson networks are derived for different mobility and transmission models. The phase transition that marks the jump of the local delay from finite to infinite is also characterized.","Title: Optimizing spatial reuse by dynamic power control.\nYear: 2012\n\nAbstract: This paper first presents a geometric analysis of the convergence condition for the Foschini-Miljanic power control algorithm. Then, based on the analysis, the Dynamic Distributed Power Control MAC ((DPC)-P-2-MAC) scheme is proposed for wireless networks. (DPC)-P-2-MAC achieves high spatial reuse, since power control enables nesting of concurrent links, thereby achieving a high density of successful links. The MAC scheme starts by trying to accommodate all links and then eliminating transmitters causing too much interference in two stages, a local stage and a global stage. Both stages operate in a fully distributed manner. Simulation results confirm the expected gains relative to standard MAC schemes: the spatial density of successful links is increased by about a factor of 4 compared to CSMA and about a factor of 8 compared to ALOHA.","Title: Throughput versus routing overhead in large ad hoc networks\nYear: 2008\n\nAbstract: Consider a wireless ad hoc network with n nodes distributed uniformly on [0, 1]2. The transport capacity (TC) of such a wireless network scales like √n. To achieve this, each node should serve about √n distinct information flows. So the routing table of each node should be of the order √n bits. We show that if the size of the routing table is restricted to be of the order O(nH(δ)), the maximum achievable pernode TC is O(nR(δ)) when the source-destination distance is n-δ/2. We show that R(δ) = min {1/2, δ/2 + H(δ)} - 1 is the optimal tradeoff.","Title: Geometry, Connectivity, and Broadcast Transport Capacity of Random Networks with Fading\nYear: 2007\n\nAbstract: In ad hoc networks with random node distribution, the underlying point process model and the channel fading process are usually considered separately. We provide a unified framework for the geometric characterization of fading by incorporating the fading process into the point process model. Concretely, assuming nodes are distributed in a stationary Poisson point process in Rd, we analyze the properties of the point processes that describe the path loss with fading. Applications include connectivity and broadcasting.","Title: Multipath fading in wireless sensor networks: measurements and interpretation\nYear: 2006\n\nAbstract: Multipath fading heavily contributes to the unreliability of wireless links, causing fairly large deviations from link quality predictions based on path loss models; its impact on wireless sensor networks is considerable. Although analytical models provide a probabilistic description, multipath fading is a deterministic phenomenon. Moreover, in the case of static nodes, fading is time-invariant. We illustrate its spatial nature with experimental evidence obtained using lower-end sensing node hardware. We also show the limitations of the supposed immunity of wideband radios to multipath fading in indoor deployments.","Title: Bandwidth- and power-efficient routing in linear wireless networks\nYear: 2006\n\nAbstract: The goal of this paper is to establish which practical routing schemes for wireless networks are most suitable for power-limited and bandwidth-limited communication regimes. We regard channel state information (CSI) at the receiver and point-to-point capacity-achieving codes for the additive white Gaussian noise (AWGN) channel as practical features, interference cancellation (IC) as possible, but less practical, and synchronous cooperation (CSI at the transmitters) as impractical. We consider a communication network with a single source node, a single destination node, and N-1 intermediate nodes placed equidistantly on a line between them. We analyze the minimum total transmit power needed to achieve a desired end-to-end rate for several schemes and demonstrate that multihop communication with spatial reuse performs very well in the power-limited regime, even without IC. However, within a class of schemes not performing IC, single-hop transmission (directly from source to destination) is more suitable for the bandwidth-limited regime, especially when higher spectral efficiencies are required. At such higher spectral efficiencies, the gap between single-hop and multihop can be closed by employing IC, and we present a scheme based upon backward decoding that can remove all interference from the multihop system with an arbitrarily small rate loss. This new scheme is also used to demonstrate that rates of O(log N) are achievable over linear wireless networks even without synchronous cooperation.","Title: A practical approach to strengthen vulnerable downlinks using superposition coding.\nYear: 2012\n\nAbstract: We propose and experimentally demonstrate a novel approach to improve the packet delivery efficiency on a vulnerable downlink (e. g., from a transmitter to a far-away receiver) using superposition coding, a multiuser transmission scheme that forgoes orthogonal transmission and deliberately introduces interference among signals at the transmitter. On a software-radio platform that uses off-the-shelf point-to-point channel codes, we show that a transmitter serving multiple links can use simple two-user superposition codes to dramatically improve (compared to time division multiplexing) the packet delivery efficiency on its most vulnerable links. Interestingly, our results suggest that superposing signals of far-away users on to those of high-traffic users yields the maximum benefits-implying that the degrees-of-freedom gain in doing so can more than compensate for the increased interference from signal superposition.","Title: Mobility and Fading: Two Sides of the Same Coin\nYear: 2010\n\nAbstract: In wireless networks, distance variations caused by node mobility generate fluctuations of the channel gains. Such fluctuations can be treated as another type of fading besides multi-path effects. In this paper, we characterize the interference statistics in mobile random networks by mapping the distance variations of mobile nodes to the channel gain fluctuations. Network performance is evaluated in terms of the outage probability. A nearest-interferer approximation is employed. This approximation provides a tight lower bound on the outage probability. Comparing to a static network, we show that the interference distribution does not change under high mobility and random walk models, but random waypoint mobility increases interference.","Title: A primer on spatial modeling and analysis in wireless networks\nYear: 2010\n\nAbstract: The performance of wireless networks depends critically on their spatial configuration, because received signal power and interference depend critically on the distances between numerous transmitters and receivers. This is particularly true in emerging network paradigms that may include femtocells, hotspots, relays, white space harvesters, and meshing approaches, which are often overlaid with traditional cellular networks. These heterogeneous approaches to providing high-capacity network access are characterized by randomly located nodes, irregularly deployed infrastructure, and uncertain spatial configurations due to factors like mobility and unplanned user-installed access points. This major shift is just beginning, and it requires new design approaches that are robust to spatial randomness, just as wireless links have long been designed to be robust to fading. The objective of this article is to illustrate the power of spatial models and analytical techniques in the design of wireless networks, and to provide an entry-level tutorial.","Title: Simplified Analysis And Design Of Mimo Ad Hoc Networks\nYear: 2006\n\nAbstract: The simple, yet powerful concept of an \"erristor\" and its \"erristance\" has recently been introduced for ad hoc networks and applied to scenarios such as retransmission (time diversity), path diversity, or a combination thereof. We extend this formalism to the case of spatial diversity, realized by employing multiple antennas at each node. Based on this framework, one can efficiently analyze and design Rayleigh-faded MIMO ad hoc networks that employ selection combining. The mathematically tractable definition of the erristor term greatly simplifies the study of a multiple-antenna network and helps solve problems based on end-to-end reliability or resource allocation easily, which we illustrate in an example. Moreover, this technique demonstrates the superiority in performance of MIMO over single-antenna routing schemes, particularly at high SNR.","Title: A Study of the Correlations Between Channel and Traffic Statistics in Multihop Networks\nYear: 2007\n\nAbstract: This paper investigates the correlations between traffic statistics and channel qualities and their impact on the performance of multihop networks. The physical channel is characterized by the probability of successful reception, which depends on the multiple access interference (MAI). Since a node contributes to interference only if it has a nonempty buffer upon being scheduled, the MAI is determ...","Title: Distributed sensor networks: a cellular nonlinear network perspective.\nYear: 2003\n\nAbstract: Large-scale networks of integrated wireless sensors become increasingly tractable. Advances in hardware technology and engineering design have led to dramatic reductions in size, power consumption, and cost for digital circuitry, and wireless communications. Networking, self-organization, and distributed operation are crucial ingredients to harness the sensing, computing, and computational capabilities of the nodes into a complete system. This article shows that those networks can be considered as cellular nonlinear networks (CNNs), and that their analysis and design may greatly benefit from the rich theoretical results available for CNNs.","Title: A Statistical Mechanics-Based Framework to Analyze Ad Hoc Networks with Random Access\nYear: 2012\n\nAbstract: Characterizing the performance of ad hoc networks is one of the most intricate open challenges; conventional ideas based on information-theoretic techniques and inequalities have not yet been able to successfully tackle this problem in its generality. Motivated thus, we promote the totally asymmetric simple exclusion process (TASEP), a particle flow model in statistical mechanics, as a useful analytical tool to study ad hoc networks with random access. Employing the TASEP framework, we first investigate the average end-to-end delay and throughput performance of a linear multihop flow of packets. Additionally, we analytically derive the distribution of delays incurred by packets at each node, as well as the joint distributions of the delays across adjacent hops along the flow. We then consider more complex wireless network models comprising intersecting flows, and propose the partial mean-field approximation (PMFA), a method that helps tightly approximate the throughput performance of the system. We finally demonstrate via a simple example that the PMFA procedure is quite general in that it may be used to accurately evaluate the performance of ad hoc networks with arbitrary topologies.","Title: Managing Interference Correlation Through Random Medium Access\nYear: 2014\n\nAbstract: The capacity of wireless networks is fundamentally limited by interference. However, little research has focused on the interference correlation, which may greatly increase the local delay (namely the number of time slots required for a node to successfully transmit a packet). This paper focuses on the question whether increasing randomness in the MAC, specifically frequency-hopping multiple access (FHMA) and ALOHA, helps to reduce the effect of interference correlation. We derive closed-form results for the mean and variance of the local delay for the two MAC protocols and evaluate the optimal parameters that minimize the mean local delay. Based on the optimal parameters, we identify two operating regimes, the correlation-limited regime and the bandwidth-limited regime. Our results reveal that while the mean local delays for FHMA with N sub-bands and for ALOHA with transmit probability p essentially coincide when p=1/N, a fundamental discrepancy exists between their variances. We also discuss implications from the analysis, including an interesting mean delay-jitter tradeoff, and convenient bounds on the tail probability of the local delay, which shed useful insights into system design.","Title: Dynamic connectivity and path formation time in Poisson networks.\nYear: 2014\n\nAbstract: The connectivity of wireless networks is commonly analyzed using static geometric graphs. However, with half-duplex radios and due to interference, static or instantaneous connectivity cannot be achieved. It is not necessary, either, since packets take multiple time slots to propagate through the network. For example, if a packet traverses a link in one time slot, it is irrelevant if the next link is available in that time slot also, but it is relevant if the next hop exists in the next time slot. To account for half-duplex constraints and the dynamic changes in the transmitting set of nodes due to MAC scheduling and traffic loads, we introduce a random multi-digraph that captures the evolution of the network connectivity in a dynamic fashion. To obtain concrete results, we focus on Poisson networks, where transmitters form a Poisson point process on the plane at all time instants. We first provide analytical results for the degree distribution of the graph and derive the distributional properties of the end-to-end connection delay using techniques from first-passage percolation and epidemic processes. Next, we prove that under some assumptions, the delay scales linearly with the source–destination distance even in the presence of interference. We also provide simulation results in support of the theoretical results.","Title: Throughput analysis of fading sensor networks with regular and random topologies\nYear: 2005\n\nAbstract: We present closed-form expressions of the average link throughput for sensor networks with a slotted ALOHA MAC protocol in Rayleigh fading channels. We compare networks with three regular topologies in terms of throughput, transmit efficiency, and transport capacity. In particular, for square lattice networks, we present a sensitivity analysis of the maximum throughput and the optimum transmit probability with respect to the signal-to-interference ratio threshold. For random networks with nodes distributed according to a two-dimensional Poisson point process, the average throughput is analytically characterized and numerically evaluated. It turns out that although regular networks have an only slightly higher average link throughput than random networks for the same link distance, regular topologies have a significant benefit when the end-to-end throughput in multihop connections is considered.","Title: A Versatile Dependent Model for Heterogeneous Cellular Networks\nYear: 2013\n\nAbstract: We propose a new model for heterogeneous cellular networks that incorporates dependencies between the layers. In particular, it places lower-tier base stations at locations that are poorly covered by the macrocells, and it includes a small-cell model for the case where the goal is to enhance network capacity.","Title: Interference and Outage in Mobile Random Networks: Expectation, Distribution, and Correlation\nYear: 2014\n\nAbstract: In mobile networks, distance variations caused by node mobility generate fluctuations in the channel gains. Such fluctuations can be treated as another type of fading besides multipath effects. In this paper, the interference statistics in mobile random networks are characterized by incorporating the distance variations of mobile nodes to the channel gain fluctuations. The mean interference is calculated at the origin and at the border of a finite mobile network. The network performance is evaluated in terms of the outage probability. Compared to a static network, the interference in a single snapshot does not change under uniform mobility models. However, random waypoint mobility increases (decreases) the interference at the origin (at the border). Furthermore, due to the correlation of the node locations, the interference and outage are temporally and spatially correlated. We quantify the temporal correlation of the interference and outage in mobile Poisson networks in terms of the correlation coefficient and conditional outage probability, respectively. The results show that it is essential that routing, MAC, and retransmission schemes need to be smart (i.e., correlation-aware) to avoid bursts of transmission failures.","Title: A Location-Based MAC Scheme for Random Wireless Network\nYear: 2011\n\nAbstract: This paper proposes a location-based MAC (LMAC) scheme for wireless networks with randomly placed nodes. This scheme regulates channel access by sharing local location information among transmitters. A lattice approximation approach is used to derive upper and lower bound for the success probability for a typical transmission attempt. Numerical results show that with the node density and link distance fixed, the optimal LMAC provides a much higher density of successful transmissions than CSMA and ALOHA.","Title: Spatial Stochastic Models and Metrics for the Structure of Base Stations in Cellular Networks.\nYear: 2013\n\nAbstract: The spatial structure of base stations (BSs) in cellular networks plays a key role in evaluating the downlink performance. In this paper, different spatial stochastic models (the Poisson point process (PPP), the Poisson hard-core process (PHCP), the Strauss process (SP), and the perturbed triangular lattice) are used to model the structure by fitting them to the locations of BSs in real cellular networks obtained from a public database. We provide two general approaches for fitting. One is fitting by the method of maximum pseudolikelihood. As for the fitted models, it is not sufficient to distinguish them conclusively by some classical statistics. We propose the coverage probability as the criterion for the goodness-of-fit. In terms of coverage, the SP provides a better fit than the PPP and the PHCP. The other approach is fitting by the method of minimum contrast that minimizes the average squared error of the coverage probability. This way, fitted models are obtained whose coverage performance matches that of the given data set very accurately. Furthermore, we introduce a novel metric, the deployment gain, and we demonstrate how it can be used to estimate the coverage performance and average rate achieved by a data set.","Title: Asymptotic Deployment Gain: A Simple Approach to Characterize the SINR Distribution in General Cellular Networks\nYear: 2014\n\nAbstract: In cellular network models, the base stations are usually assumed to form a lattice or a Poisson point process (PPP). In reality, however, they are deployed neither fully regularly nor completely randomly. Accordingly, in this paper, we consider the very general class of motion-invariant models and analyze the behavior of the outage probability (the probability that the signal-to-interference-plus-noise-ratio (SINR) is smaller than a threshold) as the threshold goes to zero. We show that, remarkably, the slope of the outage probability (in dB) as a function of the threshold (also in dB) is the same for essentially all motion-invariant point processes. The slope merely depends on the fading statistics. Using this result, we introduce the notion of the asymptotic deployment gain (ADG), which characterizes the horizontal gap between the success probabilities of the PPP and another point process in the high-reliability regime (where the success probability is near 1). To demonstrate the usefulness of the ADG for the characterization of the SINR distribution, we investigate the outage probabilities and the ADGs for different point processes and fading statistics by simulations.","Title: Interference in Large Wireless Networks\nYear: 2009\n\nAbstract: Since interference is the main performance-limiting factor in most wireless networks, it is crucial to characterize the interference statistics. The two main determinants of the interference are the network geometry (spatial distribution of concurrently transmitting nodes) and the path loss law (signal attenuation with distance). For certain classes of node distributions, most notably Poisson point processes, and attenuation laws, closed-form results are available, for both the interference itself as well as the signal-to-interference ratios, which determine the network performance. This monograph presents an overview of these results and gives an introduction to the analytical techniques used in their derivation. The node distribution models range from lattices to homogeneous and clustered Poisson models to general motion-invariant ones. The analysis of the more general models requires the use of Palm theory, in particular conditional probability generating functionals, which are briefly introduced in the appendix.","Title: Transport density vs. channel access time in wireless networks: Power control and efficient mac design.\nYear: 2012\n\nAbstract: Power control plays an important role in the design and operation of wireless networks. In this paper, we first define two critical metrics, transport density and channel access time to measure the system performance. The trade-off between these two metrics as a function of the peak power constraint is discussed in two cases, a fixed peak power case and an adjusted peak power case. Our analysis shows that the adjusted peak power constraint outperforms the fixed one in terms of both transport density and channel access time. Based on these observations, a novel and fully distributed energy-efficient MAC scheme with adjusted peak power constraint is proposed to schedule the concurrent transmissions in an efficient way. Better performance can be achieved by balancing between transport density and channel access time. Simulation results confirm the expected gains relative to standard MAC schemes: the transport density is increased by more than 50% compared to CSMA and by about a factor of 3 compared to ALOHA.","Title: Towards an end-to-end delay analysis of wireless multihop networks\nYear: 2009\n\nAbstract: In wireless multihop networks, end-to-end (e2e) delay is a critical parameter for quality of service (QoS) guarantees. We employ discrete-time queueing theory to analyze the end-to-end (e2e) delay of wireless multihop networks for two MAC schemes, m-phase TDMA and slotted ALOHA. In one-dimensional (1-D) networks, due to the lack of sufficient multiplexing and splitting, a space-time correlation structure exists, the nodes are spatially correlated with each other, and the e2e performance cannot be analyzed as in general two-dimensional networks by assuming all nodes independent of each other. This paper studies an 1-D network fed with a single flow, an extreme scenario in which there is no multiplexing and splitting. A decomposition approach is used to decouple the whole network into isolated nodes. Each node is modeled as a GI/Geo/1 queueing system. First, we derive the complete per-node delay distribution and departure characterization, accounting for both the queueing delay and access delay. Second, based on the departure process approximation, we define a parameter to measure the spatial correlation and its influence on the e2e delay variance. Our study shows that traffic burstiness of the source flow and MAC together determines the sign of the correlation.","Title: Design of a wireless assisted pedestrian dead reckoning system - the NavMote experience\nYear: 2005\n\nAbstract: In this paper, we combine inertial sensing and sensor network technology to create a pedestrian dead reckoning system. The core of the system is a lightweight sensor-and-wireless-embedded device called NavMote that is carried by a pedestrian. The NavMote gathers information about pedestrian motion from an integrated magnetic compass and accelerometers. When the NavMote comes within range of a sens...","Title: Reactive sink mobility in wireless sensor networks\nYear: 2007\n\nAbstract: The ability of a sink node to move can greatly improve the fault tolerance and load balancing properties of a sensor network. Rather than assuming extensive mobility and trying to minimize the large-scale path loss between the mobile sink and the nodes, we focus on limited-scope, arbitrary mobility triggered in response to a form of network feedback. Due to multipath fading effects, limited mobility dynamically modifies the set of sink neighbors and distributes network traffic over a larger number of nodes. We illustrate the impact of this reactive sink mobility concept on data collection by implementing it on top of a novel gradient-based routing protocol. We use Berkeley motes to present a proof of concept as well as a performance evaluation of our approach, with a particular emphasis on the advantages in terms of robustness and lifetime.","Title: Delay-optimal Power Control Policies\nYear: 2012\n\nAbstract: The delay till success (DTS) is the mean number of transmissions needed, averaged over the fading, until a single packet is successfully received (decoded) over a wireless link. This paper shows that under a mean and a peak power constraint, random power control can significantly reduce the DTS. We derive the optimal power control policies that minimize the DTS at one link of given length. For most commonly used fading distributions, these optimal power control policies are random on-off policies, whose parameters depend on the fading statistics and the link distance. We present two applications of this result in the context of noise-limited wireless networks: minimizing the local delay (mean delay for successful nearest-neighbor communication) and minimizing the local anycast delay (mean delay for a transmission to any node).","Title: Dynamic Connectivity in ALOHA Ad Hoc Networks\nYear: 2008\n\nAbstract: We consider an ad hoc network which uses multihop routing and slotted ALOHA for its MAC contention. We show that the minimum time required for a packet to reach the destination from the origin scales linearly with the distance. We define this dela y as the minimum time required for a causal path to form between the source and destination. We derive the distributional properties of the connection time using ideas from first passage percolation and epidemic processes. We show that the delay scales linearly with the distance and also provide asymptotic results (with respect to time) for the positions of the nodes which are able to receive information from a transmitter located at the origin. We also provide simulation results to support the theoretical resu lts. I. INTRODUCTION In a multihop ad hoc network, bits, frames or packets are transferred from a source to a destination in a multihop fashion with the help of intermediate nodes. This introduces a delay before a packet is delivered to the destination. For example, a five-hop route does not guarantee a delay of only five time slots. In a general setting, each node can connect to multiple nodes. So a large number of paths may form between the source and the destination. Each path may have taken a different time to form with the help of different intermediate nodes. Consider a network in which each node wants to transmit to its destination in a multihop fashion. In general in such a network, a relay node queues the packets from other nodes and its own packets and","Title: The Ginibre Point Process as a Model for Wireless Networks With Repulsion\nYear: 2014\n\nAbstract: The spatial structure of transmitters in wireless networks plays a key role in evaluating mutual interference and, hence, performance. Although the Poisson point process (PPP) has been widely used to model the spatial configuration of wireless networks, it is not suitable for networks with repulsion. The Ginibre point process (GPP) is one of the main examples of determinantal point processes that can be used to model random phenomena where repulsion is observed. Considering the accuracy, tractability, and practicability tradeoffs, we introduce and promote the β-GPP, which is an intermediate class between the PPP and the GPP, as a model for wireless networks when the nodes exhibit repulsion. To show that the model leads to analytically tractable results in several cases of interest, we derive the mean and variance of the interference using two different approaches: the Palm measure approach and the reduced second-moment approach, and then provide approximations of the interference distribution by three known probability density functions. In addition, to show that the model is relevant for cellular systems, we derive the coverage probability of a typical user and find that the fitted β-GPP can closely model the deployment of actual base stations in terms of coverage probability and other statistics.","Title: Stochastic analysis of the mean interference for the RTS/CTS mechanism\nYear: 2012\n\nAbstract: The RTS/CTS handshake mechanism in WLAN is studied using stochastic geometry. The effect of RTS/CTS is treated as a thinning procedure for a spatially point process that models the potential transceivers in a WLAN, and the resulting concurrent transmitter processes are described. Exact formulas for the intensity of the concurrent transmitter processes and the mean interference are established. The analysis yields useful results for understanding how the design parameters of RTS/CTS affect the interference in the network.","Title: Random Power Control in Poisson Networks.\nYear: 2012\n\nAbstract: This paper studies power control strategies in interference-limited wireless networks with Poisson distributed nodes. We concentrate on two sets of strategies: single-node optimal power control (SNOPC) strategies and Nash equilibrium power control (NEPC) strategies. SNOPC strategies maximize the expected throughput of the power-controllable link given that all the other transmitters do not use pow...","Title: Longest Edge Routing on the Spatial Aloha Graph\nYear: 2008\n\nAbstract: The multihop spatial reuse Aloha (MSR-Aloha) protocol was recently introduced by Baccelli et al., where each transmitter selects the receiver among its feasible next hops that maximizes the forward progress of the head of line packet towards its final destination. They identify the optimal medium access probability (MAP) that maximizes the spatial density of progress, defined as the product of the spatial intensity of attempted transmissions times the average per-hop progress of each packet towards its destination. We propose a variant called longest edge routing where each transmitter selects its longest feasible edge, and then identifies a packet in its backlog whose next hop is the associated receiver. The main contribution of this work (and of Baccelli et al.) is the use of stochastic geometry to identify the optimal MAP and the corresponding optimal spatial density of progress.","Title: Interference and outage in clustered wireless ad hoc networks\nYear: 2007\n\nAbstract: In the analysis of large random wireless networks, the underlying node distribution is almost ubiquitously assumed to be the homogeneous Poisson point process. In this paper, the node locations are assumed to form a Poisson cluster process on the plane. We derive the distributional properties of the interference and provide upper and lower bounds for its distribution. We consider the probability of successful transmission in an interference-limited channel when fading is modeled as Rayleigh. We provide a numerically integrable expression for the outage probability and closed-form upper and lower bounds. We show that when the transmitter-receiver distance is large, the success probability is greater than that of a Poisson arrangement. These results characterize the performance of the system under geographical or MAC-induced clustering. We obtain the maximum intensity of transmitting nodes for a given outage constraint, i.e., the transmission capacity (of this spatial arrangement) and show that it is equal to that of a Poisson arrangement of nodes. For the analysis, techniques from stochastic geometry are used, in particular the probability generating functional of Poisson cluster processes, the Palm characterization of Poisson cluster processes, and the Campbell-Mecke theorem.","Title: Percolation in the secrecy graph\nYear: 2011\n\nAbstract: The secrecy graph is a random geometric graph which is intended to model the connectivity of wireless networks under secrecy constraints. Directed edges in the graph are present whenever a node can talk to another node securely in the presence of eavesdroppers, which, in the model, is determined solely by the locations of the nodes and eavesdroppers. In the case of infinite networks, a critical parameter is the maximum density of eavesdroppers that can be accommodated while still guaranteeing an infinite component in the network, i.e., the percolation threshold. We focus on the case where the locations of the nodes and eavesdroppers are given by Poisson point processes, and present bounds for different types of percolation, including in-, out- and undirected percolation.","Title: Stochastic Geometry for Modeling, Analysis, and Design of Multi-Tier and Cognitive Cellular Wireless Networks: A Survey.\nYear: 2013\n\nAbstract: For more than three decades, stochastic geometry has been used to model large-scale ad hoc wireless networks, and it has succeeded to develop tractable models to characterize and better understand the performance of these networks. Recently, stochastic geometry models have been shown to provide tractable yet accurate performance bounds for multi-tier and cognitive cellular wireless networks. Given...","Title: Reliable data delivery in large-scale low-power sensor networks\nYear: 2010\n\nAbstract: In data collection applications of low-end sensor networks, a major challenge is ensuring reliability without a significant goodput degradation. Short hops over high-quality links minimize per-hop transmissions, but long routes may cause congestion and load imbalance. Longer links can be exploited to build shorter routes, but poor links may have a high energy cost. There exists a complex interplay among routing performance (reliability, goodput, energy efficiency), link estimation, congestion control, and load balancing; we design a routing architecture, Arbutus, that exploits this interplay, and perform an extensive experimental evaluation on testbeds of 100-150 Berkeley motes.","Title: Combining stochastic geometry and statistical mechanics for the analysis and design of mesh networks\nYear: 2014\n\nAbstract: We consider a two-dimensional mesh network comprising several source-destination pairs, each communicating wirelessly in a multihop fashion. First, we introduce a novel transmission policy for multihop networks according to which all the buffering in the network is performed at source nodes while relays just have unit-sized buffers. We demonstrate that incorporating this buffering scheme in conjunction with minor amendments to the medium access control (MAC) layer yields several benefits such as keeping packet delays small and helping regulate the traffic flow in a completely distributed fashion. Second, we employ a novel combination of tools from stochastic geometry and statistical mechanics to characterize the throughput and end-to-end delay performances of multihop wireless networks for two different channel access mechanisms, Carrier Sense Multiple Access (CSMA) and ALOHA. Our study also offers valuable insights from a system design stand-point such as determining the optimum density of transmitters or the optimal number of hops along a flow that maximizes the system's throughput performance. We corroborate our theoretical analyses via simulations.","Title: A Stochastic Geometry Analysis of Inter-Cell Interference Coordination and Intra-Cell Diversity.\nYear: 2014\n\nAbstract: Inter-cell interference coordination (ICIC) and intra-cell diversity (ICD) play important roles in improving cellular downlink coverage. By modeling cellular base stations (BSs) as a homogeneous Poisson point process (PPP), this paper provides explicit finite-integral expressions for the coverage probability with ICIC and ICD, taking into account the temporal/spectral correlation of the signal and...","Title: On Decoding The Kth Strongest User In Poisson Networks With Arbitrary Fading Distribution\nYear: 2013\n\nAbstract: Consider a d-dimensional network whose transmitters form a non-uniform Poisson point process and whose links are subject to arbitrary fading. Assuming interference from the k - 1 strongest users is canceled, we derive the probability of decoding the k-th strongest user in closed-form. As a special case, when k = 1, this probability is the standard coverage probability.This analytical result has immediate applications in networks with successive interference cancellation (SIC) capability. We use it to find closed-form upper and lower bounds on the probability of decoding at least k users and the mean number of successively decodable users. These bounds show that transmitter clustering is beneficial in exploiting SIC.","Title: Bounds on the information propagation delay in interference-limited ALOHA networks\nYear: 2009\n\nAbstract: Abstract—In a wireless network the set of transmitting nodes changes frequently because of the MAC scheduler and the traffic load. Analyzing the connectivity of such a network using static graphs would lead to pessimistic performance results. In this paper, we consider an ad hoc network with half-duplex radios that uses multihop routing and slotted ALOHA for the network MAC contention, and introduce a random dynamic multi-digraph to model its connectivity. We first provide analytical results about the degree distribution of the graph. Next, defining the path formation time as the minimum time required for a causal path to form between the source and destination on the dynamic graph, we derive the distributional properties of the connection delay using techniques from first passage percolation and epidemic processes. We show that the delay scales linearly with the distance and provide asymptotic results (with respect to time) for the positions of the nodes which are able to receive information from a transmitter located at the origin. We also provide simulation results to support the theoretical results.","Title: Outage And Throughput Bounds For Stochastic Wireless Networks\nYear: 2005\n\nAbstract: We derive outage expressions and throughput bounds for wireless networks subject to different sources of nondeterminism. The degree of uncertainty is characterized by the location of the network in the uncertainty cube whose three axes represent the three main sources of uncertainty in interference-limited networks: the node distribution, the channel gains, and the channel access. The range for the coordinates is [0,1], where 0 indicates complete determinism, and 1 a maximum degree of randomness (nodes distributed in a Poisson point process, fading with fading figure 1, and ALOHA channel access, respectively).","Title: Guest Editorial: Geometry and Random Graphs for the Analysis and Design of Wireless Networks\nYear: 2009\n\nAbstract: The one tutorial and 22 papers in this special issue focus on geometry and random graph for the analysis and design of wireless networks. The papers are organized into five groups: Topology; Outage, throughput, capacity, and scaling laws; Connectivity and coverage; Co-existence of disparate wireless networks and cognitive radio; and Distributed algorithms.","Title: Toward A Circuit Theory For Sensor Networks With Fading Channels\nYear: 2004\n\nAbstract: For Rayleigh fading wireless channels, there exists an interesting similarity between resistive circuits and time and path diversity mechanisms in multihop wireless sensor networks. A resistor-like circuit element, the erristor, representing the normalized noise-to-signal ratio, is introduced. Given an end-to-end packet delivery probability, the nonlinear mapping from link reception probabilities to erristor values greatly simplifies the problems of power allocation and the selection of time and path diversity schemes. Thanks to its simplicity, the formalism that is developed also provides valuable insight into the benefits of diversity mechanisms, and it reveals an interesting analogy to electric circuits as the signal-to-noise ratios exhibit resistor-like properties.","Title: The Aggregate Throughput In Random Wireless Networks With Successive Interference Cancellation\nYear: 2013\n\nAbstract: The feasibility of successive interference cancellation (SIC) depends on the received power ordering from different users, which, in turn, depends on the fading distribution, path loss function and network geometry. Using a framework based on stochastic geometry, this paper studies the aggregate throughput in d-dimensional random wireless networks with SIC capability. We consider networks with arbitrary fading distribution, power-law path loss; the network geometry is governed by a non-uniform Poisson point process (PPP). Our results demonstrate how the performance of SIC changes as a function of the network geometry, fading distribution, and the path loss law. An important observation is that, in interference-limited networks, lower per-user information rate always results in higher aggregate throughput, while in noisy networks, there exists a positive optimal per-user rate at which the aggregate throughput is maximized.","Title: A delay-minimizing routing strategy for wireless multi-hop networks\nYear: 2009\n\nAbstract: We consider a network where each route comprises a backlogged source, a number of relays and a destination at a finite distance. The locations of the sources and the relays are realizations of independent Poisson point processes. Given that the nodes observe a TDMA/ALOHA MAC protocol, our objective is to determine the number of relays and their placement such that the mean end-to-end delay in a typical route of the network is minimized. We first study an idealistic network model where all routes have the same number of hops, the same distance per hop and their own dedicated relays. Combining tools from queueing theory and stochastic geometry, we provide a precise characterization of the mean end-to-end delay. We find that the delay is minimized if the first hop is much longer than the remaining hops and that the optimal number of hops scales sublinearly with the source-destination distance. Simulating the original network scenario reveals that the analytical results are accurate, provided that the density of the relay process is sufficiently large. We conclude that, given the considered MAC protocol, our analysis provides a delay-minimizing routing strategy for random, multihop networks involving a small number of hops.","Title: A Geometry-Inclusive Fading Model For Random Wireless Networks\nYear: 2006\n\nAbstract: A new fading model is proposed and discussed that combines the uncertainties in the transmission distance as well as small-scale fading. If nodes are assumed to be distributed according to a Poisson point process and the fading is Rayleigh, the joint fading distribution is particularly simple. Interpreting fading as a stochastic mapping, we show that a node cannot infer on the presence of fading by measuring link qualities. Other applications of the fading model include connectivity, opportunistic communication, and probabilistic progress.","Title: A formalism for the analysis and design of time and path diversity schemes in wireless sensor networks\nYear: 2003\n\nAbstract: For Rayleigh fading channels, there exists an interesting similarity between resistive circuits and time and path diversity mechanisms in multihop wireless sensor networks. A resistor-like circuit element, the erristor, representing the normalized noise-to-signal ratio, is introduced. Given an end-to-end packet delivery probability (as a QoS requirement), the nonlinear mapping from link reception probabilities to erristor values greatly simplifies the problems of power allocation and the selection of time and path diversity schemes. Thanks to its simplicity, the formalism that is developed also provides valuable insight into the benefits of diversity mechanisms, which is illustrated by a number of examples.","Title: Interference and Outage in Poisson Cognitive Networks.\nYear: 2012\n\nAbstract: Consider a cognitive radio network with two types of users: primary users (PUs) and cognitive users (CUs), whose locations follow two independent Poisson point processes. The cognitive users follow the policy that a cognitive transmitter is active only when it is outside the primary user exclusion regions. We found that under this setup the active cognitive users form a point process called the Po...","Title: Toward Quasiregular Sensor Networks: Topology Control Algorithms for Improved Energy Efficiency\nYear: 2006\n\nAbstract: Uniformly random or Poisson distributions are widely accepted models for the location of the nodes in wireless sensor networks if nodes are deployed in large quantities and there is little control over where they are dropped. On the other hand, by placing nodes in regular topologies, we expect benefits both in coverage and efficiency of communication. We describe and analyze a basic localized algorithm and three modifications for topology control that provide a tradeoff between performance and deployment cost. The objective is to regularize the topology for improved energy efficiency. The basic algorithm produces quasiregular networks, which only use nodes as sentries and relays that are approximately evenly spaced, thereby emulating a regular grid topology. It is shown that quasiregular networks have a significant energy and lifetime advantage compared with purely random networks. We consider two specific types of quasiregular networks: the ones that are based on a Gaussian deviation about an ideal grid point (type A), and the ones that consist of a subset of nodes taken from a Poisson point process (type B). We show that the two types are equivalent for a certain density of the Poisson point process and, in particular, that in both cases the deviation from the ideal regular grid follows a Rayleigh distribution, whereas the distance between nearest neighbors is Ricean.","Title: The performance of successive interference cancellation in random wireless networks\nYear: 2014\n\nAbstract: This paper provides a unified framework to study the performance gain of successive interference cancellation (SIC) in d-dimensional interference-limited networks with arbitrary fading distribution and power-law path loss. We derive bounds on the mean number of users that can be successively decoded and the probability of successively decoding k users. Our results suggest that, without power control, the marginal benefit of enabling the receiver to successively decode k users diminishes very fast with k, especially in networks of high dimensions and small path loss exponent. On the other hand, SIC is more beneficial when the users are clustered around the receiver, or very low-rate codes are used.","Title: Delay Analysis Of Spatio-Temporal Channel Access For Cognitive Networks\nYear: 2011\n\nAbstract: Most channel access schemes for cognitive radio only consider using the idle periods of the primary users. Such schemes are not using the spectrum efficiently, since transmission opportunities also arise when a primary transmitter is active but its corresponding primary receiver is far away from the cognitive user. By detecting the signal power of the acknowledgments, a cognitive user is able to estimate the distance and channel condition between the primary receiver and itself. Then if the primary receiver is far away, the cognitive user can transmit simultaneously with the primary transmitter without affecting the primary link. Based on this idea, the spatio-temporal channel access scheme, utilizing both idle periods and spatial reuse, can be applied. This paper provides fundamental analysis of the channel access delays and shows the advantage of using the spatio-temporal access scheme in the carrier sense multiple access (CSMA)-based network with bi-directional links.","Title: Routing in Ad Hoc Networks-A Wireless Perspective\nYear: 2004\n\nAbstract: Traditionally, the routing problem is addressed at the network layer, an approach that has been extended to the wireless realm. In wireless multihop networks, however, strict layer-base protocol design leads to substantial inefficiencies. This paper addresses the routing problem for large wireless ad hoc networks from a fundamental point of view, not constrained by particular protocol implementations or layered architectures, but taking into account the properties of the wireless channel. First, an analytical channel model is presented that is based on Rayleigh fading. It demonstrates how noise and interference effects can be separated, and how each interfering transmission affects the packet reception probability. Second, the distribution of node distances in networks with uniformly randomly placed nodes is derived. These two ingredients are used to discuss the benefits of different routing strategies. In particular, short-hop and long-hop routing schemes are compared. Further, cooperative strategies such as multipath routing and cooperative diversity are briefly discussed as techniques that are enabled by the broadcast nature of the wireless channel.","Title: Temporal Correlation Of The Interference In Mobile Random Networks\nYear: 2011\n\nAbstract: In wireless networks, interference that is generated by undesired transmitters dominantly limits network performance. The correlation of node locations ( in mobile or static networks) makes the interference temporally correlated. Such correlation affects network performance greatly, and hence needs to be quantified. In this paper, we quantify the temporal correlation of the interference in mobile Poisson networks. More specifically, we obtain closed-form expressions for the interference correlation coefficient rho in Poisson line networks under various mobility models. When the mean speed of nodes (v) over bar increases, we show that rho is asymptotically proportional to (v) over bar (-1). Moreover, multi-path fading and random MAC schemes reduce the temporal correlation of the interference. These results are extended to higher-dimensional networks.","Title: Interference and Outage in Doubly Poisson Cognitive Networks\nYear: 2010\n\nAbstract: We consider a cognitive radio network with two types of users: primary users (PUs) and cognitive users (CUs), whose locations follow two independent Poisson point processes. The cognitive users follow the policy that a cognitive transmitter is active only when it is outside the primary user exclusion regions. Due to the interaction between the primary users and the cognitive users through exclusion regions, an exact calculation of the interference and the outage probability seems unfeasible. Instead, we derive bounds for the interference (in the form of Laplace transform) and the outage probability. Two network models, the bipolar and the nearest-neighbor model, are considered.","Title: Analysis of uncoordinated opportunistic two-hop wireless ad hoc systems\nYear: 2009\n\nAbstract: We consider a time-slotted two-hop wireless system in which the sources transmit to the relays in the even time slots (first hop) and the relays forward the packets to the destinations in the odd time slots (second hop). Each source may connect to multiple relays in the first hop. In the presence of interference and without tight coordination of the relays, it is not clear which relays should transmit the packet. We propose four decentralized methods of relay selection, some based on location information and others based on the received signal strength (RSS). We provide a complete analytical characterization of these methods using tools from stochastic geometry. We use simulation results to compare these methods in terms of end-to-end success probability.","Title: Diversity Polynomials for the Analysis of Temporal Correlations in Wireless Networks.\nYear: 2013\n\nAbstract: The interference in wireless networks is temporally correlated, since the node or user locations are correlated over time and the interfering transmitters are a subset of these nodes. For a wireless network where (potential) interferers form a Poisson point process and use ALOHA for channel access, we calculate the joint success and outage probabilities of n transmissions over a reference link. The results are based on the diversity polynomial, which captures the temporal interference correlation. The joint outage probability is used to determine the diversity gain (as the SIR goes to infinity), and it turns out that there is no diversity gain in simple retransmission schemes, even with independent Rayleigh fading over all links. We also determine the complete joint SIR distribution for two transmissions and the distribution of the local delay, which is the time until a repeated transmission over the reference link succeeds.","Title: On the End-to-End Delay Performance of Spatially Correlated Wireless Line Networks\nYear: 2008\n\nAbstract: The analytical end-to-end (e2e) performance of a wireless multihop network is largely unknown, because of the interconnections between several factors involved. Customarily, the nodes are often assumed to be spatially uncorrelated so that they can be analyzed in isolation, which is valid when all the traffic flows are independent. In practice, however, most traffic flows are correlated and cause spatial correlation among nodes. The results based on the assumption of spatially uncorrelated nodes may be far from the performance of real networks. In this paper, we aim to study the impact of the spatial correlation on the e2e delay in a wireless line network (WLN). In particular, we use queueing theory to reveal that the burstiness, the temporal correlation of the traffic flow and the underlying medium access control (MAC), together determine the spatial correlation, from which an analysis of the e2e delay of a WLN is accomplished.","Title: User-Centric Intercell Interference Nulling for Downlink Small Cell Networks.\nYear: 2014\n\nAbstract: Small cell networks are regarded as a promising candidate to meet the exponential growth of mobile data traffic in cellular networks. With a dense deployment of access points, spatial reuse will be improved, and uniform coverage can be provided. However, such performance gains cannot be achieved without effective intercell interference management. In this paper, a novel interference coordination strategy, called user-centric intercell interference nulling , is proposed for small cell networks. A main merit of the proposed strategy is its ability to effectively identify and mitigate the dominant interference for each user. Different from existing works, each user selects the coordinating base stations (BSs) based on the relative distance between the home BS and the interfering BSs, called the interference nulling (IN) range, and thus interference nulling adapts to each user's own interference situation. By adopting a random spatial network model, we derive an approximate expression of the successful transmission probability to the typical user, which is then used to determine the optimal IN range. Simulation results shall confirm the tightness of the approximation, and demonstrate significant performance gains (about 35–40 ) of the proposed coordination strategy, compared with the non-coordination case. Moreover, it is shown that the proposed strategy outperforms other interference nulling methods. Finally, the effect of imperfect channel state information (CSI) is investigated, where CSI is assumed to be obtained via limited feedback. It is shown that the proposed coordination strategy still provides significant performance gains even with a moderate number of feedback bits.","Title: On the Impact of Coordination on Local Delay and Energy Efficiency in Poisson Networks\nYear: 2015\n\nAbstract: Local delay characterizes the number of time slots required for a node to successfully transmit a packet to its target receiver. In this letter, we explore the impact of coordinated transmission on the mean local delay and the energy efficiency, in a Poisson network model. Leveraging tools from point process theory, we derive analytical expressions of the mean local delay and the long-term energy efficiency, without and with coordinated transmission. We find that coordinated transmission greatly decreases the mean local delay, and also improves the long-term energy efficiency under certain network configurations.","Title: Spatiotemporal Cooperation in Heterogeneous Cellular Networks\nYear: 2015\n\nAbstract: This paper studies downlink communication in a heterogeneous cellular network where a set of geographically separated base stations (BSs) cooperates in transmitting data to a common receiver. If a decoding error occurs, data is cooperatively retransmitted by a possibly different set of BSs, such that the receiver can benefit from spatiotemporal BS cooperation. Specific cooperation techniques studied in this paper include joint transmission, base station silencing, and the Alamouti space-time code. Using tools from stochastic geometry, the coverage probability at the typical user is characterized as an integral function of the network parameters and the sets of cooperating BSs. The expressions derived reveal the existence of two qualitatively different operating regimes. In the high-coverage regime, the typical user is diversity-limited, so cooperation techniques exploiting spatiotemporal diversity are highly effective in increasing coverage. It is shown that retransmissions always yield time diversity, while channel state information at the transmitters is required to harvest spatial diversity via joint transmission. In the low-coverage regime, on the other hand, the typical user is interference-limited, so cooperation techniques such as joint transmission and base station silencing are effective in increasing coverage as they suppress part of the interference power.","Title: Interference Functionals in Poisson Networks.\nYear: 2014\n\nAbstract: We propose and prove a theorem that allows the calculation of a class of functionals on Poisson point processes that have the form of expected values of sum–products of functions. In proving the theorem, we present a variant of the Campbell–Mecke theorem from stochastic geometry. We proceed to apply our result in the calculation of expected values involving interference in wireless Poisson networks. Based on this, we derive outage probabilities for transmissions in a Poisson network with Nakagami fading. Our results extend the stochastic geometry toolbox used for the mathematical analysis of interference-limited wireless networks.","Title: Bounding the Bethe and the Degree-M Bethe Permanents.\nYear: 2015\n\nAbstract: It was recently conjectured that the permanent of a ${P}$-lifting $\\theta^{\\uparrow{P}}$ of a matrix $\\theta$ of degree $M$ is less than or equal to the $M$th power of the permanent perm$(\\theta)$, i.e., perm$(\\theta^{\\uparrow{P}})\\leq(\\text{perm}(\\theta))^M$ and, consequently, that the degree-$M$ Bethe permanent $\\text{perm}_{M,\\mathrm{B}} (\\theta)$ of a matrix $\\theta$ is less than or equal to the permanent perm$(\\theta)$ of $\\theta$, i.e., perm$_{M, \\mathrm{B}} (\\theta)\\leq \\text{perm}(\\theta)$. In this paper, we prove these related conjectures and show in addition a few properties of the permanent of block matrices that are lifts of a matrix. As a corollary, we obtain an alternative proof of the inequality perm$_{\\mathrm{B}} (\\theta)\\leq \\text{perm}(\\theta)$ on the Bethe permanent of the base matrix $\\theta$ that uses only the combinatorial definition of the Bethe permanent.","Title: On The Optimal Block Length For Joint Channel And Network Coding\nYear: 2011\n\nAbstract: Channel coding alone is not sufficient to reliably transmit a message of finite length from a source to one or more destinations. To ensure that no data is lost, channel coding on the physical layer needs to be combined with rateless erasure correcting schemes such as automatic repeat request (ARQ) or random linear network coding (RLNC) on a higher layer. In this paper we consider channel coding on a binary symmetric channel and random linear network coding for erasure correction. Given a message of length K and network coding over a finite Galois field of size q, we obtain the optimal number of blocks for network coding that minimizes the expected number of transmissions. We consider both a single link and broadcast to n destinations. As the field size of network coding gets large and the expected coding overhead in blocks becomes small, we show that, given our assumptions, the benefit of using a larger channel coded block outweighs the advantage of employing network coding over many blocks and the optimal number of number of blocks tends to one, making RLNC equivalent to simple ARQ.","Title: Outage and capacity of heterogeneous cellular networks with intra-tier dependence\nYear: 2014\n\nAbstract: In this paper, we propose a two-tier heterogeneous cellular network (HCN) model with intra-tier dependence, where the macro base stations (MBSs) and the pico base stations (PBSs) follow a Poisson point process (PPP) and a Matern cluster process (MCP), respectively. Due to the high spatial fluctuations of the traffic demand, the users are modeled as a Cox process. Conditioning on a fixed distance between a user equipment (UE) and its nearest serving BS, exact calculations of the interference and the outage probability are derived. The per-user capacity and the area spectral efficiency are also analyzed. The results show that the model with intra-tier dependence appears closer to the real deployment than the extreme with the complete randomness (the PPP). An important conclusion is that both the per-user capacity and the area spectral efficiency improve with smaller cells, but outage does not.","Title: A heterogeneous cellular network model with inter-tier dependence\nYear: 2014\n\nAbstract: In heterogeneous cellular networks (HCNs), the macrocell network is usually assumed to be overlaid by multiple independent tiers of small cells. However, in reality, the locations of the base stations (BSs) belonging to different tiers are not fully independent. Accordingly, in this paper, we propose a two-tier HCN model with inter-tier dependence, where the macro-BS (MBS) and the pico-BS (PBS) deployments follow a Poisson point process (PPP) and a Poisson hole process (PHP), respectively. Under this setup, we derive bounds on the outage probabilities of both macro users and pico users, and then use a fitted Poisson cluster process to approximate the PHP, which is shown to provide a good approximation of the interference and outage. The results show that the model with inter-tier dependence appears closer to the real deployment than the two extremes with full regularity (the triangular lattice) and complete randomness (the PPP). An important conclusion is that significant gains can be obtained if PBSs are deployed smartly (away from MBSs).","Title: Success probabilities in Gauss-Poisson networks with and without cooperation\nYear: 2014\n\nAbstract: Gauss-Poisson processes (GPPs) are a class of clustered point processes, which include the Poisson point process as a special case and have a simpler structure than general Poisson cluster point processes. In this paper, we propose the GPP as a model for wireless networks that exhibit clustering behavior. We calculate the success probabilities and provide bounds for three kinds of GPP networks: (1) the basic model where the desired transmitter is independent of the GPP and all nodes in the GPP are interferers; (2) the non-cooperative model where the desired transmitter is one of the nodes in the GPP; (3) the cooperative model where both nodes in a two-node cluster of the GPP serve a receiver cooperatively using non-coherent joint transmission. Our results show that the bounds, especially the upper bounds, provide good approximations for different operating regimes.","Title: Cellular network coverage with inter-cell interference coordination and intra-cell diversity\nYear: 2014\n\nAbstract: Modeling cellular base stations (BSs) as a homogeneous Poisson point process (PPP), this paper provides exact expressions, in terms of a finite integral, for the coverage probability with inter-cell interference coordination (ICIC) and intra-cell diversity (ICD). Despite the fact that both ICIC and ICD can significantly improve the coverage probability, they improve coverage in drastically different ways in the high-reliability regime, where the user outage probability goes to zero. In particular, we show that ICD can provide order gain while ICIC only offers linear gain. This finding contrasts the recent result showing the absence of diversity gain in retransmission in ad hoc networks.","Title: Optimal base station density for power efficiency in cellular networks\nYear: 2014\n\nAbstract: In cellular networks, cell size reduction is an important technique for improving the spectral reuse and achieving higher data rates. In addition, it results in power savings as it leads to a decrease in transmit power. However, it is not clear if the transmit power can be indefinitely decreased with the cell sizes. In this paper, we analyze the impact of transmit power reduction (cell size reduction) on the performance of the network. More precisely, we obtain a lower bound on the transmit power such that a minimum coverage and a minimum data rate can be guaranteed. We then analyze the area power consumption metric, which denotes the total power consumed per unit area. Under the constraints of target coverage and target data rate, the area power consumption is minimized and the optimal base station density is obtained. For a path loss exponent α > 4, we observe the existence of a minimum cell size below which shrinking the cell would result in an overall increase of power. However, for α ≤ 4, there exists no such optimal cell-size, as the area power consumption increases with base station density.","Title: Traffic management in random cellular networks\nYear: 2014\n\nAbstract: We consider a cellular network where base stations are randomly deployed according to a Poisson point process and new jobs arrive in time and space according to a Poisson space-time point process, and are assigned to base stations according to different assignment schemes with/without base station cooperation. We derive “macroscopic” spatial averages of local base station performance metrics, such as traffic load, utilization factor, and delay, under different models on the service time distribution which include dependence on distance. Moreover, we determine and derive properties of the traffic capacity of the network, defined as the maximum spatio-temporal job density under a given constraint on the percentage of unstable base stations. The proposed model provides a baseline for the study of irregular cellular networks with bursty spatio-temporal data traffic.","Title: Cooperative retransmission in heterogeneous cellular networks\nYear: 2014\n\nAbstract: This paper studies spatiotemporal cooperation between base stations in the presence of interference in a heterogeneous cellular network. The focus of the paper is the cooperative retransmission scenario, where a set of randomly located base stations that are selected based on their average received powers, possibly belonging to different network tiers, jointly transmit data in each transmission. If a decoding error occurs in the first transmission, data is cooperatively retransmitted by a possibly different set of BSs, such that the receiver can benefit from spatiotemporal BS cooperation. Using tools from stochastic geometry, the coverage probability at the typical user is characterized as an integral function of the network parameters and the sets of cooperating BSs. An integral expression for the coverage probability is also derived for the case when the typical user is able to perform maximum ratio combining (MRC) of the received copies in two transmissions. Numerical evaluation shows that in the high coverage regime spatiotemporal cooperation outperforms joint transmission with no retransmissions and that the gains extend to all regimes if the user has MRC capability.","Title: Asymptotic deployment gain: A new approach to characterize coverage probability\nYear: 2014\n\nAbstract: In cellular network models, the base stations are usually assumed to form a lattice or a Poisson point process (PPP). In reality, however, they are deployed neither fully regularly nor completely randomly. Accordingly, in this paper, we consider the very general class of motion-invariant models and analyze the behavior of the coverage probability (the probability that the signal-to-interference-plus-noise-ratio (SINR) exceeds a threshold) as the threshold goes to zero. We show that, surprisingly, the slope of the outage probability (complementary to the coverage probability, in dB) as a function of the threshold (also in dB) is the same for essentially all motion-invariant point processes. The slope merely depends on the fading statistics. Using this result, we introduce the notion of the asymptotic deployment gain (ADG), which characterizes the horizontal gap between the coverage probability of the PPP and another point process in the high-reliability regime (where the coverage probability is near 1). To demonstrate the usefulness of the ADG for the characterization of coverage probabilities, we investigate the coverage properties and the ADGs for different point processes and fading statistics by simulations.","Title: Joint design of channel and network coding for star networks connected by binary symmetric channels\nYear: 2014\n\nAbstract: In a network application, channel coding alone is not sufficient to reliably transmit a message of finite length K from a source to one or more destinations as in, e.g., file transfer. To ensure that no data is lost, it must be combined with rateless erasure correcting schemes on a higher layer, such as a time-division multiple access (TDMA) system paired with automatic repeat request (ARQ) or random linear network coding (RLNC). We consider binary channel coding on a binary symmetric channel (BSC) and q-ary RLNC for erasure correction in a star network, where Y sources send messages to each other with the help of a central relay. In this scenario RLNC has been shown to have a throughput advantage over TDMA schemes as K→∞ and q→∞. In this paper we focus on finite block lengths and compare the expected throughputs of RLNC and TDMA. For a total message length of K bits, which can be subdivided into blocks of smaller size prior to channel coding, we obtain the channel code rate and the number of blocks that maximize the expected throughput of both RLNC and TDMA, and we find that TDMA is more throughput-efficient for small message lengths K and small q.","Title: The Local Delay in Poisson Networks\nYear: 2013\n\nAbstract: Communication between two neighboring nodes is a very basic operation in wireless networks. Yet very little research has focused on the local delay in networks with randomly placed nodes, defined as the mean time it takes a node to connect to its nearest neighbor. We study this problem for Poisson networks, first considering interference only, then noise only, and finally and briefly, interference plus noise. In the noiseless case, we analyze four different types of nearest-neighbor communication and compare the extreme cases of high mobility, where a new Poisson process is drawn in each time slot, and no mobility, where only a single realization exists and nodes stay put forever. It turns out that the local delay behaves rather differently in the two cases. We also provide the low- and high-rate asymptotic behavior of the minimum achievable delay in each case. In the cases with noise, power control is essential to keep the delay finite, and randomized power control can drastically reduce the required (mean) power for finite local delay.","Title: Analysis and design of diversity schemes for ad hoc wireless networks\nYear: 2005\n\nAbstract: Diversity schemes permit efficient communication over fading channels but are often hard to analyze and design in networks with many nodes. For Rayleigh-fading channels, there exists an interesting relationship between resistive circuits and time and path diversity mechanisms in wireless ad hoc networks. A resistor-like network element, the erristor, representing the normalized noise-to-signal ratio, is introduced. Given an end-to-end packet delivery probability, the logarithmic mapping from link reception probabilities to erristor values greatly simplifies the problems of power allocation and the selection of time and path diversity schemes, which is illustrated in a number of examples. We focus on transmission strategies with selection combining and simple noncoherent \"decode-and-forward\" strategies, which is motivated by their practicality. Thanks to its conceptual simplicity, the formalism that is developed provides valuable insight into the benefits of diversity mechanisms.","Title: Spatial Diversity Benefits by Means of Induced Fading\nYear: 2006\n\nAbstract: Multipath fading heavily contributes to the unreliability of wireless links and is normally seen as a negative phenomenon hindering proper radio communication. However, fading can also improve the chances of reliable communication over channels that would otherwise be unusable, as our experimental evidence shows. In the context of wireless sensor networks, we show that limited motion of the base station may be used to obtain a considerable spatial diversity benefit through the exploitation of induced fading. We compare our limited mobility approach to multi-antenna systems, which are commonly used to achieve spatial diversity, and we illustrate these concepts with the help of experimental results. Moreover, we analyze how spatial diversity relates to network lifetime","Title: Single-Hop Connectivity in Interference-Limited Hybrid Wireless Networks\nYear: 2007\n\nAbstract: We consider a hybrid wireless network, in which the low-power nodes collect data and pass it to the data aggregation nodes (base stations). The low-power nodes are assumed to form a Poisson point process and communicate to the base stations using spread spectrum. We derive bounds to the probability of sensor node isolation, when the base stations are arranged randomly or in a regular fashion. I. I NTRODUCTION AND SYSTEM MODEL A. Motivation Hybrid sensor networks consist of two groups of nodes, one group of low powered sensors which sense and forward the data to the base stations or the data aggregation nodes (1). The base stations are more powerful nodes which are connected to each other by a higher-bandwidth network. It has been shown in (2) that the throughput of hybrid networks is higher than the conventional network. The paper by Dousse et al. (3) analyzes the connectivity of hybrid networks using percolation theory. They consider a disk model without fading for connectivity and allow for multihop routing. The connectivity of an ad hoc network without interference is considered with fading in (4) and without fading in (5). Baccelli et al. (6, Sec 5.3), give a non-explicit expression for the volume fraction of stationary coverage processes, which is closely related to the node-isolation probability when the base stations are randomly deployed. In this paper we assume that the radio uses spread spectrum and that the sensor nodes simultaneously transmit data to the base stations which makes the communication interference- limited. A sensor node is said to be connected if it can directly communicate with at least one base station. We also assume that there is no power control, since the senor nodes do not know which base station to communicate with. The sensor nodes are assumed to be randomly placed on the plane. Can the base stations be also randomly placed or should they be placed in a more regular fashion to provide a certain probability of connectivity for the sensor nodes? In this paper, we shall be primarily dealing with sensor networks, but these results also apply to similar problems in cellular systems and wireless Internet access.","Title: Interference in ad hoc networks with general motion-invariant node distributions\nYear: 2008\n\nAbstract: In this paper we derive the tail properties of interference for any stationary and isotropic spatial distribution of transmitting nodes. Previously the properties of interference were known only when the nodes are distributed as a homogeneous Poisson point process on the plane. We show the effect of a singular path loss model on the tail distribution of the interference. When the path loss function has a singularity at the origin, the interference is shown to be a heavy-tailed distribution under very mild conditions. When the path loss is bounded, the distribution of the interference is predominantly dictated by the fading. We also provide asymptotically tight upper and lower bounds on the CDF of the interference, and discuss the effectiveness of using a Gaussian approximation for modelling the interference.","Title: The secrecy graph and some of its properties\nYear: 2008\n\nAbstract: A new random geometric graph model, the so-called secrecy graph, is introduced and studied. The graph represents a wireless network and includes only edges over which secure communication in the presence of eavesdroppers is possible. The underlying point process models considered are lattices and Poisson point processes. In the lattice case, analogies to standard bond and site percolation can be exploited to determine percolation thresholds. In the Poisson case, the node degrees are determined and percolation is studied using analytical bounds and simulations. It turns out that a small density of eavesdroppers already has a drastic impact on the connectivity of the secrecy graph.","Title: The transport capacity of a wireless network is a subadditive euclidean functional\nYear: 2008\n\nAbstract: The transport capacity of a dense ad hoc network with n nodes scales like radicn. We show that the transport capacity divided by radicn approaches a non-random limit with probability one when the nodes are i.i.d. distributed on the unit square. We prove that the transport capacity under the protocol model is a subadditive Euclidean functional and use the machinery of subadditive functions in the spirit of Steele to show the existence of the limit.","Title: Correction to “A Geometric Interpretation of Fading in Wireless Networks: Theory and Applications” [Dec 08 5500-5510]\nYear: 2009\n\nAbstract: In the above titled paper (ibid., vol 54, no. 12, pp. 5500-5510, Dec 08), the table containing the list of symbols was corrupted during the publication process. The proper symbol table is presented here.","Title: Lifetime Benefits through Load Balancing in Homogeneous Sensor Networks\nYear: 2009\n\nAbstract: In routing protocols for wireless sensor networks energy efficiency is of paramount importance. Reliability-oriented protocols discard lossy links to avoid the significant energy cost of packet loss. The downside is that nodes with a particularly favorable channel tend to be overused: their lifespan is curtailed and the total amount of data delivered by the network may be significantly reduced. This problem is particularly critical for the nodes that provide access to the sink, since they have to carry the weight of the whole network. The use of load balancing schemes can be expected to provide significant lifetime benefits: rather than always using the nodes with the best channel, traffic is redistributed over a larger number of relays. We quantify the benefits of load balancing by comparing a routing protocol with embedded load balancing to a reliability-oriented protocol. We present and interpret experimental evidence of the benefits that stem from load balancing, but at the same time we also show that there are situations in which load balancing does not help.","Title: Percolation in the secrecy graph: Bounds on the critical probability and impact of power constraints\nYear: 2011\n\nAbstract: Secrecy graphs model the connectivity of wireless networks under secrecy constraints. Directed edges in the graph are present whenever a node can talk to another node securely in the presence of eavesdroppers. In the case of infinite networks, a critical parameter is the maximum density of eavesdroppers that can be accommodated while still guaranteeing an infinite component in the network, i.e., the percolation threshold. We focus on the case where the location of the nodes and the eavesdroppers are given by Poisson point processes, with and without power constraints. We present bounds for different types of percolation, including in-, out - and undirected percolation.","Title: On consensus over stochastically switching directed topologies\nYear: 2009\n\nAbstract: We consider average consensus algorithms executed over stochastically varying communication topologies that may be unbalanced. It is known that the state values will reach consensus, under fairly weak conditions. However, the consensus value is a random variable. We provide concentration bounds for the distance of the state vector from the consensus subspace and for the asymptotic distribution of the value to which the various nodes converge as they reach consensus. The results allow the analysis of average consensus over wireless communication networks with more realistic assumptions than before.","Title: Outage, local throughput, and capacity of random wireless networks\nYear: 2008\n\nAbstract: Outage probabilities and single-hop throughput are two important performance metrics that have been evaluated for certain specific types of wireless networ ks. However, there is a lack of comprehensive results for larger classes of networks, and there is no syste matic approach that permits the convenient comparison of the performance of networks with different geometries and levels of randomness. The uncertainty cube is introduced to categorize the uncertainty present in a net work. The three axes of the cube represent the three main potential sources of uncertainty in interference-limited networks: the node distribution, the channel gains (fading), and the chan nel access (set of transmitting nodes). For the performance analysis, a new parameter, the so-called spatial contention, is defined. It measures the slope of the outage probability in an ALOHA network as a function of the transmit probability p at p = 0. Outage is defined as the event that the signal-to-interferen ce ratio (SIR) is below a certain threshold in a given time slot. It is shown that the spatial contention is su fficient to characterize outage and throughput in large classes of wireless networks, corresponding to diffe rent positions on the uncertainty cube. Existing results are placed in this framework, and new ones are derived. Further, interpreting the outage probability as the SIR dis tribution, the ergodic capacity of unit- distance links is determined and compared to the throughput achievable for fixed (yet optimized) trans- mission rates.","Title: Spatial Analysis of Opportunistic Downlink Relaying in a Two-Hop Cellular System\nYear: 2009\n\nAbstract: We consider a two-hop cellular system in which the mobile nodes help the base station by relaying information to the dead spots. While two-hop cellular schemes have been analyzed previously, the distribution of the node locations has not been explicitly t aken into account. In this paper, we model the node locations of the base stations and the mobile stations as a point process on the plane and then analyze the performance of two different two-hop schemes in the downlink. In one scheme the node nearest to the destination that has decoded information from the base station in the first hop is used as the relay. In the second scheme the node with the best channel to the relay that received information in the first hop acts as a relay. In both these schemes we obtain the success probability of the two hop scheme, accounting for the interference from all other cells. We use tools from stochastic geometry and point process theory to analyze the two hop schemes. Besides the results obtained a main contribution of the paper is to introduce a mathematical framework that can be used to analyze arbitrary relaying schemes. Some of the main contributions of this paper are the analytical techniques introduced for the inclusion of the spatial locations of the nodes into the math ematical analysis.","Title: Diversity Loss Due to Interference Correlation\nYear: 2012\n\nAbstract: Interference in wireless systems is both temporally and spatially correlated. Yet very little research has analyzed the effect of such correlation. Here we focus on its impact on the diversity in Poisson networks with multi-antenna receivers. Most work on multi-antenna communication does not consider interference, and if it is included, it is assumed independent across the receive antennas. Here we show that interference correlation significantly reduces the probability of successful reception over SIMO links. The diversity loss is quantified via the diversity polynomial. For the two-antenna case, we provide the complete joint SIR distribution.","Title: Random access transport capacity\nYear: 2009\n\nAbstract: We develop a new metric for quantifying end-to-end throughput in multihop wireless networks, which we term random access transport capacity, since the interference model presumes uncoordinated transmissions. The metric quantifies the average maximum rate of successful end-to-end transmissions, multiplied by the communication distance, and normalized by the network area. We show that a simple upper bound on this quantity is computable in closed-form in terms of key network parameters when the number of retransmissions is not restricted and the hops are assumed to be equally spaced on a line between the source and destination. We also derive the optimum number of hops and optimal per hop success probability and show that our result follows the well-known square root scaling law while providing exact expressions for the preconstants, which contain most of the design-relevant network parameters. Numerical results demonstrate that the upper bound is accurate for the purpose of determining the optimal hop count and success (or outage) probability.","Title: Local Delay in Static and Highly Mobile Poisson Networks with ALOHA\nYear: 2010\n\nAbstract: Communication between two neighboring nodes is the most basic operation in wireless networks. Yet very little research has focused on the local delay, defined as the mean time it takes a node to connect to its nearest neighbor. In this paper, we derive the local delay in Poisson networks with ALOHA and find the conditions for which the local delay is finite. It turns out that while the local delay is always finite in highly mobile networks, there is a phase transition in static networks, i.e., there is a maximum transmit probability above which the local delay is infinite.","Title: Delay scaling in poisson networks\nYear: 2013\n\nAbstract: The local delay, defined as the mean time it takes a node to connect to its nearest neighbor, is a fundamental performance metric in a wireless network. For a network with Poisson distributed nodes, we find its scaling behavior (as a function of the rate of transmission) for different types of nearest-neighbor and the two extreme cases of mobility (infinite mobility and no mobility). Remarkably, it turns out that the level of mobility has hardly any effect on the scaling behavior of the local delay; it affects the pre-constant only.","Title: Delay Characterization of Multihop Transmission in a Poisson Field of Interference\nYear: 2014\n\nAbstract: We evaluate the end-to-end delay of a multihop transmission scheme that includes a source, a number of relays, and a destination, in the presence of interferers located according to a Poisson point process. The medium access control (MAC) protocol considered is a combination of TDMA and ALOHA, according to which nodes located a certain number of hops apart are allowed to transmit with a certain probability. Based on an independent transmissions assumption, which decouples the queue evolutions, our analysis provides explicit expressions for the mean end-to-end delay and throughput, as well as scaling laws when the interferer density grows to infinity. If the source always has packets to transmit, we find that full spatial reuse, i.e., ALOHA, is asymptotically delay-optimal, but requires more hops than a TDMA-ALOHA protocol. The results of our analysis have applications in delay-minimizing joint MAC/routing algorithms for networks with randomly located nodes. We simulate a network where sources and relays form a Poisson point process, and each source assembles a route to its destination by selecting the relays closest to the optimal locations. We assess both theoretically and via simulation the sensitivity of the end-to-end delay with respect to imperfect relay placements and route crossings.","Title: Coordinated Packet Transmission in Random Wireless Networks\nYear: 2010\n\nAbstract: This paper studies the value of allowing multiple transmitters to share all of the available bandwidth to concurrently transmit to a single receiver with multi-packet decoding capability. While such coordination can be bandwidth-efficient, it increases the density of interferers when many such multiple-access clusters exist in the network. On the other hand, orthogonal schemes such as FDMA may not be as bandwidth-efficient but operate at lower interferer densities due to orthogonalization. We take the first step towards understanding this trade-off. In particular, we analyze equidistant transmitters sending data using a coordination scheme based on the optimum strategy for a Gaussian multiple access channel. In terms of the throughputs seen in a typical cluster in a Poisson network, this form of coordination has little or no benefit when compared to FDMA. We also find that the increased interference due to multiple coordinated transmissions reduces the efficacy of successive decoding.","Title: Optimal Spatial Reuse in Poisson Multi-Hop Networks\nYear: 2010\n\nAbstract: We consider a wireless multi-hop network with sources that are Poisson distributed and relays which are placed on the source-destination line. Given a combined TDMA/ALOHA MAC protocol, we explore the following question of optimal spatial reuse: Increasing the number of nodes that are simultaneously scheduled to transmit in a route allows nodes to transmit more often. At the same time, it results in an increase of intra-route and inter-route interference, which has a negative impact on the end-to-end delay and throughput. In a regime of large source-destination distances R, we find that it is delay-optimal for either only one node, or a number of nodes that increases linearly in R, to be scheduled in each slot, depending on the ALOHA probability. If the transmission probability is also optimized, we find that maximum spatial reuse is delay-optimal. Scaling laws for the end-to-end delay and throughput are derived in all cases.","Title: Convergence Speed of the Consensus Algorithm With Interference and Sparse Long-Range Connectivity\nYear: 2010\n\nAbstract: We analyze the effect of interference on the convergence rate of average consensus algorithms, which iteratively compute the measurement average by message passing among nodes. It is usually assumed that these algorithms converge faster with a greater exchange of information (i.e., by increased network connectivity) in every iteration. However, when interference is taken into account, it is no longer clear if the rate of convergence increases with network connectivity. We study this problem for randomly placed consensus-seeking nodes connected through an interference-limited network. We investigate the following questions: 1) How does the rate of convergence vary with increasing communication range of every node? 2) How does this result change when every node is allowed to communicate with a few selected far-off nodes? When nodes schedule their transmissions to avoid interference, we show that the convergence speed scales with r2-d , where r is the communication range and d is the number of dimensions. This scaling is the result of two competing effects when increasing r: increased schedule length for interference-free transmission versus the speed gain due to improved connectivity. Hence, although one-dimensional networks can converge faster with a greater communication range despite increased interference, the two effects exactly offset one another in two-dimensions. In higher dimensions, increasing the communication range can actually degrade the rate of convergence. Our results thus underline the importance of factoring in the effect of interference in the design of distributed estimation algorithms.","Title: Secrecy coverage\nYear: 2013\n\nAbstract: Motivated by information-theoretic secrecy, geometric models for secrecy in wireless networks have begun to receive increased attention. The general question is how the presence of eavesdroppers affects the properties and performance of the network. Previously the focus has been mostly on connectivity. Here we study the impact of eavesdroppers on the coverage of a network of base stations. The problem we address is the following. Let base stations and eavesdroppers be distributed as stationary Poisson point processes in a disk of area n. If the coverage of each base station is limited by the distance to the nearest eavesdropper, what is the maximum density of eavesdroppers that can be accommodated while still achieving full coverage, asymptotically as n → ∞?","Title: Coordinated multipoint in heterogeneous networks: A stochastic geometry approach\nYear: 2013\n\nAbstract: Motivated by the ongoing discussion on coordinated multipoint in wireless cellular standard bodies, this paper considers the problem of base station cooperation in the downlink of heterogeneous cellular networks. The focus of the paper is the joint transmission scenario, where an ideal back haul network allows a set of randomly located base stations, possibly belonging to different network tiers, to jointly transmit data, so as to mitigate intercell interference and hence improve coverage and spectral efficiency. Using tools from stochastic geometry, an exact integral expression for the network coverage probability is derived in the scenario where a typical user can coherently combine the received signal from a pool of base stations, that are selected based on their average received power strengths or distance from the receiver. In the special case where cooperation is limited to two base stations, numerical evaluations illustrate relative gains in coverage probability of up to about 30% compared to the non-cooperative case.","Title: Mean Interference in Hard-Core Wireless Networks\nYear: 2011\n\nAbstract: Matérn hard core processes of types I and II are the point processes of choice to model concurrent transmitters in CSMA networks. We determine the mean interference observed at a node of the process and compare it with the mean interference in a Poisson point process of the same density. It turns out that despite the similarity of the two models, they behave rather differently. For type I, the excess interference (relative to the Poisson case) increases exponentially in the hard-core distance, while for type II, the gap never exceeds 1 dB.","Title: The Mean Interference-to-Signal Ratio and Its Key Role in Cellular and Amorphous Networks\nYear: 2014\n\nAbstract: We introduce a simple yet powerful and versatile analytical framework for approximating the SIR distribution in the downlink of cellular systems. It is based on the mean interference-to-signal ratio and yields the horizontal gap (SIR gain) between the SIR distribution in question and a reference SIR distribution. As applications, we determine the SIR gain for base station silencing, cooperation, and lattice deployment over a baseline architecture that is based on a Poisson deployment of base stations and strongest-base station association. The applications demonstrate that the proposed approach unifies several recent results and provides a convenient framework for the analysis and comparison of future network architectures and transmission schemes, including amorphous networks where a user is served by multiple base stations and, consequently, (hard) cell association becomes less relevant.","Title: Coordinated Multipoint Joint Transmission in Heterogeneous Networks\nYear: 2014\n\nAbstract: Motivated by the ongoing discussion on coordinated multipoint in wireless cellular standard bodies, this paper considers the problem of base station cooperation in the downlink of heterogeneous cellular networks. The focus of this paper is the joint transmission scenario, where an ideal backhaul network allows a set of randomly located base stations, possibly belonging to different network tiers, to jointly transmit data, to mitigate intercell interference and hence improve coverage and spectral efficiency. Using tools from stochastic geometry, an integral expression for the network coverage probability is derived in the scenario where the typical user located at an arbitrary location, i.e., the general user, receives data from a pool of base stations that are selected based on their average received power levels. An expression for the coverage probability is also derived for the typical user located at the point equidistant from three base stations, which we refer to as the worst case user. In the special case where cooperation is limited to two base stations, numerical evaluations illustrate absolute gains in coverage probability of up to 17% for the general user and 24% for the worst case user compared with the noncooperative case. It is also shown that no diversity gain is achieved using noncoherent joint transmission, whereas full diversity gain can be achieved at the receiver if the transmitting base stations have channel state information.","Title: Heterogeneous Cellular Network Models with Dependence\nYear: 2015\n\nAbstract: Due to its tractability, a multi-tier model of mutually independent Poisson point processes (PPPs) for heterogeneous cellular networks (HCNs) has recently been attracting much attention. However, in reality, the locations of the BSs, within each tier and across tiers, are not fully independent. Accordingly, in this paper, we propose two HCN models with inter-tier dependence (Case 1) and intra-tier dependence (Case 2), respectively. In Case 1, the macro-BS (MBS) and the pico-BS (PBS) deployments follow a Poisson point process (PPP) and a Poisson hole process (PHP), respectively. Under this setup and conditioning on a fixed serving distance (distance between a user and its nearest serving BS), we derive bounds on the outage probabilities of both macro and pico users. We also use a fitted Poisson cluster process to approximate the PHP, which is shown to provide a good approximation of the interference and outage statistics. In Case 2, the MBSs and the PBSs follow a PPP and an independent Matern cluster process, respectively. Explicit expressions of the interference and the outage probability are derived first for fixed serving distance, second with random distance, and we derive the outage performance, the per-user capacity and the area spectral efficiency (ASE) for both cases. It turns out that the proposed Case 2 model is a more appropriate and accurate model for a HCN with hotspot regions than the multi-tier independent PPP model since the latter underestimates some key performance metrics, such as the per-user capacity and the ASE, by a factor of 1.5 to 2. Overall, the two models proposed provide good tradeoffs between the accuracy, tractability, and practicability.","Title: Throughput analysis for wireless networks with full-duplex radios\nYear: 2014\n\nAbstract: This paper investigates the throughput for wireless network with full-duplex radios using stochastic geometry. Full-duplex (FD) radios can exchange data simultaneously with each other. On the other hand, the downside of FD transmission is that it will inevitably cause extra interference to the network compared to half-duplex (HD) transmission. In this paper, we focus on a wireless network of nodes with both HD and FD capabilities and derive and optimize the throughput in such a network. Our analytical result shows that if the network is adapting an ALOHA protocol, the maximal throughput is always achieved by scheduling all concurrently transmitting nodes to work in FD mode instead of a mixed FD/HD mode or HD mode regardless of the network configurations. Moreover, the throughput gain of using FD transmission over HD transmission is analytically lower and upper bounded.","Title: Asymptotic outage analysis of general motion-invariant Ad Hoc Networks\nYear: 2010\n\nAbstract: The outage analysis of networks with randomly distributed nodes has been mostly restricted to the case of Poisson networks, where the node locations form a homogeneous Poisson point process. In this paper, we show that in great generality, the outage probability, as a function of the density of interfering nodes ¿, approaches ¿¿¿ as ¿ goes to zero, where ¿ and ¿ are the spatial contention and the interference scaling exponent, respectively. Interestingly, ¿ is restricted to a small range of possible values: 1 ¿ ¿ ¿ ¿/2 for a path loss exponent ¿. We also prove that for ALOHA, ¿ = 1 irrespective of the point process properties, and we demonstrate how the upper bound ¿ = ¿/2 can be achieved.","Title: Analysis of the benefits of Superposition Coding in random wireless networks\nYear: 2010\n\nAbstract: Network-wide adoption of a multipacket transmission scheme such as Superposition Coding (SC) for local “one-to-many” communication results in mutually interfering “broadcast” clusters. We analyze the benefits of SC and traditional Frequency Division (FD) with this interference via a utility function that measures the rate of information transfer per unit area. In particular, we study transmitters forming a Poisson point process and using ALOHA for medium access. For a fixed bandwidth allocation, FD allows spatial reuse to be independently optimized for each frequency band. On the other hand, with SC for a fixed power allocation, the optimal spatial reuse depends on the relative contribution of each link to the utility function. Since optimal spatial reuse is a function of the network geometry, the gains provided by SC depend on the geometry of the receiver node placement.","Title: The delay-optimal number of hops in Poisson multi-hop networks\nYear: 2010\n\nAbstract: We study the delay and throughput in a wireless multihop network with sources that form a Poisson point process and relays which are placed equidistantly on the source-destination line. A combined TDMA/ALOHA MAC protocol with intra-route TDMA and inter-route ALOHA is employed. We give bounds on the delay-optimal number of hops and derive the asymptotic delay-throughput tradeoff as the source-destination distance R gets large. The delay includes both the service times and waiting times in the buffers of the typical route. One main finding is that when the transmission probability and number of hops are jointly optimized for minimum delay, the number of hops scales as R2/3 while the delay scales as R4over3.","Title: Guest Editorial: Special section on graph theory and its application in vehicular networking\nYear: 2013\n\nAbstract: The eight papers in this special section focus on graph theory and its application in vehicular networking.","Title: Random-Access Poisson Networks: Stability and Delay\nYear: 2010\n\nAbstract: We consider a Poisson network of sources, each with a destination at a given distance and a buffer of infinite capacity. Assuming independent Bernoulli arrivals, we characterize the stability region when one or two classes of users are present in the network. We then derive a fixed-point equation that determines the success probability of the typical source-destination link and evaluate the mean delay at each buffer.","Title: Joint channel/network coding for star networks\nYear: 2013\n\nAbstract: Channel coding alone is not sufficient to reliably transmit a message of finite length from a source to one or more destinations as in, e.g., file transfer. To ensure that no data is lost, it must be combined with rateless erasure correcting schemes on a higher layer, such as a time-division multiple access (TDMA) system paired with automatic repeat request (ARQ) or random linear network coding (RLNC). We consider binary channel coding on a binary symmetric channel (BSC) and q-ary RLNC for erasure correction in a star network, where Y sources send messages to each other with the help of a central relay. We focus on finite block lengths and compare the expected throughputs of RLNC and TDMA. For a total message length of K bits, which can be subdivided into blocks of smaller size prior to channel coding, we obtain the channel coding rate and the number of blocks that maximize the expected throughput of both RLNC and TDMA, and we find that TDMA is more throughput-efficient for small K and small q.","Title: Outage Probability of General Ad Hoc Networks in the High-Reliability Regime\nYear: 2010\n\nAbstract: Outage probabilities in wireless networks depend on various factors: the node distribution, the MAC scheme, and the models for path loss, fading, and transmission success. In prior work on outage characterization for networks with randomly placed nodes, most of the emphasis was put on networks whose nodes are Poisson-distributed and where ALOHA is used as the MAC protocol. In this paper, we provide a general framework for the analysis of outage probabilities in the high-reliability regime. The outage probability characterization is based on two parameters: the intrinsic spatial contention γ of the network, introduced by Haenggi in a previous work, and the coordination level achieved by the MAC as measured by the interference scaling exponent κ introduced in this paper. We study outage probabilities under the signal-to-interference ratio (SIR) model, Rayleigh fading, and power-law path loss and explain how the two parameters depend on the network model. The main result is that the outage probability approaches γηκ as the density of interferers η goes to zero, and that κ assumes values in the range 1 ≤ κ ≤ α/2 for all practical MAC protocols, where α is the path-loss exponent. This asymptotic expression is valid for all motion-invariant point processes. We suggest a novel and complete taxonomy of MAC protocols based mainly on the value of κ. Finally, our findings suggest a conjecture that bounds the outage probability for all interferer densities.","Title: Approximate SIR Analysis in General Heterogeneous Cellular Networks\nYear: 2016\n\nAbstract: The current cellular networks have evolved to be more randomly, irregularly, and heterogeneously deployed to meet the exponential growth mobile data traffic and the demand for seamless coverage, making the signal-to-interference ratio (SIR) distribution more challenging to analyze. Therefore, in this paper, we propose two simple approximative approaches to the SIR distribution general heterogeneous cellular networks (HCNs) based on the ASAPPP method, which stands for “approximate SIR analysis based on the Poisson point process” and the MISR (mean interference-to-signal ratio)-based gain for each individual tier the HCNs. Specifically, we first establish a per-tier ASAPPP approximation to general HCNs and then present an effective gain ASAPPP method as a further simplification when the path loss exponents are the same for all the tiers, that is, we give an explicit expression for the effective gain G eff of general HCNs such that the SIR distribution is obtained by scaling the SIR threshold θ to θ/G eff . The asymptotic behavior for the tail the SIR distribution is also given. Furthermore, to highlight the simplicity and effectiveness the approximative approaches, we derive the exact distribution the SIR in the two-tier HCNs modeled by β-Ginibre and Poisson point processes and compare it with the approximate results. The results demonstrate that the proposed approaches give a simple yet excellent approximation for the SIR distribution.","Title: Asymptotics and Approximation of the SIR Distribution in General Cellular Networks.\nYear: 2016\n\nAbstract: It has recently been observed that the SIR distributions of a variety of cellular network models and transmission techniques look very similar in shape. As a result, they are well approximated by a simple horizontal shift (or gain) of the distribution of the most tractable model, the Poisson point process (PPP). To study and explain this behavior, this paper focuses on general single-tier network ...","Title: The Meta Distribution of the SIR in Poisson Bipolar and! Cellular Networks.\nYear: 2016\n\nAbstract: The calculation of the SIR distribution at the typical receiver (or, equivalently, the success probability of transmissions over the typical link) in Poisson bipolar and cellular networks with Rayleigh fading is relatively straightforward, but it only provides limited information on the success probabilities of the individual links. This paper focuses on the meta distribution of the SIR, which is ...","Title: Throughput Analysis for Full-Duplex Wireless Networks with Imperfect Self-interference Cancellation.\nYear: 2015\n\nAbstract: This paper investigates the throughput for wireless network with full-duplex radios using stochastic geometry. Full-duplex (FD) radios can exchange data simultaneously with each other. On the other hand, the downside of FD transmission is that it will inevitably cause extra interference to the network compared to half-duplex (HD) transmission. Moreover, the residual self-interference has negative effects on the network throughput. In this paper, we focus on a wireless network of nodes with both HD and FD capabilities and derive and optimize the throughput in such a network. Our analytical result shows that if the network is adopting an ALOHA protocol, the maximal throughput is achieved by scheduling all concurrently transmitting nodes to work in either FD mode or HD mode depending on one simple condition. Moreover, the effects of imperfect self-interference cancellation on the signal-to-interference ratio (SIR) loss and throughput are also analyzed based on our mathematical model. We rigorously quantify the impact of imperfect self-interference cancellation on the throughput gain, transmission range, and other metrics, and we establish the minimum amount of self-interference suppression needed for FD to be beneficial.","Title: Prototype of Virtual Full Duplex via Rapid On-Off-Division Duplex\nYear: 2015\n\nAbstract: We design and implement a software-radio system for rapid on-off-division duplex (RODD), a scheme to achieve virtual full-duplex communication at the level of a transmission frame. RODD is based on the insight that it is not necessary to separate a node's transmission and reception at the timescale of a frame. Instead, RODD allows each node in a network to follow a different random on-off signaling signature to transmit during its own on-slots and listen to its neighbors through its own off-slots. Over one frame interval, each node broadcasts its message to its neighbors while recovering its neighbors' messages from the superposed signals received via its own off-slots. In this paper, we describe an RODD prototype to prove its key concepts, including redesigned synchronization and coding schemes. Simulation results are presented for comparison with the measurements obtained from a software-defined radio implementation of RODD. The effects of on-off signaling on the performance are investigated experimentally. In particular, the bit error rate is measured and compared with the simulation results. Our results indicate that virtual full duplex is feasible on a USRPs/LabVIEW platform via RODD.","Title: Scalable transmission over heterogenous networks\nYear: 2015\n\nAbstract: Transmission of layered source information, such as scalable video coding (SVC), over heterogenous wireless networks is considered in this work. Scalable transmission enables dynamic adaption of source information to the condition of user equipments, and thus is suitable for heterogenous networks in which the transmission link quality varies substantially. Leveraging tools in stochastic geometry, a comprehensive analysis is conducted for several different transmission protocols, focusing on two key performance metrics, Standard-Definition outage probability and High-Definition probability. The proposed transmission protocols are compared in different aspects, and the benefit of interference cancellation is shown to be significant.","Title: Joint Spatial and Propagation Models for Cellular Networks.\nYear: 2015\n\nAbstract: In all current models for cellular networks, independent randomness in the positions of the base stations (BSs) and the propagation conditions is assumed. In practice, however, in coverage-oriented deployments, where the goal is to achieve good baseline coverage, cellular operators place the base stations further apart if propagation is favorable, and vice versa. We thus propose a new class of cellular model, where BSs are deployed such that all cell edge users achieve a minimum target signal power level from the serving BS. The spatial structure of the BSs is a result of the propagation environment and the target signal power. We call such network models joint spatial and propagation (JSP) models. To formulate such models, we assume the path loss follows a power law with a variable path loss exponent, so that the target signal power is achieved at the cell edges, given the distribution of the BSs. The coverage probability, defined as the probability that the signal-to-interference-plus-noise-ratio (SINR) exceeds a threshold, is evaluated and compared with the standard Poisson and lattice models. Our results show that networks with Poisson distributed BSs appear to the user like lattice networks if the dependence between BS placement and propagation is accounted for.","Title: A Throughput-Optimum Adaptive ALOHA MAC Scheme for Full-Duplex Wireless Networks.\nYear: 2015\n\nAbstract: This paper proposes an adaptive and distributed MAC scheme for wireless networks with full-duplex radios. Full-duplex (FD) radios can exchange data simultaneously using the same frequency band and potentially double the throughput. On the other hand, it will inevitably cause extra interference to the network to use FD transmission instead of half-duplex (HD) transmission. Hence, there is a tradeoff between interference and throughput for wireless networks with FD radios. In order to mitigate the interference and maximize the network throughput, we propose the Optimum Adaptive ALOHA (OA-ALOHA) MAC scheme based on the nodes' local information about their neighbors. We derive each node's optimal transmit probability that maximizes the throughput and achieves proportional fairness. Our numerical results show that if the network is using the proposed adaptive MAC scheme, significantly better network throughput can be achieved than with the non-adaptive MAC schemes. HD can achieve around 50% maximum throughput gain using adaptive ALOHA while FD more than 70%. Moreover, a Simplified Adaptive ALOHA (SA-ALOHA) MAC scheme is also included that is based on an approximation of the optimum transmit probability and achieves comparable performance.","Title: A Simple Approximative Approach to the SIR Analysis in General Heterogeneous Cellular Networks.\nYear: 2015\n\nAbstract: The crushing demands for mobile data traffic drive the current cellular networks to become more heterogeneous, making the signal-to-interference ratio (SIR) distribution more difficult to analyze. In this paper we propose a simple approximative approach to the SIR distribution of heterogeneous cellular networks (HCNs) based on the ASAPPP method which stands for \"approximate SIR analysis based on the Poisson point process\" and the MISR (mean interference-to-signal ratio)-based gain for each individual tier of the HCN. The results demonstrate that this approach gives a tight approximation and asymptotically a lower bound for the coverage probability.","Title: Delay Analysis In Static Poisson Network\nYear: 2015\n\nAbstract: We investigate the delay of the discrete-time slotted ALOHA network where the sources are distributed as a Poisson point process. Each of the sources is paired with a destination at a given distance and a buffer of infinite capacity. The network is assumed to be static, i. e., the sources and the destinations are generated at first and remain static during all the time slots. Employing tools from queueing theory as well as point process theory, we obtain upper bounds and lower bounds for the cumulative distribution function (cdf) of the conditional mean delay. Numerical results show that the gap between the upper and lower bounds is small, and the results also reveal how these bounds vary with system parameters.","Title: On the Stability of Static Poisson Networks Under Random Access.\nYear: 2016\n\nAbstract: We investigate the stable packet arrival rate region of a discrete-time slotted random access network, where the sources are distributed as a Poisson point process. Each of the sources in the network has a destination at a given distance and a buffer of infinite capacity. The network is assumed to be random but static, i.e., the sources and the destinations are placed randomly and remain static du...","Title: The Gauss-Poisson Process for Wireless Networks and the Benefits of Cooperation.\nYear: 2016\n\nAbstract: Gauss-Poisson processes (GPPs) are a class of clustered point processes, which include the Poisson point process as a special case and have a simpler structure than the general Poisson cluster point processes. A key property of the GPP is that it is completely defined by its first- and second-order statistics. In this paper, we first show the properties of the GPP and provide an approach to fit th...","Title: Ergodic Spectral Efficiency in MIMO Cellular Networks.\nYear: 2017\n\nAbstract: This paper shows how the application of stochastic geometry to the analysis of wireless networks is greatly facilitated by: ( ${i}$ ) a clear separation of time scales; (ii) the abstraction of small-scale effects via ergodicity; and (iii) an interference model that reflects the receiver’s lack of knowledge of how each individual interference term is faded. These procedures render the analysis both more manageable and more precise, as well as more amenable to the incorporation of subsequent features. In particular, the paper presents analytical characterizations of the ergodic spectral efficiency of cellular networks with single-user multiple-input multiple-output and sectorization. These characterizations, in the form of easy-to-evaluate expressions, encompass the coverage, the distribution of spectral efficiency over the network locations, and the average thereof.","Title: A Stochastic Geometry Approach to the Modeling of DSRC for Vehicular Safety Communication.\nYear: 2016\n\nAbstract: Vehicle-to-vehicle safety communications based on the dedicated short-range communication technology have the potential to enable a set of applications that help avoid traffic accidents. The performance of these applications, largely affected by the reliability of communication links, stringently ties back to the MAC and PHY layer design, which has been standardized as IEEE 802.11p. The link relia...","Title: Sir Asymptotics In Poisson Cellular Networks Without Fading And With Partial Fading\nYear: 2016\n\nAbstract: In this paper, we consider cellular networks with the base station locations modeled by a Poisson point process. However, unlike earlier works, we consider the cases of no fading and partial fading and analyze the asymptotic behavior of the distribution of the downlink signal-to-interference ratio (SIR) of a typical user. This non-fading case has been elusive since the standard Laplace trick cannot be used. We provide the asymptotics of the SIR distribution F-SIR(theta) = P(SIR < theta) as theta -> 0 for the cases of no-fading and partial fading, where only the interfering base stations are subject to fading. We also introduce a new point process-the squared relative distance process-that facilitates the asymptotic analysis of the SIR and expedites simulations.","Title: Enhanced Cellular Coverage and Throughput Using Rateless Codes.\nYear: 2017\n\nAbstract: Rateless codes have been shown to provide robust error correction over a wide range of binary and noisy channels. Using a stochastic geometry model, this paper studies the performance of rateless codes in the cellular downlink and compares it with the performance of fixed-rate codes. For the case of Rayleigh fading, an accurate approximation is proposed for the distribution of the packet transmiss...","Title: Downlink Coordinated Joint Transmission for Mutual Information Accumulation.\nYear: 2017\n\nAbstract: In this letter, we propose a new coordinated multipoint technique based on mutual information (MI) accumulation using rateless codes. Using a stochastic geometry model for the cellular downlink, we quantify the performance enhancements in coverage probability and rate due to MI accumulation. By simulation and analysis, we show that MI accumulation using rateless codes leads to remarkable improvements in coverage and rate for general users and specific cell edge users.","Title: A Message From the New Editor-in-Chief.\nYear: 2017\n\nAbstract: In recent weeks, when someone learnt that I would be the next Editor-in-Chief (EiC) of the IEEE Transactions on Wireless Communications (TWC), they were often not sure whether to offer their congratulations or their condolences. Indeed, it is a significant responsibility to be the EiC of such a prestigious journal and to follow in the footsteps of my esteemed predecessors. At the same time, it is a great honor to be given the chance to serve our community in this role, and the journal is in such a splendid shape that I am gladly accepting the responsibility. The workload is certainly high but seems manageable, especially thanks to the creation of the Executive Editorial Committee (EEC) by the outgoing EiC Jeff Andrews. The main task of the EEC is to screen incoming papers for scope and quality and assign them to area editors. Taking the EiC out of the daily critical path is a transformative change in the editorial process that allows the EiC to focus more on strategic issues—and to spend several hours per day without having to constantly monitor the flow of incoming papers. I had the opportunity to serve as the inaugural chair of the EEC for 2.5 years, which was a rewarding experience and gave me considerable insight into the journal that I can now draw on as EiC. Also, in the role of EEC Chair, I had the chance to work closely with Jeff and witness firsthand the many improvements he made to the submission process and the journal in general. They include the removal of the requirement to submit both a single-column double-spaced and a double-column single-spaced version of the paper (which I am sure every author appreciates) and a coherent policy on submissions of papers that have partially been published at conferences, plus many others “behind the scenes.” Most of all, however, I am grateful to Jeff for the appointments he made to the editorial board. I am in the enviable position of getting to work with a fantastic group of Editors, Area Editors, and Executive Editorial Committee members, all among the top experts in their fields of specialization.","Title: SIR Asymptotics in General Network Models.\nYear: 2016\n\nAbstract: In the performance analyses of wireless networks, asymptotic quantities and properties often pro- vide useful results and insights. The asymptotic analyses become especially important when complete analytical expressions of the performance metrics of interest are not available, which is often the case if one departs from very specific modeling assumptions. In this paper, we consider the asymptotics of the SIR distribution in general wireless network models, including ad hoc and cellular networks, simple and non-simple point processes, and singular and bounded path loss models, for which, in most cases, finding analytical expressions of the complete SIR distribution seems hopeless. We show that the lower tails of the SIR distributions decay polynomially with the order solely determined by the path loss exponent or the fading parameter, while the upper tails decay exponentially, with the exception of cellular networks with singular path loss. In addition, we analyze the impact of the nearest interferer on the asymptotic properties of the SIR distributions, and we formulate three crisp conjectures that -if true- determine the asymptotic behavior in many cases based on the large-scale path loss properties of the desired signal and/or nearest interferer only.","Title: Coverage Analysis for Millimeter Wave Networks: The Impact of Directional Antenna Arrays.\nYear: 2017\n\nAbstract: Millimeter wave (mm-wave) communications is considered a promising technology for 5G networks. Exploiting beamforming gains with large-scale antenna arrays to combat the increased path loss at mm-wave bands is one of the defining features. However, previous works on mm-wave network analysis usually adopted oversimplified antenna patterns for tractability, which can lead to significant deviation fr...","Title: User Point Processes in Cellular Networks.\nYear: 2017\n\nAbstract: The point process of concurrent users is critical for the analysis of cellular systems, in particular for the uplink and for full-duplex communication. We analyze the properties of two popular models. For the first one, we provide an accurate characterization of the pair correlation functions from the user and the base station point of view, which are applied to approximate the user process by Poisson and Ginibre point processes. For the second model, which includes the first model asymptotically, we study the cell vacancy probability, the mean area of vacant and occupied cells, the user-base station distance, and the pair correlation function in lightly and heavily loaded regimes.","Title: The Meta Distribution of the SIR for Cellular Networks with Power Control.\nYear: 2018\n\nAbstract: The meta distribution of the signal-to-interference ratio (SIR) provides fine-grained information about the performance of individual links in a wireless network. This paper focuses on the analysis of the meta distribution of the SIR for both the cellular network uplink and downlink with fractional power control. For the uplink scenario, an approximation of the interfering user point process with ...","Title: Towards a Tractable Delay Analysis in Large Wireless Networks.\nYear: 2016\n\nAbstract: Meeting the diverse delay requirements of next-generation wireless communication networks is one of the most critical goals of wireless system design. Though the delay of point-to-point communications has been well investigated using classical queueing theory, the delay of multi-point to multi-point communications has not been explored in depth. The main technical difficulty lies in the interacting queues problem, in which the service rate is coupled with the statuses of other queues. In this article, we elaborate on the main challenges of delay analysis in large wireless networks. Several promising approaches to bypass these difficulties are proposed and summarized to provide useful guidance.","Title: Throughput Enhancements on Cellular Downlink Channels Using Rateless Codes.\nYear: 2016\n\nAbstract: Rateless codes have been shown to provide robust error correction over wireless channels. Using a stochastic geometry model, this paper studies the performance of cellular downlink with packet transmission based on rateless codes. For the case of Rayleigh fading, a novel and accurate approximation is proposed for the distribution of the packet transmission time of rateless codes. The performance of rateless codes is compared to that of fixed rate codes by evaluating the user rate and success probability achievable with the two channel coding schemes. Based on both the proposed analysis and network simulation, the paper shows that rateless coding provides both coverage and throughput gains relative to fixed rate coding, not only for the typical user but for all users in the cellular network.","Title: Analysis of D2D Underlaid Cellular Networks: SIR Meta Distribution and Mean Local Delay.\nYear: 2017\n\nAbstract: We study the performance of device-to-device (D2D) communication underlaying cellular wireless network in terms of the meta distribution of the signal-to-interference ratio (SIR), which is the distribution of the conditional SIR distribution given the locations of the wireless nodes. Modeling D2D transmitters and base stations as Poisson point processes (PPPs), moments of the conditional SIR distr...","Title: The Effect of Spatial Interference Correlation and Jamming on Secrecy in Cellular Networks.\nYear: 2017\n\nAbstract: Recent studies on secure wireless communication have shed light on a scenario where interference has a desirable impact on network performance. Particularly, assuming independent interference power fluctuations at the eavesdropper and the receiver, opportunistic secure information transfer can occur on the legitimate link. However, interference is spatially correlated due to the common set of interfering sources, which may diminish the opportunistic secure spectrum access (OSSA) probability. We study and quantify the effect of spatial interference correlation on OSSA in cellular-networks and investigate the potential of full-duplex jamming (FDJ) solutions. The results highlight the scenarios where FDJ improves OSSA performance.","Title: Spatial Outage Capacity Of Poisson Bipolar Networks\nYear: 2017\n\nAbstract: We introduce a new notion of capacity, termed spatial outage capacity (SOC), which is defined as the maximum density of concurrently active links that have a success probability greater than a predefined threshold. For Poisson bipolar networks, we provide exact analytical and approximate expressions for the density of concurrently active links satisfying an outage constraint. In the high-reliability regime, we obtain an exact closed-form expression of the SOC, which gives its asymptotic scaling behavior.","Title: A novel approach for spectral efficiency analysis in MIMO cellular networks\nYear: 2017\n\nAbstract: The application of stochastic geometry to the analysis of wireless networks is shown to be propelled by (i) a clean separation of time scales, (ii) abstraction of small-scale effects via ergodicity, and (iii) an interference model reflecting the receiver's lack of knowledge of how individual interference terms are faded. These steps render the analysis simpler and more precise, and more amenable to incorporating subsequent features. Specifically, the paper presents easy-to-evaluate expressions for the ergodic spectral efficiency of cellular networks with single-user multiple-input multiple-output (MIMO).","Title: A Fine-Grained Analysis of Millimeter-Wave Device-to-Device Networks.\nYear: 2017\n\nAbstract: Enabling device-to-device (D2D) communications in millimeter-wave (mm-wave) networks is of critical importance for the next-generation mobile networks to support very high data rates (multi-gigabits-per-second) for mobile devices. In this paper, we provide a fine-grained performance analysis of the mm-wave D2D communication networks. Specifically, we first establish a general and tractable framewo...","Title: Sentry Selection In Sensor Networks: Theory And Algorithms\nYear: 2017\n\nAbstract: We study the geometric aspects of the sentryselection problem for a sensor network with randomlydistributed sensors each of which covers a small disc. Theproblem is to partition the sensors into a predeterminednumber of groups, so that, for each group, the sensingregions of the sensors in that group together coverthe region of interest. After presenting some theoreticalresults, we include descriptions of two fast partitioningalgorithms, together with the results of simulating themon moderate-sized networks.","Title: The Spatial Outage Capacity of Wireless Networks.\nYear: 2018\n\nAbstract: We address a fundamental question in wireless networks that, surprisingly, has not been studied before: what is the maximum density of concurrently active links that satisfy a certain outage constraint? We call this quantity the spatial outage capacity (SOC), give a rigorous definition, and analyze it for Poisson bipolar networks with ALOHA. Specifically, we provide exact analytical and approximat...","Title: Toward a Tractable Delay Analysis in Ultra-Dense Networks.\nYear: 2017\n\nAbstract: Meeting the diverse delay requirements of emerging wireless applications is one of the most critical goals for the design of ultra-dense networks. Although the delay of point-to-point communications has been well investigated using classical queueing theory, the delay of multipoint- to-multipoint communications, such as in ultra-dense networks, has not been explored in depth. The main technical di...","Title: Traffic Allocation for Low-Latency Multi-Hop Millimeter-Wave Networks with Buffers.\nYear: 2017\n\nAbstract: For millimeter-wave (mm-wave) buffer-aided tandem networks consisting of relay nodes and multiple channels per hop, we consider two traffic allocation schemes, namely local allocation and global allocation, and investigate the end-to-end latency of a file transfer. We formulate the problem for generic multi-hop queuing systems and subsequently derive closed-form expressions of the end-to-end latency. We quantify the advantages of the global allocation scheme relative to its local allocation counterpart, and we conduct an asymptotic analysis on the performance gain when the number of channels in each hop increases to infinity. The traffic allocations and the analytical delay performance are validated through simulations. Furthermore, taking a specific two-hop mm-wave network as an example, we derive lower bounds on the average end-to-end latency, where Nakagami-$m$ fading is considered. Numerical results demonstrate that, compared to the local allocation scheme, the advantage of global allocation grows as the number of relay nodes increases, at the expense of higher complexity that linearly increases with the number of relay nodes. It is also demonstrated that a proper deployment of relay nodes in a linear mm-wave network plays an important role in reducing the average end-to-end latency, and the average latency decays as the mm-wave channels become more deterministic. These findings provide insights for designing multi-hop mm-wave networks with low end-to-end latency.","Title: The Meta Distribution of the SINR in mm-Wave D2D Networks.\nYear: 2017\n\nAbstract: In this paper, the meta distribution of the signal-to-interference-plus-noise ratio (SINR) in millimeter wave (mm-wave) device-to-device (D2D) networks is studied, which is the distribution of the conditional SINR distribution given a realization of the point process modeling the network. This analysis provides much more fine-grained information on the performance of individual links than just the mean that is usually evaluated. Modeling the D2D transmitters as a Poisson point process (PPP) and considering the unique mm-wave features, moments of the conditional SINR distribution given the point process are derived in order to calculate analytical expression for the meta distribution. It turns out that when the size of the antenna array tends to infinity, the noise is totally suppressed and the node density becomes the dominating factor affecting the interference in the network. Closed-form approximations of the meta distribution with the beta distribution are also provided. Numerical results give interesting insights into the effects of mmwave features on the performance of D2D communication.","Title: Spatial Point Process Modeling of Vehicles in Large and Small Cities.\nYear: 2017\n\nAbstract: The uncertainty in the locations of vehicles on streets induced by vehicles passing and queueing make the spatial modeling of vehicles a difficult task. To analyze the performance of vehicle-to-vehicle (V2V) communication for vehicular ad hoc networks (VANETs), accurate spatial modeling is of great importance. In this paper, we concentrate on spatial point process modeling for random vehicle locations in large and small cities, performing empirical experiments with real location data of mobile taxi trajectories recorded by the global positioning system (GPS) in Beijing city of China and Porto city of Portugal. We find that the empirical probability mass functions (PMFs) of the number of taxis in test sets in different regions of Beijing or in Porto all follow a negative binomial (NB) distribution. Based on the above, we show that the Log Gaussian Cox Process (LGCP) model, whose empirical PMF nicely fits the NB distribution, accurately characterizes diverse spatial point patterns of random vehicle location in both large and small cities. This is verified by the minimum contrast method. The LGCP model can be applied to analyze performance metrics (i.e., connectivity, coverage, and capacity) and optimize the practical deployments of VANETs.","Title: Reliability Analysis of V2V Communications on Orthogonal Street Systems.\nYear: 2017\n\nAbstract: The analysis of vehicle-to-vehicle communications is generally limited to vehicles on street segments, which are modeled using 1-D point processes. However, it is essential to model the intersections which are crucial for vehicle safety. In this paper, we focus on orthogonal street systems involving intersections with Poisson distributed vehicles on each street. We derive analytical expressions for the success probabilities of two types of users-the typical general user and the typical intersection user. We show that the the orthogonal street system shares some properties of both 1-D and 2-D Poisson networks. Specifically, the vehicles on the street system behave like 1-D and 2-D Poisson point processes of vehicles in the high-reliability and low-reliability regimes, respectively. Also, we deduce that the success probability of the typical general/intersection user is upper bounded by the minimum of the success probabilities of the 1-D and 2-D Poisson networks.","Title: On the SIR Meta Distribution for Poisson Networks With Interference Cancellation.\nYear: 2018\n\nAbstract: This letter presents a theoretical framework for the analysis of the meta distribution of the SIR for Poisson networks with interference cancellation (IC) enabled at the receivers, which gives deep insight into the network performance on a link-wise basis. A simple but insightful IC model named C-IC is studied for which the exact bth moment of the meta distribution and its beta distribution approximation and some useful bounds are validated. The conditions for the mean local delay to be finite are also stated. The results show that IC improves the performance not only in terms of the mean but also in terms of the variance of the meta distribution.","Title: The SIR Meta Distribution in Poisson Cellular Networks With Base Station Cooperation.\nYear: 2018\n\nAbstract: The meta distribution provides fine-grained information on the signal-to-interference ratio (SIR) compared with the SIR distribution at the typical user. This paper first derives the meta distribution of the SIR in heterogeneous cellular networks with downlink coordinated multipoint transmission/reception, including joint transmission (JT), dynamic point blanking (DPB), and dynamic point selection...","Title: Coherent Joint Transmission in Downlink Heterogeneous Cellular Networks.\nYear: 2018\n\nAbstract: The analysis of the success probability or signal-to-interference ratio (SIR) distribution for coherent joint transmission (JT) based on stochastic geometry is an open issue. In this letter, we study coherent JT in downlink heterogeneous cellular networks and provide an upper bound of the success probability for the general user and the worst-case user (cell-corner user), and an approximation for ...","Title: Downlink Non-Orthogonal Multiple Access (NOMA) in Poisson Networks.\nYear: 2019\n\nAbstract: A network model is considered, where Poisson distributed base stations transmit to \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$N$ </tex-math></inline-formula>\n power-domain non-orthogonal multiple access (NOMA) users (UEs) each that employ successive interference cancellation (SIC) for decoding. We propose three models for the clustering of NOMA UEs and consider two different ordering techniques for the NOMA UEs: mean signal power-based and instantaneous signal-to-intercell-interference-and-noise-ratio-based. For each technique, we present a signal-to-interference-and-noise ratio analysis for the coverage of the typical UE. We plot the rate region for the two-user case and show that neither ordering technique is consistently superior to the other. We propose two efficient algorithms for finding a feasible resource allocation that maximize the cell sum rate \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$\\mathcal {R}_{\\mathrm{ tot}}$ </tex-math></inline-formula>\n, for general \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$N$ </tex-math></inline-formula>\n, constrained to: 1) a minimum throughput \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$\\mathcal {T}$ </tex-math></inline-formula>\n for each UE, 2) identical throughput for all UEs. We show the existence of: 1) an optimum \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$N$ </tex-math></inline-formula>\n that maximizes the constrained \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$\\mathcal {R}_{\\mathrm{ tot}}$ </tex-math></inline-formula>\n given a set of network parameters and 2) a critical SIC level necessary for NOMA to outperform orthogonal multiple access. The results highlight the importance in choosing the network parameters \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$N$ </tex-math></inline-formula>\n, the constraints, and the ordering technique to balance the \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$\\mathcal {R}_{\\mathrm{ tot}}$ </tex-math></inline-formula>\n and fairness requirements. We also show that interference-aware UE clustering can significantly improve performance.","Title: A tunable base station cooperation scheme for poisson cellular networks\nYear: 2018\n\nAbstract: We propose a tunable location-dependent base station (BS) cooperation scheme by partitioning the plane into three regions: the cell centers, cell edges and cell corners. The area fraction of each region is tuned by the cooperation level γ ranging from 0 to 1. Depending on the region a user resides in, he/she receives no cooperation, two-BS cooperation or three-BS cooperation. Here, we use a Poisson point process (PPP) to model BS locations and study a non-coherent joint transmission scheme, i.e., selected BSs jointly serve one user in the absence of channel state information (CSI). For the proposed scheme, we examine its performance as a function of the cooperation level using tools from stochastic geometry. We derive an analytical expression for the signal-to-interference ratio (SIR) distribution and its approximation based on the asymptotic SIR gain, along with the characterization of the normalized spectral efficiency per BS. Our result suggests that the proposed scheme with a moderate cooperation level can improve the SIR performance while maintaining the normalized spectral efficiency.","Title: Efficient Calculation of Meta Distributions and the Performance of User Percentiles.\nYear: 2018\n\nAbstract: Meta distributions (MDs) are refined performance metrics in wireless networks modeled using point processes. While there is no known method to directly calculate MDs, the moments of the underlying conditional distributions (given the point process) can often be expressed in exact analytical form. The problem of finding the MD given the moments has several solutions, but the standard approaches are...","Title: Coverage analysis of finite cellular networks: A stochastic geometry approach\nYear: 2018\n\nAbstract: This paper develops a tractable modeling and analysis framework for finite cellular wireless networks using stochastic geometry. Defining finite homogeneous Poisson point processes to model the number and locations of access points in a confined region, we study the coverage probability for an arbitrarily-located reference user that is served by the closest access point. The distance distribution and the Laplace transform (LT) of the interference are derived. We also derive a closed-form lower bound on the LT of the interference. Our analyses reveal that a higher path loss exponent improves the coverage probability and that there is a location where the coverage probability is maximized.","Title: Traffic Allocation for Low-Latency Multi-Hop Networks With Buffers.\nYear: 2018\n\nAbstract: For buffer-aided tandem networks consisting of relay nodes and multiple channels per hop, we consider two traffic allocation schemes, namely local allocation and global allocation, and investigate the end-to-end latency of a file transfer. We formulate the problem for generic multi-hop queuing systems and subsequently derive closed-form expressions of the end-to-end latency. We quantify the advant...","Title: Millimeter-Wave Device-to-Device Networks With Heterogeneous Antenna Arrays.\nYear: 2018\n\nAbstract: Millimeter-wave (mm-wave) device-to-device (D2D) communication is considered one of the most promising enabling technologies to meet the demanding requirements of future networks. Previous works on mm-wave D2D network analysis mostly considered the case that all devices were equipped with exactly the same number of antennas, whereas real networks are more complicated due to the coexistence of dive...","Title: The Benefits of Hybrid Caching in Gauss-Poisson D2D Networks.\nYear: 2018\n\nAbstract: Device caching has recently been proposed as an efficient way to offload traffic from congested cellular networks. However, previous works usually ignore the fact that, in practice, the user device may not be willing to help others due to the limited battery capacity. In this paper, we introduce cooperation among the device-to-device (D2D) transmitters and propose two novel hybrid caching strategi...","Title: A Novel Approximate Antenna Pattern for Directional Antenna Arrays.\nYear: 2018\n\nAbstract: The antenna pattern model plays a key role in evaluating the performance benefits of directional antenna arrays. In this letter, we propose a novel antenna pattern, named multi-cosine antenna pattern, to approximate the actual antenna pattern, and apply it to millimeter wave (mm-wave) networks. To get deep insights on the role of directional antenna arrays in mm-wave networks, we derive exact expr...","Title: A Unified Framework for the Tractable Analysis of Multi-Antenna Wireless Networks.\nYear: 2018\n\nAbstract: Densifying networks and deploying more antennas at each access point are two principal ways to boost the capacity of wireless networks. However, the complicated distributions of the signal power and the accumulated interference power, largely induced by various space-time processing techniques, make it highly challenging to quantitatively characterize the performance of multi-antenna networks. In ...","Title: Vehicle Distributions in Large and Small Cities: Spatial Models and Applications.\nYear: 2018\n\nAbstract: Safety applications based on vehicle-to-vehicle (V2V) communication have become a major goal toward the intelligent transportation. The performance of the applications is mainly affected by the communication links, which in turn are governed by the topology characteristics of vehicular ad hoc networks (VANETs). To analyze the performance of V2V communication for VANETs, accurate spatial modeling i...","Title: Massive MIMO Forward Link Analysis for Cellular Networks.\nYear: 2018\n\nAbstract: This paper presents analytical expressions for the signal-to-interference ratio and the spectral efficiency in macrocellular networks with massive MIMO conjugate beamforming, both with a uniform and a channel-dependent power allocation. These expressions, which apply to very general network geometries, are asymptotic in the strength of the shadowing. Through Monte-Carlo simulation, we verify their...","Title: The Meta Distribution Of The Sinr And Rate In Heterogeneous Cellular Networks\nYear: 2018\n\nAbstract: This paper focuses on the meta distribution of the signal-to-interference-plus-noise ratio (SINR) and rate in heterogeneous cellular networks (HCNs) with multiple tiers of base stations, where disjoint frequency bands are allocated among tiers and users are associated with each tier with a biased average received power. The meta distribution provides a much sharper version of the \"SINR/rate performance\" than that merely considered at the typical user through spatial averaging, which gives deep insight into the impacts of heterogeneity, resource coordination, user association, etc., on the performance of individual users. Using tools of stochastic geometry, we develop a general and tractable framework for a fine-grained analysis for HCNs with joint resource partitioning and offloading. With it, we derive exact analytical expressions as well as their asymptotic behaviors for the overall and per tier moments of the conditional SINR and rate distribution given the point processes, based on which the exact meta distributions are given. We show that although the offloaded users suffer from SINR degradation, the rate performance of all individual users can be improved via load balancing in conjunction with appropriate resource partitioning.","Title: The Energy and Rate Meta Distributions in Wirelessly Powered D2D Networks\nYear: 2019\n\nAbstract: As a key enabling technology for truly sustainable operation of devices, wireless energy and information transfer (WEIT) has attracted significant attention in wireless communication networks. Previous works on WEIT network analysis mostly concentrated on the energy outage probability or the expectation of the transferred energy at the typical wirelessly powered device using stochastic geometry. These calculations are relatively straightforward, but they only provide limited information on the energy extracted by the individual devices. This paper considers a WEIT-enabled device-to-device (D2D) network with the ambient RF transmitters distributed according to a Poisson point process and focuses on the \n<italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">meta distribution</italic>\n of the transferred energy, which is the distribution of the conditional energy outage probability given the locations of the RF transmitters, to show what fraction of devices in the network satisfy the target energy outage constraint if the required transmission energy is given. Furthermore, we derive the meta distribution of the transmission rate under an energy outage constraint and introduce a new notion of transmission efficiency, termed \n<italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">wirelessly powered spatial transmission efficiency</italic>\n, which is defined as the density of concurrently active links that rely on the wireless energy transfer technique and satisfy a certain reliability constraint that has a rate greater than a predefined threshold. Our analysis provides insightful guidelines for the most efficient way to operate a WEIT-enabled self-sustainable D2D communication network.","Title: SIR Meta Distribution in Physical Layer Security with Interference Correlation.\nYear: 2018\n\nAbstract: The meta distribution of the signal-to-interference ratio (SIR) provides more fine-grained information about the link performance than the standard success probability. This paper studies the reliability in cellular networks with physical layer security constraints using the meta distribution. We consider a cellular network where the BSs are distributed as Possion point process. We provide the distribution of the opportunistic secure spectrum access probability when connection success and secrecy success occurs simultaneously, taking into account the interference correlation. We gain insights on the links reliability in the network under different security levels and the effect on the link reliability of the distance between legitimate user and eavesdropper.","Title: Bethe and M-Bethe Permanent Inequalities.\nYear: 2015\n\nAbstract: In [1], it was conjectured that the permanent of a P-lifting theta(up arrow P) of a matrix theta of degree M is less than or equal to the Mth power of the permanent perm(theta), i.e., perm(theta(up arrow P)) <= perm(theta)(M) and, consequently, that the degree-M Bethe permanent perm(M,B)(theta) of a matrix theta is less than or equal to the permanent perm(theta) of theta, i.e., perm(M,B)(theta) <= perm(theta). In this paper, we prove these related conjectures and show some properties of the permanent of block matrices that are lifts of a matrix. As a corollary, we obtain an alternative proof of the inequality perm(B)(theta) <= perm(theta) on the Bethe permanent of the base matrix theta, which, in contrast to the one given in [2], uses only the combinatorial definition of the Bethe-permanent. The results have implications in coding theory. Since a P-lifting corresponds to an M-graph cover and thus to a protograph-based LDPC code, the results may help explain the performance of these codes.","Title: Scalable Transmission Over Heterogeneous Networks: A Stochastic Geometry Analysis.\nYear: 2017\n\nAbstract: This paper focuses on the transmission of layered source information, such as scalable video coding, over heterogeneous cellular networks. Scalable transmission enables dynamic adaption of source information to the condition of user equipment (UE) devices and, thus, is suitable for cellular networks in which the transmission link quality substantially varies over space and time. Two novel transmis...","Title: Unique coverage in Boolean models.\nYear: 2016\n\nAbstract: Consider a wireless cellular network consisting of small, densely scattered base stations. A user u is uniquely covered by a base station b if u is the only user within distance r of b. This makes it possible to assign the user u to the base station b without interference from any other user u′. We investigate the maximum possible proportion of users who are uniquely covered. We solve this problem completely in one dimension and provide bounds, approximations and simulation results for the two-dimensional case.","Title: Stochastic Geometry Modeling and Analysis of Single- and Multi-Cluster Wireless Networks.\nYear: 2018\n\nAbstract: This paper develops a stochastic geometry-based approach for the modeling and analysis of single- and multi-cluster wireless networks. We first define finite homogeneous Poisson point processes to model the number and locations of the transmitters in a confined region as a single-cluster wireless network. We study the coverage probability for a reference receiver for two strategies; closest-select...","Title: Distributed Rate Control for High Reliability in Poisson Bipolar Networks.\nYear: 2017\n\nAbstract: Reliable communication is a key requirement in wireless networks. For ad hoc networks, satisfying this requirement is challenging due to the interference caused by uncoordinated concurrent transmissions. In this regard, we provide a simple distributed way for a transmitter to meet the target reliability in an interference-limited network. Specifically, for the Poisson bipolar network with Rayleigh fading, we propose a method for a transmitter to decide on its rate (or, equivalently, the signal-to-interference ratio threshold) such that each link in the network achieves a certain reliability. Here the distributed means that the transmitter only knows the distance from its receiver to the nearest interferer and the fading statistics. Based on this local information, we present a semi-heuristic approach to find the distribution of the signal-to-interference ratio (SIR) threshold corresponding to the total interference power from all interferers. For this purpose, we use the property of the interference that it follows a stable distribution for the standard path loss model. We show that the SIR threshold follows the Weibull distribution.","Title: SIR Meta Distribution of <inline-formula> <tex-math notation=\"LaTeX\">$K$ </tex-math></inline-formula>-Tier Downlink Heterogeneous Cellular Networks With Cell Range Expansion\nYear: 2018\n\nAbstract: Heterogeneous cellular networks (HCNs) constitute a necessary step in the evolution of cellular networks. In this paper, we apply the signal-to-interference ratio (SIR) meta distribution framework for a refined SIR performance analysis of HCNs, focusing on \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$K$ </tex-math></inline-formula>\n-tier heterogeneous cellular networks based on the homogeneous independent Poisson point process (PPP) model, with range expansion bias (offloading bias) in each tier. Expressions for the \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$b$ </tex-math></inline-formula>\n-th moment of the conditional success probability for both the entire network and each tier are derived, based on which the exact meta distributions and the beta approximations are evaluated and compared. Key performance metrics, including the mean success probability, the variance of the conditional success probability, the mean local delay, and the asymptotic SIR gains of each tier are obtained. The results show that the biases are detrimental to the overall mean success probability of the whole network and that the \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$b$ </tex-math></inline-formula>\n-th moment curve of the conditional success probability of each tier can be tightly approximated by the horizontal shifted versions of the first moment curve of the single-tier PPP network. We also provide lower bounds for the region of the active probabilities of the base stations to keep the mean local delay of each tier finite.","Title: Distribution of the Number of Users per Base Station in Cellular Networks.\nYear: 2019\n\nAbstract: We consider the number of users associating with each base station in a cellular network. Extending and unifying the characterizations for certain settings available in the literature, we derive a result that is asymptotic in the strength of the shadowing, yet otherwise universally valid: it holds for every network geometry and shadowing distribution. We then illustrate how this result provides ex...","Title: Simple Approximations of the SIR Meta Distribution in General Cellular Networks.\nYear: 2019\n\nAbstract: Compared to the standard success (coverage) probability, the meta distribution of the signal-to-interference ratio (SIR) provides much more fine-grained information about the network performance. We consider general heterogeneous cellular networks (HCNs) with base station tiers modeled by arbitrary stationary and ergodic non-Poisson point processes. The exact analysis of non-Poisson network models is notoriously difficult, even in terms of the standard success probability, let alone the meta distribution. Hence, we propose a simple approach to approximate the SIR meta distribution for non-Poisson networks based on the ASAPPP (“approximate SIR analysis based on the Poisson point process”) method. We prove that the asymptotic horizontal gap \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$G_{0}$ </tex-math></inline-formula>\n between its standard success probability and that for the Poisson point process exactly characterizes the gap between the \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$b$ </tex-math></inline-formula>\nth moment of the conditional success probability, as the SIR threshold goes to 0. The gap \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$G_{0}$ </tex-math></inline-formula>\n allows two simple approximations of the meta distribution for general HCNs: 1) the \n<italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">per-tier</italic>\n approximation by applying the shift \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$G_{0}$ </tex-math></inline-formula>\n to each tier and 2) the \n<italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">effective gain</italic>\n approximation by directly shifting the meta distribution for the homogeneous independent Poisson network. Given the generality of the model considered and the fine-grained nature of the meta distribution, these approximations work surprisingly well.","Title: Per-link Reliability and Rate Control: Two Facets of the SIR Meta Distribution.\nYear: 2019\n\nAbstract: The meta distribution (MD) of the signal-to-interference ratio (SIR) provides fine-grained reliability performance in wireless networks modeled by point processes. In particular, for an ergodic point process, the SIR MD yields the distribution of the per-link reliability for a target SIR. Here we reveal that the SIR MD has a second important application, which is rate control. Specifically, we calculate the distribution of the SIR threshold (equivalently, the distribution of the transmission rate) that guarantees each link a target reliability and show its connection to the distribution of the per-link reliability. This connection also permits an approximate calculation of the SIR MD when only partial (local) information about the underlying point process is available.","Title: On the Location-Dependent SIR Gain in Cellular Networks\nYear: 2019\n\nAbstract: In wireless networks, the distances from a user to its desired transmitters and undesired interferers play a critical role in its channel quality. In this letter, we study this location-dependence in a cellular network, where a user is always served by its nearest base station. For any stationary and ergodic base station process, we partition its associated Voronoi cells into the cell centers and ...","Title: SINR and Rate Meta Distributions for HCNs with Joint Spectrum Allocation and Offloading\nYear: 2019\n\nAbstract: This paper focuses on the meta distributions of the signal-to-interference-plus-noise ratio (SINR) and user-perceived rate in heterogeneous cellular networks (HCNs) with multiple tiers of base stations. On the one hand, it is desirable to offload users to small cells to alleviate the congestion in macrocells; on the other hand, such offloading would in turn cause an SINR degradation of the offload...","Title: A Location-Dependent Base Station Cooperation Scheme for Cellular Networks\nYear: 2019\n\nAbstract: The link quality in cellular networks strongly depends on the location of the users relative to the serving and interfering base stations (BSs). This paper proposes a location-dependent BS cooperation scheme for general cellular networks, where BSs are modeled using a stationary point process and the Voronoi diagram forms the cell structure. The cooperation scheme is based on the relative average received signal strength from the three strongest BSs. For the channel model where Rayleigh fading and power-law path loss are considered, each cell is partitioned into three regions based on the relative distance to the three nearest BSs: the cell center region, cell edge region, and cell corner region. The area fraction of each region is tuned by the so-called cooperation level \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$\\gamma \\in [ {0},{1}]$ </tex-math></inline-formula>\n. We study the scheme where users in the above regions receive the non-coherent joint transmission from one, two, and three nearest BSs, respectively. As such, the scheme primarily helps users vulnerable to interference. We analyze the signal-to-interference ratio (SIR) in Poisson networks and show that a moderate \n<inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"> <tex-math notation=\"LaTeX\">$\\gamma $ </tex-math></inline-formula>\n jointly improves the average SIR performance and the network fairness.","Title: SIR Meta Distribution for Spatiotemporal Cooperation in Poisson Cellular Networks\nYear: 2019\n\nAbstract: The meta-distribution provides more fine-grained information on the signal-to-interference ratio (SIR) compared to the SIR distribution at the typical user. This paper studies the SIR meta-distribution in heterogeneous cellular networks (HCNs) for JT-DPB with two types of hybrid automatic repeat request (HARQ) techniques, where JT-DPB is a general scheme for base station cooperation that combines joint transmission (JT) and dynamic point blanking (DPB). This paper also considers two types of typical users-the general user and the worst-case user (the typical user located at the Voronoi vertex in a single-tier network). For the Type-II HARQ, a simplified network model is proposed, which leads to a tractable and accurate approach for the SIR meta-distribution analysis. The analytical results for the Type-I HARQ and Type-II HARQ are presented.","Title: Energy Correlation Coefficient in Wirelessly Powered Networks with Energy Beamforming\nYear: 2021\n\nAbstract: The spatial correlation of the energy harvested from RF transmitters, named energy correlation, plays a key role in the performance evaluation of wirelessly powered networks. This paper introduces an analytical framework with foundations in stochastic geometry to characterize the energy correlation based on the energy correlation coefficient (ECC). For a model where RI: power sources are distributed according to a Poisson point process and employ beamforming techniques to transfer energy directionally, we focus on two cases: i) each power source points the beam in a random direction; ii) each power source points the beam to an RF-powered node located in its Voronoi cell. We first analyze the ECC, of the two cases, and then give the asymptotic results with respect to the antenna array size. It turns out that in both cases the harvested energy at two locations exhibit positive correlation, and when the antenna array size tends to infinity, the correlation in the first case vanishes while the one in the second case is still positive. Numerical results give useful insights into the effect of several system parameters on the energy correlation from directed wireless energy transfer.","Title: The Transdimensional Poisson Process for Vehicular Network Analysis\nYear: 2021\n\nAbstract: A comprehensive vehicular network analysis requires modeling the street system and vehicle locations. Even when Poisson point processes (PPPs) are used to model the vehicle locations on each street, the analysis is barely tractable. That holds for even a simple average-based performance metric—the success probability, which is a special case of the fine-grained metric, the meta distribution (MD) o...","Title: The Energy Correlation Coefficient and its Key Role in Wirelessly Powered Networks\nYear: 2021\n\nAbstract: Energy correlation critically affects the performance of a wirelessly powered network due to its key effect on the spatial distribution of concurrent RF-powered transmitters. This paper introduces a powerful analytical framework with foundations in stochastic geometry to characterize the energy correlation in a general wirelessly powered network. Unlike the commonly used pair correlation function ...","Title: Performance Analysis of Inter-Cell Interference Coordination in mm-Wave Cellular Networks\nYear: 2022\n\nAbstract: In millimeter-wave (mm-wave) cellular networks, directional antenna arrays are typically adopted to mitigate the severe propagation loss. However, the interference caused by such highly directional beams may, in turn, result in a significant number of transmission failures, especially for dense networks. To tackle this problem, we propose two inter-cell interference coordination (ICIC) schemes in mm-wave bands: one is merely based on the path loss incorporating the blockage effect (PL-ICIC); the other considers both path loss and directivity gain (PG-ICIC). To fully investigate both schemes, we first derive an exact expression for the success probability (reliability) of the typical (served) user. We further provide an asymptotic analysis for the success probability and propose an effective approximation based on the asymptotic signal-to-interference ratio (SIR) gain relative to no ICIC. Secondly, to incorporate the cost of ICIC schemes, we derive the approximate normalized throughput taking into account that some users cannot be served due to limited resources. Numerical results show that the two proposed schemes provide significant reliability improvements in the low-SIR regime, and the higher the number of antennas, the wider the SIR range for which there is an improvement. In addition, compared with PL-ICIC, PG-ICIC balances the available resources among all users well.","Title: Directivity in RF Sensor Networks for Widespread Spectrum Monitoring\nYear: 2022\n\nAbstract: Widespread RF spectrum monitoring could enable data-driven modeling of spectrum usage, enhance spectral utilization, and help automate policy enforcement. Previous works in wireless sensor networks offer design insights for RF sensors, but they assume emitters that radiate omnidirectionally. This paper develops a new framework for directional sensors and emitters, which are increasingly common with the growth of millimeter wave technologies. We focus on two-dimensional random sensor deployments modeled as Poisson point processes. Specifically, we determine the probability that a sensor network detects a single emitter for a channel model including path loss, fading, and the directivity of emitters and sensors with random orientations and locations. Our results suggest that with a path loss exponent of 4, quartering the emitter half-power beamwidth doubles the required average sensor density. We also conclude that omnidirectional sensors optimize detection probability. For multiple emitters, we develop a lower bound on the probability of multi-emitter detection and find the average number of undetected emitters. Finally, assuming higher sensor quality results in higher sensor cost, we consider a fixed-budget deployment and observe that decreasing the individual sensor cost by a decade and therefore increasing the quantity of sensors reduces the missed detection probability by about a decade."],"string":"[\n \"Title: ALOHA performs optimal power control in Poisson networks\\nYear: 2012\\n\\nAbstract: This paper studies power control strategies in interference-limited wireless networks with Poisson distributed nodes. We focus on the case where each transmitter knows its distance to its desired receiver but does not know the topology of the rest of the network. We study three sets of strategies: the single-node optimal power control (SNOPC) strategy, the Nash equilibrium power control (NEPC) strategy, and the globally optimal power control (GOPC) strategy. SNOPC strategies maximize the expected throughput of the power controllable link given that all the other transmitters do not use power control. Under NEPC strategies, no individual node of the network can achieve a higher expected throughput by unilaterally deviating from these strategies. The GOPC strategy maximizes the throughput of a typical node in the network. This paper shows that under mean and peak power constraints at each transmitter, all of the three strategies are ALOHA-type random on-off power control policies in bipolar networks. For links of iid random distances, we show both SNOPC and NEPC strategies are ALOHA-type random on-off policies. These results suggest that ALOHA can be viewed not only as a MAC scheme but also as an efficient and stable power control scheme.\",\n \"Title: The TASEP: A Statistical Mechanics Tool to Study the Performance of Wireless Line Networks\\nYear: 2010\\n\\nAbstract: We consider a multihop wireless line network with a single unidirectional data flow and show that by limiting the buffer sizes at the relay nodes to unity, the flow of traffic in the system can be efficiently regulated in a completely distributed fashion. Upon exerting this simple transmission policy, we find that the transport of packets in the wireless network is analogous to the flow of particles in the totally asymmetric simple exclusion process (TASEP). Using existing results from statistical mechanics, we characterize the end-to-end delay and throughput performance of multihop wireless line networks for two different channel access schemes. Additionally, we apply our findings towards the design of long networks. This paper also aims at promoting the TASEP as a powerful tool for analyzing the performance of ad hoc networks.\",\n \"Title: Spatial and temporal correlation of the interference in ALOHA ad hoc networks\\nYear: 2009\\n\\nAbstract: Interference is a main limiting factor of the performance of a wireless ad hoc network. The temporal and the spatial correlation of the interference makes the outages correlated temporally (important for retransmissions) and spatially correlated (important for routing). In this letter we quantify the temporal and spatial correlation of the interference in a wireless ad hoc network whose nodes are distributed as a Poisson point process on the plane when ALOHA is used as the multiple-access scheme.\",\n \"Title: Interference in Lattice Networks\\nYear: 2010\\n\\nAbstract: Lattices are important as models for the node locations in wireless networks\\nfor two main reasons: (1) When network designers have control over the\\nplacement of the nodes, they often prefer a regular arrangement in a lattice\\nfor coverage and interference reasons. (2) If nodes are randomly distributed or\\nmobile, good channel access schemes ensure that concurrent transmitters are\\nregularly spaced, hence the locations of the transmitting nodes are well\\napproximated by a lattice. In this paper, we introduce general interference\\nbounding techniques that permit the derivation of tight closed-form upper and\\nlower bounds for all lattice networks, and we present and analyze optimum or\\nnear-optimum channel access schemes for one-dimensional, square, and triangular\\nlattices.\",\n \"Title: ALOHA Performs Delay-Optimum Power Control\\nYear: 2011\\n\\nAbstract: As a fundamental source of delay in wireless networks, the local delay is defined as the mean time, in number of time slots, until a packet is successfully received (decoded) over a link. This paper shows that with mean power and peak power constraints at each node, power control can significantly reduce the local delay. We show that, for links with Rayleigh fading and random length, there exists a simple power control strategy, which turns out to be optimal in reducing the local delay. This strategy acts as an ALOHA-type random on-off power control policy whose parameters depend on the link distance. The optimal power control policy as well as its variations are compared with constant power transmission and other simple random power control policies.\",\n \"Title: Distance Distributions in Finite Uniformly Random Networks: Theory and Applications\\nYear: 2010\\n\\nAbstract: In wireless networks, knowledge of internode distances is essential for performance analysis and protocol design. When determining distance distributions in random networks, the underlying nodal arrangement is almost universally taken to be a stationary Poisson point process. While this may be a good approximation in some cases, there are also certain shortcomings to this model, such as the fact that, in practical networks, the number of nodes in disjoint areas is not independent. This paper considers a more-realistic network model where a known and fixed number of nodes are independently distributed in a given region and characterizes the distribution of the Euclidean internode distances. The key finding is that, when the nodes are uniformly randomly placed inside a ball of arbitrary dimensions, the probability density function (pdf) of the internode distances follows a generalized beta distribution. This result is applied to study wireless network characteristics such as energy consumption, interference, outage, and connectivity.\",\n \"Title: The Local Delay in Mobile Poisson Networks\\nYear: 2013\\n\\nAbstract: For communication between two neighboring nodes in wireless networks, the local delay is defined as the time it takes a node to successfully transmit a packet. Previous research focuses on the local delay in static or infinitely mobile Poisson networks with ALOHA. In this paper, we extend the local delay results to Poisson networks with finite mobility. The results obtained show that mobility helps reduce the local delay. Bounds of the local delay in mobile Poisson networks are derived for different mobility and transmission models. The phase transition that marks the jump of the local delay from finite to infinite is also characterized.\",\n \"Title: Optimizing spatial reuse by dynamic power control.\\nYear: 2012\\n\\nAbstract: This paper first presents a geometric analysis of the convergence condition for the Foschini-Miljanic power control algorithm. Then, based on the analysis, the Dynamic Distributed Power Control MAC ((DPC)-P-2-MAC) scheme is proposed for wireless networks. (DPC)-P-2-MAC achieves high spatial reuse, since power control enables nesting of concurrent links, thereby achieving a high density of successful links. The MAC scheme starts by trying to accommodate all links and then eliminating transmitters causing too much interference in two stages, a local stage and a global stage. Both stages operate in a fully distributed manner. Simulation results confirm the expected gains relative to standard MAC schemes: the spatial density of successful links is increased by about a factor of 4 compared to CSMA and about a factor of 8 compared to ALOHA.\",\n \"Title: Throughput versus routing overhead in large ad hoc networks\\nYear: 2008\\n\\nAbstract: Consider a wireless ad hoc network with n nodes distributed uniformly on [0, 1]2. The transport capacity (TC) of such a wireless network scales like √n. To achieve this, each node should serve about √n distinct information flows. So the routing table of each node should be of the order √n bits. We show that if the size of the routing table is restricted to be of the order O(nH(δ)), the maximum achievable pernode TC is O(nR(δ)) when the source-destination distance is n-δ/2. We show that R(δ) = min {1/2, δ/2 + H(δ)} - 1 is the optimal tradeoff.\",\n \"Title: Geometry, Connectivity, and Broadcast Transport Capacity of Random Networks with Fading\\nYear: 2007\\n\\nAbstract: In ad hoc networks with random node distribution, the underlying point process model and the channel fading process are usually considered separately. We provide a unified framework for the geometric characterization of fading by incorporating the fading process into the point process model. Concretely, assuming nodes are distributed in a stationary Poisson point process in Rd, we analyze the properties of the point processes that describe the path loss with fading. Applications include connectivity and broadcasting.\",\n \"Title: Multipath fading in wireless sensor networks: measurements and interpretation\\nYear: 2006\\n\\nAbstract: Multipath fading heavily contributes to the unreliability of wireless links, causing fairly large deviations from link quality predictions based on path loss models; its impact on wireless sensor networks is considerable. Although analytical models provide a probabilistic description, multipath fading is a deterministic phenomenon. Moreover, in the case of static nodes, fading is time-invariant. We illustrate its spatial nature with experimental evidence obtained using lower-end sensing node hardware. We also show the limitations of the supposed immunity of wideband radios to multipath fading in indoor deployments.\",\n \"Title: Bandwidth- and power-efficient routing in linear wireless networks\\nYear: 2006\\n\\nAbstract: The goal of this paper is to establish which practical routing schemes for wireless networks are most suitable for power-limited and bandwidth-limited communication regimes. We regard channel state information (CSI) at the receiver and point-to-point capacity-achieving codes for the additive white Gaussian noise (AWGN) channel as practical features, interference cancellation (IC) as possible, but less practical, and synchronous cooperation (CSI at the transmitters) as impractical. We consider a communication network with a single source node, a single destination node, and N-1 intermediate nodes placed equidistantly on a line between them. We analyze the minimum total transmit power needed to achieve a desired end-to-end rate for several schemes and demonstrate that multihop communication with spatial reuse performs very well in the power-limited regime, even without IC. However, within a class of schemes not performing IC, single-hop transmission (directly from source to destination) is more suitable for the bandwidth-limited regime, especially when higher spectral efficiencies are required. At such higher spectral efficiencies, the gap between single-hop and multihop can be closed by employing IC, and we present a scheme based upon backward decoding that can remove all interference from the multihop system with an arbitrarily small rate loss. This new scheme is also used to demonstrate that rates of O(log N) are achievable over linear wireless networks even without synchronous cooperation.\",\n \"Title: A practical approach to strengthen vulnerable downlinks using superposition coding.\\nYear: 2012\\n\\nAbstract: We propose and experimentally demonstrate a novel approach to improve the packet delivery efficiency on a vulnerable downlink (e. g., from a transmitter to a far-away receiver) using superposition coding, a multiuser transmission scheme that forgoes orthogonal transmission and deliberately introduces interference among signals at the transmitter. On a software-radio platform that uses off-the-shelf point-to-point channel codes, we show that a transmitter serving multiple links can use simple two-user superposition codes to dramatically improve (compared to time division multiplexing) the packet delivery efficiency on its most vulnerable links. Interestingly, our results suggest that superposing signals of far-away users on to those of high-traffic users yields the maximum benefits-implying that the degrees-of-freedom gain in doing so can more than compensate for the increased interference from signal superposition.\",\n \"Title: Mobility and Fading: Two Sides of the Same Coin\\nYear: 2010\\n\\nAbstract: In wireless networks, distance variations caused by node mobility generate fluctuations of the channel gains. Such fluctuations can be treated as another type of fading besides multi-path effects. In this paper, we characterize the interference statistics in mobile random networks by mapping the distance variations of mobile nodes to the channel gain fluctuations. Network performance is evaluated in terms of the outage probability. A nearest-interferer approximation is employed. This approximation provides a tight lower bound on the outage probability. Comparing to a static network, we show that the interference distribution does not change under high mobility and random walk models, but random waypoint mobility increases interference.\",\n \"Title: A primer on spatial modeling and analysis in wireless networks\\nYear: 2010\\n\\nAbstract: The performance of wireless networks depends critically on their spatial configuration, because received signal power and interference depend critically on the distances between numerous transmitters and receivers. This is particularly true in emerging network paradigms that may include femtocells, hotspots, relays, white space harvesters, and meshing approaches, which are often overlaid with traditional cellular networks. These heterogeneous approaches to providing high-capacity network access are characterized by randomly located nodes, irregularly deployed infrastructure, and uncertain spatial configurations due to factors like mobility and unplanned user-installed access points. This major shift is just beginning, and it requires new design approaches that are robust to spatial randomness, just as wireless links have long been designed to be robust to fading. The objective of this article is to illustrate the power of spatial models and analytical techniques in the design of wireless networks, and to provide an entry-level tutorial.\",\n \"Title: Simplified Analysis And Design Of Mimo Ad Hoc Networks\\nYear: 2006\\n\\nAbstract: The simple, yet powerful concept of an \\\"erristor\\\" and its \\\"erristance\\\" has recently been introduced for ad hoc networks and applied to scenarios such as retransmission (time diversity), path diversity, or a combination thereof. We extend this formalism to the case of spatial diversity, realized by employing multiple antennas at each node. Based on this framework, one can efficiently analyze and design Rayleigh-faded MIMO ad hoc networks that employ selection combining. The mathematically tractable definition of the erristor term greatly simplifies the study of a multiple-antenna network and helps solve problems based on end-to-end reliability or resource allocation easily, which we illustrate in an example. Moreover, this technique demonstrates the superiority in performance of MIMO over single-antenna routing schemes, particularly at high SNR.\",\n \"Title: A Study of the Correlations Between Channel and Traffic Statistics in Multihop Networks\\nYear: 2007\\n\\nAbstract: This paper investigates the correlations between traffic statistics and channel qualities and their impact on the performance of multihop networks. The physical channel is characterized by the probability of successful reception, which depends on the multiple access interference (MAI). Since a node contributes to interference only if it has a nonempty buffer upon being scheduled, the MAI is determ...\",\n \"Title: Distributed sensor networks: a cellular nonlinear network perspective.\\nYear: 2003\\n\\nAbstract: Large-scale networks of integrated wireless sensors become increasingly tractable. Advances in hardware technology and engineering design have led to dramatic reductions in size, power consumption, and cost for digital circuitry, and wireless communications. Networking, self-organization, and distributed operation are crucial ingredients to harness the sensing, computing, and computational capabilities of the nodes into a complete system. This article shows that those networks can be considered as cellular nonlinear networks (CNNs), and that their analysis and design may greatly benefit from the rich theoretical results available for CNNs.\",\n \"Title: A Statistical Mechanics-Based Framework to Analyze Ad Hoc Networks with Random Access\\nYear: 2012\\n\\nAbstract: Characterizing the performance of ad hoc networks is one of the most intricate open challenges; conventional ideas based on information-theoretic techniques and inequalities have not yet been able to successfully tackle this problem in its generality. Motivated thus, we promote the totally asymmetric simple exclusion process (TASEP), a particle flow model in statistical mechanics, as a useful analytical tool to study ad hoc networks with random access. Employing the TASEP framework, we first investigate the average end-to-end delay and throughput performance of a linear multihop flow of packets. Additionally, we analytically derive the distribution of delays incurred by packets at each node, as well as the joint distributions of the delays across adjacent hops along the flow. We then consider more complex wireless network models comprising intersecting flows, and propose the partial mean-field approximation (PMFA), a method that helps tightly approximate the throughput performance of the system. We finally demonstrate via a simple example that the PMFA procedure is quite general in that it may be used to accurately evaluate the performance of ad hoc networks with arbitrary topologies.\",\n \"Title: Managing Interference Correlation Through Random Medium Access\\nYear: 2014\\n\\nAbstract: The capacity of wireless networks is fundamentally limited by interference. However, little research has focused on the interference correlation, which may greatly increase the local delay (namely the number of time slots required for a node to successfully transmit a packet). This paper focuses on the question whether increasing randomness in the MAC, specifically frequency-hopping multiple access (FHMA) and ALOHA, helps to reduce the effect of interference correlation. We derive closed-form results for the mean and variance of the local delay for the two MAC protocols and evaluate the optimal parameters that minimize the mean local delay. Based on the optimal parameters, we identify two operating regimes, the correlation-limited regime and the bandwidth-limited regime. Our results reveal that while the mean local delays for FHMA with N sub-bands and for ALOHA with transmit probability p essentially coincide when p=1/N, a fundamental discrepancy exists between their variances. We also discuss implications from the analysis, including an interesting mean delay-jitter tradeoff, and convenient bounds on the tail probability of the local delay, which shed useful insights into system design.\",\n \"Title: Dynamic connectivity and path formation time in Poisson networks.\\nYear: 2014\\n\\nAbstract: The connectivity of wireless networks is commonly analyzed using static geometric graphs. However, with half-duplex radios and due to interference, static or instantaneous connectivity cannot be achieved. It is not necessary, either, since packets take multiple time slots to propagate through the network. For example, if a packet traverses a link in one time slot, it is irrelevant if the next link is available in that time slot also, but it is relevant if the next hop exists in the next time slot. To account for half-duplex constraints and the dynamic changes in the transmitting set of nodes due to MAC scheduling and traffic loads, we introduce a random multi-digraph that captures the evolution of the network connectivity in a dynamic fashion. To obtain concrete results, we focus on Poisson networks, where transmitters form a Poisson point process on the plane at all time instants. We first provide analytical results for the degree distribution of the graph and derive the distributional properties of the end-to-end connection delay using techniques from first-passage percolation and epidemic processes. Next, we prove that under some assumptions, the delay scales linearly with the source–destination distance even in the presence of interference. We also provide simulation results in support of the theoretical results.\",\n \"Title: Throughput analysis of fading sensor networks with regular and random topologies\\nYear: 2005\\n\\nAbstract: We present closed-form expressions of the average link throughput for sensor networks with a slotted ALOHA MAC protocol in Rayleigh fading channels. We compare networks with three regular topologies in terms of throughput, transmit efficiency, and transport capacity. In particular, for square lattice networks, we present a sensitivity analysis of the maximum throughput and the optimum transmit probability with respect to the signal-to-interference ratio threshold. For random networks with nodes distributed according to a two-dimensional Poisson point process, the average throughput is analytically characterized and numerically evaluated. It turns out that although regular networks have an only slightly higher average link throughput than random networks for the same link distance, regular topologies have a significant benefit when the end-to-end throughput in multihop connections is considered.\",\n \"Title: A Versatile Dependent Model for Heterogeneous Cellular Networks\\nYear: 2013\\n\\nAbstract: We propose a new model for heterogeneous cellular networks that incorporates dependencies between the layers. In particular, it places lower-tier base stations at locations that are poorly covered by the macrocells, and it includes a small-cell model for the case where the goal is to enhance network capacity.\",\n \"Title: Interference and Outage in Mobile Random Networks: Expectation, Distribution, and Correlation\\nYear: 2014\\n\\nAbstract: In mobile networks, distance variations caused by node mobility generate fluctuations in the channel gains. Such fluctuations can be treated as another type of fading besides multipath effects. In this paper, the interference statistics in mobile random networks are characterized by incorporating the distance variations of mobile nodes to the channel gain fluctuations. The mean interference is calculated at the origin and at the border of a finite mobile network. The network performance is evaluated in terms of the outage probability. Compared to a static network, the interference in a single snapshot does not change under uniform mobility models. However, random waypoint mobility increases (decreases) the interference at the origin (at the border). Furthermore, due to the correlation of the node locations, the interference and outage are temporally and spatially correlated. We quantify the temporal correlation of the interference and outage in mobile Poisson networks in terms of the correlation coefficient and conditional outage probability, respectively. The results show that it is essential that routing, MAC, and retransmission schemes need to be smart (i.e., correlation-aware) to avoid bursts of transmission failures.\",\n \"Title: A Location-Based MAC Scheme for Random Wireless Network\\nYear: 2011\\n\\nAbstract: This paper proposes a location-based MAC (LMAC) scheme for wireless networks with randomly placed nodes. This scheme regulates channel access by sharing local location information among transmitters. A lattice approximation approach is used to derive upper and lower bound for the success probability for a typical transmission attempt. Numerical results show that with the node density and link distance fixed, the optimal LMAC provides a much higher density of successful transmissions than CSMA and ALOHA.\",\n \"Title: Spatial Stochastic Models and Metrics for the Structure of Base Stations in Cellular Networks.\\nYear: 2013\\n\\nAbstract: The spatial structure of base stations (BSs) in cellular networks plays a key role in evaluating the downlink performance. In this paper, different spatial stochastic models (the Poisson point process (PPP), the Poisson hard-core process (PHCP), the Strauss process (SP), and the perturbed triangular lattice) are used to model the structure by fitting them to the locations of BSs in real cellular networks obtained from a public database. We provide two general approaches for fitting. One is fitting by the method of maximum pseudolikelihood. As for the fitted models, it is not sufficient to distinguish them conclusively by some classical statistics. We propose the coverage probability as the criterion for the goodness-of-fit. In terms of coverage, the SP provides a better fit than the PPP and the PHCP. The other approach is fitting by the method of minimum contrast that minimizes the average squared error of the coverage probability. This way, fitted models are obtained whose coverage performance matches that of the given data set very accurately. Furthermore, we introduce a novel metric, the deployment gain, and we demonstrate how it can be used to estimate the coverage performance and average rate achieved by a data set.\",\n \"Title: Asymptotic Deployment Gain: A Simple Approach to Characterize the SINR Distribution in General Cellular Networks\\nYear: 2014\\n\\nAbstract: In cellular network models, the base stations are usually assumed to form a lattice or a Poisson point process (PPP). In reality, however, they are deployed neither fully regularly nor completely randomly. Accordingly, in this paper, we consider the very general class of motion-invariant models and analyze the behavior of the outage probability (the probability that the signal-to-interference-plus-noise-ratio (SINR) is smaller than a threshold) as the threshold goes to zero. We show that, remarkably, the slope of the outage probability (in dB) as a function of the threshold (also in dB) is the same for essentially all motion-invariant point processes. The slope merely depends on the fading statistics. Using this result, we introduce the notion of the asymptotic deployment gain (ADG), which characterizes the horizontal gap between the success probabilities of the PPP and another point process in the high-reliability regime (where the success probability is near 1). To demonstrate the usefulness of the ADG for the characterization of the SINR distribution, we investigate the outage probabilities and the ADGs for different point processes and fading statistics by simulations.\",\n \"Title: Interference in Large Wireless Networks\\nYear: 2009\\n\\nAbstract: Since interference is the main performance-limiting factor in most wireless networks, it is crucial to characterize the interference statistics. The two main determinants of the interference are the network geometry (spatial distribution of concurrently transmitting nodes) and the path loss law (signal attenuation with distance). For certain classes of node distributions, most notably Poisson point processes, and attenuation laws, closed-form results are available, for both the interference itself as well as the signal-to-interference ratios, which determine the network performance. This monograph presents an overview of these results and gives an introduction to the analytical techniques used in their derivation. The node distribution models range from lattices to homogeneous and clustered Poisson models to general motion-invariant ones. The analysis of the more general models requires the use of Palm theory, in particular conditional probability generating functionals, which are briefly introduced in the appendix.\",\n \"Title: Transport density vs. channel access time in wireless networks: Power control and efficient mac design.\\nYear: 2012\\n\\nAbstract: Power control plays an important role in the design and operation of wireless networks. In this paper, we first define two critical metrics, transport density and channel access time to measure the system performance. The trade-off between these two metrics as a function of the peak power constraint is discussed in two cases, a fixed peak power case and an adjusted peak power case. Our analysis shows that the adjusted peak power constraint outperforms the fixed one in terms of both transport density and channel access time. Based on these observations, a novel and fully distributed energy-efficient MAC scheme with adjusted peak power constraint is proposed to schedule the concurrent transmissions in an efficient way. Better performance can be achieved by balancing between transport density and channel access time. Simulation results confirm the expected gains relative to standard MAC schemes: the transport density is increased by more than 50% compared to CSMA and by about a factor of 3 compared to ALOHA.\",\n \"Title: Towards an end-to-end delay analysis of wireless multihop networks\\nYear: 2009\\n\\nAbstract: In wireless multihop networks, end-to-end (e2e) delay is a critical parameter for quality of service (QoS) guarantees. We employ discrete-time queueing theory to analyze the end-to-end (e2e) delay of wireless multihop networks for two MAC schemes, m-phase TDMA and slotted ALOHA. In one-dimensional (1-D) networks, due to the lack of sufficient multiplexing and splitting, a space-time correlation structure exists, the nodes are spatially correlated with each other, and the e2e performance cannot be analyzed as in general two-dimensional networks by assuming all nodes independent of each other. This paper studies an 1-D network fed with a single flow, an extreme scenario in which there is no multiplexing and splitting. A decomposition approach is used to decouple the whole network into isolated nodes. Each node is modeled as a GI/Geo/1 queueing system. First, we derive the complete per-node delay distribution and departure characterization, accounting for both the queueing delay and access delay. Second, based on the departure process approximation, we define a parameter to measure the spatial correlation and its influence on the e2e delay variance. Our study shows that traffic burstiness of the source flow and MAC together determines the sign of the correlation.\",\n \"Title: Design of a wireless assisted pedestrian dead reckoning system - the NavMote experience\\nYear: 2005\\n\\nAbstract: In this paper, we combine inertial sensing and sensor network technology to create a pedestrian dead reckoning system. The core of the system is a lightweight sensor-and-wireless-embedded device called NavMote that is carried by a pedestrian. The NavMote gathers information about pedestrian motion from an integrated magnetic compass and accelerometers. When the NavMote comes within range of a sens...\",\n \"Title: Reactive sink mobility in wireless sensor networks\\nYear: 2007\\n\\nAbstract: The ability of a sink node to move can greatly improve the fault tolerance and load balancing properties of a sensor network. Rather than assuming extensive mobility and trying to minimize the large-scale path loss between the mobile sink and the nodes, we focus on limited-scope, arbitrary mobility triggered in response to a form of network feedback. Due to multipath fading effects, limited mobility dynamically modifies the set of sink neighbors and distributes network traffic over a larger number of nodes. We illustrate the impact of this reactive sink mobility concept on data collection by implementing it on top of a novel gradient-based routing protocol. We use Berkeley motes to present a proof of concept as well as a performance evaluation of our approach, with a particular emphasis on the advantages in terms of robustness and lifetime.\",\n \"Title: Delay-optimal Power Control Policies\\nYear: 2012\\n\\nAbstract: The delay till success (DTS) is the mean number of transmissions needed, averaged over the fading, until a single packet is successfully received (decoded) over a wireless link. This paper shows that under a mean and a peak power constraint, random power control can significantly reduce the DTS. We derive the optimal power control policies that minimize the DTS at one link of given length. For most commonly used fading distributions, these optimal power control policies are random on-off policies, whose parameters depend on the fading statistics and the link distance. We present two applications of this result in the context of noise-limited wireless networks: minimizing the local delay (mean delay for successful nearest-neighbor communication) and minimizing the local anycast delay (mean delay for a transmission to any node).\",\n \"Title: Dynamic Connectivity in ALOHA Ad Hoc Networks\\nYear: 2008\\n\\nAbstract: We consider an ad hoc network which uses multihop routing and slotted ALOHA for its MAC contention. We show that the minimum time required for a packet to reach the destination from the origin scales linearly with the distance. We define this dela y as the minimum time required for a causal path to form between the source and destination. We derive the distributional properties of the connection time using ideas from first passage percolation and epidemic processes. We show that the delay scales linearly with the distance and also provide asymptotic results (with respect to time) for the positions of the nodes which are able to receive information from a transmitter located at the origin. We also provide simulation results to support the theoretical resu lts. I. INTRODUCTION In a multihop ad hoc network, bits, frames or packets are transferred from a source to a destination in a multihop fashion with the help of intermediate nodes. This introduces a delay before a packet is delivered to the destination. For example, a five-hop route does not guarantee a delay of only five time slots. In a general setting, each node can connect to multiple nodes. So a large number of paths may form between the source and the destination. Each path may have taken a different time to form with the help of different intermediate nodes. Consider a network in which each node wants to transmit to its destination in a multihop fashion. In general in such a network, a relay node queues the packets from other nodes and its own packets and\",\n \"Title: The Ginibre Point Process as a Model for Wireless Networks With Repulsion\\nYear: 2014\\n\\nAbstract: The spatial structure of transmitters in wireless networks plays a key role in evaluating mutual interference and, hence, performance. Although the Poisson point process (PPP) has been widely used to model the spatial configuration of wireless networks, it is not suitable for networks with repulsion. The Ginibre point process (GPP) is one of the main examples of determinantal point processes that can be used to model random phenomena where repulsion is observed. Considering the accuracy, tractability, and practicability tradeoffs, we introduce and promote the β-GPP, which is an intermediate class between the PPP and the GPP, as a model for wireless networks when the nodes exhibit repulsion. To show that the model leads to analytically tractable results in several cases of interest, we derive the mean and variance of the interference using two different approaches: the Palm measure approach and the reduced second-moment approach, and then provide approximations of the interference distribution by three known probability density functions. In addition, to show that the model is relevant for cellular systems, we derive the coverage probability of a typical user and find that the fitted β-GPP can closely model the deployment of actual base stations in terms of coverage probability and other statistics.\",\n \"Title: Stochastic analysis of the mean interference for the RTS/CTS mechanism\\nYear: 2012\\n\\nAbstract: The RTS/CTS handshake mechanism in WLAN is studied using stochastic geometry. The effect of RTS/CTS is treated as a thinning procedure for a spatially point process that models the potential transceivers in a WLAN, and the resulting concurrent transmitter processes are described. Exact formulas for the intensity of the concurrent transmitter processes and the mean interference are established. The analysis yields useful results for understanding how the design parameters of RTS/CTS affect the interference in the network.\",\n \"Title: Random Power Control in Poisson Networks.\\nYear: 2012\\n\\nAbstract: This paper studies power control strategies in interference-limited wireless networks with Poisson distributed nodes. We concentrate on two sets of strategies: single-node optimal power control (SNOPC) strategies and Nash equilibrium power control (NEPC) strategies. SNOPC strategies maximize the expected throughput of the power-controllable link given that all the other transmitters do not use pow...\",\n \"Title: Longest Edge Routing on the Spatial Aloha Graph\\nYear: 2008\\n\\nAbstract: The multihop spatial reuse Aloha (MSR-Aloha) protocol was recently introduced by Baccelli et al., where each transmitter selects the receiver among its feasible next hops that maximizes the forward progress of the head of line packet towards its final destination. They identify the optimal medium access probability (MAP) that maximizes the spatial density of progress, defined as the product of the spatial intensity of attempted transmissions times the average per-hop progress of each packet towards its destination. We propose a variant called longest edge routing where each transmitter selects its longest feasible edge, and then identifies a packet in its backlog whose next hop is the associated receiver. The main contribution of this work (and of Baccelli et al.) is the use of stochastic geometry to identify the optimal MAP and the corresponding optimal spatial density of progress.\",\n \"Title: Interference and outage in clustered wireless ad hoc networks\\nYear: 2007\\n\\nAbstract: In the analysis of large random wireless networks, the underlying node distribution is almost ubiquitously assumed to be the homogeneous Poisson point process. In this paper, the node locations are assumed to form a Poisson cluster process on the plane. We derive the distributional properties of the interference and provide upper and lower bounds for its distribution. We consider the probability of successful transmission in an interference-limited channel when fading is modeled as Rayleigh. We provide a numerically integrable expression for the outage probability and closed-form upper and lower bounds. We show that when the transmitter-receiver distance is large, the success probability is greater than that of a Poisson arrangement. These results characterize the performance of the system under geographical or MAC-induced clustering. We obtain the maximum intensity of transmitting nodes for a given outage constraint, i.e., the transmission capacity (of this spatial arrangement) and show that it is equal to that of a Poisson arrangement of nodes. For the analysis, techniques from stochastic geometry are used, in particular the probability generating functional of Poisson cluster processes, the Palm characterization of Poisson cluster processes, and the Campbell-Mecke theorem.\",\n \"Title: Percolation in the secrecy graph\\nYear: 2011\\n\\nAbstract: The secrecy graph is a random geometric graph which is intended to model the connectivity of wireless networks under secrecy constraints. Directed edges in the graph are present whenever a node can talk to another node securely in the presence of eavesdroppers, which, in the model, is determined solely by the locations of the nodes and eavesdroppers. In the case of infinite networks, a critical parameter is the maximum density of eavesdroppers that can be accommodated while still guaranteeing an infinite component in the network, i.e., the percolation threshold. We focus on the case where the locations of the nodes and eavesdroppers are given by Poisson point processes, and present bounds for different types of percolation, including in-, out- and undirected percolation.\",\n \"Title: Stochastic Geometry for Modeling, Analysis, and Design of Multi-Tier and Cognitive Cellular Wireless Networks: A Survey.\\nYear: 2013\\n\\nAbstract: For more than three decades, stochastic geometry has been used to model large-scale ad hoc wireless networks, and it has succeeded to develop tractable models to characterize and better understand the performance of these networks. Recently, stochastic geometry models have been shown to provide tractable yet accurate performance bounds for multi-tier and cognitive cellular wireless networks. Given...\",\n \"Title: Reliable data delivery in large-scale low-power sensor networks\\nYear: 2010\\n\\nAbstract: In data collection applications of low-end sensor networks, a major challenge is ensuring reliability without a significant goodput degradation. Short hops over high-quality links minimize per-hop transmissions, but long routes may cause congestion and load imbalance. Longer links can be exploited to build shorter routes, but poor links may have a high energy cost. There exists a complex interplay among routing performance (reliability, goodput, energy efficiency), link estimation, congestion control, and load balancing; we design a routing architecture, Arbutus, that exploits this interplay, and perform an extensive experimental evaluation on testbeds of 100-150 Berkeley motes.\",\n \"Title: Combining stochastic geometry and statistical mechanics for the analysis and design of mesh networks\\nYear: 2014\\n\\nAbstract: We consider a two-dimensional mesh network comprising several source-destination pairs, each communicating wirelessly in a multihop fashion. First, we introduce a novel transmission policy for multihop networks according to which all the buffering in the network is performed at source nodes while relays just have unit-sized buffers. We demonstrate that incorporating this buffering scheme in conjunction with minor amendments to the medium access control (MAC) layer yields several benefits such as keeping packet delays small and helping regulate the traffic flow in a completely distributed fashion. Second, we employ a novel combination of tools from stochastic geometry and statistical mechanics to characterize the throughput and end-to-end delay performances of multihop wireless networks for two different channel access mechanisms, Carrier Sense Multiple Access (CSMA) and ALOHA. Our study also offers valuable insights from a system design stand-point such as determining the optimum density of transmitters or the optimal number of hops along a flow that maximizes the system's throughput performance. We corroborate our theoretical analyses via simulations.\",\n \"Title: A Stochastic Geometry Analysis of Inter-Cell Interference Coordination and Intra-Cell Diversity.\\nYear: 2014\\n\\nAbstract: Inter-cell interference coordination (ICIC) and intra-cell diversity (ICD) play important roles in improving cellular downlink coverage. By modeling cellular base stations (BSs) as a homogeneous Poisson point process (PPP), this paper provides explicit finite-integral expressions for the coverage probability with ICIC and ICD, taking into account the temporal/spectral correlation of the signal and...\",\n \"Title: On Decoding The Kth Strongest User In Poisson Networks With Arbitrary Fading Distribution\\nYear: 2013\\n\\nAbstract: Consider a d-dimensional network whose transmitters form a non-uniform Poisson point process and whose links are subject to arbitrary fading. Assuming interference from the k - 1 strongest users is canceled, we derive the probability of decoding the k-th strongest user in closed-form. As a special case, when k = 1, this probability is the standard coverage probability.This analytical result has immediate applications in networks with successive interference cancellation (SIC) capability. We use it to find closed-form upper and lower bounds on the probability of decoding at least k users and the mean number of successively decodable users. These bounds show that transmitter clustering is beneficial in exploiting SIC.\",\n \"Title: Bounds on the information propagation delay in interference-limited ALOHA networks\\nYear: 2009\\n\\nAbstract: Abstract—In a wireless network the set of transmitting nodes changes frequently because of the MAC scheduler and the traffic load. Analyzing the connectivity of such a network using static graphs would lead to pessimistic performance results. In this paper, we consider an ad hoc network with half-duplex radios that uses multihop routing and slotted ALOHA for the network MAC contention, and introduce a random dynamic multi-digraph to model its connectivity. We first provide analytical results about the degree distribution of the graph. Next, defining the path formation time as the minimum time required for a causal path to form between the source and destination on the dynamic graph, we derive the distributional properties of the connection delay using techniques from first passage percolation and epidemic processes. We show that the delay scales linearly with the distance and provide asymptotic results (with respect to time) for the positions of the nodes which are able to receive information from a transmitter located at the origin. We also provide simulation results to support the theoretical results.\",\n \"Title: Outage And Throughput Bounds For Stochastic Wireless Networks\\nYear: 2005\\n\\nAbstract: We derive outage expressions and throughput bounds for wireless networks subject to different sources of nondeterminism. The degree of uncertainty is characterized by the location of the network in the uncertainty cube whose three axes represent the three main sources of uncertainty in interference-limited networks: the node distribution, the channel gains, and the channel access. The range for the coordinates is [0,1], where 0 indicates complete determinism, and 1 a maximum degree of randomness (nodes distributed in a Poisson point process, fading with fading figure 1, and ALOHA channel access, respectively).\",\n \"Title: Guest Editorial: Geometry and Random Graphs for the Analysis and Design of Wireless Networks\\nYear: 2009\\n\\nAbstract: The one tutorial and 22 papers in this special issue focus on geometry and random graph for the analysis and design of wireless networks. The papers are organized into five groups: Topology; Outage, throughput, capacity, and scaling laws; Connectivity and coverage; Co-existence of disparate wireless networks and cognitive radio; and Distributed algorithms.\",\n \"Title: Toward A Circuit Theory For Sensor Networks With Fading Channels\\nYear: 2004\\n\\nAbstract: For Rayleigh fading wireless channels, there exists an interesting similarity between resistive circuits and time and path diversity mechanisms in multihop wireless sensor networks. A resistor-like circuit element, the erristor, representing the normalized noise-to-signal ratio, is introduced. Given an end-to-end packet delivery probability, the nonlinear mapping from link reception probabilities to erristor values greatly simplifies the problems of power allocation and the selection of time and path diversity schemes. Thanks to its simplicity, the formalism that is developed also provides valuable insight into the benefits of diversity mechanisms, and it reveals an interesting analogy to electric circuits as the signal-to-noise ratios exhibit resistor-like properties.\",\n \"Title: The Aggregate Throughput In Random Wireless Networks With Successive Interference Cancellation\\nYear: 2013\\n\\nAbstract: The feasibility of successive interference cancellation (SIC) depends on the received power ordering from different users, which, in turn, depends on the fading distribution, path loss function and network geometry. Using a framework based on stochastic geometry, this paper studies the aggregate throughput in d-dimensional random wireless networks with SIC capability. We consider networks with arbitrary fading distribution, power-law path loss; the network geometry is governed by a non-uniform Poisson point process (PPP). Our results demonstrate how the performance of SIC changes as a function of the network geometry, fading distribution, and the path loss law. An important observation is that, in interference-limited networks, lower per-user information rate always results in higher aggregate throughput, while in noisy networks, there exists a positive optimal per-user rate at which the aggregate throughput is maximized.\",\n \"Title: A delay-minimizing routing strategy for wireless multi-hop networks\\nYear: 2009\\n\\nAbstract: We consider a network where each route comprises a backlogged source, a number of relays and a destination at a finite distance. The locations of the sources and the relays are realizations of independent Poisson point processes. Given that the nodes observe a TDMA/ALOHA MAC protocol, our objective is to determine the number of relays and their placement such that the mean end-to-end delay in a typical route of the network is minimized. We first study an idealistic network model where all routes have the same number of hops, the same distance per hop and their own dedicated relays. Combining tools from queueing theory and stochastic geometry, we provide a precise characterization of the mean end-to-end delay. We find that the delay is minimized if the first hop is much longer than the remaining hops and that the optimal number of hops scales sublinearly with the source-destination distance. Simulating the original network scenario reveals that the analytical results are accurate, provided that the density of the relay process is sufficiently large. We conclude that, given the considered MAC protocol, our analysis provides a delay-minimizing routing strategy for random, multihop networks involving a small number of hops.\",\n \"Title: A Geometry-Inclusive Fading Model For Random Wireless Networks\\nYear: 2006\\n\\nAbstract: A new fading model is proposed and discussed that combines the uncertainties in the transmission distance as well as small-scale fading. If nodes are assumed to be distributed according to a Poisson point process and the fading is Rayleigh, the joint fading distribution is particularly simple. Interpreting fading as a stochastic mapping, we show that a node cannot infer on the presence of fading by measuring link qualities. Other applications of the fading model include connectivity, opportunistic communication, and probabilistic progress.\",\n \"Title: A formalism for the analysis and design of time and path diversity schemes in wireless sensor networks\\nYear: 2003\\n\\nAbstract: For Rayleigh fading channels, there exists an interesting similarity between resistive circuits and time and path diversity mechanisms in multihop wireless sensor networks. A resistor-like circuit element, the erristor, representing the normalized noise-to-signal ratio, is introduced. Given an end-to-end packet delivery probability (as a QoS requirement), the nonlinear mapping from link reception probabilities to erristor values greatly simplifies the problems of power allocation and the selection of time and path diversity schemes. Thanks to its simplicity, the formalism that is developed also provides valuable insight into the benefits of diversity mechanisms, which is illustrated by a number of examples.\",\n \"Title: Interference and Outage in Poisson Cognitive Networks.\\nYear: 2012\\n\\nAbstract: Consider a cognitive radio network with two types of users: primary users (PUs) and cognitive users (CUs), whose locations follow two independent Poisson point processes. The cognitive users follow the policy that a cognitive transmitter is active only when it is outside the primary user exclusion regions. We found that under this setup the active cognitive users form a point process called the Po...\",\n \"Title: Toward Quasiregular Sensor Networks: Topology Control Algorithms for Improved Energy Efficiency\\nYear: 2006\\n\\nAbstract: Uniformly random or Poisson distributions are widely accepted models for the location of the nodes in wireless sensor networks if nodes are deployed in large quantities and there is little control over where they are dropped. On the other hand, by placing nodes in regular topologies, we expect benefits both in coverage and efficiency of communication. We describe and analyze a basic localized algorithm and three modifications for topology control that provide a tradeoff between performance and deployment cost. The objective is to regularize the topology for improved energy efficiency. The basic algorithm produces quasiregular networks, which only use nodes as sentries and relays that are approximately evenly spaced, thereby emulating a regular grid topology. It is shown that quasiregular networks have a significant energy and lifetime advantage compared with purely random networks. We consider two specific types of quasiregular networks: the ones that are based on a Gaussian deviation about an ideal grid point (type A), and the ones that consist of a subset of nodes taken from a Poisson point process (type B). We show that the two types are equivalent for a certain density of the Poisson point process and, in particular, that in both cases the deviation from the ideal regular grid follows a Rayleigh distribution, whereas the distance between nearest neighbors is Ricean.\",\n \"Title: The performance of successive interference cancellation in random wireless networks\\nYear: 2014\\n\\nAbstract: This paper provides a unified framework to study the performance gain of successive interference cancellation (SIC) in d-dimensional interference-limited networks with arbitrary fading distribution and power-law path loss. We derive bounds on the mean number of users that can be successively decoded and the probability of successively decoding k users. Our results suggest that, without power control, the marginal benefit of enabling the receiver to successively decode k users diminishes very fast with k, especially in networks of high dimensions and small path loss exponent. On the other hand, SIC is more beneficial when the users are clustered around the receiver, or very low-rate codes are used.\",\n \"Title: Delay Analysis Of Spatio-Temporal Channel Access For Cognitive Networks\\nYear: 2011\\n\\nAbstract: Most channel access schemes for cognitive radio only consider using the idle periods of the primary users. Such schemes are not using the spectrum efficiently, since transmission opportunities also arise when a primary transmitter is active but its corresponding primary receiver is far away from the cognitive user. By detecting the signal power of the acknowledgments, a cognitive user is able to estimate the distance and channel condition between the primary receiver and itself. Then if the primary receiver is far away, the cognitive user can transmit simultaneously with the primary transmitter without affecting the primary link. Based on this idea, the spatio-temporal channel access scheme, utilizing both idle periods and spatial reuse, can be applied. This paper provides fundamental analysis of the channel access delays and shows the advantage of using the spatio-temporal access scheme in the carrier sense multiple access (CSMA)-based network with bi-directional links.\",\n \"Title: Routing in Ad Hoc Networks-A Wireless Perspective\\nYear: 2004\\n\\nAbstract: Traditionally, the routing problem is addressed at the network layer, an approach that has been extended to the wireless realm. In wireless multihop networks, however, strict layer-base protocol design leads to substantial inefficiencies. This paper addresses the routing problem for large wireless ad hoc networks from a fundamental point of view, not constrained by particular protocol implementations or layered architectures, but taking into account the properties of the wireless channel. First, an analytical channel model is presented that is based on Rayleigh fading. It demonstrates how noise and interference effects can be separated, and how each interfering transmission affects the packet reception probability. Second, the distribution of node distances in networks with uniformly randomly placed nodes is derived. These two ingredients are used to discuss the benefits of different routing strategies. In particular, short-hop and long-hop routing schemes are compared. Further, cooperative strategies such as multipath routing and cooperative diversity are briefly discussed as techniques that are enabled by the broadcast nature of the wireless channel.\",\n \"Title: Temporal Correlation Of The Interference In Mobile Random Networks\\nYear: 2011\\n\\nAbstract: In wireless networks, interference that is generated by undesired transmitters dominantly limits network performance. The correlation of node locations ( in mobile or static networks) makes the interference temporally correlated. Such correlation affects network performance greatly, and hence needs to be quantified. In this paper, we quantify the temporal correlation of the interference in mobile Poisson networks. More specifically, we obtain closed-form expressions for the interference correlation coefficient rho in Poisson line networks under various mobility models. When the mean speed of nodes (v) over bar increases, we show that rho is asymptotically proportional to (v) over bar (-1). Moreover, multi-path fading and random MAC schemes reduce the temporal correlation of the interference. These results are extended to higher-dimensional networks.\",\n \"Title: Interference and Outage in Doubly Poisson Cognitive Networks\\nYear: 2010\\n\\nAbstract: We consider a cognitive radio network with two types of users: primary users (PUs) and cognitive users (CUs), whose locations follow two independent Poisson point processes. The cognitive users follow the policy that a cognitive transmitter is active only when it is outside the primary user exclusion regions. Due to the interaction between the primary users and the cognitive users through exclusion regions, an exact calculation of the interference and the outage probability seems unfeasible. Instead, we derive bounds for the interference (in the form of Laplace transform) and the outage probability. Two network models, the bipolar and the nearest-neighbor model, are considered.\",\n \"Title: Analysis of uncoordinated opportunistic two-hop wireless ad hoc systems\\nYear: 2009\\n\\nAbstract: We consider a time-slotted two-hop wireless system in which the sources transmit to the relays in the even time slots (first hop) and the relays forward the packets to the destinations in the odd time slots (second hop). Each source may connect to multiple relays in the first hop. In the presence of interference and without tight coordination of the relays, it is not clear which relays should transmit the packet. We propose four decentralized methods of relay selection, some based on location information and others based on the received signal strength (RSS). We provide a complete analytical characterization of these methods using tools from stochastic geometry. We use simulation results to compare these methods in terms of end-to-end success probability.\",\n \"Title: Diversity Polynomials for the Analysis of Temporal Correlations in Wireless Networks.\\nYear: 2013\\n\\nAbstract: The interference in wireless networks is temporally correlated, since the node or user locations are correlated over time and the interfering transmitters are a subset of these nodes. For a wireless network where (potential) interferers form a Poisson point process and use ALOHA for channel access, we calculate the joint success and outage probabilities of n transmissions over a reference link. The results are based on the diversity polynomial, which captures the temporal interference correlation. The joint outage probability is used to determine the diversity gain (as the SIR goes to infinity), and it turns out that there is no diversity gain in simple retransmission schemes, even with independent Rayleigh fading over all links. We also determine the complete joint SIR distribution for two transmissions and the distribution of the local delay, which is the time until a repeated transmission over the reference link succeeds.\",\n \"Title: On the End-to-End Delay Performance of Spatially Correlated Wireless Line Networks\\nYear: 2008\\n\\nAbstract: The analytical end-to-end (e2e) performance of a wireless multihop network is largely unknown, because of the interconnections between several factors involved. Customarily, the nodes are often assumed to be spatially uncorrelated so that they can be analyzed in isolation, which is valid when all the traffic flows are independent. In practice, however, most traffic flows are correlated and cause spatial correlation among nodes. The results based on the assumption of spatially uncorrelated nodes may be far from the performance of real networks. In this paper, we aim to study the impact of the spatial correlation on the e2e delay in a wireless line network (WLN). In particular, we use queueing theory to reveal that the burstiness, the temporal correlation of the traffic flow and the underlying medium access control (MAC), together determine the spatial correlation, from which an analysis of the e2e delay of a WLN is accomplished.\",\n \"Title: User-Centric Intercell Interference Nulling for Downlink Small Cell Networks.\\nYear: 2014\\n\\nAbstract: Small cell networks are regarded as a promising candidate to meet the exponential growth of mobile data traffic in cellular networks. With a dense deployment of access points, spatial reuse will be improved, and uniform coverage can be provided. However, such performance gains cannot be achieved without effective intercell interference management. In this paper, a novel interference coordination strategy, called user-centric intercell interference nulling , is proposed for small cell networks. A main merit of the proposed strategy is its ability to effectively identify and mitigate the dominant interference for each user. Different from existing works, each user selects the coordinating base stations (BSs) based on the relative distance between the home BS and the interfering BSs, called the interference nulling (IN) range, and thus interference nulling adapts to each user's own interference situation. By adopting a random spatial network model, we derive an approximate expression of the successful transmission probability to the typical user, which is then used to determine the optimal IN range. Simulation results shall confirm the tightness of the approximation, and demonstrate significant performance gains (about 35–40 ) of the proposed coordination strategy, compared with the non-coordination case. Moreover, it is shown that the proposed strategy outperforms other interference nulling methods. Finally, the effect of imperfect channel state information (CSI) is investigated, where CSI is assumed to be obtained via limited feedback. It is shown that the proposed coordination strategy still provides significant performance gains even with a moderate number of feedback bits.\",\n \"Title: On the Impact of Coordination on Local Delay and Energy Efficiency in Poisson Networks\\nYear: 2015\\n\\nAbstract: Local delay characterizes the number of time slots required for a node to successfully transmit a packet to its target receiver. In this letter, we explore the impact of coordinated transmission on the mean local delay and the energy efficiency, in a Poisson network model. Leveraging tools from point process theory, we derive analytical expressions of the mean local delay and the long-term energy efficiency, without and with coordinated transmission. We find that coordinated transmission greatly decreases the mean local delay, and also improves the long-term energy efficiency under certain network configurations.\"